Evidence map›Paper›PMID 33206376›Full record

ArticleEvolution; international journal of organic evolution2021

Evolution in alternating environments with tunable interlandscape correlations.

Jeff Maltas, Douglas M McNally, Kevin B Wood

Abstract read
In one paragraph

Article in Evolution; international journal of organic evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Deconstructing empirical fitness seascapes across scales of granularity.bioRxiv : the preprint server for biology · 2026
    Article
  5. The variability of evolvability: Properties of dynamic fitness landscapes determine how phenotypic variability evolves.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Article
  8. Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
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

3 authors.

Jeff MaltasDepartment of Biophysics, University of Michigan, Ann Arbor MI 48109.ORCID 0000-0001-6567-6800
Douglas M McNallyIndian Creek School, Crownsville, Maryland 21032.
Kevin B WoodDepartment of Biophysics, University of Michigan, Ann Arbor MI 48109.ORCID 0000-0002-0985-7401

Funding

Spatiotemporal Dynamics of Collective Antibiotic Resistance in Microbial CommunitiesR35GM124875 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WOOD, KEVIN · 2017 to 2024
$2.7M
NIGMS NIH HHS R35 GM124875
6 · The paper itself

Abstract

Natural populations are often exposed to temporally varying environments. Evolutionary dynamics in varying environments have been extensively studied, although understanding the effects of varying selection pressures remains challenging. Here, we investigate how cycling between a pair of statistically related fitness landscapes affects the evolved fitness of an asexually reproducing population. We construct pairs of fitness landscapes that share global fitness features but are correlated with one another in a tunable way, resulting in landscape pairs with specific correlations. We find that switching between these landscape pairs, depending on the ruggedness of the landscape and the interlandscape correlation, can either increase or decrease steady-state fitness relative to evolution in single environments. In addition, we show that switching between rugged landscapes often selects for increased fitness in both landscapes, even in situations where the landscapes themselves are anticorrelated. We demonstrate that positively correlated landscapes often possess a shared maximum in both landscapes that allows the population to step through sub-optimal local fitness maxima that often trap single landscape evolution trajectories. Finally, we demonstrate that switching between anticorrelated paired landscapes leads to ergodic-like dynamics where each genotype is populated with nonzero probability, dramatically lowering the steady-state fitness in comparison to single landscape evolution.

Indexed as

Adaptation, BiologicalBiological EvolutionEnvironmentGenetic FitnessModels, GeneticMarkov ChainsAdaptationEpistasisFitnessModels/SimulationsPopulation GeneticsSelection-Natural

Identifiers

PMID33206376
PMCPMC8246403

What OpenQuestion holds

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Registered trials

None linked

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.