Environmental covariates
Bioclimatic covariates are successfully used in SMDs since their
beginning (Booth et al., 2014), and perhaps reflect physiological
constraints. Soil type and geology have been used in SDMs for
Neotropical flying vertebrates (Ramoni-Perazzi et al., 2012, 2017, 2020)
likely echoing deeper ecological and historical constraints. Soil
characteristics modulate aspects of vegetation as functional traits,
biodiversity, and speciation, among others (Hulshof & Spasojevic, 2020;
Le Stradic et al., 2015; Mori et al., 2021; Nunes et al., 2015;
Rajakaruna, 2018). Besides, plant species distributions can shape
primary (Freeman & Mason, 2015) and secondary (Sanín & Anderson, 2018)
consumers distributions. Furthermore, geology underpins several soil
characteristics (Bockheim et al., 2014), as well as topography and
geological events that can influence biological diversification
processes over short timespans and over regional or local scales
(Antonelli et al., 2018; Gillespie & Roderick, 2014; S. L. Pereira &
Baker, 2004).
We used an ad hoc database of bioclimatic covariates for
Brazilian mainland (Ramoni-Perazzi et al., in press), information on
elevation (GMTED2010; Danielson and Gesch 2011), geological substrate
(hereafter geology; Gómez Tapias et al. 2019), and soil type (Hengl et
al. 2017). All variables were used at (or resampled to) 30 arc seconds
resolution.
We performed all the analyses using R 3.6.3 (R Core Team, 2020). To
remove collinearity, we reduced the number of continuous (bioclimatic +
elevation) covariables through a principal component analysis using
‘RStoolbox’ (Leutner et al., 2018), keeping the first four components,
whose eigenvalues were higher than one and explained 90.4% of the
variance (Supplementary material A, Appendix 1, Fig. A). The first
component (PC1) can be interpreted as a contrast between the temperature
during the most extreme conditions and its seasonality/variability
(Supplementary material A, Appendix 1, Fig. B1). Similarly, the second
component (PC2) contrasts the precipitation during extreme conditions
and its seasonality. The third component (PC3) involves the effect of
water availability, since involves temperatures under extreme
conditions/seasonality and precipitation during the warmest quarter.
Finally, the fourth component (PC4) has a weak negative association
between Mean Diurnal Temperature Range (Bio 02) and elevation.