Modeling load distribution for rural photovoltaic grid areas using image recognition

Expanding photovoltaic(PV)resources in rural-grid areas is an essential means to augment the share of solar energy in the energy landscape,aligning with the“carbon peaking and carbon neutrality”objectives.However,rural power grids often lack digitalization;thus,the load distribution within these areas is not fully known.This hinders the calculation of the available PV capacity and deduction of node voltages.This study proposes a load-distribution modeling approach based on remote-sensing image recognition in pursuit of a scientific framework for developing distributed PV resources in rural grid areas.First,houses in remote-sensing images are accurately recognized using deep-learning techniques based on the YOLOv5 model.The distribution of the houses is then used to estimate the load distribution in the grid area.Next,equally spaced and clustered distribution models are used to adaptively determine the location of the nodes and load power in the distribution lines.Finally,by calculating the connectivity matrix of the nodes,a minimum spanning tree is extracted,the topology of the network is constructed,and the node parameters of the load-distribution model are calculated.The proposed scheme is implemented in a software package and its efficacy is demonstrated by analyzing typical remote-sensing images of rural grid areas.The results underscore the ability of the proposed approach to effectively discern the distribution-line structure and compute the node parameters,thereby offering vital support for determining PV access capability.

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.

Probability Distribution Characteristics of Strong Nonlinear Waves Under Typhoon Conditions in the Northern South China Sea

The generation and propagation mechanism of strong nonlinear waves in the South China Sea is an essential research area. In this study, the third-generation wave model WAVEWATCH Ⅲ is employed to simulate wave fields under extreme sea states. The model, integrating the ST6 source term, is validated against observed data, demonstrating its credibility. The spatial distribution of the occurrence probability of strong nonlinear waves during typhoons is shown, and the waves in the straits and the northeastern part of the South China Sea show strong nonlinear characteristics. The high-order spectral model HOS-ocean is employed to simulate the random wave surface series beneath five different platform areas. The waves during the typhoon exhibit strong nonlinear characteristics, and freak waves exist. The space-varying probability model is established to describe the short-term probability distribution of nonlinear wave series. The exceedance probability distributions of the wave surface beneath different platform areas are compared and analyzed. The results show that with an increase in the platform area, the probability of a strong nonlinear wave beneath the platform increases.

Mountain permafrost distribution modeling using Multivariate Adaptive Regression Spline (MARS) in the Wenquan area over the Qinghai-Tibet Plateau

In high mountainous areas, the development and distribution of alpine permafrost is greatly affected by macro- and mi- cro-topographic factors. The effects of latitude, altitude, slope, and aspect on the distribution of permafrost were studied to under- stand the dislribution patterns of permafrost in Wenquan on the Qinghai-Tibet Plateau. Cluster and correlation analysis were per- formed based on 30 m Global Digital Elevation Model （GDEM） data and field data obtained using geophysical exploration and borehole drilling methods. A Multivariate Adaptive Regression Spline model （MARS） was developed to simulate permafrost spa- tial distribution over the studied area. A validation was followed by comparing to 201 geophysical exploration sites, as well as by comparing to two other models, i.e., a binary logistic regression model and the Mean Annual Ground Temperature model （IVlAGT）. The MARS model provides a better simulation than the other two models. Besides the control effect of elevation on permafrost distribution, the MARS model also takes into account the impact of direct solar radiation on permafrost distribution.

Modeling random telegraph signal noise in CMOS image sensor under low light based on binomial distribution

The random telegraph signal noise in the pixel source follower MOSFET is the principle component of the noise in the CMOS image sensor under low light. In this paper, the physical and statistical model of the random telegraph signal noise in the pixel source follower based on the binomial distribution is set up. The number of electrons captured or released by the oxide traps in the unit time is described as the random variables which obey the binomial distribution. As a result,the output states and the corresponding probabilities of the first and the second samples of the correlated double sampling circuit are acquired. The standard deviation of the output states after the correlated double sampling circuit can be obtained accordingly. In the simulation section, one hundred thousand samples of the source follower MOSFET have been simulated,and the simulation results show that the proposed model has the similar statistical characteristics with the existing models under the effect of the channel length and the density of the oxide trap. Moreover, the noise histogram of the proposed model has been evaluated at different environmental temperatures.

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.

Whole petroleum system and ordered distribution pattern of conventional and unconventional oil and gas reservoirs

The classical source-to-trap petroleum system concept only considers the migration and accumulation of conventional oil and gas in traps driven dominantly by buoyance in a basin,although revised and improved,even some new concepts as composite petroleum system,total petroleum system,total composite petroleum system,were proposed,but they do not account for the vast unconventional oil and gas reservoirs within the system,which is not formed and distributed in traps dominantly by buoyancedriven.Therefore,the petroleum system concept is no longer adequate in dealing with all the oil and gas accumulations in a basin where significant amount of the unconventional oil and gas resources are present in addition to the conventional oil and gas accumulations.This paper looked into and analyzed the distribution characteristics of conventional and unconventional oil/gas reservoirs and their differences and correlations in petroliferous basins in China and North America,and then proposed whole petroleum system(WPS)concept,the WPS is defined as a natural system that encompasses all the conventional and unconventional oil and gas,reservoirs and resources originated from organic matter in source rocks,the geological elements and processes involving the formation,evolution,and distribution of these oil and gas,reservoirs and resources.It is found in the WPS that there are three kinds of hydrocarbons dynamic fields,three kinds of original hydrocarbons,three kinds of reservoir rocks,and the coupling of these three essential elements lead to the basic ordered distribution model of shale oil/gas reservoirs contacting or interbeded with tight oil/gas reservoirs and separated conventional oil/gas reservoirs from source rocks upward,which is expressed as“S\T-C”.Abnormal conditions lead to other three special ordered distribution models:The first is that with shale oil/gas reservoirs separated from tight oil/gas reservoirs.The second is that with two direction ordered distributions from source upward and downward.The third is with lateral distribution from source outside.

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.

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.

Historical and current climates affect the spatial distribution of herbivorous tree insects in China

Historical and current climate impacts reshape the evolutionary trajectory and ecological dynamics of entire vegetative communities,which can drive insect species distribution.Understanding the spatial distribution of insects can enhance forest management effectiveness.The effects of historical and current climates in the spatial distribution of herbivorous tree insects in China were explored.A species distribution model simulated insect spatial distribution based on 596 species and the distribution probability and richness of these species were assessed in forest ecoregions.The explanatory power of the historical climate was stronger than that of the current climate,particularly historical annual precipitation and annual mean temperatures,for the distribution of herbivorous insects.Under both historical and current climatic conditions,herbivorous tree insects were and are mainly distributed in the North China Plain and the middle and lower reaches of the Yangtze River Plain,namely in the Huang He Plain mixed forests,Changjiang Plain evergreen forests,and Sichuan Basin evergreen broadleaf forests.The Yunnan-Guizhou Plateau and northeast China are regions with large impact differences between historical and current climates.The findings of this study provide valuable insights into herbivorous insect responses to sustained climate change and may contribute to long-term biodiversity conservation activities.

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.

Climate change impacts the distribution of Quercus section Cyclobalanopsis(Fagaceae),a keystone lineage in East Asian evergreen broadleaved forests

East Asian evergreen broadleaved forests(EBFLs) harbor high species richness,but these ecosystems are severely impacted by global climate change and deforestation.Conserving and managing EBLFs requires understanding dominant tree distribution dynamics.In this study,we used 29 species in Quercus section Cyclobalanopsis-a keystone lineage in East Asian EBLFs-as proxies to predict EBLF distribution dynamics using species distribution models(SDMs).We examined climatic niche overlap,similarity,and equivalency among seven biogeographical regions’ species using’ecospat’.We also estimated the effectiveness of protected areas in the predicted range to elucidate priority conservation regions.Our results showed that the climatic niches of most geographical groups differ.The western species under the Indian summer monsoon regime were mainly impacted by temperature factors,whereas precipitation impacted the eastern species under the East Asian summer monsoon regime.Our simulation predicted a northward range expansion of section Cyclobalanopsis between 2081 and 2100,except for the ranges of the three Himalayan species analyzed,which might shrink significantly.The greatest shift of highly suitable areas was predicted for the species in the South Pacific,with a centroid shift of over 300 km.Remarkably,only 7.56% of suitable habitat is currently inside protected areas,and the percentage is predicted to continue declining in the future.To better conserve Asian EBLFs,establishing nature reserves in their northern distribution ranges,and transplanting the populations with predicted decreasing numbers and degraded habitats to their future highly suitable areas,should be high-priority objectives.

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.

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.

Impact pressure of waves generated by landslides on bank slopes

Impulse waves that are generated by landslides in narrow reservoir areas threaten the stability of buildings and bank slopes.To discuss the action process and evolution law of the wave pressure on bank slopes,a three-dimensional physical model test that considers impulse waves generated by landslides was performed,and factors including landslide width,thickness,slope angles of the sliding surface,and bank slope angle were considered.Based on wave forms on the bank slopes,wave pressure curve characteristics,and peak value,the action process of wave pressure could be divided into the following stages:maximum pulsating pressure stage,wave impact stage(when waves break),and stationary pulsation stage.It was found that wave breaking is dependent on the value of the surf similarity parameterξ.The distribution pattern of impact pressure decays linearly on both sides of the maximum impact pressure point,and the attenuation degree decreases when it attains 40%of the maximum value.Thus,it is proposed that the prediction formula for the maximum effective impact pressure of the bank slope be related to the reciprocal of wave steepness,relative water depth,and slope rate.The prediction formula provides strong theoretical support for early safety warning and for predicting the bank slope under impulse waves generated by landslides.

A unified Minorization-Maximization approach for estimation of general mixture models

The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high,it usually brings challenges to the parameter es-timation of the mixed distribution model.The application of MM algorithm can avoid complex expectation calculations,and can also solve the problem of high-dimensional optimization by decomposing the objective function.In this paper,MM algorithm is applied to the parameter estimation problem of mixed distribution model.The method of assembly and decomposition is used to construct the substitute function with separable parameters,which avoids the problems of complex expectation calculations and the inversion of high-dimensional matrices.

Autonomous Vehicle Platoons In Urban Road Networks:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

In this paper, platoons of autonomous vehicles operating in urban road networks are considered. From a methodological point of view, the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria. To this end, a novel distributed control architecture is conceived by taking advantage of two methodologies: deep reinforcement learning and model predictive control. On one hand, the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction. On the other hand, a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle. Such tasks are here combined into a single framework:the deep reinforcement learning output(action) is translated into a set-point to be tracked by the model predictive controller;conversely, the current vehicle position, resulting from the application of the control move, is exploited by the deep reinforcement learning unit for improving its reliability. The main novelty of the proposed solution lies in its hybrid nature: on one hand it fully exploits deep reinforcement learning capabilities for decisionmaking purposes;on the other hand, time-varying hard constraints are always satisfied during the dynamical platoon evolution imposed by the computed routing decisions. To efficiently evaluate the performance of the proposed control architecture, a co-design procedure, involving the SUMO and MATLAB platforms, is implemented so that complex operating environments can be used, and the information coming from road maps(links,junctions, obstacles, semaphores, etc.) and vehicle state trajectories can be shared and exchanged. Finally by considering as operating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models, several simulations have been performed with the aim to put in light the main f eatures of the proposed approach. Moreover, it is important to underline that in different operating scenarios the proposed reinforcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.

Distributed Economic MPC for Synergetic Regulation of the Voltage of an Island DC Micro-Grid

In this paper,distributed model predictive control(DMPC) for island DC micro-grids(MG) with wind/photovoltaic(PV)/battery power is proposed,which coordinates all distributed generations(DG) to stabilize the bus voltage together with the insurance of having computational efficiency under a real-time requirement.Based on the feedback of the bus voltage,the deviation of the current is dispatched to each DG according to cost over the prediction horizon.Moreover,to avoid the excessive fluctuation of the battery power,both the discharge-charge switching times and costs are considered in the model predictive control(MPC) optimization problems.A Lyapunov constraint with a time-varying steady-state is designed in each local MPC to guarantee the stabilization of the entire system.The voltage stabilization of the MG is achieved by this strategy with the cooperation of DGs.The numeric results of applying the proposed method to a MG of the Shanghai Power Supply Company shows the effectiveness of the distributed economic MPC.

Glacier area change and its impact on runoff in the Manas River Basin,Northwest China from 2000 to 2020

Understanding the distribution and dynamics of glaciers is of great significance to the management and allocation of regional water resources and socio-economic development in arid regions of Northwest China.In this study,based on 36 Landsat images,we extracted the glacier boundaries in the Manas River Basin,Northwest China from 2000 to 2020 using eCognition combined with band operation,GIS(geographic information system)spatial overlay techniques,and manual visual interpretation.We further analyzed the distribution and variation characteristics of glacier area,and simulated glacial runoff using a distributed degree-day model to explore the regulation of runoff recharge.The results showed that glacier area in the Manas River Basin as a whole showed a downward trend over the past 21 a,with a decrease of 10.86%and an average change rate of–0.54%/a.With the increase in glacier scale,the number of smaller glaciers decreased exponentially,and the number and area of larger glaciers were relatively stable.Glacier area showed a normal distribution trend of increasing first and then decreasing with elevation.About 97.92%of glaciers were distributed at 3700–4800 m,and 48.11%of glaciers were observed on the northern and northeastern slopes.The retreat rate of glaciers was the fastest(68.82%)at elevations below 3800 m.There was a clear rise in elevation at the end of glaciers.Glaciers at different slope directions showed a rapid melting trend from the western slope to the southern slope then to the northern slope.Glacial runoff in the basin showed a fluctuating upward trend in the past 21 a,with an increase rate of 0.03×10^(8) m^(3)/a.The average annual glacial runoff was 4.80×10^(8) m^(3),of which 33.31%was distributed in the ablation season(June–September).The average annual contribution rate of glacial meltwater to river runoff was 35.40%,and glacial runoff accounted for 45.37%of the total runoff during the ablation season.In addition,precipitation and glacial runoff had complementary regulation patterns for river runoff.The findings can provide a scientific basis for water resource management in the Manas River Basin and other similar arid inland river basins.

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.