Data from: Social transmission in the wild reduces predation pressure on novel prey signals

Social transmission of information is taxonomically widespread and could have profound effects on the ecological and evolutionary dynamics of animal communities. Demonstrating this in the wild, however, has been challenging. Here we show by field experiment that social transmission among predators can shape how selection acts on prey defences. Using artificial prey and a novel approach in statistical analyses of social networks, we find that blue tit (Cyanistes caeruleus) and great tit (Parus major) predators learn about prey defences by watching others. This shifts population preferences rapidly to match changes in prey profitability, and reduces predation pressure from naïve predators. Our results may help resolve how costly prey defences are maintained despite influxes of naïve juvenile predators, and suggest that accounting for social transmission is essential if we are to understand coevolutionary processes.

Methods

This data was collected at Madingley Wood (Cambridgeshire, UK) during summer 2018. We introduced novel palatable and unpalatable foods into a wild blue tit and great tit population and recorded individuals’ food choices using RFID technology at feeding stations. We have five different datasets that include records of birds' visits to feeders with 1) plain almonds ("PooledForSocNet"), 2) palatable green and unpalatable red almonds ("GreenRedRawData2"), 3) palatable purple and unpalatable blue almonds ("PurpleBlueRawData2"), 4) palatable purple and palatable blue almonds ("PurpleBlueReversalRawData2"), and 5) palatable orange and unpalatable yellow almonds ("OrangeYellowRawData2"). In addition, we have a datasheet that includes individual attributes ("IndividualsData"). 

Usage Notes

In addition to raw data, we have provided example R scripts for 1) an analysis to test whether birds use social information during avoidance learning and whether these effects follow social network ("Example analysis scripts and data"), 2) simulations to validate our model approach ("Example validation scripts and data"), and 3) a network-based diffusion analysis to investigate social information use during avoidance learning ("NBDA scripts and data"). Avoidance learning scripts use data from the yellow/orange experiment, but the same approach applies to each colour pair.