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

博士后奖学金：OCE-PRF：利用机器学习研究海洋观测计划 (OOI) 区域有线阵列中甲烷渗漏动物群的时间动态

• 批准号: 2307504
• 负责人: Katharine Bigham
• 金额: $ 30.85万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307504&HistoricalAwards=false
• 关键词: Postdoctoral; Fellowship; OCE; PRF; Using

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.

甲烷水合物渗漏是独特的化学合成海洋环境，生物体使用海底化学物质而不是阳光来产生能量。它们在全球连续边缘发现（仅俄勒冈州和华盛顿海岸的1,300多个）提供了重要的服务，例如以甲烷水合物的形式存储以甲烷水合物和栖息地的形式用于商业上重要的鱼类种群，并为区域生物多样性和生产力做出了贡献。但是，目前尚不清楚这些关系是否是由于化学合成生产，海底栖息地的复杂性提高或其组合而产生的。这些栖息地使用者在以下操作下运作的时间不知道：他们是个人的一次性一次性互动，还是更广泛的人口的常规，可预测的互动？因此，很难量化和充分描述甲烷渗漏的服务，这是成功管理这些潜在的关键栖息地，能源资源和碳汇的重要信息。这项研究将通过使用海洋观测站计划（OOI）区域有线阵列收集的独特时间序列的图像和视频来探讨这些问题，该阵列在南部水合物山脊（Southern Hydrate Ridge）（俄勒冈州海岸的一种甲烷水合物）渗入。该项目还将涉及机器学习管道的开发，这将使研究人员和公众更容易与OOI图像一起使用，因为当前的注释图像的手动方法非常耗时。 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 - grounded 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电缆数组。为该项目开发的管道将有助于快速移动的海洋自动化成像领域，该领域旨在使每天在世界海洋中收集的大量成像，以及历史上收集的数据，更容易用于科学研究。图像中将注释可见的动物，细菌垫子和地质结构，从而量化现场随着时间的推移社区的丰富性和多样性的变化。然后，这些数据将用于研究甲烷渗水底栖巨型群岛社区的临时轨迹，并检查社区如何受环境条件的影响。洞察力将帮助我们在这些环境中每天，每月和每年的周期了解，这将更好地了解基线临时动态。总体而言，该项目将有助于甲烷水合渗水生态动力学知识，为这些生物多样性，高碳和能量丰富的栖息地提供特征和管理的重要信息。该项目由地球科学局共同资助了地球科学局，以支持AI/ML在地球科学方面的AI/ML的进步，以反映了NSF的构建范围。更广泛的影响审查标准。