In the Tibetan permafrost region, vegetation types and soil properties have been affected by permafrost degradation, but little is known about the corresponding patterns of their soil microbial communities. Thus, we analyzed the effects of vegetation types and their covariant soil properties on bacterial and fungal community structure and membership and bacterial community-level physiological patterns. Pyrosequencing and Biolog EcoPlates were used to analyze 19 permafrost-affected soil samples from four principal vegetation types: swamp meadow (SM), meadow (M), steppe (S) and desert steppe (DS). Proteobacteria, Acidobacteria, Bacteroidetes and Actinobacteria dominated bacterial communities and the main fungal phyla were Ascomycota, Basidiomycota and Mucoromycotina. The ratios of Proteobacteria/Acidobacteria decreased in the order: SM>M>S>DS, whereas the Ascomycota/Basidiomycota ratios increased. The distributions of carbon and nitrogen cycling bacterial genera detected were related to soil properties. The bacterial communities in SM/M soils degraded amines/amino acids very rapidly, while polymers were degraded rapidly by S/DS communities. UniFrac analysis of bacterial communities detected differences among vegetation types. The fungal UniFrac community patterns of SM differed from the others. Redundancy analysis showed that the carbon/nitrogen ratio had the main effect on bacteria community structures and their diversity in alkaline soil, whereas soil moisture was mainly responsible for structuring fungal communities. Thus, microbial communities and their functioning are probably affected by soil environmental change in response to permafrost degradation.
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http://dx.doi.org/10.1016/j.resmic.2014.01.002 | DOI Listing |
Sci Total Environ
January 2025
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India. Electronic address:
This study investigates the potential impact of future climate scenarios designated by different shared socioeconomic pathways (SSPs) on vegetation health. Considering the entire Indian mainland as the study region, which exhibits a diverse range of climate and vegetation regimes, we analysed long-term past (1981-2020) and future (2021-2100) changes in vegetation greenness across seven vegetation types and four seasons. In order to gain insight into the intricate interrelationships between vegetation and hydroclimatic factors (soil moisture, precipitation, solar radiation, and temperature), a Standardized Vegetation Index (SVI) is used as a proxy for vegetation health, and a bivariate copula-based probabilistic model is developed incorporating a Combined Climate Index (CCI) derived through Supervised Principal Component Analysis (SPCA) and the SVI.
View Article and Find Full Text PDFHeliyon
January 2025
Key Laboratory of Plant Resource Conservation and Germplasm Innovation in Mountainous Region, Collaborative Innovation Center for Mountain Ecology & Agro-Bioengineering, College of Life Sciences, Guizhou University, Guiyang, 550025, China.
Vegetation change significantly altered the hydrological processes and soil erosion within riparian ecosystems. It is unclear how change in managed vegetation types affect the geochemical behavior of heavy metals (HMs) and magnetic particles in karst riparian areas. Two soil depths of 0-20 cm and 20-40 cm were taken in alien species (), native species and in a typical urban plateau Lake wetland, Caohai lake, China.
View Article and Find Full Text PDFFront Plant Sci
January 2025
Yellow River Institute of Hydraulic Research, Henan Key Laboratory of Yellow Basin Ecological Protection and Restoration, Zhengzhou, China.
Vegetation productivity and ecosystem carbon sink capacity are significantly influenced by seasonal weather patterns. The time lags between changes in these patterns and ecosystem (including vegetation) responses is a critical aspect in vegetation-climate and ecosystem-climate interactions. These lags can vary considerably due to the spatial heterogeneity of vegetation and ecosystems.
View Article and Find Full Text PDFPlant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Geography, School of Environment, Education and Development, The University of Manchester, Arthur Lewis Building, Oxford Road, Manchester, M13 9PL, UK.
Urban woodland composition and configuration have strong associations with land surface temperatures (LST), but the evidence is contradictory due to different spatial scales, regional climate zones, woodland types and urban contexts. In this study, we analyse associations between urban woodland and LST within and between five cities in different Köppen climate zones. Our consistent methodology is framed around local climate zones and conducted at a fine spatial scale.
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