Ecophysiological traits of various genotypes of a green key alga in biological soil crusts from the semi-arid Colorado Plateau, USA

ABSTRACT The objective of this study was to characterize the community structure and activity of N2-fixing microorganisms in mature and poorly developed biological soil crusts from both the Colorado Plateau and Chihuahuan Desert. Nitrogenase activity was approximately 10 and 2.5 times higher in mature crusts than in poorly developed crusts at the Colorado Plateau site and Chihuahuan Desert site, respectively. Analysis of nifH sequences by clone sequencing and the terminal restriction fragment length polymorphism technique indicated that the crust diazotrophic community was 80 to 90% heterocystous cyanobacteria most closely related to Nostoc spp. and that the composition of N2-fixing species did not vary significantly between the poorly developed and mature crusts at either site. In contrast, the abundance of nifH sequences was approximately 7.5 times greater (per microgram of total DNA) in mature crusts than in poorly developed crusts at a given site as measured by quantitative PCR. 16S rRNA gene clone sequencing and microscopic analysis of the cyanobacterial community within both crust types demonstrated a transition from a Microcoleus vaginatus-dominated, poorly developed crust to mature crusts harboring a greater percentage of Nostoc and Scytonema spp. We hypothesize that ecological factors, such as soil instability and water stress, may constrain the growth of N2-fixing microorganisms at our study sites and that the transition to a mature, nitrogen-producing crust initially requires bioengineering of the surface microenvironment by Microcoleus vaginatus.

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A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China

Remote Sensing ◽

10.3390/rs11111286 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1286 ◽

Cited By ~ 3

Author(s):

Xiang Chen ◽

Tao Wang ◽

Shulin Liu ◽

Fei Peng ◽

Atsushi Tsunekawa ◽

...

Keyword(s):

Random Forest ◽

Biological Soil Crusts ◽

Data Availability ◽

Sandy Land ◽

Desert Ecosystems ◽

Mu Us Sandy Land ◽

Soil Crusts ◽

Band Combinations ◽

Semi Arid ◽

Sentinel 2

Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, which lack accuracy, and hyperspectral indices, which have lower data availability and require a higher computational effort. This study employs random forest (RF) models to optimize the extraction of BSCs using band combinations similar to the two multispectral BSC indices (Crust Index-CI; Biological Soil Crust Index-BSCI), but covering all possible band combinations. Simulated multispectral datasets resampled from in-situ hyperspectral data were used to extract BSC information. Multispectral datasets (Landsat-8 and Sentinel-2 datasets) were then used to detect BSC coverage in Mu Us Sandy Land, located in northern China, where BSCs dominated by moss are widely distributed. The results show that (i) the spectral curves of moss-dominated BSCs are different from those of other typical land surfaces, (ii) the BSC coverage can be predicted using the simulated multispectral data (mean square error (MSE) < 0.01), (iii) Sentinel-2 satellite datasets with CI-based band combinations provided a reliable RF model for detecting moss-dominated BSCs (10-fold validation, R2 = 0.947; ground validation, R2 = 0.906). In conclusion, application of the RF algorithm to the Sentinel-2 dataset can precisely and effectively map BSCs dominated by moss. This new application can be used as a theoretical basis for detecting BSCs in other arid and semi-arid lands within desert ecosystems.

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