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Published January 1, 2023 | Version v1
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Data from: Predicting and measuring decision rules for social recognition in a Neotropical frog

  • 1. Cornell University
  • 2. University of Jyväskylä
  • 3. University of Missouri
  • 4. University of Minnesota

Description

Many animals use signals to recognize familiar individuals but risk mistakes because the signal properties of different individuals often overlap. Further, 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% to 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.

Notes

See README file.

Funding provided by: Society for the Study of Evolution
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100012454
Award Number: Rosemary Grant Award

Funding provided by: American Philosophical Society
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100001461
Award Number: Lewis and Clark Fund

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: 1601493

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Additional details

Related works

Is source of
10.5061/dryad.1rn8pk0vr (DOI)