Stochastic Modeling of Extinction in Plant Populations

  • Eric S. Menges


Population viability analyses (predicting the future of small populations) have developed concepts relating largely to genetic threats, although environmental and demographic factors may be of greater immediate concern. Using twenty-eight published empirically derived projection matrices of various perennial herbs and trees, I model the behavior of plant populations by introducing temporal variation (stochasticity) in demographic parameters (mortality, growth, reproductive status, and reproductive output) into matrix projections of stage-structured populations. Stochastic modeling of population behavior allows estimation of extinction probabilities and minimum viable population.

Demographic stochasticity (DS) generates little variation in population dynamics. In contrast, moderate environmental stochasticity (ES) causes extinction risk for many populations with positive population growth under deterministic conditions. Increased ES causes increased extinction probabilities, decreased population sizes, decreased average time until extinction, and increased percentage of years with negative growth. Sensitivity to ES is greatest for populations with low finite rates of increase. Variation in mortality and growth among stages is far more important than variation in reproductive output. Simulation results generally agree with mathematical theory.

Using these analyses, I define a demographic version of minimum viable population (MVP) based on life history, degree of variation due to ES or DS, and acceptable levels of extinction probability over defined time intervals. Moderate ES may set higher MVPs than those necessary to counter short-term genetic effects of small population size. This technique could be extremely useful in conservation biology (e.g., managing endangered populations and preserve design) if data were available on temporal variation in demographic parameters.


Life History Plant Population Reproductive Output Environmental Stochasticity Conservation Biology 
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© Routledge, Chapman & Hall, Inc. and Diane C. Fiedler 1992

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  • Eric S. Menges

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