3.1 Soil physico-chemical properties
The results of the two-way ANOVA showed that the soil properties were significantly different between all vegetation types (Table 2). In both summer and winter, the soil moisture content was between 21.14% – 31.84% under all vegetation types. According to the average pH in the two seasons, the vegetation types were ranked: WN (7.92) > CPD (7.85) > CPS (7.66) > DF (7.64) > CN (7.53) > TW (7.35). In summer, the soil exchangeable Ca2+ content was highest under CPD and lowest under CN, while in winter it was highest under DF and lowest under CPS. With respect to SOC and TN contents, the vegetation types followed the order: CPS > WN > CPD > TW > DF > CN. In summer and winter, according to the soil NO3−N content, the vegetation types ranked in the order: CN > DF > CPD > CPS > WN > TW. However, regarding the soil NH4+-N content, the order was WN or CPS > CN, DF > TW or CPD. In summer and winter, the AP content under CPD was 1.4–8.6 times greater than under the other vegetation types. For all vegetation types, the range of soil AK content was lower than that for AP content. Taking the average value, the soil AK content was the highest at 139 mg/kg under DF and lowest at 74 mg/kg under CPS.
Table 2
Soil physico-chemical properties of six vegetation types in 2019
Vegetation type
|
Soil moisture
(%)
|
pH
|
Ca2+
(g/kg)
|
SOC
(g/kg)
|
TN
(g/kg)
|
NH4+-N
(mg/kg)
|
NO3−-N
(mg/kg)
|
AP
(mg/kg)
|
AK
(mg/kg)
|
Summer
|
|
|
|
|
|
|
|
|
|
DF
|
25.27±0.68b
|
7.68±0.03bc
|
2.30±0.11ab
|
11.11±0.82d
|
1.06±0.10d
|
0.22±0.03a
|
4.76±0.98ab
|
2.12±0.32b
|
112.32±3.74a
|
CPD
|
27.77±0.84ab
|
7.86±0.05ab
|
2.62±0.08a
|
24.33±0.34c
|
2.20±0.03c
|
0.19±0.02a
|
3.48±1.00abc
|
11.12±1.17a
|
109.15±6.86a
|
CN
|
23.60±1.68b
|
7.59±0.08c
|
2.11±0.09b
|
8.85±0.30d
|
0.80±0.03d
|
0.28±0.09a
|
7.28±1.40a
|
2.25±0.75b
|
108.74±4.19a
|
WN
|
27.41±0.77ab
|
7.95±0.02a
|
2.32±0.09ab
|
29.02±0.94b
|
2.65±0.07b
|
0.41±0.07a
|
2.12±0.70bc
|
2.52±0.62b
|
105.07±11.65ab
|
TW
|
24.35±2.37b
|
7.48±0.07c
|
2.38±0.20ab
|
11.66±0.16d
|
1.05±0.02d
|
0.20±0.08a
|
0.62±0.07c
|
1.20±0.23b
|
88.03±6.46ab
|
CPS
|
31.84±1.48a
|
7.67±0.06bc
|
2.12±0.04ab
|
37.49±2.14a
|
3.36±0.21a
|
0.29±0.09a
|
2.19±0.79bc
|
2.62±0.89b
|
73.09±9.81b
|
Winter
|
|
|
|
|
|
|
|
|
|
DF
|
25.5±0.96ab
|
7.60±0.05ab
|
2.80±0.10a
|
11.41±0.55c
|
1.41±0.15b
|
2.56±0.07a
|
5.83±0.77b
|
3.02±0.35b
|
166.35±7.79a
|
CPD
|
29.68±1.26a
|
7.85±0.03a
|
2.32±0.12ab
|
25.57±0.86b
|
2.97±0.16a
|
2.55±0.07a
|
4.59±0.41bc
|
7.25±0.96a
|
146.85±9.6ab
|
CN
|
27.83±1.65a
|
7.47±0.27ab
|
2.48±0.16a
|
10.60±0.50c
|
1.30±0.03b
|
2.67±0.15a
|
8.14±0.28a
|
2.43±0.37b
|
143.22±5.76ab
|
WN
|
29.95±1.09a
|
7.9±0.00a
|
1.62±0.14cd
|
30.22±1.53b
|
3.39±0.08a
|
2.70±0.10a
|
2.75±0.10d
|
2.48±0.26b
|
96.97±9.13cd
|
TW
|
21.14±2.10b
|
7.23±0.13b
|
1.88±0.10bc
|
16.33±2.25c
|
1.54±0.20b
|
2.40±0.05a
|
2.44±0.01d
|
1.70±0.06b
|
122.99±13.89bc
|
CPS
|
29.62±1.12a
|
7.66±0.08ab
|
1.30±0.10d
|
39.28±1.64a
|
3.96±0.61a
|
2.79±0.15a
|
3.42±0.30cd
|
3.03±0.50b
|
75.60±5.86d
|
Two-way ANOVA
|
|
|
|
|
|
|
|
|
|
Vegetation type
|
ns
|
***
|
***
|
***
|
***
|
*
|
***
|
***
|
***
|
Season
|
***
|
ns
|
***
|
*
|
***
|
***
|
**
|
ns
|
***
|
Vegetation type × Season
|
ns
|
ns
|
***
|
ns
|
ns
|
ns
|
ns
|
**
|
**
|
DF dragon fruit, CPD Chinese pepper in depression, CN corn, WN walnut, TW teakwood, CPS Chinese pepper on slope, Ca2+ exchangeable Ca2+, SOC soil organic carbon, TN total nitrogen, AP available phosphorus, AK available potassium. Values (Means ± standard error) followed by the different letter are significantly different within columns in the same season (P < 0.05). *, **, and *** indicate significant differences at P < 0.05, P < 0.01, and P < 0.001, respectively. ns means no significance. |
Except for soil pH and AP content, soil properties changed significantly with the seasons (Table 2). Under all vegetation types, the soil pH declined by 0.02–3.37% from summer to winter. Soil Ca2+ content increased by 22% and 17% under DF and CN, respectively, but decreased by 11% – 39% under the other vegetation types. The contents of soil SOC and TN both increased under all vegetation types, notably by 20% for SOC and 63% for TN under CN. Similarly, under TW the soil NO3-N and NH4+-N contents increased by 12% – 296% and 559% – 1217%, respectively. From summer to winter, the soil AP content increased by 43%, 42%, and 16% under DF, TW, and CPS, respectively, and decreased by 35% under CPS. Meanwhile, the soil AK content increased by 32–48% under CN, CPD and CPS, TW, and DF, and decreased by 8% under WN. The interactions between vegetation type and season were significant for soil Ca2+, AP, and AK contents, but were not significant for the other soil properties.
3.2 Soil microbial biomass and enzyme activity
Vegetation type had a very significant impact on soil MBC and MBN (Table 3). The soil MBC and MBN contents under CPD and CPS, and WN were higher than those under TW, DF, and CN. In summer, the soil MBC under CPS was the highest, and 2.77 times the lowest value under DF. Meanwhile, soil MBN under WN was the highest and 1.3 times greater than under TW. Compared to summer, the soil MBC in winter was significantly lower by 23–26% under all vegetation types except for TW, which increased by 0.72%. Meanwhile, soil MBN decreased significantly by 62% – 83% under all vegetation types. The ratios of MBC/SOC, MBN/TN, and MBC/MBN showed significant differences among the six vegetation types and the two seasons (P < 0.01, Table S1). The MBN/TN ratio was also significantly affected by the interaction of vegetation type and season (P < 0.001, Table S1).
Table 3
Soil microbial biomass and enzyme activities of six vegetation types in 2019
Vegetation type
|
MBC
(mg/kg)
|
MBN
(mg/kg)
|
ΒG
(nmol/g/h)
|
NAG
(nmol/g/h)
|
AKP
(nmol/g/h)
|
CAT
(ml/g/30min)
|
Summer
|
|
|
|
|
|
|
DF
|
130.69±6.01c
|
50.7±7.98b
|
309.76±6.27b
|
266.36±11.61b
|
346.38±9.19ab
|
11.34±0.01a
|
CPD
|
266.7±17.66b
|
89.16±3.05a
|
98.90±12.48d
|
53.72±7.46c
|
123.73±9.23c
|
11.27±0.01a
|
CN
|
132.12±8.92c
|
55.14±2.27b
|
413.38±11.99a
|
344.04±36.10ab
|
521.41±76.02a
|
11.35±0.01a
|
WN
|
356.7±21.15a
|
93.33±6.15a
|
102.93±4.14cd
|
25.69±3.96c
|
278.28±21.00bc
|
11.21±0.02a
|
TW
|
162.83±12.36c
|
40.61±1.28b
|
355.40±24.6ab
|
406.38±40.33a
|
523.55±68.15a
|
11.38±0.01a
|
CPS
|
361.68±39.77a
|
79.29±6.2a
|
187.22±39.57c
|
122.70±36.95c
|
213.43±53.77bc
|
10.97±0.10b
|
Winter
|
|
|
|
|
|
|
DF
|
99.75±15.47b
|
11.42±2.33bc
|
414.84±30.42a
|
1204.40±87.49a
|
1058.3±136.69ab
|
11.31±0.01a
|
CPD
|
203.99±4.41ab
|
29.92±2.65a
|
106.21±3.60c
|
463.37±47.29b
|
338.65±17.47c
|
11.28±0.01ab
|
CN
|
97.48±7.27b
|
9.27±0.4c
|
406.40±70.24a
|
1246.01±122.96a
|
1435.61±185.45a
|
11.30±0.00a
|
WN
|
275.05±15.75a
|
35.19±4.28a
|
81.39±10.75c
|
337.92±14.22b
|
300.49±15.21c
|
11.25±0.01c
|
TW
|
164±42.33ab
|
13.06±3.14bc
|
275.18±20.19ab
|
1286.76±99.89a
|
1418.18±147.96a
|
11.29±0.02ab
|
CPS
|
261.73±42.89a
|
23.04±3.75ab
|
173.79±23.03bc
|
495.47±174.82b
|
775.04±121.44bc
|
11.25±0.00c
|
Two-way ANOVA
|
|
|
|
|
|
|
Vegetation type
|
***
|
***
|
***
|
***
|
***
|
***
|
Season
|
***
|
***
|
ns
|
***
|
***
|
ns
|
Vegetation type × Season
|
ns
|
**
|
ns
|
***
|
***
|
***
|
DF dragon fruit, CPD Chinese pepper in depression, CN corn, WN walnut, TW teakwood, CPS Chinese pepper on slope, MBC microbial biomass carbon, MBN microbial biomass nitrogen, ΒG β-1,4-glucosidase, NAG β-1,4-N-acetylglucosaminidase, AKP alkaline phosphatase, CAT catalase. Values (Means ± standard error) followed by the different letter are significantly different within columns in the same season (P < 0.05). ** and *** indicate significant differences at P < 0.01 and P < 0.001. ns means no significance. |
Vegetation type also significantly influenced enzyme activity, including ΒG, NAG, AKP, and CAT (Table 3). All of the enzyme activities under TW, CN, and DF were generally higher than those under WN, and CPS and CPD. Regarding ΒG, the enzyme activity ranged from 80–415 nmol g−1 h−1 in both summer and winter. Compared with summer, enzyme activity was significantly elevated in winter by 34% under DF, but declined by 21% and 23% under WN and TW, respectively. NAG activity was elevated 2.2-, 2.6-, 3-, 3.5-, 7.6-, and 12-fold under TW, CN, CPS, DF, CPD, and WN, respectively. Under all vegetation types, AKP activity was also significantly elevated by 8–263%. In both summer and winter, CAT activity under CPS was significantly lower than under all other vegetation types.
3.3 Microbial community composition and network analysis
In summer, the microbial diversity indexes of bacteria and fungi, including ACE, Chao, and Shannon, showed no significant difference among all vegetation types (Table 4). From summer to winter, the bacterial ACE index declined by 0.56% – 3.98% under all vegetation types except under DF, which increased by 0.65%. The bacterial Chao index increased under DF and CPS, but decreased under the other vegetation types. Under CPD, the bacterial Shannon index significantly increased by 2.6%. For fungi, the Chao index increased by 14% and 12% under CPD and CPS, respectively, but decreased by 11% under CN. The Shannon indexes increased significantly by 46% and 17% under WN and CPS, respectively, but decreased by 28%, 22%, and 17% under DF, CN, and CPD, respectively.
Table 4
The α-diversity index of microbial community of six vegetation types in 2019
Vegetation type
|
Bacteria
|
|
Fungi
|
Ace
|
Chao
|
Shannon
|
|
Ace
|
Chao
|
Shannon
|
Summer
|
|
|
|
|
|
|
|
DF
|
1114.32±14.86a
|
1125.5±12.35a
|
8.56±0.12a
|
|
489.02±37.71a
|
483.09±38.22a
|
4.76±0.40a
|
CPD
|
1110.54±8.98a
|
1133.42±12.62a
|
8.47±0.07a
|
|
501.01±47.95a
|
492.42±44.68a
|
5.16±0.77a
|
CN
|
1118.12±8.59a
|
1137.36±9.8a
|
8.45±0.08a
|
|
572.46±26.55a
|
542.96±29.65a
|
4.53±0.73a
|
WN
|
1124.32±14.97a
|
1137.6±15.38a
|
8.65±0.05a
|
|
461.52±24.54a
|
454.8±15.84a
|
3.08±0.49a
|
TW
|
1128.2±6.66a
|
1137.88±7.23a
|
8.43±0.15a
|
|
467.92±16.99a
|
477.19±15.43a
|
4.57±0.26a
|
CPS
|
1104.98±11.12a
|
1110.77±12.81a
|
8.25±0.23a
|
|
474.04±16.32a
|
441.45±9.25a
|
4.56±0.23a
|
Winter
|
|
|
|
|
|
|
|
DF
|
1121.58±6.29a
|
1138.92±7.47a
|
8.59±0.10a
|
|
512.15±30.81a
|
487.43±16.75ab
|
3.45±0.52b
|
CPD
|
1091.79±12.12a
|
1108.68±14.56a
|
8.69±0.04a
|
|
594.51±46.16a
|
551.15±16.45a
|
4.29±0.41ab
|
CN
|
1103.7±15.66a
|
1115.04±18.61a
|
8.47±0.22a
|
|
564.59±27.12a
|
483.69±20.31ab
|
3.54±0.55b
|
WN
|
1101.36±10.29a
|
1115.06±9.95a
|
8.75±0.04a
|
|
500.23±21.11a
|
481.55±19.92ab
|
4.50±0.27ab
|
TW
|
1083.35±19.64a
|
1091.69±22.66a
|
8.33±0.16a
|
|
456.86±38.35a
|
457.24±31.26b
|
4.52±0.22ab
|
CPS
|
1098.78±13.12a
|
1112.91±13.96a
|
8.60±0.05a
|
|
500.79±21.07a
|
501.33±22.49ab
|
5.36±0.12a
|
Two-way ANOVA
|
|
|
|
|
|
|
|
Vegetation type
|
ns
|
ns
|
ns
|
|
**
|
ns
|
ns
|
Season
|
*
|
*
|
ns
|
|
ns
|
ns
|
ns
|
Vegetation type × Season
|
ns
|
ns
|
ns
|
|
ns
|
ns
|
*
|
DF dragon fruit, CPD Chinese pepper in depression, CN corn, WN walnut, TW teakwood, CPS Chinese pepper on slope. Values (Means ± standard error) followed by the different letter are significantly different within columns in the same season (P < 0.05). * and ** indicate significant differences at P < 0.05 and P < 0.01, respectively. ns means no significance. |
At the bacterial phylum level, all vegetation types were dominated by Acidobacteria (35% relative abundance), Proteobacteria (25%), and Actinobacteria (15%) (Fig. 2a). The relative abundance of Acidobacteria was highest under CPD (40%) and lowest under TW (29%). Under CN, the relative abundance of Proteobacteria decreased from 31% in summer to 22% in winter, while that of Actinobacteria increased from 10–18% over the same period. The relative abundance of Proteobacteria and Actinobacteria under WN showed the same trend as under CN. However, under CPD, the relative abundance of Proteobacteria increased from 19–26%, accompanied by a decrease in Actinobacteria from 35–30%. At the fungal phylum level, the most abundant phylum was Ascomycota under CPD and CPS, WN, and TW (average: 56%), while Mortierellomycota was most abundant under CN (38%) (Fig. 2b). From summer to winter, the relative abundance of Ascomycota decreased from 67–36%, while that of Mortierellomycota increased from 6–53% under DF. The relative abundance of Mortierellomycota was more than 33% under CN and CPD, but lower than 4% under TW and CPS.
For both bacteria and fungi, the vegetation type led to significant differences in community composition (Fig. 3, P < 0.001), and there was a little overlap among the six vegetation types in summer (Fig. 3a and c). The bacterial and fungal community structures changed from summer (Fig. 3a and c) to winter (Fig. 3b and d), and the tighter clustering can be seen in the bar plots in Fig. 3. Due to their greater artificial disturbance, the Bray-Curtis distance between DF and CN was closer than among the other vegetation types, especially in winter (Fig. 3b and d). Meanwhile, the distance between CPD and CPS was greater than that between WN and CPS due to locational differences in Chinese pepper planting. Therefore, the effect of vegetation type on the composition of the bacterial and fungal communities was greater in winter than in summer, based on the larger PERMANOVA R2 values (Fig. 3).
We then performed network analyses to assess the impact of vegetation type and season on microbial interactions. The soil microbial network patterns differed among the six vegetation types and showed clear changes from summer to winter (Fig. 4a-l, Table S4). The microbial taxa showed higher network connectivity (i.e. network degree) under CN and CPD (Fig. 4c-f) than in other vegetation types. Bacterial taxa had higher network degrees than fungal taxa, especially under CN and CPD in winter (Fig. 4d, f, Table S4). In summer and winter, the average number of nodes under CN (641) was lower than under the other vegetation types (DF 764, CPD 754, WN 746, CPS 734, TW 660), while the average number of links under CN (2333) was lower than under CPD (2764), but higher than under the other vegetation types (Fig. 4, Table S4). The average path distances under CN (5.917) and CPD (5.809) were lower than under the other vegetation types, with values > 7 (Table S4). Moreover, the proportion of negative network edges (mainly representing bacteria-fungi interkingdom correlations) sharply declined from 39.2–8.6% under CPD (Fig. 4c, d) and from 40.3–8.8% under CN (Fig. 4e, f) from summer to winter, respectively.
3.4 Structural equation model (SEM) and Pearson’s correlation heatmap
The SEM model was a reasonable fit to our data (Fig. 5). The model showed that 63% and 84% of the variance in the first and second soil fertility NMDS axes was explained by vegetation type, season, and soil moisture (Fig. 5). Soil moisture had significant and positive correlations with the SOC and TN levels (0.555 and 0.598, P < 0.01) (Table S2). The model explained 83% and 71% of the variance in the first and second soil enzyme activity NMDS axes. Season, soil fertility, soil moisture, soil pH and Ca2+, and microbial biomass directly affected soil enzyme activity. The effects of season and soil Ca2+ were positive, whereas those of soil fertility, moisture, and soil pH had a negative effect on enzyme activity, which was also supported by the Pearson’s correlation (Table S2). The model explained 56% and 75% of the variance of MBC and MBN, respectively, which were directed influenced by season, soil pH, and fertility. Regarding soil fertility, the SOC and TN showed significant correlations with MBC (0.82 and 0.717, P < 0.01, respectively) (Table S2). Vegetation type exerted only an indirect effect on soil microbial biomass and was mediated by soil fertility (NMDS1 0.511 and NMDS2 0.392, P < 0.001) (Fig. 5).
Thirty-five percent of the variance in the bacterial Shannon diversity index was explained by soil pH alone (Fig. 5a, path coefficient = 0.661***). The SEM explained 46% and 58% of the variance in the first and second bacterial community NMDS axes. Soil pH and fertility showed direct positive effects, whereas vegetation type and soil Ca2+ showed direct negative effects on bacterial community NMDS1. Vegetation type directly and positively influenced bacterial community NMDS2. Due to direct and indirect effects mediated by soil fertility, vegetation type showed a stronger effect on bacterial community composition than soil pH and Ca2+. The fungal Shannon diversity index was directly and negatively influenced by MBN (-0.659) and soil fertility NMDS2 (0.603) (Fig. 5b). The SEM explained 59% and 42% of the variance of the first and second fungal community NMDS axes. Vegetation type and soil Ca2+ showed positive effects while season and soil fertility negatively affected fungal community composition.
For bacterial phyla, the SOC and TN contents showed significant and positive correlations with the abundances of Entotheonellaeota, Armatimonadetes, and Actinobacteria (P < 0.05), but were negatively correlated with the abundances of Nitrospirae and Proteobacteria (Fig. 6a, P < 0.05). The soil Ca2+, AP, AK, and NO3−-N contents were positively correlated with Nitrospirae and Gemmatimonadetes: in particular, the NO3−-N content was strongly significantly correlated with them (r = 0.69, P < 0.001). Meanwhile, the soil Ca2+ content was negatively correlated with the abundances of Actinobacteria, Entotheonellaeota, and Firmicutes (r < -0.4, P < 0.001). The soil NH4+-N content, and the AKP and NAG enzyme activities, were positively correlated with the abundance of Actinobacteria and Chloroflexi, but negatively correlated with the abundance of Proteobacteria. CAT enzyme activity was negatively correlated with the abundance of Rokubacteria and Verrucomicrobia, while the activity of glucosidase was negatively correlated with the abundance of Entotheonellaeota. For the fungal phyla, the soil Ca2+, AP, AK, and NO3−-N contents were positively correlated with the abundance of Mortierellomycota (Fig. 6b, P < 0.001). However, the soil Ca2+, AK, and NO3−-N contents showed significant and negative correlations with the abundance of Ascomycota (P < 0.01). The soil AP content was positively correlated with the abundances of Chytridiomycota and Zoopagomycota. Glycosidase and NAG activities were positively correlated with the abundance of Glomeromycota.