AUTHOR=Xia Meng , Carruthers Tom , Kindong Richard , Dai Libin , Geng Zhe , Dai Xiaojie , Wu Feng TITLE=How Can Information Contribute to Management? Value of Information (VOI) Analysis on Indian Ocean Striped Marlin (Kajikia audax) JOURNAL=Frontiers in Marine Science VOLUME=Volume 8 - 2021 YEAR=2021 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2021.646174 DOI=10.3389/fmars.2021.646174 ISSN=2296-7745 ABSTRACT=

Fisheries researchers have focused on the value of information (VOI) in fisheries management and trade-offs since scientists and managers realized that information from different resources has different contribution in the management process. We picked seven indicators, which are log-normal annual catch observation error (Cobs), annual catch observation bias (Cbias), log-normal annual index observation error (Iobs), maximum length observation bias (Linfbias), observed natural mortality rate bias (Mbias), observed von Bertalanffy growth parameter K bias (Kbias), and catch-at-age sample size (CAA_nsamp), and built operating models (OMs) to simulate fisheries dynamics, and then applied management strategy evaluation (MSE). Relative yield is chosen as the result to evaluate the contribution of the seven indicators. Within the parameter range, there was not much information value reflected from fisheries-dependent parameters including Cobs, Cbias, and Iobs. On the other hand, for fisheries-independent parameters such as Kbias, Mbias, and Linfbias, similar tendency of the information value was showed in the results, in which the relative yield goes down from the upper bound to the lower bound of the interval. CAA_nsamp had no impact on the yield after over 134 individuals. The VOI analysis contributes to the trade-offs in the decision-making process. Information with more value is more worthy to collect in case of waste of time and money so that we could make the best use of scientific effort. But we still need to improve the simulation process such as enhancing the diversity and predictability in an OM. More parameters are on the way to be tested in order to collect optimum information for management and decision-making.