Sensitivity of landscape resistance estimates based on point selection functions to scale and behavioral state: pumas as a case study
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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.
KeywordsPuma 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.
- Bates D, Maechler M, Bolker B (2013) Linear mixed-effects models using S4 classes. (R package). http://cran.r-project.org/web/packages/lme4/index.html. Accessed 20 April 2013
- Burdett CL, Crooks KR, Theobald DM, Wilson KR, Boydston EE, Lyren LM, Fisher RN, Vickers TW, Morrison SA, Boyce WM (2010) Interfacing models of wildlife habitat and human development to predict the future distribution of puma habitat. Ecosphere 1: Article 4, p 1–21Google Scholar
- California Department of Fish and Game (1988) A guide to wildlife habitats of California. In Mayer KE, Laudenslayer WF Jr (eds) State of California Resources Agency. Department of Fish and Game, Sacramento, p 166. http://www.dfg.ca.gov/biogeodata/cwhr/wildlife_habitats.asp#Tree. Accessed 29 Jan 2013
- Compton BW, Rhymer JM, McCollough M (2002) Habitat selection by wood turtles (Clemmys insculpta): an application of paired logistic regression. Ecology 83:833–843Google Scholar
- Dormann CF, McPherson JM, Araújo MB, Bivand R, Bollinger J, Carl G, Davies RG, Hirzel A, Jetz W, Kissling WD, Kühn I, Ohlemüller R, Peres-Neto PR, Reineking B, Shröder B, Schurr FM, Wilson R (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628CrossRefGoogle Scholar
- Laundré JW, Hernández L (2003) Winter hunting habitat of pumas Puma concolor in northwestern Utah and southern Idaho, USA. Wildl Biol 9:123–129Google Scholar
- Manly BF, McDonald L, Thomas DL, McDonald TL, Erickson WP (2002) Resource selection by animals: statistical design and analysis for field studies, 2nd edn. Kluwer Academic Publishers, DordrechtGoogle Scholar
- R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/. Accessed 15 May 2013
- Ribatet M (2012) Generalized pareto distribution and peaks over threshold. (R package). http://cran.r-project.org/web/packages/POT/index.html. Accessed 15 Nov 2012
- USDA Forest Service (2007) CalVeg: FSSDE.EvegTile47A_02_v2. Pacific Southwest Region Remote Sensing Lab, McClellanGoogle Scholar