Species life-history traits, paleoenvironment, and biotic interactions likely influence speciation and extinction rates, affecting species richness over time. Birth-death models inferring the impact of these factors typically assume monotonic relationships between single predictors and rates, limiting our ability to assess more complex effects and their relative importance and interaction. We introduce a Bayesian birth-death model using unsupervised neural networks to explore multifactorial and nonlinear effects on speciation and extinction rates using fossil data. It infers lineage- and time-specific rates and disentangles predictor effects and importance through explainable artificial intelligence techniques. Analysis of the proboscidean fossil record revealed speciation rates shaped by dietary flexibility and biogeographic events. The emergence of modern humans escalated extinction rates, causing recent diversity decline, while regional climate had a lesser impact. Our model paves the way for an improved understanding of the intricate dynamics shaping clade diversification.

中文翻译：

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无监督贝叶斯神经网络揭示长鼻类特征介导的物种形成和人类驱动的灭绝


物种生活史特征、古环境和生物相互作用可能会影响物种形成和灭绝率，从而随着时间的推移影响物种丰富度。推断这些因素影响的出生-死亡模型通常假设单个预测变量和比率之间的单调关系，限制了我们评估更复杂的影响及其相对重要性和相互作用的能力。我们引入了使用无监督神经网络的贝叶斯出生-死亡模型，以利用化石数据探索对物种形成和灭绝率的多因素和非线性影响。它推断谱系和时间特定的比率，并通过可解释的人工智能技术理清预测效应和重要性。对长鼻类化石记录的分析揭示了饮食灵活性和生物地理事件影响的物种形成率。现代人类的出现加剧了灭绝速度，导致近期生物多样性下降，而区域气候的影响较小。我们的模型为更好地理解塑造进化枝多样化的复杂动态铺平了道路。