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Problems in Extinction Model Selection and Parameter Estimation.

FOLEY1

  • 1Department of Biological Sciences, California State University, Sacramento, California 95819-6077, USA

Environmental Management
|May 10, 2000
PubMed
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A pragmatic, empirical Bayesian approach offers a consensus for population viability analysis in conservation planning. This method uses population models and parameter estimation to assess extinction risks and manage uncertainty effectively.

Area of Science:

  • Ecology
  • Conservation Biology
  • Population Dynamics
  • Statistical Ecology

Background:

  • Achieving consensus on population viability assessment (PVA) for habitat conservation plans (HCPs) remains a challenge.
  • Traditional hypothesis-testing approaches may not be the most pragmatic for real-world conservation.
  • Ecological modeling requires robust methods for parameter estimation and uncertainty quantification.

Purpose of the Study:

  • To propose a pragmatic, empirical Bayesian approach for population viability assessment.
  • To outline methods for selecting population models, estimating extinction parameters, and assessing prediction uncertainty.
  • To facilitate consensus-building among stakeholders in habitat conservation planning.

Main Methods:

Related Experiment Videos

  • Utilizing a hierarchy of nested population models incorporating growth (r), carrying capacity (K), Allee threshold (N(A)), and environmental stochasticity (v(r)).
  • Employing empirical Bayesian methods for parameter estimation, including analysis of time series data and habitat/biogeographical models.
  • Quantifying uncertainty through posterior distributions and proposing the use of prior information from a population biology database.

Main Results:

  • Analytic predictions of expected extinction times are available for simple population models.
  • The approach allows for adaptive management by accumulating local information through monitoring.
  • Uncertainty in parameter estimates can be quantified, and prior distributions can accommodate stakeholder variations.

Conclusions:

  • The empirical Bayesian approach provides a flexible framework for PVA in conservation.
  • Agreement on prior information, model selection, parameter estimation, and adaptive management strategies is crucial.
  • Addressing statistical methodology unfamiliarity is key to broader adoption in ecological training and practice.