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.     

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.     

Comparison of effects of spatial autocorrelation on distribution predictions of four rare plant species in the Watarase wetland

We tested the effectiveness of distribution-prediction models for four rare herbaceous wetland species in the Watarase wetland, Japan, based on data obtained from aerial images. We used visible and near-infrared aerial images from three seasons, and elevations and vegetation heights derived from the images. Because spatial autocorrelation in species distribution data often biases the estimated effects of certain variables and reduces the prediction accuracy of distribution models, we compared the predictions of an intrinsic conditional autoregressive (CAR) model, which accounts for spatial autocorrelation, with those of a standard logistic regression model. The four study species had different distribution patterns: Ophioglossum namegatae and Impatiens ohwadae had aggregated distributions, whereas Galium tokyoense and Thalictrum simplex var. brevipes had scattered distributions. Predictions based on remote sensing images performed well for O. namegatae with the intrinsic CAR model and for I. ohwadae with both the logistic and CAR models; performance was poor for G. tokyoense and T. simplex var. brevipes with both models. Prediction accuracy improved by the CAR model in comparison to the logistic model most in O. namegatae and least in I. ohwadae. Impatiens ohwadae’s distribution was explained well by ground height. In contrast, the apparent improvement in the prediction for O. namegatae resulted from a substantial spatial random effect, suggesting the presence of determinants that could not be detected by remote sensing. The number of explanatory variables with large effects decreased in the intrinsic CAR model in three species possibly by avoiding spatial pseudoreplication, but not for T. simplex var. brevipes.     

Disentangling drivers of spatial autocorrelation in species distribution models

Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e. conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.     

Forest and Landscape Structure as Predictors of Capercaillie Occurrence

ABSTRACT Capercaillie (Tetrao urogallus) is a large, endangered forest grouse species with narrow habitat preferences and large spatial requirements that make it susceptible to habitat changes at different spatial scales. Our aim was to evaluate the relative power of variables relating to forest versus landscape structure in predicting capercaillie occurrence at different spatial scales. We investigated capercaillie-habitat relationships at the scales of forest stand and forest-stand mosaic in 2 Swiss regions. We assessed forest structure from aerial photographs in 52 study plots each 5 km². We classified plots into one of 3 categories denoting the observed local population trend (stable, declining, extinct), and we compared forest structure between categories. At the stand scale, we used presence-absence data for grid cells within the plots to build predictive habitat models based on logistic regression. At this scale, habitat models that included only variables relating to forest structure explained the occurrence of capercaillie only in part, whereas variables selected by the models differed between regions. Including variables relating to landscape features improved the models significantly. At the scale of stand mosaic, variables describing forest structure (e.g., mean canopy cover, proportion of open forest, and proportion of multistoried forest) differed between plot categories. We conclude that small-scale forest structure has limited power to predict capercaillie occurrence at the stand scale, but that it explains well at the scale of the stand mosaic. Including variables for landscape structure improves predictions at the forest-stand scale. Habitat models built with data from one region cannot be expected to predict the species occurrence in other regions well. Thus, multiscale approaches are necessary to better understand species-habitat relationships. Our results can help regional authorities and forest-management planners to identify areas where suitable habitat for capercaillie is not available in the required proportion and, thus, where management actions are needed to improve habitat suitability.     

Using a new framework of two-phase generalized additive models to incorporate prey abundance in spatial distribution models of juvenile slender lizardfish in Haizhou Bay,China

The predictive skill of species distribution models depends on the quality and quantity of input information. In addition to the physical environmental variables, prey availability is also one of the main drivers regulating spatial distribution of marine species. However, prey distribution data have rarely been considered in habitat models due to the lack of information on non-commercial prey species. This may lead to an incomplete view of species distributions and biased model predictions. In this study, we developed a new framework of two-phase generalized additive models (GAMs) based on the Tweedie distribution to incorporate the predicted prey abundance as covariates in habitat models, and applied this framework to juvenile slender lizardfish Saurida elongata in Haizhou Bay, China. This study demonstrated that the predictive skill of habitat models could be greatly improved through incorporating prey abundance as explanatory variables. The importance of prey distribution data in the habitat model confirms the essentiality of including prey data while modelling species distribution. Spatial overlap and GAM analysis demonstrated that not all dominant prey can be selected as potential explanatory variables and only those prey species showing high spatiotemporal occurrences with predators should be incorporated. The framework derived in this study could be extended to other marine organisms to improve the predictive skill of habitat models and enhance our understanding of the ecological mechanisms underlying the distribution of marine species.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

Accounting for multi‐scale spatial autocorrelation improves performance of invasive species distribution modelling (iSDM)

Aim Analyses of species distributions are complicated by various origins of spatial autocorrelation (SAC) in biogeographical data. SAC may be particularly important for invasive species distribution models (iSDMs) because biological invasions are strongly influenced by dispersal and colonization processes that typically create highly structured distribution patterns. We examined the efficacy of using a multi‐scale framework to account for different origins of SAC, and compared non‐spatial models with models that accounted for SAC at multiple levels. Location We modelled the spatial distribution of an invasive forest pathogen, Phytophthora ramorum, in western USA. Methods We applied one conventional statistical method (generalized linear model, GLM) and one nonparametric technique (maximum entropy, Maxent) to a large dataset on P. ramorum occurrence (n = 3787) to develop four types of model that included environmental variables and that either ignored spatial context or incorporated it at a broad scale using trend surface analysis, a local scale using autocovariates, or multiple scales using spatial eigenvector mapping. We evaluated model accuracies and amounts of explained spatial structure, and examined the changes in predictive power of the environmental and spatial variables. Results Accounting for different scales of SAC significantly enhanced the predictive capability of iSDMs. Dramatic improvements were observed when fine‐scale SAC was included, suggesting that local range‐confining processes are important in P. ramorum spread. The importance of environmental variables was relatively consistent across all models, but the explanatory power decreased in spatial models for factors with strong spatial structure. While accounting for SAC reduced the amount of residual autocorrelation for GLM but not for Maxent, it still improved the performance of both approaches, supporting our hypothesis that dispersal and colonization processes are important factors to consider in distribution models of biological invasions. Main conclusions Spatial autocorrelation has become a paradigm in biogeography and ecological modelling. In addition to avoiding the violation of statistical assumptions, accounting for spatial patterns at multiple scales can enhance our understanding of dynamic processes that explain ecological mechanisms of invasion and improve the predictive performance of static iSDMs.     

Primary productivity and anthropogenic disturbance as determinants of Upland Goose Chloephaga picta distribution in southern Patagonia

A species distribution may be determined by its responses to patterns of human disturbance as well as by its habitat preferences. Here we investigate the distribution of the Upland Goose Chloephaga picta, which has been historically persecuted by farmers and ranchers in Patagonia because it feeds on crops and pastures and is assumed to compete with sheep for forage. We assess whether its current breeding distribution is shaped by persecution by ranchers or whether it can be better explained by differences in habitat primary productivity and preference for wetlands, or by other anthropogenic disturbances not associated with ranching. We built species distribution models to examine the relative effect of environmental and anthropogenic predictors on the regional distribution of Upland Goose. We performed vehicle surveys in the province of Santa Cruz, Argentina, in two years, surveying 8000 km of roads and recording 6492 Geese. Generalized additive models were used to model the presence/absence of Geese in 1‐km cells. The models suggested that Upland Goose distribution is not currently affected by rancher control, as the species is more abundant in areas with high sheep stocking levels, but it is positively influenced by primary productivity and negatively influenced by urban areas. Anthropogenic disturbance caused by urban areas and oil extraction camps had a greater impact in limiting the species distribution than sheep ranching.     

The effect of aquatic and terrestrial habitat characteristics on occurrence and breeding probability in a montane amphibian: insights from a spatially explicit multistate occupancy model

Understanding species distribution and predicting range shifts are major goals of ecology and biogeography. Obtaining reliable predictions of how species distribution might change in response to habitat change requires knowledge of habitat availability, occupancy, use for breeding, and spatial autocorrelation in these parameters. Amphibians in alpine areas provide an excellent model system for disentangling habitat drivers of occupancy from that of breeding while explicitly accounting for spatial autocorrelation. We focused on the widespread common frog (Rana temporaria) inhabiting alpine lakes in the Southern Carpathians, Romania. We used single season multistate occupancy models developed to account for imperfect detection and spatial autocorrelation to estimate the occupancy and breeding probabilities and to evaluate their response to habitat characteristics. We found that frogs do not occur in all water bodies [occupancy probability: 0.697; 95% credible interval (0.614, 0.729)] and do not breed in a substantial proportion of water bodies where they occur [breeding probability conditional on occupancy: 0.707; 95% credible interval (0.670, 0.729)]. Habitat characteristics explain water body occupancy but not breeding probability; and altitude, water body surface area, water body sinuosity and permanency, presence of invertebrates, and grazing along the banks all had positive effects on occupancy. We also detected strong spatial autocorrelation in occupancy and breeding probabilities. Thus, our results indicate that habitat choice by montane amphibians is influenced by both spatial autocorrelation and habitat characteristics. Because spatial autocorrelations matter and because the presence of adults is not the same as the presence of a reproducing population, it will be difficult to predict the effects of habitat change on high altitude amphibian populations.     

Development and field validation of a regional,management‐scale habitat model: A koala Phascolarctos cinereus case study

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.     

Breeding bird diversity in relation to environmental gradients in China

Geographic variation in species richness has been explained by different theories such as energy, productivity, energy–water balance, habitat heterogeneity, and freezing tolerance. This study determines which of these theories best account for gradients of breeding bird richness in China. In addition, we develop a best-fit model to account for the relationship between breeding bird richness and environment in China. Breeding bird species richness in 207 localities (3271 km² per locality on average) from across China was related to thirteen environmental variables after accounting for sampling area. The Akaike's information criterion (AIC) was used to evaluate model performance. We used Moran's I to determine the magnitude of spatial autocorrelation in model residuals, and used simultaneous autoregressive model to determine coefficients of determination and AIC of explanatory variables after accounting for residual spatial autocorrelation. Of all environmental variables examined, normalized difference vegetation index, a measure of plant productivity, is the best variable to explain the variance in breeding bird richness. We found that species richness of breeding birds at the scale examined is best predicted by a combination of plant productivity, elevation range, seasonal variation in potential evapotranspiration, and mean annual temperature. These variables explained 47.3% of the variance in breeding bird richness after accounting for sampling area; most of the explained variance in richness is attributable to the first two of the four variables.     

Complementary strengths of spatially-explicit and multi-species distribution models

Species distribution models (SDMs) project the outcome of community assembly processes – dispersal, the abiotic environment and biotic interactions – onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock Tsuga canadensis and five additional co-occurring overstory tree species in 35 569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability. Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species–environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.     

Sex-specific population dynamics and demography of capercaillie (Tetrao urogallus L.) in a patchy environment

Modeling the population dynamics of patchily distributed species is a challenge, particularly when inference must be based on incomplete and small data sets such as those from most species of conservation concern. Here, we develop an open population spatial capture–recapture (SCR) model with sex-specific detection and population dynamics parameters to investigate population trend and sex-specific population dynamics of a capercaillie (Tetrao urogallus) population in Switzerland living in eight distinct forest patches totaling 22 km² within a region of 908 km² and sampled via scat collection. Our model accounts for the patchy distribution of habitat and the uncertainty introduced by collecting data only every third year, while producing sex by patch population trajectories. The estimated population trajectory was a decline of 2% per year; however, the sex specificity of the model revealed a decline in the male population only, with no evidence of decline in the female population. The decline observed in males was explained by the demography of just two of the eight patches. Our study highlights the flexibility of open population SCR models for assessing population trajectories through time and across space and emphasizes the desirability of estimating sex-stratified population trends especially in species of conservation concern.     

Modelling habitat use of Tetrao urogallus L. in Austria for conservation issues

Loss and deterioration of habitats are major threats for Tetrao urogallus in central Europe, where forests are highly fragmented and forest practices have distinctly changed during the last decades. Habitat models are important tools for conservation planning, often relying on presence–absence data. We mapped indirect signs of Tetrao urogallus presence as well as habitat variables over a series of seven study areas in the Austrian Alps, situated on limestone and on silicate rock. We modelled habitat use of Tetrao urogallus with one parametric approach (binary logistic regression) and two machine learning classification algorithms (classification trees and random forests) for both geological substrata separately. All three modelling approaches performed equally well in terms of accuracy or predictive power, but differed in model calibration. Three variables significantly contributed to all three habitat models on limestone and on silicate substrate, respectively, i.e. the cover of field-layer, the cover of dwarf shrubs and the proportion of deciduous trees in forest stands on limestone and the cover of field-layer, the canopy cover and the occurrence of Abies alba and/or Pinus sylvestris in forest stands on silicate rock. Some variables like the cover of Rubus sp. appeared in several models, which are not frequently mentioned in other studies. There have been some explanatory variables, which would have been missed, when applying just one single modelling approach, for example the occurrence of forest edges, the availability of canopy gaps and the supply of ant hills. Our results suggest differing habitat management strategies on limestone and on silicate rock. Considering the large spatial requirements of Tetrao urogallus the necessity of active habitat management for Tetrao urogallus becomes obvious.     

Geographical patterns of community‐based tree species richness in Chinese mountain forests: the effects of contemporary climate and regional history

The relationship between climate/productivity and historical/regional contingency and their relative influence on geographical patterns of species richness (GPSR) are still unresolved. Based on field data from 1494 plots from forests on 63 mountains across China, we document the GPSR for forest communities. Regression tree and generalized linear models were used to explore the discreteness and gradient of the distribution of tree species richness (α‐diversity), and to estimate the correlations of climate, historical floristic region, and local habitat with species richness. The collinearity between climatic variables and region were further disentangled; and the spatial autocorrelation in the patterns of α‐diversity and the residuals of alternative predictive models were compared. Overall, 75% of variation in plot‐based α‐diversity of trees was accounted for by all variables included, and about 66.5%, 64.5% and 27.9% by climate, region, and local habitat respectively. Importantly, the explanatory power of these variables differed in particular for coniferous, deciduous broadleaved and evergreen broadleaved species. Ambient temperature was more important for α‐diversity of trees than were the other climatic variables across China. Spatial autocorrelation in the pattern of α‐diversity could be accounted for mainly by spatial variation climate. The concordance between tree α‐diversity, historical flora, contemporary climate, and Quaternary climate change mode suggests the climate/productivity and historical/regional contingency both contribute to the GPSR in a complimentary manner. Taken together, our results provide unique evidence to link of the effects of contemporary climate and historical climate change on species richness across scales.     

关帝山云杉次生林树木径向生长的空间关联及其影响因子

以关帝山4 hm²云杉次生林样地为研究对象,按照CTFS(Center for Tropical Forest Science)技术规范对样地树木进行连续定位监测。利用2010至2015年间样地主要树种生长量观测数据,结合地形、土壤等环境因子调查及采样测定数据,分析了树木种群径向生长的空间关联性及其随生境的变化,并探讨了树木种群径向生长的影响因素。结果表明,青杄、华北落叶松、红桦、白桦和辽东栎为云杉次生林主要树种,在样地4个生境型(山脊生境、低海拔缓坡生境、高海拔缓坡生境、低洼地陡坡生境)中均有分布且呈现不同的径级结构。标记相关函数分析显示,同一生境型中,5树种径向生长的空间关联性各异;对于同一树种,径向生长的空间自相关性不仅具有尺度依赖性,同时生境型的不同导致树木径向生长的空间关联性发生变化。线性混合效应模型分析显示,初始胸径对树木径向生长的显著正效应在样地各类生境型的所有种群中普遍存在;生物因子对树木径向生长的显著影响只在特定生境型的青杄种群中被检测到,表明树木径向生长受同种邻体影响,但其影响显著性因树种而异;环境因子中,海拔和凹凸度对树木径向生长呈显著负效应...     

Land use changes affect distributional patterns of desert birds in the Baja California peninsula, Mexico

Reliable plans for desert bird conservation will depend on accurate prediction of habitat change effects on their distribution and abundance patterns. Predictive models can help highlight relationships between human‐related and other environmental variables and the presence of desert bird species. Presence/absence of 30 desert bird species of Baja California peninsula was modelled on the basis of explanatory variables taken from the field, maps, and digital imagery. Generalized linear models were fit to each bird species using both variables representing human activity and other environmental factors as predictors that might influence distribution. Probability of species presence was used as a habitat suitability index to evaluate the effect of human activity when the model contained a significant human activity variable. No differences were found in bird species richness between natural sites and those transformed by agriculture or urbanization. Of 59 bird species recorded in surveys, 34% were positively or negatively associated with human‐transformed habitats. Fourteen species seem to benefit from transformation of natural vegetation by agriculture or urbanization, while six were negatively affected. Sensitivity analyses of final models indicated all were robust. Results suggest that the occurrence of a large percentage of bird species inhabiting scrub habitats is sensitive to human habitat transformation. This finding has important conservation implications at regional scale as fragmentation and conversion of desert ecosystems into agricultural and urban areas affect the distribution of species that are highly selective for scrub habitat. Land use and anthropogenic activities seem to change ecological patterns at large spatial scales, but other factors could drive species richness distribution too (i.e. individual species response, species–energy relationships). The spatial modelling approach at regional scale used in this study can be useful for designing natural resource management plans in the Sonoran desert scrub.     

阿拉善荒漠啮齿动物集合群落实证研究

当生态学家探求在破碎化的栖息地中,群落物种的共存机制、多样性、局域尺度的性质和过程被放到更广阔的时空框架内时,就出现了"集合群落"这一概念。Leibold提出了集合群落概念,他们将一个集合群落定义为局域群落集,这些群落由各个潜在的相互作用的物种的扩散连接在一起。集合群落理论描述了那些发生在集合群落尺度上的过程,并且提出思考关于物种相互作用的新方法。集合群落概念为群落生态学提供了一个新的革命性的范式,集合群落研究的最基本问题是同一系统中多物种共存的机理、多样性的形成原因与维持机制。该范式强调区域范围内群落中的综合变异,强调环境特证和栖息地之间通过扩散调节的生物相互作用和空间变化。Leibold等提出了解释集合群落结果理论上的4个生态范式,即(1)中性理论;(2)斑块动态理论;(3)物种分配理论;(4)集团效应理论。之后有大量有关检验这4种生态理论的研究,但是有关陆地脊椎动物系统的集合群落的研究较少。2010—2012年,通过在内蒙古阿拉善荒漠景观中的8个固定样地中,对啮齿动物、栖息地环境因子进行调查。利用冗余分析和偏冗余分析,评估环境特征和空间特征对物种组成的影响。结果表明,环境特征独自解释72.8%的啮齿动物物种组成变化,空间特征独自解释33.8%的物种组成变化,环境特征和空间特征共同解释86.5%的啮齿动物物种组成变化,结果显著(P=0.032);去除环境特征之后,空间特征解释13.7%的变化(P=0.246),结果不显著;去除空间特征之后,栖息地变化解释52.7%的变化(P=0.016);环境特征和空间特征的交互作用解释20.1%的物种组成的变化,该区域啮齿动物群落构成集合群落,物种共存中环境特征起着主导作用,由物种分配理论解释该集合群落结构。