# Quantifying evolutionary solutions to fitness tradeoffs in fluctuating environments

> **NIH NIH F32** · STANFORD UNIVERSITY · 2023 · $71,792

## Abstract

Project Summary
 One lesson of experimental evolution is that beneficial, large-effect mutations appear rapidly in simple
laboratory environments. Given that such mutations are possible and thus have not already fixed in
wild populations, it is likely that they help the organism to specialize to a particular environment
and may display pleiotropic tradeoffs that make them deleterious in other environments. Evidence for
pleiotropy is common, and unlike constant laboratory environments, natural settings likely require
organisms to solve evolutionary tradeoffs in order to persist through intermittently harsh conditions.
 Despite the central importance to biology and medicine of the effect of environmental fluctuations on
evolution, the field has been limited by the ability to track large numbers of evolutionary paths in multiple
environmental conditions. Here, we propose to address this problem through use of a high-throughput system
of DNA-barcoded yeast. By evolving Saccharomyces cerevisiae populations in environments that impose
known biological tradeoffs on their fitness optimization, and by tracking the emergence and spread of
hundreds of thousands of adaptive mutations in both fluctuating and constant environments, we will
learn how adaptation to fluctuating environments differs from strategies that appear in the component constant
environments.
 In Aim 1, we will test the hypothesis that fluctuating environments lead to generalists that overcome
biological tradeoffs by optimizing mean fitness across conditions, while constant environments produce
specialists that are more fit in particular environments but have lower mean fitness. After identifying
generalists and specialists, we will re-barcode a collection of them in Aim 2 for subsequent evolution in
environments that fluctuate both temporally and spatially, allowing the mutants to either diversify further or
invade each other’s niches. These experiments will test the hypothesis that character displacement and
specialization should evolve as a result of competition.
 This research and training plan will prepare me to become a leader in the intersecting fields of
microbial ecology and evolution. I will ultimately apply the insights I gain from these experiments with a
highly precise model system to complex multi-species communities. My PhD training was in community
ecology, and here I will learn to work with a cutting-edge barcoding technology, as well as study molecular
biology and population genetics. Additionally, I will participate in professional development activities and do
public outreach.

## Key facts

- **NIH application ID:** 10678773
- **Project number:** 5F32GM145148-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Clare Isabel Abreu
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $71,792
- **Award type:** 5
- **Project period:** 2022-08-05 → 2024-08-04

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10678773

## Citation

> US National Institutes of Health, RePORTER application 10678773, Quantifying evolutionary solutions to fitness tradeoffs in fluctuating environments (5F32GM145148-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10678773. Licensed CC0.

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