Potential environmental drivers of Japanese anchovy (Engraulis japonicus) recruitment in the Yellow Sea

2020 
Abstract Water temperature and food availability are two environmental factors that are widely considered to be likely determinants of recruitment success of fish stocks. However, marine environmental data are commonly of limited availability. Moreover, the mechanisms by which environmental variations may act to regulate fish recruitment remain incompletely understood, especially from the point of view of prey availability. Here we report a test of the feasibility of using simulated long-term hindcast of marine environmental data based on a physical and lower-trophic-level ecosystem data-assimilative model to investigate the impacts of water temperature and prey availability on recruitment fluctuations of the Japanese anchovy (Engraulis japonicus) in the Yellow Sea. Although observational data available for model validation were limited, the model reasonably reproduced inter-annual fluctuations of phytoplankton abundance. The model results for a 15-year period (1987–2001) indicated Japanese anchovy recruitment to have been negatively correlated with July sea surface temperature in the traditional spawning ground south of the Shandong Peninsula, while being positively correlated with near-surface biomass of small zooplankton (the predominant prey for anchovy larvae) in the central Yellow Sea during the summer period. These findings imply that the Japanese anchovy recruitment in the Yellow Sea is likely regulated during its early life stages by both water temperature and food availability. The negative temperature-recruitment relationship could be explained by the “spawning temperature optima” and “optimal growth temperature” hypotheses. Furthermore, model results indicated that inter-annual variation in the near-surface biomass of small zooplankton in the central Yellow Sea during summer is mainly regulated by nutrient supply, to which both vertical stratification and horizontal advection may make contributions.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    80
    References
    5
    Citations
    NaN
    KQI
    []