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Immediate and legacy effects of snow exclusion on soil fungal diversity and community composition

Immediate and legacy effects of snow exclusion on soil fungal diversity and community composition Background: Soil fungi play crucial roles in ecosystem functions. However, how snow cover change associated with winter warming affects soil fungal communities remains unclear in the Tibetan forest. Methods: We conducted a snow manipulation experiment to explore immediate and legacy effects of snow exclusion on soil fungal community diversity and composition in a spruce forest on the eastern Tibetan Plateau. Soil fungal communities were performed by the high throughput sequencing of gene-fragments. Results: Ascomycota and Basidiomycota were the two dominant fungal phyla and Archaeorhizomyces, Aspergillus and Amanita were the three most common genera across seasons and snow manipulations. Snow exclusion did not affect the diversity and structure of soil fungal community in both snow-covered and snow-free seasons. However, the relative abundance of some fungal communities was different among seasons. Soil fungal groups were correlated with environmental factors (i.e., temperature and moisture) and soil biochemical variables (i.e., ammonium and enzyme). Conclusions: These results suggest that the season-driven variations had stronger impacts on soil fungal community than short-term snow cover change. Such findings may have important implications for soil microbial processes in Tibetan forests experiencing significant decreases in snowfall. Keywords: Winter climate change, Snow cover, Fungi, Community diversity, Community composition, Illumina sequencing * Correspondence: xuzf@sicau.edu.cn Li Zhang and Yuzhi Ren contributed equally to this work. Forestry Ecological Engineering in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province & National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River & Rainy Area of West China Plantation Ecosystem Permanent Scientific Research Base, Institute of Ecology & Forestry, Sichuan Agricultural University, Chengdu 611130, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Zhang et al. Forest Ecosystems (2021) 8:22 Page 2 of 11 Background 2013; Solly et al. 2017). In this study, we aimed to evaluate Seasonal snow cover is one of the most important the immediate and legacy effects of snow exclusion on the factors that drive biogeochemical cycling in cold regions diversity and composition of soil fungal communities. (Jusselme et al. 2016; Liu et al. 2018). Winter warming is Specifically, we hypothesized that, snow exclusion (1) will predicted to reduce the stability and thickness of snow decline soil fungal communities, and thereby change cover in snowy regions (Gobiet et al. 2014). The decline fungal community composition; (2) will result in both or absence of insulation of the snow cover would alter immediate and carry-over impacts on soil fungal commu- soil environmental conditions (e.g., temperature, moisture, nities; (3) will affect fungal communities via changes in frost intensity and duration) during winter and the grow- environmental and/or soil biochemical factors. ing season (Wipf and Rixen 2010;Kreylinget al. 2012; Aanderud et al. 2013;Liet al. 2017;Songet al. 2017). Soil Methods environmental conditions are very important to mediate Site description soil microbial community in cold ecosystems (Zinger et al. The study was conducted in a dragon spruce (Picea 2009;Voříšková et al. 2014; Morgado et al. 2016). Hence, asperata) stand at the Long-term Research Station of reduced snow cover associated with winter climate change Alpine Forest Ecosystems of Sichuan Agricultural can result in more intensive soil freezing, which may in University, which is located at the eastern Tibetan turn have profound influences on soil biological processes, Plateau of China (31°15′ N, 102°53′ E; 3021 m a.s.l.). The especially soil fungal communities. mean annual temperature is 3.0 °C, with maximum and Soil fungi are the key decomposer of soil organic minimum temperatures of 23.0 °C (July) and − 18.0 °C matter in cold soils (Baldrian 2017). The diversity and (January), respectively. Annual precipitation is about composition of fungal communities play key roles in soil 850 mm. In general, snow begins to accumulate in late carbon and nutrient cycling (Cheng et al. 2017; Asema- November and melts in late March of the following year. ninejad et al. 2018). Therefore, it is crucial to understand The understory is dominated by Salix paraplesia, Rhododen- how fungal community responds to snow cover change dron lapponicum, Cacalia sp., Carex sp., and Cyperus sp. (Li in alpine soils. Some studies have shown that snow et al. 2017). The soil is classified as Cambic Umbrisols (IUSS depth change resulted in an immediate effect on soil Working Group WRB 2007). fungal community composition in snow-covered winter (Olofsson et al. 2011; Barbeito et al. 2013;Voříšková Experimental design et al. 2014; Santalahti et al. 2016). However, winter Winter snowfall was excluded using shelters. This tech- snowpack can affect the soil moisture and nutrients in nique can effectively reduce snow cover and minimize the subsequent growing season (Wipf and Rixen 2010). unwanted environmental conditions (Li et al. 2017). In Therefore, snow cover change could also lead to carry- early November 2015, six wooden roofs (2 m height, 3 over effects on soil fungal community composition in m × 3 m ground area) were established in the Picea snow-free growing season (Buckeridge et al. 2013; Wubs asperata forest to prevent snow accumulation on the et al. 2018; Sorensen et al. 2020). To our knowledge, soil ground. One control plot that allows snow input was set fungal responses to altered snow cover have scarcely up in the vicinity of each wooden roof (3 m × 3 m been investigated both in snow-covered winter and ground area). In late winter, the accumulated snow on snow-free growing season. Obviously, exploring the the roof was added to the forest floor in order to ensure immediate and legacy effects of snow cover change on the similar water balance between the snow-free and fungal community is very essential to understand control plots. The snow manipulation began in late microbe-associated ecological processes in cold soils. November 2015 and ended in early April 2016 when As the earth’s ‘Third Pole’, the Tibetan Plateau is the seasonal snow in the control plots was melted anticipated to become warmer in the coming decades (Li et al. 2017). (Chen et al. 2013). In this region, winter snowfall has been decreasing over last decades (Wang et al. 2016; Microclimate and soil biochemical analyses Deng et al. 2017). Winter soil temperature is close to the Soil temperature (5 cm depth) and air temperature (2 m physical melting point and is sensitive to snow cover height) were measured every 1 h by the Thermochron change (Wang et al. 2007; Li et al. 2017). Our prior iButton DS1923-F5 Recorders (Maxim Dallas Semicon- study has found that snow exclusion reduced soil respir- ductor Corp., USA). The minimum daily mean soil ation and enzyme activities in wintertime, but did not temperatures of the control and snow exclusion plots result in any cross-seasonal effects in subsequent snow- were − 0.5 °C and − 2.2 °C, respectively (Fig. S1). The free season (Yang et al. 2019). Fungal communities are snow exclusion increased winter soil frost and lowered considered as key agents controlling soil C cycling in the average soil temperature (Fig. S1a). Seasonal snow cold ecosystems (Clemmensen et al. 2013; Zhang et al. began to accumulate in late November 2015 and melted Zhang et al. Forest Ecosystems (2021) 8:22 Page 3 of 11 in early April 2016 (Fig. S1). Snow depth in the control 2000) by a thermocycler PCR system (GeneAmp 9700, plots was measured approximately every 2 weeks. There ABI, USA). PCR reactions were performed in triplicate was no obvious difference in soil moisture between using a 20 μL mixture containing 4 μL of 5 × FastPfu − 1 control and snow exclusion plots (Fig. S1b). Buffer, 2 μL of 2.5 mmol∙L dNTPs, 0.8 μLofeach − 1 Soil pH was determined on field moist soil in a 1:2.5 primer (5 μmol∙L ), 0.4 μL of FastPfu Polymerase and (M/V) soil suspension using a pH meter (PHS-25CW, 10 ng of template DNA. The PCR reactions were BANTE Instruments Limited, Shanghai, China). Soil conducted using the following program: denaturation at ammonium ( NH -N) and nitrate ( NO -N) were 95 °C for 3 min, annealing at 55 °C for 30 s, and elong- 4 3 − 1 þ − ation at 72 °C for 45 s, and a final extension at 72 °C for extracted with 2 mol∙L KCl, and then NH and NO 4 3 10 min. The step from the denaturation to extension was in the extracts was determined using colorimetry (Xu run for 35 cycles. et al. 2010). Soil microbial biomass carbon (MBC) was The resulted PCR products were extracted from a 2% measured by the fumigation-extraction method (Vance agarose gel and further purified using the AxyPrep DNA et al. 1987). The released C was converted to MBC using Gel Extraction Kit (Axygen Biosciences, Union City, CA, kec 0.45 (Vance et al. 1987). USA) and quantified using QuantiFluor™-ST (Promega, We assessed the activities of three enzymes involved in USA) according to the manufacturer’s protocol. The soil C, N and P cycling: 1, 4-β-glucosidase (BG), β-N- purified amplicons were pooled in equimolar and acetyl-glucosaminidase (NAG) and acid phosphatase (AP). paired-end sequenced (2 × 300) on an Illumina MiSeq The activities were assayed using the methods described platform (Illumina, San Diego, USA) according to the by Allison and Jastrow (2006). Substrate solutions were 5 − 1 − 1 standard protocols by Majorbio Bio-Pharm Technology mmol∙L pNP-β-glucopyranoside for BG, 2 mmol∙L − 1 Co. Ltd. (Shanghai, China). The raw reads were depos- pNP-β-N-acetylglucosaminide for NAG and 5 mmol∙L ited in the NCBI Sequence Read Archive (SRA) database pNP-phosphate for AP. Activities was measured using a with accession number PRJNA562976. microplate spectrophotometer and expressed as μmol of − 1 − 1 substrate produced or consumed h ·g dry soil. Processing of sequencing data Raw FASTQ files were demultiplexed and quality- Soil sampling filtered by Trimmomatic and merged by FLASH with In 2016, three snow exclusion plots and their correspond- the following criteria: (i) the reads were truncated at any ing controls were randomly selected. Soil samples were site receiving an average quality score < 20 over a 50-bp collected from the topsoil (0–15 cm) in mid-February sliding window; (ii) Primers were exactly matched allow- (deep snow period, DSP), early April (early thawing ing two-nucleotide mismatching, and reads containing period, ETP), and mid-August (middle of the growing ambiguous bases were removed; (iii) Sequences whose season, MGS), respectively. On each sampling date, three overlap was longer than 10 bp were merged according to soil cores (5 cm in diameter, 0–15 cm deep) were their overlap sequence. randomly taken at each plot and were mixed into one All sequences acquired using the Illumina-MiSeq was composite sample per plot. The composite sample was saved in the raw fastq files. Initial processing of the raw passed through a 2-mm sieve, and any visible living plant dataset included screening to remove short and low- material was removed from the sieved soil. Subsamples of quality reads; only high-quality sequences were retained. the sieved soils were stored in the refrigerator at − 70 °C Operational taxonomic units (OTUs) were clustered with and 4 °C, respectively, for DNA and routine chemical 97% similarity cutoff using UPARSE (version 7.1, http:// analyses. drive5.com/uparse/), and chimeric sequences were identi- fied and removed using UCHIME (Edgar et al. 2011). The DNA extraction, PCR amplification and Illumina MiSeq taxonomy of each 18S rDNA gene sequence was analyzed sequencing by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) Microbial DNA was extracted from 18 soil samples by against the Silva 128/18S_eukaryota database using a using the E.Z.N.A.® Soil DNA Kit (Omega Bio Inc. confidence threshold of 70%. Rarefaction curves and alpha Norcross, GA, USA) according to manufacturer’s diversity calculations were based on OTUs with > 97% protocols. The final DNA concentration and purification identity. Rarefaction analysis and alpha-diversity indices were determined by Nanodrop® ND-1000 UV-Vis spec- (abundance-based Sobs, Chao1, Shannon and Simpson) trophotometer (Nano-Drop Technologies, Wilmington, were revealed by Mothur (Schloss et al. 2009). DE, USA), and DNA quality was checked by 1% agarose gel electrophoresis. Statistical analysis The fungal 18S rDNA gene was amplified with the Alpha diversity metrics, including the Shannon-Wiener primers SSU0817F/SSU1196R (Borneman and Hartin index, Simpson’s diversity index, richness (Sobs) and Zhang et al. Forest Ecosystems (2021) 8:22 Page 4 of 11 community coverage, were calculated to determine the OTUs (Table S2). Unclassified and norank sequences rep- “diversity” and “richness” functions of the fungal com- resented 15.57% of OTUs. munity. Repeated measures ANOVAs were performed Ascomycota were distributed across 18 orders and to test the effects of treatment, sampling date (deep dominated by Hypocreales (15.28% of all sequences), snow period, early thawing period, middle of the grow- Archaeorhizomycetales (8.55%) and Eurotiales (4.72%). ing season), and their interactions on the fungal indices. Basidiomycota were distributed across 7 orders and For specific sampling dates, Student t-tests were used to dominated by Agraicales (4.10%), Tremellales (1.36%) compare the effect of snow exclusion. The statistical and Russulales (0.25%) (Table S2). tests were considered significant at the P < 0.05 level. All At the genus level, 14 genera belonged to Ascomycota statistical analyses were performed using SPSS 20.0 and 3 genera belonged to Basidiomycota (Table 1). The (IBM Corporation, Armonk, NY, USA). most abundant genus (Saprotroph Archaeorhizomyces) One-way ANOVA with Tukey-kramer post hoc tests accounted for 8.55% of all sequences, followed by sapro- were performed to test the effects of sampling date on troph Aspergillus (4.49%) and ectomycorrhiza Amanita the abundance of fungal community at the same treat- (2.89%) (Table 1). ment. For individual sampling dates, Wilcoxon rank- sum test was used to compare the effect of snow exclu- Variation in fungal community composition sion on the abundance of fungal community structure. Across all sites, fungal communities were consistently Statistical testing among variation in fungal community dominated by Ascomycota (38.3%–55.6%) and Basidio- composition was carried out using the analysis of simi- mycota (7.92%–54.50%) (Fig. 2a). The relative abundance larity (ANOSIM). Differences in soil fungal phyla were of the Chytridiomycota (0.81%–18.1%), Glomeromycota represented on a two-dimensional ordination plot fol- (0.33%–1.18%), LKM15 (0.05%–0.84%) and Cryptomy- lowing Non-metric multidimensional scaling (NMDS) cota (0.01%–0.19%) was very low in all samples. Based analysis based on Bray-Curtis distance. Spearman correl- on Bray-Curtis, ANOSIM and NMDS were used to ation heatmap analysis was performed to examine the compare the similarity of the fungal phyla between relationships between the relative abundance of fungal treatments or seasons (Fig. 3). There were significant taxa and environmental factors or biochemical proper- differences in soil fungal communities among seasons. ties. All statistical analyses were performed using the Soil fungal community in the middle of the growing VEGAN package of the R software (Oksanen et al. 2013; season was obviously different from that in the deep R Development Core Team 2015). snow period and early thawing period (Fig. 3; ANOSIM, r = 0.527, P = 0.001). In the control plots, Basidiomycota Results was much higher in the middle of the growing season Pyrosequence data description and species alpha than in the deep snow period (P < 0.05) (Fig. S2). In the diversity treatment plots, LKM15 in the deep snow period was Pyrosequencing of 18 samples had a total of 604,373 raw much higher than that in the middle of the growing sequences. A total of 215,046 sequences were retained season (P < 0.05) (Fig. S2). However, no significant differ- after quality control. The sequence data were classified ences were found between snow regimes (Figs. 3 and S3; into 167 OTUs at 97% similarity. After normalization, ANOSIM, r = − 0.031, P = 0.539). each library had 11,947 reads and the sequence was At the genus level, 17 distinct groups were observed in clustered into 70–137 OTUs (Table S1). All rarefaction all samples which belonged to 12 classes (Fig. 2b, Table 1). curves tended to approach the saturation plateau, indi- Archaeorhizomyces (Archaeorhizomycetes), Aspergillus cating that the data volume of sequenced reads was rea- (Eurotiomycetes) and Amanita (Agaricomycetes) were the sonable (Fig. 1a). In the deep snow period, the Shannon first three abundant genera (Fig. 2b, Table 1). Snow exclu- index was higher compared to the early thawing period sion did not show any significant effects on the abundance and the middle of the growing season (Fig. 1b). How- of each genus, while sampling period had significant effect ever, the Shannon and Simpson indices were unaffected (Figs. S4 and S5). by snow exclusion (Fig. 1b). Taxonomic composition of fungal Correlation between soil biochemical properties and The OTUs were classified into 8 fungal phyla, 43 orders, fungal communities 59 families and 60 genera (Fig. 2 and Table S2). The first Spearman correlation heatmap analysis was performed three rich OTUs were Ascomycota (35.93%), Basidiomy- to examine the relationships between the phylum and cota (19.76%) and Chytridiomycota (16.17%), respectively. genus level of fungal communities and soil environmen- Other fungal groups, including Glomeromycota, LKM 15 tal and biochemical variables (Fig. 4). For the known and Cryptomycota together comprised the 12.58% of phyla, soil temperature, NH -N and enzyme activities 4 Zhang et al. Forest Ecosystems (2021) 8:22 Page 5 of 11 (a) DSP_C DSP_SE ETP_C ETP_SE MGS_SE MGS_C Number of Reads Sampled (b) Snow: P = 0.734 Date: P < 0.05 Snow × Date: P = 0.633 4 SE 0.3 Snow: P = 0.132 Date: P = 0.540 Snow × Date: P = 0.675 0.2 0.1 0.0 DSP ETP MGS Fig. 1 Rarefaction curve of the OTU number at 97% similarity cutoff (a) and α diversity index (b) of soil fungal community in control (C) and snow exclusion (SE) plots in the deep snow period (DSP), early thawing period (ETP), and in the middle of the growing season (MGS). * indicated a significant difference between control and snow exclusion on a specific same sampling date. Data shown are mean ± s.e (BG, AP and NAG) were important influence factors caused significant effects on soil fungal communities in (Fig. 4a). For the three common genera, a negative cor- arctic cold ecosystems (Morgado et al. 2016; Semenova relation was observed between Aspergillus and NO -N. et al. 2016). We utilized a snow-exclusion experiment to Moreover, Amanita showed significant correlations with test the immediate and cross-seasonal effect of snow ex- clusion on the diversity and composition of soil fungal pH, NH -N, BG and AP activities (Fig. 4b). communities in a subslpine spruce forest on the eastern Tibetan Plateau. Inconsistent with our hypotheses, snow Discussion exclusion did not affect the diversity and composition of The depth and duration of winter snow have been de- soil fungal communities in both snow-covered winter creasing on the eastern Tibetan Plateau (Li et al. 2017). and snow-free growing season, but the fungal diversity Recent studies have reported that snow cover change and composition greatly varied across seasons, indicating Simpson index Sobs index of OTU level Shannon-Wiener index Zhang et al. Forest Ecosystems (2021) 8:22 Page 6 of 11 (a) (b) norank_c_Agaricomycetes Ascomycota Basidiomycota unclassified_o_Hypocreales norank_k__Fungi norank_k_Fungi Chytridiomycota Archaeorhizomyces unclassified_k__Fungi norank_p_Chytridiomycota Glomeromycota Aspergillus LKM15 norank_o_Sordariales Cryptomycota 80 Amanita unclassified_c_Agaricomycetes norank_p_Ascomycota norank_c_Sordariomycetes norank_o_Helotiales unclassified_k_Fungi unclassified_o_Pleosporales 60 60 unclassified_o_Tremellales norank_o_Pezizales unclassified_o_Agaricales unclassified_p_Ascomycota Cladosporium unclassified_o_Pezizales norank_p_Glomeromycota unclassified_o_Orbiliales 40 40 unclassified_o_Saccharomycetales norank_o_Saccharomycetales unclassified_f_Russulaceae others DSP_C DSP_SE ETP_C ETP_SE MGS_C MGS_SE DSP_C DSP_SE ETP_C ETP_SE MGS_C MGS_SE Fig. 2 Relative abundance of different fungal phyla (a) and genus (b) in control (C) and snow exclusion (SE) plots in the deep snow period (DSP), early thawing period (ETP), and in the middle of the growing season (MGS) that soil fungal communities were not sensitive to short- Chen et al. 2017a, 2017b; Han et al. 2017; Männistö term mild increased frost associated with snow cover et al. 2018). Hypocreales, Archaeorhizomycetales and absence. Eurotiales were the dominant orders in the phylum As- Both phylum Ascomycota and Basidiomycota were the comycota. In addition, Agaricales was the dominant two dominant phyla in the study site. Similar findings order in the phylum Basidiomycota. As stated above, are observed in other cold ecosystems, such as glacier there might be similar common soil fungal communities ecosystems, alpine meadows and boreal forests (Yao among different snowy environments, indicating these et al. 2013; Antony et al. 2016; Gao and Yang 2016; fungal communities have an extensive adaptability. Table 1 List of the genera after OTU assignment. Functional group, total and relative number of sequences and OTUs are given for each genus from the total dataset Phylum Classes Genus Functional group No. of sequences Percent (%) No. of OTUs Percent (%) Ascomycota Archaeorhizomycetes Archaeorhizomyces Saprotroph 18,397 8.55 2 1.20 Eurotiomycetes Aspergillus Saprotroph 9666 4.49 2 1.20 Dothideomycetes Cladosporium Saprotroph 1779 0.83 1 0.60 Dothideomycetes Boeremia 1316 0.61 1 0.60 Dothideomycetes Aureobasidium Saprotroph 978 0.45 1 0.60 Dothideomycetes Guignardia Pathogen 955 0.44 1 0.60 Pezizomycetes Tarzetta Ectomycorrhiza 800 0.37 1 0.60 Sordariomycetes Pseudallescheria Saprotroph 670 0.31 1 0.60 Pezizomycetes Helvella Ectomycorrhiza 188 0.09 1 0.60 Saccharomycetes Galactomyces Saprotroph 120 0.06 1 0.60 Saccharomycetes Pichia Saprotroph 97 0.05 1 0.60 Eurotiomycetes Arachnomyces 49 0.02 1 0.60 Pezizomycetes Peziza Saprotroph 38 0.02 1 0.60 Dothideomycetes Cochliobolus Pathogen 15 0.01 1 0.60 Basidiomycota Agaricomycetes Amanita Ectomycorrhiza 6223 2.89 1 0.60 Agaricomycetes Camarophyllopsis 644 0.30 1 0.60 Tremellomycetes Cryptococcus Saprotroph 113 0.05 1 0.60 Relative abundance of fungal phyla (%) Relative abundance of fungal genus (%) Zhang et al. Forest Ecosystems (2021) 8:22 Page 7 of 11 Fig. 3 Non-metric multidimensional scaling (NMDS) plot showing variation in the composition (Bray-Curis distance) of soil fungal communities between snow regimes and sampling periods on phylum level. Different symbols represent different sampling date. The inverted triangle, circle and diamond represents the deep snow period (DSP), early thawing period (ETP) and in the middle of the growing season (MGS), respectively. The hollow represents Control (C) and solid represents snow exclusion (SE) plots A growing number of studies have revealed that snow 2016; Semenova et al. 2016). In contrast, the lack of cover changes leaded to significant impacts on soil snow cover resulted in slight effects on soil fungal fungal community composition and diversity in cold community structure and activity in a boreal coniferous ecosystems (Mundra et al. 2016; Semenova et al. 2016; forest (Männistö et al. 2018). Our results are consistent Männistö et al. 2018). For example, deeper snow cover with the observations found in alpine tundra (Zinger decreased saprotrophic fungi but increased ECM fungi et al. 2009) and in temperate and boreal forest ecosystems richness (Mundra et al. 2016). In addition, increased (Gao et al. 2018), which indicated snow cover change did snow cover significantly altered the composition of soil not affect soil fungal diversity and community compos- fungal communities in arctic tundra (Morgado et al. ition. This is probably because soil fungal communities in Fig. 4 Correlation heatmap of soil biochemical properties and read numbers at the phylum (a) and genus (b) level for fungal. NH -N: ammonium nitrogen; NO -N: nitrate nitrogen; MBC: microbial biomass carbon; BG: 1, 4-β-glucosidase; AP: acid phosphatase; NAG: β-N-acetyl- glucosaminidase. The color intensity in each panel indicates the relative correlation between soil property and read numbers of each group. * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001 Zhang et al. Forest Ecosystems (2021) 8:22 Page 8 of 11 cold regions have developed physiological resistance to shifted across seasons. The relative abundance of extreme conditions, such as low temperature and freeze- Archaeorhizomyces was found to be highest in the grow- thaw cycles (Stres et al. 2010;Haei et al. 2011). Addition- ing season. Studies showed that Archaeorhizomyces,asa ally, fungi can still gain energy through dissolved organic group of saprophytic fungi, are dominant in summer carbon and nitrogen in frozen soils, implying that soil fun- (Schadt et al. 2003, Fig. 3) and omnipresent in roots and gal communities had some unique adaptive strategies to rhizosphere soil (Rosling et al. 2011). Root-derived com- survive extreme cold conditions (Fitzhugh et al. 2001; pounds are the principal carbon source for these fungi Matzner and Borken 2008;Sorensen et al. 2016). As a (Rosling et al. 2011). Thus, sufficient high-quality sub- result, a short-term mild soil freezing is not powerful strates associated with root activity are favorable to enough to alter the diversity and composition of fungal Archaeorhizomyces in the growing season (Lindahl et al. communities in alpine forest soils. On the other hand, 2007). Conversely, the relative abundance of Aspergillus snow exclusion did not affect soil fungal communities in was higher in the deep snow period as compared to the the middle of the growing season, implying that the carry- other two periods, which is consistent with the observa- over effect of snow exclusion is negligible. This is mainly tions in a Pal forest soil (Rane and Gandhe 2006). because no significant differences were detected in both Environmental and biochemical variables are two environmental factors (e.g., temperature and moisture) important drivers of microbial community composition and soil variables (e.g., N pools, enzyme activities) in this (Fitzhugh et al. 2001; Matzner and Borken 2008; Chen period (Yang et al. 2019). et al. 2017b; Asemaninejad et al. 2018). Soil temperature Although no significant treatment effects were is a key factor affecting fungal communities (Asemanine- detected, fungal community diversity and composition jad et al. 2017). The abundance of Basidiomycota is rela- significantly varied across seasons. This observation is tively high in the warm growing season (Kirk et al. 2001; consistent with the results found in boreal forest soils Asemaninejad et al. 2017). In addition, soil moisture also (Voříšková et al. 2014). The Shannon-Wiener index in regulates soil fungal community in forest ecosystems the deep snow period is higher than those in the early (Bainard et al. 2014; Kim et al. 2016). The significant dif- thawing period and in the middle of the growing season, ferences in soil moisture between winter and growing which may be attributed to the low plant cover and season may, to some extent, account for the separation diversity in winter (Shi et al. 2014). A study conducted of fungi group, such as Basidiomycota and Agaricomy- in a Scots pine forest showed that saprotrophs are cetes. Soil enzyme production is mainly derived from dominant in winter, but ECM fungi grow rapidly in the soil microbes, especially soil fungi in forest ecosystems growing season (Santalahti et al. 2016). In this study, the (Schneider et al. 2012). Thus, soil enzymes can partially Basidiomycota group and genera Amanita (Agaricomy- reflect the composition and structure of fungal commu- cetes) showed a distinct transition from frozen winter to nities (Frey et al. 2004; Kivlin and Treseder 2014). In our the subsequent growing season. This is mainly because study, soil enzyme activities (e.g., BG, AP, and NAG) the carbon sources for Basidiomycota are mostly derived showed significant correlations with some specific fungal from exogenous materials, such as plant litter and wood communities. Similar observations were also found in (Kellner et al. 2010). Therefore, relatively large vegeta- the California forests ecosystem (Kivlin and Treseder tion coverage and abundance may provide more sub- 2014). Soil nutrients, especially nitrogen availability, strates during the growing season. In addition, relatively could affect soil microbial community in cold biomes rich root exudates can also favor soil fungal community, (Bardgett et al. 2002). Many studies have demonstrated especially in colder ecosystems (Shahzad et al. 2015; that nitrogen additions alter soil fungal community Delgado-Baquerizo et al. 2019). In general, Amanita has composition (Pardo et al. 2011;Morrisonetal. 2016; a mycorrhizal relationship with vascular plants (Yang Corrales et al. 2017). In this case, seasonal variations 2000). Therefore, plant growth in the growing season in N pools may partly explain the seasonal dynamic may stimulate Amanita growth. At the same time, the of fungal communities. Previous studies have demon- warmer temperature in the growing season may also strated that soil fungal communities have wider pH provide suitable conditions for Amanita reproduction ranges for optimal growth (Rousk et al. 2010). Simi- and growth (Asemaninejad et al. 2017). larly, soil pH was not correlated with fungal commu- Ascomycota did not vary among seasons in this case, nities at the phylum level, suggesting that soil pH is indicating that these fungi having strong resistance and less important to mediate soil fungal communities in adaptability to environmental stress. Some studies have subalpine coniferous forests. demonstrated that Ascomycota can adapt to harsh habitats to obtain survival advantages (Chen et al. 2017a; Conclusions Asemaninejad et al. 2018). However, the genus members This study examined the immediate and legacy effects of of Ascomycota, Archaeorhizomyces and Aspergillus snow cover change on the diversity and composition of Zhang et al. Forest Ecosystems (2021) 8:22 Page 9 of 11 fungal communities in a subalpine spruce forest on the Sichuan Excellent Youth Sci-Tech Foundation (No. 2020JDJQ0052) and the National Key Research and Development Program of China (Nos. Tibetan Plateau of China. Our findings suggested that 2016YFC0502505 and 2017YFC0505003). snow exclusion did not affect soil fungal communities in both snow-covered winter and snow-free growing sea- Availability of data and materials The datasets used and/or analysed during the current study are available son, indicating that fungal communities were insensitive from the corresponding author on reasonable request. to short-term snow cover change in Tibetan forest soils. Besides, soil fungal communities varied across seasons, Declarations implying that they had a significant shift when soil trans- Ethics approval and consent to participate formed from frozen to unfrozen. The season-driven shift Not applicable. in fungal communities may be partly explained by season-related changes in environmental factors (e.g., Consent for publication Not applicable. temperature and moisture) and biochemical variables (e.g., soil N availability and enzyme activity). Based on Competing interests our findings, the intensified and extended soil frost The authors declare that they have no competing interests. associated with winter climate change might profoundly Author details alter the phenology of soil fungal community in sub- Forestry Ecological Engineering in the Upper Reaches of the Yangtze River alpine forests experiencing significant snowfall decrease. Key Laboratory of Sichuan Province & National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River & Rainy Area of Supplementary Information West China Plantation Ecosystem Permanent Scientific Research Base, The online version contains supplementary material available at https://doi. Institute of Ecology & Forestry, Sichuan Agricultural University, Chengdu org/10.1186/s40663-021-00299-8. 2 611130, China. Global Ecology Unit CREAF-CSIC-UAB, CSIC, 08193 Barcelona, Catalonia, Spain. Forschungszentrum Jülich GmbH, Agrosphere (IBG-3), Additional file 1: Figure S1. Seasonal dynamics of air temperature (a) Jülich, Germany. Helmholtz-Centre for Environmental Research-UFZ, (2 m above the ground surface) and soil temperature (5 cm depth) from Department of Community Ecology, Theodor-Lieser-Strasse 4, 06110 Halle November 2015 to November 2016 and soil moisture (b) in control and (Saale), Germany. snow exclusion plots. The dot indicates snow depth during the winter. The asterisk indicates the sampling period. The small chart shows air and Received: 5 August 2020 Accepted: 2 March 2021 soil temperatures in winter. Figure S2. One-way ANOVA test bar plot for fungal phyla in (a) control (C) plots and (b) snow exclusion (SE) plots. DSP: deep snow period; ETP: early thawing period; MGS: middle in the References growing season. * indicated a significant difference among sampling Aanderud ZT, Jones SE, Schoolmaster DR Jr, Fierer N, Lennon JT (2013) Sensitivity dates for a same snow treatment. Figure S3. Wilcoxon rank-sum test bar of soil respiration and microbial communities to altered snowfall. Soil Biol plot for fungal phyla in the deep snow period (DSP), the early thawing Biochem 57:217–227. https://doi.org/10.1016/j.soilbio.2012.07.022 period (ETP), and the middle in the growing season (MGS). C: Control; SE: Allison SD, Jastrow JD (2006) Activities of extracellular enzymes in physically snow exclusion. * indicated a significant difference between snow re- isolated fractions of restored grassland soils. Soil Biol Biochem 38:3245–3256. gimes on a specific sampling date. Figure S4. 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Immediate and legacy effects of snow exclusion on soil fungal diversity and community composition

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DOI
10.1186/s40663-021-00299-8
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Abstract

Background: Soil fungi play crucial roles in ecosystem functions. However, how snow cover change associated with winter warming affects soil fungal communities remains unclear in the Tibetan forest. Methods: We conducted a snow manipulation experiment to explore immediate and legacy effects of snow exclusion on soil fungal community diversity and composition in a spruce forest on the eastern Tibetan Plateau. Soil fungal communities were performed by the high throughput sequencing of gene-fragments. Results: Ascomycota and Basidiomycota were the two dominant fungal phyla and Archaeorhizomyces, Aspergillus and Amanita were the three most common genera across seasons and snow manipulations. Snow exclusion did not affect the diversity and structure of soil fungal community in both snow-covered and snow-free seasons. However, the relative abundance of some fungal communities was different among seasons. Soil fungal groups were correlated with environmental factors (i.e., temperature and moisture) and soil biochemical variables (i.e., ammonium and enzyme). Conclusions: These results suggest that the season-driven variations had stronger impacts on soil fungal community than short-term snow cover change. Such findings may have important implications for soil microbial processes in Tibetan forests experiencing significant decreases in snowfall. Keywords: Winter climate change, Snow cover, Fungi, Community diversity, Community composition, Illumina sequencing * Correspondence: xuzf@sicau.edu.cn Li Zhang and Yuzhi Ren contributed equally to this work. Forestry Ecological Engineering in the Upper Reaches of the Yangtze River Key Laboratory of Sichuan Province & National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River & Rainy Area of West China Plantation Ecosystem Permanent Scientific Research Base, Institute of Ecology & Forestry, Sichuan Agricultural University, Chengdu 611130, China Full list of author information is available at the end of the article © The Author(s). 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Zhang et al. Forest Ecosystems (2021) 8:22 Page 2 of 11 Background 2013; Solly et al. 2017). In this study, we aimed to evaluate Seasonal snow cover is one of the most important the immediate and legacy effects of snow exclusion on the factors that drive biogeochemical cycling in cold regions diversity and composition of soil fungal communities. (Jusselme et al. 2016; Liu et al. 2018). Winter warming is Specifically, we hypothesized that, snow exclusion (1) will predicted to reduce the stability and thickness of snow decline soil fungal communities, and thereby change cover in snowy regions (Gobiet et al. 2014). The decline fungal community composition; (2) will result in both or absence of insulation of the snow cover would alter immediate and carry-over impacts on soil fungal commu- soil environmental conditions (e.g., temperature, moisture, nities; (3) will affect fungal communities via changes in frost intensity and duration) during winter and the grow- environmental and/or soil biochemical factors. ing season (Wipf and Rixen 2010;Kreylinget al. 2012; Aanderud et al. 2013;Liet al. 2017;Songet al. 2017). Soil Methods environmental conditions are very important to mediate Site description soil microbial community in cold ecosystems (Zinger et al. The study was conducted in a dragon spruce (Picea 2009;Voříšková et al. 2014; Morgado et al. 2016). Hence, asperata) stand at the Long-term Research Station of reduced snow cover associated with winter climate change Alpine Forest Ecosystems of Sichuan Agricultural can result in more intensive soil freezing, which may in University, which is located at the eastern Tibetan turn have profound influences on soil biological processes, Plateau of China (31°15′ N, 102°53′ E; 3021 m a.s.l.). The especially soil fungal communities. mean annual temperature is 3.0 °C, with maximum and Soil fungi are the key decomposer of soil organic minimum temperatures of 23.0 °C (July) and − 18.0 °C matter in cold soils (Baldrian 2017). The diversity and (January), respectively. Annual precipitation is about composition of fungal communities play key roles in soil 850 mm. In general, snow begins to accumulate in late carbon and nutrient cycling (Cheng et al. 2017; Asema- November and melts in late March of the following year. ninejad et al. 2018). Therefore, it is crucial to understand The understory is dominated by Salix paraplesia, Rhododen- how fungal community responds to snow cover change dron lapponicum, Cacalia sp., Carex sp., and Cyperus sp. (Li in alpine soils. Some studies have shown that snow et al. 2017). The soil is classified as Cambic Umbrisols (IUSS depth change resulted in an immediate effect on soil Working Group WRB 2007). fungal community composition in snow-covered winter (Olofsson et al. 2011; Barbeito et al. 2013;Voříšková Experimental design et al. 2014; Santalahti et al. 2016). However, winter Winter snowfall was excluded using shelters. This tech- snowpack can affect the soil moisture and nutrients in nique can effectively reduce snow cover and minimize the subsequent growing season (Wipf and Rixen 2010). unwanted environmental conditions (Li et al. 2017). In Therefore, snow cover change could also lead to carry- early November 2015, six wooden roofs (2 m height, 3 over effects on soil fungal community composition in m × 3 m ground area) were established in the Picea snow-free growing season (Buckeridge et al. 2013; Wubs asperata forest to prevent snow accumulation on the et al. 2018; Sorensen et al. 2020). To our knowledge, soil ground. One control plot that allows snow input was set fungal responses to altered snow cover have scarcely up in the vicinity of each wooden roof (3 m × 3 m been investigated both in snow-covered winter and ground area). In late winter, the accumulated snow on snow-free growing season. Obviously, exploring the the roof was added to the forest floor in order to ensure immediate and legacy effects of snow cover change on the similar water balance between the snow-free and fungal community is very essential to understand control plots. The snow manipulation began in late microbe-associated ecological processes in cold soils. November 2015 and ended in early April 2016 when As the earth’s ‘Third Pole’, the Tibetan Plateau is the seasonal snow in the control plots was melted anticipated to become warmer in the coming decades (Li et al. 2017). (Chen et al. 2013). In this region, winter snowfall has been decreasing over last decades (Wang et al. 2016; Microclimate and soil biochemical analyses Deng et al. 2017). Winter soil temperature is close to the Soil temperature (5 cm depth) and air temperature (2 m physical melting point and is sensitive to snow cover height) were measured every 1 h by the Thermochron change (Wang et al. 2007; Li et al. 2017). Our prior iButton DS1923-F5 Recorders (Maxim Dallas Semicon- study has found that snow exclusion reduced soil respir- ductor Corp., USA). The minimum daily mean soil ation and enzyme activities in wintertime, but did not temperatures of the control and snow exclusion plots result in any cross-seasonal effects in subsequent snow- were − 0.5 °C and − 2.2 °C, respectively (Fig. S1). The free season (Yang et al. 2019). Fungal communities are snow exclusion increased winter soil frost and lowered considered as key agents controlling soil C cycling in the average soil temperature (Fig. S1a). Seasonal snow cold ecosystems (Clemmensen et al. 2013; Zhang et al. began to accumulate in late November 2015 and melted Zhang et al. Forest Ecosystems (2021) 8:22 Page 3 of 11 in early April 2016 (Fig. S1). Snow depth in the control 2000) by a thermocycler PCR system (GeneAmp 9700, plots was measured approximately every 2 weeks. There ABI, USA). PCR reactions were performed in triplicate was no obvious difference in soil moisture between using a 20 μL mixture containing 4 μL of 5 × FastPfu − 1 control and snow exclusion plots (Fig. S1b). Buffer, 2 μL of 2.5 mmol∙L dNTPs, 0.8 μLofeach − 1 Soil pH was determined on field moist soil in a 1:2.5 primer (5 μmol∙L ), 0.4 μL of FastPfu Polymerase and (M/V) soil suspension using a pH meter (PHS-25CW, 10 ng of template DNA. The PCR reactions were BANTE Instruments Limited, Shanghai, China). Soil conducted using the following program: denaturation at ammonium ( NH -N) and nitrate ( NO -N) were 95 °C for 3 min, annealing at 55 °C for 30 s, and elong- 4 3 − 1 þ − ation at 72 °C for 45 s, and a final extension at 72 °C for extracted with 2 mol∙L KCl, and then NH and NO 4 3 10 min. The step from the denaturation to extension was in the extracts was determined using colorimetry (Xu run for 35 cycles. et al. 2010). Soil microbial biomass carbon (MBC) was The resulted PCR products were extracted from a 2% measured by the fumigation-extraction method (Vance agarose gel and further purified using the AxyPrep DNA et al. 1987). The released C was converted to MBC using Gel Extraction Kit (Axygen Biosciences, Union City, CA, kec 0.45 (Vance et al. 1987). USA) and quantified using QuantiFluor™-ST (Promega, We assessed the activities of three enzymes involved in USA) according to the manufacturer’s protocol. The soil C, N and P cycling: 1, 4-β-glucosidase (BG), β-N- purified amplicons were pooled in equimolar and acetyl-glucosaminidase (NAG) and acid phosphatase (AP). paired-end sequenced (2 × 300) on an Illumina MiSeq The activities were assayed using the methods described platform (Illumina, San Diego, USA) according to the by Allison and Jastrow (2006). Substrate solutions were 5 − 1 − 1 standard protocols by Majorbio Bio-Pharm Technology mmol∙L pNP-β-glucopyranoside for BG, 2 mmol∙L − 1 Co. Ltd. (Shanghai, China). The raw reads were depos- pNP-β-N-acetylglucosaminide for NAG and 5 mmol∙L ited in the NCBI Sequence Read Archive (SRA) database pNP-phosphate for AP. Activities was measured using a with accession number PRJNA562976. microplate spectrophotometer and expressed as μmol of − 1 − 1 substrate produced or consumed h ·g dry soil. Processing of sequencing data Raw FASTQ files were demultiplexed and quality- Soil sampling filtered by Trimmomatic and merged by FLASH with In 2016, three snow exclusion plots and their correspond- the following criteria: (i) the reads were truncated at any ing controls were randomly selected. Soil samples were site receiving an average quality score < 20 over a 50-bp collected from the topsoil (0–15 cm) in mid-February sliding window; (ii) Primers were exactly matched allow- (deep snow period, DSP), early April (early thawing ing two-nucleotide mismatching, and reads containing period, ETP), and mid-August (middle of the growing ambiguous bases were removed; (iii) Sequences whose season, MGS), respectively. On each sampling date, three overlap was longer than 10 bp were merged according to soil cores (5 cm in diameter, 0–15 cm deep) were their overlap sequence. randomly taken at each plot and were mixed into one All sequences acquired using the Illumina-MiSeq was composite sample per plot. The composite sample was saved in the raw fastq files. Initial processing of the raw passed through a 2-mm sieve, and any visible living plant dataset included screening to remove short and low- material was removed from the sieved soil. Subsamples of quality reads; only high-quality sequences were retained. the sieved soils were stored in the refrigerator at − 70 °C Operational taxonomic units (OTUs) were clustered with and 4 °C, respectively, for DNA and routine chemical 97% similarity cutoff using UPARSE (version 7.1, http:// analyses. drive5.com/uparse/), and chimeric sequences were identi- fied and removed using UCHIME (Edgar et al. 2011). The DNA extraction, PCR amplification and Illumina MiSeq taxonomy of each 18S rDNA gene sequence was analyzed sequencing by the RDP Classifier algorithm (http://rdp.cme.msu.edu/) Microbial DNA was extracted from 18 soil samples by against the Silva 128/18S_eukaryota database using a using the E.Z.N.A.® Soil DNA Kit (Omega Bio Inc. confidence threshold of 70%. Rarefaction curves and alpha Norcross, GA, USA) according to manufacturer’s diversity calculations were based on OTUs with > 97% protocols. The final DNA concentration and purification identity. Rarefaction analysis and alpha-diversity indices were determined by Nanodrop® ND-1000 UV-Vis spec- (abundance-based Sobs, Chao1, Shannon and Simpson) trophotometer (Nano-Drop Technologies, Wilmington, were revealed by Mothur (Schloss et al. 2009). DE, USA), and DNA quality was checked by 1% agarose gel electrophoresis. Statistical analysis The fungal 18S rDNA gene was amplified with the Alpha diversity metrics, including the Shannon-Wiener primers SSU0817F/SSU1196R (Borneman and Hartin index, Simpson’s diversity index, richness (Sobs) and Zhang et al. Forest Ecosystems (2021) 8:22 Page 4 of 11 community coverage, were calculated to determine the OTUs (Table S2). Unclassified and norank sequences rep- “diversity” and “richness” functions of the fungal com- resented 15.57% of OTUs. munity. Repeated measures ANOVAs were performed Ascomycota were distributed across 18 orders and to test the effects of treatment, sampling date (deep dominated by Hypocreales (15.28% of all sequences), snow period, early thawing period, middle of the grow- Archaeorhizomycetales (8.55%) and Eurotiales (4.72%). ing season), and their interactions on the fungal indices. Basidiomycota were distributed across 7 orders and For specific sampling dates, Student t-tests were used to dominated by Agraicales (4.10%), Tremellales (1.36%) compare the effect of snow exclusion. The statistical and Russulales (0.25%) (Table S2). tests were considered significant at the P < 0.05 level. All At the genus level, 14 genera belonged to Ascomycota statistical analyses were performed using SPSS 20.0 and 3 genera belonged to Basidiomycota (Table 1). The (IBM Corporation, Armonk, NY, USA). most abundant genus (Saprotroph Archaeorhizomyces) One-way ANOVA with Tukey-kramer post hoc tests accounted for 8.55% of all sequences, followed by sapro- were performed to test the effects of sampling date on troph Aspergillus (4.49%) and ectomycorrhiza Amanita the abundance of fungal community at the same treat- (2.89%) (Table 1). ment. For individual sampling dates, Wilcoxon rank- sum test was used to compare the effect of snow exclu- Variation in fungal community composition sion on the abundance of fungal community structure. Across all sites, fungal communities were consistently Statistical testing among variation in fungal community dominated by Ascomycota (38.3%–55.6%) and Basidio- composition was carried out using the analysis of simi- mycota (7.92%–54.50%) (Fig. 2a). The relative abundance larity (ANOSIM). Differences in soil fungal phyla were of the Chytridiomycota (0.81%–18.1%), Glomeromycota represented on a two-dimensional ordination plot fol- (0.33%–1.18%), LKM15 (0.05%–0.84%) and Cryptomy- lowing Non-metric multidimensional scaling (NMDS) cota (0.01%–0.19%) was very low in all samples. Based analysis based on Bray-Curtis distance. Spearman correl- on Bray-Curtis, ANOSIM and NMDS were used to ation heatmap analysis was performed to examine the compare the similarity of the fungal phyla between relationships between the relative abundance of fungal treatments or seasons (Fig. 3). There were significant taxa and environmental factors or biochemical proper- differences in soil fungal communities among seasons. ties. All statistical analyses were performed using the Soil fungal community in the middle of the growing VEGAN package of the R software (Oksanen et al. 2013; season was obviously different from that in the deep R Development Core Team 2015). snow period and early thawing period (Fig. 3; ANOSIM, r = 0.527, P = 0.001). In the control plots, Basidiomycota Results was much higher in the middle of the growing season Pyrosequence data description and species alpha than in the deep snow period (P < 0.05) (Fig. S2). In the diversity treatment plots, LKM15 in the deep snow period was Pyrosequencing of 18 samples had a total of 604,373 raw much higher than that in the middle of the growing sequences. A total of 215,046 sequences were retained season (P < 0.05) (Fig. S2). However, no significant differ- after quality control. The sequence data were classified ences were found between snow regimes (Figs. 3 and S3; into 167 OTUs at 97% similarity. After normalization, ANOSIM, r = − 0.031, P = 0.539). each library had 11,947 reads and the sequence was At the genus level, 17 distinct groups were observed in clustered into 70–137 OTUs (Table S1). All rarefaction all samples which belonged to 12 classes (Fig. 2b, Table 1). curves tended to approach the saturation plateau, indi- Archaeorhizomyces (Archaeorhizomycetes), Aspergillus cating that the data volume of sequenced reads was rea- (Eurotiomycetes) and Amanita (Agaricomycetes) were the sonable (Fig. 1a). In the deep snow period, the Shannon first three abundant genera (Fig. 2b, Table 1). Snow exclu- index was higher compared to the early thawing period sion did not show any significant effects on the abundance and the middle of the growing season (Fig. 1b). How- of each genus, while sampling period had significant effect ever, the Shannon and Simpson indices were unaffected (Figs. S4 and S5). by snow exclusion (Fig. 1b). Taxonomic composition of fungal Correlation between soil biochemical properties and The OTUs were classified into 8 fungal phyla, 43 orders, fungal communities 59 families and 60 genera (Fig. 2 and Table S2). The first Spearman correlation heatmap analysis was performed three rich OTUs were Ascomycota (35.93%), Basidiomy- to examine the relationships between the phylum and cota (19.76%) and Chytridiomycota (16.17%), respectively. genus level of fungal communities and soil environmen- Other fungal groups, including Glomeromycota, LKM 15 tal and biochemical variables (Fig. 4). For the known and Cryptomycota together comprised the 12.58% of phyla, soil temperature, NH -N and enzyme activities 4 Zhang et al. Forest Ecosystems (2021) 8:22 Page 5 of 11 (a) DSP_C DSP_SE ETP_C ETP_SE MGS_SE MGS_C Number of Reads Sampled (b) Snow: P = 0.734 Date: P < 0.05 Snow × Date: P = 0.633 4 SE 0.3 Snow: P = 0.132 Date: P = 0.540 Snow × Date: P = 0.675 0.2 0.1 0.0 DSP ETP MGS Fig. 1 Rarefaction curve of the OTU number at 97% similarity cutoff (a) and α diversity index (b) of soil fungal community in control (C) and snow exclusion (SE) plots in the deep snow period (DSP), early thawing period (ETP), and in the middle of the growing season (MGS). * indicated a significant difference between control and snow exclusion on a specific same sampling date. Data shown are mean ± s.e (BG, AP and NAG) were important influence factors caused significant effects on soil fungal communities in (Fig. 4a). For the three common genera, a negative cor- arctic cold ecosystems (Morgado et al. 2016; Semenova relation was observed between Aspergillus and NO -N. et al. 2016). We utilized a snow-exclusion experiment to Moreover, Amanita showed significant correlations with test the immediate and cross-seasonal effect of snow ex- clusion on the diversity and composition of soil fungal pH, NH -N, BG and AP activities (Fig. 4b). communities in a subslpine spruce forest on the eastern Tibetan Plateau. Inconsistent with our hypotheses, snow Discussion exclusion did not affect the diversity and composition of The depth and duration of winter snow have been de- soil fungal communities in both snow-covered winter creasing on the eastern Tibetan Plateau (Li et al. 2017). and snow-free growing season, but the fungal diversity Recent studies have reported that snow cover change and composition greatly varied across seasons, indicating Simpson index Sobs index of OTU level Shannon-Wiener index Zhang et al. Forest Ecosystems (2021) 8:22 Page 6 of 11 (a) (b) norank_c_Agaricomycetes Ascomycota Basidiomycota unclassified_o_Hypocreales norank_k__Fungi norank_k_Fungi Chytridiomycota Archaeorhizomyces unclassified_k__Fungi norank_p_Chytridiomycota Glomeromycota Aspergillus LKM15 norank_o_Sordariales Cryptomycota 80 Amanita unclassified_c_Agaricomycetes norank_p_Ascomycota norank_c_Sordariomycetes norank_o_Helotiales unclassified_k_Fungi unclassified_o_Pleosporales 60 60 unclassified_o_Tremellales norank_o_Pezizales unclassified_o_Agaricales unclassified_p_Ascomycota Cladosporium unclassified_o_Pezizales norank_p_Glomeromycota unclassified_o_Orbiliales 40 40 unclassified_o_Saccharomycetales norank_o_Saccharomycetales unclassified_f_Russulaceae others DSP_C DSP_SE ETP_C ETP_SE MGS_C MGS_SE DSP_C DSP_SE ETP_C ETP_SE MGS_C MGS_SE Fig. 2 Relative abundance of different fungal phyla (a) and genus (b) in control (C) and snow exclusion (SE) plots in the deep snow period (DSP), early thawing period (ETP), and in the middle of the growing season (MGS) that soil fungal communities were not sensitive to short- Chen et al. 2017a, 2017b; Han et al. 2017; Männistö term mild increased frost associated with snow cover et al. 2018). Hypocreales, Archaeorhizomycetales and absence. Eurotiales were the dominant orders in the phylum As- Both phylum Ascomycota and Basidiomycota were the comycota. In addition, Agaricales was the dominant two dominant phyla in the study site. Similar findings order in the phylum Basidiomycota. As stated above, are observed in other cold ecosystems, such as glacier there might be similar common soil fungal communities ecosystems, alpine meadows and boreal forests (Yao among different snowy environments, indicating these et al. 2013; Antony et al. 2016; Gao and Yang 2016; fungal communities have an extensive adaptability. Table 1 List of the genera after OTU assignment. Functional group, total and relative number of sequences and OTUs are given for each genus from the total dataset Phylum Classes Genus Functional group No. of sequences Percent (%) No. of OTUs Percent (%) Ascomycota Archaeorhizomycetes Archaeorhizomyces Saprotroph 18,397 8.55 2 1.20 Eurotiomycetes Aspergillus Saprotroph 9666 4.49 2 1.20 Dothideomycetes Cladosporium Saprotroph 1779 0.83 1 0.60 Dothideomycetes Boeremia 1316 0.61 1 0.60 Dothideomycetes Aureobasidium Saprotroph 978 0.45 1 0.60 Dothideomycetes Guignardia Pathogen 955 0.44 1 0.60 Pezizomycetes Tarzetta Ectomycorrhiza 800 0.37 1 0.60 Sordariomycetes Pseudallescheria Saprotroph 670 0.31 1 0.60 Pezizomycetes Helvella Ectomycorrhiza 188 0.09 1 0.60 Saccharomycetes Galactomyces Saprotroph 120 0.06 1 0.60 Saccharomycetes Pichia Saprotroph 97 0.05 1 0.60 Eurotiomycetes Arachnomyces 49 0.02 1 0.60 Pezizomycetes Peziza Saprotroph 38 0.02 1 0.60 Dothideomycetes Cochliobolus Pathogen 15 0.01 1 0.60 Basidiomycota Agaricomycetes Amanita Ectomycorrhiza 6223 2.89 1 0.60 Agaricomycetes Camarophyllopsis 644 0.30 1 0.60 Tremellomycetes Cryptococcus Saprotroph 113 0.05 1 0.60 Relative abundance of fungal phyla (%) Relative abundance of fungal genus (%) Zhang et al. Forest Ecosystems (2021) 8:22 Page 7 of 11 Fig. 3 Non-metric multidimensional scaling (NMDS) plot showing variation in the composition (Bray-Curis distance) of soil fungal communities between snow regimes and sampling periods on phylum level. Different symbols represent different sampling date. The inverted triangle, circle and diamond represents the deep snow period (DSP), early thawing period (ETP) and in the middle of the growing season (MGS), respectively. The hollow represents Control (C) and solid represents snow exclusion (SE) plots A growing number of studies have revealed that snow 2016; Semenova et al. 2016). In contrast, the lack of cover changes leaded to significant impacts on soil snow cover resulted in slight effects on soil fungal fungal community composition and diversity in cold community structure and activity in a boreal coniferous ecosystems (Mundra et al. 2016; Semenova et al. 2016; forest (Männistö et al. 2018). Our results are consistent Männistö et al. 2018). For example, deeper snow cover with the observations found in alpine tundra (Zinger decreased saprotrophic fungi but increased ECM fungi et al. 2009) and in temperate and boreal forest ecosystems richness (Mundra et al. 2016). In addition, increased (Gao et al. 2018), which indicated snow cover change did snow cover significantly altered the composition of soil not affect soil fungal diversity and community compos- fungal communities in arctic tundra (Morgado et al. ition. This is probably because soil fungal communities in Fig. 4 Correlation heatmap of soil biochemical properties and read numbers at the phylum (a) and genus (b) level for fungal. NH -N: ammonium nitrogen; NO -N: nitrate nitrogen; MBC: microbial biomass carbon; BG: 1, 4-β-glucosidase; AP: acid phosphatase; NAG: β-N-acetyl- glucosaminidase. The color intensity in each panel indicates the relative correlation between soil property and read numbers of each group. * P ≤ 0.05, ** P ≤ 0.01, *** P ≤ 0.001 Zhang et al. Forest Ecosystems (2021) 8:22 Page 8 of 11 cold regions have developed physiological resistance to shifted across seasons. The relative abundance of extreme conditions, such as low temperature and freeze- Archaeorhizomyces was found to be highest in the grow- thaw cycles (Stres et al. 2010;Haei et al. 2011). Addition- ing season. Studies showed that Archaeorhizomyces,asa ally, fungi can still gain energy through dissolved organic group of saprophytic fungi, are dominant in summer carbon and nitrogen in frozen soils, implying that soil fun- (Schadt et al. 2003, Fig. 3) and omnipresent in roots and gal communities had some unique adaptive strategies to rhizosphere soil (Rosling et al. 2011). Root-derived com- survive extreme cold conditions (Fitzhugh et al. 2001; pounds are the principal carbon source for these fungi Matzner and Borken 2008;Sorensen et al. 2016). As a (Rosling et al. 2011). Thus, sufficient high-quality sub- result, a short-term mild soil freezing is not powerful strates associated with root activity are favorable to enough to alter the diversity and composition of fungal Archaeorhizomyces in the growing season (Lindahl et al. communities in alpine forest soils. On the other hand, 2007). Conversely, the relative abundance of Aspergillus snow exclusion did not affect soil fungal communities in was higher in the deep snow period as compared to the the middle of the growing season, implying that the carry- other two periods, which is consistent with the observa- over effect of snow exclusion is negligible. This is mainly tions in a Pal forest soil (Rane and Gandhe 2006). because no significant differences were detected in both Environmental and biochemical variables are two environmental factors (e.g., temperature and moisture) important drivers of microbial community composition and soil variables (e.g., N pools, enzyme activities) in this (Fitzhugh et al. 2001; Matzner and Borken 2008; Chen period (Yang et al. 2019). et al. 2017b; Asemaninejad et al. 2018). Soil temperature Although no significant treatment effects were is a key factor affecting fungal communities (Asemanine- detected, fungal community diversity and composition jad et al. 2017). The abundance of Basidiomycota is rela- significantly varied across seasons. This observation is tively high in the warm growing season (Kirk et al. 2001; consistent with the results found in boreal forest soils Asemaninejad et al. 2017). In addition, soil moisture also (Voříšková et al. 2014). The Shannon-Wiener index in regulates soil fungal community in forest ecosystems the deep snow period is higher than those in the early (Bainard et al. 2014; Kim et al. 2016). The significant dif- thawing period and in the middle of the growing season, ferences in soil moisture between winter and growing which may be attributed to the low plant cover and season may, to some extent, account for the separation diversity in winter (Shi et al. 2014). A study conducted of fungi group, such as Basidiomycota and Agaricomy- in a Scots pine forest showed that saprotrophs are cetes. Soil enzyme production is mainly derived from dominant in winter, but ECM fungi grow rapidly in the soil microbes, especially soil fungi in forest ecosystems growing season (Santalahti et al. 2016). In this study, the (Schneider et al. 2012). Thus, soil enzymes can partially Basidiomycota group and genera Amanita (Agaricomy- reflect the composition and structure of fungal commu- cetes) showed a distinct transition from frozen winter to nities (Frey et al. 2004; Kivlin and Treseder 2014). In our the subsequent growing season. This is mainly because study, soil enzyme activities (e.g., BG, AP, and NAG) the carbon sources for Basidiomycota are mostly derived showed significant correlations with some specific fungal from exogenous materials, such as plant litter and wood communities. Similar observations were also found in (Kellner et al. 2010). Therefore, relatively large vegeta- the California forests ecosystem (Kivlin and Treseder tion coverage and abundance may provide more sub- 2014). Soil nutrients, especially nitrogen availability, strates during the growing season. In addition, relatively could affect soil microbial community in cold biomes rich root exudates can also favor soil fungal community, (Bardgett et al. 2002). Many studies have demonstrated especially in colder ecosystems (Shahzad et al. 2015; that nitrogen additions alter soil fungal community Delgado-Baquerizo et al. 2019). In general, Amanita has composition (Pardo et al. 2011;Morrisonetal. 2016; a mycorrhizal relationship with vascular plants (Yang Corrales et al. 2017). In this case, seasonal variations 2000). Therefore, plant growth in the growing season in N pools may partly explain the seasonal dynamic may stimulate Amanita growth. At the same time, the of fungal communities. Previous studies have demon- warmer temperature in the growing season may also strated that soil fungal communities have wider pH provide suitable conditions for Amanita reproduction ranges for optimal growth (Rousk et al. 2010). Simi- and growth (Asemaninejad et al. 2017). larly, soil pH was not correlated with fungal commu- Ascomycota did not vary among seasons in this case, nities at the phylum level, suggesting that soil pH is indicating that these fungi having strong resistance and less important to mediate soil fungal communities in adaptability to environmental stress. Some studies have subalpine coniferous forests. demonstrated that Ascomycota can adapt to harsh habitats to obtain survival advantages (Chen et al. 2017a; Conclusions Asemaninejad et al. 2018). However, the genus members This study examined the immediate and legacy effects of of Ascomycota, Archaeorhizomyces and Aspergillus snow cover change on the diversity and composition of Zhang et al. Forest Ecosystems (2021) 8:22 Page 9 of 11 fungal communities in a subalpine spruce forest on the Sichuan Excellent Youth Sci-Tech Foundation (No. 2020JDJQ0052) and the National Key Research and Development Program of China (Nos. Tibetan Plateau of China. Our findings suggested that 2016YFC0502505 and 2017YFC0505003). snow exclusion did not affect soil fungal communities in both snow-covered winter and snow-free growing sea- Availability of data and materials The datasets used and/or analysed during the current study are available son, indicating that fungal communities were insensitive from the corresponding author on reasonable request. to short-term snow cover change in Tibetan forest soils. Besides, soil fungal communities varied across seasons, Declarations implying that they had a significant shift when soil trans- Ethics approval and consent to participate formed from frozen to unfrozen. The season-driven shift Not applicable. in fungal communities may be partly explained by season-related changes in environmental factors (e.g., Consent for publication Not applicable. temperature and moisture) and biochemical variables (e.g., soil N availability and enzyme activity). Based on Competing interests our findings, the intensified and extended soil frost The authors declare that they have no competing interests. associated with winter climate change might profoundly Author details alter the phenology of soil fungal community in sub- Forestry Ecological Engineering in the Upper Reaches of the Yangtze River alpine forests experiencing significant snowfall decrease. Key Laboratory of Sichuan Province & National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River & Rainy Area of Supplementary Information West China Plantation Ecosystem Permanent Scientific Research Base, The online version contains supplementary material available at https://doi. Institute of Ecology & Forestry, Sichuan Agricultural University, Chengdu org/10.1186/s40663-021-00299-8. 2 611130, China. Global Ecology Unit CREAF-CSIC-UAB, CSIC, 08193 Barcelona, Catalonia, Spain. Forschungszentrum Jülich GmbH, Agrosphere (IBG-3), Additional file 1: Figure S1. Seasonal dynamics of air temperature (a) Jülich, Germany. Helmholtz-Centre for Environmental Research-UFZ, (2 m above the ground surface) and soil temperature (5 cm depth) from Department of Community Ecology, Theodor-Lieser-Strasse 4, 06110 Halle November 2015 to November 2016 and soil moisture (b) in control and (Saale), Germany. snow exclusion plots. The dot indicates snow depth during the winter. The asterisk indicates the sampling period. The small chart shows air and Received: 5 August 2020 Accepted: 2 March 2021 soil temperatures in winter. Figure S2. One-way ANOVA test bar plot for fungal phyla in (a) control (C) plots and (b) snow exclusion (SE) plots. 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