Skill (or lack thereof) of data-model fusion techniques to provide an early warning signal for an approaching tipping point

A closed form for Jacobian reconstruction from time series and its application as an early warning signal in network dynamics

The Jacobian matrix of a dynamical system describes its response to perturbations. Conversely, one can estimate the Jacobian matrix by carefully monitoring how the system responds to environmental noise. We present a closed-form analytical solution for the calculation of a system’s Jacobian from a time series. Being able to access the Jacobian enables a broad range of mathematical analyses by which deeper insights into the system can be gained. Here we consider in particular the computation of the leading Jacobian eigenvalue as an early warning signal for critical transitions. To illustrate this approach, we apply it to ecological meta-foodweb models, which are strongly nonlinear dynamical multi-layer networks. Our analysis shows that accurate results can be obtained, although the data demand of the method is still high.

Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists

Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.

The Disaster Risk, Global Change, and Sustainability Nexus

Until the 1970s, disaster risk was perceived as a direct consequence of natural hazards. Gradually, disaster risk has come to be understood as a compound event, which lies at the intersection of hazards, exposure, and vulnerability of the exposed elements. After decades of research and lessons learned from mega-disasters, social scientists have introduced the social dimension of disaster risk, and the prevailing understanding is that disasters are also a human construct. Now, due to climate and global environmental changes, even the natural component of hazards is being altered by anthropogenic activities, changing hazard susceptibility, coverage, frequency, and severity. This review retraces the brief history and evolution of the global understanding of disaster risk as a compound event, in parallel with research on global environmental change. It highlights the main milestones in this area, and shows that there are tight connections between trends of disaster risk and global change. This paper aims to demonstrate the need to better consider the role of global environmental change in disaster risk assessment. In 2015, three major new agreements were reached to improve global environmental governance: the new Sendai Framework (2015–2030), the post-2015 development agenda with the 17 Sustainable Development Goals (SDGs), and the Climate COP21 in Paris. These all include a clear focus on disaster risk reduction; however, several aspects of disaster risk linked with global environmental changes are still not clearly addressed by the main stakeholders (governments, insurers, or agencies). As the complexity of risk unfolds, more actors are getting together; the need for a holistic approach for disaster risk reduction has become clear, and is closely connected with achieving sustainable development.