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Rashid, W., Shi, J., Rahim, I. U., Dong, S., Ahmad, L. |
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Title |
Research trends and management options in human-snow leopard conflict |
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Journal Article |
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2020 |
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Biological Conservation |
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242 |
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108413 |
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1-10 |
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Snow leopards, Systematic review, Compensation, Co-existence, Livestock, Human-wildlife conflict, Mitigation |
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Conservation of the snow leopard (Panthera uncia) is challenging because of its threatened status and increase in human-snow leopard conflict (HSC). The area of occupancy of the snow leopard comprises mountainous regions of Asia that are confronted with various environmental pressures including climate change. HSCs have increased with a burgeoning human population and economic activities that enhance competition between human and snow leopard or its preys. Here we systematically review the peer-reviewed literature from 1994 to 2018 in Web of Science, Google Scholar, Science Direct and PubMed (30 articles), to evaluate the current state of scholarship about HSCs and their management. We determine: 1) the spatio-temporal distribution of relevant researches; 2) the methodologies to assess HSCs; 3) and evaluate existing interventions for conflict management; and 4) the potential options for HSC management. The aim of the current study is thus to identify key research gaps and future research requirements. Of the articles in this review, 60% evaluated the mitigation of HSCs, while only 37% provided actionable and decisive results. Compensation programs and livestock management strategies had high success rates for mitigating HSCs through direct or community-managed interventions. Further research is required to evaluate the efficacy of existing HSC mitigation strategies, many of which, while recommended, lack proper support. In spite of the progress made in HSC studies, research is needed to examine ecological and sociocultural context of HSCs. We suggest future work focus on rangeland management for HSC mitigation, thus ultimately fostering a co-existence between human and snow leopard. |
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1716 |
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Bohnett, E., Holmberg, J., Faryabi, S. P., An, L., Ahmad, B., Rashid, W., Ostrowski, S. |
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Title |
Comparison of two individual identification algorithms for snow leopards (Panthera uncia) after automated detection |
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Journal Article |
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2023 |
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Ecological Informatics |
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77 |
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102214 |
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1-14 |
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Background subtraction, Deep learning, Hotspotter, Individual identification, PIE v2, Snow leopards |
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Photo-identification of individual snow leopards (Panthera uncia) is the primary data source for density estimation via capture-recapture statistical methods. To identify individual snow leopards in camera trap imagery, it is necessary to match individuals from a large number of images from multiple cameras and historical catalogues, which is both time-consuming and costly. The camouflaged snow leopards also make it difficult for machine learning to classify photos, as they blend in so well with the surrounding mountain environment, rendering applicable software solutions unavailable for the species. To potentially make snow leopard individual identification available via an artificial intelligence (AI) software interface, we first trained and evaluated image classification techniques for a convolutional neural network, pose invariant embeddings (PIE) (a triplet loss network), and compared the accuracy of PIE to that of the HotSpotter algorithm (a SIFT-based algorithm). Data were acquired from a curated library of free-ranging snow leopards taken in Afghanistan between 2012 and 2019 and from captive animals in zoos in Finland, Sweden, Germany, and the United States. We discovered several flaws in the initial PIE model, such as a small amount of background matching, that was addressed, albeit likely not fixed, using background subtraction (BGS) and left-right mirroring (LR) techniques which demonstrated reasonable accuracy (Rank 1: 74% Rank-5: 92%) comparable to the Hotspotter results (Rank 1: 74% Rank 2: 84%)The PIE BGS LR model, in conjunction with Hotspotter, yielded the following results: Rank-1: 85%, Rank-5: 95%, Rank-20: 99%. In general, our findings indicate that PIE BGS LR, in conjunction with HotSpotter, can classify snow leopards more accurately than using either algorithm alone. |
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Alexander, J. S., Murali, R., Mijiddorj, T. N., Agvaantseren, B., Lhamo, C., Sharma, D., Suryawanshi, K. R., Zhi, L., Sharma, K., Young, J. C. |
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Applying a gender lens to biodiversity conservation in High Asia |
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2023 |
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Frontiers in Conservation Science |
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1-8 |
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gender, snow leopards, pastoralism, rights, decision-making |
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Community-based conservation efforts represent an important approach to facilitate the coexistence of people and wildlife. A concern, however, is that these efforts build on existing community structures and social norms, which are commonly dominated by men. Some biodiversity conservation approaches may consequently neglect women’s voices and deepen existing inequalities and inequities. This paper presents two community case studies that draw upon the knowledge and experience gained in our snow leopard conservation practice in pastoral and agro-pastoral settings in Mongolia and India to better understand women’s roles and responsibilities. In these settings, roles and responsibilities in livestock management and agriculture are strongly differentiated along gender lines, and significant gaps remain in women’s decision-making power about natural resources at the community level. We argue that context-specific and gender-responsive approaches are needed to build community support for conservation actions and leverage women’s potential contributions to conservation outcomes. |
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1741 |
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