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Building and Validating Bayesian Models

Building and Validating Bayesian Models. Identification of Mountain Goat Winter Range in North-central BC. Acknowledgements. Funding from the BC Min. of Environment Other participants included: R. Ellis

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Building and Validating Bayesian Models

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  1. Building and Validating Bayesian Models Identification of Mountain Goat Winter Range in North-central BC R. Scott McNay, Wildlife Infometrics Randy Sulyma, BC Min. of Forests

  2. Acknowledgements • Funding from the BC Min. of Environment • Other participants included: • R. Ellis • D. Fillier, S. Gordon, L. Vanderstar, D. Heard, G. Watts, D. Wilson, J. Vinnedge, B. Brade, R. MacDonald • Line Giguere, Robin McKinley • Concepts and ideas: • the last workshop in Chase • subsequent discussions, most notably, B. Marcot & S. Wilson, C. Apps

  3. Theory Correlative Theory Mechanistic Frequency Probability Empirical Mechanistic Empirical Correlative Modeling Context Conceptual from Bunnell 1989 • Uses: • Prediction • Management • Implications of Predictions? • Explanation • Research • Why?

  4. Rationale • General, portable model • Management & research • Prediction & explanation • Minimal resources to develop • Little information from FSJ • Insufficient resources to develop empirical or other more traditional approaches • Limited Time Frame

  5. Study Areas • Adjacent MUs • Similar but not the same • Preliminary model already built

  6. Simple UWR Model = Spatial relationships of cells processed/defined in a GIS. Typically a distance function from escape terrain.

  7. = Spatial relationships of cells processed/defined in a GIS

  8. Goats

  9. Model Construction Results Primary indicators: • Accuracy • 100% relocations • Maximize coverage • Precision • 100% alpine • Maximize area decrease

  10. Overall Results: • Basic model of EP • 65% relocations covered • 57% reduction in alpine • Spatially generalized model (nearest neighbor algorithm) • 92% relocations covered • 70% reduction in alpine

  11. Model Testing • Random sample approach applied. • Aerial reconnaissance completed. • Data collected to evaluate/verify both input parameters, and summary results. • Given funding and timing constraints, not possible to evaluate some of the spatial relationships.

  12. Model Testing Results Modeled Correct Classification Rate = 67% False Positive Error Rate = 73% False Negative Error Rate = 0% Κ = 0.50 τ = 0.32

  13. Model Testing Results Observed Modeled Correct Classification Rate = 89% False Positive Error Rate = 46% False Negative Error Rate = 0% Κ = 0.64 τ = 0.79

  14. Discussion • Were we able to restrict our search for UWR sufficiently yet remain accurate? • Was it important that we were not strictly analytical in our Bayesian learning? • Was our test protocol appropriate given the project goal?

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