Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterize currently recognized historical reference states and describe them as Pre‐Human, Indigenous Cultural, Pre‐Intensification and Hybrid‐Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values relative to other locations within the same ecosystem, regardless of the disturbance history. We acknowledge that past processes play an essential role in driving contemporary patterns of diversity. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal—to maximize biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g. native species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This ‘road map of reference states’ offers perspective needed to define and assess the status and trends in biodiversity and habitats. We demonstrate the contemporary reference state concept with an example from south‐eastern Australia. Our framework provides a tractable way for policy‐makers and practitioners to navigate biodiversity assessments to maximize conservation and restoration outcomes in contemporary ecosystems.

Questions: The cover and abundance of individual plant species have been recorded on ordinal scales for millions of plots world‐wide. Ordinal cover data often need to be transformed to a quantitative form (0%–100%), especially when scrutinising summed cover of multiple species. Traditional approaches to transforming ordinal data often assume that data are symmetrically distributed. However, skewed abundance patterns are ubiquitous in plant community ecology. The questions this paper addresses are (a) how can we estimate transformation values for ordinal data that account for the underlying right‐skewed distribution of plant cover; (b) do different plant groups require different transformations; and (c) how do our transformations compare to other commonly used transformations within the context of exploring the aggregate properties of vegetation?Location: Global.Methods: We assigned Braun‐Blanquet cover‐abundance ordinal values to continuous cover observations. We fitted a Bayesian hierarchical beta regression to estimate the predicted mean (PM) cover of each of six plant growth forms within six ordinal classes. We illustrate our method using a case study (2,809 plots containing 95,812 observations), compare the model‐derived estimates to other commonly used transformations and validate our model using an independent dataset (2,227 plots containing 51,497 observations) accessed through the VegBank database.Results: Our model found that PM estimates differed by growth form and that previous methods overestimated cover, especially of smaller growth forms such as forbs and grasses. Our approach reduced the cumulative compounding of errors and was robust when validated against an independent dataset.Conclusions: By accounting for the right‐skewed distribution of cover data, our alternate approach for estimating transformation values can be extended to other ordinal scales. A more robust approach to transforming floristic data and aggregating cover estimates can strengthen ecological analyses to support biodiversity conservation and management.

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.

Biodiversity values under the New South Wales (NSW) Biodiversity Conservation Act 2016 are assessed in part according to the number and cover of native plant species within each of six growth form groups (trees, shrubs, grasses and grass-like, forbs, ferns, and others). Here we revise 19 growth form descriptions and use an independent expert process to allocate the most common (primary) growth form to the native terrestrial vascular plant flora of NSW. Independent allocations made by three botanists concurred for 6,153 taxa (84.7 per cent of the flora) and the remaining 1,112 taxa were resolved via a structured consensus making process. Allocation of each taxon to primary growth form has generated a single point of reference for the most common growth form for each native vascular plant species, expressed in its mature state across the extent of its range in NSW. The work presented here was undertaken to support transparent, repeatable and rigorous assessments of the richness and cover of growth form groups for the NSW Biodiversity Assessment Method. However, our approach and findings will be relevant to any government agency, industry group or researcher that uses plant growth forms to simplify ecological complexity or to assess the site-scale biodiversity values of terrestrial vegetation.

Assignment of large numbers of vegetation plots to a priori vegetation classifications is increasingly being required to support natural resource management, monitoring and conservation at regional scales. Several automated systems have been developed that use quantitative synoptic tables and algorithm-based plot-to-type assignment. However, where synoptic tables do not exist, and qualitative species lists characterise vegetation type classifications, existing systems may not apply. In these situations, vegetation experts may resort to manual assignment processes that can be slow, subjective and fraught with difficulties. This study combines repeatable and objective quantitative analyses, with new software, to deliver a semi-automated plot-to-type assignment process appropriate for a priori classifications based on qualitative species lists. The flexible semi-automated assignment program (SAAP) calculates a quantitative goodness-of-fit score between plots and types, based on the species that characterise each a priori vegetation type, and the species that characterise groups of plots derived from quantitative analyses. We applied the SAAP to a case-study of 630 native vascular plant species from 930 plots, and an a priori classification of 99 vegetation types. We varied vegetation data set transforms [cover per cent (0–100%), cover score (0–6) and presence–absence (1, 0)] and analysis settings and tested the degree to which the SAAP provided plot-to-type assignment concordant with manual expert assignment. Results provided clear evidence supporting the choice of particular data set transformations and analysis settings to maximise concordance. The SAAP allocated up to 50% of plots to the same expert-assigned vegetation type, and more than 70% of plots to an expert-assigned vegetation type ranked in the top five by the SAAP. When coupled with repeatable and objective quantitative analyses, the SAAP provides vegetation experts with a new semi-automated and quantitative decision support tool to assist with the assignment of vegetation plots within a priori vegetation classifications defined by characteristic species lists.

Context: Ensembles of artificial neural network models can be trained to predict the continuous characteristics of vegetation such as the foliage cover and species richness of different plant functional groups.Objectives: Our first objective was to synthesise existing site-based observations of native plant species to quantify summed percentage foliage cover and species richness within four functional groups and in totality. Secondly, we generated spatially-explicit, continuous, landscape-scale models of these functional groups, accompanied by maps of the model residuals to show uncertainty.Methods: Using a case study from New South Wales, Australia, we aggregated floristic observations from 6806 sites into four common plant growth forms (trees, shrubs, grasses and forbs) representing four different functional groups. We coupled these response data with spatially-complete surfaces describing environmental predictors and predictors that reflect landscape-scale disturbance. We predicted the distribution of foliage cover and species richness of these four plant functional groups over 1.5 million hectares. Importantly, we display spatially explicit model residuals so that end-users have a tangible and transparent means of assessing model uncertainty.Results: Models of richness generally performed well (R²0.43–0.63), whereas models of cover were more variable (R²0.12–0.69). RMSD ranged from 1.42 (tree richness) to 29.86 (total native cover). MAE ranged from 1.0 (tree richness) to 20.73 (total native foliage cover).Conclusions: Continuous maps of vegetation attributes can add considerable value to existing maps and models of discrete vegetation classes and provide ecologically informative data to support better decisions across multiple spatial scales.