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Item Supplementation of Gaur in Bandhavgarh Tiger Reserve, Madhya Pradesh(Wildlife Institute of India, Dehradun, 2025) Nigam, Parag; Gorati, A.K.; Vishwakarma, R.; Bhandari, B.; Habib, Bilal; Mondol, Samrat; Nath, A.; Sen, S.; Krishnamoorthy, K.; Sahay, A.; Nanda, R.; Tiwari, V.R.Reintroduction and supplementation programs have been implemented worldwide to improve the conservation status of wildlife that have experienced a significant decline due to overexploitation, habitat destruction and fragmentation. Genetic drift and inbreeding are the two processes particularly relevant in reintroduction efforts that lead to reduced fitness, deceased survival rates and increased susceptibility to diseases. The MPFD in collaboration with WII has initiated a three year project (2024-2027) titled : Population management strategies for gaur (Bos gaurus gaurus) conservation: supplementation of gaur in Bandhavgarh tiger reserve, Madhya pradesh''. This project aims to ensure the long term viability of the species by enhancing its genetic diversity. To facilitate the smooth execution of field operations, an action plan was developed and released during the Inception cum planning workshop held at Bandhavgarh Tiger Reserve. Conservation translocation have become an important tool in recovering the threatened and locally extinct population. Species translocation are increasing all around the globe to reverse biodiversity loss and restore ecosystem functions. Reintroductions require careful planning as small population size experience inbreeding depression, which leads to decreased fitness and demographic stochasticity. Although genetic diversity is not directly linked to species extirpation, low gene pool results in low species recovery. To enhance the gene pool and long term viability of the restored species, supplementations are crucial, especially in small and isolated populations. The addition of new individuals amplify the gene flow in reintroduced species.Item Elephant - human conflict in the state of Jharkhand, India (2000-2003) : trends, challenges and insights(Wildlife Institute of India, Dehradun, 2025) Habib, Bilal; Pandey, R.; Nath, A.; Nigam, P.; Ganesan, A.; Roy, K.; Datta, A.Item Status of Wildlife in Sukhna Wildlife Sanctuary - 2021(Wildlife Institute of India, Dehradun, 2021) Habib, Bilal; Noor, A.; Sharma, A.; Yadav, N.; Goyal, N.Sukhna Wildlife Sanctuary (Sukhna WLS) is part of the Union Territory of Chandigarh and falls in the Great Indian Northern Plains near the foothills of the Shiwalik Hills. In process of continuing their effort of having scientific database and information on their wildlife populations and wildlife conservation, the Department of Forest and Wildlife, Union Territory of Chandigarh (DFW-CH), approached Wildlife Institute of India (WII), Dehradun (wide letter Nos. For/2021/50, dated: 06/04/21 and For/2020/0074, dated: 24/02/2020) to assist in capacity building of the field personnel in conducting wildlife surveys in Sukhna Wildlife Sanctuary, the only Protected Area (PA) of the UT. Subsequently, WII prepared an outline for the wildlife surveys for which initial training of the field personnel was considered as a prerequisite to further the survey program. Following this, WII conducted a capacity building and training workshop to train the frontline staff so that they collect scientifically robust data and get acquainted with the methods to be employed during the execution of project activities. A day-long training workshop was then held at the Chandigarh Botanical Garden, on 22nd April 2021 in the presence of 17 attendees. The DFW-CH personnel were trained in employing different field techniques such as line transect sampling, sign surveys, point counts, and use of necessary equipment (e.g. GPS units, binoculars, range finders, etc.) required during the surveys. In-field exposure to the techniques and equipment was made before starting the main survey to validate the sampling techniques. Shri Debendra Dalai, Chief Conservator of Forests and Chief Wildlife Warden (CCF&CWLW, DFW-CH), and Dr. Abdul Qayum, Deputy Chief Conservator of Forests (DCF, DFW-CH) also presided over the training workshop. Subsequently, the primary data collection process for Sukhna WLS was conducted by the DFW-CH in a five-day programme beginning 5th May to 9th May 2021. A total of 10 line transects (2 km each) inside Sukhna WLS and an additional four transects outside Sukhna WLS were sampled with an overall effort of 88 km of transect walk (80 km walk effort was made inside Sukhna WLS and 8 km walk effort in forest patches outside the Sukhna WLS). This effort yielded a total of 223 direct observations (195 inside and 28 outside Sukhna WLS) consisting of 13 wildlife species that included four ungulates, two primates, two carnivore species, one rodent, and reptile species each, two bird species, and also free-ranging stray dogs Due to low sample size of observations, the density of species other than Sambar could not be estimated with robustness. The density and other parameters of other species were not calculated because of the very low sample sizes as n>40 were considered adequate for data analysis in Distance software. Thus, data from all 14 transects of Sukhna WLS (transects which were inside as well as those monitored outside the sanctuary) were pooled for density and other parameters estimation for Sambar. Observations of other species such as Nilgai, Wild boar and Hanuman langur were pooled to estimate global detection probability which then was used to estimate the density estimates for these species, assuming they have uniform detectability in the environment. Therefore, further conservation and management strategies should consider these findings with caution. Sambar had the highest density (number of individuals/km2) of 18.08±4.22, followed by Nilgai (2.01±0.57), and Wild boar (1.17±0.33) in Sukhna WLS during the survey. The mean group size of Wild boar was the highest with 4.28±0.89 (median = 4; range = 1–12) followed by Chital (3.5±0.5; median = 4; range = 2–5), and Nilgai (3.20±0.55; median = 2; range = 1–10). Sambar had the mean group size of 2.57±0.22 (median = 2; range = 1–16). Hanuman langur’s grouping tendency averaged at 2.54±0.72 (median = 1; range = 1–8) while the Indian peafowl had mean group size of 2.42±0.29 (median = 2; range = 1–5). The detection probability varied from lowest of 0.41 to 0.56 for Sambar and pooled prey species, respectively. The population estimates obtained through extrapolation of the density estimates on the area of the sanctuary puts Sambar as the dominant species with an estimated population of ca. 290–763 individuals. The population estimate of Sambar obtained during the current survey is more robust than the estimate obtained during the previous surveys owing to several reasons including more amount of effort and area coverage undertaken this time (80 km compared to 16 km previously), more number of observations (N=138) than previously (N=21) and lower CVs associated with the estimates. Indian peafowl’s population could not be estimated due to low number observations (N=14) although an estimate of population could be made during the previous survey. Population estimates of other species such as Nilgai could not be estimated due to low number of observations. A total of 10 trails or routes of variable length (average=5.05 km; 4–5.5 km) and totalling an effort of 50.5 km (with 34:54 man hours) were walked in 10 beats across Sukhna WLS during the wildlife survey period. A total of 286 observations of indirect signs such as droppings, scats, footprints/pugmarks, scratch marks, pellets, etc. were made based on which 13 species could be identified. Interestingly, presence of barking deer was ascertained by indirect evidence only and in case of carnivores, leopard pugmarks were also recorded which could not be confirmed based on direct observations. A total of 30 points for counting birds were sampled across the 10 transects (three point count stations at each transect with inter-station distance of 400 m) during the survey. The same points were used for broad characterisation of habitat and vegetation. A total effort of 60 point samplings were conducted yielding a total of 67 species of birds belonging to 32 families and 15 orders were identified in the point transects. Of these observed species, ca. 28% of the species had more than 10 observations. Indian peafowl was the most abundant (N=59) species recorded, followed by red-wattled lapwing (N=30) and the red junglefowl (N=26). Among the species recorded with less frequency 20 species were recorded only once. Conclusions and Recommendations 1. Despite being small in area, Sukhna WLS supports good biological diversity and has the potential to be considered as one of the important wildlife and biodiversity conservation area. Therefore, steps should be undertaken to have plans for regular monitoring and research programs targeting not only the sanctuary but also its surrounding forested areas. 2. Sambar is the most abundant ungulate species in the Sukhna WLS with the highest density similar to Rajaji TR. The estimated population of Sambar in the sanctuary stands at ca. 290–763Item Telemetry based tiger corridors of Vidarbha Landscape, Maharashtra India(Wildlife Institute of India, Dehradun, 2021) Habib, Bilal; Nigam, P.; Mondal, I.; Hussain, Z.; Ghaskadbi, P.; Govekar, R.S.; Praveen, N.R.; Banerjee, J.; Ramanujam, R.M.; Ramagaonkar, J.The Vidarbha Landscape (VL) is very important as it harbours a population of about 331 tigers and forms the connecting link between the central and southern Indian tiger populations. It plays a pivotal role in exchange of individuals and thereby facilitates gene flow between these two populations increasing the viability of tiger populations in India. There are 8 protected areas or wildlife divisions where these tigers live, but these refuges are scattered like islands in a sea of human dominated landscape. Therefore, knowing the locations of tiger movement corridors and probable areas of human tiger conflict is especially important for a wildlife manager.Item 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.