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Sensitivity of resource selection and connectivity models to landscape definition

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Abstract

Context

The definition of the geospatial landscape is the underlying basis for species-habitat models, yet sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition has received little attention.

Objectives

We evaluated the sensitivity of resource selection and connectivity models to four landscape definition choices including (1) the type of geospatial layers used, (2) layer source, (3) thematic resolution, and (4) spatial grain.

Methods

We used GPS telemetry data from pumas (Puma concolor) in southern California to create multi-scale path selection function models (PathSFs) across landscapes with 2500 unique landscape definitions. To create the landscape definitions, we identified seven geospatial layers that have been shown to influence puma habitat use. We then varied the number, sources, spatial grain, and thematic resolutions of these layers to create our suite of plausible landscape definitions. We assessed how PathSF model performance (based on AIC) was affected by landscape definition and examined variability among the predicted probability of movement surfaces, connectivity models, and road crossing locations.

Results

We found model performance was extremely sensitive to landscape definition and identified only seven top models out of our suite of definitions (<1%). Spatial grain and the number of geospatial layers selected for a landscape definition significantly affected model performance measures, with finer grains and greater numbers of layers increasing model performance.

Conclusions

Given the sensitivity of habitat use inference, predicted probability surfaces, and connectivity models to landscape definition, out results indicate the need for increased attention to landscape definition in future studies.

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Acknowledgements

We thank B. Compton and E. Plunkett, for assistance with computational capacity, D. Dawn, D. Krucki, C. Bell, P. Bryant, D. Stewart, and K. Krause for field assistance and the two journal reviewers for thoughtful and insightful suggestions on improving this manuscript. We would also like to thank the following landowners/managers: The Nature Conservancy, California Department of Fish and Wildlife, Orange County Parks Department, The New Irvine Ranch Conservancy, Audubon Starr Ranch Reserve, Riverside County Parks Department, and the Cleveland National Forest. This material is based upon work supported by the Robert and Patricia Switzer Foundation, the American Association of University Women, P.E.O., UMASS Graduate School, The Nature Conservancy, Orange County Transportation Corridor Agency, The Nature Reserve of Orange County, and the McBeth Foundation.

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Authors and Affiliations

  1. Department of Environmental Conservation, University of Massachusetts, Amherst, MA, 01003, USA

    Katherine A. Zeller & Kevin McGarigal

  2. U.S. Forest Service Rocky Mountain Research Station, 2500 S Pine Knoll Dr., Flagstaff, AZ, 86001, USA

    Samuel A. Cushman

  3. School of Forestry, Northern Arizona University, Flagstaff, AZ, 86011, USA

    Paul Beier

  4. Wildlife Health Center, School of Veterinary Medicine, University of California Davis, Davis, CA, 95616, USA

    T. Winston Vickers & Walter M. Boyce

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  1. Katherine A. Zeller
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  2. Kevin McGarigal
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  3. Samuel A. Cushman
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  4. Paul Beier
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  5. T. Winston Vickers
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  6. Walter M. Boyce
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Correspondence to Katherine A. Zeller.

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Zeller, K.A., McGarigal, K., Cushman, S.A. et al. Sensitivity of resource selection and connectivity models to landscape definition. Landscape Ecol 32, 835–855 (2017). https://doi.org/10.1007/s10980-017-0489-8

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