Many marine species are shifting their distributions in response to changing ocean conditions, posing significant challenges and risks for fisheries management. Species distribution models (SDMs) are used to project future species distributions in the face of a changing climate. Information to fit SDMs generally comes from two main sources: fishery-independent (scientific surveys) and fishery-dependent (commercial catch) data. A concern with fishery-dependent data is that fishing locations are not independent of the underlying species abundance, potentially biasing predictions of species distributions. However, resources for fishery-independent surveys are increasingly limited; therefore, it is critical we understand the strengths and limitations of SDMs developed from fishery-dependent data. We used a simulation approach to evaluate the potential for fishery-dependent data to inform SDMs and abundance estimates and quantify the bias resulting from different fishery-dependent sampling scenarios in the California Current System (CCS). We then evaluated the ability of the SDMs to project changes in the spatial distribution of species over time and compare the time scale over which model performance degrades between the different sampling scenarios and as a function of climate bias and novelty. Our results show that data generated from fishery-dependent sampling can still result in SDMs with high predictive skill several decades into the future, given specific forms of preferential sampling which result in low climate bias and novelty. Therefore, fishery-dependent data may be able to supplement information from surveys that are reduced or eliminated for budgetary reasons to project species distributions into the future.

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使用渔业相关数据预测物种分布

许多海洋物种正在改变它们的分布以应对不断变化的海洋条件，给渔业管理带来重大挑战和风险。物种分布模型 (SDM) 用于在气候变化的情况下预测未来的物种分布。适合 SDM 的信息通常来自两个主要来源：独立于渔业（科学调查）和依赖于渔业（商业捕捞）的数据。与渔业相关的数据的一个问题是，捕鱼地点并不独立于潜在的物种丰度，这可能会影响物种分布的预测。然而，独立于渔业的调查资源越来越有限；因此，了解从渔业相关数据开发的 SDM 的优势和局限性至关重要。我们使用模拟方法来评估依赖渔业的数据为 SDM 和丰度估计提供信息的潜力，并量化加州洋流系统 (CCS) 中不同依赖渔业的抽样情景所产生的偏差。然后，我们评估了 SDM 预测物种空间分布随时间变化的能力，并比较了不同采样场景之间模型性能下降的时间尺度以及气候偏差和新颖性的函数。我们的结果表明，鉴于特定形式的优先抽样导致低气候偏差和新颖性，依赖渔业的抽样产生的数据仍然可以在未来几十年内产生具有高预测技能的 SDM。所以，