Skip to main content
Dryad

Bridging macroecology and macroevolution in the radiation of sigmodontine rodents

Cite this dataset

Maestri, Renan et al. (2022). Bridging macroecology and macroevolution in the radiation of sigmodontine rodents [Dataset]. Dryad. https://doi.org/10.5061/dryad.jdfn2z3d7

Abstract

Investigations of phenotypic disparity across geography often ignore macroevolutionary processes. As a corollary, the random null expectations to which disparity is compared and interpreted may be unrealistic. We tackle this issue by representing, in geographical space, distinct processes of phenotypic evolution underlying ecological disparity. Under divergent natural selection, assemblages in a given region should have empirical disparity higher than expected under an evolutionarily-oriented null model, while the opposite may indicate constraints on phenotypic evolution. We gathered phylogenies, biogeographic distributions, and data on the skull morphology of sigmodontine rodents to discover which regions of the Neotropics were more influenced by divergent, neutral, or constrained phenotypic evolution. We found that regions with higher disparity than expected by the evolutionary-oriented null model, in terms of both size and shape, were concentrated in the Atlantic Forest, suggesting a larger role for divergent natural selection there. Phenotypic disparity in the rest of South America, mainly the Amazon basin, northeastern Brazil and Southern Andes, was constrained — lower than predicted by the evolutionary model. We also demonstrated equivalence between the disparity produced by randomization-based null models and constrained-evolution null models. Therefore, including evolutionary simulations into the null modeling framework used in ecophylogenetics can strengthen inferences on the processes underlying phenotypic evolution.

Usage notes

Please see the README files.

Funding

National Council for Scientific and Technological Development, Award: 406497/2018-4

Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul, Award: 21/2551-0000620-0