Potential distributional shifts in North America of allelopathic invasive plant species under climate change models

Predictive studies play a crucial role in the study of biological invasions of terrestrial plants under possible climate change scenarios.Invasive species are recognized for their ability to modify soil microbial communities and influence ecosystem dynamics.Here,we focused on six species of allelopathic flowering plants-Ailanthus altissima,Casuarina equisetifolia,Centaurea stoebe ssp.micranthos,Dioscorea bulbifera,Lantana camara,and Schinus terebinthifolia-Xhat are invasive in North America and examined their potential to spread further during projected climate change.We used Species Distribution Models(SDMs)to predict future suitable areas for these species in North America under several proposed future climate models.ENMEval and Maxent were used to develop SDMs,estimate current distributions,and predict future areas of suitable climate for each species.Areas with the greatest predicted suitable climate in the future include the northeastern and the coastal northwestern regions of North America.Range size estimations demonstrate the possibility of extreme range loss for these invasives in the southeastern United States,while new areas may become suitable in the northeastern United States and southeastern Canada.These findings show an overall northward shift of suitable climate during the next few decades,given projected changes in temperature and precipitation.Our results can be utilized to analyze potential shifts in the distribution of these invasive species and may aid in the development of conservation and management plans to target and control dissemination in areas at higher risk for potential future invasion by these allelopathic species.

Species abundance distribution models of Toona ciliata communities in Hubei Province,China

The study of plant species abundance distribution(SAD)in natural communities is of considerable importance to understand the processes and ecological rules of community assembly.With the distribution of tree,shrub and herb layers of eight natural communities of Toona ciliata as research targets,three diff erent ecological niche models were used:broken stick model,overlapping niche model and niche preemption model,as well as three statistical models:log-series distribution model,log-normal distribution model and Weibull distribution model,to fi t SAD of the diff erent vegetation layers based on data collected.Goodness-of-fi t was compared with Chi square test,Kolmogorov–Smirnov(K–S)test and Akaike Information Criterion(AIC).The results show:(1)based on the criteria of the lowest AIC value,Chi square value and K–S value with no signifi cant diff erence(p>0.05)between theoretic and observed SADs.The suitability and goodness-of-fi t of the broken stick model was the best of three ecological niche models.The log-series distribution model did not accept the fi tted results of most vegetation layers and had the lowest goodness-of-fi t.The Weibull distribution model had the best goodness-of-fi t for SADs.Overall,the statistical SADs performed better than the ecological ones.(2)T.ciliata was the dominant species in all the communities;species richness and diversity of herbs were the highest of the vegetation layers,while the diversities of the tree layers were slightly higher than the shrub layers;there were fewer common species and more rare species in the eight communities.The herb layers had the highest community evenness,followed by the shrub and the tree layers.Due to the complexity and habitat diversity of the diff erent T.ciliata communities,comprehensive analyses of a variety of SADs and tests for optimal models together with management,are practical steps to enhance understanding of ecological processes and mechanisms of T.ciliata communities,to detect disturbances,and to facilitate biodiversity and species conservation.

Modelling the nesting-habitat of threatened vulture species in the caucasus:An ecosystem approach to formalising environmental factors in species distribution models

Abiotic factors play an important role in species localisation,but biotic and anthropogenic predictors must also be considered in distribution modelling for models to be biologically meaningful.In this study,we formalised the biotic predictors of nesting sites for four threatened Caucasian vultures by including species distribution models(wild ungulates,nesting tree species)as biotic layers in the vulture Maxent models.Maxent was applied in the R dismo package and the best set of the model parameters were defined in the R ENMeval package.Performance metrics were continuous Boyce index,Akaike's information criterion,the area under receiver operating curve and true skill statistics.We also calculated and evaluated the null models.Kernel density estimation method was applied to assess the overlap of vulture ecological niches in the environmental space.The accessibility of anthropogenic food resources was estimated using the Path Distance measure that considers elevation gradient.The availability of pine forests(Scots Pine)and wild ungulates(Alpine Chamois and Caucasian Goat)contributed the most(29.6%and 34.3%)to Cinereous Vulture(Aegypius monachus)nesting site model.Wild ungulate distribution also contributed significantly(about 46%)to the Bearded Vulture(Gypaetus barbatus)model.This scavenger nests in the highlands of the Caucasus at a minimum distance of 5–10 km from anthropogenic facilities.In contrast,livestock as a food source was most important in colony distribution of Griffon Vulture(Gyps fulvus).The contribution of distances to settlements and agricultural facilities to the model was 45%.The optimal distance from Egyptian Vulture(Neophron percnopterus)nesting sites to settlements was only 3–10 km,to livestock facilities no more than 15 km with the factor contribution of about 57%.Excluding the wild ungulate availability,the ecological niches of studied vultures overlapped significantly.Despite similar foraging and nesting requirements,Caucasian vultures are not pronounced nesting and trophic competitors due to the abundance of nesting sites,anthropogenic food sources and successful niche sharing.

Common Species Distribution Models in Biodiversity Analysis and Their Challenges and Prospects in Application

Species distribution models have been widely used to explore suitable habitats of species,the impact of climate change on the distribution of suitable habitats of species,and the construction of ecological reserves.This paper introduced species distribution models commonly used in biodiversity analysis,as well as model performance evaluation indexes,challenges in the application of species distribution models,and finally prospected the development trend of research on species distribution models.

Modelling the spatial distribution of snake species in northwestern Tunisia using maximum entropy(Maxent) and Geographic Information System(GIS)

We used GIS and maximum entropy to predict the potential distribution of six snake species belong to three families in Kroumiria（Northwestern Tunisia）： Natricidae（Natrix maura and Natrix astreptophora）, Colubridae（Hemorrhois hippocrepis, Coronella girondica and Macroprotodon mauritanicus）, and Lamprophiidae（Malpolon insignitus）. The suitable habitat for each species was modelled using the maximum entropy algorithm, combining presence field data（collected during 16 years：2000–2015） with a set of seven environmental variables（mean annual precipitation, elevation, slope gradient,aspect, distance to watercourses, land surface temperature and normalized Differential Vegetation Index. The relative importance of these environmental variables was evaluated by jackknife tests and the predictive power of our models was assessed using the area under the receiver operating characteristic. The main explicative variables of the species distribution were distance from streams and elevation, with contributions ranging from 60 to 77 and from 10 to 25%,respectively. Our study provided the first habitat suitability models for snakes in Kroumiria and this information can be used by conservation biologists and land managers concerned with preserving snakes in Kroumiria.

Mapping the potential distribution suitability of 16 tree species under climate change in northeastern China using Maxent modelling

Knowledge on the potential suitability of tree species to the site is very important for forest management planning.Natural forest distribution provides a good reference for afforestation and forest restoration.In this study,we developed species distribution model(SDM)for 16 major tree species with 2,825 permanent sample plots with natural origin from Chinese National Forest Inventory data collected in Jilin Province using the Maxent model.Three types of environmental factors including bioclimate,soil and topography with a total of 33 variables were tested as the input.The values of area under the curve(AUC,one of the receiver operating characteristics of the Maxent model)in the training and test datasets were between 0.784 and 0.968,indicating that the prediction results were quite reliable.The environmental factors affecting the distribution of species were ranked in terms of their importance to the species distribution.Generally,the climatic factors had the greatest contribution,which included mean diurnal range,annual mean temperature,temperature annual range,and iosthermality.But the main environmental factors varied with tree species.Distribution suitability maps under current(1950-2000)and future climate scenarios(CCSM4-RCP 2.6 and RCP 6.0 during 2050)were produced for 16 major tree species in Jilin Province using the model developed.The predicted current and future ranges of habitat suitability of the 16 tree species are likely to be positively and negatively affected by future climate.Seven tree species were found to benefit from future climate including B etula costata,Fraxinus mandshurica,Juglans mandshurica,Phellodendron amurense,Populus ussuriensis,Quercus mongolica and Ulmus pumila;five tree species will experience decline in their suitable habitat including B.platyphylla,Tilia mongolica,Picea asperata,Pinus sylvestris,Pinus koraiensis;and four(Salix koreensis,Abies fabri,Pinus densiflora and Larix olgensis)showed the inconsistency under RCP 2.6 and RCP 6.0 scenarios.The maps of the habitat suitability can be used as a basis for afforestation and forest restoration in northeastern China.The SDMs could be a potential tool for forest management planning.

Species distribution modeling in regions of high need and limited data： waterfowl of China

Background: A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources.Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China.Methods: Faced with limited data, we built species distribution models using a habitat suitability approach for China's breeding and non-breeding(hereafter, wintering) waterfowl.An extensive review of the literature was used to determine model parameters for habitat modeling.Habitat relationships were implemented in GIS using land cover covariates.Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available.Results: We developed suitability models for 42 waterfowl species(30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps.Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China.Wintering waterfowl suitability was highest in the lowland regions of southeastern China.Validation measures indicated strong performance in predicting species presence.Comparing our model outputs to China's protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized.Conclusions: These suitability models are the first available for many of China's waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.

Eff ects of climate change on the potential habitat distribution of swimming crab Portunus trituberculatus under the species distribution model

Over the last decades,the species distribution model(SDM)has become an essential tool for studying the potential eff ects of climate change on species distribution.In this study,an ensemble SDM was developed to predict the changes in species distribution of swimming crab Portunus trituberculatus across diff erent seasons in the future(2050s and 2100s)under the climate scenarios of Representative Concentration Pathway(RCP)4.5 and RCP8.5.Results of the ensemble SDM indicate that the distribution of this species will move northward and exhibit evident seasonal variations.Among the four seasons,the suitable habitat for this species will be signifi cantly reduced in summer,with loss rates ranging from 45.23%(RCP4.5)to 88.26%(RCP.8.5)by the 2100s.The loss of habitat will mostly occur in the East China Sea and the southern part of the Yellow Sea,while a slight increase in habitat will occur in the northern part of the Bohai Sea.These fi ndings provide an information forecast for this species in the future.Such forecast will be helpful in improving fi shery management under climate change.

Quantitative model for species distribution of hydroxy polyaluminum chloride

The species distribution of hydroxy polyaluminum chloride (PAC, Al T=0.1mol/L) solutions prepared through two different types of base injection was studied and compared quantitatively by Al Ferron timed complex colorimetric method(AFM) and 27 Al NMR spectroscopy method (ANM), and was simulated by using a quantitative calculating procedure of chemical equilibrium in MINEQL model. The results suggest that methodology of synthesis is very important for determining species distribution in the preparation of PAC solutions. In the PAC solution prepared by micro injection of base method(MIBM), there are at least five kinds of species including a kind of monomeric species Al 3+ , three kinds of polymeric species Al 2(OH) 4+ 2, Al 7(OH) 4+ 17 , Al 13 O 4(OH) (7- n )+ 24+ n ( n =0,2)and an aggregate of Al 13 or a solid phase Al(OH) 3 (aq.). Whereas in the PAC solution prepared by instantaneous injection of base method (IIBM), there are a kind of monomeric species Al 3+ , two kinds of polymeric species Al 2(OH) 4+ 2, Al 13 O 4(OH) (7- n )+ 24+ n ( n =0,2) and a solid phase Al(OH) 3(am). The change of species distribution in the PAC solution depends on preparing method, B(OH/Al) value and concentration.

Modeling Bat Species Richness and Spatial Distribution in Burkina Faso

The spatial distribution of bats in Burkina Faso is little-known. Previous studies have only described the bat species’ richness in Burkina Faso. This study was conducted to highlight bat species’ richness distribution within Burkina Faso and environmental variables that influence this distribution with the aim to give support for protection and further sampling for biodiversity. The Species Distribution Models (SDMs) were used to perform this study. To do that, species occurrences were collected throughout literature and field sampling and correlated to environmental variables through the Maxent software (Maximum Entropy). Our modeling variables included climate, vegetation cover, topography and hydrography data. The Jackknife test was performed to determine the importance of environmental variables that influence the species distribution model. The results showed that bats are present in all areas of vegetation in Burkina Faso. Species richness varies across the country. The species richness for major families increases from North to South. The total annual precipitation and topography are the main variables that positively influence bats distribution in Burkina Faso but the bare ground cover and standard deviation of the maximum temperature negatively influence this distribution. This modeling approach of bat species richness is important for policies makers and represents an invaluable tool in ecological management, particularly in the current context of climate change.

Finer topographic data improves distribution modeling of Picea crassifolia in the northern Qilian Mountains

The Qilian Mountains, a national key ecological function zone in Western China, play a pivotal role in ecosystem services. However, the distribution of its dominant tree species, Picea crassifolia (Qinghai spruce), has decreased dramatically in the past decades due to climate change and human activity, which may have influenced its ecological functions. To restore its ecological functions, reasonable reforestation is the key measure. Many previous efforts have predicted the potential distribution of Picea crassifolia, which provides guidance on regional reforestation policy. However, all of them were performed at low spatial resolution, thus ignoring the natural characteristics of the patchy distribution of Picea crassifolia. Here, we modeled the distribution of Picea crassifolia with species distribution models at high spatial resolutions. For many models, the area under the receiver operating characteristic curve (AUC) is larger than 0.9, suggesting their excellent precision. The AUC of models at 30 m is higher than that of models at 90 m, and the current potential distribution of Picea crassifolia is more closely aligned with its actual distribution at 30 m, demonstrating that finer data resolution improves model performance. Besides, for models at 90 m resolution, annual precipitation (Bio12) played the paramount influence on the distribution of Picea crassifolia, while the aspect became the most important one at 30 m, indicating the crucial role of finer topographic data in modeling species with patchy distribution. The current distribution of Picea crassifolia was concentrated in the northern and central parts of the study area, and this pattern will be maintained under future scenarios, although some habitat loss in the central parts and gain in the eastern regions is expected owing to increasing temperatures and precipitation. Our findings can guide protective and restoration strategies for the Qilian Mountains, which would benefit regional ecological balance.

Mapping the current and future distributions of Onosma species endemic to Iran

Climate change may cause shifts in the natural range of species especially for those that are geographically restricted and/or endemic species.In this study,the spatial distribution of five endemic and threatened species belonging to the genus Onosma(including O.asperrima,O.bisotunensis,O.kotschyi,O.platyphylla,and O.straussii)was investigated under present and future climate change scenarios:RCP2.6(RCP,representative concentration pathway;optimistic scenario)and RCP8.5(pessimistic scenario)for the years 2050 and 2080 in Iran.Analysis was conducted using the maximum entropy(MaxEnt)model to provide a basis for the protection and conservation of these species.Seven environmental variables including aspect,depth of soil,silt content,slope,annual precipitation,minimum temperature of the coldest month,and annual temperature range were used as main predictors in this study.The model output for the potential habitat suitability of the studied species showed acceptable performance for all species(i.e.,the area under the curve(AUC)>0.800).According to the models generated by MaxEnt,the potential current patterns of the species were consistent with the observed areas of distributions.The projected climate maps under optimistic and pessimistic scenarios(RCP2.6 and RCP8.5,respectively)of 2050 and 2080 resulted in reductions and expansions as well as positive range changes for all species in comparison to their current predicted distributions.Among all species,O.bisotunensis showed the most significant and highest increase under the pessimistic scenario of 2050 and 2080.Finally,the results of this study revealed that the studied plant species have shown an acute adaptability to environmental changes.The results can provide useful information to managers to apply appropriate strategies for the management and conservation of these valuable Iranian medicinal and threatened plant species in the future.

Climate-change habitat shifts for the vulnerable endemic oak species(Quercus arkansana Sarg.)

Quercus arkansana(Arkansas oak)is at risk of becoming endangered,as the total known population size is represented by a few isolated populations.The potential impact of climate change on this species in the near future is high,yet knowledge of its predicted effects is limited.Our study utilized the biomod2 R package to develop habi-tat suitability ensemble models based on bioclimatic and topographic environmental variables and the known loca-tions of current distribution of Q.arkansana.We predicted suitable habitats across three climate change scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5)for 2050,2070,and 2090.Our findings reveal that the current suitable habitat for Q.arkansana is approximately 127,881 km^(2) across seven states(Texas,Arkansas,Alabama,Louisiana,Mississippi,Georgia,and Florida);approximately 9.5%is encompassed within state and federally managed protected areas.Our models predict that all current suitable habitats will disap-pear by 2050 due to climate change,resulting in a northward shift into new regions such as Tennessee and Kentucky.The large extent of suitable habitat outside protected areas sug-gests that a species-specific action plan incorporating pro-tected areas and other areas may be crucial for its conserva-tion.Moreover,protection of Q.arkansana habitat against climate change may require locally and regionally focused conservation policies,adaptive management strategies,and educational outreach among local people.

Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area

Correlative species distribution models(SDMs)are important tools to estimate species’geographic distribution across space and time,but their reliability heavily relies on the availability and quality of occurrence data.Estimations can be biased when occurrences do not fully represent the environmental requirement of a species.We tested to what extent species’physiological knowledge might influence SDM estimations.Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia,we compiled a comprehensive dataset of occurrence records.We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs:a naïve model that solely depends on environmental correlates,and a physiologically informed model that further incorporates physiological information as priors.We further tested the models’sensitivity to calibration area choices by fitting them with different buffered areas around known presences.Compared with naïve models,the physiologically informed models successfully captured the negative influence of high temperature on A.japonicus and were less sensitive to the choice of calibration area.The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change(i.e.,larger range expansion and less contraction)than the physiologically informed models.Our findings highlight benefits from incorporating physiological information into correlative SDMs,namely mitigating the uncertainties associated with the choice of calibration area.Given these promising features,we encourage future SDM studies to consider species physi-ological information where available.

Potential global distribution of the guava root-knot nematode Meloidogyne enterolobii under different climate change scenarios using MaxEnt ecological niche modeling

In recent years,Meloidogyne enterolobii has emerged as a major parasitic nematode infesting many plants in tropical or subtropical areas.However,the regions of potential distribution and the main contributing environmental variables for this nematode are unclear.Under the current climate scenario,we predicted the potential geographic distributions of M.enterolobii worldwide and in China using a Maximum Entropy(MaxEnt)model with the occurrence data of this species.Furthermore,the potential distributions of M.enterolobii were projected under three future climate scenarios(BCC-CSM2-MR,CanESM5 and CNRM-CM6-1)for the periods 2050s and 2090s.Changes in the potential distribution were also predicted under different climate conditions.The results showed that highly suitable regions for M.enterolobii were concentrated in Africa,South America,Asia,and North America between latitudes 30°S to 30°N.Bio16(precipitation of the wettest quarter),bio10(mean temperature of the warmest quarter),and bio11(mean temperature of the coldest quarter)were the variables contributing most in predicting potential distributions of M.enterolobii.In addition,the potential suitable areas for M.enterolobii will shift toward higher latitudes under future climate scenarios.This study provides a theoretical basis for controlling and managing this nematode.

Modeling of cold-temperate tree Pinus koraiensis (Pinaceae) distribution in the Asia-Pacific region: Climate change impact

Background:Pinus koraiensis Siebold&Zucc.(Korean pine)is a key species of the mixed cold temperate forests of Northeast Asia.Current climate change can significantly worsen the quality of P.koraiensis habitats and therefore lead to a large-scale structural and functional transformation of the East Asian mixed forests.We built a species distribution model(SDM)for P.koraiensis using the random forest classifier–a versatile machine learning al-gorithm,to discover overlap areas of potential species occurrence in the climate condition of the Last Glacial Maximum(~21,000 year before present)and in the projected future climates(2070 year),from which possible permanent refugia for P.koraiensis were identified.Results:Using the random forest supervised learning algorithm,we developed models of the modern distribution of P.koraiensis in accordance with the five selected bioclimatic variables(Kira’s warmth and coldness indices,the index of continentality,the rain precipitation index,and the snow precipitation index).In addition to current climatic conditions,we performed this analysis for the climate of the Last Glacial Maximum and for the future projected climate(2070)under scenarios RCP2.6 and RCP8.5.Among the predictors,the rain index appears to be the most significant.The land area estimates with high suitability for P.koraiensis was 303,785 km 2 under current climatic conditions,586,499 km 2 for the Last Glacial Maximum,and 337,573 km^(2) for the future(2070)period under the RCP2.6 scenario,and 397,764 km^(2) under the RCP8.5 scenario.Conclusions:Most of the potential range of P.koraiensis during the Last Glacial Maximum was located outside the current distribution area of the species.The climatically suitable P.koraiensis habitats will likely disappear in the western part of its modern range.In the southern part of the range,which includes glacial refugia,the areas of continuous distribution of the P.koraiensis populations since the end of the Pleistocene are expected to be frag-mented,but some localities in the north of the Korean Peninsula,northeast China,southern Primorye(Russia),and central Honshu(Japan)with suitable climatic conditions for the species will support the existence of populations.

Potential Distribution of Seagrass Meadows Based on the MaxEnt Model in Chinese Coastal Waters

Seagrass meadows are generally diverse in China and have become important ecosystem with essential functions.However,the seagrass distribution across the seawaters of China has not been evaluated,and the magnitude and direction of changes in seagrass meadows remain unclear.This study aimed to provide a nationwide seagrass distribution map and explore the dynamic changes in seagrass population under global climate change.Simulation studies were performed using the modeling software MaxEnt with 58961 occurrence records and 27 marine environmental variables to predict the potential distribution of seagrasses and calculate the area.Seven environmental variables were excluded from the modeling processes based on a correlation analysis to ensure predicted suitability.The predicted area was 790.09 km^(2),which is much larger than the known seagrass distribution in China(87.65 km^(2)).By 2100,the suitable habitat of seagrass will shift northwest and increase to 923.62 km2.Models of the sum of the individual family under-pre-dicted the national distribution of seagrasses and consistently showed a downward trend in the future.Out of all environmental vari-ables,physical parameters(e.g.,depth,land distance,and sea surface temperature)contributed the most in predicting seagrass distri-butions,and nutrients(e.g.,nitrate,phosphate)ranked among the key influential predictors for habitat suitability in our study area.This study is the first effort to fill a gap in understanding the distribution of seagrasses in China.Further studies using modeling and biological/ecological approaches are warranted.

Distribution and Environmental Suitability of the Smallscaled Rock Agama, Paralaudakia microlepis(Sauria: Agamidae) in the Iranian Plateau

Predictive potential distribution modeling is of increasing importance in modern herpetological studies and determination of environmental and conservation priorities. In this article we provided results of analysis and forecasts of the potential distribution of smallscaled rock agama Paralaudakia microlepis （Blanford, 1874） using the distribution models through Maxent （www.cs.princeton.edu/- schapire / maxent）. We made an attempt for comparison of input of bioclimatic factors and characteristics of biotope distribution for three species of genus Paralaudalda. Constructed model identified dissemination of Paralaudakia microlepis enough performance （AUC = 0.972 with dispersion 0.003）. According to the map constructed, the most suitable habitats of smallscaled rock agama Paralaudakia microlepis are located in southern and eastern Iran, the west of central Pakistan and southeastern Afghanistan.

Impacts of predictor variables and species models on simulating Tamarix ramosissima distribution in Tarim Basin, northwestern China

Aims Preserving and restoring Tamarix ramosissima is urgently required in the Tarim Basin,Northwest China.Using species distribution models to predict the biogeographical distribution of species is regularly used in conservation and other management activities.However,the uncertainty in the data and models inevitably reduces their prediction power.The major purpose of this study is to assess the impacts of predictor variables and species distribution models on simulating T.ramosissima distribution,to explore the relationships between predictor variables and species distribution models and to model the potential distribution of T.ramosissima in this basin.Methods Three models—the generalized linear model(GLM),classification and regression tree(CART)and Random Forests—were selected and were processed on the BIOMOD platform.The presence/absence data of T.ramosissima in the Tarim Basin,which were calculated from vegetation maps,were used as response variables.Climate,soil and digital elevation model(DEM)data variables were divided into four datasets and then used as predictors.The four datasets were(i)climate variables,(ii)soil,climate and DEM variables,(iii)principal component analysis(PCA)-based climate variables and(iv)PCA-based soil,climate and DEM variables.Important Findings The results indicate that predictive variables for species distribution models should be chosen carefully,because too many predictors can reduce the prediction power.The effectiveness of using PCA to reduce the correlation among predictors and enhance the modelling power depends on the chosen predictor variables and models.Our results implied that it is better to reduce the correlating predictors before model processing.The Random Forests model was more precise than the GLM and CART models.The best model for T.ramosissima was the Random Forests model with climate predictors alone.Soil variables considered in this study could not significantly improve the model’s prediction accuracy for T.ramosissima.The potential distribution area of T.ramosissima in the Tarim Basin is;3.57310^(4) km^(2),which has the potential to mitigate global warming and produce bioenergy through restoring T.ramosissima in the Tarim Basin.

Distribution Changes of Chinese Skink(Eumeces chinensis) in China: the Impacts of Global Climate Change

Repaid global climate changes in temperature and rainfall influence the species distribution and diversity patterns.Chinse skink is a common species with large population and widely distribution in China.To access potential effect of climate changes on the unendangered species,we used the maximum-entropy modeling(MaxEnt)method to estimate the current and future potential distributions of Chinese Skink.Predictions were based on two periods(2050 and 2070),three general circulation models(GCMs:BCC-CSM1-1,HadGEM2-ES,MIROC5),four representative concentration pathways(RCP:2.6,4.5,6.0 and 8.0)and 28 environmental variables including topography,human impact,bio-climate and habitat.We found that the model were better fit with high values in AUC,KAPPA and TSS.The jackknife tests showed that variables of BIO9,BIO14,BIO15,HFI and GDP were relatively higher contributions to the model.Although the size of suitable areas for skink have less effect by future climate change under full and mull dispersal hypothesis,we should still focuse on the effect of human impact and climate changes on the protection and management for Chinese skink due to the variables uncertainty.