SEARCH

SEARCH BY CITATION

References

  • Aebischer, N.J., Robertson, P.A. & Kenward, R.E. (1993) Compositional analysis of habitat use from animal radio-tracking data. Ecology, 74, 13131325.
  • Begg, M.D. & Parides, M.K. (2003) Separation of individual-level and cluster-level covariate effects in regression of correlated data. Statistics in Medicine, 22, 25912602.
  • Bennington, C.C. & Thayne, W.V. (1994) Use and misuse of mixed-model analysis of variance in ecological studies. Ecology, 75, 717722.
  • Bingham, R.L. & Brennan, L.A. (2004) Comparison of type I error rates for statistical analyses of resource selection. Journal of Wildlife Management, 68, 206212.
  • Boyce, M.S., Irwin, L.L. & Barker, R. (2005) Demographic meta-analysis: synthesizing vital rates for spotted owls. Journal of Applied Ecology, 42, 3849.
  • Breslow, N.E. & Clayton, D.G. (1993) Approximate inference in generalized linear mixed models. Journal of the American Statistical Association, 88, 925.
  • Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multi-Model Inference. Springer-Verlag, New York.
  • Burnham, K.P. & White, G.C. (2002) Evaluation of some random effects methodology applicable to bird ringing data. Journal of Applied Statistics, 29, 245264.
  • Cam, E., Link, W.A., Cooch, E.G., Monnat, J.Y. & Danchin, E. (2002) Individual covariation in life-history traits: seeing the trees despite the forest. American Naturalist, 159, 96105.
  • Cooch, E.G., Cam, E. & Link, W.A. (2002) Occam's shadow: levels of analysis in evolutionary ecology − where to next? Journal of Applied Statistics, 29, 1948.
  • Follmann, D.A. & Lambert, D. (1989) Generalized logistic regression by nonparametric mixing. Journal of the American Statistical Association, 84, 295301.
  • Franklin, A.B., Anderson, D.R. & Burnham, K.P. (2002) Estimation of long-term trends and variation in avian survival probabilities using random effects models. Journal of Applied Statistics, 29, 267287.
  • Franklin, A.B., Anderson, D.R., Gutierrez, R.J. & Burnham, K.P. (2000) Climate, habitat quality, and fitness in northern spotted owl populations in northwestern California. Ecological Monographs, 70, 539590.
  • Franklin, S.E., Stenhouse, G.B., Hansen, M.J., Popplewell, C.C., Dechka, J.A. & Peddle, D.R. (2001) An integrated decision tree approach (IDTA) to mapping landcover using satellite remote sensing in support of grizzly bear habitat analysis in the Alberta Yellowhead Ecosystem. Canadian Journal of Remote Sensing, 27, 579592.
  • Garshelis, D.L. (2000) Delusions in habitat evaluation: measuring use, selection, and importance. Research Techniques in Animal Ecology: Controversies and Consequences (eds L.Boitani & T.L.Fuller), pp. 111154. Columbia University Press, New York.
  • Greenland, S. (2000) When should epidemiologic regressions use random coefficients? Biometrics, 56, 915921.
  • Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 9931009.
  • Hosmer, D.W. & Lemeshow, S. (2000) Applied Logistic Regression. John Wiley and Sons, New York.
  • Hurlbert, S.H. (1984) Pseudoreplication and the design of ecological field experiments. Ecological Monographs, 54, 187211.
  • Johnson, C.J., Seip, D.R. & Boyce, M.S. (2004) A quantitative approach to conservation planning: using resource selection functions to map the distribution of mountain caribou at multiple spatial scales. Journal of Applied Ecology, 41, 238251.
  • Johnson, D.H. (1980) The comparison of usage and availability measurements for evaluating resource preference. Ecology, 61, 6571.
  • Krawchuk, M.A. & Taylor, P.D. (2003) Changing importance of habitat structure across multiple spatial scales for three species of insects. Oikos, 103, 153161.
  • Leban, F.A., Wisdom, M.J., Garton, E.O., Johnson, B.K. & Kie, J.G. (2001) Effect of sample size on the performance of resource selection analyses. Radio Tracking and Wildlife Populations (eds J.J.Millspaugh & J.M.Marzluff), pp. 291307. Academic Press, New York.
  • Manly, B.F.J., McDonald, L.L., Thomas, D.L., McDonald, T.L. & Erickson, W.P. (2002) Resource Selection by Animals: Statistical Analysis and Design for Field Studies, 2nd edn. Kluwer, Boston.
  • McLellan, B.N. & Hovey, F.W. (2001) Habitats selected by grizzly bears in a multiple use landscape. Journal of Wildlife Management, 65, 9299.
  • McNay, R.S. & Bunnell, F.L. (1994) Characterizing independence of observations in movements of Columbian black-tailed deer. Journal of Wildlife Management, 58, 422429.
  • Mladenoff, D.J., Sickley, T.A., Haight, R.G. & Wydeven, A.P. (1995) A regional landscape analysis and prediction of favorable gray wolf habitat in the northern great-lakes region. Conservation Biology, 9, 279294.
  • Morrison, M.L. (2001) Invited paper: a proposed research emphasis to overcome the limits of wildlife–habitat relationship studies. Journal of Wildlife Management, 65, 613623.
  • Mysterud, A. & Ims, R.A. (1998) Functional responses in habitat use: availability influences relative use in trade-off situations. Ecology, 79, 14351441.
  • Natarajan, R. & Mcculloch, C.E. (1999) Modeling heterogeneity in nest survival data. Biometrics, 55, 553559.
  • Neter, J., Kutner, M.H., Wasserman, W., Nachtsheim, C.J. & Neter, J. (1996) Applied Linear Statistical Models, 4th edn. McGraw-Hill Publishers, New York.
  • Newey, W.K. & West, K.D. (1987) A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrika, 55, 703708.
  • Nielsen, S.E., Boyce, M.S., Stenhouse, G.B. & Munro, R.H.M. (2002) Modeling grizzly bear habitats in the Yellowhead ecosystem of Alberta: taking autocorrelation seriously. Ursus, 13, 4556.
  • Osko, T.J., Hiltz, M.N., Hudson, R.J. & Wasel, S.M. (2004) Moose habitat preferences in response to changing availability. Journal of Wildlife Management, 68, 576584.
  • Otis, D.L. & White, G.C. (1999) Autocorrelation of location estimates and the analysis of radiotracking data. Journal of Wildlife Management, 63, 10391044.
  • Pendergast, J.F., Gange, S.J., Newton, M.A., Lindstrom, M.J., Palta, M. & Fisher, M.R. (1996) A survey of methods of analyzing clustered binary response data. International Statistics Review, 64, 89118.
  • Pinheiro, J.C. & Bates, D.M. (2000) Mixed Effects Models in s and s-plus. Springer-Verlag, New York.
  • Rabe-Hesketh, S., Pickles, A. & Skrondal, A. (2001) gllamm Manual. Department of Biostatistics and Computing, Institute of Psychiatry, Kings College, University of London, London.
  • Rushton, S.P., Ormerod, S.J. & Kerby, G. (2004) New paradigms for modelling species distributions? Journal of Applied Ecology, 41, 193200.
  • Skrondal, A. & Rabe-Hesketh, S. (2004) Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman & Hall, New York.
  • StataCorp (2003) Stata Statistical Software, Release 8·0. Stata Corporation, College Station, Texas, USA.
  • Swihart, R.K. & Slade, N.A. (1985) Testing for independence of observations in animal movements. Ecology, 66, 11761184.
  • Ten Have, T.R., Kunselman, A.R. & Tran, L. (1999) A comparison of mixed effects logistic regression models for binary response data with two nested levels of clustering. Statistics in Medicine, 18, 947960.
  • Vaida, F. & Blanchard, S. (2005) Conditional Akaike criteria for mixed models. Biometrika, 92, 351370.
  • White, H. (1982) Maximum likelihood estimation of misspecified models. Econometrika, 50, 126.