Predicting patterns of plant species richness in megadiverse South Africa

Using new tools (boosted regression trees) in predictive biogeography, with extensive spatial 23 distribution data for >19 000 species, we developed predictive models for South African plant species richness patterns. Further, biome level analysis explored possible functional determinants of country‐wide regional species richness. Finally, to test model reliability independently, we predicted potential alien invasive plant species richness with an independent dataset. Amongst the different hypotheses generally invoked to explain species 30 diversity (energy, favorableness, topographic heterogeneity, irregularity and seasonality), results revealed topographic heterogeneity as the most powerful single explanatory variable for indigenous South African plant species richness. Some biome‐specific responses were observed, i.e. two of the five analyzed biomes (Fynbos and Grassland) had richness best explained by the “species‐favorableness” hypothesis, but even in this case, topographic heterogeneity was also a primary predictor. This analysis, the largest conducted on an almost exhaustive species sample in a species‐rich region, demonstrates the preeminence of topographic heterogeneity in shaping the spatial pattern of regional plant species richness. Model reliability was confirmed by the considerable predictive power for alien invasive species richness. It thus appears that topographic heterogeneity controls species richness in two main ways: firstly, by providing an abundance of ecological niches in contemporary space (revealed by alien invasive species richness relationships) and secondly, by facilitating the persistence of ecological niches through time. The extraordinary richness of the South African Fynbos biome, a world‐renowned hotspot of biodiversity with the steepest environmental gradients in South Africa, may thus have arisen through both mechanisms. Comparisons with similar regions of the world outside South Africa are needed to confirm the generality of topographic heterogeneity and favorableness as predictors of plant richness.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Botanical richness and endemicity patterns of Borneo derived from species distribution models

This study provides a Borneo-wide, quantitative assessment of botanical richness and endemicity at a high spatial resolution, and based on actual collection data. To overcome the bias in collection effort, and to be able to predict the presence and absence of species, even for areas where no collections have been made, we constructed species distribution models (SDMs) for all species taxonomically revised in Flora Malesiana. Species richness and endemicity maps were based on 1439 significant SDMs. Mapping of the residuals of the richness-endemicity relationship identified areas with higher levels of endemicity than can be expected on the basis of species richness, the endemicity hotspots. We were able to identify one previously unknown region of high diversity, the high mountain peaks of East Kalimantan; and two additional endemicity hotspots, the Müller Mountains and the Sangkulirang peninsula. The areas of high diversity and endemicity were characterized by a relatively small range in annual temperature, but with seasonality in temperatures within that range. Furthermore, these areas were least affected by El Niño Southern Oscillation drought events. The endemicity hotspots were found in areas, which were ecologically distinct in altitude, edaphic conditions, annual precipitation, or a combination of these factors. These results can be used to guide conservation efforts of the highly threatened forests of Borneo.     

Range limits in spatially explicit models of quantitative traits

In an influential paper, Kirkpatrick and Barton (Am Nat 150:1–23 1997) presented a system of diffusive partial differential equations modeling the joint evolution of population density and the mean of a quantitative trait when the trait optimum varies over a continuous spatial domain. We present a stability theorem for steady states of a simplified version of the system, originally studied in Kirkpatrick and Barton (Am Nat 150:1–23 1997). We also present a derivation of the system.     

A spatially explicit model for tropical tree diversity patterns

A simple two-parameter model resembling the classical voter model is introduced to describe macroecological properties of tropical tree communities. The parameters of the model characterize the speciation- and global-dispersion rates. Monte Carlo type computer simulations are performed on the model, investigating species abundances and the spatial distribution of individuals and species. Simulation results are critically compared with the experimental data obtained from a tree census on a 50 hectare area of the Barro Colorado Island (BCI), Panama. Fitting to only two observable quantities from the BCI data (total species number and the slope of the log-log species-area curve at the maximal area), it is possible to reproduce the full species-area curve, the relative species abundance distribution, and a more realistic spatial distribution of species.     

Predicting patterns of vascular plant species richness with composite variables: a meso-scale study in Finnish Lapland

This paper is an attempt, using statistical modelling techniques, to understand the patterns of vascular plant species richness at the poorly studied meso-scale within a relatively unexplored subarctic zone. Species richness is related to floristic-environmental composite variables, using occurrence data of vascular plants and environmental and spatial predictor variables in 362 1 km² grid squares in the Kevo Nature Reserve. Species richness is modelled in two different way. First, by detecting the major floristic-environmental gradients with the ordination procedure of canonical correspondence analysis, and subsequently relating these ordination axes to species richness by generalized linear modelling. Second, species richness is directly related to the composite environmental factors of explanatory variables, using partial least squares regression. The most important explanatory variables, as suggested by both approaches, are relatively similar, and largely reflect the influence of altitude or altitudinally related variables in the models. The most prominent floristic gradient in the data runs from alpine habitats to river valleys, and this gradient is the main source of variation in species richness. Some local environmental variables are also relatively important predictors; the grid squares rich in vascular plant taxa are mainly located in the lowlands of the reserve and are characterised by rivers and brooks, as well as by abundant cliff walls. The two statistical models account for approximately the same amount of variation in the species richness, with more than half of the variation unexplained. Potential reasons for the relatively modest fit are discussed, and the results are compared to the characteristics of the diversity-environment relationships at both broader- and finer-scales.     

Butterfly species richness in mainland Portugal: predictive models of geographic distribution patterns

A three-step protocol described elsewhere is used to obtain a map of butterfly species density in Portugal on a 50×50 km grid. First, all available faunistic information was compiled and analysed to explore the historic patterns of butterfly sampling in Portugal, and to determine which grid cells are sufficiently prospected to produce reliable estimates of species richness. Then, we relate the estimated species richness scores from these areas to a set of environmental and spatial variables by means of General Linear Models, obtaining a function to extrapolate of species density scores to the rest of Portugal. Finally, the model is validated, results explored and outliers identified and deleted. Any spatial autocorrelation remaining in the residuals is examined. Lastly, model parameters are recalculated in absence of deleted outliers, and the resulting function is used to predict species richness scores throughout mainland Portugal. A highly-predictive function based on some variables previously related to butterfly composition at macro-scale, such as number of sunny days per year, temperature or environmental heterogeneity, was obtained. However, in Portugal those variables are highly spatially structured along a steep latitudinal gradient, leading to difficulty in ascertaining if the latitudinal gradient detected by our analysis is due to macroecological or historic effects. Information on European and Iberian butterfly assemblages and causal processes are discussed in the light of the patterns observed. Then, previous information obtained on Portuguese scarabs is added to identify conservation areas, biogeographically important for both insect groups. Finally, the main drawbacks and advantages of this approach to mapping biodiversity for conservation are discussed briefly.     

Coupled map lattice approximations for spatially explicit individual-based models of ecology

Spatially explicit individual-based models are widely used in ecology but they are often difficult to treat analytically. Despite their intractability they often exhibit clear temporal and spatial patterning. We demonstrate how a spatially explicit individual-based model of scramble competition with local dispersal can be approximated by a stochastic coupled map lattice. The approximation disentangles the deterministic and stochastic element of local interaction and dispersal. We are thus able to understand the individual-based model through a simplified set of equations. In particular, we demonstrate that demographic noise leads to increased stability in the dynamics of locally dispersing single-species populations. The coupled map lattice approximation has general application to a range of spatially explicit individual-based models. It provides a new alternative to current approximation techniques, such as the method of moments and reaction-diffusion approximation, that captures both stochastic effects and large-scale patterning arising in individual-based models.     

Macroecological patterns of species richness in parasite assemblages

The diversity of parasite species exploiting a host population varies substantially among different host species. This review summarizes the main predictions generated by the two main theoretical frameworks used to study parasite diversity. The first is island biogeography theory, which predicts that host features, such as body size, that are associated with the probability of colonization by new parasite species, should covary with parasite species richness. The second predictive framework derives from epidemiological modelling; it predicts that host species with features that increase parasite transmission success among host individuals, such as high population density, will sustain a greater diversity of parasite species. A survey of comparative studies of parasite diversity among fish and mammalian host species finds support for most of the predictions derived from the above two theoretical perspectives. This empirical support, however, is not universal. It is often qualitative only, because quantitative predictions are lacking. Finally, the amount of variance in parasite diversity explained by host features is generally low. To move forward, the search for the determinants of parasite diversity will need to rely less on theories developed for free-living organisms, and more on its own set of hypotheses incorporating specific host–parasite interactions such as immune responses.

Zusammenfassung

Die Diversität der Parasitenarten, die eine Wirtspopulation nutzen, variiert erheblich zwischen verschiedenen Wirtsarten. Dieser Review fasst die hauptsächlichen Vorhersagen zusammen, die von den zwei wichtigsten theoretischen Rahmenkonzepten hervorgebracht werden, die für die Untersuchung der Parasitendiversität genutzt werden. Die erste ist die Inselbiogeografie, die vorhersagt, dass Wirtsmerkmale, die mit der Besiedlungswahrscheinlichkeit durch einen neuen Parasiten verknüpft sind, wie beispielsweise die Körpergröße, mit dem Artenreichtum der Parasiten kovariieren sollten. Das zweite Rahmenkonzept ist aus der epidemiologischen Modellierung abgeleitet. Es sagt vorher, dass Wirtsarten mit Merkmalen, die den Übertragungserfolg der Parasiten zwischen den Wirtsindividuen erhöhen, wie beispielsweise hohe Populationsdichten, eine größere Diversität von Parasitenarten erhalten werden. Eine Begutachtung von vergleichenden Untersuchungen über Parasitendiversität bei Fischen und Säugetieren als Wirtsarten unterstützt die meisten der Vorhersagen, die von den oben genannten zwei theoretischen Perspektiven abgeleitet sind. Diese empirische Bestätigung ist jedoch nicht allgemein gültig. Sie ist häufig nur qualitativ, da quantitative Vorhersagen fehlen. Schließlich ist der Anteil der Varianz in der Parasitendiversität, der durch die Wirtsmerkmale erklärt wird, normalerweise gering. Um vorwärts zu kommen muss sich die Suche nach den bestimmenden Faktoren der Parasitendiversität weniger auf Theorien, die für freilebende Organismen entwickelt wurden, und mehr auf ihre eigene Menge von Hypothesen verlassen, die spezifische Wirt-Parasit-Interaktionen, wie beispielsweise Immunreaktionen, mit einbeziehen.     

Topography, energy and the global distribution of bird species richness

A major goal of ecology is to determine the causes of the latitudinal gradient in global distribution of species richness. Current evidence points to either energy availability or habitat heterogeneity as the most likely environmental drivers in terrestrial systems, but their relative importance is controversial in the absence of analyses of global (rather than continental or regional) extent. Here we use data on the global distribution of extant continental and continental island bird species to test the explanatory power of energy availability and habitat heterogeneity while simultaneously addressing issues of spatial resolution, spatial autocorrelation, geometric constraints upon species' range dynamics, and the impact of human populations and historical glacial ice-cover. At the finest resolution (1 degree), topographical variability and temperature are identified as the most important global predictors of avian species richness in multi-predictor models. Topographical variability is most important in single-predictor models, followed by productive energy. Adjusting for null expectations based on geometric constraints on species richness improves overall model fit but has negligible impact on tests of environmental predictors. Conclusions concerning the relative importance of environmental predictors of species richness cannot be extrapolated from one biogeographic realm to others or the globe. Rather a global perspective confirms the primary importance of mountain ranges in high-energy areas.     

Macroecological patterns of spider species richness across Europe

We analysed the pattern of covariation of European spider species richness with various environmental variables at different scales. Four layers of perception ranging from single investigation sites to the whole European continent were selected. Species richness was determined using published data from all four scales. Correlation analyses and stepwise multiple linear regression were used to relate richness to topographic, climatic and biotic variables. Up to nine environmental variables were included in the analyses (area, latitude, elevation range, mean annual temperature, local variation in mean annual temperature, mean annual precipitation, mean July temperature, local variation in mean July temperature, plant species richness). At the local and at the continental scale, no significant correlations with surface area were found, whereas at the landscape and regional scale, surface area had a significant positive effect on species richness. Factors that were positively correlated with species richness at both broader scales were plant species richness, elevation range, and specific temperature variables (regional scale: local variation in mean annual, and mean July temperature; continental scale: mean July temperature). Latitude was significantly negatively correlated with the species richness at the continental scale. Multiple models for spider species richness data accounted for up to 77% of the total variance in spider species richness data. Furthermore, multiple models explained variation in plant species richness up to 79% through the variables mean July temperature and elevation range. We conclude that these first continental wide analyses grasp the overall pattern in spider species richness of Europe quite well, although some of the observed patterns are not directly causal. Climatic variables are expected to be among the most important direct factors, although other variables (e.g. elevation range, plant species richness) are important (surrogate) correlates of spider species richness.     

Using spatially explicit models to characterize foraging performance in heterogeneous landscapes

ABSTRACT The success of most foragers is constrained by limits to their sensory perception, memory, and locomotion. However, a general and quantitative understanding of how these constraints affect foraging benefits, and the trade-offs they imply for foraging strategies, is difficult to achieve. This article develops foraging performance statistics to assess constraints and define trade-offs for foragers using biased random walk behaviors, a widespread class of foraging strategies that includes area-restricted searches, kineses, and taxes. The statistics are expected payoff and expected travel time and assess two components of foraging performance: how effectively foragers distinguish between resource-poor and resourcerich parts of their environments and how quickly foragers in poor parts of the environment locate resource concentrations. These statistics provide a link between mechanistic models of individuals' movement and functional responses, population-level models of forager distributions in space and time, and foraging theory predictions of optimal forager distributions and criteria for abandoning resource patches. Application of the analysis to area-restricted search in coccinellid beetles suggests that the most essential aspect of these predators's foraging strategy is the "turning threshold," the prey density at which ladybirds switch from slow to rapid turning. This threshold effectively determines whether a forager exploits or abandons a resource concentration. Foraging is most effective when the threshold is tuned to match physiological or energetic requirements. These performance statistics also help anticipate and interpret the dynamics of complex spatially and temporally varying forager-resource systems.     

Three-dimensional mid-domain predictions: geometric constraints in North American amphibian, bird, mammal and tree species richness patterns

The "mid-domain effect" (MDE) has received much attention as a candidate explanation for patterns in species richness over large geographic areas. Mid-domain models generate a central peak in richness when species ranges are placed randomly within a bounded geographic area (i.e. the domain). Until now, domain limits have been described mostly in one-dimension, usually latitude or elevation, and only occasionally in two-dimensions. Here we test 1-D, 2-D and, for the first time, 3-D mid-domain models and assess the effects of geometric constraints on species richness in North American amphibian, bird, mammal and tree species. Using spatially lagged simultaneous autoregressive models, empirical richness was predicted quite well by the mid-domain predictions and the spatial autoregressive term (45–92% R²). However, our results show that empirical species richness peaks do deviate from those of the MDE predictions in 3 dimensions. Variation explained (R²) by MDE predictions generally increased with increasing mean range size of the different biotic groups (from amphibian, to tree, mammal and finally bird data), and decreased with increasing dimensions being accounted for in the models. The results suggest geometric constraints alone can explain much of the variation in species richness with elevation, specifically with respect to the larger-range taxa, birds and mammals. Our analysis addresses many of the recent methodological criticisms directed at studies testing the MDE, and our results support the hypothesis that species diversity patterns are influenced by geometric constraints.     

Global scale patterns of fish species richness in rivers

Explanations of spatial and temporal variation in species richness is a central theme in community ecology Until recently, most research has focused on small-scale phenomena, often emphasizing on local environmental factors and, thus, poorly reflecting large-scale processes that organize species richness In this paper, we analyze variations in species richness of indigeneous freshwater fish on a worldwide scale We show that factors related to species-area and species-energy theories statistically explain most of the variation in freshwater fish species richness across continents Historical events supposed to influence present distributions offish are of little assistence in explaining variations in fish species richness at the global scale Our model, which uses easily measured factors, should also be of practical value to aquatic conservation biology and natural resource management