期刊
ECOSPHERE
卷 12, 期 11, 页码 -出版社
WILEY
DOI: 10.1002/ecs2.3795
关键词
biogeography; deep learning; macroecology; monitoring; neural network; sensor; Special Feature; Harnessing the NEON Data Revolution; species
类别
资金
- National Science Foundation [1935507, 1926542]
- Department of Biological Sciences at the University of Pittsburgh
- Mascaro Center for Sustainable Development at the University of Pittsburgh
- Gordon and Betty Moore Foundation's Data-Driven Discovery Initiative [GBMF4563]
- NSF Dimension of Biodiversity program grant [DEB-1442280]
- University of Florida Informatics Institute (UFII) Graduate Fellowship
- University of Zurich's University Research Priority Programme on Global Change and Biodiversity
- Direct For Biological Sciences
- Division Of Environmental Biology [1926542] Funding Source: National Science Foundation
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1935507] Funding Source: National Science Foundation
While NEON relies heavily on automated instruments for environmental data collection, biodiversity surveys are still conducted using traditional human-centric field methods. By combining instrumentation with machine learning, NEON has the opportunity to expand the scope and scale of its biodiversity data collection while potentially reducing long-term costs. Through data science-literate staff and user community, NEON can play a unique role in supporting the growth of automated biodiversity survey methods and demonstrating their ability to answer key ecological questions.
A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30-yr horizon of the network. In contrast to NEON's extensive use of automated instruments to collect environmental data, NEON's biodiversity surveys are almost entirely conducted using traditional human-centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long-term costs. In this manuscript, we first review the current status of instrument-based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound-producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground-based measurements for plant biodiversity, and laboratory-based imaging for physical specimens and samples in the NEON biorepository. Through its data science-literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human-driven surveys.
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