Abstract
For many fish stocks, such as Pampus argenteus and Setipinna taty in China, size composition data are more accessible than catch data. Varied results can arise when different length-based stock assessment models are applied to these data, and fishery managers often need to reconcile conflicting estimates of population status. Superensemble modeling, a relatively recent innovation in fish stock assessments commonly used in other fields, may provide an effective solution to resolving uncertainties among the results from multiple length-based models. To verify potential for this approach to improve estimates of population status, we applied ensemble modeling to fit simulated data of P. argenteus and S. taty in the Bohai and Yellow Seas using predictions from a length-based integrated mixed effects (LIME) and length-based spawning potential ratio (LB-SPR) models as covariables in a superensemble model developed in this study. All simulation modeling of P. argenteus and S. taty in the Bohai and Yellow Seas was conducted using the operating model in the R package LIME. Initially, the LIME and LB-SPR performances were tested separately under three scenarios of fishing mortality and recruitment variability (“equilibrium scenario,” “endogenous scenario,” and “one-way base scenario”). Then, estimates of spawning potential ratio (SPR) were combined with the superensemble models (a linear model, a support vector machines, a random forest and a boosted regression tree). We trained our superensemble models with 80% of the simulated data and tested them with the remaining 20%. Our results showed that superensemble modeling substantially improved the estimates of SPR, with support vector machines performing the best at estimating population status: precision improved by 12.7% for S. taty and 8% for P. argenteus on average (namely, median absolute proportional error decreased by 0.127 and 0.08 on average) compared to the individual models. This finding has important implications for fisheries management in the context of species for which catch data are unavailable. Applying the size composition survey data, the results from support vector machines superensemble model suggested that neither S. taty nor P. argenteus in the Bohai Sea in 2019 are overfished, but the stock status of P. argenteus warrants vigilant monitoring.
Highlights
Stock assessment involves providing scientific, quantitative evaluations to objectively inform fisheries management (Hilborn and Walters, 1992)
Simulation testing demonstrated that the RE distributions of the LIME and LB-spawning potential ratio (SPR) methods under varying conditions were relatively dispersed compared with those under equilibrium conditions (Figure 5)
Our results showed that for P. argenteus, both LIME and LB-SPR can estimate unbiased SPR when length data are available and biological characteristics are correctly specified across various scenarios of fishing mortality and recruitment patterns (Figure 5)
Summary
Stock assessment involves providing scientific, quantitative evaluations to objectively inform fisheries management (Hilborn and Walters, 1992). Due to an urgent need for managing an increasing number of smaller less productive or marginal fish stocks as well as halting depletion and promoting recovery of long established once highly productive stocks, in recent years, data-limited methods to estimate stock status and exploitation have developed rapidly (MacCall, 2009; Dick and MacCall, 2011; Free et al, 2017). Most of these methods can be divided into catch-based methods and length-based methods. Lengthbased assessment methods, which require mean length or length composition of the catch and estimates of life history parameters, have become increasingly prevalent for evaluating the status of data-limited or data-poor fish stocks (Thorson and Cope, 2014; Hordyk et al, 2015; et al, 2015), and have great potential for application in fish species assessment where catch data are unavailable
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