262
of lineage restriction—in agreement with previous suggestions that historical climates, or the stochasticity of the processes associated with colonization or extinction, may have an important role in determining phylogenetic endemism (Carnaval
et al. 2014; Rosauer and Jetz 2015).
Although the performance of the CAR models varied across diversity measures
and taxa, the analyses decoupled from space recovered consistently lower predictive
power in bromeliads, irrespective of diversity measures (Table 11.2). Unlike the
other two groups, this clade is composed mainly of microendemics and represented
only in a relatively small region of the Atlantic Forest (Fig. 11.1). We hypothesize
that the larger influence of space, history, and chance events (particularly related to
local extinctions) may be responsible for the lower correspondence between spatial
patterns of biodiversity and climatic descriptors in groups of species that are narrowly distributed. This being true, it is expected that the predictive power of correlative models of biodiversity such as those presented here—including the use of RS
data—will perform best in tropical groups in which most species have relatively
large ranges.
The spatial distribution of the residuals of the correlation between biodiversity
metrics and climate data differed across clades. In melastomes, they were homogeneously distributed across the forest, while for both bromeliads and bignones large
residuals of SR and PD were observed in areas with low overall diversity. In bignones, residuals were especially concentrated in the south—where large geographic
extensions showed more or less PD than expected (Fig. 11.2). Particularly the
southern portion of the forest shows higher PD of bignones than expected, given the
models based on climate data. This may be related to these plants’ sensitivity to
altitude, which limits their growth (Lohmann, pers. obs.). Bignoniaceae species are
sensitive to temperature and precipitation, with abundance and species richness
responding positively in warmer climates, and strongly negative in wetter climates.
Thus, the species richness, or phylogenetic diversity, is increased in warmer and
drier dry-season habitats (Punyasena et al. 2008). The south of the Atlantic forest is
montane and the areas with larger residuals in South Brazil have dry, though cool
winters. Given that no topographic variables were included in our models, this overprediction appears reasonable.
11.5 Conclusions and Future Directions
Community-level data from three representative tropical plant groups that include
lianas, shrubs, and trees demonstrate that the use of RS data describing temperature
and precipitation accurately predicts the spatial distribution of two essential biodiversity metrics (SR and PD) in a biodiversity hotspot. This predictive power is
reduced when the approach is applied to a clade of spatially restricted (narrow
endemic) species, such as bromeliads. Across all plant groups, predictive power is
lower for diversity indices highly influenced by historical contingency and spatial
configuration, such as phylogenetic endemism. For predictive purposes, and at the
A. Paz et al.
of lineage restriction—in agreement with previous suggestions that historical climates, or the stochasticity of the processes associated with colonization or extinction, may have an important role in determining phylogenetic endemism (Carnaval
et al. 2014; Rosauer and Jetz 2015).
Although the performance of the CAR models varied across diversity measures
and taxa, the analyses decoupled from space recovered consistently lower predictive
power in bromeliads, irrespective of diversity measures (Table 11.2). Unlike the
other two groups, this clade is composed mainly of microendemics and represented
only in a relatively small region of the Atlantic Forest (Fig. 11.1). We hypothesize
that the larger influence of space, history, and chance events (particularly related to
local extinctions) may be responsible for the lower correspondence between spatial
patterns of biodiversity and climatic descriptors in groups of species that are narrowly distributed. This being true, it is expected that the predictive power of correlative models of biodiversity such as those presented here—including the use of RS
data—will perform best in tropical groups in which most species have relatively
large ranges.
The spatial distribution of the residuals of the correlation between biodiversity
metrics and climate data differed across clades. In melastomes, they were homogeneously distributed across the forest, while for both bromeliads and bignones large
residuals of SR and PD were observed in areas with low overall diversity. In bignones, residuals were especially concentrated in the south—where large geographic
extensions showed more or less PD than expected (Fig. 11.2). Particularly the
southern portion of the forest shows higher PD of bignones than expected, given the
models based on climate data. This may be related to these plants’ sensitivity to
altitude, which limits their growth (Lohmann, pers. obs.). Bignoniaceae species are
sensitive to temperature and precipitation, with abundance and species richness
responding positively in warmer climates, and strongly negative in wetter climates.
Thus, the species richness, or phylogenetic diversity, is increased in warmer and
drier dry-season habitats (Punyasena et al. 2008). The south of the Atlantic forest is
montane and the areas with larger residuals in South Brazil have dry, though cool
winters. Given that no topographic variables were included in our models, this overprediction appears reasonable.
11.5 Conclusions and Future Directions
Community-level data from three representative tropical plant groups that include
lianas, shrubs, and trees demonstrate that the use of RS data describing temperature
and precipitation accurately predicts the spatial distribution of two essential biodiversity metrics (SR and PD) in a biodiversity hotspot. This predictive power is
reduced when the approach is applied to a clade of spatially restricted (narrow
endemic) species, such as bromeliads. Across all plant groups, predictive power is
lower for diversity indices highly influenced by historical contingency and spatial
configuration, such as phylogenetic endemism. For predictive purposes, and at the
A. Paz et al.