Elephant (Elephas maximus) temporal activity, distribution, and habitat use patterns on the tiger’s forgotten trails across the seasonally dry, subtropical, hilly Churia forests of Nepal

Thapa, Kanchan; Kelly, Marcella J.; Pradhan, Narendra Man Babu

doi:10.1371/journal.pone.0216504

Cited by 16 publications

(17 citation statements)

References 37 publications

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“…Elephant attacks inside forests peaked during the afternoon (~16:00) when human activity, mainly cattle grazing, and fodder and forest resource collection would remain high inside the forests. The elephants generally rest during the mid‐day hot period and start become active with decreasing temperature in the afternoon (after 15:00) (Thapa et al., 2019 ). This increases the chance of interaction between elephants and humans.…”

Section: Discussionmentioning

confidence: 99%

Patterns and determinants of elephant attacks on humans in Nepal

Ram

Mondol

Subedi

et al. 2021

Ecology and Evolution

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Attacks on humans by Asian elephant (Elephas maximus) is an extreme form of human–elephant conflict. It is a serious issue in southern lowland Nepal where elephant‐related human fatalities are higher than other wildlife. Detailed understanding of elephant attacks on humans in Nepal is still lacking, hindering to devising appropriate strategies for human–elephant conflict mitigation. This study documented spatiotemporal pattern of elephant attacks on humans, factors associated with the attacks, and human/elephant behavior contributing to deaths of victims when attacked. We compiled all the documented incidences of elephant attacks on humans in Nepal for last 20 years across Terai and Chure region of Nepal. We also visited and interviewed 412 victim families (274 fatalities and 138 injuries) on elephant attacks. Majority of the victims were males (87.86%) and had low level of education. One fourth of the elephant attacks occurred while chasing the elephants. Solitary bulls or group of subadult males were involved in most of the attack. We found higher number of attacks outside the protected area. People who were drunk and chasing elephants using firecrackers were more vulnerable to the fatalities. In contrast, chasing elephants using fire was negatively associated with the fatalities. Elephant attacks were concentrated in proximity of forests primarily affecting the socioeconomically marginalized communities. Integrated settlement, safe housing for marginalized community, and community grain house in the settlement should be promoted to reduce the confrontation between elephants and humans in entire landscape for their long‐term survival.

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Section: Discussionmentioning

confidence: 99%

Patterns and determinants of elephant attacks on humans in Nepal

Ram

Mondol

Subedi

et al. 2021

Ecology and Evolution

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“…Wang et al [ 34 ] shows coefficient of determination, expressed as R 2 , found to be significant between NDVI and Bamboo vegetation indices at 65% and 52% with or without presence of canopy in the wet season. We used Landsat 6 Thematic Mapper imagery to derive NDVI covariate [ 23 ] and images were extracted for monsoon season that matched with the timing of the 2016 survey period. GIS data characterizing the NDVI metrics (range: -0.50–0.82) were clipped and averaged for each grid cell.…”

Section: Methodsmentioning

confidence: 99%

“…Division of the major grid cells into six sub-grid cells was primarily designed for the logistical reason (geographic complexity and terrain condition) and improving detection history of secretive animal. We chose for spatial replication over temporal [19] primarily for logistical reasons as done in previous studies for large mammals [20][21][22][23]. Adequate sign detections, replications and spatial coverage can be achieved with adequate survey efforts per grid cells and careful planning [20].…”

Section: Field Designmentioning

confidence: 99%

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

Thapa

Bista³

et al. 2020

PLoS ONE

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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 p^(SE) estimated at 0.70 (0.02). We estimated red panda site occupancy (sampled grid cells) and landscape occupancy (across the potential habitat) Ψ^(SE) 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.

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“…Due to the large numbers of images collected, researchers may save time by focusing only on those that contain animals of interest, leaving many images as 'bycatch'. These images might contain very useful data on many other species [49,50], but are often left unanalysed.…”

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

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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.

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Elephant (Elephas maximus) temporal activity, distribution, and habitat use patterns on the tiger’s forgotten trails across the seasonally dry, subtropical, hilly Churia forests of Nepal

Cited by 16 publications

References 37 publications

Patterns and determinants of elephant attacks on humans in Nepal

Patterns and determinants of elephant attacks on humans in Nepal

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

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

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