Evaluating a possible new paradigm for recruitment dynamics: predicting poor recruitment for striped bass (Morone saxatilis) from an environmental variable
Virginia Institute of Marine Science
Understanding what causes large year classes and predicting them has been called the holy grail of fisheries science, one of the last great unanswered questions. Recruitment prediction, or forecasting, is an important component for setting fishery catch limits. We propose a new approach, called the “poor-recruitment paradigm”, for predicting recruitment using environmental variables. This approach hypothesizes that it is easier to predict poor recruitment rather than good recruitment because an environmental variable affects recruitment only when its value is extreme (lethal); otherwise, the variable may be benign and not influence recruitment. Thus, good recruitment necessitates all environmental conditions not be harmful and for some to be especially favorable; poor recruitment, however, requires only one environmental variable to be extreme.
This idea was evaluated using recruitment and river discharge data for striped bass (Morone saxatilis) from seven major spawning tributaries of Chesapeake Bay. Low spring river discharge reliably resulted in poor recruitment of striped bass. Specifically, in all rivers, median recruitment and standard deviation of recruitment were lower when spring river discharge was low compared to when it was average or high; additionally, the proportion of years with poor recruitment was higher in years of low discharge than in years of average to high discharge. The consistent predictability of poor recruitment has the potential to improve stock projections, and therefore, has the potential to improve catch advice.
Forecasting recruitment, Population dynamics, Recruitment prediction, Stock projections
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Gross, Julie M.; Sadler, Philip; and Hoenig, John M., Evaluating a possible new paradigm for recruitment dynamics: predicting poor recruitment for striped bass (Morone saxatilis) from an environmental variable (2022). Fisheries Research, 252, 106329.