Data from: A rigorous comparison of sexual selection indexes via simulations of diverse mating systems

Sexual selection is a cornerstone of evolutionary theory, but measuring it has proved surprisingly difficult and controversial. Various proxy measures—e.g., the Bateman gradient and the opportunity for sexual selection—are widely used in empirical studies. However, we do not know how reliably these measures predict the strength of sexual selection across natural systems, and most perform poorly in theoretical worst-case scenarios. Here we provide a rigorous comparison of eight commonly used indexes of sexual selection. We simulated 500 biologically plausible mating systems, based on the templates of five well-studied species that cover a diverse range of reproductive life histories. We compared putative indexes to the actual strength of premating sexual selection, measured as the strength of selection on a simulated "mating trait." This method sidesteps a key weakness of empirical studies, which lack an appropriate yardstick against which proxy measures can be assessed. Our model predicts that, far from being useless, the best proxy measures reliably track the strength of sexual selection across biologically realistic scenarios. The maximum intensity of precopulatory sexual selection s′max (the Jones index) outperformed all other indexes and was highly correlated with the strength of sexual selection. In contrast, the Bateman gradient and the opportunity for sexual selection were poor predictors of sexual selection, despite their continuing popularity.

性选择是进化论的核心基石，但对其进行量化却出人意料地困难且颇具争议。各类替代指标（例如贝特曼梯度（Bateman gradient）与性选择机会（opportunity for sexual selection））已被广泛应用于实证研究当中。然而，我们尚不明确这些指标在自然系统中预测性选择强度的可靠程度，且多数指标在理论最差情景下表现欠佳。本研究对八种常用的性选择指标开展了严谨的对比分析：我们基于五种被充分研究、涵盖多样繁殖生活史的物种模板，模拟了500个符合生物学合理性的交配系统；将各类候选指标与交配前性选择的实际强度进行对比，该实际强度以模拟“交配性状”所受的选择强度来衡量。这一方法规避了实证研究的核心缺陷——后者缺乏用于评估替代指标的合适标尺。我们的模型预测，最佳替代指标非但并非毫无用处，反而能在符合生物学现实的场景中可靠地追踪性选择强度。其中，交配前性选择最大强度s′max（琼斯指数（Jones index））的表现优于所有其他指标，且与性选择强度高度相关。与之形成鲜明对比的是，尽管贝特曼梯度与性选择机会仍被广泛使用，却无法有效预测性选择强度。