Eigenbehaviors: identifying structure in routine

Longitudinal behavioral data generally contains a significant amount of structure. In this work, we identify the structure inherent in daily behavior with models that can accurately analyze, predict, and cluster multimodal data from individuals and communities within the social network of a population. We represent this behavioral structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with 79% accuracy for our test subjects. Additionally, we demonstrate the potential for this dimensionality reduction technique to infer community affiliations within the subjects’ social network by clustering individuals into a “behavior space” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96% classification accuracy of community affiliations within the population-level social network. Additionally, the distance between individuals in the behavior space can be used as an estimate for relational ties such as friendship, suggesting strong behavioral homophily amongst the subjects. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate phones, and communication of 100 subjects at MIT over the course of 9 months. As wearable sensors continue to generate these types of rich, longitudinal datasets, dimensionality reduction techniques such as eigenbehaviors will play an increasingly important role in behavioral research. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James). An erratum to this article can be found at     

Community-based scenario planning: a process for vulnerability analysis and adaptation planning to social–ecological change in coastal communities

The current and projected impacts of climate change make understanding the environmental and social vulnerability of coastal communities and the planning of adaptations important international goals and national policy initiatives. Yet, coastal communities are concurrently experiencing numerous other social, political, economic, demographic and environmental changes or stressors that also need to be considered and planned for simultaneously to maintain social and environmental sustainability. There are a number of methods and processes that have been used to study vulnerability and identify adaptive response strategies. This paper describes the stages, methods and results of a modified community-based scenario planning process that was used for vulnerability analysis and adaptation planning within the context of multiple interacting stressors in two coastal fishing communities in Thailand. The four stages of community-based scenario planning included: (1) identifying the problem and purpose of scenario planning; (2) exploring the system and types of change; (3) generating possible future scenarios; and (4) proposing and prioritizing adaptations. Results revealed local perspectives on social and environmental change, participant visions for their local community and the environment, and potential actions that will help communities to adapt to the changes that are occurring. Community-based scenario planning proved to have significant potential as an anticipatory action research process for incorporating multiple stressors into vulnerability analysis and adaptation planning. This paper reflects on the process and outcomes to provide insights and suggest changes for future applications of community-based scenario planning that will lead to more effective learning, innovation and action in communities and related social–ecological systems.     

COVID-19: From health crises to food security anxiety and policy implications

Like the rest of the world, African countries are reeling from the health, economic and social effects of COVID-19. The continent’s governments have responded by imposing rigorous lockdowns to limit the spread of the virus. The various lockdown measures are undermining food security, because stay at home orders have among others, threatened food production for a continent that relies heavily on agriculture as the bedrock of the economy. This article draws on quantitative data collected by the GeoPoll, and, from these data, assesses the effect of concern about the local spread and economic impact of COVID-19 on food worries. Qualitative data comprising 12 countries south of the Sahara reveal that lockdowns have created anxiety over food security as a health, economic and human rights/well-being issue. By applying a probit model, we find that concern about the local spread of COVID-19 and economic impact of the virus increases the probability of food worries. Governments have responded with various efforts to support the neediest. By evaluating the various policies rolled out we advocate for a feminist economics approach that necessitates greater use of data analytics to predict the likely impacts of intended regulatory relief responses during the recovery process and post-COVID-19.     

Quantifying iridescent coloration in animals: a method for improving repeatability

Quantification of animal colors is important to a variety of fields of research, especially those dealing with visual communication and sexual selection. Most animal colors are easily measured using well-established spectrophotometric techniques. However, the unique characteristics of iridescent colors present particular challenges and opportunities to quantify novel color metrics. Due to the fine-scale angle dependence of iridescent coloration, color metrics, such as hue and brightness, must be measured using methods that allow for repeatable comparison across individuals (e.g., by carefully controlling and measuring viewing geometry). Here, we explain how the optical characteristics of iridescent colors should be considered when developing measurement techniques, describe the pitfalls of some commonly used techniques, and recommend improved methods and metrics (angular degree of color change and breadth of reflectance) for quantifying iridescent color. In particular, most studies of iridescent birds to date have used less than ideal procedures and have not provided repeatability estimates for their methods. For example, we demonstrate here that measuring coloration from overlapping patches of iridescent feathers may be problematic, and we argue against methods that do not carefully control viewing geometry. We recommend measuring iridescence at maximal reflectance angles using an apparatus that allows for sample rotation, and we compare this technique to some other commonly used methods using iridescent gorget and crown feathers from Anna’s hummingbirds (Calypte anna). Our apparatus allows for the quantification of angular color change, and we found that maximal reflectance measurements using single feathers are highly repeatable both within feather samples and among samples within an individual.     

Challenges in providing residual risks in carrier testing

The probability an individual is a carrier for a recessive disorder despite a negative carrier test, referred to as residual risk, has been part of carrier screening for over 2 decades. Residual risks are calculated by subtracting the frequency of carriers of pathogenic variants detected by the test from the carrier frequency in a population, estimated from the incidence of the disease. Estimates of the incidence (and therefore carrier frequency) of many recessive disorders differ among different population groups and are inaccurate or unavailable for many genes on large carrier screening panels for most of the world's populations. The pathogenic variants detected by the test and their frequencies also vary across groups and over time as variants are newly discovered or reclassified, which requires today's residual carrier risks to be continually updated. Even when a residual carrier risk is derived using accurate data obtained in a particular group, it may not apply to many individuals in that group because of misattributed ancestry or unsuspected admixture. Missing or inaccurate data, the challenge of determining meaningful ancestry-specific risks and applying them appropriately, and a lack of evidence they impact management, suggest that patients be counseled that although carrier screening may miss a small fraction of carriers, residual risks with contemporary carrier screening are well below the risk posed by invasive prenatal diagnosis, even if one member of the couple is a carrier, and that efforts to provide precise residual carrier risks are unnecessary.     

Climate and air quality-driven scenarios of ozone and aerosol precursor abatement

In addition to causing domestic and regional environmental effects, many air pollutants contribute to radiative forcing (RF) of the climate system. However, climate effects are not considered when cost-effective abatement targets for these pollutants are established, nor are they included in current international climate agreements. We construct air pollution abatement scenarios in 2030 which target cost-effective reductions in RF in the EU, USA, and China and compare these to abatement scenarios which instead target regional ozone effects and particulate matter concentrations. Our analysis covers emissions of PM (fine, black carbon and organic carbon), SO₂, NO_x, CH₄, VOCs, and CO. We find that the effect synergies are strong for PM/BC, VOC, CO and CH₄. While an air quality strategy targeted at reducing ozone will also reduce RF, this will not be the case for a strategy targeting particulate matter. Abatement in China dominates RF reduction, but there are cheap abatement options also available in the EU and USA. The justification for international cooperation on air quality issues is underlined when the co-benefits of reduced RF are considered. Some species, most importantly SO₂, contribute a negative forcing on climate. We suggest that given current knowledge, NO_x and SO₂ should be ignored in RF-targeted abatement policies.     

Modeling Connectivity of Non‐floodplain Wetlands: Insights,Approaches, and Recommendations

Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.