Postdoctoral Fellowship: OCE-PRF: Using machine learning to investigate temporal dynamics of methane seep fauna at the Ocean Observatories Initiative (OOI) Regional Cabled Array

甲烷水合物渗漏是一种独特的化学合成海洋环境，在这种环境中，生物利用海底的化学物质而不是阳光来产生能量。它们分布在世界各地的大陆边缘(仅俄勒冈州和华盛顿州沿海就有1300多个)，提供重要的服务，如甲烷水合物形式的能量储存和具有重要商业价值的鱼类种群的栖息地，以及对区域生物多样性和生产力的贡献。然而，目前尚不清楚这些关系是由于化学合成生产生产率的提高、海底生境复杂性的增加，还是两者的结合。也不知道这些栖息地使用者在什么时间尺度下运作：他们是个人的随机一次性互动，还是更广泛人群的定期、可预测的互动？因此，很难量化和全面描述甲烷渗漏提供的服务，这是成功管理这些潜在的关键生境、能源资源和碳汇的关键信息。这项研究将利用海洋天文台倡议(OOI)区域电缆阵列十多年来在俄勒冈州海岸渗出的甲烷水合物南方水合物岭收集的独特时间序列图像和视频来探索这些问题。该项目还将涉及开发机器学习管道，这将使研究人员和公众更容易使用OOI图像的未来工作，因为目前手动注释图像的方法非常耗时。此外，该项目将支持一名早期职业博士后研究人员，让两名本科生有机会参与研究，并制作一个面向广大本科生受众的海洋技术和基于图像的海洋研究课堂模块。具体地说，该项目将开发多分类探测器，作为机器辅助注释管道的一部分，以处理由OOI区域电缆阵列收集的约37TB图像和视频。为该项目开发的管道将有助于快速发展的海洋自动成像领域，旨在使每天在世界海洋中收集的大量图像以及历史上收集的数据更容易用于科学研究。可见的动物、细菌垫和地质结构将在图像中进行注释，量化该地点群落的丰富度和多样性随时间的变化。这些数据将被用来调查甲烷渗漏处大型底栖动物群落的时间轨迹，并检查该群落是如何受到环境条件的影响的。洞察力将帮助我们了解这些环境中的每日、每月和每年周期，这将创建对基线时间动态的更好理解。总体而言，该项目将贡献甲烷水合物渗漏生态动力学的知识，为这些生物多样性、高碳和能源丰富的栖息地的特征和管理提供重要信息。该项目由地球科学局共同资助，以支持地球科学领域的AI/ML进步。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Methane hydrate seeps are unique chemosynthetic marine environments where organisms use chemicals from the seafloor rather than sunlight to create energy. They are found on continental margins worldwide (with more than 1,300 off the coast of Oregon and Washington alone) providing important services such as energy storage in the form of methane hydrates and habitat for commercially important fish stocks, as well as contributing to regional biodiversity and productivity. However, it is still unclear whether these relationships are due to enhanced productivity from chemosynthetic production, increased seafloor habitat complexity, or a combination thereof. Nor is it known what timescales these habitat users operate under: are they random one-off interactions by individuals, or regular, predictable interactions by a broader population? Therefore, it is difficult to quantify and fully describe the services methane seeps provide, which is vital information for successful management of these potentially critical habitats, energy resources, and carbon sinks. This research will explore these questions by using a unique time series of imagery and video collected by the Ocean Observatories Initiative (OOI) Regional Cabled Array over more than a decade at Southern Hydrate Ridge, a methane hydrate seep off the coast of Oregon. The project will also involve the development of machine learning pipelines which will make future work with OOI imagery more accessible for researchers and the public, since current, manual methods of annotating imagery are incredibly time consuming. Additionally, this project will support an early career post-doctoral researcher, give two undergraduate students an opportunity to participate in research, and produce a classroom module - geared to a broad undergraduate student audience - on marine technology and imagery based marine research.Specifically, this project will develop multi-taxon detectors as a part of a machine assisted annotation pipeline to process an estimated 37 TB of imagery and video collected by the OOI Regional Cabled Array. The pipeline developed for this project will contribute to a fast-moving field of marine automated imaging which aims to make the large amounts of imagery collected in the world’s oceans every day, as well as historically collected data, more readily usable for scientific research. Visible animals, bacterial mats, and geologic structures will be annotated in the imagery, quantifying the changes in abundances and diversity of the community at the site over time. This data will then be used to investigate the temporal trajectory of the benthic megafauna community at the methane seep and examine how the community is influenced by environmental conditions. Insights will help us understand daily, monthly, and yearly cycles in these environments, which will create better understanding of baseline temporal dynamics. Overall, this project will contribute knowledge of methane hydrate seep ecologic dynamics, providing vital information for characterization and management of these biodiverse, high-carbon, and energy-rich habitats.This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.