Examining assumptions of soil microbial ecology in the monitoring of ecological restoration

Hart, Miranda M.; Cross, Adam T.; D’Agui, Haylee M.; Dixon, Kingsley W.; Heyde, Mieke van der; Mickan, Bede S.; Horst, Christina; Grez, Benjamin Moreira; Valliere, Justin M.; Rossel, Raphael A. Viscarra; Whiteley, Andrew S.; Wong, Wei Chang; Zhong, Hongtao; Nevill, Paul

doi:10.1002/2688-8319.12031

Cited by 26 publications

(20 citation statements)

References 110 publications

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“…However, the soil microbiome is extremely complex because of its large taxonomic diversity and entangled biogeochemical processes (Fierer, 2017). The large number of microbial species, processes and their interactions has resulted in large uncertainties regarding predicted ecosystem services and restoration outcomes (Hart et al., 2020; Schimel & Schaeffer, 2012). For instance, a recent meta‐analysis found that including microbial taxonomic composition, in addition to environmental variables, as predictors of carbon and nitrogen process rates only improved predictive power by ~10% on average (Graham et al., 2016).…”

Section: Discussionmentioning

confidence: 99%

Does restoration of plant diversity trigger concomitant soil microbiome changes in dryland ecosystems?

Yang

Balazs

Butterfield

et al. 2021

Journal of Applied Ecology

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Drylands constitute 41% of the terrestrial surface and are home to over 38% of the human population, and these numbers are likely to increase as a consequence of desertification and projected population growth (Bestelmeyer et al., 2015;Peters et al., 2015). With 10%-20% of drylands worldwide already defined as degraded, the United Nations has declared restoring degraded drylands as a top priority for supporting human life in the coming decades (Cherlet et al., 2018). Despite time-consuming and expensive efforts, dryland restoration success remains low (Copeland et al., 2018). High temperatures, scarce and variable precipitation, low vegetation cover, and low soil nutrient availability contribute to the vulnerability, and low recovery potential, of these fragile ecosystems (Havrilla et al., 2020;.Low vegetation cover in drylands emphasizes the crucial role that soil microbial communities may play in ecological restoration because microbes provide ecosystem functions normally delivered by plants in more vegetated ecosystems (Blankinship et al., 2016;Costantini et al., 2016). Micro-organisms engineer soil structure that

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Section: Discussionmentioning

confidence: 99%

Does restoration of plant diversity trigger concomitant soil microbiome changes in dryland ecosystems?

Yang

Balazs

Butterfield

et al. 2021

Journal of Applied Ecology

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“…Our approach provides a new opportunity to infer the continuous distribution of soil fungal community at a large area with the less sampling density and consequently less laboratory analysis costs. The approach will complement molecular approaches for the assessment, characterization and improved understanding of soil fungal communities and their associated functions at different scales (Hart et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions

Yang

Shen

Bisset

et al. 2021

Preprint

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Abstract. Soil fungi play important roles in the functioning of ecosystems, but they are challenging to measure. Using a continental scale dataset, we developed and evaluated a new method to estimate the relative abundance of the dominant phyla and diversity of fungi in Australian soil. The method relies on the development of spectro-transfer functions with state-of-the-art machine learning and using publicly available data on soil and environmental proxies for edaphic, climatic, biotic and topographic factors, and visible--near infrared (vis–NIR) wavelengths, to estimate the relative abundances of the Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota and Mucoromycota and community diversity measured with the abundance-based coverage estimator (ACE) index. The machine learning algorithms tested were partial least squares regression (PLSR), random forest (RF), Cubist, support vector machines (SVM), Gaussian process regression (GPR), XG-boost (XGB) and one-dimensional convolutional neural networks (1D-CNNs). The spectro-transfer functions were validated with a 10-fold cross-validation (n = 577). The 1D-CNNs outperformed the other algorithms and could explain between 45 and 73 % of fungal relative abundance and diversity. The models were interpretable, and showed that soil nutrients, pH, bulk density, an ecosystem water balance (a proxy for aridity) and net primary productivity were important predictors, as were specific vis–NIR wavelengths that correspond to organic functional groups, iron oxide and clay minerals. Estimates of the relative abundance for Mortierellomycota and Mucoromycota produced R2 ≥ 0.60, while estimates of the abundance of the Ascomycota and Basidiomycota produced R2 values of 0.5 and 0.58, respectively. The spectro-transfer functions for the Glomeromycota and diversity were the poorest with R2 values of 0.48 and 0.45, respectively. There is no doubt that the method provides estimates that are less accurate than more direct measurements with conventional molecular approaches. However, once the spectro-transfer functions are developed, they can be used with very little cost, and could serve to supplement the more expensive and laborious molecular approaches for a better understanding of soil fungal abundance and diversity under different agronomic and ecological settings.

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“…postglacial; Napier et al, 2019). Overall, next generation sequencing has the potential to uncover long overlooked interactions and unseen diversity, with significant implications for ecosystem restoration and conservation (Williams et al, 2014; but see Hart et al, 2020).…”

Section: New Horizons and Types Of Data For The Fieldmentioning

confidence: 99%

Fifteen emerging challenges and opportunities for vegetation science: A horizon scan by early career researchers

Yannelli

Bazzichetto

Conradi

et al. 2022

J Vegetation Science

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With the aim to identify future challenges and opportunities in vegetation science, we brought together a group of 22 early career vegetation scientists from diverse backgrounds to perform a horizon scan. In this contribution, we present a selection of 15 topics that were ranked by participants as the most emergent and impactful for vegetation science in the face of global change. We highlight methodological tools that we expect will play a critical role in resolving emerging issues by providing ways to unveil new aspects of plant community dynamics and structure. These tools include next generation sequencing, plant spectral imaging, process‐based species distribution models, resurveying studies and permanent plots. Further, we stress the need to integrate long‐term monitoring, the study of novel ecosystems, below‐ground traits, pollination interactions and global networks of near‐surface microclimate data at fine spatio‐temporal resolutions to fully understand and predict the impacts of climate change on vegetation dynamics. We also emphasize the need to integrate traditional forms of knowledge and a diversity of stakeholders into research, teaching, management and policy‐making to advance the field of vegetation science. The conclusions reached by this horizon scan naturally reflect the background, expertise and interests of a representative pool of early career vegetation scientists, which should serve as basis for future developments in the field.

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Examining assumptions of soil microbial ecology in the monitoring of ecological restoration

Cited by 26 publications

References 110 publications

Does restoration of plant diversity trigger concomitant soil microbiome changes in dryland ecosystems?

Does restoration of plant diversity trigger concomitant soil microbiome changes in dryland ecosystems?

Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions

Fifteen emerging challenges and opportunities for vegetation science: A horizon scan by early career researchers

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