Quantifying resilience of humans and other animals

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.

Keywords: aging; health; livestock; resilience; tipping points.

Fig. 1.

The mechanisms that regulate systemic resilience in humans and animals. The resilience of the whole depends on the resilience of subsystems that regulate vital parameters such as temperature, glucose level, and mood. Those in turn depend, among other things, upon the functional reserves (overcapacity) of organs that inevitably wear down with aging, depending on stressors, lifestyle, and genetic make-up.

Fig. 2.

DIORs discussed in the main text. (Left) A resilient system. (Right) A frail system with low resilience. (A and B) Resilience is represented as the basin of attraction around a healthy state. Slopes correspond to rates of change. When resilience is low (B vs. A), slopes around the equilibrium are less steep, implying slower return rates to equilibrium. (C and D) Simulated recovery rates upon a small perturbation. (E and F) Simulated dynamics in a system subject to a stochastic regime of perturbations illustrating that fluctuations are larger and slower in a frail system (F vs. E), as reflected in higher variance and higher temporal autocorrelation. (G and H) Interactive dynamics of subsystems (e.g., mood, posture, cognition) are predicted to become more correlated in a network with low systemic resilience (H vs. G).

Fig. 3.

Schematic representation of possible effects of different factors on systemic resilience. While effects of some factors are only detrimental (A) or positive (C), the effect of other factors on resilience peaks at intermediate levels (B). Effects of aging on resilience (D) are moderated by the mechanisms summarized in Fig. 1.