Evidence map›Paper›PMID 40359979›Full record

ArticleProceedings. Biological sciences2025

High-throughput biodiversity surveying sheds new light on the brightest of insect taxa.

Ela Iwaszkiewicz-Eggebrecht, Robert M Goodsell, Bengt-Åke Bengsson, Marko Mutanen, Mårten Klinth, Laura J A van Dijk, Piotr Łukasik, Andreia Miraldo, Anders Andersson, Ayco Jerome Michel Tack and 2 more

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Accuracy of occurrence and abundance estimates from insect metabarcoding.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
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

12 authors.

Ela Iwaszkiewicz-EggebrechtDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.ORCID 0000-0003-1412-1711
Robert M GoodsellDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.
Bengt-Åke BengssonLokegatan 3, Färjestaden, Sweden.
Marko MutanenDepartment of Biology, University of Oulu, Oulu, Northern Ostrobothnia, Finland.
Mårten KlinthStation Linné, Färjestaden SE-38693, Sweden.ORCID 0000-0003-4755-6682
Laura J A van DijkDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.ORCID 0000-0003-1015-8496
Piotr ŁukasikFaculty of Biology, Jagiellonian University, Krakow, Poland.ORCID 0000-0002-4164-6487
Andreia MiraldoDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.
Anders AnderssonDepartment of Gene Technology, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
Ayco Jerome Michel TackDepartment of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden.
Tomas RoslinDepartment of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
Fredrik RonquistDepartment of Bioinformatics and Genetics, Swedish Museum of Natural History, Stockholm, Sweden.

Funding

European Research Council Synergy Grant 856506Knut and Alice Wallenberg Foundation 2017.088Polish National Agency for Academic Exchange PPN/PPO/2018/1/00015Polish National Science Centre 2018/31/B/NZ8/01158Swedish Research Council 2018-04620, 2021-03784, 2021-05563Swedish University of Agricultural Sciences Career Support grant to TRUppsala Multidisciplinary Center for Advanced Computational Science
6 · The paper itself

Abstract

DNA metabarcoding of species-rich taxa is becoming a popular high-throughput method for biodiversity inventories. Unfortunately, its accuracy and efficiency remain unclear, as results mostly pertain to poorly known taxa in underexplored regions. This study evaluates what an extensive sampling effort combined with metabarcoding can tell us about the lepidopteran fauna of Sweden-one of the best-understood insect taxa in one of the most-surveyed countries of the world. We deployed 197 Malaise traps across Sweden for a year, generating 4749 bulk samples for metabarcoding, and compared the results to existing data sources. We detected more than half (1535) of the 2990 known Swedish lepidopteran species and 323 species not reported during the sampling period by other data providers. Full-length barcoding confirmed three new species for the country, substantial range extensions for two species and eight genetically distinct barcode variants potentially representing new species, one of which has since been described. Most new records represented small, inconspicuous species from poorly surveyed regions, highlighting components of the fauna overlooked by traditional surveying. These findings demonstrate that DNA metabarcoding is a highly efficient and accurate biodiversity sampling method, capable of yielding significant new discoveries even for the most well known of insect faunas.

Indexed as

BiodiversityDNA Barcoding, TaxonomicLepidopteraAnimalsSwedenbiodiversity monitoringDNA barcodinghigh-throughput surveymetabarcodingspecies discoverySwedish Lepidoptera

Identifiers

PMID40359979
PMCPMC12074807

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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.