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

Liddicoat, C. ORCID: https://orcid.org/0000-0002-4812-7524, Krauss, S. L. ORCID: https://orcid.org/0000-0002-7280-6324, Bissett, A. ORCID: https://orcid.org/0000-0001-7396-1484, Borrett, R. J. ORCID: https://orcid.org/0000-0001-8663-0844, Ducki, L. C., Peddle, S. D. ORCID: https://orcid.org/0000-0003-3464-3058, Bullock, P., Dobrowolski, M. P. ORCID: https://orcid.org/0000-0001-5586-4023, Grigg, A. ORCID: https://orcid.org/0000-0002-5818-2973, Tibbett, M. ORCID: https://orcid.org/0000-0003-0143-2190 and Breed, M. F. ORCID: https://orcid.org/0000-0001-7810-9696 (2022) Next generation restoration metrics: using soil eDNA bacterial community data to measure trajectories towards rehabilitation targets. Journal of Environmental Management, 310. 114748. ISSN 0301-4797

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To link to this item DOI: 10.1016/j.jenvman.2022.114748

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Interdisciplinary centres and themes > Soil Research Centre
Life Sciences > School of Agriculture, Policy and Development > Department of Sustainable Land Management > Centre for Agri-environmental Research (CAER)
ID Code:103441
Publisher:Elsevier

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