Depicting uncertainty in wildlife habitat suitability models using Bayesian inference and expert opinion Southwest Regional GAP Project Arizona, Colorado,

Depicting uncertainty in wildlife habitat suitability models using Bayesian

inference and expert opinion

Southwest Regional GAP Project

Arizona, Colorado, Nevada, New Mexico, UtahUS-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

Lee O’BrienNatural Resource Ecology Laboratory

Colorado Sate University, Fort Collins, CO

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.


Arizona, Colorado, Nevada, New Mexico, Utah

US-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

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




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




Why Bayesian Inference ?




P(S|E) = P(S) * P(E|S)

P(E) 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)

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 uncertaintyBayes’ Theorem

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




Mountain Plover Example

Example of method incorporating expert opinion into the Colorado Gap Analysis habitat suitability model for the mountain plover (Charadrius montanus)




Wildlife Habitat Suitability Model




Prior Probability Surface




Model Review




Tools used to review habitat relationships and ranges, and collect expert opinion

Developed in ESRI ArcView and MS Excel

Range Review Tool




Elicitation by Species Experts




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.

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

Range Probability Surface




Habitat Relationship Review Tool




Colorado GAP Land Cover Map




Land Cover Probability Surface




Digital Elevation Model




Elevation Probability Surface




Bayes’ Inference Calculation




Prior Probability Surface

WHR Probability Surfaces

Range

Elevation

Land cover (x2)

Posterior Probability Surface

P(S)

P(E|S)

P(S|E)




Probability of Habitat Suitability for Mountain

Plover

Model Comparisons




Prior “Absolute” Model

Posterior Probability Model

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




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

Caveats

Land Cover Classification Accuracy Surface




Uncertainty in Mountain Plover Wildlife Habitat Suitability Model with Additional Uncertainty from Land Cover

Classification




Uncertainty Comparisons




Model Uncertainty

Model Uncertainty with Land Cover Classification Uncertainty

Distance to Water Coverage




Distance to Water Habitat Relationship as Probability




Probability of Habitat Suitability for Boreal Toad







Probability of Habitat Suitability for Boreal Toad Combining Several Expert

Reviews

Patch Size as Probability for Lynx Model




Lynx Model Comparisons




Lynx Model

Lynx Model with Patch Size Probability

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




Conclusions

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




This procedure…

Does not…

Address uncertainty from scale inconsistencies or cartographic errors

Predict species occurrence

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

ProjectArizona, Colorado, Nevada, New Mexico,

UtahUS-IALE 2004, Las Vegas, Nevada: Transdisciplinary Challenges in Landscape Ecology

Documents

Depicting uncertainty in wildlife habitat suitability models using Bayesian inference and expert opinion Southwest Regional GAP Project Arizona, Colorado,