๐Ÿ‡จ๐Ÿ‡ฆLessons from Causal Analysis: Policy Implications for Woodland Caribou Recovery in Canada

Steven F. Wilson, Ph.D., EcoLogic Research

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.

Presentation Video

About the Presenter

Steven F. Wilson, Ph.D., EcoLogic Research, 302-99 Chapel Street, Nanaimo, BC V9R 5H3, Canada, steven.wilson@ecologicresearch.ca

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.

Previous Conference Presentations

๐Ÿ‡จ๐Ÿ‡ฆpageThe Small Data Problem: Using Bayesian Networks in Endangered Species Policy Development
  • Using Bayesian Networks to Characterize Wildlife Habitat Use (Chicago, 2018)

  • The Social Graphโ€”Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)

  • Big data, small data: Bayesian networks in environmental policy analysis in Canadaโ€™s energy sector (Fairfax, 2015)

  • Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)

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