Identifying traits that enable lizard adaptation to different habitats.

Aim: Species adapt differently to contrasting environments, such as open habitats with sparse vegetation and forested habitats with dense forest cover. We investigated colonization patterns in the open and forested environments in the diagonal of open formations and surrounding rain forests (i.e. Amazonia and Atlantic Forest) in Brazil, tested whether the diversification rates were affected by the environmental conditions and identified traits that enabled species to persist in those environments. Location: South America, Brazil. Taxon: Squamata, Lizards. Methods: We used phylogenetic information and the current distribution of species in open and forested habitats to estimate ancestral ranges and identify range shifts relative to the current habitats. To evaluate whether these environments influenced species diversification, we tested 12 models using a Hidden Geographic State Speciation and Extinction analysis. Finally, we combined phylogenetic relatedness and species traits in a machine learning framework to identify the traits permitting adaptation in those contrasting environments. Results: We identified 41 total transitions between open and forested habitats, of which 80% were from the forested habitats to the open habitats. Widely distributed species had higher speciation, turnover, extinction, and extinction fraction rates than species in forested or open habitats, but had also the lower net diversification rate. Mean body temperature, microhabitat, female snout–vent length and diet were identified as putative traits that enabled adaptation to different environments, and phylogenetic relatedness was an important predictor of species occurrence. Main conclusions: Transitions from forested to open habitats are most common, highlighting the importance of habitat shift in current patterns of biodiversity. The combination of phylogenetic reconstruction of ancestral distributions and the machine learning framework enables us to integrate organismal trait data, environmental data and evolutionary history in a manner that could be applied on a global scale. [ABSTRACT FROM AUTHOR]