Landslides, floods, fires, windstorms, hailstorms, and earthquakes are major dangers in Bhutan due to historical events and their potential damage. At present, systematic collection of data is scarce and no multi-hazard zoning is reported in the existing literature for Bhutan. In addition, for proper disaster management, recognizing the existence of the hazards and identifying the vulnerable areas are the first important tasks for any multi-hazard risk studies. To fill the gap, the main objective of this study is to prepare the multi-hazard zoning and assess the multi-hazard population risk for Bhutan on seven historical hazard events. To achieve this, we first collected data on the historical events of different periods based on the data availability and created a district-level database. A total of 1224 hazard events were retrieved. We then calculated the weighted score for individual hazards based on the number of occurrences and the degree of impact through a multi-criteria decision analysis model (MCDA) using the analytic hierarchy process (AHP). The district-wise individual hazard scores are then obtained using the weighted scores. The total hazard score (THS) was aggregated and normalized to obtain the district-wise multi-hazard scores. A multi-hazard zoning map was created in the open-source software QGIS, highlighting 70% of districts with moderate to severe multi-hazard vulnerability. Considering the population distribution in each district at the local levels, the multi-hazard score is integrated and the multi-hazard population risk is mapped.
The theme of childhood remains an integral part of any life-writing narratives and, when it comes to Dalit autobiographies it is no exception. Strikingly, researchers on Dalit autobiographies have focused mostly on the ‘darker-side’ of the childhood by revealing only the socio-economic deprivations (food, clothes and shelter), plight, and the mental trauma and physical abuse, humiliation, and pain of the Dalit children, often overlooking the diversifying aspects of Dalit childhood. Though caste system pushes Dalit children to live in isolated ghettos, they still create their own imaginary world within the confines of their Dalit inhabitations by playing games with things available at hand, by role-playing some characters seen in their environs, by celebrating traditional festivals, and by listening to the elders’ stories! The article, therefore, attempts to examine how the playful activities of Dalit children, as represented in the autobiographies, embody an ecological imagination of interconnectedness. By inscribing their lived experience of subjugation in nature, Dalit children not only share a relationship of common oppression with the environment, but such an entanglement sheds new insights on the human–non-human relationship. I have chosen four Dalit autobiographies to exemplify the fact that through their games and play Dalit-children nurture an ‘intra-active’ communication between humans and the non-human environment which in turn makes ‘multispecies liveability possible’. The article draws insights from eco-criticism to reflect on the embodied experience of Dalit childhood.
India is home to about 700 Indigenous and Tribal People with a population of 104.3 million. Over the years, displacement and rapid acculturation of the Tribal Peoples in India has led to dramatic changes in their social, cultural, economic and value systems. The rapid demographic, epidemiologic, nutrition and health transitions accompanied by economic development, stresses of urbanization and westernization of lifestyle predisposes the tribal population to overweight/obesity and hypertension. A cross-sectional sample of 615 Hmar adults (18–70 years) from five rural villages of the Churachandpur sub-division and 592 urban adults were collected from Imphal East and Imphal West, Manipur. Data on socio-economic, demographic, behavioural parameters, blood pressure, height, weight, sitting height, mid-upper arm, waist and hip circumferences; and three skinfold thickness, namely, biceps, triceps and subscapular were taken from each participant. The results from the present study can be presented into two broad categories: 1. Intra-variation in hypertension by independent variables within each setting. The findings of this study strengthen the case that for men, increasing age, overweight and/or obese, alcohol consumption, and household income significantly increase the odds of developing hypertension. 2. Inter-variation in terms of rural-urban differences in hypertension for each categorical independent variable The prevalence of hypertension was significantly higher in urban than in rural areas. The urban Hmars likely have a higher level of bio-behavioural and psychological stress as compared to their rural counterparts. The rural and urban differences in the risk of developing hypertension are largely due to the differences in BMI and household income. These results should, however, be interpreted with caution. Firstly, while BMI may be a contributing factor to hypertension, the relationship between socioeconomic status and hypertension is highly inconsistent. Secondly, urbanization and economic development may be associated with hypertension, but they should not be considered the causal factors of hypertension. The overall prevalence of hypertension in the present study is about 21%. Urban areas (25%) show higher risk of developing hypertension compared with their rural counterparts (17%). Obesity is the strongest predictor of hypertension in both the rural and urban Hmar adults which might require hypertension intervention at a lower BMI. The bio-behavioural and socio-economic factors taken in the present study are unevenly distributed across rural and urban settings presenting a formidable challenge to institute public health intervention strategies specific to rural and urban areas. Early diagnosis, increasing awareness and enhancing the frequency of screenings for hypertension are suggested.
Indigenous mountain people are often marginalized from mainstream development and are not able to express their concerns over the impacts of ecosystem changes on their livelihoods. Living in geographically difficult terrains, they engage in traditional ritual practices concerning their livelihoods that build on generations of deep-rooted beliefs. Yet, the availability of literature on traditional rituals practiced in the context of farming systems is scant. We conducted an exploratory study, through structured survey questionnaires, to document the traditional ritual practices observed in farming across the country of Bhutan. The study revealed the continuing practice of diverse and unique traditional rituals being propitiated to local deities for the welfare and wellbeing of individuals and communities across all ethnic groups in Bhutan. This study documented various tangible and intangible cultural values adopted in farming practices in Bhutan that are at risk of disappearing due to anthropogenic pressures.
Liquefaction-induced damage has been observed in several moderate to large earthquakes. Intensive efforts have been made to understand the liquefaction mechanism and develop procedures for analyzing the liquefaction potential at a particular site. In the present study, we aimed to evaluate liquefaction potential of Kolkata City, located between latitudes 22°20′ N–23°00′ N and longitudes 88°04′ E–88°33′ E, based on Standard Penetration Test (SPT) N values. The deterministic approaches as per Idriss and Boulanger  and IS 1893: Part 1, have been used to determine the liquefaction potential of the city. The detailed soil data from 500 boreholes located across 75 locations of the city are analyzed at various depths below the ground surface for varying earthquake momentmagnitudes (Mw) of 6.5, 7, 7.5 and 8 at peak horizontal ground surface acceleration (amax) of 0.24 g. The evaluation is carried out in terms of factor of safety against liquefaction (FSL).
Chhukha, a southern district of Bhutan remains susceptible to landslides due to excessive temporal rainfall variability and land instability aggravated by anthropogenic factors. This has led to multiple fatalities, substantial financial losses, and damages to infrastructure, farmland, and transportation networks. This study developed the district scale Landslide susceptibility index (LSI) by a bivariate statistical approach called Probabilistic Frequency Ratio (FR) and logistic regression (LR) with the help of a geospatial technology system. A total of 236 historical landslide inventories were identified through field deputation and google earth interpretation with the rationing of 70:30. 70% of the existing landslides were used to train the models, while the remaining 30% of them were used for model validation. The FR model outperformed the LR model with an accuracy of 88.3% and 83.2% respectively. The AUC model verification shows satisfactory agreement to predict landslide susceptibility at the district scale in the Himalayan region. Both models indicated that the central and northern parts of the district account for the least susceptibility, while the southern portion of the Chhukha district accounts for the highest susceptibility to landslides. These authentic findings of the research enable the local government and other decision-making bodies in developing policies, implement innovative measures, and disseminate awareness and preparedness for the consequences of landslide disasters.
In this interview, Professor Thakur S. Powdyel, former Minister of Education, Royal Government of Bhutan, shares his reflections on the unique features of Bhutan’s educational journey through time.
The aim of this work is to search for a Convolutional Neural Network (CNN) architecture that performs optimally across all factors, including accuracy, memory footprint, and computing time, suitable for mobile devices. Although deep learning has evolved for use on devices with minimal resources, its implementation is hampered by that these devices are not designed to tackle complex tasks, such as CNN architectures. To address this limitation, a Network Architecture Search (NAS) strategy is considered, which employs a Multi-Objective Evolutionary Algorithm (MOEA) to create an efficient and robust CNN architecture by focusing on three objectives: fast processing times, reduced storage, and high accuracy. Furthermore, we proposed a new Efficient CNN Population Initialization (ECNN-PI) method that utilizes a combination of random and selected strong models to generate the first-generation population. To validate the proposed method, CNN models are trained using CIFAR-10, CIFAR-100, ImageNet, STL-10, FOOD-101, THFOOD-50, FGVC Aircraft, DTD, and Oxford-IIIT Pets benchmark datasets. The MOEA-Net algorithm outperformed other models on CIFAR-10, whereas MOEANet with the ECNN-PI method outperformed other models on CIFAR-10 and CIFAR-100. Furthermore, both the MOEA-Net algorithm and MOEA-Net with the ECNN-PI method outperformed DARTS, P-DARTS, and Relative-NAS for small-scale multi-class and fine-grained datasets.
The composition and arrangement of agroforestry species at different altitudes play a vital environmental role in the growth of Black Cardamom crop. The present study assesses agroforestry tree species composition and the relationship between the growth of Black Cardamom and associated tree species. For this, the altitudes were sorted into three altitudinal ranges; low (850-1150 m), mid (1250-1550 m), and high (1650-1950 m) and in each altitudinal range three different habitats were assessed (Timber Tree (TT), Fodder tree (FT) and Mix tree (MT) habitats). In each altitudinal band, 16 plots were made for each habitat (TT, MT, FT) with a total of 144 plots sampled systematically with 20×20 m plot size within the selected altitudinal range (850 to 1950 m). The result of the study found 56 ecologically important agroforestry tree species under 32 families that are used as shade trees for Black Cardamom. The tree species preference of Black Cardamom changes based on the basal area of trees, altitudes, and habitat types ( F (2, 33)=45.672, P =.000). The overall growth ( R ² =.95) was better in timber tree habitat at mid altitudes, where Alnus nepalensis was the dominating species having 41.40% canopy cover with low pest and disease-infested Black Cardamom stems, making it a suitable habitat for growth. The lower altitudinal band and all the mixed tree habitats were found to be unsuitable for Black Cardamom growth ( p <0.05). Thus, while cultivating Black Cardamom it is important to select appropriate tree species, canopy cover, altitude, and habitat for optimum growth .
The hypnorum-complex of bumblebees (in the genus Bombus Latreille, 1802) has been interpreted as consisting of a single widespread Old-World species, Bombus hypnorum (Linnaeus, 1758) s. lat., and its closely similar sister species in the New World, B. perplexus Cresson, 1863. We examined barcodes for evidence of species’ gene coalescents within this species complex, using the closely related vagans-group to help calibrate Poisson-tree-process models to a level of branching appropriate for discovering species. The results support seven candidate species within the hypnorum-complex (Bombus taiwanensis Williams, Sung, Lin & Lu, 2022, B. wolongensis Williams, Ren & Xie sp. nov., B. bryorum Richards, 1930, B. hypnorum, B. koropokkrus Sakagami & Ishikawa, 1972, and B. hengduanensis Williams, Ren & Xie sp. nov., plus B. perplexus), which are comparable in status to the currently accepted species of the vagans-group. Morphological corroboration of the coalescent candidate species is subtle but supports the gene coalescents if these candidates are considered near-cryptic species.
In Bhutan, where the Emergency Medical System is forming and evolving, the number of acutely ill patients requiring critical care, both in the emergency departments and intensive care units, is steadily increasing. Given the lack of baseline data and the ever-increasing number of critical care patients, this study was aimed at describing the characteristics and outcomes of patients triaged as critically ill in the emergency department. The findings from this study constitute the first ever local database, at the national referral hospital in Bhutan, of critically ill patients treated in the emergency department. It highlights the central role the emergency department plays in their management and provides information for strengthening critical care services. It also highlights the areas of improvement and identifies high yield areas of training for the emergency department.
We examine the informal exchange of labour in farming villages with the successful adoption of labour-intensive farming practices. Previous studies have characterised the network pattern of labour exchange to relate such cooperative behaviour to the community’s social structure. We use network patterns from the literature and recreate the internal network structure of the labour exchange in selected Bhutanese villages to determine the type of social enforcement mechanisms used. Results show that labour exchange networks in these villages are characterised by a high prevalence of triad closure as an underlying social structure. These are completely connected structures within the labour exchange network in which any two farmers exchanging labour have a common farmer with whom both share labour. The results from our random graph modelling imply that villages with well-functioning labour exchange institutions may be most suitable for being promoted as “organic villages” as they can adapt to the high labour requirement that comes with organic farming. Future research should analyse how villages with different network structures produce different farm outcomes and how the village and farm-specific attributes affect their social enforcement mechanisms.
Background Assessment of occupational exposures is an integral component of population-based studies investigating the epidemiology of occupational diseases. However, all the available methods for exposure assessment have been developed, tested and used in high-income countries. Except for a few studies examining pesticide exposures, there is limited research on whether these methods are appropriate for assessing exposure in LMICs. The aim of this study is to compare a task-specific algorithm-based method (OccIDEAS) to a job-specific matrix method (OAsJEM) in the assessment of asthmagen exposures among healthcare workers in a high-income country and a low- and middle- income country (LMIC) to determine an appropriate assessment method for use in LMICs for future research. Methods Data were obtained from a national cross-sectional survey of occupational asthmagens exposure in Australia and a cross-sectional survey of occupational chemical exposure among Bhutanese healthcare workers. Exposure was assessed using OccIDEAS and the OAsJEM. Prevalence of exposure to asthmagens and inter-rater agreement were calculated. Results In Australia, the prevalence was higher for a majority of agents when assessed by OccIDEAS than by the OAsJEM (13 versus 3). OccIDEAS identified exposures to a greater number of agents (16 versus 7). The agreement as indicated by κ (Cohen’s Kappa coefficient) for six of the seven agents assessed was poor to fair (0.02 to 0.37). In Bhutan, the prevalence of exposure assessed by OccIDEAS was higher for four of the seven agents and κ was poor for all the four agents assessed (-0.06 to 0.13). The OAsJEM overestimated exposures to high-level disinfectants by assigning exposures to all participants from 10 (Bhutan) and 12 (Australia) ISCO-88 codes; whereas OccIDEAS assigned exposures to varying proportions of participants from these ISCO-codes. Conclusion There was poor to fair agreement in the assessment of asthmagen exposure in healthcare workers between the two methods. The OAsJEM overestimated the prevalence of certain exposures. As compared to the OAsJEM, OccIDEAS appeared to be more appropriate for evaluating cross-country exposures to asthmagens in healthcare workers due to its inherent quality of assessing task-based determinants and its versatility in being adaptable for use in different countries with different exposure circumstances.
In the twenty-first century, there has been an increase in the use of electronic commerce networks to speed up various business activities like sales, forecasting, client product consumption , and their purchasing patterns. User interaction and suggestion systems are essential parts of the decision support system for any form of business criterion. The measurement of e-commerce buyers and sellers, along with in-depth details on the exchange of goods and purchasing behaviour, as well as the usability of the particular website, have all been provided. The standard organization of the international organization also conducts commerce. Effectiveness, efficiency, satisfaction criteria factors, attribute matrices, features, and characteristics are just a few of the usability factors that have been assigned and labelled to measure the level of involvement. It is well known that the conventional moment-generating functions of random variables and their probability distributions do not exist for all distributions and/or at all points, and that, even when they do, they require extremely challenging and time-consuming manipulations to evaluate higher central and noncentral moments. Due to their simplicity and adaptability, the traditional/conventional moment-generating functions were intended to be replaced by the generalized multivari-ate moment-generating functions created in this study for a few random vectors/matrices and their probability distribution functions. As a binomial expansion of the anticipated value of an exponent of a random vector or matrix around an arbitrarily selected constant , new functions were created for the multivariate gamma family of distributions, the multivariate normal distribution, and the distribution. To forecast the usability of a specific website using machine learning (ML) techniques, this study suggests employing 'Click Stream-based data mining' and 'Event log feature' quality of usability. For forecasting the usability of e-commerce websites, the authors have employed a variety of machine learning techniques in this work. Furthermore, the authors compared the applied machine learning approaches to the state of the art and discovered that they were exceptionally accurate in terms of ROC, AUC, recall, precision, and F1 score, among other standard performance measures. The performance is tested using statistical data mining and machine learning approaches.
Inaccessibility of veterinary and livestock extension services, and shortages of labour and forage could potentially impact the welfare of yaks (Bos grunniens) in Bhutan. The objective of this study was to assess practices relating to the welfare and management of free-ranging yaks in Bhutan and explore variations between different yak-farming regions. We interviewed herders and observed the behaviour and health status of their animals, using an adaptation of the Welfare Quality® protocol, in three yak-farming regions (east, central and west) of Bhutan. In total, for 567 cows and 549 calves, integumentary condition, body cleanliness, ocular and nasal discharge, diarrhoea, signs of damage, and gait were scored. In addition, we assessed 324cows and 272 calves for avoidance distance and examined 324 cows for subclinical mastitis. The behaviour of the herds was observed in six consecutive 20-min blocks with each block divided into two stages. The first stage (5 min) consisted of counting the number of animals eating, lying down, standing idle and walking. The second stage (15 min) consisted of counting the number of events of agonistic, allogrooming, flehming, self-licking, rubbing/scratching and playing behaviour. Avoidance distance differed between regions for calves, but not for lactating cows. Integumentary lesions, dirty body areas, nasal discharge, ocular discharge, signs of diarrhoea, subclinical mastitis and lameness were virtually absent. A few instances of agonistic behaviour (6% of all counted behavioural events) and flehming behaviour (5% of all counted behavioural events) were observed. Yaks in the central and western regions exhibited more scratching and rubbing behaviour than those in the eastern region. Herders perform a variety of painful management practices (castration, ear tagging, nasal septum piercing) without analgesia, which is a prominent welfare issue. Furthermore, mortality among yaks is relatively high and water sources often dirty, creating a health risk. Nevertheless, the welfare status of yaks living in various regions of Bhutan was assessed as good at the time of visit.
A leading cause of death from natural disasters over the last 50years is witnessed by none other than earthquake occurrences which have a negative economic impact on the world and claimed thousands of lives over the years, causing devastation to properties. In this paper, a novel Ensemble Earthquake Prediction Method (EEPM) is proposed and implemented to produce a strong learner (ensemble method) having better accuracy in prediction, less variance, and less errors. Data (parameters) which is continuous in nature is collected from two countries, India and Nepal, for five years, and surveyor’s data (precursor) which is categorical in nature is collected from three countries India, Nepal, and Kenya for five years on the specific earthquake-prone regions. The preprocessed data is generated by combining parameters and precursor data. EEPM focuses on detecting the accurate and better early signs of an earthquake and finding the probability of occurrence of an earthquake in the specified region, i.e., better prediction and robustness. The results of EEPM produced better R 2 and less variance and less error in comparison to individual machine learning methods as well as better accuracy 87.8%, compared to state-of-the-art ensemble methods. The prediction of earthquake will alarm not only the people of the society but also the different organizations to explain the appropriate range of magnitude and dynamics of occurrence of earthquake.
Background: Malnutrition is the imbalance between intake and nutritional needs, resulting in a decrease in body weight, composition, and physical function. Malnutrition causes infertility due to intestinal and liver degeneration,which may progress to testicular and ovarian degeneration. Methods: An infertile female rat model with a degenerative ovary was induced with malnutrition through a 5-day food fasting but still had drinking water. The administration of (T1) 30% (v/v) and (T2) 50% (v/v) forest honey ( Apis dorsata ) were performed for ten consecutive days, whereas the (T+) group was fasted and not administered forest honey and the (T−) group has not fasted and not administered forest honey. Superoxide dismutase, malondialdehyde, IL-13 and TNF-α cytokine expressions, and ovarian tissue regeneration were analyzed. Results: Superoxide dismutase was significantly different ( p <0.05) in T1 (65.24±7.53), T2 (74.16±12.3), and T− (65.09±6.56) compared with T+ (41.76±8.51). Malondialdehyde was significantly different ( p <0.05) in T1 (9.71±1.53), T2 (9.23±0.96), and T− (9.83±1.46) compared with T+ (15.28±1.61). Anti-inflammatory cytokine (IL-13) expression was significantly different ( p <0.05) in T1 (5.30±2.31), T2 (9.80±2.53), and T− (0.30±0.48) compared with T+ (2.70±1.57). Pro-inflammatory cytokine (TNF-α) expression was significantly different ( p <0.05) in T1 (4.40±3.02), T2 (2.50±1.65), and T− (0.30±0.48) compared with T+ (9.50±1.78). Ovarian tissue regeneration was significantly different ( p <0.05) in T− (8.6±0.69) and T2 (5.10±0.99) compared with T1 (0.7±0.95) and T+ (0.3±0.67). Conclusion: The 10-day administration of 50% (v/v) forest honey can be an effective therapy for ovarian failure that caused malnutrition in the female rat model.
The long-lived and attractive flower of Paphiopedilum fairrieanum (Lindl.) Stein has made it one of the most commercialized flowers in the world, and its distribution is restricted to small areas. Thus, it was listed as a critically endangered orchid species by the IUCN in 2015. Therefore, the aim of this study was to generate information on the abundance and distribution of Paphiopedilum fairrieanum and assess the relationship between the abundance and distribution of Paphiopedilum fairrieanum with site factors in the study area. A systematic sampling method was followed for the data collection, with a 200 m distance between the plots. A total of 34 sample plots with 5.64 m radii were established. In total, 1004 individuals of orchid species were recorded. The abundance and distribution of the Paphiopedilum fairrieanum showed statistically significant associations with the elevation (r = 0.350, p = 0.042), slope (r = 0.666, p = 0.000), precipitation (r = 0.630, p = 0.000) and temperature (r = 0.371, p = 0.031). A southeast aspect was found to have the highest number of Paphiopedilum fairrieanum, and a northwest aspect had the least. The findings of this study would help researchers to find suitable sites and the distribution of Paphiopedilum fairrieanum in unexplored areas.
Multifarious anthropogenic activities triggered by rapid urbanization has led to contamination of water sources at unprecedented rate, with less surveillance, investigation and mitigation. The use of artificial intelligence (AI) in tracking and predicting water quality parameters has surpassed the use of other conventional methods. This study presents the assessment of three main models: adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) on water quality parameters of Wangchu river located at capital city of Bhutan. The performance and predictive ability of these models are compared and the optimal model for predicting the parameters are recommended based on the coefficient correlation (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE) evaluation criteria. Overall NSE and RMSE, the ANN model predicted parameters with maximum efficiency of 97.3 percent and minimum error of 8.57. The efficiency of MLR and ANFIS model are 95.9 percent and 94.1 percent respectively. The overall error generated by MLR and ANFIS are 10.64 and 12.693 respectively. From the analysis made, the ANN is recommended as the most suitable model in predicting the water quality parameters of Wangchu river. From the six-training function of ANN, trainBR (Bayesian Regularization) achieved the CC of 99.8%, NSE of 99.3% and RMSE of 9.822 for next year data prediction. For next location prediction, trainBR achieved CC of 99.2%, NSE of 98.4% and RMSE of 6.485, which is the higher correlation and maximum efficiency with less error compared to rest of the training functions. The study represents first attempt in assessing water quality using AI technology in Bhutan and the results showed a positive conclusion that the traditional means of experiments to check the quality of river water can be substituted with this reliable and realistic data driven water models. Article highlights Total dissolved solids (TDS), electrical conductivity (EC), potential of hydrogen (pH) and dissolved oxygen (DO) are selected as main water quality parameters as data for modeling. Artificial neural network model gives highest efficiency and accuracy compared to MLR and ANFIS model. Use of artificial intelligence shows better performance to provide water quality and future predictions over conventional methods leading to conservation of water resources and sustainability.
Floristic compositions and vegetative structures are key determinants for selecting nests and roosting habitats of Critically Endangered White-bellied Heron. However, none of the Bhutanese researchers had ever studied to date. Gradient-directed transect methods were adopted using systematic sampling. Vegetation surveys were carried out inside 10 x 10 m (trees), 5 x 5 m (shrubs), and 2 x 2 m (herbs) in 48 plots across the Pochu landscape. The result shows that the Phochu landscape recorded 10 trees species belonging to six families. Pinus roxburghii is the most dominant species with relative density [RD] (86.77%), relative frequency [RF] (37.50%), relative dominance [RD] (79.93%), and IVI (204.20). While, L. ovalifolia and A. lebbeck were the lowest (RD (0.53%), RF (6.25%), RD (0.07%), (0.83%), IVI (6.85), and 7.61 respectively. While shrub constitutes 19 species and belongs to 14 families. Chromolaena odorata (32.15%, n = 933) and Cymbopogon sp. (21.26%, n = 617) were the most dominant herbs, while, Galium aparine (0.03%, n = 1) were lowest with 38 herbs species belongs to 20 families. For vegetative structures, maximum trees (38.62%, n = 73) DBH ranges from the 11-15 cm, which are found in day roosting site 1 (34.25%, n = 25). While, lowest ((0.53%, n = 1) was DBH ranges of 51-55 cm, 61-65 cm, 66-70 cm and 71-72 cm respectively. Therefore, similar vegetation composition and structure studies are suggested in other core habitats across Bhutan to deduce its habitat ecology for the long-term conservation of Critically Endangered WBH in Bhutan.
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