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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. 

Abstract Rivers are prominent landscape features, acting as key promoters of diversification among freshwater organisms. Albeit generally considered potential barriers to species movement, they may also facilitate gene flow and structure populations of semiaquatic species (Riverine Thruway Hypothesis, RTH). We evaluated the role of rivers on the processes responsible for current genetic variation in the semiaquatic frog Pseudis bolbodactyla, testing whether each hydrographic basin harbours distinct genetic lineages. We sequenced three markers on 166 samples from 13 localities along the Paraná (PR), Araguaia–Tocantins (AT), and São Francisco (SF) River basins in Brazil. We recovered three populations geographically matching each hydrographic basin. Our results indicate migration among basins, with the best model selected using approximate Bayesian computation, including migration between AT and SF and ancient gene flow from PR to the AT–SF ancestor. Our findings are likely related to the orogenic events in Central Brazil dating to the Late Miocene (5 Mya), when hydrographic basins and the geomorphological features of the Brazilian Shield were formed. This suggests that P. bolbodactyla probably represents a species complex, with each lineage occurring in a distinct hydrographic basin, matching the predictions of the RTH. 

Abstract Intraspecific genetic diversity is a key aspect of biodiversity. Quaternary climatic change and glaciation influenced intraspecific genetic diversity by promoting range shifts and population size change. However, the extent to which glaciation affected genetic diversity on a global scale is not well established. Here we quantify nucleotide diversity, a common metric of intraspecific genetic diversity, in more than 38,000 plant and animal species using georeferenced DNA sequences from millions of samples. Results demonstrate that tropical species contain significantly more intraspecific genetic diversity than nontropical species. To explore potential evolutionary processes that may have contributed to this pattern, we calculated summary statistics that measure population demographic change and detected significant correlations between these statistics and latitude. We find that nontropical species are more likely to deviate from neutral expectations, indicating that they have historically experienced dramatic fluctuations in population size likely associated with Pleistocene glacial cycles. By analyzing the most comprehensive data set to date, our results imply that Quaternary climate perturbations may be more important as a process driving the latitudinal gradient in species richness than previously appreciated. 

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.