Project 3.2.1: Deriving ecologically-relevant load targets to meet desired ... document for managing seagrass (Collier,
Light, turbidity, seagrass and water quality targets Dr Catherine Collier & Dr Alex Carter
NESP projects NESP no-regrets projects 2015 Project 3.1: Seagrass mapping synthesis: a resource for coastal management in the Great Barrier Reef World Heritage Area (Carter, McKenna, Rasheed, McKenzie, Coles) Project 3.3: Light thresholds for seagrasses of the GBR: a synthesis and guiding document for managing seagrass (Collier, Chartrand, Honchin, Fletcher, Rasheed) Project 3.4: Developing and refining biological indicators for seagrass condition assessments in an integrated monitoring program (Collier, Langlois, Zemoi, Martin, McKenzie)
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Project 3.5: Assessment of key dugong and turtle resources in the northern Torres Strait (Carter & Rasheed) 2017-2019
Project 3.2.1: Deriving ecologically-relevant load targets to meet desired ecosystem condition for the Great Barrier Reef: a case study for seagrass meadows in the Burdekin region
3.2.1 Project team Alphabetical Dr Catherine Collier, JCU Dr Matthew Adams, UQ Dr Mark Baird, CSIRO Dr Jon Brodie, JCU ARC Dr Alex Carter, JCU Dr Rob Coles, JCU Dr Katherine Martin, GBRMPA Mr Len McKenzie, JCU Dr Kate O’Brien, UQ Dr Michael Rasheed, JCU Dr Megan Saunders, UQ
RIMReP Dr James Udy, SUSA Dr Emma Lawrence, CSIRO Dr Bill Venables, CSIRO
Desired state 2008
2011
2013
1. Attributes of a healthy ecosystem: traditional values Informal interviews (Gudjuda, Mandubarra, Yuku Baja Muliku) to discuss traditional use of seagrass meadows More to do: would like to formalise/document the traditional use by Gudjuda and Wulgurukaba. Provide feedback on the work we are doing into his project.
2. Indicators Indicators – current and potential Seagrass status • Spatial extent • Above-ground biomass/percent cover • Below-ground biomass, biomass ratio • Species composition
Resistance • Carbohydrates – NESP 3.4 • Genetic diversity Recovery capacity/resilience • Reproductive effort • Seed bank
Seagrass condition • Molecular markers e.g. metabolomics and transcriptomics • Physiological indicators e.g. tissue nutrients, pigments and energetic status
Desired state indicators • Seagrass area* • Biomass* • Species composition
ERTs indicators
• Seagrass area/habitat suitability • Biomass • Species composition
*Dependent on data availability for GBR-wide assessment
Desired state Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification – Cleveland Bay
PoT funded annual surveys Davies, J. N., and M. A. Rasheed. 2016. Port of Townsville annual seagrass monitoring. 16/03, TropWATER, James Cook University, Cairns.
DRAFT Habitat classification – Cleveland Bay Individual species and community composition Using Regression tree analysis: • 10 years of seagrass biomass • Sediment category, relative intertidal exposure, depth category, water type ITEM_REL_m< 1.5
depth_cate=Dpsb,Shls
depth_cate=Intr
Sediment2=Reef,Rock,Rbbl,Sand
ITEM_REL_m>=1.5
Sediment2=CrsS,Reef,Rock,Rbbl,Sand
Sediment2=Mud
Sediment2=CrsS,Mud WT2< 19.5
WT2>=19.5
7.48e+03 : n=2526 2.16e+03 : n=631
8.21e+03 : n=2047
1.26e+04 : n=1221 1.62e+03 : n=387
5.89e+03 : n=592
Error : 0.869 CV Error : 0.872 SE : 0.0231
Hu/Hs/Cs Hu/Cs Cs/Hu/Zm high biomass Zm/Hu Zm/Ho low biomass Zm high biomass
Emma Lawrence, CSIRO
Defining desired state thresholds
We are likely to develop: • Desired state of ‘meadows’ within Cleveland Bay • Desired state of Cleveland Bay ‘region’ • GBR: Desired state of habitat type within NRM (there will be gaps) • GBR: Desired state of NRM (e.g. area of habitats and species within NRM) • Annual desired state (for monitoring etc) • Long-term (e.g. 5 or 10 year) desired state (for Outlook etc), which accounts for dynamics
Still finalising the methodological approach given the expanded scope (i.e. GBR-wide)
Light thresholds, acute
NESP Project 3.3 Final Report
• Reviewed all literature on light thresholds • Summarised in table form • Recommended light thresholds and management thresholds
Light thresholds for seagrasses of the GBRWHA: a synthesis and guiding document Including knowledge gaps and future priorities Catherine Collier, Katie Chartrand, Carol Honchin, Adam Fletcher and Michael Rasheed
• Acute light thresholds (e.g. dredging)
Collier, C. J., K. Chatrand, C. Honchin, A. Fletcher, and M. Rasheed. 2016. Light thresholds for seagrasses of the GBR: a synthesis and guiding document. Reef and Rainforest Research Centre, Cairns.
Evaluate sediment loads to meet desired state
Reef Plan ERT’s Acute light experiments (seagrass shading)
Acute light thresholds
Dredge management plans Updated Ecologicallyrelevant sediment load targets (ERTs)
This project Seagrass habitat Suitability/abundance
Sediment load Targets Light thresholds to meet desired state
ERTs 2018-2019
1. Use RECOM, eReefs to predict seagrass area in Cleveland Bay under different load scenarios (Matthew Adams, UQ & Mark Baird, CSIRO)
2. Habitat suitability using statistical models (Megan Saunders, UQ) • Quantify the relationship: • between sediment loads & seagrass area • between predicted and observed seagrass area • Predict seagrass habitat suitability based on loads • Relate to desired state
Major achievements to date 2017 • Seagrass habitat classification for the GBR and for Cleveland Bay • eReefs updates: inclusion of ‘deepwater’ seagrass, pre-Industrial and 2013 loads for boundary layer in RECOM, sheer stress mortality, nutrient uptake by leaves • Desired state analysis underway • Expanded light monitoring program in Cleveland Bay Engagement • Informal Traditional owner engagement • A number of workshops in Cairns, Townsville and Brisbane with external experts and the RIMReP working group. • Engagement with RIMReP, GBRMPA, DEE • Email updates to end-users
Activities to come
2018 – 2019 Research activities • Complete desired state analysis for Cleveland Bay and the GBR • Validate eReefs benthic light outputs with measured in situ light • Develop habitat suitability layers for Cleveland Bay • Identify ERTs that meet Seagrass Desired State (eReefs and statistical models) • Compare to existing targets and provide advice as appropriate. Engagement • Feedback to end-users (GBRMPA, OGBR, EHP, DEE, RIMReP) • Traditional owners engagement • Work on Linkages with other projects: Project 2.1.5 (Lewis et al), Project 2.3.1 (Fabricius et al), Project 2.1.9 (Jones et al), Project 3.1.7 (Brooks et al)
Defining thresholds in a dynamic habitat TSS loads from the Burdekin River, water year
TSS loads (kt)
15,000
10,000
5,000
16 20
15 20
14 20
13 20
12 20
11 20
10 20
09 20
08 20
20
07
0
Davies, J. N., and M. A. Rasheed. 2016. Port of Townsville annual seagrass monitoring. 16/03, TropWATER, James Cook University, Cairns.
Defining desired state thresholds
• Dynamic habitat • Can we expect maximum condition to improve (as per. LTSP)? • Consider providing recommendations about frequency of very good, or very poor years Setting desired state for the GBR • Data limited • We may base it on: • representation of habitat types • species composition expected in habitat types • Range of expected biomass/cover in each habitat (with some estimate of confidence).
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
Habitat classification 1. • • •
Classify by depth Intertidal Subtidal – shallow (10m)
2. Classify by water bodies • Offshore • Reef • Coastal • Estuarine 3. • •
Classify by pressures Plume water Cumulative risk
4. Classify by NRM region
5. Classify by local site conditions (Cleveland Bay) • Sediment • Relative exposure
