202
9.2.2 Where Are We Now?
Although the BAM framework was developed to understand and quantify speciesenvironment relationships (Soberón 2007; see also Soberón and Peterson 2005), the
concept and investigation of species-environment relationships are long-standing,
dating back to Wallace (Wallace 1860) and early ecologists (Grinnell 1904, 1917;
Elton 1927; Holdridge 1947; Hutchinson 1957). These early naturalists originally
established the theoretical principles to analyze and describe biogeographical distributions in relation to environmental patterns (Colwell and Rangel 2009).
Interestingly, despite the large body of theoretical advances and empirical applications, the quantification of ecological niches and estimation of species distributions
is still a challenging task (but see Sanín and Anderson 2018; Smith et al. 2018) and
one of the most active areas in macroecological and biodiversity research (Franklin
2010; Peterson et al. 2011; Anderson 2013; Guisan et al. 2017).
In fact, since the first algorithm for modeling species-environment relationship
was presented (BIOCLIM, Nix 1986), the number of publications has increased dramatically (Lobo et al. 2010; Booth et al. 2013). A simple search in Google Scholar
for the terms “ecological niche model” and “species distribution model” (last
accessed on December 30, 2018) returned 2,950 and 6,400 citations, respectively, for
1990–2018 (Fig. 9.2a). Interestingly, the number of publications on these topics
increased markedly in the past 10 years (Fig. 9.2, see also Lobo et al. 2010) and
continues to grow, particularly in studies that emphasize the application of ENMs
and SDMs to environmental assessment, forecasting, and hindcasting species distributions (Anderson 2013; Elith and Franklin 2013; Guisan et al. 2017). Interestingly,
although the number of publications increased in the last 10 years, most of the studies were performed in United States and Europe (Fig. 9.2b) in countries with a high
density of weather stations (Fig. 9.2c), with much less emphasis on the most diverse
regions of the globe. The increasing access to species occurrence data (e.g., Global
Biodiversity Information Facility, GBIF) and environmental data (climatic and satellite derived) has created the opportunity not only to model species- environment relationships but to expand the theoretical and practical applications of ENM and SDM
to different research programs and fields, including conservation biology, wildlife
and ecosystem management, evolutionary biology, and public health (Franklin 2010;
Peterson et al. 2011; Guisan et al. 2017), and to do so in remote regions where access
is limited and predictions of species distributions have disproportionate importance.
Parallel to the development and evolution of ENM and SDM theory and applications, we have witnessed the growth of technological tools and S-RS products
(Pettorelli et al. 2014a; Turner 2014). Many of these are particularly applicable for
describing, quantifying, and mapping the spatial and temporal patterns of vegetation structure and function, the impacts of human activities, and environmental
change (Turner et al. 2003; Pinto-Ledezma and Rivero 2014; Jetz et al. 2016; Cord
et al. 2017) and more recently are used as predictors of broad patterns of biodiversity, including the associations between species co-occurrence patterns and ecosystem energy availability (Phillips et al. 2008; Pigot et al. 2016; Hobi et al. 2017).
In addition, an unprecedented number of S-RS data and data products (S-RS) have
J. N. Pinto-Ledezma and J. Cavender-Bares
9.2.2 Where Are We Now?
Although the BAM framework was developed to understand and quantify speciesenvironment relationships (Soberón 2007; see also Soberón and Peterson 2005), the
concept and investigation of species-environment relationships are long-standing,
dating back to Wallace (Wallace 1860) and early ecologists (Grinnell 1904, 1917;
Elton 1927; Holdridge 1947; Hutchinson 1957). These early naturalists originally
established the theoretical principles to analyze and describe biogeographical distributions in relation to environmental patterns (Colwell and Rangel 2009).
Interestingly, despite the large body of theoretical advances and empirical applications, the quantification of ecological niches and estimation of species distributions
is still a challenging task (but see Sanín and Anderson 2018; Smith et al. 2018) and
one of the most active areas in macroecological and biodiversity research (Franklin
2010; Peterson et al. 2011; Anderson 2013; Guisan et al. 2017).
In fact, since the first algorithm for modeling species-environment relationship
was presented (BIOCLIM, Nix 1986), the number of publications has increased dramatically (Lobo et al. 2010; Booth et al. 2013). A simple search in Google Scholar
for the terms “ecological niche model” and “species distribution model” (last
accessed on December 30, 2018) returned 2,950 and 6,400 citations, respectively, for
1990–2018 (Fig. 9.2a). Interestingly, the number of publications on these topics
increased markedly in the past 10 years (Fig. 9.2, see also Lobo et al. 2010) and
continues to grow, particularly in studies that emphasize the application of ENMs
and SDMs to environmental assessment, forecasting, and hindcasting species distributions (Anderson 2013; Elith and Franklin 2013; Guisan et al. 2017). Interestingly,
although the number of publications increased in the last 10 years, most of the studies were performed in United States and Europe (Fig. 9.2b) in countries with a high
density of weather stations (Fig. 9.2c), with much less emphasis on the most diverse
regions of the globe. The increasing access to species occurrence data (e.g., Global
Biodiversity Information Facility, GBIF) and environmental data (climatic and satellite derived) has created the opportunity not only to model species- environment relationships but to expand the theoretical and practical applications of ENM and SDM
to different research programs and fields, including conservation biology, wildlife
and ecosystem management, evolutionary biology, and public health (Franklin 2010;
Peterson et al. 2011; Guisan et al. 2017), and to do so in remote regions where access
is limited and predictions of species distributions have disproportionate importance.
Parallel to the development and evolution of ENM and SDM theory and applications, we have witnessed the growth of technological tools and S-RS products
(Pettorelli et al. 2014a; Turner 2014). Many of these are particularly applicable for
describing, quantifying, and mapping the spatial and temporal patterns of vegetation structure and function, the impacts of human activities, and environmental
change (Turner et al. 2003; Pinto-Ledezma and Rivero 2014; Jetz et al. 2016; Cord
et al. 2017) and more recently are used as predictors of broad patterns of biodiversity, including the associations between species co-occurrence patterns and ecosystem energy availability (Phillips et al. 2008; Pigot et al. 2016; Hobi et al. 2017).
In addition, an unprecedented number of S-RS data and data products (S-RS) have
J. N. Pinto-Ledezma and J. Cavender-Bares