Modeling biodiversity benchmarks in variable environments

Abstract

Effective environmental assessment and management requires quantifiable biodiversity targets. Biodiversity benchmarks define these targets by focusing on specific biodiversity metrics, such as species richness. However, setting fixed targets can be challenging because many biodiversity metrics are highly variable, both spatially and temporally. We present a multivariate, hierarchical Bayesian method to estimate biodiversity benchmarks based on the species richness and cover of native terrestrial vegetation growth forms. This approach uses existing data to quantify the empirical distributions of species richness and cover within growth forms, and we use the upper quantiles of these distributions to estimate contemporary, “best‐on‐offer” biodiversity benchmarks. Importantly, we allow benchmarks to differ among vegetation types, regions, and seasons, and with changes in recent rainfall. We apply our method to data collected over 30 yr at ~35,000 floristic plots in southeastern Australia. Our estimated benchmarks were broadly consistent with existing expert‐elicited benchmarks, available for a small subset of vegetation types. However, in comparison with expert‐elicited benchmarks, our data‐driven approach is transparent, repeatable, and updatable; accommodates important spatial and temporal variation; aligns modeled benchmarks directly with field data and the concept of best‐on‐offer benchmarks; and, where many benchmarks are required, is likely to be more efficient. Our approach is general and could be used broadly to estimate biodiversity targets from existing data in highly variable environments, which is especially relevant given rapid changes in global environmental conditions.

Reference state and benchmark concepts for better biodiversity conservation in contemporary ecosystems

“…With the ever-expanding repositories of floristic data that contain inventories of plant species, there are opportunities to develop empirical benchmarks using existing vegetation data. Here, we demonstrate how floristic data were used to define the Best-on-Offer benchmarks to support and implement the Biodiversity Assessment Method under the Biodiversity Conservation Act 2016 (Yen et al, 2019). Under the New South Wales Biodiversity Conservation Act 2016, Vegetation Integrity was identified as a key biodiversity value and defined according to benchmarks for foliage cover and richness of native plant growth forms along with other habitat attributes.…”

“…Box 2 illustrates one recent attempt to identify and operationalize the contemporary Best‐on‐Offer reference state. In this operational example, sites with higher numbers of native plant species and greater structural complexity (relative to other sites of the same vegetation type) were used to identify the Best‐on‐Offer reference state as an approach to improving biodiversity conservation outcomes within south‐eastern Australia (Yen et al., 2019).…”

“…For each of the approximately 36,000 plots we allocated all native vascular plant species to one of six growth forms, trees, shrubs, grasses, forbs, ferns and others (Oliver et al, 2019), and calculated the structural (summed foliage cover of species within each growth form) and compositional (species richness within each growth form) each plot. We used modelled upper quantiles of the data distributions to estimate contemporary, Best-on-Offer benchmarks for growth form cover and richness (Yen et al, 2019).…”

“…Eighteen broad geomorphological units within the study area. Numbers shown in parentheses are the number vegetation communities (KC) with floristic plot data of the same vegetation type) were used to identify the Best-on-Offer reference state as an approach to improving biodiversity conservation outcomes within south-eastern Australia (Yen et al, 2019).…”

“…This could include year‐to‐year variation as well as longer‐term trends that might be associated with large‐scale shifts in climate, biotic interactions or disturbance regimes. Furthermore, benchmarks can be refined by ongoing assessment and monitoring of exemplar sites which has benefits of delivering the potential for dynamic benchmarks that represent seasonal and climatic changes (Yen et al., 2019). The timeframe of the reference state baseline is clearly articulated because this approach is based on the underlying data.…”