@Article{BanonBVAMRBMN:2019:PrSuNe,
               author = "Banon, Gabriela Paola Ribeiro and Banon, Gerald Jean Francis and 
                         Villamar{\'{\i}}n, Francisco and Arraut, Eduardo Moraes and 
                         Moulatlet, Gabriel Massaine and Renn{\'o}, Camilo Daleles and 
                         Banon, Lise Christine and Marioni, Boris and Novo, Evlyn 
                         M{\'a}rcia Le{\~a}o de Moraes",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas da Amaz{\^o}nia (INPA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas da Amaz{\^o}nia (INPA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Piaga{\c{c}}u} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Predicting suitable nesting sites for the Black caiman 
                         (Melanosuchus niger Spix 1825) in the Central Amazon basin",
              journal = "Neotropical Biodiversity",
                 year = "2019",
               volume = "5",
               number = "1",
                pages = "47--59",
                month = "Aug.",
             keywords = "Amazon floodplain, Amazonian caiman, ecological conservation, 
                         maximum entropy modeling, nesting habitat.",
             abstract = "After many years of illegal hunting and commercialization, the 
                         populations of the Black caiman (Melanosuchus niger) have been 
                         recovering during the last four decades due to the enforcement of 
                         a legislation that inhibits their international commercialization. 
                         Protecting nesting sites, in which vulnerable life forms (as 
                         reproductive females, eggs, and neonates) spend considerable time, 
                         is one of the most appropriate conservation actions aimed at 
                         preserving caiman populations. Thus, identifying priority areas 
                         for this activity should be the primary concern of 
                         conservationists. As caiman nesting sites are often found across 
                         the areas with difficult access, collecting nest information 
                         requires extensive and costly fieldwork efforts. In this context, 
                         species distribution modeling can be a valuable tool for 
                         predicting the locations of caiman nests in the Amazon basin. In 
                         this work, the maximum entropy method (MaxEnt) was applied to 
                         model the M. niger nest occurrence in the Mamiraua Sustainable 
                         Development Reserve (MSDR) using remotely sensed data. By taking 
                         into account the M. niger nesting habitat, the following predictor 
                         variables were considered: conditional distance to open water, 
                         distance to bare soil, expanded contributing area from drainage, 
                         flood duration, and vegetation type. The threshold-independent 
                         prediction performance and binary prediction based on the 
                         threshold value of 0.9 were evaluated by the area under the curve 
                         (AUC) and performing a binomial test, respectively. The obtained 
                         results (AUC = 0.967 +/- 0.006 and a highly significant binomial 
                         test P< 0.01) indicated excellent performance of the proposed 
                         model in predicting the M. niger nesting occurrence in the MSDR. 
                         The variables related to hydrological regimes (conditional 
                         distance to open water, expanded contributing area from drainage, 
                         and flood duration) most strongly affected the model performance. 
                         MaxEnt can be used for developing community-based sustainable 
                         management programs to provide socioeconomic benefits to local 
                         communities and promote species conservation in a much larger area 
                         within the Amazon basin.",
                  doi = "10.1080/23766808.2019.1646066",
                  url = "http://dx.doi.org/10.1080/23766808.2019.1646066",
                 issn = "2376-6808",
             language = "en",
           targetfile = "banon_predicting.pdf",
        urlaccessdate = "28 abr. 2024"
}