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Are patterns of growth adaptive?

R Sibly, P Calow, N Nichols

    Journal of Theoretical Biology
    |February 7, 1985
    PubMed
    Summary

    Sigmoid growth curves are optimal for maximizing fitness when mortality decreases with size and growth rate is constrained. Post-breeding growth may be advantageous under specific conditions related to birth and mortality rates.

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    Area of Science:

    • Evolutionary biology
    • Theoretical ecology
    • Mathematical modeling

    Background:

    • Understanding individual growth patterns is crucial in evolutionary biology.
    • Fitness is often defined by the per capita rate of increase of phenotypes.
    • Previous models have explored various growth strategies.

    Purpose of the Study:

    • To analyze individual growth patterns using fitness models based on per capita rate of increase.
    • To determine the conditions under which sigmoid growth curves represent an optimal fitness strategy.
    • To evaluate the optimality of catch-up growth and post-breeding growth.

    Main Methods:

    • Development of theoretical models defining fitness in terms of per capita rate of increase.
    • Analysis of growth curves under various assumptions regarding mortality, growth rate constraints, and reproductive strategies.
    • Mathematical evaluation of conditions favoring sigmoid growth, catch-up growth, and post-breeding growth.

    Main Results:

    • Sigmoid growth curves are predicted as optimal when mortality decreases with size and growth rate is constrained early in life.
    • Catch-up growth is generally not optimal, except in specific scenarios like seasonal breeding.
    • Post-breeding growth can be advantageous if birth or mortality rates are convex functions of investment in growth.

    Conclusions:

    • The study provides a theoretical framework for understanding optimal growth strategies in animals.
    • Predictions regarding determinate versus indeterminate growth can be evaluated using these models.
    • Further empirical data are needed for quantitative testing of the model's predictions.

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