๐Ÿ‡จ๐Ÿ‡ฆUsing Bayesian Networks to Map Winter Habitat for Mountain Goats in Coastal British Columbia

Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.

Abstract

Mountain goats are an iconic wildlife species of western North America, inhabiting steep and largely inaccessible terrain in remote areas. But they are also at risk from genetic isolation, climate change, and a variety of other stressors. Managing populations is challenging because mountain goats are difficult and expensive to inventory, and biologists have to rely on models to predict the speciesโ€™ abundance and distribution. I used landscape characteristics evident at point locations of mountain goat observations, along with an equal number of random locations, to learn the structure and parameters of a Bayesian network that predicted the suitability of habitats for mountain goats. I then used the model to process evidence scenario files of >100 million records to map the suitability of mountain goat habitat at a 25-m resolution throughout the study area. The model has subsequently been used to assess the effectiveness of current protected areas for mountain goats and to generate preliminary population estimates. Modeling the system as a Bayesian network provided a number of advantages over traditional parametric approaches because, as with many ecological studies, input variables were correlated, and animals exhibited non-linear responses to landscape conditions.

Presentation Video

Presentation Slides

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‡ฆpageLessons from Causal Analysis: Policy Implications for Woodland Caribou Recovery in Canada๐Ÿ‡จ๐Ÿ‡ฆpageThe Small Data Problem: Using Bayesian Networks in Endangered Species Policy Development

Last updated

Logo

Bayesia USA

info@bayesia.us

Bayesia S.A.S.

info@bayesia.com

Bayesia Singapore

info@bayesia.com.sg

Copyright ยฉ 2024 Bayesia S.A.S., Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd. All Rights Reserved.