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CHAPTER TEN Introduction to Applications So far in this book, we have set out a theoretical framework for modeling ecological niches and estimating abiotically suitable, potential, and occupied distributional areas (chapters 2 to 4). Then, in a more practical mode, we have described issues related to the practice of modeling, including the particulars of occurrence and environmental datasets (chapters 5 and 6), aspects of how to estimate different niches using diverse correlational modeling methods (chapter 7), the process of modifying raw model predictions to estimate geographic distributions (chapter 8), and methods by which to evaluate model performance and significance quantitatively (chapter 9). In this final section of the book, we describe a range of applications of these approaches, which include challenges in biogeography, conservation biology, ecology, evolutionary biology, and public health. Before discussing particular applications in detail, however, it is important to refer back to the theoretical framework presented earlier in the book—indeed, it is crucial to understand the theory behind the models, if one is to apply them appropriately. In chapters 3 and 7, we saw that correlative models lacking true absence data are unlikely to capture either the occupied niche (EO) or the scenopoetic existing fundamental niche (EA) perfectly; rather, these models estimate that portion of the niche represented by known occurrence records, which is likely to fall somewhere between the two extremes. Similarly, we saw that, when projected onto geographic space, correlative models identify parts of the abiotically suitable (GA) areas or even, in some cases, only the occupied area GO; in most cases, these models appear to estimate more than GO, likely including much of GI. The observation that ecological niche models are not likely to predict the full extent of either GO or GA has been cited as a critical limitation of correlative modeling approaches (Woodward and Beerling 1997, Lawton 2000, Hampe 2004). In this section of the book, however, we illustrate how these methods can be applied to interesting challenges to yield highly useful results, provided that the researcher understands exactly what is being estimated based on which data. 186 CHAPTER 10 Let us consider some potential uses of the types of predictions in G illustrated in figure 10.1. Of course, this scenario is idealized, whereby we assume that models fit neatly to known occurrence records, that occurrence records are sampled only from GO, and that none of the other complications described in chapter 7 exists. Nonetheless, this illustration characterizes three types of prediction that may be obtained. First, environmentally informed spatial interpolations “fill the gaps” around known occurrence localities, thus providing an improved estimate of the occupied area GO that is likely more informative than a minimum convex polygon based on coordinates, or than other spatial approaches that do not take underlying environmental variation into account (see predicted area labeled 1 in figure 10.1; Getz and Wilmers 2004, Mace et al. 2008). Furthermore, if predicted areas that are isolated from known occurrences by dispersal barriers are removed (see chapter 8; Peterson et al. 2002b), then model predictions provide a further improvement to estimates of GO. This prediction may be useful, for example, in conservation planning (see chapter 12). Notice that we are not expecting the model to predict the full extent of GO, but the approach certainly yields more information than is available from raw occurrence records alone (Rojas-Soto et al. 2003). Second, we move to spatial transferability predictions, which identify parts of the occupied area GO, or even GP or GA, for which no occurrence records have been collected (i.e., this part of GO or GP is unknown). Although the model does not predict areas of GO that have environmental conditions not represented among known occurrence records, this type of prediction (labeled 2 in figure 10.1) can be used to guide field surveys toward areas with high probabilities of holding new occurrence records. Accelerating discovery of unknown populations in this way has already proven particularly useful in landscapes where species’ distributions are poorly known (see chapter 11). Similarly, this approach holds considerable potential for identifying unknown vector or reservoir populations of zoonotic diseases (see chapter 14). Third, an extension of this reasoning (see figure 10.1) is that of estimating the portion of the abiotically suitable area (GI ⊂ GA) that is environmentally similar to sites where the species is known to occur, but which is not necessarily inhabited (see predicted area 3 in figure 10.1). This area is the invadable distributional area GI. This type...


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