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Plague, a highly infectious disease caused by Yersinia pestis, has killed millions of people in history and is still active in the natural foci of the world nowadays. Understanding the spatiotemporal patterns of plague outbreaks in history is critically important, as it may help facilitate the prevention and control for potential future outbreaks. This study's objective was to estimate the effect of the topography, vegetation, climate, and other environmental factors on the Y. pestis ecological niche. A maximum entropy algorithm spatially modelled plague occurrence data from 2004--2018 and the environmental variables to evaluate the contribution of the variables to the distribution of Y. pestis. Our results found that the average minimum temperature in September (--8 °C to +5 °C) and the sheep population density (250 sheep per km2) were influential in characterising the niche. The rim of Qinghai Lake showed more favourable conditions for Y. pestis presence than other areas within the study area. Identifying various factors will assist any future modelling efforts. Our suitability map identifies hotspots and will help public health officials in resource allocation in their quest to abate future plague outbreaks. [ABSTRACT FROM AUTHOR]

Bacillus anthracis is a gram-positive, rod-shaped and endospore-forming bacterium that causes anthrax, a deadly disease to livestock and, occasionally, to humans. The spores are extremely hardy and may remain viable for many years in soil. Previous studies have identified East Qinghai and neighbouring Gansu in northwest China as a potential source of anthrax infection. This study was carried out to identify conditions and areas in the Qinghai Lake basin that are environmentally suitable for B. anthracis distribution. Anthrax occurrence data from 2005–2016 and environmental variables were spatially modeled by a maximum entropy algorithm to evaluate the contribution of the variables to the distribution of B. anthracis. Principal Component Analysis and Variance Inflation Analysis were adopted to limit the number of environmental variables and minimize multicollinearity. Model performance was evaluated using AUC (area under the curve) ROC (receiver operating characteristics) curves. The three variables that contributed most to the suitability model for B. anthracis are a relatively high annual mean temperature of -2 to 0°C, (53%), soil type classified as; cambisols and kastanozems (35%), and a high human population density of 40 individuals per km2 (12%). The resulting distribution map identifies the permanently inhabited rim of the Qinghai Lake as highly suitable for B. anthracis. Our environmental suitability map and the identified variables provide the nature reserve managers and animal health authorities readily available information to devise both surveillance strategy and control strategy (administration of vaccine to livestock) in B. anthracis suitable regions to abate future epidemics. [ABSTRACT FROM AUTHOR]

Large-scale, fine, and efficient numerical simulation of a geothermal field plays an important role in geothermal energy development. Confronted with the problem of large computation and high storage requirements for complex underground models in a three-dimensional (3-D) numerical simulation of a geothermal field, a mixed space—wavenumber domain 3-D numerical simulation algorithm is proposed in this paper. According to the superposition principle of temperature field, the geothermal field is decomposed into background and abnormal temperature fields for calculation. The uniform layered model is used to solve the background field. When the abnormal field is solved, the horizontal two-dimensional (2-D) Fourier transform is used to transform the 3-D differential equation satisfied by an abnormal field into a series of one-dimensional ordinary differential equations with different wavenumbers, which greatly reduces the calculation and storage. The unit division of an ordinary differential equation is flexible, and the calculation amount is small. The algorithm fully takes advantage of the efficiency of the Fourier transform and the quickness of the catchup method to solve linear equations with a fixed bandwidth, which effectively improves the computational efficiency. Compared with the COMSOL Multiphysics professional simulation finite element software, the time consumption and memory requirements of the algorithm proposed in this paper are reduced by multiple orders of magnitude in terms of ensuring accuracy and the same mesh division. The more the number of calculated nodes is, the more obvious is the advantage. We design models to study the thermal conductivity, heat flux boundary, regional tectonic morphology, and topographic relief of the geothermal field distribution. A 3-D geophysical model is developed based on topographic elevation data, geothermal geology, and geophysical exploration data in the Qiabuqia area of Gonghe Basin, Qinghai Province, China. Numerical simulation of the geothermal field in this area is realized, which shows that the algorithm is suitable for precise and efficient simulation of an arbitrary complex terrain and geological conditions. [ABSTRACT FROM AUTHOR]

Bruce llosis is a bacterial disease caused by various Brucella species, which infect primarily cattle, swine, goats, sheep, and dogs. The disease is typically transmitted to humans through direct contact with diseased animals, consumption of contaminated animal products, or inhalation of airborne pollutants. The majority of cases are caused by consuming unpasteurized goat or sheep milk or cheese. Based on observed Brucellosis occurrence data and ecogeographic variables, a MaxEnt algorithm was used to model the current and future distribution of Brucellosis in Qinghai Lake basin, P.R. China. Our model showed the Brucellosis current distribution and predicts suitable habitat shifts under future climate scenarios. In the new representatives; SSP 2.6 and SSP 4.5 for the year 2050s and 2070s, our model predicts an expansion in the current suitable areas. This indicates that under the possible climate changes in the future, the living space of Brucellosis in Qinghai Lake basin China will expand significantly. Ecogeographic variables that contributed significantly to the distribution of Brucellosis in Qinghai Lake basin are revealed by our model. The results of our study will promote comparisons with future research and provide a new perspective to inform decision-making in the field of public health in Qinghai province. [ABSTRACT FROM AUTHOR]

• We explicitly quantified nonstationary habitat selection of snow leopards by comparing predictions across sites. • While the models developed in different areas generally agreed in terms of the important predictor variables, they differed considerably in terms of the scales that were identified as most strongly driving relationships between these variables and snow leopard occurrence. • The models also produced greatly divergent predictions of habitat suitability when cross-applied between the study areas, showing strong nonstationarity in regional habitat selection for snow leopards. • We suspect this strong nonstationary habitat response is related to the large ecological differences between the study areas, which likely lead to different regionally important limiting factors. The rapidly changing environments resulting from anthropogenic activities and climate change presented challenges to biodiversity protection efforts. China, in its pursuit of sustainable economic growth and urban development, grappled with the increasing challenge of designating national protected areas for biodiversity conservation in a proactive and ecologically effective manner. In this study, focusing on the snow leopard (Panthera uncia), a species of high conservation concern, from the A'nyamaqen and Bortala protected areas in China's Qinghai and Xinjiang provinces, respectively. Using random forest analysis, we conducted multi-scale habitat selection modeling to quantify and compared the habitats between the two sites. Three models were created utilizing data from either Qinghai, Xinjiang, or a combination of both sites, enabling investigation of nonstationarity in habitat limiting factors in different landscapes. Although there were minor differences in variable ranking and optimal scales among the models, they consistently indicated a strong negative relationship between proximity to roads and habitat suitability at broader scales. These findings provided insights into spatially varying limiting factors leading to divergent snow leopard realized habitat niches in different parts of their Chinese range. Understanding these context-dependent habitat preferences was vital for assessing the impact of infrastructure development on snow leopard populations. Overall, this study underscored the importance of understanding snow leopard habitat selection in the face of changing environments. The findings contributed to ongoing conservation efforts and emphasize the need for adaptive approaches that addressed challenges posed by urban development and environmental transformations. By integrating spatial analysis and modeling techniques, we enhanced our understanding of snow leopard ecology, enabling effective conservation strategies in China and beyond. [ABSTRACT FROM AUTHOR]