Evidence map›Paper›PMID 27881744›Full record

ArticleProceedings. Biological sciences2016

Directional selection effects on patterns of phenotypic (co)variation in wild populations.

A P A Assis, J L Patton, A Hubbe, G Marroig

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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  3. A method to predict the response to directional selection using a Kalman filter.Proceedings of the National Academy of Sciences of the United States of America · 2022
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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

4 authors.

A P A AssisDepartamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo 05508-090, Brazil paulaaprigio@usp.br.ORCID 0000-0001-8170-6352
J L PattonMuseum of Vertebrate Zoology, University of California at Berkeley, Berkeley, CA 94720, USA.
A HubbeDepartamento de Oceanografia, Instituto de Geociências, Universidade Federal da Bahia, Salvador, Bahia 40170-020, Brazil.
G MarroigDepartamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de São Paulo, São Paulo, São Paulo 05508-090, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phenotypic (co)variation is a prerequisite for evolutionary change, and understanding how (co)variation evolves is of crucial importance to the biological sciences. Theoretical models predict that under directional selection, phenotypic (co)variation should evolve in step with the underlying adaptive landscape, increasing the degree of correlation among co-selected traits as well as the amount of genetic variance in the direction of selection. Whether either of these outcomes occurs in natural populations is an open question and thus an important gap in evolutionary theory. Here, we documented changes in the phenotypic (co)variation structure in two separate natural populations in each of two chipmunk species (Tamias alpinus and T. speciosus) undergoing directional selection. In populations where selection was strongest (those of T. alpinus), we observed changes, at least for one population, in phenotypic (co)variation that matched theoretical expectations, namely an increase of both phenotypic integration and (co)variance in the direction of selection and a re-alignment of the major axis of variation with the selection gradient.

Indexed as

Biological EvolutionGenetics, PopulationGenetic VariationSelection, GeneticAnimalsModels, GeneticPhenotypeSciuridaeadaptive landscapechipmunksgenotype–phenotype mapP-matrixquantitative geneticsTamias

Identifiers

PMID27881744
PMCPMC5136582

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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.