Cyanobacterial diversity of biological soil crusts and soil properties in karst desertification area

Abstract

As important components of the biological soil crusts (BSCs) and of the primary stage of crust succession, cyanobacterial communities occupy an important ecological niche and play an important ecological role in desertification areas. In this study, we focused on the karst desertification area, which also belongs to the same category of desertification, and selected three study areas, Guanling-Zhenfeng Huajiang (HJ), Bijie Salaxi (SLX), and Shibing (SB), in the Guizhou Plateau, which represents the overall ecological environment of South China karst, to conduct surveys on the diversity of BSC species and soil properties. Analysis of the cyanobacterial communities and physicochemical properties using the Shannon-Wiener diversity index, principal component analysis, and redundancy analysis revealed that: (1) The three study areas had common cyanobacterial species, with a total of 200 species distributed across 22 genera, 2 classes, 5 orders, and 6 families belonging to the Oscillatoriales (39%), Scytonematales (24.5%), Chroococcales (23%), Nostocales (11.5%), and Rivulariales (2%), (2) The number of species increased with the intensity of karst desertification—while Oscillatoriaceae was the dominant family in HJ and moderate–severe desertification areas, Chroococcaceae and Scytonemataceae were dominant in the mild and potential desertification areas SLX and SB, (3) The Shannon-Wiener diversity indices followed the trend: SLX (3.56) > SB (3.08) > HJ (3.01), indicating that the species were more evenly distributed in mild desertification, (4) In the carbonate background, shrubland harbored the largest number of cyanobacterial species compared to grassland, bare land, and arbor woodland; however, the highest number was documented in arbor woodland in dolomite karst, (5) The soil is weathered limestone or yellow soil in all three areas, with pH ranging from 5.73 to 6.85, fine sand dominated, and soil nutrients increased with the intensity of desertification, and (6) Redundancy analysis showed that organic carbon, soil moisture content (0–5 cm), and total nitrogen substantially influenced cyanobacterial diversity. These results reveal that differences in soil nutrient content play an important role in regulating the cyanobacterial diversity and composition, thereby establishing a foundation for further research and application of soil ecological restoration of cyanobacteria in BSCs of karst desertification areas.

Terrestrial algae: pioneer organisms of carbonate rock solutional weathering in South China karst

“…The exposed Devonian, Permian, Triassic, Jurassic, Cretaceous, Tertiary and Quaternary strata ( Chen, 1984 ) spanned the geological history of more than 400 million years from the Paleozoic, Mesozoic to Cenozoic. This is an optimal research area for algae sampling ( Chen et al, 2023 ) and carbonate rock weathering and dissolution, because it has witnessed and recorded the aquatic and terrestrial plant evolution. This area is also a treasure trove of algae fossils at different strata, which can be excavated and studied.…”

Responses of Soil C, N, P and Enzyme Activities to Biological Soil Crusts in China: A Meta-Analysis

“…The curve is a fitting curve obtained by Gaussian equation fitting; Figure S2: Funnel plot of logarithmic response ratio of soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), available phosphorus (AP) content and Alkaline Phosphatase, Cellulase, Sucrase, Urease activity to biological crust, funnel plot effect value (natural logarithm of response ratio, lnR , horizontal axis) and standard error ( SE , vertical axis); Table S1: The overall heterogeneity ( Q T ) of the response of each explanatory variable to the biological crust, the percentage of heterogeneity caused by the real variation between the effect sizes ( I 2 ), and the heterogeneity between groups ( Q B ); Table S2: Kendall’s tau rank correlation tests for assessing publication bias; Table S3: Rosenthal’s fail-safe numbers for assessing publication bias; Table S4: The results of weighted regression analysis of the effect values ( lnR ) of response variables with annual average temperature, annual average rainfall and altitude; Table S5: Meta-analysis of initial data. References [ 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 ,…”