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Variation among Individuals and Reduced Demographic Stochasticity.

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  • 1Donald Bren School of Environmental Science and Management, University of California, Santa Barbara, CA 93106-5131, U.S.A., email kendall@bren.ucsb.edu.

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Summary
This summary is machine-generated.

Population viability analysis (PVA) models may overestimate extinction risk. Variation among individuals in survival rates, not accounted for in current models, can reduce this risk for small populations.

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Area of Science:

  • Ecology
  • Conservation Biology
  • Population Dynamics

Background:

  • Population viability analysis (PVA) uses stochastic demographic models to predict extinction risk.
  • Higher variance in demographic rates generally increases extinction risk.
  • Current PVA models assume identical expected fates for all individuals, modeling demographic stochasticity via distributions like the binomial.

Purpose of the Study:

  • To investigate how individual variation in expected survival affects demographic stochasticity in PVA.
  • To determine if current PVA models overestimate extinction risk due to this assumption.

Main Methods:

  • Development of a simple conceptual model to explore individual variation effects.
  • Analysis of demographic stochasticity in survival using Jensen's inequality.
  • Consideration of individual variation in fecundity and its impact on extinction risk.

Main Results:

  • Existing PVA models overestimate demographic stochasticity in survival when individual variation in expected survival exists.
  • This overestimation is linked to Jensen's inequality and the concave nature of the binomial variance function.
  • The impact of individual variation on fecundity-driven stochasticity is uncertain but could reduce extinction risk.

Conclusions:

  • Standard PVA models may inflate extinction probabilities by not accounting for individual heterogeneity in survival.
  • Incorporating individual variation in survival could lead to more accurate extinction risk assessments.
  • Further research is needed to understand individual variation in fecundity for comprehensive PVA.