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Abstract One key research goal of evolutionary biology is to understand the origin and maintenance of genetic variation. In the Cerrado, the South American savanna located primarily in the Central Brazilian Plateau, many hypotheses have been proposed to explain how landscape features (e.g., geographic distance, river barriers, topographic compartmentalization, and historical climatic fluctuations) have promoted genetic structure by mediating gene flow. Here, we asked whether these landscape features have influenced the genetic structure and differentiation in the lizard speciesNorops brasiliensis(Squamata: Dactyloidae). To achieve our goal, we used a genetic clustering analysis and estimate an effective migration surface to assess genetic structure in the focal species. Optimized isolation-by-resistance models and a simulation-based approach combined with machine learning (convolutional neural network; CNN) were then used to infer current and historical effects on population genetic structure through 12 unique landscape models. We recovered five geographically distributed populations that are separated by regions of lower-than-expected gene flow. The results of the CNN showed that geographic distance is the sole predictor of genetic variation inN. brasiliensis, and that slope, rivers, and historical climate had no discernible influence on gene flow. Our novel CNN approach was accurate (89.5%) in differentiating each landscape model. CNN and other machine learning approaches are still largely unexplored in landscape genetics studies, representing promising avenues for future research with increasingly accessible genomic datasets. 

Abstract AimSpecies 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. LocationSouth America, Brazil. TaxonSquamata, Lizards. MethodsWe 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. ResultsWe 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 conclusionsTransitions 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 The discipline of phylogeography has evolved rapidly in terms of the analytical toolkit used to analyse large genomic data sets. Despite substantial advances, analytical tools that could potentially address the challenges posed by increased model complexity have not been fully explored. For example, deep learning techniques are underutilized for phylogeographic model selection. In non‐model organisms, the lack of information about their ecology and evolution can lead to uncertainty about which demographic models are appropriate. Here, we assess the utility of convolutional neural networks (CNNs) for assessing demographic models in South American lizards in the genusNorops. Three demographic scenarios (constant, expansion, and bottleneck) were considered for each of four inferred population‐level lineages, and we found that the overall model accuracy was higher than 98% for all lineages. We then evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among the four lineages, identifying a single model with an estimated overall accuracy of 87% when using CNNs. The inferred demography of the lizard system suggests that gene flow between non‐sister populations and changes in effective population sizes through time, probably in response to Pleistocene climatic oscillations, have shaped genetic diversity in this system. Approximate Bayesian computation (ABC) was applied to provide a comparison to the performance of CNNs. ABC was unable to identify a single model among the larger set of 26 models in the subsequent analysis. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographer's toolkit. 

While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversity. However, even as both disciplines have come to utilize SNP data over the last decades, analytical approaches have remained for the most part focused on either broad-scale inferences of historical processes (phylogeography) or on more localized inferences about environmental and/or landscape features (landscape genetics). Here we demonstrate that an artificial intelligence model-based analytical framework can consider both deeper historical factors and landscape-level processes in an integrated analysis. We implement this framework using data collected from two Brazilian anurans, the Brazilian sibilator frog (Leptodactylus troglodytes) and granular toad (Rhinella granulosa). Our results indicate that historical demographic processes shape most the genetic variation in the sibulator frog, while landscape processes primarily influence variation in the granular toad. The machine learning framework used here allows both historical and landscape processes to be considered equally, rather than requiring researchers to make an a priori decision about which factors are important. 

Global climatic fluctuation has significantly impacted biodiversity by shaping adaptations across numerous species. Pleistocene climate changes notably affected species’ geographic distributions and population sizes, especially fostering post-glacial expansions in temperate regions. Evolutionary theory suggests spatial sorting of morphological traits associated with dispersal in recently expanded species. However, evidence of predicted intraspecific trait variation is scant. We investigated intraspecific trait variation in five lizard species along a forest-savanna gradient affected by Pleistocene climate. Lizards serve as an ideal group to test these ideas due to climate’s known influence on their morphological traits linked to essential functions like feeding and locomotion. We assessed two hypotheses: (i) niche variation and (ii) spatial sorting. For the niche variation hypothesis, we predicted increased intraspecific variability in head dimensions with distance from stable areas. For spatial sorting, we anticipated larger hind limb sizes with increased distance from stable areas. We gathered data on five quantitative traits from 663 samples across species. There was no evidence supporting either hypothesis across the five species. Limited sample sizes, challenges in habitat modeling, or other factors might explain this lack of support. Nonetheless, our study illuminates complexities in exploring trait variation within species. The data collected here, although inconclusive, represent a crucial test for evolutionary theory.