Effect Of Spatial Scale Research Articles

Estimates of niche position and breadth vary across spatial scales for native and alien inland fishes

AbstractAimWe estimate and compare niche position, marginality and breadth of Iberian inland fishes at three geographical extents (regional, restricted to the species’ range and global) to understand the effect of spatial scale on niche metrics. Furthermore, we investigate differences in niche metrics between native and alien fish, and test for associations with introduction date of alien species and niche characterization to better understand their invasion process.LocationIberian Peninsula and global.Time period2000–2020.Major taxa studiedFifty‐one native and 17 alien inland fish species from the Iberian Peninsula.MethodsOutlying mean index (OMI) analyses were used to estimate the niche position, marginality and breadth of Iberian inland fishes. Climatic OMI analyses were computed at three different scales (regional, restricted to the species’ range and global). Permutational analyses of variance (PERMANOVAs) were used to test for differences in niche position, marginality and breath among native and alien species.ResultsNiche metrics differed depending on the geographical extent of the investigation, as well as with respect to species origin (native versus alien). Differences in climatic niche position between native and alien species observed at the global scale were non‐existent at the regional scale. The niche breadth of widely distributed alien species was highly underestimated when only considering the invaded region, and further influenced by the first date of of species introduction.Main conclusionsEstimating niches of freshwater species, especially of alien invaders, should carefully consider the geographical extent of the investigation. We suggest that analyses that jointly consider regional and global scales will improve the estimation of niche metrics of widely distributed organisms, particularly regarding species climatic niche, and the assessment of the invasive potential of species.

Spatial scale effect of irrigation efficiency paradox based on water accounting framework in Heihe River Basin, Northwest China

Improving irrigation efficiency (IE) to alleviate water shortage has been deemed viable. However, the so-called irrigation efficiency paradox (IEP) can complicate this process, where agricultural water consumption remains high despite the enhanced irrigation efficiency, particularly in arid regions. This study developed a judgment method of IEP based on a water accounting framework and the IEP definition to detect the IEP occurring across spatial scales in the middle Heihe River Basin, the second-largest inland river basin in Northwest China. Furthermore, the scale effects of the IEP were analyzed. The results showed that evaluated by evapotranspiration (ET) and transpiration (T), the scale effect occurred because IEP does not necessarily occur at the field scale but at the irrigation district scale and basin scale. Evaluated by Inflows (I) and surface water diversion and groundwater extraction (Isg), the scale effect occurred because IEP may occur at the field and the irrigation district scales but not at the basin scale. Further, the IEP was also influenced by time scales and regional agricultural characteristics. The results can be referenced for water-saving measures, avoiding the potential IEP for actual water savings in arid regions worldwide.

Landscape effects on pollinator abundance differ among taxonomic groups

AbstractUnderstanding how landscape composition affects pollinator abundance is crucial for conserving the sustainability of the symbiotic relationship between plants and pollinators. However, field evidence on the effect of landscape composition and spatial scales on the abundance of each pollinator group is biased toward bee pollinators, and non‐bee pollinators tend to be excluded. This study aims to evaluate the effect of landscape composition at various spatial scales on diverse taxonomic groups of pollinators, including non‐bee pollinators. We selected 22 grasslands with varying landscape compositions in Shiroi City, Chiba Prefecture, Japan. We recorded the abundance of pollinators and floral resource availability at these sites. The effects of landscape composition within a 100–1000 m radius and the floral resource availability were analyzed using Bayesian generalized linear mixed models to determine whether these factors affected the abundance of each pollinator group. As a result, the difference in important factors among the pollinator groups was detected; Scoliidae, Lycaenidae, Stomorhina obsoleta, and Sphaerophoria were influenced by the landscape composition, whereas Bombus diversus and Hesperiidae were not. Scoliidae was affected by the proportion of agri‐field and forest areas within a 500–1000 m radius. St. obsoleta and Sphaerophoria showed different response patterns to the proportion of urban land within a 100 m radius. B. diversus and Hesperiidae were influenced by floral resource availability. Our study suggests that considering the spatial scales of landscape composition on the target species is essential to plan effective conservation measures.

Multi-Scale Assessment and Spatio-Temporal Interaction Characteristics of Ecosystem Health in the Middle Reaches of the Yellow River of China

Exploring the assessment methods and multi-scale spatiotemporal interaction characteristics of ecosystem health is of great significance for current ecosystem health theory and application research. Based on the regional differentiation theory and ecosystem service flow theory, the spatial weight coefficient and the modified coefficient of spatial proximity effect were introduced to improve the regional ecosystem health assessment model. Then, the improved VORS model was used to evaluate the ecosystem health level in the Middle Reaches of the Yellow River (MRYR) in China at multiple scales, and the ESTDA method was used to reveal the multi-scale spatiotemporal interaction characteristics of ecosystem health. The results show that: (1) From 1990 to 2018, the ecosystem health level at grid and county scale in the MRYR showed a trend of first decline and then increase, and experienced a slow decline and a steady rise from 1990 to 2005 and 2005 to 2018, respectively. The ecosystem health level at the grid and county scale presented a spatially hierarchical structure with alternating low-value and high-value zones. (2) Compared with the county scale, the grid scale can describe the spatial distribution characteristics of ecosystem health more refined, indicating the existence of spatial scale effects in ecosystem health assessment. (3) The rapid urbanization areas, the ecologically fragile areas in the central and western regions and the transitional zone between mountain and basin have more dynamic spatial structure, and stronger spatio-temporal interaction process. (4) In terms of LISA spatio-temporal transition, the regional system as a whole had strong path-dependent and lock-in characteristics, and the local spatial correlation structure of ecosystem health gradually tended to be stable during the study period. (5) In terms of spatio-temporal interaction network, there were strong spatio-temporal competition in the process of time evolution in six typical regions, such as the surrounding cities of provincial capitals, the fringe areas of cities, the transitional zone between mountain and basin, the transitional zone of ecologically fragile regions, the mountainous areas of western Henan Province, and the areas along rivers.

Exploring and attributing change to fractional vegetation coverage in the middle and lower reaches of Hanjiang River Basin, China.

The middle and lower reaches of Hanjiang River Basin (MLHB), areas that have an important ecological function in China, have experienced great changes in the vegetation ecosystem driven by natural environmental change and human activity. Here, we explored the spatio-temporal dynamics of fractional vegetation coverage (FVC) and quantitatively analyzed its driving factors to advance current understanding of how the ecological environment has changed. Specifically, we used the dimidiate pixel model to calculate the FVC of the MLHB from 2001 to 2018 based on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data. We then used Theil-Sen median slope (Sen's slope) and coefficient of variation (CV) to explore spatial and temporal variations, as well as characteristics in fluctuations. Finally, we utilized a geographical detector model (with spatial scale effects and spatial data discretization tests) to quantify the influence of the detected natural and human factors. Results showed that average annual FVC was 0.30-0.75 for ~90% of the study area over the 19-year study period with a heterogeneous spatial distribution. FVC variation trend displayed stability and improvement. Areas with higher FVC displayed greater stability. All 10 detected natural and anthropogenic factors were responsible for changes in FVC. The primary factors causing FVC to change were precipitation (in 2001) and slope (in 2018), followed by landform type, distance to water, and nighttime light (NTL) (in 2018). Precipitation and slope consistently displayed the largest interaction across all years. The interaction between human and topographical factors had gradually increasing significance on changes in FVC over the research period. The range and type of factors suitable for promoting vegetation growth were detected in the study area. Results of this study can provide a scientific basis for developing effective strategies for local vegetation protection, restoration, and land resource management.

Quantitative Assessment of the Spatial Scale Effects of the Vegetation Phenology in the Qinling Mountains

Vegetation phenology reflects the temporal dynamics of vegetation growth and is an important indicator of climate change. However, differences consistently exist in land surface phenology derived at different spatial scales, which hinders the understanding of phenological events and integration of land surface phenology products from different scales. The Qinling Mountains are a climatic and geographical transitional region in China. To better understand the spatial scale effect issues of land surface phenology in mountainous ecosystems, this study up-scaled vegetation start of season (SOS) and end of season (EOS) in the Qinling Mountains derived from three different Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) products to four scales (i.e., 2 km × 2 km, 4 km × 4 km, 6 km × 6 km, and 8 km × 8 km) using the spatial averaging method. Then, similarities and differences between the up-scaled SOSs/EOSs were examined using the simple linear regression, cumulative distribution function, and absolute difference. Finally, the random forest model was used to reveal the major factors influencing the spatial scale effect of land surface phenology in Qinling Mountains. Results showed that the derived basic SOS/EOS datasets using the same filtering method from the 250 m and 500 m NDVI datasets were consistent in spatial distribution, while the results from the 1000 m NDVI dataset differed. For both the basic and the up-scaled datasets, the land surface phenology derived from the Savitzky-Golay-filtered NDVI showed an advance in SOS, but a delay in EOS, compared to those derived from the asymmetric Gaussian- and double logistic-filtered NDVI. The up-scaled SOS was greatly impacted by both NDVI resolution and the filtering methods. On the other hand, EOS was mostly impacted by the filtering methods. Moreover, up-scaled SOSs usually had larger differences compared to up-scaled EOSs. While different filtering methods sometimes amplified the absolute differences between different SOS/EOS across scales, the upscaling reduced the differences. Influence factor analysis showed that spatial variations observed in SOS in Qinling Mountains were mainly caused by forest cover, uneven distribution of spring precipitation, and annual precipitation, while spatial variations in aspect, winter temperature, and autumn precipitation all strongly influenced the observed EOS across scales in the study area. These findings enhance our understanding of the effects of observational scale on vegetation phenology in mountain ecosystems and provide a reference for phenology modeling in mountainous areas.

Pore-scale hydraulic properties of virtual sandstone microstructures: spatial variations and voxel scale effects

Pore-scale hydraulic properties of virtual sandstone microstructures: spatial variations and voxel scale effects

Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China

Forest fires are among the major natural disasters that destroy the balance of forest ecosystems. The construction of a forest fire prediction model to investigate the driving mechanism of fire drivers on forest fires can help reveal the mechanism of forest fire occurrence and its risk, and thus contribute to the prevention and control of forest fires. However, previous studies on the mechanisms of forest fire drivers have not considered the effect of differences in spatial scale of action of forest fire drivers on the predicted effect. Therefore, the present study proposes a spatial prediction model of forest fires that considers the spatial scale effect of forest fire drivers to predict forest fire risk. First, based on historical forest fire data and geographic environmental data in the Yunnan Province, geographically weighted logistic regression (GWLR) was used to determine the forest fire drivers and to estimate the probability of forest fire occurrence at locations where fire observations are absent. Then, multi-scale geographically weighted regression (MGWR) was used to explore the spatial scales of action of different drivers on forest fires. The results show that meteorological factors such as relative humidity, air temperature, air pressure, sunshine hours, daily precipitation, wind speed, topographic factors such as elevation, slope, and aspect, anthropogenic factors such as population density and road network, as well as vegetation type, were significantly correlated with forest fires; thus, they are identified as important factors influencing occurrence of forest fires in the Yunnan Province. The MGWR model regression results show that the role of different forest fire drivers on forest fire occurrence has spatial scale differences. The spatial scale of drivers such as altitude, aspect, wind speed, temperature, slope, and distance from the road to the fire point was larger and their spatial influence was relatively stable, with spatial heterogeneity having less influence on the model evaluation results. The spatial scale of drivers such as relative humidity, sunshine, air pressure, precipitation, population density, and vegetation type were smaller, and spatial heterogeneity had a more obvious influence on the model evaluation results. This study provides a reference for selecting drivers and evaluating their spatial scale effects to construct predictive regional forest fire models.

Reconfirmation of the symbiosis on carbon emissions and air pollution: A spatial spillover perspective

Many studies have confirmed the co-emission characteristics of air pollution and carbon emissions. However, studies on the evolution and synergistic factors of the symbiosis of air pollution and carbon emissions over long time scales from a spatial spillover perspective are rare. Here, we identify the spatial evolution and agglomeration characteristics of carbon emissions and air pollution symbiosis by applying local autocorrelation analysis and geographical concentration and by using the dynamic spatial autoregressive model for multiple synergistic factors at city levels during 2006–2019 in China. The results are: (1) The spatial agglomeration and symbiosis of carbon emission and air pollution are similar and show strong spatial locking, as well as path-dependent properties. (2) The spatial imbalance of carbon emission agglomeration and pollution agglomeration gradually improved over time; the concentration centers are all located in Henan province, shifting northward. (3) The symbiosis between both carbon emission agglomeration and pollution agglomeration has significant “spatial and temporal scale effects”, and the economic growth is nonlinear. Additionally, innovation vitality has a negative synergistic driving effect on this relationship. In addition to the results above, rapid industrialization and urbanization are taking place in China. Hence, serious actions against greenhouse gases and air pollutants are imminently needed.

Estimating phenological sensitivity in contemporary vs. historical data sets: Effects of climate resolution and spatial scale.

Phenological sensitivity, or the degree to which a species' phenology shifts in response to warming, is an important parameter for comparing and predicting species' responses to climate change. Phenological sensitivity is often measured using herbarium specimens or local studies in natural populations. These approaches differ widely in spatiotemporal scales, yet few studies explicitly consider effects of the geographic extent and resolution of climate data when comparing phenological sensitivities quantified from different data sets for a given species. We compared sensitivity of flowering phenology to growing degree days of the alpine plant Silene acaulis using two data sets: herbarium specimens and a 6 yr observational study in four populations at Niwot Ridge, Colorado, USA. We investigated differences in phenological sensitivity obtained using variable spatial scales and climate data sources. Herbarium specimens underestimated phenological sensitivity compared to observational data, even when herbarium samples were limited geographically or to nearby weather station data. However, when observational data were paired with broader-scale climate data, as is typically used in herbarium data sets, estimates of phenological sensitivity were more similar. This study highlights the potential for variation in data source, geographic scale, and accuracy of macroclimate data to produce very different estimates of phenological responses to climate change. Accurately predicting phenological shifts would benefit from comparisons between methods that estimate climate variables and phenological sensitivity over a variety of spatial scales.

Managing landscape patterns at the riparian zone and sub-basin scale is equally important for water quality protection

Widespread attention has been given to understanding the effect of the landscape pattern on river water quality. However, which spatial scale (riparian zone versus sub-basin) has the greater impact on water quality has long been controversial, since the key metrics that affect water quality varied with spatial scale. Thus, quantifying the spatial scale effects of key landscape metrics on water quality is critical to clarifying which scale of landscape pattern is more conducive to water quality conservation. Here, we adopted variation partitioning analysis (VPA) and random forest models to quantify the landscape pattern impact on water quality at northern Erhai Lake during the 2019 rainy season (early, mid, and late), and comprehensively analyze the key landscape metrics on different scales. The results revealed that the riparian zone and sub-basin scale landscape patterns explained similar water quality variations (difference only 0.9%) in the mid (August) and late rainy season (October), but exhibited a large difference (24.1%) during the early rainy season (June). Furthermore, rivers were primarily stressed by nitrogen pollution. Maintaining the Grassland_ED > 27.99 m/ha, Grassland_LPI > 4.19%, Farmland_LSI < 3.2 in the riparian zone, and Construction_ED < 1.69 m/ha, Construction_LSI < 2.46, Farmland_PLADJ < 89.0% at the sub-basin scale could significantly reduce the TN concentration in the stream. Meanwhile, managing of these metrics can effectively prevent rapid increases of TN in rivers. Moreover, due to the low phosphorus concentration in the rivers, none of the landscape metrics significantly explained the variation in TP. This study explored the spatial scale effect of landscape patterns on water quality and revealed the driving factors of nutrient variation. This study will provide a scientific basis for aquatic environmental management in plateau watersheds.

New method for scaling nonpoint source pollution by integrating the SWAT model and IHA-based indicators

Nonpoint source (NPS) pollution shows spatial scaling effects because it is affected by topography, river networks, and many other factors. Currently, the lack of an integrated methodology for quantifying the scaling effect has become a crucial barrier in evaluating NPS pollution. In this study, a new method was proposed for scaling NPS pollution by integrating hydrological model and hydrological alteration indicators. Nested catchments were delineated by eight-direction algorithm, and a semidistributed hydrological model was used to simulate the interannual process within the drainage area and to obtain data series of runoff, sediment, and total phosphorus (TP) at different spatial scales. In addition, the average, the extrema, the change rate and feature variables of each type of indicators were proposed to quantitatively describe the pattern of NPS pollution at different spatial scales. The results show the coefficients of variation (CVs) of most runoff and TP indicators are 0.6–0.8, while those of sediment vary greatly from 0.4 to 1.6 with the threshold of those indicators being 0.33. With the increase in drainage area, the NPS load-related indicators show an increasing trend, while load intensity indicators show a decreasing trend and their changing patterns are affected by the heterogeneity of topographic or hydrological information included. Based on logarithmic variance of the change rate, 825 km2 was identified as the turning point for scaling transformation where the slope changes dramatically. The proposed methodology comprehensively describes features of the NPS scaling effect that could be utilized for targeted monitoring and control of NPS pollution in other watersheds.

Study on the Scale Effect of Spatial Variation in Soil Salinity Based on Geostatistics: A Case Study of Yingdaya River Irrigation Area

Soil salinization seriously restricts the development of agricultural economies in arid and semi-arid areas. Mastering the spatial variability characteristics of multi-scale soil salt in irrigated areas is of great significance for the improvement and utilization of saline soil and agricultural production. The middle and lower reaches of the Yingdaya River were selected as the study area, and the irrigation area was divided into three scales: the L scale (irrigation area), the M scale (township level) and the S scale (village level). A total of 131 data sets were obtained through field investigations and sampling, and the spatial variability characteristics and scale effects of the soil salt in multi-scale irrigated areas were analyzed using classical statistics, geostatistics and nested model methods. The results showed that the average soil salinities at the L, M and S scales were 1.664%, 0.263% and 0.217%, respectively, and the coefficients of variation were 2.564, 1.312 and 0.866, respectively. The soil salinities at different scales exhibited moderate spatial correlation and anisotropic characteristics, through which, the maximum variation directions for L and M were 113° and 139°, respectively, and the maximum variation direction of the S scale was 86°. The spatial distribution of the soil salinity is affected by the scale effect, but the accuracy of spatial estimations can be effectively improved by using a multi-scale nested model for interpolation. The high-value areas of soil salt in the irrigation areas were distributed in the southeastern regions of the study area, and weakened in small areas around the high-value areas. The influence of each influencing factor on the soil salinization at different scales also differed. Except for the slope, the correlations between other influencing factors and the soil salt content gradually decreased with decreases in the scale. This study provides a concise summary of the spatial variation analysis of soil characteristic variables, and also provides a scientific basis for the formulation and implementation of salinization control programs.

The effect of spatial and temporal scale on camouflage in North African rodents

Abstract Background matching, a common form of camouflage, is a widespread anti-predator adaptation that hinders detection or recognition by increasing the resemblance of prey to its environment. However, the natural environment is complex and both spatially and temporally variable, which constrains effective background matching as an anti-predator strategy. Here, using remote sensing data (publicly available satellite imagery), we investigated how variation of habitat parameters predicts background matching in 16 Sahara–Sahel rodent species across spatial and temporal scales. All fur colour parameters (hue, saturation and brightness) strongly matched the respective habitats of the different species. Background matching in terms of hue was best at the microscale, whereas results for saturation and brightness showed more variation across spatial scales among species. Camouflage across the temporal scale (from 1 to 3 years before capture) was variable among species for all colour parameters. These complex interactions suggest that, in desert rodents, colour parameters are differentially sensitive to the respective scale of the habitat, plausibly reflecting the behaviour and life history of the species and the ecological properties determining their activity patterns. Consequently, the division between habitat (camouflage) generalists and specialists might become blurred in temporally changing and spatially variable environments.

Effects of spatial scale on the built environments of community life circles providing health functions and services

The built environment surrounding communities plays an important role in maintaining resident physical and mental health and social interaction. However, although this topic has received increasing attention in recent years, its effects at different spatial scales are still poorly understood. In order to meet the daily needs of community residents at different spatial scales, the community life circle is proposed as a new concept, which is a basic unit to meet the needs of residents’ life within the scope of daily walking. This study evaluated the potential health functions and services of the built environment surrounding a community at different spatial scales in the central district of Wuhan, China. Three scales of community life circles at 5-min, 10-min and 15-min, were selected for this analysis; physical, mental and social health were evaluated at the different scales. The results show that the built environment of community life circles at a large scale can provide more social health functions, while the built environment of community life circles at a small scale can promote the physical and mental health of residents. High-level health functions of the built environment, ranked in the top 30% of all communities, are mainly concentrated around large blue and green spaces, while high-level health services are more mainly concentrated in the urban center. Moreover, lower level health functions are more easily transformed into higher level health services in large-scale built environments. However, when the scale of the built environment increases from small to large, the increase in the high level of health functions at a small scale gradually decreases with scale and even becomes negative when exceeding the bottom 30% of the value. Our findings may help policy-makers better understand the effects of health functions and services of the built environment at different scales of community life circles, which may help promote health equity in urban communities. • Health functions and health services provided by built environments surrounding the community have different scale effects. • Built environments at a large scale can provide more social health functions. • The small-scale built environment is conducive to promoting physical and mental health. • Lower level health functions are more easily transformed into higher level services in large-scale built environments. • The high health functions that communities obtain in small-scale built environments are diminished in large scale.

Scale effects of multi-medium heavy metals in response to landscape indices in the Yuan River, China

Heavy metals in river basins can affect the entire ecosystem through water recycling. Different heavy metals are significantly affected by riparian landscape patterns due to the difference of source and migration processes, while the intensity of impact is directly influenced by the spatial scale of landscape patterns. Here, we employ correlation analyses (CA) and redundancy analyses (RDA) to determine spatial scales for which multi-medium heavy metals (Cr, Mn, Cu, Zn, As, Cd and Pb) are most sensitive landscape indices in the riparian zone of the Yuan River. Results show that the most sensitive spatial scales for dissolved heavy metals were the 300 m line buffer in the wet season and catchments in the dry season. The most sensitive spatial scales for suspended particulate heavy metals were catchments in the wet season, and the 300 m and 500 m line buffers in the dry season. However, correlations between heavy metals in sediment and landscape indices were unclear at all spatial scales. The spatial scales of the 100 m point buffer and catchments showed the highest explained variances for all media. The most important landscape indices for dissolved heavy metals were ENN_MN (Mean Euclidean Nearest Neighbor Distance) and LPI (Largest Patch Index), and SHDI (Shannon's Diversity Index) and ED (Edge Density) for particulate heavy metals. More accurate spatial scale effects of landscape indicators can provide data and reference for similar watershed studies.

C2H2 selective hydrogenation over the Pd and S-modified Pd catalysts: Unraveling the role of Pd active site spatial scale caused by S atom in adjusting catalytic performance

Adjusting the active site spatial scale is an effective way to regulate catalytic performance. In this study, combining DFT calculations with microkinetic modeling, the Pd catalysts and S-modified Pd (S/Pd) catalysts were considered. Here, Pd and S/Pd catalysts were simulated by different supercells and different surface S coverage, respectively. The role of S coverage on Pd surface in adjusting Pd active site spatial scale to regulate catalytic performance of C2H2 selective hydrogenation was fully investigated. Our results showed that the catalytic performance closely depend on Pd active site spatial scale caused by the coverage of surface S species due to the change of geometric and/or electronic effects caused by S atom. S/Pd-2 × 2 catalyst with S coverage of 1/4 ML and Pd active site spatial scale of 2.691 Å is screened out to simultaneously achieve the best C2H4 activity and selectivity, as well as the effective prevention of green oil to present excellent catalyst stability, which is attributed to the synergetic geometric and electronic effects caused by S atom. The comparisons between S/Pd and S/Cu catalysts showed that the effect of metal active site spatial scale caused by S atom on catalytic performance still strongly depend on the type of metal catalysts. This study gives a theoretical insight for promoting the target reaction required smaller active site spatial scale and suppressing the side reaction required larger active site spatial scale, in which the active site spatial scale can be reasonably adjusted by changing the coverage of surface S species over the metal catalysts.

Reservoir sediment characterisation by diffuse reflectance spectroscopy in a semiarid region to support sediment reuse for soil fertilization

PurposeSoil erosion by water yields sediment to surface reservoirs, reducing their storage capacities, changing their geometry, and degrading water quality. Sediment reuse, i.e., fertilization of agricultural soils with the nutrient-enriched sediment from reservoirs, has been proposed as a recovery strategy. However, the sediment needs to meet certain criteria. In this study, we characterize sediments from the densely dammed semiarid Northeast Brazil by VNIR-SWIR spectroscopy and assess the effect of spectral resolution and spatial scale on the accuracy of N, P, K, C, electrical conductivity, and clay prediction models.MethodsSediment was collected in 10 empty reservoirs, and physical and chemical laboratory analyses as well as spectral measurements were performed. The spectra, initially measured at 1 nm spectral resolution, were resampled to 5 and 10 nm, and samples were analysed for both high and low spectral resolution at three spatial scales, namely (1) reservoir, (2) catchment, and (3) regional scale.ResultsPartial least square regressions performed from good to very good in the prediction of clay and electrical conductivity from reservoir (< 40 km2) to regional (82,500 km2) scales. Models for C and N performed satisfactorily at the reservoir scale, but degraded to unsatisfactory at the other scales. Models for P and K were more unstable and performed from unsatisfactorily to satisfactorily at all scales. Coarsening spectral resolution by up to 10 nm only slightly degrades the models’ performance, indicating the potential of characterizing sediment from spectral data captured at lower resolutions, such as by hyperspectral satellite sensors.ConclusionBy reducing the costly and time-consuming laboratory analyses, the method helps to promote the sediment reuse as a practice of soil and water conservation.

Relationships between topographic variation and plant functional trait distribution across different biomes

• Terrain factors and plant functional-trait distribution were studied in 14 biomes. • Geographically weighted regression and ordinary least squares were the methods used. • Terrain factors affected the distribution of plant functional traits. • Topography effects on plant functional-trait distribution showed biome dependency. • Topography effects on plant functional trait distributions was scale dependent. Plant functional traits can affect ecosystem functions and services. Although many studies have investigated the driving factors of plant functional-trait distribution, few have explored the relationship between terrain factors and such distribution. Here, we proposed three hypotheses to explain the above-mentioned relationship: 1) terrain factors affect the distribution pattern of plant functional traits; 2) the relationship between terrain factors and plant functional-trait distribution is biome-dependent; 3) the relationship between terrain factors and plant functional-trait distribution is scale-dependent. To test these hypotheses, we used geographically weighted regression and ordinary least squares to study the relationship between functional traits (specific leaf area, height, and wood density) and terrain factors (roughness, terrain ruggedness index, terrain position index, and vector ruggedness measure) in 14 different biomes at scales of 5 and 50 km 2 . We found that terrain factors affected the distribution of plant functional traits. Specifically, there was a significant negative correlation between terrain factors and wood density; further, terrain factors had effects on specific leaf areas but not on plant height; furthermore, roughness was the most significant among the influential terrain factors, followed by the vector ruggedness measure and the terrain ruggedness index. Additionally, the effect of biome type on the relationship between terrain factors and plant functional-trait distribution was widespread. Finally, spatial scale effects on biodiversity may depend on the transition between different biomes. Therefore, considering biome and scale effects is essential to understanding heterogeneity and biodiversity, especially in macroecological studies with important scientific and practical significance.

The Spatiotemporal Characteristics of Flow-Sediment Relationships in a Hilly Watershed of the Chinese Loess Plateau.

The flow–sediment relationship is important to understand soil erosion and sediment transport in severely eroded areas, such as Loess Plateau. Previous research focused on the variation and driving forces of runoff and sediment at the different scales in a watershed. However, the variations of the flow–sediment relationship on multispatial scales (slope, subgully, gully, and watershed scales) and multitemporal scales (annual, flood events, and flood process) were less focused. Taking the Peijiamao watershed, which includes whole slope runoff plot (0.25 ha, slope scale), branch gully (6.9 ha, subgully scale), gully (45 ha, gully scale), and watershed (3930 ha, watershed scale), four different geomorphic units located at the Chinese Loess Plateau, as the research site, a total of 31 flood events from 1986 to 2008 were investigated, and two flood process data were recorded across all the four geomorphic units. The results showed that on the annual timescale, the average sediment transport modulus and runoff depth at four scales exhibited a linear relationship, with determination coefficients of 0.81, 0.72, 0.74, and 0.77, respectively. At the flood event timescale, the relationships between sediment transport modulus and runoff depth at the gully and watershed scales could also be fitted with a linear relationship with high determination coefficients (from 0.77 to 0.99), but the determination coefficient at the slope scale was only 0.37 at the event scale. On the single rainfall event timescale, the flow–sediment relationship at the slope scale showed a figure-eight hysteretic pattern while those relationships at larger scales showed an anticlockwise loop hysteretic pattern. Under the same flow condition, the suspended sediment concentrations during the falling stage were significantly higher than those during the rising stage. Moreover, the difference was bigger as the spatial scale increased due to the wash loads in the downstream gullies, which favored the occurrence of hyper-concentration flow. The results of the study could provide useful insights into the temporal–spatial scale effects of sediment transport and their internal driving mechanisms at the watershed scale.