Space-temporal dynamics of the landscape and population structure of Euterpe precatoria Mart. in forest fragment in the municipality of Alta Floresta, Mato Grosso state, Brazil

Abstract

Euterpe precatoria Mart., is used in the cosmetic and food industry and has been increasingly exploited in the Amazonian environment. The objective of this work is to analyze the spatial-temporal dynamics of the landscape and the effect of fragmentation on the structure and pattern of spatial distribution of Euterpe precatoria, using a forest fragment in the municipality of Alta Floresta, MT state. Orbital images of Landsat-2/MSS satellite from 1975, Landsat-3/MSS from 1979, Landsat-5/TM from 1985, 1990, 1995, 2000, 2005 and 2010 and from Landsat-8/OLI from 2017 were used. From the data generated by means of remote sensing, the Anthropic Transformation Index -ITA and Linear Regression were calculated. Two plots of 200 m x 25 m were drawn in the edge-inland direction. The horizontal and vertical structure of the species was characterized by means of the analyzes: number of individuals, density, basal area, diameter distribution and spatial distribution. The spatial distribution pattern was estimated by the Morisita index, with a significance level of 5%. The first anthropic activities around the study fragment began in 1984 with the opening of the MT-325 highway. The intensification of land use contributed to the reduction of vegetation, which most critical period occurred between 1990 and 2000. We sampled 102 individuals in plot A and 218 in plot B. The density was higher in plot B. The two plots had a larger number of individuals distributed in the first diametric class. The analyzed population presented two spatial distribution patterns, random pattern in plot A and aggregate pattern in the B. Euterpe precatoria plot is balanced on the process of succession and the recruitment of individuals, since the values of the ‘q’ ratio show that there are a large number of individuals in the first height classes and DAP.

Keywords:
Açaí; Remote sensing; Spatial distribution; Amazon