A protocol for biodiversity-informed wildlife disease surveillance

Michael D Catchen , Francis Banville , Amélie C. Boutin, Cole B Brookson, Colin J. Carlson, Gabriel Dansereau , Rory Gibb, Marianne Houle, Benjamin Kaza, Hailey Robertson, David Simons, Stephanie Seifert, Timothée Poisot 

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. 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 Arena-viridae in rodents 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.