Responses of vegetation activity to the daytime and nighttime warming in Northwest China.

Though temperature over the past three decades has shown an asynchronous warming trend between daytime and nighttime, the response of vegetation activity to such non-uniform warming is still not very clear. In this study, the least squares linear trend analysis and geographic information system spatial analysis were conducted to analyze the spatiotemporal patterns of the daytime and nighttime warming based on the daily temperature data from 1982 to 2015 in Northwest China. The normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modeling System and vegetation type data were used to investigate the responses of vegetation activity to the daytime and nighttime warming using the partial correlation analysis. Our results suggested that (1) there was a very significant increasing trend in both daytime and nighttime temperatures in Northwest China from 1982 to 2015; night temperatures increased about 1.2 times faster than daytime temperatures, showing diurnal asymmetric warming; (2) the responses of vegetation activity to daytime and nighttime warming in Northwest China showed a distinct spatial pattern; the change in night temperatures had a more significant (positive in most regions) effect on vegetation; (3) various types of vegetation responded differently to asymmetric daytime and nighttime warming. Grassland NDVI, broad-leaved, and coniferous forest NDVI significantly responded to daytime warming. Shrub NDVI and desert NDVI significantly responded to night warming. These findings can deepen the understanding of the effects of the daytime and nighttime warming on vegetation activities in arid regions in the context of the current asymmetric warming.

Impact of Preseason Climate Factors on Vegetation Photosynthetic Phenology in Mid-High Latitudes of the Northern Hemisphere.

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid-high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by -0.15 days per year (days yr-1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr-1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr-1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change.

Fully integrated point-of-care blood cell count using multi-frame morphology analysis.

Point-of-care testing (POCT) of blood cell count (BCC) is an emerging approach that allows laypersons to identify and count whole blood cells through simple manipulation. To date, POCTs for BCC were mainly achieved by "stationary" images through blood smears or single-laity arranged cells in the microwell, making it difficult to obtain statistically sufficient numbers of cells. In this work, we present a fully integrated POCT device solely using "in-flow" imaging of 3 µL fingertip whole blood for improved identification and counting accuracy of BCC analysis. A miniaturized magnetic stirring module was integrated to maintain the temporal stability of cell concentration. A relatively high throughput (∼8000 cells/min) with a 30-fold dilution ratio of whole blood can be tested for as long as 1 h to examine sufficient numbers of cells, and the subclass cell concentration keeps constant. To improve the identification accuracy, multi-frame "in-flow" imaging was used to track the cell motion trails with multi-angle morphology analysis. This proof-of-concept was then validated with healthy whole blood samples and 75 cases of clinical patients with abnormal concentrations of red blood cells (RBCs), white blood cells (WBCs), and platelets (PLT). The average precision (AP) value of WBCs identification was improved from 0.8622 to 0.9934 using the multi-frame analysis method. And the high fitting degrees (>0.98) between our POCT device and the commercial clinical equipment indicated good agreement. This POCT device is user-friendly and cost-effective, making it a potential tool for diagnosing abnormal blood cell morphology or concentration in the field setting.

Varying Responses of Vegetation Greenness to the Diurnal Warming across the Global.

The distribution of global warming has been varying both diurnally and seasonally. Little is known about the spatiotemporal variations in the relationships between vegetation greenness and day- and night-time warming during the last decades. We investigated the global inter- and intra-annual responses of vegetation greenness to the diurnal asymmetric warming during the period of 1982-2015, using the normalized different vegetation index (NDVI, a robust proxy for vegetation greenness) obtained from the NOAA/AVHRR NDVI GIMMS3g dataset and the monthly average daily maximum (Tmax) and minimum temperature (Tmin) obtained from the gridded Climate Research Unit, University of East Anglia. Several findings were obtained: (1) The strength of the relationship between vegetation greenness and the diurnal temperature varied on inter-annual and seasonal timescales, indicating generally weakening warming effects on the vegetation activity across the global. (2) The decline in vegetation response to Tmax occurred mainly in the mid-latitudes of the world and in the high latitudes of the northern hemisphere, whereas the decline in the vegetation response to Tmin primarily concentrated in low latitudes. The percentage of areas with a significantly negative trend in the partial correlation coefficient between vegetation greenness and diurnal temperature was greater than that of the areas showing the significant positive trend. (3) The trends in the correlation between vegetation greenness and diurnal warming showed a complex spatial pattern: the majority of the study areas had undergone a significant declining strength in the vegetation greenness response to Tmax in all seasons and to Tmin in seasons except autumn. These findings are expected to have important implications for studying the diurnal asymmetry warming and its effect on the terrestrial ecosystem.

Responses of Forest Net Primary Productivity to Climatic Factors in China during 1982-2015.

Forest ecosystems play an important role in the global carbon cycle. Clarifying the large-scale dynamics of net primary productivity (NPP) and its correlation with climatic factors is essential for national forest ecology and management. Hence, this study aimed to explore the effects of major climatic factors on the Carnegie−Ames−Stanford Approach (CASA) model-estimated NPP of the entire forest and all its corresponding vegetation types in China from 1982 to 2015. The spatiotemporal patterns of interannual variability of forest NPP were illustrated using linear regression and geographic information system (GIS) spatial analysis. The correlations between forest NPP and climatic factors were evaluated using partial correlation analysis and sliding correlation analysis. We found that over thirty years, the average annual NPP of the forests was 887 × 1012 g C/a, and the average annual NPP per unit area was 650.73 g C/m2/a. The interannual NPP of the entire forest and all its corresponding vegetation types significantly increased (p < 0.01). The increase in the NPP of evergreen broad-leaved forests was markedly substantial among forest types. From the spatial perspective, the NPP of the entire forest vegetation gradually increased from northwest to southeast. Over the years, the proportions of the entire forest and all its corresponding vegetation types with a considerable increase in NPP were higher than those with a significant decrease, indicating, generally, improvements in forest NPP. We also found climatic factors variably affected the NPP of forests over time considering that the rise in temperature and solar radiation improved the interannual forest NPP, and the decline in precipitation diminished the forest NPP. Such varying strength of the relationship between the interannual forest NPP and climatic factors also varied across many forest types. Understanding the spatiotemporal pattern of forest NPP and its varying responses to climatic change will improve our knowledge to manage forest ecosystems and maintain their sustainability under a changing environment.

[Detection of Influencing Factors of Spatial Variability of Soil Respiration in Pangquangou Nature Reserve].

Soil respiration is an important process in maintaining global carbon balance. Taking the Pangquangou Nature Reserve as the research area, based on the field measurement of soil respiration (Rs) data combined with altitude (ELE), soil temperature (T), soil moisture (SWC), normalized vegetation index (NDVI), slope (slope), soil total carbon (C), total nitrogen (N), and soil bulk density (BD), we analyzed the main driving forces and interactions of Rs spatial differentiation by using the geographic detector model. The results showed that:① the spatial variation of Rs and its influencing factors in the study area was moderate. The Rs was significantly positively correlated with NDVI, T, and N (P<0.01) and negatively with ELE, slope, and SWC (P<0.01). The Rs was significantly correlated with BD(P<0.05) but not with C(P>0.05). ② The multivariate linear model composed of NDVI and T explained 64.3% of Rs spatial variation. ③ ELE, T, and NDVI were the dominant driving forces of Rs spatial differentiation in the study area, which could explain 64%, 59%, and 48% of the spatial variability. ④ The interaction of the two factors enhanced the explanatory power of Rs spatial differentiation, and the maximum interaction factors were ELE∩BD (q=0.73), and T∩slope (q=0.74), respectively. Therefore, in the process of Rs estimation, combined with topographical and environmental conditions, the interaction between multiple factors should be considered.

[Spatio-temporal dynamics of gross primary productivity in China from 1982 to 2017 based on different datasets]. / 不同数据集的1982­2017年中国总初级生产力的时空动态.

Understanding the spatio-temporal variations of gross primary productivity (GPP) of terrestrial ecosystem and its relationship with climatic factors can provide important basis for vegetation restoration and protection. Based on meteorological data and three public GPP datasets (EC-LUE GPP, GLASS GPP, and NIRv GPP), we syste-matically analyzed the spatial-temporal variations of GPP and its response to climate change in China during 1982-2017. All the results based on the three GPP datasets showed that the annual and seasonal GPP in China increased annually from 1982 to 2017, with that in 1998 and 2002 significantly being higher than the average level during the study period, and that in 1989 and 1992 significantly being lower than the average annual GPP. From 1982 to 2017, GPP showed a significant upward trend in most regions of China, with the regions with significant increases accounting for 75.7%, 73.0%, and 69.6% of the whole study area, respectively. There was a significant positive correlation between annual GPP and precipitation and temperature, but spatial heterogeneity was strong. Among them, the regions with positive correlation between GPP and temperature were mainly distributed in Northwest and Central China, while the regions with positive correlation between GPP and precipitation were mainly distributed in North China. There was obvious spatial-temporal heterogeneity in regions that GPP being affected by temperature and precipitation in different seasons. Temperature was the limiting factor of GPP in spring, autumn and winter, while summer GPP was mainly affected by precipitation.

Label-free detection and enumeration of rare circulating tumor cells by bright-field image cytometry and multi-frame image correlation analysis.

Identification and enumeration of circulating tumor cells (CTCs) in peripheral blood are proved to correlate with the progress of metastatic cancer and can provide valuable information for diagnosis and monitoring of cancer. Here, we introduce a bright-field image cytometry (BFIC) technique, assisted by a multi-frame image correlation (MFIC) algorithm, as a label-free approach for tumor cell detection in peripheral blood. For this method, images of flowing cells in a wide channel were continuously recorded and cell types were determined simultaneously using a deep neural network of YOLO-V4 with an average precision (AP) of 98.63%, 99.04%, and 98.95% for cancer cell lines HT29, A549, and KYSE30, respectively. The use of the wide microfluidic channel (400 µm width) allowed for a high throughput of 50 000 cells per min without clogging. Then erroneous or missed cell classifications caused by imaging angle differences or accidental misinterpretations in single frames were corrected by the multi-frame correlation analysis. This further improved the AP to 99.40%, 99.52%, and 99.47% for HT29, A549, and KYSE30, respectively. Meanwhile, cell counting was also accomplished in this dynamic process. Moreover, our imaging cytometry method can readily detect as few as 10 tumor cells from 100 000 white blood cells and was unaffected by the EMT process. Furthermore, CTCs from 8 advanced-stage cancer clinical samples were also successfully detected, while none for 6 healthy control subjects. Although this method is implemented for CTCs, it can also be used for the detection of other rare cells.

[Variation in fractional vegetation cover and its attribution analysis of different regions of Beijing-Tianjin Sand Source Region, China]. / äº¬æ´¥é£Žæ²™æºåŒºä¸åŒåˆ†åŒºæ¤è¢«è¦†ç›–åº¦å˜åŒ–åŠå½’å› åˆ†æž.

Based on the MODIS NDVI data from 2000 to 2018, we estimated the fractional vegetation cover (FVC) using the dimidiate pixel model and analyzed the spatiotemporal characteristics of FVC in the Beijing-Tianjin sand source region (BTSSR). The geographical detector model was used to estimate the impacts of natural and human factors on FVC spatial distribution at the regional scale. The results showed that the FVC of the BBTSR showed an increasing trend from 2000 to 2018, with an annual growth rate of 0.013·(10 a)-1 and a vegetation increase rate of 8.2%. The area with high FVC was concentrated in the Yanshan Mountain water source protection area, followed by the pastoral transitional zone desertified land control area and the Otindag sandy land area. The area with poor FVC was concentrated in the northern arid grassland area. The explanatory power of driving factors to FVC varied across different regions. Among the natural factors, annual precipitation was the main driving factor for the spatial distribution of FVC in the northern arid grassland area, the Otindag sandy land area and the Yanshan Mountain water source protection area. Slope was the main driving factor for the spatial distribution of FVC in the pastoral transitional zone desertified land control area. Among different human activities, the number of large livestock at the year-end was the main driving factor controlling the spatial distribution of FVC in the northern arid grassland area and the pastoral transitional zone desertified land control area, while population density was the main driving factor controlling the spatial distribution of FVC in the Otindag sandy land area and the Yanshan Mountain water source protection area. There were regional differences in the influen-ce of other factors on FVC spatial distribution. The results of the interaction detector showed that the two-factor interactions were mainly the double-synergy and nonlinear synergy. The interaction of human activities with annual precipitation and slope could more fully explain the spatial variations of FVC. The range of suitable vegetation growth identified by the risk detector was the area with annual precipitation of 316.4-486.0 mm, average relative humidity of 48.4%-57.6%, and average annual temperature of 2.5-7.9 ℃, while other driving factors were different in different zones.

Spatio-temporal pattern and driving forces of ecosystem services in coalfields of Shanxi Pro-vince, China.

Although coal has made a huge contribution to the development of the economy and socie-ty and its economic benefits have often attracted much attention, little research has focused on the ecosystem services of coalfields. Based on remote sensing data, meteorological data, and soil data in Shanxi coalfields during 1986, 2000, and 2015, we estimated soil conservation and water yield using the InVEST model, assessed the net primary productivity of vegetation using the CASA mo-del, and estimated sand fixation using the RWEQ model. Further, we simulated the spatial patterns of ecosystem services (ESs) using the k-means cluster analysis method and analyzed the influence factors of ESs using the Geodetector model in Shanxi coalfield areas. The results showed that soil conservation service, water yield service, and sand fixation service increased continuously. The high-value area of soil conservation service was mainly concentrated in the north of Hedong coalfield and the northeast of Qinshui coalfield, while the low-value area was distributed in the southwestern edge of Datong coalfield. The high-value area of water yield service was mainly concentrated in the northeast of Qinshui coalfield, while the low-value area was distributed in the northeast of Qinshui coalfield, Xishan coalfield and northwestern Qinshui coalfield. The high-value area for vegetation production service was mainly concentrated in the southeast of Qinshui coalfield, while the low-value area was distributed in Datong coalfield, Ningwu coalfield, Xishan coalfield, and northern Hedong coalfield. The distribution of low- and high-value areas of sand fixation service was unfixed. Ecosystem service bundles could be divided into four categories. The first category belonged to soil conservation service bundle, mainly distributed in the northern Ningwu coalfield, the northern Hedong coalfield, and the northern Qinshui coalfield. The second was water yield service bundle, mainly distributed in Huoxi coalfield and southern Qinshui coalfield. The third category belonged to vegetation production service bundle, mainly distributed in parts of Qinshui coalfield. The fourth category belonged to sand fixation service bundle, mainly distributed in the southern part of Hedong coalfield and Qinshui coalfield. Soil conservation service was greatly affected by temperature, digital elevation model (DEM), and industrial output value, with q values of 0.5, 0.3, and 0.2, respectively. Water yield service was greatly affected by precipitation, temperature, and DEM, with q values of 0.8, 0.3, and 0.2, respectively. The industrial output value, precipitation, and temperature q values of vegetation production service were 0.7, 0.6, and 0.2, respectively. The main influen-cing factors of sand fixation service were precipitation, temperature, and DEM, while the q values were 0.7, 0.3, and 0.3, respectively. The spatial distribution of coalfields ESs and the relationship between multiple ESs were closely related to natural and human factors. Therefore, maintaining the coordination relationship between natural-human factors and ecological services would be helpful to the management of the land reclamation, ecological reconstruction, and the sustainable development of coalfields ecosystem.

[Quantitative analysis of the effects of human activities on vegetation in the Beijing-Tianjin sandstorm source region under the climate change].

It is of great practical significance for regional ecological management to understand the quantitative impacts of human activities on vegetation under climate change. Based on GIMMS NDVI3g data, meteorological data (temperature, precipitation) and standardized precipitation evapotranspiration index (SPEI), we used correlation analysis and trend analysis to examine the spatio-temporal variation of vegetation and its driving factors in different periods from 1982 to 2014 in the Beijing-Tianjin sandstorm source region. Regression analysis and residual analysis were used to quantify the impacts of human activities on vegetation changes in different sub-regions. The results showed that from 1982 to 2014, the degradation status in 77.1% of degraded vegetation was significantly improved and 64.1% of vegetation had an increasing trend in the study area, with mean annual NDVI decreasing from southeast to northwest. Vegetation coverage increased in 74.5% of the areas after the implementation of the Beijing-Tianjin sandstorm source control project, with mountains in northern Shanxi showing the most obvious increases. Among all the climate factors, rainfall had the strongest correlation with vegetation change. Human activities, such as ecological engineering, played an active role in most areas, especially in mountains of northern Shanxi, where the contribution of human activities reached 94.9%.

Evaluation of organ function in patients with severe COVID-19 infections.

OBJECTIVE: The purpose of our study was to assess organ function in 102 patients with severe COVID-19 infections, using retrospective clinical analysis. MATERIAL AND METHODS: A retrospective analysis was conducted on 102 patients with severe COVID-19 infections. The patients were divided into a survival group (n = 73) and a non-survival group (n = 29) according to their prognosis. The age, sex, underlying diseases, clinical laboratory data within 48 h (routine blood tests, ALT, AST, TBIL, ALB, BUN, CR, D-Dimer, PT, APTT, FIB, F VIII:C, CK-MB, CK, and LDH), and ventilation status were collected. The organ functions of these severe COVID-19 patients were assessed by comparing the differences between the two groups. RESULTS: AST, BUN, CR, CK-MB, LDH, and CK in the non-survival group were higher than those in the survival group, and the differences were statistically significant (P < 0.05). D-Dimer, PT, FIB, and F VIII:C in the non-survival group were higher than the values observed in the survival group, and the differences were statistically significant (P < 0.05). PLT, AST, BUN, CR, D-Dimer, PT, FIB, F VIII:C, CK-MB, CK, and LDH predicted the area under the ROC curve (AUC) of the COVID19 endpoint events and were 0.721, 0.854, 0.867, 0.757, 0.699, 0.679, 0.715, 0.811, 0.935, and 0.802, respectively. CONCLUSION: The results showed that there were different degrees of damage to the liver, kidneys, blood coagulation, and heart function in the non-survival group. In addition, PLT, AST, BUN, CR, D-Dimer, PT, FIB, F VIII:C, CK-MB, CK, and LDH had value in evaluating disease prognosis.

OBJETIVO: Nuestro estudio tiene como objetivo evaluar la función del órgano en 102 pacientes con infección grave COVID-19 mediante análisis clínicos retrospectivos. MATERIALES Y MÉTODOS: Análisis retrospectivo de 102 pacientes con infección grave COVID-19. Los pacientes se dividieron en grupo de supervivencia (n = 73) y grupo de no supervivencia (n = 29) según la pre-fase. Edad, género, enfermedades subyacentes, datos de laboratorio clínico dentro de las 48 h (prueba de sangre de rutina, ALT, AST, TBIL, ALB, BUN, CR, dímero D, PT, APTT, FIB, F VIII: C, CK-MB, CK y LDH), y el estado de ventilación. Al comparar las diferencias entre los 2 grupos, se evaluó la función orgánica de estos pacientes graves con COVID-19. RESULTADOS: AST, BUN, CR, CK-MB, LDH y CK fueron todos más altos que el grupo de supervivencia en el grupo no sobreviviente, con una diferencia estadísticamente significativa (p < 0,05). Dímero D, PT, FIB y F VIII: C fueron mayores que el grupo de supervivencia en el grupo de no supervivencia, y la diferencia fue estadísticamente significativa (p < 0,05). PLT, AST, BUN, CR, dímero D, PT, FIB, F VIII: C, CK-MB, CK y LDH predijeron el área de curva inferior ROC (AUC) del evento final COVID-19, a 0,721, 0,854, 0,867, 0,757, 0,699, 0,679, 0,715, 0,811, 0,935 y 0,802, respectivamente. CONCLUSIÓN: Los resultados mostraron que el grupo de no supervivencia tenía diferentes grados de daño al hígado, riñón, coagulación y función cardíaca. Además, PLT, AST, BUN, CR, dímero D, PT, FIB, F VIII:C, CK-MB, CK y LDH tienen valor en la evaluación del pronóstico de la enfermedad.

Evaluation of organ function in patients with severe COVID-19 infections.

OBJECTIVE: The purpose of our study was to assess organ function in 102 patients with severe COVID-19 infections, using retrospective clinical analysis. MATERIAL AND METHODS: A retrospective analysis was conducted on 102 patients with severe COVID-19 infections. The patients were divided into a survival group (n=73) and a non-survival group (n=29) according to their prognosis. The age, sex, underlying diseases, clinical laboratory data within 48h (routine blood tests, ALT, AST, TBIL, ALB, BUN, CR, D-Dimer, PT, APTT, FIB, F VIII:C, CK-MB, CK, and LDH), and ventilation status were collected. The organ functions of these severe COVID-19 patients were assessed by comparing the differences between the two groups. RESULTS: AST, BUN, CR, CK-MB, LDH, and CK in the non-survival group were higher than those in the survival group, and the differences were statistically significant (P<0.05). D-Dimer, PT, FIB, and F VIII:C in the non-survival group were higher than the values observed in the survival group, and the differences were statistically significant (P<0.05). PLT, AST, BUN, CR, D-Dimer, PT, FIB, F VIII:C, CK-MB, CK, and LDH predicted the area under the ROC curve (AUC) of the COVID19 endpoint events and were 0.721, 0.854, 0.867, 0.757, 0.699, 0.679, 0.715, 0.811, 0.935, and 0.802, respectively. CONCLUSION: The results showed that there were different degrees of damage to the liver, kidneys, blood coagulation, and heart function in the non-survival group. In addition, PLT, AST, BUN, CR, D-Dimer, PT, FIB, F VIII:C, CK-MB, CK, and LDH had value in evaluating disease prognosis.

Recent asymmetric warming trends of daytime versus nighttime and their linkages with vegetation greenness in temperate China.

Asymmetric warming has been increasingly discussed recently, yet knowledge of this difference in warming between daytime and nighttime is still limited. Most studies of how climate warming influences the terrestrial ecosystem often ignore this asymmetric effect. We investigated the change in temperature between daytime and nighttime and analyzed the relationships between normalized difference vegetation index and the temperature in the daytime (Tmax) and the nighttime (Tmin) from 1982 to 2015 in temperate China. Results showed a faster increase in Tmin (0.46 °C dec-1, p < 0.01) during the nighttime than in Tmax (0.42 °C dec-1, p < 0.01) during the daytime, which indicated an asymmetric warming rate. The asymmetric warming during the daytime and nighttime was closely related to variations in precipitation and solar radiation. The increasing Tmin and Tmax were most pronounced over a large portion of the entire temperate China, and their warming trends displayed a non-uniform spatial distribution. The area with daytime warming was larger than that with nighttime warming, approximately accounting for 99.53% and 96.22% of temperate China, respectively. The area with warming enhancing vegetation greenness was larger during the day (71.16% of temperate China, p < 0.05) than at night (61.60% of temperate China, p < 0.05), and vice versa, which presented asymmetric warming effects on China's temperate vegetation. We also found clear differences in the responses of the normalized difference vegetation index among different vegetation biomes to this asymmetric warming. Averagely, Tmax was significantly related to the NDVI of shrub, desert, broadleaf forest, needleleaf forest, and swamp (p < 0.01). However, this similar relationship appeared only between Tmin and desert vegetation (p < 0.01). Our findings emphasized the crucial role of asymmetric warming between the daytime maxima and nighttime minima in climate change research.

Quantifying influences of physiographic factors on temperate dryland vegetation, Northwest China.

Variability in satellite measurements of terrestrial greenness in drylands is widely observed in land surface processes and global change studies. Yet the underlying causes differ and are not fully understood. Here, we used the GeogDetector model, a new spatial statistical approach, to examine the individual and combined influences of physiographic factors on dryland vegetation greenness changes, and to identify the most suitable characteristics of each principal factor for stimulating vegetation growth. Our results indicated that dryland greenness was predominantly affected by precipitation, soil type, vegetation type, and temperature, either separately or in concert. The interaction between pairs of physiographic factors enhanced the influence of any single factor and displayed significantly non-linear influences on vegetation greenness. Our results also implied that vegetation greenness could be promoted by adopting favorable ranges or types of major physiographical factors, thus beneficial for ecological conservation and restoration that aimed at mitigating environmental degradation.

Geographical Detector-Based Identification of the Impact of Major Determinants on Aeolian Desertification Risk.

Arid and semi-arid areas in North China are facing the challenge of a rising aeolian desertification risk (ADR) due to the intertwined effects of complex natural processes and intensified anthropogenic activities. An accurate quantitative assessment of the relationship between ADR and its determinants is beneficial for understanding the driving mechanisms of aeolian desertification and for controlling future desertification. Previous studies have failed to quantify the relative role of determinants driving ADR and have been limited in assessing their interactive impacts. In this study, a spatial variance analysis-based geographical detector methodology is used to quantify the effects of geological, physical, and human factors on the occurrence of ADR in an area characterized by mountains and hills in northern China. It is found that soil type, precipitation, and wind velocity are the major determinants of ADR, which implies that geological and physical elements (e.g., soil attribute) and climatic factors (e.g., precipitation and wind velocity) rather than human activities have played a greater role in the incidence of ADR. Particularly, the results show that the interaction of various determinants causes significant non-linearly enhanced impacts on the ADR. The findings of our study will assist local inhabitants and policy makers in developing measures for wind prevention and sand control to mitigate the effects of desertification in the region.