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Related Experiment Videos

Should managed populations be monitored every year?

Cindy E Hauser1, Anthony R Pople, Hugh P Possingham

  • 1Department of Mathematics, University of Queensland, St. Lucia, Queensland 4072, Australia. c.hauser@uq.edu.au

Ecological Applications : a Publication of the Ecological Society of America
|May 23, 2006
PubMed
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Adaptive wildlife population monitoring can save costs by adjusting frequency based on population size, uncertainty, and environmental factors like rainfall, outperforming fixed annual surveys.

Area of Science:

  • Ecological modeling
  • Wildlife management
  • Decision theory

Background:

  • Estimating wild population size is crucial for management actions like setting harvest quotas.
  • Traditional monitoring often occurs at fixed intervals (e.g., annually), which can be costly.
  • Predictive population models offer a potentially less expensive alternative to annual monitoring.

Purpose of the Study:

  • To investigate the optimal frequency of wildlife population monitoring within a decision-theory framework.
  • To determine if adaptive monitoring regimes can outperform fixed-interval monitoring.
  • To identify key factors influencing the decision to monitor in a given year.

Main Methods:

  • Utilized a decision-theory framework to analyze monitoring frequency.

Related Experiment Videos

  • Developed and applied a population model incorporating previous survey data and environmental variables.
  • Illustrated the approach using data from a red kangaroo (Macropus rufus) population in South Australia.

    • Main Results:

    • An adaptive monitoring strategy, varying with system state, can be more effective than fixed-interval monitoring.
    • The decision to monitor depends on previous population density estimates, their uncertainty, and rainfall patterns.
    • Monitoring is most critical when model predictions are uncertain (e.g., after long gaps or high rainfall) or when populations approach critical thresholds.

    Conclusions:

    • Adaptive monitoring offers a cost-effective approach to wildlife population management.
    • Dynamic monitoring frequency, informed by model predictions and uncertainty, optimizes resource allocation.
    • Management decisions should consider predictive uncertainty and ecological thresholds to guide monitoring efforts.