author = "Sales, Lilian Patr{\'{\i}}cia and Neves, Ol{\'{\i}}via Viana 
                         and Marco Junior, Paulo de and Loyola, Rafael",
          affiliation = "{Universidade Federal de Goi{\'a}s (UFG)} and {Universidade 
                         Federal de Goi{\'a}s (UFG)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                title = "Model uncertainties do not affect observed patterns of species 
                         richness in the Amazon",
              journal = "Plos One",
                 year = "2017",
               volume = "12",
               number = "10",
                pages = "e0183785",
                month = "Oct.",
             abstract = "Background Climate change is arguably a major threat to 
                         biodiversity conservation and there are several methods to assess 
                         its impacts on species potential distribution. Yet the extent to 
                         which different approaches on species distribution modeling affect 
                         species richness patterns at biogeographical scale is however 
                         unaddressed in literature. In this paper, we verified if the 
                         expected responses to climate change in biogeographical 
                         scale-patterns of species richness and species vulnerability to 
                         climate change-are affected by the inputs used to model and 
                         project species distribution. Methods We modeled the distribution 
                         of 288 vertebrate species (amphibians, birds and mammals), all 
                         endemic to the Amazon basin, using different combinations of the 
                         following inputs known to affect the outcome of species 
                         distribution models (SDMs): 1) biological data type, 2) modeling 
                         methods, 3) greenhouse gas emission scenarios and 4) climate 
                         forecasts. We calculated uncertainty with a hierarchical ANOVA in 
                         which those different inputs were considered factors. Results The 
                         greatest source of variation was the modeling method. Model 
                         performance interacted with data type and modeling method. 
                         Absolute values of variation on suitable climate area were not 
                         equal among predictions, but some biological patterns were still 
                         consistent. All models predicted losses on the area that is 
                         climatically suitable for species, especially for amphibians and 
                         primates. All models also indicated a current East-western 
                         gradient on endemic species richness, from the Andes foot 
                         downstream the Amazon river. Again, all models predicted future 
                         movements of species upwards the Andes mountains and overall 
                         species richness losses. Conclusions From a methodological 
                         perspective, our work highlights that SDMs are a useful tool for 
                         assessing impacts of climate change on biodiversity. Uncertainty 
                         exists but biological patterns are still evident at large spatial 
                         scales. As modeling methods are the greatest source of variation, 
                         choosing the appropriate statistics according to the study 
                         objective is also essential for estimating the impacts of climate 
                         change on species distribution. Yet from a conservation 
                         perspective, we show that Amazon endemic fauna is potentially 
                         vulnerable to climate change, due to expected reductions on 
                         suitable climate area. Climate-driven faunal movements are 
                         predicted towards the Andes mountains, which might work as climate 
                         refugia for migrating species.",
                  doi = "10.1371/journal.pone.0183785",
                  url = "http://dx.doi.org/10.1371/journal.pone.0183785",
                 issn = "1932-6203",
             language = "en",
           targetfile = "sales_model.pdf",
        urlaccessdate = "29 nov. 2020"