Human-Giraffe Interactions: Characterizing Poaching and Use of Parts as a Threat to Giraffe in Northern Kenya

Giraffe (Giraffe spp.) are iconic wildlife species to Africa, yet relatively little conservation funding and research have been directed at protection of giraffe in the wild. A growing number of national governments and conservation organizations are implementing management strategies to address the threats that giraffe face. To inform these plans, there is a need for social science that examines the human pressures associated with decline of giraffe populations, including poaching and the use of giraffe parts. As the large majority of reticulated giraffe (Giraffa reticulata) range occurs outside formally protected areas, conservation plans must be made with pastoralist communities and other actors in northern Kenya where the land is shared between people, their livestock, and wildlife. The research presented in this dissertation was conducted as part of a community-based program focused on reticulated giraffe, called the Twiga Walinzi Initiative (“Giraffe Guards” in Swahili), and represents the first quantitative study on the human dimensions of giraffe conservation. Goals of the research project were to examine key cognitions to human-giraffe interactions (i.e. attitudes, beliefs, perceptions), assess relationships between certain cognitions within areas that adopt a community-based conservation approach, and understand the extent and drivers of giraffe meat and part usage. Face-to-face interviews were conducted at two study sites over survey periods in 2016/17 (n=579) and 2019 (n=680). Results from these studies provide insights to how pastoralist communities view and act toward local giraffe. Factors that significantly influenced support for giraffe conservation differed between study sites, suggesting that local context is important to shaping human-giraffe interactions (Chapter 2). For instance, perceived benefits had stronger influence on normative belief in communities more recently connected with wildlife-based tourism. The linkages between perceived benefits, attitudes, and behaviors were further explored by assessing the relationships between these concepts within a community-based conservation setting (Chapter 3). Findings suggest a positive association between perceived benefits and attitudes toward giraffe, but there was less evidence that perceptions of wildlife-related benefits influenced use of giraffe meat/parts. As human behavior is of central interest to conservation, we also assessed levels of giraffe meat consumption (Chapter 4) and determinants of intention to consume giraffe meat (Chapter 5). Specialized questioning techniques were utilized to estimate prevalence of giraffe meat consumption preceding the two surveys. Estimated prevalence of giraffe meat consumption declined after establishment of the Twiga Walinzi. Perceived behavioral control had stronger relative influence than attitudes and subjective norms on future intention to consume giraffe meat. Collectively, these research findings are relevant for applied giraffe conservation efforts and provide a framework for understanding human-giraffe interactions and associated threats in diverse global settings

Experiences and emotional responses of farming communities living with Asian Elephants in Southern Sri Lanka

Individuals’ tolerance toward wildlife can be based on a combination of tangible benefits and costs (e.g. economic gains and losses) as well as intangible benefits and costs (e.g. shared values and risk perceptions). Asian Elephants (Elephas maximus) potentially present both types of benefits and costs for rural communities. We examined which factors were associated with emotional responses toward wild Asian Elephants among agriculturalists using a questionnaire survey of 300 households situated around the Wetahirakanda sanctuary connecting Udawalawe and Lunugamwehera National Parks, Sri Lanka. Respondents were all from the Sinhala-Buddhist ethno-religious majority with average annual household incomes of Rs. 339,335 LKR (∼$2610 USD). We found that none of the surveyed households derived any economic benefits from tourism despite the proximity of two national parks, whereas 171 (57%) had experienced crop damage by Elephants. Though the median annual income lost due to elephants was Rs.50,000 LKR (4%), 21 households (7%) had losses exceeding 100%. Only six individuals (2%) recollected any human fatalities in their communities. Only three individuals reported positive feelings toward elephants, whereas all others had negative or neutral feelings. Economic factors were not significant predictors of feelings toward elephants, whereas fear of elephants and worry about crop damage had the largest and most significant negative effects. Our findings suggest that it might not be sufficient to reduce losses solely at an individual level, but that human-elephant coexistence interventions should target communities as a whole to reduce the spill-over effects of worry and anxiety by association with others who have experienced loss

Updated geographic range maps for giraffe, Giraffa spp., throughout sub‐Saharan Africa, and implications of changing distributions for conservation

Giraffe populations have declined in abundance by almost 40% over the last three decades, and the geographic ranges of the species (previously believed to be one, now defined as four species) have been significantly reduced or altered. With substantial changes in land uses, loss of habitat, declining abundance, translocations, and data gaps, the existing geographic range maps for giraffe need to be updated. We performed a review of existing giraffe range data, including aerial and ground observations of giraffe, existing geographic range maps, and available literature. The information we collected was discussed with and validated by subject‐matter experts. Our updates may serve to correct inaccuracies or omissions in the baseline map, or may reflect actual changes in the distribution of giraffe. Relative to the 2016 International Union for Conservation of Nature Red List Assessment range map, the updated geographic range maps show a 5.6% decline in the range area of all giraffe taxa combined. The ranges of Giraffa camelopardalis (northern giraffe) and Giraffa tippelskirchi (Masai giraffe) decreased in area by 37% (122432 km2) and 4.7% (20816 km2) respectively, whereas 14% (41696 km2) of the range of Giraffa reticulata (reticulated giraffe) had not been included in the original geographic range map and has now been added. The range of Giraffa giraffa (southern giraffe) showed little overall change; it increased by 0.1% (419 km2). Ranges were larger than previously reported in six of the 21 range countries (Botswana, Ethiopia, Mozambique, South Sudan, Tanzania, and Zimbabwe), had declined in seven (Cameroon, Central African Republic, Chad, Malawi, Niger, Uganda, and Zambia) and remained unchanged in seven (Angola, Democratic Republic of Congo, eSwatini, Namibia, Rwanda, Somalia, and South Africa). In Kenya, the ranges of both Giraffa tippelskirchi and Giraffa camelopardalis decreased, but the range of Giraffa reticulata was larger than previously believed. Our updated range maps increase existing knowledge, and are important for conservation planning for giraffe. However, since rapid infrastructure development throughout much of Africa is a driver of giraffe population declines, there is an urgent need for a continent‐wide, consistent and systematic giraffe survey to produce more accurate range maps, in order to inform conservation and policy planning

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