An approach for quantifying the efficacy of ecological classification schemes as management tools

Rigorous assessments of ecological classification schemes being applied to submerged environments are needed to evaluate their utility as management tools. Verification that a scheme can quantitatively capture habitat and community variation would be of considerable value to individuals responsible for making difficult management decisions relevant to widespread environmental challenges including those in fisheries, preservation or restoration of critical habitats, and climate change. In this paper, an assessment approach that evaluates a scheme by treating it like a quantitative statistical model is presented. It couples two direct gradient, multivariate statistical techniques, multivariate regression trees (MRT) and redundancy analysis (RDA), with a modelling protocol involving model formulation, model selection, parameter estimation, and measurement of precision to produce a very flexible strategy for analyzing structure in ecological data. To illustrate the proposed approach, the assessment focused on benthic infauna and evaluating the Folk grain size classification scheme, along with some alternative grain size models. Analysis of data sets revealed that while it was fairly easy to uncover biotic-environmental relationships that were over-fitted, the community structure inherent in the data tended to be robustly discernible and preserved across all grain size models, but rigidly parameterized models (i.e., a one size fits all approach for grain size characterization with fixed boundaries) were generally ineffective. The proposed approach provided a clear, detailed, and rigorous assessment of Folk and several alternative models and can be used for the quantitative evaluation of existing ecological classification schemes and/or in the development of new schemes.