The endangered snow leopard is a large felid that is distributed over 1.83 million km2 globally. Throughout its range it relies on a limited number of prey species in some of the most inhospitable landscapes on the planet where high rates of human persecution exist for both predator and prey. We reviewed 14 published and 11 unpublished studies pertaining to snow leopard diet throughout its range. We calculated prey consumption in terms of frequency of occurrence and biomass consumed based on 1696 analysed scats from throughout the snow leopard's range. Prey biomass consumed was calculated based on the Ackerman's linear correction factor. We identified four distinct physiographic and snow leopard prey type zones, using cluster analysis that had unique prey assemblages and had key prey characteristics which supported snow leopard occurrence there. Levin's index showed the snow leopard had a specialized dietary niche breadth. The main prey of the snow leopard were Siberian ibex (Capra sibrica), blue sheep (Pseudois nayaur), Himalayan tahr (Hemitragus jemlahicus), argali (Ovis ammon) and marmots (Marmota spp). The significantly preferred prey species of snow leopard weighed 55±5 kg, while the preferred prey weight range of snow leopard was 36–76 kg with a significant preference for Siberian ibex and blue sheep. Our meta-analysis identified critical dietary resources for snow leopards throughout their distribution and illustrates the importance of understanding regional variation in species ecology; particularly prey species that have global implications for conservation.

Broad-scale models describing predator prey preferences serve as useful departure points for understanding predator-prey interactions at finer scales. Previous analyses used a subjective approach to identify prey weight preferences of the five large African carnivores, hence their accuracy is questionable. This study uses a segmented model of prey weight versus prey preference to objectively quantify the prey weight preferences of the five large African carnivores. Based on simulations of known predator prey preference, for prey species sample sizes above 32 the segmented model approach detects up to four known changes in prey weight preference (represented by model break-points) with high rates of detection (75% to 100% of simulations, depending on number of break-points) and accuracy (within 1.3±4.0 to 2.7±4.4 of known break-point). When applied to the five large African carnivores, using carnivore diet information from across Africa, the model detected weight ranges of prey that are preferred, killed relative to their abundance, and avoided by each carnivore. Prey in the weight ranges preferred and killed relative to their abundance are together termed “accessible prey”. Accessible prey weight ranges were found to be 14–135 kg for cheetah Acinonyx jubatus, 1–45 kg for leopard Panthera pardus, 32–632 kg for lion Panthera leo, 15–1600 kg for spotted hyaena Crocuta crocuta and 10–289 kg for wild dog Lycaon pictus. An assessment of carnivore diets throughout Africa found these accessible prey weight ranges include 88±2% (cheetah), 82±3% (leopard), 81±2% (lion), 97±2% (spotted hyaena) and 96±2% (wild dog) of kills. These descriptions of prey weight preferences therefore contribute to our understanding of the diet spectrum of the five large African carnivores. Where datasets meet the minimum sample size requirements, the segmented model approach provides a means of determining, and comparing, the prey weight range preferences of any carnivore species.

There is a growing recognition of the contribution that privately-owned land makes to conservation efforts, and governments are increasingly counting privately protected areas (PPAs) towards their international conservation commitments. The public availability of spatial data on countries' conservation estates is important for broad-scale conservation planning and monitoring and for evaluating progress towards targets. Yet there has been limited consideration of how PPA data is reported to national and international protected area databases, particularly whether such reporting is transparent and fair (i.e., equitable) to the landholders involved. Here we consider PPA reporting procedures from three countries with high numbers of PPAs-Australia, South Africa, and the United States-illustrating the diversity within and between countries regarding what data is reported and the transparency with which it is reported. Noting a potential tension between landholder preferences for privacy and security of their property information and the benefit of sharing this information for broader conservation efforts, we identify the need to consider equity in PPA reporting processes. Unpacking potential considerations and tensions into distributional, procedural, and recognitional dimensions of equity, we propose a series of broad principles to foster transparent and fair reporting. Our approach for navigating the complexity and context-dependency of equity considerations will help strengthen PPA reporting and facilitate the transparent integration of PPAs into broader conservation efforts.

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Key methods discussed in this chapterModelling methods: System dynamics (group model building, mediated modelling, shared vision planning), agent-based models (ARDI), role-playing games (Wat-A-Game), expert models (Bayesian networks, fuzzy cognitive maps), state-and-transition models, soft system methodologies (rich pictures, concept maps, decision trees, cognitive maps) Integrated approaches: Collaborative modelling, companion modelling, participatory system analysis Connections to other chaptersMethods for generating data and systems scoping (Chapters 5-8), specifically participatory data-collection methods (Chapter 8) or interviews and surveys (Chapter 7), may provide working material or monitoring and evaluation support within participatory modelling processes. Facilitated dialogue methods (Chapter 9) may smooth participatory modelling workshops. Future analysis (Chapter 10), scenario development (Chapter 11) or serious games (Chapter 12) may be articulated with participatory models within broader participatory resilience assessment (Chapter 14) or action research (Chapter 15) projects. Expert modelling (Chapter 16), dynamical systems modelling (Chapter 26), state-and-transition modelling (Chapter 27) and agent-based modelling (Chapter 28) cover the most common types of modelling methods used in participatory modelling, and participatory modelling may use institutional analysis (Chapter 22) conceptual frameworks.

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Private landowners in South Africa conserve roughly 40% of white rhinos globally. Given concerns that escalating poaching has caused private-rhino owners to disinvest, we used a national survey to assess 171 private-rhino owners' responses to the crisis. Twenty-eight percent of rhino owners are disinvesting in rhino, 57% are pursuing business-as-usual (largely ecotourism), and 15% are investing in more rhinos. It is currently unclear whether this diversity in private-rhino owners' responses to the crisis is increasing the resilience of the rhino population to poaching. Some rhino investors show signs of financial stress. Most owners support rhino-horn trade to fund conservation, yet international trade remains banned. By contrast, a recent national policy amendment allows rhinos to be managed as livestock, risking a shift from rhino-for-conservation to rhinofor-production on private land. Our findings highlight an urgent need to ensure policies keep pace with dynamic socioeconomic environments that influence the sustainability of wildlife use.