Using integrated wildlife monitoring to prevent future pandemics through one health approach
In the One Health context, Integrated Wildlife Monitoring (IWM) merges wildlife health monitoring (WHM) and host community monitoring to early detect emerging infections, record changes in disease dynamics, and assess the impact of interventions in complex multi-host and multi-pathogen networks. Thi...
| Autores: | , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:270435 |
| Acceso en línea: | https://ddd.uab.cat/record/270435 https://dx.doi.org/urn:doi:10.1016/j.onehlt.2022.100479 |
| Access Level: | acceso abierto |
| Palabra clave: | Anthropogenic imbalances Disease risk Host community Integrated wildlife monitoring One health Wildlife health monitoring |
| Sumario: | In the One Health context, Integrated Wildlife Monitoring (IWM) merges wildlife health monitoring (WHM) and host community monitoring to early detect emerging infections, record changes in disease dynamics, and assess the impact of interventions in complex multi-host and multi-pathogen networks. This study reports the deployment and results obtained from a nationwide IWM pilot test in eleven sites representing the habitat diversity of mainland Spain. In each study site, camera-trap networks and sampling of indicator species for antibody and biomarker analysis were used to generate information. The results allowed identifying differences in biodiversity and host community characteristics among the study sites, with a range of 8 to 19 relevant host species per point. The Eurasian wild boar (Sus scrofa) was the most connected and central species of the host communities, becoming a key target indicator species for IWM. A negative relationship between biodiversity and disease risk was detected, with a lower number and prevalence of circulating pathogens in the sites with more species in the community and larger network size. However, this overall trend was modified by specific host-community and environmental factors, such as the relative index of wild boar - red deer interactions or the proximity to urban habitats, suggesting that human-driven imbalances may favour pathogen circulation. The effort of incorporating wildlife population monitoring into the currently applied WHM programs to achieve effective IWM was also evaluated, allowing to identify population monitoring as the most time-consuming component, which should be improved in the future. This first nationwide application of IWM allowed to detect drivers and hotspots for disease transmission risk among wildlife, domestic animals, and humans, as well as identifying key target indicator species for monitoring. Moreover, anthropogenic effects such as artificially high wildlife densities and urbanisation were identified as risk factors for disease prevalence and interspecific transmission. |
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