Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data

The growing availability of genetic datasets, in combination with machine learning frameworks, offer great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we analyzed repurposed data using machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 ...

日益丰富的遗传数据集（genetic datasets）与机器学习框架（machine learning frameworks）相结合，为解答生态学与进化生物学领域中长期悬而未决的问题提供了巨大潜力。其中一个令种群遗传学家、生物地理学家与保护生物学家深感兴趣的问题是：哪些因素决定了种内遗传多样性（intraspecific genetic diversity）？该问题的解答颇具挑战，因为诸多因素均可影响遗传变异（genetic variation），包括生活史特征（life history traits）、历史影响与地理因素，且这些因素的相对重要性会随分类学尺度与地理尺度的不同而变化。此外，传统统计方法（traditional statistical approaches）难以对众多潜在相关变量的影响进行解读。为应对这些挑战，本研究采用机器学习（machine learning）方法对二次利用的数据（repurposed data）展开分析，并以新北区（Nearctic）两栖类（amphibians）为研究案例，探究了遗传多样性的预测因子。我们整合了299个物种的性状数据、分布范围特征以及超过42000条遗传序列……