Habitat suitability of Gymnocladus assamicus - A critically endangered plant of Arunachal Pradesh,India using machine learning and statistical modeling

The present study sought to identify the potential distribution range of critically endangered Gymnocladus assamicus in Arunachal Pradesh based on published data and field collection. We used the Maxent model to estimate the range of distribution and the result was then compared with three other models, i.e., the Generalized Linear Model (GLM), the Bioclim and the Random Forest model to assess the species' habitat suitability. A total of 23 different environmental variables were used, including bioclimatic ones, monthly minimum and maximum temperature, monthly precipitation and elevation data. The Maxent output listed 12 variables explaining 99.9% variation in the model. In comparison, Maxent showed the maximum region under habitat suitability criteria (1884.48 km²), followed by Random Forest (70.73 km²) and Bioclim (11.62 km²) model. Except for the Maxent model, suitable habitats predicted by other models are highly restricted within and across the study species' current distribution range. The average model prediction shows an expanded distribution range for the species up to Tawang which is the closest district of currently known distribution of the species in the state. Thus, the present study recognizes the importance of the geographic range of G. assamicus, a critically endangered species with very limited spatial distribution range and also provides some specific details to explore possible habitats for the species in new areas of potential occurrence in Arunachal Pradesh, India.     

Predicting potential impacts of climate change on freshwater fish in Korea

Climate change is expected to have profound effects on the distribution and phenology of species and the productivity of aquatic ecosystem. In this study, we projected the impacts of climate change on the distributions of 22 endemic fish species in Korea with climatic and geographical variables by using species distribution models (SDMs). Six different SDMs – linear discriminant analysis, generalized linear model, classification and regression trees, random forest, support vector machine, and multivariate adaptive regression splines – were implemented for the prediction, and compared for their prediction capacity. The results showed that the random forest displayed the highest predictive power for the prediction of current species distributions. Therefore, the random forest was used to assess the potential impacts of climate change on the distributions of 22 endemic fish species. The results revealed that five species (Acheilognathus yamatsutae, Sarcocheilichthys variegatus wakiyae, Squalidus japonicus coreanus, Microphysogobio longidorsalis, and Liobagrus andersoni) have a high probability of becoming extinct in their respective habitable sub-watersheds by the 2080s due to climate change. The sensitivity analysis of the model showed that geo-hydrological variables such as stream order and altitude and temperature-related variables such as mean temperature in January and difference between the minimum and maximum temperatures exhibited relatively higher importance in their contributions for the prediction of species occurrence than that other variables. The decline of endemic fish species richness, and their occurrence probability due to climate change, would lead to poleward and upward shifts, as well as extinctions of species. Finally, we believe that our projections are useful for understanding how climate change affects the distribution range of endemic species in Korea, while also providing the necessary information to develop preservation and conservation strategies for maintaining endemic fish.     

Suitability indices and habitat suitability index model of Caspian kutum (Rutilus frisii kutum) in the southern Caspian Sea

The relationship between species and habitat is important in ecosystem-based fisheries management. Habitat suitability index (HSI) modeling is a valuable tool in ecology and can be used to describe the relationship between fish abundance and ecological variables in order to estimate the suitability of specific habitats. In the present study, an HSI model was applied to determine suitable habitats for the Caspian kutum (Rutilus frisii kutum), an important commercial species in the southern Caspian Sea. An arithmetic mean model (AMM) was found to be the most appropriate model for describing the relationship between two of the environmental variables investigated (depth and benthos biomass). However, a geometric mean model explained the evident relationship when all four environmental variables were used (depth, benthos biomass, photosynthetically active radiation and sea surface temperature). The areas with an HSI > 0.5 had over 85 % of the total catch indicating the reliability of the prediction of the Caspian kutum habitat using the AMM. The present study showed that depth and substrate structure are the most important environmental variables for the Caspian kutum to select its habitats, and between remotely sensed data, chlorophyll a, photosynthetically active radiation and sea surface temperature are the most critical parameters for near real-time prediction of the Caspian kutum habitat.     

Habitat suitability for tsessebe Damaliscus lunatus lunatus

The distribution and numbers of tsessebe (Damaliscus lunatus lunatus) have declined considerably in South Africa, partly due to deteriorating habitat conditions. Identifying important habitat variables will assist in managing the species. The objective of this study was to identify habitat variables important for tsessebe and to develop a predictive model of habitat selection for this species in a savanna biome. The study was conducted in the Nylsvley Nature Reserve over a 2‐year period. A total of eighteen habitat variables were measured in ten plant communities at 200 sites. Logistic regression analyses were used to identify predictor variables and to construct a habitat model. Tsessebe were found <2 km from the nearest source of water, in flat areas with slopes of <3° and with <10% rockiness. Their distribution was not influenced by the woody component. Sites where tsessebe were present had significantly lower grass heights and tuft heights, with a higher grass density compared with areas not utilized by tsessebe. Nitrogen and sodium levels were also higher at present sites. Habitat type and grass height were the most significant predictors of tsessebe presence. The selected model had an overall percentage prediction of 85.0%. The model was subdivided into five vegetation‐specific models and each model was tested with independent data.     

A fine‐grained spatial prediction model for the red‐listed vascular plant Scorzonera humilis

We explored the applied use of distribution modelling as a tool for making spatial predictions of occurrences of the red‐listed vascular plant species Scorzonera humilis in a study area in southeast Norway. Scorzonera is typical of extensively managed semi‐natural grasslands. A Maxent model was trained on all known records of the species, accurately georeferenced and gridded to fine resolution (grid cells of 25×25 m). Model performance was assessed on the training data by data‐splitting (by which some records were set off for evaluation) and on independent evaluation data collected in the field. Of the eight predictor variables used in the modelling, distance to roads and to arable land were most important followed by land‐cover class and altitude. Judged from the area under curve (AUC), the model was good to excellent and a significant, positive relationship was found between relative probabilities of occurrence predicted by the model and true probability of presence provided by the independently collected evaluation data. The model was used together with the evaluation data to estimate presence of Scorzonera humilis in 0.7% of the grid cells in the study area. The grid cells in which the model predicted highest probability for Scorzonera to be present had a true probability of presence of ca 12%, i.e. 17×higher than in an average cell. The present study demonstrates that, even when only simple predictor variables are available, spatial prediction modelling contributes important knowledge about rare species such as prevalence estimates, spatial prediction maps and insights into the species’ autecology. Spatial prediction modelling also makes cost‐efficient monitoring of rare species possible. However, it is pointed out that these benefits require evaluation of the model on independently sampled evaluation data.     

Relative importance of hydrological variables in predicting the habitat suitability of Euryale ferox Salisb.

Aims Aquatic ecosystems are a priority for conservation as they have become rapidly degraded with land-use changes. Predicting the habitat range of an endangered species provides crucial information for biodiversity conservation in such rapidly changing environments. However, the complex network structure of aquatic ecosystems restricts spatial prediction variables and has hitherto limited the use of habitat models to predict species occurrence in aquatic ecosystems. We used the maximum entropy model to evaluate the potential distribution of an endangered aquatic species, Euryale ferox Salisb. We tested the relative influence of (i) climatic variables, (ii) topographic variables, and (iii) hydrological variables derived from remote sensing data to improve the prediction of occurrence of aquatic plant species.Methods We considered the southern part of the Korean Peninsula as the modeling extent for the potential distribution of E. ferox. Occurrence records for E. ferox were collected from the literature and field surveys. We applied maximum entropy modeling using remotely sensed environmental variables and evaluated their relative importance as prediction variables with variation partitioning.Important findings The species distribution model predicted potential habitats of E. ferox that matched the actual distribution well. Floodplain wetlands and shallow reservoirs were the favored habitats of E. ferox. Quantitative loss and fragmentation of wetland habitats appeared to be a major reason for the decrease of E. ferox populations. Our results also imply that hydrological variables (i.e. normalized difference water index) derived from remote sensing data greatly increased model prediction (relative contribution: 10.5–37.0%) in the aquatic ecosystem. However, interspecific competition within a similar niche environment should be considered to increase the accuracy of the distribution model.     

Species distribution modeling and machine learning in assessing the potential distribution of freshwater zooplankton in Northern Italy

Species distribution models (SDM's) are powerful tools used to describe species suitable habitats and spatial occurrences and many statistical methods and algorithms are available to model the spatial distribution of a target species. Here we explore a species distribution model framework combined with machine learning algorithms to describe the distribution of two freshwater zooplankton species Daphnia longispina (Cladocera) and Eucyclops serrulatus (Copepods) in a system of 283 shallow and ephemeral freshwater habitats in the Northern Italian Appennines. For each species, we model the habitat suitability by comparing one regression-based model, one generalized linear model (GLM) and two machine learning algorithms: random forest (RF) and artificial neural network (ANN) with one hidden layer. We used a total of 27 predictor variables. The modeling framework was used considering a scenario of future climate change in order to evaluate potential shifts in spatial distribution of the zooplankton species. For both species, the supervised machine learning algorthn (ANN) produced the highest mean values for all the performance metrics. For D. longispina and E. serrulatus, the two most important variables ranked by the shap analysis and global sensitivity and uncertainty analysis (GSUA) were temperature seasonality and precipitation of the warmest quarter. Both species, in a future climatic change scenario, are expected to shift their distribution mainly toward lower northern altitudes with an overall expansion of 7% with respect to the past/present climatic conditions. However, the spatial expansion of D. longispina and E. serrulatus was qualitatively different. In agricultural and natural areas, the expansion of E. serrulatus was greater than that of D. longispina but, in natural areas, the expansion of E. serrulatus was counterbalanced by a greater spatial contraction than that of D. longispina. As hypothesized, direct and indirect anthropogenic pressures may affect the predicted potential shift and expansion of the zooplankton species.     

Ecological modelling for the conservation of Gluta travancorica Bedd. - An endemic tree species of southern Western Ghats,India

Endemic species are highly adapted to grow exclusively in a specific geographical area. The goal of the current study is to determine the probable habitat distribution range of the narrowly endemic species Gluta travancorica. An ecological niche modelling is carried out, using four different models viz., BioClim, MaxEnt, Random Forest and Deep Neural Networks (DNN). A total of 506 G. travancorica cluster locations were surveyed and used for this study with thirty different ecogeographic, edaphic and bioclimatic environmental parameters. After a preliminary investigation using multi-collinearity analysis, soil parameter variables like pH, cation exchange capacity (CEC), silt and clay content are used for final modelling. Factor analysis of ecological niche revealed the soil parameters like pH, CEC, silt and clay content as the key predictors. The result is validated using true skill statistics, sensitivity, specificity, kappa statistic and AUC-ROC. Results of the present study show that DNN have exceptional prediction performance, demonstrated by its AUC score of 0.959. DNN model projected 32.37% (938.18 km²) of the study region to have a ‘highly suitable habitat’, whereas 67.63% (1960.82 km²) was classified as having ‘low habitat suitability’. Besides, back-to-field assessments have also proven DNN's potential in predicting the habitat suitability of G. travancorica. The study results will facilitate the prioritization of conservation and seedling restoration strategies. The forest range explored in this work is a component of one of the most important global biodiversity hotspots, and it has significant implications for regional biodiversity conservation.     

Irrigation as an important factor in species distribution models

Species distribution models (SDMs) that employ climatic variables are widely used to predict potential distribution of invasive species. However, climatic variables derived from climate datasets do not account for anthropogenic influences on microclimate. Irrigation is a major anthropogenic activity that influences microclimate conditions and alters the distribution of species in anthropogenic landuses. SDM-based studies appear to ignore the effects of irrigation on microclimatic conditions. This study incorporated irrigation as a correction to precipitation data, to improve the predictive capacity of SDM. As a case study, we examined a SDM of Wasmannia auropunctata, an invasive species that originates in South and Central America, which has invaded tropical and subtropical regions around the world. The potential distribution of W. auropunctata was predicted using Maxent. The model was built based on climatic variables and species records from non-irrigated sites in the native range and then projected on a global scale. Invasive species records were used to evaluate the performance of the model. Precipitation-related variables were modified to approximate actual water input in irrigated areas. Precipitation correction relied on an estimate of irrigation inputs. The model with irrigation correction performed better than the corresponding model without correction, on a global scale and when it was examined in five different geographical regions of the model. These results demonstrate the importance of irrigation correction for assessing the distribution of W. auropunctata in various geographical regions. Accounting for irrigation is expected to improve SDMs for a variety of species.     

Accessibility predicts structural variation of Andean Polylepis forests

High Andean mountain forests, formed almost purely by trees of the genus Polylepis, occur nowadays as scattered remnant patches of a more continuous past distribution. Apparently, the destruction of Polylepis forests has mainly been caused by millennia of human disturbance, although forest distribution may also have fluctuated according to prevailing climatic conditions. Nowadays, the remaining Polylepis forest stands are still threatened by anthropogenic disturbance, which gradually degrades the forests. The aim of our study was to test if the structural variation of Polylepis forest patches, as an indication of forest degradation, can be predicted by accessibility to humans. The study was carried out in the Cordilleras Vilcanota and Vilcabamba, Cuzco, Peru. We used indices of forest biomass and proportion of vegetative regeneration as forest structural variables. First we examined the dependence of these variables on elevation with linear regressions. We did this separately for different Polylepis species and combining the species within humid and dry areas. Thereafter, we used the residual forest structural variation to assess possible relationships with accessibility, quantified as geographical distance to the nearest village, road or market centre. We found several significant relationships between the structural variables and accessibility, which may reflect different landscape related preferences in forest use. The results suggest accessibility can be used for rapid spatial prediction of Polylepis forest degradation, which facilitates identifying Polylepis forests that are potentially the most degraded and therefore in the most urgent need of restoration or conservation activities.     

人参潜在地理分布以及气候变化对其影响预测

本文以人参为研究对象,基于人参分布点位数据和22个气候环境因子数据,运用BioMod2平台10个物种分布模型对当前我国东北地区人参潜在生境分布进行预测.以受试者工作特征曲线(ROC)为权重集成10个模型的模拟结果,构建组合模型,并基于该模型预测了IPCC 第五次评估报告中RCP 8.5、RCP 6.0、RCP 4.5和RCP 2.6等4种排放情景下21世纪50和70年代人参潜在分布范围.结果表明: 在基准气候条件下,人参适宜生境面积占研究区总面积的10.4%,此类地区主要分布于研究区东北部长白山地区以及小兴安岭东南部区域的森林地带.在未来不同的排放情景下研究区人参的适宜生境变化显著,总体上分布范围将有一定程度的缩小.同时参与建模的10种模型在统计学精度、预测结果以及变量权重上都有差异.模型精度计算结果表明,MAXENT模拟效果最好,GAM、RF和ANN次之,SRE模拟精度最低.本文构建的组合模型在一定程度上提高了现有物种分布模型的预测精度,从而使模拟效果更优.     

Identification of suitable habitat for Taxus wallichiana and Abies pindrow in moist temperate forest using maxent modelling technique

Conservation of any species necessitates knowledge of its biology and natural history, as well as prospective locations or newer adaptive landscapes where the species can survive and thrive. This study presents habitat suitability and local conservation status of Taxus wallichiana and Abies pindrow in moist temperate forest of Hazara division, Pakistan. Data was collected through field surveys based on 363 samples from field, topographical and bioclimatic variables. In the present study, we employed the MaxEnt model exclusively for each tree species along with 23 independent or environment variables (19 bioclimatic and 4 topographic). The jackknife test was used to demonstrate the significance of variables with the highest gain, and it was found that overall tree cover, annual temperature range was the factors with the highest gain, while slope was amongst the least important. The MaxEnt model produced high accuracy for each tree species, with receiver operating characteristic (ROC), area under the curve (AUC), training mean testing values for Taxus wallichiana was 0.966 followed by 0.944 for Abies pindrow. Local conservation status of Taxus wallichiana and Abies pindrow was evaluated using IUCN criteria 2001. Taxus wallichiana was declared critically endangered locally as the population size reduced by 87%. In contrast, Abies pindrow was declared as endangered as population size reduced by 69% falling under endangered criteria A of IUCN. The decline in population size of Taxus wallichiana and Abies pindrow species were due to human cause anthropogenic activities such as exploitation and loss of habitat, the extent of occurrence, and slow regeneration of tree species. Results and field-based observation revealed that suitable habitat modeling showed unsuitable (0.0–0.2), less suitable (0.2–0.4), moderately (0.4–0.6), highly (0.6–0.7), and very highly (0.7–1.0) suitable habitat for Taxus wallichiana and Abies pindrow. Results also revealed that both species were distributed irregularly in the moist temperate forest of Hazara division. Habitat suitability of Taxus wallichiana and Abies pindrow can be considered one of most significant points toward conserving these tree species. Habitat loss is a major threat to their occurrence, which should be overcome by ensuring the protection of suitable habitat and conservation approaches. Considering the species ecological and economic value, it is essential to understand how the species distribution may vary as a result of climate change to establish effective conservation policies. This study also includes significant environmental elements that influence species distribution, which could help locate regions where the species could be planted. Forest tree species require effective, scientific, and long-term management and conservation techniques in the study area. Furthermore, the formulation and implementation of protective laws and policies are required to conserve and protect both the conifer species.     

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 ~3.57 × 10 4 km 2, which has the potential to mitigate global warming and produce bioenergy through restoring T. ramosissima in the Tarim Basin.     

物种分布模型的发展及评价方法

物种分布模型已被广泛地应用于以保护区规划、气候变化对物种分布的影响等为目的的研究。回顾了已经得到广泛应用的多种物种分布模型,总结了评价模型性能的方法。基于物种分布模型的发展和应用以及性能评价中尚存在的问题,本文认为:在物种分布模型中集成样本选择模块能够避免模型预测过程中的过度拟合及欠拟合,增加变量选择模块可评估和降低变量之间自相关性的影响,增加生物因子以及将物种对环境的适应性机制(及扩散行为特征)和潜在分布模型进行结合,是提高模型预测性能的可行方案;在模型性能的评价方面,采用赤池信息量可对模型的预测性能进行客观评价。相关建议可为物种分布建模提供参考。     

Testing the Role of Climate Change in Species Decline: Is the Eastern Quoll a Victim of a Change in the Weather?

To conserve a declining species we first need to diagnose the causes of decline. This is one of the most challenging tasks faced by conservation practitioners. In this study, we used temporally explicit species distribution models (SDMs) to test whether shifting weather can explain the recent decline of a marsupial carnivore, the eastern quoll (Dasyurus viverrinus). We developed an SDM using weather variables matched to occurrence records of the eastern quoll over the last 60 years, and used the model to reconstruct variation through time in the distribution of climatically suitable range for the species. The weather model produced a meaningful prediction of the known distribution of the species. Abundance of quolls, indexed by transect counts, was positively related to the modelled area of suitable habitat between 1990 and 2004. In particular, a sharp decline in abundance from 2001 to 2003 coincided with a sustained period of unsuitable weather over much of the species’ distribution. Since 2004, abundance has not recovered despite a return to suitable weather conditions, and abundance and area of suitable habitat have been uncorrelated. We suggest that fluctuations in weather account for the species’ recent decline, but other unrelated factors have suppressed recovery.     

The effect of habitat fragmentation and abiotic factors on fen plant occurrence

Human landscape modification has led to habitat fragmentation for many species. Habitat fragmentation, leading to isolation, decrease in patch size and increased edge effect, is observed in fen ecosystems that comprise many endangered plant species. However, until now it has remained unclear whether habitat fragmentation per se has a significant additional negative effect on plant species persistence, besides habitat loss and degradation. We investigated the relative effect of isolation, habitat size, and habitat edge compared to the effect of habitat degradation by including both ‘fragmentation variables’ and abiotic variables in best subsets logistic regression analyses for six fen-plant species. For all but one species, besides abiotic variables one or more variables related to fragmentation were included in the regression model. For Carex lasiocarpa, isolation was the most important factor limiting species distribution, while for Juncus subnodulosus and Menyanthes trifoliata, isolation was the second most important factor. The effect of habitat size differed among species and an increasing edge had a negative effect on the occurrence of Carex lasiocarpa and Pedicularis palustris. Our results clearly show that even if abiotic conditions are suitable for certain species, isolation of habitat patches and an increased habitat edge caused by habitat fragmentation affect negatively the viability of characteristic fen plant species. Therefore, it is important not only to improve habitat quality but also to consider spatial characteristics of the habitat of target species when deciding on plant conservation strategies in intensively used landscapes, such as fen areas in Western Europe and North America.     

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.     

The speciation history and systematics of Carthamus (Asteraceae) with special emphasis on Turkish species by integrating phylogenetic and Ecological Niche Modelling data

To discuss the classification and possible scenarios for the speciation of Carthamus species in Turkey, 143 species occurrence data from Turkey used in Ecological Niche Modelling (ENM), ITS sequences of 23 available species gathered from the GenBank and current distribution information were used. The ENM was carried out by using MAXENT software. Among the 19 bioclimatic variables used in ENM, precipitation of coldest quarter (25 %), mean temperature of driest quarter (19 %) and annual precipitation (17 %) parameters have the highest percent contribution to the resulting prediction pattern, respectively. Bayesian-based phylogenetic analysis with divergence time estimation was implemented to obtain phylogenetic history of Carthamus species. Statistical dispersal–vicariance analysis and Bayesian binary MCMC analysis were also used to discuss biogeographical inferences. An identification key for Turkish Carthamus species that is in accordance with phylogenies was given. Ancestral area reconstruction analyses pointed out that the Western Asia region was the ancestral area for Carthamus species and in the Pliocene/Pleistocene period they started to diversify. Also ENM results clearly indicate that especially Anatolian species used Aegean and Mediterranean coastal part of Anatolia as potential refugia.     

Ecological niche modelling of Cantharellus species in Benin,and revision of their conservation status

Of the eight Cantharellus species known from Benin, seven have been encountered under similar macroecological conditions. The present work attempts to generate a more complete distribution of these seven species. Forty-eight occurrences of the target species and four explanatory variables including three bioclimatic variables and a land cover variable were used to build an ensemble model from five modelling approaches under the Biomod2 package of R software. Results showed a distribution restricted to the Bassila and Atacora mountain range phytogeographic districts with excellent statistical performance (TSS = 0.98, AUC = 0.99). This distribution is governed mainly by high soil moisture and high potential evapotranspiration, thus defining only gallery forests as the most suitable habitat for chanterelles in Sudano-guinean and Soudanese ecozones of Benin. Based on IUCN criterion B1 and sub-criteria B1a and B1c(i), these seven species were categorized under the Endangered (EN) threat category according to our results.     

Modeling potential invasion range of alien invasive species,Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP

Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India.