Browsing by Author "Nigam, P."

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Capacity building initiative on the dispersal and ranging patterns of elephants for effective management of human-elephant interactions
(Wildlife Institute of India, Dehradun, 2022) Nigam, P.; Pandav, B.; Mondol, S.; Lakshmiarayanan, N.; Kumar, A.; Nandwanshi, V.B.; Das, J.; Biswas, S.; Udhayaraj, A.D.; Vishwakarma, R.; Habib, B.; Miachieo, K.; Narasingh Rao, P.V.
Wild Asian elephant (Elephas maximus) populations are distributed in four major regions namely North West, North-East, East-Central and Southern regional meta-populations across India. Amongst them, the East-central regional population spread across the States of Odisha, Jharkhand, southern West Bengal, Chhattisgarh, and lately in Madhya Pradesh suffers disproportionately high levels of human elephant conflict. Among the myriad challenges facing management of human-elephant conflict in the region, elephant range expansion into new areas is overriding. One such range expansion that resulted in acute human-elephant conflict is being witnessed in the State of Chhattisgarh. Although northern Chhattisgarh was historically an elephant range, elephants reportedly disappeared during the period 1920 to late 1980s. While episodes of sporadic elephant occurrence in Chhattisgarh was reported during the period 1988- 1993, contemporary range expansion and concomitant human-elephant conflict began from the year 2000, and has accelerated during the last one decade. Faced with an enormous challenge of managing human-elephant conflict that is spatiotemporally dynamic unlike that of other elephant range States, constrained by limited Institutional capacities to assess and deal with the issue. Chhattisgarh Forest Department has been trying diversity its conflict mitigation strategies. Recognizing the need to objectively evaluate human-elephant conflict situation in the State, during the year 2017 Chhattisgarh Forest Department invited Wildlife Institute of India to conduct ecological research on elephants in Chhattisgarh with a three-year budget outlay. The project was a collaborative effort between Chhattisgarh Forest Department and WII. Considering the scope of the project, the project duration was further extended and eventually, the project lasted for the period July 2017 to March 2022. Being the final project report, the activities carried out as part of the project is summarized as under. Distribution and Demography In Chhattisgarh, the elephant distribution during the period 2012 to 2017 was reported from 16 Forest Divisions and four Protected Areas in the north and north-central regions of the state. The elephant population, as enumerated by Chhattisgarh Forest Department during 2021 , ranged from 250 to 300. The adult sex ratio recorded during the study was 1: 4.5. About 44% of the female segment of the population comprised of adults. Chhattisgarh elephant population is presently contiguous with other elephant populations in the neighboring states i.e., Madhya Pradesh, Jharkhand and in Odisha occurring as a meta-population 1 and thus cannot be considered as an isolated population. However, within Chhattisgarh, the population is relatively small and it occurs scattered over a large area as small and disjunctive groups facing a perpetual risk of getting isolated by ongoing linear infrastructure and other associated developmental activities in the State. If such groups get isolated, then they will not be viable in the long run. 1 Meta-population: Population of small populations that are connected through dispersals 1 O. ~ . -~ 1 -WU.d.U.fe .In.s-titu-te. o.f .In.di a Home Range, Movement Patterns & Dispersal, and Habitat Selection by Elephants During the period 2018-2022, WII-CGFD collaborative effort resulted in 10 elephant radio collaring in Chhattisgarh. The resultant effort provided 3106 elephant days of tracking information. Each of the radiocollared elephants provided an average of 310.6 (± 273) days of tracking data. As on 31 51 March 2022 when WII-CGFD collaborative project ended, two of the collared elephants (SD - Sehradev and MT - Maitri) were having functional collars. The estimated average home range (95% minimum convex polygon) of elephants in Chhattisgarh was 3172.8 km2 (± 2002.2 km2, Range: 462.3 - 6969.7 km2). The 95% kernel density home ranges of elephants were much lower averaging 512.3 km2 (± 235.3 km2, Range: 126.5 - 748.9 km2). The elephant home ranges were not wholly well defined, and marked by inter-annual shifts caused by exploratory behaviour. The elephant home ranges were relatively large. The dry season home ranges were significantly lower than monsoon and winter ranges. However, dry season home ranges of elephants are larger. The present study indicates that habitat quality in some of the forest patches - particularly those that are large and contiguous with minimal of human interference can potentially support elephants in the landscape. Thus, dry season ranges of elephants could serve as a surrogate for habitat quality. Monthly variations in home ranges were significant, and best explained by idiosyncrasies of individual elephants. Among the forest types open, moderately dense and very dense forests classified by Forest Survey of India based on crown densities, elephants selected open forests, that were predominantly juxtaposed with human-use areas. Although the crown density was low, the patches of open forests support dense stands of Sal (Shorea robusta) coppice with rank undergrowth offering adequate cover for elephants. Elephant habitat selection of these open forest patches appears to be influenced by potential foraging opportunities in human-use areas, and further facilitated by low inter-patch distance. Genetic Structure of Elephants Using 258 genetic samples collected from 9 Forest Divisions, elephant genetic structure in northern Chhattisgarh was evaluated. Analysis indicates that at least two different elephant lineages occur in Chhattisgarh. This implies that elephants occurring in Chhattisgarh have possibly come from different areas. Within the two different lineages, high relatedness amongst the individuals was observed corroborating with the general social structure of Asian elephant clans where individuals are mostly related. Crop Losses and Human Fatalities due to Elephants Crop losses caused by elephants were acute and widespread in Chhattisgarh. To draw an analogy, Karnataka's ex gratia payment towards crop losses by elephants during the period 2015-2020 was comparable with Chhattisgarh, although the former's elephant population is 93% more than the latter. The landscape-level assessment covering the whole of northern Chhattisgarh, and fine-scale assessment covering select areas in Surguja circle identified correlates of crop losses at both spatial scales. Elephant-related human deaths were widespread in the state. However, nearly 70% of incidences occurred in areas of high intensity of habitat-use by elephants. The human fatalities due to elephants were both temporally and spatially auto-correlated. 2
Current population status, distribution and threats to Indian Pangolin (Manis crissicaudata) in Terai Arc Landscape, Uttarakhand: a pilot study
(Wildlife Institute of India, Dehradun, 2020) Lyngdoh, S.; Goyal, S.P.; Nigam, P.; Kumar, V.; Badola, S.; Rasailly, S.
This pilot study to provide information on the current distribution of Indian pangolin and major poaching hotspots throughout its ranges to suggest appropriate conservation strategies and protection measures for the species. The proposed objectives for this pilot study are the following: a. To review the current status, distribution and threats to the Indian pangolin population in the study area. b. To prepare a standard protocol for the survey and population estimation of Indian pangolin. c. To formulate effective anti-poaching strategies and devise conservation measures for Indian pangolin to help Uttarakhand Forest Department.
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.
Enrichment Manual for Selected Species in Indian Zoos
(Wildlife Institute of India, Dehradun, 2015) Tyagi, P.C.; Nigam, P.; Srivastav, A.; Goswami, S.; Ningombi, M.
This manual will be an important reference source for the zoo managers for execution of welfare measures, developing innovative techniques and enrichment methods for improving the welfare of animals in Indian zoos.
Identification of human-leopard hotspot the prioritizing the mitigation measures in Junnar Forest Division, Pune, Maharashtra
(Wildlife Institute of India, Dehradun, 2020) Habib, B.; Khandekar, V.; Nigam, P.; Mondol, S.; Jayaramegowda, R.; Ghanekar, R.; Kumar, A.
Mitigation of human-carnivore conflict became a priority to wildlife managers for the conservation of large carnivores and human livelihood. Hence, for the effective mitigation measure, it is necessary to identify the priority human-carnivore conflict hotspots. In India, the growing human population, infrastructure development, and land modification are affecting the large carnivore population leading to human carnivore conflict. Among human-carnivore conflict, human-leopard conflict is common in different geographical regions due to the adaptability of species across a different environmental gradient in India. Human-leopard conflict records of 20 years (1999-2018) were collected from the different ranges of the Junnar Forest Department (JFD) in the Pune district. The area is known for the human-leopard conflict for the past three decades. The records show an abrupt surge of human-leopard conflict after the year 2014. Using these records, spatio-temporal clusters of the hot spots and cold spots were identified using optimized hotspot analysis tool in ArcGIS. Also, five different categories of hot spots in the study area namely, new hot spots, consecutive hot spots and sporadic hot spots of human-leopard conflict through emerging hot spot analysis in ArcGIS were identified. It is suggested that different management approaches and strategies focusing on the different categories of hotspots are required to deal with human-leopard conflict for effective mitigation measures. Villages have been highlighted as the new conflict hotspots i.e. which has emerged in recent years. Immediate actions like intensive night patrolling and awareness in the villages to control will help in reducing human leopard conflict.
National studbook of Phayre's leaf monkey (Trachypithecus phayrei)
(Wildlife Institute of India, Dehradun, 2014) Nigam, P.; Nilofer, B.; Srivastav, A.; Tyagi, P.C.
Patterns of Human-Wildlife Conflict in Chandrapur, Maharashtra, India
(Wildlife Institute of India, Dehradun, 2022) Habib, B.; Nigam, P.; Praveen, N.R.; Ravindran, A.
Human-wildlife conflict (HWC) is the negative interaction between human or human property and wildlife and is a growing cause for concern among conservationists and scientists globally. Although HWC is a global phenomenon, there are certain differences in its manifestation as well as magnitude in developed versus developing nations. Developed regions of the world exhibit lower levels of direct dependence on forest ecosystems and their resources, as well as exclusionary management of the wildlife habitats. India, being a developing nation, is witness to an increasing intensity of human-carnivore conflict due to the fast-shrinking percentage of forest cover, that act as natural habitats of many carnivore species, due to a combination of factors including human population explosion, agricultural expansion, and large-scale developmental activities, leading to fragmentation and destruction of forest cover all across the country. The Central Indian Landscape (CIL) is one of the regions of high tiger populations and density in India with 6 Tiger Reserves featuring heavily as source populations, including Tadoba Andhari, Pench, Kanha, Satpura, and Melghat Tiger Reserves. But there is a disproportionate decline in forest cover as well as quality, which means that even though the populations of large carnivores are thriving, there isn’t enough pristine forest to support their growing numbers. This eventually leads to a spill-over of the carnivores into surrounding human-dominated landscapes (HDL). This acts as one of the major reasons for the burgeoning number of conflict cases between humans and large carnivores. The Vidarbha Landscape (VL) of the state of Maharashtra is facing a similar decline in forest cover leading to an increase in conflict cases. Records of conflict incidents were collected from the Greater Tadoba Landscape (GTL) which covers the divisions of Brahmapuri, Chandrapur & Central Chanda, along with the Tadoba Andhari Tiger Reserve (TATR), in the Chandrapur Circle. Using these records, hotspots of livestock depredation and attacks on humans were mapped using a hotspot analysis tool in ArcGIS. Various scientific and non-scientific methods continue to be tested to slow down the increasing rate of HWC across the world. One of the major hurdles in the implementation of a universal mitigation method to curb the number and impact of HWC is the heavy influence of local factors including topography, vegetation, and human demography of the region. This requires an intensive study of the patterns and causes of conflict in a given region. Studying conflict hotspots and understanding the emerging spatial and temporal patterns is a quintessential step in the process of mitigating the HWC of any landscape. An important step in that direction is the establishment of a comprehensive database, which can be used for trend analysis and predictions. The hot spot analysis of human-carnivore conflict for tigers, leopards, and sloth bears enables visualization of the spatial distribution of events of attacks on humans as well as livestock depredation by each species, hence aiding in the development of site-specific management strategies to mitigate the effects of human-carnivore conflict
Preliminary assessment of tigers, co-predators and prey in Pranhita Wildlife Sanctuary, Maharashtra, India for exploring options for conservation translocation
(Wildlife Institute of India, Dehradun, 2020) Habib, B.; Nigam, P.; Joshi, K.; Panwar, P.
As part of the project “Preliminary assessment of tigers, co-predators and prey species in Pranhita Wildlife Sanctuary, Maharashtra, India for exploring options for conservation translocation”, the study was carried out in Pranhita Wildlife Sanctuary in Gadchiroli district of Maharashtra. The fieldwork was carried from January 2019 to June 2019 covering an area of 418.85 km2 in southern Gadchiroli. The Eastern Vidarbha Landscape (EVL) holds a high density of carnivores both inside and outside protected areas leading to an increase in human-wildlife interactions. Pranhita Wildlife Sanctuary (PWLS) is a part of EVL and could be an important corridor. To explore new habitats for carnivore species, we conducted a preliminary assessment of tigers, co-predators, and prey in PWLS. The sanctuary mainly is dominated by Southern Tropical Dry Deciduous forest. We conducted carnivore and ungulate sign surveys and deployed camera traps (n=25) in 40 km2 area in Bahmni range. The area was divided into different 1.42 × 1.42 km2 grids and at least one pair of camera trap was placed in each 2.0164 km2 grid at 20 sites and operated for 24-27 days in Bahmni range. Apart from that random camera traps were placed at 5 sites in Kamlapur and Pranhita ranges for 1 to 7 days during the study period. The camera traps sampling effort was 1030 trap nights and around 33000 images were captured. For prey species density estimation, 24 line transects of 2 km length were walked in 43 beats with 5-7 replicates. For vegetation quantification, we laid a total of 144 circular plots of a 10-meter radius and recorded 43 trees, 37 shrubs, and 13 grass species. We used both spatial and temporal data for occupancy estimation. Data were analyzed using the software Presence for occupancy estimation and Distance 7.2 for density estimation. A total of 10 carnivore species were recorded directly or indirectly during the study period. According to the IUCN Red List of threatened species, 2 are Endangered and Near Threatened, and 2 are vulnerable. The major carnivore species are leopard, Asiatic wild dog, sloth bear, Indian grey wolf, jungle cat, Indian fox, and rusty-spotted cat. The occupancy estimate (ψ) of leopard in the null model was 0.20 while for other carnivore species like sloth bear, jungle cat and wild dog were 0.70, 0.74, and 0.68 percent respectively. A total of 14 prey species were recorded during the line transect and sign survey. The major prey species are sambar, Indian gaur, chousingha, Indian giant squirrel, chital, wild pig, nilgai, barking deer, langur sp., rhesus macaque, and Indian peafowl. Among these, 3 species are listed as Vulnerable and 1 as Near Threatened by IUCN Red List. Overall density estimation of major ungulate species was 14.82/km2. The encounter rate of cattle was 0.17/km, nilgai 0.039/km, chital 0.059/km, chousingha 0.016/km, and wild pig 0.022/km. Individual density estimate of major ungulate species like chital 2.27/km2, wild pig 11.55/km2, nilgai 0.72/km2, langur 0.55/km2, Indian hare 1.78/km2, peafowl 0.44/km2, grey jungle fowl 1.87/km2, chousingha 0.28/km2 and cattle were 28.61/km2. Occupancy estimate of ungulate species like sambar 0.27%, chital 0.44%, chousingha 0.51%, Indian gaur 0.07% and nilgai were 0.59%. The major threats in the sanctuary areas are hunting for local consumption, tree cutting, livestock grazing, forest fire, roadkill, and electrocution. We have got 28 % usable images of cattle grazing and 4 % of hunting. Other administrative lacunas are impractical beat boundaries, unequipped frontline staff, lack of legal action against the guilty, inadequate infrastructure, lack of training and capacity building. There is a consistent trepidation of left-wing extremism in the minds of locals and forest officials. It prevents or demotivates them from working efficiently in the PWLS. These activities directly or indirectly affect wildlife conservation and management in PWLS. This was the first-ever scientific study conducted to document prey and predator presence in PWLS. Further detailed and long-term studies are required for a better understanding of species ecology and their habitat. Such studies will help not only in better management and conservation of species in the area but also in decision-making on conservation translocations. Based on the preliminary study and SWOT analysis following are measures to be taken before translocation of any large carnivore species to Pranhita Wildlife Sanctuary: 1. Capacity building of local staff for effective wildlife management.2. Enhancement of protection measures in the Sanctuary to reduce poaching, hunting, and other illegal activities. 3. Habitat improvement by grassland management and eradication of lantana and other invasive species. 4. Reducing threats due to electrocution by illegal power fences used for local hunting and protection of crop fields by local farmers. 5. Special forest protection force for Gadchiroli considering extremism issues.6. Augmentation of the prey base to enhance fast recovery of prey species.7. Maintaining full-strength dedicated forest staff across all range offices of the division.8. Building infrastructure such as patrolling roads, forest chowkis etc., across the sanctuary.9. Involving local people in conservation measures across the sanctuary.10. Establishment of local ecodevelopment committees. 11. Wildlife-oriented management across the Gadchiroli forest division.12. The special financial package for Gadchiroli for enhancing wildlife-oriented management.13. Mitigation measures on the existing roads through the sanctuary and other critical wildlife corridors across the division.14. Implementation of Shyamaprasad Mukherjee Jan Van Vikas Scheme for development of villages across the forested landscape of Gadchiroli to achieve sustainable development of these villages and reduce the man-animal conflict.15. Identification of potential areas within the district for designation as Sanctuary, National Park, Conservation Reserve, Community Reserve.
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.
Status of tigers, co-predator and prey in Navegaon Nagzira Tiger Reserve (NNTR) - 2021
(Maharashtra Forest Department and Wildlife Institute of India, 2022) Habib, Bilal ; Nigam, P.; Ramanujam, M.; Pate, P.; Singh, K.; Bhalavi, S.B.; Bhandari, A.; Kanishka, Akshay J.
The Phase IV monitoring for the NNTR core and buffer was conducted from December 2020 – March 2021 as part of the project “Long term Monitoring of Tiger, Co-predator and their Prey in Tiger Reserves and other Tiger Bearing Areas of Vidarbha Maharashtra”. The field site for this exercise was Navegaon-Nagzira Tiger Reserve. The core and buffer areas of the tiger reserve were covered under this exercise. The objective of 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.518 camera traps were placed in the core and buffer area of NNTR following a sampling grid of 2.01 sq. km in two blocks. An average camera trapping survey of 33 days in each block (Nagzira and Navegaon) with a sampling effort of 15,692 trap nights yielded data used for further analysis. Tiger density per 100 km2 based on the Spatially Explicit Capture-Recapture (SECR) model was 0.64 in the Navegaon-Nagzira Tiger Reserve while that of leopards based on the same method was 8.21. To estimate prey density, 172 line-transects were sampled 7 times during the sampling period, with a total walking effort of 2382 km. The individual densities in Nagzira and Navegaon Core for Sambar, Chital, Nilgai, Wild pig, and Gaur were estimated to be 1.26 ± 0.39, 8.89 ± 1.77, 9.14 ± 1.94, 8.27 ± 4.85, 5.50 ± 1.08 and 0.56 ± 0.25, NA, 10.42 ± 2.61, NA, 5.93 ± 1.68 respectively whereas the individual density estimates for Nagzira and Navegaon buffer for Sambar, Chital, Nilgai, Wild pig, and Gaur were 0.15 ± 0.13, 10.29 ± 1.96, 7.13 ± 1.43, 13.92 ± 8.29, NA and 0.49 ± 0.18, 7.61 ± 2.15, 11.75 ± 1.97, 12.03 ± 8.12, NA respectively. To study space use patterns and activity we used camera-trapping data from both core and buffer areas of Navegaon-Nagzira Tiger Reserve. 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. Species distribution was mapped seasonally using direct sighting data of wild ungulates from all the three seasons i.e. from winter 2019 to monsoon 2020 that was collected through regular patrolling using the MSTrIPES, a patrolling protocol mandated by NTCA to use in tiger reserves. MaxEnt, ArcGIS software was used for data preparation and final analysis. Factors that influence species distributions and habitat selection are of great importance to researchers and managers of wildlife. Here we used habitat variables namely: Land use Land cover (LULC), Digital Elevation Model (DEM), slope, aspect, stream delineation (Distance to streams), distance to the village, distance to road, distance to the railway line, and distance to the waterhole.
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.
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.
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.
Status of Tigers, Co-Predator and Prey in Tipeshwar Wildlife Sanctuary 2021
(Wildlife Institute of India, Dehradun, Maharashtra Forest Department, 2022) Habib, B.; Nigam, P.; Banerjee, J.; Puranik, S.; Jagtap, K.; Koley, S.
Phase IV monitoring for the Tipeshwar Wildlife sanctuary 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 148.63 km2 of the entire sanctuary. The objective of Phase IV Monitoring is to estimate the minimum number of tigers in the Tipeshwar WLS using Spatially-Explicit-Capture-Recapture Sampling and density estimation of prey base using Distance Sampling. 62 pairs of camera traps were placed in the forested area of Tipeshwar WLS following a sampling grid of 2 sq. km. in one block. The camera traps were active for 30 days yielding a sampling effort of 2206 trap nights of data which is used for further analysis. Tiger density per 100 km. sq. based on the Spatially Explicit Capture-Recapture (SECR) model was 7.07 (SE ± 0.218) in the sanctuary while that of leopards based on the same method was 3.86 (SE ±0.165). To estimate prey density, 13 line-transects were laid randomly all over the division and were sampled 7 times during the sampling period, with a total walking effort of 182 km was invested. The observations include chital (Axis axis), sambar (Rusa unicolor), nilgai (Boselaphus tragocamelus), chousingha (Tetracerus quadricornis), langur (Semnopithecus sp), wild boar (Sus scrofa), chinkara (Gazella bennetii), Blackbuck (Antilope cervicapra), Indian hare (Lepus nigricollis) and peafowl (Pavo cristatus). As per the observations, Nilgai (n=50) is the most observed species followed by Chital (n=27). The overall prey density of Tipeshwar WLS is 17.82 (SE± 3.81). Due to low number of observations densities of chousingha, chinkara, blackbuck, langur, Indian hare, peafowl, sambar, wild boar 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.
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 population
Status of tigers, copredator and prey in Tadoba Andhari Tiger Reserve (TATR) 2021
(Wildlife Institute of India, Dehradun, Maharashtra Forest Department, 2022) Habib, Bilal Habib, B., Nigam, P., Ramgaokar, J., Guruprasad, G., Kale, N., Bhagwat, S. S., Krishnan, A., Hushangabadkar, P., Sheikh, S. (2022): Status of Tigers, Co-Predator and Prey in Tadoba Andhari Tiger Reserve (TATR) 2021; Nigam, P.; Ramgaokar, J.; Guruprasad, G.; Kale, N.; Bhagwat, S.S.; Krishnan, A.; Hushangabadkar, P.; Sheikh, S.
Phase IV monitoring for the Tadoba Andhari Tiger Reserve (TATR) core and buffer was conducted from February – May 2021 covering an area of 1315 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 Phase IV Monitoring is to estimate the minimum number of tigers in the reserve using Spatially Explicit Capture-Recapture Sampling and density estimation of prey base using Distance Sampling. Camera traps were placed in 621 grids of 2.01 sq. km. area each in the core and buffer area of TATR in two blocks. In each sampling block, camera traps were active for 27 - 44 days. During 83 days of camera trapping survey with a sampling effort of 20,965 trap nights, 85 adult individual tigers were photographed in the sampled area of TATR. Estimated population (N) of tigers based on the best fit (SECR Heterogeneity) model was 86 (SE ± 0.71). Tiger density per 100 sq. km. based on the Spatially Explicit Capture-Recapture (SECR) model was 6.31 (SE ± 0.70). Along with tigers 114 adult individual leopards were photographed in the sampled area of TATR and estimated population (N) based on the best fit (SECR Heterogeneity) model was 118 (SE ± 2.17). Leopard density per 100 sq. km. based on the Spatially Explicit Capture-Recapture (SECR) model was 7.07 (SE ± 0.67). To estimate prey density, 133 line transects in core and buffer of TATR were sampled 7 times during the sampling period, with a total walking effort of 1862 km. During the sampling, a total of 1163 animal/bird observations were made. The overall individual density per km2 of major prey species in TATR was Gaur 2.16 (SE ± 0.39), Sambar 1.71 (SE ± 0.29), Chital 2.65 (SE ± 0.55), Wild Boar 3.73 (SE ± 0.84), Langur 3.35 (SE ± 0.71), Barking Deer 0.42 (SE ±0.08), Nilgai 1.04 (SE ± 0.25), Black-naped Hare 0.68 (SE ± 0.15) Peafowl 1.79 (SE ± 0.25) and Grey Jungle Fowl 8.19 (SE ± 1.02). 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 three large sympatric predators co-occur in space and in time, camera trapping was carried out. Temporal activity overlaps were derived by using kernel density. All the sympatric predators were found to co-occur in the sampled area of TATR. There was a distinct difference in the space-use pattern observed for all three carnivores and a strong spatial segregation pattern found between Tigers, Dholes, and Leopards. It showed significant segregation and avoidance of each other’s space. There was a significant overlap between the temporal activity pattern of tigers and leopards. While tigers and leopards show a strong, unimodal, nocturnal activity pattern, dholes show a bimodal, crepuscular activity pattern.
Study of genetic diversity in wild (Sus scrofa cristatus) and domestic (Sus scrofa domestica) pig to find level of hybridization between them in the vicinity of Ranthambhore National Park
(Wildlife Institute of India, Dehradun, 2015) Pandey, P.; Nigam, P.; Chauhan, N.P.S.; Goyal, S.P.
Wild pig (Sus scrofa cristatus) has a wide geographical range among all ungulates and terrestrial mammals found in the Indian subcontinent and forms an important prey-base for carnivores. Despite the variation with domestic pig in chromosome numbers, these animals can mate and produce fertile hybrids that have physical attributes similar to wild pig. A systematic study on wild pigs by the Wildlife Institute of India in Ranthambhore National Park revealed that wild pigs stray out of national park, raid agricultural crops and utilises the agro-ecosystem in peripheral villages for food resource and shelter and thus coming in contact of domestic pigs. As a result, there may be genetic hybridization between the wild and domestic pig populations. Hybridization between wild and domestic pigs may lead to introgression of alien alleles that can affect the genetic fitness and overall immune response. Thus in order to detect the hybridization and to quantify the impact on wild species, genetic assessment of wild and domestic pig is necessary with ultimate goal to find out extent of hybridization if any. Therefore, a study on genetic assessment of wild and domestic pigs and to evaluate the use and efficacy of power fence in controlling crop damage caused by wild pigs as advised by the Training, Research and Academic Council (TRAC) was undertaken from 04.02.2012 to 03.08.2014 around Ranthambhore National Park. The objectives of the study were (a) To study genetic diversity in wild and domestic pigs in the vicinity of Ranthambhore National park, (b) To find the level of hybridization between wild and domestic pigs based on genetic variability, (c) To evaluate the use and efficacy of power fence in controlling crop damage caused by wild pigs, (d) to evaluate the use and efficacy of power fence in controlling crop damage caused by wild pigs and (e) Based on the findings of the project, examine the possibility of hybridization between wild and domestic pigs in other parts of the country for further study. In view of this, report has been in two parts i.e. Section –I describes the genetic assessment of wild and domestic pigs where as Section II is related to use and efficacy of power fence. We systematically collected the biological samples of unrelated domestic pigs (n=65) from different villages surrounding the Ranthambhore National Park as a zone of interaction and also from different parts of Sawai Madhopur city as a control. For the genetic characterization and detection of event of introgression in wild pigs, we proposed collection of 30-40 blood samples in the project. The habituation of animals on baits could not been successful in spite of best efforts. This was due to the delayed receipt of permission to capture animals, in appropriate weather conditions making habituation procedure difficult and unsuccessful capture. In view of this, we collected other samples of wild pigs such as hair and faecal matter to meet the objectives of the project as these samples are equally amenable for genetic analysis but require appropriate optimization of protocols. Thus we collected blood samples (n=6) of unrelated wild pigs representing Ranthambhore National Park. Apart from blood samples, we also collected wild pigs faecal (n=26) and hair samples (n=34). For genetic diversity and detection of event of introgression, we amplified partial fragment (662 base pairs) of control region from mitochondrial genome (Asch et al., 2011) and a panel of 10 highly polymorphic microsatellite markers in domestic (n=55) and wild pig samples (n=66). We tested the applicability non-invasive faecal and hair samples for genetic assessment and evaluation of genetic introgression. Faecal DNA was of low molecular weight with PCR success rate restricted up to 200 base pair of mitochondrial DNA. We found PCI and Qiagen kit protocol for DNA extraction better for hair DNA extraction. As reported in the literature for PCR success (40 to 80%) in using such samples, our success rate in pig samples (hair and a faecal matter) was also between 60 and 90% whereas it was possible to obtain good quality data with all blood samples. Hence, collection of adequate invasive samples at large landscape would be difficult, therefore, we suggest use of non-invasive samples i.e. faecal matter and hair in future studies. Optimized protocols undertaken in this study for using noninvasive samples would have immense advantage for undertaking future cost effective studies on wild pigs. First time we report presence of two haplotypes with one segregation site in 560 base pair of amplified sequence of control region in eastern most population of wild pigs. Two haplotypes (WP_Hap-1 and WP_Hap-2) were shared equally i.e. 50% each in the population. The overall haplotype diversity of wild pigs was found 0.6 ± 0.13 whereas the nucleotide diversity was 0.001. In total 13 haplotypes with 24 segregation sites were recorded in 590 base pair of amplified sequences of domestic pigs. Two haplotype (Hap-1 and Hap-9) were shared by 80% of domestic pigs examined so far. Seven haplotype were detected only once (DP_Hap-3, DP_Hap-5, DP_Hap-8, DP_Hap-10, DP_Hap-11, DP_Hap-12 and DP_Hap-13) indicating their different geographic origin. The overall haplotype diversity of domestic pigs was found 0.79 ± 0.04 whereas the nucleotide diversity was 0.01. We selected eleven microsatellite markers for genotyping purposes. Of total samples (n=66), it has been possible to generate data complete data on multi locus genotyping only for 22 and were used for introgression purposes. Out of the eleven microsatellite markers tested on domestic and wild pig samples, three loci (S0090, S0026 and SW72) deviated from HWE whereas SW72 also showed presence of null alleles in domestic pigs. Six loci (SW122, SW24, S0090, 0225, S0226 and SW911) deviated from HWE whereas none of the loci showed sign of null alleles in wild pig samples analyzed. We found overall high genetic diversity in domestics pigs (Na=14.2, Ho=0.72 and He=0.86) as compared to the analyzed wild pig samples (Na=3.2, Ho=0.7 and He=0.7). We did not find any common haplotype between individuals of wild and domestic pigs and thus the introgression at mitochondrial level can be ruled out in the analyzed samples. We analyzed microsatellite marker data of both domestic and wild pig samples. By using Bayesian MCMC approach implemented in Structure 2.3.1. indicated admixed nature (ca.55%) of the wild pig samples. Similar results were also observed during factorial component analysis (FCA) where the wild pig individuals showed more affinity towards domestic pigs. Thus we report first time the presence of hybrid wild pig individuals (wild at mitochondrial level and domestic at nuclear level) in RTR. Therefore, absence of mitochondrial genetic introgression and presence of nuclear genetic introgression suggests unidirectional hybridization. Section II, describes the use and efficacy of power fence which was evaluated in controlling crop damage caused by wild pigs. Traditionally, wild pigs have been kept out of cultivations by scaring them away or restricting them with barriers. Scaring wild pigs with flash lights, fire, fire-crackers, crop protection guns, stone slings etc may effectively deter them sometimes. Most forms of effective barriers for wildlife such as trenches, rubble wall or conventional fences, are expensive to construct and maintain. A power fence is purely a psychological barrier. Power fence is a relatively new control technique and not fatal for animals and only restricts their movements. Power fencing is most effective and safe to animals and to humans. If properly constructed and maintained, it can effectively keep most of the animals out. Power fencing system provides an economic and a practical solution to achieve maximum protection through effective control of animal trespass. In this study, we developed the pig-proof power fences by construction of fence line around the crop fields in Jaitpur village situated on the boundary of Ranthambhore National Park, and evaluated their efficacy in reducing crop damage. We compared the extent of wild pig crop-raiding in the fenced and adjacent unfenced areas. The power unit had a solar panel, a 12 volt power battery, and an energizer to provide current to 1.5 km length of the two fences. We erected the electric fence as per the designs specified in June 2006 under different project when the fields were prepared for sowing crops. A fence design with posts at an interval of 8 meters and GI wires at the height 15, 37.5, 62.5, 100 and 135 cm were tested. Another design with posts at an interval of 8 meters and GI wires at the height 15, 37.5, 62.5, 100, 135, 165,195 and 225 cm was also constructed. The pig-proof fence was 4.5 feet in height with five strands; the first and third strands were connected to earthing, and the rest three i.e. 2nd, 4th and 5th strands were live strands. The pig and nilgai proof fence line was constructed along the forest boundary and crop fields, and it was 8.5 feet in height with 8 strands. The 1st, 3rd and 5th strands were connected to earthing system, and the rest five strands i.e. 2nd, 4th, 6th, 7th and 8th were live wires. The fences were monitored regularly by walking along the perimeter to evaluate their effectiveness and we discuss our findings . Both the fences were maintained properly. We recorded high range of voltage (7.9- 8.1 KV) at the energizer point. Average output voltage of the main fence i.e. nilgai-pig pro offence ranged from 5.5 to 6.5 KV. The voltage of small pig-proof fence was slightly on the lower side i.e. 5.2 to 6 KV. The voltage at 1000m fence length was higher than at shorter distances, which might be due to good earthing system. Overall the fences were found effective against pigs and nilgai. We conclude that the present study support the ecological finding (of previous project of WII) about genetic introgression between wild and domestic pigs around Ranthambhore National Park. This study provides valuable information on the genetic structure of indigenous wild pig which would be useful for future conservation program. Wild pig in RTR is in a vulnerable state as a distinct genetic wild resource and we suggest for appropriate measures to be undertaken to minimize the contact zone between wild and domestic pigs in and around RTR by using appropriate physical barriers as designed and tested for its efficacy. This may enable to restore genetic diversity of wild pigs after few generation through back crossing with wild pigs. We also suggest there is a need to re-visit the study in RTR with using optimized protocols to document spatial distribution of hybrids using NGS (hair and fecal matter) for developing appropriate strategies of wild pig genetic resource adapted to hot climatic conditions (Estimated expenditure would be Rs.6.0 lakhs/year). In view of valuable wild pig genetic resource for human being, there is a need to assess extent of introgression from domestic to wild pig populations of different bioclimatic zones. We also suggest for use of both mtDNA and nuclear markers which avoids inheritance bias because they detect information on both the maternally and codominatly inherited regions
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.