François Le Loc’h

The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning

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.

Rearing performances and environmental assessment of sea cage farming in Tunisia using life cycle assessment (LCA) combined with PCA and HCPC

PurposeThe present study aims to understand the influence of rearing practices and the contributions of production phases of fish farming to their environmental impacts and determine which practices and technical characteristics can best improve the farms’ environmental performance. Another objective is to identify the influence of variability in farming practices on the environmental performances of sea cage aquaculture farms of sea bass and sea bream in Tunisia by using principal component analysis (PCA) and hierarchical clustering on principal components (HCPC) methods and then combining the classification with life cycle assessment (LCA).MethodsThe approach consisted of three major steps: (i) of the 24 aquaculture farms in Tunisia, 18 were selected which follow intensive rearing practices in sea cages of European sea bass (Dicentrarchus labrax) and gilthead sea bream (Sparus aurata) and then a typology was developed to classify the studied farms into rearing practice groups using HCPC; (ii) LCA was performed on each aquaculture farm and (iii) mean impacts and contributions of production phases were calculated for each group of farms. Impact categories included acidification, eutrophication, global warming, land occupation, total cumulative energy demand and net primary production use.Results and discussionResults revealed high correlation between rearing practices and impacts. The feed-conversion ratio (FCR), water column depth under the cages and cage size had the greatest influence on impact intensity. Rearing practices and fish feed were the greatest contributors to the impacts studied due to the production of fish meal and oil and the low efficiency of feed use, which generated large amounts of nitrogen and phosphorus emissions. It is necessary to optimise the diet formulation and to follow better feeding strategies to lower the FCR and improve farm performance. Water column depth greatly influenced the farms’ environmental performance due to the increase in waste dispersion at deeper depths, while shallow depths resulted in accumulation of organic matter and degradation of water quality. Cage size influences environmental performances of aquaculture farms. Thus, from an environmental viewpoint, decision makers should grant licences for farms in deeper water with larger cages and encourage them to improve their FCRs.ConclusionsThis study is the first attempt to combine the HCPC method and the LCA framework to study the environmental performance of aquacultural activity. The typology developed captures the variability among farms because it considers several farm characteristics in the classification. The LCA demonstrated that technical parameters in need of improvement are related to the technical expertise of farm managers and workers and to the location of the farm.