Sampling procedures for inventory of commercial volume tree species in Amazon Forest.

Sylvio P Netto,Allan L Pelissari,Vinicius C Cysneiros,Carlos R Sanquetta,Marcelo Bonazza

doi:10.1590/0001-3765201720160760

Abstract

The spatial distribution of tropical tree species can affect the consistency of the estimators in commercial forest inventories, therefore, appropriate sampling procedures are required to survey species with different spatial patterns in the Amazon Forest. For this, the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling for volumetric inventories of Amazonian tree species, considering the hypotheses that the density, the spatial distribution and the zero-plots affect the consistency of the estimators, and that the adaptive cluster sampling allows to obtain more accurate volumetric estimation. We use data from a census carried out in Jamari National Forest, Brazil, where trees with diameters equal to or higher than 40 cm were measured in 1,355 plots. Species with different spatial patterns were selected and sampled with simple random sampling, systematic sampling, linear cluster sampling and adaptive cluster sampling, whereby the accuracy of the volumetric estimation and presence of zero-plots were evaluated. The sampling procedures applied to species were affected by the low density of trees and the large number of zero-plots, wherein the adaptive clusters allowed concentrating the sampling effort in plots with trees and, thus, agglutinating more representative samples to estimate the commercial volume.

Highlights

The Amazon Forest composes the richest collection of plant species on the planet, having approximately 16 thousand tree species, where approximately 50% of the trees are concentrated in only 1.4% of species (Ter Steege et al 2013) in a flora composed of many rare species of restricted distribution (Hopkins 2007)
Considering the hypotheses that (1) the low density of trees, the spatial distribution of species and the high number of zero-plots affect the consistency of the samplings in tropical forests; and that (2) the adaptive cluster sampling allows to obtain more accurate volumetric stocks; the present study aims to evaluate the conventional sampling procedures and introduce the adaptive cluster sampling to inventory of tree species in Amazon Forest
Distinct spatial distribution patterns were observed (Figure 2), where representative tree species were selected: a) Terminalia amazonica, a rare species, with density of only seven trees, and concentrated in a specific region (Figure 2a); (b) Apuleia leiocarpa, with 89 trees and concentrated in some locations (Figure 2b); c) Cedrela fissilis, with 81 trees dispersed in the area (Figure 2c); and d) Bertholletia excelsa, with high density of 904 trees (Figure 2d)

Summary

Introduction

The Amazon Forest composes the richest collection of plant species on the planet, having approximately 16 thousand tree species, where approximately 50% of the trees are concentrated in only 1.4% of species (Ter Steege et al 2013) in a flora composed of many rare species of restricted distribution (Hopkins 2007). The spatial patterns are frequently the focus of ecological researches, due to the high diversity in tropical forests that is characterized by low density of tax (Condit et al 2000). This knowledge is important to inventories, especially those intended for production and conservation forests. The spatial distributions of species are fundamental for ecological modeling (Condit et al 2000), where they reflect recruitment and mortality patterns, autoecological characteristics, syndrome of dispersion and reproductive biology (Crawley 1986, Pianka 1994, Dale 1999). The spatial patterns can affect the consistency of the sampling procedures. If the pattern is aggregate, a sample with low number of plots can result in high or low density when the results are extrapolated to the population and, appropriated sampling techniques are required (Odum and Barret 2008)

Methods

Results

Conclusion