Dissertações em Ecologia (Mestrado) - PPGECO/ICB
URI Permanente para esta coleçãohttps://repositorio.ufpa.br/handle/2011/8938
O Mestrado Acadêmico foi criado em 2015 e pertence ao Programa de Pós-Graduação em Ecologia (PPGECO) do Instituto de Ciências Biológicas (ICB) da Universidade Federal do Pará (UFPA) em parceria com a Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA).
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Item Acesso aberto (Open Access) Clima, solo e água: importância de variáveis ambientais na determinação da distribuição potencial de peixes de rios e riachos amazônicos(Universidade Federal do Pará, 2017-10-19) ALVAREZ, Facundo; MONTAG, Luciano Fogaça de Assis; http://lattes.cnpq.br/4936237097107099; GERHARD, Pedro; http://lattes.cnpq.br/5621269098705408Estimating the spatial distributions of species is one of the main objectives of macroecology, especially when sampling efforts fail to reach the demographic knowledge of the target species. In this sense, the species distribution models (SDM) allow us to approach the fundamental niche of the species from the extrapolation of predictor variables. The Amazonas-Tocantins basin is characterized by a strong environmental and physical dynamics that act differently in the regional ichthyofauna at different spatial scales. Due to the differential perception of hábitats by the species, four species of rivers were included, Ageneiosus inermis, Acestrorhynchus falcatus, Pygocentrus nattereri and Plagioscion squamosissimus, and four species of streams, Crenuchus spilurus, Helogenes marmoratus, Helogenes marmoratus and Trichomycterus hasemani. The objectives of the study were: (i) To determine which set of predictor variables allows better spatial representations for the species of rivers and streams using SDM; and (ii) To evaluate the predictive power of MaxEnt to generate SDM of rivers and streams using different sets of Predictor variables. The spatial records that presented spatial autocorrelation were processed from the spThin package. To characterize the environmental dynamics, 78 predictors were divided into three treatments: PCA1 (climatic variables), PCA2 (climatic variables, slope and accumulated flow) and PCA3 (climatic variables, slope, accumulated flow, topographic and edaphic variables). MaxEnt software was used and configured from the ENMeval package. Two aspects can be observed in the results: the use of hydrological, topographic and edaphic variables allows to obtain more precise and spatially restricted representations than only climatic variables. In the second place, it is evident that, regardless of the dimensional complexity of the system, MaxEnt allows to obtain MDEs with high predictive power for both river species and species of streams. In the case of river species, the macroscopic predictors (climatic variables - PCA1) allowed to represent their environmental requirements and their wide spatial distributions. Meanwhile, climatic, hydrological, topographic and edaphic variables (PCA3) acted as environmental filters restricting the spatial distributions of both species of rivers and streams. The dimensional complexity of the system does not affect the spatial representation capacity of Maxent, observing that, in the case of species of streams MaxEnt showed greater capacity of spatial representation.