Evidence map›Paper›PMID 40964625›Full record

ArticleEcology and evolution2025

Small Bugs, Big Data: Metagenomics for Arthropod Biodiversity Monitoring.

Samantha López Clinton, Ela Iwaszkiewicz-Eggebrecht, Andreia Miraldo, Robert Goodsell, Matthew T Webster, Fredrik Ronquist, Tom van der Valk

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Samantha López ClintonDepartment of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden.ORCID https://orcid.org/0000-0003-1364-9135
Ela Iwaszkiewicz-EggebrechtDepartment of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden.ORCID https://orcid.org/0000-0003-1412-1711
Andreia MiraldoDepartment of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden.ORCID https://orcid.org/0000-0001-6107-006X
Robert GoodsellDepartment of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden.
Matthew T WebsterDepartment of Medical Biochemistry and Microbiology, SciLifeLab Uppsala University Uppsala Sweden.
Fredrik RonquistDepartment of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden.ORCID https://orcid.org/0000-0002-3929-251X
Tom van der ValkDepartment of Bioinformatics and Genetics Swedish Museum of Natural History Stockholm Sweden.ORCID https://orcid.org/0000-0001-6582-3452

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Obtaining genome-wide data from complex samples, such as environmental material or bulk species collections, is increasingly feasible, yet inferring species presence and population genomic insights remains challenging. We applied metagenomic sequencing to 40 arthropod bulk samples collected with Malaise traps across Sweden and compared results with metabarcoding of the same material. Using a custom genome database, we achieved genus-level classification largely consistent with metabarcoding. While metagenomics detected all genera identified by metabarcoding, conservative filtering thresholds designed to minimise false positives also excluded some true signals, particularly for low-abundance taxa. Taxonomic overlap between methods was further constrained by limited reference database representation. Beyond taxonomic assignment, metagenomic sequencing yielded genome-level information: we inferred haplotype diversity, heterozygosity and geographic population structure for several abundant species, including variable degrees of hybrid origin in red wood ants and the genetic distinctiveness of Gotland bumblebees. Finally, by-catch plant DNA present in the bulk samples revealed plausible arthropod-plant interactions, several of which align with known ecological associations. Together, these results demonstrate the potential of metagenomics for biodiversity monitoring and population genomics, while underscoring the importance of filtering criteria and comprehensive reference databases.

Indexed as

biodiversity monitoringbulkDNAk‐mer classificationmetabarcodingmetagenomics

Identifiers

PMID40964625
PMCPMC12440566

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.