australian seagrass habitats. kathryn mcmahon, aceas grand 2014

Seagrass habitats: conditions and threatsData 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

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


✔

✔

✔

✔


STEPS:Develop a habitat mapIdentify risks (current & with future climate change)Acquire suitable risk layersAssign riskRun the risk assessment

✔

✔

✔

✔

Approach

Logo

Habitat mapPotential 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)

Logo

NSW

10 km grid cells

Seagrass present

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

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

Logo

Relevant Pressures

Define bioregionsIdentify habitats in each bioregion

Pressures & risk layers

Pressures & risk layers

✖✖

✖

✖✖

Pressures & risk layers✔✔✔✔✔✔

✔

✔✔✔

✖✖

✖

✖✖


✔

✔✔✔

✖✖

✖

✖✖

Industrial land-use, ABARES BRS – 1km pixel AVHRR, http://adl.brs.gov.au

http://adl.brs.gov.au/



✔

✔✔✔

✖✖

✖

✖✖

Port locations, Australian Customs & Border Protection Servicehttp://data.gov.au/dataset/australian-ports





✔

✔✔✔

✖✖

✖

✖✖

Vessel track history, Australian Maritime Safety Authoritywww.operations.amsa.gov.au/Spatial/Dataservices/CraftTrackingRequest






✔

✔✔✔

✖✖

✖

✖✖

Oil & Gas production wells, www.geoscience.gov.auDoes not include pipelines, data held by each state, restricted




http://www.geoscience.gov.au/




✔

✔✔✔

✖✖

✖

✖✖

CSIRO Modelling, http://www.csiro.au/ozclim/http://www.cmar.csiro.au/ 2070 predictions based on IPCC A1F1 scenario





http://www.cmar.csiro.au/






✔

✔✔✔

✖✖

✖

✖✖

CSIRO Modelling, http://www.csiro.au/ozclim/http://www.cmar.csiro.au/ 2070 predictions based on IPCC A1F1 scenario





No clear variable, combination of nutrient & sediment loads &resuspension






Current risk assignment: Ports

RISKHigh Cells containing ports Moderate Cells adjacent to highLow Cells adjacent to moderate No All other cells

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


Catchment condition

Greater risk if stream flow more constant

(sqrt(mean daily flow/monthly variance)

Chronic sediment and nutrients


Catchment condition




Greater risk if stream flow extremely patchy (i.e. floods)

(# days where streamflow >1SD above mean)

Acute sediment and nutrients


Catchment condition




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

Chronic or acute risk determined by nearest stream flow data

BOM streamflow data (supplemented in WA)Stations mapped to nearest bit of coastlineConfidence measure related to distance of estuary mouth to coastline-adjusted streamflow station (shown as crosshairs below).

Chronic risk :

Risk categories

10 x 10 km grid cells

SummaryChallenges 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

Logo

Thank youAcknowledgements

ACEAS working groupRobert Canto (UQ-GIS manipulation)Data custodians for data sharing

Authors AffiliationKathryn McMahon-Edith Cowan UniversityKieryn Kilminster-Department of Water, WAJames Udy-Healthy Waterways, QldMichelle Waycott-University of Adelaide, DEWNRGary Kendrick-University of WAChris Roelfsema-University of QueenslandRobert Canto-University of QueenslandMitchell Lyons-University of NSWVanessa Lucier – University of TasmaniaLynda Radke – Geosciences AustraliaPeter Scanes – Office of Environment and Heritage, NSW

Logo