Opportunities and challenges for microbiomics in ecosystem restoration.

Microbiomics is the science of characterizing microbial community structure, function, and dynamics. It has great potential to advance our understanding of plant-soil-microbe processes and interaction networks which can be applied to improve ecosystem restoration. However, microbiomics may be perceived as complex and the technology is not accessible to all. The opportunities of microbiomics in restoration ecology are considerable, but so are the practical challenges. Applying microbiomics in restoration must move beyond compositional assessments to incorporate tools to study the complexity of ecosystem recovery. Advances in metaomic tools provide unprecedented possibilities to aid restoration interventions. Moreover, complementary non-omic applications, such as microbial inoculants and biopriming, have the potential to improve restoration objectives by enhancing the establishment and health of vegetation communities.

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.