Minimum Temperature Of Coldest Month Research Articles

Predicting potential habitat distribution of the invasive species Rhynchophorus ferrugineus Olivier in China based on MaxEnt modelling technique and future climate change.

Changes in the distribution of species due to global climate change have a critically significant impact on the increase in the spread of invasive species. An in-depth study of the distribution patterns of invasive species and the factors influencing them can help to better predict and combat invasive alien species. Rhynchophorus ferrugineus Olivier is an invasive species that primarily harms plants of Trachycarpus H. Wendl. The pest invades trees in three main ways: by laying eggs and incubating them in the crown of the plant, on roots at the surface and at the base of the trunk or petiole. Most of the plants in the genus Trachycarpus are taller, and the damage is concentrated in the middle and upper parts of the plant, making control more difficult. In this paper, we combine 19 bioclimatic variables based on the MaxEnt model to project the current and future distributions of R. ferrugineus under three typical emission scenarios (2.6 W m-2 (SSP1-2.6), 4.5 W m-2 (SSP2-4.5) and 8.5 W m-2 (SSP5-8.5)) in the 2050s and 2090s. Among the 19 bioclimatic variables, five variables were screened out by contribution rates, namely annual mean temperature (BIO 1), precipitation of driest quarter (BIO 17), minimum temperature of coldest month (BIO 6), mean diurnal range (BIO 2) and precipitation of wettest quarter (BIO 16). These five variables are key environmental variables that influence habitat suitability for R. ferrugineus and are representative in reflecting its potential habitat. The results showed that R. ferrugineus is now widely distributed in the southeastern coastal area of China (high suitability zone), concentrating in the provinces of Hainan, Guangdong, Fujian, Guangxi and Taiwan. In the future, the area of high and low suitability zones will increase and the area of medium suitability zones will decrease. The area of low suitability zone will still be in the largest proportion. This study aims to provide a theoretical reference for the future control of R. ferrugineus from the perspective of geographic distribution.

The influence of climatic and human-induced factors on the spatial distribution of invasive plant species richness across the Loess Plateau

Biological invasion poses a critical global issue, leading to substantial detrimental impacts on biodiversity, the environment, and the economy. The objective of the study is to offer a thorough understanding of how both climatic and human-induced elements impact the geographic richness of invasive plant species across the Loess Plateau. We evaluate the distribution of invasive plant species at the county level across the Loess Plateau by examining herbarium records from China. We incorporate 16 climatic and anthropogenic variables to depict the local environmental settings. Furthermore, we apply a classification and regression tree approach to investigate the correlation between the richness of invasive plant species and the identified factors. Our study demonstrates that a total of 401 invasive plant species are identified, which are spread across 249 genera and 61 families. Among these, the Asteraceae family stands out as the most prevalent, trailed by Poaceae and Fabaceae. The spatial distribution of invasive plant species richness reveals a notable trend, with the highest frequencies found in the southeastern parts of the region and the lowest in the northwestern areas. It is noteworthy that regions with higher levels of economic advancement tend to harbor a more significant abundance of invasive plant species. The richness of invasive plant species on the Loess Plateau is predominantly shaped by a combination of climatic and human variables, such as annual precipitation, gross domestic product, maximum temperature of warmest month, and minimum temperature of coldest month. To fully comprehend the ecological and biological mechanisms underlying the diversity of invasive plant species on the Loess Plateau, a pioneering conceptual framework has been established. Our study suggests that achieving a harmonious equilibrium among development, conservation, and invasion mitigation is essential for recognizing emerging risks associated with habitat alterations, climate change, and socio-economic advancements in arid regions.

Diversity and distribution of bamboo-feeding true bugs in China.

The Bambusoideae subfamily, originating in the late Cretaceous, has evolved to include over 1500 species globally. Notably, China hosts the richest diversity of Bambusoideae, with 728 species documented. After a long period of coevolution, plenty of animals could feed on these plants rich in cellulose and lignin. As an important group of pests and participants in the ecosystem, bamboo-feeding true bugs (BFTBs, or bamboo-feeding Heteropteran insects) have attracted the attention of researchers. However, the diversity and distribution of BFTBs still lack systematic and generalized research. In this study, we reviewed the BFTBs in China and simulated the diversity pattern and the driving forces of this pattern. A list of 36 genera with 69 species of BFTBs in China was obtained through paper review and field surveys. And their bamboo-feeding habit had multiple independent origins. The spatial diversity pattern showed that the biodiversity hotspots of BFTBs are located in and around the tropics of southern China. Environmental driving force analysis showed that the minimum temperature of coldest month and annual precipitation were the dominant environmental factors shaping the spatial diversity of BFTBs. Our work quantified the diversity and distribution of BFTBs in China, providing fundamental data support for pest control and evolutionary research.

Ecological niche modelling guided chemotypic analysis of Ageratum conyzoides L. from varied geography of India

Ageratum conyzoides L., recognized globally as a noxious weed, holds significant value in traditional medicine, particularly in treating cuts, wounds, and tick infestations. This study focuses on identifying specific germplasms of this plant, rich in therapeutically valuable metabolites, across various phytogeographical zones in India. Using the Reverse Phase High-Performance Liquid Chromatography with Photodiode Array Detection (RP-HPLC-PDA) method, we analyzed 110 samples to quantify five key bioactive markers: Precocene-I (PRC-I), Coumarin (CM), Caryophyllene (CAR), Caryophyllene Oxide (CAO), and Humulene (HUM). Our findings revealed distinct geographical variations in these compounds. The highest concentrations of PRC-I (7.86±1.36 %) was found in sample NAC-12, CM (1.50±0.46 %) and HUM (0.0099±0.0004 %) in NAC-104, while CAR (6.33±0.87 %) and CAO (6.23±0.18 %) were predominant in NAC-01 and NAC-02, respectively. Ecological Niche Modelling (ENM) was used to predict the climatic suitability of regions for high yield of these metabolites. The ENM results showed that annual precipitation (Bio 12), precipitation of the coldest quarter (Bio 19) and minimum temperature of coldest month (Bio 6) were the principal climatic factors influencing metabolite content. Geographically, the Indo-Gangetic plain, Indus plain, and Central India were identified as suitable for higher content of PRC-I, CAO, CAR, whereas the North Eastern region favoured high content of CM and HUM. This study underscores the importance of bioactive marker-based assessment in understanding chemotypic diversity and demonstrates the effective use of ENM in predicting optimal habitats for harvesting specific chemotypes. These insights pave the way for location-specific cultivation strategies and targeted procurement of metabolite-rich germplasms, contributing to sustainable sourcing and conservation efforts, as well as to the advancement of herbal product standardization and efficacy.

Habitat suitability of Opuntia ficus-indica (L.) MILL. (CACTACEAE): a comparative temporal evaluation using diverse bio-climatic earth system models and ensemble machine learning approach.

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica. This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040-2079 (60) and 2060-2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26-30°C), mean temperature of the driest quarter (25-28°C); mean temperature of the coldest month (22-25°C); minimum temperature of coldest month (13-17°C); precipitation of the wettest month (250-500mm); potential evapotranspiration Thronthwaite (1740-1800mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Stacked machine learning models for predicting species richness and endemism for Mediterranean endemic plants in the Mareotis subsector in Egypt

An effective method for identifying species and evaluating the effects of changes caused by humans on specific species is the application of species distribution modelling (SDM) in desert environments. The fact that many dry lands and deserts throughout the world are situated in inhospitable regions may be the reason why such applications are still infrequently used on plant species in Egypt's Mediterranean region. Henceforth, the current study aims to map species richness and weighted endemism of Mediterranean endemics in the Mareotis subsector in Egypt and determine the environmental variables influencing distribution of these taxa. We produced a map of species distribution range using Ensemble SDMs. Further, stacked machine learning ensemble models derived from Random Forest (RF) and MaxEnt models were applied on 382 Mediterranean endemics distribution data to estimate and map diversity and endemism using two indices: species richness (SR) and weighted endemism index (WEI). The best models for ensemble modelling were chosen based on Kappa values and the Area Under the Receiver Operator Curve (AUC). The results showed that the models had a good predictive ability (Area Under the Curve (AUC) for all SDMs was > 0.75), indicating high accuracy in forecasting the potential geographic distribution of Mediterranean endemics. The main bioclimatic variables that impacted potential distributions of most species were wind speed, elevation and minimum temperature of coldest month. According to our models, six hotspots were determined for Mediterranean endemics in the present study. The highest species richness was recorded in Sallum, Matrouh wadis and Omayed, followed by Burg El-Arab, Ras El-Hekma and Lake Mariut. Indeed, species richness and endemism hotspots are promising areas for conservation planning. This study can help shape policy and mitigation efforts to protect and preserve Mediterranean endemics in the coastal desert of Egypt. These hotspots should be focused on by policy makers and stakeholders and declared as protectorates in the region. The largest number of species per area would be protected by focusing primarily on the hotspots with high species richness.

Climate change may drive the distribution of tribe Zyginelline pests in China and the Indo-China Peninsula to shift towards higher latitude river-mountain systems.

Tribe Zyginelline leafhoppers can transmit plant viruses and are important pests that affect agriculture, forestry, and animal husbandry, causing serious economic losses. The potential distribution patterns of Zyginellini will change under climate change. Therefore, the best-performing random forest and maximum entropy models among 12 commonly used ecological niche models, alongside an ensemble model, were selected to predict the changes in habitat suitability distribution of Zyginellini under current and future climate scenarios [represented by two shared socio-economic pathways (SSPs), namely SSP126 and SSP585, for three periods (2050s, 2070s, and 2090s)] in China and the Indo-China Peninsula for the first time. The results revealed that the distribution of Zyginellini was mainly dominated by minimum temperature of coldest month. Under current and future climate scenarios, Zyginellini was mostly distributed southeast of the 400 mm equivalent precipitation line in China, and Vietnam. Under the future SSP126 scenario, the alert areas will mainly be concentrated in Hunan, Jiangxi, Zhejiang, Anhui, and Hebei in China, alongside Myanmar and Thailand in the Indo-China Peninsula. Meanwhile, in the SSP585 scenario, the alert areas in China will increase, whereas there will be little change in the Indo-China Peninsula. Interestingly, from the current to the future, the cores of Zyginelline distribution occurred around rivers and mountains, and shifted from Guizhou along the Yuanjiang River system to higher latitudes in Hunan. Zyginellini prefers higher latitude river-mountain systems under climate change. Our results will contribute to effective pest control strategies and biogeographical research for Zyginellini alongside other Cicadellidae insects. © 2023 Society of Chemical Industry.

Predictive ecological niche modelling of an important bio-control agent: Trichoderma harzianum (Rifai) using the MaxEnt machine learning tools with climatic and non-climatic predictors

ABSTRACT Ecological niche model (ENM) pertains to a class of methodologies that utilise occurrence data alongside environmental data to formulate a correlative model of the environmental circumstances that satisfy a species’ ecological requirements. In the current study, ENM was employed to ascertain the types of habitat for Trichoderma harzianum using machine learning algorithm known as MaxEnt Entropy. Our line of reasoning posits that the efficacy of T. harzianum as a bio-control agent can be enhanced, alongside the advancement of host/crop development and metabolic processes, through its deliberate introduction into geographically appropriate habitats. ENM was performed on 92 spatially thinned presence points of this species across India, considering three bio-climatic time periods (present, 2050, and 2070) and four greenhouse gas scenarios (known as representative concentration pathways RCPs). Non-bioclimatic factors include ecosystem rooting depths (ERD), total plant available water storage capacity (TPAWSC), habitat heterogeneity indices (HHI), land use land cover (LULC) and to soil variables at four depths. Energy-related factors, like Isothermality and minimum temperature of coldest month, were shown to be the most essential for the habitat appropriateness of this species during the current bio-climatic period. Future climate predictions and their associated RCPs revealed that water-related variables, like precipitation of wettest quarter, were the most influential. Non-climatic elements that were shown to have significant impact included soil pH, maximum diversity indices, forest and grassland types, TPAWSC, ERD (95%). Our analysis showed that this species will always find optimal suitability sites in northern eastern India with almost all predictors except root zone variables.

Effects of Climate Change on the Distribution of Threatened Fishing Bat Myotis pilosus in China.

Climate change and biodiversity loss are two severe challenges that the world is facing. Studying the distribution shifts of species in response to climate change could provide insights into long-term conservation and biodiversity maintenance. Myotis pilosus is the only known fishing bat in East Asia, whereas its population has been decreasing in recent years and it is listed as a "Vulnerable" species. To assess the impact of climate change on the distribution of M. pilosus, we obtained 33 M. pilosus occurrence records within China where they are mainly distributed, and extracted 30 environmental variables. MaxEnt was applied to assess the habitat suitability, recognize the important environmental variables, predict future distribution changes, and identify the potential future climate refugia. The prediction result based on eleven dominant environmental variables was excellent. The Jackknife test showed that the "minimum temperature of coldest month", "precipitation of wettest quarter", "percent tree cover", and "precipitation of driest month" were the main factors affecting the distribution of M. pilosus. The current suitable areas were predicted to be mainly located in southwest and southeast China with a total area of about 160.54 × 104 km2, accounting for 16.72% of China's land area. Based on the CCSM4, it was predicted that the future (2050 and 2070) suitable areas of M. pilosus will expand and shift to high latitudes and altitudes with global warming, but the area of moderately and highly suitable habitats will be small. Considering the dispersal capacity of M. pilosus, the area of colonized suitable habitats in 2050 and 2070 was predicted to be only ca. 94 × 104 km2 and 155 × 104 km2, respectively. The central and southern parts of Hainan, southern Guangdong, central Guizhou, and southern Beijing were identified as potential climate refugia and could be considered as priority conservation areas for M. pilosus. Thus, we suggest long-term monitoring of the priority conservation areas, especially the areas at high latitudes and altitudes. These results contribute to our knowledge of the possible spatial distribution pattern of M. pilosus under current and future climate scenarios, which is important for the population protection and habitat management of this special piscivorous bat species.

Climate Change and Human Activities, the Significant Dynamic Drivers of Himalayan Goral Distribution (Naemorhedus goral).

The distribution of large ungulates is more often negatively impacted by the changing climate, especially global warming and species with limited distributional zones. While developing conservation action plans for the threatened species such as the Himalayan goral (Naemorhedus goral Hardwicke 1825; a mountain goat that mostly inhabits rocky cliffs), it is imperative to comprehend how future distributions might vary based on predicted climate change. In this work, MaxEnt modeling was employed to assess the habitat suitability of the target species under varying climate scenarios. Such studies have provided highly useful information but to date no such research work has been conducted that considers this endemic animal species of the Himalayas. A total of 81 species presence points, 19 bioclimatic and 3 topographic variables were employed in the species distribution modeling (SDM), and MaxEnt calibration and optimization were performed to select the best candidate model. For predicted climate scenarios, the future data is drawn from SSPs 245 and SSPs 585 of the 2050s and 2070s. Out of total 20 variables, annual precipitation, elevation, precipitation of driest month, slope aspect, minimum temperature of coldest month, slope, precipitation of warmest quarter, and temperature annual range (in order) were detected as the most influential drivers. A high accuracy value (AUC-ROC > 0.9) was observed for all the predicted scenarios. The habitat suitability of the targeted species might expand (about 3.7 to 13%) under all the future climate change scenarios. The same is evident according to local residents as species which are locally considered extinct in most of the area, might be shifting northwards along the elevation gradient away from human settlements. This study recommends additional research is conducted to prevent potential population collapses, and to identify other possible causes of local extinction events. Our findings will aid in formulating conservation plans for the Himalayan goral in a changing climate and serve as a basis for future monitoring of the species.

Prediction of Potential Suitable Distribution Areas of Quasipaa spinosa in China Based on MaxEnt Optimization Model

Quasipaa spinosa is a large cold-water frog unique to China, with great ecological and economic value. In recent years, due to the impact of human activities on the climate, its habitat has been destroyed, resulting in a sharp decline in natural population resources. Based on the existing distribution records of Q. spinosa, this study uses the optimized MaxEnt model and ArcGis 10.2 software to screen out 10 factors such as climate and altitude to predict its future potential distribution area because of climate change. The results show that when the parameters are FC = LQHP and RM = 3, the MaxEnt model is optimal and AUC values are greater than 0.95. The precipitation of the driest month (bio14), temperature seasonality (bio4), elevation (ele), isothermality (bio3), and the minimum temperature of coldest month (bio6) were the main environmental factors affecting the potential range of the Q. spinosa. At present, high-suitability areas are mainly in the Hunan, Fujian, Jiangxi, Chongqing, Guizhou, Anhui, and Sichuan provinces of China. In the future, the potential distribution area of Q. spinosa may gradually extend to the northwest and north. The low-concentration emissions scenario in the future can increase the area of suitable habitat for Q. spinosa and slow down the reduction in the amount of high-suitability areas to a certain extent. In conclusion, the habitat of Q. spinosa is mainly distributed in southern China. Because of global climate change, the high-altitude mountainous areas in southern China with abundant water resources may be the main potential habitat area of Q. spinosa. Predicting the changes in the distribution patterns of Q. spinosa can better help us understand the biogeography of Q. spinosa and develop conservation strategies to minimize the impacts of climate change.

Ecological niche shifts affect the potential invasive risk of Phytolacca americana (Phytolaccaceae) in China

BackgroundPredicting the potential habitat of Phytolacca americana, a high-risk invasive species, can help provide a scientific basis for its quarantine and control strategies. Using the optimized MaxEnt model, we applied the latest climate data, CMIP6, to predict the distribution of potential risk zones and their change patterns for P. americana under current and future (SSP126, SSP245, SSP585) climate conditions, followed by invasion potential analysis.ResultsThe predictions of MaxEnt model based on R language optimization were highly accurate. A significantly high area of 0.8703 was observed for working characteristic curve (AUC value) of subject and the kappa value was 0.8074. Under the current climate conditions, the risk zones for P. americana were mainly distributed in Sichuan, Chongqing, Guizhou, Hunan, and Guangxi provinces. The contribution rate of each climatic factor of P. americana was calculated using the jackknife test. The four factors with the highest contribution rate included minimum temperature of coldest month (bio6, 51.4%), the monthly mean diurnal temperature difference (bio2, 27.9%), precipitation of the driest quarter (bio17, 4.9%), and the warmest seasonal precipitation (bio12, 4.3%).ConclusionUnder future climatic conditions, the change in the habitat pattern of P. americana generally showed a migration toward the Yangtze River Delta region and the southeastern coastal region of China. This migration exhibited an expansion trend, highlighting the strong future invasiveness of the species. Based on the predictions, targeted prevention and control strategies for areas with significant changes in P. americana were developed. Therefore, this study emphasizes the need of an integrated approach to effectively prevent the further spread of invasive plants.

Climate change aggravates anthropogenic threats of the endangered savanna tree Pterocarpus erinaceus (Fabaceae) in Burkina Faso

Species distribution modelling is gaining popularity due to significant habitat shifts in many plant and animal species caused by climate change. This issue is particularly pressing for species that provide significant ecosystem goods and services. A prominent case is the valuable African rosewood tree (Pterocarpus erinaceus) that is threatened in sub-Saharan Africa, while its present distribution, habitat requirements and the impact of climate change are not fully understood. This native species naturally occurs in various savanna types, but anthropogenic interventions have considerably reduced its natural populations in the past decades. In this study, ensemble modelling was used to predict the current and future distribution potential of the species in Burkina Faso. Fifty-four environmental variables were selected to describe its distribution in the years 2050 and 2070 based on the greenhouse gas concentration trajectories RCP4.5 and 8.5, and the general circulation models CNRM-CM5 and HadGEM2-CC. A network of protected areas in Burkina Faso was also included to assess how many of the suitable habitats may contribute to the conservation of the species. The factors isothermality (31%), minimum temperature of coldest month (31%), pH in H2O at horizon 0–5 cm (11%), silt content at horizon 60–100 cm (9.2%) and precipitation of warmest quarter (8%) were the most influential distribution drivers for the species. Under current climate conditions, potentially highly suitable habitats cover an area of 129,695 km2, i.e., 47% of Burkina Faso. The projected distribution under RCP4.5 and 8.5 showed that this area will decrease, and that the decline of the species will be pronounced. The two models used in this study, forecast a habitat loss of up to 61% for P. erinaceus. Hence, development and implementation of a conservation programme are required to save the species in its native range. This study will help land managers prioritise areas for protection of the species, and avoid introducing it to inappropriate areas unless suitable conditions are artificially created through the management options applied.

The current and future distribution of the yellow fever mosquito (Aedes aegypti) on Madeira Island.

The Aedes aegypti mosquito is the main vector for several diseases of global importance, such as dengue and yellow fever. This species was first identified on Madeira Island in 2005, and between 2012 and 2013 was responsible for an outbreak of dengue that affected several thousand people. However, the potential distribution of the species on the island remains poorly investigated. Here we assess the suitability of current and future climatic conditions to the species on the island and complement this assessment with estimates of the suitability of land use and human settlement conditions. We used four modelling algorithms (boosted regression trees, generalized additive models, generalized linear models and random forest) and data on the distribution of the species worldwide and across the island. For both climatic and non-climatic factors, suitability estimates predicted the current distribution of the species with good accuracy (mean area under the Receiver Operating Characteristic curve = 0.88 ±0.06, mean true skill statistic = 0.72 ±0.1). Minimum temperature of coldest month was the most influential climatic predictor, while human population density, residential housing density and public spaces were the most influential predictors describing land use and human settlement conditions. Suitable areas under current climates are predicted to occur mainly in the warmer and densely inhabited coastal areas of the southern part of the island, where the species is already established. By mid-century (2041–2060), the extent of climatically suitable areas is expected to increase, mainly towards higher altitudes and in the eastern part of the island. Our work shows that ongoing efforts to monitor and prevent the spread of Ae. aegypti on Madeira Island will have to increasingly consider the effects of climate change.

Global potential distribution of Oryctes rhinoceros, as predicted by Boosted Regression Tree model

Climate change is expected to have a significant influence on species range expansion, habitat shifts, and risk of biological invasion due to changes in survival rates, and rapid reproduction. This will tend to affect their geographical distribution and dispersal patterns, thereby threatening agriculture production and food security. Therefore, it is essential to understand the impact of climate change on the range shifts of an invasive species like the Asiatic rhinoceros beetle, Oryctes rhinoceros Linnaeus (Coleoptera: Dynastinae: Scarabaeidae), to inform policy formulation and preventive measures. To achieve this, we used environmental variables and occurrence records of O. rhinoceros to predict the current and future potential distribution of the pest under two representative concentration pathways (RCPs 4.5 and 8.5) for three time periods (2030, 2050, and 2080). We employed Boosted Regression Tree (BRT) and ArcGIS to create risk maps for the pest. The BRT model predicts an expansion of O. rhinoceros outside the current known distribution. The environmental variables which contributed the most to the geographical distribution of the pest were minimum temperature of coldest month (26.81 %), followed by precipitation of wettest month (20.61 %), temperature annual range (11.34 %), mean diurnal range (11.33 %), and elevation (4.49 %). Under the different climate change scenarios, O. rhinoceros will continue to threaten the economically important host plants until 2080. As a result, there will be a need for effective strategies to prevent its spread. Our predictions are reliable and have the potential to estimate the global distribution of the pest, as well as provide suggestions for prompt of O. rhinoceros prevention and management.

Future potential distribution and expansion trends of highland barley under climate change in the Qinghai-Tibet plateau (QTP)

Highland barley is an important grain crop in the Qinghai-Tibet Plateau (QTP) of China. However, rare knowledge is available on the spatial pattern of habitat suitability across QTP and how it would be affected by future climate change. Based on 191 presence records, the probability of highland barley across the QTP under climate change is simulated by using the maximum entropy (MaxEnt) model. And the critical environmental variable (s) affecting the distribution of highland barley and the hotspots of habitat degradation/expansion during the 21st century are investigated. The results show that the daily minimum temperature of coldest month and annual precipitation are determined as the two most important environmental variables for predicting habitat distributions. Across the QTP, future climate change may result in an increase in the suitable area for planting highland barley northward, westward, and upward during the 21st century. Highland barley in river valleys of the southeastern and northeast plateau, where areas of medium and optimal suitable habitats are concentrated, may experience contractions in areas of habitat suitability during the 21st century. By the late 21st century, the average expansion and contraction of suitable habitat areas are 0.46 and 0.14 million km2, respectively, and the upper limit elevation suitable for planting barley may increase by 215 m. Our results could provide valuable distribution information to the national and local administrators for developing the special industry of highland barley in the QTP.

Prediction of Suitable Distribution of a Critically Endangered Plant Glyptostrobus pensilis

Glyptostrobus pensilis is a critically endangered living fossil plant species of the Mesozoic era, with high scientific research and economic value. The aim of this study was to assess the impact of climate change on the potential habitat area of G. pensilis in East Asia. The MaxEnt (maximum entropy) model optimized by the ENMeval data package was used to simulate the potential distribution habitats of G. pensilis since the last interglacial period (LIG, 120–140 ka). The results showed that the optimized MaxEnt model has a high prediction accuracy with the area under the receiver operating characteristic curve (AUC) of 0.9843 ± 0.005. The Current highly suitable habitats were found in the Northeast Jiangxi, Eastern Fujian and Eastern Guangdong; the main climatic factors affecting the geographic distribution of G. pensilis are temperature and precipitation, with precipitation as the temperature factor. The minimum temperature of coldest month (Bio6) may be the key factor restricting the northward distribution of G. pensilis; during the LIG, it contracted greatly in the highly suitable habitat area. Mean Diurnal Range (Bio2), Minimum Temperature of Coldest Month (Bio6), Annual Precipitation (Bio12) and Mean Temperature of Driest Quarter (Bio9) may be important climatic factors causing the changes in geographic distribution. In the next four periods, the suitable areas all migrated southward. Except for the RCP2.6-2070s, the highly suitable areas in the other three periods showed varying degrees of shrinkage. The results will provide a theoretical basis for the management and resource protection of G. pensilis.

Simulation of potential suitable distribution of Alnus cremastogyne Burk. In China under climate change scenarios

Alnus cremastogyne is a broad-leaved tree species with fast-growing and promising nitrogen-fixing capacity. Its plantation is under steady growing due to the commercial and restoration importance in China. However, little is known about the effects of projected climate change on its adaptability and future distribution. In the present study, we simulated the ecological suitability of A. cremastogyne under three climate change scenarios (CCS) (i.e., SSP1-2.6, SSP2-4.5 and SSP5-8.5) in 2050 s and 2070 s using geographic information technology (GIS) and maximum entropy model (MaxEnt). The results demonstrated that under current climate situation, the highly suitable areas were mainly located in central and eastern Sichuan, most of Hunan, central and northern Jiangxi, central and eastern Guizhou, most of Chongqing and southeast Hubei, with a total area of 52.1 × 104 km2. The areas of the moderately and poorly suitable areas were 65.52 × 104 km2 and 92.99 × 104 km2, respectively. Under the future CCS, both the highly suitable area and the total suitable area of A. cremastogyne showed increasing trends, 1.22-fold and 1.57-fold higher for highly suitable area by 2050, 1.27-fold and 1.41-fold by 2070, respectively. While the areas of the poorly suitable areas would decline. Moreover, a northwestward migration of the geometric center of the total suitable area was projected. Annual precipitation (5.9–1314.5 mm), the minimum temperature of coldest month (-0.2–16.8 ℃) and the mean temperature of coldest quarter (0.7–11.7 ℃) were the three most important environmental variables determining the distribution of A. cremastogyne. Our results highlight the plasticity of A. cremastogyne to climate change and also the feasibility of intercropping with this species to improve soil nitrogen availability.

Predicting differential habitat suitability of Rhodomyrtus tomentosa under current and future climate scenarios in China

Rhodomyrtus tomentosa, with edible and medicinal values, is a key shrub species in south China's forest understory. It maintains ecological balance, soil and water conservation, and biodiversity in the widely degraded mountain ecosystems. The distribution and population of R. tomentosa have shrunk recently due to anthropogenic impacts. At present, wild communities of R. tomentosa are rare in China's low-altitude areas. A comprehensive understanding of its current and future spatial patterns vis-à-vis changing climatic conditions can inform co-management for economic use and conservation. Based on 213 validated distribution records and nine selected environmental variables, the potential biogeographical range of R. tomentosa in China was predicted by Maxent and QGIS modeling under current and three future climate-change scenarios. The limiting factors for distribution were evaluated by Jackknife, per cent contribution and permutation importance. We found that the present actual biogeographical range was concentrated in tropical and south-subtropical China with some extensions to mid-subtropical east and southwest China, with the main occurrence in the core range of Guangdong, Guangxi, and Hainan provinces. The modeling results indicated temperature as the clinching determinant of distribution patterns, including the minimum temperature of coldest month, mean temperature of warmest quarter, and temperature seasonality. Moisture was a necessary but not critical secondary factor. Under future climate-change scenarios, habitats with excellent suitability index will expand and shift towards southwest China and high-altitude areas. The findings provide science-based evidence to adjust management and conservation plans in response to climate change protect and use R. tomentosa in suitability habitats.

Geographical distribution of As-hyperaccumulator Pteris vittata in China: Environmental factors and climate changes

Understanding the distribution of hyperaccumulators helps to implement more efficient phytoremediation strategies of contaminated sites, however, limited information is available. Here, we investigated the geographical distribution of the first-known arsenic-hyperaccumulator Pteris vittata in China and the key factors under two climate change scenarios (SSP 1-2.6 and SSP 5-8.5) at two time points (2030 and 2070). Species distribution model (MaxEnt) was applied to examine P. vittata distribution based on 399 samples from field surveys and existing specimen records. Further, among 23 environmental factors, 11 variables were used in the MaxEnt model, including temperature, precipitation, elevation, soil property, and UV-B radiation. The results show that P. vittata can grow in ~23% of the regions in China. Specifically, it is mainly distributed in 11 provinces of southern China, including Hainan, Guangdong, Guangxi, Yunnan, Guizhou, Hunan, Hubei, Jiangxi, Fujian, Zhejiang, and Jiangsu. Besides, eastern Sichuan, and southern Henan, Shaanxi, and Anhui are suitable for P. vittata growth. Under two climate change scenarios, P. vittata distribution in China would decrease by ~5.76-7.46 × 104 km2 in 2030 and ~3.22-4.68 × 104 km2 in 2070, with southern Henan and most Jiangsu being unsuitable for P. vittata growth. Among the 11 environmental variables, the minimum temperature of coldest month (bio6) and temperature annual range (bio7) are the two key factors limiting P. vittata distribution. At bio6 <-5 °C and/or bio7 >33 °C, the regions are unsuitable for P. vittata growth. Based on the MaxEnt model, precipitation had limited effects, so P. vittata can probably survive under both dry and moist environments. This study helps guide phytoremediation of As-polluted soils using P. vittata and provides an example to evaluate habitat suitability of hyperaccumulators at international scales.