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VÅRT SYSTEM ER RASKERE, SIKRERE OG RIMELIGERE!

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Omi: 28% %cels: 31,0301 Parâmetros: default AUC: 0,75 Acc: 100% Omi: 0% %cels: 45,8031 Parâmetros: default AUC: 0,91 Parâmetros: default AUC: 0,71 Acc: 84% Omi: 16% %cels: 40,8213 Parâmetros: default GARP Maxent SVM Biolclim

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Esse trabalho foi financiado pelo CNPq (bolsa de pro- dutividade e financiamento direto a PDmJr), Projeto Bioimpacto BBVA Espanha e Projeto openmodeller – Cri – FAPESP (para mFS).

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eferêncIasbIblIográfIcas

Akcakaya, H.r., S.H.m. Butchart, G.m. mace, S.N. Stuart & C. Hilton-Taylor. 2006. use and misuse of the iuCN red List Criteria in projecting climate change impacts on biodiversity. Global Change Biology 12: 2037-2043.

Anderson, r.P. 2003. real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (rodentia: muridae) in Venezuela. Journal of Biogeography 30: 591-605.

Anderson, r.P., m. Gomez-Laverde & A.T. Peterson. 2002. Geographical distributions of spiny pocket mice in South America: insights from predictive models. Global Ecology and Biogeography 11: 131-141.

Araujo, m.B., m. Cabeza, W. Thuiller, L. Hannah & P.H. Williams. 2004. Would climate change drive species out of reserves? An assessment of existing reserve-selection methods. Global Change Biology 10: 1618-1626.

Araujo, m.B. & m. New. 2006. Ensemble forecasting of species distributions. Trends in Ecology & Evolution 22: 42-47. Austin, m. 2007. Species distribution models and ecological

theory: a critical assessment and some possible new approaches. Ecological modelling 200: 1-19.

Beaumont, L.J., L. Hughes & m. Poulsen. 2005. Predicting species distributions: use of climatic parameters in BioCLim and its impact on predictions of species’ current and future distributions. Ecological modelling 186: 250-269.

Broennimann, o., u.A. Treier, H. muller-Scharer, W. Thuiller, A.T. Peterson & A. Guisan. 2007. Evidence of climatic niche shift during biological invasion. Ecology Letters 10: 701-709. Brotons, L., W. Thuiller, m.B. Araujo & A.H. Hirzel. 2004.

Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27: 437-448. Cristianini, N. & J. Shawe-Taylor. 2000. An introduction to support

vector machines and other kernel-based learning methods. Cambridge university Press, London.

Dudik, m., S.J. Phillips & r.E. Schapire. 2004. Performance guarantees for regularized maximum entropy density estimation. Proceedings of the 17th Annual Conference on Computational Learning Theory 655-662.

Elith, J. & 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.

Elith, J., C.H. Graham, r.P. Anderson, m. Dudik, 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. richardson, r. Scachetti-Pereira, r.E. Schapire, J. Soberon, S. Williams, m.S. Wisz & N.E. Zimmermann. 2006. Novel methods improve prediction of species distributions from occurrence data. Ecography 29: 129-151.

Engler, r., A. Guisan & L. 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.

Farber, o. & r. Kadmon. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the mahalanobis distance. Ecological modelling 160: 115-130. Ganeshaiah, K.N., N. Barve, N. Nath, K. Chandrashekara,

m. Swamy & r.u. Shaanker. 2003. Predicting the potential geographical distribution of the sugarcane woolly aphid using GArP and DiVA-GiS. Current Science 85: 1526-1528.

Gavin, D.G. & F.S. Hu. 2005. Bioclimatic modelling using Gaussian mixture distributions and multiscale segmentation. Global Ecology and Biogeography 14: 491-501.

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

Guisan, A., o. Broennimann, r. Engler, m. Vust, N.G. Yoccoz, A. Lehmann & N. E. Zimmermann. 2006. using niche-based models to improve the sampling of rare species. Conservation Biology 20: 501-511.

Guisan, A., T.C. Edwards & T. Hastie. 2002. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling 157: 89-100. Hargrove, W.W. & F.m. Hoffman. 2004. Potential of multivariate

quantitative methods for delineation and visualization of ecoregions. Environmental management 34: S39-S60. Heikkinen, r.K., m. Luoto, m.B. Araujo, r. Virkkala, W. Thuiller

& m.T. Sykes. 2006. methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography 30: 751-777.

Herborg, L.m., C.L. Jerde, D.m. Lodge, G.m. ruiz & H.J. macisaac. 2007. Predicting invasion risk using measures of introduction effort and environmental niche models. Ecological Applications 17: 663-674.

Hernandez, P.A., C.H. Graham, L.L. master & D.L. Albert. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29: 773-785.

Hettrich, A. & S. rosenzweig. 2003. multivariate statistics as a tool for model-based prediction of floodplain vegetation and fauna. Ecological modelling 169: 73-87.

Hijmans, r.J. & C.H. Graham. 2006. The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biology 12: 2272-2281. Hijmans, r.J., L. Guarino & E. rojas. 2002. DiVA-GiS, version 2.

A geographic information system for the analysis of biodiversity data. manual. international Potato Center, Lima, Peru. Hijmans, r.J., S.E. Cameron, J.L. Parra, P.G. Jones & A. Jarvis. 2005.

Very high resolution interpolated climate surfaces for global land areas. international Journal of Climatology 25: 1965-1978. Hirzel, A.H. & r. Arlettaz. 2003. modeling habitat suitability

for complex species distributions by environmental-distance geometric mean. Environmental management 32: 614-623. Hutchinson, G.E. 1957. Concluding remarks. Cold Spring Harbor

Symposium of Quantitative Biology 22: 415-427.

Hutchinson, G.E. 1981. introducción a la Ecología de Poblaciones. Blume Ecologia, Barcelona.

iuCN. 2004 iuCN red list of threatened species. http://www. redlist. org/ 2005.

Jimenez-Valverde, A. & J.m. Lobo. 2006. The ghost of unbalanced species distribution data in geographical model predictions. Diversity and Distributions 12: 521-524.

Joy, m.K. & r.G. Death. 2004. Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GiS and neural networks. Freshwater Biology 49: 1036-1052. Leathwick, J.r., D. rowe, J. richardson, J. Elith & T. Hastie. 2005.

using multivariate adaptive regression splines to predict the distributions of New Zealand’s freshwater diadromous fish. Freshwater Biology 50: 2034-2052.

Leathwick, J.r., J. Elith & T. Hastie. 2006. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions. Ecological modelling 199: 188-196.

Lehmann, A., J.m. overton & J.r. Leathwick. 2002. GrASP: generalized regression analysis and spatial prediction. Ecological modelling 157: 189-207.

Liu, C.r., P.m. Berry, T.P. Dawson & r.G. Pearson. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385-393.

Loo, S.E., r. mac Nally & P.S. Lake. 2007. Forecasting New Zealand mudsnail invasion range: model comparisons using native and invaded ranges. Ecological Applications 17: 181-189. Luoto, m., J. Poyry, r.K. Heikkinen & K. Saarinen. 2005. uncertainty

of bioclimate envelope models based on the geographical distribution of species. Global Ecology and Biogeography 14: 575-584.

manel, S., H.C. Williams & S.J. ormerod. 2001. Evaluating presence-absence models in ecology: the need to account for prevalence. Journal of Applied Ecology 38: 921-931.

martinez, i., F. Carreno, A. Escudero & A. rubio. 2006. Are threatened lichen species well-protected in Spain? Effectiveness of a protected areas network. Biological Conservation 133: 500-511.

Noy-meir, i., D. Wlaker & W.T. Williams. 1975. Data transformations in ecological ordination. ii. on the meaning of data standardization. Journal of Ecology 63: 779-800.

Parra-olea, G., E. martinez-meyer & G.F.P. de Leon. 2005. Forecasting climate change effects on salamander distribution in the highlands of central mexico. Biotropica 37: 202-208. Pearce, J. & S. Ferrier. 2000. Evaluating the predictive performance

of habitat models developed using logistic regression. Ecological modelling 133: 225-245.

Pearson, r.G., W. Thuiller, m.B. Araujo, E. martinez-meyer, L. Brotons, C. mcClean, L. miles, P. Segurado, T.P. Dawson & D. C. Lees. 2006. model based uncertainty in species range prediction. Journal of Biogeography 33: 1704-1708.

Pearson, r.G., C.J. raxworthy, m. Nakamura & A.T. Peterson. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in madagascar. Journal of Biogeography 34: 102-117.

Pereira, r.S. & m.F. Siqueira, m.F. no prelo. Algoritmos Genéticos. megadiversidade.

Peterjohn, B.G. 2001. Some considerations on the use of ecological models to predict species’ geographic distributions. Condor 103: 661-663.

Peterson, A.T. 2001. Predicting species’ geographic distributions based on ecological niche modeling. Condor 103: 599-605. Peterson, A.T. 2003. Predicting the geography of species’ invasions

via ecological niche modeling. Quarterly review of Biology 78: 419-433.

Peterson, A.T. & D.A. Kluza. 2003. New distributional modelling approaches for gap analysis. Animal Conservation 6: 47-54. Phillips, S.J., r.P. Anderson & r.E. Schapire. 2006. maximum

entropy modeling of species geographic distributions. Ecological modelling 190: 231-259.

raxworthy, C.J., E. martinez-meyer, N. Horning, r.A. Nussbaum, G.E. Schneider, m. ortega-Huerta & A.T. Peterson. 2003. Predicting distributions of known and unknown reptile species in madagascar. Nature 426: 837-841.

robertson, m.P., C.i. Peter, m.H. Villet & B.S. ripley. 2003. Comparing models for predicting species’ potential distributions: a case study using correlative and mechanistic predictive modelling techniques. Ecological modelling 164: 153-167. rouget, m., D.m. richardson, S.J. milton & D. Polakow. 2001.

Predicting invasion dynamics of four alien Pinus species in a highly fragmented semi-arid shrubland in South Africa. Plant Ecology 152: 79-92.

Santana, F.S., m.F. Siqueira, A.m. Saraiva & P.L.P. Correa. no prelo. A reference business process for ecological niche modelling. Ecological informatics.

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

Siqueira, m.F. 2005. uso de modelagem de nicho fundamental na avaliação do padrão de distribuição geográfica de espécies vegetais. Tese de Doutorado. universidade de São Paulo, Escola de Engenharia de São Carlos. 107pp.

Siqueira, m.F. & G. Durigan. 2007. modelagem da distribuição geográfica de espécies lenhosas de cerrado no Estado de São Paulo. revista Brasileira de Botânica 30: 249.

Soberón, J. 2007. Grinnellian and Eltonian niches and geographic distributions of species. Ecology Letters 10: 1115-1123. Stephenson, C.m., m.L. macKenzie, C. Edwards & J.m.J. Travis.

2006. modelling establishment probabilities of an exotic plant, rhododendron ponticum, invading a heterogeneous, woodland landscape using logistic regression with spatial autocorrelation. Ecological modelling 193: 747-758.

Stockman, A.K., D.A. Beamer & J.E. Bond. 2006. An evaluation of a GArP model as an approach to predicting the spatial distribution of non-vagile invertebrate species. Diversity and Distributions 12: 81-89.

Stockwell, D.r.B. 2006. improving ecological niche models by data mining large environmental datasets for surrogate models. Ecological modelling 192: 188-196.

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

Stoddard, A.m. 1979. Standardization of measures prior to cluster analysis. Biometrics 35: 765-773.

Suarez-Seoane, S., P.E. osborne & J.C. Alonso. 2002. Large- scale habitat selection by agricultural steppe birds in Spain: identifying species-habitat responses using generalized additive models. Journal of Applied Ecology 39: 755-771.

Sutherst, r.W. & G. maywald, G. 2005. A climate model of the red imported fire ant, solenopsis invicta Buren (Hymenoptera: Formicidae): implications for invasion of new regions, particularly oceania. Environmental Entomology 34: 317-335. Sutton, T., r. Giovanii & m.F. Siqueira. 2007. introducing

openmodeller. oSGeo Journal 1: 1-6.

Termansen, m., C.J. mcClean & C.D. Preston. 2006. The use of genetic algorithms and Bayesian classification to model species distributions. Ecological modelling 192: 410-424.

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

Thuiller, W., S. Lavorel & m.B. Araujo. 2005. Niche properties and geographical extent as predictors of species sensitivity to climate change. Global Ecology and Biogeography 14: 347-357.

Vapnik, V. 1995. The Nature of Statistical Learning Theory. Springer Verlag

Villordon, A., W. Njuguna, S. Gichuki, P. Ndolo, H. Kulembeka, S.C. Jeremiah, D. LaBonte, B. Yada, P. Tukamuhabwa & r.o.m. mwanga. 2006. using GiS-based tools and distribution modeling to determine sweetpotato germplasm exploration and documentation priorities in sub-Saharan Africa. Hortscience 41: 1377-1381.

CLAuDio JoSÉ BArroS DE CArVALHo

Departamento de Zoologia, universidade Federal do Paraná, Curitiba, Brasil. e-mail: [email protected]

Padrões de endemismos e a conservação

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