1. Holling 1973

2. 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

3. 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

4. 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 …

5. 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.

6. 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.

7. 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

8. 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)

9. 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.”

10. 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)

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

12. 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.”