Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
Expanding NEON biodiversity surveys with new instrumentation and machine learning approaches
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DOI：
10.1002/ecs2.3795
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发表时间：
2021-11-01
期刊：
影响因子：
2.7
通讯作者：
Yule, Kelsey
中科院分区：
文献类型：
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作者：
Kitzes, Justin;Blake, Rachael;Yule, Kelsey
国家生态观测站网络 (NEON) 的核心目标是衡量该网络 30 年范围内生物多样性的变化。与 NEON 广泛使用自动化仪器来收集环境数据相比，NEON 的生物多样性调查几乎完全使用传统的以人类为中心的实地方法进行。我们认为，将远程数据收集仪器与处理此类数据的机器学习模型相结合，为 NEON 扩大其生物多样性数据收集的范围、规模和可用性提供了重要机会，同时有可能降低长期成本。在这篇手稿中，我们首先回顾了 NEON 项目中基于仪器的生物多样性调查的现状，以及 NEON 站点之前在生物多样性、仪器和机器学习交叉点上的研究。然后，我们调查了在其他地点开发但将来可能在 NEON 站点使用的方法。最后，我们在五个案例研究中扩展了这些想法，我们认为这些案例为 NEON 站点的自动化生物多样性测量提供了特别富有成果的未来路径：用于发声类群的声学记录仪、用于中型和大型哺乳动物的相机陷阱、用于水生生物多样性的水声和远程图像、扩大的植物生物多样性的远程和地面测量，以及用于 NEON 生物储存库中物理标本和样本的实验室成像。通过其具有数据科学素养的员工和用户社区，NEON 在支持此类自动化生物多样性调查方法的发展方面发挥着独特的作用，并展示了它们有能力帮助回答在人类驱动的调查的更有限的时空尺度上无法回答的关键生态问题。
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.