2 Article structure Introduction Some real world examples Synthesis Some theorySome real world examplesSelf contained ecosystemsProcess analysisThe random worldThe spatial mosaicSynthesisSome definitionsResilience versus stabilityMeasurementApplication
3 2 views of the world 1 empirical evidence State Process Engineering viewEcosystem viewPredictable perturbationsUnpredictable perturbations(one) equilibriumMany possible statesAnalytical studyNot amenable to analytical studyInheritance from physical scienceNo inheritance from physical science1 empirical evidencePopulations are highly variable and therefore more often far from equilibrium rather than near it
4 Some theoryModels inspired from ‘isolated systems’ in physics.They lack 4 elements that are found in real systems:Multiplicity of componentsComplex (non-linear) processesSpatial and temporal processesStochasticity (randomness)Do conclusions derived from such models remain valid if these elements are considered?Predator-prey modelsStable equilibriumStable limit cycle…
5 Self-contained ecosystems Search for real system that are simpleLakes: isolated, simple controls: nutrients & fishingEmpirical 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 pointsEcological 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 fluctuationsSupport for multiple stable states & important role of variable (random) external forcing
8 The spatial mosaicSpatial processes, i.e. dispersion and limits to dispersion modify prey-predator dynamicsSpatially 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 environmentRelatively stable environmentEcosystems have evolve to cope with variable environmentEcosystems have evolve to cope with stable environmentHigh resilienceLow resilienceHigh variabilityLow 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 dynamicsStability-complexity debate (May, 1972)
11 MeasurementStability: standard mathematical tools, close to equilibriumResilience: probability of extinction, far from equilibriumSuggestion 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.”