Journal of Threatened
Taxa | www.threatenedtaxa.org | 26 November 2022 | 14(11): 22086–22097
ISSN 0974-7907
(Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.7861.14.11.22086-22097
#7861 | Received 01
February 2022 | Final received 16 November 2022 | Finally accepted 20 November
2022
A
comparative analysis of the past and present occurrences of some species of Paphiopedilum
(Orchidaceae) in northeastern India using MaxEnt and GeoCAT
Debonina Dutta 1 & Aparajita De
2
1,2 Department of Ecology and
Environmental Science, Assam University, Silchar,
Assam 788011, India.
1 deboninadtt@gmail.com, 2 aparajitade.ecology@gmail.com
(corresponding author)
Editor: Pankaj Kumar, Kadoorie
Farm and Botanic Garden Corporation, Hong Kong S.A.R., P.R. China. Date of publication: 26
November 2022 (online & print)
Citation: Dutta, D. & A. De (2022). A comparative
analysis of the past and present occurrences of some species of Paphiopedilum
(Orchidaceae) in northeastern
India using MaxEnt and GeoCAT.
Journal of Threatened
Taxa 14(11): 22086–22097. https://doi.org/10.11609/jott.7861.14.11.22086-22097
Copyright: © Dutta & De 2022. 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: DBT (BT/ENV/BC/01/2010 & 23rd March
2012).
Competing interests: The authors declare no competing interests.
Author details: Debonina Dutta is currently pursuing her PhD. Her research interest includes the study of few selected species of Paphiopedilum genus in northeastern India and their conservation using biotechnological tools. Aparajita De is a
professor. She has specialized in forest ecology, RS, and GIS applications in forest ecology and ethnobiology.
Author contributions: DD carried out the fieldwork and drafted the manuscript. AD provided guidance to conduct the field surveys and has reviewed and corrected the manuscript.
Acknowledgements: We would like to express our sincere gratitude to Department of Ecology and Environmental Science, Assam University, Silchar and The Botanical Survey of India, Eastern Regional Circle. We are grateful to the Department of Biotechnology, Govt. of India for providing financial support for the study. We are thankful to the director, BSI, Dr A.A. Mao and we convey our special regards to Dr N. Odyuo, head of office, BSI, Eastern Regional Circle, Shillong. We are thankful to Dr. Dibyendu Adhikari, principal scientist, National Botanical Research Institute for critically going through the manuscript and suggesting improvements. We are also thankful to the Dr D.K. Agarwala, BSI Gangtok. We are thankful to Miss Dukmit Lepcha, Bantei Thyrniang, M.A.M. Saikia, and Dr. Batskhem Myrboh, assistant professor, Synod College, for helping us in our field study. We duly acknowledge the following scientists who provided herbarium related information: Dr. R.K. Gupta,
in-charge, Central National Herbarium, Kolkata; Dr. Anup Chandra, head, Botany Branch, Forest Research Institute, Dehradun.
Abstract: Members of the genus Paphiopedilum
are well known for their long-lasting unique flowers. They are becoming rare
due to over-collection and habitat loss because of human disturbances and
deforestation. The present study aimed to compare the past and present
occurrences of the genus Paphiopedilum in northeastern India using MaxEnt and GeoCAT. A historical
occurrence model (HOM) was prepared using secondary data, and an actual
occurrence model (AOM) was constructed with primary field data. The HOM and AOM
revealed that bioclimatic factors, topography and precipitation play a
significant role in the survival of Paphiopedilum populations in
northeastern India in both the current and historical distributions. The other
vital environmental variables were elevation (h_dem),
mean diurnal range (bio_2), annual mean temperature (bio_1), temperature annual
range (bio_5) and annual precipitation (bio_12). The results showed a sharp
decline in the extent of occurrence and the area of occupancy of Paphiopedilum
in the study area. The extent of occurrence and area of occupancy for HOM were
170,972 km2 and 18 km2. For the AOM, they were 125,315 km2
and 12 km2, respectively. The HOM model indicated that Paphiopedilum
was earlier growing sporadically. On the other hand, the AOM result indicates
that it is presently growing sparsely in isolated pockets that are more prone
to extinction. Paphiopedilum can be conserved successfully using an
integrative conservation approach, comprising ecological modeling techniques to
search for additional locations, ex situ propagation techniques, and possible
reintroduction in selected areas.
Keywords: Ecological niche modeling,
environmental variables, lady slipper orchids, orchid conservation.
Introduction
Predictive habitat distribution
models are being used extensively in ecology as they can statistically relate
the geographic distribution of species or communities to their environment (Guisan & Zimmermann 2000). These models correlate known
species occurrence with climatic data available for relevant areas to determine
the boundaries of the multidimensional range of the species. By projecting such
conditions onto geographical space, one can predict the potential distribution
of the target species. These techniques are applied in a wide variety of
studies to: 1. predict the distribution of rare, threatened, or invasive
species (Serra et al. 2012; Silva et al. 2013, 2014; Deka et al. 2018), 2.
optimize future faunal/floristic surveys (De Siqueira et al. 2009), and 3.
inform the establishment of future protected areas (Nóbrega
& De Marco Jr 2011).
Studies related to species
distribution models have been conducted for the family Orchidaceae
by many workers (Kolanowaska & Konowalik 2014; Kolanowska & Busse 2020). Orchids are one of the most threatened
group of plants as their complex life history makes them particularly
vulnerable to global environmental change. There are more than 1,200 genera of
orchids reported in India (Misra 2019; Singh et al.
2019; Schuiteman 2022). The present study tries to
model the current distribution of the genus Paphiopedilum, family Orchidaceae in northeastern India, and compare it with the
historical occurrence data depicting its distribution in the past. The
genus Paphiopedilum is highly preferred in the horticultural market
for its exotic, large flowers on small plants (Cribb 1998). A few species are
regarded as threatened or even extinct in the wild due to over-collection from
natural areas and large-scale illegal trade (Long et al. 2010). The
genus Paphiopedilum is listed in Appendix I of the Convention on
International Trade in Endangered Species of Wild Fauna and Flora (CITES
2022). All the species under the
genus Paphiopedilum found in India come under the category of
Vulnerable to Critically Endangered according to the IUCN Red List of
Threatened Species (IUCNredlist.org/2021-3) (Table 1). Due to its high
horticultural importance (Zhen et al. 2006), the genus faces extensive
collection pressure from the wild. This, along with the rapid degradation of
its habitat has led to the drastic reduction in the population of the genus
(Cribb 1998).
Nine species of Paphiopedilums have
been reported from India: Paphiopedilum druryi (Bedd.) Stein, growing at an altitudinal range 1,400─1,550 m,
Paphiopedilum fairrieanum (Lindl.)
Stein growing at 200─1,200 m, Paphiopedilum venustum
(Wall. ex Sims) Pfitzer at 500─1,500 m,
Paphiopedilum wardii (Summerh.)
at 1,200─2,500 m, Paphiopedilum villosum (Lindl.) Stein at 1,300─2,200 m, Paphiopedilum insigne (Wall.
ex Lindl.) Pfitzer at
1,000─1,500 m, Paphiopedilum charlesworthii (Rolfe)
Pfitzer at 1,200─1,600 m, Paphiopedilum spicerianum (Rchb.f.) Pfitzer at 300─1,400 m and Paphiopedilum hirsutissimum (Lindl. ex
Hook.) Stein at 200─1,800 m. Eight species are found in the eastern Himalaya
and northeastern India, and one in southern India (Hajra
& De 2009; Chowdhery 2015). All the species of
this genus found in northeastern India have been reported to grow at altitudes
of 200─2,200 m. Several species of Paphiopedilum are found growing in
shady vertical/ limestone cliffs at varying altitudes (Averyanov
2007; Averyanov et al. 2014; Gurung et al. 2019).
The present work deals with the
following objectives: estimation of the past and the present distribution status
of some Paphiopedilum orchids in northeastern India, estimation of
changes in the area and the extent of occurrence of Paphiopedilum spp.
(if any), and determination of the environmental variables that are vital to
the distribution of the genus.
Study area
Northeastern India comprises the
eastern part of the Himalayan range, intercepted by plains, valleys, and hilly
terrains (Yadava 1990). Our study was carried out in Meghalaya, Mizoram,
Arunachal Pradesh, Sikkim, Nagaland, and Assam (Figure 1). The following
species of Paphiopedilum were recorded in the present study, viz, P. spicerianum, P. venustum, P. insigne, P. fairreanum,
and P. hirsutissimum.
Materials
and Methods
Study design
Ecological niche models (ENM)
were prepared using maximum entropy modelling (MaxEnt)
of species geographic distributions (Phillips et al. 2006). The version
3.3 of MaxEnt was used for the current study. We
executed two models for our study. Past and present data were divided into
historical occurrence points and actual occurrence points. Model 1 was executed
using the Historical occurrence points. Historical occurrence data of Paphiopedilum were
obtained from the recorded historical data such as herbarium records (Kew
Herbarium catalogue 2022; Museum National D histoire Naturelle 2022; GBIF 2022; Natural History Museum 2022) and
published literature (Table 2). We have named it as historical occurrence model
(HOM) in this paper. Model 2 was executed using the actual occurrence data
(Table 3). Therefore, model 2 is referred to as the actual occurrence model
(AOM).
The results obtained from the
ecological niche model were verified in the study area during the period of
2015─2021. The details of the historical presence sites and actual presence
sites are given in the Table 2 & 3. Based on the ENM observations, the area
of occupancy (AOO) & extent of occurrence (EOO) of historical and actual
occurrence data of Paphiopedilum were estimated. The estimation of
AOO & EOO were performed using Geospatial Conservation Assessment Tool (GeoCAT), calculated at a 1 km2 area cell size.
Figure 2 gives the details of the study design.
Data collection
Historical data were collected
from various herbaria (both offline and online) and literature sources.
Occurrence data of Paphiopedilum was obtained from herbaria of
the Botanical Survey of India, ERC, Shillong (Assam),
Global Biodiversity Information Facility (GBIF), Central National Herbarium
(CAL), Forest Research Institute, Dehradun (DD), Botanical Survey of India,
Arunachal Pradesh Regional Centre (ARUN), Natural History Museum (NHM), Museum
National d’Histoire Naturelle
(MNHN) and Kew herbarium (KEW) (Table 2). The previous occurrence reports were
also noted from the literature survey (Pradhan 1971; Pradhan 1975; Pradhan
1976, 1979; Kataki et al. 1984; Bose et al. 1999; Lucksom 2007; Misra 2007; Russel
2008a,b; Mao 2010; Bhattacharjee et al. 2018). The records obtained
from literature reviews were used for cross-referencing with the reported
locations of herbarium collections. Further the herbaria collections having
location information (nearby village name or landmark) were tagged and
digitized with the help of Google Earth following Milagros & Funk (2010).
AOM was prepared from the primary
data. The primary data were collected from field visits to respective
localities of different states (Table 3). The field surveys were conducted by
snowball sampling method (Spreen 1992; Johnson 2014).
The flowers of each species growing on the cliff were identified using a
binocular. Geo -coordinates of the location were recorded using GPS (Garmin etrex 20) and the habitat features were recorded. The
accessible sites were thoroughly surveyed for a closer view of the habitat.
Environmental variables
The dataset for ENM include NDVI
(Normalized differential vegetation index), bioclimatic variables, and
hydrological variables (i.e., slope, aspect, topography, and elevation). A
total of 12 environmental variables were selected for the study. Table 4 shows the list of the final selected
variables for the present study.
The environmental variables were
applied with principal component analysis (PCA) to avoid multi-co linearity
(correlation among the variables that could create redundancy in models)
(Chaudhary et al. 2021). The bioclimatic layers in ASCII format were used with
a resolution of 30 ARC seconds for this study. The variables for the area of
interest were obtained by masking the bioclimatic rosters with the
boundary of northeastern India using ArcView. Highly correlated variables were
excluded by performing a Pearson correlation test of variables exhibiting a
value of r <0.9 (i.e., 90%). A total of 12 environmental variables were used
post correlation (Table 4).
Results
A total of 40 specimens were
obtained from various herbaria in India and other countries (Table 2). During
the present study, we located five species of Paphiopedilum in different
sites of the study area (Table 3). They were P. spicerianum,
P. insigne, P. fairreanum, P. venustum,
and P. hirsutissimum. A total of 16 actual
occurrence sites were recorded for the five species. Image 1 shows the habitat
of a few Paphiopedilum species. The two ecological niche models were
executed based on this data and the results obtained are given as follows.
Historical occurrence model and
actual occurrence model
Two models were obtained using
the historical occurrence data and actual occurrence data. The ENM models are
represented in Figure 3. Model 1 or HOM represents the historical occurrence
distribution of Paphiopedilum, and model 2 or AOM represents the actual
occurrence distribution of Paphiopedilum in northeastern India.
AUC and jackknife interpretation
The model calibration test for Paphiopedilum
yielded satisfactory results for both models. The red line shows the ‘fit’
of the model to the training data, and the blue line indicates the ‘fit’ of the
model to the testing data (Figure 4). The area under the curve (AUC) value of
each model aids in the assessment of the model quality. In the jackknife of
AUC, the blue line depicts the real test of the predictive power of the MaxEnt model. An AUC value above 0.9 (closer to 1.0)
indicates a good model performance. The AUC values for HOM (AUCtest
= 0.972 ± 0.015) and AOM (AUCtest = 0.942 ± 0.015)
therefore indicated that the model performance was good in both cases.
The significance of environmental
variables on each model was assessed by interpretation of the jackknife of AUC
(Figure 5). The contribution of the environmental variables on the model build
was assessed from the percent contribution of variables and permutation
importance (Table 5). Among all the variables, bio_2 (mean diurnal range) and
bio_1 (annual mean temperature) were the most influential variables in the
build of HOM as evident from the percent contribution of the variables in model
build. The variable bio_2 contributed 46.6% and bio_1 contributed 19.3%
respectively on HOM (Table 5). According to the internal jackknife of AUC for
HOM, bio_1 (annual mean temperature) has the highest contribution to the model,
followed by h_dem (elevation) (Figure 5). Jackknife
of AUC shows the contribution of environmental variables in both models. The
variables collectively contributed 100% to the HOM. Aspect (h_aspect)
and topographic index (h_topoind) contributed 19.1%
and 0.7% (Table 5). Considering the permutation importance, bio_5 contributed
the highest (55.3%) to the model, followed by h_dem
(27. 5%) and bio_2 (8. 9%) (Table 5). The variable bio_2 was the most
influential environmental variable in the model build of HOM.
The percent contribution of
variables in the model build of AOM revealed that bio_12 (annual precipitation)
and bio_2 (mean diurnal range) were most influential in the model build. Bio_12
contributed 41.9% and bio_2 contributed 29.1% to the model build. The variable
bio_2 was followed by bio_5 (max temperature of the warmest month) that
contributed 24.4%, and h_topoind contributed 3.9% to
the AOM. Considering the permutation importance, bio_5 contributed 53.1%.
Jackknife of AOM infers the highest contribution of h_topoind
and bio_14 (precipitation of driest month), followed by bio_2 (mean diurnal
range) and bio_12 (annual precipitation). Amongst the bioclimatic factors,
bio_12 showed the highest contribution to the build of AOM (Table 5).
Potential habitat areas and
actual habitat areas of Paphiopedilum spp.
Figure 3 shows the Ecological
niche model for HOM and AOM. The figure depicts the probable habitats in
different colours. Areas in red are the highest
potential areas for the distribution of Paphiopedilum. Yellow represents
areas with medium potential whereas the low potential areas are represented by
green; 40 secondary occurrence records (historical occurrence records) were
recorded from literature and herbarium sources (Table 2). However, the field
survey results revealed 16 actual occurrence records of the Paphiopedilum
spp. in the study area (Table 3).
EOO & AOO of the genus Paphiopedilum
HOM shows the distribution
regions of Paphiopedilum in Assam, Mizoram, Meghalaya, Sikkim, Arunachal
Pradesh, Nagaland, and Manipur. However, according to AOM, the distribution of Paphiopedilum
is found in all the states of northeastern India, found in HOM except Assam
(Table 6). The EOO and the AOO for HOM of Paphiopedilum were 170,972 km2
and 18 km2. EOO and AOO for the AOM were 125,315 km2 and
12 km2.
DISCUSSION
Ecological niche modeling has
efficiently predicted the potential population areas of the genus in this
study. The high AUC values for training and testing (˃ 0.90) infer the high
efficiency of the niche model to differentiate between presence and absence
areas for the species.
In the Table 6, a comparison
between the historical presence sites and actual presence sites obtained
through ENM survey is presented. This comparison revealed the high predictive
value of the model. It, therefore, provides a check on the accuracy and
reliability of the ENM model in the present study.
Significant environmental
variables determining the distribution of Paphiopedilum
According to the jackknife model
and the percent contribution of variables in model build, the parameter bio_2
(mean diurnal range) shows the highest contribution to the build of HOM (Table
5). The variable bio_2 infers to the mean of monthly temperatures (max
temperature-min temperature). It contributes 46.6% to the model build of HOM,
indicating the high importance of the mean temperature in the growth of the
orchids of this genus.
In the AOM, bio_12 is the most
significant variable in the jackknife interpretation of the model (Figure 5).
The variable bio_12 indicates the annual precipitation in the model build. The
bio_12 variable is followed by bio_2 (mean diurnal range) and bio_5 (max
temperature of the warmest month). These results indicate that temperature and
rainfall are two important contributors that determine the availability of the
members of genus Paphiopedilum. The field observations also correlate
the importance of precipitation and temperature requirements of Paphiopedilum.
All the species of this genus
found in northeastern India are found between an altitude of 200─2,200 m, which
shows importance of h_dem (digital elevation model)
being one of the highest contributing variables of the internal jackknife of
HOM (Figure 5, Table 5). Similarly, mean temperature, mean diurnal range, max
temperature of the warmest month and annual precipitation play a significant
role in the model execution of Paphiopedilum in both HOM and AOM.
Other workers have also studied
the dependence of the survival, reproduction, and germination of different
plant species on temperature and precipitation. For instance, Wilkie et al. (2008) reported the influence of low
temperatures (vernalization), seasonal variations in
temperature, photoperiod, and water stress on the flowering of plants (Wilkie et al. 2008). In another study, inadequate
temperature conditions during endodormancy compromised flowering or led to
erratic and longer flowering duration with morphological disorders and flower
necrosis (Rodrigo & Herrero 2002).
AOO, EOO concerning the past and
present distribution of Paphiopedilum
Comparison of both models shows
that the species distribution of Paphiopedilum has undergone a sharp
decline over the past two decades. Field observations also indicate highly
fragmented populations. All the species of this genus have very few individuals
in the study area. A reduction in the extent of occurrence (EOO) and area of
occupancy (AOO) was observed in the AOM compared to the HOM. During the field
survey no species was located in the earlier reported sites of Assam (Table 6).
The reduction in AOO could be due
to factors like over-collection, climate change, urbanisation,
unplanned development, and habitat fragmentation. An increased frequency of
large-scale disturbances caused by extreme weather events is known to cause
increasing gaps and an overall contraction of the distribution range,
particularly in areas with relatively low levels of spatial cohesion (Paul
& Wascher 2004).
Effects of habitat fragmentation
on the persistence of populations and species play a major role in conservation
biology (Reed & Frankham 2003). Limitations of
plant species dispersal also affect plant colonization (Olivier et al. 2002).
Small population sizes, lead to decreased population fitness and eventually
make the small population sizes more vulnerable to extinction (Reed & Frankham 2003; Reed 2005, 2008). The field studies revealed
that the populations of Paphiopedilum had very few individuals. The
habitat was also highly fragmented. These factors are further exacerbating the
risk of extinction of the genus.
It was observed that Paphiopedilum
grew in the rock crevices of east-facing slopes of the habitats situated in the
hilly terrains (Image 1). They also grew in the space between tree roots and
rock layers of the habitat substratum. The prolonged filling of the conjoining
rock fissures between the rock crevices and tree roots by the dry leaves and
soil organic matter of the forests provide an excellent growth medium for these
orchids in the otherwise soil-deprived cliff sides (Phillips 2017).
Conclusion
In this study, the present status
of Paphiopedilum in northeastern India has been determined using
ENM-based surveys combined with historical data. The herbarium data provided
location history from 1857 onwards (Table 2), while the field data helped in
the present assessment of the genus. There was a significant reduction in the
EOO and AOO in the actual model as compared with the historical model. The
results of the model reveal that temperature and precipitation are the highest
contributing factors determining its availability. We were unable to locate the
plants in many locations that were earlier mentioned by previous workers.
Therefore, it can be inferred that change in the temperature and precipitation
patterns in many locations have led to its scarcity. However, this inference needs to be further
corroborated with detailed records of the climate parameter.
These orchids are becoming
increasingly rare mainly due to over collection from the wild, rising urbanization
causing habitat destruction and also global warming (Swarts & Dixon 2009;
Seaton et al. 2010; Barman & Devadas 2013; Ye et
al. 2021). Favorable climatic conditions, access to wild habitat sites, and a
conducive environment are important for the survival of plants (Hulme 2005; Ballantyne & Pickering 2015; Wraith &
Pickering 2018; Li et al. 2020; Ye et al. 2021). The comparison of
environmental requirements of the distribution of Paphiopedilum over the
years imparts an understanding of the adaptability of these orchids with
changing environmental conditions. The dwindling population size of the various
species under the genus is increasing the risk of extinction of the already
sparse populations in the study area. The over-collection of the Paphiopedilum
flowers from the wild for its high market demand results in further habitat
loss. Forest road constructions and urbanization also cause further degradation
of the Paphiopedilum habitats in different areas of Northeast India.
Such reasons have caused the Paphiopedilum orchids to become
increasingly rare with time.
Ex situ conservation techniques
for mass production of the species with higher market demand could reduce
collection pressure on already dwindling wild populations in northeastern
India. Paphiopedilum orchids are being propagated elsewhere in the world
using various techniques (Huang 1988; Hong et al. 2008; Ng & Saleh 2011).
Various individuals have been reintroduced into other suitable habitats by
various workers in China (Yang et al. 2020; Gao et al. 2020). Since, in situ
conservation is not a viable approach in many locations due to their habitat
degradation and other developmental pressures, reintroduction into potential
habitats will aid in conservation of the species. We recommend that
reintroduction of Paphiopedilum orchids should be conducted on a large
scale by both government and non-governmental agencies in northeastern India.
We recommend the conservation of Paphiopedilum orchids using an
integrative conservation approach of ecological niche modeling to search for
additional locations, ex situ propagation techniques, and possible
reintroduction in selected areas. Such schemes can be helpful to meet the
market demands of Paphiopedilum orchids and boost the conservation of
wild populations in northeastern India.
Table 1. The threat category of
genus Paphiopedilum (IUCN:
Ver.2021─3)
|
Name |
Threat category |
Population trend |
|
P. druryi |
Critically endangered |
Decreasing |
|
P. fairrieanum |
Critically endangered |
Decreasing |
|
P. venustum |
Endangered |
Decreasing |
|
P. wardii |
Endangered |
Decreasing |
|
P. villosum |
Vulnerable |
Decreasing |
|
P. insigne |
Endangered |
Decreasing |
|
P. charlesworthii |
Endangered |
Decreasing |
|
P. spicerianum |
Endangered |
Decreasing |
|
P. hirsutissimum |
Vulnerable |
Decreasing |
Table 2. Historical occurrence
records of Paphiopedilum spp.
|
|
Species |
Year |
Location |
Herbarium source |
|
1 |
P. fairreanum |
1857 |
NA |
Royal Botanic garden, Kew |
|
2 |
P. insigne |
1859 |
Mount Khasia,
Meghalaya |
Museum National d’Histoire Naturelle |
|
3 |
P. venustum |
1893 |
Sonai, Assam |
Natural History Museum |
|
4 |
P. venustum |
1893 |
Sikkim Himalaya |
Natural History Museum |
|
5 |
P. insigne |
1894 |
Cherrapunjee, Meghalaya |
Central National Herbarium,
Kolkata |
|
6 |
P. insigne |
1899 |
Shella, Meghalaya |
Central National Herbarium,
Kolkata |
|
7 |
P. insigne |
1899 |
Jaintia hills, Meghalaya |
Central National Herbarium,
Kolkata |
|
8 |
P. insigne |
1899 |
Jaintia hills, Meghalaya |
Bavarian Natural History
Collections (SNSB-GBIF) |
|
9 |
P. venustum |
1899 |
Lingzah Tolung,North Sikkim |
Naturalis Biodiversity Center
(GBIF) |
|
10 |
P. venustum |
1899 |
Sikkim |
Harvard University Herbaria
(GBIF) |
|
11 |
P. fairreanum |
1941 |
Rohlu, Sikkim |
Central National Herbarium,
Kolkata |
|
12 |
P. insigne |
1944 |
Smit, Meghalaya |
Natural History Museum |
|
13 |
P. venustum |
1952 |
Cherrapunjee, Mount Khasia, Meghalaya |
Naturalis Biodiversity Center
(GBIF) |
|
14 |
P. venustum |
1952 |
Khasia hills, Meghalaya |
Museum National d’Histoire Naturelle, , |
|
15 |
P. fairreanum |
1957 |
Dirang dzong, Arunachal
Pradesh |
Central National Herbarium,
Kolkata |
|
16 |
P. hirsutissimum |
1962 |
Khasi hills, Meghalaya |
Central National Herbarium,
Kolkata |
|
17 |
P. villosum |
1963 |
Cultivated plant |
Central National Herbarium,
Kolkata |
|
18 |
P. spicerianum |
1972 |
National orchidarium |
BSI-ERC, Shillong |
|
19 |
P. fairreanum |
1974 |
Tenga Valley, Arunachal
Pradesh |
BSI-ERC, Shillong |
|
20 |
P. insigne |
1974 |
Mount Khasia,
Meghalaya |
Natural History Museum |
|
21 |
P. insigne |
1974 |
Mount Khasia,
Meghalaya |
Museum National d’Histoire Naturelle |
|
22 |
P. insigne |
1975 |
Khasia Mountains |
Royal Botanic Garden, Kew |
|
23 |
P. villosum |
1976 |
Lunglei, Mizoram |
Forest research Institute,
Dehradun |
|
24 |
P. fairreanum |
1978 |
Jameri, Arunachal Pradesh |
Forest research Institute,
Dehradun |
|
25 |
P. venustum |
1984 |
Khasya hills, Meghalaya |
Royal Botanic Garden, Kew |
|
26 |
P. venustum |
1984 |
Sikkim Himalaya |
Royal Botanic Garden, Kew |
|
27 |
P. venustum |
1993 |
Khasia hills, Meghalaya |
Museum National d’Histoire Naturelle |
|
28 |
P. hirsutissimum |
2006 |
Maram, Manipur |
BSI-ERC, Shillong |
|
29 |
P. insigne |
2016 |
Sohra, Meghalaya |
iNaturalist (GBIF) |
|
30 |
P. fairreanum |
2017 |
Arunachal Pradesh |
Wildlife Institude
of India (GBIF) |
|
31 |
P. venustum |
2019 |
East Khasi Hills, Meghalaya |
University of Michigan
Herbarium (GBIF) |
|
32 |
P. hirsutissimum |
2019 |
Senapati, Manipur |
University of Michigan
Herbarium (GBIF) |
|
33 |
P. hirsutissimum |
NA |
NA |
Royal Botanic Garden, Kew |
|
34 |
P. fairreanum |
NA |
Rupa bridge, Arunachal Pradesh |
Central National Herbarium,
Kolkata |
|
35 |
P. fairreanum |
NA |
Gochum, Rupa |
BSI-ERC, Shillong |
|
36 |
P. insigne |
NA |
Khasiya mountains |
Royal Botanic Garden, Kew |
|
37 |
P. villosum |
NA |
Sairep, Mizoram |
BSI-ERC, Shillong |
|
38 |
P. insigne |
NA |
Khasi hills, Meghalaya |
Central National Herbarium,
Kolkata |
|
39 |
P. spicerianum |
NA |
Cachar, Assam |
Central National Herbarium,
Kolkata |
|
40 |
P. hirsutissimum |
NA |
Naga hills. Nagaland |
Central National Herbarium,
Kolkata |
Table 3. Actual occurrence
records of Paphiopedilum spp.
|
|
Species |
State |
Locality |
District |
|
1. |
P. spicerianum |
Mizoram |
Lengpui |
Mammit |
|
2. |
P. insigne |
Meghalaya |
Laimotsiang |
East Khasi Hills |
|
3. |
P. insigne |
Meghalaya |
Latara |
East Khasi Hills |
|
4. |
P. insigne |
Meghalaya |
Mawlyndiar |
East Khasi Hills |
|
5. |
P. insigne |
Meghalaya |
Mawlyndiar (Liewla) |
East Khasi Hills |
|
6. |
P. insigne |
Meghalaya |
Sohra (Nohkalikai) |
East Khasi Hills |
|
7. |
P. venustum |
Arunachal Pradesh |
Dirang |
West Kameng |
|
8. |
P. venustum |
Meghalaya |
Sohra |
East Khasi Hills |
|
9. |
P. fairreanum |
Arunachal Pradesh |
Dirang |
West Kameng |
|
10. |
P. fairreanum |
Arunachal Pradesh |
Dirang |
West Kameng |
|
11. |
P. fairreanum |
Arunachal Pradesh |
Tenga |
West Kameng |
|
12. |
P. fairreanum |
Arunachal Pradesh |
Rupa |
West Kameng |
|
13. |
P. venustum |
Sikkim |
Upper Dzongu |
North Sikkim |
|
14. |
P.hirsutissimum |
Nagaland |
Tobu |
Mon |
|
15. |
P.hirsutissimum |
Nagaland |
Meluri |
Phek |
|
16. |
P. venustum |
Sikkim |
Mangan |
North Sikkim |
Table 4. List of environmental
variables.
|
|
Variable |
Description |
|
1. |
Bio_1 |
Annual mean temperature |
|
2. |
Bio_2 |
Mean Diurnal Range (mean of
monthly (max temp – min temp)) |
|
3. |
Bio_3 |
Isothermality (P2/P7)*(100) |
|
4. |
Bio_4 |
Temperature Seasonality
(standard deviation *100) |
|
5. |
Bio_5 |
Max temperature of the warmest
month |
|
6. |
Bio_12 |
Annual precipitation |
|
7. |
Bio_14 |
Precipitation of Driest Month |
|
8. |
Bio_15 |
Precipitation of Seasonality
(coefficient of variation) |
|
9. |
h_dem |
Digital elevation model |
|
10. |
h_ topoind |
Topographic index |
|
11. |
h_aspect |
Aspect |
|
12. |
h_slope |
Slope |
Table 5. Percent contribution of
variables in model build.
|
Percent contribution of
variable in Historical occurrence model |
Percent contribution of
variables in Actual occurrence model |
||||
|
Variable |
Percent contribution |
Permutation importance |
Variable |
Percent contribution |
Permutation importance |
|
bio_2 |
46.6 |
8.9 |
bio_12 |
41.9 |
0 |
|
bio_1 |
19.3 |
0 |
bio_2 |
29.1 |
43.1 |
|
h_aspect |
19.1 |
6.6 |
bio_5 |
24.4 |
53.1 |
|
h_dem |
6 |
27.5 |
h_topoind |
3.9 |
3.8 |
|
bio_14 |
4.3 |
0.2 |
bio_14 |
0.7 |
6.7 |
|
bio_5 |
3.7 |
55.3 |
bio_15 |
0 |
0 |
|
h_topoind |
0.7 |
1.3 |
h_slope |
0 |
0 |
|
bio_12 |
0.3 |
0 |
h_dem |
0 |
0 |
|
h_slope |
0 |
0 |
h_aspect |
0 |
0 |
Table 6. Comparison between
historical occurrence data and actual presence data.
|
Species |
State |
Locality |
Source |
Nearby Positive sites as per
field findings |
|
P. spicerianum |
Mizoram |
Mammit district |
Literature review |
Present |
|
Assam |
Cachar, Sonai river Bank, Barak river bank, Narpuh
WS. |
Literature review Herbarium data ENM depiction |
Not found |
|
|
P. fairrieanum |
Sikkim |
Tinkitam |
Literature review |
Previously presence reported.
Habitat Destruction due to ongoing agricultural practices (Jhum cultivation) |
|
P. insigne |
Meghalaya |
Cherrapunjee, Mawsynram, East Khasi hills |
Literature review, Herbarium
data, ENM depiction |
Present |
|
P. venustum |
Sikkim |
Beh, Tong, Sanklang (Sikkim) |
Literature Review, Herbarium
data |
Present |
|
Meghalaya |
Jaintia Hills |
Literature Review, Herbarium
data |
Not found |
|
|
P. fairreanum |
Arunachal Pradesh |
Gacham village, Rupa, Tenga valley |
Herbarium data |
Present |
|
P. fairreanum |
Arunachal Pradesh |
Jameri village |
Herbarium data |
Absent |
|
P. villosum |
Mizoram |
Sairep, Theiriat, Lunglei |
Herbarium data, ENM depiction |
Present |
|
P. hirsutissimum |
Manipur |
Maram |
Herbarium data |
Not found |
For figures &
images - - click here for full PDF
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