NMHS Progress Report (Period April 2019 to March 2020)
1. Project Information
Project ID: NMHS/2017-18/LG09/02 Sanction Date: Dated 22.12.2017
Conservation of Threatened Vertebrate Fauna in Indian Himalayan Region through Long-Term Monitoring and Project Title: Capacity Building
BTG: Biodiversity Conservation and Management
Dr. Kailash Chandra PI and Affiliation Zoological Survey of India, Prani Vigyan Bhawan (Institution): M-Block, New Alipore, Kolkata-700053, West Bengal
Name & Address
of the Co-PI, if ----NA----
any:
In the Indian Himalayan Region (IHR) the Long-Term Monitoring
Programs and capacity-building will aid in conservation and management of threatened vertebrates. Hence, to achieve the same in the project, we used a combination of different methods/
Structured techniques including sign surveys, camera trapping, non-invasive Abstract - DNA analysis as well as questionnaire surveys. The activities of the detailing the project started during April 2019. The intensive sampling was current year progress [Word conducted in all six districts/sites in IHR. Limit 250 words]: All the six sites (districts) were divided into 10×10 km grids and further 5×5 km girds for intensive sampling. The reconnaissance surveys were conducted to know the presence/absence and abundance of various vertebrate species. Further intensive areas
were identified for long term and intensive monitoring of the faunal 1 | P a g e groups based on considering weather conditions, terrain, and/or availability of logistics into account. A total of 437 trails covering with an effort of 3848 km has been made, which resulted in collection of 3550 noninvasive samples. Furthermore, a total of 18864 camera trap night, yielded detection of 59 different vertebrate species. About 1560 samples has been processed for DNA analysis during the period. Furthermore, climate change prediction for Galliformes, Himalayan Langur and Brown bear were performed and predicted the future distribution. Based on the preliminary analysis, 10 species have been selected for the detail population genetics, demographic, phylogeography and to predict the future distribution under the climate change scenario. Among which phylogeography of the Ibex, landscape and population genetics of Red panda have been performed. Further, demographic history of Arunachal Macaque and other species have also been performed.
Information on ethno-zoological and other socio-economics aspects was collected through questionnaire surveys in 318 villages and interviewed 3804 respondents at all 6 sites. Eight state level training workshops and one national level stakeholder workshops conducted. So far 8 manuscript published as an output of the project. One booklet on the threaten vertebrates of Indian Himalayan regions is in press which included the information about the species distribution, ecology, conservation threats and other major issue for 39 mammals, 49 birds, 19 fish, 4 amphibians and 15 reptiles. Flyers and other species specific information material have also been prepared for the awareness creation among the locals.
Project Partner Name Affiliations Role & Responsibilities
-----NIL------NIL------NIL-----
2 | P a g e
2. Project Site Details
Lahaul East West Project Site Uttarkashi Darjeeling East Siang & Spiti Sikkim Kameng
IHR States North Arunachal Arunachal HP Uttarakhand Sikkim Covered Bengal Pradesh Pradesh
Long & Entire Entire Entire Entire Entire Entire district Lat. district district district district district
Site Map Annexure-I (Detailed technical Report)
Site Annexure-I (Detailed technical Report) Photograph
3. Project Activities Chart w.r.t. Timeframe [Gantt or PERT]
Project Output Work Undertaken Activity Year 2019 - 2020 Qtr 1 Qtr 2 Qtr 3 Qtr 4
Objective 1 1. Procurement 1. Meeting with 1. Further 1. Awareness was 1. The objective of related, the State Forest intensive animal created among completed where To assess and chemicals, and Department for sign surveys and villagers, majority of monitor consumables. procuring camera trapping stakeholders and marked grids have threatened permissions for conducted and others and been covered with vertebrate 2. Preparation the Year-II and about 350 camera meeting with extensive fauna in the spatial submission of traps placed at Forest sampling in the IHR and distribution report to the different sites. Department five study areas of developing maps. different state Lahaul and Spiti, spatial forest Uttarkashi 3. Field based 2. Intensive 2. Continued the database department; Darjeeling, East data collected camera trapping, camera trapping Siang and East from the sign survey, and animal sign 2. Systematic Sikkim, for the different sites. other field based surveys in all the data collection Camera trapping, from the data were sites except the 4. Recruitment sign survey and different sites of collected from Lahaul and Spiti. of the project questionnaire 10 x10 km Lahaul and Spiti Staff for vacant survey and project staff grids; 5x5 Km noninvasive positions. based on the 3.Data entry, sample collection. study area size 3 | P a g e Project Output Work Undertaken Activity Year 2019 - 2020 writing and 2. A total of 623 3. Intensive compiling of camera traps has camera trapping, Research paper been deployed sign survey and and internal which resulted to DNA based evaluation 18864 camera study. meeting. trap night, yielding 59 DNA extraction Recruitment of individual from the the project staff vertebrate species Noninvasive for vacant from all study samples positions. areas. collected from
the different 3. Developed the study sites. spatial data base for the threaten 6. Preparation of vertebrates of Geo-spatial IHR and Booklet layers is ‘in press’ with all relevant information. (Please see Detailed technical report attached for more details).
Objective 2 1. Sample 2. A total of 741 1. Sample 1. A total 693 1. A total of 3550 collection has samples has collection was in samples collected animal samples so To study the been started in been collected progress at all six so far. A total of far collected from population Lahaul and from Lahaul and proposed sites in 496 samples has all the six sites in viability of Spiti, Himachal Spiti, Darjeeling the IHR. A total also been IHR. selected Pradesh. A total and East Siang of 567 non- processed for threatened of 192 samples districts in the invasive samples DNA extraction 2. DNA extraction species in IHR have been IHR. of different and further of 1672 samples for developing collected from animals have been genetic analysis. Sambar, Goral, long-term the area. collected. Species got Snow leopard, conservation identified using Brown bear, strategies 2. DNA 2. Analysis of 2. DNA extraction Black bear, Blue species genetic analysis. extraction and and PCR Species specific sheep, Ibex and occurrence data Primer amplification of markers have other species. from the Lahaul selection and samples for been identified procurement of and Spiti to population Demographic and identify the and primer got population primers for the viability and synthesized. different conservation population genetics analysis priority areas 4. Performed the of Red Panda,
4 | P a g e Project Output Work Undertaken Activity Year 2019 - 2020 species. genetics analysis. impact of climate Ibex, Blue sheep, change data Arunachal 3. Collection of analysis for Macaque and detail data on Galliformes, other related the different 3. DNA sequencing of Brown bear and species have been habitat Himalayan langur documented. about 471 parameters, in the Indian preparation of samples from the Apart from this, different study Himalayan raster files and regions and molecular other GIS sites, identify the taxonomy of IHR species, DNA predict the future related work. distribution. mammals also sequence quality been conducted check and for the 9 ungulate performed other species using the bioinformatics mitochondrial analysis. CYT B gene.
(Please see detailed technical report attached for more details).
Objective 3: 1. Long term 1. Field sign 1. Field sign 1. By conducting 1. Partially monitoring surveys and surveys, camera the field surveys, achieved. In the To set long- plots camera trapping trapping, sample potential areas are most of the sites term established in conducted in collection and being identified to we have identified monitoring the Lahaul and Darjeeling, East Socioeconomic be chosen as plots the long term protocol for Spiti, Sikkim, data collected for long-term monitoring plots threatened Uttarkashi, Uttarkashi, West from the all the monitoring. and repetitive vertebrate Darjeeling Kameng, study sites. samples from faunal groups based on the districts. 2. Around five these areas also in different preliminary 2. One manuscript areas have been been started from parts of IHR data analysis. 2. Analysis in were published on identified in the Lahaul and and to develop comparing Monitoring of Uttarkashi, 10 in Spiti and adaptive different mammals of Lahaul and Spiti, Uttarkashi, management monitoring trans-Himalaya 5 in Darjeeling Darjeeling. strategies for protocols of region and and 5 in East long-term mammals another was to Siang, and 5 in 2. Established the conservation distributed in the identifying the West Kameng. effecting of those Darjeeling and conservation monitoring species Lahaul and Spiti priority areas for protocols using through district. the brown bear in the Camera community the Lahaul valley. trapping and engagement noninvasive genetics
5 | P a g e Project Output Work Undertaken Activity Year 2019 - 2020 sampling.
Objective 4: 1 General 1. One wildlife 1. Training 1. Training Total 5 Training awareness is conservation programmer were programme programme To enhance being created awareness conducted in the conducted in the conducted for the capacities of among locals workshops has Lahaul and Spiti Uttarkashi, West stakeholders. different during field been organized district during the Kameng and East Where about 624 stakeholders surveys. at East Sikkim month of Siang in which participants have for monitoring Meetings were district. Total September, 2019. participants were been participated. and arranged with 135 participants In which total of from the conserving State Forest participated in 95 participants Arunachal Information and threatened Department to the workshop. have participated. Pradesh Forest awareness vertebrate conduct These Including the dept. and others material prepared fauna capacity which includes training participants BRO, ITBP, stakeholders. building the species workshop for were Local programs and the different representatives Administration. 2. Preparation to specific flyers of use of modern stakeholders. from the NGOs Police and conducted the Ibex, Red Panda, science, different Forest training Pangolin and technological 2. A training programme in the Hungul with the departments Department, tools and manual and such as Forest Monastery and East Sikkim and key findings approaches. information departments, villagers. Darjeeling. along with the flyers have local training manuals, been prepared and other administration, for the various awareness civil society, stakeholders. various State materials. GIS maps have govt. depts., also been colleges and prepared for the Schools etc. stakeholders in these areas.
Objective 5: 1. 1. 450 1. Data analysis 1. Manuscript is 1. A total 3804 Questionnaire Socioeconomic of socio- prepared from the respondents were Investigating surveys surveys have economics East Siang district interviewed to get whether conducted from been completed surveys data on to understand the the information ethno- the different from the the different different on ethono- zoological states. Which Uttarkashi. aspects such as dimensions of zoology. knowledge included the Human wildlife location and practices East Siang, conflict, local perception can be a where total perception towards the conservation 2. Whereas few 2. Data has been 1079 of the study sites towards the wildlife entered and measure for questionnaire wildlife conservation. such as East primary analysis threatened conducted and conservation. Sikkim and performed. vertebrate gathered Ethnozoological fauna in IHR Darjeeling also information on use of the Monitor 2. Data analyzed 3. Manuscript and
6 | P a g e Project Output Work Undertaken Activity Year 2019 - 2020 social and been covered. lizard in the East on the Human other output are cultural Siang district. wildlife conflict under progress. practices and from the east 2. Further, data Two manuscripts beliefs. Sikkim and analysis is under Lahaul and Spiti have been progress for the of Brown bear prepared on the different sites. and other species. ethnozoological 2. Compiled use of reptiles and and analyzed fish species from the data of the East Siang and these Lahaul and Spiti. questionnaire survey to Data analysis on understand the the use of different different animal aspects of body parts is ethnozoology. under the progress from the different sites.
(Other details are provided in the Technical Report)
4. Financial and Resource Information
Note: A separate bank account is expected to be opened for NMHS Project as per the provision of Direct Beneficiary Account (DBA) as laid out by the Govt. of India and also facilitate the audit of accounts. The interest earned out of the NMHS project funds should be reported clearly in the utilization certificate.
Project Staff Information:
Sl No. Research Fellow Qualification Designation Fellowship Paid Remarks 1 Dr. Harpreet kaur Ph.D RA-I 661636 Resigned 2 Dr. Shantanu Kundu Ph.D RA-I 662400 Resigned 3 Dr. Bheem Dutt Joshi Ph.D RA-III 1275880 4 Dr. Ashutosh Singh Ph.D RA-I 1200640 5 Dr. Chethan Kumar Gandla Ph.D RA-I 557390 Resigned 6 Dr Manish Bhardwaj Ph.D RA-III 344667 Resigned 7 Dr. Vinay Kumar Singh Ph.D RA-I 159800 8 Dr. Priyamvada Bagaria Ph.D RA-I 1461268 Resigned 9 Ms. Amrita M.Sc SRF 140496 10 Mr. Tanoy Mukherjee M.Sc SRF 119000 7 | P a g e Sl No. Research Fellow Qualification Designation Fellowship Paid Remarks 11 Mr. Paromit Chatterjee M.Sc SRF 93800 12 Ms. Chandra Maya Sharma M.Sc. SRF 483226 13 Mr. Manish Kumar M.Sc. SRF 481753 14 Mr. Ram Kumaran M.Sc. JRF 119426 Resigned 15 Mr. Rittam Dutta M.Sc. SRF 464729 16 Ms. AvantikaThapa M.Sc. SRF 490129 17 Ms Amira Sharief M.Sc. JRF 424548 18 Ms. Joanica Delicia Jyrwa M.Sc. JRF 424548 19 Ms. Ashwini Dinesh Nakil M.Sc. JRF 90018 Resigned 20 Ms. Romila Devi M.Phil JRF 227116 Resigned 21 Ms. Joyashree Sarma M.Sc. JRF 273368 22 Ms. Amrita Prakashi M.Sc, JRA 148142 23 Mr. Pradipto Kumar Ghose M.Sc JRF 55916 24 Mr. Hemant Singh M.Sc. JRA 408252 25 Mr. Ritam Dutta M.Sc. SRF 464729 26 Mr. Prasad Manoj Tonde M.Sc. JRA 102629 Resigned 27 Mr. Vineet Kumar M.Sc. JRA 350709 28 Ms. Moon Debnath PGDBF(Finance) JRA 293289 29 Ms. Debaleena Chatterjee M. Sc JRA 48806 30 Mr. Vipin Kumar Tiwari M. Sc JRA 48232 31 Anshika Yadav M. Sc JRA 124007 32 Mr Pujan Kumar Pradhan M.Sc. FA 284839 33 Mr Saurav Bhattacharjee M.Sc. FA 299853 34 Ms. Papiya Bhattacharya M. Sc FA 98323 35 Mr. Inder Singh B.Sc. FA 275600 36 Mr. Yomto Mahi B.Sc. FA 274000 37 Mr. Mihin Nipa B.Sc. FA 274000 38 Ms Ngilyang Anga B.Sc. FA 272839 39 Ms. Marry Pali B.Sc. FA 271639 40 Mr.Partha Sarathi Mohanta B.Sc. FA 75329 Resigned 41 Ms. Priyadarshini Mitra M.Sc. FA 57994 42 Mr. Niloy B.Sc FA 34064 Resigned
5. Equipment and Asset Information
S. No. Equipment Details (Make/ Cost Date of Photographs Lowest Name (Qty) Model) Installati of Equipment Quotation, on IF NOT Purchased 1. NA
8 | P a g e 6. Expenditure Statement and Utilization Certificate
Financial Position/Budget Funds S. No. Expenditure % of Total cost Head Sanctioned
I Salaries/Manpower cost 12055040 7069722 58.64
II Travel 7595425 2486439 32.73 III Expendables & Consumables 1215049 100000 8.236 IV Contingencies 898623 839013 93.36 V Activities & Other Project cost 17471534 8753731 50.10 VI Institutional Charges ------VII Equipments ------39235770 19248905 Total 49.05 Interest earned 39235770.00 19248905.00 Grand Total
Period Expenditure Statement Utilization Certificate (UC)
Attached separately Attached separately
7. Project Beneficiary Groups
Beneficiary Groups Work Done Achieved (Capacity Building)
No. of stakeholders trained 1. Locals are being made aware 1. Achieved partially and the of the biodiversity around them rest commitment will be and significance of completed in the coming conservation. quarter.
2. A total of n= 624 were trained w.r.t to monitoring of threatened species in the landscapes.
No. of Capacity Building One (1) National level and 1. Ongoing and the rest three Workshops/Trainings Eight (8) state level stakeholder will be conducted after June or workshop were organized whenever allowed as per the guidelines provided by Heath Ministry and other respective department in avoidance of
9 | P a g e COVID -19.
No. of Awareness and N = 8 1. Awareness and outreach Outreach programs conducted have been stated and will be continued for the remaining project duration in all sites with focus on school and colleges.
No. of Researchers/Manpower N= 30 researchers 1. In the different sites 4-5 developed locals trained and are continuously working with us including the college students as well as the locals and forest staff.
2. In the coming field seasons, locals (especially youth from secondary schools and colleges) will be trained on different ways to monitor fauna in their surroundings.
8. Project Progress Summary (as applicable to the project)
Description Total (Numeric) Description 1. Lahaul & Spiti district 2. Uttarkashi district 3. East Sikkim IHR States Covered 6 (Six) 4. Darjeeling District 5. West Kameng District 6. East Siang District Project Site/ Field Stations Base camp established in
Developed: all the six sites in IHR No. of Patents filed (Description): NA NA Article/ Review/ Research Paper/ 6 articles 8 Publication: 10 Under review
New Methods/ Modeling Mammalian monitoring Developed 1 methodology for Trans- (description in 250 words): Himalayan landscape
10 | P a g e No. of Trainings A total of 504 participants have participated in the 5 training programmes representing different Total four (5) training so far stakeholders including conducted (1 Uttarkashi; 1 forest department, police, Lahaul and Spiti; 1 West agriculture, local (No. of Beneficiaries): Kameng, 1 East Seang, 1 communities, local eAst Sikkim) collages, school, NGOs and # 504 participants media. Apart from this quiz and Drawing competition also been conducted for school students Workshop: Nil Nil .... (attach maps about location Attached in technical Demonstration Models (Site): & Report (Annexure I) photos) Livelihood Options: NA Total five training manuals developed, one booklet on Field manuals for the threaten vertebrate monitoring of threatened species. Four species specific Training Manuals: are developed for different flyers with key finding have stakeholders with species prepared and distributed specific flyers. among the different stakeholder. Processing Units: NIL Species Collection: Annexure I Species identified: Annexure I Database/ Images/ GIS Maps: Geo-spatial database of threatened species has been Annexure I created and will be shared to funding organization through web-service.
9. Project Linkages (with nearby Institutions/ State Agencies)
S. No. Institute/ Organization Type of Linkages Brief Description
10. Additional (publication, recommendations, etc.)
1. Detailed technical report containing all details analysis, results and other information 2. Ten publications are in review process based on preliminary findings.
11 | P a g e 11. Project Concluding Remark
Kindly update the following Progress Parameters for the Reporting Period:
Progress made Project Output Project against Monitoring against each Remarks Indicators (specified Objectives objective in Sanction Letter)
Objective 1: To assess 1. The secondary as 90% of the task has The filed work from the and monitor threatened well as primary data has been completed in the different study sites is vertebrate fauna in the been compiled Spatial present year. under progress, data Indian Himalayan data base for the for entry and analysis part is Region and developing threaten vertebrate under progress. spatial database. species have been prepared and information on about 126 species under the different category is been prepared as Booklet and is under publications of the IHR region.
Objective 2: To study 1. We have been 60% of the task has We have been collecting the population viability collecting data on been archived during data on population of selected threatened populations of the reporting period. abundance and species in Indian threatened species in occupancy of the Himalayan Region for the study landscape selected threatened developing long-term systematically. species following the conservation strategies Identified the area of most robust and standard high species abundance method available in the in the Darjeeling, field. Considering the Lahaul and Sptit and vastness of the study Uttarkashi. Total 623 area we are focusing on camera traps have selected species which deployed which are relatively more resulted to capture of 59 abundant in the study vertebrate species. landscapes. So far using the genetic based study, 2. The non-invasive we studied the (n=3570) samples Phylogeogrpahy of Ibex, collected form the Population genetics different sites out of structure of the Red which 1672 samples panda and Arunachal have processed for the Macaque. Further, DNA extraction and standardization of 600 samples processed molecular markers for for the DNA
12 | P a g e Progress made Project Output Project against Monitoring against each Remarks Indicators (specified Objectives objective in Sanction Letter)
sequencing and the different species. identified ~16 species. Further, details analysis of Population Viability of the species (PVA) will be conducted. Population genetic structure and other analysis have performed for the 5 species.
3. We have deployed camera traps in the study area for collecting data on population parameters such as occupancy, abundance and density. The PVA will be carried out after understanding the standing populations of the species in coming period of the project.
Objective 3: To set 1. In the most of the 1. The long-term long-term monitoring sites, we have identified monitoring plots have protocol for threatened the long term monitoring been identified in the all vertebrate faunal groups plots and repetitive six study districts based in different parts of samples from these areas on the species Indian Himalayan also been started from abundance, diversity and Region and to develop the Lahaul and Spiti, richness in the study adaptive management Uttarkashi and landscape. strategies for long-term Darjeeling. conservation of those 2. Established the species through effecting monitoring community protocols using the engagement. Camera trapping and noninvasive genetics sampling.
Objective 4: To 1. Total 5 training 1. Total five training enhance capacities of programmme have been programme have been different stakeholders conducted in the conducted for 2019-2020
13 | P a g e Progress made Project Output Project against Monitoring against each Remarks Indicators (specified Objectives objective in Sanction Letter)
(including Forest & different sites where in the study landscape Wildlife department about 624 participants after consultation with staff, local Institutions/ have been participated. the forest department colleges, local NGOs which is the main and local communities) stakeholder. for monitoring and 2. Awareness material conserving threatened 2. So far total of 504 have been prepared for participants attended the vertebrate fauna in the the different species and Indian Himalayan training programme from distributed among the Region through the different locals and training capacity building departments, college, manuals to field staff of programs and use of NGOs and School, the forest department. modern science, Police and locals. technological tools and approaches.
Objective 5: 1. A total 3804 1. The questionnaire Investigating whether respondents were surveys were largely ethno-zoological interviewed for gathering conducted in forest knowledge and information on ethono- fringe villages which are practices can be a zoology. identified by conservation measure systematically dividing for threatened the study landscapes in vertebrate fauna in 10 x 10 km and 5x5km 2. Data has been entered IHR grids. We have and primary analysis has attempted to cover been conducted. maximum number of 3. Manuscript and other villages and the numbers output are under of respondent in the progress. villages were selected following the methodology suggested by NSSO.
2. Two manuscripts have been prepared on the ethnozoological use of reptiles and fish species.
3. Data analysis on the use of different animal body parts is under the progress from the
14 | P a g e Progress made Project Output Project against Monitoring against each Remarks Indicators (specified Objectives objective in Sanction Letter)
different sites.
4. One manuscript on the Human brown bear conflict has been performed in the Lahaul valley.
Methodology (in brief): We worked on GIS and remote sensing data to generate spatial maps for the area. We further overlaid 10 x 10 and 5 x 5 km grids for intensive study. We conducted intensive sampling and surveys at all the six sites viz., Lahaul and Spiti, Uttarkashi, East Sikkim, North Bengal, West Kameng and East Siang. Animal sign surveys and camera trapping has been conducted at all six sites in the study landscape. Point count, call counts and transect walks have been conducted for inventorying the status of bird species across all the sites. Fish sampling has also been conducted in the streams and river across the study area. Non-invasive samples were collected and processing for DNA isolation is going on for understanding the population demography and other vital parameters imperative for understanding the viability of the species standing population. Information on ethno-zoology was collected through questionnaire surveys of n=3804 respondents of n=318 villages at all 6 sites. Eight state level training workshops and one national level stakeholder workshops have been conducted. The analysis of the different type of data is under process. Annexure-I: Detailed methodology is attached and with other information. One details survey was also conducted in the Darjeeling district to develop the conservation model where we interviewed about 465 respondents in and outside the protected areas. Major Research 1. We have conducted sign surveys to understand the presence/ Achievements: absence and abundance status of the vertebrate species in the study area. We also have conducted camera trapping at five sites across all IHR. 2. We have also collected non-invasive samples and initiated processing for DNA isolation for understanding the population demography and other vital parameters imperative for
15 | P a g e understanding the viability of the species standing population. Population genetics analysis of five species Red Panda, Ibex, Blue sheep Arunachal Macaque and Chinese pangolin was performed. Further, impact of climate change on the distribution of Himalayan Grey Langur, Himalayan brown bear and 15 Galliformes have been performed. 3. Further, we also have conducted surveys for gathering information on ethno-zoology through questionnaire surveys of n=3804 respondents of n=318 villages at all 6 sites. 4. Further 5 state level training workshops also been conducted.
Brief Conclusion (the current 1. Lahaul and Spiti, Himachal Pradesh year progress) during the During the first year of study, N= 197 camera traps were installed reporting: for 4520 nights (15-30 days each). 556 individuals were interviewed in 60 villages for socio-economic, ethno-zoological practices and wildlife presence & absence. Total 1070 biological Period (Point-wise): samples (scats, pellets, hair, feather, broken egg shells etc.) were (Throughout all 4 quarters collected during the study period from May 2018 to Feb 2020. So across all six sites in IHR) far 24 species mammals; >150 species of birds and 2 fish species detected from the study area. Monitoring protocols for mammals in the Trans-Himalayan region is validated, further conservation priority areas of brown bear identified from the Lahual valley
using the occupancy modeling as species distribution models of brown bear under the climate change scenario predicted. (Please see other details in Detail Technical Report annexure -1) 2. Uttarkashi, Uttarakhand 114 cameras traps were deployed for the period of 3563 nights in 25 grids of 10×10 Km. Each camera was placed for the period of 15-90 days. In 24 villages 561 individuals were interviewed for socio-economic, ethno-zoological practices and wildlife presence & absence. 559 biological samples were collected during the study period. Occurrence of species interaction of three sympatric herbivores were also conducted in the Uttarkashi district. Where presence of one species favors the presence of another species in the particular forest type and have the different level of tolerance to human disturbance as barking deer shows the positive relation with distance to villages whereas Goral shows the negative. Further, Monal occupancy and distribution pattern were also evaluation. Also using the camera traps, total 21 species of mammals including snow leopard, flying squirrel, Asiatic black bear and sambar in the Uttarkashi district. Further, 92 species of birds also reported during the survey period. Two training programs were also conducted in the Uttarkashi district 3. Darjeeling, West Bengal In total 114 cameras traps were deployed for the period of 3296
16 | P a g e days. From 59 villages 450 respondents were surveyed for socio- economic and ethno-zoological practices. During the study period 473 biological samples were collected belonging to (Leopard, Wild pig, Barking Deer etc.). Different monitoring protocols of mammals in protected and non-protected areas have been compared and found that non protected area also harbors the equal mammalian diversity. Monitoring plots established, survey conducted to develop the conservation models near the Singalila National Park. Habitat association of four galliforms and shows the different habitat governing the distribution of these species. Using the camera traps, total 22 species of mammals and 4 species of galliforms detected. Further, one tranning programme were organized in the Darjeeling for the different stakeholders. And intensive sampling is ongoing in the Darjeeling district. (Please see details in technical Report) 4. East Sikkim, Sikkim Only 75 cameras traps were deployed for 3750 nights. From 83 villages 492 individuals were interviewed for their opinion on wildlife interaction and socio-economic studies. 271 biological samples were collected for genetic analysis. Occupancy modeling, five monitoring plots and species distribution models have developed for the different species in the East Sikkim district. About 25 mammals such as musk deer, Red panda, barking deer golden cat, sambar etc. have been detected in the camera traps. Apart from this, there are total 120 species of birds detected. One training programme conducted the east Sikkim in which around 120 participants have participated. 5. East Siang, Arunachal Pradesh 57 cameras traps were installed for 900 nights each for 15-30 nights. 1079 respondents were interacted from 43 villages to collect the data on socio-economic, ethno-zoological practices and wildlife issues. 658 biological samples were collected during the study period. About the 25 mammalian species detected using the camera trapping; 2 manuscripts on the wildlife conservation, new reports of Monitor lizard and one training programme have been organized in the Pasighat. Apart from that 170 species of birds also reported during the survey. Vegetation sampling and other DNA related work is under progress. 6. West Kameng, Arunachal Pradesh Total 66 cameras traps were deployed for 2876 nights in the study area each for 15-45 nights. Using the camera trapping data around 13 mammals have been detected. Total 120 species of birds also reported during the survey. In total 666 individuals were interviewed from 49 villages in the study site. 552 biological samples were collected during the course of study. One training
17 | P a g e programme were conducted in the West Kameng district in which 68 participants from the different departments, school, ITBP, religious and NGOs have participated. Details provided in the Technical Reports attached
Progress Achieved (%): Approx. 70%
Remaining work to be done: 1. Some of the study area required and intensive sampling and camera trapping at all the six sites including Lahaul and Spiti, East Sikkim, Uttarkashi, West Kameng and districts in IHR. 2. Abundance estimation of the focal species across all sites in the IHR. Data analysis of ethenozoological analysis conflict and other different analysis. 3. DNA analysis, PCR amplification of the newly collected samples, DNA sequences and genotyping of the different species to understand population status, gene flow and phylogeography in the IHR. 4. Geo-spatial analysis coupling with genetics as well as population viability analysis.
Submitted to: Submitted by:
Nodal Officer, NMHS-PHU National Mission on Himalayan Studies (NMHS) Project PI (Signature): G.B. Pant National Institute of Himalayan Environment Institution (Seal): and Sustainable Development, Kosi-Katarmal, Dated :26/06/2020 Almora-263643, Uttarakahnd E-mail: [email protected]
Please fill the NMHS Progress Report pro forma as applicable with respect to time and other requirements and return via post/ e-mail.
18 | P a g e Annexure-I: Unpublished only for progress assessment purpose
Annexure-I Preliminary findings of the study
Unpublished information only for progress assessment purpose of the project Copyright © 2020 All rights reserved, Director Zoological Survey of India. Any reproduction in full or part of this document must mention the title and credit the mentioned publisher as the copyright owner. Contents Executive Summary
1. Introduction ...... 8 2. Study area ...... 10 2.1 Uttarkashi District ...... 10 2.2 Lahaul and Spiti ...... 11 2.3 Darjeeling ...... 12 2.4 East Sikkim ...... 13 2.5 East Siang...... 13 2.6 West Kameng ...... 14 3. Methodology in Brief ...... 14 3.1 Methodology opted for surveying Mammals ...... 15 3.2 Methodology opted for surveying Birds ...... 16 3.3 Methodology for questionnaire survey data collection ...... 16 3.4 DNA based analysis ...... 17 4. Data Analysis ...... 18 4.1 Camera traps data analysis ...... 18 4.2 Habitat covariates...... 18 4.3 Occupancy modelling framework ...... 19 4.4 Activity pattern ...... 20 4.5 Generalized linear modelling (GLM) ...... 20 4.6 Climate change, geo-spatial and species distribution modeling ...... 21 4.7 Variable preparation and selection: ...... 21 4.8 Model Preparation, Evaluation and Change Dynamics of Ensemble Models ...... 23 4.9 Biological connectivity and patch level habitat evaluation ...... 24 4.10 Species clustering and cohort formation ...... 25 4.11 Questionnaire survey (Human–wildlife conflict & locals perception analysis) ... 25 4.12 Data Analysis of mitochondrial DNA...... 26 4.13 Data analysis of microsatellites ...... 26 5. Preliminary Finding ...... 27 5.1 Preliminary finding of objective 1: To assess and monitor threatened vertebrate fauna in the Indian Himalayan Region and developing spatial database...... 27 Sample Collection, Species diversity and diversity indices...... 27 Captures rates of different species using camera trapping ...... 28 Geospatial data of threaten vertebrate‟s species in the Indian Himalayan region ...... 36
ZSI, Kolkata [1]
Annexure-I: Unpublished only for progress assessment purpose
5.2 Preliminary finding of Objective 2: To study the population viability of selected threatened species in Indian Himalayan Region for developing long-term conservation strategies...... 37 Occupancy modeling of different species from different sites ...... 38 Activity pattern ...... 49 DNA based analysis ...... 53 5.3 Preliminary finding for Objective 3: To set long-term monitoring protocol for threatened vertebrate faunal groups in different parts of Indian Himalayan Region and to develop adaptive management strategies for long-term conservation of those species through community engagement...... 69 5.3.1. Field testing of different methods for monitoring mammals in Trans-Himalayas 69 5.3.2. Monitoring and risk assessment of feral dog populations Lahaul and Spiti region. 71 5.3.3. Species Distribution models for the different species...... 79 5.3.4. Prediction of climate change on different species ...... 83 5.3.5. Long term monitoring plots ...... 102 5.3.6. Vegetation Sampling ...... 103 5.3.7. Micro-histological analysis of the faecal samples ...... 106 5.4 Preliminary finding for the objective 4: To enhance capacities of different stakeholders (including Forest & Wildlife department staff, local Institutions/ colleges, local NGOs and local communities) for monitoring and conserving threatened vertebrate fauna in the Indian Himalayan Region through capacity building programs and use of modern science, technological tools and approaches. 114 5.4.1. Capacity Building ...... 114 5.5 Preliminary finding for Objective 5: Investigating whether ethno-zoological knowledge and practices can be a conservation measure for threatened vertebrate fauna in IHR ...... 125 5.5.1. Questionnaire survey ...... 125 5.5.2. Human-Wildlife Conflicts ...... 132 5.5.3. Ethnological Studies ...... 138 Referencess ...... 144
List of Figures
Figure 1. Study area map of six study district ...... 10 Figure 2. Elevation wise placement of cameras ...... 30 Figure 3. Camera Trap images of mammalian species in the Salu top in Mukhem Range: A) Barking deer, B) Himalayan porcupine, C) Sambar female, D) Sambar male ...... 32 Figure 4. Camera Trap images of different species A&B) Asiatic black bear C) Kalij Pheasant, C) Himalayan Grey Langur in Gangotri range...... 32 Figure 5. Camera Trap images of A and B) Leopard in Mukhem and Taknor range in the Uttarkashi Forest Division C) Yellow throated marten and D) Monal in Dayra in the Uttarkashi Forest Division...... 33 Figure 6. Camera Trap images of Sambar male & female and goral in different days in the Gangotri range in the Uttarkashi Forest Division...... 34
ZSI, Kolkata [2]
Annexure-I: Unpublished only for progress assessment purpose
Figure 7. Figure 10. A) Female extracted algae from the oak tree, B) Livestock grazing of goat and sheet, C) Male (Locally known Gujjar) owning mass grazing of buffalo in the Uttarkashi district D) Buffelo grazing in the Badkot Forest division E) Cow grazing in the forest F) female collecting fodder for Cattel’s ...... 35 Figure 8. Number of trails species and kilometers walk in the different study sites...... 35 Figure 9. Direct sighting of different species: From right top to left bottom, Monal, Himalayan tahr, Serow, and Goral...... 36 Figure 10. Examples of booklet and geospatial data for the Threatened vertebrates’ species in the Indian Himalayan regions...... 37 Figure 11. Himalayan brown bear occupancy probability based on top model in the Lahaul Valley, Himachal Pradesh...... 39 Figure 12. Covariates effect on the occupancy of three ungulate species. Influence of forest type (West Himalayan Sub-alpine birch/fir Forest (FT 188) and distance to village covariates on the occupancy of ungulates (barking deer, goral and sambar) in uttarkashi, showing the estimated beta values of species specific and pair wise interaction estimated from multi species occupancy models...... 44 Figure 13. Temporal activity pattern of three ungulate species (a) goral,(b)barking deer, (c) sambar...... 49 Figure 14. Overlapping activity pattern of (a) barking deer-goral (b) Goral-sambar, (c) sambar- barking deer...... 50 Figure 15. Temporal activity pattern of (a) Himalayan monal (b) Human & (c) overlapping activity pattern of Himalayan monal-humans in Uttarkashi...... 51 Figure 16. Temporal activity: a) Satyr Tragopan b) Hill Partridge c) Blood Pheasant d) Kalij Pheasant e) Temporal overlap between Blood Pheasant and Satyr Tragopan f) Temporal overlap between Kalij Pheasant and Hill Partridge...... 52 Figure 17. Visualization of isolated DNA from different ungulates pellet on 0.8% Agarose. Lane no. 1-13 scrap of outer surface of pellet and Lane no. 14-15 for use of half pellet...... 53 Figure 18. In-silico PCR multiplexing to labeled the dye of microsatellite markers that was tested in the present study to select the microsatellite loci...... 54 Figure 19. Phylogenetic tree identifying the 9 species of herbivores ...... 55 Figure 20. Phylogenetic tree identifying the 9 species of herbivores ...... 55 Figure 21. Map showing the sample locality from India used in this study ...... 59 Figure 22. Phylogeographic relationship in the genus Capra throughout its distribution. A) Bayesian based phylogenetic analysis performed using BEAST v. 2.1.3. (Molecular dating was undertaken using the normal prior with a mean 2%/My following the universal evolutionary rate of vertebrate Cyt b mitochondrial DNA. Above the node- divergence time is given and below the node is the posterior probabilities. Light green color on map depicts the entire distribution of Siberian Ibex as per IUCN which is overlaid by three colors with respect to three clades, i.e. purple representing I-T clade, light pink representing KZ clade and Sandy brown representing AMR clade. IUCN distribution of Capera ibex and Capera nubiana are represented by the dark pink and dark red color on the map. On the left on the map, haplotypes, H1 to H24 are represented in different color representing different locality as per their geographical origin). B). the mini tree of the detailed BEAST based phylogeny is representing sequence divergence between the major clades of the ibex ...... 60 Figure 23. a) Maximum likelihood and b) Bayesian phylogenetic relationships of the red panda clade based onand CR (436 bp) DNA sequences. Values at nodes correspond toposterior
ZSI, Kolkata [3]
Annexure-I: Unpublished only for progress assessment purpose
probabilities > 0.50. Yellow clade A, Red clade B, Sky blue clade C, Orange clade D, Green clade E, Blue clade F ...... 63 Figure 24. Median-joining network based on maximum parsimony among mtDNA CR haplotypes of red pandas. Nodes contain the haplotype name and are proportional to haplotype frequencies. Length of branches is proportional to the number of changes from one haplotype to the following, the lines next to the branch representing more than one mutation step...... 64 Figure 25. Demographic history of red panda populations estimated using Bayesian skyline plot. (a) the Himalayan red pandas and (b) the Chinese red pandas. Bayesian skyline plot showing a population decline during recent past for the Indian population whereas the Chinese population showing a growth. The solid line shows the median estimates of Ne τ (Ne = effective population size; τ = generation time), and the blue area around median estimates show the 95% highest posterior density (HPD) estimate of the historic effective population size. The timing of events was estimated assuming a substitution rate of 1.9 × 10–8 substitutions/site/year (Hu et al., 2020)...... 65 Figure 29. Population assignment inferred by the STRUCTURE from the four seizure of Indian and Chinese pangolin...... 68 Figure 30. Graphical representation of most possible clusters (delta K) inferred by STRUCTURE HARVESTER...... 68 Figure 31. Study area map showing surveyed grids in Lahaul & Spiti District, B). Info graphic showing valley wise presence of mammalian species in the Lahaul and Spiti district of Himachal Pradesh...... 71 Figure 32. Feral dog depredating on Marmot Carcass in the Chandratal Lake...... 73 Figure 33. Images capture of feral dogs in the different areas of Spiti valley...... 73 Figure 34. DNA extracted from scat samples of carnivores using Qiagen kit on 0.8 % agarose gel. M, MW marker 100bp ladder...... 74 Figure 35. PCR products of nearly 150 bp from ATP6 primer with DNA templates (n=1) on 2% agarose gel. M, MW marker 100bp ladder -ve, negative control...... 74 Figure 36. DNA sequences chromatogram of Canis lupus familiarisusing the ATP6 gene from the samples collected from the Lahaul and Spiti regions...... 75 Figure 37: A) Graph of ensembled model depicting trends of distribution of feral dogs. Models evaluated with AUC (>0.75), and then weighted with previous metrics means, B) Graph depicting variables which govern the distribution of feral dogs in Lahaul and Spiti. Variable relative contribution evaluated with Pearson...... 77 Figure 38: Showing areas of Lahaul and Spiti infested by feral dogs categorized into five classes ...... 77 Figure 39. Feral dogs chasing the Ruddy shelduck in the Chandratallake: 1. Dogs started chasing duck after pointing the location; 2 Dogs about to reache at the location of Ruddy shelduck and duck moved apart from there; 3. Dog finally reached at location and went inside the water for few steps...... 78 Figure 40. Image shows the presence of feral dogs in the same location where ibex captures in the Ladrcha top at 4100 meter altitude...... 79 Figure 41. Species distribution models for the different species in the Uttarkashi and Lahaul and Spiti region...... 81 Figure 42. Species distribution maps of five dominant species in the West Kameng district...... 81 Figure 43. Species distribution maps of five dominant species in the East Sikkim district...... 82 Figure 44. Species distribution maps of five dominant species in the Darjeeling district...... 83
ZSI, Kolkata [4]
Annexure-I: Unpublished only for progress assessment purpose
Figure 45. Map showing the study area boundary and the presence records of HBB (Himalayan Brown Bear). Protected areas (PAs) within the study area boundary was represented by crossed boundary polygon. The clolour scale represents the elevation gradient of the study landscape, with lower elevation represented by (Green) and the higher elevation represented by (Red)...... 84 Figure 46. Represents the Pearson’s correlation among the variable...... 86 Figure 47. Model evaluation plots, representing average training ROC of both training and cross- validation (CV) for the replicate runs under four models were a. showing ROC plot of boosted regression tree (BRT) b. generalized linear model (GLM), c. multivariate adaptive regression spines (MARS), d. Random forest (RF)...... 89 Figure 48. Showing the possible biological connectivity for HBB in different habitat suitability scenarios. The current flow in the maps has been represented by the colour tamp from low to high. a. represents the possible biological connectivity in the present habitat conditions. Figure b and c represent the possible biological connection the projected habitat conditions in the future habitat conditions for the year 2050 in RCP 4.5 and RCP 8.5 respectively...... 94 Figure 49. (a) Principal Component Analysis based ordination of the species with respect to the attached environmental variables showing associations among groups of species (b) Euclidean distance based K-means clustering of the species. Note: 1-Black Francolin, 2- Blood Pheasant, 3-Blyths Tragopan, 4-Cheer Pheasant, 5-Chestnut-breasted Partridge, 6- Grey Peacock, 7-Hill Partridge, 8-Himalayan Snowcock, 9-Humes Pheasant, 10-Kalij Pheasant, 11-Koklass Pheasant, 12-Himalayan Monal Pheasant, 13-Mountain Bamboo Partrdige, 14-Rufous-throated Partridge, 15-Satyr Tragopan, 16-Sclater’s Monal, 17- Snow Partridge, 18-Temnick’s Tragopan, 19-Tibetan Partridge, 20-Western Tragopan, 21-Wihte-cheeked Partridge...... 96 Figure 50. Distributions predicted by the SDM and BEM (current) frameworks, for the three Galliformes clusters. Note: WRVG - Wide ranging, all Vegetation type Galliformes; EHDVG - Eastern Himalaya, Dense Vegetation affinity Galliformes; WHSVG - West Himalaya, Sparse to moderate Vegetation Galliformes; SDM – Species Distribution Model; BEM – Bioclimatic Envelope Model. . The base map is a Digital Elevation Model (DEM) from Shuttle Radar Topographic Mission (SRTM), NASA...... 99 Figure 51. Predicted gains, losses and habitat retentions of the bioclimatic envelopes among the three clusters, in the face of predicted climate change for the year 2070 (RCP 4.5 and RCP 8.5). Note: WRVG - Wide ranging, all Vegetation type Galliformes; EHDVG - Eastern Himalaya, Dense Vegetation affinity Galliformes; WHSVG - West Himalaya, Sparse to moderate Vegetation Galliformes. . The base map is a Digital Elevation Model (DEM) from Shuttle Radar Topographic Mission (SRTM), NASA...... 100 Figure 52. Monitoring plosts laid in the different study sites...... 103 Figure 53. Systematic vegetation sampling using quadrate method (1x1m for Herb, 5 m for Shrub and 10 m for Tree) following every 500 m in Trail/Transect...... 104 Figure 54. Some selected images of plant material collected and maintained records as herbarium sheets...... 105 Figure 55. Reference slides of different plants species ...... 106 Figure 56. Image showing microscopic pattern of monocots and dicots remains in the faecal samples of ungulates ...... 107 Figure 57. Microscopic structure of hair of different prey species found in snow leopard scat samples from the Lahaul and Spiti valley...... 111
ZSI, Kolkata [5]
Annexure-I: Unpublished only for progress assessment purpose
Figure 58. Lecture on the Human Wildlife Conflict Mitigation emphasized the role of Rapid Response Team in Rescue and Rehabilitation of the Conflict Wild Animals...... 115 Figure 59. Banner of two-day training programme held in Uttarkashi...... 117 Figure 60. Different images of activity during the training program...... 118 Figure 61. Participants are also intracting with the hands on training on the cametra trapping and other euipments...... 118 Figure 62. Different activities during the training programmes in the different sites...... 119 Figure 63. Training manual developed for workshop conducted in the Uttarkashi ...... 120 Figure 64. Project flyer on aim and objectives and other information of the project...... 121 Figure 65. Format for the knowledge testing and feedback form about participants knowledge on local conservation issue and wildlife species...... 122 Figure 66. Drawing competition of students during the training programme ...... 122 Figure 67. Local and College students participating in the Extempore speech on the Wildlife and wildlife conservation...... 123 Figure 68. College students participating in the Extempore speech and Gram Budha expressing his views on the wildlife and wildlife conservation...... 123 Figure 69. Training programme covered in the different print media...... 124 Figure 70. Local perception in the different Himalayan ranges ...... 126 Figure 71. Distribution of opinions on wildlife conservation according to the socio-economic status (modified Kuppuswamy scale, 2018) of the 1079 respondents...... 129 Figure 72. A likert-scale distribution of opinions concerning wildlife conservation (n=1079) .... 130 Figure 73. Human-Wildlife Conflict reported by locals in East Siang District, Arunachal Pradesh ...... 133 Figure 74. Distribution of animal conflict cases admitted by the respondents in their respective circles ...... 133 Figure 75. Human wildlife conflict percentage in the Uttarkashi district...... 134 Figure 76. Kuppuswamy Socio-economic status of respondents (n=400)...... 135 Figure 77. Timing of raiding of Himalayan brown bear in Lahaul and Spiti as reported by respondent (n=400) ...... 136 Figure 78. Season wise crop damage and livestock depredation by Himalayan brown bear in Lahaul as reported by respondents (n=400)...... 137 Figure 79. Himalayan brown bear crop damage and livestock depredation by distance from forest/brown bear habitat...... 137 Figure 80. At different altitudinal zone crop damage and livestock depredation by Himalayan brown bear in Lahaul as reported by respondents (n=400)...... 138 Figure 81. Graph showing hunting pressure due to various use in ethno-zoological aspect ..... 140 Figure 82. Graph showing hunting pressure due to various use of wild animal body parts ...... 140 Figure 83. Glaucostegus granulatus skin (a) and bone (b). Maximum Likelihood tree based on 12S rRNA (c) and cyt b (d) sequence showing the relationship between the collected specimen (in box) and other suspected close relative species...... 142
ZSI, Kolkata [6]
Annexure-I: Unpublished only for progress assessment purpose
Executive Summary
Reconnaissance surveys in all the district were conducted where the potential sites were identified for undertaking extensive survey for the monitoring of threaten vertebrate. Initially the information about the wildlife presence was obtained through having interactions with locals from the different villages which were located in fringes forested areas. We used different survey methods to explore vertebrate diversity which comprises of camera trapping, sign survey and DNA based monitoring.
During the month of June, 2018 to February, 2020, a total n=437 trails were walked systematically in the different six study sites an effort of 3848 km. We placed a total of 643 cameras traps in the selected grids of 10×10 km each for the Uttarkashi and Lahaul and Spiti and 5×5km for the rest of sites. In these cameras, we captured 50 mammalian species and few bird with the capture rate of 0.01-0.0.47. Among different species captured, Sambar and barking deer and wild boar were captured in most of the sites. Whereas, rare species such as Snow leopard, brown bear, Musk deer, Red panda and Chinese pangolin were also captures from the different sites. In addition, we collected a total of 3550 samples of different species using non invasive methods.
During the systematic field surveys, we also made observations of the avian fauna in the study area. A total of 250 species of bird were observed so far, of which the details climate changes prediction of 16 Galliformes species were conducted in the Indian Himalayan Region. Comprehensive DNA protocols for the identifying fecal samples were standardized. A set of different mitochondrial and microsatellite loci were selected for undertaking the phylogenetics, phylogeography and population genetics analysis. Populaitn genetics analysis of 5 species have been done from the different sites. Apart from this, systematic vegetation sampling was also carried out and histological analysis of collected samples is under progress to understand the feeding habits and foraging behavior of the mammalian species in the area. Feeding analysis of Snow leopard and Leopard from the Darjeeling and Lahaul and Spiti district also been conducted where total of 11 and 10 wild prey species were identified.
ZSI, Kolkata [7]
Annexure-I: Unpublished only for progress assessment purpose
Understanding, the dependency of local on the forest resources and severity of conflict faced by local a total of n=3804 interviews in 318 villages were conducted in all study district. Of these 200 respondent were analyzed indicates that about 70% respondents reported human-wildlife conflicts, and majority of them reported crop depredation by wild boar, porcupine, monkeys, followed by livestock killing and rare human attack instances by large carnivores most prominently the common leopard and Asiatic black bear. However, despite of the existing human-wildlife conflict situation most of the respondents have positive attitude towards the wildlife conservation and wildlife which is a welcome for the long term conservation and management of the species. Further details analysis on the ethnozoology, human–brown bear conflict in the Lahual and Spiti and other sites also conducted.
Total eight two days training programme were organized at different study sites where total of 794 participants have attendant the training programme. The training programme was conducted for the different Stakeholders. The training programme had the invited lectures from the experts and demonstration sessions on the camera trapping, use of GPS, spotting scope, and other equipments. These participants belonged to the Forest Department, Gram Panchayat, School students, BRO, Police, Veterinary Doctors ITBP, district administration, NGOs, Religious Institute and College & School teachers and Van Sarpanch
1. Introduction
The Mountain range of the Himalayas is biodiversity rich, and has evolutionary and ecological significance due to its complex history of evolution (Nayar, 1996; Pandit et al, 2014). The region falls in different states of India i.e., Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, Northern part of West Bengal and Arunachal Pradesh. Together these states are known as Indian Himalayan Region (IHR). Geologically these regions are divided into two areas, the Western Himalayas containing the states of Jammu and Kashmir, Himachal Pradesh, Uttarakhand and the Eastern Himalayas containing the states of Sikkim, Northern part of West Bengal and Arunachal Pradesh. But over the last few decades IHR has experienced rapid land use change due to rapid human population growth through activities like agriculture, forest encroachment for settlements, tourism
ZSI, Kolkata [8]
Annexure-I: Unpublished only for progress assessment purpose etc. (Pandit et al, 2007). There have been studies to document and quantify the faunal diversity and their biological extinctions and threat levels to those animals partly or whole (Behera, 2005; Xu et al, 2009). But no studies have been ever made to assess and monitor the faunal groups, especially threatened ones focusing long-term conservation. Therefore, understanding the various factors influencing the biodiversity such as climate change, global temperature increase, habitat fragmentation, hiding of biological corridors are serious threats for the biodiversity in this regions. Mitigating these threats are serious challenges and requires a series of ecological, evolutionary and other multidisciplinary studies, which gather the information on the spatial distribution, evolutionary history, demography, population viability and landscape feature.
Objective of Project
1. To assess and monitor threatened vertebrate fauna in the Indian Himalayan Region and developing spatial database.
2. To study the population viability of selected threatened species in Indian Himalayan Region for developing long-term conservation strategies.
3. To set long-term monitoring protocol for threatened vertebrate faunal groups in different parts of Indian Himalayan Region and to develop adaptive management strategies for long-term conservation of those species through community engagement.
4. To enhance capacities of different stakeholders (including Forest & Wildlife department staff, local Institutions/ colleges, local NGOs and local communities) for monitoring and conserving threatened vertebrate fauna in the Indian Himalayan Region through capacity building programs and use of modern science, technological tools and approaches.
5. Investigating whether ethno-zoological knowledge and practices can be a conservation measure for threatened vertebrate fauna in IHR
ZSI, Kolkata [9]
Annexure-I: Unpublished only for progress assessment purpose
2. Study area
Total six study sites were selected for surveying vertebrate fauna. all the six sites (districts) were divided into 10×10 km grids and further these grids were divided into the 5×5 km girds for the intensive sampling. The reconnaissance surveys were conducted to know the presence/ absence and abundance of various vertebrate species in the different study sites (Fig. 1).
Figure 1. Study area map of six study district
2.1 Uttarkashi District
The district Uttarkashi is for the most part known as a sacred town near Rishikesh. It is situated in the territory of Uttarakhand in India. Situated on the banks of Bhagirathi, and is located at 30.73°N 78.45°E. Covering mostly hilly terrain with an average elevation of
ZSI, Kolkata [10]
Annexure-I: Unpublished only for progress assessment purpose
1,165 m, many rivers flows within the district, in which the Yamuna and the Ganges (Bhagirathi) are greatest and holiest among them, their starting point is Yamunotri and Gangotri (Gomukh) individually. Uttarkashi is rich in term of biological resources, biodiversity and other places of religious, cultural and eco-tourism. Besides there are several mammals species occur such as Asiatic black bear, Brown bear, Musk deer, Common leopard, Snow leopard, Blue sheep, Himalayan tahr and Serow. Moreover, the district also harbors the populations of the Himalayan monal and many other important bird species. Whereas the floral resource of the district contains western Himalayan, broadleaf woods at its most reduced heights, changing to western Himalayan subalpine conifer backwoods and western Himalayan alpine shrub and meadows at its most noteworthy rises. Trees exhibit in the lower parts are pine, deodar cedar, oak and different deciduous species. The entire district is divided into three Forest Divisionsi) Uttarkashi Forest Division ii) Upper Yamuna Badkot Forest Division & iii) Tons Forest Division) and also has two Protected Areas (PAs) i) Gangotri National Park & ii) Govind Pashu Vihar National Park. The forested habitats of the study landscape are home to top conservation priority species including Snow Leopard, Musk deer, Himalayan brown, Tibetan wolf, bear and Western Tragopan Himalayan monal. The whole district is chanractized the different forest types ranged from mixed tropical forest, Pine forests (1,100 and 1,800 m). Mixed oak forests (1800 and 2800 m) and posses the high biodiversity, coniferous forests (2800 and 3400m) and vast alpine pasturelands (>3400m) which are home for the Himalayan brown bear, Tibetan wolf, Snow leopard etc. (Fig. 1). 2.2 Lahaul and Spiti
The Trans-Himalayan district of Lahaul and Spiti (L&S) extends from 31°44'57" to 32°59'57"N latitudes, 76°46'29" to 78°41'34"E longitudes between the Pir Panjal Mountain chains of the Greater Himalaya and Trans Himalaya (Aswal and Mehrotra 1994). It is the largest district in Himachal Pradesh (HP) with a total area of 13,841 km2 posses forest cover of about (1.11%) with varying elevation from 2400 to 6400 m (Fig. 1). The forested areas of the district are managed under two Forest Division viz., Lahaul Forest Division (Territorial) and Spiti Forest Division (Wildlife). The Spiti region presents typical arid or xeric conditions whereas the Pattern valley posses a mix of great Himalayan as well as Trans-Himalayan conditions. The Spiti region is bestowed with
ZSI, Kolkata [11]
Annexure-I: Unpublished only for progress assessment purpose three Protected Areas (Pin Valley National Park, Kibber Wildlife Sanctuary, and Chandratal Wildlife Sanctuary), whereas the Lahaul region does not have any protected area. The physiographic structure is peculiar with perpetual snow-covered peaks and valleys with high steep and undulating terrain. The land cover is largely represented by subalpine vegetation, rolling grassland meadows, agricultural land and snow-covered permafrost area. Owing to harsh climatic conditions, poor rainfall, and short growing season, the Trans-Himalayan mountains slopes of the landscape support low vegetation cover (<20%), and are known to harbour a unique assemblage of wild flora and fauna (Joshi et al. 2006). 2.3 Darjeeling
Darjeeling is the northern most district of the state of West Bengal in India and is a part of the Eastern Himalayan Hotspot mostly mentioned in conjugation with the adjacent state of Sikkim as “Sikkim-Darjeeling Himalayas” (Bose & Mukherjee, 2019; Chakraborty et al., 2011; Hazra et al., 2002). The district shares its boundaries with countries like Nepal, Bhutan and the Indian state of Sikkim (Fig. 1). The annual precipitation is characterized by monsoon with average annual precipitation of 2547 mm; with the highest average precipitation i.e., 781.5 mm in July. The temperature of Darjeeling ranges from 14 to 24° C in summers and during winters between 5 to 8 °C with its highest hill tops covered with snow from December to February (Darjeeling Climatological Table 1901–2000, 2015). The Darjeeling district has four protected areas (PAs) viz., Singalila National Park, Senchal Wildlife Sanctuary, Mahananda Wildlife Sanctuary and Jorepokhri Salamander Sanctuary. Out of which this study spans the following: Singalila National Park (SNP) which has an area of 108.8 square km has an altitudinal range starting from 2400 m to 3636 m. It shares an open boundary with Nepal throughout its western border and with the state of Sikkim in the north. It has temperate and alpine forest. It allows tourism in the boundaries and has scanty settlements for the said purpose. Senchal Wildlife Sanctuary (SWLS) is spread over 38.97 square km area across an altitudinal range of 1500 m to 2600 m. The forest types here range from tropical to temperate. It has few settlements in the core and is surrounded by small settlements on the edges with roads running through the forests. Territorial forests (TERR) are forested land on which moderate and planned extraction of domestic fuel
ZSI, Kolkata [12]
Annexure-I: Unpublished only for progress assessment purpose wood and fodder for cattle is permitted. These forests are planted with exotic species like Cryptomeria japonica, Pinus petula, other non-exotic species like Alnus nepalensis, Eury aacuminata. These forests are found as mosaics with small to large settlements and roads and are maintained by the forest department.
2.4 East Sikkim
Sikkim, one of the smallest States of India, is situated at the western extremities of the Eastern Himalaya, nestled in the lap of Mount Khangchendzonga, the 3rd highest peak (8,585 m) in the world. These forest systems harbour some of the unique and endemic species of Himalayan flora and fauna. East Sikkim located in Sikkim, covers 964sq. kilometers with three subdivisions namely, Gangtok, Pakyong and Rongli. Gangtok is the districts headquarter (Fig. 1). The climate of the district has been roughly divided into the tropical, temperature and alpine zones. The major drainage system in east district is Teesta, Rangpo Chhu and Dik Chhu. The district host three protected area namely Pangolakha Wildlife Sanctuary, Fambong Lho Wildlife Sanctuary and Kyongnosla Wildlife Sanctuary which covers area of 190 sq km. Along with Wildlife Sanctuary the district also host reserve forest which also supports both floral and faunal diversity very significantly. East Sikkim Faunal diversity includes red panda, Asiatic black beer, Chinese pangolin, clouded leopard, leopard cat, marbled cat, snow leopard, red fox, wild boar, barking deer, ghoral, serow, Himalayan thar and other small mammals include large Indian civet, Small Indian Civet, Masked Palm Civet, Small Clawed Otter, Common Otter Indian, Crested Porcupine and many more. The reserve forest in Sikkim also holds significant faunal diversity. This study considered two wildlife sanctuary and reserve forest under six territorial range in east Sikkim. 2.5 East Siang
East Siang is one of the oldest districts of the state of Arunachal Pradesh, India and is the part of Indian Himalayan region (IHR). The main town, Pasighat lies about 155 meters above the sea level and is known to be the first connecting link between the state and the mainland India. Recently, the district had experienced changes in the boundary as the authorities have proposed a reformation. The district has now shrunk from 4,005 Km2 to 1800 Km2 and remains with 7 circles viz., Bilat, Mebo, Namsing, Pasighat, Sille-Oyan,
ZSI, Kolkata [13]
Annexure-I: Unpublished only for progress assessment purpose
Ruksin and Yagrung. People who live here comprise mainly of Adi, Mishing and Galo tribes with few inhabitants from adjacent villages and the state of Assam (Fig. 1). The locals are mainly dependent for livelihood on agriculture, followed by government services and education. The mode of communication is generally in Hindi and Adi.
2.6 West Kameng
West Kameng is a district from Arunachal Pradesh, 7,095.06 Sq.km West Kameng covering a total area of 7422 sq. km that is located approximately between 91° 30' to 92° 40' East longitudes and 26° 54' to 28° 01' North latitudes (Fig. 1). Kameng river, tributary of Brahmaputra flows within the district. International boundary of this district is shared with Tibet in the north, Bhutan in the west, Tawang District in the northwest, and East Kameng district in the east. Sessa orchid wildlife sanctuary and Eaglenest Wildlife sanctuary covering an area of 217 sq km are the only protected area located in this district. Having very rich bird diversity, it is very famous for its three unique Tragopan species. Hepetofauna includes several species of lizrads, skinks and different types of snakes. Endangered mammal species includes capped langur, Asian elephant, red panda, Asiatic black bear, Endangered Arunachal macaque, Namdapha Flying Squirrel, Chinese Pangolin, Himalayan Musk Deer, Himalayan Serow, Himalayan Palm Civet etc.
3. Methodology in Brief
To collect the information from the local people about the wildlife presence multiple- choice question (MCQ) were designed to undertake the questionnaire survey. We undertook reconnaissance survey and interaction with the locals, and other sources of information to know the presence of wildlife species, level of human disturbance and other factors that affecting the wildlife presence. We identified the key sites for the species occurrence and planned to our subsequent extensive and intensive sampling/survey. Further, selected grids were surveyed to understand and explore the diversity of vertebrates‟ species using the different survey methods for the targeted species. The study area was divided into 5×5 Km grids (Darjeeling, East Sikkim and East Siang) and 10×10 Km grids (Lahaul and Spiti, Uttarkashi and West Kameng) to
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Annexure-I: Unpublished only for progress assessment purpose maximize our effort so that all logistically accessible grids could be covered. The grids fallen in heavy human settlements were not covered. The field surveys were conducted during 2018-2020 in a different month in the in the all study sites. The selected grids were visited by a team of researchers systematic for the detection/non-detection of different vertebrates species. Four sign survey all selected grids trails of 2-10 km length were walked in the selected grids for documenting mammalian fauna from July 2018– Febeuary-2020. For all direct or indirect signs (direct sighting, faecal matter, hair, pug mark, hoof mark) of mammals locality name, GPS coordinates, altitude, sign type, terrain, forest type along with photographs were recorded. Camera traps were deployed in selected grids.
3.1 Methodology opted for surveying Mammals a) Sign survey: Sign surveys of mammals were recorded in forest trails (3-14 km long) along the ridges or small streams covering the different elevation gradients (100- 540000m) of the study areas. During the sign survey, direct sightings, tracks and signs of ungulates, carnivores and small mammals were recorded. Den sites of carnivores were also taken into account for recording their presence. The sign includes pugmarks of felids, canids, primates and ursids and hoof marks of ungulates. The other sign includes scratch and scrape marks on trees and the trails. Whereas, pellets, scats and fecal matter were collected as an indirect evidences of different species. Direct sightings of mammals during sign survey were also recorded along with GPS location, habitat types and other important ecological information. c) Scanning: Scanning from vantage points was carried out for open slope dwelling mountain ungulates such as Himalayan tahr, blue sheep and goral in mid-elevation zones. Scanning an open meadow/ cliff was carried out using binoculars and sighting of ungulates was recorded. Wherever it was possible, individual‟s sex and other demographic information was noted for the ungulates groups. d) Camera trapping: Camera trapping for carnivores and elusive forest ungulates species was carried out in the selected grids of different study district. All the cameras were installed in the animal trails, resting sites and vantage points. Each camera trap was placed at 1 meter above the ground to ensuring the maximum area and trail covered (Guil, 2010). Important information was
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Annexure-I: Unpublished only for progress assessment purpose noted during the installation of each camera such as time and date of placement, GPS location, number of camera, habitat characterization and topography etc. We used two type of cameras 1– ultra-compact SPYPOINT FORCE-11D trail camera (SPYPOINT, GG Telecom, Canada, QC) during the study and 2– Browning trail camera (Defender 850, 20 MP, Prometheus Group, LLC Birmingham, Alabama, https://browningtrailcameras.com). e) Opportunistic records: The presence of mammal was also documented opportunistically wherever we found any evidence. Therefore, animals‟ presence was also recorded apart from the selected area while travelling from one area to other area by vehicle or other mode.
3.2 Methodology opted for surveying Birds a) Spot-mapping: Spot-mapping (Ralph et al. 1993) was carried out in a 1 ha plot (200 m x 200 m plot) at every 500 m interval on a trail selected for sampling in a study site. Spot-mapping was targeted for the threatened birds in forest and open habitats as this is based on the territorial behavior of the birds (Ralph et al. 1993). b) Variable radius point count: Along the elevation gradient, variable radius point count (Bibby et al. 1992) was carried out to detect species distribution and community composition at every 200 m interval on the selected trail for sampling in a particular study site. Birds were counted for 10 minutes duration and the sighting distance (at which the individual was first observed) was recorded using range finder. c) Trail sampling: Trails of 1 to 1.5 km length are selected to conduct trail sampling to detect the presence of galliformes. Camera trapping was also carried out to detect the presence of galliformes in the study area. d) Opportunistic records: Records of bird species presence and abundance before and after sampling period was also documented and daily logs were maintained where information on species, number, timing, habitat and observations on behavior was recorded throughout the area visited.
3.3 Methodology for questionnaire survey data collection
For data collection on the local perception we sampled the study area after systematically covering the maximum logistically possible villages located on the forest fringe in East
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Siang. We used semi-structured opened ended questionnaire surveys during 2018-2019. Efforts were made to samples minimum thirty per cent of the total number of household in each village (Cochran, 1997) and followed the guidelines of the National Sample Survey Organization (NSSO, 2011), Government of India. The interviews were only conducted with one representative individual of household above age >18 years, preferably older. These households were selected based on the formula keeping in the mind the large number of households to complete 30% response rate of the total household from a particular village which applies two measures first the margin of error (5%) and second the confidence level (Sukhatme, and Sukhatme, 1970; Cochran, 1997)
N=(CV(Y)*t/ ∈)2
Where CV = Coefficient of Variation of character under study, Y = Character under study, t = Value of t statistics at 5% level of significance (95% Confidence Interval) and ∈= Margin of Error or permissible error, which is generally 5% to 25%, as where the permissible error is increase, the sample size will also increase.
3.4 DNA based analysis
Non-invasive samples were collected for DNA based analysis. All sampling locations recorded with GPS coordinates, locality name and marked species name if it was known. All the samples first air dried directly in Sun or hot air oven. After drying these samples, all the samples were transferred in the 100ml sterilized vials for the further analysis and transported to ZSI, Kolkata for the DNA based analysis. All the samples were processed in the dedicated room available for the DNA based analysis. DNA was extracted using the Qiagen Stool DNA extraction Kit (Qiagen, Germany). PCR was amplified with ATPase 6 (Chaves et al. 2012) for the species identification of carnivores samples whereas, Cyt b (Verma and Singh, 2003) were used for the herbivores samples. Furthermore, in the population genetics analysis, use of microsatellites has revolutionized the field of conservation biology (DeSalle and Amato; Arruga et al. 2001). Microsatellite DNA, or short sequence repeat (SSR), is a PCR based co-dominant DNA marker (Tautz 1989) which describes the bi-parental genetic structure. Microsatellite loci have proven to be particularly valuable for parentage, stock discrimination, population genetics and genome mapping because of the high levels of polymorphism (Clarke et al.
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2015; Smith et al. 2015; Williams et al. 2015). Therefore, we selected the different microsatellite loci to carry out the population genetics analysis of the selected vertebrate species.
4. Data Analysis
4.1 Camera traps data analysis
The photographs taken in a days were sorted into folders for each species then tagged with their respective Scientific names with the help of software “Digikam” for the package “camtrapR” in R studio (R Development Core Team, 2019) to generate data sheet containing the date and time information of every capture in 60 minutes interval (Li et al., 2010). Each camera traps image was carefully visualized to identify the species where any doubt and unclear image were excluded from the analysis. Location details (GPS coordinates, altitude, sign type, terrain type, forest type, dominant vegetation, canopy cover) and photographs of direct sightings and confirmed signs (feathers, dust bathing sites (wallowing sites), scratch marks, droppings) were taken into account (Joshi et al., 2020). The sheets generated of camera capture then used to calculate the capture rates (Total captures/number of camera trap nights) of species other analysis.
4.2 Habitat covariates
We hypothesized habitat variables may influence the distribution, occupancy and detection probability of species. A total of 31 variables were extracted either from the field or using the ArcGIS v. 10.1 software (ESRI, Redlands, CA). These covariates were classified into following categories (Topographic variables, Habitat variables and anthropogenic variables). The topographic variables (elevation, slope and aspect) were generated using 30x resolution SRTM image downloaded from EarthExplorer (https://earthexplorer.usgs.gov/). The habitat/ land cover classification was carried out using Landsat 8 satellite imagery (Spatial resolution = 30 m) downloaded from Global Land Cover Facility was classified following Singh et al. (2005) using the program ERDAS Image version 9.0 (ERDAS®, Inc, Atlanta, Georgia). The study area was classified into nine Land use/land cover (LULC) classes viz., West Himalayan Sub- alpine birch/fir Forest (FT 188), West Himalayan upper oak/fir forest (FT 162), West
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Himalayan Dry juniper forest (FT 180), Ban oak forest (FT 152), Moist Deodar forest (FT 155), Western mixed coniferous forest (FT 156), Moist temperate Deciduous forest (FT 157) were used for further analysis considering their importance to species ecology and behavior. The values for all the covariates were extracted at 30m resolution, and a single value per site was obtained by averaging all the pixel values within each sampling site (camera trap locations).
4.3 Occupancy modelling framework
We fitted occupancy and detectability models using programme Mark. Presence and absence of each of three species combined in a single sheets as following the Rota et al. (2016). We modelled occupancy and detection probability for each species assuming the result from any given survey was the outcome of two binomial processes acting simultaneously 1) the probability a species was present at a site (ψ) over long time period; and, 2) the probability a species was present within the site and observed in any given survey visit (ρ). Distinguishing the true presence/absence of a species from detection/non-detection (i.e. species present and captured or species present but not captured) requires spatially or temporally replicated data. We treated after 15 trap nights as a repeat survey at a particular camera station resulting in ~7 sampling occasions per camera station. We incorporated site-level characteristics affecting species-specific occurrence and detection probabilities using a generalized linear modelling approach (Dorazio & Royle 2005). Our rationale for including these variables in the occupancy and detectability component of the model is that we expected these variables to influence occupancy and detectability of species. We assumed a closed population over the survey period. All covariates were standardized prior to model fitting. We fitted the most complex model to each species and considered all possible combinations of covariates. We modelled the influence of covariates on occupancy and detectability using the logit link function.
We fit a set of models including the detection probability as a constant, p(.), and variable function to occupancy (covariate) for site-specific covariates and models include occupancy as constant (.) and variable function of the detection p(covariates) for the respective site covariates. For the multispecies occupancy, we also modelled the
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Annexure-I: Unpublished only for progress assessment purpose three species occupy the same site varies as a function of these variables. Even though the model assumes constant pairwise interactions between species, we also model the probability two species occur together vary as a function of covariates. Although we did not include higher order interactions in any of our models, we will examine how the variables of each camera site will influence the pairwise interaction of the three ungulate species. We assumed f123 = 0, hence we did not include higher order interactions in any of our models, we assumed the conditional probability 3 species occurred together was purely a function of species-specific (f1, f2, f3) and pairwise interaction (f12, f13, f23) parameters. The best-supported model was identified by selecting the model with the lowest AICc value and highest model weights (Anderson 2008) where higher model weights indicate a better fit of the model to the data. Second order. Information Criterion (AICc; Akaike 1973) values were used to rank the occupancy models and all the models whose ΔAICc< 2 were considered as equivalent models. The Akaike weight represents the ratio of ΔAICc values for the whole set of candidate models, providing a strength of evidence for each model. The summed model weight of each covariate in these models was used to determine the most influential variables for each species. The sign of logistic coefficient of each variable (positive or negative) was used to determine the direction of influence of the variables on the occupancy and detection probability of the three ungulate species.
4.4 Activity pattern
We have compared activity patterns among species to see how overlapping patterns may relate to competition using package “Overlap” in R (R Development Core Team). The time and date printed on the photographs has been used to determine the daily activity pattern of individual species (Pei 1998). We used a Daily Activity Index (DAI) of half an hour duration to examine the daily activity. The coefficient of overlap is denoted by “Dhat1” values, ranging between zero (no overlap) and 1.0 (complete overlap).
4.5 Generalized linear modelling (GLM)
To understand the habitat use pattern and association Generalized linear modelling (GLM) analysis were performed in package “Rcmdr” of R studio (Fox et al., 2019) to estimate model β-coefficient values with the help of binomial distribution model using
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Annexure-I: Unpublished only for progress assessment purpose the most accepted “logit” link function for the purpose of studying habitat of species (Boyce et al., 2002; Guisan and Thuiller, 2005). Out of the numerous models that were run with the habitat variables significantly associated with the occurrence of pheasants and other mammal‟s species, the model with the lowest Akaike‟s Information Criterion (AIC) was selected as the best fit (Burnham and Anderson, 1998 and Boyce et al., 2002).
4.6 Climate change, geo-spatial and species distribution modeling
We understand the impact the climate changes on the different species and primarily we developed these models for the Himalayan Grey Langur, Himalayan Brown bear and Galliformes species from Indian Himalayan region. For that the presence location have extracted from the different field with range of species distribution apart from that published locations of different species also extracted from the different sources (Nawaz, 2008; Su et al., 2018, Birdlife GBIF etc.). 4.7 Variable preparation and selection:
The selection of the environmental variables is the most crucial step for performing SDM and climate change ranger modeling. These variables should be based on ecological relevance to the species for which SDM is performed (Mukherjee et al., 2020; Sharma et al., 2019). We started with n=35 environmental variables (Table 1) which were categorized into four categories i.e. land cover, topographic, anthropogenic and environmental variable.
Further, for making climate change projections in different (4.5 and 8.5 RCP) future scenarios for the year 2050 we have used the MIROC5 (Model for Interdisciplinary Research On Climate) General Circulation Model (GCM) developed by the University of Tokyo, National Institute for Environmental Studies, Japan Agency (Su et al. 2018).
Table 1. Total predictor variable selected for the correlation coefficient test. A total of n=35 predictor variables have been selected for the correlation test. The predictors were divided into four major groups namely. Land-Cover Level variable, Bioclimatic, Topographic, Anthropogenic.
Variable Sl. No. Code Description Type Land-Cover 1. EG_NL_frst1 Euclidian distance to Evergreen Needle leaf Forests (Dominated Level by evergreen conifer trees (canopy >2m). Tree cover >60%) variable 2. EG_BL_Fores Euclidian distance to Evergreen Broadleaf Forests (Dominated
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t by evergreen broadleaf and palmate trees (canopy >2m). Tree cover >60%) 3. Mixed_Frst1 Euclidian distance to Mixed Forests (Dominated by neither deciduous nor evergreen (40-60% of each) tree type (canopy >2m) Tree cover) 4. Wdy_Savana Euclidian distance to Woody Savannas (Tree cover 30-60% (canopy >2m)) 5. Savannas Euclidian distance to Savannas (Tree cover 10-30% (canopy >2m)) 6. Grasslands Euclidian distance to Grasslands (Dominated by herbaceous annuals (<2m)) 7. Croplands Euclidian distance to Croplands (At least 60% of area is cultivated cropland) 8. Crop_VegMos Euclidian distance to Cropland/Natural Vegetation Mosaics (Mosaics of small-scale cultivation 40-60% with natural tree, shrub, or herbaceous vegetation) 9. permnt_snow Euclidian distance to Permanent Snow and Ice (With 60% of area is covered by snow and ice for at least 10 months of the year) 10. barren Euclidian distance to Barren (At least 60% of area is non- vegetated barren (sand, rock, soil) areas with less than 10% vegetation) 11. water Euclidian distance to Water Bodies (At least 60% of area is covered by permanent water bodies) Bioclimatic 12. bio_1 Annual Mean Temperature 13. bio_2 Mean Diurnal Range (Mean of monthly (max temp - min temp) 14. bio_3 Isothermality (BIO2/BIO7) (* 100) 15. bio_4 Temperature Seasonality (standard deviation *100) 16. bio_5 Max Temperature of Warmest Month 17. bio_6 Min Temperature of Coldest Month 18. bio_7 Temperature Annual Range (BIO5-BIO6) 19. bio_8 Mean Temperature of Wettest Quarter 20. bio_9 Mean Temperature of Driest Quarter 21. bio_10 Mean Temperature of Warmest Quarter 22. bio_11 Mean Temperature of Coldest Quarter 23. bio_12 Annual Precipitation 24. bio_13 Precipitation of Wettest Month 25. bio_14 Precipitation of Driest Month 26. bio_15 Precipitation Seasonality (Coefficient of Variation) 27. bio_16 Precipitation of Wettest Quarter
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28. bio_17 Precipitation of Driest Quarter 29. bio_18 Precipitation of Warmest Quarter 30. bio_19 Precipitation of Coldest Quarter Topographic 31. elevation Elevation 32. slope Slope 33. aspect Aspect Anthropogeni 34. hfp Human footprint c 35. Builtup Euclidian distance to Urban and Built-up Lands
The Land cover classes were generated using the MODIS (Moderate Resolution Imaging Spectroradiometer) Land Cover Type Product (MCD12Q1) with 500-meter resolution, with seventeen different categories (Table 1). These categories were converted into the vector files followed by generating euclidian distances of the respective land cover class. The topographic variables (elevation, slope and aspect) were obtained using SRTM data with 90meter spatial resolution (http://srtm.csi.cgiar.org/srtmdata/ via Diva-Gis). The environmental variables comprised of 19 bioclimatic variables which were extracted from Worldclim (https://www.worldclim.org/). The Global Human Footprint Dataset under Human Influence Index (HII) was downloaded from Socioeconomic Data and Applications Centre (SEDAC), NASA and used as anthropogenic variable. After data processing all the variables were resampled at 30 arc sec ~1km2 spatial resolution using the Spatial-Analyst Extension in ArcGIS 10.6. Further the spatial multi-collinearity among the variables was tested using the SAHM package in VisTrails software and the variables with Pearson Correlation Coefficients (r) more than 0.8 were dropped from the analysis (Warren, Glor & Turelli, 2010).
4.8 Model Preparation, Evaluation and Change Dynamics of Ensemble Models
We have used the ensemble modeling approach for evaluating the suitable habitats considered to be the robust methodologies for species distribution modeling and have been used in various studies throughout the globe (Elith, & Leathwick, 2009; Miller, 2010; Mukherjee et al., 2020, Guisan et al., 2007). We used four modelling algorithms i.e., Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Boosted Regression Trees (BRT) and Random Forest (RF) methods, implemented through the VisTrails pipeline of Software for Assisted Habitat Modeling (SAHM) package (Morisette et al., 2013; Talbert, & Talbert, 2012). All the participating models of the present simulation resulted in probability surfaces, scaled from 0 (least suitable) to 1 (most suitable), indicating the probability of suitable habitat for HBB in the study landscape. Furthermore, individual binary maps were prepared by using minimum training
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Annexure-I: Unpublished only for progress assessment purpose presence as the threshold (Hayes, Cryan & Wunder, 2015). Followed, by the selection and elimination of models based on the AUC threshold (0.75) and by generating both the ensemble probability and count maps. The ensemble maps were considered to be the running averages for the binary estimates of the participating models categorising the landscape in 0 (non-habitat) and 1 (habitat). The ensemble count maps range from 0-4, which depicts the number of models agreed for suitability in respective pixel (Mukherjee et al., 2020). The highest model agreement i.e., 4 have been considered for the comparison of habitat configuration and area change calculation in both present and future climate change scenarios. Further, for the comparative model evaluation AUC (area under the receiver operator curve), True Skill Statistic (TSS) (Allouche, Tsoar, & Kadmon, 2006), Cohen‟s Kappa (Cohen, 1968), Proportion Correctly Classified (PCC), specificity, and sensitivity were estimated for training data and (n=10) cross-validation sets. (Jiménez-Valverde, Acevedo, Barbosa, Lobo & Real, 2013; Phillips, & Elith, 2010).
4.9 Biological connectivity and patch level habitat evaluation
For assessment of the biological connectivity between the high suitable patches, we have used circuit model which is one of the widely used approaches for animal corridor design (Ruiz- González et al., 2014; Wang et al., 2014). We simulated the possible biological corridor for all the three species by using Circuitscape toolbox for ArcGIS Ver. 10.1. (Mcrae et al., 2008; McRae, & Shah, 2009; Wang et al., 2014). The habitat suitability model output probability surfaces for both the present and future habitat conditions have been used as conductance surfaces for generating connectivity in the landscape (Sharma et al. 2019). However, for reducing the computational complexity we have used centroids of highly suitable patches located in PAs for build the connections. Further the same centroids were used for building the predicting the biological connectivity for the species in future scenarios.
The landscape configuration matrices have been used by several researchers and are found useful in providing vital information on the habitat quality (Bagharia et al., 2020; Haila, 2002; Kupfer et al., 2006; Laurance, 2008; Mukherjee, Sharma, Thakur, Saha, & Chandra, 2019). Thus for comparative analysis of habitat configuration of HBB in the present and in future RCPs we have used FRAGSTATS Ver. 4.2 (McGarigal, Cushman, & Ene, 2012). We estimated the Largest patch index (LPI), Landscape shape Index (LSI) and Aggregation Index (AI) which are useful in qualifying and identifying the level of fragmentation for present and future habitat for HBB (Bagharia et al., 2020; Mukherjee et al., 2020).
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4.10 Species clustering and cohort formation
We clustered the species and made cohorts by grouping species based on variables which are found to be of significance in predicting the group for each entity. The ideal number of clusters were determined using the within-sum of squares method (Kassambara, 2017) in R environment v 3.6.2 (R Core Team, 2019), with package „cluster‟ (Kaufman & Rousseeuw, 2009). Further, for ascertaining the clusters based on ordination methods, we used the selected variables and ordinated them in R package „FactoMineR‟ (Le, Jose & Husson, 2008) using Principle Component Analysis (PCA, Comon, 1994) and Non-Metric Dimensional Scaling (NMDS, Kruskal, 1964). Species were clustered using hierarchical clustering method (Hartigan, 1975), agnes method (Struyf, Hubert & Rousseeuw, 1997), partitioning around medoids (PAM) method (Struyf et al., 1997) and k-means clustering method (Hartigan, 1979).
4.11 Questionnaire survey (Human–wildlife conflict & locals perception analysis)
To address the different questions from the data collected through questionnaire survey such as human wildlife conflict, local perception towards the wildlife conservation and ethno–zoological use of wildlife, different statistical approaches were used. All the statistical analysis were performed using SPSS v. 22.0 (IBM Corp, 2013). Depending on the nature of the variables, measures of central tendency and dispersion were calculated for all socio-economic, socio-demographic and perception data characteristics. The data was tested for normalcy, homoscedasticity. The used ANCOVA (analysis of covariance) test based on the linked identity to test the model effects on the dependent variable (Goldberg and Shneider, 1993). The analysis was performed after dropping the uniform variables to remove possible bias in data. The collinearity among the variables was tested using the Pearson‟s (r), Spearman‟s rho among the non-categorical variables. Whereas, Chi-square (X2) was adopted for categorical variables accompanied by Cramer‟s V test and adjusted standardized residual (asr) for computing the strength of the association and the most significant cell (s), respectively. Our null hypothesis stating that there are no significant differences across our independent variables (village, gender, circle, education level, human attacks by wildlife) with respect to perception on wildlife conservation. The alpha limit was set at 0.05 following Sukanan and Anthony (2019).
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4.12 Data Analysis of mitochondrial DNA
Generated sequences of the mitochondrial markers were were examined and validated using Sequencher 4.7 (Gene Codes Corporation, USA). Generated sequences were also validated using reference data through the NCBI, BLAST at GenBank (http://www.ncbi.nlm.nih.gov) for species identification. The sequences were aligned using Bioedit (Hall 1999). Polymorphic sites and haplotypes were identified using DnaSP 5.10 (Librado and Rozas 2009), and Bioedit (Hall 1999). Genetic diversity within and among the populations was estimated by computing nucleotide (π) and haplotype (hd) diversity. Haplotype diversity measures that the probability of two randomly sampled alleles are different while nucleotide diversity is defined as the average number of nucleotide differences per site in pairwise comparisons among DNA sequences (Saitou and Nei 1987). Diversity indices and neutrality test were calculated using the DnaSP 5.10 (Librado and Rozas 2009). Mismatch distribution was also used to demonstrate the pattern of population stability or population expansion in the past under the sudden demographic expansion models using the software Arlequin v. 3.1.1 (Excoffier et al. 2005). Patterns of historical demography can also be inferred from estimates of the effective population size over time using the BSP method implemented in BEAST v. 2.1.3 (Bouckaert et al. 2014).
4.13 Data analysis of microsatellites
The electropherograms produced by the genetic analyser were scored using GENEMAPPER v. 5.0 after manual scrutiny of each allele call. We used MICRO- CHECKER v. 2.2.3 (Van Oosterhout et al. 2004)to examine the presence of null alleles in the markers, GENEPOP v. 4.2 (Rousset 2008) was used to detect the presence of linkage disequilibrium. The diversity indices and probabilities of identities were calculated using GenAlEx v. 6.5(Peakall and Smouse 2006, 2012). To understand the distribution of genetic variations, GENETIX v. 4.05 (Belkhir et al. 2004) and the R package ADEGENET v. 2.0.0 (Jombart 2008) was used to perform two and three-dimensional Factorial Correspondence Analysis (FCA) and Discriminant Analysis of Principal Components (DAPC) respectively. POPULATIONS v. 1.2.32 (Langella 1999) and TREEVIEW v. 1.6.6 (Page 1996)was used to compute and visualize a neighbour-joining
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Annexure-I: Unpublished only for progress assessment purpose dendrogram of individuals based on Nei‟s standard genetic distance (Ds). BOTTLENECK v. 1.2.02 (Piry et al. 1999) was used to detect any recent genetic bottlenecks in the study population. We used a Bayesian statistical approach as implemented by STRUCTURE v. 2.2.3 (Pritchard et al. 2000)to perform clustering and assignment of individuals to detect population structure and ancestry. GENECLASS v. 2.0 (Piry et al. 2004) was used to detect any individual that might show a genetic signature of a distinctly different population. We also used ML-RELATE (Kalinowski et al. 2006) to understand the distribution of related individuals in the study populations.
5. Preliminary Finding
We described here the preliminary results for all the five objectives in which we observed the different parameters such as collected data of species diversity, questionnaire surveys to understand the human wildlife interaction, impact of climate changes of species distribution, occupancy analysis some selected species, genetics based analysis to understand the demography, population genetics and phylogeography studies in the six state of Indian Himalayan region. Through which we placed about 623 cameras (18664 camera trapping nights), collected 3550 non-invasive samples, interviewed 3804 respondents from 318 villages and 437 trails of total 3848 km trails walk, 200 samples analyzed for the feeding behaviors, and collected 450 plants and tree species to understand the vegetation pattern.
5.1 Preliminary finding of objective 1: To assess and monitor threatened vertebrate fauna in the Indian Himalayan Region and developing spatial database.
Sample Collection, Species diversity and diversity indices
We collected total 3550 fecal samples of different species from the different sites (Table 2). In which maximum samples were collected from the Lahaul and Spiti region. Further after screening of good quality of samples, 1672 samples were processed for the DNA based analysis for the species identification. Table 2. Table 1. Noninvasive samples collected from the different locality.
Sites Samples collected DNA analysis Lahual and Spiti 1037 950
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Uttarkashi 559 229 Darjeeling 473 28 East Sikkim 271 75 East Siang 658 75 West Kameng 552 315 Total 3550 1672
In the all six sites we reported the total of total 50 species of mammals, of 22 in Darjjeling 25 in East Sikkim, 19 species Lahaul and Spiti and 16 in the Uttarkashi District. Among the mammalian species using the camera trapping we observed the Shannon diversity index 1.5 to 2.75 in the all study sites (Table 3). Whereas, total 203 birds detected in the Darjeling, 120 in the East Sikkim, 170 species in the East Siang, 98 species in the Uttarkashi district, 120 in the West Kameng district. Apart from these 6 species of fish and 7 species of reptiles and 3 amphibians were recorded so far based on the sign survey, camera traps and direct sighting of species.
Table 3. Table 2. Species diversity indices for the mammals and other species captured in the camera trapping.
East Sikkim Darjeeling East Siang West Kameng Lahaul and Spiti Uttarkashi Simpson_1 -D 0.911 0.8644 0.8637 0.6463 0.8462 0.8999 Shannon_H 2.759 2.403 2.527 1.551 2.482 2.588 Evenness_e^H/S 0.4781 0.3947 0.5004 0.3628 0.3627 0.532
Captures rates of different species using camera trapping
For the development of baseline information on the presence of vertebrate fauna of the study districts we deployed the camera traps in the different study sites (Table 4). These camera traps were placed strategically at different localities after the stratification of the study area as described in the material and methods. The camera trap site selection was based on the three major criteria forest type, forest density covers and the topography. These cameras were placed on different elevation ranges from 300-5500 meters (Fig. 2). Total of 623 camera traps deployed in the all 6 sites which resulted to total of 18864
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Annexure-I: Unpublished only for progress assessment purpose camera traps nights. We observed total of 50 species of mammals (15-25) in all the six sites and details are given below (Fig. 3-6). In the Lahaul and Spiti district, total of 23 confirmed and 1 unidentified mammal were identified. We deployed total of 195 camera traps which yielded total 4520 camera traps nights. Among which highly captured species were Red fox (0.10±0.02) and least captured species was the Leopard cat (0.0001±0.0001). In the Uttarkashi district we deployed total of 120 cameras which resulted total of 3563 camera traps night and captured 21 species of mammals. Among these mammals, barking deer were highly captured species (0.15±0.02) and least captured species was Leopard cat (0.002±0.002; Table 5). In the Darjeeling district we, deployed total of 114 cameras which yielded total of 3296 camera traps night. In the Darjeeling, total of 22 mammals detected in which the barking deer (0.47±0.43) was the most detected species and least detected species was the weasel and Chinese pangolin (Only single captures; 0.000±0.000). Apart from these, 4 Garliformes was also detected in the Darjeeling district. In the East Sikkim district, we deployed total of 75 cameras and detected total of 25 mammalian species along with 4 Garliformes species. East Sikkim we also found the barking deer as dominant species with capture rate of (0.348±0.02) and least captures species was Golden cat (0.002±0.002). In the East Siang district, we placed total of 57 cameras with total of 900 camera traps night which has detected total of 25 mammals‟ species with high capture rate of Asiatic Elephant and low was leopard cat (Table 5). Further in the West Kameng district, we placed a total of 66 camera traps with the 2876 camera trap night. We detected total of 15 mammals‟ species with barking deer (0.079±0.035) as most abundant species followed by the (0.015±0.07). Apart from that our camera also captures the feral dogs, human presence and different livestock grazing on filed (Fig. 7). Table 4. Table 3. Number of camera deployed in the different study sties.
Study sites Total Cameras Trap nights Lahaul and Spiti 197 4520 Uttarkashi 114 3563 Darjeeling 114 3296 East Sikkim 75 3709 East Siang 57 900
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West Kameng 66 2876 Total 623 18864
Figure 2. Elevation wise placement of cameras
Table 5. Table 3. Capture rates of species from the different study sites
SN East Sikkim East Sikkim Darjeeling East Siang WK L&S Uttarkashi 1 Snow Leopard 0.01(±0.002) 2 Leopard 0.04 (±0.04) 0.085 (±0.015) 0.0 03(±0.002) 3 Dhole 0.003(±0.002) 0.4 (±0.00) 4 Himalayan Musk Deer 0.009(±0.009) 0.003(±0.002) 0.004(±0.003) 5 Red Panda 0.004(±0.004) 0.003(±0.003) 6 Asiatic Black Bear 0.007(±0.007) 0.005(±0.005) 0.4 (±0.00) 0.00115(±0.00084) 0.0006(±0.000 6) 0.013(±0.012) 7 Clouded Leopard 0.004(±0.004) 8 Gaur 0.064(±0.045) 9 Sambar Deer 0.01(±0.003) 0.24(±0.06) 0.07(±0.045) 10 Assamese macaque 0.033(±0.027) 0.37(±0.07) 11 Golden Cat 0.002(±0.002) 0.003(±0.003) 0.00113(±0.00113) 12 Himalayan brown Goral 0.153(±0.132) 0.005(±0.006) 0.56(±0 .00) 0.00044(±0.00044) 0.066(±0.042) 13 Marble Cat 0.003(±0.002) 0.002(±0.003) 14 Serow 0.068(±0.039) 0.049(±0.05) 0.09(±0.012) 0.00081(±0.00056) 0.015(±0.01) 15 Barking Deer 0.348(±0.2) 0.477(±0.473) 0.096(±0.045) 0.07858(±0.03529) 0.116(±0.084)
ZSI, Kolkata [30]
Annexure-I: Unpublished only for progress assessment purpose
16 Himalayan Palm Civet 0.018(±0.013) 0.012(±0.013) 0.4(±0.00) 0.01234(±0.0078) 0.0005(±0.0004) 17 Indian Hare 0.002(±0.002) 0.035(±0.037) 0.09(±0.01) 18 Large Indian Civet 0.126(±0.119) 0.044(±0.047) 0.02(±0.009) 0.00131(±0.00096) 19 Leopard cat 0.024(±0.024) 0.058(±0.06) 0.01(±0.01) 0.0071(±0.00319) 0.0001(±0.0001) 0.002(±0.002) 20 Indian/ Royle's Pika 0.037(±0.037) 0.02(±0.01) 0.006(±0.004) Himalayan Crestless 21 Porcupine 0.022(±0.022) 0.028(±0.031) 0.03(±0.075) 0.00167(±0.001) 0.005(±0.004) 22 Red Fox 0.002(±0.002) 0.1 0(±0.02) 23 Rhesus Macaque 0.16(±0.151) 0.01(±0.013) 0.002(±0.001) 0.043(±0.036) 24 Wild Boar 0.065(±0.054) 0.13(±0.131) 0.21(±0.06) 0.00845(±0.00333) 0.048(±0.038) 25 Yellow Throated Martin 0.033(±0.024) 0.004(±0.004) 0.05(±0.037) 0.00968(±0.00548) 0.001(±0.001) 0.011(±0.009) 26 Squirrel 0.065(±0.048) 0.021(±0.023) 0.07(±0.005) 0.00039(±0.00039) 0.009(±0.006) 27 Rhodent (Unidentified) 0.004(±0.004) 0.053(±0.057) 0.04(±0.02) 0.009(±0.003) 0.005(±0.005) 28 Pangolin 0(±0) 29 Mongoose 0(±0) 30 Weasel 0(±0) 0.0004(±0.0003) 31 Cat (unidentified) 0.003(±0.004) 0.13(±0.03) 32 Wild Water Buffelo 0.738(±0.3705) 33 Small Indian Civet 0.18(±0.04) 34 Asiatic Elephant 2.15(±1.41) 35 Hog Deer 0.11(±0.0) 36 Melanistic cat species 0.11(±0.0) 37 Jungle Cat 0.2(±0.03) 0.0003(±0.0003) 38 Golden Jackal 39 Langur 0.00085(±0.00085) 40 Arunachal Macaque 0.01456(±0.00694) 41 Himalayan Brown Bear 0.008(±0.003) 42 Tibetans Wolf 0.006(±0.006) 43 Himalayan Red Fox 0.02(±0.005) 0.014(±0.01) 44 Wooly hare 0.01(±0.01) 45 Blue Sheep 0.005(±0.001) 46 Himalayan Ibex 0.011(±0) 47 Himalayan Tahr 0.006(±0.006) 0.014(±0.011) 48 Wild dog 0.007(±0.005) 49 Flying squirrel 0.001(±0.001) 50 Stone martin 0.002(±0.001)
ZSI, Kolkata [31]
Annexure-I: Unpublished only for progress assessment purpose
Figure 3. Camera Trap images of mammalian species in the Salu top in Mukhem Range: A) Barking deer, B) Himalayan porcupine, C) Sambar female, D) Sambar male
Figure 4. Camera Trap images of different species A&B) Asiatic black bear C) Kalij Pheasant, C) Himalayan Grey Langur in Gangotri range.
ZSI, Kolkata [32]
Annexure-I: Unpublished only for progress assessment purpose
Figure 5. Camera Trap images of A and B) Leopard in Mukhem and Taknor range in the Uttarkashi Forest Division C) Yellow throated marten and D) Monal in Dayra in the Uttarkashi Forest Division.
.
ZSI, Kolkata [33]
Annexure-I: Unpublished only for progress assessment purpose
Figure 6. Camera Trap images of Sambar male & female and goral in different days in the Gangotri range in the Uttarkashi Forest Division.
Sign Survey:
In the sign survey we travel total of 437 trails of 3848 Km walk resulted to detect 15-24 species in each sites (Fig. 8 and 9). As most of the signs those collected as non-invasive samples were unidentified and under process to identify through DNA based techniques to assigned species. Among the different sites, indirect evidences of barking deer were found high in the Darjeeling, East Siang, Uttarkashi, whereas in the Lahaul and Spiti district Ibex Red fox signs were recorded as most abundant.
ZSI, Kolkata [34]
Annexure-I: Unpublished only for progress assessment purpose
Figure 7. Figure 10. A) Female extracted algae from the oak tree, B) Livestock grazing of goat and sheet, C) Male (Locally known Gujjar) owning mass grazing of buffalo in the Uttarkashi district D) Buffelo grazing in the Badkot Forest division E) Cow grazing in the forest F) female collecting fodder for Cattel‟s
Figure 8. Number of trails species and kilometers walk in the different study sites.
ZSI, Kolkata [35]
Annexure-I: Unpublished only for progress assessment purpose
Figure 9. Direct sighting of different species: From right top to left bottom, Monal, Himalayan tahr, Serow, and Goral.
Geospatial data of threaten vertebrate’s species in the Indian Himalayan region
For the threaten vertebrates‟ species, we gathered the data of around 126 threatened vertebrates species. A total of 126 species classified as threatened under different threat categories (CE, EN, and VU). Further, under the different threaten category there was around 39 species of mammals, 49 species of birds, 15 species of reptiles, 19 species of fish and four species of amphibians. Each of species have assessed for the their IUCN, CITES and IWPA and provided the information on the taxonomic keys, distribution, conservation action etc (Fig.10).
ZSI, Kolkata [36]
Annexure-I: Unpublished only for progress assessment purpose
Figure 10. Examples of booklet and geospatial data for the Threatened vertebrates‟ species in the Indian Himalayan regions.
5.2 Preliminary finding of Objective 2: To study the population viability of selected threatened species in Indian Himalayan Region for developing long- term conservation strategies.
Understanding of the occupancy, population genetics, demography and climate change analysis is an important aspect for designing the conservation strategies for the different species. Based on the primary survey, occupancy modeling of the species is important criteria for the identifying the conservation priority areas within species range and how the species are distributed in the area. Whereas, genetics is helpful to understand the demography and population genetics structure is important to identify the different
ZSI, Kolkata [37]
Annexure-I: Unpublished only for progress assessment purpose management units (MUs), Conservation Units or Evolutionary Significant units (ESUs) along with genetics based population viability analysis.
Occupancy modeling of different species from different sites
a) Identify the conservation priority area of Brown Bear in Lahaul and Spiti valley
Large carnivores that occur in low densities, particularly in the high-altitude areas are globally threatened because of habitat loss and anthropogenic disturbances. Among the eight bear species, brown bear has the largest distribution range, where the Himalayan brown bear distribution is restricted to Himalayan high lands with relatively small and fragmented populations. In the Indian Himalayan regions, the brown bear is mostly distributed in the high-altitude ranges of Jammu and Kashmir Union Territory (UT), Ladakh UT, Himachal Pradesh and Uttarakhand but poorly studied due to elusive nature and rugged landscape. So far, very little information is available on the species except for few distribution records and short-term studies focused on bear-human conflict. Much of its distribution range in India is largely unexplored and hence, no scientific information is available which is vital for the conservation of the species and management of its habitats.
Therefore, we understand the distribution and occupancy of brown bear in the Lahaul Valley of Himachal Pradesh using both sign survey and camera trapping. For this, the study landscape was divided into 10 km x 10 km grids, and in each grid, at least 4 camera traps were deployed strategically and extracted the 11 habitat variables (Table 6). Further, a total of 56 trails were also surveyed in the selected grids. The total effort of n=758 camera nights and 544 km trail walk resulted with naïve occupancy of 0.54 in the Lahaul Valley, which is slightly lower than the estimated one (0.562–0.757; Table 7). Out of 34 models, those were run, only 5 models were found useful in explaining the influence of covariates on brown bear occupancy and detection based on the lowest AIC values (Table 8). Out of 34 single-season occupancy models run for the brown bear with different site co-variants, only „agriculture land‟ (β = 24; ±14) and combined effect of „agriculture land + alpine grassland‟ (β= 28.0±10) showed a positive association with occupancy of brown bear in the Lahaul Valley. Whereas, detection probability was mostly explained by habitat covariates such as „human settlement‟ (β = 0.00 ± 0.00) and „alpine grassland‟ (β
ZSI, Kolkata [38]
Annexure-I: Unpublished only for progress assessment purpose
= -0.73, ±0.31) which showed a negative association (Table 9). The positive relationship of occupancy with agriculture land indicated that Himalayan brown bears are using an agriculture land which is leading to increasing bear-human conflict. Through the present study, we identified few areas in Lahaul Valley for priority conservation actions. Amongst the different forest ranges, the mean estimated occupancy for all ranges was 0.01-1.0 (Fig. 11).
Figure 11. Himalayan brown bear occupancy probability based on top model in the Lahaul Valley, Himachal Pradesh.
The top two models indicated the detection probabilities of the brown bear influenced by the human settlements and alpine grassland in the Lahaul Valley (Table 3). Further, for occupancy, both the top rank models have shown weak relationships of Agriculture land alone with brown bear occupancy (β = 24; ±14), followed by combined model psi (Agriculture land + alpine grassland), p(.) (β = 28.0±10). Whereas, the detection probability of brown bear was negatively influenced by the Human settlements (β = 0.00 ± 0.00) and the alpine grassland (β = -0.73, ±0.31) (Table 4).
ZSI, Kolkata [39]
Annexure-I: Unpublished only for progress assessment purpose
Table 6. Habitat covariates used for the occupancy analysis of Himalayan brown bear (Ursus arctos isabellinus) in the Lahaul Valley, Himachal Pradesh.
Sr. Variables Code Data Source no.
Topographic 1 Slope SLO SRTM USGS 2 Aspect ASP 3 Elevation ELV Land Cover and Land Use (LULC) Type Western Mixed Coniferous 4 Forest (spruce, Blue Pine, silver WMCF fir) 5 Dry alpine Scrub DAS LANDSAT8 USGS 6 Alpine Grassland AG 7 Moist Deodar Forest MDF 8 Agriculture & Horticulture land AH 9 Human Settlement HS Distance human settlement (m) was calculated using log 10 Distance to Settlement SAL LULC map Euclidean distance function using ArcGIS 10x Distance to nearest stream Extracted from 11 Distance from River RIV (m) was (DEM) calculated using log Euclidean
ZSI, Kolkata [40]
Annexure-I: Unpublished only for progress assessment purpose
distance function using ArcGIS 10x Table 7. Occupancy estimates of Himalayan brown bear in the different forest ranges in the Lahaul Valley, Himachal Pradesh CODES ψ ±SE 95% CI Keylong 0.562 0.02 0.5191-0.6127 Pattan 0.757 0.01 0.7328-0.7848 Udaipur 0.757 0.01 0.7328-0.7848 Tindi 0.663 0.02 0.6300-0.7020 Table 8. Summary of model selection procedure for Himalayan brown bears in the Lahaul Valley, Himachal Pradesh
Model AIC DeltaAIC K AIC wgt psi(AH),p(.) 120.29 0.0 2 0.36 psi(AH+AG),p(.) 121.82 1.5 3 0.17 psi(.),p(SAL) 134.74 14.45 3 0.00 psi(.),p(AG) 135.96 15.67 3 0.00 psi(AH),p(AG) 121.08 0.8 3 0.24
Table 9. Models estimating Himalayan brown bear influences of different site covariates with beta (b) coefficient and standard error (SE) estimates for the top-ranked models.
Models β (AH) β (AG) β (SAL) β(AG,AH) 27.52 - psi(AH),p(.) (SE ±4.75) - - 28.08 0.510 - psi (AH+AG),p(.) (SE±10.15) (SE±0.826) - -0.00015 - psi(.),p(SAL) - - (SE±0.000097) psi(.),p(AG) - -0.738 - -
ZSI, Kolkata [41]
Annexure-I: Unpublished only for progress assessment purpose
(SE±0.319) -0.438 psi(AH),p(AG) - - - (SE±0.307)
b) Occupancy Modeling of three species of Ungulates in Uttakashi
Ungulates influence ecosystem dynamics through prey-predator relationship and helps in maintaining ecosystem balance. Among the three ungulate species (barking deer, goral and sambar) are distributed sympatrically in the Uttarkashi district and important to identify the areas of high occupancy to understand the see impact of recent land use and land changes in the Uttarkashi district due high anthropogenic activity and other factors. Assessing coexistence and role of interspecific interactions along with landscape variables is necessary to know these species co-occur in space. Hence, the current study was conducted aiming at multispecies occupancy modelling of three sympatrically distributed ungulate species in Uttarkashi. We used both sign survey and camera trapping to evaluate the role of species specific and interspecific interactions and effects of environmental variables on their habitat use. Through multispecies occupancy modelling we investigated whether these species occur together more or less frequently than would be expected by chance, while dealing with imperfect detection and incorporating possible habitat variables into the models. We also investigated whether species occurrence is influenced by environmental conditions and the presence of other species. We examined how the variables of each camera site will influence the species specific and pairwise interaction of the three ungulate species. We assumed f123 = 0, hence we did not include higher order interactions in any of our models, we assumed the conditional probability 3 species occurred together was purely a function of species-specific (f1, f2, f3) and pairwise interaction (f12, f13, f23).
A sampling effort of 2819 camera trap nights across 62 camera sites was achieved in the Uttarkashi district. We obtained 30, 24, 9 detections of barking deer, goral and sambar across 62 sites. Model selection provides clear evidence of interspecific dependence of the three species. The top models assume pairwise dependence between the species. The top model assumed the occupancy of the three species at different sites varied as a function of (forest type 188 and distance to village) keeping detection
ZSI, Kolkata [42]
Annexure-I: Unpublished only for progress assessment purpose
probability as constant p(.). This model suggests that mean marginal occupancy probability of barking deer was positively influenced by distance to village (β =2.62 ± 1.13) and negatively influenced by forest type 188 (β= -7.79 ± 0.00). We found that marginal occupancy of goral exhibits a positive relationship with variable forest type 188 (β= 5.76± 0.00) and negative relationship with variable distance to village (β= -2.43± 1.17). Sambar exhibit a very strong relationship between marginal occupancy and West Himalayan sub alpine birch/fir forest (β= 22.80 ± 0.00) and negative relationship between marginal occupancy and distance to village (β= -0.22 ± 0.59). The results of our top model also showed evidences of interspecific interaction among species pairs, which makes it very clear that occupancy probability of species one varied in presence or absence of another species (Fig 12). We also found occupancy probability that two species occurred together varied as a function of forest type 188. The relationship between occurrence of barking deer and variable forest type 188 varied markedly depending on whether sambar and goral was present. This model suggests that barking deer was more likely to occur together at sites were sambar was present (β= 12.80±0.00), having West Himalayan sub alpine birch/fir forest as a variable function of occupancy, while both barking deer and goral (β= -13.38± 0.00), and goral and sambar (β= -11.05± 0.00), are less likely to occur together at sites with forest type 188 (Fig 12).
FT 188
ZSI, Kolkata [43]
Annexure-I: Unpublished only for progress assessment purpose
Figure 12. Covariates effect on the occupancy of three ungulate species. Influence of forest type (West Himalayan Sub-alpine birch/fir Forest (FT 188) and distance to village covariates on the occupancy of ungulates (barking deer, goral and sambar) in uttarkashi, showing the estimated beta values of species specific and pair wise interaction estimated from multi species occupancy models.
c) Occupancy Modeling of Monal in the Uttarkashi District
Total effort of 2819 camera trap nights in 69 camera traps and 54 trails covering 650 km of different locations resulted in 40 independent captures of Himalayan monal (Lophophorus impegans) through camera trapping and 99 independent signs recorded through sign survey in the study area. The highest capture was in Sankari range (n=22) followed by Yamnotri (n=13) then Gangotri (n=5). The highest signs recorded were in Taknor (n=42), followed by Sankari range (n=25), Gangotri (n=16), Mukhem (n=11), Yamnotri (n=3) then Dharasu (n=2). The capture rate and encounter rate of Himalayan
ZSI, Kolkata [44]
Annexure-I: Unpublished only for progress assessment purpose monal was found to (0.08±0.06) individuals per trap night and (3.58±1.15) individuals per km in the study area covering an elevation ranging between 2000-4067 m in the study area. The combination of both, i.e., camera traps and sign survey yielded in the 0.69 naïve occupancy of Himalayan monal in Uttarkashi. Amongst the different forest ranges e.g., Gangotri, Taknor, sankari, Dharasu, yamnotri, Mukhem), the mean estimated occupancy for all ranges was 0.1-1.0 (Table 10). Out of 38 models, only 5 top models were selected found which were useful in explaining the influence of covariates on Himalayan monal occupancy and detection probability based on the lowest AIC values (Table 11). The top model based on the AIC value indicated that the slope and reserve forest (RF) influenced the occupancy of Himalayan monal the most (Table 12) keeping detection probability constant p(.). This model assumed the occupancy of the Himalayan monal at different sites varied as a function of (slope and reserve forest) keeping detection probability as constant p(.). The results of our top model suggests that occupancy probability of Himalayan monal was positively influenced by slope (β =27.52±16.25) and negatively influenced by reserve forest (RF) (β= -8.14±4.99) (Table 12). Table 10. Occupancy estimates of Himalayan monal in different forest ranges of Uttarkashi, Dehradun.
Codes Ψ ±SE 95% CI Gangotri 0.98 0.02 0.007-0.07 Taknor 0.00 1-1 Badahat 0.99 0.001 0.75-1 Mukhem 0.77 0.06 0.51-0.88 Darasu 0.79 0.09 0.46-0.94 Yamnotri 1 0.00 1-1 Singtur 0.11 0.085 0.02-0.42 Rupin 0.67 0.06 0.02-0.77 Sankari 0.84 0.03 0.65-0.91
ZSI, Kolkata [45]
Annexure-I: Unpublished only for progress assessment purpose
Table 11. Summary of top five models selected for Himalayan monal occupancy in Uttarkashi, Uttarakhand
Model AIC Delta AIC Model K - AIC weights likelihood 2*loglikelihood psi(Slope+RF),p(.) 391.91 0 0.58 1 3 385.91 psi(Slope),p(.) 393.76 1.85 0.23 0.39 2 389.76 psi(RF),p(.) 396 4.09 0.076 0.12 2 392 psi(Slope+RF),p(RF) 398.1 6.19 0.02 0.04 3 392.1 Psi(.),p(RF) 400.66 8.75 0.007 0.01 2 396.66
Table 12. Models with beta coefficient values of different site covariates influencing Himalayan monal occupancy and detection probability
Model ψ (Slope) ψ (RF) Ψ(.) p (.) p(RF) psi(Slope+RF),p(.) 27.52±16.25 -8.14±4.99 - -1.29±0.13 - psi(Slope),p(.) 3.60±1.30 - - -1.20±0.16 - psi(RF),p(.) 1.74±0.65 - - -1.18±0.67 - psi(Slope+RF),p(RF) 1.08±1.37 1.22±1.00 - - 1.22±0.18 Psi(.),p(RF) - - - 1.25±0.38 -1.16±0.16
d) Habitat Association of Garlifomrs in the Darjeeling district
The Indian Sub-Continent is home to 51 species of pheasants and 16 of these are endemic to the Indian Himalayan region (Poudyal, 2014). The members of family Phasianidae holds prime position in the food wed and are invaluable biological indicators, they are also known to be very susceptible to the on-going phenomenon of habitat degradation and rising anthropogenic pressure (Fuller & Garson, 2000). Very few attempt have been made to understand the habitat ecology, distribution and abundance pattern of these species in the Indian Himalayan regions. Therefore, this study aims to focus on the preliminary understanding of the distribution of pheasants in different habitat types and their preferences in the study area through generalized linear modelling (GLM) with the
ZSI, Kolkata [46]
Annexure-I: Unpublished only for progress assessment purpose help of camera trapping. Eventually, this type of study would throw light on the necessitous conservation plans of these very valuable biological entities. We used the cameras trapping and deployed 58 cameras in the different location in the 15 grids of 5X5 grids separated by an average distance of 2 km operating on an average of 31.96 days in 58 different locations. Camera traps were placed in 13 locations in SNP, 33 in SWLS and 13 locations in TERR. The unevenness of efforts in camera trapping was largely due to the inaccessibility in SNP due to very dense vegetation. These camera traps results in to effort of 2550 camera trap days in 58 different locations resulted in 378 independent captures of four pheasant species (Lophura leucomelanos, Arborophila torqueola, Tragopan satyra and Ithaginis cruentus) in the study area. The highest capture was of Lophura leucomelanos (n=223) followed by Arborophila torqueola (n=142) then Tragopan satyra (n=9) and Ithaginis cruentus (n=5). Altitudinal range overlap between Kalij pheasant (1689 m a.s.l to 2604 m a.s.l) and Hill partridge (1689 m a.s.l to 3114 m a.s.l) and Satyr tragopan (2592 m a.s.l to 3377 m a.s.l) were prominent. Blood pheasant was captured in only one location at 3377 in the Singalila National Park. In this effort two camera traps were stolen, one in SWLS and one in TERR.
Since only two species out of four pheasants present in our study area yielded more than 100 events (records) so univariate habitat modelling was only possible for these two (Lophura leucomelanos and Arborophila torqueola). For Kalij pheasant habitat dominated by oak was a positively and strongly influencing variable, whereas pine trees was not as strongly associated with them as oak. Altitude, presence of dense bamboo, distance from water and presence of rhododendron species were significantly and negatively correlated with the trapping events of this species. In case of Hill Partridge buk oak forest and distance from water, were the most influential variables (Table 13). Here, distance from road, grass cover on the ground, grazing and human disturbance was the variables that were negatively correlated with the trapping events of this species. In addition, altitude and aspect also have positive influences on the occurrence of this species (Table 13). Table 13. β-coefficient values of best model selected based on the AICc values for understanding the habitat variables influencing the occurrence of Kalij pheasant and Hill partridge significantly.
Species Variable β- Std. z value Pr(>|z|) Significance
ZSI, Kolkata [47]
Annexure-I: Unpublished only for progress assessment purpose
Estimate Error Kalij Altitude -0.91402 0.489618 -1.867 0.061928 . Presence of Bamboo -1.99553 0.885426 -2.254 0.024212 * Presence of Dog 2.795355 1.117281 2.502 0.012352 * Distance from Water -68.9467 27.89597 -2.472 0.013452 * Presence of Oak 2.660005 0.795285 3.345 0.000824 *** Presence of Pine 1.551478 0.865939 1.792 0.073186 . Presence of Rhododendron -2.77732 1.494013 -1.859 0.063032 . Plantation -2.21195 0.914156 -2.42 0.015535 * Hill Partridge Altitude 2.106193 0.865048 2.435 0.014901 * Aspect 0.012449 0.005124 2.429 0.015126 * Distance from Road -92.9215 55.7474 -1.667 0.095548 . Distance from Water -115.859 38.69737 -2.994 0.002754 ** Grass cover on ground -2.8557 1.336751 -2.136 0.032655 * Grazing present -3.4374 1.895365 -1.814 0.069742 . Low human disturbance -3.31847 1.451431 -2.286 0.022234 * Medium human -5.05644 2.14727 -2.355 0.018532 *
ZSI, Kolkata [48]
Annexure-I: Unpublished only for progress assessment purpose
disturbance No human disturbance -3.55946 1.817003 -1.959 0.050116 . Buk Oak Forest 4.886659 1.384062 3.531 0.000415 ***
Activity pattern
a) Ungulates species in the Uttarkashi district
The overall activity patterns were similar among all the three ungulate species. All the a species were active throughout the day, with decreased activity between early morning and afternoon and late evening. Goral was active throughout the day with decreased activity in early morning and evening (Fig 13). While both barking deer and sambar were more active in late morning and early evening (Fig 14). The overlap of activity between, sambar - barking deer (Dhat1 value=0.85) was considerably higher than barking deer – goral (Dhat1 value=0.78), and to a very lesser extent than goral - sambar (Dhat1=0.81) (Fig 14).
c Figure 13. Temporal activity pattern of three ungulate species (a) goral,(b)barking deer, (c) sambar.
a) Monal
The daily activity pattern of Himalayan monal and Humans on visualisation showed marked difference in the peaks of their activity. Himalayan monal showed greater peak activities in the hours after sunrise and the activity gradually decreased towards mid-day
ZSI, Kolkata [49]
Annexure-I: Unpublished only for progress assessment purpose and gave a smaller peak before sunset and remains a little active till 18:00 hrs (Fig 15). The activity of Humans was mostly restricted to hours with daylight i.e., from 05:47 to 18:26 hrs and showed greater peak activity in mid-day. The overlap plot of temporal activity between Himalayan monal and humans showed marked difference in the activity of Himalayan monal due to the presence of humans (Dhat1=0.77) (Fig 15).
Figure 14. Overlapping activity pattern of (a) barking deer-goral (b) Goral-sambar, (c) sambar-barking deer.
ZSI, Kolkata [50]
Annexure-I: Unpublished only for progress assessment purpose
Figure 15. Temporal activity pattern of (a) Himalayan monal (b) Human & (c) overlapping activity pattern of Himalayan monal-humans in Uttarkashi.
b) Galliformes in Darjeeling
The daily activity pattern of these four pheasant species on visualisation showed marked difference in the peaks of their activity. Kalij pheasant showed greater activities in the hours after sunrise and the activity gradually decreased towards mid-day and gave a smaller peak before sunset (Fig.16). The activity of Hill partridge was mostly restricted to hours with daylight i.e., from 05:47 to 18:26 hrs. There were only nine captures of Satyr Tragopan and they suggest that this species was the most active in the hours before sunset. Whereas, there were only five captures of Blood pheasant and according to that they are more active during the hours just after sunrise till mid-day. Kalij and Hill partridge showed high overlap of temporal activity (Dhat1=0.76) and Tragopan and Blood pheasant showed lower overlap (Dhat1=0.41).
ZSI, Kolkata [51]
Annexure-I: Unpublished only for progress assessment purpose
Figure 16. Temporal activity: a) Satyr Tragopan b) Hill Partridge c) Blood Pheasant d) Kalij Pheasant e) Temporal overlap between Blood Pheasant and Satyr Tragopan f) Temporal overlap between Kalij Pheasant and Hill Partridge.
ZSI, Kolkata [52]
Annexure-I: Unpublished only for progress assessment purpose
DNA based analysis
Total of the 3550 samples collected from the different sites of Indian Himalayan region and all samples have been cataloged and stored in the silica gel and kept for the further DNA based analysis (Table 14). Out of these samples, 1672 good quality of samples have been processed for the DNA extraction (Fig. 17). All the samples were preserved in 70% ethanol and DNA was extracted using QIAamp Fast DNA Stool Mini Kit (QIAGEN Germany) following manufacturer protocol.All the extracted samples were amplified with Cyt b gene for the herbivores species identification and ATP6 gene used for the carnivores‟ species identification.
Figure 17. Visualization of isolated DNA from different ungulates pellet on 0.8% Agarose. Lane no. 1-13 scrap of outer surface of pellet and Lane no. 14-15 for use of half pellet.
a) Selection of microsatellite primers for mutlilocus genotyping
Total 100 microsatellite markers were selected and run in the multiplex manager (Holleley and Geerts 2009)to select loci for the multiplex PCR (Fig. 18). Multiplex manager helps us to identify the overlapped region that falls into different size ranges, and we labeled microsatellite loci accordingly (Fig. 18). Based on different primer have been selected and for the different species such as 10 microsatellite loci for the Musk deer (Zuo et al.2005), 7 loci for the Deer species, 16 microsatellite loci selected for the bovids (Barendse et al., 1992), 12 microsatellite loci selected for the canids, 15 selected for the fishes species (Matura et al. 2010) and 15 for the primates species.
ZSI, Kolkata [53]
Annexure-I: Unpublished only for progress assessment purpose
Figure 18. In-silico PCR multiplexing to labeled the dye of microsatellite markers that was tested in the present study to select the microsatellite loci.
b) Species identification
Based on the DNA based species identification total 9 species of herbivores and 7 species of carnivores‟ preliminary identified (Figure 19&20). After identifying the species, 10 species have been selected for the detailed population genetic structure, demography, landscape genetics and phylogenetics analysis.
Table 14. Samples collected from the different study sites
Sites Samples collected DNA analysis Lahual and Spiti 1037 950 Uttarkashi 559 229 Darjeeling 473 28 East Sikkim 271 75 East Siang 658 75 West Kameng 552 315 Total 3550 1672
ZSI, Kolkata [54]
Annexure-I: Unpublished only for progress assessment purpose
Indian Barking deer Blue sheep Blue
Himalyan tahr
35
99 30
Sambar
Serow
35
15
Ibex
Musk Deer
Sheep
Domestic goat
Figure 19. Phylogenetic tree identifying the 9 species of herbivores
Marten Snow Leopard Snow
Leopard cat
40
48
Tibetan Wolf Brow n bear
100 100
79
Black bear
Domestic dog Red Fox
Figure 20. Phylogenetic tree identifying the 9 species of herbivores
ZSI, Kolkata [55]
Annexure-I: Unpublished only for progress assessment purpose
c) Population genetics, demography and phylogeography
For the population genetics based analysis ten species were selected and these species includes: Snow leopard, Brown bear, Blue sheep, Ibex, Musk deer, Chinese Pangolin, Red Panda, Arunachal Macaque, Leopard and Black bear. These species were tested with the different sets of mitochondrial and microsatellites markers and address the different questions related to population genetics analysis, phylogeography, and demographic analysis. Details of few of studied species are as follow: 1. Blue sheep and Ibex Ungulates in trans-Himalayan region is usually neglected and are rarely studied due to difficult terrain and inaccessibility. Himalayan Ibex and Blue sheep are two important species of Caprinae family which plays an important ecological role in Himalayas and are also important prey species for elusive snow leopard and Tibetan wolf species. We collected around 150 pellet samples from herds (4-50) individuals of both Ibex and Blue sheep. All pellet samples collected were sun-dried and kept in collection vials with silica. Five tissues of both Ibex and Blue sheep each were procured from locals having trophies from past years. We tested the cross-species amplification of 22 microsatellite markers on both Ibex and Blue sheep. For individual identification of ibex and blue sheep we established panel of nine and eight microsatellite markers respectively. All established loci are found to be polymorphic and exhibited PIC > 0.5 except for BM203F for Ibex and CSSM14 for blue sheep. The most polymorphic locus for ibex is Haut14 with HE =
0.816 and CSSM 14 for blue sheep with HE = 0.923. A total of 53 individuals of ibex were identified with nine markers and 23 individuals of blue sheep were identified with a set of eight markers. Overall observed Heterozygosity and expected heterozygosity were
HO = 0.431 ±0.15, and HE = 0.679 ±0.12 respectively for ibex (Table 15) and HO=0.451 ±
0.25 and HE= 0.749± 0.10 respectively for the blue sheep (Table 16). However, more in- depth study of the population needs to be done to understand the demography and trend of population genetics.
There is hardly any genetic study done on Indian Himalayan mountain ungulates (Ahmed et al., 2016), However, this study is first to document genetic diversity of two ibex and blue sheep of Indian trans-Himalayan region. Detailed genetic study of these
ZSI, Kolkata [56]
Annexure-I: Unpublished only for progress assessment purpose ungulates needs to be done to understand their fragmented population and management. This study ascertains the efficiency of marker panel in population genetics of Ibex and Blue sheep. Cross-species amplification of these markers are also shown in other studies such as., Kim et al., 2004 and thus can be used to study about genetic diversity of other ungulate species.
Table 15. Microsatellite markers amplified in the test panel and genetic diversity summary for Himalayan Ibex
PCR (% No. of F Null Locus H H PIC F success) alleles O E IS allele
Haut14 86.79 12 0.587 0.816 0.796 0.291 0.1631
INRA35 92.45 14 0.571 0.798 0.779 0.293 0.1558
CSPS115 75.47 8 0.475 0.701 0.650 0.334 0.1798
ETH10 90.57 14 0.208 0.567 0.554 0.639 0.4783
BM1824 88.68 9 0.319 0.754 0.705 0.608 0.4329
SPS115 79.24 7 0.619 0.716 0.670 0.145 0.0478
SR-CSRP6 100 7 0.396 0.693 0.649 0.436 0.2757
BM203F 90.56 10 0.271 0.457 0.445 0.416 0.2707
BM415F 86.79 8 0.435 0.607 0.553 0.294 0.1799
MEAN 87.84 9.889 0.431 0.688 0.647 0.384 0.2427
SD 0.15 0.12 0.11 0.16 0.14
Table 16. Microsatellite markers amplified in the test panel and genetic diversity summary for Blue sheep
Locus PCR No. of HO HE PIC FIS F null (%succes alleles allele s)
Haut14 92 7 0.320 0.746 0.707 0.585 0.4031
INRA35 92 8 0.120 0.842 0.823 0.863 0.7496
CSSM14 96 3 0.923 0.567 0.472 -0.615 -0.2616
BM1824 96 6 0.538 0.680 0.628 0.227 0.1008
SPS115 77 5 0.190 0.747 0.706 0.756 0.5883
ZSI, Kolkata [57]
Annexure-I: Unpublished only for progress assessment purpose
SR-CSRP6 100 9 0.556 0.738 0.716 0.265 0.1470
ETH152F 96 7 0.462 0.797 0.768 0.437 0.2646
BM203F 96 12 0.500 0.875 0.863 0.444 0.2732
Mean 93.125 7.125 0.451 0.764 0.710 0.370 0.2831
SD 0.25 0.10 0.12 0.46 0.31
2. Phylogeography of Ibex:
Montane systems act as „biogeographic barrier‟ and have been considered as one of the prominent drivers causing for the vicariance speciation (Baker & Bradley 2006; Drovetski et al. 2013; Yang et al. 2016). Elevated ridges in the montane system often alter habitat and force changes in the floral composition, consequently imposing challenges on the native species to cope up for their survival under the changing climatic conditions. Wild ungulates which prefer to live in groups with smaller home ranges, retain relatively lower ecological resilience and so during the climatic oscillations and temporal topographic transfiguration in the past, they resorted longer in the deeper cleft of the mountains which facilitated intra-specific variation and drove for allopatric speciation (Xing & Ree 2017). Montane systems, formed by a series of climatic oscillations and temporal topographic metamorphosis, have broken off the contiguous distribution of wide spread species and accelerated allopatric speciation. We used mitochondrial partial cyt b region to address the speciation in the Sibreian ibex from the entire range. We used Bayesian phylogeny based approach and molecular dating to understand the species diversification in the entire landscape. We collected 30 fresh pellets (#9 from Kibber Wildlife Sanctuary, Spiti valley and 21 from the Lahaul valley) from Himachal Pradesh (Fig. 21). All the samples were processed for DNA extraction using the QIAamp DNA Stool Mini Kit (QIAGEN, Germany) in dedicated room. The Polymerase Chain Reaction (PCR) to amplifying mitochondrial Cyt b gene was performed using the universal primers developed by Verma and Singh (2003).
ZSI, Kolkata [58]
Annexure-I: Unpublished only for progress assessment purpose
Figure 21. Map showing the sample locality from India used in this study
To unravel the complexity in species recognition of Indian ibex, we collected thirty faecal samples of ibex from the Lahaul and Spiti, Himachal Pradesh, India that yielded two haplotypes with intra-specific sequence divergence (SD) of 0.00-0.004. However, the inclusion of the available sequences of Siberian ibex from various ranges countries/areas viz. Altai Mountain, Kazakhstan, Kyrgyzstan, Russia, Mongolia, Tajikistan: Pamirs regions showed intra-specific SD of 0.028-0.118 (Table 17).
Table 17. Genetic distance matrix among the Clades of Ibex
IT KZ IT-KZ AMR CapIbex CapNub IT KZ 0.089 IT-KZ - - AMR 0.110 0.036 0.063 CapIbex 0.041 0.073 0.061 0.093 CapNub 0.061 0.093 0.081 0.108 0.057 0.000
IT-India and Tajikistan Clade; KZ-Kazakhstan Clade; AMR- Altai mountain and Mongolia Russia; IT-KZ- India and Tajikistan and Kazakhstan clade; Capibex-Capra ibex, CapNub-Capra nubiana; ’ –‘ -Sequence divergence not calculated
ZSI, Kolkata [59]
Annexure-I: Unpublished only for progress assessment purpose
All sequences of Siberian ibex yielded 20 haplotypes, and Bayesian-based phylogeny clustered them into three major clades i.e. I-T, KZ and AMR Clades. I-T clade represented India and Tajikistan; the KZ clade represented Kazakhstan and AMR clade represented sequences from Altai mountain, Mongolia and Russia with high posterior probability (0.9; Fig. 22a). Surprisingly, I-T clade (presently known as Siberian ibex) was estimated to have diverged from the Alpine ibex during the early Pleistocene epoch (~ 2.4 mya, 95% CI 1.4–3.8; SD 0.041) than the Siberian ibex during Miocene-Pliocene boundary (~6.6 mya, 95% CI 4.8–9.4; SD 0.083, Fig 22 a&b). I-T clade was found to be adequately diverged from the KZ clade found in Kazakhstan (SD 0.083) and AMR clade found in Mongolia (SD 0.110; Fig 1 a & b) on the set 10x threshold of mean intra- specific distance criterion (Kerr et al. 2009; Tobe et al. 2010). This piloted observation indicated that I-T clade, found in India and Tajikistan is not the same species of Siberian ibex distributed in KZ and AMR clade. This unique observation was corroborated by the species divergence during the Pliocene (~ 3.9 mya, CI 2.4-5.7; SD 0.061) between I-T clade and the Nubian ibex.
Figure 22. Phylogeographic relationship in the genus Capra throughout its distribution. A) Bayesian based phylogenetic analysis performed using BEAST v. 2.1.3. (Molecular dating was undertaken using the normal prior with a mean 2%/My following the
ZSI, Kolkata [60]
Annexure-I: Unpublished only for progress assessment purpose universal evolutionary rate of vertebrate Cyt b mitochondrial DNA. Above the node- divergence time is given and below the node is the posterior probabilities. Light green color on map depicts the entire distribution of Siberian Ibex as per IUCN which is overlaid by three colors with respect to three clades, i.e. purple representing I-T clade, light pink representing KZ clade and Sandy brown representing AMR clade. IUCN distribution of Capera ibex and Capera nubiana are represented by the dark pink and dark red color on the map. On the left on the map, haplotypes, H1 to H24 are represented in different color representing different locality as per their geographical origin). B). the mini tree of the detailed BEAST based phylogeny is representing sequence divergence between the major clades of the ibex .
3. Phylogeographic and demographic history of Red panda
Knowledge of the genetic structure and population history of a species is critical for conservation and management planning at the population level within a species‟ range. The red panda (Ailurus fulgens) is an endangered species distributed at the Central and Eastern Himalayan biotic province is of special interest in evolutionary studies due to its taxonomic uniqueness (Glatston et al., 2015). Recent study based on genomic data provided evidences to classify red panda in two different species, Ailurus fulgens and Ailurus styani, of which the former species distributed in Himalayas have already been reported to retain low genetic diversity and higher genetic load (Hu et al. 2020). Therefore, we examine the evolutionary history of Himalayan red pandas across its current range in India, and to assess the genetic divergence and population structure among different geographic locations using mitochondrial control region DNA analysis. We collected 132 faecal samples from the protected areas (PAs) of North West Bengal (#29) (northern part), Sikkim (#28) and Arunachal Pradesh (#75) of India. We sequenced 436 bp of the mitochondrial control region (CR) of 44 individuals across the Indian range and downloaded 44 sequences from its Chinese range. CR alignment of the 44 newly determined sequences identified 18 unique haplotypes and 34 polymorphic sites with an average nucleotide difference of 7.25. The overall Haplotype diversity and Nucleotide diversity were 0.925 and 0.017 respectively (Table 18).
ZSI, Kolkata [61]
Annexure-I: Unpublished only for progress assessment purpose
Phylogeographic analysis revealed significant divergent clades with geographic breaks corresponding to steep mountains and rivers and identified Brahmaputra as the boundary between the two species. The estimated time at which the both species diverged was traced back to the middle to late Pleistocene (0.49 mya).
Table 18. Summary of mitochondrial genetic diversity and neutrality tests of demographicexpansion of red panda population in India. N- Number of samples; P- Polymorphic sites; H-Number of Haplotypes; K- Average number of nucleotide differences; Hd-Haplotype diversity; π- Nucleotide diversity; SSD- Sums of squared deviations; Rg- Harpending‟s raggedness index; * P >0.10
N P H K Hd Pi Tajima‟s Fu's Fu and Fu and SSD Rg D* Fs* Li‟s D* Li‟s F* 44 34 18 7.25 0.925 0.017 -0.247 -1.717 0.370 0.147 0.021 0.039 P=0.44 P=0.29 P=0.65 P=0.56 P=0.24 P=0.12
Tree topology based on the CR dataset was similar for the ML and BI analyses, and both consistently separated the Indian and Chinese population (labelled A to F in Fig. 23). Clade A (Yellow) is restricted to Kanchenjunga landscape (KL), including SNP, ES, WS and NVNP with two samples from the study of Su et al. previously recognized as A. fulgens. Clade B (Red) and Clade F (Blue) is comprised of haplotypes from mountainous areas of southeast China and Dibang valley of India. Clade C (Sky blue) includes the West and Central Arunachal population group. Clade D (Orange) corresponds to the Chinese population and Clade E (Green) contains the samples from the ES and NVNP (Fig. 23). Most clades themselves and the relationships between them were strongly supported by Bayesian probabilities and ML bootstrap values. According to the current species classification, Clade A,C, E, and D, E, F collectively correspond to the A. fulgens and A. styani.
ZSI, Kolkata [62]
Annexure-I: Unpublished only for progress assessment purpose
Figure 23. a) Maximum likelihood and b) Bayesian phylogenetic relationships of the red panda clade based onand CR (436 bp) DNA sequences. Values at nodes correspond toposterior probabilities > 0.50. Yellow clade A, Red clade B, Sky blue clade C, Orange clade D, Green clade E, Blue clade F
ZSI, Kolkata [63]
Annexure-I: Unpublished only for progress assessment purpose
Population Structure assignment We used different strategies to identify population genetic structure. Haplotype networks based on mitochondrial control region sequences showed presence of genetic structure. The haplotype distribution was geographically significant (Fig. 24). Haplotype 1-6 and 8 were distributed in Kanchenjunga landscape comprised of West Bengal and Sikkim populations. Haplotype 7, 11-14 and 16 comprised of Tawang (Taw), West Kameng(WK) and Menchuka (M) individuals and the rest of the haplotypes comprised of single individuals from Dibang H9, H10, H15, H17 and H18.
Figure 24. Median-joining network based on maximum parsimony among mtDNA CR haplotypes of red pandas. Nodes contain the haplotype name and are proportional to haplotype frequencies. Length of branches is proportional to the number of changes from one haplotype to the following, the lines next to the branch representing more than one mutation step.
Historical demography The historical population trend inferred by the Bayesian skyline plot showed that the Indian population has experienced a recent sharp decline of the effective population size over the last 40k years (Fig. 25a). whereas the Chinese population showed a population
ZSI, Kolkata [64]
Annexure-I: Unpublished only for progress assessment purpose expansion and thereafter demographic stability (Fig. 25b). Our study did not support population expansion in the demographic history of the Indian population the multimodal pattern of mismatch distribution indicated that the population is under demographic equilibrium. Recent population growths of the Chinese population compared to the Indian population were also supported by maximum likelihood estimates of the exponential growth rate in LAMARC (positive corrected g values: 225.78 for Chinese population and 98.78 for Indian population) and higher number of effective population size Ne= 1389.24 compared to Ne= 326.85 of Indian population.
Figure 25. Demographic history of red panda populations estimated using Bayesian skyline plot. (a) the Himalayan red pandas and (b) the Chinese red pandas. Bayesian skyline plot showing a population decline during recent past for the Indian population whereas the Chinese population showing a growth. The solid line shows the median estimates of Ne τ (Ne = effective population size; τ = generation time), and the blue area around median estimates show the 95% highest posterior density (HPD) estimate of the historic effective population size. The timing of events was estimated assuming a substitution rate of 1.9 × 10–8 substitutions/site/year (Hu et al., 2020).
4. Pangolin Indexing System (PIS): implications in forensic surveillance of large seizures Demand for pangolin scales in East Asia has increased dramatically in past two decades, raising concerns to the pangolin survival and bringing pangolin species to the brink of local extinction. Enumerating the number of individuals from the seized pangolin scales
ZSI, Kolkata [65]
Annexure-I: Unpublished only for progress assessment purpose largely go undocumented, mostly due to the unavailability of the appropriate methods. In this study, we developed a Pangolin Indexing System (PIS), a multi-locus STR panel of eight di-nucleotide microsatellites that proved promising in individualization and assignment of Chinese and Indian Pangolin scales. We received 12 reference samples of known pangolins i.e. eight CP and four IP from West Bengal Forest Department and four seizures of pangolin scales (S1 to S4) from various law enforcement agencies and we scrutinized 74 individual scales based on their physical intactness and possibility of tissue remains. We identify the species using the partial fragment of Cytochrome b gene (Verma and Singh, 2003). We selected 15 loci from Luo et al., (2006) based on the extent of polymorphism (higher He, number of alleles and smaller fragment size) in Manis javanica and Manis pentadactyla hoping for their positive applicability in Cinese Pangolin and Indian Pangolin. The individual locus was first amplified in two independent replicates with the reference sample of CP and IP in an uniplex PCR reaction at six different annealing temperatures (50ºC-60ºC) in Veriti thermal cycler (Applied Biosystems, USA). Based on the high amplification success, degree of polymorphism, allelic variability, lower stutter characteristics, power of discrimination (PD) and the polymorphic information content (PIC), we validated a Pangolin Indexing System, comprising of eight STR loci, and checked its reproducibility and reliability in profiling pangolin scales. The combined power of discrimination (PD) of Chinese and Indian Pangolin samples were same i.e. 1.00. The combined power of exclusion was 0.83 and 0.99 for Chinese and Indian Pangolin. The PIS of eight polymorphic microsatellite STRs exhibited the cumulative probability of identity 3.7×10-9 for Indian pangolin and 3.6×10-7 for Chinese pangolin and identified 51 unique genotypes from the 74 scales selected from the four pangolin seizures (Table 21). The study demonstrated the first report of cross- species validation of STRs developed from Malayan pangolin to Indian pangolin and showed the potential application in wildlife forensics through DNA profiling of Indian and Chinese pangolin seizures.
Based on the membership of coefficient (q) ≥0.80 as well as Delta K, Bayesian-based STRUCTURE analysis clustered 51 unique genotypes in two distinct clusters, the seizures S1, S2 in cluster 1 (CP) and S3 and S4 in cluster 2 (IP) (Fig.29; Fig. 30). However, to understand the sub-structuring within the identified clusters of CP and IP
ZSI, Kolkata [66]
Annexure-I: Unpublished only for progress assessment purpose seizures, we evaluated population assignment patterns at K 8 and observed meta- populations/ sub-structuring within the seizures (Fig. 29). Since we do not have the actual localities of the origin of these seizures, we conservatively presume that this sub- structuring reflected possible historic gene flow between the individuals of CP and IP.
Table 19. Genetic diversity indices and forensic parameters of Indian and Chinese pangolin (reference and seizure samples).
Parameters Indian Pangolin Chinese Pangolin Na 9.50±1.76 8.13±1.57 Ne 5.10±1.18 3.42±0.72 Ho 0.59±0.08 0.41±0.09 He 0.69±0.07 0.61±0.08 UHe 0.70±0.07 0.62±0.08 0.84 (MJA10) to 0.06 PE range 0.51 (MJA26) to 0.01 (MJA22, MJA27) (MJA11)
Combined. PE 0.99 0.83
0.92 (MJA18) to 0.57 PD range 0.92 (MJA18) to 0.30 (MJA22) (MJA11)
Combined. PD 1.00 1.00
0.88 (MJA27) to 0.37 PIC range 0.86 (MJA18) to 0.20 (MJA22) (MJA22)
Combined. PM 1.88×10-6 4.66×10-6
Most MJA18 (PD-0.92, PIC- discriminative and MJA18 (PD-0.92, PIC- 0.86) 0.87) Polymorphic loci
*Na - No. of Different Alleles, *Ne- No. of Effective Alleles, Ho – Observed Heterozygosity, *He – Expected Heterozygosity, UHe – Unbiased Heterozygosity, *PE – Power of Exclusion, *PD – Power of Discrimination, *PIC – Polymorphism information content, *PM – Probability of Match
ZSI, Kolkata [67]
Annexure-I: Unpublished only for progress assessment purpose
Figure 26. Population assignment inferred by the STRUCTURE from the four seizure of Indian and Chinese pangolin.
Figure 27. Graphical representation of most possible clusters (delta K) inferred by STRUCTURE HARVESTER.
ZSI, Kolkata [68]
Annexure-I: Unpublished only for progress assessment purpose
5.3 Preliminary finding for Objective 3: To set long-term monitoring protocol for threatened vertebrate faunal groups in different parts of Indian Himalayan Region and to develop adaptive management strategies for long-term conservation of those species through community engagement.
– In the Indian Himalayan region many species found those are having the high conservation priority and as we have to develop the effective conservation strategies for ten selective species from the IHR fond some keystone species under which all species of IHR ecosystem can be conserve. Because elusive behavior and financial constraints hamper the long-term monitoring of these species in the IHR. Such monitoring is also becoming difficult because most complex terrain that remain inaccessible for most of the time throughout the Year. Therefore, we compared different methods to monitor, tested how many plots are needed for long term monitoring and how different methods coupled with advanced tools and techniques can be use full in long term monitoring of these species.
4.3.1. Field testing of different methods for monitoring mammals in Trans- Himalayas
Combined surveys based on non-invasive genetic methods and camera trapping increase chances of capturing most of the elusive species which are otherwise challenging to document, especially in high altitude areas due to tough terrains and inaccessibility. The Trans–Himalayan ranges of India are neglected ecosystem in the context of faunal assessment and their monitoring. The present study provides a comparative performance of non-invasive genetics and different field surveying methods in enumerating the mammalian diversity of Lahaul and Spiti (L&S) an Indian Trans Himalayan region. We undertook sign surveys, field questionnaire, camera trapping, and collected faeces (n=471) from the trails/transects for DNA analysis. For conducting the field study the entire landscape of Lahaul and Spiti was stratified into seven blocks or forest ranges. Three were in Spiti valley (1-Kibber Wildlife Sanctuary, 2- Pin Valley National Park, and 3 - Chandertal Wetland Sanctuary) and four forest range of Lahaul valley (1-Keylong, 2- Tindi, 3-Pattan, and 4-Udaipur). These seven blocks/forest ranges (hereafter called as blocks) represent the entire topographic as well as physiographic variability of the region.
ZSI, Kolkata [69]
Annexure-I: Unpublished only for progress assessment purpose
We used all four survey methods in all seven-block/ranges of the study landscape for comparative analysis (Fig. 31a). A total effort of 688 km transect walk, 1115 camera trap night, 650 questionnaires and 471 fecal samples in 34 (10 x 10 km) grids covering seven blocks of L&S. Together all methods resulted in the identification of 23 species of mammals with two new records, i.e. Asiatic Black bear (Ursus thibetanus) and Leopard cat (Prionailurus bengalensis) in the region (Fig. 31b). The camera traps performed best with a maximum number of species (19) than the other three methods, i.e. Questionnaire survey (16), indirect sign survey (15) and DNA based (10). However, no significant differences (Kruskal-Wallis, K=10; P>0.05) was observed among the methods used for species detection. The camera trap detection varied significantly among the different forest ranges (K=12; P<0.004). The DNA based detection aided in overcoming the issue of wrong identification of the faecal samples of closely related species. Through this article, we discussed the performance of different methods in detecting mammalian diversity for developing field-tested monitoring protocol specifically for the Trans- Himalayan region. We recommend use of camera trap along with non-invasive sampling for monitoring mammals in the Trans-Himalayas.
ZSI, Kolkata [70]
Annexure-I: Unpublished only for progress assessment purpose
Figure 28. Study area map showing surveyed grids in Lahaul & Spiti District, B). Info graphic showing valley wise presence of mammalian species in the Lahaul and Spiti district of Himachal Pradesh.
4.3.2. Monitoring and risk assessment of feral dog populations Lahaul and Spiti region.
Human associated threats to the wildlife have now turned to another dimension relating through feral dogs which are now becoming one of the prominent factor to
ZSI, Kolkata [71]
Annexure-I: Unpublished only for progress assessment purpose biodiversity loss. Globally, very less but noticeable studies are available on the impact of feral dogs on wildlife either through direct predation, changing their natural behaviour and disease transmission. In this study, we aimed to understand the habitat use and sharing pattern of feral dogs with wildlife species, extant of threats due to the feral dogs in the Lahaul and Spiti valley and identify the infested areas in the valley by feral dogs using species distribution modelling. We used camera trapping, trail sampling, non- invasive genetics and questionnaire survey to gather the information on the feral dogs. Further species distribution modelling was used to identify the areas prone to high risk with available patches for the feral dog occurrence.
Total of 167 cameras placed in the Lahual and Spiti valley in which 7 cameras (15 captures) detected the presence of feral dogs near to the wildlife habitat of Snow Leopard, Ibex and Blue sheep. Among these different locality, 5 captures were detected in the Chandratal lake. Whereas in the Kibber wildlife sanctuary, Takshan Nala (Lossar) and Harsar valley at Lahaul valley a feral dog was also reported (Fig. 32-35).
ZSI, Kolkata [72]
Annexure-I: Unpublished only for progress assessment purpose
Figure 29. Feral dog depredating on Marmot Carcass in the Chandratal Lake.
The maximum capture found in Chandratal regions, in which one feral dog is depredating on the Marmot carcass (Fig. 32) whereas different dogs were capture on different occasion. Whereas of the 67 trails walked (688 km) 9 trails resulted in direct sighting of feral dogs in groups of 2 to 4. Out of 615 questionnaires conducted from both the valleys10% respondents admitted the presence of feral dogs.
Figure 30. Images capture of feral dogs in the different areas of Spiti valley.
Total of 471 fecal samples collected from Lahaul and Spiti valleys in which 295 samples were belongs to carnivores. Further the DNA was extraction from these samples and most of them shown degraded quality ranges 100bp to 1Kb of DNA fragments (Fig. 34). Out of 295 samples of carnivores further processed for the species identification using the primers of ATPase 6 gene. Out of these, 103 (34.9%) samples were amplified
ZSI, Kolkata [73]
Annexure-I: Unpublished only for progress assessment purpose
(Fig. 35) and for only 58 samples (19%) produces good quality sequences. Out these 58 samples, 14 samples were identified as feral dogs using these samples generated good quality of sequences with the Q value of >30 (Fig. 36; Table 22).
Figure 31. DNA extracted from scat samples of carnivores using Qiagen kit on 0.8 % agarose gel. M, MW marker 100bp ladder.
Figure 32. PCR products of nearly 150 bp from ATP6 primer with DNA templates (n=1) on 2% agarose gel. M, MW marker 100bp ladder -ve, negative control.
ZSI, Kolkata [74]
Annexure-I: Unpublished only for progress assessment purpose
Figure 33. DNA sequences chromatogram of Canis lupus familiarisusing the ATP6 gene from the samples collected from the Lahaul and Spiti regions.
Table 20. Non-invasive samples identified as Canis lupus familiaris
SN SID Valley Locality Name 1 Canis lupus familiaris ZS185 Spiti Mudh(PIN valley) 31.90542 78.02002778 2 Canis lupus familiaris ZS188 Spiti Mudh (PIN valley) 31.89549 78.01161111 3 Canis lupus familiaris ZS257 Spiti Near Lossar Bridge 32.43392 77.7568 4 Spiti Canis lupus familiaris ZS270 Cave Area,Lossar 32.44791 77.74786 5 Spiti Canis lupus familiaris ZS292 TundaNala, Lossar 32.45838 77.87811 6 Spiti Canis lupus familiaris ZS295 TundaNala, Lossar 32.45887 77.87788 7 Spiti Canis lupus familiaris ZS297 TundaNala, Lossar 32.45977 77.87716 8 Spiti Canis lupus familiaris ZS298 TundaNala, Lossar 32.46004 77.87754 9 Spiti Canis lupus familiaris ZS303 TundaNala, Lossar 32.46304 77.87659 10 Spiti Hansa Valley, Canis lupus familiaris ZS358 Lossar 32.44576 77.90399 11 Spiti Hansa Valley, Canis lupus familiaris ZS360 Lossar 32.4452 77.90431 12 Spiti Canis lupus familiaris ZS368 Chandertal Lake 32.4735 77.61761 13 Canis lupus familiaris ZS382 Lahaul Neelkanth 32.75692 76.88733333 14 Canis lupus familiaris ZS389 Lahaul Neelkanth 32.77703 76.93058333
ZSI, Kolkata [75]
Annexure-I: Unpublished only for progress assessment purpose
Among these 14 samples, 2 samples found in the Neelkanth lake, 2 were from Mudh, PIN valley National Park and 5 from Tunada Nala, Losar and 2 were from Hansa Valley and one from the Chnandrtal Lake whereas two samples were from different location of Losar region.
To identify the area of high invasion of feral dogs in the Lahaul and Spiti found different models were run and out of the four models, ANN (Artificial neural network) and RF (Random forest) were found best fitted which predicts the trends of distribution of feral dogs in the valley. These four models (GLM, RF, ANN and GAM; details described in methods) were ensembled to predict the distribution of feral dogs (Fig. 37a). Among the 9 variables selected to identify that are associated to make a suitable condition for the feral dogs invasion in the area, Bio-19 (precipitation of coldest quarter) and human footprints were found highly governing variable that influence the distribution of feral dogs in the valley (Fig. 37b). The areas which are under risk of feral dogs are and shown in Figure 38 (the areas comes under the least risk: 11988, low risk: 1950, moderate: 1247, High: 2506, very high: 1381). Out of total available area, 1381 Km2 area is highly prone for the feral dog invasion in the valley. Map also depicts very high risk of feral dogs in Spiti valley in comparison to Lahaul valley. In Spiti valley areas such as Chandertal and Losar are more prone to high risk of feral dogs whereas in Lahaul valley the areas such as Miyar, Jispa and Darcha are the areas which are under high risk of feral dogs presence.
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Figure 34: A) Graph of ensembled model depicting trends of distribution of feral dogs. Models evaluated with AUC (>0.75), and then weighted with previous metrics means, B) Graph depicting variables which govern the distribution of feral dogs in Lahaul and Spiti. Variable relative contribution evaluated with Pearson.
Figure 35: Showing areas of Lahaul and Spiti infested by feral dogs categorized into five classes
During the present survey we identified a large number of samples of feral/domestic dogs in wild-species habitat through DNA based techniques as well as in the Camera traps images. As per the respondent's majority of the ungulate species in the area are severely threatened, because of depredation by feral dogs and unsustainable grazing. Furthermore, there are photographic records of wild dogs chasing Ruddy shelduck in Chandertal and feeding on carcase of marmot (Fig. 39&32).
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We also got capture of feral dogs sharing habitat with Himalayan Ibex in the Kibber wildlife sanctuary (Fig. 40). As ibex is the main prey species of top carnivore (snow leopard) if the population of its prey species declines the population of the top carnivore species also declines which is already listed in category of threatened species will lead the species (snow leopard) at the verge of extinction. For threatened species that are already suffering from serious population declines due to other causes, the impact of dogs may seriously impede population recovery efforts.
Figure 36. Feral dogs chasing the Ruddy shelduck in the Chandratallake: 1. Dogs started chasing duck after pointing the location; 2 Dogs about to reache at the location of Ruddy shelduck and duck moved apart from there; 3. Dog finally reached at location and went inside the water for few steps.
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Figure 37. Image shows the presence of feral dogs in the same location where ibex captures in the Ladrcha top at 4100 meter altitude.
4.3.3. Species Distribution models for the different species
Lahaul and Spiti: From each sites, we have selected five most dominant species and prepared the species distribution models for 3-5 dominant species based on the records sighted using the camera trapping location and sign survey. From Lahaul and Spiti region, total three species viz., brown bear, Red fox and Ibex were selected. Brown bear distribution were found mostly in the Lahaul Valley and some part of the PIN valley National Park. High probability of ibex were found both in the Lahaul and Spiti valley whereas Red fox mostly distributed in the human dominated areas (Fig. 41).
Uttarkashi: In the Uttarkashi Sambar Goral and Barking deer was found more abundant initially most of the location were obtained from the Uttarkashi forest division in the which Taknor and Mukhem range was found high distribution probability of Sambar and Goral and Barking deer have high distribution probability in the Dunda, Dhrashu, Taknor and Barhat ranges (Fig 41).
West Kameng district: In the West Kameng district we generated the species distribution maps for the five dominated species such as Yellow throated marten, Arunachal Macaque, Wild boar, Leopard cat and Barking deer (Fig. 42).
East Sikkim: In the East Sikkim district have generated the species distribution models for Barking deer, Asiatic Black bear, Golden Jackal, Goral and Yellow Throated Marten (Fig. 43).
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Darjeeling district: In the Darjeeling district we have generated the species distribution maps for the species Leopard Cap, Common leopard, Serow, Wild boar and Barking deer (Fig. 44).
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Figure 38. Species distribution models for the different species in the Uttarkashi and Lahaul and Spiti region.
Figure 39. Species distribution maps of five dominant species in the West Kameng district.
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Figure 40. Species distribution maps of five dominant species in the East Sikkim district.
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Figure 41. Species distribution maps of five dominant species in the Darjeeling district.
4.3.4. Prediction of climate change on different species
a. Himalayan brown bear
The high-altitude ecosystem of Himalayan regions is highly sensitive to climate change. These impacts are quite visible in the form of changing hydrology, biodiversity, glacial melting, altitudinal shift in species, habitat reduction (Shrestha, & Bawa, 2014). The diverse habitat of the region is home to some of the top conservation priority species and also been listed among the global biodiversity hotpots (Palni, & Rawal, 2010; Pandit, Kumar, & Koh, 2014). For implementing the present study, we have used the entire distribution range of HBB which includes two Northern Himalayan states of India (Himachal Pradesh and Uttarakhand), two
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Union territories (Jammu & Kashmir and Ladakh) and Pakistan occupied Kashmir region and few areas of North-Eastern region of Pakistan (Figure 45).
Figure 42. Map showing the study area boundary and the presence records of HBB (Himalayan Brown Bear). Protected areas (PAs) within the study area boundary was represented by crossed boundary polygon. The clolour scale represents the elevation gradient of the study landscape, with lower elevation represented by (Green) and the higher elevation represented by (Red).
Model performance and Temporal Change Dynamics of Suitability for HBB
After spatial autocorrelation rarefaction analysis only n=717 spatially uncorrelated presence location were used to model the habitat suitability of the study species. Subsequently after removing the multicollinearity among the predictor variables, out of the initial 35 variables, only 11 uncorrelated predictors were selected and used in modeling environment (Figure 46). The resultant model performance for all the participating models was found excellent on both training and cross-validation data. The resulted AUC value of the models was found to range between 0.93-0.98 for training and between 0.91- 0.95 for cross-validation, thus by considering the AUC
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Annexure-I: Unpublished only for progress assessment purpose threshold of 0.75 all the participating models have been considered to be used for building ensembles (Table 23, Figure 47).
Table 21. Model evaluation metrics for each of participating distribution models. Boosted Regression Tree (BRT), Random Forest (RF), Generalized Linear Model (GLM), and Generalized Additive Model (GAM). Model evaluation metrics included area under the receiver operator curve (AUC), Proportion Correctly Classified (PCC), True Skill Statistic (TSS), Cohen‟s kappa, sensitivity, and specificity. Model evaluation was assessed on the training data used to the model as well as the withheld CV (Cross Vallidation) data used for model evaluation.
Model Dataset AUC Δ PCC TSS KAPPA Specificity Sensitivity AUC
BRT TRAIN 0.98 0.04 93.80 0.87 0.84 0.93 0.93
CV 0.94 89.90 0.77 0.75 0.91 (sd 0.03) 0.86
(sd 0.02) (sd 2.64) (sd 0.06) (sd 0.06) (sd 0.06)
GLM TRAIN 0.95 0.02 89.60 0.79 0.74 0.89 0.89
CV 0.92 87.32 0.73 0.69 0.87 0.86
(sd 0.02) (sd 2.16) (sd 0.06) (sd 0.05) (sd 0.02) (sd 0.06)
MAR TRAIN 0.93 0.01 86.21 0.72 0.67 0.86 0.86 S CV 0.91 85.55 0.70 0.65 0.86 0.84
(sd 0.03) (sd 3.35) (sd 0.08) (sd 0.07) (sd 0.02) (sd 0.06)
RF TRAIN 0.95 0.001 90.8622 0.81 0.77 0.90 0.90
CV 0.95 93.80 0.82 0.83 0.85 0.96
(sd 0.03) (sd 1.78) (sd 0.07) (sd 0.05) (sd 0.07) (sd 0.00)
Predictors with score more than the threshold (0.8) have been selected for the suitability modeling. The red and blue dots represents the distribution of presence and absence location with the predictor variable. The Response curve along with the percentage of
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Annexure-I: Unpublished only for progress assessment purpose deviation explained by the respective predictor variable have been given in the far left of the plot.
Also the results of evaluation metrics i.e., TSS, PCC, Kappa, Sensitivity and Specificity signify the quality of the model for both training and CV (Table 23).
Figure 43. Represents the Pearson‟s correlation among the variable.
The ΔAUC was found lowest for RF with 0.001 and the highest value of 0.047 for BRT indicating the sensitivity of the data used for model fitting. Among all the model runs, GLM has retained all the provided predictors, whereas the BRT model selected only six predictors. Out of all the participating predictors, crop and vegetation mosaic (Crop_VegMos) were selected by all the four modeling methods, as they contributed most with average contribution of 26.78%,
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Annexure-I: Unpublished only for progress assessment purpose followed by elevation and precipitation seasonality (bio_15) with 15.53% and 13.84%, respectively.
Thus, indicating the most determining environmental envelop for the species (Table 24). The comparative analysis of present and future models suggests a massive decline of about 73.38% (RCP 4.5) and 72.87% (RCP 8.5) in 2050 based on the highest model argument (Table 25).
Table 22. Percentage of variable contribution for ensemble model of HBB.
Predictor Code GLM MARS BRT RF μ (Mean)
Precipitation bio_15 17.29 0 13.95 24.12 13.84 seasonality (coefficient of variance)
Euclidian distance to Grasslands 7.81 0.50 2.88 4.85 4.01 grassland cover type
Euclidian distance to barren 19.01 27.90 0.00 5.59 13.12 barren cover type
Euclidian distance to Crop_VegMos 11.91 10.20 44.36 40.66 26.78 crop and vegetation
Euclidian distance to permnt_snow 7.49 23.10 0 11.47 10.52 permanent snow cover
Elevation elevation 11.96 24.67 15.27 10.22 15.53
Euclidian distance to water 5.83 9.13 23.54 0 9.63 Water bodies
Slope slope 1.80 0 0 0.96 0.69
Human footprint hfp 2.03 2.34 0 0 1.09
Euclidian distance to Builtup 8.14 0.00 0 1.10 2.31 build-up areas
Isothermally bio_3 6.73 2.15 0 1.03 2.48 [(bio2/bio7)*100]
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The highest suitable areas for HBB on the basis of the highest model argument, was found to be 20642 km2, for the present habitat and climatic scenario, which have been reduced to 5494 km2 and 5600 km2 in RCP 4.5 and 8.5 respectively for the year 2050. A total of 33 Pas were identified in the study landscape, to have the representativeness of HBB habitat in the present scenario, of which Gulmarg WLS exhibits the highest mean suitability value of 3.523, followed by Kugti WLS, Lippa Asrang WLS and Tundah WLS with 2.352, 2.333 and 2.307 mean value respectively. However, for the future climatic condition the representativeness of the PAs have shown a considerable reduction, of which Gulmarg WLS suffers a loss of more than 21% in suitable habitat in both the RCP. Kugti WLS exhibits a habitat loss of 36.85 under 4.5 RCP and up to 43.38% loss under 8.5 RCP, followed by a reduction by 71.43% for Lippa Asrang WLS in 4.5 RCP for the year 2050.
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Figure 44. Model evaluation plots, representing average training ROC of both training and cross-validation (CV) for the replicate runs under four models were a. showing ROC plot of boosted regression tree (BRT) b. generalized linear model (GLM), c. multivariate adaptive regression spines (MARS), d. Random forest (RF).
Table 23. Change in the area in km2 according to the different number of model argument in the present and future RCP scenarios (4.5 and 8.5) for the year 2050.
No. of model Present 2050 2050 argument (RCP 4.5)
(RCP 8.5)
0 model argument 377466 417856 416752 (Unsuitable)
1 model argument 46016 369746 37462
2 model argument 19443 11392 11628
3 model argument 13847 5926 5972
4 model argument 20642 5494 5600 (Highest suitable)
Habitat quality estimation for HBB
The comparative evaluation for the habitat quality estimation suggests, further degradation of habitat for the species (Table 26). Using the highest model agreement output, configuration analysis result suggests more than 40% decline in NP (No. of patches) for both the RCP (4.5 and 8.5) scenarios in 2050 with reference to the present. Moreover, the quality and habitability of these remaining patches are being questionable, as suggested by the following results of LPI and AI. The LPI have shown a reduction by more than 50% in the future with reference to the present, thus indicating the formation of new smaller patches (Table 26). Furthermore, these resulted smaller isolated patches were found to be sparsely located as compared to present habitat for HBB, as aggregation index (AI) shown a reduction of more than 9% in both the RCPs for the year 2050 (Table 26).
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Table 24. Quality estimation result for suitable area for HBB based on ensemble models in both present and future scenarios. Table show metrics quantifying the area and fragmentation of grid cells in the ensemble model, categorised on no. of model argument where the HBB was projected to be present, including number of patches (NP), Largest Patch Index (LPI) and Aggregation Index (AI).
Present 2050 % Change from 2050 % Change from Present to 2050 Present to 2050 (RCP 4.5) (RCP 8.5) (RCP 4.5) (RCP 8.5)
NP
1 model 4113 2795 -32.04 2715 -33.98 argument
2 model 3446 1834 -46.77 1111 -67.75 argument
3 model 2445 1060 -56.64 1840 -24.74 argument
4 model 562 315 -43.95 334 -40.56 argument
LPI
1 model 0.7932 0.6619 -16.55 0.642 -19.06 argument
2 model 0.3643 0.1062 -70.84 0.1424 -60.91 argument
3 model 0.1431 0.1142 -20.19 0.1512 5.66 argument
4 model 0.5299 0.2312 -56.36 0.2386 -54.97 argument
AI
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1 model 60.8415 63.3583 4.13 63.90 5.04 argument
2 model 46.5745 49.7829 6.88 45.38 -2.56 argument
3 model 45.2655 46.2045 2.07 49.19 8.68 argument
4 model 74.9878 67.9491 -9.38 67.63 -9.80 argument
Representativeness of Himalayan Protected Area for HBB conservation in climate change scenarios
The results suggest that PAs with the highest suitable habitat for the present situation is Gulmarg WLS with mean suitability of 3.52, followed by Kugti WLS and Lippa Asrang WLS (Table 27). Whereas in 2050 decline is projected in the mean suitable patches of the species among the PAs within the study landscape, with an average of 58.23% in RCP 4.5 and 60.35% in RCP 8.5. In RCP 4.5 more than 80% decline in the mean habitat value was observed in 12 PAs, out of which 8 PAs may loss 90-100% suitable habitat patches. Furthermore, in case of RCP 8.5, the decline in suitable patches will aggravate to 13 PAs, and 3 PAs will become completely inhabitable for the HBB (Table 27).
The connectivity analysis of the present suitable habitat of HBB with future 2050 in both the climate change scenarios depicts that, connectivity resides in the central portion of Jammu Kashmir and Ladakh, connecting Kargil to the southern region of Jammu and Kashmir through Doda district (Figure 48). The Doda (J&K) was also found to be connected with the Chamba district of Himachal Pradesh (HP). The highest level of connectivity was found to exist in areas of Lahul and Spiti (L&S) and Kulu districts of HP, via southern section of Chamba, HP.
Table 25. Representing the mean suitability of protected areas in the present and future RCP scenarios for the year 2050. The mean suitability values have been extracted from the protected areas based on the count map of model arguments.
Protected area Mean suitability of HBB
Present 2050 Percentage 2050 Percentage
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RCP_4. change RCP_8. change 5 (Present to 5 (Present to 2050 RCP 2050 RCP 4.5) 8.5)
1. Gulmarg WLS 3.523 2.758 -21.72 2.769 -21.41
2. Kugti WLS 2.352 1.486 -36.85 1.332 -43.38
3. Lippa Asrang 2.333 0.667 -71.43 0.821 -64.84 WLS
4. Tundah WLS 2.307 1.402 -39.25 1.255 -45.61
5. Sainj WLS 2.277 0.685 -69.93 0.592 -73.99
6. Limbar WLS 2.190 1.379 -37.01 1.293 -40.94
7. Manali WLS 1.980 0.392 -80.20 0.294 -85.15
8. Sangla (Raksham 1.868 0.675 -63.84 0.764 -59.07 Chitkul) WLS
9. Govind Pashu 1.775 0.452 -74.55 0.531 -70.10 Vihar WLS
10. Dhauladhar WLS 1.535 0.777 -49.39 0.645 -57.95
11. Chandra Tal WLS 1.412 0.627 -55.56 0.490 -65.28
12. Rupi Bhaba WLS 1.392 0.505 -63.76 0.455 -67.35
13. Govind NP 1.377 0.191 -86.12 0.194 -85.89
14. Sechu Tuan Nala 1.373 1.141 -16.88 1.105 -19.50 WLS
15. Overa -Aru WLS 1.371 0.438 -68.03 0.465 -66.11
16. Great Himalayan 1.263 0.309 -75.54 0.265 -78.99 NP
17. Tirthan WLS 1.120 0.087 -92.23 0.054 -95.15
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18. Pin Valley NP 0.762 0.216 -71.62 0.193 -74.67
19. Kedarnath WLS 0.718 0.250 -65.19 0.231 -67.87
20. Gangotri NP 0.695 0.092 -86.73 0.086 -87.64
21. Valley of Flowers 0.543 0.244 -55.07 0.205 -62.32 NP
22. Nanda Devi NP 0.425 0.085 -79.95 0.081 -81.02
23. Kanawar WLS 0.424 0.000 -100.00 0.000 -100.00
24. Dachigam NP 0.380 0.051 -86.52 0.051 -86.52
25. Gamgul Siahbehi 0.218 0.000 -100.00 0.000 -100.00 WLS
26. Hemis NP 0.191 0.016 -91.87 0.013 -93.41
27. Trikuta WLS 0.136 0.182 33.33 0.159 16.67
28. Pong Dam Lake 0.134 0.006 -95.74 0.003 -97.87 WLS
29. Changthang WLS 0.076 0.005 -93.14 0.005 -93.70
30. Karakoram 0.033 0.000 -99.25 0.000 -99.87 (Nubra Syyok) WLS
31. Nargu WLS 0.017 0.000 -100.00 0 -100.00
Furthermore, the southern section of (L&S) has connectivity with the central portions of Kinnaur, HP, which further connects with the northern part of Uttarkashi district of Uttarakhand. However, in case of future projections, the overall trend suggesting a contraction in biological connectivity from both the north and southern region of the entire study landscape (Figure 48). In 2050 the biological connections between the populations of northern Pakistan will be negligible and in India connectivity may get confined to central regions, i.e. L&S and Kinnaur districts, HP. However, comparative results showed that the corridors from Lahaul to Kullu to Kinnaur will
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Annexure-I: Unpublished only for progress assessment purpose reduce considerably. Moreover, the corridor starting from Kinnaur to Uttarkashi further unto Rudraprayag may contraction significantly because of climate change impact. However, the corridor in the Perpanjal landscape which connects Pangi Valley, HP, Doda, Kistwar NP, Kugti WLS, Daksum WLS and L&S may remain intact with minimal negative impacts. The HBB population of Kargil region may get isolated with marginal connectivity with Kistwar NP of J&K state.
Figure 45. Showing the possible biological connectivity for HBB in different habitat suitability scenarios. The current flow in the maps has been represented by the colour tamp from low to high. a. represents the possible biological connectivity in the present habitat conditions. Figure b and c represent the possible biological connection the projected habitat conditions in the future habitat conditions for the year 2050 in RCP 4.5 and RCP 8.5 respectively.
The present analysis indicates that much of the suitable habitat of HBB will be lost in climate change scenarios (Table 23; Figure 47). The projected climate change may result in the
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Annexure-I: Unpublished only for progress assessment purpose loss of particular combinations of climatic conditions that are suitable for the species. In the present scenario most of the PAs which possess suitable habitats for HBB may not retain suitable geographic isotherm suitable for the species. Hence, there is a need to take adopt preemptive spatial planning of PAs in Himalayan region for the long term viability of the species. The suitable habitats mapped outside the PAs and are closely placed may be prioritized for bring them into the PA network. We have presented our results from Western and Northwestern Himalayan while taking HBB as an indicator species and we suggest more studies may also be taken up in other ecosystems vulnerable to climate change.
b. Assessing the current and future distributions of rare Himalayan Galliformes species using archetypal data abundant cohorts, a macroecological approach Few range restricted Himalayan Galliformes are rare and data deficient and it is difficult to model their distributions. The data abundant species or archetypal cohorts can be used as proxies for the data deficient species, to model their distributions; and thereupon successful modelling, climate change impacts on their distribution can be assessed. We classified 21 Galliformes species from the Himalayas into clusters of species with similar habitat associations (Table 28). We used best performing non collinear variables consisting of topography, vegetation, soil, anthropogenic indices and bioclimatic factors for ordination (PCA and NMDS) and clustering (hierarchical clustering, agnes, partitioning around medoids and k–means clustering) of the archetypal cohorts. The clusters were used for two separate modelling frameworks- Species Distribution Models (SDMs) with a combination of biophysical and topographical parameters; and Bioclimatic Envelope Models (BEMs) with only bioclimatic variables. Predicted climate- driven changes in species ranges (year 2070, RCP 4.5 and 8.5) were assessed. The 21 species were clustered in three groups. Precipitation emerged as the overall significant driving factor for all the three clusters (Table 28).
Species clustering
The ordination of the species with PCA and NMDS, with respect to the selected variables reveal species assemblages and environmental associations resulted in the formulation of three groups. The ordination and clustering methods revealed similar group affiliations
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Annexure-I: Unpublished only for progress assessment purpose among the Galliformes species (Fig. 49a, 49b; Table 28). Moreover, the three clusters obtained were consistent across the ordination and clustering techniques used in the study. Hence, we use these cluster groups for performing SDM and BEM analysis (Table 28). Henceforth, we refer to these three groups as Wide ranging, all Vegetation type Galliformes (WRVG); Eastern Himalaya, Dense Vegetation affinity Galliformes (EHDVG); and West Himalaya, Sparse to moderate Vegetation Galliformes (WHSVG), (Table 28).
Figure 46. (a) Principal Component Analysis based ordination of the species with respect to the attached environmental variables showing associations among groups of species (b) Euclidean distance based K-means clustering of the species. Note: 1-Black Francolin, 2-Blood Pheasant, 3-Blyths Tragopan, 4-Cheer Pheasant, 5-Chestnut-breasted Partridge, 6-Grey Peacock, 7-Hill Partridge, 8-Himalayan Snowcock, 9-Humes Pheasant, 10-Kalij Pheasant, 11-Koklass Pheasant, 12-Himalayan Monal Pheasant, 13-Mountain Bamboo Partrdige, 14-Rufous-throated Partridge, 15-Satyr Tragopan, 16-Sclater‟s Monal, 17- Snow Partridge, 18-Temnick‟s Tragopan, 19-Tibetan Partridge, 20-Western Tragopan, 21-Wihte-cheeked Partridge.
Table 26. Cluster allocations of the Galliformes species based on the different clustering algorithms.
Species Distributio Vegetation Elevati h agne pa k- Cluste
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n on (m) c s m mean r s named as 2500- WRV 1 1 1 1 Black Francolin Wide Sparse 5000 G 1200- WRV Grey Peacock East Dense 5000 1 1 1 1 G 400- WRV Hill Partridge Wide Dense 4000 1 1 1 2 G 1200- WRV Hume‟s Pheasant East Sparse 3000 1 1 1 1 G All 245- vegetation 3050 WRV Kalij Pheasant Wide type 1 1 1 1 G Mountain-bamboo 2000- WRV Partridge East Dense 5000 1 1 1 1 G Rufous-throated East- 460- WRV Partridge Central Dense 2500 1 1 1 1 G Wihte-cheeked Moderately 1500- WRV Partridge East Dense 5000 1 1 1 1 G Rhododendr 1500- East- on 4700 EHDV Blood Pheasant Central dominated 1 1 1 2 G 1800- EHDV Blyth‟s Tragopan East Dense 3500 2 2 2 2 G Chestnut-breasted 350- EHDV Partridge East Dense 2500 2 2 2 2 G 2000- EHDV Satyr Tragopan Central Dense 3800 2 2 2 2 G Rhododendr 3000- on 4000 EHDV Sclater‟s Monal East dominated 2 2 2 2 G Temnick‟s 2100- EHDV Tragopan East Dense 3600 2 2 2 2 G
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2800- EHDV Tibetan Partridge East Sparse 5200 2 2 2 2 G Himalayan West- 3000- WHSV Snowcock Central Sparse 5800 2 2 3 1 G 1500- WHSV Cheer Pheasant West Sparse 3050 3 3 3 3 G West- Moderately 2100- WHSV Koklass Pheasant Central Dense 3300 3 3 3 3 G Himalayan Monal Moderately 2000- WHSV Pheasant Wide Dense 4875 3 3 3 3 G Rhododendr 3000- West- on 3500 WHSV Snow Partridge Central dominated 3 3 3 3 G 2000- WHSV Western Tragopan West Dense 2800 3 3 3 3 G Note: hc – Heirarchical clustering, agnes – Agnes clustering, pam – Partioning Around Medoids, k-means – K-means clustering
Species Distribution Models and Bioclimatic Envelope Models The SDMs and BEMs for the identified clusters WRVG, EHDVG and WHSVG were developed using ensembles of selected models for each group, variable importance was assessed, accuracy of modelled outputs in each cluster were evaluated and threshold values for conversion of probability surfaces to binary maps were calculated. The ROC based accuracies for all groups varied between 0.881 and 0.988 (Table 29).
Area of predicted habitat according to SDM and BEM The probability surfaces of the current (SDM and BEM) and future (BEM) distributions were converted into binary maps (Fig. 50) of presence and absence using the respective threshold values (S4: R2). The SDMs predicted 47.779% (440379.53 km2), 8.663% (79851.97 km2) and 55.290% (509608.88 km2) of area of the entire landscape to be suitable for WRVG, EHDVG and WHSVG respectively. On the other hand, according to the BEMs, the projected percentage of climatically suitable habitat in the landscape was 27.386% (WRVG, 252424.05 km2), 7.165% (EHDVG, 66043.69 km2) and 15.564% (WHSVG, 143453.24 km2) for the respective clusters of Galliformes (S4:R2, Table 30).
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Annexure-I: Unpublished only for progress assessment purpose
Climate driven predicted changes
The binary maps of the current and future climate projections were used for estimating the gain, and loss in the bioclimatic envelopes of the three Galliformes clusters (Table 30, Fig. 51). The cluster WRVG (Fig. 51) was predicted to lose about 54.10% of its climatically suitable habitat by 2070 at RCP 4.5, and almost entire bioclimatic envelope loss (98.58%) at RCP 8.5 (Table 30). The habitat gain or range shifts were minimal (2.6%, RCP 4.5 and 1.5%, RCP 8.5), indicating range contraction. In case of WRVG cluster, it is projected to retain almost half of its bioclimatic envelope (46.04%) at RCP 4.5 by 2070, but a drastic decline (98.58%) is expected at the higher emission scenario of RCP 8.5. The second cluster EHDVG (Fig. 51), was predicted to suffer significant losses in both the scenarios (91.88%, RCP 4.5 and 96.90%, RCP 8.5), indicating significant range contraction (Table 4). However, in case of third cluster WHSVG (Fig. 4), about one-third of its climatic envelope will be retained in both the RCP scenarios (31.64%, RCP 4.5 and 28.08%, RCP 8.5) (Table 30).
Figure 47. Distributions predicted by the SDM and BEM (current) frameworks, for the three Galliformes clusters. Note: WRVG - Wide ranging, all Vegetation type Galliformes; EHDVG - Eastern Himalaya, Dense Vegetation affinity Galliformes; WHSVG - West Himalaya, Sparse to moderate Vegetation Galliformes; SDM – Species Distribution Model; BEM – Bioclimatic Envelope Model. . The base map is a Digital Elevation Model (DEM) from Shuttle Radar Topographic Mission (SRTM), NASA.
ZSI, Kolkata [99]
Annexure-I: Unpublished only for progress assessment purpose
Figure 48. Predicted gains, losses and habitat retentions of the bioclimatic envelopes among the three clusters, in the face of predicted climate change for the year 2070 (RCP 4.5 and RCP 8.5). Note: WRVG - Wide ranging, all Vegetation type Galliformes; EHDVG - Eastern Himalaya, Dense Vegetation affinity Galliformes; WHSVG - West Himalaya, Sparse to moderate Vegetation Galliformes. . The base map is a Digital Elevation Model (DEM) from Shuttle Radar Topographic Mission (SRTM), NASA.
Table 27. Model evaluation scores on test data for the clusters and respective models selected for each cluster for the SDMs and BEMs
SDM BEM Model ROC KAPPA TSS Model ROC KAPPA TSS WRVG GAM 0.661 0.217 0.279 GBM 0.678 0.249 0.303 GAM 0.766 0.348 0.412 RF 0.673 0.245 0.295 GBM 0.766 0.348 0.412 Mean 0.671 0.237 0.292 Mean 0.766 0.348 0.412 Ensemble 0.881 0.566 0.601 Ensemble 0.891 0.569 0.611 EHDVG
ZSI, Kolkata [100]
Annexure-I: Unpublished only for progress assessment purpose
ANN 0.765 0.517 0.246 SRE 0.776 0.552 0.451 CTA 0.801 0.570 0.443 FDA 0.807 0.474 0.552 GAM 0.830 0.601 0.427 GAM 0.833 0.515 0.546 MARS 0.839 0.585 0.469 GBM 0.899 0.639 0.689 FDA 0.840 0.596 0.441 MARS 0.829 0.494 0.551 GBM 0.902 0.732 0.517 RF 0.927 0.712 0.771 RF 0.907 0.793 0.524 Mean 0.859 0.567 0.622 Mean 0.833 0.618 0.440 Ensemble 0.951 0.726 0.783 Ensemble 0.955 0.75 0.828 WHSVG GBM 0.676 0.363 0.384 GBM 0.702 0.364 0.425 RF 0.700 0.375 0.406 RF 0.733 0.401 0.476 SRE 0.686 0.319 0.372 Mean 0.717 0.382 0.451 Mean 0.687 0.353 0.387 Ensemble 0.988 0.907 0.92 Ensemble 0.902 0.609 0.611
Table 28. Predicted changes in the climatically suitable area (km2) for the Galliformes clusters in 2070 under RCP scenarios 4.5 and 8.5
WRVG EHDVG WHSVG Current status of suitable habitat (km2) 55.290 SDM 440379.53 47.77% 79851.97 8.663% 509608.88 % 15.564 BEM 252424.05 27.386% 66043.69 7.165% 143453.24 % Predicted change in area (km2) 2070 under RCP 4.5 Absence 644685.87 69.95% 845420.40 91.72% 747774.18 81.13% Habitat gain 24422.25 2.65% 10243.01 1.11% 30416.06 3.30% Habitat loss 136467.72 54.10% 60634.22 91.88% 98147.72 68.47% Retained habitat 116121.71 46.04% 5399.92 8.18% 45359.60 31.64% Predicted change in area (km2) 2070 under RCP 8.5 Absence 663864.31 72.03% 851625.23 92.40% 735878.16 79.84%
ZSI, Kolkata [101]
Annexure-I: Unpublished only for progress assessment purpose
Habitat gain 5200.96 0.56% 3984.09 0.43% 42206.46 4.58% Habitat loss 248648.75 98.58% 63947.57 96.90% 103359.40 72.11% Retained habitat 3983.53 1.58% 2140.56 3.24% 40253.52 28.08% Note: Percentages of suitable areas according to the SDM and BEM models are with respect to the total area of the landscape (921697 km2). Percentage of retained habitat and habitat loss are calculated with respect to the respective areas of climatically suitable habitat of each cluster under the current climate. And percentages of absence and habitat gain are calculated with respect to the total area of the landscape.
4.3.5. Long term monitoring plots
In the each of six sites, monitoring plots have been placed to monitor the vertebrates diversity for the longer duration. In the Lahaul and Spiti so far 10 monitoring plots have been placed. These plots selected based on the primary data collected and presence of the conservation priority species (Fig. 52). These monitoring plots will regularly monitor each year and intensive sampling in these plots will be done. In the Uttarkashi district, we have laid 5 monitoring plots in the Uttarkashi Forest division and Tons Forest Division primarily in the habitat of dominant species such as Sambar, Himalayan Monal, Brown bear, Goral and common leopard. In the Darjeeling district so far 5 monitoring placed in which 1 plot was placed in the Singalila National Park and other are placed in the Non protected areas. These plots were basically placed in the habitat of Red Panda and Chinese Pangolin, Leopard, and some of the important Galliformes species. In the East Sikkim 5 monitoring plots have marked in the habitat of the Tibetan wolf, Musk deer, leopard, Red panda etc. Whereas, in the West Kameng and East Siang district of Arunachal Pradesh, we identified the 4 and 2 monitoring plots basically in the habitat of Hog deer, elephants in the East Siang and Musk deer, Goral and Arunachal Macaque in the West Kameng district. These monitoring plots will be regularly samples through the Camera Trapping and noninvasive sampling as these two methods have provided the best results for the long term monitoring of mammals and Galliformes species in the Indian Himalayan regions (Joshi et al. 2020).
ZSI, Kolkata [102]
Annexure-I: Unpublished only for progress assessment purpose
Figure 49. Monitoring plosts laid in the different study sites.
4.3.6. Vegetation Sampling
The systematic vegetation samples conducted in the different sites and data analysis is under progress to understand the availability of food resources of the different species. So far 499 plots for the vegetation samples have been placed (Table 31). In which total of 415 herbarium sheets prepared of different plant species (Fig. 53, 54).
ZSI, Kolkata [103]
Annexure-I: Unpublished only for progress assessment purpose
Table 29. Number of vegetation plots and tree, shrubs and grasses observed in the different study sites.
Sites Total plots Total Tree Total shrubs Grasses Lahaul and Spiti 52 15 18 5 Uttarkashi 125 30 20 17 Darjeeling - - - - East Sikkim 25 72 50 30 East Siang - - - - West Kameng - - - -
Figure 50. Systematic vegetation sampling using quadrate method (1x1m for Herb, 5 m for Shrub and 10 m for Tree) following every 500 m in Trail/Transect.
The vegetation analysis is under process to understand distribution of different vegetation in range of conservation priority species. Further this will also help to understand the habitat ecology of the threatened vertebrate species. Furthermore, the vegetation data will play significant role in understanding the food habits and foraging
ZSI, Kolkata [104]
Annexure-I: Unpublished only for progress assessment purpose behavior of the priority herbivores species. For understanding the food habitat of ungulates we are collecting palatable voucher samples for developing reference slides (Fig. 55). These slides will be helpful in identification of the species during the micro- histological analysis of the fecal samples of ungulates.
Figure 51. Some selected images of plant material collected and maintained records as herbarium sheets.
References Slides Reference slides from the plant material collected from the field being under progress which will help to identify the feeding compassion of herbivorous species from fecal samples. So far 56 references slides have prepared from the different sites (Fig. 55).
ZSI, Kolkata [105]
Annexure-I: Unpublished only for progress assessment purpose
Figure 52. Reference slides of different plants species
4.3.7. Micro-histological analysis of the faecal samples
a) Ungulates species
So far 46 samples from the Uttarkashi district and 15 samples from the Lahaul and Spiti have processed for the analysis. Total of 184 slides have been prepared of these ungulate species and rest are under progress. One the bases of preliminary analysis and inadequacy of references data, these samples initially screened at monocot and dicot level (Table 32; Fig. 56) and further will be identified once all reference data will be available. Among these highest fragment of the 718 monocots were identified for the sambar deer (Table 32).
ZSI, Kolkata [106]
Annexure-I: Unpublished only for progress assessment purpose
Table 30. Species wise detection of monocots and dicots in fecal samples collected from the different sites.
Species name Monocots Dicots Sambar 718 610 Barking deer 77 131 Goral 169 57 Musk deer 32 35 Blue sheep 524 1
Figure 53. Image showing microscopic pattern of monocots and dicots remains in the faecal samples of ungulates
b) Feeding analysis of the carnivores’ species
ZSI, Kolkata [107]
Annexure-I: Unpublished only for progress assessment purpose
Feeding analysis of Snow Leopard from Lahaul and Spiti district
A total of 51 scats samples of snow leopard were collected from the Lahaul valley and prey items were identified in the samples. In 51 samples we identified total 140 prey items belonging to nine wild and five domestic mammals (Fig. 57; Table 33). About the 35.29% scats samples contained remains of two species while four species were present in 27.45%. About 13.72% scat samples contained the remains of four species and five species occurred in 9.80% of scats whereas single species appeared in 11.76% scats.
In the study area snow leopard consumed most frequently on cattle/cow which was found in relative frequency 24.28% and identified in the total of 67% of all scats, followed by goat with the relative frequency of 16.42% in relative frequency (Table 33). In the wild prey species, Snow leopard predated most frequently on Blue sheep and was detected in 12.14 % of relative frequency and was detected in the 34% of all scats followed by gray langur which was detected in 8.57% as relative frequency of occurrence while occurred in 23.52% of all scats (Table 33). Wild prey contributed 33.53% of snow leopard diet and
ZSI, Kolkata [108]
Annexure-I: Unpublished only for progress assessment purpose while domestic prey contributed 66.47% of diet. Vegetable matter was recorded in 21.42 % of scats.
Table 31. Composition of Snow leopard diet in Lahaul and Spiti area.
Prey item Number of scats Frequency of Relative frequency of N=51 occurrence (%) occurrence (%) Blue sheep (Pseudois nayaur) 17 33.33 12.14 Himalayan tahr (Hemitragus 3 5.88 2.14 jemlahicus) Argali (Ovis ammon) 2 3.92 1.42 Red fox ( Vulpes vulpes ) 4 7.84 2.85 Rhesus macaque (Macaca 1 1.96 0.71 mulatta) Musk deer ( Moschus spp) 1 1.96 0.71 Marten (Martes flavigula) 3 5.88 2.14 Marmots (Marmorata spp) 3 5.88 2.14 Cow (Boss pp) 34 66.66 24.28 Sheep (Ovis aries) 15 29.41 10.71 Goat (Capra aegagrus hircus) 23 45.09 16.42 Yak (Boss grunniens) 12 23.52 8.57 Horse (Equus caballus) 9 17.64 6.42 Squirrel ( Squiridae spp) 1 1.96 0.71 Gray langur (Semnopithecus) 12 23.52 8.57
Relative biomass and relative number of individual consumed-
In terms of relative biomass consumed, domestic livestock played important role in diet of snow leopard. As domestic prey species cow contributed maximum biomass comprising 40.50% in all scats. In average domestic livestock contributed more biomass in diet of Snow leopard which is 80.65% while wild prey species comprising 19.27% of biomass consumed (Table 34).
Table 32. Estimation of relative biomass and relative quantity of prey consumed by Snow leopard. Where X= Live body weight of prey species and correction factor Y = biomass
ZSI, Kolkata [109]
Annexure-I: Unpublished only for progress assessment purpose deposited per scat, D= Relative biomass consumed, E= Relative quantity of prey consumed.
Prey species Body Correction Relative biomass Relative quantity of weight (kg) factor(Y) consumed prey consumed (X) (kg/scat) D ( %) E (%) Blue sheep (Pseudois nayaur) 55 3.90 8.79 6.43
Himalayan tahr (Hemitragus 50 3.73 1.48 5.19 jemlahicus) Argali (Ovis ammon) 80 4.78 1.26 8.20 Red fox ( Vulpes vulpes ) 10 2.33 1.23 1.13 Rhesus macaque (Macaca 10 2.33 0.30 1.03 mulatta) Musk deer ( Moschus spp) 10 2.33 0.30 1.03 Marten (Martes flavigula) 4 0.84 0.48 Marmots (Marmorata spp) 6 0.87 0.69 Cow (Boss pp) 200 8.98 40.50 24.97 Sheep (Ovis aries) 30 3.03 6.02 3.63 Goat (Capra aegagrus hircus) 35 3.20 9.76 4.51
Yak (Boss grunniens) 200 8.98 14.29 21.62 Horse (Equus caballus) 185 8.45 10.08 19.68 Squirrel ( Squiridae spp) 1.5 2.03 0.26 0.17 Gray langur (Semnopithecus) 14.5 2.48 3.94 1.86
ZSI, Kolkata [110]
Annexure-I: Unpublished only for progress assessment purpose
Figure 54. Microscopic structure of hair of different prey species found in snow leopard scat samples from the Lahaul and Spiti valley.
Feeding analysis of Common leopard from Darjeeling district
Total 42 number of leopard scats collected from the Darjeeling district during the 2018 and 2019 and processed for the diet analysis in which total of 105 prey items were identified. Whereas, we identified total 52 prey items belonging to 11 wild prey species and three livestock viz., domestic dog, cow and horse from the scat analysis during 2018. We found leopard most frequently predated on large Indian civet as wild prey which is present 47.61% of all scats with the relative frequency of 19.23% followed by cow 35.09 % (relative frequency; 15.38%) (Table 35). In the scat collected during the 2019, we identified 52 prey items belonging to the 13 wild species were detected and three livestock. We observed that leopard predated on wild boar which is found 28.57 % of all scats with the relative frequency of 11.32%, followed (Table 35).
Table 33. Table-1. Frequency of occurrence and relative frequency of occurrence of prey species in Leopard scats in Darjeeling district (2018 – 2019)
ZSI, Kolkata [111]
Annexure-I: Unpublished only for progress assessment purpose
Species name 2018 2019 Wild prey species No. of % Relative No. of % Relative scats Frequency frequency of scats Frequency frequency of N=21 of occurrence N=21 of occurrence occurrence occurrence
Barking deer 0 0.00 0.00 1 4.76 1.88 Wild boar 1 4.76 1.92 6 28.57 11.32 Large Indian civet 10 47.61 19.23 2 9.52 3.77
Small Indian civet 5 23.81 9.61 6 28.57 11.32 Asian palm civet 5 23.81 9.61 6 28.57 11.32 Masked palm civet 1 4.76 1.92 1 4.76 1.88
Rhesus macaque 0 0.00 0.00 1 4.76 1.88 Wild dog 0 0.00 0.00 1 4.76 1.88 Common langur 5 23.81 9.61 3 14.28 5.66 Leopard cat 2 9.52 3.84 3 14.28 5.66 Bengal fox 0 0.00 0.00 1 4.76 1.88 Serow 1 4.76 1.92 0 0.00 0.00 Yellow throated 2 9.52 3.84 0 0.00 0.00 marten Indian hare 0 0.00 0.00 1 4.76 1.88 Bengal slow loris 0 0.00 0.00 1 4.76 1.88 Rodent sp 2 9.52 3.84 1 4.76 1.88 Unid Primate sp 1 4.76 1.92 0 0.00 0.00 Livestock Cow 8 35.09 15.38 9 42.85 16.98 Horse 3 14.28 5.75 4 19.04 7.54 Dog 6 28.57 11.53 6 28.57 11.32
Relative biomass and relative no. of individual consumed
ZSI, Kolkata [112]
Annexure-I: Unpublished only for progress assessment purpose
Estimated relative biomass contribution of different prey species to the diets of leopard using the equation developed by Ackerman et al., (1984) gave the better evaluation of the of prey contribution in diet. In terms of relative biomass contribution from the study of 2018, large Indian civet contributed maximum biomass 13.08% as wild prey species followed by common langur 6.54%, small Indian civet 5.98% respectively. Whereas, cow contributed maximum biomass that is 30.86 %, as livestock in the Darjeeling district (Table 36). In 2019, estimated biomass contribution and wild boar contributed maximum biomass 10.5 % as wild prey species followed by small Indian civet 6.3%, Asian palm civet 6.3%, large Indian civet 2.31% respectively. Livestock contributed maximum diet which is 58.58%, from that cow contributed 33.29%, horse 17.28% and dog 8.01% (Table 36).
Table 34. Estimation of relative biomass and relative number of prey individuals consumed by leopard in Darjeeling district (2018 – 2019)
Species name 2018 2019 Wild prey species Average Correction Relative Relative no. Relative Relative body factor (Y) biomass of individual biomass no.of weight (kg/scat) D (%) consumed D (%) individual (X) kg E (%) consumed E (%) Barking deer 20 2.68 0.00 0.00 1.37 3.71 Wild boar 42 3.45 1.99 7.62 10.5 9.13 Large Indian civet 8 2.26 13.08 3.65 2.31 1.79 Small Indian civet 2.5 2.06 5.98 1.46 6.3 1.62 Asian palm civet 3 2.08 6.03 1.56 6.3 1.62 Masked palm civet 3 2.08 1.2 0.72 1.06 0.70 Rhesus macaque 7 2.22 0.00 0.00 1.1 1.40 Wild dog 15.1 2.50 0.00 0.00 1.27 2.84 Common langur 8 2.26 6.54 2.52 3.46 1.99 Leopard cat 5 2.15 2.48 1.29 3.29 1.44 Bengal fox 3.2 2.09 0.00 0.00 1.06 0.74 Serow 38 3.31 1.91 6.91 0.00 0.00 Yellow throated 4 2.12 2.45 1.11 0.00 0.00 marten
ZSI, Kolkata [113]
Annexure-I: Unpublished only for progress assessment purpose
Indian hare 2 2.05 0.00 0.00 1.04 0.52 Bengal slow loris 2 2.05 0.00 0.00 1.04 0.52 Rodent sp 0.50 1.99 2.31 0.48 1.01 0.26 Unid Primate sp 10 2.33 1.34 1.96 0.00 0.00 Cow 150 7.23 30.86 31.34 33.29 31.90 Horse 185 8.45 14.67 34.61 17.28 35.21 Dog 18 2.61 9.07 4.69 8.01 4.52
Where X= average body weight of prey species and correction factor Y = biomass deposited per scat, D= Relative biomass consumed, E= Relative quantity of prey consumed.
5.4 Preliminary finding for the objective 4: To enhance capacities of different stakeholders (including Forest & Wildlife department staff, local Institutions/ colleges, local NGOs and local communities) for monitoring and conserving threatened vertebrate fauna in the Indian Himalayan Region through capacity building programs and use of modern science, technological tools and approaches.
5.4.1. Capacity Building
We conducted total of 8 workshops in the different study sites. Two training programme in the Lahaul and Spiti, two in the Uttarkashi district, single training programme have been conducted in the Darjeeling, East Sikkim, East Siang and West Kameng District. One national meeting has been conducted in the Dehradun. These training programme was conducted for the different Stakeholders in which we organized invited lectures, lecture from the expert available in our team members two lectures from the experts and two training session on demonstration on the camera trapping, use of GPS, spotting scope, and other equipment. Experts has also delivered lecture on the Human Wildlife Conflict Mitigation emphasized the role of Rapid Response Team in Rescue and Rehabilitation of the Conflict Wild Animals (Fig. 58). A total of 794 participants 477 male and 249 females attendant the have attended the training cum workshop among which total 29 participants were females and 69 participants were of males. These
ZSI, Kolkata [114]
Annexure-I: Unpublished only for progress assessment purpose participants belonged to the Forest Department, Gram Panchayat, School students, College & School teachers and Van Sarpanch (Table 37).
Figure 55. Lecture on the Human Wildlife Conflict Mitigation emphasized the role of Rapid Response Team in Rescue and Rehabilitation of the Conflict Wild Animals.
Table 35. Detail of Capacity Building programme conducted under NMHS project 2018- 2020
National Level 1 Dehradun (April 2018) workshop Sites wise workshop Uttarkashi Lahaul & Spiti Darjeeling Sikkim East Siang West Kameng 1 (2019) 1 (2019) Total Number of 2 2 (1 Proposed (1 Proposed 1 (2020) 1 (2020) workshops 2018 & 2020 2018 & 2019 25-26 March 30-31 March 20) 20) Total Number of 179 212 120 135 80 68 Participants Total Number of 148 175 52 60 42 -
ZSI, Kolkata [115]
Annexure-I: Unpublished only for progress assessment purpose
Male Total Number of 31 37 68 75 38 - Female Forest Departme 110 36 21 16 30 32 nt ITBP 0 5 - - Police 0 12 - 4 Locals 30 104 22 5 7 2 Tourism 0 9 1 1 NGO 2 3 10 2 3 7 School and 20 35 61 72 30 23 Numb Colleges er of Local partici Administr 12 2 2 1 4 4 pants ation from BRO - 3 - - - - differe Religious - 2 - -
nt SSB - - 4 - - Stake Self Help holder - - - 15 3 - Group: s Joint Forest Managem - - 1 4 - - ent Committe e Eco Developm ent - - - 2 1 - Committe e Panchayat 5 1 2 3 2
Total 794 Locals have 2 locals and have Hands on Provided field Hands on Hands on been provided a kept and training training with training to training for training for specific training have been camera trap and conduct sign camera camera in survey and provided to them sampling kit for survey including trapping, trapping, Any specific monitoring tools on the vertebrate scat and pellet identification of sample sample training provided monitoring collection birds and collection, collection, mammals sign vocalization vocalization recording recording and sign and sign surveys surveys.
ZSI, Kolkata [116]
Annexure-I: Unpublished only for progress assessment purpose
Birds and A manual on A manual on A manual on Plantation A manual on mammals field field monitoring field monitoring field monitoring awareness by field guides have techniques, techniques, techniques, NGO (Aadi monitoring Any capacity been provided sample sample sample Banekebang techniques, material provided collection and collection and collection and Community), sample Forensic Forensic Forensic Wildlife collection Investigation Investigation Investigation awareness and Forensic Calendar Investigation
Figure 56. Banner of two-day training programme held in Uttarkashi.
ZSI, Kolkata [117]
Annexure-I: Unpublished only for progress assessment purpose
Figure 57. Different images of activity during the training program.
Figure 58. Participants are also intracting with the hands on training on the cametra trapping and other euipments.
ZSI, Kolkata [118]
Annexure-I: Unpublished only for progress assessment purpose
Figure 59. Different activities during the training programmes in the different sites.
ZSI, Kolkata [119]
Annexure-I: Unpublished only for progress assessment purpose
Furthermore, for the benefit of the participants a manual on field monitoring techniques was also distributed during the training programme. This manual provides basics of various wildlife monitoring techniques (Fig. 63-64).
Figure 60. Training manual developed for workshop conducted in the Uttarkashi
ZSI, Kolkata [120]
Annexure-I: Unpublished only for progress assessment purpose
Figure 61. Project flyer on aim and objectives and other information of the project.
ZSI, Kolkata [121]
Annexure-I: Unpublished only for progress assessment purpose
Figure 62. Format for the knowledge testing and feedback form about participants knowledge on local conservation issue and wildlife species.
Figure 63. Drawing competition of students during the training programme
ZSI, Kolkata [122]
Annexure-I: Unpublished only for progress assessment purpose
Figure 64. Local and College students participating in the Extempore speech on the Wildlife and wildlife conservation.
Figure 65. College students participating in the Extempore speech and Gram Budha expressing his views on the wildlife and wildlife conservation.
ZSI, Kolkata [123]
Annexure-I: Unpublished only for progress assessment purpose
Figure 66. Training programme covered in the different print media.
ZSI, Kolkata [124]
Annexure-I: Unpublished only for progress assessment purpose
5.5 Preliminary finding for Objective 5: Investigating whether ethno-zoological knowledge and practices can be a conservation measure for threatened vertebrate fauna in IHR
5.5.1. Questionnaire survey
A total of n=3804 interviews in 318 villages were conducted in all six study area of Indian Himalayan region. The questionnaire surveys aim at gather the information on the perception of the peoples related to wildlife conservation, ethno zoological issues, human wildlife interaction and conservation status (Table 38).
Table 36. Total of respondent from the different sites and villages.
Respondent Villages Lahual and Spiti 556 60 Uttarkashi 561 24 Darjeeling 450 59 East Sikkim 492 83 East Siang 1079 43 West Kameng 666 49 3804 318
Local Perception about wildife conservation
Using the set of questionnaire survey, we understad the locals perception towrds the wildife conservaiton from the different sites based on their socioeconomic status. To evaluate the house socioeconomic status, we used Kuppuswamy scale (Saleem, 2018). In which the upper lower class was more interested in the conservation of wildlife in all sites (Fig. 70).
ZSI, Kolkata [125]
Annexure-I: Unpublished only for progress assessment purpose
Figure 67. Local perception in the different Himalayan ranges
a) Meta-analysis of local perception towards the wildlife conservation in the East Siang district
A perception towards the wildlife conservation of locals determines the future of conservation programs which is associated with establishing a symbiotic relationship between local attitudes and the coexisting wildlife. In this analysis we used data from 1079 interviews held at East Siang District, Arunachal Pradesh. The aim of this analysis was to examine the influences of several socio-demographic, socio-economic and human- wildlife interactions on their opinions concerning wildlife conservation. The respondents gave wide and diversified responses on significance of wildlife for the different purposes such as food consumption (hunting) to aesthetic, moral and future values and the ecological balance (Table 39). The responses were recorded and tested with 11 independent variables namely age, gender, household size, education, house type, crop damages, livestock attacks, human attacks, village, circles and occupation.
ZSI, Kolkata [126]
Annexure-I: Unpublished only for progress assessment purpose
Table 37. Distribution of opinions proposed by the locals towards Wildlife Conservation
Opinions on Wildlife Conservation Description Rank No significance. Disagree highest degree of repugnance 1 towards wildlife protection/ belief that there is no role of fauna in the wild or the ecosystem
Food Resources Disagree promotes hunting/ belief fauna is 2 solely for human consumption
Vague Statements Unsure uncommon ideas of wildlife 3 significance.
Aesthetic, future Agree animals are a part of an aesthetic 4 and moral values world and should be preserved (AFM). for the future generation
Ecological balance Agree specifically aware of the 5 importance of each species in the ecosystem, in which, if a small change occurs a negative feedback will be reciprocated.
Do not know Unsure as the option indicates, this group 6 were not aware or reluctant to hold an opinion on wildlife conservation.
Socio-demographic and Socio-economic profile of households
A total of 1079 respondents were surveyed from 43 villages, of whom 77.2% were male and 22.8% were females. The average age was 44.67 (range 18–80). The household size ranged from 1–18 (median=6, skewness= 0.844). Among the respondents, majority were from the Adi tribe with few numbers from other tribes or states.
A huge majority of the households had access to electricity (99.6%). Piped water (90%) connected from pools and rivers dominated their source of drinking water followed by underground water (8.1%) and streams (1.3%). Although some of the respondents had access to LPG gas provided by the government, the respondents
ZSI, Kolkata [127]
Annexure-I: Unpublished only for progress assessment purpose preferred wood (75.6%) as their main source of energy for cooking. Education level of the respondents is in homogeneous with 38.5% as illiterates, 11.5% have completed primary school; 14.1% with middle school; 21.2% had attained high school education; 3.7% completed higher secondary education while the remaining 10.7% had a bachelor‟s degree. In terms of occupation 83.5% of the respondents identified themselves as part time/ full-time farmers, 7.5% were professionals either in government or private sectors, 1.7% were semi-professionals, 3.0% were semi field workers, 2.5% were skilled workers and 1.3% were daily wager or tea estate workers.
Houses were mostly made of bamboo and wood (85.3%), followed by 10.1 % semi pucca and 4.6% with pucca houses. On an average, each household owned 3.5 livestock with poultry (mode=10) as the most common. Others include cows, goats, pigs and mithun. Personal consumptions were claimed by 90.6%, while commercialisations agriculture purposes were from a small number 15.3% and 12.5% respectively. Average land holding is 0.49 bighas. Livestock deaths occur with an average 16.63 (Keep ranges here) per year on account of old age or epidemic.
Income states, occupation and education level of the household were cumulatively combined to extract an estimation of the socioeconomic status of each household. The scale that has been used is of Kuppuswamy (Saleem, 2018). A staggering 78.97% of the households interviewed falls in the lower middle category followed by upper middle with 12.97 %, relatively smaller proportion in upper lower and upper class with 3.24% and 4.81% respectively (Fig. 1). Socioeconomic status of a household does indeed play a role in shaping an opinion. Upper classes (Upper middle & Upper) opposed hunting and promoted conservation while lower categories were reluctant or unsure to comment upon their opinion.
ZSI, Kolkata [128]
Annexure-I: Unpublished only for progress assessment purpose
Figure 68. Distribution of opinions on wildlife conservation according to the socio- economic status (modified Kuppuswamy scale, 2018) of the 1079 respondents.
Local Attitudes, Awareness and Knowledge of the respondents
Based on the Likert Scale, most of the respondents, figuratively, 54.34% were seen to be unsure on the need of animal protection and conservation, followed by 33.64% who agreed on wildlife conservation while the rest 12.04% did not (Fig. 71). However, the reasons varied across individuals when asked on the significance. Negative attitudes included “no significance” (5.83%) and “food resources” (6.21%) while respondents believing on Aesthetic, future and moral values (AFM) (20.48%) and “ecological balance” (13.16%) were considered as positive attitudes. The remaining proportion of respondents produced unsure (51.54%) and vague statements (2.78%).
ZSI, Kolkata [129]
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Figure 69. A likert-scale distribution of opinions concerning wildlife conservation (n=1079)
ANCOVA indicates strong significant relation (p<0.001) with village, circles followed by human attacks while gender and education were moderately (p<0.01) significant models against locals opinion on wildlife conservation (Table 40). Further, Pearson‟s Chi square revealed significant variable subsets. Amongst 43 villages (Cramer‟s V=0.283, p<0.01), 5 (Aoholi [A] Berung [B] Bilat, [C] Bodak [D] and Mer [E] represented as alphabetically for privacy reasons) had a significant influences on the six proposed local views. An opinion that wildlife protection has no significance was exhibited by Mer [E] village (asr=7.4) followed by Bodak [D] (asr=2.7) while Aoholi [A] (asr=4.4) had a strong view on wildlife as a food resource. Aesthetic (5.1%), future (9.36%) and moral (4.7%) values was appreciated by Bilat [C] village (asr=4.3) followed by Bodak [D] (asr=2.2). When we compare the Circles (Cramer‟s V= 0.142, p>0.000), Bilat Circle (asr=5.7) promoted nature‟s beauty and future importance (AFM), while Mebo (asr=4.8) and Namsing (asr=4.4) held an opinion of food resources and no significance respectively. The education was moderately association with locals opinions (Cramer‟s V= 0.125, p<0.000) in which graduates (asr=6.6) have positive opinion to conserve the wildlife for ecological balance, followed by middle school (asr=2.7) for food resources, Illiterates for aesthetic and future values, and primary school level have
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“no significance”. Although majority of the respondents admitted that wildlife causes crop damages (74.05%), and livestock attacks (52.4%). However, “human attacks” (21.8%) responses seems to play a huge role (p<0.01) (Table 40) in shaping their negative perception (p<0.001) (Table 41).
Table 38. The ANCOVA test for interaction effects of independent variables on local perception of wildlife conservation
Dependent Variable: Opinions Independent Variables Type III Sum of df Mean Square F Sig. Squares Corrected Model 351.406a 61 5.761 2.753 .000 Intercept 411.136 1 411.136 196.491 .000 Age 6.020 1 6.020 2.877 .090 Household Size 1.262 1 1.262 .603 .437 Village*** 201.629 34 5.930 2.834 .000 Circles*** 25.578 1 25.578 11.230 .001 Education* 30.649 5 6.130 2.930 .012 Occupation 13.445 7 1.921 .918 .492 House Type .641 2 .321 .153 .858 Crop Damage 1.285 1 1.285 .614 .433 Human Attacks** 18.931 1 18.931 9.047 .003 Livestock Attacks 3.731 1 3.731 1.783 .182 Gender* 9.500 1 9.500 4.540 .033 Error 2125.871 1016 2.092 Total 27667.000 1078 Corrected Total 2477.277 1077 a. R Squared = .142 (Adjusted R Squared = .090) ***village and circles are strongly siginificant at p<0.001
**human attacks and education are moderately significant at p<0.01
*Gender and is significant at p<0.05
During the interview, the respondents were also tested on their knowledge about Wildlife Protection (Act), 1972 of India, of which, ~59.2 % could answer correctly while only 10.1% knew distinctly about WPA Act, 1972.
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Table 39. Test for significant correlations between perception and independent categorical variables
Food Significance of No Vague Ecological consumption AFM Do not Know. Conservation. significance. statements Balance. .
Village*** Mer Aoholi Bilat HQ Berung ns ns (Cramer’s V= 0.283) (asr=7.4) (asr=4.4) (asr=4.3) (asr=2.9)
Circles*** Namsing Mebo Bilat Pasighat(asr=2.6) ns ns (Cramer’s V= 0.142) (asr=4.4) (asr=4.8) (asr=5.7) Sille-Oyan(asr=2.6)
Male Gender** ns ns ns ns ns (asr=3.7) (Cramer’s V=0.127)
Primary Medium Education level*** Illiterates Graduates School School ns ns (Cramer’s V=0.125) (asr=2.4) (asr=6.6) (asr=2.3) (asr=2.7)
Human Attacks*** Yes Yes No No ns ns (Cramer’s V= 0.198) (asr=4.5) (asr=2.9) (asr=2.9) (asr=2.6)
***village, education and human attacks are strongly significant at p<0.001
**gender is moderately significant at p<0.01
ns is not significant, p>0.05
5.5.2. Human-Wildlife Conflicts
Human-Wildlife Conflict issues are also prevalent in forest fringes areas. We have performed some of the preliminary analysis in some of the selected sites indicates that majority of the respondents reported crop depredation by wild boar, elephants, and Asiatic black bear while livestock attacks are caused by canids and common leopard. Few of the respondents also reported human attack cases by elephants. In general, problems faced by the villagers caused by animals is depicted in Fig 73-75.
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Figure 70. Human-Wildlife Conflict reported by locals in East Siang District, Arunachal Pradesh
Figure 71. Distribution of animal conflict cases admitted by the respondents in their respective circles
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Figure 72. Human wildlife conflict percentage in the Uttarkashi district.
Human– brown bear conflict in the Lahaul valley, Himachal Pradesh
The study on assessment of human-brown bear conflict was carried out in Lahaul valley of district L&S of Himachal Pradesh during July-December 2018. We designed a questionnaire survey format which covers almost every aspect of Human-wildlife conflict. Total of 400 households (male n=301, female n=99) were interviewed to understand the human-brown bear conflicted area. Total of 400 respondents were interviewed to understand the human-brown bear conflict on the livestock depredation, crop damage, horticultural damage and human casualties were gathered. The age of interviewees ranged from 15-84 years (average age=45.60). Among these respondent, about 70.25% respondent admitted their dependency upon the forest either for livestock grazing or collection of non-timber forest products. Human-brown bear conflicts were very high near brown bear habitat/forest especially for the livestock or crop damage. Based on the information obtained, no human casualties were caused by brown bear in Lahaul valley but only two incidences where brown bear display assault to nomadic shepherd in the Lahaul valley. We mainly noticed two prominent types of human-brown bear conflict in Lahaul valley (1) crop damage (57.50%) (agricultural/horticultural), (2) Livestock-depredation (27%).
ZSI, Kolkata [134]
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Socio-economic status crop wise pattern of damage Socioeconomic status derived from Kuppuswamy‟s socio-economic scale of interviewers (n=400). We took 3 parameters to analyses the socio-economic status of the interviewer viz. (1) Occupation of the head of the family (2) Education of the head (3) Total monthly income of the family and divided in to 5 socio-economic classes. Out of 400 respondents, we found upper lower (IV) (42.25%) consisted with high percentage followed by lower (V) (29%) Whereas, for the other classes different percentage composition were found viz., upper middle (II) (10%) lower middle (III) (9.75%) and upper (I) (9%) (Fig. 4). Among these categories, upper lower (IV) (27.50-14.50%) were highly impacted by crop or livestock damage followed by lower (V) (15.50-5.25%) upper middle (II) (5.50- 3.75%) middle lower (III) (5-2%) and lowest one were of upper (I) (4-1.50%; Fig. 76)
Figure 73. Kuppuswamy Socio-economic status of respondents (n=400).
.
Crop damage pattern by brown bear In the Lahaul valley the human settlement is close to brown bear habitat which often leads to high rate of crop damage. Lahual valley is highly productive in term of agriculture and horticulture crops. Both local and exotic vegetables cultivated in the valley which give them high price value. These crops includes Maize (Zea mays), pea (Pisumsativium), potato (Solanumtuberosum), Beans (Phaseolus vulgaris), Iceberg
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(Lactuca sativa), cabbage (Brassica oleracea var. capitata), broccoli (Brassica oleracea var. italic) Wheat (Triticumaestivum). Among the horticultural crops, apple (Pyrusmalus), plum (Prunusarmeniaca), apricot (Prunuspadus), peach (Prunuspersica) were found abundant in the study area. Among agricultural crops iceberg (18%) was mostly damaged by brown bear. Out of 57.5% crop depredation by brown bear, highest was recording during the summer (23.38%; Fig. 1). The activity of brown bear crop damage mostly begins from midnight 12–6 am (37.5%; Fig.77).
Figure 74. Timing of raiding of Himalayan brown bear in Lahaul and Spiti as reported by respondent (n=400)
We found, areas those are closer to the forested or brown bear habitat are more prone to Human brown bear conflict. As the distance decreases from the nearest brown bear habitat, crop damage were high (24.5%) in distance <500 m from forest/brown bear habitat followed by 500 –1000 m (20.75%) (>1000 m (12%) (Fig. 79). There were also varying extent of crop damage by altitudinal zone. Highest crop damage was found to be in the altitudinal zone ranging between 2800–3000 m (15.75%) and least at <2600 m (8%; Fig. 80).
ZSI, Kolkata [136]
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Figure 75. Season wise crop damage and livestock depredation by Himalayan brown bear in Lahaul as reported by respondents (n=400).
Figure 76. Himalayan brown bear crop damage and livestock depredation by distance from forest/brown bear habitat.
Livestock depredation
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The data on livestock depredation was collected interviewing the villagers and shepherds. Amongst livestock sheep and goats and cow were predated by the brown bear. Livestock‟s depredation was found to be 27% in Lahaul valley in highest reported during the summer (11.25%) and least was in spring (2.88%; Fig. 78). Livestock damage was varying with distance from forest or brown bear habitat such as <500 m (8%), 500 m- 1000 m (14.25%) and >1000 m (4.75%; Fig. 80). Livestock depredation was almost in similar trend with altitudinal range. <2600 m (6.75%), 2600 m-2800 m (6.5%), 2800 m- 3000 m (4.25%), 3000 m- 3200 m (6.5%) and lowest at >3200 m (3%) (Fig. 80). Protection measure Only 10.25% (41 out of 400) use protective measure against possible bear damage such as electric or solar fences, human clothes hanged around apple orchid and agricultural lands.
Figure 77. At different altitudinal zone crop damage and livestock depredation by Himalayan brown bear in Lahaul as reported by respondents (n=400).
5.5.3. Ethnological Studies
Ethno zoological Utilization in East Siang District The tribals in the East Siang district (mostly Adi) are known to celebrate Arran festival and Kirup Puja, both which involves recreational hunting. Hunting practices are common in both sites. Though earlier these hunting practices were means of survival. Gradually, with livestock farming and other sources of income group activities, it helps the communities to continue the legacy of their forefathers and also to get to know each
ZSI, Kolkata [138]
Annexure-I: Unpublished only for progress assessment purpose other better. Locals were also asked on the ethno zoological use of animal species if animals or animal were used for religious, medicinal, food, ornamental purposes. Comparing the five Ethno Zoological uses and consumption of wildlife as food and their detailed account is given in Table 42.
Table 40. Account on ethno zoological use by wildlife by respondents with examples
EAST SIANG DISTRICT
Ethno No. of Figures in zoological Examples respondents percentage Knowledge Monkey- skull; elephant- tooth; pigs- Religious 101 6.17 animal sacrifice; poultry- animal sacrifice. Porcupine- stomach, intestine; black bear- Medicinal 126 7.45 gall bladder Kidney. Wild boar, Barking deer, Sambar Deer, Food 988 58.46 Squirrels, Bats, Rats. Wild boar- jaws, horns, tooth; Barking deer- Ornamental 472 27.92 antlers; Sambar Deer-antlers.
Others 3 0.17 As pets- Barking Deer, Hog Deer.
Ethno-zoological Utilization in the West Kameng:
The Monpa, Sardupan, Miji tribes are the common inhabitants in this district. Lossar is the biggest festival in this area. They celebrate this festival as the beginning of New Year and for new fortune. Hunting is a traditional practice among the tribes, but now it becomes less day by day. They hunt Barking Deer, Wild boar, Serow for their meat, on the other hand, Black Bear, porcupine, pangolin are used for medicinal purposes. Animal exhibits such as Black bear skin, barking deerskin, skulls of wild animals are the pride, symbolizing the power and wealth of the houses. Raptors feathers, bones of bats are used in religious purposes. Skulls of Serow, Wild boar, Monkey are used to protect them and their houses from negative vibes (Fig. 81, 82).
ZSI, Kolkata [139]
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Figure 78. Graph showing hunting pressure due to various use in ethno-zoological aspect
Figure 79. Graph showing hunting pressure due to various use of wild animal body parts
Ethno medicinal use of Sharpnose guitarfish in Lahaul and Spiti
Illegal trade of fishes is common and have been in practice since ages for the support of livelihood and as dietary supplements. However, several species are protected in the
ZSI, Kolkata [140]
Annexure-I: Unpublished only for progress assessment purpose
Wildlife (Protection) Act, 1972 of India and their trade is restricted under CITES. On May 29, 2018, two persons were observed selling skin and bone of fish (referring them to starfish) as remedy for body pain at Keylong (32° 31́ 0.42°N; 76° 58́ 37.48°E). While interacting with the seller, it was informed that the fishes were captured in Bay of Bengal and brought to Lahul and Spiti through a network of people involved in illegal killing, capturing and trading. The seller mentioned the involvement in trading the fish organs that includes fins, meat, liver oil & skin in India and abroad to earn their livelihood. Therefore, we report a trade of Sharpnose guitarfish (Glaucostegus granulatus) for the ethnomedicinal remedy, identified using DNA barcoding in the Keylong district of Lahaul and Spiti, Himachal Pradesh (Fig.83). This study provides the first DNA barcode of Sharpnose guitarfish and raise concerns to generate a large reference database for the other fishes in trade in order to handle the wildlife offence cases using multigene approach and resolving ambiguity in the species identification.
ZSI, Kolkata [141]
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Figure 80. Glaucostegus granulatus skin (a) and bone (b). Maximum Likelihood tree based on 12S rRNA (c) and cyt b (d) sequence showing the relationship between the collected specimen (in box) and other suspected close relative species.
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Publication from the Project
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