The alpha–beta–regional relationship: providing new insights into local–regional patterns of species richness and scale dependence of diversity components

Ecologists frequently regress local species richness on regional species richness to draw inferences about the processes that structure local communities. A more promising approach is to quantify the contributions of alpha and beta diversity to regional diversity (the ABR approach) using additive partitioning. We applied this approach to four local–regional relationships based on data from 583 arboreal beetle species collected in a hierarchically nested sampling design. All four local–regional relationships exhibited proportional sampling, yet the ABR approach indicated that each was produced by a different combination of alpha and beta richness. Using the results of the ABR analysis, we also analysed the scale dependence of alpha and beta using a hierarchical linear model. Alpha diversity contributed less than expected to regional diversity at the finest spatial scale and more than expected at the broadest spatial scale. A switch in relative dominance from beta to alpha diversity with increasing spatial scale suggested scale transitions in ecological processes. Analysing the scale dependence of diversity components using the ABR approach furthers our understanding about the additivity of species diversity in biological communities.     

Flea (Siphonaptera) species richness in the Great Basin Desert and island biogeography theory

Numbers of flea (Siphonaptera) species (flea species richness) on individual mammals should be higher on large mammals, mammals with dense populations, and mammals with large geographic ranges, if mammals are islands for fleas. I tested the first two predictions with regressions of H. J. Egoscue's trapping data on flea species richness collected from individual mammals against mammal size and population density from the literature. Mammal size and population density did not correlate with flea species richness. Mammal geographic range did, in earlier studies. The intermediate‐sized (31 g), moderately dense (0.004 individuals/m²) Peromyscus truei (Shufeldt) had the highest richness with eight flea species on one individual. Overall, island biogeography theory does not describe the distribution of flea species on mammals in the Great Basin Desert, based on H. J. Egoscue's collections. Alternatively, epidemiological or metapopulation theories may explain flea species richness.     

A critical analysis of the ubiquity of linear local–regional richness relationships

Synthesis Despite theoretical criticisms, the ubiquity of linear relationships between local and regional species richness has long been used to justify it as a valid framework to conclude that local communities are not saturated with species. However, we reanalyzed published studies with a new unbiased method and found no prevalence of linear relationships and more than 40% of misclassifications, including textbook examples. We thus conclude that the prevailing argument in favor of associating a valid ecological interpretation to local–regional species richness plots, its ubiquity, is not sustained, and that ecologists could use for instance metacommunity theory to make inference on the strength of local and regional processes. Identifying the relative importance of regional and local processes to local species diversity is a central issue to many questions in basic and applied ecology. One widely‐used method is to plot local species richness against its regional richness to infer whether regional or local processes determine local diversity. However, this method increases the tendency to find regional prevalence as suggested by a recent simulation. We reanalyzed studies in the literature with an unbiased method and found no prevalence of either regional or local processes. In addition, almost 40% of the studies and 50% of the ecology textbook examples using the traditional method were misclassified. Our findings reinforce the need of alternative, novel tools identified by for instance metacommunity theory to go beyond the studies of local–regional relationships in the ecological literature that focus on the interdependence of regional and local processes.     

The relationships between local and regional species richness and spatial turnover

Aim To determine the empirical relationships between species richness and spatial turnover in species composition across spatial scales. These have remained little explored despite the fact that such relationships are fundamental to understanding spatial diversity patterns. Location South‐east Scotland. Methods Defining local species richness simply as the total number of species at a finer resolution than regional species richness and spatial turnover as turnover in species identity between any two or more areas, we determined the empirical relationships between all three, and the influence of spatial scale upon them, using data on breeding bird distributions. We estimated spatial turnover using a measure independent of species richness gradients, a fundamental feature which has been neglected in theoretical studies. Results Local species richness and spatial turnover exhibited a negative relationship, which became stronger as larger neighbourhood sizes were considered in estimating the latter. Spatial turnover and regional species richness did not show any significant relationship, suggesting that spatial species replacement occurs independently of the size of the regional species pool. Local and regional species richness only showed the expected positive relationship when the size of the local scale was relatively large in relation to the regional scale. Conclusions Explanations for the relationships between spatial turnover and local and regional species richness can be found in the spatial patterns of species commonality, gain and loss between areas.     

Trophic theory of island biogeography

MacArthur and Wilson's Theory of Island Biogeography (TIB) is among the most well-known process-based explanations for the distribution of species richness. It helps understand the species-area relationship, a fundamental pattern in ecology and an essential tool for conservation. The classic TIB does not, however, account for the complex structure of ecological systems. We extend the TIB to take into account trophic interactions and derive a species-specific model for occurrence probability. We find that the properties of the regional food web influence the species-area relationship, and that, in return, immigration and extinction dynamics affect local food web properties. We compare the accuracy of the classic TIB to our trophic TIB to predict community composition of real food webs and find strong support for our trophic extension of the TIB. Our approach provides a parsimonious explanation to species distributions and open new perspectives to integrate the complexity of ecological interactions into simple species distribution models.     

The island biogeography of languages

Aim To examine the degree to which area, isolation, environmental conditions and time since first settlement explain variation in language richness among islands. Location Pacific islands ranging east–west from Rapa Nui to Indonesia and north–south from Hawaii to New Zealand. Methods We constructed a dataset of 264 Pacific islands that support 1640 languages (c. 24% of the world's languages). We examined possible predictors of language richness using three different types of models: linear regression models, linear mixed models that included random effects for language phylogeny and simultaneous autoregressive models. We tested whether the following variables, alone or in combination, predict language richness: island area and isolation, climate (rainfall, temperature), mean growing season, soil fertility, habitat heterogeneity (elevation, number of ecoregions), time since first human settlement. Results We identified two optimal models (delta Akaike information criterion < 2). One (R²= 0.52) included area, with 86% of remaining variation accounted for by random effects for phylogeny. The other (R²= 0.56) included a spatial component, area and a suite of other variables (of which isolation and settlement scale were significant). Of the hypotheses tested (mean growing season, ecological risk, habitat heterogeneity, climate, time since settlement, area–isolation theory), area–isolation performed best, alone explaining 44% of variation in language richness. Main conclusions Language diversity relates strongly to island area, and, after controlling for area, with variables linked to isolation (e.g. distance to continent, time since settlement). The influence of environmental productivity may be scale and context dependent. Although environmental productivity may shape language diversity patterns at a global scale, it plays little role on Pacific islands. Approximately half the variance in language richness remains unexplained. Unlike other taxa, for which area, isolation and environmental conditions explain up to 90% of variation in richness, human diversity patterns appear to also be influenced by other variables (e.g. economic, political and social factors).     

Predicting continental-scale patterns of bird species richness with spatially explicit models

The causes of global variation in species richness have been debated for nearly two centuries with no clear resolution in sight. Competing hypotheses have typically been evaluated with correlative models that do not explicitly incorporate the mechanisms responsible for biotic diversity gradients. Here, we employ a fundamentally different approach that uses spatially explicit Monte Carlo models of the placement of cohesive geographical ranges in an environmentally heterogeneous landscape. These models predict species richness of endemic South American birds (2248 species) measured at a continental scale. We demonstrate that the principal single-factor and composite (species-energy, water-energy and temperature-kinetics) models proposed thus far fail to predict (r(2) < or =.05) the richness of species with small to moderately large geographical ranges (first three range-size quartiles). These species constitute the bulk of the avifauna and are primary targets for conservation. Climate-driven models performed reasonably well only for species with the largest geographical ranges (fourth quartile) when range cohesion was enforced. Our analyses suggest that present models inadequately explain the extraordinary diversity of avian species in the montane tropics, the most species-rich region on Earth. Our findings imply that correlative climatic models substantially underestimate the importance of historical factors and small-scale niche-driven assembly processes in shaping contemporary species-richness patterns.     

Modeling directional spatio‐temporal processes in island biogeography

A key challenge in island biogeography is to quantity the role of dispersal in shaping biodiversity patterns among the islands of a given archipelago. Here, we propose such a framework. Dispersal within oceanic archipelagos may be conceptualized as a spatio‐temporal process dependent on: (1) the spatial distribution of islands, because the probability of successful dispersal is inversely related to the spatial distance between islands and (2) the chronological sequence of island formation that determines the directional asymmetry of dispersal (hypothesized to be predominantly from older to younger islands). From these premises, directional network models may be constructed, representing putative connections among islands. These models may be translated to eigenfunctions in order to be incorporated into statistical analysis. The framework was tested with 12 datasets from the Hawaii, Azores, and Canaries. The explanatory power of directional network models for explaining species composition patterns, assessed by the Jaccard dissimilarity index, was compared with simpler time‐isolation models. The amount of variation explained by the network models ranged from 5.5% (for Coleoptera in Hawaii) to 60.2% (for Pteridophytes in Canary Islands). In relation to the four studied taxa, the variation explained by network models was higher for Pteridophytes in the three archipelagos. By the contrary, small fractions of explained variation were observed for Coleoptera (5.5%) and Araneae (8.6%) in Hawaii. Time‐isolation models were, in general, not statistical significant and explained less variation than the equivalent directional network models for all the datasets. Directional network models provide a way for evaluating the spatio‐temporal signature of species dispersal. The method allows building scenarios against which hypotheses about dispersal within archipelagos may be tested. The new framework may help to uncover the pathways via which species have colonized the islands of a given archipelago and to understand the origins of insular biodiversity.     

Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography

We investigate how variation in patch area and forest cover quantified for three different spatial scales (buffer size of 500, 1500 and 3000 m radius) affects species richness and functional diversity of bat assemblages in two ecosystems differing in fragment–matrix contrast: a landbridge island system in Panama and a countryside ecosystem in the Brazilian Amazon. Bats were sampled on 11 islands and the adjacent mainland in Panama, and in eight forest fragments and nearby continuous forest in Brazil. Species–area relationships (SAR) were assessed based on Chao1 species richness estimates, and functional diversity–area relationships (FAR) were quantified using Chao1 functional diversity estimates measured as the total branch length of a trait dendrogram. FARs were calculated using three trait sets: considering five species functional traits (FAR_ALL), and trait subsets reflecting ‘diet breadth’ (FAR_DIET) and ‘dispersal ability’ (FAR_DISPERSAL). We found that in both study systems, FAR_ALL was less sensitive to habitat loss than SAR, in the sense that an equal reduction in habitat loss led to a disproportionately smaller loss of functional diversity compared to species richness. However, the inhospitable and static aquatic matrix in the island ecosystem resulted in more pronounced species loss with increasing loss of habitat compared to the countryside ecosystem. Moreover, while we found a significant FAR_DISPERSAL for the island ecosystem in relation to forest cover within 500 m landscape buffers, FAR_DIET and FAR_DISPERSAL were not significant for the countryside ecosystem. Our findings highlight that species richness and functional diversity in island and countryside ecosystems scale fundamentally differently with habitat loss, and suggest that key bat ecological functions, such as pollination, seed dispersal and arthropod suppression, may be maintained in fragments despite a reduction in species richness. Our study reinforces the importance of increasing habitat availability for decreasing the chances of losing species richness in smaller fragments.     

Predicting lichen hydration using biophysical models

Two models for predicting the hydration status of lichens were developed as a first step towards a mechanistic lichen productivity model. A biophysical model included the water potential of the air, derived from measurements of air temperature, relative humidity and species-specific rate constants for desiccation and rehydration. A reduced physical model, included only environmental parameters, assuming instantaneous equilibration between the lichen and the air. These models were developed using field and laboratory data for three green algal lichens: the foliose epiphytic Platismatia glauca (L.) W. Culb., the fruticose epiphytic Alectoria sarmentosa (Ach.) Ach. and the fruticose, terricolous and mat-forming Cladina rangiferina (L.) Weber ex Wigg. The models were compared and validated for the same three species using data from a habitat with a different microclimate. Both models predicted the length and timing of lichen hydration periods, with those for A. sarmentosa and P. glauca being highly accurate—nearly 100% of the total wet time was predicted by both the biophysical and physical models. These models also predicted an accurate timing of the total realized wet time for A. sarmentosa and P. glauca when the lichens were wet. The model accuracy was lower for C. rangiferina compared to the epiphytes, both for the total realized wet time and for the accuracy of the timing for the hydration period. These results demonstrate that the stochastic and continually varying hydration status of lichens can be simulated from biophysical data. Further development of these models to also include water-related activity, light and temperature conditions during the hydration events will then be a potent tool to assess potential lichen productivity in landscapes and habitats of various microclimatic conditions.     

The island biogeography of exotic bird species

Aim   A recent upsurge of interest in the island biogeography of exotic species has followed from the argument that they may provide valuable information on the natural processes structuring island biotas. Here, we use data on the occurrence of exotic bird species across oceanic islands worldwide to demonstrate an alternative and previously untested hypothesis that these distributional patterns are a simple consequence of where humans have released such species, and hence of the number of species released.
Location   Islands around the world.
Methods   Statistical analysis of published information on the numbers of exotic bird species introduced to, and established on, islands around the world.
Results   Established exotic birds showed very similar species–area relationships to native species, but different species–isolation relationships. However, in both cases the relationship for established exotics simply mimicked that for the number of exotic bird species introduced. Exotic bird introductions scaled positively with human population size and island isolation, and islands that had seen more native species extinctions had had more exotic species released.
Main conclusion   The island biogeography of exotic birds is primarily a consequence of human, rather than natural, processes.     

Predicting mountain plant richness and rarity from space using satellite-derived vegetation indices

Can species richness and rarity be predicted from space? If satellite‐derived vegetation indices can provide us with accurate predictions of richness and rarity in an area, they can serve as an excellent tool in diversity and conservation research, especially in inaccessible areas. The increasing availability of high‐resolution satellite images is enabling us to study this question more carefully. We sampled plant richness and rarity in 34 quadrats (1000 m²) along an elevation gradient between 300 and 2200 m focusing on Mount Hermon as a case study. We then used 10 Landsat, Aster, and QuickBird satellite images ranging over several seasons, going up to very high resolutions, to examine the relationship between plant richness, rarity, and vegetation indices calculated from the images. We used the normalized difference vegetation index (NDVI), one of the most commonly used vegetation indexes, which is strongly correlated to primary production both globally and locally (in more seasonal and in drier and/or colder environments that have wide ranges of NDVI values). All images showed a positive significant correlation between NDVI and both plant species richness and percentage tree cover (with R² as high as 0.87 between NDVI and total plant richness and 0.89 for annual plant richness). The high resolution images enabled us to examine spatial heterogeneity in NDVI within our quadrats. Plant richness was significantly correlated with the standard deviation of NDVI values (but not with their coefficient of variation) within quadrats and between images. Contrary to richness, relative range size rarity was negatively correlated with NDVI in all images, this result being significant in most cases. Thus, given that they are validated by fieldwork, satellite‐derived indices can shed light on richness and even rarity patterns in mountains, many of which are important biodiversity centres.