Modeling biodiversity benchmarks in variable environments.

Area of Science:

• Ecology
• Environmental Science
• Conservation Biology

Background:

• Quantifiable biodiversity targets are crucial for effective environmental assessment and management.
• Biodiversity benchmarks, often based on species richness, face challenges due to spatial and temporal variability.
• Existing expert-elicited benchmarks are limited and may not capture environmental dynamics.

Purpose of the Study:

• To develop and apply a novel statistical method for estimating biodiversity benchmarks.
• To establish contemporary, data-driven benchmarks for native terrestrial vegetation growth forms.
• To accommodate spatial, temporal, and environmental variations in biodiversity metrics.

Main Methods:

• A multivariate, hierarchical Bayesian approach was employed.
• Empirical distributions of species richness and cover within growth forms were quantified using existing data.
• Upper quantiles of these distributions were used to estimate "best-on-offer" biodiversity benchmarks.

Main Results:

• Estimated benchmarks were consistent with expert-elicited benchmarks for some vegetation types.
• The data-driven approach proved transparent, repeatable, and updatable.
• Benchmarks successfully accounted for variation across vegetation types, regions, seasons, and rainfall.

Conclusions:

• The proposed Bayesian method provides a robust and adaptable framework for setting biodiversity targets.
• This approach offers a more efficient and data-aligned alternative to traditional methods, especially in variable environments.
• The method is broadly applicable for estimating biodiversity targets amidst global environmental change.