Journal of Threatened Taxa | www.threatenedtaxa.org | 26 March 2023 | 15(3): 22874–22882

 

 

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

https://doi.org/10.11609/jott.7814.15.3.22874-22882

#7814 | Received 01 January 2022 | Final received 08 April 2022 | Finally accepted 11 March 2023

 

 

Ecological niche modeling to find potential habitats of Vanda thwaitesii, a notified endangered orchid of Western Ghats, India

 

S. William Decruse

 

KSCSTE-Jawaharlal Nehru Tropical Botanic Garden and Research Institute, Palode, Thiruvananthapuram, Kerala 695562, India.

swdecruse@gmail.com

 

 

 

Abstract: Conservation planning of a threatened species essentially requires information on its present population and extent of distribution. Ecological niche modeling (ENM) is a suitable machine learning technique to predict potential distribution of a species enabling to identify suitable habitat for conservation action. The present study undertook distribution modeling of Vanda thwaitesii, a notified endangered orchid species of the Indian Western Ghats and Sri Lanka using maxent software. Geographical coordinates of 54 occurrence points at 1 km resolutions gathered during the study were utilized for modeling. A total of 37 variables including bioclimatic, topographical, and seasonal climate subjected to principal component analysis extracted into three components based on temperature and precipitation. Four representative variables from each component in all possible combinations resulted consistent output showing distribution of the species extending from Gavi in Periyar Tiger Reserve of Kerala to Chikkamagalur of Karnataka. Habitat suitability was confined to the cooler regions receiving an average 3,400 mm annual mean precipitation, 22.70C annual mean temperature, and 290 mm summer precipitation. A total of 2,557 km2 in Kerala and Karnataka mostly outside protected forests demonstrated as the highly suitable habitats. Silent Valley National Park, Idukki Wildlife Sanctuary, Periyar Tiger Reserve, and Brahmagiri Wildlife Sanctuary in addition to a few reserve forests hold sufficient area for reinforcement of diversity of V. thwaitesii from vulnerable locations. The present study revealed niche modeling as a useful tool to find suitable habitats for V. thwaitesii in the Western Ghats.

 

Keywords: Bioclimatic variables, co-linearity, conservation, endangered species, jack knife test, maxent modeling, Orchidaceae, QGis, summer precipitation, Wayanad.

 

 

Editor: Anonymity requested.            Date of publication: 26 March 2023 (online & print)

 

Citation: Decruse, S.W. (2023). Ecological niche modeling to find potential habitats of Vanda thwaitesii, a notified endangered orchid of Western Ghats, India. Journal of Threatened Taxa 15(3): 22874–22882. https://doi.org/10.11609/jott.7814.15.3.22874-22882

 

Copyright: © Decruse 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: Department of Biotechnology, Government of India.

 

Competing interests: The author declares no competing interests.

 

Author details: Dr. S. William Decruse is Principal Scientist and HOD, Biotechnology and Bioinformatics Division, KSCSTE-JNTBGRI, Thiruvananthapuram. Dr. Decruse is working on conservation of RET orchids of Western Ghats India through micropropagation and restoration since 1991. As a prerequisite for restoration, worked on population study and niche modeling of a few orchid species.

 

Acknowledgements: Chief Wildlife Warden, Department of Forests and Wildlife, Government of Kerala is acknowledged for granting permission to enter forest of Kerala. The study was supported by DBT Government of India through a Research Project No. BT/PR-10457/ BCE/ 08/649/2008; 30-07-2010. 

 

 

 

INTRODUCTION

 

Orchids are a group of plants belonging to Orchidaceae with 29,335 accepted species worldwide (POWO 2021). They have wide climatic preferences ranging from tropical to alpine habitats. However, due to habitat loss, habitat fragmentation, over-exploitation, and unrestrained illegal activities, many orchids have limited distribution range and population strength as other threatened plant species (Agustini et al. 2016; Warghat et al. 2016; Bachman et al. 2019; Lughadha et al. 2020). The existence of biodiversity have prime importance for the stability of an ecosystem and thus developing effective strategies for their conservation is the serious concern of conservation biologists (Singh et al. 2017). Besides, conservation planning of a threatened species essentially requires information on its present population and extent of distribution (Radosavljevic & Anderson 2014; Štípková et al. 2020). Mapping the identified geographical locations and predicting potential distribution of a species out of that is known to be useful in identifying critical regions that may need conservation action (Warren & Seifert 2011).

Vanda thwaitesii Hook.f. is one among the 81 species of Vanda reported worldwide (POWO 2021). It is endemic to the southern Western Ghats and Sri Lanka and is endangered mainly due to habitat loss and fragmentation. It was first collected by Thwaites from Sri Lanka in 1898 and remained elusive for over a century which forced to declare the species as extinct in 1981 (Sathishkumar & Sureshkumar 1998). During the period from 1982 to 1997, the species was collected from Silent Valley and Wayanad, Kerala thus confirming its presence in India (Sathishkumar & Sureshkumar 1998). The latter authors could also locate reference sample of V. thwaitesii collected in 1885 from Mananthavady in Wayanad District of Kerala. As per reports, the species have distribution in seven localities in Kerala. The species is distributed in narrow pockets with restricted numbers and later under section 38 of the Biological Diversity Act 2002, the Central Government notified that V. thwaitesii is on the verge of extinction and prohibited/regulated collection (MOEF 2009). The ministry also called for studies on all aspects of the notified species for holistic understanding and propagation of the species for the purpose of in situ and ex situ conservation and rehabilitation.

Plant distribution modeling/ ecological niche modeling (ENM) is recognized as an efficient tool to understand potential distribution of a plant/animal species and maximum entropy method (MEM; Phillips et al. 2006) is widely used for the purpose (Elith et al. 2011; Peterson et al. 2011). These models establish relationships between occurrences of species and biophysical and environmental conditions in the study area thus predicting suitable habitat for survival and existence of a species. This technique was successfully applied to find potential distribution and identify environmental niches for several plant species including orchids Vanda wightii (Decruse 2014), V. bicolor (Deb et al. 2017), Paphiopedilum javanicum (Romadlon et al. 2021), and Habenaria suaveolens (Jalal & Singh 2017). Based on the current understanding and the requirement of devising conservation action, the present study is framed to understand the habitat suitability of the species in localities other than that described earlier and the reported localities in Western Ghats region of Kerala, Tamil Nadu, and Karnataka states in India.

 

 

MATERIALS AND METHODS

 

Study site and field survey

As per the cited literature, Wayanad, Silent Valley, and Periyar Tiger Reserve in Kerala are the reported localities of V. thwaitesii in India (Agustine 1995; Sathishkumar & Sureshkumar 1998). The species have distribution at altitudes 500–1,060 m in moist deciduous to evergreen forests. Therefore, similar habitats in Idukki District of Kerala to Coorg District of Karnataka were surveyed to record geographical coordinates of occurrence points and score population status. Periyar Tiger Reserve, Idukki WS, Thirunelli, and Silent Valley are the protected forests surveyed. The inhabited land mainly covered is along roadways in North and South Forest Division of Wayanadu and adjoining places in Coorg District of Karnataka and Nilgiri District of Tamil Nadu. The surveys were conducted during 2011–2014 periods. The presence of V. thwaitesii was confirmed through close observation of the morphological feature (Image 1) of the species including fruit and flowers with the help of binoculars. Geographical co-ordinates of the presence location were recorded using Garmin GPSMap 60CSx.

 

Distribution modeling

A total of 37 environmental variables including 19 bioclimatic, six topographic, 11 seasonal climatic, and one vegetation variable (Table 1) influencing survival of a plant species were analyzed for their importance in modeling studies. As peninsular India receive monsoon in four distinct season, i.e., January–February (Dry period), March–May (Summer precipitation), June–September (South-west monsoon), and October–December (North-east monsoon) the monthly precipitation data obtained from world climate (https://www.worldclim.org) was reconstructed to obtain seasonal precipitation variables of the region. All extraction, clipping, and recalculation were undertaken using QGIS software version 3.10

The climatic variables are derived from temperature and precipitation data and thus multi co-linearity always exists among different variables. Therefore, principal component analysis (PCA) is often recommended (Junior & Nóbrega 2018) to control the negative effects of co-linearity and as a more objective solution for the problem of variable selection. The data points of all the 37 variables corresponding to the geo-reference points of V. thwaitesii occurrence in 1-km spatial resolution were extracted using point sampling tool in QGIS and PCA analysis undertaken in SPSS 16 software to sort out the variables having significant contribution to the model. Accordingly, three major components were extracted (Table 2) and one important variable representing each component and all those with VIF less than 10 (Naimi et al. 2014) in each component were selected. Thus the variables Bio_1, Bio_9, Bio_12 and summer precipitation (PMM) were included in the final model.

The reported localities are confined to southern Indian peninsula and Sri Lanka and thus the region falling under longitudes (E) 67.5 & 89 and latitudes (N) 5.5 & 24.5 was extracted from the world climate to prepare distribution model for peninsular India and Sri Lanka. Maxent software version 3.40 (Philips et al. 2006) was used to build a habitat suitability model. In this modeling, 75% of the encounter data was used for the training set and the rest for the test set. The modeling used auto features with 500 iterations and other default values. For validating model robustness, 10 replicated model runs was executed with a threshold rule of 10 percentile training presence and employed bootstrapping method for dividing the samples into replicate folds. The output of the Maxent software predicts habitat suitability in the range 0 (not suitable) to 1 (appropriate) (Phillips & Dudik 2008). For selection of most important environmental variable, Jack knife test was performed. The output was imported to QGIS and a distribution map was created and prediction area calculated.

 

 

RESULTS

 

Field Survey

Extensive field surveys revealed the distribution of V. thwaitesii altitudes from 489 m to 1,168 m mostly on evergreen trees exposed to sunlight. A total of 93 occurrence points were recorded in Kerala, Tamil Nadu, and Karnataka. The surveys in Periyar Tiger Reserve (PTR), Idukki Wildlife Sanctuary, and Wayanad revealed inhabited land in Wayanad Plateau as the dominant distribution area and their population near forest segments is meager. Therefore, it is clear that the dominant population in the most ideal habitat was lost due to habitat loss and extension of their population was withheld due to habitat fragmentation.

 

Modeling studies

Out of the 93 occurrence points recorded during our field surveys, 54 geo-reference points were at 1-km spatial resolution (Table 3). Hunnasgiria in Sri Lanka the reported type locality retrieved from Google Earth was also included for modeling. Thus 55 occurrence points were available for modeling. The 37 variables (19 bioclimatic, six topography, 11 seasonal, and one vegetation) subjected to principal component analysis, extracted into three components explaining 96.5% variance (Table 2). The final model based on the four selected variables revealed summer precipitation in the months of March to May (PMM), annual precipitation (Bio_12), and annual mean temperature (Bio_1), have significant contribution to the model (Table 4). Jack Knife test (Figure 2) revealed PMM as the environmental variable with highest gain and significant drop when it is omitted. The species flowers during April–May when receives summer showers, after a dry period and thus precipitation during March–May may be critical for the survival and spread of the species. In the distribution model generated (Figure 3), one occurrence point at 489 m altitude in Kannur District was in the least probable region. This is the only one occurrence record below 700 m still establishing a solitary colony with 15 individuals with the incidence of fruit set and new recruit. However, 41% of the points fall in high probable (0.8–1) region and 32.6% in the 0.6–0.8 region (Figure 4). The whole peninsular India was modeled where only 0.59% area (11,561 km2; Figure 4) confined to the Western Ghats region of Kerala, Karnataka, and Tamil Nadu emerged as suitable habitats for V. thwaitesii. Out of the total area (2,557 km2) in the high probable region (0.6–1.0), Wayanad District of Kerala and Coorg District of Karnataka together constitute 1,461 km2 (57%). Idukki District (353), Nelliyampathy (195), and Sholayar (137) of Kerala are the other regions having habitat suitability of 0.6–1.0 class. Suitable habitats extend from cooler regions in Thirunelveli districts of Tamil Nadu to Chikkamagalur district of Karnataka (Figure 3) covering protected forests as Idukki Wildlife Sanctuary, Silent Valley National Park, Periyar Tiger Reserve, and Brahmagiri Wildlife Sanctuary. In addition, a small area in Central Province of Sri Lanka also has habitat suitability.

Different variables extracted in the two principal components have equal variance of temperature and precipitation in the occurrence localities and thus the distribution models appeared very similar; V. thawaitesii inhabits cooler regions receiving annual mean temperature in the range 19.75–24.3 oC. Similarly, annual precipitation in the occurrence points are in the range 2020–4794 (Table 5). The occurrence points also received 293.5±28.4 mm rain fall (233–461 mm) during the summer months (March–May). Both annual mean temperature and summer precipitation are contributing significantly to the model as revealed in the model output (Table 4) and jackknife test (Figure 2).

 

 

DISCUSSION

 

Conservation assessment essentially requires sufficient field surveys and gathering of primary data on the distribution of species and their population attributes. This is an exhaustive process requiring substantial effort and investment of human and financial resources which has not been taken seriously in India and therefore the availability of primary data is limited. Conservation planning, action monitoring and evaluation of a species initially require prioritization through threat assessment (Mace & Lande 1991; Master 1991; Moran & Kanemoto 2017). Vanda thwaitesii is an epiphytic orchid endemic to Indian peninsula and Sri Lanka hitherto unknown for over 100 years until 1998 when a few populations were discovered from Kerala (Sathishkumar & Sureshkumar 1998). Based on the information on herbarium data, the Government of India notified the species as endangered and invited studies on all aspects of the notified species for propagation and conservation. Based on the extensive field surveys, we found significant populations of the species outside protected forests and the present habitats are highly fragmented. Thus conservation through rehabilitation or translocation into safer localities appeared very essential, preventing further loss before their economic value is deciphered. In spite of its unique aromatic and exquisite flowers it is underutilized in breeding as it is lesser known to orchid breeders. The present study delivers a habitat suitability model for conservation of the species prepared as part of a sponsored project supported by DBT, Government of India during 2010–16.

Niche modelling is an economical and effective tool to prepare guide maps for intended plant survey and delineation of conservation areas for selected species (Adhikari et al. 2012) thus improving the availability of primary data on the distribution of species and their population attributes for improved threat assessment and more accurate categorization of endangered species (Adhikari et al. 2018). Georeferenced occurrence points and environmental data pertaining to the distribution area are the two prerequisites for habitat modeling. The 55 occurrence points used are more than sufficient to undertake such study. Environmental variable as 19 bioclimatic variables and digital elevation are observed as biologically meaningful to define ecophysiological tolerances of a species (Graham & Hijimans 2006; Murienne et al. 2009) and generally utilized for modeling studies. Topographic variables are derived from digital elevation model from satellite data and are also known to influence the distribution of plant species (Wang et al. 2014). However, those variables and normalized digital vegetation index, another remote sensing data variable are least influencing the distribution of V. thwaitesii as they did not extract as a significant factor in PCA. Nevertheless, the bioclimatic variables and seasonal climatic variables are derived from temperature and precipitation data and thus often expresses multi co-linearity and thus often difficult to select the decisive environmental variables and their contributions. Principal component analysis and regression analysis executed on them reduced the number of factors into three still explaining 96.5% variance. Environmental variables showing VIF less than 10 are also included as the existing co-linearity is less significant (Naimi et al. 2014). In this study, we could extract three components with six, four, and one variable and excluding variables with VIF greater than 10, retained four variables (Table 2) to determine habitat suitability for V. thwaitesii in peninsular India.

The distribution map thus obtained showed high resolution with AUC 0.997 and therefore is having high prediction efficiency. The AUC ranges from 0.5–1.0 for models that are no better than random to perfect predictive ability. It is also clear that 73.3% of the occurrence records falls in the high suitability region (0.6–1.0) and only 1% in the least suitable region (0–0.2). Besides, a few occurrence records in Chikkamagallur, Hassan and Nelliyampathy later gathered from online resources and not used for modeling fall in the suitable area predicted. Thus, the presence of V. thwaitesii could be confirmed in most of the prediction area proving the robustness of the model.

Elevation and temperature are often the most determining variable in habitat modeling as revealed in the terrestrial orchids as Dactylorhiza hatagirea (Wani et al. 2021) and Oeceoclades maculata (Kolanowska 2013). Precipitation is also an important variable that influences habitat modeling in some species, such as Habenaria suaveolens (Jalal & Singh 2017) and Zanthoxylum armatum (Xu et al. 2019). However, it seems that the determining factor in habitat modeling is species-specific and not exclusively confined to any of the variables. Work undertaken in epiphytic orchids as Vanda wightii from Western Ghats, India (Decruse 2014) and Vanda bicolor from northeastern region of India (Deb et al. 2017) indicate that precipitation warmest quarter (Bio_18) is the most influential factor in the model. While Polystachya concreta, a pantropic epiphytic orchid (Kolanowska et al. 2020), is reported to prefer different temperature and precipitation factors as far as Asian, African, and American regions are concerned. As reported, temperature seasonality (Bio_4), isotheramality (Bio_3), and precipitation seasonality (Bio_15) have significant contribution in Asian region while temperature factors alone (Bio_2, 4, 1) in American region and precipitation factors alone (Bio_12, 18, 14) in African region. Therefore, a single factor can’t be considered important for global distribution of a particular species. The model output for peninsular India and Sri Lanka in the present study revealed that temperature and precipitation contribute significantly to the distribution of V. thwaitesii providing a robust model with high prediction efficiency. Therefore, the predicted areas in protected forests are highly suitable for conservation of V. thwaitesii. The generated model is also a guide map to find new populations from locations other than those reported earlier.

 

 

CONCLUSION

 

Vanda thwaitesii can sustain in the regions receiving 3,400 mm average annual precipitation and 290 mm in summer. In addition, they prefer cool climate with an average annual mean temperature 22.27oC. The most ideal climatic conditions (0.6–1.0 class) prevail mostly in Wayanad, Idukki, and Palakkad districts of Kerala in addition to Coorg District of Karnataka. However, most of the modeled area in Western Ghats is outside protected forests. Still, there are sufficient locations in reserve forests in Kerala and Karnataka in addition to the sanctuaries as Silent Valley National Park, Idukki Wildlife Sanctuary, Periyar Tiger Reserve, and Brahmagiri Wildlife Sanctuary for reinforcement of diversity from vulnerable locations as inhabited land, plantations, and wayside trees.

 

 

Table 1. Climatic and topography variables utilized for analysis of their contributions in plant distribution modeling.

Bioclimatic Variablesa

Topography b

Specific Climate (Western Ghats)

Vegetationc

Bio_1–19

Elevation

Slope

Topical wetness index

Vertical distance from channel network

Convexity

Aspect

Precipitatione:

Jan–Feb

Mar–May (PMM, Summer)

Jun–Sep (SW monsoon)

Oct–Dec (NE monsoon)

Precipitation humid months (PHM, Apr–Nov )

Precipitation dry months (Dec–Mar)

Evapotraspirationd:

Annual (aieto)

Humid months (Apr–Nov)

Dry months (Dec–Mar)

Temperaturee

Average humid months (THM)

Average dry months

Normalized digital vegetation index (NDVI)

a https://www.worldclim.org/data/worldclim21.html.

b Digital Elevation Model (DEM) data downloaded from http://www.earthexplorer.usgs.gov/ and derived  the topographic variables in  QGIS 3.10.7

cBuchhorn et al. (2020) Copernicus Global land Service, http://www.land.copernicus.eu/g;pbal/products/ndvi

d CGIAR, FAO, http://www. cgiar-csi.org. Monthly data downloaded and  computed the seasonal value in in  QGIS 3.10.7

e https://www.worldclim.org/data/worldclim21.html). Monthly data downloaded and computed the seasonal data in  QGIS 3.10.7

 

Table 2. Output of principal component analysis (PCA) and variance inflation factor (VIF) among the variables.

Total variance explained and component variables

Principal components and VIF against one variable taken as constant

Component

PCA output

PC1

PC2

PC3

Eigen values

% of Variance

  Variables

  VIF

Variables

  VIF

Variable

1

7.14

59.5

Bio_1

 

Bio_12

 

PMM

2

3.62

30.3

Bio_8

17.7

Bio_13

199.0

 

3

0.8

06.7

Bio_9

5.4

Bio_16

443.9

 

Total

96.5

Bio_11

57.3

PHM

61.6

 

 

 

 

THM

32.0

aieto

52.2

 

 

 

Table 3. Geo-reference points (~1 km spatial data) of Vanda thwaitesii occurrence in Kerala, Tamil Nadu, and Karnataka used for distribution modeling.

Population

Latitude

Longitude

Altitude (m)

Location

District

1

9.4826166

77.144733

910

PTR 

Idukki

2

9.46885

77.142266

931

PTR

Idukki

3

9.4653

77.143766

931

PTR

Idukki

4

9.4552

77.1426

1004

PTR

Idukki

5

11.098583

76.442203

920

Silent Valley

Palakkad

6

11.105

76.421345

1149

Silent Valley

Palakkad

7

11.079016

76.441608

1168

Silent Valley

Palakkad

8

11.46208

76.413495

839

Nadugani

Nilagiri

9

9.7479333

76.9709

986

Idukki WLS

Idukki

10

9.83825

76.92895

830

Kulamavu

Idukki

11

9.79245

76.8809

788

Kulamavu

Idukki

12

11.735766

75.9297

990

Vellamunda

Wayanad

13

11.71355

75.919216

991

Vellamunda

Wayanad

14

11.721683

75.935333

751

Pulinjal

Wayanad

15

11.9098

75.989583

793

Thirunelli

Wayanad

16

11.911316

75.98155

787

Thirunelli

Wayanad

17

11.827433

76.03125

774

Onedayangadi

Wayanad

18

11.83295

75.853733

727

Periya

Wayanad

19

11.835716

75.834616

772

Periya

Wayanad

20

11.838516

75.8295

727

Periya

Wayanad

21

11.8385

75.880516

772

Peria Peak

Wayanad

22

11.839583

75.891266

741

Varayal

Wayanad

23

11.84

75.902283

721

Varayal

Wayanad

24

11.838866

75.912816

781

Boys town

Wayanad

25

11.842583

75.868833

781

Peria Peak

Wayanad

26

11.802316

75.865283

754

Aalattil

Wayanad

27

11.76565

75.8666

721

Kunhome

Wayanad

28

11.7638

75.855216

727

Kunhome

Wayanad

29

11.753316

75.855716

720

Kunhome

Wayanad

30

11.7401

75.8398

720

Niravilpuzha

Wayanad

31

11.5139

76.019233

739

Lakkidi

Wayanad

32

11.526066

76.023116

739

Lakkidi

Wayanad

33

11.541

76.037516

735

Vythiri

Wayanad

34

11.83995

75.849278

735

Periya

Wayanad

35

11.839071

75.8535

735

Periya

Wayanad

36

12.154783

75.569166

489

Paithalmala

Kannur

37

12.35375

75.6586

925

Karugunda

Coorg

38

12.3545

75.652566

925

Karugunda

Coorg

39

12.3554

75.649316

925

Karugunda

Coorg

40

12.370266

75.621983

957

Cherambane

Coorg

41

12.376433

75.569266

901

Chettimani

Coorg

42

11.595216

76.01625

727

Achooranam

Wayanad

43

11.619966

76.001316

770

Edathara

Wayanad

44

11.62865

75.989016

817

Thariode

Wayanad

45

11.718533

75.92335

918

Mangalas-sery mala

Wayanad

46

11.69515

75.9385

863

Valaram-kunnu

Wayanad

47

11.712516

75.94255

772

Pulinjal

Wayanad

48

11.63262

76.01695

756

Thariode

Wayanad

49

11.54169

76.22731

898

Vaduvanchal

Wayanad

50

11.53397

76.24425

869

Vaduvanchal

Wayanad

51

11.49433

76.33644

992

Pandallur

Nilagiri

52

11.84624

76.02874

776

Thrissileri

Wayanad

53

7.294328

80.85475

880

Hunnasgiria

Srilanka

54

11.6621

75.94205

764

Kuttiyam-vayal

Wayanad

55

11.676533

75.935517

914

Meenmutty

Wayanad

 

 

Table 4. Contributions of variables in the model.

Bioclim as variables

Percent contribution

Permutation importance

Annual mean temperature (Bio 1)

30.6

34.8

Annual precipitation (Bio 12)

18.6

5.1

Precipitation Mar–May (PMM)

48.4

52.7

Mean temperature of driest quarter (Bio 9)

2.4

7.4

 

 

Table 5. Temperature and precipitation data at occurrence points of V. thwaitesii. The data were retrieved from world climatic data using point sampling tool in QGis.

Temperature (oC)

Precipitation (mm)

Variable

Mean±SD

Min–Max in occurrence points

Variable

Mean±SD

Min–Max in occurrence points

Annual mean temperature

22.27±0.68

20.60–24.3

Annual

3398.98 ±608.4

2020–4794

Mean wettest quarter

21.3±0.6

20.20–23.1

Wettest Period

1153.65±325.4

376–1713

Mean driest quarter

22.5±0.86

20.85–22.4

Wettest quarter

2429.37±650.9

873–3680

Mean coldest quarter

21.2±0.69

19.75–23.2

Humid months

3307.67±687.1

1197–4744

Mean humid months

22.27±0.67

20.75–23.2

Summer (Mar–May)

292.67±30.1

218–461

 

 

For figures & image - - click here for full PDF

 

 

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