@Article{KlippelAmarVinh:2016:DeEvSp,
               author = "Klippel, Sandro and Amaral, Silvana and Vinhas, L{\'u}bia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Development and evaluation of species distribution models for five 
                         endangered elasmobranchs in southwestern Atlantic",
              journal = "Hydrobiologia",
                 year = "2016",
               volume = "779",
               number = "1",
                pages = "11--33",
                month = "Oct.",
             keywords = "Boosted regression trees, Chondrichthyes, Essential fish habitat, 
                         Remote sensing, Species–environment relationships, Threatened 
                         species.",
             abstract = "Species distribution models (SDMs) are tools to obtain habitat 
                         suitability maps based on historical species occurrences and 
                         environmental variables. Those maps can be used to restrict 
                         fishing grounds or to assist in planning and reserve selection. 
                         This is especially important for species at risk of extinction. We 
                         developed SDMs for five endangered elasmobranch species, namely 
                         Squatina guggenheim, S. occulta, Rhinobatos horkelii, Galeorhinus 
                         galeus, and Mustelus schmitti, using Boosted Regression Trees. 
                         Data from 1,704 bottom trawls carried out between 1972 and 2005 as 
                         part of research surveys on the southern Brazilian shelf between 
                         28°36\′S and 33°45\′S, combined with satellite 
                         imagery and environmental atlases, were used in the models. Based 
                         on 10-fold cross-validation statistics, all models had a 
                         reasonable performance, though S. guggenheim models had an 
                         excellent discrimination (AUC > 0.9) and R. horkelii models had 
                         just a fair discriminatory power (AUC 0.70.8). Except for R. 
                         horkelii, all models showed good association between observed and 
                         predicted occurrences (PBC > 0.5). Squatina guggenheim models 
                         provided the greatest explained deviance (4954%), whereas R. 
                         horkelii models the smallest (1417%). Models predictions were 
                         consistent with the current knowledge of all species. Moreover, 
                         those models made reasonable predictions using the great spatial 
                         and temporal coverage of satellite data.",
                  doi = "10.1007/s10750-016-2796-5",
                  url = "http://dx.doi.org/10.1007/s10750-016-2796-5",
                 issn = "0018-8158 and 1573-5117",
             language = "en",
           targetfile = "klippel_development.pdf",
        urlaccessdate = "27 abr. 2024"
}