From the Forest to the Dish: A Comprehensive Study of the Wildmeat Value Chain in Yangambi, Democratic Republic of Congo

Given the importance of wildmeat for local and national economies, understanding the structure and operations of the informal wildmeat value chains is necessary to provide recommendations for a sustainable wildmeat sector. However, the limited number of case studies available hinders our capacity to understand general patterns in wildmeat trade chains and provide recommendations that apply in different contexts. This study contributes to our understanding of wildmeat trade chains with another case study from the Yangambi landscape, in the Democratic Republic of Congo. We use a value chain approach to explore the structure and functioning of the trade and identify the main barriers to entry into the business, as well as the main levers that can be used to reverse unsustainable use. Bushmeat remains the most consumed source of meat both in the main urban area and in surrounding villages. Urban consumption generates a trade of about 103–145 tons of bushmeat per year for a human population of 37,997 inhabitants. Yangambi combines all the factors for a depletion scenario: a quasi-open access system and high levels of dependency on the resource at all levels of the chain (from consumers to hunters). Despite this, emblematic species such as the chimpanzee, buffalo, okapi, red colobus and giant pangolin are still present in the area. The trade chain follows a “redundant” structure with few barriers to participation in the sector: (1) many hunters and rights holders; (2) many traders; (3) significant demand. Hunters, on average, obtain a higher profit than traders, who bear the highest costs of transportation, fines and bribes. Reducing unsustainable trade in this context, will necessarily imply reducing the burden on natural ecosystems as the main providers of animal protein. Moreover, supporting processes to re-structure local governance systems in this post-conflict context will also support efforts to reverse unsustainable use. The differences observed in Yangambi as compared to other well-studied wildmeat trade chains illustrates that no two bushmeat market chains are alike. Recommendations to reduce unsustainable trade in urban areas need to be tailored to specific contexts, taking into consideration differences in terms of whether markets are open or underground, the length of the trade chains (from local to international trade chains), the existence and type of barriers to entry, the number and type of stakeholders involved and the factors influencing the demand–supply equilibrium

Understanding Factors that Shape Exposure to Zoonotic and Food-Borne Diseases Across Wild Meat Trade Chains

The rise of zoonotic disease-related public health crises has sparked calls for policy action, including calls to close wildlife markets. Yet, these calls often reflect limited understanding of where, precisely, exposure to risk occurs along wildlife and wild meat trade chains. They also threaten to negatively impact food security and livelihoods. From a public health perspective, it is important to understand the practices that shape food safety all along the trade chain, resulting in meat that is either safe to eat or managed as a potential vector of pathogens. This article uses ethnographic methods to examine the steps that lead a wild animal from the forest to the plate of an urban consumer in Yangambi and Kisangani in the Democratic Republic of Congo (DRC). Focusing on hunters, village-level consumers, transporters, market traders and urban consumers, we highlight specific practices that expose different actors involved in the trade chain to wild meat related health risks, including exposure to food borne illnesses from contaminated meat and zoonotic pathogens through direct contact with wild animals, and the local practices in place to reduce the same. We discuss interventions that could help prevent and mitigate zoonotic and food borne disease risks associated with wild meat trade chains

Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5°C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity)

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 × 10−8), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution