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

Autor: Liddicoat C; College of Science and Engineering, Flinders University, Adelaide, Australia; School of Public Health, The University of Adelaide, Adelaide, Australia. Electronic address: craig.liddicoat@flinders.edu.au., Krauss SL; Kings Park Science, Western Australia Department of Biodiversity Conservation and Attractions, Perth, Australia; School of Biological Sciences, University of Western Australia, Perth, Australia., Bissett A; CSIRO Oceans and Atmosphere, Hobart, Australia., Borrett RJ; College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia., Ducki LC; College of Science and Engineering, Flinders University, Adelaide, Australia; College of Science, Health, Engineering and Education, Murdoch University, Perth, Australia., Peddle SD; College of Science and Engineering, Flinders University, Adelaide, Australia., Bullock P; South32 Worsley Alumina, Perth, Australia., Dobrowolski MP; School of Biological Sciences, University of Western Australia, Perth, Australia; Iluka Resources Limited, Perth, Australia; Harry Butler Institute, Murdoch University, Perth, Australia., Grigg A; Alcoa of Australia Limited, Perth, Australia., Tibbett M; School of Biological Sciences, University of Western Australia, Perth, Australia; Department of Sustainable Land Management & Soil Research Centre, School of Agriculture, Policy and Development, University of Reading, Berkshire, United Kingdom., Breed MF; College of Science and Engineering, Flinders University, Adelaide, Australia.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2022 May 15; Vol. 310, pp. 114748. Date of Electronic Publication: 2022 Feb 19.
DOI: 10.1016/j.jenvman.2022.114748
Abstrakt: 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.
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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