Subject(s): CX.34

Category: Brief Article

Abstract:

Soil ecosystems are inherently complex: space, time and biological diversity interact giving way to emergence of dynamic, complex features such as distribution patterns, abundance profiles, nutrient paths, etc. Specifically, sampling for soil diversity is fraught with problems that arise form the very different spatial scales that involve biological populations’ aggregates and subpopulations. Typical sampling techniques tend either to be ineffective for complexity assessment (i.e. too small to capture a representative subset of most populations and their distributions) or overshot their target with very large samples that can be cost-ineffective. Optimized sampling techniques that use the species-area curves may be inadequate for the purpose of measuring diversity, as they typically focus on the species accumulation rather than on the measurement of structure. Also, species-area curves are sensitive to the accumulation mechanism: the order in which subsamples accumulate matters. We propose an algorithmic method that tries to capture enough data for a cost-effective diversity (complexity) assessment while statistically ensuring consistency. Tests have been done with actual, species-level soil mesofauna fauna data. A C program implements the algorithm.

Retrieve Manuscript

Retrieve Previous Revision's Abstract

Submit referee report/comment