The business of saving species: New study explores fresh models for conservation

Kingston, Ont. — Business and wildlife conservation may seem worlds apart, but both face the same challenge: managing high-stakes systems with limited resources. In both arenas, success means doing more with less.

In a cross-collaborative study published on Sept. 15 in PNAS, research leads from Smith School of Business at Queen’s University and the College of Veterinary Medicine (CVM) at Cornell University developed a novel decision model derived from business operations that detects emerging wildlife disease -months earlier or with -lower costs than the current traditional strategies.

Jue Wang, associate professor of management science at Smith and a visiting scholar at the Johnson Graduate School of Management at the time of the study, worked alongside a team at CVM lead by Krysten Schuler, associate research professor and wildlife disease ecologist at Cornell, to devise an AI-driven optimization model to maximize financial recourses and minimize the spread of disease.

“The biggest challenge in tackling emerging diseases is that they can appear almost anywhere across vast landscapes, making it difficult to decide where to focus attention,” explains Wang.

The development of this model comes at a time when Schuler says, “wildlife populations are seeing increased risk from emerging diseases just as the agencies that manage then are experiencing ever-tightening budgets.”

Typically, wildlife management agencies focus on reactive management strategies, with resources allocated only after the threat is detected. However, “By the time the first case is detected, the disease or species may have been spreading unnoticed for an extended period,” says Schuler.

But being proactive is often expensive, presenting a dilemma for underfunded management groups. Wildlife managers must make careful calculations to balance the more costly prevention activities, with surveillance activities that are expensive initially but become cheaper over time.

The new model developed and applied in their study allocates a given budget across many geographical sites, to minimize unnoticed disease spread before the first case even detected. Using chronic wasting disease (CWD) in New York State as a case study, the researchers tested the model with a theoretical budget of $500,000 across all 62 counties in the state over a 10-year period. They compared the outcomes of this optimized AI model with the one currently being used in New York State.

Leveraging simulated data representing a realistic scenario of CWD spread, the researchers showed that the model was able to detect CWD in deer on average 8.4 months earlier when compared to the traditional models, or reduce the current spending by 22 per cent without compromising the performance.

“With limited budgets, this new model provides clear guidance on when and where to invest in prevention or surveillance,” Wang says.

Wildlife professionals are not the only ones who stand to benefit from this new approach. Any large-scale system that is at risk of the introduction and spread of a rare foreign agent can benefit from the AI tool, with such applications as fighting invasive species, infectious disease in agriculture or zoonotic disease in public health.

“The applications of this model are endless,” says Schuler.