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      From Metaphors to Formalism: A Heuristic Approach to Holistic Assessments of Ecosystem Health

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      PLoS ONE
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          Abstract

          Environmental policies employ metaphoric objectives such as ecosystem health, resilience and sustainable provision of ecosystem services, which influence corresponding sustainability assessments by means of normative settings such as assumptions on system description, indicator selection, aggregation of information and target setting. A heuristic approach is developed for sustainability assessments to avoid ambiguity and applications to the EU Marine Strategy Framework Directive (MSFD) and OSPAR assessments are presented. For MSFD, nineteen different assessment procedures have been proposed, but at present no agreed assessment procedure is available. The heuristic assessment framework is a functional-holistic approach comprising an ex-ante/ex-post assessment framework with specifically defined normative and systemic dimensions (EAEPNS). The outer normative dimension defines the ex-ante/ex-post framework, of which the latter branch delivers one measure of ecosystem health based on indicators and the former allows to account for the multi-dimensional nature of sustainability (social, economic, ecological) in terms of modeling approaches. For MSFD, the ex-ante/ex-post framework replaces the current distinction between assessments based on pressure and state descriptors. The ex-ante and the ex-post branch each comprise an inner normative and a systemic dimension. The inner normative dimension in the ex-post branch considers additive utility models and likelihood functions to standardize variables normalized with Bayesian modeling. Likelihood functions allow precautionary target setting. The ex-post systemic dimension considers a posteriori indicator selection by means of analysis of indicator space to avoid redundant indicator information as opposed to a priori indicator selection in deconstructive-structural approaches. Indicator information is expressed in terms of ecosystem variability by means of multivariate analysis procedures. The application to the OSPAR assessment for the southern North Sea showed, that with the selected 36 indicators 48% of ecosystem variability could be explained. Tools for the ex-ante branch are risk and ecosystem models with the capability to analyze trade-offs, generating model output for each of the pressure chains to allow for a phasing-out of human pressures. The Bayesian measure of ecosystem health is sensitive to trends in environmental features, but robust to ecosystem variability in line with state space models. The combination of the ex-ante and ex-post branch is essential to evaluate ecosystem resilience and to adopt adaptive management. Based on requirements of the heuristic approach, three possible developments of this concept can be envisioned, i.e. a governance driven approach built upon participatory processes, a science driven functional-holistic approach requiring extensive monitoring to analyze complete ecosystem variability, and an approach with emphasis on ex-ante modeling and ex-post assessment of well-studied subsystems.

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          Most cited references19

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          Assessing ecosystem health.

          D. Rapport (1998)
          Evaluating ecosystem health in relation to the ecological, economic and human health spheres requires integrating human values with biophysical processes, an integration that has been explicitly avoided by conventional science. The field is advancing with the articulation of the linkages between human activity, regional and global environmental change, reduction in ecological services and the consequences for human health, economic opportunity and human communities. Increasing our understanding of these interactions will involve more active collaboration between the ecological, social and health sciences. In this, ecologists will have substantive and catalytic roles.
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            Application of multicriteria decision analysis in environmental decision making.

            Decision making in environmental projects can be complex and seemingly intractable, principally because of the inherent trade-offs between sociopolitical, environmental, ecological, and economic factors. The selection of appropriate remedial and abatement strategies for contaminated sites, land use planning, and regulatory processes often involves multiple additional criteria such as the distribution of costs and benefits, environmental impacts for different populations, safety, ecological risk, or human values. Some of these criteria cannot be easily condensed into a monetary value, partly because environmental concerns often involve ethical and moral principles that may not be related to any economic use or value. Furthermore, even if it were possible to aggregate multiple criteria rankings into a common unit, this approach would not always be desirable because the ability to track conflicting stakeholder preferences may be lost in the process. Consequently, selecting from among many different alternatives often involves making trade-offs that fail to satisfy 1 or more stakeholder groups. Nevertheless, considerable research in the area of multicriteria decision analysis (MCDA) has made available practical methods for applying scientific decision theoretical approaches to complex multicriteria problems. This paper presents a review of the available literature and provides recommendations for applying MCDA techniques in environmental projects. A generalized framework for decision analysis is proposed to highlight the fundamental ingredients for more structured and tractable environmental decision making.
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              Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.

              Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                10 August 2016
                2016
                : 11
                : 8
                : e0159481
                Affiliations
                [001]Thünen Institute of Sea Fisheries, Hamburg, Germany
                US Army Engineer Research and Development Center, UNITED STATES
                Author notes

                Competing Interests: The authors have declared no competing interests exist.

                • Wrote the paper: HOF GK.

                • Developed the concept: HOF.

                Author information
                http://orcid.org/0000-0003-2902-3559
                Article
                PONE-D-15-38220
                10.1371/journal.pone.0159481
                4980027
                27509185
                1d1d5f8f-88df-4535-9ccf-9a80edac2c17
                © 2016 Fock, Kraus

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 30 August 2015
                : 5 July 2016
                Page count
                Figures: 9, Tables: 2, Pages: 29
                Funding
                Funded by: BMBF
                Award ID: FKZ 03F0671A
                Award Recipient :
                This work was supported by the Federal Ministry of Research and Education (BMBF; www.bmbf.de). Project name:‘NOAH – Assessment of seafloor state in the German North Sea’, Grant number FKZ 03F0671A. The work was carried out within NOAH, but authors sere not funded through the project. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecosystem Functioning
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Ecosystem Functioning
                Biology and Life Sciences
                Ecology
                Ecosystems
                Marine Ecosystems
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Marine Ecosystems
                Ecology and Environmental Sciences
                Sustainability Science
                Biology and Life Sciences
                Computational Biology
                Ecosystem Modeling
                Biology and Life Sciences
                Ecology
                Ecosystems
                Ecosystem Modeling
                Ecology and Environmental Sciences
                Ecology
                Ecosystems
                Ecosystem Modeling
                Ecology and Environmental Sciences
                Conservation Science
                Biology and Life Sciences
                Ecology
                Marine Ecology
                Ecology and Environmental Sciences
                Ecology
                Marine Ecology
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                Marine Biology
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                Earth Sciences
                Marine and Aquatic Sciences
                Marine Biology
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                All relevant data are within the paper and its Supporting Information files.

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