A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data

Abstract

Background Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is urgently needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. Methods We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ⇠ 85% of the individuals have been directly monitored. Results Through the protocol, we demonstrated how pre-network data permu tations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. Conclusions The protocol allows researchers to analyse GPS-based radio telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under di↵erent conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions