Premium
Management of Data‐Limited Fisheries: Identifying Informative Data to Achieve Sustainable Fisheries
Author(s) -
Sun Ming,
Li Yunzhou,
Zhang Chongliang,
Xu Binduo,
Ren Yiping,
Chen Yong
Publication year - 2020
Publication title -
north american journal of fisheries management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 72
eISSN - 1548-8675
pISSN - 0275-5947
DOI - 10.1002/nafm.10438
Subject(s) - stock assessment , fisheries management , maximum sustainable yield , stock (firearms) , data management , sustainable management , fishery , business , fish stock , marine fisheries , management by objectives , management strategy , proxy (statistics) , environmental resource management , computer science , sustainability , data mining , fishing , geography , environmental science , ecology , biology , marketing , business administration , archaeology , machine learning
Sustainable fisheries management is built on effective management strategies and informative data that are usually well defined for data‐rich fisheries. However, their counterparts in data‐limited fisheries remain inadequately evaluated due to insufficient data for stock assessments. This raises two questions: (1) can data‐limited management strategies achieve sustainable fisheries and (2) are certain types of data more informative than others are in developing sustainable data‐limited fisheries? To address these questions, we performed management strategy evaluation with data‐rich stocks to simulate data‐limited fisheries management. The performance of data‐limited management procedures ( MP s) was compared with the performance of management strategies that are based on data‐rich stock assessments. We examined the informative nature of six data categories in data‐limited situations by developing a tier system that mimicked different data availability levels and evaluating their contributions to management success. Our study showed that it is possible to achieve maximum sustainable yield ( MSY ) with limited data, given the proper choice of MP s. In addition, the data categories were not equally informative to the development of sustainable data‐limited fisheries management. In our case study, the MP s using the data on stock size and quantitative management targets (target proxy) demonstrated the highest management success rates. The most effective (100% management success rate) while least data‐demanding MP s included minlenLopt1, SPMSY , MCD , Itarget1, and ICI . We also confirmed that the performance of the data‐limited MP s was closely related to historical stock dynamics and levels of data availability. We suggest considering historical stock trends and data availability levels as critical prerequisites in the management of data‐limited fisheries.