Forest Diversity and Management (Topics in Biodiversity and Conservation)

Forest Diversity and Management

TOPICS IN BIODIVERSITY AND CONSERVATION Volume 2

The titles published in this series are listed at the end of this volume.

Forest Diversity and Management

Edited by

David L. Hawksworth and Alan T. Bull

Reprinted from Biodiversity and Conservation, volume 15:4 (2006)

123

A C.I.P. Catalogue record for this book is available from the library of Congress.

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Contents

Forest Diversity and Management Introduction

1

D. CLOSSET-KOPP, A. SCHNITZLER and D. ARAN / Dynamics in natural mixed-beech forest of the Upper Vosges

3–33

OSWALDO TÉLLEZ-VALDÉS, PATRICIA DÁVILA-ARANDA and RAFAEL LIRA-SAADE / The effects of climate change on the long-term conservation of Fagus grandifolia var. mexicana, an important species of the Cloud Forest in Eastern Mexico

35–47

OLIVARIMBOLA ANDRIANOELINA, HERY RAKOTONDRAOELINA, LOLONA RAMAMONJISOA, JEAN MALEY, PASCAL DANTHU and JEAN-MARC BOUVET / Genetic diversity of Dalbergia monticola (Fabaceae) an endangered tree species in the fragmented oriental forest of Madagascar

49–68

HÉLÈNE GONARD, FRANÇOIS ROMANE, IGNACIO SANTA REGINA and SALVATORE LEONARDI / Forest management and plant species diversity in chestnut stands of three Mediterranean areas

69–82

FRIEDRICH PATRICK GRAZ / Spatial diversity of dry savanna woodlands Assessing the spatial diversity of a dry savanna woodland stand in northern Namibia using neighbourhood-based measures

83–97

BÄRBEL BLEHER, DANA USTER and THOMAS BERGSDORF / Assessment of threat status and management effectiveness in Kakamega Forest, Kenya

99–117

MASASHI OHSAWA and TAKUO NAGAIKE / Influence of forest types and effects of forestry activities on species richness and composition of Chrysomelidae in the central mountainous region of Japan

119–131

ANDREAS HEMP / The banana forests of Kilimanjaro: biodiversity and conservation of the Chagga homegardens

133–157

M.G.P. TCHOUTO, M. YEMEFACK, W.F. DE BOER, J.J.F.E. DE WILDE, L.J.G. VAN DER MAESEN and A.M. CLEEF / Biodiversity hotspots and conservation priorities in the Campo-Ma‘an rain forests, Cameroon

159–192

R. KINDT, P. VAN DAMME and A.J. SIMONS / Tree diversity in western Kenya: using profiles to characterise richness and evenness

193–210

GERHARD LANGENBERGER, KONRAD MARTIN and JOACHIM SAUERBORN / Vascular plant species inventory of a Philippine lowland rain forest and its conservation value

211–241

WEIBANG SUN, YUAN ZHOU, CHUNYUAN HAN, CHUNXIA ZENG, XIAODONG SHI, QIBAI XIANG and ALLEN COOMBES / Status and conservation of Trigonobalanus doichangensis (Fagaceae)

243–258

NIALL G. BURNSIDE, DAN J. METCALFE, ROGER F. SMITH and STEVE WAITE / Ghyll woodlands of the Weald: characterisation and conservation

259–278

AI-LIAN ZHAO, XIAO-YONG CHEN, XIN ZHANG and DONG ZHANG / Effects of fragmentation of evergreen broad-leaved forests on genetic diversity of Ardisia crenata var. bicolor (Myrsinaceae)

279–291

M.G.P. TCHOUTO, W.F. DE BOER, J.J.F.E. DE WILDE and L.J.G. VAN DER MAESEN / Diversity patterns in the flora of the Campo-Ma’an rain forest, Cameroon: do tree species tell it all?

293–314

JEAN-REMY MAKANA and SEAN C. THOMAS / Impacts of selective logging and agricultural clearing on forest structure, floristic composition and diversity, and timber tree regeneration in the Ituri Forest, Democratic Republic of Congo

315–337

ALFONSO GARMENDIA, SUSANA CÁRCAMO and OSCAR SCHWENDTNER / Forest management considerations for conservation of Black Woodpecker Dryocopus martius and White-backed Woodpecker Dendrocopos leucotos populations in Quinto Real (Spanish Western Pyrenees)

339–355

STEVEN M. VAMOSI / A reconsideration of the reproductive biology of the Atlantic forest in the Volta Velha Reserve

357–364

ALESSIO MORTELLITI and LUIGI BOITANI / Patterns of rodent species diversity and abundance in a Kenyan relict tropical rainforest

365–380

J. LUIS HERNANDEZ-STEFANONI / The role of landscape patterns of habitat types on plant species diversity of a tropical forest in Mexico

381–397

JANE HERBERT / Distribution, habitat and Red List status of the New Caledonian endemic tree Canacomyrica monticola (Myricaceae)

399–406

GRACE NANGENDO, HANS TER STEEGE and FRANS BONGERS / Composition of woody species in a dynamic forest–woodland–savannah mosaic in Uganda: implications for conservation and management

407–435

JÖRN THEUERKAUF and SOPHIE ROUYS / Do Orthoptera need human land use in Central Europe? The role of habitat patch size and linear corridors in the Bialowiez·a Forest, Poland

437–448

BENIGNO GONZÁLEZ-RIVAS, MULUALEM TIGABU, KARIN GERHARDT, GUILLERMO CASTRO-MARÍN and PER CHRISTER ODÉN / Species Composition, diversity and local uses of tropical dry deciduous and gallery forests in Nicaragua

449–467

B. BALAGURU, S. JOHN BRITTO, S.J., N. NAGAMURUGAN, D. NATARAJAN and S. SOOSAIRAJ / Identifying conservation priority zones for effective management of tropical forests in Eastern Ghats of India

469–483

SWEN C. RENNER, MATTHIAS WALTERT and MICHAEL MÜHLENBERG / Comparison of bird communities in primary vs. young secondary tropical montane cloud forest in Guatemala

485–515

T.R. SHANKAR RAMAN / Effects of habitat structure and adjacent habitats on birds in tropical rainforest fragments and shaded plantations in the Western Ghats, India

517–547

Biodiversity and Conservation (2006) 15:1061–1061 DOI 10.1007/s10531-006-0009-7


Springer 2006

Introduction

Topics in Biodiversity and Conservation: Forest diversity and management Natural forests, with a history of ecological continuity extending back for thousands of years, are unrivalled as the treasure store of terrestrial biodiversity on Earth. Yet to date there is no fully comprehensive inventory of the above- and below-ground biota of any forest available, even in western Europe. However, in conserving natural forests, it is reasonable to assume that the myriads of unnamed bacteria, fungi, insects, mites and nematodes present will be safeguarded along with the trees, provided that the forest structure is maintained. But forests are also a key player in the global carbon cycle, and so in the maintenance of the composition of the atmosphere crucial to Life as we know it. To endanger and clear forests is at the peril of future generations, but as so many peoples depend on forests for food and wood, the issue of how forests can be used sustainably, in a way that protects the full spectrum of organisms they contain, has to be addressed. Sadly, the compensatory planting of new forests, whether for exploitation or conservation, does not fully address the need; the trees, other plants and vertebrates may be secured by such approaches, but the full soil biota, and complete spectrum of organisms associated with a natural forest are unlikely ever to be regained. This compilation of peer-reviewed papers, drawn from researchers around the world, examines many different aspects of forest diversity and management. They consider forests in diverse locations, including Australia, Cameroon, China, France, Kenya, the Philippines, Poland, Uganda, and the UK. The forest types considered vary from banana forests, savannah forests, and tropical rainforest to the much revered ancient oak forest of Bialowieza in Poland. The emphasis is on the trees themselves, including effects of logging, changes in management practices, and climate change. In some cases the consequences of forest disturbance or destruction on other plants, birds, or vertebrates are reported for particular forests. Given the wide range of topics brought together here, this collection should be of particular interest to those involved in teaching forest conservation and management, and requiring a cross-section of current work in the field. DAVID L. HAWKSWORTH The Yellow House, Calle Aguila 12, Colonia La Maliciosa, Mataelpino, Madrid 28492, Spain. E-mail: [email protected]

[1]

Biodiversity and Conservation (2006) 15:1063–1093 DOI 10.1007/s10531-004-1874-6


Springer 2006

-1

Dynamics in natural mixed-beech forest of the Upper Vosges D. CLOSSET-KOPP*, A. SCHNITZLER and D. ARAN LBFE, University of Metz, Campus Bridoux, rue du Ge´ne´ral Delestraint, 57070 Metz-Borny, France; *Author for correspondence (e-mail: [email protected]) Received: 10 February 2004; accepted in Revised form 22 July 2004

Key words: Mixed beech forests, Age-structure, Architecture, Soil, Light regime, Stand history Abstract. Forest dynamics were analysed in the Upper Vosges mountains of north-eastern France in two reserve areas, Frankenthal-Missheimle (FM) and Grand Ventron (GV), located in the Ballons des Vosges Natural Regional Park (Parc Naturel Re´gional des Ballons des Vosges). Two plots of 3000 m2 each were established in mixed beech woodlands located just below sub-alpine beech forests for long-term monitoring. The main aim of the study was to interpret how the different species populations in mixed-beech woodlands in the Vosges grow and interact over the long term, and to determine the disturbance history. The study combined vegetation description, dendrological and structural data, architectural descriptions and drawings and light distribution and soil analysis. Historical information was also taken into consideration. Soils in the two plots showed available phosphate P values > 0.14 g kg
1, indicating good levels of phosphorus supply for plants, except for A1/C horizon (1Va soil) which corresponds to a medium-fertility soil. However, soils were found to be shallow because of the slope, a factor that may limit water availability for adult trees and seedlings. As the canopy (composed of existing trees) consists of shade trees, the growth rates for seedlings and saplings (potential trees) depends on the canopy architecture: when growing in sunlit gaps, saplings reach full daylight (canopy height) in less than 100 years. When developing in shade (suppressed state), saplings may need up to 150 years before reaching full daylight. Alternating periods of rapid and slow growth explain why some trees present a wide range of stem diameters and ages in the area leading up to the canopy (some trees are more than 300-years-old), in contrast with the relatively homogeneous height classes distribution, indicating suppression periods. Trees in the FM and GV plots were found to have different growth rates. Both study plots developed with similar past disturbance events, the two most important being at the beginning of the 18th century. In addition, the forests were regularly affected by smaller disturbances until present. Because of the spatial heterogeneity and large range of ages represented, the forest stands within the two natural reserve areas are presently considered to be the bestpreserved sites in the upper Vosges, but their situation near the timber line prevents them from becoming models for forest management at lower altitudes.

Introduction Unmanaged forests are the last representatives of the pristine landscapes of Europe. Unfortunately, they have practically disappeared from European forest panels, with a total of only 3 millions hectares (i.e. 1.7% of the total forest area; COST Action E4 1999). These forest relics are located primarily in remote, inaccessible areas, in unproductive regions, hunting reserves or along frontier borders (Peterken 1996; COST Action 4 1999; Motta et al. 2002; [3]

1064 Schnitzler 2002). Apart from Russia, where old-growth forests may reach up to 20,000 km2 (Sittler et al. 2000), they are small patches (a mean range of 20– 100 ha) within a larger landscape patchwork of managed forests and various land uses. For example, France, with 15 million hectares of metropolitan forest cover, possesses the third largest woody domain in Europe, but only 0.2% (300 km2) of this total are natural forests (Vallauri and Poncet 2003). Most of these are concentrated in mountain regions (the Vosges, the Jura, the Pyrenees and the Alps). Studies have demonstrated that these vestiges cannot remain completely independent of their managed surroundings, and are unable to preserve their potential biodiversity (Helle and Ja¨rvinen 1986). Pollen diagrams have, however, demonstrated that even small ‘virgin’ forests remain stable in composition over hundred of years (Bradshaw and Holmqvist 1999). The main causes of such stability and resilience are the high complexity in structure and architecture associated with the complexity of biotic interactions, which lead to remarkable resistance to climatological events (White 1978; Franks and McNaughton 1991). All remaining natural forests urgently need protection in order to preserve their cultural and scientific value, to protect their wildlife and genetic diversity and to ensure sites for basic research in ecology. They also provide the necessary reference data for applied research in forest management and environmental monitoring (Leibendguth 1959; Peterken 1996). The present study looked at several aspects of the forest dynamics and biodiversity of mixed-beech woodlands of the ‘Parc Naturel Re´gional des Ballons des Vosges’ (PNRBV) (Upper Vosges, north-eastern France). The PNRBV has two natural reserves within its boundaries: the Grand Ventron (GV), created in 1995 covering over 1647 ha, and the Frankenthal–Missheimle (FM), created in 1989 with over 746 ha. Their respective elevations (GV: 720– 1204 m; FM : 690–1363 m) correspond to the submontane (400–800) and montane belts (800–1100 altitude). Both reserves harbour typical, often endangered, plant communities (Schwoehrer and Despert 1999; Schwoehrer 1999; Untereiner et al. 2002). The impact of human activity has been significant since the French Revolution and includes logging, local fires and the planting of non-indigenous spruce since the 1850s (Garnier 1994, 1998). Ungulate densities are also closely linked to human activity and their populations have increased considerably during the 20th century (ONF 2000; Heuze´ 2002). One of the main objectives of the creation of the PNRBV, within the framework of which the present research was carried out, was to contribute to the conservation and sustainable management of the forests through basic and applied research and the development of innovative methodologies. For this reason, our research focused on long-term, forest-monitoring plots situated in strictly protected woodlands of the FM and GV natural reserves which still include small stands of nearly natural woodlands (Gilg 1997). The aim of the study is: (i) to propose and innovate a sampling protocol for long-term studies,

[4]

1065 (ii) to interpret how different species populations of Vosgian mixed-beech woodlands grow and interact over the long-term in light of their disturbance history, (iii) to propose a diagnostic of forest naturalness.

Materials and methods Study sites The Vosges form a long ridge with a continuous crest line, linked to the west and to the Rhine valley to the east by steep slopes. In the southern Vosges, the crest oscillates from 1000 to 1425 m. The climate is oceanic (1600 to 3000 + mm rainfall; mean annual temperature of 4 C above 1000 m). The hercynian bedrock is mainly composed of granite and metamorphic rock, partially covered with morainic material. They include biotitic granite rock, locally porphyroid, with acid plagioclase (An10-20), K-Feldspar, quartz and some apatite (Mansuy 1992). Soils range from acidic browns to podzols (Souchier 1971; Bonneau et al. 1978). The mixed-beech woods found between 600 and 1000 m are part of the Fagion alliance. The main plant community is the Luzulo–Fagetum (Meusel 37) which includes two sub-associations (group Vaccinium myrtillus; group Festuca altissima, Oberdorfer 1992) typical of Central European mountains and hills north of the Alps (Ellenberg 1988; Oberdorfer 1991, 1992; Bogenrieder 2001). The three plant communities include the same tree species (Fagus sylvatica, Abies alba, Acer pseudoplatanus, Sorbus aucuparia) and shrubs (Rubus tereticaulis, Rubus idaeus, Lonicera nigra). Beech (Fagus sylvatica) occupies a central position in the ecology of the Vosgian forests, outcompeting the other tree species. Silver fir (Abies alba) is regular but suffers from browsing (Heuze´ 2002). Acer pseudoplatanus is competitive in shallow soils found in rocky habitats. Spruce (Picea excelsa)is rare, despite the importance of neighbouring plantations. Ground-level flora is dominated by Vaccinium myrtillus, Luzula luzuloides, Deschampsia flexuosa and Prenanthes purpurea. The GV and FM reserves are 8 km apart (Figure 1). They are close (8– 15 km) to a third natural reserve, the Guebwiller (700–950 m), which also includes some stands of nearly natural woodlands (Renaud et al. 2000).

Permanent plots Two plots measuring 3000 m2 (50 · 60 m, i.e. 30 quadrants of 10 · 10 m) each were selected within the less disturbed mixed-beech woodlands of the two reserves. Stand coordinates were referenced by means of a global positioning [5]

1066

Figure 1. Location of FM and GV natural reserves in the Upper Vosges.

system (GPS) (GV: latitude 4827¢N; longitude 665¢E; the plot is situated near the ‘Grand Ventron’ farm; FM: latitude 4830¢N ; longitude 710¢E: the plot is situated below the ‘Trois-Fours’ farm). Metallic boundary markers were buried (forced in the soil) in the ground in order to pursue the study over the very long term. The FM plot is located between 900 and 950 m while the GV plot is 50 m higher (950–1025 m), very close to the multi-stemmed beech forests that form the timber-line (Carbiener 1966). Both plots face East. Plot GV is characterized by a succession of steep slopes (50–60%), and flatter (5–30%), moister zones while the FM plot exhibits more regular slopes, averaging 65%. Plot FM is adjacent to a large, permanent gap resulting from the accumulation of boulders of glacial origin. The fieldwork started in 2000 and ended in 2003.

Soil data Soil-units were defined according to local topography, regeneration patches and humus type characteristics. For each soil-unit, one representative profile was described (horizons, colour, texture, structure, stoniness, root development). All horizons of the representative profiles were sampled, with special attention to the first few centimetres (A11 horizon) where seed germination takes place. In some cases, A11 horizons were also sampled within one soil-unit, with regard to different slopes, regeneration or humus characteristics. [6]

1067 In 2002, five soil units were defined in the GV plot. In 2003, three soil-units were identified and sampled in the FM plot. All samples were air-dried, passed through a 2 mm sieve and then analysed for chemical characteristics (analysis performed by INRA Laboratory for Soil Analysis, Arras, France): residual moisture content at 105 C, organic carbon and total nitrogen (dry combustion method), ‘available’ phosphorus (extracted by H2SO4 and NaOH; Duchaufour and Bonneau 1959), and exchangeable cations (based on cobalthexamine method, Orsiny and Remy 1976). Calculations were performed for C/N ratio, cation exchange capacity (CEC: sum of exchangeable cations), base saturation (ration of exchangeable ‘basic’ cations – Ca2+, Mg2+, K+ and Na+ – to CEC), and exchangeable Mg/Al and Ca/Al ratios.

Stand characteristics Ground flora The ground flora coverage was identified and the coverage of each species estimated, (Braun–Blanquet cover coefficient were converted in percentage cover). Tree seedling densities (height < 130 cm with d.b.h less than 10 cm) were identified and quantified per 10 · 10 m quadrant (30 per stand). Size variables and architecture Tree height, stem diameter and crown area are variables of a tree’s architecture, and an expression of its growth strategy (Halle´ and Oldeman 1970; Halle´ et al. 1978; Oldeman 1979, 1990; Oosterhius et al. 1982). Tree height yields information about ecological conditions and growth strategies: the harsher the climate, the shorter the trees. Tree height distribution is also related to regeneration processes: theoretically, in continuously regenerating stands, the number of individuals in each height-class is expected to follow an exponential decline in numbers from shorter to taller trees. Curves are modified under shady canopies due to the stagnation in growth for much of the tree’s life cycle. In these cases, stem diameter increases when height progress is nearly at a standstill (Peters 1992). Clear allometric relationships exist between these variables. The hf/H ratio (k) compares the height of the tree trunk (hf) up to the first main fork to the total height (H). The inversion point k, where the architectural tendencies are inverted (the axes become smaller and smaller, terminating at the periphery of the crown), is important in forest stand diagnosis: the higher the inversion point (i.e. the shallower the crown depth), the higher the competition with neighbouring trees. Growth strategy can also be interpreted using tree architecture. Architecture corresponds to the visible, morphological expression of hereditary growth development. For tree species, orthotropic versus plagiotropic axes, the ability to reiterate (i.e. the capacity to partially or totally repeat hereditary architec[7]

1068 ture in the same tree, by stimulation of resting meristems, Oldeman 1979) may explain differences in size variables. Fagus sylvatica grows according to Troll’s model with plagiotropic differentiation in all axes. The flattened and highly organized leaf layers (monolayers), as well as the plagiotropy are necessary for intercepting light over large surfaces. Beech is also very flexible, forming shoots of different lengths in response to environmental conditions (Halle´ and Oldeman 1970; Nicolini 1997; Roloff 1999). Acer pseudoplatanus follows Rauh’s model, characterized by orthotropic axes and faster growth than the beech. However, Acer pseudoplatanus is a monolayer, which increases its ability to intercept sunlight (Halle´ et al. 1978). Abies alba follows Massart’s model, characterized by a specialized plagiotropic organization of the branches that confer them high individual survival in the lower forest storeys (Edelin 1977). Architectural criteria also allow us to define the different phases of tree development. Oldeman (1990) has defined three main phases (‘potential tree’, ‘tree of the present’ and ‘tree of the past’), considered as the principal social states in the forest ecosystem. Potential trees and trees of the present can be seen as two distinct phases in the growth of a tree. In potential trees, the growth in height is relatively more important than growth in stem diameter and crown extension. Potential trees may grow faster under the high light levels found in canopy gaps (‘released-growing trees’) or be suppressed by shading from canopy trees. After successive intervals of suppression and released growth, many potential trees reach a minimum height above which they cannot be suppressed. In this case, they develop their architecture by reiteration and reach the tree of the present phase. The transition between these two growth phases is gradual, depending on the site and the forest architecture. To distinguish the two steps in our study, an empirical threshold was set at the height of 20 m, which corresponds to the minimum needed to no longer be suppressed on the steep slopes of the Vosges (suppression is visible when trees present shallow crowns and reiterations along the trunk, and the h/d.b.h. ratio is greater than 100). Tall tree species that have gone beyond this threshold are considered full-grown. For our purposes, living and dead trees of more than 10 cm d.b.h. and height >1 m 30 were identified in the plot and measured (diameter at breast height, total height, height of the main fork. Each (living or dead) tree was carefully drawn according to its exact proportions and morphology. Crown area was defined using four perpendicular directions, including one facing the slope. Tree architecture was represented visually by vertical (six, 10 · 50 m parallel drawings per stand, oriented towards the slope) and horizontal drawings (one for living and one for dead trees in each plot). Relationship between size variables and age The age distribution of the trees depends on the plot history (past and present natural disturbances, impact of historical practices). Stem diameter growth depends on altitude, light intensity, temperature, water and nutrient supply, neighbouring trees, possible pollution as well as the period in the tree’s life [8]

1069 cycle (‘potential’ versus ‘present’). Thus, tree ring analysis is useful for determining the influence of ecological conditions within the particular context of the Upper Vosges. The ring widths of 156 trees (93 in the GV plot and 63 in FM, including beech, silver fir and Acer pseudoplatanus were noted. Two cores were extracted at breast height from each tree, one up-slope and one down-slope. Tree-ring widths were measured using a computer-assisted device (Becker et al. 1995) and cross-dated against previously existing site chronologies (data from INRA, Champenoux), in order to correct for missing or duplicate rings. Stem diameter growth was calculated by dividing core length by the total number of ring widths. Growth rate comparisons were done at each site for each state and each species using non-parametric Mann–Whitney U-tests (Statistica software).

Canopy geometry and light distribution Since tree growth, architecture and size variables are largely explained by light dynamics (e.g. Chazdon and Pearcy 1986; Denslow et al. 1990; Vester 1997), variations in light distribution at points within the canopy were also studied. Relationships between the tree, the forest and the light level in old-growth forests have been studied by Koop (1989). The methodology deals with the architectural parameters of the canopy (canopy geometry) that are commonly used in the literature (i.e. gap fraction and foliage area index LAI), and the light variables (i.e. direct and diffuse solar radiation transmitted throughout the canopy). The simultaneous treatment of canopy geometry and distribution of incident light (PAR, understood as QPAR, quantum irradiance, expressed in mol m
2 d
1, Varlet-Granchet et al., 1989) was studied using hemispherical canopy photography. For each plot, 25 canopy photographs were taken in each 100 m2 section, 10 m apart from each other, at 50 cm above the ground, in overcast conditions. Because some photographs were of poor quality, the final raw data included only 21 photographs for GV and 24 for FM. The camera was equipped with a fish-eye lens with a view angle of 180, carefully levelled and oriented for true North. The raw data from each photograph consisted of a matrix with 18 intervals of 5 zenith angles and 24 sectors of 15 azimuth angles which contained the gap fraction. All values were corrected for latitude, slope, orientation and topographic mask in the GLA model (Frazer et al. 1999). Calculations were performed at hourly intervals, then integrated daily, during the vegetative season (June–September). The radiation intercepted depended upon values calculated for each canopy element involved, over the whole hemisphere and along the solar tracks. Hemispherical photographs therefore present spatial auto-correlations with each other. Values found in FM and GV will be compared with data obtained using similar methods in the Guebwiller mixed-beech forest. Data concern one plot [9]

1070 of 1 ha chosen in a nearly natural forest stand growing in deep soil (slope from 20 to 30)(Renaud et al., 2000; Pierrel 2001).

Results Soil characteristics In both plots, soils are dark coloured, stony or gravely, with a fluffy to massive structure and are finely textured (coarser with depth). Soils are shallow. FM soils show less variability in type than those in GV. Soils belong mostly to the Rankosol (Ranker) type, except for 2V soil which belong to Alocrisol and 5V soil which presents hydromorphic features (located in bench slope). Humus type ranges from oligo-mull to hemi-moder (Brethes et al. 1992). Chemical analysis (Table 1) shows a humose trend, with relatively high organic carbon content, particularly for the 5V soil due to waterlogged conditions. Less organic matter accumulation is observed in FM soils which may be related to better biodegradability of organic materials. In both stands, rather low C/N ratios, from 14.9 to 17.7 in uppermost horizons (A11), accounted for a rapid evolution of plant material added to soil and good nitrogen nutrition for trees. C/N ratios increasing with depth (1Va, 4V and 1F soils) probably indicate a cryptopodzolisation process, morphologically hidden by the humose character of soils. Available phosphorus obtained using the Duchaufour and Bonneau (1959) method is a good indicator of soil fertility. All samples showed available P2O5 values > 0.14 g kg
1, indicating a good phosphorus supply for plants, except for A1/C horizon (1Va soil) which corresponded to a medium-fertility soil (Bonneau 1995). No notable differences were observed in regeneration spots, or between GV and FM plots. Soils show a medium cation exchange capacity (CEC), mostly related to organic matter content (organic carbon), decreasing with depth and lower in FM soils. Low base saturation was always observed in depth, while it exceeded 35% in uppermost horizons in 4V and 3F soils and reached 60% in 2F soil, due to active nutrient cycling. Among basic exchangeable cations, Ca generally predominates over Mg and K (low Na). Similarly, among acid exchangeable cations, Al largely predominates over H (low Mn), except in 1Va, 1Vd, 4V and 2F soils’ uppermost horizons showing a higher H proportion, probably related to cryptopodzolisation. A plentiful supply of Al on exchange sites is known to disturb Mg and Ca supply. The lowest Mg/Al and Ca/Al ratios were found in all deeper horizons (Bw, A1/C and C) except 2F soil, suggesting a possible Mg and Ca deficiency. On the other hand, all uppermost horizons showed higher ratios, apart from 3V soil (Mg) and 5Va soil (Ca). On the whole, no pronounced differences in soil property features were observed between the two plots. [10]

Table 1. Chemical characteristics of soils in GV and FM plots. Stand

Soil

Horizon

Depth cm

Organic carbon g kg
1

C/N

Available P2O5 g kg
1

Exchangeable cations Ca

2+

Mg

2+

K

+

Na

+

Al

3+

2+

CEC

Base saturation%

Mg/ Al

Ca/ Al

pH H 2O

10.73 7.18 5.70 9.47 11.09 11.63 13.17 8.36 5.39 4.37 12.67 9.52 4.45 18.21 13.77 7.80 13.58 3.74 11.77 5.91 4.50 2.72 9.42 7.46 7.20 5.49

29.0 12.6 3.6 15.9 26.0 20.7 30.7 8.4 6.9 5.9 11.1 9.4 6.3 46.0 21.3 5.6 12.7 8.7 15.4 22.8 6.3 5.2 60.2 23.2 37.9 12.1

0.148 0.064 0.012 0.053 0.088 0.098 0.090 0.026 0.021 0.020 0.027 0.032 0.023 0.216 0.072 0.020 0.041 0.032 0.044 0.056 0.020 0.012 0.495 0.075 0.134 0.035

0.490 0.084 0.008 0.086 0.276 0.237 0.393 0.036 0.024 0.018 0.073 0.032 0.018 1.032 0.222 0.021 0.059 0.038 0.106 0.203 0.022 0.020 2.073 0.225 0.574 0.074

3.6 3.6 4.1 4.3 3.7 3.6 3.9 4.0 4.3 4.5 4.2 4.3 4.5 3.7 3.6 4.0 4.5 4.6 4.3 4.4 4.5 4.7 4.2 4.0 4.2 4.3

+

Mn

H

0.068 0.013 0.006 0.117 0.159 0.036 0.298 0.042 0.027 0.014 0.275 0.092 0.028 0.168 0.028 0.006 0.140 0.011 0.289 0.214 0.033 0.010 0.362 0.036 0.263 0.110

3.56 2.09 0.33 0.55 1.50 3.58 1.60 0.33 0.17 – 0.35 0.30 0.08 3.60 2.66 0.50 0.31 0.16 0.55 0.31 – – 1.39 1.30 0.84 0.31

cmol+kg
1 GV

[11] FM

A11 A12 A1/C A11 A11 A11 A11 A12 Bw C A11 A12 C A11 A12 C A11 A12 A11 A11 A12 A1/C A11 A1/C A11 A12

0–2 2–7 7–30 0–2 0–2 0–2 0–2 2–10 10–25 25–50 0–2 2–12 12–40 0–2 2–10 10–33 0–2 2–20 0–2 0–2 2–15 15–25 0–2 2–10 0–2 2–20

14.84 7.91 5.79 18.48 15.41 16.23 18.03 12.65 6.75 6.61 29.24 17.92 6.15 30.77 19.51 8.06 34.37 5.58 18.30 6.66 4.29 3.25 12.45 8.85 7.87 5.29

17.7 17.0 21.8 15.4 16.4 17.3 17.0 15.1 15.4 15.5 17.2 16.0 14.6 17.7 17.2 21.7 17.6 15.9 14.9 15.9 15.4 17.4 16.6 14.9 16.5 14.8

0.386 0.195 0.125 0.35 0.316 0.289 0.404 0.3 0.184 0.16 0.454 0.365 0.219 0.503 0.404 0.197 0.54 0.254 0.438 0.412 0.208 0.286 0.388 0.317 0.46 0.333

1.95 0.35 0.04 0.63 1.81 1.33 2.84 0.26 0.11 0.07 0.78 0.27 0.07 6.25 1.81 0.14 0.68 0.12 0.97 0.82 0.09 0.05 4.13 0.99 1.93 0.33

0.59 0.27 0.06 0.38 0.57 0.55 0.65 0.19 0.10 0.08 0.28 0.27 0.09 1.31 0.59 0.13 0.47 0.10 0.40 0.23 0.08 0.03 0.99 0.33 0.45 0.15

0.49 0.24 0.07 0.42 0.45 0.44 0.49 0.20 0.11 0.07 0.31 0.31 0.08 0.75 0.46 0.11 0.49 0.08 0.39 0.28 0.09 0.04 0.49 0.34 0.32 0.13

0.08 0.05 0.03 0.07 0.05 0.08 0.05 0.05 0.04 0.03 0.03 0.05 0.03 0.08 0.06 0.04 0.09 0.02 0.05 0.02 0.02 0.02 0.06 0.07 0.03 0.05

Data in g kg
1 of dry matter, except for available P2O5 in g kg
1 of air-dried soil: below detection limits.

3.99 4.17 5.16 7.29 6.54 5.61 7.23 7.28 4.83 4.10 10.65 8.23 4.06 6.06 8.16 6.86 11.41 3.24 9.12 4.05 4.18 2.57 1.99 4.40 3.37 4.41

1071

1Va 1Va 1Va 1Vb 1Vc 1Vd 2V 2V 2V 2V 3V 3V 3V 4V 4V 4V 5Va 5Va 5Vb 1F 1F 1F 2F 2F 3F 3F

1072 Stand characteristics Density and spatial patterns Plot GV is more crowded (322 stems ha
1) than FM (275 stems ha
1), but FM volume values were more important (677.7 m3 ha
1 and 949 m3 ha
1 for GV and FM respectively)(Table 2). The dominant species is beech, which accounts for 66% in GV and 70% in FM. Multi-stemmed beech trees were also more important in GV. Abies alba and Acer pseudoplatanus are relatively minor components of the stands, accounting for 18.6 and 13.3% respectively in GV, and 18.1 and 9.4% in plot FM. Acer pseudoplatanus typically occupies rocky, sunnier sites. Sapling density is higher in FM than in GV. In plot FM, the dominant species is Acer pseudoplatanus (42%), followed by Abies alba (32.6%) and Fagus sylvatica (23.7%). Beech is largely dominant in the GV plot, representing 80% of the total number of saplings. Silver fir development is limited by ungulate predation and its sensitivity to frost and drought. Picea excelsa and Sorbus aucuparia are rare. Woody regeneration is strongly clumped in several ellipsoid patches at gap margins, extending towards the slope (Figure 2a, b). The biggest gaps in both plots are caused by rocky areas where tree regeneration is difficult. These gaps are mainly colonised by herbaceous species: Luzula luzuloides, ferns in GV; Festuca altissima, Oxalis acetosella, Galeopsis tetrahit, small woody species (Rubus tereticaulis, Rubus idaeus, Vaccinium myrtillus). Tree and forest architecture Because of the steep slopes, nearly all the trees present asymmetrical shapes: crowns develop marked supplementary axes (reiterations, Oldeman 1974) on trunks, and secondary axes facing the other end of the valley, while there are no secondary, supplementary axes on the opposite side (Figure 3a, a1, b, b1). Asymmetry is particularly developed in Fagus sylvatica and Acer pseudoplatanus, very flexible species which reiterate their initial architectural model easily. Trees of the present reiterate more often than potential trees because they have sufficient energy for crown expansion. Asymmetrical shapes are explained by insertion points k which are low (0.32–0.42) on one side of the trunks and high on the opposite side. There is strong inter-penetration of crowns laterally between the middle and upper-third of tree height, while foliage is much less dense above and below. Potential trees often present triangular crown shapes (suppressed trees growing below healthy, shading trees of the present). To compensate for the small foliar volume, some potential trees have developed small reiterations along the trunk. Firs exhibit many kinds of traumas, including loss of major branches and double forks resulting from loss of apical buds (frost, wind). In plot GV, 23.4% of beech trees are multi-stemmed, with 2–14 trunks of different dimensions. Most trunks have reached the canopy, with some big trunks reaching more than 30 m high. Within one type of individual, the range in height varied from 0 to 6 m, with stem diameters varying from 3 to 16 cm. In [12]

Table 2. Densities and volumes of tree species in FM and GV. Number saplings 4

Number, Fagus sylvatica

[13]

GV 8983 7184 FM 52300 12395 4 stems ha
1; height < 1m30; d.b.h. < 4 cm Number Number, living trees 1 Fagus sylvatica GV 322 213 FM 275 193 Number Number, dead trees 1 Fagus sylvatica GV 63 16 FM 16 0 1 2 3 4 5

Number, Abies alba

Number, Acer pseudoplatanus

Number, Picea excelsa

Number, Sorbus aucuparia

592 17049

1194 22122

18 680

18 52

Number, Abies alba 43 50 Number, Abies alba 47 16

Number, Picea excelsa 6 26 Total volume dead trees 2, 4 23.3 24.8

Total volume living trees 2 677.7 949

Basal area living trees 3 22.8 53.3

Stems ha
1, d.b.h. > 4 cm. m3 ha
1 based on the calculation formula of volume used in Renaud et al. 2000. V = diameter2 · height · 0.35 for coniferous and V = diameter2 · height · 0.5 for broad-leaved tree. m2 ha
1. Dead trunks of multi-stemmed beech trees excluded.

1073

1074

Figure 2. (a) Crown projection map (50 · 60 m) in FM (shaded areas represent regeneration). Fa for Fagus sylvatica; Ac for Acer pseudoplatanus; Ab for Abies alba. (b) Crown projection map (50 · 60 m) in VN (shaded areas represent regeneration) Fa for Fagus sylvatica; Ac for Acer pseudoplatanus; Ab for Abies alba.

[14]

1075

Figure 3a. Examples of vertical profile (10 m · 50 m) in FM (tree species and age are represented for potential trees and trees of the present. (Fa: Fagus sylvatica; Ab: Abies alba; Ac: Acer pseudoplatanus).

general, one or two were big and healthy, one to three smaller and the remaining trunks (big or small) dead. The proportion of dead trunks increased as the total number of trunks increased. Size-distribution, social status and age-distribution In both plots, tree height distribution presented a simple, global architecture with foliage concentrated in the canopy:canopy trees (i.e. trees of the present) accounted for 60% of the registered trees in FM and 68.8% in GV. In spite of a regular, evenly spaced distribution of trees of the present, canopy stratification [15]

1076

Figure 3a. (Continued)

was rather complex, with the imbrication of three main layers: 20–25 m, 25– 30 m and some emergents between 35 and 40 m. Plot GV had a nearly equal number of trees between 20–25 and 25–30 m (Figure 3a, a1), while FM included more trees in the upper canopy (Figure 3b, b1). Size-distribution is not related to age-distribution, indicating that shadetolerant species cohorts can survive for long periods in a suppressed state. Correlations are better for older trees in the upper parts of the canopy; i.e. [16]

1077

Figure 3b. Examples of vertical profile (10 m · 50 m) in GV (tree species and age are represented for potential trees and trees of the present. (Fa: Fagus sylvatica; Ab: Abies alba; Ac: Acer pseudoplatanus).

maximum ages are closer to sizes: 40 m high and 111 cm d.b.h. for a 345-yearold silver fir, 29 m high and 41 cm d.b.h. for a 214-year-old beech; 23 m high and 54 cm d.b.h. for a 231-year-old Acer pseudoplatanus (Table 3). Most trees of the present had a rotten heart, particularly the Acer pseudoplatanus. However, their foliage and axes were well-developed, without any sign of bark or leaf loss. Trees can be considered as reaching the stage ‘of the present’ at various ages. In general, potential trees in FM reach the canopy earlier than those in GV: a range of 48–162 years in FM compared to a range of 102 (very rare)–223 years in GV. Thus, under good light conditions, averages of 2.2 mm growth per year [17]

1078

Figure 3b. (Continued)

were recorded for three young 45- to 52-year-old beeches in FM. In this plot, potential trees and trees of the present also had similar growth rates for stem diameter while in GV, trees of the present grew significantly (p < 0.001) faster than potential trees (Table 4). These data indicate that suppression was more marked in GV, which limits correlations between stem diameter growth and age (Figure 4). For beech trees, the Spearman correlation coefficient (Rs) is 0.48 while the correlation is above 0.65 in FM. The silver fir presents similar tendencies (Rs = 0.71 in FM; 0.29 in GV). Average growth rates of suppressed beech trees in GV are only 0.3– 0.6 mm per year and 0.7 mm for silver fir. This explains why some GV saplings may be rather old: 119 years for a 4.5 m high silver fir; 135 years for a 7.5 mhigh beech. One potential beech was still in a suppressed state at 215 year of age. Multi-stemmed beech trees present a broad range of ages (from 20 to 78 years) within one individual. [18]

1079 Table 3. Ranges of stem diameter and age for beech, silver fir and sycamore in FM and GV. FM plot n

GV plot

Range of DBH (cm)

Range of ages (years)

n

Range of DBH (cm)

Range of ages (years)

Potential trees < 10 m Fagus sylvatica Abies alba Acer pseudoplatanus

6 3 2

8–11 11–79 5–7

54–102 30–59 21–68

6 2 1

5–19 6 10

41–135 37–119 55

10–20 m Fagus sylvatica Abies alba Acer pseudoplatanus

4 3 1

13–32 21–30 67

35–52 54–80 77

5 14 1

16–32 14–54 11

119–160 149–226 66

Trees of the present 20–30 m Fagus sylvatica Abies alba Acer pseudoplatanus

13 1 5

32–76 45 30–73

146–193 135 117–162

36 4 14

29–64 48–70 10–57

102–321 107–193 78–231

30–40 m Fagus sylvatica Abies alba Acer pseudoplatanus

18 6 1

45–83 57–111 54

150–281 132–345 154

8 2 14

29–54 62–64 10–57

147–223 101–200 78–231

Correlations between stem diameter and age of Acer pseudoplatanus trees were significant for GV (0.87). For FM, Acer pseudoplatanus trees are too rare to be analysed. Dead trees Dead trees (trees of the past) represented 11.2 and 3.1% of the total volume of trees in GV and FM, respectively (Table 2). Most of them were silver firs. In FM no death was recorded among beech trees. Dead trees generated only very small gaps because they were rarely very large: average stem diameters ranged from 10–80 cm. The three dead silver fir trees analysed in GV (d.b.h of 13, 13.6 and 25 cm) died at 79, 89 and 191 year of age respectively. Two beech trees (d.b.h of 25 and 22 cm) died at 145 and 149 years respectively. These trees were either snapped off at different heights (from 2 to 24 m) or uprooted. Dead trees presented different degrees of rot. Standing dead trees had woodpecker holes and were often infected by Fomes fomentarius. Tree establishment Tree distribution by species (Fagus sylvatica, Abies alba and Acer pseudoplatanus) and age-class (Figure 5) in FM and GV indicates a pattern of establishment and mortality during the last 350 years. There was a peak in establishment between 1800 and 1840 in FM (a total of 46 trees in 40 years). In [19]

1080 Table 4. Stem diameter growth for beech, silver fir and sycamore in FM and GV. Potential versus present in each plot Potential n

Present Growth rate

n

Growth rate

FM Fagus sylvatica Abies alba Acer pseudoplatanus

10 6 3

1.37 1.6 1.3

31 7 6

1.47 1.7 1.26

NS NS NS

GV Fagus sylvatica Abies alba Acer pseudoplatanus

11 16 2

0.64 0.83 0.59

44 6 12

0.95 2.02 0.96

*** *** NS

11 16 2

Growth rate 0.64 0.83 0.59

** ** NS

44 6 12

0.95 2.02 0.96

*** NS NS

Potential versus potential per plot FM n 10 6 3

Growth rate 1.37 1.6 1.3

Present versus present per plot Fagus sylvatica 31 Abies alba 7 Acer pseudoplatanus 6

1.47 1.7 1.26

Fagus sylvatica Abies alba Acer pseudoplatanus

GV n

***p < .001 **p < .01 *p < .05 NS: not significant

GV, the peak occurred 20 years earlier, between 1780 and 1820 (a total of 62 trees). Beech was the colonizing tree in more than 50% of the cases. After 1840, recruitment was continuous but weak (approximately 2–4 trees per 20 yearperiod except in 1920–1940 in FM). Before 1780–1800, trees were very sparse (probably most of them have died since that time): two silver firs born respectively in 1640 and 1680, two beech trees born in 1680 and 1720 and one Acer pseudoplatanus in 1760. Figure 6a, b illustrate age distribution in the two plots. Trees from 100 to 200 years of age were regularly distributed as small groups of similar ages. Trees in the 200- 300-year-old category, which had survived stress, pathogens or windstorms, were scattered as relics among these smaller groups. Trees under 100 years of age were clumped around gaps or at the margins of canopy trees. Canopy geometry and light pattern The two plots presented differences in canopy geometry and light patterns (Figure 7a, b; Table 5). Values from FM indicate a more open habitat than [20]

1081

Figure 4. Age-stem diameter distribution for Fagus sylvatica, Abies alba, Acer pseudoplatanus in FM and GV. Rs: Spearmann correlation coeffiecient, ***: p < 0.001, **: p < 0.01, *: p < 0.05, NS: Not significant

[21]

1082

Figure 5.

Tree establishment in GV and FM.

[22]

1083

Figure 6. (a) Tree age and spatial distribution in FM. (b) Tree age and spatial distribution in GV.

that in GV: canopy openness of 14.6% compared to 11.5% in GV; percentages of total incident light of 14.5% compared to 6.7% in GV. The differences can also be visualized when considering the horizontal variations in the gap [23]

1084

Figure 7a. Variations in canopy geometry and light condition in FM.

fraction and the total incident light (trans total): (i) for gap fraction, the highest value in FM reaches 22% compared to 16.5% in GV; (ii) for the total incident light, the highest value is 14.5% compared to 6.7% in GV.

[24]

1085

Figure 7b. Variations in canopy geometry and light condition in GV.

[25]

1086

Table 5. Mean and range of values of canopy geometry and of the distribution of light in GV and FM. n [26]

GV

21

FM

24

Mean values Range of extreme values Mean values Range of extreme values

CO%

LAI m2 m
2

Trans direct mole m
2 d
1

Trans direct%

Trans diffuse mole m
2 d
1

Trans diffuse%

Trans total mole m
2 d
1

Trans total%

11.5 7–16.5 14.6 9.8–22

2.6 1.9–3.8 2.5 1.8–3

0.65 0.07–1.54 0.29 0.07–0.91

6.1 0.67–14.3 14.7 5.3–43.3

0.77 0.27–1.65 0.89 0.5–1.84

7.3 2.6–15.8 13.5 5.9–28

1.42 0.36–2.77 1.14 0.71–1.8

6.7 1.8–11.8 14.5 7.7–24.3

1087

Figure 8. Distribution of sun flecks in FM and GV.

The sun fleck distribution points to marked differences between FM and GV (Figure 8). In GV, the distribution is much more patchy, with a high number of small 5–10¢ sun flecks (970 compared to only 201 in FM) in relation to the higher tree density and more complex forest architecture. There were some very long sun flecks in GV (reaching 155¢) which were not observed in FM (maximum: 65¢). These long sun flecks, that originated from the horizon, occurred in only one photo site near the border of the plot. Canopy geometry and PAR values vary according to the solar zenith angles. In GV, gap fraction values ranged from 22.5 to 32.5, while in FM the angles where gap fractions were highest ranged from 2.5 to 22.5. The zenithal angles where incident light was highest was, in both cases, between 32.5 and 72.5 degrees, corresponding to the slope and the topographic mask. Discussion Forest dynamics near the timber-line The examples given in plots FM and GV indicate that mixed-beech forests are spatially and temporally heterogeneous. Both plots show wide variations in stem density, size-class distribution and age distribution that are ecological traits of woodlands in a nearly natural state. Despite a higher stem density, the GV site exhibited a lower volume of stem wood than the FM site. The importance of deadwood and discontinuities in the distribution of saplings and seedlings are also ecological features regularly observed in natural, shady woodlands in Europe and North America (Jones 1945; Leme´e 1978; Mayer and Neumann 1981; Peterken 1996; Schnitzler 2002). These stand characteristics, combined with the remarkable resistance of mixed-beech forests in the FM and GV reserves (as compared to trees in the surrounding [27]

1088 managed forest stands) to the historical december 1999 storm (Schwoehrer personal observation) indicate that forest stands in the two reserves have retained a relatively high degree of ‘naturalness’ (for a detailed discussion of the word ‘naturalness’, see Peterken 1996). This finding is of interest for the interpretation of forest dynamics. The mechanism of gap formation and development is linked to the scale of disturbance events and biotic processes (pathogens, predation, mast years). In the two reserves, mixed-beech forests near the timber-line included chronic, small-scale gap creation associated with the death of single, large trees or small groups of trees. The causes of death among relatively young trees were unknown, but no doubt multiple and thought to have resulted from either natural causes (windbreaks, pathogens, stem exclusion, severe drought) or anthropogenic influences (logging, air pollution). Natural and anthropogenic causes can also have a combined effect. Significant impacts were felt from the severe growth declines of silver fir in 1917–1923; 1943–1951 and 1976–1983 due to a combination of reduced rainfall (particularly in 1976) and increased air pollution (Becker 1985, 1989; Ulrich and Williot 1994). The air pollution is worst on the eastern slopes of the Upper Vosges near the timber line, coming from Eastern Europe (Becker 1985). These events explain the large numbers of dead silver firs in the two plots. Fir decline may be due to direct acid deposition on leaves, as well as acidification processes and nutrient deficiencies in soils with low buffering capacity. In GV and FM stands, however, soils derive from biotitic granite rock, the weathering of which is supposed to partially compensate for Ca and Mg losses. In both plots, soils should thus be less sensitive to acidification than in other parts of the Vosges mountains, like those with base-poor sandstone and acid granite catchments. Some small-scale gaps have probably expanded and coalesced in the past, thus explaining the succession of trees close in age. But there is also evidence of large-scale disturbance events during the period from 1780 to 1840 which might be of anthropogenic origin: there is historical evidence of frequent logging in forests just below summit pastures at the beginning of the 19th century (Garnier 1994, 1998) The deep shade cast by the beech and silver fir canopy explains why potential trees and regeneration are largely confined to gap margins. The tendency for regeneration and ground flora to form ellipsoid patches below canopy trees is typical of forest stands growing on very steep slopes into which sun flecks penetrate obliquely through foliage, thus displaying a multitude of sun flecks far from the gap (Pierrel 2001). Steep slopes play a role in the mutual influences seen between the light regime in understoreys and the canopy architecture. The steeper the slope, the shallower the soil and the lower the stem density (also related to altitude), the higher the canopy openness and penetration of incident light. In the Guebwiller mixed-beech forest canopy, openness averages 9.7% and only 2–15.7% of the total incident light is transmitted within the plot (Pierrel 2001). Light values are more important in GV and still more in FM. In the latter, the proximity of a [28]

1089 permanent gap further increases the lateral penetration of incident light. This explains why seedling densities are higher in FM and Acer pseudoplatanus can regenerate more easily there than in GV. Presence of that specie in FM have a retroactively impact and directly influences light arrival in the underlayer. A lot of Acer pseudoplatanus seeds is probably coming from outside, and thus Acer pseudoplatanus is probably invading the woodlands plots. Better light conditions also explain why potential trees and trees of the present have similar growth rates in FM: potential trees reach the canopy in 100 years, and their growth continues thereafter in the canopy at the same rhythm. In GV many potential trees have grown in shade, and growth rates are lower: the duration of the potential state lasts lasts 200 years or more. When potential trees arrive at full light, stem growth increases with the development of axes and foliage. Beech trees of the present however, grow less rapidly in GV than in FM which suggests less favorable growth conditions, probably due to the higher altitude. The present-day, large range of ages and sizes recorded between trees of the present in both plots can be interpreted as differences in growth patterns during tree development: (i.e. alternating suppressed and released-growing periods). Such growth processes have been recorded in all forests composed of shade trees (Leme´e 1978; Koop and Hilgen 1987; Peters 1992; Korpel 1995; Peterken 1996). Acer pseudoplatanus presents a different strategy based on its rather low tolerance for shade. Young Acer pseudoplatanus trees are numerous in open, rocky areas or at margins, where there is no suppression phase. Harsher conditions near the summits (for example, only 3 months of age at 900–1000 m) explain why canopy trees are smaller than in forests at lower altitudes: only 40 m high for 300-year-old silver firs near the crests as compared to 52–55 m for 180-year-old silver firs in the Guebwiller natural reserve (Renaud et al. 2000). In managed stands in the Vosges, some beech trees have been known to reach 42 m in 120 years (a 2–3 mm annual growth rate between ages 20–70 according to Seynave 1999) compared to only 29 m in height for a 214year-old beech in plots of similar density. In the virgin forest of Dobroc (720– 1000 m altitude, granite), Slovakia, there are 45 m beeches that are 230-yearsold (Korpel 1995). Multi-stemmed beech shape is a particularity of forests growing near the timber line. Such architecture only occurs under conditions of stress (Carbiener 1966; Peters 1992; Closset 2000). These trees are not lower in stature than single-stemmed trunks as suggested by Givnish (1984), but they are more slender than single tree trunk of similar age. Actually, competition between genetically identical trees has an impact of the lower volume of stem wood. They form large, very stable individuals in the canopy because a multistemmed growth form ensures better mechanical stability for the tree (ClossetKopp and Schnitzler (2000b), thus improving resistance to windthrow. Multi-stemmed individuals form clusters of genetically similar stems, with the

[29]

1090 potential for separate existence. This explains why stems may have different sizes and growth rates as related to age and social state. At the present stage in the evolution of the two plots, old trees are very rare. The three trees which are more than 300-years-old discovered in the plots are the only ones recorded in the upper Vosges to date, but clearly more studies could be done on this subject. The absence of very old trees differentiates forests of the Upper Vosges from other virgin mixed-beech forests in Europe where there are silver fir trees more than 400-years-old, and many more beech trees above the age of 350 (Mayer and Neumann 1981; Korpel 1995; Cenusa 2001; Schnitzler 2002). Given the present day composition of potential tree, we can predict that beech will dominate the canopy.

Objectives and priorities of woodland nature conservation in the upper Vosges Forest stands in the two reserves represent lesser-managed stands in the Upper Vosges, but human impacts have nonetheless been multiple, and often irreversible. Remnants of more natural forest stands are located near the summits and on steeper slopes, an inaccessibility which limits the data needed for a comprehensive analysis of forest dynamics. The present-day surface of strictly protected forests is also too small and too intermixed with managed forests and open landscapes to serve as reference points for management principles, because they are not representative enough of a completely pristine landscape. Given their rarity in Western Europe, these small areas must however be regarded as of utmost importance as a class of woodlands for nature conservation, research and education. A worthy objective of long-term conservation efforts would be to re-create more substantial examples of missing types of mixed-beech forests in the upper Vosges, and in the meantime, to leave unmanaged the remaining forests located in natural reserves.

Acknowledgment We gratefully acknowledge the Parc Naturel Regional des Ballons des Vosges for financial support through the study project from C. Schwoehrer. We are also much indebted to J.L. Dupouey for his invaluable assistance in dendrology and the logistical support of his laboratory (INRA Champenoux). We also wish to express their gratitude to Y. Despert, L. Domergue, C. Kieffer and P. Behr who have contributed core and data sampling.

References Becker M. 1985. Le de´pe´rissement du sapin dans les Vosges. Quelques facteurs lie´s a` la de´te´rioration des cıˆ mes. Revue Forestie`re Franc¸aise 37: 281–287.

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Biodiversity and Conservation (2006) 15:1095–1107 DOI 10.1007/s10531-004-1868-4


Springer 2006

-1

The effects of climate change on the long-term conservation of Fagus grandifolia var. mexicana, an important species of the Cloud Forest in Eastern Mexico OSWALDO TE´LLEZ-VALDE´S*, PATRICIA DA´VILA-ARANDA and RAFAEL LIRA-SAADE Laboratorio de Recursos Naturales, Unidad de Biologı´a, Tecnologı´a y Prototipos, Facultad de Estudios Superiores Iztacala UNAM., Av. de los Barrios 1, Los Reyes Iztacala, Tlalnepantla, C.P. 54090, Estado de Me´xico, Me´xico; *Author for correspondence (e-mail: [email protected]; phone: +01-55-56-23-11-27; fax: 01-55-56-23-12-25) Received 23 July 2003; accepted in revised form 22 July 2004

Key words: BIOCLIM, Bioclimatic modeling, Climate change, Cloud forest, Fagus, Sierra Madre Oriental Abstract. We examined the effects of climate change on the future conservation and distribution patterns of the cloud forests in eastern Mexico, by using as a species model to Fagus grandifolia Ehr. var. mexicana (Martı´ nez) Little which is mainly located in this vegetation type, at the Sierra Madre Oriental. This species was selected because it is restricted to the cloud forest, where it is a dominant element and has not been considered for protection in any national or international law. It is probably threatened due to the fact that it plays an important social role as a source of food and furnishing. We used a floristic database and a bioclimatic modeling approach including 19 climatic parameters, in order to obtain the current potential distribution pattern of the species. Currently, its potential distribution pattern shows that it is distributed in six different Mexican Priority Regions for Conservation. In addition, we also selected a future climate scenario, on the basis of some climate changes predictions already proposed. The scenario proposed is characterized by +2 C and
20% rainfall in the region. Under this predicted climatic condition, we found a drastic distribution contraction of the species, in which most of the remaining populations will inhabit restricted areas located outside the boundaries of the surrounding reserves. Consequently, our results highlight the importance of considering the effects of possible future climate changes on the selection of conservation areas and the urgency to conserve some remaining patches of existing cloud forests. Accordingly, we believe that our bioclimatic modeling approach represents a useful tool to undertake decisions concerning the definition of protected areas, once the current potential distribution pattern of some selected species is known.

Introduction The cloud forests represent one of the most interesting biological systems in the Neotropical region (Luna et al. 1999). They are usually rare, vulnerable and threatened in the world. Its northern distribution limit is the Sierra Madre Oriental, in the state of Tamaulipas, Mexico (Briones 1991) and its southern one reaches Argentina (Webster 1995). [35]

1096 In Mexico, the cloud forests are characterized by being island-like or archipelagic. In other words, they are arranged in isolated patches that usually bear a rich flora, with many endemic species (Rzedowski 1996; Luna et al. 2001). In the last years, the interest for studying the cloud forests, in particular their species richness and conservation, has been raised (Churchill et al. 1995). The reason for this interest is based on the high rates of deforestation and loss of cloud forests due to the introduction of cultivars, especially coffee (Moguel and Toledo 1999), but also to its irrational use for other agricultural activities, as well as for forestry and cattle farming purposes. It is recognized that these forests are threatened all over the world and that the damage that they have suffered is irreversible, due to their high disturbance vulnerability (Luna et al. 1988; McNeely et al. 1995). Fortunately, many of these forests are restricted to inaccessible sites in the mountains and consequently, they are still present and reasonably well conserved. In contrast, those located in places where human being has access have been drastically transformed to secondary pasture and cultivated lands. A few former studies have attempted the identification of priority areas for the conservation of the Mexican cloud forests, using Parsimony Analysis of Endemicity and other biogeographic approaches (Morrone and Crisci 1995; Morrone and Espinosa 1998). Even though these studies have highlighted the importance and need to protect the cloud forests, they have not considered either the probable effects that the climatic change might cause in their future survival, conservation and distribution patterns, nor the proposal of some general conservation strategies to be undertaken in the coming years. We believe this information is very relevant, in order to focus our efforts and resources to undertake accurate long-term conservation actions that can assure the survival of these unique plant communities. In particular, we decided to use Fagus grandifolia var. mexicana as our study model, due to its restricted distribution to the cloud forests. Although, this taxon has been also treated as F. mexicana (Lo´pez and Cha´zaro 1995), F. grandifolia Ehrh. var. mexicana (Martı´ nez) Little (Little 1965; Alca´ntara and Luna 2001), F. grandifolia Ehrh. (Johnston et al. 1989), or even as the subspecies Fagus subsp. mexicana (Shen 1992) that has not yet been published, we recognize the former as the accepted name. In accordance with the fossil record, Fagus grandifolia was present in eastern Asia during the late Oligocene and in western North America, including Alaska, during late Oligocene and early Miocene. However, its current distribution pattern is restricted to eastern North America (Canada and United States) and small patches of Mexico (Tamaulipas, Hidalgo, Veracruz y Puebla). The latter represent relictual areas of a former extensive cloud forest of Fagus grandifolia (Pe´rez 1994). Fagus grandifolia Ehrh. var. mexicana used to be a dominant and common tree representative of some of the Mexican cloud forests (Williams et al. 2003). Some of these cloud forests are restricted to the Sierra Madre Oriental, from the state of Tamaulipas in northeastern Mexico to the states of San Luis [36]

1097 Potosı´ , Quere´taro, Hidalgo, Puebla and Veracruz in central-eastern Mexico. In addition, we suspect that the species might be also present in the state of Oaxaca (Figure 1), but further fieldwork should be done to prove it. Even though, Fagus grandifolia var. mexicana is restricted to the cloud forests and plays an important social role, as a source of food and for furnishing activities (Malda 1990), it has not been considered as either a rare, threatened or endangered species (Vovides et al. 1997; Oldfield et al. 1998; Williams et al. 2003). However, some authors have already suggested the species rareness (Malda 1990; Lo´pez and Cha´zaro 1995). In particular, Perez (1994, 1999) considers that the species is endangered at the national level. He estimates that the total number of individuals of the species existing at the present is below 20,000. He also points out that the largest and most heterogeneous, genetically speaking, population is located at the state of Hidalgo, where 50% of the total number of individuals estimated for the country is located in this area. In addition, all these authors have highlighted the lack of nation and international laws for protecting and/or conserving the species.

Figure 1. Model of the potential distribution of Fagus grandifolia var. mexicana, on relationship to the known records. On the right corner the potential distribution of the species in the state of Oaxaca is shown. [37]

1098 Some recent data documenting the wild populations status of the species have been generated, especially in the states of Tamaulipas, Hidalgo and Veracruz (Williams et al. 2003). In some sites the species is considered extinct, whereas, in other places there are still some small patches of what used to be a cloud forest of Fagus grandifolia. So far, the species has not been recorded in the cloud forests of Quere´taro, which is a neighbor state of Hidalgo and San Luis Potosı´ and bears similar environmental conditions for hosting the species. Probably the absence of Fagus in Quere´taro is due to physiographic differences as suggested by Cartujano et al. (2002). However, it might be also possible that the species has been misidentified due to its morphological similarity to Carpinus sp., Ostrya sp. or Ulmus sp., as has been suggested by Lo´pez and Cha´zaro (1995). Thus, the purpose of this work is to undertake a comprehensive review of the current situation of the cloud forests in eastern Mexico by using Fagus grandifolia var. mexicana as our species model. Consequently, we attempted to undertake the following actions: (1) to document the current recorded distribution of the species in Mexico; (2) to obtain the potential distribution patterns of the species; (3) to assess the effects that the potential distribution pattern of the species might have, under a climatic change scenario; (4) to evaluate the role that the Protected Natural Areas and the Priority Regions of Mexico will be playing for the long-term conservation of cloud forests; (5) to propose a general strategy for attempting the conservation of the oriental Mexican cloud forests. Accordingly, the approach of this work includes the utilization of bioclimatic models that enable to explain the current situation of the eastern cloud forests of Mexico, on the basis of the potential distribution pattern of a representative species (Fagus grandifolia var. mexicana) that is used as a model. In addition, we present an attempt to assess the future distribution of the cloud forests, using the species data, once a predicted scenario due to climatic change is included (Te´llez and Da´vila 2003).

Methods The plant geographic distribution information that we used in this analysis was obtained from the database of the World Information Network of Biodiversity (REMIB) (http://www.conabio.gob.mx/remib/doctos/remibnodosdb.html). The herbarium data were obtained from the National Herbarium of Mexico (MEXU), from 29 specimens that beard geo-referenced information (i.e. complete latitude, longitude, and elevation). The taxonomical identification of the specimens was undertaken by Drs. Shen Shung-Fu and Kevin Nixon who are important specialists of the Fagaceae. On the other hand, the information concerning the vegetation structure and ecological attributes of the species that is included in the discussion of this work was obtained from relevant literature (Malda 1990; Lo´pez and Cha´zaro 1995; Luna et al. 2000; Williams et al. 2003). [38]

1099 The bioclimatic modeling approach used in this work was that of the program ANUCLIM (Houlder et al. 2000). The program uses mathematically and statistically interpolated climatic surfaces (digital files in raster format) that were estimated using the information obtained from a standard network of meteorological stations. The climatic surfaces or digital files were generated using thin plate smoothing spline methods in the ANUSPLIN package (Hutchinson 1991, 1995a, b, 1997; Hutchinson and Gessler 1994). These surfaces include long-term monthly mean values of precipitation and temperature from more than 6200 stations (4000 stations including temperature data and 6000 including precipitation data from the same set of stations). The estimated mean errors for those surfaces were between 8 and 13% for monthly precipitation values and about 0.4–0.5 C for temperature values. These errors are similar to those found in the standard meteorological instruments (Nix 1986). We produced a bioclimatic profile for Fagus grandifolia var. mexicana, using the program BIOCLIM. The derivation of the bioclimatic profile was based on selected-simple-matching thresholds. The values for each of the 19 bioclimatic parameters (Table 1), were assessed by a systematic scanning throughout a grid of data points. We used the profile to predict potential distribution pattern of the species. Using the homoclime matching principle, we identified those points on the climate grid, where the climatic conditions were present within the limits summarized in the bioclimatic profile of the species (Booth et al. 1987). We matched the bioclimatic profiles against a grid of data points that contained climatic data from the existing network of stations (bioclimatic

Table 1. Bioclimatic profile of Fagus grandifolia var. mexicana (Fagaceae). Parameter

Minimum–maximum (Mean ± SD)

Annual mean temperature (C) Mean diurnal range (C) Isothermality (2/7) (C) Temperature seasonality (C of V) (%) Maximum temperature of warmest period (C) Minimum temperature of coldest period (C) Temperature annual range (5–6) (C) Mean temperature of wettest quarter (C) Mean temperature of driest quarter (C) Mean temperature of warmest quarter (C) Mean temperature of coldest quarter (C) Annual precipitation (C) Precipitation of wettest period (C) Precipitation of driest period (C) Precipitation seasonality (C of V) (%) Precipitation of wettest quarter (C) Precipitation of driest quarter (C) Precipitation of warmest quarter (C) Precipitation of coldest quarter (C)

13.4–22.2 (16.6 ± 2.09) 8.2–15 (11.5 ± 1.88) 0.54–0.62 (0.59 ± 0.02) 0.61–1.1 (0.78 ± 0.17) 22.4–33.5 (26.3 ± 3.04) 5–9.9 (6.8 ± 1.16) 14.5–24.4 (19.5 ± 2.93) 14.3–24.7 (18 ± 2.72) 12.3–19.5 (14.6 ± 1.59) 15.5–25.6 (19.1 ± 2.48) 11–17.6 (13.4 ± 1.4) 824–2458 (1401 ± 367.19) 46–127 (75 ± 17.59) 0–15 (1 ± 3.67) 66–88 (77 ± 7.28) 418–1164 (691 ± 168.35) 52–201 (109 ± 42.39) 243–647 (397 ± 78.18) 52–239 (126 ± 54.41)

[39]

1100 parameters file). We used a regular grid of 30 arc seconds (0.00083 or approximately 1 km2) of spatial resolution. The geocoding errors were detected using the program ArcView 3.2. In addition, for a more detailed detection of anomalies and potential errors on the bioclimatic profiles, we used the program BIOCLIM (Houlder et al. 2000). Whenever possible, we corrected errors by using a 1:50,000 scale topographic maps. Fortunately, a single anomalous record was detected and removed. Finally, although the magnitude of climate change is uncertain and many different future scenarios have been proposed, we generated just one climate scenario, as proposed by Karl (1998) and some other authors, whom have predicted similar future climatic conditions (Canziani and Diaz 1998; Giorgi et al. 1998; Neilson 1998). The program BIOCLIM was used, in order to set up the proposed future climate change scenario (year 2050), which shows a temperature increment of 2 C and a precipitation decrement of 20%, for any given present point, at the latitude and longitude where the range and the localities of the species are located. For inserting the climate change scenario, we produced a grid of indices in ARCINFO ASCIIGRID format through the BIOCLIM program and the Digital Elevation Model (DEM). The predicted distribution patterns of the selected species were plotted to represent the future potential distribution patterns found, after climate change conditions were entered. In this paper we only present the results of an extreme scenario for assessing the role the Priority Regions for Conservation (PRCs) proposed by CONABIO (Arriaga et al. 2000), will play in the future. The area covered by the potential distribution of the species was calculated with ArcView 3.2 (ESRI 2000).

Results The results obtained suggest that the present distribution pattern known for Fagus grandifolia var. mexicana, is indeed correct and complete, due to the fact that in all cases, the collecting sites fitted within the limits of the potential distribution area obtained in the analysis (Figure 1). Thus, the species is restricted to the Sierra Madre Oriental from the state of Tamaulipas to southern Veracruz, as has been stated by Williams et al. (2003). However, on the basis of the potential distribution assessment of the species, we believe that probably its southern limit might extend to the state of Oaxaca. However, field verifications should be done before we can assure it (Figure 1). The results also point out that the species is restricted to unique climatic conditions in the Sierra Madre Oriental, as it is shown in its bioclimatic profile (Table 1). Its climatic uniqueness represents the specific spots or areas along the Oriental Sierra Madre where it can grow. In other words, although we state that Fagus grandifolia var. mexicana grows along the Sierra Madre, the fact is that it only grows in some specific areas that have a unique combination of climatic attributes and do not grow in others that have other climatic features. [40]

1101 On the basis of the species current potential geographic range, it is evident that it would be distributed in six Priority Regions for Conservation (Arriaga et al. 2000): (1) El Cielo Biosphere Reserve in the State of Tamaulipas, (2) Sierra Gorda-Rı´ o Moctezuma in the State of Quere´taro, (3) Cloud Forest of the Sierra Madre Oriental in the States of Hidalgo, Veracruz and Puebla, (4) Cuetzalan in the State of Puebla, (5) Pico de Orizaba-Cofre de Perote in the State of Veracruz and, (6) Oaxacan northern Sierra. The current potential distribution model of Fagus (the climatically suitable environments for the development of this species), covers about 5800 km2. However, once the climate change scenario was introduced, its potential distribution pattern contracts drastically in more than 66%. The remaining sites that will be suitable for the establishment of Fagus populations will be covering about 1700 km2 or in other words, about 1/3 of the original potential distribution range, including parts of the states of Quere´taro, Hidalgo, Puebla, and a very small portion of the state of Veracruz (Figure 2). Due to its drastic distribution pattern contraction, the remaining Fagus patches will probably coincide with only three of the Priority Regions for Conservation (PRCs 2, 3 and 4) in the states of Quere´taro, Hidalgo and Puebla and none will be present in the state of Veracruz (Figure 2).

Figure 2. Model of the potential distribution of Fagus grandifolia var. mexicana on relationship to the Priority Regions for Conservation (CONABIO), once the proposed climate change scenario was entered. [41]

1102 Discussion Independently of the taxonomical uncertainty of the studied taxon (whether it is a species, a variety or a subspecies), evidently, it is seriously threatened due to its intensive wood extraction, habitat fragmentation and the expansion of the agricultural land use in areas where it naturally grows. In addition, its restricted presence in the cloud forests increases its risk. Currently, the populations of Fagus grandifolia var. mexicana are distributed within the boundaries of at least five Priority Regions for Conservation (Arriaga et al. 2000), although the one from Oaxaca, remains to be proved. From them, the El Cielo Biosphere Reserve represents the only Protected Natural Area that has been officially declared. Consequently, the future protection of the cloud forest, Fagus grandifolia var. mexicana and other animal and plant species of the area is uncertain. The protection uncertainty of Fagus, has already been pointed out by Williams et al. (2003) and mentioned the extinction of the species populations from Teziutla´n, Puebla. In the case of the populations located at the Biosphere Reserve of El Cielo, in the state of Tamaulipas, the agricultural and cattle farming activities have caused a dramatic reduction of the cloud forest. Now, when the climatic change scenario is added to the current situation, the questions to be answered are the following: (1) Is it feasible to have a long-term conservation strategy to protect the cloud forest of the state of Tamaulipas and Puebla? and (2) Where do we have higher probabilities of conserving wellpreserved cloud forests in Mexico? It is clear that the cloud forest of Tamaulipas is already under strong disturbance pressures and consequently its structure and diversity has been already drastically altered. On the other hand, we believe that these communities are currently less modified in the states of Quere´taro and Hidalgo. Now, if in addition, the climate changes occur as it is proposed, the results obtained show that these states also seem to be the adequate cloud forest reservoirs, as has been partially suggested formerly by Luna et al. (2000). Alcantara and Luna (1997), mentioned that Hidalgo represents the state where the cloud forests in Mexico reach their larger extent. They also pointed out that in the central-eastern part of Hidalgo, this plant community still remains in the form of wealthy patches that cover around 100 km2 or more. In these patches, a total of 114 families, 301 genera and 452 species have been recorded by them. Several species of the region are listed in the Mexican Norm NOM-059-ECOL-2000 (Ano´nimo 2000), as vulnerable or in danger of extinction, such as Cyathea fulva, Deppea hernandezii, Nopalxochia phyllanthoides, Magnolia schiedeana, Rhynchostele rosii, Chamaedorea elegans, Psilotum complanatum, Symplocos coccinea and Ceratozamia mexicana (Vovides et al. 1997; Alcantara and Luna 1997). Consequently, Luna and Alca´ntara (2002) emphasize the need to focus the cloud forest conservation efforts in the state of Hidalgo, where many endemic plant species for Mexico have been recorded, such as Bouvardia martinezii, [42]

1103 Carya palmeri, Ceratozamia mexicana, Cyathea mexicana, Dalbergia palo-escrito, Deppea hernandezii, D. microphylla and Magnolia dealbata, among others. In addition, these authors pointed out that some other taxa of the cloud forests that are disjunct between Mexico and the United States show very restricted distribution ranges in Mexico, as in the case of Illicium floridanum, Nyssa sylvatica and Schizandra glabra. In summary, this mixture of hardly known, rare and threatened species is part of a unique natural system that not only bears taxa from different ancestral biotas, but also has high rates of species richness and endemicity, as well as, a very fragile habitat that unfortunately do not have any kind of protection. In the case of the cloud forests of the states of Quere´taro, it is documented that it bears a very rich flora and plant communities. Cartujano et al. (2002), recorded 130 families, 465 genera and 774 species of vascular plants in the cloud forests of the eastern portion of this State. Among this diverse flora, a number of endemics to Mexico or restricted endemics to the Sierra Madre Oriental are included (Cinnamomum bractefoliaceum, Clethra kenoyeri, C. pringlei, Ilex condensata and Inga huastecana, among others), as well as, some species listed as vulnerable, rare, or threatened (Magnolia dealbata, Tilia mexicana, Carpinus caroliniana and Litsea glaucescens, among others) under the Mexican Norm of Endangered Species NOM-059-ECOL2000. Despite the floristic richness and rareness of the cloud forests, timber extraction, livestock grazing and conversion of forest to farmland, which is risking its long-term conservation, represent the main recent disturbance sources of these forests. Unfortunately, precise assessments of the current destruction rate of these forests have not been done (Pe´rez 1994, 1999). Although, in the particular case of cloud forests there is not any former record documenting their probable shifts due to climate change in Mexico. A similar exercise assessing future distribution patterns of some cacti species was done by Te´llez and Da´vila (2003), in a semiarid region of central Mexico. They showed the drastic contraction of some of the cacti species potential distribution patterns, once the climatic changes conditions were included. In summary, in this work we attempted to highlight the importance of including the best biological knowledge available (geographic distribution, vegetation structure and ecology) and a bioclimatic modeling technique to assess the possible present and future role of any reserve or protected area. We also wish to emphasize the need to include Information concerning current and future environmental conditions and the potential distribution patterns of plants and animals, should be included in the decisions for selecting and establishing any reserve or protected area. Due to the methodology and the available data used, it is important to consider that the results obtained in this study might be slightly biased by some unrecorded errors or even by the lack of enough information. The

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1104 inclusion of only 29 records data for the model generation, might seems not representative of the species distribution pattern. However the records used cover, in general terms, all the environmental conditions that theoretically the species might occupy (the geographic, ecological and altitudinal range of the taxon). In addition, natural systems complexity represents a challenge for undertaking a modeling approach. In particular, the evident limitation of the bioclimatic models is the lack of inclusion of information concerning biotic interactions, evolutionary changes, as well as relevant biological processes such as dispersion (Pearson and Dawson 2003). Consequently, the existence of certain degree of errors is probably unavoidable. Also, the bioclimatic data, due to its own nature, shows two kinds of errors: (1) the omission (= the lack of consideration of the space that is occupied by the niche; (2) commission (= the consideration of a space that is not occupied by the niche). Consequently, each algorithm used to model a species ecological niche, has a combination of commission and omission errors (Peterson and Vieglais 2001). Even though, the existence of these errors is recognized, we believe that the bioclimatic modeling represents a useful tool or starting point for understanding the current and potential distribution patterns of animals and plants. Its usefulness has already been proved for some species at certain scales, in which this approach has generated relevant information (Pearson and Dawson 2003). In the case of this study, the model clearly reflects that the spatial climatic resolution used to correlate it to the species records that were included, enabled a precise and solid bioclimatic profile of Fagus. Finally, we believe that with the present biological information, it is feasible and recommendable to carry out a similar exercise for other plant groups. Endemic species and main elements of plant communities should be especially important to be submitted to a bioclimatic modeling. By this means, we can increase the probability of proposing adequate conservation strategies. In the particular case of this study, the results obtained show that through the bioclimatic approach, we can be able to focus in long-term management, planning, and development of new, flexible, and dynamic forms of wildlife and resource conservation (Nix 1986; Lindenmayer et al. 1991; Te´llez and Da´vila 2003).

Acknowledgements We thank to the anonymous reviewers for their valuable comments and corrections. To PAPCA 2002 program of the FES Iztacala UNAM for the financial support to carry out part of this study.

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1106 Hutchinson M.F. and Gessler P.E. 1994. Splines – more than just a smooth interpolator. Geoderma 62: 45–67. Johnston M.C., Nixon K., Nesom G.L., and Martı´ nez M. 1989. Listado de plantas vasculares conocidas de la Sierra de Guatemala, Go´mez Farı´ as, Tamaulipas, Me´xico. Biotam 1: 21–33. Kappelle M., Van Vuuren M.M.I. and Baas P. 1999. Effects of climate change on biodiversity. A review and identification of key research issues. Biodiv. Conserv. 8: 1383–1397. Karl T.A. 1998. Regional trends and variation of temperature and precipitation. In: Watson R.T., Zinyowera M.C., Moss R.H. and Dokken D.J. (eds), The Regional Impacts of Climate Change: An Assessment of Vulnerability. Special Report of IPCC Working Group II. Cambridge University Press, Cambridge, UK, pp. 411–425. Lindenmayer D.B., Nix H.A., McMahon J.P., Hutchinson M.F. and Tanton M.T. 1991. The conservation of Leadbeater’s possum, Gymnobelideus leadbeateri (McCoy): a case study of the use of bioclimatic modelling. J. Biogeogr. 18: 371–383. Little E.L. Jr. 1965. Mexican beech, a variety of Fagus grandifolia. Castanea 30: 167–170. Lo´pez M.L. and Cha´zaro B.M. 1995. Plantas len˜osas raras del bosque meso´filo de montan˜a. I. Fagus mexicana Martı´ nez (Fagaceae). Boletı´ n de la Sociedad Bota´nica de Me´xico 57: 113–115. Luna V.I., Almeida L., Villers L. and Lorenzo L. 1988. Reconocimiento florı´ stico y consideraciones fitogeogra´ficas del bosque meso´filo de montan˜a de Teocelo, Veracruz. Boletı´ n de la Sociedad Bota´nica de Me´xico 48: 35–63. Luna V.I., Alca´ntara A.O., Espinosa O.D.E. and Morrone J.J. 1999. Historical relationships of the Mexican cloud forests: a preliminary vicariance model applying Parsimony Analysis of Endemicity to vascular plant taxa. J. Biogeogr. 26: 1299–1306. Luna V.I., Alca´ntara A.O., Morrone J.J. and Espinosa O.D.E. 2000. Track analysis and conservation priorities in the cloud forests of Hidalgo, Mexico. Div. Distribut. 6: 137–143. Luna V.I., Morrone J.J., Ayala A.O. and Organista D.E. 2001. Biogeographical affinities among Neotropical cloud forests. Plant Systemat. Evol. 228: 229–239. Malda G.B. 1990. Plantas vasculares raras, amenazadas y en peligro de extincio´n en Tamaulipas. Biotam 2: 55–61. McNeely J.A., Gadgil M., Leveque C., Padoch C. and Reedford K. 1995. Human influences on Biodiversity. In: Heywood V.H. and Warton R.T. (eds), Global Diversity Assessment. Cambridge University Press, Cambridge, UK pp. 711–821. Moguel P. and Toledo M.V.M. 1999. Biodiversity conservation in traditional coffee systems of Mexico. Conserv. Biol. 13(1): 11–21. Morrone J.J. and Crisci J.V. 1995. Historical biogeography: introduction to methods. Annu. Rev. Ecol. Systemat. 26: 373–401. Morrone J.J. and Espinosa M.D. 1998. La relevancia de los atlas biogeogra´ficos para la conservacio´n de la biodiversidad mexicana. Ciencia (Me´xico) 49: 12–16. Nix H.A. 1986. A Biogeographic analysis of Australian elapid snakes. In: Longmore R. (ed.), Atlas of the Elapid snakes of Australia. Flora and Fauna. 7: 4–15. Neilson R.P. 1998. Simulation of regional climate change with global coupled climate models and regional modelling techniques. In: Watson R.T., Zinyowera M.C., Moss R.H. and Dokken D.J. (eds), The Regional Impacts of Climate Change: An Assessment of Vulnerability. Special Report of IPCC Working Group II. Cambridge University Press, Cambridge, UK, pp. 439–456. Oldfield S.F., Lusty C. and MacKinven A. 1998. The World List of Threatened Trees. World Conservation Press. Pearson R.G. and Dawson T.P. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol. Biogeogr. 12: 361–371. Pe´rez P.M. 1994. Revisio´n sobre el conocimiento dendrolo´gico, silvı´ cola y un censo de las poblaciones actuales del ge´nero Fagus en Me´xico. Tesis de maestrı´ a (Biologı´ a). Facultad de Ciencias. Universidad Nacional Auto´noma de Me´xico, Me´xico, DF, 146 pp.

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1107 Pe´rez P.M. 1999. Las hayas de Me´xico.Monografı´ a de Fagus grandifolia spp. mexicana. Universidad Auto´noma Chapingo, Chapingo, Me´xico, 51 pp. Peterson A.T. and Vieglais D.A. 2001. Predicting species invasions using ecological niche modeling: new approaches from bioinformatics attack a pressing problem. BioScience 51: 363–371. Rzedowski J. 1996. Ana´lisis preliminar de la flora vascular de los bosques meso´filos de montan˜a de Me´xico. Acta Bota´nica mexicana 35: 25–44. Pe´rez P.M. 1999. Las hayas de Me´xico.Monografı´ a de Fagus grandifolia spp. mexicana. Universidad Auto´noma Chapingo, Chapingo, Me´xico, 51 pp. Shen C.F. 1992. A monography of the genus Fagus Tourn. ex. L. (Fagaceae). Dissertation, City University of New York, New York. Te´llez V.O. and Da´vila A.P. 2003. Protected areas and climate change: a case study of the cacti in the Tehuaca´n-Cuicatla´n biosphere reserve, Me´xico. Conserv. Biol. 17(3): 846–853. Vovides A.P., Luna V. and Medina G. 1997. Relacio´n de algunas plantas y hongos mexicanos raros, amenazados o en peligro de extincio´n y sugerencias para su conservacio´n. Acta Bota´nica Mexicana 39: 1–42. Webster G.L. 1995. The Panorama of Neotropical Cloud Forests. In: Churchill S.P., Balslev H., Forero E. and Luteyn J.L. (eds), Biodiversity and Conservation of Neotropical Montane Forestes. Proceedings of the Neotropical Montane Forest Biodiversity and Conservation Symposium. The New York Botanical Garden, 21–26 June 1993, New York, pp. 53–77. Williams L.G., Rowden A. and Newton A.C. 2003. Distribution and stand characteristics of relict populations of Mexican beech (Fagus grandifolia var mexicana). Biol. Conserv. 109: 27–36.

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Biodiversity and Conservation (2006) 15:1109–1128 DOI 10.1007/s10531-004-2178-6


Springer 2006

Genetic diversity of Dalbergia monticola (Fabaceae) an endangered tree species in the fragmented oriental forest of Madagascar OLIVARIMBOLA ANDRIANOELINA1, HERY RAKOTONDRAOELINA2, LOLONA RAMAMONJISOA1, JEAN MALEY3, PASCAL DANTHU4 and JEAN-MARC BOUVET5,* 1

Silo national des Graines Forestie`res, Ambatobe BP 5091, Antananarivo, Madagascar; 2PCP Foreˆts et Biodiversite´/Fofifa DRFP BP 904, Antananarivo, Madagascar; 3Institut des Sciences de l’Evolution Universite´ de Montpellier II, cc 065, Universite´ Montpellier 2, Place Euge`ne Bataillon, 34095 Montpellier Cedex 05, France; 4PCP Foreˆts et Biodiversite´/Cirad, BP 853, Antananarivo, Madagascar; 5Cirad-Foreˆt, Campus international de Baillarguet TA10/C, BP 5035, 34398 Montpellier Cedex, France; *Author for correspondence (e-mail: [email protected]; phone: +33-467593728; fax: +33-467593733) Received 25 February 2004; accepted in revised form 2 August 2004

Key words: Chloroplast microsatellites, Conservation, Gene flow, Genetic structure, Post-glacial recolonisation, RAPD Abstract. There is an urgent need to maintain and restore a broad genetic base for the management of Dalbergia monticola, a very economically important but endangered tree species in Madagascar. Random amplified polymorphism DNAs (RAPDs) and chloroplast microsatellite markers were used to quantify the genetic variation and to analyse the geographic distribution of diversity. Ten locations covering most of the natural range were sampled. Sixty-three RAPD polymorphic and 15 monomorphic loci were obtained from 122 individuals. Genetic diversity was low and very close among populations and regions. The unrooted neighbour-joining tree exhibited 4 groups, representing 6% (p = 0.000) of the total variation. The greater part of the variance, 81%, was observed within populations. A Mantel test suggested that genetic distances between populations were weakly correlated with geographic distances (R = 0.46, p = 0.12). The three chloroplast microsatellite primers assayed on 100 individuals gave 13 chlorotypes. Most of the populations showed 2 or 3 haplotypes. Haplotype diversity for the total population was equal to HeCp = 0.83 and ranged from 0.00 to 0.80 among the populations. The unrooted neighbour-joining tree exhibited 4 groups corresponding to the four regions representing 80% (p = 0.0000) of the total variation. Genetic diversity varies with regions, the north and south being less variable. Chlorotype distribution, the phylogenetic tree and historical information suggest that putative refugias in the centre-north region originating from the early Holocene could explain the pattern of variation observed today. By combining the results obtained at nuclear and organellar loci, a strategy of conservation based on evolutionarily significant units is proposed.

Introduction The separation from Gondwana, 158–160 million years ago, has led to high endemism in Madagascar recognised as one of the most original in the world (Myers et al. 2000; Briggs 2003). About 80% of the plant species are endemic and the richness of fauna and flora is great. Present patterns of the Malagasy [49]

1110 ecosystem have been determined by numerous factors such as climate change and human practices. Glaciation cycles, and especially the last maximum glaciation, are known to have had a strong impact on Malagasy species distributions (Burney 1996; Gasse and Van Campo 2001). Although known human presence is not very ancient in the island, 2000 years BP, practices such as fire have also markedly influenced the distribution of species, especially since the 15th century (Straka 1996). The highlands would have first undergone the action of fire and today are covered with grass. More recently, over the two last centuries, the oriental forests have decreased dramatically due mainly to ‘‘slash and burn’’ practices. Today, primary vegetation probably still covers about 10% of the original area (Myers et al. 2000), so dense forest has been reduced to a fragmented landscape. In addition, forest exploitation has greatly increased over the last 50 years due to rising demand for wood as energy and saw timber. The combination of fragmentation and overexploitation threatens some economically and ecologically important tree species and a conservation strategy for these forest trees is urgently needed. Much research remains to be done to improve basic biological knowledge but, as a broad genetic base is required to maintain the evolutionary process and to preserve the gene pool, assessment of within-species genetic variation can be a useful tool when starting a conservation strategy (Newton et al. 1999; Cavers et al. 2003). Among methodologies employed to assess variation, those based on molecular markers are widely used with forest tree species (Newton et al. 1999). Random amplified polymorphism DNA (RAPD) is one of the most popular DNA-based approaches (Bekessy et al. 2002). It is the least technically demanding and offers a fast method of providing information from a large number of loci, particularly in species where no study has been undertaken. Moreover, the diversity assessed by RAPD is comparable to that obtained with allozymes or RFLP (Wu et al. 1999; Esselman et al. 2000). There are some limitations, however, owing to their lack of reproducibility, and dominance prevents the distinction between homozygous and heterozygous individuals (Gillies et al. 1997). Chloroplast DNA markers are often used to study genetic diversity and structure, especially in combination with nuclear ones (Viard et al. 2001) and have provided useful information on colonisation and dispersal in plant species. Chloroplasts are maternally inherited in most angiosperms and paternally inherited in gymnosperms, so the level of differentiation is greater than bi-parental inheritance. Chloroplast microsatellite markers (cpSSRs) present high polymorphism and are now frequently used in phylogeographic forest tree analyses (Marshall et al. 2002; Palme´ and Verdramin 2002; Collevatti et al. 2003; Grivet and Petit 2003). Although molecular techniques have been widely used in tree species, no studies have been undertaken for Malagasy tree species. In this study we used both RAPD and cpSSRs to investigate the pattern of variation at the natural range scale of a species of rosewood: Dalbergia monticola Baker. This species is one of the major components of the oriental [50]

1111 forest of Madagascar but, for the reasons mentioned previously, it is threatened in its natural stands. Little research has been undertaken that provides information on ecological patterns and no information is available on genetic variation across the species range. The aims of this study were then (i) to quantify the genetic variation within and between populations using these two molecular markers, (ii) to analyse the geographic distribution of diversity in the natural range, and (iii) to define a conservation strategy based on molecular diversity.

Material and methods Species description – plant material Dalbergia monticola’s natural range extends from the northern part of Madagascar (region of Antalaha 15 latitude south) to the southern part (region of Fort Carnot 22 latitude south) forming a fragmented belt 1000 km long and 100 km wide (see the map in Figure 4). Adult trees frequently reach 20 m in height and 1 m in diameter at chest height. Recognised as a long-lived tree species (over 200 years), the natural populations are situated in two main climaxes: the submontane evergreen seasonal forest and the dense rainforest, of altitude ranging from 350 to 1600 m, mean temperature from 18 to 23 C, and mean annual rainfall from 750 to 2500 mm. Dalbergia monticola reproduces mostly sexually and is mainly insect pollinated, flowering and fruiting from August to November, with some geographical variations. It fruits between July and September. The species is mainly barochorous, but its seeds can also be dispersed by animals such as birds, monkeys, and rodents, although no research has been conducted on this. Ten locations were identified, covering most of the natural range from the north to south of the island (Table 1), to sample the species. Within each location, between 6 and 25 trees were chosen randomly in fragmented forest. Due to the lack of trees in some sampling areas, the minimum distance between two consecutive trees was in some cases only 20 m (see range of distance in Table 1). The trees were generally small, most of them having a total height of 5–20 m and a diameter of 15–30 cm. This observation stresses the lack of adult trees due to the intense exploitation of the species. Five healthy leaves were collected from each tree and dried rapidly in the field using silica gel.

DNA extraction DNA was extracted from dried leaves, following the modified protocol described by (Bousquet et al. 1990). Leaves (100 mg) were ground to a fine powder with a [51]

1112 Table 1. Characteristics of the Dalbergia monticola populations sampled in the natural range. Region Location

N

Latitude Longitude Rainfall (mm)

T Elevation Distancea min–max C (m) (m)

North

12 10 17 11 11 10 7 6 25 20 139

1657¢S 1705¢S 1810¢S 1755¢S 1745¢S 1906¢S 1902¢S 1918¢S 2116¢S 2134¢S

12–28 12–28 12–29 12–29 12–29 10–26 10–26 10–26 17–27 10–26

Tsaramolotra Ampitsongona Centre- Didy north Antsevabe Ambohijahanary Centre Bekorakaka Madiorano Ankeniheny South Ranomafana Tolongoina Total a

4844¢E 4842¢E 4835¢E 4832¢E 4835¢E 4821¢E 4812¢E 4823¢E 4726¢E 4732¢E

750 750 1240 1240 1240 1790–2190 1790–2190 1790–2190 2900 2800

900–1200 900–1200 800–900 800–900 800–900 850–900 800–1100 800–1100 900–1000 800–1200

50–500 50–500 20–50 20–100 50–1500 50–1000 100–1500 100–500 20–200 20–1000

range of distance between two consecutive sampled trees.

mortar and pestle in a 1.5 ml Eppendorf tube under liquid nitrogen. DNA extraction buffer (5 ml) was added (100 mM Tris–HCl (pH 8.0), 20 mM EDTA, 1.4 M NaCl, 1% PEG 6000, 2% MATAB, 0.5% sodium sulphite). The tube was then incubated at 74 C for 20 min. Samples were washed with wet chloroform (CIAA, 24:1) to remove cellular debris and protein. After 15 min of centrifugation at 5000 · g, the liquid phase was transferred to 15 ml tubes. Isopropanol (5 ml) was added and mixed gently to precipitate the DNA. The resulting DNA pellets were resuspended in 400 ll of sterile water overnight at 37 C and stored at
20 C until required.

RAPD methods PCR amplifications were performed in a 20 ll reaction mix with 5 ll of DNA (3 ng/ll), 2 · buffer, 0.2 lM of each primer (OPB7, OPB11, OPN15, OPR15, OPW9, OPW12, OPW13, OPW14, OPX3, OPX6, OPX10), 5 U/ll of DNA Taq polymerase, completed with sterile water. The reaction mixture was overlaid with 40 ll of sterile mineral oil to prevent fluid evaporation. All reactions were performed in Techne Cyclogene. Optimal amplification conditions for RAPDs were 1 cycle of 3 min at 94 C (initial denaturation), followed by 45 cycles of 4 min at 94 C (denaturation), 1 min at 36 C (annealing) and 2 min at 72 C (extension). A final step of 10 min at 72 C ensured full extension of all amplified products. RAPD bands were separated in 1.5% agarose gel, staining in ethidium bromide and visualised by UV transillumination. To reduce errors in comparison of RAPD profiles between different PCR runs, the same 10 individuals were included in all PCR runs. Only RAPD bands that could be unequivocally scored were counted in the analysis. Bands of weight higher than 1700 bp and molecular weight lower than 300 bp were [52]

1113 not used so as to ensure good repeatability of the RAPD process and avoid misscoring.

Chloroplast microsatellite method Three universal microsatellite primers (Ccmp4, Ccmp6 and Ccmp7) described by Weising and Gardner (1999), and 1 tobacco microsatellite (Ntcp9) described by (Bryan et al. 1999) were tested over a subset of the total population. Among the 4 primer pairs tested in a sample of 8 individuals, 3 were polymorphic (Ccmp4, Ccmp6, and Ccmp7). For the primers Ccmp, PCR amplifications were done in a 8 ll reaction mix with 2 ll of DNA, 2 · buffer, 10 lM of each primer (R and F), 5 U/ll of DNA Taq polymerase, completed with sterile water. All reactions were performed in a ‘‘Stratagene’’ Thermocyclor. Optimal amplification conditions were 1 cycle of 4 min at 94 C (initial denaturation), following by 30 cycles of 30 s at 94 C (denaturation), 1 min at 56 C (annealing) and 1 min at 72 C (extension). A final step of 5 min at 72 C ensured full extension of all amplified products. PCR amplifications were done for the 9 primers in a 20 ll reaction mix with 5 ll of DNA, 10 · buffer, 2 lM of each primer (R and F), 5 mM of dNTPs, 50 mM of MgCl2, 99% of glycerol, 5 U/ll DNA Taq polymerase, completed with sterile water. Amplification conditions are similar, except for the annealing temperature which is 55 C. Bands were separated and visualised in acrylamide gel.

Data analysis In the case of RAPD data, amplified DNA marker bands were scored in a binary manner as either present (1) or absent (0) and entered into a binary data matrix. Each PCR product was assumed to represent a single locus because homology is generally high at the intraspecific level. The frequency of each band and the percentage of polymorphic loci (%P) were calculated in each population. Shannon’s diversity index was P used to assess molecular variation. This parameter, defined as IRAPD =
i¼2 i¼1 pi log2 pi, where pi is the frequency of the RAPD phenotype (presence (1) or absence (0) of the band), is frequently used in the absence of assumptions concerning the Hardy–Weinberg equilibrium (Gillies et al. 1997; Martin and Hernandez Bermejo 2000). It was calculated for each locus and averaged over loci to provide the degree of variation within each population, IRAPDpop. Shannon’s index was also estimated for the whole sample considered as a single population, IRAPDtot. The expected genetic heterozygosity P Her was estimated with the fixation index F equal to zero. HeRAPD ¼ 1
ni¼1 p2i (where pi is the frequency of the allele i in a population), [53]

1114 and the other diversity parameters, number of haplotypes (na), effective number of haplotypes (ne ¼ 1 Pn1 2 , where pi is the frequency of the allele i in p i¼1 i

a population), percent of polymorphic RAPD loci (%P) and their standard errors were calculated with Popgene 1.32. (Yeh and Boyle 1997). For cpSSRs, because of the non-recombining nature of the chloroplast genome, cpDNA haplotypes were treated as alleles at a single locus. Chloroplast haplotype variation within populations was calculated with the same parameters as for RAPD (ICp HeCp ne) with Popgene software version 1.32. (Yeh and Boyle 1997). The genetic structure, for RAPD and cpSSRs, was estimated using analysis of molecular variance, AMOVA (Excoffier et al. 1992), with Arlequin software version 2000 (Schneider et al. 2000). The percentage variance within and among populations were estimated to partition the variation, and the associated P value was estimated with permutation techniques. To illustrate the genetic structure obtained with the two markers, a cluster analysis using the neighbour-joining method was conducted with the software package DARwin 4.0 (Perrier et al. 2003). The matrix of genetic distances was calculated using the AMOVA-derived Fst. The levels of differentiation among populations estimated from AMOVA for nuclear markers (FstRAPD) and chloroplast markers (FstCp) could be used to derive the pollen-to-seed migration ratio, using the following formula of (Ennos 1994): r ¼ mp =ms ¼ ½ðð1=FstRAPD Þ
1Þ
2ðð1=Fstcp Þ
1Þ=½ð1=Fstcp Þ
1Þ where mp and ms are pollen and seed migration rates, respectively. In the case of RAPDs the value is biased, and probably overestimated, due to the amplification of the cytoplasmic genome. The association between geographic and genetic distances was estimated as a Spearman’s rank correlation coefficient (q). The null hypothesis of the association was tested with the Mantel test using Fstat software (Goudet 2001). Minimum spanning networks between haplotypes (each network embedding all minimum spanning trees for a given distance matrix) were computed with the MINSPNET (Excoffier and Smouse 1994), provided with Arlequin software version 2000 (Schneider et al. 2000). The distance matrix between haplotypes was calculated using a distance matrix based on the square of the difference in microsatellite size with the formula Dij ¼

L X

ðail
ajl Þ2

l¼1

where aij and ajl give the allele size in base pairs at the lth locus of individuals i and j, respectively. [54]

1115 Results Genetic diversity and structure with RAPD markers The 15 random primers generated a total of 65 RAPD polymorphic and 13 monomorphic loci ranging in size from 152 to 340 bp. This set of loci is expected to give a good sampling of the total genome and a good assessment of the genetic diversity. The number of bands per primer varied from 1 to 7. Table 2 shows that the diversity parameter for the total population was equal to 0.19 (0.15) and varied from 0.09 (0.16) for the population of Ankeniheni in the centre to 0.19 (0.21) for the population of Ambohijanahary in the centre north. Shannon’s diversity index followed the same pattern of variation, with a value for the total population equal to 0.30 (0.21) and a range between 0.15 (0.24) in Ankeniheni to 0.28 (0.27) in Ambohijanahary. The percentage of polymorphic loci varied from P = 30% in Ankeniheni to P = 65% in Didy (Table 2) and the total population value was 83%. Although differences between populations for na, ne, HeRAPDand IRAPD were marked, they were smaller than the standard deviation. No pattern of variation for na, ne, IRAPD, HeRAPD and %P was observed among the set of populations. For example, the relationship between diversity parameters and latitude was low, and the coefficient of correlation between latitude and IRAPD was equal to R = 0.08, (associated p value = 0.80). When the four regions were compared, no specific trend was observed for the diversity parameter (Table 3). The differences between the parameters were lower than the standard error. The differentiation assessed among populations was marked. The analysis of molecular variance showed that 76% (p = 0.000) of the variation was present

Table 2. Population size (N), number of haplotypes (na), effective number of haplotypes (ne), Shannon’s index (IRAPD), percent of polymorphic RAPD loci (%P), RAPD diversity (HeRAPD) for each population. The standard error of each parameter is given between brackets. Country

Population

N

na

ne

HeRAPD

IRAPD

%P

North

Tsaramalotro Ampisotgoina Ambohijanahary Antsevabe Didy Bekorokaka Madiorano Ankeniheny Ranomafana Tologoina

11 10 11 11 17 10 6 6 21 19 122

1.51(0.50) 1.49(0.50) 1.60(0.49) 1.43(0.50) 1.65(0.48) 1.52(0.50) 1.41(0.50) 1.30(0.46) 1.64(0.48) 1.52(0.50) 1.83(0.38)

1.21(0.30) 1.23(0.31) 1.30(0.35) 1.24(0.34) 1.25(0.29) 1.25(0.32) 1.20(0.30) 1.16(0.29) 1.20(0.24) 1.25(0.34) 1.27(0.27)

0.14(0.17) 0.14(0.17) 0.18(0.19) 0.14(0.19) 0.16(0.16) 0.15(0.18) 0.12(0.17) 0.09(0.16) 0.14(0.15) 0.19(0.19) 0.19(0.15)

0.22(0.24) 0.22(0.25) 0.28(0.27) 0.21(0.27) 0.26(0.24) 0.24(0.25) 0.19(0.25) 0.15(0.24) 0.23(0.22) 0.23(0.26) 0.30(0.21)

51 49 60 44 65 53 41 30 64 53 83

Centre-north

Centre

South Total

[55]

1116 Table 3. Population size (N), number of haplotypes (na), effective number of haplotypes (ne), Shannon’s index (IRAPD), percent of polymorphic RAPD loci (%P), RAPD diversity (HeRAPD) for each region. The standard error of each parameter is given in brackets. Region

N

na

ne

IRAPD

HeRAPD

%P

North Centre-north Centre South

21 39 22 40

1.59(0.49) 1.73(0.44) 1.61(0.49) 1.74(0.44)

1.24(0.30) 1.29(0.29) 1.23(0.30) 1.25(0.28)

0.15(0.17) 0.19(0.16) 0.15(0.16) 0.16(0.16)

0.24(0.25) 0.30(0.24) 0.24(0.24) 0.27(0.23)

59 73 61 74

within populations, 18% (p = 0.000) among populations within region, and 6% (p = 0.000) among regions. As a result, the unrooted neighbour-joining tree obtained with RAPD markers (Figure 1) exhibited three main clusters corresponding to the three regions south, centre and the combination of

Figure 1. Unrooted neighbour-joining tree based on RAPD markers. The four regions are: the South (S), the Centre (C), the Centre-north (C-N), and the North (N). The tree was drawn with the Fst matrix given by analysis of molecular variance (Excoffier et al. 1992) with Arlequin software version 2000 (Schneider et al. 2000). [56]

1117 centre-north and north. The link of Antsevabe with the cluster of the south is difficult to explain. This clustering suggested a good relationship between the geographical distance and the genetic distance. This was partly confirmed by the Mantel test showing a coefficient of correlation between genetic and geographic distances moderately high (R = 0.46), but not significantly different from zero (p = 0.12), this relationship is illustrated in Figure 2a.

Genetic diversity and structure with chloroplast microsatellite markers The three chloroplast microsatellite primers assayed on 100 individuals gave 10 different alleles: Ccmp4, 2 alleles, Ccmp6, 4 alleles and Ccmp7, 4 alleles. The combination of the 3 loci and the 10 alleles gave 13 chlorotypes (Table 4). Except for Ampitsongoina, each population exhibited several haplotypes and generally one chlorotype was predominant. In the total population, the C11

Figure 2. Relation between genetic and geographical distances for the RAPD and chsloroplast microsatellite markers. Matrices of genetic distances were calculated using the AMOVA-derived Fst (Arlequin software version 2000 (Schneider et al. 2000)). [57]

1118 Table 4. Allelic characteristics in base pairs for the three loci, allelic combination of each chlorotype and frequencies of the chlorotypes present in each population and in the total population of Dalbergia monticola. Zone

Population

North

Tsaramalotro

N

Chlorotype

Ccmp4

Ccmp6

Ccmp7

Frequency

7

C3 121 120 152 0.14 C5 122 120 152 0.86 Ampitsongoina 6 C5 122 120 152 1.00 Centre-north Ambohijanahary 10 C1 122 119 151 0.20 C2 122 120 151 0.10 C4 122 119 152 0.20 C6 122 121 152 0.10 C9 122 120 153 0.30 C10 122 121 153 0.10 Antsevabe 11 C2 122 120 151 0.64 C4 122 119 152 0.36 Didy 15 C1 122 119 151 0.07 C2 122 120 151 0.66 C4 122 119 152 0.07 C5 122 120 152 0.07 C9 122 120 153 0.13 Centre Bekorokaka 6 C3 121 120 152 0.17 C8 121 120 153 0.67 C12 121 120 154 0.16 Madiorano 6 C8 121 120 153 0.83 C12 121 120 154 0.17 Ankeniheny 2 C2 122 120 151 0.50 C9 122 120 153 0.50 South Ranomafana 23 C7 122 122 152 0.04 C11 122 122 153 0.96 Tolongoina 14 C7 122 122 152 0.21 C11 122 122 153 0.58 C13 122 122 154 0.21 Frequencies in total population: C1(0.03), C2 (0.19), C3 (0.02), C4 (0.07), C5 (0.13), C6 (0.01), C7 (0.04), C8 (0.09), C9 (0.06), C10 (0.01), C11 (0.30) C12 (0.02), C13 (0.03)

haplotypes exhibited the highest frequency (30%), followed by C2 (19%) and C5 (13%) while others were lower than 10%. Haplotype diversity varied markedly among the populations and ranged from HeCp = 0.00 (Ampitsongoina) to HeCp = 0.80 (Ambohijanahary) (Table 5). For the total population it was equal to HeCp = 0.71. The number of alleles (na), effective number of haplotypes (ne) and Shannon’s diversity index (ICp) followed the same pattern. No clear pattern of variation with latitude was observed when considering the within-population diversity. However, when the regional level was taken into account the results showed that the diversity parameters were higher in the centre-north and centre than in the north and south. For example, HeCp = 0.68 and 0.55 in the centre-north and centre, while HeCp = 0.14 and 0.32 in the north and south. [58]

1119 Table 5. Diversity parameters assessed with chloroplast microsatellite markers for each population and the total population of Dalbergia monticola. Population size (N), number of haplotypes (na), effective number of haplotypes (ne), Shannon’s index (ICp), haplotypic diversity (HCp). Region

Population

N

na

ne

HeCp

ICp

North

Tsaramalotro Ampisongoina Ambohijanahary Antsevabe Didy Bekorokaka Madiorano Ankeniheny Ranomafana Tologoina

7 6 10 11 15 6 6 2 23 14 100

2 1 6 2 5 3 2 2 2 3 13

1.32 1 5 1.86 2.10 2 1.38 2 1.09 2.39 6.10

0.25 0.00 0.80 0.46 0.52 0.50 0.28 0.50 0.08 0.58 0.83

0.41 0.00 1.69 0.66 1.08 0.87 0.45 0.69 0.18 0.98 2.10

Centre-north

Centre

South Total

With cpSSR, the unrooted neighbour-joining tree exhibited four clusters corresponding to the four regions more distinctly than with RAPD data (Figure 3). Ankeniheny from the centre, however, was included in the cluster of the centre-north region, but the poor sample size of this population (2 individuals) can explain this unexpected position. This strong differentiation among populations was confirmed by the analysis of molecular variance. The variation among groups represented 80% (p = 0.0000) of total variation, the within-group variation among populations 4% (p = 0.000) and the betweenindividual variation within populations 16% (p = 0.000).

Phylogenetic relation and geographic distribution of the haplotypes The distribution of the 13 haplotypes across the natural range showed a geographical structure (Figure 4). Some haplotypes were present in contiguous populations and regions but none was scattered among distant populations and regions. The network of haplotypes in Figure 4b shows that haplotype C5 has the highest number of connections and C5 is among the most frequent (13%). Generally, haplotypes that are closely related to each other, differing by a single mutation at a microsatellite repeat, are geographically close. For example, the set of haplotypes C7, C11 and C13 is located in the south, haplotypes C1, C2, C4, C1 and C5 are in the centre-north, haplotypes C8, C9, C12, and C10 are in the centre and haplotypes C3, C5 in the north. This relation of isolation by distance is confirmed by the significant correlation between genetic distance (Fst) and geographic distance (q = 0.57; p = 0.02). Figure 2b shows that this linear relationship increased strongly up to 300 km and then reached a plateau, emphasising that Fst is more or less constant and close to 1 after this distance. [59]

1120

Figure 3. Unrooted neighbour-joining tree based on chloroplast microsatellites markers. The four regions are: the South (S), the Centre (C), the Centre-north (C-N), and the North (N). The tree was drawn with the Fst matrix given by analysis of molecular variance (Excoffier et al. 1992) with the Arlequin software version 2000 (Schneider et al. 2000).

Discussion Genetic diversity of Dalbergia monticola Although the oriental forest is strongly fragmented, the impact of fragmentation due to human agriculture on the dynamics of the genetic diversity within the remaining fragments of Dalbergia monticola is likely to be still limited because it is a very recent phenomenon. It takes several generations before the impact of genetic drift can be observed. However, the natural range of Dalbergia monticola was likely to be wider in the recent past and the second wave of settlement from the 15th century had probably reduced the whole population in Madagascar and consequently the genetic diversity of the species. With polymorphic and monomorphic loci used for calculation, the mean intraspecific genetic diversity of Dalbergia monticola obtained with RAPD markers is smaller than that estimated for other tree species (Bekessy et al. [60]

1121

Figure 4. (a) Geographic distribution of the 13 chlorotypes in the natural range of Dalbergia monticola (the limit of the natural range is represented by the dark line). The size of the circle is proportional to the size of the sample. (b) Minimum spanning network among the 13 haplotypes found in the natural range of Dalbergia monticola. The size of the circle is proportional to the frequency of the haplotype in the total population. The connection length is equal to one for the dark line and two for the dotted line.

2002; Newton et al. 2002; Bouvet et al. 2004). Shannon’s diversity index for these species ranged from 0.42 to 0.65, whereas it is 0.30 (0.21) for D. monticola. Values of expected heterozygosity are similar to those estimated for perennial species growing on smaller islands, but the percentage of polymorphic loci in Dalbergia monticola is higher (Kwon and Morden 2002). Although RAPD markers should be considered with caution as they are not a good predictor of total genetic diversity (Nybom and Bartish 2000), the low values of estimated diversity parameters in this species could be explained by the limited range of the species. An island population contains less genetic diversity than a mainland population (Barett 1998). Although Madagascar is a single large island the isolation from other sources gene could favour the effect of genetic drift. Another factor that can explain the low diversity is a recent and rapid expansion after a bottleneck (Savolainen and Kuittinen 2000). There are few published studies concerning the diversity of cpSSrs in angiosperm tree species. The present study shows, however, that the results varied according to the species and the number of primers used. With 3 primers, 100 individuals distributed in 10 populations, the 13 haplotypes identified in Dalbergia monticola are consistent with previous studies on angiosperms (Palme´ and Verdramin 2002; Grivet and Petit 2003). In addition, the haplotypic heterozygosity HeCp = 0.88 is high compared with estimates for other tree species (Palme´ and Verdramin 2002) and suggest high diversity compared to a number of temperate tree species studied. [61]

1122 Genetic structure and gene flow The distribution of genetic diversity with RAPD markers indicates that most of the variation is present within populations (81%). This can be explained by some biological patterns such as long-lived woody perennials and outcrossed insect pollination species according to Nybom and Bartish (2000). Nested analysis of variance with RAPDs shows that only 6% of the total variation is attributed to among-group variation, 16% to among populations within groups, and 76% among individuals within populations. This is low but significant differentiation among regions and populations is closely related to the correlation (moderately high but not significant) between the geographic and genetic distance. This pattern of isolation by distance is often observed in tree species wind or insect-pollinated when long distances within the natural range are considered (Bekessy et al. 2002), while no significant correlation is seen when short distances are taken into account (Schierenbeck et al. 1997). The distribution of the diversity is very different using chloroplast microsatellites. We note a strong differentiation between regions (80%) and a low percentage of variance within populations (16%). This is a classical result for angiosperm forest trees (Raspe´ et al. 2000) because of the maternal inheritance of the chloroplast DNA, and seeds are dispersed over shorter distances compared with pollen (Echt et al. 1998). In Dalbergia monticola, seed dispersal is mainly barochorous and contributes to this strong structure. The relationship between genetic distance and geographic distance for cpSSr (Figure 2b) confirms this strong structure and shows that above 300 km Fst is close to 1. The different patterns of genetic structure of RAPD and cpSSr can also be viewed through the relative rate of pollen flow to seed flow. With FstCp = 0.84 and FstRAPD = 0.23, the calculation of r showed that gene flow by pollen is 15 times greater than by seeds. The difference is marked but is smaller than for temperate species such as Fagus silvatica (r = 84), Quercus robur (r = 286), Quercus petraea (r = 500) (King and Ferris 1998). This can be explained by the fact that they are wind-pollinated whereas D. monticola is insect-pollinated.

Marker distribution and historical factors One of the most consistent factors that strongly influenced the partition of genetic diversity of tree species over the natural range is the last glaciation period and the subsequent migrations from the refuges (Aide and Riviera 1998; Willis and Whittaker 2000). In Europe (Verdramin et al. 1998), South America (Dutech et al. 2000; Bekessy et al. 2002) and the Sudano-Sahelian region of Africa (Bouvet et al. 2004), the genetic variability of tree species has been analysed in connection with the last glaciation period, 15–20,000 years ago, but no studies, to our knowledge, have attempted to establish this relation in Madagascar. As many tropical regions, the island underwent climate fluctuation during the Pleistocene and Holocene periods, but also the impact of human activities. [62]

1123 Three critical periods in the Quaternary should be mentioned. The first was the Last Glaciation Maximum around 20,000–21,000 years ago which led to an extremely cold and arid climate. According to (Adams and Faure 1997), during this episode the rainforest in Madagascar was limited to the north-eastern part of the country (between latitudes 12 and 16), the high plateaus were covered with ericoid, graminoid and composite-dominated vegetation and the oriental escarpment and coastal plain presented tropical woodland characterised by low, open canopy and usually deciduous trees. Other authors have concluded that tropical montane vegetation belts must have been vertically displaced 900– 1500 m (Burney 1996). Such a cold-driven displacement of vegetation zones would have confined the island’s humid forest zones to the relatively small land area along the east coast, with isolated patches elsewhere (low elevation humid refugia). From the early Holocene (8000 14C years ago) the rainforest occupied a broader zone than today. Early Holocene warming led to a gradual replacement of ericoid vegetation in the mid-elevation with forest in wetter locations of the eastern escarpment and a rise in the level of Lake Alaotra (Burney 1996). Some analyses, based on pollen records in high plateau locations (Lake Itasy, Ankatra, Andasibe) suggested that the rain forest was present up to elevations of 1800–2000 m (Straka 1996). The third period corresponds to human settlements 2000 years ago and the effect of burning which increased the frequency of fire and resulted in disturbance of the too wet or too dry ecosystems which generally support a natural fire regime (Burney 1996). Assuming a limitation of the rainforest in the northern part of the range during LGM, we can hypothesise that this region may be the zone of putative refuges for sub-montane rainforest tree species. The assumption cannot be verified by the results of this study because no sample was collected in the northern part of the range in the latitude 15. However, our study proposes another putative refuge. Generally, the highest diversity regions are considered as putative refuges. The higher diversity in the centre-north region and the decrease in diversity in the southern and northern limits of the natural range (Table 6) suggest that the zone of Didy, Ambohijanahary and Antsevabe could be a putative refuge for this species. Phylogenetic trees based on haplotypes (Figures 4b) and their distribution across the natural range (Figure 4a) also point to a pattern of migration from the centre-north region.

Table 6. Diversity parameters assessed with chloroplast microsatellite markers for the four main regions of the natural range of Dalbergia monticola. Population size (N), number of haplotypes (na), effective number of haplotypes (ne), Shannon’s index (ICp), haplotypic diversity (HCp). Region

N

na

ne

HeCp

ICp

North Centre-north Centre South

13 36 14 37

2 7 5 3

1.17 3.16 2.22 1.48

0.14 0.68 0.55 0.32

0.27 1.44 1.13 0.61

[63]

1124 Haplotype C5 occupies a central position in the phylogenetic trees with the highest number of connections (Figure 4b) and is present in the centre-north region. It is likely to be the ancestor of most of the other chlorotypes which mutate during the northward and southward expansion. A putative refuge in the centre-north region, close to Lake Alaotra, is also supported by the study of Reyes (1993) cited by (Burney 1996) who inferred the Late Quaternary climatic changes in north-eastern and central Madagascar using diatom spectra. In this study, Reyes (1993) indicated that early Holocene warming led to the gradual replacement of ericoid vegetation in the mid-elevation with forest developing in wetter locations along the eastern escarpments and a rise in the level of Lake Alaotra. Human settlement probably started 2000 years ago and the impact on the forest ecosystem of human practices has increased over the last two millennia. The probable effect of burning by humans has been to reduce the natural range of Dalbergia monticola. Without an uncontrolled use of burning, the natural range of Dalbergia monticola would be wider than today. Our study does not allow us to measure this effect. More recently, 50 years ago, the impact of human activity led to a marked increase in fragmentation in the oriental rainforest. Fragmentation is a factor which can seriously disturb the evolutionary process and which has to be taken into consideration in conservation. New investigations are needed to measure its impact on the genetic diversity of Dalbergia monticola.

Consequences for conservation of the species Forest genetic resources in Madagascar constitute an essential component of the biological diversity of forest ecosystems. They supply a wide range of goods and services essential to the life of rural communities and efforts to promote sustainable management are required. Strategies involving genetic studies are still rare and exploration of the genetic diversity of Malagasy trees is expected. Newton et al. (1999) emphasise that intraspecific diversity has become a parameter fundamental to the management of species with the aim of maintaining their evolutionary potential. In this study we used neutral markers to assess diversity. The use of such markers to predict the diversity of adaptive genes or economic traits within populations has to be considered with caution (Lynch et al. 1999). RAPD or cpSSrs are different from genes and may not be a good predictor of gene function diversity or of complex traits. However, molecular markers can give indications of the genetic distance between populations and of their different evolutionary histories (Holsinger 1996), and may indicate a similar genetic structure for morphological traits (Nesbitt et al. 1995). Different genes could have been selected, appeared by mutation or have been eliminated by genetic drift in separated populations which do not exchange genes. Some authors use intraspecific diversity to elaborate conservation strategies (Rajagodal et al. [64]

1125 2000). The concept of evolutionary significant units (ESUs) has received increasing support as providing a rational basis for identifying suitable units for conservation (Moritz 1994; Newton et al. 1999) and has been implemented in forest genetic resources when nuclear and organelle haplotypes are available (Cavers et al. 2003). This approach can be proposed given the methodology and the results of the current study. Chloroplast markers can provide a basis for defining ESUs using the main lineages, their distribution in the natural range and the result showing the differentiation between populations (AMOVA and Mantel test). The set of regions north and centre-north, the centre region and the south region can be identified as ESUs. Significant divergence between these three groups with RAPD markers confirms the identification of the three ESUs. Although pollen gene flow is important, isolation by distance was observed by means of the Mantel test, and a significant genetic differentiation among populations and regions was shown by AMOVA. RAPD markers indicate that most of the variability is present within populations, stressing the importance of gene flow among individuals. This result could help to define a strategy to elaborate ex and in situ conservation stands. To start such a programme, the population should consist of many individual trees selected within a few natural populations within the ESUs to capture a large proportion of variation. Much work is needed to elaborate an optimal strategy. As stressed by Cavers et al. (2003), the best strategy will concord with use of neutral markers and adaptive information (also ecological information), but a first step in the identification of ESUs with molecular markers provides a first practical framework.

Acknowledgements We are very grateful to colleagues from ‘‘Silo National des Graines Forestie`res’’ for assistance in collecting samples, to Alexandre Vaillant and Ce´line Cardi from Cirad-foreˆt for their technical assistance in the laboratory, and to the anonymous reviewers. These results are part of master of science thesis of O. Andrianoelina, which was financially supported by the Millennium Seed Bank (MSB) project, managed by the Seed Conservation Department, RBG Kew. The study was carried out at SNGF, which receives funds from the Malagasy Government, as well as at Cirad-Foreˆt, where laboratory work was done. L. Ramamonjisoa, head of the technique division in SNGF and J.M. Bouvet, head of the Research unit on forest genetic resources, were responsible for supervision.

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Biodiversity and Conservation (2006) 15:1129–1142 DOI 10.1007/s10531-004-3103-8


Springer 2006

-1

Forest management and plant species diversity in chestnut stands of three Mediterranean areas HE´LE`NE GONDARD1,*, FRANC¸OIS ROMANE1, IGNACIO SANTA REGINA2 and SALVATORE LEONARDI3 1

CEFE (UMR 5175), 1919 route de Mende, 34293 Montpellier cedex 5, France; 2Instituto de Recursos Naturales y Agrobiologia de Salamanca, P. box 257 C/Cordel de Merinas 40, E-37071 Salamanca, Spain; 3Dipartimento di Metodologie Fisiche e Chimiche per l’Ingegneria, Facolta` di Ingegneria, Universita` di Catania, viale A. Doria 6, 95125 Catania, Italy; *Author for correspondence (e-mail:[email protected]; phone: +33-4-67613276; fax: +33-4-67412138) Received 6 February 2004; accepted in revised form 31 August 2004

Key words: Castanea sativa, Coppice stand, Diversity index, Functional trait, Grove Abstract. Over many centuries, chestnut fruits had an important role as food, while chestnut wood was used for local purposes. Today sweet chestnut stands are very common around the western Mediterranean Basin, and it is necessary to analyze the dynamic of plant species diversity in different chestnut stand types (groves and coppices) to guide management strategies that will allow the conservation of biodiversity. Our objective was to analyze consequences on plant species diversity of various management strategies in chestnut stands of three Mediterranean areas, Salamanca (Spain), the Ce´vennes (France), and Etna volcano (Italy). We found that plant species diversity is different according to management types; it is higher in groves than in coppice stands. We also demonstrated that Castanea sativa cultivated groves were characterized by small heliophillous therophytes. C. sativa abandoned groves, mixed C. sativa–Quercus pyrenaica coppice stands, Q. pyrenaica coppice stands, and young C. sativa coppice stands were characterized by hemicryptophytes with anemochorous dispersal mode and chamaephytes. Medium and old C. sativa coppice stands (that differ by the shoot age) were characterized by phanerophytes with zoochorous dispersal mode. Human perturbations maintain a quite high level of species diversity. In contrast, the abandonment of chestnut stands leads to homogeneous vegetation with decreasing diversity. One solution could be to maintain a landscape mosaic constituted of diverse chestnut stands modified by human activities (groves, cultivated or abandoned, and coppice stands). This could enhance regional plant diversity.

Nomenclature – Flora Europaea (Tutin et al. 1964–1980)

Introduction Sweet chestnut (Castanea sativa Mill) stands are very common around the western Mediterranean Basin. Over many centuries, chestnut fruits had an important role as food for humans and as feed for domestic animals, while chestnut wood was used for local purposes such as wine barrels, vineyard pegs, tool handles and carpentry (Arnaud and Bouchet 1995). Today, chestnut [69]

1130 stands cover large areas particularly in Portugal, Spain, France, Italy and Greece. Thus, it is necessary to analyze the dynamic of plant species diversity in different chestnut stand types (groves and coppices) to guide management strategies that will allow the conservation of biodiversity and at the same time to optimize productivity and profitability. The characterization of community response to different management types in terms of functional traits appears as a promising tool to achieve this goal (McIntyre et al. 1995; Hadar et al. 1999; Lavorel et al. 1999; Gondard et al. 2003). Indeed, from an ecosystem perspective, species richness (number of species), which is the conventional metric of biodiversity, is not as important as functional trait richness. This approach analyzes the functioning of the ecosystem, and its response to abandonment, by focusing on vegetation description defined by functional traits not necessarily linked with taxonomic attribution (Pillar 1999). Functional traits fall into three biological categories: morphological traits describing aspect, life history traits indicating plant behavior in the environment, and regeneration traits (Lavorel et al. 1997). The use of functional traits for the comprehension and analysis of plant species dynamics in relation with perturbation is clearly demonstrated by many authors (Dı´ az and Cabido 1997; Lavorel and Cramer 1999; McIntyre et al. 1999; McIntyre and Lavorel 2001; Dı´ az et al. 2002; Gondard and Deconchat 2003). Consequently, our objective was to analyze consequences on plant species diversity of various management strategies in chestnut stands of three Mediterranean areas, Salamanca (Spain), the Ce´vennes (France), and Etna volcano (Italy). We hypothesized that, whatever area, species diversity between groves and coppice stands is different essentially according to dendrometric characteristics and management types. Indeed, groves have, in general, large trees with regular pruning, understorey cleaning, etc., and coppices have many shoots without clearing but logging. We assumed that species diversity is highest in groves. We focused on understorey stratum which is sensitive to changes of ecosystem conditions (Pregitzer and Barnes 1982; Strong et al. 1991; Mitchell et al. 1997, 1998) and recognized like a very important component in ecosystem functioning (Host and Pregitzer 1991; Arsenault and Bradfield 1995; Brakenhielm and Lui 1998).

Materials and methods The experiment was carried out in three Mediterranean areas, in the Honfrı´ a forest, located in the southern of Salamanca province in Spain, in the Ce´vennes in southern France, and on Etna volcano in Italy (Table 1). The Honfrı´ a forest is representative of traditional chestnut (Castanea sativa) management over many centuries in Spain, but also a model of possible sustainable management in the future. In this forest, chestnut is considered as a paraclimax species and the deciduous oak (Quercus pyrenaica) as a climax species. Thus, we selected five stands that are representative of this forest: a chestnut cultivated grove, a [70]

1131 chestnut abandoned grove, a chestnut coppice stand, a mixed chestnut-oak stand, and an oak pure stand. In the Ce´vennes, we identified a succession following agricultural abandonment from chestnut cultivated grove to chestnut old coppice stand. Thus, we selected five stages that form the successional gradient: a cultivated grove, an abandoned grove, a young coppice (<25 years old), a medium coppice stand (between 26 and 50 years old), and an old coppice stand (>51 years old). On Etna volcano, the tradition is coppice management and not grove. Thus, we selected five coppice stands (Fornazzo, Trisciala, Balilla, Monte Crisimo, Piano Lepre) that are representative of the study area and differ by their stand characteristics (Table 2). In each stand, we established five 10 · 10 m plots. The plots were contiguous because there was only little area available at the site with relatively homogeneous topographic conditions, and in order to respect 100 m2 plot size minimum. In each plot, we recorded all plant species occurring in the understorey stratum. The plant cover of each species was estimated by the point quadrat method (Gounot 1969), using 100 points, i.e. one point each 10 cm, along a 10 m line traversing each plot. According to previous observations, realized by one of us, 100 m2 plots appeared to be suitable for monitoring this kind of vegetation. Data were collected during June month in 2001 in the Ce´vennes, 2002 in the Honfrı´ a forest, and 2003 in Italy. Moreover, plant species recorded were characterized by functional traits such as plant height and life form that refer to morphology, light tolerance that refers to life history traits, and dispersal mode to regeneration traits (Appendix 1).

Data analyses The criteria to compare stands were species richness (number of taxa per 100 m2) and species diversity (Pielou 1975; Magurran 1988). Among the many diversity indices available, we chose P the Shannon index (H’), which was recommended by Pielou (1975): H0 ¼
i¼1;n ðpi log2 ðpi ÞÞ where pi is the abundance ratio of species (i) in the square, and n is the species number in the square. Forest stands in the three geographical areas are submitted to different silvicultural management and also to contrasting environment and climate conTable 1. Characteristics of the three Mediterranean areas studied.

Altitude (m) Mean annual rainfall (mm yr
1) Mean annual temperature (C) Parent material Soil

Honfrı´ a Forest

Ce´vennes

Etna volcano

Spain

France

Italy

900 1500 11 Schist Cambisol

650 1400 11 Schist Cambisol

1000 1100 12 volcanic ash, lava regosol volcanic

[71]

1132 Table 2. Main characteristics of chestnut stands selected in the Ce´vennes in France, on Etna volcano in Italy and in Honfrı´ a Forest in Spain. Confidence intervals p = 0.05. For each site, mean values in the same column followed by different letters are significantly different. p < 0.05, Mann– Whitney test. Tree age Tree (years) height (m)

Diameter at breast height (cm)

Shoot density Basal area (shoot ha
1) (m 2ha
1)

Site

Stand

Honfrı´ a Forest Spain

C. sativa 90 cultivated grove

11.30 ± 1.3b 18.30 ± 2.1c

295 ± 20a

23 ± 5b

C. sativa 85 abandoned grove C. sativa 70 coppice stand Mixed C. sativa–Q. 60 pyrenaica stand Q. pyrenaica 75 pure stand Ce´vennes Cultivated grove 70 France Abandoned grove 75 Young coppice 16 Medium coppice 39 Old coppice 56 Etna volca Fornazzo coppice 31 Italy Trisciala coppice 28 Balilla coppice 37 Monte Crisimo 26 coppice Piano 27 Lepre coppice

8.90 ± 0.8a 20.40 ± 3.0c

382 ± 30a

19 ± 4a

15.3 ± 1.3c 12.90 ± 1.7b 1892 ± 100b

28 ± 8c

10.7 ± 0.8b

8.90 ± 1.2a 3208 ± 150c

21 ± 5a

12.2 ± 1.0b 11.60 ± 1.5b 2960 ± 125c

27 ± 7c

18.00 ± 1.0a 45.00 ± 7.1a 17.40 11.20 12.40 16.40 17.67

± ± ± ± ±

0.5a 0.8b,c 0.9c 0.5d 0.7a

44.60 ± 11.5a 9.40 ± 1.5b 17.80 ± 7.8c,d 24.00 ± 4.8d 9.20 ± 0.5a

120 ± 45a 440 ± 195a 1040 ± 611b 1080 ± 396b 840 ± 488b 4680 ± 1242a,c

26 ± 18a 45 ± 21a 8 ± 4b 17 ± 14c,d 35 ± 13d 38 ± 8a,b

12.17 ± 0.3a 16.67 ± 2.3a 16.00 ± 0.0a

7.20 ± 0.3b 6020 ± 895a 20.9 ± 1.7c 1140 ± 331b 7.10 ± 1.2d 2900 ± 919c

29 ± 3a 43 ± 8b 24 ± 6c

15.67 ± 3.2a

9.90 ± 0.5a 4180 ± 394d,c 38 ± 1a,b

ditions, so differences are expected between them. However, due to the low number of stands analyzed, we used non-parametric test that allows to work with low size samples. We chose the Mann–Whitney non-parametric test that allows to compare means pairwise (Falissard 1998). In each Mediterranean area, we used Correspondence Analysis (CA) and Canonical Correspondence Analysis (CCA, ter Braak 1987) to quantify the effects of management types with species functional traits. We performed a Correspondence Analysis (CA,Greenacre 1984) of plant species observed on the entire point quadrat set (67 in Honfrı´ a forest in Spain, 41 in the Ce´vennes in France, and 40 on Etna volcano in Italy) and management types (coppice stands and groves in Honfrı´ a forest and in the Ce´vennes, and different coppice stand types on the Etna volcano). We used CCA to determine the fraction of variance of the species among management types explained by the species and functional traits. For each Mediterranean area, we carried out the CCA by confronting the CA table with another table composed by the same species [72]

Species richness

1133 60 55 50 45 40 35

a

30 25 20 15 10 5 0

bc

C. sativa cultivated grove

b

b

C. sativa abandoned grove

C. sativa coppice stand

c

Mixed C. sativaQ. pyrenaica stand

Q. pyrenaica stand

Figure 1. Mean species richness in the understorey of the C. sativa and Q. pyranaica stands of the Honfrı´ a forest in the southern of Salamanca province in Spain. Error bars at ±95% confidence limits. Two different letters between the coppice stands indicated significant statistical difference (Mann–Whitney non-parametric test, p < 0.05).

number and functional traits separated in subclasses. Moreover, hierarchical ascending classification was used to make easier the identification of groups in factorial plans (Roux 1985).

Results Species richness and species diversity (Shannon index) In the Honfrı´ a forest and the Ce´vennes, species richness was highest in cultivated groves, (Figures 1, 2). On Etna volcano, species richness was highest in

Species richness

45

a

40 35 30

b

25 20

c

cd d

15 10 5 0

Cultivated Abandoned Grove Grove

Young Coppice

Medium Coppice

Old Coppice

Figure 2. Mean species richness along a successional gradient from cultivated chestnut grove to old C. sativa coppice stand (Le Cros site in the Ce´vennes). Error bars at ±95% confidence limits. Two different letters between the coppice stands indicated significant statistical difference (Mann– Whitney non-parametric test, p < 0.05). [73]

Species richness

1134 45 40 35 30 25 20 15 10 5 0

a b

bc cd

Monte Crisimo

Trisciala

Fornazzo

d

Balilla

Piano Lepre

Figure 3. Mean species richness in the understorey in the five coppice C. sativa stands on the Etna volcano in Italy. Error bars at ±95% confidence limits. Two different letters between the coppice stands indicated significant statistical difference (Mann–Whitney non-parametric test, p < 0.05).

the Trisciala coppice stand (Figure 3), and not significantly different from the abandoned grove in the Ce´vennes (p > 0.05). Species diversity was also highest in cultivated groves and Trisciala coppice stand.

Plant functional traits and management types Consequences of various management types on plant species in term of functional traits were analyzed with CCA, and hierarchical ascending classification allowed the identification of several groups in each Mediterranean area 1 Axis 2 (inertia 15%) 0,8

Honfría forest - Group 2 C.sativa coppice stands Arpa, Casa, Celo, Prav, Qupy, Tesc

Geophyte 0,6

0,4

> 50 cm Axis 1 (inertia 48%) -1

Honfría forest - Group 3 C. sativa abandoned groves & Mixed C. sativa-Q. pyrenaica coppice stands & Q. pyrenaica coppice stands Anod, Caof, Capa, Daca, Gehi, Gepi, Haha, Jamo, Loco, Meme, Feru, Himu Barochorous

0,2

Heliophillous

Hydrochorous Anemochorous

Zoochorous

Therophyte

0

-0,8

-0,6 -0,4 Phanerophyte

Shade tolerant

-0,2

0 Chamaephyte -0,2

0,2 30-50 cm

0,4

0,6

0,8

1

1,2

1-30 cm

Autochorous

Honfría forest - Group 1 C. sativa cultivated groves Anpr, Boma, Brho, Cegl, Cran, Crvi, Cyec, Gaap, Tran, Trar, Trca, Scan, Vubr

-0,4 Hemicryptophyte -0,6

Figure 4. Ordination in the plane of the two axes of functional traits after a canonical correspondence analysis from a matrix composed by the 67 plant species observed on the line point quadrat of the 25 plots in the Honfrı´ a forest in Spain and a matrix composed by the same plant species and their functional traits. Groups were identified by an hierarchical ascending classification. Plots, and some plant species associated to each group were indicated on the figure. Codes of plant species are indicated in Appendix 1. The total variation explained by CCA is 39%. [74]

1135 The Cévennes - Group 2 C. sativa abandoned groves & young coppice stands Armo, Clvu, Dagl, Erci, Hima, Himu, Pone, Prvu, Trpr, Veof, Visa

0,8 Axis 2 (inertia 28%) Autochorous

Barochorous 0,4

Phanerophyte > 50 cm

0,2

Chamaephyte

Zoochorous Shade tolerant

0 -0,8

-0,6 30-50 cm

-0,4

-0,2

0

Heliophillous 1-30 cm

Hemicryptophyte

The Cévennes - Group 3 C. sativa medium and & old coppice stands Bepe, Csa, Hede, Qupu, Rops

0,6

Therophyte

0,2 Hydrochorous

0,4 Geophyte

0,6

0,8 Axis 2 (inertia 37%)

1

-0,2

The Cévennes - Group 1 C. sativa cultivated groves Ruac, Orpe, Ptaq, Tesc

-0,4 Anemochorous -0,6

Figure 5. Ordination in the plane of the two axes of functional traits after a canonical correspondence analysis from a matrix composed by the 41 plant species observed on the line point quadrat of the 25 plots in the Ce´vennes in France and a matrix composed by the same plant species and their life traits. Groups were identified by an hierarchical ascending classification. Plots, and some plant species associated to each group were indicated on the figure. Codes of plant species are indicated in Appendix 1. The total variation explained by CCA is 46%.

studied. In the Honfrı´ a forest, the C. sativa cultivated groves (group 1) were characterized by small heliophillous therophytes (Figure 4). The C. sativa coppice stands (group 2) were characterized by shade tolerant phanerophytes with zoochorous dispersal mode and geophytes. The C. sativa abandoned groves, mixed C. sativa–Q. pyrenaica coppice stands and Q. pyrenaica coppice stands (group 3) were composed essentially by hemicryptophytes and chamaephytes with anemochorous or barochorous dispersal mode. In the Ce´vennes, the C. sativa cultivated groves (group 1) were characterized by therophytes with anemochorous dispersal mode and geophytes (Figure 5). The C. sativa abandoned groves and the young coppice stands (group 2) were characterized by heliophillous hemicryptophytes and chamaephytes. The C. sativa medium and old coppice stands (group 3) were composed more particularly by phanerophytes with zoochorous dispersal mode. In the case of C. sativa coppice stands on Etna volcano in Italy, Monte Crisimo (group 1) were more particularly characterized by therophytes and chamaephytes (Figure 6), Triciala (group 2) by hemicryptophytes with anemochorous dispersal mode, Piano Lepre (group 3) by geophytes with barochorous dispersal mode, and Balilla and Fornazzo (group 4) by shade tolerant phanerophytes with zoochorous dispersal mode.

Discussion and concluding remarks A main trend emerging from our species richness data was higher species richness in the chestnut cultivated groves than in coppice stands; both in the [75]

1136 1,5

Etna volcano - Group 2 C. sativa coppice stands Trisciala Acli, Brsy, Crle, Himu, Rane, Sivu, Trpu

Axis 2 (inertia 29%)

Etna volcano - Group 1 C. sativa coppice stands 1 Monte Crisimo Arth, Avba, Homu, Lasp, Sete, Stme, Vidi, Vite

Anemochorous

Heliophillous 30-50 cm

Chamaephyte Hemicryptophyte 1-30 cm -0,8

-0,6

Barochorous Geophyte

0,5

-0,4

Etna volcano - Group 3 C. sativa coppice stands Piano Lepre Door, Epmi, Lagr, Lave, Leco, Muco, Ptaq

Therophyte Hydrochorous 0 -0,2 Autochorous

0

0,2

0,4

0,6

0,8 Axis 1 (inertia 43%)

-0,5

Shade tolerant -1

> 50 cm Phanerophyte Zoochorous

Etna volcano - Group 4 C. sativa coppice stands Balilla & Fornazzo Casa, Hehe, Prsp, Quda, Ruid, Ruul

-1,5

Figure 6. Ordination in the plane of the two axes of life traits after a canonical correspondence analysis from a matrix composed by the 40 plant species observed on the line point quadrat of the 25 plots on Etna volcano in Italy and a matrix composed by the same plant species and their life traits. Groups were identified by an hierarchical ascending classification. Plots, and some plant species associated to each group were indicated on the figure. Codes of plant species are indicated in Appendix 1. The total variation explained by CCA is 49%.

Honfrı´ a forest in Spain and in the Ce´vennes in France. However, species richness in the cultivated groves of the Honfrı´ a forest (53 ± 4 species) was significantly higher than in the cultivated groves of the Ce´vennes (38 ± 4 species) (p < 0.01). The strawberry culture in some years under chestnut groves in the Honfrı´ a forest can explain this difference. Indeed, the high biodiversity among plants was always related to perturbations (pruning, grazing, fire, etc.), and often observed in Mediterranean areas (Romane et al. 1992). The species diversity decrease observed along the successional gradient in the Ce´vennes appears to be a general trend in Mediterranean Basin (Tatoni and Roche 1994; Debussche et al. 1996). On Etna volcano, tradition is coppice management and not grove, and species diversity in coppice stands was lowest than in the chestnut cultivated groves of the Honfrı´ a forest and the Ce´vennes. Nevertheless, species diversity in Trisciala coppice stand was not significantly different from chestnut abandoned groves in the Ce´vennes (p>0.05). In Trisciala, stumps have regular and large spacing and thus light, which is recognized as a factor linked positively with species richness (Grime and Jarvis 1975; Gilliam et al. 1995; Yorks and Dabydeen 1999), is available in the understorey and favour growth. Like in the study of Rubio et al. (1999) in Extremadure (Central Spain), or Kitazawa and Ohsawa (2002) in Chiba (Central Japan), the difference according to management type was well observed. The species composition differences among management type showed us that generally small heliophillous therophytes characterized C. sativa cultivated groves. Low intensity disturbance can explain the persistence of annual species in cultivated groves (Lavorel 1999). Hemicryptophytes with anemochorous [76]

1137 dispersal mode and chamaephytes characterized C. sativa abandoned groves, mixed C. sativa–Q. pyrenaica coppice stands, Q. pyrenaica coppice stands, and young C.sativa coppice stands. Phanerophytes with zoochorous dispersal mode characterized more particularly medium and old coppice stands (coppice stands that differ by the shoot age). This pattern coincides with the general trend described in Southern France; annual plants are substituted by perennial grasses and shrubs with canopy closure (Houssard et al. 1980; Escarre´ et al. 1983; Tatoni and Roche 1994). Perturbations were necessary to maintain a quite high level of species diversity. In contrast, the abandonment of chestnut stands, for decades or even centuries, will turn into closed and homogeneous vegetation with decreasing plant diversity. One solution could be to maintain a landscape mosaic consisting of diverse chestnut stands modified by human activities (chestnut groves, abandoned chestnut groves and chestnut coppice stands) (Gondard et al. 2001; Rubio and Escudero 2003). This could enhance regional plant diversity. However, in our study we recorded only common species, thus if rare species have been observed, the estimation of biodiversity would be review, and quality aspect take into account. Moreover, due to some of the unsatisfactory aspects of the experimental design (replication number), our study only indicates, but does not validate, several possible management techniques, of which remain to be tested further.

Acknowledgements We thank the European Union (MANCHEST contracts, DG XII). We also warmly thank Maria Failla, Giuseppe Siracusa, Antonino La Mantia, Mirella Clausi, Sergio Salazar Iglesias, Alvaro Peix Geldart, Jesu´s Herna´ndez, Zuheir Shater, Alain Renaux, Michel Grandjanny, Marie Maistre, Maurice Rapp, and Franc¸ois Jardon for their participation in collecting data in the chestnut stands.

Appendix 1. Functional traits (life form, dispersal mode, plant height and light tolerance) of plant species observed along the point quadrat line and used in the Canonical Correspondence Analysis according to available data: Molinier and Mu¨ller 1938; Pignatti 1982; van der Pijl 1982; Bonnier 1990; De Bolos et al. 1993. Th – therophyte, G – geophyte, H – hemicryptophyte, Ch – chamaephyte, Ph – phanerophyte. Code

Species

Life form

Dispersal mode

Height

Light tolerance

Acli Acmi Alpe Anod Anpr Armo

Achillea ligustica Achillea millefolium Alliaria petiolata Anthoxantum odoratum Anthemis pratensis Arenaria montana

H H H H Th H

Anemochorous Anemochorous Anemochorous Anemochorous Anemochorous Anemochorous

30–50 cm 30–50 cm 30–50 cm 30–50 cm 1–30 cm 1–30 cm

shade tolerant shade tolerant shade tolerant heliophillous heliophillous heliophillous

[77]

1138 Appendix 1. Continued. Code

Species

Life form

Dispersal mode

Height

Light tolerance

Arpa Arth Asal Astr Avba Avsu Bepe Brsy Brma Brho Cadi Caof Capa Casa Cavu Cegl Celo Chju Coma Clvu Coar Coav Coli Cran Crle Crvi Cyec Cysc Daca Dagl Deme Door Epla Epmi Erar Erci Feov Feru Gaap Gamo Gasa Gefl Gehi Gepi Gero Gnlu Haha

Aristolochia pallida Asplenium trichomanes Asphodelus albus Asplenium trichomanes Avena barbata Avena sativa Betula pendula Brachypodium sylvaticum Bromus maximus Bromus hordeaceus Carex distachia Calamintha officinalis Campanula patula Castanea sativa Calluna vulgaris Cerastium glomeratum Cephalanthera longifolia Chondrilla juncea Conopodium majus Clinopodium vulgare Convolvulus arvensis Corylus avellana Corrigiola littoralis Crucianella angustifolia Crepis leontodontoides Crepis virens Cynosurus echinatus Cytisus scoparius Daucus carota Dactylis glomerata Deschampsia media Doronicum orientale Epilobium lanceolatum Epipactis microphylla Erica arborea Erica cinerea Festuca ovina Festuca rubra Galium aparine Galium mollugo Galium saccharatum Genista florida Genista hispanica Genista pilosa Geranium robertianum Gnaphalium lutescens Halimium lasianthumsubsp .alyssoides Hedera helix Hippocrepis comosa

G Th G H Th Th Ph H Th Th H H H Ph Ch Th G H G H H Ph H Th H Th Th Ph H H H G H G Ch Ch H H Th H Th Ch Ch Ch Th H Ch

Barochorous Anemochorous Barochorous Zoochorous Anemochorous Zoochorous Anemochorous Anemochorous Anemochorous Zoochorous Barochorous Zoochorous Anemochorous Zoochorous Anemochorous Anemochorous Anemochorous Anemochorous Barochorous Hydrochorous Barochorous Zoochorous Autochorous Anemochorous Anemochorous Anemochorous Anemochorous Autochorous Anemochorous Anemochorous Zoochorous Anemochorous Anemochorous Anemochorous Barochorous Barochorous Anemochorous Anemochorous Zoochorous Barochorous Zoochorous Autochorous Autochorous Autochorous Autochorous Autochorous Zoochorous

30–50 cm 1–30 cm 30–50 cm 1–30 cm >50 cm >50 cm >50 cm 30–50 cm 30–50 cm 1–30 cm 1–30 cm 30–50 cm 30–50 cm >50 cm 30–50 cm 1–10 cm 1–30 cm >50 cm >50 cm 30–50 cm 30–50 cm >50 cm 1–30 cm 1–30 cm 30–50 cm 30–50 cm 1–30 cm >50 cm 30–50 cm >50 cm 30–50 cm 30–50 cm 30–50 cm 1–30 cm >50 cm 30–50 cm 30–50 cm 30–50 cm 1–30 cm 30–50 cm 1–30 cm 1–30 cm 1–30 cm 1–10 cm 1–30 cm 1–30 cm 30–50 cm

shade tolerant shade tolerant heliophillous shade tolerant heliophillous heliophillous shade tolerant shade tolerant heliophillous heliophillous shade tolerant heliophillous heliophillous shade tolerant shade tolerant heliophillous shade tolerant shade tolerant shade tolerant heliophillous heliophillous shade tolerant shade tolerant heliophillous heliophillous heliophillous heliophillous shade tolerant heliophillous heliophillous shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant heliophillous shade tolerant heliophillous shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant heliophillous shade tolerant

Ph H

Zoochorous Autochorous

>50 cm 1–30 cm

shade tolerant shade tolerant

Hehe Hico

[78]

1139 Appendix 1. Continued. Code

Species

Life form

Dispersal mode

Height

Light tolerance

Hihi Hima Himu Hipi Hium Hola Homo Homu Hyra Ilaq Jamo Laan Lagr Lasp Lave Leco Lihe Litr Loco Loet Luca Lufo Lusi Meme Meun Muco Orco Orpe Pehi Pimu Plla Pobu Pone Posy Prav Prgr Prsp Prvu Ptaq Quda Qupu Qupy Rabu Rane Rops Ruac Ruid Rupe

Hispidella hispanica Hieracium maculatum Hieracium murorum Hieracium pilosella Hieracium umbellatum Holcus lanatus Holcus mollis Hordeum murinum Hypochaeris radicata Ilex aquifolium Jasione montana Lathyrus angulatus Lathyrus grandiflorus Lathyrus sphaericus Lathyrus venetus Leopoldia comosa Linaria heterophylla Linaria triornitophora Lotus corniculatus Lonicera etrusca Luzula campestris Luzula forsteri Luzula sieberi Melittis melissophyllum Melica uniflora Muscari commutatum Ornithopus compressus Ornithopus perpusillus Petrorhagia hispanica Piptaterum multiflorum Plantago lanceolata Poa bulbosa Poa nemoralis Poa trivialis subsp. sylvicola Prunus avium Prunella grandiflora Prunus spinosa Prunella vulgaris Pteridium aquilinum Quercus dalechampii Quercus pubescens Quercus pyrenaica Ranunculus bulbosus Ranunculus neapolitanus Robinia pseudo-acacia Rumex acetosella Rubus idaeus Rubia peregrina var . longifolia

H H H H H H H Th H Ph H Th G Th G G H H H Ph H H H H H G Th Th H H H H H H Ph H Ph H G Ph Ph Ph H H Ph G Ph H

Anemochorous Anemochorous Anemochorous Anemochorous Anemochorous Anemochorous Zoochorous Anemochorous Anemochorous Anemochorous Anemochorous Barochorous Autochorous Autochorous Barochorous Anemochorous Anemochorous Autochorous Autochorous Zoochorous Zoochorous Anemochorous Zoochorous Zoochorous Anemochorous Anemochorous Zoochorous Zoochorous Autochorous Anemochorous Anemochorous Barochorous Anemochorous Anemochorous Zoochorous Barochorous Zoochorous Hydrochorous Anemochorous Zoochorous Zoochorous Zoochorous Barochorous Anemochorous Autochorous Anemochorous Zoochorous zoochorous

1–30 cm 30–50 cm 30–50 cm 1–30 cm 30–50 cm >50 cm 30–50 cm 1–30 cm 30–50 cm >50 cm 1–30 cm 30–50 cm >50 cm 1–30 cm 30–50 cm 1–30 cm 1–30 cm 30–50 cm 1–30 cm 30–50 cm 1–30 cm 1–30 cm 1–30 cm 30–50 cm 30–50 cm 1–30 cm 30–50 cm 1–30 cm 1–30 cm >50 cm 1–30 cm 1–30 cm 30–50 cm 30–50 cm >50 cm 1–30 cm >50 cm 1–30 cm >50 cm >50 cm >50 cm >50 cm 30–50 cm 1–30 cm >50 cm >50 cm >50 cm >50 cm

heliophillous shade tolerant shade tolerant shade tolerant heliophillous heliophillous heliophillous heliophillous heliophillous shade tolerant shade tolerant heliophillous heliophillous heliophillous heliophillous heliophillous heliophillous shade tolerant heliophillous shade tolerant heliophillous shade tolerant shade tolerant shade tolerant shade tolerant heliophillous heliphillous shade tolerant heliophillous heliophillous heliophillous heliophillous shade tolerant heliophillous shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant shade tolerant heliophillous shade tolerant heliophillous heliophillous shade tolerant shade tolerant

[79]

1140 Appendix 1. Continued. Code

Species

Life form

Dispersal mode

Height

Light tolerance

Ruul Scan Sete Siin Sivu

Rubus ulmifolius Scleranthus annuus Sedum tenuifolium Silene inflata Silene vulgaris subsp. angustifolia Solidago virgaurea Stellaria media Teucrium scorodonia Thapsia garganica Torilis arvensis Trifolium angustifolium Trifolium arvense Trifolium campestre Trifolium pratense Trifolium pratense subsp. semi-purpureum Trifolium repens Veronica officinalis Vicia disperma Vicia lutea Vicia pseudocracca Vicia sativa Vicia tenuifolia Vulpia bromoides

Ph Th Ch H H

zoochorous zoochorous anemochorous anemochorous anemochorous

>50 cm 1–30 cm 1–30 cm 30–50 cm 30–50 cm

shade tolerant heliophillous heliophillous shade tolerant shade tolerant

H Th G H Th Th Th Th H H

anemochorous anemochorous barochorous anemochorous zoochorous anemochorous anemochorous anemochorous anemochorous anemochorous

30–50 cm 1–30 cm 30–50 cm >50 cm 30–50 cm 1–30 cm 1–30 cm 1–30 cm 1–30 cm 1–30 cm

shade tolerant shade tolerant shade tolerant heliophillous shade tolerant heliophillous heliophillous heliophillous heliophillous heliophillous

H H Th Th Th H Th Th

anemochorous hydrochorous anemochorous autochorous barochorous autochorous anemochorous zoochorous

1–30 cm 1–30 cm 30–50 cm 30–50 cm 30–50 cm 30–50 cm 30–50 cm 1–30 cm

shade tolerant shade tolerant heliophillous heliophillous heliophillous heliophillous heliophillous heliophillous

Sovi Stme Tesc Thga Toar Tran Trar Trca Trpr Trpu Trre Veof Vidi Vilu Vips Visa Vite Vubr

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Biodiversity and Conservation (2006) 15:1143–1157 DOI 10.1007/s10531-004-3105-6


Springer 2006

-1

Spatial diversity of dry savanna woodlands Assessing the spatial diversity of a dry savanna woodland stand in northern Namibia using neighbourhood-based measures FRIEDRICH PATRICK GRAZ 1 Department of Land Management Conservation, Polytechnic of Namibia, P/Bag 13388, Windhoek, Namibia; (e-mail: [email protected]; phone: +264-061-207-2215; fax: +264-061-2072123)

Received 8 March 2004; accepted in revised form 3 August 2004

Key words: Ecology, Mingling index, Namibia, Spatial diversity, Spatial structure, Uniform angle index, Woodland savanna Abstract. The dry woodland savannas of Namibia are of significant socio-economic importance. The paper tests the suitability of a number of diversity indicators developed for species poor systems in Europe in the woodland context. The indicators that were tested included the species specific mingling index, MSp, the measure of surround and the uniform angle index. The simple application of the methods permit relatively unschooled crews to conduct an enumeration in the field.The results show that the indicators do not only display current diversity status, but also reflect the ecological context of the individual species.

Introduction and background The dry woodland savannas of northern Namibia are of significant socioeconomic importance to many rural communities, providing a variety of timber and non-timber products. The woodland resources that are used range from building material and wood fuel to food, medicine and grazing (NFSP 1996). The quantities of the different products that are extracted are considerable. Ollikainen (1992) estimated for example, that firewood alone amounted to a total of 1.5 million cubic metres of wood during 1992. No quantity or value estimates of non-wood products are available for the Namibian dry woodland savannas, although these may be considerable. The various woodland products differ in their importance to the various communities, and at different times. Although exact quantities were not indicated, Lee (1973) reported, for instance, that the intake of Schinziophyton rautanenii nuts could comprise up to 90% of the total food intake of some San communities. While this percentage will have changed in the meantime, Bu¨schel (1999) indicated that the nuts still represent the staple diet of nomadic and sedentary San groups. The importance of the nuts increases particularly when agricultural crops are insufficient to meet normal requirements. [83]

1144 Other communities in the Kavango region of Namibia depend almost entirely on the production and sale of carvings for the tourism industry, although no studies seem to have been published in this regard. Some carvings are also produced in the Caprivi region, but the local population does not appear to depend as much on this form of income although no estimates are available. The emphasis on different species for the carving industry is also shifting. In the early 1990s the industry in the Okavango region made use of Pterocarpus angolensis almost exclusively. Now, however, species like Guibourtia coleosperma, Baikiaea plurijuga and even S. rautanenii (despite its light weight) are being utilized extensively. This is primarily due to the overexploitation of P. angolensis. Further woodland species, such as Burkea africana or Terminalia sericea serve mainly for poles or as firewood, although they support a caterpillar that also represents an important source of food (Leger 1997).

Spatial diversity and woodland structure The word ‘‘structure’’ generally considers the composition of a population of trees in terms of specific characteristics. These may include tree age, size, species or sex (in the case of dioceous trees). Spatial structure, on the other hand looks at the arrangement of such characteristics in space. Spatial diversity refers to the arrangement of the characteristics in relation to each-other or in relation to a particular point on the ground. The woodland savanna in northern Namibia is supported by coarse Aeolian sands with poor water holding capacity and nutrient status. The trees that occur here need to cope with highly variable precipitation and high evaporation rates. Frequent fires and exploitation further affect the environment. Taken in combination, trees and especially their seedling have to cope with a wide variety of conditions over a very short period of time and have adapted accordingly. A number of the woodland species are frequently, though not exclusively, found in almost monospecific stands. This may be due to regeneration requirements, as in the case of P. angolensis (Graz 1996), the ability to compete, especially for water, as in the case of B. plurijuga (Mitlo¨hner 1997) or superior fire tolerance as in the case of B. africana (Rutherford 1981). The monospecificity of stands of S. rautanenii and T. sericea have not been investigated. T. sericiea, however, is a pioneer that may quickly colonize open areas where it may actually form thickets (Shackleton 2001). Bu¨schel (1999) reported on the other hand that stands dominated by S. rautanenii were comprised of trees of different sizes and species in the Okavango region of Namibia. Similarly, Mitlo¨hner (1997) also described stands of mixed species, comprising of P. angolensis, B. africana and B. plurijuga, while observations near the study site also showed mixed stands (unpublished data). [84]

1145 In addition to being almost monospecific, trees within many stands often seem to be of similar size although not necessarily of similar age. Childes (1984) reported, for instance, that B. plurijuga stands were of variable age despite the equal size of the trees. Plants remain small for a number of years until environmental conditions are suitable for further development. This is probably also the case for B. africana and S. rautanenii, although nothing seems to have been documented. The restriction of growth described by Childes for B. plurijuga is similar to the suffrutex development stage of P. angolensis reported by Vermeulen (1990). During this period seedlings from a number of years may accumulate in this developmental stage and develop together to the sapling stage when environmental conditions permit. In such cases the above ground parts are not of the same age as the roots. It is unclear if the differences in the ages of the roots will be reflected in the survival rate of the above ground parts of the trees. It is also uncertain whether or not whole stands of any of the above species will die off and be replaced by others at a different location, or whether the existing regeneration is sufficient to replace those trees that have died. The data pertaining to the structure of stands in northern Namibia currently available is superficial, despite its significant importance for management. Spatial diversity, or a lack of spatial diversity, has important implications. Consider for instance the effect of exploitation on an even sized, monospecific stand; selection based on a minimum diameter may result in a local clear-felling (Von Breitenbach 1968; Graz 1996). The resulting vegetation structure would be increasingly prone to fire that may cause further vegetation change, as well as subsequent erosion and nutrient loss (see Graz 1996). Causes of mortality are not necessarily only of human origin, however. The different sizes of a number of species have, for example, their own degree of fire tolerance. This means that trees up to a particular size class may be removed from a stand by a sufficiently intense fire. Wilson and Witkowski (2003) found that the bark-thickness of B. africana increases with tree circumference between 0 and 400 mm. The thickness of the bark is the primary protector against the effect of fire on the cambium. Fire tolerance may be overcome if the bark of trees is breached by animals (Yeaton 1988) or growth stresses (Graz 2003). Studies relating to spatial aspects have in the past concentrated on the dispersion of plants using measures such as the nearest neighbour of Clark and Evans (1954) or point to plant distances after Pielou (1977). More recently the uniform angle index (UAI) (Gadow 1999; Staupendahl 2001; Gadow et al. 2003) has been implemented to describe complex forest structures. The aggregation of tree attributes have only been addressed more recently by other measures, such as the ‘‘measure of surround’’ (Hui et al. 1998) or the spatial ‘‘mingling’’ (Gadow 1999). [85]

1146 The mingling measure is used to quantify the degree of interspersion or mingling of tree characteristics, as illustrated in Figure 1. Trees that are surrounded by others of similar characteristic are aggregated in terms of the characteristic, implying a lower degree of mingling of this characteristic. On the other hand, trees surrounded by others of dissimilar characteristic imply a higher degree of mingling. Mingling should not only be considered in terms of categorical data, such as species or sex, or whether a tree is alive or dead, but should be expanded to include any measure with which a tree might be described, including height or diameter. Albert and Gadow (1998) reported on the use of these neighbourhood-based measures to assess the effect of selective thinning on the diversity of a beech stand in Germany. The authors had found the measures to be sensitive to small-scale differences and changes of woodland structure, and were able to provide more intuitively acceptable results than the segregation index of Pielou (1977, p. 227 ff). This study aims to achieve two main objectives. The first objective is to assess the applicability of indicators that were developed and assessed in Europe to the Southern African context where little or no basic stand information is available for non-plantation areas. In addition, the study intends to generate information that will promote the understanding of the ecology of Namibia’s woodland resources. Description of the study area The woodland area that was enumerated covers approximately 70 ha and is situated between 1930¢ E, 1915¢ S and 1945¢ E, 1930¢ S near the Kanovlei Forestry Research Station in the western Tsumkwe district of the Otjozondjupa region, northeastern Namibia. The area is dominated by linear fossil dunes or sandy plains on calcareous deposition, similar to those in the adjoining Kavango region described by Graz (1999). The soils are Kalahari sands, classified as unconsolidated aeolian material by Coetzee (2001), with very poor water holding capacity and nutrient status, and subsequently a very low potential for any agricultural development (Department of Water Affairs 1991).

Figure 1. The mingling of black, grey and white ‘trees’ within two square stands (after Gadow, 1999). [86]

1147 The region is traversed by a system of omuramba (vegetated dry riverbed), with the soils classified as unconsolidated fluvial material (Coetzee 2001). These soils are shallower and have a heavier texture than the dunes (Department of Water Affairs 1971). Precipitation is mostly in the form of thunderstorms amounting to an average rainfall of between 500 and 600 mm per year (Amakali 1992). However, the distribution of precipitation is highly variable and prominently positively skewed. Expected rainfall is therefore significantly lower than the long-term averages. Rain generally falls in the period September to May, with most rain occurring between December and March. Average annual evaporation rates are between 2600 and 2800 mm (Crerar and Church 1988) resulting in an overall moisture deficit. de Pauw and Coetzee (1999) have determined an approximate growing period of between 91 and 120 days, based on the relationship between available moisture, the amount of evapotranspiration and the average air temperature. Although the general vegetation is described as tree savanna and woodland by Giess (1998), there is some significant variation in species and structural composition. The Directorate of Forestry identifies a number of dissimilar patches of forest or savanna (Chakanga 1995). While the sandy planes and dunes are dominated by Burkea africana, various species of Combretum, Pterocarpus angolensis, Schinziophyton rautanenii and Terminalia sericea. Scattered patches of Baikiaea plurijuga also occur. The lower lying omuramba vegetation is comprised primarily of Acacia erioloba, Dichrostachys cinerea and Philenoptera nelsii. Nuts from the S. rautanenii trees within the stand are harvested by local communities to augment their food supply, and by the Directorate of Forestry to obtain material for the National Tree Seed Centre and for ex situ conservation of genetic material. Additionally the stand shows signs of periodic wood harvesting of B. plurijuga stems, as well as for firewood. Dry season fires are frequent (Graz 2003).

Material and methods The interspersion of tree attributes The original measure of mingling and its derivatives are based on the proportion of trees with dissimilar characteristics to those of a selected sample tree. The species mingling index Mi for a given sample tree, i, using n neighbours is, for example, obtained through: ð1Þ

Mi ¼

n 1X mij ; n j¼1

[87]

1148 where


mij ¼

1; if the tree is of another species; 0; if the tree is of the same species:

When four neighbours are used to determine Mi the index may obtain one of five possible values: 0/4 none of the neighbours are of a different species, 1/4 one of the neighbours is of a different species, 2/4 two of the neighbours are of a different species, 3/4 three of the neighbours are of a different species, and 4/4 all of the neighbours are of a different species. The arithmetic mean (MSp) of the Mi values that were obtained for a particular species sp provides a measure of the degree of interspersion of the species in the area. MSp provides a value between 0 and 1. Values close to 0 indicate that trees of the reference species sp occur in groups therefore implying a low degree of mingling and high degree of aggregation. High values of MSp, closer to 1, on the other hand, imply a high degree of mingling, i.e. trees of the reference species do not occur together. As is the case when examining the distribution of data around a mean value, additional information may be extracted from the distribution of Mi values of individual species. When the proportion that a species contributes to a stand is known, as assumed in the studies reported on by Lewandowski and Pommerening (1997) and Hui et al. (1998) a theoretical distribution of Mi values may be calculated based on the hypergeometric probability distribution. The distribution reflects the number of expected Mi values that would be obtained if all trees were interspersed randomly. The hypergeometric distribution is used to determine the probability, P, that a number of trees of a particular species may occur in a given sample of n trees taken from a population of N trees containing k trees of the species of interest. The probability that x trees in the sample will be of the species of interest is then determined after Newmark (1997) as:     k N
k  x n
x   P¼ for x ¼ 0; 1; 2; . . . ; n; N n which expands to: P¼

k xðk
xÞ

ðN
kÞ  ðn
xÞðN
k
ðn
xÞÞ N nðN
nÞ

for x ¼ 0; 1; 2; . . . ; n:

The resulting probability multiplied by the total number of samples that were taken provides the expected number of Mi values for that species. The [88]

1149 observed and expected distributions of Mi values may then be compared with the application of standard statistical methods to test for significance of deviations from the theoretical (random) distribution. Although no detailed data is available for any of the woodland areas in Namibia, and the extent of the woodland areas hampers the collection of such information, the sample size provided a suitable estimate of the species composition of the stand. The simulation study reported on by Graz (2004) has shown that the mingling index is sensitive to the species composition of a stand. In a stand of trees interspersed randomly, for example the aggregation of a species, 1
MSp, approximates the proportion that a species Sp contributes to the stand. This may be more intuitively understood if we consider each sample tree to provide an estimate of the proportion that its species contributes to the stand. Values of 1
MSp which are greater than the proportion contribution therefore indicate an overaggregation of the species, while lower values imply overdispersion within the stand. This relationship provides an important base from which the index may be interpreted. This study investigated the interspersion of a number of tree characteristics. In addition to the mingling of species described above, the interspersion of tree dominance is quantified on the basis of diameter (TSp) and height (HSp) using the ‘‘measure of surround’’ described by Hui et al. (1998), and which is applied in a method analogous to that of the mingling index. More particularly: ð2Þ

Ti ¼

where


tij ¼

1;

n 1X tij ; n j¼1

if the tree; j; is thicker than the sample tree i; 0; otherwise:

The species specific mean interspersion of tree diameter, TSp, is then the arithmetic mean of the values of Ti for that species. Similarly, the interspersion of tree height, Hi, is obtained through: ð3Þ

Hi ¼

where


hij ¼

1;

n 1X hij ; n j¼1

if the tree; j; is higher than the sample tree i; 0; otherwise:

The species specific interspersion of tree height, HSp, is then again determined as the mean of the values of Hi for the species. An equivalent measure was used to quantify the interspersion of dead trees (DSp) by counting the number of dead neighbours for each sample tree. [89]

1150 Uniform angle index The UAI was initially described by Gadow et al. (1998) and later by Staupendahl (2001) to provide a measure of the overall contagion of trees within a forest stand. The index is obtained by identifying the n nearest neighbours of a sample tree. Starting with the closest neighbour and moving in a clockwise direction around the sample tree the angle, aj, between two adjacent neighbours is determined in relation to the sample tree. The number of angles smaller than, or equal to, a given critical angle, a0, are then counted, i.e. ð4Þ

Wi ¼

where


wij ¼

1; 0;

n 1X wij ; n j¼1

if aj  a0 ; otherwise:

The critical angle (in degrees) is determined as: ð5Þ

a0 ¼

360 : Number of neighbours

Four neighbours would therefore be evaluated in terms of a 90 critical angle1. Since all of the indexes used to measure the interspersion of tree characteristics were based on four trees, the same neighbours could be used for the UAI. A practical advantage of choosing a0 = 90 is that two adjoining sides of a record book or clipboard may be used to determine whether or not an angle is greater than or less than the critical angle. Effectively, the index describes the spatial distribution around a particular reference tree. If the species of the reference tree is noted we may obtain the mean value for either for the whole population or for a particular species of interest. The mean value of the index is strongly correlated with the nearest neighbour index of dispersion of Clark and Evans (1954) that has long been used in ecological studies. Together with the number of trees in a stand, the UAI may be used to estimate the distribution of distances between a tree and its neighbours (Gadow et al. 2003). This information is generally not available and comparison of observed index values are compared to the simulation results of Gadow et al. (1998) are used.

1

More recent studies have shown that this statement needs to be modified; a more suitable critical angle is 72 (see Gadow et al. 2003). [90]

1151 Sampling The extent of the stand was recorded in the field using a Garmin Venture GPS. The track-log was stored for subsequent mapping. A regular sample grid of one geographic second was then superimposed on the stand amounting to a sample point approximately every 30 m at that latitude. Sampling points were located using a standard GPS receiver. The accuracy of autonomous GPS readings was considered adequate for the purpose of the study. While a dense canopy reduces the reliability of a GPS reading within a stand (Dominy and Duncan 2001), many of the trees in the area had already shed their leaves and canopy interference was considered negligible after initial comparison of signal strengths in wooded and in open areas. Since the enumeration coincided with the war in Iraq it is uncertain whether GPS readings were affected by selective availability on some days. It was felt, however, that this was acceptable. At each sample point the closest tree with a dbh of 5 cm or more was identified to serve as reference tree. Although trees had, in a few cases, snapped off below breast height, such trees were nevertheless sampled, since they play a role in the interspersion of plants. For each sample tree the four nearest neighbouring trees with a diameter of greater than 5 cm were determined and compared with the reference tree in terms of species, mortality, height and diameter, and the UAI was established. Time was kept short by assigning two persons to each sampling team. While the enumerator collected the measures, a navigator moved to find the next sample point. A total of 1121 sample points were assessed. The data was entered into a spreadsheet and the indexes were calculated for each species using cross tables.

Results and discussion The species specific indexes are summarized in Table 1. The table also shows a surrogate species of ‘Dead’ created to record trees that were still standing but had been burnt beyond a stage where they might be identified. Also, species of the genus Combretum and Comiphora were lumped, as individual species could not readily be identified. The row marked ‘overall’ provides the each index as calculated over the entire data set. The overall shows a contagion (Wi) greater than 0.6, here indicating a tendency towards non-random (clumped) dispersion of trees (after Gadow et al. 1998). The dispersion around trees of the individual species does not seem to diverge very much from the mean value of 0.665, if Philenoptera nelsii and Securidaka longipedunculata are discounted because of their very low overall occurrence. This is in line with general observations in the field. The table shows that most of the species have a tendency to aggregate.

[91]

1152 To compare the proportion of a given species within the stand and the value 1
MSp consider Table 2. The table omits those species with very few observations (less than 5% of the total). The final column in the table reflects the parameter M proposed by Graz (2004) to determine the degree of interspersion. The value of M is larger than 0 and less than or equal to 1. Values close to 0 indicates a very low degree of mingling, and 1 indicates a more random distribution of the species in the stand. Table 2 shows that B. plurijuga has the highest degree of aggregation followed by Terminalia sericea. More T. sericea seedlings survive in open areas, i.e. away from conspecific trees (Smith and Grant 1986) where it has the ability to form thickets (Shakelton 2001). This was evident in the field. T. sericea would colonize gaps in the canopy, thus causing the aggregation. The dispersion of B. plurijuga is also shown in Figure 2. The figure shows that the species occurs in a very limited area. The accompanying graph shows the relative distribution of Mi values (bar) and the theoretical hypergeometric distribution. The graph shows a clear difference between the two, due to the clumping of the species, reflected by the low value of M (Table 2). The cause of the aggregation of B. plurijuga is uncertain, since the trees had few larger neighbours as evidenced by the low value of TSp in Table 1. It is possible that the patch of B. plurijuga is a remnant of a larger stand that has been subject to high degrees of mortality. This possibility stems from reports by Von Breitenbach (1968) who suggested that the almost pure stands in the Caprivi region developed towards mixed stands as a result of fire. The possibility is corroborated by the high degree of mortality (DSp) associated with the species (see Figure 3). The dead trees within the B. plurijuga Table 1. Mean of the various indicators for each of the identified species. Species of sample tree

N

P(Sp)

WSp

DSp

MSp

TSp

HSp

Burkea africana Baikiaea plurijuga Combretum species Comiphora species Ochna pulchra Philenoptera nelsii Pterocarpus angolensis Schinziophyton rautanenii Securidaka longipedunculata Strychnos pungens Terminalia sericea Unidentifiable dead tree Overall

116 194 214 36 26 16 178 75 2 26 96 142 1121

0.103 0.173 0.191 0.032 0.023 0.014 0.159 0.067 0.002 0.023 0.086 0.127 1.0000

0.688 0.665 0.657 0.639 0.635 0.750 0.647 0.653 0.625 0.683 0.641 0.701 0.665

0.226 0.116 0.148 0.201 0.087 0.141 0.163 0.137 0.125 0.163 0.130 0.285 0.169

0.751 0.653 0.697 0.875 0.962 0.820 0.813 0.875 0.962 0.893 0.781

0.323 0.249 0.484 0.382 0.567 0.563 0.198 0.243 0.375 0.462 0.565

0.332 0.256 0.479 0.556 0.673 0.625 0.218 0.300 0.625 0.548 0.602

0.794

0.360

0.399

P(Sp) denotes the proportion that a species contributes to the stand as a whole. The species specific indicators are: WSp = mean UAI, DSp = mean mortality, MSp = mean mingling, TSp = mean diameter dominance, and HSp = mean height dominance. The overall values for each indicator was calculated using the entire data set. [92]

1153 Table 2. Comparing the proportion P(Sp) that a species contributes to the population with (1
MSp). Species of sample tree

N

P(Sp)

MSp

1
MSp

P ðpÞ M ¼ 1
M Sp

Baikiaea plurijuga Burkea africana Combretum species Pterocarpus angolensis Schinziophyton rautanenii Terminalia sericea Unidentifiable dead tree Total

116 194 214 178 75 96 142 1121

0.103 0.173 0.191 0.159 0.067 0.086 0.127

0.653 0.751 0.697 0.813 0.875 0.781

0.347 0.249 0.303 0.188 0.125 0.219 0.285*

0.298 0.696 0.631 0.883 0.627 0.391 0.447

*Note that the value of DSp is used here (the mean proportion of dead neighbours), rather than the mingling index.

patch, shown in figure 3, are generally large trees. This is not evident from the indexes but supports the suggestion by Von Breitenbach cited above. Actual tree mortality may be caused directly by repeated burning of the stem, as well as changes in the osmotic potential of the top-soil that is caused by the accumulation of ash in the upper soil layers (Mitlo¨hner pers. comm.) In contrast to B. plurijuga, P. angolensis is interspersed almost randomly according to Table 2 and in Figure 2. As is evident in the figure, the observed distribution of Mi values (bars) follow the theoretical distribution much more closely than those of B. plurijuga. It must be noted, that P. angolensis occurs comparatively seldom within the B. plurijuga patch. This exclusion from the patch is more pronounced for B. africana. The reason or cause for this is not readily apparent. Outside this patch B. africana is more aggregated resulting in the lower value of M. The random distribution of P. angolensis is probably a reflection of the regeneration requirements of the species. Vermeulen (1990) reports that P. angolensis is especially sensitive to competition in the seedling and establishment phases. The species therefore often regenerates in areas that have been cleared by human or other action. Other species would then establish themselves later. The interspersion of trees of different size is reflected in the columns TSp (diameter specific) and HSp (height specific) in Table 1. Preliminary simulation results have shown that a random interspersion of tree sizes would result in an overall average of TSp = 0.5 and HSp = 0.5. The table shows, therefore, that size classes are not interspersed randomly. P. angolensis, S. rautanenii and B. plurijuga need to be highlighted. The low values of TSp and HSp for these species imply that few neighbouring trees are larger than the reference tree. This is supported by general observations in the field. The species therefore dominate in the area in which they occur. It also reflects the regeneration requirements of P. angolensis noted previously, but highlights the importance of further research into the demography of the other two species. [93]

1154

Figure 2. The dispersion of Baikiaea plurijuga, Pterocarpus angolensis and Burkea africana, within the study area. High values of Mi are shown in large circles and vice versa. The graphs depict the observed relative distribution of Mi values (bars), and the theoretical hypergeometric distribution (lines) of the values that would indicate a completely random interspersion of the species.

Table 1 also shows a similarity between the values of TSp and HSp of the individual species. Unpublished data shows a high degree of correlation between the dbh and height of B. africana (r2 = 0.8352), as well as for P. angolensis (r2 = 0.7317) for nearby stands. [94]

1155

Figure 3. The aggregation of dead trees within the stand. The degree of interspersion is reflected by the size of the points, with a high degree of aggregation shown by larger points.

Differences between the two indexes are due to the number of species found in the stand, and the differences in their respective diameter
height relationships. A larger difference occurs for the Comiphora species, however, reflecting the squat form of the trees; a relatively thick-trunked but short tree.

Conclusions In the past the applications of neighbourhood-based spatial measures were supported by detailed knowledge of the stands that were assessed, as noted above. This was not the case in this study, where only the extent of the stand was known. However, despite their simple application the indexes are able to provide information about the stands they describe, being able to reflect much of what is currently known about the individual tree species and their ecological circumstances. The results have also highlighted gaps in our knowledge of the ecology of a few of the important trees, such as Schinziophyton rautanenii, Baikiaea plurijuga, and Burkea africana, as well as the various Combretum species that occur in the area. These include regeneration requirements and species succession, and highlights the need for further investigation. The application of the measures described here has shown that they are easily applied in the field with relatively little training required, although the field crews will have to be able to identify the different tree species. This is particularly useful in view of the trend towards community based natural resource management in Namibia, where community members will have to assess their own resources. Since most of the rural community members are able to identify different plants in their vernaculars, species identification should not be a problem, despite sometimes limited literacy levels. [95]

1156

Acknowledgements I would like to thank my sister Ms. H. Riehmer and Ms. R. Haipinge as well as the late Mr. H. Roth for their assistance with data collection in the field. My sincere thanks also to the Directorate of Forestry, Namibia, for allowing me to use the Kanovlei Forest Station as a base, and the Polytechnic of Namibia who funded the field work. I would particularly like to thank Prof. K. von Gadow, Institute of Forest Management, Univeristy of Goettingen for comments.

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Biodiversity and Conservation (2006) 15:1159–1177 DOI 10.1007/s10531-004-3509-3


Springer 2006

-1

Assessment of threat status and management effectiveness in Kakamega Forest, Kenya BA¨RBEL BLEHER1,2,*, DANA USTER3 and THOMAS BERGSDORF 4 Institut fu¨r Zoologie, Abt. V- O¨kologie, Johannes Gutenberg-Universita¨t Mainz, Becherweg 13, 55099 Mainz, Germany; 2Department of Ornithology, National Museums of Kenya, Nairobi, Kenya; 3 Universita¨t Bielefeld, Fakulta¨t fu¨r Biologie, Abt. O¨kologie, Universita¨tsstrasse 25, 33615 Bielefeld, Germany; 4Zoologisches Forschungsinstitut & Museum Alexander Koenig, Adenauerallee 160, 53113 Bonn, Germany; *Author for correspondence (e-mail: [email protected]; phone: +49-(0)61313926108; fax: +49-(0)6131-3923731) 1

Received 6 May 2004; accepted in revised form 6 September 2004

Key words: Conservation, Disturbance indicator, Forest degradation, Logging, Management Abstract. To counteract an increasing biodiversity decline, parks and protected areas have been established worldwide. However, many parks lack adequate management to address environmental degradation. To improve management strategies simple tools are needed for an assessment of human impact and management effectiveness of protected areas. This study quantifies the current threats in the heavily fragmented and degraded tropical rainforest of Kakamega, western Kenya. We recorded seven disturbance parameters at 22 sites in differently managed and protected areas of Kakamega Forest. Our data indicate a high level of human impact throughout the forest with illegal logging being most widespread. Furthermore, logging levels appear to reflect management history and effectiveness. From 1933 to 1986, Kakamega Forest was under management by the Forest Department and the number of trees logged more than 20 years ago was equally high at all sites. Since 1986, management of Kakamega Forest has been under two different organizations, i.e. Forest Department and Kenya Wildlife Service. The number of trees logged illegally in the last 20 years was significantly lower at sites managed by the Kenya Wildlife Service. Finally, logging was lower within highly protected National and Nature Reserves as compared to high logging within the less protected Forest Reserves. Reflecting management effectiveness as well as protection status in Kakamega Forest, logging might therefore provide a valuable quantitative indicator for human disturbance and thus an important tool for conservation managers. Logging might be a valuable indicator for other protected areas, too, however, other human impact such as e.g. hunting might also prove to be a potential indicator.

Introduction Recent decades have seen a serious biodiversity decline due to habitat loss and alteration especially of tropical forests leading to a profound species-extinction crisis (Heywood 1995; Pimm et al. 1995; Whitmore 1997). Thus, much of tropical biodiversity is unlikely to survive without effective protection (Pimm et al. 1995; Myers et al. 2000). To counteract the anthropogenic impact and conserve biodiversity and ecosystem processes parks and protected areas have been established worldwide. Some studies demonstrate that parks can indeed provide basic safeguard against land-clearing in the context of high land-use pressure (Brunner et al. 2001). However, more often parks appear to lack [99]

1160 adequate management to address a host of threats within their borders (Brunner et al. 2001; Putz et al. 2001; Ervin 2003a, b). Protected areas face increasing levels of environmental degradation with more than 70% of 201 parks surveyed across 16 tropical countries being affected by poaching, encroachment and logging (van Schaik et al. 1997). Consequently, the improvement of management strategies of protected areas is of top priority for conservation practitioners. To improve and optimise management strategies methods to assess the threat status of protected areas and to measure management effectiveness of conservation efforts have become a major environmental concern (Margoluis and Salafsky 1998; Salafsky and Margoluis 1999; Hockings et al. 2000; Salafsky et al. 2002; Ervin 2003c; Hockings 2003). These assessments are an essential component of systematic conservation planning (Margules and Pressey 2000); they can enable conservation managers and policymakers to identify management strengths and weaknesses, reveal severity and distribution of levels of human impact, respond to pervasive management problems, refine their conservation strategies and reallocate budget expenditures (Brunner et al. 2001; Ervin 2003 a, c; Parrish et al. 2003). Therefore, the development of simple tools to monitor and assess whether conservation succeeds for protected areas are of great importance and require indicators that are measurable, scientifically sound, and comparable among protected areas over time, but also practical and cost-effective (Margoluis and Salafsky 1998). Traditionally, biological indicators have been used to assess the level of human impact in protected areas and measure management success. Ideally, they are supposed to serve as indicators for changes in the overall biodiversity of a site (Noss 1990; Sparrow et al. 1994). However, relationships between potential indicator species and total biodiversity as well as critical ecosystem processes are not that well established (Lindenmayer et al. 2000). Few of these methods using biologically based indicators are practical and cost-efficient, especially for use in the developing countries as they require substantial effort and resources beyond day-to-day project activities (Salafsky and Margoluis 1999). Finally, their results are often difficult to interprete for non-specialists and generally require the presence of baseline data against which to compare changes (Salafsky and Margoluis 1999). Another approach to assess human impact in protected areas and to assess management effectiveness is to identify and monitor threats directly as a proxy measurement of conservation success such as e.g. implemented in the Threat Reduction Assessment (TRA) (Salafsky and Margoluis 1999) and the Rapid assessment and Prioritization of Protected Area Management (RAPPAM) program recently established by WWF’s Forest for Life program (Ervin 2003c). This approach of directly identifying threats is sensitive to changes over short time periods and throughout a site, comparisons among projects and sites are possible, data can be collected through simple techniques and the method is practical and cost-effective. Furthermore, the results can be readily interpreted by conservation staff and can provide detailed, adaptive management guidance [100]

1161 to protected area managers. The primary tool for RAPPAM is the rapid assessment questionnaire which covers management planning, input and processes, and the identification of future threats and past pressures (Ervin 2003c). However, quantitative and objective approaches are still urgently required for the assessment of threat status and management effectiveness of protected areas to provide reliable, scientifically sound data. In this paper we present results from a survey quantifying human impact and evaluating management effectiveness in Kakamega Forest, western Kenya. Kakamega Forest is one of the last remaining indigenous forests of Kenya situated in an agricultural area with a high human density of more than 175 individuals per km2 (Tsingalia 1988). Like many other countries Kenya harbours an on-going conflict between forest conservation and land use needs of its increasing population (Tsingalia 1988; Wass 1995). This has put a longterm pressure on Kakamega Forest leading to its severe reduction and fragmentation in the last century. Additionally, it has suffered increasing degradation through both, extensive commercial and local exploitation of timber (Tsingalia 1988; Fashing et al. 2004; Mitchell, 2004). Large-scale commercial logging was reduced in the last decades, mostly through official presidential decree banning all indigenous tree species exploitation in the forest in the early 1980s (Tsingalia 1988; Mitchell, 2004), the transfer of the northern part of the forest under the rigorous authority of the Kenya Wildlife Service (KWS), the establishment of forest stations and ranger patrols, and through tourism and long-term research (e.g. Zimmerman 1972; Cords 1987; Mutangah 1996; Fashing et al. 2004). However, illegal activities including logging, fuelwood collection and extraction of bark for medicinal purposes occur to this day and appear to be heterogeneously throughout the forest with some sites providing more protection than others (Kiama and Kiyiapi 2001; Fashing et al. 2004; Fashing, in press). Our study presents a quantitative assessment of the current threats in the main forest block of Kakamega Forest and its fragments comprising areas of different management regimes and different protection priorities. In order to evaluate effectiveness of conservation measures we asked how differently managed and protected areas differ in their level of human impact. With this assessment we aim to provide a quantitative, simple site-level monitoring tool and a first guidance to management planners and decision makers on problems related to human impact and management in Kakamega Forest.

Kakamega Forest and its forest history Study site We conducted the study at Kakamega Forest (between latitudes 0008¢30.5¢¢ N (41,236 in UTM 36 N) and 0022¢12.5¢¢ N (15,984) and longitudes 3446¢08.0¢¢ E (696,777) and 3457¢26.5¢¢ E (717,761), G. Schaab, personal communication), [101]

1162 western Kenya, at an altitude of 1500–1700 m (Figure 1). Kakamega Forest is a mid-altitudinal tropical rainforest and considered to be the eastern most remnant of the lowland Congo Basin rainforests of Central Africa (Kokwaro

Figure 1. Satellite image (channel 5 of Landsat 7 ETM+, 05 Feb 2001) of Kakamega main forest and its five fragments in western Kenya with official forest boundaries as gazetted in 1933 (dashed line) and official boundaries of National and Nature Reserves (white line). Coordinates in UTM 36 N. [102]

1163 1988). Annual rainfall in Kakamega Forest is approximately 2007 mm (as averaged from FD records at Isecheno Forest Station from 1982 to 2001) and highly seasonal with a rainy season from April to November and a short dry season from December to March. The average monthly maximum temperature ranges from 18 to 29 C while the average monthly minimum ranges from 4 to 21 C (Muriuki and Tsingalia 1990).

Management history Kakamega Forest was first gazetted as Trust Forest under proclamation No. 14 in 1933 and has since been managed by the FD; in 1964 it was declared to be a Central Forest (Blackett 1994). Three small Nature Reserves, Isecheno, Kisere and Yala, were established and gazetted within the Forest Reserve in 1967 (Blackett 1994). In 1986, the northern part of Kakamega Forest called Buyangu together with the adjacent Kisere Forest was gazetted as Kakamega National Reserve and fell under management of the KWS. Today, Kakamega Forest is part Forest Reserve, part Nature Reserve and part National Reserve, and management is under the authority of both, FD and KWS, on behalf of the state.

Fragmentation and disturbance history Kakamega Forest is a highly fragmented and disturbed forest and has been continually exploited for many years due to the high surrounding population pressure (Kokwaro 1988; Wass 1995). The main forest block gazetted in 1933 by the FD to control human activities covered 23,777 ha (Kokwaro 1988, for original forest boundaries see Figure 1). The FD aimed mostly at provision of timber for local communities and commercial demand. Clear-felling of indigenous forest to make way for fast-growing exotic tree and softwood plantations was extensive under colonial forest service. Especially the southern parts of Kakamega were exploited until the late 1980s (Bennun and Njoroge 1999; Mitchell, 2004). Clearance for settlement and tea plantations slowed over the 1980s as forest protection was better enforced, but more areas were cleared south of the Yala river (Brooks et al. 1999). Furthermore, selective logging was intense in the past and the general trend of timber extraction showed a continued rise from 1933 to 1981 (Tsingalia 1988; Mutangah 1996). Consequently, the main agents of forest degradation have been mostly logging and extraction of commercially valuable timber, followed by charcoal burning, cattle grazing, shamba system farming, hunting for bush-meat, tree debarking and removal of dead trees for firewood (Oyugi 1996; Mitchell, 2004). In the early 1980s a presidential decree banned all indigenous tree species exploitation, leading to a halt of commercial logging, however, tree poaching and other illegal activities still exist. [103]

1164 Table 1. List of 22 disturbance survey sites in Kakamega Forest. Site No. Site name

Main forest/ Area Transect Area Management Protection fragmenta (ha)b length (m) surveyed (ha) regimec statusd

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

f f mf mf mf mf mf mf mf mf mf mf mf mf mf f f f f f f f

Malava Kisere Colobus Buyangu Shikusa Salazar I Salazar II Shamiloli Central II Central I Chemneko Vihiga Sawmill Isecheno II Isecheno I Chepsugor Ikuywa I Ikuywa II Yala Kibiri Ishiru Kaimosi

77 420 8245 8245 8245 8245 8245 8245 8245 8245 8245 8245 8245 8245 8245 1370 1370 1370 1178 1178 1178 65

1200 2600 2400 1600 1000 1000 2000 1000 1000 1000 1000 1000 1000 1000 1600 1000 1000 1600 2000 1000 1000 280

2.4 5.2 4.8 3.2 2.0 2.0 4.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 3.2 2.0 2.0 3.2 4.0 2.0 2.0 0.6

fd kws kws kws kws kws kws fd fd fd fd fd fd fd fd fd fd fd fd fd fd fd

fr nr nr nr nr nr nr fr fr fr fr fr fr nr nr fr fr fr nr nr fr fr

a

Abbreviationss: f, fragment; mf, main forest block. Area sizes obtained from satellite image 05 Feb 2001 Landsat 7 ETM+. c Abbreviations: fd, Forest Department; kws, Kenya Wildlife Service. d Abbreviations: fr, Forest Reserve; nr, National or Nature Reserve. b

As a consequence of the long fragmentation and disturbance history Kakamega Forest was reduced and broken up in several fragments over the last century and today the main forest block covers only 8245 ha (G. Schaab, personal communication; for fragment sizes see Table 1) comprising a heterogenous mixture of different succession stages including disturbed primary forest, secondary forest, clearings and glades, as well as tea and timber plantations (Bennun and Njoroge 1999). For more detailed information on the fragmentation and disturbance history of Kakamega Forest see Tsingalia (1988) and Mitchell (2004).

Methods Disturbance survey In February and April 2002 and in June and July 2003, disturbance surveys were carried out at 22 forested sites in Kakamega main forest and its peripheral fragments (for a complete list of all sites see Table 1). Twelve of the 22 sites [104]

1165

Figure 2. Location of 22 disturbance survey sites in Kakamega Forest (left) and the number of trees logged per hectare in the last 20 years for both, trees 610 cm in diameter and >10 cm in diameter for each site, respectively (right).

chosen for surveys were close to the 12 sites where Mutangah (1996) carried out his disturbance surveys in 1992/1994 (see Table 1); an additional 10 new sites where chosen where Mutangah (1996) had not carried out any surveys in the past (e.g. in the fragments Kisere, Malava and Kaimosi). This was done in order to obtain a large sample size of representative sites distributed over the whole Kakamega Forest. The sites chosen were not necessarily near points of easy access (Figure 2); in fact, many of the sites are located in the centre of the forest (e.g. No. 5, 9, 10). With many trails running through the whole of Kakamega Forest, it is easily accessible for local exploitation. At each site except for the smallest fragment (Kaimosi), transects were run at least 1000 m in length (Table 1). Transects sometimes followed existing trails, e.g. at Colobus site we chose some of the former overgrown monkey research transects established by Gathua in 1996 (Fashing and Gathua, in press). In all other cases where trails did not exist, we made our way through undergrowth along a line. Surveys included recording any of seven disturbance parameters in a belt [105]

1166 of 10 m on each side of the transect thereby covering a total area of 56.6 ha with a median of 2 ha per site (range 0.6–5.2). All disturbances recorded are thought to present mostly illegal activities. Disturbance parameters recorded were 1. the number of trees logged: For each tree stump the circumference was measured to calculate its diameter. Trees with a diameter of less than 10 cm were assumed to be collected mostly by women and used as firewood, whereas trees with a diameter of more than 10 cm were assumed to be cut mostly by men and used as polewood or timber. For each stump the approximate time since cutting was estimated to be either less than 20 years or more than 20 years for a distinction between recent and past logging, respectively. Age was estimated according to the degree of decomposition and the shape of the remaining tree stump, i.e. stumps with smooth surfaces or freshly cut stumps with clear cutting profiles were estimated to be logged in the last 20 years, whereas stumps with wavy or semi-decayed cut surfaces were estimated to be logged more than 20 years ago (following Mutangah 1996). Tree species were identified when possible; however, for trees having been logged more than 20 years ago, species identification was not always possible due to a higher level of decomposition. 2. the number of trees exhibiting any signs of debarking for medicinal use. 3. the number of charcoal kilns, i.e. areas with charcoal remains such as black half-burned pieces of wood and in some cases still burning charcoal heaps. 4. the number of sawing pits, i.e. pits used for cutting large trees. 5. the number of honey gathering sites, i.e. tree stems with bee-hives from which honey had been extracted. 6. the number of abandoned and current paths used by locals e.g. for firewood collection. 7. the number of cattle tracks used to bring cattle to the glades for grazing.

Data analysis We tested the influence of management regime and protection status on the disturbance parameters 1–7 for all 22 sites. For disturbance parameter 1, we tested the influence separately on the number of logged trees of two different age classes (trees logged in the last 20 years, trees logged more than 20 years ago) and two different size classes (diameter  10 cm, diameter >10 cm). For management regime we distinguished between sites being managed by the KWS (n = 6) and the FD (n = 16) (Table 1). For protection status we distinguished between sites with high protection priority i.e. National or Nature Reserves (n = 10) and sites with low protection priority i.e. Forest Reserves (n = 12) (Table 1). Consequently, 6 National/Nature Reserves are managed by the KWS and 4 National/Nature Reserves and 12 Forest Reserves by the FD. Furthermore, we tested the influence of fragmentation on the number of logged trees for both, the two different age and size classes. We calculated [106]

1167 t-tests (t) for normally distributed data with an adjustment in case variances were unequal and Mann–Whitney U-tests (U) for non-normally distributed data. We correlated the different disturbance parameters calculating nonparametric pairwise Spearman correlations. We compared our data from 12 sites (i.e. site No. 5, 7, 8, 11–13, 15–17, 19– 21) with data from Mutangah (1996) who quantified the same disturbance parameters in 1992/1994 at the same 12 sites. Although the sites were the same, transects were not; thus, data are not dependent data. For comparisons of the two data sets we calculated Pearson and Spearman rank correlations for normally and non-normally distributed data, respectively. Data analysis was carried out using JMP (1995).

Results Evidence for human impact was found at all our 22 sites with a median number of 21.1 disturbance events per hectare (q1 = 9.8, q3 = 44.6, range 1.8–81.5, n = 22). The sites Salazar II (No. 7) situated in the northern Kakamega National Reserve and managed by the KWS as well as Yala (No. 19) situated in the Yala Nature Reserve managed by the FD showed lowest disturbance levels with 2.8 and 4.9 disturbances per hectare, respectively (Table 2). The site Ishiru (No. 21) had highest disturbance levels with 68.5 disturbances per hectare; is situated at the southern forest edge and is managed by the FD.

Management, protection status and logging Of all seven disturbance parameters, logging of trees was the most widespread at all 22 sites (Table 2), thus providing the most useful indicator of forest disturbance in this study. Over a total survey area of 56.6 ha, we found 1023 logged trees from 68 species. The most frequent tree species logged were Funtumia africana (2.1 trees logged per hectare), Prunus africana (1.3), Celtis new species name: Celtis gomphophylla (1.0), Trilepisium madagascariensis (0.9), Diospyros abyssinica (0.8) and Aningeria altissima (0.7). The average diameter of tree stumps was 27.0 cm ± 9.6 (if not otherwise noted mean ± 1 SD). Past and present logging We could not find differences in the number of logged trees for past logging (i.e. more than 20 years ago) between different sites indicating that logging levels in the past might have been similar throughout Kakamega Forest (Figures 3a, b; management regime, U: Z = 0.26, p = 0.79; protection status, t: t = 2.32, df = 1, p = 0.15). In contrast, a significant effect of management regime and protection status was found for present logging (i.e. in the last [107]

1168

Table 2. Summary data of disturbance survey for all 22 sites.

[108]

Site No.

No. trees logged/ha >20 years

No. trees logged/ha <20 years

Total No. trees logged/ha

No. trees with signs of debarking/ha

No. charcoal kilns/ha

No. sawing pits/ha

No. honey gathering sites/ha

No. paths/ha

No. cattle tracks/ha

Total no. trees logged/ha in 1992/1994

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

3.8 1.9 2.9 5.0 1.5 3.0 2.0 6.0 2.0 2.0 1.5 6.0 1.0 0 0 8.0 0 3.5 1.3 3.0 10.5 0

5.4 8.5 2.3 4.1 1.5 2.0 0 53.5 8.0 10.0 40.5 15.0 36.0 9.0 12.5 34.5 3.8 33.5 1.5 45.4 65.0 30.0

9.2 10.4 5.2 9.1 3.0 5.0 2.0 59.5 10.0 12.0 42.0 21.0 37.0 9.0 12.5 42.5 3.8 37.0 2.8 48.9 75.5 30.0

1.7 1.7 0 0 0 0 0.3 0.5 0 0 0 3.0 0 1.0 2.5 0.5 1.3 0 1.3 0 0.5 6.7

0 0 0 0 1.0 0 0 2.0 0 0 4.5 2.5 0.5 0 0 0 1.3 1.0 0 0 0.5 0

2.1 1.0 0 0 0.5 3.0 0 1.0 2.5 0 0 0 0 0.5 0.3 0 0.3 0 0.8 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 5.0 0 0 0 0 0 0 0 0 0

8.8 2.3 0.2 0 1.5 2.0 0.5 1.0 7.5 1.0 6.5 7.0 0 2.0 0 6.5 5.9 0.5 0 13.0 5.0 0

0.4 0.2 0 0 0 0 0 4.5 0 0 0 0 0.5 0 11.3 0 1.9 0 0 0 0 0

– – – – 14.0 – 1.0 52.0 – – 74.0 29.0 8.0 – 16.0 83.0 23.0 – 11.0 99.0 77.0 –

For logging, numbers per hectare are given separately for trees logged >20 years ago, trees logged <20 years ago and for all trees logged. For a comparison of logging between today and 1992/1994, the total number of trees logged as recorded by Mutangah (1996) are given.

1169

Figure 3. Number of trees logged more than 20 years ago (a, b) and in the last 20 years (c, d) for sites under management by the Kenya Wildlife Service (KWS, n = 6) and the Forest Department (FD, n = 16) (a, c) and for sites having high protection priority (i.e. situated within National/ Nature Reserves, n = 10) and low protection priority (i.e. situated within Forest Reserves, n = 12) (b, d). Given are medians, quartils, minimum and maximum values and significance levels. n.s., not significant; * p < 0.05; **p < 0.005.

20 years) with fewer trees logged at sites managed by the KWS and with high protection priority (Figures 3c, d; management regime, t: t = 19.21, df = 1, p = 0.0004; protection status, U: Z =
2.54, p = 0.0111). Firewood and polewood/timber use in the last 20 years Traces of firewood collection (tree diameter  10 cm) were often found at sites managed by the FD but rarely at sites managed by the KWS (Figure 2). The FD-managed sites situated within Nature Reserves (No. 14, 15, 19), in the centre of the forest (No. 9, 10) and in central Ikuywa (No. 17) had the lowest levels of firewood collection (Figure 2). In contrast, sites at the forest edge (No. 8, 11, 12, 16, 18, 20, 21) adjacent to local settlements had the highest logging [109]

1170 levels (Figure 2). Tree cutting for polewood/timber (tree diameter >10 cm) had low levels at KWS sites, but high levels at FD sites (Figure 2). Both, firewood collection and logging for polewood/timber was significantly higher at FD sites as compared to KWS sites (firewood/ha: KWS median = 0, q1 = 0, q3 = 0.3, range 0–1.2, FD median = 5.5, q1 = 1.6, q3=17.3, range 0–26.0; U: Z =
3.04, p = 0.0024; polewood/ha: KWS 2.9 ± 2.6; FD 16.9 ± 12.7; t: F = 16.54, df = 1, p = 0.0007). Similarly, significant differences in the number of logged trees for both firewood and polewood/timber, were found between sites of high and low protection priority (firewood/ha: high protection median = 0, q1 = 0, q3 = 2.0, range 0–19.0, low protection median = 10.0, q1 = 1.8, q3 = 17.3, range 0–26.0; U: Z =
2.55, p = 0.011; polewood/ha: high protection median = 3.2, q1 = 1.5, q3 = 8.1, range 0–26.5, low protection median=15.4, q1 = 7.4, q3 = 26.3, range 2.5–47.5; U: Z =
2.61, p = 0.0092). Fragmentation and logging No differences were found between the main forest and the fragments for the number of trees logged more than 20 years ago and less than 20 years ago, as well as for the number of trees logged for firewood and for polewood/timber (trees logged >20 years ago: U: Z = 0.44, p = 0.64; trees logged < 20 years ago: U: Z = 0.83, p = 0.4037; firewood/ha: U: Z = 1.57, p = 0.1164; polewood/ha: U: Z = 0.03, p = 0.97). Re-assessment of logging: 1992/1994 and today No differences for the number of logged trees per hectare were found between our data and those of Mutangah’s (1996) survey in 1992/1994. This suggests a similar overall logging level (1992/1994: 40.6 ± 34.4, today: 29.2 ± 25.2; t: t = 0.92, df = 22 p = 0.37). Furthermore, we found a significant positive correlation between both data sets suggesting that transects at the same sites still have same logging levels after 10 years (Pearson: r = 0.72, p = 0.0088).

Management, protection status and other human impact For all other disturbance parameters differences between differently managed and protected sites were only found for the number of charcoal kilns with significantly lower numbers in highly protected sites (high protection: median = 0, q1 = 0, q3 = 0, range 0–1.0, low protection: median = 0.5, q1 = 0, q3 = 1.8, range 0–4.5; U: Z =
2.26, p = 0.024). However, in contrast to the number of logged trees, all other disturbance parameters were mostly rare events and appear to be indicators only for localized threats (Table 2). Burning of charcoal, e.g., seems to be a serious threat at the eastern (No. 11, 12) and western edge (No. 8) of the main forest block, whereas cattle tracks appear to be a problem mostly at Isecheno I (No. 15) and at the eastern edge of the forest (No. 8) (Table 2). In [110]

1171 general, no correlation could be found between the disturbance parameters when calculating non-parametric pairwise Spearman correlations (p > 0.05).

Discussion Status quo of human impact According to our survey human impact is found everywhere in Kakamega Forest with logging being most widespread. This confirms the expressions of alarm over the misuse and overexploitation of Kakamega’s forest resources through illegal human activities (Kowkaro 1988; Emerton 1991; Mutangah et al. 1992; Wass 1995; Oyugi 1996; Fashing et al. 2004). Our data support Mutangah’s (1996) survey from 1992/1994 indicating the highest logging levels occur in the most southerly part of the forest as well as along the western edge. Furthermore, some of the disturbed sites (e.g. No. 11, 21) in Mutangah’s (1996) survey have been degraded heavily in the meantime and the canopy cover reduced substantially (N. Saijita and C. Analo, personal communication). In both, Mutangah’s (1996) and our survey, the lowest logging levels were found in the northern Kakamega National Reserve, central Ikuywa and Yala.

Human impact in differently managed areas Our data do not only show the current status quo of the human impact on Kakamega Forest, but also reflect its management history in the last 20 years. Before 1986, when all of Kakamega Forest was managed by the FD, Colobus, Buyangu and Salazar sites (No. 3, 4, 6, 7) in the northern part were well known for intensive commercial logging through timber companies (Tsingalia 1988; Mitchell, 2004). Correspondingly, the number of trees logged more than 20 years ago appears to be equally high at those sites as compared to others. In 1986, the KWS took over the northern part of Kakamega Forest as a National Reserve and the changes in management appear to have resulted in changes in logging numbers in the last 20 years. Illegal tree poaching was reduced at sites under KWS management probably due to tightened security, whereas FD sites still experience higher tree poaching rates today. Furthermore, FD sites show various other local threats such as e.g. charcoal burning and cattle grazing. Under FD management, sites with high protection priority such as Yala and Isecheno Nature Reserves (No. 14, 15, 19) still show lower overall threat levels as compared to sites with low protection priority. For example, Fashing et al.’s (2004) results of a long-term study of tree populations in Kakamega Forest indicate that their study plots in Isecheno remained relatively undisturbed over the last 20 years. A decrease of pioneer species density by 21% in these sites are taken as evidence that the [111]

1172 forest is maturing towards a climax forest and that at least the conservation measures applied to Isecheno appear to have succeeded Fashing et al. 2004). Nevertheless, prospects for other severly disturbed sites are assumed to be bad, as is the general prognosis for Kakamega Forest if protection efforts are not increased and illegal exploitation by local people remains high, particularly on its periphery (Cords and Tsingalia 1982; Kokwaro 1988; Tsingalia 1988; Fashing et al. 2004). How do the two conservation boards KWS and FD differ in their management aims and strategies? The overall aim of the KWS is ‘to conserve, protect and sustainably manage the wildlife resources’ and its areas are set aside for conservation and tourism only (Wass 1995). People are not allowed to collect any forest products and these policies are strictly enforced through regular patrols by up to five game rangers (E.W. Kiarie, personal communication). The overall aim of the FD is to ‘enhance conservation and protection of indigenous forest, to improve the production of timber and fuelwood and to establish a framework for the long-term development forestry’ (Wass 1995). Some sites are also set aside for conservation, however, some used to be plantations of exotic tree species or mixtures of indigenous species, while others experienced enrichment planting (A. Oman, personal communication). Logging, tree debarking and charcoal burning is prohibited, whereas fuelwood collection was licenced until recently (A. Oman, personal communication). It appears that the FD has been largely restricted in its capacity to implement conservation policies effectively due to the lack of adequate resources in contrast to the better funded KWS, leading to insufficient levels of staffing, patrols, weaponry etc. These differences in resources might have led to different disturbance levels as found in our survey. Besides overall funding and the number of staff, other potential factors associated with management regime and effectiveness might be e.g. accessibility to the forest or proximity of the forest to neighbouring settlements, population density, community relations and compensation programs to locals. In a recent assessment of the impact of anthropogenic threats on 93 protected areas of 22 tropical countries park effectiveness was shown to correlate most strongly with density of guards i.e. the more guards the higher effectiveness (Brunner et al. 2001). Furthermore, effectiveness correlated with the level of deterrence of illegal activities in the parks and with the degree of border demarcation and existence of direct compensation programs for local communities (Brunner et al. 2001). However, it did not correlate with enforcement capacity (i.e. a composite variable of training, equipment and salary), accessibility, budget, number of staff working on economic development or education, or the local involvement of communities in park management (Brunner et al. 2001). To obtain more information on the factors influencing management effectiveness in Kakamega Forest more studies are highly recommended following the RAPPAM guidelines.

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1173 Management recommendations for Kakamega Forest The high human impact on Kakamega Forest especially along the western and eastern edge of the main forest block indicates an imminent danger of further fragmentation. The main forest block might fall into two separate forest blocks, i.e. Kakamega National Reserve in the North and Isecheno Nature Reserve in the South. To prevent this from happening in the near future, we strongly recommend following the management plan of forest zoning as outlined by the Kenya Indigenous Forest Conservation Programme (KIFCON 1994; Wass 1995): establishing a protection zone to provide a core for biodiversity conservation extending from the North to South; setting up a rehabilitation zone with enrichment planting where degradation has reached high levels; and establishing a subsistence use zone flanking the protection zones where local people are allowed to extract forest products. This forest zoning aims both, to maintain as much indigenous forest cover as possible and to permit optimal use of forest resources on a sustainable basis (Wass 1995). We recommend placing the protection zone under strict KWS management as our survey indicates that areas of Kakamega Forest managed by the KWS appear to hold surprisingly low disturbance levels despite high land-use pressure. The degradation and logging levels in the suggested subsistence zones are already alarming, so that we suggest enrichment planting there. Finally, encouragement of on-farm-forestry projects might provide resources in the long-term and thus might relieve the subsistence use zone. This is supported by the fact that a tree nursery run by the local grassroot conservation organization KEEP (Kakamega Environmental Education Program) at Isecheno Forest Station has been successfully nursing seedlings of both, indigenous and exotic tree species, for sale to local farmers. Beyond conservation measures for the main forest block, high protection priority must also be given to the low-disturbance sites central Ikuywa (No. 17), Yala (No. 19) and the 400 ha fragment of Kisere (No. 2). Kisere Nature Reserve is of particurlar conservation significance because it has been relatively undisturbed in the past and still harbours species-rich forest communities that include the rare DeBrazza monkeys (Cercopithecus neglectus) (Muriuki and Tsingalia 1990; Chism and Cords 1997). Although managed by KWS, it appears to have experienced increasing disturbance levels in the last few years (N. Saijita and C. Analo, personal communication). This might be due to the lack of ranger outposts in Kisere (KWS headquarters is at Buyangu), and the fact that the number of rangers (10–20) might not be sufficient to cover both, Buyangu and Kisere. Therefore, an immediate increase of regular ranger patrols to control logging more effectively is highly recommended, as suggested by previous authors (Kokwaro 1988; Mutangah 1996; Chism and Cords 1997).

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1174 Logging as indicator for quantitative threat assessment In our survey only logging appeared to be an effective indicator for human impact on the forest and might offer a valuable tool to conservation managers. First, the recording of the number of logged trees provides a quantitative, objective measure of the human impact on protected areas. Most other assessments of threat status and management effectiveness used qualitative rather than quantitative approaches (see e.g. Salafsky and Margoluis 1999; Brunner et al. 2001; Ervin 2003c). For example, following the RAPPAM methodology and using a questionnaire, the question arises whether the protected area managers themselves answering questions on their own management will supply objective answers. Consequently, our method collecting empirical data on the number of logged trees is more objective. Second, methods using logging as a disturbance indicator assess disturbance directly and not through biological indicators. Often, human impact is inferred from long-term studies on plant species composition or population structure as a biological indicator (e.g. Fashing et al. 2004). However, biological indicators can only assess the present situation resulting from past human impact. In contrast, quantifying disturbances directly can provide empirical data on the present human impact. Furthermore, logging as a disturbance indicator can enable us to differentiate between recent and past disturbance and might consequently help to evaluate past management policies. Third, despite its quantitative approach this method provides a simple, lowbudget method important especially for rapid and repeated assessment of disturbed forests. Repeated assessments are crucial especially in protected areas such as Kakamega Forest where heterogeneity in forest condition occurs over small spatial scales (Fashing et al. 2004). Consequently, surveys using logging as a disturbance indicator can provide the maximum amount of current up-todate and scientifically sound information for management planners in return for the effort and time involved. Finally, although the list of potential threats facing protected areas worldwide is long, logging appears to affect nearly 70% of more than 200 parks throughout the tropics (van Schaik et al. 1997) and emerges as one of the most hotly debated issues in tropical forest conservation (Rice et al. 1997; Bowles et al. 1998; Laurance 2001). Consequences of logging do not only include loss of habitat, but also changes in the microclimatic environment, erosion of soil and modification of fire regimes (Barlow et al. 2002; Cochrane and Laurance 2002) with the impact depending on the type of logging, i.e. whether commercial mechanized logging with heavy equipment or local exploitation of timber through e.g. pit-sawying and firewood collection. Furthermore, secondary effects of logging might be increased access to remote forested areas through the creation of roads and paths leading to further logging, forest colonization and hunting (Wilkie et al. 1992; Rice et al. 1997; Laurance 1998; Robinson et al. 1999). Consequently, logging appears to be a serious constant [114]

1175 threat to tropical forests worldwide making its validity as an useful indicator even more probable. Disturbance or impact assessments in combination with long-term studies on forest structure and composition after logging (e.g. Plumptre 1996; Chapman and Chapman 1997; Struhsaker 1997; Fashing et al. 2004) can provide important information on regeneration dynamics after human impact. Studies from Kakamega Forest indicate that regeneration from the severe human impact of the last century might be possible though not without rigorous conservation measures (Fashing et al. 2004, this study). Finally, repeated disturbance assessments are important to keep track of the human impact in protected areas and can provide feedback to management planners when evaluating past management decisions and setting up new conservation goals.

Acknowledgements The study was funded by the German Federal Ministry of Education and Research within the framework of BIOTA East Africa (01LC0025 / subprojects E03, E10 & E11). We thank the Kenyan Ministry for Education and Research for the permission to carry out research in Kakamega Forest, and the KWS and FD for granting us access to their reserves. We appreciate information on management by E.W. Kiarie (Senior Warden, Kakamega National Reserve) and A. Oman (Assistant District Forest Officer, Kakamega). We highly acknowledge field assistance by C. Analo and N. Saijita. G. Schaab kindly provided maps and data on forest patch sizes. We wish to thank the paperclub at Mainz, N. Mitchell, L. Todt, H.Todt, and two anonymous referees for comments on earlier drafts of the manuscript and K. Boehning-Gaese, H. Dalitz and M. Kraemer for overall support.

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Biodiversity and Conservation (2006) 15:1179–1191 DOI 10.1007/s10531-004-4693-x


Springer 2006

-1

Influence of forest types and effects of forestry activities on species richness and composition of Chrysomelidae in the central mountainous region of Japan MASASHI OHSAWA* and TAKUO NAGAIKE Yamanashi Forest Research Institute 2290-1 Saishoji, Masuho-cho, Minamikoma-gun, Yamanashi 400-0502, Japan; *Author for correspondence (e-mail: [email protected]; phone: +81-556-22-8001; fax: +81-556-22-8002) Received 4 May 2004; accepted in revised form 28 October 2004

Key words: Batophila acutangula, Biodiversity, Insect diversity, Larix kaempheri, Leaf beetles, Species composition, Species richness, Sphaeroderma tarsatum Abstract. Species richness and composition of the Chrysomelidae (Coleoptera) were studied in larch (Larix kaempheri [Lamb.] Carrie`re) plantations, secondary forests, and primary forests. In addition, the effects of forest management practices, such as thinning and long rotation, were examined in the larch plantation. The species richness of Chrysomelidae was higher in the larch plantation than in the secondary forest or in the primary forest. Among the larch plantations, the species richness in old-aged plantations was higher than that in middle-aged plantations. The composition of the beetle assemblages in the larch plantation differed from that in the secondary forest or in the primary forest. Exosoma akkoae (Chujo), Batophila acutangula Heikertinger, and Calomicrus nobyi Chujo were caught with a bias toward the larch plantation. Longitarsus succineus (Foudras) and Sphaeroderma tarsatum Baly were caught more in the secondary forest and the primary forest, respectively. More B. acutangula and S. tarsatum were caught in stands where their host plants occurred at higher rates. Species richness of understory plants was an important factor for chrysomlid species richness, and frequency of host occurrence affected the number of individuals of leaf beetles examined. It seems that forest types and forest management practices affect host plants as well as Chrysomelidae, and that these effects on the host plants also influence chrysomelid assemblages.

Introduction The original vegetation of forestal area in the central mountainous region in Japan is considered as mixed forest of broad-leaved trees and coniferous trees dominated by Quercus crispula Blume. Since the 1940s, however, primary forests have rapidly diminished as a result of forestry activities, and now the area is known for its larch plantations. To achieve ecologically sustainable forest management in this region, studies were started to elucidate the status of biodiversity in forests and effects of forest management practices in the area, i.e., a basic information necessary for ecologically susutainable management. The study area is largely occupied by Japanese larch (Larix kaempferi [Lamb.] Carrie`re) plantations and secondary broad-leaved forests, dominated by [119]

1180 Quercus crispula Blume, (41 and 33% of the total area, respectively). Larch plantations were established for timber production; thinning has been conducted twice within a 45-year period in this area, in order to enhance growth of dominant trees. Long rotations have been adopted for some larch plantations to obtain high quality wood. Secondary forests were formerly used to produce firewood, charcoal, etc. Now, however, they are mostly left unattended. Because larch plantations and secondary forests occupy large areas, their role in conserving biodiversity is important. Primary forests, though fragmented today, should also be considered important because of the potential presence of rare indigenous species inhabiting in them. Three forest types (larch plantation, secondary forest, and primary forest) and two management practices frequently used in larch plantations (thinning and long rotations) were chosen for this study. Leaf beetles (Chrysomelidae, Coleoptera) are small in size (less than 1.5 cm in general) and their antennas are usually less than half the length of their bodies. They distribute worldwide and about 500 species have so far been reported in Japan (Kimoto and Takizawa 1994). Though they are related with cerambycid beetles which are saproxylic in their larval stage, leaf beetles feed on leaves, stems or roots in both larval and adult stages and are regarded as phytophagous herbivores. The diversity of Cerambycidae, saproxylic beetles, was investigated in this area, and it was reported that higher species richness of the beetles was observed in secondary forests than in larch plantations or primary forests, and that thinning increased cerambycid diversity in larch plantations (Ohsawa 2004). This time, leaf beetles were chosen for the study of diversity to clarify the effects of different forest types and forestry activities on the phytophagous herbivores. There has been one report published on the subject of the diversity and conservation of Chrysomelidae (Greatorex-Davies and Sparks 1994). In this study, the diversity of leaf beetles and some other insects was investigated in the rides of woodlands, and it has been reported that both species richness and abundance of leaf beetles declined with increasing levels of shade, and that rides must be actively managed to keep light levels high if species richness and abundance are to be maintained. The purpose of this study was to compare the species richness and composition of Chrysomelidae, phytophagous herbivores, in larch plantations, secondary forests, and primary forests, and assess the effects of forest practices, such as thinning and long rotations, on the diversity of this family in order to obtain information for conservation of leaf beetles in forest area.

Methods Study site This study was conducted in forests at altitudes ranging from 1390–1770 m (mean annual precipitation: ca. 1120 mm; mean annual temperature: ca. 9.9 C) [120]

1181 in the central mountainous region of Japan (Figure 1 of Ohsawa 2004). Three forest types in this area, i.e., larch plantations, secondary broad-leaved forest (secondary forest), and primary or near-primary broad-leaved forest (primary forest), were selected for the investigation. A total of 46 stands were chosen: 24 stands of larch plantations, aged 21–79 years; 11 stands of secondary forest, dominated by Q. crispula, with other broad-leaved trees such as Ilex macropoda Miq., Fraxinus sieboldiana Blume var. serrata Nakai, Betula platyphylla Sukatchev var. japonica (Miq.) Hara, Acer spp ., and Prunus spp.; and 11 stands of primary forest dominated by Q. crispula, but mixed with conifers such as Abies homolepis Sieb. et Zucc. and Tsuga diversifolia (Maxim.) Masters, with a high proportion of conifers in two of the stands. Larch plantations consisted of three types: 13 stands of middle-aged (21–45 years old), six stands of recently thinned (middle-aged, 1.5 and 2.5 years after thinning), and five stands of old (58– 79 years old) plantations. Survey of leaf beetles A Malaise trap was set in each of the 46 stands to capture insects, and leaf beetles were separated from among those trapped. To minimize forest edge effects and micro-topographical differences within each stand, efforts were made to set each trap in a typical spot for the stand on a simple slope (or in a flat area if there was no slope) in the interior of the stand. Leaf beetles were captured in three 14-day periods: in middle to late June, in middle to late July, and in early to mid-August. Because of high elevation, June to August is an appropriate period for capturing many beetles including Chrysomelidae, according to the results of an investigation conducted on insects through spring to autumn in this area (Ohsawa unpublished data). Dry specimens were prepared for all species of leaf beetles trapped, and were kept in the Yamanashi Forest Research Institute. Environmental factors Environmental factors examined were altitude, gradient, larch plantation versus natural broad-leaved forest mostly dominated by Q. crispula, i.e., secondary forest and primary forest (larch plantation vs. natural broadleaved forest), openness of the canopy, the numbers of vascular plants (<2 m and 2 m in height), and age of larch plantations. In addition, for two chrysomelid species, Batophila acutangula Heikertinger and Sphaeroderma tarsatum Baly, commonly found in this area, the occurrence of their host plants was investigated as an environmental factor. For the openness of the canopy, a digital camera with a fish-eye converter (Nikon Cool Pix 950) was used to take a hemispherical photograph of the canopy from a height of 1 m above the ground at five locations. The openness of the canopy was [121]

1182 an average of five values calculated from the hemispherical photographs using HEMIPHOTO (ter Steege 1993). The number of vascular plant species was investigated in 11 stands. Vascular plants were divided into two groups by their height: the one shorter than 2 m and the other 2 m or more. Counted in the numbers of vascular plants were, for the first group (<2 m), all species found in 40 plots (1 · 1 m), and for the second group (2 m), all species found in 40 plots (5 · 5 m) for each stand (Nagaike et al. 2003). Rubus spp. (Rubus mesogaeus Focke, R. koehneanus Focke, R. palmatus Thunb. var. coptophyllus A. Gray, and R. pungens Cambesse`des var. oldhamii [Miq.] Maxim.) and bamboo grass (Sasa nipponica Makino et Shibata and Sasamorpha borealis [Hack.] Nakai) were host plants of B. acutangula and S. tarsatum, respectively. In each 46 stand, the occurrences of Rubus spp. and bamboo grass were investigated in an area of 10 · 30 m including in the center a spot where a Malaise trap was set in. The occurrence of Rubus spp. in stands was ranked at four degrees (0: not found; 1: found in less than 10% of the area; 2: found in at least 10% and less than 25% of the area; and 3: found in at least 25%). The occurrence of bamboo grass was also ranked at four degrees (0: not found; 1: occupied less than 10% of the area; 2: occupied at least 10% and less than 25%; and 3: occupied at least 25%).

Statistical analyses One way ANOVA with Bonferroni’s post-hoc test (Bonferroni test) was conducted to compare the number of species, the number of individuals, and Shannon-Wiener diversity index (H¢, Magurran 1988) among the forest types. Logarithmic transformation was performed for the number of individuals before Bonferroni test. Simple regression analysis was conducted to identify the environmental factors that affected the species richness of leaf beetles. The regression analysis was also applied to the relationship between the numbers of two chrysomelid species and occurrence of their host plants. Detrended correspondence analysis (DCA) was applied to compare the composition of leaf beetles in all investigated stands. The analysis was conducted with the software package, PC-ORD (McCune and Mefford 1999). Bonferroni test was applied to compare DCA scores (the first and second axes) among forest types. To define the meaning of the first two axes of DCA, simple correlation coefficients were calculated between the two axes and stand-structure parameters, such as the number of species, the number of individuals, H¢, larch plantation vs. natural broad-leaved forest, altitude, gradient, openness of the canopy, the number of vascular plants (<2 m and 2 m in height, for 11 stands) and forest age (for larch plantations), Indicator species analysis (Dufreˆne and Legendre 1997) was carried out to compare species of Chrysomelidae among the forest types and to identify species associated with each forest type. [122]

1183 Results Number of species, individuals, and diversity index In this study, 1303 individuals of 41 species were captured. The species with the highest number of individuals captured was Aphthona perminuta Baly (635), followed by S. tarsatum (201), Exosoma akkoae (Chujo) (92), Zipangia lewisi (Jacoby) (54), Syneta adamsi Baly (51), B. acutangula (47), Cryptocephalus amiculus Baly (30), Stenoluperus nipponemsis (Laboissiere) (29), Pseudoliprus nigritus (Baly) (26), and Zeugophora annulata (Baly) (25). The average number of species, the average number of individuals, and the average H¢ in each forest type are shown in Table 1. The number of species and H¢ in the larch plantation was significantly higher than those in the secondary forest or in the primary forest (Bonferroni test, p < 0.05). The number of individuals was not significantly different among three forest types. In the larch plantation, the number of species and that of individuals were significantly higher in the old plantation than in the middle-aged plantation (Bonferroni test, p < 0.05, Table 2). H¢ was not significantly different among three types of larch plantations.

Environmental factors Among seven environmental factors, openness of the canopy, larch plantation vs. natural broad-leaved forest, age of larch plantation, and the number of plant species (<2 m in height) had a significant regression coefficient with the number of chrysomelid species (Table 3). The species caught in larch plantations were in higher numbers than in natural broad-leaved forests. Among larch plantations, older plantations had more chrysomelid species. Chrysomelid species were caught in higher number in stands with wider openness of the canopy and with more plant species (<2 m in height). Other factors such as gradient, altitude, and plant species (2 m in height) did not significantly affect the number of species.

Table 1. Numbers of chrysomelid species, numbers of individuals, and diversity index (H¢) in three forest types.

Larch plantation Secondary forest Primary forest

n

Number of species (Mean1) ± SD)

Number of individuals2) (Mean1) ± SD)

H¢ (Mean1) ± SD)

24 11 11

5.9a ± 2.1 3.6b ± 2.1 3.6b ± 2.1

28.6 ± 26.3 19.2 ± 30.5 36.8 ± 41.0

1.8a ± 0.5 1.2b ± 0.9 0.8b ± 0.8

1) For each comparison, different alphabetical letters indicate mean with a significant difference (Bonferroni test, p < 0.05). 2) Bonferroni test was performed after logarithmic transformation.

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1184 Table 2. Numbers of chrysomelid species, numbers of individuals and diversity index (H¢) in three types of larch plantations.

Middle aged plantation Recently thinned plantation Old plantation

n

Number of species (Mean1) ± SD)

Number of individuals2) (Mean 1) ± SD)

H¢ (Mean1) ± SD)

13 6 5

5.4a ± 2.1 5.2ab ± 1.3 8.0b ± 1.2

19.0a ± 13.8 21.8ab ± 10.4 61.8b ± 39.2

1.9 ± 0.6 1.9 ± 0.3 1.7 ± 0.7

1) For each comparison, different alphabetical letters indicate mean with a significant difference (Bonferroni test, p < 0.05). 2) Bonferroni test was performed after logarithmic transformation.

Significant correlation coefficients were obtained by calculation between the number of B. acutangula captured and the occurrence of Rubus spp., and between the number of S. tarsatum and the occurrence of bamboo grass (Table 4).

Species composition The results of detrended correspondence analysis are shown in Figure 1. The scores of the second axis were higher in the larch plantation than those in the secondary forest or in the primary forest (Bonferroni test, p < 0.05). There was no significant difference between the scores in the secondary forest and those in the primary forest. A stand-structure parameter which possesses a significant correlation with the first axis was H¢, and that with the second axis was larch plantation vs. natural broad-leaved forest (Table 5). Correlations between the other stand-structure parameters and the two axes were not significant. Table 3. Relationship between number of species and environmental factors (simple regression analysis). Environmental factors

Standardized partial regression coefficient

Openness of the canopy Altitude Gradient Larch plantation vs. Natural broad-leaved forest Age of foresta Number of vascular plant species (<2 m in height)b Number of vascular plant species (2 m in height)b a

The analysis was applied for larch plantations. The analysis was applied for 11 stands. *p < 0.05; **p < 0.01. b

[124]

0.39** 0.13 0.01
0.49** 0.50* 0.74** 0.13

1185

Figure 1. Detrended correspondence analysis ordination (first and second axes) performed on leaf beetles in 46 stands in central Japan.

Indicator species analysis showed that E. akkoae, B. acutangula, and C. nobyi each had a significant indicator value (Ind Val) for the larch plantation (Table 6). Longitarsus succineus (Foudras) and S. tarsatum showed a significant Ind Val for the secondary forest and the primary forest, respectively. Among three types of larch plantation, A. perminuta, S. adamsi, and C. amiculus each had a significant Ind Val for the old larch plantation, and E. akkoae had a significant Ind Val for the recently thinned plantation (Table 7).

Discussion Species richness Species richness of leaf beetles in the larch plantation was significantly higher than that in the secondary forest or in the primary forest. Nagaike (2002) Table 4. Simple correlation coefficients for two chrysomelid speices between the number of individuals and occurrence of host plants. Host plants

Bamboo grass Rubus spp.

Correlation coefficients S. tarsatum

B. acutangula

0.69** –a

– 0.33*

a Not analyzed. *p < 0.05; **p < 0.01.

[125]

1186 Table 5. Simple correlaton coefficients between scores of detrended corespondence analysis (first two axes) and stand-structure parameters. Parameter

Axis 1

Number of leaf beetle species Number of leaf beetle individuals H¢ Larch plantation vs. Natural broad-leaved forest Openess of the canopy Altitude Gradient Number of vascular plant species (<2 m in height)a Number of vascular plant species (2 m in height)a Age of treeb

0.22
0.14 0.31* 0.07 0.12 0.06 0.06 0.50
0.15
0.02

Axis 2 0.25
0.06 0.28
0.53** 0.28
0.10
0.09 0.54 0.22
0.21

a

For 11 stands. For larch plantation. *p < 0.05; **p < 0.01. b

studied diversity of vascular plants in this area and reported that species richness was significantly higher in larch plantations than in secondary forests. This result in higher species richness of vascular plants coincides with higher species richness of Chrysomelidae in the larch plantation than in the secondary forest. In this study, species richness of leaf beetles was higher in stands with higher species richness of understory plants (<2 m in height), though the relationship between species richness of leaf beetles and species richness of the plants (2 m) was not clear. Because leaf beetles feed on leaves, stems, or roots of various plants, and because host plants of the beetles vary with the beetle species, it seems that forests with more plant species enable chrysomelid species to inhabit in them in higher numbers. Among larch plantations, the old larch plantation had a higher species richness of leaf beetles than did the middle-aged plantation. This result corresponds with the result of regression analysis that the higher number of chrysomelid species was caught in older larch plantations. Species richness of vascular plants was higher in the old plantation than in the middle-aged plantation, though the difference was not significant (Nagaike et al. 2003). They also found that plant species composition was different between the middle-aged plantation and the old plantation. This difference may have resulted in a higher species richness of Chrysomelidae in the old plantation than in the middle-aged plantation. Further investigations will be necessary to clarify this point. Thinning did not cause significant difference in species richness of leaf beetles, though the recently thinned plantation had wider openness of the canopy than the middle-aged plantation did (Bonferroni test, p < 0.01). S. tarsatum and B. acutangula had a significant Ind Val for the primary forest and the larch plantation, respectively. However, more individuals of both beetles were found in stands with higher occurrences of host plants. For [126]

1187 both species, significant correlations were ascertained by calculation between the number of the beetles and the occurrence of the host plants. This, therefore, seems to suggest that the numbers of both chrysomelid species are affected by the occurrence of their host plants.

Table 6. Indicator value (Ind Val) of chrysomelid species for three forest types. Species

Ind Val

Forest type

Exosoma akkoae (Chujo) Batophila acutangula Heikertinger Sphaeroderma tarsatum Baly Calomicrus nobyi Chujo Aphthona perminuta Baly Zeugophora annulata (Baly) Pseudoliprus nigritus (Baly) Longitarsus succineus (Foudras) Stenoluperus nipponensis (Laboissiere) Syneta adamsi Baly Zipangia lewisi (Jacoby) Cryptocephalus amiculus Baly Oomorphoides cupreatus (Baly) Stenoluperus cyaneus (Baly) Stenoluperus bicarinatus (Weise) Sphaeroderma placidum Harold Basilepta balyi (Harold) Syneta brevitibialis Kimoto Hesperomorpha hirsuta (Jacoby) Cryptocephalus confusus Suffrian Chrysomelidae sp.1 Aspidomorpha indica Boheman Oomorphus japanus Jacoby Lochmaea capreae (Linnaeus) Agelasa nigriceps Motschulsky Calomicrus cyaneus (Jacoby) Cryptocephalus fortunatus Baly Asiorestia obscuritarsis (Motschulsky) Zeugophora unifasciata (Jacoby) Lilioceris lewisi (Jacoby) Pyrrhalta annulicornis (Baly) Zeugophora bicolor (Kraatz) Lema cirsicola Chujo Chaetocnema concinna (Marsham) Chaetocnema ingenua (Baly) Lipromela minutissima (Pic) Sphaeroderma nigricolle Jacoby Japonitata nigrita (Jacoby) Sangariola punctatostriata (Motschulsky) Taumacera tibialis (Jacoby) Chrysolina virgata (Motschulsky)

53.0* 47.7* 47.3* 45.8* 42.5 34.4* 29.2 25.7* 23.8 16.8 16.0 15.5 14.5 14.1 14.0 12.5 9.1 9.1 9.1 9.1 9.1 9.1 8.3 8.3 8.3 6.8 6.2 6.2 6.2 4.2 4.2 4.2 4.2 4.2 4.2 4.2 4.2 4.2 4.2 4.2 4.2

Larch plantation Larch plantation Primary forest Larch plantation Primary forest Larch plantation Larch plantation Secondary forest Primary forest Primary forest Larch plantation Larch plantation Larch plantation Secondary forest Primary forest Larch plantation Secondary forest Secondary forest Secondary forest Secondary forest Primary forest Primary forest Larch plantation Larch plantation Larch plantation Secondary forest Primary forest Secondary forest Secondary forest Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation Larch plantation

*p < 0.05. [127]

1188 Table 7. Indicator value (Ind Val) of chrysomelid species for larch plantation types. Species

Ind Val

Plantation type

A. perminuta S. adamsi E. akkoae C. amiculus S. tarsatum S. nipponensis Z. annulata B. acutangula P. nigritus C. nobyi P. annulicornis Z. bicolor C. fortunatus L. minutissima S. nigricolle J. nigrita S. punctatostriata L. succineus O. cupreatus Z. lewisi L. cirsicola C. concinna A. obscuritarsis S. bicarinatus C. cyaneus L. capreae A. nigriceps O. japanus S. cyaneus S. placidum C. ingenua L. lewisi T. tibialis Z. unifasciata C. virgata

71.5* 69.1* 67.3* 45.7* 38.2 36.2 31.6 24.3 23.3 23.1 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 20.0 18.1 16.7 16.7 16.7 15.4 15.4 14.4 14.4 11.3 11.3 9.3 7.7 7.7 7.7 7.7 7.7

Old plantation Old plantation Recently thinned plantation Old plantation Old plantation Old plantation Recently thinned plantation Recently thinned plantation Middle-aged plantation Recently thinned plantation Old plantation Old plantation Old plantation Old plantation Old plantation Old plantation Old plantation Old plantation Middle-aged plantation Old plantation Recently thinned plantation Recently thinned plantation Recently thinned plantation Middle-aged plantation Middle-aged plantation Old plantation Old plantation Old plantation Old plantation Old plantation Middle-aged plantation Middle-aged plantation Middle-aged plantation Middle-aged plantation Middle-aged plantation

*p < 0.05.

Species composition As the results of DCA showed that scores in the second axis was higher in the larch plantation than in the secondary forest or in the primary forest, the composition of Chrysomelidae in the larch plantation differed from that in the secondary forest or in the primary forest. Six chrysomelid species had significant Ind Vals in relation to the three forest types in this study. E. akkoae, B. acutangula, and C. nobyi each showed a significant Ind Val for the larch plantation. B. acutangula and C. nobyi feed on Rubus spp. and Clematis apiifolia DC., respectively (Kimoto and Takizawa [128]

1189 1994). The host plants for E. akkoae are unknown. L. succineus having a significant Ind Val for the secondary forest feeds on Artemisia spp. S. tarsatum had a significant Ind Val for the primary forest and feeds on bamboo grass. It is interesting that host plants of these chrysomelid species having significant Ind Vals for certain forest types in this study were not dominant tall trees, but understory herbs.

Comparison with a saproxylic family Cerambycid beetles, though closely related to Chrysomelidae, are mostly saproxylic in their larval stage. Cerambycidae had higher species richness in the secondary forest than in the larch plantation or in the primary forest (Ohsawa 2004). On the other hand, more species of Chrysomelidae inhabited in the larch plantation than in the secondary forest or in the primary forest. Among forest management practices for larch plantations, long-rotations brought about high species richness for Chrysomelidae, whereas thinning increased species richness for Cerambycidae (Ohsawa 2004). It seems that ecological differences between these families, i.e., saproxylic (Cerambycidae) and phytophagous herbivores (Chrysomelidae), can be attributed to the difference in species richness with these forest types and forest management practices. The amount of dead wood or decayed wood affects the species richness of saproxylic beetles (Martikainen et al. 2000), and the secondary forest (unattended) has more amount and variety of dead or decayed wood than does the larch plantation (well managed) (Green and Peterken 1997; Kirby et al. 1998; Fridman and Walheim 2000). The recently thinned larch plantation contains more newly-made stumps and woody debris as compared with the middle-aged plantation. Consequently, there are more cerambycid species in the secondary forest and in the recently thinned larch plantation (Ohsawa 2004). On the other hand, the larch plantation had higher chrysomelid species richness than did the secondary forest or the primary forest, probably because leaf beetles are phytophagous herbivores and larch plantations have higher species richness of the plants. In this study, higher species richness of understory plants positively influenced the species richness of Chrysomelidae, and some understory plants were important hosts of leaf beetles characteristic to each forest type. More precise attention should be paid to understory plants for diversity conservation of leaf beetles, phytophagous herbivores, than for that of saproxylic beetles.

Conclusion The larch plantation had a higher species richness of Chrysomelidae than did other two natural forests. However, the composition of chrysomelid assemblage in the larch plantation was different from that in the primary forest which is considered to have indigenous species in the area. Among the three types of [129]

1190 larch plantation, the species richness of Chrysomelidae was higher in the old plantation than in the middle-aged plantation. Long rotations of larch plantations seem favorable for chrysomelid diversity. No difference in chrysomelid species composition between the secondary forest and the primary forest could be detected by DCA. Since the primary forest which is thought to contain indigenous species has diminished and fragmented, the secondary forest is expected to maintain these leaf beetles. However, considering that the secondary forest contained only 43% (6/14 species) of species caught in the primary forest in this study, the forest type may not be sufficient to maintain the beetle assemblage in the primary forest. This study showed that species richness of understory plants is an important factor for chrysomelid species richness, and that the frequency of host occurrence significantly affects the number of individuals. Because forest types affect plant diversity (Qian et al. 1997; Nagaike 2002; Nagaike et al. 2003), it is likely that both forest types and forest management practices affect host plants as well as Chrysomelidae, and that these effects on the host plants also influence chrysomelid assemblages.

Acknowledgements We wish to thank Mr. Akihiro Ohashi, Gifu Forest Science Institute, and Dr. Masahiro Isono, Forestry and Forest Products Research Institute, Japan, for identifying several leaf beetles and helpful advice. We are also grateful to Mr. Masao Osawa for correction of English for the manuscript and helpful advice. We deeply appreciate technical assistance given me by Mrs Miki Saso, and the helpful advice and encouragement by members of Yamanashi Forest Research Institute.

References Dufreˆne M. and Legendre P. 1997. Species assemblages and indicator species: the need for a flexible asymmetrical approach. Ecol. Monogr. 67: 345–366. Fridman J. and Walheim M. 2000. Amount, structure, and dynamics of dead wood on managed forestland in Sweden. Forest Ecol. Manage. 131: 23–36. Greatorex-Davies J.N. and Sparks T.H. 1994. The response of Heteroptera and Coleoptera species to shade and aspect in rides of coniferised lowland woods in Southern England. Biol. Conserv. 67: 255–273. Green P. and Peterken G.F. 1997. Variation in the amount of dead wood in the woodlands of the Lower Wye Valley, UK in relation to the intensity of management. Forest Ecol. Manage. 98: 229–238. Kimoto S. and Takizawa H. 1994. Leaf Beetles (Chrysomelidae) of Japan. Tokai University Press, Tokyo. Kirby K.J., Reid C.M., Thomas R.C. and Goldsmith F.B. 1998. Preliminary estimates of fallen dead wood and standing dead trees in managed and unmanaged forests in Britain. J. Appl. Ecol. 35: 148–155. [130]

1191 Magurran A.E. 1988. Ecological Diversity and its Measurement. Princeton University Press, Princeton, NJ. Martikainen P., Siitonen J., Punttila P., Kaila L. and Rauh J. 2000. Species richness of Coleoptera in mature managed and old-growth boreal forests in southern Finland. Biol. Conserv. 94: 199–209. McCune B. and Mefford M.J. 1999. PC-ORD. Multivariate Analysis of Ecological data, version 4. MjM Software Design, Gleneden Beach, Oregon. Nagaike T. 2002. Differences in plant species diversity between conifer (Larix kaempferi) plantations and broad-leaved (Quercus crispula) secondary forests in central Japan. Forest Ecol. Manage. 168: 111–123. Nagaike T., Hayashi A., Abe M. and Arai N. 2003. Differences in plant species diversity in Larix kaempferi plantations of different ages in central Japan. Forest Ecol. Manage. 183: 177–193. Ohsawa M. 2004. Species richness of Cerambycidae in larch plantations and natural broad-leaved forests of the central mountainous region of Japan. Forest Ecol. Manage. 189: 375–385. Qian H., Klinka K. and Sivak B. 1997. Diversity of the understory vascular vegetation in 40 year-old and old-growth forest stands on Vancouver Island, British Columbia. J. Veg. Sci. 8: 773–780. ter Steege H. 1993. HEMIPHOTO, A Programme to Analyze Vegetation Indices, Light Quality from Hemispherical Photographs. The Tropebos Foundation, Wageningen.

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Biodiversity and Conservation (2006) 15:1193–1217 DOI 10.1007/s10531-004-8230-8


Springer 2006

-1

The banana forests of Kilimanjaro: biodiversity and conservation of the Chagga homegardens ANDREAS HEMP Department of Plant Physiology, Universita¨t Bayreuth, Universita¨tsstr. 30, 95440 Bayreuth (e-mail: [email protected]; phone: 0921-552630; fax: 0921-552642) Received 18 May 2004; accepted in revised form 7 December 2004

Key words: Agroforestry, Apophytes, Coffee–banana planations, East Africa, Epiphytes, Kilimanjaro, Neophytes, Ruderal vegetation, Tropical montane forest Abstract. Natural flora, vegetation, diversity and structure of 62 traditional coffee–banana plantations on Kilimanjaro were investigated and compared with the other vegetation formations on this volcano on basis of over 1400 plots following the method of Braun-Blanquet. The vegetation of the so-called Chagga homegardens belongs floristically to the formation of ruderal vegetation forming two main communities that are determined by altitude. These coffee–banana plantations maintain a high biodiversity with about 520 vascular plant species including over 400 non-cultivated plants. Most species (194) occurring in the Chagga homegardens are forest species, followed by 128 ruderal species, including 41 neophytes. Typical of the agroforestry system of the Chagga homegardens is their multilayered vegetation structure similar to a tropical montane forest with trees, shrubs, lianas, epiphytes and herbs. Beside relicts of the former forest cover, which lost most of their former habitats, there are on the other hand (apophytic) forest species, which were directly or indirectly favoured by the land use of the Chagga people. High demand of wood, the introduction of coffee varieties that are sun-tolerant and low coffee prizes on the world marked endanger this effective and sustainable system.

Introduction Humans have continuously inhabitated the slopes of Mt. Kilimanjaro for the last 2000 years (Schmidt 1989). However, during the last decades the human population increased dramatically. According to ethnographic studies of Widenmann (1899) 50,000–60,000 Chagga people were estimated to live on Mt. Kilimanjaro in 1895. In 2002 the census counted 1,053,204 people (National Bureau of Statistics 2003). As such, the population has multiplied 20 times since 1895. Most of the population is concentrated at an altitude between 1000 and 1800 m, with densities varying from 500 to 1000 people per km2 in some areas (FAO 1986; Timberlake 1986). Here, within the submontane zone, a very remarkable kind of land use prevails: dense ‘banana forests’ with a scattered upper tree layer, the so-called Chagga homegardens, in Chagga language ‘vihamba’ (the term ‘homegarden’ refers to the small size and subsistence-level of the farms, cp. Nair 1993). Due to this sustainable and well developed agroforestry system (cp. e.g. Fernandes et al. 1984) degradation in this vegetation belt is rare, despite the enormous population. In their homegardens the [133]

1194 Chagga use four vegetation layers. Under a tree layer, which provides firewood, fodder and shadow, banana trees (in about 25 varieties, cp. Simmonds 1966) are grown and under the bananas coffee trees, and under these vegetables. This multilayer system maximizes the use of limited land. The area is irrigated by a network of canals fed by main furrows originating from the montane forest. Rough estimates give over one thousand furrows of varying lengths and capacities (Ramsay 1965) some dating back to the 17th century (Hemp and Winter 1999). This farming system evolved over several centuries and did not change much over the last decades compared with the land uses in the lower zones. This densely populated coffee–banana belt stretches on the climatically most favourable zone of the southern and south-eastern slopes (Figure 1) over an area of 1000 km2. The Chagga live within their homegardens in single dwellings; villages as such do not exist. But, along the main roads centres with church, village council, schools and some shops are situated. The average size of a homegarden varies between 0.5 and 1.7 ha (O’Kting’ati and Kessy 1991; Mdoe and Wiggins 1997). Livestock – cattle, goats, sheep and pigs – and sometimes poultry are kept in stalls. Women and children spend a great part of the day collecting grass along paths, fields and forest edges and on steep meadow slopes. Pasture farming is rare in submontane zone due to intensive agriculture. Therefore (based on the nature of their components) the Chagga homegardens can be classified as an agrisilvicultural system (cp. Nair 1993). Bee-keeping plays an important role. The chagga homegardens were the subject of different studies dealing mainly with socio-economic (Clemm 1963; Brewin 1965; Fernandes et al. 1984; O’Kting’ati and Kessy 1991; Mdoe and Wiggins 1997) or ethnobotanical and ethnozoological aspects (O’Kting’ati et al. 1984; Hemp 1999; Hemp 2001). The array of cultivated species was already described in detail by the first scientists on Kilimanjaro e.g. Volkens (1897) or Widenmann (1899). The aim of the present study is to describe natural flora, vegetation and structure of the Chagga homegardens and to highlight their function for biodiversity and as a refuge area for natural plant species. For this purpose the species composition of this man-made habitat is compared with all vegetation formations of Mt. Kilimanjaro as presented by Hemp (2001a). In a parallel study the function of the Chagga homegardens as a habitat of endangered and endemic grasshopper species was investigated on the same plots (Hemp in press).

Study area Mt. Kilimanjaro, a relic of an ancient volcano, rising from the savanna plains at 700 m elevation to a snow-clad summit of 5895 m altitude is located 300 km south of the equator in Tanzania on the border with Kenya. Its climate is characterized by a bimodal rainfall pattern with the long rains from March to May forming the main rainy season, and the short rains centred around the [134]

1195

Figure 1. Land use and vegetation cover of the study area with location of plots (dots).

month of November of the small rainy season. The foothills of the southern slopes receive an annual rainfall of 800–900 mm and the lower slopes at 1500 m receive 1500–2000 mm. The forest belt between 2000 and 2300 m receives partly over 3000 mm (Hemp 2001a), which is more than on other high mountains of East Africa. In the alpine zone the precipitation decreases to 200 mm. According to the different climatic conditions several vegetation zones are apparent on the southern slopes of Mt. Kilimanjaro (Figure 1). Between 700 and 1000 m a.s.l. the dry and hot colline savanna zone stretches around the mountain base, where most areas are farmed with maize, beans and sunflowers, in West Kilimanjaro with wheat. Around Lake Chala at the eastern foot of the mountain, and around Ngare Nairobi of West Kilimanjaro, savanna grasslands are still intact. The main cultivation zone with its coffee–banana plantations, the actual study area, is located between 1000 and 1800 m. Natural forests cover an area of about 1000 km2 on Mt. Kilimanjaro. In the lower parts of the southern slope the montane forests are characterized by the tree Ocotea usambarensis and higher up in the cloud forest zone by Podocarpus latifolius, Hagenia abyssinica and Erica excelsa. On the drier northern slope the vegetation zonation starts with Croton-Calodendrum forests, Cassipourea forests at midaltitudes and Juniperus forests at higher altitudes. At around 3100 m the forests are replaced by Erica bush. At an altitude of about 3900 m the Erica heathlands grade into Helichrysum cushion vegetation that reaches up to 4500 m. Higher altitudes are very poor in vegetation while the highest [135]

1196 elevations of Kibo peak are covered with glaciers. For a more detailed description of these vegetation types see Hemp (2001a). Methods Data have been collected since 1996. Over 1400 releve´s (plots) of all vegetation types were produced on the whole mountain using the method of Braun-Blanquet (1964), 62 of them representing banana gardens (location see in Figure 1). Special attention was given to homogeneity and representation of the stands. The releve´ size was chosen with respect to the minimum area, which was 1000 m2 in the banana gardens. In the releve´s a herb layer (<1 m tall), a shrub layer (1–10 m tall) and a tree layer (>10 m tall) were differentiated. Climbers (stranglers, scramblers, tendrillar plants, hook and root climbers and twiners, cp. Lind and Morrison 1974) reaching into the shrub and tree layer were defined as lianas. Epiphytes (accidental, casual, hemi- and holo-epiphytes, cp. Kress 1986) were treated as an additional layer. To portray the structure of the banana gardens, a standprofile diagram according to Hammen et al. (1989) was produced. The releve´s were clustered according to floristic similarity and the resulting plant communities were united into nine formations: rocks; ruderal vegetation; vegetation of trampled grounds; grasslands; salt marshes; freshwater swamps; forest clearings; forests, and; heathlands. Constancy tables of these formations (except the vegetation of trampled ground) are presented by Hemp (2001a). Based on their constancy in the different formations (or vegetation classes) character species of these formations were determined. pH was measured in the main root horizon in 5–10 cm depth of selected plots using a WTW pH-metre (pH 330). Two parallel samples were taken and measured in distilled water and 0.01 M CaCl2 solution, respectively. Nomenclature follows FTEA (1952–2003) and (Beentje 1994). Results Based on Landsat ETM images from the year 2000 (source: USGS/UNEPGRID-Sioux Falls) extend and distribution of the Chagga homegardens on Kilimanjaro was determined. Excluding other vegetation types of the submontane cultivated zone (e.g. riverine forests, meadows, maize fields etc.) banana fields cover an area of about 675 km2, which is about two thirds of the extend of the montane forest belt. Phytosociological and ecological aspects The vegetation of the Chagga homegardens belongs floristically to the formation (or vegetation class) of ruderal vegetation (Table 1, cp. Table 1 in [136]

Table 1. Vegetation of the Chagga homegardens.

[137]

1197

1198

Table 1. Continued.

[138]

[139]

1199

1200 Hemp 2001a). Ruderal species form in respect of species number and vegetation cover of the herb layer the most important ecological group. Widespread ruderal species (character species of the vegetation class) that can be found as well on fallow arable land, waste places in towns and roadsides from the colline savanna area up to the montane zone are e.g. Bidens pilosa, Oxalis corniculata, Commelina benghalensis and Galinsoga parviflora. Phyllanthus odontadenius, Didymodoxa caffra and Celosia schweinfurthiana occur in different ruderal vegetation types within the submontane zone. Finally, a characteristic weed species confined to the banana gardens on Kilimanjaro is Oxalis latifolia. Based on the floristic composition, two distinct plant communities were distinguished. Diagnostic species of community 1 are the ruderal species Euphorbia heterophylla, Amaranthus hybridus, Sida acuta, Blainvillea gayana and Malvastrum coromandelianum. Impatiens walleriana, Drymaria cordata and Plectranthus parvus are diagnostic for community 2; the latter species is restricted in the study area to this community. Each of these two main communities can be divided into a vicarious vegetation unit of the southern and the (north-) eastern slope, respectively, governed by the rainfall regime. This partition into a wet southern and drier northern slope is also reflected by other vegetation formations such as forests (cp. Hemp in press a). Determining factors for the differentiation of the two main communities 1 and 2 are altitude and factors related to altitude: community 1b of the southern slope occurs between 800 m and about 1300 m, receiving 900–1580 mm rainfall, and community 2b between 1300 and 1800 m, receiving 1580–2200 mm rainfall. Mean annual temperature varies between 23.4 and 18.8 C in community 1b and between 18.8 and 16.1 C in community 2b (climate data from Hemp in press a). As a result of the increasing precipitation mean pH-values decrease from 6.4 (CaCl2) and 6.7 (H2O), respectively in community 1b to 5.5 (CaCl2) and 6.0 (H2O), respectively in community 2b. As in the forests of Kilimanjaro (Hemp in press a, b) the influence of precipitation is reflected by the distribution of epiphytes and pteridophytes. Vascular epiphytes are nearly missing in community 1b and start above 1300 m in community 2b. The same holds for pteridophytes. Mean cover values of epiphytes (including mosses and lichens) in both communities are 0 and 8%, respectively. On the drier eastern slope the lowland community 1a occurs between 1100 and about 1500 m, and community 2b is less distinctly developed than on the wetter southern slope, occurring only in a narrow strip above 1500 m. Such a rising and condensation of vegetation zones due to the ‘Massenerhebungseffekt’ was also observed in the forest vegetation on the dryer leeward slope of Kilimanjaro (Hemp in press a).

Diversity Five hundred and twenty-three vascular plant species were recorded in the releve´s. These are about three quarter of the species occurring in the ruderal [140]

1201

Figure 2. Vascular plant species richness in the main vegetation formations (as presented with constancy tables by Hemp 2001a) of Mt. Kilimanjaro, based on the evaluation of about 1400 vegetation plots.

vegetation formation on Kilimanjaro (Figure 2). With over 700 species this formation holds rank three in respect of species richness after the forests and grasslands. Mean vascular plant species number per plot is 54. Figure 3 shows the floristic composition of the banana fields in relation to the different vegetation formations on Kilimanjaro. Most species (194) occurring in the Chagga homegardens are forest species, followed by 128 ruderal species. Cultivated plants contribute 19% to this floristic spectrum with 99 species. A characteristic feature of the ruderal vegetation on Kilimanjaro is the high contribution of neophytes. In the Chagga homegardens nearly a quarter of the species is introduced, with 99 cultivated species and 41 neophytes (Figure 4, Table 2).

Vegetation structure Figure 5 shows a vegetation profile of a Chagga homegarden in the area of Kidia (Old Moshi). Typical of the agrisilvicultural system of the Chagga homegardens is their multilayered vegetation structure similar to a tropical montane forest. Therefore the growth form spectrum (Figure 6) displays beside herbs also trees, shrubs, lianas and epiphytes. Apart from some cultivated fruit trees, e.g. Persea americana, Mangifera indica and Syzygium cumini or introduced timber trees such as Grewillea robusta and Cupressus lusitanica, most of the 55 encountered tree species (including young trees of the shrub and herb [141]

1202

Figure 3. Floristic composition of the banana fields in respect of the different vegetation formations on Kilimanjaro.

Figure 4. Share of cultivated, neophytic and indigenous plants in the Chagga homegardens.

layer this number adds to 82) are remnants of the former forest cover (Table 3). Most widespread are Albizia schimperiana, Rauvolfia caffra, Cordia africana, Commiphora eminii and Margaritaria discoidea. Nearly all banana fields are [142]

1203 Table 2. Neophytes of the Chagga home gardens. Name

Frequency

Ageratrum conyzoides Oxalis corniculata Galinsoga parviflora Oxalis latifolia Euphorbia heterophylla Conyza sumatrensis Amaranthus hybridus ssp. hybridus Tagetes minuta Galinsoga quadriradiata Solanum nigrum Richardia scabra Nicandra physalodes Lantana camara Tridax procumbens Adiantum raddianum Euphorbia hirta Bryophyllum pinnatum Lepidium bonariense Senna occidentalis Stachytarpheta jamaicensis Galium aparine Ipomoea hederifolia Lagascea mollis Lantana camara Sonchus oleraceus Acanthospermum hispidum Senna bicapsularis Apium leptophyllum Argemone mexicana Caesalpinia decapetala Chenopodium ambrosioides Tradescantia zebrina Acmella oleracea Brassica juncea Crambe hispanica Datura stramonium Ipomoea nil Sambucus nigra Senna septentrionalis Solanum seaforthianum Spermacoce assurgens

57 45 39 38 33 32 31 22 20 20 14 12 8 8 7 7 5 5 5 5 4 4 4 4 4 3 3 2 2 2 2 2 1 1 1 1 1 1 1 1 1

covered by at least some trees (Figure 7); mean coverage of the tree layer is 28%, mean species number 4.5. Twenty-nine species were found growing epiphytically in the plots (Table 4). Twenty-two were holo-epiphytes, mainly restricted in their occurrence to the epiphyte layer of the forests on Kilimanjaro (cp. Hemp 2001b). Most wide[143]

1204

Figure 5. Profile (27 · 2.5 m) and ground plan (27 · 5 m; bold lines indicate the area used for the profile) of a typical Chagga homegarden in Kidia (Old Moshi) at 1400 m a.s.l. Exposition: south west, inclination: 25. An open light upper canopy is formed by Albizia schimperiana var. amaniensis, on which epiphytes such as the fern Drynaria volkensii and Telphairia pedata, a liana with oilcontaining seeds, find habitats. Bananas form a dense upper shrub layer of 4–6 m height, coffee trees a lower shrub layer of 1.5–2 m, intermingled with 1–1.5 m high Coco Yam (Colocasia esculenta). The lower side of the banana field borders a road; here Dracaena fragans is planted as a hedge.

spread epiphytes are the petridophytes Pleopeltis macrocarpa (growing quite often on coffee trees), Asplenium aethiopicum, A. theciferum, Lepisorus excavatus and Drynaria volkensii. Vascular epiphytes start to appear above 1300 m in community 2 (see above). Forty-one climbers reaching into the shrub and tree layer were found in the plots (Table 5). Including young climbers found in the herb layer this number adds to 52 species. Eleven species were cultivated plants with important

[144]

1205

Figure 6. Growth form spectrum of the Chagga homegardens; (a) species number of the respective stratum in the vegetation plots; (b) species number of all representatives of a growth form, e.g. of trees including young trees occurring in the shrub and herb layer or e.g. of herbs excluding young trees etc.

agricultural crop plants such as three Dioscorea and Passiflora species and the Cucurbitaceae Telphairia pedata belonging to this growth form. One hundred twenty-six species were encountered in the shrub layer of the plots. Excluding young trees, 63 species remain (Table 6). Similar to the trees, epiphytes and lianas most of the shrubs in the Chagga homegardens were forest species. However, in the shrub layer the most important cultivated plants occurred: Different varieties of Musa · sapientium (dessert bananas) and M. · paradisiaca (cooking bananas) and Coffea arabica. Bananas form a dense (mean cover value 50%) upper shrub layer of about 4–6 m height and coffee trees a lower layer of 1.5–2 m. Already Volkens (1897) and Widenmann (1899) reported that the bananas on Kilimanjaro are most luxuriant with heights of 6– 8 m in the area of Kibosho and Kilema. This may be due to the fact that the bedrocks in these areas of the wet central southern slope consist of rhomb porphyry instead of porous tuff and ashes as in the adjacent regions of the southern slope (Downie and Wilkinson 1972) and that the eastern slope receives less precipitation. Four hundred nine species were found in the herb layer of the homegardens. Excluding young trees, shrubs and lianas 304 herbs and grasses occurred in the plots. In contrast to the other strata the main species group of the herb layer consisted of representatives of ruderal vegetation. This is shown in Figure 8. In this figure species of grasslands and wet habitats are lumped together into ‘semi-natural’ species, as such habitats are mostly influenced by cutting and grazing of cattle. It appears that the tree and epiphyte layer are the most [145]

1206 Table 3. Trees of the Chagga home gardens. Name

Frequency t

Grevillea robusta Albizia schimperiana Persea americana Rauvolfia caffra Cordia africana Mangifera indica Commiphora eminii ssp. zimmermannii Margaritaria discoidea Markhamia lutea Bridelia micrantha (Citrus aurantium) Croton macrostachyus Albizia petersiana Alangium chinense Ficus sur Casearia battiscombei Ficus thoningii Jacaranga mimosiaefolia Milicia excelsa Syzygium cumini Turraea robusta Albizia gummifera (Annona muricata) Annona reticulata Artocarpus heterophyllus (Azaridachta indica) (Cassia siamea) Cassia spectabilis (Citrus limon) Cupressus lusitanica Cussonia holstii Eriobotrya japonica Ficus vallis-choudae Olea capensis ssp. welwitschii Trema orientalis Croton megalocarpus (Deinbollia kilimandscharica) Ficus lutea Prunus africana Trichilia emetica (Allophylus abyssinicus) (Bersama abyssinica) (Bombacopsis glabra) Ceiba pentandra Celtis africana (Celtis mildbraedii) Cinnamomum verum (Citrus paradisi) Citrus sinensis

f

f

f

t f

f f f x o o f t f

x

x f f

[146]

37 36 21 18 14 13 11 9 8 7 7 7 6 5 5 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 1 1 1 1 1 1 1 1 1

1207 Table 3. Continued. Name

Frequency

(Clausena anisata) Cordia goetzei Diospyros abyssinica (Ehretia cymosa) Ekebergia capensis Erythrina abyssinica Eucalyptus spec. (Euclea divinorum) Ficus exasperata Kaya anthotheca Lecaniodiscus fraxinifolius ((Lepidotrichilia volkensii)) (Macaranga capensis) (Maesopsis eminii) Manihot glaziovii (Maytenus undata) Melia azedarach (Mystroxylon aethiopicum) (Neoboutonia macrocalyx) ((Ochna holstii)) (Olea europaea ssp. africana) Phoenix reclinata (Polyscias fulva) Psidium guajava Rapanea melanophloeos ((Sorindeia madagascariensis)) Synadenium cf. volkensii Syzygium cordatum Syzygium guineense (Tabernaemontana stapfiana) (Tabernaemontana ventricosa) Teclea nobilis (Vepris spec.)

t

t

t x x

f

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

f = fruit tree; o = ornamental tree; t = timber tree; x = introduced tree of other uses; no label: indigenous tree; in brackets = only found in the shrub layer; in two brackets = only found as young tree in the herb layer.

natural vegetation strata in the Chagga homegardens whereas the herb layer is dominated by (128) ruderal species followed by 117 forest species and 64 cultivated plants. Plants of the semi-natural grasslands and wet habitats contributed 51 and 15 species, respectively. Discussion The presented distinction of two main communities is confirmed by the composition of Saltatoria species of the banana gardens, which show a parallel [147]

1208

Figure 7. Typical Chagga homegarden in Kidia (Old Moshi) with an open tree canopy and a dense banana undergrowth.

distinction of two coenoses (Hemp, C. unpub. data). The congruence of vegetation communities and Saltatoria coenoses was already proofed on Kilimanjaro by grassland communities (Hemp and Hemp 2003). The Chagga homegardens maintain a high biodiversity with over 400 not cultivated plants. However, their majority serves as forage, for household and agricultural purposes, for medical applications, as drugs and for magic purposes (Hemp 1999). Most areas of the submontane coffee–banana belt resemble a woodland with a dense undergrowth of bananas (Figures 5 and 7). Thus, 193 forest species were found in the studied plots, species that need a forest-like habitat structure for surviving. These are about 17% of the 1155 forest plants of Kilimanjaro (30% of the forest trees – including young trees 45%—and 17% of the forest epiphytes, cp. Hemp in press a). Compared with large scale coffee plantations this conserving function becomes evident: Four surveyed commercial plantations harboured only 6 forest species, and three quarter of the species were widespread ruderal or cultivated species (Hemp unpub. data). This holds not only for plants but also for insects such as Saltatoria. The Chagga homegardens serve as a refuge not only for forest grasshoppers, which contribute the majority of the species but also for endemics (Hemp in press). These findings are in line with the fact that biodiversity in general on Kilimanjaro culminates at 1000–1300 m with over 900 vascular plant species (Hemp in press c) inside the coffee–banana belt, the most densely populated region of the mountain. This is due to the high variety of (moderately) cultivated areas (the Chagga homegardens), forest patches, river gorges and grasslands at this altitude. This (mostly man-made) variety of habitats, the high [148]

1209 Table 4. Epiphytes of the Chagga home gardens. Name

Frequency

Pleopeltis macrocarpa Asplenium aethiopicum Asplenium theciferum Lepisorus excavatus Drynaria volkensiii Peperomia tetraphylla Drynaria volkensii Rhipsalis baccifera Impatiens walleriana Phragmanthera usuiensis Loxogramme abyssinica Polystachia simplex Erianthemum dregei Loranthaceae spec. Plicosepalus curviflorus Rangaeris amaniensis (Achyranthes aspera) Aerangis amaniensis Asplenium megalura Asplenium sandersonii Asplenium strangeanum Chamaeangis sarcophylla (Didymodoxa caffra) (Dorstenia zansibarica) (Drymaria cordata) Ficus thoningii (Pilea tetraphylla) (Poa schimperana) (Setaria homonyma)

28 21 12 8 7 6 5 5 4 4 3 3 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1

in brackets: accidental epiphytes

beta-diversity, promotes alpha-diversity, allowing species from lower altitudes to climb up the mountain. A similar phenomenon was observed in the Saltatoria fauna of Kilimanjaro (Hemp and Hemp 2003). From the forest species found in the banana gardens it can be assumed that the forest, which covered the lower slopes of Kilimanjaro before human settlement, resembled in some aspects the Cassipourea forests of the western and northern slopes (cp. vegetation table in Hemp 2001a). However, due to the much higher precipitation on the southern slope, major differences to these forests can be expected; and above 1500 m probably elements of the camphor forests constituted to their floristic composition. As nearly all of these former submontane forests disappeared, it is difficult to reconstruct their full floristic composition. Since humans have continuously inhabitated the lower slopes of Mt. Kilimanjaro at least for the last 2000 years (Schmidt 1989), it can be assumed that many submontane forest species were extinguished together with [149]

1210 Table 5. Lianas of the Chagga home gardens. Name

Frequency

Dioscorea lecardii (Glycine wightii) Dioscorea bulbifera Passiflora edulis Telphairia pedata Passiflora laurifolia Zehneria scabra Toddalia asiatica Ficus thoningii Smilax anceps Dioscorea alata Passiflora quadrangularis Solanecio angulatus Thunbergia alata Vigna spec. Cyphostemma masukuense ssp-masukuense Cyphomandra betacea Clerodendron johnstonii Basella alba Caesalpinia decapetala Cissus oliveri Cyphostemma kilimandscharicum Diplocyclos schliebenii (Ipomoea obscura) Momordica foetida Pentarrhinum insipidum (Stephania abyssinica) (Teramnus labialis ssp. labialis) Vigna membranacea (Adenia gummifera) Adenia spec. (Begonia meyeri-johannis) Clematis brachiata Dahlbegia lactea (Diplocyclos palmatus) Embelia schimperi Helinus mystacinus Ipomoea hederifolia Ipomoea indica Ipomoea nil Landolphia buchananii (Merremia palmata) (Merremia spec.) Mondia whytei Monstera spec. Oreosyce africana Paullinia pinnata Piper nigrum (Rourea thomsonii) Rubus rosifolius Sechium edule Solanum seaforthianum c = cultivated; in brackets = only found in the herb layer.

[150]

c c c c c

c c

c

c

c c

21 13 12 8 7 6 6 5 5 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1211 Table 6. Shrubs of the Chagga home gardens. Name

Frequency

Musa Sapientium / M. X paradisiaca Coffea arabica Dracaena fragrans Manihot esculenta Carica papaya Ricinus communis Cyphomandra betacea Dracaena steudneri Morus alba Prunus persica Capsicum fruticosum Adhatoda engleriana Lantana camara Cajanus cajan Cestrum nocturnum Synadenium compactum Tetradenia riparia Colocasia esculenta Euphorbia pulcherrima Ochna insculpta Aloe volkensii Datura suaveolens Elettaria cardamomum Keetia gueinzii Rubus pinnatus Senna bicapsularis Solanum spec. Bauhinia tomentosa Capsicum cf. baccatum Dracaena steudneri Ensete edule Flacourtia indica Leucas grandis Phyllanthus ovalifolius Thevetia peruviana Allophylus rubifolius Arundinaria alpina Bougainvillea spec. (Cyathea manniana) Dombeya rotundifolia Euclea racemosa Grewia bicolor Hibiscus calyphyllus Jatropha curcas Lonchocarpus eriocalyx Mirabilis spec. Oxyanthus speciosus ssp. globosus Psychotria lauracea Punica granatum Rhus vulgaris

62 57 29 21 18 14 13 11 11 10 9 2 8 6 6 5 5 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

i

i

i i

i i

i i

i i i i i i i

i i i i i i i i

[151]

1212 Table 6. Continued. Name

Frequency i

Rubus rosifolius Sambucus nigra Senna septentrionalis Senna spec. (Sesbania sesban) Solanecio cydonifolius Solanecio mannii Sterculia cf. stenocarpa Tephrosia vogelii Vernonia lasiopus Vernonia myriantha Ximenia americana var. caffra Ziziphus mucronata

i i i i i i i i

1 1 1 1 1 1 1 1 1 1 1 1 1

i = indigenous species; in brackets = only found as young shrub in the herb layer.

Figure 8. Forest species, semi-natural species (i.e. species of grasslands and wet habitats), cultivated and ruderal species (including species of trampled ground) with their share in the different strata of the Chagga homegardens.

the forest cover. Only in deep inaccessible gorges remnants of floristically very rich forests can be found, resembling the intermediate forests of the Eastern Arc. Mts. (a chain of ancient crystalline mountains in East Africa), e.g. of the Pare and Usambara Mts. Here many species, which were believed to be endemic to the Eastern Arc. Mts, were encountered on Kilimanjaro (Hemp 2002). These findings suggest a rich forest flora inhabiting the southern slopes of Mt. Kilimanjaro in former times. Thus, the lower diversity of the forest flora and [152]

1213

Figure 9. Records of Christella dentata on Mt. Kilimanjaro, at the base of the UTM grid. The scale of the squares is 4 km2. This apophytic fern, which thrives naturally in riverine forests was able to spread over the whole submontane banana plantation belt due to the network of over one thousand furrows. Gaps within the distribution area inside the banana plantations (especially in its eastern part) are mostly due to lack of data.

the lower share of endemic plant species of Kilimanjaro can be explained by the widely destroyed submontane (intermediate) forest rather than by the higher age and greater ecoclimatic stability of the Eastern Arc. Mts. as suggested by e.g. Rodgers and Homewood (1982) or Fjeldsa˚ et al. (1997). This is corroborated by the fact, that forest inhabitants, which become less affected by forest devastation like Saltatoria, have similar numbers of endemic forest species in the submontane and montane zone on Mt. Kilimanjaro (including Mt. Meru) and the East Usambara Mts. (Hemp, C. unpub. data). Endemic grasshopper species like Ixalidium sjo¨stedti, Parepistaurus deses and Altiusambilla modicicrus have coped with the habitat change from forest to plantations (Hemp and Hemp 2003). On the other hand there are forest species, which were directly or indirectly favoured by the Chagga people. An example of an intentionally dispersed forest plant is Dracaena fragrans. This shrub or small tree has lost almost all natural habitats on Kilimanjaro, except very few submontane river gorges (cp. Hemp in press a). However it is one of the most characteristic species in the banana plantations, where it is used as a hedge plant (cp. Figure 5). Dracaena [153]

1214 hedges are protected as they serve also as burial ground. Anthropogenic influence does not only destroy natural habitats but sometimes it enlarges the distribution of indigenous species by increasing habitat diversity. An example of such apophytes sensu Rikli (1903) (i.e. indigenous species, which could extend their natural distribution area due to human influence and occupy anthropogenic habitats; for examples in the middle European flora cp. e.g. Sukopp and Kowarik 1987; Sukopp and Langer 1996) is the fern Christella dentata, a species of riverine forests in the colline and submontane zone of Kilimanjaro. Its main habitat and main distribution area are nowadays the coffee–banana plantations with their ramified irrigation system and forest-like structure on the southern and eastern slopes (Figure 9). The same holds for Impatiens walleriana and the fern Adiantum poiretii. Another apophyte is e.g. Pellaea viridis, which naturally thrives on Kilimanjaro in submontane CrotonCalodendrum forests (Hemp in press b). Some forest plants (e.g. Pilea tetraphylla) were only encountered in the banana plantations but in none of the 583 forest plots, highlighting the important conserving function of the Chagga homegardens. The agroforestry system of the Chagga homegardens is a unique feature of Kilimanjaro, distributed over an area of about 1000 km2. If one passes from north-east to the south-western end of this belt, one could drive for 120 km through a closed ‘banana forest’ containing about 225 Million banana ‘trees’ (calculated on the base of the vegetation profile) – if there was a continuous road. The same type of land use, however with a smaller extension, occurs on

Figure 10. Tschibo estate at Mweka, southern slope of Kilimanjaro in November 2003. Hundreds of old trees were felled to grow new coffee varieties that do not need shade. [150]

1215 the Pare Mountains and Mt. Meru, which shows nearly exact the same floristic and structural composition (Hemp unpub. data). The Chagga homegardens maintain not only a high biodiversity, they are an old and very sustainable way of land use that meets several different demands. Beside crop production, the sparse tree layer provides people with fire wood, fodder and timber. But the high demand of wood, low coffee prizes on the world marked and the introduction of coffee varieties that are sun-tolerant endanger this effective system (Hemp et al. in press). In some areas of the mountain (e.g. on the eastern slopes) the trees in the banana fields are very scattered or already missing. A very bad example in this respect was observed at Mweka on the central southern slope: Here a large foreign coffee company felled hundreds of old trees in November 2003 to grow coffee (Figure 10). In order to reduce the pressure on the forest, it is necessary, to support the tree planting in the Chagga homegardens with their unique agroforestry system. Similar to environmental programmes for farmers in the European Union (e.g. for the protection of wetlands or dry meadows), there should be a programme that rewards farmers to have a certain share of their land covered by trees. It can be estimated that a homegarden supplies 1/4 to 1/3 of the fuelwood requirements of a family (Fernandes et al. 1984). As the banana belt is nearly as extensive as the forest reserve, this will of course have major effects in terms of forest protection and the water balance. In combination with new marketing and farming strategies for growing organic coffee through traditional methods an advertising campaign should be started especially in European countries where the awareness of environmental problems is high. A concept with this idea was already proposed to the OECD (Hemp 2003).

Acknowledgements I gratefully acknowledge grants by the Deutsche Forschungsgemeinschaft and the Tanzanian Commission for Science and Technology for permitting research. For support in getting permits I owe gratitude to the Chief Park Wardens of Kilimanjaro National Park, Mr Lomi Ole Moirana and Mr Nyamakumbati Mafuru, to the Catchment Forest officers and to my counterpart Mr Jacob Mushi (Tanzania Association of Foresters), Moshi. I further thank the keepers of the East African Herbarium, Nairobi (Kenya) Dr Beatrice Khayota and Kew Herbarium, England, Prof Dr Owens for permission to study their collections, Quentin Luke and Simon Mathenge (both Nairobi) for help in identifying difficult species and Prof Dr Inge Lenski, Marburg (Germany) for valuable comments on the manuscript. References Beentje H.J. 1994. Kenya Trees, Shrubs and Lianas. National Museums of Kenya, Nairobi. Braun-Blanquet J. 1964. Pflanzensoziologie. Springer, Wien. [151]

1216 Brewin D.R. 1965. Kilimanjaro agriculture. Tanganyika Notes Records 64: 115–117. Clemm V.M. 1963. Agricultural productivity and sentiment on Kilimanjaro. Econ. Bot. 18: 99–121. Downie C. and Wilkinson P. 1972. The Geology of Kilimanjaro. University of Sheffield. FAO 1986. Rehabilitation of traditional schemes (Phase I), Tanzania AG: TCP/URT/4522, Mission Report, Rome. Fernandes E.C.M., Okting’ati A. and Maghembe J. 1984. The Chagga homegardens: a multistoried agroforestry cropping system on Mt. Kilimanjaro (Northern Tanzania). Agroforest. Syst. 2: 73– 86. Fjeldsa˚ J., Ehrlich D., Lambin E. and Prins E. 1997. Are biodiversity ‘hotspots’ correlated with current ecoclimatic stability? A pilot study using the NOAA-AVHRR remote sensing data. Biodivers. Conserv. 6: 401–422. FTEA 1952–2003. Flora of Tropical East Africa. Royal Botanic Garden, Kew. Hammen T.V.D., Mueller-Dombois D. and Little M.A 1989. Manual of Methods for Mountain Transect Studies. IUBS, Paris. Hemp A. and Winter J.C. 1999. Ergebnisse der ethnobotanischen Forschung am Kilimanjaro. Bayreuther Forum O¨kologie 64: 117–144. Hemp A. 1999. An ethnobotanical study on Mt. Kilimanjaro. Ecotropica 5: 147–165. Hemp A. 2001a. Ecology of the pteridophytes on the southern slopes of Mt. Kilimanjaro. Plant Biol. 3: 493–523. Hemp A. 2001b. Life form and strategies of forest ferns on Mt. Kilimanjaro. In: Gottsberger G. and Liede S. (eds), Life forms and Dynamics in Tropical Forests. Dissertationes Botanicae 346, pp. 95–130. Hemp A. 2002. Ecology of the pteridophytes on the southern slopes of Mt. Kilimanjaro. Part I: Altitudinal distribution. Plant Ecol. 159: 211–239. Hemp A. 2003. Climate impacts and responses on Mount Kilimanjaro. In: Agrawala S. and Moehner A. (eds), Development and Climate Change in Tanzania: Focus on Mount Kilimanjaro. OECD, Paris. Hemp A. in press a. Continuum or zonation? Altitudinal gradients in the forest vegetation of Mt. Kilimanjaro Plant Ecol. Hemp A. in press b. Ecology and altitudinal zonation of pteridophytes on Mt. Kilimanjaro. In Proceedings of the XVIIth AETFAT Congress 21–26 September 2003. Hemp A. in press c. The impact of fire on diversity, structure and composition of Mt. Kilimanjaro’s vegetation. In Proceedings of the GMBA Symposium 19–24 August 2002 in Moshi, Tanzania. Hemp A., Lambrechts C. and Hemp C. (in press). Global Trends in Africa: The Case of Mt. Kilimanjaro. UNEP, Nairobi. Hemp C. and Hemp A. 2003. Saltatoria coenoses of high-altitude grasslands on Mt. Kilimanjaro, Tanzania (Orthoptera: Saltatoria). Ecotropica 9: 71–97. Hemp C. in press. The Chagga home gardens – relict areas for endemic Saltatoria species (Insecta: Orthoptera) on Mt. Kilimanjaro. Biol. Conserv. Hemp C. 2001. Ethnozoological research on invertebrates on Mt. Kilimanjaro, Tanzania. Ecotropica 7: 139–149. Kress W.J. 1986. The systematic distribution of vascular epiphytes: an update. Selbyana 9: 2–22. Lind E.M. and Morrison M.E.S. 1974. East African Vegetation. Longman, London. Mdoe N. and Wiggins S. 1997. Returns to smallholder dairying in the Kilimanjaro region, Tanzania. Agr. Econ. 17: 75–87. Nair P.K.R. 1993. An introduction to agroforestry. Kluwer, Dordrecht. National Bureau of Statistics and Central Census Office. 2003. 2002 Population and Housing census. Genaral report. The United Republic of Tanzania, Dar es Salaam. O’Kting’ati A. and Kessy J.F. 1991. The farming system on Mount Kilimanjaro. In: Newmark W.D. (ed), The Conservation of Mount Kilimanjaro. The IUCN Tropical Forest Programme, pp. 71–80. O’Kting’ati A., Maghembe J.A., Fernandes E.C.M. and Weaver G.H. 1984. Plant species in the Kilimanjaro agroforestry system. Agroforest. Syst. 2: 177–186.

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Biodiversity and Conservation (2006) 15:1219–1252 DOI 10.1007/s10531-005-0768-6


Springer 2006

-1

Biodiversity hotspots and conservation priorities in the Campo-Ma‘an rain forests, Cameroon M.G.P. TCHOUTO1,*, M. YEMEFACK2, W.F. DE BOER3, J.J.F.E. DE WILDE4, L.J.G. VAN DER MAESEN4 and A.M. CLEEF5 1

Limbe Botanic Garden, BP 437, Limbe, Cameroon; 2International Institute for Geo-Information Science and Earth Observation (ITC), PO Box 6, 7500 AA Enschede, The Netherlands; 3Resource Ecology Group, Wageningen University, Bornsesteeg 69, 6708 PD, Wageningen, The Netherlands; 4 Biosystematics Group, Wageningen University, Generaal Foulkesweg 37, 6703 BL, Wageningen, The Netherlands; 5Institute for Biodiversity and Ecosystem Dynamics (IBED) Research Group, Palynology and Paleo/Actuo-ecology, University of Amsterdam, Kruislaan 318, 1090 GB Amsterdam, The Netherlands; *Author for correspondence (e-mail: [email protected]) Received 3 February 2004; accepted 7 January 2005

Key words: Biodiversity, Cameroon, Campo-Ma’an, Central Africa, Conservation, Endemic species, Forest refuge, Genetic Heat Index, Pioneer Index, Plant diversity, Tropical rain forest Abstract. Until recently, patterns of species richness and endemism were based on an intuitive interpretation of distribution maps with very limited numerical analyses. Such maps based solely on taxonomic collections tend to concentrate on collecting efforts more than biodiversity hotspots, since often the highest diversity is found in well-collected areas. During the last decades, there has been an overwhelming concern about the loss of tropical forest biological diversity, and an emphasis on the identification of biodiversity hotspots in an attempt to optimise conservation strategies. Furthermore, the concept of sites of high diversity, or hotspots, has attracted the attention of conservationists as a tool for conservation priority settings. With the development of GIS tools, geostatistics, phytosociological and multivariate analysis software packages, more rigorous numerical analyses of distributional and inventory data can be used for assessing conservation priorities. In the Campo-Ma’an rain forest, inventory data from 147 plots of 0.1 ha each and 7137 taxonomic collections were used to examine the distribution and convergence patterns of strict and narrow endemic species. We analysed the trends in endemic and rare species recorded, using quantitative conservation indices such as Genetic Heat Index (GHI) and Pioneer Index (PI), together with geostatistic techniques that help to evaluate and identify potential areas of high conservation priority. The results showed that the Campo-Ma’an area is characterised by a rich and diverse flora with 114 endemic plant species, of which 29 are restricted to the area, 29 also occur in southwestern Cameroon, and 56 others that are also found in other parts of Cameroon. Although most of the forest types rich in strict and narrow endemic species occur in the National Park, there are other biodiversity hotspots in the coastal zone and in areas such as Mont d’Ele´phant and Massif des Mamelles that are located outside the National Park. Unfortunately, these areas, supporting 17 strict endemic species that are not found in the park, are under serious threat and do not have any conservation status for the moment. Taking into consideration that with the growing human population density, pressure on these hotspots will increase in the near future, it is suggested that priority be given to the conservation of these areas and that a separate management strategy be developed to ensure their protection.

[159]

1220 Introduction Central African rain forests are among the top conservation priority areas in the world (Davis et al. 1994; Heywood and Watson 1995; Myers et al. 2000). The Campo-Ma’an rain forest, in the southern part of Cameroon, falls under the Guineo–Congolian Regional Centre of Endemism that is reported to be species-rich with high levels of endemism (White 1979, 1983; Davis et al. 1994). It is situated in the middle of the Atlantic Biafran forest zone that extends from Southeast Nigeria to Gabon and the Mayombe area in Congo (Letouzey 1968, 1985). The vegetation in the Campo-Ma’an area is determined by climate, especially rainfall, altitude, soils, proximity to the sea and human disturbance (Tchouto 2004). The structure and composition of the forest, as well as the vegetation types change progressively from the mangrove or coastal forest on sandy shorelines through the endemic lowland evergreen forest rich in Caesalpinioideae with Calpocalyx heitzii and Sacoglottis gabonensis, to the submontane forest on hilltops and the mixed evergreen and semi-deciduous forest in the drier Ma’an area. Other vegetation types/sub-types include swamps, seasonally flooded forests, riverine and secondary forests. The forest in the Ma’an area is described as transitional between the coastal evergreen forest and the semi-deciduous forest of the interior. In view of the rich and diverse flora of the Campo-Ma’an rain forest, as well as its high level of endemism, it has been identified as one of the key conservation sites in Cameroon (Gartlan 1989; Foahom and Jonkers 1992). The Campo-Ma’an area is a Technical Operational Unit (TOU) that comprises a National Park, five forest management units, two agro-industrial plantations, and a multi-uses zone (Tchouto 2004). Despite the low population density, there are many stakeholders and different types of land use. Activities such as logging, industrial and shifting agriculture exert varying ecological impact on the forest ecosystem. This has led to deforestation, habitat fragmentation and alteration of the coastal forests. With the increasing destruction of natural ecosystems, it is important to identify biodiversity hotspots and conservation priorities in order to enable an effective management. To achieve this, we need to study the species composition and species distribution, so that we can target conservation resources and efforts to rich and diverse areas with a high number of endemic species. Endemism is commonly regarded as an important criterion for assessing the conservation value of a given area. In this study, forest inventory data and taxonomic collections will be used to examine the distribution and convergence patterns of strict and narrow endemic species. We will use new quantitative conservation indices such as GHI (Genetic Heat Index) and Pioneer Index (PI) to analyse trends in endemic and rare species in the various forest types. Finally, geostatistic analysis and techniques will help to evaluate and identify potential areas of high conservation priority. [160]

1221 Materials and methods Study area The study was conducted in the Campo-Ma’an rain forest in south Cameroon. The site covers about 7700 km2 and it is located between latitudes 210¢– 252¢ N and longitudes 950¢–1054¢ E. Following the FAO classification system, soils in the Campo-Ma’an area are generally classified as Ferrasols and Acrisols (Franqueville 1973; Muller 1979; van Gemerden and Hazeu 1999). They are strongly weathered, deep to very deep and clayey in texture (except at the seashores and in river valleys where they are mainly sandy), acid and low in nutrients with pH (H2O) values generally around 4. The topography ranges from undulating to rolling in the lowland area, to steeply dissect in the more mountainous areas. In the Campo area, altitudes are mostly low, ranging from sea level to about 500 m. In the eastern part, which is quite mountainous, the altitude varies between 400 and 1100 m and the rolling and steep terrain brings about a more variable landscape. The area has a typical equatorial climate with two distinct dry seasons (November–March and July to mid-August) and two wet seasons (April–June and mid-August to October). The average annual rainfall generally decreases with increasing distance from the coast, ranging from 2950 mm/year in Kribi and 2800 mm in Campo to 1670 mm in Nyabissan in the Ma’an area. The average annual temperature is about 25 C and there is little variation between years. The hydrography of the area shows a dense pattern with many rivers, small river basins, fast-flowing creeks and rivers in rocky beds containing many rapids and small waterfalls. Generally, the area has a low population density of about 10 inhabitants per square kilometre and is sparsely populated (ca. 61,000 inhabitants) with most people living around Kribi, along the coast, and in agro-industrial and logging camps (ERE De´veloppement 2002; de Kam et al. 2002). Despite the low population density, there are few employment opportunities. The local people are very poor and so far rely solely on the forest resources to meet their basic needs. As a result, local pressure on the CampoMa’an rain forest is increasing and there are several activities that are carried out in the area with varying ecological impacts on the forest ecosystem. These activities include agriculture, logging, poaching and hunting.

Botanical assessment Sampling was carried out between 2000 and 2003 in 147 plots of 0.1 ha (50 m · 20 m) in representative and homogeneous vegetation types (see Table 6 for an overview of the plots per vegetation type). In each 0.1-ha plot, all trees, shrubs, herbs and lianas with DBH ‡1 cm (diameter at breast height, about 1.3 m above ground) were measured, recorded and identified. These plots were established in undisturbed forests or matured secondary forests within 12 vegetation [161]

1222 Table 1. Star categories and GHI weight classes as defined for Cameroon. Star category Weight for GHI Comments Black (BK)

27

Gold (GD)

9

Blue (BU)

3

Scarlet (SC)

1

Red (RD) Pink (PK) Green (GN)

1 1 –

Species which are only found in Campo-Ma’an area (strictly endemic) or near endemics (species which also occur in some localities around Campo-Ma’an such as Bipindi, Edea-Kribi, Lolodorf or southern part of Cameroon). Urgent attention to conservation of population is needed. Cameroon endemics, rare and threatened Lower Guinea endemics. Cameroon has definitely responsibility for preserving these species. Lower Guinea and Guineo–Congolian endemics which are widespread internationally but rare in Cameroon, or vice versa. Common but under serious pressure from heavy exploitation. Exploitation needs to be curtailed if usage is to be sustainable. Protection of all scales vital. Common but under pressure from exploitation. Common and moderately exploited. Widespread Guineo–Congolian, pantropical and tropical African species that are not under pressure. No particular conservation concern.

Adapted from Hawthorne and Abu-Juam (1995), Hawthorne (1996) and Tchouto et al. (1998).

types ranging from coastal forest, mangrove, swamp, lowland evergreen forest, mixed evergreen and semi-deciduous forest to submontane forest at higher elevations (800–1100 m above sea level). Most of the plots were located in the National Park and the forest management units which are less affected by human activities. Furthermore, in each representative vegetation type, a provisional plant species checklist was made in the field with information on their growth form, guild and frequency. A guild refers to a group associated with a common way of life (Table 2). For unknown species, a voucher specimen was collected. The study also involved the collection of fertile specimens encountered in plots, vegetation types and specific habitats such as exposed rocks and riverbanks. The geographic co-ordinates of each plot, sample or specimen were recorded using the Global Positioning System (GPS, Garmin 12XL model with estimated precision of ±10 m). These co-ordinates were used for mapping main vegetation types, species distribution, and biodiversity hotspots. A duplicate of each specimen was mounted and preserved in the Kribi Herbarium. Others duplicates were sent to the National Herbarium in Yaounde, Cameroon (YA) and the Nationaal Herbarium Nederland, Wageningen University Branch (WAG) for further identification and preservation. Criteria for taxa inclusion A plant species checklist was generated from the inventory data, from the plant collections made during the study, and from specimens previously collected in the area by other scientists, stored in the Cameroon and Wageningen herbaria. Furthermore, a taxonomic search for potential taxa of high conservation [162]

1223 Table 2. Guild and weight classes. Guild

Weight for PI

Comment

Pioneer (PI)

2

Non-Pioneer light demanding (NP)

1

Shade-bearers (SB)

0

Regenerating only in forest gaps and therefore indicating disturbed forest (e.g. Ceiba, Musanga, Harungana, Macaranga). Although some juveniles are also found in the understorey of undisturbed forest, they require gaps to develop to full maturity. Generally, non-pioneer light demanding are abundant in matured disturbed forest (e.g. Albizia, Entandrophragma, Piptadeniastrum, Pycnanthus). Understorey herbs, shrubs and trees which grow, flower and fruit in undisturbed forest (e.g. Cola, Diospyros, Psychotria, Rinorea).

Adapted from Hawthorne and Abu-Juam (1995), Hawthorne (1996) and Cable and Cheek (1998).

priority such as endemic, rare, new and threatened species was carried out using existing floras and monographs (Keay and Hepper 1954–1972; Aubre´ville and Leroy 1961–1992; Aubre´ville and Leroy 1963–2000; Lebrun and Stork 2003; Satabie´ and Leroy 1980–1985; Satabie´ and Morat 1986–2001), the IUCN (2002) red list categories, and the WCMC (1998) world list of threatened trees. On the basis of this information, a list of 141 species of high conservation values was produced with information on their habit, guild, star category (Table 1) and chorology. In this list, priority was given to taxa that are strictly endemic to the Campo-Ma’an area. Followed by species that are endemic to southwestern Cameroon (also occurring in Bipindi and Lolodorf areas) or Cameroon and Lower Guinea endemics (especially if they reach their northern or southern limit of distribution in Campo-Ma’an). Furthermore, species that reach their northern or southern limit of distribution in the Campo-Ma’an area were also included in the list. Star rating of species and measurement of forest conservation value A star rating system, based on the work of Hawthorne and Abu-Juam (1995) and Hawthorne (1996) in Ghana, Cable and Cheek (1998) and Tchouto et al. (1998) in Cameroon, was used to define the conservation status of each species recorded (Table 1). The factors considered when categorising species into star categories are their distribution, ecology, local abundance, taxonomy, life history, interaction with ecosystem parameters and economic importance (Hawthorne 1996). Therefore, species that are endemic, rare, threatened, or likely to represent a scarce genetic resource, are more valuable than others are. Hence, forests richer in such species receive a higher score than others. The GHI concept was developed by Hawthorne (1996) to express the conservation value of a given forest, and the PI concept to express the level of [163]

1224 disturbance in a given forest. GHI is an attempt to provide a scale, on which to measure the genetic ‘temperature’ or value of the forest. A plot/forest with an average GHI >150 will be considered warm or hot. In general, for species with completed monographs, black stars occupy about 1–3 filled degree squares on a standard distribution map, gold stars 4–14, blue stars 15–30, and green star more than 30 degree squares. Hawthorne (1996) defined the guild as a flexible concept used to circumscribe a group of plant species with a similar ecology and way of life. All the species were grouped into guild classes as defined in Table 2 and a PI score was calculated as an expression of the relative contribution of pioneers. Five classes of human disturbance were used to evaluate the forest quality and condition as defined in Table 3. These classes were mainly based on the field observation of the level of human disturbance and the state of forest degradation. The GHI and PI values of each sample were calculated using the TREMA database as follows: Genetic Heat Index (GHI) ¼ ½ð(BK  BK weightÞ þ ðGD  GD weightÞ þ ðBU  BU weightÞ þ ðRD  RD weight))= ðBK þ GD þ BU þ GN þ RDÞ  100; where BK = number of black star species; GD = number of gold star species; BU = number of blue star species; GN = number of green star species; and RD = number of red, scarlet and pink star species. Pioneer Index (PI) ¼

ðPioneer  PI weightÞ þ (NP  NP weightÞ  100; Total number of species

where PI = number of pioneer species and NP = number of non-pioneer light demanding species.

Table 3. Forest condition classes showing the degree of human disturbance on the natural forest cover. Forest condition

Classes

Notes

Excellent

Virtually undisturbed

Good

Less than 25% disturbed 25–50% disturbed

Undisturbed forest, with good canopy and few signs of human disturbance except for hunting and NTFPs collection. Small patches of recent disturbances (<25%) with good canopy cover. Obviously disturbed with significant patches (25–50%) of recent degradation but with good predominant forest and broken upper canopy. Considerable are (>50%) of recent degradation. Patchy with heavily disrupted canopy. No significant forest left (<2% good forest). Massive land conversion for plantation or farm.

Slightly degraded Mostly degraded Very poor

More than 50% disturbed Farm land

[164]

1225 Geostatistical analysis Conservation indices such GHI and PI are likely to vary throughout a region. Geostatistics (Isaaks and Srivastava 1989; Webster and Oliver 2001) were applied to quantify the spatial distribution of GHI within the Campo-Ma’an forest. Geographic analyses were done using ILWIS software (ILWIS 2001) and GSTAT package (Pebesma and Wesseling 1998) of R software (R Development Core Team 2002). The semivariance was calculated for GHI data on a minimum lag distance of 1250 m and each lag distance class contained at least 105 pairs of points. The semivariogram parameters (nugget, sill and range) were computed using the GSTAT fit variogram function. During the study of GHI spatial variability, the main objective was to obtain a map from point observations. Since this also required the estimation of a value at un-visited locations, the technique commonly used is known as kriging (Isaaks and Srivastava 1989). The semivariogram function was then used to extrapolate the GHI values in the CampoMa’an forest at 100 m · 100 m grid, using ordinary kriging. The output map was reclassified into five classes of conservation value (Hawthorne 1996).

Results Species richness and endemism A plant species checklist made of 2297 species of vascular plants, ferns and fern allies was generated from inventory data and from 2348 herbarium specimens and 4789 ecological specimens collected in the various plots. They belonged to 851 genera and 155 families. More than 67% of the specimens were identified at species level, 28% at generic level, 4% at family level and 1% remained unidentified. The 20 most important families and genera are shown in Tables 4 and 5. In terms of growth form, tree species contributed for 26% to the total number of 2297 species recorded, followed by herbs (24%), shrubs (23%) and climbers (17%), respectively. About 72% of the total number of species recorded was also found in the Campo-Ma’an National Park and the remaining 28% were only found in the coastal forest and the semi-deciduous forests located outside the park. In addition to a list of 92 threatened species (Appendix 2) recorded in IUCN (2002) and WCMC (1998), a list with 141 plant species of high conservation priorities was produced, with information on their growth forms, guild, chorology and star categories (Appendix 1). Only species that are endemic to Cameroon and species that reach their northern or southern limit of distribution are included in this list. The Campo-Ma’an area has about 114 endemic species, 29 of which are only known from the area, 29 only occur in the southwestern part of Cameroon, and 56 near endemics that also occur in other parts of Cameroon (Appendix 1 and Figure 1). Shrubs contributed for 38% of the 114 endemic species (Appendix 1), herbs 29%, trees 20% and climbers 11%. Moreover, 540 species (23% of the total [165]

1226 Table 4. Most important families recorded in the Campo-Ma’an area. No.

Family

No. of species

Predominant growth forms

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Rubiaceae Euphorbiaceae Leguminosae-Caesalpinioideae Apocynaceae Annonaceae Acanthaceae Leguminosae-Papilionaideae Sterculiaceae Gramineae Orchidaceae Melastomataceae Moraceae Celastraceae Cyperaceae Dichapetalaceae Sapindaceae Araceae Loganiaceae Sapotaceae Begoniaceae Others(135 families)

279 117 96 80 69 68 65 62 54 54 46 40 39 39 39 36 34 33 30 29 988

Trees, shrubs, herbs and climbers Trees, shrubs, herbs and climbers Trees and shrubs Trees, shrubs, herbs and climbers Trees, shrubs and climbers Herbs Trees, herbs and climbers Shrubs and trees Herbs Terrestrial and epiphytic herbs Shrubs and herbs Trees, shrubs and herbs Climbers Herbs Climbers Trees and shrubs Herbs and hemi-epiphytes Shrubs and climbers Trees and shrubs Terrestrial and epiphytic herbs Trees, shrubs, climbers and herbs

Table 5. Most important genera recorded in the Campo-Ma’an area. No.

Genus

No. of species

Predominant growth forms

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Dichapetalum (Dichapetalaceae) Psychotria (Rubiaceae) Cola (Sterculiaceae) Begonia (Begoniaceae) Diospyros (Ebenaceae) Salacia (Celastraceae) Strychnos (Loganiaceae) Rinorea (Violaceae) Drypetes (Euphorbiaceae) Combretum (Combretaceae) Dorstenia (Moraceae) Campylospermum (Ochnaceae) Bulbophyllum (Orchidaceae) Ficus (Moraceae) Garcinia (Guttiferae) Asplenium (Aspleniaceae) Culcasia (Araceae) Landolphia (Apocynaceae) Tricalysia (Rubiaceae) Bertiera (Rubiaceae) Others (831 genera)

37 35 32 29 27 27 24 23 21 18 18 17 16 16 16 15 15 15 15 14 1867

Climbers Shrubs Trees and shrubs Terrestrial and epiphytic herbs Trees and shrubs Climbers Climbers Trees and shrubs Trees and shrubs Climbers Herbs Shrubs Terrestrial and epiphytic herbs Trees and stranglers Trees and shrubs Epiphytic herbs Herbs and hemi-epiphytes Climbers Trees an shrubs Shrubs Trees, shrubs, climbers and herbs

[166]

1227

Figure 1. Distribution of 114 strict and narrow endemic plant species recorded in the CampoMa’an area (gray circle). Black circle represents the distribution of 17 threatened strict endemics that are not found in the National Park. The size of the circle represents the relative density of endemics at a given point.

number of species) recorded are endemic to the Lower Guinea Centre of Endemism, 1123 species (49%) are Guineo–Congolian endemics and 105 species (5%) are Guinea endemics as described by White (1979). Overall, there was a high concentration of strict and narrow endemic species in the lowland evergreen forest rich in Caesalpinioideae, coastal and submontane forests located in the western and northern parts of Ma’an and a relatively low concentration of these species in Ma’an area (Figure 1). Although more than 70% of the total endemic species recorded were also found in the National Park, 17 of the 29 strict endemic species were not recorded in the park (Appendix 1). The distribution patterns of these 17 taxa showed a high concentration of species around Campo, Lobe, Massif des Mamelles, Mont d’Ele´phant and Zingui and a very poor representation in the Ma’an area (Figure 1). GHI and measurement of forest conservation value More than 57% of the plots have a high GHI score with the highest score recorded in the submontane forest (GHI = 294.4) and the lowest score in mangrove (GHI = 3.1). As shown in Figure 2, the submontane forest had the highest average GHI score of 214.7, followed by the lowland evergreen forest rich in Caesalpinioideae with Calpocalyx heitzii and Sacoglottis gabonensis [167]

1228

Figure 2. The average Genetic Heat Index (GHI = bars) and average Pioneer Index (PI = line) for the various vegetation types as defined in Table 6.

(GHI = 194.1). The mangrove and the coastal forest on sandy shorelines had the lowest average GHI score (GHI = 3 and 120.2, respectively). The average PI was very high in the mangrove forest (PI = 125), coastal forest on sandy shorelines (PI = 66.9) and in the forest rich in Aucoumea klaineana (PI = 60). Generally, there was a significant decrease in average GHI with increasing average PI (Figure 2). As shown in Figure 3, there was a very strong significant negative correlation between the average GHI scores and the PI scores recorded in the various vegetation types (F1–10 = 111.71, R2 = 0.918, p < 0.0001). However, the correlation was rather weak with a low explanatory factor when the analysis was carried out using all plots as individual data points (F1–45 = 94.00, R2 = 0.393, p<0.0001). Most of the forest types within the National Park were virtually undisturbed or less than 25% disturbed (Figure 4). The coastal forest between Campo and Kribi, as well as the forests around Massif des Mamelles, Mont d’Ele´phant, agro-industrial plantations, logging concessions and settlements were much more affected by human activities (Figure 4). These forests were often more than 25% disturbed by human activities and were characterised by a high PI scores (Figures 2 and 4).

Geostatistical results The analysis of the spatial structure of the dataset did not show any preferential spatial trend. Therefore, an omni-directional analysis of the semivariance (best described by a spherical model) was applied. Figure 5 shows the semivariogram and its characteristics. The GHI variable showed a strong spatial dependence [168]

1229

Figure 3. Correlation between the average GHI scores and the average PI scores for the various vegetation types.

Figure 4.

Impact of human disturbance on the Campo-Ma’an rain forest.

within a range of 10,500 m. The nugget (645) was low compared to the total variance or sill (3700). This suggests that more than 82% (100*(Sill-Nugget)/Sill) of the semivariance of GHI could be modelled by the variogram over a range of 10 km. The output map of the ordinary kriging (Figure 6) was reclassified into [169]

1230

Figure 5. Spherical variogram model for GHI in the Campo-Ma’an rain forest (estimated from 147 points of 0.1 ha each).

Figure 6. Ordinary kriging map showing the distribution of GHI scores and conservation hotspots within the Campo-Ma’an rain forest The following GHI values are defined for the various conservation classes (Hawthorne 1996): Very high conservation value for GHI >200; High conservation value (150 ‡ GHI <200); Moderate conservation value (100 ‡ GHI <150); Low conservation value (50 < GHI <100) and very low conservation value (GHI <50). [170]

1231 five GHI classes, partitioning the conservation value of the Campo-Ma’an forest. This partition showed that 1% of the area was characterised by a very high conservation value, 45% by a high conservation value, 30% by an average conservation value, 15% by a low conservation value and 9% by a very low conservation value. A considerable portion of the National Park and the forests around Massif des Mamelles and Mont d’Ele´phant was characterised by a high conservation value, with highest values found in Dipikar Island, Massif des Mamelles, Mont d’Ele´phant and in the submontane forest on hilltops. The forests in the Ma’an area, around Campo and agro-industrial plantations, near villages and along the roads had a low conservation value. Similar patterns were observed for the distribution of strict and narrow endemic species (Figure 1).

Discussion General vegetation patterns The Campo-Ma’an has a diverse range of vegetation that changes progressively from sea level to 1100 m at higher altitudes. The wetter Campo area was dominated by the lowland evergreen forest rich in Caesalpinioideae and the drier Ma’an area by the mixed evergreen and semi-deciduous forest. More than 65% of the forest types recorded had at least 250 species (Table 6). The submontane forest had the highest frequency of species-rich plots (93% of the plots had above 100 species/0.1 ha). Other rich vegetation types included the lowland evergreen forest rich in Caesalpinioideae with Calpocalyx heitzii and Sacoglottis gabonensis (81%), the lowland evergreen forest rich in Caesalpinioideae with Sacoglottis gabonensis (78%), the lowland evergreen forest rich in Caesalpinioideae (76%) and the mixed evergreen and semi-deciduous forest (67%). The mangroves, swamps and the coastal vegetations on sandy shorelines were species-poor. The explanation for the diverse range of forest types and habitats might stem partly from the fact that the Campo-Ma’an vegetation is influenced by several environmental factors such as rainfall, altitude, soil, the proximity to the sea and the degree of human disturbance (Tchouto 2004). As a result there was a gradual variation in dominant species, forest type and structure from the coast to the hilltops and the drier forest in the Ma’an region.

Species richness and endemism The Campo-Ma’an area is characterised by a rich and diverse flora with more than 2297 species of vascular plants, ferns and fern allies. The site has about 114 endemic plant species out of which 29 are strictly endemic to the site. The number of endemic plant species is relatively high considering the size of the area, and more than 75% of the current vegetation cover was characterised [171]

1232 Table 6. Number of plots, number of species and number of stem/ha recorded within the various vegetation types for all plants with DBH ‡1 cm. Vegetation types

No of plots

No of species

Average No of stems/ha

Caesalp (2.3 ha) Caesalpcasa (2.5 ha) Caesalpsa (1.4 ha) Cosaga (0.9 ha) Cosaca (0.9 ha) Cosas (0.4 ha) Mixevergreen (2.1 ha) Mixsemideci (1.6 ha) Submontane (1.4 ha) Swamps (0.5 ha) Mangrove (0.2 ha) Okoume´ forests (0.5 ha) Total for the Campo-Ma’an area

23 25 14 9 9 4 21 16 14 5 2 5 147

557 (75–128) 555 (93–139) 474 (86–138) 303 (81–140) 326 (78–108) 100 (27–55) 523 (63–135) 481 (86–147) 499 (79–148) 246 (18–108) 4 (3–4) 234 (18–107) 1116 (3–148)

4380 5326 5935 5810 5864 4710 4983 5460 6094 4276 8630 4802 5312

(2500–5750) (3120–7020) (4350–7120) (4940–8010) (4740–7570) (3630–5700) (3890–6980) (4390–6340) (3680–8449) (2070–5960) (8150–9100) (3720–5800) (2070–9100)

Minimum and maximum values are given betweem brackets. Submontane = Submontane forest; Caesalpcasa = Lowland evergreen forest rich in Caesalpinioideae, Calpocalyx heitzii and Sacoglottis gabonensis; Casealpsa= Lowland evergreen forest rich in Caselpinioideae and Sacoglottis gabonensis; Caesalp= Lowland evergreen forest rich in Caesalpinioideae; Cosaga= Coastal forest with Sacoglottis gabonesis; Mixsemideci= Mixed evergreen and semi-deciduous forest with elements predominant; Mixevergreen= Mixed evergreen and semideciduous forest with semi-deciduous elements of evergreen forest predominant; Okoume´= forest rich in Aucoumea klaineana; Cosaca= Coastal forest with Sacoglottis gabonensis and Calpocalyx heitzii; and Cosas= Coastal forest on sandy shorelines.

either by very high, high or average GHI values (Figure 6). Furthermore, the distributions of strict and narrow endemic species showed a high concentration of these species in the submontane forest between Ebianemeyong and Akom II, in Dipikar Island, and in the forests in and around Massif des Mamelles, Lobe, Mont d’Ele´phant and Zingui. Surprisingly, the mixed evergreen and semideciduous forest in the Ma’an area showed a relatively low concentration of these species (Figure 1). The explanation for the high occurrence of endemics might stem partly from the fact that the area falls within a series of postulated rain forest refugia in Central Africa (Hamilton 1982; White 1983; Maley 1987, 1989, 1990, 1993, 1996; Sosef 1994, 1996). In such refugia, the unique combination of climatic and geological histories, contemporary ecological factors, and inherent biological properties of taxa and their combinations, may have contributed to survival and/or speciation (Barbault and Sastrapradja 1995; Hawksworth and Kalin-Arroyo 1995). Furthermore, the Campo-Ma’an area forms part of the Guineo–Congolian Regional Centre of Endemism (White 1983). All families endemic to this biogeographic region are also found in the Campo-Ma’an area (White 1983). They include Hoplestigmataceae, Huaceae, Lepidobotryaceae, Medusandraceae, Pandaceae, Pentadiplandraceae and Scytopetalaceae. Moreover, 82% of endemic genera cited by White (1983) also occur in the area. [172]

1233 Forest richness and biodiversity hotspots Considering the fact that the occurrence of endemic species contributes significantly to the conservation value of a forest, it is important to study their distribution and abundance prior to any conservation initiatives. This is mainly due to the fact that strict and narrow endemic species are restricted to small areas, and are therefore highly vulnerable to human disturbance and other forms of environmental changes (Myers 1988; Williams 1993; Heywood and Watson 1995). A study carried out in the Campo-Ma’an rain forest has revealed that the submontane forest, the lowland evergreen forest rich in Caesalpinioideae with Calpocalyx heitzii and Sacoglottis gabonensis, and the lowland evergreen forest rich in Caesalpinioideae are richer in strict and narrow endemics compared to the other forest types found in secondary forest and along the coast. This is confirmed by the high average GHI scores recorded in these forest types (Figure 2). Most of these forest types were located in the National Park and the lowland evergreen forests around Massif des Mamelles and Mont d’Ele´phant. They were virtually undisturbed or less than 25% disturbed by human activities (Figure 4). This implies that the Massif des Mamelles and the Mont d’Ele´phant areas represent other biodiversity hotspots, located outside of the Park (Figure 6). There was a strong significant negative correlation between the average GHI scores and the average PI scores recorded in the various vegetation types. Most plots located near settlements and in secondary forests were characterised by a low conservation value with low GHI scores and high PI scores. This confirmed that disturbed forests are rich in pioneer species but poor in plant species with high conservation priority. It is worth reiterating that a considerable portion of the Campo-Ma’an area has been selectively logged at least twice during the past 30 years. Although logging damages were moderate and had low impact on the total forest biodiversity, it has created forest gaps that allowed the development of many pioneer species. This might have contributed to the high average PI scores registered in the coastal forest types. In these areas with conflicts between human and conservation activities, there is an urgent need to develop participatory approaches to sustainable natural resource management that integrates the objectives of conservation with local development.

Threatened species During the selection of species of high conservation priority, taxa were chosen on a global rather than a Cameroonian or a Campo-Ma’an perspective of conservation importance. Of the 29 strict endemic species that are only known from the Campo-Ma’an area, 17 were not recorded in the National Park illustrating the need for conservation activities outside the park. Although these 17 strict endemics are not immediately threatened with extinction, the [173]

1234 most threatened are probably those occurring in the coastal zone and in areas located at the vicinity of large agro-industrial plantations, since these areas are heavily exploited. As shown in Figure 4, their habitats are fragmented and degraded because these areas are surrounded by farms and heavily disturbed forests. Considering the fact that extinct species are taxa that are no longer known to exist in the world after repeated search in their type localities (WCMC 1998; IUCN 2002), we cannot yet talk about extinction because no attempt has been made to search for these species. Furthermore, only 67% of the total amount of specimens collected was identified at species level. However, with the ongoing speed of forest degradation noticed in the coastal area, eight of these strict endemics (Beilschmiedia dinklagei, Deinbollia macroura, Ledermanniella batangensis, Psychotria aemulans, P. batangana, P. dimorphophylla, P. oligocarpa, and Strychnos canthioides) that are only known from the coastal zone can be categorised as endangered species. While the nine others that are located inland around Efoulan, Fenda, Massif des Mamelles, Mont d’Ele´phant and Zingui can be categorised as vulnerable. They are Afrotrewia kamerunica, Bulbophyllum alinae, Begonia montis-elephantis, Calvoa stenophylla, Dorstenia dorstenioides, Guaduella mildbraedii, Hypolytrum sp. nov., Scaphopetalum acuminatum and S. brunneo-purpureum. Some of them so far are only known from type specimens or from a few collections made in the type locality before the 60s. Others such as Afrotrewia kamerunica, Begonia montis-elephantis and Hypolytrum sp. nov. have a restricted range with a small and restricted population. Furthermore, habitat fragmentation may convert a previously more continuous population structure to a metapopulation structure, with local populations becoming so small that they may have a substantial risk of extinction (Hawksworth and Kalin-Arroyo 1995).

Implications for biodiversity conservation The Campo-Ma’an National Park The National Park is the core conservation area of the Campo-Ma’an Technical Operational Unit. It is surrounded by areas under several land uses that have varying ecological impact on the park and the surrounding forests. The park is of high conservation priority with about 72% of the 2297 species of vascular plants, ferns and fern allies recorded so far in the Campo-Ma’an area. More than 70% of the total endemic species recorded were also found in the National Park, and most of the forest types with high GHI scores, low PI scores and high conservation priority species were also found in the park (Figures 2 and 6). The most important one is the endemic lowland evergreen forest rich in Caesalpinioideae with Calpocalyx heitzii and Sacoglottis gabonensis, a vegetation type that only occurs in the Campo area (Letouzey 1985; Gartlan 1989; Thomas and Thomas 1993). Other forest types such as the submontane forest on hilltops, the lowland evergreen forest rich in [174]

1235 Caesalpinioideae and the mixed evergreen and semi-deciduous forests are also well represented. So far the National Park is the only area with a legal conservation status. It is a permanent state forest that is protected by law and solely used for forest and wildlife conservation. However, its boundaries have not been marked, the management plan has not yet been produced and protection is weak. Therefore, it is of urgent need to demarcate the boundary of the park, to reinforce its protection, and to complete and implement its management plan as soon as possible.

Massif des Mamelles and Mont d’Ele´phant This study has demonstrated that other hotspots for biodiversity conservation, such as Mont d’Ele´phant and Massif des Mamelles, are located outside the National Park (Figure 6). These areas are non-permanent forest estates that can be allocated for human activities such as logging, agro-industry, agriculture, agro-forestry, community forest, communal forest or private forest. Moreover, hunting, fishing, mineral exploitation or any other form of economic activities is allowed if done in accordance to the 1994 forest law. Unfortunately, these areas do not have any conservation status and a number of ongoing human activities have negative impacts on the forest ecosystem (Tchouto 2004). In addition to the construction of the Tchad-Cameroon oil pipeline terminal at Grand Batanga and the rock exploitation on Mont d’Ele´phant, there exists a plan to exploit the iron ore deposits of the Massif des Mamelles. All these activities, if realized, would affect the vegetation and thus impact the biodiversity. As shown in Figure 4, these fragmented forest patches with high conservation priorities are more seriously exposed to forest degradation and habitat loss since they are surrounded by disturbed and degraded forests. Furthermore, they are the type localities for some rare endemic species such as Afrotrewia kamerunica, Begonia montis-elephantis, Bulbophyllum alinae and Hypolytrum sp. nov. that are so far only known from the type specimens or from few collections made in these areas. Pressure on these fragmented hotspots will increase in the future with the growing human population density, the few local employment opportunities and the poverty of the local people, for whom the forest is a major resource. In order to ensure the protection of these areas, it is suggested that local community be encouraged to create community forests with several management zones. Each community forest should have the identified biodiversity hotspot as the core conservation area, surrounded by a buffer zone stimulating the sustainable management of non-timber forest products and hunting practices.

The coastal zone, Ntem basin, Lobe and Memve’ele waterfalls The coastal zone is a narrow strip (65 km long) along the Atlantic Ocean from the Lobe waterfalls to the Ntem estuary in the Dipikar Island that extends about 2–3 km inland. It has suffered and continues to suffer from intense [175]

1236 human pressure that has led to the destruction of most of its natural vegetation (Figure 4). However, it is worth mentioning that some rare endemic species such as Deinbollia macroura, Psychotria batangana, P. dimorphophylla, P. oligocarpa, and Strychnos canthioides are so far only known from this zone. Furthermore, there is an impressive network of rivers and streams in the Campo-Ma’an area that presents a number of very specialised riparian habitats. Our study confirmed that the Lobe, Bongola, Memve’ele waterfalls and Ntem basin (Boucle du Ntem) support a rich riparian flora with many endemic and rare rheophile species (Cusset 1987; Thomas and Thomas 1993). Most of the endemic rheophytes are of the genus Ledermanniella in the Podostemaceae family. These rheophytes which are found on exposed rocks in streambeds, are seasonally submerged by fast-flowing water, and normally reproduce in drier periods when the water level recedes. The Ntem basin is also reported to constitute an important refuge for wildlife and fish fauna because of the presence of many rare species of freshwater fishes (Vivien 1991; Matthews and Matthews 2000; Djama 2001). Therefore, it is suggested to develop a separate management strategy in order to protect these riparian habitats.

Conclusion The study provides important information on the abundance and distribution of endemic species, as well as the location of biodiversity hotspots in the Campo-Ma’an area. This information is essential for any decision-making process for biodiversity conservation and sustainable natural resource management. Our study has revealed that the area is characterised by a rich flora with more than 2297 species of vascular plants and about 114 endemic plant species, of which 29 are only found in the area. Although most of the forest types with plant species of high conservation priorities appeared to occur in the Campo-Ma’an National Park, there are several human activities in the area with varying negative ecological impacts on the forest ecosystem. Therefore, the successful management and long-term sustainability of the Park will largely depend on the ability to reconcile the objectives of conservation and other uses at its vicinity. The study also demonstrated that there are other biodiversity hotspots in the coastal zone and areas such as Mont dEle´phant and Massif des Mamelles that are located outside the National Park. These areas support 17 strict endemic species that are not found in the park. Unfortunately, these strict endemics are the most threatened since their habitats are fragmented and degraded as a result of past and present land conversion to subsistence and industrial plantations. Furthermore, these hotspots are the type localities for some rare endemic species that are so far only known from type specimens or from a few collections made in these areas. Contrary to the National Park, these hotspots do not yet have any conservation status per se. However, although the park is a [176]

1237 permanent state forest which is protected by law and should be solely used for forest and wildlife conservation, its boundaries have not been marked, the management plan has not yet been produced and protection is weak. It is, therefore, of urgent need to demarcate its boundary, reinforce its protection, and complete and implement its management plan as soon as possible. Furthermore, in view of the fact that pressure on these fragmented hotspots is likely to increase in the future with the growing human population density, it is suggested that a separate management strategy be developed to ensure the protection of these biodiversity hotspots and their endemic species.

Acknowledgements This study was carried out in the framework of the Campo-Ma’an Biodiversity Conservation and Management Project, Cameroon, and was financially supported by Tropenbos International, The Netherlands. We will like to thank G. Achoundong, J.M. Onana, B. Sonke, L. Zapfack and P. Mezili at the National Herbarium, Cameroon, and F.J. Breteler and C.C.H. Jongkind at the Nationaal Herbarium Nederland, Wageningen University Branch, who assisted in plant identification. The staff of Campo-Ma’an Project is also acknowledged with gratitude for their assistance and support during the fieldwork. Particular thanks are for my field assistants Elad Maurice and Ossele Mathilde for their enthusiastic support and cooperation. We will also like to extend our sincere thanks to all chiefs and village representatives, for their active participation in the organisation and collection of field data.

[177]

No. Family

[178]

1

Acanthaceae

2

Annonaceae

3

Annonaceae

4

Apocynaceae

5

Apocynaceae

6

Apocynaceae

7

Apocynaceae

8 9

Araceae Araceae

Species

Guild Star Habit Chorology

Notes

sb

GD Hb

Cam

Akom II, Dipikar Island, Western and South Cameroon

sb

GD Sh

Sw-Cam

sb

GD Sh

Cam

Akom II, Dipikar Island, Massif des Mamelles, Bipindi and Lolodorf Massif des Mamelles, Bipindi and Lolodorf

sb

GD Sh

Cam

Ma’an, South, Centre and East Cameroon

Np

GD Lwcl

Cam

Efoulan, Bipindi, Makak and Mt. Cameroon

sb

BK

Sw-Cam

Campo, Bipindi and Lolodorf

sb

GD Sh

Lg

sb sb

BK He GD Hb

Sw-Cam Cam

Np

GD Swcl

Cam

Northern limit of distribution, from Gabon to Akom II, Onoyong and Ma’an Massif des Mamelles, Dipikar Island, Ma’an and Bipindi Bifa, Zingui, Akom II, Dipikar Island, Bipindi, Mt Cameroon and Eseka Dipikar Island, Ebolowa and Mt. Cameroon

sb

bu

Hb

Lg

Northern limit of distribution, from Gabon to Bipindi, Zingui

sb

bu

Hb

Lg

Northern limit of distribution, from Gabon to Ebianemeyong

sb

bu

Hb

Lg

sb

bu

Hb

Lg

Northern limit of distribution, from Gabon to Bipindi, Zingui and Grand Batanga Northern limit of distribution, from Congo, Gabon to Mvini and Efoulan

10

Aristolochiaceae

11

Balsaminaceae

12

Balsaminaceae

13

Begoniaceae

Stenandrium thomense (Milne-Redh.) Vollesen Monanthotaxis elegans (Engl. and Diels) Verdc. Monodora zenkeri Engl. and Diels* Callichilia monopodialis (K.Schum.) Stapf* Landolphia flavidiflora (K. Schum.) Persoon* Petchia africana Leeuwenb.* Tabernaemontana hallei (Boiteau) Leeuwenb. Culcasia bosii Ntepe-Nyame Culcasia panduriformis Engl. and Krause Pararistolochia preussii (Engl.) Hutch. & Dalziel Impatiens hians Hook.f. var. bipindensis (Gilg) Grey- Wilson Impatiens gongolana N.Halle´ Begonia anisosepala Hook.f.

14

Begoniaceae

Begonia clypeifolia Hook.f.

Sh

1238

Appendix 1 List of 141 plant species that are either strictly endemic to the Campo-Ma’an area (only found in Carapo-Ma’an) or near endemic (also occur in the western parts of south Cameroon or other parts of Cameroon).

15

Begoniaceae

16

Begoniaceae

17 18 19

Begoniaceae Begoniaceae Begoniaceae

20

Begoniaceae

21

Burseraceae

22

Burseraceae

23 [179]

24

25

26 27 28 29 30

Begonia mbangaensis Sosef Begonia microsperma Warb. Begonia montis’-elephantis J.J.de Wilde* Begonia zenkeriana Smith and Wassh. Aucoumea klaineana Pierre

Dacryodes buettneri (Engl.) Lam. Capparaceae Ritchiea simplicifolia Oliv. var. Caloneura (Gilg) Kers Celastraceae Pristimera luteoviridis (Exell) N.Halle´ var. kribiana N.Halle´ Chrysobalanaceae Dactyladenia cinera (Engl. ex de Wild) Prance and F.White** Chrysobalanaceae Dactyladenia icondere (Baill.) Prance and F.White Combretaceae Combretum cinnabarinum Engl. and Diels Cyperaceae Hypolytrum sp. nov. ined.* Dichapetalaceae Dichapetalum altescandens Engl. * Dichapetalaceae Dichapetalum cymulosum (Oliv.) Engl.* Dichapetalaceae Dichapetalum librevillense Pellegr.*

bu

Ep

bu

Hb

Lg

Northern limit of distribution, from Gabon to Mvini, Efoulan and around Kom River Lg Northern limit of distribution, from Gabon to Lolabe and around Kribi Sw-Cam Akom II, Efoulan, Bipindi and Lolodorf Cam Ebianemeyong, Ma’an, South-west and South Cameroon Campo-Ma’an Rare species, only known from a small population on Mt d’Ele´phant Sw-Cam Campo, Massif des Mamelles, Dipikar Island, Bipindi and Lolodorf Lg Northern limit of distribution, from Gabon to Ma’an and Ebianemeyong Lg Northern limit of distribution, from Gabon to Ma’an and Ebianemeyong Cam Lobe, Campo, Kienke, Dipikar Island, Bipindi, Lolodorf and Ebolowa Campo-Ma’an Rare species, only known from few collections on Mt d’Ele´phant and Dipikar Island

sb sb sb

BK Hb GD Hb BK Hb

sb

BK

Hb

Pi

bu

Tr

Np

bu

Tr

sb

BK

Sh

Np

BK

Swcl

sb

BK

Tr

Sw-Cam

sb

bu

Sh

Lg

np

bu

Lwcl

sb np

BK bu

Hb Lwcl

np

GD Lwcl

Northern limit of distribution, from Congo, Gabon to Grand Batanga, Campo and Dipikar Island Lg Northern limit of distribution, from Gabon to Bipindi and Dipikar Island Campo-Ma’an New species only known from Mont d’Ele´phant Lg Northern limit of distribution, from Gabon to Efoulan and Zingui Cam Grand Batanga, Campo, Bipindi, Lolodorf and Douala

np

bu

Lg

Lwcl

Rare species, only known from type specimens (Bipindi) and a record from Grand Batanga

Northern limit of distribution, from Gabon to Mt d’Ele´phant and Campo

1239

31

Begonia elaeagnifolia Hook. ep f. Begonia heterochroma Sosef sb

[180]

No. Family

Species

Guild Star Habit Chorology

32

Dichapetalaceae

np

BK

Lwcl

33

Dichapetalaceae

Dichapetalum oliganthum Breteler* Tapura tchoutoi Breteler

sb

BK

Sh

34

Dryopteridaceae

sb

bu

He

35

Ebenaceae

sb

BK

Tr

36

Ebenaceae

sb

Bu

Tr

37

Euphorbiaceae

Sh

38

sb

BK

Gnetaceae

Lastreopsis davalliaeformis (Tardieu) Tardieu* Diospyros alboflavescens (Gu¨rke) F.White Diospyros soyauxii Gu¨rke and K. Schum. Afrotrewia kamerunica Pax and Hoffm.* Gnetum buchholzianum Engl.

np

GD Hcl

39

Gramineae

Guaduella mildbraedii Pilg.*

sb

BK

Hb

40

Gramineae

pi

bu

Hb

41 42

Guttiferae Guttiferae

Hyparrhenia wombaliensis (Vanderyst ex Robyns) Clayton* Garcinia conrauana Engl. Garcinia densivenia Engl.

Sb ri

GD Tr GD Tr

43

Icacinaceae

Alsodeiopsis zenkeri Engl.

rh

GD Sh

44 45

Icacinaceae Icacinaceae

46

Icacinaceae

Iodes kamerunensis Engl. sb Rhaphiostylis ovalifolia Engl. sb ex Sleumer* Rhaphiostylis subsessilifolia sb Engl.

GD Swcl GD Swcl BK

Swcl

Notes

Grand Batanga, Campo, Mt d’Ele´phant, Kribi, Longi and Lolodorf. Campo-Ma’an Rare species, only known from few collections around Bifa and Dipikar Island Lg Northern limit of distribution, from Gabon to Bipindi and Zingui Sw-Cam Rare species, only known from few collections from Bifa, Zingui and Bipindi Lg Northern limit of distribution, from Gabon to Campo and Zingui Campo-Ma’an Rare species, only known from Massif des Mamelles Sw-Cam

Cam

Dipikar Island, Onoyong, Ma’an, Littoral, South-west and South provinces of Cameroon Campo-Ma’an Rare species, only known from few collections in the Campo area Lg Northern limit of distribution, from Congo to Campo

Cam Cam

Akom II, South-west and South Cameroon Dipikar Island, Ebianemeyong, Mvini, Littoral and South Cameroon Cam Frequent along the Bongola and Ntem rivers, and other rivers in Littoral, East and South Cameroon Cam Akom II, Dipikar Island, Bipindi, Bertoua and Nanga Eboko Cam Coastal forest around Kribi, Grand Batanga, Lolabe, Elabi Massif des Mamelles, Littoral and South Cameroon Campo-Ma’an Rare species, only known from Grand Batanga, Ebianemeyong and Mt d’Ele´phant

1240

Appendix 1 (Continued)

[181]

Ixonanthaceae

48

Lauraceae

49

Lauraceae

50

Lauraceae

51

Lauraceae

52

Lauraceae

53

Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae LeguminosaeCaesalpinioideae Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae

54 55 56 57 58 59 60 61

np

bu

Tr

sb

BK

Tr

sb

BK

Tr

Campo-Ma’an Rare species, only known from few records around Grand Batanga

sb

BK

Tr

Sw-Cam

sb

BK

Tr

Sw-Cam

sb

BK

Tr

Sw-Cam

np

bu

Tr

Lg

sb

GD Tr

Cam

np

GD Tr

Cam

sw

bu

Tr

Lg

np

bu

Tr

Lg

ri

bu

Tr

Lg

sb

BK

Tr

Sw-Cam

Gilbertiodendron pachyant- np hum (Harms) J.Le´onard Plagiosiphon longitubus sb (Harms) J.Le´onard

BK

Tr

Sw-Carm

BK

Tr

Sw-Cam

Ochthocosmus calothyrsus (Mildbr.) Hutch. and Dalziel Beilschmiedia cuspida (K. Krause) Robyns and Wilczek Beilschmiedia dinklagei (Engl.) Robyns and Wilczek* Beilschmiedia klainei Robyns and Wilczek Beilschmiedia papyracea (Stapf) Robyns and R.Wilczek Beilschmiedia welczekii Fouilloy Amphimas ferrugineus Pierre ex Pellegr. Anthonotha leptorrhachis (Harms) J.Le´onard Aphanocalyx hedinii (A.Chev.) Wieringa Aphanocalyx ledermannii (Harms) Wieringa Copaifera religiosa J.Le´onard Daniellia klainei A.Chev. Dialium zenkeri Harms

Lg

Northern limit of distribution, from Gabon to Cameroon (frequent in the Campo-Ma’an area) Campo-Ma’an Rare species, only known from Fenda and Akom II

Rare species, only known from few records from Akom II, Ebianemeyong and Bipindi Rare species, only known from Ebianemeyong, Akom II, Fenda and Bipindi Akom II, Mvini, Nkoelon, Dipikar Island, Ebianemeyong, Ma’an, Bipindi and Lolodorf Northern limit of distribution, from Gabon to Dipikar Island, Ma’an, Onoyong and Akom II Bifa, Campo, Dipikar Island, Lobe, Massif des Mamelles, Mt d’Ele´phant, Bipindi, Lolodorf and Mt Cameroon Akom II, Ebianemeyong, Kom, Ma’an, Bipindi and Eseka Northern limit of distribution, occurs along rivers from Gabon, Equatorial Guinea to the Dipikar Island Northern limit of distribution, from Congo to Akom II and Efoulan Northern limit of distribution, from Congo to Akom II, Eoulan and Ma’an Campo, Dipikar Island, Onoyong, Bipindi and Lolodorf Ebianemeyong, Kom, Massif des Mamelles, Bipindi and Lolodorf Akom II, Efoulan, Ma’an, Bipindi, and Lolodorf

1241

47

No. Family

Species

Guild Star Habit Chorology

Notes

62

Plagiosiphon multijugus (Harms) J.Le´onard Tetraberlinia moreliana Aubre´v.* Chlorophytum petrophyllum K.Krause Mostuea neurocarpa Gilg

sb

GD Tr

Cam

Akom II, Dipikar Island, Ma’an, Bipindi and Kribi-Edea areas

sb

bu

Lg

sb

GD Hb

Cam

Northern limit of distribution, from Gabon, Bidou and Mt. D’Ele´phant Bifa, Dipikar Island, Mvini, Littoral and South Cameroon

sb

bu

Sh

Lg

np

BK

Lwcl

ri

GD Tr

np

GD Lwcl

pa

GD Pa

pi

GD Hb

Northern limit of distribution, from Gabon to Bifa, Campo and Dipikar Island Campo-Ma’an Rare species, only known from few collections around Grand Batanga and Lolabe Cam Akom II, Dipikar Island, Ebianemeyong, Onoyong, Bipindi, Lolodorf, Kribi-Edea and South-west Cameroon Cam Dipikar Island, Mvini, Ma’an, Bipindi, Masok, Douala-EdeaKribi areas. Cam Grand Batanga, Bongola, Bipindi, Eseka, Barombi and along the Lokoundje and Nyong rivers. Cam Ma’an, Onoyong and South Cameroon

sb

BK

Hb

Sw-Cam

sb

BK

Hb

sb

BK

Hb

sb

bu

Hb

BK

Sh

64

Leguminosae -Caesalpinioideae Leguminosae -Caesalpinioideae Liliaceae

65

Loganiaceae

66

Loganiaceae

63

67 [182]

68 69 70 71 72 73 74 75

Strychnos canthioides Leeuwenb. * Loganiaceae Strychnos elaeocarpa Gilg ex Leeuwenb. Loganiaceae Strychnos mimfiensis Gilg ex Leeuwenb. Loranthaceae Tapinanthus preussii (Engl.) Tiegh. Marantaceae Hypselodelphys zenkeriana (K.Schum.) Milne-Redh. Melastomataceae Amphiblemma letouzeyi Jacq.-Fe´l.* Melastomataceae Calvoa calliantha Jacq.-Fe´l. Melastomataceae Calvoa stenophylla Jacq.-Fe´l* Melastomataceae Guyonia tenella Naud.

Melastomataceae Memecylon arcuato-margin- sb atum Gilg ex Engl. var. arcuatomarginatum

Tr

Rare species, only known from few collections recorded on hills around Akom II, Efoulan and Bipindi Sw-Cam Rare species, only known from Ebianemeyong, Akom II and Bipindi Campo-Ma’an Rare species, only known from type specimens collected in Zingui Lg Northern limit of distribution, from Equatorial Guinea to Lobe and Bongola Cam Akom II Dipikar Island, Kom, Mt. D’Ele´phant, Kienke, Longi and Kribi-Edea

1242

Appendix 1 (Continued)

[183]

76

Menispermaceae

77

Menispermaceae

78

Moraceae

79

Moraceae

80

Myrsinaceae

Albertisia glabra (Diels and Troupin) Forman Penianthus camerounensis A.Dekker Dorstenia dorstenioides (Engl.) Hijman and C.C.Berg* Dorstenia involuta M.Hijman Ardisia dolichocalyx Taton

sb

BK

81

Myrtaceae

Eugenia kameruniana Engl.* sb

82

Ochnaceae

83

Ochnaceae

84

Ochnaceae

Campylospermum letouzeyi Farron Campylospermum zenkeri (Engl. ex Tiegh.) Farron Testulea gabonensis Pellegr.

85

Olacaceae

86

Orchidaceae

87

Orchidaceae

88

Orchidaceae

89

Orchidaceae

Swcl

sb

GD Sh

sb

BK

Hb

sb

BK

Hb

sb

GD Hb

Cam

BK

Sh

Cam

sb

GD Sh

Cam

sb

GD Sh

Cam

np

bu

Octoknema dinklagei Engl.

sb

GD Tr

Bulbophyllum alinae Szlachetko* Corymborkis minima P.J.Cribb* Podandriella batesii (la Croix) Szlachetko and Olszewski* Polystachya letouzeyana Szlachetko and Olszewski*

ep

BK

sb

GD Hb

sb

BK

Hb

ep

BK

Ep

Tr

Ep

Sw-Cam

Rare species, only known from Dipikar Island and Bipindi

Cam

Afan, Akom II, Dipikar Island, Ebianemeyong, Mekok, Littoral, South and South-west Cameroon Campo-Ma’an Rare species, only known from few collection around Kienke and Fenda Campo-Ma’an Rare species, only known from Dipikar Island and Ma’an Bifa, Campo, Dipikar Island, Onoyong, Littoral, South and South-west Cameroon Rare species, only known from Ebianemeyong, Ma’an, Nyabissan Dipikar Island and South Cameroon

Campo, Massif des Mamelles, Kribi-Edea and South Cameroon Lg Northern limit of distribution, from Gabon to Dipikar Island, Ma’an and Onoyong Cam Akok, Grand Batanga, Lolabe, South and South-west Cameroon Campo-Ma’an Rare species, only known from few collections on Mt d’Ele´phant Cam Rare species, only known from few collections around Campo, Lolabe and Korup National Park Campo-Ma’an Rare species, only known from Akom II, Efoulan and Ebianemeyong Campo-Ma’an Rare species, only known from Efoulan

1243

No. Family 90

91 92 93 94 [184]

95 96 97 98 99 100

101 102

Orchidaceae

Species

Vanilla africana Lindley subsp. cucullata (Kraenzlin and K. Shum.) Szlachetko and Olszewski * Podostemaceae Ledermanniella annithomae C. Cusset* Podostemaceae Ledermanniella batangensis (Engl.) C. Cusset* Podostemaceae Ledermanniella bosii C.Cusset Podostemaceae Ledermanniella boumiensis C. Cusset Podostemaceae Ledermanniella kamerunensis (Engl.) C. Cusset Podostemaceae Ledermanniella linearifolia Engl. Podostemaceae Ledermanniella variabilis (G.Taylor) C.Cusset Rhizophoraceae Cassipourea kamerunensis (Engl.) Alston Rhizophoraceae Cassipourea zenkeri (Engl.) Alston Rubiaceae Chazaliella sciadephora (Hiern) Petit and Verdc, var. condensata Verdc. Rubiaceae Ecpoma apocynaceum K.Schum. Rubiaceae Hymenocoleus glaber Robbr.

Guild Star Habit Chorology

Notes

np

BK

Hcl

Sw-Cam

Campo, Massif des Mamelles, Mt d’Ele´phant and Bipindi

rh

BK

Hb

Campo-Ma’an Rare species, only known from Memve’ele water falls

rh

BK

Hb

Campo-Ma’an Rare species, only known from Lobe water falls

rh

BK

Hb

rh

bu

Hb

rh

BK

Hb

rh

GD Hb

rh

GD Hb

sb

GD Sh

Campo-Ma’an Rare species, only known from the Ntem Basin, Bongola, Lobe and Memve’ele waterfalls Lg Northern limit of distribution, from Gabon to the Bongola and Memve’ele water falls Campo-Ma’an Rare species, only known from the Bongola water falls in Dipikar Island Cam Lobe and Bongola falls in the Campo-Ma’an area, and in the Nkam river in Yabassi Cam Bongola and Lobe water falls, and in Mamfe river in Southwest Cameroon Cam Akom II, Littoral and South Cameroon

sb

GD Sh

Cam

sb

GD Sh

Cam

Akom II Bifa, Ebianemeyong, Eboundja, Lobe, Ma’an, Bipindi, Lolodorf and South Cameroon Mvini, Onoyong, Ma’an, Littoral and South Cameroon

pi

BK

Sw-Cam

Rare species, only known from Bifa, Zingui and Bipindi

sb

GD Hb

Cam

Akom II, Dipikar Island, Massif des Mamelles, Mvini, Littoral, South and South-west Cameroon

Sh

1244

Appendix 1 (Continued)

[185]

103

Rubiaceae

104 105

Rubiaceae Rubiaceae

106

Rubiaceae

107 108

Rubiaceae Rubiaceae

109

Rubiaceae

110

Rubiaceae

111

Rubiaceae

112

Rubiaceae

113

Rubiaceae

114

Rubiaceae

115

Rubiaceae

116

Rubiaceae

117

Rubiaceae

118

Rubiaceae

lxora aneimenodesma K.Schum. subsp. aneimenodesma Ixora synactica De Block* Oxyanthus oliganthus K.Schum. Pavetta camerounensis S.Manning subsp. camerounensis Pavetta kribiensis J.Manning Pavetta mpomii S.Manning

GD Sh

Cam

Akom II, Dipikar Island, Bipindi and Lolodorf

sb sb

BK Sh GD Sh

Sw-Cam Cam

Rare species, only known from Efoulan, Zingui and Bipindi Akom II, Ma’an and South Cameroon

sb

GD Sh

Cam

Akom II, Bifa, Campo, Dipikar Island, Massif des Mamelles, Mt d’Ele´phant, Littoral and South Cameroon

sb sb

BK BK

Sw-Cam Sw-Cam

sb

GD Sh

Cam

Rare species, only known from Mvini, Bipindi and Lolodorf Mt d’Ee´lephant, Mvini, Nkoelon, Ebianemeyong, Bipindi and Lolodorf Dipikar Island, Mvini, Nkoelon and South Cameroon

np

GD Hb

Cam

sb

BK

Sh

Campo-Ma’an

sb

BK

Sh

Campo-Ma’an

sb

GD Sh

Cam

ri

BK

Sh

Campo-Ma’an

sb

BK

Sh

Sw-Cam

sb

BK

Sh

Campo-Ma’an

sb

GD Sh

Cam

sb

GD Sh

Cam

Sh Sh

Ebianemeyong, Nyabissan, Ma’an, Centre and South Cameroon Rare species, only known from few collections around Grand Batanga Rare species, only known from few collections around Grand Batanga Akom II, Bifa, Ma’an, Bipindi, Lolodorf, Centre and South Cameroon Rare species, only known from few collections from Grand Batanga and Lobe Rare species, only known from Akom II, Onoyong, Bipindi and Lolodorf Rare species, only known from few collections around Grand Batanga Akok, Bifa, Campo, Dipikar Island, Kom, Mvini and South Cameroon Akom II, Dipikar Island, Massif des Mamelles, Mvini, and South Cameroon

1245

Pavetta staudtii Hutch. and Dalziel Pseudosabicea medusula (K.Schum.) N.Halle´ Psychotria aemulans K. Schum.** Psychotria batangana K. Schum.* Psychotria camerunensis Petit Psychotria dimorphophylla K. Schurm.* Psychotria lanceifolia K.Schum. Psychotria oligocarpa K.Schum.* Psychotria sadebeckiana K.Schum.var. elongata Petit Psychotria sadebeckiana K.Scham. var. sadebeckiana

sb

No. Family

Guild Star Habit Chorology

Notes

sb

GD

Sh

Cam

sb

GD

Sh

Cam

Dipikar Island, Massif des Mamelles, Centre and South Cameroon Ebianemeyong, Mvini, Centre and South Cameroon

sb

GD

Swcl

Cam

Mvini, Nkoelon, Centre and South Cameroon

sb

BK

Sh

Campo-Ma’an Rare species, only known from few collections around Campo

sb

BK

Sh

sb

bu

Sh

Campo-Ma’an Rare species, only known from Bifa, Massif des Mamelles and Dipikar Island Lg Northern limit of distribution, from Gabon to Dipikar Island

sb

GD

Sh

Cam

BK

Sh

BK

Tr

128

Scytopetalaceae Rhaptopetalum sessilifoIium sb Engl.* Sterculiaceae Cola fibrillosa Engl. and sb Krause Sterculiaceae Cola letouzeyana Nkongm. sb

GD

Sh

129

Sterculiaceae

sb

GD

Sh

130

Sterculiaceae

sb

BK

Sh

131

Sterculiaceae

sb

BK

Sh

132

Sterculiaceae

sb

BK

Sh

119 120 121 122 123 124 [186]

125 126 127

Species

Rubiaceae

Tricalysia amplexicaulis Robbr. Rubiaceae Tricalysia talbotii (Wemham) Keay Rubiaceae Vangueriella laxiflora (K.Schum.) Verdc. Sapindaceae Deinbollia macroura Gilg ex Radlkofer* Sapindaceae Deinbollia mezilii D.W.Thomas and D.J.Harris Sapindaceae Deinbollia pycnophylla Gilg ex Radlk. Scytopetalaceae Pierrina zenkeri Engl.

Cola praeacuta Brenan and Keay Scaphopetalum acuminatum Engl. and K. Krause* Scaphopetalum brunneo-purpureum Engl. and K. Krause** Scaphopetalum zenkeri K-Schum.

Bifa, Campo, Ebianemeyong, Ma’an, Nyabissan, Littoral and South Cameroon Sw-Cam Rare species, only known from few collections around Efoulan and Bipindi Sw-Cam Rare species, only known from few collections around Dipikar Island and Bipindi Cam Akora II, Dipikar Island, Ebianemeyong, Onoyong, Centre and South Cameroon Cam Bifa, Dipikar Island, Massif des Mamelles, South and Southwest Cameroon Carapo-Ma’an Rare species, only known from few collections from Efoulan and Fenda Campo-Ma’an Rare species, only known from few collections from Fenda and Zingui Sw-Cam

Akom II, Dipikar Island, Ebianemeyong, Bipindi and Lolodorf

1246

Appendix 1 (Continued)

133

Thymelaeaceae

134

Urticaceae

135

Violaceae

136

Violaceae

137 138

Dicranolepis glandulosa H.H.W.Pearson Urera gravenreuthi Engl.

sb

GD

Sh

pi

GD

Hcl

sb

BK

Sh

[187]

sb

BK

Sh

Violaceae Violaceae

Allexis zygomorpha Achoundong and Onana* Rinorea campoensis M. Brandt ex Engl. Rinorea microglossa Engl.* Rinorea sp. nov. 1 ined.*

sb sb

BK GD

Sh Sh

139

Violaceae

Rinorea sp. nov. 2 ined.*

sb

GD

Sh

140

Zingiberaceae

Sb

BK

Hb

141

Zingiberaceae

Aulotandra kamerunensis Loes. Renealmia densispica Koechlin

Sb

BK

Hb

Cam

Akom II, Dipikar Island, Grand Batanga, Campo, Littoral South, and South-west Cameroon Cam Dipikar Island, Ma’an, Littoral, South and South-west Cameroon Cam Coastal forest between Edea and Campo, Bidou, Akok, Longi, Bipindi and Lolodorf Campo-Ma’an Rare species, only known from Campo, Dipikar Island, Lobe and Massif des Mamelles Sw-Cam Efoulan, Bipindi, Lolodorf, Centre and South Cameroon Cam Coastal forest between Kribi and Campo, Dipikar Island, and Douala-Edea-Kribi regions Cam Kienke, Massif des Mamelles, Dipikar Island, Kribi, KribiEdea, Douala- Yaounde, and Eseka regions Sw-Cam Rare species, only known from few collections from Ebianemeyong, Nyabissan and Bipindi Sw-Cam Rare species, only known from few collections from Dipikar Island, Ebianemeyong and Ambam

Those species that reach their northern or southern limit of distribution in the Campo-Ma’an area are also included in the list. *Species strictly endemic to the Campo-Ma’an area that were not recorded in the National Park. **Species for which the status or range needs more investigation. Guild: ep, epiphyte; np, non-pioneer light demanding; pi, pioneer; rh, rheophyte; ri, riverine; sb, shade-bearer; and sw, swamp. Star: as defined in Table 1. Habit: Ep, epiphyte; Hb, herb; Hcl, herbaceous climber; He, hemi-epiphyte; Lwcl, large woody climber; Swcl, small woody climber; Pa, parasite; Sh, shrub; and Tr, tree. Chorology: Campo-Ma’an, strict endemic to Campo-Ma’an; Sw-Cam, endemic to southwestern part of Cameroon; Cam, endemic to Cameroon; Lg, Lower Guinea endemic (especially those species that reach either their northern or southern limit of distribution in the Campo-Ma’an area).

1247

[188]

No. Family

Species

Guild Habit Chorology IUCN/WCMC

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 29 30

Afrofittonia silvestris Lindau Sclerochiton preussii (Lindau) C.B.Clarke Antrocaryon micraster A. Chev. and Guillaum. Trichoscypha bijuga Engl. Trichoscypha mannii Hook. f. Boutiquea platypetala Le Thomas Pachypodanthium barteri (Benth.) Hutch. and Dalziel Uvariastrum zenkeri Engl. and Diels Uvariodendron connivens (Benth.) R.E.Fr. Tylophora cameroonica N.E.Br. Cordia platythyrsa Baker Aucoumea klaineana Pierre Dacryodes igaganga Aubrev. And Pellegr. Salacia lehmbachii Loes. var. pes-ranulae N.Halle´ Dactyladenia cinera (Engl. ex de Wild) Prance and F.White Terminalia ivorensis A.Chev. Hemandradenia mannii Stapf Diospyros barteri Hiern Diospyros crassiflora Hiern Amanoa strobilacea Mu¨ll.Arg. Crotonogyne manniana Mu¨ll.Arg. Drypetes preussii (Pax) Hutch. Drypetes tessmanniana (Pax) Pax and K.Hoffm. Neoboutonia mannii Benth. Pseudagrostistachys africana (Mu¨ll.Arg.) Pax and K.Hoffm. Garcinia brevipedicellata (Baker f.) Hutch. and Dalziel Garcinia kola Heckel Garcinia staudtii Engl. Hoplestigma pierreanum Gilg Afrostyrax kamerunensis Perkins and Gilg

sb sb pi sb sb sb sw sb sb pi pi pi np np sb np sb sb sb sb sb sb sb pi sb sb sb sb np sb

Acanthaceae Acanthaceae Anacardiaceae Anacardiaceae Anacardiaceae Annonaceae Annonaceae Annonaceae Annonaceae Asclepiadaceae Boraginaceae Burseraceae Burseraceae Celastraceae Chrysobalanaceae Combretaceae Connaraceae Ebenaceae Ebenaceae Euphorbiaceae Euphorbiaceae Euphorbiaceae Euphorbiaceae Euphorbiaceae Euphorbiaceae Guttiferae Guttiferae Guttiferae Hoplestigmataceae Huaceae

Hb Hb Tr Tr Tr Sh Tr Sh Tr Swcl Tr Tr Tr Swcl Tr Tr Tr Tr Tr Sh Sh Tr Sh Tr Tr Tr Tr Tr Tr Tr

Lg Lg Lg Lg Lg Lg Lg Lg Lg Lg Gc Lg Lg Lg Sw-Cam Gu Lg Gu Gc Gu Lg Lg Lg Gu Lg Lg Gc Lg Lg Lg

VU A1c + 2c EN B1 + 2e VU A1cd CR A1c + 2abc VU A1C, B1 + 2c EN A1c + 2c VU A1c VU A1c, B1 + 2c LR/nt LR/nt VU A1d VU A1cd VU A1cd + 2cd VU B1 + 2c CR B1 + 2c VU A1cd LR/nt VU A1c EN A1d VU A1c, B1 + 2c LR/nt VU B1 + 2c CR A1c + 2c LR/nt VU A1c, B1 + 2c VU A1c, B1 + 2c VU A1cd VU A1c, B1 + 2c CRA1c + 2c VU A1c, B1 + 2c

1248

Appendix 2 IUCN (1994) threat categories for 92 plant species recorded in the Campo-Ma’an area that are listed in The IUCN (2002) Red List of Threatened Species and The World List of Threatened Trees (WCMC 1998).

[189]

Huaceae Irvingiaceae Irvingiaceae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Caesalpinioideae Leguminosae-Mimosoideae Leguminosae-Mimosoideae Leguminosae-Mimosoideae Leguminosae-Papilionoideae Leguminosae-Papilionoideae Leguminosae-Papilionoideae Leguminosae-Papilionoideae Liliaceae Melastomataceae Melastomataceae Melastomataceae Meliaceae Meliaceae Meliaceae Meliaceae Meliaceae

Afrostyrax lepidophyllus Mildbr. Irvingia excelsa Mildbr. Irvingia gabonensis (Aubry-Lecomte ex O’Rorke) Baill. Afzelia bipindensis Harms Afzelia pachyloba Harms Anthonotha leptorrhachis (Harms) J.Le´onard Aphanocalyx hedinii (A.Chev.) Wieringa Daniellia klainei A.Chev. Daniellia oblonga Oliv. Dialium bipindense Harms Dialium tessmannii Harms Didelotia unifoliolata J.Le´onard Gilbertiodendron pachyanthum (Harms) J.Le´onard Guibourtia ehie (A. Chev.) J. Le´onard Loesenera talbotii Baker f. Pellegriniodendron diphyllum (Harms) J.Le´onard Plagiosiphon longitubus (Harms) J.Le´onard Swartzia fistuloides Harms Calpocalyx heitzii Pellegr. Calpocalyx le
testui Pellegr. Calpocalyx ngouniensis Pellegr. Craibia atlantica Dunn Millettia laurentii De Wild. Millettia macrophylla Benth. Ormocarpum klainei Tisser. Chlorophytum petrophyllum K.Krause Memecylon candidum Gilg Memecylon dasyanthum Gilg ex Lederman and Engl. Warneckea wildeana Jacq.-Fe´l. Entandrophragma angolense (Welw.) C.DC. Entandrophragma candollei Harms Entandrophragma cylindricum (Sprague)Sprague Entandrophragma utile (Dawe and Sprague) Sprague Guarea cedrata (A.Chev.) Pellegr.

sb np np np np sb np ri np np sb sb np np sb sb sb sb np sb sb sb np pi sb sb sb sb sb np np np np np

Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Tr Sh Hb Sh Tr Sh Tr Tr Tr Tr Tr

Lg Gc Gc Gc Gc Cam Cam Lg Lg Lg Lg Lg Sw-Cam Gc Lg Gu Sw-Cam Gc Lg Gc Gc Gc Gc Lg Lg Cam Lg Lg Lg Tra Gc Gc Gc Gc

VU A1c, B1 + 2c LR/nt LR/nt VU A1cd VU A1d CR A1c + 2c CR B1 + 2abcd, C1 + 2ab LR/nt VU A1c LR/nt LR/nt LR/nt VU D2 VU A1c VU A1c, B1 + 2c LR/nt CR A1 + 2c EN A1cd VU A1c, B1 + 2c VU D2 VU A1c VU A1c EN A1cd VU A1c, B1 + 2c CR A1c CR A1c + 2c VU B1 + 2c VU B1 + 2c VU D2 VU A1cd VU A1 cd VU A1cd VU A1cd VU A1c

1249

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64

[190]

No.

Family

Species

Guild

Habit

Chorology

IUCN/WCMC

65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92

Meliaceae Meliaceae Meliaceae Meliaceae Meliaceae Moraceae Myrtaceae Ochnaceae Ochnaceae Rhizophoraceae Rubiaceae Rubiaceae Rutaceae Sapotaceae Sapotaceae Sapotaceae Sapotaceae Simaroubaceae Sterculiaceae Sterculiaceae Sterculiaceae Sterculiaceae Sterculiaceae Sterculiaceae Sterculiaceae Sterculiaceae Violaceae Violaceae

Guarea thompsonii Sprague and Hutch. Khaya anthotheca (Welw.) C. DC. Khaya ivorensis A.Chev. Lovoa trichilioides Harms Turraeanthus africanus (Welw. ex C DC.) Pellegr. Milicia excelsa (Welw.) C.C.Berg Eugenia kameruniana Engl. Lophira alata Banks ex Gaertn.f. Testulea gabonensis Pellegr. Anopyxis klaineana (Pierre) Engl. Hallea stipulosa (DC.) Leroy Nauclea diderrichii (De Wild. And T.Durand) Merrill Vepris heterophylla Letouzey Autranella congolensis (De Wild.) A.Chev. Baillonella toxisperma Pierre Gluema ivorensis Aubre´V. and Pellegr. Tieghemella africana Pierre Nothospondias staudtii Engl. Cola hypochrysea K.Schum. Cola philipi
jonesii Brenan and Keay Cola praeacuta Brenan and Keay Cola semecarpophylla K.Schum. Mansonia altissima (A.Chev.) A.Chev. var. kamerunica Jacq.-Fe´l. Pterygota bequaertii De Wild. Pterygota macrocarpa K.Schum. Sterculia oblonga Mast. Allexis cauliflora (Oliv.) Pierre Allexis obanensis (Baker f.) Melchior

np np np np sb pi sb pi np np sw pi sb np np np np np sw sb sb sb np np np pi sb sb

Tr Tr Tr Tr Tr Tr Sh Tr Tr Tr Tr Tr Sh Tr Tr Tr Tr Tr Tr Sh Sh Sh Tr Tr Tr Tr Sh Sh

Gc Gc Gc Gc Gc Tra Cam Gc Lg Gc Gc Gc Gc Gc Lg Gc Lg Gc Lg Lg Cam Lg Gu Gc Gc Gc Lg Lg

VU A1c VU A1cd VU A1cd VU A1cd VU A1cd LR/nt CR A1c VU A1cd EN A1cd VU A1cd VU A1cd VU A1cd EN A1c, B1 + 2c CR AIcd VU A1cd VU B1 + 2c EN A1cd VU B1 + 2c VU A1c EN B1 + 2c CR A1c + 2c LR/cd EN A1cd VU A1cd VU A1cd VU A1cd VU A1c, B1 + 2c VU B1 + 2c

NB: Guild, habit and chorology categories as defined in Appendix 1.

1250

Appendix 2 (Continued)

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Biodiversity and Conservation (2006) 15:1253–1270 DOI 10.1007/s10531-005-0772-x


Springer 2006

-1

Tree diversity in western Kenya: using profiles to characterise richness and evenness R. KINDT1,*, P. VAN DAMME2 and A.J. SIMONS1 1

ICRAF, PO Box 30677-00100, Nairobi, Kenya; 2Ghent University, Laboratory of Tropical and Subtropical Agriculture and Ethnobotany, Coupure Links 653, B-9000 Gent, Belgium; *Author for correspondence (e-mail: [email protected]; phone: 254-2-524000 or 1-650-8336645; fax: 254-2524001 or 1-650-8336646) Received 18 May 2004; accepted in revised form 7 January 2005

Key words: Accumulation, Agroforestry, Diversification, Domestication, Evenness, Rarefaction, Re´nyi diversity profile, Richness Abstract. Species diversity is a function of the number of species and the evenness in the abundance of the component species. We calculated diversity and evenness profiles, which allowed comparing the diversity and evenness of communities. We applied the methodology to investigate differences in diversity among the main functions of trees on western Kenyan farms. Many use-groups (all trees and species that provide a specific use) could not be ranked in diversity or evenness. No usegroup had perfectly even distributions. Evenness could especially be enhanced for construction materials, fruit, ornamental, firewood, timber and medicine, which included some of the most species-rich groups of the investigated landscape. When considering only the evenness in the distribution of the dominant species, timber, medicine, fruit and beverage ranked lowest (>60% of trees belonged to the dominant species of these groups). These are also use-groups that are mainly grown by farmers to provide cash through sales. Since not all communities can be ranked in diversity, studies that attempt to order communities in diversity should not base the ordering on a single index, or even a combination of several indices, but use techniques developed for diversity ordering such as the Re´nyi diversity profile. The rarefaction of diversity profiles described in this article could be used in studies that compare results from surveys with different sample sizes.

Introduction One of the objectives of tree domestication research, in general and more specific, in western Kenya is the diversification of tree species composition in agroecosystems (Kindt and Lengkeek 1999; Kindt et al. 2004). In the realm of agroforestry, underpinning the need for diversification is the desire to enhance the stability and productivity of agroecosystems (ICRAF 1997; Atta-Krah et al. 2004). Diversity means different things to different people. Most often in natural or agricultural systems, species counts (species richness) are provided as the measure of diversity. Continuing this logic, diversification means adding more species. Species diversity, however, is a function of the number of species, and the evenness in distribution of species’ abundances (Magurran 1988; Purvis and Hector 2000). Options for diversification can therefore be dissociated into interventions that target richness and those that target evenness. [193]

1254 We investigated the diversity of various groups of trees that contributed to similar service or production functions on farms in western Kenya, as our target was diversification within these functions, and not of overall diversity. Since heterogeneity in characteristics of species results in saturating effects of increased diversity on ecosystem functioning (e.g., Hector et al. 1999; Loreau et al. 2001; Tilman et al. 2001), we expect larger effects from increasing diversity within communities of lower diversity. Ranking of use-groups in diversity, therefore, allows prioritising their scope for diversification measured as the average expected effect of adding one species. In addition, such approaches provide the opportunity to model effects of replacement, substitution and expansion at fixed and varying tree densities. Appropriate techniques for measuring diversity and evenness were used in this article. Single indices of diversity or evenness can result in wrong interpretation since not all communities can be ranked in diversity or evenness, whereas they can always be ranked on the basis of a single index (Taillie 1979; To´thme´re´sz 1995; Ricotta and Avena 2002; Ricotta 2003). Since diversity research should not be based on single indices of diversity or evenness, techniques for diversity and evenness ordering were used that produce diversity and evenness profiles.

Material and methods Study area Complete tree inventories were made on 201 stratified-randomly selected farms (taken to mean all land managed by a single household) in the Vihiga and Kakamega districts of western Kenya. The study area is inhabited predominantly by the Luhya (Luyia) ethnic group and belongs to the same agroecological zone where altitude ranges 1500–1800 m above sea level, annual mean temperature ranges 18.1–20.4 C, and annual bimodal rainfall ranges 1600– 2000 mm (Jaetzhold 1982). Four villages were selected within the area, each located in a different stratum that mainly differed in farm sizes and arrangement of woody biomass in the landscape (Bradley et al. 1985; Bradley 1991). The selection of villages coincided with a gradient towards the species-rich Kakamega Forest National Reserve.

Information recorded on tree species All trees (woody perennials) were censused using Beentje (1994) as the key reference. For each tree species encountered on a farm, its abundance (the total number of trees) and uses (see below) were recorded by participatory interviews with household informants involving farm walks, tree counting by the interviewer and data recording on a species-by-species basis. [194]

1255 Households listed all the products or services (uses) that are provided by the different species encountered on their farm. Free responses were obtained on tree uses (primary and all secondary uses) that were postcoded during data entry and checking. In total, 60 use categories were recorded, but analyses were only conducted for the 12 use categories that occurred on more than 20% of farms. Because it is possible that some informants could have forgotten some uses of particular species, information was adjusted (increasing species–farm-use combinations from 6859 to 7526) by always including all trees of a species in a use-group if more than 50% of farmers and minimum five farmers with the species mentioned the use. Use-groups were defined as all the trees encountered in the survey that provided one particular type of use. These use-groups were analysed by matrices with as rows the information collected on a particular farm and as columns the various species encountered. Each cell of a use-group matrix provides the number of trees of each species that was used for a particular function on a particular farm. Diversity and evenness profiles The Re´nyi diversity profile is one of the techniques for diversity ordering that were specifically designed to rank communities from low to high diversity. Re´nyi diversity profile values (Ha) are calculated from the frequencies of each component species (proportional abundances pi = abundance of species i/ total abundance) and a scale parameter (a) ranging from zero to infinity (To´thme´re´sz 1995; Legendre and Legendre 1998) as: P a  ln pi Ha ¼ 1
a It can be demonstrated that values of the Re´nyi profile at the respective scales of 0, 1, 2 and ¥ are related to species richness S, the Shannon diversity index H, the Simpson diversity index D
1 and the Berger–Parker diversity index d
1 (Magurran 1988; Legendre and Legendre 1998; Shaw 2003): H0 ¼ lnðSÞ

H1 ¼ H ¼


X

H2 ¼ lnðD
1 Þ ¼ ln

pi log pi

X 
1 ðp2i Þ

H1 ¼ lnðd
1 Þ ¼ lnðp
1 max Þ [195]

1256 Community A is more diverse than a community B if the diversity profile for community A is everywhere above the diversity profile for community B. Communities that have intersecting profiles cannot be ordered in diversity. The fact that intersecting profiles (partial diversity ordering) could occur explains why ordering techniques such as the Re´nyi series are needed, since a single diversity index will not provide sufficient information. The values of the series for the various use-groups were calculated for scales a 2{0,0.25, 0.5, 1, 2, 4, 8, ¥}. From the diversity profile we derived an evenness profile (ln Ea,0) that orders communities in evenness in a similar way that diversity profiles order communities in diversity (Kindt et al. 2001; Ricotta and Avena 2002): ln Ea;0 ¼ Ha
H0 Partial ordering may occur where evenness profiles intersect, indicating that intrinsic evenness ordering is not possible. For example, community A with species abundances of (70, 20, 10) can only be partially ordered with community B with species abundances of (60, 35, 5) and it is therefore not possible to identify the community of largest evenness. The example above is a rare case where diversity ordering equals evenness ordering since species richness is equal in community A and B. Since species richness differs between most communities, diversity and evenness ordering can only be inferred through separate ordering techniques that conform to diversity ordering (such as the Re´nyi diversity series) or to evenness ordering (such as Hill’s Ea,0 subfamily, see below). Since H0 = ln S (as shown above), rearranging the above formula shows that the Re´nyi diversity profile can be decomposed as: Ha ¼ ln S þ ln Ea;0 This decomposition has the attractive feature in showing mathematically that diversity combines information on species richness and evenness, which conforms to the definition of diversity (Magurran 1988; Purvis and Hector 2000; Shaw 2003). The decomposition of the Re´nyi profile is a generalisation of the decomposition of the Shannon index H1 as ln S + ln E (Hayek and Buzas 1997; Kindt et al. 2001). Ea,0 is a subfamily of Hill’s parametric evenness (Hill 1973; Ricotta and Avena 2002; Ricotta 2003) defined as: Ea;0 ¼

Na exp Ha ¼ N0 exp H0

Ea,0 has several desirable properties as an evenness function: it is consistent with the Lorenz ordering and therefore meets the general requirements for an evenness index (Taillie 1979; Rousseau et al. 1999; Ricotta 2003), it is formally related to diversity (Ricotta and Avena 2002; Ricotta 2003), and it is normalised between zero and one. [196]

1257 Since ln Ea,0 is a monotone transformation of Ea,0, both these measures provide the same rank order when communities are compared for evenness despite the fact that ln Ea,0 is not normalised between zero and one. In analogy with diversity profiles (To´thme´re´sz 1995), the various techniques provide the same ordering, but differ in the resolution of the graphs that they produce.

Accumulation patterns for the Re´nyi series We calculated the Re´nyi diversity profile for each use-group by calculating the frequencies of each species in the entire survey. These profiles allow comparing the total diversity of the various use-groups at the scale of the total survey. Since some use-groups did not occur on each farm, another comparison was made by only including farms where a specific use-group occurred. Because the total number of farms per use-group differed, profiles were rarefied to the same number of farms (the number of farms of the smallest use-group) so that effects from differences in sample size could be removed from the analysis. The rarefaction of the diversity profile was achieved through a Monte-Carlo approach of 1000 random additions of farms (selecting the first farm at random, adding the second farm at random,…) using sampling without replacement. We obtained accumulation surfaces for diversity profiles by calculating the average Ha for each subset of 1, 2, …, all farms combined, and for each a2{0,0.25, 0.5, 1, 2, 4, 8, ¥} (Kindt et al. 2001). A similar approach of rarefaction is used to calculate species accumulation curves for random pooling of sample units (e.g., Colwell 1997; Gotelli and Colwell 2001). FORTRAN and R statistical programmes developed to carry out the computations can be obtained from the authors (Kindt 2001, 2004). H¥ was the only value for which 95% confidence interval (CI) limits for the expected values for the entire survey area could be calculated as it is obtained solely from the proportion of the dominant species, while other values in the diversity series include an effect of species richness. Hayek and Buzas (1997) propose to calculate the variance of species frequency (p) in the case of cluster sampling as n P _2 r pclus

¼

i¼1

m2i ðpi
pÞ2

ðn
1Þm2 n
1

(mi = total abundance of farm i, pi = species frequency in farm i, p = species frequency of the total survey, n = number of farms, and m = total abundance). 95% CI limits for the proportion of the dominant species can then be calculated as: _

p  t  r pclus [197]

1258 The limits of the CI for the proportion were y=ln(x
1) transformed to calculate a CI for H¥.

Results Diversity ordering and examination of richness and evenness contributions Figure 1 shows the Re´nyi diversity ordering of all trees and the 12 most frequent use-groups. By examining the values at scales 0, 1, 2, and ¥, species richness and values of Shannon, Simpson, and Berger–Parker diversity indices can be inferred. The many intersections in the figure show the difficulties to order most groups in diversity. Figure 2 shows many groups with intersecting evenness profiles, which is an indication that many groups cannot be ranked in evenness. Table 1 provides some more precise statistics for certain profile values than can be inferred from the figures, and also gives some parameters that describe the pattern of the various diversity profiles (see below). We differentiated between five pools of use-groups based on their total species richness (H0) (which is an ordering of use-group only based on richness): (i) pool A – very high richness, including all trees and firewood; (ii) pool B – high richness, including shade; (iii) pool C – medium richness, including

Figure 1. Diversity profiles based on the Re´nyi series Ha for all trees and trees belonging to particular use-groups. [198]

1259

Figure 2. Evenness profiles for all trees and trees belonging to particular use-groups.

Table 1. Values for specific profile values of the diversity and evenness profiles depicted in Figures 1 and 2 with for all trees and for the most frequent use-groups in western Kenya. Use-group

H0

H1

H2

H¥

ln E1,0

E1,0

H¥,I

H¥,u

All trees Firewood Shade Medicine Ornamental Timber Boundary Soil fertility Charcoal Fruit Construction Fodder Beverage

5.16 5.04 4.43 4.06 3.97 3.89 3.53 3.30 3.26 3.22 3.00 1.95 1.39

2.79 2.69 3.00 1.59 1.34 1.11 1.95 1.44 1.29 1.08 0.66 1.61 0.50

2.38 2.29 2.60 0.79 0.94 0.77 1.69 1.08 0.95 0.61 0.43 1.40 0.38

1.78 1.71 2.04 0.41 0.68 0.48 1.17 0.64 0.70 0.32 0.24 0.87 0.22


2.37
2.35
1.43
2.47
2.63
2.78
1.58
1.86
1.97
2.13
2.33
0.33
0.89

0.09 0.10 0.24 0.08 0.07 0.06 0.21 0.16 0.14 0.12 0.10 0.72 0.41

1.61 1.54 1.43 0.15 0.37 0.36 0.97 0.17 0.29 0.24 0.17 0.44 0.04

1.98 1.92 3.84 0.77 1.12 0.61 1.44 1.57 1.41 0.41 0.32 1.63 0.44

H¥;l and H¥;u are 95% CI limits for H¥. See methods for the formulas.

medicine, ornamental and timber; (iv) pool D – moderate richness, including boundary demarcation, soil fertility enhancement, charcoal, fruit and construction wood; and (v) pool E – low richness, including fodder and beverage. Some differences and similarities in diversity and evenness can subsequently be detected between and within pools (Figures 1 and 2). [199]

1260 When comparing diversity and evenness patterns for pools, it can be observed that use-groups of pool A and B were more diverse than the other use-groups. The evenness profile (Figure 2) shows that shade (pool B) has a more even distribution than use-groups of pool A. Diversity profiles of use-groups of pool C intersected all use-groups of pool D and E, with the exception of fruit and construction (pool D) and beverage (pool E). The evenness profiles, however, show that ornamental and timber (pool C) had lower evenness than all use-groups of pool D, with exception of construction for which intersection can be observed. Fodder and beverage (pool E) had the most even distribution of all use-groups, with the exception of an intersection for beverage and shade. When comparing diversity for use-groups that belong to the same pool, it can be seen that all trees are more diverse but that firewood is more even within pool A although the profiles were almost parallel to each other. The evenness profile of medicine intersected the other use-groups of pool C. All use-groups of pool C had intersecting evenness profiles. These groups can therefore not be ranked in evenness within pool C. Most use-groups within pool D could be ranked in diversity, since their diversity profiles did not intersect. Boundary demarcation is the most diverse and evenly distributed use-group within the pool. Construction is the least diverse use-group and fruit the second least diverse of pool D. Their evenness profiles intersect, however. Charcoal and soil fertility had intersecting diversity and evenness profiles. Within pool E, fodder is more diverse and more evenly distributed. Values of H¥ (Figure 1 and Table 1) lower than 0.5 indicate that the dominant species of timber, medicine, fruit, construction and beverage contains more than 60% of all trees of these use-groups. Values of H¥ between 0.5 and 1.5 indicate that the dominant species contains between 22 and 60% of all trees for soil fertility, charcoal, ornamental, fodder and boundary demarcation. The dominant species contains a smaller percentage of trees for the other usegroups. However, values of ln E¥,0 (Figure 2) show that the dominant species for all trees and firewood are less evenly distributed when comparing with many other use-groups (the formula ln E¥,0 = H¥
H0 implies that when species richness increases, the frequency of the dominant species should decrease to maintain the same ln E¥,0). Medicine had the least evenly distributed dominant species.

Effects of sample size on diversity Figure 3 shows the average profile values and associated 95% CI for subsamples of 47 farms (the number of farms with fodder, the least frequent usegroup). Figure 3 indicates that, on average, similar profiles are obtained as for the full sample. Diversity profiles, especially medicine and shade, had large 95% CI, however. The 95% CI still allow classifying use-groups in the richness [200]

1261 pools that we differentiated above. Construction of the original pool D obtained richness values closer to the range of pool E. Within pools, most 95% CI of diversity profile values overlap, although the averages showed a similar pattern in Figures 1 and 3. Figures 4–6 show that several use-groups (especially all trees, firewood, boundary, timber, fruit and construction) obtain stable (asymptotic) values for H1, H2 and H¥ after 40 farms were sampled. Some other use-groups showed some strong non-linear patterns throughout the range of pooled farms. These use-groups were especially shade, fodder, medicine, and soil fertility. Whereas the non-linear patterns were for the larger part increments of the index with increasing sample size, a declining pattern could be observed for medicine for H2 and H¥. The observed patterns suggest that to extrapolate profile values, in some cases (asymptotic patterns) similar values can be expected, whereas in other cases different values would be expected (non-linear patterns). However, extrapolation should be used carefully since we had no actual data for larger sample sizes. Table 1 (H¥,l and H¥,u) indicates the CI that is expected for the average value of H¥ when all farms would be sampled in the survey area (assuming random sampling of the farms). Relatively large CI were even obtained for

Figure 3. Averages of rarefied diversity profiles based on the Re´nyi series Ha for all trees and trees belonging to particular use-groups calculated from 1000 random subsamples of 47 farms, with 95% CI. All profile values were calculated for the same scales, but groups were presented at different scales for better discrimination. [201]

1262

Figure 4. Accumulation curve for the Shannon diversity index for all trees and trees belonging to particular use-groups. The vertical reference indicates the sample size of 47 farms depicted in Figure 3.

groups where stable values were obtained at the respective scale in Figure 6 (for example timber, fruit, construction, and beverage). However, these CI were among the smallest. Large CI corresponded to non-linear patterns for the Berger–Parker index (Figure 6, see discussion above). Medicine which had a non-linear negative pattern for the Berger–Parker index had a relatively small CI, however.

Discussion Using diversity and evenness profiles We compared the diversity of various use-groups by using the Re´nyi diversity profile. The numerous intersections of diversity profiles indicate situations of partial ordering in our data set. If we would have used a single diversity index such as the Shannon, Simpson or Berger–Parker index, then we have obtained erroneous results. Studies that attempt to order communities in diversity (e.g., Dougall and Dodd 1997; Slik et al. 2002; Mishra et al. 2004; Zilihona et al. 2004) should therefore not base the ordering on a single index, or even a [202]

1263

Figure 5. Accumulation curve for H2 for all trees and trees belonging to particular use-groups. The vertical reference indicates the sample size of 47 farms depicted in Figure 3.

combination of several indices, but use techniques developed for diversity ordering such as the Re´nyi diversity profile. Although the aim of our study was not to compare the performance of several diversity ordering techniques, we agree with To´thme´re´sz (1995) that the Re´nyi profile is one of the most useful methods for diversity ordering. Various researchers have used models of rank-abundance curves to study biodiversity (e.g., Magurran 1988; Hayek and Buzas 1997; Hubbell 2001; Belaoussoff et al. 2003; Magurran and Henderson 2003; McGill 2003). Because rank-abundance curves do not provide a direct graphical method for diversity ordering (some diversity ordering techniques are based on cumulative frequencies, but none use the frequencies of the individual species; To´thme´re´sz 1995), and since information is lost when rank-abundance curves are modelled, we do not recommend using (models of) rank-abundance distribution for diversity ordering. As for the diversity profile, we observed various intersections in the evenness profiles for our dataset. For this reason, a single index of evenness (e.g., Zilihona et al. 2004) is not sufficient information to order communities in evenness in the same way that a single index of diversity is not sufficient for diversity ordering. [203]

1264

Figure 6. Accumulation curve for H¥ for all trees and trees belonging to particular use-groups. The vertical reference indicates the sample size of 47 farms depicted in Figure 3.

The influence of sample size on the diversity profile It has been observed for a long time in ecological research that sample size has an influence on species richness (e.g., Arrhenius 1921). Since diversity is influenced by richness, sample size also has an effect on diversity. We were able to investigate the influence of sample size on diversity by studying the range of values observed in subsets of the data by using a randomisation approach similar to the randomisation approach to calculate species accumulation curves (Kindt et al. 2001). This approach allowed rarefaction to the same number of sample units (farms in our example), thus removing the effects from differences in sample size. This approach could be useful in other studies that compare results obtained from surveys with different sample sizes, es