@InProceedings{NicasioMasHern:2017:UnEvDe,
               author = "Nicasio, Sergio and Mas, Jean-Fran{\c{c}}ois and Hern{\'a}ndez, 
                         Gabriela",
                title = "Una evaluaci{\'o}n del sesgo de muestreo sobre el an{\'a}lisis 
                         ROC de modelos de nicho",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7483--7488",
         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 = "During the last decades, ecological niche modeling has become a 
                         very popular methodologyin the exploration and analysis of 
                         biodiversity data. ROC analysis is widely used to assess the 
                         modelsand high performance is often reported in the literature. 
                         However, datasets derived from opportunisticobservations often 
                         exhibit a strong geographic bias, mainly due to accessibility. 
                         This unequal coverageof a species distribution can strongly affect 
                         the quality of the model when important parts of theenvironmental 
                         space suitable to a specie are poorly represented in the survey 
                         dataset. This study aimsat assessing the performance of ROC 
                         analysis in evaluating niche models. We elaborated 
                         independantniche models for Romerolagus diazzi using MaxEnt and 
                         data obtained during different decadesseparately. Each decade 
                         based model was trained using 75% of the data and assessed using 
                         theremaining 25%. ROC analysis based on the 25% of test data 
                         presented high scores for all the models.However, ACP analysis and 
                         the comparison between the species distribution derived from the 
                         modelspresented important differences. These results suggest that 
                         ROC analysis based on a subset of the datatend to be 
                         optimistically biased because the test set is not independant from 
                         the training set and presentsoften the same bias.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59569",
             language = "es",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMFR5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMFR5",
           targetfile = "59569.pdf",
                 type = "Modelagem espacial",
        urlaccessdate = "27 abr. 2024"
}