Abstract The considerable interest in detecting global vegetation changes based on satellite observations is increasing. However, studies rely on single indices to explore the driving mechanisms of the greening trend might exacerbate uncertainties of global ecosystem change. Thus, vegetation growth dynamics from various biophysical properties required to be monitored comprehensively. In this study, a consistent framework for evaluating vegetation growth trends was developed based on five widely used satellite‐derived products of MODIS Collection 6; the consistency in vegetation growth was mapped; and the factors that affected the consistency of vegetation growth were explored. The results showed that, during 2000‐2015, 45.6% of global vegetated area experienced inconsistent trends in vegetation greenness, cover and productivity, especially in evergreen broadleaf forests, grasslands, open shrublands, woody savannas and croplands. Only 5.4% of global vegetated area exhibited simultaneous trends in greenness, cover and productivity, and the inconsistent areas were expanding in the study period. Contradictory vegetation changes were mainly reflected in the opposite trends of vegetation greenness and productivity in evergreen broadleaf forests. Moreover, the inconsistency change was mainly manifested in the greenness‐dominated vegetation enhancement, without enhanced productivity. The increment difference between NPP and GPP also showed respiration losses greatly offset the effect of vegetation greenness or cover on productivity. This study provides integrated insights for understanding the inconsistency of vegetation structural and functional changes in the context of global greening.
Abstract Objectives Few studies have investigated the association between social capital and quality of life (QoL) among stroke patients. To address this research gap, we aimed to explore the association between social capital and QoL among stroke patients in Anhui Province, China. Study design Cross-sectional study. Methods This cross-sectional study was conducted using a multi-stage stratified random sampling method. The following data including demographic characteristics, health-related conditions, five dimensions of social capital status, and quality of life (QoL) were collected using a questionnaire. Generalized linear models were then used to determine the relationship between social capital and QoL after adjusting for confounding factors. Results A total of 390 participants were included for the final analysis in this study. Our results indicated that subjects with higher social capital including social connection (coefficient: 28.28, 95% CI: 19.39–37.16), social support (coefficient: 21.17, 95% CI: 10.63–31.71), trust (coefficient: 13.46, 95% CI: 2.73–24.19), reciprocity (coefficient: 25.56, 95% CI: 15.97–35.15), and cohesion (coefficient: 19.30, 95% CI: 9.90–28.70) had increased odds of reporting poor QoL when compared with lower social capital group. We also observed that the association between social capital and QoL varied across cities. Conclusions Our findings show that social capital is associated with QoL in adult stroke patients, suggesting that social capital may be significant for enhancing QoL among adults with stroke.
Abstract Dispersal, rather than species sorting, is widely recognized as the dominant driver for determining meta‐community structure at fine geographical scales in running water ecosystems. However, this view has been challenged by a recently proposed “fine‐scale species sorting hypothesis,” where community structure can be largely determined by an environmental gradient formed by local pollution at fine scales. Here, we tested this hypothesis by studying community composition and geographical distribution of metazoan zooplankton in a heavily polluted river—the North Canal River in the Haihe River Basin, China. Analysis of similarity (ANOSIM) showed that the community composition of metazoan zooplankton differed significantly ( p = .001) along the environmental gradient. Ammonium (NH 4 ‐N) was the leading factor responsible for changes in zooplankton community structure and geographical distribution, followed by total dissolved solid (TDS), Na, dissolved oxygen (DO) and temperature (T). Variation partitioning revealed a larger contribution of environmental variables (21.6%) than spatial variables (1.1%) to the total explained variation of zooplankton communities. Our results support that species sorting, rather than dispersal, played a key role in structuring communities. Threshold Indicator Taxa ANalysis (TITAN) also revealed significant change points at both taxon and community levels along the gradient of NH 4 ‐N, providing further support for the influence of environmental variables on zooplankton communities. Collectively, we validate the fine‐scale species sorting hypothesis when an environmental gradient exists in running water ecosystems at fine geographical scales. However, future studies on interactions between pollutants and zooplankton communities are still needed to better understand mechanisms responsible for the meta‐community dynamics.
Measures to characterize the spatial patterns of vegetation change can provide important information for understanding and assessing habitat fragmentation and its causes. This study developed a new framework to assess fragmentation by integrating the spatial patterns of vegetation coverage and its change across the giant panda habitat ecosystem of China. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, we detected the historical vegetation disturbances and used "abrupt," "gradual," and "total" to characterize the vegetation change processes from 2000 to 2017. The spatial patterns of vegetation coverage and its change were described by spatial clusters and outliers, and the framework was established to find the potential area for reduction of habitat fragmentation. The results indicated that 1.9% of the study area experienced disturbances during 2000–2017. Most of the disturbed area (78.9%) experienced negative abrupt vegetation change, and the undisturbed area mainly showed an increase in vegetation (85.8%). The spatial clusters of high and low total changes accounted for 22.7% and 18.2% of the study area, respectively. The high change clusters were primarily along the northern and southeastern borders of the giant panda habitat, while the low change clusters spread over the central and southern areas. Considering the spatial patterns of both vegetation coverage and its change, 12.7% of the study area, located mainly in southern Minshan and eastern Qionglaishan, needs careful management to reduce habitat fragmentation. This study provides new insight to understand habitat fragmentation in terms of spatial and temporal characteristics of vegetation status and change, and will benefit future habitat management efforts.
Deng, J. J.; Harff, J.; Li, Y. F., Zhao, Y., and Zhang, H., 2016. Morphodynamics at the coastal zone in the Laizhou Bay, Bohai Sea. Morphogenetic processes of the Yellow River Delta and the Laizhou Bay, Bohai Sea, China, have to be described by the interrelation of riverine sediment supply, relative sea level change and the effects of wind driven waves and nearshore currents. The research area is regarded a natural laboratory of the development of river dominated coastal zone with interfering natural and anthropogenic forcing factors. As well for the historical hindcast as for future projections of the morphogenetic coastal processes the Dynamic Equilibrium Shore Model (DESM) can be applied. This model generalizes the standard Bruun rule model and generates Digital Elevation Models (DEMs) scenarios on decadal to centennial time scales for the geological past and future. The basic concept is a dynamic equilibrium coastal profile evolution in adaption to the sediment budget in a spatially three-dimensional domain. By adding parameters to account for sediment mass contribution from riverine sediment flux, the DESM can be used to explore coastal morphological equilibrium states responding to sediment budget changes for river-dominated coastal zones. For the parameterization historical maps of the southern Bohai Sea have been applied to reconstruct paleo-coastlines for the 19th century for the comparison with a modern DEM. Gauge measurements provided the data for an estimation of trends in sea level change for the Laizhou Bay on the decadal scale. Modern sea level rise together with reduced riverine sediment supply caused by anthropogenic activities such as damming up-streams may change the depositional environment at the Yellow River Delta and related areas of the Laizhou Bay from river dominated (progrational) to wave dominated (regressive) environment.
With the acceleration of urbanization rate over 50%, the initiative of Ecological Restoration and Urban Regeneration tends to be the focus of future planning and design in China. However, how to identify where and what to be regenerated is challenging. This paper calls for the integration of ecosystem disservice (EDS) research into urban problem diagnosis. Ecosystem disservice is raised up in opposite to ecosystem services, referring to ecosystem’s uncomfortable or negative influences to humans. The investigation of EDS should distinguish causes (natural factors and human intervention), influenced status (actual disservices and latent disservices), and influenced level (relative disservices and absolute disservices). Using EDS as a diagnosing framework, cities could systematically analyze key issues and hotspots, summarize the checklist of disservices, and utilize varied strategies for solutions including enhancing services, mitigating disservices and tradeoff between services and disservices.
Abstract In view of the key factor in regional hydrological processes and water resource management, the temporal patterns of precipitation anomalies and oscillations were detected by the Quantile Perturbation Method (QPM) and the Singular Spectrum Analysis (SSA) Method, and the spatial patterns were identified using the Principal Component Analysis (PCA) Method. In addition, the teleconnections and lagged influence with large-scale climate oscillations in the Yangtze River Delta (YRD) of China from 1957 to 2016 were also analyzed. Results showed that, temporally, the main oscillations of precipitation were all found to be 2, 7–11 and 3–4 years in the annual and seasonal scales. Precipitation quantiles are subject to strong temporal oscillations at (multi-)decadal time scales, with high and low anomalies at specific periods. Spatially, the whole region could be divided into two main sub-regions in annual and seasonal scales, respectively. Among the selected large-scale climate oscillations in this study, the Pacific Decadal Oscillation (PDO) has a stronger influence on precipitation in March, July and September, but significant correlations were detected in more than 18% of the total stations. These stations were mainly in the southeast regions. The North Pacific index (NP) controlled the precipitation in February (13.95% of the total stations) and October (37.21% of the total stations) in the north region. Generally, the indicators of the Southern Oscillation Index (SOI) and Oceanic Niño 4 SST Index (ONI) had the strongest influence in regional precipitation variations, but significant correlations were detected in more than 20% of the total stations in March, September, October and November. Also, large-scale climate oscillations have a delayed way on precipitation. Among the oscillations, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) showed that significant cross-correlations on precipitation were 0 and 3–5 months, respectively. NP showed significant cross-correlations with precipitation in many stations when the lag time was 0–3 months. Generally, the PDO, SOI and ONI have a greater influence in the south region, mainly with the lag time of 0–3, 2–3 and 1–5 months, respectively. The results will provide a basis for taking relevant measures to deal with problems of meteorological disaster and water supplement under climate change.
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