Discriminating conspecifics from heterospecifics can help avoid costly interactions between closely related sympatric species. The guenons, a recent primate radiation, exhibit high degrees of sympatry and form multi-species groups. Guenons have species-specific colorful face patterns hypothesized to function in species discrimination. Here, we use a machine learning approach to identify face regions most essential for species classification across fifteen guenon species. We validate these computational results using experiments with live guenons, showing that facial traits critical for accurate classification influence selective attention toward con- and heterospecific faces. Our results suggest variability among guenon species in reliance on single-trait-based versus holistic facial characteristics for species discrimination, with behavioral responses and computational results indicating variation from single-trait to whole-face patterns. Our study supports a role for guenon face patterns in species discrimination, and shows how complex signals can be informative about differences between species across a speciose and highly sympatric radiation.

中文翻译：

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遍及灵长类动物辐射的物种识别信号的结构

将异种与异种区别开可以帮助避免密切相关的同胞物种之间代价高昂的相互作用。刚牛是最近的灵长类动物辐射，表现出高度的交感性并形成多物种群体。Guenons具有特定于物种的彩色面孔图案，被认为可以在物种歧视中发挥作用。在这里，我们使用一种机器学习方法来识别对15种古农物种进行分类最重要的面部区域。我们使用实弹枪进行实验验证了这些计算结果，结果表明，对于准确分类至关重要的面部特征影响对有选择性和异性面部的选择性注意。我们的研究结果表明，基于单性状与整体面部特征进行区分的豚鼠物种之间存在差异，行为反应和计算结果表明，从单性状到全脸模式都有变化。我们的研究支持扁豆人脸模式在物种识别中的作用，并显示复杂信号如何在特定和高度同伴辐射中揭示物种之间的差异。