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Approximations of population growth in a noisy environment: on the dichotomy of non-age and age structure
Univ Skovde, Sweden.
Linköping University, Department of Physics, Chemistry and Biology, Theoretical Biology. Linköping University, Faculty of Science & Engineering.
2019 (English)In: Theoretical Ecology, ISSN 1874-1738, E-ISSN 1874-1746, Vol. 12, no 1, p. 99-110Article in journal (Refereed) Published
Abstract [en]

By simulations of population growth exposed to environmental noise, we compared realised long-run growth rate of age structured populations of four different life histories, with four approximations. One approximation used a non-structured population model, including specific population growth rates for each time step, determined by actual vital rates, while the other three used age-structured data to estimate a mean growth rate, then applicable for all time steps. In general, approximations were reasonable accurate. Yet some were completely erroneous and inaccurate enough to move stationary populations to become species on the red list as an endangered species according to International Union for Conservation of Nature (IUNC). The inaccuracies depended, in the following decreasing order, on: life history, what part of the demography the noise was acting on, and noise colour. The non-structured growth approximation had smaller errors with red noise while the three age-structured approximations had their largest errors with red noise. Since it is generally understood that the most common noise in nature is red noise, we conclude that the non-structured approximation will be the best predictor of population growth in most cases. We also conclude that evenness in distribution over age classes is a possible predictor for the sensitivity of long-run growth rate to type of approximation and therefore a promising object for further studies. Finally, our results indicate that in general, more focus ought to be on reducing the error in the data collection on population densities, especially for studies over longer time periods, than of collecting age-specific data.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG , 2019. Vol. 12, no 1, p. 99-110
Keywords [en]
Long-run growth; Growth approximation; Environmental noise; Age structure; Life history; Data collection
National Category
Ecology
Identifiers
URN: urn:nbn:se:liu:diva-154979DOI: 10.1007/s12080-018-0391-2ISI: 000459514000008OAI: oai:DiVA.org:liu-154979DiVA, id: diva2:1297567
Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-08-22

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CiteExportLink to record
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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • text
  • asciidoc
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