Holling 1973

Article structure Introduction Some real world examples Synthesis Some theory Some real world examples Self contained ecosystems Process analysis The random world The spatial mosaic Synthesis Some definitions Resilience versus stability Measurement Application

2 views of the world 1 empirical evidence State Process Engineering view Ecosystem view Predictable perturbations Unpredictable perturbations (one) equilibrium Many possible states Analytical study Not amenable to analytical study Inheritance from physical science No inheritance from physical science 1 empirical evidence Populations are highly variable and therefore more often far from equilibrium rather than near it

Some theory Models inspired from ‘isolated systems’ in physics. They lack 4 elements that are found in real systems: Multiplicity of components Complex (non-linear) processes Spatial and temporal processes Stochasticity (randomness) Do conclusions derived from such models remain valid if these elements are considered? Predator-prey models Stable equilibrium Stable limit cycle …

Self-contained ecosystems Search for real system that are simple Lakes: isolated, simple controls: nutrients & fishing Empirical evidence for multiple stable states and rapid transitions ≠ equilibrium point or limit cycle.

Process analysis Ricker demographic models + realism based on empirical evidence of predation processes -> complex models with multiple stable points Ecological conclusions from models with simplified processes are different from those from more realistic models and…evidence for multiple stable states ≠ equilibrium point or limit cycle.

The random world Examples with budworms and pink salmon Transitions between different regimes triggered by random climate fluctuations Support for multiple stable states & important role of variable (random) external forcing

The spatial mosaic Spatial processes, i.e. dispersion and limits to dispersion modify prey-predator dynamics Spatially structured populations can persist while fluctuating greatly at local scale. Ecological conclusions from models with spatial processes are different from those from more realistic models and… Stability≠persistence (~resilience)

Some definitions “I propose that the behavior of ecological systems could well be defined by two distinct properties: resilience and stability. Resilience determines the persistence of relationships within a system and is a measure of the ability of these systems to absorb changes of state variables, driving variables, and parameters, and still persist. In this definition resilience is the property of the system and persistence or probability of extinction is the result. Stability, on the other hand, is the ability of a system to return to an equilibrium state after a temporary disturbance… In this definition stability is the property of the system and the degree of fluctuation around specific states the result.”

Resilience versus stability Highly dynamic environment Relatively stable environment Ecosystems have evolve to cope with variable environment Ecosystems have evolve to cope with stable environment High resilience Low resilience High variability Low variability “…some Arctic ecosystems thought of as fragile may be highly resilient, although unstable.” Needs to be considered for systems with multiple equilibriums and for transitory dynamics Stability-complexity debate (May, 1972)

Measurement Stability: standard mathematical tools, close to equilibrium Resilience: probability of extinction, far from equilibrium Suggestion to use negative binomial distribution

Application Implication for management: “A management approach based on resilience … would emphasize the need to keep options open, the need to view events in a regional rather than a local context, and the need to emphasize heterogeneity. Following from this would be not the presumption of sufficient knowledge, but the recognition of our ignorance; not the assumption that future events are expected, but that they will be unexpected. The resilience framework can accommodate this shift of perspective, for it does not require a precise capacity to predict the future, but only a qualitative capacity to devise systems that can absorb and accommodate future events in \whatever unexpected form they may take.”