Snow leopards (Panthera uncia) are elusive predators inhabiting high-altitude and mountainous rugged habitats. The current study was conducted in the Yanchiwan National Nature Reserve, Gansu Province, China, to assess the habitat suitability of snow leopards and identify key environmental factors inducing their distribution. Field data collected between 2019 and 2022 through scat sampling and camera trapping techniques provided insights into snow leopard habitat preferences. Spatial distribution and cluster analyses show distinct hotspots of high habitat suitability, mostly concentrated near mountainous landscapes. While altitude remains a critical determinant, with places above 3300 m showing increased habitat suitability, other factors such as soil type, human footprint, forest cover, prey availability, and human disturbance also play important roles. These variables influence ecological dynamics and are required to assess and manage snow leopard habitats. The MaxEnt model has helped us to better grasp these issues, particularly the enormous impact of human activities on habitat suitability. The current study highlights the importance of altitude in determining snow leopard habitat preferences and distribution patterns in the reserve. Furthermore, the study underscores the significance of considering elevation in conservation planning and management strategies for snow leopards, particularly in mountainous regions. By combining complete environmental data with innovative modeling tools, this study not only improves local conservation efforts but also serves as a model for similar wildlife conservation initiatives around the world. By understanding the environmental factors driving snow leopard distribution, conservation efforts can be more efficiently directed to ensure the long-term survival of this endangered species. This study provides valuable insights for evidence-based conservation efforts to safeguard the habitats of snow leopards amidst emerging anthropogenic pressure and environmental fluctuations.

Schisandrae Sphenantherae Fructus (SSF), the dry ripe fruit of Schisandra sphenanthera Rehd. et Wils., is a traditional Chinese medicine with wide application potential. The quality of SSF indicated by the composition and contents of secondary metabolites is closely related to environmental factors, such as regional climate and soil conditions. The aims of this study were to predict the distribution patterns of potentially suitable areas for S. sphenanthera in China and pinpoint the major environmental factors influencing its accumulation of medicinal components. An optimized maximum entropy model was developed and applied under current and future climate scenarios (SSP1-RCP2.6, SSP3-RCP7, and SSP5-RCP8.5). Results show that the total suitable areas for S. sphenanthera (179.58×104 km2) cover 18.71% of China\'s territory under the current climatic conditions (1981-2010). Poorly, moderately, and highly suitable areas are 119.00×104 km2, 49.61×104 km2, and 10.98×104 km2, respectively. The potentially suitable areas for S. sphenanthera are predicted to shrink and shift westward under the future climatic conditions (2041-2070 and 2071-2100). The areas of low climate impact are located in southern Shaanxi, northwestern Guizhou, southeastern Chongqing, and western Hubei Provinces (or Municipality), which exhibit stable and high suitability under different climate scenarios. The contents of volatile oils, lignans, and polysaccharides in SSF are correlated with various environmental factors. The accumulation of major secondary metabolites is primarily influenced by temperature variation, seasonal precipitation, and annual precipitation. This study depicts the potential distribution of S. sphenanthera in China and its spatial change in the future. Our findings decipher the influence of habitat environment on the geographical distribution and medicinal quality of S. sphenanthera, which could have great implications for natural resource conservation and artificial cultivation.

UNASSIGNED: The re-emergence of scrub typhus in the southern provinces of China in recent decades has been validated, thereby attracting the attention of public health authorities. There has been a spatial and temporal expansion of scrub typhus in Hainan Province, but the epidemiological characteristics, environmental drivers, and potential high-risk areas for scrub typhus have not yet been investigated.
UNASSIGNED: The aims of this study were to characterize the spatiotemporal epidemiology of scrub typhus, identify dominant environmental risk factors, and map potential risk areas in Hainan Province from 2011 to 2020.
UNASSIGNED: The spatiotemporal dynamics of scrub typhus in Hainan Province between 2011 and 2020 were analyzed using spatial analyses and seasonal-trend decomposition using regression (STR). The maximum entropy (MaxEnt) model was applied to determine the key environmental predictors and environmentally suitable areas for scrub typhus, and the demographic diversity of the predicted suitable zones was evaluated.
UNASSIGNED: During 2011-2020, 3260 scrub typhus cases were recorded in Hainan Province. The number of scrub typhus cases increased continuously each year, particularly among farmers (67.61%) and individuals aged 50-59 years (23.25%) who were identified as high-risk groups. A dual epidemic peak was detected, emerging annually from April to June and from July to October. The MaxEnt-based risk map illustrated that highly suitable areas, accounting for 25.36% of the total area, were mainly distributed in the northeastern part of Hainan Province, where 75.43% of the total population lived. Jackknife tests revealed that ground surface temperature, elevation, cumulative precipitation, evaporation, land cover, population density, and ratio of dependents were the most significant environmental factors.
UNASSIGNED: In this study, we gained insights into the spatiotemporal epidemiological dynamics, pivotal environmental drivers, and potential risk map of scrub typhus in Hainan Province. These results have important implications for researchers and public health officials in guiding future prevention and control strategies for scrub typhus.

Sclerotinia sclerotiorum, a fungal pathogen, causes world-wide crop losses and additional disease management strategies are needed. Modeling the climate niche of this fungus may offer a tool for the selection of biological control organisms and cultural methods of control. Maxent, a modeling technique, was used to characterize the climate niche for the fungus. The technique requires disease occurrence data, bioclimatic data layers, and geospatial analysis. A cross-correlation was performed with ArcGIS 10.8.1, to reduce nineteen bioclimatic variables (WorldClim) to nine variables. The model results were evaluated by AUC (area under the curve). A final model was created with the random seed procedure of Maxent and gave an average AUC of 0.935 with an AUC difference of -0.008. The most critical variables included annual precipitation (importance: 14.1%) with a range of 450 mm to 2500 mm and the mean temperature of the coldest quarter (importance: 55.6%) with a range of -16 °C to 24 °C, which contributed the most to the final model. A habitat suitability map was generated in ArcGIS 10.8.1 from the final Maxent model. The final model was validated by comparing results with another occurrence dataset. A Z-Score statistical test confirmed no significant differences between the two datasets for all suitability areas.

Species distribution modeling (SDM) has become an increasingly common approach to explore questions about ecology, geography, outbreak risk, and global change as they relate to infectious disease vectors. Here, we conducted a systematic review of the scientific literature, screening 563 abstracts and identifying 204 studies that used SDMs to produce distribution estimates for mosquito species. While the number of studies employing SDM methods has increased markedly over the past decade, the overwhelming majority used a single method (maximum entropy modeling; MaxEnt) and focused on human infectious disease vectors or their close relatives. The majority of regional models were developed for areas in Africa and Asia, while more localized modeling efforts were most common for North America and Europe. Findings from this study highlight gaps in taxonomic, geographic, and methodological foci of current SDM literature for mosquitoes that can guide future efforts to study the geography of mosquito-borne disease risk.

BACKGROUND: Despite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated.
METHODS: A negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk.
RESULTS: The final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus.
CONCLUSIONS: These findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

The genus Sanghuangporus is well-known for its edible and medicinal values. In this study, the most comprehensive occurrence records of Sanghuangporus with accurate species identification are subjected to MaxEnt, to model the current geographic distribution and future range shifts under multiple climate change scenarios in China. The current potential distribution model of Sanghuangporus is excellently predicted as indicated by the value of Area Under Receiver Operator Characteristic Curve. The current potential distribution basically corresponds to the known occurrence records of Sanghuangporus, and provides clues to new suitable habitats. The critical environmental variables to the distribution are annual precipitation, host plant, annual mean temperature and elevation. Host plant is not the most critical contribution to the model, but it indeed plays a decisive role in restricting the distribution of Sanghuangporus. This role is further confirmed by the distribution area of the highly suitable habitat increasing by 155.468%, when excluding host plant from environmental variables. For future scenarios, generally the area of highly suitable habitat for Sanghuangporus extremely increases, but the locations do not change a lot. In conclusion, this study provides important ecological information for the utilization and conservation of the edible and medicinal fungus Sanghuangporus.

Using the 260 geographical distribution records of Polygonatum cyrtonema in China, combined with 53 environmental factors, the maximum entropy modeling(MaxEnt) was used to study the ecological factors affecting the suitability distribution of P. cyrtonema. The ArcGIS software was used to predict the potential distribution of the population of P. cyrtonema. The dominant factors were chosen by using the Jackknife test and the Receiver Operating Characteristic(ROC) curve was used to evaluate the simulation. The results showed that high value of area under curve(AUC) denoted good results, which significantly differed from random predictions. Based on the evaluation criterion, the accuracies of the predictions of P. cyrtonema potential distribution in the current periods were excellent. The main environmental factors affecting the suitable growth of P. cyrtonema were the monthly precipitation, the wettest monthly precipitation, the annual average temperature range and the precipitation of November, March, February, April, May and October. There are 9 environmental factors in soil type. The potential fitness of P. cyrtonema in China is high, mainly concentra-ted in Hunan, western Hubei, Guangdong, northeastern Guangxi, southeastern Guizhou, Jiangxi, southwestern Anhui, Fujian, Zhejiang, Shaanxi, southwestern Henan and Chongqing. The growth distribution of the potential distribution area of P. cyrtonema was divided, and the zoning map of the growth suitability of P. cyrtonema was formed. Through the comparative analysis of the potential distribution range based on MaxEnt and the distribution range of literature records, the understanding of the distribution range of P. cyrtonema was expanded.

Dozens of mutations throughout the sequence of the gene encoding superoxide dismutase 1 (SOD1) have been linked to toxic protein aggregation in the neurodegenerative disease amyotrophic lateral sclerosis (ALS). A parsimonious explanation for numerous genotypes resulting in a common phenotype would be mutation-induced perturbation of the folding free-energy surface that increases the populations of high-energy states prone to aggregation. The absence of intermediates in the folding of monomeric SOD1 suggests that the unfolded ensemble is a potential source of aggregation. To test this hypothesis, here we dissected SOD1 into a set of peptides end-labeled with FRET probes to model the local behavior of the corresponding sequences in the unfolded ensemble. Using time-resolved FRET, we observed that the peptide corresponding to the Loop VII-β8 sequence at the SOD1 C terminus was uniquely sensitive to denaturant. Utilizing a two-dimensional form of maximum entropy modeling, we demonstrate that the sensitivity to denaturant is the surprising result of a two-state-like transition from a compact to an expanded state. Variations of the peptide sequence revealed that the compact state involves a nonnative interaction between the disordered N terminus and the hydrophobic C terminus of the peptide. This nonnative intramolecular structure could serve as a precursor for intermolecular association and result in aggregation associated with ALS. We propose that this precursor would provide a common molecular target for therapeutic intervention in the dozens of ALS-linked SOD1 mutations.

Among the 13 Mico species recognized by the IUCN Red List of Threatened Species, six are listed as \"Data Deficient\". The geographic range of most of the Mico species has been estimated from only a few records. We report new localities and the geographic extension of Mico chrysoleucos. In addition, we confirmed the presence of the species in two distinct protected areas. We modeled the habitat suitability of M. chrysoleucos using the maximum entropy method and including new records obtained by the authors in the state of Amazonas, Brazil. From the total area of occurrence calculated for the species, 22.8% is covered by protected areas and indigenous lands. The annual mean deforestation rate estimated between 2000 and 2015 was 2.95%, and the total area deforested by 2015 was 3354 km2 or 8.6% of the total distribution limits of the species. The habitat lost between 2000 and 2015 was 3.2% (1131 km2) of the total potential distribution, while the habitat loss area legally protected was 31 km2, and the habitat loss in settlements was equal to 691 km2. Our results extend the geographic distribution of the species about 100 km farther south, with the Maracanã River being a possible geographic barrier for the species. The significantly low rate of habitat loss inside protected areas and indigenous land, when compared to unprotected areas, points out the importance of these areas to M. chrysoleucos conservation. The species is relatively wide-ranging, legally protected, and resilient to regional anthropic threats. However, the hydroelectric schemes and the improvement of the road system in southern Amazonia pose an imminent threat to the species.