Australian seagrass habitats: condition and threats. ACEAS Grand 2014 Kathryn McMahon and Kieryn Kilminster

1. Seagrass habitats: conditions and
threats
Data identification and acquisition
Kathryn McMahon, Kieryn Kilminster, James Udy, Michelle
Waycott, Gary Kendrick, Chris Roelfsema, Robert
Canto, Mitchell Lyons, Vanessa Lucier, Lynda Radke, Peter
Scanes

2. GOAL: Nation-wide, spatially
explicit, risk assessment for seagrass
habitat
WHY?
• Seagrass habitat – significant ecosystem services
• Globally declining at a significant rate
• Australia – high diversity, biggest meadows, large
losses, some species being considered as
threatened ecological community
• Risk assessment to identify areas to focus
management

3. GOAL: Nation-wide, spatially
explicit, risk assessment for seagrass
habitat
✔
✔
✔
✔

4. GOAL: Nation-wide, spatially
explicit, risk assessment for seagrass
habitat
STEPS:
Develop a habitat map
Identify risks (current & with future climate
change)
Acquire suitable risk layers
Assign risk
Run the risk assessment
✔
✔
✔
✔

5. Approach

6. Approach

7. Approach

8. Habitat map
Potential seagrass presence
Base-map, 10 x 10 km, presence/absence, compiled
from multiple sources
(Mount and Bricher 2008, Estuarine, Coastal and Marine Habitat Mapping
Project – Dept Climate Change, National Land & Water Resources Audit )
+
Maps sourced (SA Government; SA, Dept Water &
UWA, WA; UQ, Qld).
+
Expert opinion (ACEAS working group)

9. NSW
10 km grid cells
Seagrass present

10. Habitat map
Identification challenges
• lack of metadata
• limited open access data exchange
• consistency of approach i.e. assumptions with
combining, error propagation
Acquisition challenges
• permissions
• aware more data available but considerable time to
source & assess quality

11. Risk layers
• Identify relevant pressures / threats
• Identify data-sets that reflect these
risks or relevant proxies of these risks
• Assign categories of risk
None, Low, Moderate, High

12. Relevant
Pressures
Define bioregions
Identify habitats in each bioregion

13. Pressures & risk layers

14. Pressures & risk layers
✖
✖
✖
✖
✖

15. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖

16. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖
Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.gov.au

17. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖
Port locations, Australian Customs & Border Protection Service
http://data.gov.au/dataset/australian-ports
Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.gov.au

18. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖
Vessel track history, Australian Maritime Safety Authority
www.operations.amsa.gov.au/Spatial/Dataservices/CraftTrackingRequest
Port locations, Australian Customs & Border Protection Service
http://data.gov.au/dataset/australian-ports
Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.gov.au

19. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖
Oil & Gas production wells, www.geoscience.gov.au
Does not include pipelines, data held by each state, restricted
Vessel track history, Australian Maritime Safety Authority
www.operations.amsa.gov.au/Spatial/Dataservices/CraftTrackingRequest
Port locations, Australian Customs & Border Protection Service
http://data.gov.au/dataset/australian-ports
Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.gov.au

20. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖
CSIRO Modelling, http://www.csiro.au/ozclim/
http://www.cmar.csiro.au/
2070 predictions based on IPCC A1F1 scenario
Oil & Gas production wells, www.geoscience.gov.au
Does not include pipelines, data held by each state, restricted
Vessel track history, Australian Maritime Safety Authority
www.operations.amsa.gov.au/Spatial/Dataservices/CraftTrackingRequest
Port locations, Australian Customs & Border Protection Service
http://data.gov.au/dataset/australian-ports
Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.gov.au

21. Pressures & risk layers
✔
✔
✔
✔
✔
✔
✔
✔
✔
✔
✖
✖
✖
✖
✖
CSIRO Modelling, http://www.csiro.au/ozclim/
http://www.cmar.csiro.au/
2070 predictions based on IPCC A1F1 scenario
Oil & Gas production wells, www.geoscience.gov.au
Does not include pipelines, data held by each state, restricted
Vessel track history, Australian Maritime Safety Authority
www.operations.amsa.gov.au/Spatial/Dataservices/CraftTrackingRequest
Port locations, Australian Customs & Border Protection Service
http://data.gov.au/dataset/australian-ports
Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.gov.au
No clear variable, combination of nutrient & sediment loads &
resuspension

22. Current risk assignment: Ports
RISK
High Cells containing ports
Moderate Cells adjacent to high
Low Cells adjacent to moderate
No All other cells

23. Current risk assignment: Ports
RISK
High Cells containing ports
Moderate Cells adjacent to high
Low Cells adjacent to moderate
No All other cells

24. Current risk assignment: Ports
RISK
High Cells containing ports
Moderate Cells adjacent to high
Low Cells adjacent to moderate
No All other cells

25. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)

26. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)
Catchment condition

27. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)
Catchment condition

28. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)
Catchment condition

29. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)
Catchment condition
Greater risk if stream flow more
constant
(sqrt(mean daily flow/monthly variance)
Chronic sediment and nutrients

30. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)
Catchment condition
Greater risk if stream flow more
constant
(sqrt(mean daily flow/monthly variance)
Chronic sediment and nutrients
Greater risk if stream flow extremely patchy
(i.e. floods)
(# days where streamflow >1SD above mean)
Acute sediment and nutrients

31. Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used NLWRA of estuarine condition combined with flow data (BOM)
Catchment condition
Greater risk if stream flow more
constant
(sqrt(mean daily flow/monthly variance)
Chronic sediment and nutrients
Greater risk if stream flow extremely patchy
(i.e. floods)
(# days where streamflow >1SD above mean)
Acute sediment and nutrients
Spatial extent of impact
Spatial extent of impact greater if annual stream flow (GL) greater

32. Chronic or acute risk determined by nearest
stream flow data
BOM streamflow data (supplemented in WA)
Stations mapped to nearest bit of coastline
Confidence measure related to distance of estuary
mouth to coastline-adjusted streamflow station
(shown as crosshairs below).
Chronic risk :

33. Risk categories
10 x 10 km grid cells

34. Summary
Challenges for data identification & acquisition
• lack of metadata – corporate knowledge important
• limited open access data exchange
• consistency of approach i.e. assumptions with combining, error
propagation
• identifying relevant data-sets – reflect pressure/risk, iterative
• appropriate spatial & temporal scale (i.e. 10 x 10 km, Australian-
wide)
• strategies for dealing with data-gaps
• assumptions for each layer
• identifying risk not predicting response

35. Thank you
Acknowledgements
ACEAS working group
Robert Canto (UQ-GIS manipulation)
Data custodians for data sharing
Authors Affiliation
Kathryn McMahon-Edith Cowan University
Kieryn Kilminster-Department of Water, WA
James Udy-Healthy Waterways, Qld
Michelle Waycott-University of Adelaide, DEWNR
Gary Kendrick-University of WA
Chris Roelfsema-University of Queensland
Robert Canto-University of Queensland
Mitchell Lyons-University of NSW
Vanessa Lucier – University of Tasmania
Lynda Radke – Geosciences Australia
Peter Scanes – Office of Environment and Heritage, NSW