Coletânea sobre bibliografias referentes à temática Modelagem de Nicho Ecológico

Anderson, R. P. 2003. Real vs artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuala. 30:591-605.

Anderson, R. P., M. Laverde, and A. T. Peterson. 2002a. Geographical distributions of spiny pocket mice in South America: Insights from predictive models. Global Ecology and Biogeography 11:131-141.

Anderson, R. P., M. Laverde, and A. T. Peterson. 2002b. Using niche-based GIS modeling to test geographic predictions of competitive exclusion and competitive release in South American pocket mice. Oikos 93:3-16.

Anderson, R. P., D. Lew, and A. T. Peterson. 2003. Evaluating predictive models of species' distributions: criteria for selecting optimal models. Ecological Modelling 162:211-232.

Anderson, R. P., and E. Martinez-Meyer. 2004. Modeling species' geographic distributions for preliminary conservation assessments: an implementation with the spiny pocket mice (Heteromys) of Ecuador. Biological Conservation 116:167-179.

Araújo, M. B., and A. Guisan. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogegraphy 33:1677-1688.

Araújo, M. B., and P. H. Williams. 2000. Selecting areas for species persistence using occurrence data. Biological Conservation 96:331-345.

Austin, M. 2002a. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157:101-118.

Austin, M. P. 2002b. Case studies of the use of environmental gradients in vegetation and fauna modelling: theory and practice in Australia and New Zealand. Pages 73-82 in J. M. Scott, P. J. Heglund, F. Samson, J. Haufler, M. Morrison, M. Raphael, and B. Wall, editors. Predicting Species Occurences: Issues of Accuracy and Scale. Island Press, Covelo, CA.

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

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

Barry, S., and J. Elith. 2006. Error and uncertainty in habitat models. Journal of Applied Ecology 43:413-423.

Bazzaz, F. A. 1998. Plant in changing environments: Linking physiological, population, and comunity ecology. Cambridge University Press, Cambridge, UK.

Bojorquez-Tapia, L. A., I. Azuara, E. Ezcurra, and O. A. Flores V. 1995. Identifying conservation priorities in Mexico through geographic information systems and modeling. Ecological Applications 5:215-231.

Busby, J. R. 1986. A biogeographical analysis of Nothofagus cunninghamii (Hook.) Oerst. in southeastern Australia. Australian Journal of Ecology 11:1-7.

Carpenter, G., A. N. Gillison, and J. Winter. 1993. DOMAIN: A flexible modeling procedure for mapping potential distributions of animals and plants. Biodiversity and Conservation 2:667-680.

Chapman, A. D., M. E. S. Munoz, and I. Koch. 2005. Environmental information: placing biodiversity phenomena in an ecological and environmental context. Biodiversity Informatics 2:24-41.

Chen, G., and A. T. Peterson. 2002. Prioritization of areas in China for biodiversity conservation based on the distribution of endangered bird species. Bird Conservation International 12:197-209.

Collingham, Y. 2000. Predicting the spatial distribution of non-indigenous riparian weeds: issues of spatial scale and extent. 37:13-27.

Corsi, F., J. de Leeuw, and A. Skidmore. 2000. Modeling species distribution with GIS. Pages 389-434 in L. Boitani and T. Fuller, editors. Research Techniques in Animal Ecology. Controversies and consequences. Columbia University Press, New York.

Cristianini, N., and J. Shawe-Taylor. 2000. An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press.

DEH. 2004. Normalized Difference Vegetation Index. in. Department of Environmental and Heritage. http://www.deh.gov.au/erin/ndvi/ndvi.html.

Efron, B. 1979. "Bootstrap Methods: Another Look at the Jackknife". The Annals of Statistics 7:1-26.

Egbert, S. L., M. A. Ortega-Huerta, E. Mart¡nez-Meyer, K. P. Price, and A. T. Peterson. 2000. Time-series analysis of high-temporal resolution AVHRR NDVI imagery of Mexico. Pages 1978-1980 in.

Egbert, S. L., A. T. Peterson, V. Sanchez-Cordero, and K. P. Price. 1998. Modeling conservation priorities in Veracruz, Mexico. Pages 141-150 in S. Morain, editor. GIS in natural resource management: Balancing the technical-political equation. High Mountain Press, Santa Fe, New Mexico.

Elith, J., and M. Burgman. 2002. Predictions and their validation: Rare plants in the Central Highlands, Victoria. in J. M. Scott, P. J. Heglund, and M. L. Morrison, editors. Predicting Species Occurrences: Issues of Scale and Accuracy. Island Press, Washington, D.C.

Elith, J., C. H. Graham, R. P. Anderson, M. Dudík, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M. Nakamura, Y. Nakazawa, J. M. Overton, A. T. Peterson, S. J. Phillips, K. S. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberon, S. Williams, M. S. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129-151.

Elton, C. S. 1927. Animal Ecology. Sidgwich and Jackson, London.

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

Fawcett, T. 2003. ROC graphs: notes and practical considerations for data mining researchers. Palo Alto, CA: HP Laboratories.

Feria, T. P., and A. T. Peterson. 2002. Prediction of bird community composition based on point-occurrence data and inferential algorithms: a valuable tool in biodiversity assessments. Diversity and Distributions 8:49-56.

Ferrier, S., and G. Watson. 1996. An evaluation of the effectiveness of environmental surrogates and modelling techniques in predicting the distribution of biological diversity. in. Canberra, Australia: NSW National Parks and Wildlife Service.

Ferrier, S., G. Watson, J. Pearce, and M. Drielsma. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. 1. Species-level modeling. Biological Conservation 11:2275-2307.

Fielding, A. H., and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49.

Gause, G. F. 1934. The struggle for existence. Williams and Wilkins.

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

Grinnell, J. 1917. Field tests of theories concerning distributional control. American Naturalist 51:115-128.

Guisan, A., T. C. Edwards Jr, and T. Hastie. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 157:89-100.

Guisan, A., C. H. Graham, J. Elith, and F. Huettmann. 2007. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions 13:332-340.

Guisan, A., and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8:993-1009.

Guisan, A., S. B. Weiss, and A. D. Weiss. 1999. GLM versus CCA spatial modelling of plant species distribution. 143:107-122.

Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135:147-186.

Hijmans, R. J. 2005. Very high resolution interpolated climate surfaces for global land areas. 25:1965-1978.

Hijmans, R. J., S. Cameron, and J. Parra. 2004. WorldClim, a new high-resolution global climate database. in Inter-American Workshop on Environmental Data Access.

Hirzel, A. H., and A. Guisan. 2002. Wich is the optimal sampling strategy for habitat suitability modelling? Ecological Modelling 157:331-341.

Hirzel, A. H., J. Hausser, D. Chessel, and N. Perrin. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027-2036.

Hirzel, A. H., V. Helfer, and F. Metral. 2001. Assessing habitat-suitability models with a virtual species. Ecological Modelling 145:111-121.

Huntley, B., P. M. Berry, W. Cramer, and A. P. McDonald. 1995. Modelling present and potential future ranges of some European higher plants using climate response surfaces. JOURNAL OF BIOGEOGRAPHY 22:967-1001.

Hutchinson, G. E. 1957. Concluding remarks. Cold Spring Harbor Symposia on Quantitative Biology 22:415-427.

Isaaks, E. H., and R. M. Srivastava. 1989. Applied Geostatistics, 2 edition. Oxford University Press, Oxford.

Iwashita, F. 2007. Sensibilidade de modelos de distribuição de espécies a erros de posicionamento de dados de coleta. master degree. Instituto de Pesquisas Espaciais - INPE, São José dos Campos, SP.

Liu, C., P. M. Berry, T. P. Dawson, and R. G. Pearson. 2005. Selecting thresholds of occurence in the prediction of species distributions. Ecography 28:385-393.

Magana, V., C. Conde, O. Sanchez, and C. Gay. 1997. Assessment of current and future regional climate scenarios for Mexico. Climate research 9:107-114.

Manel, S., H. C. Williams, and S. J. Ormerod. 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. 38:921-931.

Metz, C. E. 1986. ROC methodology in radiologic imaging. Investigative Radiolgy 21:720-733.

Netter, J., M. N. Kutner, C. J. Nachtssheim, and W. Wasserman. 1996. Applied linear statistical models, 4 edition. WCB/McGraw-Hill, Boston.

Nix, H. A. 1986. A biogeographic analysis of Australian elapid snakes. Pages 4-15 in R. Longmore, editor. Atlas of Australian Elapid Snakes. Australian Government Publishing Service, Canberra.

Oberhauser, K., and A. T. Peterson. 2003. Modelling current and future potencial wintering distributions of eastern North American monarch butterflies. PNAS (Proceedings of the National Academy of Sciences of the United States of America) 100:14063-14068.

Oksanen, J., and P. R. Minchin. 2002. Continuum theory revisited: what shape are species responses along ecological gradients? Ecological Modelling 157:119-129.

Ortega-Huerta, M. A., and A. T. Peterson. 2004. Modelling spatial patterns of biodiversity for conservation prioritization in North-eastern Mexico. Diversity and Distributions 10:39-54.

Parra, J. L., C. C. Graham, and J. F. Freile. 2004. Evaluating alternative data sets for ecological niche models of birds in the Andes. Ecography 27:350-360.

Paruelo, J. M., E. G. Jobb gy, and O. E. Sala. 2001. Current distribution of ecosystem functional types in temperate South America. Ecosystems 4:683-698.

Pearce, J., and S. Ferrier. 2000. Evaluating the predictive perfomance of habitat models developed using logistic regression. Ecological Modelling 133:225-245.

Pearson, G. R., C. J. Raxworthy, M. Nakamura, and A. T. Peterson. 2006a. Predicting species distributios from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogegraphy 34:102-117.

Pearson, R. G., W. Thuiller, M. B. Araújo, E. Martinez, L. Brotons, C. McClean, L. Miles, P. Segurado, T. Dawson, and D. Lees. 2006b. Model-based uncertainty in species' range prediction. JOURNAL OF BIOGEOGRAPHY 33:1704-1711.

Pereira, R. S. 2002. Desktop Garp. in. University of Kansas Biodiversity Research Center, Lawrence, Kansas.

Petersen, L. R., and J. T. Roehrig. 2001. West Nile virus: A reemerging global pathogen. Emerging Infectious Diseases 7:611-614.

Peterson, A. T., M. A. Ortega-Huerta, J. Bartley, V. Sanchez-Cordero, J. Soberón, R. H. Buddemeier, and D. R. B. Stockwell. 2002a. Future projections for Mexican faunas under global climate change scenarios. Nature 416:626-629.

Peterson, A. T., M. Papes, and D. A. Kluza. 2003a. Predicting the potential invasive distributions of four alien plant species in North America. Weed Science 51:863-868.

Peterson, A. T., M. Papes, and J. Soberón. 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modelling 213:63-72.

Peterson, A. T., V. Sanchez-Cordero, C. B. Beard, and J. M. Ramsey. 2002b. Ecologic niche modeling and potential reservoirs for Chagas disease, Mexico. Emerging Infectious Diseases 8:662-667.

Peterson, A. T., R. Scachetti-Pereira, and D. A. Kluza. 2003b. Assessment of Invasive Invasive Potential of Homalodisca coagulata in Western North America and South America. Biota Neotropica 3.

Peterson, A. T., J. Soberón, and V. Sanchez-Cordero. 1999. Conservatism of ecological niches in evolutionary time. Science 285:1265-1267.

Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259.

Phillips, S. J., M. Dud¡k, and R. E. Schapire. 2004. A maximum entropy approach to species distribution modeling. Pages 655-662 in Proceedings of the 21st International Conference on Machine Learning

21st International Conference on Machine Learning. ACM Press, New York.

Polasky, S., and A. R. Solow. 2001. The value of information in reserve site selection. Biodiversity and Conservation 10:1051-1058.

Raes, N., and H. t. Steege. 2007. A null-model for significance testing of presence-only species distribution models. Ecography 30:727-736.

Reddy, S., and L. M. Dávalos. 2003. Geographical sampling bias and its implications for conservation priorities in Africa. 30:1719-1727.

Roura-Pascual, N., A. SUAREZ, C. G¢mez, P. Pons, Y. Touyama, A. L. Wild, and A. T. Peterson. 2005. Geographic potential of Argentine ants (Linepithema humile Mayr) in the face of global climate change. Proceedings of the Royal Society of London B 271:2527-2535.

Rushton, S. P., S. J. Ormerod, and G. Kerby. 2004. New paradigms for modelling species distributions? Journal of Applied Ecology 41:193-200.

Sala, O. E., F. S. Chapin-III, J. J. Armesto, E. Berlow, J. Bloomfield, R. Dirzo, E. Huber-Sanwald, L. F. Huenneke, R. B. Jackson, A. Kinzig, R. Leemans, D. M. Lodge, H. A. Mooney, M. n. Oesterheld, N. L. Poff, M. T. Sykes, B. H. Walker, M. Walker, and D. H. Wall. 2000. Global biodiversity scenarios for the year 2100. Science 287:1770-1774.

Scott, J. M., P. J. Heglund, F. Samson, J. Haufler, M. Morrison, M. Raphael, and B. Wall. 2002. Predicting Species Occurrences: Issues of Accuracy and Scale. Pages 868 in. Island Press, Covelo, CA.

Segurado, P., and M. B. Araújo. 2004. An evaluation of methods for modelling species distributions. JOURNAL OF BIOGEOGRAPHY 31:1555-1568.

Segurado, P., M. B. Araújo, and W. E. Kunin. 2006. Consequences of spatial autocorrelation for niche-based models. Journal of Applied Ecology 43:433-444.

Sing, T., O. Sander, N. Beerenwinkel, and T. Lengauer. 2005. ROCR: visualizing classifier performance in R. Bioinformatics 21:3940-3941.

Siqueira, M. F., and G. Durigan. 2007. Modelagem da distribuição geográfica de espécies lenhosas de cerrado no Estado de São Paulo. Revista Brasileira de Botânica 30:239-249.

Siqueira, M. F. d., and A. T. Peterson. 2003. Consequences of Global Climate Change for Geographic Distributions of Cerrado Tree Species. Biota Neotropica 3.

Soberon, J. M., and A. T. Peterson. 2005. Interpretation of models of fundamental ecological niches and species' distributional areas. Biodiversity Informatics 2:1-10.

Stockwell, D. R. B. 2006. Improving ecological niche models by data mining large environmental datasets for surrogate models. Ecological Modelling.

Stockwell, D. R. B., and D. Peters. 1999. The GARP modelling system: Problems and solutions to automated spatial prediction. International Journal of Geographic Information Systems 13:143-158.

Stockwell, D. R. B., and A. T. Peterson. 2002. Effects of sample size on accuracy of species distribution models. Ecological Modelling 148:1-13.

Stoms, D. M., and W. W. Hargrove. 2000. Potential NDVI as a baseline for monitoring ecosystem functioning. International Journal of Remote Sensing 21:401-407.

Strahler, A. H., W. Lucht, C. B. Shaaf, T. Tsang, F. Gao, X. Li, J. P. Muller, P. Lewis, and M. J. Barnsley. 1999. MODIS BRDF/Albedo Product: Algorithm Theoretical Basis Document (Version 5). in.

Sutton, T., R. Giovanii, and M. F. Siqueira. 2007. Introducing openModeller. OSGeo Journal 1:1-6.

Thomas, C. D., A. Cameron, R. E. Green, M. Bakkenes, L. J. Beaumont, Y. C. Collingham, B. F. N. Erasmus, M. F. d. Siqueira, A. Grainger, L. Hannah, L. Hughes, B. Huntley, A. S. v. Jaarsveld, G. F. Midgley, L. Miles, M. A. Ortega-Huerta, A. T. Peterson, O. L. Phillips, and S. E. Williams. 2004. Extinction risk from climate change. Nature 427:145-148.

Thuiller, W. 2003. BIOMOD - optimizing prediction of species distributions and projecting potential future shifts under global change. Global Change Biology 9:1353-1362.

UMD. 2001. AVHRR NDVI Data Set. in. University of Maryland, http://glcf.umiacs.umd.edu/index.shtml, College Park, Maryland.

Vapnik, V. 1995. The Nature of Statistical Learning Theory. SpringerVerlag.

Verhoef, W., M. Menenti, and S. Azzali. 1996. A colour composite of NOAA-AVHRR-NDVI based on time series analysis (1981-1992). International Journal of Remote Sensing 17:231-235.

Wiley, E. O., K. M. McNyset, A. T. Peterson, and C. R. Robins. 2003. Niche modeling and geographic range predictions in the marine environment using a machine-learning algorithm. Oceanography 16:120-127.

Yee, T. W., and N. D. Mitchell. 1991. Generalized additive models in plant ecology. Journal of Vegetation Science 2:587-602.

Zaniewski, A. E., A. Lehmann, and J. M. Overton. 2002. Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns. Ecological Modelling 157:261-280.