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Updated: Jun 2, 2026
20:36
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Integrating info-gap decision theory with robust population management: a case study using the Mountain Plover
Max Post van der Burg1, Andrew J Tyre
1School of Natural Resources, University of Nebraska-Lincoln, Hardin Hall, 3310 Holdrege Street, Lincoln, Nebraska 68583-0974, USA. mzp0022@auburn.edu
Summary
Wildlife managers can improve conservation decisions under extreme uncertainty by combining robust population management with information-gap decision theory. This approach helps prioritize actions, like nest marking for the Mountain Plover, to maintain stable populations.
Area of Science:
- Ecology
- Conservation Biology
- Wildlife Management
Background:
- Wildlife management decisions are often hampered by significant uncertainty, particularly when data is scarce.
- Existing methods like robust population management and information-gap decision theory address uncertainty but are rarely used together.
Purpose of the Study:
- To develop and apply a novel integrated approach combining robust population management with information-gap decision theory for conservation under extreme uncertainty.
- To assess nest survival management strategies for the endangered Mountain Plover (Charadrius montanus).
Main Methods:
- Integrated robust population management for matrix models with the information-gap decision theory framework.
- Applied the combined model to nest survival management in the Mountain Plover population.
Main Results:
- Matrix sensitivities indicated limited impact of nest management on population growth rate.
- High uncertainty in adult and juvenile survival suggested maximizing nest marking effort as a robust strategy for population stability.
Conclusions:
- The integrated information-gap model offers a valuable framework for evaluating conservation decisions under severe uncertainty.
- Balancing opportunity and robustness is key for effective wildlife population management when data is limited.
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