Evidence map›Paper›PMID 41140306›Full record

ArticleEcology and evolution2025

StrataSeq: A Workflow for Rapid Development of Molecular Databases for Hard-To-Identify Species.

Anna K Merges, Peter Manning, Dennis Baulechner, Katharina John, Andrey Zaitsev, Volkmar Wolters, Damian Baranski, Hans-Peter Grossart, Jason Woodhouse, Clément Schneider and 1 more

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

11 authors.

Anna K MergesInstitute of Insect Biotechnology Justus-Liebig University Giessen Germany.
Peter ManningSenckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany.ORCID https://orcid.org/0000-0002-7940-2023
Dennis BaulechnerInstitute of Animal Ecology Justus-Liebig-University Giessen Germany.ORCID https://orcid.org/0000-0003-1839-9657
Katharina JohnInstitute of Animal Ecology Justus-Liebig-University Giessen Germany.
Andrey ZaitsevInstitute of Animal Ecology Justus-Liebig-University Giessen Germany.
Volkmar WoltersInstitute of Animal Ecology Justus-Liebig-University Giessen Germany.
Damian BaranskiSenckenberg Biodiversity and Climate Research Centre Frankfurt am Main Germany.
Hans-Peter GrossartDepartment of Plankton and Microbial Ecology Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) Stechlin Germany.ORCID https://orcid.org/0000-0002-9141-0325
Jason WoodhouseDepartment of Plankton and Microbial Ecology Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) Stechlin Germany.ORCID https://orcid.org/0000-0003-1129-5396
Clément SchneiderLOEWE Centre for Translational Biodiversity Genomics Frankfurt am Main Germany.ORCID https://orcid.org/0000-0003-3743-9319
Miklós BálintInstitute of Insect Biotechnology Justus-Liebig University Giessen Germany.ORCID https://orcid.org/0000-0003-0499-8536

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biodiversity loss necessitates improved monitoring of small, species-rich taxa, such as protists, phyto- and zooplankton and terrestrial invertebrates. Traditional biomonitoring is often infeasible for these taxa due to complex morphology and few taxonomists. DNA-based approaches offer promising solutions by enabling rapid species identification. However, the effectiveness of these methods depends on the completeness of molecular reference databases, which remain incomplete, particularly for remote and biodiverse regions. To address this, we propose the StrataSeq workflow, a systematic approach to optimise the generation of DNA reference databases for hard-to-identify taxa. Reference sequences allow us to connect molecular operational taxonomic units to a wealth of information available for many described taxa. StrataSeq consists of four key steps: (1) Habitat-stratified sample subsetting selects a minimal but ecologically representative sample set by stratifying along key environmental gradients. (2) Prioritising morphospecies involves sorting specimens into morphospecies and ranking them based on their occurrence across samples, prioritising common taxa for detailed identification. (3) Detailed morphological identification focuses on common morphospecies to maximise taxonomic coverage while minimising effort. (4) Reference DNA sequence generation targets taxa lacking molecular references, with sequenced specimens deposited as museum vouchers. We benchmarked the StrataSeq workflow using two datasets of Collembola from grassland soils in Germany. In comparison with a species list generated by a more labour-intensive traditional approach (identification of randomly selected individuals from all samples), the StrataSeq workflow captured 69% of species but required only 22% of the effort. StrataSeq is adaptable to various organism groups and environmental settings, including both spatial and temporal gradients. The workflow enhances the cost-effectiveness of generating reference DNA databases, supporting improved biodiversity monitoring and ecological research. StrataSeq offers a scalable solution to accelerate the completion of molecular databases, thereby improving biomonitoring and ecosystem assessments under global change pressures.

Indexed as

biodiversity monitoringDNA reference databasehabitat‐stratified samplingmorphological identificationmorphospeciesspecies turnover

Identifiers

PMID41140306
PMCPMC12547479

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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.