Evidence map›Paper›PMID 40342700›Full record

ArticleEcology and evolution2025

Robot-Aided Measurement of Insect Diversity on Vegetation Using Environmental DNA.

Darina Koubínová, Steffen Kirchgeorg, Christian Geckeler, Sarah Thurnheer, Martina Lüthi, Théophile Sanchez, Stefano Mintchev, Loïc Pellissier, Camille Albouy

Abstract read
In one paragraph

Article in Ecology and evolution, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Darina KoubínováEcosystems and Landscape Evolution, Department of Environmental Systems Science ETH Zürch Zürich Switzerland.
Steffen KirchgeorgSwiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland.
Christian GeckelerSwiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland.
Sarah ThurnheerEcosystems and Landscape Evolution, Department of Environmental Systems Science ETH Zürch Zürich Switzerland.
Martina LüthiEcosystems and Landscape Evolution, Department of Environmental Systems Science ETH Zürch Zürich Switzerland.
Théophile SanchezEcosystems and Landscape Evolution, Department of Environmental Systems Science ETH Zürch Zürich Switzerland.
Stefano MintchevSwiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland.
Loïc PellissierEcosystems and Landscape Evolution, Department of Environmental Systems Science ETH Zürch Zürich Switzerland.ORCID https://orcid.org/0000-0002-2289-8259
Camille AlbouyEcosystems and Landscape Evolution, Department of Environmental Systems Science ETH Zürch Zürich Switzerland.ORCID https://orcid.org/0000-0003-1629-2389

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional methods of biodiversity monitoring are often logistically challenging, time-consuming, require experienced experts on species identification, and sometimes include destruction of the targeted specimens. Here, we investigated a non-invasive approach of combining the use of drones and environmental DNA (eDNA) to monitor insect biodiversity on vegetation. We aimed to assess the efficiency of this novel method in capturing insect diversity and comparing insect composition across different vegetation types (grassland, shrub and forest) in Switzerland. A commercial, off-the-shelf drone was equipped with a specialised probe that autonomously swabbed vegetation and collected eDNA. Then, samples were processed using rapid third-generation Oxford Nanopore sequencing. The obtained data were analysed for insect diversity, comparing taxonomic richness, evenness and community composition across the three habitat types using statistical techniques. Sequencing of the samples yielded 76 hexapod taxa, revealing an insect community with notable differences in taxonomic richness but not in evenness across grassland, shrub and forest habitats. Our study demonstrates the potential of drone-based sampling integrated with eDNA and nanopore sequencing for biodiversity monitoring, offering a non-destructive method for detecting insect occurrence on plant surfaces. Integrating robotics and eDNA technology provides a promising solution for fast, large-scale, non-invasive biodiversity monitoring, potentially improving conservation efforts and ecosystem management.

Indexed as

dronesenvironmental DNAinsectsmetabarcodingnon‐invasive biodiversity monitoringOxford nanopore sequencingvegetation swabbing

Identifiers

PMID40342700
PMCPMC12058354

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.