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The patterns of soil physicochemical properties at large-scales such as buffer zone have been extensively studied, while at fine-scales of vegetation buffer strips (represent a trade-off between economic and ecology benefits of land) and the relationship with vegetation biodiversity are yet to be deepened. This study focused on the discrepancy between soil physicochemical properties (categorizing as soil properties, soil texture, soil stoichiometric ratio, soil microbial activity) and vegetation biodiversity indices in the buffer strips of newly constructed Chushandian reservoir, China. The results showed that the woodland buffer strips had higher soil microbial activity and carbon sequestration capacity than grassland and abandoned cropland. Soil physicochemical properties at 0 m in the reservoir buffer strips with three land use types were more influenced by the overlying water. Distance from watercourse is an important factor affecting soil properties in the reservoir buffer strips with different land use types, mainly mediated by affecting the vegetation biodiversity indices. The width of the existing grassland and abandoned cropland (20 m) is not enough for ecological restoration and should be appropriately widened. Finally, we suggest that the restoration of reservoir buffer strips should be set with flexible width according to the previous land use types. [ABSTRACT FROM AUTHOR]

Daphnia galeata is a common and dominant species in warmer waters, and has a strong top-down effect on both phytoplankton and bacteria. The knowledge of its temporal and spatial patterns of genetic diversity is fundamental in understanding its population dynamics and potential ecological function in ecosystems. Its population genetics have been investigated at regional scales but few within regions or at smaller spatial scales. Here, we examined the fine-scale spatial genetic variation of D. galeata within four large, deep reservoirs in wet and dry seasons and the six-year variation of genetic diversity in one of the reservoirs by using cytochrome c oxidase subunit I and microsatellites (simple sequence repeat). Our study shows that fine-scale spatial genetic variation commonly occurred within the reservoirs, indicating strong environmental selection at least in the two of reservoirs with strong longitudinal gradients. Since the environmental gradients established in the dry season was largely reduced in the wet season, the fine-scale spatial genetic variation was much higher in the dry season. The dynamics of local genetic diversity did not follow the theoretical pattern of rapid erosion but peaked in mid or mid-late growth season. The local genetic diversity of D. galeata appears to be shaped and maintained not only by recruitment from resting egg banks but also by gene flow within reservoirs. The temporal and fine-scale genetic variation within a water body suggests that it is necessary to pay attention to sampling periods and locations of a given water body in regional studies. [ABSTRACT FROM AUTHOR]

The spatial decomposition of demographic data at a fine resolution is a classic and crucial problem in the field of geographical information science. The main objective of this study was to compare twelve well-known machine learning regression algorithms for the spatial decomposition of demographic data with multisource geospatial data. Grid search and cross-validation methods were used to ensure that the optimal model parameters were obtained. The results showed that all the global regression algorithms used in the study exhibited acceptable results, besides the ordinary least squares (OLS) algorithm. In addition, the regularization method and the subsetting method were both useful for alleviating overfitting in the OLS model, and the former was better than the latter. The more competitive performance of the nonlinear regression algorithms than the linear regression algorithms implies that the relationship between population density and influence factors is likely to be non-linear. Among the global regression algorithms used in the study, the best results were achieved by the k-nearest neighbors (KNN) regression algorithm. In addition, it was found that multi-sources geospatial data can improve the accuracy of spatial decomposition results significantly, and thus the proposed method in our study can be applied to the study of spatial decomposition in other areas. [ABSTRACT FROM AUTHOR]

To identify township-level high-risk foci of malaria transmission in Yunnan Province, China, along the international border, we retrospectively reviewed data collected in hospitals and clinics of 58 townships in 4 counties during 2011-2013. We analyzed spatiotemporal distribution, especially hot spots of confirmed malaria, using geographic information systems and Getis-Ord Gi*(d) cluster analysis. Malaria incidence, transmission seasonality, and Plasmodium vivax:P. falciparum ratio remained almost unchanged from 2011 to 2013, but heterogeneity in distribution increased. The number of townships with confirmed malaria decreased significantly during the 3 years; incidence became increasingly concentrated within a few townships. High-/low-incidence clusters of P. falciparum shifted in location and size every year, whereas the locations of high-incidence P. vivax townships remained unchanged. All high-incidence clusters were located along the China-Myanmar border. Because of increasing heterogeneity in malaria distribution, microgeographic analysis of malaria transmission hot spots provided useful information for designing targeted malaria intervention during the elimination phase. [ABSTRACT FROM AUTHOR]

Energy intensity is an important representative of energy efficiency. Currently, most countries lack fine-scale energy intensity data, taking China as an example, it only published provincial energy intensity data. However, the published large-scale energy intensity cannot support the formulation of local policies. What's more, the research work about the evaluation of fine-scale energy intensity is rare. To solve this problem, a "top-down" method based on machine learning is proposed to evaluate the fine-scale energy intensity. Appropriate features were extracted from multi-source satellite data, then the performances of multiple machine learning models were compared. It is found that deep neural network reaches the highest level among these models. Therefore, it was selected to estimate city-scale energy intensity from the year of 2001–2017. It turns out that the energy efficiency of southeast cities is higher than that of northwest cities in China, and most cities are developing towards the direction of improving energy efficiency. Among all cities, the central ones are the fastest to improve energy efficiency. However, the energy efficiency of a few cities is found to reduce during this period. The proposed method can also be used in other countries to help governments save energy and reduce emissions. • Energy intensity at city scale was estimated in China using satellite data and machine learning. • The temporal and spatial characteristics of Chinese cities' energy intensity were analyzed. • Nighttime light is most important feature in estimating energy intensity. [ABSTRACT FROM AUTHOR]

Accurate prediction of forest carbon sequestration potential requires a comprehensive understanding of tree growth relationships. However, the studies for estimating carbon sequestration potential concerning tree growth relationships at fine spatial-scales have been limited. In this paper, we assessed the current carbon stock and predicted sequestration potential of Lushan City, where a region has rich vegetation types in southern China, by introducing parameters of diameter at breast height (DBH) and tree height in the method of coupling biomass expansion factor (BEF) and tree growth equation. The partial least squares regression (PLSR) was used to explore the role of combined condition factors (e.g., site, stand, climate) on carbon sequestration potential. The results showed that (1) in 2019, the total carbon stock of trees in Lushan City was 9.22 × 10 5 t, and the overall spatial distribution exhibited a decreasing tendency from northwest to south-central, and the carbon density increased with elevation; (2) By 2070, the carbon density of forest in Lushan City will reach a relatively stable state, and the carbon stock will continue to rise to 2.15 × 10 6 t, which is 2.33 times of the current level, indicating that Lushan forest will continue to serve as a carbon sink for the next fifty years; (3) Excluding the effect of tree growth, regional forest carbon sequestration potential was significantly influenced on site characteristics, which achieved the highest Variable Importance in Projection (VIP) value (2.19) for slope direction. Our study provided a better understanding of the relationships between forest growth and carbon sequestration potential at fine spatial-scales. The results regarding the condition factors and how their combination characteristics affect the potential for carbon sequestration could provide crucial insights for Chinese carbon policy and global carbon neutrality goals.

Leaf economics, hydraulic, and shade-tolerance traits involve different resource-use strategies, and are critical for interactive plants, yet it remains unclear whether co-occurring individuals modulate the coupling relationships between these trait dimensions. To address this knowledge gap, we measured four leaf economics, five hydraulic, and two shade-tolerant traits in leaf and wood tissues across 90 individuals from 26 tree species in two subtropical forests in Eastern China. Confirmatory factor analysis (CFA) and Principal Component Analysis (PCA) were employed to explore multi-trait relationships. With respect to leaf economics traits, leaf nitrogen concentrations were positively associated with leaf stomatal density and conductance, leaf-specific hydraulic conductivity, and twig vessel diameter. However, leaf phosphorus concentrations were negatively associated with leaf-specific hydraulic conductivity. The specific leaf area was negatively associated with the light exposure index, light saturation point, and leaf Huber value. Regarding hydraulic traits, twig vessel diameters were positively correlated with the light saturation point and light exposure index. Stomatal conductance was positively associated with the light saturation point, whereas leaf-specific hydraulic conductivity was positively associated with the light exposure index. The first two PCA axes explained 54.23% of the total trait variation. The first axis of the multi-trait variation was mainly explained by leaf habits and represented the trade-offs between acquisitive hydraulic economics and cheap leaf economics. The second axis of the multi-trait variation was mainly explained by plant growth form, and defined the trade-offs between weak shade tolerance and cheap leaf area construction. In conclusion, our results provide evidence for the coupling relationships between leaf economics, hydraulic, and shade-tolerant traits among co-occurring individuals. This study also highlights that bivariate trait relationships based on large-scale interspecific surveys may be the opposite of that within-community scales. • The coupling relationship among leaf economics, hydraulic and shade-tolerant traits hold at within-community scale. • The locally integrated trait dimensions capture variations in plant growth form and leaf habit. • Trait-trait trade-offs presented at local scale differ from those at the broad-spatial scale. • Evergreen plants with great hydraulic efficiency are particularly conservative in leaf phosphorus concentration. • Plants with a great capacity for light interception tend to construct low specific leaf area. [ABSTRACT FROM AUTHOR]

Exploring spatio-temporal patterns of disease incidence can help to identify areas of significantly elevated or decreased risk, providing potential etiologic clues. The study uses the retrospective analysis of space-time scan statistic to detect the clusters of COVID-19 in mainland China with a different maximum clustering radius at the family-level based on case dates of onset. The results show that the detected clusters vary with the clustering radius. Forty-three space-time clusters were detected with a maximum clustering radius of 100 km and 88 clusters with a maximum clustering radius of 10 km from 2 December 2019 to 20 June 2020. Using a smaller clustering radius may identify finer clusters. Hubei has the most clusters regardless of scale. In addition, most of the clusters were generated in February. That indicates China's COVID-19 epidemic prevention and control strategy is effective, and they have successfully prevented the virus from spreading from Hubei to other provinces over time. Well-developed provinces or cities, which have larger populations and developed transportation networks, are more likely to generate space-time clusters. The analysis based on the data of cases from onset may detect the start times of clusters seven days earlier than similar research based on diagnosis dates. Our analysis of space-time clustering based on the data of cases on the family-level can be reproduced in other countries that are still seriously affected by the epidemic such as the USA, India, and Brazil, thus providing them with more precise signals of clustering.