Collaborative Research: Harvesting Long-term Survey Data to Develop Zooplankton Distribution Models for the Antarctic Peninsula

合作研究：收集长期调查数据以开发南极半岛浮游动物分布模型

• 批准号: 2203177
• 负责人: Deborah Steinberg
• 金额: $ 14.28万
• 依托单位: College of William & Mary Virginia Institute of Marine Science
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203177&HistoricalAwards=false
• 关键词: Collaborative; Research; Harvesting; Long; term

This project is co-funded by a collaboration between the Directorate for Geosciences and Office of Advanced Cyberinfrastructure to support Artificial Intelligence/Machine Learning and open science activities in the geosciences.Machine learning model will be used in this project to predict the distributions of five zooplankton species in the western Antarctic Peninsula (wAP) based on oceanographic properties. The project will take advantage of a long-term series collected by the Palmer Long-Term Ecological Research (LTER) program that collects annual data on physics, chemistry, phytoplankton (or food), zooplankton and predators (seabirds, whales and seals). By analyzing this dataset and combining it with other data collected by national and international programs, this project will provide understanding and prediction of zooplankton distribution and abundance in the wAP. The machine learning models will be based on environmental properties extracted from remote sensing images thus providing ecosystem knowledge as it decreases human footprint in Antarctica. The relationship between species distribution and habitat are key for distinguishing natural variability from climate impacts on zooplankton and their predators. This research benefits NSF mission by expanding fundamental knowledge of Antarctic systems, biota, and processes as well as aligning with data and sample reuse strategies in Polar Research. The project will benefit society by supporting two female early-career scientists, a post-doctoral fellow and a graduate student. Polar literacy will be promoted through an existing partnership with Out Of School activities that target Science, Technology, Engineering and Mathematics (STEM) education, expected to reach 120,000 students from under-represented minorities in STEM annually. The project will also contribute to evaluate the ecosystem in the proposed Marine Protected Area in the wAP, subject to krill fishery. Results will be made available publicly through an interactive web application. The Principal Investigators propose to address three main questions: 1) Can geomorphic features, winter preconditioning and summer ocean conditions be used to predict the austral summer distribution of zooplankton species along the wAP? 2) What are the spatial and temporal patterns in modeled zooplankton species distribution along the wAP? And 3) What are the patterns of overlap in zooplankton and predator species? The model will generate functional relationships between zooplankton distribution and environmental variables and provide Zooplankton Distribution Models (ZDMs) along the Antarctic Peninsula. The Palmer LTER database will be combined with the NOAA AMLR data for the northern wAP, and KRILLBASE, made public by the British Antarctic Survey’s Polar Data Center. This project will generate 1) annual environmental spatial layers on the Palmer LTER resolution grid within the study region, 2) annual species-specific standardized zooplankton net data from different surveys, 3) annual species-specific predator sightings on a standardized grid, and 4) ecological model output. Ecological model output will include annual predictions of zooplankton species distributions, consisting of 3-dimensional fields (x,y,t) for the 5 main zooplankton groups, including Antarctic krill, salps and pteropods. Predictions will be derived from merging in situ survey data with environmental data, collected in situ or by remote sensing.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.

该项目由地球科学局与高级网络基础设施办公室之间的合作共同资助，以支持人工智能/机器学习和地球科学中的开放科学活动。该项目将在该项目中使用基于海洋学属性的西部南极（WAP）中的五个浮游动物学物种的分布，以预测基于海洋学特性的五个浮游动物。该项目将利用帕尔默长期生态研究（LTER）计划收集的长期系列，该计划收集了有关物理，化学，浮游植物（或食物），浮游动物和捕食者（海鸟，鲸鱼和密封群）的年度数据。通过分析该数据集并将其与国家和国际计划收集的其他数据相结合，该项目将提供对WAP中浮游动物分布和抽象的理解和预测。机器学习模型将基于从遥感图像中提取的环境特性，从而在减少南极人的人足迹时提供生态系统知识。物种分布与栖息地之间的关系是区分自然变异性与气候对浮游动物及其捕食者的影响的关键。这项研究通过扩展南极系统，生物群和过程的基本知识，以及与数据和样本重用策略保持一致，从而使NSF的任务受益。该项目将通过支持两位女性早期科学家，一名博士后研究员和一名研究生来使社会受益。极性素养将通过与针对科学，技术，工程和数学（STEM）教育的学校活动的现有合作伙伴关系来促进，预计每年将吸引来自STEM中代表不足的少数群体的120,000名学生。该项目还将有助于评估WAP中拟议的海洋保护区域中的生态系统，但要受磷虾渔业的影响。结果将通过交互式Web应用程序公开提供。首席研究人员建议解决三个主要问题：1）地貌特征，冬季预处理和夏季海洋条件可以用来预测WAP沿WAP浮游生物的澳大利亚夏季分布吗？ 3）浮游动物和捕食者物种的重叠模式是什么？该模型将在浮游动物分布和环境变量之间产生功能关系，并在南极半岛提供浮游动物分布模型（ZDMS）。 Palmer lter数据库将与北WAP的NOAA AMLR数据相结合，而英国南极调查的极地数据中心公开了磷虾。该项目将生成1）研究区域内的Palmer lter分辨率网格上的年度环境空间层，2）来自不同调查的年度规格特定于年度标准化的浮游动物净数据，3）3）年度规格在标准化网格上的特异性捕食者目击，以及4）生态模型输出。生态模型输出将包括浮游动物物种分布的年度预测，该预测由5个主要浮游动物组的3维田（x，y，t）组成，包括南极磷虾，萨尔普斯和pteropods。预测将来自将原位调查数据与环境数据合并，本地或远程敏感性收集。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来评估，被认为是宝贵的支持。