Tree Diversity,Environmental Heterogeneity,and Productivity in a Mexican Tropical Dry Forest1

Species diversity–environmental heterogeneity (D–EH) and species diversity–productivity (D–P) relationships have seldom been analyzed simultaneously even though such analyses could help to understand the processes underlying contrasts in species diversity among sites. Here we analyzed both relationships at a local scale for a highly diverse tropical dry forest of Mexico. We posed the following questions: (1) are environmental heterogeneity and productivity related?; (2) what are the shapes of D–EH and D–P relationships?; (3) what are individual, and interactive, contributions of these two variables to the observed variance in species diversity?; and (4) are patterns affected by sample size, or by partitioning into average local diversity and spatial species turnover? All trees (diameter at breast height ≥5 cm) within twenty‐six 0.2‐ha transects were censused; four environmental variables associated with water availability were combined into an environmental heterogeneity index; aboveground standing biomass was used as a productivity estimator. Simple and multiple linear and nonlinear regression models were run. Environmental heterogeneity and productivity were not correlated. We found consistently positive log‐linear D–EH and D–P relationships. Productivity explained a larger fraction of among‐transect variance in species diversity than did environmental heterogeneity. No effects of sample size were found. Different components of diversity varied in sensitivity to environmental heterogeneity and productivity. Our results suggest that species' differentiation along water availability gradients and species exclusion at the lowest productivity (driest) sites occur simultaneously, independently, and in a scale‐dependent fashion on the tree community of this forest.     

The second Southern African Bird Atlas Project: Causes and consequences of geographical sampling bias

Using the Southern African Bird Atlas Project (SABAP2) as a case study, we examine the possible determinants of spatial bias in volunteer sampling effort and how well such biased data represent environmental gradients across the area covered by the atlas. For each province in South Africa, we used generalized linear mixed models to determine the combination of variables that explain spatial variation in sampling effort (number of visits per 5′ × 5′ grid cell, or “pentad”). The explanatory variables were distance to major road and exceptional birding locations or “sampling hubs,” percentage cover of protected, urban, and cultivated area, and the climate variables mean annual precipitation, winter temperatures, and summer temperatures. Further, we used the climate variables and plant biomes to define subsets of pentads representing environmental zones across South Africa, Lesotho, and Swaziland. For each environmental zone, we quantified sampling intensity, and we assessed sampling completeness with species accumulation curves fitted to the asymptotic Lomolino model. Sampling effort was highest close to sampling hubs, major roads, urban areas, and protected areas. Cultivated area and the climate variables were less important. Further, environmental zones were not evenly represented by current data and the zones varied in the amount of sampling required representing the species that are present. SABAP2 volunteers' preferences in birding locations cause spatial bias in the dataset that should be taken into account when analyzing these data. Large parts of South Africa remain underrepresented, which may restrict the kind of ecological questions that may be addressed. However, sampling bias may be improved by directing volunteers toward undersampled regions while taking into account volunteer preferences.     

Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effects

Aim To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine‐scale bird atlas data set. Location The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ‘pure’ environmental variation, (b) spatially‐structured environmental variation, (c) ‘pure’ spatial variation and (d) unexplained, non‐spatial variation. Results The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ‘pure’ spatial and spatially‐structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one‐third of the variation for 12 species. Main conclusions The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ‘noise’ in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all‐scale spatial components of this variation (the source of ‘false correlation’) and to bring out the ‘pure’ environmental variation for ecological interpretation.     

Spatial and environmental effects on a rock‐pool metacommunity depend on landscape setting and dispersal mode

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Bird and mammal species composition in distinct geographic regions and their relationships with environmental factors across multiple spatial scales

Global patters of species distributions and their underlying mechanisms are a major question in ecology, and the need for multi‐scale analyses has been recognized. Previous studies recognized climate, topography, habitat heterogeneity and disturbance as important variables affecting such patterns. Here we report on analyses of species composition – environment relationships among different taxonomic groups in two continents, and the components of such relationships, in the contiguous USA and Australia. We used partial Canonical Correspondence Analysis of occurrence records of mammals and breeding birds from the Global Biodiversity Information Facility, to quantify relationships between species composition and environmental variables in remote geographic regions at multiple spatial scales, with extents ranging from 10⁵ to 10⁷ km² and sampling grids from 10 to 10,000 km². We evaluated the concept that two elements contribute to the impact of environmental variables on composition: the strength of species' affinity to an environmental variable, and the amount of variance in the variable. To disentangle these two elements, we analyzed correlations between resulting trends and the amount of variance contained in different environmental variables to isolate the mechanisms behind the observed relationships. We found that climate and land use‐land cover are responsible for most explained variance in species composition, regardless of scale, taxonomic group and geographic region. However, the amount of variance in species composition attributed to land use / land cover (LULC) was closely related to the amount of intrinsic variability in LULC in the USA, but not in Australia, while the effect of climate on species composition was negatively correlated to the variability found in the climatic variables. The low variance in climate, compared to LULC, suggests that species in both taxonomic groups have strong affinity to climate, thus it has a strong effect on species distribution and community composition, while the opposite is true for LULC.     

The influence of different spatial‐scale variables on caddisfly assemblages in Flemish lowland streams

1. Patterns in species assemblages are the result of the combined influence of processes acting on different spatial scales. Various studies describe the distribution of macroinvertebrate communities and their relationship with environmental factors at different geographical scales, but only a few of these studies concentrate on Western European lowlands. 2. Using Flanders as representative for the densely populated Western‐European lowlands, the specific aims of this study are: (i) to identify the different trichopteran species assemblages and to characterise them biologically using indicator species; (ii) to determine which environmental gradients most influence the observed species assemblages; and (iii) to analyse the relative importance of different spatial scale variables in constraining the Trichoptera distributions. 3. Assessment of the main environmental gradients suggested that the absence of Trichoptera from certain locations was mainly due to elevated nutrient concentrations and lower oxygen contents, confirming their sensitivity to anthropogenic disturbance. 4. Five Trichoptera species assemblages were distinguished based on Bray–Curtis dissimilarity coefficients. These assemblages did not differ significantly in species richness, but a shift in stream zonation preference was observed. In the ordination analysis 11 variables that were selected using a stepwise model building function manifested themselves as upstream–downstream and size‐related gradients. The Trichoptera assemblages in lowland streams thus appear to follow a longitudinal succession pattern that corresponds with the species‐specific preferences. 5. Partitioning the variance over the different spatial scales indicated that the reach‐scale variables were far more important in explaining the variation in species composition. The study design, which limited the minimum–maximum range of catchment‐scale characteristics, however, may have led to an overestimation of the impact of the local‐scale variables.     

Temporal variability in the spatial and environmental determinants of functional metacommunity organization – stream fish in a human‐modified landscape

1. Quantifying the relative importance of environmental filtering versus regional spatial structuring has become an intensively studied area in the context of metacommunity ecology. However, most studies have evaluated the role of environmental and spatial processes using taxonomic data sets of single snapshot surveys. 2. Here, we examined temporal changes in patterns and possible processes behind the functional metacommunity organization of stream fishes in a human‐modified landscape. Specifically, we (i) studied general changes in the functional composition of fish assemblages among 40 wadeable stream sites during a 3‐year study period in the catchment area of Lake Balaton, Hungary, (ii) quantified the relative importance of spatial and environmental factors as determinants of metacommunity structure and (iii) examined temporal variability in the relative role of spatial and environmental processes for this metacommunity. 3. Partial triadic analysis showed that assemblages could be effectively ordered along a functional gradient from invertebrate consuming species dominated by the opportunistic life‐history strategy, to assemblages with a diverse array of functional attributes. The analysis also revealed that functional fish assemblage structure was moderately stable among the sites between the sampling periods. 4. Despite moderate stability, variance partitioning using redundancy analyses (RDA) showed considerable temporal variability in the contribution of environmental and spatial factors to this pattern. The analyses also showed that environmental variables were, in general, more important than spatial ones in determining metacommunity structure. Of these, natural environmental variables (e.g. altitude, velocity) proved to be more influential than human‐related effects (e.g. pond area, % inhabited area above the site, nutrient enrichment), even in this landscape with relatively low variation in altitude and stream size. 5. Pond area was, however, the most important human stressor variable that was positively associated with the abundance of non‐native species with diverse functional attributes. The temporal variability in the relative importance of environmental and spatial factors was probably shaped by the release of non‐native fish from fish ponds to the stream system during flood events. 6. To conclude, both spatial processes and environmental control shape the functional metacommunity organization of stream fish assemblages in human‐modified landscapes, but their importance can vary in time. We argue, therefore, that metacommunity studies should better consider temporal variability in the ecological mechanisms (e.g. dispersal limitation, species sorting) that determine the dynamics of landscape‐level community organization.     

Regional and local influence of grazing activity on the diversity of a semi-arid dung beetle community

This study analyses the effect of resource availability (i.e. sheep dung) on dung beetle communities in an arid region of Central Spain, both at regional and at local scales. A total of 18 sites within 600 km² were sampled for the regional analysis and 16 sites within the 30 km² of an Iberian municipality were sampled for the local analysis. Spatial and environmental characteristics of sampling sites were also compiled at both scales, including measures of grazing activity (livestock density at regional scale, and two counts of rabbit and sheep dung at local scale). At a regional scale, any environmental or spatial variable can help to explain the variation in abundance. However, species richness was related to summer precipitation and composition was related to elevation. At local scale, abundance is not significantly related to any of the environmental variables, but species richness was related to the local amount of sheep dung (27% of variance). The amount of dung in a 2‐km buffer around the site accounts for 27–32% of variance in abundance and 60–65% of variance in species richness. The presence of the flock with the highest sheep density explains 53% of abundance variability and 73% of species richness variance. A cluster analysis of localities identified two main groups, one characterized by a lower abundance and species richness that can be considered a nested subsample of the species‐rich group. The mean and maximum amount of sheep dung in the sites separated by less than 2 km are the only significant explanatory variables able to discriminate both groups. These results suggest that grazing intensity (and the associated increase in the amount of trophic resources) is a key factor in determining local variation in the diversity and composition of dung beetle assemblages. However, dung beetle assemblages are not spatially independent at the analysed resolution, and the amount of dung in the surroundings seems to be more important for locally collected species than the dung effectively found in the site. Although differences in the availability and quantity of trophic resources among nearby sites could be affecting the population dynamics and dispersion of dung beetles within a locality, sites with larger populations, and greater species numbers would not be able to exercise enough influence as to bring about a complete local faunistic homogenization.     

A model of geographical, environmental and regional variation in vegetation composition of pyrogenic grasslands of Florida

Aim To develop a landscape‐level model that partitions variance in plant community composition among local environmental, regional environmental, and purely spatial predictive variables for pyrogenic grasslands (prairies, savannas and woodlands) throughout northern and central Florida. Location North and central Florida, USA. Methods We measured plant species composition and cover in 271 plots throughout the study region. A variation‐partitioning model was used to quantify components of variation in species composition associated with the main and interaction effects of soil and topographic variables, climate variables and spatial coordinates. Partial correlations of environmental variables with community variation were identified using direct gradient analysis (redundancy analysis and partial redundancy analysis) and Monte Carlo tests of significance. Results Community composition was most strongly related to edaphic variables at local scales in association with topographic gradients, although geographically structured edaphic, climatic and pure spatial effects were also evident. Edaphic variables explained the largest portion of total variation explained (TVE) as a main effect (48%) compared with the main effects of climate (9%) and pure spatial factors (9%). The remaining TVE was explained by the interaction effect of climate and spatial factors (13%) and the three‐way interaction (22%). Correlation analyses revealed that the primary compositional gradient was related to soil fertility and topographic position corresponding to soil moisture. A second gradient represented distinct geographical separation between the Florida panhandle and peninsular regions, concurrent with differences in soil characteristics. Gradients in composition corresponded to species richness, which was lower in the Florida peninsula. Main conclusions Environmental variables have the strongest influence on the species composition of Florida pyrogenic grasslands at both local and regional scales. However, the limited distributions of many plant taxa suggest historical constraints on species distributions from one physiographical region to the other (Florida panhandle and peninsula), although this pattern is partially confounded by regionally spatially structured environmental variables. Our model provides insight into the relative importance of local‐ and regional‐scale environmental effects as well as possible historical constraints on floristic variation in pine‐dominated pyrogenic grasslands of the south‐eastern USA.     

Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent

Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence‐only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data‐sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point‐process modelling framework (GLMs with subset selection, GLMs fitted with an elastic‐net penalty, and Maxent). To correct for sampling bias, we applied model‐based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial‐block cross‐validation and a holdout data set. When evaluated with independent, but also sampling‐biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic‐net models have intermediate performance, with slight advantage for GLMs on cross‐validation and Maxent on hold‐out evaluation. Model‐based bias correction is very useful in data‐sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than the effort covariate, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias‐free presence–absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed.     

Plant composition associated with environmental gradients in tropical montane forests (Cueva de Los Guacharos National Park,Huila, Colombia)

Niche differentiation among tropical forest plants can generate species turnover along gradients of soil, topography, climate, and land use history. In this study we explore the relative importance of these variables as drivers of floristic composition in Cueva de Los Guacharos National Park. We established twenty 0.1‐ha plots, within which trees, lianas, and shrubs (diameter ≥ 2.5 cm) were censused. We selected plot locations in primary and disturbed forests, and we measured topography and soil variables. Despite their structural similarity, primary and disturbed forests differed floristically, and also differed in environmental variables measured. A NMDS ordination showed that variation in the floristic composition across plots is highly correlated to the exchangeable acidity, elevation, temperature, and magnesium availability. Variance partitioning analysis shows that together spatial and environmental variables explain 24.2 percent of the variation in species composition. ‘Pure environmental’ variables were more important in explaining compositional variability than ‘pure spatial’ processes (9.8% and 1.4%, respectively). Residual variance may be attributed to stochastic process or non‐measured biotic effects.     

Environmental drivers of species composition and functional diversity of dung beetles along the Atlantic Forest–Pampa transition zone

Human activities are causing a rapid loss of biodiversity, which impairs ecosystem functions and services. Therefore, understanding which processes shape how biodiversity is distributed along spatial and environmental gradients is a first step to guide conservation and management efforts. We aimed to determine the relative explanatory importance of biogeographic, environmental, landscape and spatial variables on assemblage dissimilarities and functional diversity of dung beetles along the Atlantic Forest–Pampa (i.e. forest–grassland) transition zone located in Southeast South America. We described each site according to their biogeographic position, environmental conditions, landscape features and spatial patterns. The compositional dissimilarity was partitioned into turnover and nestedness components of β‐diversity. Mantel tests and generalised dissimilarity models were used to relate β‐diversity and its components to biogeographic, environmental, landscape and spatial variables. Variation partitioning analysis was used to estimate the pure and shared variation in species composition and functional diversity explained by the four categories of predictors. Biome domain was the main factor causing dung beetle compositional dissimilarity, with a high species replacement between Atlantic Forest and Pampa. Biogeographic, environmental, landscape and spatial distances also affected the patterns of dung beetle dissimilarity and β‐diversity components. The shared effects of the four sets of predictors explained most of the variation in dung beetle composition. A similar response pattern was found for dung beetle functional diversity, which excluded biogeographic effects. Only the pure effects of environmental and spatial predictors were significant for species composition and functional diversity. Our results indicate that dung beetle species composition and functional diversity are jointly driven by environmental, landscape and spatial predictors with higher pure environmental and spatial effects. The forest–grassland transition zone promotes a strong species and trait replacement highly influenced both by environmental filtering and dispersal limitation.     

Spatial autocorrelation and red herrings in geographical ecology

Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro‐scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence. Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East. Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least‐squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared. Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non‐detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de‐emphasized predictors with strong autocorrelation and long‐distance clinal structures, giving more importance to variables acting at smaller geographical scales. Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.     

Hydrological and environmental variables outperform spatial factors in structuring species,trait composition,and beta diversity of pelagic algae

There has been increasing interest in algae‐based bioassessment, particularly, trait‐based approaches are increasingly suggested. However, the main drivers, especially the contribution of hydrological variables, of species composition, trait composition, and beta diversity of algae communities are less studied. To link species and trait composition to multiple factors (i.e., hydrological variables, local environmental variables, and spatial factors) that potentially control species occurrence/abundance and to determine their relative roles in shaping species composition, trait composition, and beta diversities of pelagic algae communities, samples were collected from a German lowland catchment, where a well‐proven ecohydrological modeling enabled to predict long‐term discharges at each sampling site. Both trait and species composition showed significant correlations with hydrological, environmental, and spatial variables, and variation partitioning revealed that the hydrological and local environmental variables outperformed spatial variables. A higher variation of trait composition (57.0%) than species composition (37.5%) could be explained by abiotic factors. Mantel tests showed that both species and trait‐based beta diversities were mostly related to hydrological and environmental heterogeneity with hydrological contributing more than environmental variables, while purely spatial impact was less important. Our findings revealed the relative importance of hydrological variables in shaping pelagic algae community and their spatial patterns of beta diversities, emphasizing the need to include hydrological variables in long‐term biomonitoring campaigns and biodiversity conservation or restoration. A key implication for biodiversity conservation was that maintaining the instream flow regime and keeping various habitats among rivers are of vital importance. However, further investigations at multispatial and temporal scales are greatly needed.     

Bird diversity patterns in Neotropical temperate farmlands: The role of environmental factors and trophic groups in the spring and autumn

Productivity, habitat heterogeneity and environmental similarity are of the most widely accepted hypotheses to explain spatial patterns of species richness and species composition similarity. Environmental factors may exhibit seasonal changes affecting species distributions. We explored possible changes in spatial patterns of bird species richness and species composition similarity. Feeding habits are likely to have a major influence in bird–environment associations and, given that food availability shows seasonal changes in temperate climates, we expect those associations to differ by trophic group (insectivores or granivores). We surveyed birds and estimated environmental variables along line‐transects covering an E‐W gradient of annual precipitation in the Pampas of Argentina during the autumn and the spring. We examined responses of bird species richness to spatial changes in habitat productivity and heterogeneity using regression analyses, and explored potential differences between seasons of those responses. Furthermore, we used Mantel tests to examine the relationship between species composition similarity and both the environmental similarity between sites and the geographic distance between sites, also assessing differences between seasons in those relationships. Richness of insectivorous birds was directly related to primary productivity in both seasons, whereas richness of seed‐eaters showed a positive association with habitat heterogeneity during the spring. Species composition similarity between assemblages was correlated with both productivity similarity and geographic proximity during the autumn and the spring, except for insectivore assemblages. Diversity within main trophic groups seemed to reflect differences in their spatial patterns as a response to changes between seasons in the spatial patterns of food resources. Our findings suggest that considering different seasons and functional groups in the analyses of diversity spatial pattern could contribute to better understand the determinants of biological diversity in temperate climates.     

Matching data sets from two different spatial samples

Abstract. Methods for coupling two data sets (species composition and environmental variables for example) are well known and often used in ecology. All these methods require that variables of the two data sets have been recorded at the same sample stations. But if the two data sets arise from different sample schemes, sample locations can be different. In this case, scientists usually transform one data set to conform with the other one that is chosen as a reference. This inevitably leads to some loss of information. We propose a new ordination method, named spatial‐RLQ analysis, for coupling two data sets with different spatial sample techniques. Spatial‐RLQ analysis is an extension of co‐inertia analysis and is based on neighbourhood graph theory and classical RLQ analysis. This analysis finds linear combinations of variables of the two data sets which maximize the spatial cross‐covariance. This provides a co‐ordination of the two data sets according to their spatial relationships. A vegetation study concerning the forest of Chizé (western France) is presented to illustrate the method.     

Habitat distribution models,spatial autocorrelation,functional traits and dispersal capacity of alpine plant species

Abstract. We evaluate the potential influence of disturbance on the predictability of alpine plant species distribution from equilibrium‐based habitat distribution models. Firstly, abundance data of 71 plant species were correlated with a comprehensive set of environmental variables using ordinal regression models. Subsequently, the residual spatial autocorrelation (at distances of 40 to 320 m) in these models was explored. The additional amount of variance explained by spatial structuring was compared with a set of functional traits assumed to confer advantages in disturbed or undisturbed habitats. We found significant residual spatial autocorrelation in the habitat models of most of the species that were analysed. The amount of this autocorrelation was positively correlated with the dispersal capacity of the species, levelling off with increasing spatial scale. Both trends indicate that dispersal and colonization processes, whose frequency is enhanced by disturbance, influence the distribution of many alpine plant species. Since habitat distribution models commonly ignore such spatial processes they miss an important driver of local‐ to landscape‐scale plant distribution.     

Beta diversity of rock-restricted cichlid fishes in Lake Malawi: importance of environmental and spatial factors

The rock-restricted cichlid fish assemblages of Lake Malawi exhibit high spatial diversity in their species composition and relative abundance. However the extent to which this is due to the effects of local environmental differences, dispersal limitation of constituent taxa, and the assignment of allopatric populations to species is uncertain. We examined the factors associated with diversity within an assemblage from the north-western shores, encompassing a spatial scale of 170 km. For both the whole assemblage, and all constituent species-complexes, spatial variance in community structure was significantly dependent upon both geographic distances between locations and local habitat variables. Pronounced effects of distance indicate limited dispersal, but our results also show that that the spatial variance explained by geographic distance alone was strongly linked to proportion of allopatric populations within a species-complex with species status. Thus, the taxonomic status of allopatric populations underlies, at least partially, the biogeographical structure of this assemblage. Substrate composition and habitat depth were also significant determinants of community structure, although spatial variance attributed to these variables was less than that associated with distance alone. Substantial unexplained variance may be a consequence of the effects of unmeasured habitat variables, high ecological similarity between co-occurring species, stochastic influences on population abundance, and the effects of local adaptation. Despite low spatial variance explained by the assessed environmental variables, significant environmental influence on cichlid assemblage structure across a wide spatial scale indicates that even slight future environmental changes may have the capacity to significantly alter species composition.     

Role of environmental and spatial processes in structuring anuran communities across a tropical rain forest

This study aimed to evaluate if anuran species distributions in riparian and non‐riparian areas are influenced by environmental factors (i.e. niche) and/or by spatial factors (i.e. dispersal). The environmental variables analysed were altitude, distance from the stream and leaf litter depth. Spatial factors were represented by the eigenvectors extracted from geographical coordinates by eigenfunction analysis. The study was conducted in 24 km² of terra‐firme forest in Central Amazonia, Manaus – Amazonas, Brazil. Between November 2008 and May 2009, three samples were taken from 41 plots, 21 plots being placed at non‐riparian areas and another 20 placed in riparian areas. We submitted the assemblage dataset to a partial redundancy analysis to evaluate the contributions of environmental and spatial variables (selected with a forward selection procedure). In addition, we tested if communities differ from riparian and non‐riparian areas using a db‐MANOVA. Species richness and species composition differed between riparian and non‐riparian plots. Some species were restricted to riparian areas. Altitude was the only significant variable (P = 0.005) explaining 21% of the total variance. When analysing the data from all plots using the partial redundancy analysis, 27% of the variance was explained by spatial and environmental variables. The environmental variables explained exclusively 4% of the variance in assemblage composition, and 13% was explained by environmental variables that were also structured in space (i.e. the shared fraction), while 10% was explained exclusively by spatial variables. In conclusion, our results showed differences between the assemblages of riparian and non‐riparian areas which can be explained by the distribution of anuran species along environmental gradients altitude and distance to streams, with little evidence of dispersal limitation.     

Can niche‐based distribution models outperform spatial interpolation?

Aim Distribution modelling relates sparse data on species occurrence or abundance to environmental information to predict the population of a species at any point in space. Recently, the importance of spatial autocorrelation in distributions has been recognized. Spatial autocorrelation can be categorized as exogenous (stemming from autocorrelation in the underlying variables) or endogenous (stemming from activities of the organism itself, such as dispersal). Typically, one asks whether spatial models explain additional variability (endogenous) in comparison to a fully specified habitat model. We turned this question around and asked: can habitat models explain additional variation when spatial structure is accounted for in a fully specified spatially explicit model? The aim was to find out to what degree habitat models may be inadvertently capturing spatial structure rather than true explanatory mechanisms. Location We used data from 190 species of the North American Breeding Bird Survey covering the conterminous United States and southern Canada. Methods We built 13 different models on 190 bird species using regression trees. Our habitat‐based models used climate and landcover variables as independent variables. We also used random variables and simulated ranges to validate our results. The two spatially explicit models included only geographical coordinates or a contagion term as independent variables. As another angle on the question of mechanism vs. spatial structure we pitted a model using related bird species as predictors against a model using randomly selected bird species. Results The spatially explicit models outperformed the traditional habitat models and the random predictor species outperformed the related predictor species. In addition, environmental variables produced a substantial R² in predicting artificial ranges. Main conclusions We conclude that many explanatory variables with suitable spatial structure can work well in species distribution models. The predictive power of environmental variables is not necessarily mechanistic, and spatial interpolation can outperform environmental explanatory variables.