Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth

Wei, Xinzhu and Zhang, Jianzhi and Pál, Csaba (2019) Environment-dependent pleiotropic effects of mutations on the maximum growth rate r and carrying capacity K of population growth. PLOS Biology, 17 (1). e3000121. ISSN 1545-7885

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Abstract

Maximum growth rate per individual (r) and carrying capacity (K) are key life-history traits that together characterize the density-dependent population growth and therefore are crucial parameters of many ecological and evolutionary theories such as r/K selection. Although r and K are generally thought to correlate inversely, both r/K tradeoffs and trade-ups have been observed. Nonetheless, neither the conditions under which each of these relationships occur nor the causes of these relationships are fully understood. Here, we address these questions using yeast as a model system. We estimated r and K using the growth curves of over 7,000 yeast recombinants in nine environments and found that the r–K correlation among genotypes changes from 0.53 to −0.52 with the rise of environment quality, measured by the mean r of all genotypes in the environment. We respectively mapped quantitative trait loci (QTLs) for r and K in each environment. Many QTLs simultaneously influence r and K, but the directions of their effects are environment dependent such that QTLs tend to show concordant effects on the two traits in poor environments but antagonistic effects in rich environments. We propose that these contrasting trends are generated by the relative impacts of two factors—the tradeoff between the speed and efficiency of ATP production and the energetic cost of cell maintenance relative to reproduction—and demonstrate an agreement between model predictions and empirical observations. These results reveal and explain the complex environment dependency of the r–K relationship, which bears on many ecological and evolutionary phenomena and has biomedical implications.

Item Type: Article
Subjects: Open Digi Academic > Biological Science
Depositing User: Unnamed user with email support@opendigiacademic.com
Date Deposited: 07 Jan 2023 10:18
Last Modified: 27 Apr 2024 10:54
URI: http://publications.journalstm.com/id/eprint/1

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