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Phylogenetic turnover of arbuscular mycorrhizal fungal communities across steppe grasslands

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  • Received: 06 July 2024
    Revised: 22 September 2024
    Accepted: 14 October 2024
    Published online: 30 October 2024
    Agrobiodiversity  2024, 1(2): 16−22  |  Cite this article
  • Ecological interactions are evolutionarily conserved, indicating a tendency of closely related species to interact with similar partners. Arbuscular mycorrhizal (AM) fungi form obligate symbioses with the roots of most land plants. Local host preference is frequently reported as a factor in structuring AM fungal communities. There lacks study about whether AM fungi-host preference could structure AM fungal communities at the regional scales. Here, AM fungal communities of 296 root samples were revealed, encompassing 76 plant species from 29 plant families, which were sampled in steppe in the Xilingol Grassland in northern China. The relative importance of plant phylogeny, geographical distance, and environmental variables were characterized on phylogenetic turnover of AM fungal communities with GLMM-MCMC (the generalized linear mixed model using Markov chain Monte Carlo) and Mantel test approaches. Geographic distance appeared to be more important to the phylogenetic turnover of AM fungal communities than plant phylogeny and environmental variables, evidencing the role of dispersal limitation in shaping the root AM fungal communities. A great majority of phylogenetic beta diversity (betaNTI and betaNRI) is distributed between −2 and +2, which also suggested a random pattern of AM fungal communities. Here, empirical evidence supporting that dispersal limitation is the main determinant of AM fungal communities at the landscape scale is provided and it is suggested that AM fungal communities are mainly structured by stochastic events.
  • Starting in the early 2000s, China has experienced rapid growth as an emerging wine market. It has now established itself as the world's second-largest grape-growing country in terms of vineyard surface area. Furthermore, China has also secured its position as the sixth-biggest wine producer globally and the fifth-most significant wine consumer in terms of volume[1]. The Ningxia Hui autonomous region, known for its reputation as the highest quality wine-producing area in China, is considered one of the country's most promising wine regions. The region's arid or semiarid climate, combined with ample sunlight and warmth, thanks to the Yellow River, provides ideal conditions for grape cultivation. Wineries in the Ningxia Hui autonomous region are renowned as the foremost representatives of elite Chinese wineries. All wines produced in this region originate from grapes grown in their vineyards, adhering to strict quality requirements, and have gained a well-deserved international reputation for excellence. Notably, in 2011, Helan Mountain's East Foothill in the Ningxia Hui Autonomous Region received protected geographic indication status in China. Subsequently, in 2012, it became the first provincial wine region in China to be accepted as an official observer by the International Organisation of Vine and Wine (OIV)[2]. The wine produced in the Helan Mountain East Region of Ningxia, China, is one of the first Agricultural and Food Geographical Indications. Starting in 2020, this wine will be protected in the European Union[3].

    Marselan, a hybrid variety of Cabernet Sauvignon and Grenache was introduced to China in 2001 by the French National Institute for Agricultural Research (INRA). Over the last 15 years, Marselan has spread widely across China, in contrast to its lesser cultivation in France. The wines produced from Marselan grapes possess a strong and elegant structure, making them highly suitable for the preferences of Chinese consumers. As a result, many wineries in the Ningxia Hui Autonomous Region have made Marselan wines their main product[4]. Wine is a complex beverage that is influenced by various natural and anthropogenic factors throughout the wine-making process. These factors include soil, climate, agrochemicals, and human intervention. While there is an abundance of research available on wine production, limited research has been conducted specifically on local wines in the Eastern Foot of Helan Mountain. This research gap is of significant importance for the management and quality improvement of Chinese local wines.

    Ion mobility spectrometry (IMS) is a rapid analytical technique used to detect trace gases and characterize chemical ionic substances. It achieves this through the gas-phase separation of ionized molecules under an electric field at ambient pressure. In recent years, IMS has gained increasing popularity in the field of food-omics due to its numerous advantages. These advantages include ultra-high analytical speed, simplicity, easy operation, time efficiency, relatively low cost, and the absence of sample preparation steps. As a result, IMS is now being applied more frequently in various areas of food analysis, such as food composition and nutrition, food authentication, detection of food adulteration, food process control, and chemical food safety[5,6]. The orthogonal hyphenation of gas chromatography (GC) and IMS has greatly improved the resolution of complex food matrices when using GC-IMS, particularly in the analysis of wines[7].

    The objective of this study was to investigate the changes in the physicochemical properties of Marselan wine during the winemaking process, with a focus on the total phenolic and flavonoids content, antioxidant activity, and volatile profile using the GC-IMS method. The findings of this research are anticipated to make a valuable contribution to the theoretical framework for evaluating the authenticity and characterizing Ningxia Marselan wine. Moreover, it is expected that these results will aid in the formulation of regulations and legislation pertaining to Ningxia Marselan wine in China.

    All the grapes used to produce Marselan wines, grow in the Xiban vineyard (106.31463° E and 38.509541° N) situated in Helan Mountain's East Foothill of Ningxia Hui Autonomous Region in China.

    Folin-Ciocalteau reagent, (±)-6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), 2,20-azino-bis-(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS), 2,4,6-tris (2-pyridyl)-s-triazine (TPTZ), anhydrous methanol, sodium nitrite, and sodium carbonate anhydrous were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. (Shanghai, China). Reference standards of (+)-catechin, gallic acid, and the internal standard (IS) 4-methyl-2-pentanol were supplied by Shanghai Yuanye Bio-Technology Co., Ltd (Shanghai, China). The purity of the above references was higher than 98%. Ultrapure water (18.2 MΩ cm) was prepared by a Milli-Q system (Millipore, Bedford, MA, USA).

    Stage 1−Juice processing: Grapes at the fully mature stage are harvested and crushed, and potassium metabisulfite (5 mg/L of SO2) was evenly spread during the crushing process. The obtained must is transferred into stainless steel tanks. Stage 2−Alcoholic fermentation: Propagated Saccharomyces cerevisiae ES488 (Enartis, Italy) are added to the fresh must, and alcoholic fermentation takes place, after the process is finished, it is kept in the tanks for 7 d for traditional maceration to improve color properties and phenolics content. Stage 3−Malolactic fermentation: When the pomace is fully concentrated at the bottom of the tanks, the wine is transferred to another tank for separation from these residues. Oenococcus oeni VP41 (Lallemand Inc., France) is inoculated and malic acid begins to convert into lactic acid. Stage 4−Wine stabilization: After malolactic fermentation, potassium metabisulfite is re-added (35 mg/L of SO2), and then transferred to oak barrels for stabilization, this process usually takes 6-24 months. A total of four batches of samples during the production process of Marselan wine were collected in this study.

    Total polyphenols were determined on 0.5 mL diluted wine sample using the Folin-Ciocalteu method[8], using gallic acid as a reference compound, and expressed as milligrams of gallic acid equivalents per liter of wine. The total flavonoid content was measured on 0.05 mL of wine sample by a colorimetric method previously described[9]. Results are calculated from the calibration curve obtained with catechin, as milligrams of catechin equivalents per liter of wine.

    The antioxidative activity was determined using the ABTS·+ assay[10]. Briefly, the ABTS·+ radical was prepared from a mixture of 88 μL of potassium persulfate (140 mmol/L) with 5 mL of the ABTS·+ solution (7 mmol/L). The reaction was kept at room temperature under the absence of light for 16 h. Sixty μL samples were mixed with 3 mL of ABTS·+ solution with measured absorption of 0.700 ± 0.200 at 734 nm. After 6 min reaction, the absorbance of samples were measured with a spectrophotometer at 734 nm. Each sample was tested in triplicate. The data were expressed as mmol Trolox equivalent of antioxidative capacity per liter of the wine sample (mmol TE/L). Calibration curves, in the range 64.16−1,020.20 μmol TE/L, showed good linearity (R2 ≥ 0.99).

    The FRAP assay was conducted according to a previous study[11]. The FRAP reagent was freshly prepared and mixed with 10 mM/L TPTZ solution prepared in 20 mM/L FeCl3·6H2O solution, 40 mM/L HCl, and 300 mM/L acetate buffer (pH 3.6) (1:1:10; v:v:v). Ten ml of diluted sample was mixed with 1.8 ml of FRAP reagent and incubated at 37 °C for 30 min. The absorbance was determined at 593 nm and the results were reported as mM Fe (II) equivalent per liter of the wine sample. The samples were analyzed and calculated by a calibration curve of ferrous sulphate (0.15−2.00 mM/mL) for quantification.

    The volatile compounds were analyzed on a GC-IMS instrument (FlavourSpec, GAS, Dortmund, Germany) equipped with an autosampler (Hanon Auto SPE 100, Shandong, China) for headspace analysis. One mL of each wine was sampled in 20 mL headspace vials (CNW Technologies, Germany) with 20 μL of 4-methyl-2-pentanol (20 mg/L) ppm as internal standard, incubated at 60 °C and continuously shaken at 500 rpm for 10 min. One hundred μL of headspace sample was automatically loaded into the injector in splitless mode through a syringe heated to 65 °C. The analytes were separated on a MxtWAX capillary column (30 m × 0.53 mm, 1.0 μm) from Restek (Bellefonte, Pennsylvania, USA) at a constant temperature of 60 °C and then ionized in the IMS instrument (FlavourSpec®, Gesellschaft für Analytische Sensorsysteme mbH, Dortmund, Germany) at 45 °C. High purity nitrogen gas (99.999%) was used as the carrier gas at 150 mL/min, and drift gas at 2 ml/min for 0−2.0 min, then increased to 100 mL/min from 2.0 to 20 min, and kept at 100 mL/min for 10 min. Ketones C4−C9 (Sigma Aldrich, St. Louis, MO, USA) were used as an external standard to determine the retention index (RI) of volatile compounds. Analyte identification was performed using a Laboratory Analytical Viewer (LAV) 2.2.1 (GAS, Dortmund, Germany) by comparing RI and the drift time of the standard in the GC-IMS Library.

    All samples were prepared in duplicate and tested at least six times, and the results were expressed as mean ± standard error (n = 4) and the level of statistical significance (p < 0.05) was analyzed by using Tukey's range test using SPSS 18.0 software (SPSS Inc., IL, USA). The principal component analysis (PCA) was performed using the LAV software in-built 'Dynamic PCA' plug-in to model patterns of aroma volatiles. Orthogonal partial least-square discriminant analysis (OPLS-DA) in SIMCA-P 14.1 software (Umetrics, Umeă, Sweden) was used to analyze the different volatile organic compounds in the different fermentation stages.

    The results of the changes in the antioxidant activity of Marselan wines during the entire brewing process are listed in Table 1. It can be seen that the contents of flavonoids and polyphenols showed an increasing trend during the brewing process of Marselan wine, which range from 315.71−1,498 mg CE/L and 1,083.93−3,370.92 mg GAE/L, respectively. It was observed that the content increased rapidly in the alcoholic fermentation stage, but slowly in the subsequent fermentation stage. This indicated that the formation of flavonoid and phenolic substances in wine mainly concentrated in the alcoholic fermentation stage, which is consistent with previous reports. This is mainly because during the alcoholic fermentation of grapes, impregnation occurred to extract these compounds[12]. The antioxidant activities of Marselan wine samples at different fermentation stages were detected by FRAP and ABTS methods[11]. The results showed that the ferric reduction capacity and ABST·+ free radical scavenging capacity of the fermented Marselan wines were 2.4 and 1.5 times higher than the sample from the juice processing stage, respectively, indicating that the fermented Marselan wine had higher antioxidant activity. A large number of previous studies have suggested that there is a close correlation between antioxidant activity and the content of polyphenols and flavonoids[1315]. Previous studies have reported that Marselan wine has the highest total phenol and anthocyanin content compared to the wine of Tannat, Cabernet Sauvignon, Merlot, Cabernet Franc, and Syrah[13]. Polyphenols and flavonoids play an important role in improving human immunity. Therefore, Marselan wines are popular because of their high phenolic and flavonoid content and high antioxidant capacity.

    Table 1.  GC-IMS integration parameters of volatile compounds in Marselan wine at different fermentation stages.
    No. Compounds Formula RI* Rt
    [sec]**
    Dt
    [RIPrel]***
    Identification
    approach
    Concentration (μg/mL) (n = 4)
    Stage 1 Stage 2 Stage 3 Stage 4
    Aldehydes
    5 Furfural C5H4O2 1513.1 941.943 1.08702 RI, DT, IS 89.10 ± 4.05c 69.98 ± 3.22c 352.16 ± 39.06b 706.30 ± 58.22a
    6 Furfural dimer C5H4O2 1516.6 948.77 1.33299 RI, DT, IS 22.08 ± 0.69b 18.68 ± 2.59c 23.73 ± 2.69b 53.39 ± 9.42a
    12 (E)-2-hexenal C6H10O 1223.1 426.758 1.18076 RI, DT, IS 158.17 ± 7.26a 47.57 ± 2.51b 39.00 ± 2.06c 43.52 ± 4.63bc
    17 (E)-2-pentenal C5H8O 1129.2 333.392 1.1074 RI, DT, IS 23.00 ± 4.56a 16.42 ± 1.69c 18.82 ± 0.27b 18.81 ± 0.55b
    19 Heptanal C7H14O 1194.2 390.299 1.33002 RI, DT, IS 17.28 ± 2.25a 10.22 ± 0.59c 14.50 ± 8.84b 9.11 ± 1.06c
    22 Hexanal C6H12O 1094.6 304.324 1.25538 RI, DT, IS 803.11 ± 7.47c 1631.34 ± 19.63a 1511.11 ± 26.91b 1526.53 ± 8.12b
    23 Hexanal dimer C6H12O 1093.9 303.915 1.56442 RI, DT, IS 588.85 ± 7.96a 93.75 ± 4.67b 92.93 ± 3.13b 95.49 ± 2.50b
    29 3-Methylbutanal C5H10O 914.1 226.776 1.40351 RI, DT, IS 227.86 ± 6.39a 33.32 ± 2.59b 22.36 ± 1.18c 21.94 ± 1.73c
    33 Dimethyl sulfide C2H6S 797.1 193.431 0.95905 RI, DT, IS 120.07 ± 4.40c 87.a02 ± 3.82d 246.81 ± 5.62b 257.18 ± 3.04a
    49 2-Methylpropanal C4H8O 828.3 202.324 1.28294 RI, DT, IS 150.49 ± 7.13a 27.08 ± 1.48b 19.36 ± 1.10c 19.69 ± 0.92c
    Ketones
    45 3-Hydroxy-2-butanone C4H8O2 1293.5 515.501 1.20934 RI, DT, IS 33.20 ± 3.83c 97.93 ± 8.72b 163.20 ± 21.62a 143.51 ± 21.48a
    46 Acetone C3H6O 836.4 204.638 1.11191 RI, DT, IS 185.75 ± 8.16c 320.43 ± 12.32b 430.74 ± 3.98a 446.58 ± 10.41a
    Organic acid
    3 Acetic acid C2H4O2 1527.2 969.252 1.05013 RI, DT, IS 674.66 ± 46.30d 3602.39 ± 30.87c 4536.02 ± 138.86a 4092.30 ± 40.33b
    4 Acetic acid dimer C2H4O2 1527.2 969.252 1.15554 RI, DT, IS 45.25 ± 3.89c 312.16 ± 19.39b 625.79 ± 78.12a 538.35 ± 56.38a
    Alcohols
    8 1-Hexanol C6H14O 1365.1 653.825 1.32772 RI, DT, IS 1647.65 ± 28.94a 886.33 ± 32.96b 740.73 ± 44.25c 730.80 ± 21.58c
    9 1-Hexanol dimer C6H14O 1365.8 655.191 1.64044 RI, DT, IS 378.42 ± 20.44a 332.65 ± 25.76a 215.78 ± 21.04b 200.14 ± 28.34b
    13 3-Methyl-1-butanol C5H12O 1213.3 414.364 1.24294 RI, DT, IS 691.86 ± 9.95c 870.41 ± 22.63b 912.80 ± 23.94a 939.49 ± 12.44a
    14 3-Methyl-1-butanol dimer C5H12O 1213.3 414.364 1.49166 RI, DT, IS 439.90 ± 29.40c 8572.27 ± 60.56b 9083.14 ± 193.19a 9152.25 ± 137.80a
    15 1-Butanol C4H10O 1147.2 348.949 1.18073 RI, DT, IS 157.33 ± 9.44b 198.92 ± 3.92a 152.78 ± 10.85b 156.02 ± 9.80b
    16 1-Butanol dimer C4H10O 1146.8 348.54 1.38109 RI, DT, IS 24.14 ± 2.15c 274.75 ± 12.60a 183.02 ± 17.72b 176.80 ± 19.80b
    24 1-Propanol C3H8O 1040.9 274.803 1.11042 RI, DT, IS 173.73 ± 4.75a 55.84 ± 2.16c 80.80 ± 4.99b 83.57 ± 2.34b
    25 1-Propanol dimer C3H8O 1040.4 274.554 1.24784 RI, DT, IS 58.20 ± 1.30b 541.37 ± 11.94a 541.33 ± 15.57a 538.84 ± 9.74a
    28 Ethanol C2H6O 930.6 231.504 1.11901 RI, DT, IS 5337.84 ± 84.16c 11324.05 ± 66.18a 9910.20 ± 100.76b 9936.10 ± 101.24b
    34 Methanol CH4O 903.6 223.79 0.98374 RI, DT, IS 662.08 ± 13.87a 76.94 ± 2.15b 61.92 ± 1.96c 62.89 ± 0.81c
    37 2-Methyl-1-propanol C4H10O 1098.5 306.889 1.35839 RI, DT, IS 306.91 ± 4.09c 3478.35 ± 25.95a 3308.79 ± 61.75b 3313.85 ± 60.88b
    48 1-Pentanol C5H12O 1257.6 470.317 1.25222 RI, DT, IS 26.13 ± 2.52c 116.50 ± 3.71ab 112.37 ± 6.26b 124.17 ± 7.04a
    Esters
    1 Methyl salicylate C8H8O3 1859.6 1616.201 1.20489 RI, DT, IS 615.00 ± 66.68a 485.08 ± 31.30b 470.14 ± 23.02b 429.12 ± 33.74b
    7 Butyl hexanoate C10H20O2 1403.0 727.561 1.47354 RI, DT, IS 95.83 ± 17.04a 62.87 ± 3.62a 92.59 ± 11.88b 82.13 ± 3.61c
    10 Hexyl acetate C8H16O2 1298.6 524.366 1.40405 RI, DT, IS 44.72 ± 8.21a 33.18 ± 2.17d 41.50 ± 4.38c 40.89 ± 4.33b
    11 Propyl hexanoate C9H18O2 1280.9 499.577 1.39274 RI, DT, IS 34.65 ± 3.90d 70.43 ± 5.95a 43.97 ± 4.39b 40.12 ± 4.05c
    18 Ethyl hexanoate C8H16O2 1237.4 444.749 1.80014 RI, DT, IS 55.55 ± 5.62c 1606.16 ± 25.63a 787.24 ± 16.95b 788.91 ± 28.50b
    20 Isoamyl acetate C7H14O2 1127.8 332.164 1.30514 RI, DT, IS 164.22 ± 1.00d 243.69 ± 8.37c 343.51 ± 13.98b 365.46 ± 1.60a
    21 Isoamyl acetate dimer C7H14O2 1126.8 331.345 1.75038 RI, DT, IS 53.61 ± 4.79d 4072.20 ± 11.94a 2416.70 ± 49.84b 2360.46 ± 43.29c
    26 Isobutyl acetate C6H12O2 1020.5 263.605 1.23281 RI, DT, IS 101.65 ± 1.81a 15.52 ± 0.67c 44.87 ± 3.21b 45.96 ± 1.41b
    27 Isobutyl acetate dimer C6H12O2 1019.6 263.107 1.61607 RI, DT, IS 34.60 ± 1.05d 540.84 ± 5.64a 265.54 ± 8.31c 287.06 ± 3.66b
    30 Ethyl acetate dimer C4H8O2 885.2 218.564 1.33587 RI, DT, IS 1020.75 ± 6.86d 5432.71 ± 6.55a 5052.99 ± 9.65b 5084.47 ± 7.30c
    31 Ethyl acetate C4H8O2 878.3 216.574 1.09754 RI, DT, IS 215.65 ± 3.58a 38.29 ± 2.37c 71.59 ± 2.99b 69.32 ± 2.85b
    32 Ethyl formate C3H6O2 838.1 205.127 1.19738 RI, DT, IS 175.48 ± 3.79d 1603.20 ± 13.72a 1472.10 ± 5.95c 1509.08 ± 13.26b
    35 Ethyl octanoate C10H20O2 1467.0 852.127 1.47312 RI, DT, IS 198.86 ± 36.71b 1853.06 ± 17.60a 1555.51 ± 24.21a 1478.05 ± 33.63a
    36 Ethyl octanoate dimer C10H20O2 1467.0 852.127 2.03169 RI, DT, IS 135.50 ± 13.02d 503.63 ± 15.86a 342.89 ± 11.62b 297.28 ± 14.40c
    38 Ethyl butanoate C6H12O2 1042.1 275.479 1.5664 RI, DT, IS 21.29 ± 2.68c 1384.67 ± 8.97a 1236.52 ± 20.21b 1228.09 ± 5.09b
    39 Ethyl 3-methylbutanoate C7H14O2 1066.3 288.754 1.26081 RI, DT, IS 9.70 ± 1.85d 200.29 ± 4.21a 146.87 ± 8.70b 127.13 ± 12.54c
    40 Propyl acetate C5H10O2 984.7 246.908 1.48651 RI, DT, IS 4.57 ± 1.07c 128.63 ± 4.28a 87.75 ± 3.26b 88.49 ± 1.99b
    41 Ethyl propanoate C5H10O2 962.1 240.47 1.46051 RI, DT, IS 10.11 ± 0.34d 107.08 ± 3.50a 149.60 ± 5.39c 167.15 ± 12.90b
    42 Ethyl isobutyrate C6H12O2 971.7 243.229 1.56687 RI, DT, IS 18.29 ± 2.61d 55.22 ± 1.07c 98.81 ± 4.67b 104.71 ± 4.73a
    43 Ethyl lactate C5H10O3 1352.2 628.782 1.14736 RI, DT, IS 31.81 ± 2.91c 158.03 ± 2.80b 548.14 ± 74.21a 527.01 ± 39.06a
    44 Ethyl lactate dimer C5H10O3 1351.9 628.056 1.53618 RI, DT, IS 44.55 ± 2.03c 47.56 ± 4.02c 412.23 ± 50.96a 185.87 ± 31.25b
    47 Ethyl heptanoate C9H18O2 1339.7 604.482 1.40822 RI, DT, IS 39.55 ± 6.37a 38.52 ± 2.47a 28.44 ± 1.52c 30.77 ± 2.79b
    Unknown
    1 RI, DT, IS 15.53 ± 0.18 35.69 ± 0.80 12.70 ± 0.80 10.57 ± 0.86
    2 RI, DT, IS 36.71 ± 1.51 120.41 ± 3.44 198.12 ± 6.01 201.19 ± 3.70
    3 RI, DT, IS 44.35 ± 0.88 514.12 ± 4.28 224.78 ± 6.56 228.32 ± 4.62
    4 RI, DT, IS 857.64 ± 8.63 33.22 ± 1.99 35.05 ± 5.99 35.17 ± 3.97
    * Represents the retention index calculated using n-ketones C4−C9 as external standard on MAX-WAX column. ** Represents the retention time in the capillary GC column. *** Represents the migration time in the drift tube.
     | Show Table
    DownLoad: CSV

    This study adopted the GC-IMS method to test the volatile organic compounds (VOCs) in the samples from the different fermentation stages of Marselan wine. Figure 1 shows the gas phase ion migration spectrum obtained, in which the ordinate represents the retention time of the gas chromatographic peaks and the abscissa represents the ion migration time (normalized)[16]. The entire spectrum represents the aroma fingerprints of Marselan wine at different fermentation stages, with each signal point on the right of the relative reactant ion peak (RIP) representing a volatile organic compound detected from the sample[17]. Here, the sample in stage 1 (juice processing) was used as a reference and the characteristic peaks in the spectrum of samples in other fermentation stages were compared and analyzed after deducting the reference. The colors of the same component with the same concentration cancel each other to form a white background. In the topographic map of other fermentation stages, darker indicates higher concentration compared to the white background. In the 2D spectra of different fermentation stages, the position and number of peaks indicated that peak intensities are basically the same, and there is no obvious difference. However, it is known that fermentation is an extremely complex chemical process, and the content and types of volatile organic compounds change with the extension of fermentation time, so other detection and characterization methods are needed to make the distinction.

    Figure 1.  2D-topographic plots of volatile organic compounds in Marselan wine at different fermentation stages.

    To visually display the dynamic changes of various substances in the fermentation process of Marselan wine, peaks with obvious differences were extracted to form the characteristic fingerprints for comparison (Fig. 2). Each row represents all signal peaks selected from samples at the same stage, and each column means the signal peaks of the same volatile compound in samples from different fermentation stages. Figure 2 shows the volatile organic compounds (VOCs) information for each sample and the differences between samples, where the numbers represent the undetermined substances in the migration spectrum library. The changes of volatile substances in the process of Marselan winemaking is observed by the fingerprint. As shown in Fig. 2 and Table 2, a total of 40 volatile chemical components were detected by qualitative analysis according to their retention time and ion migration time in the HS-GC-IMS spectrum, including 17 esters, eight alcohols, eight aldehydes, two ketones, one organic acid, and four unanalyzed flavor substances. The 12 volatile organic compounds presented dimer due to ionization of the protonated neutral components before entering the drift tube[18]. As can be seen from Table 2, the VOCs in the winemaking process of Marselan wine are mainly composed of esters, alcohols, and aldehydes, which play an important role in the construction of aroma characteristics.

    Figure 2.  Fingerprints of volatile organic compounds in Marselan wine at different fermentation stages.
    Table 2.  Antioxidant activity, total polyphenols, and flavonoids content of Marselan wine at different fermentation stages.
    Winemaking stage TFC (mg CE/L) TPC (mg GAE/L) FRAP (mM FeSO4/mL) ABTs (mM Trolox/L)
    Stage 1 315.71 ± 0.00d 1,083.93 ± 7.79d 34.82c 38.92 ± 2.12c
    Stage 2 1,490.00 ± 7.51c 3,225.51 ± 53.27c 77.32b 52.17 ± 0.95b
    Stage 3 1,510.00 ± 8.88a 3,307.143 ± 41.76b 77.56b 53.04 ± 0.76b
    Stage 4 1,498.57 ± 6.34b 3,370.92 ± 38.29a 85.07a 57.46 ± 2.55a
    Means in the same column with different letters are significantly different (p < 0.05).
     | Show Table
    DownLoad: CSV

    Esters are produced by the reaction of acids and alcohols in wine, mainly due to the activity of yeast during fermentation[19], and are the main components of fruit juices and wines that produce fruit flavors[20,21]. In this study, it was found that they were the largest detected volatile compound group in Marselan wine samples, which is consistent with previous reports[22]. It can be observed from Table 2 that the contents of most esters increased gradually with the extension of fermentation time, and they mainly began to accumulate in large quantities during the stage of alcohol fermentation. The contents of ethyl hexanoate (fruity), isoamyl acetate (banana, pear), ethyl octanoate (fruity, pineapple, apple, brandy), ethyl acetate (fruity), ethyl formate (spicy, pineapple), and ethyl butanoate (sweet, pineapple, banana, apple) significantly increased at the stage of alcoholic fermentation and maintained a high level in the subsequent fermentation stage (accounting for 86% of the total detected esters). These esters can endow a typical fruity aroma of Marselan wine, and played a positive role in the aroma profiles of Marselan wine. Among them, the content of ethyl acetate is the highest, which is 5,153.79 μg/mL in the final fermentation stage, accounting for 33.6% of the total ester. However, the content of ethyl acetate was relatively high before fermentation, which may be from the metabolic activity of autochthonous microorganisms present in the raw materials. Isobutyl acetate, ethyl 3-methyl butanoate, propyl acetate, ethyl propanoate, ethyl isobutyrate, and ethyl lactate were identified and quantified in all fermentation samples. The total contents of these esters in stage 1 and 4 were 255.28 and 1,533.38 μg/mL, respectively, indicating that they may also have a potential effect on the aroma quality of Marselan wine. The results indicate that esters are an important factor in the formation of flavor during the brewing process of Marselan wine.

    Alcohols were the second important aromatic compound in Marselan wine, which were mainly synthesized by glucose and amino acid decomposition during alcoholic fermentation[23,24]. According to Table 2, eight alcohols including methanol, ethanol, propanol, butanol, hexanol, amyl alcohol, 3-methyl-1-butanol, and 2-methyl-1-propanol were detected in the four brewing stages of Marselan wine. The contents of ethanol (slightly sweet), 3-methyl-1-butanol (apple, brandy, spicy), and 2-methyl-1-propanol (whiskey) increased gradually during the fermentation process. The sum of these alcohols account for 91%−92% of the total alcohol content, which is the highest content of three alcohols in Marselan wine, and may be contributing to the aromatic and clean-tasting wines. On the contrary, the contents of 1-hexanol and methanol decreased gradually in the process of fermentation. Notably, the content of these rapidly decreased at the stage of alcoholic fermentation, from 2,026.07 to 1,218.98 μg/mL and 662.08 to 76.94 μg/mL, respectively, which may be ascribed to volatiles changed from alcohols to esters throughout fermentation. The reduction of the concentration of some alcohols also alleviates the strong odor during wine fermentation, which plays an important role in the improvement of aroma characteristics.

    Acids are mainly produced by yeast and lactic acid bacteria metabolism at the fermentation stage and are considered to be an important part of the aroma of wine[22]. Only one type of acid (acetic acid) was detected in this experiment, which was less than previously reported, which may be related to different brewing processes. Acetic acid content is an important factor in the balance of aroma and taste of wine. Low contents of volatile acids can provide a mild acidic smell in wine, which is widely considered to be ideal for producing high-quality wines. However, levels above 700 μg/mL can produce a pungent odor and weaken the wine's distinctive flavor[25]. The content of acetic acid increased first and then decreased during the whole fermentation process. The content of acetic acid increased rapidly in the second stage, from 719.91 to 3,914.55 μg/mL reached a peak in the third stage (5,161.81 μg/mL), and decreased to 4,630.65 μg/mL in the last stage of fermentation. Excessive acetic acid in Marselan wine may have a negative impact on its aroma quality.

    It was also found that the composition and content of aldehydes produced mainly through the catabolism of amino acids or decarboxylation of ketoacid were constantly changing during the fermentation of Marselan wines. Eight aldehydes, including furfural, hexanal, heptanal, 2-methylpropanal, 3-methylbutanal, dimethyl sulfide, (E)-2-hexenal, and (E)-2-pentenal were identified in all stage samples. Among them, furfural (caramel bread flavor) and hexanal (grass flavor) are the main aldehydes in Marselan wine, and the content increases slightly with the winemaking process. While other aldehydes such as (E)-2-hexenal (green and fruity), 3-methylbutanol (fresh and malt), and 2-methylpropanal (fresh and malt) were decomposed during brewing, reducing the total content from 536.52 to 85.15 μg/mL, which might potently affect the final flavor of the wine. Only two ketones, acetone, and 3-hydroxy-2-butanone, were detected in the wine samples, and their contents had no significant difference in the fermentation process, which might not affect the flavor of the wine.

    To more intuitively analyze the differences of volatile organic compounds in different brewing stages of Marselan wine samples, principal component analysis was performed[2628]. As presented in Fig. 3, the points corresponding to one sample group were clustered closely on the score plot, while samples at different fermentation stages were well separated in the plot. PC1 (79%) and PC2 (18%) together explain 97% of the total variance between Marselan wine samples, indicating significant changes in volatile compounds during the brewing process. As can be seen from the results in Fig. 3, samples of stages 1, 2, and 3 can be distinguished directly by PCA, suggesting that there are significant differences in aroma components in these three fermentation stages. Nevertheless, the separation of stage 3 and stage 4 samples is not very obvious and both presented in the same quadrant, which means that their volatile characteristics were highly similar, indicating that the volatile components of Marselan wine are formed in stage 3 during fermentation (Fig. S1). The above results prove that the unique aroma fingerprints of the samples from the distinct brewing stages of Marselan wine were successfully constructed using the HS-GC-IMS method.

    Figure 3.  PCA based on the signal intensity obtained with different fermentation stages of Marselan wine.

    Based on the results of the PCA, OPLS-DA was used to eliminate the influence of uncontrollable variables on the data through permutation test, and to quantify the differences between samples caused by characteristic flavors[28]. Figure 4 revealed that the point of flavor substances were colored according to their density and the samples obtained at different fermentation stages of wine have obvious regional characteristics and good spatial distribution. In addition, the reliability of the OPLS-DA model was verified by the permutation method of 'Y-scrambling'' validation. In this method, the values of the Y variable were randomly arranged 200 times to re-establish and analyze the OPLS-DA model. In general, the values of R2 (y) and Q2 were analyzed to assess the predictability and applicability of the model. The results of the reconstructed model illustrate that the slopes of R2 and Q2 regression lines were both greater than 0, and the intercept of the Q2 regression line was −0.535 which is less than 0 (Fig. 5). These results indicate that the OPLS-DA model is reliable and there is no fitting phenomenon, and this model can be used to distinguish the four brewing stages of Marselan wine.

    Figure 4.  Scores plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.
    Figure 5.  Permutation test of OPLS-DA model of volatile components in Marselan wine at different fermentation stages (n = 200).

    VIP is the weight value of OPLS-DA model variables, which was used to measure the influence intensity and explanatory ability of accumulation difference of each component on classification and discrimination of each group of samples. In previous studies, VIP > 1 is usually used as a screening criterion for differential volatile substances[2830]. In this study, a total of 22 volatile substances had VIP values above 1, indicating that these volatiles could function as indicators of Marselan wine maturity during fermentation (see Fig. 6). These volatile compounds included furfural, ethyl lactate, heptanal, dimethyl sulfide, 1-propanol, ethyl isobutyrate, propyl acetate, isobutyl acetate, ethanol, ethyl hexanoate, acetic acid, methanol, ethyl formate, ethyl 3-methylbutanoate, ethyl acetate, hexanal, isoamyl acetate, 2-methylpropanal, 2-methyl-1-propanol, and three unknown compounds.

    Figure 6.  VIP plot of OPLS-DA model of volatile components in Marselan wine at different fermentation stages.

    This study focuses on the change of volatile flavor compounds and antioxidant activity in Marselan wine during different brewing stages. A total of 40 volatile aroma compounds were identified and collected at different stages of Marselan winemaking. The contents of volatile aroma substances varied greatly at different stages, among which alcohols and esters were the main odors in the fermentation stage. The proportion of furfural was small, but it has a big influence on the wine flavor, which can be used as one of the standards to measure wine flavor. Flavonoids and phenols were not only factors of flavor formation, but also important factors to improve the antioxidant capacity of Marselan wine. In this study, the aroma of Marselan wines in different fermentation stages was analyzed, and its unique aroma fingerprint was established, which can provide accurate and scientific judgment for the control of the fermentation process endpoint, and has certain guiding significance for improving the quality of Marselan wines (Table S1). In addition, this work will provide a new approach for the production management of Ningxia's special wine as well as the development of the native Chinese wine industry.

  • The authors confirm contribution to the paper as follows: study conception and design: Gong X, Fang L; data collection: Fang L, Li Y; analysis and interpretation of results: Qi N, Chen T; draft manuscript preparation: Fang L. All authors reviewed the results and approved the final version of the manuscript.

  • The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

  • This work were supported by the project of Hainan Province Science and Technology Special Fund (ZDYF2023XDNY031) and the Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences in China (Grant No. 1630122022003).

  • The authors declare that they have no conflict of interest.

  • Supplementary Table S1 Brief description of the sampling sites in a steppe grassland in northern China.
    Supplementary Fig. S1 Plant phylogenetic tree with the tree in Zanne et al. (2014) as the backbone. Numbers are estimated divergence time (million years).
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  • Cite this article

    Zhang Q, Duan J, Goberna M, Jin Y, Xing J, et al. 2024. Phylogenetic turnover of arbuscular mycorrhizal fungal communities across steppe grasslands. Agrobiodiversity 1(2): 16−22 doi: 10.48130/abd-0024-0004
    Zhang Q, Duan J, Goberna M, Jin Y, Xing J, et al. 2024. Phylogenetic turnover of arbuscular mycorrhizal fungal communities across steppe grasslands. Agrobiodiversity 1(2): 16−22 doi: 10.48130/abd-0024-0004

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Phylogenetic turnover of arbuscular mycorrhizal fungal communities across steppe grasslands

Agrobiodiversity  1 2024, 1(2): 16−22  |  Cite this article

Abstract: Ecological interactions are evolutionarily conserved, indicating a tendency of closely related species to interact with similar partners. Arbuscular mycorrhizal (AM) fungi form obligate symbioses with the roots of most land plants. Local host preference is frequently reported as a factor in structuring AM fungal communities. There lacks study about whether AM fungi-host preference could structure AM fungal communities at the regional scales. Here, AM fungal communities of 296 root samples were revealed, encompassing 76 plant species from 29 plant families, which were sampled in steppe in the Xilingol Grassland in northern China. The relative importance of plant phylogeny, geographical distance, and environmental variables were characterized on phylogenetic turnover of AM fungal communities with GLMM-MCMC (the generalized linear mixed model using Markov chain Monte Carlo) and Mantel test approaches. Geographic distance appeared to be more important to the phylogenetic turnover of AM fungal communities than plant phylogeny and environmental variables, evidencing the role of dispersal limitation in shaping the root AM fungal communities. A great majority of phylogenetic beta diversity (betaNTI and betaNRI) is distributed between −2 and +2, which also suggested a random pattern of AM fungal communities. Here, empirical evidence supporting that dispersal limitation is the main determinant of AM fungal communities at the landscape scale is provided and it is suggested that AM fungal communities are mainly structured by stochastic events.

    • Ecological interactions are evolutionarily conserved, indicating a tendency of closely related species to interact with similar partners[1]. Thus, phylogenies can significantly contribute to the understanding of interactions, even across trophic levels[2,3]. In particular, plant phylogeny leaves an imprint in root-associated microbial communities[48]. The magnitude of the plant's phylogenetic signature belowground is especially strong on host-dependent microbial partners based on their history of coevolution[3]. Ultimately, such a phylogenetically structured top-down control can further affect the rates of essential ecosystem processes[9].

      Arbuscular mycorrhizal (AM) fungi form obligate symbioses with the roots of most land plants[10]. The AM symbiosis is arguably the world's most prevalent and ancient mutualism known, traced back to early land colonization by plants[11]. Local host preference is frequently reported as a factor structuring AM fungal communities[12,13], as closely related woody plants tend to interact with closely related AM fungi[14]. The woody plant–AM fungi networks are highly interconnected and explained by plant and AMF phylogenies. This suggests that the phylogenetic niche conservatism in woody plants and their AM fungal symbionts could contribute to interdependent AM fungi and plant community assembly. Also on a local scale, the phylogenetic composition of plant communities, as well as plant life history traits, have been shown to impact the phylogenetic structure of AM fungal assemblages[15,16]. For example, the effect of plant life history traits was depicted as that in the order annual herbs–perennial herbs–semi-woody plant species, there was a transition from phylogenetic clustered to over-dispersed AM fungal communities. Most of the AM fungi preferentially associated with annual plants belonged to Glomeraceae, showing a phylogenetic clustering pattern. However, conflicting results have been reported on a broader scale. At the global scale, variations in AM fungal communities are similar to the levels of variations seen in plant families[17], as plants within the same family may have inherited similar traits or may share similar ecological niches that select for particular AM fungal communities. A global meta-analysis however indicates that plant-fungal association patterns are poorly influenced by host phylogeny[18]. Further, there are few studies about whether AM fungi-host plant preference could exert selection within AM fungal communities.

      In addition to host plant effects, environmental filtering (e.g., soil physical and chemical properties) and geographical dispersal are also capable of affecting AM fungal communities. However, the relative importance of AM fungi-host preference, environmental filtering and geographical dispersal has yet to be tested to check whether AM fungi-host preference is a central mechanism structuring AM fungal communities. In the present study, a sampling of the roots of plant individuals across the Xilingol steppe in northern China were manipulated. Whether the phylogenetic divergence of host plants may be a main driver for variations of AM fungal communities at the regional scale was tested, at which spatial and environmental drivers can concurrently play a relevant role.

    • The sampling area represents a typical steppe in the Xilingol Grassland in northern China, which is located in the mid-latitude inland area with a semi-arid climate and covers a total area of 179,600 km2 (Fig. 1; Supplementary Table S1). Ten sites were randomly chosen across the sampling area in August 2022 (Fig. 1; Supplementary Table S1). To collect roots, soil was excavated to a depth of 0.4 m because this is where fine roots are most abundant. Then roots that were connected to shoots were identified, and one plant individual was randomly chosen and its root samples were collected manually with sterile gloves (sprayed with 70% EtOH) put in plastic bags, and refrigerated. Root samples were transported to the laboratory, washed free of soil, and stored for molecular analysis. The location of each sampled individual was recorded in GPS. At each of these sampling sites, 29–30 plant individuals were sampled. Only adult plant individuals were chosen and seedlings and juveniles were not included. A total of 296 root samples (plant individuals) were collected, encompassing 76 plant species from 29 plant families (see the phylogenetic tree in Supplementary Fig. S1).

      Figure 1. 

      Geographic distribution of the 10 sampling sites containing 296 sampling individuals. Sampling sites are described in detail in Supplementary Table S1.

      For each sampled plant individual, the longitude, latitude, and altitude were recorded with an eXplorist 210 GPS (Magellan, San Dimas, CA, USA). Mean annual temperature (MAT) and mean annual precipitation (MAP) were compiled from the National Meteorological Bureau of China database. Data were compiled by interpolating data of daily mean temperature and daily precipitation records (1961–2016) from 716 climate stations across China (http://data.cma.cn). Rhizosphere soil was collected by gently shaking off the soil adhering to the roots for measuring soil characteristics. For each root sample, there is a corresponding rhizosphere soil sample, leading to a total of 296 rhizosphere soil samples.

    • To remove AM fungal spores or hyphae from the root surface, roots were sonicated at low frequency for 3 min (30-s bursts followed by 30-s rests performed three times). Genomic DNA was extracted from a 0.2 g (dry) subsample of fine roots with the FastDNA SPIN Kit (MP Biomedicals, Santa Ana, CA, USA) following the manufacturer's protocol. The extracted DNA was dissolved in 50 μL TE buffer, quantified by spectrophotometer, and then stored at −20 °C before further use. The root AM fungal community was described using the Illumina Miseq platform with the primer set AMV4.5NF (5′-AAGCTCGTAGTTGAATTTCG-3′) / AMDGR (5′-CCCAACTATCCCTATTAATCAT-3′) which targets the 18S SSU rRNA gene region[19]. PCR was performed with 35 cycles (95 °C for 45 s, 58 °C for 45 s, and 72 °C for 1 min) and a final extension at 72 °C for 7 min in 50-μL reaction mixtures (1.25 mM deoxynucleoside triphosphate, 2 U Taq DNA polymerase (TaKaRa, Shiga, Japan), 15 μM AMF primers, and 50 ng genomic DNA). PCR products were purified using a QIAquick PCR Purification kit (QIAGEN), quantified using a NanoDrop ND-1000 instrument (Thermo Scientific, USA) (confirmed using the ratio of A260/280 between 1.8–2.0), and were normalized in equimolar amounts before sequencing. Sequencing reads were assigned to samples, and the corresponding paired reads were merged if the overlap was 100% identical using FLASH (V1.2.7, http://ccb.jhu.edu/software/FLASH/). The reads were quality-filtered with QIIME 1.6.0[20]. Default settings for Illumina processing in QIIME were used [minimum number of consecutive high-quality base > 75% total read length, maximum number of consecutive low-quality base = 3, last quality score considered low-quality = 3, maximum number of ambiguous (N) characters = 0] as recommended by Bokulich and colleagues[21]. After removing chimera sequences with UCHIME (Version 4.2)[22], OTU (operational taxonomic units) classification at 0.97 similarity and taxonomic assignment were acquired by blasting against the MaarjAM database (http://maarjam.botany.ut.ee)[23] following the criteria of Davison et al.[24]. After removing singletons, the longest sequence for each OTU was chosen as the representative sequence. The representative sequences were aligned with ClustalW[25]. Because an even sequencing depth per sample is required for beta diversity calculations, the OTU table was rarefied to 502 sequences per sample. There were 188 and 167 OTUs in the OTU table before and after the rarefaction, respectively. The maximum likelihood phylogenetic trees of AM fungi were inferred with RAxML v7.0.3[26] using the GTRCAT model and 1,000 bootstrap replicates. Six independent phylogenetic trees were constructed based on different seeds. Phylogenetic trees of AM fungi are provided as Supplementary Fig. S1.

    • The phylogenetic relationships between the 76 studied plant species was resolved by firstly matching the species names to the tips of the megaphylogeny of vascular plants (i.e., GBOTB.extended.TPL) embedded within the R software package V.PhyloMaker2[27]. A total of 54 of the 76 species names were matched. Secondly, the remaining 22 unmatched species were bound to the megaphylogeny by adopting the 'S3' scenario of the function 'phylo.maker' in V.PhyloMaker2. Thirdly, the expanded megaphylogeny was trimmed using the 'drop.tip' function in the ape package for R[28], to retain the 76 study species only (Supplementary Fig. S1).

    • The characteristics of the rhizospheric soil samples were assessed by determining their total nitrogen (TN), extractable phosphorus, pH, and soil organic carbon (SOC). Soil TN was determined by performing elemental analyses (Elementar Vario MACRO, Germany). Extractable P concentrations were determined using a spectrophotometer (UV-1600 spectrophotometer, Beijing). The standard Walkley-Black potassium dichromate oxidation method was employed for obtaining SOC. Then, a 1:1 ratio of soil to water slurries was used to measure soil pH with an acidometer (HANNA, Padova, Italy).

    • Several indices of phylogenetic community structure and turnover were calculated. First, the phylogenetic structure of AM fungal communities was described by calculating a matrix (matrix P) that contains the composition of species fuzzy‐weighed by their pairwise phylogenetic similarities[29] with the R package PCPS[30]. In matrix P, each OTU has a value per sample that increases as the phylogenetic distance between neighbouring OTUs decreases. Ordination techniques allow reducing matrix P to represent the phylogenetic structure at the sample level. Principal coordinate analysis was performed with Euclidean distances and extracted the sample scores along the first axis, which represents the principal component phylogenetic structure (PCPS1). This axis captures the deepest phylogenetic divergences among lineages[31,32].

      Second, the phylogenetic turnover of AM fungal communities from each plant individual was computed as betaMNTD (between-assemblage analogs of mean nearest taxon distance) and betaMPD (between-assemblage analogs of mean pairwise distance) as described by Fine & Kembel[33]. BetaMNTD is the mean nearest taxon distance between pairs of species drawn from two distinct communities and is sensitive to the changes of lineages close to the phylogenetic tips, while betaMPD is sensitive to tree-wide distributions of lineages. Further, betaNTI (between-assemblage analogs of the nearest taxon index) and betaNRI (between-assemblage analogs of net relatedness index) were computed as the number of standard deviations that observed betaMNTDs or betaMPDs departed from the mean of the null model of random shuffling of taxa labels across the phylogeny[33]. BetaNTI and betaNRI were computed as the number of standard deviations that observed betaMNTDs and betaMPDs departed from the mean of the null distribution, respectively. To calculate betaNTI or betaNRI, a null distribution of betaMNTDs or betaMPDs were generated by randomizing OTUs across the phylogeny (999 null iterations) based on random shuffling of OTU labels across the tips of the phylogeny. BetaNTIs and betaNRIs less than −2 indicates less than expected phylogenetic turnover (communities are more similar than expected), while betaNTIs and betaNRIs greater than +2 indicates greater than expected phylogenetic turnover. BetaNTIs and betaNRIs indicates random patterns when the values are distributed between −2 to +2 as this means distribution deviation from the null model was not significant. All four phylogenetic beta-diversity values across six replicated trees were found to be highly correlated (r > 0.90, p < 0.001) indicating reproducibility in the phylogenetic reconstructions.

    • Two approaches were used to determine the relative importance of plant phylogeny, geographic distance, and environmental variables in driving the phylogenetic community structure and turnover in AMF communities.

    • The Bayesian version of GLMM-MCMC (the generalized linear mixed model using Markov chain Monte Carlo) was performed with the six AMF phylogenetic trees with the geographic coordinates as the random factors, following the method of Stone et al.[34]. GLMMs are used for analyzing correlated non-Gaussian data, but their likelihood function is only available as a high dimensional integral, making closed-form inference and prediction impossible. Consequently, Bayesian GLMMs also have intractable posterior densities, and MCMC algorithms are typically used for conditional simulation and exploring these densities in GLMMs. In the present analysis, the three phylogenetic beta dissimilarities, PCPS1, betaMNTD, and betaMPD were used as the dependent variables in the GLMM-MCMC analysis, respectively. As betaMNTD and betaMPD were two-dimensional data in dissimilarity format, the first axis of principal coordinate ordinations was extracted and used as the dependent variable in Bayesian GLMM-MCMC analysis. Principal coordinate ordinations were also manipulated on plant phylogenetic distance and distance of environmental variable, and the first axis of each distance matrix was extracted as the independent variables. GLMM-MCMC was performed with the R package MCMCglmm[35]. The effect of predictors was estimated by calculating the 95% confidential interval of their posterior distributions[36]. 13,000 MCMC iterations were ran with a burn-in period of 3,000 iterations and default priors. Convergence of the chain was verified by the autocorrelation function of the Markov chain.

    • The Mantel test was used to explore the relationship between the phylogenetic turnover of AM fungal communities (measured as betaMNTD, betaMPD, and the distance of matrix P) and plant phylogenetic distance of host plant (betaMNTD and betaMPD of plant phylogenetic distance), geographical distance, and distance of environmental variables. As the PCPS1 is in column data format, Euclidean distance of the matrix P was used in the Mantel test. Further, partial Mantel tests were done between the distance of the matrix P/betaMNTD/ betaMPD and plant phylogeny, geographic, and environmental distance, respectively, after controlling each of the other two factors. Results were corrected for multiple testing as one series using a false discovery rate (FDR). As the phylogenetic beta-diversity values across six replicated trees showing reproducibility in the phylogenetic reconstructions, a single phylogenetic tree was used in Mantel and partial Mantel analysis. Geographic distance between samples was calculated from the latitude and longitude coordinates using the 'geosphere' packages[37]. The environmental distance was calculated as the Bray-curtis dissimilarity of environmental variables including climate (MAP and MAT) and soil physicochemical characteristics (pH, TN, extractable P, and SOC). The Mantel test examines the correlation relationship between the two matrices. In the partial Mantel test, control was implemented by calculating the correlation between the residuals of each of the two primary distance matrices after performing a linear regression on the third distance matrix.

    • A total of 167 OTUs were detected in the rarefied OTU table, among which Glomeraceae (113 OTUs) was the most dominant at the family level, followed by the Diversisporaceae (12 OTUs).

      There were 43660 paired phylogenetic beta-diversities (betaMNTD or betaMPD) across the 296 samples. Calculation of the number of standard deviations that observed betaMNTDs or betaMPDs departed from the mean of the null model (i.e., betaNTI or betaNRI, respectively) showing a large proportion of both betaNTI (89.19%, 38,941 out of 43,660 paired samples) and betaNRI (93.38%, 40,770 out of 43,660 paired samples) values distributed between −2 to +2 (Fig. 2).

      Figure 2. 

      Distribution of betaNTI and betaNRI values.

      For the relationship between phylogenetic turnover of AM fungal communities and plant phylogeny, geographical distance, and environmental variables, the Bayesian GLMM-MCMC analysis identified geographical distance as the only significant factor in explaining phylogenetic beta-diversity of AM fungal communities (Table 1). For all the three phylogenetic beta-diversity of AM fungal communities (i.e., PCPS1, betaMNTD, and betaMPD), the 95% credible intervals of Bayesian postmean estimates explained by geographical distance did not overlap with 0 (Table 1), which suggested a significant role of geographical distance in determining beta diversity of AM fungal communities. Plant phylogeny and environmental variables were not significant in Bayesian GLMM-MCMC analysis in explaining either of the beta-diversity of AM fungal communities (Fig. 2, Table 1).

      Table 1.  GLMMMCMC analysis on effects of plant phylogeney, geographical distance and environmental variables on the phylogenetic turnover of AMF communities with sampling site as a random factor. Bayesian postmean estimates and 95% credible intervals are shown. Geographical distance was the only factor significantly explaining phylogenetic turnover of AMF communities, as the 95% credible interval did not overlap with 0 and p < 0.05.

      PCPS1 BetaMNTD BetaMPD
      Post mean 95% CI Post mean 95% CI Post mean 95% CI
      Geographical distance 2.2 × 10−10 (1.3 × 10−12, 6.1 × 10−10) 1.9 × 10−9 (1.9 × 10−16, 8.5 × 10−9) 1.4 × 10−8 (5.0 × 10−14, 4.7 × 10−8)
      Plant phylogeny 4.5 × 10−3 (−1.5 × 10−2, 2.5 × 10−2) −4.6 × 10−6 (−1.1 × 10−5, 1.8 × 10−6) −7.8 × 10−6 (−2.3 × 10−5, 6.9 × 10−6)
      Environment 1.5 × 10−3 (−3.0 × 10−3, 5.9 × 10−3) 3.7 × 10−3 (−9.6 × 10−3, 2.0 × 10−2) −5.3 × 10−4 (−3.5 × 10−2, 3.8 × 10−2)
      Values in bold represent the 95% credible interval did not overlap with 0 and p < 0.05.

      Mantel tests showed a significant correlation between betaMNTD of AM fungal communities and plant phylogeny (FDR adjusted p < 0.05; Table 2), but the correlation between PCPS1 and betaMPD of AM fungal communities and plant phylogeny was not significant (Table 2). Geographical distance showed a significant correlation with all three phylogenetic beta diversity metrics of AM fungal communities (i.e., PCPS1, betaMNTD, and betaMPD ) (FDR adjusted p < 0.05; Table 2).

      Table 2.  Mantel test between unweighted betaMNTD/betaMPD of AMF communities and plant phylogeny, geographical distance, or environmental variable.

      PCPS1 BetaMNTD BetaMPD
      r p r p r p
      Plant phylogeny 0.008 0.600 0.053 0.043 0.049 0.252
      Geographical distance 0.051 0.004 0.179 0.003 0.058 0.012
      Environment 0.002 0.998 0.021 0.037 0.022 0.924
      p values were FDR adjusted. Values in bold represent the 95% credible interval did not overlap with 0 and p < 0.05.

      In the partial Mantel test, betaMNTD of AM fungal communities was significantly correlated with plant phylogeny after controlling for environmental variables (Table 3). When the geographic distance was accounted for in the partial Mantel test, the correlation between AM fungal communities and plant phylogeny was not significant (Table 3). The betaMNTD of AMF communities was significantly correlated with environment and geographical distance after controlling for each of the other factors (Table 3). The betaMPD of AMF communities was only significantly correlated with geographical distance after controlling plant phylogeny or environmental variables (Table 3). Correlation of betaMPD of AMF with plant phylogeny or environment was not significant after controlling geographical distance in that partial Mantel test (Table 3). The PCPS1 of AMF communities was only significantly correlated with geographical distance after controlling plant phylogeny or environmental variables (Table 3), but showed no significant correlation with plant phylogeny or environment (Table 3).

      Table 3.  Partial Mantel test between unweighted betaMNTD/betaMPD of AMF communities and plant phylogeny, geographical distance, or environmental variable, after controlling one of these three factors.

      Controlling PCPS1 BetaMNTD BetaMPD
      r p r p r p
      Plant phylogeny Geographical distance 0.007 0.598 0.046 0.068 0.046 0.237
      Environment 0.008 0.597 0.055 0.045 0.047 0.312
      Geographical distance Plant phylogeny 0.051 0004 0.177 0.006 0.056 0.009
      Environment 0.067 0.001 0.185 0.006 0.072 0.006
      Environment Plant phylogeny 0.028 0.996 0.023 0.048 0.019 0.999
      Geographical distance 0.005 0.999 0.051 0.999 0.049 0.999
      p values were FDR adjusted. Values in bold represent the 95% credible interval did not overlap with 0 and p < 0.05.
    • The present results showed that betaMNTD of AM fungal communities was significantly correlated with plant phylogeny, but the correlation between PCPS1 and betaMPD and plant phylogeny was not significant. The Bayesian GLMM-MCMC analysis also suggested a poor influence of plant phylogeny in explaining the phylogenetic turnover of AMF communities. The betaMNTD is the mean nearest taxon distance between pairs of species drawn from two distinct communities[33] and is sensitive to the changes in lineages close to the phylogenetic tips. The betaMPD tends to be more sensitive to the tree-wide distributions of lineages, compared to betaMNTD. PCPS1 was considered to capture the deepest phylogenetic divergences among lineages. Thus, the present results indicate that AM fungal communities colonizing plant species with close relatedness might contain taxa that are phylogenetically clustered towards the tips of phylogeny[33]. However, this correlation is reduced when controlling for geographical distance and environmental variables in the partial Mantel test, but the role of geographical distance was emphasized in both GLMM-MCMC analysis and partial Mantel analysis.

      Dispersal limitation can be stochastic - when occurring through passive processes like wind - or deterministic when driven by differences in dispersal traits among taxa (or lineages)[38]. In the present study, the role of stochasticity in shaping AM fungal communities in the steppe grassland was suggested by the great importance of geographical distance, a conclusion confirmed by analyses of deviations of phylogenetic turnover from the stochastic expectation. The majority of both betaNTI and betaNRI values distributed between −2 to +2, suggesting a random pattern. This result also suggests that AM fungal communities were mainly structured by stochastic events (e.g., dispersal limitation). Plant phylogenetic distance also showed a stronger correlation with geographical distance (p < 0.001 of partial Mantel test controlling environment) than environment (p > 0.05 of partial Mantel test controlling geographical distance), indicating that stochastic events (e.g., dispersal limitation) also affected the current phylogenetic structure of plant communities. This suggested that past events generate and maintain biogeographic patterns of plants might similarly operate in the microbial world, e.g., AM fungal communities.

      Dispersal limitation represents an inability of taxa to reach potentially suitable habitats in a given time frame. Nonetheless, AM fungi are found on all continents, and many approximate species-level phylogroups (phylogenetically defined groupings of taxa described by DNA sequences) have been shown to exhibit wide distributions, frequently spanning multiple continents[17,39,40]. Such broad distribution patterns suggest a highly effective long-distance dispersal strategy. However, the dispersal ability of AM fungi (e.g., dispersal of spores by wind or animals) was considered to directly influence its ability to colonize a location and therefore its geographic distribution[4144]. Long-distance dispersal of AM fungi might be difficult since they have hypogeous and relatively large spores (0.01−1 mm) with limited dispersal ability compared to other fungal groups[45]. On this aspect, the passive dispersal of AM fungi supported the role of stochasticity in shaping AM fungal communities here. On another aspect, AM fungi disperse by large spores in the soil, mycelial fragments, and colonized root pieces[10]. Spore number, volume, and shape may be correlated with dispersal ability in fungi[46,47]. Certain functional traits of AM fungi associated with dispersal (spore number and volume) or spatial niches (e.g. allocation of hyphal biomass to soil or roots) tend to be similar among closely related species, i.e. they are phylogenetically conserved[4648]. It might therefore be hypothesized that AM fungi with similar dispersal-related traits are more likely to co-occur than functionally dissimilar taxa. Thus, the correlation between spatial distance and both fungal and host plant phylogenetic structure suggests that distance effects on AM fungal communities may not be purely neutral, but also may reflect correlated differences in dispersal traits among lineages of the plants and fungi. The role of dispersal limitation in structuring AM fungal communities has previously been recognized on a local-[49] and global-scale[17]. Here, the analysis represents the first empirical evidence that dispersal limitation is the main determinant of AM fungal communities on a landscape scale.

      Whether co-evolution has an important ongoing role in most mycorrhizal relationships is unknown[50]. AM fungi are typically considered as host generalists, although patterns of partner preference have been clearly documented[12,13,51], and plant growth responses to AM fungi depend partly on specificity between plant phylogenetic lineages and fungal taxa[52]. It has also been documented that host plant phylogeny influences the local composition of AM fungi[17]; however, tests of null models found that plant phylogeny-AM fungi relationships can be scale-dependent[13] or due to spatial effect[53]. Moreover, Põlme et al.[18] found that there was no overall evidence for a phylogenetic signal in plant-AM fungal associations on a broad scale, although the phylogenetic correlation of plant-AM fungal assemblages was detected on a local scale[15]. Here, significant correlation was found between plant phylogeny and betaMNTD of AM fungal communities in Mantel test. However, further analysis (partial Mantel test and Bayesian GLMM-MCMC) showed this AM fungi-plant relationship seems to be attributed to the similar effect of dispersal limitation on both AM fungal communities and plant phylogenetic lineages, driving high positive spatial auto-correlation. AM fungi are obligate symbionts and, therefore, successful colonization is contingent on the coordinated arrival of fungal spores and suitable plant hosts to new locations. Here it has been shown that the stochastic dispersal processes seem to be important in the assembly of plant-AM fungal communities, but these dispersal processes may be correlated with traits in both AM fungi and their host plants that are influenced by phylogenetic history.

    • A recent study revealed the dominant role of stochasticity in structuring AM fungal communities on a regional scale, with drift generally being the major process[54], which aligned with the present findings. Thus, these results together with previous studies[12,13,55] acknowledge that the relationship between plant phylogeny and AM fungal communities may be scale-dependent, with stronger correlations at local scales, and a strong spatial structuring on regional scales[55]. In common with their plant hosts, AM fungi are essentially sessile and require external vectors. There is evidence for multiple potential dispersal vectors for AM fungi – both abiotic (water and air/wind) and biotic (animals, including humans)[56], e.g., birds are endozoochorous co-dispersers, transporting viable propagules of both partners – plants and AM fungi[56]. As the present study site is a steppe grassland, it is reasonable to speculate that besides wind and wild animals, grazing activities for storing cattle and sheep could be important in generating the regional distribution pattern of AM fungi. This was supported by that grazing substantially increased the stochasticity of AM fungal community assembly in the steppe by reducing the deterministic effects of plant richness on a regional scale[57] in favor of those AM fungi with disturbance-tolerant traits.

      Due to the vast area of the Xilingol grassland, the 10 sampling sites in this study might not fully represent the AM fungal diversity of the whole grassland. Further, there is a lack of comparative studies with AM fungal communities in other grassland areas or different ecosystems (e.g. forest) which might limit the understanding of the driving mechanism of AM fungal community assemblage. Third, as sampling was manipulated at only one time, it may be limited to specific seasons and do not reflect the dynamics of arbuscular mycorrhizal fungal communities at different time scales. Thus, for future studies, sampling should be manipulated with more sites in different ecosystems and various temporal dynamics to detect the phylogenetic signal of plant-AM fungal interactions, and to test whether the dominant role of stochasticity in structuring AM fungal communities on the regional scale is a generalized rule or not.

    • The present results showed that geographic distance was the most important factor explaining the phylogenetic β-diversity in AM fungal communities, while the effects of plant phylogeny and environmental variables was not significant. This suggests that at the landscape scale, AMF community structure is largely determined by stochastic events (e.g., dispersal limitation). Furthermore, significant correlations were found between plant phylogeny and the β-MNTD of AMF communities, but this correlation was attenuated after controlling for geographic distance and environmental variables. This suggests that the role of dispersal limitation in influencing the relationship between AM fungi and host plants may be related to the phylogenetic history of plants and AM fungi. This study provides new insights into understanding the evolution and ecology of the plant-AM fungal symbiosis and highlights the important role of dispersal limitation in AM fungal community structure.

    • The authors confirm contribution to the paper as follows: study conception and design: Zhang Q, Zhou J, Chen X; data collection: Zhang Q, Duan J, Xing J, Du S, Yu M, Qi G; analysis and interpretation of results: Zhang Q, Duan J, Goberna M, Jin Y, Chu J, Yang H, Chen X; draft manuscript preparation: Zhang Q, Duan J, Yang H. All authors reviewed the results and approved the final version of the manuscript.

    • All data generated or analyzed during this study are included in this published article and its supplementary information files.

      • This work was supported by the Central Public-interest Scientific Institution Basal Research Fund (CAFYBB2019QB001, CAFYBB2020ZB001), the Natural Science Foundation of China (Grant No. 31870099) and the Strategic Priority Research Program of Chinese Academy of Sciences (XDA26020102).

      • The authors declare that they have no conflict of interest.

      • Supplementary Table S1 Brief description of the sampling sites in a steppe grassland in northern China.
      • Supplementary Fig. S1 Plant phylogenetic tree with the tree in Zanne et al. (2014) as the backbone. Numbers are estimated divergence time (million years).
      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press on behalf of Yunnan Agricultural University. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (2)  Table (3) References (57)
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    Zhang Q, Duan J, Goberna M, Jin Y, Xing J, et al. 2024. Phylogenetic turnover of arbuscular mycorrhizal fungal communities across steppe grasslands. Agrobiodiversity 1(2): 16−22 doi: 10.48130/abd-0024-0004
    Zhang Q, Duan J, Goberna M, Jin Y, Xing J, et al. 2024. Phylogenetic turnover of arbuscular mycorrhizal fungal communities across steppe grasslands. Agrobiodiversity 1(2): 16−22 doi: 10.48130/abd-0024-0004

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