Evidence map›Paper›PMID 35060465›Full record

ArticleParasitology2021

Temporal dynamics of freshwater planktonic parasites inferred using a DNA metabarcoding time-series.

Kingsly C Beng, Justyna Wolinska, Elisabeth Funke, Silke Van den Wyngaert, Alena S Gsell, Michael T Monaghan

Abstract read
In one paragraph

Article in Parasitology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

3 citing papers in PubMed.

  1. Long-term trends in parasite diversity and infection levels: approaches and patterns.Biological reviews of the Cambridge Philosophical Society · 2026
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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

6 authors.

Kingsly C BengDepartment of Ecosystem Research, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.ORCID 0000-0002-0745-7930
Justyna WolinskaDepartment of Ecosystem Research, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.
Elisabeth FunkeDepartment of Ecosystem Research, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.
Silke Van den WyngaertDepartment of Experimental Limnology, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.
Alena S GsellDepartment of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands.
Michael T MonaghanDepartment of Ecosystem Research, Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parasites are important components of biodiversity and contributors to ecosystem functioning, but are often neglected in ecological studies. Most studies examine model parasite systems or single taxa, thus our understanding of community composition is lacking. Here, the seasonal and annual dynamics of parasites was quantified using a 5-year metabarcoding time-series of freshwater plankton, collected weekly. We first identified parasites in the dataset using literature searches of the taxonomic match and using sequence metadata from the National Center for Biotechnology Information (NCBI) nucleotide database. In total, 441 amplicon sequence variants (belonging to 18 phyla/clades) were classified as parasites. The four phyla/clades with the highest relative read abundance and richness were Chytridiomycota, Dinoflagellata, Oomycota and Perkinsozoa. Relative read abundance of total parasite taxa, Dinoflagellata and Perkinsozoa significantly varied with season and was highest in summer. Parasite richness varied significantly with season and year, and was generally lowest in spring. Each season had distinct parasite communities, and the difference between summer and winter communities was most pronounced. Combining DNA metabarcoding with searches of the literature and NCBI metadata allowed us to characterize parasite diversity and community dynamics and revealed the extent to which parasites contribute to the diversity of freshwater plankton communities.

Indexed as

ParasitesPlanktonAnimalsBiodiversityDNA Barcoding, TaxonomicEcosystemFresh WaterRNA, Ribosomal, 18SRNA, Ribosomal, 18S18S rRNA geneaquatic food webGenbanklong-term monitoringmetabarcodingMüggelseeplanktonic parasites

Identifiers

PMID35060465
PMCPMC8573423

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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.