Landscape Ecology

, Volume 29, Issue 3, pp 541–557 | Cite as

Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study

  • Katherine A. Zeller
  • Kevin McGarigal
  • Paul Beier
  • Samuel A. Cushman
  • T. Winston Vickers
  • Walter M. Boyce
Research Article


Estimating landscape resistance to animal movement is the foundation for connectivity modeling, and resource selection functions based on point data are commonly used to empirically estimate resistance. In this study, we used GPS data points acquired at 5-min intervals from radiocollared pumas in southern California to model context-dependent point selection functions. We used mixed-effects conditional logistic regression models that incorporate a paired used/available design to examine the sensitivity of point selection functions to the scale of available habitat and to the behavioral state of individual animals. We compared parameter estimates, model performance, and resistance estimates across 37 scales of available habitat, from 250 to 10,000 m, and two behavioral states, resource use and movement. Point selection functions and resistance estimates were sensitive to the chosen scale of the analysis. Multiple characteristic scales were found across our predictor variables, indicating that pumas in the study area are responding at different scales to different landscape features and that multi-scale models may be more appropriate. Additionally, point selection functions and resistance estimates were sensitive to behavioral state; specifically, pumas engaged in resource use behavior had an opposite selection response to some land cover types than pumas engaged in movement behavior. We recommend examining a continuum of scales and behavioral states when using point selection functions to estimate resistance.


Puma concolor Conditional logistic regression Resistance surface Cost-surface Connectivity Resource selection function 



We thank A. Allyn, B. Compton, E. Plunkett, H. Robinson, and B. Timm for helpful conversations, ideas, and assistance with computational capacity, D. Dawn, D. Krucki, C. Bell, P. Bryant, D. Stewart, and K. Krause for field assistance, and three anonymous reviewers for thoughtful and constructive comments. 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 National Science Foundation under NSF DGE-0907995, a Kaplan Graduate Award, The Nature Conservancy, Orange County Transportation Corridor Agency, The Nature Reserve of Orange County, and the McBeth Foundation.

Supplementary material

10980_2014_9991_MOESM1_ESM.docx (64 kb)
Supplementary material 1 (DOCX 64 kb)
10980_2014_9991_MOESM2_ESM.docx (119 kb)
Supplementary material 2 (DOCX 119 kb)
10980_2014_9991_MOESM3_ESM.docx (265 kb)
Supplementary material 3 (DOCX 265 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Katherine A. Zeller
    • 1
    • 2
  • Kevin McGarigal
    • 1
  • Paul Beier
    • 3
  • Samuel A. Cushman
    • 4
  • T. Winston Vickers
    • 5
  • Walter M. Boyce
    • 5
  1. 1.Department of Environmental ConservationUniversity of MassachusettsAmherstUSA
  2. 2.PantheraNew YorkUSA
  3. 3.School of ForestryNorthern Arizona UniversityFlagstaffUSA
  4. 4.U.S. Forest Service Rocky Mountain Research Station2500 S Pine Knoll Dr.FlagstaffUSA
  5. 5.Wildlife Health Center, School of Veterinary MedicineUniversity of California DavisDavisUSA

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