Modelos de distribuição potencial em escala fina: metodologia de validação em campo e aplicação para espécies arbóreas
Ferreira, Larissa Campos
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Some conservation actions require the knowledge of the geographical distribution of species, however, this knowledge is far from being achieved for most species. The species distribution models (SDMs) have proved a useful tool to predict the distribution of species and guide field research to find new records. The SDMs using field data occurrence and environmental variables to indicate potential sites for the occurrence of a species. The quality and quantity of the data used are important to a successful result prediction models and application to conservation. The choice of environmental data and the algorithm and their settings are important for the development of models, the choice of these variables have directly influences to the quality of the models. Another very important step in modeling is the quality assessment and validation of the model, is that it may decrease the risk of accepting as true models with gross errors. The objective of this study is to evaluate the applicability of models generated by MaxEnt to find new populations of plants considering different data configurations used. For this, considering that the field validation is the most appropriate in the literature, but the most costly, the first chapter proposes a validation methodology of the models as easy application field. The methodology was able to find new records in the field, therefore, indicated for the validation of models. In the second chapter, knowing of the existence of a wide variety of variables that influence the performance of the models, the aim was to test the influence of the sample size, the spatial bias, the set of climate data and settings available for the MaxEnt algorithm in the areas of prediction potential distribution. The results demonstrated that the use of sampling and climate data restricted to the limit of the study area and also the use of soil data generate more accurate models.