The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning
Type | Article | ||||||||
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Date | 2013-10 | ||||||||
Language | English | ||||||||
Author(s) | Hattab Tarek1, 2, Ben Rais Lasram Frida1, Albouy Camille3, Sammari Cherif4, Romdhane Mohamed Salah1, Cury Philippe2, Leprieur Fabien5, Le Loc'h Francois2 | ||||||||
Affiliation(s) | 1 : UR 03AGRO1 Ecosyste`mes et Ressources Aquatiques, INAT (Institut National Agronomique de Tunisie), Tunis, Tunisia 2 : UMR 212 Ecosyste`mes Marins Exploite´ s, IRD (Institut de Recherche pour le De´veloppement), Se` te, France 3 : Departement de biologie, Chimie et Ge´ographie, UQAR (Universite´ du Que´bec a` Rimouski), Que´bec, Canada 4 : Laboratoire du milieu marin, INSTM (Institut National des Sciences et Technologies de la Mer), Salammboˆ , Tunisia 5 : Laboratoire Ecologie des Systemes Marins Co tiers UMR 5119, UM2 (Universite´ de Montpellier 2), Montpellier, France |
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Source | Plos One (1932-6203), 2013-10 , Vol. 8 , N. 10 , P. e76430 (1-13) | ||||||||
DOI | 10.1371/journal.pone.0076430 | ||||||||
WOS© Times Cited | 40 | ||||||||
Abstract | ottom trawl survey data are commonly used as a sampling technique to assess the spatial distribution of commercial species. However, this sampling technique does not always correctly detect a species even when it is present, and this can create significant limitations when fitting species distribution models. In this study, we aim to test the relevance of a mixed methodological approach that combines presence-only and presence-absence distribution models. We illustrate this approach using bottom trawl survey data to model the spatial distributions of 27 commercially targeted marine species. We use an environmentally- and geographically-weighted method to simulate pseudo-absence data. The species distributions are modelled using regression kriging, a technique that explicitly incorporates spatial dependence into predictions. Model outputs are then used to identify areas that met the conservation targets for the deployment of artificial anti-trawling reefs. To achieve this, we propose the use of a fuzzy logic framework that accounts for the uncertainty associated with different model predictions. For each species, the predictive accuracy of the model is classified as ‘high’. A better result is observed when a large number of occurrences are used to develop the model. The map resulting from the fuzzy overlay shows that three main areas have a high level of agreement with the conservation criteria. These results align with expert opinion, confirming the relevance of the proposed methodology in this study. |
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