Predicting species distribution: ...
REVIEWS AND SYNTHESES Predicting species distribution: offering more than simple habitat models Antoine Guisan1* and Wilfried Thuiller2,3 1Laboratoire de Biologie de la Conservation (LBC), Departement �� d���Ecologie et d���Evolution (DEE), Universite �� de Lausanne, Batiment �� de Biologie, CH-1015 Lausanne, Switzerland 2 Climate Change Research Group, Kirstenbosh Research Center, South African National Biodiversity Institute, Post Bag x7, Claremont 7735, Cape Town, South Africa 3 Macroecology and Conservation Unit, University of Evora, �� Estrada dos Leoes, �� 7000-730 Evora, �� Portugal *Correspondence: E-mail: email@example.com Abstract In the last two decades, interest in species distribution models (SDMs) of plants and animals has grown dramatically. Recent advances in SDMs allow us to potentially forecast anthropogenic effects on patterns of biodiversity at different spatial scales. However, some limitations still preclude the use of SDMs in many theoretical and practical applications. Here, we provide an overview of recent advances in this field, discuss the ecological principles and assumptions underpinning SDMs, and highlight critical limitations and decisions inherent in the construction and evaluation of SDMs. Particular emphasis is given to the use of SDMs for the assessment of climate change impacts and conservation management issues. We suggest new avenues for incorporating species migration, population dynamics, biotic interactions and community ecology into SDMs at multiple spatial scales. Addressing all these issues requires a better integration of SDMs with ecological theory. Keywords Dispersal, ecological niche theory, future projections, habitat suitability maps, population dynamics, prediction errors, predictive biogeography, spatial scales, species distribution models. Ecology Letters (2005) 8: 993���1009 IN T R OD U C TI ON The fascinating question of how plants and animals are distributed on Earth in space and time has a long history which has inspired many biogeographers and ecologists to seek explanations. Most modelling approaches developed for predicting plant or animal species distributions have their roots in quantifying species���environment relation- ships. Three phases seem to have marked the history of species distribution models (SDMs) (S. Ferrier, personal communication): (i) non-spatial statistical quantification of species���environment relationship based on empirical data, (ii) expert-based (non-statistical, non-empirical) spatial modelling of species distribution, and (iii) spatially explicit statistical and empirical modelling of species distribution. Earliest found examples of modelling strategies using correlations between distributions of species and climate seems to be those of Johnston (1924), predicting the invasive spread of a cactus species in Australia, and Hittinka (1963) assessing the climatic determinants of the distribution of several European species (quoted in Pearson & Dawson 2003). Earliest developments in computer-based predictive modelling of species distribution seem to originate in the mid-1970s, stimulated by the numerous quantification of species���environment available at that time (Austin 1971). The earliest species distribution modelling attempt found so far in the literature seems to be the niche-based spatial predictions of crop species by Henry Nix and collaborators in Australia (Nix et al. 1977). These were succeeded, in the early 1980s, by the pioneering simulations of species distribution by Ferrier (1984). At about the same time, the publication of two seminal books (Verner et al. 1986 Margules & Austin 1991, resulting from a workshop in 1988) also contributed largely to promote this new approach, resulting in a growing number of species distributions models proposed in the literature. These advances were largely supported by the Ecology Letters, (2005) 8: 993���1009 doi: 10.1111/j.1461-0248.2005.00792.x ��2005 Blackwell Publishing Ltd/CNRS
parallel developments in computer and statistical sciences, and by strong theoretical support to predictive ecology as ��more rigorously scientific, more informative and more useful ecology�� (Peters 1991). As a result, the number of related publications increased very significantly since the early 1990s, and the first partial reviews, such as those published by Franklin (1995) and Austin (1998), appeared shortly before the turn of the century. A large symposium on modelling species occur- rence, organized in Snowbird, Utah, in September 1999, additionally provided a large review of the twentieth century state-of-the-art in this field (Scott et al. 2002). A synthesis review of this pre-2000 period can be found in Guisan & Zimmermann 2000). In recent years, predictive modelling of species distribu- tion has become an increasingly important tool to address various issues in ecology, biogeography, evolution and, more recently, in conservation biology and climate change research (see Table 1). In this paper, we review the recent achievements in developing species distribution models (SDMs) and address some of their limitations. We devote particular attention to the challenge of projecting the impacts of climate change on the distribution of biodiversity, which currently yields some of the most spectacular progress in SDM research. To set the scene, we first define SDMs and provide an overview of basic ecological theory and working assumptions underpin- ning them. We then discuss some methodological issues, decisions to be made during the process of model building and evaluation, and the implications for conservation and management. We then summarize important challenges that must be addressed to overcome the limitations of SDMs. W H A T A R E S D M S A N D H O W DO TH E Y WO R K ? Species distribution models are empirical models relating field observations to environmental predictor variables, based on statistically or theoretically derived response surfaces (Guisan & Zimmermann 2000). Species data can be simple presence, presence���absence or abundance obser- vations based on random or stratified field sampling, or observations obtained opportunistically, such as those in natural history collections (Graham et al. 2004a). Environ- mental predictors can exert direct or indirect effects on species, arranged along a gradient from proximal to distal predictors (Austin 2002), and are optimally chosen to reflect the three main types of influences on the species (modified from Guisan & Zimmermann 2000 Huston 2002 Fig. 1): (i) limiting factors (or regulators), defined as factors controlling species eco-physiology (e.g. temperature, water, soil com- position) (ii) disturbances, defined as all types of perturbations affecting environmental systems (natural or human-induced) and (iii) resources, defined as all compounds that can be assimilated by organisms (e.g. energy and water). These relationships between species and their overall environment can cause different spatial patterns to be observed at different scales (Fig. 1), often in a hierarchical manner (Pearson et al. 2004). For instance, a gradual distribution observed over a large extent and at coarse resolution is likely to be controlled by climatic regulators, whereas patchy distribution observed over a smaller area and at fine resolution is more likely to result from a patchy distribution of resources, driven by micro-topographic variation or habitat fragmentation (Fig. 1 see examples in Scott et al. 2002). The environmental data related to these three main Table 1 Some possible uses of SDMs in ecology and conservation biology Type of use References Quantifying the environmental niche of species Austin et al. (1990), Vetaas (2002) Testing biogeographical, ecological and evolutionary hypotheses Leathwick (1998), Anderson et al. (2002), Graham et al. (2004b) Assessing species invasion and proliferation Beerling et al. (1995), Peterson (2003) Assessing the impact of climate, land use and other environmental changes on species distributions Thomas et al. (2004), Thuiller (2004) Suggesting unsurveyed sites of high potential of occurrence for rare species Elith & Burgman (2002), Raxworthy et al. (2003), Engler et al. (2004) Supporting appropriate management plans for species recovery and mapping suitable sites for species reintroduction Pearce & Lindenmayer (1998) Supporting conservation planning and reserve selection Ferrier (2002), Araujo �� et al. (2004) Modelling species assemblages (biodiversity, composition) from individual species predictions Leathwick et al. (1996), Guisan & Theurillat (2000), Ferrier et al. (2002) Building bio- or ecogeographic regions No published example found Improving the calculation of ecological distance between patches in landscape meta-population dynamic and gene flow models No published example found 994 A. Guisan and W. Thuiller ��2005 Blackwell Publishing Ltd/CNRS
types of influence on species distribution are best manipu- lated in a geographical information system (GIS). The procedure of SDM building ideally follows six steps (modified from Guisan & Zimmermann 2000 see Table 2): (i) conceptualization, (ii) data preparation, (iii) model fitting, (iv) model evaluation, (v) spatial predictions, and (vi) assessment of model applicability. Many important decisions are made during the initial conceptual phase, which can be split into two subphases: (i) theory and data: define an up-to-date conceptual model of the system to be simulated based on sound ecological thinking and clearly defined objectives (Austin 2002 Huston 2002), setting multiple working hypotheses (e.g. pseudo- equilibrium Guisan & Theurillat 2000 see next section), assessing available and missing data and the relevance of environmental predictors for the focal species and the given scale (Thuiller et al. 2004a), identifying an appropriate sampling strategy for collecting new data (Hirzel & Guisan 2002) or for complementing existing sets, and choosing the appropriate spatio-temporal resolution and geographic extent for the study. (ii) Modelling methods: identify the most appropriate method(s) for modelling the response variable (e.g. ordinal GLM for semi-quantitative species abundance Guisan & Harrell 2000) and identifying both the framework (e.g. resampling techniques vs. truly independent observa- tions) and the statistics needed for evaluating the predictive accuracy of the model (Pearce & Ferrier 2000 Fielding 2002). In current practice, however, few decisions are made at the very start of a study, because of the lack of knowledge of the target organism or of the study area and related data. For instance, the choice of an appropriate resolution might depend on the size of the species home range and the way the species uses resources in the landscape. The choice of the geographical extent might also depend on a prior knowledge of environmental gradients in the study area (to ensure including complete gradients Austin 2002 Van Horn 2002) or, for animal species, males/females, or summer/winter habitats might need separate models (Jaberg & Guisan 2001). Answers to these questions usually require either the collection of preliminary field observations, running sensitivity analyses, or conducting experiments to, for example, quantify the fundamental range of tolerance of an organism to predictors (e.g. Kearney & Porter 2004). Many other features ��� methodological, statistical or theoretical ��� need to be additionally controlled or consid- ered at each step of SDM building (Table 2). Solid criteria need to be used for detecting potential problems, such as overfitting (when number of predictors ��� number of observations), overdispersion (i.e. greater dispersion than expected from the probability distribution) or multicoline- arity (i.e. high correlations between several predictors). Careful consideration of these factors must be made to ensure successful predictions (Table 2). For more details on the different steps of SDM building, we refer readers to Guisan & Zimmermann (2000). E C OL O G I C A L T H EO R Y A N D A S SU M P T I O N S B E H I N D S D M S Species distribution models ��� and their output habitat suitability maps ��� have been used with relatively good success to investigate a variety of scientific issues (Table 1). However, despite the rapid improvement of methods, Presence/absence models Limiting climatic factors (regulators: too hot, too cold, too dry, etc..) Gradual distribution (mostly geographic gradients) Abundance models Resource factors (nutriments, food, etc..) Patchy distribution Realized distribution Bioclimatic range modulated by dispersal, disturbance and biotic interactions Other species' distribution (competitors, facilitators, dispersal vectors, distur- bators, preys, predators) Requirements Impacts Global scale Local scale Disturbance models Dynamic modelling Density independ- dence Density depen- dence Bioclimatic range Potential distribution based on the bioclimatic envelope Dispersal/migration models Dispersal factors (vectors, barriers, history, etc..) Disturbance factors (extreme events, disturbing species, etc..) Local extinctions Figure 1 General hierarchical modelling framework illustrating the way to integrate disturbance, dispersal and population dynamics within currently static species distribution models (SDMs). See text for explanations. Predicting species distribution 995 ��2005 Blackwell Publishing Ltd/CNRS