Journal of Threatened Taxa | www.threatenedtaxa.org | 26 March 2026 | 18(3): 28455–28467

 

ISSN 0974-7907 (Online) | ISSN 0974-7893 (Print) 

https://doi.org/10.11609/jott.9958.18.3.28455-28467

#9958 | Received 27 May 2025 | Final received 18 February 2026| Finally accepted 03 March 2026

 

 

Predicting the potential habitat of Tragopan blythii (Jerdon, 1870) (Aves: Galliformes: Phasianidae) in Mehao Wildlife Sanctuary of Arunachal Pradesh, India

 

Eba Tapo 1   & Gibji Nimasow 2          

 

1,2 Department of Geography, Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh 791112, India.

1 eba.tapo@rgu.ac.in, 2 gibji.nimasow@rgu.ac.in (corresponding author)

 

 

Abstract: The Blyth’s Tragopan Tragopan blythii is a medium-sized pheasant endemic to the eastern Himalaya and is classified as ‘Vulnerable’. This species thrives in dense forest ecosystems at higher altitudes. Species distribution modelling (SDM) helps identify potential suitable habitats by relating species occurrence to key environmental variables, especially in areas with limited field data. The present study aims to predict the potential habitat of T. blythii in Mehao Wildlife Sanctuary, Arunachal Pradesh, using the maximum entropy (MaxEnt) method. The study offers valuable insights into the ecological and environmental conditions necessary for the survival of this vulnerable species. The results showed 3.93% (11.09 km²) of the total area as suitable, followed by 4.94% (13.91 km²) as moderately suitable, 18.55% (52.22 km²) as least suitable, and 72.58% (204.30 km²) as unsuitable. Model performance was good with a mean area under the curve (AUC) of 0.915 (SD = 0.040) and a true skill statistic (TSS) value of 0.798. The jackknife test revealed that the distribution of T. blythii is primarily determined by the mean diurnal range (BIO2), with additional influence from the temperature annual range (BIO7) and precipitation seasonality (BIO15). An analysis of the model output revealed a restricted distribution of T. blythii in the northern parts of the study area. These results support habitat prioritization and conservation planning for the long-term protection of the species. Thus, the model results can be used in further investigation to explore the natural habitat of this vulnerable species.

 

Keywords: Climate change, community awareness, conservation, eastern Himalaya, endemic, environmental variables, habitat, pheasant, species distribution modelling, vulnerable.

 

 

 

Editor: Aditya Srinivasulu, Zoo Outreach Organisation, Hyderabad, India. Date of publication: 26 March 2026 (online & print)

 

Citation: Tapo, E. & G. Nimasow (2026). Predicting the potential habitat of Tragopan blythii (Jerdon, 1870) (Aves: Galliformes: Phasianidae) in Mehao Wildlife Sanctuary of Arunachal Pradesh, India. Journal of Threatened Taxa 18(3): 28455–28467. https://doi.org/10.11609/jott.9958.18.3.28455-28467

  

Copyright: © Tapo & Nimasow 2026. Creative Commons Attribution 4.0 International License. JoTT allows unrestricted use, reproduction, and distribution of this article in any medium by providing adequate credit to the author(s) and the source of publication.

 

Funding: The study does not receive any funding.

 

Competing interests: The authors declare no competing interests.

 

Author details: Eba Tapo is currently pursuing a PhD at the Department of Geography, Rajiv Gandhi University, Arunachal Pradesh, on the topic “Status of population, habitat occupancy and conservation of Tragopan blythii in Mehao Wildlife Sanctuary, Lower Dibang Valley District of Arunachal Pradesh”. His research interest is in biogeography, specifically ornithogeography. Gibji Nimasow is currently a professor and head of the Department of Geography at Rajiv Gandhi University, Arunachal Pradesh. He has guided around ten Ph.D. theses and published about 60 research articles in reputed journals. His research interests include biogeography, mountain ecology, human geography, forest resources, traditional ecological knowledge, and remote sensing & GIS.

 

Author contributions: ET conceived, designed, and drafted the paper. GN interpreted, edited the language/ grammar, and revised the manuscript. Both authors read the revised manuscript and approved the submitted version.

 

Acknowledgements: The authors are grateful to Rajiv Gandhi University, Rono Hills, Doimukh, Arunachal Pradesh, for providing necessary infrastructural support to carry out doctoral-related work of the first author. The authors are also thankful to Dr. Dhoni Bushi and Dr. Ranjit Mahato for their invaluable assistance in making this study successful. Finally, we acknowledge the Soil and Limnological Laboratory, Department of Geography, for providing essential laboratory facilities to accomplish this study.

 

 

 

Introduction

 

Species distribution modelling (SDM) has become a powerful tool for understanding the spatial patterns of bird species, informing conservation efforts, and predicting responses to environmental changes. This approach combines species occurrence data with environmental variables to predict where species are likely to be found. SDMs rely on high-quality data obtained from various sources like eBird and bird atlases, telemetry data from tracked migratory birds, museum historical records, and satellite imagery (Papeş 2007; Jiguet et al. 2011; Santos et al. 2023; Shirley et al. 2013). The commonly used predictors include bioclimatic variables such as temperature, precipitation, climate extremes, elevation, slope, terrain complexity, forest structure, wetland extent, agricultural areas, and the availability of prey species or competitors (Heikkinen et al. 2007; Shirley et al. 2013; Prosser et al. 2018; Pshegusov & Chadaeva 2023; Tamang et al. 2023). SDMs have been applied to a wide range of bird species, from migratory waterfowl to threatened vultures, and have proven essential in addressing conservation challenges (Papeș 2007; Prosser et al. 2018; Pshegusov & Chadaeva 2023). SDM is a cornerstone of conservation planning, helping to identify priority areas for habitat protection, assess the coverage of protected areas, identify gaps, and predict future habitat shifts due to climate change (Papeş 2007; Wu et al. 2014; Briscoe et al. 2021). By assessing habitat suitability, SDMs help design protected areas and corridors, support restoration efforts for degraded habitats, and aid in managing human-impact landscapes, viz., agricultural regions (Pshegusov & Chadaeva 2023; Tamang et al. 2023). SDMs can estimate population trends by modelling how environmental changes influence species distributions and predict areas susceptible to invasion by non-native bird species, allowing for pre-emptive control measures (Prosser et al. 2018; Briscoe et al. 2021).

Various SDM techniques are applied in birds, with maximum entropy (MaxEnt) being a prominent presence-only method known for its simplicity and effectiveness (Pshegusov & Chadaeva 2023; Tamang et al. 2023). MaxEnt enables researchers to conduct analyses with little programming expertise. It promotes reproducible research and offers features for model comparison and cross-validation (Mayer et al. 2024). It is also used to forecast invasive species and to predict future habitat expansions due to climate change scenarios (Schmid et al. 2024). Recent developments in MaxEnt modelling feature innovative optimisation algorithms that effectively manage large-scale, non-smooth data, especially in wildfire science. These algorithms greatly enhance convergence rates and computational efficiency (Langlois et al. 2024).

Tragopan blythii (Image 1) is listed as ‘Vulnerable’ under criterion ‘C2a(i)’ by the IUCN Red List of Threatened Species in 2020 (BirdLife International 2020a). Tragopan blythii is distributed from Bhutan through Arunachal Pradesh, Nagaland, Mizoram, and Manipur in northeastern India, extending into northern Myanmar and southeastern Tibet, as well as northwestern Yunnan, China (Birdlife International 2001). The adult male T. blythii differs from other species within the tragopan group by having a restricted patch of red on its upper breast (Madge & McGowan 2002). It is vulnerable primarily due to its declining population size across small subpopulations (BirdLife International 2020b). In some regions, its population is estimated to comprise around 38 individuals only (Ghose et al. 2003). Tragopan blythii inhabits moist, evergreen, broadleaf forests featuring thick understorey, dense scrub, and montane bamboo on steep slopes, typically occurring either singly or in pairs or small groups of four to five individuals (Sathyakumar & Kaul 2007). The documented altitudinal range of the species is from 1,400 m (winter) to 3,300 m (summer), but the majority of records come from a narrower range of 1,800–2,400 m (BirdLife International 2020b). The vocalisations of T. blythii are significant for courtship and territorial displays, featuring distinct calls that assist in species identification. Acoustic analyses reveal that these calls are subject to sexual selection pressure, acting as a mechanism for species isolation (Islam & Crawford 2010). Recent research has concentrated on the phylogenetic analysis of T. blythii using mitochondrial DNA and multi-locus analyses (Randi et al. 2000; Zou et al. 2021), resulting in the discovery of a new phylogeographic population of T. blythii in Mount Kennedy, Myanmar (Zou et al. 2021). These findings underscore the need to comprehend the genetic diversity and population structure of T. blythii for effective conservation strategies. It is rare throughout much of India, with an estimated 50% of the population in the Nagaland area (Eastern Mirror Nagaland 2017). Studies on T. blythii emphasize the necessity of genetic analyses, population, and conservation measures to safeguard this vulnerable species and its unique populations (Randi et al. 2000; Zou et al. 2021). To ensure long-term survival, further research is essential to understand its phylogeography, behaviour, and ecology.

Arunachal Pradesh is the largest state in North East India and falls within the Himalayan global biodiversity hotspots (Sen & Mukhopadhyay 1999; Meyers et al. 2000; Sinha et al. 2005). The state is also considered India’s biodiversity frontier (Borges 2005; Mishra & Datta 2007; Borang et al. 2008). However, due to its remote location and mountainous topography, the rich terrestrial biodiversity, including wildlife, has been inadequately documented or relatively unexplored. The state is home to over 850 bird species, representing nearly two-thirds of India’s avifauna. In particular, the Dihang Dibang Biosphere Reserve is noted for 492 species, including 37 that are globally threatened (Rangini et al. 2014). The population of Tragopan blythii is small, declining, and fragmented into minor subpopulations within the heavily disrupted habitat. Thus, the species is designated as a Schedule I bird under the Wildlife (Protection) Act 1972 (2022 amendment), India. The vulnerability is likely to intensify due to hunting practices and habitat degradation. As per reports, Tragopan blythii have been sighted in the Mishmi Hills of Arunachal Pradesh (King et al. 2008). Hence, this study attempts to understand the habitat and predict the distribution of Tragopan blythii in Mehao Wildlife Sanctuary, Arunachal Pradesh, which falls under the Mishmi Hills, using field methods and geospatial technology.

 

 

Materials and Methods

 

Study area

The study area, Mehao Wildlife Sanctuary (MWS), is located between 28º 05ʹ to 28º 15ʹN and 93º 30ʹ to 95º 45ʹ E in the Lower Dibang Valley District of Arunachal Pradesh (Figure 1). It spans approximately 282 km2 and features a diverse topography with elevations of 500–3,000 m. The climate is predominantly temperate, with significant rainfall and various vegetation types, including subtropical forests, bamboo groves, and temperate forests. MWS is one of the oldest of the 13 sanctuaries in Arunachal Pradesh. Koronu, Injuno, Balek, Cheta, Ejengo, Rayang, and Tiwarigaon villages are located on the periphery of the sanctuary. The sanctuary hosts vital plant species such as Terminalia myriocarpa, Duabanga grandiflora, Phoebe cooperiana, Bombax ceiba, Canarium strictum, Lagerstroemia speciosa, Michelia champaca, Gmelina arborea, Coptis teeta, Messua ferrea, Dillenia indica, Castanopsis indica, and Bischofia javanica, along with various orchids. The wildlife includes Tigers, Black Bear, Leopards, Elephants, Wild Boar, Capped Langur, White-browed Gibbon, Musk Deer, and Mishmi Takin. Furthermore, numerous bird species inhabit the area, such as hornbills, babblers, bulbuls, warblers, flycatchers, pigeons, and a range of reptiles, snakes, insects, and leeches (Murali et al. 2012).

 

Occurrence data

The occurrence data were collected from both primary and secondary sources. Field surveys were conducted for two years (2022–2024) following the point count  methods by strategically selecting the survey sites and using handheld Garmin global positioning system (GPS) devices (Volpato et al. 2009). The point counts were spaced at least 200 m apart, and each count captured species seen or heard within a radius of approximately 20 m. The survey covered various altitudinal zones and seasons during the morning hours when birds were most active. The surveys were conducted seasonally, in 18 locations during autumn (September), winter (December), spring (March), and summer (June) each year, totalling 144 counts over two years. Additionally, camera trapping techniques were employed to record T. blythii and assess its habitat conditions. The bird was identified in consultation with the ornithologists from the Department of Zoology, Rajiv Gandhi University. To limit spatial autocorrelation and sampling bias, occurrence records were filtered using the spThin package in R by applying a 10 km minimum distance between points. This reduced clustering caused by uneven sampling effort and improved the reliability of the species distribution model (Kramer-Schadt et al. 2013; Aiello-Lammens et al. 2015). After removing the coordinates falling outside boundary of MWS, only 36 occurrence records were used to run the final model.

 

Environmental parameters

To predict the distribution of T. blythii, a set of environmental data, such as topographical (12.5 m) and climatic (30 arc-second) spatial resolution, has been acquired from various sources using ArcGIS 10.3. The elevation data was sourced from the Alaska Satellite Facility (n.d.), which provided key topographic parameters, including altitude, slope, and aspect. This high-resolution DEM provides a detailed dataset that offers insights into earth’s surface, capturing the elevation data for producing topographic attributes. Additionally, 19 bioclimatic variables were downloaded from the WorldClim website (Fick & Hijmans 2017).   These variables offer extensive global climate layers for various applications, such as mapping and spatial modelling. Multicollinearity among predictor variables was evaluated using the Variance Inflation Factor (VIF) in R, and variables with VIF values greater than seven were excluded to reduce redundancy and improve model performance (Zuur et al. 2010; Dormann et al. 2013; James et al. 2013; Manzoor et al. 2018). According to Parolo et al. (2008), Dormann et al. (2013), Merow et al. (2013), and Manzoor et al. (2018), highly correlated parameters affect model performance negatively and lead to inaccurate predictions. Hence, such parameters were removed by performing a multicollinearity (Pearson’s R) test using the usdm package in R (Naimi & Araújo 2016). After the test, 12 predictor variables, namely Mean diurnal range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), minimum temperature of the coldest month (BIO6), temperature annual range (BIO7), mean temperature of the coldest quarter (BIO11), precipitation seasonality (BIO15), precipitation of the driest quarter (BIO17), precipitation of the warmest quarter (BIO18), altitude, slope, and aspect were included in the final model (Figure 2).

 

Model settings and evaluation

The species distribution model of T. blythii was generated using MaxEnt (version 3.4.1). MaxEnt (Phillips et al. 2006; Phillips & Dudík 2008) is regarded as one of the leading species distribution modelling techniques and has been widely used (Elith et al. 2011). It is deterministic and approaches the probability distribution linked to maximum entropy (Berger et al. 1996; Phillips et al. 2006; Baldwin 2009). All model parameters were kept at their default settings in MaxEnt. Variable importance was assessed using the jackknife test, which evaluates the relative contribution of each environmental predictor by sequentially excluding individual variables and by running models with each variable in isolation. This approach helps identify variables that contain unique information and those that most strongly influence model performance, based on changes in training gain. The jackknife test is particularly important for understanding predictor relevance and reducing redundancy among correlated variables in species distribution modelling (Phillips et al. 2006; Baldwin 2009; Elith et al. 2011). Five replicate runs were used with cross-validation to ensure and evaluate the model’s reliability (Pearson et al. 2004). Model performance was assessed using the threshold-independent area under the curve (AUC) of the receiver operating characteristic (ROC) curve, and true skill statistic (TSS). The values of AUC range from models with no predictive ability (AUC ≤ 0.5) to models having perfect predictions (AUC = 1.0), where 0.9–1 = excellent; 0.8–0.9 = good; 0.7–0.8 = satisfactory; 0.6–0.7 = poor and < 0.6 = very poor or model failed (Araújo et al. 2005; Lissovsky & Dudov 2021). The TSS takes into account both omission and commission errors, and success due to random guessing, and ranges from -1 to +1, where +1 indicates perfect agreement and values of zero or less indicate a performance no better than random (Allouche et al. 2006). The methodology applied is shown in Figure 3.

 

 

Results

 

Model performance

The model results showed that the distribution of T. blythii is mostly influenced by the mean diurnal range of temperature (33.7%), temperature annual range (17.8%), precipitation seasonality (15.1%), altitude (11.1%), and temperature seasonality (9.2%). Slope (4.7%), aspect (2.7%), precipitation of the driest quarter (2.5%), and precipitation of the warmest quarter (2.25%) also exerted some influence on the distribution of T. blythii. Model evaluation indicated excellent predictive performance, with a mean AUC of 0.915 (SD = 0.040) and a high TSS value of 0.798, reflecting robust model reliability and classification accuracy (Figure 4). The jackknife test revealed that the distribution of T. blythii was primarily influenced by the mean diurnal range of temperature (BIO2), which accounted for 33.7% of the explained variable, followed by temperature annual range (BIO7) and precipitation seasonality (BIO15) with 17.8% and 15.1%, respectively. The contribution of other parameters was relatively lesser with altitude (11.1%), temperature seasonality (9.2%), slope (4.7%), aspect (2.7%), precipitation of the driest quarter (2.5%), and precipitation of the warmest quarter (2.2%). Isothermality (BIO3), minimum temperature of the coldest month (BIO6), and mean temperature of the coldest quarter (BIO11) showed meagre influence on the distribution of T. blythii (Table 1).                                                                 

 

Species distribution modelling of T. blythii

The model predicted the potential distribution of T. blythii in the MWS within a range of 0–0.9, which was categorised into five suitability categories, viz. highly suitable (>0.8), suitable (0.6–0.8), moderately suitable (0.4–0.6), least suitable (0.2–0.4), and not suitable (< 0.2). The results showed an area of 2.48 km² (0.88%) as highly suitable and 8.59 km² (3.05%) as suitable. Further, the model predicted an area of 13.91 km² (4.94%) as moderately suitable, 52.22 km² (18.55%) as least suitable, and the largest area of 204.30 km² (72.58%) as not suitable (Table 2). An examination of the final model revealed that the northern parts of the study area are suitable for T. blythii, owing to higher altitude, thick understorey, pronounced slopes, and cooler temperatures. On the other hand, the southern parts, mostly characterized by lower elevations, dense canopy, and hot temperatures, have been predicted as unsuitable habitat for T. blythii. The suitable habitats are mostly found throughout the Mayodia Pass, which is characterized by higher elevation, cooler temperatures, and winter snowfall (Image 2).

 

 

Discussion

 

Model performance was high, with a mean AUC of 0.924 and a TSS value of 0.798, indicating excellent discriminatory power and reliable prediction of suitable habitats for T. blythii (Swets 1988; Elith et al. 2006; Phillips et al. 2006). Among the predictor variables, mean diurnal temperature range, annual temperature range, and precipitation seasonality emerged as the most influential climatic factors shaping the species’ distribution. The results support earlier findings of climate as a primary determinant of species’ geographic limits, especially for montane and habitat-specialist birds (Parmesan & Yohe 2003; Root et al. 2003; Reside et al. 2010; Hill & Preston 2015).

The diurnal temperature range, representing the difference between daytime and nighttime temperatures, is biologically important because it influences metabolic expenditure, thermoregulation, and activity patterns in birds (McKechnie & Wolf 2010; Wang et al. 2023). Large fluctuations in daily temperature can impose physiological stress and reduce habitat suitability for forest-dwelling pheasants adapted to stable microclimatic conditions. Similar patterns have been reported for the Western Tragopan Tragopan melanocephalus in the western Himalayas, where diurnal temperature range strongly influenced habitat suitability (Singh et al. 2020). Experimental and field studies further indicate that increased thermal variability can negatively affect growth, survival, and reproductive success across taxa (Vasseur et al. 2014; Stoks et al. 2017), suggesting that T. blythii may be particularly vulnerable to ongoing climatic instability.

Precipitation seasonality also played a major role in determining habitat suitability. Rainfall regimes regulate forest structure, understorey density, and availability of food resources such as seeds, shoots, and invertebrates (Choudhury 2001; Guisan & Thuiller 2005; Sathyakumar & Kaul 2007). High seasonal variability in precipitation may disrupt breeding cycles and reduce nesting success through habitat degradation and changes in vegetation phenology (Both et al. 2006; Soria-Auza et al. 2010). Comparable relationships between precipitation patterns and habitat suitability have been documented for pheasants and other montane birds across the Himalayas and southeastern Asia (Chhetri et al. 2018; Cohen et al. 2020; Li et al. 2022).

Topographic variables, particularly altitude and slope, were also significant predictors of T. blythii distribution. The species was predicted to occur primarily at 1,000–2,500 m, which closely matches earlier observations ranging 1,400–3,300 m (BirdLife International 2008; Ghosh 2003). Altitude integrates multiple environmental gradients such as temperature, humidity, vegetation type, and human disturbance, all of which influence species occupancy (Körner 2007; Elsen & Tingley 2015). Steeper slopes may provide refugia from anthropogenic pressures such as agriculture and logging, thereby enhancing habitat persistence for forest-dependent species (Jetz et al. 2007; Laurance et al. 2011). The strong association of T. blythii with primary evergreen broadleaf forests observed in this study corroborates previous findings emphasizing its dependence on intact forest ecosystems (Choudhury 1997; Ghose et al. 2003; Sathyakumar & Kaul 2007).

Model projections revealed that only a small fraction of the study area was classified as highly suitable (0.88%) or suitable (3.05%), whereas most regions were categorized as least suitable (18.55%) or unsuitable (72.58%). This limited availability of suitable habitat corresponds with the species’ current vulnerability status and narrow ecological niche requirements (BirdLife International 2020b; IUCN 2023). Narrow climatic tolerances have been associated with heightened extinction risk under climate change, particularly for montane endemics (Thomas et al. 2004; Freeman et al. 2018). The results, therefore, suggest that even modest climatic shifts could lead to further habitat contraction and population decline in T. blythii.

Although this study employed MaxEnt due to its robustness with presence-only data and small sample sizes (Phillips et al. 2006; Elith et al. 2011), recent studies emphasize the advantages of ensemble ecological niche models that combine multiple algorithms such as random forest, generalized linear models, and boosted regression trees (Araújo & New 2007; Marmion et al. 2009). Ensemble approaches reduce uncertainty and improve predictive performance, particularly for rare and elusive species with limited occurrence records (Thuiller et al. 2009; Feng et al. 2019). Such methods have already been applied successfully to forecast habitat shifts of Himalayan pheasants under climate change scenarios (Chhetri et al. 2018; Singh et al. 2020).

Following the reproducibility checklist proposed by Feng et al. (2019), this study recognizes several methodological limitations, including small sample size, potential sampling bias, and reliance on a single modelling algorithm. These factors may influence model transferability and prediction uncertainty (Warren & Seifert 2011; Merow et al. 2013). Future research should, therefore, incorporate ensemble modelling frameworks, bias-corrected occurrence data, and independent validation datasets to enhance robustness and reproducibility (Araújo et al. 2019; Feng et al. 2019; Zurell et al. 2020). Despite these limitations, the present findings provide an important baseline for understanding the climatic and topographic drivers of T. blythii distribution and offer valuable guidance for conservation planning and climate-adaptive management strategies.

Conclusion

 

The study applied the MaxEnt method to predict the suitable habitats of T. blythii in the Mehao Wildlife Sanctuary, located in the Lower Dibang Valley of Arunachal Pradesh. The model used 36 occurrence records and 12 environmental variables for the targeted species. The model performance was good. The model predicted only 3.93% of the total area as suitable, which may be due to its restricted distribution range. Besides, the model also predicted about 5% of the area as moderately suitable, which remains to be explored to confirm species occurrence. The suitable areas of T. blythii were mostly located in the northern portion of the MWS at altitudes above 1,700 m. The occurrences of the species were most frequently observed at 1,000–2,500 m in the study area. The study area represents a key natural habitat of the vulnerable T. blythii. Thus, safeguarding areas recognised as suitable habitats can ensure conservation of T. blythii in the long run. Educating local communities on the significance of T. blythii can greatly aid such conservation efforts. These findings indicate the need to develop effective strategies for identifying potential habitats, supporting government policies to protect vulnerable species, and reducing human activities like overexploitation, hunting, and deforestation in the preferred habitats of T. blythii. The study revealed the potential distribution range of T. blythii and laid the groundwork for future research. There is an essential need for initiatives to raise public awareness and build capacity by governmental agencies and NGOs, involving the local communities, to avert further decline in the population of  Blyth’s Tragopan.

 

 

Table 1. Parameter contributions based on the MaxEnt model, codes, units, and source of the database.

Parameters

Contribution (%)

Code

Units

Source

Mean diurnal range

33.7                          

BIO2

°C

WorldClim

Temperature annual range

17.8

BIO7

°C

WorldClim

Precipitation seasonality

15.1

BIO15       

Unitless

WorldClim

Altitude

11.1

Elevation

Meter

ALOS PALSAR

Temperature seasonality

9.2

BIO4

Unitless

WorldClim

Slope

4.7

Slope        

Degree

ALOS PALSAR

Aspect

2.7

Aspect

Degree

ALOS PALSAR

Precipitation of the driest quarter

2.5

BIO17

Mm

WorldClim

Precipitation of the warmest quarter

2.2

BIO18

Mm

WorldClim

Isothermality

0.5

BIO3

Unitless

WorldClim

Min. temperature of the coldest month

0.4

BIO6

°C

WorldClim

Mean temperature of the coldest quarter

0.2

BIO11

°C

WorldClim

 

 

Table 2: Suitable categories of T. blythii in the study area.

Suitable categories

Value

Area (km2)

Area (%)

Not Suitable

0 - 0.20

204.30

72.58

Least Suitable

0.20 - 0.40

52.22

18.55

Moderate Suitable

0.40 - 0.60

13.91

4.94

Suitable

0.60 - 0.80

8.59

3.05

Highly Suitable

0.60 - 0.80

2.48

0.88

Total

 

281.50

100.00

 

 

For figures & images - - click here for full PDF

 

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