2 datasets found
  1. B

    Data from: Fitness costs in spatially structured environments

    • borealisdata.ca
    • open.library.ubc.ca
    Updated May 19, 2021
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    Florence Débarre (2021). Data from: Fitness costs in spatially structured environments [Dataset]. http://doi.org/10.5683/SP2/EGN7LV
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 19, 2021
    Dataset provided by
    Borealis
    Authors
    Florence Débarre
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    AbstractThe clustering of individuals that results from limited dispersal is a double-edged sword: while it allows for local interactions to be mostly among related individuals, it also results in increased local competition. Here I show that, because they mitigate local competition, fitness costs such as reduced fecundity or reduced survival are less costly in spatially structured environments than in non spatial settings. I first present a simple demographic example to illustrate how spatial structure weakens selection against fitness costs. Then, I illustrate the importance of disentangling the evolution of a trait from the evolution of potential associated costs, using an example taken from a recent study investigating the effect of spatial structure on the evolution of host defense. In this example indeed, the differences between spatial and non-spatial selection gradients are due to differences in the fitness costs, thereby undermining interpretations of the results made in terms of the trait only. This illustrates the need to consider fitness costs as proper traits in both theoretical and empirical studies. Usage notesDebarre_2015_EvolutionZipped folder containing the scripts to re-run and plot all the figures presented in the article.

  2. Data from: Fitness costs in spatially structured environments

    • data.niaid.nih.gov
    • datadryad.org
    • +1more
    zip
    Updated Mar 17, 2015
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    Florence Débarre (2015). Fitness costs in spatially structured environments [Dataset]. http://doi.org/10.5061/dryad.6m761
    Explore at:
    zipAvailable download formats
    Dataset updated
    Mar 17, 2015
    Dataset provided by
    University of Exeter
    Authors
    Florence Débarre
    License

    https://spdx.org/licenses/CC0-1.0.htmlhttps://spdx.org/licenses/CC0-1.0.html

    Description

    The clustering of individuals that results from limited dispersal is a double-edged sword: while it allows for local interactions to be mostly among related individuals, it also results in increased local competition. Here I show that, because they mitigate local competition, fitness costs such as reduced fecundity or reduced survival are less costly in spatially structured environments than in non spatial settings. I first present a simple demographic example to illustrate how spatial structure weakens selection against fitness costs. Then, I illustrate the importance of disentangling the evolution of a trait from the evolution of potential associated costs, using an example taken from a recent study investigating the effect of spatial structure on the evolution of host defense. In this example indeed, the differences between spatial and non-spatial selection gradients are due to differences in the fitness costs, thereby undermining interpretations of the results made in terms of the trait only. This illustrates the need to consider fitness costs as proper traits in both theoretical and empirical studies.

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Florence Débarre (2021). Data from: Fitness costs in spatially structured environments [Dataset]. http://doi.org/10.5683/SP2/EGN7LV

Data from: Fitness costs in spatially structured environments

Related Article
Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
May 19, 2021
Dataset provided by
Borealis
Authors
Florence Débarre
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Description

AbstractThe clustering of individuals that results from limited dispersal is a double-edged sword: while it allows for local interactions to be mostly among related individuals, it also results in increased local competition. Here I show that, because they mitigate local competition, fitness costs such as reduced fecundity or reduced survival are less costly in spatially structured environments than in non spatial settings. I first present a simple demographic example to illustrate how spatial structure weakens selection against fitness costs. Then, I illustrate the importance of disentangling the evolution of a trait from the evolution of potential associated costs, using an example taken from a recent study investigating the effect of spatial structure on the evolution of host defense. In this example indeed, the differences between spatial and non-spatial selection gradients are due to differences in the fitness costs, thereby undermining interpretations of the results made in terms of the trait only. This illustrates the need to consider fitness costs as proper traits in both theoretical and empirical studies. Usage notesDebarre_2015_EvolutionZipped folder containing the scripts to re-run and plot all the figures presented in the article.

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