Stochastic effects contribute to population fitness differences

Raziel Davison, Marc Stadman and Eelke Jongejans

Ecological Modelling, 2019, vol. 408, issue C, -

Abstract: Demographic rates differ between populations and also fluctuate over time, sometimes driving large fitness differences, but the strength of stochastic effects remain heretofore unresolved. We demonstrate the importance of stochastic processes by comparing the drivers of long-term population growth. We quantify stochastic contributions to differences in population growth rates among 218 plant and two animal populations representing 62 species (all records from the COMPADRE and COMADRE matrix databases suitable for our analyses) using the Small Noise Approximation Life Table Response Experiment (SNA-LTRE), a recently developed matrix model tool for decomposing the stochastic contributions of elasticities, variability and correlations. Stochastic influences comprise over a quarter of all contributions to population growth variation among populations (mean ± SD = 28 ± 14%). The relative importance of stochastic effects decreases with generation time and lifespan, confirming predictions that longevity buffers populations against the negative effects of variability. Stochastic effects are larger when populations differ widely in growth rates, suggesting that stochasticity is likely to be important where ecological conditions vary greatly, and are larger among herbaceous perennials than among woody plants, ferns and succulents, possibly reflecting phenotypic plasticity in response to fluctuating environments. Overall, we show that stochastic effects are often strong enough to warrant the additional effort required to characterize their contributions to population growth.

Keywords: Correlation; Covariance; Elasticity; Life Table Response Experiment; Small Noise Approximation; Stochastic matrix model (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:408:y:2019:i:c:5

DOI: 10.1016/j.ecolmodel.2019.108760

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