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Zellweger et al. 2016). Temperature, for example, has been repeatedly shown to be
a good predictor of the species that inhabit a given area (the taxonomic dimension
of biodiversity, e.g., Peters et al. 2016). However, the power to predict the distinct
dimensions of biodiversity varies within and across groups of organisms. For
instance, the contribution of different measures of temperature and precipitation
appears to be idiosyncratic when multiple taxa are compared (Rompré et al. 2007;
Laurencio and Fitzgerald 2010; Peters et al. 2016; Zellweger et al. 2016). Moreover,
and in contrast to species richness (SR), the relationships between climate and the
geographic distribution of evolutionary diversity in a region (i.e., the phylogenetic
dimension of biodiversity), as well as the relationships between climate and endemism, have been less explored. Still, those relationships appear weaker due to the
relatively larger contribution of history, biogeography, and contingency in the spatial distribution of lineages (da Silva et al. 2012; Barratt et al. 2017).
Most of those advances have relied on the use of climatic data sets that are interpolated from weather station data (Hijmans et al. 2005), summarizing spatial patterns of temperature and precipitation. These include the widely used WorldClim
data set (Hijmans et al. 2005), country-specific data sets (e.g., Cuervo- Robayo et al.
2014), and the hybrid CHELSA database (Karger et al. 2017). The ease by which
biodiversity scientists can access and download these databases, and the fact that
they provide global-scale climatic information at biologically relevant scales (up to
1 km), have resulted in a sharp increase in the number of studies that explore the
correlations between climate and biodiversity patterns. Yet the accuracy and the
effectiveness of these global climatic descriptors have been questioned (Soria-Auza
et al. 2010). Because the distribution of weather stations around the world is unequal,
the confidence in those data sets is reduced in undersampled areas, which frequently
correspond to the most biodiverse areas on Earth (see Pinto-Ledézma and CavenderBares, Chap. 9).
In this chapter, we explore the use of bioclimatic variables built from long-term
climatologies derived from remote sensing (RS) as predictors of biodiversity patterns. We focus in a megadiverse region, with high topographic complexity: the
Brazilian Atlantic Forest hotspot. We evaluate whether climate, inferred from RS
sources, predicts which areas accumulate the highest diversity of species, evolutionary lineages, and endemism. For that, we use distribution and phylogenetic data
from three plant clades representing different life forms, that are commonly found
in the Brazilian Atlantic Forest: melastomes (178 species of shrubs and trees), bromeliads (43 species of epiphytes), and bignones (131 species of lianas). We also
evaluate what (if any) gains emerge from the use of climatic descriptors based on
RS, rather than weather stations, for this area. Given the sharp altitudinal changes
observed in the Brazilian Atlantic Forest hotspot, it has been proposed that interpolated weather station data may perform more poorly than variables derived from RS
(Waltari et al. 2014).
A. Paz et al.
Zellweger et al. 2016). Temperature, for example, has been repeatedly shown to be
a good predictor of the species that inhabit a given area (the taxonomic dimension
of biodiversity, e.g., Peters et al. 2016). However, the power to predict the distinct
dimensions of biodiversity varies within and across groups of organisms. For
instance, the contribution of different measures of temperature and precipitation
appears to be idiosyncratic when multiple taxa are compared (Rompré et al. 2007;
Laurencio and Fitzgerald 2010; Peters et al. 2016; Zellweger et al. 2016). Moreover,
and in contrast to species richness (SR), the relationships between climate and the
geographic distribution of evolutionary diversity in a region (i.e., the phylogenetic
dimension of biodiversity), as well as the relationships between climate and endemism, have been less explored. Still, those relationships appear weaker due to the
relatively larger contribution of history, biogeography, and contingency in the spatial distribution of lineages (da Silva et al. 2012; Barratt et al. 2017).
Most of those advances have relied on the use of climatic data sets that are interpolated from weather station data (Hijmans et al. 2005), summarizing spatial patterns of temperature and precipitation. These include the widely used WorldClim
data set (Hijmans et al. 2005), country-specific data sets (e.g., Cuervo- Robayo et al.
2014), and the hybrid CHELSA database (Karger et al. 2017). The ease by which
biodiversity scientists can access and download these databases, and the fact that
they provide global-scale climatic information at biologically relevant scales (up to
1 km), have resulted in a sharp increase in the number of studies that explore the
correlations between climate and biodiversity patterns. Yet the accuracy and the
effectiveness of these global climatic descriptors have been questioned (Soria-Auza
et al. 2010). Because the distribution of weather stations around the world is unequal,
the confidence in those data sets is reduced in undersampled areas, which frequently
correspond to the most biodiverse areas on Earth (see Pinto-Ledézma and CavenderBares, Chap. 9).
In this chapter, we explore the use of bioclimatic variables built from long-term
climatologies derived from remote sensing (RS) as predictors of biodiversity patterns. We focus in a megadiverse region, with high topographic complexity: the
Brazilian Atlantic Forest hotspot. We evaluate whether climate, inferred from RS
sources, predicts which areas accumulate the highest diversity of species, evolutionary lineages, and endemism. For that, we use distribution and phylogenetic data
from three plant clades representing different life forms, that are commonly found
in the Brazilian Atlantic Forest: melastomes (178 species of shrubs and trees), bromeliads (43 species of epiphytes), and bignones (131 species of lianas). We also
evaluate what (if any) gains emerge from the use of climatic descriptors based on
RS, rather than weather stations, for this area. Given the sharp altitudinal changes
observed in the Brazilian Atlantic Forest hotspot, it has been proposed that interpolated weather station data may perform more poorly than variables derived from RS
(Waltari et al. 2014).
A. Paz et al.