Patterns Of Spatial Variation Research Articles

Species identification and spatial diversity patterns of the Giant Panda National Park (GPNP) in Ya’an, Sichuan, China

Species identification and spatial diversity patterns of the Giant Panda National Park (GPNP) in Ya’an, Sichuan, China

Detection and Attribution of Vegetation Dynamics in the Yellow River Basin Based on Long-Term Kernel NDVI Data

Detecting and attributing vegetation variations in the Yellow River Basin (YRB) is vital for adjusting ecological restoration strategies to address the possible threats posed by changing environments. On the basis of the kernel normalized difference vegetation index (kNDVI) and key climate drivers (precipitation (PRE), temperature (TEM), solar radiation (SR), and potential evapotranspiration (PET)) in the basin during the period from 1982 to 2022, we utilized the multivariate statistical approach to analyze the spatiotemporal patterns of vegetation dynamics, identified the key climate variables, and discerned the respective impacts of climate change (CC) and human activities (HA) on these variations. Our analysis revealed a widespread greening trend across 93.1% of the YRB, with 83.2% exhibiting significant increases in kNDVI (p < 0.05). Conversely, 6.9% of vegetated areas displayed a browning trend, particularly concentrated in the alpine and urban areas. With the Hurst index of kNDVI exceeding 0.5 in 97.5% of vegetated areas, the YRB tends to be extensively greened in the future. Climate variability emerges as a pivotal determinant shaping diverse spatial and temporal vegetation patterns, with PRE exerting dominance in 41.9% of vegetated areas, followed by TEM (35.4%), SR (13%), and PET (9.7%). Spatially, increased PRE significantly enhanced vegetation growth in arid zones, while TEM and SR controlled vegetation variations in alpine areas and non-water-limited areas such as irrigation zones. Vegetation dynamics in the YRB were driven by a combination of CC and HA, with relative contributions of 55.8% and 44.2%, respectively, suggesting that long-term CC is the dominant force. Specifically, climate change contributed to the vegetation greening seen in the alpine region and southeastern part of the basin, and human-induced factors benefited vegetation growth on the Loess Plateau (LP) while inhibiting growth in urban and alpine pastoral areas. These findings provide critical insights that inform the formulation and adaptation of ecological conservation strategies in the basin, thereby enhancing resilience to changing environmental conditions.

Environment and management jointly shape the spatial patterns of plant species diversity of moist grasslands in the mountains of Northeastern Yunnan

Grasslands account for about a quarter of the Earth's land area and are one of the major terrestrial ecosystems, with significant ecological and economic values. The influence of environmental factors and management types on grassland biodiversity has garnered considerable attention. This study investigated how patterns of species richness are influenced by geographical distance, environmental gradients, and management type in the moist mountain grasslands of northeastern Yunnan, China. We used structural equation modeling to disentangle the impacts of environment and management on phylogenetic community structure, and using partial Mantel tests estimated the roles of dispersal limitation and environmental filtering on taxonomic and phylogenetic beta diversity of three types of grasslands. Our results show that taxonomic alpha diversity increased in grazed grasslands and decreased in mowed grasslands, compared with protected grasslands. However, the phylogenetic structure of both grazed and mowed grassland communities was clustered, whereas that of protected communities was random. Moreover, both grazing and mowing significantly reduced the taxonomic and phylogenetic beta diversity of grasslands, with the lowest values observed in mowed grasslands. Both taxonomic and phylogenetic beta diversity were dominated by species turnover under different management types. The taxonomic and phylogenetic beta diversities of protected and grazed grasslands were simultaneously affected by environmental filtering and dispersal limitation, with the later playing a stronger role. In addition, mowing and following management measures had a stronger filtering effect on grassland community structure, as reflected by changes in community composition.

Spatial Pattern and Driving Mechanism of Urban Taxi Fares in China

Taxi fare is related to the daily life of residents. Reasonable taxi fare not only meets the travel demand of residents but also improves the income of drivers and promotes employment. The spatial variation of taxi fares exists between different regions and cities. Previous studies on taxi fares have been conducted mostly in individual cities, and there has been no study on the spatial differentiation pattern of taxi fares on a national scale. Taking 336 cities across China as the research object, a multiple linear regression model of taxi fares was established by demonstrating the spatial variation pattern of taxi fares, the global differentiation index, spatial autocorrelation analysis, and kernel density analysis, etc. The significance of the study is to explore the law of spatial differentiation of taxi fares in China and to provide a stenographic record of taxi fare adjustment. The results show that: (1) the spatial variation of taxi fares across the country is significant, with the starting taxi fare range being between RMB 4 and RMB 14. (2) The global differentiation index of taxi fares is large, with two low-low clusters and three high-high clusters appearing spatially, and the results of the kernel density analysis surface a dispersion distribution centered on provincial capitals. (3) The divergence pattern of taxis nationwide is influenced by several factors. A multiple linear regression model is selected to establish a multiple linear regression model of urban disposable income per capita, regional GDP, urbanization rate, and urban population density, which shows that urban disposable income per capita has the greatest influence on taxi fares. The model shows that urban disposable income per capita has the greatest influence on taxi fares, and the fare of a 5 km taxi ride is 6.07. Taxi fares have a clear pattern of spatial differentiation in China and are most affected by urban disposable income per capita. Through this study, we can gain a deeper understanding of the variation in taxi fares across the country and provide data and theoretical support for the rationality of taxi fare adjustments.

Spatial Pattern of Genetic Diversity and Demographic History Revealed by Population Genomic Analysis: Resilience to Climate Fluctuations of Acer truncatum Bunge

Whole genome sequencing techniques are capable of providing insights into plant genetic adaptation to climate oscillations. Acer truncatum Bunge is a new resource tree for food with ornamental, timber and medicinal value. However, it has been listed as a near-threatened species because of the decreasing number of natural populations. In order to develop conservation strategies and sustainable innovative utilization for genetic resources, spatial pattern of genetic diversity and demographic history of A. truncatum from 13 natural populations, which cover the entire range, were analyzed by 9,086,353 single nucleotide polymorphisms (SNPs) through whole genome resequencing. The genetic diversity of natural populations was high (He = 0.289, I = 0.449), and genetic variations mainly resided within populations (82.47%) by AMOVA analysis. Cluster analysis showed that 13 natural populations mainly originated from three common gene pools. Therefore, it is recommended that the natural populations (LBGM, BTM, WLS and DQT) with high genetic diversity in different groups were given priority for in situ conservation and the genetic resources from these populations were collected for ex situ conservation. The effective population size of A. truncatum experienced two large-scale sharp contractions and currently decreased to its bottom in history. Nonetheless, A. truncatum could have expanded its effective population size to its historical peak after the last glacial period, indicating that it is highly resilient to fluctuations of temperature and humidity.

Abundance and diversity patterns and environmental drivers of Peracarida (Arthropoda, Crustacea) macrofauna from the deep sea of the southwestern Gulf of Mexico

Abundance and diversity patterns and environmental drivers of Peracarida (Arthropoda, Crustacea) macrofauna from the deep sea of the southwestern Gulf of Mexico

StDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics.

Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.

Spatiotemporal evolution patterns and underlying formation mechanisms of monsoon rainfall across eastern China: A complex network perspective

Spatiotemporal evolution patterns and underlying formation mechanisms of monsoon rainfall across eastern China: A complex network perspective

Evolution and Optimization Simulation of Coastal Chemical Industry Layout: A Case Study of Jiangsu Province, China

In the face of geopolitical challenges and climate change, economic progress, safe production, and environmental protection have emerged as important directions for chemical industry development. However, the rational optimization of the chemical industry layout under the backdrop of ecological environmental protection necessitates further exploration. This study explores the evolution and future development direction of the chemical industry layout within the coastal region of Jiangsu Province, China, using the CA–Markov model. The findings reveal a trend of spatial agglomeration growth among coastal chemical enterprises, with Moran’s Index increasing from 0.109 in 2007 to 0.206 in 2017. The petrochemical industry, in particular, demonstrated the most significant agglomeration effect, with approximately 52.10% being concentrated in 14 coastal industrial parks in 2017. Under the constraints of the ecological environment and policy guidance, the land area allocated for the chemical industry experienced a reduction of over 10%, further strengthening the emphasis on spatial agglomeration. Chemical industries along Jiangsu’s coast have become agglomerated and concentrated near industrial parks and ports. Their spatial distribution and connectivity were mainly influenced by factors such as convenient transportation, the ecological environment, local policies, the distance from residential areas, and industrial agglomeration. Under different scenarios—including natural growth, ecological environment constraints, and policy guidance—chemical industries show diverse spatial patterns. Ecological environmental constraints and policy guidance can provide various intervention methods for the government to promote the optimization direction and focus of the chemical industry layout while minimizing its impact on the ecological environment.

Remotely sensed seasonal dynamics of an estuarine plume in the coastal zone of El Rincón system (Southwestern Atlantic Ocean)

Remotely sensed seasonal dynamics of an estuarine plume in the coastal zone of El Rincón system (Southwestern Atlantic Ocean)

Spatial and temporal analysis of haemorrhagic septicaemia outbreaks in India over three decades (1987–2016)

Haemorrhagic septicaemia (HS) is an economically important disease affecting cattle and buffaloes and the livelihoods of small-holder farmers that depend upon them. The disease is caused by Gram-negative bacterium, Pasteurella multocida, and is considered to be endemic in many states of India with more than 25,000 outbreaks in the past three decades. Currently, there is no national policy for control of HS in India. In this study, we analysed thirty year (1987–2016) monthly data on HS outbreaks using different statistical and mathematical methods to identify spatial variability and temporal patterns (seasonality, periodicity). There was zonal variation in the trend and seasonality of HS outbreaks. Overall, South zone reported maximum proportion of the outbreaks (70.2%), followed by East zone (7.2%), Central zone (6.4%), North zone (5.6%), West zone (5.5%) and North-East zone (4.9%). Annual state level analysis indicated that the reporting of HS outbreaks started at different years independently and there was no apparent transmission between the states. The results of the current study are useful for the policy makers to design national control programme on HS in India and implement state specific strategies. Further, our study and strategies could aid in implementation of similar approaches in HS endemic tropical countries around the world.

Quantifying the Spatial Distribution Pattern of Soil Diversity in Southern Xinjiang and Its Influencing Factors

Soil diversity plays an important role in maintaining ecological balance and ensuring the sustainability of the land. Xinjiang is a typical arid and semi-arid region of China, and the study of Xinjiang soils is significant for understanding soil properties in all such environments. This study applied the moving window technique and the species–area curve model from ecology to establish optimal analysis windows, calculate landscape pattern indices, and reveal soil distribution characteristics in Southern Xinjiang. Additionally, we used geographic detectors to identify the primary influencing factors in different geomorphic regions. The results indicate a positive correlation between soil richness and area in the Southern Xinjiang region. The Tarim Basin, despite being the largest area, shows the lowest diversity and evenness indices. Overall, mountainous areas have higher soil evenness when compared to basins. In terms of natural factors, temperature, precipitation, and topography play a crucial role in the variation of soil diversity in mountainous areas, while parent material has a greater influence in the basin regions. The characteristics of soil diversity vary by region and are influenced by the interactive effects of various natural factors. However, the impact of human activities also requires consideration. The low evenness poses a greater challenge for soil restoration in the basin regions. Soil conservation efforts in arid regions are of paramount importance. The research findings can provide valuable insights for the development of sustainable agriculture, soil conservation, and for addressing climate change challenges in arid regions.

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

Effects of land use and land cover change under shared socioeconomic pathways on future climate in the Yellow River basin, China

Choice of molecular marker influences spatial patterns of phylogenetic diversity.

Spatial patterns of phylogenetic diversity (PD) are increasingly becoming relevant for conservation decisions. PD measures are based on phylogenies estimated from molecular data. This paper addresses the question of how different molecular markers impact PD spatial patterns. We first conducted a simple simulation to explore the effect of deep and shallow changes in topology (simulating variations in molecular markers), using ultrametric and non-ultrametric trees, and then used a dataset of Chilean flora with four sets of markers to assess potential differences in spatial patterns of PD ranks using different markers and types of trees. The simulation consistently showed that the difference in PD rank was lower for ultrametric trees than for phylograms. A similar trend was observed using the Chilean flora dataset, with among-markers variability in spatial patterns of the PD metrics lower for ultrametric than for non-ultrametric trees, depicted as top 2.5 and 5% hotspots. Frequency distribution of PD values differed among markers as well, with this variation less apparent for ultrametric trees. We conclude that the choice of markers impacts spatial patterns of PD, and these results vary more strongly for phylograms, suggesting that ultrametric trees are more robust to the choice of marker.

North Atlantic Response to Observed North Atlantic Oscillation Surface Heat Flux in Three Climate Models

Abstract We investigate how the ocean responds to 10-yr persistent surface heat flux forcing over the subpolar North Atlantic (SPNA) associated with the observed winter NAO in three CMIP6-class coupled models. The experiments reveal a broadly consistent ocean response to the imposed NAO forcing. Positive NAO forcing produces anomalously dense water masses in the SPNA, increasing the southward lower (denser) limb of the Atlantic meridional overturning circulation (AMOC) in density coordinates. The southward propagation of the anomalous dense water generates a zonal pressure gradient overlying the models’ North Atlantic Current that enhances the upper (lighter) limb of the density-space AMOC, increasing the heat and salt transport into the SPNA. However, the amplitude of the thermohaline process response differs substantially between the models. Intriguingly, the anomalous dense-water formation is not primarily driven directly by the imposed flux anomalies, but rather dominated by changes in isopycnal outcropping area and associated changes in surface water mass transformation (WMT) due to the background surface heat fluxes. The forcing initially alters the outcropping area in dense-water formation regions, but WMT due to the background surface heat fluxes through anomalous outcropping area decisively controls the total dense-water formation response and can explain the intermodel amplitude difference. Our study suggests that coupled models can simulate consistent mechanisms and spatial patterns of decadal SPNA variability when forced with the same anomalous buoyancy fluxes, but the amplitude of the response depends on the background states of the models.

Epigenomic tomography for probing spatially defined chromatin state in the brain

Spatially resolved epigenomic profiling is critical for understanding biology in the mammalian brain. Single-cell spatial epigenomic assays were developed recently for this purpose, but they remain costly and labor intensive for examining brain tissues across substantial dimensions and surveying a collection of brain samples. Here, we demonstrate an approach, epigenomic tomography, that maps spatial epigenomes of mouse brain at the scale of centimeters. We individually profiled neuronal and glial fractions of mouse neocortex slices with 0.5mm thickness. Tri-methylation of histone 3 at lysine 27 (H3K27me3) or acetylation of histone 3 at lysine 27 (H3K27ac) features across these slices were grouped into clusters based on their spatial variation patterns to form epigenomic brain maps. As a proof of principle, our approach reveals striking dynamics in the frontal cortex due to kainic-acid-induced seizure, linked with transmembrane ion transporters, exocytosis of synaptic vesicles, and secretion of neurotransmitters. Epigenomic tomography provides a powerful and cost-effective tool for characterizing brain disorders based on the spatial epigenome.

High diversity within and low but significant genetic differentiation among geographic and temporal populations of the global Streptococcus pneumoniae.

Streptococcus pneumoniae is the major cause of invasive pneumococcal disease. However, the global population structure remains largely unexplored. In this study, we investigated the spatial and temporal patterns of genetic variation of S. pneumoniae based on archived multilocus sequence typing data from PubMLST.org. Our analyses demonstrated both shared and unique distributions of sequence types (STs) and allele types among regional populations. Among the 17 915 global STs, 36 representing 15 263 isolates were broadly shared among all six continents, consistent with recent clonal dispersal and expansion of this pathogen. The analysis of molecular variance revealed that>96% genetic variations were found within individual regional populations. However, though low (<4%), statistically significant genetic differentiation among regional populations was observed. Comparisons between non-clone-corrected and clone-corrected datasets showed that localized clonal expansion contributed significantly to the observed genetic differentiations among regions. Temporal analyses of the isolates showed that implementation of pneumococcal conjugate vaccine impacted the distributions of STs, but the effect on population structure was relatively limited. Linkage disequilibrium analyses identified evidence for recombination in all continental populations; however, the inferred recombination was not random. We discussed the limitations and implications of our analyses to the global epidemiology and future vaccine developments for S. pneumoniae.

GEOSPATIAL TECHNIQUES FOR MAPPING AND ANALYSIS OF CLIMATIC VARIABLES IN GOMBE STATE, NORTH-EAST NIGERIA.

This study was carried out to apply satellite- based climatic data in geospatial environment to downscale climatic data into smaller political units and to generate and analyse thematic maps of climatic variables in Gombe State. Spatial climatic data on rainfall, minimum and maximum temperature were obtained from DivaGIS climatic data, while water vapour, solar radiation and wind speed were derived from the WorldClim climatic data. Monthly climatic records from 1981-2021 in twenty-four points evenly spread across Gombe State on rainfall, minimum/maximum temperature and relative humidity were obtained from NASA Power Project to generate the trends and seasonal patterns of temperature and rainfall. Daily climatic records of rainfall were also acquired from globalweather climatic data between 1979 and 2013 and processed to derive the values of rainfall indices using Nieuwolt formula. The monthly climatic records were processed using the kriging module of ArcGIS to transform the point climatic data to spatial data. The result revealed that rainfall in Gombe State occurs mainly between May and September with the peak in August, while the trends in rainfall was decreasing at the southern and northern parts of the state but increasing at the central region. Rainfall onset occurs in the 1st or 2nd week of May in the south, but late (1st week of June) in the north. Rainfall cessation at the central and northern parts of the state occurs in the 3rd or 4th weeks of September, while the southern part experiences late cessation between the 1st and 3rd week of October. The LRS ranges from 95-111 and 160-175 days in the northern and southern parts respectively. High relative humidity was recorded between May and October with the peak in August. Spatial patterns of solar radiation and wind speed were also generated and analyzed. Awareness of the use of satellite based climatic data and geospatial techniques for downscaling climatic data were recommended in order to ease the problems of lack or inadequate climatic data for spatial mapping. Causal factors for the spatial patterns of climatic variables in Gombe state was suggested for further studies.

Spatial disparities and sources analysis of co-benefits between air pollution and carbon reduction in China

Spatial misallocation of resource elements impedes the coordination in regional co-benefit in carbon emission and pollution reduction. Guided by scope economy principles, this study aims to quantifies co-benefits and explores spatial variation patterns to facilitate mutual synergy. The findings offer valuable insights for identifying optimal focus points and shaping targeted collaborative policies. Based on the multi-source provincial panel data spanning 2000 to 2021, this paper quantitatively assesses the co-benefit through the lens of marginal abatement cost. Then, the Dagum Gini coefficient was employed to investigate the disparities at the spatial level. Additionally, geographical detector is introduced to analyze the source of disparities at the factor level. Results indicate: (1) Under joint reduction, the marginal abatement cost reduces by 57.86% and 79.97% respectively, with an overall 68.77% increase in co-benefit fluctuation. (2) Provinces with low co-benefit significantly decreased after 2007, while provinces with high co-benefit concentrated in the northwest, southwest, and east. (3) Overall disparities decreased, east-central regions hold the lowest spatial disparities in co-benefit and east-west hold the most; intragroup differences and supervariable density primarily contributes to the disparity. (4) FDI significantly influence the co-benefit over the past two decades, with spatial disparities influenced by both endogenous and exogenous factors across development stages.

Diversity of an Odonata assemblage from a tropical dry forest in San Buenaventura, Jalisco, Mexico (Insecta, Odonata).

The patterns of richness, diversity, and abundance of an odonate assemblage from San Buenaventura, Jalisco are presented here. A total of 1087 specimens from seven families, 35 genera and 66 species were obtained through monthly samplings of five days each during a period of one year. Libellulidae was the most diverse family (28 species), followed by Coenagrionidae (21), Gomphidae (7), Aeshnidae (6), Calopterygidae (2), Lestidae (1) and Platystictidae (1). Argia was the most speciose genus. The highest species richness and Shannon diversity were found during August and September, whereas the highest abundance was observed in June and the highest Simpson diversity was recorded in September - all of which were associated with the rainy season. The highest values of phylogenetic diversity were found from June to October. The different diversity facets of this assemblage were positively correlated with precipitation and minimum temperature, whereas maximum temperature showed no influence. In addition, we found that this odonate diversity was higher than most Mexican localities with tropical dry forest (TDF) studied. We continue our efforts to describe the patterns of richness, diversity and abundance of some insect groups associated with the tropical dry forest ecosystem in Mexico, following a latitudinal gradient of the distribution of this ecosystem in the country. Our emphasis here was to evaluate the spatial and temporal patterns of richness and diversity of an Odonata assemblage from Jalisco, Mexico.