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Sea cucumbers and sea urchins are promising candidates for aquaculture since they are high market value and low-trophic organisms. However, although they often co-exist in many marine habitats showing feeding interactions, there is currently a lack of investigations available regarding the co-culture of these organisms in Integrated Multi-Trophic Aquaculture (IMTA). The present study investigated, for the first time, the laboratory-scale feasibility of an integrated aquaculture between P. lividus and H. tubulosa, two of the most valuable Mediterranean echinoderms, through a four-month experiment. More specifically, three food sources with different fish meal concentrations were tested separately to sustain the integrated production in a land-based RAS (Recycling aquaculture system) of both co-cultured species: 1) completely vegetable diet (D-0), 2) vegetable diet with 20% of fish meal (D-20) and 3) vegetable diet with 40% of fish meal (D-40). Among these experimental diets D-20 (with 20% of fish meal supplement) was consumed more efficiently and sustained high growth rates for both co-cultured species. However, significant growth was detected with all experimental diets, indicating successful integrated aquaculture between sea urchins and sea cucumbers. The present study, therefore, suggested the existence of substantial benefits of an integrated aquaculture between these echinoderm species, that could promote the environmental and economic sustainability of their production on a large-scale. Our results showed, in fact, that less than 24% of the organic matter administered with the food remained as waste in our IMTA system after being ingested by the two trophic levels. The sea urchins ingested 87% of the food administered, absorbing 64% of the organic matter, whilst in the second step, the sea cucumbers consumed 54% of organic matter present in the sea urchin feces. Hence the aquaculture model investigated here was highly effective in reducing the total waste, at the same time providing added value in the form of sea cucumber biomass.

Logbook data constitute a key element within the electronic recording and reporting system of the European Fisheries Control Technologies Framework and are used to record, report, process, store and send information about fishing operations, including landings and fishing gear. A relevant application of logbook data is to account for the heterogeneity of fishing practices (e.g., by gear or metier), which is a key aspect of the Common Fishery Policy. However, despite their importance, few published studies have explored the potential and pitfalls of logbook data, even in combination with other powerful data sources such as the Vessel Monitoring System (VMS). Here, a new approach to characterizing the composition of landings for the different types of gear based on the use of Self-Organizing Maps (SOMs − a particular type of Artificial Neural Network) is applied to the Italian fleet logbook dataset. The SOM is trained on the landings composition and the resulting patterns are interpreted using some measures obtained from the analysis of the corresponding VMS data. Namely, the mean sea bottom depth and the area of activity are obtained for each fishing trip. Moreover, the ability of the trained SOM to predict gear from landings is tested using a new dataset. The trained SOM classifies logbook records according to the ecological, taxonomical, and trophic characteristics of the species caught, and the depth of fishing activities plays an important role in diversifying the landings associated with certain widely used fishing gear such as the bottom otter trawl. The clustering of SOM units allows the identification of a set of 12 groups, which are strongly related to the types of gear used by the Italian fleet. Furthermore, the trained SOM shows a high ability to recognize gear from logbook data, thus confirming the robustness of the landings profiles detected.

Publisher Summary This chapter describes some applications of neural networks (NN) to modeling processes in coastal ecology to demonstrate the potential of this approach and to identify some of its limitations. Neural networks is introduced as computational tools, providing a general and accessible description of their main features. Dynamic modeling in coastal ecosystems is being advocated and is placed on steady state models because they are the most common NN ecological applications in coastal ecosystems. A comparison between conventional, dynamic models and neural networks is outlined using some phytoplankton biomass models for a coastal embayment as examples. The state-of-the-art in neural network modeling is discussed, as well as some perspectives in their future development. Useful links to neural network resources are also presented in chapter.