Reconciling resource utilization and resource selection functions

Journal of Animal Ecology

Volumn 82, Issue 6, 2013, Pages 1146-1154

  Hooten, Mevin B ^a   Hanks, Ephraim M ^b   Johnson, Devin S ^c   Alldredge, Mat W ^d  

^a Colorado State University ^*  (United States)

^b Department of Environmental and Radiological Health Sciences   (United States)

^c NOAA National Marine Mammal Laboratory   (United States)

^d NONE   (United States)

Author keywords

Kernel density estimation; Space use; Spatial statistics; Utilization distribution

Indexed keywords

ANIMAL; DATA SET; RESOURCE SELECTION; SIMULATION; SPACE USE; TELEMETRY;

ANIMALIA;

ANIMAL; ARTICLE; BIOLOGICAL MODEL; ECOLOGY; ECOSYSTEM; KERNEL DENSITY ESTIMATION; METHODOLOGY; PHYSIOLOGY; PUMA; SPACE USE; SPATIAL STATISTICS; TELEMETRY; UNITED STATES; UTILIZATION DISTRIBUTION;

KERNEL DENSITY ESTIMATION; SPACE USE; SPATIAL STATISTICS; UTILIZATION DISTRIBUTION;

ANIMALS; COLORADO; ECOLOGY; ECOSYSTEM; MODELS, BIOLOGICAL; PUMA; TELEMETRY;

EID: 84886306163     PISSN: 00218790     EISSN: 13652656     Source Type: Journal    
DOI: 10.1111/1365-2656.12080     Document Type: Article

References (25)

1. Aarts, G., Fieberg, J. & Matthiopoulos, J. (2012) Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods in Ecology and Evolution, 3, 177-187.
2. Beers, T., Dress, P. & Wensel, L. (1966) Aspect transformation in site productivity research. Journal of Forestry, 64, 691-692.
3. Clayton, D., Bernardinelli, L. & Montomoli, C. (1993) Spatial correlation in ecological analysis. International Journal of Epidemiology, 22, 1193-1202.
4. Cressie, N.A.C. (1993) Statistics for Spatial Data. John Wiley & Sons, Inc., New York, USA.
5. Hepinstall, J.A., Marzluff, J.M., Handcock, M.S. & Hurvitz, P. (2004) Incorporating resource utilization distributions into the study of resource selection: dealing with spatial autocorrelation. Resource Selection Methods and Applications (ed. S. Huzurbazar ), pp. 12-19. Omnipress, Madison, WI.
6. Hodges, J.S. & Reich, B.J. (2010) Adding spatially-correlated errors can mess up the fixed effect you love. The American Statistician, 64, 325-334.
7. Hughes, J. & Haran, M. (2013) Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. Journal of the Royal Statistical Society: Series B, Statistical Methodology, 75, 139-159.
8. Johnson, C.J., Nielson, S.E., Merrill, E.H., McDonald, T.L. & Boyce, M.S. (2006) Resource selection functions based on use-availability data: theoretical motivation and evaluation methods. Journal of Wildlife Management, 70, 347-357.
9. Johnson, D.S., Thomas, D.L., Ver Hoef, J.M. & Christ, A. (2008) A general framework for the analysis of animal resource selection from telemetry data. Biometrics, 64, 968-976.
10. Krebs, C. (1978) Ecology: The Experimental Analysis of Distribution and Abundance. Harper & Row Publishers Inc., New York, USA.
11. Lele, S.R. & Keim, J.L. (2006) Weighted distributions and estimation of resource selection probability functions. Ecology, 87, 3021-3028.
12. Lele, S.R. (2009) A new method for estimation of resource selection probability function. The Journal of Wildlife Management, 71, 122-127.
13. Manly, B.F.J., McDonald, L.L., Thomas, D.L., McDonald, T.L. & Erickson, W.P. (2002) Resource Selection by Animals. Kluwer Academic Publishers, Dordrecht.
14. Marzluff, J.M., Millspaugh, J.J., Hurvitz, P. & Handcock, M.S. (2004) Relating resources to a probabilistic measure of space use: forest fragments and stellar's jays. Ecology, 85, 1411-1427.
15. Millspaugh, J.J., Nielson, R.M., McDonald, L.L., Marzluff, J.M., Gitzen, R.A., Rittenhouse, C.D., Hubbard, M.W. & Sheriff, S.L. (2006) Analysis of resource selection using utilization distributions. Journal of Wildlife Management, 70, 384-395.
16. Otis, D.L. & White, G.C. (1999) Autocorrelation of location estimates and the analysis of radiotracking data. Journal of Wildlife Management, 63, 1039-1044.
17. Paciorek, C.J. (2010) The importance of scale for spatial-confounding bias and precision of spatial regression estimators. Statistical Science, 25, 107-125.
18. R Core Team. (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
19. Reich, B.J., Hodges, J.S. & Zadnik, V. (2006) Effects of residual smoothing on the posterior of the fixed effects in disease-mapping models. Biometrics, 62, 1197-1206.
20. Ribeiro, P.J. & Diggle, P.J. (2001) geoR: a package for geostatistical analysis. R-NEWS, 1, 15-18.
21. Silverman, B.W. (1986) Density Estimation for Statistics and Data Analysis. Chapman & Hall, London.
22. Swihart, R.K. & Slade, N.A. (1985) Testing for independence of observations in animal movements. Ecology, 66, 1176-1184.
23. Venables, W.N. & Ripley, B.D. (2002) Modern Applied Statistics with S, 4th edn. Springer, New York.
24. Warton, D.I. & Shepherd, L.C. (2010) Poisson point process models solve the "pseudo-absence problem" for presence-only data in ecology. The Annals of Applied Statistics, 4, 1383-1402.
25. Wilson, R.R., Hooten, M.B., Strobel, B.N. & Shivik, J.A. (2010) Accounting for individuals, uncertainty, and multi-scale clustering in core area estimation. Journal of Wildlife Management, 74, 1343-1352.