Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Niche variability and its consequences for species distribution modeling

When species distribution models (SDMs) are used to predict how a species will respond to environmental change, an important assumption is that the environmental niche of the species is conserved over evolutionary time-scales. Empirical studies conducted at ecological time-scales, however, demonstrate that the niche of some species can vary in response to environmental change. We use habitat and locality data of five species of stream fishes collected across seasons to examine the effects of niche variability on the accuracy of projections from Maxent, a popular SDM. We then compare these predictions to those from an alternate method of creating SDM projections in which a transformation of the environmental data to similar scales is applied. The niche of each species varied to some degree in response to seasonal variation in environmental variables, with most species shifting habitat use in response to changes in canopy cover or flow rate. SDMs constructed from the original environmental data accurately predicted the occurrences of one species across all seasons and a subset of seasons for two other species. A similar result was found for SDMs constructed from the transformed environmental data. However, the transformed SDMs produced better models in ten of the 14 total SDMs, as judged by ratios of mean probability values at known presences to mean probability values at all other locations. Niche variability should be an important consideration when using SDMs to predict future distributions of species because of its prevalence among natural populations. The framework we present here may potentially improve these predictions by accounting for such variability.     

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.     

Identifying the relevant spatial and temporal scales in plant species occurrence models: The case of arable weeds in landscape mosaic of crops

Species distribution models (SDMs) represent potential valuable tools to explore factors underlying species occurrence over a large range of spatial scales. However, a recurrent problem with this approach is identifying the appropriate spatial and temporal scales for modeling. This problem is reinforced in plant populations for which it is often difficult to evaluate the limits of habitat patches. In this study, we aimed at developing SDMs for 13 arable weeds in highly dynamic small agricultural region. Although weed dynamic is widely thought to result from local processes, we explored the spatial and temporal scales that would best explain species occurrence over the area. Models were developed using weed occurrence data in 58 fields over four consecutive years (2008–2011) and spatial organization of management practices over the landscape for eight consecutive years (2004–2011). We used a model selection approach based on the minimum AIC criteria to select the best SDMs. Results showed that SDMs can successfully be applied to model weed occurrence over a small region. The appropriate temporal scale to consider in weed SDMs should encompass several years to reflect the effect of management history while the relevant spatial scale should extend beyond the crop field itself and include the field border and neighboring fields. This study illustrates that adopting a multiple scale approach is successful to model plant occurrence over a highly dynamic landscape.     

Disentangling scale dependencies in species environmental niches and distributions

Understanding species’ responses to environmental conditions, and how these ­species–environment associations shape spatial distributions, are longstanding goals in ecology and biogeography. However, an essential component of species–environment relationships – the spatial unit, or grain, at which they operate – remains unresolved. We identify three components of scale‐dependence in analyses of species–environment associations: 1) response grain, the grain at which species respond most strongly to their environment; 2) environment spatial structure, the pattern of spatial autocorrelation intrinsic to an environmental factor; and 3) analysis grain, the grain at which analyses are conducted and ecological inferences are made. We introduce a novel conceptual framework that defines these scale components in the context of analyzing species–environment relationships, and provide theoretical examples of their interactions for species with various ecological attributes. We then use a virtual species approach to investigate the impacts of each component on common methods of measuring and predicting species–environment relationships. We find that environment spatial structure has a substantial impact on the ability of even simple, univariate species distribution models (SDMs) to recover known species–­environment associations at coarse analysis grains. For simulated environments with ‘fine’ and ‘intermediate’ spatial structure, model explanatory power, and the frequency with which simple SDMs correctly estimated a virtual species’ response to the simulated environment, dramatically declined as analysis grain increased. Informed by these results, we use a scaling analysis to identify maximum analysis grains for individual environmental factors, and a scale optimization procedure to determine the grain of maximum predictive accuracy. Implementing these analysis grain thresholds and model performance standards in an example east African study system yields more accurate distribution predictions, compared to SDMs independently constructed at arbitrary analysis grains. Finally, we integrate our conceptual framework with virtual and empirical results to provide practical recommendations for researchers asking common questions about species–environment relationships.     

Microclimate species distribution models estimate lower levels of climate-related habitat loss for salamanders

Species distribution models (SDMs) largely rely on free-air temperatures at coarse spatial resolutions to predict habitat suitability, potentially overlooking important microhabitat. Integrating microclimate data into SDMs may improve predictions of organismal responses to climate change and support targeting of conservation assets at biologically relevant scales, especially for small, dispersal-limited species vulnerable to climate-change-induced range loss. We integrated microclimate data that account for the buffering effects of forest vegetation into SDMs at a very high spatial resolution (3 m²) for three plethodontid salamander species in Great Smoky Mountains National Park (North Carolina and Tennessee). Microclimate SDMs were used to characterize potential changes to future plethodontid habitat, including habitat suitability and habitat spatial patterns. Additionally, we evaluated spatial discrepancies between predictions of habitat suitability developed with microclimate and coarse-resolution, free-air climate data. Microclimate SDMs indicated substantial losses to plethodontid ranges and highly suitable habitat by mid-century, but at much more conservative levels than coarse-resolution models. Coarse-resolution SDMs generally estimated higher mid-century losses to plethodontid habitat compared to microclimate models and consistently undervalued areas containing highly suitable microhabitat. Furthermore, microclimate SDMs revealed potential areas of future gain in highly suitable habitat within current species’ ranges, which may serve as climatic microrefugia. Taken together, this study highlights the need to develop microclimate SDMs that account for vegetation and its biophysical effects on near-surface temperatures. As microclimate datasets become increasingly available across the world, their integration into correlative and mechanistic SDMs will be imperative for accurately estimating organismal responses to climate change and helping environmental managers tasked with spatially prioritizing conservation assets.     

Pushing the limits in marine species distribution modelling: lessons from the land present challenges and opportunities

Aim Species distribution models (SDMs) have been used to address a wide range of theoretical and applied questions in the terrestrial realm, but marine‐based applications remain relatively scarce. In this review, we consider how conceptual and practical issues associated with terrestrial SDMs apply to a range of marine organisms and highlight the challenges relevant to improving marine SDMs. Location We include studies from both marine and terrestrial systems that encompass many geographic locations around the globe. Methods We first performed a literature search and analysis of marine and terrestrial SDMs in ISI Web of Science to assess trends and applications. Using knowledge from terrestrial applications, we critically evaluate the application of SDMs in marine systems in the context of ecological factors (dispersal, species interactions, aggregation and ontogenetic shifts) and practical considerations (data quality, alternative modelling approaches and model validation) that facilitate or create difficulties for model application. Results The relative importance of ecological factors to be considered when applying SDMs varies among terrestrial and marine organisms. Correctly incorporating dispersal is frequently considered an important issue for terrestrial models, but because there is greater potential for dispersal in the ocean, it is often less of a concern in marine SDMs. By contrast, ontogenetic shifts and feeding have received little attention in terrestrial SDM applications, but these factors are important to many marine SDMs. Opportunities also exist for applying more advanced SDM approaches in the marine realm, including mechanistic ecophysiological models, where water balance and heat transfer equations are simpler for some marine organisms relative to their terrestrial counterparts. Main conclusions SDMs have generally been under‐utilized in the marine realm relative to terrestrial applications. Correlative SDM methods should be tested on a range of marine organisms, and we suggest further development of methods that address ontogenetic shifts and feeding interactions. We anticipate developments in, and cross‐fertilization between, coupled correlative and process‐based SDMs, mechanistic eco‐physiological SDMs, and spatial population dynamic models for climate change and species invasion applications in particular. Comparisons of the outputs of different model types will provide insight that is useful for improved spatial management of marine species.     

Do species distribution models explain spatial structure within tree species ranges?

Aim To evaluate the ability of species distribution models (SDMs) to predict the spatial structure of tree species within their geographical ranges (how trees are distributed within their ranges). Location Continental Spain. Methods We used an extensive dataset consisting of c. 90,000 plots (1 plot km^?2) where presence/absence data for 23 common Mediterranean and Atlantic tree species had been surveyed. We first generated SDMs relating the presence or absence of each species to a set of 16 environmental predictors, following a stepwise modelling process based on maximum likelihood methods. Superimposing spatial correlograms generated from the predictions of the SDMs over those generated from the raw data allowed a model–observation comparison of the nature, scale and intensity (level of aggregation) of spatial structure with the species ranges. Results SDMs predicted accurately the nature and scale of the spatial structure of trees. However, for most species, the observed intensity of spatial structure (level of aggregation of species in space) was substantially greater than that predicted by the SDMs. On average, the intensity of spatial aggregation was twice that predicted by SDMs. In addition, we also found a negative correlation between intensity of aggregation and species range size. Main conclusions Standard SDM predictions of spatial structure patterns differ among species. SDMs are apparently able to reproduce both the scale and intensity of species spatial structure within their ranges. However, one or more missing processes not included in SDMs results in species being substantially more aggregated in space than can be captured by the SDMs. This result adds to recent calls for a new generation of more biologically realistic SDMs. In particular, future SDMs should incorporate ecological processes that are likely to increase the intensity of spatial aggregation, such as source–sink dynamics, fine‐scale environmental heterogeneity and disequilibrium.     

Using Species Distribution Models For Fungi

Species distribution models (SDMs) are an emerging tool in the study of fungi, and their use is expanding across species and research topics. To summarise progress to date and to highlight important considerations for future users, we review 283 studies that apply SDMs to fungi. We found that macrofungi, lichens, and pathogenic microfungi are most often studied. While many studies only aim to model species response to environmental covariates, the use of SDMs for explicitly predicting fungal occurrence in space and time is growing. Many studies collect fungal occurrence data, but the use of pre-collected records from reference collections and citizen science programs is increasing. Challenges of applying SDMs to fungi include detection and sampling biases, and uncertainties in identification and taxonomy. Further, finding environmental covariates at appropriate spatial and temporal scales is important, as fungi can respond to fine-scale environmental patterns. Fine-scale covariate data can be difficult to gather across space, but we show remote-sensing measurements are viable for fungi SDMs. For those fungi interacting with host species, host information is also important, and can be used as covariates in SDMs. We also highlight that competition among fungi, and dispersal, can affect observed distributions, with the latter particularly prominent for invasive fungi. We show how one can account for these processes in models, when suitable data are available. Finally, we note that environmental DNA records create new opportunities and challenges for future modelling efforts, and discuss the difficulties in predicting invasions and climate change impacts. The application of SDMs to fungi has already provided interesting lessons on how to adapt modelling tools for specific questions, and fungi will continue to be relevant test subjects for further technical development of SDMs.     

Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes

Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we propose to derive environmental data layers by mapping ecological indicator values in space. We combined ~6 million plant occurrences with expert-based plant ecological indicator values (EIVs) of 3600 species in Switzerland. EIVs representing local soil properties (pH, moisture, moisture variability, aeration, humus and nutrients) and climatic conditions (continentality, light) were modelled at 93 m spatial resolution with the Random Forest algorithm and 16 predictors representing meso-climate, land use, topography and geology. Models were evaluated and predictions of EIVs were compared with soil inventory data. We mapped each EIV separately and evaluated EIV importance in explaining the distribution of 500 plant species using SDMs with a set of 30 environmental predictors. Finally, we tested how they improve an ensemble of SDMs compared to a standard set of predictors for ca 60 plant species. All EIV models showed excellent performance (|r| > 0.9) and predictions were correlated reasonably (|r| > 0.4) to soil properties measured in the field. Resulting EIV maps were among the most important predictors in SDMs. Also, in ensemble SDMs overall predictive performance increased, mainly through improved model specificity reducing species range overestimation. Combining large citizen science databases to expert-based EIVs is a powerful and cost–effective approach for generalizing local edaphic and climatic conditions over large areas. Producing ecologically meaningful predictors is a first step for generating better predictions of species distribution which is of main importance for decision makers in conservation and environmental management projects.     

Avian SDMs: current state,challenges, and opportunities

Quantifying species distributions using species distribution models (SDMs) has emerged as a central method in modern biogeography. These empirical models link species occurrence data with spatial environmental information. Since their emergence in the 1990s, thousands of scientific papers have used SDMs to study organisms across the entire tree of life, with birds commanding considerable attention. Here, we review the current state of avian SDMs and point to challenges and future opportunities for specific applications, ranging from conservation biology, invasive species and predicting seabird distributions, to more general topics such as modeling avian diversity, niche evolution and seasonal distributions at a biogeographic scale. While SDMs have been criticized for being phenomenological in nature, and for their inability to explicitly account for a variety of processes affecting populations, we conclude that they remain a powerful tool to learn about past, current, and future species distributions – at least when their limitations and assumptions are recognized and addressed. We close our review by providing an outlook on prospects and synergies with other disciplines in which avian SDMs can play an important role.     

Weak climatic associations among British plant distributions

Aim Species distribution models (SDMs) are used to infer niche responses and predict climate change‐induced range shifts. However, their power to distinguish real and chance associations between spatially autocorrelated distribution and environmental data at continental scales has been questioned. Here this is investigated at a regional (10 km) scale by modelling the distributions of 100 plant species native to the UK. Location UK. Methods SDMs fitted using real climate data were compared with those utilizing simulated climate gradients. The simulated gradients preserve the exact values and spatial structure of the real ones, but have no causal relationships with any species and so represent an appropriate null model. SDMs were fitted as generalized linear models (GLMs) or by the Random Forest machine‐learning algorithm and were either non‐spatial or included spatially explicit trend surfaces or autocovariates as predictors. Results Species distributions were significantly but erroneously related to the simulated gradients in 86% of cases (P < 0.05 in likelihood‐ratio tests of GLMs), with the highest error for strongly autocorrelated species and gradients and when species occupied 50% of sites. Even more false effects were found when curvilinear responses were modelled, and this was not adequately mitigated in the spatially explicit models. Non‐spatial SDMs based on simulated climate data suggested that 70–80% of the apparent explanatory power of the real data could be attributable to its spatial structure. Furthermore, the niche component of spatially explicit SDMs did not significantly contribute to model fit in most species. Main conclusions Spatial structure in the climate, rather than functional relationships with species distributions, may account for much of the apparent fit and predictive power of SDMs. Failure to account for this means that the evidence for climatic limitation of species distributions may have been overstated. As such, predicted regional‐ and national‐scale impacts of climate change based on the analysis of static distribution snapshots will require re‐evaluation.     

Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges

Species distribution models (SDMs) use spatial environmental data to make inferences on species' range limits and habitat suitability. Conceptually, these models aim to determine and map components of a species' ecological niche through space and time, and they have become important tools in pure and applied ecology and evolutionary biology. Most approaches are correlative in that they statistically link spatial data to species distribution records. An alternative strategy is to explicitly incorporate the mechanistic links between the functional traits of organisms and their environments into SDMs. Here, we review how the principles of biophysical ecology can be used to link spatial data to the physiological responses and constraints of organisms. This provides a mechanistic view of the fundamental niche which can then be mapped to the landscape to infer range constraints. We show how physiologically based SDMs can be developed for different organisms in different environmental contexts. Mechanistic SDMs have different strengths and weaknesses to correlative approaches, and there are many exciting and unexplored prospects for integrating the two approaches. As physiological knowledge becomes better integrated into SDMs, we will make more robust predictions of range shifts in novel or non-equilibrium contexts such as invasions, translocations, climate change and evolutionary shifts.     

种间作用对物种分布模拟的影响及建模方法

物种分布模型(SDMs)通过量化物种分布和环境变量之间的关系,并将其外推到未知的景观单元,模拟、预测地理空间中生物的潜在分布,是生态学、生物地理学、保护生物学等研究领域的重要工具.然而,目前物种分布模型主要采用非生物因素作为预测变量,由于数据量化和建模表达困难,生物因素特别是种间作用在物种分布模型中常被忽略,将种间作用...     

Behavioural context of multi-scale species distribution models assessed by radio-tracking

Incorporating ecological processes and animal behaviour into Species Distribution Models (SDMs) is difficult. In species with a central resting or breeding place, there can be conflict between the environmental requirements of the ‘central place’ and foraging habitat. We apply a multi-scale SDM to examine habitat trade-offs between the central place, roost sites, and foraging habitat in Myotis nattereri. We validate these derived associations using habitat selection from behavioural observations of radio-tracked bats. A Generalised Linear Model (GLM) of roost occurrence using land cover variables with mixed spatial scales indicated roost occurrence was positively associated with woodland on a fine scale and pasture on a broad scale. Habitat selection of radio-tracked bats mirrored the SDM with bats selecting for woodland in the immediate vicinity of individual roosts but avoiding this habitat in foraging areas, whilst pasture was significantly positively selected for in foraging areas. Using habitat selection derived from radio-tracking enables a multi-scale SDM to be interpreted in a behavioural context. We suggest that the multi-scale SDM of M. nattereri describes a trade-off between the central place and foraging habitat. Multi-scale methods provide a greater understanding of the ecological processes which determine where species occur and allow integration of behavioural processes into SDMs. The findings have implications when assessing the resource use of a species at a single point in time. Doing so could lead to misinterpretation of habitat requirements as these can change within a short time period depending on specific behaviour, particularly if detectability changes depending on behaviour.     

Forecasting species range dynamics with process‐explicit models: matching methods to applications

Knowing where species occur is fundamental to many ecological and environmental applications. Species distribution models (SDMs) are typically based on correlations between species occurrence data and environmental predictors, with ecological processes captured only implicitly. However, there is a growing interest in approaches that explicitly model processes such as physiology, dispersal, demography and biotic interactions. These models are believed to offer more robust predictions, particularly when extrapolating to novel conditions. Many process–explicit approaches are now available, but it is not clear how we can best draw on this expanded modelling toolbox to address ecological problems and inform management decisions. Here, we review a range of process–explicit models to determine their strengths and limitations, as well as their current use. Focusing on four common applications of SDMs – regulatory planning, extinction risk, climate refugia and invasive species – we then explore which models best meet management needs. We identify barriers to more widespread and effective use of process‐explicit models and outline how these might be overcome. As well as technical and data challenges, there is a pressing need for more thorough evaluation of model predictions to guide investment in method development and ensure the promise of these new approaches is fully realised.     

Temporal resolutions in species distribution models of highly mobile marine animals: Recommendations for ecologists and managers

While ecologists have long recognized the influence of spatial resolution on species distribution models (SDMs), they have given relatively little attention to the influence of temporal resolution. Considering temporal resolutions is critical in distribution modelling of highly mobile marine animals, as they interact with dynamic oceanographic processes that vary at time‐scales from seconds to decades. We guide ecologists in selecting temporal resolutions that best match ecological questions and ecosystems, and managers in applying these models. We group the temporal resolutions of environmental variables used in SDMs into three classes: instantaneous, contemporaneous and climatological. We posit that animal associations with fine‐scale and ephemeral features are best modelled with instantaneous covariates. Associations with large scale and persistent oceanographic features are best modelled with climatological covariates. Associations with mesoscale features are best modelled with instantaneous or contemporaneous covariates if ephemeral processes are present or interannual variability occurs, and climatological covariates if seasonal processes dominate and interannual variability is weak.     

Ecological differentiation and habitat unsuitability maintaining a ground beetle hybrid zone

Exogenous selection via interactions between organisms and environments may influence the dynamics of hybrid zones between species in multiple ways. Two major models of a hybrid zone allowed us to hypothesize that environmental conditions influence hybrid zone dynamics in two ways. In the first model, an environmental gradient determines the mosaic distribution at the boundary between ecologically differentiated species (mosaic hybrid zone model). In the second model, a patch of unsuitable habitat traps a hybrid zone between species whose hybrids are unfit (tension zone model). To test these, we examined the environmental factors influencing the spatial structure of a hybrid zone between the ground beetles Carabus maiyasanus and C. iwawakianus using GIS‐based quantification of environmental factors and a statistical comparison of species distribution models (SDMs). We determined that both of the hypothetical processes can be important in the hybrid zone. We detected interspecific differences in the environmental factors in presence localities and their relative contribution in SDMs. SDMs were not identical between species even within contact areas, but tended to be similar within the range of each species. These results suggest an association between environments and species, and provide evidence that ecological differentiation between species plays a role in the maintenance of the hybrid zone. Contact areas were characterized by a relatively high temperature, low precipitation, and high topological wetness. Thus, the contact areas were regarded as being located in an unsuitable habitat with a drier climate, where those populations are likely to occur in patches with limited precipitation concentrated. A comparison of spatial scales suggests that exogenous selection via environmental factors may be weaker than endogenous selection via genitalic incompatibility.     

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.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.