Spatial Differences Research Articles

Optimization of Rural Residential Areas in Flood Detention Basins Based on Dual Minimum Accumulation Resistance Model: A Case Study of Xun County in Central China

Optimizing rural residential areas (RRAs) in environmentally fragile areas such as flood detention basins is of great significance for improving the human–land relationship and achieving sustainable rural development. This study took Xun County in central China as a case study, established a dual minimum cumulative resistance model (DMCR) that considered the dual factors of natural attributes and policy regulations to evaluate the optimization resistance of RRAs and determined the optimization directions and strategies accordingly. The main results are: (1) RRAs are relatively small and scattered, and there are spatial conflicts with policy regulations such as flood detention basin and urban development boundaries. (2) The spatial difference in optimization resistance of RRAS is higher in the central and northern towns and lower in the western and eastern towns. The factors with significant effects include policy resistance, location resistance, and production resistance. (3) The optimization of RRAs is divided into three directions: annexation or evacuation, consolidation and improvement, and clustering and upgrading, with their respective area proportions of 31.17%, 48.12%, and 20.72%. (4) RRAs in the direction of clustering and upgrading allow for moderate expansion; RRAs in the direction of consolidation and improvement achieve smart reduction through the renovation of homesteads; RRAs in the direction of annexation or evacuation are gradually integrated into the urban area in the suburbs and completely demolished in the outer suburbs.

Measurement and optimization paths of the multidimensional development levels of counties in the Yellow River Basin: based on the sustainable livelihoods framework

The Yellow River Basin (YRB) faces intense man-land conflicts. However, existing studies rarely focus on the comprehensive and multidimensional development levels in the YRB, and there is a lack of refined county-level studies, making it difficult to fully support the implementation of ecological protection and high-quality development strategy in the YRB. Under the Sustainable Livelihoods Framework, the spatial distribution, differences, and correlation characteristics of the multidimensional development levels of counties in the YRB are disclosed. This is achieved by comprehensively utilizing geospatial, socio-economic, and other multi-source data in combination with methods such as the entropy weight method, the Theil index, and spatial analysis. Optimal development paths are proposed with a focus on the development types of counties. The findings are as follows: (1) Counties in the YRB have a low overall multidimensional development level, presenting a spatial distribution pattern of “high in the east and low in the west” overall. In terms of each dimension, the average value ranking of lower reach > middle reach > upper reach is observed in each dimension; the exception is the financial dimension (with the ranking of middle reach > lower reach > upper reach). (2) Differences between counties in the development indices vary across different dimensions. The differences in the multidimensional development indices and in the development indices of each dimension (except for the natural dimension) are mainly attributable to differences between counties within each reach. Differences between reaches are non-significant. (3) The multidimensional development levels of counties in the YRB and their development levels in each dimension show strong spatial correlation. And significant counties are mainly identified as LL-type (clusters of low value areas) and HH-type (clusters of high value areas). (4) Depending on the measurement results and advantageous factors of the county multidimensional development index, counties are classified into four development types: weak development type, single-dimension-led development type, potential coordinated development type, and multidimensional coordinated development type. Based on this classification, targeted optimal development paths are proposed with a focus on the specific characteristics of different development types. The research findings can provide effective scientific support for ecological protection and high-quality development in the YRB.

Analysis of Time–Frequency Characteristics and Influencing Factors of Air Quality Based on Functional Data in Fujian

Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO2, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control.

Distribution dynamics, spatial differences, and convergence of synergistic pollution and carbon reduction in China.

Carbon and pollution reduction are highly synergistic elements of green development, and the detailed derivation of the synergistic relationship is urgently needed. Based on the research perspective of coupling coordination, this paper firstly bases on the research perspective of coupling synergy, takes 30 provinces in China as the research object from 2011 to 2022, and constructs a coupling coordination degree model to measure the synergistic effect of pollution reduction and carbon reduction; secondly, kernel density and Dagum coefficient are used to analyze the spatial and temporal characteristics, spatial differences and sources of differences in the synergistic effects of pollution reduction and carbon reduction in China as a whole, in the three major regions and in each province; finally, the Moran index is applied to test the spatial agglomeration characteristics of the synergistic effect of pollution reduction and carbon reduction, and the spatial panel model is applied to test its β-convergence. The study shows that (1) during the study period, the level of synergistic effect on carbon reduction in China changed from good coordination to good coordination, and the synergistic effect in the western region was significantly better than that in the eastern and central regions; (2) the synergistic effect of pollution reduction and carbon reduction in China as a whole and the three regions showed obvious spatial differences, but the differences were gradually narrowing, and the supervariable density was the dominant factor of the overall differences; (3) as a whole, the synergistic effect of pollution reduction and carbon reduction among provinces has a spatial agglomeration effect, and the level of synergistic effect of pollution reduction and carbon reduction across the country and east and west regions has a significant absolute β convergence trend, while there is a significant conditional β convergence trend across the country and the three major regions, and the eastern and central regions with a low synergy level have a higher convergence rate, indicating that as time goes by, under the influence of various factors, the synergistic effect will converge to the steady state level. The purpose of this paper is to analyze the spatial patterns and regional differences in the synergistic effects of pollution reduction and carbon reduction in Chinese provinces and also to provide a quantitative basis for the full implementation of the synergistic regulation of pollution reduction and carbon reduction.

Multi-Scale Effect of Land Use Landscape on Basin Streamflow Impacts in Loess Hilly and Gully Region of Loess Plateau: Insights from the Sanchuan River Basin, China

The gullies and valleys of the Loess Plateau, as key ecological zones for soil erosion control, play a critical role in the region’s sustainable development under increasing urbanization. This study employed the Soil and Water Assessment Tool (SWAT) to analyze the impacts of land use/cover changes (LUCC) on runoff at multiple spatial scales and locations within the Sanchuan River Basin (SRB) in the loess hilly and gully region. The methodology integrates SWAT modeling with LUCC scenario analysis, focusing on spatial and scale effects of land use changes on hydrological processes. The results revealed distinct spatial differences, with diminishing LUCC impacts on streamflow from the upper to lower reaches of the basin, regardless of land use type. Scale effects were also evident: grassland effectively controlled runoff within 300 m of riparian zones, while forest land was most effective beyond 750 m. A relatively insensitive range for runoff changes was observed between 300 and 750 m. These findings highlight the critical role of LUCC in influencing runoff patterns and underscore the importance of region-specific and scale-sensitive land use management strategies. This research provides valuable guidance for sustainable land planning, particularly in riparian zones, to enhance runoff control and optimize ecological benefits.

Analysis on Spatial-temporal Characteristics and Driving Factors of PM2.5 in Heilongjiang Province in the Past 20 years

PM2.5 is an important indicator for measuring the degree of air pollution. Studying the space-time variation and the driving factors of spatial heterogeneity is important for controlling air pollution and improving regional air quality. Based on PM2.5 remote sensing data from 2000 to 2021, the Theil-Sen Median trend analysis, Mann-Kendall significant inspection, and spatial auto correlation were used to analyze the characteristics of space-time changes in PM2.5 concentration, and geographical detectors were combined with a multi-scale geographical weighted regression model to explore the key driver factor and its influence and direction of the impact and role of PM2.5 spatial differences. The results showed that： ① The average PM2.5 value of Heilongjiang Province was between 22.01 and 41 μg·m-3 from 2000 to 2021. From 2008 to 2015, the average PM2.5 value was higher than the secondary concentration limit （35 μg·m-3） of the "Environmental Air Quality Standard." The turning point of the PM2.5 concentration change that occurred in 2013 generally showed the trend of change and then a downward trend. Winter was the high incidence season for PM2.5 pollution. The PM2.5 concentration space was a distributed pattern in the south and north and the high-value zone was mainly based on Harbin, Daqing City, and the surrounding area. The low-value areas were distributed in the northern regions such as the Great Khingan Mountains Region and Heihe City. ② Factor detection results indicated that the average annual temperature was the most important driving factor that affected PM2.5 spatial differences. The remaining key driver factors were in turn： high-end, population density, average annual wind speed, land use, night lights, annual years precipitation, slope, annual relative humidity, and NDVI. Interactive detection showed that the interpretation of PM2.5 points after interaction was higher than a single factor after interaction, indicating that affecting PM2.5 spatial difference was the result of the common effect of each driver factor. The effect of natural factors was more obvious than that of social and economic factors. ③ The effect of different influence factors on PM2.5 had a significant spatial difference. The average annual temperature, average annual relative humidity, population density, and night lighting played a promotion effect on PM2.5 pollution and NDVI and land use played an inhibitory effect on PM2.5 pollution. PM2.5 was significantly different from the action role of various influencing factors and the average annual temperature, annual average wind speed, and NDVI impact scale were the smallest, with a variable bandwidth of 43； population density and land use impact scale were the largest, with a variable bandwidth of 140.

Contributon-Informed Approach to RPV Irradiation Study Using Hybrid Shielding Methodology

An important aspect of pressurized water reactor (PWR) lifetime monitoring is supporting radiation shielding analyses which can quantify various in-core and out-core effects induced in reactor materials by varying neutron–gamma fields. A good understanding of such a radiation environment during normal and accidental operating conditions is required by plant regulators to ensure proper shielding of equipment and working personnel. The complex design of a typical PWR is posing a deep penetration shielding problem for which an elaborate simulation model is needed, not only in geometrical aspects but also in efficient computational algorithms for solving particle transport. This paper presents such a hybrid shielding approach of FW-CADIS for characterization of the reactor pressure vessel (RPV) irradiation using SCALE6.2.4 code package. A fairly detailed Monte Carlo model (MC) of typical reactor internals was developed to capture all important streaming paths of fast neutrons which will backscatter the biological shield and thus enhance RPV irradiation through the cavity region. Several spatial differencing and angular segmentation options of the discrete ordinates SN flux solution were compared in connection to a SN mesh size and were inspected by VisIt code. To optimize MC neutron transport toward the upper RPV head, which is a particularly problematic region for particle transport, a deterministic solution of discrete ordinates in forward/adjoint mode was convoluted in a so-called contributon flux, which proved to be useful for subsequent SN mesh refinement and variance reduction (VR) parameters preparation. The pseudo-particle flux of contributons comes from spatial channel theory which can locate spatial regions important for contributing to a shielding response.

Projections of urban land exposure to drought and flood in China.

With global warming, the risk of human exposure to extreme climates increases, profoundly impacting sustainable development. Using rapidly developing China as an example, this study estimates the exposure of urban land to drought and flood based on historical and future climate and land data. The results show that the area of urban land exposed to drought in China generally decreases in the future. However, the area exposed to flood sharply increases in the mid- and long-term future, except for the near future. There is heterogeneity in the changes in urban land exposure under different levels of drought and flood. The changes in exposure areas under different future scenarios show spatial differences and an overall trend of spatial aggregation. Climate factors are the main drivers of changes in the area exposed to drought and flood. The findings can provide important references for policy-making and urban land management in response to extreme climate change.

Connectivity of scamp Mycteroperca phenax populations in the United States Gulf of Mexico and Atlantic Ocean

Connectivity among marine fish populations not only arises through the movement of adults but also through larval dispersal facilitated by ocean currents. For species with long pelagic larval duration and high site fidelity as adults, larval dispersal can be the dominant mechanism of connectivity among otherwise spatially distinct populations. Therefore, when assessing the recruitment dynamics or population structure of such species, it is essential to evaluate how larval biology and reproductive ecology interact with oceanographic circulation. To investigate this interaction for scamp Mycteroperca phenax, a grouper found in the Gulf of Mexico and Atlantic Ocean, we simulated the dispersal of millions of virtual larvae throughout the scamp’s range near the southeastern USA. We found a pattern of local retention, with larvae tending to settle close to their spawning location. There was also, however, long-distance dispersal that consistently crossed the boundary between spatial management units. Due to directional patterns in oceanographic transport and spatial differences in abundance, almost one-third of the virtual larvae that settled in the Atlantic came from spawning locations in the Gulf of Mexico, although most of these settled near the boundary between the regions. These patterns were robust to a variety of sensitivity analyses, but the magnitude of connectivity between management units varied. This connectivity has significant implications for stock assessment and fishery management. It may increase the resilience of Atlantic populations, but it also means that the sustainability of Atlantic populations may rely, in part, on the health of spawning populations in the Gulf of Mexico.

Persistent organic pollutant accumulation in Pacific abyssal plain sediments and biota: Implications for sources, transport, and deep-sea mining

Despite its vast size and ecological and economic importance, the deep sea is among the least understood ecosystems on Earth. While much remains to be discovered, researchers have established that the deep sea is being exposed to numerous anthropogenic factors including chemical pollution. Persistent organic pollutants (POPs), defined by their persistence in the environment, bioaccumulation, and toxicity, are continually discharged and transported into the deep sea despite efforts to ban or reduce their production under global and regional conventions. Here, we provide the first evidence of these POPs in sediment and biota in the Pacific abyssal plain, an area currently targeted for deep-sea mining. Sediment and fish tissue (Coryphaenoides sp., a deep-ocean predator and scavenger fish) collected from two sites in the eastern Clarion-Clipperton Zone of the Pacific abyssal plain were tested for three POPs: polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and per- and polyfluoroalkyl substances (PFAS). Similarities between the sites in PCB congener concentrations suggested that PCBs were distributed evenly over the region. Conversely, higher variability in PBDE congener concentrations and PFAS concentrations from the same sites suggested that these chemicals had more patchy distributions across the region. Sediment PFAS were especially variable, detected at a high concentration (1.5 ng/g wet weight) in only one of five samples but measured in all fish muscle samples at levels comparable to some freshwater ecosystems. Results suggest that raining particulates (e.g., particulate organic matter and microplastics) dominate vertical transport of PCBs, resulting in more uniform distribution, while episodic events such as carrion-fall and vertically migrating species may drive PFAS transport processes resulting in patchy spatial distributions and differences in scavenging biota versus sediment. Unintentional PCBs (e.g., pigment components) comprised a large proportion of total PCBs in sediment and muscle tissue, suggesting that unregulated PCB releases are accumulating in the deep Pacific Ocean.

Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning

Urban greening plays a crucial role in maintaining environmental sustainability and enhancing people’s well-being. However, limited by the shortcomings of traditional methods, studying the heterogeneity and nonlinearity between environmental factors and green view index (GVI) still faces many challenges. To address the concerns of nonlinearity, spatial heterogeneity, and interpretability, an interpretable spatial machine learning framework incorporating the Geographically Weighted Random Forest (GWRF) model and the SHapley Additive exPlanation (Shap) model is proposed in this paper. In this paper, we combine multi-source big data, such as Baidu Street View data and remote sensing images, and utilize semantic segmentation models and geographic data processing techniques to study the global and local interpretation of the Beijing region with GVI as the key indicator. Our research results show that: (1) Within the Sixth Ring Road of Beijing, GVI shows significant spatial clustering phenomenon and positive correlation linkage, and at the same time exhibits significant spatial differences; (2) Among many environmental variables, the increase of green coverage rate has the most significant positive effect on GVI, while the increase of building density shows a strong negative correlation with GVI; (3) The performance of the GWRF model in predicting GVI is excellent and far exceeds that of comparison models.; (4) Whether it is the green coverage rate, urban built environment or socioeconomic factors, their influence on GVI shows non-linear characteristics and a certain threshold effect. With the help of these non-linear influences and explicit threshold effects, quantitative analyses of greening are provided, which can help to assist urban planners in making more scientific and rational decisions when allocating greening resources.

Effect of spatial differentiation of plant communities on PM2.5 and O3 in urban green spaces in Beijing, China

Urban green space can improve the air quality of urban human settlements. This study aimed to investigate the spatial differences of air quality among the different plant community structures and types of urban park green spaces. We select 17 sample sites in Beijing Olympic Forest Park, and they are located in different areas of plant community structures and types. The study entailed an analysis of the interrelationships between the plant community structures, types, and PM2.5, O3, and PM2.5–O3 compound data. The results showed that PM2.5 was lower in tree-shrub-grass, tree-shrub, and tree-grass than in shrub-grass and grass plant community areas; PM2.5 was lower in evergreen coniferous, mixed coniferous and broadleaved, and deciduous broadleaved plant communities than that in grass or shrub ones. In different plant community structures, types areas, O3 was higher than 100 μg·m–3, and there were no significant differences among the plant community areas. The air quality index with PM2.5–O3 composite pollution value as the main parameter reached the level of “moderate pollution”, and the result that deserves further attention. The research results provide a basic scientific basis for the planning, design, and updating optimization of functional urban green spaces based on evidence-based design.

Deepfake Detection Model Combining Texture Differences and Frequency Domain Information

In recent years, public security incidents caused by deepfake technology have occurred frequently around the world, which makes an efficient and accurate deepfake detection model crucial. The existing advanced methods use the manipulation features in the image to realize the binary classification of real and fake images by training complex neural network models. However, these models rely on a single manipulation feature, and the detection accuracy of these methods will be greatly reduced when the forgery technology or image quality of the training data set and the validation data set are different. Inspired by the existing work, we propose a two-stream collaborative learning framework that combines spatial texture differences and frequency information. The average difference convolution (ADC) is designed to extract the spatial texture difference information of the image, and the gray image frequency-aware decomposition (GFAD) is used to extract the artifact information of the image in the frequency domain. At the same time, the ViT idea is combined with cross attention mechanism for feature fusion to comprehensively mine forged features in forged images. Experimental results show that the proposed model has good detection effects on three benchmark data sets. In terms of cross-dataset evaluation, the AUC on Celeb-DF dataset reaches 82.86%, which is better than the existing advanced methods.

Identifying the impact of water constraints on agricultural productivity in hillside contexts

AbstractUsing a unique panel dataset based on two census rounds, we estimate the impact of irrigation gaps and drought on yields of paddy, maize, and other crops in Bhutan, an understudied country. We utilize a recently‐developed spatial first differences (SFD) approach to identify the impacts of water‐related constraints and compare these results from a panel fixed effects (FE) approach. We find that irrigation gaps reduce paddy yields and droughts reduce maize yields. Estimates from the SFD model are found to be consistent compared to those from the panel FE model. Water‐related constraints also reduce yields of vegetable crops, and other constraints such as labor shortages, wild animals, insects, and diseases reduce yields of cereal crops.

Study on the spatio-temporal coupling and drivers of agricultural carbon emission efficiency and food security

In light of global climate change and environmental challenges, reducing agricultural carbon emissions while maintaining food security has become a critical concern for sustainable agricultural development. This study examines 13 provinces in primary grain-producing regions, utilising data from 1999 to 2022 to analyse the spatio-temporal heterogeneity and driving factors of the coupled and coordinated development of agricultural carbon emission efficiency and food security. The findings indicate that: 1) The proportion of carbon emissions from agricultural inputs in primary grain-producing regions exceeds 80%, whereas the contribution of carbon sinks from rice, maise, and wheat surpasses 95%. Both agricultural carbon emissions and carbon sinks in the Yellow River Basin are significantly elevated; 2) The general trend of agricultural carbon emission efficiency and food security is increasing, spatially characterised by a pattern of high levels in the north and south, and low levels in the south and high levels in the north, respectively. The integration of the two systems is progressing positively, and the trailing form of food security has emerged as a developmental trend, with the degree of food security limiting the coordinated advancement of both. 3) The overall spatial disparity exhibits a declining tendency, with hypervariable density being the primary contributor to this spatial difference. The overall polarisation of the primary grain-producing regions has diminished. The level of heterogeneity in the Songhua River basin progressively intensifies; 4) Rural human capital and financial support for agriculture, urbanisation rate and soil erosion control, agricultural machinery power and soil erosion control are the main interaction factors. The economic status and rural human capital will facilitate the integrated and harmonious development of the two systems, while the agricultural disaster rate will impede this integrated and harmonious development. Ultimately, policy solutions are proposed to optimise agricultural inputs and raise their utilisation efficiency, adopt diversified regional development strategies, promote regional connection and coordinated development, and reinforce the management of drivers and policy support.

Channel and Spatial Enhancement Network for human parsing

The dominant backbones of neural networks for scene parsing consist of multiple stages, where feature maps in different stages often contain varying levels of spatial and semantic information. High-level features convey more semantics and fewer spatial details, while low-level features possess fewer semantics and more spatial details. Consequently, there are semantic-spatial gaps among features at different levels, particularly in human parsing tasks. Many existing approaches directly upsample multi-stage features and aggregate them through addition or concatenation, without addressing the semantic-spatial gaps present among these features. This inevitably leads to spatial misalignment, semantic mismatch, and ultimately misclassification in parsing, especially for human parsing that demands more semantic information and more fine details of feature maps for the reason of intricate textures, diverse clothing styles, and heavy scale variability across different human parts. In this paper, we effectively alleviate the long-standing challenge of addressing semantic-spatial gaps between features from different stages by innovatively utilizing the subtraction and addition operations to recognize the semantic and spatial differences and compensate for them. Based on these principles, we propose the Channel and Spatial Enhancement Network (CSENet) for parsing, offering a straightforward and intuitive solution for addressing semantic-spatial gaps via injecting high-semantic information to lower-stage features and vice versa, introducing fine details to higher-stage features. Extensive experiments on three dense prediction tasks have demonstrated the efficacy of our method. Specifically, our method achieves the best performance on the LIP and CIHP datasets and we also verify the generality of our method on the ADE20K dataset.

Study on Spatial Difference and Its Convergence of Urban Assessment: Based on 286 Cities in China

Study on Spatial Difference and Its Convergence of Urban Assessment: Based on 286 Cities in China

Icelandic glacial dissolved organic carbon fluxes, composition and variability - relevance for the global glacial carbon budget

Despite their relatively large size, organic carbon (OC) fluxes from Icelandic glaciers have not been explicitly considered in current global glacial OC flux calculations. Most global glacial OC estimates are based on limited individual flux estimates, which show considerable spatial differences, are often based on melt season fluxes, and rarely account for seasonal and diurnal variability of glacial dissolved organic matter (DOM). Using an annual dataset of 25 Icelandic glaciers (and their glacial streams) we investigate DOM concentration and composition, calculating an estimate for downstream OC fluxes from Icelandic glaciers, considering diurnal and seasonal variability. DOM source and composition distinctly changed from a terrestrial character toward a more proteinaceous character as melt increased, both on a seasonal and diurnal basis, likely reflecting the flow path of the meltwater. While DOC concentration did not change on a diurnal basis, DOM composition was more labile in the afternoons, possibly indicating photochemical or biological transformation processes. Overall, the glacial streams predominantly acted as CO2 sinks. However, higher DOC concentrations, along with contributions of more proteinaceous DOM in proglacial streams, led to a decrease in the uptake potential for CO2. Finally, we estimated an export flux of 0.0026 ± 0.0029 Gg C yr−1 km−2 of DOC, and 0.011 ± 0.007 Tg C yr−1 km−2 of POC, from Icelandic glaciers. We reveal larger than previously assumed DOC and POC fluxes from Icelandic glaciers, with higher-than-global-average areal fluxes (~3 % and 9 % of global glacial C flux respectively). Our findings underscore the importance of revising current global estimates to include the not-fully-accounted-for contribution of Icelandic, and other glaciers. This is particularly important considering ongoing climatic changes will likely affect glacial meltwater discharge and sources, leading to altered DOM composition and DOC concentration, having potentially considerable consequences for glacial OC export and CO2 uptake potentials of glacial streams.

Comparison of flea diversity in the burrows of Richardson's ground squirrels (Urocitellus richardsonii) in urban and rural sites in central Saskatchewan, Canada.

Spatial and temporal differences in the relative abundance of arthropod vectors are important factors that influence the risk of disease for mammalian hosts. Seasonal changes in the diversity and abundance of fleas (Siphonaptera) in Richardson's ground squirrel (Urocitellus richardsonii) burrows were studied at two sites in central Saskatchewan. A total of 225 fleas (151 at an urban site and 74 at a rural site) were collected. Flea prevalence differed among seasons at the urban site but not at the rural site. Of the nine flea species detected (eight at the urban site and six at the rural site), Oropsylla rupestris, O. bruneri, O. labis, O. tuberculata, and Aetheca wagneri are vectors of Yersinia pestis, the causative agent of plague. The presence and abundance of some fleas differed between sites and seasons. Neopsylla inopina and O. rupestris were the most abundant species at the urban site during the spring and summer, respectively, while O. bruneri was the most abundant species at the rural site. Our findings may have implications for the management of the black-tailed prairie dogs (Cynomys ludovicianus) in southwestern Saskatchewan because they coexist with U. richardsonii, are hosts for Oropsylla, and are at great risk of plague exposure/infection.

Understanding sediment and carbon accumulation in macrotidal minerogenic saltmarshes for climate resilience

Coastal saltmarshes play an essential role in providing services such as sediment and carbon storage, coastal protection and support for biodiversity. Despite their importance, understanding the factors controlling sediment and carbon accumulation in these minerogenic saltmarshes remains challenging due to their diversity and site-specific characteristics. Understanding the respective role of these drivers is essential for effective coastal management, particularly for mitigating the impacts of climate change. This study evaluates the control of forcing factors on the lateral and vertical morphological evolution and carbon burial rates of three minerogenic saltmarshes located on the French Atlantic coast (Pertuis Charentais region). By focusing on these sites, the study isolates specific factors such as wind and wave exposure, inundation frequency, and sediment availability, while minimizing confounding influences like climate and tidal range. Results reveal significant lateral expansion of saltmarsh boundaries towards the sea across all sites, with the highest rates of progradation observed in the protected areas influenced by geomorphological features such as sand spits and sheltered bay heads. Sediment and mass accumulation rates (SAR; MAR), derived from 210Pb and 137Cs profiles of sediment cores (n = 14), range from 0.48 to 2.22 cm yr−1, among the highest reported globally, with notable variability within and between sites. Inundation frequency and accommodation space explain SAR variability within sites, while sediment availability predominantly determines spatial differences in vertical accumulation rates between sites. Organic carbon burial rates range from 75 to 345 gC m−2 yr−1, and show a strong correlation with SAR (r = 0.9, p < 0.001, n = 13) but no dependence on carbon content or density (r = 0.2, p > 0.05, n = 13). This highlights the role of sediment input in the accumulation and sequestration of carbon by minerogenic saltmarshes. Furthermore, isotopic analysis indicates a marine source dominance in organic carbon sediment. This research provides insights into how different environmental conditions affect saltmarsh morphological evolution and carbon sequestration rates, informing targeted coastal management strategies focused on enhancing ecosystem resilience and climate resilience.