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Acta Amazonica

Print version ISSN 0044-5967

Abstract

FIGUEIREDO, Symone Maria de Melo; VENTICINQUE, Eduardo Martins; FIGUEIREDO, Evandro Orfanó  and  FERREIRA, Evandro José Linhares. Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil. Acta Amaz. [online]. 2015, vol.45, n.2, pp.167-174. ISSN 0044-5967.  https://doi.org/10.1590/1809-4392201402834.

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

Keywords : modeling; Maxent; forest inventory; modeflora; Amazon.

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