1. CASE STUDIES – RED LIST OF ECOSYSTEMS
IUCN Red List of Ecosystems Training
@redlisteco
IUCN Red List of Ecosystems

2. Assessing ecosystem change: Criteria
Threatening
processes
Risk of loss
of characteristic
native biota
Ecosystem distribution
Ecosystem function
D Altered biotic
B Restricted
distribution
A Declining
C Degradation
of abiotic
environment
E Quantitative
risk analysis
Risk model for ecosystems
Threats to defining features (distribution, biota & function)
Multiple mechanisms
4 symptoms of decline
= 4 criteria
Plus one overarching criterion: probability of collapse
Spatial processes
Rates of spatial decline (rapid habitat loss ~ high risk)
land use change: mores, tropical forests
Vulnerability to catastrophes (spatially explicit threat)
Easter Island forests
Functional processes
Degradation of physical environment (suitability/heterogeneity - niche theory)
Aral Sea
Disruption of biotic processes (decline of functional complementarity, facilitation)
kelp forests, arid shrublands
Each criterion has sub-criteria that represent different measures of risk, e.g., different timeframes or distribution metrics

3. https://iucnrle.org/assessments/

4. https://iucnrle.org/assessments/

5. Example: Coorong Lagoon, Australia
Keith et al., 2013 / Contribution by Rebecca Lester et Peter Fairweather

6. Example: Coorong Lagoon, Australia
Keith et al., 2013.

7. Example: Coorong Lagoon, Australia
Keith et al., 2013.

8. Example: Coorong Lagoon, Australia
State & transition framework
8 states (healthy & unhealthy)
Transition to ‘unhealthy’ states across the region  collapsed
Interactions between water levels, flow and salinity

9. Example: Coorong Lagoon, Australia
Criterion A: Reduction in geographic distribution
A1: Past (past 50 y)
No declines in the past 50 years

10. Example: Coorong Lagoon, Australia
Criterion A: Reduction in geographic distribution
A1: Past (past 50 y)
No declines in the past 50 years
A2: Future (next 50 y)
Extent very unlikely to decline in the future. Low barrage flows would be compensated by seawater flowing
in through the Murray Mouth

11. Example: Coorong Lagoon, Australia
Criterion A: Reduction in geographic distribution
A1: Past (past 50 y)
No declines in the past 50 years
A2: Future (next 50 y)
Extent very unlikely to decline in the future. Low barrage flows would be compensated by seawater flowing
in through the Murray Mouth
A3: Historic (since 1750)
No estimates of historic extent available

12. Example: Coorong Lagoon, Australia
Criterion B: Restricted geographic distribution
B1: Extent of occurrence - EOO
121 km long, average width of 1.9 km
EOO = 205 km2 (< 2000 km2)
- Evidence of increasing salinity since 1996, with extremely high salinities observed since 2006.
- Further increases in salinity forecast under median and dry future climate projections.
- One location.

13. Example: Coorong Lagoon, Australia
Criterion B: Restricted geographic distribution
B2: Area of occupancy - AOO
AOO = 17
- Evidence of increasing salinity since 1996, with extremely high salinities observed since 2006.
- Further increases in salinity forecast under median and dry future climate projections.
- One location.

14. Example: Coorong Lagoon, Australia
Criterion B: Restricted geographic distribution
B3: Small number of locations
The Coorong occupies a single location and is prone to the effects of both human activities (i.e. water extraction and changes to flow regimes upstream) and stochastic events (e.g. drought) simultaneously.

15. Example: Coorong Lagoon, Australia
Criterion B: Restricted geographic distribution
B1a,b,c: Extent of occurrence
B2a,b,c: Area of occupancy
B3: Small number of locations

16. Example: Coorong Lagoon, Australia
Criterion C: Environmental degradation
Two main components in the abiotic environment reduce habitat quality for characteristic biota of the Coorong ecosystem: small volumes of fresh water delivered to the Coorong via the River Murray, and the extreme salinity of the South Lagoon
Variables (threshold)
Average volumes of annual barrage flows (1000 μS cm-1)
Average annual salinity (117 g L-1)
Maximum annual salinity (100 g L-1)

17. Example: Coorong Lagoon, Australia
Criterion C: Environmental degradation
Variables (threshold)
Average volumes of annual barrage flows (1000 μS cm-1)
Average annual salinity (117 g L-1)
Maximum annual salinity (100 g L-1)
Barrage flows decline affect the whole Coorong (extent of impact is always 100%).

18. Example: Coorong Lagoon, Australia
Criterion C: Environmental degradation
C1: Past (past 50 y)
Flows below threshold in 36% of years and this affects 100%
of ecosystem extent.

19. Example: Coorong Lagoon, Australia
Criterion C: Environmental degradation
C1: Past (past 50 y)
Flows below threshold in 36% of years and this affects 100%
of ecosystem extent.
C2: Future (next 50 y)
Different climate scenarios used.
Median Future: Flows below threshold in 45% of years and this affects 100%
Dry Future: Flows below threshold in 86% of years and this affects 100%

20. Example: Coorong Lagoon, Australia
Criterion C: Environmental degradation
C1: Past (past 50 y)
Flows below threshold in 36% of years and this affects 100%
of ecosystem extent.
C2: Future (next 50 y)
Flows below threshold in 86% of years and this affects 100%
C3: Historic (since 1750)
No estimates of historic change available.

21. Example: Coorong Lagoon, Australia
Criterion D: Disruption of biotic processes
Ruppia megacarpa and R. tuberosa are a critical component in the structure and functioning of the Coorong: provision of food and habitat for birds, fish and macroinvertebrates + modify physical and biogeochemical processes in the lagoon.
Variables (threshold)
Spatial coverage of Ruppia spp. (abundance = 0)
estimates of historic change available.

22. Example: Coorong Lagoon, Australia
Criterion D: Disruption of biotic processes
D1: Past (past 50 y)
Ruppia megacarpa once dominated the Murray Mouth and North Lagoon. Not observed in the Coorong since the mid-1990s.
Ruppia tuberosa has traditionally dominated in the South Lagoon.
1999, present in 33 to 91% samples from four sites
2005, absent from two sites
2008, absent from the South Lagoon
Extent and severity of the decline in both Ruppia species > 80%

23. Example: Coorong Lagoon, Australia
Criterion D: Disruption of biotic processes
D1: Past (past 50 y)
Extent and severity of the decline in both Ruppia species > 80%
D2: Future (next 50 y)
No simulations for future declines in R. tuberosa exist, while R. megacarpa is already extinct from the system, and no evidence of ecolonisation has been observed

24. Example: Coorong Lagoon, Australia
Criterion D: Disruption of biotic processes
D1: Past (past 50 y)
Extent and severity of the decline in both Ruppia species > 80%
D2: Future (next 50 y)
No simulations for future declines in R. tuberosa exist, while R. megacarpa is already extinct from the system, and no evidence of ecolonisation has been observed
D3: Historic (since 1750)
No estimate of long-term changes biotic interactions exists.

25. Example: Coorong Lagoon, Australia
Criterion E: Quantitative risk analysis
State & transition framework
8 states (healthy & unhealthy)
Transition to ‘unhealthy’ states across the region  collapsed
Interactions between water levels, flow and salinity

26. Example: Coorong Lagoon, Australia
Criterion E: Quantitative risk analysis
Modelled hundreds of future scenarios of water extraction & climate change
Probability of collapse:
30-100% in 50 years
( )

27. Example: Coorong Lagoon, Australia
Criterion A B C D E
Overall
Subcriterion 1 LC CR VU
CR(EN-CR)
CR B1a,b,c,C2,E
Subcriterion 2 EN DD
Subcriterion 3

28. Example: Mountain ash forest
Dominated by Eucalyptus regnans, the world’s tallest flowering plant.
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

29. Example: Mountain ash forest

30. Example: Mountain ash forest
Criterion A: Reduction in geographic distribution
A1: Past (past 50 y)
3 Victorian Government spatial layers:
(i) the Statewide Forest Resource Inventory dataset;
(ii) the Logging History dataset;
(iii) the Ecological Vegetation Classes dataset.
- Current distribution estimated at ha
- 96.4% on public land (stable land tenure)
- Assumed no change in distribution since 1964

31. Example: Mountain ash forest
Criterion A: Reduction in geographic distribution
A1: Past (past 50 y)
Current distribution estimated at ha. No change since 1964
A2: Future (next 50 y)
96.4% on public land (stable land tenure). No change predicted for 2064.

32. Example: Mountain ash forest
Criterion A: Reduction in geographic distribution
A1: Past (past 50 y)
Current distribution estimated at ha. No change since 1964
A2: Future (next 50 y)
96.4% on public land (stable land tenure). No change predicted for 2064
A3: Historic (since 1750)
Models suggest a decrease from ha in 1750 to ha in 2014

33. Example: Mountain ash forest
Criterion B: Restricted geographic distribution
B1c: Extent of occurrence
EOO= 11,000 km2; ≤2 locations

34. Example: Mountain ash forest
Criterion B: Restricted geographic distribution
B2: Area of occupancy
96 occupied cells, 23 ≤ 1km2
AOO = 76

35. Example: Mountain ash forest
Criterion B: Restricted geographic distribution
B1c: Extent of occurrence
EOO= 11,000 km2; ≤2 locations
B2: Area of occupancy
AOO = 76
B3: Number of locations
≤2 locations; prone to collapse within short period

36. Example: Mountain ash forest
Criterion C: Environmental degradation
Collapse: 100% of area where ecosystem occurs no longer bioclimatically suitable
C1: Past (past 50 y)
Insufficient temperature and precipitation data since 1964

37. Example: Mountain ash forest
Criterion C: Environmental degradation
Collapse: 100% of area where ecosystem occurs no longer bioclimatically suitable
C1: Past (past 50 y)
Insufficient temperature and precipitation data since 1964
C2: Future (next 50 y)
Used IPCC emission scenarios to calculate the predicted extent loss
45% reduction (extent), 100% relative severity

38. Example: Mountain ash forest
Criterion C: Environmental degradation
Collapse: 100% of area where ecosystem occurs no longer bioclimatically suitable
C1: Past (past 50 y)
Insufficient temperature and precipitation data since 1964
C2: Future (next 50 y)
Used IPCC emission scenarios to calculate the predicted extent loss
45% reduction (extent), 100% relative severity
C3: Historic (since 1750)

39. Example: Mountain ash forest
Criterion D: Disruption of biotic processes
Collapse: less than 1% old growth forest remains
Used long-term field survey data, fire-history records and mapped old-growth forest. Investigated 39 scenarios based on varying harvesting and fire regimes
D1: Past (past 50 y)
Estimated a change in the number of hollow-bearing trees (using old-growth as a surrogate) ≥80% relative severity (averaged across 100% extent)

40. Example: Mountain ash forest
Criterion D: Disruption of biotic processes
Collapse: less than 1% old growth forest remains
Used long-term field survey data, fire-history records and mapped old-growth forest. Investigated 39 scenarios based on varying harvesting and fire regimes
D1: Past (past 50 y)
Estimated a change in the number of hollow-bearing trees (using old-growth as a surrogate) ≥80% relative severity (averaged across 100% extent)
D2: Future (next 50 y)
Projected decline of ≥78% with ≥100% relative severity (averaged across
100% extent of the ecosystem

41. Example: Mountain ash forest
Criterion D: Disruption of biotic processes
Collapse: less than 1% old growth forest remains
Used long-term field survey data, fire-history records and mapped old-growth forest. Investigated 39 scenarios based on varying harvesting and fire regimes
D1: Past (past 50 y)
Estimated a change in the number of hollow-bearing trees (using old-growth as a surrogate) ≥80% relative severity (averaged across 100% extent)
D2: Future (next 50 y)
Projected decline of ≥78% with ≥100% relative severity (averaged across
100% extent of the ecosystem
D3: Historic (since 1750)
Decline with ≥99% relative severity over 100% extent

42. Example: Mountain ash forest
Ecosystem collapse:
when Hollow Bearing Trees HBT<1/ha
<1 year
Oldgrowth forest
>1 HBT/ha
Regrowth forest
<1 HBT/ha
>120 years x
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

43. 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

44. Example: Mountain ash forest
Criterion A B C D E
Overall
Subcriterion 1 LC EN DD CR
CR D1,D2,D3,E
Subcriterion 2 VU
Subcriterion 3
A single metric may not provide a detailed picture of the status of a given ecosystem!
Ecosystems with little change in extent may be experiencing severe functional changes.

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