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ABSTRACT  In recognition of unavoidable changes that human actions are producing in our environment, the term adaptation has become ubiquitous in the environmental and climate‐change literature. Human adaptation is a field with a significant history in anthropology, yet anthropological contributions to the burgeoning field of climate change remain limited. This "In Focus" section presents studies of local adaptations to climate variation and change. Each is concerned with current environmental challenges and future environmental change, and each study is placed within a wider context that includes processes of globalization and integration into market economies, formal and informal institutions, and disasters. These studies highlight the challenges involved in understanding complex adaptations under conditions of stress. They also illustrate how anthropologists engage the larger climate‐change and human‐adaptation discussions and enhance our ability to respond to the challenges of a changing environment.

AbstractOne major challenge of social impact assessment concerns the implementation of multicriteria decision analysis (MCDA) to ascertain the vulnerability of households to environmental change. While MCDA has been widely used to combine vulnerability indicators into an aggregated vulnerability score, the sensitivity of vulnerability indices to uncertain appraisals and judgements of the magnitudes and weights of indicators has been largely ignored so far. In this work, based on vulnerability indicators previously selected and ranked using the analytic hierarchy process (AHP) technique, for household Brazil surveys carried out in 1998 and 2012, a sensitivity analysis (SA) was implemented to account for the variation of vulnerability indicators over time and space. In particular, two techniques were applied: the indicator removal and the threshold value tests. The indicator removal test involved setting to zero a particular indicator weight and rescaling the remaining indicator weights linearly. The threshold value test aimed to identify which indicators had the most relative influence on both indices. Finally, the critical threshold value showed the most important vulnerability indicators and allowed to summarise and contrast the standardized scores differences of the indicators between the two surveys. The results showed which indicators were the most important in increasing or decreasing the vulnerability and improved the understanding of how the overall vulnerability of rainfed farming households changed through time as a function of changes in sensitivity and adaptive capacity.

During the 21st century, human-environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human-environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data. ; SESYNC under National Science Foundation [DBI-1639145]; National Science FoundationNational Science Foundation (NSF); National Science Foundation through the NEON Program ; Published version ; This article emerged from a workshop titled People, Land, & Ecosystems: Leveraging NEON for SocioEnvironmental Synthesis that was held at the National Socio-Environmental Synthesis Center (SESYNC). This work was supported by SESYNC under funding received from the National Science Foundation DBI1639145. The National Ecological Observatory Network is a program sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute. This material is based in part upon work supported by the National Science Foundation through the NEON Program. The conclusions in this publication are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy.