The application of predictive modelling of species distribution to biodiversity conservation

Predictive modelling of species geographical distributions is a thriving ecological and biogeographical discipline. Major advances in its conceptual foundation and applications have taken place recently, as well as the delineation of the outstanding challenges still to be met (Araújo & Guisán, 2006; Guisan et al., 2006; Moisen et al., 2006; accompanying papers). In this article, we focus on the application of predictive distribution modelling to biodiversity conservation. We begin by outlining a series of ‘real-life’ conservation problems that have been addressed with predictive modelling. Next, we consider the need to integrate these techniques with the development of systematic baseline data sets and models, in order to effectively monitor biodiversity change. The following section considers the availability of remotely sensed ecological data for use in predictive modelling, and introduces the readers to their major on-line sources. These general remarks provide a backdrop for a series of articles collected in this issue of Diversity and Distributions that emerged from a workshop on Predictive Modelling of Species Distributions, New Tools for the XXI Century, carried out under the auspices of the Universidad Internacional de Andalucía, sede Antonio Machado in Baeza, Spain, on 2–4 November 2005. The final section of this article provides an introduction to the collection of papers. Keeping up-to-date with the conceptual and technical advances of predictive modelling of species distribution is a challenge in itself, because it comprises a rapidly evolving set of versatile tools with a wide range of applications (Guisan & Zimmermann, 2000; Guisan & Thuiller, 2005). Predictive models have been successfully used in a variety of conservation biology studies, with the main purpose of addressing pressing conservation problems (e.g. Brotons et al., 2004; Linkie et al., 2006). Confronted with extremely limited data on the distribution, abundance, and dynamics of most species on Earth (Rodríguez, 2003; Mace et al., 2005), models allow for the extrapolation of relatively few field samples and museum records to the entire potential range of a species, thus creating predicted distribution maps (e.g. Guisan & Zimmermann, 2000; Heglund, 2002; Anderson et al., 2003; Guisan & Thuiller, 2005). To carry out this task, detailed and reliable environmental data are needed to establish the species–environment relationships required to estimate species distribution outside sampling sites. The collection of field data using environmentally stratified sampling designs assures that the range of environmental variation is covered, and improves these predictions (Austin & Heyligers, 1991; Margules & Austin, 1991; Hortal et al., 2001; Hortal & Lobo, 2005; J.R. Ferrer-Paris & J.P. Rodríguez, unpubl. data). Once data on species distribution have been collected in the field or derived from museum collections, and adequate environmental models have been fitted to the data, it is possible to generate predictive distribution maps. A number of potential applications of these generated maps rapidly arise in the field of biodiversity conservations and planning (provided that beyond statistical evaluations, also managers and planners are satisfied with the predictions provided by modellers). It is possible, for example, to assess the representation of species in nature reserve networks, evaluate their response to global climate change (of both the species and the reserves), quantify the impact of expected land cover change, and evaluate the influence of expanding exotic species, among other applications. Growth and potential of predictive modelling techniques and approaches is boosting the applications of biodiversity mapping in recent years. A good example of structure construction of a discipline that has grown in parallel to biodiversity mapping is reserve design. Inspired by pioneering work carried out in Australia in the 1980s (Kirkpatrick, 1983), the Gap Analysis Program (GAP) in the USA provided a major thrust to the application of predicted modelling to conservation (Scott et al., 1993; Jennings, 2000; Peterson & Kluza, 2003). This programme has the main objective of creating and updating maps of the predicted distribution of native vertebrate species in the USA. By collating information from field research programmes and modelling their distribution using a variety of tools, GAP has become a powerful source of basic cartographic information for a number of planning and management decisions at the local, state, and national scale (Pearlstine et al., 2002; Noon et al., 2003). After generating predicted ranges, the next step in the process consists of overlaying these distributions with the geographical extent of the protected areas network, thus identifying existing ‘gaps’— species that are not represented in the network — and providing potential locations for the future expansion or creation of new protected areas. Gap analysis has been proven highly successful and has become a very popular conservation tool. Its use is not limited to predicting geographical distributions, but also takes advantage of other sources of data on species ranges as well (e.g. De Klerk et al., 2004; Rodrigues et al., 2004). The ample literature on reserve selection has been primarily devoted to optimizing the number, size, location, and cost of reserve networks, in order to assure that they include the largest possible fraction of biodiversity within the smallest amount of protected land and, ultimately, identify priority areas for conservation (Margules et al., 2002; Wilson et al., 2006; Alagador & Cerdeira, in press). Predictive modelling has enriched the discussion by including habitat suitability as an important characteristic to consider in the design of a network of reserves, because habitat suitability has been shown to correlate with the persistence of populations (Araújo et al., 2002; Cabeza et al., 2004). In an analogous way, habitat suitability has been incorporated into metapopulation models, which are primarily concerned with patch shape, size, and isolation (Akçakaya & Atwood, 1997; Kramer-Schadt et al., 2005). Regardless of how well the current reserve networks represent extant biodiversity, however, they are all threatened by global change, a series of complex forces that combine both climate change and land-use change (Araújo, 2004; Thomas et al., 2004; Whittaker et al., 2005; Araújo & Rahbek, 2006; Araújo et al., 2006; Root & Schneider, 2006; Schlesinger, 2006; Sutherland, 2006; Thuiller et al., 2006a,b). A number of studies, many based on habitat modelling, have raised the concern that species distributions may shift in response to climate change (mainly towards higher latitudes and altitudes), so that some may be forced to abandon reserves that were designed to protect them, and move to unprotected areas — if they can. The problem is that the current distribution of a particular species may be constrained by unsuitable surrounding habitats (e.g. perturbed areas, topographical barriers) or there may be a physical limit to their range, as is the case of species that live on mountains and cannot continue to shift upwards indefinitely (Dirnbock et al., 2003; Thuiller et al., 2006a). In fact, extensive modelling exercises predict an overall loss of biodiversity over the next 50 years as a result of climate change, in particular for mountain species (Bakkenes et al., 2002; Thomas et al., 2004). These predictions have been challenged, however, because the results tend to vary with the technique applied (Thuiller et al., 2004; Pearson et al., 2006), and broad-scale models do not capture local interactions between organisms and their environment (Hampe, 2004), such as dispersal, interspecific interactions, or the existence of refuges at a finer spatial resolution than the climate change scenarios on which the models are based (Post et al., 1999; Araújo & Pearson, 2005; Pearson, 2006). Although most studies are pessimistic about the extent of future range contractions, this is still an ongoing debate that relies heavily on habitat modelling. Another promising but less explored line of research is the use of predictive habitat models to forecast conflicts between human activities and biodiversity conservation, and to try to minimize these conflicts. This is the case of the assessment of the impact of land-use changes linked to evolving agricultural practices. In Europe, for example, the simultaneous abandonment of traditional agriculture (areas that harbour several endangered species) and the intensification of agriculture on more profitable lands have led to a net decrease of biodiversity (Donald et al., 2002; Newton, 2004). Environmental and agricultural policies tend to aim at different targets, and predictive models have been helpful for decision-makers to identify areas where these opposing forces may clash (Brotons et al., 2004; Nogues-Bravo & Agirre, 2006; Seoane et al., 2006). Similarly, maps that predict the likelihood of conflicts between carnivores and humans (due to livestock predation) have also been developed, guiding planners towards geographical locations where predation events are less likely to occur (Treves et al., 2004). Another common conflict arises when human infrastructures, such as airports, roads, and power lines, are in the path of the regular movement of animals. For example, large birds pose a threat to airplanes because of bird strikes, particularly during the migratory season when birds gather in large flocks (http://ecogrid.sara.nl/bambas/distribution/index.php). Collision with power lines and road kills have been linked to the local decline of endangered species (Ferrer & Janss, 1999; Guzmán et al., 2004), while some studies have modelled higher risk areas where corrective actions should be prioritized (Garthe & Huppop, 2004; Malo et al., 2004). Again, predictive models stand out as a useful tool for land-use planners seeking to make better decisions about biodiversity management and conservation. Invasive species are one of the major drivers of global change, threatening numerous other species with extinction (Vitousek et al., 1996; Chapin et al., 2000). Biologists and agronomists initially began to search for traits that made species more aggressive colonizers, and for characteristics of ecosystems that made them more susceptible and vulnerable to invaders, in order to predict, mitigate, and perhaps prevent the negative consequences of biological invaders on native taxa (Crawley, 1986, 1987; D’Antonio & Vitousek, 1992; Crawley et al., 1996; Mack, 1996; Veltman et al., 1996; Williamson & Fitter, 1996; Williamson, 1999; Mack et al., 2000; Huston, 2004; Richardson, 2004; Callaway & Maron, 2006). More recently, predictive habitat models have been used to map areas of high risk of being invaded (or that are currently more affected) both regionally (Mercado-Silva et al., 2006; Muñoz & Real, 2006) and globally (Thuiller et al., 2005). The resulting picture can hardly be more worrying: six global biodiversity hotspots are highly susceptible of being invaded (Thuiller et al., 2005). On a relatively less ambitious scale, there are a number of additional applied questions that are being addressed with predictive models. Ecologists and field biologists, for example, are keen to use predictive models to generate potential habitat maps to plan for the reintroduction of species, and to evaluate reintroduction schemes underway (Mladenoff et al., 1999; Schadt et al., 2002; Hirzel et al., 2004; Pellet et al., 2004). Although reintroducing species into a portion of their historical range tends to be more successful than placing them elsewhere (Griffith et al., 1989), the ‘original’ habitat is not always available (in fact this may be the cause of the disappearance of the species), and it may be necessary to look for new sites. But even when the original range is still a viable option, maps of potential suitable habitat help to recognize barriers to the dispersal of released individuals, to identify isolated areas unlikely to be colonized naturally, and to quantify the carrying capacity of the managed territory. These maps have also helped to detect species behavioural responses to fragmentation that could be used as early warnings of human perturbation (Laiolo & Tella, 2005). Conservation, planning, and species management need basic monitoring data in order to gather critical information about where a species occurs and how its abundance changes through time (Underhill & Gibbons, 2002). Basic monitoring is aimed at sampling populations in order to describe their distribution in space (Donald & Fuller, 1998) and time (Bibby et al., 2000). Monitoring of species distribution has a strong link to predictive, habitat-based modelling techniques because both have the explicit objective of producing a spatial representation of a species’ range. In fact, the development of predictive, habitat based models has been possible due the large amounts of data accumulated in large-scale distribution monitoring programmes such as atlases (Osborne & Tigar, 1992; Tobalske & Tobalske, 1999; Araújo & Pearson, 2005). But predictive distribution modelling has not only grown due to the availability of monitoring data. More recently, these techniques have started to play a key role in producing basic quantitative information on species distributions, allowing atlases move beyond their classic ‘black and white’ maps derived from grid mapping of presence–absence data. These new methodological approaches also offer testable predictive models that allow a more adequate representation of species distributions in a context of recognized but often unquantified uncertainty (Elith et al., 2002; Barry & Elith, 2006). Predictive models have already been applied to a number of atlases, for example, mammals (Hausser, 1995) and birds (Estrada et al., 2004). Since atlases are a primary source of species distribution data for planning and species management conducted by governmental agencies, non-governmental organizations (NGOs), academia, and the private sector, the progressive inclusion of predictive habitat modelling will allow for the production of an increasing number of detailed and testable maps, therefore increasing their potential impact and reliability. For a number of conspicuous species, monitoring often involves counts to estimate their abundance in a given area. Population size is a fundamental ecological parameter, often required by local and international organizations and legislations, to assess the conservation status of a species, on the grounds that it is inversely correlated with the probability of extinction (IUCN, 2001; O’Grady et al., 2004). However, the development of a standard sampling scheme to correctly make inferences from the in the field to the entire is For example, studies on mammals to the technique of sampling from or of are and estimate of abundance may be et al., 2006). In are to the problem and have the advantage that they provide maps at a higher spatial resolution in schemes are than in as well as potential into the abundance et al., 2003; & 2004). These spatial modelling applied to field data have a large number of applications to conservation for example Another promising application of habitat-based predictive modelling techniques to basic monitoring is less monitoring programmes aim at changes in However, they are based on a spatial of these data also have the potential for producing species maps through or habitat modelling techniques et al., J.R. Ferrer-Paris & J.P. Rodríguez, unpubl. data). of using this of data have promising results as mapping tools et al., 2006; Brotons et al., including the of sampling techniques et al., 2006). models have link to monitoring predictions may help in areas in or to identify promising areas for future such for example, potential for species in to be explored (e.g. Seoane et al., 2003; et al., 2006; Brotons et al., These spatial models have been shown to be an on traditional and & 2004; et al., 2006). the maps for species relies on characteristics is particularly and habitat models with information is a to distributions et al., 2005). 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