Quantifying Feral Pig Interactions to Inform Disease Transmission Networks

Abstract

Feral pigs threaten biodiversity in 54 countries worldwide and cause an estimated $120 billion of damage annually in the United States of America (USA). Feral pigs imperil over 600 native species and have directly driven 14 species into extinction. Moreover, feral pig populations pose a significant zoonotic disease threat to humans such as Japanese encephalitis, and act as reservoir for endemic pathogens such as Brucella and leptospirosis. Efforts to understand and control disease spread by feral pigs rely on models of social dynamics, how the animals interact with one another. Yet social dynamics are known to vary enormously from place to place, so knowledge generated for example in USA and Europe might not easily transfer to locations such as Australia. Here, we fill a continental gap in our understanding of feral pig social dynamics by developing a proximity-based social network analysis approach to rapidly assess social interactions using animal tracking data. This method, applied to the continent of Australia, included 146 GPS-monitored feral pigs, and revealed distinct patterns influenced by sex and season, with females demonstrating higher group cohesion (female-female) and males acting as crucial connectors between independent groups. Contact rates are remarkably high within groups, indicating rapid intra-group disease spread that contrasts with much slower potential for inter-group disease spread. Seasonal variations further complicate this dynamic, with contact rates being much higher in summer. The results show that, in Australia, targeting adult males in feral pig control programs could enhance efforts to contain disease outbreaks. Concern over the economic and human health impacts of animal diseases is higher than ever before. We urge a rapid global effort to use models of feral pig social interactions to develop efficient control strategies tailored to local conditions.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* Figure labels were updated. We found that a General Linear Mixed Effect Model (Table 1) was not optimal for analysing the effect of sex on the network measures, and therefore this analysis has been done again using a non-parametric test (Wilcoxon rank-sum test) for direct and indirect networks based on a 5 metres threshold (Table 1).

* https://github.com/Tatianaproboste/Feral-Pig-Interactions