Evidence map›Paper›PMID 35768403›Full record

ArticleParasitology2022

Fish-parasite interaction networks reveal latitudinal and taxonomic trends in the structure of host-parasite associations.

Robert Poulin, Cameron McDougall

Abstract read
In one paragraph

Article in Parasitology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

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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

2 authors.

Robert PoulinDepartment of Zoology, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand.ORCID 0000-0003-1390-1206
Cameron McDougallDepartment of Zoology, University of Otago, P.O. Box 56, Dunedin 9054, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, treating host–parasite associations as bipartite interaction networks has proven a powerful tool to identify structural patterns and their likely causes in communities of fish and their parasites. Network analysis allows for both community-level properties to be computed and investigated, and species-level roles to be determined. Here, using data from 31 host–parasite interaction networks from local fish communities around the world, we test for latitudinal trends at whole-network level, and taxonomic patterns at individual parasite species level. We found that while controlling for network size (number of species per network), network modularity, or the tendency for the network to be subdivided into groups of species that interact mostly with each other, decreased with increasing latitude. This suggests that tropical fish–parasite networks may be more stable than those from temperate regions in the event of community perturbations, such as species extinction. At the species level, after accounting for the effect of host specificity, we observed no difference in the centrality of parasite species within networks between parasites with different transmission modes. However, species in some taxa, namely branchiurans, acanthocephalans and larval trematodes, generally had higher centrality values than other parasite taxa. Because species with a central position often serve as module connectors, these 3 taxa may play a key role in whole-network cohesion. Our results highlight the usefulness of network analysis to reveal the aspects of fish–parasite community interactions that would otherwise remain hidden and advance our understanding of their evolution.

Indexed as

AcanthocephalaFish DiseasesParasitesAnimalsFishesHost-Parasite InteractionsBipartite networkcoevolutionconnectancemodularitynestednesstaxonomy

Identifiers

PMID35768403
PMCPMC10090588

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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.