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Invasive alien plants (IAPs) have serious environmental and economic impacts, especially in vulnerable areas of China. However, IAP richness distribution patterns, their driving factors, and the dynamic shifts in potential distribution areas remain elusive. We assessed IAP richness distribution patterns and drivers using 402 IAPs recorded in China at 88 926 occurrence points, and then predicted their potential distribution areas. The results show that IAP hotspots were mainly located in southeastern China, especially coastal areas of the South and East and large inland cities. Population density, gross domestic product (GDP), and four climate variables associated with precipitation and temperature jointly influenced the richness distribution pattern of all IAPs. Specifically, population density and GDP impacted the richness distribution pattern of narrow‐range IAPs, and population density, GDP, distance to the nearest national highway, and five climate variables affected the richness distribution pattern of widespread IAPs. Only GDP contributed significantly to the richness distribution pattern of the top 5% hotspot grid cells, whereas population density, GDP, and precipitation in the driest month (BIO14) significantly influenced the richness distribution patterns of hotspots for both the top 10% and top 20%. Prediction analysis demonstrated that southeastern China would have particularly high invasion risk under both current and future climate scenarios. Regions with increases in predicted species richness are more common (44.83–64.97%) than those with decreases, except under the Representative Concentration Pathway (RCP) 4.5 scenario. Climate change will contribute greatly to the expansion of potential IAP distribution areas under both optimistic (RCP 2.5) and pessimistic scenarios (RCP 8.5). The results of this study provide insights into the priority management of IAPs by developing promising strategies for the control and prevention of IAP invasion. [ABSTRACT FROM AUTHOR]

Climate is a key factor affecting the potential distribution of insects, and the host is another important constraint for the distribution of pests. To elucidate changes in the potential distribution of Beauveria bassiana under climate change scenarios, this paper used the data of two different greenhouse gas (GHG) emission scenarios (RCP2.6, RCP8.5) to predict the potential distribution of B. bassiana and its typical host, Bactrocera dorsalis (Hendel), based on the MaxEnt model. Then, the potential distribution of B. bassiana and B. dorsalis (Hendel) was compared, and their suitable growth area’s change and expansion trend under two different GHG emission scenarios were mastered. The results of this study show that the potential distribution area of B. bassiana will increase by 2,050 under the RCP8.5 climate scenario, mainly in central Europe and southwestern Asia, with an increased area of 3.28 × 105 km2. However, under the climate scenario of RCP2.6, the potential distribution area for B. bassiana decreased by 2.0 × 105 km2, mainly in North America. This study will provide a theoretical basis for the control of B. dorsalis (Hendel) with B. bassiana.

The impact of climate change on ecosystems, especially at the species level, can be currently observed in many parts of the world. Species distribution models (SDMs) are widely used to predict the likely changes in the distribution of species in future climate change scenarios. The aim of the present study is to predict the effect of climate change on a valuable threatened tree species Dysoxylum binectariferum in the northeastern part of Bangladesh using the maximum entropy (MaxEnt) model on species’ occurrence data. The future distribution of D. binectariferum was predicted under two scenarios from the IPCC 5th assessment (RCP 4.5, and RCP 8.5) in 2050 and 2070. Model results showed that approximately 32% (2177 km2) of the studied area is currently suitable for this species to grow. However, future predictions obtained by the model projected a complete loss of suitable habitat for D. binectariferum in the studied area by both 2050 and 2070. Therefore, urgent measures are required for the conservation of D. binectariferum in northeastern Bangladesh. The application of species distribution models to simple inventory data (such as the occurrence of the species) may provide policymakers and conservationists with a useful tool for the prediction of future distribution (at both local and regional scales) of poorly known species with high preservation concerns.

Abstract: Only ca. 3000 individuals of Ilex khasiana Purk. are surviving today. The tree species is endemic to Khasi hills of northeast India, and is critically endangered. For improving the conservation status of the species, potential area and habitat for reintroduction were predicted using Maximum Entropy (MaxEnt) distribution modelling algorithm. The model was developed using 16 locality data in the native range of Khasi hills, and 16 environmental parameters including enhanced vegetation index (EVI) and digital elevation data. The model predicted that the suitable habitats of I. khasiana was restricted to an area of ≈500km2 in the Khasi hills of Meghalaya. The distribution of potential habitats was strongly influenced by elevation and the EVI layers for the period April–May, which corresponds to the flowering phase of the species, thus indicating the importance of flowering stage in determining the species distribution. Population status was positively correlated with higher model thresholds in the undisturbed habitats confirming the usefulness of the habitat model in population monitoring, particularly in predicting the successful establishment of the species. The study delineated the potential habitats in the higher elevations of Khasi hills within the current home range where the species can be reintroduced. [Copyright &y& Elsevier]

In order to study the ecology suitability of Pterocephalus hookeri, and provide a reference for GAP planting location and regional development, the Maxent model and GIS technology were used to investigate ecology suitability regions for P. hookeri based on the distribution points collected from Chinese virtual herbarium, the references and field trips. The potential distribution areas mainly concentrated in the eastern Tibet, western Sichuan, southern Qinghai, northwest Yunnan, and southern Gansu. There were 7 major environmental factors to have obvious influence on ecology suitability distributions of P. hookeri, including altitude (contribution rate of 62%), precipitation of warmest quarter (contribution rate of 14.4%), coefficient of variation of precipitation seasonality (contribution rate of 7.2%), mean temperature of driest quarter (contribution rate of 3.5%), the electrical conductivity of top and sub-soil (contribution rate of 3%), the total exchangeable bases in the top- and subsoil (contribution rate of 2.4%) and SD of temperature seasonality (contribution rate of 2.2%). The study of the ecological suitability regionalization of P. hookeri based on Maxent model can provide scientific basis for the selection of artificial planting base and GAP planting location., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Chinese Pharmaceutical Association.)

Wildlife tending and artificial cultivation is an important way to protect the wild resources of Rhodiola crenulata. It is a study hotspot at present. The distribution information of R. crenulata was collected by query data and field survey, the ecological suitability regionalization was conducted based on maximum entropy model combine with ecological factors, including climate, soil and altitude. To provide the reference for production layout, suitable planting area and the selection of artificial planting base by studying the ecological suitability regionalization of R. crenulata. The potential distribution areas mainly concentrated in the easen Tibet, western Sichuan, southern Qinghai, and Gansu Gannan Tibetan Autonomous Prefecture, Yunnan Diqing Tibetan Autonomous Prefecture. There were 5 major environmental factors to have obvious influence on ecology suitability distributions of R. crenulata, including altitude (contribution rate of 61.8%), precipitation of warmest quarter (contribution rate of 19%), the coefficient of variation of precipitation seasonality (contribution rate of 4.7%), the SD of temperature seasonality (contribution rate of 4%), mean temperature of driest quarter (contribution rate of 2.5%). The AUCs of ROC curve were both above 0.9, indicating that the predictive results with the Maxent model were highly precise. The study of the ecological suitability regionalization of R. crenulata based on Maxent can provide a scientific basis for the selection of artificial planting base., Competing Interests: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose., (Copyright© by the Chinese Pharmaceutical Association.)