Multivariate analysis of stress in experimental ecosystems by Principal Response Curves and similarity analysis

Abstract

Experiments in microcosms and mesocosms, which can be carried out in an advanced tier of risk assessment, usually result in large data sets on the dynamics of biological communities of treated and control cosms. Multivariate techniques are an accepted tool to evaluate the community treatment effects resulting from these complex experiments. In this paper two methods of multivariate analysis are discussed on their merits: 1) the canonical ordination technique Principal Response Curves (PRC) and 2) the similarity indices of Bray-Curtis and Stander. For this, the data sets of a microcosm experiment were used to simultaneously study the impact of nutrient loading and insecticide application.

Both similarity indices display, in a single graph, the total effect size against time and do not allow a direct interpretation down to the taxon level. In the PRC method, the principal components of the treatment effects are plotted against time. Since the species of the example data sets, react in qualitatively different ways to the treatments, more than one PRC is needed for a proper description of the treatment effects. The first PRC of one of the data sets describes the effects due to the chlorpyrifos addition, the second one the effects as a result of the nutrient loading. The resulting principal response curves jointly summarize the essential features of the response curves of the individual taxa. This paper goes beyond the first PRC to visualize the effects of chemicals at the community level. In both multivariate analysis methods the statistical significance of the effects can be assessed by Monte Carlo permutation testing.