Evidence map›Paper›PMID 25631990›Full record

ArticleProceedings. Biological sciences2015

Trait dimensionality explains widespread variation in local adaptation.

Ailene MacPherson, Paul A Hohenlohe, Scott L Nuismer

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 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

3 authors.

Ailene MacPhersonProgram in Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844, USA.
Paul A HohenloheProgram in Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844, USA Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA.
Scott L NuismerProgram in Bioinformatics and Computational Biology, University of Idaho, Moscow, ID 83844, USA Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA snusimer@uidaho.edu.

Funding

The Evolution of Protein FlexibilityP20RR016448 · NCRR · UNIVERSITY OF IDAHO · PI FORNEY, LARRY J · 2002 to 2011
$19.3M
Genomics Resources Core (GRC)P30GM103324 · NIGMS · UNIVERSITY OF IDAHO · PI SETTLES, MATTHEW · 2013 to 2017
$5.0M
NCRR NIH HHS P20RR016448NIGMS NIH HHS P30GM103324
6 · The paper itself

Abstract

All species are locked in a continual struggle to adapt to local ecological conditions. In cases where species fail to locally adapt, they face reduced population growth rates, or even local extinction. Traditional explanations for limited local adaptation focus on maladaptive gene flow or homogeneous environmental conditions. These classical explanations have, however, failed to explain variation in the magnitude of local adaptation observed across taxa. Here we show that variable levels of local adaptation are better explained by trait dimensionality. First, we develop and analyse mathematical models that predict levels of local adaptation will increase with the number of traits experiencing spatially variable selection. Next, we test this prediction by estimating the relationship between dimensionality and local adaptation using data from 35 published reciprocal transplant studies. This analysis reveals a strong correlation between dimensionality and degree of local adaptation, and thus provides empirical support for the predictions of our model.

Indexed as

Gene FlowAdaptation, PhysiologicalAnimalsGenetics, PopulationGenetic VariationHumansModels, GeneticPhenotypePopulation Dynamicsecological differentiationecological geneticsgene flowreciprocal transplantspecies rangetransplant experiment

Identifiers

PMID25631990
PMCPMC4344135

What OpenQuestion holds

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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.