THE ECOLOGY OF MONTANE BENGAL TIGERS (Panthera tigris tigris) IN THE HIMALAYAN KINGDOM OF BHUTAN

Large carnivores are endangered across the globe. Loss of habitat and habitat fragmentation, prey depletion, and direct poaching for the illegal wildlife trade are the major causes driving them towards extinction. Although tigers (Panthera tigris) once roamed across Asia, they are now restricted to 7 % of their historical range and experiencing rapid population declines. This warrants a concerted, multipronged strategy that will halt further declines of tigers in the wild. One approach put forth by some scientists is to focus conservation on 6% of the presently occupied tiger habitat identified as tiger sources sites. Other scientists argued for a broader strategy to enhance tiger populations outside of tiger sources sites. Bhutan, for example, was not included in this 6% solution. Here we evaluate whether Bhutan is a potential tiger source site using spatially-explicit mark recapture models to estimate tiger density and spatial distribution in Bhutan. We used large scale remote-camera trapping across n=1,129 sites in 2014 – 2015 to survey all potential tiger range in Bhutan. We estimated 90 (95% CI 80 – 103) individual tigers with 45 females (95% CI 49 – 80) and with a mean density of 0.23 (0.21 – 0.27) adult tigers per 100 km2. Thus, Bhutan has significantly higher numbers of tigers than almost all identified source sites (mean=54) in the 6% solution. We used N-mixture models to estimate spatial distribution and relative abundance of primary prey species of tigers in Bhutan, and the effects of anthropogenic disturbance on tigers and their prey. Gaur (Bos gaurus) and sambar (Rusa unicolor) are concentrated in the southern part of Bhutan and were strong determinants of tiger occupancy. Wild pigs (Sus scrofa) and muntjac (Muntiacus muntjak) are wildly distributed across Bhutan, but did not affect tiger occupancy. In contrast to many other tiger ranges, anthropogenic disturbance did not show consistent negative impacts on tigers and their prey. We show how important the landscape of Bhutan and adjacent northeast India is to regional tiger conservation. With low human density and large swaths of forest cover, this landscape is a promising stronghold for tigers in future

Royal Manas National Park, Bhutan: A Hot Spot for Wild Felids

The non-uniformity of the distribution of biodiversity makes allocation of the limited resources available for conservation of biodiversity a difficult task. Approaches such as biodiversity hotspot identification, endemic bird areas, crisis ecoregions, global 200 ecoregions, and the Last of the Wild are used by scientists and international conservation agencies to prioritize conservation efforts. As part of the biodiverse Eastern Himalayan region, Bhutan has been identified as a conservation priority area by all these different approaches, yet data validating these assessments are limited. To examine whether Bhutan is a biodiversity hot spot for a key taxonomic group, we conducted camera trapping in the lower foothills of Bhutan, in Royal Manas National Park, from November 2010 to February 2011. We recorded six species of wild felids of which five are listed on the IUCN Red List: tiger Panthera tigris, golden cat Pardofelis temminckii, marbled cat Pardofelis marmorata, leopard cat Prionailurus bengalensis, clouded leopard Neofelis nebulosa and common leopard Panthera pardus. Our study area of 74 km(2) has c. 16% of felid species, confirming Bhutan as a biodiversity hot spot for this group

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard \u3ci\u3ePanthera pardus\u3c/i\u3e

Many large carnivores occupy a wide geographic distribution, and face treats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated data sets and camera-trapping data from 22 leopards captured 82 times during winter 2010-2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the true explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25-15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest

TEACHING WILDLIFE BIOLOGY IN BHUTAN: DEVELOPMENT OF WILDLIFE BIOLOGY CURRICULUM AND TEACHING MODULES

Bhutan has always been proactive while dealing with conservation issues and has sound policies and guidelines in place because of our visionary and farsighted leaders. However, with the recent political development and government focus shifting more towards economic development, conservation in Bhutan will be confronted with new challenges and issues. The stakes are very high as Bhutan, with an area of only 38,394 square kilometers (NSB 2007), has amazingly very rich biodiversity and is the most important part of the Eastern Himalaya- a region recognized as a global biodiversity hotspot. In light of such increasing challenges, the need for strong scientific evidence to justify the conservation and management of our rich biodiversity is of paramount importance. However, the major impediment to acquire such kind of scientific evidence has been due to lack of scientific rigor and objectivity in the field. The shortage of trained manpower has been the major setback for the Department of Forest, which is the primary organization mandated for managing and conserving the natural resources in the country. The Ministry of Agriculture and the department of forests in particular, has launched the establishment of Bhutan’s first premier Institute of Environmental and Forestry studies (Ugyen Wangchuck Environmental and Forestry Institute). Wildlife biology along with other courses will be taught as compulsory subjects for both Rangers and Guards in this new institute. The graduates from this institute will work either in Park services or in the forestry services at different levels. This institute will also provide special training “refresher course” for those already working in the field to upgrade their knowledge and skills. For my professional paper, in fulfillment of my M.Sc. Degree, I am working towards developing curriculum and detailed teaching modules for wildlife biology and allied courses for the new institute in Bhutan. I have developed wildlife biology modules in two parts; the first part consist of 13 chapters covering basic ecological concepts and principles, the second part consists of 7 chapters for wildlife management and conservation covering basic techniques of wildlife survey and monitoring in the field. However, for this professional paper I have included only the first eight chapters of the first parts. Each module (chapter) is written in the form of lecture notes and student will be provided with lecture handouts for each module for the class. I have included one field lab exercise and will be developing additional detailed field and lab exercise for the subsequent chapters

New distribution record of the Bhutan Takin Budorcas taxicolor whitei Hodgson, 1850 (Cetartiodactyla: Bovidae) in Bhutan

A camera trapping survey in eastern Bhutan in 2015 has yielded a picture of the Bhutan Takin in the uplands of Kurichu River watersheds in east of Wangchuck Centennial National Park, and is the easternmost documented  distribution of the species in Bhutan. The photograph was taken on 30th June 2015 at 9:24 AM in the site located on 27056’03.8’’E &amp; 91004’53.7”N at 3,898m. The habitat is dominated by Fir and Rhododendron.</p

Data from: Examining temporal sample scale and model choice with spatial capture-recapture models in the common leopard Panthera pardus

Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (Panthera pardus) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km2 (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest

Leopard Camera Trapping Data (for Dryad)

Individual identities, sex, date and location of capture for all leopards included in this study

Examining Temporal Sample Scale and Model Choice with Spatial Capture-Recapture Models in the Common Leopard <i>Panthera pardus</i>

<div><p>Many large carnivores occupy a wide geographic distribution, and face threats from habitat loss and fragmentation, poaching, prey depletion, and human wildlife-conflicts. Conservation requires robust techniques for estimating population densities and trends, but the elusive nature and low densities of many large carnivores make them difficult to detect. Spatial capture-recapture (SCR) models provide a means for handling imperfect detectability, while linking population estimates to individual movement patterns to provide more accurate estimates than standard approaches. Within this framework, we investigate the effect of different sample interval lengths on density estimates, using simulations and a common leopard (<i>Panthera pardus</i>) model system. We apply Bayesian SCR methods to 89 simulated datasets and camera-trapping data from 22 leopards captured 82 times during winter 2010–2011 in Royal Manas National Park, Bhutan. We show that sample interval length from daily, weekly, monthly or quarterly periods did not appreciably affect median abundance or density, but did influence precision. We observed the largest gains in precision when moving from quarterly to shorter intervals. We therefore recommend daily sampling intervals for monitoring rare or elusive species where practicable, but note that monthly or quarterly sample periods can have similar informative value. We further develop a novel application of Bayes factors to select models where multiple ecological factors are integrated into density estimation. Our simulations demonstrate that these methods can help identify the “true” explanatory mechanisms underlying the data. Using this method, we found strong evidence for sex-specific movement distributions in leopards, suggesting that sexual patterns of space-use influence density. This model estimated a density of 10.0 leopards/100 km² (95% credibility interval: 6.25–15.93), comparable to contemporary estimates in Asia. These SCR methods provide a guide to monitor and observe the effect of management interventions on leopards and other species of conservation interest.</p></div

Model estimates for effect of body mass and body size index (mass/tarsus length) on migratory status of males.

<p>Model estimates for effect of body mass and body size index (mass/tarsus length) on migratory status of males.</p

Variation in Median Leopard Density Estimates and 95% Credibility Interval Width for Different Sampling Intervals.

<p>Effects of changing sample period duration on median estimated densities (in leopards/100 km²; A) and 95% credibility interval width (in leopards/100 km²; B) of common leopards in Royal Manas National Park, Bhutan during winter 2010–2011 for 16 spatial capture-recapture models. The 16 models include all possible combinations of the four sampling periods—Quarterly, Monthly, Weekly and Daily—and four model specifications—Distance (dark gray), Sex (medium gray), σ_sex (light gray) and Sex + σ_sex (white). Error bars represent 95% credibility intervals.</p