Month: January 2023

Please find details below of a new article added to our Bibliography:

Title: Research trends and management options in human-snow leopard conflict

Author: Rashid, W., Shi, J., Rahim, I. U., Dong, S., Ahmad, L.

Abstract: 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.

URL: https://snowleopardnetwork.org/b/show.php?record=1716

Please find details below of a new article added to our Bibliography:

Title:    Human expertise combined with artificial intelligence improves performance of snow leopard camera trap studies

Author:   Bohnett, E., Faryabi, S. P., Lewison, R., An, L., Bian, X., Rajabi, A. M., Jahed, N., Rooyesh, H., Mills, E., Ramos, S., Mesnildrey, N., Perez, C. M. S., Taylor, J., Terentyev, V., Ostrowski, S.

Abstract:    Camera trapping is the most widely used data collection method for estimating snow leopard (Panthera uncia) abundance; however, the accuracy of this method is limited by human observer errors from misclassifying individuals in camera trap images. We evaluated the extent Whiskerbook (www.whiskerbook.org), an artificial intelligence (AI) software, could reduce this error rate and enhance the accuracy of capture-recapture abundance estimates. Using 439 images of 34 captive snow leopard individuals, classification was performed by five observers with prior experience in individual snow leopard ID (“experts”) and five observers with no such experience (“novices”). The “expert” observers classified 35 out of 34 snow leopard individuals, on average erroneously splitting one individual into two, thus resulting in a higher number than true individuals. The success rate of experts was 90 %, with less than a 3 % error in estimating the population size in capture-recapture modeling. However, the “novice” observers successfully matched 71 % of encounters, recognizing 25 out of 34 individuals, underestimating the population by 25 %. It was found that expert observers significantly outperformed novice observers, making statistically fewer errors (Mann Whitney U test P = 0.01) and finding the true number of individuals (P = 0.01). These differences were contrasted with a previous study by Johansson et al. 2020, using the same subset of 16 individuals from European zoos. With the help of AI and the Whiskerbook platform, “experts” were able to match 87 % of encounters and identify 15 out of 16 individuals, with modeled estimates of 16 ± 1 individuals. In contrast, “novices” were 63 % accurate in matching encounters and identified 12 out of 16 individuals, modeling 12 ± 1 individuals that underestimated the population size by 12 %. When comparing the performance of observers using AI and the Whiskerbook platform to observers performing the tasks manually, we found that observers using Whiskerbook made significantly fewer errors in splitting one individual into two (P = 0.04). However, there were also a significantly higher number of combination errors, where two individuals were combined into one (P = 0.01). Specifically, combination errors were found to be made by “novices” (P = 0.04). Although AI benefited both expert and novice observers, expert observers outperformed novices. Our results suggest that AI effectively reduced the misclassification of individual snow leopards in camera trap studies, improving abundance estimates. However, even with AI support, expert observers were needed to obtain the most accurate estimates.

URL: https://snowleopardnetwork.org/b/show.php?record=1715

Please find details below of a new article added to our Bibliography:

Title: A review of the ecological and socioeconomic characteristics of trophy hunting across Asia

Author: Parker, B. G., Khanyari, M., Ambarli, H., Buuveibaatar, B., Kabir, M., Khanal, G., Mirzadeh, H. R., Onon, Y., Farhadinia, M. S.

Abstract: The continuing debates about trophy hunting should be underpinned by an understanding of at least the basic characteristics of the practice (e.g. species, quotas, areas, prices). Whilst many countries in Asia have established trophy hunting programmes of considerable importance to conservation and local livelihoods, there remains some ambiguity over the extent of trophy hunting in Asia as its basic characteristics in each country have not been compiled. In this study, we compile information on various ecological and socioeconomic characteristics of trophy hunting of mammals for countries across Asia by reviewing published and unpublished literature, analysing trade data, and obtaining contributions from in-country contacts. Across Asia, established trophy hunting programmes exist in at least 11 countries and target at least 30 species and one hybrid (incl., five Vulnerable and one Endangered species). Trophy hunting in these countries varies markedly in areas (e.g. >1 mi
llion km2 in Kazakhstan, 37% of country, vs. 1325 km2 in Nepal, <1% of country) and annual offtakes (e.g. Kazakhstan: 4500 individuals from 4 of 5 trophy species; Pakistan: 229 from 4 of 7; Mongolia: 155 from 6 of 9; Tajikistan: 126 from 3 of 6; Nepal: 22 from 3 of the 4 that are trophy hunted in practice). Permit prices also vary across species and countries, with domestic and international hunters sometimes charged different rates. Hunters from the USA appear overwhelmingly prominent among international clients. National legislations typically mandate a proportion of trophy hunting revenue to accrue locally (range: 40–100%). We provide five key recommendations for research to inform trophy hunting policy in Asia: (1) Ecological impact assessments; (2) Socioeconomic impact assessments; (3) Evaluations of the contributions of trophy hunting to conservation spending; (4) Evaluations of the contributions of trophy hunting to the post-2020 Global Biodiversity Framework; (5) Further e
xaminations of perceptions of trophy hunting.

URL: https://snowleopardnetwork.org/b/show.php?record=1714