Species Distribution Models Algorithms Research Articles

Modeling the Spatial Distribution of Acacia decurrens Plantation Forests Using PlanetScope Images and Environmental Variables in the Northwestern Highlands of Ethiopia

Small-scale Acacia decurrens plantation forests, established by farmers on degraded lands, have become increasingly prevalent in the Northwestern Highlands of Ethiopia. This trend has been particularly notable in Fagita Lekoma District over the past few decades. Such plantations play a significant role in addressing concerns related to sustainable agricultural land use, mitigating the adverse effects of deforestation, and meeting the livelihood and energy requirements of a growing population. However, the spatial distribution of Acacia decurrens and the essential remote sensing and environmental variables that determine its distribution are not well understood. This study aimed to model the spatial distribution of Acacia decurrens plantation forests using PlanetScope data and environmental variables combined with a species distribution model (SDM). Employing 557 presence/absence points, noncollinear variables were identified and utilized as input for six SDM algorithms, with a 70:30 split between training and test data, and 10-fold bootstrap replication. The model performance was evaluated using the receiver operation characteristic curve (AUC) and true skill statics (TSS). The ensemble model, which combined results from six individual algorithms, was implemented to predict the spatial distribution of Acacia decurrens. The highest accuracy with the values of 0.93 (AUC) and 0.82 (TSS) was observed using random forest (RF), followed by SVM with values of 0.89 (AUC) and 0.71 (TSS), and BRT with values of 0.89 (AUC) and 0.7 (TSS). According to the ensemble model result, Acacia decurrens plantation forests cover 22.44% of the district, with the spatial distribution decreasing towards lower elevation areas in the northeastern and western parts of the district. The major determinant variables for identifying the species were vegetation indices, specifically CVI, ARVI, and GI, with AUC metric values of 39.3%, 16%, and 7.1%, respectively. The findings of this study indicate that the combination of high-resolution remote sensing-derived vegetation indices and environmental variables using SDM could play a vital role in identifying Acacia decurrens plantations, offering valuable insights for land use planning and management strategies. Moreover, comprehending the spatial distribution’s extent is crucial baseline information for assessing its environmental implications at a local scale.

The interaction between temperature and precipitation on the potential distribution range of Betula ermanii in the alpine treeline ecotone on the Changbai Mountain

Alpine treeline ecotones are highly sensitive to climate warming. The low temperature-determined alpine treeline is expected to shift upwards in response to global warming. However, little is known about how temperature interacts with other important factors to influence the distribution range of tree species within and beyond the alpine treeline ecotone. Hence, we used a GF-2 satellite image, along with bioclimatic and topographic variables, to develop an ensemble suitable habitat model based on the species distribution modeling algorithms in Biomod2. We investigated the distribution of suitable habitats for B. ermanii under three climate change scenarios (i.e., low (SSP126), moderate (SSP370) and extreme (SSP585) future emission trajectories) between two consecutive time periods (i.e., current–2055, and 2055–2085). By 2055, the potential distribution range of B. ermanii will expand under all three climate scenarios. The medium and high suitable areas will decline under SSP370 and SSP585 scenarios from 2055 to 2085. Moreover, under the three climate scenarios, the uppermost altitudes of low suitable habitat will rise to 2,329 ​m a.s.l., while the altitudes of medium and high suitable habitats will fall to 2,201 and 2,051 ​m a.s.l. by 2085, respectively. Warming promotes the expansion of B. ermanii distribution range in Changbai Mountain, and this expansion will be modified by precipitation as climate warming continues. This interaction between temperature and precipitation plays a significant role in shaping the potential distribution range of B. ermanii in the alpine treeline ecotone. This study reveals the link between environmental factors, habitat distribution, and species distribution in the alpine treeline ecotone, providing valuable insights into the impacts of climate change on high-elevation vegetation, and contributing to mountain biodiversity conservation and sustainable development.

Insights from the past: Invasion trajectory and niche trends of a global freshwater invader.

Freshwater ecosystems are invaded by a non-random selection of taxa, among which crayfish stand out with successful examples worldwide. Species distribution models (SDMs) have been used to detect suitable areas for invasive species and predict their potential distributions. However, these prediction exercises assume the stability of realized environmental niches, which is uncertain during invasion. Worldwide evaluations involving cosmopolitan invaders may be particularly useful but have seldom been considered. Focusing on the successful invasion history of the red swamp crayfish, Procambarus clarkii, we assessed its geographic expansion and niche trends over time. Based on global occurrences from 1854 to 2022, multiple sequential SDMs have been implemented based on a set of bioclimatic variables. The environmental suitability for each period was projected through to the next period(s) using an ensemble procedure of commonly used SDM algorithms. As the records of the species are known, it was possible to check whether the modelling projections were concordant with the observed expansion of red swamp crayfish at a global scale. This also permitted analysis of its realized niche, and its dynamics, during different expansion phases. SDM maps based on past species records showed concordance with the known crayfish distributions and yielded similar spatial patterns with outputs overperforming random combinations of cells in term of suitability. The results also reflect the stability of the species niche, which despite some expansions during the invasion process, changed little in terms of main position in functional space over time. SDMs developed in the early stages of invasion provide useful insights but also tend to underpredict the potential range compared to models that were built for later stages. Our approach can be easily transferable to other well-documented taxa and represents valuable evidence for validating the use of SDMs, considering a highly dynamic world where biogeographical barriers are often bypassed.

Differing sensitivity of species distribution modelling algorithms to climate data source

The increasing availability of contemporary climate databases calls for comparative studies testing their performance in ecological modelling. We evaluated how contemporary climate data sources interact with the modelling algorithm and affect the predictive accuracy of Species Distribution Models (SDMs). We modelled the distribution of 37 woody plants in the Iberian Peninsula using three global climate databases: WorldClim (WC), CHELSA (CH) and MERRAclim (MER); a regional climate database (the Iberian Climate Atlas, ICA), and three modelling algorithms: Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and Partial Least Squared Regressions (PLSRs). The predictive capacity of the models was estimated by performing an external evaluation while controlling for extrapolation issues. The climate database source variable and its interaction with the modelling algorithm variable showed significant effects on a model's predictive performance. Post hoc tests revealed that MER produced the lowest discrimination capacity scores, especially using GLMs as algorithm. PLSR models were significantly better than GLMs when MER was used. GAMs and PLSR models with MER were significantly worse than some ICA and CH models but not worse than any of the WC models. These results indicate that contemporary climate data sources need to be considered as a seedbed of uncertainty in SDMs and that poorly flexible algorithms are unable to deal with suboptimal data. CH is a reliable global climate database and PLSR is a technique worth considering in SDMs. The best practice is to select the most accurate climate data available and to choose the algorithm based on the purpose of the study and on context-dependent technical details.

Distribution characteristics of the free-roaming dog population of the western area of Tongatapu, Kingdom of Tonga: Developing a GIS for biosecurity and animal welfare projects

The free-roaming dog population of Tongatapu, Kingdom of Tonga, is a ubiquitous, well-known and persistent feature of the island. Information on the quantity, condition and spatial distribution characteristics of these dogs is needed for projects aimed at improving biosecurity conditions in different impacted sectors of society, while addressing the management of an animal associated with humans. Health and safety of the human population, agriculture and biodiversity are directly affected by the presence of free-roaming dogs in Tongan communities. A survey conducted over western Tongatapu recorded locations of free-roaming dogs sighted when driving along the roads of 12 towns and villages. Locations were recorded using an iPad and the mobile application Collector for ArcGIS. A total of 1152 dogs was recorded, mostly within villages and residential roads. Patterns of spatial distribution of dog locations were described using kernel density, hotspot analysis, and cluster and outlier analysis tools available in ArcGIS Pro. Using the species distribution modelling algorithms Maxent and random forest (RF), the suitability of the entire island for free-roaming dogs was determined using as input the dog locations and environmental variables consisting of population, land use and road types. Results of both algorithms show that roads and residential areas consistently show the highest probability of free-roaming dog presence. The RF algorithm also produced maps that show predictions of the values of other variables collected during the survey, including body-condition scores, age group and sex types over the entire island. Overall, the maps produced and the database created contribute a baseline biosecurity knowledge-base supporting rational decision-making and governance for the island.

Anthropogenic and natural fragmentations shape the spatial distribution and genetic diversity of roe deer in the marginal area of its geographic range

Habitat destruction and fragmentation are major factors in the destruction of genetic diversity and affect the movement behavior of the Roe deer population in the remaining habitats. Here, we study the population and landscape genetics of Capreolus capreolus (roe deer) in northern and northwestern Iran using twelve polymorphism microsatellite markers. From 111 total specimens, 63 had successful extraction (6 feces, 35 tissues, 9 bones, and 13 antlers). We considered 30 microsatellite polymorphic loci, of which only 12 were amplified for our further analysis. For genetic diversity analysis, the Weir-Cockerham method was applied to measure the inbreeding coefficient (FIS) and fixation index (FST) for each locus as well as for each population. For landscape genetics, the susceptibility patterns of genetic variations were assessed using three hypotheses including isolation by distance (IBD), isolation by environment (IBE), isolation by resistance (IBR), and individual landscape genetic analysis. A habitat suitability map as an indicator of landscape resistance was constructed from several species distribution models (SDMs) algorithms including Generalized Boosting Models (GBM), Maximum Entropy (Maxent), Random Forest (RF), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS) and artificial neural networks (ANN) and an ensemble model. Our estimated FIs index showed that the Golestan, Arasbaran, and Guilan populations had the highest and lowest genetic diversity among roe deer populations. According to the Fst criterion, our results showed that Golestan and East Azarbaijan (Arasbaran) had the highest and Mazandaran had the lowest genetic distance patterns. Our results do not suggest that there is high genetic differentiation for roe deer in the region, with high levels of gene flow between study areas. We found that geographic distance has no significant relationship with genetic distance and that there is no significant relationship between the ecological niche non-similarity matrix and the genetic distance matrix. The most influential factors affecting gene flow in roe deer were aspect and elevation variables. The analysis suggests that the landscape has no significant influence on the structuring of the studied population and shows little genetic differentiation.

Interannual climate variability improves niche estimates for ectothermic but not endothermic species

Climate is an important limiting factor of species’ niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.

Climate‐driven decline in the habitat of the endemic spiny babbler (Turdoides nipalensis)

AbstractClimate change could amplify the extinction risk of endemic species, and the risk is even greater for species occupying high elevations and mountain ranges. In this study, we assessed the climatically suitable habitat of the only endemic Nepalese bird species, the spiny babbler (Turdoides nipalensis), and predicted the extent of the future (2050 and 2070) habitat of this species under two climate change scenarios (SSP2‐4.5 and SSP5‐8.5). We used georeferenced occurrence points alongside ecologically meaningful climatic and topographic variables to develop an ensemble suitable habitat model using different species distribution modeling algorithms in BIOMOD2. We identified 22,488.83 km2 (15%) of Nepal's total land area as suitable habitat for this endemic species, where the nonprotected regions incorporated the largest extent of suitable habitat (88%), with a majority of this suitable area within the central Mid‐Hill region. Under the SSP2‐4.5 scenario, 21.58% and 34.08% of the current suitable habitat range are projected to be lost by 2050 and 2070, respectively. Whereas under the SSP5‐8.5 scenario, our projections suggest that 40.45% and 52.18% of habitat will be lost by 2050 and 2070, respectively. Habitat suitability increased with a rise in warmest quarter precipitation (above 1000 mm), coldest quarter precipitation between 50 and 100 mm, and warmest quarter temperature between 20 and 30°C. Given our results, it is crucial to review the conservation policy of nonprotected areas and formulate a spiny babbler‐specific conservation action plan with a special focus on protecting their primary habitat in human‐dominated landscapes and nonprotected areas.

Potential Distribution of Pilocarpus microphyllus in the Amazonia/Cerrado Biomes under Near-Future Climate Change Scenarios.

Pilocarpus microphyllus Stapf. ex Wardlew. (Rutaceae) is an endemic and threatened medicinal plant species from tropical Brazil. Popularly known as "jaborandi", it is the unique natural source of pilocarpine, an alkaloid used to medical treat glaucoma and xerostomia. Based on Species Distribution Models (SDMs), we modeled the suitability of P. microphyllus's geographical distribution considering three Global Circulation Models (GCMs) under two future climate change scenarios (SSP2-4.5 and SSP5-8.5). The quantitative analyses carried out using ten different SDM algorithms revealed that precipitation seasonality (Bio15) and precipitation of the driest month (Bio14) were the most important bioclimatic variables. The results evidenced four main key areas of continuous occurrence of the plant spreading diagonally over tropical Brazilian biomes (Amazon, Cerrado and Caatinga). The near-future (2020 to 2040) ensemble projections considering all GCMs and scenarios have indicated negative impacts for the potential loss or significant reduction in suitable habitats for P. microphyllus in the transition region between the Amazon and Cerrado into central and northern Maranhão state, and mainly in the Caatinga biome over the northern Piaui state. On the other hand, positive impacts of the expansion of the plant habitat suitability are projected over forest cover protected areas of the Amazon biome in the southeastern Pará state. Since the jaborandi is of socioeconomic importance for many families in the north/northeast Brazil, it is urgent to implement public policies for conservation and sustainable management, thus mitigating the impacts of global climate change.

Habitat Suitability and Conflict Zone Mapping for the Blue Bull (Boselaphus tragocamelus) across Nepal.

Rapidly changing environmental conditions (bioclimatic, anthropogenic, topographic, and vegetation-related variables) are likely to alter the spatial distribution of flora and fauna. To understand the influence of environmental variables on the Blue bull's distribution and to identify potential conflict zones, the habitat suitability analysis of the Blue bull was performed using ensemble modeling. We modelled the distribution of the Blue bull using an extensive database on the current distribution of the Blue bull and selected 15 ecologically significant environmental variables. We used ten species distribution modeling algorithms available in the BIOMOD2 R package. Among the ten algorithms, the Random Forest, Maxent, and Generalized linear model had the highest mean true skill statistics scores, ensuring better model performance, and were considered for further analysis. We found that 22,462.57 km2 (15.26%) of Nepal is suitable for the Blue bull. Slope, precipitation seasonality, and distance to the road are the environmental variables contributing the most to the distribution of Blue bull. Of the total predicted suitable habitats, 86% lies outside protected areas and 55% overlaps with agricultural land. Thus, we recommend that the future conservation initiatives including appropriate conflict mitigation measures should be prioritized equally in both protected areas and outside protected areas to ensure the species' survival in the region.

Моделирование распространения, численности и выживаемости видов: новые возможности и методы

Моделирование распространения, численности и выживаемости видов: новые возможности и методы

Climate change affects Galliformes taxonomic, phylogenetic and functional diversity indexes, shifting conservation priority areas in China

AbstractAimComprehensive biodiversity protection necessitates the consideration of multiple indexes of diversity, and how the distribution patterns of priority areas may shift under climate change. Galliformes is a globally endangered avian order vulnerable to climate change that provide an important indicator for wildlife conservation effectiveness. Here, we identified priority areas for conserving Galliformes taxonomic, phylogenetic, and functional diversity in China and their spatial dynamics subject to climate change, and examined how well existing protected areas align with current and future priority areas.LocationChina.MethodsWe applied species distribution modelling and Zonation algorithms to identify conservation priority area dynamics for 47 galliform species across three biodiversity indexes subject to three future climate change scenarios to 2050s and 2070s. We overlaid these identified priority areas onto existing national nature reserves and national parks to assess and project their effectiveness.ResultsCurrent priority areas proved spatially incongruent between indexes, with an optimal area overlap comprising just 10.3% of China's land area, lying largely outside of existing protected areas. Furthermore, over 80% of modelled optimal priority areas currently lacked formal conservation status. Future priority areas will shift substantially under climate change, to an extent dependent on greenhouse gas emission scenarios. Nevertheless, we identified five large regions where optimal Galliformes diversity indexes should remain stable under all scenarios, thus providing potential climatic refugia, if protected from human encroachment.Main ConclusionsThe current deficits we identified for Galliformes protection in China resonate with a broader need for hierarchical conservation strategic planning across regions and ecosystems to ensure long‐term biodiversity protection, accommodating for climate change.

How could climate change influence the distribution of the black soldier fly, Hermetiaillucens (Linnaeus) (Diptera, Stratiomyidae)?

The black soldier fly, Hermetiaillucens (Linnaeus, 1758), is a saprophagous species used to decompose organic matter. This study proposes a distribution model of H.illucens to illustrate its current and future distribution. The methodology includes data collection from the Global Biodiversity Information Facility (GBIF), complemented with iNaturalist, manual expert curation of occurrence records, six species distribution models algorithms and one ensemble model. The average temperature of the driest annual quarter and the precipitation of the coldest annual quarter were the key variables influencing the potential distribution of H.illucens. The distribution range is estimated to decrease progressively and their suitable habitats could change dramatically in the future due to global warming. On the other hand, current optimal habitats would become uninhabitable for the species, mainly at low latitudes. Under this scenario, the species is projected to move to higher latitudes and elevations in the future. The results of this study provide data on the distribution of H.illucens, facilitating its location, management and sustainable use in current and future scenarios.

Raptors at risk: Attributes of mortality within an anthropogenic landscape in the Mid-Hills region of Nepal

Wildlife rescue and rehabilitation centers offer important insights regarding the threats and attributes associated with raptor casualties. We utilized a data set on raptors admitted to a single wildlife rescue center (WRC) in the Mid-Hills region of Nepal by applying two approaches. First, we explored the overview of species admitted and the cause of admission at WRC, and modeled the outcome (survival, mortality) of frequently admitted raptor species (FARS) by analyzing the cause of admittance, the season of admittance, and age category through binary logistic regression. Second, we identified the high-risk zone for raptors through an ensemble approach using multiple species distribution modeling algorithms in BIOMOD2. A total of 17 species of raptors with 830 cases of admittance; Black Kite (Milvus migrans) 47%, Barn Owl (Tyto alba) 24%, Eurasian Eagle-Owl (Bubo bubo) 12%, and other species (17%) were recorded at the WRC over the course of five years (2017–2021). Unknown causes (39%) comprised the largest proportion of admittance, followed by illegal captivity (17%), injured by dogs (10%), and injured by humans (10%). Among the three FARS, the mortality rate was highest in the Barn Owl (62%), followed by the Eurasian Eagle-Owl (55%) and the Black Kite (47%). The model with an additive effect of species, cause, and age category was the best fit model to predict the outcome for FARS. Out of the total study area (705 km2), 19% was under high-risk, 41% was under medium-risk, and 40% was under low-risk. Most of the high-risk zones were incorporated within agricultural areas (57%) and built-up areas (31%). The variation in mortality rate among the FARS indicates that species-specific conservation strategies need to be developed to reduce casualty risk. Identified potential anthropogenic threats and their impact on the survivability and mortality of raptors in this study could help to mitigate casualties. Moreover, the high-risk areas associated with raptor casualties in this study need to be prioritized to implement the conservation endeavors for the raptors.

The status of honeybees (Apis mellifera scutellata) colonies in Hurugwe Safari Area-Rifa section, northern Zimbabwe

ABSTRACT Species Distribution Models (SDM) play an essential role in establishing factors affecting honeybee species spatial population distribution and ensuring effective niche habitat protection. This study aimed to assess the precision of the triangulation bee lining technique in locating Apis mellifera scutellata colonies within wild habitats, locate A. m. scutellata colony site population distribution and develop a predictive model of A. m. scutellata habitat suitability using SDM algorithms within Hurugwe Safari Area (HSA)-Rifa section, northern Zimbabwe. A survey of A. m. scutellata was carried out between February and October 2019. The triangulation bee lining technique was used to track wild bees to their colony sites. The bee lining technique was evaluated to assess its precision in locating honeybee colonies. Ensemble models were used to develop a predictive suitability niche ecosystem for A. m. scutellata nesting sites. The study located wild honeybee colonies using the triangulation bee lining technique with an 18% accuracy. Bee colonies were found at a mean distance of 27.7 m ± 2.06 from triangulated points. The predicted suitable areas were primarily areas close to the water sources and low-lying areas. The study recommended the use of the triangulation honeybee hunting method in locating wild honeybee colonies for research purposes, wild bee colony inspection for pests and diseases and sustainable organic honey collection.

Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables

Species distribution models (SDMs) have been used extensively in the field of landscape ecology and conservation biology since its origin in the late 1980s. But there is still a void for a universal modeling approach for SDMs. With recent advancements in satellite data and machine learning algorithms, the prediction of species occurrence is more accurate and realistic. Presently, four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas. To establish complex relation between the occurrence data and regional climatic and land use parameters, several satellite products, namely, MODIS, Sentinel-5p, GPM, ECOSTRESS, and shuttle radar topography mission (SRTM), are acquired and used as predictor variables to the different SDM algorithms. The performance evaluation has been conducted using the area under curve (AUC), which showed the best result for Maxent (AUC = 0.871) and poor result was observed for RF (AUC = 0.755) among all. The overall prediction confirmed the distribution of Rhododendron arboreum in the mid to high altitudes of central and southern parts of the Garhwal Division. We provide crucial evidence that combining multisatellite products using machine learning algorithms can provide a much better understanding of species distribution that can eventually help the researchers and policymakers to take the necessary step toward its conservation.

Identifying the environmental and anthropogenic causes, distribution, and intensity of human rhesus macaque conflict in Nepal

Reducing conflict between humans and wildlife is considered a top conservation priority. However, increasingly human-induced disturbances across natural landscapes have escalated encounters between humans and wildlife. In Nepal, forests have been destroyed, fragmented, and developed for human settlements, agricultural production, and urban centers for decades. As a result, human-wildlife conflict, in the form of crop-raiding, livestock predation, and injuries to humans and wildlife, is common throughout the country. In particular, crop-raiding by macaques is an increasingly common form of human-wildlife conflict. Rhesus macaques (Macaca mulatta) have been identified as a top ten crop-raiding wildlife species in Nepal. In order to better understand the nationwide distribution and intensity of human-rhesus macaque conflict (HRMC), we conducted an extensive literature review of reported incidences of HRMC during the period 2000 to 2021 in Nepal. We also created an online survey to obtain nationwide data on the location and severity of HRMC, and modeled the set of ecological factors that affect habitat suitability for rhesus macaques. An ensemble of three different species distribution model (SDM) algorithms were used to analyze these data. We found that almost 44% of Nepal's land area contains suitable habitat for rhesus macaques, with less than 8% of all suitable habitat located in protected national parks. As humans continue to alter and fragment natural landscapes, HRMC in Nepal has intensified. At present, nearly 15% of the country's land area in which human settlements are permitted, is characterized by moderate or high rates of HRMC. We argue that prioritizing programs of forest restoration, strategic management plans designed to connect isolated forest fragments with high rhesus macaque population densities, creating government programs that compensate farmers for income lost due to crop-raiding, and educational outreach that informs local villagers of the importance of conservation and protecting biodiversity, offer the most effective solutions to reduce HRMC in Nepal.

Modeling the effect of climate change on the distribution of threatened medicinal orchid Satyrium nepalense D. Don in India.

It is vital to understand the distribution area of a threatened plant species for its better conservation and management planning. Satyrium nepalense (family: Orchidaceae) is a threatened terrestrial orchid species with valuable medicinal and nutritional properties. The survival of S. nepalense in wild conditions has been challenged by increasing global surface temperature. Hence, understanding the impact of climate change on its potential distribution is crucial to conserve and restore this species. In present study, Maxent species distribution modeling algorithm was used to simulate the current distribution of S. nepalense in India and predict the possible range shift in projected future climate scenarios. A set of 19 bioclimatic variables from WorldClim database were used to predict the potential suitable habitats in current climatic condition and four Representative Concentration Pathway (RCP 2.6, 4.5, 6.0, and 8.5) scenarios by integrating five General Circulation Models (GCMs) for future distribution modeling of species for the years 2050 and 2070. Furthermore, change analysis was performed to identify the suitable habitat in current and future climate for delineating range expansion (gain), contraction (loss), and stable (no change) habitats of species. The Maxent model predicted that ~ 2.38% of the geographical area in India is presently climatically suitable for S. nepalense. The key bioclimatic variables affecting the distribution of studied species were the mean temperature of warmest quarter, mean temperature of wettest quarter, precipitation of warmest quarter, and temperature seasonality. Under future climate change scenarios, the total suitable habitat of S. nepalense will increase slightly in the Himalayan region and likely to migrate towards northward, but in the Western Ghats region, the suitable areas will be lost severely. The net habitat loss under four RCP scenarios was estimated from 26 to 39% for the year 2050, which could further increase from 47 to 60% by the year 2070. The finding of the predictive Maxent modeling approach indicates that warming climates could significantly affect the potential habitats of S. nepalense and hence suitable conservation measures need to be taken to protect this threatened orchid species in wild conditions.

Predicted declines in suitable habitat for greater one-horned rhinoceros (Rhinoceros unicornis) under future climate and land use change scenarios.

Rapidly changing climate is likely to modify the spatial distribution of both flora and fauna. Land use change continues to alter the availability and quality of habitat and further intensifies the effects of climate change on wildlife species. We used an ensemble modeling approach to predict changes in habitat suitability for an iconic wildlife species, greater one‐horned rhinoceros due to the combined effects of climate and land use changes. We compiled an extensive database on current rhinoceros distribution and selected nine ecologically meaningful environmental variables for developing ensemble models of habitat suitability using ten different species distribution modeling algorithms in the BIOMOD2 R package; and we did this under current climatic conditions and then projected them onto two possible climate change scenarios (SSP1‐2.6 and SSP5‐8.5) and two different time frames (2050 and 2070). Out of ten algorithms, random forest performed the best, and five environmental variables—distance from grasslands, mean temperature of driest quarter, distance from wetlands, annual precipitation, and slope, contributed the most in the model. The ensemble model estimated the current suitable habitat of rhinoceros to be 2610 km2, about 1.77% of the total area of Nepal. The future habitat suitability under the lowest and highest emission scenarios was estimated to be: (1) 2325 and 1904 km2 in 2050; and (2) 2287 and 1686 km2 in 2070, respectively. Our results suggest that over one‐third of the current rhinoceros habitat would become unsuitable within a period of 50 years, with the predicted declines being influenced to a greater degree by climatic changes than land use changes. We have recommended several measures to moderate these impacts, including relocation of the proposed Nijgad International Airport given that a considerable portion of potential rhinoceros habitat will be lost if the airport is constructed on the currently proposed site.

Predicting the distributional range shifts of Rhizocarpon geographicum (L.) DC. in Indian Himalayan Region under future climate scenarios.

Himalaya, the highest mountain system in the world and house of important biodiversity hotspot, is sensitive to projected warming by climate change. Rhizocarpon geographicum (map lichen), a crustose lichen, grows in high mountain ranges, is a potential indicator species of climate change. In the present study, MaxEnt species distribution modeling algorithm was used to predict the suitable habitat for R. geographicum in current and future climate scenarios. Nineteen bioclimatic variables from WorldClim database, along with elevation, were used to predict the current distribution and three representative concentration pathway (RCP) scenarios by integrating three general circulation models (GCMs) for future distribution of species covering years 2050 and 2070. Furthermore, we performed change analysis to identify the precise difference between the current and future distribution of suitable areas of the species for delineating habitat range expansion (gain), habitat contraction (loss), and stable habitats. The final ensemble model obtained had average test value 0.968, and its predicted ~ 27.5% of the geographical area in the Indian Himalayan Region is presently climatically suitable for the species. The predicted highly suitable area for R. geographicum is observed to be declining in Northwestern Himalaya, and it is shifting towards the higher elevation areas of the Eastern Himalaya. The projected distribution in future under the RCP scenarios (RCP 4.5, 6.0, and 8.5) showed the range expansion towards higher elevations, and it is more pronounced for the extreme future scenarios (RCP 8.5) than for the moderate and intermediate climate scenarios (RCP 4.5 and RCP 6.0). However, assuming that species can migrate to previously unoccupied areas, the model forecasts a habitat loss of 10.86-16.51% for R. geographicum, which is expected due to increase in mean annual temperature by 1.5-3.7 °C. The predictive MaxEnt modeling approach for mapping lichen will contribute significantly to the understanding of the impact of climate change in Himalayan ecosystems with wide implications for drawing suitable conservation plans and to take adaptation and mitigation measures.