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: 2203176
• 负责人: Megan Cimino
• 金额: $ 56.7万
• 依托单位: University of California-Santa Cruz
• 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=2203176&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.

该项目由地球科学理事会和先进网络基础设施办公室之间的合作共同资助，以支持地球科学领域的人工智能/机器学习和开放科学活动。该项目将使用机器学习模型来预测五种浮游动物的分布该项目将利用帕尔默长期生态研究 (LTER) 计划收集的年度数据的长期系列。通过分析该数据集并将其与国家和国际项目收集的其他数据相结合，该项目将提供对浮游动物分布和捕食者的了解和预测。 wAP 中的丰富度将基于从遥感图像中提取的环境属性，从而提供生态系统知识，因为它减少了南极洲的人类足迹。物种分布和栖息地之间的关系是区分自然变化的关键。这项研究通过扩大南极系统、生物群和过程的基础知识以及与极地研究中的数据和样本再利用策略相一致，使 NSF 的使命受益。该项目将通过支持两名早期雌性动物来造福社会。 -将通过与针对科学、技术、工程和数学（STEM）教育的校外活动的现有合作伙伴关系，提高职业科学家、博士后研究员和研究生的极地素养。该项目每年还将吸引 120,000 名来自 STEM 中代表性不足的少数族裔的学生参与评估 wAP 中拟议的海洋保护区的生态系统，结果将通过交互式网络应用程序公开发布。建议解决三个主要问题：1）能否利用地貌特征、冬季预处理和夏季海洋条件来预测沿 wAP 的南方夏季浮游动物物种分布？ 3）浮游动物和捕食者物种的重叠模式是什么？该模型将生成浮游动物分布和环境变量之间的函数关系，并提供南极沿线的浮游动物分布模型（ZDM） Palmer LTER 数据库将与英国南极调查局极地数据中心公布的北部 wAP 和 KRILLBASE 的 NOAA AMLR 数据相结合。该项目将生成 1) 研究区域内 Palmer LTER 分辨率网格上的年度环境空间图层，2) 来自不同调查的年度特定物种标准化浮游动物网数据，3) 标准化网格上的年度特定物种捕食者目击数据，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，由 5 个主要浮游动物类群（包括南极磷虾、樽海鞘）的 3 维字段 (x,y,t) 组成。预测将通过合并原位调查数据得出，这些数据反映了原位或通过遥感收集的环境数据。该奖项符合 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。