2019
DOI: 10.3389/fenvs.2019.00173
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Primer and Database Choice Affect Fungal Functional but Not Biological Diversity Findings in a National Soil Survey

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Cited by 33 publications
(38 citation statements)
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“…3D) compared to undisturbed control (ANOVA, p < 0.05 after FDR correction). However, we acknowledge that AMF are better characterized by the 18S rRNA region rather than the ITS region (George et al 2019). Detected AMF belonged to class Glomeromycetes (genera Diversispora, Racocetra, Claroideoglomus, Dominikia, Funneliformis, Glomus, Kamienskia, Rhizophagus) and class Archaeosporomycetes (genus Archaeospora).…”
Section: Resultsmentioning
confidence: 90%
“…3D) compared to undisturbed control (ANOVA, p < 0.05 after FDR correction). However, we acknowledge that AMF are better characterized by the 18S rRNA region rather than the ITS region (George et al 2019). Detected AMF belonged to class Glomeromycetes (genera Diversispora, Racocetra, Claroideoglomus, Dominikia, Funneliformis, Glomus, Kamienskia, Rhizophagus) and class Archaeosporomycetes (genus Archaeospora).…”
Section: Resultsmentioning
confidence: 90%
“…This is consistent with studies that have found that ectomycorrhizal fungi were predominant in forested sites (Nguyen et al ; Detheridge et al ) and arbuscular mycorrhizal fungi in grassland sites (Nguyen et al ). Interpretation of these results needs to be approached with caution, however, as Acacia species can obfuscate data through their dual association with both arbuscular and ectomycorrhizal fungal species, and resolution of Glomeralean sequences using ITS alone has not proven comprehensive (Lekberg et al ; George et al ). The high abundance of ectomycorrhizal fungi sequences in our study was correlated to the high stem density (>3,000 stems/ha) of Acacia and eucalypt species (the main woody vegetation) (Ngugi et al ), and has been reported to be associated with accumulation of leaf litter (Glen et al ).…”
Section: Discussionmentioning
confidence: 99%
“…Animal richness was governed by intensive land use and unaffected by soil properties, while microbial richness was driven by environmental properties across land uses. The efficacy of 18S and ITS1 barcodes in capturing fungal biological and functional diversity has been compared, revealing barcode biases that influenced metrics of functional but not biological diversity (George et al, 2019b). Investigations of bacterial functional groups in the 16S marker gene dataset showed changes in sulphate-reducing bacterial community across land uses, with highest richness in grasslands (George et al, 2020).…”
Section: Additional Published Work To Datementioning
confidence: 99%