Modelagem de distribuição para espécies sul-americanas de Podicipedidae

Abstract

Podicipedidae is a family from the Order Podicipediformes, characterized by their morphological adaptations to aquatic environments. Many species of this group are threatened and are exposed to a lot of extinction pressure, mainly due to anthropogenic actions, like invasive and exotic species introduction, hunting and habitat loss. Three of the twenty-two species are extinct, and eight of the living species are considered threatened by IUCN.A tool that helps conservation is the Species Distribution Modelling. This technique makes it possible to predict potential habitat areas for the species of this family. The main goal of this project was to identify variables who could influence South American Podicipedidae’s distribution and forecast potential distribution. To achieve these goals we did two species distribution modeling for South American Grebes using the software R, first, we created models using bioclimatic variables and land use, then we created a second series of models, using only bioclimatic variables from Wordclim. Our results show that precipitation on driest months and temperature during coldest months are the variables that most influence Grebe's distribution. Even though we assumed that land use would affect the species distribution, our results didn’t prove that, on the contrary, these variables influenced the least. For each species we created 4 different models. The first model was with the BIOCLIM algorithm and included both bioclimatic and land use variables, the second one also used both variables, but it was the MaxEnt algorithm, the third and fourth models were both only using bioclimatic variables, one for BIOCLIM and other to MaxEnt. BIOCLIM was also able to predict some extinctions, it considers Podiceps taczanowskii as a non existing species and Podiceps gallardoi as a species with almost no chance to be found. Meanwhile, MaxEnt predicted more occurrence areas for all the species, especially when the areas shared similarities in their climate conditions.