Identifying conservation areas on the basis of alternative distribution data sets

Area of Science:

• Conservation Biology
• Spatial Ecology
• Biodiversity Informatics

Background:

• Accurate biodiversity distribution data is crucial for effective conservation planning.
• Inaccurate data leads to over- or underrepresentation of species, impacting reserve network design and effectiveness.

Purpose of the Study:

• To investigate how different distribution data sets affect conservation reserve selection.
• To propose new methods for minimizing uncertainty in achieving conservation targets within reserves.

Main Methods:

• Utilized common prioritization methods: richness mapping and systematic reserve design.
• Developed a novel approach integrating multiple distribution data types.
• Simulated reserve establishment using predicted distribution models, occurrence records, and combined data for Arizona mammals.

Main Results:

• Spatial overlap between reserve networks varied greatly depending on data type, with pairwise overlap never exceeding 50% even with combined data.
• Reserve size required to meet conservation targets fluctuated significantly, with the largest system being 10 times the smallest.
• Different data sets led to substantial variability in reserve placement and size.

Conclusions:

• The choice of biodiversity distribution data profoundly impacts conservation planning outcomes.
• Novel integration methods can minimize uncertainty and improve the reliability of biodiversity reserve networks.
• Accurate and integrated data are essential for effective biodiversity feature protection.