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References

  • 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, 177187.
  • Aarts, G., MacKenzie, M., McConnell, B., Fedak, M. & Matthiopoulos, J. (2008) Estimating space-use and habitat preference from wildlife telemetry data. Ecography, 31, 140160.
  • Albert, C.H., Yoccoz, N.G., Edwards, T.C., Graham, C.H., Zimmermann, N.E. & Thuiller, W. (2010) Sampling in ecology and evolution – bridging the gap between theory and practice. Ecography, 33, 10281037.
  • Arthur, S.M., Manly, B.F.J., McDonald, L.L. & Garner, G.W. (1996) Assessing habitat selection when availability changes. Ecology, 77, 215227.
  • Baddeley, A. & Turner, R. (2000) Practical maximum pseudolikelihood for spatial point patterns. Australian and New Zealand Journal of Statistics, 42, 283315.
  • Baddeley, A., Berman, M., Fisher, N.I., Hardegen, A., Milne, R.K., Schuhmacher, D., Shah, R. & Turner, R. (2010) Spatial logistic regression and change-of-support in Poisson point processes. Electronic Journal of Statistics, 4, 11511201.
  • Bastille-Rousseau, G., Fortin, D. & Dussault, C. (2010) Inference from habitat-selection analysis depends on foraging strategies. Journal of Animal Ecology, 79, 11571163.
  • Begon, M., Harper, J.L. & Townsend, C.R. (1996) Ecology: Individuals, Populations and Communities Blackwell Publishing, Oxford.
  • Beyer, H.L., Haydon, D.T., Morales, J.M., Frair, J.L., Hebblewhite, M., Mitchell, M. & Matthiopoulos, J. (2010) The interpretation of habitat preference metrics under use-availability designs. Philosophical Transactions of the Royal Society B-Biological Sciences, 365, 22452254.
  • Boyce, M.S. & McDonald, L.L. (1999) Relating populations to habitats using resource selection functions. Trends in Ecology and Evolution, 14, 268272.
  • Boyce, M.S., Vernier, P.R., Nielsen, S.E. & Schmiegelow, F.K.A. (2002) Evaluating resource selection functions. Ecological Modelling, 157, 281300.
  • Buckland, D.A. & Elston, S.T. (1993) Empirical models for the distribution of wildlife. Journal of Applied Ecology, 30, 478495.
  • Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multimodel Inference: A Practical Information-theoretic Approach 2nd edn., Springer, New York.
  • Chakraborty, A., Gelfand, A.E., Wilson, A.M., Latimer, A.M. & Silander, J.A. (2011) Point pattern modeling for degraded presence-only data over large regions. Journal of the Royal Statistical Society, Series C (Applied Statistics), 60, 757776.
  • Cressie, N.A.C. (1993) Statistics for Spatial Data. Wiley, New York.
  • Embling, C.B., Gillibrand, P.A., Gordon, J., Shrimpton, J., Stevick, P.T. & Hammond, P.S. (2010) Using habitat models to identify suitable sites for marine protected areas for harbour porpoises (Phocoena phocoena). Biological Conservation, 143, 267279.
  • Forester, J.D., Kyung, I.M. & Rathouz, P.J. (2009) Accounting for animal movement in estimation of resource selection functions: sampling and data analysis. Ecology, 90, 35543565.
  • Fotheringham, A.S., Brunsdon, C. & Charlton, M.E. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester.
  • Gaillard, J.-M., Hebblewhite, M., Loison, A., Fuller, M., Powell, R., Basille, M. & Van Moorter, B. (2010) Habitat-performance relationships: finding the right metric at a given spatial scale. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 365, 22552265.
  • Geisser, S. (1993) Predictive Inference: An Introduction. Chapman and Hall, New York.
  • Guisan, A. & Zimmermann, N.E. (2000) Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147186.
  • Hastie, T.J. & Tibshirani, R.J. (1990) Generalized Additive Models. Chapman and Hall, London.
  • Heisey, D.M. (1985) Analyzing selection experiments with log-linear models. Ecology, 66, 17441748.
  • Horne, J.S. & Garton, E.O. (2006) Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kernel home-range analysis. Journal of Wildlife Management, 70, 641648.
  • Johnson, C.J., Nielsen, 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, 347357.
  • Johnson, D.S., Thomas, D.L., Hoef, J.M.V. & Christ, A. (2008) A general framework for the analysis of animal resource selection from telemetry data. Biometrics, 64, 968976.
  • Kneib, T., Knauer, F. & Küchenhoff, H. (2011) A general approach to the analysis of habitat selection. Environmental and Ecological Statistics, 18, 125.
  • Krausman, P.R. (1999) Some basic principles of habitat use. Grazing Behavior of Livestock and Wildlife (eds K. Launchbaugh, K. Sanders & J. Mosley), pp. 8590. University of Idaho Forest, Wildlife & Range Exp. Sta. Bull. #70, Univ. of Idaho, Moscow, ID.
  • Lele, S.R. & Keim, J.L. (2006) Weighted distributions and estimation of resource selection probability functions. Ecology, 87, 30213028.
  • MacKenzie, D.I., Nichols, J.D., Royle, J.A., Pollock, K.H., Hines, J.E. & Bailey, L.L. (2005) Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Elsevier, San Diego.
  • Manly, B.F.J., McDonald, L.L. & Thomas, D.L. (1993) Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Chapman and Hall, London.
  • Manly, B.F.J., McDonald, L.L., Thomas, D.L., McDonald, T.L. & Erickson, W.P. (2002) Resource Selection by Animals. Statistical Design and Analysis for Field Studies, 2nd edn. Kluwer Academic Publishers, Dordrecht.
  • Matthiopoulos, J. (2003) Model-supervised kernel smoothing for the estimation of spatial usage. Oikos, 102, 367377.
  • Matthiopoulos, J., Hebblewhite, M., Aarts, G. & Fieberg, J. (2011) Generalized functional responses for species distributions. Ecology, 92, 583589.
  • Mauritzen, M., Belikov, S.E., Boltunov, A.N., Derocher, A.E., Hansen, E., Ims, R.A., Wiig, O. & Yoccoz, N. (2003) Functional responses in polar bear habitat selection. Oikos, 100, 112124.
  • McCracken, M.L., Manly, B.F.J. & Heyden, M.V. (1998) The use of discrete-choice models for evaluating resource selection. Journal of Agricultural Biological and Environmental Statistics, 3, 268279.
  • McDonald, L.L., Manly, B.F.J. & Raley, C.M. (1990) Analyzing foraging and habitat use through selection functions. Studies in Avian Biology, 13, 325331.
  • Moorcroft, P.R. & Barnett, A.H. (2006) Mechanistic home range models and resource selection analysis: a reconciliation and unification. Ecology, 89, 11121119.
  • Moreau, G., Fortin, D., Couturier, S. & Duchesne, T. (2012) Multi-level functional responses for wildlife conservation: the case of threatened caribou in managed boreal forests. Journal of Applied Ecology, 49, 611620.
  • Mysterud, A. & Ims, R.A. (1998) Functional responses in habitat use: availability influences relative use in trade-off situations. Ecology, 79, 14351441.
  • Nelder, J.A. & Mead, R. (1965) A simplex algorithm for function minimization. Computer Journal, 7, 308313.
  • Olsson, O. & Rogers, D.J. (2009) Predicting the distribution of a suitable habitat for the white stork in Southern Sweden: identifying priority areas for reintroduction and habitat restoration. Animal Conservation, 12, 6270.
  • Páez, A., Uchida, T. & Miyamoto, K. (2002) A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific kernel bandwidths and a test for locational heterogeneity. Environment and Planning A, 34, 733754.
  • Patil, G.P. (2002) Weighted distributions. Encyclopedia of Environmetrics, Volume 4 (eds A.H. El-Shaarawi & W.W. Piegorsch), pp. 23692377. John Wiley & Sons, Chichester.
  • Patil, G.P. & Rao, C.R. (1978) Weighted distributions and size-biased sampling with applications to wildlife populations and human families. Biometrics, 34, 179189.
  • Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231259.
  • R Development Core Team (2011) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  • Ramsay, J.O. & Silverman, B. (2005) Functional Data Analysis. Springer-Verlag, New York.
  • Randin, C.F., Dirnböck, T., Dullinger, S., Zimmermann, N.E., Zappa, M. & Guisan, A. (2006) Are niche-based species distribution models transferable in space? Journal of Biogeography, 33, 16891703.
  • Renner, I. & Warton, D. (2013) Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, DOI: 10.1111/j.1541-0420.2012.01824.x.
  • Rue, H., Martino, S. & Chopin, N. (2009) Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71, 319392.
  • Sinclair, A.R.E., Fryxell, J.M. & Caughley, G. (2006) Wildlife Ecology, Conservation, and Management. Blackwell Publishing, London.
  • Tilman, D. (1986) A consumer-resource approach to community structure. American Zoologist, 26, 522.
  • Warton, D.I. & Shepherd, L.C. (2010) Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology. Annals of Applied Statistics, 4, 13831402.