Mapping Wildlife Disease Risk to Guide Surveillance

The Problem

It is difficult to predict how wildlife diseases will spread across landscapes. Environmental conditions, host movement patterns, and landscape connectivity all affect how pathogens move through wildlife populations. For managers and conservation agencies, understanding where disease risk is highest is critical for designing effective monitoring programs.

A wildlife management agency needed to improve how it monitored a potentially emerging wildlife disease across several watersheds. Field teams collected biological samples and observations from wildlife populations, but funding for monitoring had decreased. Survey teams could only sample a small fraction of the landscape each season.

Monitoring efforts were not able to determine the locations where disease emergence was most likely. Managers needed a more systematic way to evaluate disease risk across the landscape so that surveillance efforts could be targeted where they would provide the greatest benefit.

QSC’s Modeling Approach

QSC developed a spatial disease risk modeling framework designed to identify areas where wildlife disease transmission was most likely to occur.

The model integrates multiple sources of ecological and environmental data. Wildlife observation records provide information about host species presence and distribution, while historical monitoring data offer insight into previous disease detections. Environmental variables such as temperature, vegetation patterns, and landscape features are also incorporated because these factors can affect both host behavior and pathogen persistence.

Landscape connectivity is another important component of the model. Wildlife movement patterns often follow natural corridors such as river systems, forest edges, and habitat transitions. By incorporating spatial relationships between habitats, the model estimates how disease risk propagates across the landscape.

Testing error in serosurveillance is another complication. Sometimes an animal has a disease but tests negative. Conversely, some individuals test positive when they do not carry the disease. Both types of error are accounted for in the model.

QSC developed a probabilistic spatial model that estimates the relative likelihood of disease presence across the region. Instead of producing a single deterministic prediction, the model generates a continuous risk surface that highlights areas where disease transmission is more likely under current environmental conditions.

The modeling approach builds on research published by a QSC scientist in the peer-reviewed literature on wildlife disease risk modeling.

Decision Support in Practice

The model outputs are translated into static risk maps that wildlife managers use to guide surveillance planning.

These maps highlight areas where environmental conditions, host presence, and landscape connectivity combine to create elevated disease risk. Managers compare these high-risk areas with their existing monitoring locations to determine the best surveillance strategy.

Because the risk maps are developed at the scale of individual watersheds, they allow monitoring programs to adapt their strategies locally. In some watersheds, the model identifies specific habitat corridors where wildlife movement can facilitate disease spread. In others, environmental conditions suggest that disease persistence might be more likely in particular habitat types.

This spatial perspective helps agencies deploy their limited monitoring resources more strategically. Field teams focus sampling efforts in locations where disease detection is most informative, rather than distributing surveys evenly across the entire region.

The maps also help guide longer-term planning by identifying areas where environmental changes, such as shifts in habitat conditions or wildlife movement patterns, might alter disease risk in the future.

Outcome

The most important outcome of the project is a clearer understanding of how disease risk varies across the landscape.

Instead of relying solely on broad monitoring coverage, the wildlife agency uses a spatial framework for prioritizing surveillance efforts within each watershed. Sampling teams focus on areas where disease emergence is most likely while still maintaining baseline monitoring elsewhere.

The risk maps also improve communication between scientists and resource managers. Complex modeling results are translated into clear spatial outputs that decision-makers interpret easily when planning monitoring activities.

The result is not simply a new ecological model. It is a practical decision-support tool that connects environmental data, wildlife observations, and landscape structure to guide disease surveillance across a large and ecologically complex region.