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
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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 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|