Evidence map›Paper›PMID 31993085›Full record

ArticleEvolutionary applications2020

Northern European

Dorte Bekkevold, Johan Höjesjö, Einar Eg Nielsen, David Aldvén, Thomas Damm Als, Marte Sodeland, Matthew Peter Kent, Sigbjørn Lien, Michael Møller Hansen

Abstract read
In one paragraph

Article in Evolutionary applications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Introgression affectsEvolutionary applications · 2024
    Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Northern EuropeanEvolutionary applications · 2020
    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

9 authors.

Dorte BekkevoldNational Institute of Aquatic Resources Technical University of Denmark Silkeborg Denmark.ORCID https://orcid.org/0000-0002-5297-032X
Johan HöjesjöDepartment of Biological & Environmental Sciences University of Gothenburg Gothenburg Sweden.
Einar Eg NielsenNational Institute of Aquatic Resources Technical University of Denmark Silkeborg Denmark.
David AldvénGenerations R&D Vattenfall AB Älvkarleby Sweden.
Thomas Damm AlsDepartment of Biomedicine Aarhus University Aarhus Denmark.
Marte SodelandDepartment of Natural Sciences University of Agder Kristiansand Norway.
Matthew Peter KentFaculty of Biosciences Norwegian University of Life Sciences Ås Norway.
Sigbjørn LienFaculty of Biosciences Norwegian University of Life Sciences Ås Norway.
Michael Møller HansenDepartment of Bioscience - Genetics, Ecology and Evolution Aarhus University Aarhus Denmark.ORCID https://orcid.org/0000-0001-5372-4828

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The salmonid fish Brown trout is iconic as a model for the application of conservation genetics to understand and manage local interspecific variation. However, there is still scant information about relationships between local and large-scale population structure, and to what extent geographical and environmental variables are associated with barriers to gene flow. We used information from 3,782 mapped SNPs developed for the present study and conducted outlier tests and gene-environment association (GEA) analyses in order to examine drivers of population structure. Analyses comprised >2,600 fish from 72 riverine populations spanning a central part of the species' distribution in northern Europe. We report hitherto unidentified genetic breaks in population structure, indicating strong barriers to gene flow. GEA loci were widely spread across genomic regions and showed correlations with climatic, abiotic and geographical parameters. In some cases, individual loci showed consistent GEA across the geographical regions Britain, Europe and Scandinavia. In other cases, correlations were observed only within a sub-set of regions, suggesting that locus-specific variation was associated with local processes. A paired-population sampling design allowed us to evaluate sampling effects on detection of outlier loci and GEA. Two widely applied methods for outlier detection (

Indexed as

brown troutgenotype‐environment associationlocal adaptationoutlier testsalmonid

Identifiers

PMID31993085
PMCPMC6976966

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.