Entropy Model Research Articles

Distribution changes of Ormosia microphylla under different climatic scenarios

Ormosia microphylla is a nationally prioritized wild plant in China but effects of likely future climate change have been poorly studied. Here distribution data of O. microphylla and environmental data with an optimized MaxEnt maximum entropy model were used to predict potentially suitable areas under current and future climate scenarios. The results showed that with future climate warming, the total area suitable for O. microphylla might gradually increase. In the three future periods (2030s (2021–2040), 2050s (2041–2060) and 2090s (2081–2100)), the medium and high suitable areas under different climate scenarios generally showed an expanding trend, while the low suitable areas mostly showed a decreasing trend. At the same time, the potential suitable areas for O. microphylla in China have shown a certain degree of migration trend towards higher latitudes in the north and northwest, as well as a trend towards higher altitudes. The research results will provide data support for the protection of germplasm resources and the development of artificial cultivation techniques for O. microphylla, and provide a theoretical basis for the protection of other rare and endangered plants.

Adapting distribution patterns of desert locusts, Schistocerca gregaria in response to global climate change.

The desert locust (Schistocerca gregaria) is a destructive migratory pest, posing great threat to over 60 countries globally. In the backdrop of climate change, the habitat suitability of desert locusts is poised to undergo alterations. Hence, investigating the shifting dynamics of desert locust habitats holds profound significance in ensuring global agricultural resilience and food security. In this study, we combined the maximum entropy modelling and geographic information system technology to conduct a comprehensive analysis of the impact of climate change on the distribution patterns and habitat adaptability of desert locusts. The results indicate that the suitable areas for desert locusts (0.2976 × 108 km2) are concentrated in northern Africa and southwestern Asia, accounting for 19.97% of the total global land area. Key environmental variables affecting the desert locust distribution include temperature annual range, mean temperature of the coldest quarter, average temperature of February, and precipitation of the driest month. Under the SSP1-2.6 and SSP5-8.5 climate scenarios, potential suitable areas for desert locusts are estimated to increase from 2030 (2021-2040) to 2090 (2081-2100). By 2090, highly suitable areas for SSP1-2.6 and SSP5-8.5 are projected to be 0.0606 × 108 and 0.0891 × 108 km2, respectively, reflecting an expansion of 1.84 and 2.77% compared to existing ones. These research findings provide a theoretical basis for adopting prevention and control strategies for desert locusts.

Phylogenetic relationships of Smilax astrosperma (Smilacaceae) with other Smilax species and its potential distribution patterns in karst areas of south China

The genus Smilax is adapted for growth in karst areas of south China, likely because of the karst habitat heterogeneity of the area and climate factors. However, the phylogenetic relationships among different species in this genus and impact of environmental changes on its geographic distribution patterns remain unclear. Therefore, we assembled the 11 chloroplast genomes of the genus Smilax and reconstructed its phylogeny using the maximum‐likelihood method to confirm the phylogenetic relationships of Smilax astrosperma among 28 other Smilax genus species. In addition, a maximum entropy (MaxEnt) model was applied to evaluate and predict the potential distribution of S. astrosperma using bioclimatic variables, soil environment, elevation, slope degree and aspect, and human footprints under present and predicted future climate scenarios. The results show that S. astrosperma has a close relationship with S. glabra in the genus Smilax. The average temperature of the coldest quarter is a crucial factor influencing the distribution of S. astrosperma in karst areas. The potential distribution range of S. astrosperma increases and decreases under the SSP1‐2.6 and SSP5‐8.5 scenarios, respectively. These findings highlight that S. astrosperma and other species of the karst areas of south China need special attention under climate warming since both warming itself and extreme weather events threaten their survival.

A Framework for Developing Biodiversity Conservation Networks Based on Morphological Spatial Pattern Analysis and the Maximum Entropy Model: A Case Study of the Jianghan Plain, China

Constructing ecological networks in urban areas improves ecosystem stability and biodiversity protection. However, most studies focus on optimizing ecological environments through objective assessments, often neglecting species diversity. This study developed a biodiversity grading framework for the Jianghan Plain using species observation and ecosystem diversity data. Supported by ArcGIS, ecological sources were identified via MSPA and graded using the Guidelines and MaxEnt model. The MCR model was used to simulate connectivity barriers between ecological sources and calculate the minimum cumulative resistance distance, thereby generating corridors and ultimately constructing a hierarchical biodiversity conservation network for the Jianghan Plain. Our findings indicated the following: (1) The Jianghan Plain hosts 21 major ecological sources, primarily natural water bodies at the plain’s edge, which can be classified into five primary and 16 secondary sources based on biodiversity grades. (2) The recessive corridors, comprising 10 primary and 95 secondary ones, are mainly concentrated in the central Jianghan Plain, with primary corridors located centrally and westward, characterized by a large overall span. (3) Changhu Lake and Honghu Lake, two critical water bodies with high-quality habitats and significant biodiversity, were identified as key ecological nodes from the ecological sources, bridging and guiding the central and southern corridors. (4) Based on the ecological network distribution and key nodes and corridors, a “three zones, three belts, and two points” strategy was proposed for optimizing the Jianghan Plain’s ecological network. This study provides a novel framework and theoretical support for regional habitat, biodiversity conservation, and sustainable development.

Identification of Environmental Determinants Involved in the Distribution of Burkholderia pseudomallei in Southeast Asia using MaxEnt software.

Burkholderia pseudomallei (Bp), causing melioidosis, is becoming a major global public health concern. It is highly endemic in Southeast Asia (SEA) and Northern Australia and is persisting beyond the established areas of endemicity. This study aimed to determine the environmental variables that would predict the most suitable ecological niche for this pathogenic bacterium in SEA by maximum entropy (MaxEnt) modeling. Systematic review and meta-analysis of data for melioidosis were obtained from public databases such as PubMed, Harmonized World Soil (HWSD) and WorldClim. The potential map showing the environmental layers was processed by ArcGIS, and the prediction for the probability of habitat suitability using MaxEnt software (version 3·4·4) and ENMeval R-based modeling tools was utilized to generate the distribution map with the best-fit model. Both bioclimatic and edaphic predictors were found to be the most important niche-determining environmental variables affecting the geographical distribution of Bp. The highest probability of suitability was predicted in areas with mean temperature of the wettest quarter at ≥26°C, annual precipitation of <2300 mm and Acrisol soil type. Combining those significantly influential variables, our predictive modeling generated a potential distribution map showing the concentration of areas and its location names with high suitability for Bp presence. The predicted distribution of Bp is extensive in the mainland part of SEA. This can be used to draw appropriate measures to safeguard public health and address the true disease burden of melioidosis in the region under the current climate scenario.

Presettlement Tree Distributions and Forest Types of Northeast Ohio, USA, Mapped With Species Distribution Models

ABSTRACTQuestionsEuropean colonization radically transformed the landscapes of eastern North America. Understanding this legacy is vital to managing current ecological communities. To do so requires spatially explicit information about presettlement vegetation. Here we test the ability of species distribution models, which have rarely been used with historical data like land survey records, to generate useful predictions of presettlement tree distributions. These models also allow us to assess pre‐disturbance vegetation‐environment relationships.LocationCuyahoga County, Ohio, USA.MethodsGeneralized linear models, generalized boosting models, random forests, and maximum entropy models related the distributions of 17 tree taxa to elevation, slope, aspect, and soil type, based on 4234 tree observations from circa‐1800 surveys. Cluster analysis defined forest types and created a prediction map of forest types using random forests.ResultsThis study generated high‐resolution predictions of presettlement tree distributions. Fagus grandifolia and Carya spp. were predicted to have found suitable habitat in over half the area. Elevation was by far the most important predictor, followed by slope. Many taxa were more likely to occur at higher elevations, corresponding to the Allegheny Plateau, while others followed river valleys. Broadly, 51% of the county was predicted to support forest types with Fagus grandifolia and/or Acer spp., and 48% of the county was predicted to support forest types with Quercus spp., Carya spp., and/or Castanea dentata.ConclusionsThis study demonstrates that species distribution modeling with historical data provides insights into presettlement vegetation and vegetation‐environment relationships. Our results reveal striking ecological patterns not apparent in today's landscape, such as the sharp difference in vegetation between the Central Lowland and Allegheny Plateau. The maps created here offer a historical perspective that can inform conservation, education, and further research.

Energy of Functional Brain States Correlates With Cognition in Adolescent-Onset Schizophrenia and Healthy Persons.

Adolescent-onset schizophrenia (AOS) is relatively rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems were fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared with controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Severity of psychopathology was correlated positively with energy. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. The MEM's integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

Remotely-Sensed Game Trails Are a Behavioral Footprint That Explains Patterns of Herbivore Habitat Use.

Trade-offs between food acquisition and predator avoidance shape the landscape-scale movements of herbivores. These movements create landscape features, such as game trails, which are paths that animals use repeatedly to traverse the landscape. As such, these trails integrate behavioral trade-offs over space and time. Here, we used remotely sensed imagery to analyze the density of game trails with spatial environmental variables to understand landscape-scale patterns of herbivore habitat use in an African savanna. Woody plant cover was the best predictor of game trail density, with the highest densities correlating with intermediate woody plant cover. We also explored how patterns of game trail density compared to two known measures of herbivore habitat use (i.e., dung counts and maximum entropy modeling) and found strong quantitative fits. To understand the patterns revealed by the density of game trails, we explored the trade-off between food acquisition and perceived predation risk across a woody plant cover gradient. Using behavioral observations, we found that the relationship between woody plant cover and the distribution of game trails was likely driven by the risk and reward trade-off, with less vigilance and more feeding occurring in areas with a high density of game trails and intermediate woody cover. Ultimately, we show that game trails are a novel data source that can be used to identify broadly-occurring patterns of herbivore habitat use over large spatial scales.

GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India

GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India

Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios

For effective management and prevention, wildfire risk prediction needs to consider the substantial impacts of climate change on wildfire patterns. This study analyzed the probability of wildfire occurrence in South Korea using the Maximum Entropy (MaxEnt) model and predicted future wildfire occurrence under shared socioeconomic pathway (SSP) climate change scenarios. The model utilized historical fire occurrence data and was trained using 12 environmental variables encompassing climate, topography, vegetation, and socioeconomic factors. Future wildfire risk was predicted under the SSP2-4.5 and SSP5-8.5 scenarios for 2041–2060 and 2081–2100. Increased average temperature and solar radiation were key drivers of elevated wildfire risk, whereas increased precipitation and relative humidity reduced this risk. Under current conditions, 367,027 ha (6.52%) within the study area were classified as high-risk based on the MaxEnt model output (p &gt; 0.6). Under both SSP scenarios, a decline in the at-risk area was observed over time. This study provides fundamental data for wildfire management and prevention strategies in South Korea and provides quantitative evidence on the potential impact of climate-related environmental changes on wildfires.

Predicting the Potential Distribution of Cheirotonus jansoni (Coleoptera: Scarabaeidae) Under Climate Change.

Cheirotonus jansoni (Jordan, 1898), a beetle species of ecological and ornamental significance, is predominantly found in southern China. With limited dispersal ability, it is classified as a Class 2 protected species in China. In this study, the widely employed maximum entropy (MaxEnt) model and the ensemble Biomod2 model were applied to simulate C. jansoni habitat suitability in China under current environmental conditions based on available distribution data and multiple environmental variables. The optimized MaxEnt model demonstrated improved accuracy and robust predictive capabilities, making it the preferred choice for simulating dynamic changes in potentially suitable habitats for C. jansoni under future climate scenarios. Protection gaps were further identified through analyses of the overlap between nature reserves and highly suitable areas for C. jansoni. The established models indicated that this species primarily resides in southeastern mountainous regions of China below 2000 m, with a preferred altitude of 1000-2000 m. Future climate scenarios suggest a reduction in the overall suitable habitat for C. jansoni with an increase in temperature, underscoring the urgent need for enhanced conservation efforts for this beetle species.

Using maximum entropy modeling to predict the potential distributions of genus Copaifera L. in a conservation unit in the Brazilian Amazon

Using maximum entropy modeling to predict the potential distributions of genus Copaifera L. in a conservation unit in the Brazilian Amazon

Phylogeny, antiquity, and niche occupancy of Trinomia (Hymenoptera: Halictidae), an Afrotropical endemic genus of Nomiinae.

The Afrotropical region is home to many endemic bee groups, yet almost none have been studied from an integrated, holistic perspective. Among them, the halictid subfamily Nomiinae contains exceptional African diversity with variable distributions and life histories. Here, we combine phylogenomics, molecular dating, and distributional modelling to explore the evolutionary ecology of the genus Trinomia. We analyzed a matrix of 59 species of Nomiinae using ultraconserved element (UCE) and whole genome data, including all six species of Trinomia, and estimated divergence times for the subfamily. We then generated distribution models for all six species of Trinomia using Maximum Entropy models (MaxEnt) and 671 spatial data points. From these methods, we discovered a monophyletic Trinomia with an unexpected sister group relationship to the Asian-endemic genus Gnathonomia, as well as a recent origin of Trinomia in the late Miocene (∼5.8 million years ago). From our results, we found hints of phylogenetic conservatism in distribution among sister-groups of Trinomia, however, our results also highlight the need for additional efforts inventorying, identifying, and sharing data on African bees. This study represents an exemplary first step into studying bee spatial phylogenomics of African endemic bees.

Prediction of potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest and maximum entropy models

To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.

Environmental Suitability Predictions for the Distribution and Potential Cultivation of Artemisia afra in South Africa

Cultivation is advocated as a solution for the sustainable exploitation of medicinal plants. Understanding environmental factors influencing plant species distribution will eliminate the indiscriminate introduction of medicinal plants to inappropriate cultivation regions. This study investigated environmental conditions for the distribution of Artemisia afra and mapped out potential areas for its cultivation in South Africa. Soil samples were collected for analysis in the Free State Province in South Africa. To identify suitable environmental conditions for the natural distribution of A. afra, the South African National Botanical Institute database and physically collected Global Positioning System points were used in a maximum entropy model. Monthly long-term average interpolated weather surfaces were used to estimate the effect of climate change on future climate suitability for A. afra distribution. Sixty-one percent of soil samples from different A. afra populations were clay loam soils with a slightly acidic to neutral pH. The carbon source utilization, Shannon Weaver Index, and species richness were positively correlated with one group of fourteen soil samples, and species evenness was positively correlated with the second group, consisting of four samples. Climate change will only affect the distribution of A. afra in the very long term. The current study provides critical information for identifying suitable cultivation areas for A. afra while supporting conservation efforts from an ecological point of view.

Habitat Suitability Modeling for the Invasive Opuntia stricta Using Remote Sensing and Maxent in Tsavo East National Park, Kenya

Biological invasions represent a major threat to ecosystem services and products, with the potential to disrupt ecosystems across a broad spectrum of bioclimatic regions. Consequently, it is essential to monitor the spread of invasive species systematically and over extensive areas. Remote sensing and geographic information systems have long been recognized as valuable tools for achieving this goal. This paper examines the efficacy of optical satellite data from different seasons in detecting the invasive species Opuntia stricta (Australian pest pear) within the southern portion of the Tsavo East National Park. Maximum Entropy (MaxEnt) modelling was employed to determine the most relevant environmental variables, with a total of ten predictors tested. The results demonstrated that ndvi2017dry, rvi2018wet, rvi2017wet, msavi2018dry and rvi2018dry were the most effective Vegetation Indices (Vis) for detection of Opuntia stricta. The study also found that seasonal variations played a significant role in enhancing detection accuracy. The fine-scale MaxEnt modelling predicted core areas of invasion, yielding a mean AUC of 0.718. Suitable habitat within the study area was classified into high, medium, low, and very low categories, with 51 km² identified as highly suitable for Opuntia stricta growth. Further classifications included 37 km², 83 km², and 3,589 km² for medium, low, and very low suitability, respectively. Opuntia stricta was detected in 4.15% of the study area. From this study we concluded that the invasive species remain a big risk to protected areas especially in the Tsavo East National Park. Continued monitoring is recommended especially in areas predicted to have high invasion in future. This study provides baseline data for prioritizing invasive species monitoring and management strategies.

A Model for Defining the Boundaries of Complex Sentences and Clauses in English Translation Based on the Huffman Tree and Objective Function

To address the issues of high redundancy in sentence features and poor semantic similarity analysis in English translation, a boundary definition model for complex sentence clauses in English translation based on the Huffman tree and objective function is proposed. This model analyzes the boundary structure of complex sentences and clauses in English translation, determines the mathematical expected value of the probability distribution of boundary features, introduces the maximum entropy model to assess the key degree of different features, and utilizes conditional random fields to mine hidden features. Additionally, by employing a hierarchical clustering algorithm to analyze the distance between boundary features of complex sentences and clauses in English translation, similar images are merged based on the minimum distance between data points. Feature redundancy scores are obtained through an attention mechanism, and the weight of boundary features of complex sentences and clauses in English translation is calculated. By using the edit distance in semantic similarity to determine the boundary distance of clauses, and then using cosine similarity to calculate the similarity between the boundary features of complex clauses in English translation, a Huffman tree and objective function are introduced to construct a model for defining the boundary of complex clauses in English translation. Input the boundary feature values of complex sentences and clauses in English translation to complete the final definition. The experimental results show that the proposed method performs well in the task of defining the boundaries of complex sentences and clauses in English translation, with semantic similarity analysis results remaining above 95% and reaching up to 99%, significantly better than the comparative methods. Meanwhile, the recall curve obtained by the proposed method is closest to the ideal curve and has a small fluctuation range, stable between 90% and 98%, further verifying its accuracy and robustness in boundary delineation. In addition, when the sample data size is 1000, the confidence level of the proposed method is as high as 99.6%, which is higher than the 95.6% and 95.1% of the comparison methods. As the sample size increased to 2500, the confidence level of the proposed method remained at a high level of 99.4%, while the confidence level of the comparative method decreased to 94.2% and 94.1%, respectively. These data results fully demonstrate the effectiveness of the proposed method in reducing redundant interference and improving confidence. In summary, the proposed method has improved the performance of defining the boundaries of complex sentences and clauses in English translation.

Predicting the potential distribution of Astragali Radix in China under climate change adopting the MaxEnt model.

Astragali Radix is the dried root of Astragalus mongoliae or Astragalus membranaceus, a leguminous plant. Since ancient times, Astragali Radix has been widely used in Chinese traditional Chinese medicine. As people become more health-conscious, the market demand for Astragali Radix grows and its popularity is increasing in the international market. As an important medicinal plant, the growth of Astragali Radix is strongly influenced by environmental conditions. In order to meet the market demand for high quality Astragali Radix herbs, it is necessary to search and find areas suitable for the growth of Astragali Radix. In this study, we assessed the potential impacts of climate change on the distribution of the Chinese medicinal plant Astragali Radix using the maximum entropy (MaxEnt) model in combination with a geographic information system(GIS). Distribution data and environmental variables were analyzed to predict suitable areas for Astragali Radix under the SSP126, SSP245 and SSP585 scenario for current and future (2041-2060, 2061-2080, 2081-2100). Jackknife is used to assess the importance of environmental variables, and environmental variables with a model contribution greater than 5% were considered to be the main drivers. The results showed that the current area of suitable area for Astragali Radix is 188.41 km2, and the three climate scenarios show an increasing trend in the future, with a maximum of 212.70 km2. North China has always been the main suitable area, while the area of suitable area in Southwest China is decreasing, and Xinjiang will be developed as a new suitable area in the future. Annual precipitation (41.6%), elevation (15.9%), topsoil calcium carbonate (14.8%), annual mean temperature (8.3%), precipitation seasonality (8%) and topsoil pH (6%) contributed more to the model and were the main environmental influences on the distribution of Astragali Radix. In addition, the centroids of the suitable areas shifted northward under all three climate scenarios, indicating a migratory response to global warming. Our study found that suitable area of Astragali Radix has been expanding for most of the time in each period of the three climate scenarios compared with the current situation. In the future, humans can focus on enhancing the cultivation techniques of Astragali Radix in these suitable areas. This study provide a scientific basis for the development of planting strategies and spatial distribution management of Astragali Radix. It helps to optimize the selection of planting areas and resource conservation of Chinese herbs.

Pattern and driver of Rosa roxburghii Tratt suitability and its effect on ecological services in karst desertification control areas

The eco-industry is an effective path to achieve ecological and economic sustainable development. However, the role played by Rosa roxburghii Tratt (R. roxburghii) industry in karst desertification control still needs to be determined. In this study, we predict the suitability probability and driving variables of R. roxburghii's distribution through a Maximum Entropy model, combining climate, population, vegetation, and soil data, and conducted a sample plot survey in a high-suitability area to explore the role of the R. roxburghii industry in ecological services by using adjacent non-R. roxburghii patches as comparisons. The results showed that South China Karst, with its simultaneous rain and heat climate characteristics, was the most suitable area for the industrialization of R. roxburghii in the world. In South China Karst, the area of no, low, medium, and high suitability zones for R. roxburghii was 4.2 × 105, 6.12 × 105, 4.64 × 105, and 4.42 × 105 km2, respectively, and population distribution was the dominant variable controlling its distribution. The net primary productivity, soil conservation, water conservation, mean species abundance, and aboveground biomass of the R. roxburghii monoculture plot in the high-suitability area were significantly lower than those of adjacent non-R. roxburghii lands and forest patches. Furthermore, the monoculture had higher trade-offs between provision, regulation, and support service. This study found that the monoculture of R. roxburghii industry, dominated by human activities, failed to improve ecological services and exacerbated the trade-offs between services. Therefore, future karst desertification control still needs to follow the agroforestry development path.

Distribution of the schistosome intermediate snail host Biomphalaria pfeifferi in East Africa's river systems and the prevalence of Schistosoma mansoni infection.

There is a need for current and more detailed information on the distribution of Biomphalaria pfeifferi snails in East Africa's river systems. B. pfeifferi is arguably the most important snail intermediate host in the transmission of schistosomiasis, a disease ranked second to malaria in terms of tropical diseases of public health importance. We assessed the occurrence and geographical distribution of B. pfeifferi snails in Kenya, Uganda and Tanzania. Maximum entropy modelling was used to predict the potential distribution of B. pfeifferi snails and malacological surveys were conducted guided by MaxEnt predictions and information from previous studies. Malacological surveys were conducted at a total of 172 sites, including streams, rivers, dams, irrigation schemes and springs over a 3-y period from 2018 to 2020, with geospatial, ecological and physicochemical information recorded for each site. B. pfeifferi snails were found at 23 of the 172 sites and inhabited a variety of habitat types. Of the 23 sites where B. pfeifferi snails were found, 15 (65.2%) were streams, 3 rivers (13.04%), 2 dams (8.7%), 2 springs (8.7%) and 1 an irrigation scheme (4.35%). B. pfeifferi abundance showed a significant positive correlation with increasing water temperature and decreasing water depth. In Kenya, B. pfeifferi snails were found around the Lake Victoria basin, the Mwea irrigation scheme and in parts of the former Eastern Province of Kenya. In Uganda, B. pfeifferi snails were found in Jinja District, Ntoroko District and Soroti District. In Tanzania, B. pfeifferi snails were found in the Iringa, Tabora and Kigoma Regions. We observed moderate to high prevalence of Schistosoma mansoni infection, with S. mansoni-infected snails found at 11 of 23 sites and with an average prevalence of 24.9% at infected sites. In Kenya, S. mansoni-infected snails were found in the Lake Victoria basin (22.5% prevalence at infected sites) and the former Eastern Province (13.5% prevalence at infected sites). In Uganda, infected snails were found in Ntoroko District (100% infected) and Soroti District (20% infected). In Tanzania, infected snails were found in the Kigoma Region, with a prevalence of 10% at the infected site. This information on the distribution of B. pfeifferi snails and S. mansoni infection in East Africa's river systems can aid in developing better prevention and control strategies for human schistosomiasis. Regular surveys of the river systems for snail intermediate hosts followed by molecular detection of schistosome infection could form a basis for the development of a prompt and cost-effective surveillance system for schistosomiasis in the region.