200
(2) S-RS products. Live oaks are a small lineage, descended from a common ancestor (for a discussion on phylogenetics, see Meireles et al., Chap. 7) that includes
seven species that vary in geographic range size and climatic breadth and are
distributed in both temperate and tropical climates from the southeastern United
States, Mesoamerica, and the Caribbean (Cavender-Bares et al. 2015). Their variation in range size and climatic distributions, their distributions in both highly studied and understudied regions of the globe, and the second author’s expert knowledge
of their distributions make them an interesting case study for comparing SDM/
ENMs that rely on classic data sources to those that use remotely sensed data
sources, which have more consistent data accuracy and resolution. We used the live
oaks as a test clade to evaluate the relationships among the modeled niches estimated from both sources of environmental data.
Given that the interpolated climate surfaces from WorldClim (Hijmans et al.
2005) are the most widely used data set for the study of species-environment relationships, we compare the performance of SDMs based on S-RS products to those
based on WorldClim data. If there is a tight relationship between models from the
two sources, this would indicate that the resultant models from S-RS products
have similar performance to the resultant models from the WorldClim climatic
predictors. Remotely sensed data products may provide an advantage in predicting
species distributions in regions where climatic data is sparsely sampled. Although
WorldClim provides interpolated climate surfaces for land areas across the world
at multiple spatial resolutions, from 30 arc seconds (~1 km) to 10 arcmin
(~18.5 km) (Hijmans et al. 2005), the spatial distribution of the base information
(i.e., weather or climatic stations) used for interpolations is unevenly distributed
across the world (Fig. 9.2c). This is not a small issue given the uncertainty associated with interpolated climatic variables when modeling species-environment
relationships, especially in many tropical countries, where weather stations are
frequently few and far apart (Soria-Auza et al. 2010). Given that tropical regions
are precisely the regions where most species occur (Fig. 9.2c), finding alternative
means to predict species is important for efforts to monitor and manage biodiversity globally. S-RS products, which provide quasi-global coverage of land and sea
surfaces at high temporal and spatial resolution, represent promising alternatives
that may be particularly important in the world’s most biologically diverse regions.
Our aim here is to provide an understanding of the potential of S-RS products to
quantify species ecological niches and estimate species distributions rather than to
develop a definitive ecological and geographical profile for the live oaks themselves. If the consistent accuracy and high spatial resolution of S-RS products can
actually improve estimates of species distributions, they will represent an advance
in our ability to predict where species are likely persist under changing environments. Ultimately, such predictions can be combined with other remote sensing
(RS) means of detecting species and biodiversity (Meireles et al., Chap. 7; Bolch
et al., Chap. 12; Record et al., Chap. 10) to enable global-scale biodiversity change
detection.
J. N. Pinto-Ledezma and J. Cavender-Bares
(2) S-RS products. Live oaks are a small lineage, descended from a common ancestor (for a discussion on phylogenetics, see Meireles et al., Chap. 7) that includes
seven species that vary in geographic range size and climatic breadth and are
distributed in both temperate and tropical climates from the southeastern United
States, Mesoamerica, and the Caribbean (Cavender-Bares et al. 2015). Their variation in range size and climatic distributions, their distributions in both highly studied and understudied regions of the globe, and the second author’s expert knowledge
of their distributions make them an interesting case study for comparing SDM/
ENMs that rely on classic data sources to those that use remotely sensed data
sources, which have more consistent data accuracy and resolution. We used the live
oaks as a test clade to evaluate the relationships among the modeled niches estimated from both sources of environmental data.
Given that the interpolated climate surfaces from WorldClim (Hijmans et al.
2005) are the most widely used data set for the study of species-environment relationships, we compare the performance of SDMs based on S-RS products to those
based on WorldClim data. If there is a tight relationship between models from the
two sources, this would indicate that the resultant models from S-RS products
have similar performance to the resultant models from the WorldClim climatic
predictors. Remotely sensed data products may provide an advantage in predicting
species distributions in regions where climatic data is sparsely sampled. Although
WorldClim provides interpolated climate surfaces for land areas across the world
at multiple spatial resolutions, from 30 arc seconds (~1 km) to 10 arcmin
(~18.5 km) (Hijmans et al. 2005), the spatial distribution of the base information
(i.e., weather or climatic stations) used for interpolations is unevenly distributed
across the world (Fig. 9.2c). This is not a small issue given the uncertainty associated with interpolated climatic variables when modeling species-environment
relationships, especially in many tropical countries, where weather stations are
frequently few and far apart (Soria-Auza et al. 2010). Given that tropical regions
are precisely the regions where most species occur (Fig. 9.2c), finding alternative
means to predict species is important for efforts to monitor and manage biodiversity globally. S-RS products, which provide quasi-global coverage of land and sea
surfaces at high temporal and spatial resolution, represent promising alternatives
that may be particularly important in the world’s most biologically diverse regions.
Our aim here is to provide an understanding of the potential of S-RS products to
quantify species ecological niches and estimate species distributions rather than to
develop a definitive ecological and geographical profile for the live oaks themselves. If the consistent accuracy and high spatial resolution of S-RS products can
actually improve estimates of species distributions, they will represent an advance
in our ability to predict where species are likely persist under changing environments. Ultimately, such predictions can be combined with other remote sensing
(RS) means of detecting species and biodiversity (Meireles et al., Chap. 7; Bolch
et al., Chap. 12; Record et al., Chap. 10) to enable global-scale biodiversity change
detection.
J. N. Pinto-Ledezma and J. Cavender-Bares