Inference on diversity from forest inventories: a review

Abstract

A number of international agreements and commitments emphasize the importance of appropriate monitoring protocols and assessments as prerequisites for sound conservation and management of the world’s forest ecosystems. Mandated periodic surveys, like forest inventories, provide a unique opportunity to identify and properly satisfy natural resource management information needs. Distinctively, there is an increasing need for detecting diversity by means of unambiguous diversity measures. Because all diversity measures are functions of tree species abundances, estimation of tree diversity indices and profiles is inevitably performed by estimating tree species abundances and then estimating indices and profiles as functions of the abundance estimates. This strategy can be readily implemented in the framework of current forest inventory approaches, where tree species abundances are routinely estimated by means of plots placed onto the surveyed area in accordance with probabilistic schemes. The purpose of this paper is to assess the effectiveness of this strategy by reviewing theoretical results from published case studies. Under uniform random sampling (URS), that is when plots are uniformly and independently located on the study region, consistency and asymptotic normality of diversity index estimators follow from standard limit theorems as the sampling effort increases. In addition, variance estimation and bias reduction are achieved using the jackknife method. Despite its theoretical simplicity, URS may lead to uneven coverage of the study region. In order to avoid unbalanced sampling, the use of tessellation stratified sampling (TSS) is suggested. TSS involves covering the study region by a polygonal grid and randomly selecting a plot in each polygon. Under TSS, the diversity index estimators are consistent, asymptotically normal and more precise than those achieved using URS. Variance estimation is possible and there is no need to reduce bias.