Field testing of different methods for monitoring mammals in Trans-Himalayas: A case study from Lahaul and Spiti

Joshi, Bheem Dutt; Sharief, Amira; Kumar, Vineet; Kumar, Manish; Dutta, Ritam; Devi, Romila; Singh, Ashutosh; Thakur, Mukesh; Sharma, Lalit Kumar; Chandra, Kailash

doi:10.1016/j.gecco.2019.e00824

Cited by 22 publications

(19 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thirdly, for recording the species presence, we adopted a three-pronged approach i.e. transects/trail surveys, questionnaire surveys, and also the remote camera traps following Pradhan et al 20 , Buckland et al 39 and Joshi et al 54 . Further, it was not logistically feasible to cover the entire area systematically due to the rugged terrain, unpredictable weather and poor resources availability.…”

Section: Methodsmentioning

confidence: 99%

Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India

Dalui

Khatri

Singh

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

Wildlife management in rapid changing landscapes requires critical planning through cross cutting networks, and understanding of landscape features, often affected by the anthropogenic activities. The present study demonstrates fine-scale spatial patterns of genetic variation and contemporary gene flow of red panda (Ailurus fulgens) populations with respect to landscape connectivity in Kangchenjunga Landscape (KL), India. The study found about 1,309.54 km2 area suitable for red panda in KL—India, of which 62.21% area fell under the Protected Area network. We identified 24 unique individuals from 234 feces collected at nine microsatellite loci. The spatially explicit and non-explicit Bayesian clustering algorithms evident to exhibit population structuring and supported red panda populations to exist in meta-population frame work. In concurrence to the habitat suitability and landscape connectivity models, gene flow results supported a contemporary asymmetric movement of red panda by connecting KL—India in a crescent arc. We demonstrate the structural-operational connectivity of corridors in KL—India that facilitated red panda movement in the past. We also seek for cooperation in Nepal, Bhutan and China to aid in preparing for a comprehensive monitoring plan for the long-term conservation and management of red panda in trans-boundary landscapes.

show abstract

Section: Methodsmentioning

confidence: 99%

Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India

Dalui

Khatri

Singh

et al. 2020

Sci Rep

Self Cite

View full text Add to dashboard Cite

show abstract

“…Standardized protocols are available for human observations (Sutherland, 2008;White & Edwards, 2000), camera traps (Kays et al, 2020;Meek et al, 2014;Rovero & Zimmermann, 2016;Scotson et al, 2017, Wearn & Glover-Kapfer, 2017, and PAM (Abrahams, 2018;Browning et al, 2017;Darras et al, 2018). There is also much literature comparing the outcomes of particular methods (e.g., human observation versus camera trapping: Bessone et al, 2020, Cappelle et al, 2019, Greene et al, 2016Joshi et al, 2020, Roberts, 2011. However, few articles compare all three field methods (Stephenson, 2020;Wrege et al, 2017), and none do so in depth.…”

Section: Introductionmentioning

confidence: 99%

Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors

Zwerts

Stephenson

Maisels

et al. 2021

Conservat Sci and Prac

View full text Add to dashboard Cite

Wildlife monitoring is essential for conservation science and data‐driven decision‐making. Tropical forests pose a particularly challenging environment for monitoring wildlife due to the dense vegetation, and diverse and cryptic species with relatively low abundances. The most commonly used monitoring methods in tropical forests are observations made by humans (visual or acoustic), camera traps, or passive acoustic sensors. These methods come with trade‐offs in terms of species coverage, accuracy and precision of population metrics, available technical expertise, and costs. Yet, there are no reviews that compare the characteristics of these methods in detail. Here, we comprehensively review the advantages and limitations of the three mentioned methods, by asking four key questions that are always important in relation to wildlife monitoring: (1) What are the target species?; (2) Which population metrics are desirable and attainable?; (3) What expertise, tools, and effort are required for species identification?; and (4) Which financial and human resources are required for data collection and processing? Given the diversity of monitoring objectives and circumstances, we do not aim to conclusively prescribe particular methods for all situations. Neither do we claim that any one method is superior to others. Rather, our review aims to support scientists and conservation practitioners in understanding the options and criteria that must be considered in choosing the appropriate method, given the objectives of their wildlife monitoring efforts and resources available. We focus on tropical forests because of their high conservation priority, although the information put forward is also relevant for other biomes.

show abstract

“…In the last decade, many new field survey projects have been carried out and more data are now available [30]. In particular, new and widely used camera-trapping surveys have provided a large amount of highquality data for snow leopard occurrences e.g., [49,[50][51][52]. Here, we showed that snow leopard occurrence data compiled from published literature, camera-trap surveys, and line transect surveys can be applied using Random Forest to effectively model snow leopard habitats and project the suitable habitats of snow leopards under future climate scenarios.…”

Section: Discussionmentioning

confidence: 99%

Potential Range Shift of Snow Leopard in Future Climate Change Scenarios

Dazhi³

et al. 2022

Sustainability

View full text Add to dashboard Cite

The snow leopard (Panthera uncia) lives in alpine ecosystems in Central Asia, where it could face intensive climate change and is thus a major conservation concern. We compiled a dataset of 406 GPS-located occurrences based on field surveys, literature, and the GBIF database. We used Random Forest to build different species distribution models with a maximum of 27 explanatory variables, including climatic, topographical, and human impact variables, to predict potential distribution for the snow leopard and make climate change projections. We estimated the potential range shifts of the snow leopard under two global climate models for different representative concentration pathways for 2050 and 2070. We found the distribution center of the snow leopard may move northwest by about 200 km and may move upward in elevation by about 100 m by 2070. Unlike previous studies on the range shifts of the snow leopard, we highlighted that upward rather than northward range shifts are the main pathways for the snow leopard in the changing climate, since the landform of their habitat allows an upward shift, whereas mountains and valleys would block northward movement. Conservation of the snow leopard should therefore prioritize protecting its current habitat over making movement corridors.

show abstract

Field testing of different methods for monitoring mammals in Trans-Himalayas: A case study from Lahaul and Spiti

Cited by 22 publications

References 21 publications

Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India

Fine-scale landscape genetics unveiling contemporary asymmetric movement of red panda (Ailurus fulgens) in Kangchenjunga landscape, India

Methods for wildlife monitoring in tropical forests: Comparing human observations, camera traps, and passive acoustic sensors

Potential Range Shift of Snow Leopard in Future Climate Change Scenarios

Contact Info

Product

Resources

About