Unveiling the diversity, composition, and dynamics of phyllosphere microbial communities in Alhagi sparsifolia across desert basins and seasons in Xinjiang, China

Abstract

Phyllosphere microbes residing on plant leaf surfaces for maintaining plant health have gained increasing recognition. However, in desert ecosystems, knowledge about the variety, composition, and coexistence patterns of microbial communities in the phyllosphere remains limited. This study, conducted across three basins (Turpan-TLF, Tarim-CL, and Dzungaria-MSW) and three seasons (spring, summer, and autumn) in Xinjiang, China, aimed to explore the diversity and composition of microbial communities in the phyllosphere, encompassing both bacteria and fungi in Alhagi sparsifolia. We also investigated the co-occurrence patterns, influencing factors, and underlying mechanisms driving these dynamics. Results indicate that phyllosphere bacteria exhibited lower diversity indices (ACE, Shannon, Simpson, Fisher phylogenetic diversity, and Richness) in spring compared to summer and autumn, while the Goods Coverage Index (GCI) was higher in spring. Conversely, diversity indices and GCI of phyllosphere fungi showed an opposite trend. Interestingly, the lowest level of multi-functionality and niche width in phyllosphere bacteria occurred in spring, while the highest level was observed in phyllosphere fungi. Furthermore, the study revealed that no significant differences in multi-functionality were found among the regions (CL, MSW, and TLF). Network analysis highlighted that during spring, phyllosphere bacteria exhibited the lowest number of nodes, edges, and average degree, while phyllosphere fungi had the highest. Surprisingly, the multi-functionality of both phyllosphere bacteria and fungi showed no significant correlation with climatic and environmental factors but displayed a significant association with the morphological characteristics and physicochemical properties of leaves. Structural Equation Model indicated that the morphological characteristics of leaves significantly influenced the multi-functionality of phyllosphere bacteria and fungi. However, the indirect and total effects of climate on multi-functionality were greater than the effects of physicochemical properties and morphological characteristics of leaves. These findings offer new insights into leaf phyllosphere microbial community structure, laying a theoretical foundation for vegetation restoration and rational plant resource utilization in desert ecosystems.

Keywords: climate effects; co-occurrence patterns; desert ecosystems; phyllosphere microbiome; seasonal dynamics.

Figure 1

The three sampling sites at Cele, Turpan, and Mosuowan are located in Tarim Basin, Turpan Basin, and Junggar Basin, respectively.

Figure 2

Precipitation and temperature plots at sampling points.

Figure 3

Seasonal changes of phyllosphere microbial community diversity. Seasonal changes of microbial community diversity [A and G (ACE), B and H (Shannon), C and I (Simpson), D and J (Fisher phylogenetic diversity), E and K (Goods coverage), F and L (Richness)] of the phyllosphere bacteria, and [M and S (ACE), N and T (Shannon), O and U (Simpson), P and V (Fisher phylogenetic diversity), Q and W (Goods coverage), R and X (Richness)] of the phyllosphere fungi. Different lowercase letters indicate significant differences among elevations at the p < 0.05 level (ANOVA and Duncan’s test). When comparing different seasons, samples from different locations were mixed into one group. When comparing different sites, samples from different seasons are taken as a group. CL, Cele; MSW, Mosuowan; TLF, Turpan; SP, spring; SU, summer; AU, autumn; FPD, Fisher phylogenetic diversity.

Figure 4

Seasonal changes of phyllosphere microbial community multi-functionality. [A, B, and C (phyllosphere bacterial multifunctionality)] and [D, E, and F (phyllosphere fungal Multifunctionality)]. Different lowercase letters indicate significant differences among elevations at the p < 0.05 level (ANOVA and Duncan’s test). The differences between groups between sites were also compared (p < 0.05). When comparing different seasons, samples from different locations were mixed into one group. When comparing different sites, samples from different seasons are taken as a group. CL, Cele; MSW, Mosuowan; TLF, Turpan; SP, spring; SU, summer; AU, autumn.

Figure 5

The first two dimensions (PCA1-2) of the multidimensional multi-functionality [A, B, C, and D (phyllosphere bacterial multifunctionality)] and [E, F, G, and H (phyllosphere fungal multifunctionality)] were selected by principal component analysis (PCA). When comparing different seasons, samples from different locations were mixed into one group. When comparing different sites, samples from different seasons are taken as a group. CL, Cele; MSW, Mosuowan; TLF, Turpan; SP, spring; SU, summer; AU, autumn.

Figure 6

Network co-occurrence model of phyllosphere microbial community [A (phyllosphere bacterial network)] and [B (phyllosphere fungal network)]. We mixed the bacteria and fungi in the phylloplane and leaf endosphere, respectively. In different seasons and regions, a bacterial network was constructed using 8(4 leaf phylloplane+4 leaf endosphere) replicas, as was the fungal network. CL, Cele; MSW, Mosuowan; TLF, Turpan; SP, spring; SU, summer; AU, autumn.

Figure 7

Seasonal and regional changes of phyllosphere microbial niche width [A and B (phyllosphere bacterial and fungal niche width)]. Different lowercase letters indicate significant differences among elevations at the p < 0.05 level (ANOVA and Duncan’s test). The bacteria and fungi were performed by t.test function in different seasons (SP, SU, and AU) and regions (CL, MSW, and TLF). When comparing different seasons, samples from different locations were mixed into one group. When comparing different sites, samples from different seasons are taken as a group. CL, Cele; MSW, Mosuowan; TLF, Turpan; SP, spring; SU, summer; AU, autumn. Significance codes, “**,” p < 0.01; “***,” p < 0.001.

Figure 8

The relationship between leaf microbial community multi-functionality [A, C, and E (phyllosphere bacterial multifunctionality)] and [B, D, and F (phyllosphere fungal multifunctionality)] and leaf morphological characteristics, physical and chemical properties, climate, and environmental factors. LOC, leaf organic carbon (g⋅kg⁻¹); TN, total nitrogen (g⋅kg⁻¹); TP, total phosphorus (g⋅kg⁻¹); TK, total potassium (g⋅kg⁻¹); EC, electrical conductivity (mS⋅cm⁻¹); LAR, leaf area (cm²); LDW, leaf dry weight (g); SLA, specific leaf area (cm²⋅g⁻¹); SLW, specific leaf weight (g⋅cm⁻²). Temp, temperature (°C); Hum, humidity (%); Atm, atmospheric pressure (hPa); Prep, precipitation (mm); WS, wind speed (m⋅s⁻¹); WD, wind direction (°); HR, horizontal radiation(w⋅m⁻²); DR, direct radiation(w⋅m⁻²); SR, scattered radiation (w⋅m⁻²); FPD, Fisher phylogenetic diversity. Significance codes, “*,” p < 0.05; “**,” p < 0.01; “***,” p < 0.001.

Figure 9

The relationship between leaf microbial diversity [A and B (influencing factors of phyllosphere bacterial diversity)] and [C and D (influencing factors of phyllosphere fungal diversity)] and leaf morphological characteristics, physicochemical properties, climate, and environmental factors. When comparing the factors influencing the diversity of bacteria and fungi separately, samples from different locations and seasons were mixed into one group. CL, Cele desert; MSW, Mosuowan desert; TLF, Turpan desert; SP, spring; SU, summer; AU, autumn. LOC, leaf organic carbon (g⋅kg⁻¹); EC, electrical conductivity (mS⋅cm⁻¹); SLA, specific leaf area (cm²⋅g⁻¹). Significance codes, “*,” p < 0.05; “***,” p < 0.001.

Figure 10

The path analysis between the multi-functionality of leaf microbial community [A (phyllosphere bacteria multifunctionality path analysis) and B (phyllosphere fungi multifunctionality path analysis) and leaf morphological characteristics, physicochemical properties, and climatic environmental factors. LOC, leaf organic carbon (g⋅kg⁻¹); TN, total nitrogen (g⋅kg⁻¹); TP, total phosphorus (g⋅kg⁻¹); TK, total potassium (g⋅kg⁻¹); EC, electrical conductivity (mS⋅cm⁻¹); LAR, leaf area (cm²); LDW, leaf dry weight (g); SLA, specific leaf area (cm²⋅g⁻¹); SLW, specific leaf weight (g⋅cm⁻²). Temp, temperature (°C); Hum, humidity (%); Atm, atmospheric pressure (hPa); Prep, precipitation (mm); WS, wind speed (m⋅s⁻¹); WD, wind direction (°); HR, horizontal radiation (w⋅m⁻²); DR, direct radiation(w⋅m⁻²); SR, scattered radiation (w⋅m⁻²). Significance codes, “*,” p < 0.05; “**,” p < 0.01; “***,” p < 0.001.