(searched for: doi:10.17352/2455-8400.000053)
Published: 1 February 2022
Sustainability, Volume 14; https://doi.org/10.3390/su14031691
Productivity susceptibility analysis (PSA) is a semi-quantitative ecological risk assessment tool, widely used to determine the relative vulnerability of target and non-target species to fishing impacts. Considering the available information on species-specific life-history and fishery-specific attributes, we used PSA to assess the relative risk of the 60 species interacting with the shrimp trawl fishery in the Bay of Bengal, Bangladesh. Penaeus monodon, the most important target, and Metapenaeus monoceros, the highest catch contributor, along with other 15 species were in the moderate-risk category, while seven non-target bycatch species were in the high-risk category. PSA-derived vulnerability results were validated with IUCN extinction risk, exploitation rate and stocks’ catch trend. The majority of the identified species showed high productivity (37%) and high susceptibility (46%), and all the moderately and highly vulnerable species were subjected to overfishing conditions by shrimp trawl fishery, which coincided with the vulnerability scores (V ≥ 1.8). Species with V ≥ 1.8 mostly showed a decreasing catch trend, while the species with a stable or increasing catch trend had a V ≤ 1.72. Data quality analysis of productivity and susceptibility attributes indicated that the majority of species were considered data-limited, which emphasizes the acquisition of data on spatio-temporal abundance, catch and effort, and biological information specifically relating to species age, growth, and reproduction. However, our findings can assist fishery administrators in implementing an ecosystem approach to ensure the sustainability and conservation of marine biodiversity in the Bay of Bengal.
Published: 16 October 2021
Journal of Marine Science and Engineering, Volume 9; https://doi.org/10.3390/jmse9101137
Stock assessment is necessary to understand the status of fishery stocks. However, for the data-poor fishery, it is very challenging to assess the stock status. The length-based Bayesian biomass (LBB) technique is one of the most powerful methods to assess the data-poor fisheries resources that need simple length frequency (LF) data. Addressing the present gap, this study aimed to assess the stock status of three sardines (Sardinella fimbriata, Dussumieria acuta, and D. elopsoides) in the Bay of Bengal (BoB), Bangladesh using the LBB method. The estimated relative biomass for S. fimbriata was B/B0< BMSY/B0, indicating the overfished biomass, while the assessed B/B0 >BMSY/B0 for D. acuta and D. elopsoides indicates healthy biomass. Additionally, for S. fimbriata, the length at first landing was smaller than the optimum length at first landing (Lc< Lc_opt), indicating an overfishing status, but a safe fishing status was assessed for D. acuta and D. elopsoides (Lc >Lc_opt). Therefore, increasing the mesh size of fishing gears may help to ensure the long-term viability of sardine populations in the BoB, Bangladesh.