Plant-root pathogenic fungal and plant-mycorrhizal fungal association networks in a subtropical forest

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.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. Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 32101281Funding provided by: Basic Research Project of Guizhou Provincial Science and Technology Department Fund of China*Crossref Funder Registry ID: Award Number: QKHJC ZK[2021] 096Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31925027Funding provided by: National Natural Science Foundation of ChinaCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100001809Award Number: 31901106This 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) >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)