Depicting uncertainty in wildlife habitat suitability models using Bayesian inference and expert opinion

1. Depicting uncertainty in wildlife habitat suitability models using Bayesian inference and expert opinion Lee O’Brien Natural Resource Ecology Laboratory Colorado Sate University, Fort Collins, CO Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

2. Acknowledgments This project was funded by the USGS, National Gap Analysis Program I would also like to thank… David Theobald, Natural Resource Ecology Laboratory Ken Burnham, Fishery and Wildlife Department at Colorado State University Fritz Agterberg, Geological Survey of Canada Donald Schrupp, Colorado Division of Wildlife …and the species experts who agreed to be “guinea pigs” for the project: Brad Lambert, Lauren Livo, Erin Muths, Rick Scherer, Tanya Shenk and Michael Wunder. Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

3. Project Goals • Develop alternative for “absolute” predictions of habitat suitability • Quantify expert reviews of wildlife habitat suitability models • Compile and depict the cumulative uncertainty in wildlife habitat suitability models • Easily update models as new data become available • Honestly relate the “state of knowledge” about predicted habitat distributions to natural resource planners and managers Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

4. Wildlife Habitat Suitability Models • Expert models based upon Wildlife Habitat Relationships (WHR) • Usually binary, without indication of strengths or certainty of relationships • Examples from Colorado Gap Analysis Project (Schrupp et al. 2000) GIS Layers - Land cover - Elevation - Range limits - Distance to water - Soils Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

5. Why Bayesian Inference ? • The revision of orderly opinion in light of relevant new information • Allows the combination of empirical and knowledge-based data • Method is transparent and straightforward; species experts, and natural resource planners and managers can fully understand and interpret • In Bayesian framework probabilities are measures of uncertainty Bayes’ Theorem P(S|E) = P(S) * P(E|S) P(E) Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Where: P(S) = probability of habitat being suitable (prior probability) P(E) = habitat element probabilities (for suitable and unsuitable habitat) P(E|S) = probability of habitat elements given suitable habitat (averaged across elements and experts) P(S|E) = probability of habitat being suitable given habitat elements (posterior probability) US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

6. Methods • Use best available data (literature and expert) to build habitat suitability models • Have species experts review model parameters and provide opinions on the certainty of the habitat relationships • Re-code raster GIS data layers to create probability surfaces • Combine habitat probability surfaces by averaging expert probabilities for each corresponding pixel • Use Bayes’ Theorem to combine the expert probabilities with the prior model to create a posterior probability surface, which depicts the uncertainty in the predicted distribution of suitable habitat Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

7. Mountain Plover Example • Example of method incorporating expert opinion into the Colorado Gap Analysis habitat suitability model for the mountain plover (Charadrius montanus) Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

8. Wildlife Habitat Suitability Model Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

9. Prior Probability Surface Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

10. Tools used to review habitat relationships and ranges, and collect expert opinion • Developed in ESRI ArcView and MS Excel Model Review Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

11. Range Review Tool Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

12. You are asked to review the range maps and add your opinion about the range of the species, by selecting hydro-units and providing a value for how certain you are that the species habitat can be found in the selected hydro-units. The value entered should be between 0 and 1 inclusive, with 0 meaning that you are absolutely certain species habitat does not occur in the hydro-unit and 1 meaning that you are absolutely certain that the species habitat does occur in the hydro-unit. A value of 0.5 would indicate that you are not certain whether the species habitat occurs in the hydro-unit or not. The value should reflect both your knowledge about the particular species and how certain you are that suitable habitat occurs in a particular hydro-unit. Elicitation by Species Experts Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah 1) 0.5 is “non-informative” probability value = “I don’t know” 2) modeling distribution of suitable habitat; not species occurrence 3) two types of uncertainty: habitat relationships & knowledge about species US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

13. Range Probability Surface Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

14. Habitat Relationship Review Tool Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

15. Colorado GAP Land Cover Map Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

16. Land Cover Probability Surface Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

17. Digital Elevation Model Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

18. Elevation Probability Surface Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

19. WHR Probability Surfaces Bayes’ Inference Calculation Range Prior Probability Surface Posterior Probability Surface Elevation P(S) P(S|E) Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Land cover (x2) P(E|S) US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

20. Probability of Habitat Suitability for Mountain Plover Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

21. Model Comparisons Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Prior “Absolute” Model Posterior Probability Model US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

22. Land Cover Classification Accuracy • Acknowledged spatial and classification inaccuracies in land cover map • Identify per cover class via some accuracy assessment procedure • Accuracy assessment for Colorado land cover map (Reiners et al. 2000) included a fuzzy assessment of classification accuracy (i.e., degrees of “rightness” and “wrongness” - Gopal and Woodcock 1994) • “RIGHT” fuzzy assessment converts nicely into probabilities (certainty) • Multiply habitat suitability probability map and land cover certainty map Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Caveats • There was not enough data to assess accuracy of some land cover classes, these were assigned an “un-informative” probability of 0.5 • There was an unknown level of uncertainty added by using air-videography interpretation as “truth” to assess classification accuracy • Need robust accuracy assessment to produce reliable certainty map US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

23. Land Cover Classification Accuracy Surface Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

24. Uncertainty in Mountain Plover Wildlife Habitat Suitability Model with Additional Uncertainty from Land Cover Classification Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

25. Uncertainty Comparisons Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Model Uncertainty with Land Cover Classification Uncertainty Model Uncertainty US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

26. Distance to Water Coverage Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

27. Distance to Water Habitat Relationship as Probability Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

28. Probability of Habitat Suitability for Boreal Toad Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

29. Probability of Habitat Suitability for Boreal Toad Combining Several Expert Reviews Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

30. Patch Size as Probability for Lynx Model Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

31. Lynx Model Comparisons Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Lynx Model with Patch Size Probability Lynx Model US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

32. Findings • The expert reviewers who I contacted agreed with the utility of the project, were willing to participate and quickly learned the procedures for quantifying their certainty of the habitat relationships • It took an average of 1 hour per species for range and model reviews • The reviews were done in workshops or the tools were given to experts to do reviews on their own (need ESRI ArcView and MS Excel); each method had advantages and disadvantages • Needed robust accuracy assessment of land cover classes to assign reliable uncertainty contributed by this layer Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

33. Conclusions This procedure… • Depicts accumulated uncertainty in habitat suitability models • Provides a way to incorporate knowledge from many species experts • Provides a way to incorporate uncertainty of land cover classification • Provides a way to incorporate new modeling elements and reveal the additional associated uncertainty • Provides an easy way to update models with new information • Relates “state of knowledge” about predicted suitable habitat distribution Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah Does not… • Address uncertainty from scale inconsistencies or cartographic errors • Predict species occurrence US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

34. Usefulness for Gap Analysis • Provides a way to incorporate species expert knowledge into models • “Honest” depiction of uncertainty in predicted habitat distributions • Time and effort involved per review is reasonable • The resulting continuous surface probability map would have to be divided into categories to be used in gap analysis (e.g., areas with probabilities over 0.75 could be considered “suitable” habitat and used in the analysis of ‘gaps’ in networks of conservation lands) • The habitat suitability surfaces can be used in other “what if” planning scenarios and used to direct future habitat analysis • Verifying models vs. showing current “state of knowledge” ? Southwest Regional GAP Project Arizona, Colorado, Nevada, New Mexico, Utah US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology