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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
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Related Experiment Video

Updated: May 21, 2026

Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli
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Daily Transfers, Archiving Populations, and Measuring Fitness in the Long-Term Evolution Experiment with Escherichia coli

Published on: August 18, 2023

Survivability is more fundamental than evolvability.

Michael E Palmer1, Marcus W Feldman

  • 1Department of Biology, Stanford University, Stanford, California, USA. mep@charles.stanford.edu

Plos One
|June 23, 2012
PubMed
Summary

Lineages must adapt to survive. We introduce k-survivability, a predictive measure of evolutionary success, incorporating variation and environmental factors to forecast lineage survival over time.

Area of Science:

  • Evolutionary biology
  • Quantitative biology

Background:

  • Evolvability, the tendency to produce adaptive variation, is crucial for lineage survival.
  • Current interpretations of evolvability are insufficient for predicting evolutionary outcomes.
  • Deleterious variation and its interplay with adaptive variation significantly impact evolutionary success.

Purpose of the Study:

  • To define a predictive measure of evolutionary success, termed k-survivability.
  • To quantify the probability of a lineage avoiding extinction over k generations.
  • To provide a metric for studying the evolution of various biological phenomena.

Main Methods:

  • Estimating k-survivability using multiple experimental replicates.
  • Incorporating initial standing variation, generated variation spectrum, and heritability.

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  • Accounting for spatial dispersal and lifestyle diversification of sub-lineages.
  • Illustrating measurement with in silico models and suggesting in vivo application.

    • Main Results:

    • K-survivability provides a quantitative measure of evolutionary success.
    • The metric integrates standing variation, generated variation, heritability, and environmental factors.
    • Spatial dispersal and lifestyle diversification were shown to decrease extinction likelihood.

    Conclusions:

    • K-survivability offers a predictive framework for evolutionary outcomes.
    • This metric enables quantitative studies on the evolution of mutation rates, dispersal, genotype-phenotype maps, and sexual reproduction.
    • K-survivability addresses the macroevolutionary constraint of long-term lineage persistence in fluctuating environments.