216
Nevertheless, it seems that overall statistical model accuracy in this example is not
improved (Pearson et al. 2004; Thuiller 2004; see also Table 9.2). Given that climatic and S-RS data provide information at different spatial and temporal scales, a
promising option would be to use both sources of environmental predictors to model
species distributions to achieve “the best of both worlds” (Saatchi et al. 2008;
Pradervand et al. 2013).
More accurate predictions of species distributions are critical for the development of conservation and management actions if we are to meet the challenges
posed by global change (Coudun et al. 2006; Zimmermann et al. 2007; Cord et al.
2013). Our point here is to facilitate and demonstrate the potential for the use of
S-RS data for predicting species distributions and modeling environmental niches.
The results we show here and those of others (e.g., Saatchi et al. 2008; Waltari et al.
2014) indicate that S-RS data provide a valuable complement to other environmental variables for ENM/SDM.
Another potential and important research direction is the use of S-RS products
that have high temporal resolution, such as LAI (Fig. 9.3b), as biophysical variables
that represent ecosystem functions (Cord et al. 2013, 2017). These products allow
the exploration of dynamics of vegetation growth and seasonality in vegetation
function, fundamental features that characterize vegetation form and function
(Myneni et al. 2002; Hobi et al. 2017). Here, using metrics derived from MODIS
LAI in combination with other S-RS products (Table 9.1), we show that, using relevant biophysical variables, it is possible to predict distributions similar to those
predicted from climate data alone (Fig. 9.5, Tables 9.2 and 9.3). In fact, a recent
study (Simões and Peterson 2018) found that including biotic predictors can improve
ENMs even while increasing model complexity, such that the combination of abiotic and biotic predictors improves model performance (Simões and Peterson 2018).
To confirm this conclusion, substantial effort would be needed, including new methodological and conceptual approaches, to disentangle the real contribution of S-RS
products—spatial and temporal features of S-RS products that improve statistical
model performance—as predictors of species distributions. Nonetheless, our results
highlight an advance on the use of relevant predictors for modeling speciesenvironment relationships.
In addition, recent macroecological studies have used these products to relate
annual vegetation productivity to continental and global patterns of species richness
(Pigot et al. 2016; Hobi et al. 2017; Coops et al. 2018), providing spatially explicit
support for the use of satellite data products in predicting biodiversity. These
advances point to an exciting avenue for the study of the distribution and assembly
of biological communities (Ferrier and Guisan 2006). For example, S-RS products
can be used for the development of stacked species distribution models (S-SDM,
see Fig. 9.6) that can be integrated into novel biodiversity modeling frameworks,
such as Spatially Explicit Species Assemblage Modelling (SESAM, Guisan and
Rahbek 2011) or the Hierarchical Modelling of Species Communities (HMSC,
Ovaskainen et al. 2017), aimed at predicting composition and distribution of species
and communities (Mateo et al. 2017).
J. N. Pinto-Ledezma and J. Cavender-Bares
Nevertheless, it seems that overall statistical model accuracy in this example is not
improved (Pearson et al. 2004; Thuiller 2004; see also Table 9.2). Given that climatic and S-RS data provide information at different spatial and temporal scales, a
promising option would be to use both sources of environmental predictors to model
species distributions to achieve “the best of both worlds” (Saatchi et al. 2008;
Pradervand et al. 2013).
More accurate predictions of species distributions are critical for the development of conservation and management actions if we are to meet the challenges
posed by global change (Coudun et al. 2006; Zimmermann et al. 2007; Cord et al.
2013). Our point here is to facilitate and demonstrate the potential for the use of
S-RS data for predicting species distributions and modeling environmental niches.
The results we show here and those of others (e.g., Saatchi et al. 2008; Waltari et al.
2014) indicate that S-RS data provide a valuable complement to other environmental variables for ENM/SDM.
Another potential and important research direction is the use of S-RS products
that have high temporal resolution, such as LAI (Fig. 9.3b), as biophysical variables
that represent ecosystem functions (Cord et al. 2013, 2017). These products allow
the exploration of dynamics of vegetation growth and seasonality in vegetation
function, fundamental features that characterize vegetation form and function
(Myneni et al. 2002; Hobi et al. 2017). Here, using metrics derived from MODIS
LAI in combination with other S-RS products (Table 9.1), we show that, using relevant biophysical variables, it is possible to predict distributions similar to those
predicted from climate data alone (Fig. 9.5, Tables 9.2 and 9.3). In fact, a recent
study (Simões and Peterson 2018) found that including biotic predictors can improve
ENMs even while increasing model complexity, such that the combination of abiotic and biotic predictors improves model performance (Simões and Peterson 2018).
To confirm this conclusion, substantial effort would be needed, including new methodological and conceptual approaches, to disentangle the real contribution of S-RS
products—spatial and temporal features of S-RS products that improve statistical
model performance—as predictors of species distributions. Nonetheless, our results
highlight an advance on the use of relevant predictors for modeling speciesenvironment relationships.
In addition, recent macroecological studies have used these products to relate
annual vegetation productivity to continental and global patterns of species richness
(Pigot et al. 2016; Hobi et al. 2017; Coops et al. 2018), providing spatially explicit
support for the use of satellite data products in predicting biodiversity. These
advances point to an exciting avenue for the study of the distribution and assembly
of biological communities (Ferrier and Guisan 2006). For example, S-RS products
can be used for the development of stacked species distribution models (S-SDM,
see Fig. 9.6) that can be integrated into novel biodiversity modeling frameworks,
such as Spatially Explicit Species Assemblage Modelling (SESAM, Guisan and
Rahbek 2011) or the Hierarchical Modelling of Species Communities (HMSC,
Ovaskainen et al. 2017), aimed at predicting composition and distribution of species
and communities (Mateo et al. 2017).
J. N. Pinto-Ledezma and J. Cavender-Bares