Evidence map›Paper›PMID 31594502›Full record

ArticleProceedings. Biological sciences2019

A continuum of biological adaptations to environmental fluctuation.

Ming Liu, Dustin R Rubenstein, Wei-Chung Liu, Sheng-Feng Shen

Abstract read
In one paragraph

Article in Proceedings. Biological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

4 authors.

Ming LiuBiodiversity Research Center, Academia Sinica, Taipei 11529, Taiwan, Republic of China.
Dustin R RubensteinDepartment of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY 10027, USA.
Wei-Chung LiuInstitute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan, Republic of China.
Sheng-Feng ShenBiodiversity Research Center, Academia Sinica, Taipei 11529, Taiwan, Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bet-hedging-a strategy that reduces fitness variance at the expense of lower mean fitness among different generations-is thought to evolve as a biological adaptation to environmental unpredictability. Despite widespread use of the bet-hedging concept, most theoretical treatments have largely made unrealistic demographic assumptions, such as non-overlapping generations and fixed or infinite population sizes. Here, we extend the concept to consider overlapping generations by defining bet-hedging as a strategy with lower variance and mean per capita growth rate across different environments. We also define an opposing strategy-the rising-tide-that has higher mean but also higher variance in per capita growth. These alternative strategies lie along a continuum of biological adaptions to environmental fluctuation. Using stochastic Lotka-Volterra models to explore the evolution of the rising-tide versus bet-hedging strategies, we show that both the mean environmental conditions and the temporal scales of their fluctuations, as well as whether population dynamics are discrete or continuous, are crucial in shaping the type of strategy that evolves in fluctuating environments. Our model demonstrates that there are likely to be a wide range of ways that organisms with overlapping generations respond to environmental unpredictability beyond the classic bet-hedging concept.

Indexed as

Adaptation, BiologicalAnimalsBiological EvolutionDemographyPopulation DensityPopulation Dynamicsbet-hedging strategyenvironmental fluctuationspecialization

Identifiers

PMID31594502
PMCPMC6790776

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.