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Contributions of covariance: decomposing the components of stochastic population growth in Cypripedium calceolus.

ABSTRACT: Although correlations between vital rates can have important effects on evolution and demography, few studies have investigated their effects on population dynamics. Here, we extend life-table response experiments (LTREs) to variable environments, showing how to quantify contributions made by (1) mean vital rates, (2) variability driven by environmental fluctuations, (3) correlations implying demographic trade-offs and reflecting stage transition synchrony, and (4) elasticities reflecting local selection pressures. Applying our methods to the lady's slipper orchid Cypripedium calceolus, we found that mean rates accounted for 77.1% of all effects on the stochastic growth rate, variability accounted for 12.6%, elasticities accounted for 6.6%, and correlations accounted for 3.7%. Stochastic effects accounted for 17.6%, 15.3%, and 35.9% of the total in our three populations. Larger elasticities to transitions between dormancy states and stronger correlations between emergence and survival suggest that one population was under greater pressure to remain active while the other two showed survival payoffs for dormancy in poor years. Strong negative correlations between dormancy, emergence, and stasis balanced opposing contributions, resulting in near stationarity in two populations. These new methods provide an additional tool for researchers investigating stochastic population dynamics and should be useful for a broad range of applications in basic ecology and conservation biology.

SUBMITTER: Davison R

PROVIDER: S-EPMC3810155 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Contributions of covariance: decomposing the components of stochastic population growth in Cypripedium calceolus.

Davison Raziel R Nicolè Florence F Jacquemyn Hans H Tuljapurkar Shripad S

The American naturalist 20130118 3

Although correlations between vital rates can have important effects on evolution and demography, few studies have investigated their effects on population dynamics. Here, we extend life-table response experiments (LTREs) to variable environments, showing how to quantify contributions made by (1) mean vital rates, (2) variability driven by environmental fluctuations, (3) correlations implying demographic trade-offs and reflecting stage transition synchrony, and (4) elasticities reflecting local ...[more]

PMID: 23448889

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Project description:A few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature for Escherichia coli, Listeria innocua, and Salmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

Dataset Information

Contributions of covariance: decomposing the components of stochastic population growth in Cypripedium calceolus.

Publications

Contributions of covariance: decomposing the components of stochastic population growth in Cypripedium calceolus.

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