| Literature DB >> 35464943 |
Jingshang Xiao1, Shubin Lan2, Zulin Zhang1,3, Lie Yang1, Long Qian1, Ling Xia1, Shaoxian Song1, María E Farías4, Rosa María Torres5, Li Wu1.
Abstract
As the critical ecological engineers, biological soil crusts (biocrusts) are considered to play essential roles in improving substrate conditions during ecological rehabilitation processes. Physical disturbance, however, often leads to the degradation of biocrusts, and it remains unclear how the physical disturbance affects biocrust microorganisms and their related metabolism. In this study, the photosynthetic biomass (indicated by chlorophyll a), nutrients, enzyme activities, and bacterial communities of biocrusts were investigated in a gold mine tailing of Central China to evaluate the impact of physical disturbance on biocrusts during the rehabilitation process of gold mine tailings. The results show that physical disturbance significantly reduced the photosynthetic biomass, nutrient contents (organic carbon, ammonium nitrogen, nitrate nitrogen, and total phosphorus), and enzyme activities (β-glucosidase, sucrase, nitrogenase, neutral phosphatase, and urease) of biocrusts in the mine tailings. Furthermore, 16S rDNA sequencing showed that physical disturbance strongly changed the composition, structure, and interactions of the bacterial community, leading to a shift from a cyanobacteria dominated community to a heterotrophic bacteria (proteobacteria, actinobacteria, and acidobacteria) dominated community and a more complex bacterial network (higher complexity, nodes, and edges). Altogether, our results show that the biocrusts dominated by cyanobacteria could also develop in the tailings of humid region, and the dominants (e.g., Microcoleus) were the same as those from dryland biocrusts; nevertheless, physical disturbance significantly reduced cyanobacterial relative abundance in biocrusts. Based on our findings, we propose the future work on cyanobacterial inoculation (e.g., Microcoleus), which is expected to promote substrate metabolism and accumulation, ultimately accelerating the development of biocrusts and the subsequent ecological restoration of tailings.Entities:
Keywords: bacterial community; biological soil crusts; enzyme activity; mine tailing; nutrient content; physical disturbances
Year: 2022 PMID: 35464943 PMCID: PMC9019783 DOI: 10.3389/fmicb.2022.811039
Source DB: PubMed Journal: Front Microbiol ISSN: 1664-302X Impact factor: 6.064
FIGURE 1Location of the gold tailings (A,B), landscapes of the experimental area (C), the diagram of the position where the samples were collected and a sample image of different disturbance areas (D). DH- Heavily disturbed area; DB- disturbed boundary area; UB- Undisturbed boundary area approximately 1–1.5 m away from the disturbed boundary area; U-Undisturbed area.
Basic characteristics of experimental plots.
| Plot number | Disturbed time | Biocrust color | Developed level of biocrusts | Degree of disturbance |
| DH | 1 Year ago | Gray | Cyanobacteria (disturbed 1 year ago) | Heavy (mechanical overturn) |
| DB | 1 Year ago | Gray-green | Cyanobacteria (disturbed boundary) | Slight (people trample) |
| UB | 0 Year | Green | Cyanobacteria (undisturbed boundary) | None |
| U | 0 Year | Black | Cyanobacteria; Moss (undisturbed) | None |
Biocrust physiochemical properties (n = 3).
| DH | DB | UB | U | |
| EC (μs/cm) | 117.5 ± 11.88a | 64.93 ± 14.54b | 61.93 ± 3.61b | 48.6 ± 4.98b |
| pH | 8.61 ± 0.23a | 7.56 ± 0.21b | 7.10 ± 0.05b | 7.37 ± 0.41b |
| Chl-a (μg/g) | 2.09 ± 0.84d | 3.78 ± 0.21c | 7.74 ± 0.58b | 8.99 ± 0.15a |
| Scytonemin (μg/g) | 5.72 ± 3.16d | 33.48 ± 4.08c | 149.21 ± 4.57b | 201.06 ± 5.73a |
| NO3-N (mg/kg) | 9.74 ± 1.17d | 14.38 ± 0.80c | 21.19 ± 1.69b | 24.34 ± 1.62a |
| NH4-N (mg/kg) | 0.45 ± 0.04d | 2.53 ± 0.01c | 3.39 ± 0.03b | 3.62 ± 0.01a |
| OC (g/kg) | 8.00 ± 1.47b | 13.24 ± 0.54b | 22.97 ± 5.32a | 25.57 ± 1.72a |
| EPS (mg/g) | 1.30 ± 0.15a | 1.40 ± 0.10a | 1.32 ± 0.05a | 1.27 ± 0.03a |
| TK (g/kg) | 4.08 ± 0.31a | 3.29 ± 0.25b | 2.96 ± 0.21b | 3.89 ± 0.23a |
| TP (g/kg) | 0.20 ± 0.03d | 0.45 ± 0.04c | 0.63 ± 0.05b | 0.75 ± 0.07a |
| Clay (<2 μm) (%) | 9.82 ± 1.28b | 9.11 ± 1.53b | 15.84 ± 1.74a | 16.63 ± 0.56a |
| Silt (2–20 μm) (%) | 59.52 ± 3.13b | 53.18 ± 2.43c | 66.09 ± 2.31a | 66.37 ± 2.01a |
| Sand (>20 μm) (%) | 30.66 ± 4.40a | 37.71 ± 0.90a | 18.06 ± 3.93b | 17.01 ± 2.51b |
Chl-a, Chlorophyll a; EC, electrical conductance; EPS, extracellular polysaccharide; NO
The letters indicate statistical differences in the results of analysis of variance between different tissues with a significant difference of P < 0.05.
FIGURE 2Changes in the enzyme activity of the biocrusts under different disturbance levels (n = 3). (A) α-glucosidase. (B) β-glucosidase. (C) neutral protease. (D) neutral phosphatase. (E) peroxidase. (F) polyphenol oxidase. (G) sucrase. (H) nitrogenase. (I) urease. Significant differences (P < 0.05) are marked by different letters.
FIGURE 3Bacterial α-diversity at different disturbance levels of biocrusts (n = 3). (A) Sobs index. (B) ACE index. (C) Chao1 index. (D) Shannon index. (E) Simpson index. (F) Coverage index. Significant differences (P < 0.05) are marked by different letters.
FIGURE 4Non-metric multidimensional scaling of bacteria community structure between biocrusts at different disturbance levels.
FIGURE 5Average relative abundance of bacteria at phylum (A) and genus (B) level (n = 3). P-values are listed at right, * represents the significant differences at 0.05.
FIGURE 6Co-occurrence networks of biocrusts bacterial communities in different disturbance biocrusts. (A) DH network. (B) DB network. (C) UB network. (D) U network. The nodes are colored by the phylum level. The size of each node is proportional to the node degree. The link between each pair of nodes represents positive (pink) and negative (green) correlation.
Network topological properties between different disturbance biocrusts.
| Network properties | DH | DB | UB | U |
| Total nodes | 120 | 122 | 100 | 92 |
| Total edges | 1,069 | 1,159 | 770 | 541 |
| Negative edges (percentage) | 201 (18.8) | 487 (42.0) | 308 (40.0) | 222 (41.0) |
| Positive edges (percentage) | 868 (81.2) | 672 (58.0) | 462 (60.0) | 319 (59.0) |
| Average clustering coefficient | 0.744 | 0.758 | 0.772 | 0.74 |
| Average path distance | 4.927 | 4.507 | 5.228 | 5.803 |
| Modularity | 0.569 | 0.606 | 0.567 | 0.634 |
| Complexity | 8.91 | 9.50 | 7.70 | 5.88 |