Building robust conservation plans.

Area of Science:

• Conservation science
• Environmental planning
• Biodiversity management

Background:

• Systematic conservation planning balances biodiversity goals with socioeconomic costs.
• Highly cost-efficient plans may be dissimilar to other valid plans, posing risks under implementation uncertainty.
• Implementation uncertainty, where plans may fail, challenges traditional conservation prioritization.

Purpose of the Study:

• To identify conditions where optimal conservation plans have low implementation success.
• To develop a flexible method for conservation prioritization that accounts for implementation uncertainty.
• To compare the performance of the new method against traditional cost-efficiency approaches.

Main Methods:

• Simulations of conservation plan implementation and habitat loss.
• A novel method generating multiple alternative plans and selecting based on similarity density in "similarity space".
• Comparison with classic cost-efficiency maximization using synthetic and real datasets.

Main Results:

• The new flexible method enhances the probability of achieving conservation targets under implementation uncertainty.
• The measure of shortfall (χ) in objectives significantly influences the performance of flexibility-enhanced prioritization.
• The proposed method provides a more robust approach when plans face potential failure or unavailability.

Conclusions:

• Flexible conservation prioritization improves success rates when facing uncertain implementation.
• Planners should consider plan similarity and flexibility to mitigate risks associated with implementation uncertainty.
• This approach aids in prioritizing conservation efforts effectively in dynamic and uncertain environments.