Stochastic Modelling to Assess External Environmental Drivers of Atlantic Chub Mackerel Population Dynamics

The population dynamics of small and middle-sized pelagic fish are subject to considerable interannual and interdecadal fluctuations in response to fishing pressure and natural factors. However, the impact of environmental forcing on these stocks is not well documented. The Moroccan Atlantic coast is characterized by high environmental variability due to the upwelling phenomenon, resulting in a significant abundance and variation in the catches of small and middle-sized pelagic species. Therefore, understanding the evolution of stock abundance and its relationship with different oceanographic conditions is a key issue for fisheries management. However, because of the limited availability of independent-fishery data along the Moroccan Atlantic coast, there is a lack of knowledge about the population dynamics. The main objective of this study is to test the correlation between the environment conditions and the stock fluctuations trends estimated by a stock assessment model that does not need biological information on growth, reproduction, and length or age structure as input. To achieve this objective, the fishery dynamics are analyzed with a stochastic surplus production model able to assimilate data from surveys and landings for a biomass trend estimation. Then, in a second step, the model outputs are correlated with different environmental (physical and biogeochemical) variables in order to assess the influence of different environmental drivers on population dynamics. This two-step procedure is applied for chub mackerel along the Moroccan coast, where all these available datasets have not been used together before. The analysis performed showed that larger biomass estimates are linked with periods of lower salinity, higher chlorophyll, higher net primary production, higher nutrients, and lower subsurface oxygen, i.e., with an enhanced strength of the upwelling. In particular, acute anomalies of these environmental variables are observed in the southern part presumably corresponding to the wintering area of the species in the region. The results indicate that this is a powerful procedure, although with important limitations, to deepen our understanding of the spatiotemporal relationships between the population and the environment in this area. Moreover, once these relationships have been identified, they could be used to generate a mathematical relationship to simulate future population trends in diverse environmental scenarios.Postprin

ICES. 2021. Working Group on Southern Horse Mackerel Anchovy and Sardine (WGHANSA).

The ICES Working Group on Southern horse mackerel, anchovy and sardine (WGHANSA) assessed the status of anchovy in Atlantic Iberian waters (ane.27.9a; western and southern components) and horse mackerel in Atlantic Iberian waters (hom.27.9a) in the May meeting and of anchovy in Bay of Biscay (ane.27.8), sardine in southern Celtic Seas and the English Channel (pil.27.7), sardine in Bay of Biscay (pil.27.8abd) and sardine in Cantabrian Sea and Atlantic Iberian waters (pil.27.8c9a) in the November meeting. In addition, to answer a special request from Portugal and Spain, in May the working group updated the assessment of sardine in Atlantic Iberian waters (pil.27.8c9a) based on the most recent data available and included as catch scenarios, the harvest control rule evaluated in the Workshop for the evaluation of the Iberian sardine harvest control rules (WKSARHCR 2021). Deviations from the stock annex caused by missing surveys and deteriorated catch data due to the Covid-19 were described and sensitivity analyses of their impact were provided whenever possibl

Gadget for anchovy 9a South: Model description and results to provide catch advice and reference points (WGHANSA-1 2021)

The model speci fications presented below correspond to those benchmarked in WKPELA 2018. The main difference is that results are presented now for the end of the second quarter of each year instead of being presented at the end of the fourth quarter. This responds to practical modi cations in the de nition of the assessment year, now it goes from July 1st to June 30th of the next year. Model speci fications for this year are presented in section 2.2 and ??, as well as estimated parameters after optimization in Table 2