Quantifying resilience of humans and other animals

• Type of Material: Article
• Authors: Marten Scheffer, Wageningen University; J. Elizabeth Bolhuis, Wageningen University; Denny Borsboom, University of Amsterdam; Timothy Buchman, Emory University; Sanne M. W. Gijzel, Wageningen University; Dave Goulson, University of Sussex; Jan E. Kammenga, Wageningen University; Bas Kemp, Wageningen University; Ingrid A. van de Leemput, Wageningen University; Simon Levin, Princeton University; Carmel Mary Martin, Monash Health Community; Rene J. Melis, Radboud University Nijmegen; Egbert H. van Nes, Wageningen University; L. Michael Romero, Tufts University; Marcel G. M. Olde Rikkert, Radboud University Nijmegen
• Language: English
• Date: 2018-11-20
• Publisher: National Academy of Sciences
• Publication Version: Final Published Version
• Copyright Statement: © 2018 National Academy of Sciences. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1073/pnas.1810630115
• Title of Journal or Parent Work: Proceedings of the National Academy of Sciences
• ISSN: 0027-8424
• Volume: 115
• Issue: 47
• Start Page: 11883
• End Page: 11890
• Keywords: EARLY-WARNING SIGNALS; OLDER-ADULTS; Science & Technology - Other Topics; Science & Technology; CATASTROPHIC SHIFTS; livestock; RANDOMIZED CONTROLLED-TRIAL; CRITICAL TRANSITIONS; health; POPULATION COLLAPSE; Multidisciplinary Sciences; tipping points; LIFE-SPAN; SAFE OPERATING SPACE; TIPPING POINT; CRITICAL SLOWING-DOWN; resilience; aging
• Research Categories: Psychology, Behavioral

Abstract

All life requires the capacity to recover from challenges that are as inevitable as they are unpredictable. Understanding this resilience is essential for managing the health of humans and their livestock. It has long been difficult to quantify resilience directly, forcing practitioners to rely on indirect static indicators of health. However, measurements from wearable electronics and other sources now allow us to analyze the dynamics of physiology and behavior with unsurpassed resolution. The resulting flood of data coincides with the emergence of novel analytical tools for estimating resilience from the pattern of microrecoveries observed in natural time series. Such dynamic indicators of resilience may be used to monitor the risk of systemic failure across systems ranging from organs to entire organisms. These tools invite a fundamental rethinking of our approach to the adaptive management of health and resilience.