Growth and fatty acid composition of Octopus vulgaris fed different diets based on bogue (Boops boops) in comparison to wild individuals

[EN] Octopus vulgaris is a suitable candidate to diversify marine aquaculture (Iglesias et al., 2000; Vaz Pires et al. 2004). Actually, wild sub-adults are on-growing in floating cages showing promising results (Chapela et al., 2006; Rodríguez et al., 2006). Even though octopus industrial development is still limited, mainly associated to the dependence of wild catch individuals for ongrowing (Iglesias et al., 2007) and a lack of an appropriate formulated diet (García García and Cerezo, 2006). In addition, essential macronutrient requirements for this species are still not well known. Used of discarded bogue as single food for Octopus on-growth results in similar growth than co-fed diets with the crab (Portunus pelagic). FA content of Muscle and DG showed important ARA content, suggesting the important functions of this FA in this specie.

Reconstruction of spatiotemporal capture data by means of orthogonal functions: the case of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic

[EN] The information provided by the International Commission for the Conservation of Atlantic Tunas (ICCAT) on captures of skipjack tuna (Katsuwonus pelamis) in the central-east Atlantic has a number of limitations, such as gaps in the statistics for certain fleets and the level of spatiotemporal detail at which catches are reported. As a result, the quality of these data and their effectiveness for providing management advice is limited. In order to reconstruct missing spatiotemporal data of catches, the present study uses Data INterpolating Empirical Orthogonal Functions (DINEOF), a technique for missing data reconstruction, applied here for the first time to fisheries data. DINEOF is based on an Empirical Orthogonal Functions decomposition performed with a Lanczos method. DINEOF was tested with different amounts of missing data, intentionally removing values from 3.4% to 95.2% of data loss, and then compared with the same data set with no missing data. These validation analyses show that DINEOF is a reliable methodological approach of data reconstruction for the purposes of fishery management advice, even when the amount of missing data is very high.