The population response to environmental noise: population size, variance and correlation in an experimental system

Timothy Guy Benton, C. T. Lapsley, A. P. Beckerman

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    1. Variation in an organism's environment can influence its life history and therefore its population size. Understanding the interplay between noise and population dynamics is of considerable importance, especially for prescribing management of economically important or threatened species. The impact of noise on population biology has been the subject of frequent theoretical investigations and discussed in posthoc analyses of time-series. However, there is a dearth of experimental investigations.

    2. Here we report results from a replicated laboratory study of soil mites, Sancassania berlesei, kept in controlled environments but with food supplied randomly, with different synchrony and variances, while maintaining the same mean rate.

    3. Increasing environmental variance increases population variance, but decreases mean population size. therefore changing the observed shape of the distribution of population sizes: the shape becomes more skewed with more environmental variance.

    4. The distribution of population sizes are best described by negative binomial or gamma distributions. Log-normal and normal distributions rarely fit and Poisson distributions never fit.

    5. The correlation between populations is sensitive to the correlation in environmental noise but insensitive to its variance.

    6. Different life stages (eggs, juveniles and adults) respond differently to noise. in that there are significant differences in the way that environmental variation changes the mean, variance, shape of distribution and relationship between environmental and population synchrony.

    Original languageEnglish
    Pages (from-to)320-332
    Number of pages12
    JournalJournal of Animal Ecology
    Publication statusPublished - 2002


    • gamma distribution
    • Moran effect
    • negative binomial distribution
    • Sancassania berlesei
    • stochasticity
    • MODELS

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