Vegetation quality assessment: A sampling-based loss-gain accounting framework for native, disturbed and reclaimed vegetation.

• Vegetation Quality Assessment measures losses and gains in vegetation quality and extent. • An objective, repeatable workflow for quantifying impacts and monitoring restoration progress. • Stratified-random sampling reduces bias and enables estimation of error and uncertainty. • Quality measured by overlap between indicator distributions in impacted and undisturbed vegetation. • Distribution overlap provides a novel and intuitive index of similarity to benchmark. Governments and society increasingly are demanding that industrial projects result in a net positive impact (NPI) on biodiversity. Impacts are commonly measured in terms of losses and gains of area and quality of vegetation, where quality refers to how closely a site matches the condition of native vegetation in its undisturbed state. Existing vegetation quality frameworks share a number of limitations, including little or no replication, uncertain scope of inference, vulnerability to bias, and inability to measure error. Here we present the Vegetation Quality Assessment (VQA) framework, a sampling-based extension of Quality Hectares that measures vegetation quality in terms of overlap between the probability distributions of ecological indicators at a project site and in undisturbed (benchmark) vegetation of the same kind. Distribution overlap incorporates natural variation at the landscape scale and provides an intuitive measure of quality that varies between 0 and 1. Indicators are measured using a stratified-random sampling design that minimizes bias and supports inference at the scale of the project landscape. Confidence limits of quality and quality hectares are determined by bootstrapping; power and minimum sample sizes are estimated by Monte Carlo simulation. Multiple assessments track losses and gains of quality hectares and enable accurate accounting of progress to NPI. The VQA framework can be implemented using a variety of vegetation sampling methods, allowing existing vegetation databases to be leveraged as sources of data. We conclude by demonstrating the application of VQA at several mining operations in the Elk Valley of southeastern British Columbia, Canada. [ABSTRACT FROM AUTHOR]