Ecological equivalence assessment methods: what trade-offs between operationality, scientific basis and comprehensiveness? | Méthodes d'évaluation de l'équivalence écologique : quels compromis entre opérationnalité, base scientifique et exhaustivité ?
2017
Bezombes, L. | Gaucherand, S. | Kerbiriou, C. | Reinert, M.E. | Spiegelberger, T. | Ecosystèmes montagnards (UR EMGR) ; Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA) | Centre d'Ecologie et des Sciences de la COnservation (CESCO) ; Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS) | European Institute For Energy Research (EIFER) ; Universität Karlsruhe (TH)-EDF R&D (EDF R&D) ; EDF (EDF)-EDF (EDF)
[Departement_IRSTEA]Territoires [TR1_IRSTEA]SEDYVIN
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Afficher plus [+] Moins [-]anglais. In many countries, biodiversity compensation is required to counterbalance negative impacts of development projects on biodiversity by carrying out ecological measures, called offset when the goal is to reach "no net loss" of biodiversity. One main issue is to ensure that offset gains are equivalent to impact-related losses. Ecological equivalence is assessed with ecological equivalence assessment methods (EAMs) taking into account a range of key considerations that we summarized as ecological, spatial, temporal and uncertainty. When EAMs take into account all considerations, we call them "comprehensive". EAMs should also aim to be science-based and operational, which is challenging. Many EAMs have been developed worldwide but none is fully satisfying. In the present study, we examine 13 EAMs in order to identify i) their general structure and ii) the synergies and trade-offs between EAMs characteristics related to operationality, scientific-basis and comprehensiveness (called "challenges" in his paper). We evaluate each EAM on the basis of 12 criteria describing the level of achievement of each challenge. We observe that all EAMs share a general structure, with possible improvements in the choice of target biodiversity, the indicators used, the integration of landscape context and the multipliers reflecting time lags and uncertainties. We show that no EAM combines all challenges perfectly. There are trade-offs between and within the challenges: operationality tends to be favored while scientific basis are integrated heterogeneously in EAMs development. One way of improving the challenges combination would be the use of offset dedicated data-bases providing scientific feedbacks on previous offset measures.
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