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CoPe RCN: New Technology to Inform Coastal Science and Management
项目编号1940269
Robert Miller
项目主持机构University of California-Santa Barbara
开始日期2020-08-01
结束日期07/31/2023
英文摘要This grant is for the planning and development of Regional Ecosystem Services Observation Networks. RESONs are regionally-focused networks of sensors and data sources necessary to take the pulse of coastal ecosystems, spanning their structure, function, and services provided to people. They extend the framework of the regional Ocean Observing Systems (OOSs) to explicitly consider biodiversity, ecosystem function, and species migration in the context of fisheries and coastal infrastructure needs. This Research Coordination Network (RCN) will bring together marine scientists, modelers, engineers, spatial statisticians, and computer scientists from academia and industry in collaboration with stakeholders and managers. In order to broaden participation in these activities, the team will coordinate with the NSF GOLD Active Societal Participation In Research and Education (ASPIRE) program, which aims to cultivate a generation of geoscientists with the leadership knowledge and skills, scholarship, and material support to reframe and rebrand the geosciences as socially relevant. They will also work with the UCSB Engineering Capstone program, a year-long, project-based course offered to students that pairs fourth-year engineering students with research projects to create an engineered solution for real and meaningful problems across the disciplines of mechanical, electrical and computer engineering. UCSB is a Hispanic Serving Institution and an Asian American and Native American Pacific Islander Serving Institution with a population of over 40 percent first generation college students that is uniquely located directly on the coast, which, together with its strengths in marine science, engineering, and environmental science and management, makes it an ideal location as an inclusive national network hub for coastal science.

This RCN is needed because time series data have been collected in coastal systems at great expense, but the spatial and temporal scales of these monitoring efforts are inadequate to address regional and global shifts in coastal communities, biodiversity and ecosystem services that result from climate change, pollution, and fishing. Urgently needed are cheaper, faster, higher-quality ways to collect time series data with broad spatial coverage that captures time scales of ecosystem processes and moving marine animals. Filling this need will greatly accelerate progress on Rules of Life and climate change research in marine ecosystems and enable management responses and community adaptation to change. This effort will focus on three data targets: biodiversity, ecosystem function, and species migration, using the California Current Large Marine Ecosystem (CC LME) as an initial model, and will leverage existing networks including Southern California Coastal OOS, LTER, MBON (Marine Biodiversity Observation Network) and ATN (Animal Telemetry Network). Variables considered will include sound, imagery, environmental DNA, biomass, movement, and physiological performance. The goals of the RCN will be to 1) develop a series of workshops to identify effective combinations of sensors and measurements and plan spatial and temporal sampling regimes necessary for building and validating models adequate to forecast future ecosystem change; 2) identify specific data products, sensor and sensor platforms needed to address gaps, engaging stakeholders and managers to ensure relevance to policy; and 3) build a sustained collaborative network with the technology sector focused on coastal ecosystem health.

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.
资助机构US-NSF
项目经费$499,997.00
项目类型Standard Grant
国家US
语种英语
文献类型项目
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/211517
推荐引用方式
GB/T 7714
Robert Miller.CoPe RCN: New Technology to Inform Coastal Science and Management.2020.
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