A comparison of the species–time relationship across ecosystems and taxonomic groups

The species–time relationship (STR) describes how the species richness of a community increases with the time span over which the community is observed. This pattern has numerous implications for both theory and conservation in much the same way as the species–area relationship (SAR). However, the STR has received much less attention and to date only a handful of papers have been published on the pattern. Here we gather together 984 community time-series, representing 15 study areas and nine taxonomic groups, and evaluate their STRs in order to assess the generality of the STR, its consistency across ecosystems and taxonomic groups, its functional form, and its relationship to local species richness. In general, STRs were surprisingly similar across major taxonomic groups and ecosystem types. STRs tended to be well fit by both power and logarithmic functions, and power function exponents typically ranged between 0.2 and 0.4. Communities with high richness tended to have lower STR exponents, suggesting that factors increasing richness may simultaneously decrease turnover in ecological systems. Our results suggest that the STR is as fundamental an ecological pattern as the SAR, and raise questions about the general processes underlying this pattern. They also highlight the dynamic nature of most species assemblages, and the need to incorporate time scale in both basic and applied research on species richness patterns.     

Spatio‐temporal scaling of biodiversity and the species–time relationship in a stream fish assemblage

1. The increase of species richness with the area of the habitat sampled, that is the species–area relationship, and its temporal analogue, the species–time relationship (STR), are among the few general laws in ecology with strong conservation implications. However, these two scale‐dependent phenomena have rarely been considered together in biodiversity assessment, especially in freshwater systems. 2. We examined how the spatial scale of sampling influences STRs for a Central‐European stream fish assemblage (second‐order Bernecei stream, Hungary) using field survey data in two simulation‐based experiments. 3. In experiment one, we examined how increasing the number of channel units, such as riffles and pools (13 altogether), and the number of field surveys involved in the analyses (12 sampling occasions during 3 years), influence species richness. Complete nested curves were constructed to quantify how many species one observes in the community on average for a given number of sampling occasions at a given spatial scale. 4. In experiment two, we examined STRs for the Bernecei fish assemblage from a landscape perspective. Here, we evaluated a 10‐year reach level data set (2000–09) for the Bernecei stream and its recipient watercourse (third‐order Kemence stream) to complement results on experiment one and to explore the mechanisms behind the observed patterns in more detail. 5. Experiment one indicated the strong influence of the spatial scale of sampling on the accumulation of species richness, although time clearly had an additional effect. The simulation methodology advocated here helped to estimate the number of species in a diverse combination of spatial and temporal scale and, therefore, to determine how different scale combinations influence sampling sufficiency. 6. Experiment two revealed differences in STRs between the upstream (Bernecei) and downstream (Kemence) sites, with steeper curves for the downstream site. Equations of STR curves were within the range observed in other studies, predominantly from terrestrial systems. Assemblage composition data suggested that extinction–colonisation dynamics of rare, non‐resident (i.e. satellite) species influenced patterns in STRs. 7. Our results highlight that the determination of species richness can benefit from the joint consideration of spatial and temporal scales in biodiversity inventory surveys. Additionally, we reveal how our randomisation‐based methodology may help to quantify the scale dependency of diversity components (α, β, γ) in both space and time, which have critical importance in the applied context.     

Which function describes the species–area relationship best? A review and empirical evaluation

Aim The aims of this study are to resolve terminological confusion around different types of species–area relationships (SARs) and their delimitation from species sampling relationships (SSRs), to provide a comprehensive overview of models and analytical methods for SARs, to evaluate these theoretically and empirically, and to suggest a more consistent approach for the treatment of species–area data. Location Curonian Spit in north‐west Russia and archipelagos world‐wide. Methods First, I review various typologies for SARs and SSRs as well as mathematical models, fitting procedures and goodness‐of‐fit measures applied to SARs. This results in a list of 23 function types, which are applicable both for untransformed (S) and for log‐transformed (log S) species richness. Then, example data sets for nested plots in continuous vegetation (n = 14) and islands (n = 6) are fitted to a selection of 12 function types (linear, power, logarithmic, saturation, sigmoid) both for S and for log S. The suitability of these models is assessed with Akaike’s information criterion for S and log S, and with a newly proposed metric that addresses extrapolation capability. Results SARs, which provide species numbers for different areas and have no upper asymptote, must be distinguished from SSRs, which approach the species richness of one single area asymptotically. Among SARs, nested plots in continuous ecosystems, non‐nested plots in continuous ecosystems, and isolates can be distinguished. For the SARs of the empirical data sets, the normal and quadratic power functions as well as two of the sigmoid functions (Lomolino, cumulative beta‐P) generally performed well. The normal power function (fitted for S) was particularly suitable for predicting richness values over ten‐fold increases in area. Linear, logarithmic, convex saturation and logistic functions generally were inappropriate. However, the two sigmoid models produced unstable results with arbitrary parameter estimates, and the quadratic power function resulted in decreasing richness values for large areas. Main conclusions Based on theoretical considerations and empirical results, I suggest that the power law should be used to describe and compare any type of SAR while at the same time testing whether the exponent z changes with spatial scale. In addition, one should be aware that power‐law parameters are significantly influenced by methodology.     

The power of time: spatiotemporal scaling of species diversity

The species–area relationship (SAR) provides the foundation for much of theoretical ecology and conservation practice. However, by ignoring time the SAR offers an incomplete model for biodiversity dynamics. We used long‐term data from permanent plots in Kansas grasslands, USA, to show that the increase in the number of species found with increasing periods of observation takes the same power‐law form as the SAR. A statistical model including time, area, and their interaction explains 98% of variation in mean species number and demonstrates that while the effect of time depends on area, and vice versa, time has strong effects on species number even at relatively broad spatial scales. Our results suggest equivalence of underlying processes in space and time and raise questions about the diversity estimates currently used by basic researchers and conservation practitioners.     

Effects of sampling protocol on the shapes of species richness curves

Aim Scheiner (Journal of Biogeography, 2009, 36 , 2005–2008) criticized several issues regarding the typology and analysis of species richness curves that were brought forward by Dengler (Journal of Biogeography, 2009, 36 , 728–744). In order to test these two sets of views in greater detail, we used a simulation model of ecological communities to demonstrate the effects of different sampling schemes on the shapes of species richness curves and their extrapolation capability. Methods We simulated five random communities with 100 species on a 64 × 64 grid using random fields. Then we sampled species–area relationships (SARs, contiguous plots) as well as species–sampling relationships (SSRs, non‐contiguous plots) from these communities, both for the full extent and the central quarter of the grid. Finally, we fitted different functions (power, quadratic power, logarithmic, Michaelis–Menten, Lomolino) to the obtained data and assessed their goodness‐of‐fit (Akaike weights) and their extrapolation capability (deviation of the predicted value from the true value). Results We found that power functions gave the best fit for SARs, while for SSRs saturation functions performed better. Curves constructed from data of 32² grid cells gave reasonable extrapolations for 64² grid cells for SARs, irrespective of whether samples were gathered from the full extent or the centre only. By contrast, SSRs worked well for extrapolation only in the latter case. Main conclusions SARs and SSRs have fundamentally different curve shapes. Both sampling strategies can be used for extrapolation of species richness to a target area, but only SARs allow for extrapolation to a larger area than that sampled. These results confirm a fundamental difference between SARs and area‐based SSRs and thus support their typological differentiation.     

Species–energy relationship in the deep sea: a test using the Quaternary fossil record

Little is known about the processes regulating species richness in deep‐sea communities. Here we take advantage of natural experiments involving climate change to test whether predictions of the species–energy hypothesis hold in the deep sea. In addition, we test for the relationship between temperature and species richness predicted by a recent model based on biochemical kinetics of metabolism. Using the deep‐sea fossil record of benthic foraminifera and statistical meta‐analyses of temperature‐richness and productivity‐richness relationships in 10 deep‐sea cores, we show that temperature but not productivity is a significant predictor of species richness over the past c. 130 000 years. Our results not only show that the temperature‐richness relationship in the deep‐sea is remarkably similar to that found in terrestrial and shallow marine habitats, but also that species richness tracks temperature change over geological time, at least on scales of c. 100 000 years. Thus, predicting biotic response to global climate change in the deep sea would require better understanding of how temperature regulates the occurrences and geographical ranges of species.     

Separating the effects of number of individuals sampled and competition on species diversity: an experimental and analytic approach

1 Species richness typically increases with the number of individuals sampled, although many ecological processes that influence species richness are also well known to depend on density of individuals. We separated the effects of density on species richness that are due to sampling, from those due to density-dependent ecological processes such as competition or predation, by manipulating the density of an entire community.
2 A seed bank from a community of desert annual plants that occur on semi-stabilized sand dunes in Israel was collected from the field and sown in an experimental garden at a range of densities from 1/16 to eight times the natural density. The species pool observed in the lowest density plots was used as the null community, which was repeatedly sampled to calculate the species richness (and other diversity indices) in higher density plots that would be expected from sampling considerations alone. The significance of deviations of observed diversity from this expected diversity was then evaluated.
3 Both observed and expected number of species increased substantially with the experimental increase in density. However, observed species richness, the Shannon–Wiener diversity index and Simpson's diversity index were often significantly lower than that expected based on sampling considerations. The magnitude of the deviation from expected increased significantly with increasing density for richness and the Shannon–Wiener index. This provides some of the first direct experimental evidence from diverse natural assemblages that increasing competition among all the individuals in a community can lead to competitive exclusion.     

Patterns of species richness on very small islands: the plants of the Aegean archipelago

Aim To investigate the species–area relationship (SAR) of plants on very small islands, to examine the effect of other factors on species richness, and to check for a possible Small Island Effect (SIE). Location The study used data on the floral composition of 86 very small islands (all < 0.050 km²) of the Aegean archipelago (Greece). Methods We used standard techniques for linear and nonlinear regression in order to check several models of the SAR, and stepwise multiple regression to check for the effects of factors other than area on species richness (‘habitat diversity’, elevation, and distance from nearest large island), as well as the performance of the Choros model. We also checked for the SAR of certain taxonomic and ecological plant groups that are of special importance in eastern Mediterranean islands, such as halophytes, therophytes, Leguminosae and Gramineae. We used one‐way anova to check for differences in richness between grazed and non‐grazed islands, and we explored possible effects of nesting seabirds on the islands’ flora. Results Area explained a small percentage of total species richness variance in all cases. The linearized power model of the SAR provided the best fit for the total species list and several subgroups of species, while the semi‐log model provided better fits for grazed islands, grasses and therophytes. None of the nonlinear models explained more variance. The slope of the SAR was very high, mainly due to the contribution of non‐grazed islands. No significant SIE could be detected. The Choros model explained more variance than all SARs, although a large amount of variance of species richness still remained unexplained. Elevation was found to be the only important factor, other than area, to influence species richness. Habitat diversity did not seem important, although there were serious methodological problems in properly defining it, especially for plants. Grazing was an important factor influencing the flora of small islands. Grazed islands were richer than non‐grazed, but the response of their species richness to area was particularly low, indicating decreased floral heterogeneity among islands. We did not detect any important effects of the presence of nesting seabird colonies. Main conclusions Species richness on small islands may behave idiosyncratically, but this does not always lead to a typical SIE. Plants of Aegean islets conform to the classical Arrhenius model of the SAR, a result mainly due to the contribution of non‐grazed islands. At the same time, the factors examined explain a small portion of total variance in species richness, indicating the possible contribution of other, non‐standard factors, or even of stochastic effects. The proper definition of habitat diversity as pertaining to the taxon examined in each case is a recurrent problem in such studies. Nevertheless, the combined effect of area and a proxy for environmental heterogeneity is once again superior to area alone in explaining species richness.     

Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling

Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species‐level Poisson processes and estimate patch‐level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early‐successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.     

Random sampling, abundance–extinction dynamics and niche-filtering immigration constraints explain the generation of species richness gradients

Aim  The paradigm that species' patterns of distribution, abundance and coexistence are the result of adaptations of the species to their niches has recently been challenged by evidence that similar patterns may be generated by simple random processes. We argue here that a better understanding of macroecological patterns requires an integration of both ecological and neutral stochastic approaches. We demonstrate the utility of such an integrative approach by testing the sampling hypothesis in a species–energy relationship of forest bird species.
Location  A Mediterranean biome in Catalonia, Spain.
Methods  To test the sampling hypothesis we designed a metacommunity model that reproduces the stochastic sampling from a regional pool to predict local species richness variation. Four conceptually different sampling procedures were evaluated.
Results  We showed that stochastic sampling processes predicted a substantial part (over 40%) of the observed variation in species richness, but left considerable variation unexplained. This remaining variation in species richness may be better understood as the result of alternative ecological processes. First, the sampling model explained more variation in species richness when the probability that a species colonises a new locality was assumed to increase with its niche width, suggesting that ecological differences between species matter when it comes to explaining macroecological patterns. Second, extinction risk was significantly lower for species inhabiting high-energy regions, suggesting that abundance–extinction processes play a significant role in shaping species richness patterns.
Main conclusions  We conclude that species–energy relationships may not simply be understood as a result of either ecological or random sampling processes, but more likely as a combination of both.     

Disentangling the effects of area, energy and habitat heterogeneity on boreal forest bird species richness in protected areas

Aim  One of the few general laws in ecology is that species richness is a positive function of area. However, it has been proposed that area would merely be a proxy for energy. Additionally, habitat heterogeneity has been found to be an important factor determining species richness. Yet the relative importance of those relationships is little known, and it is still unclear how they are brought about. We aimed to dissect which factors drive the species richness of boreal forest birds, and to identify the most probable mechanisms.
Location  Forested protected areas in Finland.
Methods  Using bird line census data collected in 104 protected areas, we ran simultaneous autoregressive models to explain the species richness of forest birds. We explored the value of forest area, tree volume, tree growth, mean degree days and habitat heterogeneity as explanatory variables and used the species richness within different species groups, based on the predictions of hypothesized mechanisms, as a response variable.
Results  Energy, rather than area or habitat heterogeneity, seems to be the main driver of species richness in boreal forest birds. More specifically, productive energy was a better predictor of total species richness than solar energy. Among the tested hypothetical mechanisms, the sampling hypothesis received strong support. After accounting for sampling, solar energy had an effect on species richness.
Main conclusions  As productive energy, such as tree volume, is associated with species richness, high-energy areas should be prioritized in forest conservation planning. Reductions in productive energy may first lead to the disappearance of the rarest species due to the random sampling process. Climate change may result in increased species richness due to increasing amount of productive and solar energy in forests. However, the range shifts of bird species may not be fast enough to keep up with the temperature increases.     

Linking species-area and species-energy relationships in Drosophila microcosms

Resource availability is an important constraint on community structure. Some authors have suggested it conceptually links two of the most basic patterns in ecology, the species–area relationship and the latitudinal gradient in species richness. I present the first experimental test of this conjecture, by manipulating both the area and resource concentration of artificial larval drosophilid fly habitats and then allowing colonization from a natural species pool. Both the abundance and species richness of these habitats depended upon the total quantity of resources available, regardless of whether those resources were contained within smaller high-quality habitats or larger poor-quality habitats. While the intercepts of species–area relationships varied with resource concentration, they all collapsed onto the same species–energy curve. These results support the view that energetic constraints are of fundamental importance in structuring ecological communities, and that such constraints may even help explain ecological patterns such as the species–area relationship that do not explicitly address resource availability.     

Determinants of regional species richness: an empirical analysis of the number of hawkmoth species (Lepidoptera: Sphingidae) on the Malesian archipelago

Aims Major patterns and determinants of the species richness of Sphingidae in the Malesian archipelago were investigated, including a distinction of richness patterns between subfamilies and range‐size classes. Location Southeast Asia, Malesia. Methods Using a compilation of specimen‐label data bases, geographic information system (GIS)‐supported estimates of distributional ranges for all Sphingidae species of Southeast Asia were used to assess the species richness of islands. Range maps for all species and checklists for 114 islands can be found at http://www.sphingidae‐sea.biozentrum.uni‐wuerzburg.de . Potential determinants of the species richness of islands were tested with general linear models. Results The estimated species richness of islands in the region is determined by biogeographical association, seasonality, availability of rain forest and island size. Species–area relationships are linear on a semi‐logarithmic representation, but not on a double‐logarithmic scale. Species richness of all sphingid subfamilies is influenced by biogeography. The presence of large rain‐forest areas affects mainly Smerinthinae, whereas distance from continental Asia is conspicuously irrelevant for this group. Widespread rather than geographically restricted species shape the overall distribution patterns of species richness. The altitudinal range of islands does not significantly affect species‐richness patterns, but its potential effects on geographically restricted species are discussed. Main conclusions As well as being affected by climatic and vegetation parameters, sphingid species richness is strongly influenced by a historical, directional dispersal process from continental Southeast Asia to the Pacific islands. This process did not apply equally to species of different taxonomic groups or range sizes. Widespread species decline in species richness towards the south‐east, whereas geographically restricted species exhibit an inverse pattern of species richness, probably because speciation becomes more important in this group within the more isolated island groups.     

Decomposing the process of species accumulation into area dependent and time dependent parts

In 1960, Preston predicted that the process of species accumulation in time (species–time relationship, STR) should be similar to the species–area relationship (SAR) and follow a power function with a slope of about 0.26. Here these two conjectures are tested using data of the spatiotemporal species accumulation in a local community of beech forest Hymenoptera. A power function species–area–time model of the form S = S₀ A^z t gave better fits to observed species numbers than a simple power function SAR model, and was able to predict similar species turnover rates (about 9% per year) to those inferred by other methods. The STR was well fitted by a power function, although due the limited time span (8 years) a logarithmic STR pattern cannot be ruled out. STR slopes ranged between 0.01 and 0.23 and were lower than predicted by Preston. Temporal species turnover appeared to be negatively correlated to species densities and positively correlated to species body weights. Ecological guild and taxon membership did not significantly influence temporal species turnover.     

Spatiotemporal scaling of plant species richness and functional diversity in a temperate semi‐natural grassland

The accumulation of biodiversity in space and time has been modelled extensively using the species–area relationship and the species–time relationship, respectively. Recently, these models have been combined into time–area curves in order to investigate spatiotemporal scaling of species richness. This study expands on previous research by applying these spatiotemporal models to functional diversity. Understanding spatiotemporal dynamics of ecological traits is important due to their crucial role in ecosystem functioning and mediating species responses to environmental change. We present a new function based on the semi‐logarithmic species–area relationship, which was applied with a power function to vegetation survey data from Scottish machair grassland for both species richness and two measures of functional diversity. When taking a whole‐study approach using non‐linear mixed effects models, the semi‐logarithmic function used here shows a positive time–area interaction for species richness, contrasting with the negative interaction of the power law found in previous investigations. Although there was a negative time–area interaction for functional diversity measures at the whole‐study scale, parameter estimates were inconsistent at the individual site level. Overall, the results reveal differing spatiotemporal dynamics of species and their traits and suggest that the appropriate scale for space‐for‐time substitutions depends on the aspect of biodiversity being investigated. The new model developed in this study, and the novel application to functional diversity, opens up future possible research into spatiotemporal dynamics of biodiversity.     

Biogeographical patterns of blood parasite lineage diversity in avian hosts from southern Melanesian islands

Aim (1) To describe the species–area relationships among communities of Plasmodium and Haemoproteus parasites in different island populations of the same host genus (Aves: Zosterops). (2) To compare distance–decay relationships (turnover) between parasite communities and those with potential avian and dipteran hosts, which differ with respect to their movement and potential to disperse parasite species over large distances. Location Two archipelagos in the south‐west Pacific, Vanuatu and New Caledonia (c. 250 km west of Vanuatu) and its Loyalty Islands, with samples collected from a total of 16 islands of varying sizes (328–16,648 km²). Methods We characterized parasite diversity and distribution via polymerase chain reaction (PCR) from avian (Zosterops) blood samples. Bayesian methods were used to reconstruct the parasite phylogeny. In accordance with recent molecular evidence, we treat distinct mitochondrial DNA lineages as equivalent to species in this study. Path analysis and parasite lineage accumulation curves were used to assess the confounding effect of inadequate sampling on the estimation of parasite richness. Species–area and species–distance relationships were assessed using linear regression: distance–decay relationships were assessed using Mantel tests. Results Birds and mosquito species and Plasmodium lineages exhibited significant species–area relationships. However, Plasmodium lineages showed the weakest ‘species–area’ relationship; no relationship was found for Haemoproteus lineages. Avian species richness influenced parasite lineage richness more than mosquito species richness did. Within individual avian host species, the species–area relationship of parasites showed differing patterns. Path analysis indicated that sampling effort was unlikely to have a confounding effect on parasite richness. Distance from mainland (isolation effect) showed no effect on parasite richness. Community similarity decayed significantly with distance for avifauna, mosquito fauna and Plasmodium lineages but not for Haemoproteus lineages. Main conclusions Plasmodium lineages and mosquito species fit the power‐law model with steeper slopes than found for the avian hosts. The lack of species–distance relationship in parasites suggests that other factors, such as the competence of specific vectors and habitat features, may be more important than distance. The decay in similarity with distance suggests that the sampled Plasmodium lineages and their potential hosts were not randomly distributed, but rather exhibited spatially predictable patterns. We discuss these results in the context of the effects that parasite generality may have on distribution patterns.     

Local–regional boundary shifts in oribatid mite (Acari: Oribatida) communities: species–area relationships in arboreal habitat islands of a coastal temperate rain forest, Vancouver Island, Canada

Aim This study investigates the species–area relationship (SAR) for oribatid mite communities of isolated suspended soil habitats, and compares the shape and slope of the SAR with a nested data set collected over three spatial scales (core, patch and tree level). We investigate whether scale dependence is exhibited in the nested sampling design, use multivariate regression models to elucidate factors affecting richness and abundance patterns, and ask whether the community composition of oribatid mites changes in suspended soil patches of different sizes. Location Walbran Valley, Vancouver Island, Canada. Methods A total of 216 core samples were collected from 72 small, medium and large isolated suspended soil habitats in six western redcedar trees in June 2005. The relationship between oribatid species richness and habitat volume was modelled for suspended soil habitat isolates (type 3) and a nested sampling design (type 1) over multiple spatial scales. Nonlinear estimation parameterized linear, power and Weibull function regression models for both SAR designs, and these were assessed for best fit using R² and Akaike's information criteria (ΔAIC) values. Factors affecting oribatid mite species richness and standardized abundance (number per g dry weight) were analysed by anova and linear regression models. Results Sixty‐seven species of oribatid mites were identified from 9064 adult specimens. Surface area and moisture content of suspended soils contributed to the variation in species richness, while overall oribatid mite abundance was explained by moisture and depth. A power‐law function best described the isolate SAR (S = 3.97 × A^0.12, R² = 0.247, F_1,70 = 22.450, P < 0.001), although linear and Weibull functions were also valid models. Oribatid mite species richness in nested samples closely fitted a power‐law model (S = 1.96 × A^0.39, R² = 0.854, F_1,18 = 2693.6, P < 0.001). The nested SAR constructed over spatial scales of core, patch and tree levels proved to be scale‐independent. Main conclusions Unique microhabitats provided by well developed suspended soil accumulations are a habitat template responsible for the diversity of canopy oribatid mites. Species–area relationships of isolate vs. nested species richness data differed in the rate of accumulation of species with increased area. We suggest that colonization history, stability of suspended soil environments, and structural habitat complexity at local and regional scales are major determinants of arboreal oribatid mite species richness.     

Island biogeography of bats in Baja California, Mexico: patterns of bat species richness in a near-shore archipelago

Aim We studied the relationship between the size and isolation of islands and bat species richness in a near‐shore archipelago to determine whether communities of vagile mammals conform to predictions of island biogeography theory. We compared patterns of species richness in two subarchipelagos to determine whether area per se or differences in habitat diversity explain variations in bat species richness. Location Islands in the Gulf of California and adjacent coastal habitats on the Baja California peninsula in northwest Mexico. Methods Presence–absence surveys for bats were conducted on 32 islands in the Gulf of California using acoustic and mist‐net surveys. We sampled for bats in coastal habitats of four regions of the Baja peninsula to characterize the source pool of potential colonizing species. We fitted a semi‐log model of species richness and multiple linear regression and used Akaike information criterion model selection to assess the possible influence of log₁₀ area, isolation, and island group (two subarchipelagos) on the species richness of bats. We compared the species richness of bats on islands with greater vegetation densities in the southern gulf (n = 20) with that on drier islands with less vegetation in the northern gulf (n = 12) to investigate the relationship between habitat diversity and the species richness of bats. Results Twelve species of bats were detected on islands in the Gulf of California, and 15 species were detected in coastal habitats on the Baja peninsula. Bat species richness was related to both area and isolation of islands, and was higher in the southern subarchipelago, which has denser vegetation. Log₁₀ area was positively related to bat species richness, which increased by one species for every 5.4‐fold increase in island area. On average, richness declined by one species per 6.25 km increase in isolation from the Baja peninsula. Main conclusions Our results demonstrate that patterns of bat species richness in a near‐shore archipelago are consistent with patterns predicted by the equilibrium theory of island biogeography. Despite their vagility, bats may be more sensitive to moderate levels of isolation than previously expected in near‐shore archipelagos. Differences in vegetation and habitat xericity appear to be associated with richness of bat communities in this desert ecosystem. Although observed patterns of species richness were consistent with those predicted by the equilibrium theory, similar relationships between species richness and size and isolation of islands may arise from patch‐use decision making by individuals (optimal foraging strategies).     

Species–area relationships in the Andaman and Nicobar Islands emerge because rarer species are disproportionately favored on larger islands

The island species–area relationship (ISAR) describes how the number of species increases with increasing size of an island (or island‐like habitat), and is of fundamental importance in island biogeography and conservation. Here, we use a framework based on individual‐based rarefaction to infer whether ISARs result from passive sampling, or whether some processes are acting beyond sampling (e.g., disproportionate effects and/or habitat heterogeneity). Using data on total and relative abundances of four taxa (birds, butterflies, amphibians, and reptiles) from multiple islands in the Andaman and Nicobar archipelago, we examine how different metrics of biodiversity (total species richness, rarefied species richness, and abundance‐weighted effective numbers of species emphasizing common species) vary with island area. Total species richness increased for all taxa, as did rarefied species richness controlling for a given sampling effort. This indicates that the ISAR did not result because of passive sampling, but that instead, some species were disproportionately favored on larger islands. For birds, frogs, and lizards, this disproportionate effect was only associated with species that were rarer in the samples, but for butterflies, both more common and rarer species were affected. Furthermore, for the two taxa for which we had plot‐level data (reptiles and amphibians), within‐island β‐diversity did not increase with island size, suggesting that within‐island compositional effects were unlikely to be driving these ISARs. Overall, our results indicate that the ISARs of these taxa are most likely driven by disproportionate effects, that is, where larger islands are important sources of biodiversity beyond a simple sampling expectation, especially through their influence on rarer species, thus emphasizing their role in the preservation and conservation of species.     

Biome transitions as centres of diversity: habitat heterogeneity and diversity patterns of West African bat assemblages across spatial scales

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.