Zooplankton
Ocean
Observations &
Modelling
Task Team
Jason Everett, Mark Baird, Anthony Richardson
Co-convenors
with
Iain Suthers, Ryan Heneghan, Wayne Rochester, Hector Lozano-Montes, Joanna Strzelecki, Barbara Robson,
Paul van Ruth, Kerrie Swadling, Pearse Buchanan, Ana Lara Lopez, Julia Blanchard, Richard Matear, Claire
Davies, Jenny Skerratt, Ryan Downie, Leonardo Laiolo, Chris Griffiths, Felicity Mcennulty et al
ZOOM Objectives
• Improved communication and collaboration between zooplankton
observational and modelling communities
• Review literature for use of zooplankton observations in models
• Develop and make available zooplankton fields and datasets that
are directly applicable in ecosystem models
• Initiate observation and model developments that bring model
outputs closer to observations
• Develop methods to incorporate zooplankton obs. into models.
• Investigate the synergies / agreement between data streams.
• Outline recommended methods for using IMOS observations for
assessing models.
Why study zooplankton?
• Key food source for higher trophic levels
• Trophic level between phytoplankton and fish
• Interface between bottom-up and top-down control
• First trophic level that introduces substantial behavior – DVM
• Zooplankton are not usually the focus of models, or data is scare, so less
effort goes into parameterisation/assessment:
– Important trophic regulator of the abundance of small pelagic fish (Lassalle et al., 2013)
– Variation in zooplankton productivity drives yields of upper trophic level in large marine
ecosystems (Friedland et al., 2012)
– Changing the representation of zooplankton in biogeochemical models substantially
changes the fate of primary production (Mitra 2009)
• ZOOM is addressing 2 main gaps in zooplankton research
Gap 1: The Zooplankton Gap!
• Relative to our knowledge of phytoplankton and fisheries, we
have little understanding of the zooplankton that link them.
• A legacy of the evolution of observational technology and
oceanography
• Physical variables, phytoplankton and fisheries have been
easier to collect
• Exacerbated by the difficulty of
measuring zooplankton and their
phylogenetic complexity.
Gap 2: The Observation-Modelling Gap
• Little zooplankton data used in models (n=153)
– 20 % for zooplankton (Arhonditsis and Brett 2004)
– 95 % for phytoplankton
Observations sparse in time and space
Work in different units (wet weight vs nitrogen)
Publish in separate journals
Modellers often aim for simplification and
observationalists often highlight diversity
• Zooplankton model parameters are nearly always
poorly constrained due to the limited data
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CPR
Opportunities for IMOS
• Bring together observational and
modelling research communities
– Different languages and backgrounds
NRS
• IMOS zooplankton observations:
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Discrete yet complementary
Across multiple scales
Diverse methods
Quantify uncertainty between methods
• Zooplankton Models:
– Improve uptake of zooplankton
observations into models
– Quantify sources of uncertainty
in models.
LOPC+ZooScan – Value addng to NRS
1st ZOOM Workshop
• 9 modellers and 9 observationalists in one room for 2
days in Feb 2016
Better integrating zooplankton observations and
models
1. Quantify how zooplankton represented in models
2. Review sampling platforms available
3. Case Study: Integrating models and data using
‘Observation Models’
Lessons from Optical Modelling:
• Models are best combined with observations when
they share common quantities.
• Model assessment of chlorophyll a using satellitederived Chlorophyll a
•
Go back and understand what you are
measuring/modelling and how to assess it.
•
Model Chl. a ≠ Satellite Reflectance
(Phytoplankton+CDOM+Detritus)
• Model Reflectance ~ Satellite Reflectance
Observation Models: Size-Based
Observations
Models
Observation Models: Bioacoustics
Generate modelled echograms from
ecosystems models
Observation Models: Sampling the Model
3 case studies: How ZOOM is proposing to use
existing IMOS data streams to better assess the
zooplankton component of our models
Sample the model at the
same temporal and spatial
resolution as the observations
The z-score allows direct
comparison of characteristics
of intrinsically different
quantities.
ZOOM Initiatives
Encouraging uptake of zooplankton into models
Identified a range of models where there are available zooplankton data
ZOOM members interested in assessing zooplankton in their models
Model (Implementation)
# Zoo groups
Collaborator
Atlantis (SE, Coral Sea, NSW,
Gladstone, GBR, SE-Tas, NW, SW,
GAB)
4
Fulton, Hutton, Dichmont, Lozano-Montes
e-Reefs
2
Baird, Skerratt
1-4
ECOPATH (GoC, GBR, Coastal Qld,
ETBF, NSWS, Bass Strait, Phillip Is.,
PPB, Tas, Tas reefs, S Tas seamounts,
Jurien Bay, NWS, Ningaloo, Darwin,
Kimberley, GAB, Gulf St Vincent)
Bulman, Bustamante, Gribble, Gehrke,
Griffiths, Lozano-Montes, Watson,
Metcalfe, Julie, Forrest
NPZD (global, Spencer Gulf)
1-2
Matear, Doubell
CAEDYM (Swan Estuary)
5
Robson
ZOOM Initiatives
Creating zooplankton biomass fields
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n = 10,000
Creating a gridded CARS-type product
Model ready
Initial conditions for models
Seasonal assessment of model output
Northeast U.S.A
ZOOM Objectives
• Improved communication and collaboration between zooplankton
observational and modelling communities In Progress
• Review literature use of zooplankton observations in models
In Progress
• Develop and make available zooplankton fields and datasets that
In Progress
are directly applicable in ecosystem models
• Initiate observation and model developments that bring model
outputs closer to observations In Progress
• Develop methods to incorporate zooplankton obs. into models.
• Investigate the synergies / agreement between data streams.
• Outline recommended methods for using IMOS observations for
2016/17
assessing models.
2016/17
2016/17
Where to from here?
Keep the discussion going
Presentation of progress at ACOMO
Leverage ACOMO to run a short ZOOM meeting
Another workshop in early 2017
“Facilitating zooplankton data uptake into
Australian numerical models”
• Address our remaining objectives
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– Demonstrate uptake of zooplankton into models
– Provide a framework for making this easier