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

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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.

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slide1

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

acknowledgments

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

project goals

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

wildlife habitat suitability models

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

why bayesian inference

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

methods

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

mountain plover example

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

wildlife habitat suitability model

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

prior probability surface

Prior Probability Surface

Southwest Regional GAP Project

Arizona, Colorado, Nevada, New Mexico, Utah

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

model review

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

range review tool

Range Review Tool

Southwest Regional GAP Project

Arizona, Colorado, Nevada, New Mexico, Utah

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

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.

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

range probability surface

Range Probability Surface

Southwest Regional GAP Project

Arizona, Colorado, Nevada, New Mexico, Utah

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

habitat relationship review tool

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

colorado gap land cover map

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

land cover probability surface

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

digital elevation model

Digital Elevation Model

Southwest Regional GAP Project

Arizona, Colorado, Nevada, New Mexico, Utah

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

elevation probability surface

Elevation Probability Surface

Southwest Regional GAP Project

Arizona, Colorado, Nevada, New Mexico, Utah

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

bayes inference calculation

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

probability of habitat suitability for mountain plover

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

model comparisons

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

land cover classification accuracy

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

land cover classification accuracy surface

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

slide24

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

uncertainty comparisons

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

distance to water coverage

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

distance to water habitat relationship as probability

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

probability of habitat suitability for boreal toad

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

probability of habitat suitability for boreal toad combining several expert reviews

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

patch size as probability for lynx model

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

lynx model comparisons

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

findings

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

conclusions

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

usefulness for gap analysis

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

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