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  Spatial and temporal heterogeneity alter the cost of plasticity in Pristionchus pacificus

Kalirad, A., & Sommer, R. (2024). Spatial and temporal heterogeneity alter the cost of plasticity in Pristionchus pacificus. PLOS Computational Biology, 20(1): e1011823. doi:10.1371/journal.pcbi.1011823.

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Kalirad, A1, Author                 
Sommer, RJ1, Author                 
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1Department Integrative Evolutionary Biology, Max Planck Institute for Biology Tübingen, Max Planck Society, Max-Planck-Ring 5, 72076 Tübingen, ou_3371685              

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 Abstract: Phenotypic plasticity, the ability of a single genotype to produce distinct phenotypes under different environmental conditions, has become a leading concept in ecology and evolutionary biology, with the most extreme examples being the formation of alternative phenotypes (polyphenisms). However, several aspects associated with phenotypic plasticity remain controversial, such as the existence of associated costs. While already predicted by some of the pioneers of plasticity research, i.e. Schmalhausen and Bradshaw, experimental and theoretical approaches have provided limited support for the costs of plasticity. In experimental studies, one common restriction is the measurement of all relevant parameters over long time periods. Similarly, theoretical studies rarely use modelling approaches that incorporate specific experimentally-derived fitness parameters. Therefore, the existence of the costs of plasticity remains disputed. Here, we provide an integrative approach to understand the cost of adaptive plasticity and its ecological ramifications, by combining laboratory data from the nematode plasticity model system Pristionchus pacificus with a stage-structured population model. Taking advantage of measurements of two isogenic strains grown on two distinct diets, we illustrate how spatial and temporal heterogeneity with regard to the distribution of resources on a metapopulation can alter the outcome of the competition and alleviate the realized cost of plasticity.

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 Dates: 2024-01
 Publication Status: Issued
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 Identifiers: DOI: 10.1371/journal.pcbi.1011823
PMID: 38289972
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Title: PLOS Computational Biology
  Abbreviation : PLOS Comput Biol
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: 21 Volume / Issue: 20 (1) Sequence Number: e1011823 Start / End Page: - Identifier: ISSN: 1553-734X
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000017180_1