Uncertainties and Robustness in Fisheries Stock Assessment and Management: Data Processing, Modeling and Socioeconomic Aspects

The main uncertainties that affect the quality of fisheries stock assessment and pose great challenges to fisheries management can stem from a wide range of sources including observation errors associated with model input data, dynamic model process errors, model structure misspecifications, and/or volatile fishery-related socioeconomic environment. Many assessment and management failures are in part attributed to inappropriate consideration of different uncertainties. For many traditional stock assessment models, the observation error is the only source of uncertainty that modelers explicitly deal with. The observation error is usually assumed to be random. The objective functions are formulated by the corresponding distributions (e.g., log-normal distribution for biomass index data; multinomial distribution for size composition data) and minimized to estimate the model parameters (i.e., observation-error-estimators). However, the distributional assumption regarding observation error is often violated by the fact that outliers caused by atypical observation error frequently occur in fishery data. The traditional observation-error-estimators are also challenged by advocators of state-space models. Although previous simulation studies have shown the state-space model outperformed the observation-error-estimator in various cases, it is still unclear how the performance of the two estimators is affected when there are model misspecifications. In addition, despite the fact that the state-space models are advertised as providing the means to differentiate process error from observation error, the estimates of the two errors tend to be biased. How to improve the accuracy of error estimates remains a major question for state-space models. Data-limited methods (DLM) coupled with empirical harvest strategies, considered as an alternative to the stock assessment modeling approach, have been drawing extensive attention in recent decades. However, more research is needed to better understand the robustness of certain DLMs and empirical harvest strategies to key uncertainties. A biologically sustainable fishery is not warranted to be immune to socioeconomic uncertainties that may impair the wellbeing of fishermen. The exceptional slow price recovery of the American lobster in the wake of socioeconomic shocks is a living example. Analyzing the socioeconomic shocks in history can provide insights on how to help the fishery industry confront future uncertainties. Using simulations or case studies, this project aims to evaluate the performance of existing approaches (including estimators, data processing methods, management strategies) when confronting uncertainties from different sources and develop new approaches that can better quantify the level of, or are more robust to the key uncertainties. This study shows that using robust distributions in the likelihood function can locate outliers caused by atypical observation error in biomass index data. The advantage of the state-space production model over the observation-error-estimator diminishes with increased model specification errors. Using multiple time series instead of one time series of biomass index as model inputs can substantially improve the performance of state-space production models and especially improves the accuracy of the error estimates. A new indicator (i.e., Bhighest_S: the biomass at which surplus production is at its highest) is proposed for identifying stock status when only biomass and catch data are available. Simulation studies show that Bhighest_S is potentially more robust to observation errors or model misspecification than the modeling approach for stock status identification. Understanding the reason for the fluctuation of American lobster price suggests that providing resources gradually through the extent of price recovery rather than large and immediate injections of resources may be more efficient for fishing sectors experiencing crises. This study provides several approaches that can better quantify or be more robust to the uncertainties commonly seen in fisheries stock assessment. While the results of the simulations and case studies are produced approximating conditions for the particular stocks (jumbo flying squid, pacific saury, American lobster), the findings regarding the uncertainty issues are relevant to many stocks that have the similar characteristics. This study evaluates error estimation in state-space production models in considerably more depth than previous studies. The recommendations made in this study can help address uncertainties in stock assessment and management

An evaluation of the effects of sample size on estimating length composition of catches from tuna longline fisheries using computer simulations

Length composition analysis can provide insights into the dynamics of a fish population. Accurate quantification of the size structure of a population is critical to understand the status of a fishery and how the population responds to environmental stressors. A scientific observer program is a reliable way to provide such accurate information. However, 100% observer coverage is usually impossible for most fisheries because of logistic and financial constraints. Thus, there is a need to evaluate observer program performance, identify suitable sample sizes, and optimize the allocation of observation efforts. The objective of this study is to evaluate the effects of sample size on the quality of length composition data and identify an optimal coverage rate and observation ratio to improve the observation efficiency using an onboard observer data set from China's tuna longline fishery in the western and central Pacific Ocean. We found that the required sample size varies with fish species, indices used to describe length composition, the acceptable accuracy of the estimates, and the allocation methods of sampling effort. Ignoring other information requirements, 1000 individuals would be sufficient for most species to reliably quantify length compositions, and a smaller sample size could generate reliable estimates of mean length. A coverage rate of 20% would be sufficient for most species, but a lower coverage rate (5% or 10%) could also be effective to meet with the accuracy and precision requirement in estimating length compositions. A non-random effort allocation among fishing baskets within a set could cause the length composition to be overestimated or underestimated for some species. The differences in effective sample sizes among species should be included in the consideration for a rational allocation of observation effort among species when there are different species management priorities. Keywords: Length composition, Sample size, Onboard observer, Accuracy and precision, WCPO and computer simulatio

Using Bayesian Bio-economic model to evaluate the management strategies of Ommastrephes bartramii in the Northwest Pacific Ocean

The neon flying squid (Ommastrephes bartramii) in the Northwest Pacific Ocean is one economically important cephalopod, largely exploited by squid jigging fleets from Chinese Mainland, Japan, and Chinese Taibei. In this study, a Bayesian Bio-economic model was developed using fishery data from Chinese Mainland, Japan, and Chinese Taibei, and relevant fishery economic data from Chinese Mainland. The stock assessment and risk analysis of alternative management strategies for O. bartramii were carried out. Three prior distributions (i.e., uniform, normal and logarithmic normal) for model parameters were assumed in different scenarios. The results showed that the estimated model parameters and reference points such as maximum sustainable yield (MSY), maximum economic yield (MEY), bio-economic balance point (BE) and fishing mortality were similar in the scenarios of normal and logarithmic normal prior assumptions. However, the estimates were larger in the scenario of uniform prior assumption. The fishing mortalities and annual catches from 1996 to 2008 were lower than the reference points F0.1 and MSY in all the three scenarios, indicating that O. bartramii stock is at sustainable exploited level. The results of decision analysis indicated that under the same harvest rate, the catch and biomass in 2023 from the uniform assumption were the highest. However, the highest probability of the collapse was found for squid resources after 2023. Our findings suggested that the harvest rate of 0.4 appeared to be the best management regulation under the uniform assumption, and the MSY would be 200 thousand tons. In addition, the harvest rate of 0.5 would be the best management regulation under the other two assumptions, and the MSY would be 180 thousand tons, which balanced the desire for high yields and the healthy population. The results of this study could be used to provide management suggestions for neon flying squid in the Northwest Pacific Ocean

Comparing a suite of surplus-production-based stock status identification approaches and management procedures

Different approaches have been used to identify fishery stock status when only biomass and catch data are available. However, the performance of the approaches may be affected by the uncertainties derived from different sources (e.g., model misspecification, stock productivity changing, observation error). Here, we propose that the observed biomass associated with the highest calculated surplus production can be used as an indicator (Bhighest_S) to identify stock status. We develop a management procedure (MP) atop a widely used method (i.e., Gcontrol) by incorporating Bhighest_S in the harvest control rule. Two simulations are conducted to compare the stock status identification approaches and corresponding MPs. Using Bhighest_S to identify stock status performs better than surplus production modeling approaches in simulated regime shift scenarios. Compared with the old version of Gcontrol, incorporating Bhighest_S or estimated BMSY in the harvest control rule provides more stable and higher yields. This study contributes to the development and evaluation of indicator-based stock status identification approaches and MPs that only require biomass and catch data.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author