The secrets in our soils: Using soil bacteria to monitor mine site restoration across a 40-year chronosequence in an arid environment in Western Australia

Very little is known about the patterns of recovery and processes of soil biota following land ecological restoration of native ecosystems cleared for mining. Despite this, there is increasing recognition of the importance of soil biota for restoration of critical ecosystem processes. The emergence of new eDNA metabarcoding technologies now enable the high throughput assessment and characterisation of these previously hidden belowground communities. Here, I sampled soil bacterial communities from a 40-year rehabilitation chronosequence of a mineral sands mine in an arid environment in southwest Western Australia. The assemblages displayed strong differences in composition across differently aged rehabilitated sites. A general recovery trend toward the native, undisturbed state was observed, with some notable phyla and orders showing strong trajectories of recovery to native assemblages with increasing time since rehabilitation. These shifting taxonomic patterns were accompanied by changes in putative functional assignments of ecological processes including carbon and nitrogen cycling. This analysis of spatial and temporal patterns of diversity and association with environmental conditions in natural and differently aged rehabilitated ecosystems can be used to monitor recovery progress and trajectories, and aid future restoration goal setting and decision making

Soil DNA chronosequence analysis shows bacterial community re‐assembly following post‐mining forest rehabilitation

Mining activities modify both aboveground and belowground ecological communities, presenting substantial challenges for restoration. The soil microbiome is one of these impacted communities and performs important ecosystem functions but receives limited focus in restoration. Sequencing soil DNA enables accurate and cost-effective assessment of soil microbiota, allowing for comparisons across land use, environmental, and temporal gradients. We used amplicon sequencing of the bacterial 16s rRNA gene extracted from soil samples across a 28-year post-mining rehabilitation chronosequence to assess soil bacterial composition and diversity following rehabilitation at a bauxite mine in Western Australia's jarrah forest. We show that while bacterial alpha diversity did not differ between reference and rehabilitated sites, bacterial community composition changed dramatically across the chronosequence, suggesting strong impacts by mining and rehabilitation activities. Bacterial communities generally became increasingly similar to unmined reference sites with time since rehabilitation. Soil from sites rehabilitated as recently as 14 years ago did not have significantly different communities to reference sites. Overall, our study provides evidence indicating the recovery of soil bacterial communities toward reference states following rehabilitation. Including several ecological reference sites revealed substantial natural variability in bacterial communities from within a single mine site. We urge future restoration chronosequence studies to sample reference sites that geographically span the restored sites and/or are spatially paired with restored sites to ensure this variability is captured and to improve any inferences on recovery

Next generation restoration metrics: using soil eDNA bacterial community data to measure trajectories towards rehabilitation targets

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. Soil microbiota are fundamentally linked to the restoration of degraded ecosystems, helping to underpin ecological functions and plant communities. High-throughput sequencing of soil eDNA to characterise these communities offers promise to help monitor and predict ecological progress towards reference states. Here we demonstrate a novel methodology for monitoring and evaluating ecological restoration using three long-term (>25 year) case study post-mining rehabilitation soil eDNA-based bacterial community datasets. Specifically, we developed rehabilitation trajectory assessments based on similarity to reference data from restoration chronosequence datasets. Recognising that numerous alternative options for microbiota data processing have potential to influence these assessments, we comprehensively examined the influence of standard versus compositional data analyses, different ecological distance measures, sequence grouping approaches, eliminating rare taxa, and the potential for excessive spatial autocorrelation to impact on results. Our approach reduces the complexity of information that often overwhelms ecologically-relevant patterns in microbiota studies, and enables prediction of recovery time, with explicit inclusion of uncertainty in assessments. We offer a step change in the development of quantitative microbiota-based SMART metrics for measuring rehabilitation success. Our approach may also have wider applications where restorative processes facilitate the shift of microbiota towards reference states