{ "abstract": "Land use and climate change are increasing the risk of spillover of zoonotic disease into human populations. However, we lack actionable information about the prevalence of pathogens in wildlife populations for most of the globe, challenging our ability to implement strategies to prevent zoonoses. Even when this data exists, it has historically been sampled opportunistically and without guidance based on known geographic distributions of hosts of zoonotic pathogens. Biosurveillance is essential to mitigating zoonotic spillover risk, but given the expensive nature of monitoring pathogens in wildlife, we need to be strategic about deciding where and what to sample to obtain as much useful information as possible — particularly to identify regions where spillover is most probable and implement preventative measures to mitigate outbreak risk. The field of biodiversity monitoring has established many practices that can directly inform optimal biosurveillance efforts. One such concept is the Biodiversity Observation Network (or BON), which aims to select monitoring locations that most effectively and efficiently capture the status and trends of biodiversity. We present a protocol for integrating data on host biodiversity into sampling priority for wildlife disease surveillance based on host species distribution models, with optional potential to integrate pathogen prevalence data (if available). This protocol has the flexibility to target different forms of sampling (collecting host occurrence vs. pathogen prevalence data) to adapt to different levels of data availability, but still makes adaptive sampling recommendations based on a principled understanding of host distribution and pathogen biology. We illustrate this flexibility with two case studies, prioritizing sampling for Hanta- and Arenaviridae in rodents and shrews in India and South Korea, respectively representing data poor and data rich contexts. We view this framework as a basis for integrating long-term biosurveillance and biodiversity monitoring programs, and maximizing the useful information available for public health decision making. Author summary: The majority of infectious diseases that infect humans are zoonotic, meaning they circulate in wildlife populations and human infections arise from contact with wildlife, leading to transmission of disease in humans (called zoonotic spillover). Zoonotic disease presents a major challenge for public health, and in order to target preventative measures and outbreak response, public health practitioners need a robust understanding of what and where pathogens circulate in wildlife to anticipate spillover risk. However, given the limited budgets of wildlife disease surveillance programs, public health researchers must be strategic in choosing where and when to sample to best understand the status of zoonotic disease in wildlife populations. Biodiversity plays a major role in structuring the geographic structure of where zoonotic pathogens circulate, and the last few decades have seen a massive increase in the amount and types of biodiversity data that is available to researchers. Biodiversity has the potential to play a vital role in guiding sampling effort for wildlife disease, but is largely underutilized in biosurveillance programs. In this manuscript, we introduce a standardized protocol for incorporating data on the hosts of zoonotic pathogens to better target sampling effort to improve our understanding of zoonotic pathogens in wildlife and better map spillover risk to predict and prevent future outbreaks.", "abstract_shown": true, "affiliations": [ "Département de sciences biologiques, Université de Montréal, Montréal, Québec, Canada", "Department of Biology, Carleton University, Ottawa, Ontario, Canada", "Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, Connecticut, United States of America", "Department of Genetics, Evolution and Environment, University College London, London, United Kingdom", "Department of Public & Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, New York, United States of America", "Department of Anthropology & Center for Infectious Disease Dynamics, Pennsylvania State University, State College, Pennsylvania, United States of America", "College of Veterinary Medicine, Washington State University, Pullman, Washington, United States of America" ], "article_type": "Research Article", "arxiv": null, "author_total": 13, "authors": [ { "affs": [ 0 ], "name": "Michael D. Catchen" }, { "affs": [ 1 ], "name": "Francis Banville" }, { "affs": [ 2 ], "name": "Amélie C. Boutin" }, { "affs": [ 3 ], "name": "Cole B. Brookson" }, { "affs": [ 4 ], "name": "Colin J. Carlson" }, { "affs": [ 5 ], "name": "Gabriel Dansereau" }, { "affs": [ 6 ], "name": "Rory Gibb" }, { "name": "Marianne Houle" }, { "name": "Benjamin Kaza" }, { "name": "Hailey Robertson" }, { "name": "David Simons" }, { "name": "Stephanie N. Seifert" }, { "name": "Timothée Poisot" } ], "bibtex": "https://research.draconitic.com/paper/834nk797j4ra7.bib", "cited_by": 1, "doi": "10.1371/journal.pesy.0000023", "fulltext_url": null, "id": "834nk797j4ra7", "issns": [ "3070-8141" ], "landing_url": "https://doi.org/10.1371/journal.pesy.0000023", "language": null, "licence": "CC-BY-4.0", "licence_label": "CC BY 4.0", "licence_source": "journal", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "2739|Zoonotic diseases and public health" ], [ "plos", "/Biology and life sciences/Ecology/Biodiversity" ], [ "plos", "/Biology and life sciences/Organisms/Eukaryota/Animals/Wildlife" ], [ "plos", "/Biology and life sciences/Zoology/Animals/Wildlife" ], [ "plos", "/Ecology and environmental sciences/Ecology/Biodiversity" ], [ "plos", "/Medicine and health sciences/Epidemiology/Disease surveillance" ], [ "plos", "/Medicine and health sciences/Epidemiology/Medical risk factors" ], [ "plos", "/Medicine and health sciences/Medical conditions/Infectious diseases/Zoonoses" ], [ "plos", "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens" ], [ "plos", "/Medicine and health sciences/Pathology and laboratory medicine/Pathogens/Animal pathogens/Zoonotic pathogens" ], [ "plos", "/Medicine and health sciences/Pharmaceutics/Dose prediction methods" ] ], "openalex": "W7114994694", "pdf_url": "https://journals.plos.org/ecosystems/article/file?id=10.1371/journal.pesy.0000023&type=printable", "pmcid": null, "pmid": null, "published": "2026-09-08", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/834nk797j4ra7.ris", "sources": [ { "first_seen": "2026-10-30T12:17:56Z", "native_id": "10.1371/journal.pesy.0000023", "source": "plos", "url": "https://doi.org/10.1371/journal.pesy.0000023" } ], "subjects": [ { "field": "biology", "origin": "plos:ecology", "subfield": "ecology" }, { "field": "biology", "origin": "plos:zoology", "subfield": "zoology-behaviour" }, { "field": "medicine", "origin": "plos:epidemiology", "subfield": "epidemiology-public-health" } ], "title": "A protocol for biodiversity-informed wildlife disease surveillance", "title_text": "A protocol for biodiversity-informed wildlife disease surveillance", "type": "peer-reviewed", "updated_at": "2026-10-30T12:17:56Z", "url": "https://research.draconitic.com/paper/834nk797j4ra7", "venue": "PLOS Ecosystems", "versions": [] }