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Setting population-size targets for geese causing socio-economic conflicts.

Fred A Johnson1, Henning Heldbjerg2, Szabolcs Nagy3

  • 1Department of Bioscience - Kalø, Aarhus University, Grenåvej 14, 8410, Rønde, Denmark. fred.johnson@bios.au.dk.

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Summary
This summary is machine-generated.

To manage human-wildlife conflicts, multi-criteria decision analysis (MCDA) helped set population targets for abundant greylag geese (Anser anser). The study recommends a 20% population reduction, requiring significant management effort.

Keywords:
ConflictConsensus-convergence modelGeeseMulti-criteria decision analysisObjectivesStakeholders

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Area of Science:

  • Wildlife management
  • Conservation biology
  • Decision analysis

Background:

  • Increasing European goose populations are causing conflicts with human activities.
  • Effective management strategies are needed to mitigate these human-wildlife conflicts.

Purpose of the Study:

  • To utilize multi-criteria decision analysis (MCDA) for setting population targets for greylag geese (Anser anser).
  • To achieve stakeholder consensus on managing super-abundant goose populations.

Main Methods:

  • Expert elicitation was used to assess impacts of goose abundance on ecological, economic, and societal objectives.
  • Stakeholder representatives weighted objectives, and a consensus-convergence model facilitated agreement on tradeoffs.
  • Population targets were established for two distinct management units.

Main Results:

  • The preferred population targets suggest approximately a 20% reduction from current greylag goose abundances.
  • Achieving these targets necessitates substantial management efforts beyond existing population-control measures.
  • Multi-criteria decision analysis (MCDA) proved effective in building consensus among diverse stakeholders.

Conclusions:

  • MCDA offers a systematic and transparent framework for resolving human-wildlife conflicts.
  • The study provides actionable population targets for greylag geese management.
  • Stakeholder involvement is crucial for successful wildlife population management.