Abdulla, F. A. & Lettenmaier, D. P. (1997) Development of regional parameter estimation equations for a macroscale hydrologic model. Journal of Hydrology, 197, 230–257.CrossRefGoogle Scholar

Accad, A. & Neil, D. T. (2006) Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: a case study from the wet tropics of Northeastern Australia. Ecological Modelling, 198, 85–100.CrossRefGoogle Scholar

Ackerly, D. D. (2003) Community assembly, niche conservatism, and adaptive evolution in changing environments. International Journal of Plant Sciences, 164, S165–S184.CrossRefGoogle Scholar

Ackerly, D. D. (2004) Adaptation, niche conservatism, and convergence: comparative studies of leaf evolution in the California chaparral. American Naturalist, 163, 654–671.CrossRefGoogle ScholarPubMed

Adjemian, J. C. Z., Girvetz, E. H., Beckett, L., & Foley, J. E. (2006) Analysis of Genetic Algorithm for Rule-Set Production (GARP) modeling approach for predicting distributions of fleas implicated as vectors of plague, Yersinia pestis, in California. Journal of Medical Entomology, 43, 93–103.Google ScholarPubMed

Agresti, A. (1996) An Introduction to Categorical Data Analysis. New York: John Wiley and Sons.Google Scholar

Ahlqvist, O. (2005) Using uncertain conceptual spaces to translate between land cover categories. International Journal of Geographical Information Science, 19, 831–857.CrossRefGoogle Scholar

Akçakaya, H. R. (2001) Linking population-level risk assessment with landscape and habitat models. Science of the Total Environment, 274, 283–291.CrossRefGoogle ScholarPubMed

Akçakaya, H. R. (2000) Viability analyses with habitat-based metapopulation models. Population Ecology, 42, 45–53.CrossRefGoogle Scholar

Akçakaya, H. R. & Atwood, J. L. (1997) A habitat based metapopulation model of the California gnatcatcher. Conservation Biology, 11, 422–434.CrossRefGoogle Scholar

Akçakaya, H. R., Butchart, S. H. M., Mace, G. M., Stuart, S. N., & Hilton-Taylor, C. (2006) Use and misuse of the IUCN Red List Criteria in projecting climate change impacts on biodiversity. Global Change Biology, 12, 2037–2043.CrossRefGoogle Scholar

Akçakaya, H. R., Franklin, J., Syphard, A. D., & Stephenson, J. R. (2005) Viability of Bell's Sage Sparrow (Amphispiza belli ssp. belli) under altered fire regimes. Ecological Applications, 15, 521–531.CrossRefGoogle Scholar

Akçakaya, H. R., Radeloff, V. C., Mladenoff, D. J., & He, H. S. (2004) Integrating landscape and metapopulation modeling approaches: viability of the sharp-tailed grouse in a dynamic landscape. Conservation Biology, 18, 526–537.CrossRefGoogle Scholar

Albert, P. S. & McShane, L. M. (1995) A generalized estimating equations approach for spatially correlated binary data: applications to the analysis of neuroimaging data. Biometrics, 51, 627–638.CrossRefGoogle ScholarPubMed

Aldridge, C. L. & Boyce, M. S. (2007) Linking occurrence and fitness to persistence: habitat-based approach for endangered Greater Sage-Grouse. Ecological Applications, 17, 508–526.CrossRefGoogle ScholarPubMed

Alexander, S. M., Logan, T. B., & Paquet, P. C. (2006) Spatio-temporal co-occurrence of cougars (Felis concolor) and wolves (Canis lupis) and their prey during winter: a comparison of two analytical methods. Journal of Biogeography, 33, 2001–2012.CrossRefGoogle Scholar

Allouche, O., Tsoar, A., & Kadmon, R. (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43, 1223–1232.CrossRefGoogle Scholar

Andersen, M. C., Adams, H., Hope, B., & Powell, M. (2004) Risk analysis for invasive species: general framework and research needs. Risk Analysis, 24, 893–900.CrossRefGoogle ScholarPubMed

Anderson, D. R. & Burnham, K. P. (2002) Avoiding pitfalls when using information-theoretic methods. Journal of Wildlife Management, 66, 912–918.CrossRefGoogle Scholar

Anderson, R. P., Lew, D., & Peterson, A. T. (2003) Evaluating predictive models of species distributions: criteria for selecting optimal models. Ecological Modelling, 162, 211–232.CrossRefGoogle Scholar

Anselin, A., Meire, P. M., & Anselin, L. (1989) Multicriteria techniques in ecological evaluation: an example using the analytical hierarchy process. Biological Conservation, 49, 215–229.CrossRefGoogle Scholar

Anselin, L. (2002) Under the hood – Issues in the specification and interpretation of spatial regression models. Agricultural Economics, 27, 247–267.CrossRefGoogle Scholar

Anselin, L. & Rey, S. J. (1991) Properties of tests for spatial dependence in linear regression models. Geographical Analysis, 23, 112–131.CrossRefGoogle Scholar

Anselin, L., Florax, R. J. G. M., & Rey, S. J. (2004) Advances in Spatial Econometrics: Methodology, Tools and Applications. Berlin: Springer.CrossRefGoogle Scholar

AraÚjo, M. B. & Guisan, A. (2006) Five (or so) challenges for species distribution modeling. Journal of Biogeography, 33, 1677–1688.CrossRefGoogle Scholar

AraÚjo, M. B. & Luoto, M. (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecology and Biogeography, 16, 743–753.CrossRefGoogle Scholar

AraÚjo, M. B. & New, M. (2007) Ensemble forecasting of species distributions. Trends in Ecology & Evolution, 22, 42–47.CrossRefGoogle ScholarPubMed

AraÚjo, M. B. & Williams, P. H. (2000) Selecting areas for species persistence using occurrence data. Biological Conservation, 96, 331–345.CrossRefGoogle Scholar

AraÚjo, M. B., Pearson, R. G., Thuiller, W., & Erhard, M. (2005a) Validation of species-climate impact models under climate change. Global Change Biology, 11, 1504–1513.CrossRefGoogle Scholar

AraÚjo, M. B., Thuiller, W., & Pearson, R. G. (2006) Climate warming and the decline of amphibians and reptiles in Europe. Journal of Biogeography, 33, 1712–1728.CrossRefGoogle Scholar

AraÚjo, M. B., Thuiller, W., Williams, P. H., & Reginster, I. (2005b) Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography, 14, 17–30.CrossRefGoogle Scholar

Armstrong, J. S. & Collopy, F. (1992) Error measures for generalizing about forecasting methods: empirical comparisons. International Journal of Forecasting, 8, 69–80.CrossRefGoogle Scholar

Ashcroft, M. B. (2006) A method for improving landscape scale temperature predictions and the implications for vegetation modelling. Ecological Modelling, 197, 394–404.CrossRefGoogle Scholar

Asher, J., Warren, M., Fox, R., Harding, P., Jeffcoate, G., & Jeffcoat, S. (Eds.) (2001) The Millennium Atlas of Butterflies in Britain and Ireland. Oxford, UK: Oxford University Press.

Asner, G. P., Martin, R. E., Ford, A. J., Metcalfe, D. J., & Liddell, M. J. (2009) Leaf chemical and spectral diversity in Australian tropical forests. Ecological Applications, 19, 236–253.CrossRefGoogle ScholarPubMed

Aspinall, R. & Veitch, N. (1993) Habitat mapping from satellite imagery and wildlife survey data using a Bayesian modeling procedure in GIS. Photogrammetric Engineering and Remote Sensing, 59, 537–543.Google Scholar

Aspinall, R. J. & Pearson, D. M. (1996) Data quality and spatial analysis: analytical use of GIS for ecological modeling. In Goodchild, M. F., Steyaert, L. T., Parks, B. O.et al. (Eds.) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO: GIS World Books, pp. 35–38.Google Scholar

Augustin, N., Mugglestone, M., & Buckland, S. (1996) An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology, 33, 339–347.CrossRefGoogle Scholar

Augustin, N., Mugglestone, M., & Buckland, S. (1998) The role of simulation in modelling spatially correlated data. Environmetrics, 9, 175–196.3.0.CO;2-2>CrossRefGoogle Scholar

Austin, M. (1971) The role of regression analysis in plant ecology. Proceedings of the Ecological Society of Australia, 6, 63–75.Google Scholar

Austin, M. P. (1980) Searching for a model for use in vegetation analysis. Vegetatio, 42, 11–21.CrossRefGoogle Scholar

Austin, M. P. (1985) Continuum concept, ordination methods, and niche theory. Annual Review of Ecology and Systematics, 16, 39–61.CrossRefGoogle Scholar

Austin, M. P. (1990) Community theory and competition in vegetation. In Grace, J. B. & Tilman, D. (Eds.) Perspectives on Plant Competition. San Diego, CA: Academic Press, pp. 215–238.Google Scholar

Austin, M. P. (1992) Modelling the environmental niche of plants: some implications for plant community response to elevation CO2 levels. Australian Journal of Botany, 40, 615–630.CrossRefGoogle Scholar

Austin, M. P. (1998) An ecological perspective on biodiversity investigations: examples from Australian eucalypt forests. Annals of the Missouri Botanical Garden, 85, 2–17.CrossRefGoogle Scholar

Austin, M. (1999) A silent clash of paradigms: some inconsistencies in community ecology. Oikos, 86, 170–178.CrossRefGoogle Scholar

Austin, M. P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101–118.CrossRefGoogle Scholar

Austin, M. P. (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200, 1–19.CrossRefGoogle Scholar

Austin, M. & Cunningham, R. (1981) Observational analysis of environmental gradients. Proceedings of the Ecological Society of Australia, 11, 109–119.Google Scholar

Austin, M. P. & Heyligers, P. C. (1989) Vegetation survey design for conservation: gradsect sampling of forests in North-eastern New South Wales. Biological Conservation, 50, 13–32.CrossRefGoogle Scholar

Austin, M. P. & Heyligers, P. C. (1991) New approach to vegetation survey design: gradsect sampling. In Margules, C. R. & Austin, M. P. (Eds.) Nature Conservation: Cost Effective Biological Surveys and Data Analysis. East Melbourne, Australia: CSIRO, pp. 31–36.Google Scholar

Austin, M. P. & Meyers, J. (1996) Current approaches to modelling the environmental niche of eucalypts: implication for management of forest biodiversity. Forest Ecology and Management, 85, 95–106.CrossRefGoogle Scholar

Austin, M. P. & Smith, T. M. (1989) A new model for the continuum concept. Vegetatio, 83, 35–47.CrossRefGoogle Scholar

Austin, M. P., Belbin, L., Meyers, J. A., Doherty, M. D., & Luoto, M. (2006) Evaluation of statistical models used for predicting plant species distributions: Role of artificial data and theory. Ecological Modelling, 199, 197–216.CrossRefGoogle Scholar

Austin, M. P., Cunningham, R. B., & Fleming, P. M. (1984) New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio, 55, 11–27.CrossRefGoogle Scholar

Austin, M. P., Nicholls, A. O., Doherty, M. D., & Meyers, J. A. (1994) Determining species response functions to an environmental gradient by means of a beta-function. Journal of Vegetation Science, 5, 215–228.CrossRefGoogle Scholar

Austin, M. P., Nicholls, A. O., & Margules, C. R. (1990) Determining the realised qualitative niche: environmental niches of five Eucalyptus species. Ecological Monographs, 60, 161–177.CrossRefGoogle Scholar

Austin, M. P., Pausas, J. G., & Nicholls, A. O. (1996) Patterns of tree species richness in relation to environment in southeastern New South Wales, Australia. Australian Journal of Ecology, 21, 154–164.CrossRefGoogle Scholar

Bach, M., Breuer, L., Frede, H. G., Huisman, J. A., Otte, A., & Waldhardt, R. (2006) Accuracy and congruency of three different digital land-use maps. Landscape and Urban Planning, 78, 289–299.CrossRefGoogle Scholar

Bailey, L. L., Simons, T. R., & Pollock, K. H. (2004) Estimating site occupancy and species detection probability parameters for terrestrial salamanders. Ecological Applications, 14, 692–702.CrossRefGoogle Scholar

Bailey, T. C. & Gatrell, A. C. (1995) Interactive spatial data analysis, Harlow, England, Longman.Google Scholar

Band, L. E. (1986) Topographic partitioning of watersheds with digital elevation models. Water Resources Research, 22, 15–24.CrossRefGoogle Scholar

Banks, W. E., D'errico, F., Peterson, A. T.et al. (2008) Human ecological niches and ranges during the LGM in Europe derived from an application of eco-cultural niche modeling. Journal of Archaeological Science, 35, 481–491.CrossRefGoogle Scholar

Barnes, T. K., Volety, A. K., Chartier, K., Mazzotti, F. J., & Pearlstine, L. (2007) A habitat suitability index model for the eastern oyster (Crassostrea virginica), a tool for restoration of the Caloosahatchee Estuary, Florida. Journal of Shellfish Research, 26, 949–959.CrossRefGoogle Scholar

Barnett, V. (2002) Sample Survey: Principles and Methods. London: Arnold.Google Scholar

Barrows, C. W. (1997) Habitat relationships of the Coachella Valley fringe-toed lizard (Uma inornata). Southwestern Naturalist, 42, 218–223.Google Scholar

Barrows, C. W., Swartz, M. B., Hodges, W. L.et al. (2005) A framework for monitoring multiple-species conservation plants. Journal of Wildlife Management, 69, 1333–1345.CrossRefGoogle Scholar

Barry, S. & Elith, J. (2006) Error and uncertainty in habitat models. Journal of Applied Ecology, 43, 413–423.CrossRefGoogle Scholar

Barry, S. C. & Welsh, A. H. (2002) Generalized additive modelling and zero inflated count data. Ecological Modelling, 157, 179–188.CrossRefGoogle Scholar

Bazzaz, F. A. & Pickett, S. T. A. (1980) Physiological ecology of tropical succession: A comparative review. Annual Review of Ecology and Systematics, 11, 287–310.CrossRefGoogle Scholar

Beale, C. M., Lennon, J. J., Elston, D. A., Brewer, M. J., & Yearsley, J. M. (2007) Red herrings remain in geographical ecology: a reply to Hawkins et al. (2007). Ecography, 30, 845–847.CrossRefGoogle Scholar

Beaumont, L. J. (2008) Why is the choice of future climate scenarios for species distribution modelling important?Ecology Letters, 11, 1135–1146.CrossRefGoogle ScholarPubMed

Beck-Worner, C., Raso, G., Vounatsou, P.et al. (2007) Bayesian spatial risk prediction of Schistosoma mansoni infection in western Côte d'Ivoire using a remotely-sensed digital elevation model. American Journal of Tropical Medicine and Hygiene, 76, 956–963.Google ScholarPubMed

Beerling, D., Huntley, B., & Bailey, J. (1995) Climate and the distribution of Fallopia japonica: use of an introduced species to test the predictive capacity of response surfaces. Journal of Vegetation Science, 6, 269–282.CrossRefGoogle Scholar

Beger, M. & Possingham, H. P. (2008) Environmental factors that influence the distribution of coral reef fishes: modeling occurrence data for broad-scale conservation and management. Marine Ecology-Progress Series, 361, 1–13.CrossRefGoogle Scholar

Beissinger, S. R. & McCullough, D. R. (Eds.) (2002) Population Viability Analysis. Chicago: University of Chicago Press.

Bell, J. F. (1996) Application of classification trees to the habitat preference of upland birds. Journal of Applied Statistics, 23, 349–359.CrossRefGoogle Scholar

Bellis, L. M., Pidgeon, A. M., Radeloff, V. C., St-Louis, V., Navarro, J. L., & Martella, M. B. (2008) Modeling habitat suitability for greater rheas based on satellite image texture. Ecological Applications, 18, 1956–1966.CrossRefGoogle ScholarPubMed

Benedict, M. Q., Levine, R. S., Hawley, W. A., & Lounibos, L. P. (2007) Spread of the tiger: global risk of invasion by the mosquito Aedes albopictus. Vector-Borne and Zoonotic Diseases, 7, 76–85.CrossRefGoogle ScholarPubMed

Benediktsson, J. A., Swain, P. H., & Ersoy, O. K. (1993) Conjugate-gradient neural networks in classification of multisource and very-high-dimensional remote sensing data. International Journal of Remote Sensing, 14, 2883–2903.CrossRefGoogle Scholar

Benito Garzón, M., Blazek, R., Neteler, M., Sónchez de Dios, R., Ollero, H. S., & Furlanello, C. (2006) Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling, 197, 383–393.CrossRefGoogle Scholar

Benito Garzón, M., Sanchez de Dios, R., & Sainz Ollero, H. (2008) Effects of climate change on the distribution of Iberian tree species. Applied Vegetation Science, 11, 169–178.CrossRefGoogle Scholar

Besag, J. (1972) Nearest-neighbour systems and the autologistic model for binary data. Journal of the Royal Statistical Society B, 34, 75–83.Google Scholar

Besag, J. (1974) Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B, 36, 192–236.Google Scholar

Bickford, S. A. & Laffan, S. W. (2006) Multi-extent analysis of the relationship between pteridophyte species richness and climate. Global Ecology and Biogeography, 15, 588–601.CrossRefGoogle Scholar

Bio, A. M. F., Alkemade, R., & Barendregt, A. (1998) Determining alternative models for vegetation response analysis: a non-parametric approach. Journal of Vegetation Science, 9, 5–16.CrossRefGoogle Scholar

Borcard, D., Legendre, P., & Drapeau, P. (1992) Partialling out the spatial component of ecological variation. Ecology, 73, 1045–1055.CrossRefGoogle Scholar

Boitani, L., Sinibaldi, I., Corsi, F.et al. (2008) Distribution of medium- to large-sized African mammals based on habitat suitability models. Biodiversity and Conservation, 17, 605–621.CrossRefGoogle Scholar

Bojorquez-Tapia, L. A., Brower, L. P., Castilleja, G.et al. (2003) Mapping expert knowledge: redesigning the Monarch Butterfly Biosphere Reserve. Conservation Biology, 17, 367–379.CrossRefGoogle Scholar

Bolker, B. M., Brooks, M. E., Clark, C. J.et al. (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127–35.CrossRefGoogle ScholarPubMed

Bolstad, P. V. & Lillesand, T. M. (1992) Improved classification of forest vegetation in northern Wisconsin through a rule-based combination of soils, terrain, and Landsat Thematic Mapper data. Forest Science, 38, 5–20.Google Scholar

Bond, J. E. & Stockman, A. K. (2008) An integrative method for delimiting cohesion species: finding the population-species interface in a group of Californian trapdoor spiders with extreme genetic divergence in geographic structuring. Systematic Biology, 57, 628–646.CrossRefGoogle Scholar

Botkin, D. B., Saxe, H., AraÚjo, M. B.et al. (2007) Forecasting the effects of global warming on biodiversity. Bioscience, 57, 227–236.CrossRefGoogle Scholar

Bourgeron, P. S., Humphries, C. J., & Jensen, M. E. (2001) General data collection and sampling design considerations for integrated ecological assessment. In Bourgeron, P., Jensen, M. & Lessard, G. (Eds.) An Integrated Ecological Assessment Protocols Guidebook. New York: Springer-Verlag, pp. 92–107.CrossRefGoogle Scholar

Box, E. (1981) Macroclimate and Plant Forms: An Introduction to Predictive Modeling in Phytogeography. The Hague: Dr. W. Junk.CrossRefGoogle Scholar

Boyce, M. S. (2006) Scale for resource selection functions. Diversity and Distributions, 12, 269–276.CrossRefGoogle Scholar

Boyce, M. S., Vernier, P. R., Nielsen, S. E., & Schmiegelow, F. K. A. (2002) Evaluating resource selection functions. Ecological Modelling, 157, 281–300.CrossRefGoogle Scholar

Bradbury, R. B., Kyrkos, A., Morris, A. J., Clark, S. C., Perkins, A. J., & Wilson, J. D. (2000) Habitat associations and breeding success of yellowhammers on lowland farmland. Journal of Applied Ecology, 37, 789–805.CrossRefGoogle Scholar

Bradley, B. A. & Fleishman, E. (2008) Can remote sensing of land cover improve species distribution modelling?Journal of Biogeography, 35, 1158–1159.CrossRefGoogle Scholar

Braunisch, V., Bollmann, K., Graf, R. F., & Hirzel, A. H. (2008) Living on the edge – modelling habitat suitability for species at the edge of their fundamental niche. Ecological Modelling, 214, 153–167.CrossRefGoogle Scholar

Breiman, L. (1996) Bagging predictors. Machine Learning, 26, 123–140.CrossRefGoogle Scholar

Breiman, L. (2001a) Statistical modeling: the two cultures. Statistical Science, 16, 199–215.CrossRefGoogle Scholar

Breiman, L. (2001b) Random forests. Machine Learning, 45, 15–32.Google Scholar

Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984) Classification and Regression Trees. Belmont, CA: Wadsworth.Google Scholar

Brigham, C. A., Thomson, D. M., Brigham, C. A., & Schwartz, M. W. (2003) Approaches to modeling population viability in plants: an overview. Population viability in plants: Conservation, management and modeling of rare plants, 145–171.CrossRefGoogle Scholar

Brodley, C. & Utgoff, P. (1995) Multivariate decision trees. Machine Learning, 19, 45–77.CrossRefGoogle Scholar

Broennimann, O. & Guisan, A. (2008) Predicting current and future biological invasions: both native and invaded ranges matter. Biology Letters, 4, 585–589.CrossRefGoogle ScholarPubMed

Broennimann, O., Thuiller, W., Hughes, G., Midgley, G. F., Alkemade, J. M. R., & Guisan, A. (2006) Do geographic distribution, niche property and life form explain plants' vulnerability to global change?Global Change Biology, 12, 1079–1093.CrossRefGoogle Scholar

Broennimann, O., Treier, U. A., Muller-Scharer, H., Thuiller, W., Peterson, A. T., & Guisan, A. (2007) Evidence of climatic niche shift during biological invasion. Ecology Letters, 10, 701–709.CrossRefGoogle ScholarPubMed

Brotons, L., Herrando, S., & Pla, M. (2007) Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long-term monitoring programs. Diversity and Distributions, 13, 276–288.CrossRefGoogle Scholar

Brotons, L., Thuiller, W., AraÚjo, M. B., & Hirzel, A. H. (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography, 27, 437–448.CrossRefGoogle Scholar

Brown, D. (1994) Predicting vegetation types at treeline using topography and biophysical disturbance variables. Journal of Vegetation Science, 5, 641–656.CrossRefGoogle Scholar

Brown, D. B. & Bara, T. J. (1994) Recognition and reduction of systematic error in elevation and derivative surfaces from 7 1/2-minute DEMs. Photogrammetric Engineering and Remote Sensing, 60, 189–194.Google Scholar

Browning, D. M., Beaupre, S. J., & Duncan, L. (2005) Using partitioned Mahalanobis D-2(K) to formulate a GIS-based model of timber rattlesnake hibernacula. Journal of Wildlife Management, 69, 33–44.2.0.CO;2>CrossRefGoogle Scholar

Brzeziecki, B., Kienast, F., & Wildi, O. (1993) A simulated map of the potential natural forest vegetation of Switzerland. Journal of Vegetation Science, 4, 499–508.CrossRefGoogle Scholar

Buckley, L. B. & Roughgarden, J. (2004) Biodiversity conservation: effects of changes in climate and land use. Nature, 430, 2 pp. following 33; discussion following 33.CrossRefGoogle ScholarPubMed

Buermann, W., Saatchi, S., Smith, T. B.et al. (2008) Predicting species distributions across the Amazonian and Andean regions using remote sensing data. Journal of Biogeography, 35, 1160–1176.CrossRefGoogle Scholar

Burgman, M. A. (2005) Risks and Decisions for Conservation and Environmental Management. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar

Burgman, M. A. & Fox, J. C. (2003) Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning. Animal Conservation, 6, 19–28.CrossRefGoogle Scholar

Burgman, M. A., Lindenmayer, D. B., & Elith, J. (2005) Managing landscapes for conservation under uncertainty. Ecology, 86, 2007–2017.CrossRefGoogle Scholar

Burnham, K. P. & Anderson, D. R. (1998) Model Selection and Multimodel Inference: A Practical Information – Theoretic Approach. New York, USA: Springer-Verlag.CrossRefGoogle Scholar

Burrough, P. & McDonnell, R. (1998) Principles of Geographical Information Systems, Oxford: Oxford University Press.Google Scholar

Busby, J. R. (1986) A biogeoclimatic analysis of Nothofagus cunninghamii (Hook.) Oerst. in southeastern Australia. Australian Journal of Ecology, 11, 1–7.CrossRefGoogle Scholar

Busby, J. R. (1991) BIOCLIM – a bioclimatic analysis and prediction system. In Margules, C. R. & Austin, M. P. (Eds.) Nature Conservation: Cost Effective Biological Surveys and Data Analysis. East Melbourne, Australia: CSIRO, pp. 64–68.Google Scholar

Callaway, R. M. & Pennings, S. C. (2000) Facilitation may buffer competitive effects: Indirect and diffuse interactions among salt marsh plants. American Naturalist, 156, 416–424.CrossRefGoogle ScholarPubMed

Cantor, S. B., Sun, C. C., Tortolero-Luna, G., Richards-Kortum, R., & Follen, M. (1999) A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. Journal of Clinical Epidemiology, 52, 885–892.CrossRefGoogle ScholarPubMed

Card, D. H. (1982) Using known map category marginal frequencies to improve estimates of thematic map accuracy. Photogrammetric Engineering and Remote Sensing, 48, 431–439.Google Scholar

Carlson, K. M., Asner, G. P., Hughes, R. F., Ostertag, R., & Martin, R. E. (2007) Hyperspectral remote sensing of canopy biodiversity in Hawaiian lowland rainforests. Ecosystems, 10, 536–549.CrossRefGoogle Scholar

Carmel, Y. & Flather, C. H. (2006) Constrained range expansion and climate change assessments. Frontiers in Ecology and the Environment, 4, 178–179.Google Scholar

Carpenter, G., Gillison, A. N., & Winter, J. (1993) DOMAIN: a flexible modeling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation, 2, 667–680.CrossRefGoogle Scholar

Carpenter, G. A., Gopal, S., Macomber, S., Martens, S., Woodcock, C. E., & Franklin, J. (1999) A neural network method for efficient vegetation mapping. Remote Sensing of Environment, 70, 326–338.CrossRefGoogle Scholar

Carroll, C. & Johnson, D. S. (2008) The importance of being spatial (and reserved): Assessing Northern Spotted Owl habitat relationships with hierarchical Bayesian Models. Conservation Biology, 22, 1026–1036.CrossRefGoogle ScholarPubMed

Carroll, C., Phillips, M. K., Schumaker, N. H., & Smith, D. W. (2003) Impacts of landscape change on wolf restoration success: Planning a reintroduction program based on static and dynamic spatial models. Conservation Biology, 17, 536–548.CrossRefGoogle Scholar

Carroll, C., Zielinski, W. J., & Noss, R. F. (1999) Using presence-absence data to build and test spatial habitat models for the fisher in the Klamath Region, U.S.A. Conservation Biology, 13, 1344–1359.CrossRefGoogle Scholar

Carstens, B. C. & Richards, C. L. (2007) Integrating coalescent and ecological niche modeling in comparative phylogeography. Evolution, 61, 1439–1454.CrossRefGoogle ScholarPubMed

Carter, G. M., Stolen, E. D., & Breininger, D. R. (2006) A rapid approach to modeling species–habitat relationships. Biological Conservation, 127, 237–244.CrossRefGoogle Scholar

Cawsey, E. M., Austin, M. P., & Baker, B. L. (2002) Regional vegetation mapping in Australia: a case study in the practical use of statistical modelling. Biodiversity and Conservation, 11, 2239–2274.CrossRefGoogle Scholar

Chambers, J. M. & Hastie, T. J. (1992) Statistical Models in S. Pacific Grove, CA: Wadsworth.Google Scholar

Chase, J. M. & Leibold, M. A. (2003) Ecological Niches: Linking Classical and Contemporary Approaches. Chicago: Chicago University Press.CrossRefGoogle Scholar

Chatfield, C. (1995) Model uncertainty, data mining and statistical inference. Journal of the Royal Statistical Society, 158, 419–466.CrossRefGoogle Scholar

Chefaoui, R. M. & Lobo, J. M. (2008) Assessing the effects of pseudo-absences on predictive distribution model performance. Ecological Modelling, 210, 478–486.CrossRefGoogle Scholar

Chen, D. M. & Stow, D. (2003) Strategies for integrating information from multiple spatial resolutions into land-use/land-cover classification routines. Photogrammetric Engineering and Remote Sensing, 69, 1279–1287.CrossRefGoogle Scholar

Ciarniello, L. M., Boyce, M. S., Seip, D. R., & Heard, D. C. (2007) Grizzly bear habitat selection is scale dependent. Ecological Applications, 17, 1424–1440.CrossRefGoogle ScholarPubMed

Civco, D. L. (1993) Artificial neural networks for land-cover classification and mapping. International Journal of Geographic Information Systems, 7, 173–186.CrossRefGoogle Scholar

Clark, J. D., Dunn, J. E., & Smith, K. G. (1993) A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management, 57, 519–526.CrossRefGoogle Scholar

Clark, L. A. & Pregibon, D. (1992) Tree-based models. In Chambers, J. & Hastie, T. J. (Eds.) Statistical Models in S. PacificGrove, CA: Wadsworth and Brooks/Cole Advanced Books and Software, pp. 377–419.Google Scholar

Clark, M. L., Roberts, D. A., & Clark, D. B. (2005) Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales. Remote Sensing of Environment, 96, 375–398.CrossRefGoogle Scholar

Clarke, K. C. (1997) Getting Started with Geographic Information Systems. Upper Saddle River, NJ: Prentice Hall.Google Scholar

Clarke, K. C. & Gaydos, L. J. (1998) Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographic Information Science, 12, 699–714.CrossRefGoogle ScholarPubMed

Clevenger, A. P., Wierzchowski, J., Chruszcz, B., & Gunson, K. (2002) GIS-generated, expert-based models for identifying wildlife habitat linkages and planning mitigation passages. Conservation Biology, 16, 503–514.CrossRefGoogle Scholar

Cochran, W. G. (1977) Sampling Techniques. New York: Wiley.Google Scholar

Collins, S. L., Glenn, S. M., & Roberts, D. W. (1993) The hierarchical continuum concept. Journal of Vegetation Science, 4, 149–156.CrossRefGoogle Scholar

Congalton, R. G. (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46.CrossRefGoogle Scholar

Cohen, W. B. & Goward, S. N. (2004) Landsat's role in ecological applications of remote sensing. Bioscience, 54, 535–545.CrossRefGoogle Scholar

Cooperrider, A. Y., Boyd, R. J., & Stuart, H. R. (Eds.) (1986) Inventory and Monitoring of Wildlife Habitat. Denver, CO: US Department of the Interior, Bureau of Land Management, Service Center.

Cord, P. L. (2008) Using species-environmental amplitudes to predict pH values from vegetation. Journal of Vegetation Science, 19, 437–444.Google Scholar

Cordellier, M. & Pfenninger, M. (2009) Inferring the past to predict the future: climate modelling predictions and phylogeography for the freshwater gastropod Radix balthica (Pulmonata, Basommatophora). Molecular Ecology, 18, 534–544.CrossRefGoogle Scholar

Corsi, F., Dupre, E., & Biotani, L. (1999) A large-scale model of wolf distribution in Italy for conservation planning. Conservation Biology, 13, 150–159.CrossRefGoogle Scholar

Costa, J., Peterson, A. T., & Beard, C. B. (2002) Ecologic niche modeling and differentiation of populations of Triatoma brasiliensis neiva, 1911, the most important Chagas' disease vector in northeastern Brazil (Hemiptera, Reduviidae, Triatominae). American Journal of Tropical Medicine and Hygiene, 67, 516–520.CrossRefGoogle Scholar

Coudun, C. & Gégout, J.-C. (2007) Quantitative prediction of the distribution and abundance of Vaccinium myrtillus with climatic and edaphic factors. Journal of Vegetation Science, 18, 517–524.CrossRefGoogle Scholar

Coudun, C., Gégout, J.-C., Piedallu, C., & Rameau, J.-C. (2006) Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. Journal of Biogeography, 33, 1750–1763.CrossRefGoogle Scholar

Cramer, J. S. (2003) Logit Models from Economics and Other Fields. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar

Cramer, W., Bondeau, A., Woodward, F. I.et al. (2001) Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Global Change Biology, 7, 357–373.CrossRefGoogle Scholar

Crawley, M. J. (2005) Statistics: An Introduction Using R. Chichester, UK: John Wiley & Sons, Ltd.CrossRefGoogle Scholar

Cressie, N. (1991) Statistics for Spatial Data. New York: John Wiley and Sons.Google Scholar

Crossman, N. D. & Bass, D. A. (2008) Application of common predictive habitat techniques for post-border weed risk management. Diversity and Distributions, 14, 213–224.CrossRefGoogle Scholar

Culvenor, D. S. (2002) TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Computers & Geosciences, 28, 33–44.CrossRefGoogle Scholar

Cumming, G. S. (2000a) Using between-model comparisons to fine-tune linear models of species ranges. Journal of Biogeography, 27, 441–455.CrossRefGoogle Scholar

Cumming, G. S. (2000b) Using habitat models to map diversity: pan-African species richness of ticks (Acari: Ixodida). Journal of Biogeography, 27, 425–440.CrossRefGoogle Scholar

Cumming, G. S. (2002) Comparing climate and vegetation as limiting factors for species ranges of African ticks. Ecology, 83, 255–268.CrossRefGoogle Scholar

Cunningham, H. R., Rissler, L. J., & Apodaca, J. J. (2009) Competition at the range boundary in the slimy salamander: using reciprocal transplants for studies on the role of biotic interactions in spatial distributions. Journal of Animal Ecology, 78, 52–62.CrossRefGoogle ScholarPubMed

Curran, P. (1980) Multispectral remote sensing of vegetation amount. Progress in Physical Geography, 4, 315–341.CrossRefGoogle Scholar

Currie, D. J. (1991) Energy and large-scale patterns of animal-species and plant-species richness. American Naturalist, 137, 27–49.CrossRefGoogle Scholar

Cutler, D. R., EdwardsJr., T. C., Beard, K. H.et al. (2007) Random forests for classification in ecology. Ecology, 88, 2783–2792.CrossRefGoogle ScholarPubMed

Daly, C. (2006) Guidelines for assessing the suitability of spatial climate data sets. International Journal of Climatology, 26, 707–721.CrossRefGoogle Scholar

Daly, C. & Taylor, G. H. (1996) Development of a new Oregon precipitation map using the PRISM model. In Goodchild, M. F., Steyaert, L. T., Park, B. O.et al. (Eds.) GIS and Environmental Modeling: Progress and Research issues. Fort Collins, CO: GIS World Books, pp. 91–92.Google Scholar

Daly, C., Halbleib, M., Smith, J. I.et al. (2008) Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. International Journal of Climatology, 28, 2031–2064.CrossRefGoogle Scholar

Daly, C., Neilson, R. P., & Phillips, D. L. (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Applied Meteorology, 33, 140–158.2.0.CO;2>CrossRefGoogle Scholar

Das, A., Lele, S. R., Glass, G. E., Shields, T., & Patz, J. (2002) Modeling as a discrete spatial response using generalized linear mixed models: application to Lyme disease. International Journal of Geographic Information Science, 16, 152–166.CrossRefGoogle Scholar

Davis, F. W. & Goetz, S. (1990) Modeling vegetation pattern using digital terrain data. Landscape Ecology, 4, 69–80.CrossRefGoogle Scholar

Davis, F. W., Seo, C., & Zielinski, W. J. (2007) Regional variation in home-range-scale habitat models for fisher (Martes pennanti) in California. Ecological Applications, 17, 2195–2213.CrossRefGoogle Scholar

Davis, F. W., Stine, P. A., Stoms, D. M., Borchert, M. I., & Hollander, A. (1995) Gap analysis of the actual vegetation of California: 1. The southwestern region. Madroño, 42, 40–78.Google Scholar

De'ath, G. (2002) Multivariate regression trees: a new technique for modeling species–environment relationships. Ecology, 83, 1105–1117.Google Scholar

De'ath, G. (2007) Boosted trees for ecological modeling and prediction. Ecology, 88, 243–251.CrossRefGoogle ScholarPubMed

De'ath, G. & Fabricius, K. E. (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 3178–3192.CrossRefGoogle Scholar

Dechka, J. A., Franklin, S. E., Watmough, M. D., Bennett, R. P., & Ingstrup, D. W. (2002) Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan. Canadian Journal of Remote Sensing, 28, 679–685.CrossRefGoogle Scholar

Dedecker, A. P., Goethals, P. L. M., Gabriels, W., & De Pauw, N. (2004) Optimization of Artificial Neural Network (ANN) model design for prediction of macroinvertebrates in the Zwalm river basin (Flanders, Belgium). Ecological Modelling, 174, 161–173.CrossRefGoogle Scholar

DeFries, R. S. & Townshend, J. R. G. (1999) Global land cover characterization from satellite data: from research to operational implementation?Global Ecology and Biogeography, 8, 367–379.CrossRefGoogle Scholar

DeMatteo, K. E. & Loiselle, B. A. (2008) New data on the status and distribution of the bush dog (Speothos venaticus): evaluating its quality of protection and directing research efforts. Biological Conservation, 141, 2494–2505.CrossRefGoogle Scholar

DeMers, M. (1991) Classification and purpose in automated vegetation maps. Geographical Review, 81, 267–280.CrossRefGoogle Scholar

DeMers, M. (2009) Fundamentals of Geographic Information Systems, Hoboken, NJ: Wiley.Google Scholar

Deng, Y., Wilson, J. P., & Bauer, B. O. (2007) DEM resolution dependencies of terrain attributes across a landscape. International Journal of Geographical Information Science, 21, 187–213.CrossRefGoogle Scholar

Dettmers, R., Buehler, D. A. & Bartlett, J. B. (2002) A test and comparison of wildlife-habitat modeling techniques for predicting bird occurrence at a regional scale. In Scott, J. M., Heglund, P. J., Morrison, M., Raphael, M., Haufler, J., & Wall, B. (Eds.) Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press, pp. 607–615.Google Scholar

Diggle, P. (1983) Statistical Analysis of Spatial Point Patterns. London: Academic Press.Google Scholar

Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., & Arnold, J. G. (2008) Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States. Journal of Applied Meteorology and Climatology, 47, 475–497.CrossRefGoogle Scholar

Diniz-Filho, J. A. F. & Bini, L. M., (2005) Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecology and Biogeography, 14, 177–185.CrossRefGoogle Scholar

Diniz-Filho, J. A. F., Bini, L. M., & Hawkins, B. A. (2003) Spatial autocorrelation and red herrings in geographical ecology. Global Ecology & Biogeography, 12, 53–64.CrossRefGoogle Scholar

Diniz-Filho, J. A. F., Hawkins, B. A., Bini, L. M., De Marco, P., & Blackburn, T. M. (2007a) Are spatial regression methods a panacea or a Pandora's box? A reply to Beale et al. (2007). Ecography, 30, 848–851.CrossRefGoogle Scholar

Diniz-Filho, J. A. F., Rangel, T. F. L. V. B., Bini, L. M., & Hawkins, B. A. (2007b) Macroevolutionary dynamics in environmental space and the latitudinal diversity gradient in New World birds. Proceedings of the Royal Society Biological Sciences Series B, 274, 43–52.CrossRefGoogle ScholarPubMed

Dirnböck, T., Dullinger, S., Gottfried, M., Ginzler, C., & Grabherr, G. (2003) Mapping alpine vegetation based on image analysis, topographic variables and Canonical Correspondence Analysis. Applied Vegetation Science, 6, 85–96.Google Scholar

Donald, P. F. & Fuller, R. J. (1998) Ornithological atlas data: a review of uses and limitations. Bird Study, 45, 129–145.CrossRefGoogle Scholar

Dormann, C. F. (2007a) Assessing the validity of autologistic regression. Ecological Modelling, 207, 234–242.CrossRefGoogle Scholar

Dormann, C. F. (2007b) Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 16, 129–138.CrossRefGoogle Scholar

Dormann, C. F. (2007c) Promising the future? Global change projections of species distributions. Basic and Applied Ecology, 8, 387–397.CrossRefGoogle Scholar

Dormann, C. F., McPherson, J. M., AraÚjo, M. B.et al. (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30, 609–628.CrossRefGoogle Scholar

Dormann, C. F., Purschke, O., Garcĺa-Marquez, J., Lautenbach, S., & Schröder, B. (2008) Components of uncertainty in species distribution analysis: a case study of the Great Grey Shrike. Ecology 89, 3371–3386.CrossRefGoogle ScholarPubMed

Doswald, N., Zimmermann, F., & Breitenmoser, U. (2007) Testing expert groups for a habitat suitability model for the lynx Lynx lynx in the Swiss Alps. Wildlife Biology, 13, 430–446.CrossRefGoogle Scholar

Dozier, J. (1980) A clear-sky spectral solar radiation model for snow-covered mountainous terrain. Water Resources Research, 16, 709–718.CrossRefGoogle Scholar

Dozier, J. & Frew, J. (1990) Rapid calculation of terrain parameters for radiation modeling from digital elevation data. IEEE Transactions on Geoscience and Remote Sensing, 28, 963–969.CrossRefGoogle Scholar

Drake, J. M., Randin, C., & Guisan, A. (2006) Modelling ecological niches with support vector machines. Journal of Applied Ecology, 43, 424–432.CrossRefGoogle Scholar

Dray, S., Legendre, P., & Peres-Neto, P. R. (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, 196, 483–493.CrossRefGoogle Scholar

Dreisbach, T. A., Smith, J. A., & Molina, R. (2002) Challenges of modeling fungal habitats: when and where do you find chanterelles? In Scott, J. M., Heglund, P. J., Morrison, M. L.et al. (Eds.) Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press, pp. 475–481.Google Scholar

Dubayah, R. & Rich, P. M. (1995) Topographic solar radiation for GIS. International Journal of Geographic Information Systems, 9, 405–419.CrossRefGoogle Scholar

Dubayah, R. & Rich, P. M. (1996) GIS-based solar radiation modeling. In Goodchild, M. F., Steyaert, L. T., Parks, B. O.et al. (Eds.) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO: GIS World Books, pp. 129–134.Google Scholar

Dudĺk, M., Phillips, S. J., & Schapire, R. E. (2007) Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. Journal of Machine Learning Research, 8, 1217–1260.Google Scholar

Duncan, B. W., Breininger, D. R., Schmalzer, P. A., & Larson, V. L. (1995) Validating a Florida scrub jay habitat suitability model using demographic data on Kennedy Space Center. Photogrammetric Engineering & Remote Sensing, 61, 1361–1370.Google Scholar

Dunk, J. R., Zielinski, W. J., & Preisler, H. K. (2004) Predicting the occurrence of rare mollusks in northern California forests. Ecological Applications, 14, 713–729.CrossRefGoogle Scholar

Dutilleul, P. (1993) Modifying the t-Test for assessing the correlation between two spatial processes. Biometrics, 49, 305–314.CrossRefGoogle Scholar

Early, R., Anderson, B., & Thomas, C. D. (2008) Using habitat distribution models to evaluate large-scale landscape priorities for spatially dynamic species. Journal of Applied Ecology, 45, 228–238.CrossRefGoogle Scholar

Echarri, F., Tambussi, C., & Hospitaleche, C. A. (2009) Predicting the distribution of the crested tinamous, Eudromia spp. (Aves, Tinamiformes). Journal of Ornithology, 150, doi:10.1007/s10336–008-0319–5.CrossRefGoogle Scholar

Edwards, T. C.., Cutler, D. R., Zimmermann, N. E., Geiser, L., & Alegria, J. (2005) Model-based stratifications for enhancing the detection of rare ecological events. Ecology, 86, 1081–1090.CrossRefGoogle Scholar

Edwards, T. C.., Cutler, D. R., Zimmermann, N. E., Geiser, L., & Moisen, G. G. (2006) Effects of sample survey design on the accuracy of classification tree models in species distribution models. Ecological Modelling, 199, 132–141.CrossRefGoogle Scholar

Eisen, R. J., Reynolds, P. J., Ettestad, P.et al. (2007) Residence-linked human plague in New Mexico: a habitat-suitability model. American Journal of Tropical Medicine and Hygiene, 77, 121–125.Google ScholarPubMed

Elder, J. F. (2003) The generalization paradox of ensembles. Journal of Computational and Graphical Statistics, 12, 853–864.CrossRefGoogle Scholar

Elith, J. & Burgman, M. A. (2002) Predictions and their validation: rare plants in the Central Highlands, Victoria, Australia. In Scott, J. M., Heglund, P. J., Morrison, M. L.et al. (Eds.) Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press, pp. 303–313.Google Scholar

Elith, J. & Graham, C. (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography, 32, 1–12.CrossRefGoogle Scholar

Elith, J., Graham, C. H., Anderson, R. P.et al. (2006) Novel methods improve prediction of species distributions from occurrence data. Ecography, 29, 129–151.CrossRefGoogle Scholar

Elith, J. & Leathwick, J. (2007) Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Diversity and Distributions, 13, 265–275.CrossRefGoogle Scholar

Elith, J., Burgman, M. A., & Regan, H. M. (2002) Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecological Modelling, 157, 313–329.CrossRefGoogle Scholar

Elith, J. & Leathwick, J. (2009) Conservation prioritisation using species distribution modelling. In Moilanen, A., Wilson, K. A., & Possingham, H. (Eds.) Spatial Conservation Prioritization: Quantitative Methods and Computational Tools. Oxford, UK: Oxford University Press, pp. 70–93.Google Scholar

Elith, J., Leathwick, J. R., & Hastie, T. (2008) A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802–813.CrossRefGoogle ScholarPubMed

Ellenberg, H. (1988) Vegetation Ecology of Central Europe. Cambridge, UK: Cambridge University Press.Google Scholar

Ellenberg, H., Weber, H. E., Duell, R., Wirth, V., Werner, W., & Paulissen, D. (1991) Indicator Values of Plants in Central Europe. Goettingen (Germany, F.R.): Goltze.Google Scholar

Ellison, A. M. (2004) Bayesian inference in ecology. Ecology Letters, 7, 509–520.CrossRefGoogle Scholar

Elton, C. (1927) Animal Ecology. Chicago: University of Chicago Press.Google Scholar

Engler, R., Guisan, A., & Rechsteiner, L. (2004) An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41, 263–274.CrossRefGoogle Scholar

Epstein, H. E., Lauenroth, W. K., Burke, I. C., & Coffin, D. P. (1997) Productivity patterns of C-3 and C-4 functional types in the US Great Plains. Ecology, 78, 722–731.Google Scholar

Erasmus, B. F. N., van Jaarsveld, A. S., Chown, S. L., Kshatriya, M., & Wessels, K. J. (2002) Vulnerability of South African animal taxa to climate change. Global Change Biology, 8, 679–693.CrossRefGoogle Scholar

Ertsen, A. C. D., Alkemade, J. R. M., & Wassen, M. J. (1998) Calibrating Ellenberg indicator values for moisture, acidity, nutrient availability and salinity in the Netherlands. Plant Ecology, 135, 113–124.CrossRefGoogle Scholar

Estes, J. E. & Mooneyhan, D. W. (1994) Of maps and myths. Photogrammetric Engineering and Remote Sensing, 60, 517–524.Google Scholar

Estes, L. D., Okin, G. S., Mwangi, A. G., & Shugart, H. H. (2008) Habitat selection by a rare forest antelope: a multi-scale approach combining field data and imagery from three sensors. Remote Sensing of Environment, 112, 2033–2050.CrossRefGoogle Scholar

Evangelista, P. H., Kumar, S., Stohlgren, T. J.et al. (2008) Modelling invasion for a habitat generalist and a specialist plant species. Diversity and Distributions, 14, 808–817.CrossRefGoogle Scholar

Fairbanks, D. H. K. & McGwire, K. C. (2004) Patterns of floristic richness in vegetation communities of California: regional scale analysis with multi-temporal NDVI. Global Ecology and Biogeography, 13, 221–235.CrossRefGoogle Scholar

Farber, O. & Kadmon, R. (2003) Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecological Modelling, 160, 115–130.CrossRefGoogle Scholar

Fels, J. E. (1994) Modeling and mapping potential vegetation using digital terrain data. Unpublished Ph.D. Thesis, College of Forest Resource, North Carolina State University.

Feranec, J., Hazeu, G., Christensen, S., & Jaffrain, G., (2007) CORINE land cover change detection in Europe (case studies of the Netherlands and Slovakia). Land Use Policy, 24, 234–247.CrossRefGoogle Scholar

Fernandez, N., Delibes, M., & Palomares, F. (2006) Landscape evaluation in conservation: Molecular sampling and habitat modeling for the Iberian lynx. Ecological Applications, 16, 1037–1049.CrossRefGoogle ScholarPubMed

Ferrer-Castan, D. & Vetaas, O. R. (2005) Pteridophyte richness, climate and topography in the Iberian Peninsula: comparing spatial and nonspatial models of richness patterns. Global Ecology and Biogeography, 14, 155–165.CrossRefGoogle Scholar

Ferrier, S. (2002) Mapping spatial pattern in biodiversity for regional conservation planning: where to from here?Systematic Biology, 51, 331–363.CrossRefGoogle Scholar

Ferrier, S. & Guisan, A. (2006) Spatial modelling of biodiversity at the community level. Journal of Applied Ecology, 43, 393–404.CrossRefGoogle Scholar

Ferrier, S., Drielsma, M., Manion, G., & Watson, G. (2002a) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modeling. Biodiversity and Conservation, 11, 2309–2338.CrossRefGoogle Scholar

Ferrier, S., Manion, G., Elith, J., & Richardson, K. (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions, 13, 252–264.CrossRefGoogle Scholar

Ferrier, S., Watson, G., Pearce, J., & Drielsma, M. (2002b) Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling. Biodiversity and Conservation, 11, 2275–2307.CrossRefGoogle Scholar

Fertig, W. & Reiners, W. A. (2002) Predicting Presence/Absence of plant species for range mapping: a case study from Wyoming. In Scott, J. M., Heglund, P. J., Morrison, M. L.et al. (Eds.) Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press, pp. 483–489.Google Scholar

Ficetola, G. F., Thuiller, W., & Miaud, C. (2007) Prediction and validation of the potential global distribution of a problematic alien invasive species – the American bullfrog. Diversity and Distributions, 13, 476–485.CrossRefGoogle Scholar

Ficetola, G. F., Thuiller, W., & Padoa-Schioppa, E. (2009) From introduction to the establishment of alien species: bioclimatic differences between presence and reproduction localities in the slider turtle. Diversity and Distributions, 15, 108–116.CrossRefGoogle Scholar

Fielding, A. H. & Bell, J. F. (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24, 38–49.CrossRefGoogle Scholar

Fielding, A. H. (Ed.) (1999) Machine learning methods for ecological applications, Boston, Kluwer.CrossRef

Fielding, A. H. & Haworth, P. F. (1995) Testing the generality of bird-habitat models. Conservation Biology, 9, 1466–1481.CrossRefGoogle Scholar

Fischer, H. (1990) Simulating the distribution of plant communities in an alpine landscape. Coenoses, 5, 37–43.Google Scholar

Fisher, R. N., Suarez, A. V., & Case, T. J. (2002) Spatial patterns in the abundance of the coastal horned lizard. Conservation Biology, 16, 205–215.CrossRefGoogle Scholar

Fitzgerald, R. W. & Lees, B. G. (1992) The application of neural networks to floristic classification of remote sensing and GIS data in complex terrain. Proceedings of the XVII Congress ISPRS. Bethesda, MD, American Society of Photogrammetry and Remote Sensing.Google Scholar

Fitzpatrick, M. C., Gove, A. D., Sanders, N. J., & Dunn, R. R. (2008) Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. Global Change Biology, 14, 1337–1352.CrossRefGoogle Scholar

Fleishman, E., Murphy, D. D., & Sjögren-Gulve, P. (2002) Modeling species richness and habitat suitability for taxa of conservation interest. In Scott, J. M., Heglund, P. J., Morrison, M. L.et al. (Eds.) Predicting Species Occurrences: Issues of Accuracy and Scale. Covelo, CA: Island Press, pp. 507–517.Google Scholar

Fleming, G., Merwe, M., & McFerren, G. (2007) Fuzzy expert systems and GIS for cholera health risk prediction in southern Africa. Environmental Modelling & Software, 22, 442–448.CrossRefGoogle Scholar

Flesch, A. D. & Hahn, L. A. (2005) Distribution of birds and plants at the western and southern edges of the Madrean Sky Islands in Sonora, Mexico. U S Forest Service Rocky Mountain Research Station Proceedings RMRS-P, 36, 80–87.Google Scholar

Florinsky, I. V. (1998) Combined analysis of digital terrain models and remotely sensed data in landscape investigation. Progress in Physical Geography, 22, 33–60.CrossRefGoogle Scholar

Foley, J. A., DeFries, R., Asner, G. P.et al. (2005) Global consequences of land use. Science, 309, 570–574.CrossRefGoogle ScholarPubMed

Fonseca, R. L., Guimaraes, P. R., Morbiolo, S. R., Scachetti-Pereira, R., & Peterson, A. T. (2006) Predicting invasive potential of smooth crotalaria (Crotalaria pallida) in Brazilian national parks based on African records. Weed Science, 54, 458–463.CrossRefGoogle Scholar

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 Ecology and Biogeography, 13, 315–320.CrossRefGoogle Scholar

Foody, G. M. & Cutler, M. E. J. (2003) Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. Journal of Biogeography, 30, 1053–1066.CrossRefGoogle Scholar

Fornes, G. L. (2004) Habitat use by loggerhead shrikes (Lanius ludovicianus) at Midewin National Tallgrass Prairie, Illinois: an application of Brooks and Temple's habitat suitability index. American Midland Naturalist, 151, 338–345.CrossRefGoogle Scholar

Fortin, M.-J. & Dale, M. R. T. (2005) Spatial Analysis: A Guide for Ecologists. Cambridge, UK: Cambridge University Press.Google Scholar

Fortin, M. J., Keitt, T. H., Maurer, B. A., Taper, M. L., Kaufman, D. M., & Blackburn, T. M. (2005) Species geographic ranges and distributional limits: pattern analysis and statistical issues. Oikos, 108, 7–17.CrossRefGoogle Scholar

Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002) Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester, UK: Wiley & Sons.Google Scholar

Franklin, J. (1995) Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography, 19, 474–499.CrossRefGoogle Scholar

Franklin, J. (1998) Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. Journal of Vegetation Science, 9, 733–748.CrossRefGoogle Scholar

Franklin, J. (2001) Geographic information science and ecological assessment. In Bourgeron, P., Jensen, M. & Lessard, G. (Eds.) An Integrated Ecological Assessment Protocols Guidebook. New York: Springer-Verlag, pp. 151–161.CrossRefGoogle Scholar

Franklin, J. (2002) Enhancing a regional vegetation map with predictive models of dominant plant species in chaparral. Applied Vegetation Science, 5, 135–146.CrossRefGoogle Scholar

Franklin, J. (in press) Spatial point pattern analysis of plants. In Rey, S. J. & Anselin, L. (Eds.) Perspectives on Spatial Data Analysis. New York: Springer.

Franklin, J. & Woodcock, C. E. (1997) Multiscale vegetation data for the mountains of Southern California: spatial and categorical resolution. In Quattrochi, D. A. & Goodchild, M. F. (Eds.) Scale in Remote Sensing and GIS. Boca Raton, FL, CRC/Lewis Publishers Inc., pp. 141–168.Google Scholar

Franklin, J., Keeler-Wolf, T., Thomas, K.et al. (2001) Stratified sampling for field survey of environmental gradients in the Mojave Desert Ecoregion. In Millington, A., Walsh, S., & Osborne, P. (Eds.) GIS and Remote Sensing Applications in Biogeography and Ecology. Netherlands: Kluwer Academic Publishers, pp. 229–253.CrossRefGoogle Scholar

Franklin, J., Logan, T., Woodcock, C. E., & Strahler, A. H. (1986) Coniferous forest classification and inventory using Landsat and digital terrain data. IEEE Transactions on Geoscience and Remote Sensing, GE-24, 139–149.CrossRefGoogle Scholar

Franklin, J., McCullough, P., & Gray, C. (2000) Terrain variables used for predictive mapping of vegetation communities in Southern California. In Wilson, J. & Gallant, J. (Eds.) Terrain Analysis: Principles and Applications. New York: Wiley & Sons, pp. 331–353.Google Scholar

Franklin, J., Phinn, S. R., Woodcock, C. E., & Rogan, J. (2003) Rationale and conceptual framework for classification approaches to assess forest resources and properties. In Wulder, M. A. & Franklin, S. E. (Eds.) Remote Sensing of Forest Environments: Concepts and Case Studies. Boston, MA: Kluwer Academic Publishers, pp. 279–300.CrossRefGoogle Scholar

Franklin, J., Syphard, A. D., He, H. S., & Mladenoff, D. J. (2005) The effects of altered fire regimes on patterns of plant succession in the foothills and mountains of southern California. Ecosystems, 8, 885–898.CrossRefGoogle Scholar

Franklin, J., Wejnert, K. E., Hathaway, S. A., Rochester, C. J., & Fisher, R. N. (2009) Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California. Diversity and Distributions 15, 167–177.CrossRefGoogle Scholar

Franklin, S. E. & Wulder, M. A. (2002) Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26, 173–205.CrossRefGoogle Scholar

Freeman, E. A. & Moisen, G. G. (2008a) A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence. Ecological Modelling, 217, 48–58.CrossRefGoogle Scholar

Freeman, E. A. & Moisen, G. (2008b) PresenceAbsence: An R package for presence absence analysis. Journal of Statistical Software, 23.CrossRefGoogle Scholar

Frescino, T. S., Edwards, T. C.., & Moisen, G. G. (2001) Modeling spatially explicit forest structural attributes using Generalized Additive Models. Journal of Vegetation Science, 12, 15–26.CrossRefGoogle Scholar

Freund, Y. & Schapire, R. (1996) Experiments with a new boosting algorithm. Machine Learning: Proceedings of the 13th International Conference. San Francisco: Morgan Kauffman, pp. 148–156.Google Scholar

Friedman, J. H. (1991) Multivariate adaptive regression splines. Annals of Statistics, 19, 1–141.CrossRefGoogle Scholar

Friedman, J. H. (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 1189–1232.CrossRefGoogle Scholar

Friedman, J. H. (2002) Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 367–378.CrossRefGoogle Scholar

Friedman, J. H. & Roosen, C. B. (1995) An introduction to multivariate adaptive regression splines. Statistical Methods in Medical Research, 4, 197–217.CrossRefGoogle ScholarPubMed

Gahegan, M. (2003) Is inductive machine learning just another wild goose (or might it lay the golden egg)?International Journal of Geographical Information Science, 17, 69–92.CrossRefGoogle Scholar

Gallagher, R. V., Beaumont, L. J., Downey, P. O., Hughes, L., & Leishman, M. R. (2008) Projecting the impact of climate change on bitou bush and boneseed distributions in Australia. Plant Protection Quarterly, 23, 37.Google Scholar

Gamon, J. A. & Qiu, H.-L. (1999) Ecological applications of remote sensing at multiple scales. In Pugnaire, F. I. & Valladares, F. (Eds.) Handbook of Functional Plant Ecology. New York: Marcel Dekker, Inc., pp. 805–846.Google Scholar

Gasc, J. P., Cabela, A., Crnobrnja-Isailovic, J.et al. (1997) Atlas of amphibians and reptiles in Europe. Atlas of amphibians and reptiles in Europe, 1–496.Google Scholar

Gaston, K. J. (2003) The Structure and Dynamics of Geographic Ranges. Oxford, UK: Oxford University Press.Google Scholar

Gauch, H. G. & Whittaker, R. H. (1972) Coencline simulation. Ecology, 53.CrossRefGoogle Scholar

Gause, G. F. (1934) The Struggle for Existence. Baltimore, MD: Williams and Wilkins.CrossRefGoogle ScholarPubMed

Gégout, J.-C., Coudun, C., Bailly, G., & Jabiol, B. (2005) EcoPlant: a forest site database to link floristic data with soil resources and climate conditions. Journal of Vegetation Science, 16, 257–260.CrossRefGoogle Scholar

German, G. & Gahegan, M. (1996) Neural network architectures for the classification of temporal image sequences. Computers & Geosciences, 22, 969–979.CrossRefGoogle Scholar

Gerrard, R., Stine, P., Church, R., & Gilpin, M., (2001) Habitat evaluation using GIS: a case study applied to the San Joaquin Kit Fox. Landscape and Urban Planning, 52, 239–255.CrossRefGoogle Scholar

Gibson, L., Barrett, B., & Burbidge, A. (2007) Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot. Diversity and Distributions, 13, 704–713.CrossRefGoogle Scholar

Gibson, L. A., Wilson, B. A., Cahill, D. M., & Hill, J. (2004) Modelling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia. Biological Conservation, 117, 143–150.CrossRefGoogle Scholar

Gillespie, T. W. (2005) Predicting woody-plant species richness in tropical dry forests: A case study from south Florida, USA. Ecological Applications, 15, 27–37.CrossRefGoogle Scholar

Gillespie, T. W., Foody, G. M., Rocchini, D., Giorgi, A. P., & Saatchi, S. (2008) Measuring and modelling biodiversity from space. Progress in Physical Geography, 32, 203–221.CrossRefGoogle Scholar

Gillison, A. N. & Brewer, K. R. W. (1985) The use of gradient directed transects or gradsect in natural resources survey. Journal of Environmental Management, 20, 103–127.Google Scholar

Gleason, H. A. (1926) The individualistic concept of the plant association. Bulletin of the Torrey Botanical Club, 53, 7–26.CrossRefGoogle Scholar

Godinho, R., Teixeira, J., Rebelo, R.et al. (1999) Atlas of the continental Portuguese herpetofauna: an assemblage of published and new data. Revista Espanola de Herpetologia, 13, 61–82.Google Scholar

Goetz, S., Steinberg, D., Dubayah, R., & Blair, B. (2007) Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA. Remote Sensing of Environment, 108, 254–263.CrossRefGoogle Scholar

Goodchild, M. F. (1988) Stepping over the line: technological constraints and the new cartography. American Cartographer, 15, 311–319.CrossRefGoogle Scholar

Goodchild, M. F. (1992) Geographical data modeling. Computers & Geosciences, 18, 401–408.CrossRefGoogle Scholar

Goodchild, M. F. (1994) Integrating GIS and remote sensing for vegetation analysis and modeling: methodological issues. Journal of Vegetation Science, 5, 615–626.CrossRefGoogle Scholar

Goodchild, M. F. (1996) The spatial data infrastructure of environmental modeling. In Goodchild, M. F., Steyaert, L. T., Parks, B. O.et al. (Eds.) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO: GIS World Books, pp. 11–15.Google Scholar

Goodchild, M. F. & Gopal, S. (1989) The accuracy of spatial databases, London, Taylor & Francis.Google Scholar

Goodchild, M. F. & Tate, N. J. (1992) Description of terrain as a fractal surface, and application to digital elevation model quality assessment. Photogrammetric Engineering and Remote Sensing, 58, 1568–1570.Google Scholar

Goodchild, M. F., Parks, B. O., & Steyaert, L. T. (1993) Environmental Modeling with GIS: New York: Oxford University Press.Google Scholar

Goodchild, M. F., Steyaert, L. T., Parks, B. O.et al. (Eds.) (1996) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO: GIS World Books.

Gottschalk, T. K., Huettmann, F., & Ehlers, M. (2005) Thirty years of analysing and modelling avian habitat relationships using satellite imagery data: a review. International Journal of Remote Sensing, 26, 2631–2656.CrossRefGoogle Scholar

Gotway, C. & Stroup, W. (1997) A generalized linear model approach to spatial data analysis and prediction. Journal of Agricultural, Biological, and Environmental Statistics, 2, 152–178.CrossRefGoogle Scholar

Gould, W. (2000) Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots. Ecological Applications, 10, 1861–1870.CrossRefGoogle Scholar

Graham, C. H. & Blake, J. G. (2001) Influence of patch- and landscape-level factors on bird assemblages in a fragmented tropical landscape. Ecological Applications, 11, 1709–1721.CrossRefGoogle Scholar

Graham, C. H. & Fine, P. V. A. (in press) Phylogenetic beta diversity: linking ecological and evolutionary processes across space in time. Trends in Ecology & Evolution.

Graham, C. H. & Hijmans, R. J. (2006) A comparison of methods for mapping species ranges and species richness. Global Ecology and Biogeography, 15, 578–587.CrossRefGoogle Scholar

Graham, C. H., Elith, J., Hijmans, R. J.et al. (2008) The influence of spatial errors in species occurrence data used in distribution models. Journal of Applied Ecology, 45, 239–247.CrossRefGoogle Scholar

Graham, C. H., Ferrier, S., Huettman, F., Moritz, C., & Peterson, A. T. (2004) New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology & Evolution, 19, 497–503.CrossRefGoogle ScholarPubMed

Granadeiro, J. P., Andrade, J., & Palmeirim, J. M. (2004) Modelling the distribution of shorebirds in estuarine areas using generalised additive models. Journal of Sea Research, 52, 227–240.CrossRefGoogle Scholar

Greenberg, J. A., Dobrowski, S. Z., Ramirez, C. M., Tuil, J. L., & Ustin, S. L. (2006) A bottom-up approach to vegetation mapping of the Lake Tahoe Basin using hyperspatial image analysis. Photogrammetric Engineering and Remote Sensing, 72, 581–589.CrossRefGoogle Scholar

Greig-Smith, P. (1983) Quantitative Plant Ecology. Oxford, UK: Blackwell Scientific Publications.Google Scholar

Gret-Regamey, A. & Straub, D. (2006) Spatially explicit avalanche risk assessment linking Bayesian networks to a GIS. Natural Hazards and Earth System Sciences, 6, 911–926.CrossRefGoogle Scholar

Griffith, D. A. (2006) Assessing spatial dependence in count data: Winsorized and spatial filter specification alternatives to the auto-Poisson model. Geographical Analysis, 38, 160–179.CrossRefGoogle Scholar

Griffith, D. A. & Peres-Neto, P. R. (2006) Spatial modeling in ecology: The flexibility of eigenfunction spatial analyses. Ecology, 87, 2603–2613.CrossRefGoogle ScholarPubMed

Grinnell, J. (1917a) The niche-relationships of the California thrasher. Auk, 34, 427–433.CrossRefGoogle Scholar

Grinnell, J. (1917b) Field tests and theories concerning distributional control. The American Naturalist, 51, 115–128.CrossRefGoogle Scholar

Grossman, D. H., Faber-Langendeon, D., Weakley, A. S.et al. (1998) International Classification of Ecological Communities: Terrestrial Vegetation of the United States Volume 1. The National Vegetation Classification System: Development, Status and Applications. http://consci.tnc.org/library/pubs/class/index.html. Washington, DC: The Nature Conservancy.Google Scholar

Gu, W. D. & Swihart, R. K. (2004) Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation, 116, 195–203.CrossRefGoogle Scholar

Guisan, A. & Harrell, F. E. (2000) Ordinal response regression models in ecology. Journal of Vegetation Science, 11, 617–626.CrossRefGoogle Scholar

Guisan, A. & Thuiller, W. (2005) Predicting species distributions: offering more than simple habitat models. Ecology Letters, 8, 993–1009.CrossRefGoogle Scholar

Guisan, A. & Zimmermann, N. E. (2000) Predictive habitat distribution models in ecology. Ecological Modelling, 135, 147–186.CrossRefGoogle Scholar

Guisan, A., Edwards, T. C.., & Hastie, T. (2002) Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling, 157, 89–100.CrossRefGoogle Scholar

Guisan, A., Lehmann, A., Ferrier, S.et al. (2006) Making better biogeographical predictions of species distributions. Journal of Applied Ecology, 43, 386–392.CrossRefGoogle Scholar

Guisan, A., Theurillat, J.-P., & Kienast, F. (1998) Predicting the potential distribution of plant species in an alpine environment. Journal of Vegetation Science, 9, 65–74.CrossRefGoogle Scholar

Guisan, A., Weiss, S., & Weiss, A. (1999) GLM versus CCA spatial modeling of plant species distributions. Plant Ecology, 143, 107–122.CrossRefGoogle Scholar

Guisan, A., Zimmermann, N. E., Elith, J., Graham, C. H., Phillips, S., & Peterson, A. T. (2007) What matters for predicting the occurrences of trees: Techniques, data, or species characteristics?Ecological Monographs, 77, 615–630.CrossRefGoogle Scholar

Gumpertz, M., Graham, J., & Ristaino, J. (1997) Autologistic model of spatial pattern of Phytophthora epidemic in bell pepper: Effects of soil variation on disease presence. Journal of Agricultural, Biological and Environmental Statistics, 2, 131–156.CrossRefGoogle Scholar

Gumpertz, M., Wu, C., & Pye, H. (2000) Logistic regression for southern pine beetle outbreaks with spatial and temporal autocorrelation. Forest Science, 46, 95–107.Google Scholar

Guo, Q. H., Kelly, M., Gong, P., & Liu, D. S. (2007) An object-based classification approach in mapping tree mortality using high spatial resolution imagery. GIScience & Remote Sensing, 44, 24–47.CrossRefGoogle Scholar

Guo, Q. H., Kelly, M., & Graham, C. H. (2005) Support vector machines for predicting distribution of sudden oak death in California. Ecological Modelling, 182, 75–90.CrossRefGoogle Scholar

Gurevitch, J., Scheiner, S. M., & Fox, G. A. (2006) The Ecology of Plants, Sunderland, MA, Sinauer Associates.Google Scholar

Gustafson, E. J. (1998) Quantifying landscape spatial pattern: What is the state of the art?Ecosystems, 1, 143–156.CrossRefGoogle Scholar

Hagemeijer, W. J. M. & Blair, M. J. (1997) The EBCC Atlas of European Breeding Birds. Their Distribution and Abundance. London: Poyser.Google Scholar

Haining, R. P. (1990) Spatial Data Analysis in the Social and Environmental Sciences. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar

Hanley, J. A. & McNeil, B. J. (1982) The meaning and use of the area under a receiver operating characteristics curve. Radiology, 143, 29–36.CrossRefGoogle Scholar

Hannah, L. & Phillips, B. (2004) Extinction-risk coverage is worth inaccuracies. Nature, 430, 141.CrossRefGoogle ScholarPubMed

Hanski, I. (1999) Metapopulation Ecology. Oxford, UK: Oxford University Press.Google Scholar

Harrell, F. E. (2001) Regression Modelling Strategies: with Applications to Linear Models, Logistic Regression and Survival Analysis. New York: Springer.CrossRefGoogle Scholar

Hastie, T. J. & Pregibon, D. (1992) Generalized linear models. In Chambers, J. & Hastie, T. (Eds.) In Statistical Models in S. Pacific Grove, CA: Wadsworth, pp. 195–247.Google Scholar

Hastie, T., Tibshirani, R. J., & Buja, A. (1994) Flexible discriminant analysis by optimal scoring. Journal of the American Statistical Association, 89, 1255–1270.CrossRefGoogle Scholar

Hastie, T., Tibshirani, R., & Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction. 2nd edn., New York: Springer-Verlag.CrossRefGoogle Scholar

Hastie, T., Tibshirani, R., & Friedman, J. (2001) The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag.CrossRefGoogle Scholar

Hastie, T. J. & Tibshirani, R. J. (1990) Generalized Additive Models. London: Chapman and Hall.Google Scholar

Hathaway, S. A. (2000) An exploratory analysis of the biogeographic distribution of herpetofauna (reptiles and amphibians) and environmental variation in San Diego County using museum records and survey data. Unpublished Masters Thesis, Geography Department, San Diego State University.

Hawkins, B. A., Diniz-Filho, J. A. F., Bini, L. M., Marco, P., & Blackburn, T. M. (2007) Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology. Ecography, 30, 375–384.CrossRefGoogle Scholar

Hazin, H. & Erzini, K. (2008) Assessing swordfish distribution in the South Atlantic from spatial predictions. Fisheries Research, 90, 45–55.CrossRefGoogle Scholar

He, H. S. & Mladenoff, D. J. (1999) Spatially explicit and stochastic simulation of forest-landscape fire disturbance and succession. Ecology, 80, 81–90.CrossRefGoogle Scholar