Benthic primary productivity in the KimberleyDR RENAE HOVEY - UWA
AcknowledgmentsWA State Government and WAMSI partners for supporting this research.
Links with sub-projects:
2.2.3 – Benthic community production (Ryan Lowe)
2.2.4 – Seagrass (Gary Kendrick)
1.3.1 – Reef geomorphology (Mick O’Leary)
1.2.5 – Dugong (Peter Bayliss)
Raw data: Gary Kendrick, Ryan Lowe, Renee Gruber, Jim Falter, Mat Vanderklift, Doug Bearham, James McLaughlin, Lucie Chovrelat, (CSIRO), Andrea Zavala Perez, Bonnie Laverock (UWA), Christin Säwström (ECU), and the Bardi JawiRangers
Analysis: Leonardo Ruiz Montoya
AIMS
Develop a GIS for spatially and temporally explicit baseline information on habitats and processes.
Develop benthic habitat distribution maps
Map benthic primary production◦ Effects of season and grazing
Mapping MethodsBayesian Belief Network – GIS framework
◦ Framework underpins project
◦ Quantitative and qualitative data
◦ Captures both scientific and traditional ecological knowledge
◦ Interdisciplinary tool – policymakers, planners, managers
◦ Effective in storing data
◦ Visual representation of relationships
Spatial modelling (traditional method)
◦ Develop statistical models that explain the relationship between benthic primary producers and environmental conditions (= predictive model)
◦ Upscale and extend across areas that we not visited
Mapping coastal seagrasses
GBR: Bayesian belief network for coastal seagrass habitats
(seagrass: present and absent) (Grech and Coles 2010)
Collating spatially explicit data
Bathymetry Light climate Sediment
Also Benthic Shear StressREI – Fetch (wind velocity, direction and % frequency)Tides (range)
Related to seagrass presence –> predictor variables
Benthic habitats – presence/absence data
Collating spatially explicit data
Seagrass Distribution Map
Probably seagrass
Possible seagrass
Substrate veg - seagrass &/or algae
Probably coral/reef
Probably sand
1.2.5 DugongsPeter Bayliss and Chris Wilcox
Depth driven model based on seagrass depth distributions from literature
*Benthic shear and REI models are almost ready to add to the BNN
- Final seagrass distribution map
Validation using animal tracking data
Possibly more data:• 2.2.4 – turtle tagging• Chris Wilcox, Richard Campbell,
Peter Bayliss
Historical data:
Mapping biomass production
Geo-referenced photo quadrats
Mapping biomass production
Validation
Mapping biomass production
Effects of grazing
Biomass Production
Map of primary production on intertidal reef
Points of interest: Not a strong seasonal signalInter-annual variability needs further investigation*Sargassum (algae) estimates need converting to biomass
Data from projects 2.2.3 and 2.2.4
&
GPP
Mapping biomass production- seagrass
(Max)0.0050.005 – 0.010.014
Biomass production per day
Per shoot Per m2 (shoot density)
Mapping biomass production- seagrass
Biomass production per day
Annual DW biomass (t)
Annual DW biomass (t) Consumed
Enhalus 19.7 3.2
Thalassia 48.5 3.2
Mix seagrass 152.9 17.9
Thalassia mix 0.146 0.009
Total 221.2 24.3
(Conservative values)
Can covert to carbon production using literature derived values
Next step: spatially extend productivity estimates
Kordi & O’Leary 2016-> ReefKIM
Next step: spatially extend productivity
1.3.1 Geomorphology (Mick O’Leary)
*Seagrass and Algae a single category
Management implications
•Seagrasses in the Kimberley deal with extreme physical conditions, yet maintain high productivity
•This productivity supports a diverse animal community
•Growth rates and productivity more valuable than traditional biomass or cover measurements
•Even more important to understand the spatial extent and arrangement of benthic productivity • appropriate monitoring designs and spatial
management
•These maps provide a baseline – a starting point or base layer to which more information can be added to improve accuracy and understanding of processes driving productivity in the region• Community outreach and citizen science will
be critical
Questions?
THANK YOU…
Halophila Thalassia Enhalus Cymodocea
WAMSI 2.2.4WAMSI 2.2.4