Towards a toolkit for global insect biodiversity monitoring

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2024, Volume and Issue: 379(1904)

Published: May 5, 2024

Insects are the most diverse group of animals on Earth, yet our knowledge their diversity, ecology and population trends remains abysmally poor. Four major technological approaches coming to fruition for use in insect monitoring ecological research—molecular methods, computer vision, autonomous acoustic radar-based remote sensing—each which has seen advances over past years. Together, they have potential revolutionize ecology, make all-taxa, fine-grained feasible across globe. So far, within among technologies largely taken place isolation, parallel efforts projects led redundancy a methodological sprawl; yet, given commonalities goals approaches, increased collaboration integration could provide unprecedented improvements taxonomic spatio-temporal resolution coverage. This theme issue showcases recent developments state-of-the-art applications these technologies, outlines way forward regarding data processing, cost-effectiveness, meaningful trend analysis, open requirements. papers set stage future automated monitoring. article is part ‘Towards toolkit global biodiversity monitoring’.

Language: Английский

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Insect detect: An open-source DIY camera trap for automated insect monitoring

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(4), P. e0295474 - e0295474

Published: April 3, 2024

Insect monitoring is essential to design effective conservation strategies, which are indispensable mitigate worldwide declines and biodiversity loss. For this purpose, traditional methods widely established can provide data with a high taxonomic resolution. However, processing of captured insect samples often time-consuming expensive, limits the number potential replicates. Automated facilitate collection at higher spatiotemporal resolution comparatively lower effort cost. Here, we present Detect DIY (do-it-yourself) camera trap for non-invasive automated flower-visiting insects, based on low-cost off-the-shelf hardware components combined open-source software. Custom trained deep learning models detect track insects landing an artificial flower platform in real time on-device subsequently classify cropped detections local computer. Field deployment solar-powered confirmed its resistance temperatures humidity, enables autonomous during whole season. On-device detection tracking estimate activity/abundance after metadata post-processing. Our classification model achieved top-1 accuracy test dataset generalized well real-world images. The software highly customizable be adapted different use cases. With custom models, as accessible programming, many possible applications surpassing our proposed method realized.

Language: Английский

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The transformative potential of eDNA-based biodiversity impact assessment

Current Opinion in Environmental Sustainability, Journal Year: 2025, Volume and Issue: 73, P. 101517 - 101517

Published: March 11, 2025

Language: Английский

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Drivers and benefits of natural regeneration in tropical forests

Published: April 21, 2025

Language: Английский

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Causes and consequences of insect decline in tropical forests

Published: April 4, 2025

Language: Английский

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