Long‐ and short‐read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities

Abstract

Fungi are key drivers of nutrient cycling in terrestrial ecosystems.One important guild of fungi form ectomycorrhizas (ECM), a symbiosis between fungi and plants in which fungal hyphae enclose the plant's fine root tips. The fungi provide nutrients and protection from pathogens in exchange for carbon from the plant (Smith & Read, 2008). Approximately 8% of described fungal species are thought to take part in ECM symbiosis (Ainsworth, 2008;Rinaldi et al., 2008).Although only about 2% of land plant species form ECM, these include ecologically and economically important stand-forming trees belonging to both temperate and boreal groups such as Pinaceae and Fagaceae, and tropical groups such as Dipterocarpaceae, Uapaca (Phyllanthaceae) and Fabaceae tr. Amherstiae (Brundrett, 2017), together representing approximately 60% of tree stems globally (Steidinger et al., 2019).Although ECM fungi form many well-known mushrooms (e.g. Amanita, Cantharellus, Boletus), some instead produce inconspicuous (e.g. Tomentella) or no (e.g. Cenococcum) fruitbodies. Even when fruitbodies are large, they are ephemeral, so study of ECM communities

Soil eukaryote community shift but not composition is consistently recovered by different OTU inference methods applied to long read metabarcoding data

“…The LSU, 5.8S, ITS2, and full ITS (ITS1-5.8S-ITS2) regions were extracted using LSUx (version 0.99.6; Furneaux et al, 2021) from the OTU consensus sequences generated by all three OTU generation methods. Three datasets were used for assigning taxonomy: the SILVA LSU NR 99 dataset (version 138.1, eukaryotes only; Quast et al, 2012) and RDP fungal LSU training set (version 11; Liu et al, 2012) for the extracted LSU sequences, and the UNITE all-eukaryotes dataset (version 8.2, including singletons; Nilsson et al, 2019) for the extracted ITS sequences.…”

“…While different bioinformatic tools capture different representations of a sequenced soil microeukaryotic community, large scale ecological patterns are similarly represented with set threshold OTUs compared to denoised ASVs for short read amplicons (Glassman & Martiny, 2018). Further, spatio-temporal turnover patterns are consistently captured using both different sequencing technologies and different amplicon lengths (Furneaux et al, 2021). However, suitable species proxies and taxonomic assignments are important to go beyond large-scale patterns.…”

“…This approach captures both within and between species variation, even as little as one base pair difference, and so ASVs may be further clustered in order to serve as proxies for species (Frøslev et al, 2017). However, DADA2 does rely on the presence of at least two identical sequences as seeds for generating ASVs, so the method can perform poorly when the majority of reads are singletons (Furneaux et al, 2021). For consistency, instead of ASV we use OTU_A to refer to the output of this denoising method.…”

“…The development of PacBio sequencing technology (Pacific Biosciences, Menlo Park, CA, SA) has allowed longer eDNA amplicons, including both variable spacers and more conserved functional rDNA regions, to be sequenced from complex samples. In the absence of matching reference sequences, taxonomic assignment of novel lineages is possible based on phylogenetic inference using the more conserved rDNA small subunit (SSU) (Jamy et al, 2020) and/or large subunit (LSU) sequences (Furneaux et al, 2021;Leho Tedersoo et al, 2017). The benefit of phylogenetically supported taxonomic assignment of OTUs is particularly relevant in communities consisting mostly of poorly characterized lineages (Kalsoom Khan et al, 2020).…”

“…Apart from providing the first belowground community observations from this study site, we outline an analysis of detection limits and phylogenetic resolution of three different OTU generation methods applied to long read metabarcoding data. This approach is potentially important for the field of eDNA community analysis as long read metabarcoding is becoming an increasingly applied methodology (Furneaux et al, 2021;Jamy et al, 2020;Tedersoo et al, 2017). Burki et al 2021.…”