204
Zimmermann et al. 2007), attention has only recently turned to using these data in
studies of species-environment relationships (Cord et al. 2013; West et al. 2016),
and most studies use bioclimatic data such as WorldClim (but see Paz et al.,
Chap. 11; Record et al., Chap. 10). Although early attempts indicated that S-RS
products do not seem to improve the accuracy in estimating species distributions
(Pearson et al. 2004; Thuiller 2004; Zimmermann et al. 2007), more recent publications (Kissling et al. 2012; Cord et al. 2013) suggest that despite these apparent
limitations, S-RS products provide better spatial resolution that allow the discrimination of habitat characteristics not captured when bioclimatic data are used (Saatchi
et al. 2008; Cord et al. 2013), and they can be used as surrogates of biotic and/or
functional predictors such as LAI that increase the performance of individual species models (Kissling et al. 2012; Cord et al. 2013).
9.3 Modeling Ecological Niches and Predicting Geographic
Distributions
Although the terms ENM and SDM are often used synonymously in the literature, the
two are not equivalent (Anderson 2012; Soberón et al. 2017). A comprehensive discussion of this topic is beyond the scope of this chapter but is provided elsewhere (see
Peterson et al. 2011; Anderson 2012; Soberón et al. 2017). A crucial step in differentiating the two terms is to establish a distinction between environmental space and geographical space (Hutchinson’s duality; Colwell and Rangel 2009). On the one hand,
environmental space corresponds to a suite of environmental conditions at a given time
(e.g., climate, topography); on the other hand, geographical space is the extent of a
particular region or study area (Soberón and Nakamura 2009; Peterson et al. 2011) and
includes important historical context. Thus, when modeling species ecological niches,
we are modeling the existing abiotically suitable conditions for the species or the biotically reduced niche (Peterson et al. 2011; see also Fig. 9.1). However, when modeling
species distributions, the intent is to project objects into geographical space (Fig. 9.1),
and, depending on the factors considered, it is possible to estimate the occupied distributional area or the invadable distributional area (Soberón and Nakamura 2009;
Peterson et al. 2011; Anderson 2012; Soberón et al. 2017).
9.3.1 Methods
9.3.1.1 Oak Species Data Sets
Occurrence data were downloaded from iDigBio between 20 and 24 July 2018,
including localities collected by the authors, and cleaned for accuracy. Any botanical garden localities were discarded. All points were visually examined, and any
localities that were outside the known range of the species, or in unrealistic locations (e.g., water bodies), were discarded.
J. N. Pinto-Ledezma and J. Cavender-Bares
Zimmermann et al. 2007), attention has only recently turned to using these data in
studies of species-environment relationships (Cord et al. 2013; West et al. 2016),
and most studies use bioclimatic data such as WorldClim (but see Paz et al.,
Chap. 11; Record et al., Chap. 10). Although early attempts indicated that S-RS
products do not seem to improve the accuracy in estimating species distributions
(Pearson et al. 2004; Thuiller 2004; Zimmermann et al. 2007), more recent publications (Kissling et al. 2012; Cord et al. 2013) suggest that despite these apparent
limitations, S-RS products provide better spatial resolution that allow the discrimination of habitat characteristics not captured when bioclimatic data are used (Saatchi
et al. 2008; Cord et al. 2013), and they can be used as surrogates of biotic and/or
functional predictors such as LAI that increase the performance of individual species models (Kissling et al. 2012; Cord et al. 2013).
9.3 Modeling Ecological Niches and Predicting Geographic
Distributions
Although the terms ENM and SDM are often used synonymously in the literature, the
two are not equivalent (Anderson 2012; Soberón et al. 2017). A comprehensive discussion of this topic is beyond the scope of this chapter but is provided elsewhere (see
Peterson et al. 2011; Anderson 2012; Soberón et al. 2017). A crucial step in differentiating the two terms is to establish a distinction between environmental space and geographical space (Hutchinson’s duality; Colwell and Rangel 2009). On the one hand,
environmental space corresponds to a suite of environmental conditions at a given time
(e.g., climate, topography); on the other hand, geographical space is the extent of a
particular region or study area (Soberón and Nakamura 2009; Peterson et al. 2011) and
includes important historical context. Thus, when modeling species ecological niches,
we are modeling the existing abiotically suitable conditions for the species or the biotically reduced niche (Peterson et al. 2011; see also Fig. 9.1). However, when modeling
species distributions, the intent is to project objects into geographical space (Fig. 9.1),
and, depending on the factors considered, it is possible to estimate the occupied distributional area or the invadable distributional area (Soberón and Nakamura 2009;
Peterson et al. 2011; Anderson 2012; Soberón et al. 2017).
9.3.1 Methods
9.3.1.1 Oak Species Data Sets
Occurrence data were downloaded from iDigBio between 20 and 24 July 2018,
including localities collected by the authors, and cleaned for accuracy. Any botanical garden localities were discarded. All points were visually examined, and any
localities that were outside the known range of the species, or in unrealistic locations (e.g., water bodies), were discarded.
J. N. Pinto-Ledezma and J. Cavender-Bares