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Biogeographic patterns of ectomycorrhizal fungal communities associated with Castanopsis sieboldii across the Japanese archipelago

Biogeographic patterns in ectomycorrhizal (ECM) fungal communities and their drivers have been elucidated, including effects of host tree species and abiotic (climatic and edaphic) conditions. At these geographic scales, genotypic diversity and composition of single host tree species change with spatial and environmental gradients, reflecting their historical dispersal events. However, whether the host genotypes can be associated with the biogeographic patterns of ECM communities remains unclear. We investigated the biogeographic pattern of ECM fungal community associated with the single host species Castanopsis sieboldii (Fagaceae), whose genotypic diversity and composition across the Japanese archipelago has already been evaluated, and we quantified the effect of host genotypes on the biogeographic pattern. Richness and community composition of ECM fungi changed with latitude and longitude; these biogeographic changes of ECM community were significantly explained by host genotypic variables. Quantitative analyses showed a higher relative explanatory power of climatic and spatial variables than that of host genotypic variables for the biogeographic patterns in the ECM community. Our results suggest the importance of historical events of host dispersal in determining the biogeographic patterns of the ECM fungal community, while their explanation power was lower than that for climatic filtering and/or fungal dispersal.


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Ectomycorrhizal (ECM) fungi are symbionts of tree species in Fagaceae, Betulaceae, Dipterocarpaceae, and Pinaceae and are dominant in tropical, temperate, and and collected a block of fermentation-humus (FH) layer (10 × 10 cm, 10 cm in depth) 142 including tree roots within 3 m from each tree trunk. The soil blocks were collected within 143 the expanse of the canopies of each individual C. sieboldii tree and the sampling points 144 were at least 3 m from each other. The blocks were stored in plastic bags and frozen at -145 20°C during transport to the laboratory. A total of 240 blocks (12 study sites × samples 146 from 20 individual of C. sieboldii) were used for the study. 147 In the laboratory, FH materials in the blocks were slightly melted at room 148 temperature (20-25°C) and gently loosened. Then fine roots of trees were extracted from 149 the samples using a 2-mm mesh sieve and gently washed with tap water to remove soil 150 particles and debris. The sieved FH materials were used for the measurement of chemical 151 properties as described below. In each block, 20 individual root segments (approximately 152 5 cm in length) were selected, and one root tip (1 to 2 mm in length) was collected from 153 each root segment under a binocular microscope at 20× magnification. The 20 root tips 154 were pooled for each block and kept in a microcentrifuge tube containing 70% ethanol 155 (w/v) at -20°C. Before extracting DNA, the root tips were washed to remove small 156 particles on the root surface by 0.005% aerosol OT (di-2-ethylhexyl sodium underestimation of ITS richness. Thus, the remaining 129,725 reads from 233 samples 206 were used for further analyses. The number of sequencing reads per sample ranged from 207 50 to 3058 (mean, 551). All sequences were assembled across the samples using Assams 208 at a threshold similarity of 97%, which is widely used for fungal ITS region (Osono,209 2014), and the resulting consensus sequences represented molecular operational 210 taxonomic units (OTUs). Then singleton OTUs were removed. Consensus sequences of 211 the OTUs are listed in (Supporting Information Table S1).

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To systematically annotate the taxonomy of the OTUs, we used Claident  Table S1. To screen for ECM fungi, we referred to reviews by Tedersoo & Smith 220 (2013) to assign OTUs to the genera and/or families that were predominantly ECM fungi.

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The resultant ECM fungal OTUs (ECM OTUs) were used for further analyses (see 222   Supporting Information Table S1).

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Environmental and host genetic data 225 To investigate relationships between environmental factors (i.e., edaphic, 226 climatic, and host biogeographic variables) and ECM fungal community (richness and 227 composition of ECM OTUs), we measured and calculated the following variables (Table   228 1 and Supporting Information Method S1). For each sample, water content (WC), pH, 229 and carbon (C) and nitrogen (N) concentrations of the FH materials were measured.

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Carbon and nitrogen concentrations were determined by the combustion method using 231 automatic gas chromatography (Sumigraph NC-22, Sumika Chemical Analysis Service, 232 Ltd, Tokyo, Japan). The methods for WC, pH, and C and N concentrations followed those 233 described in Matsuoka et al. (2016a). We calculated four climatic variables for each site: 234 the mean annual temperature (MAT) (10-year averages before each sampling date), the 235 mean annual precipitation (MAP) (10-year averages before each sampling date), the mean 236 daily temperature during the 2 weeks before each sampling date (t2w), and the cumulative amount of precipitation during the 2 weeks before each sampling date (p2w). Data of air 238 temperature and rainfall were obtained from the nearest station of the Automatic 239 Metrological Data Acquisition System (Japan Meteorological Agency) to each study site.  al., 2000;Falush et al., 2003). This method has been widely used for many population Method S1 and Table S2. The correlations between latitude and longitude, and each 267 environmental variable are provided in Supporting Information Table S3.  Table S4).

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To examine the biogeographic pattern of the rarefied ECM OTU richness,    Table S1).  Table S5). The best model showed that the OTU richness

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It is important to note that approximately 64% of the variation remained 477 unexplained (Fig. 3). Several factors may be invoked to explain this unexplained which is short considering the distances between sampling sites (Fig. 1).

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OTU richness of ECM fungi 494 We observed that OTU richness decreased at lower latitudes. From the results of GLM, this geographic pattern of OTU richness could be attributed partly to climatic 496 factors, especially temperature and precipitation (Table 2 and Supporting Information   497   Table S3). Operational taxonomic unit richness decreased at sites with high MAT. This 498 pattern is consistent with the result from a meta-analysis of global patterns of ECM 499 communities (Tedersoo et al., 2012), which showed that ECM fungal richness decreases 500 at tropical and subtropical regions. This lower richness in tropical and subtropical region 501 may be related to the difference in thickness of the soil organic layer. In low latitude 502 regions, soil organic layer is thinner than that in high latitudinal regions because of active 503 organic matter decomposition, which can make vertical habitat segregation of ECM fungi 504 difficult (Dickie et al., 2002;Tedersoo et al., 2012). Indeed, it was reported from a 505 previous study that, in Japan, the soil organic layer is thinner in sub-tropical forests than 506 temperate forests (Osono, 2015), though in C. sieboldii forests, such comparison has 507 never been conducted.

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The GLM results showed that OTU richness could be related to precipitation.

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First, the OTU richness decreased with p2w. The potential effect of water stress (e.g., the 510 availability of oxygen in soil decreases with increasing precipitation) on the OTU richness 511 is discussed as a reason for the negative influence of rainfall (e.g., Tedersoo et al., 2012).

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In our study sites, p2w had a wide range from 0 to 400 mm (Table 1), and water stress 513 could affect OTU richness in the sites with high p2w. Whereas, MAP showed a positive 514 relationship with OTU richness, though the explanatory value was lower than p2w. A 515 negative relationship between OTU richness and MAP at a global-scale was reported by 516 Tedersoo et al. (2012), which is contradictory to our result. The reason for this 517 inconsistency is unclear, but the difference in the range of MAP may partly have 518 contributed to this difference. The MAP value in our study (1,700-3,300 mm) was higher 519 than that of the previous study (mostly around 0-2,000 mm), and this might have led to 520 a different response of the community owing to their adaptation to high precipitation.  temperature, MAP mean annual precipitation, t2w mean daily temperature during the 2 weeks before each sampling date, p2w cumulative 762 amount of precipitation during 2 weeks before each sampling date.