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Evaluating the Performances of Size‐Frequency‐Based Methods for Estimating Fishing Mortality of Pholis fangi
Marine and Coastal Fisheries ( IF 1.7 ) Pub Date : 2019-08-06 , DOI: 10.1002/mcf2.10085 Ning Chen 1 , Chongliang Zhang 1 , Ming Sun 1 , Binduo Xu 1 , Ying Xue 1 , Yiping Ren 1, 2 , Yong Chen 3
Marine and Coastal Fisheries ( IF 1.7 ) Pub Date : 2019-08-06 , DOI: 10.1002/mcf2.10085 Ning Chen 1 , Chongliang Zhang 1 , Ming Sun 1 , Binduo Xu 1 , Ying Xue 1 , Yiping Ren 1, 2 , Yong Chen 3
Affiliation
Age‐structured data are commonly needed in stock assessments but are unavailable for the majority of existing fisheries, while fish size (length or weight) data are most readily accessible and carry critical information of population status. However, the performances of assessment methods based on size‐frequency data have been less well understood. This study evaluated the reliability of two size‐frequency‐based assessment methods in R package, s6model and TropFishR, for estimating the exploitation status of one data‐poor stock, Pholis fangi in Haizhou Bay, China. We used both simulation and empirical approaches to compare the performances of the two assessment methods in estimating F and Fmsy and examined their robustness to the stochasticity and amount of sampling data. In the simulation test, s6model and TropFishR yielded satisfactorily accurate and consistent estimates of F and Fmsy. In the empirical test, the estimated stock exploitation status varied substantially among seasons. TropfishR and s6model estimated the F/Fmsy to be 2.40 and 2.05 in spring, respectively, indicating remarkable overfishing, and 0.43 and 0.41 in fall, respectively, indicating an optimistic exploitation level. The reflected pattern was consistent with the fishery characteristics of this species. The s6model method was substantially influenced by sample size and might lead to unreasonable estimations in small samples, whereas TropFishR was relatively robust on the changes of sample size. This study highlighted the usefulness of size‐frequency methods in guiding fisheries management and provided references for their applications in data‐limited situations.
更新日期：2019-08-06
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