2.5. Climatic niche divergence
The study utilized the framework introduced by Broennimann et al. in 2012, which is available as part of the R package Ecospat (Di Cola et al., 2017). This framework analyzed the overlap in climatic niches between two distinct species. The analysis involved extracting data from 19 bioclimatic variables at the locations where these species were recorded. This data extraction was done using the kernel density function developed by Broennimann et al. in 2012, implemented within the modified principal component analysis (PCA-env) multivariate space.
The resulting density grids were the foundation for calculating the climatic niche overlap, assessed using Schoener’s D metric, a well-established method (Schoener, 1970; Warren et al., 2008). Schoener’s D metric ranges from 0 to 1, where 0 indicates no overlap between the niches of the two species, and 1 signifies complete overlap (Broennimann et al., 2012; Hemami et al., 2020; Sillero et al., 2022).
Two randomization tests were applied to determine whether there was climatic niche divergence or conservation between the two species: niche equivalency (or identity) and niche similarity (or background). The niche equivalency test aimed to assess whether the climatic niches of the two species in their respective geographic regions were similar (Broennimann et al., 2012; Hemami et al., 2020; Oboudi et al., 2021). However, since the niche equivalency test did not consider the environmental background, the niche similarity test was conducted in two directions. This test aimed to determine whether the climatic niche of one range was significantly more similar to the niche of another range than would be expected by chance (Warren et al., 2008; Broennimann et al., 2012). The null hypothesis of the niche similarity test was rejected when the observed overlap (Schoener’s D) was significantly lower than the simulated values (P < 0.05 ) (Broennimann et al., 2012), indicating that the climatic niche of the two species was not more similar than what would occur randomly (DeChaine et al., 2014).
In order to provide a more comprehensive understanding of climate niche dynamics, three additional indicators were computed: ”niche stability,” ”niche expansion,” and ”niche unfilling” (Datta et al., 2019; Bates & Bertelsmeier, 2021).
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