Multivariate and univariate asymmetrical analyses in environmental impact assessment: a case study of Mediterranean subtidal sessile assemblages

Characterizing the potential effects of human activities on natural systems is a central problem in applied ecology. This requires the development of analytical procedures able to separate human perturbation from natural spatio-temporal variability displayed by most populations. Beyond-BACI experimental designs provide a framework to address these issues but, to date, their use is limited to the analysis of human impacts on the abundance of single species or other univariate measures. Here, we describe in detail an asymmetrical design that included 1 impact location (I) and a set of 3 controls (Cs), sampled at a hierarchy of spatial scales 4 times over a period of 15 mo. We focused on shallow subtidal assemblages of sessile organisms exposed to sewage discharge along a stretch of coast in southern Italy. The purpose of this paper is to illustrate (1) the comparison of variance components for the assessment of impacts and (2) the use of recently developed multivariate methods (distance-based premutational MANOVA) in the analysis of multivariate species data in response to a complex asymmetrical design. Results indicated that temporal changes in the whole assemblage at I were distinct from those occurring at Cs, and that the nature of this difference (although not its size) was fairly consistent through time. A suite of taxa was identified as important in characterizing the differences found between I and Cs. Some algae (e.g. Colpomenia sinuosa, Gelidium sp. and Pterocladiella sp.), in particular, occurred uniquely at I. Univariate analyses indicated significant Time × I-v-Cs interactions for several taxa, and significantly smaller spatial variation at the scale of quadrats at I compared to Cs. In contrast, the small-scale spatial variation in the number of taxa was significantly greater at I than at Cs. The findings of this study have important implications for future multivariate and univariate analyses in environmental impact assessment.