Analyzing tropical forest tree species abundance distributions using a nonneutral model and through approximate Bayesian inference

The neutral theory of biodiversity challenges the classical niche-based view of ecological communities, where species attributes and environmental conditions jointly determine community composition. Functional equivalence among species, as assumed by neutral ecological theory, has been recurrently falsified, yet many patterns of tropical tree communities appear consistent with neutral predictions. This may mean that neutral theory is a good first-approximation theory or that species abundance data sets contain too little information to reject neutrality. Here we present a simple test of neutrality based on species abundance distributions in ecological communities. Based on this test, we show that deviations from neutrality are more frequent than previously thought in tropical forest trees, especially at small spatial scales. We then develop a nonneutral model that generalizes Hubbell's dispersal-limited neutral model in a simple way by including one additional parameter of frequency dependence. We also develop a statistical method to infer the parameters of this model from empirical data by approximate Bayesian computation. In more than half of the permanent tree plots, we show that our new model fits the data better than does the neutral model. Finally, we discuss whether observed deviations from neutrality may be interpreted as the signature of environmental filtering on tropical tree species abundance distributions.     

Multi-scale analysis on species diversity within a 40-ha old-growth temperate forest

In order to better explore the maintenance mechanisms of biodiversity,data collected from a 40-ha undisturbed Pinus forest were applied to the Individual SpecieseArea Relationship model (ISAR) to determine distribution patterns for species richness.The ecological processes influencing species abundance distribution patterns were assessed by applying the same data set to five models:a LogNormal Model (LNM),a Broken Stick Model (BSM),a Zipf Model (ZM),a Niche Preemption Model (NPM),and a Neutral Model (NM).Each of the five models was used at six different sampling scales (10 m×10 m,20 m×20 m,40 m×40 m,60 m×60 m,80 m×80 m,and 100 m×100 m).Model outputs showed that:(1) Accumulators and neutral species strongly influenced species diversity,but the relative importance of the two types of species varied across spatial scales.(2) Distribution patterns of species abundance were best explained by the NPM at small scales (10 me20 m),whereas the NM was the best fit model at large spatial scales.(3) Species richness and abundance distribution patterns appeared to be driven by similar ecological processes.At small scales,the niche theory could be applied to describe species richness and abundance,while at larger scales the neutral theory was more applicable.     

Between geometry and biology: the problem of universality of the species-area relationship

The species-area relationship (SAR) is considered to be one of a few generalities in ecology, yet a universal model of its shape and slope has remained elusive. Recently, Harte et al. argued that the slope of the SAR for a given area is driven by a single parameter, the ratio between total number of individuals and number of species (i.e., the mean population size across species at a given scale). We provide a geometric interpretation of this dependence. At the same time, however, we show that this dependence cannot be universal across taxa: if it holds for a taxon composed from two subsets of species and also for one of its subsets, it cannot simultaneously hold for the other subset. Using three data sets, we show that the slope of the SAR considerably varies around the prediction. We estimate the limits of this variation by using geometric considerations, providing a theory based on species spatial turnover at different scales. We argue that the SAR cannot be strictly universal, but its slope at each particular scale varies within the constraints given by species' spatial turnover at finer spatial scales, and this variation is biologically informative.     

Ecological Resilience, Biodiversity, and Scale

We describe existing models of the relationship between species diversity and ecological function, and propose a conceptual model that relates species richness, ecological resilience, and scale. We suggest that species interact with scale-dependent sets of ecological structures and processes that determine functional opportunities. We propose that ecological resilience is generated by diverse, but overlapping, function within a scale and by apparently redundant species that operate at different scales, thereby reinforcing function across scales. The distribution of functional diversity within and across scales enables regeneration and renewal to occur following ecological disruption over a wide range of scales. Received 11 April 1997; accepted 9 July 1997.     

Evaluating the reliability of species distribution models with an indirect measure of bird reproductive performance

Measures of fitness such as reproductive performance are considered reliable indicators of habitat quality for a species. Such measures are, however, only available in a restricted number of sites, which prevents them from being used to quantify habitat quality across landscapes or regions. Alternatively, species presence records can be used along with environmental variables to build models that predict the distribution of species across larger spatial extents. Model predictions are often used for management purposes as they are assumed to describe the quality of the habitats to support a species. Yet, given that species are often present both in optimal and suboptimal areas, the use of data collected during the breeding season to build these models may potentially result in misleading predictions of habitat quality for the reproduction of the species, with potentially significant conservation consequences. In this study we analysed the relationship between fitness parameters informing on habitat quality for reproduction and predictions of species distribution models at multiple spatial scales using two independent sets of data. For 19 passerine bird species, we compared an indirect measure of reproductive performance (ratio of juveniles‐to‐adults) – obtained from Constant Effort Sites (CES) mist‐netting data in Catalonia – with the predictions of models based on bird presence records collected during the Catalan Breeding Bird Atlas (CBBA). A positive relationship between the predictions derived from species distribution models and the reproductive performance of the species was found for almost half of the species at one or more spatial scales. This result suggests that species distribution models may help to predict habitat quality for some species over some extents. However, caution is needed as this is not consistent for all species at all scales. Further work based on species‐ and scale‐specific approaches is now required to understand in which situations species distribution models provide predictions that are in line with reproductive performance.     

Connecting geographical distributions with population processes

The geographical distribution of a species is determined by a large number of complex processes operating over spatial scales spanning 10 orders of magnitude. Patterns in population processes have been described at numerous scales. We show that two patterns, measured at different scales, jointly allow us to infer heretofore unknown patterns in the distribution of demographic patterns across the geographical range of a species. The resulting model describes three fundamentally different modes of geographical variation in vital rates of populations. One mode is characterized by a positive nonlinear relationship between the maximum rate of population growth and the intensity of intraspecific competition across a geographical range. That is, populations that grow rapidly are also those where individuals experience the greatest per capita negative effect of the presence of other individuals. The second mode of behaviour is described by a negative nonlinear relationship between maximum growth rate and density dependence. Under this scenario, populations with low capacity to grow rapidly have highest intensities of intraspecific competitive effects. A third mode of behaviour is characterized by a weak positive relationship between growth rate and intraspecific competition, with very little geographical variation in maximum growth rate. A survey of studies relating temporal means and variances in population abundance for a variety of species indicate that the second mode of geographical variation in population dynamics across species ranges is the most common, though a few species appear to be characterized by the third mode.     

Diversity and productivity of plant communities across the Inland Northwest,USA

No definitive explanation for the form of the relationship between species diversity and ecosystem productivity exists nor is there agreement on the mechanisms linking diversity and productivity across scales. Here, we examine changes in the form of the diversity–productivity relationship within and across the plant communities at three observational scales: plots, alliances, and physiognomic vegetation types (PVTs). Vascular plant richness data are from 4,760 20 m² vegetation field plots. Productivity estimates in grams carbon per square meter are from annual net primary productivity (ANPP) models. Analyses with generalized linear models confirm scale dependence in the species diversity–productivity relationship. At the plot focus, the observed diversity–productivity relationship was weak. When plot data were aggregated to a focus of vegetation alliances, a hump-shaped relationship was observed. Species turnover among plots cannot explain the observed hump-shaped relationship at the alliance focus because we used mean plot richness across plots as our index of species richness for alliances and PVTs. The sorting of alliances along the productivity gradient appears to follow regional patterns of moisture availability, with alliances that occupy dry environments occurring within the increasing phase of the hump-shaped pattern, alliances that occupy mesic to hydric environments occurring near the top or in the decreasing phase of the curve, and alliances that occupy the wettest environments having the fewest species and the highest ANPP. This pattern is consistent with the intermediate productivity theory but appears to be inconsistent with the predictions of water–energy theory.     

Patterns of species density and productivity at different spatial scales in herbaceous plant communities

A major challenge in evaluating patterns of species richness and productivity involves acquiring data to examine these relationships empirically across a range of ecologically significant spatial scales. In this paper, we use data from herb‐dominated plant communities at six Long‐Term Ecological Research (LTER) sites to examine how the relationship between plant species density and above‐ground net primary productivity (ANPP) differs when the spatial scale of analysis is changed. We quantified this relationship at different spatial scales in which we varied the focus and extent of analysis: (1) among fields within communities, (2) among fields within biomes or biogeographic regions, and (3) among communities within biomes or biogeographic regions. We used species density (D=number of species per m²) as our measure of diversity to have a comparable index across all sites and scales. Although we expected unimodal relationships at all spatial scales, we found that spatial scale influenced the form of the relationship. At the scale of fields within different grassland communities, we detected a significant relationship at only one site (Minnesota old‐fields), and it was negative linear. When we expanded the extent of analyses to biogeographic regions (grasslands or North America), we found significant unimodal relationships in both cases. However, when we combined data to examine patterns among community types within different biogeographic regions (grassland, alpine tundra, arctic tundra, or North America), we did not detect significant relationships between species density and ANPP for any region. The results of our analyses demonstrate that the spatial scale of analysis – how data are aggregated and patterns examined – can influence the form of the relationship between species density and productivity. It also demonstrates the need for data sets from a broad spectrum of sites sampled over a range of scales for examining challenging and controversial ecological hypotheses.     

Ranked species occupancy curves reveal common patterns among diverse metacommunities

Aim Community ecologists often compare assemblages. Alternatively, one may compare species distributions among assemblages for macroecological comparisons of species niche traits and dispersal abilities, which are consistent with metacommunity theory and a regional community concept. The aim of this meta‐analysis is to use regressions of ranked species occupancy curves (RSOCs) among diverse metacommunities and to consider the common patterns observed. Location Diverse data sets from four continents are analysed. Methods Six regression models were translated from traditional occupancy frequency distributions (OFDs) and are distributed among four equation families. Each regression model was fitted to each of 24 data sets and compared using the Akaike information criterion. The analysed data sets encompass a wide range of spatial scales (5 cm–50 km grain, 2–7000 km extent), study scales (11–590 species, 6–5114 sites) and taxa. Observed RSOC regressions were tested for the differences in scale and taxa. Results Three RSOC models within two equation families (exponential and sigmoidal) are required to describe the very different data sets. This result is generally consistent with OFD research, but unlike OFD‐based expectations the simple RSOC patterns are not related to spatial scale or other factors. Species occupancy in diverse metacommunities is efficiently summarized with RSOCs, and multi‐model inference reliably distinguishes among alternative RSOCs. Main conclusions RSOCs are simple to generate and analyse and clearly identified surprisingly similar patterns among very different metacommunities. Species‐specific hypotheses (e.g. niche‐based factors and dispersal abilities) that depend on spatial scale may not translate to diverse metacommunities that sample regional communities. A novel set of three metacommunity succession and disturbance hypotheses potentially explain RSOC patterns and should be tested in subsequent research. RSOCs are an operational approach to the regional community concept and should be useful in macroecology and metacommunity ecology.     

At what spatial scale(s) do mammals respond to urbanization?

Spatial scale is fundamental in understanding species–landscape relationships because species’ responses to landscape characteristics typically vary across scales. Nonetheless, such scales are often unidentified or unreliably predicted by theory. Many landscapes worldwide are urbanizing, yet the spatial scaling of species’ responses to urbanization is poorly understood. We investigated the spatial scaling of urbanization effects on a community of 15 mammal species using ~60 000 wildlife detections collected from a constellation of 207 camera traps across an extensive urban park system. We embedded a bivariate Gaussian kernel in hierarchical multi-species models to determine two scales of effect (a scale of maximal effect and a broader scale of cumulative landscape effect) for two biological responses (occupancy and site visit frequency) across two seasons (winter and summer) for each species. We then assessed whether scales of effect varied according to theoretical predictions associated with biological responses and species traits (body size and mobility). Scales of effect ranged from < 50 m to > 9000 m and varied among species, but not as predicted by theory. Species’ occupancy generally showed a weak response to urbanization and the scale of this effect was both highly uncertain and consistent across species. We did not detect any relationship between scales of effect and species’ body size or mobility, nor was there any evident pattern of scaling across biological response or seasons. These results imply that 1) urbanization effects on mammals manifest across a very broad spectrum of spatial scales, and 2) current theories that a priori predict the scale at which urbanization affects mammals may be of limited use within a given system. Overall, this study suggests that developing general theory regarding the scaling of species–landscape relationships requires additional empirical work conducted across multiple species, systems and timescales.     

Landscape heterogeneity at multiple spatial scales enhances spider species richness in an agricultural landscape

Landscape supplementation, which enhances densities of organisms by combination of different landscape elements, is likely common in heterogeneous landscapes, but its prevalence and effects on species richness have been little explored. Using grassland-dwelling spiders in an agricultural landscape, we postulated that richness and abundances of major constituent species are both highest in intermediate mixtures of forests and paddy fields, and that this effect derives from multi-scale landscape heterogeneity. We collected spiders in 35 grasslands in an agricultural landscape in Japan and determined how species richness and abundances of major species related to local and landscape factors across different spatial scales. We used a generalized linear model to fit data, created all possible combinations of variables at 15 spatial scales, and then explored the best models using Akaike's information criterion. Species richness showed a hump-shaped pattern in relation to surrounding forest cover, and the spatial scale determining this relationship was a 300–500-m radius around the study sites. Local variables were of minor importance for species richness. Abundances of major species also exhibited a hump-shaped pattern when plotted against forest cover. Thus, a combination of paddy fields and forests is important for enhancement of grassland spider species richness and abundance, suggesting habitat supplementation. The effective spatial scales determining abundances varied, ranging from 200 to >1000 m, probably representing different dispersal abilities. Landscape compositional heterogeneity at multiple spatial scales may be thus crucial for the maintenance of species diversity.     

From richer to poorer: successful invasion by freshwater fishes depends on species richness of donor and recipient basins

Evidence for the theory of biotic resistance is equivocal, with experiments often finding a negative relationship between invasion success and native species richness, and large‐scale comparative studies finding a positive relationship. Biotic resistance derives from local species interactions, yet global and regional studies often analyze data at coarse spatial grains. In addition, differences in competitive environments across regions may confound tests of biotic resistance based solely on native species richness of the invaded community. Using global and regional data sets for fishes in river and stream reaches, we ask two questions: (1) does a negative relationship exist between native and non‐native species richness and (2) do non‐native species originate from higher diversity systems. A negative relationship between native and non‐native species richness in local assemblages was found at the global scale, while regional patterns revealed the opposite trend. At both spatial scales, however, nearly all non‐native species originated from river basins with higher native species richness than the basin of the invaded community. Together, these findings imply that coevolved ecological interactions in species‐rich systems inhibit establishment of generalist non‐native species from less diverse communities. Consideration of both the ecological and evolutionary aspects of community assembly is critical to understanding invasion patterns. Distinct evolutionary histories in different regions strongly influence invasion of intact communities that are relatively unimpacted by human actions, and may explain the conflicting relationship between native and non‐native species richness found at different spatial scales.     

Evolutionary signal in the gut microbiomes of 74 bird species from Equatorial Guinea

How the microbiome interacts with hosts across evolutionary time is poorly understood. Data sets including many host species are required to conduct comparative analyses. Here, we analyzed 142 intestinal microbiome samples from 92 birds belonging to 74 species from Equatorial Guinea, using the 16S rRNA gene. Using four definitions for microbial taxonomic units (97%OTU, 99%OTU, 99%OTU with singletons removed, ASV), we conducted alpha and beta diversity analyses. We found that raw abundances and diversity varied between the data sets but relative patterns were largely consistent across data sets. Host taxonomy, diet and locality were significantly associated with microbiomes, at generally similar levels using three distance metrics. Phylogenetic comparative methods assessed the evolutionary relationship between the microbiome as a trait of a host species and the underlying bird phylogeny. Using multiple ways of defining “microbiome traits”, we found that a neutral Brownian motion model did not explain variation in microbiomes. Instead, we found a White Noise model (indicating little phylogenetic signal), was most likely. There was some support for the Ornstein‐Uhlenbeck model (that invokes selection), but the level of support was similar to that of a White Noise simulation, further supporting the White Noise model as the best explanation for the evolution of the microbiome as a trait of avian hosts. Our study demonstrated that both environment and evolution play a role in the gut microbiome and the relationship does not follow a neutral model; these biological results are qualitatively robust to analytical choices.     

Use of simulated data from a process‐based habitat model to evaluate methods for predicting species occurrence

Much research has centered on determining which habitat model best predicts species occurrence. However, previous work typically used data sets that are inherently biased for evaluation. The use of simulated data provides a way of testing model performance using un‐biased data where the true relationships between species occurrence and population processes are predefined using sound ecological theory. We used a process‐based habitat model to generate simulated occurrence data to evaluate presence–absence and presence–only methods: generalized linear and generalized additive models (GLM, GAM), maximum entropy model (Maxent), and discrete choice models (DCM). This is the first study to use a DCM for predicting species distributions. We varied the effect that habitat quality had on fecundity and reported the model responses to these changes. When the effect of habitat quality on fecundity was weak, model performance was no better than random for all methods, however, performance increased as the habitat/fecundity relationship became stronger. For each level of habitat quality effect, there was little variation in performance between the presence–absence and presence–only methods. The use of a process‐based habitat model to generate occurrence data for evaluating model performance has a distinct advantage over other testing methods, because no errors are made during sampling and the true ecological relationships between population process and species occurrence are known. This leads to un‐biased results and increased confidence in assessing model performance and making management recommendations.     

Characterizing and predicting species distributions across environments and scales: Argentine ant occurrences in the eye of the beholder

Aim Species distribution models (SDMs) or, more specifically, ecological niche models (ENMs) are a useful and rapidly proliferating tool in ecology and global change biology. ENMs attempt to capture associations between a species and its environment and are often used to draw biological inferences, to predict potential occurrences in unoccupied regions and to forecast future distributions under environmental change. The accuracy of ENMs, however, hinges critically on the quality of occurrence data. ENMs often use haphazardly collected data rather than data collected across the full spectrum of existing environmental conditions. Moreover, it remains unclear how processes affecting ENM predictions operate at different spatial scales. The scale (i.e. grain size) of analysis may be dictated more by the sampling regime than by biologically meaningful processes. The aim of our study is to jointly quantify how issues relating to region and scale affect ENM predictions using an economically important and ecologically damaging invasive species, the Argentine ant (Linepithema humile). Location California, USA. Methods We analysed the relationship between sampling sufficiency, regional differences in environmental parameter space and cell size of analysis and resampling environmental layers using two independently collected sets of presence/absence data. Differences in variable importance were determined using model averaging and logistic regression. Model accuracy was measured with area under the curve (AUC) and Cohen's kappa. Results We first demonstrate that insufficient sampling of environmental parameter space can cause large errors in predicted distributions and biological interpretation. Models performed best when they were parametrized with data that sufficiently sampled environmental parameter space. Second, we show that altering the spatial grain of analysis changes the relative importance of different environmental variables. These changes apparently result from how environmental constraints and the sampling distributions of environmental variables change with spatial grain. Conclusions These findings have clear relevance for biological inference. Taken together, our results illustrate potentially general limitations for ENMs, especially when such models are used to predict species occurrences in novel environments. We offer basic methodological and conceptual guidelines for appropriate sampling and scale matching.     

Akaike information criterion should not be a “test” of geographical prediction accuracy in ecological niche modelling

Model complexity in ecological niche modelling has been recently considered as an important issue that might affect model performance. New methodological developments have implemented the Akaike information criterion (AIC) to capture model complexity in the Maxent algorithm model. AIC is calculated based on the number of parameters and likelihoods of continuous raw outputs. ENMeval R package allows users to perform a species-specific tuning of Maxent settings running models with different combinations of regularization multiplier and feature classes and finally, all these models are compared using AIC corrected for small sample size. This approach is focused to find the “best” model parametrization and it is thought to maximize the model complexity and therefore, its predictability. We found that most niche modelling studies examined by us (68%) tend to consider AIC as a criterion of predictive accuracy in geographical distribution. In other words, AIC is used as a criterion to choose those models with the highest capacity to discriminate between presences and absences. However, the link between AIC and geographical predictive accuracy has not been tested so far. Here, we evaluated this relationship using a set of simulated (virtual) species. We created a set of nine virtual species with different ecological and geographical traits (e.g., niche position, niche breadth, range size) and generated different sets of true presences and absences data across geography. We built a set of models using Maxent algorithm with different regularization values and features schemes and calculated AIC values for each model. For each model, we obtained binary predictions using different threshold criteria and validated using independent presence and absences data. We correlated AIC values against standard validation metrics (e.g., Kappa, TSS) and the number of pixels correctly predicted as presences and absences. We did not find a correlation between AIC values and predictive accuracy from validation metrics. In general, those models with the lowest AIC values tend to generate geographical predictions with high commission and omission errors. The results were consistent across all species simulated. Finally, we suggest that AIC should not be used if users are interested in prediction more than explanation in ecological niche modelling.     

A consistent occupancy–climate relationship across birds and mammals of the Americas

At broad spatial scales, species richness is strongly related to climate. Yet, few ecological studies attempt to identify regularities in the individual species distributions that make up this pattern. Models used to describe species distributions typically model very complex responses to climate. Here, we test whether the variability in the distributions of birds and mammals of the Americas relates to mean annual temperature and precipitation in a simple, consistent way. Specifically, we test if simple mathematical models can predict, as a first approximation, the geographical variation in individual species’ probability of occupancy for 3277 non‐migratory bird and 1659 mammal species. We find a Gaussian model, where the probability of occupancy of a 10⁴ km² quadrat decreases symmetrically and gradually around a species ‘optimal’ temperature and precipitation, was generally the best model, explaining an average of 35% of the deviance in probability of occupancy. The inclusion of additional terms had very small and idiosyncratic effects across species. The Gaussian occupancy–climate relationship appears general among species and taxa and explains nearly as much deviance as complex models including many more parameters. Therefore, we propose that hypotheses aiming to explain the broad‐scale distribution of species or species richness must also predict generally Gaussian occupancy–climate relationships. Synthesis Science aims to identify regularities in a complex natural world. General patterns should be identified before one searches for potential mechanisms and contingencies. However, species geographic distributions are often modelled as complex (sometimes black box), species‐specific, functions of their environment. We asked whether a simple model could account for as much of the geographic variation in a species' probability of occupancy, and be widely applicable across thousands of species. As a first approximation, we found that a simple Gaussian occupancy‐climate relationship is very common in Nature, whether it be causal or not.     

Modes of speciation and the neutral theory of biodiversity

Hubbell's neutral theory of biodiversity has generated much debate over the need for niches to explain biodiversity patterns. Discussion of the theory has focused on its neutrality assumption, i.e. the functional equivalence of species in competition and dispersal. Almost no attention has been paid to another critical aspect of the theory, the assumptions on the nature of the speciation process. In the standard version of the neutral theory each individual has a fixed probability to speciate. Hence, the speciation rate of a species is directly proportional to its abundance in the metacommunity. We argue that this assumption is not realistic for most speciation modes because speciation is an emergent property of complex processes at larger spatial and temporal scales and, consequently, speciation rate can either increase or decrease with abundance. Accordingly, the assumption that speciation rate is independent of abundance (each species has a fixed probability to speciate) is a more natural starting point in a neutral theory of biodiversity. Here we present a neutral model based on this assumption and we confront this new model to 20 large data sets of tree communities, expecting the new model to fit the data better than Hubbell's original model. We find, however, that the data sets are much better fitted by Hubbell's original model. This implies that species abundance data can discriminate between different modes of speciation, or, stated otherwise, that the mode of speciation has a large impact on the species abundance distribution. Our model analysis points out new ways to study how biodiversity patterns are shaped by the interplay between evolutionary processes (speciation, extinction) and ecological processes (competition, dispersal).     

The spatial scaling of beta diversity

Beta diversity is an important concept used to describe turnover in species composition across a wide range of spatial and temporal scales, and it underpins much of conservation theory and practice. Although substantial progress has been made in the mathematical and terminological treatment of different measures of beta diversity, there has been little conceptual synthesis of potential scale dependence of beta diversity with increasing spatial grain and geographic extent of sampling. Here, we evaluate different conceptual approaches to the spatial scaling of beta diversity, interpreted from ‘fixed’ and ‘varying’ perspectives of spatial grain and extent. We argue that a ‘sliding window’ perspective, in which spatial grain and extent covary, is an informative way to conceptualize community differentiation across scales. This concept more realistically reflects the varying empirical approaches that researchers adopt in field sampling and the varying scales of landscape perception by different organisms. Scale dependence in beta diversity has broad implications for emerging fields in ecology and biogeography, such as the integration of fine‐resolution ecogenomic data with large‐scale macroecological studies, as well as for guiding appropriate management responses to threats to biodiversity operating at different spatial scales.