The importance of the role of cephalopods in marine ecosystems and commercial fisheries has increased over recent years. There is now evidence that the distribution of cephalopods is expanding latitudinally. Nevertheless, information about the spatial distribution of cephalopods and its implications in the Yellow Sea (YS) is not well known. In an attempt to redress this deficiency, we firstly conducted a simple analysis of geospatial patterns in fishing effort in the YS during 2012–2016 to ascertain if changes in fishing intensity (across all species) might be responsible for creating an apparent latitudinal shift in cephalopod distribution. Although fishing intensity increased in the YS over the 5-year period, there are no significant differences among years within each latitude, implying that all latitudes respond in a similar way each year. We then used long-term scientific survey data (2000, 2009, 2014, and 2017) of cephalopods (Todarodes pacificus, Loliolus spp., Octopus variabilis, Octopus ocellatus, Sepiola birostrata, and Euprymna spp.) collected each October in the YS, combined with oceanographic variables including sea surface temperature (SST) and chlorophyll-a concentration (CHLA), to model relationships to establish habitat suitability indices (HSIs) using both an arithmetic mean method (AMM) and a geometric mean method (GMM). Cross-validation, standard deviation, and mean squared error of prediction (MSEP) were used to evaluate the performance of the HSI. Abundance index data from surveys of 2018 were overlaid on maps of predicted HSI for the same year to visualize the correspondence of the modeled HSI. Spatiotemporal mapping of oceanographic variables showed that SST and CHLA change dramatically around 34°N, which may relate to the spatial distribution of cephalopods. CHLA is the most important oceanographic variable for most squid and octopus species, while SST is the most important for bobtail squid. The MSEP showed that the AMM-based HSI performed better than the GMM-based HSI. Future studies should take weighting of oceanographic variables into account and ideally integrate them into a more holistic model to obtain increased precision in predictions when establishing HSIs.

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模拟海洋学对黄海秋季常见头足类动物空间分布的影响

近年来，头足类动物在海洋生态系统和商业渔业中的作用越来越重要。现在有证据表明头足类动物的分布正在纬度上扩大。然而，关于头足类动物的空间分布及其在黄海 (YS) 中的影响的信息尚不清楚。为了弥补这一缺陷，我们首先对 2012-2016 年期间 YS 捕捞努力的地理空间模式进行了简单分析，以确定捕捞强度的变化（所有物种）是否可能导致头足类动物发生明显的纬度变化分配。尽管在 5 年期间，YS 的捕捞强度有所增加，但每个纬度内的年份之间没有显着差异，这意味着所有纬度每年的响应方式相似。然后，我们使用了每年 10 月在 YS 收集的头足类动物（Todarodes pacificus、Loliolus spp.、Octopus variabilis、Octopus ocellatus、Sepiola birostrata 和 Euprymna spp.）的长期科学调查数据（2000、2009、2014 和 2017 年）。结合海洋学变量，包括海面温度 (SST) 和叶绿素-a 浓度 (CHLA)，使用算术平均法 (AMM) 和几何平均法 (GMM) 对关系进行建模，以建立栖息地适宜性指数 (HSI)。交叉验证、标准偏差和预测均方误差 (MSEP) 用于评估 HSI 的性能。将 2018 年调查的丰度指数数据叠加在同年预测的 HSI 地图上，以可视化建模 HSI 的对应关系。海洋变量的时空制图显示，SST 和 CHLA 在 34°N 附近发生显着变化，这可能与头足类动物的空间分布有关。CHLA 是大多数鱿鱼和章鱼物种最重要的海洋变量，而 SST 是短尾鱿鱼最重要的海洋变量。MSEP 显示基于 AMM 的 HSI 表现优于基于 GMM 的 HSI。未来的研究应考虑海洋变量的权重，并在理想情况下将它们整合到一个更全面的模型中，以便在建立 HSI 时提高预测精度。MSEP 显示基于 AMM 的 HSI 表现优于基于 GMM 的 HSI。未来的研究应考虑海洋变量的权重，并在理想情况下将它们整合到一个更全面的模型中，以便在建立 HSI 时提高预测精度。MSEP 显示基于 AMM 的 HSI 表现优于基于 GMM 的 HSI。未来的研究应考虑海洋变量的权重，并在理想情况下将它们整合到一个更全面的模型中，以便在建立 HSI 时提高预测精度。