Future spatial distribution of Diaphorina citri in Mexico under climate change models

Climate change may modify environmental conditions creating suitable environments for phytopathogen vectors in places that were not suitable before. The present study aimed to contrast current and future spatial distribution of Diaphorina citri in Mexico under two climate change scenarios, Shared Socioeconomic Pathways (SSP) 4.5 and 8.5 for years 2050 and 2070. Non-correlated bioclimatic variables from eight General Circulation Models derived from the Coupled Model Intercomparison Project-6 and presence point data were used to generate distribution models with MaxEnt. Future projections showed that current suitable areas, equivalent to a 38.6% of coverage persist across all scenarios, new suitability areas appear, and no reduction is expected. All the models coincide on a potential increase in relation to the current national distribution of 11.1, 14.8, 13.8 and 25.5% for SSP2 4.5–50 SSP2 4.5–70 SSP5 8.5–50, and SSP5 8.5–70 respectively. Most of the new areas are not currently dedicated to citriculture; however, an increase in the risk of Huanglongbing is expected because most of the new areas are contiguous to the current presence areas, and cover urban zones where there may exist rutaceous hosts, from which the vector may spread the disease to the production zones.     

A MaxEnt modelling approach to understand the climate change effects on the distributional range of White-bellied Sholakili Sholicola albiventris (Blanford, 1868) in the Western Ghats,India

Each species is uniquely influenced by anthropogenic climate change. Change in temperature and precipitation due to climate change may lead to species adaptation or extinction, or in some cases, a range shift. To know the influence of climate change on a restricted and endemic bird species of the Western Ghats (WG), White-bellied Sholakili (WBS) Sholicola albiventris (Blanford, 1868), we conducted a study by using species distribution modelling. We considered 73 spatial bias-corrected occurrence points of WBS along with environmental variables like the mean temperature of coldest quarter (Bio 11), precipitation of driest month (Bio 14) and mean precipitation of warmest quarter (Bio 18). We used the MaxEnt application with ENM evaluate tool in R statistical package for developing a climate model for WBS. Bio 11 was observed to be the most crucial climate variable shaping the habitat of WBS. The current study predicts that only 2823km² in WG is suitable for WBS. One-third of this area falls under the protected area network, of which 52% is becoming unsuitable to this narrow endemic due to climate warming. The model also predicts 26% to 45% habitat loss under different climate change scenarios by the 2050s.     

Mopane (Colophospermum mopane): A potential winner under climate change in southern Africa

Distribution and abundance under climate change of particularly non-timber forest product tree species is vital since they sustain many livelihoods, especially in rural sub-Saharan Africa. The aim of the study was to determine the current and future natural range of mopane (Colophospermum mopane (J. Kirk ex Benth.) J. Léonard, Fabaceae), a dominant tree species in mopane woodlands of southern Africa. An ensemble model was built in ‘biomod2’ from eight algorithms and used to estimate the current and future distribution. Seven bioclimatic variables and 269 occurrence records were used to calibrate individual models that were later combined into an ensemble model. The ensemble model was projected to two time periods, 2041–2060 and 2081–2100, under two shared socio-economic pathways (SSPs), SSP2-4.5 and SSP5-8.5, and three general circulation models (GCMs). The ensemble model showed high performance (KAPPA = 0.770, ROC = 0.961, TSS = 0.792, ACCURACY = 0.900). A map of the current distribution shows occurrence predominantly in low-lying areas, including the Zambezi, Save and Limpopo valleys, Okavango and Cuvelai basins, and in southern and central Mozambique. Projection maps show expansion under all SSPs, GCMs and time periods. Averaged across GCMs in 2041–2060, the range expanded by 22.37% under SSP2-4.5, and by 19.94% under SSP5-8.5. In 2081–2100, the range expanded by 20.43% under SSP2-4.5, and by 27.62% under SSP5-8.5. Notably, the range expansion was highest under SSP5-8.5, an SSP that envisages unmitigated greenhouse gas release and the largest mean global temperature increase. It is highly likely that mopane is not directly threatened by climate change. Indirect climate change threats, however, remain uncertain.     

Climate and habitat availability determine 20th century changes in a butterfly's range margin

Evidence of anthropogenic global climate change is accumulating, but its potential consequences for insect distributions have received little attention. We use a ''climate response surface'' model to investigate distribution changes at the northern margin of the speckled wood butterfly, Pararge aegeria. We relate its current European distribution to a combination of three bioclimatic variables. We document that P. aegeria has expanded its northern margin substantially since 1940, that changes in this species'' distribution over the past 100 years are likely to have been due to climate change, and that P. aegeria will have the potential to shift its range margin substantially northwards under predicted future climate change. At current rates of expansion, this species could potentially colonize all newly available climatically suitable habitat in the UK over the next 50 years or more. However, fragmentation of habitats can affect colonization, and we show that availability of habitat may be constraining range expansion of this species at its northern margin in the UK. These lag effects may be even more pronounced in less-mobile species inhabiting more fragmented landscapes, and highlight how habitat distribution will be crucial in predicting species'' responses to future climate change.     

Identifying refugia and corridors under climate change conditions for the Sichuan snub‐nosed monkey (Rhinopithecus roxellana) in Hubei Province,China

Using a case study of an isolated management unit of Sichuan snub‐nosed monkey (Rhinopithecus roxellana), we assess the extent that climate change will impact the species’ habitat distribution in the current period and projected into the 2050s. We identify refugia that could maintain the population under climate change and determine dispersal paths for movement of the population to future suitable habitats. Hubei Province, China. We identified climate refugia and potential movements by integrating bioclimatic models with circuit theory and least‐cost model for the current period (1960–1990) and the 2050s (2041–2060). We coupled a maximum entropy algorithm to predict suitable habitat for the current and projected future periods. Suitable habitat areas that were identified during both time periods and that also satisfied home range and dispersal distance conditions were delineated as refugia. We mapped potential movements measured as current flow and linked current and future habitats using least‐cost corridors. Our results indicate up to 1,119 km² of currently suitable habitat within the study range. Based on our projections, a habitat loss of 67.2% due to climate change may occur by the 2050s, resulting in a reduced suitable habitat area of 406 km² and very little new habitat. The refugia areas amounted to 286 km² and were located in Shennongjia National Park and Badong Natural Reserve. Several connecting corridors between the current and future habitats, which are important for potential movements, were identified. Our assessment of the species predicted a trajectory of habitat loss following anticipated future climate change. We believe conservation efforts should focus on refugia and corridors when planning for future species management. This study will assist conservationists in determining high‐priority regions for effective maintenance of the endangered population under climate change and will encourage increased habitat connectivity.     

Species distribution models for predicting the habitat suitability of Chinese fire‐bellied newt Cynops orientalis under climate change

Climate change influences species geographical distribution and diversity pattern. The Chinese fire‐bellied newt (Cynops orientalis) is an endemic species distributed in East‐central China, which has been classified as near‐threatened species recently due to habitat destruction and degradation and illegal trade in the domestic and international pet markets. So far, little is known about the spatial distribution of the species. Based on bioclimatic data of the current and future climate projections, we modeled the change in suitable habitat for C. orientalis by ten algorithms, evaluated the importance of environmental factors in shaping their distribution, and identified distribution shifts under climate change scenarios. In this study, 46 records of C. orientalis from East China and 8 bioclimatic variables were used. Among the ten modeling algorithms, four (GAM, GBM, Maxent, and RF) were selected according to their predictive abilities. The current habitat suitability showed that C. orientalis had a relatively wide but fragmented distribution, and it encompassed 41,862 km². The models suggested that precipitation of warmest quarter (bio18) and mean temperature of wettest quarter (bio6) had the highest contribution to the model. This study revealed that C. orientalis is sensitive to climate change, which will lead to a large range shift. The projected spatial and temporal pattern of range shifts for C. orientalis should provide a useful reference for implementing long‐term conservation and management strategies for amphibians in East China.     

Ensemble modeling approach to predict the past and future climate suitability for two mangrove species along the coastal wetlands of peninsular India

Mangroves support numerous ecosystem services and help in reducing coastal ecological risks, yet they are declining rapidly due to climate change, sea level fluctuations and human activities. It is important to understand their responses to climate and sea level changes and identify conservation target areas at spatio-temporal scales, specifically in regions of rich mangrove biodiversity. In this study, we predicted the potential impact of past (Middle Holocene, ∼6000 years), current and future (2050s, 2070s; RCP 2.6 and RCP 8.5) climate change scenarios on the two dominant species in the coastal mangrove forest wetlands of India, i.e., Rhizophora mucronata and Avicennia officinalis through an ensemble species distribution modeling approach. The ensemble modeling has been carried out by integrating eight single algorithm methods. Based on the receiver operating characteristics of area under the curve (AUC) and true skill statistics (TSS) values the ensemble modeling has yielded the highest predictive performance for SVM for both the species and lowest by CART for R. mucronata and BIOCLIM for A. officinalis. The internal evaluation metrics of the resulting Species distribution models (SDMs) tested its robustness with AUC-0.97 and TSS-0.89 for A. officinalis and AUC-0.99 and TSS-0.90 for R. mucronata. Precipitation of Wettest Month (Bio 13) and Mean Temperature of Warmest Quarter (Bio 10) was the most important variable (54–67%) for the distribution of A. officinalis and Precipitation Seasonality (Bio 15) and Precipitation of Warmest Quarter (Bio 18) for R. mucronata. High precipitation and sea-level highstand during middle Holocene led to the maximum range expansion of suitable habitat for the mangrove species which is also validated in the present study by the fossil pollen datasets. Total mangrove habitat in current and future climatic scenarios decreased in 2.6 and 8.5 Representative Concentration Pathways (RCPs) for 2050 and 2070 which indicates the vulnerability of the species to climate change impacts. Mangrove species are projected to shift their ranges more towards land in future experiencing a decrease in the amount of suitable coastal area available to them throughout the Indian coastline. The plausible cause for this range shift may be due to higher precipitation that is usually associated with longer period of soil inundation and because of the rise in sea level. Our findings will assist in formulating species-specific restoration plans for these keystone species in context of climate change in the Indian Subcontinent.     

Identifying climate refugia and its potential impact on Tibetan brown bear (Ursus arctos pruinosus) in Sanjiangyuan National Park,China

Climate change has direct impacts on wildlife and future biodiversity protection efforts. Vulnerability assessment and habitat connectivity analyses are necessary for drafting effective conservation strategies for threatened species such as the Tibetan brown bear (Ursus arctos pruinosus). We used the maximum entropy (MaxEnt) model to assess the current (1950–2000) and future (2041–2060) habitat suitability by combining bioclimatic and environmental variables, and identified potential climate refugia for Tibetan brown bears in Sanjiangyuan National Park, China. Next, we selected Circuit model to simulate potential migration paths based on current and future climatically suitable habitat. Results indicate a total area of potential suitable habitat under the current climate scenario of approximately 31,649.46 km², of which 28,778.29 km² would be unsuitable by the 2050s. Potentially suitable habitat under the future climate scenario was projected to cover an area of 23,738.6 km². Climate refugia occupied 2,871.17 km², primarily in the midwestern and northeastern regions of Yangtze River Zone, as well as the northern region of Yellow River Zone. The altitude of climate refugia ranged from 4,307 to 5,524 m, with 52.93% lying at altitudes between 4,300 and 4,600 m. Refugia were mainly distributed on bare rock, alpine steppe, and alpine meadow. Corridors linking areas of potentially suitable brown bear habitat and a substantial portion of paths with low‐resistance value were distributed in climate refugia. We recommend various actions to ameliorate the impact of climate change on brown bears, such as protecting climatically suitable habitat, establishing habitat corridors, restructuring conservation areas, and strengthening monitoring efforts.     

Patterns and Variability of Projected Bioclimatic Habitat for Pinus albicaulis in the Greater Yellowstone Area

Projected climate change at a regional level is expected to shift vegetation habitat distributions over the next century. For the sub-alpine species whitebark pine (Pinus albicaulis), warming temperatures may indirectly result in loss of suitable bioclimatic habitat, reducing its distribution within its historic range. This research focuses on understanding the patterns of spatiotemporal variability for future projected P.albicaulis suitable habitat in the Greater Yellowstone Area (GYA) through a bioclimatic envelope approach. Since intermodel variability from General Circulation Models (GCMs) lead to differing predictions regarding the magnitude and direction of modeled suitable habitat area, nine bias-corrected statistically down-scaled GCMs were utilized to understand the uncertainty associated with modeled projections. P.albicaulis was modeled using a Random Forests algorithm for the 1980–2010 climate period and showed strong presence/absence separations by summer maximum temperatures and springtime snowpack. Patterns of projected habitat change by the end of the century suggested a constant decrease in suitable climate area from the 2010 baseline for both Representative Concentration Pathways (RCPs) 8.5 and 4.5 climate forcing scenarios. Percent suitable climate area estimates ranged from 2–29% and 0.04–10% by 2099 for RCP 8.5 and 4.5 respectively. Habitat projections between GCMs displayed a decrease of variability over the 2010–2099 time period related to consistent warming above the 1910–2010 temperature normal after 2070 for all GCMs. A decreasing pattern of projected P.albicaulis suitable habitat area change was consistent across GCMs, despite strong differences in magnitude. Future ecological research in species distribution modeling should consider a full suite of GCM projections in the analysis to reduce extreme range contractions/expansions predictions. The results suggest that restoration strageties such as planting of seedlings and controlling competing vegetation may be necessary to maintain P.albicaulis in the GYA under the more extreme future climate scenarios.     

气候变化对祁连山蒙古扁桃潜在适生区的影响

气候变化将改变物种的生存环境,影响其分布范围,甚至威胁到某些物种的生存。本文通过ArcGIS软件和最大熵(MaxEnt)模型模拟蒙古扁桃(Amygdalus mongolica)在祁连山当前(1970—2000年)和未来(2081—2100年)2个气候时期背景下的地理分布格局,并分析其主要的环境影响因素。结果表明：(1)在当前气候条件下,蒙古扁桃在祁连山的东南部有较好的适生性;(2)未来4种气候情景下(SSP126,SSP245,SSP245和SSP585),蒙古扁桃在祁连山南部及东南部的适生区有消失的风险,扩张区主要集中在祁连山中北部的国家公园附近;(3)蒙古扁桃的分布格局主要向祁连山北部和高纬度地区迁移;(4)最湿月降水量(Bio13)、坡度(Slope)、最冷季度均温(Bio11)和最热月最高温(Bio5)的累计贡献率达到了80%以上,是影响蒙古扁桃适生分布的主要因子。本研究模拟、分析、预测了当前和未来不同情景下蒙古扁桃在祁连山的潜在分布及其变化,为祁连山生态及物种多样性的保护提供科学依据。     

Prediction of potential distribution of soybean in the frigid region in China with MaxEnt modeling

Soybean (Glycine max (L.) Merr.) is one of the most important grains and oil-producing plants grown in China. Understanding the potential suitable characteristics of areas where soybean is grown and predicting its potential habitat under different climate scenarios are a significant part of ensuring food security. This study compiled 65 occurrence locations of soybean and 32 environmental variables obtained from the WorldClim database. Nine environmental variables were selected for model training. We identified potential suitable distribution areas for soybean in the frigid region and predicted changes in its geographical distribution under four shared socioeconomic pathways, SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5, for the periods from 2021 to 2040, 2041 to 2060, 2061 to 2080, and 2081 to 2100 using the MaxEnt model. The results showed that annual mean temperature, elevation, and April solar radiation were the dominant factors affecting the distribution of soybean, contributing 48.8%, 17.9%, and 15.7% of the variability in the data, respectively. Highly suitable habitats (defined as having a suitability variable P of 0.66–1.0) for the current conditions included the Songnen and Sanjiang plains, covering about 2.36 × 10⁵ km². The total areas of highly (as defined above) and moderately suitable (0.33–0.66) habitats would be reduced under the four climate scenarios. However, the centroids of the highly suitable habitat had a small mobile range under different scenarios. These results along with previous research on the potential distribution of soybean offer useful information; ecological modeling approaches need to be considered in future crop planting management and land use.     

Impact of Climate Change on Potential Distribution of Chinese Caterpillar Fungus (Ophiocordyceps sinensis) in Nepal Himalaya

Climate change has already impacted ecosystems and species and substantial impacts of climate change in the future are expected. Species distribution modeling is widely used to map the current potential distribution of species as well as to model the impact of future climate change on distribution of species. Mapping current distribution is useful for conservation planning and understanding the change in distribution impacted by climate change is important for mitigation of future biodiversity losses. However, the current distribution of Chinese caterpillar fungus, a flagship species of the Himalaya with very high economic value, is unknown. Nor do we know the potential changes in suitable habitat of Chinese caterpillar fungus caused by future climate change. We used MaxEnt modeling to predict current distribution and changes in the future distributions of Chinese caterpillar fungus in three future climate change trajectories based on representative concentration pathways (RCPs: RCP 2.6, RCP 4.5, and RCP 6.0) in three different time periods (2030, 2050, and 2070) using species occurrence points, bioclimatic variables, and altitude. About 6.02% (8,989 km²) area of the Nepal Himalaya is suitable for Chinese caterpillar fungus habitat. Our model showed that across all future climate change trajectories over three different time periods, the area of predicted suitable habitat of Chinese caterpillar fungus would expand, with 0.11–4.87% expansion over current suitable habitat. Depending upon the representative concentration pathways, we observed both increase and decrease in average elevation of the suitable habitat range of the species.     

闽楠(Phoebe bournei, Lauraceae)地理分布及随气候变化的分布格局模拟

掌握气候变化对珍稀濒危物种的分布和适应性变化趋势的影响,是开展保护生物学研究的基础。闽楠（Phoebe bournei）是我国东部亚热带森林的优势树种,也是金丝楠木的主要来源树种。它具有重要的经济、园林与生态价值,目前已被列为国家II级保护植物。预测不同气候背景下该物种的地理分布格局可为这一珍贵树种的资源保护、合理利用与开发提供指导依据,同时也为闽楠的起源与地理分化研究奠定基础。本研究基于闽楠的123个分布点信息与19个气候因子,采用最大熵模型（MaxEnt）与ArcGIS空间分析,构建闽楠于末次冰期（距今22000年）、当前（1950-2000年）以及未来（2050年与2070年）相应地潜在分布区格局,并确定未来受威胁的适生区、面积与影响分布的气候因子。结果表明：闽楠的适生区覆盖浙江、福建、江西、广东、广西、湖南、湖北、贵州及重庆,制约闽楠地理分布的气候因子主要是温度季节性变化标准差（Bio4）、最暖月最高温（Bio5）与最干季降水量（Bio17）;在末次盛冰期闽楠退缩到我国东部的许多山区,诸如武夷山、浙闽丘陵、武陵山、雪峰山、湘黔桂毗邻的山区;随着全球气候变暖,到2050年与2070年闽楠的适生区有着破碎化甚至丧失的风险。     

Future Risks of Pest Species under Changing Climatic Conditions

Most agricultural pests are poikilothermic species expected to respond to climate change. Currently, they are a tremendous burden because of the high losses they inflict on crops and livestock. Smallholder farmers in developing countries of Africa are likely to suffer more under these changes than farmers in the developed world because more severe climatic changes are projected in these areas. African countries further have a lower ability to cope with impacts of climate change through the lack of suitable adapted management strategies and financial constraints. In this study we are predicting current and future habitat suitability under changing climatic conditions for Tuta absoluta, Ceratitis cosyra, and Bactrocera invadens, three important insect pests that are common across some parts of Africa and responsible for immense agricultural losses. We use presence records from different sources and bioclimatic variables to predict their habitat suitability using the maximum entropy modelling approach. We find that habitat suitability for B. invadens, C. cosyra and T. absoluta is partially increasing across the continent, especially in those areas already overlapping with or close to most suitable sites under current climate conditions. Assuming a habitat suitability at three different threshold levels we assessed where each species is likely to be present under future climatic conditions and if this is likely to have an impact on productive agricultural areas. Our results can be used by African policy makers, extensionists and farmers for agricultural adaptation measures to cope with the impacts of climate change.     

The identification and conservation of climate refugia for two Colombian endemic titi (Plecturocebus) monkeys

Natural resource managers face the challenge of developing conservation plans for key species and given that anthropogenic climate change (CC) effects on biodiversity are becoming increasingly evident, the new challenge is to properly incorporate CC adaptation strategies into such plans. Thus, the objective of this study is to evaluate the potential CC effects on the climatically suitable areas for two Colombian endemic titi monkeys Plecturocebus ornatus and P. caquetensis and to identify the prospective climate refugia as macro-ecological adaptation strategies for each species. A detailed ecological niche modeling (ENM) approach was applied with the maximum entropy algorithm, using presence records and different sets of bioclimatic variables describing baseline (1960–1990) and future climates (∼2070). Models of future climatic suitability were generated using projections of variables under a stabilization (RCP4.5) and business as usual (RCP8.5) scenarios with data from two general circulation models (GCMs) describing storylines of increasing (CESM1_CAM5) and decreasing (CSIRO_ACCESS1_3) rainfall patterns. The results for both species indicate that in a warmer future, opposite rainfall patterns and choice of the bioclimatic variables may lead to divergent responses on the extent and geographic distribution of their climatic niche, which varied from regions gaining, losing, and retaining suitability in potential climate refugia. Moreover, CC represents a serious threat for P. caquetensis and P. ornatus since their ranges may be largely exposed to novel climates. Their baseline climatic suitability area is projected to shrink and shift to higher elevations in the Andes mountains, and the climate refugia identified for both species are poorly covered by protected areas. Therefore, the climate refugia identified in this work and the management recommendations offered should be considered by species conservation plans to contribute to the selection of priority regions for conservation actions. The modeling approach reveals the uncertainties arising from the selection of bioclimatic variables and GCMs in ENM, which can be replicated to identify climate refugia targeting different species of conservation concern.     

Using MaxEnt modeling to predict the potential distribution of the endemic plant Rosa arabica Crép. in Egypt

Climate change poses negative impacts on plant species, particularly for those of restricted ecology and distribution range. Rosa arabica Crép., an exclusive endemic species to Saint Catherine Protectorate in Egypt, has severely declined and become critically endangered in the last years. In this paper, we applied the maximum-entropy algorithm (MaxEnt) to predict the current and future potential distribution of this species in order to provide a basis for its protection and conservation. In total, 32 field-based occurrence points and 22 environmental variables (19 bioclimatic and three topographic) were used to model the potential distribution area under current and two future representative concentration pathways (RCP2.6 and RCP8.5) for the years 2050 and 2070. Annual temperature, annual precipitation and elevation were the key factors for the distribution of R. arabica. The response curves showed that this species prefers habitats with an annual temperature of 8.05–15.4 °C, annual precipitation of 36 to 120 mm and elevation range of 1571 to 2273 m a.s.l. Most of the potential current suitable conditions were located at the middle northern region of Saint Catherine. Prediction models under two future climate change scenarios displayed habitat range shifts through the disappearance of R. arabica in sites below 1500 m a.s.l., an altitudinal range contraction at 1500–2000 m and possible expansions towards higher elevation sites (2000–2500 m a.s.l.). Our findings can be used to define the high priority areas for reintroduction or for protection against the expected climate change impacts and future modifications.     

Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy‐deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate‐only model shows that only 11.64% (17,190 km²) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km² (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate‐only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.     

Precipitation of the warmest quarter and temperature of the warmest month are key to understanding the effect of climate change on plant species diversity in Southern African savannah

In this study, we test for the key bioclimatic variables that significantly explain the current distribution of plant species richness in a southern African ecosystem as a preamble to predicting plant species richness under a changed climate. We used 54,000 records of georeferenced plant species data to calculate species richness and spatially interpolated climate data to derive nineteen bioclimatic variables. Next, we determined the key bioclimatic variables explaining variation in species richness across Zimbabwe using regression analysis. Our results show that two bioclimatic variables, that is, precipitation of the warmest quarter (R² = 0.92, P < 0.001) and temperature of the warmest month (R² = 0.67, P < 0.001) significantly explain variation in plant species richness. In addition, results of bioclimatic modelling using future climate change projections show a reduction in the current bio‐climatically suitable area that supports high plant species richness. However, in high‐altitude areas, plant richness is less sensitive to climate change while low‐altitude areas show high sensitivity. Our results have important implications to biodiversity conservation in areas sensitive to climate change; for example, high‐altitude areas are likely to continue being biodiversity hotspots, as such future conservation efforts should be concentrated in these areas.     

Modeling impacts of climate change on the geographic distribution of medicinal plant Fritillaria cirrhosa D. Don

Fritillaria cirrhosa D. Don is a renowned traditional Chinese medicine plant that is mainly distributed in the southeastern margin of the Qinghai–Tibet Plateau. The overexploitation in the recent years has led to a sharp decline of this undomesticated resource. Analyzing the impact of climate change on the geographic distribution of F. cirrhosa is meaningful for its conservation and domestication. In this study, the maximum entropy model (Maxent) was used to simulate the distribution of F. cirrhosa in relation to the current and future climatic conditions. The maximum temperature of the warmest month (Bio 5) and the precipitation of the warmest quarter (Bio 18) were the two most important bioclimatic variables determining the distribution of F. cirrhosa. Based on the predicted level of climatic warming, a further reduction of the geographic distribution of F. cirrhosa is to be expected. This study demonstrated the necessity and urgency of establishing more effective ways to protect the natural F. cirrhosa resources and developing artificial cultivation methodology.     

Shifts in potential geographical distribution of Pterocarya stenoptera under climate change scenarios in China

Climate change poses a serious threat to biodiversity. Predicting the effects of climate change on the distribution of a species' habitat can help humans address the potential threats which may change the scope and distribution of species. Pterocarya stenoptera is a common fast‐growing tree species often used in the ecological restoration of riverbanks and alpine forests in central and eastern China. Until now, the characteristics of the distribution of this species' habitat are poorly known as are the environmental factors that influence its preferred habitat. In the present study, the Maximum Entropy Modeling (Maxent) algorithm and the Genetic Algorithm for Ruleset Production (GARP) were used to establish the models for the potential distribution of this species by selecting 236 sites with known occurrences and 14 environmental variables. The results indicate that both models have good predictive power. Minimum temperature of coldest month (Bio6), mean temperature of warmest quarter (Bio10), annual precipitation (Bio12), and precipitation of driest month (Bio14) were important environmental variables influencing the prediction of the Maxent model. According to the models, the temperate and subtropical regions of eastern China had high environmental suitability for this species, where the species had been recorded. Under each climate change scenario, climatic suitability of the existing range of this species increased, and its climatic niche expanded geographically to the north and higher elevation. GARP predicted a more conservative expansion. The projected spatial and temporal patterns of P. stenoptera can provide reference for the development of forest management and protection strategies.