Wild boar as a sentinel species for ecosystem health surveillance: A review

Sentinel surveillance uses selected species that are widely distributed, easily sampled, and exposed to multiple health threats as indicators of ecosystem health. The Eurasian wild boar (Sus scrofa) is a broadly distributed game species that meets these conditions. We conducted a systematic review to assess the suitability of the wild boar as a sentinel or indicator species for monitoring health hazards relevant to ecosystem health, focusing on the hazards monitored, methods, justification for use, perceived performance, limitations, and proposed improvements. We identified 86 studies explicitly framing the wild boar as a sentinel species, covering a wide range of hazards, including 30 pathogens, 44 environmental contaminants, two biological toxins and 13 antimicrobial resistance (AMR) indicators. It was used to monitor pathogens (57%), environmental contaminants (31.4%), AMR (12.8%), and biological toxins (3.5%). Research effort was concentrated in Europe (84.9%) and relied mostly on hunted animals (90.7%). Wild boar were frequently selected as sentinels due to their previous use as indicators of ecosystem health in the literature, omnivorous diet, wide distribution, and accessibility as game species. However, hunting-based sampling may introduce biases, while post-mortem conditions and ecological variability may affect results. Despite this, 74.4% of studies supported their use as sentinels, particularly for pathogens and environmental contaminants. In contrast, their perceived suitability for AMR was more variable, and no quality assessment was available for biological toxins. Overall, wild boar sentinel surveillance is considered logistically efficient and valuable for monitoring ecosystem health hazards. However, its effectiveness is context-dependent and influenced by methodological and ecological limitations. Improving surveillance design through systematic sampling, alternative sample sources (e.g., found-dead animals or non-invasive methods), integration of multi-species data, and inclusion of covariates could enhance the reliability of findings within a One Health framework.