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Extinction dynamics in experimental metapopulations.

Jane Molofsky1, Jean-Baptiste Ferdy

  • 1Department of Botany, University of Vermont, Burlington, VT 05405-0086, USA. jane.molovsky@uvm.edu

Proceedings of the National Academy of Sciences of the United States of America
|March 2, 2005
PubMed
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Metapopulation theory suggests migration aids persistence, but this study found a nonlinear relationship. Too much migration can increase extinction risk by causing synchronized population cycles.

Area of Science:

  • Ecology
  • Population Biology
  • Conservation Biology

Background:

  • Metapopulation theory is crucial for conservation in fragmented landscapes.
  • Classical models often overlook local population structure and dynamics, which can influence extinction.
  • Understanding migration's role is key to managing fragmented populations.

Purpose of the Study:

  • To investigate metapopulation dynamics under varying migration rates.
  • To examine how migration influences population persistence and extinction risk.
  • To test predictions of classical metapopulation theory using experimental setups.

Main Methods:

  • Utilized experimental metapopulations of the annual plant Cardamine pensylvanica.
  • Manipulated migration rates between local populations.

Related Experiment Videos

  • Employed stochastic simulations and calculated metapopulation capacity and population coherence.

    • Main Results:

    • Connected populations persisted longer than isolated ones, but the effect of migration was nonlinear.
    • Extinction risk increased sharply above a migration threshold, consistent with metapopulation capacity calculations.
    • High migration rates and population coherence increased extinction risk due to synchronized population cycles.

    Conclusions:

    • Classical metapopulation theory partially predicts persistence, but local dynamics and coherence are critical.
    • There is an optimal migration level for population persistence; exceeding it can be detrimental.
    • Empirically determining optimal migration for natural populations is complex due to nonlinear effects and coherence risks.