Monitoring and assessment of natural resources often require inputs from multiple data sources. In fisheries science, for example, the inference of a species’ abundance distribution relies on two main data sources, namely commercial fisheries and scientific survey data. Despite efforts to combine these data into an integrated statistical model, their coupling is frequently hampered due to differences in their sampling designs, which imposes distinct bias sources in the estimator of the abundance distribution. We developed a flexible species distribution model (SDM) that can integrate both data sources while filtering out their relative bias contributions. We applied the model on three different age groups of the western Baltic cod stock. For each age group, we tested the model on (1) survey data and (2) integrated data (survey + commercial) as a means to compare their differences and investigate how the inclusion of commercial fisheries data improved the spatiotemporal abundance estimator and parameter estimates. Moreover, we proposed a novel validation approach to evaluate whether the inclusion of commercial fisheries data in the integrated model is not in direct contradiction with the survey data. Following our approach, the results indicated that the use of commercial fisheries data is suitable for the integrated model. Across all age groups, our results demonstrated how commercial fisheries supplied additional information on cod’s spatiotemporal abundance dynamics, highlighting sometimes abundance hot spots that were not detected by the survey model alone. Additionally, the integrated model provided a reduction of up to 20% and 10% in the uncertainty (SE) of the predicted abundance fields and fixed-effect parameters, respectively. The proposed model represents thus a valuable benchmark for evaluating spatiotemporal dynamics of fish, and strengthens the science-based advice for marine policymakers.

中文翻译：

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弥合商业渔业和调查数据之间的差距，以模拟海洋物种的时空动态

自然资源的监测和评估通常需要来自多个数据源的输入。例如，在渔业科学中，物种丰度分布的推断依赖于两个主要数据源，即商业渔业和科学调查数据。尽管努力将这些数据组合成一个集成的统计模型，但由于采样设计的差异，它们的耦合经常受到阻碍，这在丰度分布的估计器中强加了不同的偏差源。我们开发了一个灵活的物种分布模型 (SDM)，它可以整合两个数据源，同时过滤掉它们的相对偏差贡献。我们将该模型应用于三个不同年龄组的西波罗的海鳕鱼种群。对于每个年龄段，我们在 (1) 调查数据和 (2) 综合数据（调查 + 商业）上测试了模型，作为比较它们差异的一种手段，并研究包含商业渔业数据如何改进时空丰度估计量和参数估计量。此外，我们提出了一种新的验证方法来评估在综合模型中包含商业渔业数据是否与调查数据不直接矛盾。按照我们的方法，结果表明商业渔业数据的使用适合集成模型。在所有年龄组中，我们的结果展示了商业渔业如何提供有关鳕鱼时空丰度动态的额外信息，突出有时单独调查模型未检测到的丰度热点。此外，集成模型使预测的丰度场和固定效应参数的不确定性 (SE) 分别降低了 20% 和 10%。因此，提议的模型代表了评估鱼类时空动态的宝贵基准，并加强了对海洋政策制定者的科学建议。