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Using elasticity analysis of demographic models to link toxicant effects on individuals to the population level: an example

  1. F. HANSEN1,
  2. V. E. FORBES1,†,
  3. T. L. FORBES2

Article first published online: 27 MAR 2002

DOI: 10.1046/j.1365-2435.1999.00299.x

Issue

Functional Ecology

Functional Ecology

Volume 13, Issue 2, pages 157–162, April 1999

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How to Cite

HANSEN, F., FORBES, V. E. and FORBES, T. L. (1999), Using elasticity analysis of demographic models to link toxicant effects on individuals to the population level: an example. Functional Ecology, 13: 157–162. doi: 10.1046/j.1365-2435.1999.00299.x

Author Information

  1. 1

    Department of Life Sciences and Chemistry, Roskilde University, Universitetsvej 1, DK-4000 Roskilde, Denmark, and,

  2. 2

    Department of Marine Ecology and Microbiology, National Environmental Research Institute, Frederiksborgvej 399, PO Box 358, DK-4000 Roskilde, Denmark

Publication History

  1. Issue published online: 27 MAR 2002
  2. Article first published online: 27 MAR 2002

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Keywords:

  • Capitella;
  • life history;
  • nonylphenol;
  • population dynamics

1. A simple two-stage population model was applied to data from a previously published life-table response experiment (LTRE), which examined the toxicity of 4-n-nonylphenol to life-history traits of the polychaete Capitella sp. I. Population growth rates (λ) and the relative sensitivities (= elasticities) of λ to changes in each of the individual life-history traits were calculated.

2. In the present study, the life-history parameters measured in laboratory-reared individuals were manipulated to simulate potential effects of competition and predation on fecundity, time to reproductive maturity and juvenile survival to explore how such factors might influence the sensitivity of population growth rate to toxicant-caused changes in individual life-history traits.

3. Dramatic changes in elasticity patterns among simulations indicate that population growth rates may respond very differently to toxicant exposure depending on the extent to which other demographically limiting factors (e.g. competitors and/or predators) are operating on the population.

4. Effectively predicting the population-level consequences arising from toxicant effects measured on individuals can be improved by exploring the elasticity pattern of λ for the population over a range of realistic ecological situations.

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