Zooplankton plays a major role in ocean food webs and biogeochemical cycles, and provides major ecosystem services as a main driver of the biological carbon pump and in sustaining fish communities. Zooplankton is also sensitive to its environment and reacts to its changes. To better understand the importance of zooplankton, and to inform prognostic models that try to represent them, spatially-resolved biomass estimates of key plankton taxa are desirable. In this study we predict, for the first time, the global biomass distribution of 19 zooplankton taxa (1-50 mm Equivalent Spherical Diameter) using observations with the Underwater Vision Profiler 5, a quantitative in situ imaging instrument. After classification of 466,872 organisms from more than 3,549 profiles (0-500 m) obtained between 2008 and 2019 throughout the globe, we estimated their individual biovolumes and converted them to biomass using taxa-specific conversion factors. We then associated these biomass estimates with climatologies of environmental variables (temperature, salinity, oxygen, etc.), to build habitat models using boosted regression trees. The results reveal maximal zooplankton biomass values around 60°N and 55°S as well as minimal values around the oceanic gyres. An increased zooplankton biomass is also predicted for the equator. Global integrated biomass (0-500 m) was estimated at 0.403 PgC. It was largely dominated by Copepoda (35.7%, mostly in polar regions), followed by Eumalacostraca (26.6%) Rhizaria (16.4%, mostly in the intertropical convergence zone). The machine learning approach used here is sensitive to the size of the training set and generates reliable predictions for abundant groups such as Copepoda (R2 ≈ 20-66%) but not for rare ones (Ctenophora, Cnidaria, R2 < 5%). Still, this study offers a first protocol to estimate global, spatially resolved zooplankton biomass and community composition from in situ imaging observations of individual organisms. The underlying dataset covers a period of 10 years while approaches that rely on net samples utilized datasets gathered since the 1960s. Increased use of digital imaging approaches should enable us to obtain zooplankton biomass distribution estimates at basin to global scales in shorter time frames in the future.

浮游动物在海洋食物网和生物地球化学循环中发挥着重要作用，并作为生物碳泵和维持鱼类群落的主要驱动力提供主要的生态系统服务。浮游动物对其环境也很敏感，并对环境的变化做出反应。为了更好地了解浮游动物的重要性，并为试图代表它们的预测模型提供信息，需要对关键浮游生物分类群进行空间分辨的生物量估计。在这项研究中，我们首次使用水下视觉剖面仪 5 的观测结果预测了 19 个浮游动物类群（1-50 毫米等效球直径）的全球生物量分布，这是一个定量的原位成像仪。在对 2008 年至 2019 年间在全球范围内获得的超过 3,549 个剖面（0-500 m）中的 466,872 个生物进行分类后，我们估计了它们的个体生物量，并使用分类群特定的转换因子将它们转换为生物量。然后，我们将这些生物量估计与环境变量（温度、盐度、氧气等）的气候学相关联，以使用增强回归树建立栖息地模型。结果揭示了在 60°N 和 55°S 附近的最大浮游动物生物量值以及在海洋环流周围的最小值。预计赤道的浮游动物生物量也会增加。全球综合生物量 (0-500 m) 估计为 0.403 PgC。它主要由桡足类（35.7%，主要在极地地区）占主导地位，其次是 Eumalacostraca（26.6%）Rhizaria（16.4%，主要在热带辐合带）。这里使用的机器学习方法对训练集的大小很敏感，并为丰富的群体（如桡足类（R2 ≈ 20-66%））生成可靠的预测，但对于稀有的群体（栉水母、刺胞动物、R2 < 5%）则不然。尽管如此，这项研究还是提供了第一个协议来估计全球、空间分辨的浮游动物生物量和群落组成。原位单个生物体的成像观察。基础数据集涵盖 10 年，而依赖净样本的方法使用自 1960 年代以来收集的数据集。越来越多地使用数字成像方法应该使我们能够在未来更短的时间内获得流域到全球范围内的浮游动物生物量分布估计。