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

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dc.contributor.authorLiddicoat, C.
dc.contributor.authorKrauss, S.L.
dc.contributor.authorBissett, A.
dc.contributor.authorBorrett, R.J.
dc.contributor.authorDucki, L.C.
dc.contributor.authorPeddle, S.D.
dc.contributor.authorBullock, P.
dc.contributor.authorDobrowolski, M.P.
dc.contributor.authorGrigg, A.
dc.contributor.authorTibbett, M.
dc.contributor.authorBreed, M.F.
dc.date.issued2022
dc.description.abstractIn post-mining rehabilitation, successful mine closure planning requires specific, measurable, achievable, rele- vant 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 stan- dard 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.
dc.description.statementofresponsibilityCraig Liddicoat, Siegfried L. Krauss, Andrew Bissett, Ryan J. Borrett, Luisa C. Ducki, Shawn D. Peddle, Paul Bullock, Mark P. Dobrowolski, Andrew Grigg, Mark Tibbett, Martin F. Breed
dc.identifier.citationJournal of Environmental Management, 2022; 310:114748-1-114748-12
dc.identifier.doi10.1016/j.jenvman.2022.114748
dc.identifier.issn0301-4797
dc.identifier.issn1095-8630
dc.identifier.orcidLiddicoat, C. [0000-0002-4812-7524]
dc.identifier.urihttps://hdl.handle.net/2440/135251
dc.language.isoen
dc.publisherElsevier BV
dc.relation.granthttp://purl.org/au-research/grants/arc/LP190100051
dc.rights© 2022 Elsevier Ltd. All rights reserved.
dc.source.urihttps://doi.org/10.1016/j.jenvman.2022.114748
dc.subjecteDNA
dc.subjectMine closure assessment
dc.subjectRestoration genomics
dc.subjectRehabilitation trajectory
dc.subjectSoil microbiota
dc.subjectSpatial autocorrelation
dc.subject.meshBacteria
dc.subject.meshSoil
dc.subject.meshSoil Microbiology
dc.subject.meshBenchmarking
dc.subject.meshMicrobiota
dc.titleNext generation restoration metrics: Using soil eDNA bacterial community data to measure trajectories towards rehabilitation targets
dc.typeJournal article
pubs.publication-statusPublished

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