1,721,008 research outputs found

    Whiting (Merlangius merlangus) Grows Slower and Smaller in the Adriatic Sea: New Insights from a Comparison of Two Populations with a Time Interval of 30 Years

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    Nowadays, overexploitation and climate change are among the major threats to fish production all over the world. In this study, we focused our attention on the Adriatic Sea (AS), a shallow semi-enclosed sub-basin showing the highest exploitation level and warming trend over the last decades within the Mediterranean Sea. We investigated the life history traits and population dynamics of the cold-water species whiting (Merlangius merlangus, Gadidae) 30 years apart, which is one of the main commercial species in the Northern AS. The AS represents its southern limit of distribution, in accordance with the thermal preference of this cold-water species. Fish samples were collected monthly using a commercial bottom trawl within the periods 1990–1991 and 2020–2021. The historical comparison highlighted a recent reduction in large specimens (>25 cm total length, TL), which was not associated with trunked age structures, therefore indicating a decrease in growth performance over a period of 30 years (L∞90–91 = 29.5 cm TL; L∞20–21 = 22.8 cm TL). The current size at first sexual maturity was achieved within the first year of life, at around 16 cm TL for males and 17 cm TL for females. In the AS, whiting spawns in batches from December to March, showing a reproductive investment (gonadosomatic index) one order of magnitude higher in females than in males. Potential fecundity (F) ranged from 46,144 to 424,298, with it being heavily dependent on fish size. We hypothesize that the decreased growth performance might be related to a metabolic constraint, possibly related to the increased temperature and its consequences. Moreover, considering the detrimental effects of size reduction on reproductive potential, these findings suggest a potential endangerment situation for the long-term maintenance of whiting and cold-related species in the AS, which should be accounted for in setting management strategies

    Going Beyond Counting First Authors in Author Co-citation Analysis

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    The present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed

    Modeling landings profiles of fishing vessels: An application of Self-Organizing Maps to VMS and logbook data

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    Logbook data constitute a key element within the electronic recording and reporting system of the Euro- pean 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 log- book data is to account for the heterogeneity of fishing practices (e.g., by gear or métier), 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 com- position 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
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