Resource Patchiness as a Resolution to the Food Paradox in the Sea.

AbstractAverage concentrations of biota in the ocean are low, presenting a critical problem for ocean consumers. High-resolution sampling, however, demonstrates that the ocean is peppered with narrow hot spots of organism activity. To determine whether these resource aggregations could provide a significant solution to the ocean's food paradox, a conceptual graphical model was developed that facilitates comparisons of the role of patchiness in predator-prey interactions across taxa, size scales, and ecosystems. The model predicts that predators are more reliant on aggregated resources for foraging success when the average concentrations of resources is low, the size discrepancy between predator and prey is great, the predator has a high metabolic rate, and/or the predator's foraging time is limited. Size structure differences between marine and terrestrial food webs and a vast disparity in the overall mean density of their resources lead to the conclusion that high-density aggregations of prey are much more important to the survival of oceanic predators than their terrestrial counterparts, shaping the foraging decisions that are available to an individual and setting the stage on which evolutionary pressures can act. Patches of plenty may be rare, but they play an outsized role in behavioral, ecological, and evolutionary processes, particularly in the sea.

Environmental DNA metabarcoding reveals the effect of environmental selection on phytoplankton community structure along a subtropical river.

The Dongjiang River, a major tributary of the Pearl River system that supplies water to more than 40 million people in Guangdong Province and neighboring regions of China, harbors rich biodiversity, including many endemic and endangered species. However, human activities such as urbanization, agriculture, and industrialization have posed serious threats to its water quality and biodiversity. To assess the status and drivers of phytoplankton diversity, which is a key indicator of aquatic ecosystem health, this study used Environmental DNA (eDNA) metabarcoding combined with machine learning methods to explore spatial variations in the composition and structure of phytoplankton communities along the Dongjiang River, including its estuary. The results showed that phytoplankton diversity exhibited spatial distribution patterns, with higher community structure similarity and lower network complexity in the upstream than in the downstream regions. Environmental selection was the main mechanism shaping phytoplankton community composition, with natural factors driving the dominance of Pyrrophyta, Ochrophyta, and Cryptophyta in the upstream regions and estuaries. In contrast, the downstream regions was influenced by high concentrations of pollutants, resulting in increased abundance of Cryptophyta. The random forest model identified temperature, dissolved oxygen, chlorophyll a, NO2-, and NH4+ as the main factors influencing the primary phytoplankton communities and could be used to predict changes during wet periods. This study provides valuable insights into the factors influencing phytoplankton diversity and community composition in the Dongjiang River, and demonstrates the application value of eDNA metabarcoding technique in large-scale, long-distance river biodiversity monitoring.

Multi-elemental stoichiometric ratios of atmospheric wet deposition in Chinese terrestrial ecosystems.

Intense human activities have significantly altered the concentrations of atmospheric components that enter ecosystems through wet and dry deposition, thereby affecting elemental cycles. However, atmospheric wet deposition multi-elemental stoichiometric ratios are poorly understood, hindering systematic exploration of atmospheric deposition effects on ecosystems. Monthly precipitation concentrations of six elements-nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), and magnesium (Mg)-were measured from 2013 to 2021 by the China Wet Deposition Observation Network (ChinaWD). The multi-elemental stoichiometric ratio of atmospheric wet deposition in Chinese terrestrial ecosystems was N: K: Ca: Mg: S: P = 31: 11: 67: 5.5: 28: 1, and there were differences between vegetation zones. Wet deposition N: S and N: Ca ratios exhibited initially increasing then decreasing inter-annual trends, whereas N: P ratios did not exhibit significant trends, with strong interannual variability. Wet deposition of multi-elements was significantly spatially negatively correlated with soil nutrient elements content (except for N), which indicates that wet deposition could facilitate soil nutrient replenishment, especially for nutrient-poor areas. Wet N deposition and N: P ratios were spatially negatively correlated with ecosystem and soil P densities. Meanwhile, wet deposition N: P ratios were all higher than those of ecosystem components (vegetation, soil, litter, and microorganisms) in different vegetation zones. High input of N deposition may reinforce P limitations in part of the ecosystem. The findings of this study establish a foundation for designing multi-elemental control experiments and exploring the ecological effects of atmospheric deposition.

Spatiotemporal evolution and development path of healthcare services supply in China.

OBJECT: Promoting the accessibility and equity of healthcare services, as well as enhancing service capacity, are crucial for building a sound healthcare system. Particularly in the past two years of the normalized COVID-19 situation, this issue has garnered widespread attention in the academic community. This study aims to investigate and analyze the characteristics and trends of the spatial-temporal evolution of healthcare service supply levels in China. It also seeks to explore the influencing factors and pathways for development, with the goal of optimizing the allocation of healthcare resources. METHODS: This article uses the entropy weight TOPSIS method combined with Dagum Gini coefficient and Kernel density to evaluate the supply level of healthcare services in 31 provinces and cities in China from 2012 to 2020, and explores its development and spatial pattern characteristics. Then, through Moran index, panel regression model and spatial econometric testing, the spatial correlation problem and its influencing factors are further analyzed, and targeted policy recommendations are proposed based on it, laying the foundation for further promoting the balanced development of healthcare service supply capacity. RESULTS: (1) Healthcare services supply levels in various provinces and cities in China have significantly increased, with a shift in spatial distribution from 'higher in the east and lower in the west' to 'convergence between east and west, with lower levels in the central regions.' (2) Relative differences among regions are narrowing annually, primarily due to interactions between the four regions rather than within each region, with expanding impact of overlapping regions. (3) Absolute differences among regions are also decreasing, moving towards uniformity with a contraction of extension and a restraint on the trend towards multipolarization. (4) Spatial correlation between adjacent regions is weakening, eventually becoming non-significant, with fading spatial effects. (5) The correlation between local economic development, population factors, institutional arrangements, and the current state of supply is significant, and the research design and conclusions remain robust even after thorough consideration of spatial effects. The study explores the development pathways based on the objective existence of regional development and the controllable government actions. CONCLUSION: The overall level of healthcare service supply in China has improved, but regional differences still exist. The objective level of regional development and the subjective behavior of local governments have a significant impact on the supply of healthcare services. Therefore, it is recommended that each region adapt to local conditions, identify its own strengths and weaknesses, coordinate resource supply and demand, consider the impact of key factors, and optimize the allocation of healthcare development resources.

GIS-based spatiotemporal mapping of malaria prevalence and exploration of environmental inequalities.

Malaria poses a significant threat to global health, with particular severity in Nigeria. Understanding key factors influencing health outcomes is crucial for addressing health disparities. Disease mapping plays a vital role in assessing the geographical distribution of diseases and has been instrumental in epidemiological research. By delving into the spatiotemporal dynamics of malaria trends, valuable insights can be gained into population dynamics, leading to more informed spatial management decisions. This study focused on examining the evolution of malaria in Nigeria over twenty years (2000-2020) and exploring the impact of environmental factors on this variation. A 5-year-period raster map was developed using malaria indicator survey data for Nigeria's six geopolitical zones. Various spatial analysis techniques, such as point density, spatial autocorrelation, and hotspot analysis, were employed to analyze spatial patterns. Additionally, statistical methods, including Principal Component Analysis, Spearman correlation, and Ordinary Least Squares (OLS) regression, were used to investigate relationships between indicators and develop a predictive model. The study revealed regional variations in malaria prevalence over time, with the highest number of cases concentrated in northern Nigeria. The raster map illustrated a shift in the distribution of malaria cases over the five years. Environmental factors such as the Enhanced Vegetation Index, annual land surface temperature, and precipitation exhibited a strong positive association with malaria cases in the OLS model. Conversely, insecticide-treated bed net coverage and mean temperature negatively correlated with malaria cases in the same model. The findings from this research provide valuable insights into the spatiotemporal patterns of malaria in Nigeria and highlight the significant role of environmental drivers in influencing disease transmission. This scientific knowledge can inform policymakers and aid in developing targeted interventions to combat malaria effectively.

Monitoring agricultural drought using different indices based on remote sensing data in the Brazilian biomes of Cerrado and Atlantic Forest.

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

Morphological spatial clustering of high-density central areas and their coupling relationship with thermal environment--a case study of the wuyi road hatchback in changsha.

Intense urban development and high urban density cause the thermal environment in urban centers to deteriorate continuously, affecting the quality of the living environment. In this study, 707.49 hectares of land in the central area of Changsha were divided into 121 plots. 11 microclimate-related morphological indicators were comprehensively selected, and the K-means method was used for cluster analysis. Then, the relationship between morphological clusters and the thermal environment was explored by simulating the thermal environment of the study area with ENVI-met. First, five spatial types were found to characterize the area: high-level with high floor area ratio, low density, and low greenery; middle-level with high floor area ratio high density; medium-capacity with high density and small volume; low-level with low density and high greenery; and low floor area ratio, low density, and high greenery. Second, the building windward surface density, sky openness, building density, floor area ratio and green space rate affect the thermal environment. Third, Cluster3 had the highest average air temperature (Ta), followed by Cluster5, furthermore Clusters4, 1, and2 had relatively low Ta. The spatial vitality index and green space rate in Cluster1; the area-weighted building shape index, average building volume and sky openness in Cluster2; green space rate in Cluster3; indicators such as the floor area ratio and green space rate in Cluster4; indicators such as the impervious surface rate and green space rate in Cluster5 had greater influences on Ta. Fourthly, simply increasing the area of green space cannot maximize the cooling effect of green spaces. Instead, constructing an equalized greening network can better regulate the thermal environment. Fifthly, the results provide a scientific basis for the design and the regulation of urban centers.

Motor Imagery EEG Signal Classification Using Distinctive Feature Fusion with Adaptive Structural LASSO.

A motor imagery brain-computer interface connects the human brain and computers via electroencephalography (EEG). However, individual differences in the frequency ranges of brain activity during motor imagery tasks pose a challenge, limiting the manual feature extraction for motor imagery classification. To extract features that match specific subjects, we proposed a novel motor imagery classification model using distinctive feature fusion with adaptive structural LASSO. Specifically, we extracted spatial domain features from overlapping and multi-scale sub-bands of EEG signals and mined discriminative features by fusing the task relevance of features with spatial information into the adaptive LASSO-based feature selection. We evaluated the proposed model on public motor imagery EEG datasets, demonstrating that the model has excellent performance. Meanwhile, ablation studies and feature selection visualization of the proposed model further verified the great potential of EEG analysis.

Heterogeneity of otolith chemical composition from two-dimensional mapping: Relationship with biomineralization mechanisms and implications for microchemistry analyses.

Although otoliths are widely used as archives to infer life-history traits and habitat use in fishes, their biomineralization process remains poorly understood. This lack of knowledge is problematic as it can lead to misinterpretation of the different types of signals (e.g., optical or chemical) that provide basic data for research in fish ecology, fisheries management, and species conservation. Otolith calcification relies on a complex system involving a pericrystalline fluid, the endolymph, whose organic and inorganic compositions are spatially heterogeneous for some constituents. This property stems from the particular structure of the calcifying saccular epithelium. In this study, we explored the spatial heterogeneity of elemental incorporation in otoliths for two species of high economic interest, European hake Merluccius merluccius (L. 1758) and European sea bass Dicentrarchus labrax (L. 1758). Two-dimensional mappings of chemical elements were obtained using UV high-repetition-rate femtosecond laser ablation (fs-LA) system coupled to a high-resolution inductively coupled plasma sector field mass spectrometer analyses on transverse sections of sagittae. Results highlighted a clear asymmetry between proximal (sulcus) and distal (antisulcus) concentrations for elements such as magnesium (Mg), phosphorus (P), manganese (Mn), and potassium (K) with concentration gradient directions that varied depending on the element. Strontium (Sr) and barium (Ba) did not show a proximo-distal gradient. These results are discussed in light of current knowledge on the endolymph composition and the mechanisms that lead to its compartmentalization, highlighting the need for further research on otolith biomineralization. Operational implications for studies based on otolith chemical composition are also discussed with emphasis on advice for sampling strategies to avoid analytical biases and the need for in-depth analyses of analytical settings before comparing otolith signatures between species or geographical areas.

Updated loss factors and high-resolution spatial variations for reactive nitrogen losses from Chinese rice paddies.

Anthropogenic reactive nitrogen (Nr) loss has been a critical environmental issue. However, due to the limitations of data availability and appropriate methods, the estimation of Nr loss from rice paddies and associated spatial patterns at a fine scale remain unclear. Here, we estimated the background Nr loss (BNL, i.e., Nr loss from soils without fertilization) and the loss factors (the percentage of Nr loss from synthetic fertilizer, LFs) for five loss pathways in rice paddies and identified the national 1 × 1 km spatial variations using data-driven models combined with multi-source data. Based on established machine learning models, an average of 23.4% (15.3-34.6%, 95% confidence interval) of the synthetic N fertilizer was lost to the environment, in the forms of NH3 (17.4%, 10.9-26.7%), N2O (0.5%, 0.3-0.8%), NO (0.2%, 0.1-0.4%), N leaching (3.1%, 0.8-5.7%), and runoff (2.3%, 0.6-4.5%). The total Nr loss from Chinese rice paddies was estimated to be 1.92 ± 0.52 Tg N yr-1 in 2021, in which synthetic fertilizer-induced Nr loss accounted for 69% and BNL accounted for the other 31%. The hotspots of Nr loss were concentrated in the middle and lower regions of the Yangtze River, an area with extensive rice cultivation. This study improved the estimation accuracy of Nr losses and identified the hotspots, which could provide updated insights for policymakers to set the priorities and strategies for Nr loss mitigation.

Topic modeling discovers trending topics in global research on the ecosystem impacts of microplastics.

The ecological threats of microplastics (MPs) have sparked research worldwide. However, changes in the topics of MP research over time and space have not been evaluated quantitatively, making it difficult to identify the next frontiers. Here, we apply topic modeling to assess global spatiotemporal dynamics of MP research. We identified nine leading topics in current MP research. Over time, MP research topics have switched from aquatic to terrestrial ecosystems, from distribution to fate, from ingestion to toxicology, and from physiological toxicity to cytotoxicity and genotoxicity. In most of the nine leading topics, a disproportionate amount of independent and collaborative research activity was conducted in and between a few developed countries which is detrimental to understanding the environmental fates of MPs in a global context. This review recognizes the urgent need for more attention to emerging topics in MP research, particularly in regions that are heavily impacted but currently overlooked.

Changes in urban green space configuration and connectivity using spatial graph-based metrics in Ardabil developing city, Iran.

Urban planning is essential for managing the diverse impacts of urban green spaces, such as public access, stormwater control, urban life quality, and landscape aesthetics, promoting sustainable urban development and urban residents' well-being by integrating green space considerations into city planning. The aim of this study is to use graph-based metrics to calculate the connectivity of UGS across the main municipal zones of Ardabil city over consecutive periods under different population growth rates. Another objective of this study is to compare the connectivity values of UGS in the four municipal zones and to evaluate changes in the connectivity indices at various distance thresholds of UGS patches. After identifying UGS in different periods, the changes in graph-based connectivity indices at various distance thresholds of UGS patches were analyzed. Additionally, the changes in connectivity indices over different periods and across various municipal zones were compared and analyzed. The findings reveal that UGS areas were larger in the past but have recently had smaller patch sizes. Connectivity between UGS nodes (dNL) decreased at various distances over the study years, showing a declining trend in different connectivity indices. UGS connectivity decreased in municipal zones 1, 2, and 3 but increased in recent years after a decline until 2012 across all four zones of Ardabil city. Zone 4 had the highest UGS connectivity due to newly developed urban areas and well-allocated UGSs. Integrating the ecological impacts of UGS connectivity in urban development and design will enhance trade-offs between conservation, public health, and social equity. New urban areas should allocate sufficient land for UGS and parks, ensuring accessibility to support health and leisure through municipal planning. The study highlights the need for sustainable urban development policies that prioritize the allocation and maintenance of UGSs.

Characterizing heterogeneous forest structure in ponderosa pine forests via UAS-derived structure from motion.

Increasingly, dry conifer forest restoration has focused on reestablishing horizontal and vertical complexity and ecological functions associated with frequent, low-intensity fires that characterize these systems. However, most forest inventory approaches lack the resolution, extent, or spatial explicitness for describing tree-level spatial aggregation and openings that were characteristic of historical forests. Uncrewed aerial system (UAS) structure from motion (SfM) remote sensing has potential for creating spatially explicit forest inventory data. This study evaluates the accuracy of SfM-estimated tree, clump, and stand structural attributes across 11 ponderosa pine-dominated stands treated with four different silvicultural prescriptions. Specifically, UAS-estimated tree height and diameter-at-breast-height (DBH) and stand-level canopy cover, density, and metrics of individual trees, tree clumps, and canopy openings were compared to forest survey data. Overall, tree detection success was high in all stands (F-scores of 0.64 to 0.89), with average F-scores > 0.81 for all size classes except understory trees (< 5.0 m tall). We observed average height and DBH errors of 0.34 m and - 0.04 cm, respectively. The UAS stand density was overestimated by 53 trees ha-1 (27.9%) on average, with most errors associated with understory trees. Focusing on trees > 5.0 m tall, reduced error to an underestimation of 10 trees ha-1 (5.7%). Mean absolute errors of bole basal area, bole quadratic mean diameter, and canopy cover were 11.4%, 16.6%, and 13.8%, respectively. While no differences were found between stem-mapped and UAS-derived metrics of individual trees, clumps of trees, canopy openings, and inter-clump tree characteristics, the UAS method overestimated crown area in two of the five comparisons. Results indicate that in ponderosa pine forests, UAS can reliably describe large- and small-grained forest structures to effectively inform spatially explicit management objectives.

Spatial-temporal evolution characteristics of PM2.5 and its driving mechanism: spatially explicit insights from Shanxi Province, China.

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial-temporal evolution of PM2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM2.5 concentration within Shanxi Province, with PM2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m3 overall. The Hurst exponent indicated a projected decrease in PM2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM2.5 and provided a reference for similar studies.

Effects of a long-term operation wetland for wastewater treatment on the spatial pattern and function of microbial communities in groundwater.

Constructed wetlands have been used globally for wastewater treatment owing to low energy inputs and operating costs. However, the impact of their long-term operation on groundwater microbial communities is still unclear. This study aims to investigate the effects and further reveal the linkage between a large-scale surface flow constructed wetland (in operation for 14 years) and groundwater. Changes in the characteristics of groundwater microbial communities and their potential influencing factors were studied based on hydrochemical analysis, Illumina MiSeq sequencing, and multivariate statistical analysis methods. Results show that the long-term operation wetland significantly elevated groundwater nutrient levels and increased the risk of ammonia nitrogen pollution compared to background values. An apparent heterogeneity of microbial communities exhibited in the vertical direction and a similarity in the horizontal direction. Wetland operations substantially altered the structure of microbial communities at 3, 5, and 12 m depths, particularly a reduced abundance of denitrifying and chemoheterotrophic functional genera. The formation and evolution of groundwater microbial community structure mainly subjected to the contributions of dissolved oxygen (33.70%), total nitrogen (21.40%), dissolved organic carbon (11.09%), and pH (10.60%) variations resulted from the wetland operation and largely differed in depths. A combined effect of these factors on the groundwater should be concerned for such a long-term running wetland system. This study provides a new insight into the responses of groundwater microbial community structure driving by wetland operation and a better understanding of corresponding variation of microbial-based geochemical processes.

Can placebo or nocebo pills improve or impair cognition performance?

OBJECTIVE: Although the placebo effect is well known to affect many behaviors, the effects on cognitive performance are less well investigated. METHODS: In this study, the effects of a placebo and a nocebo manipulation on cognitive performance was investigated in healthy young participants in an unblinded between-subjects study. In addition, the participants were asked about their subjective experience in the placebo and nocebo condition. RESULTS: The data suggested that the placebo condition induced the feeling of being more attentive and more motivated and the nocebo condition induced a feeling of being less attentive and alert and that they performed less well than normal. However, no placebo or nocebo effects were found on the actual performance on word learning, working memory, Tower of London task, or spatial pattern separation. CONCLUSIONS: These findings further support the notion that placebo or nocebo effects are not likely to occur in young healthy volunteers. However, other studies suggest that placebo effects can be found in implicit memory tasks and in participants with memory problems. Further placebo/nocebo studies are indicated using different experimental designs and different populations in order to better understand the placebo effect on cognitive performance.

Spatial associations of long-term exposure to diesel particulate matter with seasonal and annual mortality due to COVID-19 in the contiguous United States.

BACKGROUND: People with certain underlying respiratory and cardiovascular conditions might be at an increased risk for severe illness from COVID-19. Diesel Particulate Matter (DPM) exposure may affect the pulmonary and cardiovascular systems. The study aims to assess if DPM was spatially associated with COVID-19 mortality rates across three waves of the disease and throughout 2020. METHODS: We tested an ordinary least squares (OLS) model, then two global models, a spatial lag model (SLM) and a spatial error model (SEM) designed to explore spatial dependence, and a geographically weighted regression (GWR) model designed to explore local associations between COVID-19 mortality rates and DPM exposure, using data from the 2018 AirToxScreen database. RESULTS: The GWR model found that associations between COVID-19 mortality rate and DPM concentrations may increase up to 77 deaths per 100,000 people in some US counties for every interquartile range (0.21 µg/m3) increase in DPM concentration. Significant positive associations between mortality rate and DPM were observed in New York, New Jersey, eastern Pennsylvania, and western Connecticut for the wave from January to May, and in southern Florida and southern Texas for June to September. The period from October to December exhibited a negative association in most parts of the US, which seems to have influenced the year-long relationship due to the large number of deaths during that wave of the disease. CONCLUSIONS: Our models provided a picture in which long-term DPM exposure may have influenced COVID-19 mortality during the early stages of the disease. That influence appears to have waned over time as transmission patterns evolved.

Detected climatic change in global distribution of tropical cyclones.

Owing to the limited length of observed tropical cyclone data and the effects of multidecadal internal variability, it has been a challenge to detect trends in tropical cyclone activity on a global scale. However, there is a distinct spatial pattern of the trends in tropical cyclone frequency of occurrence on a global scale since 1980, with substantial decreases in the southern Indian Ocean and western North Pacific and increases in the North Atlantic and central Pacific. Here, using a suite of high-resolution dynamical model experiments, we show that the observed spatial pattern of trends is very unlikely to be explained entirely by underlying multidecadal internal variability; rather, external forcing such as greenhouse gases, aerosols, and volcanic eruptions likely played an important role. This study demonstrates that a climatic change in terms of the global spatial distribution of tropical cyclones has already emerged in observations and may in part be attributable to the increase in greenhouse gas emissions.

The Spatio-Temporal Patterns of Regional Development in Shandong Province of China from 2012 to 2021 Based on Nighttime Light Remote Sensing.

As a major coastal economic province in the east of China, it is of great significance to clarify the temporal and spatial patterns of regional development in Shandong Province in recent years to support regional high-quality development. Nightlight remote sensing data can reveal the spatio-temporal patterns of social and economic activities on a fine pixel scale. We based the nighttime light patterns at three spatial scales in three geographical regions on monthly nighttime light remote sensing data and social statistics. Different cities and different counties in Shandong Province in the last 10 years were studied by using the methods of trend analysis, stability analysis and correlation analysis. The results show that: (1) The nighttime light pattern was generally consistent with the spatial pattern of construction land. The nighttime light intensity of most urban, built-up areas showed an increasing trend, while the old urban areas of Qingdao and Yantai showed a weakening trend. (2) At the geographical unit scale, the total nighttime light in south-central Shandong was significantly higher than that in eastern and northwest Shandong, while the nighttime light growth rate in northwest Shandong was significantly highest. At the urban scale, Liaocheng had the highest nighttime light growth rate. At the county scale, the nighttime light growth rate of counties with a better economy was lower, while that of counties with a backward economy was higher. (3) The nighttime light growth was significantly correlated with Gross Domestic Product (GDP) and population growth, indicating that regional economic development and population growth were the main causes of nighttime light change.

Microplastic migration and distribution in the terrestrial and aquatic environments: A threat to biotic safety.

Plastics production has been increasing over years, while their recycling rate is lower, resulting in huge amounts of microplastics (MP) accumulating in the environment. Although the environmental behaviors of MPs have been focused on in recent years, the migration, distribution and adverse effects of MPs in terrestrial and aquatic environments are still not systematically understood. In this review, based on the newest publications from the core database of the Web of Science, both the migration and distribution of MPs were summarized, as well as MPs transfer in biota and their biological effects were also focused on. Generally, the complicated and numerous pathways of MPs migration lead to their distribution throughout or nearly all environments on a global scale. However, the migration mechanisms of MPs with various sizes, shapes, and colors by physicochemical and biological processes, and the prediction models of MP migration and distribution, are deficient, despite these properties being highly related to MPs migration and bio-safety. Although MPs have already invaded microorganisms, plants, animals, and even human beings, the biological effects still need more study, so far as their sizes and shapes and also their composition and adsorption are concerned. Moreover, based on the highlights and deficiencies of current studies, further studies have also been proposed. This review aims to help people re-evaluate the uncertain behaviors of MPs in various environments, and could be helpful to fully understand their biological effects in different environmental conditions.