Multiple Spatial Scale Analysis of the Niche Characteristics of the Rhododendron dauricum Plant Communities in Northeast China

This study aims to verify the concept of niches at multiple spatial scales in plant communities.To this end,we analyzed the niche characteristic of Rhododendron dauricum plant communities in Northeast China at three spatial scales.At the local scale,we calculated the Importance Value(IV)of species in five communities in the north of the Da Hinggan Mountains.At the intermediate scale,we examined five communities in their entirety,calculated the niche breadth of the species,and integrated niche overlap and interspecific association to analyze interspecific relationships.Further,the generalized additive model(GAM)was used to analyze the impact of topography and soil factors on niche characteristics.At the regional scale,we analyzed the geographical distribution of dominant species of R.dauricum plant communities in Northeast China and used principal component analysis(PCA)to analyze the impact of geographical and climate factors on species distribution.The results show that at the local scale,the IV of the species in each community varies widely.At the intermediate scale,species with a wide niche breadth tend to have a high value for IV.Larix gmelinii,Betula platyphylla,R.dauricum,Ledum palustre,and Vaccinium vitis-idaea had a relatively wide niche breadth and a high niche overlap,and the interspecific associations were almost all positive.Elevation and soil nutrients were the most dominant environmental factors.At the regional scale,species with a wide niche breadth tend to have a wide range of distribution,and temperature and precipitation were the most dominant environmental factors.This study suggests that the niche characteristics at three scales are both related and different.Niche characteristics at the local scale were various and labile,and niche characteristics at the intermediate and regional scales were relatively regular.These results show some degree of consistency with previous studies from an evolutionary perspective.The action mechanisms of these communities are related to differences in the dominant environmental factors.In addition,the integration of niche overlap and interspecific association determine interspecific relationships more accurately.

Dependence of the Accuracy of Precipitation and Cloud Simulation on Temporal and Spatial Scales

Precipitation and associated cloud hydrometeors have large temporal and spatial variability, which makes accurate quantitative precipitation forecasting difficult. Thus, dependence of accurate precipitation and associated cloud simulation on temporal and spatial scales becomes an important issue. We report a cloud- resolving modeling analysis on this issue by comparing the control experiment with experiments perturbed by initial temperature, water vapor, and cloud conditions. The simulation is considered to be accurate only if the root-mean-squared difference between the perturbation experiments and the control experiment is smaller than the standard deviation. The analysis may suggest that accurate precipitation and cloud simulations cannot be obtained on both fine temporal and spatial scales simultaneously, which limits quanti- tative precipitation forecasting. The accurate simulation of water vapor convergence could lead to accurate precipitation and cloud simulations on daily time scales, but it may not be beneficial to precipitation and cloud simulations on hourly time scales due to the dominance of cloud processes.

Issues with Spatial Scale in Urban Research

Scale is the range or measurement unit of the characteristics of natural or human ontology in the temporal or spatial dimension and is widely used in daily life and the study of various disciplines.Scale effect pertains to certain laws and characteristics that can only be reflected on a specific scale,so choosing the appropriate scale remains the basic premise of scientific research.The concept of the urban spatial system is complex and has the characteristics of scale dependence,and the selection of an appropriate spatial scale is important for the accurate estimation and description of urban issues.In this paper,we discuss spatial scale in urban research using cases that primarily come from the Chinese experience,provide some examples that demonstrate the importance of appropriate scale in urban research,including urban shrinkage,and highlight problems in spatial research.Ultimately,we suggest that scale consciousness should be the basic consciousness required in empirical research.

Temporal and Spatial Scale Dependence of Precipitation Analysis over the Tropical Deep Convective Regime

Data from Goddard cumulus ensemble model experiment are used to study temporal and spatial scale dependence of tropical rainfall separation analysis based on cloud budget during Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment （TOGA COARE）. The analysis shows that the calculations of model domain mean or time-mean grid-scale mean simulation data overestimate the rain rates of the two rainfall types associated with net condensation but they severely underestimate the rain rate of the rainfall type associated with net evaporation and hydrometeor convergence.

Quantifying spatial scale of positive and negative terrains pattern at watershed-scale:Case in soil and water conservation region on Loess Plateau

The positive and negative terrains(P-N terrains) widely distributed across China's Loess Plateau constitute the dual structure characteristic of loess landforms. Analysis of loess P-N terrains at the watershed scale can serve to elucidate the structural characteristics and spatial patterns of P-N terrains, which benefits a better understanding of watershed evolution and suitable scales for loess landform research. The Two-Term Local Quadrat Variance Analysis(TTLQV) is calculated as the average of the square of the difference between the block totals of all possible adjacent pairs of block size, which can be used to detect both the scale and the intensity of landscape patches(e.g., plant/animal communities and gully networks). In this study, we determined the latitudinal and longitudinal spatial scale of P-N terrain patterns within 104 uniformly distributed watersheds in our target soil and water conservation region. The results showed that TTLQV is very effective for examining the scale of P-N terrain patterns. There were apparently three types of P-N terrain pattern in latitudinal direction(i.e., Loess Tableland type, Loess Hill type, and Transitional Form between Sand and Loess type), whereas there were both lower and higher values for P-N terrain pattern scales in all loess landforms in the longitudinal direction. The P-N terrain pattern alsoclearly presented anisotropy, suggesting that gully networks in the main direction were well-developed while others were relatively undeveloped. In addition, the relationships between the first scales and controlling factors(i.e., gully density, nibble degree, watershed area, mean watershed slope, NDVI, precipitation, loess thickness, and loess landforms) revealed that the first scales are primarily controlled by watershed area and loess landforms. This may indicate that the current spatial pattern of P-N terrains is characterized by internal force. In selecting suitable study areas in China' Loess Plateau, it is crucial to understand four control variables: the spatial scale of the P-N terrain pattern, the watershed area, the main direction of the watershed, and the loess landforms.

Quantitative method for evaluating detailed volatility of wind power at multiple temporal-spatial scales

With the increasing proportion of wind power integration, the volatility of wind power brings huge challenges to the safe and stable operation of the electric power system. At present, the indexes commonly used to evaluate the volatility of wind power only consider its overall characteristics, such as the standard deviation of wind power, the average of power variables, etc., while ignoring the detailed volatility of wind power, that is, the features of the frequency distribution of power variables. However, how to accurately describe the detailed volatility of wind power is the key foundation to reduce its adverse influences. To address this, a quantitative method for evaluating the detailed volatility of wind power at multiple temporal-spatial scales is proposed. First, the volatility indexes which can evaluate the detailed fluctuation characteristics of wind power are presented, including the upper confidence limit, lower confidence limit and confidence interval of power variables under the certain confidence level. Then, the actual wind power data from a location in northern China is used to illustrate the application of the proposed indexes at multiple temporal(year–season–month–day) and spatial scales(wind turbine–wind turbines–wind farm–wind farms) using the calculation time windows of 10 min, 30 min, 1 h, and 4 h. Finally, the relationships between wind power forecasting accuracy and its corresponding detailed volatility are analyzed to further verify the effectiveness of the proposed indexes. The results show that the proposed volatility indexes can effectively characterize the detailed fluctuations of wind power at multiple temporal-spatial scales. It is anticipated that the results of this study will serve as an important reference for the reserve capacity planning and optimization dispatch in the electric power system which with a high proportion of renewable energy.

Neutral and accumulator species in determining the spatial structure of tree species at different spatial scales

In 2014,a 40-ha undisturbed plot was established in the Beijing Songshan Nature Reserve.The spatial distribution and spatial associations of five dominant tree species of different height classes were examined using spatial point pattern analysis and the effects of these species on local community diversity assemblages were examined using the ISAR method.The dominant species were characterized by an aggregated distribution at spatial scales of 0–50 m.At smaller spatial scales,trees in larger height classes had significant positive and negative effects on the diversity of the lower height classes.However,at larger spatial scales this effect was diminished.At small scales(0–10 m),accumulator species had positive effects on species diversity,maintained an over-representative proportion of diversity in their proximity,and supported the ecological niche theory.At the larger scale(10–50 m),neutral species were dominant and had positive effects on species diversity,though repeller species,which had negative effects on species diversity,also contributed to diversity.Neutral and accumulator species together determined local species diversity,but the relative importance of the two was closely related to spatial scale.A combination of the ecological niche theory and neutral processes together determines species coexistence and biodiversity of an undisturbed pine forest.

Spatial Scale Effects of Water Erosion Dynamics:Complexities, Variabilities, and Uncertainties

Severe water erosion is notorious for its harmful effects on land-water resources as well as local societies. The scale effects of water erosion, however, greatly exacerbate the difficulties of accurate erosion evaluation and hazard control in the real world. Analyzing the related scale issues is thus urgent for a better understanding of erosion variations as well as reducing such erosion. In this review article, water erosion dynamics across three spatial scales including plot, watershed, and regional scales were selected and discussed. For the study purposes and objectives, the advantages and disadvantages of these scales all demonstrate clear spatial-scale dependence. Plot scale studies are primarily focused on abundant data collection and mechanism discrimination of erosion generation, while watershed scale studies provide valuable information for watershed management and hazard control as well as the development of quantitatively distributed models. Regional studies concentrate more on large-scale erosion assessment, and serve policymakers and stakeholders in achieving the basis for regulatory policy for comprehensive land uses. The results of this study show that the driving forces and mechanisms of water erosion variations among the scales are quite different. As a result, several major aspects contributing to variations in water erosion across the scales are stressed: differences in the methodologies across various scales, different sink-source roles on water erosion processes, and diverse climatic zones and morphological regions. This variability becomes more complex in the context of accelerated global change. The changing climatic factors and earth surface features are considered the fourth key reason responsible for the increased variability of water erosion across spatial scales.

Impact of Spatial Scale and Building Exposure Distribution on Earthquake Insurance Rates:A Case Study in Tangshan,China

In order to examine the effect of spatial scale and building exposure distribution on the pure rate of earthquake catastrophe insurance,this study described three modules for rate determination,put forward the general assumptions and principles for calculating the pure insurance rate,and introduced three types of building distribution and their calculation.Taking Tangshan City of Hebei Province in China as an example,we analyzed the pure rate of regional earthquake insurance in terms of spatial scale and building exposure distribution by using the method of control variables.The results show that for districts(or counties)with large differences in seismic risk,the risk areas can be further divided to apply differential rates.In areas with a diverse distribution of potential earthquake source areas and large differences in building density,there is a risk of overestimating or underestimating the pure rate of earthquake insurance when buildings are distributed evenly or partially evenly.This violates the break-even principle of rate setting.This study also provides a reference for earthquake catastrophe insurance companies to choose the spatial scale and the detailed level of exposure distribution in rate determination.

Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context

The combination of spatial distribution,semantic characteristics,and sometimes temporal dynamics of POIs inside a geographic region can capture its unique land use characteristics.Most previous studies on POI-based land use modeling research focused on one geographic region and select one spatial scale and semantic granularity for land use characterization.There is a lack of understanding on the impact of spatial scale,semantic granularity,and geographic context on POI-based land use modeling,particularly large-scale land use modeling.In this study,we developed a scalable POI-based land use modeling framework and examined the impact of these three factors on POI-based land use characterization using data from three geographic regions.We developed a unified semantic representation framework for POI semantics that can help fuse heterogeneous POI data sources.Then,by combining POIs with a neural network language model,we developed a spatially explicit approach to learn the embedding representation of POIs and AOIs.We trained multiple supervised classifiers using AOI embeddings as input features to predict AOI land use at different semantic granularities.The classification performance of different land use classes was analyzed and compared across three geographic regions to identify the semantic representativeness of POI-based AOI embedding and the impact of geographic context.

Increased community compositional dissimilarity alleviates species loss following nutrient enrichment at large spatial scales

Aims Anthropogenic activities have drastically increased nutrient availabil-ity,resulting in declines in species richness in many plant communi-ties.However,most previous studies have explored only species-loss patterns and mechanisms over small sampling areas,so their results might overestimate species loss at larger spatial scales.The aim of this research was to explore species diversity change patterns and species-loss rates at multiple scales in alpine meadow communities following nutrient enrichment.Specifically,we asked two closely related ques-tions:(i)do changes in species diversity and species-loss patterns differ among spatial scales?and(ii)how does community compositional dissimilarity and species turnover change among spatial scale?Methods This study was implemented in an alpine meadow community,which is regarded as one of the most sensitive and vulnerable ter-restrial ecosystems to anthropogenic nutrient enrichment.We conducted a fertilization experiment that involved the addition of nitrogen(N),phosphorus(P)and a mixture of both to a series of quadrats ranging from 1 to 16 m2 over 5 years to study the variations in the patterns of species diversity in response to nutrient additions at different spatial scales.Important Findings Our results showed that the changes in species diversity and species loss were dependent on the type of fertilization and the spatial scale.After N and NP fertilization,species diversity significantly decreased at the small scale but not at the large scale,and the rate of species loss decreased as the spatial scale increased.In contrast,the differences between the P addition and control communities were negligible at both the small and large spatial scales.N fertilization caused species to be lost from the small sampling scale,but because different species were lost from dif-ferent samples,there was an increase in compositional dissimilarity at larger spatial scales,which reduced the total number of species lost when measured at larger scales.These findings highlight spatial scale in evalu-ating the biodiversity loss after fertilization and suggest that the compo-sitional dissimilarity might play an important role in mediating species loss after fertilization.Our study significantly improved our understanding of changes in species diversity and species loss at different spatial scales under nutrient-enrichment scenarios.

A Spatial Evapotranspiration Tool at Grid Scale

The drastic decline in groundwater table and many other detrimental effects in meeting irrigation demand, and the projected population growth have force to evaluate consumptive use or evapo-transpiration (ET), the rate of liquid water transformation to vapor from open water, bare soil, and vegetation, which determines the irrigation demand. As underscored in the literature, Pen-man-Monteith method which is based on aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the estimation of ET is oftentimes carried out using meteorological data from climate stations. Therefore, such estimation of ET may vary spatially and thus there exists a need to estimate ET spatially at different spatial or grid scales/resolutions. Thus, in this paper, a spatial tool that can geographically encompass all the best available climate datasets to produce ET at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010.

The Spatial Sensitivity Analysis of Evapotranspiration using Penman-Monteith Method at Grid Scale

The need to allocate the existing water in a sustainable manner, even with the projected population growth, has made to assess the consumptive use or evapotranspiration (ET), which determines the irrigation demand. As underscored in the literature, Penman-Monteith method which is a combination of aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the application of Penman-Monteith relies on many climate parameters such as relative humidity, solar radiation, temperature, and wind speed. Therefore, there exists a need to determine the parameters that are most sensitive and correlated with dependent variable (i.e., ET), to strengthen the knowledge base. However, the sensitivity of ET using Penman-Monteith is oftentimes estimated using meteorological data from climate stations. Such estimation of sensitivity may vary spatially and thus there exists a need to estimate sensitivity of ET spatially. Thus, in this paper, based on One-AT-A-Time (OAT) method, a spatial sensitivity tool that can geographically encompass all the best available climate datasets to produce ET and its sensitivity at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010. To summarize the outcome of the sensitivity analysis using OAT method, sensitivity indices are developed for each raster cell. The demonstration of the tool shows that, among the considered parameters, the computed ET using Penman-Monteith is highly sensitive to solar radiation followed by temperature for the state of Nebraska, as depicted by the sensitivity index. The computed sensitivity index of wind speed and the relative humidity are not that significant compared to the sensitivity index of solar radiation and temperature.

Change of precipitation intensity spectra at different spatial scales under warming conditions

The long-term change of the whole spectra of precipitation intensity in China is examined using observed daily data recorded at 477 surface stations for the period from 1961 to 2008. The results show a spatially coherent decrease of trace precipitation despite different reduction magnitudes among the regions. For measurable precipitation, significant regional and seasonal characteristics are observed. In autumn, the whole measurable precipitation decreased over Eastern China (east of 98°E). In summer and winter, a significant increase of heavy precipitation and decrease of light precipitation are detected south of Eastern China. In Western China, measurable precipitation is found to have increased in all four seasons. Composite analysis reveals a quasi-linear relationship between increasing surface temperature and precipitation on a global scale. The responses of precipitation at different intensities to the increased temperature are distinct, with a significant spectra-shifting from light to heavy precipitation. Compared with precipitation over the ocean, the amplification of heavy precipitation over land is relatively less, most likely constrained by the limited water supply. The response of regional precipitation to global warming shows greater uncertainties compared with those on the global scale, perhaps due to interference by more complex topography and land cover, as well as human activities, among other factors.

Response of sediment yield to vegetation restoration at a large spatial scale in the Loess Plateau

The impact of vegetation coverage on erosion and sediment yield in the Loess Plateau has been extensively studied,but the research has been primarily based on observations from slope runoff plots or secondary forest regions;the scaling method remains unresolved when it is applied at a large spatial scale,and it is difficult to apply to regions with severe soil and water loss given the predominance of herbs and shrubs.To date,there is little data on the quantitative impact of changes to vegetation on sediment concentration at a large spatial scale.This paper is based on vegetation information from remote sensing images,measured rainfall and sediment data over nearly 60 years,and results from previous runoff and sediment variation research on the Yellow River.We introduce the concepts of a sediment yield coefficient and the percentage of effective vegetation and erodible area,analyze the impact of different vegetation conditions on the flood sediment concentration and sediment yield,and evaluate the effect of rainfall intensity on sediment yield under different vegetation conditions at the watershed scale.We propose models to evaluate the impact of vegetation on sediment yield in the loess gully hilly region,which are based on remote sensing data and support an application at a large spatial scale.The models can be used to assess sediment reduction that results from the current significant improvement of vegetation in the Loess Plateau.

Multi-scale Spatial Patterns and Influencing Factors of Rural Poverty:A Case Study in the Liupan Mountain Region, Gansu Province, China

The important role of spatial scale in exploring the geography of poverty as well as its policy implications has been noticed but with limited knowledge. To improve such limited understanding, we mainly investigated the spatial patterns and influencing factors of rural poverty(indicated by poor population and poverty incidence) at three different administrative levels in the Liupan Mountain Region, one of the fourteen poorest regions in China. Our results show that from a global perspective, poor areas are clustered significantly at the county-, township-, and village-level, and more greatly at a lower level. Locally, there is spatial mismatch among poverty hotspots detected not only by the same indicator at different levels but also by different indicators at the same level. A scale effect can be found in the influencing factors of rural poverty. That is, the number of significant factors increases, but the degree of their association with poverty incidence decreases at a lower level. Such scale effect indicates that poverty incidence at lower levels may be affected by more complex factors, including not only the new local ones but also the already appeared non-local ones at higher levels. However, the natural conditions tend to play a scale-independent role to poverty incidence. In response to such scale-dependent patterns and factors, anti-poverty policies can be 1) a multilevel monitoring system to reduce incomplete or even misleading single-level information and understanding; 2) the village-based targeting strategy to increase the targeting efficiency and alleviate the mentioned spatial mismatch; 3) more flexible strategies responding to the local impoverishing factors, and 4) different task emphasises for multilevel policymakers to achieve the common goal of poverty reduction.

Changes in the spatial scale of Beijing UHI and urban development

The seasonal and interannual variations of Beijing urban heat island (UHI) are investigated in this paper using the temperature data from 1960 to 2000 at 20 meteorological stations in the Beijing region, and then the relationship between the intensity and spatial scale of UHI and Beijing urbanization indices is analyzed and discussed. Main conclusions are the followings. First, Beijing UHI shows obvious seasonal variations, and it is strongest in winter, next in spring and autumn, and least in summer. The seasonal variation of the UHI mainly occurs in the urban area. The UHI intensity at the center of Beijing is more than 0.8℃ in winter, and only 0.5℃ in summer. Second, the intensity of Beijing HUI exhibits a clear interannual warming trend with its mean growth rate (MGR) being 0.3088℃/10 a. The MGR of HUI is largest in winter, next in spring and autumn, and least in summer, and the urban temperature increase makes a major contribution to the growth of HUI intensity. Third, since the Reform and Opening, the urbanization indices have grown several ten times or even one hundred times, the intensity of HUI has increased dramatically, and its spatial scale also expanded distinctively along with the expansion of urban architectural complexes. Fourth, the interannual variation of urbanization indices is very similar with that of HUI intensity, and their linear correlation coefficients are significant at a more than 0.001 confidence level.

Fundamental Theories of Spatial Similarity Relations in Multi-scale Map Spaces

Similarity relation is one of the spatial relations in the community of geographic information science and cartography.It is widely used in the retrieval of spatial databases, the recognition of spatial objects from images, and the description of spatial features on maps.However, little achievements have been made for it by far.In this paper, spatial similarity relation was put forward with the introduction of automated map generalization in the construction of multi-scale map databases;then the definition of spatial similarity relations was presented based on set theory, the concept of spatial similarity degree was given, and the characteristics of spatial similarity were discussed in detail, in-cluding reflexivity, symmetry, non-transitivity, self-similarity in multi-scale spaces, and scale-dependence.Finally a classification system for spatial similarity relations in multi-scale map spaces was addressed.This research may be useful to automated map generalization, spatial similarity retrieval and spatial reasoning.

The evolution of tea spatial agglomeration in China:An analysis based on different geographical scales

The spatial agglomeration of agricultural production is conducive to reducing planting costs, increasing production efficiency and improving product quality. It is an important way to promote the transformation and upgrad of tea industry and realize the modernization of China's tea industry. This study used Gini coefficient and spatial autocorrelation analysis to explore the characteristics of tea spatial agglomeration in China from three geographical scales: regional level, provincial level and prefecture level from the year 2005 to 2015. The results indicated that there was a significant scale effect on the tea spatial agglomeration. The agglomeration degree increased from the regional level, provincial level to prefecture level. The types of spatial agglomeration evolution of the three scales were Ushaped, continuous diffusion, and continuous agglomeration. The spatial autocorrelation of tea production could only be found at the prefecture level. Meanwhile, at the prefecture scale, we could not only reveal the pattern changes at the regional and provincial levels, but also identify tea production agglomeration regions. Compared with the large scale, the small scale could reveal the characteristics of tea spatial agglomeration in more details. Factors such as natural resource endowments, cost factor, technological advancement, agglomeration economy, and agricultural policy influenced the evolution of tea spatial agglomeration from different geographical scales. Finally, from the perspectives of spatial transfer of tea production, promoting spatial agglomeration, building tea production bases, and breaking administrative boundaries, we proposed several policy suggestions for optimizing the spatial layout of tea production.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.