Abstract
A method for wildlife habitat evaluation, based on statistical methods and Decision Theory is developed. The aim is to provide tools for an adequate management of wildlife resources, replacing empirical appreciation of habitats with scientific evaluation, increasing predictability and reducing dependence on the empirical knowledge of experienced practitioners. The method uses multiple logistic regression to predict the probability of occurrence of the studied species, based on a set of environmental variables. The transformation from probability values to occurrence predictions is done using Decision Theory, which also establishes the conditions of applicability of the model. The method is easily integrable with Geographic Information Systems, allowing the efficient use of large sets of environmental data and the application of different decision criteria for each management unit. The method is illustrated by the application to a case study: the distribution in a private game reserve of wild rabbit (Oryctolagus cuniculus L. 1758), one of the most important Portuguese game species.
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References
Berry, K.H. (1984) Introduction: development, testing and application of Wildlife-Habitat Models, in J. Verner, M. Morrison and C.J. Ralph (eds.), Wildlife 2000. Modelling Habitat Relationships of Terrestrial Vertebrates, The University of Wisconsin Press, pp. 3–4.
Borralho, R., Rego, F., Palomares, F., and Hora, A. (1996) The distribution of the Egyptian mongoose Herpestes ichneumon (L.) in Portugal, Mammal Rev. 26 (1), 1–8.
Cardoso, J.C. (1965) Os solos de Portugal. Sua classificaçâo, caracterizaçäo e génese. /- A sul do rio Tejo, Secretaria de Estado da Agricultura, Direcçäo-Geral dos Serviços Agricolas, Lisboa.
Cardoso, J.C. (1974) A classificaçäo dos solos de Portugal. Boletim de Solos do Serviço de Reconhecimento e Ordenamento Agrdrio 17, 14–46.
Carmo, P.L., Romdo C.O. and Santos, A.M. (1986) Avaliaçâo de potencialidades cinegéticas - método HEP, Comunicaçóes do Congresso Florestal Nacional, Lisboa.
Cochran, W. G. (1954) Some methods for strengthening the common xz Tests, Biometrics 10, 417–451.
Daniel, W.W. (1978) Applied Nonparametric Statistics,Houghton Mifflin Company.
Donovan, M.L., Rabe, D.L., and Olson, C.E. (1987) Use of Geographic Systems to develop Habitat Suitability Models, Wildl. Soc. Bull. 15, 574–579.
Draper, N.R. and Smith, H. (1981) Applied regression analysis, Second Edition, Wiley Series in Probability and Mathematical Statistics, New York.
Egan, J.P. (1975) Signal Detection Theory and ROC analysis, Academic Press. New York.
ERENA (1994) Plano de Ordenamento e de Explorav6o Cinegéticos e Plano de Aproveitamento Turistico, TERRAPRIMA - Sociedade Agricola, Lda. Lisboa.
Eby, J.R. and Bright, L.R. (1985), A digital GIS based on LANDSAT and other data for elk habitat efectiveness analysis, Proceedings of the 19th International Symposium of Remote Sensing and Environment pp. 855–864.
Fabricius, C. and Mentis, M.T. (1990) Seasonal habitat selection by eland in arid savanna in South Africa, S. Afr. J. Zool. 25, 238–244 (cit. in Fabricius e Coetzee 1992 ).
Fabricius, C. and Coetzee, K. (1992), Geographic information systems and artificial intelligence for predicting the presence or absence of mountain reedbuck, S.–Afr. Tydskr. Natuurnav 22 (3) 80–86.
Gutzwiller, K.J. and Anderson, S.H. (1984), Improving Vertebrate-Habitat Regression Models in Verner, J., Morrison, M., and Ralph, C.J. (eds.), Wildlife 2000. Modeling habitat relationships of terrestrial vertebrates, The University of Wisconsin Press, pp. 161–164.
Marzluff, J.M. (1984) Assumptions and design of regression experiments: the importance of lack-of-fit testing, in J. Verner, M. Morrison and C.J. Ralph (eds.), Wildlife 2000. Modeling Habitat Relationships of Terrestrial Vertebrates, The University of Wisconsin Press, pp. 165–170.
Maynard, P.F. (1981) The logit classifier: a general maximum likelihood discriminant for remote sensing applications,Unpublished M. A. Thesis, Department of Geography, University of California, Santa Barbara, California (cit. in Pereira 1989).
Mladenoff, D. J., Sickley, T.A., Haight, R.G., and Wydeven, A.P. (1995), A regional landscape analysis and prediction of favorable gray wolf habitat in the northern Great Lakes region, Conservation Biology 9 (2), 279–294.
Norton, T.W. and Williams, J.E. (1992) Habitat modelling and simulation for nature conservation., Mathematics and Computers in Simulation 33, 379–384.
Norton, T.W. and Possingham, H.P. (1993) Wildlife modelling for biodiversity conservartion. Modelling Change in Environmental Systems. John Wiley and Sons.
Palmeirim, J.M. (1985), Using LANDSAT TM imagery and spatial modelling in automatic habitat evaluation and release site selection for the Ruffed Grouse (Galliformes: Tetraonidae), Proceedings /9th International Symposium of Remote Sensing and Environment pp. 729–738.
Pereira, J.M.C. (1989) A spatial approach to statistical habitat suitability modeling: The Mt. Graham Red Squirrel Case Study, Unpublished Ph. D. Dissertation, School of Renewable Natural Resources, University of Arizona, Tucson, Arizona.
Pereira, J.M.C. and Itami, R.M. (1991) GIS-Based habitat modeling using logistic multiple regression: A study of the Mt. Graham red squirrel, Photogrammetric Engineering and Remote Sensing 57(11), 14751486.
Pereira, J.M.C. and Duckstein, L. (1993). A multiple criteria decision-making approach to GIS-based land suitability evaluation. Int. J. Geographical Information Systems 5 (7), 407–424.
Poole, R.W. (1974) An Introduction to Quantitative Ecology, McGraw-Hill Book Company, New York.
Rice, S.M., Guthery, F.S., Spears, G.S., DeMaso, S.J. and Koerth, B.H. (1993) A precipitation-habitat model for northern Bobwhites on semiarid rangeland, J. Wildl. Manage. 57 (1), 92–192.
Saveland, J.M. and Neuenschwander, L. F. (1990) A signal detection fremework to evaluate models of tree mortality following fire damage, Forest Science, 36 (1), 66–76.
Schamberger, M.L. and O’Neil, L.1. (1984) Concepts and constraints of habitat-model testing, in J. Verner, M. Morrison and C.J. Ralph (eds.), Wildlife 2000. Modeling Habitat Relationship.s of Terrestrial Vertebrates, The University of Wisconsin Press, pp. 5–10.
Shugart (Jr.), H.H. (1981) An overview of multivariate methods and their application to studies of wildlife habitat, in D.E. Caper (ed.), The use of multivariate statistics in studies of wildlife habitat. Proceedings of a workshop, Burlington, pp. 4–10.
Williams, G.L., Russel, K.R. and Seitz, W.K. (1978) Pattern recognition as a tool in the ecological analysis of habitat, in A. Marwelstein (ed.), Classification, inventory and analysis of fish and wildlife habitat–The Proceedings of a National Symposium, Phoenix, Ariz. U. S. Dep. Inter., Fish and Wildlife Serv., Off. of Biol. Serv., FWS/OBS-78/76 USD, FWS, OBS, Washington, D.C., pp. 521–531.
Winterfeldt (von), D. and Edwards, W., (1986), Decision Analysis and Behavioral Research, Cambridge University Press, New York, 604 pp.
Zar, J.H. (1996) Biostatistical Analysis, Prentice-Hall, Third edition, London.
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Martins, H., Domingos, T., Rego, F., Borralho, R., Bugalho, J. (1997). Habitat Evaluation Using Logistic Regression. In: Soares, A., Gómez-Hernandez, J., Froidevaux, R. (eds) geoENV I — Geostatistics for Environmental Applications. Quantitative Geology and Geostatistics, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1675-8_34
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DOI: https://doi.org/10.1007/978-94-017-1675-8_34
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