Resilience Science

About Resilience

windmill in sandhills

Resilience is a feature of complex systems like ecosystems, human systems, or coupled social-ecological systems such as an agriculture production system. It captures the ability of a system to stay organized around similar structures, processes, and functions, also known as staying in a regime. Resilience is enabled by adaptive capacity, learning, system memory, and other system features that provide both a diversity and redundancy of response options when confronting disturbances. A focus on the whole system is called general resilience and is concerned with a system’s ability to stay in the same regime, or through human agency to deliberately transform to a more desired regime. A grassland’s resilience to woody shrub invasion is an example of general resilience. Alternatively, scientists, practitioners, and stakeholders can focus on specific resilience, which is the resilience of a particular system feature, like a crop, to a particular type of disturbance, like drought.

It is easier to manage for specific resilience, such as the optimization of a particular feature like primary productivity or stability in production, but this often has negative impacts on general system resilience and can push a system closer to a critical tipping point. Management goals that seek to hold one component of a system in a fixed state tend to ignore the implications on other system components that may not be immediately obvious because they operate at different spatial and temporal scales. One important lesson of resilience science has been the recognition that even highly managed systems like agricultural production systems are also complex adaptive systems, which means they are nonstationary (subject to constant change) and have many interacting components with feedbacks across scales. We have learned the hard way that attempting to hold one system component in a fixed state can cause long-term degradation and undesired negative impacts. Forest fires in the American West, for example, were historically suppressed in order to prevent the loss of valuable timber product to fire. Managing forests to optimize this specific system feature—reducing biomass loss to fire—has had catastrophic consequences. Fires are crucial to the long-term health of forests, and their suppression reduced forest resilience and unwittingly set the stage for the collapse of entire forests. The American West is increasingly experiencing catastrophic fires that burn more severely as a result of many decades of preventing smaller-scale, less intense fires from burning.

Why Resilience Matters

a crowd of people crossing the street before the pandemic versus a few people crossing a street wearing masks

1

Regime shifts are often unexpected and abrupt, and the outcomes are largely unknown; they tend to be highly disruptive for humans.

Consider the impacts of transitioning from a pre-pandemic regime to a pandemic-defined regime.​

healthy, colorful coral compared to bleached coral

2

Regime shifts are often, but not always, negative from a human perspective, as the movement from one regime to another can result in a reduction in goods or services that were valuable to humans.

Consider coral-dominated reef systems, versus algae-dominated reefs.​

dustbowl comparison photos taken from same location during dustbowl and many years after the dustbowl

3

Regime shifts can be irreversible. Even when they are not, the amount of resources and effort required to shift them back is highly disproportionate to the amount of disturbance that triggered the initial shift.

Consider the US Dust Bowl event of the 1930’s, which took decades and a great deal of resources to even partially reverse some of the damage.

Resilience Models

Below are competing models representing the resilience response of systems over time and to perturbations. In A, B, and C, resilience is shown in terms of hypothetical system trajectory and is shown on the Y-axis and time on the X-axis. In D, E, and F resilience is considered from a complex adaptive systems point of view.

A. Stationary system without perturbation.
straight line on diagram

System trajectory does not change or vary.

B. Stationary single equilibrium system with perturbation.
straight line that dips down and returns back to same

System trajectory drops with perturbation but bounces back with time. Here, the only metric is the time required to bounce back to equilibrium. Use of this model could lead to the erroneous conclusion that all systems will recover given sufficient time

C. Stationary single equilibrium system with an alternative configuration of trajectory.
A stationary single equilibrium system with an alternative configuration of trajectory.

This model, as with Grafton et al. Figure 1, fails to capture the potential for systemic changes between regimes that lead to completely different trajectories following perturbation.

D. Ball and cup diagram of alternative states (cups) in a non-stationary non-equilibrium system without perturbation.
This shows the state of the system (circle), which emphasizes its complex adaptive nature, rather than a specific system structure.

This shows the state of the system (circle), which emphasizes its complex adaptive nature, rather than a specific system structure.

E. Ball and cup diagram of alternative states in a non-stationary non-equilibrium system with perturbation.
In this case, perturbation does not exceed the resilience of the system. System trajectories are expected to vary but are maintained within a single basin of attraction (i.e., it has adaptive capacity conferred by ecological stability measures).

In this case, perturbation does not exceed the resilience of the system. System trajectories are expected to vary but are maintained within a single basin of attraction (i.e., it has adaptive capacity conferred by ecological stability measures).

F. Ball and cup diagram of alternative states in a non-stationary non-equilibrium system with perturbation that exceeds the resilience of the system.
Ball and cup diagram of alternative states in a non-stationary non-equilibrium system with perturbation that exceeds the resilience of the system.

The system is moved into an alternative basin of attraction, with completely different system-level properties (performance, function, structures, processes, and feedbacks).

Grand Challenges

Balancing general and specific resilience is a grand challenge with regards to agriculture and systems of people and agricultural food production. It is clearly desirable to maintain resilience in individual system components, such as farms and farmers and food production, but doing so without harming the resilience of agricultural food systems or of the surrounding landscapes through negative agriculture impacts requires tradeoffs. It is crucially important to understand where the limits of resilience lie—where efforts to maximize production in one arena may reduce resilience in the larger food system or surrounding landscape and risk collapse and a regime shift. This requires a better understanding of thresholds in both the biophysical and human components of agricultural food systems, a core NIARR focus.