Distinguishing error from chaos in ecological time series

DOI PDF Web Site 被引用文献3件
  • George Sugihara
    Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093, U. S. A.
  • Bryan Thomas Grenfell
    Department of Zoology, Cambridge University, Cambridge, U. K.
  • Robert Mccredie May
    Department of Zoology, Oxford University, Oxford, OX1 3PS, U. K. And Imperial College, Exhibition Road, London SW7 2AZ, U. K.

書誌事項

公開日
1990-11-29
権利情報
  • https://royalsociety.org/journals/ethics-policies/data-sharing-mining/
DOI
  • 10.1098/rstb.1990.0195
公開者
The Royal Society

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説明

<jats:title>Abstract</jats:title> <jats:p>Over the years, there has been much discussion about the relative importance of environmental and biological factors in regulating natural populations. Often it is thought that environmental factors are associated with stochastic fluctuations in population density, and biological ones with deterministic regulation. We revisit these ideas in the light of recent work on chaos and nonlinear systems. We show that completely deterministic regulatory factors can lead to apparently random fluctuations in population density, and we then develop a new method (that can be applied to limited data sets) to make practical distinctions between apparently noisy dynamics produced by low-dimensional chaos and population variation that in fact derives from random (high-dimensional)noise, such as environmental stochasticity or sampling error. To show its practical use, the method is first applied to models where the dynamics are known. We then apply the method to several sets of real data, including newly analysed data on the incidence of measles in the United Kingdom. Here the additional problems of secular trends and spatial effects are explored. In particular, we find that on a city-by-city scale measles exhibits low-dimensional chaos (as has previously been found for measles in New York City), whereas on a larger, country-wide scale the dynamics appear as a noisy two-year cycle. In addition to shedding light on the basic dynamics of some nonlinear biological systems, this work dramatizes how the scale on which data is collected and analysed can affect the conclusions drawn.</jats:p>

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