@article{fdi:010061274,
  title = {{T}he use of a predictive habitat model and a fuzzy logic approach for marine management and planning},
  author = {{H}attab, {T}. and {L}asram, {F}. {B}. and {A}lbouy, {C}. and {S}ammari, {C}. and {R}omdhane, {M}. {S}. and {C}ury, {P}hilippe and {L}eprieur, {F}. and {L}e {L}oc'h, {F}ran{\c{c}}ois},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ottom trawl survey data are commonly used as a sampling technique to assess the spatial distribution of commercial species. {H}owever, 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. {I}n this study, we aim to test the relevance of a mixed methodological approach that combines presence-only and presence-absence distribution models. {W}e illustrate this approach using bottom trawl survey data to model the spatial distributions of 27 commercially targeted marine species. {W}e use an environmentally-and geographically-weighted method to simulate pseudo-absence data. {T}he species distributions are modelled using regression kriging, a technique that explicitly incorporates spatial dependence into predictions. {M}odel outputs are then used to identify areas that met the conservation targets for the deployment of artificial anti-trawling reefs. {T}o achieve this, we propose the use of a fuzzy logic framework that accounts for the uncertainty associated with different model predictions. {F}or 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. {T}he map resulting from the fuzzy overlay shows that three main areas have a high level of agreement with the conservation criteria. {T}hese results align with expert opinion, confirming the relevance of the proposed methodology in this study.},
  keywords = {},
  booktitle = {},
  journal = {{P}los {O}ne},
  volume = {8},
  numero = {10},
  pages = {e76430},
  ISSN = {1932-6203},
  year = {2013},
  DOI = {10.1371/journal.pone.0076430},
  URL = {https://www.documentation.ird.fr/hor/fdi:010061274},
}