Extinction risk depends strongly on factors contributing to stochasticity (original) (raw)

Nature volume 454, pages 100–103 (2008) Cite this article

Abstract

Extinction risk in natural populations depends on stochastic factors that affect individuals, and is estimated by incorporating such factors into stochastic models1,2,3,4,5,6,7,8,9. Stochasticity can be divided into four categories, which include the probabilistic nature of birth and death at the level of individuals (demographic stochasticity2), variation in population-level birth and death rates among times or locations (environmental stochasticity1,3), the sex of individuals6,8 and variation in vital rates among individuals within a population (demographic heterogeneity7,9). Mechanistic stochastic models that include all of these factors have not previously been developed to examine their combined effects on extinction risk. Here we derive a family of stochastic Ricker models using different combinations of all these stochastic factors, and show that extinction risk depends strongly on the combination of factors that contribute to stochasticity. Furthermore, we show that only with the full stochastic model can the relative importance of environmental and demographic variability, and therefore extinction risk, be correctly determined. Using the full model, we find that demographic sources of stochasticity are the prominent cause of variability in a laboratory population of Tribolium castaneum (red flour beetle), whereas using only the standard simpler models would lead to the erroneous conclusion that environmental variability dominates. Our results demonstrate that current estimates of extinction risk for natural populations could be greatly underestimated because variability has been mistakenly attributed to the environment rather than the demographic factors described here that entail much higher extinction risk for the same variability level.

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Figure 1: A family of stochastic Ricker models based on Ricker’s 26 assumptions about the life cycle of a fish species that cannibalises its eggs.

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Figure 2: Variance in the number of individuals in the next generation ( N t + 1 ) as a function of the number of individuals in the current generation ( N t ) for the stochastic Ricker models.

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Figure 3: Intrinsic mean time to extinction 30 (T m ) for the stochastic Ricker models as a function of the finite rate of increase ( R).

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Acknowledgements

We thank M. Gibson, D. Hodgkiss, C. Koenig, T. McCabe, D. Paulus, D. Smith, N. Tcheou, R. Villalobos and M. Wu for assistance. This study was funded by the National Science Foundation.

Author Contributions B.A.M. derived and analysed the models, and analysed the data. B.A.M. and A.H. conceived the study, planned and directed the experiments, and wrote the paper.

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Authors and Affiliations

  1. Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado 80309, USA,
    Brett A. Melbourne
  2. Department of Environmental Science and Policy, University of California, Davis, California 95616, USA,
    Alan Hastings

Authors

  1. Brett A. Melbourne
  2. Alan Hastings

Corresponding author

Correspondence toBrett A. Melbourne.

Supplementary information

Supplementary information (download PDF )

This file contains Supplementary Figures 1-4, Supplementary Methods, Supplementary Table 1, Supplementary Discussion, and Supplementary Notes. The Supplementary Figures show stochastic realisations of the models (Fig. S1), the best model fitted to the Tribolium data (Fig. S2), extensions to the models (Fig. S3) , and measurement error bias (Fig. S4). The Supplementary Methods provide a detailed derivation of the stochastic Ricker models, and equations to equate the total variance for environmental stochasticity and demographic heterogeneity. Supplementary Table 1 provides pmfs for the stochastic Ricker models. The Supplementary Discussion considers extensions to the stochastic Ricker models, the robustness of the model fit, and measurement error bias. The Supplementary Notes include additional references. (PDF 1068 kb)

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Melbourne, B., Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity.Nature 454, 100–103 (2008). https://doi.org/10.1038/nature06922

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Editorial Summary

Increased risk of extinction

The risk that a natural population can become extinct is a fundamental biological process, and is central to our understanding of biodiversity and evolution. But Brett Melbourne and Alan Hastings contend that existing mathematical models of extinction risk ascribe variability in population numbers to the wrong processes. In work that combines a new mathematical theory with experimental data, they show that different kinds of random-ness in the life of an animal combine in such a way that the risk of extinction is many times higher than previously thought possible, and that estimated risks of extinction for endangered species need to be raised.