Response of Water Quality to Land Use and Landscape Pattern in the Ganjiang River Watershed.

Analysing the impact of landscape composition and structure on water quality at different scales is of great significance to water quality protection. The aim of this study was to determine scale-dependent impacts of land use/landscape patterns on water quality. The Ganjiang River, the largest water system in the Poyang Lake watershed, the largest freshwater lake in China. The response of water quality to land use and landscape patterns in the Ganjiang River watershed was explored based on land use and water quality data using redundancy and Spearman correlation analyses. Considering upstream monitoring of the entire Ganjiang River watershed; watersheds at the county level administrative region; and 1, 2, 5, 10, 15, 20, and 30 km-radius circular buffer zones, a total of nine scales of land use/landscape patterns that influence water quality in the Ganjiang River watershed were analysed. Results indicated that the county-level scale and the land use type of the 20 km-radius buffer zone upstream of the monitoring site were closely linked to water quality (96.28% and 93.23%, respectively). Among the land use types, construction land and cultivated land were the main output sources of pollutants. Regarding landscape pattern index, the greater the fragmentation of the landscape, the heavier was the water pollution load; the more the patches per unit area, the more stable was the ecosystem and the lower was the pollutant concentration. In addition, the eco-hydrological system of the Ganjiang River watershed was revealed to some extent through multi-angle analysis. These conclusions can serve as a reference for government departments to formulate land management and water quality protection measures.

Addressing inconsistency in functional neuroimaging: A replicable data-driven multi-scale functional atlas for canonical brain networks.

The advent of various neuroimaging methodologies has greatly aided in the conceptualization of large-scale brain networks in the field of cognitive neuroscience. However, there is inconsistency across studies in both nomenclature and the functional entities being described. There is a need for a unifying framework which standardizes terminology across studies while also bringing analyses and results into the same reference space. Here we present a functional whole-brain atlas of canonical brain networks derived from more than 100k resting-state fMRI datasets. These data-driven networks are highly replicable across datasets as well as multiple spatial scales. We have organized, labeled, and described them with terms familiar to the fields of cognitive and affective neuroscience in order to optimize their utility in future neuroimaging analyses and enhance the accessibility of new findings. The benefits of this atlas are not limited to future template-based or reference-guided analyses, but also extend to other data-driven neuroimaging approaches across modalities, such as those using blind independent component analysis (ICA). Future studies utilizing this atlas will contribute to greater harmonization and standardization in functional neuroimaging research.

Effects of changing spatial scale on debris-flow hazard assessment: A case study in the Dadu River basin, China.

Debris flows are a prevalent mountain hazard that poses severe risks to human life and property. Debris-flow hazard assessments at the regional scale are vital for risk management, which establish spatial associations between debris flows and their influencing factors based on specific evaluation units. Different spatial scales of evaluation units can influence the spatial attributes and associations obtained by statistics, and further affect the accuracy of hazard assessments. However, there is limited consensus regarding the optimal spatial scale of evaluation units for debris-flow hazard assessment. To address this issue, six different scales of grid cells and forty influencing factors related to topography, material sources, hydrology, and human activities are analyzed by the geographical detector model to assess the debris-flow hazards in the Dadu River basin, China. The results reveal that over 92 % of debris-flow points fall within hazardous zones across all spatial scales, confirming the effectiveness of the assessment model. Topography, particularly local gully topography, dominates the debris-flow occurrence in the study area, while human activities also significantly contribute. As the spatial scale of evaluation units increases, the explanatory power of the influencing factors improves, with the 90 % quantile ranging from 0.23 to 0.46. This result suggests that larger spatial scales weaken the spatial characteristics of the factors. The finer and more informative the factors are, the more sensitive to spatial scale effects. The 10 km × 10 km is identified as the optimal spatial scale, which effectively preserves the local spatial characteristics while avoiding information loss or overload. These findings provide valuable insights for enhancing the accuracy of hazard assessments and improving the efficiency of risk management.

Plant virus community structuring is shaped by habitat heterogeneity and traits for host plant resource utilisation.

Host plants provide resources critical to viruses and the spatial structuring of plant communities affects the niches available for colonisation and disease emergence. However, large gaps remain in the understanding of mechanisms that govern plant-virus disease ecology across heterogeneous plant assemblages. We combine high-throughput sequencing, network, and metacommunity approaches to test whether habitat heterogeneity in plant community composition corresponded with virus resource utilisation traits of transmission mode and host range. A majority of viruses exhibited habitat specificity, with communities connected by key generalist viruses and potential host reservoirs. There was an association between habitat heterogeneity and virus community structuring, and between virus community structuring and resource utilisation traits of host range and transmission. The relationship between virus species distributions and virus trait responses to habitat heterogeneity was scale-dependent, being stronger at finer (site) than larger (habitat) spatial scales. Results indicate that habitat heterogeneity has a part in plant virus community assembly, and virus community structuring corresponds to virus trait responses that vary with the scale of observation. Distinctions in virus communities caused by plant resource compartmentalisation can be used to track trait responses of viruses to hosts important in forecasting disease emergence.

Domain knowledge-enhanced multi-spatial multi-temporal PM2.5 forecasting with integrated monitoring and reanalysis data.

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

Atmospheric nitrogen deposition is related to plant biodiversity loss at multiple spatial scales.

Due to various human activities, including intensive agriculture, traffic, and the burning of fossil fuels, in many parts of the world, current levels of reactive nitrogen emissions strongly exceed pre-industrial levels. Previous studies have shown that the atmospheric deposition of these excess nitrogen compounds onto semi-natural terrestrial environments has negative consequences for plant diversity. However, these previous studies mostly investigated biodiversity loss at local spatial scales, that is, at the scales of plots of typically a few square meters. Whether increased atmospheric nitrogen deposition also affects plant diversity at larger spatial scales remains unknown. Here, using grassland plant community data collected in 765 plots, across 153 different sites and 9 countries in northwestern Europe, we investigate whether relationships between atmospheric nitrogen deposition and plant biodiversity are scale-dependent. We found that high levels of atmospheric nitrogen deposition were associated with low levels of plant species richness at the plot scale but also at the scale of sites and regions. The presence of 39% of plant species was negatively associated with increasing levels of nitrogen deposition at large (site) scales, while only 1.5% of the species became more common with increasing nitrogen deposition, indicating that large-scale biodiversity changes were mostly driven by "loser" species, while "winner" species profiting from high N deposition were rare. Some of the "loser" species whose site presence was negatively associated with atmospheric nitrogen deposition are listed as "threatened" in at least some EU member states, suggesting that nitrogen deposition may be a key contributor to their threat status. Hence, reductions in reactive nitrogen emissions will likely benefit plant diversity not only at local but also at larger spatial scales.

Environmental and host factors underlying tick infestation in invasive raccoons (Procyon lotor) in Hokkaido, Japan.

Revealing interactions between ticks and wild animals is vital for gaining insights into the dynamics of tick-borne pathogens in the natural environment. We aimed to elucidate the factors that determine tick infestation in wild animals by investigating ticks on invasive raccoons (Procyon lotor) in Hokkaido, Japan. We first examined the composition, intensity, and seasonal variation of ticks infesting raccoons in six study areas in Hokkaido from March 2022 to August 2023. In one study area, ticks infesting tanukis (raccoon dog, Nyctereutes procyonoides albus) were collected in May to July in both 2022 and 2023, and questing ticks were collected from the vegetation by flagging every other week in the same period. Next, we screened 17 environmental and host variables to determine factors that affect the number of ticks infesting raccoons using generalized linear (mixed) models. From 245 raccoons, we identified a total of 3,917 ticks belonging to eight species of two genera: the most prominent species were Ixodes ovatus (52.9 %), followed by Haemaphysalis megaspinosa (14.4 %), Ixodes tanuki (10.6 %), and Ixodes persulcatus (9.5 %). Ixodes ovatus was also predominant among questing ticks and ticks infesting tanukis. Although I. tanuki was frequently collected from raccoons and tanukis, it was rarely collected in the field. The variables that significantly affected the infestation on raccoons differed by genus, species and developmental stage of the tick. For instance, the infestation of adult I. ovatus was significantly affected by four variables: night-time temperature during nine days before capturing the raccoon, the size of forest area around the capture site, sex of the raccoon, and sampling season. The first two variables were also responsible for the infestation on raccoons of almost all species and stages of ticks. Our study revealed that the number and composition of ticks infesting raccoons can be affected not only by landscape of their habitats but also by weather conditions in several days before capturing.

Major overlap in plant and soil organic carbon hotspots across Africa.

Terrestrial plant and soil organic carbon stocks are critical for regulating climate change, enhancing soil fertility, and supporting biodiversity. While a global-scale decoupling between plant and soil organic carbon has been documented, the hotspots and interconnections between these two carbon compartments across Africa, the second-largest continent on the planet, have been significantly overlooked. Here, we have compiled over 10,000 existing soil organic carbon observations to generate a high-resolution map, illustrating the distribution pattern of soil organic carbon in Africa. We then showed that above- and below-ground plant carbon are significantly and positively correlated with soil organic carbon across Africa. Both soil and plant carbon compartments shared major hotspots in the tropical regions. Our study provides critical insights into the spatial distribution of carbon hotspots across Africa, essential for soil conservation and safeguarding terrestrial carbon stocks amidst the challenges of climate change.

Antibiotic resistome dynamics in agricultural river systems: Elucidating transmission mechanisms and associated risk to water security.

Usage of antibiotics in agriculture has increased dramatically recently, significantly raising the influx of antibiotic resistance genes (ARGs) into river systems through organic manure runoff, seriously threatening water security. However, the dynamics, transmission mechanisms, and potential water security risk of ARGs, as well as their response to land use spatial scale and seasonal variations in agricultural river systems remain unclear. To address these challenges, this work employed metagenomic technique to systematically evaluate the pollution and dissemination of ARGs in overlying water and sediment within a typical agricultural catchment in China. The results demonstrated significant differences between overlying water and sediment ARGs. Overlying water dominated by multidrug ARGs exhibited higher diversity, whereas sediment predominantly containing sulfonamide ARGs had higher abundance. The dynamics of ARGs in overlying water were more responsive to seasonal variations compared to sediment due to greater changes in hydrodynamics and nutrient conditions. The profiles of ARGs in overlying water were largely regulated by microbiota, whereas mobile genetic elements (MGEs) were the main forces driving the dissemination of ARGs in sediment. The variation in dissemination mechanisms led to different resistance risks, with sediment presenting a higher resistance risk than overlying water. Furthermore, Mantel test was applied to discover the impact of land use spatial scale and composition on the transmission of ARGs in river systems. The findings showed that cultivated land within 5 km of the riverbank was the key influencing factor. Cultivated land exacerbated ARGs spread by increasing MGEs abundance and nutrient concentrations, resulting in the abundance of ARGs in high-cultivated sites being twice that in low-cultivated sites, and raising the regional water security risk, with a more pronounced effect in sediment. These findings contribute to a better understanding of ARGs dissemination in agricultural watersheds, providing a basis for implementing effective resistance control measures and ensuring water security.

Integrating very high resolution environmental proxies in genotype-environment association studies.

Landscape genomic analyses associating genetic variation with environmental variables are powerful tools for studying molecular signatures of species' local adaptation and for detecting candidate genes under selection. The development of landscape genomics over the past decade has been spurred by improvements in resolutions of genomic and environmental datasets, allegedly increasing the power to identify putative genes underlying local adaptation in non-model organisms. Although these associations have been successfully applied to numerous species across a diverse array of taxa, the spatial scale of environmental predictor variables has been largely overlooked, potentially limiting conclusions to be reached with these methods. To address this knowledge gap, we systematically evaluated performances of genotype-environment association (GEA) models using predictor variables at multiple spatial resolutions. Specifically, we used multivariate redundancy analyses to associate whole-genome sequence data from the plant Arabis alpina L. collected across four neighboring valleys in the western Swiss Alps, with very high-resolution topographic variables derived from digital elevation models of grain sizes between 0.5 m and 16 m. These comparisons highlight the sensitivity of landscape genomic models to spatial resolution, where the optimal grain sizes were specific to variable type, terrain characteristics, and study extent. To assist in selecting variables at appropriate spatial resolutions, we demonstrate a practical approach to produce, select, and integrate multiscale variables into GEA models. After generalizing fine-grained variables to multiple spatial resolutions, a forward selection procedure is applied to retain only the most relevant variables for a particular context. Depending on the spatial resolution, the relevance for topographic variables in GEA studies calls for integrating multiple spatial scales into landscape genomic models. By carefully considering spatial resolutions, candidate genes under selection by a more realistic range of pressures can be detected for downstream analyses, with important applied implications for experimental research and conservation management of natural populations.

Spatial scale effects on the value of ecosystem services in China's terrestrial area.

Identifying the response characteristics of ecosystem service value (ESV) to changes in spatial scales, known as spatial scale effects, is crucial in guiding the development of corresponding management strategies. This paper examines ESV in China's terrestrial area during the year 2020, revealing the spatial aggregation characteristics of ESV and the trade-off and synergistic relationships of ecosystem services at different spatial scales, ranging from 1 km × 1 km-10 km × 10 km, with a gradient of 1 km. The results indicate: 1) The distribution pattern of ESV in China's terrestrial area is "high in the southeast and low in the northwest." 2) The spatial characteristics of ESV in China's terrestrial area undergo a distinct transition at the 3 km × 3 km scale. In detail, the spatial clustering features show a trend of first rising and then falling with the increase in spatial scale, while the synergistic relationships between different ecosystem services strengthen and the trade-off relationships weaken with the increase of the spatial scale. These findings can inform the formulation of differentiated ecological protection compensation policies and enable cross-area trading of ecological values in China.

[Spatial scale effects of landscape patterns on non-point source pollution: A case study of Taizi River Basin in Northeast China]. / éžç‚¹æºæ±¡æŸ“å¯¹æ™¯è§‚æ ¼å±€å“åº”çš„ç©ºé—´å°ºåº¦æ•ˆåº”­­ä»¥ä¸œåŒ—太子河流域为例.

River water quality is influenced by natural processes and human activities. Multi-scale landscape patterns can affect river water quality by altering the generation and transport processes of pollutants at different spatial scales. Taking Taizi River Basin in Northeast China as an example, we analyzed the relationship between landscape patterns and non-point source pollution in rivers based on water quality monitoring data and land use data by using correlation analysis and redundancy analysis methods. We aimed to determine the key spatial scales for the responses of landscape patterns to non-point source pollution and identify the key landscape indices influencing river non-point source pollution. The results showed that water quality of Taizi River Basin had seasonal differences, with better water quality during the flood season than non-flood season. Spatially, total nitrogen (TN) and total phosphorus (TP) were higher at the confluence points of tributaries and downstream areas. The impact of landscape patterns on non-point source pollution was stronger during the non-flood season than the flood season, while the influence on TN was stronger than on TP. At the spatial scale of within 500 m buffer zone during the flood season and at the sub-watershed scale during the non-flood season, landscape patterns showed the highest explanatory power for the variations of TN and TP. At the type level, built-up land, cropland, and bare land were positively correlated with TN and TP, while forest was negatively correlated with TN and TP, which were the key types influencing non-point source pollution. At the landscape level, patch density, percentage of like adjacencies, and contagion index were key indicators affecting watershed water quality. Lower patch density was associated with better connectivity and aggregation of "sink" landscapes, leading to better purification effects on TN, but more pronounced retention effects on TP. Conversely, higher landscape diversity and denser pattern of multiple types would cause the deterioration of water quality. Our results suggested that rational allocation of landscape types within the watershed and riparian buffer zones, appropriately enriching landscape diversity, and optimizing landscape aggregation and connectivity would be effective measures for improving water quality and achieving sustainable ecological management.

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media.

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin-destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales-10-100 m and 100-3000 m-to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

[Spatial Patterns of Soil Bacterial Communities and N-cycling Functional Groups Along an Altitude Gradient in Datong River Basin].

The altitude distribution patterns of soil microorganisms and their driving mechanisms are crucial for understanding the consequences of climate change on terrestrial ecosystems. There is an obvious altitude difference in Datong River Basin in the Qilian Mountains. Two spatial scale transections were set up along the mountain slope (with altitude spanning 1 000 m) and the mainstream direction (with altitude spanning 300-500 m), respectively. The distribution characteristics of the soil bacterial community structure and diversity along the altitude gradients were examined using high-throughput sequencing technology. Based on the FAPROTAX database, the altitude distribution patterns of nitrogen cycling functional groups were analyzed to investigate the major environmental factors influencing the altitude distribution patterns of soil bacterial communities. The findings revealed that:① Soil physicochemical characteristics varied significantly with altitude. The content of total nitrogen (TN) and nitrate nitrogen (NO3-) were positively correlated with the altitude (P < 0.01), whereas the soil bulk density and pH were negatively connected (P < 0.001). ② The abundance of OTU increased significantly along the altitude (P < 0.01), and the richness and diversity indices increased along the altitude, although the trend was not statistically significant (P > 0.05). ③ The predominant bacterial communities were Acidobacteria, Proteobacteria, and Bacteroidetes, and as altitude climbed, their relative abundances varied between increasing, decreasing, and slightly decreasing, respectively. ④ The nitrogen cycling processes involved 13 functional groups, primarily nitrification, aerobic ammonia oxidation, aerobic nitrite oxidation, etc. As the altitude increased, the response law changed, with an increase in the abundance of nitrobacteria (P < 0.01), a slight increase in the abundance of aerobic ammonia-oxidizing bacteria and nitrite-oxidizing bacteria, and a hump-back tendency in bacteria abundance for nitrogen respiration. ⑤ Redundancy analysis revealed that the key determinants influencing soil bacterial populations at the phylum level were altitude, pH, and the content of NH4+. Mantel analysis showed that the dominant groups of soil bacterial nitrogen cycling were all statistically and significantly driven by altitude (P < 0.01). ⑥ The α-diversity of the bacterial community with increasing altitude were both increased along the mountain slope and the mainstream direction, but the soil properties, the abundance of N-cycling functional groups, and the main environmental factors differed. Therefore, it is of great significance to explore the altitude distribution pattern of soil microorganisms at different spatial scales.

Variation in fine-scale water table depth drives abundance of a unique semi-terrestrial crayfish species.

With anthropogenic changes altering the environment and the subsequent decline of natural habitats, it can be challenging to predict essential habitats for elusive and difficult to study taxa. Primary burrowing crayfish are one such group due to the complexity in sampling their semi-terrestrial, subterranean habitat. Sampling burrows usually requires a labor-intensive, time-consuming excavation or trapping process. However, limited information on burrowing crayfish suggests that fine-scale habitat variation may drive burrowing crayfish habitat choice. This project aimed to evaluate the fine-scale habitat characteristics that influence burrowing crayfish presence and abundance at a large, restored-remnant grassland preserve in north-central Illinois. We documented burrow abundance and quadrat-specific habitat variables such as root biomass, canopy cover, apparent seasonal high-water table (water table) depth and dominant vegetation at sites with and without burrowing crayfish populations. Data was recorded at every quadrat and analyzed using generalized linear mixed models. A total of 21 models were created to determine what habitat variables affected burrow presence and abundance. We found that the water table depth was a significant driver of burrow presence and abundance. Root biomass and vegetation cover were not significant drivers, although they did show up in the final models, explaining the data. These findings demonstrate empirical support for previous observations from other burrowing crayfish research and demonstrate the influence of fine-scale habitat when modeling elusive taxa requirements.

Spatial distribution of benthic taxonomic and functional diversity in the Yellow River Basin: From ecological processes to associated determinant factors.

One of the fundamental objectives in ecology is to investigate the ecological processes and associated factors governing the abundance and spatial distribution patterns of biodiversity. However, the reaction of biological communities to environmental degradation remains relatively unknown, even for ecologically crucial communities like macroinvertebrates in aquatic ecosystems. Here, we sampled 117 locations to quantify relative contributions of geographical and environmental factors, including water quality, land use, climate, and hydrological factors, to determine the absolute and relative compositions of macroinvertebrate communities and their spatial distribution in the Yellow River Basin (YRB), the sixth-longest river system on Earth. We assessed relative roles of species sorting and dispersal in determining macroinvertebrate community structure along YRB. Our results demonstrated that alpha and beta diversity indices showed an increase from the up- to low-reaches of YRB. The middle and low-reaches exhibited elevated species diversity and both regions exhibited relatively stable community compositions. The biodiversity of macroinvertebrates was influenced by a combination of geographical factors and environmental variables, with environmental factors predominantly serving as the principal determinants. Results of multiple linear regression and variance decomposition showed that the effect of environmental factors was approximately three times greater than that of spatial factors. These findings provide support for the hypothesis that species sorting, driven by environmental gradients, plays a significant role in shaping the community structure of macroinvertebrates in running water ecosystems at the basin scales. Moreover, the factors contributing to substantial shifts in biodiversity across different segments of YRB indicate that distinct river sections have been influenced by varying stressors, with downstream areas being more susceptible to the impacts of water pollution and urbanization resulting from human activities.

Variability of soft-bottom macrobenthic invertebrates at different spatial scales: Comparisons between habitats and seasons.

Studies focusing on patterns of spatial variation in marine soft-bottom assemblages suggest that variability is mainly concentrated at small spatial scale (from tens of centimeters to few meters), but there is still a lack of knowledge about the consistency of this spatial pattern across habitats and seasons. To address this issue, we quantified the variability in the structure of macrozoobenthic assemblages and in the abundance of dominant macroinvertebrate species in the Mellah Lagoon (Algeria) at three spatial scales, i.e., Plot (meters apart), Station (10's m apart) and Site (kms apart) scale, in Ruppia maritima (Ruppia) beds and unvegetated sediments (Unvegetated), and in two dates in winter and two dates in summer 2016. Spatial variability of the most dominant bivalve Mytilaster marioni varied significantly between habitats, but consistent across the two seasons, with a more heterogeneous distribution in Ruppia than in Unvegetated at the Station scale. Furthermore, a second-order interaction among the hierarchical nature of spatial variability, season and habitat emerged for the assemblage structure. Spatial variability between habitats varied significantly in winter, with the largest variation at the Plot scale in Unvegetated and more heterogenous assemblages at the Plot and Site scales than at the Station scale in Ruppia, but did not vary in summer when most of the variance was at the Site scale. We demonstrate that the scales of influence of the processes operating in the Mellah Lagoon are contingent on the specific habitat and/or period of the year at which the study was conducted, highlighting the importance of examining all these sources of variation simultaneously to increase the accuracy of explanatory models derived from the observed patterns in sedimentary environments.

Spatial difference on nitrogen removal in the water based on different resolutions for Sanhuanpao wetland, Northeast China.

The research on the deviations caused by different resolutions is relevant to the study of spatial scale effects. In 2018, spatial interpolations were performed using the removal ratios of the TN, NH4-N, and NO3-N of the layers of different resolutions, respectively. Based on the mean and the standard deviation, the area, shape, and position were obtained for four levels related to the removal ratios of the three nitrogen forms. The linear and 6th function fitting methods were used to reveal the differences in nitrogen removal in wetland water at different spatial resolutions. The results showed that a resolution of 25 times the original was the key scale of the spatial effects. Due to the fact that 52 of the 72 functions did not reach a significant level (P < 0.05), the spatial scale effect of the nitrogen removal was mainly characterized by disorderly fluctuations. The results have a certain extrapolation value for the analysis of spatial scale effects. PRACTITIONER POINTS: The resolution difference was not sufficient to change the spatial pattern of the geographic phenomena. The resolution of 25 times the original was the important scale for determining spatial effects. When studying the spatial scale effects caused by differences in resolution, it was necessary to comprehensively consider various resolutions.

[Effects of Land Use Structure and Spatial Pattern at Different Temporal and Spatial Scales on Water Quality in Suzhou Creek].

Relationships between land use and water quality of rivers and lakes vary spatially and temporally. These variations were analyzed using spatial analysis and mathematical statistical methods for the Suzhou Creek in Shanghai. Based on the data of water quality and land use in 2001， 2005， 2010， 2015， and 2020， five spatial scales （200， 500， 1 000， 2 000， and 5 000 m reach buffer） of the landscape pattern were extracted using correlation and redundancy analysis to explore the impact of land use composition and spatial pattern on water quality at different spatial and temporal scales. The results showed that： ① the water quality of Suzhou Creek has gradually improved in the past 20 years； other indicators were between Class II to Class IV in 2020 except TN， and TN was the main pollutant. ② The main land use type of the buffer zone was construction land， and the proportion of greenland and woodland showed a small growth trend. ③ The water quality was closely related to landscape pattern， showing temporal and spatial scale effects. On the time scale， indicators such as construction land， agricultural land， landscape dominance， aggregation， and diversity had significant correlations with various water quality parameters， and there was an inverse correlation in 2010 compared with that in other years for NH4+-N， TP， and TN. The landscape pattern in 2001 had the greatest explanation for water quality， with an explanation rate of 93.65%. The impact of greenland and woodland on water quality has begun to emerge in the past 10 years. ④ On the spatial scale， there were significant correlations between greenland and woodland， patch number， landscape shape index， diversity index， and water quality. There was a strong positive regulatory effect of greenland and woodland on NH4+-N， TP， and TN at the scale of 2 000 m. The patch number and landscape shape index had relatively strong regulatory effects on water quality on a larger spatial scale， whereas the Shannon diversity index had a better positive regulatory effect on water quality on a small scale. The landscape pattern within a buffer of 2 000 m had the highest interpretation degree for all factors， with an explanation rate of 68.47%. The study showed that rationally planning the proportion of greenland and woodland within the 2 000 m buffer zone and optimizing its landscape configuration is an important measure to purify the surface water quality of Suzhou Creek.

Effects of landscape pattern on water quality at multi-spatial scales in Wuding River Basin, China.

Urbanization and agricultural land use have led to water quality deterioration. Studies have been conducted on the relationship between landscape patterns and river water quality; however, the Wuding River Basin (WDRB), which is a complex ecosystem structure, is facing resource problems in river basins. Thus, the multi-scale effects of landscape patterns on river water quality in the WDRB must be quantified. This study explored the spatial and seasonal effects of land use distribution on river water quality. Using the data of 22 samples and land use images from the WDRB for 2022, we quantitatively described the correlation between river water quality and land use at spatial and seasonal scales. Stepwise multiple linear regression (SMLR) and redundancy analyses (RDA) were used to quantitatively screen and compare the relationships between land use structure, landscape patterns, and water quality at different spatial scales. The results showed that the sub-watershed scale is the best spatial scale model that explains the relationship between land use and water quality. With the gradual narrowing of the spatial scale range, cultivated land, grassland, and construction land had strong water quality interpretation abilities. The influence of land use type on water quality parameter variables was more distinct in rainy season than in the dry season. Therefore, in the layout of watershed management, reasonably adjusting the proportion relationship of vegetation and artificial building land in the sub-basin scale and basin scope can realize the effective control of water quality optimization.