Arid and semi-arid ecosystems are among the world's most fragile biomes, where woody plants underpin ecological stability yet face increasing pressures from habitat fragmentation, overexploitation, and climate change. Conventional IUCN Criterion B assessments based on Extent of Occurrence (EOO) and Area of Occupancy (AOO) provide standardized spatial thresholds but do not explicitly incorporate demographic or structural processes influencing persistence. This study develops a Conservation Priority Index (CPI), a process-informed prioritization framework that integrates carrying capacity (K), metapopulation connectivity (lambda), and fragmentation within a diagnostic structure complementary to IUCN Criterion B. Using six ecologically significant Thar Desert species (Acacia jacquemontii, Anogeissus sericea var. nummularia, Calligonum polygonoides, Commiphora wightii, Haloxylon salicornicum, and Tecomella undulata), we combined geospatial analyses with Structural Equation Modelling (SEM) and Canonical Correspondence Analysis (CCA) to evaluate structural relationships among demographic and environmental parameters. Although all species exceeded EOO thresholds, their AOOs were <2,000 km(2), indicating spatial restriction under Criterion B. SEM results highlighted the structural interplay between patch configuration and demographic capacity, while CCA identified temperature, precipitation, and soil carbon as key dimensions of environmental alignment. CPI outcomes revealed marked variation in prioritization: T. undulata exhibited the highest concern (CPI = 0.92) due to restricted occupancy and edaphic specialization, whereas H. salicornicum showed lower relative vulnerability (CPI = 0.28) associated with strong spatial cohesion and environmental alignment. Sensitivity analyses demonstrated that CPI rankings were robust to alternative weighting and spatial parameter configurations. CPI thus differentiates structural and demographic fragility among species with similar Red List classifications and serves as a complementary, scalable, and dynamically updatable (Delta CPI) framework for conservation prioritization and monitoring in data-limited arid systems.

Cowpea (Vigna unguiculata L. Walp.) is a climate-resilient legume essential for food security and dryland farming in semi-arid regions of India. This study delineates climate-smart suitability zones by integrating Ensemble Species Distribution Modelling (ESDM) with Analytic Hierarchy Process-Multi-Criteria Evaluation (AHP-MCE) to incorporate climatic, soil, and land-use determinants. Seven modelling algorithms (GLM, GAM, MARS, CTA, RF, ANN, SVM) were applied under baseline, 2050, and 2070 climates (RCP 4.5 and 8.5), achieving strong ensemble performance (AUC = 0.84-0.88; TSS = 0.62-0.67). Precipitation variables dominated current suitability, while temperature extremes shaped future patterns. Integrating soil and LULC data through AHP-MCE substantially improved spatial precision, expanding optimum suitability zones and identifying new target regions in central, southern and eastern India. The integrated framework offers practical value for breeding programs (identifying heat- and drought-prone target environments), agricultural policy and investment (site-specific irrigation, diversification, climate-risk planning), and farm-level decision making. By quantitatively combining climatic, soil and land-use predictors in a unified GIS-based ensemble system, an advancement over existing legume suitability studies, this work provides a scalable tool for climate-resilient crop planning and adaptive agricultural development.

Endemism is essential for biodiversity conservation, as species with limited ranges are more susceptible to habitat loss due climate change. This study evaluates the Area of Endemism (AoE) for Acacia jacquemontii, Anogeissus sericea var. nummularia, Calligonum polygonoides, Commiphora wightii, and Tecomella undulata, which are found in the arid and semi-arid areas of western India. The Area of Endemism (AoE) for these species was assessed using Machine Learning-Staking Species Distribution Modelling (SSDM). Study indicated that the mean temperature of the wettest quarter (Bio-8, 30 to 33 degrees C), the maximum temperature of the warmest month (43.3 to 46.2 degrees C), the annual temperature range (Bio-7, 35.1 to 35.4 degrees C), and the precipitation of the coldest quarter (Bio-19, 11-13 mm) significantly influence the AoE and percentage of endemism. The Enhanced Vegetation Index (EVI) indicated that the examined endemic species inhabited both homogeneous and diverse settings. The topographical features, including elevation (10 to 30 m), slope 1 (10-15 % gradient), and slope 2 (30 to 45 % gradient), were deemed more appropriate. SSDM modeling indicated that the present optimum AoE is located in the Marwar region of the Thar Desert, covering the western districts of Rajasthan. This investigation revealed the following key trends: (a) as climatic conditions change, suitable habitats are shifting northward, whereas southern Rajasthan (Barmer, Jaisalmer, Nagaur, Pali, Jalore) is seeing a reduction in optimal habitat, (b) optimal AoE is decreasing at a more accelerated rate than the moderate, particularly in north-central Rajasthan and Gujarat, (c) the western limit of Jaisalmer district shows indications of recovery under RCP 8.5, with expected new habitat patches arising in Churu, Bikaner, and Sikar. The results identified specific sites for biodiversity protection, so enabling adaptive management measures to reduce species extinction in vulnerable habitats.

Pergularia daemia (Forssk.) Chiov. exhibits dual ecological and socio-economic roles, functioning both as a valued ethnomedicinal plant and an invasive weed across tropical and subtropical drylands. Despite its wide adaptability, the species climatic niche dynamics and future distribution under climate change remain poorly quantified.This study provides the first global assessment of the climatic suitability, key environmental drivers, and potential future distribution of P. daemia under multiple emission scenarios.A total of 710 spatially thinned occurrence records were modelled using an Ensemble Species Distribution Modelling (ESDM) framework incorporating eight algorithms (GLM, GAM, MARS, ANN, CTA, SVM, MaxEnt, and Random Forest). Predictors included harmonized bioclimatic and non-bioclimatic variables for baseline modelling, while future projections (2050 and 2070 under RCP 4.5 and RCP 8.5) were generated using the BCC-CSM2-MR GCM. Niche dynamics were further evaluated using ellipsoid niche hypervolume (ENH) analysis. Current estimates highlight high suitability in semi-arid regions of Sub-Saharan Africa and peninsular India, strongly influenced by annual precipitation (Bio12), precipitation seasonality (Bio15), and isothermality (BIO3). Future projections indicate a temporary increase in climatically suitable area by 2050; however, this potential expansion may not translate into realized habitat occupancy. By 2070, suitability declines markedly, with pronounced habitat fragmentation and a substantial rise in low-suitability zones, particularly under high-emission scenarios. Peripheral and marginal suitability corridors critical for dispersal connectivity show >90% contraction. ENH analyses reveal initial niche expansion under moderate warming, followed by niche contraction and displacement under stronger warming trajectories.

Avocado (Persea americana Mill.), a nutrient-rich tropical fruit, is gaining prominence in India due to rising domestic demand and export potential. However, its cultivation remains fragmented, largely confined to southern states, with limited knowledge of ecological requirements under diverse agro-climatic zones and climate change scenarios. This study aimed to identify key bioclimatic and non-bioclimatic factors influencing avocado suitability, model its current and future distribution using ensemble species distribution modelling (ESDM), assess niche dynamics under four Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0, and 8.5) for 2050 and 2070, and evaluate implications for climate-resilient agroforestry planning. Using 35 spatially thinned occurrence records and high-resolution environmental predictors, ESDM integrating eight machine learning algorithms was applied. Model performance was robust (AUC: 0.86-0.91), with Random Forest and Maxent performing best. Critical predictors included isothermality, minimum temperature of the coldest month, precipitation in the coldest quarter, urbanization, and forest cover. Current suitability hotspots were concentrated in Kerala and Tamil Nadu. Future projections under RCPs 2.6 and 6.0 indicated northward and altitudinal expansion into the Western Ghats, northeastern hills, and eastern India, whereas RCP 8.5 suggested increased fragmentation and instability. Niche analysis revealed ecological breadth expansion under low to moderate emissions, but contraction and displacement under high-emission conditions. These findings highlight scope for expanding avocado cultivation under low to moderate emissions, provided thermal and precipitation stability is maintained. The study offers a geospatial foundation for climate-smart avocado production, conservation, and policy, emphasizing the protection of climatic refugia in southern India and adaptive agroecological strategies for long-term sustainability.

This study was conducted to assess the habitat suitability of Carissa carandas in India, which is crucial for its sustainable integration into agriculture under changing climatic conditions. We utilized Maximum Entropy (MaxEnt) modelling to evaluate the species' distribution across current and future scenarios (2050 and 2070) under four Representative Concentration Pathways (RCPs: 2.6, 4.5, 6.0, and 8.5). Results indicated that temperature-related variables, particularly the Minimum Temperature of the Coldest Month (MiTCM, contributing 46.8% in 2070 RCP 2.6) and Isothermality (contributing up to 35.2% in 2070 RCP 8.5), are the dominant climatic drivers. Land Use and Land Cover (LULC) factors such as urbanization (49.8%), total cultivated land (28.1%), and grassland (9.0%) significantly influence habitat suitability. Under the current conditions, optimal habitat spans 4,588 km(2), decreasing by 38.95% under LULC scenarios. Projected habitat changes indicate 2.04% gain under 2070, but 11.06% decline under 2050 RCP 2.6. Southern and western regions, including Karnataka, Tamil Nadu, Maharashtra, and Gujarat exhibit high suitability. Habitat fragmentation is projected in northern and western India due to climate change and land use modifications. These findings underscore the need for proactive conservation planning and climate-adaptive agricultural strategies to optimize the cultivation of C. carandas. Policymakers and stakeholders should focus on preserving suitable regions while mitigating urbanization-induced habitat loss.

Prosopis cineraria (L.) Druce, a keystone species in hot arid and semi-arid ecosystems of India, contributes significantly to ecological stabilization, carbon sequestration, and traditional agroforestry systems. This study employs an integrated geospatial and statistical framework to assess the spatial dominance and habitat suitability of P. cineraria across diverse ecological gradients. Using field-based relative importance value (RIV) data from 322 sites, inverse distance weighting (IDW) was applied to interpolate species dominance patterns. Ensemble species distribution modelling (ESDM) was implemented with seven machine learning algorithms (e.g., random forest, MaxEnt, artificial neural network) and environmental predictors—bioclimatic, edaphic, topographic, and anthropogenic—to delineate habitat suitability. Results indicated that bioclimatic variables, particularly precipitation seasonality (Bio15) and temperature extremes (Bio5, Bio6), were the most influential, with random forest achieving the highest predictive accuracy (AUC = 0.98; TSS = 0.89). IDW interpolation identified strong P. cineraria dominance in western-central districts (Jodhpur, Pali, Nagaur), while ESDM projected 428,407 km2 of suitable habitat, largely overlapping with field-derived hotspots. Niche hypervolume analysis revealed a broad core niche but a more restricted realized distribution, constrained by human pressures and environmental factors. These findings provide evidence that could guide zone-specific conservation strategies, including the prioritization of high-RIV areas for in situ protection and ecological restoration in low-dominance regions. While the results highlight the ecological importance of P. cineraria, further field validation and socio-economic assessments are recommended before direct policy application.

Flower colour is a key trait shaping pollination, reproduction and plant–environment interactions. In arid ecosystems, it may also signal adaptations to heat and (Ultraviolet) UV stress. Tecomella undulata , a threatened keystone tree of the Indian Desert, exhibits striking flower colour polymorphism with yellow, orange and red morphs. This study tested whether artificial intelligence (AI) can reliably classify these morphs, thereby supporting conservation efforts. Field surveys were conducted across natural populations in the Thar Desert. An accessible no-code AI platform (Google Teachable Machine) was used for supervised classification of flower and tree images, with unsupervised clustering applied for validation. The AI classifier achieved high accuracy in distinguishing morphs at both flower and tree scales. Morphs showed consistent separation, with orange functioning as an intermediate form. Despite red morphs being more frequent, the presence of yellow and orange morphs contributes essential functional diversity important for pollinator interactions and reproductive resilience. This study demonstrates that no-code AI provides an effective, scalable approach to documenting intraspecific variation in threatened species. By enabling rapid and reliable identification of flower colour morphs, the approach offers practical applications for ex situ conservation, restoration and morph-aware biodiversity management in T. undulata and other arid-zone trees.

The pre-installation assessment criteria for solar energy parks have been simulated through a variety of machine learning algorithms, with predictors categorized into three different climatic time frames (present, 2050, and 2070 bio-climatic time frames) and four distinct Socio-Economic Emission Scenarios, namely, RCPs 2.6, 4.5, 6.0, and 8.5, which represent projections for future levels of radiative forcing and greenhouse gas emissions W/m2. A promising new location identification was speedily achieved through the development of an ensemble distribution model using a machine learning algorithm. The total capacity (in MW) and covered area of 78 different solar parks across India from various agro-climatic zones were examined (Sq. KM). Predictions about the future viability of existing solar parks are made in this study, and the best places for new ones are suggested. It was found that 2.08

Haloxylon salicornicum is a keystone shrub of the Indian arid zone, valued for dune stabilization, fodder, and ecosystem restoration, yet its climatic resilience remains poorly understood. This study assessed the ecological thresholds, current habitat suitability, and future distribution of H. salicornicum under changing environmental conditions. An Ensemble Species Distribution Modelling (ESDM) framework, integrating seven machine learning algorithms with 152 spatially filtered occurrence records, was applied. Predictor variables included bioclimatic, edaphic, land-use, and anthropogenic factors, weighted through an Analytic Hierarchy Process–Multi-Criteria Evaluation (AHP–MCE) to enhance ecological realism. Current suitable habitat occupies 12–15

Ensemble species distribution modelling was compared to the two best-performing individual algorithms using climatic and non-climatic predictors for the critically endangered plant species Commiphora wightii, looking at habitat suitability, niche overlap, and IUCN categories like Extent of occurrence (EOO) and Area of occupancy (AOO) in India. We selected ensemble, Random Forest, and Support Vector Machine algorithms with current and two future climatic time frames (2050 and 2070) along with aspect and slope predictors based on the model quality tools. Using the ensemble methodology and the SVM technique, we found that the seasonality of precipitation had a stronger influence on the habitat suitability of this species in both the present and the 2070 time frames. The SVM was able to capture the magnitude of their effects better than the ensemble technique. However, for the 2050 climate projection, both the ensemble and SVM imply that the wettest quarter’s precipitation has a greater impact. This was found that Rajasthan’s flood-prone eastern plain, internal drainage dry zone, and irrigated north-western plains were less ideal places for this species to survive under the current climate.

Climate change and other extinction facilitators have caused significant shifts in the distribution patterns of many species during the past few decades. Restoring and protecting lesser-known species may be more challenging without adequate biogeographical information. To address this knowledge gap, the current study set out to determine the global spatial distribution patterns of Indigofera oblongifolia (Forssk) a relatively lesser-known leguminous species. This was accomplished by utilizing three distinct bioclimatic temporal frames (current, 2050, and 2070) and four greenhouse gas scenarios (RCPs 2.6, 4.5, 6.0, and 8.5), in addition to non-climatic predictors such as global livestock population, human modification of terrestrial ecosystems, and global fertilizers application (nitrogen and phosphorus). Furthermore, we evaluate the degree of indigenousness using the geographical area, habitat suitability categories, and number of polygons. This research reveals that climatic predictors outperform non-climatic predictors in terms of improving model quality. Precipitation Seasonality is one of the most important factors influencing this species' optimum habitat suitability up to 150 mm for the current, 2050 RCP 8.5 and 2070-RCPs 2.6, 4.5, and 8.5. Our ellipsoid niche modelling extends the range of precipitation during the wettest quarter and maximum temperature during the warmest month to 637 mm and 26.5–31.80 degrees Celsius, respectively. India has a higher indigenous score in the optimal class than the African region. This findings suggest that the species in question tends to occupy contiguous regions in Africa, while in India, it is dispersed into several smaller meta-populations.

A comprehensive evaluation of the habitat suitability across the India was conducted for the introduced species Opuntia ficus-indica . This assessment utilized a newly developed model called BioClimInd, takes into account five Earth System Models (ESMs). These ESMs consider two different emission scenarios known as Representative Concentration Pathways (RCP), specifically RCP 4.5 and RCP 8.5. Additionally, the assessment considered two future time frames: 2040–2079 (60) and 2060–2099 (80). Current study provided the threshold limit of different climatic variables in annual, quarter and monthly time slots like temperature annual range (26–30 °C), mean temperature of the driest quarter (25–28 °C); mean temperature of the coldest month (22–25 °C); minimum temperature of coldest month (13–17 °C); precipitation of the wettest month (250–500 mm); potential evapotranspiration Thronthwaite (1740–1800 mm). Predictive climatic habitat suitability posits that the introduction of this exotic species is deemed unsuitable in the Northern as well as the entirety of the cooler eastern areas of the country. The states of Rajasthan and Gujarat exhibit the highest degree of habitat suitability for this particular species. Niche hypervolumes and climatic variables affecting fundamental and realized niches were also assessed. This study proposes using multi-climatic exploration to evaluate habitats for introduced species to reduce modeling uncertainties.

Background The aim of this study is to examine the effects of four different bioclimatic predictors (current, 2050, 2070, and 2090 under Shared Socioeconomic Pathways SSP2-4.5) and non-bioclimatic variables (soil, habitat heterogeneity index, land use, slope, and aspect) on the habitat suitability and niche dimensions of the critically endangered plant species Commiphora wightii in India. We also evaluate how niche modelling affects its extent of occurrence (EOO) and area of occupancy (AOO). Results The area under the receiver operating curve (AUC) values produced by the maximum entropy (Maxent) under various bioclimatic time frames were more than 0.94, indicating excellent model accuracy. Non-bioclimatic characteristics, with the exception of terrain slope and aspect, decreased the accuracy of our model. Additionally, Maxent accuracy was the lowest across all combinations of bioclimatic and non-bioclimatic variables (AUC = 0.75 to 0.78). With current, 2050, and 2070 bioclimatic projections, our modelling revealed the significance of water availability parameters (BC-12 to BC-19, i.e. annual and seasonal precipitation as well as precipitation of wettest, driest, and coldest months and quarters) on habitat suitability for this species. However, with 2090 projection, energy variables such as mean temperature of wettest quarter (BC-8) and isothermality (BC-3) were identified as governing factors. Excessive salt, rooting conditions, land use type (grassland), characteristics of the plant community, and slope were also noticed to have an impact on this species. Through distribution modelling of this species in both its native (western India) and exotic (North-east, Central Part of India, as well as northern and eastern Ghat) habitats, we were also able to simulate both its fundamental niche and its realized niche. Our EOO and AOO analysis reflects the possibility of many new areas in India where this species can be planted and grown. Conclusion According to the calculated area under the various suitability classes, we can conclude that C. wightii 's potentially suitable bioclimatic distribution under the optimum and moderate classes would increase under all future bioclimatic scenarios (2090 > 2050 ≈ current), with the exception of 2070, demonstrating that there are more suitable habitats available for C. wightii artificial cultivation and will be available for future bioclimatic projections of 2050 and 2090. Predictive sites indicated that this species also favours various types of landforms outside rocky environments, such as sand dunes, sandy plains, young alluvial plains, saline areas, and so on. Our research also revealed crucial information regarding the community dispersion variable, notably the coefficient of variation that, when bioclimatic + non-bioclimatic variables were coupled, disguised the effects of bioclimatic factors across all time frames.

Weed species have the potential to alter the structure and functions of the ecosystem and besides their antagonistic ecological relationships with main crops, simultaneously they are also valued for their secondary metabolites of pharmaceutical and nutraceutical values. Climate and community-associated changes may alter the presence of such species as well as the concentration and quality of their active chemical constituents. In the present study, we carried out a comparative study to assess the proportional performance of different algorithms (both regression and machine learning based) for the assessment of habitat suitability of Tribulus terrestris within Indian arid and semi-arid areas. Furthermore, the impact of niche modeling on the Extent of Occurrence (EOO) and Area of Occupancy (AOO) of this species with three bioclimatic timeframe projections was also quantified. We hypothesized that these objectives will enable us to identify the major bioclimatic and community predictors that determine the habitat suitability of T. terrestris and also give projected area cover with this species under different suitability classes. For the above objectives, we implemented the ensemble techniques in which different algorithms (General linear model; GLM), (Generalized additive model; GAM), (Classification tree analysis; CTA), (Artificial neural network; ANN), (Support vector machine; SVM), (Multivariate adaptive spline; MARS), (Random forest; RF), and (Maximum entropy; MAXENT) were utilized and their prediction performance was assessed by using Kappa statistic, Area Under the receiver operating characteristic Curve (AUC), sensitivity, specificity, and True Skill Statistic (TSS). Niche overlap was carried out to visualize the amount of area retained by this species under different predictions. Comparative evaluation of different approaches revealed the best performance of random forest among all other algorithms that produced excellent model qualities for all three studied bioclimatic variables while good model quality for Habitat Heterogeneity Indices (HHI). Our results also revealed that HHI are less dynamic for species distribution modeling (SDM) of this species as compared to bioclimatic variables. Precipitation of Coldest Quarter (BC-19), Precipitation Seasonality (BC-15), and Annual Precipitation (BC-12) were the most significant variables that affect the SDM of this species. With current climatic conditions, we observed that optimum areas are located in the northern region of the arid and semi-arid areas of India covering 92,400 km2 areas. While during 2050 projection area under this class increases up to 100,800 km2 which suggests a 9.09% increase. While during 2070, this class covers 91,900 km2 which showed −8.83% area decreases with respect to the previously projected timeframe and only 0.54% decrease compared to the current BC. With HHI variables, we found the disintegration of different classes in small patches as compared to bioclimatic variables. Overall, 111.25 km centroid shifting will be anticipated from the current to 2070-time era. In this analysis, we also find a significant negative pattern between EOO and AOO (R2 = 0.87). Our results can be used to enhance ecologically (regarded as weed species) as well as economic (regarded as medicinally most important species) management in order to curb this or for harvesting the higher biomass (standing state) for its important secondary metabolites.

Species Distribution Modelling (SDM) involves utilizing observations of a given species and its surrounding environment to produce a sound approximation of the species' potential distribution. The intricate relationships between organisms and their surroundings, coupled with the profusion of data, have captured the attention of ecologists and statisticians alike. Consequently, they have directed their efforts towards exploring the potential of machine learning techniques. Our study employs an ensemble machine learning approach to simulate the global ecological niche modelling of Ganoderma lucidum fungus. This involves the utilization of various environmental predictors and the averaging of multiple algorithms to achieve a comprehensive analysis. 563 spatially thinned presence points of G. lucidum were projected with three bio-climatic time frames, namely current, 2050, and 2070, and four Representative Concentration Pathways (RCPs), namely 2.6, 4.5, 6.0, and 8.5, as well as non-climatic variables (surface soil features, land use, rooting depth and water storage capacity at rooting zone). We observed excellent model qualities as the Area Under the receiver operating Curve (AUC) approached 0.90. Random Forest was identified as the best individual algorithm, while the Maxent entropy was identified as the least effective for Ecological Niche Modelling (ENM) of G. lucidum. Globally, under the current bio-climatic and non-bioclimatic projection, optimum habitat for this fungus covers 12510876.3 km2 area while, maximum area (13248546.9 Sq. km.) under this habitat class with future projections was recorded with RCP of 8.5 in 2070. The primary determinants of its current global distribution were ecosystem rooting depth, water storage capacity, and precipitation seasonality. While, with two future bioclimatic time frames and RCPs, Isothermality was identified as the most influential predictor. Based on our assessment, it has been determined that this particular fungus is exhibiting a persistent pattern of proliferation across the regions of Europe, America, and certain areas of India. The present investigation sought to underscore the importance of discerning the native habitats of this species, taking into account both current and anticipated climatic shifts. This knowledge is essential for effectively coordinating the artificial cultivation and natural harvesting of G. lucidum, which is necessary to meet the ever-increasing industrial demands.

The goal of this study was to identify the global geographical distribution patterns of a lesser known indigenous legume species, Indigofera oblongifolia, using three bio-climatic timeframes (current, 2050, and 2070) and four greenhouse gas scenarios (RCPs 2.6, 4.5, 6.0, and 8.5), as well as non-climatic predictors like global livestock population, human modification of terrestrial ecosystem (GHMTE), global fertilizers application (nitrogen and phosphorus). In addition, we assess the degree of indigenousness using the area, habitat suitability categories, and number of polygons, and we identify the temporal effects of various bio-climatic variables on its fundamental and realized niche. The AUC for models built using current climate data and RCPs for the years 2050 and 2070 was 0.90. This research reveals that climatic predictors outperform non-climatic predictors in terms of improving model quality. Precipitation Seasonality is one of the most important factors influencing this species’ optimum habitat suitability up to 150 mm for the current, 2050-RCP 8.5, and 2070-RCPs 2.6, 4.5, and 8.5. The range of this parameter has altered from 79–176.9 to 85–196 as the climatic conditions and RCPs have improved. Our ellipsoid niche modelling extends the range of these bioclimatic variables to 637 mm and 26.5-31.80 degrees Celsius, respectively. India has a higher indigenous score in the optimal class than the African region. These findings indicate that this species inhabits more continuous areas in Africa, whereas it is fragmented into a number of smaller meta-populations in India (group of spatially separated population of the same species).

Macrophomina phaseolina , a soil saprophytic plant pathogen of global distribution and wide host range, was studied in relation to current and future (2050 and 2070) climate change scenarios, soil variables, and habitat heterogeneity indices (HHI). On 285 geographically thinned, presence-only data, we used R program-based Ensemble Species Distribution Modelling (ESDM) and eight individual algorithms to do ensemble modelling. When compared to other algorithms and ensemble outcomes, our study demonstrated that Random Forest (RF) was the best predictive individual algorithm. As a consequence, we utilized RF to assess this species’ habitat appropriateness, niche width, niche overlap, and area occupied within pre-defined habitat classes. In the present and 2050 Bio-Climatic (BC) periods, isothermality was recognized as the most significant element, whereas annual mean temperature was indicated as the most important regulating factor during BC-2070. According to HHI, the population of this species drops monotonically as the coefficient of variation increases. With the exception of 15 to 30 cm, depth soil predictor demonstrated that sand percentage had the least influence on the pathogen’s habitat at all examined depths. Silt played a vital function at varied depths. The findings of ESDM with the combined current data set demonstrated that climatic factors outperformed HHI and soil variables in terms of dispersion.

The objective of this study was to utilize niche modelling techniques and predictors, including bioclimatic, soil, habitat heterogeneity indices, and land-use land cover (LULC), to ascertain the present and potential distribution of Tecomella undulata in India. The bio-climatic variables of 2050 and 2070 timeframes were employed to forecast future occurrences. The study also examined the level of indigeneity of T. undulata and analysed the factors that impact its fundamental and realized niche. The Maxent model utilized for forecasting the distribution of T. undulata demonstrated a high level of precision, incorporating both bioclimatic and non-bioclimatic variables. The study highlights the significance of mean and maximum temperatures during the warmest quarter and month, as well as the wettest months and years’ worth of precipitation. In addition, threshold values for these predictors were calculated. In contrast to the limiting effects of climatic factors, the species in question was found to exhibit a greater degree of facilitation in response to soil conditions (including rooting conditions, nutrient availability, and salt excess), habitat heterogeneity indices (such as range, maximum, and coefficient of variance of diversity), and lLULC predictors (including urban areas, residential and infrastructure development, forested regions, and sparsely vegetated areas). As a result, this species was able to expand its range across a wider expanse of India. The Churu and Jhunjhunu districts and a transact region including Pali, Jalor, Jodhpur, Sanchor, and Barmer have been identified as the best possible locations for its occurrences. Shrinkage would begin around 2050 in all of these areas. By 2070, the Churu and Jhunjhunu regions had become significantly more fragmented, while the Jodhpur region and the surrounding areas of Barmer, Sanchor, Jalor, and Vav had grown. Specific coordinates were also identified pertains to zone of extinction, zone of re-occurrence and zone of maximum occurrence. The aforementioned discoveries enable us to ascertain the extent of land that is conducive to the growth of T. undulata across diverse ecological niches, as well as the underlying factors and critical points that impact its dispersion dynamics both presently and prospectively. This shall aid us in determining the necessity of extensive captive cultivation for the preservation of the species and its consequential ecological advantages.