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 species Norops 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 in N. 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.

进化生物学的一个关键研究目标是了解遗传变异的起源和维持。在主要位于巴西中部高原的南美洲稀树草原塞拉多，人们提出了许多假设来解释景观特征（例如地理距离、河流屏障、地形分区和历史气候波动）如何通过调节基因来促进遗传结构流动。在这里，我们询问这些景观特征是否影响了蜥蜴物种Norops brasiliensis（有鳞目：Dactyloidae）的遗传结构和分化。为了实现我们的目标，我们使用遗传聚类分析并估计有效的迁徙表面来评估焦点物种的遗传结构。然后，使用优化的电阻隔离模型和基于模拟的方法与机器学习（卷积神经网络；CNN）相结合，通过 12 个独特的景观模型来推断当前和历史对种群遗传结构的影响。我们恢复了五个地理分布的种群，这些种群被基因流低于预期的区域分开。 CNN 的结果表明，地理距离是巴西猪笼草遗传变异的唯一预测因素，而坡度、河流和历史气候对基因流没有明显的影响。我们新颖的 CNN 方法在区分每个景观模型方面是准确的 (89.5%)。在景观遗传学研究中，CNN 和其他机器学习方法在很大程度上尚未得到探索，随着基因组数据集的日益普及，它们代表了未来研究的有希望的途径。