Linkages among forest, water, and wildlife: a case study from Kalapani community forest in Lamahi bottleneck area in Terai Arc Landscape.

Forest and water are important entities for sustaining life on earth. In a terrestrial ecosystem, linkages between the entities creates a mosaic benefiting the wildlife by creating the suitable habitat. In turn, communities get benefits stemming up from ecosystem services such as fodder, fuelwood, and water. We present a case study from a forest restoration project to assess the linkages between forest, water and wildlife across Lamahi bottleneck area in Terai Arc Landscape. We used combination of surveys such as forest area and canopy cover change (2001-2016) analysis followed by household questionnaire, water hole, camera trapping including process documentation. Forest area has increased by ~20 km2 in last 16 yrs. followed by number of water spouts along the identified tributaries. Water spouts are conserved in the form of conservation pond by the communities living downstream and utilized in the vegetable farming. Communities have benefited financially (~ US$ 1,252) contributing to their income level from the sale of fresh season vegetables in nearby market. Camera trap survey including the assessment of historical records showed presence of wildlife including elephant, hyena and other small carnivores in and around bottleneck forest. Both, motivation and enthusiastic support from local communities followed by the conducive government policies led to improve condition of natural resources over the period. This has also created a mosaic habitat for wildlife forming functional connectivity along the linear Terai Arc Landscape

Carbonated tiger-high above-ground biomass carbon stock in protected areas and corridors and its observed negative relationship with tiger population density and occupancy in the Terai Arc Landscape, Nepal.

Healthy natural forests maintain and/or enhances carbon stock while also providing potential habitat and an array of services to wildlife including large carnivores such as the tiger. This study is the first of its kind in assessing relationships between above-ground biomass carbon stock, tiger density and occupancy probability and its status in protected areas, corridors, and forest connectivity blocks. The dataset used to assess the relationship were: (1) Converged posterior tiger density estimates from camera trap data derived from Bayesian- Spatially Explicit Capture-Recapture model from Chitwan National Park; (2) Site wise probability of tiger occupancy estimated across the Terai Arc Landscape and (3) Habitat wise above-ground biomass carbon stock estimated across the Terai Arc Landscape. Carbon stock maps were derived based on eight habitat classes and conservation units linking satellite (Landsat 7 ETM+) images and field collected sampling data. A significant negative relationship (r = -0.20, p<0.01) was observed between above-ground biomass carbon stock and tiger density in Chitwan National Park and with tiger occupancy (r = -0.24, p = 0.023) in the landscape. Within protected areas, we found highest mean above-ground biomass carbon stock in high density mixed forest (~223 tC/ha) and low in degraded scrubland (~73.2 tC/ha). Similarly, we found: (1) highest tiger density ~ 0.06 individuals per 0.33 km2 in the riverine forest and lowest estimates (~0.00) in degraded scrubland; and (2) predictive tiger density of 0.0135 individuals per 0.33 km2 is equivalent to mean total of 43.7 tC/ha in Chitwan National Park. Comparatively, we found similar above-ground biomass carbon stock among corridors, large forest connectivity blocks (~117 tC/ha), and within in tiger bearing protected areas (~119 tC/ha). Carbon conservation through forest restoration particularly in riverine habitats (forest and grassland) and low transitional state forests (degraded scrubland) provides immense opportunities to generate win-win solutions, sequester more carbon and maintain habitat integrity for tigers and other large predators

Distribution and habitat use of red panda in the Chitwan-Annapurna Landscape of Nepal

In Nepal, the red panda (Ailurus fulgens) has been sparsely studied, although its range covers a wide area. The present study was carried out in the previously untapped Chitwan-Annapurna Landscape (CHAL) situated in central Nepal with an aim to explore current distributional status and identify key habitat use. Extensive field surveys conducted in 10 red panda range districts were used to estimate species distribution by presence-absence occupancy modeling and to predict distribution by presence-only modeling. The presence of red pandas was recorded in five districts: Rasuwa, Nuwakot, Myagdi, Baglung and Dhading. The predictive distribution model indicated that 1,904.44 km of potential red panda habitat is available in CHAL with the protected area covering nearly 41% of the total habitat. The habitat suitability analysis based on the probability of occurrence showed only 16.58% (A = 315.81 km ) of the total potential habitat is highly suitable. Red Panda occupancy was estimated to be around 0.0667, indicating nearly 7% (218 km ) of the total habitat is occupied with an average detection probability of 0.4482±0.377. Based on the habitat use analysis, altogether eight variables including elevation, slope, aspect, proximity to water sources, bamboo abundance, height, cover, and seasonal precipitation were observed to have significant roles in the distribution of red pandas. In addition, 25 tree species were documented from red panda sign plots out of 165 species recorded in the survey area. Most common was Betula utilis followed by Rhododendron spp. and Abies spectabilis. The extirpation of red pandas in previously reported areas indicates a need for immediate action for the long-term conservation of this species in CHAL

Landscape variables affecting the Himalayan red panda Ailurus fulgens occupancy in wet season along the mountains in Nepal.

The Himalayan red panda is an endangered mammal endemic to Eastern Himalayan and South Western China. Data deficiency often hinders understanding of their spatial distribution and habitat use, which is critical for species conservation planning. We used sign surveys covering the entire potential red panda habitat over 22,453 km2 along the mid-hills and high mountains encompassing six conservation complexes in Nepal. To estimate red panda distribution using an occupancy framework, we walked 1,451 km along 446 sampled grid cells out of 4,631 grid cells in the wet season of 2016. We used single-species, single-season models to make inferences regarding covariates influencing detection and occupancy. We estimated the probability of detection and occupancy based on model-averaging techniques and drew predictive maps showing site-specific occupancy estimates. We observed red panda in 213 grid cells and found covariates such as elevation, distance to water sources, and bamboo cover influencing the occupancy. Red panda detection probability [Formula: see text] estimated at 0.70 (0.02). We estimated red panda site occupancy (sampled grid cells) and landscape occupancy (across the potential habitat) [Formula: see text] at 0.48 (0.01) and 0.40 (0.02) respectively. The predictive map shows a site-specific variation in the spatial distribution of this arboreal species along the priority red panda conservation complexes. Data on their spatial distribution may serve as a baseline for future studies and are expected to aid in species conservation planning in priority conservation complexes

Model comparison for detection probability.

<p>Model comparison for detection probability.</p

Relative abundance of red panda in CHAL.

<p>Relative abundance of red panda in CHAL.</p

Red panda habitat suitability classes with available area in CHAL.

<p>Red panda habitat suitability classes with available area in CHAL.</p

Variables influencing red panda distributions.

<p>Variables influencing red panda distributions.</p

Chitwan Annapurna Landscape (CHAL).

<p>Chitwan Annapurna Landscape (CHAL).</p

Grid distribution for field survey in CHAL.

<p>Grid distribution for field survey in CHAL.</p