Economic shocks and health resilience: lessons from the Russian Federation

Background: Despite extensive research on determinants of health, there is much less information on factors protecting health among those exposed to economic shocks. Using longitudinal data from the Russian Federation in the post-Soviet period, we examined individual-level factors that enhance resilience of health to economic shocks. Methods: Logistic regression analysed factors associated with good self-assessed health (SAH) and health resilience, using pooled samples from the Russia Longitudinal Monitoring Survey-Higher School of Economics (1994–2012). Results: The general population consistently reported ‘average’ SAH, indicating almost invariant trends over the years. Male gender was the strongest predictor of good SAH and health resilience. Other factors positively associated with good SAH were age, higher education, employment, residing in rural areas, living in a larger and/or non-poor household. Among unemployed and those remaining unemployed, residing in rural areas, living in a larger and/or non-poor household remained the strongest predictors of good SAH and health resilience. These same factors were also important for males with recent job loss. Conclusions: Several factors predicting good SAH in the general population also influence health resilience factors among those remaining unemployed and experiencing a job loss. Such factors help to identify those most vulnerable and aid targeting assistance during economic crises

Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Past abrupt changes, tipping points and cascading impacts in the Earth system

The geological record shows that abrupt changes in the Earth system can occur on timescales short enough to challenge the capacity of human societies to adapt to environmental pressures. In many cases, abrupt changes arise from slow changes in one component of the Earth system that eventually pass a critical threshold, or tipping point, after which impacts cascade through coupled climate–ecological–social systems. The chance of detecting abrupt changes and tipping points increases with the length of observations. The geological record provides the only long-term information we have on the conditions and processes that can drive physical, ecological and social systems into new states or organizational structures that may be irreversible within human time frames. Here, we use well-documented abrupt changes of the past 30 kyr to illustrate how their impacts cascade through the Earth system. We review useful indicators of upcoming abrupt changes, or early warning signals, and provide a perspective on the contributions of palaeoclimate science to the understanding of abrupt changes in the Earth system

Challenges to adaptation: a fundamental concept for the shared socio-economic pathways and beyond

The framework for the new scenarios being developed for climate research calls for the development of a set of Shared Socioeconomic Pathways (SSPs), which are meant to differ in terms of their challenges to mitigation and challenges to adaptation. In order for the scenario process to fulfill its goals, the research and policy communities need to develop a shared understanding of these concepts. This paper focuses on challenges to adaptation. We begin by situating this new concept in the context of the rich literatures related to inter alia adaptation, vulnerability, and resilience. We argue that a proper characterization of challenges to adaptation requires a rich, exploration of the concept, which goes beyond mere description. This has a number of implications for the operationalization of the concept in the basic and extended versions of the SSPs. First, the elements comprising challenges to adaptation must include a wide range of socioeconomic and even some (non-climatic) biophysical factors. Second, careful consideration must be given to differences in these factors across scales, as well as cross-scale interactions. Third, any representation of the concept will require both quantitative and qualitative elements. The scenario framework offers the opportunity for the SSPs and full scenarios to be of greater value than has been the case in past exercises to both Integrated Assessment Modeling (IAM) and Impacts,Adaptation, and Vulnerability (IAV) researchers, but this will require a renegotiation of the traditional, primarily unidirectional relationship between the two communities

The Vulnverability Cube: A Multi-Dimensional Framework for Assessing Relative Vulnerability

The diversity and abundance of information available for vulnerability assessments can present a challenge to decision-makers. Here we propose a framework to aggregate and present socioeconomic and environmental data in a visual vulnerability assessment that will help prioritize management options for communities vulnerable to environmental change. Socioeconomic and environmental data are aggregated into distinct categorical indices across three dimensions and arranged in a cube, so that individual communities can be plotted in a three-dimensional space to assess the type and relative magnitude of the communities’ vulnerabilities based on their position in the cube. We present an example assessment using a subset of the USEPA National Estuary Program (NEP) estuaries: coastal communities vulnerable to the effects of environmental change on ecosystem health and water quality. Using three categorical indices created from a pool of publicly available data (socioeconomic index, land use index, estuary condition index), the estuaries were ranked based on their normalized averaged scores and then plotted along the three axes to form a vulnerability cube. The position of each community within the three-dimensional space communicates both the types of vulnerability endemic to each estuary and allows for the clustering of estuaries with like-vulnerabilities to be classified into typologies. The typologies highlight specific vulnerability descriptions that may be helpful in creating specific management strategies. The data used to create the categorical indices are flexible depending on the goals of the decision makers, as different data should be chosen based on availability or importance to the system. Therefore, the analysis can be tailored to specific types of communities, allowing a data rich process to inform decision-making