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To evaluate the relative importance of climate, productivity, environmental heterogeneity, biotic associations and habitat use by cattle to account for the species richness of trees, shrubs and herbs across the Subantarctic-Patagonian transition. An area of c. 150 x 150 km, within the transition zone between the Subantarctic and Patagonian subregions on the eastern slope of the Andes (c. 39-42° S, 70-72° W). All vascular plants found at each one of 50 (10 x 10 m) sampling plots were counted to estimate the local tree, shrub and herb species richness. Path analysis was used to evaluate the relationship between the richness of the three life-forms and plant cover, dried litter biomass, mean annual temperature, annual precipitation, daily temperature range, substrate heterogeneity and number of faecal pats. Principal coordinates of neighbour matrices was used to model the spatial autocorrelation of the data. Total plant species richness showed a unimodal pattern of spatial variation across the transition. Richness responded positively to indirect effects of precipitation mediated through plant cover, but there was a negative overall effect of precipitation on richness towards the west of the transition, most strongly for trees. An increase in substrate heterogeneity promoted a local increase in herb and shrub richness; the richness of trees increased in sites with steeper slopes. Canopy closure had a direct negative impact on herb richness; it also increased the local accumulation of litter, which negatively affected shrub and herb richness. The impact of habitat use by cattle negatively affected herb richness in areas to the east of the biogeographical transition. We suggest that the importance of indirect climatic effects mediated by vegetation cover can account for species richness patterns across this transition, most strongly for woody species, which supports the productivity hypothesis. The southern temperate forests towards the west may represent a deviation from the predictions of the water-energy dynamics hypothesis. Dissimilar spatial patterns of variation in the richness of woody and herbaceous species, and their different responses to climatic and heterogeneity variables across the transition, suggest that plant life-form influences the plant species richness-environment relationships.

Aim: Our goal was to use the occurrence of the influenza A virus in wild birds in Japan to create a potential risk map for the spread of avian influenza by migratory birds. Our modelling included a consideration of the multicollinearity and spatial autocorrelation of environmental variables and an examination of the reproducibility of the model results. Location: Japan. Methods: We used the maximum entropy approach to generate potential distribution models from presence-only data. Independent variables in the model included environmental factors such as winter temperature and precipitation, host factors such as duck population size and habitat abundance and artificial factors such as size of urban areas and poultry density. We used eigenvector-based spatial filters to alleviate spatial autocorrelation. To explore the reliability of the model, we compared the risk indices of localities positive in past winters for the influenza A virus in wild birds with those of all localities. Results: The model alleviated spatial autocorrelation with a high degree of accuracy. Dabbling duck population, size of urban area, diving duck population and altitude were the variables that were most strongly correlated with the potential distribution of avian influenza. We used the frequency of occurrence of the influenza A virus in five recent years in localities where wild birds were infected to estimate the repeatability of the high-risk indices; the potential risk indices for avian influenza in wild birds were high in localities where wild birds were infected in past. Main conclusions: The dabbling duck population in an area appeared to be the best indicator of high risk for the introduction of avian influenza from abroad. Priority monitoring localities for avian influenza carried by wild birds should be designated in western Japan and along the Pacific coast, which we estimated to be high-risk areas. Poultry farms in these areas should increase their biosecurity to prevent vectors from introducing avian influenza.

Web service can bring together applications running on diverse platforms, users can access and share various data, information and models more effectively and conveniently from certain web service platform. Cloud computing emerges as a paradigm of Internet computing in which dynamical, scalable and often virtualized resources are provided as services. With the rampant growth of massive data and restriction of net, traditional web services platforms have some prominent problems existing in development such as calculation efficiency, maintenance cost and data security. In this paper, we offer a spatial statistics service based on Microsoft cloud. An experiment was carried out to evaluate the availability and efficiency of this service. The results show that this spatial statistics service is accessible for the public conveniently with high processing efficiency.

Geospatial phenomena often have spatial heterogeneity, which is caused by differences in the data generation process from place to place. There are two types of spatial heterogeneity: continuous and discrete, and there has been much discussion about how to analyze one type of spatial heterogeneity. Although geospatial phenomena can have both types of spatial heterogeneities, previous studies have not sufficiently discussed how to consider these two different types of spatial heterogeneity simultaneously and how to detect them separately, which may lead to biased estimates and the wrong interpretation of geospatial phenomena. This study proposes a new approach for the analysis of spatial data with both heterogeneities by combining the eigenvector spatial filtering-based spatially varying coefficient (ESF-SVC) model, which assumes the continuous spatial heterogeneity and generalized lasso (GL) estimation, which assumes discrete spatial heterogeneity and proposes the ESF-GL-SVC model. The performance of ESF-GL-SVC was evaluated through experiments based on a Monte Carlo simulation and confirms that the ESF-GL-SVC showed better performance in estimating coefficients with both types of spatial heterogeneity than the previous two models. The application of the apartment rent data showed that the ESF-GL-SVC outputs the result with the smallest BIC value, and the estimated coefficients depict continuous and discrete spatial heterogeneity in the dataset. Reasonable coefficients were estimated using the ESF-GL-SVC, although some coefficients by ESF-SVC were not.

The novel coronavirus disease (COVID-19) has become a public health problem at a global scale because of its high infection and mortality rate. It has affected most countries in the world, and the number of confirmed cases and death toll is still growing rapidly. Susceptibility studies have been conducted in specific countries, where COVID-19 infection and mortality rates were highly related to demographics and air pollution, especially PM2.5, but there are few studies on a global scale. This paper is an exploratory study of the relationship between confirmed COVID-19 cases and death toll per million population, population density, and PM2.5 concentration on a worldwide basis. A multivariate linear regression based on Moran eigenvector spatial filtering model and Geographically weighted regression model were undertaken to analyze the relationship between population density, PM2.5 concentration, and COVID-19 infection and mortality rate, and a geostatistical method with bivariate local spatial association analysis was adopted to explore their spatial correlations. The results show that there is a statistically significant positive relationship between COVID-19 confirmed cases and death toll per million population, population density, and PM2.5 concentration, but the relationship displays obvious spatial heterogeneity. While some adjacent countries are likely to have similar characteristics, it suggests that the countries with close contacts/sharing borders and similar spatial pattern of population density and PM2.5 concentration tend to have similar patterns of COVID-19 risk. The analysis provides an interpretation of the statistical and spatial association of COVID-19 with population density and PM2.5 concentration, which has implications for the control and abatement of COVID-19 in terms of both infection and mortality.