Whales, Seals, Fish and Man

Developments in Marine Biology 4

Whales, seals, fish and man

Developments in Marine Biology 1.

Toxic dinoflagellate blooms edited by D.L. Taylor and H.H. Seliger, 1979 (out of print)

2.

Phytoflagellates edited by E.R. Cox, 1980 (out of print)

3.

Toxic phytoplanktonblooms in the sea edited by T.J. Smayda and Y. Shimizu, 1993

Developments in Marine Biology 4

Whales, seals, fish and man Proceedings of the International Symposium on the Biology Marine Mammals in the North East Atlantic Tromsg, Norway, 29 November-1 December 1994

Editors:

Arnoldus Schytte Blix Department of Arctic Biology, University of Tromse, Tromse, Norway Lars Wallge Institute of Physiology, University of Oslo, Blindern, Oslo, Norway 0yvind Ulltang Institute of Marine Research, Nordnes, Bergen, Norway

1995

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0 1995 Elsevier Science B.V. All rights reserved.

No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher, Elsevier Science B.V., Permissions Department. P.O. Box 521, lo00 AM Amsterdam, The Netherlands. No responsibility is assumed by the Publisher for any injury andor damage to persons or property as a matter of products liability, negligence or otherwise, or from use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, the Publisher recommends that independent verification of diagnoses and drug dosages should be made. Special regulations for readers in the USA - This publication has been registered with the Copyright Clearance Center Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, USA. Information can be obtained from the CCC about conditions under which photocopies of parts of this publication may be made in the USA. All other copyright questions, including photocopying outside the USA, should be referred to the copyright owner, Elsevier Science B.V., unless otherwise specified. Developments in Marine Biology 4 ISBN 0 444 82070 1 This book is printed on acid-free paper. Published by: Elsevier Science B.V. P.O. Box 21 1 lo00 AE Amsterdam The Netherlands

Library of Congress Cataloging in Publication Data: I n t e r n a t i o n a l S y m p o s i u m on t h e B i o l o g y o f M a r i n e M a m m a l s in t h e N o r t h E a s t A t l a n t i c (1994 T r o m s s , N o r w a y ) Whales. seals. fish, and man proceedings o f the International S y m p o s i u m on t h e B i o l o g y o f M a r i n e M a m m a l s i n t h e N o r t h E a s t A t l a n t i c . T r o m s c . N o r n a y . 29 N o v e m b e r - 1 D e c e m b e r 1994 / e d l t o r s . A r n o l d u s S c h y t t e - B I i x . L a r s WalIcre. 0 1 v i n d U l t a n g . p. c m . -- ( D e v e l o p m e n t s i n m a r i n e b i o l o g y 4) I n c l u d e s indexes. I S B N 0-444-82070-1 1. M a r i n e m a m m a l s - - N o r t h A t l a n t l c O c e a n - - C o n g r e s s e s . 2. F i s h e r i e s - - N o r t h A t l a n t i c O c e a n - - C o n g r e s s e s . I. S c h y t t e - B l l x . Arnoldus. 11. Wallse. Lars. 111. Ultang. Oivind. IV. Title. V. S e r i e s . 0~713.25.157 1994 599.5'09163'3--dC20 95-91 10 CIP

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In order to ensure rapid publication this volume was prepared using a method of electronic text processing known as Optical Character Recognition (OCR). Scientific accuracy and consistency of style were handled by the author. Time did not allow for the usual extensive editing process of the Publisher. Printed in the Netherlands

Preface In February 1988, the Board of the Norwegian Fisheries Research Council agreed to establish an integrated research programme on whales and seals in Norwegian waters. A planning group was appointed to draw up the framework for the programme and recommend how it should be organised. Members of the planning group were Professor Lars WallCe, University of Oslo; (chairman) Professor Amoldus S. Blix, University of Tromsr (senior lecturer) Per Grotnes, Norwegian College of Fishery Science, Tromsr (research officer) Morten Ryg, University of Oslo; (research director) Oyvind Ulltang, Institute of Marine Research, Bergen. A plan was presented in June 1988 and approved by both the Board of the Norwegian Fisheries Research Council, and the two relevant ministries (Environment and Fisheries). The planning group was supplemented by one representative from each of the two ministries, and was appointed as the steering group for the research programme. Dr. Nina H. Markussen later replaced Dr. Morten Ryg, Dr. Ame J. BjCrge and Mr. Helge Lorentzen were representatives of the ministries for most of the programme period, but were temporarily replaced by others. Ms. Sidsel GrCnvik was employed as secretary to the steering group. In 1988, there were two main reasons for the establishment of an extensive research programme on marine mammals. Firstly, in the preceding few years there had been considerable scientific disagreement concerning the information on which Norwegian minke whaling was based. In particular, there had been controversy over the size of the north-east Atlantic minke whale stock, and disagreement as to whether continued harvesting of the stock could be justified. In 1986, the International Whaling Commission decided to classify the northeast Atlantic minke whale stock as a "protection stock", and the Norwegian government subsequently appointed an independent group of scientists to assess the state of our knowledge and review the scientific controversy (Roy M. Anderson, UK; Raymond J.H. Beverton, UK; Ame Semb-Johansson, Norway; and Lars WallCe, Norway). The group concluded that our knowledge of the biology, population structure, and stock sizes of this species was inadequate and recommended a number of research activities designed to provide more data. Secondly, during the winter of 1986-1987 large numbers of harp seals appeared along the Norwegian coast. Altogether, about 60,000 animals were caught in fishing gear. The same happened on a somewhat smaller scale in the winter of 1987-1988. These migrations resulted in heavy economic losses for fishermen. The reasons for the mass migrations were not clear, although many hypotheses were put forward. The steering group gave highest priority to research activities which could provide more information on the questions mentioned above, but funding was also provided for research proposals related to other aspects of the management of marine mammals. The research programme was originally planned to last for five years, but was later extended for another two years until the end of 1994. The total research council

vi spending on the programme added up to more than NOK 100 million (approximately 15 million US$). We believe that the programme has resulted in significant advances in our understanding of marine mammals both in Norwegian waters and elsewhere. Since marine mammal research is a topic of international interest, the steering group decided to arrange an international symposium to mark the conclusion of the programme, to present the results and to provide an opportunity for criticism and discussion. The symposium was organised in Tromsr Norway, at 70~ in the middle of the Arctic winter (November 29 to December 1, 1994), when snow and darkness prevail, but it nevertheless attracted 150 participants from no less than 17 countries. This volume presents the proceedings of the symposium. It outlines the major findings from the Norwegian programme, and in addition contains invited contributions from international authorities on specific relevant topics as well as other communications by non-Norwegian participants. We hope that this volume will be of interest to scientists and managers as well as to the environmentally aware members of the general public.

Arnoldus Schytte Blix Lars WallOe Oyvind Ulltang

vii

Acknowledgements The editorial committee gratefully acknowledges the work of the organising committee for the symposium: Ame J. BjCrge (convener), Oyvind Ulltang, Erling S. NordCy, Sidsel Grcnvik (secretary), and the many helpers from the Department of Arctic Biology, the Norwegian College for Fishery Science, and the Norwegian Institute of Fisheries and Aquaculture, as well as the financial contributions from the Research Council of Norway and the many sponsors, all of whom contributed to a successful symposium. We would also like to express our appreciation of the secretarial assistance provided by Ms. Elin Giaever during the production of this volume.

viii

Sponsors The Research Council of Norway, Oslo The University of Tromsr The Roam Amundsen Centre for Arctic Research, University of Tromsr The Norwegian Institute of Fisheries and Aquaculture, Tromsr TromsO Kommune Norges Fiskarlag, Trondheim Norges R&fisklag, Tromsr Sildemelfabrikkenes Landsforening, Oslo Nord-Norges Sildolje- og Sildemelfabrikkers Forening, Vadsr G.C. Rieber & Co., A/S, Bergen L. Macks Olbryggeri og Mineralvandsfabrik A/S, Tromsr Odd Berg Gruppen, Tromsr

ix

Contents Preface Acknowledgements Sponsors

V

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Stock assessment Estimating the abundance of marine mammals: a North Atlantic perspective P.S. Hammond Effective search width in shipboard surveys of minke whales in the northeastern Atlantic: concepts and methods T. Schweder and G. Hagen Point clustering of minke whales in the northeastern Atlantic G. Hagen and T.Schweder Use of mark-recapture experiments to monitor seal populations subject to catching N. @en and T. @-itsland Estimation of grey seal Halichoerus grypus pup production from one or more censuses S.-H. Lorentsen and Bakke Increased accuracy in the estimation of harp seal (Phoca groenlandica) abundance in whelping patches V.I. Chernook, V.A. Potelov and N. V. Kuznetsov Haul-out behaviour of the Norwegian harbour seal during summer R. Roen and A. Bj@rge Influences on spatial patterns of Gulf of Maine harbor porpoises D. Palka Aerial surveys of harp and hooded seal pups in the Greenland Sea pack-ice T.@itsland and N. @en

a.

Stock identity and social organization Genetic markers and whale stocks in the North Atlantic ocean: a review A. Arnason Genetic variation in northeastern Atlantic minke whales (Balaenoptera acutorostrata) A. K. Danielsddttir, S. D. Hallddrsson, S. Gudlaugsddttir and A. Arnason Preliminary results of a DNA-microsatellite study of the population and social structure of the harbour porpoise L. W. Andersen, L.-E. Holm, B. Clausen and C.C. Kinze Photo-identification of the minke whale Balaenoptera acutorostrata off the Isle of Mull, Scotland A. Gill and R.S. Fairbairns

3

13 27

35 47

53 61 69 77

91 105

119 129

X

Parasites as indicators of social structure and stock identity of marine mammals J.A. Balbuena, F.J. Aznar, M. Ferndndez and J.A. Raga Marine mammalian fatty acids: a source of information 0. Grahl-Nielsen and 0. Mjaavatten Fatty acid composition in blubber, heart and brain from phocid seals B. Fredheim, S. Holen, K.I. Ugland and 0. Grahl-Nielsen Studies of the social ecology of Norwegian killer whales (Orcinus orca) A. Bisther and D. Vongraven Possible effects of previous catch on the present population of Norwegian killer whales (Orcinus orca) D. Vongraven and A. Bisther

Distribution, diet and feeding ecology New approaches to studying the foraging ecology of small cetaceans A.J. Read Distribution and diving behaviour of hooded seals L.P. Folkow and A.S. Blix Distribution and abundance of walruses (Odobenus rosmarus) in Svalbard I. Gjertz and @. Wiig Habitat use and diving behaviour of harbour seals in a coastal archipelago in Norway A. Bjorge, D. Thompson, P. Hammond, M. Fedak, E. Bryant, H. Aarefjord, R. Roen and M. Olsen Spatial and temporal variations in northeast Atlantic minke whale Balaenoptera acutorostrata feeding habits T. Haug, H. Gj@sa?ter,U. LindstrGm, K.T. Nilssen and I. Rottingen Seasonal distribution, condition and feeding habits of Barents Sea harp seals (Phoca groenlandica) K.T. Nilssen Food consumption of the Northeast Atlantic stock of harp seals E.S. Nordoy, P-.E. Mirtensson, A.R. Lager, L.P. Folkow and A.S. Blix Historic variation in the diet of harp seals (Phoca groenlandica) in the northwest Atlantic J. W. Lawson and G.B. Stenson Seasonal and regional variations in the diet of harbour seal in Norwegian waters M. Olsen and A. Bjorge Feeding ecology of harp and hooded seals in the Davis Strait - Baffin Bay region F. 0. Kapel

133 141 153 169

177

183 193 203

21 1

225

24 1 255

26 1

27 1

287

xi

Energetics and other physiological aspects Food requirements of Northeast Atlantic minke whales E.S. Nordgy, L.P. Folkow, P.-E. MGrtensson and A.S. Blix Energetics of pregnancy, lactation and neonatal development in ringed seals (Phoca hispida) C. Lyde rsen Harp and hooded seals - a case study in the determinants of mating systems in pinnipeds K.M. Kovacs Consumption of cod by the Northwest Atlantic grey seal in Eastern Canada M.O. Hammill, M.S. Ryg and B. Mohn Digestive physiology of minke whales S.D. Mathiesen, T.H. Aagnes, W. Sgrmo, E.S. Nordgy, A.S. Blix and M.A. Olsen Body condition of fin whales during summer off Iceland G.A. Vikingsson Seal adaptations for long dives: recent studies of ischemia and oxygen radicals R. Elsner, S. Qyaseter, O.D. Saugstad and A.S. Blix Pineal functions in newborn seals K.-A. Stokkan Changes in metabolic rate and body composition during starvation and semistarvation in harbour seals N.H. Markussen Variation in the metabolic rates of captive harbour seals D. Rosen and D. Renouf Population dynamics Population dynamics: species traits and environmental influence C.W. Fowler Interpretation of growth layers in the periosteal zone of tympanic bulla from minke whales Balaenoptera acutorostrata I. Christensen On the life history and autecology of North Atlantic rorquals J. Sigurjdnsson Aspects of the biology of the harbour porpoise, Phocoena phocoena, from British waters C. Lockyer Aspects of reproduction and seasonality in the harbour porpoise from Dutch waters M.J. Addink, T.B. Sgrensen and M. Garcia Hartmann Migration strategy of southern minke whales to maintain high reproductive rate H. Kato

307 319 329 337

35 1 36 1 37 1 377 383 393

403 413 425 443 459 465

xii Overview of cetacean life histories: an essay in their evolution T. Kasuya Modelling the school structure of pilot whales in the Faroe Islands, 18321994 D. Bloch and L. Lustein Harp seals as indicators of the Bqrents Sea ecosystem Y.K. Timoshenko

Interactions with fisheries Interactions between marine mammals and fisheries: an unresolved problem for fisheries research T.D. Smith Strategies to reduce the incidental capture of marine mammals and other species in fisheries M.A. Hall Ecological implications of harp seal Phoca groenlandica invasions in northern Norway T. Haug and K.T. Nilssen Aspects of the sealworm Pseudoterranova decipiens life-cycle and sealfisheries interactions along the Norwegian coast K. Andersen, S. des Clers and T. Jensen Grey seal (Halichoerus grypus Fabr.), population biology, food and feeding habits, and importance as a final host for the life-cycle of sealworm (Pseudoterranova decipiens Krabbe) in Icelandic Waters E. Hauksson and D. Olafsddttir Pollutants, toxicology and epizootics Toxicological and epidemiological significance of pollutants in marine mammals P.J.H. Reijnders and E.M. de Ruiter-Dijkman Organochlorine contaminants in marine mammals from the Norwegian Arctic J.U. Skaare Seasonal variation of organochlorine concentrations in harp seal (Phoca groenlandica) L. Kleivane, 0. Espeland, K.I. Ugland and J. U.Skaare Biomarkers in blood to assess effects of polychlorinated biphenyls in freeliving grey seal pups B.M. Jenssen, J. U.Skaare, S. Woldstad, A.T. Nastad, 0. Haugen, B. Kloven and E.G. Sormo Toxic, essential and non-essential metals in harbour porpoises of the Polish Baltic Sea P. Szefer, M. Malinga, W. Czarnowski and K. Skdra

48 1 499 509

527 537 545 557

565

575

5 89 599

607 617

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Assessment of the vulnerability of grey seals to oil contamination at Froan, Norway M.Ekker, D. Vongraven, B.M. Jenssen and M. Silverstone Cytochrome P450 in marine mammals: isozyme forms, catalytic functions, and physiological regulations A. GoksQyr Serological investigation of morbillivirus infections in minke whales (Balaenoptera acutorostrata) S. Stuen and P. Have

Management and cultural, social and economic aspects of exploitation The International Whaling Commission’s Revised Management Procedure as an example of a new approach to fishery management J.G. Cooke Multispecies modelling and management with reference to the Institute of Marine Research’s multispecies model for the Barents Sea 0.Ulltang The management of Irish waters as a whale and dolphin sanctuary E. Rogan and S. D. Berrow The scientific background for the management of monodontids in West Greenland M.P. Heide-JQrgensen Marine mammals in the culture of Norwegian coastal communities A. Kalland Management of whaling in coastal communities R. Gambell Impacts of modern seal invasions S. Eikeland

623 629 64 1

647 659 671 683 689 699 709

Index of authors

715

Keyword index

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Stock assessment

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9 1995 ElsevierScienceB.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 13. Ulltang,editors

Estimating the abundance of marine mammals: a North Atlantic perspective P.S. H a m m o n d Sea Mammal Research Unit, Natural Environment Research Council, Cambridge, UK Abstract. This paper investigates how studies to estimate the abundance of marine mammals have used the characteristics and behaviour of the target population to help determine the most appropriate method for any specific study. It also addresses how these studies have attempted to overcome the problems associated with ensuring that important assumptions of the method are not violated. Three basic methods used for estimating marine mammal abundance are described: extrapolating counts; mark-recapture analyses of photo-identification data; and sightings surveys. Recent studies in the North Atlantic to estimate abundance include: harbour seals and bottlenose dolphins in the Moray Firth, Scotland; grey seals in the North Sea and the breeding population in Britain; North Atlantic humpback whales; and the NASS and SCANS sightings surveys for whales and small cetaceans in the North Atlantic and North Sea, respectively. The paper demonstrates the wide range of applicability of the available methods, the way that the example studies addressed and (in some cases) overcame potential problems, and the modification or extension of "standard" methodology to fit particular circumstances. Key words: population size, extrapolating counts, sightings surveys, line transect, photo-identification, mark-recapture, model assumptions, North Atlantic

Introduction There are a number of ways to estimate animal abundance that are applicable to marine mammals. But any application of these methods must take into account the inconvenient fact that pinnipeds and cetaceans spend most (or all) of their time at sea and a large proportion of that time underwater. However, studies to estimate marine mammal abundance can take advantage of the variation in the physical and behavioural characteristics of species to arrive at the most appropriate method for a particular case. In this context, pinnipeds spend a proportion of their time on land (or ice) and some of these haulout periods are predictable, particularly during the pupping season. Being out of the water avoids the complication of animals diving out of sight. This means that they can actually be counted. Methods for estimating seal numbers make use of haulout behaviour thus avoiding the problems of sampling at sea. In contrast, cetaceans spend their entire lives at sea. Direct counts are not possible and methods for estimating their abundance must rely on sampling. This has received increasing attention during the last 15 years, particularly through the forum of the IWC Scientific Committee [1,2]. This paper investigates how studies to estimate the abundance of marine mammals have used the characteristics and behaviour of the target population to help deAddress for correspondence: Sea Mammal Research Unit, Natural Environment Research Council, High Cross, Madingley Road, Cambridge CB3 0ET, UK.

termine the most appropriate method for any specific study. But even the most appropriate method will have its drawbacks, particularly the difficulties in ensuring that important assumptions of the estimation model are not violated. This paper also addresses, therefore, how these studies have attempted to overcome these difficulties. The examples chosen are all drawn from the North Atlantic, reflecting the focus of this Symposium and the experience of the author. This paper is not a review but, in describing these example studies, it is also intended to give the reader an idea of the range of work undertaken in the North Atlantic in recent years.

Methods for estimating abundance A fundamental assumption of any method of estimating abundance is that the data must be representative of the study population or area. This applies to all the methods described below.

Extrapolation of counts Rarely, if ever, can it be possible to enumerate an entire population of animals. This is certainly true for marine mammals which are effectively invisible for a large proportion of their lives. However, seals give birth and wean their pups on land or ice, and also haul out during their annual moult and on other occasions, and it is possible during these periods to count the number of animals present in an area. But all the animals in the population will not be hauled out at the same time so it is necessary to extrapolate the number of seals counted to obtain an estimate of the entire population. There are a number of ways of doing this. One is to choose a class of animal which can be counted (pups of the year, for example) and to use those numbers as input to an age- or stage-structured population model which calculates total abundance. An example of this method is given below. For a reliable estimate the population model must be realistic with parameter values estimated from relevant data. Another method of extrapolation is to count all the animals hauled within a given period and to divide this number by an estimate of the proportion of the population which is hauled out during this time. One way that this proportion can be estimated is by using telemetry data. An example of this method is given below. Assuming accurate counts can be made, these methods are most sensitive to the quality of data used to extrapolate counts to total abundance.

Mark-recapture and photo-identification Mark-recapture methods use data on the number of animals marked and the proportion of marked animals in samples of recaptured animals to estimate population size. They makes several basic assumptions about the data including:

marks are unique, cannot be lost and are always reported on recovery; and marking does not affect the subsequent catchability of an animal. In the past, mark-recapture analyses have been used to estimate the population size of marine mammals using data from artificially applied marks. More recently, studies have concentrated on populations of animals in which unique individual natural markings can be photographed and identified. Records of re-identifications from a series of photographic samples (capture histories) can then be used as data for mark-recapture analyses to estimate population size. Three examples of this are given below. Photo-identification has some advantages over the use of artificial marks [1] but obviously cannot be used for those populations whose individuals do not possess recognisable natural markings. In mark-recapture analyses, the assumption most likely to be violated is that each animal must have the same probability of being captured within a sampling occasion. It is very likely that the behaviour of individual animals will lead to heterogeneity of capture probabilities resulting in population estimates which are biased downwards. In the extreme, some animals may never be available to be sampled and will not be included in the population estimate. There are ways to account for this analytically if closed population models can be used but it is clearly better to minimise the problem during data collection. This means designing a study which gives every animal a chance of being captured, and capturing as many animals as possible. This can be achieved more easily if the distribution of the study population is concentrated in a limited area for a period during the year.

Sightings surveys Line transect methods [2,3] were first developed for terrestrial animals but are now widely used to estimate the abundance of cetacean populations via shipboard or aerial surveys. In these sightings surveys, the study area is sampled by the survey platform searching along predetermined transects, placed so that the whole area is representatively sampled. When sightings are made, data are collected which allow the calculation of perpendicular distance from the sighting to the transect line. These data are used to estimate the effective width of the strip searched so that sample density can be estimated and extrapolated to give an estimate of abundance in the whole study area. Sighting surveys thus provide an estimate of the number of animals in an area at a given time, not an estimate of the size of a biological population unless the whole of that population was in the study area during the survey period. The most important assumption made by the line transect sampling method is that every animal (or group of animals if groups are the sighting target) on the transect line itself is seen. That is, the probability of detecting an animal at zero perpendicular distance is unity. Clearly, this can never be the case for cetaceans, which are underwater most of the time. In order to arrive at an unbiased estimate of abundance, therefore, it is necessary to estimate this probability (known as g(0) in the literature). The best way to do this is through the analysis of duplicate sightings data collected

from independent sightings platforms. However, sightings surveys do not always collect data for estimating g(0) and in these cases, abundance will be negatively biased by an unknown amount, notwithstanding other potential biases. An alternative method of data collection and analysis for sightings surveys is that of "cue counting" [4]. This method involves estimating the density of cues (whale blows, for example) per unit area searched per unit time and dividing by an estimate of the rate at which animals of the study species provide cues. This approach can only be used for species which exhibit clear cues. But it has the advantage that the equivalent of g(0) is the probability that all cues at zero (radial) distance from the survey platform will be seen, which is far more likely to be satisfied. Cue counting works best from aerial platforms.

Studies to estimate abundance in the North Atlantic

Harbour seals in the Moray Firth The Moray Firth in northeast Scotland is home to a population of harbour seals which regularly haul out on inter-tidal sandbanks, particularly during the June/July pupping season. This has made it possible to count seals at all known sites in the area during this period of the year with the aim of estimating population size (personal communication). Sex-specific estimates of the proportion of seals hauled out during this time were estimated from VHF telemetry data collected from 26 seals of a range of sizes. Pups were not included in the estimate because they were being born throughout the counting period. The estimate of the average number of seals hauled out of 1007 was extrapolated to an estimate of population size (excluding pups) of 1651 (CV = 0.104). The important factors which had to be taken into account in this study were" the timing of the counts with respect to time of year, time of day and stage of tide; the need to ensure that double counting was not occurring; and the representativeness of the telemetry data. Peak haulout counts were made during the pupping season and 2 h either side of low tide. There was no diurnal pattern. This was in contrast to a similar study in Orkney where peak haulout counts were made during the moult, there was no tidal relationship but time of day had to be taken into account [5]. Double counting was judged minimal based on the movement patterns of individuals. The telemetry data were collected over a number of years and the data for females was biased towards pregnant females so there is a possibility that they were not representative and that the population estimate may be biased. Obtaining representative telemetry data for any study is often difficult and is, perhaps, the major drawback of this method of estimating abundance.

The British grey seal breeding population The number of grey seals breeding around the coasts of Britain is estimated each

year based on counts of pups born during autumn [6]. High resolution aerial photography is used to record all the pups on land at all known major breeding sites, several times during the pupping season. Pup production is estimated by fitting a model, with parameters defining birth, death and time of leaving the site, to the pup counts for each site. The number of females in the population is estimated by feeding the annual pup production figures into an age-structured population model. Total population size is obtained via a simple sex ratio calculation. The most important factors affecting this study are: the quality of the aerial photographs; the adequacy of the model used to estimate pup production from the pup counts (including the number of counts made each year at each site); and the adequacy of the data in the population model. The development of a purpose-built camera system including image-motion compensation [7] has resulted in excellent quality aerial photographs. The model to estimate pup production is sensitive to the timing and length of the pupping season. These vary from site to site, and so pup production is estimated separately for each site. Estimates of population size from the model are sensitive to the fecundity rate data and the sex ratio. The fecundity data were collected from one area in 1981 and it is not known how representative they are to other areas and the present day. There are few data on the sex ratio, which is assumed to be 1:1. Grey seals in the North Sea

The method described above estimates the size of the grey seal population breeding around Britain. But the distribution of seals outside the pupping season, when they are building up energy reserves for the next season, may be different. And it is the number of seals foraging in an area which is of most relevance to concerns about interactions with fisheries. To address this, a study using photo-identification methods to obtain data for mark-recapture analyses has been conducted to estimate the number of seals associated with haulouts along the central North Sea coast of Britain [8]. The pelage patterns of grey seals (Fig. l a) were photographed at 2-week intervals during the summer months at three major haulout areas in 1991-1993. Normally, only adult female grey seals are well enough marked to be recognisable in a subsequent photograph. This is an extreme form of heterogeneity of capture probabilities and to account for it the proportion of well-marked animals present was recorded during each sampling occasion. The pelage pattern was extracted from a standard area of the seal's head and neck by specifically developed image processing software which compensates for differences in the viewpoint of the photograph and the posture of the seal [9]. These standard images served as "marks". All pairs of photographs were compared by another computer program which generated a set of similarity measures, the highest of which were compared by eye to determine genuine matches. In this way, capture histories were established for all individually recognisable seals. A mark-recapture model was developed specifically for these data, structured to

Fig. 1. Examples of natural markings used for photo-identification studies in the North Atlantic: (a) grey seal (photo: Lex Hiby, Sea Mammal Research Unit); (b) bottlenose dolphin (photo: Ben Wilson, University of Aberdeen); (c) humpback whale (photo: Tony Martin, Sea Mammal Research Unit).

allow for movement between the three sampling areas and to account for animals identified from the left or right side [8]. The data showed that mixing of animals in the population occurred rapidly so that it was possible to estimate population size within each year using a model ignoring births and deaths. The estimates were corrected by an estimate of the proportion of well-marked seals in the population. This assumes that males and immature seals spend the same proportion of their time at haulout sites as do adult females. Limited telemetry data on grey seals indicate that this is the case but if it is not, the estimate will be biased. Heterogeneity of capture probabilities within the well-marked part of the population was probably not a problem because sampling covered all major haulout sites in the area and over 50% of this class of animals were identified. In addition, data collected outside the study area indicated little exchange of animals.

Bottlenose dolphins in the Moray Firth In the Moray Firth, one of the aims of a study of a small and isolated population of bottlenose dolphins is to estimate population size. The dolphins can be identified by nicks on the dorsal fin and/or by pigmentation patterns on the skin (Fig. l b) [10]. This is thus an ideal situation for mark-recapture analysis of photo-identification data. As in the case of grey seals described above, left and right sides of an animal are different and were sampled separately. Not all animals have natural markings so data were collected to estimate the proportion which do. The population was sampled twice each month during the summers of 1990-1992 and matches were determined by eye. The first population estimates have recently been calculated (personal communication). Analysis was via a multi-sample closed population model applied to the data for each summer. This allowed the use of identifying marks which were known to last longer than a summer but not necessarily more than a year, thus increasing sample sizes, and allowed heterogeneity of capture probabilities to be taken into account using the program, CAPTURE [11 ]. The model allowing heterogeneity gave consistently higher estimates indicating that this did need to be accounted for. The estimate in 1992, when sampling was extended to cover a larger proportion of the known range, was higher indicating that there were some site preferences within the study area and that results from this year should be used. The estimate for the right side (lower CV than left side) for 1992 using the heterogeneity model was selected as the best estimate. This study is a good example of the use of an extensive and detailed data set to address and take account of some of the potential problems of applying mark-recapture methods of photo-identification data.

North Atlantic humpback whales At the other extreme of scale is an ongoing multi-national study of humpback whales in the North Atlantic. One aim of project YoNAH (Years of the North Atlantic Humpback) is to estimate the size of the whole population via mark-recapture

10 analyses of data from photo-identification of fluke patterns (Fig. l c). Humpbacks breed in winter in the West Indies, migrating to summer feeding areas in high latitudes. There is known to be a high degree of fidelity of an animal to a particular feeding area [ 12]. Project YoNAH sampled whales in the breeding areas and again in all known major feeding areas (Gulf of Maine, Canada, West Greenland, Iceland and Norway) in 1992 and 1993. Although humpback whales occur across the whole North Atlantic, the chosen abundance estimation method is the most appropriate because all animals are well-marked and available to be sampled in relatively discrete and accessible areas. The photographs are currently being processed by eye. In 1992, 819 whales were identified in the West Indies and 855 in feeding areas. In 1993, there were 773 identifications from the West Indies and over 1000 are expected from the feeding areas. Next, the most appropriate mark-recapture model to analyze this particular data set will be developed. The options are limited by the number of sampling occasions but there is a large amount of detailed data which it is hoped can be used to test whether some of the standard assumptions have been violated and to take account of this, if necessary. For example, sampling in the feeding areas was unbalanced; how important was this? In addition, it is expected that analyses of data in the breeding areas may be affected by different behavioural characteristics of animals in different social groups, a form of heterogeneity of capture probabilities. North Atlantic sightings surveys

In 1987 and 1989, several countries in the North Atlantic collaborated in the NASS (North Atlantic Sightings Survey) projects, and a third is planned for 1995. The area covered by the 1987 and 1989 surveys is shown in Fig. 2. Ships were the main survey platform but some areas were covered by aerial survey to estimate the abundance of the target species: fin, minke, sei and pilot whales. The ship surveys used line transect sampling and the aerial surveys used cue-counting methods. On the ships, no data were collected to allow the estimation of g(0) in 1987, but in 1989 duplicate sightings data were collected on some vessels. A new "maximum simulated likelihood" method of analysis was developed to estimate g(0) for minke whales by integrating experimental and survey data [13]. NASS is a good example of one way to enable a large area of ocean to be surveyed in order to estimate cetacean abundance. Estimates of the target species could only be obtained from sightings surveys and a collaborative effort was important in order to cover as much of the North Atlantic as possible. Estimates of abundance for all the target species have been accepted by the IWC Scientific Committee although there is currently a debate about the estimate of g(0) for minke whales. For the other species, g(0) was not estimated and was assumed to be equal to one. Small cetacean abundance in the North Sea

Another, recently conducted, multi-national sightings survey in the North Atlantic

11

% % .

@

O'o/. zr \

6~'~

20.t,/

I 20"~ o* Fig. 2. Areas covered by the 1987 and 1989 NASS sightings surveys in the central and eastern North Atlantic and the 1994 SCANS sightings survey in the North Sea and adjacent waters. was SCANS (Small Cetacean Abundance in the North Sea). SCANS targeted harbour porpoises and other small cetaceans (and including minke whales) in the North Sea and adjacent waters in summer 1994. The area surveyed (shown in Fig. 2) was sampled by nine ships and two aircraft. Harbour porpoises are difficult to detect and it was expected that the probability of detecting them on the transect line would be small, so it was important to obtain good estimates of g(0). Duplicate sightings data were, therefore, collected on all ships. The two searching platforms operated all the time; one searched in "normal" mode, the other tracked sightings detected further ahead. The two aircraft flew in tandem (one directly behind the other) for much of the time, allowing duplicate sightings data to be collected during the aerial survey. New methodology was developed to analyze the duplicate sightings data from the ships and the aircraft. At the time of writing, the data are still being analyzed. It is hoped that the intensive coverage of the survey area and the extensive duplicate sightings data collected will allow the calculation of abundance estimates of higher than usual precision and accuracy.

Concluding remarks

The studies described above demonstrate the range of and variation within the methods available to estimate the abundance of marine mammal populations. In each example, the method was chosen which was most appropriate to the physical and

12

behavioural characteristics of the study population. In all cases, those conducting the research were aware of the particular difficulties of the chosen method and in some cases were able to investigate potential violations of the assumptions. And in several examples, "standard" methods were modified to adapt existing techniques to fit the particular situation. The studies described also demonstrate that there has been much work in recent years to increase our knowledge of marine mammal abundance in the North Atlantic. And the examples given, although important ones, are not exhaustive. Future attempts to estimate pinniped or cetacean population size should take note of the successes, and problems, detailed in the work described above.

References 1. Hammond PS. Estimating the size of naturally marked whale populations using capture-recapture techniques. Rep Int Whal Commn 1986;(Special Issue 8):253-282. 2. Hiby AR, Hammond PS. Survey techniques for estimating abundance of cetaceans. Rep Int Whal Commn 1989;(Special Issue 11):47-80. 3. Buckland ST, Anderson DR, Burnham KP, Laake JL. Distance Sampling: Estimating Abundance of Animal Populations. London: Chapman and Hall, 1993. 4. Hiby AR. An approach to estimating population densities of great whales from sightings surveys. IMA J Math Appl Med Biol 1985;2:201-220. 5. Thompson PM, Harwood J. Methods for estimating the population size of common seals (Phoca vitulina). J Appl Ecol 1990;217:281-294. 6. Ward AJ, Thompson D, Hiby AR. Census techniques for grey seal populations. Symp Zool Soc London 1987;58:181-191. 7. Hiby AR, Thompson D, Ward AJ. Census of grey seals by aerial photography. Photogram Rec 1988;12:589-594. 8. Hiby AR. Abundance estimates for grey seals in summer based on photo-identification data. In: Hammond PS (ed.) Grey Seals in the North Sea and their Interactions with Fisheries. Report to UK Ministry of Agriculture, Fisheries and Food under contract MF 0503, 1994. 9. Hiby AR, Lovell, P. Computer-aided matching of natural markings: a prototype system for grey seals. Rep Int Whal Commn 1990;(Special Issue 12):57-61. 10. Thompson PM, Hammond PS. The use of photography to monitor dermal disease in wild bottlenose dolphins (Tursiops truncatus). Ambio 1992;21:135-137. 11. White GC, Anderson DR, Burnham KP, Otis DL. Capture-recapture and removal methods for sampling closed populations. Los Alamos, NM: Los Alamos National Laboratory, 1982. 12. Katona SK, Beard JA. Population size, migrations and feeding aggregations of the humpback whale (Megaptera novaeangliae) in the western North Atlantic Ocean. Rep Int Whal Commn 1990;(Special Issue 12):295-305. 13. Schweder T, HCst G. Integrating experimental data and survey data to estimate g(0): a first approach. Rep Int Whal Commn 1992;42:575-582.

9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 0. Ulltang,editors

13

Effective search width in shipboard surveys of minke whales in the northeastern Atlantic: concepts and methods Tore Schweder ~ and Gro Hagen 2

_

1University of Oslo, Blindern, Oslo, Norway; and 2Norwegian Computing Center, Blindern, Oslo,

Norway

A b s t r a c t . Concepts and methods for estimating the effective search width from survey data of the line

transect type, independent observer data, dive time data and other data sources are reviewed and further developed. It is argued that effective search width is the primary parameter, which should not be estimated by separate estimates of g(0) and f(0) in cases where the hazard of sighting has been estimated. To estimate the hazard probability of sighting in complex models, systematic simulation is used. Occurrence exposure data for estimating the hazard probability of sighting are obtained from independent observer experiments by an automatic identification rule based on measurement error data. To remove bias introduced by imperfections in the chosen rule, the independent observer experiments are simulated for spatially distributed whales with density and clustering estimated from the survey data. Measurement errors are included in the model when estimating the hazard probability of sighting. The effective search width is calculated as the sighting rate in the fitted simulation model. Emphasis is on methodology for future surveys. Key words: baleen whales, abundance estimation, sighting, line transect survey

Introduction

Shipborne surveys of whales are cases of line transect experiments (see [1,2]). In standard line transect theory, the assumptions are that (i) objects on the line are detected with certainty; (ii) objects are detected at their initial location, and (iii) measurements of time and positions are exact. An additional assumption is: (iv) cues are, with certainty, identified with individual objects. When the objects are minke whales in the north Atlantic, and when the survey is carried out by ship, none of the four assumptions are likely to be completely satisfied. A whale is only available for sighting in the brief moments it breaks the surface for breathing. Observations are made from the barrel, some 16 m above sea surface, with two trained observers using the naked eye. In the Norwegian surveys, the vessels move at a speed of 10 knots, when the sighting condition is satisfactory (ca. 25% of the time). The northern minke whale does not show itself by a visible blow, and it is not easy to see the black back of a surfacing whale. Whales dive, and it is quite possible to pass a whale which is submerged during the period of possible sighting. With g(y) being the probability of detecting a whale at perpendicular distance y from the transect, assumption (i) is that g(0) = 1. When this assumption fails, g(0) must be estimated. Data, concepts and methods for estimating g(0) are discussed in some detail. We argue, however, that the effective search width, 2w, is the parameter of

Address for correspondence: T. Schweder, University of Oslo, Box 1095 Blindern, 0317 Oslo, Norway.

14 primary interest, and this parameter should rather be estimated directly than through separate estimates of g(0) and the probability density at zero of observed perpendicular distances, f(0) = g(O)/w. The basic concept in our model is the hazard probability of sighting. This is the conditional probability of successfully sighting a surfacing whale, given that the whale was not previously sighted. Having estimated this probability as a function of surfacing position, and possibly other observables, the effective search width is estimated by counting the successes in a simulation experiment. Here, whales scattered over the area are surfacing according to observed dive time data, and they are sighted according to the estimated hazard probability of sighting. Whales are moving about. Assumption (ii) is essentially that their movements are random, with no specific directional drift, or without being affected by the observer. If whales are attracted by the approaching vessel, or if they try to avoid it, assumption (ii) is broken. This problem is not further discussed. When a whale has been sighted, the observer presses a button connected to a computer to record the time of the sighting. He then measures the radial distance to the surfacing by eye, and he measures the angle between the sighting line and the transect line with the help of an angle board fixed at the barrel rim. None of these measurements are taken without error. Separate experiments are needed to estimate the error distribution, with sighting of radar buoys or other objects similar to a surfacing minke whale at known positions, and under conditions similar to the survey. In the experiment reported in [3], the vessel moved towards radar buoys at a speed of 10 knots, and the observer was allowed to observe the buoys at discrete points in time. Such experiments serve the additional purpose of training the observers. We use data from Ref. [3] for various purposes, but we do not discuss measurement error experiments any further. We do, however, stress the importance of consistency: if measurement errors are present, substantial errors can occur if it is switched inconsistently between measured and true coordinates for relative whale positions throughout the analysis. In our case, the cues are visible surfacings of minke whales. Cues might be misinterpreted. The problem is not that other species of whales are mistaken for minke whales, but rather that series of minke whale cues erroneously might be linked together as tracks for individual whales. The identification of tracks of cues is done online, and it is based on whale behaviour, direction of movement, size and other visual aspects of the cue. This identification might be affected by two types of errors: the series of sighted cues belonging to one whale is broken into several tracks; and the series of more than one whale are linked together in one track. It is possible to develop a cue counting method which will help to remove bias introduced by erroneous track identification. Space does not, however, allow this to be done in the present paper. The primary purpose of this paper is to present concepts and methods for the shipborn survey of minke whales in Norwegian water, planned for the summer of 1995, and for similar surveys. The plan is to cover the complete northeastern Atlantic management area. We go a bit further than in Ref. [4], where the independent ob-

15 server experiments in 1989 and 1990 were analysed, and an estimate of abundance was presented. In the 1995 survey, the plan is to have two barrels in place on each vessel. This will allow the independent observer experiments to be concurrent with the survey. Heterogeneity in observational conditions, and in sighting efficiency between vessels and teams, will thus be less of a concern than assumed in Ref. [ 11 ]. Due to the criticism raised in Ref. [6], the interpretation of data from independent observer experiments is discussed in extra detail. We present an automated procedure for so-called duplicate identification. This procedure is used on the observed data from the 1990 independent observer experiment (see [5]). The classification found by using the automated rule agrees well with the classification actually used in Ref. [4]. To remove bias due to subjectivity in the classification rule, simulation based estimation is required. This approach is explained, but not carried out. The method to use is that of maximum simulated likelihood [7], which was used in Ref. [4] to estimate g(0).

The search width problem The detection curve, g(y), represents the conditional probabilities of detecting a whale, given that it is at perpendicular distance y _> 0 from the line. The effective search width, 2w, is that width for which the strip of area a = 2wL contains the same expected number of objects as is observed during a transect of length L. When the transect line is randomly placed in the area, and D is the average whale density per unit area, the expected number of sightings is

DL2~g(y) dy The effective half-width is therefore W--

~g(y) dy

If n whales are observed during a transect of length L through an area of size A, and an estimate ff is available for w, the abundance of whales in the area is estimated by ^

An

NA = 2~L

(1)

Perpendicular distances between observed whales and the line has probability density, f(y), which is proportional to the detection curve, f ( y ) = g(y)/w. The effective half-width therefore satisfies g(0) w = ~ f(0)

(2)

16 This relation has been used to estimate w, by first estimating f(0) by fitting a parametric probability density to the observed perpendicular distances in the survey, and then separately to estimate g(0) from data gathered in an independent observer experiment [1,2,4]. The effective search width is, however, a more fundamental concept than g(0). If a direct estimate of w is available, as is the case when the spatial hazard probability of sighting has been estimated, this estimate should be used in density or abundance estimation. There are two main reasons for not estimating f(0) and g(0) separately, and then computing w: (i) it is notoriously difficult to estimate the density at the extreme end, 0, of the range. Erroneous model specification for the observed perpendicular distances can actually introduce quite severe bias in the estimate of f(0). The second reason is (ii) that extra variability is introduced in the abundance estimate. If the hazard probability has to be estimated, both w, f(0) and g(0) are implicitly determined. To estimate f(0) separately from the positions of initial observations, which already have been used when estimating the hazard probability, amounts to adding variability, and it will make the estimation of uncertainty more difficult. In Ref. [4], we were in partial breach with this advice (see section 7). The abundance estimate is sensitive to the estimation of g(0). For the previous surveys of minke whales in the northeastern Atlantic, g(0) was estimated to 0.36 [4]. The abundance estimate not corrected for g(0) (setting g(0)= 1, as assumed in the standard theory) was therefore increased by a factor of 1/0.36 = 2.78.

Hazard

probability

and simulation

Minke whales are available for sighting only at the discrete points in time when they are up breathing. The concept of hazard probability is therefore appropriate when modelling the observational process. Let Q(p) be the conditional probability that a whale surfacing at position p relative to the observer platform is sighted, given that the whale was not previously sighted from the platform. If Pi is the relative position of the whale at its ith surfacing, I I ( 1 - Q(Pi)) is the probability that the whale is passed without being sighted at any of its surfacings. If the whale stays at a fixed absolute position, the relative positions a r e Pi=(Xo-vTi,y), where the first coordinate is its position along the transect direction and the second coordinate is the perpendicular position. Here, Xo is the forward distance at time 0, Ti is the time of the ith surfacing and v is the speed of the observer platform. If the surfacings follow a Poisson process in time, with surfacing rate a, the probability generating functional of the Poisson point process yields (E is the expectation operator)

g(y) =

1 - Eli

(1 -

Q(x o -

vT/, y)) = 1 - e x p ( - c t v

IoQ(X,y) dx)

(3)

It is here assumed that no whale is sighted behind the observer platform, Q (x,y) = 0, x<0.

17 There is a distinct rhythm in the surfacings of a minke whale, and the Poisson process provides only an approximation to its surfacing pattern. By simulation one obtains a better estimate than eq. (3) of the detection curve and the effective search width. The method is to scatter points representing whales over an area. Each whale is given a surfacing pattern, which might be a random selection of dive time data obtained from observing radio tagged whales (see [8]). An observer platform is simulated to move at speed v through the middle of the area, and each time a hitherto unobserved whale at relative position p, at visible range, is simulated to surface, a pseudo random experiment is carried out to determine whether the whale is observed at this exposure. The success probability is Q (p). The effective search width is calculated from the number of sighted whales and the transect length in a simulation where the spatial density of whales is set to 1. This is done as follows. Let n be the number of sighted whales in a simulated transect of length L through an area of size A containing N whales. The direct estimate of the effective search width is, by inverting eq. (1), given by An =~ 2LN

(4)

By eq. (2), a count-estimate of g(0) is found as

~(0) = ~ ( 0 )

(5)

where an appropriate distance model is used for estimating the probability density at the origin, f(0), of the perpendicular distances for sighted objects in the simulation. The simulation approach has several advantages. The simulation model can, for example, be expanded to embody measurement errors, whale movement, heterogeneity in sighting conditions, etc., when data allow valid specifications of such elements. Since the effective search width simply is estimated by counting the number of successful sightings in the simulation experiments, such modifications of the model will not make the calculation of w difficult.

Independent observer experiments In the North Sea experiment in 1990, two barrels were mounted on the mast of the vessel, one above the other [5]. The observer teams in the two barrels could not communicate, but both were linked to a common computer for coherent time measurements, and to the coordinator at the bridge. This will also be the setup for all the vessels participating in the survey in 1995. Independently of each other, the observations of surfacing minke whales are made from the two platforms, both with respect to time and relative position. These cues are linked together in tracks, supposedly belonging to individual whales. This is done on the spot, and the track identification is based on all "information" available to the

18 observer at the time. Platform A produces one set of observations, and so does platform B. At the stage of analysis, the two independent, but concurrent, sets of observed cue-tracks are converted to two sets of spatial occurrence/exposure data, one for each platform. An exposure for platform A is a surfacing seen by B of a whale which A had not sighted before. This exposure is an occurrence if A successfully sights the cue. The occurrence/exposure data for B are defined in the same way, but now with A as the control platform. The identification of occurrence/exposure data in Ref. [4] was done by visual inspection of graphical representation of the data from the two platforms. This was done independently by several scientists. For the 1990 data, the four judgements were in good agreement. This was taken as evidence for the agreed occurrence/ exposure identification to be close to correct. It has, however, been argued that the judgements could be wrong, in spite of the agreement. A different occurrence/ exposure identification was, in fact, presented in Ref. [6]. This identification was more explicitly based on observed error distributions in measurements of times and relative positions, but a substantial subjective element remained in the judgements.

Duplicate identification To avoid subjectivity in the analysis, the following approach is possible. An automated rule for identifying exposures and occurrences from the parallel stream of observations in the independent observer experiment is developed. The procedure should be consistent with information on errors in time and spatial measurements. The aim should be to have the true exposures and occurrences identified, without contaminating the data with false identifications. To prevent possible classification errors to introduce bias in the estimated hazard probability function, the automated rule is applied both to the observed data and to data obtained from simulation of the independent observer experiment. This is explained in some detail below. In the 1990 independent observer data, some positions were not recorded, but could be inferred by interpolation from other positions in the track of cues. Another problem was that some time measurements were accurate only to the minute, while other were accurate to the second. The less accurate times are given in rounded minutes, with 0 s, but it is unknown which of the time measurements with 0 s are rounded and which are accurate. From the frequency of 0 s, one finds that about 7% of the time measurements are in rounded minutes. From the measurement error experiment reported in Ref. [3], we estimated the expected value,/z, and the standard deviation, a, of the distance AP = IP,- P2l between two independently recorded positions, P~ and P2, for a duplicate sighting. These values are represented as two spatial functions of the mean of the two observed positions, P and they are used in the automated identification rule given below.

19 1.

Cues from platform A and B are matched with respect to time. The track of an A-cue and that of a B-cue are classified as a possible duplicate pair if the time difference between the two cues is less than 30 s. For a possible duplicate pair of tracks, the A-cue and B-cue closest in time are classified as a possible pair of duplicate sightings. If this pair is not unique, choose arbitrarily one of the pairs. The cues for the two tracks of a possible duplicate pair are matched in serial order, with the possible duplicate sighting as the reference. If all the matched cues have time difference less than 30 s, and if at least some of those with non-zero seconds are closer than e seconds (e = 8, 10 or 15 s), the pair remains a possible duplicate pair of tracks. 2. A pair of tracks remains a possible duplicate pair if A P < I* ( P ) + a a ( P ) for at least one matched pair of cues. The coefficient a is set to 1 if position is recorded for both cues, to 2 if position is recorded for one of the cues, while the other position is found by interpolation, and to a = 3 if both positions are found by interpolation. To further remain a possible duplicate pair of tracks, none of the matched pairs of cues must have one of its cues more than 0.2 nm away to the port side of the track line while the other is more than 0.2 nm away to the starboard side. 3. If an A-track is paired with more than one B-track as possible duplicates, the following pruning is done. Pair the A-track with that or those B-tracks which minimize the minimal time difference in matched pairs of cues. If multiple pairs remain, choose that or those which minimize the maximum time difference in matched pairs. If there still are multiples, choose that or those with minimum minimal spatial distance within matched pairs. If multiples remain, choose that with minimum maximal spatial distance within matched pairs. If the pruning is not complete at this stage, make an arbitrary choice. The remaining pairs of tracks are classified as duplicate observations of the same whale. 4. A-tracks not a member of any duplicate pair provide exposures for B. The exposures have spatial coordinates from the A-cues, and they are all classified as failures (non-occurrences). An A-track which is a member of a duplicate pair provides failure exposures for B if it has cues before those matched with Bcues. If the first B-cue in the pair is matched with an A-cue, this is classified as a duplicate sighting or an occurrence for B. The position of this successful exposure is that of the A-cue. Exposures and occurrences for A are found in the same way, with A and B interchanged. This duplicate rule applied to the 1990 data identified all exposures and occurrences used in Ref. [4], except one. Table 1 displays the result of using the above automatic role as compared to the classification used in Ref. [4]. The classification used is well characterised by the role, with e = 15 s.

Simulation-based parameter estimation The basic idea of statistical estimation is to fit a model so that its predictions are in

20 Table 1. Classification of the 1990 independent observer data by the automated rule for three critical levels Limit

Exposures

8s

+5

10 s

+5

15 s

+2

Occurrences -0 -0 -0

+0 +0 +0

-3 -3

-1

The classifications are compared to that based on visual inspection, with 219 exposures and 44 occurrences. The positive (negative) numbers indicate how many of the category have been added (missed).

the best possible agreement with observed data. In nice statistical models, it is possible to find an explicit formula for computing the estimate from the data. In more complex models, one might be able to find the estimate by numerical optimization. When, for example the parameters tr and b of the popular model

])

(6)

are estimated by the program distance [1], this is usually done by numerical maximum likelihood estimation on appropriately grouped data. In many cases, however, the model is too complex to allow the likelihood function, or some other appropriate criterion function, to be analytically or numerically maximized. In such cases, one might find an acceptable estimate of the parameter of the model by systematic simulation. The idea is to simulate the model for many different values of the parameter in a region of high simulated likelihood. The simulated likelihood of the observed data is calculated from a descriptive statistical model applied to both the observed data and the simulated data for a given value of the parameter. After having found the region of high simulated likelihood, and having computed the simulated likelihood for sufficiently many parameter points in this region, the likelihood surface is estimated by quadratic regression, possibly in a generalized linear model framework. The maximum simulated likelihood estimate is then computed by maximizing this fitted likelihood surface. Theory and methodology of maximum simulated likelihood is discussed in Ref. [9]. A version of this method was used to estimate the hazard probability of sighting minke whales in the Norwegian shipborne survey in 1989, Q = 1 - exp(- exp(flo + fl x log(x)+

fl rr))

(7)

in forward track distance x and radial distance r (see [4,7]). The maximum simulated likelihood method was actually developed for this particular problem, because the "true" likelihood was unavailable since the data consisted both of occurrence/ exposure data from independent observer experiments and positions of initial sightings in the survey.

21 When measurement errors are included in the model (see below) and an automatic classification rule is used to process the data from the independent observer experiment, the simulation based estimation goes as follows. From the observed data, one has positions of initial sightings Pj = (Xj, Yj), j = 1 ..... J. The occurrence/exposure data are (Ei,Di), i= 1 ..... I, where Ei = (Xi, Yi) is the relative position of the ith exposure, and D i is 1 if the object was successfully sighted at the ith exposure, and 0 if it was missed. Writing Q(x,y Ifl) for the hazard probability of sighting at relative position (x,y) when the hazard parameter has value fl, one might use as descriptive log likelihood I

L(fl) -" ~_~ [O i log(Q( X i , Y/lfl) + (1- D i ) log(1- Q(X i , Y/]fl))] i=1

+ ~ [log(Q(Xj, Yj Ifl))- a--v~~ Q(x / y j) dz]- 7 log(w) j=l

(8)

The first likelihood component is the binomial likelihood of the occurrence/exposure data. The second component is obtained by the Poisson approximation, eq. (3), as explained in Ref. [6]. This last component is due to the positions of initial sightings, and is preferable to the likelihood component L1(fl), which was used in Ref. [4]. The maximum simulated likelihood approach is to estimate the independent observer experiment and the survey for a number of values b for the hazard probability parameter ft. In the formulation of the log likelihood eq. (8), the coordinates are taken to be measured and are not true coordinates. The simulation is therefore done by adding a randomly drawn measurement error to the simulated surfacing position before the pseudo random experiment is carried out to see whether the surfacing leads to a successful sighting. This is elaborated in the next section. Each simulation yields data of the same format as the observed data. After having classified the simulated independent observer data into occurrences and exposures by the chosen rule, the maximum likelihood estimator fl(b) is calculated by maximizing eq. (8), but with simulated data in place of observed data. When this has been done for sufficiently many values, b, in the parameter region of high likelihood, a quadratic surface in b is fitted to the responses L(fl(b)) by generalized linear regression. Generalized linear regression [10] is used since log likelihood is known to be F-distributed and not normally distributed. Finally, the fitted log likelihood is maximized, and the maximizing value of b is taken as the maximum simulated likelihood estimate ft. The covariance matrix of this estimate is calculated from the curvature of the fitted log likelihood in the usual way. More complicated models than that indicated above, or that used in Ref. [4], are probably needed to satisfy the Scientific Committee of the 1WC. In models based on observed dive-time data, measurement errors in the observed relative positions, automated occurrence/exposure identification from independent observer experimental data, and errors in cue track identification, simulation based estimation seems indispensable.

22 Measurement

errors

Errors in radial sighting distance and angle measurements were identified as a potential source of bias in the Norwegian independent observer experiments (see [11]). An experiment [3] revealed that substantial variability was present in these measurements, and also some bias. The structure of these measurement errors is summarized as follows [3,4] (where typos occur in the formula for errors in the angles): Let A be the measured angle, and a the true angle between the sighting line and the track line, and let R and r be the measured and true radial distance from the observer to the whale, respectively. Then, given a and r, R = 0.815r + 0.301(0.05 - 0.05 e x p ( - r / 0.05)) + 0.117(r + 0.01) TMZ 1 and A = s i g n ( a ) ( 1 . 1 4 4 7 x ~ - 0.1892 + 0.761Z 2 + 3 ( 1 - I)Z 3 )2 where Zl, Z 2 and Z3 are independent standard normal variates, independent of the mixing indicator, I, Pr (I = 1) = 1 - Pr(l = 0) = 0.98. The hazard probability (eq. (7)) in Ref. [4] was estimated on occurrence/exposure data and positional data for initial sightings. These data are given in observed coordinates. In the simulation, one keeps to observed coordinates as follows. Each time a hitherto unobserved whale is simulated to surface at visible range, a positional error vector, e, is drawn from the above error distribution and added to the true relative position vector, p. The relative position in measured coordinates is then P = p + e. The Bernoulli experiment to determine whether this surfacing is sighted has success probability Q(P). It is also possible to work with true coordinates instead of observed ones. Then, the success probability in the simulation is Q(p), and measurement errors are added to positions where sightings are made, but after the sighting. If the measurement errors are added after the sighting, the hazard probability parameter, fl, relates to the true position of the surfacing. When estimating this parameter by way of simulation, the log likelihood (eq. (8)) for simulated data must be computed in measured coordinates, exactly in the same way as when the hazard parameter relates to measured coordinates. The estimate of fl will depend on whether it relates to true or measured coordinates. The resulting estimate of the effective search width should, however, not differ substantially between the two approaches - provided one keeps consistently to either measured or true coordinates in the analysis consisting both of parameter estimation and calculation of effective search width. When the hazard model has been fitted in true coordinates, a direct estimate of g(0) is available through eq. (3) or by simulating the sighting process for whales located on the track line. If, however, the hazard probability parameter relates to measured coordinates, only the count estimate (eq. (5)) makes sense.

23 Figure 1 shows the distribution of perpendicular distances from the track line of initial sightings during the 1989 survey. The dotted line is the distance model (eq. (6)) fitted to data with y < 0.5 nm, and the other curve is the exponential density f(y) = a e x p ( - a y ) fitted to y < 0.3 nm. Figure 2 shows the same for the simulated perpendicular distances in the fitted model including measurement errors. This model was fitted by maximum simulated likelihood, as described in Ref. [4]. The shape of the simulated distribution is somewhat different from the observed one close to 0. The exponential distribution fits actually slightly better than the distance model (eq. (6)). The numbers inserted on the graphs are estimates off(0) for the two models. In Ref. [4] we used the model (eq. (6)) when calculating the count estimate of g(0). The discrepancy in distributional shape near 0 was not noticed in Ref. [4]. It is probably due to imperfections in the measurement error model, which was estimated separately. This lack of fit illustrates the importance of combining all elements of the analysis in one model, in order to ensure consistency in the analysis. The above difficulty also illustrates the sensitivity of the abundance estimate on the model chosen for the perpendicular distance distribution. Since estimation of a probability density at the extreme end of the range is notoriously difficult, it should be avoided if possible. When the hazard model has been fitted, the count estimate (eq. (4)) of the effective search width is available, and there is in fact no need for any estimation off(0). In the 1995 survey, it is planned to have two barrels on each vessel, to allow independent observations to be made concurrently with the survey for each vessel and

Observed

1989

CO

r~

-4.88 4.55

'I\L~ .....13

i.

iiii 0'.0 Fig. 1.

[Inn

i

012

014 016 Perpendicular distance

n nn

018

n

1'.0

24

Simulated

i

4.91

--...

r]m•

O

olo

012

014

016

Perpendicular distance

m r v . v . - ~ FI

o'.8

,-1

1'.o

Fig. 2.

each stratum. It will then be possible to estimate the hazard probability function for each stratum. Thus, the effective search width can be estimated by counting the sightings in simulation models for each stratum. This will allow the abundance estimates to be less hampered with bias introduced by choosing the wrong distance model, and it will allow the variability of the abundance estimates to be less - and more easily understood. More systematic measurement error experiments are also needed, both for the purpose of training the observers, and for obtaining a better model for measurement errors.

Conclusion The standard assumptions in line transect analysis are not always satisfied. When surveying minke whales by ship in the northeastern Atlantic, it is clear that the probability is less than 1 of observing an object on the track line, and positions are measured with error. To tackle these difficulties, models formulated in terms of the hazard probability of sighting have been useful for this type of discrete line transect experiments. To estimate the parameters of these models, methods based on systematic simulation have been developed. Independent observer experiments and experiments for gathering data on the errors in positional data provide an empirical basis for the abundance estimate. To calculate this estimate, the effective search width should be estimated directly through the hazard probability model, and not

25

indirectly through g(0) and f(0). To remove subjectivity, an automated rule to identify exposures and occurrences is needed, and this rule should be applied to both the observed data from the independent observer experiment, and to simulated data in the course of the estimation. Statistical uncertainty in the estimate of effective search width can be studied by a parametric bootstrap approach, as in Ref. [4]. The methodology developed for discrete line transect experiments is essentially applicable also for the more usual continuous time experiments. Here, the concept of hazard rate replaces the hazard probability of sighting [1,13]. To estimate the hazard rate from independent observer experiments, one will need models of censored survival times: the time one observer is aware of an object until the other observer sights the object. In the continuous case, the detection curve is exactly determined by eq. (3).

References 1. Buckland ST, Anderson DR, Burnham KP, Laake JL. Distance Sampling: Estimating Abundance of Biological Populations. London: Chapman and Hall, 1993. 2. Hiby AR, Hammond PS. Survey techniques for estimating current abundance of cetateans. Rep Int Whal Commn 1989;(Special Issue 11):47-80. 3. ~ien N, Schweder T. Estimates of bias and variability in visual distance measurements made by observers during shipboard surveys of northeastern Atlantic minke whales. Rep Int Whal Commn 1992;42:407-412. 4. Schweder T, Oien N, HCst G. Estimates of abundance of northeastern Atlantic minke whales in 1989. Rep Int Whal Commn 1993;43:323-331. 5. Schweder T, Oien N, H~st G. Estimates of g(0) for the northeastern Atlantic minke whales based on independent observer experiments in 1989 and 1990, found by the hazard probability approach. Rep Int Whal Commn 1992;42:399-405. 6. Cooke, J. A re-analysis of experimental data to estimate G(0) for shipboard surveys of minke whales in the North Atlantic. Paper SC/46/NA8 presented to the Scientific Committee of the International Whaling Commission, 1994 (unpublished). 7. Schweder T, HCst G. Integrating experimental data and survey data to estimate g(0): a first approach. Rep Int Whal Commn 1992;42:557-582. 8. Folkow LP, Blix AS. Daily changes in surfacing rates of minke whales (Balaneoptera acutostrata) in Norwegian waters. Rep Int Whal Commn 1993;43:311-314. 9. Schweder T. Maximum simulated likelihood, 1994 (unpublished). 10. McCullagh P, Nelder JA. Generalized Linear Models. London: Chapman and Hall, 1983. 11. International Whaling Commission. Report of the Scientific Committee, Annex L. Report of the ad-hoc working group on g(0). Rep Int Whal Commn 1992;42:252-258. 12. Oien N. Abundance of the northeastern Atlantic stock of minke whales based on shipboard surveys conducted in July 1989. Rep Int Whal Commn 1991;41:499-511. 13. Schweder T. Transformations of point processes: applications to animal sighting and catch problems, with special emphasis on whales. Dissertation, University of California, Berkeley, CA, USA, 1974 (unpublished).

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9 1995 ElsevierScience B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand D. Ulltang,editors

27

Point clustering of minke whales in the northeastern Atlantic G r o H a g e n 1 and T o r e S c h w e d e r 2 1Norwegian Computing Center, Blindern, Oslo, Norway; and 2University of Oslo, Blindern, Oslo, Norway A b s t r a c t . A statistical model for spatial whale distribution in the Northeastern Atlantic is presented.

The model, a Neyman-Scott cluster process, is fitted separately for 12 strata, based on data from the Norwegian shipborn survey in 1989. The spatial distribution differs considerably between areas, with respect to both cluster size, intensity of clusters and density of whales within clusters. It seems obvious that information about the clustering is relevant when planning new surveys, and when analyzing survey data. K e y w o r d s : survey data, spatial distribution, cluster process

Introduction

The spatial distribution of minke whales is of interest for various reasons. The degree of clustering is of independent biological interest, with respect to both behaviour and feeding. The variability in the line transect surveys will depend on the degree of clustering, and the interpretation of survey data will be facilitated if a spatial point process model has been fitted. We will use transect data from the Norwegian shipborne survey in 1989 [6] to fit a Neyman-Scott cluster process, separately for each stratum. The estimation is done by fitting the theoretical K-function [ 1] to its empirical counterpart by numerical optimization. The K-function summarizes the interpoint distances in the point process.

Statistical model for whale distribution

The point process model for spatial whale distribution in the Northeastern Atlantic is a Neyman-Scott cluster model. When defining such a model, we need to specify a statistical model that covers both the cluster units and the individual whales belonging to each cluster. Separate models are fitted to each of the survey blocks used in Dien [6]. The model, and simulation, is based on: a. Cluster units: the clusters form a stationary Poisson process with intensity 2 per unit area. This means that the number of clusters in a region A, with area v(A) is Poisson distributed with parameter (and expected value) m = 2 . v(A). The clusters' positions inside the region, represented by the positions of cluster centers, are randomly distributed over the total region according to a uniform probability distribution. Address for correspondence: G. Hagen, Norwegian Computing Center, Box 114, Blindern, N-0314 Oslo, Norway.

28 b~

Individual whales belonging to a cluster: the individual whales of a given cluster is a Poisson process, but with intensity decreasing as the distance from the cluster center increases. The Poisson intensity of whales in a cluster is

/texp -

X2

+y2]

2p z

(1)

where x is the distance from the center in the x-direction and y is the distance in the y-direction. The intensity model (1) specifies that the number of points in a cluster is Poisson distributed with parameter M =/t2arp 2

(2)

The points are independently scattered around the cluster center, according to the spherical bivariate normal distribution with variance in both x- and ydirection p2. The parameter p might be interpreted as the radius of the cluster, and the parameter/~ is the whale intensity in the center. If p is small relative to the size of the area, the method of simulation indicated above works well. If, however, p is large, edge effects come into play. Poisson cluster processes are described in Diggle [1 ] and Ripley [3]. The process consists of parent events (cluster centers), and to each parent there are a number of offspring (individual whales) with relative positions independently and identically distributed according to a probability distribution (bivariate normal).The intensity of parent events is 2, and the number of offspring for each parent is a Poisson variable with parameter M, given in eq. (2). To summarize the interpoint distances in a Poisson cluster process, one introduces the so-called K-function, defined by yK(t) = expected number of further points within distance t from an arbitrary point in the process where y is the overall intensity of points, y = 2M = 2/t2~p 2

(3)

In the present model, "points" are the individual whales, and yK(t) thus is defined as the expected number of further whales within an interpoint distance not exceeding t from a randomly chosen whale. From the theory in Diggle [1] we find that in the present model, K(t) = art 2

4-

"~- 1 -- exp - 4P 2

and so the kt-parameter disappears.

(4)

29 Estimation To estimate the model parameters we will use the K-function, and we therefore need an empirical point estimate for K(t) based on observed data. The estimator R ( t ) is described in Diggle [1] and Ripley [3], and we only include a short summary. The function ~,K(t) is the expected number of points (whales) within a distance t from a randomly chosen point (whale). For a general region A with area v(A), the expected number of points is w(A). The expected number of ordered pairs of points, with the first point being inside A thus become Np (t) = ~ v ( A ) . ~,K(t) = ~ , 2 v ( A ) K ( t )

(5)

The method for estimating K(t) is to estimate Np(t), and then to combine eq. (5) with the common estimator for overall intensity p = n/v(A), to obtain an estimator for the K-function: K ( t ) = v ( A ) IVp (t) n 2

where n is the total number of points observed inside the region A. Let P~ ..... Pz be the process points inside the region A, and let u U be the distance between Pi and Pj. Following Ripley [3], we estimate Np as a weighted sum of ordered pairs, with weights being the inverse of the probability that a point at distance u U from the point Pi will be observed (i.e. is inside the region A). When the process is stationary and isotropic, as is the case in our model, this probability is the proportion of the circumference with radius u U and center at Pi that lies inside the observed region A. Our observed points are from line transects through relatively large areas. It seems realistic to define the observed region (region A in the text above) to be a small strip of width b around the transect line. The total observed area in a block (stratum) is v(A) = hTb

where h = intended vessel speed during transect, T = transect duration inside the actual block and b is the effective search width on the transect. In 1989 the intended vessel speed was h = 10 knots, and the effective search width has been estimated to be approximately 0.18 nautical miles [4]. When estimating the model parameters, we therefore use these values in all formulas. In several of the blocks, the data consist of observations from more than one continuous transect. We have chosen to regard these as independent observation series from the same model. They are therefore linked together in one continuous transect, covering the actual block. The only relevant parameters are block identification and relative time of observation. From these we can compute a theoretical position of each observation, and perform the estimation. For one specific block, let the relative points of time for whale observations be 0 < rl < ra < "'" < rn < T. Relative forward

30 positions in the observed strip will then be 0 < hrl < hr2 < "'" < hrn < hT, and these can also be regarded as the observed absolute positions along the transect strip. The reason for this is that the effective search width is very small compared to the forward distances between observations. The K-function and its estimator R(t) thus become

K(t) = K(hr)= zc(hr) 2 + ~ 1-

exp/h,242// (6)

n2

i=1

E['gi-'gJ j~i

(uij

where It(u) = 1 if u < t, and zero else. To stabilize the variance, Ripley [3] suggests using L(t)= ~l(K(t)/z~) when estimation is done by least squares. For a given survey block, the estimates of 2 and p are those values which minimize the squared error:

Z (L(t) -/~(t)) 2 t
z + W(2lt2gp2hTb- n) 2

(7)

t
31

Table 1. Blockwise parameter estimates based on observations from the Norwegian survey in 1989, see Oien [6] for block identification Block

~t

VSN VSS SV BJ BA GA KO FI LO NO SN NS Median value

jb

3.131 5.951 0.292 1.630 9.765 2.351 6.446 1.363 1.642 2.024 0.932 0.762 1.833

~.

1.230 1.244 133.979 2.161 5.815 2.617 5.720 1.886 2.156 2.861 2.667 16.684 2.642

8.896 6.539 5.074 2.569 1.181 9.951 6.601 5.327 4.425 6.966 2.479 1.049 1.737

x x x x x x x x x x x x x

10 -3 10-3 10 -6 10-3 10 -4 10-4 10-4 10- 3 10-3 10 --4 10 -3 10-4 10-3

Discussion

The model parameters (U, P, 2) were estimated by numerical minimization of eq. (7), separately for each of the 12 blocks. The parameter estimates are presented in Table 1, and from this table we observe that the parameter estimates differ a lot. Figure 1 shows that the L-functions are well fitted to the empirical estimates, and it also illustrates the dilemma when deciding the actual window size At. In the upper tail of

Estimated and fitted L-functi0n o

.j

0

.-I

0.0

0.2

0.4 0.6 time (hours)

0.8

1.0

O v.-

0.0

0.2

0.4 0.6 time (hours)

0.8

1.0

Fig. 1. Empirical L-function (dots) and fitted L-function (smooth curve), from the blocks VSS (west of Spitsbergen, south) and KO (Kola).

32 VSS-estimated model, simulated (no

{Do

E

.~..

o .l=.,

.,.

~-':

~t--00 o ......

...,,-

/~~

,;:~.,~

..r

.~...... .

:~".

~ .~.

@..-.

.r

-.~-

,~;:

:!~,,,

~ ~r .~.

-

~'

-~

",":

...~...:

~:~..

%'r.:

~176

r~.~.. 9 .

"- ~~ . .,

.,~.'..,

"(~,"

...... ".~.'o 9

0

20

40

6'0

80

100

nautical miles

KO-estimated model, simulated ~ 0 ~ 0

E

. . ;;~' :'":'".

- " ":9." ~L~ f~ f' ~" ' ~, ":i ' ~ " ;'. ;" _:,-'7. :, " .

. " :,~:

.o_ ,r

9

~

"~

":

"~.,~..':(,'::'.....~.:.rff,,,,~

.~, .....

9

~0 r-"00

9

.. . . . . .

.

.

.

.

.

9

9

.......;.,.-,.,. ~r 9

.

..

....

.. ,,:~.o.,~ ~ ; 7 : - . ":..~.-.~:,~ -..., . . . . .

9 .

.

" ~ ...... ." ".~.~,~"..';

"

.

~-

::~..

,

. ....

..

.

......,~.,-..,,.o~...o~.... , . , .-.. . . . . . ...

~ '~-,-:.~~:,.- .-

,...~%-..,

..-,

' : .~,~=.': ~~4.~~::;,..'.:-... 9 9

0 {.o

..

: . "...'.:.?:~r

. . . . 2"..:-.~.'...."

~~ ::='.~;.Z

0 ',:3"

:~,: .--.

9 %.. . . . s .~ 9 .','.. ""

"" ...

9

:.'

.....: :..,.,.:...: 9

"

"

"'..

.

..

"

9 :...,-.,,..:

~.~,'-,',.'0 ,'r . ",-.: :.-,:.~~' .... "!i.i,:, . ". 9

,-.,...-...'~9*':'-T~": -;.,,,: 9

0 o,,I

"

9

"~'" "i.

:';"'~i,'._" 9

0

,".

20

40

60

.

80 100 nautical miles

2 . Two regions of 10,000 square nautical miles simulated according to the estimated model from block VSS (west of Spitsbergen, south) and KO (Kola), respectively. Fig.

33 the left figure there is a substantial increase in the empirical values, which is not reflected in the fitted function. This sudden increase can be interpreted as an inclusion of points from different clusters in the sum defining the empirical Lfunction, and is therefore of minor interest when investigating the individual clusters. The different sets of parameter estimates give rise to varying spatial whale distributions. The model estimated for block VSS (west of Spitsbergen, south) is an example of a model with many small clusters of high whale intensity, while the KOmodel (Kola block) is an example of a model with relatively few and large clusters in terms of radius, and of high whale intensity. Block SV (Svalbard area, southwest of the Spitsbergen blocks) has parameter estimates very different from the other blocks. The fitted model for this block has very few clusters of enormous radius but with very small whale density. When the models are to be interpreted, one must have in mind that the estimated whale distribution is a theoretical model for regions of unlimited area. The actual blocks then must be regarded as a smaller region, randomly placed in the infinite area of the theoretical model. However, the result is clearly that the whale distribution in the Northeastern Atlantic can be modelled as a Neyman-Scott process, with substantial clustering. Information on degree of clustering within the various survey blocks will be of help when designing future surveys. It is important to optimize the design in order to obtain abundance estimates of minimal variance, given total costs. Since variability depends on the degree of clustering, such information is helpful. From data gathered in a new survey, the Neyman-Scott model should be estimated for the individual survey blocks. These fitted models will be useful when the survey process is to be simulated, in order to estimate hazard probability parameters and effective search width [5].

References 1. 2. 3. 4.

Diggle PJ. Statistical Analysis of Spatial Point Patterns. London: Academic Press, 1983. Neyman J, Scott EL. A theory of spatial distribution of galaxies. Astrophys J 1952;116:144-163. Ripley BD. Spatial Statistics. New York: Wiley, 1981. Schweder T, Oien N, Hr G. Estimates of abundance of northeastern Atlantic minke whales in 1989. Rep Int Whal Commn 1993;43:323-331. 5. Schweder T, Hagen G. Effective search width in shipborne surveys of minke whales in the northeastern Atlantic: concepts and methods. Contribution to the International Symposium on the Biology of Marine Mammals in the Northeast Atlantic, Tromsr 1994. 6. Oien N. Abundance of the northeastern Atlantic stock of minke whales based on shipboard surveys conducted in July 1989. Rep Int Whal Commn 1991;41:499-511.

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9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand t3. Ulltang, editors

35

Use of mark-recapture experiments to monitor seal populations subject to catching Nils Oien and Torger Oritsland Institute of Marine Research, Nordnes, Bergen, Norway Abstract. During the period 1977-1994 about 17,000 harp seal pups have been tagged in the Greenland Sea and about 18,000 in the White Sea. The numbers of recoveries since 1977 have been approximately 870 and 50, respectively, from the two tagging operations. The recaptures indicate that Greenland Sea harp seals share common feeding grounds with both Northwest Atlantic and White Sea harp seals, albeit not breeding grounds. Recaptures made 1 year or more after tagging during the commercial catch operations in the Greenland Sea have been used to estimate pup production for several years during the period 1977-1991. It is evident from the analyses that the assumptions of mark-recaptUre estimates are violated, probably most seriously so by violations of the randomness assumption and non-uniform distribution of tagged animals. A mechanism of non-permanent emigration is suggested to explain the source of the bias, but no data are available to correct it although a truncation of recapture data assuming that temporary emigrated seals have returned to the population upon reaching sexual maturity might prove useful. Although mark-recapture experiments as a tool to estimate pup production certainly involve many problems which need to be accounted for, they should at least be considered as valuable supplements to other direct methods such as aerial surveys. In the case of ice-breeding seals in the Greenland Sea, the logistics and operational constraints on aerial surveys might even leave markrecaptures experiments the only feasible alternative at present. Key words: harp seals, mark-recapture, distribution, pup production, Greenland Sea, White Sea

Background Mark-recapture experiments might be useful in studying several aspects of the monitoring of seal populations subject to catching. When such experiments were introduced into the studies of ice-breeding seals in the North Atlantic long ago, the main purpose was to gain knowledge on migrations and distributions to resolve questions on population structure and stock identity. Norwegian taggings of harp (Phoca groenlandica) and hooded (Cystophora cristata) seals in the North Atlantic performed during the years 1951-1963 have been summarized by Rasmussen and Oritsland [ 1]. Moreover, known-age teeth from recaptured animals have been used to establish age reading procedures, for example the relationship between growth layer groups in dentine and absolute age in harp seals [2]. More recently, mark-recapture experiments have also been used for estimating pup production (e.g. [3]). Harp seals in the northern North Atlantic are separated into three populations based on the location of their breeding sites, namely the Northwest Atlantic, the Greenland Sea (traditionally known as the West Ice) and the White Sea (East Ice) populations. Pup production of the Northwest Atlantic population has successfully Address for correspondence: N. Oien, Institute of Marine Research, P.O. Box 1870 Nordnes, N-5024 Bergen, Norway.

36 been estimated from aerial surveys [4]. In the White Sea, regular surveys have been conducted over a long period of years to estimate the number of females in the breeding lairs there. In recent years, aerial surveys have also been conducted to estimate pup production of harp and hooded seals in the Greenland Sea [5] but with limited success due to the very difficult logistics associated with operations in that remote area as well as unfavourable weather and ice conditions in past years. A survey estimate of harp seal pup production in 1991 has been presented [6], but the status for the West Ice hooded seal population is still unknown. As part of the studies of harp and hooded seals in the northeast Atlantic extensive efforts have been made through the years to tag pups in the breeding lairs in the Greenland Sea, and since 1987 also as a collaborative effort with Russian scientists in the White Sea. Relatively little information has been returned from the hooded seal taggings, but for harp seals the marking programme has been most informative. This has motivated the use of the mark-recapture data from the Greenland Sea to try to estimate harp seal pup production in that area. In this paper we present a summary of earlier presentations [6-8] on the markrecapture experiments conducted on harp seals in the northeastern Atlantic, in some cases including updated information.

Tagging of harp seals in the Greenland and White Seas Since the late 1970s, Norwegian tagging efforts have concentrated on the breeding lairs in the Greenland Sea area and then mainly on harp seals. In the Greenland Sea, tagging has been conducted in breeding patches which are usually found within an area broadly delimited by the latitudes 70~ and 75~ and longitudes 20~ and 0~ Through most years, tagging has been carried out on an opportunistic basis as part of the research activities on board commercial vessels during their catch operations. This has restricted the possibilities of distributing tagging effort throughout the breeding season and area. During the years 1983-1988, governmental subsidies, partly allocated to facilitate research, made it easier to tag a larger number of pups. During the years 1989-1991, tagging was carried out from research vessels as part of the Sea Mammal Research Programme, mainly by using helicopters to distribute marking effort. In the White Sea a cooperative project with Russia on tagging harp seals started in 1987, and especially since 1989 a considerable number of pups have been tagged annually. The number of harp seal pups tagged in the Greenland Sea and the White Sea over the period 1977-1994 is shown in Fig. 1. The total numbers tagged within this period are about 17,000 and 18,000 for the two areas, respectively. However, while the last large tagging operation in the Greenland Sea took place in 1991, tagging has continued on an annual basis in the White Sea breeding lairs. As is seen in Fig. 2, the overwhelming number of recaptures have come from the Greenland Sea taggings, despite the fact that about the same total numbers of pups

37

45OO

I

4OOO

West Ice

r--1 East Ice

lo 350O O}

oo~, 3000 D. :3

2500

~20[~ i 15oo 1000 500

,,,, ,, , ,I,I i r-. r..

co ,-.. O,

o,-..

o co

O,

,co

O,

e~ co

O,

O,

co co ~

~ co

(~.

to co

,o co

C~,

O,

,I..,.,II,I .. v... co O-

co co ~

o, co O,

Year

o

,

,

,

8:

Fig. 1. Numbers of harp seal pups tagged in the Greenland Sea area (the West Ice) and in the White Sea (the East Ice) over the period 1977-1994.

have been tagged. Until now, a total of 870 recoveries has been obtained from the Greenland Sea taggings after 1977, and approximately 50 from the White Sea taggings. As a specific example, in 1991 approximately 3300 and 4200 pups were tagged in the Greenland and White Seas, respectively, but during the next catching season in 1992, 71 recaptures were made in the West Ice and only one in the East Ice. The difference is therefore only partly related to the fact that the West Ice tags have been available for recapture over a longer period; the most important factor is the very different age structure in catches of moulting harps from the two areas. While there is typically about 50% immatures in the moulting catches in the West Ice, this percentage is much lower in the East Ice.

180

.IdO '~ 140 :3

m West Ice East Ice

~. 120 0 U

I00

0

8O

.n c 13 =i... 0

60

6

,-

40 20 0

CO

C~

~-"

CO

~

CO

u') co

O,

,0

co

O-

co

co

~1"

(3-

Year

Fig. 2. Accumulated numbers since 1977 of recaptured harp seals by cohort tagged in the West Ice and in the East Ice.

38

Distribution patterns Most of the recoveries have been made during catch operations in moulting patches in the West and East Ices. The recoveries from outside these areas predominantly comprise very young seals (0 and 1 age groups). Although young animals are inexperienced and may be more prone to getting caught in fishing gear, they may also have other migration patterns and feeding ground preferences than the older harp seals. In particular, several seals have been recaptured off the northern coasts of Iceland 1-2 months after tagging in the Greenland Sea. As discussed by Rasmussen and Oritsland [1], this probably reflects a passive drift of pups with prevailing currents. Further evidence for distribution dependence on prevailing weather conditions during the pupping season is found in the recoveries from the 1984 cohort tagging in the Greenland Sea (Fig. 3). There have been five recaptures north of 65~ at West Greenland. One was tagged in 1985, the others were tagged in 1984, and all recaptured from 6 months to 3 years and 9 months after tagging. Moreover, a harp seal from the 1984 tagging was recaptured in 1990 at Newfoundland. Bad weather conditions with heavy storms from the northeast were reported during the pupping season in 1984, and five tagged pups were caught in fishing gear north of Iceland during May and early June that year. However, recoveries of 1 year or even older seals might indicate that they also actively migrate to areas not commonly thought to be part of their normal distributional range, and that they might stay in these areas for a while.

6000 80oo

4500 ,

3000 . ,

.

15oo ,,..., t .

00oo .. '

.

15oo 3000 .,.,,), . . . . z

4500 '

700o

6000 . .

80oo

70oo

o

6000

6000

5000

lrJ ~ 6000

....

i

4 500

'l 3000

......

I 1 500

5000 00oo

1 500

3000

4 5oo

6000

Fig. 3. Recapture sites of harp seals tagged as pups in the West Ice area in 1984 and recaptured outside the tagging area.

39

6000 8000 ......

4500

t

,,

3000 n

1 500 i ,

00oo i

1 500 ,,

3000 i

9

Q

}

4500

t

.,

6000

8000

~

O 7000

70oo

6000

6000

50oo~ 600o

I

45oo

I

3000

'

I

15oo

j.,...~_r" I 00oo

I

15oo

I

30oo

I

45oo

'500o I

6000

Fig. 4. Recapture sites of harp seals tagged as pups in the White Sea in the period 1987-1994. Recaptures outside the tagging area only.

Recoveries from outside the East Ice of harp seals tagged in the White Sea have above all been made along the Norwegian coastline, but also at East Greenland (Fig. 4). Most of these seals have been in their first year at the date of recapture. Several recaptures are worthy of further comment. In 1994 two harp seals tagged in the White Sea, one in 1990 and one in 1991, were caught in a moulting patch in the West Ice northwest of Jan Mayen. In 1993, a 2-year-old was caught late in the season southwest of Spitsbergen. Three young harps were also caught at East/Southern Greenland during the autumn of 1992. In Fig. 5 are shown recoveries of harp seals tagged in the Greenland Sea but recovered outside the breeding and moulting patches there. Most of the recoveries are located within a band from the Ammasalik area at East Greenland, passing through the coastal areas of Iceland, especially the northern coastline, and ending off the coast of northern Norway. Thus this might be considered a southern limit of their distribution although it might well be that also the southern part of East Greenland, extending into the Nanortalik area west of Cape Farewell, should be considered part of the normal range. The areas at East Greenland (generally the Denmark Strait) are apparently important feeding grounds both for harp seals breeding in the West Ice and at Newfoundland, and as mentioned above, even stragglers from the East Ice occur. Many recaptures, most of them young immature seals, have been reported from the Nanortalik and Ammasalik districts of seals tagged at the Front, Gulf and Davis Strait breeding patches. Similarly, frequent recaptures of young seals along the

40 6000 8000 t

45oo

30oo

,

,

1 5o0 ,., = ~,

00oo !

1 50o

30oo I

45oo !

60oo

8000

70oo

60oo-

EI

q~

- 6000

, i

50oo~ 6000

, 4 500

'

, 30oo

, ~ 1 500

i 00oo

, 1 500

i 30oo

, 4 500

5000 6000

Fig. 5. Recapture sites of harp seals tagged as pups in the West Ice area in the period 1977-199]. Rec a p t u r e s o u t s i d e the t a g g i n g a r e a only.

Norwegian coast, tagged in the Greenland Sea as well as in the White Sea, indicate that those two breeding units share common feeding grounds off northern Norway. The management implications are, however, unclear but do not seem to call for changes in current practice. There is no hard evidence from the mark-recapture experiments that the three North Atlantic harp seal populations (Northwest Atlantic, Greenland Sea and White Sea) intermingle at the breeding grounds, as we do not have recaptures of breeding animals. But there are records from the moulting season indicating that intermixing is probable: (1) in late May 1990 a seal tagged in the West Ice in 1984 was shot 40 miles southeast of Gray Islands, Newfoundland; (2) a seal tagged in the West Ice in 1977 was recaptured in late April in 1983 in the White Sea, presumably during moulting; (3) three harps tagged in the Greenland Sea, one in each of the years 1989-1991, were caught in 1992 during moulting in the East Ice; (4) the two harp seals tagged in the White Sea and recaptured in the West Ice (mentioned above). In addition, Rasmussen and Oritsland [ 1] report on a seal tagged in the West Ice in 1952 and recaptured in the White Sea in late April 1953. That there is little positive evidence of breeding mixing does not mean that exchanges do not occur at all, and we also know that very small migration rates are sufficient to erase genetic differences between populations. So far, studies of genetic variation of enzymes have not been able to reveal differences between West Ice and East Ice animals [9], while a study of underwater vocalizations of harp seals [10] concluded that harps of the northwest Atlantic population breeding in the Gulf of St. Lawrence were reproductively isolated from harps breeding in the Greenland Sea.

41 The large seal invasions in 1986-1988 were correlated with an increase in reported recaptures off the Norwegian coastline. All the 13 recaptures of current interest were of seals tagged in 1985 and 1987 (no seals were tagged in 1986). Of these, 11 were recaptured in winter (one in December, four each in January and February and two in March) and two in May. A rough calculation indicates that these recaptures would mean that West Ice harp seals would account for at most a few percent of the total catches of some 100,000 animals caught in fishing gear on the Norwegian coast. It is, however, difficult to be too specific about numbers of recaptures as indicators of availability of harp seals in different areas. Along the Norwegian coast, for example, some fishing effort is distributed throughout the year, but there are peaks in effort corresponding to spawning migrations of cod, saithe and haddock. These species seem to spawn from winter to early summer, with the most intense periods being from mid-March to May [ 11 ].

Pup production estimates By considering each marked cohort over the years as a separate entity from which catches and recaptures are removed, an estimate of the original pup production can be made based on a Petersen mark-recapture estimator. In addition to a series of assumptions underlying such an estimator [12], this approach also requires that the age distributions in the moulting catches are known, which introduces the question of whether the age samples are representative and whether errors associated with age determinations are negligible. Over the period relevant to the pup production estimates (i.e. from 1978 onwards), age samples have usually been collected annually onboard one or two commercially operating vessels. The absolute sample sizes have varied as have the catches, but usually the age samples have comprised 25% to

1613[]03 140000 0

o

1[x3[x30

o

8OOOO

:3 "13 c~

a. 6[XX~

a=

I I

F

4 ~ 20000 r'-.. r',.. (3".

I

co r~. O-

I

oo co O-

I

~ co O-

I

u~ co O,

I

l'.,. ao (3'.

I

ao ao (3-

I

Oao O-

I

(~

I

,.-

8:

Cohort

Fig. 6. Pup production estimates by year based on accumulated data up to and including 1993. The bars illustrate the 95 % confidence regions around the point estimates.

42 more than 90% of the Norwegian catches of 1-year-old and older seals. Evaluations of age determinations [13] based on dental annuli indicate that they might be subject to both measurement and systematic errors. Such errors have not been accounted for in the estimates presented here, but it is certain that measurement errors would add to the uncertainty in the pup production estimates, while it is not at all clear in which direction estimates would be biased if there are systematic errors of the kind mentioned above. Pup production estimates for harp seals in the Greenland Sea from recoveries accumulated over the years since tagging are summarized in Fig. 6 together with their associated 95% confidence intervals. The coefficients of variation for the estimates are typically less than 0.15 (range 0.10--0.27 [8]). Although the estimates span a wide range, they can hardly be told apart by their 95% confidence intervals with the possible exception of the highest estimates. A striking feature of these pup production estimates is that they seem to fall into two broad categories; one with estimates around 40,000-55,000 (seven cohort estimates) and one with estimates two to three times that, i.e. 100,000-115,000 (three cohort estimates). In Fig. 7, the cohort estimates have been plotted as a function of number of years with accumulated data. An apparent feature of the point estimates is that they tend to either increase or decrease for a specific cohort as years pass, while the ratio of marked to unmarked animals within a cohort should be constant if the underlying assumptions hold. There is reason to believe that the underlying assumptions of mark-recapture estimates are all undoubtedly violated to some extent. However, some of the assumptions, like those on population closure and that marking does not affect catchability, do not seem to influence the estimates. It might be suggested that tag loss could explain why we observe an increasing trend in most of the cohort estimates as data accumulate over the years. Unfortunately, double tagging experiments have not been carried out routinely to check the

--"--

250000

o

r-

._o 2[][][][]O 0 ::3 "13

o

15OOOO

\

--*--

O.

u9 l [ ] [ ] [ x ~ d'

E

..,,.,.,---"

I ,--

0,,I

I ~

I ~

a

~

D-'---o---o

o

I

I

I

I

I

I

I

I

I

I

i

I

t.O

',0

I'~

CO

0'.

0 ,,.-.

'-,,=-

0,I ,,=-

~ ,.-

~ i=.-

~ ,...

,,0 ,=-

Years

after lagging

1977

1978 1983

<>

1984

J'

1985

"9

1987

-"

1988

----o

1989

--x--

1990

--~:--

1991

Fig. 7. Cohort pup production estimates plotted as a function of years with accumulated data.

43 tag loss assumption. Bowen and Sergeant [3] made the conclusion from their data that loss of Rototags in harp seals occurs shortly after the tags are applied and subsequent losses are negligible, at least until the tags become worn or brittle. If the tags are lost only during the first year after application and at a probability of 0.25, all the serial estimates of, say, the 1978 cohort are positively biased, although this should not induce a trend in the series. The bias in the final 1978 estimate would then be about +20%. Bowen and Sergeant [3], who conducted a double-tagging experiment for Rototags on harp seals living under similar conditions in the Northwest Atlantic, estimated a tag loss rate of 0.051_ 0.0081 (SE) from data pooled over the first 3 months after tag application, with no evidence of increasing tag loss with time. If tag loss is to account for increasing serial trends in estimates, there has to be further tag losses as years pass. A topic closely related to tag loss is non-reporting of recoveries. During the period covered by this study, only a few vessels have participated in the catching operations. Institute personnel have often been on board these vessels, and the crew members have been well aware of the tagging programme. If not detected at an earlier stage, the tag is usually recognized when the seals are skinned. However, tags might occasionally be overlooked. During the catching season in 1989 we received reports on two occasions and in 1990 on one occasion that the remains of processed seal had been thrown overboard when it was recognized that it had a tag, however, too late to identify it. We therefore have to realize that some unknown positive bias is introduced to the estimates from such losses. Segregational behaviour and non-random mixing would be detrimental to markrecapture estimates. It is, however, unavoidable that the tagging process itself is a clustered operation thereby implying that each pup does not have the same probability of being marked. This is something which could be remedied if uniform mixing took place before recapture. Although the pooled recapture rates are similar for the different vessels operating within the same year, there are examples of clustering of recaptures, thus questioning the randomness assumption. While the discussions so far have failed to explain the observed results entirely satisfactorily, a mechanism that could possibly explain both serial trends (positive and negative) as well as large variations in estimates might be suggested. The basis for these speculations is that recoveries of harp seals tagged in the Greenland Sea have been reported from a vast area extending from Newfoundland to the Barents Sea, most of them young immature animals. Tagged animals from some of the cohorts (for example 1984) seem to have been more prone to go to other areas, depending on ice and prevailing weather conditions, as discussed earlier. For simplicity, we will assume that the pup production in any year can be divided into two components, one "normal" component and one "emigrating" component. The normal component is supposed to follow a normal migration cycle, whatever that might be, but at any rate returning to the West Ice next year for moulting. We suggest that the emigrating component at least temporarily (>1 year) leaves the normal cycle, and its members are therefore not available for the next year's moulting season. The assignment of a pup to either of these components takes place at birth, for example determined by

44 location in a breeding patch, where those at the outskirts might be more prone to be taken away by prevailing weather and ice conditions. It can then be shown [8] that: (i) the return rate of temporarily emigrated animals will determine how fast the mark-recapture estimates approach the true value over years; and (ii) the proportional distribution of tags on emigrants and non-emigrants during tagging determines whether the true estimate will be approached from below or above. This motivates the following procedures for alternative estimations of pup production: (1) given that the first year after tagging generally is the more sensitive to the assumptions of uniform mixing, estimates might be calculated by deleting data from the first year after tagging; (2) by the assumption that all emigrants will return to the population when sexually mature, estimates might be calculated by deleting data from all the 5 years following a tagging. Although with a scanty data series, such tentative estimates were presented by Oien and Oritsland [8] and indicate that the return rate is rather small the first year, but increasing with the years, giving some merit to the suggested method (2). Unfortunately, several years will pass until the cohorts arrive at the truncation point, and thereafter several years are needed to obtain reasonable sample sizes for the estimates. This procedure might of course be of limited value for management questions in the short term, but in a longer time perspective might prove to be the only way of monitoring on a regular basis such remote seal populations as the harp seals breeding in the Greenland Sea.

Acknowledgements The tagging of harp seals during the Sea Mammal Research Programme has been funded by the Norwegian Council of Fisheries Research (NFFR), project number 4001-701.304.

References 1. RasmussenB, Oritsland T. Norwegian tagging of harp and hooded seals in North Atlantic waters. Fiskeridir Skr 1964;13(7):43-55. 2. BowenWD, Sergeant DE, Oritsland T. Validation of age estimation in the harp seal, Phoca groenlandica, using dentinal annuli. Can J Fish Aquat Sci 1983;40:1430-1441. 3. Bowen WD, Sergeant DE. Mark-recapture estimates of harp seal pup (Phoca groenlandica) production in the Northwest Atlantic. Can J Fish Aquat Sci 1983;40:728-742. 4. Stenson GB, Myers RA, Hammill MO, Ni I-H, Warren WG, Kingsley MCS. Pup production of harp seals, Phoca groenlandica, in the northwest Atlantic. Can J Fish Aquat Sci 1993;50:24292439. 5. Oritsland T, Oien N. Aerial surveys of harp and hooded seal pups in the Greenland Sea pack-ice. In: Blix AS, WallOe L, Ulltang 0 (eds) Whales, Seals, Fish and Man. Amsterdam: Elsevier, 1995;77-83. 6. Oien N. Update of mark-recapture estimates of harp seal pup production in the Greenland Sea. Working paper to the Joint ICES/NAFO Working group on harp and hooded seals, WP SEA-47, Copenhagen, September, 1993.

45 7. Oien N, Oritsland T. Recaptures of harp seals (Phoca groenlandica) tagged as pups in the Greenland Sea; pup production and dispersion patterns. Working paper to the Joint ICES/NAFO Working group on harp and hooded seals, WP SEA-33, Copenhagen, October, 1991. 8. Oien N, Oritsland T. Using mark-recapture methods to estimate pup production of harp seals (Phoca groenlandica) in the Greenland Sea. ICES CM 1992/N:10- Ref.D. 9. Meisfjord J, Fyllingen I, N~evdal, G. A study of genetic variation in Northeast Atlantic harp seals (Pagophilus groenlandicus). ICES CM 1991/N:5. 10. Terhune JM. Geographical variation of harp seal underwater vocalizations. Can J Zool 1994;72:892-897. 11. Bergstad OA, Jr T, Dragesund O. Life history and ecology of the Gadoid resources of the Barents Sea. Fish Res 1987;5:119-161. 12. Seber GAF. The Estimation of Animal Abundance and Related Parameters. London: Charles Griffin, 1982. 13. Oien N. Age compositions in 1982 to 1984 samples from breeding and moulting harp seals in the West Ice, with an evaluation of the age determinations. Working paper to the ICES Working group on harp and hooded seals in the Greenland Sea, SGS WP 12, Copenhagen, October, 1987.

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9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCe and ~. Ulltang, editors

47

E s t i m a t i o n of grey seal Halichoerus grypus p u p p r o d u c t i o n f r o m o n e or m o r e c e n s u s e s Svein-HS.kon Lorentsen and O y v i n d B a k k e Norwegian Institute for Nature Research, Tungasletta, Trondheim, Norway Halichoerus grypus~ the spread of pupping dates exceeds the length of the stay ashore of individual pups. Thus, no single census will include all pups born. To cover the whole pupping season is time consuming and expensive. We therefore developed a method, based on maximum likelihood estimation, to estimate annual pup production from one or more censuses in which the pups are classified according to age. The results of using this method on single censuses in the 1993 breeding season in the Froan Nature Reserve, Central Norway, are presented. The total pup production estimates are within 20% of the estimate based on all 8 censuses, except for those based on single censuses, which deviated more. A b s t r a c t . In the grey seal

K e y w o r d s : maximum likelihood, age group duration, stage structure

Introduction

Proper management of animal populations requires rational methods for monitoring population development. The methods used should give a confident estimate of the population size, or of the fraction that is being monitored, and should be easy to carry out, cheap, and not too time consuming. For seals the only fraction of the population that can be accurately counted is the number of pups born. Seals use the same pupping grounds annually and the number of pups born each year can be accurately counted, and compared with previous counts. However, in the grey seal Halichoerus grypus, the spread of pupping dates exceeds the length of the stay ashore of individual pups, and thus, no single census will include all pups born. To cover the whole pupping season is time consuming and expensive. We therefore developed a method for estimating annual pup production from a limited number of censuses in which the pups are classified according to age. After birth grey seal pups go through five age groups defined by external morphology [ 1]. They typically migrate to sea at 3-4 weeks old. A procedure for estimating grey seal pup production from a single census was given by Radford et al. [2] who assumed deterministic age group durations. We wanted to develop a procedure where the number of censuses may be greater than one, as the breeding season seems to be too long at Froan to obtain sensible estimates from one census. We also wanted to take into account the variation in age group durations and take advantage of today's computing power, which makes maximum likelihood estimation possible.

Address for correspondence: S.-H. Lorentsen, Norwegian Institute for Nature Research, Tungasletta 2, N-7005 Trondheim, Norway. Tel: (+47) 73 580500. Fax: (+47) 73 915433. Emaih [email protected].

48 Ward et al. [3] gave a similar estimation procedure, but without going into detail about how to utilize knowledge of age group durations or applying the estimation procedure to data.

Materials and Methods Data were collected in Froan Nature Reserve (64~ 9~ Central Norway, during the breeding seasons of 1990--1993. Froan is one of the main grey seal pupping grounds in Norway and each year about 300 females gather to give birth to their single pup [4]. The whole area used by breeding grey seals was searched approximately every 5 days. All pups encountered were identified by a Rototag mark in their hind flipper and were aged according to Kovacs and Lavigne [1]. In addition, the number of days since birth was determined for pups that were less than 3 days old. We used a procedure for estimating the total grey seal pup production during a breeding season based on one or more censuses in each of which counts of pups of each defined age group are obtained [5]. The procedure resembles the Bellow and B irley model described by Manly [6]. Estimates were obtained for the peak time for pup production, the spread of the breeding season, the observability (invisibility due to stay at sea or death), and the total number of pups produced. We needed to know the distribution and parameters for the duration of the first four age groups. Parameter estimates were obtained by a method described by Bakke and Lorentsen [5]. It is based on repeated counts of marked pups throughout the breeding season, and thus requires a great deal of field work. It seems reasonable to assume that the duration of stay in each age group varies less from season to season than the four parameters mentioned in the preceding paragraph, so that these estimates may also be used in other years. The age group duration parameters of the fifth age group are not used because it is difficult to obtain estimates for the duration of this age group since pups have migrated to sea and are not easily observable when this age group ends. However, the first observation of a pup in the fifth age group is used when estimating the parameters of the fourth age group. We have assumed that the time of birth To is normally distributed with expected value # and standard deviation or. Thus # may be thought of as the "time of peak pup production" and cr as the "spread of the breeding season". Further, we have assumed that the probability of a pup being observable t days after birth is qt, where q is a parameter that may be less than 1 because the pup is at sea or dead. The motivation for choosing this probability is that the probability of being observed t days after birth would be qt if all live pups are observed and they have a constant probability q of surviving from one day to the next, and it is also reasonable to believe that invisibility due to pups entering the sea increases as they get older. The age group durations Tj, 1 < j < 4 were assumed to be log-normally distributed with parameters as estimated in the Results using the method of Bakke and Lorentsen [5].

49 The uncertainty of the estimates may be assessed by bootstrap analysis [7]. For each census, a new census is then simulated by drawing the same number of pups as in the original census. Each pup is assigned a specific age group with probability equal to the ratio of pups in this age group to the total number of pups in the original census. Then the parameters are estimated again on the basis of this simulated data set. This procedure is repeated a number of times, so that a number of bootstrap estimates of the parameters are obtained. The sample standard deviations of those parameters are estimates of the standard deviations of the original ones.

Results

Pup production The data from the 1993 breeding season were organized into 8 census data sets (Table 1). Information on single pups obtained by marking was thus lost. Several subsets of the censuses were used to estimate pup production parameters (Table 2). When all 8 censuses were used to obtain estimates, a total production of 306 pups was estimated. We simulated birth, aging and observability according to the model (Table 1) to see if deviation from the model in the original data had any effect on the estimates (Table 2).

Age group durations The age group durations were assumed to be log-normally distributed. The expected values and standard deviations were estimated to 4.4 _+ 0.8, 2.4 _+ 1.4, 4.5 _+ 4.0 and 6.5 _ 2.5 days for age groups 1, 2, 3 and 4, respectively, using data from 465 pups.

Table 1. Results of eight censuses made during 45 days (with the last count on day 0) at Froan in 1993 Census no.

Day no.

No. of pups in age group 1

2

1

-45

8 (20)

2 3 4 5 6 7 8

-41 -37 -31 -25 -14 -8 0

16 (22) 39 (24) 48 (41) 21 (37) 9 (12) 4 (3) 3 (1)

Numbers in parentheses were simulated by the model.

14 (2)

10 (10) 15 (13) 19 (11) 31 (13) 14 (11) 4 (2) 2 (1)

3

4 0 (4)

5 (7) 15 (7) 18 (10) 19 (16) 18 (9) 9 (10) 4(2)

0 (1) 0(3) 4 (5) 5(8) 21 (13) 18(17) 22 (9) 8 (5)

50 Table 2. Estimates of expected time of birth (/z), standard deviation of this (a), observability parameter (q) and total pup production (N) for various subsets of counts made at Froan in 1993 (Table 1)

No. of censuses

Censuses included

Parameterestimates /z

a

q

N

All real All simulated 2, 4, 6, 8 1, 4, 7 3, 6

-32.2 -33.0 -30.9 -31.8 _ 0.4 -33.5

10.6 11.3 10.5 9.9 _+0.4 9.9

0.93 0.90 0.91 0.93 _ 0.01 0.92

1

3

-41.3

3.4

1.00

1 1 1

4 5 6

-35.2 -32.3 -22.7

3.4 3.3 3.6

1.00 1.00 1.00

306 312 348 328 _ 39 366 84 105 95 61

8 8 4 3 2

An estimate based on the simulated counts is also included. Bootstrapping was used to estimate the standard deviation for the estimate based on 3 censuses.

Discussion

In 1993, a total of 226 pups were found in the Froan Nature Reserve. However, due to a long period of bad weather during the peak of the pup production, it is highly likely that we missed several pups which probably were washed into the sea and drifted out of the study area. Also taking into consideration the probability of failing to see a pup during its stay ashore, the estimated total production of 306 pups using all 8 censuses seems reasonable. All estimates based on more than one census are within 20% of the estimate based on all 8 censuses. In contrast, the mean for the single census estimates (86 pups) is only one-fourth of the total estimate and no single census estimate is more than onethird of the total estimate. This may be due to both the extended spread of pupping dates at Froan, and/or inaccuracies in our model. Nevertheless, these results clearly indicate that total pup production estimates based on a single census should be avoided. To obtain more accurate estimates, it is necessary to know more about the distribution of births and the observability of pups, although the closeness of the estimates derived from the simulated censuses to those from the original data indicates that the estimates are quite robust. No assessment of the age group duration parameters has been made, and more accurate information on the distribution of the age group durations should be acquired before using the pup production estimates for management purposes. The procedure used for age group parameter estimation is extremely computer intensive. The duration of age groups could, thus, be better determined by following individual pups, e.g. by radiotelemetry. The observability q is influenced by mortality, invisibility when the pups are at sea and when being overlooked by the observer. Clearly it would have been desir-

51 able to have separated the first factor from the last two. Again, more information could be obtained by studying individual pups.

Acknowledgements This study was financed by the Norwegian Research Council through grant no. 4001.713.030. We want to thank Steinar Engen for helpful discussions. We are thankful to T. Anker-Nilssen who made helpful comments on the manuscript and to R. Binns who improved the English language. Thanks are also due to the field personnel G. Dahl, H. Hoel, J.M. Meland, M. Olsen, T. Opdahl, T. Rodahl, P.T. Smiseth, O. Vie, and I.J. Oyen. Part of the data was collected by M. Ekker, B.M. Jenssen and D. Vongraven from SINTEF/UNIMED and the University of Trondheim.

References 1. Kovacs KM, Lavigne DM. Growth of Grey seal Halichoerus grypus neonates: differential maternal investment. Can J Zool 1986;64:1937-1943. 2. Radford PJ, Summers CF, Young KM. A statistical procedure for estimating grey seal pup production from a single census. Mammal Rev 1978;8:35--42. 3. Ward AJ, Thompson D, Hiby AR. Census techniques for grey seal populations. Symp Zool Soc London 1987;58:181-191. 4. Wiig 0, Ekker M, Ekker T, RCv N. Trends in the pup production of grey seals Halichoerus grypus in Froan, Norway, from 1974 to 1987. Holarctic Ecol 1990;13:173-175. 5. Bakke 0, Lorentsen S-H. Estimation of offspring production from a limited number of stage structured censuses. Manuscript. 6. Manly BFJ. Stage-structured Populations: Sampling, Analysis and Simulation. London: Chapman and Hall, 1990. 7. Efron B, Tibshirani RJ. An Introduction to the Bootstrap. New York: Chapman and Hall, 1993.

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9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand O. Ulltang,editors

53

Increased accuracy in the estimation of harp seal (Phoca groenlandica) abundance in whelping patches V.I. Chernook, V.A. Potelov and N.V. Kuznetsov Knipovich Polar Research Institute of Marine Fishery and Oceanography (PINRO), Murmansk, Russian Federation A b s t r a c t . The course and results of experimental aerial surveys of harp seal whelping patches con-

ducted in March 1993-1994 in the White Sea u s i n g an MI-8 helicopter are presented. Recommendations concerning improvements of the on-board equipment, the method of aerial survey and possible automation of the aerial counting work are given. Key words: seal, white coat pup, air survey, infrared, transect

Introduction

Aerial surveys of harp seal whelping patches are part of the complex research which also includes the distribution and migration analysis (geographical aspects) and biological aspects, and which leads to a rational use of the stocks of this animal. Aerial surveys have been used to estimate the number of harp seals in the White Sea since the middle 1920s. Since the early 1960s scientists have surveyed only suckling females, while estimations of the number of pups themselves, the main indication of the seals' stock condition, have not been made. While suckling their pups the females need to feed themselves and must occasionally leave their pups. According to observations made by Popov [1] in early March (when the patches are formed and the suckling begins) 70-80% of females are in the water in the early morning hours on bright sunny days. During the second part of the day no more than 30--40% stay in the water. After 1 or 2 weeks of suckling, the time the females spend in the water increases greatly. This may cause aerial surveys when only females are counted to err by 50% or more. The number of females on the ice also decreases during snow-storms, or when the ice breaks up [1], but increases during ice compressions. Taking into account that the number of suckling females varies greatly both during a 24-h daily cycle and during the whelping period, the harp seal researchers in the White Sea have introduced a correction factor [2]. Thus it is assumed that 80% of females are on the ice and 20% are in the water during aerial surveys conducted in early March. The validity of the correction factor has not yet been confirmed. Estimates based on aerial surveys of harp seal whelping patches during the first days of suckling have some advantages compared to estimates made at other periods of their life (e.g. when the suckling period is over or on the moulting grounds). Then

Address for correspondence: V.I. Chernook, Knipovich st. 6, Murmansk 183763, Russian Federation.

54 the density of the seals' patch is at its highest; furthermore, the age and sex structures of the patch are stable for a long time (females + pups + males). After approximately 3 weeks the whelping patches break up leaving only the pups on the ice. Because of the ice drift, however, the density of pups in the patch greatly decreases, thus resulting in increases in the cost of flights designed to survey them. For that reason we set out to improve the method of aerial surveys of seals in the suckling period. The main point of the work was to improve the accuracy in the methods used to estimate harp seal pup numbers. The means was a survey where the seals were synchronously recorded visually and in the IR part of the spectrum. The stages of the work were: data collection; determination of optimal survey conditions by considering the technical specifications of the on-board equipment; improving the methods for a synchronous survey using the visual and IR part of the spectrum; simultaneous processing of the shots and images obtained from the two parts of spectrum.

Methods

and

Equipment

In March 1993 and 1994 experimental aerial surveys of harp seal whelping patches were carded out using an MI-8 helicopter in the entrance of the White Sea. Technical facilities used in 1993 included (see Table 1): "Vulkan" thermovision set; aerial photo camera AFA-TES- 10; video set JVC GF-500. It is necessary to emphasize that, unlike photo and video cameras, the thermovision set surveys the space by linear scanning with a narrow momentary sighting angle perpendicular to the flight direction. Therefore the given combined survey may be considered as a "quasisynchronous" one.

Table 1.

Specifications of the equipment used in the 1993 survey

Sensor

Thermovisiona Video Photo

Sight angle

Track width

Ground resolution (m)b

(~

(km)

Across

Along

80 40 84

0.72 1.34

1.29

0.40 0.33 0.15

0.4 0.4 0.2

aThermovision-scanner with scanning frequency of 180 lines/s, sensitivity 0.3~ bGround resolutions are given for a flight altitude of 200 m.

55

Table 2. Specifications of the equipment used in the 1994 survey. Sensor

Thermovisiona Video Photo

Sight angle

Track width

Ground resolution (m)b

(o)

(km)

Across

Along

20 40 44

0.35 0.72 0.80

0.26 0.33 0.08

0.3 0.4 0.1

aThermovision sensitivity is 0.2~ bGround resolutions are given for a flight altitude of 200 m.

Technical facilities used in 1994 included (see Table 2): "Insight 80" series thermal imager; video camera JVC TK-880; videocassette recorders JVC; aerial photo camera PA-39; lap-top computer Toshiba; GPS positioning system Raytheon R-900. To determine the number of whitecoats reliably, the survey should be conducted when the majority of the females have whelped. The experiments were conducted when the number of whelped females exceeded 95%. The experiments both in 1993 and in 1994 were carried out using the following design: 1st flight: searching for the whelping patch and choosing the optimum flight parameters (height and speed; synchronous surveys were carried out successively from 400, 200 and 100 m height, speeds ranging between 100 and 180 km/h). 2ndflight: surveying the seal patch along a net of transects at a predetermined height and speed. In 1993 the flights were carried out on 5 and 7 March, and in 1994 on 8 and 9 March. With the equipment used in 1993, the optimum height and speed was 100 m and 180 km/h. The corresponding values in 1994 were 200 m and 130 km/h. The essence of the method is synchronous surveying of the harp seal whelping patches in the visual and IR parts of the spectrum followed by joint processing of the images, when the number of whitecoat pups lying on the ice is determined. Interpretation of the visual images was unproblematic because this information is the same as obtained through the human eye. Thermal images were formed by three main objects: snow-and-ice cover, water and seals. Snow-and-ice cover and water were hardly distinguishable on IR images and served as background for the surveyed seals. These objects had a temperature close to that of the air which at the beginning of March is usually - 3 to -5~ It is known that the temperature of adult seals and whitecoat pups substantially exceeds 0~ Such thermal contrast is quite enough to image them distinctly by means of thermovision techniques (Fig. 1). -

-

-

-

56

a).

b).

Fig. 1. Computer image processing of an IR frame (there are 3 harp seals on the frame) (a) incoming IR frame; (b) IR frame (a) after image processing. 1, Water; 2, ice; 3, harp seals.

-

-

-

-

The succession of analyses of the photographs obtained was as follows: general analysis of images, sorting of the films according to tracks and heights; selection of image areas and ice-floes surveyed in both the visual and IR parts of the spectrum; identification and calculation of seals on the photos in the visual part of the spectrum; joint analysis and calculation of seals in both the visual and IR parts of the spectrum (Fig. 2);

Fig. 2. (1) Video image; (2) processed IR image; bright (warm) spots on IR image are seals; clark (cold) background is ice and water.

57 studies of spatial distribution and determination of errors in seal calculations; In the 1993 experiment, the number of seals was determined by counting the animals on the same ice floes on visual and IR images. The seal count was made in the following order: counts of adult seals on the photos; counts of total number of seals on the IR images; counts of adult seals and whitecoat pups while analysing both photo and IR images. To ensure the reliability of the survey results, video films were also used. The 1994 results have not yet been analysed, so that the following only presents the 1993 results. -

Results

In Table 3 the results of aerial survey of seals on three tracks from a height of 100 m are presented. On these tracks some ice-floes with clearly visible edges were selected and on these ice-floes seals were counted on photos and IR images. A total 272 females and 60 males (332 in total) were counted on the three tracks. According to presently used methods of estimation of seal numbers in aerial surveys, these data correspond to 272 whitecoat pups, 272 females and 60 males (totally 604 seals). Use of a correction factor of 20% [2] yields a corrected number of seals of 340 females, 60 males and 340 pups; in total 740 animals. On IR images 522 seals were counted. Thus the results obtained by one method alone may include large errors. Combination of the two methods, however, may help to increase the accuracy. As can be seen from Table 4, the number of adults remained the same, 332, but the distribution among males and females was altered when the photos and IR images were analysed in combination. In particular, two new groups of seals appeared: single ones (without pups), which were classified as "females with pups" according to Table 3, and dead ones (no spots on IR image). The number of females with pups decreased from 272 to 188 (by 31%), the number of males increased from 60 to 72 (by 20%). In the combined analysis of the images, 69 pups lying on ice-floes without Number of harp seals detected during photo and IR surveys on whelping patches in the White Sea, 6.03.93, 100 m height (corrected numbers include a surplus of 20% [2]) Table 3.

No. of track 10

Visual survey Females 96

11

109

12 Total Corrected

67 272 340

IR survey total no. of seals

Males

Total

9 27 24 60 60

105

194

136

194

91 332 (604) 400 (740)

134 522

58 Table 4. Results of harp seal numbers estimated on whelping patches in the White Sea obtained by combined analysis of the photo and IR images obtained 6.03.93, 100 m height No. of track

10 11 12 Total

Adults

Pups

Total number

Females with pups

Single seals

Males

Dead or ice-floes

With mothers

Single

88 62 38 188

6 47 17 70

9 27 36 72

2 2

88 62 38 188

28 28 13 69

221 226 142 589

mothers were detected. The total number of pups established by the visual and IR method of survey on the three tracks equalled 257 which was 6.5% less than in Table 3 (or 32% less if the correction factor is taken into consideration). The data of Table 4 also let one determine the number of females in the water, which is equal to the number of single pups. On track 10 they amount to 24.1%, on track 11, 31.1% and on track 12, 25.5%.

Discussion When conducting such surveys, the spatial resolution of the survey equipment is of utmost importance. The ground resolution depends on the angle of view, the equipment's angular resolution and the altitude of the flight. Due to some technical problems the resolution of the thermovision equipment obtained during the present survey was less than that of the video camera and included 200-250 lines in the frame. The technical characteristics of the infrared imager are determined when conducting the survey (video + IR). An infrared imager temperature sensitivity of 0.1 o, a spatial resolution of 300-400 elements in the line, and an angle of view of 15-20 ~ are desirable. The following errors appear in processing of the infrared images: a seal scared away from the ice in the patch can be seen as a luminous spot (heated ice), which may be erroneously interpreted as a second seal; a seal only observed in the water has a very weak infrared contrast and may be not detected in the infrared image, but may be observed in the video image; pup and female lying together may be seen as a large spot; pups lying under standing ice-floes are not observed in the infrared still pictures. The main shortcoming of the infrared equipment (infrared imager "Vulkan") used in 1993 was that the film recording the infrared information was not suitable for operational analysis and correction of the survey results in situ. Furthermore, its sensitivity was insufficient. The infrared equipment used in 1994 had better sensitivity, with an output signal of television standard, making it possible to observe the results

59 of the survey during the flight at once. Additionally, the use of a satellite navigational system made it possible to have a precise spatial attachment of all the data from the survey and to show the route of the flight on the monitor screen. The 1994 results will be published at a later stage. Synchronous video and IR surveys of harp seals whelping patches allow the determination of the number of seals on the ice. A combined image analysis in the visible and IR parts of spectrum increases the accuracy of the number of adult seals determined. Using this method, the pupless females (sterile and non-pregnant), separate males, females moving inside the patch, dead animals (or spots resembling animals) and pups separated from their mothers may be detected on the photos.

Acknowledgements The authors thank the on-board operator V.Yu. Bogomolov for his participation in the experimental survey of the seals. We also thank A.B. Gvozd for his aid in data processing and forming the present report.

References 1. Popov LA. On biological substantiation of aerial photography of harp seals' whelping patches in the White Sea (in Russian). In: IV All-Union Conference on Studying of Sea Animals, Kaliningrad, Moscow, 1969;102-105. 2. Timoshenko Yu K. Observations on harp seal females' behaviour on whelping patches. In: Coil "Sea mammals". Moscow: USSR Academy of Science, 1978;323-324 (in Russian).

This Page Intentionally Left Blank

9 1995 ElsevierScience B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. Wallce and O. Ulltang,editors

61

Haul-out behaviour of the Norwegian harbour seal during summer Randi R o e n and Arne BjCrge Norwegian Institute for Nature Research, Blindern, Oslo, Norway Abstract. Background: the haul-out behaviour of the Norwegian harbour seals had previously not been studied thoroughly. Methods: the intent was to examine the haul-out behaviour in relation to three factors: (1) the diel light cycle, (2) the tidal cycle and (3) the interaction between these two cycles. Observations were therefore made on days with low tide around noon, and later repeated on days with high tide around noon. The number of hauled-out seals was counted every hour, day and night. Three places were selected on the basis of their differing characteristics in diel light and tidal variation during summer. Results: the diel light cycle, the tidal cycle, and the interaction between the two cycles, all showed a significant relation with the haul-out behaviour both at Froan and in Kongsfjord. At Hvaler, the haulout pattern was very inconsistent, with large fluctuations from day to day. Conclusions: the higher numbers of hauled-out seals in the daytime, especially in Froan but to a certain degree also in Kongsfjord, may indicate that most seals feed at night. Similarly, the higher numbers hauled out at low tide, especially in Kongsfjord but also in Froan, may indicate that most seals feed around high tides. At Hvaler, the seals may be less at ease by the great deal of commercial and recreational boat traffic. K e y w o r d s : seals, diurnal activity, time series, haul-out pattern

Introduction

Tide is often thought to be a highly important factor influencing the haul-out behaviour of harbour seals [1-3]. Others, however, have found time of day to be the most significant factor [4,5]. Prior to this study, only occasional observations of the haul-out behaviour of harbour seals have been made in Norway. These observations suggest that harbour seals in the Oslofjord haul out in the very early morning, while the seals in West- and North-Norway haul out at low tide [6].

Materials and Methods Three harbour seal colonies at three different locations were selected for the study. At all three locations, haul-out sites are accessible throughout the tidal cycle. Hvaler (59~ 10~ has the smallest tidal amplitude, on average <30 cm, but a large variation in the diel light during summer; the sun is down for about 5 h. Kongsfjord (70~ 29~ has the opposite characteristics, 24 h daylight and an average tidal amplitude around 200 cm, while Froan (63~ 9~ comparably has a moderate tidal amplitude, on average about 130 cm, as well as a moderate variation in the diel light; the sun is down for about 3 h. At Froan a maximum of 100 hauledAddress for correspondence: R. Roen, Norwegian Institute for Nature Research, P.O. Box 1037, Blindern, N-0315 Oslo, Norway.

62 out harbour seals was observed; at Kongsfjord the maximum was 45, and at Hvaler it was 42. According to the principles described by Heide-JCrgensen [7] and Calambokidis et al. [8], the harbour seals were monitored from an elevated view point and counted more than once, before a mean was made. The variation from this mean was generally found to be +_1 seal. The diel light and tidal cycles may interact, so that different combinations of these cycles may influence the haul-out behaviour differently. In order to assess possible interactions, each harbour seal colony was monitored for 2 x 24 h on two occasions with low tide around noon (later referred to as "out of phase" sequences, since the tide falls as the sun rises in the daytime), and similarly on two other occasions with high tide around these hours ("in phase" sequences). The tidal state was recorded, in addition to the time of day, as a measure for the diel light cycle. The seals were viewed with Hartmann Wetzlai 25 x 80 binoculars on a tripod, Fuji 7 x 50 binoculars and a Mirador 60m/m spotting scope with a 20--45 x zoom lens. The official tide tables for the Norwegian coast for 1991 (produced by Norges SjCkartverk) were used to find the high and low tides. Times of sunset and sunrise were obtained from the tables for 1991 of the sun's height and azimuth (produced by Department of Astrophysics, University of Oslo). An analysis of discrete time series by searching for hidden periodicities with periodogram analyses was applied according to Wei [9]. A regression estimation technique was used to assess the deterministic periodic components "diel light" with frequency 1/24 day -~ and "tidal height" with frequency 2/24 day -~. The significance of the contribution of a given frequency to the total variability was tested by an ordinary F-test by comparing with the variability not accounted for by the model, i.e. the residual variability.

Results

The diel light cycle The diel light cycle did show a significant effect on the haul-out behaviour of the harbour seals both at Froan (P < 0.05, r2=0.38) and in Kongsfjord (P < 0.05, r 2 = 0.09) (Fig. 1). At Froan the mean number of hauled-out seals peaked around midday (1000-1700 h; 39-49 seals) and was lowest at night (2300-0500 h; 4-9 seals) (Fig. l a). In Kongsfjord the mean number was slightly larger in the morning (0500-1000 h; 17-21 seals), than in the evening and night (6-13 seals) (Fig. lb). The r 2 for Kongsfjord is conspicuously low. However, by applying hourly means based on observations from all days instead of the individual hourly observations, one finds that r 2 rises to 0.97 for Froan and 0.72 for Kongsfjord. The variance connected with these hourly means, however, conceals both a tidal and a stochastic component. At Hvaler, the diel light cycle did not show a significant effect on the haul-out behaviour of the harbour seals (P = 0.12). There were great fluctuations in the daily haul-out pattern.

63 a

b 80

.

Ill

~o

Sea,, ,o

20 . . . . . . o ~~.~.a~

l

~

'"

ii II

II

'l

h~.t.

sea,s

8~t

60

I

401 20

I ,,llllll~l,

.

.

.

.

,,

0

Time

Time

Fig. 1. Mean numbers of hauled-out seals ( , ) , 1 standard deviation, a n d the numbers predicted by the model of the diel light cycle ( - - ) , in relation to time of day, for Froan (a) and Kongsfjord (b). For Froan Ypred. = 25.5 + 22.1 cos 211/24 (t - 14), r 2 = 0.38; for Kongsfjord Ypred. = 14.1 + 5.1 cos 2H/24(t 8), r 2 = 0,09.

The tidal cycle The tidal cycle also showed a significant effect on the haul-out behaviour of the harbour seals both at Froan (P < 0.05, r 2 = 0.26) and in Kongsfjord (P < 0.05, r 2 = 0.33) (Fig. 2). At Froan the mean number of hauled-out seals was lowest around high tide (7.9 seals) and peaked at low tide (41.2 seals) (Fig. 2a). The same was true for Kongsfjord (high tide 3.2 seals; low tide 23.9 seals) (Fig. 2b). When applying the hourly means, one finds that r 2 rises to 0.94 for Froan and 0.87 for Kongsfjord. The variance connected with these hourly means conceals both a diel light and a stochastic component. At Hvaler, the tidal cycle did not show a significant effect on the haul-out behaviour of the harbour seals (P = 0.08). There was no distinct haulout pattern related to the tidal cycle.

Analysis combining the diel light and tidal cycles Both at Froan and in Kongsfjord, the diel light and tidal cycles did show a significant coupled effect on the haul-out behaviour of the harbour seals, both in the "out of

a

80

b

I

60

80 60

Seals 40

Seals 40

20

20

0

1

2

3 4 h.t.

5

6

7

Tidal state

8

9 10 11 12 I.t.

l

t

0

l/_/-_c 1

2

3 4 h.t.

5

6

7

Tidal state

8

9 10 11 12 I.t.

Fig. 2. Mean numbers of hauled-out seals ( , ) , 1 standard deviation, and the numbers predicted by the model of the tidal cycle ( - - ) , in relation to tidal state, for Froan (a) and Kongsfjord (b). For Froan Ypred. = 25.2 + 18.5 cos 2 H / 1 2 ( t - 8), r 2 = 0 . 2 6 ; for Kongsfjord Ypred. = t3.9 + 10.1 cos 2 I I t 1 2 ( t - 9), r 2 = 0.33. (h.t., high tide; l.t., low tide.)

64

Seals

100

100~

80

80=

60

Seals

40

i

601

40 !

i

20

ol

0 l.t.

h.t.

Tim~.t.

h.t.

h.t.

Seals

I.t.

Timeh.t.

c~

I.t.

4 0 1 ~

40 30

o

. . . . . . . . . .

I

20

Seals

30 20

10 o

o

I.t.

o

o

o

h.t.

.o..~..~..o..~.

Time

I.t.

.~..o. c~

h.t.

I

0

c~

h.t.

I.t.

Time

h.t.

I.t.

Fig. 3. Mean numbers of hauled-out seals ( , ) , 1 standard deviation, and the numbers predicted by the model combining the diel light and tidal cycles (m), in relation to time of day and high and low tides, for the "in phase" sequence (a,c) and the "out of phase" sequence (b,d) at Froan (a,b) and in Kongsfjord (c,d). In (a) Ypred. = 31.3 + 35.1 cos 2 I I / 2 4 ( t - 13) + 18.1 cos 2I'I/24(2t), r 2 = 0.86; in (b) Ypred. = 21.7 + 17.0 cos 2 I I / 2 4 ( t - 16) + 16.0 cos 2 I I / 2 4 ( 2 t - 13), r 2 = 0.69. In (c) Ypred. = 10.6 + 4.2 cos 2 F l / 2 4 ( t - 10) + 6.9 cos 2 I I / 2 4 ( 2 t - 2), r 2 = 0.32; in (d) Ypred. = 17.7 + 6.6 cos 2 F l / 2 4 ( t - 7) + 11.5 cos 2 F l / 2 4 ( 2 t - 14), r 2 = 0.56.

phase" sequences (Froan, P < 0.05, r 2 = 0.86; Kongsfjord, P < 0.05, r z = 0.32) and in the "in phase" sequences (Froan, P < 0.05; r 2 = 0.69; Kongsfjord, P < 0.05, r 2 = 0.56) (Fig. 3). At Froan, the highest numbers of hauled-out seals were always observed in the "out of phase" sequences, at which time they occurred around noon (Fig. 3a). The lowest numbers in the "out of phase" sequences could be found at the high tides and at night (2000-0500 h). The diel light component explained 68% of the total variation in the observed number of hauled-out seals, while the tidal component explained 18%. Utilisation of hourly means raises the r 2 for the diel light component to about 0.77, and the r 2 for the tidal component slightly to about 0.20. In the "in phase" sequences, the number of hauled-out seals peaked around the afternoon low tide, with fewer seals hauling out around the morning low tide (Fig. 3b). The lowest mean numbers could be found at night (2300-0300 h). The diel light component explained 36% of the total variation in the observed data, and the tidal component explained 33%. Again, employment of hourly means increases the r 2 for the diel light component to about 0.51, and the r 2 for the tidal component to about 0.46. In Kongsfjord, the number of hauled-out seals generally peaked at or around the two low tides in the "out of phase" sequences (Fig. 3c). However, the noon low tide peak was much larger and more distinct than the midnight one. The diel light component only explained 9% of the total variation in the observed data, while the tidal

65 component explained 23%. Utilisation of hourly means increases the r 2 for the diel light component to about 0.23, and the r 2 for the tidal component to about 0.62. In the "in phase" sequences, the morning low tide peak was somewhat larger than the aftemoon low tide peak (Fig. 3d). The diel light component explained only 13% of the total variation in the observed data, while the tidal component explained 43%. Usage of hourly means increases the r 2 for the diel light component to about 0.21, and the r 2 for the tidal component to about 0.74. The largest numbers of hauled-out seals always occurred in the "in phase" sequences, most often around a morning low tide. At Hvaler, only the tidal component showed a significant influence on the haulout behaviour in the "out of phase" sequences (P < 0.05, r 2 = 0.16), and only the diel light component showed a significant influence in the "in phase" sequences (P < 0.05, r 2 = 0.19).

Discussion The harbour seals at Froan preferred to haul out during the day (Fig. l a). The diel light cycle is therefore important for the seal's haul-out behaviour in this area. In Kongsfjord, there are only very small variations in the diel light during summer, due to the midnight sun. The diel light cycle may therefore only have a very weak effect on the haul-out behaviour in Kongsfjord, as indicated by the almost negligible higher numbers of hauled-out seals in the early morning (Fig. l b). At Hvaler, the diel light cycle seemed to be of no importance for the haul-out behaviour of the harbour seal colony. The tidal cycle is of importance for the haul-out behaviour of the harbour seals both at Froan and in Kongsfjord (Fig. 2). At Hvaler, however, there are only very small differences in tidal height. The tidal cycle, therefore, does not seem to have any influence on the haul-out behaviour of the harbour seals in this area. In the analysis of the interaction between the diel light and tidal cycles, the diel light seemed to be the most important component at Froan, since maximum numbers of hauled-out seals always occurred in the daytime (Fig. 3a,b). The tidal component, however, was important for the size and timing of this daily maximum. Thus, most seals hauled out around the noon low tide in the "out of phase" sequences (Fig. 3a), and around the afternoon low tide in the "in phase" sequences (Fig. 3b). The results may indicate that most seals at Froan probably feed primarily during the night, and secondly also around the high tides. In Kongsfjord, however, the tide seemed to be the most important component, since maximum numbers always occurred at low tides (Fig. 3c,d). Yet, the time at which a tidal stage occurred was also of some importance. Thus, a somewhat larger number of hauled-out seals occurred around the noon low tide in the "out of phase" sequences (Fig. 3c), and around the morning low tide in the "in phase" sequences (Fig. 3d). The results may suggest that most seals in Kongsfjord primarily feed around high tides. At Hvaler, the haul-out behaviour of. the harbour seals was very inconsistent. Only the diel light component was of impor-

66 tance for the haul-out pattern in the "in phase" sequences, and only the tidal component was of importance in the "out of phase" sequences. The diel haul-out pattern observed in Froan is in accordance with the patterns found in other studies of the harbour seal [e.g. 4,10,11 ]. Harbour seals at Hvaler and Froan feed on several species of fish (e.g. Trisopterus esmarkii, Pollachius virens, Clupea harengus [ 12]) which are considered to be either nocturnally active fishes, or have vertical migrations at night [13-16], and may therefore occur closer to the surface during these hours. The fishes may thereby be more available to the harbour seals at night, resulting in a higher nocturnal activity in the seals. Similarly, the pattern of higher numbers of hauled-out seals at or around high tides observed both at Froan and in Kongsfjord is in agreement with the results found in other studies of harbour seals [e.g. 3,11,17]. Harbour seals may respond to increased local food availability as the high tide enters [ 11 ]. Some of the fishes that migrate in and out of the intertidal zone with the tide [18] are known to be among the prey of harbour seals, e.g. Pleuronectidae, Clupeidae and Salmonidae [e.g. 12,19,20]. The Salmonidae, however, are especially interesting in the case of Kongsfjord, where there is a river with populations of salmon, sea trout and sea char. In addition, extensive intertidal fiats in Kongsfjord may impede high tide haul-out at sites available further up on the fjord banks, since harbour seals often prefer to haul out at places where a swift flee to deeper water is possible [ 1,21 ]. Seals in Kongsfjord moved during haulout and remained close to the tide line as it fell, a type of behaviour reported for hunted and disturbed populations of harbour seals elsewhere [21 ]. At Hvaler the tidal range is very small. In such areas, the activity patterns of the fish may be synchronised with the diel light cycle, rather than with the tides [24]. The lack of a distinct daily haul-out pattern at Hvaler was unexpected, considering the large variations in diel light. However, it may be a consequence of the seals not being at ease, possibly as a result of a combination of the very open topography and the boat traffic in the area. Hvaler has a heavier traffic burden than either of the other two locations, due both to commercial shipping and to recreational utilisation. Generally, however, seals that haul out on smooth sandy beaches or continuous ice [e.g. 1,22,23] do not seem to be influenced by the states of the tide. On the other hand, seals hauling out in habitats consisting of rocks and intertidal ledges do appear to be influenced by the tide [e.g. 2,3,17]. The habitats at Hvaler, Froan and in Kongsfjord belong to the latter group. The haul-out behaviour of the harbour seals in Kongsfjord and at Froan may therefore be expected to be influenced by the tide, whereas the tidal range at Hvaler is too small to have an effect on the haul-out behaviour.

Acknowledgements This study has been a part of the project "Comparative feeding ecology of marine mammals in Norwegian waters" under the Norwegian Marine Mammal Research Program. Financial support was provided by the Norwegian Council for Fisheries Research and WWF.

67

References 1. Sullivan RM. Seasonal occurrence and haul-out use in pinnipeds along Humboldt county, California. J Mammal 1980;61:754-760. 2. Thompson PM, Miller D. Summer foraging activity and movements of radio-tagged common seals (Phoca vitulina) in the Moray Firth, Scotland. J Appl Ecol 1990;27:492-501. 3. Terhune JM, Albon M. Variability in harbour seal numbers on haul-out sites. Aquat Mammal 1983;10:71-78. 4. Stewart BS. Diurnal hauling patterns of harbor seals at San Miguel Island, California. J Wildlife Manage 1984;48:1459-1461. 5. Thompson PM, Harwood J. Methods for estimating the population size of common seals, Phoca vitulina. J Appl Ecol 1990;27:924-938. 6. BjCrge A. Ecology of common seal, Phoca vitulina, in Norway. In: Proceedings, Coastal Seal Symposium, Oslo. Paris: International Council for Game and Wildlife Conservation, 1987;159161. 7. Heide-Jr M-P. Sp~ettet s~el (Phoca vitulina vitulina L.) ph Anholt 1977-78. Flora Fauna 1979;85:59-70. 8. Calambokidis J, Taylor BL, Carter SD, Steiger GH, Dawson PK, Antrim LD. Distribution and haul-out behavior of harbor seals in Glacier Bay, Alaska. Can J Zool 1987;65:1391-1396. 9. Wei WWS. Time Series Analysis. Sec. 12-1. Reading, MA: Addison-Wesley, 1990;i-xv, 478 pp. 10. Yochem PK, Stewart BS, DeLong RL, DeMaster DP. Diel haul-out patterns and site fidelity of harbor seals (Phoca vitulina richardsi) on San Miguel Island, California, in autumn. Mar Mammal Sci 1987;3:323-332. 11. Thompson PM, Fedak MA, McConnell B J, Nicholas KS. Seasonal and sex-related variation in the activity patterns of common seals (Phoca vitulina). J Appl Ecol 1989;26:521-535. 12. Olsen M. N~eringsvalg og n~eringsstrategi hos steinkobber (Phoca vitulina). Cand. Scient.-oppgave i marin zoologi. Biologisk Institutt, Universitetet i Oslo, 1993. 13. Albert OT. Oyephl i Norskerenna: Utbredelse, biologi og n~eringsCkologi. Hovedfagsoppgave i fiskeribiologi. Institutt for fiskeribiologi, Univeristetet i Bergen, 1989, 80 pp. 14. Gordon JDM. The fish populations in inshore waters of the west of Scotland. The Biology of the Norway Pout (Trisopterus esmarkii). J Fish Biol 1977;10:417--430. 15. Woodhead, PMJ. The behaviour of fish in relation to light in the sea. Oceanogr Mar Biol Annu Rev 1966;4:337--403. 16. Helfman GS. Fish behaviour by day, night and twilight. In: Pitcher TJ (ed) Behaviour of Teleost Fishes. London: Chapman and Hall, 1993;479-512. 17. Schneider DC, Payne PM. Factors affecting haul-out of harbor seals at a site in south-eastern Massachusetts. J Mammal 1983;64:518-520. 18. Gibson RN. Intertidal teleosts: life in a fluctuating environment. In: Pitcher TJ (ed) Behaviour of Teleost Fishes. London: Chapman and Hall, 1993;513-536. 19. Bigg MA. Harbour seal Phoca vitulina Linnaeus, 1758 and Phoca largha Pallas, 1811. In: Ridgway SH, Harrison RJ (eds) Handbook of Marine Mammals, Vol 2. Seals. London: Academic Press, 1993; 1-27. 20. Brown RF, Mate BR. Abundance, movements and feeding habits of harbor seals, Phoca vitulina, at Netarts and Tillamook Bays, Oregon. Fish Bull 1983;81:291-301. 21. Vaughan RW. A study of common seals in the Wash. Mammal Rev 1978;8:25-34. 22. Finley KJ. Haul-out behaviour and densities of ringed seals (Phoca hispida) in the Barrow Strait area, NWT. Can J Zool 1979;57:1985-1997. 23. Smith MSR. Seasonal movements of the Weddell seal in McMurdo Sound, Antarctica. J Wildlife Manage 1965;29:464--470. 24. Koppel VH. Habitat selection and space partitioning among two Mediterranean Blenniid species. Mar Ecol Publ Stazione Zool Napoli 1988;9:329-346.

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69

Influences on spatial patterns of Gulf of Maine harbor porpoises Debra Palka National Marine Fisheries Service, Woods Hole, MA, USA Abstract. Background: inter-annual spatial and temporal differences in the sighting rates of harbor porpoises in the Gulf of Maine/Bay of Fundy region have been documented since 1970. This paper explores a possible reason for these differences. Methods: using data collected in 1991 and 1992, harbor porpoise densities adjusted for sea state were related to environmental factors (surface temperature, water depth, density index of prey species, and spatial location) by inspecting contour maps of kriged values of each factor and by fitting generalized additive models (GAMs) to all the factors simultaneously. Results: on a large spatial scale, high density aggregations of harbor porpoises were located in the same general regions during both years. However, on a smaller spatial scale, exact location and magnitude of the aggregations were correlated with the small scale distributions of environmental factors. High densities of harbor porpoises were associated with waters that had surface temperatures of 10-13.5~ contained fish densities of 1.5-11 fish caught per minute of trawling, and were 30-70 fathoms deep. Conclusions: inter-annual changes in surface temperature and fish density may be a reason for changes in the distribution and abundance of harbor porpoises. Key words: habitat description, generalized additive models

Introduction

Line transect surveys conducted in the summers of 1991 and 1992 revealed differences in the small spatial scale distribution and abundance of harbor porpoises (Phocoena phocoena) in the Gulf of Maine/Bay of Fundy/Scotian slope region (GOM/BOF). These surveys used the same ship in nearly the same region, at the same time of the year, and used the same sighting procedures, analysis methods, and half the same observers. Yet, in addition to small scale distribution differences, the estimated abundance for 1992 was nearly twice that for 1991 [1,2]. Such differences may not be abnormal. During 1970-1978, Gaskin and Watson [3] documented interannual and spatial differences in sighting rates in part of the Bay of Fundy. To explain these inter-annual differences, this study expands a previous Bay of Fundy habitat study [4] to the entire GOM/BOF. In this larger region, small scale densities of harbor porpoises were examined relative to small scale environmental and physical factors: water temperature, depth, index of density of herring and silver hake, and spatial location.

Materials and Methods

Harbor porpoise density and most of the environmental data were obtained during Address for correspondence: National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543, USA.

70 line transect sighting surveys conducted in the GOM/BOF region during 22 July-30 August, 1991 and 29 July-6 September, 1992. During both years, the two "independent" team methodology was used to estimate density corrected for g(0) [ 1]. Environmental data were collected at the beginning of "legs of effort", which were defined as a period in which all sighting conditions were the same. A leg usually lasted 30 min and was about 5 nautical miles (nmi) long. Depth was not collected in 1991. Therefore, using the software package SURFER [5], depths measured in 1992 were kriged and used to predict the depths at the beginning of legs surveyed in 1991. Simple kriging was used with a spherical variogram model, no drift or nugget, and anisotropy was at a 45 ~ angle tilted NE-SW, which followed the coastline. The predicted depths were validated by inspection of a nautical chart. Density indices of a combination of several fish species were estimated from data collected on research trawl surveys conducted by the National Marine Fisheries Service (NMFS) and Canadian Department of Fisheries and Oceans (DFO). Trawl surveys were conducted during 23 July-1 August, 1991 (NMFS), 5-10 July, 1991 (DFO), 27 July-10 August, 1992 (NMFS), and 24 June-2 July, 1992 (DFO). Fish species that were investigated are primary harbor porpoise prey species: Atlantic herring (Clupea harengus), alewife herring (Alosa pseudoharngus), blueback herring (A. aestivalis), and silver hake (Merluccius bilinearis). Fish density indices were calculated as the ratio of total number of fish of the above species that were caught in a trawl to the length (in minutes) of that trawl. Fish density indices at the beginning of each leg of effort of the harbor porpoise sighting survey were predicted from kriged spatial distributions of the fish density index from the trawl surveys. Simple kriging was used with the same conditions as for depth: a spherical variogram model, no drift or nugget, and anisotropy at a 45 ~ angle tilted NE-SW. Sighting rates from a leg of a line transect survey depend not only on the true local density, but also on sightability of harbor porpoises, which may be affected by observers (i.e. year of survey), sea state or size of harbor porpoise groups. Size bias was checked for by using DISTANCE [6] and was not significant. Density along a leg of effort was, therefore, estimated by multiplying the following factors: sighting rate of groups from a leg of effort, average group size from that leg, and the sightability corresponding to the year and sea state experienced during that leg. Sightability was expressed as f(O)/g(O), where f(0) is the probability density of observed perpendicular distances at perpendicular distance zero, and g(0) is the probability of detecting a group on the track line. These parameters were estimated independently for each combination of sea state and year using DISTANCE to estimate f(0) and the direct duplicate method [1 ] to estimate g(0). The resulting adjusted estimates of local density were fitted to environmental factors (water depth, surface temperature, latitude, longitude, and fish density index) using generalized additive models (GAMs) [7] in SPLUS [8] and by visual inspection of contour plots of the spatial distribution of an environmental factor overlaid with harbor porpoise density for each leg of effort. Smooth contour plots were generated by kriging values of one environmental factor, which was collected on the irregularly spaced beginning of legs of sighting survey effort. Simple kriging was

71 used under the same conditions as was used for depth. These contour plots were also used to measure the amount of planar area which was covered by a range of values of a particular factor. Planar area was computed by projecting the desired portions of the predicted surface onto a plane and calculating the area of the projection. An assumption of GAMs is that the dependent variable is distributed according to a distribution from the exponential family which includes the Normal, Poisson, Binomial and Gamma distributions. The dependent variable in this study, density of harbor porpoises, is always greater than or equal to zero, but may not be an integer. Distributions of normalized residuals (Anscombe residuals) were used to determine which distribution was most appropriate. Because no harbor porpoises were observed on some legs of effort (65% and 52% in 1991 and 1992, respectively), only those legs with positive densities were used. To assess goodness-of-fit and compare GAM models, the F-test, AIC [7] and a pseudo-R 2 [9] were used. The pseudo-R 2 was 1.0 minus the ratio of deviance of the model to deviance for the model with just the overall mean.

Results

During 1991, 472 and 375 groups of harbor porpoises were seen by the two "independent" teams while surveying 1,962 nmi of track line. During 1992, there were 726 and 599 groups observed by the two teams on 2,003 nmi of track line. There was no evidence of size bias in either year. The estimates of f(0) and g(0) differed by year and sea state, although not significantly (ANOVA, P > 0.05). To insure an assumption of GAMs, the dependent variable, harbor porpoise density, was log transformed and the gaussian link was used because log(density) was more closely normally distributed than were the Gamma or Poisson normalized (Anscombe) residuals of density. Fish density indices were transformed to log(fish density index + 1) for the same reason. Adding one to the fish density index allowed the areas of zero fish density (which were infrequent) to be used. Using the F-test and AIC for each separate year, the following GAM model was chosen which used the lowest possible order smoothed functions while still capturing the shape of the relationship: log(density) = s(temperature,2) + s(depth, 2) + s(log(fish + 1),2) +/o(latitude,longitude, 1/3) where s(factor, df) indicates a smoothed function of the factor with df degrees of freedom, and/o(factorl,factor2,span) indicates a lowest smoothed fit of factorl and factor2, where the span is the fraction of data used to compute the local regression. Separate smoothed functions of latitude and longitude were investigated but did not fit as well as the bivariate relationship, indicating an interaction between latitude and longitude. The pseudo-R 2 for 1991 and 1992 were 0.25 and 0.32, respectively.

72

Fig. 1. Plots of CAM smooth fits of the environmental factors: temperature (temp), depth and log transformed fish density index (logcalcallnum) for 1991 (top line) and 1992 (bottom line). Solid line is best fit, dashed lines are 2 standard error confidence limits, hash lines on x-axis are the observed values of that factor.

73 GAM models suggested that high densities of harbor porpoises were in waters that were between 10 ~ and 13.5~ especially between 10 ~ and 12 ~ in 1991 (Fig. 1). Spatial contour plots of temperature overlaid with harbor porpoise density (Fig. 2) illustrated that 1992 harbor porpoise density was higher than that in 1991, as was the percentage of the planar area that was covered with water temperatures of 10-13.5 ~ (41% in 1991 versus 59% in 1992). GAMs demonstrated that harbor porpoises were most often found in waters that were between 30 and 70 fathoms deep. Also, more animals were found in shallower waters (down to 10 fathoms) and in deeper waters (out to 100 fathoms) in 1992 than in 1991 (Fig. 1). The relationship between harbor porpoise and fish density is less clear. The GAM models indicated that, for both years, high densities of harbor porpoises were associated with intermediate levels of the fish density indices (1.5-11.0 fish/min; Fig. 1), even though harbor porpoises were distributed in waters with a larger range of fish density indices in 1992 than in 1991 (0-53 fish/min in 1991 versus 0-244 in 1992). In fact, legs with very high fish density indices were in hot waters, generally in temperatures higher than the "preferred" surface temperatures described above. The planar area of the study region that was covered with waters containing the "preferred" fish index values was greater in 1992 than in 1991 (10% in 1991 versus 16% in 1992; Fig. 2). The bivariate relationship of latitude and longitude in the GAM indicated that highest densities during both years were found around Grand Manan Island and offshore of Penobscot and Frenchmans Bays of central Maine (Fig. 1). The Maine area had lower harbor porpoise densities in 1991 than in 1992.

Discussion

The GAM models of harbor porpoise density did predict small scale patterns; however, the magnitude was underestimated. For example, when using environmental data from 1991 in the 1992 model, the density in 1991 was correctly predicted to be lower than that predicted for 1992. However, the predicted 1991 densities were also lower than the actual 1991 densities. This may be due to several possibilities, such as: the smoothing procedure was too smooth; zero density legs should have been incorporated; data should have been collected on a finer spatial scale; there were additional factors influencing density and/or sightability of harbor porpoises that were not incorporated; or density in the summer depends not only on concurrent small scale environmental factors, but also on events which occurred in regions where harbor porpoises spent the winter. Some of these factors can be investigated in the future. Even though the GAM models provided very smooth surfaces, these data indicated that on a large spatial scale, harbor porpoise aggregations were in the same general vicinity during both years. On a smaller scale, the exact location and magnitude of those aggregations were correlated to small scale distributions of water tem-

74 Fig. 2. Contour plots of spatial distribution of temperature and log transformed fish density index for 1991 and 1992. Dark areas of contour plots are warmer ternperatures. Black dots and + marks are the locations of the beginning of legs of effort from the harbor porpoise line transect survey. + indicates no observed harbor porpoises. Size of black dot indicates magnitude of harbor porpoise density; the larger the dot size the greater the density. Total black areas are either land or water that was not surveyed.

75

perature and fish density indices. This can be seen by a close inspection of the two highest density areas. The Maine aggregation extended farther offshore and south in 1992 than in 1991. However, in both years, the aggregation coincided with a finger of water that was 13~ and contained intermediate fish density indices of 2 - 1 0 or more fish per minute. The same trends can be seen in the Grand Manan Island area, although not as clearly. South of the island (along 44.3~ from 67.5~ to 66.5~ temperature, harbor porpoise and fish densities were higher in 1991 than in 1992. Inter-annual changes in the surface temperature and fish density patterns may be a reason for the inter-annual changes in harbor porpoise abundance. Although it is difficult to demonstrate a cause-and-effect relationship, it can be noted that the abundance of the region for 1992 was estimated to be 1.8 times higher than that for 1991 [2], which coincides with a similar magnitude of increase in the planar area covered by "preferred" water temperatures (1.5 x ) and fish density indices (1.6 x ).

References 1. Palka D. Abundance estimate of the Gulf of Maine harbor porpoise. Rep Int Whal Commn 1994;(Special Issue 16) (in press). 2. Smith T, Palka D, Bisack K. Biological significant of bycatch of harbor porpoise in the Gulf of Maine demersal groundfish fishery. NOAA/NMFS/NEFSC Ref Doc 1993;23:1-15. 3. Gaskin DE, Watson AP. The harbor porpoise, Phocoena phocoena, in Fish Harbour, New Brunswick, Canada: occupance, distribution and movements. Fish Bull 1985;83:427-442. 4. Watts P, Gaskin DE. Habitat index analysis of the harbor porpoise (Phocoena phocoena) in the southern coastal Bay of Fundy, Canada. J Mammal 1985;66:733-744. 5. Keckler D. SURFER for Windows User Guide. Golden, CO, USA: Golden Software Inc, 1994. 6. Buckland S, Anderson DR, Burnham KP, Laake JL. Distance Sampling. Estimating Abundance of Biological Populations. New York: Chapman and Hall, 1993. 7. Hastie TJ, Tibshirani RJ. Generalized Additive Models. New York: Chapman and Hall, 1991. 8. SPLUS. Seattle, WA, USA: Statistical Sciences, 1991. 9. Swartzman G, Huang C. Spatial analysis of Bering Sea groundfish survey data using generalized additive models. Can J Fish Aquat Sci 1992;49:1366--1378.

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77

Aerial surveys of harp and hooded seal pups in the Greenland Sea pack-ice Torger Oritsland and Nils Oien Institute of Marine Research, Nordnes, Bergen, Norway A b s t r a c t . After experimental surveys by ship-borne helicopter and fixed-wing aircraft in 1990, harp

seal breeding patches were surveyed in the Greenland Sea pack-ice (the West Ice) in the spring of 1991. Combined estimates based on data from visual helicopter transects and aerial photographic transect surveys indicate a minimum total production in excess of 55,000 harp seal pups in the West Ice in 1991. Extreme weather conditions and an exceptional westerly distribution of the pack-ice impeded attempts to survey West Ice hooded seal pups in 1994. Classified counts suggest late births of harp seal pups in 1990 when compared to 1991. Less precise data suggest comparable timing of peak pupping but a wider temporal distribution of hooded seal births in 1994 than in 1991. Published results from analyses of still-camera and video material confirm that image-analysis may be used to obtain useful information on environmental conditions and the spatial distribution and sizes of seals in pack-ice breeding patches. Key words: visual, video and photographic transects, pup development, production

Introduction Harp and hooded seals, Phoca groenlandica and Cystophora cristata, were selected as key species for studies under the Norwegian Marine Mammal Research Programme 1989-1993, and surveys of pups in the West Ice, the breeding grounds in the Greenland Sea pack-ice, were among the chosen priority projects planned for the programme. Experience in the operation of a ship-based helicopter was obtained on an expedition to tag harp seal pups in the West Ice during the breeding season of 1989 [1,2]. The project was continued on an expedition to pursue the tagging programme and to test alternative techniques and select methods for aerial transect surveys of pups in the West Ice breeding lairs in 1990 [3]. Harp seal pups were then surveyed and tagged during the breeding season in 1991 [4,5]. Further efforts were delayed by a temporary scarcity of funds, and a postponed expedition dedicated to surveys of hooded seal pups was organized by the Institute of Marine Research in prolongation of the Marine Mammal Research Programme in 1994. Activities and results related to the surveys in 1990, 1991 and 1994 are summarized in this report. Results from the tagging programme [6] are reported separately to this symposium [7].

Logistics The mutually trustful relationship established between the Institute of Marine ReAddress for correspondence: T. Dritsland, Institute of Marine Research, P.O. Box 1870, Nordnes, N5024 Bergen, Norway.

78 search and the companies and crews during the tagging expedition in 1989, formed the basis for further cooperation with Rieber Shipping A/S, Bergen, and Helikopterteneste a.s., Kinsarvik, for the use of the same ship and helicopter on the expeditions in 1990, 1991 and 1994. The "Polarsyssel" is a 50-m long vessel of 499 gross tonnes with reinforced hull for navigation in ice and fitted with a 2,500 hp main engine. She is fully classified as an icebreaker-sealer (DnV + 1AJ). Large flush hatches over the aft shelterdeck are particularly useful because they permit sheltered stowage of the helicopter during crossings in rough seas to and from the ice, as well as sheltered instalment of a container lab. The Ecureuil AS 350 B 1 helicopter proved to be well suited for our purpose, both with regard to ease of handling and stowage on board the ship and because of capacity and range. Both the ship and the helicopter were fitted with satellite navigation systems. The global positioning system (GPS) in the helicopter proved to be useful both for positioning of observations during search flights and for navigation along preplanned transect lines during visual and video surveys. A fixed-wing twin-engine Partenavia P68TC Observer aircraft, operated by Fotonor A.S., Oslo, was based on Jan Mayen Island to be used for search flights and photographic transect surveys of seal patches in all the 3 years covered by this report. For navigation, the Partenavia was fitted with Loran C in 1990 and 1991 and with GPS in 1994, and carded a mounted Wild RC 20 camera with a 15 cm lens for vertical photography. In 1994 an additional fixed-wing twin-engined aircraft operated by the same company, a Piper PA31 fitted with GPS and a vertically mounted Zeiss LMK camera with a 15 cm lens, was based at Longyearbyen airport, Svalbard, to improve the efficiency of search and photographic surveys during the expected brief spells of acceptable weather conditions.

Field work 1990

The primary purpose of the expedition in 1990 was to test alternative techniques for selection of methods for transect surveys of harp and hooded seal pups in the West Ice breeding lairs. A secondary objective was to continue the tagging program for harp seal pups which was started in 1989. "Polarsyssel", with the helicopter on board, spent 20 days (20 March to 9 April) in the ice in 1990. Continued northerly winds prior to and during the breeding season dispersed the pack-ice and also caused a rapid southerly drift of the floes. This drift continued throughout the season. Except for three early days with strong winds and poor visibility, the weather conditions were tolerable or good during the period in the ice. The Partenavia aircraft was available for search and survey flights for 13 days, but

79 the operations were restricted by local weather conditions at Jan Mayen, and flying was limited to a total of 29.5 h. However, the pack-ice between 71~ and 75~ was searched on three flights before the end of March, and another search with intervallic photography from 1,000 ft (305 m) was carded out from 70~ to 75~ during the first few days of April (Fig. 1). Short intervallic and overlapping photo-transects from 500 ft (152 m) and 1,000 ft were also obtained over two small harp seal breeding lairs. Additional information on ice conditions was supplied from two search flights by a Soviet aircraft which covered the area between 72~ and 74~ on 21 March and 23 March. Breeding seals were not discovered during the Soviet flights. "Polarsyssel", in the meantime, worked its way from about 71~ up to 75~ using available time on helicopter search and tagging of harp seal pups. Scattered breeding harp seals were recorded throughout the area from 70~ to 74~ with two small patches of breeding harps which were located around 73~ 12~ in late March. Widely dispersed families of hoods were found all over the open pack-ice from 70~ to 75~ with only one minor concentration of some 300 families at 73~ 13~ on 25 March. In addition to search flights, the helicopter was also used to deploy personnel for tagging and for experimental transect surveys. Seven series of video transects were carried out over the two patches of harps at 100 ft (30 m) altitude intervals between 200 ft and 600 ft (61 and 183 m). A special Dage MTI camera, selected for maximum resolution, proved to be hopelessly inadequate for the purpose, but good images were obtained by an off-the-shelf Sony 8 mm handycam, provisionally mounted for vertical recording through a front-bottom window in the helicopter cockpit [8]. In 1990 the helicopter was also used for visual transects with two observers at 200 ft (61 m) and a series of age (developmental stage) determinations of harp seal pups from altitudes around 50 ft (15 m) in breeding patches between 31 March and 8 April. 1991

In 1991 the Partenavia aircraft was available on Jan Mayen Island for 26 days from 15 March. Operations were impeded by unsuitable or unstable weather for 16 days, but a total of seven search flights and three photosurveys were completed over the pack-ice between 71~ 18~ and 74~ 03~ "Polarsyssel", with the helicopter onboard, was engaged in search, taggings and surveys in the ice between 71 ~ 18'W and 75~ 03'W for 28 days from 16 March. Again the weather was highly variable and less than ideal with winds at gale force or stronger for 11 days, and moderate or poor visibility for 16 days through the period "Polarsyssel" was working in the pack-ice (Fig. 2). Young ice covered extensive areas between strips and patches of consolidated older ice. Currents and changing winds led to frequent rearrangements of the ice with an unexpected easterly drift in late March and a resultant fairly fast drift towards SSW in early April. Scattered harp seals with pups were recorded throughout the area between 71 ~ and 75~ a distance of about 350 nautical miles (650 km), with the largest concen-

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Fig. 2. Ice edges and the distribution of breeding harp and hooded seals, recorded by ship-borne helicopter and spotting aircraft in the West Ice 16 March to 12 April, 1991" (1) hooded seal breeding patch; (2) harp seal breeding patch; (3) scattered breeding hooded seals; (4) drift; (5) ice edge.

Table 1. Estimates of harp seal pup production in four separate breeding patches in the Greenland Sea pack-ice in 1991, based on visual transect counts from helicopter and photographic transect surveys by fixed-wing aircraft, uncorrected for bias

Patch No. 01 02 03 04 Total over all patches 95% confidence interval

Visual 7,100 (0.075) 3,800 52,500 (0.159)

Photographic 2,021 (0.128) 5,905 31,917 (0.271)

Combined 2,991 (0.078) 3,800 5,905 (0.195) 42,574 (0.141) 55,270 (0.141) 44,500-68,500

The estimate for patch No 2 was provided from visual shipboard transects by the Soviet research vessel "Varzuga" without information on uncertainties. Available visual and photographic data were combined for each patch by weighting of variances. Coefficients of variation are given in parentheses.

82 trations towards the north. Four separate harp seal breeding patches were found and three of them were covered by aerial still photo transects and two of these three patches also by visual and video helicopter transects (Table 1). The fourth and smallest patch (No. 02) was covered only by visual shipboard transects carried out by the Soviet research vessel "Varzuga". Age or developmental stages were determined for a total of 4,711 harp seal pups in two of the breeding patches. An additional 20 dead pups (0.4% of the total sum) were recorded during these low-altitude helicopter flights. 1994

As mentioned under logistics, the expedition in 1994 included an extra fixed-wing aircraft stationed at Longyearbyen airport, Svalbard. "Polarsyssel", with the helicopter on board, had 19 days in the ice from 16 March, and both aircraft were available for 18 days from the same date. However, boisterous weather was even more restrictive this year than during any of the previous expeditions. Northerly winds prevailed with strengths from strong breeze (Beaufort 6) to violent storm (B.11) with occasional hour-long gusts of hurricane force (B.12). Visibility was poor to moderate for 16 days through the time spent in the ice. Also the ice conditions were extreme with the ice edge far to the west of the expected position and with a rapid drift of the pack-ice towards SSW-SW (Fig. 3). An Argos satellite buoy deployed on a large floe on 26 March drifted at an average speed of 1.3 knots until it was resighted 116 nautical miles further to the SSW 4 days later, and continued roughly in the same direction along the East Greenland coast until the last signals were received in early June. Because of the ice conditions, the expedition in 1994 operated under special permit from Greenlandic authorities. "Polarsysser' operated south of 72~ towards 68~ throughout the period in the ice and was unable to proceed further north. The helicopter was used on 12 search flights between 68~ and 73~ and two attempts at visual transect surveys of hooded seals with pups on 26 March and 30 March. An attempt to use the helicopter for tagging of harp seal pups at 69~ 20~ on 3 April was discontinued because of icing and poor visibility. The difficult weather conditions and the westerly location of the pack-ice also impeded the operation of the two fixed-wing aircraft. They needed about 1 and up to 3 h to reach the ice from their bases on Jan Mayen and at Longyearbyen, respectively, and therefore required exceptionally stable weather and reliable forecasts. However, three search flights from Jan Mayen covered the ice from 68~ 23~ to 74~ 10~ and the area from 74~ 15~ to 76~ 09~ was searched on four flights from Longyearbyen. Only small numbers of scattered breeding harps and hoods were found north of 74~ A total of 395 photographs were taken at intervals from 800 ft (245 m) during these seven search flights. The Partenavia from Jan Mayen also obtained 180 images from the same altitude in four transects over scattered weaned hooded seal pups around 68~ 23~ on 26 March. On the same date scattered female hoods

83 76 ~

_

74 o

72 ~

'~176 Lf -

6B o -/--

. . . . . .

03,26~ /

Y

=

3

ooo

/-,,

::x

5

--'03.25-03.26

2 "0 o

-

'

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'

Fig. 3. Ice edges and the distribution of breeding harp and hooded seals recorded by spotting aircraft

and ship-borne helicopter in the West Ice 20 March to 4 April, 1994: (1) hooded seal breeding patch; (2) harp seal breeding patch; (3) scattered weaned hooded seal pups; (4) scattered weaned harp seal pups; (5) ice edge. with relatively young pups were recorded around 74~ 14~ Another hooded seal breeding patch with newly born pups unevenly distributed over an area of about 5 x 10 nautical miles (170 km 2) was discovered the next day at 71~ 18~ A few pictures were obtained but the available flight-time did not permit coverage by transects.

Results and Discussion

Still-camera and video images obtained during the exploratory survey in 1990 have been used in image analyses to assess ice-conditions and measure sizes of seals [8].

84

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Fig, 4. The relative distribution of harp seal pups by developmental stage and date (] = newborn; 5 = moulted beater), recorded by classified counts in the West Ice 28 March to 8 April, 1990 (upper graph) and 22 March to 12 April, 1991 (lower graph). Sample sizes for each count are given along the upper horizontal axis of each graph. The vertical stippled line in the lower graph divides between counts in two separate breeding patches.

85 108 100

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I

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.

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x

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148

306

6O

U ~ e,.

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0 40

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Fig. 5. The relative distribution of hooded seal pups by developmental stage and date (0 = unborn/ parturient females; 5 = weaned), recorded by classified counts in the West Ice 22 March to 7 April, 1991 (upper graph) and 16 March to 31 March, 1994 (lower graph). Sample sizes for each count are given along the upper horizontal axis of each graph.

86 Results indicate that seals prefer medium-sized floes, ranging from 13 to 38 rn in diameter, compared to the size range 0.2--49 rn for all ice floes. Floe shape was also characteristic of occupied floes: seals selected more rounded floes rather than elongated floes. The presence of seals was also related to the ice conditions over a larger area. In the transects that were analysed seals were always found in areas with greater than 60% ice coverage. Data from videos resulted in values of 1.32-2.40 m for lengths of adult and subadult seals, and 1.02 m for pup length. No attempt was made to distinguish between species, but these measurements fall within the combined range of lengths for harp and hooded seals obtained from previous studies [8]. Abundance estimates of harp seal pups calculated from the basic transect data obtained in 1991 are listed in Table 1, with a provisional best estimate of total production exceeding 55,000 pups. For each patch the available estimates have been combined by weighting of variances. The variance associated with the Soviet count of patch No. 02 is not known, and has therefore not been accounted for in the confidence interval for the combined total. Both the visual and the photographic survey estimates have an inherent negative bias caused by the fact that no correction has been made for scattered pups between the patches, nor for the temporal distribution of births. A specific problem with the photographic surveys is caused by errors in the interpretation and reading of the photographic material. Experimental reading of subsamples by alternative techniques indicates that the adopted procedure may underestimate the true counts of pups by about 9% [5]. The results from low altitude developmental stage determinations of harp seal pups in 1991 [4] are compared to corresponding data collected in 1990 in Fig. 4. These graphs suggest that breeding may have occurred earlier by 5-7 days in 1991 than in 1990. To accommodate the sealers' lore, it may be mentioned that the moon occurred in its first quarter on 23 March in 1991 and 10 days later, on 2 April, in 1990. Developmental stages of hooded seal pups [4] were not determined on dedicated flights in 1994, but classified counts recorded on helicopter search flights suggest an exceptionally wide temporal spacing of births; centered roughly around the same dates as in 1991 when stage determinations were recorded on three dedicated low altitude helicopter flights and from the ship in passing through the ice (Fig. 5). Because they covered only limited areas with unevenly distributed hooded seals, representing a minor but unknown proportion of the total, the visual helicopter transects of hooded seal pups in 1994 were methodological exercises rather than surveys which could be applied in assessments. Data from the expedition in 1994 are indicative of possible distributions of breeding hooded seals under extreme conditions in the Greenland Sea. They are, however, totally inadequate for any assessment of production.

Acknowledgements We acknowledge with gratitude the assistance of the crews on the "Polarsyssel" and

87 the crews manning the helicopter and fixed-wing aircraft, and appreciate the support of Rieber Shipping A/S, Helikopterteneste a.s. and Fotonor AS. We also extend our thanks to BjCrn Bergflcdt, Kjell Arne Fagerheim and Karl Tellnes of the Institute of Marine Research, and to Tore Haug, Kjell T. Nilssen and Nils-Erik Skavberg of the Norwegian Institute of Fisheries and Aquaculture in Tromsr who all contributed by enthusiastic participation in the field work. Kjell Arne Fagerheim also handled all visual analyses of the photographic material and Siri Hartvedt provided assistance with the punching and analysis of data. Visitors who joined individual expeditions to pursue independent studies and contributed to activities in the field included Jonny Beyer, University of Bergen, Robert Eisner, University of Alaska, Lars Folkow and Per-Erik MArtensson, University of Tromsr V.A. Potelov and V.F. Prishchemikhin, sevPINRO, Arkhangelsk and Jack Terhune, University of New Brunswick. The project was funded by the Norwegian Fisheries Research Council (NFFR No. 4001701.304) and in 1994 by the Norwegian Research Council (NFR No. 104.500/110).

References 1. Oritsland T, Folkow L. Selmerking i Vesterisen. Rapport Fiskeridirektoratets havforskningsinstitutt, SPS 8904. Bergen: 1989; 1-4. 2. Folkow LP, Blix AS. Tracking harp and hooded seals in the Greenland and Barents Seas. Working Paper ICES Working Group on Harp and Hooded Seals, 16-18 October. SEA- 17. Bergen: 1989; 16. 3. ~ritsland T, Haug T, Oien N, BergflCdt B. Telling, merking og undersCkelser av sel i Vesterisen. Rapport Havforskningsinstituttet, SPS 9004. Bergen: 1990;1-11. 4. IDritsland T, Fagerheim KA, Oien N. West Ice seal survey and tagging in 1991. Working Paper Joint ICES/NAFO Working Group on Harp and Hooded Seals, 14-18 October, SEA-23. Copenhagen: 1991;1-13. 5. Oien N, Oritsland T. Aerial and visual surveys to estimate harp seal pup production in the Greenland Sea. ICES Coun Meet 1993; N9:1-9. 6. ~ien N, 13ritsland T. Recaptures of harp seals (Phoca groenlandica) tagged as pups in the Greenland S e a - pup production and dispersion patterns. Working Paper Joint ICES/NAFO Working Group on Harp and Hooded Seals, 14-18 October, SEA-33. Copenhagen: 1991 ;1-21. 7. Oien N. Use of mark-recapture experiments to monitor seal populations subject to catching. In: Blix AS, WallCe L, Ulltang 13 (eds) Whales, Seals, Fish and Man. Amsterdam: Elsevier, 1995;3545. 8. Estep KW, Maclntyre F, Noji TT, Stensholt B, Oritsland T. Seal sizes and habitat conditions assessed from aerial photography and video analysis. ICES J Mar Sci 1994;51:253-261.

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Stock identity and social organization

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© 1995 ElsevierScienceB.V. All rightsreserved Whales, seals, fish and man A.S. Blix, L. Wallceand 0. Ulltang,editors

Genetic markers

91

and whale stocks in the North Atlantic ocean:

a review

Alfrec3 Arnason lmmunogenetics Unit, Department of Pathology, University Hospital, Reykjavfk, Iceland Abstract. The discovery of protein electrophoresis and later of applying electrophoresis to DNA fragments is a powerful tool in comparing animals from different areas. We discuss our experience of using these techniques in comparing stocks of whales. The emphasis is laid on fin (Balaenoptera physalus), sei (B. borealis) and minke whales (B. acutorostrata). Of 31 enzyme systems encoded by 40 loci in fin whale liver samples from Iceland and Spain, 11 loci were found to be polymorphic. The average heterozygosity for these two stocks of fin whales was 0.074 and 0.083, respectively. There were between years differences observed in the fin whales suggesting substructures of the populations. The Nei's genetic distance between Icelandic and Spanish fins was 0.016. In a limited study of fin whale samples from Norway, Iceland and Canada there were statistical differences between enzyme loci tested. No between years differences were found in seis that came only from Iceland. The isozyme study of minke whales revealed genetic differentiation between Norway, Iceland and West Greenland. DNA fingerprinting showed similar differentiation. Earlier morphometric comparisons and tagging experiments pointed in the same direction. Immunogenetical studies of the MHC region of fin and sei show lack of polymorphism in these species. The C4 is more similar to terrestrial animals regarding polymorphism. The C4 typing revealed hybridisation between blue and fin whales; three hybrids were found, one fertile female in her second pregnancy and two males. This demonstrates how closely related these two species are. The conclusion from our study is that fin whales from Norway, Iceland, Canada and Spain are separate stocks. The same applies to minke whales from Norway, Iceland and West Greenland; they represent different populations. Key words: fin, sei, minke, isozymes, DNA, MHC, hybrids

Introduction In this short review we concentrate mainly on our investigation over the past 18 years or so. This is bound to reflect the technical advances during the period. The advent of electrophoresis made possible the separation of different proteins and later of DNA fragments. The patterns acquired give the picture of different genes or their products, the proteins; these are the so-called genetic markers. We have applied a variety of these techniques to study several species of whales from different areas of the North-Atlantic Ocean. R/Srvik and Jonsgaard [1] divide fin whales in the North Atlantic Ocean into six stocks: 1. North Norway and Arctic Eastern North-Atlantic; 2. East Greenland and Iceland; 3. West-Norway and the Faroes; 4. British Isles, Spain and Portugal; Address for correspondence: Immunogenetics Unit, Department of Pathology, University Hospital, Reykjav~, Iceland.

92 5. 6.

West Greenland; Nova Scotia, Newfoundland and Labrador. The IWC divides the fin whales of the North Atlantic into seven management stocks, the divisions the same as above, except stock 6 is further subdivided into two stocks: Newfoundland-Labrador and Nova Scotia stocks [2]. Other ideas have been put forward, e.g. that the fin whales in the North Atlantic form a patchy continuum [3]. The sei whales are divided into three management stocks by the IWC [2]: 1. Iceland-Denmark Strait; 2. Eastern stock (Norway, Scottish Isles, Spain and Portugal); 3. Nova Scotia. The IWC division of minke whales into four management stocks is as follows [2]: 1. the Canadian East Coast stock; 2. West Greenland stock; 3. Central stock (Iceland); 4. Northeastern stock (Norway). The IWC stock divisions are mainly based on catch statistics, and some biological characters such as morphometry and tag returns. The aim of the present study was to use genetic marker systems in order to clarify, if possible, the stock identity of the above-mentioned whale species in the North Atlantic Ocean. This review also deals with some comparative immunogenetical markers in the major histocompatibility complex (MHC region) as well as complement factor 4 (C4).We also discuss species hybridization between the blue and the fin whale and how this reflects the close genetic relationship between these species.

Material and Methods

Electrophoresis was the separation method used, adjusted to each marker system. The proteins investigated were mostly isozymes, each locus recognized by specific substrates. The DNA fragments were recognized by specific probes and restriction enzymes and in some cases by PCR magnification. Otherwise we refer to the original papers cited in the text. The samples came from the following areas: fin whales (Balaenoptera physalus) mainly from Icelandic and Spanish waters, but smaller numbers for a pilot study from Canadian and Norwegian waters; sei whales (Balaenoptera borealis) only from Icelandic waters; minke whales (Balaenoptera acutorostrata) from Norway, Iceland and West Greenland. Only a few individuals from various other whale species were studied for the purpose of species comparison.

Results

The results presented here are a compilation from our earlier publications from dif-

93 ferent times thus being a review of what we know today about the whale stocks in the North Atlantic The fin whale

Both protein and DNA markers were used in the study. Protein markers

We started investigating esterases of fin whale blood in 1971, concentrating on carbonic anhydrases of the erythrocytes [4]. The total of 1,099 fin whales caught off Iceland were studied in the years 1971, 1981-1989. This system was in HardyWeinberg equilibrium all years except 1985 and 1986. Young males seemed to be responsible for this deviation from the Hardy-Weinberg equilibrium [4,5]. Liver esterases. The liver esterases gave a complex pattern and only the fast moving esterases were of use in this respect. No significant differences were observed between years or locations [4,6]. Liver esterase patterns were on the other hand useful for species comparison. Cardiac esterases. The electrophoretic esterase patterns of cardiac muscle homogenates of fin whales are highly polymorphic. This is discussed in detail in our earlier reports [4,7]. There is a highly significant difference between Iceland and Spain in frequencies of some fractions [4]. If we use the most frequent phenotypes as markers shown in Fig. 1, differences between Iceland and Spain seem obvious and are statistically significant. The differences between years could probably reflect herd structure. It should be borne in mind that we do not know the genetics behind the cardiac esterase patterns, so we are only using these as markers. Genetic variation at 40 enzyme loci in fin whales. Fin whale samples taken in 1985 and under special permit in 1986-1989 were used for an extensive study of isozymes and other markers [4,8]. A total of 31 out of the 38 enzyme systems examined gave adequate staining and resolution. The zymograms have been described

Phenotypes iI

0,8 0,7

II III

0,6 % 0,5 0,4

0,3

i~ !iiiii

0,2 0,1

!.

Fig.

1981 n=169

1982 1983 n=72 n=71 Iceland

1984 n=129

I

I

1983 n=29

1984 n=12 Spain

I

1. A bar chart demonstrating the frequencies of cardiac esterase phenotypes in fin whales caught off Iceland in the years 1981-1984 and in fin whales caught off Spain 1983-1984 [4].

94 Table 1. Allele frequencies of 11 polymorphic enzymes in fin whales caught off Iceland and Spain Locus

Subunit number

Alleles

Allele frequencies Spain 1985

Sample size Ada

(n) 1

Ak-I

(n) 1

Ca

(n) 1

Gpd

(n) 2

Ldh-A

(n) 4

Mdh-S

(n) 2

Mpi

(n) 1

Pep-A

(n) 1

Pgm-1

(n) 1

Pgi

(n) 2

Sod-A

(n) 2

1 2 3 4 1 2 F S 1 2 A A' 1 2 1 2 3 1 2 3 1 2 1 2 3 1 2

46 0.098 0.293 0.152 0.457 33 0.636 0.364** 24 0.187 0.813 46 0.533 0.467 46 0.674 0.326** 40 0.562 0.438 0.000 45 0.611 0.389 0.000 45 0.100 0.900 46 0.858 0.109 0.033 46 0.315 0.685

Iceland 1985

Iceland 1986

65 49 0.085 0.069 0.231 0.139 0.330 0.271 0.354*** 0.521"** 64 67 0.164 0.172 0.836*** 0.828*** 145 0.276 0.724* 65 0.138 0.862 65 72 0.531 0.729 0.469 0.271 65 72 0.669 0.299 0.331 0.701"* 60 62 0.133 0.113 0.867 0.887 0.000 0.000 20 71 0.725 0.676 0.275 0.317 0.000 0.007*** 65 59 0.108 0.153 0.892 0.847 63 70 0.937 0.915 0.063 0.064 0.000 0.021 65 72 0.208 0.312 0.792 0.688

Iceland 1987

78 0.032 0.346 0.109 0.513"** 73 0.192 0.808*** 78 0.814 0.186 78 0.147 0.853 78 0.038 0.949 0.013 73 0.610 0.370 0.020*** 74 0.101 0.899* 78 0.911 0.051 0.038 78 0.391 0.609

Iceland 1988

68 0.081 0.279 0.051 0.588** 61 0.492 0.508*** 68 0.801 0.198 68 0.044 0.956 68 0.140 0.838 0.022 58 0.715 0.241 0.430** 65 0.262 0.738 68 0.919 0.037 0.044 68 0.140 0.860

Danfelsd6ttir et al. [8]. Significant deviations in genotypic frequency from Hardy-Weinberg I (*) and the sample size (n). *0.01 < P < 0.05; **0.001 < P < 0.01; ***P < 0.001; -, not screened.

p r e v i o u s l y . I n fin w h a l e s a m p l e s c a u g h t o f f I c e l a n d a n d S p a i n , t h e 31 e n z y m e s r e p r e s e n t 4 0 l o c i o f w h i c h 11 ( 2 7 . 5 % ) w e r e p o l y m o r p h i c b y t h e 0 . 9 5 c r i t e r i o n f o r p o l y m o r p h i s m , i.e. t h e r a r e a l l e l e h a d a f r e q u e n c y o f at l e a s t 0.05. A l l e l e f r e q u e n c i e s f o r t h e p o l y m o r p h i c loci in fin w h a l e s a m p l e s are l i s t e d in T a b l e 1. In f i n w h a l e s , t h e

95 loci suitable for the chi-square goodness-of-fit test for the Hardy-Weinberg equilibrium were: Ada, Ak-1, Ca, Gpd, Ldh-A, Mdh-S, Mpi-1, Pep-A, Pgm-1, Pgi and Sod-A. Where the expected number of a given genotypic frequency was <5 and df = 1, the Yates correction was applied. In cases where the expected number was ~<3 and df > 1, the frequency values for certain genotypes were pooled [9]. Most of the chi-square values were found to be non-significant, indicating no deviation from the Hardy-Weinberg equilibrium. There were, however, significant differences between observed proportions and Hardy-Weinberg expectations in 16 of the 48 comparisons. It can be seen from Table 1 that the majority of these differences were observed at the Ada, Ak-1 and Pep-A loci. Some deviations between expected and observed values were also noted at the Ca, Pgm-1 and Mdh-S loci. All the significant deviations from the expected proportions are due to an excess of homozygotes. The amount of genetic variation found at the enzyme loci in fin whales is shown in Table 2. The observed average heterozygosity values were calculated for 12 variable enzyme loci plus 28 invariant loci and ranged from 0.064 to 0.077 in fin whales caught in different years off Iceland (1985-1988) with a mean of 0.071 (SD = 0.005). The average heterozygosity was 0.089 in those caught off Spain in the year 1985. The genetic differences between the samples were calculated using the genetic distance coefficient (D) of Nei [10] based on allele frequencies at all loci examined. The mean genetic distance between Icelandic fin whale samples from the years 1985-1988 was 0.010. The genetic distance between the Icelandic and the Spanish samples caught in 1985 was 0.013. When comparing Icelandic samples from all the years (1985-1988) with the Spanish samples from 1985, the mean D value was higher at 0.020. An unweighted pair group arithmetic average (UPGMA) cluster analysis [11] of the D values gave the dendrogram in Fig. 2.

Spain 1985 Iceland 1985 Iceland 1986 Iceland 1987 Iceland 1988 I

,

0.03

,

,

,

I

,

0.02

i

i

i

I

0.01

. . . .

t

0.00

Genetic distance Fig. 2. An UPGMAdendogramshowingthe relationships of North Atlantic fin whale samples based on Nei's unbiased geneticdistance coefficient(D) of 39 loci [8].

96 A pilot study was carried out on samples from Nova Scotia, Newfoundland (kept frozen since 1972), Iceland (scientific catch 1988) and from the Barents Sea (biopsy samples, 1991). These preliminary results indicate that the fin whales from these areas are different populations (Danfelsd6ttir, personal communication) judged from the statistical analysis using the BIOSYS-1, Fortran-77 computer program by Swofford and Selander [12]. DNA markers The M H C genes. We have demonstrated limited polymorphism of MHC genes of

two cetacean species [13]. Human class I and class II probes were used to study major histocompatability complex (MHC) genes from fin and sei whales. Stronger signals were obtained on whale than on an equivalent concentration of mouse DNA. Evidence was obtained for several DRB-related genes, a DNA gene, one DQA gene and multiple class I genes in the two species. The whale genes were less polymorphic than those of human and mice. No difference was found between the few samples from fins caught off Spain and those caught off Iceland. The sample size was too small for a population study. The importance of this system is mainly related to immunogenetics, and this may reflect the lack of polymorphism in fins and seis due to different immunological stimulus in the sea compared with terrestrial mammals. The C4 genes. We used human C4 cDNA probes to investigate the complement component C4 gene in fin, sei and minke [14]. Restriction mapping of genomic DNA shows the presence of only one gene (locus) in these species. The C4 gene is polymorphic in fin and sei, which could be used in population studies. Only 31 fins and 22 seis were investigated for the same restriction enzyme. The study of DNA samples available from mother-foetus pairs from the two polymorphic species showed a simple two allele transmission. Sei whales

We could only study sei whales from Icelandic waters. In our first investigation in 1981-1984 we found less variation in cardiac esterases in sei whales than fin whales [7]. In another study [8] of 40 isozyme loci from 101 animals caught in the years 1985-1988, seven loci proved to be polymorphic or 17.5%. There was less heterozygosity than in fins, H = 0.047 (see Table 2). No significant differences in Table 2. The average heterozygosity in fin whales caught and Spain and in sei whales caught off Iceland

Fin whales Sei whales

Spain

Iceland

Iceland

Iceland

Iceland

Iceland

1985

1985

1986

1987

1988

Mean

SD

0.089 -

0.077 0.039

0.070 0.049

0.064 0.042

0.071 0.036

0.071 0.047

0.005 0.011

Danfelsd6ttir et al. [8]. - , no data available.

97 Table 3. The multilocus G-test values for the pairwise comparison of all frequencies between Icelandic sei whale samples from different sample years Locus

1985-1986

Ldh-A Ada Mdh-S Mpi Pep-A Pgm-1 Total

1986-1987

1987-1988

1985-1988

G

df

G

df

G

df

G

df

5.15 2.72 1.15 0.19 7.02 1.12 17.35

2 2 2 2 2 5 15

0.30 0.59 0.62 9.43 4.88 1.64 17.46

2 2 2 2 2 5 15

1.57 1.22 1.44 1.68 1.10 0.03 7.04

2 2 2 2 2 5 15

6.83 9.70 11.37 15.42 10.33 2.13 55.78

6 6 6 6 6 15 45

Danfelsddttir et al. [8]. No significant differences between sample years were observed.

allele frequencies between years were observed nor any deviation from HardyWeinberg equilibrium as shown in Table 3. The sei whale population is very uniform. Minke whales

This species is discussed only briefly in this review as this is dealt with in this volume by Danfelsd6ttir. The isozyme studies [15] indicate that the minke whales from West Greenland, Iceland and Norway represent different populations (Fig. 3). We used human hypervariable region probe and Alu I endonuclease to study variability in minke whales from these regions. The fingerprinting results are shown in Table 4 and support the existence of separate populations in those areas [16].

W. Greenland

Iceland

Norway

I

i

0.020

.

,

.

r

.

0.015

.

.

.

t

.

0.010

.

.

.

I

.

0.005

.

.

.

I

0.000

Genetic distance Fig. 3. An U P G M A dendogram showing the relationships of North Atlantic minke whale samples based on Nei's unbiased genetic distance coefficient (D) of 27 loci [15].

98

Table 4. DNA fingerprinting of minke whales using Alu I endonuclease alpha-globin 3'HVR probe (range 4.3-24kb) Country

Plate No.

No. of f animals

x

q

h

p

Iceland

45 43 46 46

19 10 7 36 11

1.79 _+0.42 1.90 _+0.32 2.00 _+0 1.86 _+0.35 1.54 _+0.50

0.15 0.16 0.21 0.16 0.18

0.0724 0.0770 0.1000 0.0789 0.0863

0.9624 0.9600 0.9470 0.9589 0.9549

0.0335 0.0307 0.0441 0.0346 0.0713

48

20

1.85 _+0.37

0.027

0.0134

0.9935

0.0013

Iceland total Norway (Barent Sea) West Greenland (Davis Strait)

,/~rnason and Spilliaert [ 16]. f, mean number of resolvable fragments obtained after digestion of minke whale genomic DNA with Alu I + SD. x, probability that a fragment present in one individual is also present in a randomly chosen individual. q, calculated mean population frequency of the resolvable alleles, assuming x = 2q - q2 h, mean probability for a fragment to be in heterozygous state, h = 2q(1 - q)/(2q q 2 ) = 2(lq)/(2 - q). p, mean probability that all fragments detected in an individual A are also present in another individual B,p =xf. -

Hybridization between blue and fin whale W h a l e r s have c l a i m e d for a long time that there existed h y b r i d s b e t w e e n blue and fin w h a l e s [17]. In the scientific catch under special p e r m i t in 1986 was a f e m a l e b a l a e n o p t e r i d w h a l e with external characteristics o f both blue and fin whale. This a n i m a l carried a foetus. It was also clear that this was its second p r e g n a n c y as j u d g e d f r o m the status o f the ovary. M o l e c u l a r analysis o f c o m p l e m e n t factor 4 (C4) and several other m a r k e r s d e m o n s t r a t e d clearly that the animal was indeed a h y b r i d b e t w e e n the two largest animals on earth, the blue and the fin w h a l e (Fig. 4), and m t D N A analysis s h o w e d that the father o f the foetus was a blue w h a l e [18]. T w o b a l a e n o p t e r i d males caught in 1983 and 1989 p r o v e d to b e h y b r i d s b e t w e e n the t w o a b o v e - m e n t i o n e d species. O n e o f these had a blue w h a l e m o t h e r and a fin w h a l e father, the other had a fin w h a l e m o t h e r and a blue w h a l e father j u d g e d f r o m the m t D N A which is u s u a l l y inherited f r o m the m o t h e r only. T h e difference b e t w e e n the m i t o c h o n d r i a l c y t o c h r o m e b gene o f the blue and fin suggests that the two species s e p a r a t e d / > 3 . 5 million y e a r s ago. The sequences o f the m i t o c h o n d r i a l control region o f blue and fin w h a l e s differ b y 7%. The difference in the m t D N A control r e g i o n b e t w e e n three blue w h a l e m t D N A h a p l o t y p e s was ~< 1%, about one-tenth o f the difference b e t w e e n the two species [19]. F i g u r e s 5 and 6 show the ventral side of male blue/fin h y b r i d and a m a l e fin, respectively.

DNA fingerprinting of fin whales in the North Atlantic Ocean using human hypervariable region probe, alpha-globin 3 'HVR A s d e m o n s t r a t e d earlier [20], the h u m a n a l p h a - g l o b i n 3 ' H V R p r o b e was useful for D N A fingerprinting fin and sei. The e n d o n u c l e a s e H i n f I gave the highest n u m b e r o f

99

kb 3.5 3.0 2.7 2.5

1.8 1

2

3

4

Fig. 4. Southern blot assay of DNA, digested with the restriction endonuclease Taq I, and probed with a C4 cDNA of human origin: 1, fin whale; 2, blue/fin hybrid; 3, blue whale; 4, foetus of the hybrid whale.

DNA fractions in fin whales, Alu I and Rsa I in sei whales. It can be deduced from this that the probability of finding two identical fins is 1:1.3 x 106 and for sei 1:6,250, when the endonucleases Hinf I and Alu I were used, respectively. This demonstrates great genetic variation in fins, which is also found in the enzyme systems [8].

Discussion

It can be seen from Fig. 2 that the different sample years of Icelandic fin whales show more genetic similarities than the Icelandic and Spanish fin whale samples. The genetic variation observed in fin whales caught off Iceland and off Spain is higher than that described in general for other mammals. Ayala [21] has summarised the genetic variation in 30 mammalian species and finds the average heterozygosity H = 0.051 and the average proportion of polymorphic loci P = 0.206. In fin whales caught off Iceland, H = 0.071 and for fin whales caught off Spain the value is higher at H = 0.089. The proportion of loci polymorphic is the same for both groups at P = 0.275 (40 loci investigated). Our H-values for fin whales are also higher than previously reported values by Wada and Numachi [22] for the North Pacific and the Antarctic fin whale stocks (H=0.017 and P=0.138, 13 loci investigated). The

100

Fig. 5. The ventral side of a male blue/fin hybrid whale caught in Icelandic waters in 1989. Compare the ventral coloration with that of the fin whale in Fig. 6 (photo by ,~rni Alfre~sson, 1989).

Fig. 6. A male fin whale in the same position as the hybrid whale in Fig. 5 (photo by Sveinn GuSmundsson, 1989).

101 observed genotypic frequencies at most of the loci revealed non-significant deviations from those expected by the Hardy-Weinberg equilibrium in Icelandic and Spanish fin whales. However, at three loci the observed genotypic frequencies between years in Icelandic fin whales differed significantly from that expected in most of the comparisons which were made. The deviations from the HardyWeinberg expectations were all due to deficiency in heterozygotes, which could point to substructure in the population. The G-values of the genetic homogeneity tests show significant differences between all the sample years in fin whales from Iceland and also between the samples from Iceland 1985 and Spain 1985. The G-values between the sample years 1985-1986 and 1986-1987 in fin whales from Iceland, show less difference than the G-value between samples from Iceland 1985 and Spain 1985, although the sample years 1987-1988 show a greater difference. This is hard to interpret at the population level, but may indicate that some variation exists in sampling of different breeding herds between whaling seasons. The genetic distance between samples of fin whales from Iceland 1985 and Spain 1985 (D = 0.013) is greater than the mean D value calculated between Icelandic sample years 1985-1988 (D = 0.010). The D value (D = 0.013) between the Icelandic and Spanish fin whales is smaller than that found between geographically isolated populations in other mammalian species, D = 0.058 [21], but within the range of previously reported values between populations of Cetacea [22], the values ranging from 0.0003 to 0.0885. The finding that the genetic distance between samples from the two areas (Iceland 1985 and Spain 1985) is greater that that found between Icelandic sample years, suggests that the Icelandic and Spanish samples represent two different stocks. Most population genetic studies involve only a single year comparison between populations. Nevo [24] reports that marine vertebrates have significantly higher average heterozygosity (H=0.061, 21 species) than terrestrial vertebrate species ( H = 0.041, 70 species) but that they share similar polymorphism, P = 0.203 (15 species) and P = 0.180 (74 species), respectively. The amount of genetic variation in fin and sei whales in Danfelsd6ttir et al. [8] (H = 0.069 and P = 0.225) agrees with Nevo' s findings and the large value of H implies that the fin and the sei whales have an unbroken history in the Northeast Atlantic. The value of genetic distance between fin and sei whales (D = 0.098) is lower than the mean value reported on species of mammals in general, D = 0.232 [23] but higher than the smallest genetic distance found between two whale species, striped dolphin (Stenella coeruleoalba) and spotted dolphin (Stenella attenuata), D = 0.026 and also higher than the D value between sei and Bryde's whales, D = 0.047 [23]. The genetic distance between cetacean species, as demonstrated by this review and other studies [8,22,23] appears to be less than the genetic distances previously reported between other mammalian species. The above-mentioned results support that fin whales and minke whales from the sample areas represent different populations; this is also supported by morphometric studies [25,26] and tagging experiments [27].

102

Conclusions The genetic marker studies indicate that: 1. fin whales from Canada, Iceland, Norway and Spain are different populations with substructures; 2. sei whales in Icelandic waters are a homogenous population; 3. minke whales from West Greenland, Iceland and Norway are separate populations; 4. heterogeneity is high in the three species in protein as well as DNA markers indicating an unbroken history in the area; 5. genetical relationship is close between the three species; 6. hybridization between blue whales and fin whales occurs and the hybrid can be fertile; 7. MHC genes are less polymorphic in fin and sei whales than in most terrestrial mammals reflecting a different immunogenic environment in the sea.

Acknowledgements I wish to thank all my co-authors over the years who have made this review possible. I also want to thank those working on the flencing platform for their assistance as well as the crews of the whaling boats for their help. Grants from Hvalur Ltd. over the years, which made these studies possible, are gratefully acknowledged.

References 1. R6rvik C J, Jonsgard A. Review of balaenopterids in the North Atlantic Ocean. FAO Fish Ser (Mammals in the Seas) (5) 1981 ;3:269-286. 2. Donovan GP. A review of IWC stock boundaries. Rep Int Whal Commn 1991;(Special Issue 13):39~58. 3. Sergeant DE. Stocks of fin whales Balaenopteraphysalus L. in the North Atlantic Ocean. Rep Int Whal Commn 1977;27:460-473. 4. Arnason A, Danielsd6ttir AK, Spilliaert R, SigurSsson JH, J6nsd6ttir S, P~ilsd6ttir A, Duke EJ, Joyce P, Groves V, Trowsdale J. A brief review of protein and DNA marker studies in relation to the stock identity of fin whales (Balaenoptera physalus) from Iceland and Spain (SC/F91/F16). Rep Int Whal Commn 1992;42:701-705. 5. Arnason A, Spilliaert R. Study of carbonic anhydrase polymorphism in fin whales (Balaenoptera physalus) caught off Iceland and comparison with five other species of whales: Sei (B. borealis), minke (B. acutorostrata), sperm (Physeter catodon), killer whale (Orcinus orca) and pilot whale (Globicephala melaena) (SC/39/O18). Rep Int Whal Commn 1988;38:514. 6. Spilliaert R, Arnason A. A progress report on an electrophoretic study of liver esterases in fin whales from Icelandic and Spanish waters (SC/40/Ball). Rep Int Whal Commn 1989;39: 463. 7. Arnason A, J6nsd6ttir S. An electrophoretic study of cardiac esterases and proteins of fin whales (Balaenoptera physalus) from Icelandic and Spanish waters and sei whales (Balaenoptera borealis) caught off Iceland (SC/39/Ba6). Rep Int Whal Commn 1988;38:507.

103 8. Danfelsd6ttir AK, Duke EJ, Joyce P,/trnason A. Preliminary studies on genetic variation at enzyme loci in fin whales (Balaenoptera physalus) and sei whales (B. borealis) from the North Atlantic. Rep Int Whal Commn 1991;(Special Issue 13):115-124. 9. Sokal PR, Rohlf FJ. Biometry, 2nd edn. San Francisco, CA: Freeman, 1981 ;859 pp. 10. Nei M. Genetic distance between populations. Am Nat 1972;106:283-292. 1 I. Sneath PHA, Sokal RR. Numerical Taxonomy. San Francisco, CA: Freeman, 1973;573 pp. 12. Swofford DL, Selander RB. BIOSYS-1. A computer program for the analysis of allelic variation in population genetics and biochemical systematics. Champaign, IL: Illinois Natural History Survey, 1989;43 pp. 13. Trowsdale J, Groves V, /~rnason A. Limited MHC polymorphism in whales. Immunogenetics 1989;29:19-24. 14. Spilliaert R, P~lsd6ttir ,/~, Arnason A. Analysis of the C4 genes in baleen whales using a human cDNA probe. Immunogenetics 1990;32:73-76. 15. Danfelsd6ttir AK, Duke EJ, ,~rnason A. Genetic variation at enzyme loci in North Atlantic minke whales (Balaenoptera acutorostrata). Biochem Genet 1992;30:189-202. 16. ,~rnason A, Spilliaert R. A study of variability in minke whales (Balaenoptera acutorostrata) in the North Atlantic using a human hypervariable region probe, a-globin 3'HVR. Rep Int Whal Commn 1991 ;41:439-443. 17. Cocks AH. The fin whale fishery of 1886 on the Lapland coast. The Zoologist 1887;11:207-222. 18. Spilliaert R, Vfkingsson G, ,~rnason lJ, P~ilsd6ttir A, Sigurj6nsson J,/~rnason A. Species hybridization between a female blue whale (Balaenoptera musculus) and a male fin whale (B. physalus): molecular and morphological documentation. J Hered 1991;82:269-274. 19. Arnason 0, Spilliaert R, P~ilsd6ttir A, Arnason A. Molecular identification of hybrids between the two largest whale species, the blue and the fin whale. Hereditas 1991 ;115:183-189. 20. Arnason A. Hvalastofnar og erf6amork. Landvernd: Hi6 fslenska n~ittfirufr~eOif61ag, 1993;351 pp. 21. Ayala FJ. Population and Evolutionary Genetics. Menlo Park, CA: Benjamin/Cumming, 1982;268 PP. 22. Wada S, Numachi K. Allozyme analysis of genetic differentiation among the populations and species in Balaenoptera. Rep Int Whal Commn 1991 ;(Special issue 13): 125-154. 23. Wada S. Genetic differentiation between two forms of short-finned pilot whales off the Pacific coast of Japan. Sci Rep Whal Res Inst 1988;39:91-101. 24. Nevo E. Genetic variation in natural populations: patterns and theory. Theoret Popul Biol 1978;13:121-177. 25. Jover L. Biometrical discrimination between Icelandic and Spanish stocks of fin whales (Balaenoptera physalus). Rep Int Whal Commn 1987;37:394. 26. Christensen I, Haug T, Wiig O. Morphometric comparison of minke whales Balaenoptera acutorostrata from different areas of the North Atlantic. Mar Mammal Sci 1990;6:327-338. 27. Gunnlaugsson Th, Sigurj6nsson J. Analysis of the North-Atlantic fin whale marking data from 1979-1988 with special reference to Iceland. Rep Int Whal Commn 1989;39:383-388.

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9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 0. Ulltang, editors

105

Genetic variation in northeastern Atlantic minke whales (Balaenoptera ac utorostrata ) Anna K. Danfelsd6ttir l, Sverrir D. Halld6rsson ~, Sigfrf6ur Gu61augsd6ttir 1 and Alfre6/krnason 2 1Marine Research Institute, Reykjavfk, Iceland; and 21mmunogenetics Unit, Department of Pathology, University Hospital, Reykjavfk, Iceland Abstract. Background: the population structure of North Atlantic minke whales (Balaenoptera acutorostrata) was studied in a previous study by Danfelsd6ttir et al. [1]. It was concluded that bigger sample sizes were needed in order to determine if whales from the northeastern Atlantic represent a different population to the West Greenland and Icelandic whales, respectively. Methods: genetic variation within northeastern Atlantic (Norwegian) minke whales was investigated and compared with genetic variation in minke whales from West Greenland and Iceland using isozyme analysis. Results: in the Norwegian minke whale liver samples, 37 enzyme loci were examined, of which eight were found to be polymorphic. The average heterozygosity was 0.060 (SE = 0.021). Significant deviations from the Hardy-Weinberg genotypic frequencies were observed at two loci in the liver samples. In gonad samples, 28 enzyme loci were examined and significant deviations from expected frequencies were observed at two loci, which could be due to small sample size of the gonads. Contingency Z2 analysis revealed significant differences in allele frequencies between the Norwegian minke whales and minke whales from West Greenland and Iceland, respectively. Wright's FST value was 0.111 and the genetic distance was 0.015 between minke whale liver samples from West Greenland and Norway. The FST value was 0.149 and the genetic distance was 0.028 between minke whale gonad samples from Iceland and Norway. Conclusions: the genetic differences between minke whales from Norway, West Greenland and Iceland indicated that the Norwegian minke whales represent a different breeding population from the West Greenland population and the Icelandic population, respectively. Key words: population genetics, isozyme analysis, population structure, stocks, genetic variability, genetic distance

Introduction

The minke whale (Balaenoptera acutorostrata) is widely distributed in the North Atlantic but their division into populations is unclear. Their northern geographical range is limited by the polar ice and their southern limit is thought to reach as far as the Mediterranean on the eastern side of the North Atlantic and south of the coast of Florida on the western side [2]. They migrate north in the spring and early summer to their feeding grounds and south again in the autumn to their breeding grounds. The North Atlantic minke whales have been divided into four management stocks by the International Whaling Commission [3]: (1) the Canadian East coast stock, (2) the West Greenland stock, (3) the Central stock (Icelandic whales) and (4) the

Address for correspondence: A.K. Danfelsd6ttir, Marine Research Institute, c/o Biotechnology IceTec, Keldnaholt, IS-112 Reykjav~, Iceland.

106 northeastern stock (Norwegian whales). This division is based on methods such as catch statistics, studies on biological characteristics and tagging. A study by Christensen et al. [4] using statistical analysis of morphometric data to identify North Atlantic minke whale populations revealed some differences in parameters measured between minke whales from West Greenland, Icelandic and Norwegian waters. However, it was considered that no clear stock division could be identified because of the high degree of overlapping characters between the groups. An isozyme study of the genetic variation in North Atlantic minke whales, including samples from West Greenland (West Greenland stock), Icelandic (Central stock) and Norwegian waters (northeastern stock) indicated that the genetic differences between samples from the three areas represented different breeding populations. However, due to small sample size of Norwegian minke whales, it was noted that more extensive study using larger sample sizes was needed, and that possible tissue specificity of zymograms could result in larger deviation of the relative genetic distances between Icelandic samples and samples from the other areas [ 1]. Differences in variability in DNA fingerprinting of minke whale samples from West Greenland, Iceland and Norway (Barents Sea) using human a-globin 3'HVR probe, supported the existence of three different populations in these areas [5]. Population genetic studies of minke whales using electrophoretic analysis of isozymes have been carried out on North Atlantic minke whales by S imonsen et al. [6] and Arnason et al. [7], on Antarctic minke whales by Wada and Numachi [8], on Japanese coastal minke whales by Wada [9] and on Pacific and Antarctic minke whales by Wada and Numachi [ 10]. Other population genetic studies on minke whales include methods such as mtDNA D-loop sequencing, mtDNA restriction fragment analysis (mtDNA RFLP), ribosomal DNA RFLP, repetitive satellite DNA and single locus DNA variation [ 11-

15]. The present study describes the genetic variation in minke whale samples from Norway (northeastern stock) using electrophoretic analysis of isozymes. The Norwegian samples came from four main areas, along the west of Spitsbergen and Bear Island (ES area), Barents Sea (EB area), Norwegian coast (EC area) and the North Sea (EN area). The genetic variation in Norwegian minke whales is compared with the isozyme data of North Atlantic minke whales (West Greenland and Central stock) contained in Danfelsd6ttir et al. [ 1]. The genetic variation in liver and kidney samples from West Greenland and Norway and gonad samples from Iceland and Norway were examined in order to investigate the genetic variation and population structure of North Atlantic minke whale samples.

Materials and Methods

Minke whale samples were collected during the Norwegian scientific catch 1988 (EB and EC areas), 1989 (ES and EC areas), 1990 (EB and EC areas), 1992 (ES, EB and EC areas), 1993 (ES, EB, EC and EN areas) and 1994 (EN area). The samples

107 f r o m the 1985 w h a l i n g season in N o r w a y , Barents Sea (EB) w e r e supplied b y Ivar Christensen, Institute of M a r i n e Research, B e r g e n , N o r w a y . S a m p l e s w e r e stored at - 2 0 ~ in N o r w a y , transported to Iceland by air on dry ice and stored a t - 2 0 ~ until analysed. A total of 279 N o r w e g i a n m i n k e w h a l e tissue s a m p l e s o f liver, k i d n e y and g o n a d f r o m 220 individuals were analysed (Table 1). T h e i s o z y m e data on W e s t G r e e n l a n d / D a v i s strait and Icelandic coastal m i n k e whales w e r e obtained f r o m Danielsd6ttir et al. [ 1]. A total of 40 individuals of m i n k e w h a l e s a m p l e s f r o m W e s t G r e e n l a n d and Iceland w e r e also r e - a n a l y s e d in this study, as a control for the c o m p a r i s o n of the i s o z y m e data c o n t a i n e d in Danfelsd6ttir et al. [1]. T h e s a m e m e t h o d s as described in Danfelsd6ttir et al. [1 ] and Danfelsd6ttir [16] w e r e used for p r e p a r i n g the samples for electrophoresis and c a r r y i n g out the i s o z y m e analysis. Table 2 lists the 29 e n z y m e s y s t e m s e x a m i n e d , the a b b r e v i a t i o n s used, the

Table 1. The location, tissue type and number of North Atlantic minke whale samples

Location

Year

Tissue type Liver

West Greenland b

1980 1982 1983 Total

Iceland b

1981 1982 1983 1984 1985 Total

28 22 50

Kidney

Gonads a

8 2 19 29 18 22 25 41 8 114

Norway

Barent's Sea EB

Spitsbergen ES Coastal EC

North Sea EN

1985 1988 1990 1992 1993 1989 1992 1993 1988 1989 1990 1992 1993 1993 1994 Total

12 1 2 39 12 15 35 27 25 1 3 18 18 5 7 220

2 12 24 1 2 41

aNumbers in parentheses are samples that are being analysed at present. bSamples analysed in the study by Danfelsd6ttir et al. [ 1]

(39) (12) (35) (27) 15 3 (18) (18) 167

108

Table 2. Enzymes screened for in North Atlantic minke whales (Balaenoptera acutorostrata), their Enzyme Commission numbers (EC No.), minimum number of loci, the optimal electrophoresis methods employed and the staining method references Enzyme

Abbrev.

EC No.

No. of loci

Optimum buffer system (%)a

Staining Tissue method reference T O L K

Adenosine deaminase Adenylate kinase Alcohol dehydrogenase Aspartate aminotransferase Creatine kinase Esterase Fructose- 1.6-diphosphatase Fumarase Glucose dehydrogenase Glucose-6-Ph. dehydrogenase Glutamate dehydrogenase Glyceraldehyde phosph, deh. a-Glycerophosphate dehydrog. Hexokinase Isocitric dehydrogenase Lactate dehydrogenase Malate dehydrogenase Malic enzyme Mannose phosphate isomerase Nucleoside phosphorylase Peptidase A Peptidase B Peptidase C Phosphoglucomutase 6-Phosphogluconate dehydrog. Phosphoglucose isomerase Sorbitol dehydrogenase Superoxide dismutase Xanthine oxidase

ADA AK ADH AAT CK EST F16DP FH GDH G6PD GLUD GAPDH GPD HK IDH LDH MDH ME MPI NP PEPA PEPB PEPC PGM 6PGDH PGI SDH SOD XO

3.5.4.4 2.7.4.3 1.1.1.1 2.6.1.1 2.7.3.2 3.1.1.1 3.1.3.11 4.2.1.2 1.1.1.47 1.1.1.49 1.4.1.3 1.2.1.12 1.1.1.8 2.7.1.1 1.1.1.42 1.1.1.27 1.1.1.37 1.1.1.40 5.3.1.8 2.4.2.1 3.4.11 .* 3.4.11 3.4.11 2.7.5.1 1.1.1.44 5.3.1.9 1.1.1.14 1.15.1.1 1.2.3.2

1 2 1 2 2 1

PAGE(15) PAGE(12) PAGE(5) PAGE(4) AM PAGE(7)

[21] [22] [22] [22] [24] [22]

+ + + -+ -

+ + + + + + + + + + + + + + - + +

1

AM

[22]

-

-

+

+

1 1 1 1

PAGE (3) TCB PAGE(7) TVB

[20] [22] [20] [23]

+ + + +

w + + +

+ + + +

+ + w +

1

AM

[22]

+

+

+

w

1

AM

[24]

+

+ -

1

AM

[22]

+

+

+

+

2 2 2 2 1

AM PAGE(5) PAGE(5) AM PAGE(12)

[22] [25] [20] [20] [21]

+ + + + +

+ + + + +

+ + + + +

+ + + + +

1

AM

[21]

+

+

+

+

1 1 1 1

PAGE(7) PAGE(9) PAGE(9) PAGE(9)

[22] [22] [22] [22]

+ + + +

+ + + +

+ + + +

+ + + w

1

AM

[22]

+

+

+

+

1

PAGE(4)

[22]

+ + + +

1

AM

[22]

- -

4 1

PAGE(7) PAGE (5)

[26] [27]

+ + + + - - + -

+

+

w

aoptimum buffer system: AM, citrate-aminopropylmorpholine (pH 6.3), modified from [17]; PAGE, polyacrylamide gel electrophoresis; Tris-glycine (pH 8.9) [ 18]; TCB, Tris-citrate-borate (pH 8.6) [ 19]; TVB, Tris-versene-borate (pH 8.0) [20]. Tissues: T, testis; O, ovaries; L, liver; K, kidney. Staining appearance: +, staining,-, no staining; w, weak staining. o p t i m a l e l e c t r o p h o r e t i c m e t h o d s a n d r e f e r e n c e s f o r t h e s t a i n i n g m e t h o d s . A l l t h e statistical a n a l y s i s o f t h e e l e c t r o p h o r e t i c d a t a w a s c a r r i e d o u t u s i n g B I O S Y S - 1 ,

Fortran-

7 7 c o m p u t e r p r o g r a m b y S w o f f o r d a n d S e l a n d e r [28].

Results

Genetic variation within samples T h e 29 e n z y m e s y s t e m s e x a m i n e d in t h e N o r w e g i a n m i n k e w h a l e s a m p l e s r e p r e s e n t 3 9 loci. S o m e o f t h e e n z y m e s y s t e m s f a i l e d to g i v e a d e q u a t e s t a i n i n g a n d r e s o l u t i o n

109 required in this study. They were a-glycerophosphatase (GPD) and peptidase C (PEPC) in liver samples, 6-phosphogluconate dehydrogenase (6PGDH) and xanthin oxidase (XO) in kidney samples and aspartate aminotransferase (AAT), esterase (EST), fructose-l,6-diphosphatase (F16DP), sorbitol dehydrogenase (SDH) and XO in gonad samples. In the Norwegian liver, kidney and gonad samples 27, 27 and 24 enzyme systems represent 37, 37 and 28 loci, respectively. The following six loci were found to be polymorphic in all three tissue types of the Norwegian minke whales: Ada, Ak-1, Mdh-S, Mpi and Pep-B. In addition, the liver samples were polymorphic at Adh, AatS and Ldh-A loci (eight loci in all) and variable at three loci: Est, Pep-A and Pgi. The kidney samples, in addition, were polymorphic at Aat-S, Ldh-A and Pep-A (eight loci in all) and the gonad samples at Pep-A (six loci in all). Two of the loci (Ldh-A and Mdh-S) were excluded from statistical analyses of minke whale liver samples, as the readings of these loci were ambiguous in some of the Norwegian samples and their analysis needs to be repeated. Table 3 summarises the amount of genetic variation found at the enzyme loci in the West Greenland, Icelandic and Norwegian liver, kidney and gonad samples. The mean sample size per locus, the mean number of alleles per locus, number of loci examined, the percentage of polymorphic loci by the 0.95 criterion for polymorphism and the average heterozygosity, both average observed (direct count) and the Table 3. The average heterozygosity at enzyme loci in North Atlantic minke whales

Tissue

Mean sample size per locus

West Greenland Liver 49.9 (0.1) Kidney 28.2 (0.6) Iceland Gonads

112.0 (1.1)

Norway Liver

216.0

Kidney

43.5

Gonads

15.0

(1.0)

Mean no. of alleles per locus

No. of loci

1.2 (0.1) 1.2 (0.1)

37

Percent of polymorphic loci, pa

Average heterozygosity (H) H (SE) direct count

H (SE) H-W expected b

16.2

0.061 (0.025)

0.057 (0.023)

29

17.2

0.045 (0.023)

0.044 (0.019)

1.3 (0.1)

29

20.7

0.074 (0.027)

0.076 (0.028)

1.4

37

21.6

0.053 (0.018)

0.060 (0.021)

1.3

35

22.9

0.081 (0.029)

0.083 (0.028)

1.3

28

21.4

0.086 (0.038)

0.074 (0.029)

(0.1)

(0.3)

(0.1)

(o.o)

(o.1)

P, polymorphism by the 0.95 criterion; H, average heterozygosity (including invariant loci); SE, standard error. aA locus is considered polymorphic if the frequency of the most common allele does not exceed 0.95. b Hardy-Weinberg expected: unbiased estimate [29].

110

Table 4. Allele frequencies at variable loci in North Atlantic minke whales and x2-values (df = 1) for deviation from Hardy-Weinberg equilibrium Locus

Subunit no.

Alleles

Ada

1

(n)

Ak-1

1

~ d h

2

Aar-S

1

Est

1

West Greenland liver Obs. freq.

I 2 3 (n) 1 2 3

(4 I

2 (n) 1 2 3

(4

1 2 3

50 0.3 10 0.610 0.080 50 0.210 0.780 0.010 50 0.040 0.960 50 0.060 0.940 0.000 50 0.000 1.000 0.000

X2

P

2.19

0.14

0.31

0.58

0.06

0.08

0.17

0.68

Iceland gonadsa Obs. freq.

x2

111 0.113 0.887 0.000 0.12 109 0.500 0.500 0.000 1.21 87 0.236 0.764 0.20 87#

114#

Norway gonads

Norway liver

P

0.73

0.27 0.66

Obs. freq. 213 0.042 0.484 0.474 215 0.126 0.874 0.000 211 0.066 0.934 208 0.065 0.933 0.002 207 0.022 0.973 0.005

X2

P

1.17

0.28

6.95

0.008**

0.24

0.62

0.25

0.61

Obs. freq15 0.033 0.333 0.633 15 0.067 0.933 0.000 15 0.000 1.OOO 15#

15# 0.00

I.00

x2

P

1.01

0.20

0.04

0.84

Ldh-A

4

(n)

Mdh-S

2

(n) 1

Mpi

1

Pep-A

1

Pep4

1

A A*

2 (n) 1 2 3 (n) 1 2 (n) 1

Pgi

NP

2

2

2

(4

1 2 8 (4 1

2

df, degrees of freedom. n, number of individuals analysed. #, Aat-S and Est did not stain in gonad samples; !, Samples need to be re-analysed for Ldh-A and Mdh-S loci. *0.01 < P < 0.05, **0.001 < P < 0.01, ***P < 0.001. aData from DanielsdBttir et al. [I].

111

112 average calculated heterozygosity according to the unbiased H by Nei [29]) are listed. In the Norwegian minke whales, the average percentage of polymorphic loci was 21.6% in liver, 22.9% in kidney and 21.4% in gonad samples. The average heterozygosity for the Norwegian samples was 0.060 (0.021) in liver, 0.083 (0.028) in kidney and 0.074 (0.029) in gonad samples. The contingency Z2 statistical analyses of sex heterogeneity of allele frequencies were carried out. Significant differences were observed between males and females in the Norwegian liver samples, at the Ada and Ak-1 loci (Z2 at 34 loci = 45.52, df = 12, P < 0.001). The locus Pep-B was not in H-W equilibrium within the male sample group and was therefore excluded from this comparison. The sex ratio was 0.491 for females. The variable loci in liver and gonad samples from West Greenland, Iceland and Norway, their allele frequencies and the Z2 goodness of fit test for the HardyWeinberg equilibrium are listed in Table 4. Kidney samples were also analysed but the data are not represented here. The liver samples were chosen for the comparison between West Greenland and Norwegian areas on the grounds of bigger sample size. Significant differences in observed frequencies from those expected by the HardyWeinberg equilibrium were detected at Ak-1 and Pep-B loci (P < 0.01) in Norwegian liver samples both due to heterozygote deficiency and at Mdh-S and Pep-A locus (P < 0.05) due to heterozygote excess and deficiency, respectively, in Norwegian gonad samples (Table 4). The Norwegian liver samples (n = 220) had seven unique alleles at the Aat-S, Est, Pep-A, Pep-B and Pgi loci, not found in the West Greenland liver samples (n = 50). The Norwegian gonad samples (n = 15) had three unique alleles at the Ada, Pep-A and Pep-B loci not found in the Icelandic gonad samples (n = 114).

Genetic differentiation The heterogeneities in allele frequencies between liver samples from West Greenland and Norway and gonad samples from Iceland and Norway were tested using the contingency Z2 table and the Wright's F-statistics (Fsx) analyses [28]. The heterogeneities of Z2 values between areas are presented in Table 5. The total Z2 values for the differences in allele frequencies between samples were highly significant (P < 0.001) both between liver samples from West Greenland and Norway (Z2= 105.15, d f - 11) and between gonad samples from Iceland and Norway (Z2= 289.46, d f - 11) (Table 5). No contingency Z2 analysis was done between West Greenland and Icelandic samples as common tissue types were not available. Wright's Fsv values for the polymorphic loci in the West Greenland-Norwegian comparison varied between 0.000 and 0.241 and the mean value was 0.111 which means that 11.1% of the total gene diversity (HT = 0.106) is attributed to differences between sample areas and 88.9% of the genetic variation is due to within total sample variability. In the Icelandic-Norwegian comparison, the FsT values for the polymorphic loci varied between 0.009 and 0.342 and the mean value was 0.149. The total gene diversity was HT = 0.135.

113 Table 5. Contingency ~2 analysis of allele frequencies at enzyme loci between North Atlantic minke whale samples

Locus

Minke whale samples West Greenland and Norway liver

Ada Ak-1 Adh

Aat-S Est Ldh-A Mdh-S Mpi Np Pep-A Pep-B Pgi

Total for all loci

)(;2 df )(;2 df ~2 df )(;2 df )(;2 df )~2

df Z2 df )~2 df ~2 df ~2 df )C2 df ~2 df ~2 df

96.40"** 2 _ 0.97 1 0.27 2 2.71 2 _

_ 0.04 1 _ 3.82 1 0.93 2 105.15"** 11

Iceland and Norway gonads 152.09" 2 20.03*** 1 8.85* 1 _

0.99 1 4.76 2 1.51 1 50.03*** 1 46.69*** 1 289.46*** 11

df, degrees of freedom. * 0.01 < P < 0.05, ** 0.001 < P < 0.01, *** P < 0.001. T h e g e n e t i c differentiations b e t w e e n the s a m p l e s f r o m W e s t G r e e n l a n d a n d N o r w a y and also b e t w e e n I c e l a n d and N o r w a y w e r e c a l c u l a t e d u s i n g the u n b i a s e d genetic d i s t a n c e c o e f f i c i e n t (D) of Nei [29] b a s e d on the allele f r e q u e n c y o f 37 a n d 27 c o m m o n loci, r e s p e c t i v e l y . T h e D v a l u e c a l c u l a t e d b e t w e e n s a m p l e s f r o m W e s t G r e e n l a n d a n d N o r w a y was 0.015. T h e D v a l u e b e t w e e n s a m p l e s f r o m I c e l a n d a n d N o r w a y w a s 0.028. T h e U P G M A d e n d r o g r a m in Fig. 1 w a s b a s e d on the a b o v e listed D values.

Discussion Genetic variation within samples

T h e g e n e t i c variation in the N o r w e g i a n m i n k e w h a l e s , H = 0 . 0 6 0 ( S E = 0 . 0 2 1 ) a n d

114

0.03 I

0.02 i

I

0.01

I

I

0.00

I

I

I

West Greenland (liver)

Norway (liver) Norway (gonads)

Iceland (gonads)

i

0.03

i

i

0.02

I

I

0.01

I

I

0.00

Nei's genetic distance (D).

Fig. 1. A UPGMA dendrogram showing the relationships of North Atlantic minke whale samples from West-Greenland, Iceland and Norway, based on Nei's [29] unbiased genetic distance coefficient (D).

P = 0 . 2 1 6 is within the range of previously reported values in minke whale populations (Table 6). The average H of North Atlantic minke whales from this study and Danfelsd6ttir et al. [1] is H = 0.064 (SE = 0.023). Wada and Numachi [10] reported a range from H = 0.020 to 0.087 in three minke whale populations and Simonsen et al. [6] reported H = 0 . 0 4 6 ( S E = 0 . 0 1 0 ) in West Greenland minke whales. The average heterozygosity in 19 baleen whale populations ranged from H = 0.003 to 0.087 (mean average H = 0.042, SE = 0.029) (Table 6). The low H value for the small form minke whale, H = 0.003 [10] could be due to the small sample size (n = 8). Table 6 summarises the average heterozygosity for four baleen whale species, H = 0.043 [1,6,10,16,30] and for 13 toothed whale species, H = 0.056 [31-35]. The genetic variation in marine mammals is H = 0.041 (30 species) and P = 0.150 (26 species) [ 1,6,10,16,30-44], which is similar to what Nevo et al. [45] summarised for mammals in general, H = 0.041, SD = 0.035 (184 species) and P = 0.191 (181 species). The amount of genetic variation in North Atlantic minke whales is within the range of previously reported average values for H and P in marine mammals and mammals. The range of heterozygosity values appears to be broad in whale populations which agrees with Nevo et al. [45] who report that the intraspecific and interspecific H and P values range widely in mammals (184 species) and that the level of genetic variation may differ more within than between taxa.

115 Table 6. Summary of the genetic variation in mammalian species and populations

Groups

No. of species

No. of loci

P0.95

H (SE)

References

0.191 0.150 0.175 0.179 0.171

0.041 0.041 0.049 0.056 0.043

(0.035) (0.035) (0.038) (0.046) (0.029)

[1,6,10,16,30--44] [1,6,10,16,30-35] [31-35] [1,6,10,16,30]

0.222 0.044 0.179 0.095 a 0.162 0.207 0.216 0.204

0.087 0.020 0.052 0.046 0.057 0.074 0.060 0.057

(0.024) (0.011) (0.018) (0.010) b (0.023) (0.028) (0.021) (0.021)

[ 10] [ 10] [10] [6] [1] [ 1] This study [1,6,10 and this study]

P/H

Mammals Marine mammals Whales Toothed whales Baleen whales Minke whales Antarctic ocean Japan coastal Korean coastal West Greenland West Greenland Iceland Norway Mean

181/184 26/30 17 13 4 45 45 39 21 37 29 37

[45]

P, polymorphism by the 0.95 criterion; H, average heterozygosity, unbiased estimate [29]" SE, standard error. a By the 0.99 criterion. b By direct count method.

The observed significant deviations in observed genotypic frequencies from those expected by the Hardy-Weinberg equilibrium were detected at two of the eight polymorphic loci in Norwegian liver samples and were due to heterozygote deficiency, suggesting that the samples are not taken from a homogeneous population. Samples were taken in different areas in the Northeast Atlantic, in different seasons and years and might contribute to the observed heterogeneity within the Norwegian samples. Further statistical analyses on the subdivided samples are needed, in order to study the observed heterogeneity. Deviations from the H-W equilibrium at the Mdh-S and Pep-A loci in gonad samples could be explained by a small sample size (n = 15).

Genetic differentiation The contingency Z2 analyses revealed highly significant differences in allele frequencies between liver samples from West Greenland and Norway and between gonad samples from Iceland and Norway. This indicates that the samples from Norway are from a different population than the samples from West Greenland and Iceland, respectively. The degree of heterogeneity between samples of minke whales measured by Wright's FST indicated restricted gene flow between the samples. The FST was similar to fin whale samples from two locations (Iceland and Spain, FST = 0.10) and higher than found between fin whale sample years (FsT = 0.07) [ 16]. This is similar to the findings of Arnason and Spilliaert [5]. They examined the variability in the North Atlantic minke whales by using the human a-globin 3'HVR (hypervariable region) probe. Morphological studies on minke whales from these

116 three areas [4] also revealed some differences in samples between areas. PalsbOll [12] used restriction fragment analysis of mtDNA and ribosomal DNA to identify samples of minke whales from West Greenland and the northeastern Atlantic (Norway). His findings revealed no significant difference between the two areas and he concluded that either there was a co-existence of two matemal lineages within one random mating population or an existence of two distinct breeding aggregations in one common feeding area. Our findings, however, do not indicate any mixing of breeding populations at the feeding areas. The genetic distance between minke whale liver samples from West Greenland and Norway, D = 0.015 was less than the genetic distance between gonad samples from Iceland and Norway, D = 0.028. The genetic distance between North Atlantic minke whale populations is within the lower range of what was reported between populations of Antarctic and Pacific minke whales, D = 0.0125 to D = 0.0885 by Wada and Numachi [ 10]. The distance between Icelandic and West Greenland minke whale samples was not calculated because no common tissue types were available. Only gonad samples were available from the Icelandic minke whales and liver and kidney samples from West Greenland. Deviations of the relative genetic distance between Norway and the other areas could occur since the tissue types are not the same between the two comparisons. Also it should be noted that only 15 gonad samples were available from Norway and more samples need to be examined for better evaluation of the genetic differences between minke whales from Iceland and Norway. The level of genetic differentiation between minke whales from Norway and both West Greenland and Iceland indicates that the Norwegian minke whale samples (northeastern stock) represent a different population from that of West Greenland and Icelandic (Central stock) minke whales, respectively. Study of the population structure of northeastem Atlantic minke whales is underway and further statistical analyses of the different sample areas, seasons and years are in process, as well as the study of more gonad samples from the northeastern area for comparisons with Icelandic gonad samples.

Acknowledgements We would like to thank Mr. Sverrir Sverrisson for assisting with the laboratory work and Dr. J6n M. Einarsson for professional support and encouragement. This research was supported by funding from the Norwegian Fisheries Research Council.

References 1. Danfelsd6ttirAK, Duke EJ, Arnason A. Genetic variation at enzyme loci in North Atlantic minke whales, Balaenoptera acutorostrata. Biochem Genet 1992;30:189-202. 2. ROrvikCJ, Jonsghrd A. Review of balaenopterids in the North Atlantic ocean. F.A.O. Fish Ser (5) Mammals in the Seas 1981;3:269-286.

117 3. Donovan GP. A review of IWC stock boundaries. Rep Int Whal Commn 1991;(Special Issue 13):39-68. 4. Christensen I, Haug T, Wiig 0. Morphometric comparison of minke whales (Balaenoptera acutorostrata) from different areas of the North Atlantic. Mar Mammal Sci 1990;6:327-338. 5. A,rnason A, Spilliaert R. A study of variability in minke whales (Balaenoptera acutorostrata) in the North Atlantic using a human hypervariable region probe, alpha-globin 3'HVR. Rep Int Whal Commn 1991;41:439-443. 6. Simonsen V, Kapel F, Larsen F. Electrophoretic variation in the minke whale, Balaenoptera acutorostrata Lacepede. Rep Int Whal Commn 1982;32:275-278. 7. Arnason A, Danfelsd6ttir AK, Spilliaert R, SigurOsson JH, J6nsd6ttir S, P~ilsd6ttir A, Duke EJ, Joyce P, Groves V, Trowsdale J. A brief review of protein and DNA marker studies in relation to the stock identity of fin whales (Balaenoptera physalus) from Iceland and Spain. Rep Int Whal Commn 1991 ;42:701-705. 8. Wada S, Numachi K. External and biochemical characters as an approach to stock identification for the Antarctic minke whale. Rep Int Whal Commn 1979;29:421-432. 9. Wada S. Genetic structure and taxonomic status of minke whales in the coastal waters of Japan. Rep Int Whal Commn 1983;33:361-363. 10. Wada S, Numachi K. Allozyme analyses of genetic differentiation among the populations and species of the Balaenoptera. Rep Int Whal Commn 1991;(Special Issue 13): 125-154. 11. Hoelzel AR, Dover GA. Mitochondrial D-loop DNA variation within and between populations of the minke whale (Balaenoptera acutorostrata). Rep Int Whal Commn 1991;(Special Issue 13):171-182. 12. PalsbOll PJ. Restriction fragment length polymorphism in the ribosomal genes of minke whales, Balaenoptera acutorostrata, from the Barents sea and West Greenland and restriction fragment pattern analysis of mitochondrial DNA in minke whales, Balaenoptera acutorostrata, from the Davis Strait and the Northeast Atlantic. Master Thesis, University of Copenhagen, 1989. 13. Wada S, Kobayashi T, Numachi KI. Genetic variability and differentiation of mitochondrial DNA in minke whales. Rep Int Whal Commn 1991;(Special Issue 13):203-216. 14. Amos B, Dover GA. The use of satellite DNA sequences in determining population differentiation in the minke whale. Rep Int Whal Commn 1991;(Special Issue 13):235-244. 15. vanPijlen I, Amos W, Dover GA. Multilocus DNA fingerprinting applied to population studies of the minke whale Balaenoptera acutorostrata. Rep Int Whal Commn 1991;(Special Issue 13):245254. 16. Danfelsd6ttir AK. Genetic variation among different species and populations of baleen whales from the North Atlantic Ocean. Ph.D. Thesis, University College Dublin, Ireland, 1994. 17. Clayton JW, Tretiak DN. Amine-citrate buffers for pH control in starch gel electrophoresis. J Fish Res Board Can 1972;29:1169-1172. 18. Davis BJ. Disk electrophoresis- II. Methods and application to human serum proteins. Ann NY Acad Sci 1964; 121:404--427. 19. Taggart J, Ferguson A, Mason FM. Genetic variation in Irish populations of brown trout (Salmo trutta L.): electrophoretic analysis of allozymes. Comp Biochem Physiol 1981;69B:393-4 12. 20. Siciliano MJ, Shaw SR. Separation and visualisation of enzymes on gels. In: Smith I (ed) Chromatographic and Electrophoretic Techniques, Vol. II. London: Heinemann, 1976; 185-209. 22. Harris H, Hopkinson DA. Handbook of Enzyme Electrophoresis in Human Genetics. Amsterdam: North-Holland, 1976. 23. Shaw CR, Prasad R. Starch gel electrophoresis of enzymes. A compilation of recipes. Biochem Genet 1970;4:297-320. 24. Brewer GJ. An Introduction to Isozyme Techniques. London: Academic Press, 1970. 21. Meera Khan P, Rijken H, Wijnen JT, Wijnen LMM, De Boer LEM. 4. Red cell enzyme variation in the orangutan: electrophoretic characterisation of 45 enzyme systems in cellogel. In: de Boer LEM, Junk W (eds) The Orang Utan. Its Biology and Conservation. The Hague, 1982;61-108.

118 25. Shaklee JB, Kepes KL, Whitt GS. Specialised lactate dehydrogenase isozymes: the molecular and genetic basis for the unique eye and liver LDHs of teleost fishes. J Exp Zool 1973;185:217-240. 26. Crouch R, Priest DG, Duke EJ. Superoxide dismutase activities of bovine ocular tissues. Exp Eye Res 1978;27:503-509. 27. Yen TTT, Glassman E. Electrophoretic variants of xanthine dehydrogenase in Drosophila melanogaster. Genetics 1965 ;52:977-981. 28. Swofford DL, Selander RB. BIOSYS-1. A computer program for the analysis of allelic variation in population genetics and biochemical systematics. Champaign, IL: Illinois Natural History Survey, 1989. 29. Nei M. Estimation of average heterozygosity and genetic distance from a small number of individuals. Genetics 1978;89:583-590. 30. Danfelsd6ttir AK, Duke EJ, Joyce P, /~rnason A. Preliminary studies on the genetic variation at enzyme loci in fin whales (Balaenoptera physalus) and sei whales (Balaenoptera borealis) from North-Eastern Atlantic. Rep Int Whal Commn 1991;(Special Issue 13): 115-124. 31. Shimura E, Numachi K. Genetic variability and differentiation in the toothed whales. Sci Rep Whales Res Inst 1987;38:141-163. 32. Andersen LW. Electrophoretic differentiation among local population of the long-finned pilot whale, Globicephala melaena, at the Faroe Islands. Can J Zool 1988;66:1884-1892. 33. Wada S. Genetic heterozygosity in the striped dolphin off Japan. Rep Int Whal Commn 1983;33:617-619. 34. Wada S. Genetic differentiation between two forms of short-finned pilot whales off the Pacific coast of Japan. Sci Rep Whales Res Inst Tokyo 1988;39:91-101. 35. Winans GA, Jones LL. Electrophoretic variability in Dall's porpoise (Phocoenoides dalli) in the North Pacific ocean and Bering Sea. J Mammal 1988;69:14-21. 36. Bonnell ML, Selander RK. Elephant seals: genetic variation and near extinction. Science 1974; 184:908-909. 37. Gales NJ, Adams M, Burton HR. Genetic retardness of two populations of the southern elephant seal, Mirounga leonina. Mar Mammal Sci 1989;5:57-67. 38. McDermid EM, Ananthakrishnan R, Agar NS. Electrophoretic investigation of plasma and red cell proteins and enzymes of Macquarie Island elephant seals. Anim Blood Groups Biochem Genet 1972;3:85-94. 39. Shaughnessy PD. An electrophoretic study of blood and milk proteins of the southern elephant seal, Mirounga leonina. J. Mammal 1974;55:796-808. 40. McDermid EM, Bonner WN. Red cell and serum protein systems of grey seals and harbour seals. Comp Biochem Physiol 1975;50B:97-101. 41. Testa JW. Electromorph variation in weddell seals (Leptonychotes weddelli). J Mammal 1986;67:606-610. 42. Lidicker WZ, Sage RD, Calkins DG. Biochemical variation in northern sea lions from Alaska. In: Smith MH, Joule J (eds) Mammalian Population Genetics. Athens, GA: University of Georgia Press, 1981 ;231-241. 43. Simonsen V, Allendorf FW, Eanes WF, Kapel FO. Electrophoretic variation in large mammals. III. The ringed seal, Pusa hispida, the harp seal, Pagophilus groenlandicus, and the hooded seal, Cystophra cristata. Hereditas 1982;97:87-90. 44. Simonsen V, Born EW, Kristensen T. Electrophoretic variation in large mammals. IV. The Atlantic walrus, Odobenus rosmarus rosmarus (L.). Hereditas 1982;97:91-94. 45. Nevo E, Beiles A, Ben-Shlomo R. The evolutionary significance of genetic diversity: ecological, demographic and life history correlates. In: Mani GS (ed) Evolutionary Dynamics of Genetic Diversity, Proceedings, Manchester, 1983. Berlin: Springer-Verlag, 1984; 13-213.

9 1995 ElsevierScience B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand ~. Ulltang, editors

119

Preliminary results of a DNA-microsatellite study of the population and social structure of the harbour porpoise Liselotte Wesley Andersen l, Lars-Erik Holm 2, Bjarne Clausen 3 and Carl C. K i n z e 4 l lnstitute of Biological Sciences, Department of Genetics and Ecology, University of Aarhus, Denmark; 2Department of Animal Physiology and Biochemistry, National Institute of Animal Science, Foulum, Denmark; 3Danish Environmental Research Institute, Denmark; and 4Zoological Museum, University of Copenhagen, Denmark Abstract. A total of 106 specimens were used in the analysis of the subdivision into genetically differentiated subpopulations of the harbour porpoise, Phocoena phocoena, occurring in inner Danish waters (IDW), North Sea and West Greenland using PCR amplified DNA microsatellites. To date, two polymorphic microsatellites, 417/418 and insulin-like growth factor-I (IGF-I), were detected, consisting respectively of 8 and 15 alleles. Deviations from the expected Hardy-Weinberg distribution were observed at the IGF locus in the North Sea sample and subsamples from the North Sea, and in the West Greenland male sample. All the North Sea samples showed a surplus of homozygotes which could be explained by a Wahlund effect; the mixing of several subpopulations or non-random mating illustrated by straying males from different breeding areas in the North Sea. The analysis of the genetical population structure performed by Monte-Carlo simulations showed that harbour porpoises from West Greenland are geographically differentiated from harbour porpoises in IDW and North Sea. It was not possible with the present sample size and number of microsatellite loci to differentiate between harbour porpoises in IDW and the North Sea. Key words: cetaceans, Danish waters, genetic structure, nuclear markers, polymerase chain reaction, 417/418 primer set, insulin-like growth factor-I, Monte Carlo simulations

Introduction Growing concern about the health of the porpoise populations in the Northeast Atlantic is accentuated by the apparently high incidental mortality of porpoises and by the lack of knowledge about the impact this may cause to the natural growth rates of the populations [1]. To evaluate the potential impact, it is at least necessary to estimate the magnitude of the incidental mortality and the pregnancy rate, to estimate the abundance and distribution of the harbour porpoise population in the given region and to estimate genetical differentiation among subpopulations and the gene flow between them. The only analysis available at present on the genetical population structure of the Danish harbour porpoises is that of Andersen [2], which was based on allozyme data. The present preliminary study concerns the above-mentioned estimate of the subdivision into genetically differentiated subpopulations and the processes that influence subdivision of the harbour porpoise occurring in inner Danish waters (IDW) Address for correspondence: L.W. Andersen, Institute of Biological Sciences, Department of Genetics and Ecology, University of Aarhus, Denmark.

120 and the North Sea using DNA microsatellites. Microsatellite loci vary in the number of repeats consisting of usually di-nucleotide sequences and are highly polymorphic, often with up to more than a dozen alleles per locus [3,4]. Because the array of microsatellite repeats is generally shorter than 300 base pairs [5], it is possible to use the polymerase chain reaction (PCR) to amplify a single locus.

Materials and Methods Muscle tissues from 106 stranded and incidentally caught harbour porpoises were used in the analysis representing three supposed populations/subpopulations, West Greenland, inner Danish waters and the North Sea. All the individuals in the Greenlandic sample, the North Sea sample and most of the individuals in the IDW sample were analyzed by allozyme electrophoresis by Andersen [2]. The additional individuals were collected by H.H. Dietz, National Veterinary Laboratory, Aarhus, Denmark. Mating in harbour porpoises takes place in July to August [6] suggesting that porpoises collected in summer represent an interbreeding subpopulation. Two microsatellite primers were used. One flanks the simple sequence locus 417/418 described by Schl0tterer et al. [7] and Amos et al. [8] in long-finned pilot whales (Globicephala melas) and the other is situated in the promoter region of the insulin-like growth factor I (IGF-1) in cattle and pigs, where it is polymorphic [9]. The microsatellites were fluorescently labelled and analyzed on a 6% denaturing polyacrylamide gel using the ALF TM DNA Sequencer (Pharmacia LKB) (Fig. 1). At the 417/418 locus, 8 alleles were detected ranging in size from 162 to 180 base pairs (upper fluorogramme, Fig. 1). This microsatellite was originally found to be polymorphic with 3 alleles in the long-finned pilot whales [8]. At the IGF-I locus 15 alleles were detected ranging in size from 94 to 154 base pairs (lower fluorogramme). Figure 1 gives an example of four different genotypes at each locus and the top indicated at 118 base pairs is one of the internal markers.

Statistical analysis The test for deviations from expected Hardy-Weinberg distributions was performed with a permutation test. The genes are distributed independently into genotypes by shuffling and the array found is one of several under the assumed Hardy-Weinberg equilibrium with allele frequencies of the array identical to the original sample. This test was conducted using a computer-package developed by H.R. Siegismund, The Arboretum, The Royal Veterinary and Agricultural University, HCrsholm, Denmark. Monte-Carlo simulations were also used in the analysis of the geographical differentiation to generate the expected Chi-square distribution if the homogeneity, Ho, was true for a given data set [10]. This was performed by using the MONTEprogram from the REAP-package on each locus [11 ] and testing the combined probability of the exact probabilities of the two loci. Where multiple tests were run, the sequential Bonferroni test was applied [ 12] to give table-wide significance levels.

121

locus 417/418

17/Oct/94

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0 8 ' 5 5 ' 37

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Data n Base Pa~r ]

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"

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120

160

140

180

2O0

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Genotypes: 33=8/9; 34=4/8; 35=6/8; 36=9/11 Fig. 1. Fluorogramme.

2OO

122 T h e g e o g r a p h i c d i f f e r e n t i a t i o n w a s a l s o a n a l y z e d w i t h W r i g h t ' s F - s t a t i s t i c s [13] f r o m w h i c h it is p o s s i b l e to o b t a i n a g e n e f l o w e s t i m a t e ( e f f e c t i v e n u m b e r o f m i grants

per

generation).

Fs.r--1/(1 + 4 N m )

in W r i g h t ' s

infinite

Island

model,

if

m < < 1. T h e F s x o v e r t h e t w o m i c r o s a t e l l i t e l o c i w a s e s t i m a t e d u s i n g W e i r a n d Cockerham's

[ 14] u n b i a s e d e s t i m a t o r , O . T h i s e s t i m a t o r g i v e s a r e a s o n a b l e m e a s u r e

f o r N m w i t h m o d e r a t e l e v e l s o f g e n e f l o w a n d r e a s o n a b l y l a r g e s a m p l e s i z e s [ 15].

Table 1. Tests for Hardy-Weinberg expectations based on permutation tests [18,19]. The observed and expected G-values from the goodness of fit to the Hardy-Weinberg distribution are compared to simulated values. N = sampler size, H o and H e = observed and expected heterozygosity, F = (H e Ho)IHe, P = probability of the goodness of fit after 1,000 simulations. Locality

Locus

N

Ho

He

Inner Danish waters (IDW)

IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418 IGF 417/418

57 57 49 49 45 45 54 54 32 32 23 23 34 34 18 18 25 25 21 21 33 33 10 10 16 16 106 106 37 37 11 11 17 17

0.8247 0.5263 0.7755 0.6122 0.7778 0.6444 0.8148 0.5185 0.7500 0.6563 0.7826 0.5217 0.8529 0.5294 1.0000 0.5556 0.6800 0.5600 0.7619 0.5238 0.8485 0.5152 1.0000 0.5000 0.6875 0.6250 0.8019 0.5660 0.7838 0.6757 0.7273 0.4545 0.8235 0.6471

0.8717 0.6131 0.8669 0.5891 0.8686 0.6062 0.8673 0.6118 0.8616 0.6255 0.8478 0.6219 0.8689 0.6051 0.8549 0.5664 0.8525 0.5808 0.8424 0.6145 0.8650 0.6070 0.8200 0.5700 0.8457 0.6230 0.8728 0.6053 0.8046 0.6775 0.7769 0.6116 0.7958 0.6765

North Sea North Sea a IDW summer North Sea summer IDW females IDW males North Sea females North Sea males IDW summer females IDW summer males North Sea summer females North Sea summer males Total IDW + North Sea Greenland summer W. Greenland summer females W. Greenland summer males

F 0.0540 0.1416 0.1055 -0.0392 0.1046 -0.0631 0.0605 0.1525 0.1297 -0.0492 0.0769 0.1611 0.0184 0.1251 -0.1697 0.0191 0.2026 0.0358 0.0956 0.1476 0.0191 0.1513 -0.2195 0.1228 0.1871 -0.0031 0.0813 0.0648 0.0259 0.0027 0.0638 0.2568 -0.0348 0.0435

P 0.1200 0.4180 0.0190" 0.4240 0.0590 0.4650 0.0930 0.5100 0.0240* 0.3280 0.2640 0.5560 0.1820 0.5330 0.9730 0.7010 0.0030* 0.2090 0.2470 0.6050 0.2110 0.4560 0.8530 0.5080 0.0080* 0.2000 0.2590 0.7330 0.0520 0.2020 0.7910 0.0770 0.0130* 0.4360

aNorth Sea sample minus 4 individuals landed on the same date (17 November 1980) and in the same harbour.

123 Results and Discussion

Table 1 shows the significant deviations from the Hardy-Weinberg expectations for the harbour porpoise in the North Sea, IDW and West Greenland as totals and when they are divided into locality, season and sex. Significant deviations from HardyWeinberg expectation were observed only at the IGF-I locus in 5 cases. In 4 cases, all subsamples of the North Sea total sample based either on season or sex, a clear Wahlund-effect was observed, as found by Andersen [2]. This observation could be explained by a mixing of several subpopulations in the North Sea or non-random mating. The homozygote excess is caused by males in the North Sea which also supports earlier findings. One hypothesis that explains this observation is that female harbour porpoises return to the same breeding area every year while males stray, returning to different breeding areas in the North Sea, indirectly suggesting the existence of several breeding areas and several subpopulations. Males in the IDW sample, on the contrary, seem to return to the breeding areas in the inner Danish waters indicating that this group is a reminiscence of a Baltic Sea subpopulation. Figure 2 pictures the distribution of the allele frequencies of the sample totals and when dividing into summer-samples for IDW and North Sea. At the IGF-I locus, one private allele was observed in the North Sea sample and one in the IDW sample. At the 417/418 locus, a private allele was found only in the IDW sample. Private alleles can be used to estimate gene flow in terms of Nm [ 16] in species with low to moderate gene flow (Nm < 10). Rare and private alleles might also imply population structure, i.e. the occurrence of allele 15 at the IGF-I locus and allele 8 at the 417/418 locus in the IDW sample might be diagnostic for an IDW subpopulation. Meanwhile since the frequencies of these alleles are equal to the probability of the occurrence of individuals carrying the alleles in the population (IDW) and the sample sizes are small, it is impossible to conclude that the allele does not also exist elsewhere. More polymorphic microsatellite loci and a larger sample size should be analyzed to evaluate this possibility. In Table 2 the geographical heterogeneity among the three assumed populations of harbour porpoises based on permutation tests [10,11] is given whenever allele counts could be used. In three incidences, genotypes were used because deviation from Hardy-Weinberg expectation was observed. The test for geographical heterogeneity between the West Greenland, IDW and North Sea samples was significant (P < 0.007) indicating the existence of geographical subpopulations. In Andersen [2], a seasonal effect influenced the genotypic distribution at the PGM locus, which corresponded well to a formerly described seasonal migration of harbour porpoises out of the Baltic and into the North Sea in winter [17]. As a result of this observation, the geographical differentiation was tested among summer samples giving a highly significant difference (P < 0.00009). A pairwise comparison between IDW, North Sea and West Greenland and the respective subgroups based on season showed that both total and summer samples of the IDW and North Sea are geographically differentiated from the West Greenland sample. This was expected but it was not possible to detect this differentiation in the allozyme study. In the pairwise

124

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125 Table 2. Tests for allele-frequency differences among the three supposed populations of harbour porpoise based on permutation tests [ 10] Locality

Chi-square

P

W. Greenland-IDW-North Sea a W.Greenland-IDWSU-North Sea summer genotypes North Sea a Inner Danish waters - W. Greenland summer Inner Danish waters North Sea a W. Greenland summer - W. Greenland summer I D W summer - North Sea summer genotypes I D W summer - W. Greenland summer genotypes North Sea summer North Sea female I D W female - W. Greenland summer female I D W summer female North Sea summer female - W. Greenland summer female I D W summer female - North Sea summer female

16.29 18.53 4.72 15.02 Undef. Undef. 10.11 18.28 3.13 9.10 5.39 2.68

0.0026* 0.00097** 0.3173 0.0046* 0.0000"** 0.0000"** 0.0386 0.00109** 0.5350 0.0580 0.2402 0.6127

-

-

-

The probability for each locus was combined and the Chi-square values are given along with the new P values (df = 4). a North Sea sample minus 4 individuals landed on the same date (17 November 1980) and in the same harbour. For table-wide significance ***P < 0.00009, **P < 0.0011, *P < 0.007.

comparison, on the other hand, it was not possible to differentiate between the IDW summer sample and the North Sea summer sample. Geographic differentiation of the three populations also was estimated in terms of Wright's F-statistics. An Fsx = 0.008 was found, which indicated a low degree of genetic substructure in the total sample of the three assumed subpopulations. The gene flow estimate was Nm = 31 which is high. One problem here is the high number of alleles at each locus and the following lack of some genotypes in many cases. This might require a larger sample size when Weir and Cockerham's estimator for Fsx is used. Another question is whether the harbour porpoise is capable of migrating over very long distances such as between West Greenland and IDW and the North Sea, so the estimated gene flow might only be between IDW and the North Sea.

Conclusion

The preliminary analysis of the genetic population structure of the harbour porpoise in Danish waters and West Greenland based on two microsatellite loci showed that harbour porpoises from West Greenland are geographically differentiated from harbour porpoises in inner Danish waters and the North Sea. It is not possible with the present sample size and number of microsatellite loci to differentiate between harbour porpoises in the IDW and North Sea. The allozyme study showed a significant deviation between the genotypic distribution of the IDW and North Sea sample. Excess of homozygotes was observed in both studies in the North Sea sample males indicating that the North Sea sample probably consists of several subpopulations figuratively illustrated by males straying from different breeding areas.

126

Acknowledgements We thank all the people involved in collecting material from the harbour porpoises for this study. A special thanks to the staff in the laboratory at the National Institute of Animal Science, Department of Animal Physiology and Biochemistry, Foulum, Denmark for their help during the study. Thanks also to H.R. Siegismund, The Arboretum, Royal Veterinary and Agricultural University, HCrsholm, Denmark for invaluable advice with the statistical analysis, to V. Loeschcke, Department of Genetics and Ecology, University of Aarhus for use of the laboratory and help, to M.M. Hansen and M. Niclasson for constructive discussions and to R. Krebs for reviewing and editing the English. This study was funded by the Danish Natural Science Research Council, Jr.Nr. 11-0642-1. References 1. NMFS, National Marine Fisheries Service. Harbour porpoise in eastern North Atlantic: status and research needs. Northeast Fisheries Science Center Reference Document 92-06. NOAA/NMFS/ NEFSC, Woods Hole, USA: 1992. 2. Andersen LW. The population structure of the harbour porpoise, Phocoena phocoena, in Danish waters and part of the North Atlantic. Mar Biol 1993;116:1-7. 3. Tautz D. Hypervariability of simple sequences as a general source for polymorphic DNA markers. Nucleic Acids Res 1989;17:6463-6471. 4. Fornage M, Chan L, Siest G, Boerwinkle E. Allele frequency distribution of the (TG)n(AG)m microsatellite in the apolipoprotein C-II gene. Genomics 1992;12:6368. 5. Rassmann K, Schl6tterer C, Tautz D. Isolation of simple-sequence loci for use in polymerase chain reaction-based DNA fingerprinting. Electrophoresis 1991 ;12:113-118. 6. SCrensen TB, Kinze CC. Reproduction and reproductive seasonality in Danish harbour porpoises, Phocoena phocoena. Ophelia 1994;39:159-176. 7. Schl6tterer C, Amos B, Tautz D. Conservation of polymorphic simple sequence loci in cetaceans species. Nature 1991 ;354:5463-5467. 8. Amos B, Schl6tterer C, Tautz D. Social structure of pilot whales revealed by analytical DNAprofiling. Science 1993;260:670--672. 9. Kirkpatrick BW. Identification of a conserved microsatellite site in porcine and bovine insulin-like growth factor-I gene 5' flank. Anim Genet 1992;23:543-548. 10. Roff DA, Bentzen P. The statistical analysis of mitochondrial DNA polymorphism: chi-square and the problem of small samples. Mol Biol Evol 1989;6:539-545. 11. McElroy D, Moran P, Bermingham E, Kornfield I. REAP. The restriction enzyme analysis package, Version 4.0. Department of Zoology, Migratory Fish Research Institute, Center for Marine Studies, University of Maine, USA, 1991. 12. Rice WR. Analyzing tables of statistical tests. Evolution 1989;43:223-225. 13. Wright S. The genetical structure of populations. Ann Eugen 1951 ;15:323-354. 14. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution 1984;38:1358-1370. 15. Slatkin M, Barton NH. A comparison of three indirect methods for estimating average levels of gene flow. Evolution 1989;43:1349-1368. 16. Slatkin, M. Rare alleles as indicators of gene flow. Evolution 1985;39:53-65. 17. MChl-Hansen, BU. Investigations on reproduction and growth of the harbour porpoise, Phocoena phocoena (L.), from the Baltic. Vidensk Meddr Dansk Naturh Foren 1954;116:369-396.

127 18. Chakraborty R, Fornage M, Gueguen R, Boerwinkle E. Population genetics of hypervariable loci: analysis of PCR based VNTR polymorphism within a population. In: Burke T, Doff G, Jeffreys AJ, Wolf R (eds) DNA Fingerprinting: Approaches and Applications. Basel, Switzerland: Birkh~iuser Verlag, 1991;127-143. 19. Guo SW, Thompson EA. Performing the exact test for Hardy-Weinberg proportion for multiple alleles. Biometrics 1992;48:361-372. 20. Gaskin, D.E. The harbour porpoise, Phocoena phocoena, (L.). regional populations, status and information on direct and indirect catches. Rep Int Whal Commn 1984;34:569-586.

This Page Intentionally Left Blank

9 1995 Elsevier Science B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand ~. Ulltang,editors

129

Photo-identification of the minke whale Balaenoptera acutorostrata off the Isle of Mull, Scotland A. Gill 1 and R.S. Fairbairns 2 1Department of Zoology, Aberdeen University, Aberdeen, UK; and 2Sea Life Surveys, Dervaig, Isle of Mull UK Abstract. Sea Life Surveys, set up by Richard Fairbairns in 1990, conducts research and whale-

watching trips in the waters around the Isle of Mull on the West Coast of Scotland. The main focus of this research is the Minke Whale Balaenoptera acutorostrata and since 1990 a photographic database has been collected containing photographs and sightings information. This database was analysed to evaluate the potential of using photo-identification to identify individual minke whales. Twenty-eight minke whales have been identified using distinguishing features such as dorsal fin notches, scars to the body and lateral body pigmentation. Fifteen whales have been seen at least twice, eleven of which have been sighted in more than 1 year. This suggests that some minke whales show site fidelity in returning to the area in successive years. Ten of the identified whales have been sighted at least twice within 1 year, with eight of these sighted in at least two different months of that year. This suggests that some minke whales are seasonally resident in the waters around Mull. There appears to be no evidence of any exclusive ranges as shown by minke whales in the eastern North Pacific. However, the data suggest that the whales tend to move progressively northwards throughout the season. This could be explained by spatial changes in prey abundance or temporal changes in the whales' selection of diet. Key words: baleen whales, natural marking, fidelity, migration

Introduction The waters to the north and west of Mull in Scotland contain an ecologically diverse marine life. Regularly sighted cetaceans include the minke whale (Balaenoptera acutorostrata), harbour porpoise (Phocoena phocoena), Risso's dolphin (Grampus griseus), common dolphin (Delphinus delphis), and occasional sightings of the killer whale (Orcinus orca). "Sea Life Surveys" was set up in 1990 by Richard Fairbairns conducting research and whale-watching trips covering the waters between Mull, the Scottish mainland (Ardnamurchan Point), Coil, Tiree, Eigg, Muck, Rum and The Treshnish Isles. The main focus of this research is the minke whale and data collected on these trips include details of location, photographs, behavioural observations and details of environmental conditions. The aim of this study is to evaluate the potential for identifying individual minke whales using photographic techniques and to use this photo-identification as a tool to answer various questions about the ecology of the minke whale in the coastal waters around Mull. In particular the aim was to investigate whether individual whales are returning to the study area in successive years, whether they are seasonally resident or transient and to determine the ranges of individual whales in the survey area. Address for correspondence: A. Gill, Department of Zoology, Aberdeen University, Tillydrone Avenue, Aberdeen, UK.

130 Materials and Methods The photographic catalogue consisted of photographs taken during whale-watching seasons from May to October in 1990-1994 by Richard Fairbaims. The survey trips were conducted aboard the 10.5 m M.V. Alpha Beta, equipped with a flying bridge and a global positioning system (GPS). Photographs were taken with a Canon EOS10 35 mm single lens reflex camera equipped with a 300 mm f2.8 lens and a motor drive. Shutter speed was set at 1/1000 s. The film used was Fujicolour 200 ASA Print film and photographs were processed at a commercial photographic laboratory. Black and white prints were sometimes produced from the colour negatives to highlight certain features to aid individual identification, for example, by varying the contrast of the prin-ts, scars and pigmentation of the whale can be enhanced. All the photographs were examined and initially sorted by selecting those photographs which were of sufficient quality for identification purposes. A photograph is considered to be of good quality if the major axis of the whale in the photograph is perpendicular to the photographer and the image is large enough to show various distinguishing features such as fin shape, lateral body pigmentation (three distinct swaths of lighter pigmentation on each side of the minke's body) and body scars. These were then studied for various features in order to distinguish between individual whales and to match resightings. A catalogue of identified whales was established containing all the photographs from the identified sighting and any subsequent resightings. The locations of all the sightings for each identified whale were plotted to investigate the possibility of exclusive ranges and intraseasonal movements.

Results Twenty-eight whales were identified from a total of 671 sightings. The whales were identified by year (1990-1994), and the number of identified sightings per year and the total number of sightings per identified whale is shown in Table 1. Whale no. 11 had a calf with her in 1992 and they were seen together throughout this season. Of the 28 individually identified whales 16 were identified by dorsal fin characteristics, 7 identified by scarring and 5 identified from a combination of lateral body pigmentation patterns, fin shape and small white scars. Fifteen of the identified whales have been sighted at least twice, the greatest number of resightings for one whale is 11. Eleven of these whales have been sighted in more than 1 year and this illustrates that whales are returning to the same area in successive years showing temporal site fidelity. Ten of the identified whales have been sighted at least twice within one or more years, nine of which were sighted in at least two different months in any one year. This suggests that these whales maybe be resident at least seasonally in the waters surrounding Mull. The locations of the identified whales reveal that there is considerable overlap in the ranges of these whales and so there appears to be no evidence of any exclusive

131 Table 1. Whales identified by year, showing the year of sighting and the total number of sightings for each whale ID no.

1990

1991

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Total id/year a

1

3

1

3 1 1 1 1 1 2

1992 2 3 1 1 1 1 1

1993 4 2

1994 1 3

3 1 3 1

4 1 1 1

1

1 1

1 1

1 2

1 2 1 1

1 1 1 1

2

11

11

11

2 1 1 12

Total no. 11 8 1 1 4 5 3 4 1 1 5 3 2 2 1 2 1 2 1 3 2 1 1 1 1 2 1 1

aTotal number of individually identified whales seen in that year.

ranges as shown by minke whales in the eastern North Pacific [1 ]. However, the data suggest that some whales tend to move progressively northwards throughout the season (mean change in latitude from first sighting to last sighting of an individual whale within a year is +9.6404 min north (n = 10)). Discussion This study has shown that photo-identification is a feasible technique for the individual recognition of minke whales in the coastal waters around Mull. The data reveal that from using such techniques, some important aspects of life-history patterns can be understood. The individuals recognised in this study were mainly identified from dorsal fin characteristics such as notches or unusual fin shapes. Dorsey et al. [2] studying minke whales in the eastern north Pacific identified 40% of these whales from dorsal fin features compared to 57% of identified individuals in

132 this study. Dorsey et al. [2] in their study used a smaller boat with greater manoeuvrability than the boat used in our study. The higher percentage of whales thus identified from dorsal fin characteristics in this study may reflect the greater difficulty in obtaining the optimal position for taking photographs that will highlight other features such as lateral body pigmentation and body scarfing. In the eastern north Pacific, Dorsey [2] reports that minke whales have numerous oval scars, thought to be of biological origin. These scars proved to be very useful for identifying individuals in this eastern north Pacific population, but were less useful in this study as very few of the whales photographed in our survey area have these scars. Despite this, several resightings have been achieved allowing some aspects of the life history of the minke whale to be determined. It is very important that this photoidentification work is continued to maintain the photographic database and with further resightings, our knowledge of the life history of the minke whales will be enhanced. The possibility that minke whales show site fidelity may have important implications for the identification of stocks and for the setting of catch quotas because if whaling is resumed it could have a severe effect on local populations. The results from this study imply that a small population of minke whales are resident during the summer around Mull and some of these return year after year. There is a possibility that some of these whales may remain all year round and these whales will probably have different feeding strategies to that of migratory whales. Residency of such a large mobile predator would have important implications in terms of energy and material flow and material cycling in the local ecosystem. The distribution of minke whales throughout the survey area over the season is likely to be in relation to their prey species. The northward progression of the minke whales throughout the season could be explained by a shift in the abundance of prey species or temporal changes in the whale's selection of diet.

References 1. Dorsey EM. Exclusive adjoining ranges in individually identified minke whales (Balaenoptera acutorostrata) in Washington State. Can J Zool 1983;61:174-181. 2. Dorsey EM, Stern SJ, Hoelzel AR, Jacobsen J. Minke whales (Balaenoptera acutorostrata) from the West Coast of North America: individual recognition and small-scale site fidelity. SC/A88/ID21. Rep Int Whal Commn 1990;(Special Issue 12):357-368.

9 1995 Elsevier Science B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 13. Ulltang, editors

133

Parasites as indicators of social structure and stock identity of marine m a m m a l s J.A. Balbuena, F.J. Aznar, M. Fernfindez and J.A. R a g a Department of Animal Biology, University of Valencia, Burjasot, Valencia, Spain A b s t r a c t . Introduction: the use of parasites as biological indicators of marine mammals has not yet received all the necessary attention. The aim of this review is to show the value of parasite data in studies of stock identity and social structure. Methods: the application of the technique to marine mammals is hampered by the lack of control over sampling conditions and the paucity of information about the biology of their parasites. Relevant criteria for suitable parasite tags are discussed. Case studies: previous work on marine mammals is presented to illustrate the usefulness and limitations of parasite data. A study of pilot whales revealed that differences in helminth infections between two groups of pods conformed with previous evidence suggesting separate stocks. Other investigations have provided valuable information about behavioural features of marine mammals. Studies of whale-lice exemplify the advantages of using directly transmitted ectoparasites. Conclusions: the technique has so far proven more successful in behavioural than in population studies. However, parasite analyses have yet to reach their full potential. Improved statistical rigour and the use of molecular techniques applied to the parasites may provide new tools for further studies.

Key words: parasitology, cetaceans, pinnipeds, wildlife, management, conservation

Introduction Parasites have been used successfully as indicators or "markers" of stocks, migrations and other biological aspects of aquatic animals [1 ]. Research in this area is increasing at a remarkable rate. Williams et al. [1] revealed over 140 relevant references from the 1980s versus about 50 from the 1970s, most studies being focussed on commercially important species. Nevertheless, the interest in parasites as biological indicators of marine mammals has experienced a comparable increase. We have found nine references published between 1990 and 1994, whereas there were five from the 1980s and two from previous years. Although these modest figures possibly show the logistic and methodological constraints on research on marine mammals, perhaps they also reflect the fact that this technique has not yet received all the necessary attention from marine mammalogists. Parasite data have been used to study a wide range of biological features of marine mammals, including phylogeny [2], local migrations [3], distribution [4], disease [5], stock identity [6-8] and social behaviour [8-10]. A thorough discussion of all these approaches would probably exceed the space available for this review. This paper is mainly concerned with studies of stock identity and social structure because a good knowledge of these two aspects is essential for management and conservation

Address for correspondence: Juan A. Balbuena, Department of Animal Biology, University of Valencia, Dr. Moliner 50, 46100-Burjassot, Valencia, Spain. Email: [email protected].

134 of wild populations. However, parasitologists are not often aware of this implication. Lester [11] noted that only few papers dealing with fish parasites as tags contained useful information for fisheries managers. Likewise, conservation biologists and wildlife managers frequently do not seem to understand the value of parasite data. The aim of this study is to bridge the gap between parasitologists, marine mammalogists and wildlife managers by showing the value of parasites as indicators of stock identity and social behaviour of sea mammals. We discuss some methodological problems, present previous work to illustrate the advantages and limitations of the technique and provide some guidelines for further work.

Methods

The technique consists basically of the comparison of infections with one or more parasite species between several host groups, which can be arranged according to ecological (stock identity studies) or behavioural (social structure studies) criteria. The analysis is usually based on prevalence (proportion of individuals infected with a particular parasite species [12]), intensity (number of individuals of a given parasite species in each infected host [12]), abundance (mean number of individuals of a particular parasite species per host examined [12]) or presence/absence of parasite species. Occasionally, biochemical or morphometrical differences in the parasites have also been used [ 1,13]. Although studies of parasites as biological indicators are now standard for fishes, caution should be taken in applying the same methods to marine mammals mainly because of sampling limitations and the paucity of information about the parasites' biology.

Sampling Unlike parasitological investigations of fish, there is often little control over sampling conditions. Generally researchers have to rely on unpredictable, occasional strandings or by-catches, which may result in severe sampling biases. For instance, comparisons of parasite infections between host groups might be unreliable if sampling is not simultaneous. There is a risk of mistaking spatial for temporal patterns because parasite infections can vary seasonally or annually. Fish parasitologists can partly overcome this problem by restricting the analysis to one cohort of hosts [ 14]. However, this might be impractical in marine mammal studies because of the usually low sample sizes. Perhaps a more realistic approach is to use data from several parasites simultaneously [11] or limit the study to qualitative (presence/ absence) rather than quantitative differences (e.g. [7]). Generally, parasite occurrences are less sensitive than prevalence and intensity of infection to temporal factors [15]. However, unless this assumption is supported by previous field evidence, the conclusions drawn from these studies are questionable. Variation between samples in sex, age or other host factors may also compromise

135 the analyses. Researchers should ensure that such factors either do not differ significantly between samples or do not affect parasite infections [8].

Parasite selection The success of the analyses depends to a great extend on choosing suitable parasite species as markers. In marine mammal studies, this can be difficult because data on the biology of most parasites are scarce. Different authors have provided the criteria for appropriate parasite tags [1,11,13,14], although in practice some might be too restrictive [11]. Here, we discuss those more relevant to studies of marine mammals.

Parasite life span This is probably the most important factor, particularly in stock identity studies, determining the aims, design and conclusions of the survey. Short-lived parasites can show short-term or seasonal segregation of stocks, while long-lived parasites can reveal long-term segregation or permanent stocks. Little is known about the longevity of marine mammal parasites. However, it can be assessed in different ways, such as experimental infections of laboratory animals [16] or captive marine mammals [ 17] or comparison with similar parasites in terrestrial hosts [ 18].

Source of infection The composition of the parasite fauna of a particular individual can give indications of the habitats previously occupied by the host. Data about the distribution of parasites of marine mammals are fragmentary, although there is evidence concerning some common species. For instance, the nematode Anisakis typica occurs in warm and tropical waters between 40~ and 36~ [19]. The finding of this species in a striped dolphin (Stenella coeruleoalba) stranded on the French Atlantic coast suggested that the animal was a vagrant from southern latitudes [4].

Parasite life cycles Parasites with complex life cycles should not be discarded as biological markers because many successful studies include parasites with more than one host in the life cycle [1,11]. However, directly transmitted parasites with one-host cycle, such as whale-lice in cetaceans or suckling lice in pinnipeds, are simpler to use. In this case, it is not necessary to consider potential fluctuations in environmental conditions influencing the abundance and distribution of both free-living stages and intermediate hosts essential to the completion of the cycles. In addition, directly transmitted parasites are especially appropriate in behavioural studies because their infections tend to reflect the social patterns of their hosts [9].

Recovery of parasites Parasites should be easily detected involving a minimum of dissection. This condition is not essential but might be limiting, especially in studies of large whales or

136 under restricted time or resources. In this case, the use of ectoparasites or epizoites is advantageous over that of endoparasites.

Case studies

We present some examples to illustrate the value and limitations of parasite analyses. In a recent study on intestinal helminths of pilot whales (Globicephala melas) off the Faroe Islands, abundance of intestinal helminths of 101 animals from seven pods was used to evaluate their potential as indicators of stock and social identity [8]. The study showed differences between a group of three pods and another formed by four pods (Fig. 1). These results might be interpreted as prima facie evidence for temporal or spatial segregation of whale stocks. The first group consisted of pods caught within a few days in July and August showing high abundance of the acanthocephalan Bolbosoma capitatum, whereas the second included pods from September to June with low abundance of this species. Since B. capitatum is presumably a short-lived parasite [8], the variation observed could also result from seasonal fluctuations of parasite abundance, or from the interaction of spatial and temporal factors. However, the pattern of pod differences con-

1

Q r C

1~I 6 ~

0

w "~ 41-

m

O.i

1

9

2

9

9

3

5

i

7

I

I

4

6

School No.

Fig. 1. Dendrogram based on Mahalanobis' distances between the centroids of seven pods of pilot whales. From Balbuena and Raga [8].

137 formed with previous evidence from pollutant load and genetic analyses, supporting the hypothesis of whale segregation. Nevertheless, parasites can provide more conclusive evidence, particularly about behavioural features of marine mammals. In the same study, resampling experiments revealed that adult males were more difficult to allocate into their pods than the remainder, suggesting male exchanges between pods. This agreed with genetic studies showing that adult males do not breed within their natal pods [8]. Similarly, studies of whale-lice have provided valuable insights into the social behaviour of cetaceans and exemplify the advantages of using directly transmitted ectoparasites. Best [10] revealed that differences in the relative frequencies of two whale-louse species on sperm whales were indicative of segregation of adult males. Based on these data, he suggested that male sperm whales attained puberty at a length of 39-40 ft (i.e. about 11.9-12.2 m), which proved a very good estimate. Rice [20] based on histological analyses of gonadal tissue reckoned that males reach sexual maturity at a body length of 11-12 m. A recent survey of the whale-louse, lsocyamus delphini on Faroese pilot whales also yielded some interesting results [9]. Mature male whales showed higher prevalence and abundance of whale-lice than the rest. These data conformed with the polygynous mating system proposed for the species. Social fights between sexually mature males would result in a higher incidence of wounds and scars, providing additional shelter for whale-lice. Mass infections consisting of thousands of whalelice always occurred on unhealed wounds on the bulls. Perhaps these infections can be used to identify dominant males in sighting surveys.

Conclusions

This review shows that despite methodological limitations, parasites can be useful as biological indicators of marine mammals. Contrary to other alternative methods (genetics, pollutant analyses, etc.), parasite studies have the advantage of being relatively cheap and easy to implement. They do not require sophisticated equipment and although a knowledge of parasite systematics is desirable, it is not essential. These features make them particularly suitable for studies in developing countries or in remote field stations. The technique has so far proven more conclusive in behavioural than in population studies of marine mammals. Apparently, parasite surveys lack the precision of genetic, morphometric or biochemical studies to establish stock boundaries. It is important to note, however, that the latter techniques usually reveal "true stocks" or populations with a genetic basis, while parasite surveys tend to indicate "ecological stocks" (i.e. spatial patterns of distribution or abundance in an ecological time scale [21]). This can explain some contradictions between genetic and parasite analyses. For instance, Lester [11] recently showed that parasite data showed distinct groups of the fish, Hoplostethus atlanticus off New Zealand, while previous genetic analyses suggested a single stock. Therefore, parasite surveys should not be considered as a surrogate but as a complement to other studies.

138 Parasite analyses of marine mammals have yet to reach full potential, both in development and application. Increased statistical rigour and the use of sophisticated multivariate methods can provide better evidence relevant to management and conservation of their hosts [8]. In addition, as molecular techniques are perfected and applied to parasites, new tools to separate marine mammal stocks or discover more about their activities will be available. We believe that further integrated studies combining host and parasite information are most necessary to meet the ever increasing demand for correct conservation and management policies of marine mammals.

Acknowledgement This study was funded by grants from the DGICYT (project Nos PB87-997 and PB92-875) and CICYT (projects Nos NAT 1254/90E and NAT91-1128-C04-01) of the Spanish Government.

References 1. Williams HH, MacKenzie K, McCarthy AM. Parasites as biological indicators of the population biology, migrations, diet, and phylogenetics of fish. Rev Fish Biol Fish 1992;2:144-176. 2. Hoberg EP, Adams AM. Phylogeny, historical biogeography, and ecology of Anophryocephalus spp. (Eucestoda: Tetrabothriidae) among pinnipeds of the Holarctic during the late Tertiary and Pleistocene. Can J Zool 1992;70:703-719. 3. Helle E, Valtonen ET. Comparison between spring and autumn infection by Corynosoma (Acanthocephala) in the ringed seal Pusa hispida in the Bothnian Bay of the Baltic Sea. Parasitol 1981 ;82:287-296. 4. Abril E, Almor P, Raga JA, Duguy R. Parasitisme par Anisakis typica (Diesing, 1860) chez le dauphin bleu et blanc (Stenella coeruleoalba) dans le Nord-Est Atlantique. Bull Soc Zool Fr 1986;111:131-133. 5. Aznar FJ, Balbuena JA, Raga JA. Are epizoites biological indicators of a western Mediterranean striped dolphin die-off? Dis Aquat Org 1994;18:159-163. 6. Delyamure SL, Yurakhno MV, Popov VN, Shults LM, Fay FH. Helminthological comparison of subpopulations of the Bering Sea spotted seals, Phoca largha Pallas. In: Fay FH, Fedoseev GA (eds) Soviet-American Cooperative Research on Marine Mammals. Vol 1 - Pinnipeds. NOAA Tech Rep NMFS, 1984;61-65. 7. Dailey MD, Vogelbein WK. Parasite fauna of three species of Antarctic whales with reference to their use as potential stock indicators. Fish Bull 1991;89:355-365. 8. Balbuena JA, Raga JA. Intestinal helminths as indicators of segregation and social structure of pods of long-finned pilot whales (Globicephala melas) off the Faeroe Islands. Can J Zool 1994;72:443-448. 9. Balbuena JA, Raga JA. Ecology and host relationships of the whale-louse Isocyamus delphini (Amphipoda: cyamidae) parasitizing long-finned pilot whales (Globicephala melas) off the Faroe Islands (Northeast Atlantic). Can J Zool 1991;69:141-145. 10. Best PB. The sperm whale (Physeter catodon) off the west coast of South Africa 3. Reproduction in the male. Invest Rep Div Fish S Afr 1969;72:1-20. 11. Lester RJG. Reappraisal of the use of parasites of fish stock identification. Aust J Mar Freshwater Res 1990;41:855-864.

139 12. Margolis L, Esch GW, Holmes JC, Kuris AM, Schad GA. The use of ecological terms in parasitology (report of an ad hoc committee of the American Society of Parasitologists). J Parasitol 1982;68:131-133. 13. Moser M. Parasites as biological tags. Parasitol Today 1991;7:182-185. 14. MacKenzie K. Parasites as indicators of host populations. Int J Parasitol 1987;17:345-352. 15. Pence DB. Helminth communities of mammalian hosts: concepts at the infracommunity, component and compound community levels. In: Esch GW, Bush AO, Aho JM (eds) Parasite Communities: Patterns and Processes. London: Chapman and Hall, 1990;233-260. 16. Valtonen ET, Helle E. Experimental infection of laboratory rats with Corynosoma semerme (Acanthocephala). Parasitology 1982;85:9-19. 17. McCleUand G. Phocanema decipiens: growth, reproduction, and survival in seals. Exp Parasitol 1980;49:175-187. 18. von Bonsdorff B. Diphyllobothriasis in Man. London: Academic Press, 1977. 19. Davey JT. A revision of the genus Anisakis Dujardin, 1845 (Nematoda: Ascaridata). J Helminthol 1971;14:51-72. 20. Rice DW. Sperm whale Physeter macrocephalus Linnaeus, 1758. In: Ridgway SH, Harrison RJ (eds) Handbook of Marine Mammals, Vol 4. London: Academic Press, 1989;177-233. 21. Spanakis E, Tsimedines N, Zouros E. Genetic differences between populations of sardine, Sardina pilchardus, and anchovy, Engraulis encrasicolus, in the Aegean and Ionian Seas. J Fish Biol 1989;35:417-437.

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9 1995 Elsevier Science B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand t3. Ulltang, editors

141

M a r i n e m a m m a l i a n fatty acids: a source of information Otto Grahl-Nielsen and Olav Mjaavatten Department of Chemistry, University of Bergen, Norway A b s t r a c t . Background: fatty acids have been determined in tissues, especially blubber, of seals and

whales in a large number of investigations. None of these investigations have used a sufficient number of animals, nor have they treated the data in a proper statistical manner to allow quantitative conclusions on the internal (such as species, age, reproduction) and external (such as dietary) effects on the composition of the fatty acids. With good availability of seal samples through the Norwegian research program on marine mammals, we have by way of a chemometric method determined the fatty acids in blubber, hair and heart tissue of harp seals, Phoca groenlandica, hooded seals, Cystophora cristata, and harbor seals, Phoca vitulina. Methods: carefully selected samples of tissue were methanolysed in anhydrous methanolic HC1, the fatty acid methyl esters formed were gas chromatographed and the results were treated with multivariate principal component analysis. Conclusions: we have demonstrated the advantages of multivariate analysis, not previously applied on data on fatty acids in marine mammals. By this method, species differences in fatty acid composition of blubber, hair and heart tissue can be detected despite considerable individual variation. Differences between pups and adults were also clear for all three tissues. It is not likely that these differences are caused by the diet; distinct differences were observed in the pattern of fatty acids in the blubber of harp seals caught near Svalbard and in their main prey, the amphipod Parathemisto libellula. The pattern in the hairs from hooded seals changed from the time the hairs were new until they were shed 1 year later. The fatty acids in the heart tissue of harp seals differed between the western and eastern populations of harp seals. It is thus apparent that the fatty acid profile may be used for identification purposes. Key words: seals, blubber, hair, heart, species, populations

Introduction

The fatty acid composition of marine mammalian tissue, blubber and milk in particular, has been the subject of many investigations [1]. Species, age and dietary effects appear to have influence on the composition. However, it is not possible to extract any general conclusions about the quantitative measures of these differences from the many publications. This is mainly due to the small number of individuals which have been analysed in each case. With large individual differences, it has been impossible to apply quantitative, statistical treatment on the analytical data. We have developed a chemometric method for analysis of fatty acid composition, based on direct methanolysis of the sample, gas chromatography of the methyl esters formed, followed by multivariate, statistical treatment of the gas chromatographic data. With good availability of samples of seals through the Norwegian research program on marine mammals, we wanted to apply our chernometric method to study the fatty acids of blubber, hair and heart tissue. The main goal was to search for dif-

Address for correspondence: 0. Grahl-Nielsen, Department of Chemistry, University of Bergen, N5007 Bergen, Norway.

142 ferences between populations within the same species, but in this context it was necessary to look for differences between species, the effect of the age of the seals, and also to consider if variation in the diet could influence the fatty acid composition.

Materials and Methods

Seals were shot at various locations in the Northern Atlantic and in the Barents Sea according to the map in Fig. 1. Selected tissues were wrapped in aluminum foil and frozen onboard. After thawing in the laboratory, smaller samples of tissue were retrieved for analysis" approximately 2 mg of liquid blubber were collected with the tip of a small spatula from 1 cm beneath the skin from the dorsal part of the animal and transferred to a thick-walled glass tube for methanolysis. For sampling of hair, the pelt was washed carefully with water and air-dried. Approximately 50 mg of hair were picked out. In the pelts from four of the hooded seals caught in June, old hairs could be distinguished from new hairs, and old and new hairs were sampled separately. The hair sample was left for 24 h in a mixture of chloroform and methanol (3:1) and thereafter washed repeatedly with methanol and dichloromethane in a filter to get rid of extraneous lipids. The integral fatty acids of the hair were then obtained by methanolysis. Samples of heart tissue were taken from the tip of the heart. The outer "skin" was removed, and approximately 20 mg were cut off, carefully rinsed in distilled water to

~:::::i:i:::i:i:i: ::::::::::::::::::::::::::::::::::::::::::::::::::: ...... .............................................. .................................................. .................................................. :::.:::.:.:::: :.: .. ::::::::::::::::::::::::::::::::: :.;::: ===========================================

Fig. 1. The seals were shot in the following areas" E, harp seals in April 1989; W, harp seals in April 1989; S, harp seals in September 1990; H, harbor seals in June and October 1990; 90, harp and hooded seals in March and April 1990, and hooded seals in July 1990.

143 remove blood, thereafter dried on filter paper and transferred to the glass tube for methanolysis. Methanolysis was achieved by 2 N HC1 in dry methanol at 100~ for 15 h. The fatty acid methyl esters formed were extracted into hexane and gas chromatographed as described earlier [2]. To obtain the combined information from all fatty acids simultaneously, they were subjected to multivariate treatment based on principal component analysis. Their relative values were logarithmically transformed, thereby levelling out differences among fatty acids present in large and small amounts. With each sample positioned in the multi-dimensional space described by the log-transformed variables (fatty acids), the two coordinates (principal components, PCs) that described the largest and second largest variance among the samples were computed using the program package SIRIUS [3]. In this manner the relationship between the samples was displayed in two dimensions without considerable loss of the total original variance. The original variables, i.e. the fatty acids, were also displayed in the PC coordinate system, resulting in so-called biplots. Fatty acids with high, positive or negative, values along a PC in a biplot have high importance for that PC, and consequently for the position of the samples in the plot. Close positioning of two fatty acids means that they are positively correlated, while two acids on opposite sides of the origin are negatively correlated. The more perpendicular the direction from the origin towards two fatty acids, the less they are correlated.

Results and Discussion

Due to the clear difference between pup and adult harp seals (Table 1), only adult seals were used for inter-species evaluation: 27 harp seals, 16 hooded seals and 17 harbor seals. Within the species there were large differences between the seals, resulting in high standard deviations of the averages of the fatty acids (Table 1). This was also observed in other investigations [4,5]. A discussion of inter-species differences in the fatty acid composition of seal blubber based on determinations from single animals, as has been the case in some investigations, is therefore futile. In comparison of the three species, one acid at a time, i.e. a univariate approach, clear differences were found: 13 acids were significantly different, on the 99% level, between the harp seals and the hooded seals (Table 1). The differences in four of these corresponded with differences found by Jangaard and Ke [5]: 16:1 and 22:5n3 were lower, and 18:1 and 20:1 were higher in hooded seals than in harp seals. Nine acids differed significantly between the harp seals and the harbor seals, and comparison of hooded and harbor seals also revealed nine fatty acids with significant differences in the relative amounts (Table 1). When all 18 fatty acids were taken into consideration simultaneously in a principal component analysis, the three species fell into three groups (Fig. 2). Large individual differences within each group are obvious. While the hooded and harbor seals are well separated in this plot, the group of harp seals falls between the other two

144 Table 1. Relative amounts (as % of sum) • SD of fatty acids in blubber fat of pups and adult harp seals, adult hooded seals and adult harbor seals Fatty acids

Harp

Pups

14:0 14:1n5 16:0 16:1n7

18:0 18:1n9 18:1n7 18:1n5

18:2n6 18:3n3

18:4n3 20:1n9 20:4n6 20:5n3 22:1nl 1 22:1n9 22:5n3 22:6n3 Saturated Monousaturated Polyunsaturated

N=9

Adults N = 27

5.4 _ 0.5 0.9 _+ 0.2 1 4 . 2 _ 1.3 16.3 _ 2.3 1.9 _ 0.4 25.3 _ 1.4 6.1 _ 0.7 0.5 • 0.1 2.0 + 0.3 0.8 _ 0.1 2.7 _ 0.6 5.8 _ 2.4 0.4 • 0.1 8.5 _ 1.3 0.6 _ 0.5 0.2 • 0.1 3.0 • 0.4 5.7 • 0.9 22 56 23

5.8 1.2 11.1 15.0 1.0 20.4 5.1 0.5 1.7 0.9 3.7 8.2 0.3 9.0 3.4 0.4 4.0 8.3 18 54 28

> > >

> < > < < < <

_ 0.7 _ 0.4 _ 2.5 _ 2.4 _ 0.2 __. 3.0 • 1.4 _ 0.1 _ 0.3 _ 0.2 +_.0.7 _ 3.1 • 0.1 _ 1.4 _ 2.3 • 0.2 • 0.7 • 2.7

< > > > < < >

> < > < < >

Hooded

Harbor

N = 15

N = 17

5.1 0.7 9.1 9.3 1.5 23.6 3.6 0.5 1.7 0.9 2.4 15.4 0.3 5.2 7.3 1.0 2.4 10.1 16 61 23

_ 0.6 _ 0.2 • 1.7 • 1.7 _ 0.3 _-4-2.2 _ 0.6 _ 0.1 _ 0.2 _ 0.2 • 0.5 _ 2.4 • 0.1 • 1.3 _ 1.5 • 0.3 • 0.5 • 1.8

> < >

< > < > > <

5.1 _ 0.4 2.0 _ 0.5 9 . 6 _ 1.6 18.2 _ 4.5 0.9 _ 0.2 22.1 _ 2.6 3.1 • 0.7 0.5 _ 0.1 1.8 _ 0.2 1.5 • 0.3 2.6 _ 0.5 9.5 _ 2.7 0.7 _ 0.3 4.8 +_ 0.7 3.0 _ 1.1 0.2 • 0.1 4.2 • 0.8 9.8 • 0.9 16 59 25

< > >

<

> < > < <

The cases in which the amounts of fatty acids were significantly (P < 0.01) different between the groups are marked with inequality signs. The signs to the far right indicate significant differences between harbor and harp seals (adults).

species and is partly overlapping those groups. However, this plot does not show the total variance between the samples, since the two first principal components represent 67% of the variance, and the samples are projected onto the plane made up of these two principal components. By computing the groups pairwise, and only using the fatty acids which discriminate best according to Table 1, a complete separation of the three species of seals was evident (the PC-plots are not shown). The three species of seals have different feeding habits. Of the two oceanic species, the hooded seals feed at greater depths than the harp seals. The harbor seal feeds in coastal areas. However, all have an opportunistic feeding nature, consuming the prey items which are available, from squid and crustacea to a wide variety of fish species [6-8]. Many of these prey species are consumed by all three species of seals. The fatty acid composition of the prey species vary widely [9]. With such a composite menu, which is partly overlapping among the three seal species, any dietary effect should be levelled out. Examination of the stomachs of the harp seals shot at Svalbard in September 1990 showed that their diet was dominated by the amphipod Parathemisto libellula [10]. This was also the most abundant prey organism in most water layers in the area. P.

145

PC 2 (25%)

PC 1 (42%) Fig. 2. PC plot of blubber samples from hooded seals shot in the West Ice in 1990 (K), from harp seals shot in the West ice in 1989 and 1990 and in the East Ice in 1989 and near Svalbard in 1990 (G), and from harbor seals shot on the Norwegian coast in 1990. Each letter represents one seal. Eight replicate samples from one hooded seal are shown with outlined Ks.

Table 2. Relative amounts (as % of sum) _+SD of fatty acids in blubber fat of harp seals shot near Svalbard in September 1990 and in the amphipod Parathemisto libellula, their dominating diet Fatty acids

Blubber

P. libellula

14:0 14:1n9 16:0 16:1n7 18:0 18:1n9 18:ln7 18:1 n5 18:2n6 18:3n3 18:4n3 20:1n9 20:5n3 22:1nl 1 22:1n9 22:5n3 22:6n3

5.3 1.6 7.8 14.6 0.8 19.1 3.9 0.6 1.9 1.0 3.4 10.8 8.5 5.2 0.6 4.3 10.9

5.7 0.1 12.4 10.3 0.9 11.6 2.9 1.0 2.7 1.4 5.1 12.4 13.9 3.6 1.8 0.4 13.9

_ 1.0 _ 0.7 _ 2.2 _ 2.3 _ 0.2 _+4.5 _+ 1.0 _-4-0.1 _+0.4 _ 0.1 -+ 0.5 -+ 2.8 _+ 1.1 _+3.2 _+0.3 _+0.8 _+ 1.6

_ 1.2 _ 0.03 _+ 1.9 _+2.9 _ 0.1 _+ 1.9 _+ 1.2 -+ 0.2 _ 0.4 _ 0.3 -+ 0.6 -+ 3.0 _+ 1.3 _+0.8 _+0.6 _+0.07 _+ 1.7

146

P C 2 2o%

S

S

P

S

Ppp P

P C 1 73~

Fig. 3. PC plot of blubber samples from harp seals from Svalbard in September 1990 (S), and from the amphipod Parathemisto libellula caught in the same area (P). The two outlined Ps are replicates from the same animal.

libellula was also found to dominate the diet of harp seals captured in the same area in 1987 [6]. The composition of fatty acids in four P. libeUula caught by trawl in the area was determined and compared with the pattern of fatty acids in the blubber of the harp seals (Table 2). Clear differences are obvious, particularly for the polyunsaturated acid 22:5n3, which has approximately a ten times higher relative abundance in the blubber than in the prey organism, and the monounsaturated acid 14: ln9, which is approximately 15 times more abundant in the blubber. A principal component analysis of the fatty acid composition of the blubber and of P. libellula confirms the large difference between the two sample types (Fig. 3). With P. libellula being a major food item during excessive feeding of the harp seals in late summer and early spring, increasing their average blubber thickness from less than 2 cm to 8 cm [10], the large difference in fatty acid composition is a strong argument against the dietary effect on the fatty acid composition of the blubber.

Table 3. Relative amounts (as % of sum) • SD of fatty acids in hairs from pups and adults of harp seals caught in March 1990 and from adult hooded seals caught in March and June 1990 Fatty acids

Harp

Hooded

Pups

14:0 16:0 16:ln7 ul u2 18:0 18:1n9 u3 20:0 21:0a

2.9 19.7 4.9 4.0 1.3 11.9 29.5 2.6 0.6 22.7

Adults _ 1.5 • 1.6 +_.2.5 _ 0.7 _ 0.3 • 1.3 _ 3.8 • 2.1 • 0.1 • 4.2

3.5 34.0 3.6 2.6 1.0 26.9 10.7 2.4 1.2 14.2

_ _ _ _ • _ • • • •

2.1 5.3 2.7 0.7 0.2 4.9 3.7 1.4 0.3 5.6

March

June: new

June: old

3.8 34.0 4.2 3.8 0.7 21.8 16.6 1.2 1.0 13.1

2.2 30.3 3.8 6.2 0.9 13.6 17.2 2.1 1.4 22.3

2.2 45.3 1.7 1.3 0.7 26.1 18.1 0.2 1.6 2.9

__. 1.2 __.5.7 __. 1.9 • 0.6 • 0.1 __.5.2 • 6.3 • 0.4 • 0.4 • 2.7

_ _ _ _ _ _ • • • •

0.4 2.3 0.4 1.8 0.1 1.8 3.2 0.5 0.2 2.0

New and old hairs were determined separately from the hooded seals caught in June.

• _ • _ _ • • • • •

0.4 2.9 0.6 0.2 0.1 2.6 6.0 0.2 0.4 0.6

147

unid

21:0ap/./pS~77 p / 23/unid 18: l n 9 ~ a / / ~

unid 20:0 8:0

16:1n7

14:0

Fig. 4. Biplot of samples of hair from pup and adult harp seals shot in the West Ice in April 1990 and

the fatty acids in the samples. The composition of fatty acids in hairs also showed large individual differences (Table 3). Nevertheless, there were obvious systematic differences: the saturated acids were less abundant in the pups than in the adult harp seals, while the monounsaturated and the branched acids were higher in the pups. The composition of fatty acids in the hair of adult harp and hooded seals was less different; none of the acids differed on the 95% level due to the large individual deviation. The differences between new and old hairs from the hooded seal were large; the most prominent differences being the higher content of the saturated acids, 16:0 and 18:0 in particular, in the old hairs, and the much lower amount of the anteiso 21:0 in the old hairs. The relative amounts of fatty acids in the hairs from the hooded seals caught in March apparently fell between the amounts in the new and old hairs. With principal component analysis, the pups and adult harp seals formed two different groups even if the spread among the individuals within each group was substantial (Fig. 4). In this biplot the fatty acids are displayed together with the samples, showing how the fatty acids influence the distribution of the samples. The saturated acids, 16:0, 18:0 and 20:0 are associated with the adult seals, indicating higher relative values of these acids in adults than in the pups. This is in accordance with the values in Table 3. The two monoenic acids, 16:1n7 and 18:1n9 and the branched acid, anteiso 21"0, are associated with the pups on the left side of the plot, showing that they are negatively correlated with the three aforementioned saturated acids, with high relative values in the pups and small relative value in the adults. The remaining four acids are located between the two groups of samples and have less importance for the separation between the groups. The difference in the fatty acid pattern of the hairs from adults of the two seal species, harp seals and hooded seals, was less than the difference between adults and

148 pups of the harp seal (Table 1). However, multivariate computation revealed that the fatty acid pattern of the hairs of the seals is species specific. In an investigation on mammalian hair, Wertz and Downing [ 11 ] found fatty acid compositions in sheep, pig, dog, cow and human to be "nearly identical". However, the relative amounts reported by them differ more among the species than the differences between the two seal species (Table 1). Moreover, they have not sampled from different individuals, and are therefore precluded from the use of statistics in their comparisons. It is therefore impossible to compare the results of the two investigations. The new and 1-year-old hairs of hooded seals differed substantially in fatty acid patterns (Table 1). The new hairs were much richer in anteiso 21:0 and two branched acids, while the two saturated acids 16:0 and 18:0 were present in higher amounts in the old hairs. Wertz and Downing [12] have suggested that the function of the branched fatty acids in hair is to fluidize membranes, similar to the function of unsaturated fatty acids in other tissues. Since hair is exposed to air, unsaturated acids have been replaced by branched acids because they exhibit greater resistance towards oxidative damage. We have now observed that the relative abundance of the branched fatty acids decreases as the hairs are aging. By comparing the composition of fatty acids in the new and old hairs from the hooded seals caught in July with the composition in the hairs from the seals caught in March (Table 1), it was clear that the new hairs had a closer resemblance to the hairs from March than the old hairs. PC analysis of the samples from March together with the new and old hairs from July also showed that the March samples were more closely related to the new hairs than to the old (Fig. 5). Consequently, the change in fatty acid composition which occurs during the 1-year lifetime of hair is much larger during the last 3-4 months before the hairs are shed than during the preceding 8-9 months. Even if the fatty acid composition in blubber tissue and in hairs was species specific, discrimination on a population level appears not to be possible. We therefore considered heart tissue, which could be expected to contain a more specific fatty acid

.3 21 4 r

Fig. 5. PC plot of samples of new (left) and old (right) hairs from four hooded seals shot in July 1990 in

the West Ice, together with samples of hair from six hooded seals shot in late March 1990 in the same area.

149 Table 4. Relative amounts (as % of sum) • SD of fatty acids in heart tissue of pups and adult harp seals, adult hooded seals and adult harbor seals Fatty acids

Harp

14:0 14:1n5 16:0 16:ln7 18:0 18:1n9 18:1n7 18:1n5 18:2n6 18:3n3 20:0 20:1n9 20:4n6 20:5n3 22:0 22:1nl 1 22:1n9 22:6n3 24:1n9 Saturated Monounsaturated Polyunsaturated

Pups N = 19

Adults N = 37

0.9 0.1 16.3 4.8 20.7 23.9 6.4 0.3 5.1 0.3 1.3 1.8 9.6 4.9 0.5 0.3 0.2 2.0 0.6 39.7 38.4 21.9

1.1 0.1 15.9 5.0 16.5 23.0 4.7 0.2 7.4 0.4 1.1 3.7 6.0 9.9 0.4 1.4 0.2 2.1 0.7 35.1 39.1 25.8

_ 0.3 _ 0.04 _ 1.3 • 0.6 • 2.6 _ 1.6 • 0.6 __ 0.1 _ 0.8 • 0.1 • 0.3 _ 0.8 • 1.7 __. 1.5 • 0.1 • 0.2 • 0.09 • 1.1 • 0.1

• • • _ • • _ • _ • _ • • • • • • • •

0.4 0.06 1.1 0.9 1.7 3.0 0.9 0.09 1.2 0.1 0.2 1.7 1.4 2.2 0.1 1.1 0.18 0.7 0.1

>

< < > < < < >

Hooded

Harbor

N = 26

N = 17

1.1 0.1 15.1 3.2 15.8 24.4 4.5 0.2 7.3 0.5 1.1 4.9 6.9 7.6 0.5 3.1 0.4 2.6 0.6 33.6 41.5 24.9

• 0.2 __ 0.03 • 1.3 __. 1.2 • 2.6 __. 3.3 • 0.7 _.+0.07 • 1.5 • 0.2 __.0.2 • 2.1 • 2.6 • 1.7 • 0.1 • 2.0 • 0.3 • 1.3 • 0.1

>

>

< <

< >

>

1.0 _ 0.5 0.04 _ 0.02 14.8 • 0.7 2.7 _ 0.7 15.5 • 2.1 19.7 _ 2.4 4.5 • 0.9 0.2 • 0.03 8.8 _ 1.7 1.3 • 0.3 1.2 _ 0.2 4.2 • 1.7 12.3 • 3.3 6.2 • 1.7 0.5 • 0.1 3.0 • 2.1 0.2 • 0.11 3.1 • 1.0 0.7 • 0.1 33.0 35.3 31.7

< < <

> >

> < > > >

The cases in which the amounts of fatty acids were significantly (P < 0.01) different between the adult seals are marked with inequality signs. The signs to the far right indicate significant differences between harbor and harp seals.

PC2

13%

Q

~3

"~

. . . .

K I~_

~)

o/i I

G

PC 1 63%1

Fig. 6. PC plot of samples of heart tissue from hooded seals shot in the West Ice in 1990 (K), from harp seals shot in the West Ice in 1989 and 1990 and in the East Ice in 1989 and near Svalbard in 1990 (G), and from harbor seals shot on the Norwegian coast in 1990. Each letter represents one seal.

150

HOODED

\

HARP \

/

G

.,'~ ~

G/777

G

/

/

9

\.

/ r

Fig. 7. PC plot of samples of heart tissue from pups and adults of hooded (K) and harp (G) seals shot in the West Ice in 1990.

~ PC 2 (25%)

ADULTS

I

I

PUPS

(42%) , '

Fig. 8. PC plot of samples of heart tissue from adult (bold numbers on the left) and pups (numbers in italics on the right) of harp seals from the West Ice, 1-4, and the East Ice, 11-12 and 21-25. Two or three replicate samples were taken from each animal.

151 profile than the other two types of tissues due to a high proportion of phospholipids in the lipid fraction. As was the case for the two other tissues, a large individual variation in the composition of the fatty acids in the heart tissue was observed, giving high standard deviations of the mean values (Table 4) and a large spread in the PC plot (Fig. 6). It appears from the plot that the fatty acid profile of the harbor seals is distinct from the profile of the other two species, while there is an overlap between hooded and harp seals. However, a test of the two latter species shows a clear distinction for animals shot in the same area off Greenland in the spring of 1990 (Fig. 7). This plot not only shows a species difference, but also an age difference, in that pups are clearly different from the adults. When differences between the western and eastern populations of harp seals are considered, it turns out that the difference between the adults and pups is larger than the difference between the two populations. This is seen in the PC plot in Fig. 8, where the age difference is along PC 1, which describes 42% of the total variance, while the population difference is along the second principal component with 25% of the variance among the samples. Adults and pups therefore have to be treated separately. The two populations are then well distinguished (Fig. 9). As a control, pups and adults from the western population, shot in the West Ice in 1990, were compared with the seals from 1989 (Fig. 9). For both pups and adults, the

PUPS E

E

ADULTS

E

E

EE

X

X

X

I IS I I I I I

S

X

xs

Fig. 9. PC plots of the same samples as in Fig. 7, computed separately for pups and adults. E and W represent seals from the East and West Ice, respectively, from 1989, X represent seals from the West Ice in 1990, and S represent seals shot near Svalbard in September 1990.

152 samples f r o m the w e s t e m population from the 2 years overlap. This indicates that the fatty acid profile of the heart tissue is typical for the two populations of harp seals. Thus, the fatty acid profile may be regarded as an intrinsic marker which may be used to identify to which population a seal belongs. This was applied to 22 harp seals shot near Svalbard in September 1990. At this time of the year the harp seals are foraging far away from their breeding areas. Seals from the two populations are expected to mix in the n o r t h e m areas. The overlap of the Svalbard seals with the western seals suggest that they belong to this population (Fig. 8). Although our findings are based on a very restricted n u m b e r of seals, the corresponding results for adults and pups, and the successful test of the 1990 seals from the w e s t e m population, do strengthen the implications that the fatty acid profile m a y be used for identification purposes.

References 1. Ackman RG, Lamothe F. Marine mammals. In: Ackman RB (ed) Marine Biogenic Lipids, Fats and Oils II. Boca Raton, FL: CRC Press, 1989;179-381. 2. Viga A, Grahl-Nielsen O. Genotypic and phenotypic fatty acid composition in the tissues of salmon, Salmo salar. Comp Biochem Physiol 1990;96B:721-727. 3. Kvalheim OM, Karstang T. A general-purpose program for multivariate data analysis. Chemom Intel Lab Syst 1987;2:235-237. 4. Engelhardt FR, Walker BL. Fatty acid composition of the harp seal, Pagophilus groenlandicus (Phoca groenlandica). Comp Biochem Physiol 1974;47B: 169-179. 5. Jangaard PM, Ke PJ. Principal fatty acids of depot fat and milk lipids from harp seal (Pagophilus groenlandica) and hooded seal (Cystophora cristata). J Fish Res Bd Can 1968;25:2419-2426. 6. Lydersen C, Angantyr LA, Wiig O, Oritsland T. Feeding habits of northeast Atlantic harp seals Phoca groenlandica along summer ice edge of the Barents Sea. Can J Fish Aquat Sci 1991 ;48:2180-2183. 7. Wiig 0. Selenes n~eringsopptak og konsum. Fiskets Gang 1988;6/7:12-13. 8. Olsen M, BjCrge A. Diet of the harbor seal Phoca vitulina, in the Hvaler area in 1990 and 1991, compared to the abundance of fish in the area. ICES Coun Meet 1992/n:19:1-15. 9. Gunstone FD, Harwood JL, Padley FB. The Lipid Handbook, 2nd edn. London: Chapman and Hall, 1994:167-180. 10. Nilssen KT, Haug T, Potelov V, Timoshenko YK. Feeding habits of harp seals (Phoca groenlandica) during early summer and autumn in the northern Barents Sea. Polar Biol 1994;in press. 11. Wertz PW, Downing DT. Integral lipids of mammalian hair. Comp Biochem Physiol 1989;92B:759-761. 12. Wertz PW, Downing DT. Integral lipids of human hair. Lipids 1988;9:878-881.

9 1995 Elsevier Science B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 13. Ulltang, editors

153

Fatty acid composition in blubber, heart and brain from phocid seals BjCrge Fredheim 1, Siv Holen 2, Karl Inne Ugland 3 and Otto Grahl-Nielsen 4 1Norsk Hydro a.s., Research Centre, Sectionfor Environmental Technology, Norway; 2Bergen College, Department of Construction and Engineering, Norway; 3Department of Biology, University of Oslo, Norway and 4Department of Chemistry, University of Bergen, Norway Abstract. The fatty acid composition of phocid seals was investigated in order to map the possible stratification of fatty acids in the depot fat and to detect possible differences between different tissues and between the four species grey seal (Halichoerus grypus), harbour seal (Phoca vitulina), ringed seal (Phoca hispida) and harp seal (Phoca groenlandica). We used a chemometric method in this analysis: the fatty acids were determined by gas chromatography, and the relative amounts of fatty acids were treated by principal component analysis. Analysis of the depot fat in the four species indicates that the fatty acid composition of the blubber varies along a gradient from the epidermis. We are able to distinguish between three distinct vertical layers. With increasing depth from the epidermis, the amount of saturated and long-chained monounsaturated fatty acids in the blubber fat increased, while the amount of short-chained monounsaturated fatty acids decreased. The four seal species exhibited differences in fatty acid composition in the outermost layer of the blubber. In contrast, the middle and inner layers did not show differences between species. We also analysed the fatty acid composition of heart and brain tissue from harp seals. The three tissues had distinctly different profiles.

Key words: grey seal, harbour seal, ringed seal, harp seal, fatty acids

Introduction The fatty acid composition of blubber fat from marine mammals has been thoroughly investigated considering the commercial value of their oils [1 ]. In baleen whales the fatty acid composition varies with the different cross-sectional areas sampled [2-5]. In contrast, most of the analyses of blubber fat from seals are based on the assumption that the blubber has a homogenous composition [6-8]. However, this assumption is based on the analysis of only one harp seal [9]. In this work we apply a chemometric method developed by Kvalheim [10] and Grahl-Nielsen [11] in an analysis of three layers in a cross-section of the blubber from four phocid seals: grey seal (Halichoerus grypus, Fabricius 1791), harbour seal (Phoca vitulina L.), harp seals (Phoca groenlandica, Erxleben 1777) and ringed seals (Phoca hispida, Schreber 1775), and in an analysis of differences in the fatty acid profile from blubber, heart and brain tissue from harp seals. This method consists of direct methanolysis of the samples, followed by high-resolution gas chromatography and multivariate analysis of the data. Thus, instead of using three steps, (i.e. extraction, saponification, and esterification) the methanolysis requires only a single

Address for correspondence: B. Fredheim, Norsk Hydro a.s., P.O. Box 2560, N-3901 Porsgrunn, Norway.

154 step. This technique allows a large number of samples to be analysed. The purpose of our study was to identify the individual and interspecific variation in the blubber composition. Harp seals for analysis of heart and brain tissue were sampled in the Greenland Sea and in the Barents Sea. For analysis of the blubber, seals were sampled in the Varangerfjord (Finnmark county in northern Norway). Both the coastal species (grey and harbour seal) are represented by a small population in this fjord. In addition, the two arctic species (ringed and harp seal) visit the area regularly during the winter. All four species are known to be opportunistic feeders, hence competing for the available food resources in the sampling area.

Materials and Methods

We analysed the blubber of eight grey seals, four harbour seals, four harp seals and four ringed seals. All seals were collected during a period of 6 months from November 1991 to April 1992 in the Varangerfjord area in the eastern part of Finnmark (70~ 30~ (Fig. 1) during the seal invasions [12]. We also analysed

~fjorden

~,.

Os

Fig. 1. Map showing the sampling localities for harbour seal, grey seal, harp seal and ringed seal off the Norwegian coast.

155 brain and heart tissue from 80 harp seals collected ultimo April and primo May in the breeding area north of Jan Mayen (74~ 11 ~ and ultimo March and primo April in the breeding area in the southern part of the Barents Sea (69~ 44~ The blubber sections (skin to muscle) were taken from the area immediately overlying the sternum. Heart tissue was taken from the apex, and brain tissue was taken from the area close to foramen magnum. All tissue-samples were carefully wrapped and subsequently frozen and stored (temperature <-20~ Samples were taken in a frozen or partially frozen state. The cross-section of the blubber was sectioned into three equal pieces; outer-, middle- and inner-layer. In order to avoid contact with skin and muscle and also to minimise the influence of autooxidation, we extracted approximately 2 mg of oil from inside each blubber section. For each of the samples of heart and brain, we used approximately 20 mg of tissue. The pericardium was removed from the heart before sampling and the sample was then rinsed in water to remove blood from the tissue. Samples from brain tissue were taken from the grey matter. All samples were transferred to thick-walled glass tubes for methanolysis (100~ in 15 h), followed by gas chromatography of the resuiting methyl esters as described by Grahl-Nielsen and Barnung [11 ]. Peaks in the chromatograms from all samples were identified by comparison with chromatograms of standards. The areas of well-defined peaks from the chromatograms were integrated and the total relative amounts of the fatty acids, as a percentage of the sum, were calculated. Differences between the tissues can be seen by comparing the relative values for the fatty acids. In order to separate the information from noise, the data matrix was simplified by a multivariate technique called principal component analysis (PCA) first described by Pearson [13]. PCA looks for a few linear correlations (principal components or PCs) which can be used to summarise the data, losing as little information in the process as possible. This attempt to reduce dimensionality can be described as "parsimonious summarisation" of the data. If the data can be summarised by two linear combinations, we say that the dimensionality is reduced from 23 (original number of fatty acids) to 2 (number of PCs). In this investigation PCA was used to extract the two coordinates (PCs) that described the largest and the second-largest variance among the samples, using the software package SIRIUS [14]. In this manner the relationship between the samples was displayed in two dimensions without considerable loss of the total original variance. Classification of the various groups of samples was achieved in the second multivariate step using SIMCA analysis [15,16] also available in the SIRIUS package. Principal components were fitted to each group of samples. The number of components used to describe each group was determined by cross-validation [ 17]. A tolerance interval, with 95% significance level, around the principal components, was then determined for each group. This is the maximum relative standard deviation (RSDmax). Each sample was then classified by determination of its relative standard deviation (RSD) from the principal components of the group. A lower RSD than RSDmaxfor a group indicates that the sample belongs to that group.

156

kPC 2 (17%t8:1 (o9 Blubber

18:1 (o7 20:1 (o9

ain ' 1

"~L~.'5 3

\~

rt

14:0 A/

16:1 ~

PC 1 (69%) ,jr

Fig. 2. Classification of all tissue samples by PCA based on 11 fatty acids. The importance of the various fatty acids is given by their position in the biplot.

Table 1. Relative amounts (as % of sum) • SD of fatty acids, and sum of saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids in the samples from heart and brain (harp seals) and blubber (grey seal, harbour seal, ringed seal and harp seal) Fatty acids 14:0 14:1095 16:0 16:1o97 18:0 18:1o99 18:1097 18:1095 18:2096 18:3(o3 18:4o93 20:0 20:1099 20:3096 20:4w6 20:5093 22:0 22:10911 22:1099 22:5093 22:6093 24:0 24:1099 SFA MUFA PUFA

Heart (n = 80) 0.52 _ 0.20 11.74 0.88 16.76 22.89 4.14 0.13 0.15

• 2.46 • 0.43 • 2.54 • 1.68 • 1.05 _ 0.02 _+0.11

1.80 5.05 0.24 3.44 0.63 1.41 0.15 0.91 0.66 3.83 6.19 10.26 38 44 9

+_.0.60 • 2.13 • 0.05 +_0.86 +_.0.38 • 0.39 __.0.15 _ 0.39 +_0.24 _ 1.78 • 2.25 _ 2.40

Brain (n = 80)

Blubber (n = 20)

0.90 0.10 13.19 4.04 16.06 20.16 4.05 0.25 7.21 0.32

_+0.31 +_0.06 • 1.40 • 0.80 __.2.17 • 2.37 _ 0.60 _ 0.07 _ 1.42 _+0.09

5.16 1.39 9.83 15.73 1.14 20.80 4.67 0.60 2.15 1.35 2.37

1.06 3.30 0.19 8.44 8.29 0.36 1.03 0.17 0.44 2.42

+_.0.32 _ 1.38 _ 0.05 _ 1.78 +_2.12 _.+0.10 __.0.68 _ 0.08 _ 0.28 • 0.88

1.98 _ 6.67 32 35 27

The number of animals analysed (n) is given for each tissue.

_ 1.18 _ 0.59 • 2.26 • 6.00 _ 0.42 _+ 3.70 _ 1.19 • 0.11 +_0.39 _+0.47 +_.0.63

6.42 • 2.52 0.58 _+0.24 6.17 _ 1.62 2.63 0.35 4.92 12.55

16 53 30

__.2.19 _ 0.24 +_0.83 • 2.02

157 Results

For the multivariate treatment we used 23 fatty acids, ranging from 14:0 to 24: lo~9, and two unidentified peaks (noid2 and noid3) which were represented in detectable amounts in the samples. Analysis of the relative amounts of 11 fatty acids represented in detectable amounts in both heart, brain and blubber showed significant differences between the three tissues (Fig. 2). The total amount of saturated acids in heart and brain was found to be more than twice the amount of saturated acids found in blubber (Table 1). Of the total fatty acid content, heart and brain were found to contain 38% and 32% saturated acids, respectively, with 16:0 and 18:0 being the main contributors (Table 1). Blubber had a low relative amount of saturated fatty acids; in fact, the only saturated acids detected in the blubber were the short-chained acids 14:0, 16:0 and 18:0, while both short- and long-chained saturated acids were found in heart and brain. However, blubber had the highest amount of monounsaturated and polyunsaturated fatty acids. Heart and brain also had the highest number of detectable fatty acids, 20 and 21, respectively, while blubber only had 18. There was also a higher number of unidentified peaks in the heart and brain samples. Table 2. Relative amounts (as % of sum) of fatty acids, and the sum of saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids in blubber samples from four seal species caught in Varangerfjorden in northern Norway (short MUFA have 18 or less carbon atoms and long MUFA have more than 18 carbon atoms in the chain) Fatty acids

14:0 14:1 095 16:0 16:1 ~o7 18:0 18:1 ~o9 18:1 co7 18:1 095 18:2 a~6 18:3 w3 18:4o93 20:1 ~o9 20:4 ~o6 noid2 20:5 a~3 22:1 ~oll 22:1 ~o9 noid3 22:5 ~o3 22:6 ~o3 SFA Short MUFA Long MUFA PUFA

Total blubber Grey seal

Harbour seal

Ringed seal

Harp seal

5.04 -+ 1.33 1.22 _ 0.48 9.65 _ 2.09 12.87 _ 3.44 1.18 __.0.42 23.35 _+ 3.29 4.71 _+ 1.12 0.60 _+0.08 2.33 _+0.32 1.61 -+ 0.51 2.36 _+0.55 6.76 _+2.33 0.65 _+0.29 1.07 _+0.16 5.41 _+0.72 2.52 _+2.18 0.36 _+0.26 0.35 _+0.06 4.93 _+0.88 13.00_+ 1.33 15.88 _+ 1.16 42.75 _+ 1.13 9.64 _+ 1.19 30.29 _+ 1.37

5.03 1.66 11.68 18.53 1.27 19.05 3.99 0.62 2.16 1.35 2.38 5.84 0.59 0.71 5.13 2.44 0.32 0.32 4.62 12.31 17.97 43.85 8.60 28.54

5.00 1.45 8.73 21.26 0.99 17.67 5.32 0.68 1.79 1.08 2.01 4.44 0.55 0.74 8.02 2.09 0.21 0.35 5.26 12.35 14.72 46.39 6.74 31.05

5.62 1.42 9.57 13.79 1.11 19.65 4.63 0.51 2.04 1.00 2.73 8.19 0.42 0.56 7.20 3.56 0.48 0.39 4.91 12.25 16.29 39.99 12.23 30.54

-+ 0.81 _+0.85 _ 1.28 _ 6.77 _+0.38 _+2.69 _+0.89 _+0.07 +_.0.42 -+ 0.31 _+0.53 _+ 1.97 _+0.16 _+0.13 _+0.99 _+ 1.84 _+0.18 _+0.06 _+0.92 _+2.25 _+ 1.17 _+ 1.07 _+ 1.19 _+ 1.19

_ 1.25 _ 0.43 -+ 2.69 _ 7.09 _+0.47 _+2.10 _ 1.44 _+0.16 -+ 0.28 _+0.28 _+0.63 _+2.30 _+0.14 _+0.29 _+ 1.32 _+2.38 _+0.19 _+0.09 _+0.61 _+2.84 _+ 1.22 _+ 1.11 _+ 1.42 _+ 1.30

_ 0.95 _ 0.59 _+2.13 _ 3.06 _+0.40 _+ 3.05 -+ 1.03 _+0.06 -+ 0.22 _+0.21 _+0.71 _+2.23 _+0.14 _+0.12 _+ 1.76 _+2.16 _+0.23 _+0.10 _+0.69 _+2.26 _+ 1.10 _+ 1.10 _+ 1.19 _+ 1.24

158

PC2 (28%)

14:10)5 16:10)7

Gray seal

Qs

~8

18:30)3

~7

~5 18:~

06

18:40)3

18:30)3"]1;~ m n~ 0 ~2 .~9 ~J: 1,r 3

20:40)6

"~q~l

PC2 (13%) noid2

14:10)5

~1,6:,=7 01

14:0 ~ll8:10)7~r'?tll ~22:50)3l!14~2__% ~ 2

Harbour seal

'PC2 (17%) . !8 : 4 o ~ 1~4

3

22:60)3 22:50)3 18:0

20:40)6

18:0 -PC1 (53~

PC1 (67=/o)

Ringed seal

4m

Im 18:3r ~2

~

14:0 r~Jd3 16:1~i10)5

20:1o)9

2

~/~

16:0

2~~ 18:0

01

22:60)3 20:40)6 0

Outer layer

noid3

1~

l..O

18:10)7

04

20:50)3 ~6:10)7 14:10)5

22:60)3

"~1 PC1 ( 6 6 % ) gg Middle layer

20:40)6 ~

PC1 (39%)

r

Inner layer

Fig. 3. Classification of blubber samples by PCA based on fatty acids (the importance of the various fatty acids is given by their position in the biplot) from (a) grey seal, (b) harbour seal, (c) ringed seal and (d) harp seal. Each number represents one individual seal and the symbols indicate the position of replicate samples from outer, middle and inner blubber layer (replicate samples from one individual indicates the low analytical variance).

Table 2 presents the total relative amount of the 18 fatty acids represented in all blubber samples (n = 40). A closer examination of Table 2 reveals several differences between the species. However, neither a univariate statistical test nor PCA was able to separate the species significantly at this level. By the use of direct methanolysis and PCA we were able to analyse accurate samples from three layers in a blubber cross-section from 20 animals. The biplots from the four species analysed are presented in Fig. 3. The plots indicate that both harbour, ringed, grey and harp seals exhibit interspecific and individual or intraspecific variation in the fatty acid composition of the blubber. The plots also show that the intraspecific variation displayed by PC1 accounts for most of the variance in the data; 67%, 66%, 53% and 39% of the total variation, respectively (Fig. 2). In addition, the intraspecific variation displayed by PC2 only accounts for 17%, 13%, 28% and 27%, respectively.

159

Table 3. Relative amounts (as % of sum) _ SD of fatty acids from three layers in a cross section of the blubber from four seal species caught in Varangerfjord in northern Norway Grey seal

14:0 14:1095 16:0 16:1097 18:0 18:1099 18:1097

18:1095 18:2to6 18:3093 18:4093 20:1o99 20:4096 noid2 20:5093 22:10911 22:1099 noid3 22:5093 22:6~3

Harbour seal

Middle

Inner

Outer

Middle

Inner

Outer

3.81 +- 039 1.60 +- 0.39 8.05 _ 1.34 16.89 _ 1.34 0.79 _ 0.15 25.80 +- 1.85 5.55 ---0.68 0.66 • 0.07 2.35 +- 0.30 1.42--.0.41 2.07 ---0.32 4.71 +- 0.84 0.83 _ 0.34 1.02 _ 0 . 1 0 5.67 _ 0.44 0.59---0.35 0.17 _ 0.09 0.34 _ 0.02 5.01 _ 0.71 12.66 _ 0.77

4.86 _ 082 1.21 _ 0.40 9.45 +- 1.41 12.44 +- 1.84 1.20 +- 0.29 23.37 +- 3.11 4.78 ---0.98 0.60 _ 0.08 2.41 +- 0.31 1.58--,0.50 2.41 ---0.54 6 . 7 2 _ 1.20 0.63 +- 0.24 1.11 • 5.49 +- 0.69 2.04--,1.13 0.38 +- 0.16 0.36 +- 0.04 5.28 +- 0.88 13.65 _ 1.42

6.33 _ 1.22 0.88 • 0.39 11.17 _ 2.22 9.40 +- 1.65 1.50 +- 0.41 21.60 • 3.31 4.04 _ 1.25 0.56 _ 0.09 2.26 _ 0.34 1.76--,0.62 2.53 --.0.71 8.86 +- 2.57 0.51 _ 0.25 1.08 --.0.19 4.88 +- 0.84 4.65---2.07 0.55 _ 0.32 0.37 +- 0.10 4.66 • 1.19 12.40 +- 1.22

4.81 _ 0.10 2.74 • 0.16 11.46 • 0.60 27.06 • 1.11 0.95 +- 0.05 17.75 +- 1.43 3.86---0.58 0.68 • 0.04 1.91 • 0.18 1.24• 2.28 • 4.31 _ 0.64 0.62 +- 0 . I 0 0.65 • 4.40 _ 0.10 1.02--,0.42 0.19 +- 0.04 0.29 +- 0.02 3.79 +- 0.24 9.99 +- 0.34

5.02 _ 0.54 1.39 • 0.31 11.63 • 0.87 16.85 +- 2.85 1.21 +- 0.15 19.70 +- 1.47 3.86---0.68 0.60 • 0.04 2.42 • 0.42 1.59--,0.23 2.73 ---0.44 6.07 _ 1.05 0.53 +- 0.08 0.78 • 5.42 +- 0.59 2.33--.1.16 0.29 _ 0.08 0.34 +- 0.03 4.68 +- 0.35 12.54 +- 0.93

5.00 _ 1.09 0.79 +_0.12 12.77 +- 0.99 12.05 +- 0.59 1.68 +- 0.37 19.86 +- 2.91 3.99--- 1.10 0.55 • 0.06 2.41 _ 0.51 1.40_+0.34 2.38 ---0.63 6.40 +- 1.10 0.54 +- 0.15 0.74 +_0.14 5.87 _ 1.27 3.53---0.89 0.39 +- 0.06 0.31 +- 0.05 4.95 +- 0.73 14.37 • 2.34

Ringed seal

14:0 14:1095 16:0 16:1097 18:0 18:1o99 18:1097 18:1095 18:2xo6 18:3093 18:4o93 20:1099 20:4096 noid2 20:5093 22:10911 22:1099 noid3 22:5093 22:6093

Harp seal

Middle

Inner

Outer

Middle

Inner

Outer

3.81 ---0.27 1.80--.0.16 6.26 +- 0.52 27.31 ---2.79 0.56 +- 0.09 1 9 . 0 7 _ 1.49 6.76 +- 0.40 0.80 +- 0.13 1.77 ---0.30 0.86 --,0.13 1.46--,0.25 2.64 +- 1.51 0.60 +- 0.13 0.87 +- 0.40 8.64 _ 1.06 0.14 +- 0.14 0.03 +- 0.05 0.37 _ 0.09 5.16 +- 0.39 11.09 _ 1.30

5.21 _ 1.11 1.60--.0.27 7.89 _ 1.49 23.90---4.04 0.88 _ 0.21 16.33 +-2.16 5.35 • 0.67 0.72 +- 0.14 1.68--,0.22 1.11 --.0.18 2.17 ---0.37 3.68 +- 1.14 0.49 _ 0.10 0.74 +- 0.21 8.90 _ 0.54 1.15 _ 0.55 0.18 _ 0.09 0.40 _ 0.06 5.81 +- 0.33 11.80 +- 2.65

6.19--,0.77 1.01 ---0.04 11.56 +- 0.77 12.42--. 1.50 1.56 _ 0.22 1 7 . 3 6 _ 1.28 3.57 +- 0.37 0.56 +- 0.06 1.76--.0.24 1.28 ---0.35 1.88 • 7.02 • 1.08 0.72 +- 0.06 0.55 +- 0.09 6.02 • 0.44 5 . 8 9 _ 1.02 0.45 +- 0.03 0.23 _ 0.04 4.82 +- 0.32 15.14 _ 2.43

4.76---0.60 2.11 --,0.41 7.21 __. 1.29 16.35 +- 1.92 0.72 • 0.14 21.73__. 1.46 4.81 +- 0.34 0.51 __.0.08 2.20__.0.19 1.14 • 3.13 • 7.25 __.0.88 0.39 +- 0.10 0.59 +- 0.07 8.47 __. 1.17 1.78 • 1.07 0.31 +- 0.09 0.46 +- 0.10 4.86 +- 0.85 11.21 +- 0.50

6.01 ---0.34 1.24 ---0.25 10.20 +- 0.86 13.40--.2.41 1.05 +- 0.12 18.72 +- 3.39 4.63 +- 1.24 0.49 +- 0.05 2.01 --,0.19 0.95 ---0.06 3.02• 8.12 +- 1.96 0.35 +- 0.05 0.55 +- 0.08 7.44 _ 1.62 3.67 _ 1.80 0.50 +- 0.17 0.41 +- 0.02 5.00 _ 0.64 12.22 +- 2.21

6.64 ---0.56 0.93 ---0.24 10.98 +- 1.34 11.47--,3.39 1.43 _ 0.32 18.16 • 2.32 4.58 __. 1.49 0.51 • 0.08 1.94 • 0.88 ---0.29 2.36---0.86 10.20 +- 2.86 0.41 +- 0.14 0.53 -4-_0.18 5.70 +- 1.52 5.51 _ 2.19 0.70 +- 0.29 0.33 +- 0.06 4.87 +- 0.80 11.86 +- 2.93

160 Table 4. Relative amounts (as % of sum) _+SD of saturated (SFA), monounsaturated ( M U F A ) and polyunsaturated (PUFA) fatty acids in the samples from three layers in a cross section of the blubber from four seal species caught in Varangerfiord in northern Norway Fatty acids

Blubber section Outer

Middle

Inner

Grey seal ZSFA ZShort M U F A ZLong M U F A ZPUFA

12.66_ 50.50 • 5.46 _ 30.00 •

1.04 0.74 1.08 1.45

15.51 42.40 9.14 31.46

_ 1.14 • 0.99 • 1.19 _+ 1 33

18.99 _+ 1.13 36.49 • 1.01 14.05 _+ 1.18 2 9 . 0 0 _ 1.44

17.21 52.08 5.53 24.23

_ 1.02 _+0.59 _ 1.15 • 1.39

17.86 42.40 8.69 29.92

• 1.15 • 0.91 • 1.19 _+ 1.34

19.45 37.25 10.32 31.91

_ 1.22 _+0.87 _+ 1.18 _ 1.21

10.63 55.73 2.81 29.57

_ • _ _

1.12 0.89 1.00 1.24

13.99 47.90 5.01 31.95

• 1.37 • 1.09 • 1.21 _+ 1.09

19.32 34.92 13.35 31.62

+ 1.47 • 0.39 • 1.41 _+ 1.12

12.70 45.51 9.34 31.40

_ _ _ •

1.09 0.75 1.18 1.26

17.26 38.48 12.29 31.00

• 1.08 _ 1.07 • 1.19 +_ 1.20

19.05 35.65 16.41 28.02

• 1.10 +_ 1.08 _+ 1.17 _ 1.18

Harbour seal ZSFA ZShort M U F A ZLong MUFA ZPUFA

Ringed seal YSFA ZShort M U F A ZLong MUFA ZPUFA

Harp seal ZSFA ZShort M U F A ZLong M U F A ZPUFA

In the biplots, the fatty acids (variables) are located in accordance with their influence on the distribution or grouping of the samples (objects). Only fatty acids with high loadings on both PC1 and PC2, and hence influence the distribution of the samples, are presented in the biplots. Fatty acids with low loadings (close to the origin in the biplots) have little or no influence on the distribution of the samples and are removed from the figure to simplify the plots. Fatty acids located adjacent to the samples from the outer blubber layer (Fig. 3) are those with significantly higher relative amount in these samples (Table 3). In the biplot from grey and ringed seals, these fatty acids are located farthest to the left (Fig. 3a,c). In the same manner, fatty acids located adjacent to the samples from the inner layer are found in significantly higher relative amount in the samples from this layer, and are located to the right in the same plots (Fig. 3a,c). The outermost layer displays a significantly higher level of 14:1~o5 and 16:1co7, while 20:1~o9 and 18:0 are found in significantly higher levels in the innermost layer (Table 3). These fatty acids thus discriminate between the outer and inner blubber layer in all four species. Table 4 summarises the amount of saturated (SFA), short-chained (18 carbon atoms or fewer) and long-chained (more than 18 carbon atoms) monounsaturated (MUFA) and polyunsaturated fatty acids (PUFA) for the three blubber-layers in each

161

I D Outerlayer [] Middlelayer []

Innerlayer I

Grey seal

60-

Harbour seal

-60

50-

-50

40-

-40

30-

-30

20-

-20 -10

c-

0

i

O

E >

-

i

Harp seal

50

0

-60

Ringed seal

-50 -40

4O

rr

-30 -20

20

10

1

v

I

I

I

I

i

I

I

I

0

Fig. 4. The relative amounts of different groups of fatty acids determined in the blubber cross section

from each of the four species (values are given in Table 4). of the four species. With increasing depth from the epidermis, the amount of SFA in the blubber fat increased, while the amount of MUFA decreased. The relative amount of SFA was found to range from 8.2 in the outermost layer, to 22.6 in the innermost layer, a difference of more than 14 in the cross-section. In the same matter relative values for unsaturated acids were found to range from 91.4 in the outermost layer, decreasing with depth. The lowest value found in the innermost layer was 78, a difference of 13.4 in the cross-section. However, by dividing the monounsaturated acids into short-chained and long-chained, nuances appear (Fig. 4). Long-chained monounsaturated acids increase while short-chained monounsaturated acids decrease with increasing depth in the blubber fat. The relative amount of PUFA shows no significant variation in the stratified blubber samples (Fig. 3).

162 Discussion and Conclusions

In this investigation we use the relative amounts of fatty acids to analyse inter- and intraspecific differences in fatty acid profiles. By using a chemometric method we were able to analyse a large number of samples. The interpretation of a large sample size calls for the use of multivariate techniques. This is in contrast with most earlier investigations of marine lipids which are based on a relatively small number of samples (i.e. one animal) using univariate techniques [5,7,9,18]. Interpretation of one fatty acid at a time does not give a total picture of how the different samples are related to each other. However, this can be obtained by taking all fatty acids into account simultaneously by a multivariate approach.

Tissue differences Considering the saturated acids, the difference between the heart and brain tissue is mainly caused by the lack of 24:0 in brain, while the amount of 24:0 was found to be 6% of the total fatty acid composition in the heart. Similar values have been found in heart and brain from harbour and grey seals [ 19] and from hooded seal (Crystophora cristae), harbour seal and harp seal [20]. However, Grahl-Nielsen and Mjaavatten [20] found a higher value of saturated acids in brain tissue. In fact, the total relative amount of saturated acids was found to be 45 mainly due to higher values of 16:0 and 18:0. This higher value could be due to samples taken from both the grey and white matter. White matter is higher in lipid content than grey matter used in our investigation. Samples from both grey and white matter would probably cause a great variance in the data. However, this variance was not examined. The importance for future examinations of sampling not only the same tissue but also the same part of the tissue should therefore be emphasised. The fatty acid composition of different tissues from marine mammals has been thoroughly investigated, and differences have been found in several species [18]. Ackman et al. [21] found differences between heart and lung tissue from harbour seal. Differences between heart, liver and blubber from grey and harbour seals have also been demonstrated [19]. Engelhardt and Walker [22] demonstrated differences in tissues like heart, brain, liver, aorta, muscles and testes from harp seal. However, an investigation of the black right whale (Eubalena glacialis) concluded that the blubber had the same fatty acid composition as other tissues [23]. All of the above investigations are based on a small sample size, mainly one or at most two animals, and were analysed using univariate methods. Only Grahl-Nielsen and Mjaavatten [20] used multivariate techniques (PCA) to analyse the differences in the fatty acid profile between heart, brain, blubber, jaw-bone, hair and eye-lens. By applying PCA on all the samples from heart, brain and blubber, the analysis shows that blubber has a greater variability than heart and brain (Fig. 2). This variability is possibly due to the function of the different tissues analysed. Blubber is used as the main storage of fat reserves, and in natural conditions synthesis and hydrolysis of fat occur continually [24]. This implies that the fatty acid composition of

163 the blubber possibly will be influenced by the diet. Schweigert et al. [25] assumed that the fatty acid composition in the blubber from grey seal is possibly related to the diet. The same conclusion was also proposed by Ackman and Hooper [19] by comparing fatty acids found in the blubber from harbour and grey seal, and by West et al. [7] by comparing blubber from ringed seal, largha seal (Phoca largha), baikal seal (Phoca fasciata) and bearded seal (Erignathus barbatus). The total relative amount of unsaturated fatty acids in blubber was found to be 82.7% in our investigation. This is in accordance with earlier investigations of blubber from harp seal [6], grey seal [25-27], harbour seal [19] and ringed and bearded seal [28]. The relative amounts of unsaturated fatty acids in prey organisms of grey seal and other marine mammals have also been found to be approximately 80% [25]. By monitoring the fatty acid composition in blubber from harbour and grey seals in captivity, Grahl-Nielsen and Mjaavatten [29] found no significant differences in the composition of the blubber when the diet was switched from herring to mackerel. In spite of the differences in the fatty acid composition of the two prey species, it did not alter the composition of the depot fat significantly. Investigations of subcutaneous adipose tissue from humans indicate that the fatty acid composition of adipose tissue is related to the composition of the diet [30], and that the turnover of fatty acids in humans is approximately 600 days [31]. The fatty acid profile from human adipose tissue will therefore give an indication of the mean fatty acid composition of the diet during the last 3 years [32]. During starvation caused by moulting, pupping and also periods with lack of food, marine mammals utilise the stored fat reserves [33-36]. Investigation of lactating grey seals shows that the mother loses as much as 40% of total body mass during lactation [34], and that approximately 94% of the weight loss is due to decline in blubber mass [37]. This implies that the turnover time of fatty acids in sea mammals is possibly different, being much faster in sea mammals than in humans. A shorter turnover time of the fatty acids implies that the composition of blubber fat in marine mammals could be more influenced by the composition of their diet, especially when the animals are encountering periods of starvation. The investigation done by Grahl-Nielsen and Mjaavatten [29] was based on animals living in captivity. These animals will not encounter natural conditions with periods of starvation, and will possibly have a much lower activity than seals living under more strenuous condition in their natural habitat. This work based on 120 animals concludes that there are significant differences in the fatty acid composition between heart, brain and blubber. The most obvious difference is the amount of polyunsaturated fatty acids of linolenic (o~3) structure typically being associated with marine mammal depot fat. These fatt~ acids are greatly reduced or lacking in the heart and brain. However, it cannot be negated that the diet is of importance when considering the fatty acid profile especially in depot fat.

Layering of the blubber In this work we have demonstrated significant differences in the fatty acid composition in a vertical cross-section by separating the blubber into several layers

164

(Fig. 3). The mean values of the fatty acids found in the blubber in this investigation compare well with earlier investigations [6,7,18,19,25-28]. However, all earlier investigations are based on the assumption of a homogenous blubber in seals. This assumption is based on conclusions drawn by Jangaard and Ke [9] after analysing the blubber from one harp seal. We have analysed 20 animals from four species and they all revealed the same pattern. In all animals, a gradient of the relative amount of fatty acids was found. This gradient may have masked or shaded the results from the work of Jangaard and Ke [9]. Layering of the blubber in marine mammals has been demonstrated earlier in fin whale (Balaenoptera physalis) [2,5], sei whale (Balaenoptera borealis) [3-5], and walrus (Odobenus rosmarus divergens) [8]. This layering of the blubber in seals has, as far as we know, never been demonstrated. One characteristic represented in all the species analysed is the increasing amount of short-chained monounsaturated fatty acids from the inner layer to the skin or outer layer (Fig. 4). Investigation of blubber from fin whale [2,5] and walrus [8] revealed the same pattern. In our investigation the amount of short-chained monounsaturated acids constituted more than 46% of the total lipid content in the outer blubber layer, the total amount of monounsaturated acids in the same layer being more than 55%. However, the long-chained monounsaturated acids do not show the same pattern. In fact, they are decreasing in the same direction. In fin whale [2,6], and in sei whale, blue whale (Balaenoptera musculus) and humpback whale (Megaptera novaengliae) [3,4] the highest level of unsaturated fatty acids was found in the inner layer. This was also demonstrated in walrus [8]. In addition to its function as storage of fat reserves, the blubber plays an important role in thermoregulation [38]. Sea mammals are known to be homeothermal animals, and the blubber is thus heterothermal displaying a temperature gradient from skin to muscle with the temperature ranging from environmental temperature, which can be below 0~ to the body temperature, normally 36-38~ [39]. Compared to whales, seals have a larger surface to volume ratio. In addition, seals are also known to have a thinner insulating blubber layer. The seals may therefore have a large heat loss over the body surface, and it is of great relevance to keep the melting point of the fatty acids at a low level [6]. In fish, an inverse relationship has been demonstrated between the proportion of unsaturated fatty acids and the water temperature that the fish encounter [40]. Unsaturated fatty acids have a lower melting point (-11~ for C18) than saturated fatty acids of the same chain length (79~ for C~8), the melting point increasing with increasing chain length [41]. It should therefore be of great value to have high levels of monounsaturated fatty acids in the blubber, especially when living in arctic and high-boreal regions. Ringed seals were found to have the highest level of short-chained monounsatu-

Fig. 5. Classification of blubber samples by PCA based on 20 fatty acids (the importance of the various fatty acids is given by their position in the biplot), from (a) outer blubber layer, (b) middle blubber layer and (c) inner blubber layer from grey seal (Hg), harbour seal (Pv), ringed seal (Ph) and harp seal (Pg).

165

a)

~PCZ(Z6%) 16:1 to7 1

b) kPCZ(Zl~)

20:4 (06

[8: [ ~ 9

18:1 (o5 2~.5 m3

18:I m7

(o3

PC1(33%)

,

~b,

c) kPCZ(ZO~)

16:0

18:0

phPh

Hg

20.5~ ~

20:4 m6

Pv

Pv

Pg

139

Pgg

Hg

22:6 0~3

L

g

16:1 m 7

20:1 (o9

Hg t~g

Hg

Pu ~)~:4~3Hg

noid2

22:51~ (o5 18:1 m7

%

Pv

Hg

14:l co5

18:1(~9

18:2 (o~"l:)id3

H~b

PC1(30%)"~. ,

166 rated acids in the outer layer (Fig. 4), the amount increasing by 20% from the inner layer. In grey seal and harbour seal the increase was found to be 14%, and 10% in harp seal. Ringed seal is known to be the northernmost seal species, observed as far north as the north pole. The high level of monounsaturated fatty acids may therefore be related to the habitat (arctic and sub-arctic region). We expected to find high levels of monounsaturated fatty acids in harp seal, having boreal and arctic distribution and also greater surface to volume ratio. However, we found higher levels of monounsaturated fatty acids in the sub-arctic and boreal coastal-living grey and harbour seals than in harp seal. It should be noticed that the harp seals analysed were generally leaner than the other species, possibly caused by migration, and this may have masked or disguised the gradient.

Species differences By dividing the blubber into three layers we were able to separate the species by PCA analysis and SIMCA classification. However, the separation between all four species was only significant in the outermost layer (Fig. 5). Species differences in fatty acid composition of blubber have been indicated in earlier investigations [6,7,18,19]. These investigations are all based on univariate treatment of data from few animals, often only one, and without replicates. We have analysed 20 animals with replicate samples from each individual in three blubber layers, a total of 120 blubber samples. This work shows the relevance of newer techniques, i.e. multivariate statistics, in fatty acid analysis of different tissues from marine mammals. The assumption of a homogenous blubber must be discarded and the layering in adipose tissue has to be advised in further analysis for dietary fatty acids as well as in analysis of toxic compounds.

Acknowledgements This study was supported by the Norwegian Research Council. We thank T. Oritsland and K.A. Fagerheim, Institute of Marine Research, Bergen, T. Haug and S. Kjelquist, Fisheries Research Institute, Tromsr for the collection of samples.

References 1. Ackman RG, Lamothe F. Marine mammals. In: Ackman RG (ed) Marine Biogenic Lipids, Fats and Oils II. Boca Raton, FL: CRC Press, 1989;179-381. 2. Ackman RG, Eaton CA, Jangaard PM. Lipids of the fin whale (Balaenoptera physalus) from North Atlantic waters. I. Fatty acid composition of whale blubber and blubber sections. Can J Biochem 1965;43:1513-1520. 3. Ackman RG, Hingley JH, Eaton CA, Logan VH, Odense PM. Layering and tissue composition in the blubber of the North-West Atlantic sei whales (BaIaenoptera borealis). Can J Zool 1975;53:1340-1344.

167 4. Ackman RG, Hingley JH, Eaton CA, Sipos JC. Blubber fat deposition in mysticeti whales. Can J Zool 1975;53:1332-1339. 5. Lockyer CH, MacConnell LC, Waters TD. The biochemical composition of fin whale blubber. Can J Zool 1984;62:2553-2562. 6. Ackman RG, Epstein S, Eaton CA. Differences in the fatty acid composition of blubber fats from northwestern Atlantic fin whales (Balanoptera physalus) and harp seals (Phoca groenlandica). Comp Biochem Physiol 1971;40:683-697. 7. West GC, Burns JJ, Modafferi M. Fatty acid composition of blubber from the four species of Bering Sea phocid seals. Can J Zool 1979;57:189-195. 8. West GC, Burns JJ, Modafferi M. Fatty acid composition of Pacific walrus skin and blubber fats. Can J Zool 1979;57:1249-1255. 9. Jangaard PM, Ke PJ. Principal fatty acids of depot fat and milk lipids from Harp seals (Pagophilus groenlandica) and Hooded seals (Cystophora cristata). J Fish Res Bd Can 1968;25:2419-2426. 10. Kvalheim OM, Oygard K, Grahl-Nielsen O. SIMCA multivariate data analysis of blue mussel components in environmental pollution studies. Anal Chim Acta 1983;150:145-152. 11. Grahl-Nielsen O, Barnung T. Variations in the fatty acid profile of marine animals caused by environmental and developmental changes. Mar Environ Res 1985; 17:218-221. 12. Ugland KI, JCdestr KA, Aspholm PE, KrCyer AB, Jacobsen T. Fish consumption by invading harp seals off the Norwegian coast in 1987 and 1988. International Council for the Exploration of the Sea, J Mar Sci 1993;50:27-38. 13. Pearson K. On lines and planes of closest fit to a system of points in space. Philos Mag 1901 ;2:227-572. 14. Kvalheim O, Kvarstang TV. A general-purpose program for multivariate data analysis. Chemom Intel Lab Sys 1987;2:235-237. 15. Wold S, Sj6str6m M. SIMCA a method for analyzing chemical data in terms of similarity and analogy. In: Kowalski BR, Chemometrics: Theory and Applications. American Chemical Society Symposium Series, 1977;243-282. 16. Albano C et al. Pattern recognition by means of disjoint principal component models (SIMCA). Philosophy and methods. Proceeding Symposium on Applied Statistics, Copenhagen, 1981;182217. 17. Wold S. Cross-validatory estimation of the number of components in factor and principal component models. Technometrics 1978;20:397-405. 18. Ackman RG. Marine Biogenic Lipids, Fats, and Oils II. Boca Raton, FL: CRC Press, 1989. 19. Ackman RG, Hooper SN. Long-chain monoethylenic and other fatty acids in heart, liver, and blubber lipids of two harbour seals (Phoca vitulina) and one gray seal (Halichoerus grypus). J Fish Res Bd Can 1974;31:333-341. 20. Grahl-Nielsen O, Mjaavatten O. BestandsundersCkelse av sjCpattedyr ved hjelp av fettsyreprofiler. Sluttrapport sjr 1991. 2i. Ackman RG, Hooper SN, Hingley J. The Harbor Seal Phoca vitulina concolor De Kay: comparative details of fatty acids in lung and heart phospholipids and triglycerides. Can J Biochem 1972;50:833-838. 22. Engelhardt FR, Walker BL. Fatty acid composition of the harp seal, Pagophilus groenlandicus (Phoca groenlandica). Comp Biochem Physiol 1974; 169-179. 23. Morris RJ, Culkin F. Marine lipids: analytical techniques and fatty acid ester analysis. Oceanogr Mar Biol Annu Rev 1976;14:391-433. 24. Stryer L. Biochemistry. New York: W.H. Freeman, 1988. 25. Schweigert FJ, Stobo WT, Zucker H. Vitamin E and fatty acids in gray seal (HaUchoerus grypus). J Comp Physiol Ser B 1990; 159:649-654. 26. Ackman RG, Jangaard PM. The gray (Atlantic) seal; fatty acid composition of the blubber from a lactating female. Can J Biochem 1965;43:251-255. 27. Ackman RG, Eaton CA. n-3 Docosapentaenoic acid in blubber of dam and pup gray seals

168

(Halichoerus grypus): implications in the inuit diet and for human health. Can J Zool 1988;66:2428-2431. 28. Innis SM, Kuhnlein HV. The fatty acid composition of northern Canadian marine and terrestrial mammals. Acta Med Scand 1987;222:105-109. 29. Grahl-Nielsen O, Mjaavatten O. Dietary influence on fatty acid composition of blubber fat of seals as determined by biopsy: a multivariate approach. Mar Biol 1991;110:59-64. 30. Plakk6 T, Berkel J, Beynen AC, Hermus RJJ, Katan MB. Relationship between the fatty acid composition of the diet and that of the subcutaneous adipose tissue in individual human subjects. Hum Nutr Appl Nutr 1983;37A:365-372. 31. Dayton JA, Hashimoto S, Dixon W, Pearce ML. Composition of lipids in human serum and adipose tissue during prolonged feeding of a diet high in unsaturated fat. J Lipid Res 1966;8:508. 32. Beynen AC, Hermus RJJ, Hautvast JGAJ. A mathematical relationship between the fatty acid composition of the diet and that of the adipose tissue in man. Am J Clin Nutr 1980;33:81. 33. Bonner WN. Gray seal Halichoerus grypus Fabricius, 1791. In: Ridgeway SH, Harrison FR (eds) Handbook of Marine Mammals, Vol 2 Seals. London: Academic Press, 1981;11-144. 34. Fedak MA, Anderson SS. The energetics of lactation; accurate measurements from a large wild animal, the gray seal (Halicoerus grypus). J Zool London 1982;198:473-479. 35. NordCy ES, Blix AS. Energetics of Gray seal pups during the post-weaning fast. Acta Physiol Scand 1985;124:542. 36. Sergeant DE. Feeding, growth and productivity of northwest Atlantic harp seals (Phoca groenlandica). J Fish Res Bd Can 1973;17-29. 37. NordCy ES, Ingebretsen OC, Blix AS. Depressed metabolism and low protein catabolism in fasting Gray seal pups. Acta Physiol Scand 1990;139:361-369. 38. Ryg M, Smith TG, ~ritsland NA. Thermal significance of the topographical distribution of blubber in ringed seals (Phoca hispida). Can J Fish Aquat Sci 1988;45:985-992. 39. Irving L. Temperature regulations in marine mammals. In: Andersen HT (ed) The Biology of Marine Mammals. New York: Academic Press, 1969;168-174. 40. Geraci JR. Pinniped nutrition. International Council for the Exploration of the Sea 1975;169:312323. 41. Hart H. Organic Chemistry. Boston, MA: Houghton Mifflin, 1987.

9 1995 ElsevierScience B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. Wall0eand O. Ulltang,editors

Studies of the social ecology of Norwegian

169

killer whales

(Orcinus

orca) Anna Bisther ~ and Dag Vongraven 2 1University of GOteborg, Kristineberg Marine Research Station, Fiskebdckskil, Sweden; and 2University of Trondheim, Department of Zoology, Dragvoll, Norway Abstract. Killer whales along the Norwegian coast have been studied since 1990 with methods based on photo-identification. Preliminary insights of the social characteristics of Norwegian killer whales are presented generally. About 400 killer whales are identified, and 65% of the whales have been resighted. The whales occur in groups of moderate sizes, all containing adult males and breeding females. The groups seem to be social units based at least partly on stable membership. Acoustic analyses revealed both uniqueness and a variable degree of similarity in the vocalization of different groups of whales. The social integrity of groups is further indicated by cooperative feeding on herring, communal care of young, competitive interactions between groups and specialized feeding on marine mammals by one small group of whales. Some adult males have a nomadic occurrence, and might eventually represent a mating strategy. Ritualized interactions between males appear to be affiliation acts. Large males and female-sized whales do not seem to differentiate in diving behaviour during cooperative feeding due to synchrony in respiration patterns. Norwegian killer whales resemble the well-studied Canadian killer whales, but there are also indications of some intraspecific variability.

Key words: mammals, male strategies, acoustic communication, communal care

Introduction This presentation is a brief summary of behavioural studies conducted within the present project on Norwegian killer whales. It introduces methods and approaches that have been utilized during the past few years, and contains a synoptic discussion of some findings. Detailed results are to be published elsewhere. Killer whales occur regularly along the Norwegian coast and are reported to follow the migration of their main prey item, the Atlanto-Scandic herring (Clupea harengus) [1]. The impact of killer whale predation upon the herring stock caused concern in the early 1970s, and resulted in a protective hunt for killer whales in Norwegian coastal waters [2]. Norwegian authorities then issued a provisional ban on killer whale catches in 1982 because of incomplete knowledge of the status of the whale population. The project on Norwegian killer whales was initiated in 1990, and one objective is to contribute with ecological and life history data for assessment of the population. The social life of the whales is also being studied, where different behavioural aspects are combined into themes. These are partly based on results from the compre

Address for correspondence: A. Bisther, University of G6teborg, Kristineberg Marine Research Station, 450 34 Fiskeb~ickskil, Sweden.

170 hensive research on Canadian killer whales [3,4], but also on theoretical work on large mammals in general.

Methods

Field work has been conducted in both of the areas where killer whales concentrate seasonally; the spawning grounds of herring off the coast of Mere (63~ during early spring, and in the fjords off Ofoten (68~ where the herring reside during the winter months [5]. The winter season provides a unique scenario, since about 500 killer whales enter the fjords and stay until the end of January when the herring starts to migrate south towards the spawning grounds. The project is based on photo-identification where individual whales are identified by their natural and permanent markings [6]. The principle of photo-ID is illustrated in Fig. 1. Identified whales are given alphanumeric codes, which are stored on file together with information on time, location and demographic characters. The database is then utilized for several ecological objectives such as migrational pattern and population size. Behavioural studies include the underwater vocalization of the whales, where acoustic recordings have been obtained with a hydrophone system covering frequencies up to 20 kHz. Data on social events and respiration patterns have been sampled from direct observations and logged utilizing a computer-based recorder or dictaphones. Progressive analyses of behavioural and photographic data refer to (i) the social integrity of groups, (ii) male strategies and (iii) social determinants for large carnivores (e.g. cooperative hunting tactics and communal care of young).

Results

Results are described in general terms. Each behavioural aspect is still based on quantitative field work, and includes both the completed and the continuous studies of killer whale behaviour.

Social integrity of groups About 400 killer whales have been identified within the project, and 65% of these whales have been re-sighted during the years. The whales occur in groups of moderate sizes (-15 animals), all containing adult males and breeding females. Long-term associations (years) are present both between and within the two sexes, and the groups seem to be social units based at least partly on stable membership. Competitive interactions between groups of whales are occasionally observed as "feeding patch take-overs", where one feeding group is forced to leave because of the rapid approach of another. The interactions contrast the usual tolerance ex-

171

~.

.,.~

~

' -~. '

t',,~

.,~. ,..

9 ~.4

9

,

~

o

~

,

,

~

Fig. 1. Examples of photographic identification of Norwegian killer whales (photo" Dag Vongraven).

pressed between killer whale groups feeding independently of each other within close range. Analyses of the vocalization of four different groups of whales revealed stereotyped repetitions of a limited repertoire of acoustic signals classified as "discrete

172

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calls" (defined and illustrated in Fig. 2). Each group could be acoustically distinguished even if a variable degree of similarity was present [7].

Male strategies Males are identified as being either permanent companions to females, or nomadic. Male-male pairs are frequently observed as distinct units, where they might spend several hours in homosexual and highly ritualized activities. "Courtship trios", temporary constellations of two adult males and a female, have occasionally been observed to be engaged in sexual activities. Adult males are also observed to act as "baby-sitters", where a calf or a juvenile is closely attached to the male. This "echelon" position gives the smaller whale energetic benefits through assisted locomotion [8]. A few females are identified as alloparents because of the large number of young at their sides. Respiration profiles were obtained on pairs of instantly identifiable whales, a male and a female-sized whale simultaneously, to investigate if the two sexes differentiated in their diving behaviour during cooperative feeding. An example of a set of profiles is given in Fig. 3.

173 I

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Discussion The social system and behavioural ecology of Norwegian killer whales are gradually pieced together with photographic, bioacoustic and behavioural data. As for most studies on free-ranging whales, this project also has a long-term perspective. Considering the preliminary insights that have been obtained so far, the killer whales off the Norwegian coast seem to have social characteristics in common with their Canadian conspecifics. The cooperative nature of Norwegian killer whales is stated both in their coordinated feeding activities [9], and in the communal care of young where small individuals are hydrodynamically carried by adult group members. The genealogy of the whales is unknown, but the photographic data still indicate stability in group com-

174 position. If Norwegian killer whales are socially organized in the same way as Canadian killer whales, the groups would be matrilineal kin units where even the adult males refrain from dispersing from their natal "pod" [3]. However, intraspecific variation might be found among the strategies of the males. Some of the Norwegian killer whale males seem to have a nomadic lifestyle where they repeatedly occur together with different groups of whales. The presence of other males in the groups does not prevent these males entering the group, participating in cooperative feeding or leaving the group together with a female. These nomads are few, compared to males that are consistently identified together with the same females, and they might represent one of two strategies in accordance with the mating system of the brown hyena (Hyaena brunnea) [10]. Among these animals, the males either become nomadic and mate with unrelated females, or stay in their natal clans and gain reproductive success through alloparenting the cubs of their female relatives. Another possibility, if the killer whale males do have a nomadic strategy, might be the importance of age, size and social rank upon dispersal from the natal group. Because of the previous catch of Norwegian killer whales, it is also possible that the nomadic males are not representing any strategy but are merely a consequence of disrupted groups. Male mammals usually compete for access to receptive females [ 11 ]. Among bottlenose dolphins (Tursiops truncatus), male competition takes the form of physical fighting resulting in heavily scarred bodies of the oldest animals [ 12]. Male aggression has not been observed from the surface behaviour of Norwegian killer whales, but the males frequently occur in pairs, detached from other whales in their vicinity. At these instances, the males might spend a considerable amount of time in a ritualized activity descriptively termed "body contact-belly up-beak genital". Due to the quantified reciprocity and symmetry of roles in the display, the behaviour resembles the sexual gestures used as ritualized greetings between adult male baboons (Papio cynocephalus anubis) [13]. These male primates form stable coalitions where they take the oestrus females of a troop away from other males. In that respect, male baboons have a similar strategy to male bottlenose dolphins in Shark Bay, western Australia [ 14]. The behavioural studies of Norwegian killer whales are too preliminary to be conclusive about the formation of alliances or coalitions among males. The sexual activities observed to occur between two males and a female might however indicate some form of cooperative relationship between adult males. The size differences between male and female killer whales were discussed by Bain [15]. He quotes the work of Peters [16], and since the larger sized males are able to swim more efficiently at higher speeds than females, they would also have the ability to feed at greater depths. Bain suggests that the philopatric males in the Canadian killer whale population might "disperse" ecologically rather than geographically. Adult males would thereby reduce the cost of sharing food resources with their maternal groups. In a Norwegian version of the idea of ecological dispersal, respiration profiles were measured for pairs of killer whales while they were cooperatively feeding on

175 herring with their groups. The diving behaviour of males and female-sized whales during feeding was however highly synchronous. No differences were found that could attribute a specific role for the larger males when measuring the respiration patterns. However, these data were sampled in an area where food is superabundant, and only in a final stage of the feeding when the herring has been herded towards the surface. The males might still compensate for their larger size and higher energetic needs. Solitary feeding on fish has only been observed to include males, which is also described to occur among Canadian killer whales [ 17]. The acoustic communication of killer whales consists of high frequency whistles and pulsed signals that can be either variable or discrete [18]. Discrete "calls" are produced by the whales in a stereotyped way, and each Canadian killer whale pod was found to have a limited but unique and stable repertoire of discrete calls [19]. The pod-specific vocalization was termed "dialects", and is suggested to have evolved among Canadian killer whales as a result of the social structure of the population [4]. The primary function of dialects might be to promote group cohesiveness and to coordinate behavioural activities. The acoustic properties of Norwegian killer whale vocalization resemble dialects. Different groups can be acoustically distinguished, repertoire sizes of discrete calls are comparable, there is a variable degree of similarity between groups and the relative use of calls is about the same [7]. The knowledge of the social organization of Norwegian killer whales is however incomplete. It remains to be demonstrated that all groups are stable units with no long-term exchange of individuals or sub-groups, which is the prerequisite of dialects. Many years of field work on Norwegian killer whales are still required before the results can be adequately compared with the insights revealed from the Canadian killer whale population. Knowledge of the Canadian whales is meanwhile being used as a "model" in the Norwegian research, to search for similarities or variations. Sometimes both occur, as in a small group of Norwegian killer whales that seem to have developed specialized feeding on marine mammals. In that respect, they are "transient" as the carnivorous killer whales in the Canadian population are termed [20]. The Norwegian "transients" still seem to diverge from the Canadian definition, since they are not socially isolated from fish-feeding groups of whales. Further aspects on comparative social ecology will clarify differences between characteristic traits for the killer whale and intraspecific variations due to various ecological factors.

Acknowledgements For the enthusiastic assistance on board M/S Bella, we acknowledge Marianne Olsen, Bente Brekke, Elle Lettevall, Tyri Askl6f, Leffe Strandh, brother and sister Bisther. The project was financed in the period 1990-1993 by The Norwegian Fisheries Research Council as part of the national research program on marine mammals.

176

References 1. Christensen I. Distribution, movements and abundance of killer whales (Orcinus orca) in Norwegian coastal waters, 1982-1987, based on questionnaire survey. Rit Fiskideildar 1988;11:7988. 2. ~ien N. The distribution of killer whales (Orcinus orca) in the North Atlantic based on Norwegian catches, 1938-1981, and incidental sightings, 1967-1987. Rit Fiskideildar 1988;11:65-78. 3. Bigg, MA, Olesiuk PF, Ellis GM, Ford JKB, Balcomb KC. Social organization and genealogy of resident killer whales (Orcinus orca) in the coastal waters of British Columbia and Washington State. Rep Int Whal Commn 1990;(Special Issue 12):383-405. 4. Ford JKB. Vocal traditions among killer whales (Orcinus orca) in coastal waters of British Columbia. Can J Zool 1991 ;69:1454-1483. 5. RCttingen I. A review of variability in the distribution and abundance of Norwegian spring spawning herring and Barents Sea capelin. Polar Res 1990;8:33-42. 6. Bigg MA, Ellis G, Balcomb KC. The photographic identification of individual cetaceans. Whalewatcher (J Am Cetacean Soc) 1986;20(2):10-12. 7. Bisther A. The acoustic communication of social groups of photographically identified killer whales (Orcinus orca) at the coast off Norway. Master thesis, University of Gtiteborg, Sweden, 1991. 8. Lang TG. Hydronamic analysis of cetacean performance. In: Norris KE (ed) Whales, Dolphins and Porpoises. Berkley, CA: University of California Press, 1966;410--432. 9. Simil~i T, Ugarte F. Surface and underwater observations of cooperatively feeding killer whales, Orcinus orca, in northern Norway. Can J Zool 1993;71:1494-1499. 10. Mills MGL. The comparative behavioral ecology of hyenas: the importance of diet and food dispersion. In: Gittleman JL (ed) Carnivore Behavior, Ecology and Evolution. London: Chapman and Hall, 1989;125-143. 11. Poole T. Social Behaviour in Mammals. New York: Chapman and Hall, 1985. 12. Scott MD, Irvine AB, Wells RS. A long-term study of bottlenose dolphins on the west coast of Florida. In: Leatherwood S, Reeves RR (eds) The Bottlenose Dolphin. San Diego, CA: Academic Press, 1990;235-243. 13. Smuts BB, Watanabe JM. Social relationships and ritualized greetings in adult male baboons (Papio cynocephalus anubis). Int J Primatol 1990; 11 (2): 147-172. 14. Connor RC, Smolker RA, Richards AF. Two levels of alliance formation among male bottlenose dolphins (Tursiops sp.). Proc Natl Acad Sci USA 1992;89:987-990. 15. Bain DE. An evaluation of evolutionary processes: studies of natural selection, dispersal and cultural evolution in killer whales (Orcinus orca). Ph.D. dissertation, University of California, Santa Cruz, 1989. 16. Peters RH. The Ecological Implications of Body Size. New York: Cambridge University Press, 1983. 17. Hoelzel RA. Foraging behaviour and social group dynamics in Puget Sound killer whales. Anim Behav 1993;45:581-591. 18. Ford JKB. A catalogue of underwater calls produced by killer whales (Orcinus orca) in British Columbia. Can Data Rep Fish Aquat Sci No 633, 1987. 19. Ford JKB, Fisher HD. Group-specific dialects of killer whales (Orcinus orca) in British Colombia. In: Payne R (ed) Communication and Behavior of Whales. AAAS Sel Symp Series 76, Boulder, CO: Westview Press, 1983;129-161. 20. Baird RW. Foraging behaviour and ecology of transient killer whales (Orcinus orca). PhD thesis, Simon Fraser University, Canada, 1994.

9 1995Elsevier Science B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand O. Ulltang, editors

177

Possible effects of previous catch on the present population of Norwegian killer whales (Orcinus orca) D a g V o n g r a v e n 1 and A n n a Bisther 2 1Department of Zoology, University of Trondheim-A VH, Dragvoll, Norway; and 2Kristineberg Marine Research Station, University of Gothenburg, 45 034 Fiskebiickskil, Sweden A b s t r a c t . Intensive coastal killer whale catches undertaken in the two decades after 1960 (especially in

1969, 1970 and 1979), might have had effects on reproduction and social behaviour in the present Norwegian killer whale community. The catch was both sex- and age-biased, and this might have triggered compensatory mechanisms. Our approach when studying the social ecology of Norwegian killer whales must take into account the possible presence of such mechanisms. Further modelling studies are in progress. K e y w o r d s : killer whales, catch data, reproduction, social ecology

Introduction

The killer whale population off the Norwegian coast has previously been subjected to a hunting pressure with unknown effects upon the present population. Results and insights from an on-going photo-ID study indicate that this population is stationary and has evolved complex social behaviour. Other species of long-lived, slow-reproducing mammals that live in socially structured societies have been shown to respond to hunting with changes in social strategies, either as an effect of a reduced population density (i.e. simakobu monkey Nasalis concolor [1 ]) or as an effect of selective takes of large and old individuals (i.e. elephants Loxodonta africana [2]). Could peaks in the catch of killer whales one generation ago have had any other effects than a reduced size of the present population?

Materials and Results

There are official catch records of 2435 killer whales caught in the North Atlantic in the period from 1938 to 1981. Of these, 64% were caught in the coastal waters off Norway. Twenty-nine percent of the total catch in the whole period occurred in the three seasons 1969 (231), 1970 (246) and 1979 (221), and 91% of these were caught in the coastal fishery zones. Temporal and spatial intensity is especially characteristic of the catch during these years, during which the majority of the whales were caught

Address for correspondence: D. Vongraven, Department of Zoology, University of Trondheim-AVH, 7055 Dragvoll, Norway.

178 a

1969/1970

b

1979 Sex ratio in total catch (F : M)

Sex ratio in total catch (F : M)

1:2.1 20

1.2:1 20

IMMATURE WHALES

~ ~o

"~

IMMATURE WHALES

10

s 0

MALES

FEMALES BOTHSEXES

0

Fig. 1. Characteristics of the Norwegian killer whale catch, depicting the sex- and age-biases in the peak seasons (a) 1969 and 1970; and (b) 1979.

in the space of 1-2 months within a single fishery zone every season. The 1969/1970 catch was concentrated in the MOre region, while the 1979 catch was concentrated in Lofoten. Females are sexually mature at an average length of 15 ft, and males at 18 ft [3]. Seventy-one percent of the total catch was recorded after 1960, and the fraction of immature whales in the same period was 11.9%. The corresponding fraction for the two high intensity years 1969 and 1970 was 6.9%, and for 1979 15.9%. Of all the whales caught after 1960, a fraction of only 4.2% were smaller than 15 ft (Fig. 1). The overall sex ratio for sexually mature whales is close to 1:1.3 (females/males) for all seasons. However, there are large variations between years. The sex ratio for the two seasons 1969/1970 was 1:2.1, whereas the sex ratio for the 1979 season was 1.2:1 (Fig. 1). There was a switch from a female-biased to a male-biased sex ratio at lengths above 19 ft.

Discussion There are two factors that indicate that the catch in the peak seasons could be regarded as high compared to the total size of the killer whale community. First, a total number of identified whales in the range of 5-700 individuals and fractions of resightings of 65% and 90% in the two on-going photo-ID studies off the Norwegian coast indicate that a population size much bigger than this is improbable (Simil~i personal communication; Bisther and Vongraven, unpublished data). Second, the female bias in the catch from 1979 could be considered as a direct effect of the malebiased catch from previous years, and especially the 1969/1970 catch, if population size was in the size range previously suggested.

179 There is an obvious size bias in the catch from these peak seasons. Given that young whales rely on nursing and care-taking from adult whales for survival, the relative absence of calves and juveniles in the latest catch records could have led to an increase in their future mortality as many parents and potential care-giving individuals were removed. In the Pacific Northwest, when comparing two populations of killer whales with different exploitation histories, Bain [4] found a neonate mortality of 63% in the cropped population and 41% in the uncropped population, whereas adult survivorship was similar. A tendency towards a higher juvenile mortality (up to an age of 15 years) in cropped than in uncropped pods has also been shown by Olesiuk et al. [5]. Live-capture fishery for killer whales in the North-American Pacific Northwest removed approx. 25% of the initial population in the period 1964-1975 [6]. If the population size of Norwegian killer whales in the 1960s was in the order of magnitude previously suggested, then the "coastal" fraction of the 477 whales caught in 1969 and 1970 would at least represent a similar fraction of the population at that time. Findings like these point to the fact that killer whale reproduction depends on social as well as on density dependent determinants. It is also easier to comprehend compensatory mechanisms being induced on a group level rather than on an overall reduced density in the whole area inhabited by the population [7]. Destruction of social structures caused by biased removal of individuals from the population could account for some of the loose group structure suggested by our data. Further modelling studies with basis in the catch data will be carried out. 13y means of different scenarios for population status and catch regime, and previously published vital rates, we will try to investigate what possible effects the catch might have had on the social structure of the population. Finally, we wish to place the emphasis on the potential importance of the catch, and on the influence this ought to have on our approach when studying the social system of Norwegian killer whales. References 1. Watanabe K. Variations in group composition and population density of two sympatric mentawaian leaf-monkeys. Primates 1981 ;22:145-160. 2. Eltringham SK. Elephants. Blandford Books, 1982;52-56. 3. Christensen I. 1982. Killer whales in Norwegian coastal waters. Rep Int Whal Commn 1982;32:633--641. 4. Bain DE. An evaluation of evolutionary processes. Ph.D. Thesis, University of California, Santa Cruz, 1988. 5. Olesiuk PF, Bigg MA, Ellis GM. Life history and population dynamics of resident killer whales O r c i n u s o r c a in the coastal waters of British Columbia and Washington State. Rep Int Whal Commn 1990;(Special Issue 12):209-243. 6. Bigg MA, MacAskie IB, Ellis G. Abundance and movements of killer whales off eastern and southern Vancouver Island with comments on management. Preliminary Report, Arctic Biological Station, Ste. Anne de Bellevue, Quebec, 1976. 7. Fowler CW. Density dependence in cetacean populations. Rep Int Whal Commn 1984;(Special Issue 6):373-379.

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Distribution, diet and feeding ecology

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9 1995 ElsevierScience B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand t3. Ulltang,editors

183

New approaches to studying the foraging ecology of small cetaceans A n d r e w J. R e a d Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA Abstract. Dolphins and porpoises spend the majority of their lives underwater, out of the view of human observers. Consequently, scientists have relied on indirect means to study the foraging ecology of these animals. These indirect methods, such as examining the stomach contents of carcasses, provide an incomplete and often biased view of their feeding ecology. Promising new technological developments with data loggers and satellite telemetry are beginning to offer alternative methods to studying the foraging ecology of small cetaceans. Data loggers have long been used with other marine vertebrates but have not been suitable for dolphins or porpoises because the devices must be retrieved to recover stored data. Recent advances in their recovery have overcome some of these difficulties and loggers have now been successfully deployed on several species of small cetaceans. A new generation of small satellitelinked transmitters has allowed researchers to follow the movements and diving behaviour of animals from their offices. Continuing developments in these fields will afford new opportunities to study the lives of dolphins and porpoises and better understand how they find and obtain food at sea.

Key words: foraging, ecology, small cetaceans

Introduction Dolphins and porpoises spend most of their lives underwater, out of the view of human observers. Many species also live in remote habitats where direct observation of their behaviour is difficult and often impossible. Consequently, for most of these animals, our knowledge of their foraging ecology is limited to inferences made from the examination of carcasses obtained as strandings, incidental catches in commercial fisheries, or from directed catches. Even when we are able to observe dolphins and porpoises catch their prey, we are usually limited to events at or near the surface that are unlikely to be representative of the true range of predator-prey interactions. The traditional approach to studying the foraging ecology of small cetaceans has been to examine stomach contents and attempt to reconstruct the diet from samples of hard parts [21,22]. With this dietary information in hand, it is then possible to make some inferences regarding the foraging ecology and behaviour of the marine mammal. A preponderance of benthic and demersal prey items in the diet would indicate that an animal was feeding at or near the sea floor, for example. This is a valuable exercise that provides important information on the diet of small cetaceans. It is also fraught with potential pitfalls and biases that must be taken into account when reconstructing the diet and making inferences about behaviour. Most of these poten-

Address for correspondence: Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA.

184 tial biases are well known (see the review of Pierce and Boyle [21] for a full description). A few examples will illustrate some of the problems associated with such dietary analysis. First, the sample may not be representative of the diet of the population because of the manner in which specimens were obtained. For example, observations of the stomach contents of stranded animals may not reflect their true diet, particularly if the animals were sick for some time before their death. The stomach contents of animals killed incidentally in fishing operations may be biased towards the target species of the fishery and not represent the diet of the population when it is not interacting with fishing gear. A second class of biases arises through the differential ingestion, digestion, and retention of hard parts. Some animals may ingest only the bodies of prey, without consuming the heads (and otoliths). The rates at which hard parts digest in the stomachs of small cetaceans are not known, but it is clear that these rates differ among prey taxa. Squid beaks are resistant to digestion, for example, and are believed to be retained in the stomach much longer than otoliths [5]. It is particularly difficult, therefore, to reconstruct the diet of a marine mammal that feeds on both squid and fish with any accuracy. Finally, the presence of a particular item in the diet tells us little of how, when or where the marine mammal came to locate and capture the prey. A mid-water fish that undertakes pronounced vertical migration could be captured at depth during the day, or near the surface between dusk and dawn; its presence in the stomach tells us little about the foraging behaviour of the predator. A considerable amount of research has helped to overcome some of these biases. Indirect means of examining the diet of marine mammals have been developed, such as studying the fatty acid composition [ 11 ] or stable isotope ratios [ 1] of tissues and comparing them to potential prey. The digestion rates of hard parts of various sizes and shapes have been measured in the laboratory to help understand the biases associated with differential digestion times [22]. And biochemical techniques have been developed to identify the presence of different prey species without using hard parts [21 ]. Taken together, these methods offer researchers a powerful array of tools with which to study the diet of small cetaceans. More promising, however, is the development of methods that allow researchers to follow the activities of individual animals at sea and thus make more direct observations of foraging behaviour [7]. Early attempts to use conventional radio telemetry to study the diving behaviour of common dolphins (Delphinus delphis) were promising [9], but the tags available at that time were too cumbersome for widespread application. Recent advances in the miniaturization, attachment and recovery of data Table 1. Deployments of time-depth recorders (TDRs) on small cetaceans

Species

N

Maximum depth (m)

Reference

Harbour porpoises Spotted dolphins Orcas

7 5 7

226 203 173

[27] [24] [3]

185 loggers and satellite-linked tags have allowed researchers to begin to use these tools to study how marine mammals find and obtain their food at sea [6,7]. The basic technology for both these approaches has been available for some time, but only recently have they been suitable for use with dolphins and porpoises. These applications will revolutionize the way that we study the foraging ecology of small cetaceans in the next decade.

Data loggers Data loggers are microprocessor-controlled devices that record information obtained from external sensors. The most common and best known application of this system is the time-depth recorder (TDR), which has been used on a variety of marine vertebrates and developed extensively with pinnipeds [13]. The earliest TDRs mechanically recorded depth profiles on film [6]. The current generation of TDRs sample ambient pressure at pre-set intervals and store this information as hexadecimal code on a microchip [6,7]. The data are downloaded to a computer after the TDR is retrieved and the code converted to depth information. The need to recover the TDR has been one of the major obstacles to the use of these devices on dolphins and porpoises. TDRs can be attached to and recovered from pinnipeds when they haul out, often in a predictable location. Unfortunately, dolphins and porpoises are not inclined to haul out and allow researchers to attach devices to them. Three innovative approaches have overcome this obstacle, however, and TDRs have now been successfully deployed on harbour porpoises (Phocoena phocoena), spotted dolphins (Stenella attenuata) and orcas (Orcinus orca) (Table 1). The first successful deployment of a TDR on a cetacean was made by Westgate et al. [27], who studied the diving behaviour of harbour porpoises in the Bay of Fundy, Canada. These researchers developed a tag that incorporated a TDR and a small VHF radio tag in a buoyant epoxy package. The tags were attached to porpoises released from herring weirs with the help of commercial fishermen. The attachment mechanism incorporated two small magnesium nuts that corroded rapidly in salt water, allowing the package to detach from the porpoise and float to the surface, where it could be located by conventional radio telemetry. Seven of eight tags were recovered, with one tag retrieved 17 days after it was deployed. Scott et al. [24] tagged and tracked spotted dolphins associated with yellowfin tuna (Thunnus albacares) in the Eastern Tropical Pacific. These dolphins were captured with a chartered commercial purse seiner, tagged with a saddle-mounted TDR and VHF radio assembly and followed from a research vessel. After a period of several days the animals were recaptured with the purse seiner and the saddles removed. In addition, these researchers investigated the bond between dolphins and yellowfin tuna by attaching acoustic tags to the tuna and simultaneously tracking the movements of fish and mammals. Another unique approach was taken by Baird and Goodyear [3,4], who attached a buoyant TDR-VHF package to orcas in the waters of Vancouver Island, British Co-

186 lumbia. The tags were attached to the whales with suction cups, using a crossbow or long pole. A magnesium disc was incorporated into the wall of the suction cup; when the magnesium corroded and the suction was broken, the package detached and floated to the surface. These tags were recovered after a deployment period of up to 8.4 h [3]. In each of these three cases, TDRs provided a wealth of new information on the diving behaviour of the species in question. For example, in the study of Westgate et al. [27], one harbour porpoise dove to the deepest part of the Bay of Fundy (226 m) and most of the tagged porpoises routinely made dives to over 100 m. Most dives were flat-bottomed, with rapid descent and ascent rates and prolonged periods of time at a relatively constant depth during the mid-portion of the dive. The authors interpreted this behaviour as foraging, probably at or near the sea floor, although it was not possible to obtain synoptic data on water depth. This supports the observations of Recchia and Read [23], who found a preponderance of Atlantic herring (Clupea harengus) and silver hake (Merluccius bilinearis) in the stomach contents of harbour porpoises killed in bottom-set gill nets. Herring and silver hake are found predominantly at depth during the day, although both are believed to migrate upwards in the water column at night. Surprisingly, however, the tagged porpoises made longer and deeper dives at night, when their prey are thought to be closer to the surface. This diel variation in diving behaviour is not currently understood, but will undoubtedly spur further study of the foraging behaviour of this species. These three studies demonstrate some of the potential for using data loggers, and particularly TDRs, to study the foraging ecology of small cetaceans. We are beginning to gain an appreciation for the diversity of diving behaviour in dolphins, porpoises, and small whales and to consider some of the factors responsible for this variation. The three studies also demonstrate some of the limitations of this approach as it is currently employed. The use of TDRs with small cetaceans requires researchers to both attach and recover the tags. The non-invasive attachment of Baird and Goodyear [3,4] obviates the requirement of capturing an animal to attach a TDR, but their approach does not allow for a long-term deployment that will provide information on diel patterns in behaviour. In addition, we are still limited in our understanding of the foraging ecology of these animals because TDRs give us only an indirect view into their feeding behaviour. We assume that flat-bottomed dives represent foraging excursions, but have no way of testing this assumption. I shall return to this point below.

Satellite telemetry The use of conventional radio tracking techniques, such as VHF telemetry, has greatly increased our ability to track the movements and behaviour of small cetaceans at sea. Limiting this methodology, however, is the need to stay in radio contact with tagged animals. During the last two decades, a new technique has revolutionized the ability of biologists to track the movements of animals in remote

187 areas. This technique, known as satellite telemetry, utilizes radio transmitters that send signals to receivers aboard the US National Oceanographic and Atmospheric Administration (NOAA) Tiros-N weather satellites. The receivers are operated by Service ARGOS, of Toulouse, France, which makes the system available to commercial and scientific users. The system has proven extremely effective in tracking large terrestrial mammals [12] and used with great success to telemeter information about the foraging ecology of pinnipeds [7,20]. The ARGOS system uses the Doppler shift to estimate the position of the transmitter or Platform Transmitting Terminal (PTI'). All PTI's transmit on a stable frequency of 401.650 mHz. The accuracy of the estimated position depends on the number and quality of transmissions received during a satellite pass. The transmission rate is limited both by the ARGOS system and by the surfacing behaviour of the animal. PTFs used with marine mammals typically employ a salt water switch to restrict transmissions to periods when an animal is at the surface and the signal can be received by the satellite. Each transmitter is identified by a unique code that is sent at the beginning of every message. In addition to the ID code, the transmitter can send up to 256 bits of data, which typically includes information from environmental sensors aboard the tag. The most frequent type of recorded data is information on depth and temperature, although velocity sensors are also employed on pinniped PTrs [20]. Until very recently, PTI's were too large for use with small cetaceans. In 1987, Mate [ 17] tagged a rehabilitated stranded pilot whale (Globicephala melaena) with a P T r and followed its movements and diving behaviour for 95 days. The whale made long dives at night, presumably to forage on the squid that is believed to comprise its primary prey. During the daylight hours, the pilot whale made shallow dives, perhaps resting or feeding on near-surface prey. Since this early success, advances in miniaturization have allowed PTrs to be used to study the movements and activities of bottlenose dolphins (Tursiops truncatus), narwhals (Monodon monoceros), belugas (Delphinapterus leucas), white-sided dolphins (Lagenorhynchus acutus), common dolphins (Delphinus sp.), and harbour porpoises (Table 2). The most successful programme has been the study of high Arctic belugas by Martin and colleagues [15,16]. Eighteen belugas were tagged with PTTs between 1988 and 1992, yielding a tremendous amount of information on movement patterns Table 2. Deployments of satellite-linked radio tags (PTTs) on small cetaceans

Species

N

Maximum duration (days)

Reference

Bottlenose dolphins Pilot whales Narwhals Belugas White-sided dolphins Bottlenose dolphins Common dolphins Harbour porpoises

14 1 3 18 1 1 2 3

35 95 19 75 6 25 ? 21

[25] [17] [14] [15] [19] [18] P. Thorson, personal communication A. Read, unpublished data

188 and diving behaviour. These belugas made dives of up to 18 min in duration and to depths of 440 m. As was the case with harbour porpoises, most of the long dives made by belugas were fiat-bottomed, with several minutes spent at a constant depth near the sea bed. These dives were interpreted as foraging excursions, supporting previous observations from stomach contents that belugas feed on benthic and demersal prey. The diving capabilities of these animals were unexpected, however, and the results demonstrate that belugas have access to much more of the Arctic sea floor than previously believed. Once again, this new technology has radically changed the way that we view the foraging ecology of these animals. The use of satellite telemetry has overcome the need to recover tags to obtain data, one of the main problems with the use of data loggers. But satellite tags have some of the same other limitations possessed by data loggers, particularly the need to capture animals to attach tags. And, as with data loggers, satellite telemetry provides only an indirect view of foraging behaviour. In addition, satellite-linked tags have their own limitations imposed by the ARGOS system. The maximum limit of 256 bits of data per transmission is a serious impediment to the collection of fine-scale environmental data from more than a single sensor. Even with data compression algorithms and duty cycling of different sensors, it is difficult or impossible to collect the kind of information that can be obtained with data loggers. The number of successful uplinks is also limited by the difficulty of coordinating transmissions with satellite passes; this is a particular problem in equatorial regions where the number of passes per day is very limited. The ARGOS system is currently limited to the two Tiros satellites which are both in polar orbits. Finally, the recent success of long-term tag attachments used with PTI's means that we are approaching the maximum transmission life of the current generation of batteries. It may be possible to address some of these limitations with new technological developments, as discussed below.

Future directions

Recent developments with both data loggers and satellite telemetry have produced impressive advances in our understanding of the foraging ecology of small cetaceans. Nevertheless, many important questions remain. For example, how important is echolocation in finding food? The prey of many dolphins and porpoises are known to produce sound, and it is likely that passive acoustics and vision are also important senses in the initial detection process. How do deep-diving animals find their prey several hundred metres below them near the sea floor? How important are cooperative feeding strategies in group-living species? These and many other questions will require more direct means of observing the behaviour of dolphins and porpoises. Below I list some avenues of technological research and development that could yield the answers to such questions. As with the development of data loggers and satellite telemetry, it is likely that many of these developments will first be tested on pinnipeds, before the constraints of deployment on small cetaceans are overcome. One area of future work that holds great promise is the development of new sen-

189 sors for use with data loggers and satellite-linked tags. Researchers are already experimenting with sensors that record stomach temperature [28] and heart rate [ 10]. It should soon be possible to determine when prey are ingested by monitoring reductions in stomach temperature and then link this information to diving patterns to test the hypothesis that long, fiat-bottomed dives are foraging excursions. Video may also make a dramatic impact on our ability to document foraging behaviour, at least for shallow water species [8]. Finally, loggers are now available that allow researchers to record the occurrence of echolocation clicks and thus relate acoustic behaviour to feeding events. Preliminary work has been conducted on several species in captivity [2,26] and it should soon be possible to adapt some of these techniques to the study of free-ranging animals. At present, it seems likely that the simultaneous use of several sensors will be best achieved with data loggers rather than PTTs. Data loggers are not as power-hungry as PTI's because they do not have to transmit data, thus the loggers can be considerably smaller than PTI's. The fact that loggers record rather than transmit data allows them to store information on a much finer scale. Such detailed records will be critical when we attempt to integrate information recorded simultaneously from several sensors (depth, stomach temperature and echolocation trains, for example). The possibility of obtaining such rich data records is tremendously exciting and will leave us struggling for analytical tools to cope with a wealth of information. The second type of technical developments that will further our study of foraging ecology lies in the development, attachment and recovery of the tags themselves. As noted above, for the most part we still need to capture animals to attach tags and, for many species, this is not possible. More work on non-invasive, remote attachments, like those of Baird and Goodyear [3,4], is required. We also need to incorporate active release mechanisms into the attachments used with data loggers, so that we can retrieve them when it is convenient to do so. Although this is a relatively straightforward task, at present we are limited by the need either to recapture a tagged animal or to wait for a passive mechanism to trigger before we can recover the data logger. Finally, we will need considerable advances in our ability to transmit large quantities of data rapidly to receiving stations, either aboard satellites or other platforms. Clearly, in all these cases a productive collaboration between engineers and biologists will be essential to progress. The last aspect of development in the study of foraging ecology of small cetaceans rests not in our ability to understand what dolphins and porpoises are doing, but in the fine-scale distribution and behaviour of their prey. As we increase our ability to monitor the activities of marine mammals, we rapidly outstrip the ecological information base required to interpret this behaviour. Those of us who have searched vainly through databases on commercial fisheries and stock assessments know too well that the scale of this information is much too coarse to be of value. Even when small cetaceans feed on commercially valuable prey species, we seldom have distributional data on a scale that is fine enough to interpret movement or diving patterns. In other cases, such as the pelagic dolphins that feed on mid-water squid and fishes of the deep scattering layer, even the taxonomy of prey species may be unresolved.

190 To test the hypotheses that we will formulate about the feeding strategies of these animals, it will be necessary to collect synoptic information about the distribution and movements of their prey. This may involve sophisticated means of detecting the presence of prey aggregations, such as the use of research sonar systems, and tracking the prey themselves to monitor movements and activity patterns.

Conclusions To those of us involved in studying the foraging ecology of small cetaceans, this is a tremendously exciting time to be conducting field research. Each data logger and satellite uplink holds the promise of new insight into the ecology and behaviour of these animals. As our toolbox of research techniques grows, we will be able to piece together the puzzle that describes how dolphins and porpoises make a living at sea. Our experience to date has demonstrated that with increased knowledge comes a myriad of unexpected questions and apparent paradoxes. Our present challenge is to develop new and benign tools to resolve these questions and better understand the role of small cetaceans in their ecosystems.

Acknowledgement I would like to thank my colleagues who work on harbour porpoises in the Bay of Fundy and on bottlenose dolphins in Sarasota, Florida for their dedication and friendship in the field. In particular, Andrew Westgate, Michael Scott, Randy Wells and Forrest Townsend have been instrumental in developing safe and effective tagging systems. Peter Tyack, Kurt Fristrup and Bill Watkins of the Woods Hole Oceanographic Institution have generously shared their insight and experiences. I also thank Tony Martin and Bruce Mate for their encouragement and advice. This paper was substantially improved by comments from Andrew Westgate. My research has been supported by the US National Marine Fisheries Service, US Office of Naval Research and World Wildlife Canada. Finally, thanks to Arne BjCrge for inviting me to Tromsr and to prepare this paper.

References 1. Abend A, Finn J, Smith TD. Diet prediction of the long-finned pilot whale (Globicephala melas) using carbon and nitrogen stable isotope tracers. Abstracts, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993; 19. 2. Akamatsu T, Hatakeyama Y, Kojima T, Soeda H. Echolocation rates of two harbor porpoises (Phocoena phocoena). Mar Mammal Sci 1994;10:401-411. 3. Baird RW. Foraging behaviour and ecology of transient killer whales (Orcinus orca). PhD Dissertation, Simon Fraser University, Vancouver, Canada, 1994. 4. Baird RW, Goodyear JD. An examination of killer whale diving behaviour using a recoverable,

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5.

6. 7. 8.

9. 10.

11. 12. 13. 14. 15. 16. 17. 18.

19.

20. 21. 22. 23. 24.

25. 26. 27. 28.

suction-cup attached TDR/VHF tag. Abstracts, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993;25. Bigg MA, Fawcett I. Two biases in diet determination of northern fur seals (Callorhinus ursus). In Beddington JR, Beverton RJH, Lavigne DM (eds) Marine Mammals and Fisheries. London: George Allen and Unwin, 1985;284--299. Costa DP. Methods for studying the energetics of freely diving animals. Can J Zool 1988;66:4552. Costa DP. The secret life of marine mammals. Oceanography 1993;6:t20-128. Davis RW, LeBoeuf BJ, Marshall G, Crocker D, Williams J. Observing the underwater behavior of elephant seals at sea by attaching a small video camera to their backs. Abstracts, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993;40. Evans WE. Orientation behavior of delphinids: radio telemetric studies. Ann NY Acad Sci 1971;188:142-160. Goodyear JD, Andrews R. The first whale ECG with a self-contained tag and radio and satellitelinked depth and heart-monitoring tags for whales. Abstracts, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993;54. Iverson S. Milk secretion in marine mammals in relation to foraging: can milk fatty acids predict diet? Symp Zool Soc London 1993;66:263-291. Keating KA, Brewster WG, Key CH. Satellite telemetry: performance of animal tracking systems. J Wildlife Manage 1991;55:160-171. Kooyman G. Diverse Divers: Physiology and Behaviour. Berlin: Springer-Verlag, 1989. Martin AR, Kingsley MCS, Ramsay MA. Diving behaviour of narwhals (Monodon monoceros) on their summer grounds. Can J Zool 1994;66:446--458. Martin AR, Smith TG. Deep diving in wild, free-ranging beluga whales, Delphinapterus leucas. Can J Fish Aquat Sci 1992;49:462-466. Martin AR, Smith TG, Cox OP. Studying the behaviour and movements of high Arctic belugas with satellite telemetry. Symp Zool Soc London 1993;66:195-210. Mate BR. Watching habits and habitats from Earth satellites. Oceanus 1989;32:14-18. Mate BR, Rossbach KA, Nieukirk SL, Wells RS, Irvine AB, Scott MD, Read AJ. Satellitemonitored movements and dive-behavior of a bottlenose dolphin (Tursiops truncatus) in Tampa Bay, Florida. Mar Mammal Sci (in press). Mate BR, Stafford KM, Nawojchik R, Dunn JL. Movements and dive behavior of a satellitemonitored Atlantic white-sided dolphin (Lagenorhynchus acutus) in the Gulf of Maine. Mar Mammal Sci 1994;10:116-121. McConnell B J, Chambers C, Fedak MA. Foraging ecology of southern elephant seals in relation to the bathymetry and productivity of the Southern Ocean. Antarctic Sci 1992;4:393-398. Pierce GJ, Boyle PR. A review of methods for diet analysis in piscivirous marine mammals. Oceanogr Mar Biol Annu Rev 1991;29:409-486. Pierce GJ, Boyle PR, Watt J, Grisley M. Recent advances in diet analysis of marine mammals. Symp Zool Soc London 1993;66:241-261. Recchia CA, Read AJ. Stomach contents of the harbour porpoise, Phocoena phocoena (L.), from the Bay of Fundy, Canada. Can J Zool 1989;67:2140-2146. Scott MD, Chivers SJ, Olson RJ, Lindsay RJ. Radiotracking of spotted dolphins associated with tuna in the Eastern Tropical Pacific. Abstracts, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993;97. Tanaka S. Satellite radio tracking of bottlenose dolphins Tursiops truncatus. Bull Jap Soc Sci Fish 1987 ;53:1327-1338. Tyack P. A data logger to identify vocalizing dolphins. J Acoust Soc Arm 1991;90:1668-1671. Westgate AJ, Read AJ, Berggren P, Koopman HN, Gaskin DE. Diving behaviour of harbour porpoises, Phocoena phocoena. Can J Fish Aquat Sci (in press). Wilson RP, Cooper J, Plotz J. Can we determine when marine endotherms feed? A case study with seabirds. J Exp Biol 1992;167:267-275.

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9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand ~. Ulltang,editors

193

Distribution and diving behaviour of hooded seals L.P. Folkow and A.S. Blix Department of Arctic Biology and Institute of Medical Biology, University of Tromsr Tromsr Norway Abstract. Hooded seals, Cystophora cristata, are abundant in the North Atlantic. This paper reviews current knowledge on the distribution and dive behaviour of these seals. The stock which breeds in sea ice near Jan Mayen may count about 250,000 animals, but little is known about where they stay and what they eat outside the pupping season (March/April) and the moult (July). We used satellite tags to monitor movements and/or dive depths and durations of 19 seals, and we obtained data on -12,000 locations and -120,000 dives, between July 1992 and July 1993. After the moult, most of the seals dispersed to travel, once or repeatedly, between the ice off Greenland and the distant waters off the Faeroe Islands, south of Bear Island, or the Irminger Sea. After breeding, all seals again returned to sea to travel to the waters off northern Ireland, the Faeroes or the Norwegian coast. Hooded seals may dive repeatedly to >1,000 m and stay submerged for >52 min, but usually dive to 100-600 m depth. We suggest that the dietary preferences, and even the fish consumption of hooded seals in different areas may be assessed by comparing their dive depths with the distribution of potential prey. Key words: arctic, feeding ecology, fisheries resources, North Atlantic

Introduction

Hooded seals (Cystophora cristata) are abundant in the ice-filled waters of the North Atlantic. Average lengths and weights of adult males and females are about 2.5 and 2.0 m, and 300 and 160 kg, respectively [1 ]. Previous studies of hooded seals have almost exclusively been conducted in connection with breeding, which takes place in the second half of March/early April, and moulting, which occurs between late June and early August. During these events, hooded seals aggregate in the drift ice and are accessible in large numbers for both studies and harvesting. During the remainder of the year, however, the seals disperse and are seldom found in large concentrations. For this reason, studies of the distribution and feeding ecology of the species during these periods have been difficult to carry out. However, modem satellite tracking techniques have recently made it possible to remotely monitor movements of radiotagged seals, via satellite. Moreover, such satellite tags, which are attached to the seal by gluing it to the fur, may also include different types of sensors allowing collection and subsequent transmission of data on for example dive depth and duration, heart rate, etc. This review of the distribution and dive behaviour of hooded seals integrates previous knowledge based on traditional techniques (field studies on breeding and moulting animals, incidental observations, and interviews with sealers and hunters) with recent data obtained by use of satellite telemetry and tracking techniques.

Address for correspondence: L.P. Folkow, Department of Arctic Biology, University of Tromsr N9037 Tromsr Norway.

194 Distribution and Migration Hooded seals aggregate to give birth and mate in relatively heavy pack ice near Jan Mayen (the West Ice), off Newfoundland (the Front) and in the Gulf of St. Lawrence (the Gulf) (Fig. 1). In addition, whelping also takes place in some areas in the Davis Strait [2]. The sizes of the different breeding stocks are not known, but based on current estimates of pup production rates it appears that the Northwest Atlantic stocks count about 400,000 animals [3], while the West Ice stock may count in the order of 250,000 animals. The segregation into different breeding areas has caused speculation as to whether different breeding stocks represent separate populations.

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195 Based on migration patterns, Rasmussen [4] concluded that there is only one stock of hooded seals in the North Atlantic, and this conclusion is supported by Wiig and Lie [5], who in comparing morphometric data from hooded seal skulls from the different breeding stocks, found no significant differences. Still, the two main breeding stocks appear to inhabit different geographical areas during most of the year. From an ecological and managemental point of view, the two stocks should therefore still be looked upon as separate, despite the fact that there is some exchange of genetic material between them. After breeding, hooded seals have been reported to start migrating towards their moulting areas. The main moulting area is located in the Denmark Strait, where animals from both the Northwest Atlantic and the West Ice stocks have been presumed to moult [4,6,7] (Fig. 1). That migration of hooded seals from Newfoundland to the Denmark Strait takes place has been confirmed by mark-recapture data [2,8], as well as in recent satellite tracking studies [9]. All stocks of hooded seals were for long considered to moult in the Denmark Strait. However, Nansen [6] described another lair which was located further north, between 72 and 76~ Folkow, Mhrtensson and Blix (submitted to Polar Biology) observed a lair somewhat further southeast to this (northwest of Jan Mayen), but concluded that this probably corresponded to that described by Nansen [6], and suggested that a large fraction of the West Ice stock is moulting there. Previous reviews of hooded seal migration patterns seem to imply that the seals start their migration towards the moulting lairs as soon as breeding is completed, and that they all travel in one general direction while feeding on their way [4]. However, data obtained by Folkow, Mhrtensson and Blix (submitted to Polar Biology) from eight satellite tagged hooded seals in the period between breeding and moult show that West Ice hooded seals leave the drift ice and distribute widely soon after breeding, to perform long excursions to distant waters (e.g. off the Faeroe Islands, off northern Ireland and in the Norwegian Sea) (Fig. 1), before heading towards the moulting lair northwest of Jan Mayen in the summer. Also in the Northwest Atlantic, satellite tracking studies show that excursions at high sea take place before the moult migration starts [9]. Data on the distribution of hooded seals after the moult off the east coast of Greenland are few, primarily because such studies have remained impossible until the development of satellite tracking techniques. The general opinion has been that the seals then distribute widely, the seals from the Northwest Atlantic either returning to waters off Newfoundland, or following the Greenland coast around Kap Farvel, and north as far as to the Thule district [10], while West Ice hooded seals have been assumed to mainly range north, in the ice between Greenland, Svalbard and Bear Island [4]. However, some seals have been documented to migrate over long distances to other areas, and records exist of observations or catches of hooded seals, from Alaska in the west [11], to the Barents Sea in the east [7], along the coast of Norway [7,12], south of Iceland and off the Faeroe Islands [13]. In fact, stranded specimens have been found on remote locations as far south as the Portuguese coast in the east [14] and the coast of Florida in the west [15], and a stranded female

196 hooded seal has even been found in the Pacific, as far south as the Califomian coast [ 16]. Such seals have usually been considered as "stragglers" and "strays". However, recent satellite tracking data have shown that long travels in the central and eastern North Atlantic are roles rather than exceptions for these mammals. Folkow, MArtensson and Blix (submitted to Polar Biology) have collected data from 15 subadult and adult hooded seals of both sexes which were equipped with satellite transmitters after the moult in lairs at about 71~ 12~ and which were tracked for about 200 days, on average. They found that all but one of the tagged seals on one or several occasions made an excursion which lasted for about 3-7 weeks to distant areas, and then returned to the drift ice edge, somewhere between 65 and 77~ along the east coast of Greenland. The most frequently visited area off the ice edge were the waters off the Faeroe Islands, where altogether eight of the seals stayed at some time during the tracking period. Other areas of importance were the Irminger Sea (southwest of Iceland), the continental shelf break between the Norwegian mainland and Bear Island, and areas in the Norwegian sea and north/northeast of Iceland (Fig. 1). One individual, which was a male, travelled to waters off the west and north coast of Svalbard (up to 81 ~ and remained there throughout the remainder of the tracking period. Apart from this, the study by Folkow, MArtensson and Blix (submitted to Polar Biology) did not reveal any differences in distribution pattems of males and females. Long excursions at high sea were conducted at all times of the year, but the seals always returned to the ice edge and were never recorded to haul out on any coast. In this sense, the hooded seal appears to be a truly pelagic species. At the time of breeding (second half of March), 7 of the 15 tags which were deployed in July were still active. Judging by the size of the animals, which was compared with length-age data presented by Rasmussen [4], one female may have been sexually mature, and this animal retumed to the breeding area southwest of Jan Mayen and spent 6 days hauled-out on the ice before returning to sea. Given the short nursing period of hooded seals [ 17], this animal may well have given birth and nursed a pup in this period.

Diving behaviour It is easier to summarize previous data on the dive behaviour of hooded seals than on their distribution. In 1890, Nansen [6] stated that hooded seals are deep divers, and Oynes [12] reported that hooded seals occasionally were caught in nets off the coast of Norway at depths of several hundred meters. The first actual recording of dive depths of hooded seals was made by Scholander [18] who found that captive young seals could dive down to about 75 m. Data on dive depths of free-swimming hooded seals did not exist until such information could be collected by satellite telemetry. The first confirmation of Nansen's [6] assumption was made by Folkow and Blix [19], who found that their satellite-tagged hooded seals could dive to depths beyond the depth range of the pressure sensor employed, which was 0-1,000 m. Dives as

197

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deep as this were usually not performed on a daily basis, but could account for more than 40% of all dives in some areas (Fig. 2), at some times of the year (Folkow and Blix, submitted to Polar Biology). Data showed that hooded seals regularly dive to depths between 300 and 600 m (Fig. 3), but that the dive depth pattern may vary significantly from area to area, and between seasons. Thus, diving in ice-covered waters east of Greenland (north of 70~ and north/northeast of Iceland, was characterized by high proportions of dives to depths of less than 300 m, while diving in the Denmark Strait (off the east coast of Greenland, south of 70~ included high fractions of dives to more than 300 m depth. In the waters off the Faeroe Islands, dive depths showed seasonal changes, with depths of 100-300 m dominating in

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duration (rain) Fig. 3. Overall dive depths and durations of 16 hooded seals, between July 1992 and July 1993 (from

manuscript by L.P. Folkow and A.S. Blix, submitted to Polar Biology). autumn (August-November), while there was a shift towards deeper dives (300600 m) in winter and spring (December-May). Areas in which particularly deep dives were performed were the waters off Jan Mayen (Fig. 2), and in the Irminger Sea, but even in these areas dives to between 300 and 600 m usually constituted the largest fractions. Comparison of dive depths with water depths in different areas showed that most dives must have been pelagic. Folkow and Blix (submitted to Polar Biology) also found that dive durations generally were well correlated with dive depths and a few dives of more than 52 min duration were recorded in areas where deep dives (>1,000 m) were performed. The average dive duration of hooded seals appears to be between 5 and 15 min (Fig. 3), and Folkow and Blix (submitted to Polar Biology) estimated the aerobic dive limit of

199 hooded seals to be about 15-17 min, which implies that as much as 36% of all dives by hooded seals are in part anaerobic. Dive depth data have also been collected, by use of the same technique, from Northwest Atlantic hooded seals during the period between breeding and moult. These studies showed that the depths of dives were less in this area (<530 m in the Laurentian Channel) [9] which is not surprising since water depth is also less here. The study by Stenson and co-workers [9] also revealed that hooded seals stay submerged for more than 80% of the time, while at sea. The diving rates recorded by Folkow and Blix (submitted to Polar Biology) from West Ice hooded seals were low to intermediate in ice-covered areas, where the seals presumably hauled out at intervals, while the rate at high sea appeared to be inversely proportional to dive depths, with highs of 125-142 dives per day in areas characterized by shallow diving, and lows of 56 dives per day in deep diving areas. The latter dive rate compares well with those recorded in southern elephant seals (Mirounga leonina) [20] and northern elephant seals (Mirounga angustirostris) [21 ], which are both known as record deep divers.

Prey selection and food consumption Data on the food selection of hooded seals are few, primarily because they fast during both breeding and moult [4], which is when they have been available for studies. Hooded seals with stomach contents have nevertheless been collected, although only in small numbers and from a few selected areas and times of the year. Nansen [6] reported that hooded seals feed on redfish (Sebastes sp.), gadoids, halibut and squid (presumably Gonatus fabricii, as reported by Sergeant [22]). Wolleba~k [23] suggested that they may also take Atlantic salmon (Salmo salar), when available. Greenland halibut (Reinhardtius hippoglossoides), herring (Clupea harengus), capelin (Mallotus villosus), redfish and various gadoids (including Atlantic cod (Gadus morhua) and Arctic cod (Boreogadus saida)) were found in stomachs of hooded seals collected in waters northwest, south and southeast of Greenland [24], as well as in Newfoundland waters [25], with substantial variation in frequency of occurrence between areas. Obviously, these records are not sufficiently detailed to allow a general assessment of the dietary preferences of these mammals at different times of the year and in different areas. Dive depth pattems reflect the depths at which hooded seals forage, and this information can presumably therefore be used, together with fisheries resource survey data, to predict which prey items these animals are primarily eating in different areas. The approach can be illustrated by the following two examples, taken from Folkow and Blix (submitted to Polar Biology): 1. Satellite-tagged hooded seals frequently visited waters off the Faeroe Islands, where, in fact, 14.5% or 550, of the total of almost 3800 seal days which were recorded during the tracking period, were spent. In this area, dive depths of 100-300 m dominated in early autumn, while there was a shift towards deeper

200 (300-600 m) dives in late autumn and winter. The spatial and temporal distribution of the seals correlates very well with the known distribution of the blue whiting (Micromesistius poutassou) which is found in large numbers in these waters (Hjalti J~ikupsstovu, Fisheries Laboratory of the Faeroes (personal communication)). Furthermore, the blue whiting spawns in April and May in waters west of the British Isles, at depths of 300-600 m (JLkupsstovu, personal communication). We found that satellite-tagged hooded seals were also present in this area (off northern Ireland) for 57 seal days (or 1.5% of all seal days) at exactly that time of the year (April and May), and that they mainly were diving to depths of 300-600 m. These data strongly suggest that blue whiting was the main prey of hooded seals in both areas. 2. About 3.8% of all seal days were spent in the Irminger Sea, where dives deeper than 300 m accounted for 30-60% of all dives. In this area, hooded seals were most likely feeding mainly on redfish, which are distributed pelagically at depths of 100-500 m and sometimes down to 900 m [26]. In the following, we have attempted to illustrate how the consumption of fish by hooded seals in different areas may be calculated from this type of data. Such calculations require that we also know the size of the relevant seal stock and that we assume that the behaviour of satellite-tagged seals is representative for this stock. Also, we need to know the energy requirements of hooded seals, as well as the energy density of the relevant prey species. The population size of the West Ice stock of hooded seals is unknown, but may count about 250,000 animals, which spend 250,000 x 365 = 91,250,000 seal days in different areas, on an annual basis. Data on energy expenditure of hooded seals are few. However, Elias [27] reports that adult hooded seals maintain basal metabolic rates that are similar to those predicted by Kleiber [28] for mammals of similar size (i.e. 3.4 W kg-~ It is therefore reasonable to assume that free-swimming hooded seals maintain an average field metabolic rate of about 2.3 x B MR [29], which, for an average-sized hooded seal of about 170 kg would correspond to 31,900 kJ per day. Hooded seals which spend 14.5 + 1.5% = 16% of 91,250,000 (i.e. 14,600,000) seal days off the Faeroes and off Ireland would, thus, need to cover energy expenses totalling 31,900,000 x 14,600,000 = 4.65 x 1014 J by feeding on the blue whiting. The energy density of the blue whiting is unknown, but can be assumed to be similar to that of Atlantic cod, being 4.74 kJ/g, on average (M~trtensson, NordCy, Lager and B lix, unpublished), but only about 93% of the energy content of a fish meal is available as metabolizable energy to the seal, due to energy loss as faecal material [30]. Based on these considerations, hooded seals would be expected to consume 105,000 tonnes of blue whiting in these waters. This figure can be compared with the total fishery of blue whiting in the same (spawning) area of about 346,000 tonnes in 1993 (J~ikupsstovu, personal communication). When using the same approach for redfish (having an average energy density of 6.65 kJ/g, as determined in Sebastes marinus specimens (M~rtensson, NordCy, Lager and Blix, unpublished)), a consumption of almost 18,000 tonnes during a total of 3,467,500 seal days in the Irminger Sea is indicated.

201 The satellite-tagged seals spent most (38%) of their time near the sea ice edge (off Greenland). We have not yet completed the analyses of seal dive depth data in relation to data on available prey items in these areas, but it seem highly likely that hooded seals consume large amounts of capelin and polar cod there. Obviously, the approach described above yields fairly rough estimates of the consumption of fish by hooded seals. However, at present there is no alternative to this approach, since the dispersed offshore distribution of this species makes any systematic analysis of food selection based on collected stomach contents virtually impossible. These calculations set the potential impact of this largely overseen pinniped species into perspective, and illustrate the importance of obtaining better data to predict its effect on fisheries resources in the North Atlantic.

Acknowledgements The authors wish to thank cand. real. Hjalti J~ikupsstovu, Fisheries Laboratory of the Faeroes, for supplying data on the biology of the blue whiting, cand. scient. Per-Erik MArtensson and cand. scient. Anne R. Lager for assistance in the field, and the crews of M/V "Polarfangst" and KNM "Senja" for their helpful cooperation. This study was financed by the Norwegian Research Council, grant nos. 408.006 and 104503/ 110.

References 1. Lavigne DM, Kovacs KM. Harps and hoods: ice-breeding seals of the Northwest Atlantic. Waterloo, Canada: University of Waterloo Press, 1988. 2. Sergeant DE. A rediscovered whelping population of hooded seals Cystophora cristata Erxleben and its possible relationship to other populations. Polarforschung 1974;44:1-7. 3. Anonymous. Report of the joint ICES/NAFO working group on harp and hooded seals. International Council for the Exploration of the Sea, Copenhagen, September, 1993. C.M. 1994/Assess: 5. 4. Rasmussen B. Om klappmyssbestanden i det nordlige Atlanterhav. Fisken Havet 1960; 1:1-23. 5. Wiig ~, Lie RW. An analysis of the morphological relationships between the hooded seals (Cystophora cristata) of Newfoundland, the Denmark Strait, and Jan Mayen. J Zool London 1984;203:227-240. 6. Nansen F. Paa ski over Gr6nland. Kristiania, Oslo: Aschehoug and Co, 1890. 7. Oritsland T. Klappmyss. Fauna Oslo 1959;12:70-90. 8. Hammill MO. Seasonal movements of hooded seals tagged in the Gulf of St. Lawrence, Canada. Polar Biol 1993;13:307-310. 9. Stenson GB, Hammill MO, Fedak MA, McConnell BJ. The diving behaviour and seasonal migration of adult hooded seals. Abstract, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993; 103. 10. Kapel FO. Recent research on seals and seal hunting in Greenland. Rapp P-V R6un Cons Int Explor Mer 1975;169:462-478. 11. Burns JJ, Gavin A. Recent records of hooded seals, Cystophora cristata Erxleben, from the western Beaufort Sea. Arctic 1980;33:326-329. 12. Oynes P. Sel p~ norskekysten fra Finnmark til Mere. Fisk Gang 1964;48:694-707.

202 13. Mohr E. Die Klappmtitze. Kosmos Stuttgart 1955;51:546-551. 14. Reiner F. Nota sobre a segunda ocorrencia de una foca de mitra, Cystophora cristata (Erxleben, 1777) nas costas de Portugal. Mus Mar Cascais Mem Ser Zool 1980;1(6). 15. Miller GS. A hooded seal in Florida. Proc Biol Soc Washington 1917;30:121. 16. Dudley M. First Pacific record of a hooded seal, Cystophora cristata Erxleben, 1777. Mar Mammal Sci 1992;8:164-168. 17. Bowen WD, Oftedal OT, Boness DJ. Birth to weaning in 4 days: remarkable growth in the hooded seal, Cystophora cristata. Can J Zool 1985 ;63:2841-2846. 18. Scholander PF. Experimental investigations on the respiratory function in diving mammals and birds. Hvalrhdets Skr 1940;22:1-131. 19. Folkow LP, Blix AS. Satellite tracking of hooded seals (Cystophora cristata) in the Greenland and Norwegian Seas. Abstract, Tenth Biennial Conference on the Biology of Marine Mammals, Galveston, TX, 1993;48. 20. Hindell MA, Slip DJ, Burton HR. The diving behaviour of adult male and female southern elephant seals, Mirounga leonina (Pinnipedia: Phocidae). Aust J Zool 1991 ;39:595-619. 21. LeBoeuf BJ, Naito Y, Huntley AC, Asaga T. Prolonged, continuous, deep diving by northern elephant seals. Can J Zool 1989;67:2514-2519. 22. Sergeant DE. History and present status of populations of harp and hooded seals. Biol Conserv 1976; 10:95-118. 23. Wolleb~ek A. Uber die Biologie der Seehunde und die Seehundjagd im europ~iischen Eismeer haupts~ichlich nach norwegischen Quellen. Rapp Cons Explor Mer 1907;8:5-82. 24. Kapel FO. Studies on the hooded seal, Cystophora cristata, in Greenland, 1970-1980. NAFO Sci Coun Studies 1982;3:67-75. 25. Ross S-A. Food and feeding of the hooded seal (Cystophora cristata) in Newfoundland. M Sc Thesis, Memorial University of Newfoundland, St. John's, Newfoundland, 1993. 26. Muus B. Fisk (Pisces). In: Salomonsen F (ed) GrCnlands fauna. Copenhagen: Gyldendalske Boghandel, 1981 ;23-158. 27. Elias M. Swimming energetics of hooded seals (Cystophora cristata). MSc Thesis, University of Guelph, Guelph, Ontario, 1985. 28. Kleiber M. The Fire of Life. New York: Krieger, 1975. 29. Stewart REA, Lavigne DM. Energy and female condition in nursing harp seals Phoca groenlandica. Holarctic Ecol 1984;7:182-194. 30. MLrtensson P-E, Nordr ES, Blix AS. Digestibility of crustaceans and capelin in harp seals (Phoca groenlandica). Mar Mammal Sci 1994;10:325-331.

9 1995 Elsevier Science B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand O. Ulltang, editors

Distribution and a b u n d a n c e of walruses Svalbard

203

(Odobenus rosmarus) in

Ian Gjertz ~ and Oystein Wiig 2 1Norwegian Polar Institute, Majorstuen, Oslo, Norway; and 2Zoological Museum, Sarsgt. 1, Oslo, Norway Abstract. Information on the distribution and abundance of walruses in Svalbard is reviewed. Former

and present haul-out sites were mapped based on skeletal remains, drag marks and/or hauled out animals. Walruses in Svalbard are sexually segregated. Males are found in the more accessible areas in the north and south-east while females are found in the north-east and possibly in Franz Josef Land. The males seem to be site tenacious, but occasionally undertake trips to neighbouring areas within both Svalbard and Franz Josef Land. A conservative estimate of the number of walruses in Svalbard, which also includes parts of Franz Josef Land, exceeds 2,000 animals. Key words: marine mammals, arctic, haul-out sites, migration, sexual segregation

Introduction Walruses (Odobenus rosmarus) were once very common in Svalbard [ 1]. Their preharvest number has never been established, but the size of the original population must have been very large. Walrus hunting in Svalbard started in 1604, and by the late 19th century the stock showed clear signs of decrease [1]. Hunting continued until 1952, when walruses were given total protection in Norwegian waters [2]. They were then believed to be on the verge of extinction [3]. Born [4] concluded, based on opportunistic observations and surveys, that walruses in Svalbard consisted of a summering stock of about 100 animals in the beginning of the 1980s. Walruses are site-tenacious and annually return to the same haul-out sites on land. This was a major cause of their decline. LCnr [1] summarised historical catches of walruses in Svalbard and the adjacent European Arctic waters, and indicated the areas in Svalbard which were considered best for hunting. Other authors have indicated where walrus observations have been most numerous in the latter part of this century [3-5]. This information either indicates the distribution in the present early stages of a recovering stock or possibly reflects the distribution of observational effort. Tsalkin [6] suggested, based on catch statistics, that walruses in Franz Josef Land and Novaya Zemlya were part of a larger general stock inhabiting the European Arctic (Fig. 1). Born [5] supported this view and suggested that walruses summering in Svalbard belonged to a population with its main distribution in Franz Josef Land. In 1989 we started a research project in order to document the present status of wal-

Address for correspondence: I. Gjertz, Norwegian Polar Institute, P.O.B. 5072 Majorstuen, N-0301 Oslo, Norway.

204 -1-1

90

~_

"

t

~hrans

Josef ~

"-"r~f" - "7

"0

GREENLAND~

Fig. 1. Map of the European Arctic [ 10].

ruses at Svalbard. This paper is a review of the major results obtained during that project.

Distribution Based on historical material and observations obtained in recent years, it has been possible to map the most important distribution areas of walruses in Svalbard. The presence of former haul-out sites, as well as haul-out sites now in use were mapped based on skeletal remains, drag marks and/or hauled-out animals. Three main areas were evident, in the north, north-east and south-east [7] (Fig. 2). From the beginning of the walrus harvest in Svalbard we know that walruses were very abundant at the southernmost island, BjCmCya, and along the west coast of Spitsbergen. Walruses have started to recolonize the latter areas, but they have not yet returned to BjCmCya [7]. Several ground surveys were conducted in south-east Svalbard each year from 1989 to 1993 [7]. All the animals observed, with the exception of a few single cows with calves, were males of varying ages. The surveys have also shown that at least some of the walruses are site tenacious. This could indicate that a large part of the walruses either return to the same sites each year or stay there all year. Few observations of large numbers of females and calves are known from Svalbard. Observations from after 1982 indicate that the only area where they are found in significant numbers is in the extreme north-eastem part of the archipelago [7]. The herds of walruses observed in other areas in summer are almost exclusively males, both adults and immatures [5,7].

205

Svalbard "

.

O

O

O

0 ,

i

1

60 9

i

i

120 km #

Fig. 2. Map of Svalbard, excluding BjCrnCya, indicating locations of known walrus haul-out sites [7].

We know that from the turn of the last century sealing vessels caught large numbers of walruses in Franz Josef Land [8]. These were predominantly females and calves. In 1990, 1991 and 1992 the Norwegian Polar Institute undertook field trips to Franz Josef Land and found a large number of females and calves in the southcentral parts of this archipelago [8,9]. Compared with knowledge on walruses in Svalbard [7], this information suggests that there may be a close connection between the animals of these two neighbouring archipelagos, and that the majority of walruses in Svalbard in summer are males from one common Svalbard-Franz Josef Land stock, as suggested by Born [5]. If walruses in Svalbard and Franz Josef Land are from one common stock then they will necessarily have to undertake annual migrations to meet and mate. Some mention of such migration is found in the literature, but it is not well documented. LCnr [1] dismissed the idea of an annual migration, and showed that even though walruses were exterminated in one area in Svalbard they could still be caught in other areas. He therefore suggested that the walrus population in Svalbard consisted of partially stationary groups which, because of close distances had some contact

206 with each other. Born [5] discussed the possibility of walruses wintering in polynias along the north-east coasts of Svalbard. He suggested that since walruses are usually first observed at northern Spitsbergen late in July, they must come from wintering grounds far away, for example from Franz Josef Land.

Telemetry A total of 34 satellite transmitters (PTI's) were deployed on walruses over a 4-year period (Wiig and Gjertz, unpublished data). All were tagged in the period from midJuly to early September and the PTTs lasted at best to mid-winter. Of these, 28 were deployed on males in Svalbard, the rest were deployed in Franz Josef Land on five males and one female. The results of the satellite trackings show that there is a migration of male walruses between Svalbard and Franz Josef Land [8] (Fig. 3). In particular it seems that an autumn migration of walruses from south-east Svalbard to north-east Svalbard, Victoria Island and Franz Josef Land is common [ 10]. Similarly one of the males from Franz Josef Land moved to Victoria Island [8] (Fig. 4). Four walruses in Svalbard were tracked from late summer to January/February and were seen to stay in that area for the winter. The one walrus that was tracked in Franz Josef Land until mid-winter similarly stayed in that area (Wiig and Gjertz, unpublished data). The telemetry results do not indicate a seasonal migration, following the edge of the drift ice, southwards in the autumn and northwards in spring. They are, however,

/

/

/

i

\

\

\

~'\J

\ .

/ /

/

!

"\

\

/

/

Fig. 3. Walrus tracked from south-east Svalbard to Franz Josef Land and back to northern Svalbard [10].

207

Fig. 4. Two separate walrus trackings. One from western Spitsbergen to Hinlopenstretet, the other from eastern Franz Josef Land to Victoria Island [ 10].

partly in accordance with LCnr [1] view that walruses tend to stay in particular areas, but may undertake long migrations to other areas and then return. The telemetry results for the walruses at south-east Svalbard indicate that the migrations are regular or at least common. We assume that these migrations are connected with breeding activity. Based on the results of the satellite telemetry, it is evident that walruses winter in the Svalbard area, both in the southern parts of Svalbard as well as in the winter pack ice. This is possible because there are numerous open leads within the Barents Sea ice during winter. The only walrus at Franz Josef Land that was followed until midwinter stayed in that area and therefore supports Born's [5] view that walruses also winter there. Born and Knutsen [11], using satellite telemetry in Northeast Greenland, have shown that bull walruses from this area disperse far out to sea, but they found no indication of a connection between the walruses in Greenland and Svalbard. Such a connection was, however, proven in July 1992 [12]. This indicates a connection between the walruses in these two geographical areas, and that the walruses from eastern Greenland to eastern Franz Josef Land may belong to one common stock.

Aerial surveys

In September 1992 surveys using fixed wing aircraft covered most of Svalbard's coastline. Attempts were made to cover areas considered of special interest at least

208 twice. In September-October 1993 similar surveys were conducted at the sites shown to be most important in 1992. In 1992 numbers of walruses observed varied from 0 to 288 on a single day while in 1993 they varied from 0 to 245 animals (Gjertz and Wiig, unpublished data). Cows and calves were only observed in the ice in north-eastern-most Svalbard, however, not in large numbers, in accordance with Gjertz and Wiig [7]. When considering the abundance of walrus in Svalbard, it is important to bear in mind that the animals observed seem to be predominantly males. If we assume that the different herds all behave like the ones in the Southeast, i.e. that they are site tenacious, then at least 750 male walruses are found in Svalbard, excluding the northeastern-most areas (Gjertz and Wiig, unpublished data). These latter areas have been the most important area for walruses in Svalbard in this century [7]. We must therefore assume that a considerable number of walruses are found here. If we assume that the sex ratio among walruses in Svalbard is similar to that in other areas, i.e. 1"1 [13] then the population of walruses belonging to the Svalbard area must, in a conservative estimate, be at least 1500 animals plus calves, i.e. exceed 2000 animals.

Acknowledgement This study was supported by the Norwegian Polar Institute, The Governor of Svalbard and the Norwegian Fisheries Research Council grant 4001-735.004.

References 1. LCnO O. The catch of walrus (Odobenus rosmarus) in the areas of Svalbard, Novaja Zemlja, and Franz Josef Land. Nor Polarinst Arbok 1972; 1970:199-212. 2. Anonymous. Fredning av hvalross. Konglig Resolusjon (Royal Decree) 20, Juni 1952. 3. Norderhaug M. Hvalrossens (Odobenus rosmarus) forekomst i Svalbardomr~det 1960-1967. Nor Polarinst Arbok 1969; 1967:146-150. 4. Oritsland T. Walrus in the Svalbard area. IUCN Publ New Ser Suppl 1973;39:59--68. 5. Born EW. Status of the Atlantic walrus Odobenus rosmarus rosmarus in the Svalbard area. Polar Res 1984;2:27-45. 6. Tsalkin, V. Materials on the biology of the walrus of Franz Josef Archipelago. Byull Mosk Ova Ispyt Prir 1937;46:43-51 (in Russian). 7. Gjertz I, Wiig 0. Past and present distribution of walruses in Svalbard. Arctic 1994;47:34--42. 8. Gjertz I, Hansson R, Wiig 0. The historical distribution and catch of walrus in Franz Josef Land. Nor Polarinst Medd 1992;120:67-81. 9. Knutsen LO. Walrus studies in the Franz Josef Land archipelago during August 1992. Nor Polarinst Medd 1993;126:1-16. 10. Gjertz I, Wiig 0. Status of walrus research in Svalbard and Franz Josef Land in 1992. A review. In: Stewart REA, Richard PR, Stewart BE (eds) Report of the 2nd Walrus International Technical and Scientific (WITS) Workshop, Winnipeg, Manitoba, Canada. Can Tech Rep Fish Aquat Sci 1993; 1940:68-84. 11. Born EW, Knutsen LO. Satellite-linked radio tracking of Atlantic walruses (Odobenus rosmarus rosmarus) in northeastern Greenland, 1989-1991. Z S~iugetierkd 1993;57:275-287.

209 12. Born EW, Gjertz I. A link between walruses (Odobenus rosmarus) in northeast Greenland and Svalbard. Polar Rec 1993;29:329. 13. Fay FH. Walrus Odobenus rosmarus (Linnaeus, 1758). In: Ridgeway SH, Harrison RJ (eds) Handbook of Marine Mammals, Vol 1, The Walrus, Sea Lions, Fur Seals and Sea Otter. London: Academic Press, 1981; 1-23.

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9 1995 Elsevier Science B.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 0. Ulltang, editors

211

Habitat use and diving behaviour of harbour seals in a coastal archipelago in Norway Arne BjCrge ~, Dave T h o m p s o n 2, Philip H a m m o n d 2, Michael F e d a k 2, E d m u n d Bryant 2, Hilde Aarefjord 1, Randi Roen 1 and Marianne Olsen ~ 1Norwegian Institute for Nature Research, Blindern, Oslo, Norway; and 2Sea Mammal Research Unit, Cambridge, UK A b s t r a c t . Background: the harbour seal is coastal and non-migratory and suitable for behavioural

studies using short-range telemetry and tracking. Methods: a combination of VHF radio telemetry and underwater ultrasonic telemetry was used to obtain behavioural and physiological data from 13 harbour seals tagged at an archipelago in Norway. VHF signals were used to locate seals, and they were tracked at close proximity by inflatable boats. Results: transit and foraging activity were identified based on differences in dive profiles. When foraging, all tagged seals operated solitarily, and they returned repeatedly to the same or approximately the same foraging sites. The radio tagged seals used different types of foraging habitats, ranging from shallow kelp areas located 20 km offshore to 150-200 m deep basins with muddy sea bed located a few kilometres from the respective haul-out sites. The seals were typically foraging at or close to the sea bed. Display behaviour including underwater vocalization was recorded for sexually mature males in June and July. Conclusion: the combination of VHF and ultrasonic telemetry is useful for studies of resident seals. Tracking free ranging seals at close proximity made it possible to identify and describe their resting, foraging and display areas. Key words: Phoca vitulina, telemetry, display behavior, foraging ecology

Introduction The harbour seal (Phoca vitulina) has typically been regarded as non-migratory and littoral in distribution [1], and exhibiting a diurnal haul-out pattern [2,3]. Recent studies however, e.g. by Thompson and co-workers [4-6], have revealed seasonal and year to year movements between a few haul-out sites and between foraging locations. Thompson [7] nevertheless concluded that harbour seals are resident in the same geographical area throughout the year. The diurnal haul-out patterns and the limited travelling speed deployed indicate that harbour seals forage within a few kilometres of their haul-out sites. This is supported by studies of foraging movements of radio tagged seals where most foraging activity was less than 50 km from haul-out sites [5-8]. A typical harbour seal habitat should provide, therefore, suitable haul-out sites, shelter during the parturition and lactation periods and sufficient food within reach of the haul-out sites to sustain the population throughout the year. In Norway three distinct types of habitats are utilized by harbour seals: coastal archipelagos, deep fjords and fjords with estuarine sandbanks [9]. The population of

Address for correspondence: A. BjCrge, Norwegian Institute for Nature Research, P.O. Box 1037, Blindern, N-0315 Oslo, Norway.

212 harbour seals in Norway is estimated to be at least 4,100 seals; >95% of these occur in coastal archipelagos, hauling out on intertidal rocks or small islands [9]. In the Northeast Atlantic, harbour seal breeding occurs in June and early July [1,10-13] with most pups born in mid-June [11]. Parturition is followed by a 35 week lactation period. The females ovulate towards the end of the lactation period, and copulation takes place in the water [14,15]. Peak moulting occurs between midJuly and mid-September. Females apparently complete their moult about 3 weeks earlier than males and the whole moulting season is completed within 2-3 months after the end of the breeding season [ 16]. Site fidelity and restricted foraging movements make the harbour seal suitable for studies of haul-out and foraging distribution using short range VHF radio telemetry, and such studies have been carried out at several locations since the late 1970s [5,6,17,18]. In this study, we conducted detailed investigations of the habitat use and at-sea behaviour of seals before and during the breeding season, and after the moult in a coastal archipelago typical for harbour seals in Norway. In order to obtain detailed real-time information on the at-sea behaviour of individual seals, VHF telemetry was combined with underwater ultrasonic telemetry to collect data on dive depth and behaviour when seals were at sea. Materials and Methods

The study was carried out in Froan Nature Reserve off the coast of Central Norway at 64~ 9~ Froan is a coastal archipelago with a large number of small islands and islets on a shallow water plateau. It is separated from the mainland coast to the east by a 50 km wide and 500 m deep basin and separated from larger islands to the south by 300 m deep and about 5 km wide channels. To the north and west, Froan is exposed to the North Atlantic. The harbour seals haul-out in the southern and central part of the archipelago and the number of animals in the area at the time of the study probably exceeded 200 animals [9].

Seal capture and handling Seals were captured in nets with mesh size of 17-19 cm (stretched mesh). The nets were set close to haul-out sites and watched continuously to minimize the risk of drowning seals. A total of 13 harbour seals were tagged: 2 males and 2 females in June-July 1990; 3 males in June 1991; 1 female and 5 males in August-September 1993 (not all seals had a complete set of transmitters). When necessary, seals were tranquillized by Zoletil (Laboratories Reading, Z.A.C. 17 Rue des Marronniers, 94240 L'Hay-les-Roses) before further handling. After handling, seals were left on land until they voluntarily entered the water.

Tags and tagging Small VHF radio tags transmitting on frequencies between 142.0 and 142.5 MHz

213 (produced by Marine Radar Ltd, UK) were attached to the head of the seals. This position was chosen to ensure that the aerial was exposed when seals were at the surface. Depth-velocity tags were attached behind and above the fight fore flipper. These tags were composed of a pressure sensor, paddle wheel, alkaline battery and an ultrasonic transmitter. Components were made by VEMCO Ltd, Halifax, Canada, and assembled at SMRU. A temperature tag (produced for this project by SINTEF, Trondheim, Norway) composed of a temperature sensor, alkaline battery and an ultrasonic transmitter was lowered though the oesophagus and placed in the stomach of the seal. Experiments on captive harbour seals at NINA-University of Oslo showed that the temperature tags would remain in the stomach from 2 to about 20 days. Ultrasonic tags transmitted on frequencies between 60 and 99.9 kHz and all external tags were glued to the fur using an epoxy resin as described by Fedak et al. [ 19].

Data recording and tracking An automatic land-based VHF radio receiving station received and stored radio signals from the seals when they were hauled out or at the surface. The VHF radio station is described by Nicholas et al. [20] and was composed of a set of aerials covering 360 ~ a YAESU VHFAJHF Communication Receiver FRG-9600, and a SANYO 16LT portable computer used as data logger. The station could be programmed to switch from one frequency to another and thus record information from more than one animal. Tracking seals at sea was conducted by a directional VHF radio (a purpose built directional aerial and display unit and a YAESU VHF/UHF Communication Receiver FRG-9600) mounted on board an inflatable boat, type Zodiac MK IV with twin 30 hp Johnson outboard engines. The VHF signals were used to locate and close in on seals at sea. Information on dive duration and dive intervals and positions of the tracking boat were automatically logged on a SANYO 16LT portable computer. Position of the tracking boat was obtained by a GARMIN GPS 65 Personal Navigator. When tracking the seal at close proximity, detailed behavioural and physiological data from the ultrasonic transmitters were received by hydrophone and logged on VEMCO VR60 Ultrasonic Receivers (manufactured and marketed by VEMCO Ltd, Halifax, Canada).

Results

Haul-out patterns and haul-out sites We considered that continuous VHF signals for 8 min or more indicated that seals were hauled out. Although haul-out bouts were recorded during both day and night

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and at high and low tide, there was a tendency for seals to haul-out more frequently during the day and at low tide. After a trip to sea, seals regularly returned to the same

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haul-out rock or to adjacent rocks in the same area. The radio tagged seals typically hauled out among other seals on intertidal rocks in clusters of small islands (e.g. at Hestv~er, Sandskj~er and M~sskj~er in Fig. 1). One seal (91M2) tagged at Hestv~er, however, regularly hauled out alone just outside the harbour of S~rbur~y (Fig. 1).

Dive profiles as indicators of transit and foraging activity When the seals were at sea, we used dive profiles to characterize their behaviour. Travelling to and from the foraging grounds, the seals moved in typical V-shaped dives (Fig. 2). Usually we observed several consecutive V-shaped dives in a constant direction, and we defined these dives as transit dives. These dives did not always reach to the sea floor. When seals ceased directed travel at sea, a different type of dives was observed. The dive profile was U-shaped (Fig. 3), the dives usually reached the sea floor and the swimming direction frequently changed during and between dives. We defined these dives as foraging dives. The stomach temperature was used as further support in identification of foraging activity. Significant drops in temperature were interpreted as ingestion of food. An example is given in Fig. 4. On this occasion the stomach temperature of seal 93M3 was lowered stepwise from about 37~ to about 30~ over a period of 30 min during a trip at sea. The temperature gradually returned to about 37~ over a longer period of time. On a few occasions, in particular when seals foraged in areas with complex topography, transit and foraging type dives may be mingled.

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Dive durations of transit and foraging dives were not significantly different, and the average dive duration was 3.3 min (SD 1.9 min). The longest dive recorded was 14.3 min. For both transit and foraging dives the swimming speed was typically between 1.1 and 1.6 m/s.

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217

Examples offoraging trips All recorded foraging activity was at or close to the sea floor. At the sea floor, the seals continued swimming, presumably searching for food, at their usual swim speed. The seals foraged at water depths between 15 and 200 m and in areas with different substrate. Examples of foraging behaviour of typical seals are given below. One male seal tagged June 22, 1991 (91M3) travelled regularly about 20 km from his haul-out site at Sandskj~er to feed at some shallow offshore rocks (Fig. 1). He fed predominantly at depths between 15 and 50 m. Between his haul-out site and foraging grounds he passed through a 200 m deep basin where other radio tagged seals were foraging (see below). Seal 91M3, however, showed no sign of foraging activity until he reached the shallow rocks. His foraging trips were approximately 8-22 h long and most of his foraging activity was during night hours (between 1800 and 0600 h). The seal remained in the water until he was back at his haul-out site. However, during the longer foraging trips, periods of little movement were recorded. We interpreted these periods as periods with resting in water. Another male seal (90M2) tagged July 6, 1990 foraged in a deep basin 3-6 km from his haul-out sites during July (Fig. 1). His foraging trips were 3-8 h long, predominantly during day hours, and he repeatedly returned to his haul-out site or to a site were he remained stationary in water for several hours (see description of display sites below). A male seal tagged June 13, 1991 (91M2) foraged off the east side of the archipelago. This seal often hauled out outside the harbour of SCrburCy and was foraging within distances of 5 km from his haul-out site (Fig. 1). Most of his foraging activity was at depths of about 100 m. This particular seal started or finished his foraging trips in shallow sandy bays (depth of 15-30 m), where he caught flatfish, as verified by visual observation when he brought his catch to the surface. This seal foraged predominantly during night hours, and the swimming speeds and dive profiles of his nocturnal foraging trip from June 17 to June 18 are shown in Fig. 5. One male seal (93M3) tagged at Hestv~er August 28, 1993 foraged on slopes at depths between 30 and 120 m in the waters mainly north of the haul-out site. He foraged predominantly during the day and regularly returned to Hestv~er to haul-out. However, on September 2, when he foraged on the east side of the archipelago about 5-8 km from Hestv~er, he hauled out about 5 km east of his usual haul-out site. On September 4 he foraged about 15 km northeast of Hestv~er and he hauled out about 10 km from his normal haul-out site.

Foraging habitats Using the dive profiles as indicators of foraging activity, we were able to identify the foraging grounds of the individual seals. Foraging was recorded at or close to the sea floor at depths ranging from 15 to 200 m and on different substrates. The complex topography at Froan creates a diversity of habitats and the harbour seals used a variety of these for foraging.

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Fig. 5. Swim speed and dive profiles of an adult male harbour seal (91M2) during a nocturnal foraging trip from June 17 to June 18, 1991. The X-axis is time in minutes from start of tracking. From 0 to 120 min the seal moved at the surface close to the islands. Between 120 and 248 min he foraged at about 100 m depth about 0.5 km from shore. From 248 min he moved into shallow water where he foraged between 362 and 480 min. The pause in tracking between 253 and 260 min was due to echosounding the first foraging grounds which prevented receiving ultrasonic transmitted data from the seal.

The foraging grounds of seal 91M3 can be described as shallow rocks, 15-50 m below surface. At depths of less than 35 m the rocks were covered by kelp forest. The main kelp species was Laminaria hyperborea where adult plants may have a stipes of 2 m and a leaf of 1-1.5 m. The behavioural data did not provide information on whether the seal was foraging within or just above the kelp forest. Echo-surveys showed concentrations of fish surrounding the top of these shallow rocks. The fish were thought to be young saithe Pollachius virens. Seal 93M3 foraged on slopes from the deeper part of the kelp forest to depths of about 120 m. The substrate was characterized by rocks and stones in the kelp zone and stones, gravel, shells, sand and mud as the depth increased. The foraging grounds of seal 93M3 were not echo-surveyed, but concentrations of fish and single fishes were recorded at similar habitat types in 1991. The recorded concentrations of fish were probably young saithe. In shallow bays and narrows between islands, the substrate consisted of shells, crushed shells, sand and clay. In shallow parts, such bays and narrows were often dominated by sea grass Zostera marina. No concentrations of fish were recorded when echo-surveying this habitat type. Seal 91M2 used this habitat type for parts of his foraging activity. However, during most of his foraging, seal 91M2 searched for food at depths of about 100 m on substrate of gravel, sand and mud where the sea floor was gently sloping down into the 500 m deep basin east of the archipelago.

219 Echosurveys showed dense schools of fish at these foraging grounds of seal 91M2, and based on the characteristics of the echogram the fish species was thought to be herring Clupea harengus. Seal 90M2 and several of the tagged seals not described here foraged at the sea bed in 100-200 m deep basins, 2-15 km from their haul-out sites. The substrate in these basins was soft, consisting of mud and ooze. No concentrations of fish were recorded at these sites.

Diet composition In 1991 the diet of harbour seals was examined by identifying fish otoliths in 20 faecal samples collected at the haul-out sites. Seven species of fish were identified. The species are listed below according to their relative importance in the diet. Numbers in parentheses are percent frequency of occurrence: Norway pout Trisopterus esmarkii (25), saithe (20), Norwegian haddock Sebastes viviparus (15), dab Limanda limanda (5), herring (5), greater argentine Argentina silus (5), poor cod Trisopterus minutus (5). More detailed information on diet of harbour seals at Froan is given by Olsen and BjCrge [21 ].

Breeding habitats and display behaviour In June 1991 three adult males were tagged (91M1 on June 6; 91M2 on June 13; 91M3 on June 22). Until about June 27 they alternated between foraging trips and hauling out. From June 28 and July 3, seals 91M1 and 91M2, respectively, changed their activity patterns. Between foraging trips they spent much of their time in water

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220

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I~5 CALLSI MINUTE Fig. 7. Display vocalization made by male harbour seals at southern Froan in late June early July, 1991. The height of the bars indicates the number of calls per minute. Highest calls rates were recorded in the channels between the most frequented haul-out rocks in the southwestern part of Froan.

at particular sites with repetitive dives uniform in both profile and duration (Fig. 6). The mean duration of these dives was 3.34 min (SD 0.34) and mean surface interval was 0.53 min (SD 0.16). The seals vocalized at 45-60 s intervals during each dive giving 3-5 calls per dive. One seal was monitored for periods of 3 h but VHF reception indicated that he performed similar dive patterns for up to 7 h continuously. The characteristics of these calls are described in detail by Thompson et al. [22]. We interpreted this behaviour as sexual display activity. Similar calls were recorded over large areas in Froan. At some sites, we heard calling at rates up to 6 calls/min. Dividing the call detection rates by the calling rate of our tagged seals indicates that between five and nine seals were calling within the audible distance of our hydrophone at some sites. These sites were mainly in the deeper channels penetrating into the shallow water plateau between the haul-out sites which were used by females with pups and other seals (Fig. 7).

Discussion

At Froan, seals haul-out on rocks and apparently suitable rocks are available throughout the tidal cycle. However, the haul-out pattern of harbour seals in this area was influenced by the diel cycle and tidal cycle. The number of hauled out seals peaked at low tide, but according to Roen and BjCrge [3], the peaks were higher during the day than during the night. This may indicate a preference for foraging at night and a tendency to skip one haul-out bout at low tide during the night. The radio tagged seals had different timing to their foraging trips, but at least two seals (91M2 and 91M3) showed preference for foraging at night while other seals were foraging

221 during the day and night. The number of successfully tracked seals was too limited to evaluate the 24 h distribution of foraging activity. In a sample of 17 radio tagged seals in California, 6 seals hauled out most often during day, 7 hauled out most frequently during the night and for the rest of the tagged seals, no preferences were recorded [23]. In most studies, the predominant diurnal pattern indicates that seals are foraging at night. However, Thompson [7] found that harbour seals in the Moray Firth fed more often during the day when feeding on wintering clupeids. He suggested that these seals preferred to forage when the herring were concentrated in tight schools. The radio tagged seals at Froan were foraging close to the sea floor. Several fish species do migrate vertically and are distributed deeper or closer to the sea floor during the day. This indicates that seals may forage during the night if they are feeding on deep water fish, such as the greater argentine, which may be available to the seals only during their nocturnal vertical migrations. Foraging during the day may be advantageous if seals are feeding on species which are more easily caught when they are concentrated on the sea bed and this limits the range of escape routes. During June and July the tracked seals normally returned to their usual haul-out (or display) sites after a foraging trip. The tracking of seal 93M3 after moult in September 1993 indicated that this seal frequently hauled out at alternative sites, e.g. after a period of active foraging and before returning to the usual site. However, the present data provide no basis to conclude whether this was due to individual behaviour, or Jf it reflects a change in behaviour and site fidelity after moult. However, Thompson [7] stated that breeding may influence movement patterns of harbour seals. This is in agreement with the conclusions drawn by Thompson et al. [22] based on tracking of mature males during breeding season in the present study. In June and July the seals showed strong site fidelity towards haul-out sites and also towards display sites in sexually mature males. Seals tracked during this period were solitary when foraging and they regularly returned to the same or approximately the same feeding grounds for several trips during the period of tracking. Most of the foraging activity was within a few kilometres of the haul-out sites. Under such conditions, intraspecific competition for food and feeding grounds may develop. The variety of different types of foraging habitats utilized by the harbour seals and the pattern of returning to the same feeding grounds may be an indication of individual specialization and a means to minimize intraspecific competition and optimize foraging in periods of the year when site fidelity may cause constraints on the foraging movements of these seals.

Acknowledgements This study was made possible by funding from the Norwegian Council for Fisheries Research as part of the Norwegian Marine Mammal Research Programme. Additional funding was available from the Natural Environment Research Council, UK.

222

We would like to thank Sr Kaarstad, Kevin Nicholas, and the fishermen Rolf Heggelund, BjCrn Gaarden and Magne Werkland for assisting at our field work in Froan.

References 1. Bigg MA. Harbour seal Phoca vitulina Linnaeus, 1758 and Phoca largha Pallas, 1811. In: Ridgway SH, Harrison RJ (eds) Handbook of Marine Mammals, Vol 2: Seals. London: Academic Press, 1981; 1-27. 2. Stewart BS. Diurnal hauling patterns of harbor seals at San Miguel Island, California. J Wildlife Manage 1984;48:1459-1461. 3. Roen R, BjCrge A. Haul-out behaviour of the Norwegian harbour seal in summer. In: Blix AS, Walltae L, Ulltang 0 (eds) Whales, Seals, Fish and Man. Amsterdam, The Netherlands: Elsevier, 1995;61-67. 4. Thompson PM. Seasonal changes in the distribution and composition of common seal (Phoca vitulina) haul-out groups. J Zool 1989;217:281-294. 5. Thompson PM, Miller D. Summer foraging activity and movements of radio-tagged common seals (Phoca vitulina L. ) in the Moray Firth, Scotland. J Appl Ecol 1990;27:492-501. 6. Thompson PM, Pierce GJ, Hislop JRG, Miller D, Diack JWS. Winter foraging by common seals (Phoca vitulina) in relation to food availability in inner Moray Firth, N.E. Scotland. J Anim Ecol 1991 ;60:283-294. 7. Thompson PM. Harbour seal movement patterns. In: Boyd IL (ed) Marine Mammals. Advances in Behavioural and Population Biology. Symp Zool Soc London 66. Oxford: Clarendon Press, 1993;225-239. 8. Stewart BS, Leatherwood S, Yochem PK, Heide-Jlargensen MP. Harbor seal tracking and telemetry by satellite. Mar Mammal Sci 1989;5:361-375. 9. Bjtarge A. Status of the harbour seal Phoca vitulina L. in Norway. Biol Conserv 1991;58:229-238. 10. Veneables UM, Veneables LS. Observations on a breeding colony of the seal Phoca vitulina in Shetland. Proc Zool Soc London 1955;128:387-396. 11. Thompson PM. Timing of mating in the common seal (Phoca vitulina). Mammal Rev 1988;18:105-112. 12. H~irk6nen T, Heide-JCrgensen MP. Comparative life histories of East Atlantic and other harbour seal populations. Ophelia 1990;32:211-235. 13. Temte JL, Bigg MA, Wiig 0. Clines revisited: timing of pupping in the Harbour seal (Phoca vitulina). J Zool 1991;224:617-632. 14. Fisher HD. Delayed implantation in the harbour seal (Phoca vitulina). Nature 1954;173:879-880. 15. Boulva J, McLaren IA. Biology of the harbor seal, Phoca vitulina, in eastern Canada. Bull Fish Res Bd Can 1979;200:1-24. 16. Thompson P, Rothery P. Age and sex differences in the timing of moult in the common seal, Phoca vitulina. J Zool 1987;212:597-603. 17. Brown F, Mate BR. Abundance, movements, and feeding habits of harbor seals, Phoca vitulina, at Netarts and Tillamook Bays, Oregon. Fish Bull US Fish Wildl Serv 1983;81:291-301. 18. Pitcher KW, Mcallister DC. Movements and haul out behaviour of radio-tagged harbour seals, Phoca vitulina. Can Field Nat 1981;95:292-297. 19. Fedak MA, Anderson SS, Curry MG. Attachment of a radio tag to the fur of seals. J Zool 1983 ;200:298-300. 20. Nicholas KS, Fedak MA, Hammond PS. An automatic recording station for detecting and storing radio signals from free ranging animals. In: Priede IG, Swift SM (eds) Wildlife Telemetry: Remote Monitoring and Tracking of Animals. Chichester, UK: Ellis Horwood, 1992;76-78.

223 21. Olsen M, BjCrge A. Seasonal and regional variation in the diet of harbour seal Phoca vitulina in Norwegian waters. In: Blix AS, WallCe L, Ulltang 0 (eds) Whales, Seals, Fish and Man. Amsterdam, The Netherlands: Elsevier, 1995 ;271-275. 22. Thompson D, Fedak MA, BjCrge A, Bryant E, Aarefjord H, Hammond PS. Diving and calling behaviour of male harbour seals (Phoca vitulina) during the breeding season. Mar Mammal Sci (submitted). 23. Yochem PK, Stewart BS, DeLong RL, DeMaster DP. Diel haul-out patterns and site fidelity of harbor seals on San Miguel Island, California, in autumn. Mar Mammal Sci 1987;3:323-333.

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9 1995 ElsevierScience B.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 13. Ulltang,editors

225

Spatial and temporal variations in northeast Atlantic minke whale BalaenopCera acur feeding habits Tore Haug ~, Harald Gjcs~eter2, Ulf LindstrCm3, Kjell T. Nilssen ~ and Ingolf RCttingen 2 1Norwegian Institute of Fisheries and Aquaculture, Tromsr Norway; 2Institute of Marine Research, Nordnes, Bergen, Norway; and 3Norwegian College of Fisheries Science, University of Tromsr Tromsr Norway Abstract. Stomach content samples from northeast Atlantic minke whales Balaenoptera acutorostrata, caught in scientific whaling operations in selected subareas during summer in 1992 and during spring, summer and autumn in 1993 revealed a diet where fish played a very prominent role. Considerable heterogeneity in prey species composition occurred both between areas and seasons and from year-toyear. In the 1992 summer survey, capelin dominated the whale diets in the two northmost study areas (Spitsbergen and Bear Island), while the dietary contribution of krill was much more conspicuous in both summer and autumn in 1993. This is consistent with an increase in krill and severe decrease in capelin availability in these areas from 1992 to 1993. The southern coastal areas (Kola, Finnmark, Lofoten/Vester~ilen) were different from the north in that herring was the dominant planktivorous fish. In 1992, this species, 0-group in particular, was documented to be very abundant both in the resource surveys and in the whale stomach analyses. Herring was also the most important food item for the whales in the southern coastal areas in both summer and autumn in 1993. To some extent, the herring was accompanied by gadoid species during summer in both years, and the gadoids fish species dominated the whale diet in spring in 1993. K e y words: scientific whaling, stomach analysis, prey availability

Introduction

In the management of fish stocks in the Barents Sea (and other areas), increased attention has been paid to account for multispecies interactions. Although the state of the art for multispecies assessment is not very advanced, the Multispecies Working Group of the International Council for the Exploration of the Sea (ICES) is actively working in the field. The modelling effort in the Barents Sea multispecies model (MULTSPEC) [ 1] has mainly focussed on the predation on capelin Mallotus villosus by cod Gadus morhua. Recently, however, the model has been expanded to include other top predators such as harp seals Phoca groenlandica and minke whales Balaenoptera acutorostrata. Recent attempts to analyse multispecies interactions and ecosystem functions have, however, highlighted obvious gaps and deficiencies in both data and knowledge, and this applies in particular to marine mammals [2]. For this reason, studies of the feeding ecology of important predators, including harp seals [3-5], are currently

Address for correspondence: T. Haug, Norwegian Institute of Fisheries and Aquaculture, P.O. Box 2511, N-9002 Troms0, Norway.

226 being carried out. The minke whale is a frequent marine mammal in the Northeast Atlantic, the abundance, as estimated by the International Whaling Commission (IWC), being 86,736 (CV =0.1655, 95% CI 61,000-117,000) [6]. Supplementary studies of the role of this species as a top predator are considered important [7]. The minke whale is a boreo-arctic species which, in the North Atlantic, migrates regularly to feeding areas in the high north in spring and early summer, and southwards to breeding areas in the autumn [9]. The Northeast Atlantic stock is known to

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Fig. 2. Food composition, expressed as relative biomass (by calculated fresh weight) of prey organisms, in minke whales sampled in five subareas in the Northeast Atlantic in summer in 1992, and in four subareas in the Northeast Atlantic in spring, summer and autumn in 1993.0-group herring and gadoids are presented separately. "Others" include amphipods of the genus Parathemisto and several fish species: glacier lantern fish Benthosema glaciale, blue whiting Micromesistius poutassou, redfish Sebastes sp., great silver smelt Argentina silus, witch flounder Glyptocephalus cynglossus and unidentifiable remains of gasteroids, trigliids and cottids. N = numbers of stomachs examined.

228 feed on various species of zooplankton and fish, particularly herring Clupea harengus, capelin and cod [9-13]. The collapse of two important stocks of potential prey species (Norwegian spring spawning herring in the early 1970s and Barents Sea capelin in the mid-1980s [14]) is likely to have had an impact on the feeding habits and possibly also the migratory behaviour of Northeast Atlantic minke whales. Reports from stomach inspections made during previous commercial catches are, therefore, difficult to put into present-day perspective because they relate to periods and areas with changing prey availability or with prey abundance much different from today. During a limited Norwegian scientific catch of minke whales in 1988-1990, some pilot studies of the diet of the captured animals were conducted [15]. In order to evaluate the present ecological significance of the Northeast Atlantic minke whale more thoroughly, a scientific whaling program, addressing particularly questions concerning feeding ecology by using stomach analyses and concurrent estimates of prey availability, was initiated in Norway in 1992 [16]. To fulfill the scientific objectives of this program, a scientific catch of whales at different times of the year (spring, summer and autumn) over a period of 3 years was required. The extent of the program and the many activities which have to be coordinated made it necessary to test the methodology on a reduced scale including only the summer period (July 4-August 17) during the first year (1992) of operations. In 1993, however, all three periods were included (spring, April 15-May 15; summer, June 15-July 12; autumn, August 25-September 20). This paper presents results from minke whale stomach analyses and records of potential prey abundance from these 2 years of field work. Minke whales were sampled in different subareas in Norwegian and adjacent waters. The 1992 field work included five subareas (Fig. 1). Unfortunately, Russian authorities refused any scientific whaling in the Russian economic zone in 1993. This left one of the 1992 subareas (the southeastern Barents Sea) unsurveyed and reduced the 1993 field work to incorporate only four subareas (Fig. 2). The substructured design with respect to both sampling area and time of the year enables evaluation of potential geographical and temporal variations in the minke whale diet. Furthermore, samples in the same areas from two different years enable evaluation of potential year-to-year variations.

Materials and Methods

Sampling of whales An important goal of scientific whaling is to obtain samples representative for each area; all whales present in the area should have the same probability of being caught. This calls for procedures of random sampling that ensure geographical scattering within each area and avoids preference for any particular size, sex, behavior or other attribute [16]. To obtain this randomization, sampling procedures of searching for

229 whales along predetermined transects, randomly laid out in each area, were used. In addition, when a whale was observed during a search, an all-out attempt was made at catching the whale, regardless of catchability. The transects were designed in sawtooth patterns, mainly according to the principles used during the previous shipboard sightings surveys NASS-89 [17]. Experience gained during the 1992 operations was used to perform slight modifications in the 1993 transects. In order to make the searching operations as efficient as possible, a certain amount of freedom was given to modify transect lines during the course of operation, depending on factors such as ice-cover, weather conditions and observations of minke whale abundances [18,19]. The minke whales were hunted and killed according to the whaling procedures described by Haug et al. [ 18,19]. Killed whales were immediately taken on board the vessel for dissection and biological sampling. In 1992 stomach contents data were obtained from 92 whales (51 males and 41 females, ranging in size between 485 and 883 cm). The 1993 material included samples from 63 whales (28 males and 35 females, ranging in size between 442 and 880 cm). Difficult weather conditions and low whale abundance (no whales observed at Spitsbergen, very few observed in the other areas [19]) severely hampered the 1993 spring operations when only 4 whales were obtained. During the 1993 summer period, the whale abundance situation appeared similar to the observations made in 1992, and 32 whales were obtained. Changes in relative distribution of the whales appeared to have occurred between summer and autumn, particularly in the northmost areas where the animals seemed to have left both the Spitsbergen and Bear Island subareas in favour of areas further to the east in the Barents Sea [19]. To secure samples from whales in the northern areas also during autumn, the Bear Island subarea was extended in a northeastward direction during the course of operations. A total of 27 whales were sampled in the autumn period.

Analyses of minke whale stomachs The complete digestive tract was taken out of the whale as soon as possible (1-3 h post mortem). Minke whale stomachs consist of a series of four chambers [20]. Experience from pilot studies performed during the scientific whaling in 1988-1990 suggested that sampling from the first chamber (the fore-stomach) would give sufficient data to evaluate the diet of the animals [15]. Therefore, only contents from this stomach chamber were used in the present analyses. The on board and laboratory treatment of the fore-stomach contents were as described in detail by Haug et al. [21]. Otoliths were collected and identified to the lowest possible taxon, preferably to species [22,23]. The total number of each fish species was determined by adding the number of fresh specimens, the number of intact skulls and half the number of free otoliths. Random subsamples of otoliths were measured, and otolith length-fish length/weight correlations were used to estimate the original fish weight. Erosion of

230 otoliths, which is a problem in studies of seal stomachs [24], is probably not a problem in these minke whale diet studies as the analyses were restricted to the contents in the fore-stomach where digestive glands are completely absent and no gastric acids are produced [20]. For crustaceans, the total weight and the number of individuals were recorded for each species in subsamples, and this was used to obtain crude estimates of the numerical contribution of each prey species. Known mean weights of fresh crustaceans were used to obtain crude estimates of the original biomass of the crustaceans eaten by the minke whales. Several feeding indices are commonly used in stomach analyses of top predators [24,25]. In this presentation, only the relative contribution of each prey species to the total diet expressed in terms of calculated fresh weight, was used.

Estimation of relative prey abundance In 1992, the estimation of minke whale prey abundance was performed in a survey especially designed for the purpose. One of the chartered whaling vessels was fitted with trawl equipment (but no scientific acoustic instrumentation) and was used to trawl pelagically in sound scattering layers in the two northmost sampling areas (Spitsbergen and Bear Island, Fig. 1). The remaining three sampling areas (Kola, Finnmark and Lofoten/Vesterhlen) were subjected to more thorough resource surveys using a research vessel (R/V "Johan Ruud") during the period 11-20 July. R/V "Johan Ruud" carried out an acoustic survey using standard methods [26], where a Simrad EK 500 scientific echo sounder [27] and a BEI post-processing system [28] were used. A minimum acoustic threshold o f - 8 8 dB SV was applied in order also to record larger zooplankton acoustically. Partitioning of the acoustic data and allocation of these to species were done on the basis of the acoustic character of each species and on the results from trawling. Both pelagic and demersal trawls were used to sample the observed scatterers. The standard echo integration method used is described in detail by MacLennan and Simmonds [29]. The acoustic parameter measured by the echo integrator is the area backscattering coefficient (SA), an absolute, acoustic linear unit, proportional to fish (and plankton) area density. Rough estimates of relative differences in the density of the scatterers may be made by use of the SA-Values only. This procedure is followed in this presentation, and the recorded SA-Value per nautical mile and 50 m depth channel was averaged over 5 nautical miles, and distributed on the following groups of targets: 0-group fish, plankton, cod + haddock Malanogrammus aeglefinus, herring, capelin, other pelagic fish, and other demersal fish. The 1993 prey abundance estimations were based on data drawn from regular Norwegian resource surveys designed for the mapping of relevant resources, not all of them necessarily of interest as potential whale prey (Table 1). These surveys form part of a time series, and are carried out at a time of the year which is thought to give the most reliable estimates of abundance of each actual species.

231 Table 1. Annual surveys carried out to estimate absolute abundance of fish stocks in 1993 (operating institution: Institute of Marine Research, Bergen, Norway)

Period

Area

Type of survey

Target species

Feb-Mar

Barents Sea

Cod and haddock

Mar-Apr Jun Aug-Sep Sep Dec-Jan

Lofoten-Vesterhlen Barents Sea Bear Island-Spitsbergen Barents Sea Vestfjord area

Combined acoustics and bottom trawl Acoustic survey Acoustic Bottom trawl Acoustics Acoustics, tagging

Cod, haddock and saithe Immature herring Cod Capelin, immature herring Adult herring

The resource surveys were not always fully synoptic with the minke whale sampiing (Table 1). Further, the surveys aimed to survey the entire fish stock in question while the transects applied in the minke whale sampling program surveyed only part of the distributional area of the prey. Absolute abundances of actual species were estimated for areas according to a classification proposed for use in the Barents Sea multispecies modelling (MULTSPEC) [30]. To estimate the abundance for potential minke whale prey, the following MULTSPEC areas were used to represent the minke whale sampling areas: Bear Island and Spitsbergen, MULTSPEC area VI; Finnmark, MULTSPEC areas II and III; Lofoten/Vesterhlen, MULTSPEC area I. Thus, interpolation in time and space was necessary, utilizing all available knowledge of migration and distribution. In addition to the surveys listed in Table 1, an annual international 0-group fish survey is carried out in the Barents Sea in late August and early September [31 ]. Some additional surveys, with aims other than absolute abundance estimation of fish stocks (0-group, immature and adults), were carried out, concomitantly in area and time with the sampling of minke whales. During spring, relative data on prey abundance were collected in the Bear Island and Finnmark subareas during a cruise designed to assess prawn resources, and in the Bear Island and Lofoten/Vester~len subareas during a cruise to study hydrography and the occurrence of planktonic species. During the summer period, the Spitsbergen subarea was surveyed during a prawn assessment cruise, and the Finnmark subarea during plankton surveys. Data collection during the 1993 resource mappings was generally conducted using devices and methodology comparable to those used during the 1992 "Johan Ruud" resource survey. Relevant plankton surveys to observe annual production and year to year variability were generally conducted in late summer [32]. Standard plankton nets were used to sample the whole water column from bottom to surface. Collected samples were filtered (through 2000/tm, 1000/tm and 180/~m meshes) in order to separate the different size fractions of plankton. The biomass determinations were based on dry weights.

232 Results

The 1992 data Whale stomach contents Fourteen different prey species were identified in the stomachs of the minke whales (Fig. 2). Of these, 12 were fish species. In terms of calculated fresh weight, the relative contribution of krill in all areas (except Bear Island) was small compared with fish. In the two northernmost areas (Spitsbergen and Bear Island), the fish component was completely dominated by capelin (70-90%). In the coastal areas of North Norway (Finnmark and Lofoten/Vester~len), herring (0-group, I-group, and older individuals) comprised the main bulk (77-83%) of prey biomass. One-year-old and older herring also made an important contribution to the prey biomass (27%) in the Kola area, where the prey also included considerable weights of sand eels (30%), large cod (30%) and haddock (9%). A component of large cod (9%) was observed also in the Finnmark material, while large saithe Pollachius virens were found to contribute significantly (15%) to the prey biomass in Lofoten/Vesterhlen. Prey abundance West of Spitsbergen. The pelagic amphipod Parathemisto libellula dominated the pelagic trawl hauls north in the Spitsbergen area. Some few individuals of polar cod Boreogadus saida and capelin were also found. Further south, the catches were totally dominated by 0-group cod. Some specimens of capelin were also observed. Bear Island. The area was dominated by 0-group cod. Additionally, some krill and herring were found, together with a few capelin. Simultaneously with the present survey, a cod fishery took place along the bottom in the area. Coast of Kola. The majority of echo recordings were found in the upper 50 m. Trawling revealed a total dominance of young herring in the Kola area, mainly of the 1991 year class (Table 2). Some 0-group fish were found, although in lower concentrations than observed in Norwegian coastal areas further to the west. The only plankton organism found in the pelagic trawl was krill, and only in small amounts. Coast of Finnmark. On the North Cape Bank (approximate position 71-72~ 26~ a few schools of capelin were found at 100-200 m depth. The densest fish concentrations in Finnmark were located east of 28~ and the concentrations decreased with distance from the coast. North of 71 ~ the 0-group layer was totally dominating. The 0-group fish layer in the eastem parts of this area consisted almost exclusively of cod. To the west of North Cape (26~ however, the 0-group fish layer was dominated by herring. Lofoten/Vestertden. The area was totally dominated by echoes in the upper 50 m. The largest concentrations of echoes were found in the northernmost offshore parts of the area. In the more inshore Vestfjorden, relatively small registrations were detected. A dense layer of 0-group fish dominated (Table 2), but the species composition varied. In Vestfjorden, this layer consisted of cod, haddock, saithe, herring and

233 Table 2. Ecological studies of minke whales 1992: resource surveys, mean echo abundance (SA) of prey groups in the three coastal areas Kola, Finnmark and Lofoten/Vesterfilen

Species or group

Bottom fish Pelagic fish Plankton Herring Capelin 0-group fish Cod + haddock Total

Area Kola

Finnmark

Lofoten/Vesterfilen

3.6 45.8 27.3 280.3 6.9 28.9 1.9 395.7

10.1 61.1 31.2 150.5 38.2 314.4 2.2 607.8

60.1 172.8 28.2 0.0 0.0 1700.0 42.8 2003.8

sand eel. In areas with high echo densities the layer was dominated by herring. Outside Vestfjorden this dominance of herring was nearly total.

The 1993 data Whale stomach contents During the summer period a minimum of 10 different prey species were identified in the stomachs of the minke whales, while the corresponding numbers during spring and autumn were 9 and 7, respectively (Fig. 2). Based on calculated fresh biomass, gadoid fish (cod in Bear Island and Finnmark, haddock and saithe in Lofoten/ Vesterhlen) contributed most to the diet of the minke whales taken during the spring period when only fish was found in the stomachs. During the summer, krill was particularly conspicuous (92%) in the Spitsbergen area. In all other areas, fish contributed most to the summer diet biomass: cod and haddock with 63% in Bear Island, herring with nearly 100% in Finnmark, and haddock, herring and saithe with 90% in Lofoten/Vesterhlen. In the autumn, krill contributed most to the whale diet in Spitsbergen (88%) and in Bear Island/Hopen (80%), while herring dominated the diet biomass in the two coastal areas (74% in Finnmark, 96% in Lofoten/Vesterhlen). Prey abundance: fish species Spitsbergen and Bear Island. During the 1993 spring period, winter conditions prevailed in the water masses both in the Spitsbergen and Bear Island subareas where only very small biomasses of zooplankton and pelagic fish species were available [33]. There are no absolute abundance estimates of prey organisms available for the spring and summer periods in the Spitsbergen and Bear Island region. The biomass of fish is known, however, to be rather small in both periods. The availability of fish increases in the northmost areas from July on, mainly caused by an increasing amount of 0-group fish (herring and cod in particular) which drift northwards with the warm North Atlantic Current. In August 1993, 0-group herring and cod were distributed over a major part of the two northern minke whale sampling areas

234

Table 3. Ecological studies of minke whales 1993" estimated abundance (in tonnes) of different fish stocks in the minke whale sampling areas (x = estimates not available) Period

Prey Cod

Spitsbergen Spring Summer Autumn Bear Island Spring Summer Autumn Finnmark Spring Summer Autumn

68,210

Haddock Saithe

Capelin Herring Herring (adult) (immat.)

8,453

0

0

335,421 19,784

57,000

0

64,000 34,500 x

0 1,500,000 0 1,930,000

x

Lofoten/Vester~len Spring 767,800 25,700 Summer Autumn

7,000

89,100

500,000 600,000 2,500,000

400,000 700,000 700,000

[ 14,32]. An autumn acoustic survey [31 ] also found some capelin in the eastern parts of the Bear Island area and east of Hopen (Table 3). Finnmark. The most important planktivorous fish in this area in 1993 was young and adolescent Norwegian spring spawning herring. In spring, immature herring started feeding migrations into the Finnmark area from areas further to the east [32]. Furthermore, capelin spawn in March-April along the coasts of the Kola Peninsula and Finnmark. Apparently, there will also be considerable amounts of young cod and haddock available in the area, feeding on the herring and also on some spent capelin (Table 3). In the 1993 summer period, the Finnmark area was dominated by immature herring, and from the end of July the fish biomass was further augmented by influx of 0-group fish species which were transported into the area by currents from the south west. In 1993 these 0-group fish concentrations were dominated by cod and herring [ 14,32]. The autumn period in this area was similar to the summer with a predominance of young herring and 0-group fish. The estimates of immature herring in the summer and autumn periods (Table 3) are from surveys performed in June and September, respectively (Table 1). There are no reliable estimates of cod and haddock in Finnmark for the 1993 summer period. Lofoten/Vester&len. In spring, stocks of adult fish (cod, haddock, saithe and herring) migrate northwards through this area on their way from spawning grounds to summer feeding areas. The spring stocks of cod, haddock and saithe (Table 3) were mapped on a spawning ground survey (Table 1) conducted in Lofoten and Vesterhlen from early March to early April [34]. There are also concentrations of

235 immature herring in the area, usually distributed close to the coast. Adult herring, however, migrate at 200--400 m depth to the west of the continental slope during spring. The herring estimates from the spring and summer period are interpolated from stock size estimates made in the wintering area (Table 3) and data on the relative herring distribution in April and July/August. In summer, some fish species (saithe, cod, immature herring) feed in the Lofoten/Vestergden area, while the amount of fish biomass increases substantially in autumn due to the arrival of adult herring which migrate towards their wintering area in the inner Vestfjord. The biomass data from the autumn period are estimated from acoustic estimates on the wintering areas and from tagging estimates [32].

Prey abundance: zooplankton The overall zooplankton production in 1993 in the areas of interest to the minke whale surveys was probably above average and considerably larger than in 1992 [32]. The yearly production of krill in the Barents Sea has been estimated as 50-70 million tonnes [35]. The main species of krill in this area are Thysanoessa inermis, T. raschii, T. longicaudata and Meganyctiphanes norwegica. However, the exact standing stock biomass t.in tonnes) of krill in the particular areas and times of minke whale sampling cannot be given.

Discussion

Piscivorous whales The diet of minke whales in Norwegian and adjacent waters, as observed in JulyAugust 1992, was almost completely dominated by fish while the contribution of planktonic crustaceans was very small. Fish were the most conspicuous constituents of the diet of minke whales examined also in 1993 when surveys were performed both in spring, summer and autumn. The present investigation, therefore, confirms the euryphagous nature of North Atlantic minke whales [9,10,15,36,37], similar to those in Japanese waters [38], but quite unlike the rather stenophagous krill eating minke whales in the Antarctic [39,40]. Considerable heterogeneity in diet occurred among the geographical areas investigated in both 1992 and 1993. In 1993 variation in diet composition was observed also between the investigation periods.

Restricted spring data The 1993 results may point towards a dominant role of gadoid fish (cod, haddock and saithe) in the spring diet of minke whales in the areas investigated. This seems consistent with previous observations made in Lofoten in the 1940s [9,10]. The very few whale observations made during the spring period in the northmost areas (no whales seen at Spitsbergen, very few seen at Bear Island) [19] seem consistent with observations of a typical winter situation prevailing in these waters during the whole

236 spring period [33]. The present material from the spring period is, however, very restricted, and more firm conclusions must await more data.

Capelin and krill in the north In July-August 1992 capelin dominated the whale diet in the northmost parts of the area investigated (Spitsbergen and Bear Island), while the dietary contribution of krill was much more conspicuous in the north in both summer and autumn in 1993. The prominent role of krill in the Spitsbergen area in 1993 is consistent with previous summer observations made in 1950 [9,10] and in 1989 [15]. Stomach inspections in 1950 in the Bear Island area also revealed pelagic crustaceans to be the main food item, although often mixed with capelin [10]. Interestingly, in 1989 the Barents Sea capelin stock still remained at an extremely low abundance level following a severe collapse in 1985/1986 while in 1992 it had recovered completely to its precollapse levels [14]. Furthermore, from 1992 to 1993 the capelin stock was again subjected to a dramatic decrease which occurred simultaneously with a marked increase in zooplankton production, in particular in these northern areas [32]. These ecosystem changes may have contributed to the observed changes in minke whale diets in the northernmost areas between 1989 and 1993. The large amounts of 0-group cod observed during the 1992 resource surveys in the Spitsbergen and Bear Island areas were further confirmed during the international 0-group fish survey in the area [41]. Interestingly, this vast amount of small cod in the upper water layers does not seem to have attracted the attention of the minke whales. The same was true for the pelagic amphipod P. libellula, which dominated in the northernmost trawl hauls in the Spitsbergen area but were only found in very small amounts in minke whale stomachs. Capelin was only found sporadically in the trawl hauls performed in the Spitsbergen and Bear Island areas. The same was true for krill.

Herring and gadoids in the south The capelin stock is mainly confined to the central and northern parts of the Barents Sea [42], while the dominant planktivorous fish along the Norwegian coast and in the southern Barents Sea is the Norwegian spring spawning herring. Contrasting the now very decreased capelin stock, the stock of Norwegian spring spawning herring has been increasing in recent years [32]. The difference in prey abundance situation in the southern coastal areas compared with the north was also reflected in different whale diets. Herring was clearly the most important minke whale prey species in the coastal subareas of North Norway both during summer in 1992 and during summer and autumn in 1993. Summer and early autumn predation of minke whales upon herring was observed in Lofoten and Vesterhlen both in the 1940s [9,10] and in 1988 [15,43]. In 1992 the whale diets in the southern areas were varied and included a broader spectrum of fish species than those in the north. Correlation seemed to exist between

237 prey availability and minke whale diets: 0-group herring was very abundant in the resource surveys in Lofoten/VesterAlen, less abundant in Finnmark and nearly absent off Kola. A similar west-to-east distribution of 0-group herring was found in the minke whale stomachs from these three areas. Apparently, the Kola diet was least dominated by herring, and included considerable amounts of sand eels and cod. An apparent importance of 0-group herring on the whale diet, as observed during summer in 1992, was not found in the Finnmark and the Lofoten/VesterAlen subareas in any parts of the 1993 surveys. The 1993 autumn diet of minke whales from the Lofoten/Vester~len subarea was completely dominated by adult herring, while during summer the diet was more mixed with particular large representation of haddock, to some extent also saithe. The vast autumn appearance of adult Norwegian spring spawning herring in Lofoten/VesterAlen is a relatively new phenomenon, related to the recent rebuilding of this stock [44]. Prior to the collapse of this stock in the late 1960s, the adults wintered in the open sea northeast of Iceland. In Finnmark, where herring dominated the whale diet in both periods, the whales were also observed to have eaten considerable quantities of 0-group gadoid fish during autumn in 1993. Similar minke whale predation upon 0-group fish was observed in Finnmark in August in 1988 [15]. Krill was very scarce in the diets of minke whales sampled in coastal areas of Russia (1992) and North Norway (1992 and 1993). This contradicts some earlier observations: during summer in 1972-1973, krill was found to be the main minke whale prey on the Kola coast [11,12]. Although few minke whale stomach data are available from the Finnmark coast, there is some recent evidence (from 1988 and 1990) that they may also prey upon krill and possibly also on 0-group specimens of herring, cod and haddock [ 15].

Acknowledgements Sincere thanks are due to field assistants and crews on board the chartered whaling vessels "Ann Brita", "AsbjCm Selsbane", "BrandsholmbCen", "Havliner", "Leif Junior", "Nybraena", "Rango" and "Reinebuen", and on board the research vessels "GO Sars", "Jan Mayen", "Johan Hjort" and "Johan Ruud". Assistance was received also from N. Oien in transect constructions, V. Frivoll and L. Svensson in laboratory treatment of whale stomach contents, P. Grotnes in biomass calculations, and S. Hartvedt, F. Strand and G. Granaas in figure production. R.T. Barrett is acknowledged for comments on the manuscript and improvements to the English. The ecological studies of Northeast Atlantic minke whales are supported economically by the Norwegian Council of Research (dept. NFFR), projects 40012800.084 and 4001-701.421.

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9 1995 ElsevierScienceB.V. All rights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand 121.Ulltang,editors

241

Seasonal distribution, condition and feeding habits of Barents Sea harp seals (Phoca groenlandica) Kjell Tormod Nilssen Norwegian Institute of Fisheries and Aquaculture, Tromsr Norway In early summer (May-June) Barents Sea harp seals (Phoca groenlandica) migrate to northern areas of the Barents Sea. Here they feed in open waters and along the drifting pack-ice during summer and autumn. In late autumn (November) the seals migrate southwards, and in winter and spring the stock is usually concentrated in the southeastern areas of the Barents Sea and in the White Sea. Breeding (late February to early March) and moult (April-May) occur in these southern areas. Seasonal variations in the condition of adults indicate that late summer and autumn are the most intensive feeding periods, and the pelagic amphipod Parathemisto libellula appears to be the dominant prey from September until mid-October. During October, a shift in the diet from pelagic crustaceans to fish seems to occur. Capelin, and to a lesser extent polar cod, are major prey during the autumn. In later winter (February) herring has been found to be the main harp seal prey in the southeastern Barents Sea. The energy reserves stored during summer and autumn are maintained until February. During breeding (March) and moult (April-May) the stores of blubber decrease rapidly, indicating restricted food intake at this time. Abstract.

Key words: migrations, feeding intensity, prey, prey abundance

Introduction

In the management of marine resources, increased attention has recently been directed towards the study of multispecies interactions. This has encouraged studies of the feeding ecology of important top predators within marine ecosystems. The harp seal (Phoca groenlandica) is the most abundant seal species in the Barents Sea. Harp seals are important predators in the Barents Sea, and studies of their role as top predators are considered important within a management context [1 ]. The species is now included in a multispecies model, MULTSPEC, which may provide the basis for future management of marine resources in the area [2]. In addition to the biological input required by multispecies modelling, data on harp seal ecology would contribute to a better understanding of environmental processes that affect feeding opportunities for the species. Furthermore, such information may be able to shed some light on density-dependent changes observed in growth and fecundity in Barents Sea harp seals over the past 30 years [3], and reveal potential causes for the extensive harp seal invasions into coastal waters of North Norway in 1986-1988 [4,5].

Address for correspondence: K.T. Nilssen, Norwegian Institute of Fisheries and Aquaculture, P.O. Box 2511, N-9002 Tromsr Norway.

242 Pilot studies of harp seal feeding along the Barents Sea pack-ice edge were carried out by the Institute of Marine Research, Bergen, in autumn 1981-1983 (unpublished data), and the investigations were continued in 1987 [6]. To provide more data on the ecological significance of harp seals in the Barents Sea, further studies designed to gather information about the feeding habits of the animals throughout the year have been carried out more recently [4,7-11 ]. Field sampling was designed to take account of the migratory pattern of the harp seal in the Barents Sea (see Fig. 1). The purposes of the studies were to determine

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Fig. 1. Map showing the seasonal migration pattern of Barents Sea harp seals, and indicating the sampiing areas in the period 1989-1994. From [16].

243 Table 1. Summary of the Barents Sea harp seals collected for ecological studies in the period 1989-

1994

Year

Season

Area

Sampling type

N u m b e of r seals

1989 1990 1991 1991 1992 1992 1992 1992 1993 1993 1994 Total

Mar Sep Jun Sep Mar-Apr Apr Apr-May Oct Feb Mar May

White Sea (1) East of Svalbard (5) East of Hopen (4) East of Svalbard (6) Varangerfjord(8) East Ice (2) White Sea (1) East of Svalbard (7) Southeastern Barents Sea (3) White Sea (1) White Sea (1)

CS SEa SEa SEa SE/GN CS SE SE SEa CS SE

300 22 239 40 44 438 302 51 110 145 52 1,743

cs, commercial sealing; SE, scientific expedition; GN, gill net by-catches. The numbers in parentheses indicate the sampling areas shown in Fig. 1. aAbundance of potential prey was assessed by trawling in the areas where seals were observed and caught. seasonal variations in condition of the harp seals and their feeding habits throughout the year. Material collected included weight, length, girth and blubber thickness measurements, contents from stomachs and intestines and, in some instances estimates of prey abundances [8-10,12]. The following represents an overview of the studies highlighting the most important results.

S a m p l i n g A r e a s a n d Periods

Harp seals were sampled in the southern Barents Sea and White Sea from February to early May 1989-1994, and along the pack-ice belt in the northern parts of the Barents Sea in June, September and October 1990-1992 (Table 1, Fig. 1). Sampling in the White Sea was carded out during Russian commercial sealing in March 1989 and 1993, and Russian research surveys in April-May 1992 and 1994. In the East Ice, sampling was carried out during Norwegian commercial sealing. In March-April 1992 in Varangerfjord, in February 1993 in the areas east of Cape Kanin and in June 1991 and September-October 1990-1992 in the northern parts of the Barents Sea, sampling of harp seals was carried out during research surveys [8-10]. The capture and on-board treatment of the harp seals, and the laboratory analyses of stomach and intestinal contents were described in detail by Nilssen et al. [8,10].

Seasonal Distribution

The annual migration route of the Barents Sea harp seals is summarized in Fig. 1. In winter and spring (December-May), the entire seal stock seems to be concentrated at

244 the southern end of its range, primarily in the southeastern parts of the Barents Sea and in the White Sea where breeding (February-March) and moult (April-May) occur [13,14]. Whelping in the White Sea is followed by 12 days of intensive lactation after which the females mate and desert the pups. After a lapse of a further 4 weeks the adults and immatures then moult. Adult males and immatures of both sexes are the first to haul out on to the ice to moult [15]. Between breeding and moult the mature females undertake a feeding migration from the White Sea westwards along the Kola coast, and some of the migrating females reach Varangerfjord in North Norway [8]. In May, during and after the moult, the seals start to migrate northwards in the Barents Sea, following the receding ice edge. From June onwards the seals are widely distributed in open and pack-ice waters from Novaja Zemlja in the east, along the pack-ice belt in the north and along the western coast of Spitsbergen [13,16]. The distribution of the harp seals at this time seems to be closely linked to the configuration and location of the drifting pack-ice during summer and autumn (June-October). The exact timing and route of the late autumn migration from the northern parts of the Barents Sea are not known, but recent observations suggest that the migration takes place during November before the main southward advance of the drifting pack-ice [16]. By December almost all harp seals appear to have moved southwards and seals are found between the southwest coast of Novaja Zemlja and Cape Kanin.

Seasonal Variations in Adult Condition

Previous observations suggest that both blubber thickness and condition of adult harp seals vary on a seasonal basis [15,17,18]. Recent analyses of changes in harp seal condition in the Barents and White Seas confirm this seasonality [9]. Harp seals are generally in poor condition in spring and early summer (MayJune), their condition improves during the course of summer, and seals are in good condition in September-October (Fig. 2). The energy stores built up during the summer and autumn are maintained until February, but then the seals become thinner as the stores of blubber decrease rapidly during the breeding period (late February to early March). There may be a slight increase in condition in the short period between lactation and moult (late March to early April), but the stores of blubber decrease during moult, which occurs from late April to June (Fig. 2). The increase in condition between mid-June and September, and a further increase to October, indicate that summer and autumn must be very intensive feeding periods. The apparent stability in condition during late autumn and early winter suggests that the seals are able to consume sufficient prey to meet energy requirements during this period. The very poor condition observed in March and June provides clear indications that feeding by the seals must be restricted during breeding and moult. Seasonal changes in feeding have also been observed in captive harp seals, with Lager et al. [19] reporting that food intake in subadults was generally low during moult (May-June), to be followed by a pronounced increase in feeding in the

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Fig. 2. The condition of adult (standard body length 150 cm or more) Barents Sea harp seals (both sexes pooled). Condition factor, C = (L/M)0"Sd,where L is standard body length in m, M is total body mass in kg and d is dorsal blubber thickness in cm, measured between the front flippers (Ryg et al. 1990). From [12]. period July-October. During the period October-April the food intake of captive seals was observed to be relatively stable.

Feeding Habits Winter-spring in the southern Barents and White Seas Our knowledge about the feeding habits of harp seals in the period N o v e m b e r January is still limited. In February 1993, harp seals were captured in the southeastern Barents Sea (Fig. 1, areas 3) and prey abundance was examined by trawling and acoustic echo-integration survey methods. In the area northeast of Cape Kanin, herring (Clupea harengus) was found to be both the dominant harp seal prey and also the dominant species in the trawl catches (Fig. 3). Herring was also the dominant prey of seals in the Pechora Sea, although polar cod (Boreogadus saida) dominated in the trawl catches [9]. The stock of Norwegian spring spawning herring collapsed in the late 1960s. However, since 1988 the stock size has increased gradually to a level comparable to that in the early 1960s. Stock size was estimated to be about 2.8 million tonnes in

246 CAPE KANIN-KOLGUYEV 100%

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1994 [20]. Thus, increasing quantities of young herring (0-group and recruits up to 4 years old) are recorded in the southern Barents Sea [21 ], and immature herring are, at present, probably the major winter prey for harp seal in this area [9]. The decline in condition in spring and early summer (Fig. 2) suggests that food intake is decreased in this period. However, some feeding does occur among lactating female harp seals. In the White Sea breeding grounds in early March 1989 and 1993, the main food items were found to be crustaceans [8]. Comparable observations were made of lactating harp seals in the White Sea in 1962 [22]. Nocturnal

247

feeding, and subsequent rapid digestion [23], may account for the low frequency of food remains found in stomachs of lactating harp seal females shot during the day [24]. The localisation of breeding grounds is determined by the drift and distribution of ice, and this may vary both seasonally and from year to year. This may result in considerable variations in food availability for the female seals which spend a lot of time on the ice nursing their pups [15,25]. In his 1962 investigations in the White Sea, Timoshenko [22] found that if food was available, lactating harp seals fed quite intensively. In contrast to the situation in early March in the White Sea, seals examined in Varangerfjord in late March to early April 1992 had stomachs well filled with undigested prey, almost exclusively capelin (Fig. 4). These seals were mostly adult females, and some still had milk in their mammary glands. These females were probably on a westward feeding migration following the lactation period. Capelin (Mallotus villosus) has also been found to dominate the diet of harp seals in this area during late winter also in 1991 [8]. The increased dominance of capelin in the seal

100

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Fig. 4. Diet composition from stomach contents analyses of harp seals caught in Varangerfjord (MarchApril) and East Ice (April) 1992. Dietary composition is given, in terms of relative frequency of occurrence of each prey item as numerical frequency and as per cent total biomass. From [8].

248 diet during late winter in eastern Finnmark in recent years differs from observations made during the extensive harp seal invasions in 1986-1988. At that time the seal diet consisted mainly of prawns (Pandalus borealis) and codfish (Gadidae) [4,11]. Earlier, in 1978-1981, harp seals taken as by-catches in winter gill-net fisheries in Finnmark were reported to have eaten mainly capelin [26]. The recovery of the Barents Sea capelin stock in 1990, following the severe collapse in 1985/1986 [20], probably accounts for the increase in importance of capelin as prey for harp seals in Finnmark during the winters of 1991 and 1992. The Barents Sea capelin spawning grounds stretch from the White Sea to the North Norwegian coast (particularly Finnmark) and spawning takes place (March-April) when harp seals are in the Varangerfjord area [27-30]. There was a new collapse in the capelin stock during 1993 [20] and the stock is again at a low level. This, combined with the substantial increase of immature Norwegian spring spawning herring in the southern Barents Sea, has probably resulted in herring becoming the most important harp seal prey in this area during the winter. Restricted feeding by harp seals was observed during the moult. In the East Ice in April 1992 the seals had eaten mainly prawns and capelin (Fig. 4), but codfish, sculpins (Cottidae), snailfish (Liparidae) and flatfish (Pleuronectidae) also occurred in the diet [8]. In April 1993 in this area, harp seal stomachs well-filled with herring were observed (K.A. Fagerheim, Institute of Marine Research, Bergen, Norway, pers. comm.). During moult in the White Sea, the harp seal diet comprised amphipods (Parathemisto spp.), prawns, capelin, herring, eelpout (Zoarces viviparus), sandeels (Ammodytes spp.) and stickleback (Gasterosteus aculeatus) [8]. Results of the Barents and White Seas studies agree with previous observations of harp seals in the western North Atlantic. In late winter and early spring, food intake tends to be reduced [15,18]. The exception seems to be in the brief period between breeding and moult when increased condition of adult seals (Fig. 2) and the occurrence of well-filled stomachs in adult females indicates that feeding is more intense. This observation is supported by previous White Sea data which showed a slight increase in adult female blubber thickness in the time period between whelping and moult [ 17].

Early summer to autumn in the northern Barents Sea The Barents Sea harp seals are found in large numbers along the drifting pack-ice and in open waters in early summer [13,16,31,32]. In the course of a research survey conducted along the ice fringe from Novaja Zemlja to Hopen in June 1991 (Table 1), almost all harp seals were observed to be confined to the pack-ice southeast of Hopen [ 10]. The stomachs of seals captured from ice floes in this area (Fig. 1, area 4) were usually empty, with only a few fragments of prawns and otoliths of long rough dab (Hippoglossoides platessoides), sculpin (Triglops pingelii), capelin and polar cod being found. There was also little sign of harp seal faeces on the ice floes where the seals had hauled out. The seals were in poor condition (Fig. 2), suggesting that little feeding had taken place during the early summer period [ 10].

249 Pelagic and bottom trawling carried out concomitantly with the capture of the seals revealed little or no potential prey in the water column at shallow depths. Prawns, capelin, polar cod and other fish species were, however, abundant in bottom waters at depths of approximately 330 m [10]. These prey items were probably unavailable to the harp seals, due to the great depth of occurrence. The fact that adult harp seals improve in condition from June to September (Fig. 2) indicates that the summer and autumn are periods of intensive feeding. However, no data are available about harp seal feeding habits in the Barent Sea during the summer period (July-August). In autumn the harp seals disperse along almost the entire ice edge, from the east of Spitsbergen to the northeastern parts of the Kara Sea [13,16]. In September 1990 and 1991 harp seals were captured whilst in water close to the pack-ice edge (Fig. 1, area

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250 5 in 1990 and area 6 in 1991). The seals had well-filled stomachs suggesting that this was a period of intensive feeding. In September 1990 the seal stomachs contained large amounts of the pelagic amphipod Parathemisto libellula. This amphipod also dominated the diet in September 1991, but prawns and various fish species were also found to contribute to the diet (Fig. 5). Pelagic and bottom trawling revealed that the amphipod P. libellula was most abundant in surface waters (surface and 20 m depth) in September 1990 (Fig. 6). This amphipod was also taken in considerable quantities in the trawl samples made

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251 10-15 m above the bottom, where krill was also present in large amounts. The volumetric contribution of fish to all pelagic hauls was low, whereas the bottom hauls comprised almost exclusively fish, particularly capelin, polar cod and flatfishes (Fig. 6). P. libellula dominated the biomass in the pelagic trawl catches taken at the surface and at 20 rn depth also in September 1991. This amphipod was also abundant in the hauls made approximately 20 m above the bottom, but krill and capelin tended to dominate these samples. Sculpin, polar cod and snailfish (Liparis spp.) were also taken [ 10]. The results of the present study agree with those of investigations carried out in the same area in September 1987 [6]. The possible importance of P. libellula as a prey species for harp seals in other Arctic areas, e.g. Greenland and Canada, has been indicated previously [ 18,33-35]. During October, the temperature in the Barents Sea region falls, and strong winds from the north result in a rapid extension of pack-ice cover towards the south. This forces the harp seals to move southwards, and the seals also seem to move towards Novaja Zemlja further to the east [13,16]. In the pack-ice areas of the central northern parts of the Barents Sea (Fig. 1, area 7) a shift in the diet of the harp seals was observed in October. Thus, the diet consisted mainly of P. libellula up to midOctober, but changed to fish, mainly capelin and to a lesser extent polar cod, as ice cover forced the seals to move southwards during late October (Fig. 5). The amphipod P. libellula is known to be the dominant species in cold water plankton communities in the upper 50 m of the water column of the northwest Atlantic [36]. The abundance of the amphipod may reach a peak in late August, and start to decline in early September [37-39]. The species is known to have a high energy content in September, and in the northwest Atlantic the lipid content increases from 18% to 35% of the dry weight from early August to mid-September [40]. The amphipod has been suggested to be an important link in the food chain between herbivorous zooplankton and marine fish, birds and mammals [ 18,33,37,38,41-45]. The observed consumption of large quantities of P. libellula by the Barents Sea harp seals would seem to confirm the importance of this amphipod species in Arctic food chains. The polar cod is known to be an important prey species in marine Arctic ecosystems [42,46]. In the Barents Sea polar cod spawn in early winter along the west and south coast of Novaja Zemlja [46]. Harp seals are known to feed on polar cod during winter [9,10,13]. Russian studies conducted during the 1930s [13] provided evidence that polar cod was the most important prey of harp seal in eastern parts of the Barents Sea during late autumn. The present stock size of polar cod in the Barents Sea is unknown, but results of annual acoustic surveys conducted since 1986 suggest that it is probably small [47]. Results of recent studies into the feeding habits of harp seals do not suggest that polar cod is currently an important prey species for the harp seal during late autumn and winter. Following the collapse of the Barents Sea capelin stock in 1985/1986 [48], the stock had recovered by 1990-1992 and stock size was comparable to that observed before 1981 [49]. During late summer and autumn the capelin is distributed throughout the central and northern parts of the Barents Sea [27]. If abundant the capelin

252 may represent a potential prey for the seals, and the results of stomach content analyses conducted in late October 1992 tend to support this (Fig. 5). Apparently, there are several fish species that may serve as prey for harp seals during the late autumn and early winter. Given the variability in abundances of species such as capelin and herring, and the currently low level of the Barents Sea capelin stock [20], the catholic taste of the harp seals for different species of fish prey is probably important in ensuring adequate winter feeding in the southern Barents Sea.

Acknowledgements Thanks are due to the crews and field assistants on board the vessels "Johan Ruud", "Polarfangst", Michael Sars", "Selis", "Meridian", "Melshorn", "Jan Mayen" and "Varzuga". The following field assistants participated during the sampling in Varangerfjord and in the White Sea: A. Orjebu, T.O. Rudi, S.A. Kjellqwist, G. Henriksen, Y. Nazarenko and V. Adrianov. Thanks are also due to B. BergflCdt, K.A. Fagerheim, N.E. Skavberg, L. Lindblom, F. Strand, L. Olsen, G. Granaas, S. Hartvedt, N. Stasenkova and S. Slonova for technical and laboratory assistance, and to T. Haug and M. Jobling who commented upon the manuscript and the latter corrected the English. The ecological studies of harp seals were supported by funding from the Norwegian Council of Research, project no. 4001-701.260.

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9 1995ElsevierScienceB.V. All fights reserved Whales, seals, fish and man A.S. Blix, L. WallCeand t3. Ulltang,editors

255

Food consumption of the Northeast Atlantic stock of harp seals E.S. Nord~y, P-.E. MS.rtensson, A.R. Lager, L.P. Folkow and A.S. Blix Department of Arctic Biology and Institute of Medical Biology, University of Tromsr Breivika, Tromsr Norway Abstract. The present report estimates the yearly food consumption of the White Sea population

(--600,000 animals) of the Northeast Atlantic stock of harp seals. Daily gross energy intake (GEI) was determined by studies of captive harp seals (Phoca groenlandica) offered capelin (Mallotus villosus) with an energy density of 9.6 kJ/g (wet mass). Daily food intake averaged 3.3 % of body mass, and GEl was 25,600 kJ/day. Using a chemical marker method (Mn 2+) digestible energy (DE) of capelin was found to be 93% of GEl, while the DE of krill (Thysanoessa sp.) or amphipods was 82%. Data on GEI, DE, diet composition (based on analysis of stomach contents from seals mainly caught in the pack ice) and seasonal changes in energy density of prey, were used to arrive at an annual food consumption of this population of 1 million tonnes. Of this, about 700,000 tonnes is likely to consist of various fish species, of which capelin and herring (Clupea harengus) appear to be the most important. Our calculations may underestimate fish consumption, however, since feeding experiments and preliminary data from satellite telemetry indicate that harp seals rely less on crustaceans than hitherto presumed. Key words:

Phoca groenlandica, food cnnsumption, Barent's Sea

Introduction

Harp seals in the Northeast Atlantic are divided into two apparent subpopulations, one which breeds in the Greenland Sea (Jan Mayen area) and one breeding in the White Sea of Russia. After breeding and moulting in the pack ice, the populations disperse and probably spend much of the time in open waters of the Greenland and the Barents Seas, respectively, as well as along the northern pack ice edge. In the following, food consumption estimates will be made for the White Sea population only, since there are no recent data on the diet of the Greenland Sea population. The pup production of the White Sea population has been estimated by visual surveys to be about 140,000 pups during the spring of 1991 [1], which indicates a total population size in the order of 600,000 seals (1 year and older) [2]. The rather high number of animals, combined with a mature body mass of 120-150 kg, makes the harp seal an important top predator in the Northeast Atlantic. At the Department of Arctic Biology we have kept harp seals in captivity for several years and during this period, studied food intake and digestibility of different foods to obtain estimates of food consumption on the individual level. The purpose of the present paper is to use results obtained from experiments on captive harp seals, together with current knowledge on the seasonal changes in diet composition and the

Address for correspondence: E.S. Nord~y, Department of Arctic Biology, Breivika, 9037 Tromsr Norway.

256 energy content of prey, to estimate the yearly consumption of various prey groups by harp seals of the White Sea population.

Gross Energy Intake of Captive Seals Since data on the average field metabolic rate and food consumption of free-living harp seals are difficult and expensive to obtain, we have performed a food intake study of captive harp seals for an entire year under changing light conditions which simulates the light regime which harp seals would normally encounter in the wild at 70~ [3]. The great importance of seasonal changes in photoperiod in body mass regulation, appetite and activity levels has previously been demonstrated for other polar species at our department [4-6]. Using four growing harp seals with an age of 3-4 years and an average body mass of 81.3 kg, Lager and co-workers [3] found that food intake varied seasonally from a high level of 5-6% of body mass in August-September to a low level of 1-2% of body mass in April-June, demonstrating the importance of obtaining such measurements throughout the year. Since the energy density of food (--9.6 kJ/g) was continuously monitored, the food intake data could be translated into the average amount of energy ingested, or gross energy intake (GEI), for harp seals during growth. Thus, the average daily GEI per animal was 25,600 kJ/day. Since these measurements were based on medium-sized harp seals that were in an age class probably representing a rather high proportion of the population [7], GEI of these four seals was assumed to be representative of an average, free-living harp seal. Multiplying the all-year average of 25,600 kJ/day with a total population of 600,000 seals, gives an estimate of the total GEI of the population in the order of 5.6 • 1012 kJ/year, if capelin was the only food source. Moreover, if harp seals eat significant amounts of crustaceans, GEI must be adjusted upwards due to a lower digestibility of this prey (DE--82% of GEI), compared with capelin (DE ~92% of GEI) [8].

Diet Composition and Energy Density of Prey In order to convert our estimated GEI into total food intake, one important premise is that seasonal changes in diet composition are known. The 250 stomachs used in this analysis were collected in February-April in Varanger [10,11], during the period February-April in the pack ice off Cape Kanin and in the Pechora Sea [12] and in the period August-October in the pack ice between Svalbard and Franz Josefs Land [13,14]. Thus, almost all our data so far were obtained from seals which resided in the pack ice. The different data for different subareas and seasons were combined to obtain the approximate average yearly % mass contribution from different prey groups. These data suggest that Parathemisto sp. and other crustaceans contribute about 35% of

257 Table 1. The diet composition (as % mass), the average energy density of the prey (in the period 1 January to 31 December) and the % energy contribution of the various prey to gross energy intake (GEI) Prey

% mass

Energy density (kJ/g) wet mass

% of GEl

Parathemisto sp. Herring Capelin Fish, various Pandalus sp. Crustacea Polar cod Sand eel

27.1 20.7 24.2 15.7 4.8 3.0 1.8 2.6

3.9 7.1 6.9 4.9 5.1a 5.0 5.3 6.0

18.8 26.2 29.8 13.7 4.4 2.7 1.7 2.8

aTaken from Keiver et al. [9]. ingested mass of prey, while fish, in particular capelin (Mallotus villosus) and herring (Clupea harengus), contribute the remaining 65% (Table 1). Another critical factor during this process of conversion from GEI to food intake is the seasonal changes in energy density of prey. Many of the important prey groups of harp seals display substantial seasonal changes in energy density (Mhrtensson PE, et al., submitted to Mar Mamm Sci). For example, capelin varies from 4.8 kJ/g (wet mass) in mid-summer to 9.5 kJ/g in late autumn and mid-winter, with a yearly average of 6.9 kJ/g (Table 1).

Food Consumption By combining data on energy density of prey, diet composition, digestibility of the prey and the assumed GEI (Table 1) of a population of the order of 600,000 animals, the consumption of different prey groups was estimated (Fig. 1). These calculations suggest that in the order of 300,000 tonnes of Parathemisto sp., 200,000 tonnes of herring and 250,000 tonnes of capelin are consumed. The total fish consumption is in the order of 700,000 tonnes, while the total consumption of prey, including crustaceans, most likely exceeds 1 million tonnes. To put these numbers into perspective, the estimated food consumption by harp seals was compared with the estimated stock size and the total commercial catch of herring and capelin in 1993 in the Norwegian and Barents Seas (Fig. 2). These numbers suggest that the consumption of herring by harp seals may be at least of the same order of magnitude as the total commercial harvest of herring which was estimated at 8% of the total spawning stock in 1993 in the Norwegian and Barents Sea [15]. Similarly, the estimated consumption of capelin by harp seals may have been about 50% of the commercial take (--586,000 tonnes) in the same area in 1993, when the spawning population was assumed to be 2.2 million tonnes, based on the estimation of the mature stock of capelin during the autumn of 1992 [15]. These numbers show that harp seals may have considerable impact on fish stocks and that it is of

258

0 0 0 '%'"

500

X 09

ttO cO ...,E

to tO 0

>,, (1) t_

n

400 -

[----] Parathemisto sp. Pandalus borealis Crustaceans

300 -

2o0

-

100

-

Fishes

Crustacea, various Herring Capelin IIT[]T~ Sand eel Polar cod Fish, various

[rl]~

0

Prey groups Fig. 1. The estimated mass (1000 tonnes) of various prey species consumed by 600,000 harp seals of the White Sea population in the Northeast Atlantic. The values are based on gross energy intake of captive harp seals, the energy density of prey and the % mass of different prey groups as found by stomach content analysis.

3000

0 0 0

1----] Spawning stock size Commercial catch Harp seal consumption

2500 -

X o~

2000

C C 0

1500 -

-4,--o

O'J o~ t13

-

1000

-

500

-

E 0

83 9- -

Capelin Herring

Fig. 2. The biomass (1000 tonnes) of the spawning population of capelin (3 years +) and herring (3 years +), compared with the total commercial catch of these species [15] and the estimated harp seal consumption (this paper) in the Northeast Atlantic in 1993. To be noted is an additional consumption of capelin and herring by other marine mammals (minke whales in particular) and cod (Gadus morhua). It is emphasised that the biomass of younger age-classes of fish (pre-recruits), which is probably considerable, is not included in the biomass of fish stocks.

259 importance to consider the harp seal population food consumption in future predictions of stock size developments of different important commercial species, like herring and capelin. However, this analysis may be biased due to the fact that most of the stomach material was collected from harp seals from the pack-ice area in the northern and southeastern Barents Sea. As a consequence, the crustacean species Parathemisto libellula, being particularly abundant in close vicinity to pack ice, may be overrepresented in the final food consumption estimates. This suggestion is based on the following arguments. First, extensive feeding experiments have shown that young harp seals which were fed a mixed diet of P. libellula and krill (Thysanoessa sp.), two apparently important crustaceans in the diet, were not able to obtain a positive energy balance [8,16]. In fact, despite access to food ad libitum and a daily food intake of up to 3.7 kg, body mass was lost. After replacing the krill with a diet of capelin, however, an immediate increase in body mass was observed. This pattern of body mass development may be attributed to the rather low energy density and a lower digestibility of crustaceans (M~trtensson P-E et al., submitted to Mar Mamm Sci), compared with capelin [8], and, perhaps, that more energy was wasted during a more timeconsuming feeding behaviour, when eating crustaceans. Secondly, in July 1992 three harp seals were tagged with satellite transmitters to follow their movements after moulting in the Greenland Sea (Folkow and Blix, unpublished). The tags were attached to the fur on the back of the seals, and since these often swim on their backs, locations could only be obtained while the seals hauled out on ice-floes. Initially, all seals stayed within the pack ice. After a few days, however, contact was lost with all seals, in some cases for several months, before they were again relocated along the northern pack-ice edge. We believe that the seals stayed in open water at high sea when they were "incommunicado". This pattern of alternating haul-outs in the pack-ice and long travels somewhere in the open waters was observed throughout the study period (July-April), suggesting that the search for food was mainly concentrated on open waters where fish is more abundant. Based on these findings we conclude that a future priority research area is to obtain unbiased information about the autumn and winter distribution and dive behaviour of harp seals by use of satellite telemetry, in order to improve our underst