Length-based assessment of five small pelagic fishes in the Senegalese artisanal fisheries

PLOS ONE RESEARCH ARTICLE Length-based assessment of five small pelagic fishes in the Senegalese artisanal fisheries Bocar Sabaly Baldé ID1*, Patrice Brehmer1,2,3, Penda Diop Diaw1 1 Institut Sénégalais de Recherche Agricole, ISRA, Centre de Recherche Océanographique de DakarThiaroye, CRODT, Dakar, Sénégal, 2 IRD, Univ Brest, CNRS, Ifremer, UMR Lemar, Dakar, Sénégal, 3 Commission Sous Régionale des Pêches, CSRP, Secrétariat Permanent de la CSRP, Dakar, Sénégal * a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Baldé BS, Brehmer P, Diaw PD (2022) Length-based assessment of five small pelagic fishes in the Senegalese artisanal fisheries. PLoS ONE 17(12): e0279768. https://doi.org/10.1371/ journal.pone.0279768 Editor: Vitor Hugo Rodrigues Paiva, MARE – Marine and Environmental Sciences Centre, PORTUGAL Received: March 8, 2022 Accepted: December 11, 2022 Published: December 30, 2022 Copyright: © 2022 Baldé et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Fisheries management is an important strategy for ensuring sustainable use of resources. However, in West Africa, in the absence of quality data for many stocks and effective stock assessment models, the cases where this has been truly successful are notable for their rarity. In West Africa, small pelagic fish are of great socio-economic importance, as well as good indicators of fish stressors. Here, historical data (2004–2019) of five small pelagic species (Sardina pilchardus, Ethmalosa fimbriata, Trachurus trecae, Scomber colias and Mugil cephalus) were collected in Senegalese waters. The B/BMSY results showed stocks to be collapsed (B/BMSY = 0.13 and 0.1 for M. cephalus and S. pilchardus, respectively) and heavily overfished (B/BMSY = 0.24; E. fimbriata). Only S. colias and T. trecae stock were considered to be in good condition (B/BMSY = 1.7 and 1.4 respectively). The Lc/Lc_opt ratio was � 1 for E. fimbriata and M. cephalus, suggesting that the individuals caught for these species were too small. To reverse these bad stock statuses, catching individuals at Lc_opt, 25, 21, 43 and 18 cm for S. colias, E. fimbriata, M. cephalus and S. pilchardus, respectively should be a natural guarantee against recruitment failure and allow individuals to ensure the long-term survival of populations, in a context of data poor fisheries. In conclusion, this study shows that, despite limitations, the LBB model can provides indicators of stock status for species to encourage management measures, especially in data poor countries. It is hoped that these results can help to better assess many stocks currently considered too data poor to be assessed or at least encourage data collection effort on stocks discerned as in bad or critical status. Data Availability Statement: All relevant data are within the manuscript and its Supporting Information files. Funding: Yes. Data analysis was done inside the AWA project funded by IRD and the BMBF (grant 01DG12073E), www.awa.ird.fr (SRFC: Sub Regional Fisheries Commission), and the Preface project funded by the European Commission’s Seventh Framework Program (2007-2013) under Grant Agreement number 603521, https://preface. b.uib.no. Paper redaction was done within the 1. Introduction In West Africa, the fishing sector plays an important role by providing food security and nutrition [1, 2]. It’s also the main resource for traditional processing activities (drying or smoking) and plays a vital role in the diet of population [3]. However, with poorly adapted and ineffective fisheries management policies, this sector is confronted with the effects of overfishing and the collapse of fisheries in terms of local consumption, food security, and economic value [2, 4, 5]. Attempts to regulate fisheries in the region through the establishment of marine protected PLOS ONE | https://doi.org/10.1371/journal.pone.0279768 December 30, 2022 1 / 15 PLOS ONE MAVA foundation for OAP8 actions in West Africa including AGD-pelagic project (SRFC/CSRP). Competing interests: The authors have declared that no competing interests exist. Assessment of the status of small pelagic stocks in data poor-fisheries context areas and gear restrictions have been limited by the lack of scientific data and inadequate infrastructure and human capacity to effectively monitor and assess marine resources [5, 6]. Indeed, the data available have some major drawbacks. Nominal catches are sometimes inaccurate [2, 5]. Indeed, they often contain inaccurate transcriptions of weights and sizes of individuals. Insufficient data on fishing effort, in addition to unreliable and outdated statistical data also form part of the problem [2, 5]. This affects their use in the formulation of relevant policies for the sector [2]. Consequently, management guidelines and controls must be simple, but also robust against uncertainties, as well as being proportionate to the information available [7]. Thus, it is possible to apply generic management procedures, which are not necessarily the best for a given fishery, but which might be better than taking no action [8]. Recently, several stock assessment methods have been developed and applied to many stocks with poor data [9–11]. The Length-based Integrated Mixed Effects (LIME) model uses a dynamic age-structured model and assumes that biological input parameters are known without error, length at age is normally distributed, natural mortality rates are constant over time and growth rates are constant between cohorts with the ability to account for fishing mortality [12]. However, the estimation of some parameters such as recruitment (r) and fishing mortality (F/M) may be uncertain as they are assessed over a single year of length [12]. The Catch Maximum Sustainable Yield model [13] estimates reference points (FMSY, BMSY) as well as relative stock size (B/BMSY) and exploitation (F/FMSY) while the Depletion-Based Stock Reduction Analysis model [14] estimates maximum sustainable yield. However, the prediction of the CMSY method is only accurate when validated with real data from simulated stocks or evaluated against the BMSY estimate for real stocks, whereas the DB-SRA model is limited by the inability to cope with the uniform decrease in abundance as well as an underestimation of the overfishing limit values depending on stock characteristics [14]. The LBB model is a LengthBased Bayesian (LBB) biomass estimate based on processes for analysing length frequency (LF) or width frequency data of fish or invertebrate populations [11]. According to Froese et al. [11], this model works for species that grow throughout their lifetime. It estimates asymptomatic length (L1), length at first capture (Lc), natural mortality (M/K) and fishing mortality (F/K). The LBB model is (...truncated)