Spatial Association Patterns Research Articles

Mapping multi-dimensional variability in water stress strategies across temperate forests

Increasing water stress is emerging as a global phenomenon, and is anticipated to have a marked impact on forest function. The role of tree functional strategies is pivotal in regulating forest fitness and their ability to cope with water stress. However, how the functional strategies found at the tree or species level scale up to characterise forest communities and their variation across regions is not yet well-established. By combining eight water-stress-related functional traits with forest inventory data from the USA and Europe, we investigated the community-level trait coordination and the biogeographic patterns of trait associations for woody plants, and analysed the relationships between the trait associations and climate factors. We find that the trait associations at the community level are consistent with those found at the species level. Traits associated with acquisitive-conservative strategies forms one dimension of variation, while leaf turgor loss point, associated with stomatal water regulation strategy, loads along a second dimension. Surprisingly, spatial patterns of community-level trait association are better explained by temperature than by aridity, suggesting a temperature-driven adaptation. These findings provide a basis to build predictions of forest response under water stress, with particular potential to improve simulations of tree mortality and forest biomass accumulation in a changing climate.

Examining the Effects of Environment, Geography, and Elevation on Patterns of DNA Methylation Across Populations of Two Widespread Bumble Bee Species.

Understanding the myriad avenues through which spatial and environmental factors shape evolution is a major focus in biological research. From a molecular perspective, much work has been focused on genomic sequence variation; however, recently there has been increased interest in how epigenetic variation may be shaped by different variables across the landscape. DNA methylation has been of particular interest given that it is dynamic and can alter gene expression, potentially offering a path for a rapid response to environmental change. We utilized whole genome enzymatic methyl sequencing to evaluate the distribution of CpG methylation across the genome and to analyze patterns of spatial and environmental association in the methylomes of two broadly distributed montane bumble bees (Bombus vancouverensis Cresson and Bombus vosnesenskii Radoszkowski) across elevational gradients in the western US. Methylation patterns in both species are similar at the genomic scale with ∼1% of CpGs being methylated and most methylation being found in exons. At the landscape scale, neither species exhibited strong spatial or population structuring in patterns of methylation, although some weak relationships between methylation and distance or environmental variables were detected. Differential methylation analysis suggests a stronger environment association in B. vancouverensis given the larger number of differentially methylated CpG's compared to B. vosnesenskii. We also observed only a handful of genes with both differentially methylated CpGs and previously detected environmentally associated outlier SNPs. Overall results reveal a weak but present pattern in variation in methylation over the landscape in both species.

The Spatial Patterns and Building Policies of Rural Settlements in the Context of Demolition: The Case of Xian’an, China

In China, the “land-restructuring” policy provides balanced land for urban settlements that is strictly limited in expansion. Therefore, reassessing and adjusting the layout of rural settlements is of great practical significance for promoting rural revitalization. In this paper, taking Xian’an district in Hubei Province as an example, we use the weighted rank-sum ratio comprehensive evaluation method and spatial association analysis method to analyze the development level and spatial pattern of settlements. The results show that: (1) The development level of settlements in Xian’an shows obvious spatial differences, with a spatial pattern of ‘high in the core–low in the periphery’ and ‘high in the northwest–low in the southeast’, which is the result of the combined effect of natural geographical conditions and socioeconomic conditions; (2) The comprehensive development level of settlements, evaluated based on four major indicators—population size, resource endowment, spatial characteristics, and material construction—reveals the presence of cluster effects, distance decay effects, administrative hierarchy effects, and “long board” effects; (3) Within village communities, settlements with significantly high levels and settlements with significantly low levels have a similar geographic distribution and mosaic spatial patterns. Lastly, based on the overall development level and spatial association patterns of settlements, this article presents possible options for governmental settlement governance from the standpoint of rural building management.

Production factors, regional economic development and agriculture in brazil: A spatial econometrics approach

Agriculture has a great effect on the development process of a region and production land, labor, and technology are determinant factors in its dynamism. This work carries out a spatial analysis for each production factor of Brazilian municipalities in 2006 and 2017 and intends to verify if there are patterns of association and agglomerations. This work is applied research of quantitative and exploratory nature. The Exploratory Analysis of Spatial Data (ESDA) was used to perform spatial analysis for each production factor in Brazilian municipalities in 2006 and 2017 and to check the patterns of association and agglomerations. The main agricultural activities and practice are related to exports as proposed by North. The formation of clusters verified both in the use of the factors of land, labor and technology, and in the value of agricultural production has a polarized role in the Brazilian space, as described by Perroux. This polarization and the formation of clusters expanded at certain points over time, indicating the formation of new links with neighboring municipalities (a la Hirschman). The spatial association pattern was significant in all analyzed variables, with emphasis on the non-physical technology factor, presenting the highest spatial autocorrelation coefficient in both periods analyzed.

Patterns of spatial and temporal association between Zootoca vivipara, Anguis fragilis, Vipera berus and Natrix helvetica at artificial refuges

A 15-year data set of reptile observations at corrugated iron refuges was analysed to describe the spatial and temporal associations between species pairs of viviparous lizards Zootoca vivipara, slow worms Anguis fragilis, northern vipers Vipera berus and grass snakes Natrix helvetica. Of the two snake species, only the viper is known as a routine lizard predator. We analysed two variables expressing pairwise reptile associations at refuges. The first analysis was of monthly spatial overlap assessed as the proportion of unique refuge positions used in common, but not necessarily used simultaneously. The second analysis was of a more precise spatial and temporal assessment based on counts of reptile pairs cohabitating at the refuges (i.e. using the same refuges simultaneously). Apart from viviparous lizards paired with either of the two snake species, the frequencies for both variables were either as expected or significantly greater than expected by chance; in particular grass snakes cohabited much more frequently than expected with both vipers (+327%) and slow worms (+218%). In contrast, for the viviparous lizard/viper pair both variables were statistically significantly less frequent than expected by chance - monthly overlap (-35%) and cohabitation (-87%). As viviparous lizards are predated by vipers, cohabiting would be much reduced but the significantly lowered monthly spatial overlap may indicate active avoidance. For the viviparous lizard/grass snake pair the spatial overlap was as expected by chance but, as with vipers, the frequency of cohabiting was significantly less than expected by chance (-75%). These observations are discussed in relation to the known kairomonal responses of this lizard to viper and grass snake deposits. Viper deposits are also believed to act as a kairomone for slow worms, but we found no evidence that they avoided vipers, supporting existing literature showing that V. berus is unlikely to be an important slow worm predator. Keywords: species associations, cohabitation, corrugated iron refuges, viper kairomone, chalk grassland

The Use of the GWPCA-MGWR Model for Studying Spatial Relationships between Environmental Variables and Longline Catches of Yellowfin Tunas

The yellowfin tuna represents a significant fishery resource in the Pacific Ocean. Its resource endowment status and spatial variation mechanisms are intricately influenced by marine environments, particularly under varying climate events. Consequently, investigating the spatial variation patterns of dominant environmental factors under diverse climate conditions, and understanding the response of yellowfin tuna catch volume based on the spatial heterogeneity among these environmental factors, presents a formidable challenge. This paper utilizes comprehensive 5°×5° yellowfin tuna longline fishing data and environmental data, including seawater temperature and salinity, published by the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) for the period 2000–2021 in the Pacific Ocean. In conjunction with the Niño index, a multiscale geographically weighted regression model based on geographically weighted principal component analysis (GWPCA-MGWR) and spatial association between zones (SABZ) is employed for this study. The results indicate the following: (1) The spatial distribution of dominant environmental factors affecting the catch of Pacific yellowfin tuna is primarily divided into two types: seawater temperature dominates in the western Pacific Ocean, while salinity dominates in the eastern Pacific Ocean. When El Niño occurs, the area with seawater temperature as the dominant environmental factor in the western Pacific Ocean further extends eastward, and the water layers where the dominant environmental factors are located develop to deeper depths; when La Niña occurs, there is a clear westward expansion in the area with seawater salinity as the dominant factor in the eastern Pacific Ocean. This change in the spatial distribution pattern of dominant factors is closely related to the movement of the position of the warm pool and cold tongue under ENSO events. (2) The areas with a higher catch of Pacific yellowfin tuna are spatially associated with the dominant environmental factor of mid-deep seawater temperature (105–155 m temperature) to a greater extent than other factors, the highest correlation exceeds 70%, and remain relatively stable under different ENSO events. The formation of this spatial association pattern is related to the vertical movement of yellowfin tuna as affected by subsurface seawater temperature. (3) The GWPCA-MGWR model can fully capture the differences in environmental variability among subregions in the Pacific Ocean under different climatic backgrounds, intuitively reflect the changing areas and influencing boundaries from a macro perspective, and has a relatively accurate prediction on the trend of yellowfin tuna catch in the Pacific Ocean.

Global patterns of human-wildlife spatial associations and implications for differentiating conservation strategies.

Understanding the global patterns of human and wildlife spatial associations is essential for pragmatic conservation implementation, yet analytical foundations and indicator-based assessments that would further this understanding are lacking. We integrated the global distributions of 30,664 terrestrial vertebrates and human pressures to map human-nature index (HNI) categories that indicate the extent and intensity of human-wildlife interactions. Along the 2 dimensions of biodiversity and human activity, the HNI allowed placement of terrestrial areas worldwide in one of 4 HNI categories: anthropic (human-dominated areas), wildlife-dominated (little human influence and rich in wildlife), co-occurring (substantial presence of humans and wildlife), and harsh-environment (limited presence of humans and wildlife) areas. The HNI varied considerably among taxonomic groups, and the leading driver of HNI was global climate patterns. Co-occurring regions were the most prevalent (35.9%), and wildlife-dominated and anthropic regions encompassed 26.45% and 6.50% of land area, respectively. Our results highlight the necessity for customizing conservation strategies to regions based on human-wildlife spatial associations and the distribution of existing protected area networks. Human activity and biodiversity should be integrated for complementary strategies to support conservation toward ambitious and pragmatic 30×30 goals.

Genetic architecture of the structural connectome

Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.

Detecting the Spatial Association between Commercial Sites and Residences in Beijing on the Basis of the Colocation Quotient

Identifying the spatial association between commercial sites and residences is important for urban planning. However, (1) the patterns of spatial association between commercial sites and residences across an urban space and (2) how the spatial association patterns of each commercial format and different levels of residences vary remain unclear. To address these gaps, this study used point-of-interest data of commercial sites and residences in Beijing, China, to calculate colocation quotients, which were used for identifying the spatial association characteristics and patterns of commercial sites and residences in the city. The results show that (1) the global colocation quotient of commercial sites and residences in Beijing is below 1, indicating relatively weak spatial association. The spatial association between each commercial format and residences varies greatly and shows the characteristics of integration of high-frequency consumption and separation of low-frequency consumption. Additionally, the spatial associations between high-grade residences and commercial formats are relatively weak, whereas those between low-grade residences and commercial formats are relatively strong. (2) The local spatial association patterns of various commercial formats and residences exhibit obvious spatial heterogeneity. Overall, the proportions of various commercial formats attracted by residences are considerably higher than those of residences attracted by various commercial formats, revealing spatial asymmetry. Within the Fourth Ring Road, commercial formats are mainly attracted by residences, showing a spatial association pattern of “distribute commercial sites according to the location of residences”. The proportions of residences attracted by commercial formats increase outside the Fourth Ring Road, presenting a spatial association pattern of “commercial formats attracting residences”. The findings offer valuable insights into the development mechanisms of commercial and residential spaces and provide valuable information for urban planning.

Quantifying the association between Arctic Sea ice extent and Indian precipitation

AbstractThe unprecedented sea ice loss in the Arctic is due to its stronger surface warming than other parts across the globe, resulting in far‐flung effects on weather and climate at different spatial and temporal scales. We investigate how Arctic Sea ice and Indian summer monsoon rainfall (ISMR) are associated at seasonal scale and when preconditioned by Arctic Oscillation (AO) phases. This study also examines the potential predictability using correlation and composite analysis. The results indicate a significant decline in sea ice extent (SIE) at a rate of 0.055 Mkm2/year (p < 0.05). The strong association between SIE and precipitation in the Indian region is confirmed by correlation values ranging from −0.6 to 0.6 (p < 0.05). The spatial patterns of seasonal SIE and precipitation association remain consistent for 1979–2021. We found that the prevailing AO phases influence the association of sea ice with ISMR precipitation. The high correlation between SIE and ISMR anomalies suggests that Arctic Sea ice could be a reliable predictor for ISMR.

Spatio-temporal characteristics of land ecological security and analysis of influencing factors in cities of major grain-producing regions of China

The increasingly critical ecological environment in the major grain-producing regions (MGPRs) of China has attracted widespread attention, but there is limited research at the city level. This study presented a land ecological security (LES) evaluation index system based on the pressure, state, response (PSR) model. It used the panel entropy weight method, kernel density function method, Moran index model, and spatial econometric model to measure LES of 158 cities in MGPRs from 2005 to 2020, and analyzed the spatial and temporal evolutionary trends, spatial divergence characteristics, and influencing factors of LES. The results are as follows. (1) From 2005 to 2020, the overall trend of urban LES level was rising, but it experienced an N-shape fluctuation trajectory of “rise-fall-rise”. The state index and response index were on an upward trend, while the pressure index had a certain degree of decline. (2) LES was characterized by the spatial distribution of “high in the north and low in the south” and “high in the east and low in the west”, while spatial heterogeneity became increasingly significant, with greater spatial differences within the northern cities and within the central cities. (3) There was a significant spatial spillover effect of LES, but the effect decreased with the expansion of spatial divergence. The spatial association patterns mainly exhibited “high-high” (HH) and “low-low” (LL) agglomerations. (4) The response layer was the main obstacle subsystem, while green space per capita, environmental governance investment, precipitation per capita, arable land per capita, and agricultural development were the main obstacle factors. (5) Economic development, fiscal decentralization, industrial advancement, and innovation input were the positive factors of LES, whereas financial development and industrial development were the negative factors of LES. The findings provide effective policy reference for the sustainable utilization of urban land resources.

Within-field spatial patterns of Helicoverpa zea (Lepidoptera: Noctuidae) and spatial associations with stink bugs and their injury in field corn.

The corn earworm, Helicoverpa zea (Boddie) (Lepidoptera: Noctuidae), is a cosmopolitan pest in the field crop landscape in the southeastern United States. Field corn (Zea mays L.) is the most important midseason host for H. zea where intensive selection pressure occurs for resistance to insecticidal toxins from Bacillus thuringiensis (Bt). Because spatial patterns of H. zea in field corn have not been extensively studied, field corn was sampled for H. zea larvae and injury in 2021 and 2022. Patterns of spatial aggregation were identified in a number of fields in both larval populations and injury. Aggregation of H. zea larvae was less common at R5 than at R2. Associations between the spatial patterns of H. zea and the variability in crop phenology were identified in some fields, with positive associations between plant height and H. zea larvae, indicating that ovipositing H. zea moths avoid areas with reduced plant height and delayed reproductive maturity. Additionally, negative spatial associations between stink bug ear injury and H. zea larvae and their injury were found in a small number of cases, indicating some spatial interactions between the two pest complexes and their injury. Results from these studies provide valuable insight into the spatial patterns of H. zea in field corn. An understanding of the local dispersal and population dynamics of H. zea can be used to help further improve integrated pest management and insect resistance management programs for this major polyphagous pest.

Exploring and evaluating the spatial association between commercial and residential spaces using Baidu trajectory data

The spatial association between commercial and residential spaces strongly influences residents' daily lives, social equity, and economic performance. Based on Baidu trajectory dataset of Zhuhai and the dynamic consumption travel flow perspective, this study proposes a research framework to explore the spatial association patterns between commercial and residential spaces and to evaluate the commercial accessibility of different social groups. Findings indicate that the formation of spatial association communities is partially limited by natural barriers and administrative boundaries. The spatial association patterns follow a paradigm: a polycentric spatial association formed in the main urban area with medium travel distance, a monocentric spatial association formed in the suburbs with short travel distance, and a bicentric spatial association formed in the outer suburbs with long travel distance. For social justice in consumption, the mid-high-level income social group has the highest commercial accessibility, whereas the low-level income social group suffers from long consumption travel distance. Moreover, social disparities in commercial accessibility are also found among urban circles and spatial association patterns. This study has implications for governments in terms of designing policies to enhance commercial accessibility, mitigate social inequality in consumption, and achieve their goal of self-containment during urbanization.

Spatial Distribution of Pinus koraiensis Trees and Community-Level Spatial Associations in Broad-Leaved Korean Pine Mixed Forests in Northeastern China.

Investigating the spatial distributions and associations of tree populations provides better insights into the dynamics and processes that shape the forest community. Korean pine (Pinus koraiensis) is one of the most important tree species in broad-leaved Korean pine mixed forests (BKMFs), and little is known about the spatial point patterns of and associations between Korean pine and community-level woody species groups such as coniferous and deciduous trees in different developmental stages. This study investigated the spatial patterns of Korean pine (KP) trees and then analyzed how the spatial associations between KP trees and other tree species at the community level vary in different BKMFs. Extensive data collected from five relatively large sample plots, covering a substantial area within the natural distribution range of KP in northeastern China, were utilized. Uni- and bivariate pair correlation functions and mark correlation functions were applied to analyze spatial distribution patterns and spatial associations. The DBH (diameter at breast height) histogram of KP trees in northeastern China revealed that the regeneration process was very poor in the Changbai Mountain (CBS) plot, while the other four plots exhibited moderate or expanding population structures. KP trees were significantly aggregated at scales up to 10 m under the HPP null model, and the aggregation scales decreased with the increase in size classes. Positive or negative spatial associations were observed among different life stages of KP trees in different plots. The life history stages of the coniferous tree group showed positive spatial associations with KP saplings and juvenile trees at small scales, and spatial independence or negative correlations with larger KP trees at greater scales. All broad-leaved tree groups (canopy, middle, and understory layers) exhibited only slightly positive associations with KP trees at small scales, and dominant negative associations were observed at most scales. Our results demonstrate that mature KP trees have strong importance in the spatial patterns of KP populations, and site heterogeneity, limited seed dispersal, and interspecific competition characterize the spatial patterns of KP trees and community-level spatial associations with respect to KP trees, which can serve as a theoretical basis for the management and restoration of BKMFs in northeastern China.

The Urban Facilities Before and After the COVID-19 Pandemic: Spatial Association Patterns Mining in Wuhan, China

The Urban Facilities Before and After the COVID-19 Pandemic: Spatial Association Patterns Mining in Wuhan, China

Macroecology of rocky intertidal benthic communities along the southwestern Atlantic: Patterns of spatial variation and associations with natural and anthropogenic variables

Assessing spatial variability in biodiversity and its relationships with potential drivers is necessary for understanding and predicting changes in ecosystems. Here, we evaluated spatial patterns in sessile macrobenthic communities in rocky intertidal habitats along the southwestern Atlantic (SE Brazil), spanning over 500 km of coastline. We applied a rapid-survey approach focusing on the main space occupiers and habitat-forming taxa. We partitioned community variance into spatial scales ranging from metres to hundreds of kilometres and assessed whether community patterns were associated with variation in shore topography, nearshore ocean, and human influence. The communities from the mid-midlittoral level exhibited equivalent variation (31–35%) at the scales of quadrats (metres), sites (kilometres), and sub-regions (tens of kilometres). For the communities from the low-midlittoral and infralittoral fringe levels, most variability occurred at the scales of quadrats and sites (30–42%), followed by sub-regions (22%). Wave fetch, sea surface temperature (SST), and shore inclination were the variables that best explained community structure at the mid-midlittoral. At the low-midlittoral and infralittoral fringe, the most influential variables were related to oceanic forcing (SST, total suspended solids, particulate organic carbon, chlorophyll-a concentration) and human influence. Univariate analyses also revealed strong associations between the abundance of the main components of the communities and the predictor variables evaluated. Our results suggest that urbanised estuarine bays and coastal upwelling regimes have a strong influence on adjacent benthic communities, driving macroecological patterns in the study area. This study advances the knowledge in macroecology and biogeography of rocky shores in an understudied coastline and globally and provides valuable insights for future assessments of ecological changes resulting from unfolding human impacts.

Spatial–Temporal Dynamic Graph Differential Equation Network for Traffic Flow Forecasting

Traffic flow forecasting is the foundation of intelligent transportation systems. Accurate traffic forecasting is crucial for intelligent traffic management and urban development. However, achieving highly accurate traffic flow prediction is challenging due to road networks’ complex dynamic spatial and temporal dependencies. Previous work using predefined static adjacency matrices in graph convolutional networks needs to be revised to reflect the dynamic spatial dependencies in the traffic system. In addition, most current methods ignore the hidden dynamic spatial–temporal correlations between road network nodes as they evolve. We propose a spatial–temporal dynamic graph differential equation network (ST-DGDE) for traffic prediction to address the above problems. First, the model captures the dynamic changes between spatial nodes over time through a dynamic graph learning network. Then, dynamic graph differential equations (DGDE) are used to learn the spatial–temporal dynamic relationships in the global space that change continuously over time. Finally, static adjacency matrices are constructed by static node embedding. The generated dynamic and predefined static graphs are fused and input into a gated temporal causal convolutional network to jointly capture the fixed long-term spatial association patterns and achieve a global receiver domain that facilitates long-term prediction. Experiments of our model on two natural traffic flow datasets show that ST-DGDE outperforms other baselines.

Spatial Association Patterns with Cultural and Behaviour with the Situations of COVID-19

This study was a cross-sectional study. The study of spatial association patterns and the influences on the Coronavirus Disease 2019 (COVID-19) epidemic situation in Thailand was performed using secondary data from the COVID-19 interactive dashboard, Department of Disease Control, Ministry of Public Health, between January 1st, 2020, and December 31st, 2021. Moran’s I, Local Indicators of Spatial Association (LISA), and Spatial Regression was applied for statistical analysis. In the epidemic situation of COVID-19, the highest of 11,512.65 per one hundred thousand population, and the spatial association between the nighttime light average, the prevalence of smokers in Thailand, the proportion of population per village health volunteer, and the proportion of population per health care center with the epidemic situation of COVID-19 has Moran’s I = 0.309, 0.396, 0.081 and 0.424, respectively. From the Spatial Lag Model (SLM), a factor that has a spatial association with the epidemic situation of COVID-19 is the nighttime light average, the prevalence of smokers in Thailand, and the proportion of population per healthcare center, which can predict the epidemic situation of COVID-19 by 47.8 percent (R2 =0.478). The growth factor of a large city is an important factor for population density which is a major cause of spread of the coronavirus easily. Moreover, smoking behavior has encouraged the epidemic to spread rapidly. The situation is serious as the number of hospitals is not enough to support the treatment and screening of patients to cover the entire population of Thailand. Therefore, it is urgent that the government plan to mitigate the situation with maximum efficiency by having Covid-19 centers and increase the number of beds and facilities.

Phylotype resolved spatial variation and association patterns of planktonic Thaumarchaeota in eastern Chinese marginal seas

The majority of marine ammonia oxidizers belong to Thaumarchaeota, a phylum of Archaea, which is distributed throughout the water column. Marine surface waters contain distinct thaumarchaeotal phylotypes compared to the deeper ocean, but spatial dynamics of the surface-associated lineages are largely unsolved. This study of 120 seawater samples from the eastern Chinese marginal seas identified contrasting distribution and association patterns among thaumarchaeotal phylotypes across different dimensions. Horizontally, Nitrosopumilus-like and Nitrosopelagicus-like phylotypes dominated the surface water (3 m) of the Yellow Sea (YS) and East China Sea (ECS), respectively, along with increased abundance of total free-living Thaumarchaeota in ECS. Similar compositional changes were observed in the surface microlayer. The spatial heterogeneity of particle-attached Thaumarchaeota was less clear in surface microlayers than in surface waters. Vertically, the Nitrosopelagicus-like phylotype increased in abundance from surface to 90 m in ECS, which led to an increase in the proportion of Thaumarchaeota relative to total prokaryotes. This occurred mainly in the free-living fraction. These results indicate a clear size-fractionated niche partitioning, which is more pronounced at lower depths than in the surface water/surface microlayer. In addition, associations of Thaumarchaeota with other microbial taxa varied between phylotypes and size fractions. Our results show that a phylotype-resolved and size-fractionated spatial heterogeneity of the thaumarchaeotal community is present in surface oceanic waters and a vertical variation of the Nitrosopelagicus-like phylotype is present in shallow shelf waters.

Spatial distribution and determinants of stunting, wasting and underweight in children under-five in Ethiopia

BackgroundThe burden of malnutrition in children under five remains an important child health issue where significant regional variations are observed in Ethiopia. The main aim of the current study was to explore the spatial distribution of stunting, wasting, and underweight in children under five in Ethiopia.MethodsThe secondary data from EDHS, 2016, was employed, and a total of 7960 under-five children were included in the analysis. The general spatial analysis was performed to explore the spatial distribution of malnutrition among under-five within and between the regions of Ethiopia. The Spatial Lag and Spatial Error models were used to examine the spatial dependence between stunting, wasting, and being underweight. The Geographically weighted regression analysis was performed to estimate the types and strength of relationships between malnutrition and associated determinant factors across the regions and within the clusters or Zones of each region.ResultsAmong the under-five children included in the study, 36.6% were stunted, 12.2% were wasted and 25.2% were underweight. The Global Moran Index's value indicates (Stunting, I = 0.3135, p-value < 0.00001, Wasting, I = 0.1948, p-value < 0.0001 and Underweight, I = 0.5291, p-value < 0.0001) that there was a significant spatial variation of malnutrition across the regions and Zones of Ethiopia. The significant source of spatial variation of malnutrition in children under five was associated with the mother's education level, drinking water facility, toilet facilities, number of children under-five in the household, household’s wealth index, breastfeeding duration of the child, child size at birth, Body Mass Index of Mothers (BMI), region, and place of residence.ConclusionsThe spatial association and spatial patterns of stunting, wasting, and being underweight were clustered within and between the regions of Ethiopia. The hotspot areas of stunting, wasting, and being underweight were detected in the regions where there were poor health facilities and limited socioeconomic indicators.