4.6 Article

Ecosystem-based forecasts of recruitment in two menhaden species

期刊

FISH AND FISHERIES
卷 19, 期 5, 页码 769-781

出版社

WILEY
DOI: 10.1111/faf.12287

关键词

ecosystem-based management; empirical dynamic modelling; nonparametric forecasting; recruitment dynamics

资金

  1. Lenfest Ocean Program [00028335]
  2. Department of Defense Strategic Environmental Research and Development Program [15 RC-2509]
  3. EPA- STAR Fellowship Program
  4. National Science Foundation [DEB-1020372]
  5. National Science Foundation ABI-Innovation [DBI-1667584]
  6. Sugihara Family Trust
  7. Deutsche Bank-Jameson Complexity Studies Fund
  8. Leslie and John McQuown Gift
  9. McQuown Chair in Natural Sciences, University of California, San Diego

向作者/读者索取更多资源

Gulf (Brevoortia patronus, Clupeidae) and Atlantic menhaden (Brevoortia tyrannus, Clupeidae) support large fisheries that have shown substantial variability over several decades, in part, due to dependence on annual recruitment. Nevertheless, traditional stock-recruitment relationships lack predictive power for these stocks. Current management of Atlantic menhaden explicitly treats recruitment as a random process. However, traditional methods for understanding recruitment variability carry the very specific hypothesis that the effect of adult biomass on subsequent recruitment occurs independently of other ecosystem factors such as food availability and predation. Here, we evaluate the predictability of menhaden recruitment using a model-free approach that is not restricted by these strong assumptions. We find that menhaden recruitment is predictable, but only when allowing for interdependence of stock with other ecological factors. Moreover, while the analysis confirms the presence of environmental effects, the environment alone does not readily account for the complexity of menhaden recruitment dynamics. The findings set the stage for revisiting recruitment prediction in management and serve as an instructive example in the ongoing debate about how to best treat and understand recruitment variability across species and fisheries.

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