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Ecoforecasting in real time for commercial fisheries: the Atlantic white shrimp as a case study

机译：商业渔业的实时生态预测：以大西洋白虾为例

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摘要

Predictive modeling of natural resources has long relied on mechanistic descriptions incorporating various population attributes and to a lesser extent environmental conditions. A radical departure from this tradition is proposed, advocating the data-driven analysis and forecasting of population cycles from historical records, and using the Atlantic white shrimp, Litopenaeus setiferus, as a case study. The time series data were collected in the Charleston Harbor (32°47′00″N, 79°56′00″W), South Carolina, USA, and from the database of the National Marine Fisheries Service (http://www.st.nmfs.gov/st1/commercial/index.html), for the period between January 1986 and December 2004. Correlations between shrimp population cycles and environmental hydrological parameters were established by phase space reconstruction, a technique central to most nonlinear time series analysis methods. Predictive models of future shrimp population levels were built using feedforward artificial neural networks, a well-known machine learning technique. From several attempted strategies, predicting the state commercial harvest from the sampling of populations in the Charleston Harbor conducted by the South Carolina Department of Natural Resources proved to be optimal, with an accuracy of 92% for 1-month and 79% for 3-month ahead predictions, as measured by the nonparametric and nonlinear Spearman's correlation coefficient. In addition, the shrimp population levels seem to be more sensitively to changes in surface water temperature than salinity, but the latter is also an important consideration. These models also suggest that catch-per-unit-effort data are important indicators of commercial harvest and, thus, provide an important linkage between monitoring programs and commercial returns, enabling accurate predictions of natural resources to be made in near real time and extended beyond the critical time frames within which resource managers operate.
机译：长期以来，对自然资源的预测建模一直依赖于对各种人口属性以及较小程度的环境条件进行综合的机械描述。提出了与该传统的根本性偏离，主张从历史记录中进行数据驱动的分析和对种群周期的预测，并以大西洋白对虾Litopenaeus setiferus为例进行研究。时间序列数据是在美国南卡罗来纳州的查尔斯顿港（北纬32°47′00″，北纬79°56′00″）中收集的，并从国家海洋渔业局（http：// www。国家海洋渔业局）的数据库中收集。 st.nmfs.gov/st1/commercial/index.html），时间为1986年1月至2004年12月。通过相空间重构，建立了虾种群周期与环境水文参数之间的相关性，这是大多数非线性时间序列分析的核心技术方法。使用前馈人工神经网络（一种著名的机器学习技术）建立了未来虾种群水平的预测模型。从几种尝试的策略中，由南卡罗来纳州自然资源部在查尔斯顿港进行的抽样调查中预测的州商业性收获被证明是最佳的，1个月的准确度为92％，3个月的准确度为79％由非参数和非线性Spearman相关系数测得的超前预测。此外，虾的种群水平似乎比盐度对地表水温的变化更为敏感，但后者也是一个重要的考虑因素。这些模型还表明，单位捕捞量数据是商业收成的重要指标，因此，在监测计划和商业回报之间提供了重要的联系，从而可以对自然资源进行近乎实时的准确预测，并扩展到资源管理器运行的关键时间范围。

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