Ecosystem Sciences Division, Social-Ecological and Economic Systems Program, Pacific Islands Fisheries Science Center, National Marine Fisheries Service, Honolulu Hawaii, Hawaii, United States of America.
PLoS One. 2021 Sep 7;16(9):e0257027. doi: 10.1371/journal.pone.0257027. eCollection 2021.
Fishing trip cost is an important element in evaluating economic performance of fisheries, assessing economic effects from fisheries management alternatives, and serving as input for ecosystem and bioeconomic modeling. However, many fisheries have limited trip-level data due to low observer coverage. This article introduces a generalized linear model (GLM) utilizing machine learning (ML) techniques to develop a modeling approach to estimate the functional forms and predict the fishing trip costs of unsampled trips. GLM with Lasso regularization and ML cross-validation of model are done simultaneously for predictor selection and evaluation of the predictive power of a model. This modeling approach is applied to estimate the trip-level fishing costs using the empirical sampled trip costs and the associated trip-level fishing operational data and vessel characteristics in the Hawaii and American Samoa longline fisheries. Using this approach to build models is particularly important when there is no strong theoretical guideline on predictor selection. Also, the modeling approach addresses the issue of skewed trip cost data and provides predictive power measurement, compared with the previous modeling efforts in trip cost estimation for the Hawaii longline fishery. As a result, fishing trip costs for all trips in the fishery can be estimated. Lastly, this study applies the estimated trip cost model to conduct an empirical analysis to evaluate the impacts on trip costs due to spatial regulations in the Hawaii longline fishery. The results show that closing the Western and Central Pacific Ocean (WCPO) could induce an average 14% increase in fishing trip costs, while the trip cost impacts of the Eastern Pacific Ocean (EPO) closures could be lower.
钓鱼旅行成本是评估渔业经济绩效、评估渔业管理替代方案的经济影响以及作为生态系统和生物经济模型输入的一个重要因素。然而，由于观察员的覆盖范围有限，许多渔业的旅行级别数据有限。本文介绍了一种广义线性模型（GLM），利用机器学习（ML）技术来开发一种建模方法，以估计未采样旅行的功能形式并预测其钓鱼旅行成本。GLM 与 Lasso 正则化和 ML 交叉验证同时进行，以进行预测因子选择和模型的预测能力评估。该建模方法用于使用经验采样旅行成本以及夏威夷和美属萨摩亚延绳钓渔业中的相关旅行级别钓鱼作业数据和船只特征来估计旅行级别钓鱼成本。当没有关于预测因子选择的强有力的理论指导时，使用这种方法构建模型尤为重要。此外，与之前在夏威夷延绳钓渔业的旅行成本估算中进行的建模工作相比，该建模方法解决了旅行成本数据偏态的问题，并提供了预测能力的衡量。因此，可以估计渔业中所有旅行的旅行成本。最后，本研究应用估计的旅行成本模型进行实证分析，以评估夏威夷延绳钓渔业中空间法规对旅行成本的影响。结果表明，关闭中西太平洋（WCPO）可能导致钓鱼旅行成本平均增加 14%，而关闭东太平洋（EPO）的旅行成本影响可能较低。
