Seagrass habitats: conditions and
threats
Data identification and acquisition
Kathryn McMahon, Kieryn Kilminster, James Ud...
GOAL: Nation-wide, spatially
explicit, risk assessment for seagrass
habitat
WHY?
• Seagrass habitat – significant ecosyste...
GOAL: Nation-wide, spatially
explicit, risk assessment for seagrass
habitat
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GOAL: Nation-wide, spatially
explicit, risk assessment for seagrass
habitat
STEPS:
Develop a habitat map
Identify risks (c...
Approach
Approach
Approach
Habitat map
Potential seagrass presence
Base-map, 10 x 10 km, presence/absence, compiled
from multiple sources
(Mount and ...
NSW
10 km grid cells
Seagrass present
Habitat map
Identification challenges
• lack of metadata
• limited open access data exchange
• consistency of approach i.e...
Risk layers
• Identify relevant pressures / threats
• Identify data-sets that reflect these
risks or relevant proxies of t...
Relevant
Pressures
Define bioregions
Identify habitats in each bioregion
Pressures & risk layers
Pressures & risk layers
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Pressures & risk layers
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Pressures & risk layers
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Industrial land-use, ABARES BRS – 1km pixel AVHRR,
http://adl.brs.go...
Pressures & risk layers
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Port locations, Australian Customs & Border Protection Service
http:...
Pressures & risk layers
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Vessel track history, Australian Maritime Safety Authority
www.opera...
Pressures & risk layers
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Oil & Gas production wells, www.geoscience.gov.au
Does not include p...
Pressures & risk layers
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CSIRO Modelling, http://www.csiro.au/ozclim/
http://www.cmar.csiro.a...
Pressures & risk layers
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CSIRO Modelling, http://www.csiro.au/ozclim/
http://www.cmar.csiro.a...
Current risk assignment: Ports
RISK
High Cells containing ports
Moderate Cells adjacent to high
Low Cells adjacent to mode...
Current risk assignment: Ports
RISK
High Cells containing ports
Moderate Cells adjacent to high
Low Cells adjacent to mode...
Current risk assignment: Ports
RISK
High Cells containing ports
Moderate Cells adjacent to high
Low Cells adjacent to mode...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Sediment and nutrient delivery
•No Australia-wide data set (SEDNET had no modeled loads for >60% catchments)
•Instead used...
Chronic or acute risk determined by nearest
stream flow data
BOM streamflow data (supplemented in WA)
Stations mapped to n...
Risk categories
10 x 10 km grid cells
Summary
Challenges for data identification & acquisition
• lack of metadata – corporate knowledge important
• limited open...
Thank you
Acknowledgements
ACEAS working group
Robert Canto (UQ-GIS manipulation)
Data custodians for data sharing
Authors...
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Australian seagrass habitats. Kathryn McMahon, ACEAS Grand 2014

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

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Australian seagrass habitats. Kathryn McMahon, ACEAS Grand 2014

  1. 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. 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. 3. GOAL: Nation-wide, spatially explicit, risk assessment for seagrass habitat ✔ ✔ ✔ ✔
  4. 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. 5. Approach
  6. 6. Approach
  7. 7. Approach
  8. 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. 9. NSW 10 km grid cells Seagrass present
  10. 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. 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. 12. Relevant Pressures Define bioregions Identify habitats in each bioregion
  13. 13. Pressures & risk layers
  14. 14. Pressures & risk layers ✖ ✖ ✖ ✖ ✖
  15. 15. Pressures & risk layers ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✖ ✖ ✖ ✖ ✖
  16. 16. Pressures & risk layers ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✖ ✖ ✖ ✖ ✖ Industrial land-use, ABARES BRS – 1km pixel AVHRR, http://adl.brs.gov.au
  17. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 33. Risk categories 10 x 10 km grid cells
  34. 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. 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

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