Predicting and Measuring Decision Rules for Social Recognition in a Neotropical Frog

Many animals use signals to recognize familiar individuals but risk making mistakes because the signal properties of different individuals often overlap. Furthermore, outcomes of correct and incorrect decisions yield different fitness payoffs, and animals incur these payoffs at different frequencies depending on interaction rates. To understand how signal variation, payoffs, and interaction rates shape recognition decision rules, we studied male golden rocket frogs, which recognize the calls of territory neighbors and are less aggressive to neighbors than to strangers. We first quantified patterns of individual variation in call properties and predicted optimal discrimination thresholds using signal variation. We then measured thresholds for discriminating between neighbors and strangers using a habituation-discrimination field playback experiment. Territorial males discriminated between calls differing by 9%-12% in temporal properties, slightly higher than the predicted thresholds (5%-10%). Finally, we used a signal detection theory model to explore payoff and interaction rate parameters and found that the empirical threshold matched those predicted under ecologically realistic assumptions of infrequent encounters with strangers and relatively costly missed detections of strangers. We demonstrate that receivers group continuous variation in vocalizations into discrete social categories and that signal detection theory can be applied to understand evolved decision rules.

Automated summary

Many animals use signals to recognize familiar individuals but may make mistakes due to overlapping signal properties. This study focused on male golden rocket frogs and investigated how signal variation, payoffs, and interaction rates shape recognition decision rules. The study found that the frogs group signal variation into discrete social categories and that signal detection theory can be applied to understand their evolved decision rules.