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It is widely accepted that species diversity is contingent upon the spatial scale used to analyze patterns and processes. Recent studies using coarse sampling grains over large extents have contributed much to our understanding of factors driving global diversity patterns. This advance is largely unmatched on the level of local to landscape scales despite being critical for our understanding of functional relationships across spatial scales. In our study on West African bat assemblages we employed a spatially explicit and nested design covering local to regional scales. Specifically, we analyzed diversity patterns in two contrasting, largely undisturbed landscapes, comprising a rainforest area and a forest-savanna mosaic in Ivory Coast, West Africa. We employed additive partitioning, rarefaction, and species richness estimation to show that bat diversity increased significantly with habitat heterogeneity on the landscape scale through the effects of beta diversity. Within the extent of our study areas, habitat type rather than geographic distance explained assemblage composition across spatial scales. Null models showed structure of functional groups to be partly filtered on local scales through the effects of vegetation density while on the landscape scale both assemblages represented random draws from regional species pools. We present a mixture model that combines the effects of habitat heterogeneity and complexity on species richness along a biome transect, predicting a unimodal rather than a monotonic relationship with environmental variables related to water. The bat assemblages of our study by far exceed previous figures of species richness in Africa, and refute the notion of low species richness of Afrotropical bat assemblages, which appears to be based largely on sampling biases. Biome transitions should receive increased attention in conservation strategies aiming at the maintenance of ecological and evolutionary processes.
Quantifying and explaining the spatial distribution of life on Earth is a central focus of contemporary ecological research. In most taxa, species richness increases from the poles towards the equator (Hillebrand 2004). Since standardized data collection has been rarely achieved over broad spatial extents, many studies analyzed drivers of species richness using large sampling units such as gridded range maps or point records generalized to larger areas (Lyons and Willig 1999, 2002, Ceballos and Ehrlich 2006, Orme et al. 2006, Davies et al. 2007). Accordingly, these studies focused on the regional scale as their underlying data do not account for lacunarity or range porosity, that is an increasing loss of species with increasing spatial resolution (Hurlbert and White 2005, Hurlbert and Jetz 2007). The causative mechanisms driving species richness are still hotly debated, and some of the conflicting results might be explained by the scale-dependency of species richness (Rahbek 2005).
A major conceptual advancement has been the recognition that local and regional processes act in concert to result in a community or, more neutrally defined, a point estimate of overlapping regional species distributions (Ricklefs 2004). At regional scales, speciation, extinction, and immigration create, over evolutionary time, regional species pools. At local scales, habitat selection and species interactions as well as stochastic processes may be important. To predict species richness in relation to environmental conditions requires an understanding of the relative contribution of these processes along a spatially nested hierarchy (Ricklefs 1987, 2004, Cornell and Lawton 1992, Loreau 2000). The landscape scale connects local and regional scales and thus is of immense interest for studying patterns and causes of species richness (Böhning-Gaese 1997, Whittaker et al. 2001).
Within ecological time, regional diversity sets the limit for species richness at the local scale. To identify processes that determine local diversity, we need to ask how beta diversity, or species turnover, links regional and local scales. In Whittaker's (1960) multiplicative approach, regional diversity (in his terms gamma diversity) is the product of beta diversity and local (or alpha) diversity. However, this approach does not allow direct comparison of the relative contribution of these factors because regional and local diversities are measured as the number of species (or related units that incorporate the abundance of species), while beta diversity is dimensionless. Alternatively, diversities can be partitioned additively where regional diversity=local diversity+beta diversity (Lande 1996, Loreau 2000, Veech et al. 2002). This additive approach defines beta diversity as species turnover and therefore is well suited to analyze the relative contributions of diversity components across spatial scales (Wagner et al. 2000, Crist et al. 2003, Summerville et al. 2003, Freestone and Inouye 2006, Veech and Crist 2007).
Habitat heterogeneity is considered an important mechanistic factor driving species richness: only few species are found in all habitats, hence an increase in habitat types should lead to more species when sampled across a landscape (Rosenzweig 1995, Kerr et al. 2001, Tews et al. 2004). Several studies assessed this relationship across large spatial extents and employed variables such as altitudinal range or number of land cover classes per grid cell as proxies for habitat heterogeneity (Kerr and Packer 1997, Rahbek and Graves 2001, Van Rensburg et al. 2002, Ruggiero and Kitzberger 2004). Since these studies employed relatively coarse grain, habitat heterogeneity might have been missed as an explanatory variable of species diversity because ecologically relevant heterogeneity is likely to be perceived by organisms at finer grain depending on factors such as body size and dispersal ability.
We assessed diversity and assemblage structure of bats (Chiroptera) in two largely undisturbed areas in Ivory Coast, West Africa, and asked which factors drive species diversity from local to regional scales. We employed a spatially explicit and nested design that ranged from local to regional scales to account for the influence of spatial grain and extent (Wiens 1989, Whittaker et al. 2001, Ricklefs 2004, Rahbek 2005). We analyzed constant sample units (plots) that were hierarchically grouped within landscapes, hence keeping the sample grain invariant while changing the sample “focus” or area of inference (Scheiner et al. 2000). The extent of the landscape scale was chosen to match the dispersal abilities of our study group. As bats show a broad suite of habitat-related adaptations, most notably in their sensory systems (echolocation, passive listening, vision, and smell) and morphology (wing shape) (Norberg and Rayner 1987, Neuweiler 1989, Schnitzler and Kalko 2001, Safi and Dechmann 2005), we hypothesized that species richness of bats should be positively related to environmental heterogeneity as heterogeneous habitats should offer more niches than uniform ones. We differentiated between habitat complexity and habitat heterogeneity (August 1983), where heterogeneity is defined as the horizontal variability or patchiness of a habitat and complexity refers to the development of vertical strata within a habitat. In our approach, heterogeneity of vegetation types is taken as the most relevant habitat parameter for the majority of bat species as well as the most commonly used variable in previous studies (Tews et al. 2004).
Our study was conducted in two contrasting landscapes along the steep climatic gradient of West Africa, which is characterized by the staggered arrangement of biomes that stretch from the rainforest zone in the south through various savanna types up to the Sahara Desert in the north. Variables such as annual precipitation, actual evapotranspiration, and net primary productivity decrease along this S-N-gradient while seasonality increases (Tateishi and Ahn 1996, Imhoff et al. 2004). If water-related variables best explain broad-scale patterns of species richness of animals in the (sub)tropics (Hawkins et al. 2003), species richness of bats should monotonically increase from deserts to forests. If habitat heterogeneity drives species richness, one would expect a unimodal gradient with a peak at intermediate latitudes corresponding to the structurally most heterogeneous region along the biome transition (“Guinea Zone”) between forests and savannas (Goetze et al. 2006).
We hypothesized first that species diversity increases with habitat heterogeneity through the effects of beta diversity. Second, we postulated a positive relation between habitat complexity and species diversity. Third, habitat type rather than geographic distance should explain diversity patterns across spatial scales. Fourth, we expected that the structure of functional groups within a habitat type is not a random draw from the combined landscape assemblage but a selectively filtered subset. Fifth, we hypothesized that the reputed impoverishment of Afrotropical bat assemblages (Findley 1993) is largely due to sampling biases.
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We greatly appreciate the contribution of Stefan Pettersson, Njikoha Ebigbo and Katja Soer to the data set analyzed here. We thank Kouakou Kouamé, Koffi Kouadio and Kouadio Kouakou (all CNP) and Georges Gbamlin (TNP) for their dedicated assistance in the field. Vital logistical support was provided by K. Eduard Linsenmair, Frauke Fischer and the employees of the “Projét Biodiversité” at CNP, and by the “Centre de Recherche en Ecologie”, the “Projet Autonome pour la Conservation du Parc National de Taï”, and the “Taï Monkey Project” at TNP. Research permits to work in CNP and TNP were kindly granted by the “Ministère de l'Agriculture et des Ressources Animales” and the “Ministère de l'Enseignement Supérieur et de la Recherche Scientifique”, République de Côte d'Ivoire. Dieter Kock, Senckenberg Museum Frankfurt, offered invaluable help with species identification. Thomas Crist, Lou Jost, Martin Pfeiffer, Erica Sampaio and Joseph Veech helped in various ways with the development and interpretation of our analyses and in shaping our ideas. We appreciate general support by Hans-Ulrich Schnitzler and Mark-Oliver Rödel. This is a contribution of the BIOTA program, funded by the German Federal Ministry of Education and Research (BMBF, project 01LC0017, 01LC0411 and 01LC0617E1). We acknowledge additional funding by the Landesgraduiertenförderung BW and the German Academic Exchange Service (DAAD). Douglas Kelt, Egbert Leigh, Christoph Meyer and three reviewers helped to improve previous versions of the manuscript.