Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning
Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning
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DOI：
10.3389/fmars.2022.894372
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发表时间：
2022-08-09
影响因子：
3.7
通讯作者：
Kiko, Rainer
中科院分区：
文献类型：
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作者：
Drago, Laetitia;Panaiotis, Thelma;Kiko, Rainer
浮游动物在海洋食物网和生物地球化学循环中发挥重要作用，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。浮游动物对环境也很敏感，并对环境的变化做出反应。为了更好地理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，关键浮游生物种类的空间分辨生物量估计是可取的。在这项研究中，我们利用水下视觉剖面仪5(一种定量的原位成像仪器)的观测，首次预测了19个浮游动物类群(相当于球径1-50 mm)的全球生物量分布。在对2008年至2019年全球3,549个剖面(0-500米)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，并使用特定于分类群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)的气候学联系起来，使用增强回归树建立栖息地模型。结果表明，浮游动物生物量在北纬60度和S 55度附近有最大值，在大洋环流附近有极小值。赤道浮游动物生物量的增加也被预测出来。全球总生物量(0-500m)估计为0.403 ppc。主要以桡足类(35.7%，主要分布在极地)为主，其次是真足类(26.6%)和根霉(16.4%，主要分布在热带辐合带)。这里使用的机器学习方法对训练集的大小很敏感，并对桡足纲(R2约为20-66%)等丰富的类群产生可靠的预测，但对稀有类群(Ctenophora，Cnidaria，R2<；5%)不产生可靠的预测。尽管如此，这项研究提供了第一个方案，通过对单个生物的原位成像观测来估计全球、空间分辨的浮游动物生物量和群落组成。基本数据集涵盖10年的时间，而依赖净样本的方法利用的是自1960年代以来收集的数据集。数字成像方法的更多使用应该使我们能够在未来更短的时间范围内获得流域到全球范围内浮游动物生物量分布的估计。
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 degrees N and 55 degrees 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 approximate to 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.