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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. 

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.