Age‐specific sensitivity analysis of stable, stochastic and transient growth for stage‐classified populations

Giaimo, Stefano; Traulsen, Arne

doi:10.1002/ece3.9561

Cited by 2 publications

(2 citation statements)

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“…In an interesting parallel with our own study, this result disproves the then commonly accepted assumption that averaging growth rates across realizations of the fluctuating environment would lead to an accurate estimate of the effective growth rates. Following on from Tuljapurkar's original work on sensitivity analyses to variance in vital rates, recent studies performed an age‐specific sensitivity analysis of stable, stochastic, and transient growth in structured populations (Giaimo & Traulsen, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

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Solving the stochastic dynamics of population growth

Marrec,

Bank,

Bertrand

2023

Ecology and Evolution

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Population growth is a fundamental process in ecology and evolution. The population size dynamics during growth are often described by deterministic equations derived from kinetic models. Here, we simulate several population growth models and compare the size averaged over many stochastic realizations with the deterministic predictions. We show that these deterministic equations are generically bad predictors of the average stochastic population dynamics. Specifically, deterministic predictions overestimate the simulated population sizes, especially those of populations starting with a small number of individuals. Describing population growth as a stochastic birth process, we prove that the discrepancy between deterministic predictions and simulated data is due to unclosed‐moment dynamics. In other words, the deterministic approach does not consider the variability of birth times, which is particularly important with small population sizes. We show that some moment‐closure approximations describe the growth dynamics better than the deterministic prediction. However, they do not reduce the error satisfactorily and only apply to some population growth models. We explicitly solve the stochastic growth dynamics, and our solution applies to any population growth model. We show that our solution exactly quantifies the dynamics of a community composed of different strains and correctly predicts the fixation probability of a strain in a serial dilution experiment. Our work sets the foundations for a more faithful modeling of community and population dynamics. It will allow the development of new tools for a more accurate analysis of experimental and empirical results, including the inference of important growth parameters.

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Section: Discussionmentioning

confidence: 99%

“…Following on from Tuljapurkar's original work on sensitivity analyses to variance in vital rates, recent studies performed an age-specific sensitivity analysis of stable, stochastic, and transient growth in structured populations (Giaimo & Traulsen, 2022).…”

Section: Discussionmentioning

confidence: 99%

Solving the stochastic dynamics of population growth

Marrec,

Bank,

Bertrand

2023

Ecology and Evolution

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Age‐specific sensitivity analysis of stable, stochastic and transient growth for stage‐classified populations

Giaimo

Traulsen

2022

Ecology and Evolution

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Sensitivity analysis in ecology and evolution is a valuable guide to rank demographic parameters depending on their relevance to population growth. Here, we propose a method to make the sensitivity analysis of population growth for matrix models solely classified by stage more fine‐grained by considering the effect of age‐specific parameters. The method applies to stable population growth, the stochastic growth rate, and transient growth. The method yields expressions for the sensitivity of stable population growth to age‐specific survival and fecundity from which general properties are derived about the pattern of age‐specific selective forces molding senescence in stage‐classified populations.

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Age‐specific sensitivity analysis of stable, stochastic and transient growth for stage‐classified populations

Cited by 2 publications

References 56 publications

Solving the stochastic dynamics of population growth

Solving the stochastic dynamics of population growth

Age‐specific sensitivity analysis of stable, stochastic and transient growth for stage‐classified populations

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