scholarly journals Functional Diversity Can Predict Ecosystem Functions Better Than Dominant Species: The Case of Desert Plants in the Ebinur Lake Basin

2021 ◽  
Vol 13 (5) ◽  
pp. 2858
Author(s):  
Zhufeng Hou ◽  
Guanghui Lv ◽  
Lamei Jiang
Keyword(s):  
Functional Diversity ◽  
Ecosystem Function ◽  
Dominant Species ◽  
Ecosystem Functions ◽  
Available Phosphorus ◽  
Lake Basin ◽  
Relative Importance ◽  
Soil Available Phosphorus ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

Studying the impact of biodiversity on ecosystem multifunctionality is helpful for clarifying the ecological mechanisms (such as niche complementary effects and selection) of ecosystems providing multiple services. Biodiversity has a significant impact on ecosystem versatility, but the relative importance of functional diversity and dominant species to ecosystem functions needs further evaluation. We studied the desert plant community in Ebinur Lake Basin. Based on field survey data and experimental analysis, the relationship between the richness and functional diversity of dominant species and the single function of ecosystem was analyzed. The relative importance of niche complementary effect and selective effect in explaining the function of plant diversity in arid areas is discussed. There was no significant correlation between desert ecosystem functions (soil available phosphorus, organic matter, nitrate nitrogen, and ammonium nitrogen) and the richness of the dominant species Nitraria tangutorum (p < 0.05). Soil organic matter and available phosphorus had significant effects on specific leaf area and plant height (p < 0.05). Functional dispersion (FDis) had a significant effect on soil available phosphorus, while dominant species dominant species richness (SR) had no obvious effect on single ecosystem function. A structural equation model showed that dominant species had no direct effect on plant functional diversity and ecosystem function, but functional diversity had a strong direct effect on ecosystem function, and its direct coefficients of action were 0.226 and 0.422. The results can help to explain the response mechanism of multifunctionality to biodiversity in arid areas, which may provide referential significance for vegetation protection and restoration for other similar areas.

scholarly journals Examining the Driving Factors of the Direct Carbon Emissions of Households in the Ebinur Lake Basin Using the Extended STIRPAT Model

2021 ◽  
Vol 13 (3) ◽  
pp. 1339
Author(s):  
Ziyuan Chai ◽  
Zibibula Simayi ◽  
Zhihan Yang ◽  
Shengtian Yang
Keyword(s):  
Energy Consumption ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Driving Factors ◽  
Series Data ◽  
Lake Basin ◽  
Stirpat Model ◽  
Consumption Structure ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

In order to achieve the carbon emission reduction targets in Xinjiang, it has become a necessary condition to study the carbon emission of households in small and medium-sized cities in Xinjiang. This paper studies the direct carbon emissions of households (DCEH) in the Ebinur Lake Basin, and based on the extended STIRPAT model, using the 1987–2017 annual time series data of the Ebinur Lake Basin in Xinjiang to analyze the driving factors. The results indicate that DCEH in the Ebinur Lake Basin during the 31 years from 1987 to 2017 has generally increased and the energy structure of DCEH has undergone tremendous changes. The proportion of coal continues to decline, while the proportion of natural gas, gasoline and diesel is growing rapidly. The main positive driving factors affecting its carbon emissions are urbanization, vehicle ownership and GDP per capita, while the secondary driving factor is residents’ year-end savings. Population, carbon intensity and energy consumption structure have negative effects on carbon emissions, of which energy consumption structure is the main factor. In addition, there is an environmental Kuznets curve between DCEH and economic development, but it has not yet reached the inflection point.

scholarly journals Digital Mapping of Soil Organic Carbon Using Sentinel Series Data: A Case Study of the Ebinur Lake Watershed in Xinjiang

2021 ◽  
Vol 13 (4) ◽  
pp. 769
Author(s):  
Xiaohang Li ◽  
Jianli Ding ◽  
Jie Liu ◽  
Xiangyu Ge ◽  
Junyong Zhang
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Prediction Accuracy ◽  
Lake Basin ◽  
Arid Areas ◽  
Annual Average ◽  
Machine Learning Methods ◽  
Ebinur Lake ◽  
Ebinur Lake Basin

As an important evaluation index of soil quality, soil organic carbon (SOC) plays an important role in soil health, ecological security, soil material cycle and global climate cycle. The use of multi-source remote sensing on soil organic carbon distribution has a certain auxiliary effect on the study of soil organic carbon storage and the regional ecological cycle. However, the study on SOC distribution in Ebinur Lake Basin in arid and semi-arid regions is limited to the mapping of measured data, and the soil mapping of SOC using remote sensing data needs to be studied. Whether different machine learning methods can improve prediction accuracy in mapping process is less studied in arid areas. Based on that, combined with the proposed problems, this study selected the typical area of the Ebinur Lake Basin in the arid region as the study area, took the sentinel data as the main data source, and used the Sentinel-1A (radar data), the Sentinel-2A and the Sentinel-3A (multispectral data), combined with 16 kinds of DEM derivatives and climate data (annual average temperature MAT, annual average precipitation MAP) as analysis. The five different types of data are reconstructed by spatial data and divided into four spatial resolutions (10, 100, 300, and 500 m). Seven models are constructed and predicted by machine learning methods RF and Cubist. The results show that the prediction accuracy of RF model is better than that of Cubist model, indicating that RF model is more suitable for small areas in arid areas. Among the three data sources, Sentinel-1A has the highest SOC prediction accuracy of 0.391 at 10 m resolution under the RF model. The results of the importance of environmental variables show that the importance of Flow Accumulation is higher in the RF model and the importance of SLOP in the DEM derivative is higher in the Cubist model. In the prediction results, SOC is mainly distributed in oasis and regions with more human activities, while SOC is less distributed in other regions. This study provides a certain reference value for the prediction of small-scale soil organic carbon spatial distribution by means of remote sensing and environmental factors.

Analysis of spatiotemporal changes in the drying of the Ebinur Lake Basin and its influencing factors based on MODIS and Landsat 8 satellites

2018 ◽  
Vol 38 (8) ◽  
Author(s):  
朱小强 ZHU Xiaoqiang ◽  
塔西甫拉提·特依拜 TASHPOLAT·Tiyip ◽  
丁建丽 DING Jianli ◽  
依力亚斯江·努尔麦麦 Ilyas Nurmemet ◽  
夏楠 XIA Nan ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Lake Basin ◽  
Landsat 8 ◽  
Ebinur Lake ◽  
Spatiotemporal Changes ◽  
Ebinur Lake Basin

scholarly journals Uncertainty in gap filling and estimating the annual sum of carbon dioxide exchange for the desert Tugai forest, Ebinur Lake Basin, Northwest China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8530
Author(s):  
Dexiong Teng ◽  
Xuemin He ◽  
Jingzhe Wang ◽  
Jinlong Wang ◽  
Guanghui Lv
Keyword(s):  
Carbon Dioxide ◽  
Northwest China ◽  
Desert Ecosystem ◽  
Carbon Dioxide Exchange ◽  
Lake Basin ◽  
Gap Filling ◽  
Ebinur Lake ◽  
Annual Carbon ◽  
Arid Desert ◽  
Ebinur Lake Basin

In most eddy covariance (EC) studies, carbon flux measurements have a high defect rate for a variety of reasons. Obtaining the annual sum of carbon dioxide exchange requires imputation of data gaps with high precision and accuracy. This study used five methods to fill the gaps in carbon flux data and estimate the total annual carbon dioxide exchange of the Tugai forest in the arid desert ecosystem of Ebinur Lake Basin, Northwest China. The Monte Carlo method was used to estimate the random error and bias caused by gap filling. The results revealed that (1) there was a seasonal difference in the friction velocity threshold of nighttime flux, with values in the growing season and non-growing season of 0.12 and 0.10 m/s, respectively; (2) the five gap-filling methods explained 77–84% of the data variability in the fluxes, and the random errors estimated by these methods were characterized by non-normality and leptokurtic heavy tail features, following the Laplacian (or double-exponential) distribution; (3) estimates of the annual sum of carbon dioxide exchange using the five methods at the study site in 2015 ranged from −178.25 to −155.21 g C m−2 year−1, indicating that the Tugai forest in the Ebinur Lake Basin is a net carbon sink. The standard deviation of the total annual carbon dioxide exchange sums estimated by the five different methods ranged from 3.15 to 19.08 g C m−2 year−1, with bias errors ranging from −13.69 to 14.05 g C m−2 year−1. This study provides a theoretical basis for the carbon dioxide exchange and carbon source/sink assessment of the Tugai forest in an arid desert ecosystem. In order to explore the functioning of the Tugai forest at this site, a greater understanding of the underlying ecological mechanisms is necessary.

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