Journal of Threatened Taxa | www.threatenedtaxa.org | 26 April 2023 | 15(4): 23061–23074

 

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

https://doi.org/10.11609/jott.8330.15.4.23061-23074

#8330 | Received 17 December 2022 | Final received 11 March 2023 | Finally accepted 24 March 2023

 

 

Westward range extension of Burmese Python Python bivittatus in and around the Ganga Basin, India: a response to changing climatic factors

 

Pichaimuthu Gangaiamaran 1, Aftab Alam Usmani 2, C.S. Vishnu 3, Ruchi Badola 4 & Syed Ainul Hussain 5

 

1–5 Wildlife Institute of India, P.O. Box 18, Chandrabani, Dehradun, Uttarakhand 248002, India.

1 bnhsgangai@gmail.com, 2 aftab.a.usmani@gmail.com, 3 vishnusreedharannair@gmail.com, 4 ruchi@wii.gov.in, 5 ainul.hussain@gmail.com (corresponding author)

 

 

 

Editor: Raju Vyas, Vadodara, Gujarat, India. Date of publication: 26 April 2023 (online & print)

 

Citation: Gangaiamaran, P., A.A. Usmani, C.S. Vishnu, R. Badola & S.A. Hussain (2023). Westward range extension of Burmese Python Python bivittatus in and around the Ganga Basin, India: a response to changing climatic factors. Journal of Threatened Taxa 15(4): 23061–23074. https://doi.org/10.11609/jott.8330.15.4.23061-23074

 

Copyright: © Gangaiamaran et al. 2023. 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 Natonal Mission for Clean Ganga, Ministry of Jal Shakthi, Government of India.

 

Competing interests: The authors declare no competing interests.

 

Author details: Pichaimuthu Gangaiamaran is a research biologist (NMCG-Birds) at the Wildlife Institute of India. Aftab Alam Usmani is a research associate (NMCG-Birds) at the Wildlife Institute of India. C.S. Vishnu is a Ph.D. scholar at the Wildlife Institute of India. Ruchi Badola is a principal investigator (NMCG), Dean, and scientist-G at the Wildlife Institute of India. Syed Ainul Hussain is a project manager (NMCG) and former scientist-G at the Wildlife Institute of India.

 

Author contributions: SAH and RB supervised the study. PG and SAH conceived the idea.  PG, AAU, and CSV collected field data. PG prepared the initial draft, and CSV wrote the final draft. SAH reviewed the manuscript. CSV did the analysis and visualization.  All authors revised subsequent versions. All authors agreed upon the final version.

 

Acknowledgements: The NMCG Project, Government of India, supported this work. Our heartfelt thanks to Mr Vivek Sharma, herpetologist, for identifying the species first. We sincerely thank Mr Sanjay Kumar, IAS, district magistrate of Bijnor. Also, we thank Mr Sher Singh, the field assistant, for the location details. Besides, we acknowledge Mr Navaneeth Krishnan for his support during the preparation of photo plates. Our deepest gratitude to the registrar, dean and the director at Wildlife Institute of India, Dehradun, for their support and encouragement. We strongly thank the people who helped us obtain the information.

 

 

Abstract: The range extension of animals is influenced by various factors, particularly environmental variables and ecological requirements. In this study, we have attempted to quantify the potential current distribution range of the Burmese Python Python bivittatus in and around the Ganga Basin. We collected the Burmese Python sightings between 2007 and 2022 from various direct and indirect sources and recorded 38 individuals, including eight females and five males; the rest were not examined for their sex. Out of these, 12 individuals were rescued from human habitations. Most python sightings were observed in Uttarakhand and Uttar Pradesh (n = 12 each), followed by Bihar (n = 6). The expanded minimum convex polygon (MCP) range was calculated as 60,534.2 km2. In addition, we quantified the potential current distribution status of this species using 19 bioclimatic variables with the help of MaxEnt software and the SDM toolbox in Arc GIS. The suitable area for the python distribution was calculated as 1,03,547 km2. We found that the following variables influenced the python distribution in the range extended landscape: Annual Mean Temperature (20.9 %), Precipitation of Wettest Quarter (6.4 %), Precipitation of Driest Quarter (30.1 %), Precipitation of Warmest Quarter (0.3%), Isothermality (0.1%), Temperature Annual Range (18.7 %), Mean Temperature of Wettest Quarter (11.4 %), Mean Temperature of Driest Quarter (2.2 %), Land use/land cover (3.3 %), and Elevation (6.6 %). These results will support the field managers in rescuing individuals from conflict areas and rehabilitating them based on the appropriate geographical region.

 

Keywords: Distribution, expansion, habitat, prediction, reptiles, suitability, survivorship, temperature, topography, vulnerable.

 

 

 

INTRODUCTION

 

Reptiles are poikilothermic and are extremely sensitive to the thermal features of the environment (Carranza et al. 2018); hence highly vulnerable to climate change (Sinervo et al. 2016). Minute changes in the environmental temperatures also affect their daily activities, biology, and survival (Wilms et al. 2011; Ribeiro et al. 2012). Several studies have recorded the influence of climatic variables in the distribution of species, i.e., altitude (El-Gabbas et al. 2016), precipitation (Sanchooli 2017), temperature (Javed et al. 2017), and vegetation cover (Fattahi et al. 2014). Studies have concluded that reptiles are more influenced by climate-related variables rather than topographical variables (Guisan & Hofer 2003). Reptiles are being threatened for many reasons, including conversion and loss of habitat, invasive species, and the pet trade, apart from the changes in climate and topographical features, which adversely disturb their spatial distribution (Cox et al. 2012). Pythons, one of the largest reptile groups and apex predators, perform a significant role in the ecological system like other carnivores (Pearson et al. 2005), by controlling the population of ungulates, reptiles, birds, and other small mammals (Bhupathy et al. 2014). Identifying the potential distribution range of species and predicting future potential distribution based on changing environmental conditions have become necessary due to population declines and expansion (Todd et al. 2010; Urban 2015). Many species appear to adapt to rising temperatures associated with climate changes by shifting their ranges to higher latitudes or elevations (Chen et al. 2011; Jose & Nameer 2020)

The Burmese Python Python bivittatus is considered one of the largest snake species in the world (Barker & Barker 2008), and it can grow up to a length of 6 m (20 ft) (Clark 2012). Kuhl (1820) has formally distinguished the Burmese Pythons from other python species. P. bivittatus is a squamate reptile of the Pythonidae family, the top of the body is dark brownish- or yellowish-grey, with a series of 30 to 40 large irregular squarish, black-edged, dark chocolate-grey blotches on the top and sides of the body; it has dark and dark grey dorsal and lateral spots; it has a sub-ocular stripe; and the belly is greyish with dark spots on the outer scale rows (Das 2012). The body is thick and cylindrical; the head is lance-shaped and distinct from the neck; sensory pits can be found in the rostrals as well as on some supralabials and infralabials (Das 2012). The spurs are small; the tail is short and prehensile; and there are cloacal spurs (Das 2012).

 Python bivittatus is one of three native python species found in India along with Python molurus and Malayopython reticulatus (Rashid & Khan 2018). The Burmese Python is native to the tropical rainforests and subtropical jungles of India, Myanmar, southern China, southeastern Asia, and some extent of the Indonesian archipelago (McDiarmid et al. 1999). The distribution of P. bivittatus in Southeastern Asia encompasses eastern parts of India, Nepal, Bhutan, Bangladesh, Myanmar, Thailand, Cambodia, Vietnam, northern Malaysia, and southern China (Barker & Barker 2008, 2010). Some isolated observations in the Gangetic plain have recently been reported by Rashid & Khan (2018). The P. bivittatus is an invasive species in the United States. Due to climatic suitability, the pythons in the everglades might spread quickly into many parts of the U.S. (Dorcas et al. 2012; McCleery et al. 2015; Sovie et al. 2016). Global warming trends were predicted to increase suitable habitats significantly that promotes the range expansion among them (Pyron et al. 2008).

In the native range, P. bivittatus has been listed under the ‘Vulnerable’ category by the IUCN Red List of Threatened Species (Stuart et al. 2012). Also, they are included in Schedule-I (Part II) of the Indian Wild Life (Protection) Act, 1972 (IWPA) and Appendix II of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). Burmese Pythons occupy habitats ranging from hardwood forests to mangrove swamps in the introduced range in the USA (Walters et al. 2016), however in the native range, they dwell in the tropical lowlands, grassland forests and within areas modified for human use (Barker & Barker 2008; Cota 2010; Rahman et al. 2014). 

In this study, we have attempted to quantify the potential current distribution range of Burmese Pythons in and around the Ganga Basin. Also, identified the bioclimatic variables that contributed to their range expansion.

 

 

MATERIALS AND METHODS

 

Study Area

The P. bivittatus live in subtropical or tropical forests, which include dry forests, mangrove vegetation, swamps, moist montane grasslands, wetlands, and permanent freshwater marshes/pools (Stuart et al. 2012). According to the IUCN, the Burmese Python’s distribution range as being in northeastern states of India, including West Bengal. The current study focuses on six major Indian states: Uttarakhand, Uttar Pradesh, Bihar, Jharkhand, West Bengal, and Odisha; all apart from Odisha are situated in the Gangetic Basin, however, some Burmese Python sighting records have gathered from the Odisha as well, since it is a neighbouring state of West Bengal.

Ganga is the national river of India which passes through three separate biogeographic zones, the Himalaya, the Gangetic Plain, and the eastern coast, which has a unique biodiversity assemblage (NMCG-WII GBCI 2019). The Ganga River Basin occupies nearly one-third of the geographical area of India (Jain et al. 2007). Presently this region is experiencing a high urbanisation rate and almost 45% of India’s population lives in the Ganga basin (Quadir 2022). The temperature of the Gangetic plain doesn’t fall under an average of 21⁰C, the daily maximum temperature in the warmest month rises to 40⁰C (EMSF 2019); thus, the atmospheric temperature is very suitable for P. bivittatus. Here, we report the extended native range of P. bivittatus in and around the Gangetic Basin.

 

Methods and Analysis

The direct sightings of Burmese Pythons have been obtained with photographic evidence from various parts of the study area, with the help of forest staff, researchers, and local people (Image 1). Also, we collected secondary pieces of information from the published works (Table 2). With the available coordinates, a range extension map has been made and the expansion area was estimated by the minimum convex polygon (MCP) in Arc GIS (Supplementary Figure 4). Additionally, the current potential distribution status of this species has been identified with 19 bioclimatic layers, which were obtained from Worldclim dataset. Further, the layers were prepared with the SDM toolbox in Arc GIS and run the model with the help of MaxEnt (Figure 1).

Species distribution for the Burmese Python was modelled using MaxEnt (version 3.4.1.; Phillips et al. 2004, 2006) because it is the most widely used and popular choice for species distribution modelling, providing high extrapolative accuracies even with low presence-only data (Bosso et al. 2018; Soucy et al. 2018; Zhang et al. 2018). This study has only used presence data and to generate pseudo-absences, 10,022 background points were randomly selected by the MaxEnt model.

The presence data was split into 75% random samples for calibrating the model and 25% for evaluating model performance. We used a subsampling technique to generate a stable model because of its advantages over cross-validation (Anderson & Raza 2010), and bootstrap (Rospleszcz et al. 2014), and three replications were chosen to run the model. Regularization multipliers are used to prevent overfitting of predicted values and to balance the model fit (Phillips & Dudík 2008). The model provides settings for assessing model complexity by varying feature classes and regularisation multipliers. Threshold selection was done, the logistic output format ranging between 0 (unsuitable) and 1 (maximum suitability), was used for the model results, which shows habitat suitability (presence probability) of targeted species (Phillips et al. 2004). Binary suitable/unsuitable map was prepared accordingly.

 

 

RESULTS AND DISCUSSION

 

We collected the details of Burmese Pythons in the Ganga Basin and adjacent areas. The data has been collected from both direct and indirect sources (Table 2). A total of 38 sighting records were obtained, including eight females, five males, and the remaining unsexed. The pythons were identified using photographs and morphological features from the field guide by Whitaker & Captain (2004).

The Burmese Pythons are known as the sister species of Indian Rock Python P. molurus and the Burmese Python differs from the Rock Python in several ways. Supralabials touching the eye, the tongue, and some parts of the head are pale pinkish in Indian Rock Python. The supralabials are separated from the eye by subocular scales in the Burmese Python and the tongue is bluish-black with no pink colour on the head (Whitaker & Captain 2004). Also, the Indian Python being ‘yellowish’ while the Burmese Python is ‘greyish’ in colour (Whitaker & Captain 2004).

From these, 10 individuals were rescued from human habitations. Also, a mating event was observed in August by the NMCG Team of WII, and  a brooding female was observed by Rashid & Khan (2018) in May. Das et al. (2012) reported earlier breeding records of Burmese Python such as egg shell remains and earlier nesting activities in the Gangetic Basin, at the Katerniaghat and Dudhwa regions. Most of the python sightings were recorded from the state of Uttarakhand and Uttar Pradesh (n = 12 each), followed by Bihar (n = 6), West Bengal (n = 4), Odisha (n = 3), and Jharkhand (n = 1) respectively. The expanded MCP range was calculated as 60,534.2 km2 (Supplementary Figure 4). The most python sighting records were obtained in the year of 2017 (n = 8), followed by the year 2021 (n = 6) (Figure 3).

In addition, we found that some environmental variables have a considerable role in the distribution of P. bivittatus (Table 1 and Supplementary Table 1). The suitable area for the potential distribution of P. bivittatus was predicted as 1,03,547 km2. The Gangetic Plain and its adjacent places have a favourable temperature for the species rapid expansion.

From the Jackknife evaluation, these results were consistent. The model output yielded satisfactory results with the training and test data; the final model had accuracy with an AUC value of 0.865.

The present model outputs show that 10 variables influence the python distribution. Some variables, however, have a high proportion. Temperature and precipitation both play a significant role in their distribution.

An optimum temperature is essential for their survival and dispersal. According to research, excessively cold  temperatures make it difficult for pythons to survive (Mazzotti et al. 2011). The reported lethal temperature in the low land species is approximately 38–42˚C (Brattstrom 1968; Snyder & Weathers 1975), and the increased temperatures can affect the population sex ratios of reptiles (Bickford et al. 2010). A study conducted in China among 50 snake species found that the distribution of species was related to changes in the thermal index and precipitation or potential evapotranspiration (Wu  2016).

The Jackknife evaluation results revealed that the Wettest Quarter Mean Temperature, Annual Mean Temperature, and Driest Quarter Precipitation were the primary factors influencing the P. bivittatus distribution (Figure 1).  The percent contribution values are given in Table 1. A proper field survey in the remaining area would yield more sightings across the basin.

According to the findings, the Driest Quarter Precipitation (30.1%) is a significant influencing factor for the range extension of the Burmese Python in the Gangetic Basin, however, Penman et al. (2010) discovered that the Driest Quarter Precipitation is a major bioclimatic variable that has a significant impact on the distribution of the most endangered Hoplocephalus bungaroides snake species in Australia.

Similarly, Annual Mean Temperature is a significant variable that influences species distribution. Annual Mean Temperature contributed 20.9% to the Burmese Python distribution in the study area. Annual Mean Temperature is a significant bioclimatic factor for the species (Gül et al. 2015); according to a study on Xerotyphlops vermicularis from the western and central Black Sea Region, Annual Mean Temperature contributed 55.3% of the species’ distribution (Afsar et al. 2016).

Rödder & Lötters (2010) has reported that the annual mean temperature contributes to the distribution of Greenhouse Frog Eleutherodactylus planirostris (13.8%). Mean Temperature of the Wettest Quarter (11.4 %) plays an important role in the distribution of the P. bivittatus. Studies on the invasive California Kingsnake Lampropeltis californiae in the Canary Islands have reported that the Mean Temperature of the Wettest Quarter and the Mean Temperature Driest Quarter have influenced its distribution.

The contribution of elevation was 6.6% and landcover was found to have 3.3%. The elevation also plays a role ecologically since it affects the temperature (Ananjeva et al. 2014; Hosseinzadeh et al. 2014). Studies have concluded that with a gain in elevation, species richness among reptiles would decline (Chettri et al. 2010).

Our findings show a trend in the westward range extension of the Burmese Python in the study area, which could be attributed to a response to changing climatic factors. In the United States, some studies have proven that less body temperature during the cold snap leads to physiological stress on this species and may lead to mortality (Mazzotti et al. 2011; Stahl et al. 2016). Jacobson et al. (2012) observed that the Burmese Pythons are projected to spread northward in response to warming winter temperature regimes. Nevertheless, Van Moorter et al. (2016) stated that animal movement is directly connected to resource use, such as habitat selection. However, recent records justify that this species having a good population along the Gangetic plain (Rashid & Khan 2018; Shafi et al. 2020).  

Scarce SDM studies were conducted among reptile species in India; the primary reason is the only way to know about the occurrence localities of their collections is through publications of researchers. In many cases, a direct visit to the particular institutes is the only way to get the required data, which takes considerable time (Das & Pramanic 2018). In addition, finding them in the field is very difficult due to their highly camouflaged behaviour.

 

CONCLUSION

 

According to prediction results, the potential distribution of the Vulnerable Burmese Python has expanded westward from the northeastern region to the Ganga Basin. The Burmese Python’s expanding range can be interpreted as a bioindicator of changing climate. A comprehensive study on future predictions, habitat suitability, and phylogeny will aid their conservation in the range-extended landscape and reveal the population divergence. This research will also assist field managers in successfully reintroducing Burmese Pythons into suitable habitats.

 

 

Table 1. The list of environmental variables used in the model and their percent contribution and permutation importance in the model.

Variable

Description

Unit

Percent contribution (%)

Permutation importance (%)

bio17

Precipitation of Driest Quarter

mm

30.1

17.7

bio1

Annual Mean Temperature

oC

20.9

22

bio7

Temperature Annual Range (bio5-bio6)

oC

18.7

8.9

bio8

Mean Temperature of Wettest Quarter

oC

11.4

36.3

dem

Digital Elevation Model

m

6.6

1.8

bio16

Precipitation of Wettest Quarter

mm

6.4

2

landcover

Land Cover

-

3.3

3.2

bio9

Mean Temperature of Driest Quarter

oC

2.2

3.3

bio18

Precipitation of Warmest Quarter

mm

0.3

2.9

bio3

Isothermality (bio2/bio7)(×100)

-

0.1

2.1

 

 

Table 2. Burmese Python location details.

 

Place

Latitude

Longitude

Date

Observers

1

Rajaji National Park, Uttarakhand

29.8974

78.26666667

31-03-2007

Joshi & Singh 2015

2

Chilla Forest, Haidwar-Chilla-Rishikesh, Uttarakhand

29.9710

78.21327778

09-08-2007

Joshi & Singh 2015

3

Haridwar Forest Range, Rajaji National Park

29.9397

78.12827778

09-08-2007

Joshi & Singh 2015

4

Katerniaghat WS, Railway Station, Uttar Pradesh

28.3069

81.15638889

00-02-2009

Das et al. 2012

5

Katerniaghat WS, Uttar Pradesh

28.3373

81.12080833

00-06-2009

Das et al. 2012

6

Hastinapur Range, Uttar Pradesh

29.0809

78.06425

14-11-2009

Yadav et al. 2017

7

Forest Rest Hosue, Hastinapur Range, Uttarakhand

29.1546

77.99613889

28-12-2009

Yadav et al. 2017

8

Rispna River, Jakhan, Uttarakhand

30.3660

78.0829

15-09-2010

Joshi & Singh 2015

9

Bhitarkanika, Wildlife Sanctuary

20.7355

86.87741667

18-08-2010

Gopi 2010 (Unpubl.)

10

Timli Forest Range, Kalsa Forest Division, Uttarakhand

30.3333

77.67332778

14-10-2011

Joshi & Singh 2015

11

Lacchiwala Forest Range, Uttarakhand

30.2553

78.01666667

08-11-2011

Joshi & Singh 2015

12

Sanghagara Forest, Odisha

21.6323

85.55245278

00-00-2015

Nayak 2015 (Unpubl.)

13

Manguraha Range, Valmiki Tiger Reserve, Bihar

27.3288

84.53578611

11-03-2017

Shafi et al. 2020

14

Bijaligarh, Jawan, Aligarh, Uttar Pradesh

28.0407

78.11541667

18-05-2017

Rashid & Khan 2018

15

Narainapur village, Bihar

27.3387

83.96441667

11-08-2017

Shafi et al. 2020

16

Manor River, Ganauli Range, Bihar

27.3570

83.92586111

14-08-2017

Shafi et al. 2020

17

Dhaltangarh Forest, Odisha

20.3118

86.23827778

00-09-2017

Dwibedy 2017

18

Gautam Buddha, Wildlife Sanctuary, Bihar, Jharkhand

24.5797

85.54165556

00-09-2017

WII team 2017(Unpubl.)

19

Gandak barrage, Valmiki Nagar Range, Bihar

27.4333

83.92374444

04-11-2017

Shafi et al. 2020

20

Buxa, North Bengal

26.5667

89.45494444

00-11-2017

Dash 2017 (Unpubl.)

21

Udaipur Wildlife Sanctuary, Bettiah, Valmiki Tiger Reserve, Bihar

26.8137

84.43378056

14-01-2018

Shafi et al. 2020

22

Manguraha Range, Valmiki Tiger Reserve, Bihar

27.3189

84.46808333

24-01-2018

Shafi et al. 2020

23

WII, Campus, Chandrabani, Dehradun, Uttarakhand

30.2810

77.97494167

13-03-2018

Singh 2018 (Unpubl.)

24

Rooth Bangar, Anupshahr, Uttar Pradesh

28.3129

78.289875

14-10-2018

NMCG-WII  2019 (Unpubl.)

25

Gorumara, North Bengal

26.7185

88.77230278

00-08-2019

Dash 2019 (Unpubl.)

26

Buxa, North Bengal

26.6000

89.51839444

00-10-2019

Dash 2020 (Unpubl.)

27

Amangarh, Bijnor, Uttar Pradesh

29.4027

78.85969167

09-12-2020

Hushangabadkar 2019 (Unpubl.)

28

Barkala Range, Shivalik Forest Division

30.3946

77.63166667

20-02-2020

Pawar 2020 (Unpubl.)

29

Bijnor barrage, Uttar Pradesh

29.3733

78.03776944

26-07-2020

NMCG-WII 2020 (Unpubl.)

30

Dhumpara forest, West Bengal

26.6983

89.80184444

11-04-2021

Sarkar 2021 (Unpubl.)

31

Narora, Ganga Bas, Bulandshahr, Uttar Pradesh

28.1973

78.40184444

18-08-2021

NMCG-WII 2021 (Unpubl.)

32

Rajaji National Park, Uttarakhand

29.9685

78.20027778

12-11-2021

Kumar 2021 (Unpubl.)

33

Nawalpur, Bijnor, Uttar Pradesh

29.4037

78.02267778

20-11-2021

NMCG-WII 2021(Unpubl.)

34

Narora, Bulandshahr, Uttar Pradesh

28.2079

78.35157778

27-11-2021

NMCG-WII 2021(Unpubl.)

35

Narora, Barrage downstream, Bulandshahr, Uttar Pradesh

28.1891

78.39691389

19-12-2021

NMCG-WII 2021(Unpubl.)

36

Khatauli, Muzaffarnagar, Uttar Pradesh

29.2860

77.67954722

21-01-2022

Yadav 2022 (Unpubl.)

37

Corbett Tiger Reserve

29.5335

78.77368

10-10-2022

NMCG-WII 2022(Unpubl.)

38

Corbett Tiger Reserve

29.4924

78.762801

11-10-2022

NMCG-WII 2022(Unpubl.)

 

 

For figures & images - - click here for complete PDF

 

 

REFERENCES

 

Afsar, M., K. Çiçek, Y. Tayhan & C.V. Tok (2016). New records of Eurasian Blind Snake, Xerotyphlops vermicularis (Merrem, 1820) from the Black Sea region of Turkey and its updated distribution. Biharean Biologist 10(2): 98–103.

Ananjeva, N.B., E.A. Golynsky, S.S. Hosseinian Yousefkhani & R. Masroor (2014). Distribution and environmental suitability of the small scaled rock agama, Paralaudakia microlepis (Sauria: Agamidae) in the Iranian Plateau. Asian Herpetological Research 5(3): 161–167. https://doi.org/10.3724/SP.J.1245.2014.00161

Anderson, R.P. & A. Raza (2010). The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution, preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography (37): 1378–1393. https://doi.org/10.1111/j.1365-2699.2010.02290.x

Barker, D.G. & T.M. Barker (2008). The Distribution of the Burmese Python, Python molurus bivittatus. Bulletin of the Chicago Herpetological Society 43(3): 33–38.

Barker, D.G. & T.M. Barker (2010). The distribution of the Burmese python, Python bivittatus, in China. Bulletin Chicago Herpetological Society 45(5): 86–88.

Bhupathy, S., C. Ramesh & A. Bahuguna (2014). Feeding habits of Indian rock pythons in Keoladeo National Park, Bharatpur, India. The Herpetological Journal 24(1): 59–64.

Brattstrom, B.H. (1968). Thermal acclimation in anuran amphibians as a function of latitude and altitude. Comparative Biochemistry and physiology 24(1): 93–111.

Bickford, D., S.D. Howard, D.J. Ng & J.A. Sheridan (2010). Impacts of climate change on the amphibians and reptiles of Southeast Asia. Biodiversity and conservation 19(4): 1043–1062. https://doi.org/10.1007/s10531-010-9782-4

Bosso, L., S. Smeraldo, P. Rapuzzi, G. Sama, A.P. Garonna & D. Russo (2018). Nature protection areas of Europe are insufficient to preserve the threatened beetle Rosalia alpina (Coleoptera: Cerambycidae): evidence from species distribution models and conservation gap analysis. Ecological Entomology 43(2): 192–203. https://doi.org/10.1111/een.12485

Carranza, S., M. Xipell, P. Tarroso, A. Gardner, E.N. Arnold, M.D. Robinson, M. Simó-Riudalbas, R. Vasconcelos, P. de Pous, F. Amat & J. Šmíd (2018). Diversity, distribution and conservation of the terrestrial reptiles of Oman (Sauropsida, Squamata). PloS one 13(2): p.e0190389. https://doi.org/10.1371/journal.pone.0190389

Chen, I.C., J.K. Hill, R. Ohlemüller, D.B. Roy & C.D. Thomas. (2011). Rapid range shifts of species associated with high levels of climate warming. Science 333: 1024–1026. https://doi.org/10.1126/science.1206432

Chettri, B., S. Bhupathy & B.K. Acharya (2010). Distribution pattern of reptiles along an eastern Himalayan elevation gradient, India. Acta Oecologica 36(1): 16–22. https://doi.org/10.1016/j.actao.2009.09.004

Clark. B. (2012). Burmese Python Care sheet. Reptiles. https//:Burmese Python Care Sheet - Reptiles Magazine. Downloaded on 17 October 2022.

Cota, M. (2010). Geographical distribution and natural history notes on Python bivittatus in Thailand. The Thailand Natural History Museum Journal 4(1): 19–28.

Cox, N.A., D. Mallon, P. Bowles, J. Els & M.F. Tognelli (2012). The Conservation Status and Distribution of Reptiles of the Arabian Peninsula. IUCN, and Sharjah, UAE: Environment and Protected Areas Authority, Cambridge, UK and Gland, Switzerland.

Das, A., D. Basu, L. Converse & S.C. Choudhury (2012). Herpetofauna of Katerniaghat Wildlife Sanctuary, Uttar Pradesh, India. Journal of Threatened Taxa 4(5): 2553–2568. https://doi.org/10.11609/JoTT.o2587.2553-68

Das, I. (2012). A Naturalist’s Guide to the Snakes of South-east Asia: Including Malaysia, Singapore, Thailand, Myanmar, Borneo, Sumatra, Java and Bali. John Beaufoy Publishing, 160 pp.

Das, S. & K. Pramanick (2018). Review on the use of Species Distribution Modeling as a tool for assessing climate change mediated extinction risk in Indian Squamate reptiles, pp. 11–29. In: Environment and Sustainable Development: Strategies and Initiatives. NECTAR Publishers, 29 pp.

Dorcas, M.E., J.D. Willson & J.W. Gibbons (2011). Can invasive Burmese pythons inhabit temperate regions of the southeastern United States?. Biological Invasions 13(4): 793–802. https://doi.org/10.1007/s10530-010-9869-6

Dorcas, M.E., J.D. Willson, R.N. Reed, R.W. Snow, M.R. Rochford, M.A. Miller, W.E. Meshaka, P.T. Andreadis, F.J. Mazzotti, C.M. Romagosa & K.M. Hart (2012). Severe mammal declines coincide with proliferation of invasive Burmese pythons in Everglades National Park. Proceedings of the National Academy of Sciences 109(7): 2418–2422.

 El-Gabbas, A., S. Baha El Din, S. Zalat & F. Gilbert (2016). Conserving Egypt’s reptiles under climate change. Journal of Arid Environment 127: 211–221. https://doi.org/10.1016/j. jaridenv.2015.12.007

Fattahi, R., G.F. Ficetola, N. Rastegar-Pouyani, A. Avci, Y. Kumlutaş, C. Ilgaz & S.S.H. Yousefkhani (2014). Modelling the potential distribution of the Bridled skink, Trachylepis vittata (Olivier, 1804), in the Middle East. Zoology in the Middle East 60: 208–216. https://doi.org/10.1080/09397140.2014.944428

Guisan, A. & U. Hofer (2003). Predicting reptile distributions at the mesoscale: relation to climate and topography. Journal of Biogeography 30: 1233–1243. https://doi.org/10.1046/j.1365-2699.2003.00914.x

Gül, S., Y. Kumlutaş & C. Ilgaz (2015). Climatic preferences and distribution of six evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling. Turkish Journal of Zoology 39: 235–243. https://doi.org/10.3906/zoo-1311-9

Hosseinzadeh, M.S., M. Aliabadian, E. Rastegar-Pouyani & N. Rastegar-Pouyani (2014). The roles of environmental factors on reptile richness in Iran. Amphibia-Reptilia 35(2): 215–225.

Jacobson, E.R., D.G. Barker, T.M. Barker,R. Mauldin, M.L. Avery, R. Engeman & S. Secor (2012). Environmental temperatures, physiology and behavior limit the range expansion of invasive Burmese pythons in southeastern USA. Integrative Zoology 7(3): 271–285. https://doi.org/10.1111/j.1749-4877.2012.00306.x

Jain, S.K., P.K. Agarwal & V.P. Singh (2007). Ganga Basin. In: Hydrology and Water Resources of India. Water Science and Technology Library, vol 57. Springer, Dordrecht. https://doi.org/10.1007/1-4020-5180-8_8

Javed, S.M., M. Raj & S. Kumar (2017). Predicting potential habitat suitability for an endemic gecko Calodactylodes aureus and its conservation implications in India. Tropical Ecology 58: 271–282.

Jose, S.V. & P.O. Nameer (2020). The expanding distribution of the Indian Peafowl (Pavo cristatus) as an indicator of changing climate in Kerala, southern India: A modelling study using MaxEnt. Ecological Indicators 110: 105930. https://doi.org/10.1016/j.ecolind.2019.105930

Joshi, R. & A. Singh (2015). Range Extension and Geographic Distribution Record for the Burmese Python (Python bivittatus,Kuhl 1820) (Reptilia: Pythonidae) in north-western India. IRCF Reptiles and Amphibians 22(3): 102–105.

Kuhl, H. (1820). Beiträge zur Zoologie und vergleichenden Anatomie. Frankfurt am Main: Hermannsche Buchhandlung, 152 pp.

Mazzotti, F.J., M.S. Cherkiss, K.M.  Hart,  R.W. Snow, M.R. Rochford, M.E.  Dorcas &  R.N. Reed (2011). Cold-induced mortality of invasive Burmese pythons in south Florida. Biological Invasions 13(1): 143–151. https://doi.org/10.1007/s10530-010-9797-5

McCleery, R.A., A. Sovie, R.N. Reed, M.W. Cunningham, M.E. Hunter & K.M. Hart (2015). Marsh rabbit mortalities tie pythons to the precipitous decline of mammals in the Everglades. Proceedings of the Royal Society B: Biological Sciences 282(1805): 20150120. https://doi.org/10.1098/rspb.2015.0120

McDiarmid, R.W., J.A. Campbell & T.A. Toure (1999). Snake Species of the World: A Taxonomic and Geographic Reference. Herpetologists’ League, Washington, 511 pp.

NMCG-WII GBCI (2019). Planning and Management for Aquatic Species Conservation and Maintenance of Ecosystem Services in the Ganga River Basin for a Clean Ganga. Wildlife Institute of India, 37 pp.

Pearson, D., R. Shine & A. Williams (2005). Spatial ecology of a threatened python (Morelia spilota imbricata) and the effects of anthropogenic habitat change. Austral Ecology 30: 261–274. https://doi.org/10.1111/j.1442-9993.2005.01462.x

Penman, T.D., D.A. Pike, J.K. Webb & R. Shine (2010). Predicting the impact of climate change on Australia’s most endangered snake, Hoplocephalus bungaroides. Diversity and Distributions 16(1): 109–118. https://doi.org/10.1111/j.1472-4642.2009.00619.x

Phillips, S.J., M. Dudík & R.E. Schapire (2004). A maximum entropy approach to species distribution modeling, pp. 655–662. In: Proceedings of the 21st International Conference on Machine Learning. Learning. ACM Press, New York, USA.

Phillips, S.J., R.P. Anderson & R.E. Schapire (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S.J. & M. Dudík (2008). Modelling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161–175. https://doi.org/10.1111/j.0906-7590.2008.5203.x

Pyron, R.A., F.T. Burbrink & T.J. Guiher (2008). Claims of potential expansion throughout the US by invasive python species are contradicted by ecological niche models. PLoS One 3(8): e2931. https://doi:10.1371/journal.pone.0002931.g001

Quadir, A. (2022). Urbanization and its Impact on Ganga Basin. International Journal of Scientific and Research Publications 12(3): 371–375. https://doi.org/10.29322/IJSRP.12.03.2022.p12351

Rahman, S.C., C.L. Jenkins, S.J. Trageser & S.M.A. Rashid (2014). Radio-telemetry study of Burmese python (Python molurus bivittatus) and elongated tortoise (Indotestudo elongata) in Lawachara National Park, Bangladesh: a preliminary observation. In: Khan M.A.R., M.S. Ali, M.M. Feeroz & M.N. Naser (eds.). The Festschrift on the 50th Anniversary of the IUCN Red List of Threatened. Species, 54–62 pp.

Rashid, S.R. & J.A. Khan (2018). Burmese Python: New sighting record of Python bivittatus in Sumera Block, Jawan, Aligarh, Uttar Pradesh, India. Reptile Rap #184. In: Zoo’s Print 33(3): 19–22.

Rödder, D. & S. Lötters (2010). Explanative power of variables used in species distribution modelling: an issue of general model transferability or niche shift in the invasive Greenhouse frog (Eleutherodactylus planirostris). Naturwissenschaften 97: 781–796. https://doi.org/10.1007/s00114-010-0694-7

Rospleszcz, S., S. Janitza & A.L. Boulesteix (2014). The Effects of Bootstrapping on Model Selection for Multiple Regression. Technical Report. Department of Statistics, University of Munich, 164 pp.

Ribeiro, P.L., A. Camacho & C.A. Navas (2012). Considerations for assessing maximum critical temperatures in small ectothermic animals: Insights from leaf-cutting ants. PLoS One 7: e32083. https://doi.org/10.1371/journal.pone.0032083

Sanchooli, N. (2017). Habitat suitability and potential distribution of Laudakia nupta (De Filippi, 1843) (Sauria: Agamidae) in Iran. Russian Journal of Ecology 48: 275–279. https://doi.org/10.1016/j.chemosphere.2017.07.130

Shafi, S., K.K. Maurya, G. Ojha, A. Mall & H. Roy (2020). Sightings of Burmese Pythons (Python bivittatus) in and around the Valmiki Tiger Reserve, Bihar, India. Reptiles & Amphibians 27(3): 519–521.

Sinervo, B., F. Méndez-de-la-Cruz, D.B. Miles, B. Heulin, E. Bastiaans, M. Villagrán-Santa Cruz, R. Lara-Resendiz, N. Martínez-Méndez, M.L. Calderón-Espinosa, R.N. Meza- Lázaro, H. Gadsden, L.J. Avila, M. Morando, I.J. De la Riva, R.V. Sepulveda, C.F.D. Rocha, N. Ibargüengoytía, C.A. Puntriano, M. Massot, V. Lepetz, T.A. Oksanen, D.G. Chapple, A.M.  Bauer, W.R. Branch, J. Clobert & J.W. Sites Jr. (2016). Erosion of lizard diversity by climate change and altered thermal niches. Science (2010) 328: 894–9. https://doi.org/10.1126/science.1184695

Snyder, G.K. & W.W.Weathers (1975). Temperature adaptations in amphibians. The American Naturalist 109(965): 93–101.

Soucy, J.P.R., A.M. Slatculescu, C. Nyiraneza, N.H. Ogden, P.A. Leighton, J.T. Kerr & M.A. Kulkarni (2018). High-resolution ecological niche modeling of Ixodes scapularis ticks based on passive surveillance data at the northern frontier of Lyme Disease emergence in North America. Vector-Borne and Zoonotic Diseases 18(5): 235–242. https://doi.org/10.1089/vbz.2017.2234

Sovie, A.R., R.A. McCleery, R.J. Fletcher & K.M. Hart (2016). Invasive pythons, not anthropogenic stressors, explain the distribution of a keystone species. Biological Invasions 18(11): 3309–3318. https://doi.org/10.1007/s10530-016-1221-3

Stahl, R.S., R.M. Engeman, M.L. Avery & R.E. Mauldin (2016). Weather constraints on Burmese python survival in the Florida Everglades, USA based on mechanistic bioenergetics estimates of core body temperature. Cogent Biology 2(1): 1239599. https://doi.org/10.1080/23312025.2016.1239599

Stuart, B., T.Q. Nguyen, N. Thy, L. Grismer, T. Chan-Ard, D. Iskandar, E. Golynsky & M.W.N. Lau ( 2012). Python bivittatus (errata version published in 2019). The IUCN Red List of Threatened Species 2012: e.T193451A151341916. https://doi.org/10.2305/IUCN.UK.2012-1.RLTS.T193451A151341916.en

Todd, B.D., J.D. Willson & J.W. Gibbons (2010). The global status of reptiles and causes of their decline. Ecotoxicology of Amphibians and Reptiles 47: 67.

Urban, M.C. (2015). Accelerating extinction risk from climate change. Science 348(6234): 571–573. https://doi.org/10.1126/science.aaa4984

Van Moorter, B., C.M. Rolandsen, M. Basille & J.M. Gaillard (2016). Movement is the glue connecting home ranges and habitat selection. Journal of Animal Ecology 85(1): 21–31. https://doi.org/10.1111/1365-2656.12394

Walters, T. M., F.J. Mazzotti & H.C. Fitz (2016). Habitat selection by the invasive species Burmese python in southern Florida. Journal of Herpetology 50(1): 50–56. https://doi.org/10.1670/14-098

Whitaker, R. & A. Captain (2004). Snakes of India. Draco Books, 481 pp.

Wilms, T.M., P. Wagner, M. Shobrak, D. Rödder & W. Böhme (2011). Living on the edge? - on the thermobiology and activity pattern of the large herbivorous desert lizard Uromastyx aegyptia microlepis Blanford, 1875 at Mahazat as-Sayd Protected Area, Saudi Arabia. Journal of Arid Environment 75: 636–647. https://doi.org/10.1016/j.jaridenv. 2011.02.003

Wu, J. (2016). Detecting and Attributing the Effects of Climate Change on the Distributions of Snake Species Over the Past 50 Years. Environmental Management 57: 207–219. https://doi.org/10.1007/s00267-015-0600-3

Yadav, S.K., A. Khan & M.S. Khan (2017). Burmese Python: Python bivittatus: An addition to the reptiles of Hastinapur Wildlife Sanctuary, Uttar Pradesh, India. Reptile Rap #175. In: Zoo’s Print 32(8): 25–29.

Zhang, K., L. Yao, J. Meng & J. Tao (2018). Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment 634: 1326–1334. https://doi.org/10.1016/j.scitotenv.2018.04.112

 

 

Supplementary Table 1. Correlation matrix of 19 bioclimatic variables for the study area.

 

bio1

bio2

bio3

bio4

bio5

bio6

bio7

bio8

bio9

bio10

bio11

bio12

bio13

bio14

bio15

bio16

bio17

bio18

bio19

bio1

 

-0.14779      

0.08760     

-0.42869      

0.90153      

0.92267     

-0.14922      

0.93389

0.81279      

0.95163

0.95119     

-0.21245     

-0.16153

-0.81734

0.34291

-0.17860     

-0.81841

-0.51654     

-0.88703

bio2

 

 

-0.31464      

0.81630      

0.26648     

-0.46219      

0.95392      

0.04286

-0.08663

0.12559     

-0.37663     

  -0.54222

-0.20902

-0.11059

0.71472     

-0.20688     

-0.13034     

-0.24952

0.31944

bio3

 

 

 

-0.67003

-0.09922      

0.33385     

-0.57486

-0.15161

0.02123

-0.06728

0.31439

0.44121

0.31085

0.08710

-0.43401

0.31909

0.14107

0.05773

-0.20499

bio4

 

 

 

 

-0.07045     

-0.73224

0.90814

-0.13176

-0.31550

-0.17097

-0.68083

-0.43057

-0.17476

  0.11897

0.55119

-0.17553

0.10056

0.09052

0.57134

bio5

 

 

 

 

 

0.70464      

0.26739      

  0.89828

  0.77905

0.98525

0.76266

-0.47444

-0.27734

-0.81398

0.66332     

-0.29209     

-0.84760     

-0.65693

-0.72143

bio6

 

 

 

 

 

 

-0.49526      

0.74698

0.74665   

  0.78795

0.99414

  0.02268

-0.03898     

-0.64307

0.04283

-0.05124

-0.64382

-0.41728

 -0.89227

bio7

 

 

 

 

 

 

 

0.08541     

-0.06010

0.13623

-0.41631

-0.61165

-0.28660

-0.12328

0.75396     

-0.28802

-0.16343

-0.23760

0.32843

bio8

 

 

 

 

 

 

 

 

0.73185      

0.93858

0.79022

-0.30953

-0.20348

-0.84934

0.47888

-0.22488     

-0.83284

-0.42588     

-0.76364

bio9

 

 

 

 

 

 

 

 

 

0.80841      

0.76968     

-0.22410     

-0.13860     

-0.64973 

0.38786     

-0.15809     

-0.66989     

-0.44216     

-0.71539

bio10

 

 

 

 

 

 

 

 

 

 

0.83473     

-0.38515     

-0.22971

-0.82624

0.58044     

-0.24640     

-0.84637     

-0.59264     

-0.78426

bio11

 

 

 

 

 

 

 

 

 

 

 

-0.03064     

-0.06362     

-0.69210   

0.11125     

-0.07652

-0.68949     

-0.45954     

-0.90313

bio12

 

 

 

 

 

 

 

 

 

 

 

 

0.89080      

0.33208     

-0.47576      

0.89701      

0.38875

0.74506      

0.08170

bio13

 

 

 

 

 

 

 

 

 

 

 

 

 

0.19523

  -0.06113

  0.99339

0.23411

0.67848

0.10474

bio14

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-0.51010      

0.21465      

0.89777

0.43282      

0.77657

bio15

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-0.07259

-0.57791

-0.34106

-0.17024

bio16

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.25888

0.67644

0.12540

bio17

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.47692      

0.80538

bio18

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

0.39553

bio19