Soil microbiota are fundamentally linked to the restoration of degraded ecosystems, as they are central to important ecological functions including the support of plant communities. High throughput sequencing of environmental DNA used to characterise soil microbiota offers promise to monitor ecological progress towards reference states. In post-mining rehabilitation, successful mine closure planning requires specific, measurable, achievable, relevant and time-bound (SMART) completion criteria, such as returning ecological communities to match a target level of similarity to reference sites. We analysed patterns of surface soil bacterial community similarity to reference (9rehabilitation trajectory9) data from three long-term (> 25 year) post-mining rehabilitation chronosequence case studies from south-west Western Australia. We examined the influence of different ecological distance measures, sequence grouping approaches, and eliminating rare taxa on rehabilitation trajectories and predicted recovery times. We also explored the issue of spatial autocorrelation in our rehabilitation trajectory assessments and trialled a first-pass approach for correcting its undue influence. We found considerable variation in bacterial communities among reference sites within each case study minesite, providing valuable context for setting targets and evaluating recovery. Median Bray-Curtis similarities among references within each minesite ranged from 30-36%, based on amplicon sequence variant-level data. Median predicted times for rehabilitated sites to recover to these levels ranged from around 40 to over 100 years. We discuss strengths and limitations of the different approaches and offer recommendations to improve the robustness of this assessment method. Synthesis and applications. We demonstrate a proof-of-concept, complexity-reducing application of soil eDNA sequence-based surveys of bacterial communities in restoration chronosequence studies to quantitatively assess progress towards reference communities and corresponding rehabilitation targets. Our method provides a step towards developing microbiota-based SMART metrics for measuring rehabilitation success in post-mining, and potentially other, restoration contexts. Our approach enables prediction of recovery time, explicitly including uncertainty in assessments, and assists examination of potential barriers to ecological recovery, including biologically-associated variation in soil properties.
