Comment on page
Lessons from Causal Analysis: Policy Implications for Woodland Caribou Recovery in Canada
Presented at the 8th Annual BayesiaLab Conference on October 26, 2020.
As global conservation actions become more urgent, informed decision-making requires robust analyses of the costs and benefits of policy options, based on available evidence. Recovery planning for endangered species must assume a cause-and-effect relationship between proposed management interventions and population responses; however, most current ecological knowledge is derived from observational studies because experiments are largely infeasible or unethical. Weak and conflicting inferences about causal mechanisms have created debate and confusion among decision-makers, planners and stakeholders. While causal modelling techniques are well-developed and common in other policy domains that face similar challenges, the approach is nearly absent in conservation biology. I examine the challenge of woodland caribou recovery efforts in Canada through the lens of causal modelling, highlighting recent, high-profile debates and illustrating how a causal modelling approach can help to bring resolution while supporting robust forecasting and decision support.
Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.
- Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector (Fairfax, 2015)
- Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)
- The Social Graph—Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)
- Using Bayesian Networks to Characterize Wildlife Habitat Use (Chicago, 2018)
- The Small Data Problem: Using Bayesian Networks in Endangered Species Policy Development (Durham, 2019)