Emerging technologies for pollinator monitoring

Høye, Toke T. ORCID: https://orcid.org/0000-0001-5387-3284; Montagna, Matteo; Oteman, Bas; Roy, David B. ORCID: https://orcid.org/0000-0002-5147-0331. 2025 Emerging technologies for pollinator monitoring. Current Opinion in Insect Science, 101367. 10.1016/j.cois.2025.101367

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Official URL: https://doi.org/10.1016/j.cois.2025.101367

Abstract/Summary

Efficient tools for monitoring pollinator populations are urgently needed to address their reported declines. Here, we review advanced technologies focusing on image recognition and DNA-based methods to monitor bees, hoverflies, moths and butterflies. Insect camera traps are widely used to record nocturnal insects against uniform backgrounds, while cameras studying diurnal pollinators in natural vegetation are in early stages of development. Depending on context, insect camera traps can assess occurrence, phenology and proxies of abundance for easily recognizable and common species. DNA-based techniques can drastically decrease the costs of sample processing and speed of specimen identification but strongly depend on the completeness of reference DNA databases, which are continually improving. Molecular analyses are becoming more affordable as uptake increases. Image-based methods for identification of dead specimens show promising results for some invertebrates but image reference databases for pollinators are far from complete. Building image reference databases with expert entomologists is a priority. Lidar and acoustic sensors are emerging technologies although which insect taxa can be separated in data from these sensors and how well is still uncertain. By improving accessibility to novel technologies and integrating them with existing approaches, monitoring of pollinators and other insects could deliver richer, more standardized and possibly cheaper data with benefits to future insect conservation efforts.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.cois.2025.101367
UKCEH and CEH Sections/Science Areas:	Biodiversity and Land Use (2025-)
ISSN:	2214-5745
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	bees, butterflies, computer vision, deep learning, DNA barcoding, DNA metabarcoding, hoverflies, insects, moths
NORA Subject Terms:	Ecology and Environment Electronics, Engineering and Technology Data and Information
Date made live:	24 Mar 2025 12:48 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539138

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1016/j.cois.2025.101367

UKCEH and CEH Sections/Science Areas:

Biodiversity and Land Use (2025-)

ISSN:

2214-5745

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

bees, butterflies, computer vision, deep learning, DNA barcoding, DNA metabarcoding, hoverflies, insects, moths