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Autocorrelated environmental variation and the establishment of invasive species

K. Cuddington,A. Hastings

Published 2016 in Oikos

ABSTRACT

Environmental parameters such as temperature and rainfall have a positively autocorrelated variance structure which makes it likely that runs of good or bad conditions will occur. It has previously been demonstrated that such autocorrelated environmental variance can increase the probability of extinction in small populations, in much the same way that increased variance without autocorrelation can increase extinction risk. As a result, it has also been suggested that positive autocorrelation will decrease the probability that a species will establish in a novel location. We suggest that describing the probability of invasion success as the probability of indefinite persistence may be an inappropriate definition of risk. Economic or ecological damage may be associated with a population that initially reaches high densities before going extinct in the new location. In addition, such populations may spread to new locations before extirpation. We use a modeling approach to examine the effect of positively autocorrelated conditions on the probability that small populations will reach large size before extinction. We find that where variance is high and the geometric mean of the population growth rate is low, autocorrelation increases the risk that a population will pass a an upper threshold density, even when extinction probability is unaffected. Therefore species classified as having low probability of invasion risk on the basis of population growth rates measured in low variance environments may actually have quite a substantial probability of establishing a large population for a period of time. The mechanism behind the effect is the disproportionate influence of short runs of good conditions initially following introduction.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    Oikos

  • Publication date

    2016-07-01

  • Fields of study

    Biology, Economics, Environmental Science

  • Identifiers
    DOI 10.1111/OIK.02859
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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