CRITERION E: QUANTITATIVE RISK ANALYSIS IUCN Red List of Ecosystems Training www.iucnrle.org @redlisteco IUCN Red List of Ecosystems

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

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

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 50-100 years Proportion of model runs in which the ecosystem collapses

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

Process for developing models

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

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

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

Further reading … Models RLE case studies Guidelines

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