Evidence map›Paper›PMID 41700656›Full record

ArticleMolecular ecology resources2026

Scoop That Poop: Optimising Faecal Sample Pre-Processing for Parasite Metabarcoding.

Madison E Patch, Graham B Goodman, Sara B Weinstein

Abstract readEvaluation Study
In one paragraph

Article in Molecular ecology resources, 2026. 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

3 authors.

Madison E PatchDepartment of Biology, Utah State University, Logan, Utah, USA.ORCID https://orcid.org/0009-0007-2213-6741
Graham B GoodmanDepartment of Biology, Utah State University, Price, Utah, USA.ORCID https://orcid.org/0000-0001-5811-5837
Sara B WeinsteinDepartment of Biology, Utah State University, Logan, Utah, USA.ORCID https://orcid.org/0000-0002-8363-1777

Funding

USU Undergraduate Research and Creative Opportunity GrantUtah State University Extension Grant Ext00172
6 · The paper itself

Abstract

Molecular techniques such as DNA metabarcoding are increasingly used to characterise parasite communities. However, relatively few studies have examined how sample processing methods influence detection rates. We used faecal samples collected from free-roaming horses to evaluate how parasite density and pre-processing methods influenced quantification of parasite richness, community composition, and detection of different taxa. Methods that concentrated parasites substantially increased parasite detection, especially at the low infection levels often seen in wildlife. Specifically, we found that DNA extracts from larval coproculture and a newly developed egg concentration approach detected approximately twice as many species and genera as extracts made directly from faecal matter. Although parasite richness was consistently lower in these faecal subsamples, overall parasite communities were still similar between pre-processing methods. Ultimately, the optimal method depends on research constraints and goals. Working with parasite larvae is more time intensive, but lower cost as larval parasites can be extracted using a lysis buffer approach which performs similarly to commercial extraction kits. DNA extraction from faecal subsamples misses rare and common taxa but minimises field processing. Our novel egg concentration method offers a compromise between relatively rapid processing and high sensitivity.

Indexed as

DNA Barcoding, TaxonomicFecesParasitesSpecimen HandlingAnimalsHorsesequidmetabarcodingNemabiomeparasitewildlife

Identifiers

PMID41700656
PMCPMC12911221

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.