Modeling biodiversity benchmarks in variable environments

Effective environmental assessment and management requires quantifiable bio-diversity targets. Biodiversity benchmarks define these targets by focusing on specific biodiver-sity metrics, such as species richness. However, setting fixed targets can be challenging becausemany biodiversity metrics are highly variable, both spatially and temporally. We present a mul-tivariate, hierarchical Bayesian method to estimate biodiversity benchmarks based on the spe-cies richness and cover of native terrestrial vegetation growth forms. This approach usesexisting data to quantify the empirical distributions of species richness and cover withingrowth forms, and we use the upper quantiles of these distributions to estimate contemporary,“best-on-offer”biodiversity benchmarks. Importantly, we allow benchmarks to differ amongvegetation types, regions, and seasons, and with changes in recent rainfall. We apply ourmethod to data collected over 30 yr at~35,000 floristic plots in southeastern Australia. Ourestimated benchmarks were broadly consistent with existing expert-elicited benchmarks, avail-able for a small subset of vegetation types. However, in comparison with expert-elicited bench-marks, our data-driven approach is transparent, repeatable, and updatable; accommodatesimportant spatial and temporal variation; aligns modeled benchmarks directly with field dataand the concept of best-on-offer benchmarks; and, where many benchmarks are required, islikely to be more efficient. Our approach is general and could be used broadly to estimate bio-diversity targets from existing data in highly variable environments, which is especially relevantgiven rapid changes in global environmental conditions.This research was supported by the Australian Research Council Centre of Excellence for Environmental Decisions (CE11001000104) and the New South Wales Office of Environment and Heritag