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References

  • Albert, P. S. and McShane, L. M.. 1995. A generalized estimating equations approach for spatially correlated binary data: with an application to the analysis of neuroimaging data. Biometrics 51: 627638.
  • Anon. 2005. R: a language and environment for statistical computing. – R Foundation for Statistical Computing.
  • Anselin, L.. 1988. Spatial econometrics: methods and models. Kluwer.
  • Anselin, L.. 2002. Under the hood: issues in the specification and interpretation of spatial regression models. Agricult. Econ. 17: 247267.
  • Araújo, M. B. and Williams, P. H.. 2000. Selecting areas for species persistence using occurrence data. Biol. Conserv. 96: 331345.
  • Araújo, M. B. and Rahbek, C.. 2006. How does climate Change affect biodiversity?. Science 313: 13961397.
  • Augustin, N. H. et al. 1996. An autologistic model for the spatial distribution of wildlife. J. Appl. Ecol. 33: 339347.
  • Augustin, N. H. et al. 1998. The role of simulation in modelling spatially correlated data. Environmetrics 9: 175196.
  • Augustin, N. H. et al. 2005. Analyzing the spread of beech canker. For. Sci. 51: 438448.
  • Bai, Z. et al. 1996. Some large-scale matrix computation problems. J. Comput. Appl. Math. 74: 7189.
  • Beerling, D. J. et al. 1995. Climate and the distribution of Fallopia japonica–use of an introduced species to test the predictive capacity of response surfaces. J. Veg. Sci. 6: 269282.
  • Besag, J.. 1974. Spatial interaction and the statistical analysis of lattice systems. J. Roy. Stat. Soc. B 36: 192236.
  • Besag, J. et al. 1991. Bayesian image restoration with two applications in spatial statistics (with discussion). Ann. Inst. Stat. Math. 43: 159.
  • Bivand, R. 2005. spdep: spatial dependence: weighting schemes, statistics and models. – R package version 0.3–17.
  • Bjørnstad, O. N. and Falck, W.. 2000. Nonparametric spatial covariance functions: estimation and testing. Environ. Ecol. Stat. 8: 5370.
  • Borcard, D. and Legendre, P.. 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol. Modell. 153: 5168.
  • Breslow, N. E. and Clayton, D. G.. 1993. Approximate inference in generalized linear mixed models. J. Am. Stat. Assoc. 88: 925.
  • Brooks, S. P.. 2003. Bayesian computation: a statistical revolution. Phil. Trans. R. Soc. A 361: 26812697.
  • Brownstein, J. S. et al. 2003. A climate-based model predicts the spatial distribution of the Lyme disease vector Ixodes scapularis in the United States. Environ. Health Persp. 111: 11521157.
  • Brunsdon, C. et al. 1996. Geographically weighted regression: a method for exploring spatial non-stationarity. Geogr. Analys. 28: 281298.
  • Carey, V. J. 2002. gee: generalized estimation equation solver. Ported to R by Thomas Lumley (ver. 3.13, 4.4) and Brian Ripley. – <www.r-project.org>.
  • Carl, G. and Kühn, I.. 2007a. Analyzing spatial autocorrelation in species distributions using Gaussian and logit models. Ecol Modell. 207: 159170.
  • Carl, G. and Kühn, I. 2007b. Analyzing spatial ecological data using linear regression and wavelet analysis. – Stochast. Environ. Res. Risk Assess., in press.
  • Casella, G. and George, E. I.. 1992. Explaining the Gibbs sampler. Am. Stat. 46: 167176.
  • Cliff, A. D. and Ord, J. K.. 1981. Spatial processes: models and applications. Pion.
  • Clifford, P. et al. 1989. Assessing the significance of the correlation between two spatial processes. Biometrics 45: 123134.
  • Cressie, N. A. C.. 1993. Statistics for spatial data. Wiley.
  • Dark, S. J.. 2004. The biogeography of invasive alien plants in California: an application of GIS and spatial regression analysis. Div. Distrib. 10: 19.
  • Davies, R. G. et al. 2006. Human impacts and the global distribution of extinction risk. Proc. R. Soc. B 273: 21272133.
  • Diggle, P. J. et al. 1995. Analysis of longitudinal data. Clarendon.
  • Diniz-Filho, J. A. and Bini, L. M.. 2005. Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecol. Biogeogr. 14: 177185.
  • Diniz-Filho, J. A. F. et al. 1998. An eigenvector method for estimating phylogenetic inertia. Evolution 52: 12471262.
  • Diniz-Filho, J. A. F. et al. 2003. Spatial autocorrelation and red herrings in geographical ecology. Global Ecol. Biogeogr. 12: 5364.
  • Dobson, A. J.. 2002. An introduction to generalized linear models. Chapman and Hall.
  • Dormann, C. F.. 2007a. Assessing the validity of autologistic regression. Ecol Modell. 207: 234242.
  • Dormann, C. F.. 2007b. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecol. Biogeogr. 16: 129138.
  • Dormann, C. F.. 2007c. Promising the future? Global change predictions of species distributions. Basic Appl Ecol. 8: 387397.
  • Dray, S. et al. 2006. Spatial modeling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecol. Modell. 196: 483493.
  • Dutilleul, P.. 1993. Modifying the t test for assessing the correlation between two spatial processes. Biometrics 49: 305314.
  • Elith, J. et al. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129151.
  • Ferrier, S. et al. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling. Biodiv. Conserv. 11: 22752307.
  • Foody, G. M.. 2004. Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecol. Biogeogr. 13: 315320.
  • Fortin, M. J. and Dale, M. R. T.. 2005. Spatial analysis – a guide for ecologists. Cambridge Univ. Press.
  • Fotheringham, A. S. et al. 2002. Geographically weighted regression: the analysis of spatially varying relationships. Wiley.
  • Gavin, D. G. and Hu, F. S.. 2006. Spatial variation of climatic and non-climatic controls on species distribution: the range limit of Tsuga heterophylla. J. Biogeogr. 33: 13841396.
  • Gelfand, A. E. and Vounatsou, P.. 2003. Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics 4: 1125.
  • Gelfand, A. E. et al. 2005. Modelling species diversity through species level hierarchical modelling. Appl. Stat. 54: 120.
  • Griffith, D. A.. 2000a. Eigenfunction properties and approximations of selected incidence matrices employed in spatial analyses. Lin. Algebra Appl. 321: 95112.
  • Griffith, D. A.. 2000b. A linear regression solution to the spatial autocorrelation problem. J. Geogr. Syst. 2: 141156.
  • Griffith, D. A. and Peres-Neto, P. R.. 2006. Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses in exploiting relative location information. Ecology 87: 26032613.
  • Guisan, A. and Zimmermann, N. E.. 2000. Predictive habitat distribution models in ecology. Ecol. Modell. 135: 147186.
  • Guisan, A. and Thuiller, W.. 2005. Predicting species distributions: offering more than simple habitat models. Ecol. Lett. 8: 9931009.
  • Gumpertz, M. L. et al. 1997. Autologistic model of spatial pattern of Phytophthora epidemic in bell pepper: effects of soil variables on disease presence. J. Agricult. Biol. Environ. Stat. 2: 131156.
  • Haining, R.. 2003. Spatial data analysis – theory and practice. Cambridge Univ. Press.
  • Hastie, T. J. and Tibshirani, R. J.. 1990. Generalized additive models. Chapman and Hall.
  • Hawkins, B. A. et al. 2007. Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology. Ecography 30: 375384.
  • He, F. L. et al. 2003. Autologistic regression model for the distribution of vegetation. J. Agricult. Biol. Environ. Stat. 8: 205222.
  • Hoeting, J. A. et al. 2000. An improved model for spatially correlated binary responses. J. Agricult. Biol. Environ. Stat. 5: 102114.
  • Hooten, M. B. et al. 2003. Predicting the spatial distribution of ground flora on large domains using a hierarchical Bayesian model. Landscape Ecol. 18: 487502.
  • Huffer, F. W. and Wu, H. L.. 1998. Markov chain Monte Carlo for autologistic regression models with application to the distribution of plant species. Biometrics 54: 509524.
  • Hurlbert, S. H.. 1984. Pseudoreplication and the design of ecological experiments. Ecol. Monogr. 54: 187211.
  • Isaaks, E. H. and Shrivastava, R. M.. 1989. An introduction to applied geostatistics. Oxford Univ. Press.
  • Jetz, W. and Rahbek, C.. 2002. Geographic range size and determinants of avian species richness. Science 297: 15481551.
  • Jetz, W. et al. 2005. Local and global approaches to spatial data analysis in ecology. Global Ecol. Biogeogr. 17: 9798.
  • Kaboli, M. et al. 2006. Avifaunal gradients in two arid zones of central Iran in relation to vegetation, climate, and topography. J. Biogeogr. 33: 133144.
  • Kaluzny, S. P. et al. 1998. S-plus spatial stats user's manual for Windows and Unix. Springer.
  • Keitt, T. H. et al. 2002. Accounting for spatial pattern when modeling organism-environment interactions. Ecography 25: 616625.
  • Kissling, W. D. and Carl, G. 2007. Spatial autocorrelation and the selection of simultaneous autoregressive models. – Global Ecol. Biogeogr., in press.
  • Klute, D. S. et al. 2002. Autologistic regression modeling of American woodcock habitat use with spatially dependent data. – In: Scott, J. M. et al (eds), Predicting species occurrences: issues of accuracy and scale. Island Press, pp. 335343.
  • Knapp, R. A. et al. 2003. Developing probabilistic models to predict amphibian site occupancy in a patchy landscape. Ecol. Appl. 13: 10691082.
  • Kupfer, J. A. and Farris, C. A.. 2007. Incorporating spatial non-stationarity of regression coefficients inti predictive vegetation model. Landscape Ecol. 22: 837852.
  • Kühn, I.. 2007. Incorporating spatial autocorrelation may invert observed patterns. Div. Distrib. 13: 6669.
  • Kühn, I. et al. 2006. Relating geographical variation in pollination types to environmental and spatial factors using novel statistical methods. New Phytol. 72: 127139.
  • Latimer, A. M. et al. 2006. Building statistical models to analyze species distributions. Ecol. Appl. 16: 3350.
  • Legendre, P.. 1993. Spatial autocorrelation: trouble or new paradigm?. Ecology 74: 16591673.
  • Legendre, P. and Fortin, M.-J.. 1989. Spatial pattern and ecological analysis. Vegetatio 80: 107138.
  • Legendre, P. and Legendre, L.. 1998. Numerical ecology. Elsevier.
  • Legendre, P. et al. 2002. The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography 25: 601615.
  • Lennon, J. J.. 2000. Red-shifts and red herrings in geographical ecology. Ecography 23: 101113.
  • Liang, K. Y. and Zeger, S. L.. 1986. Longitudinal data analysis using generalized linear models. Biometrika 73: 1322.
  • Lichstein, J. W. et al. 2002. Spatial autocorrelation and autoregressive models in ecology. Ecol. Monogr. 72: 445463.
  • Liebhold, A. M. and Gurevitch, J.. 2002. Integrating the statistical analysis of spatial data in ecology. Ecography 25: 553557.
  • Link, W. A. and Barker, R. J.. 2006. Model weights and the foundations of multimodel inference. Ecology 87: 26262635.
  • Littell, R. C. et al. 1996. SAS system for mixed lodels. SAS Publ.
  • Luoto, M. et al. 2001. Determinants of distribution and abundance in the clouded apollo butterfly: a landscape ecological approach. Ecography 24: 601617.
  • McCullough, P. and Nelder, J. A.. 1989. Generalized linear models. Chapman and Hall.
  • McPherson, J. M. and Jetz, W.. 2007. Effects of species’ ecology on the accuracy of distribution models. Ecography 30: 135151.
  • Miller, J. et al. 2007. Incorporating spatial dependence in predictive vegetation models. Ecol. Modell. 202: 225242.
  • Moore, J. E. and Swihart, R. K.. 2005. Modeling patch occupancy by forest rodents: incorporating detectability and spatial autocorrelation with hierarchically structured data. J. Wildl. Manage. 69: 933949.
  • Myers, R. H. et al. 2002. Generalized linear models. Wiley.
  • Orme, C. D. L. et al. 2005. Global hotspots of species richness are not congruent with endemism or threat. Nature 436: 10161019.
  • Osborne, P. E. et al. 2001. Modelling landscape-scale habitat use using GIS and remote sensing: a case study with great bustards. J. Appl. Ecol. 38: 458471.
  • Osborne, P. E. et al. 2007. Non-stationarity and local approaches to modelling the distributions of wildlife. – Div. Distribut., in press.
  • Palma, L. et al. 1999. The use of sighting data to analyse Iberian lynx habitat and distribution. J. Appl. Ecol. 36: 812824.
  • Pearson, R. G. and Dawson, T. P.. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Global Ecol. Biogeogr. 12: 361371.
  • Perry, J. N. et al. 2002. Illustrations and guidelines for selecting statistical methods for quantifying spatial paterrns in ecological data. Ecography 25: 578600.
  • Pinheiro, J. C. and Bates, D. M.. 2000. Mixed-effect models in S and S-plus. Springer.
  • Rangel, T. F. L. V. B. et al. 2006. Towards an integrated computational tool for spatial analysis in macroecology and biogeography. Global Ecol. Biogeogr. 15: 321327.
  • Reich, R. M. et al. 2004. Predicting the location of northern goshawk nests: modeling the spatial dependency between nest locations and forest structure. Ecol. Modell. 176: 109133.
  • Segurado, P. and Araújo, M. B.. 2004. An evaluation of methods for modelling species distributions. J. Biogeogr. 31: 15551568.
  • Segurado, P. et al. 2006. Consequences of spatial autocorrelation for niche-based models. J. Appl. Ecol. 43: 433444.
  • Smith, P. A.. 1994. Autocorrelation in logistic regression modelling of species’ distributions. Global. Ecol. Biogeogr. Lett. 4: 4761.
  • Sokal, R. R. and Oden, N. L.. 1978a. Spatial autocorrelation in biology. I. Methodology. Biol. J. Linn. Soc. 10: 199228.
  • Sokal, R. R. and Oden, N. L.. 1978b. Spatial autocorrelation in biology. II. Some biological implications and four applications of evolutionary and ecological interest. Biol. J. Linn. Soc. 10: 229249.
  • Stephenson, C. M. et al. 2006. Modelling establishment probabilities of an exotic plant, Rhododendron ponticum, invading a heterogeneous, woodland landscape using logistic regression with spatial autocorrelation. Ecol. Modell 193: 747758.
  • Sykes, M. T.. 2001. Modelling the potential distribution and community dynamics of lodgepole pine (Pinus contorta Dougl. ex. Loud.) in Scandinavia. For. Ecol. Manage. 141: 6984.
  • Teterukovskiy, A. and Edenius, L.. 2003. Effective field sampling for predicting the spatial distribution of reindeer (Rangifer tarandus) with help of the Gibbs sampler. Ambio 32: 568572.
  • Thogmartin, W. E. et al. 2004. A hierarchical spatial model of avian abundance with application to Cerulean warblers. Ecol. Appl. 14: 17661779.
  • Tiefelsdorf, M. et al. 1999. A variance-stabilizing coding scheme for spatial link matrices. Environ. Plann. A 31: 165180.
  • Tobler, W. R.. 1970. A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 46: 234240.
  • Tognelli, M. F. and Kelt, D. A.. 2004. Analysis of determinants of mammalian species richness in South America using spatial autoregressive models. Ecography 27: 427436.
  • Venables, W. N. and Ripley, B. D.. 2002. Modern applied statistics with S. Springer.
  • Ver Hoef, J. M. et al. 1993. Spatial models for spatial statistics: some unification. J. Veg. Sci. 4: 441452.
  • Wall, M. M.. 2004. A close look at the spatial structure implied by the CAR and SAR models. J. Stat. Plann. Infer. 121: 311324.
  • Waller, L. A. and Gotway, C. A.. 2004. Applied spatial statistics for public health data. Wiley.
  • Wood, S. N.. 2006. Generalized additive models. Chapman and Hall/CRC.
  • Worm, B. et al. 2005. Global patterns of predator diversity in the open oceans. Science 309: 1365136.
  • Wu, H. L. and Huffer, F. W.. 1997. Modelling the distribution of plant species using the autologistic regression model. Environ. Ecol. Stat. 4: 4964.
  • Yamaguchi, N. et al. 2003. Habitat preferences of feral American mink in the Upper Thames. J. Mammal. 84: 13561373.
  • Yan, J.. 2002. geepack: yet another package for generalized estimating equations. R News 2: 1214.
  • Yan, J. 2004. geepack: generalized estimating equation package. – R package version 0.2–10.