The fact that, at certain scales, climatic and physical factors affect profoundly the distributions of species has been known for a very long time. In the last two decades, mathematical techniques designed to estimate the geographic extent of the “fundamental ecological niche” (FN), or subsets of it, defined mostly in coarse-scale climatic dimensions [the “bioclimatic envelope” or climatic niche (Pearson & Dawson 2003)], have seen increasing use. Although of interest in itself regarding the evolutionary ecology of species, estimating the FN is often taken as an intermediate step towards estimating the geographic distribution of the species. The FN has been estimated in two ways: (1) direct measurement or physical modeling of responses of individuals to temperature, humidity, and other physical parameters, and inferring from them fitness values of different combinations of physical variables. Then, using GIS technology, geographic regions of positive fitness can be displayed (Porter et al. 2000; Porter et al. 2002). This line of work has been referred to as the ‘mechanistic approach’ to niche modeling (Guisan & Zimmermann 2000). (2) Niches may be reconstructed by relating data on species’ occurrences with data sets summarizing climatic, topographic, edaphic, and other ‘ecological’ dimensions (in the form of GIS layers); combinations of environmental variables most closely associated with observed presences of species can then be identified and projected onto landscapes to identify appropriate regions, as above. The inferential steps in this manifestation of niche modeling have been achieved using diverse algorithms, including range rules (Nix 1986), DOMAIN (Carpenter et al. 1993), FloraMap (Jones & Gladkov 1999), multiple regression and other generalized linear and additive models (Guisan & Zimmermann 2000), neural networks (Pearson et al. 2002), and genetic algorithms (Stockwell 1999; Stockwell & Noble 1992; Stockwell & Peters 1999), and several others. All, in essence, extrapolate from associations between point occurrences and environmental data sets to identify areas of predicted presence on the map. These areas are (in some sense determined by the algorithm) similar ecologically to those where the species is known to occur, and this procedure can be termed as the ‘correlative approach’ to ecological niche modeling. Whether the result is interpreted as the species’ distribution, the spatial extent of its fundamental niche, or some other phraseology, it is important to remember that (strictly speaking) these extrapolation algorithms only find regions that “resemble,” in terms of the layers provided, those where occurrence points are located. The rest of the process is interpretation. Often (but not always), tools used to perform these niche estimations require information about absence of species. At certain spatio-temporal scales, and for certain taxa, this information may be available, perhaps in the form of well-surveyed sites at which the species was not detected. Alternatively, and at cost of restrictive assumptions, “absence data” may be generated in the form of pseudoabsences—areas without definite information regarding the species’ occurrence, but assumed to be lacking the species for the purpose of providing statistical contrasts upon which analyses may be based. The mechanistic approach, being based on direct measures of physiological variables, ignores biotic interactions, and indeed has little hope of taking them into account. The correlative approach, in contrast, is based on observations that already include effects of biotic interactions on distributions of species—here, of course, the challenge is removing the effects of those same interactions. The two approaches thus estimate quite-different phenomena, and should be interpreted carefully before being used interchangeably in applications. In what follows, we clarify the logic behind the correlative approach, and review its key limitations—our purpose is to provide a more formal discussion of what is being modeled in ‘ecological niche modeling.’ Pearson and Dawson (2003) discussed characteristics and limitations of these two methods as regards estimating niches and predicting distributions under scenarios of climate change.
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