ISSN: 2577-4050
Authors: Hamwi N* , Ali-Basha N and Altajer H
From January 2021 to December 2023, a total of 222 random samples of Rhinobatos rhinobatos were collected from the Syrian coast in the eastern Mediterranean Sea, spanning a three-year period. These samples underwent advanced analysis techniques, including artificial neural networks and fuzzy logic. The largest individual captured during the study had a total length of 115.73 cm and was estimated to be 9 years. By applying the von Bertalanffy growth equation (TLt = 149.46 (1-e-0.145 (t + 1.201))), it was determined that the species exhibited positive allometric growth (b = 3.17). The growth performance index (Φ’) was calculated as 3.51, indicating growth efficiency. The study also estimated several mortality coefficients for Rhinobatos rhinobatos. The coefficients were as follows: Z = 0.45 y-1 (total mortality), F = 0.15 y-1 (fishing mortality), M = 0.30 y-1 (natural mortality), and E = 0.33 y-1 (exploitation rate). The survival coefficient (S) was found to be 0.64 y-1. The analysis of population growth (FP = 49.7) of Rhinobatos rhinobatos from the Syrian coast indicated a moderate growth pattern within the local marine environment. However, the study also revealed a high vulnerability to fishing, with a vulnerability score of 65.6 FV. This vulnerability poses a significant threat to fish populations along the Syrian coast. The results of this study provide valuable insights into the population dynamics of Rhinobatos rhinobatos in the Syrian coastal region. They emphasize the importance of implementing conservation measures for the sustainable management of this species. Additionally, the results enhance our understanding of the growth, mortality, and vulnerability of Rhinobatos rhinobatos to fishing, laying the groundwork for future research and management strategies.
Keywords: Fishing Vulnerability; Fuzzy logic; Growth; Mortality; Rhinobatos rhinobatos; Syrian Coast
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