<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hattab, Tarek</style></author><author><style face="normal" font="default" size="100%">Ben Rais Lasram, Frida</style></author><author><style face="normal" font="default" size="100%">Albouy, Camille</style></author><author><style face="normal" font="default" size="100%">Sammari, Chérif</style></author><author><style face="normal" font="default" size="100%">Romdhane, Mohamed Salah</style></author><author><style face="normal" font="default" size="100%">Cury, Philippe</style></author><author><style face="normal" font="default" size="100%">Leprieur, Fabien</style></author><author><style face="normal" font="default" size="100%">Le Loc’h, François</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1371/journal.pone.0076430</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Bottom 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.</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue></record></records></xml>