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采用节约方法进行全球昆虫生物监测。

Towards global insect biomonitoring with frugal methods.

机构信息

Dept. Physics, Lund University, Sölvegatan 14c, 22362 Lund, Sweden.

Dept. Biology, Lund University, Sölvegatan 35, 22362 Lund, Sweden.

出版信息

Philos Trans R Soc Lond B Biol Sci. 2024 Jun 24;379(1904):20230103. doi: 10.1098/rstb.2023.0103. Epub 2024 May 6.

Abstract

None of the global targets for protecting nature are currently met, although humanity is critically dependent on biodiversity. A significant issue is the lack of data for most biodiverse regions of the planet where the use of frugal methods for biomonitoring would be particularly important because the available funding for monitoring is insufficient, especially in low-income countries. We here discuss how three approaches to insect biomonitoring (computer vision, lidar, DNA sequences) could be made more frugal and urge that all biomonitoring techniques should be evaluated for global suitability before becoming the default in high-income countries. This requires that techniques popular in high-income countries should undergo a phase of 'innovation through simplification' before they are implemented more broadly. We predict that techniques that acquire raw data at low cost and are suitable for analysis with AI (e.g. images, lidar-signals) will be particularly suitable for global biomonitoring, while techniques that rely heavily on patented technologies may be less promising (e.g. DNA sequences). We conclude the opinion piece by pointing out that the widespread use of AI for data analysis will require a global strategy for providing the necessary computational resources and training. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

摘要

目前,没有任何保护自然的全球目标得到实现,尽管人类严重依赖生物多样性。一个主要问题是,对于地球上大多数生物多样性丰富的地区,缺乏数据,而在这些地区,节俭的生物监测方法将特别重要,因为用于监测的可用资金不足,特别是在低收入国家。在这里,我们讨论了昆虫生物监测的三种方法(计算机视觉、激光雷达、DNA 序列)如何变得更加节俭,并敦促在成为高收入国家的默认方法之前,应对所有生物监测技术进行全球适用性评估。这要求在更广泛地实施之前,在高收入国家流行的技术应经历一个通过简化进行创新的阶段。我们预测,那些以低成本获取原始数据且适合人工智能分析的技术(例如图像、激光雷达信号)将特别适合全球生物监测,而那些严重依赖专利技术的技术可能不太有前途(例如 DNA 序列)。我们在观点文章的结尾指出,广泛使用人工智能进行数据分析需要制定一个提供必要计算资源和培训的全球战略。本文是主题为“迈向全球昆虫生物多样性监测工具包”的一部分。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/448d/11070255/6442bc6f542e/rstb20230103f01.jpg

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