DOI: 10.5061/dryad.kwh70rz5z
title: Plant-root pathogenic fungal and plant-mycorrhizal fungal association networks in a subtropical forest

description: Although rhizosphere fungi are essential for plant survival and ecosystem
 functioning, little is known about the processes that structure
 plant–fungal association networks. In this study, we constructed
 association networks between 43 plant species and two groups of
 root-associated fungi (mycorrhizal and pathogenic fungi; MF and PF,
 respectively) in a diverse subtropical forest. We then evaluated the
 modularity of plant–MF and plant–PF networks and linked them to the
 functional traits and phylogenies of both plants and fungi. We observed
 strong modularity in both plant–MF and plant–PF networks. Phylogenetically
 related fungi tended to emerge in the same modules. MF from distinct
 modules associated with plants with different specific root length and
 specific root area in plant–MF networks. PF from distinct modules
 associated with plants with different dark respiration rate and light
 compensation point in plant–PF networks. Plant affiliation to modules was
 explained by both plant traits and phylogeny (22% for plant–MF and 37% for
 plant–PF networks). In contrast, fungal affiliation to modules was
 explained by fungal phylogeny (16% for plant–MF and 29% for plant–PF
 networks). Our results elucidate the link between modularity in plant–root
 fungal networks and the functional traits and phylogeny of the plants and
 fungi. Our study highlights the importance of traits and phylogeny in
 governing root fungal community assembly from network perspective.
description: This study was conducted in a 50-ha plot in a
 subtropical forest in Heishiding Nature Reserve, Southern China
 (23°25′–23°29′ N，111°49′–111°55′ E). The mean annual temperature
 of this area is 19.7 °C, and the annual precipitation is 1,750 mm. The
 total area of this nature reserve is 4,200 ha, including a 2,202 ha core
 area and a 1,660 ha experimental area. We established the 50-ha forest
 plot in 2012, and identified all trees with a diameter at breast height
 (DBH) &gt;1 cm. In total, this plot included approximately 2,69,000
 stems of 213 woody plant species (Wang et al. 2019).
 We compiled the dataset of
 fungal communities from 512 root samples of the 43 plant species (no less
 than 5 sampled individuals for each plant species) in the Heishiding plot
 (Wang et al., 2019). These plant species were selected based
 on their taxonomic placement and abundance.  For each plant species, we randomly selected
 5–15 individuals for fine root sampling. At least three root fragments (each
 approximately 2 cm in length) around an individual tree were traced from
 different directions and then pooled to create a single sample. The fine
 root samples were immediately cooled on ice in the field and stored at -20
 °C in a refrigerator until processing. More sampling details can be found
 in a previously published paper (Wang et al. 2019). Of 100 fine-root samples
 (randomly selected from all root samples), the tree species of 97 root
 samples traced in the field were correctly confirmed by rbcLa sequences
 obtained from a Sanger sequencing platform. Thus, we considered the
 tracing method as an accurate strategy to capture the taxonomic (species
 level) information of sampled fine roots. Root-associated fungi were
 identified by the internal transcribed spacer (ITS) region of fungal
 rDNA.  Root-associated fungi were identified by the
 internal transcribed spacer (ITS) region of fungal rDNA. After removing
 chimeric sequences, we obtained 11,000,000 high-quality reads of the ITS
 region of fungal rDNA. The operational taxonomic units (OTUs) of root
 fungi were discriminated using a threshold of 97% sequence identity. Each
 sequence was assigned to a taxonomic label based on the UNITE database
 using the Ribosomal Database Project (RDP) classifier
 (Wang, Garrity, Tiedje,
 & Cole, 2007). Each fungal genus was then assigned into
 functional categories. We identified EM fungi by blasting our fungal
 genera against the fungal genera in a database of EM taxa and lineages
 (Tedersoo and Smith
 2013). We assigned all
 OTUs in Glomeromycota to AM fungi (Schüßler 2002). Because we could only identify 21 OTUs of AM,
 EM and AM were pooled to represent the MF guild. Identifying fungal plant
 pathogens is challenging, because identification can only take place after
 the plants are diseased. Therefore, pathogenic genera were initially
 identified using the FUNGuild database (Nguyen et al. 2016). We then consulted the literature and retained
 only potential pathogens (OTUs) that had been identified to the species
 level and are known to be pathogenic to woody
 plants. To evaluate network modularity, we constructed a
 plant–PF association network including 113 fungal plant pathogens and 43
 plant species, as well as a plant–MF association network including 883
 mycorrhizal fungi (862 EM and 21 AM) and 43 plant species. To account for
 the sampling inequality, each cell in each network matrix was filled with
 the mean abundance (sequenced reads) of each fungal OTU (species) on each
 sampled tree, and the numbers were rounded to the nearest integer.
 Abundance of fungal OTUs on each sampled tree was calculated after
 subsampling each sample to 3000 sequence reads to eliminate the effects of
 sample size (Wang et al.
 2019).
description: Plant-fungal interaction matrices are filled in the mean
 abundance (sequence reads) of each fungal OTU (species) on each host plant
 individual after subsampling each sample to 3000 sequence reads, with the
 numbers rounded to the nearest integer.