1. CRITERION E: QUANTITATIVE RISK ANALYSIS
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2. Purpose Criterion E: Quantitative Risk Analysis
Threatening processes
Risk of loss
of characteristic
native biota
A Declining
distribution
C Degradation
of abiotic
environment
D Altered biotic
processes
Ecosystem distribution
Ecosystem function
B Restricted
Purpose
Enable synthesis across all major threats and mechanisms of collapse
Formal integration of interactions between spatial & functional processes
An overarching framework for the RLE criteria
Anchors the risk model

3. Criterion E: Quantitative Risk Analysis
E. Quantitative analysis that estimates the probability of ecosystem collapse to be: CR
≥ 50% within 50 years EN
≥ 20% within 50 years VU
≥ 10% within 100 years

4. How to quantify risk of collapse
Use simulation models that:
Incorporate key features & processes
Key species, structural features
Major processes that sustain ecosystem identity
Major threats and their effects on 1 & 2
Incorporate stochasticity in key processes & threats
e.g. effects of variable climate, probability of fires, etc.
Simulate plausible future scenarios
Many replicate simulations
Produce quantitative estimates of the risk of ecosystem collapse over years
Proportion of model runs in which the ecosystem collapses

5. How to estimate probability of collapse
Some candidate model types:
Spatial models (e.g. cellular automata)
State-and-transition models
Mass-balance models
Bifurcation plots
Network theory
Graph theory
Dynamic species distribution and population models
General ecosystem models (e.g. the Madingley model)
“Ecosystem viability analysis” (EVA)
Model selection will depend on:
Ecosystem type
Data availability
How to represent uncertainty
How to incorporate stochasticity

6. Process for developing models

7. Example: Mountain Ash Forest
Tall eucalypt forest ecosystem
Key features
Large trees (>50m), dense understory
Diverse tree-dependent fauna
Temperature and precipitation determine distribution (‘wet and cool’)
Key processes
Recurring wildland fires
Timber harvest
Burns et al. 2015

8. Example: Mountain Ash Forest
Ecosystem collapse:
When Hollow Bearing Trees HBT <1/ha x
Oldgrowth forest
>1 HBT/ha
Regrowth forest
<1 HBT/ha
< 1 year
> 120 years
Model - calculates future (50yrs) HBT density as a function of:
Initial HBT density
Probability of fire
Probability of logging
Projected climate suitability
Burns et al. 2015
39 modelled scenarios of logging and fire under future climate
Logging: none/regrowth only/unrestricted
Fire: none/small/medium/large extent
10,000 simulations

9. Example: Mountain Ash Forest
Results:
All scenarios: ≥92% chance of reaching a collapsed state (<1 HBT/hectare) in 50 years
Scenarios with unrestricted logging, medium and large fires produced most severe effects

10. Further reading …
Models
RLE case studies
Guidelines

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