author = "Motta, Alline Zagnoli Villela and Braga, Sollano Rabelo and Silva, 
                         Nathalia Drummond Marques da and Christofaro, Cristiano",
                title = "Avalia{\c{c}}{\~a}o do desempenho de modelos de 
                         distribui{\c{c}}{\~a}o potencial da esp{\'e}cie Wunderlichia 
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6874--6881",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Potential distribution models, when allowing the occurrence 
                         mapping of species, can be a powerful tool for conservation of 
                         natural resources programs. The objective of this study is to 
                         evaluate the performance of many modeling algorithms utilizing 
                         distribution data of the Wunderlichia azulenzis species. The 
                         species is listed in the Ministry of Environment''s National list 
                         of endangered species of flora in the Caatinga biome. Two groups 
                         of algorithms, classified according to two types of entry data 
                         (presence and absence), were evaluated using the Area Under the 
                         Curve - AUC. From the registered occurrences for the species on 
                         database Global Biodiversity Information Facility GBIF, and 
                         utilizing six temperature and precipitation variables selected 
                         from the Worldclim project, species distribution maps were 
                         created. Six different algorithms were used to create the 
                         distribution maps of the species. The Mahalanobis Distance (0,978) 
                         and the Random Forest (0,0993) algorithms presented the greatest 
                         AUC values among its respective groups, while the Bioclim (0,931) 
                         and General Linear Model - GLM (0,807) algorithms presented the 
                         lowest values. The algorithms that are a part of the group of 
                         models that use only presence registers (Bioclim, Domain and 
                         Mahalanobis Distance) were considered efficient.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59861",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMDMR",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3PSMDMR",
           targetfile = "59861.pdf",
                 type = "Monitoramento e modelagem ambiental",
        urlaccessdate = "24 jan. 2021"