Variety Of Spatial Scales Research Articles

Propagating spatially varying multiplicative shear bias to cosmological parameter estimation for stage-IV weak-lensing surveys

ABSTRACT We consider the bias introduced by a spatially varying multiplicative shear bias (m-bias) on tomographic cosmic shear angular power spectra. To compute the bias in the power spectra, we estimate the mode-coupling matrix associated with an m-bias map using a computationally efficient pseudo-Cℓ method. This allows us to consider the effect of the m-bias to high ℓ. We then conduct a Fisher matrix analysis to forecast resulting biases in cosmological parameters. For a Euclid-like survey with a spatially varying m-bias, with zero mean and rms of 0.01, we find that parameter biases reach a maximum of $\sim 10 {{\ \rm per\ cent}}$ of the expected statistical error, if multipoles up to ℓmax = 5000 are included. We conclude that the effect of the spatially varying m-bias may be a subdominant but potentially non-negligible contribution to the error budget in forthcoming weak lensing surveys. We also investigate the dependence of parameter biases on the amplitude and angular scale of spatial variations of the m-bias field, and conclude that requirements should be placed on the rms of spatial variations of the m-bias, in addition to any requirement on the mean value. We find that, for a Euclid-like survey, biases generally exceed $\sim 30 {{\ \rm per\ cent}}$ of the statistical error for m-bias rms ∼0.02–0.03 and can exceed the statistical error for rms ∼0.04–0.05. This allows requirements to be set on the permissible amplitude of spatial variations of the m-bias that will arise due to systematics in forthcoming weak lensing measurements.

Remote sensing scene image classification model based on multi-scale features and attention mechanism

Remote sensing scene classification has received more and more attention as important fundamental research in recent years. However, the redundant background information and complex spatial scale variability of remote sensing scene images make the existing convolutional neural network models, which mainly concentrate on global features, perform poorly. To effectively alleviate these problems, we proposed an MSRes-SplitNet model based on multiscale features and attention mechanisms for remote sensing scene image classification. First, MSRes blocks are constructed for the extraction of multi-scale features. Then, the multi-channel local features are fused by the Split-Attention block. Finally, the global and local feature information is aggregated by convolution, thus obtaining multi-scale features while alleviating the small-sample learning problem. Experiments are conducted on three publicly available datasets and compared with other state-of-the-art methods, showing that the proposed method MSRes-SplitNet has better performance while effectively reducing a large number of parameters.

Atmospheric wet deposition of trace elements in Bangladesh: A new insight into spatiotemporal variability and source apportionment

The interaction between water vapor and natural/anthropogenic airborne particles deposits a massive amount of trace elements in the ecosystem. As the principal source region of the Indian monsoon originated from the Bay of Bengal, atmospheric trace elements in Bangladesh have impacted atmospheric wet deposition along the pathway, even reaching the headwaters in the Asian water tower. However, no study reports the atmospheric wet deposition of trace elements at the spatiotemporal scale. Thus, this study investigated the concentration, sources, and deposition of eighteen trace elements (Al, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Rb, Sr, Mo, Cd, Sn, Sb, Ba, and Pb) from 232 precipitation samples at four sites in Bangladesh. Results showed that the VWM concentrations of the eighteen measured trace elements ranged from 0.03 to 535.6 μg L−1. Zn, Fe, and Al were the principal elements of the atmosphere at four sites with mean values of 207.9 ± 227.8, 18.2 ± 9.3, and 16.3 ± 6.8 μg L−1, respectively. Besides, the eighteen trace elements showed significant variation in spatial scale with distinct seasonality. Enrichment factors of Zn, Sb, and Cd indicated serious anthropogenic influences. The major sources of trace elements were fossil fuel combustion, brick kilns, crustal dust, fugitive Pb, metal smelters, and battery recycling. Both the concentration and precipitation amount played a pivotal role in the deposition. Most of the air masses during the monsoon season came from marine sources passing over southern India and Sri Lanka. Meanwhile, the air masses during the non-monsoon season were from West Asia and the northwestern Indian subcontinent. The air masses are transported over a long range and deposit massive amounts of particulate matter in the Third Pole Himalayan region. This first-hand work on spatiotemporal variation provides a reference dataset for future targeting of the scientific community and policymakers for the development of strategies and action plans.

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.

Post-fire seed dispersal of a wind-dispersed shrub declined with distance to seed source, yet had high levels of unexplained variation.

Plant-population recovery across large disturbance areas is often seed-limited. An understanding of seed dispersal patterns is fundamental for determining natural-regeneration potential. However, forecasting seed dispersal rates across heterogeneous landscapes remains a challenge. Our objectives were to determine (i) the landscape patterning of post-disturbance seed dispersal, and underlying sources of variation and the scale at which they operate, and (ii) how the natural seed dispersal patterns relate to a seed augmentation strategy. Vertical seed trapping experiments were replicated across 2 years and five burned and/or managed landscapes in sagebrush steppe. Multi-scale sampling and hierarchical Bayesian models were used to determine the scale of spatial variation in seed dispersal. We then integrated an empirical and mechanistic dispersal kernel for wind-dispersed species to project rates of seed dispersal and compared natural seed arrival to typical post-fire aerial seeding rates. Seeds were captured across the range of tested dispersal distances, up to a maximum distance of 26 m from seed-source plants, although dispersal to the furthest traps was variable. Seed dispersal was better explained by transect heterogeneity than by patch or site heterogeneity (transects were nested within patch within site). The number of seeds captured varied from a modelled mean of ~13 m-2 adjacent to patches of seed-producing plants, to nearly none at 10 m from patches, standardized over a 49-day period. Maximum seed dispersal distances on average were estimated to be 16 m according to a novel modelling approach using a 'latent' variable for dispersal distance based on seed trapping heights. Surprisingly, statistical representation of wind did not improve model fit and seed rain was not related to the large variation in total available seed of adjacent patches. The models predicted severe seed limitations were likely on typical burned areas, especially compared to the mean 95-250 seeds per m2 that previous literature suggested were required to generate sagebrush recovery. More broadly, our Bayesian data fusion approach could be applied to other cases that require quantitative estimates of long-distance seed dispersal across heterogeneous landscapes.

Associations between greenness and hemodynamic markers at varied spatial scales and metrics of greenness

Background and aim: Residential greenness, often measured through NDVI, has been linked to cardiovascular disease (CVD) outcomes and associated risk factors. However, physiological mechanisms underlying these associations are not well understood, nor is the influence of differing types and scales of greenness. Therefore, we examined associations between CVD risk markers of systolic blood pressure (SBP) and arterial index (AI), a measure of arterial stiffness, with residential greenness at multiple greenness metrics and spatial scales. Methods: We recruited 723 adult participants from a 4.5sqmi area of Louisville, Kentucky, USA, and measured SBP and AI. We collected high resolution greenness metrics of NDVI, canopy, and leaf area in spatial radii of 20m to 500m around participants’ residence via Aerial LiDAR and Sentinel-2 satellite imagery. We utilized adjusted linear regression with a hierarchical modeling approach to examine associations between greenness and hemodynamic markers. Results: We observed inverse associations between SBP and multiple greenness metrics at radii of 300m and 500m (-0.26 to -2.66% per IQR), but not 20m or 100m. We observed inverse associations between AI and metrics of greenness with all models at a 100m radius (-1.41 to -2.25% per IQR), but only leaf area and models with basic adjustments at 300m and 500m radii (-1.79 and -1.9% per IQR). When stratified, we observed significant associations between greenness and SBP among only females and those with higher levels of education. Among stratified results between greenness and AI, we observed significant associations only in females, participants <50 years of age, and higher educational attainment. Conclusions: Greenness is associated with hemodynamic markers of SBP and AI with inconsistent associations between metrics and spatial scales. Future investigations of such high-resolution data may inform links between greenness and health and could help to design targeted greening interventions to address CVD. Keywords: Greenness, Blood Pressure, Hypertension, CVD

Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series

Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.

Scales of spatial variability in Lake Michigan glider-based limnological observations

This study quantified spatial variability in temperature, colored dissolved organic matter, and chlorophyll-α fluorescence in the epilimnion and hypolimnion of southern Lake Michigan using observations collected by an autonomous underwater vehicle (glider) deployed multiple times per year during 2012–2019. We also include similar analyses of the deep chlorophyll maximum during the summer of those years. Ranges based on semivariance analysis and decorrelation distances generally were 5–15 km, with greater differences observed among seasons in the epilimnion and less unresolved error found for the temperature calculations. Our analysis provides information on submesoscale variation in limnological conditions that may be useful for several applications, including planning field sampling, observation networks, and data assimilation.

GEDI launches a new era of biomass inference from space

Accurate estimation of aboveground forest biomass stocks is required to assess the impacts of land use changes such as deforestation and subsequent regrowth on concentrations of atmospheric CO2. The Global Ecosystem Dynamics Investigation (GEDI) is a lidar mission launched by NASA to the International Space Station in 2018. GEDI was specifically designed to retrieve vegetation structure within a novel, theoretical sampling design that explicitly quantifies biomass and its uncertainty across a variety of spatial scales. In this paper we provide the estimates of pan-tropical and temperate biomass derived from two years of GEDI observations. We present estimates of mean biomass densities at 1 km resolution, as well as estimates aggregated to the national level for every country GEDI observes, and at the sub-national level for the United States. For all estimates we provide the standard error of the mean biomass. These data serve as a baseline for current biomass stocks and their future changes, and the mission’s integrated use of formal statistical inference points the way towards the possibility of a new generation of powerful monitoring tools from space.

Exploring spatial nonstationarity for four mammal species reveals regional variation in environmental relationships

AbstractBroad‐scale ecological research on species distributions commonly presumes that the correlative relationships discovered are stationary over space. This is an assumption of most species distribution models (SDMs) that combine observations of species occurrence with environmental characteristics to understand current ecological correlates and to predict distributions based on those relationships. However, ecological relationships may vary spatially because of changes in the environment (i.e., resource availability) or the organism itself (i.e., local adaptation). Discovering this within‐species variation typically requires dense datasets over large geographic areas, which are now being provided by the recent proliferation of open‐access biodiversity occurrence records. Using nearly 4000 sampling locations from an open‐access, state‐wide camera‐trapping project, we explore the space‐varying effects of covariates on the distribution of four mammal species at two scales: region‐specific and fine resolution, with the latter estimated using spatially varying coefficients (SVC) models, to understand the scale of spatial variation in ecological relationships. Among the four species tested, the ecological relationships for two were best explained with the regional models, equivocal results for one species, while the SVC model had superior fit and predictive performance for the final species (white‐tailed deer, Odocoileus virginianus). Spatial nonstationarity was more common in relationships with landscape composition characteristics, such as housing density, than in landscape configuration metrics, such as patch richness density. One of the most appealing results of an SVC approach is not only the improved predictions across large landscapes but also understanding how animals are responding to the environment differently at the management unit level. For example, we found that deer's spatially varying relationship with forest cover was best explained by an interactive effect of deer management units (i.e., differences in deer populations) and predator pressure. These findings lead to a new hypothesis about how deer may be differentially using forested environments across space and could be a promising area of future research. Given sufficient data, accounting for nonstationarity in SDMs can show large‐scale ecological patterns while also detecting local level changes in animal ecology in areas small enough that management or protection can be readily implemented.

Sunny-side up: ontogenetic variation in egg mass temperatures of the wood frog Rana sylvatica

The efficacy of most biological processes is temperature dependent and, within physiological limits, on average, warmer is better. This axiom of biology has led to a wide range of adaptations for dealing with temperatures that are outside of an organism’s preferred temperature. Many pond-breeding amphibians lay their eggs during early spring, when water temperatures are near freezing. Communal nest-site selection has been proposed as a mechanism to increase developmental temperatures, and temperatures near the center of egg-mass aggregations are elevated relative to egg-masses on the aggregation’s periphery. It is unclear whether this spatial variation in temperature is due to concentration of metabolic heat, absorption of solar radiation, or both. Here, we explore finer scale spatial variation within egg masses of the wood frog Rana sylvatica, one of the earliest amphibians to breed during the North American spring. We compared peripheral and core temperatures of egg masses that were exposed either to 1) ambient sunlight from above, or 2) sunlight reflected by a mirror from below. We found that differences between core and peripheral temperatures were higher in the control than in the mirror treatment, but core and peripheral temperatures were statistically indistinguishable in both cases. Moreover, the difference in peripheral and internal temperatures increased significantly over the course of development. However, these trends were only significant in ambient sunlight and actually decreased in the mirror group. Our results suggest that the benefits of communal nesting are also experienced by individual egg masses, albeit to a lesser extent. In addition, the lack of effect in shaded egg masses suggests that the thermal advantage is tied to sun exposure and not due to concentration of metabolic heat.

Field scale variability in soil properties and silage corn yield

Field scale spatial variability of soil properties, crop quality parameters and yield are needed to evaluate the efficiency of management practices in crop production. The purpose of this study was to determine the magnitude of field variability in soil properties, silage yield of corn (Zea mays L.) varieties, and to characterize their spatial structures, and map the stated attributes. The experiment was conducted in an alluvial flood plain of lower Kazova watershed in Tokat province of Turkey. Several physical and chemical soil properties and silage corn yield were determined. Coefficient of variation (CV%) varied from 1.0% (pH) to 38.1% (P2O5) in herbicide not applied plots and from 0.9% (pH) to exchangeable Na (55.1%) in herbicide applied plots. Calcium carbonate, organic matter and clay displayed well defined spatial structure. Sand, pH and electrical conductivity (EC) showed moderate spatial dependency. However, silt, moisture content, bulk density, plant available phosphorus and potassium had weak spatial structure. Silage corn yield distribution map successfully distinguished the three corn hybrids planted. The difference in vegetation period among three corn hybrids was effective in distinguishing the location of hybrids within the field. However, the variability in each of the hybrids blocks was assumed to occur due to the difference in short range soil properties. The longest range values were obtained for silage corn yield at both herbicide applied and herbicide unapplied plots.

Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales

To explore how well large spatial scale digital soil mapping can contribute to efforts to monitor soil organic carbon (SOC) stocks and changes, we reviewed regional and national studies quantifying SOC within lands dominated by agriculture using SCORPAN approaches that rely on soil (S), climate (C), organisms (O), relief (R), parent material (P), age (A), and space (N) covariates representing soil forming factors. After identifying 79 regional (> 10,000 km2) and national studies that attempted to estimate SOC, we evaluated model performances with reference to soil sampling depth, number of predictors, grid-distance, and spatial extent. SCORPAN covariates were then investigated in terms of their frequency of use and data sources. Lastly, we used 67 studies encompassing a variety of spatial scales to determine which covariates most influenced SOC in agricultural lands using a subjective ranking system. Topography (used in 94% of the cases), climate (87%), and organisms (86%) covariates that were the most frequently used SCORPAN predictors, aligned with the factors (precipitation, temperature, elevation, slope, vegetation indices, and land use) currently identified to be most influential for model estimate at the large spatial extent. Models generally succeeded in estimating SOC with fits represented by R2 with a median value of 0.47 but, performance varied widely (R2 between 0.02 and 0.86) among studies. Predictive success declined significantly with increased soil sampling depth (p < 0.001) and spatial extent (p < 0.001) due to increased variability. While studies have extensively drawn on large-scale surveys and remote sensing databases to estimate environmental covariates, the absence of soils data needed to understand the influence of management or temporal change limits our ability to make useful inferences about changes in SOC stocks at this scale. This review suggests digital soil mapping efforts can be improved through greater use of data representing soil type and parent material and consideration of spatio-temporal dynamics of SOC occurring within different depths and land use or management systems.

A multiscale finite element method for coupled heat and water transfer in heterogeneous soils

Large-scale modeling of coupled heat and water transfer (CHWT) is challenging due to the spatial variabilities of soil hydraulic and thermal properties. A multi-scale finite element method (MsFEM) has been designed for simulating liquid water fluxes in unsaturated soils. In this study, the MsFEM approach is expanded as a new scheme that can handle CHWT in soils. Two groups of MsFEM basis functions are constructed to incorporate the heterogeneities in soil hydraulic conductivity and thermal conductivity, and a Petrov-Galerkin formulation is adopted to implement the proposed MsFEM scheme. The MsFEM scheme is also exploited as a sequential solver when heat transfer and water transfer are expressed in a partially coupled formulation (Wang et al., 2022a). Numerical examples illustrate that, without essentially increasing the computing load, the MsFEM scheme can improve the accuracy by up-to 30% compared to the standard finite element method (FEM), especially for thermally driven water transfer. Some fine scale spatial variations in soil temperature can only be revealed with the MsFEM scheme. If the MsFEM sequential solver is applied, the coupled heat and water transfer model can be rewritten into a sequence of modules, where liquid water transfer, heat transfer and vapor transfer are solved step-by-step, but the thermally driven liquid water is omitted due to its relatively small value (Wang et al., 2022a). With the MsFEM sequential solver, a flexible modeling architecture can be achieved at the cost of a relatively small increase of error (<5%). Therefore, the MsFEM scheme presented in this study is an effective numerical approach to simulating CHWT in heterogeneous soils.

Assessing national-level provision of conservation capacity building: lessons learnt from a case study of Kenya

AbstractAs global environmental pressures grow, the need for delivering relevant and sustainable capacity building in conservation has never been greater. Individuals, organizations and communities need the skills, knowledge and information that allow them to address environmental issues at a variety of spatial scales and in diverse contexts. Capacity is currently built through a range of activities, including tertiary education, training courses, online learning, mentoring and continuing professional development. However, a significant proportion of the current capacity-building provision is non-strategic, project-based and reactive. The conservation sector still lacks a coordinated approach to capacity building linked to broader conservation goals. Without an assessment of current capacity-building provision and future capacity needs, the delivery of capacity building in conservation will remain fundamentally ad hoc. The need for strategic conservation capacity building in sub-Saharan Africa has been identified and here we report on the first collation of online material to assess current conservation capacity provision in Kenya (the country with the greatest online capacity-building presence). We reviewed a total of 177 capacity-building initiatives delivered during 2014–2019 and recorded 55 separate metrics for each initiative. We present: (1) a broad overview of the data collation methods developed, (2) examples of data that will support strategic capacity-building strategies, and (3) the lessons learnt from this assessment.

High spatial and temporal variability in Antarctic ice discharge linked to ice shelf buttressing and bed geometry

Antarctica’s contribution to global mean sea level rise has been driven by an increase in ice discharge into the oceans. The rate of change and the mechanisms that drive variability in ice discharge are therefore important to consider in the context of projected future warming. Here, we report observations of both decadal trends and inter-annual variability in ice discharge across the Antarctic Ice Sheet at a variety of spatial scales that range from large drainage basins to individual outlet glacier catchments. Overall, we find a 37 ± 11 Gt year−1 increase in discharge between 1999 and 2010, but a much smaller increase of 4 ± 8 Gt year−1 between 2010 and 2018. Furthermore, comparisons reveal that neighbouring outlet glaciers can behave synchronously, but others show opposing trends, despite their close proximity. We link this spatial and temporal variability to changes in ice shelf buttressing and the modulating effect of local glacier geometry.

Coralligenous assemblages of continental shelf: Multiple spatial scale variability in the western Sardinia

Mediterranean mesophotic hard bottoms are mostly characterized by coralligenous banks. Although investigations on this habitat have increased in the last years, knowledge about its structure and spatial variability is still very limited as most studies are focused on the description of assemblages of peculiar and small locations such as biodiversity hotspots. This study aimed at i) describing coralligenous megafauna on the western Sardinian continental shelf between 80 and 120 m of depth, ii) assessing the distribution of the dominant taxa, and iii) estimating patterns of spatial variability at different scales. The coralligenous banks were studied along five sectors of the western Sardinian continental shelf by means of Remote Operated Vehicles (ROVs) and images were analyzed to identify the main taxa or morphological groups and their abundance. Results highlighted several common patterns among sectors, with assemblages dominated by the gorgonians Eunicella cavolini and Callogorgia verticillata. On the other hand, some taxa (e.g. Callogorgia verticillata, Paramuricea hirsuta, Antipathella subpinnata, Corallium rubrum Paramuricea clavata, Poecillastra compressa and Aplidium spp.) mainly contribute to segregate the coralligenous assemblages within the considered sectors of this study. A high small-scale variability was also observed. The presence of extensive and well diversified mesophotic animal forests stresses the importance to improve the conservation of the western Sardinian continental shelf.

Geostatistical filtering to map a 3D anthropogenic pedo-geochemical background for excavated soil reuse

The safe reuse of excavated soils in urban areas requires the guarantee of geochemical compatibility between this scarce resource and the local pedo-geochemical background. Previous works have shown that the usual statistical tools (high order quantiles, upper whiskers) are not always adequate for separating anomalies from background contents, especially when there are many anomalies.This paper presents an application of factorial co-kriging (geostatistical filtering) used to separate anomalies from the pedo-geochemical background, according to their different scales of spatial variability. The hypothesis is that the background corresponds to long distance variations, while the anomalies, which can be either positive or negative,- represent short distance variations.The variable of interest is the lead content (Pb) in the subsoils of the “Ile de Nantes” urban area, where some areas were previously dedicated to industrial activities. The data were collected from different surveys, not specifically aimed at determining the pedo-geochemical background, but rather in a context of pollution diagnostics. The lengths of the samples were strongly variable. In a first step, only the samples having approximately the same length were retained for the estimation, which significantly reduced the amount of data. In a second step, the geostatistical estimation was performed using all the data. The resulting maps are significantly more accurate for the total Pb and its background component. The distinction between anomalies and background is discussed based on the calculated maps.Factorial co-kriging appears to be relevant to map the anthropogenic pedo-geochemical background, using data acquired for other purposes. It allows separating anomalies and background for any parameter presenting significant contrasts.

Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China

Investigating urban vibrancy and factors that impact urban vibrancy aids the understanding of urban land use policies, provides solid foundation for scientific urban planning. The boom in information and communication technologies and the advancement of big data extraction provides new sources of data and make it possible to measure and analyze urban vibrancy at a finer spatial and temporal scale. This study aims to portray the spatiotemporal variation patterns of urban vibrancy in 24 h and investigate the potential influence mechanism of it. The central districts of Beijing consisting of 135 subdistricts are selected as the study area. Massive and spontaneous geo-tagged check-in data released from social media platforms has attracted increasing attentions in urban vibrancy studies because it reflects well people’s activities at a certain time, which is a good proxy for urban vibrancy. This study hence uses the check-in data from Weibo, the largest microblogging platform in China, to proxy urban vibrancy. We also extract from multisource spatial big data to explore potential determinants of urban vibrancy. This study seeks to reveal the global and local varying impacts of different factors on urban vibrancy by employ spatial lag model (SLM) and multiscale geographically weighted regression (MGWR) model. Results show that the increase in the number of different point of interests (POIs) improves urban vibrancy. Their effects on vibrancy vary at different times but have no obvious spatial scale variation. Splitting effect and attraction effect of land use diversity are introduced to explain its significantly negative effect on the intensity and fluctuation of urban vibrancy. It requires the wisdom of urban planners to balance these two effects of land use diversity in the process of urban construction. The guidance strategy of “highlighting the main functions and enriching the auxiliary functions” is helpful to build vibrant cities. Socioeconomical conditions, location and accessibility have different spatial scale effects on urban vibrancy at subdistrict level. These findings enable us to have a deeper understanding of the variation patterns and influence mechanism of urban vibrancy in China’s megacities and benefit the urban land use policy research and management community.

Avifaunal feeding guilds’ response to landscape compositional heterogeneity and their drivers in forest mosaic, Uttarakhand, India

AbstractThe positive association between landscape compositional heterogeneity and avian species richness is widely accepted, however, birds of different feeding guilds are expected to respond differently because of diverse resource utilisation patterns and ecological tolerance. In the present study, we assessed the response of avian species and avifaunal feeding guilds to landscape compositional heterogeneity and factors associated with their richness. Bird richness and landscape variables, edge density, landscape diversity, and area of land cover types were evaluated at 30 sampling sites in the Terai-Arc landscape of Uttarakhand, India. Univariate regression was performed to investigate the response of birds and various feeding guilds to landscape compositional heterogeneity. Average weighted models of most parsimonious generalised linear regression models (&lt;Δ 2AICc) were developed for various feeding guilds to identify significant predictors of species richness. The richness of overall birds and most feeding guilds, except piscivores and frugivores, responded positively to landscape compositional heterogeneity at variable spatial scales. The scale of effect was largest for carnivore (1.5 km), followed by granivore (1 km), insectivore (0.75 km), and frugi-insectivore, nectarivore, and omnivore (0.5 km). Overall bird species richness was positively associated with landscape diversity, teak plantation, and Sal-mixed forest. The average-weighted models identified edge density and dry riverine forest for frugi-insectivore, barren land and water body for the carnivore, teak plantation, Sal-mixed forest and dry-riverine forest for insectivore, edge density, human habitation/agriculture, teak plantation, barren land and scrubland for granivore, human habitation/agriculture for omnivore and waterbodies for piscivore and frugivore guilds as the significant drivers of species richness. The study concludes that the response to landscape compositional heterogeneity differs among feeding guilds and varies with the spatial scale of analysis. The results of our study are expected to serve as a reference for future studies, exploring the landscape relationship to the avian community in similar environmental conditions.