Spatial non-stationarity effects of driving factors on soil respiration in an arid desert region

Wang, Jinlong; Teng, Dexiong; He, Xuemin; Lu, Qin; Yang, Xiaodong; Lv, Guanghui

doi:10.1016/j.catena.2021.105617

Cited by 12 publications

(24 citation statements)

References 101 publications

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“…According to the groundwater level and vegetation development, the gradient belt from southwest to northeast can be divided into riverbank habitat, transitional zone habitat, and desert margin habitat [19]. The riverbank habitat is located at the southern end of the gradient zone (0~1.50 km from the riparian zone), and the groundwater table is relatively shallow due to its proximity to the channel.…”

Section: Experimental Designmentioning

confidence: 99%

“…The soil particle size is large, the organic matter content (1.35 g kg −1 ) is low, and the soil water content (1.04%) is also low, making plant survival challenging. The species growing in this area mainly include Haloxylon ammodendron, Nitraria tangutorum, and Suaeda salsa [19].…”

Section: Experimental Designmentioning

confidence: 99%

“…The 10 m × 10 m sampling scale was measured before the 5 m × 5 m sampling scale. To eliminate the influence of above-ground plant respiration, the ground vegetation and large branches in the collar were all removed one day before the measurement [19]. Each PVC collar was measured three times, and the average value was taken as the Rs value at the measuring point [22,60].…”

Section: Rs and Soil Indicatorsmentioning

confidence: 99%

“…Soil pH was determined using a PHS-3C Precision pH meter (Shanghai Etorch Electro-Mechanical Technology Co., Ltd., Shanghai, China). The soil organic carbon (SOC), total nitrogen (TN), nitrate nitrogen (NN), ammonium nitrogen (AN), total phosphorus (TP), and available phosphorus (AP) were measured using the methods described by Wang et al [19].…”

Section: Rs and Soil Indicatorsmentioning

confidence: 99%

“…At the regional scale, landscape [10,11], land use type [12,13], and vegetation type [14,15] affected Rs. At the plot scale, stand and canopy structure [16,17], root distribution [18], and the heterogeneity of major environmental factors and soil characteristics [19] lead to the spatial heterogeneity of Rs [20,21]. Currently, the dynamic chamber-infrared CO 2 analyzer (IRGA) method is mostly used to measure Rs in ecosystems with equipment such as LI-COR series instruments (including the 6400 and 8100 instruments: LI-COR Biosciences, Lincoln, NE, USA) [22].…”

Section: Introductionmentioning

confidence: 99%

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Spatial Scale Effects of Soil Respiration in Arid Desert Tugai Forest: Responses to Plant Functional Traits and Soil Abiotic Factors

Wang

et al. 2022

Forests

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Understanding the spatial variation law of soil respiration (Rs) and its influencing factors is very important when simulating and predicting the terrestrial carbon cycle process. However, there are still limitations in understanding how different sampling scales affect the spatial heterogeneity of Rs and whether the spatial scale effect will change with habitat types. Our objectives were to explore the effects of different sampling scales on the spatial variability of Rs and the relative importance of soil abiotic characteristics and plant traits in influencing the spatial variability of Rs. The Rs, soil properties, and plant traits were measured through field investigation and indoor analysis in the Tugai forest desert plant community in the Ebinur Lake Basin in northwest China. The Rs showed significant water gradient changes, with a coefficient of variation of 35.4%–58%. Plot types had significant effects on Rs, while the change of sampling scale did not lead to significant differences in Rs. At the plot scale, Rs spatial variation at the 5 m × 5 m sampling scale mainly depended on plant traits (leaf length, leaf thickness, leaf dry matter content, and leaf phosphorus content, p < 0.05), while Rs spatial variation at the 10 m × 10 m scale mainly depended on soil properties (soil total phosphorus, ammonium nitrogen, soil water content, and pH, p < 0.05). At the local scale, soil nutrients (soil available phosphorus and ammonium nitrogen) and plant traits (maximum plant height, leaf length, and phosphorus content) at the 5 m × 5 m scale jointly explained 49% of the spatial change of Rs. In contrast, soil microclimate (soil water content), soil nutrients (soil pH, available phosphorus, and nitrate nitrogen), and plant traits (leaf thickness) jointly explained 51% of the spatial variation of Rs at the 10 m × 10 m scale. These results demonstrate the potential to predict the spatial variability of Rs based on the combination of easily measured aboveground functional traits and soil properties, which provides new ideas and perspectives for further understanding the mechanism of Rs change in Tugai forests.

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Section: Experimental Designmentioning

confidence: 99%

Section: Experimental Designmentioning

confidence: 99%

Section: Rs and Soil Indicatorsmentioning

confidence: 99%

Section: Rs and Soil Indicatorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spatial Scale Effects of Soil Respiration in Arid Desert Tugai Forest: Responses to Plant Functional Traits and Soil Abiotic Factors

Wang

et al. 2022

Forests

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Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019

Ding

Jin

2022

Intl Journal of Climatology

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Knowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas.

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Effects of aerosol on terrestrial gross primary productivity in Central Asia

Ding

Wang

et al. 2022

Atmospheric Environment

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Spatial non-stationarity effects of driving factors on soil respiration in an arid desert region

Cited by 12 publications

References 101 publications

Spatial Scale Effects of Soil Respiration in Arid Desert Tugai Forest: Responses to Plant Functional Traits and Soil Abiotic Factors

Spatial Scale Effects of Soil Respiration in Arid Desert Tugai Forest: Responses to Plant Functional Traits and Soil Abiotic Factors

Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019

Effects of aerosol on terrestrial gross primary productivity in Central Asia

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