A network approach for managing ecosystem services and improving food and nutrition security on smallholder farms

Smallholder farmers are some of the poorest and most food insecure people on Earth. Their high nutritional and economic reliance on home-grown produce makes them particularly vulnerable to environmental stressors such as pollinator loss or climate change which threaten agricultural productivity. Improving smallholder agriculture in a way that is environmentally sustainable and resilient to climate change is a key challenge of the 21st century. Ecological intensification, whereby ecosystem services are managed to increase agricultural productivity, is a promising solution for smallholders. However, smallholder farms are complex socio-ecological systems with a range of social, ecological and environmental factors interacting to influence ecosystem service provisioning. To truly understand the functioning of a smallholder farm and identify the most effective management options to support household food and nutrition security, a holistic, systems-based understanding is required. In this paper, we propose a network approach to understand, visualise and model the complex interactions occurring among wild species, crops and people on smallholder farms. Specifically, we demonstrate how networks may be used to (a) identify wild species with a key role in supporting, delivering or increasing the resilience of an ecosystem service; (b) quantify the value of an ecosystem service in a way that is relevant to the food and nutrition security of smallholders; and (c) understand the social interactions that influence the management of shared ecosystem services. Using a case study based on data from rural Nepal, we demonstrate how this framework can be used to connect wild plants, pollinators and crops to key nutrients consumed by humans. This allows us to quantify the nutritional value of an ecosystem service and identify the wild plants and pollinators involved in its provision, as well as providing a framework to predict the effects of environmental change on human nutrition. Our framework identifies mechanistic links between ecosystem services and the nutrients consumed by smallholder farmers and highlights social factors that may influence the management of these services. Applying this framework to smallholder farms in a range of socio-ecological contexts may provide new, sustainable and equitable solutions to smallholder food and nutrition security. A free Plain Language Summary can be found within the Supporting Information of this article

The Dopamine Transporter Gene, a Spectrum of Most Common Risky Behaviors, and the Legal Status of the Behaviors

This study tests the specific hypothesis that the 9R/9R genotype in the VNTR of the dopamine transporter gene (DAT1) exerts a general protective effect against a spectrum of risky behaviors in comparison to the 10R/9R and 10R/10R genotypes, drawing on three-time repeated measures of risky behaviors in adolescence and young adulthood on about 822 non-Hispanic white males from the Add Health study. Our data have established two empirical findings. The first is a protective main effect in the DAT1 gene against risky behaviors. The second finding is that the protective effect varies over age, with the effect prominent at ages when a behavior is illegal and the effect largely vanished at ages when the behavior becomes legal or more socially tolerated. Both the protective main effect and the gene-lifecourse interaction effect are replicated across a spectrum of most common risky behaviors: delinquency, variety of sexual partners, binge drinking, drinking quantity, smoking quantity, smoking frequency, marijuana use, cocaine use, other illegal drug use, and seatbelt non-wearing. We also compared individuals with the protective genotype and individuals without it in terms of age, physical maturity, verbal IQ, GPA, received popularity, sent popularity, church attendance, two biological parents, and parental education. These comparisons indicate that the protective effect of DAT1*9R/9R cannot be explained away by these background characteristics. Our work demonstrates how legal/social contexts can enhance or reduce a genetic effect on risky behaviors

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

This paper provides a multi-disciplinary overview of the research issues and achievements in the field of Big Data and its visualization techniques and tools. The main aim is to summarize challenges in visualization methods for existing Big Data, as well as to offer novel solutions for issues related to the current state of Big Data Visualization. This paper provides a classification of existing data types, analytical methods, visualization techniques and tools, with a particular emphasis placed on surveying the evolution of visualization methodology over the past years. Based on the results, we reveal disadvantages of existing visualization methods. Despite the technological development of the modern world, human involvement (interaction), judgment and logical thinking are necessary while working with Big Data. Therefore, the role of human perceptional limitations involving large amounts of information is evaluated. Based on the results, a non-traditional approach is proposed: we discuss how the capabilities of Augmented Reality and Virtual Reality could be applied to the field of Big Data Visualization. We discuss the promising utility of Mixed Reality technology integration with applications in Big Data Visualization. Placing the most essential data in the central area of the human visual field in Mixed Reality would allow one to obtain the presented information in a short period of time without significant data losses due to human perceptual issues. Furthermore, we discuss the impacts of new technologies, such as Virtual Reality displays and Augmented Reality helmets on the Big Data visualization as well as to the classification of the main challenges of integrating the technology.publishedVersionPeer reviewe