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Item Tiger corridors of the Eastern Vidarbha landscape(NTCA and Wildlife Institute of India, Dehradun, 2016) Mondal, Indranil; Habib, Bilal; Nigam, Parag; Talukdar, GautamItem Ecology of clouded leopard (Neofelis nebulosa) in an East Himalayan Biodiversity Hotspot - Carnivore Co-existence in Manas National Park, Assam, India(Wildlife Institute of India, Dehradun, 2021) Lyngdoh, Salvador; Habib, Bilal; Bhatt, UrjitSympatric species occupying similar niche can result in competitive exclusion of subordinate species. However, species are able to avoid interspecific competition through morphological, physiological, or behavioural trade-offs, which in turn leads to differences in resource use. A guild of wild species of Felidae comprising various combinations of up to eight species is distributed across South-east Asia, with species ranging in size from the tiger (Panthera tigris) to the flat-headed cat (Prionailurus planiceps). Little is known of the ecology of most of these species, and less of their guilds. Large felids such as tigers and leopards coexist in most of their ranges. The sympatric association of such large cats has been studied and debated in most tropical forests of India. The clouded leopard (Neofelis nebulosa), a potent ambassador species for conservation, is among the least known. The clouded leopard is the smallest of the large felids and is least studied due to its secretive nature and nocturnal behaviour. The species is an umbrella species for the Asian forest ecosystem and can be found along the foothills of the Himalayas through Nepal, Bhutan, and India to South China down to Peninsular Malaysia, and on the islands of Sumatra and Borneo. The clouded leopard is vulnerable on the IUCN Red List of Threatened Species and faces a global decline in population and contraction in its geographic range. The species occupies areas undergoing some of the most rapid deforestations and is threatened by poaching and wildlife trafficking. Clouded leopards are apex predators in many Southeast Asian rainforests, although they cooccur with larger predators such as tigers, leopards, dholes; their density, activity, and habitat use may vary. Although there have been discoveries regarding the felid guilds and habitat use of the Sunda clouded leopard, and the threat to the species from habitat loss, little is known for the mainland clouded leopard and the felids with which it is sympatric. Despite the fact that tropical rainforests are known for its high biodiversity and species richness, the scarcity and/or the cryptic behaviour of some of the species have resulted in the scarcity of information about these species. The tendency of many rainforest species to avoid humans on existing tracks (where most transect surveys are done) is well known. These conventional methods include surveys on the footprints, dung, calls, live-trapping, den counts and direct observation. All these surveys are usually performed along transects, and in the past, they were the preferred method in various countries. However, walking along transects to observe terrestrial mammals in tropical rainforests can be extremely challenging. The observers' different abilities to detect and recognize the species may lead to a bias during data collection, increasing the likelihood of animals fleeing unobserved. Presence-absence survey using transects lines or logging tracks may not yield substantial evidence of species diversity. Thus, if any survey were to be conducted without considering these factors, most wildlife surveys could expect a biased trend. In a dense tropical rainforest, camera-traps are useful to detect cryptic species, estimating species diversity, movement, interactions, habitat associations, abundances using individual recognition and, recently, without individual recognition in various countries. A good image from the camera trap is indisputable regarding a certain species' presence compared to an interview or conventional survey methods. The utilization of camera-traps has revealed the presence of secretive rainforest dwelling species, which have been overlooked by applying the traditional transect surveys. In India, this method has been used in estimating densities and abundances of various carnivore species in several protected areas, but few attempts have so far been made in the dense forests of tropical evergreen habitats of the north-eastern part. The use of camera trapping rate as an index of abundance is both promising and cost-effective for the rapid assessment of animal abundance in remote areas or where alternative methods are unfeasible.The study was conducted in tropical semi-evergreen forests of Manas National Park (MNP), Assam, India. The objectives of the study were to (1) estimate the status of clouded leopard and other carnivores, (2) assess prey status and feeding ecology of clouded leopard, and (3) determine the factors governing coexistence of carnivores.Item Status of tigers, copredator and prey in Bor Tiger Reserve, Maharashtra, India 2021(Wildlife Institute of India, Dehradun, 2022) Habib, Bilal ; Nigam, P.; Govekar, R.; Ramanujam, M.; Gawai, R.; Dabholkar, Y.; Bhowmick, I.SummaryThe Phase IV monitoring exercise as a part of the project “Long Term Monitoring of Tigers-predators and prey in tiger reserves and other bearing areas of Vidarbha, Maharashtra, for Bor Tiger Reserve was conducted from December 2020-May 2021. This exercise, having three main objectives, the status of prey, estimation of minimum tiger and leopard numbers, and capacity building among staff flagged off with a capacity-building workshop in December 2020. Line transects surveys aimed to estimate the density of prey species were carried out in two blocks with an effort of 7 days for each transect line. In the core area among all the prey species, the highest density was recorded for Chitals 7.14 (±4.44)/km2 followed by Sambar 6.45 (±2.26)/km. sq. and Nilgai 2.53(±0.67)/km2.In the buffer area, the highest density was recorded for wild pigs 5.75 (±1.26)/km2 among all the ungulate species. The density of other species includes Chital 0.81 (±0.22), Sambar 0.40 (±0.15), Nilgai 4.72 (±0.58), Peafowl 2.56 (±0.54), Langur 19.09 (±2.49).Camera trapping based on the spatial capture-recapture framework was conducted on the same locations of the same grids (2 km2) similar to the previous cycle (2020) which were selected based on a rigorous sign survey that provided sign encounters of tiger, leopard, and other co-predators. This year both core and buffer areas were covered in two blocks with 211 active camera trap stations during February 2021-May 2021. The effort resulted in 7572 trap nights. The number of individual tigers captured was 9 along with 46 leopards. Tiger density based on the Spatially Explicit Capture-Recapture framework was 1.10(±0.37)/100km2 and the density of leopard was 6.68 (±0.80)/100km2. To study space use and activity patterns we have used camera-trapping data from both core and buffer areas of Pench Tiger Reserve. Higher activity overlap was recorded between tigers and leopards (Dhat1=0.88) among predators. Camera trap locations with the number of captures of each species were modeled in a GIS domain using IDW (Inverse distance weighted) interpolation technique to generate spatially explicit capture surfaces. The times recorded on camera trap photos provide information on the period during the day that a species is most active. Species active at the same periods may interact as predator and prey, or as competitors. Sensors that record active animals (e.g. camera traps) build up a record of the distribution of activity over the day. Records are more frequent when animals are more active and less frequent or absent when animals are inactive. The area under the distribution of records thus contains information on the overall level of activity in a sampled populationItem Status of tigers, co-predator and prey in Akola Wildlife Division, Maharasthra, India 2021(Wildlife Institute of India, Dehradun, 2022) Habib, Bilal ; Nigam, P.; Banerjee, J.; Reddy, M.S.; Nimje, A.; Khairnar, M.N.; Patil, J.; Ray, S.Phase IV monitoring for Akola Wildlife Division was conducted from February – May 2021 covering an area of 300 sq. km. as a part of the project “Long-term Monitoring of Tigers, Co-Predators and Prey in Tiger Reserves and other Tiger bearing areas of Vidarbha, Maharashtra”. The objective of the Phase IV Monitoring is to estimate the minimum number of tigers in the reserve using Capture-Recapture Sampling and density estimation of prey base using Distance Sampling. A total of 103 camera traps (pairs) were placed in the 4 wildlife sanctuaries (viz. Dnyanganga WLS, Katepurna WLS, Karanja-Sohol WLS and Lonar WLS) of Akola Wildlife Division following a sampling grid of 2 sq. km. In each wildlife sanctuary, camera traps were active for 25-30 days. During 90 days of camera trapping survey with a sampling effort of 3,090 trap nights, 42 adult individual leopards were photographed in Akola Wildlife Division. 28 adult individual leopards were photographed in Dnyanganga WLS and population size (N) based on the best fit (SECR Heterogeneity) model was 28 (SE ± 1.0). 9 adult individual leopards were photographed in Katepurna WLS and population size (N) based on the best fit (SECR Null) model was 10 (SE ± 1.27). 3 and 2 adult individual leopards were photographed in Karanja-Sohol WLS and Lonar WLS respectively. Leopard density per 100 sq. km. based on the Spatially Explicit Capture-Recapture (SECR) model was 13.42 (SE ± 2.56) and 25.61 (SE ± 8.85) for Dnyanganga WLS and Katepurna WLS respectively. To estimate prey density in Dnyanganga WLS, 42 line transects were sampled times 6-7 during the sampling period, with a total walking effort of 513 km. Overall during the sampling, 336 animal/bird observations were made. The overall density of major prey species (Wild Boar 14.90/sq. km., Nilgai 12.51/sq. km., Peafowl 2.79/sq. km., Chinkara 1.40/sq. km. and Four Horned Antelope 1.33/sq. km.) as estimated using distance sampling was 24.19 /sq. km. A basic understanding of sympatric carnivore ecology with asymmetric competition enables us to hypothesize that to coexist and not just co-occur there must be niche segregation on at least one of the three axes: space, time, and/or diet. To understand how large sympatric predators co-occur in space and in time, camera trapping was carried out. Temporal activity overlaps were derived by using kernel density. Leopards were found in all 4 wildlife sanctuaries. There was a distinct difference in the space-use pattern observed for all three carnivores and a strong spatial segregation pattern found between Leopards, Hyenas and Dholes. It showed significant segregation and avoidance of each other’s space. While leopards show a strong, bimodal, nocturnal activity pattern, Hyenas have a strong, unimodal activity pattern in Dnyanganga WLS. In Katepurna WLS, leopards show a strong unimodal, nocturnal activity pattern and dholes show a bimodal, crepuscular activity pattern.Item Status of Tigers, Co-Predator and Prey in Pench Tiger Reserve (PTR) 2021(Wildlife Institute of India, Dehradun, Maharashtra Forest Department, 2022) Habib, Bilal; Nigam, P.; Ramanujam, M.; Pathak, A.; Shukla, P.; Dabholkar, Y.; Bhowmick, I.The Phase IV monitoring exercise as a part of the project “Long Term Monitoring of Tigers-predators and prey in tiger reserves and other bearing areas of Vidarbha, Maharashtra, for Pench Tiger Reserve was conducted from January 2021-July 2021. This exercise, having three main objectives, the status of prey, estimation of minimum tiger and leopard numbers, and capacity building among staff flagged off with a capacity-building workshop in January 2021. Line transects surveys aimed to estimate the density of prey species were carried out in two blocks with an effort of 7 days for each transect line. Among all the prey species highest density was recorded for Chitals 24.28 (±4.83)/km2 in the core. The density of other species are as follows Sambar 6.08 (±0.98), and Gaur 1.56 (±0.39)/km2, Wild pig 4.31 (±0.90), Langur 17.02 (±3.56), Nilgai 1.91 (±0.41), Barking Deer 0.59 (±0.15), Hare 0.81 (±1.12), Peafowl 2.49 (±0.60). In the buffer area, the density of Chital was 8.63 (±4.15) and of Sambar was 1.36 (±0.40). Camera trapping based on the spatial capture-recapture framework was conducted on the same locations of the same grids (2 km2) similar to the previous cycle (2020) which were selected based on a rigorous sign survey that provided sign encounters of tiger, leopard, and other co-predators. This year the trapping was completed in a single block with 311 camera stations resulted in 8415 trap nights during May 2021-June 2021. The minimum number of individual tigers captured was 44 along with 60 leopards. Tiger density based on the Spatially Explicit Capture-Recapture framework was 4.78(±0.7)/100km2 and the density of leopard was 7.55 (±1.02)/100km2. To study space use and activity patterns we have used camera-trapping data from both core and buffer areas of Pench Tiger Reserve. Higher activity overlap was recorded between tigers and leopards (Dhat1=0.88) among predators. Camera trap locations with the number of captures of each species were modeled in a GIS domain using IDW (Inverse distance weighted) interpolation technique to generate spatially explicit capture surfaces. The times recorded on camera trap photos provide information on the period during the day that a species is most active. Species active at the same periods may interact as predator and prey, or as competitors. Sensors that record active animals (e.g. camera traps) build up a record of the distribution of activity over the day. Records are more frequent when animals are more active and less frequent or absent when animals are inactive. The area under the distribution of records thus contains information on the overall level of activity in a sampled population.Item Status of Tigers, Co-Predator and Prey in Pandharkawada Forest Division (Territorial) 2021(Wildlife Institute of India, Dehradun, Maharashtra Forest Department, 2022) Habib, Bilal ; Ramarao, S.V.; Jagtap, K.P.; Nigam, P.; Koley, S.The Phase IV monitoring for the Pandharkawada Forest Division (Territorial) was conducted from March –April (2021) as part of the project “Long Term Monitoring of Tigers, Co-Predators and Prey species in Vidarbha Landscape, Maharashtra, India”. The exercise aimed to cover an area of 655.336 km2 of the forested area of the entire division. The objective of the Phase IV Monitoring is to estimate the minimum number of tigers in the Pandharkawada Forest Division using Spatially-Explicit-Capture-Recapture Sampling and density estimation of prey species using Line transect based Distance Sampling. 110 pairs of camera traps were placed in the forested area of Pandharkawada Forest Division following a sampling grid of 2 km2 in all four blocks. The camera traps were active for average 30 days in each block yielding a sampling effort 3508 of trap nights of data which is used for further analysis. From the camera trap photographs 11 tigers (unique to Pandharkawada Forest Division) and 10 leopards have been identified. Tiger density per 100 km2 based on the Spatially Explicit Capture-Recapture (SECR) model was 2.356 (SE ± 0.727) in the forest division while that of leopards based on the same method was 2.99 (SE ±1.03). To estimate prey density, 84 line-transects were laid randomly all over the division and were sampled 7 times during the sampling period, with a total walking effort of 1176 km was invested. The observations include chital (Axis axis), nilgai (Boselaphus tragocamelus), chousingha (Tetracerus quadricornis), langur (Semnopithecus sp), wild boar (Sus scrofa), chinkara (Gazella bennetii), Indian hare (Lepus nigricollis) and peafowl (Pavo cristatus). As per the observations, Nilgai (n=278) is the most observed species followed by Wild boar (n=77), Peafowl (n= 54), Indian hare (n=45) and Chital (n=44). The overall prey density of Pandharkawada Forest Division is 10.977 (SE± 1.19). Due to very low observations (n<20) densities of chousingha and chinkara could not be estimated. To study the activity, we used the camera trap images. The times recorded on camera trap photos provide information on the period during the day that a species is most active. Species active at the same periods may interact as predator and prey, or as competitors. Sensors that record active animals (e.g. camera traps) build up a record of the distribution of activity over the day. Records are more frequent when animals are more active and less frequent or absent when animals are inactive. The area under the distribution of records thus contains information on the overall level of activity in a sampled population. We used IDW (Inverted distance weighted) to map the intensive area used by different animal species.Item Status of tigers, co-predator and prey in Painganga Wildlife Sanctuary 2021(Maharashtra Forest Department, Wildlife Institute of India, 2022) Habib, Bilal ; Banerjee, J.; Reddy, M.S.; Nigam, P.; Jagtap, K.; Puranik, S.; Koley, S.Phase IV monitoring for the Painganga Wildlife Sanctuary was conducted from February – April 2021 as part of the project “Long Term Monitoring of Tigers, Co-Predators and Prey species in Vidarbha Landscape, Maharashtra, India”. The exercise aimed to cover an area of 399.98 km2 of the entire sanctuary. The objective of Phase IV Monitoring is to estimate the minimum number of tigers in the sanctuary using Spatially-Explicit-Capture-Recapture Sampling and density estimation of prey base using Distance Sampling. 45 pairs of camera traps were placed in the forested area of Painganga Wildlife Sanctuary following a sampling grid of 2 sq. km. in one block. The camera traps were active for 30 days yielding a sampling effort of 1722 trap nights of data which is used for further analysis. The minimum number of tigers and leopards individuals identified are 2 and 10 respectively. Tiger density per 100 sq. km. based on the Spatially Explicit Capture-Recapture (SECR) model could not be estimated due to low sample size while that of leopards based on the same method was 3.86 (SE ±0.165). To estimate prey density, 66 line-transects were laid randomly all over the division and were sampled 7 replicates during the sampling period, with a total walking effort of 924 km. The observations include Chital (Axis axis), Sambar (Rusa unicolor), Nilgai (Boselaphus tragocamelus), Chousingha (Tetracerus quadricornis), Langur (Semnopithecus sp), Wild Boar (Sus scrofa), Chinkara (Gazella bennettii), Blackbuck (Antilope cervicapra), Indian Hare (Lepus nigricollis) and Peafowl (Pavo cristatus). As per the observations, Nilgai (n = 236) is the most observed species followed by Langur, Chital, and Wild Boar. The overall prey density of Painganga WLS is 35.142 (SE ± 4.2723). Due to a low number of observations density estimation was not carried out for Chousingha, Chinkara, Blackbuck, Indian Hare, Peafowl, Sambar. To study the activity, we used the camera trap images. The times recorded on camera trap photos provide information on the period during the day that a species is most active. Species active at the same periods may interact as predator and prey, or as competitors. Sensors that record active animals (e.g. camera traps) build up a record of the distribution of activity over the day. Records are more frequent when animals are more active and less frequent or absent when animals are inactive. The area under the distribution of records thus contains information on the overall level of activity in a sampled population. We used IDW (Inverted distance weighted) to map the intensive area used by different animal species.Item Cheetah landscape in India(NTCA and Wildlife Institute of India, Dehradun, 2024) Qureshi, Qamar; Bipin, C.M.; Rautela, Nupor; Jain, Dhruv; Habib, Bilal; Sharma, Uttam K.; Bhardwaj, G.S.; Mallick, Amit; Yadav, S.P.; Gopal, Rajesh; Shrivastav, Aseem; Sen, Subharanjan; Krishnamoorthy, L.; Gupta, Rajesh; Upadhyay, Pawan K.; Tiwari, Virendra R.Based on data collected during the All India Tiger estimation conducted in 2022 information on human disturbances and the presence of invasive species in the forest divisions and protected areas were collated for the landscape along with the human footprint index and mapped to identify the areas that require management as well as planning for prioritizing actions.