Am Nat. 2021 Feb;197(2):147-163. doi: 10.1086/712246. Epub 2020 Dec 23.
AbstractSignal detection theory (SDT) has been used to model optimal stimulus discrimination for more than four decades in evolutionary ecology. A popular standard model that maximizes payoff per encounter was recently criticized for being too simplistic, leading to erroneous predictions. We review a number of SDT models that have received less attention but have explicitly taken repeated encounters into account, focusing on prey choice, mate search, aggressive mimicry, and the aiding of kin. We show how these models can be seen as variants of a second standard model that can be analyzed in a unified framework. In contrast to the simpler model, in this second model a higher probability of an undesirable or dangerous event occurring may either decrease or increase the receiver's acceptance rates. In each instance, the latter outcome requires undesirable events to be undesirable in a relative rather than an absolute sense. Increasing the abundance of desirable signalers or the payoff from accepting them may also either raise or reduce acceptance rates. Our synthesis highlights fundamental similarities among models previously studied on a case-by-case basis and challenges some long-held beliefs. For example, some classic predictions of Batesian mimicry can be reversed when model prey are protected by low profitability rather than harmful defense.
摘要 信号检测理论(SDT)在进化生态学中已被用于模拟最佳刺激辨别已有四十多年。最近,一种收益最大化的流行标准模型因其过于简单而受到批评,导致错误的预测。我们回顾了一些受到较少关注但明确考虑了重复遭遇的 SDT 模型,重点关注猎物选择、配偶搜索、攻击性模仿和亲属援助。我们展示了如何将这些模型视为可以在统一框架中分析的第二个标准模型的变体。与更简单的模型相比,在第二个模型中,发生不良或危险事件的可能性增加可能会降低或提高接收者的接受率。在后一种情况下,这种结果需要不良事件在相对而非绝对意义上是不良的。增加理想信号者的丰度或接受它们的收益也可能会提高或降低接受率。我们的综合分析强调了之前在逐个案例基础上研究的模型之间的基本相似性,并挑战了一些长期存在的观点。例如,当受保护的模型猎物的收益较低而不是有害防御时,贝氏拟态的一些经典预测可以被反转。