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• W2024194472 abstract "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." @default.
• W2024194472 created "2016-06-24" @default.
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• W2024194472 date "2013-10-11" @default.
• W2024194472 modified "2023-09-23" @default.
• W2024194472 title "The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning" @default.
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• W2024194472 doi "https://doi.org/10.1371/journal.pone.0076430" @default.
• W2024194472 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3795769" @default.
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