Random subsamples of animal populations can reveal intrinsic differences in sociality with key implications in ecology, conservation and disease transmission

Abstract

Animal populations are under mounting stress from the dual threats of climate change and rapid global human population growth, raising significant concerns about declining wildlife and the rising risk of zoonotic diseases. In many species, social interactions can be a highly plastic suite of behaviours that are responsive to these disturbances and are consequential to other processes like disease transmission and population dynamics. Studying social interactions can be challenging in that researchers often rely on wildlife population subsamples due to practical constraints and costs, which can introduce biases in the reliability of social network metrics. We investigated the extent to which subsamples can depict intrinsic characteristics of wildlife populations using data from three distinct species: peri-urban fallow deer, Alpine ibex and Angolan giraffe. We showed that random subsamples of these populations could still reveal differences in their social behaviour, indicating that, as long as researchers have a reliable estimate of population size, subsampling animal populations can be an effective and precise method to infer their sociality and offer valuable empirical data for management, conservation and zoonotic disease ecology. Furthermore, we demonstrate that non-random sampling, influenced for instance by animal personality and related trappability, can introduce significant biases in social network estimates. These findings underscore the importance of accounting for sampling biases in social network analysis and offer a robust framework for using partial networks in ecological studies and conservation management.