Choosing a sensible cut-off point: assessing the impact of uncertainty in a social network on the performance of NBDA

Sonja Wild,William Hoppitt

doi:10.1007/s10329-018-0693-4

Abstract

Network-based diffusion analysis (NBDA) has become a widely used tool to detect and quantify social learning in animal populations. NBDA infers social learning if the spread of a novel behavior follows the social network and hence relies on appropriate information on individuals’ network connections. Most studies on animal populations, however, lack a complete record of all associations, which creates uncertainty in the social network. To reduce this uncertainty, researchers often use a certain threshold of sightings for the inclusion of animals (which is often arbitrarily chosen), as observational error decreases with increasing numbers of observations. Dropping individuals with only few sightings, however, can lead to information loss in the network if connecting individuals are removed. Hence, there is a trade-off between including as many individuals as possible and having reliable data. We here provide a tool in R that assesses the sensitivity of NBDA to error in the social network given a certain threshold for the inclusion of individuals. It simulates a social learning process through a population and then tests the power of NBDA to reliably detect social learning after introducing observational error into the social network, which is repeated for different thresholds. Our tool can help researchers using NBDA to select a threshold, specific to their data set, that maximizes power to reliably quantify social learning in their study population.

Highlights

Cultural behavior, broadly defined, is behavior that is passed on among individuals through social learning (Boyd and Richerson 1995)
Network-based diffusion analysis (NBDA), first developed by Franz and Nunn (2009), infers social learning if the diffusion of a behavior follows the social network, as it is based on the assumption that more closely associated individuals are more likely to learn from each other (Coussi-Korbel and Fragaszy 1995)
In an NBDA, social learning is inferred if the AICc for the model including social learning is lower than for a model without social learning

Summary

Introduction

Broadly defined, is behavior that is passed on among individuals through social learning (Boyd and Richerson 1995). Recent years have seen the development of novel methods that quantify the importance of social learning on the spread of a behavior in freely interacting groups of animals. Tool that can quantify the effect of social learning among a group or population of animals (including humans) (Franz and Nunn 2009; Hoppitt et al 2010). NBDA, first developed by Franz and Nunn (2009), infers social learning if the diffusion of a behavior follows the social network (i.e., a representation of connections among individuals within a social group or population), as it is based on the assumption that more closely associated individuals are more likely to learn from each other (Coussi-Korbel and Fragaszy 1995). Diffusion data can either be the order with which individuals acquire a behavior

Methods

Results

Conclusion