ZOOM Workshop Outcomes November 2017

Transcription

1 ZOOM Workshop Outcomes November 2017 Attendees: Jason Everett, Anthony Richardson, Mark Baird, Jenny Skerratt, Ryan Heneghan, Julia Blanchard, Rowan Trebilco, Hector Lozano-Montes, Joanna Strzelecki, Cathy Bulman, Trevor Hutton, Heidi Pethybridge, Claire Davies, Felicity McEnnulty, Ruth Eriksen, Wayne Rochester, Kerrie Swadling, Patti Virtue, Beth Fulton, Richard Matear, Andrew Lenton, Ryan Downie, Ana Lara-Lopez What have we learnt? 1. Time series data are valuable for model assessment better than one-off cruises 2. No gridded plankton products are currently available (except for remotely-sensed ones), but are valuable for regional, national and global models 3. Gridded products are generated from statistical models the overall mean and monthly or seasonal climatology are more robust than an inter-annual product 4. There is a push for including greater plankton complexity and realism in model components. There is greater scope and desire to increasing zooplankton complexity in ecosystem (EwE and Atlantis) models than in BGC models 5. What is the correlation scale (footprint) of plankton data at NRS sites (i.e., is it indicative of 1 km2, 10 km2, 100 km2 or 1000 km2?). We can use CPR data to look at the correlation scale of zooplankton, or assess it from an NPZD model 6. There has been a consistent message that diets are important for modelling trophic linkages. 7. There is a strong interest in IMOS providing zooplankton data in a range of units, including Nitrogen, Carbon, Wet Weight, Dry Weight. We would also provide a list of equations so others can convert their own data 8. All plankton data have strengths and weaknesses and this needs to be acknowledged and available to those using the data. 9. Size spectrum models with plankton functional groups suggest that the relative proportions of different groups are set predominantly by size 10. Likely key areas for future model development: a) SE Tasmania (aquaculture), b) GBR (ongoing need), c) Kimberley/Pilbara (oil and gas development), d) SE Shark & Scale fishery region 11. Developing national products now is timely because we are close to having BGC ereef-type models around all of Australia; BGC Wombat models are global 12. The CARS approach of using harmonics for physical and chemical data is appropriate, but less appropriate for plankton data with more complex seasonal cycles phytoplankton in Australia 13. Historical data on phytoplankton and zooplankton is available through the AODN (some health warnings) The vision is that this data becomes more accessible to users through the above processes but extra time and effort will be required with this data.

2 Next steps ZOOM Meetings 14. Meet up for a ZOOM discussion in February in Hobart for those who are in town for the IMOS APM (26-28 th February). Date: Tuesday 27 th February, 1.15pm, Hotel Grand Chancellor, Hobart 15. Plan for the next ZOOM meeting to be in Hobart at the end of The theme will be Confronting Models with Data. Updated IMOS data products 16. Mapped Zooplankton Data: The observationalists are developing maps of zooplankton biomass, abundance, size-spectra and species distribution based upon statistical models. These maps need to be made available: a. in netcdf format; b. in a range of formats, including 1) global, 2) national and 3) marine bioregions (south-west, north-west, north, temperate east and south east); c. as an overall mean and as monthly climatologies; d. the raw data-points plotted on the statistical maps so users can see where the data is from; e. with value-added data-points including variables such as Omnivore:Carnivore ratio etc f. with measures of uncertainty/confidence: independent data sets (e.g., zooplankton biomass maps from nets, CPR, and LOPC), uncertainty estimates (standard errors), withhold data to use for prediction, include data distribution g. with depth-based estimates of zooplankton biomass (probably not individual species) 17. Zooplankton Samples: Value add to all the existing zooplankton species dataproducts (NRS, CPR, Historical data etc) with additional data fields of: h. total (meso) zooplankton, i. zooplankton functional groups (total copepods, omnivores/carnivorous copepods, larvaceans, salps, chaeognaths, calcifying zooplankton, jellyfish) j. Community Temperature Index of samples k. Species diversity l. An index of mean size of zooplankton m. An index of invertebrate predators n. Ratio of meroplankton:holoplankton o. Ratio of carnivore:herbivore p. Calculate correlation scales of CPR samples (and use to infer correlation scales of NRS zooplankton samples. 18. Phytoplankton Samples: Value add to all the existing phytoplankton species dataproducts (NRS, Historical data etc) with additional data fields of:

3 q. phytoplankton functional groups (diatoms, dinoflagellates, diatom:dinoflagellate ratios, Trichodesmium, Noctiluca), r. An index of mean size of phytoplankton s. Ratio of autotrophic:heterotrophic plankton t. Biomass of bacteria u. Explore the ability to deliver value-added data on Trichodesmium such as filament length, puffs and tufts v. Indication of HAB species and their potential impact 19. Size-structured zooplankton: We can value-add to the NRS samples by processing them through the LOPC and getting size-spectra data w. Produce size-spectra to initialise and assess size-spectra models (e.g. Mizer) x. Bin size-spectra data into size-classes corresponding to BGC (e.g. ereefs) and Ecosystem Models (e.g. Atlantis and EwE). y. Estimate of microzooplankton from size spectrum slope 20. Southern Ocean Zooplankton: We are aiming to improve the representation of the Southern Ocean within our database z. Talk with AAD data managers to get more zooplankton data in the Southern Ocean to improve global and regional maps of zooplankton functional groups aa. Work with Southern Ocean researchers (e.g., Kerrie ;-) to uncover more data to improve global and regional maps of zooplankton functional groups Enhancing data delivery (Partnering with AODN) 21. Work with AODN to better deliver plankton data including: a. the full CSIRO database in database format allowing AODN to query and produce products given codes, better visualisations etc. b. visualisation products such as on NOAA COPEPOD ( c. value added data fields (as above) d. the ability to pair physical variables (Temperature, Salinity etc with zooplankton data in in one file as required by the user 22. There is a discrepancy of what data is required for different models so the eventual goal is to provide an R-package (Ecologists) and a MATLAB Toolbox (Modellers) for users to manipulate the data in their own ways and create different datasets for different models (modellers) or questions (ecologists). This should be done in conjunction with an implementation of a standards-based way of querying biological data, 23. Produce regularly updated data products (e.g., plankton time series data for the NRS) named appropriately ( latest ) so modellers can scrape it automatically 24. To help data users, write a paragraph or two on the AODN for each NRS on key plankton species, their life history, and seasonality (See Attachment 1) 25. Need for improved meta-data such as strengths and weaknesses of biomass (e.g., underestimate small and large zooplankton, but overestimate the detritus) e.g., ereefs paper 26. The conversions used for making data products need to be well documented. A

4 standardised list of recommended/best-practice C and N conversions for plankton data and publish this as metadata on the AODN and in the NRS Methods Paper 27. Continue consultation over plankton products during the ongoing development of the Research Data Cloud (Jason) and the National Mooring Network and value-added products discussion (Claire) 28. Workshop output: publish a suite of data products in a multi-authored data paper? (Jason and Claire to co-ordinate) e. Package the data products f. Discuss limitations of the various methods et al g. Take up by models Updated Model Products 29. Produce Phytoplankton and Zooplankton reanalysis product over the past 20 years 30. Use zooplankton biomass map to set the background quadratic mortality for large zooplankton in the BGC models (e.g. ereefs) 31. Assess whether to put zooplankton parameters in ECOBASE (like FishBase for fish) 32. Consider changes to the C:N in the models. It is currently parameterised as the Redfield Ratio (about 8) but crustacean zooplankton C:N is ~4.5. How do the zooplankton in models remove more C? 33. Consider including Trichodesmium in tropical ecosystem models 34. Consider including larvaceans in tropical ecosystem models 35. The models have generally not been confronted by data for the lower trophic levels Additional Funding 36. In order to expedite the delivery of some of the priority data products (Attachment 2), we will approach IMOS for a small amount of extra funding. This funding request is outlined in Attachment 3. This funding will be requested for 2 particular projects: i. To enable the production of size-spectra information from the NRS samples using the Laser Optical Plankton Counter at the University of New South Wales. ii. To provide some travel assistance to Jason and Claire to get together with Anthony at CSIRO, Brisbane in order to develop the statistical products required for mapping the zooplankton data.

5 Attachment 1: A summary card for each NRS (and possibly each CPR route) to highlight the key facts and observations from the data. The card must also summarise the physical data collected (e.g. temperature, nutrients or chlorophyll a) in order to place the data in context. Zooplankton at the Maria Island NRS Maria Island ( S E) is one of seven National Reference Stations (NRS) that comprise the Australian Integrated Marine Observing System s (IMOS). The Maria Island NRS is sited at an historical mooring operating since 1944 where ocean temperatures are rising faster than anywhere else in Australia due to the increasing incursion of the East Australian Current. Water temperature at Maria Island has warmed by 1.5 C since 1944, and is a consequence of global warming and its influence on the intensification of the warm, poleward-flowing East Australian Current (EAC). Of Maria Island, there is a decline of copepod species with preferences for colder water and an increase in those that prefer warmer water. The dominant species are. Changes in zooplankton ranges in response to warming are consistent with what is happening in other systems including the North Sea, Northeast Atlantic and Northwest Atlantic.

6 Attachment 2: Data priorities for models where 1 (Green) = Important, 2 (Yellow) = Would like to have, and 3 (Orange) = Not really important due to model configuration. ** Indicates data development priorities based on scoring. Global BGC (i.e. WOMBAT) Regional BGC (i.e. ereefs) Size- Spectra EcoPath w/ EcoSim Atlantis PHYTOPLANKTON Phytoplankton functional groups (e.g. diatoms, dino) ** An index of mean size of phytoplankton Ratio of autotrophic:heterotrophic plankton 1 2 N/A Biomass of bacteria ** 1 N/A N/A Further info on Trichodesmium (e.g. filament length, puffs & tufts) 3 2 N/A Indication of HAB species 3 3 N/A ZOOPLANKTON SPECIES ID Total zooplankton biomass/abundance ** Zooplankton functional groups ** Community Temperature Index of samples Species diversity 1 3 N/A An index of mean size of zooplankton ** An index of invertebrate predators ** Ratio of meroplankton:holoplankton 2 N/A Ratio of carnivore:herbivore zooplankton ** ZOOPLANKTON SIZE SPECTRA Produce size-spectra from NRS Samples ** Bin size-spectra data into size-bins corresponding to models 3 1 N/A Estimate of microzooplankton from size spectrum slope 3 3 N/A Mean Score

7 Attachment 3: Additional funding request to support the delivery of data products to IMOS We are requesting some additional support in order to expedite the delivery of the priority data products (Attachment 2) required by the modelling community. This funding will be requested for 2 particular components: 1. Travel assistance The development of the value-added and statistical data products (e.g. biomass maps or ratios of carnivores to herbivores) will require some additional workshops between Jason Everett, Claire Davies and Anthony Richardson. We are requesting travel assistance for Jason and Claire to travel to Brisbane 1 week in 2018 in order to meet face-to-face and expedite the product development. Requested Support: 1x return flight (HBA-BNE) for Claire = ~$600 1x return flight (SYD-BNE) for Jason = ~$400 5x nights accommodation in Brisbane for Claire = ~$1000 5x nights accommodation in Brisbane for Jason = ~$1000 In-kind Support: UNSW and CSIRO will provide salary support for the week. TOTAL = $ Size-Spectra Analysis. Simon Jennings and Julia Blanchard, at ACCOMO 2016, made a strong case for the delivery of sizebased data as part of the IMOS data-stream. The modelling community at ZOOM 2017 also saw significant value in size-based plankton measurements. We propose to process the historical NRS samples in bulk to quantify the size-distribution as a value-added product for modellers to use. We will then process the future samples in 6-month batches to provide an ongoing data-stream. This can be achieved in the new Plankton Lab at UNSW. Requested Support (2018): Once-off Sample Processing: 350 remaining samples x $30 per sample = $12,000 (~200 samples processed by UNSW in 2015). Shipping: $500 Requested Support (2019 onwards): Ongoing Sample Processing: 7 stations x 12 samples x $30 per sample = $2520 per year Shipping: $200 In-kind Support: UNSW will cover the ongoing maintenance costs of the Laser Optical Plankton Counter. UNSW will provide lab space and computing resources. Jason Everett and Iain Suthers will provide the ~200 NRS Samples (MAI, PHB, NSI) which were processed as part of a 2015 Honours Project (valued at ~$6000) Jason Everett will provide his MATLAB software which processes the raw data files. One-off 2018 TOTAL = $ 12,500 Ongoing (2019 onwards) TOTAL = $ 2,720