Collaborative Research: Automated observations of phytoplankton communities from open water moorings

浮游植物是生活在海洋表面附近的一组不同的微生物。它们在海洋食物网和全球生物地球化学循环中发挥着重要作用。成像FlowCytobot（IFCB）已使用多年，但由于高功率要求，部署仅限于这些类型的具有电源和互联网连接的近岸站点。EAGER的这一提议将在一个开放的海洋系泊处测试部署成像FlowCytobot。确定浮游植物群落的组成和时间动态的能力很重要，但传统的样本收集方法很耗时。成像FlowCytobot（IFCB）是在WHOI开发的一种潜水式流式细胞仪，可以分辨10-150微米尺寸范围内的颗粒，包括硅藻，甲藻和颗石藻等关键浮游植物群体，每小时可以捕获高达30，000张高分辨率图像。迄今为止，国际林业局？已在沿海固定结构（码头，塔）部署了电力，互联网和定期维护。EAGER的这一提议将通过在公海系泊处进行测试来扩大国际森林管理局的业务能力。如果成功的话，新一代的自主浮游生物采样器可以部署在全球海洋的不同地理位置，远离海岸线的生态和地球化学重要位置。该项目将资助SIO的一名研究生，他们将接受培训，以操作和维护IFCB，并使用机器学习工具和图像分析软件分析数据。

Phytoplankton are a diverse group of microscopic organism living near the ocean surface. They play essential role in marine food webs and global biogeochemical cycles. The Imaging FlowCytobot(IFCB) has been used for a number of years but because of the high power requirements deployment has been limited to these types of nearshore sites with power and internet connection. This EAGER proposal will test deploy the Imaging FlowCytobot on an open ocean mooring.The ability to determine the composition and temporal dynamics of phytoplankton communities is important but traditional methods for collecting samples is time consuming. The Imaging FlowCytobot (IFCB) was developed at WHOI as a submersible flow cytometer that can resolve particles in the 10-150 micrometer size range, including critical phytoplankton groups such as diatoms, dinoflagellates, and coccolithophores, and can capture up to 30,000 high resolution images per hour. To date IFCB?s have been deployed at coastal fixed structures (piers, tower) with power, internet, and regular maintenance. This EAGER proposal will expand the operational capabilities of IFCB by testing it on an open ocean mooring. If successful, a new generation of autonomous plankton samplers could be deployed in geographically diverse locations throughout the global ocean in ecologically and biogeochemically significant locations far from the coastlines. This project will fund a graduate student from SIO who will be trained to operate and maintain the IFCB, as well as analyze data using machine learning tools and image analysis software.