215
predictors might be anticipated to affect model performance, different lines of
evidence indicate that model performance is not affected by grain resolution, but
rather by species response to the environmental conditions in the study region
(Guisan et al. 2007, see also Fig. 9.5). Our results show that enhancing spatial resolution of interpolated climatic data does not improve the spatial resolution at which
species distributions can be accurately predicted. The quality of interpolated climate
surfaces such as WorldClim, which depends on climatic stations as data sources, has
been ignored as a source of uncertainty in studies of species-environment relationships—for example, Hijmans et al. (2005) used a variable number of weather stations for their interpolations, 47.554, 24.542, and 14.930 for precipitation, mean
temperature, and maximum and minimum temperature, respectively—especially in
the tropics (Fig. 9.2c), where weather stations are sparse (Hijmans et al. 2005;
Soria- Auza et al. 2010). This source of uncertainty can be avoided using S-RS products, which have continuous (from daily to monthly) and quasi-global environmental information, including precipitation, temperature, and biophysical variables that
represent different components of vegetation and ecosystems (Funk et al. 2015;
Cord et al. 2017; Radeloff et al. 2019).
Fine and broad spatial and temporal scale data derived from S-RS, which have
only been available in the last ~20 years (Turner 2014), can be used to improve the
evaluation of species-environment relationships. A number of research avenues
remain to be pursued to better understand the potential of S-RS data and their
products in quantifying species ecological niches and estimating species distributions. For example, applying the same framework presented here to other species
or clades (including vertebrates and invertebrates) or applying more complex
frameworks (e.g., Peterson and Nakazawa 2008; Waltari et al. 2014) may shed
light on the potential of S-RS products as predictors for the analysis of speciesenvironment relationships. This is important because ENMs/SDMs are used as
predictive models that can be extrapolated across space and time to forecast and
monitor biodiversity under a changing global climate (Peterson and Nakazawa
2008; Warren 2012).
9.4.1 Should We Use S-RS Data for ENM/SDM?
Whether S-RS data should replace other environmental data in modeling niches and
projecting species distribution depends on the modeling purposes (Peterson et al.
2011). In fact, modeling species niches and projecting distributions involves relating a set of species occurrences to relevant environmental predictors. In essence,
ENM/SDM based only on climatic variables would tend to return broad predictions
(Coudun et al. 2006, see also right panel in Fig. 9.5), particularly because climatic
data are useful in describing macroecological patterns of species distributions and
communities (Lin and Wiens 2017; Manzoor et al. 2018), while ENM/SDM based
on S-RS data alone allows the discrimination of local features not captured by climatic information (Coudun et al. 2006; Saatchi et al. 2008; Radeloff et al. 2019).
9 Using Remote Sensing for Modeling and Monitoring Species Distributions
predictors might be anticipated to affect model performance, different lines of
evidence indicate that model performance is not affected by grain resolution, but
rather by species response to the environmental conditions in the study region
(Guisan et al. 2007, see also Fig. 9.5). Our results show that enhancing spatial resolution of interpolated climatic data does not improve the spatial resolution at which
species distributions can be accurately predicted. The quality of interpolated climate
surfaces such as WorldClim, which depends on climatic stations as data sources, has
been ignored as a source of uncertainty in studies of species-environment relationships—for example, Hijmans et al. (2005) used a variable number of weather stations for their interpolations, 47.554, 24.542, and 14.930 for precipitation, mean
temperature, and maximum and minimum temperature, respectively—especially in
the tropics (Fig. 9.2c), where weather stations are sparse (Hijmans et al. 2005;
Soria- Auza et al. 2010). This source of uncertainty can be avoided using S-RS products, which have continuous (from daily to monthly) and quasi-global environmental information, including precipitation, temperature, and biophysical variables that
represent different components of vegetation and ecosystems (Funk et al. 2015;
Cord et al. 2017; Radeloff et al. 2019).
Fine and broad spatial and temporal scale data derived from S-RS, which have
only been available in the last ~20 years (Turner 2014), can be used to improve the
evaluation of species-environment relationships. A number of research avenues
remain to be pursued to better understand the potential of S-RS data and their
products in quantifying species ecological niches and estimating species distributions. For example, applying the same framework presented here to other species
or clades (including vertebrates and invertebrates) or applying more complex
frameworks (e.g., Peterson and Nakazawa 2008; Waltari et al. 2014) may shed
light on the potential of S-RS products as predictors for the analysis of speciesenvironment relationships. This is important because ENMs/SDMs are used as
predictive models that can be extrapolated across space and time to forecast and
monitor biodiversity under a changing global climate (Peterson and Nakazawa
2008; Warren 2012).
9.4.1 Should We Use S-RS Data for ENM/SDM?
Whether S-RS data should replace other environmental data in modeling niches and
projecting species distribution depends on the modeling purposes (Peterson et al.
2011). In fact, modeling species niches and projecting distributions involves relating a set of species occurrences to relevant environmental predictors. In essence,
ENM/SDM based only on climatic variables would tend to return broad predictions
(Coudun et al. 2006, see also right panel in Fig. 9.5), particularly because climatic
data are useful in describing macroecological patterns of species distributions and
communities (Lin and Wiens 2017; Manzoor et al. 2018), while ENM/SDM based
on S-RS data alone allows the discrimination of local features not captured by climatic information (Coudun et al. 2006; Saatchi et al. 2008; Radeloff et al. 2019).
9 Using Remote Sensing for Modeling and Monitoring Species Distributions