Automated in situ Plankton Imaging and Classification System (APICS)

海洋浮游生物是指游动速度为零或很小的生物，它们随洋流被动移动。这一定义涵盖了一组在分类学和形态学上具有多样性的生物，跨越许多门和数万个物种。单就大小而言，浮游生物跨越了许多数量级，从亚微米尺度的单细胞生命形式到直径可达一米的大型水母。浮游生物维持着海洋中所有其他形式的多细胞生命。光合成员约占全球初级生产的50%。光合浮游生物捕获的能量通过捕食性相互作用传递到食物链上，支持具有重要商业价值的鱼类和贝类物种的生长。浮游生物还通过它们在全球碳循环中的作用来调节地球的气候。了解浮游生物生态学及其在海洋生态系统中的功能作用的关键是能够及时跟踪浮游生物丰度和群落组成的变化。这通常需要对生物体进行可视化和分类。从历史上看，这种测量通常是在偏远地区的研究船上进行的。测量是昂贵和耗时的。多年来，自动化过程的潜在好处一直受到重视。然而，合适的、可商购的仪器的出现需要时间。在这个项目中，我们将配置一个自动化的，在原位浮游生物成像和分类系统（APICS）的研究浮游生物动态在一个长期的时间序列网站，站L4在西英吉利海峡。图像将使用机器学习软件自动分类并公开提供。该系统将使收集浮游生物数据的频率增加100倍。此外，通过自动化，该系统将大大降低运营成本，与目前的人工船舶取样和分类程序相比。APICS将在世界上首次自动获取浮游生物的现场图像数据，其大小从10微米到20毫米不等，跨越3-4个数量级，它将嵌入定期测量其他物理和化学变量的西海峡观测站，为研究海洋环境的高频时间变化提供一个独特的系统。过去30年来，世界海关组织的数据显示，浮游生物丰度发生了显著变化，硅藻和大型桡足类数量减少，而部分浮游生物数量增加。APICS将使这些趋势得到详细研究，从而更好地了解浮游生物群落动态以及浮游生物与整个海洋生态系统之间的关系。

Marine plankton are defined as organisms with zero or small swimming velocities, which move passively with ocean currents. The definition covers a taxonomically and morphologically diverse group of organisms that span many phyla and tens of thousands of species. In size alone, plankton span many orders of magnitude, ranging from sub-micron scale unicellular life forms up to large jelly fish that can measure up to a meter in diameter. Plankton sustain all other forms of multicellular life in the ocean. Photosynthetic members account for approximately 50 % of global primary production. The energy captured by photosynthetic plankton is passed up the food chain through predatory interactions, where it supports the growth of commercially important species of fish and shellfish. Plankton also regulate Earth's climate, through their role in the global carbon cycle. Key to understanding the ecology of plankton and the functional role they play in marine ecosystems, is the ability to follow how plankton abundances and community composition change in time. This usually requires that organisms be visualised and classified. Historically, such measurements have been made aboard research vessels in often remote locations. The measurements are expensive and time consuming to make. The potential benefits of automating the process have been appreciated for years. However, it has taken time for suitable, commercially available instruments to become available. In this project, we will configure an Automated, in situ Plankton Imaging and Classification System (APICS) for studying plankton dynamics at a long-term time series site, Station L4 in the Western English Channel. Images will be automatically classified using machine learning software and made publicly available. The system will enable a 100-fold increase in the frequency at which plankton data is collected. Furthermore, through automation, the system will dramatically reduce operating costs compared to current, manual ship-based sampling and classification procedures. In a world first, APICS will automatically acquire image data in situ for plankton spanning 3-4 orders of magnitude in size, ranging from 10 microns up to 20 mm. Embedded within the Western Channel Observatory (WCO), where other physical and chemical variables are routinely measured, it will provide a unique system for studying high-frequency temporal changes in the marine environment. Over the past 30 years, data from the WCO have shown marked changes in plankton abundances, with reductions in the number of diatoms and large copepods, and an increase in the number of meroplankton. APICS will enable such trends to be studied in fine detail, leading to improved understanding of plankton community dynamics, and the relationship between plankton and marine ecosystems as a whole.