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 genus Norops. 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.

中文翻译：

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使用卷积神经网络的系统地理学模型选择

系统地理学学科在用于分析大型基因组数据集的分析工具包方面发展迅速。尽管取得了重大进展，但尚未充分探索可能解决模型复杂性增加带来的挑战的分析工具。例如，深度学习技术在系统地理学模型选择方面没有得到充分利用。在非模式生物中，缺乏有关其生态和进化的信息会导致不确定哪些人口模型是合适的。在这里，我们评估了卷积神经网络 (CNN) 在评估Norops属南美蜥蜴人口统计模型方面的效用. 四个推断的人口水平谱系中的每一个都考虑了三种人​​口统计方案（常量、扩展和瓶颈），我们发现所有谱系的整体模型准确度均高于 98%。然后，我们评估了一组 26 个模型，这些模型考虑了四个谱系之间的进化关系、基因流和有效种群大小的变化，确定了一个模型，当使用 CNN 时，其总体准确度估计为 87%。蜥蜴系统推断的人口统计学表明，非姐妹种群之间的基因流动和有效种群大小随时间的变化，可能是对更新世气候振荡的反应，已经塑造了该系统的遗传多样性。应用近似贝叶斯计算 (ABC) 来提供与 CNN 性能的比较。在随后的分析中，ABC 无法在更大的 26 个模型中识别出单个模型。我们的结果表明，CNN 可以轻松有效地纳入系统地理学家的工具包中。