Evidence map›Paper›PMID 41522517›Full record

ArticlePeerJ2026

Improving taxonomic resolution, biomass and abundance assessments of aquatic invertebrates by combining imaging and DNA megabarcoding.

Philipp M Rehsen, Mia S Honka, Mikko Impiö, Iris Madge Pimentel, Florian Leese, Arne J Beermann

Abstract read
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In one paragraph

Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Philipp M RehsenDepartment of Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.
Mia S HonkaDepartment of Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.
Mikko ImpiöDepartment of Method Development, Finnish Environment Institute (SYKE), Helsinki, Finland.
Iris Madge PimentelDepartment of Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.
Florian LeeseDepartment of Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.
Arne J BeermannDepartment of Aquatic Ecosystem Research, University of Duisburg-Essen, Essen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding biodiversity change requires a comprehensive assessment of not only the identity of species inhabiting an ecosystem but also their biomass and abundance. However, assessing biodiversity on the species level with precise biomass information is a time-consuming process and thus rarely applied. While DNA-based approaches like DNA barcoding offer precise species identification, they lack information on specimen size and biomass. In contrast, high-throughput imaging techniques enable rapid measurements of a specimen's size and morphological features but may have low taxonomic resolution. In this study, we combined DNA megabarcoding,

Indexed as

Aquatic OrganismsBiomassDNA Barcoding, TaxonomicInvertebratesAnimalsBiodiversityNeural Networks, ComputerAquatic invertebratesAutomated imagingBarcodingBiodiversity assessmentBiomass assessmentDeep learningDNA megabarcodingMachine learning

Identifiers

PMID41522517
PMCPMC12782037

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Registered trials

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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.