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Aim: Addressing how woody plant species are distributed in space can reveal inconspicuous drivers that structure plant communities. The spatial structure of conspecifics varies not only at local scales across co‐existing plant species but also at larger biogeographical scales with climatic parameters and habitat properties. The possibility that biogeographical drivers shape the spatial structure of plants, however, has not received sufficient attention. Location: Global synthesis. Time Period: 1997–2022. Major Taxa Studied: Woody angiosperms and conifers. Methods: We carried out a quantitative synthesis to capture the interplay between local scale and larger scale drivers. We modelled conspecific spatial aggregation as a binary response through logistic models and Ripley's L statistics and the distance at which the point process was least random with mixed effects linear models. Our predictors covered a range of plant traits, climatic predictors and descriptors of the habitat. Results: We hypothesized that plant traits, when summarized by local scale predictors, exceed in importance biogeographical drivers in determining the spatial structure of conspecifics across woody systems. This was only the case in relation to the frequency with which we observed aggregated distributions. The probability of observing spatial aggregation and the intensity of it was higher for plant species with large leaves but further depended on climatic parameters and mycorrhiza. Main Conclusions: Compared to climate variables, plant traits perform poorly in explaining the spatial structure of woody plant species, even though leaf area is a decisive plant trait that is related to whether we observe homogenous spatial aggregation and its intensity. Despite the limited variance explained by our models, we found that the spatial structure of woody plants is subject to consistent biogeographical constraints and that these exceed beyond descriptors of individual species, which we captured here through leaf area. [ABSTRACT FROM AUTHOR]

Objective To understand the changes in spatial aggregation of HIV cases among men who have sex with men (MSM) in Chengdu from 2010 to 2021, providing a scientific basis for the regional prevention and control of MSM cases. Methods Data were collected from the Chinese Disease Prevention and Control Information System. Spatial-temporal auto-correlation and aggregation analyses were conducted using ArcGIS 10.6 software, while StatsCan software was utilized for spatial-temporal scanning analysis. Results The average annual growth rate of homosexual transmission cases in Chengdu was 27.41% from 2010 to 2015 and -5.03% from 2015 to 2021. The HIV epidemic affected 370 towns/street areas, demonstrating spatial aggregation, with the epidemic concentrated in the central urban area. Over time, the northeastern region spread southeast, the southwestern area expanded southwest and south, and the northwestern region spread northward. The hotspot areas exhibited similar aggregation patterns. Spatial-temporal scanning analysis indicated that from 2013 to 2018, the primary aggregation areas comprised 40 streets in the central urban area. Conclusion During the period from 2010 to 2021, the trend of the HIV epidemic among MSM in Chengdu showed a rapid increase followed by a slow decline. The spatial distribution exhibited significant imbalances, with the epidemic spreading from the central urban area to the suburban areas. Increased efforts are needed to enhance HIV awareness in schools and areas with concentrated mobile populations. [ABSTRACT FROM AUTHOR]

The role of soil organic carbon (SOC) is crucial not only for numerous soil functions and processes but also for addressing various environmental crises and challenges we face. Consequently, the demand for information on the spatiotemporal variability of SOC is increasing, posing new methodological challenges, such as the need for information on SOC and SOC changes with quantified uncertainty across a wide variety of spatial scales and temporal periods. Our objective was to present a methodology based on a combination of machine learning and space–time geostatistics to predict the spatiotemporal variability of SOC stock with quantified uncertainty at various spatial supports (i.e., point support, 1 × 1 km, 5 × 5 km, 10 × 10 km, 25 × 25 km, counties, and the entire country) for Hungary, between 1992 and 2016. The role of geostatistics is pivotal, as it accounts for the spatiotemporal correlation of the interpolation errors, which is essential for reliably quantifying the uncertainty associated with spatially aggregated SOC stock and SOC stock change predictions. Five times repeated 10-fold leave-location-out cross-validation was used to evaluate the point support predictions and uncertainty quantifications, yielding acceptable results for both SOC stock (ME = −0.897, RMSE = 19.358, MEC = 0.321, and G = 0.912) and SOC stock change (ME = 0.414, RMSE = 16.626, MEC = 0.160, and G = 0.952). We compiled a series of maps of SOC stock predictions between 1992 and 2016 for each support, along with the quantified uncertainty, which is unprecedented in Hungary. It was demonstrated that the larger the support, the smaller the prediction uncertainty, which facilitates the identification and delineation of larger areas with statistically significant SOC stock changes. Moreover, the methodology can overcome the limitations of recent approaches in the spatiotemporal modelling of SOC, allowing the prediction of SOC and SOC changes, with quantified uncertainty, for any year, time period, and spatial scale. This methodology is capable of meeting the current and anticipated demands for dynamic information on SOC at both national and international levels.

Objective To analyze the epidemiological characteristics, changing trends, and spatial -temporal aggregation of hand-foot-mouth disease (HFMD) in Wenzhou from 2013 to 2022, so as to scientifically guide the decision-making of prevention and controL Methods The infectious disease report data for HFMD in Wenzhou city were collected from the China Disease Prevention and Control Information System for the period 2013--2022. Spatial -temporal scanning analysis was conducted using StatsCan software, and spatial autocorrelation analysis was performed using ArcMap 10.2 software to identify spatial -temporal aggregation patterns. Results A total of 260 401 cases of HFMD were reported in Wenzhou from 2013 to 2022, with an average annual incidence of 276.93 per 100 000 and a severe disease rate of 13,82 per 100 000, The incidence exhibited a decreasing trend (X2t«nd=26 402.606, P < 0.05). The incidence rate among individuals over 6 years old increased (X2tnd=l 814.455, P < 0.05), as did the rates among nursery children, students, and teachers (%2=5 493.085, P < 0.05; x2t«nd=l 752.584, P < 0.05;忙剧二9 424, P < 0,05). Spatial analysis revealed aggregation of HFMD cases in 2017, with a Moran I value of 0.433 514 (Z=2.42 775 and P=0.015 193), indicating positive spatial correlation and hot spots concentrated in Wencheng county and Pingyang county. Spatial-temporal scanning identified a primary aggregation covering RuiJ an city, Cangnan county, Pingyang county, and Wencheng county, from January 1, 2014, to December 31, 2018, with a radiation radius of 44.67km (LLR=12 534.45, RR=L9& P < 0,01). The proportion of enterovirus type 71 (EV-A71) in group A showed a downward trend (%2=171-245, P < 0.05), while the proportion of other enteroviruses, including coxsackievirus group A 16 (CV-A16), increased (%2tMnd=93.481!1 P < 0.05). Conclusion The incidence of HFMD in Wenzhou remains high, although both the incidence and severity rates have decreased. Other enteroviruses, including coxsackievirus group A 6 (CV-A6), have become the dominant pathogens, and there has been an increase in incidence among teachers and individuals over 6 years old. The main aggregation areas were identified as RuiJ an city, Cangnan county, Pingyang county, and Wencheng county. [ABSTRACT FROM AUTHOR]

Plant reproductive failure is a critical concern for conserving rare and endangered species that typically have low‐density and sparse populations. One important factor contributing to reproductive failure is the spatial arrangement of plants within a population, which can lead to isolation and negatively affect seed production, particularly in obligate outcrossers. Additionally, plant size can compound this effect, influencing seed production via multiple processes. Here, we investigate how spatial distribution and size influence the reproductive success of Vasconcellea chilensis, an endemic‐threatened papaya species in Chile. We first examined whether V. chilensis can produce seeds via apomixis using pollinator exclusion experiments. We then used Spatial Point Pattern Analysis (SPPA) in three populations to explore the spatial arrangement of plants. Finally, we assessed whether plant size and neighbor distance influence the reproductive success V. chilensis is a dioecious shrub unable to produce fruits through apomixis. The SPPA revealed significant clustering of female and male plants at different spatial scales, indicating a non‐random distribution. Moreover, a significant spatial association between the sexes was observed. In two populations, closer proximity to male plants was linked to higher seed production. Our study revealed that the reproductive system of V. chilensis is susceptible to distance‐dependent reproductive failure due to pollen limitation. While the species' spatial structure may partially mitigate this risk, female plants isolated from male counterparts will likely experience reduced seed set. [ABSTRACT FROM AUTHOR]

ObjectiveThis study aimed to provide suggestions for the prevention and control of bacterial food poisoning. The pattern of occurrence and epidemiological characteristics of bacterial food poisoning in China were determined， and its spatial aggregation was explored.MethodsDescriptive epidemiology was conducted to analyze and compare national bacterial food poisoning events from 2010 to 2019， and ArcGIS 10.6 was used to analyze the data spatially and draw relevant maps.ResultsA total of 1 110 cases of bacterial food poisoning were reported in China from 2010 to 2019， involving 49 457 poisonings and 87 deaths. Most bacterial food poisoning incidents occurred in the third quarter and in September. The highest number of incidents with identified causes was attributed to the contamination of meat and meat products， accounting for 19.10% of the total incidents. Non-typhoid Salmonella， Vibrio parahaemolyticus， and Bacillus cereus were the main causative agents of bacterial food poisoning， accounting for 56.04% and 61.17% of the total number of incidents and poisonings， respectively. The number of bacterial food poisoning incidents （39.37%） and morbidity incidents （34.76%） caused by collective canteens was the highest. The highest number of deaths （83.91%） from bacterial food poisoning occurred in households. Guangdong， Sichuan， Yunnan， Guangxi， and Shandong reported the highest number of incidents with the highest number of people poisoned in the country. The national bacterial food poisoning incidents exhibited a clustered spatial distribution， with “hot spots” mainly concentrated in parts of Yunnan， western and northern Guangxi， southern Sichuan， Chongqing， western and northern Hubei， western and southern Hu’nan， and central and northern Guangdong.ConclusionThe number of bacterial food poisoning incidents ranks first among all kinds of food poisoning incidents in China， suggesting the need for relevant departments to strengthen supervision and training， particularly in crucial settings such as collective canteens and catering service units. Various food safety standards and requirements should be strictly implemented， and publicity and education should be strengthened for key populations. It is recommended that hotspot regions of bacterial food poisoning explore the underlying causes and mechanisms of their spatial aggregation and implement corresponding measures to reduce the occurrence of such incidents.

In contrast to much of the literature that focuses on the issue of spatial dependence in housing price research, this study addresses the spatial aggregation of housing transactions and analyzes the effects of transaction hotspots and short-term flipping hotspots on housing prices by using real housing transaction data in Taipei City, Taiwan. The empirical results show that after controlling for the effects of spatial dependence and individual housing attributes, the impact of transaction hotspot areas on housing prices is significantly negative, while the impact of flipping hotspot areas on housing prices is significantly positive. The results verify that the key to driving up housing prices lies in flipping activities. Furthermore, the results of the spatial quantile regression model show that low-priced residential properties are more sensitive to the spatial concentration of housing transactions and flipping transactions in the housing market. Our results have implications for the government’s policy intending to control hot trade volumes to cool skyrocketing housing prices in a booming housing market. It is suggested that the government should pay attention to restraining short-term flipping activities in the housing market rather than setting constrains on housing transactions.

Channel attention has been shown to improve the performance of deep convolutional neural networks efficiently. Channel attention adaptively recalibrates the importance of each channel, determining what to attend to. However, channel attention only encodes inter-channel information but neglects the importance of positional information. Positional information is crucial in determining where to attend to. To address this issue, we propose a novel channel-spatial attention method named Spatial-Decomposition-Aggregation Attention (SDAA) method. First, a high-axis spatial direction is decomposed into multiple low-axis spatial directions. Then, a shared transformation sub-unit establishes attention in each low-axis space direction. Next, all the low-axis attention masks are aggregated into a high-axis attention mask. Finally, the generated high-axis attention mask is fused into the input features, thus enhancing the input features. Essentially, our method is a divide-and-conquer process. Experimental results demonstrate that our SDAA method outperforms the existing channel-spatial attention methods. [ABSTRACT FROM AUTHOR]

Dominance of neotropical tree communities by a few species is widely documented, but dominant trees show a variety of distributional patterns still poorly understood. Here, we used 503 forest inventory plots (93,719 individuals ≥2.5 cm diameter, 2609 species) to explore the relationships between local abundance, regional frequency and spatial aggregation of dominant species in four main habitat types in western Amazonia. Although the abundance‐occupancy relationship is positive for the full dataset, we found that among dominant Amazonian tree species, there is a strong negative relationship between local abundance and regional frequency and/or spatial aggregation across habitat types. Our findings suggest an ecological trade‐off whereby dominant species can be locally abundant (local dominants) or regionally widespread (widespread dominants), but rarely both (oligarchs). Given the importance of dominant species as drivers of diversity and ecosystem functioning, unravelling different dominance patterns is a research priority to direct conservation efforts in Amazonian forests. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of ecosystem service values (ESVs) and ecological risk are critical indicators to represent the regional ecological protection level and potential of sustainable development, which largely depend on land-use patterns. Aiming to contribute to global climate mitigation, China has proposed dual-carbon goals that would remarkably influence the land-use/cover change (LUCC) distribution. Based on the Landsat land cover data of 2000, 2010 and 2020 and multisource satellite products, several driving factors are integrated into the patch-generating land use simulation (PLUS) model to simulate future LUCC patterns for the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) under rapid urbanization, cropland protection and carbon neutral (CN) scenarios from 2020 to 2050. Spatial–temporal ecosystem service and ESVs are allocated using INVEST and the equivalent factor method and thus ecological risks are evaluated using the entropy method. Results indicate that forest growth is the largest under the CN scenario, especially in the northwestern and northeastern GBA, exceeding 25,800 km2 in 2050, which results in both the highest habitat quality and carbon storage. The largest ESVs, reaching higher than 5210 yuan/pixel, are found in the CN scenario, particularly expanding toward the suburban area, leading to the lowest ecological risks. From 2020 to 2050, habitat quality, carbon storage and ESVs improve, while ecological risks decline in the CN scenario. This research provides implications for economic and ecological balanced development and gives references to the carbon-neutral pathway for the GBA. [ABSTRACT FROM AUTHOR]

The conservation of traditional village clusters, especially those within traditional village aggregation areas, is an important part of historical cultural landscape preservation. With the hilly regions of Zhejiang province as the study area, this study innovatively proposed a traditional village aggregation area identification method from the perspective of historical layering. Because the influence of social economics on the spatial development of villages would gradually lead to spatially geographical features in the village evolution process, this study evaluated eight spatial characteristic indicators from natural geography, socioeconomics, and historical culture to identify ordinary traditional villages based on the threshold value extracted from protective traditional villages. In order to avoid the spatial identification deviation caused by only taking protective traditional villages as samples, we chose them together with ordinary traditional villages to identify 14 traditional village aggregation areas, which were categorized into four types based on regional protection conditions and development potential. This study expands the perspective for understanding the formation of the traditional villages' spatial characteristics and provides development guidance for traditional village aggregation areas in hilly regions of Zhejiang province. [ABSTRACT FROM AUTHOR]

Land degradation under the influence of global warming and ecological environmental destruction due to poor land management is the main challenge facing the Mongolian Plateau, and its future ecological risk change trends and drivers are also unclear. Therefore, to address the context relevant to this challenge, planning based on measured information from land use patterns is required. Based on land use and land cover (LULC), this study evaluates the landscape ecological risk (LER) of the Selenga River Basin by calculating the landscape pattern index. The spatiotemporal pattern and influencing factors of landscape ecological risk in the Selenga River Basin from 1990 to 2040 were analyzed. According to the results of LULC analysis, forest and grassland were the primary land use types in the Selenga River Basin. The built area, forest, and cropland showed an increasing trend, while the grassland area showed a fluctuating decreasing trend. From 1990 to 2010, the comprehensive land use dynamic degree showed a trend of rising first and then falling, specifically from 0.13% in 1990 to 0.29% in 2010, and will drop to 0.06% by 2040, indicating that the range of land use change is becoming more and more stable. The landscape ecological risk assessment shows a distribution pattern of "low at the edge and high in the middle". The landscape ecological risk index (LER) first increases and then decreases, with the peak value in 2010 (0.085). By calculating the spatial aggregation of LER and the partial correlation with climate, we found that the Moran's I index showed an "anti-V"-shaped change trend from 1990 to 2040, and the average landscape ecological risk presents positive spatial correlation, primarily with high-value aggregation, and peaked in 2010. Precipitation had a negative correlation with landscape ecological risk controlling for temperature, while there was a positive relationship between temperature and landscape ecological risk under the influence of controlling precipitation. This study provides a scientific basis for LULC planning in the Selenga River Basin, and is of great significance for maintaining the ecological security of the Mongolian Plateau. [ABSTRACT FROM AUTHOR]

The high-quality development of new agricultural business entities (NABEs) is an important driving force for realizing rural revitalization and accelerating the modernization of agriculture and rural areas. The main purpose of the study is to investigate the spatial distribution pattern, aggregation scales, development mechanism, and internal differences of various types of NABEs in different regions. It provides targeted ideas for alleviating regional differences in the development of NABEs in different agricultural regions. Kernel density estimation, nearest neighbor distance analysis, Tyson's polygon coefficient of variation, and Ripley's K function are used to study the spatial and temporal evolution, spatial aggregation, and scale divergence of various types of NABEs, and Pearson correlation analysis is incorporated to explore the specific factors affecting the development of various types of NABEs. The study results: First, family farms are the most widely distributed, and agricultural enterprises are the most sparsely distributed, being distributed "more in the southeast and less in the northwest" in all three categories. Second, the strongest aggregation scales of different NABEs are increasing, and the strongest aggregation scales of agricultural enterprises are larger than those of family farms and cooperatives in all agricultural areas. Third, the development of specialized farmers' cooperatives (abbreviated as 'cooperatives') is more constrained by traditional agricultural inputs and is a kind of agricultural input-oriented development. Family farms are more constrained by the living standards of rural residents in the region and are a kind of rural economy-oriented development. Agricultural enterprises are more subject to the economic level of the region, which is a kind of market economy-oriented development. Finally, in the process of developing NABEs, regional differences should be emphasized, and a small number of agriculturally leading enterprises and model cooperatives should drive a large number of small-scale family farms and smallholder farmers in order to become a characteristic path for China's agricultural development. [ABSTRACT FROM AUTHOR]

The identification, evaluation, and spatial distribution of "Production–Living–Ecological" space (PLEs) have been widely studied, but there is still little attention paid to whether their distribution characteristics will vary with scale changes. This article takes the organic whole of the PLEs composed of production space, living space, and ecological space on Hainan Island as the research object. Starting from the perspective of spatial heterogeneity, it quantitatively evaluates its spatial functions and explores the regularities of its aggregation and coordination characteristics with changes in scale, revealing the evolution of its distribution pattern with changes in scale. The results show that: (1) The distribution of PLEs in Hainan Island has obvious heterogeneity. The functional values of production and living space are distributed in a pattern of high in the south and north, low in the middle; The ecological space shows a high distribution pattern in the middle and low around it. (2) The PLEs in Hainan Island is significantly affected by scale effects. The degree of aggregation decreases as the scale increases, with the ecological space showing the most obvious downward trend, the living space showing a weaker downward trend and the production space being moderate. (3) The overall level of coupling-coordination of PLEs in Hainan Island is low, which decreases as the scale increases, with 500 m × 500 m being the peak value. The research results of this article indicate that there are scale effects in the functional distribution characteristics of PLEs, which can provide decision support for the national spatial planning at different scales. [ABSTRACT FROM AUTHOR]

Abstract Background Cardiovascular disease (CVD) is the leading cause of mortality worldwide and in Colombia. The analysis of CVD mortality has been mainly relied on individual factors and rates, but occurrence is also related to contextual conditions. Understanding the distribution of CVD in a region will contribute to implement more focused-preventive and care interventions. Methods Using the national mortality register established by the Department of National Statistics, standardized rates and spatial distribution of CVD mortality were estimated for Cali, Colombia, between 2010–2017. Global and local spatial aggregation was assessed using the Geary’s C test and for each district standardized mortality ratios smoothed by the Bayesian empirical were estimated. Results Over the period, CVD was the main cause of mortality with 28,804 deaths accounting for 23,8% of total deaths. The global CVD mortality rate varied from 235.9 to 257.4 per 100.000 habitants, with an average increase of 9.1% in the percentage change. The main cause of mortality were hypertensive diseases following by ischemic heart diseases and stroke. The standardized mortality ratios smoothed by the Bayesian empirical method showed that the districts 7, 13, 14, 15 and 16 located at the eastern area with the lowest socio-economic strata and the district 22 at the south of the city with the highest socio-economic strata had the high risks of CVD mortality. All these areas were at the boundary of the city. The the lowest risk was observed in districts 1 and 2 at the northwest area with the upper socio-economic strata. Over the study period, a spatial autocorrelation was found in the districts 1,9 10, 11, 12, 13, 14, 15, 19, and 21 by using the Geary’s C test. The highest significant spatial association was found in the districts 1 and 21. Conclusion Of 22 districts in Cali, the highest risk of CVD mortality was found in three at the lowest and one in the upper socio-economic strata between 2013 and 2017. Over the period a global spatial aggregation was identified due mainly to districts peripherical located suggesting that there could be contextual conditions influencing the risk. Therefore, there is a need for considering local conditions to prevent CVD mortality.

Background: Cardiovascular disease (CVD) is the leading cause of mortality worldwide and in Colombia. The analysis of CVD mortality has been mainly relied on individual factors and rates, but occurrence is also related to contextual conditions. Understanding the distribution of CVD in a region will contribute to implement more focused-preventive and care interventions. Methods: Using the national mortality register established by the Department of National Statistics, standardized rates and spatial distribution of CVD mortality were estimated for Cali, Colombia, between 2010–2017. Global and local spatial aggregation was assessed using the Geary’s C test and for each district standardized mortality ratios smoothed by the Bayesian empirical were estimated. Results: Over the period, CVD was the main cause of mortality with 28,804 deaths accounting for 23,8% of total deaths. The global CVD mortality rate varied from 235.9 to 257.4 per 100.000 habitants, with an average increase of 9.1% in the percentage change. The main cause of mortality were hypertensive diseases following by ischemic heart diseases and stroke. The standardized mortality ratios smoothed by the Bayesian empirical method showed that the districts 7, 13, 14, 15 and 16 located at the eastern area with the lowest socio-economic strata and the district 22 at the south of the city with the highest socio-economic strata had the high risks of CVD mortality. All these areas were at the boundary of the city. The the lowest risk was observed in districts 1 and 2 at the northwest area with the upper socio-economic strata. Over the study period, a spatial autocorrelation was found in the districts 1,9 10, 11, 12, 13, 14, 15, 19, and 21 by using the Geary’s C test. The highest significant spatial association was found in the districts 1 and 21. Conclusion: Of 22 districts in Cali, the highest risk of CVD mortality was found in three at the lowest and one in the upper socio-economic strata between 2013 and 2017. Over the period a global spatial aggregation was identified due mainly to districts peripherical located suggesting that there could be contextual conditions influencing the risk. Therefore, there is a need for considering local conditions to prevent CVD mortality. [ABSTRACT FROM AUTHOR]

The conservation of traditional village clusters, especially those within traditional village aggregation areas, is an important part of historical cultural landscape preservation. With the hilly regions of Zhejiang province as the study area, this study innovatively proposed a traditional village aggregation area identification method from the perspective of historical layering. Because the influence of social economics on the spatial development of villages would gradually lead to spatially geographical features in the village evolution process, this study evaluated eight spatial characteristic indicators from natural geography, socioeconomics, and historical culture to identify ordinary traditional villages based on the threshold value extracted from protective traditional villages. In order to avoid the spatial identification deviation caused by only taking protective traditional villages as samples, we chose them together with ordinary traditional villages to identify 14 traditional village aggregation areas, which were categorized into four types based on regional protection conditions and development potential. This study expands the perspective for understanding the formation of the traditional villages’ spatial characteristics and provides development guidance for traditional village aggregation areas in hilly regions of Zhejiang province.

Energy system models that consider variable renewable energy sources (VRESs) are computationally complex. The greater spatial scope and level of detail entailed in the models exacerbates complexity. As a complexity-reduction approach, this paper considers the simultaneous spatial and technological aggregation of energy system models. To that end, a novel two-step aggregation scheme is introduced. First, model regions are spatially aggregated to obtain a reduced region set. The aggregation is based on model parameters such as VRES time series, capacities, etc. In addition, spatial contiguity of regions is considered. Next, technological aggregation is performed on each VRES, in each region, based on their time series. The aggregations' impact on accuracy and complexity of a cost-optimal, European energy system model is analyzed. The model is aggregated to obtain different combinations of numbers of regions and VRES types. Results are benchmarked against an initial resolution of 96 regions, with 68 VRES types in each. System cost deviates significantly when lower numbers of regions and/or VRES types are considered. As spatial and technological resolutions increase, the cost fluctuates initially and stabilizes eventually, approaching the benchmark. Optimal combination is determined based on an acceptable cost deviation of <5% and the point of stabilization. A total of 33 regions with 38 VRES types in each is deemed optimal. Here, the cost is underestimated by 4.42 % , but the run time is reduced by 92.95 % . [ABSTRACT FROM AUTHOR]

The identification, evaluation, and spatial distribution of “Production–Living–Ecological” space (PLEs) have been widely studied, but there is still little attention paid to whether their distribution characteristics will vary with scale changes. This article takes the organic whole of the PLEs composed of production space, living space, and ecological space on Hainan Island as the research object. Starting from the perspective of spatial heterogeneity, it quantitatively evaluates its spatial functions and explores the regularities of its aggregation and coordination characteristics with changes in scale, revealing the evolution of its distribution pattern with changes in scale. The results show that: (1) The distribution of PLEs in Hainan Island has obvious heterogeneity. The functional values of production and living space are distributed in a pattern of high in the south and north, low in the middle; The ecological space shows a high distribution pattern in the middle and low around it. (2) The PLEs in Hainan Island is significantly affected by scale effects. The degree of aggregation decreases as the scale increases, with the ecological space showing the most obvious downward trend, the living space showing a weaker downward trend and the production space being moderate. (3) The overall level of coupling-coordination of PLEs in Hainan Island is low, which decreases as the scale increases, with 500 m × 500 m being the peak value. The research results of this article indicate that there are scale effects in the functional distribution characteristics of PLEs, which can provide decision support for the national spatial planning at different scales.

Human-environment relationship, as the interaction between human society and natural environment, is the inevitable way for sustainable development. The coordinated relationship between human and environment determines the quality of regional economic development and ecological environment. To coordinate human-environment relationship for natural resources can support human society development, we investigated the dynamic evolutionary characteristics of the human-environment coordination of the Yellow River Delta over the past 30 years using the coupled coordination model, artificial classification and spatial autocorrelation analysis. From the analytical results, the findings are as follows: (1) Human activity intensity in the Yellow River Delta increased to 35.24 % from 1990 to 2018, which spatial distribution pattern showed low in the coastal and high inland. Ecological carrying capacity was decreased by 1.61 × 104 hm2. The spatial distribution pattern showed the same characteristics as human activity intensity. (2) The area of human-environment coordination showed increasing trend from 1990 to 2018. Among them, “Severe Uncoordinated” and “High-quality Corrdianted” area changed the most obviously, the area of this two coordinated levels were increased by 731.15 km2 and 674.33 km2 respectively. “Good Coordinated” area dramatically decreased 1 479.61 km2, “Relatively Uncoordinated” and “Low Coordianted” were stable in the study area; (3) The coordination of human-environment indicates clear spatial agglomeration characteristics that, high aggregation in the coastal but low inland. Moreover, the research provides references for coordinating human-environment relationship.

This study proposes a human body motion prediction model that can adapt to the disorders in various human motion patterns and represent the kinematic constraints. In human motion prediction, the acquisition of features that capture inter-motion and inter-joint linkages is considered effective. To generate links that are adaptive to the reference time of dominant features and their crossing-over joints, we construct an attention-based network that aggregates motion sequences temporally and spatially. We evaluated the motion prediction results using the Human3.6M dataset with the indices of mean angle error and mean per joint position error and showed that our method outperforms other state-of-the-art methods.

It is imperative to rapidly and precisely acquire crop lodging area and severity for disaster prevention and yield prediction. However, estimation of crop lodging area at a large scale remains challenging due to the relatively low sensitivity of remote sensing signal to the lodging variation, limited availability of remote sensing images, and lodging statistical data. This study proposes a new method for lodging area estimation based on the optimal grid cell of Sentinel-2 and crop lodging percentage, overcoming the limitation of traditional pixel-based mapping approaches that fail to obtain quantitative lodging information. Basing the spatial aggregation method, we analyzed the optimal grid size of Sentinel-2 data for lodging percentage estimation. Then we investigated the spectral response for different lodging percentage levels and analyzed the potential of lodging percentage estimation for Sentinel-2 metrics (including selected spectral bands and their derived vegetation indexes (VIs)). A quantitative model was established between the training set and the Sentinel-2 metrics using the random forest (RF) algorithm. Finally, around 1462.62 ha fields from six counties or districts in Heilongjiang province in China were estimated for lodging percentage. Results indicate that the proposed method can estimate the crop lodging percentage on the testing set with an R2 and RMSE of 0.64 and 25.24, respectively, which can explain around 95 % spatial variation of lodging crop. Moreover, the overall magnitude of reflectance increased with the increase in lodging percentage. Among all Sentinel-2 optimal metrics, the Green, SWIR1, and Red edge 1 bands are the most crucial indicators for lodging percentage estimation. Our results on lodging percentage estimation in the study area indicate that there is more lodging maize in the Meilisidawoerzu district than in other areas. Although typhoons passed over Fuyu and Lindian counties, the lodging percentage in these areas is relatively low. The lodging percentage map has great value in agriculture management and insurance claim.

Based on the epidemic data of COVID-19 in 50 states of the United States (the US) from December 2021 to January 2022, the spatial and temporal clustering characteristics of COVID-19 in the US are explored and analyzed. First, the retrospective spatiotemporal analysis is performed by using SaTScan 9.5, and 17 incidence areas are obtained. Second, the reliability of the results is tested by the circular distribution method in the time latitude and the clustering method in the spatial latitude, and it is confirmed that the retrospective spatiotemporal analysis accurately measures in time and reasonably divides regions according to the characteristics in space. Empirical results show that the first-level clustering area of the epidemic has six states with an average relative risk of 1.28 and the second-level clustering area includes 18 states with an average relative risk of 0.86. At present, the epidemic situation in the US continues to expand. It is necessary to do constructive work in epidemic prevention, reduce the impact of epidemic, and effectively control the spread of the epidemic.

Abstract Land management is known to have consequences for biodiversity; however, our synthetic understanding of its effects is limited due to highly variable results across studies, which vary in the focal taxa and spatial grain considered, as well as the response variables reported. Such synthetic knowledge is necessary for management of agroecosystems for high diversity and function. To fill this knowledge gap, we investigated the importance of scale‐dependent effects of land management (LM) (pastures vs. meadows), on plant and soil microbe diversity (fungi and bacteria) across 5 study sites in Central Germany. Analyses included diversity partitioning of species richness and related biodiversity components (i.e., density of individuals, species‐abundance distribution, and spatial aggregation) at two spatial grains (α‐ and γ‐scale, 1 m2 and 16 km2, respectively). Our results show scale‐dependent patterns in response to LM to be the norm rather than the exception and highlight the importance of measuring species richness and its underlying components at multiple spatial grains. Our outcomes provide new insight to the complexity of scale‐dependent responses within and across taxonomic groups. They suggest that, despite close associations between taxa, LM responses are not easily extrapolated across multiple spatial grains and taxa. Responses of biodiversity to LM are often driven by changes to evenness and spatial aggregation, rather than by changes in individual density. High‐site specificity of LM effects might be due to a variety of context‐specific factors, such as historic land management, identity of grazers, and grazing regime. Synthesis and applications: Our results suggest that links between taxa are not necessarily strong enough to allow for generalization of biodiversity patterns. These findings highlight the importance of considering multiple taxa and spatial grains when investigating LM responses, while promoting management practices that do the same and are tailored to local and regional conditions.

Introduction: Data processing is a crucial step in energy system modelling which prepares input data from various sources into a format needed to formulate a model. Multiple open-source web-hosted databases offer pre-processed input data within the European context. However, the number of documented open-source data processing workflows that allow for the construction of energy system models with specified spatial resolution reduction methods is still limited. Methods: The first step of the data-processing method builds a dataset using web-hosted pre-processed data and open-source software. The second step aggregates the dataset using a specified spatial aggregation method. The spatially aggregated dataset is used as input data to construct sector-coupled energy system models. Results: To demonstrate the application of the data processing process, three power and heat optimisation models of Germany were constructed using the proposed data processing approach. Significant variation in generation, transmission and storage capacity of electricity were observed between the optimisation results of the energy system models. Conclusions: This paper presents a novel data processing approach to construct sector-coupled energy system models with integrated spatial aggregations methods.

Airborne measurements of sun-induced chlorophyll fluorescence (SIF) are a promising tool for monitoring plant functioning on different scales. However, currently operational airborne imaging spectrometers for SIF measurements still have limited spatial resolution and pointing accuracy. This is challenging in terms of the practical use of SIF maps for crop breeding and plant phenotyping. We developed and tested two spatial aggregation approaches to make airborne SIF data usable in experimental settings with a high number of small experimental plots. The two aggregation approaches generating representative SIF values for experimental plots demonstrated the potential to be used in crop phenotyping. The first aggregation approach (Approach A) aggregates pixel values directly on SIF maps, whereas the second approach (Approach B) aggregates at-sensor radiance before SIF retrieval. The statistical analysis showed that Approaches A and B led to significantly different SIF products for single experimental plots (p

Understanding how species are non‐randomly distributed in space and how the resulting spatial structure responds to ecological, biogeographic, and anthropogenic drivers is a critical piece of the biodiversity puzzle. However, most metrics that quantify the spatial structure of diversity (i.e., community differentiation), such as Whittaker's β‐diversity, depend on sampling effort and are influenced by species pool size, species abundance distributions, and numbers of individuals. Null models are useful for identifying the degree of differentiation among communities due to spatial structuring relative to that expected from sampling effects, but do not accommodate the influence of sample completeness (i.e., the proportion of the species pool in a given sample). Here, we develop an approach that makes use of individual‐ and coverage‐based rarefaction and extrapolation, to derive a metric, βC, which captures changes in intraspecific aggregation independently of changes in the species pool size. We illustrate the metric using spatially explicit simulations and two case studies: (1) a re‐analysis of the "Gentry" plot data set consisting of small forest plots spanning a latitudinal gradient from North to South America and (2) comparing a large plot in high diversity tropical forests of Barro Colorado Island, Panama, with a plot in a lower diversity temperate forest in Harvard Forest, Massachusetts, USA. We find no evidence for systematic changes in spatial structure with latitude in these data sets. As it is rooted in biodiversity sampling theory and explicitly controls for sample completeness, our approach represents an important advance over existing null models for spatial aggregation. Potential applications range from better descriptors of biogeographic diversity patterns to the consolidation of local and regional diversity trends in the current biodiversity crisis. [ABSTRACT FROM AUTHOR]

Abstract Understanding how grazing activity drives plant community structure or the distribution of specific species in a community remains a major challenge in community ecology. The patchiness or spatial aggregation of specific species can be quantified by analyzing their relative coordinates in the community. Using variance and geostatistical analysis methods, we examined the quantitative characteristics and spatial distribution of Stipa breviflora in a desert steppe in northern China under four different grazing intensities (no grazing, NG, light grazing, LG, moderate grazing, MG, and heavy grazing, HG) at three small spatial scales (10 × 10 cm, 20 × 20 cm, 25 × 25 cm). We found that grazing significantly increased cover, density, and proportion in standing crop of S. breviflora, but decreased height. The spatial distribution of S. breviflora was strongly dependent upon the sampling unit and grazing intensity. The patchiness of S. breviflora reduced with sampling scale, and spatial distribution of S. breviflora was mainly determined by structural factors. The intact clusters of S. breviflora were more fragmented with increasing grazing intensity and offspring clusters spread out from the center of the parent plant. These findings suggest that spatial aggregation can enhance the ability of S. breviflora to tolerate grazing and that smaller isolated clusters are beneficial to the survival of this dominant species under heavy grazing.

Ecosystem services of Amazonian forests are disproportionally produced by a limited set of hyperdominant tree species. Yet the spatial variation in the delivery of ecosystem services by individual hyperdominant species across their distribution ranges and corresponding environmental gradients is poorly understood. Here, we use the concept of habitat quality to unravel the effect of environmental gradients on seed production and aboveground biomass (AGB) of the Brazil nut, one of Amazonia’s largest and most long-lived hyperdominants. We find that a range of climate and soil gradients create trade-offs between density and fitness of Brazil nut trees. Density responses to environmental gradients were in line with predictions under the Janzen–Connell and Herms–Mattson hypotheses, whereas tree fitness responses were in line with resource requirements of trees over their life cycle. These trade-offs resulted in divergent responses in area-based seed production and AGB. While seed production and AGB of individual trees (i.e., fitness) responded similarly to most environmental gradients, they showed opposite tendencies to tree density for almost half of the gradients. However, for gradients creating opposite fitness-density responses, area-based seed production was invariable, while trends in area-based AGB tended to mirror the response of tree density. We conclude that while the relation between environmental gradients and tree density is generally indicative of the response of AGB accumulation in a given area of forest, this is not necessarily the case for fruit production.

Ecosystem services of Amazonian forests are disproportionally produced by a limited set of hyperdominant tree species. Yet the spatial variation in the delivery of ecosystem services by individual hyperdominant species across their distribution ranges and corresponding environmental gradients is poorly understood. Here, we use the concept of habitat quality to unravel the effect of environmental gradients on seed production and aboveground biomass (AGB) of the Brazil nut, one of Amazonia's largest and most long-lived hyperdominants. We find that a range of climate and soil gradients create trade-offs between density and fitness of Brazil nut trees. Density responses to environmental gradients were in line with predictions under the Janzen–Connell and Herms–Mattson hypotheses, whereas tree fitness responses were in line with resource requirements of trees over their life cycle. These trade-offs resulted in divergent responses in area-based seed production and AGB. While seed production and AGB of individual trees (i.e., fitness) responded similarly to most environmental gradients, they showed opposite tendencies to tree density for almost half of the gradients. However, for gradients creating opposite fitness-density responses, area-based seed production was invariable, while trends in area-based AGB tended to mirror the response of tree density. We conclude that while the relation between environmental gradients and tree density is generally indicative of the response of AGB accumulation in a given area of forest, this is not necessarily the case for fruit production. [ABSTRACT FROM AUTHOR]

β‐diversity is a primary biodiversity pattern for inferring community assembly. A randomized null model that generates a standardized β‐deviation has been widely used for this purpose. However, the null model has been much debated and its application is limited to abundance data.Here we derive analytical models for β‐diversity to address the debate, clarify the interpretation and extend the application to occurrence data.The analytical analyses show unambiguously that the standardized β‐deviation is a quantification of the effect size of non‐random spatial distribution of species on β‐diversity for a given species abundance distribution. It robustly scales with sampling effort following a power law with exponent of 0.5. This scaling relationship offers a simple method for comparing β‐diversity of communities of different sizes.Assuming log‐series distribution for the metacommunity species abundance distribution, our model allows for calculation of the standardized β‐deviation using occurrence data plus a datum on the total abundance.Our theoretical model justifies and generalizes the use of the β null model for inferring community assembly rules. [ABSTRACT FROM AUTHOR]

Abstract Untangling organisms' multi‐scale spatial distributions is challenging due to their interactions with environments at multiple spatial and temporal scales. We deployed an Adaptive Resolution Imaging Sonar (ARIS) in a Chesapeake Bay sub‐estuary to investigate multi‐scale spatial distributions of the bay nettle (Chrysaora chesapeakei) in May–September of 2016 and 2017. Nettles were found to be dispersed in aggregations of multiple individuals. The average density of bay nettles in 2017 was higher than in 2016. Small aggregations (30 m) contributed ~40% of the total observed nettles. Large patches were more common in creek habitat where nettle density was higher. Nettle density was found to hit a peak value once in 2016, while there were two density peaks in 2017. Different aggregation patterns were observed during the second peak period in 2017 in which the number of large patches increased dramatically. Within the surveyed waterscape, the spatial patterns were consistent over time with higher abundance in the source creek than in the river channel, which underscores that C. chesapeakei requires hard substrate in shallow creeks for its benthic polyp stage. Using the ratio between nettles in the creek and near creek mouth as a proxy for dispersal rate, more nettles were transported out of the creek in 2017 than in 2016. The increase in patch size and high dispersion rate in peak periods in 2017 suggests that individuals were moving out of the creek habitat as density increased. Results highlight the complex spatial structure of bay nettles, which has major impacts on density estimates and subsequently affects our understanding of jellyfish population dynamics and long‐term trends.

Land management is known to have consequences for biodiversity; however, our synthetic understanding of its effects is limited due to highly variable results across studies, which vary in the focal taxa and spatial grain considered, as well as the response variables reported. Such synthetic knowledge is necessary for management of agroecosystems for high diversity and function.To fill this knowledge gap, we investigated the importance of scale‐dependent effects of land management (LM) (pastures vs. meadows), on plant and soil microbe diversity (fungi and bacteria) across 5 study sites in Central Germany. Analyses included diversity partitioning of species richness and related biodiversity components (i.e., density of individuals, species‐abundance distribution, and spatial aggregation) at two spatial grains (α‐ and γ‐scale, 1 m2 and 16 km2, respectively).Our results show scale‐dependent patterns in response to LM to be the norm rather than the exception and highlight the importance of measuring species richness and its underlying components at multiple spatial grains.Our outcomes provide new insight to the complexity of scale‐dependent responses within and across taxonomic groups. They suggest that, despite close associations between taxa, LM responses are not easily extrapolated across multiple spatial grains and taxa. Responses of biodiversity to LM are often driven by changes to evenness and spatial aggregation, rather than by changes in individual density. High‐site specificity of LM effects might be due to a variety of context‐specific factors, such as historic land management, identity of grazers, and grazing regime.Synthesis and applications: Our results suggest that links between taxa are not necessarily strong enough to allow for generalization of biodiversity patterns. These findings highlight the importance of considering multiple taxa and spatial grains when investigating LM responses, while promoting management practices that do the same and are tailored to local and regional conditions. [ABSTRACT FROM AUTHOR]

Untangling organisms' multi‐scale spatial distributions is challenging due to their interactions with environments at multiple spatial and temporal scales. We deployed an Adaptive Resolution Imaging Sonar (ARIS) in a Chesapeake Bay sub‐estuary to investigate multi‐scale spatial distributions of the bay nettle (Chrysaora chesapeakei) in May–September of 2016 and 2017. Nettles were found to be dispersed in aggregations of multiple individuals. The average density of bay nettles in 2017 was higher than in 2016. Small aggregations (<5 m) were persistent in both years but only contributed <10% of total observed nettles. Large patches (>30 m) contributed ~40% of the total observed nettles. Large patches were more common in creek habitat where nettle density was higher. Nettle density was found to hit a peak value once in 2016, while there were two density peaks in 2017. Different aggregation patterns were observed during the second peak period in 2017 in which the number of large patches increased dramatically. Within the surveyed waterscape, the spatial patterns were consistent over time with higher abundance in the source creek than in the river channel, which underscores that C. chesapeakei requires hard substrate in shallow creeks for its benthic polyp stage. Using the ratio between nettles in the creek and near creek mouth as a proxy for dispersal rate, more nettles were transported out of the creek in 2017 than in 2016. The increase in patch size and high dispersion rate in peak periods in 2017 suggests that individuals were moving out of the creek habitat as density increased. Results highlight the complex spatial structure of bay nettles, which has major impacts on density estimates and subsequently affects our understanding of jellyfish population dynamics and long‐term trends. [ABSTRACT FROM AUTHOR]

Areas of improving and degrading groundwater-quality conditions in the State of California were assessed using spatial weighting of a new metric for scoring wells based on constituent concentrations and the direction and magnitude of a trend slope (Sen). Individual well scores were aggregated across 2135 equal-area grid cells covering the entire groundwater resource used for public supply in the state. Spatial weighting allows results to be aggregated locally (well or grid cell), regionally (groundwater basin), provincially, or statewide. Results differentiate degrading (increasing concentration trends) areas with low to moderate concentrations (unimpaired) from degrading areas with moderate to high concentrations (impaired). Results also differentiate improving areas (decreasing concentration trends) in the same manner. Multi-year to decadal groundwater-quality trends were computed from periodic, inorganic water-quality data for 38 constituents collected between 1974 and 2014 for compliance monitoring of nearly 13,000 public-supply wells (PSWs) in the State of California. Mann-Kendall (MK) rank correlations and Sen's slope estimator were used to detect statistically significant trends for the entire period of recorded data (long-term trend), for the period since 2000 (recent trend), for different pumping seasons (seasonal trend), and for reversals of trends. Statewide, the most frequently detected trends since 2000 were for nitrate (36%), gross alpha/uranium (10%), arsenic (14%), total dissolved solids (TDS) (23%), and the major ions that contribute to TDS (19–28%). The Transverse and Selected Peninsular Ranges (TSPR) and the San Joaquin Valley (SJV) hydrogeologic provinces had the largest percentage of areas with moderate to high nitrate concentrations and groundwater quality trends. Improving nitrate concentrations in parts of the TSPR is associated with long-term managed aquifer recharge that has replaced historical, agriculturally affected groundwater with low-nitrate recharge in parts of the TSPR. This example suggests that application of dilute, excess surface water to agricultural fields during the winter could improve groundwater-quality in the SJV over the long term. [ABSTRACT FROM AUTHOR]

Objective: The identification of neighborhoods where registered sex offenders (RSOs) reside may facilitate evidence-based service delivery and increase neighborhood capacity building. This study aims to determine if there is a patterned spatial aggregation of RSOs and to identify the neighborhood characteristics of RSO locations. Method: We used ArcGIS to match residential information from 938 Ohio ZIP Code areas with census data and conducted spatial autocorrelation analyses to explore and map spatial clustering of RSO residential locations. We used ordinary least squares and spatial regression models to estimate the association between neighborhood characteristics and the rate of RSOs. Results: The percentages of non-Hispanic Black population, Hispanic population, and poverty were positively associated with the rate of RSO residences; percentages of female-headed households, having a bachelor's degree, and owner-occupied households were negatively associated with the rate of RSO residences. Overall, RSOs were spatially aggregated in socially disorganized areas. Conclusions: The findings can be used to promote evidence-informed policies to manage decarcerated RSOs, distribute limited resources to assist former offenders' rehabilitation, and empower communities in disadvantaged neighborhoods to advance smart decarceration. [ABSTRACT FROM AUTHOR]

Abstract Background Life expectancy at birth (LE) has been calculated for states and counties. LE estimates at these levels mask health disparities in local communities. There are no nationwide estimates at the sub-county level. We present a stepwise approach for calculating LE using census tracts in New York state to identify health disparities. Methods Our study included 2751 census tracts in New York state, but excluded New York City. We used population data from the 2010 United States Census and 2008–2010 mortality data from the state health department. Tracts were assigned to 99.97% of the deaths. We removed tracts which had a majority of people living in group quarters. Deaths in these tracts are often recorded elsewhere. Of the remaining 2679 tracts, 6.6% of the tracts had standard errors ≥ 2 years. A geographic aggregation tool was used to aggregate tracts with fewer than 60 deaths, and then aggregate areas that had standard errors of ≥ 2 years. Results Aggregation resulted in a 9.9% reduction in the number of areas. Tracts with

As one of the few generalities in ecology, Taylor’s power law admits a power function relationship V = aMb between the variance V and mean number M of organisms in a quadrat. We examine the spatial distribution data of seven urban service facilities in 37 major cities in China, and find that Taylor’s Law is validated among all types of facilities. Moreover, Taylor’s Law is robust if we shift the observation window or vary the size of the quadrats. The exponent b increases linearly with the logarithm of the quadrat size, i.e. b(s) = b0 + A log (s). Furthermore, the ANOVA test indicates that b takes distinct values for different facilities in different cities. We decompose b into two different factors, a city-specific factor and a facility-specific factor (FSF). Variations in b can be explained to a large extent by the differences between cities and types of facilities. Facilities are more evenly distributed in larger and more developed cities. Competitive interchangeable facilities (e.g. pharmacy), with larger FSFs and smaller bs, are less aggregated than complementary services (e.g. restaurants).

Most plant species are spatially aggregated. Local demographic and ecological processes (e.g. vegetative growth and limited seed dispersal) result in a clustered spatial pattern within an environmentally homogenous area. Spatial aggregation should be considered when modelling plant abundance data.Commonly, plant abundance is quantified by measuring cover within multiple areal plots or along multiple lines randomly placed within a study area. A common practice for analysing plant cover is to use statistical methods that rely on the normal distribution for quantifying uncertainty. This is problematic because plant cover data tend to be left‐skewed (J‐shaped), right skewed (L‐shaped) or U‐shaped and, therefore, commonly violate classic statistical assumptions, such as normality.We outline statistical analyses that explicitly account for spatial aggregation by assuming that plant cover is beta‐distributed. The beta distribution is a flexible choice because within the open unit interval it can take on a wide range of shapes (L, J, U, or a bell‐shaped). We discuss and introduce extensions to the beta distribution that address common analysis issues encountered in plant cover datasets, such as (a) the treatment of zero and one cover values, (b) hierarchical data structures and (c) observations errors. For heuristic purposes, we focus on single species analyses, but we demonstrate how the outlined methods can be generalized to more species.The assumption that plant cover is beta‐distributed allows us to estimate the degree of spatial aggregation, and the ecological significance of this new knowledge is discussed. We provide a summary of available software for analyses (emphasizing standard R packages) and include worked examples and a simulation study comparing analysis options as supplemental information.Synthesis. Previously, the state of the statistical software made it practically difficult for empirical plant ecologists to analyze their cover data correctly, but new theory and R‐packages have been developed, and this difficulty no longer exists. We recommend that empirical plant ecologists embrace the new statistical possibilities for exploring the exciting ecological features in spatial variation of plant cover. [ABSTRACT FROM AUTHOR]

Aim: The interaction of land use with local versus regional processes driving biological homogenization (β‐diversity loss) is poorly understood. We explored: (a) stream β‐diversity responses to land cover (forest versus agriculture) in terms of physicochemistry and physicochemical heterogeneity; (b) whether these responses were constrained by the regional species pool, i.e. γ‐diversity, or local assembly processes through local (α) diversity; (c) whether local assembly operated through the regional species abundance distribution (SAD) or intraspecific spatial aggregation; and (d) the dependence on body size, dispersal capacity and trophic level (producer versus consumer). Location: USA, Canada and France. Time period: 1993–2012. Major taxa studied: Stream diatoms, insects and fish. Methods: We analysed six datasets totalling 1,225 stream samples. We compared diversity responses to eutrophication and physicochemical heterogeneity in forested versus agricultural streams with regression methods. Null models quantified the contribution of local assembly to β‐diversity (β‐deviance, βDEV) for both types of land covers and partitioned it into fractions explained by the regional SAD (βSAD) versus aggregation (βAGG). Results: Eutrophication explained homogenization and more uneven regional SADs across groups, but local and regional biodiversity responses differed across taxa. The βDEV was insensitive to land use. The βSAD largely exceeded βAGG and was higher in agriculture. Main conclusions: Eutrophication but not physicochemical heterogeneity of agricultural streams underlay β‐diversity loss in diatoms, insects and fish. Agriculture did not constrain the magnitude of local versus regional effects on β‐diversity but controlled the local assembly mechanisms. Although the SAD fraction dominated in both land covers, it increased further in agriculture at the expense of aggregation. Notably, the regional SADs were more uneven in agriculture, exhibiting excess common species or stronger dominance. Diatoms and insects diverged from fish in terms of biodiversity, SAD shape and βDEV patterns, suggesting an overriding role of body size and/or dispersal capacity compared with trophic position. [ABSTRACT FROM AUTHOR]

Many ecological applications, like the study of mortality rates, require the estimation of proportions and confidence intervals for them. The traditional way of doing this applies the binomial distribution, which describes the outcome of a series of Bernoulli trials. This distribution assumes that observations are independent and the probability of success is the same for all the individual observations. Both assumptions are obviously false in many cases.I show how to apply bootstrap and the Poisson binomial distribution (a generalization of the binomial distribution) to the estimation of proportions. Any information at the individual level would result in better (narrower) confidence intervals around the estimation of proportions. As a case study, I applied this method to the calculation of mortality rates in a forest plot of tropical trees in Lambir Hills National Park, Malaysia.I calculated central estimates and 95% confidence intervals for species‐level mortality rates for 1,007 tree species. I used a very simple model of spatial dependence in survival to estimate individual‐level risk of mortality. The results obtained by accounting for heterogeneity in individual‐level risk of mortality were comparable to those obtained with the binomial distribution in terms of central estimates, but the precision increased in virtually all cases, with an average reduction in the width of the confidence interval of ~20%.Spatial information allows the estimation of individual‐level probabilities of survival, and this increases the precision in the estimates of mortality rates. The general method described here, with modifications, could be applied to reduce uncertainty in the estimation of proportions related to any spatially structured phenomenon with two possible outcomes. More sophisticated approaches can yield better estimates of individual‐level mortality and thus narrower confidence intervals. [ABSTRACT FROM AUTHOR]

Aim: The relationship between the proportion of sites occupied by a species and the area of a site [occupancy–area relationship (OAR)] offers key information for biodiversity management and has long fascinated ecologists. We quantified the variation in OAR for 3,157 woody species in 17 forest plots worldwide and tested the relative importance of environment and species traits for explaining this variation and evaluated overall model predictive ability. Location: Global. Time period: Early 21st century. Major taxa studied: Woody plants. Methods: We used mixed‐effect regression to examine the observed shape of the OAR (its "slope") against species‐specific and plot‐wide predictors: coarse‐grain occupancy, tree size, plot species richness, energy availability and topographic complexity. Results: We found large variation in OAR slopes, and the variation was strongest among species within plots. The OAR slopes showed a latitudinal trend and were steeper near the equator. As predicted, coarse‐grain occupancy and tree size negatively affected OAR slopes, whereas species richness had a positive effect and explained most of the variance between plots. Although hypothesized directionalities were broadly confirmed, traits and environment had relatively limited overall predictive power. Main conclusions: These results document the variation of the OAR for 3,157 species at near‐global extent. We found a latitudinal gradient in OAR slopes and confirmed key hypothesized predictors. But at this global extent and over the large set of species analysed, the remaining unexplained variation in OAR slopes was substantial. Nevertheless, this large‐scale empirical analysis of the OAR offers an initial step towards a more general use of OARs for the fine‐scale prediction of species distributions and abundance. [ABSTRACT FROM AUTHOR]

Understanding how grazing activity drives plant community structure or the distribution of specific species in a community remains a major challenge in community ecology. The patchiness or spatial aggregation of specific species can be quantified by analyzing their relative coordinates in the community. Using variance and geostatistical analysis methods, we examined the quantitative characteristics and spatial distribution of Stipa breviflora in a desert steppe in northern China under four different grazing intensities (no grazing, NG, light grazing, LG, moderate grazing, MG, and heavy grazing, HG) at three small spatial scales (10 × 10 cm, 20 × 20 cm, 25 × 25 cm). We found that grazing significantly increased cover, density, and proportion in standing crop of S. breviflora, but decreased height. The spatial distribution of S. breviflora was strongly dependent upon the sampling unit and grazing intensity. The patchiness of S. breviflora reduced with sampling scale, and spatial distribution of S. breviflora was mainly determined by structural factors. The intact clusters of S. breviflora were more fragmented with increasing grazing intensity and offspring clusters spread out from the center of the parent plant. These findings suggest that spatial aggregation can enhance the ability of S. breviflora to tolerate grazing and that smaller isolated clusters are beneficial to the survival of this dominant species under heavy grazing. [ABSTRACT FROM AUTHOR]

Carbon dioxide emissions within regions affect one another in space, and factors that influence carbon dioxide emissions show different spatial characteristics. To show the spatial aggregation of carbon dioxide emissions and the heterogeneity of the influencing factors, the spatial aggregation analysis model and spatial heterogeneity analysis model were constructed in this study. The Global Moran-Index (Moran’sI) was used to construct the spatial aggregation analysis model of carbon dioxide emissions, and the spatial geographic weighted regression analysis was used to construct the heterogeneous model of the influencing factors. The aggregation and heterogeneity were analyzed, and the validity of the model results was verified by unbiased estimation test and significance analysis. Results demonstrate that, the Moran'sI value of carbon dioxide emissions is greater than 0.6, and carbon dioxide emissions indicate strong spatial correlation and clear spatial agglomeration characteristics; By using the weighted regression of spatial geography, it is also found that the spatial affect of influencing factors on carbon dioxide emissions presents different distribution characteristics, thereby confirming the existence of spatial heterogeneity. The study provides references for the implementation of regional energy conservation and carbon emission reduction policies. [ABSTRACT FROM AUTHOR]

Scale effects are inherent in spatial analysis. Quantitative knowledge about them is necessary for properly interpreting and scaling analysis results. The objective of this study was to systematically model patch area scaling and the associated uncertainty. A hybrid approach was taken to tackle the difficulty involved. Recognizing that patch’s size and shape play the key role in shaping its scaling behavior, a function model of patch area scaling based on patch morphology was first conceptually formulated. It was then substantiated by sampling and interpolating in the scale-integrated domain of patch morphology, which is characterized by a one-dimensional size index, namely the relative support range (RSR), and a compactness index, namely filling. The area scaling model obtained unveils a simple consistent scaling pattern of all patches and an overall fading range between 0.12 and 3.16 in terms of RSR. The uncertainty model built exhibits a filling-dependent pattern of the variance of patch area, which can be as large as 0.67 (i.e., 67%) in terms of standard deviation. The models were validated by using them to predict patch and class area scaling of the test patches and landscapes. This study demonstrated the basic feasibility of analytically modeling scaling behavior. It also revealed the uncertainty of scale effects is very significant due to the inevitable randomness in rasterization.