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兑现承诺:为自动化昆虫监测方法做好未来准备。

Delivering on a promise: futureproofing automated insect monitoring methods.

机构信息

German Centre for Integrative Biodiversity Research Halle-Jena-Leipzig, Puschstrasse 4, 04103 Leipzig, Germany.

Department of Computer Science, Martin-Luther-University, Halle-Wittenberg, 06099 Halle, Germany.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230105. doi: 10.1098/rstb.2023.0105. Epub 2024 May 6.

Abstract

Due to rapid technological innovations, the automated monitoring of insect assemblages comes within reach. However, this continuous innovation endangers the methodological continuity needed for calculating reliable biodiversity trends in the future. Maintaining methodological continuity over prolonged periods of time is not trivial, since technology improves, reference libraries grow and both the hard- and software used now may no longer be available in the future. Moreover, because data on many species are collected at the same time, there will be no simple way of calibrating the outputs of old and new devices. To ensure that reliable long-term biodiversity trends can be calculated using the collected data, I make four recommendations: (1) Construct devices to last for decades, and have a five-year overlap period when devices are replaced. (2) Construct new devices to resemble the old ones, especially when some kind of attractant (e.g. light) is used. Keep extremely detailed metadata on collection, detection and identification methods, including attractants, to enable this. (3) Store the raw data (sounds, images, DNA extracts, radar/lidar detections) for future reprocessing with updated classification systems. (4) Enable forward and backward compatibility of the processed data, for example by in-silico data 'degradation' to match the older data quality. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

摘要

由于技术的快速创新,昆虫群落的自动化监测成为可能。然而,这种持续的创新危及到未来计算可靠生物多样性趋势所需的方法学连续性。在很长一段时间内保持方法学的连续性并不容易,因为技术在不断改进,参考资料库在不断扩大,而且现在使用的硬件和软件在未来可能不再可用。此外,由于许多物种的数据是同时收集的,因此将无法简单地校准新旧设备的输出。为了确保使用收集到的数据计算出可靠的长期生物多样性趋势,我提出了四项建议:(1)构建可以持续数十年的设备,并在设备更换时设置五年的重叠期。(2)构建新设备以类似于旧设备,特别是当使用某种引诱剂(例如光)时。保存关于收集、检测和识别方法的极其详细的元数据,包括引诱剂,以实现这一点。(3)为将来使用更新的分类系统对原始数据(声音、图像、DNA 提取物、雷达/激光探测)进行重新处理而进行存储。(4)实现处理后数据的向前和向后兼容性,例如通过“模拟数据降级”来匹配旧数据的质量。本文是“迈向全球昆虫生物多样性监测工具包”主题专刊的一部分。

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引用本文的文献

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Towards a toolkit for global insect biodiversity monitoring.迈向全球昆虫生物多样性监测工具包。
Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230101. doi: 10.1098/rstb.2023.0101. Epub 2024 May 6.

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