Understanding the prevalence of bear part consumption in Cambodia:A comparison of specialised questioning techniques

The trade in bear parts for medicine and for status is a conservation challenge throughout Asia. The Asiatic black bear (Ursus thibetanus) and the sun bear (Helarctos malayanus) are endemic to this region, and populations are estimated to have declined throughout their ranges due to widespread illegal killing of bears and trade in parts, combined with loss of habitat. Previous studies have indicated that legislation alone is insufficient to prevent illegal hunting and trade, indicating instead a need to address demand for bear parts and products. We conducted mixed-method surveys in Cambodia to understand the key motivators for individuals to consume bear parts, and to understand whether specialised questioning techniques are applicable in this context. Bear part use is illegal in Cambodia and may therefore be considered a sensitive behaviour, in that individuals may be reluctant to admit to it. To counteract possible biases, four specialised questioning techniques were used in this study: randomised response technique (RRT), unmatched count technique (UCT), nominative technique (NT), and false consensus bias (FCB). All four methods serve to shield a respondent’s admittance of a sensitive behaviour from the interviewer. The results presented here show that great variability exists in anonymous methods’ efficacy in certain contexts. However, the results overall indicate that individuals in Cambodia are under-reporting their consumption of bear parts when directly asked, and that the prevalence of bear part use in Cambodia may be as high as 15% of the population, representing a significant conservation challenge

Understanding the prevalence of bear part consumption in Cambodia:A comparison of specialised questioning techniques

<div><p>The trade in bear parts for medicine and for status is a conservation challenge throughout Asia. The Asiatic black bear (Ursus thibetanus) and the sun bear (Helarctos malayanus) are endemic to this region, and populations are estimated to have declined throughout their ranges due to widespread illegal killing of bears and trade in parts, combined with loss of habitat. Previous studies have indicated that legislation alone is insufficient to prevent illegal hunting and trade, indicating instead a need to address demand for bear parts and products. We conducted mixed-method surveys in Cambodia to understand the key motivators for individuals to consume bear parts, and to understand whether specialised questioning techniques are applicable in this context. Bear part use is illegal in Cambodia and may therefore be considered a sensitive behaviour, in that individuals may be reluctant to admit to it. To counteract possible biases, four specialised questioning techniques were used in this study: randomised response technique (RRT), unmatched count technique (UCT), nominative technique (NT), and false consensus bias (FCB). All four methods serve to shield a respondent’s admittance of a sensitive behaviour from the interviewer. The results presented here show that great variability exists in anonymous methods’ efficacy in certain contexts. However, the results overall indicate that individuals in Cambodia are under-reporting their consumption of bear parts when directly asked, and that the prevalence of bear part use in Cambodia may be as high as 15% of the population, representing a significant conservation challenge.</p></div

Use of specialized questioning techniques to detect decline in giraffe meat consumption

Biodiversity conservation depends on influencing human behaviors, but when activities are illegal or otherwise sensitive, e.g. because the behavior in question is taboo to a particular society, actors can be hesitant to admit engagement with illicit behaviors. We applied Specialized Questioning Techniques (SQT) to estimate and compare the behavioral prevalence of giraffe meat consumption from 2017 to 2019 in northern Kenya, Laikipia and Samburu County, between direct questioning and two SQTs: Randomized Response Technique (RRT) and Unmatched Count Technique (UCT). Comparisons between the two samples (2017 and 2019) yielded significant differences across all three methods, with confidence intervals distinctly divergent between years. The significant disparity between the two samples for all three methods suggests that there was a true reduction in giraffe meat usage in our study area, from 2017 to 2019. A key change in the study area between the two time periods was the introduction of a community-based program for giraffe conservation. Primary program activities, including ecological monitoring, community outreach and education, and collaboration with wildlife security teams, align with other conservation programs that have demonstrated reduced poaching pressures. This study demonstrates an application of SQTs to detect a decline of giraffe meat consumption, providing an alternative to self-reported data for monitoring sensitive behaviors related to direct exploitation and illegal uses of wildlife

Wildlife Trade in 2021: Still start with the consumer

The trade in and consumption of wildlife is indisputably a threat to both human health and global biodiversity (Can et al., 2019 and ‘t Sas-Rolfes et al., 2019). Consumer-focused demand reduction is one proposed family of strategies designed to mitigate trade and consumption through the application of robust wildlife consumer research, which is then used to design behavior change initiatives that (ideally) reduce consumer demand (e.g. Veríssimo, 't Sas-Rolfes, and Glikman, 2020). In 2012, Veríssimo et al. published a foundational editorial within this journal that was one of the first articles to explicitly call for consumer-focused demand reduction measures. Veríssimo et al. (2012) noted such positives as overcom-ing persistent failures in enforcement (e.g. Rasphone et al., 2019), and promoting greater inclusivity by bringing diverse stakeholders, such as indigenous peoples, into these efforts. Since that article, consumer-focused de-mand reduction has received growing attention and broader application (e.g. Davis et al., 2020, Veríssimo et al., 2020). Although it has been heartening to see an increase in robust, applied consumer research to reduce demand, the COVID-19 pandemic—which almost certainly originated due to wildlife trade and consumption (Roe et al., 2020)—underscores that there is still a long way to go, and meaningful, multipronged and collabora-tive steps must still be taken to address this threat to human health and global biodiversity (the “OneHealth” concept (El Zowalaty and Järhult, 2020). Measures that must be implemented include more well-defined laws in emerging infectious disease (EID) hotspots (Huang et al., 2020) and decentralized disease surveillance at known source sites, such as wet markets (Watsa et al., 2020). However, consumer-focused demand reduction efforts are arguably more powerful due to the nature of the wildlife trade chain. As the chain is marked by multiple entry-points for disease, from the initial poaching event to the final consumption of an infected animal (Watsa et al., 2020), removing the end point of consumption through demand reduction causes the entire chain to collapse, consequently removing those multiple EID points

Specialized questioning techniques and their use in conservation: A review of available tools, with a focus on methodological advances

Conservationists measuring noncompliance with measures limiting the exploitation of natural resources often ask sensitive questions. However, respondents may distort their answers due to the risk of sanctions, taboos or social norms. Specialized Questioning Techniques (SQTs) can encourage honest answering through protecting respondent's privacy by design. This study provides a complete overview of the main SQTs, as well as their most recent advances. We performed a scoping review of existing SQTs, starting with those covered in Nuno and St. John (2015). We covered techniques which advanced over the last few years (n = 14), and included techniques that have never been explained in a conservation context before (n = 2), or that were invented after 2015 (n = 4), like the parallel model, the pair method, the list method and the person count technique. We explained each technique with real, or hypothetical, examples and also discussed advantages and limitations, emphasizing the need to understand the context where they are applied and to imagine the potential repercussions of SQTs, to protect both respondents and their communities. SQTs developed impressively over the last few years, and many different techniques are available to elicit sensitive behaviors with implications for conservation. This research offers a summary to conservationists and practitioners who want to approach this topic and integrate them in their work

An updated analysis of the consumption of tiger products in urban Vietnam

Tigers are indisputably in danger of extinction due to habitat loss and demand for their parts. Tigers are extirpated in the wild from every country bar one in mainland East and Southeast Asia. Although consumption of tiger products is known to be established in China, less is known about demand for tiger products in Southeast Asia. In this study, we investigate tiger product demand in Vietnam, a major illegal wildlife consumer country. There has been little research into consumption, in particular the level of use, the products being consumed, variation in use of products between areas, and the motivations of consuming tiger products. Through a quantitative survey of 1120 individuals, we show that use of tiger products could be as high as ~11% of the sample in both urban centers of Vietnam, Hanoi and Ho Chi Minh City. Tiger bone glue is the predominant product used, for medicinal purposes. In Hanoi, it is generally purchased by the individual for self-use, while in Ho Chi Minh City it is generally purchased as a gift. In both cities, individuals were generally highly satisfied with the product, indicating entrenched belief in efficacy among consumers. Ultimately, our results show that tiger product use is relatively pervasive. We suggest that conservation organizations should focus on behavior change campaigns that are informed by the results here, and that are specific to each area and to the specific use of tiger product glue for medicine. By reducing demand, beleaguered tiger populations will have a greater chance of stabilization and eventual growth

Correction: Understanding the prevalence of bear part consumption in Cambodia: A comparison of specialised questioning techniques.

[This corrects the article DOI: 10.1371/journal.pone.0211544.]

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data

Camera settings and biome influence the accuracy of citizen science approaches to camera trap image classification.

Scientists are increasingly using volunteer efforts of citizen scientists to classify images captured by motion-activated trail cameras. The rising popularity of citizen science reflects its potential to engage the public in conservation science and accelerate processing of the large volume of images generated by trail cameras. While image classification accuracy by citizen scientists can vary across species, the influence of other factors on accuracy is poorly understood. Inaccuracy diminishes the value of citizen science derived data and prompts the need for specific best-practice protocols to decrease error. We compare the accuracy between three programs that use crowdsourced citizen scientists to process images online: Snapshot Serengeti, Wildwatch Kenya, and AmazonCam Tambopata. We hypothesized that habitat type and camera settings would influence accuracy. To evaluate these factors, each photograph was circulated to multiple volunteers. All volunteer classifications were aggregated to a single best answer for each photograph using a plurality algorithm. Subsequently, a subset of these images underwent expert review and were compared to the citizen scientist results. Classification errors were categorized by the nature of the error (e.g., false species or false empty), and reason for the false classification (e.g., misidentification). Our results show that Snapshot Serengeti had the highest accuracy (97.9%), followed by AmazonCam Tambopata (93.5%), then Wildwatch Kenya (83.4%). Error type was influenced by habitat, with false empty images more prevalent in open-grassy habitat (27%) compared to woodlands (10%). For medium to large animal surveys across all habitat types, our results suggest that to significantly improve accuracy in crowdsourced projects, researchers should use a trail camera set up protocol with a burst of three consecutive photographs, a short field of view, and determine camera sensitivity settings based on in situ testing. Accuracy level comparisons such as this study can improve reliability of future citizen science projects, and subsequently encourage the increased use of such data