Affiliation:
1. Universidade Estadual de Campinas, Brazil
Abstract
After 500 years of exploitation and destruction, the Brazilian Atlantic Forest has been reduced to less the 8% of its original cover, and climate change may pose a new threat to the remnants of this biodiversity hotspot. In this study we used modelling techniques to determine present and future geographical distribution of 38 species of trees that are typical of the Brazilian Atlantic Forest (Mata Atlântica), considering two global warming scenarios. The optimistic scenario, based in a 0.5% increase in the concentration of CO2 in the atmosphere, predicts an increase of up to 2 °C in the Earth's average temperature; in the pessimistic scenario, based on a 1% increase in the concentration of CO2 in the atmosphere, temperature increase may reach 4 °C. Using these parameters, the occurrence points of the studied species registered in literature, the Genetic Algorithm for Rule-set Predictions/GARP and Maximum entropy modeling of species geographic distributions/MaxEnt we developed models of present and future possible occurrence of each species, considering Earth's mean temperature by 2050 with the optimistic and the pessimistic scenarios of CO2 emission. The results obtained show an alarming reduction in the area of possible occurrence of the species studied, as well as a shift towards southern areas of Brazil. Using GARP, on average, in the optimistic scenario this reduction is of 25% while in the pessimistic scenario it reaches 50%, and the species that will suffer the worst reduction in their possible area of occurrence are: Euterpe edulis, Mollinedia schottiana, Virola bicuhyba, Inga sessilis and Vochysia magnifica. Using MaxEnt, on average, in the optimistic scenario the reduction will be of 20% while in the pessimistic scenario it reaches 30%, and the species that will suffer the worst reduction are: Hyeronima alchorneoides, Schefflera angustissima, Andira fraxinifolia and the species of Myrtaceae studied.
Subject
General Agricultural and Biological Sciences
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