FN Archimer Export Format
PT J
TI The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning
BT 
AF HATTAB, Tarek
   BEN RAIS LASRAM, Frida
   ALBOUY, Camille
   SAMMARI, Cherif
   ROMDHANE, Mohamed Salah
   CURY, Philippe
   LEPRIEUR, Fabien
   LE LOC'H, Francois
AS 1:1,2;2:1;3:3;4:4;5:1;6:2;7:5;8:2;
FF 1:;2:;3:;4:;5:;6:;7:;8:;
C1 UR 03AGRO1 Ecosyste`mes et Ressources Aquatiques, INAT (Institut National Agronomique de Tunisie), Tunis, Tunisia
   UMR 212 Ecosyste`mes Marins Exploite´ s, IRD (Institut de Recherche pour le De´veloppement), Se` te, France
   Departement de biologie, Chimie et Ge´ographie, UQAR (Universite´ du Que´bec a` Rimouski), Que´bec, Canada
   Laboratoire du milieu marin, INSTM (Institut National des Sciences et Technologies de la Mer), Salammboˆ , Tunisia
   Laboratoire Ecologie des Systemes Marins Co tiers UMR 5119, UM2 (Universite´ de Montpellier 2), Montpellier, France
C2 INAT, TUNISIA
   IRD, FRANCE
   UNIV QUEBEC (UQAR), CANADA
   INSTM, TUNISIA
   UNIV MONTPELLIER, FRANCE
IN DOAJ
IF 3.534
TC 40
UR https://archimer.ifremer.fr/doc/00391/50282/50937.pdf
LA English
DT Article
AB 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. 
PY 2013
PD OCT
SO Plos One
SN 1932-6203
VL 8
IS 10
UT 000325819400057
DI 10.1371/journal.pone.0076430
ID 50282
ER

EF