Predicting negative human-tiger (Panthera tigris) interactions in mosaic landscapes around Dudhwa and Pilibhit tiger reserves in India

Chatterjee, Mayukh; Chatterjee, Nilanjan; Chandel, Poonam; Bhattacharya, Tapajit; Kaul, Rahul

doi:10.3389/fcosc.2022.999195

Cited by 3 publications

(1 citation statement)

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“…On the basis of this data, predictive algorithms, which frequently employ recurrent neural networks (RNNs), can predict future tiger behavior. These predictions are useful for organizing conservation strategies and mitigating conflicts between humans and tigers (Chatterjee et al, 2022).. In Sumatra, Indonesia, machine learning and thermal imaging drones were utilized to monitor leopards at night.…”

Section: Image and Video Analysis Techniquesmentioning

confidence: 99%

Application of Machine Learning in Drone Technology for Tracking of Tigers

AlZubi,

Alkhanifer

2024

IJAR

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Background: Tigers, as iconic apex predators and symbols of biodiversity conservation, face numerous threats to their existence. Effective tracking and monitoring are essential for understanding and preserving these majestic creatures and their habitats. The convergence of machine learning and drone technology has emerged as transformative tools in the field of tiger tracking. Drones, or Unmanned Aerial Vehicles (UAVs), have rapidly become invaluable assets in wildlife conservation. Machine learning algorithms, with their capacity to analyze complex datasets, make predictions and automate decision-making processes, offer a novel approach to processing the massive amounts of data generated by drones, including images, sounds and sensor readings. Methods: This paper explores the historical significance of tiger tracking, the pivotal role of drones in conservation and the transformative capabilities of machine learning in wildlife monitoring. In this work, an accurate framework for tiger detection based on YOLOv8 is utilized. Result: By examining the interplay between machine learning, drone technology and tiger conservation, this paper highlights the potential for innovation and the challenges that lie ahead, promising a brighter future for these iconic creatures and their ecosystems. The fine-tuned YOLOv8 model demonstrates exceptional object detection performance, boasting a mAP50 of 0.9820 and a mAP50-95 of 0.6856, coupled with precise classification (precision 0.9646) and robust instance capture (recall 0.9580).

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Section: Image and Video Analysis Techniquesmentioning

confidence: 99%

Application of Machine Learning in Drone Technology for Tracking of Tigers

AlZubi,

Alkhanifer

2024

IJAR

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Human–Carnivore Conflict in and Around Kafta Sheraro National Park, Northern Ethiopia: Determinants, Costs, and Conservation Implications

Kidane,

Gebremichael Weldetnsae,

Siyum

et al. 2024

International Journal of Zoology

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Resolving human–carnivore conflict is crucial for the sustainable coexistence of humans and wildlife. Achieving this, however, requires a comprehensive understanding of the causes and complexities associated with the conflict. This study aimed to assess the nature, underlying causes and costs of human–carnivore conflict, as well as the conservation challenges and potential mitigation measures in and around Kafta Sheraro National Park, Ethiopia. In 2020, we conducted 210 questionnaire interviews with villagers surrounding the park. The surveys gathered data on respondents’ reports of carnivore depredation, the extent and patterns of human–carnivore conflicts, incidents of carnivore killings, the economic impact of livestock depredation, threats to wildlife conservation, potential traditional mitigation measures for human–carnivore conflicts, and their socio‐economic characteristics. A logistic regression model was used to identify factors contributing to livestock depredation. Seventy‐one percent of respondents reported conflict with carnivore species in the 5 years preceding the study. A total of 1390 heads of stock were reported killed by carnivore species, resulting in an economic loss of US$ 170,741. Spotted hyenas (Crocuta crocuta) were responsible for most livestock depredation (37.8%), followed by leopards (Panthera pardus) (29.5%), caracals (Felis caracal) (8%), and common jackals (Canis aureus) (7.3%). Spotted hyenas killed all types of livestock [cattle (Bos taurus), goat (Capra hircus), sheep (Ovis aries), and donkey (Equus asinus)] across all the studied villages, while leopards, caracals, and jackals primarily targeted goats and sheep. Our findings indicated that most livestock depredations occurred at night and during the dry season. Although villagers employed several mitigation measures to prevent carnivore attacks, vigilance and bush fences were reported as the most effective methods. The study also revealed that agricultural land expansion and human settlements are major threats to wildlife conservation. Approximately 75% of surveyed households admitted to retaliatory killings of carnivore species in direct response to livestock losses. The study suggests that effective conflict mitigation and community conservation education should incorporate strategies to promote sustainable wildlife conservation and rural development.

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Predator in proximity: how does a large carnivore respond to anthropogenic pressures at fine-scales? Implications for interface area management

Mohan,

Sathyakumar,

Krishnamurthy

2024

PeerJ

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Background Driven by habitat loss and fragmentation, large carnivores are increasingly navigating human-dominated landscapes, where their activity is restricted and their behaviour altered. This movement, however, raises significant concerns and costs for people living nearby. While intricately linked, studies often isolate human and carnivore impacts, hindering effective management efforts. Hence, in this study, we brought these two into a common framework, focusing on an interface area between the critical tiger habitat and the human-dominated multiple-use buffer area of a central Indian protected area. Methods We employed a fine-scale camera trap survey complemented by GPS-collar movement data to understand spatio-temporal activity patterns and adjustments of tigers in response to anthropogenic pressures. We used an occupancy framework to evaluate space use, Bayesian circular GLMs to model temporal activity, and home range and step length analyses to assess the movement patterns of tigers. Further, we used predation-risk models to understand conflict patterns as a function of tiger presence and other habitat variables. Results Despite disturbance, a high proportion of the sampled area was occupied by 17 unique tigers (ψ = 0.76; CI [0.73–0.92]). The distance to villages (β ± SE = 0.63 ± 0.21) and the relative abundance of large-bodied wild prey (β ± SE = 0.72 ± 0.37) emerged as key predictors of tiger space use probability, indicating a preference for wild prey by tigers, while human influences constrained their habitat utilisation. Distance to villages was also identified as the most significant predictor of the tigers’ temporal activity (μ ± σ = 3.03 ± 0.06 rad) that exhibited higher nocturnality near villages. A total of 11% of tiger home ranges were within village boundaries, accompanied by faster movement in these areas (displacement 40–82% higher). Livestock depredation probability by tigers increased with proximity to villages (P = 0.002) and highway (P = 0.003). Although tiger space use probability (P = 0.056) and wild prey abundance (P = 0.134) were non-significant at the 0.05 threshold, their presence in the best-fit predation-risk model suggests their contextual relevance for understanding conflict risk. The results highlight the importance of appropriately managing livestock near human infrastructures to effectively mitigate conflict. Conclusions Shared space of carnivores and humans requires dynamic site-specific actions grounded in evidence-based decision-making. This study emphasises the importance of concurrently addressing the intricate interactions between humans and large carnivores, particularly the latter’s behavioural adaptations and role in conflict dynamics. Such an integrated approach is essential to unravel cause-effect relationships and promote effective interface management in human-dominated landscapes.

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Predicting negative human-tiger (Panthera tigris) interactions in mosaic landscapes around Dudhwa and Pilibhit tiger reserves in India

Cited by 3 publications

References 48 publications

Application of Machine Learning in Drone Technology for Tracking of Tigers

Application of Machine Learning in Drone Technology for Tracking of Tigers

Human–Carnivore Conflict in and Around Kafta Sheraro National Park, Northern Ethiopia: Determinants, Costs, and Conservation Implications

Predator in proximity: how does a large carnivore respond to anthropogenic pressures at fine-scales? Implications for interface area management

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