Mammal richness and diversity in a Himalayan hotspot: the role of protected areas in conserving Bhutan’s mammals

Dorji, Sangay; Rajaratnam, Rajanathan; Vernes, Karl

doi:10.1007/s10531-019-01821-9

Cited by 30 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biological corridors also displayed high variability in terms of mean relative species richness and proportion of total cumulative relative species richness. A similar finding was reported by Dorji et al (2019), who found that mammal diversity in biological corridors and non-protected areas were lower than in protected areas, confirming that PAs are critical to large mammal conservation in Bhutan. But we believe that this does not necessarily mean that the current BCs do not serve their purpose.…”

Section: Effectiveness Of the Existing Pan In Protecting Species Richsupporting

confidence: 87%

“…CA|TS certifies and credits important tiger conservation sites with high standards in protecting tigers, their prey and habitat (Pasha et al 2018). Thus, our findings provide evidence that these PAs are not only important for large species but also medium-sized and yet smaller terrestrial mammals (Dorji et al 2019) and these additional conservation areas could bolster the protected landscape from anthropogenic pressure and perhaps also foster the maintenance of the ecological integrity of this important wildlife conservation landscape in the eastern Himalayas.…”

Section: Effectiveness Of the Existing Pan In Protecting Species Richmentioning

confidence: 64%

See 1 more Smart Citation

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

et al. 2021

View full text Add to dashboard Cite

Context Understanding the environmental and anthropogenic factors influencing habitat selection of multiple species is a foundation for quantifying human impacts on biodiversity and developing effective conservation measures. Objectives To determine the effect of multiple scales of environmental/topographic and anthropogenic variables and landscape patterns on habitat suitability of terrestrial mammals in Bhutan, assess the effectiveness of the current protected area network, identify areas of high species richness outside of the existing protected area, and evaluate the potential effectiveness of indicator and umbrella species for conservation planning. Methods We modelled multi-scale habitat selection of sixteen species of terrestrial mammals across Bhutan using data from a nation-wide camera trap survey. We used the predicted species distribution maps to assess the multi-species conservation effectiveness of the existing protected area network. We performed simulations to identify high priority areas for multiple species based on their habitat suitability, proximity to existing protected areas and overall connectivity within the predicted distribution of species. We used correlation analysis among predicted occurrence maps and multivariate cluster analysis to identify potential indicator species. We evaluated the potential utility of each species as umbrella species by assessing how well optimal protected areas for that species would protect suitable habitat for all 16 species simultaneously. Results Protected areas and forest cover were strongly associated with habitat use of most modelled species. Additionally, topographical features, like terrain roughness and slope position, contributed to habitat selection of multiple species, but often in different ways. Environmental and topographical variables were mostly selected at medium to broad scales. Anthropogenic variables (agriculture and built-up areas) were negatively associated with habitat suitability of most species at both fine and broad scales. Conservation effectiveness assessment of existing protected areas found protected areas in south-central Bhutan have high effectiveness in terms of both mean and total richness protected. Similarly, biological corridors in the south-central region offered high mean richness protection. Our simulation of optimal areas for additional protection found areas abutting protected areas in southern Bhutan offered high relative species richness protection. Our umbrella species analysis found muntjac, wild pig, serow, sambar and Asian golden cat are the most effective umbrella species for broader biodiversity protection. Our indicator species analysis found tiger, gaur, dhole, clouded leopard, Asian black bear and common leopard as effective indicator species. Conclusions This study highlights the need to protect optimally located species-rich areas outside the current protected areas. This kind of multi-species habitat assessment provides important information to optimize future conservation and development plans at national and regional scales.

show abstract

Section: Effectiveness Of the Existing Pan In Protecting Species Richsupporting

confidence: 87%

Section: Effectiveness Of the Existing Pan In Protecting Species Richmentioning

confidence: 64%

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Camera traps have also been increasingly employed as part of long-term, wide-scale monitoring programs [46]. This has led to some programs managing millions of images, including the Snapshot Serengeti project [7], the dataset collated by the Tropical Ecology Assessment and Monitoring Team (TEAM, now part of the 'Wildlife Insights' project, https://www.wildlifeinsights.org/) [47] and that collected by Dorji et al in Bhutan [48]. While some projects monitor communities of species, many other surveys target a single species or taxon.…”

Section: Camera Traps and Citizen Sciencementioning

confidence: 99%

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Green

Rees

Stephens

et al. 2020

Animals

View full text Add to dashboard Cite

Simple Summary: Camera traps, also known as "game cameras" or "trail cameras", have increasingly been used in wildlife research over the last 20 years. Although early units were bulky and the set-up was complicated, modern camera traps are compact, integrated units able to collect vast digital datasets. Some of the challenges now facing researchers include the time required to view, classify, and sort all of the footage collected, as well as the logistics of establishing and maintaining camera trap sampling arrays across wide geographic areas. One solution to this problem is to enlist or recruit the public for help as 'citizen scientists' collecting and processing data. Artificial Intelligence (AI) is also being used to identify animals in digital photos and video; however, this process is relatively new, and machine-based classifications are not yet fully reliable. By combining citizen science with AI, it should be possible to improve efficiency and increase classification accuracy, while simultaneously maintaining and promoting the benefits associated with public engagement with, and awareness of, wildlife.Abstract: Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as 'citizen science'. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.

show abstract

“…Although these non-protected areas harbour significant mammalian biodiversity (Dorji et al, 2019;Harihar, 2011;Punjabi and Rao, 2017) and act as important corridor habitats for freeranging animals (Neelakantan et al, 2019;Sunderraj et al, 1995), they are often overexploited (land conversion, grazing, unregulated poaching and hunting practices etc.) (Macdonald et al, 2013;Ripple et al, 2015Ripple et al, , 2014 and experience strong human-wildlife conflict (crop raiding, damage to infrastructures and injuries to humans) (Gubbi et al, 2014;Kissui, 2008).…”

Section: Introductionmentioning

confidence: 99%

Assessment of endemic northern swamp deer (Rucervus duvaucelii duvaucelii) distribution and identification of priority conservation areas through modeling and field surveys across north India

Paul

Sarkar

Patil

et al. 2020

Preprint

View full text Add to dashboard Cite

Recent declines in large herbivores have led to significant conservation efforts globally. However, the niche-specific megaherbivores residing outside protected areas face more imminent extinction threats. Swamp deer, the obligate grassland-dwelling endemic cervid is the most extinction-prone megaherbivore in the Indian subcontinent. Limited information on distribution and habitat status pose significant conservation and management challenges for the remaining fragmented populations in north, north-east and central India. To this end, we combined exhaustive field surveys and Maximum Entropy (MaxEnt) modeling to generate the most detailed distribution map for the northern swamp deer subspecies. We used primary data from more than 6000 km2 field surveys and eight ecologically relevant covariates for model predictions. Grassland cover, annual mean temperature and distance from water were the major factors that predicted the species distribution. Models predicted swamp deer distribution in only ~3% of the entire landscape, covering both protected (~1.4%) as well as non-protected (~1.6%) areas. Our validation surveys in some of these predicted areas confirmed swamp deer presence and indicated ~85% model accuracy. Finally, we identified four ‘Priority Conservation Area’ still retaining adequate grassland habitat and species presence that require immediate attention to ensure population connectivity across this landscape. These results highlight the importance of the marginalized grassland ecosystems of northern India that still retains high biodiversity. We suggest a swamp deer-centric conservation approach to protect these human-dominated habitats and emphasize in generating such information for other endemic, habitat-specialist species across the globe.

show abstract

Mammal richness and diversity in a Himalayan hotspot: the role of protected areas in conserving Bhutan’s mammals

Cited by 30 publications

References 45 publications

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

Prioritizing areas for conservation outside the existing protected area network in Bhutan: the use of multi-species, multi-scale habitat suitability models

Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Assessment of endemic northern swamp deer (Rucervus duvaucelii duvaucelii) distribution and identification of priority conservation areas through modeling and field surveys across north India

Contact Info

Product

Resources

About