A roadmap for a quantitative ecosystem-based environmental impact assessment. (20th February 2017)

• Record Type: Journal Article
• Authors: Coston-Guarini, J.; Guarini, J.-M.; Hinz, Shawn; Wilson, Jeff; Chauvaud, L.
• Editors: Link, Jason
• Is Part Of: ICES journal of marine science. Volume 74:Number 7(2017)
• Page Start: 2012
• Page End: 2023
• Publication Date: 2017-02-20
• Subjects: drivers of change -- ecosystem -- environmental impact assessment -- modelling -- socio-ecological system; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsx015
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: A new roadmap for quantitative methodologies of Environmental Impact Assessment (EIA) is proposed, using an ecosystem-based approach. EIA recommendations are currently based on case-by-case rankings, distant from statistical methodologies, and ecological ideas that lack proof of generality or predictive capacities. These qualitative approaches ignore process dynamics, scales of variations and interdependencies and are unable to address societal demands to link socio-economic and ecological processes (e.g. population dynamics). We propose to re-focus EIA around the systemic formulation of interactions between organisms (organized in populations and communities) and their environments but inserted within a strict statistical framework. A systemic formulation allows scenarios to be built that simulate impacts on chosen receptors. To illustrate the approach, we design a minimum ecosystem model that demonstrates nontrivial effects and complex responses to environmental changes and validated with case study. We suggest that an Ecosystem-Based EIA—in which the socio-economic system is an evolving driver of the ecological one—is more promising than a socio-economic-ecological system where all variables are treated as equal. This refocuses the debate on cause-and-effect, processes, identification of essential portable variables, and allows for quantitative comparisons between projects, which is critical in cumulative effects determinations.