1. Towards community-based approaches to estimating NPP & NCP from remotely-sensed optical properties Rick A. Reynolds Scripps Institution of Oceanography University of California San Diego rreynolds@ucsd.edu

2. NPP models  Optical measurements are a key component to estimating NPP & NCP at multiple temporal and spatial scales  General form of NPP models Net Production = [Biomass * Light absorption * Quantum yield] – (Respiration) Can be coupled with additional models to estimate NCP  All phytoplankton “Biomass” is considered equal!  Differences in community composition, photophysiology, and size structure are ignored ICESOCC | SIO | 22 Sep 2014 2

3. The ocean is not homogenous ICESOCC | SIO | 22 Sep 2014 3 57 biogeochemical provinces identified by Longhurst et al. (1995) 81 provinces classified from satellite data (Oliver and Irwin, 2008)

4. Can optics be used to identify communities? ICESOCC | SIO | 22 Sep 2014 4  Significant advances in discriminating oceanic communities from optical measurements  Several approaches  abundance based  single species blooms  dominant functional groups  size structure  Potential for mapping communities and improving NPP & NCP model estimates available at www.ioccg.org

5. Example community-based approach to NPP ICESOCC | SIO | 22 Sep 2014 5 Uitz et al. 2006; IOCCG 2014  Chla distributions partitioned into 3 distinct phytoplankton size classes based on total abundance  Micro (diatoms and dinoflagellates)  Nano (prymnesiophytes)  Pico (prokaryotes and picoeukaryotes)

6.  Extended to NPP  Class-specific photophysiology  NPP partitioned among size classes  SO results seem reasonable  Micro 30-50% of NPP in spring-summer  Nano dominate seasonal blooms  But  No SO data in parameterization  Valid with changing ocean? ICESOCC | SIO | 22 Sep 2014 6 Example community-based approach to NPP Uitz et al. 2010 TOTAL Micro Nano Pico Dec-Feb climatology of NPP for 1998-2007

7. Optical-based classifications  Discrimination of communities through HCA of optical data  Pigment-based clusters used as a reference  Non-bloom conditions  Chla range 0.1-0.6 mg m -3 ICESOCC | SIO | 22 Sep 2014 7 Torrecilla et al. 2011

8. Good agreement between pigments and optics ICESOCC | SIO | 22 Sep 2014 8  High degrees of similarity between classifications derived from pigments and optics  Phytoplankton absorption coefficient better than R rs  Best results obtained using derivatives of high spectral resolution data Pigment-based clusters Dominant marker pigmentsStation Fuco ≈ MV-ChlbA DV-Chla > ZeaB DV-Chla ≈ ZeaC1, C2, C3, C4 19’-Hexfuco > ZeaD 19’-Hexfuco > FucoE Zea ≈ 19’-HexfucoF Torrecilla et al. 2011 -based clusters

9. IOPs and planktonic community  Particle IOPs closely linked to planktonic constituents  spectral absorption coefficient directly linked to phytoplankton pigments and cell size  spectral scattering coefficient sensitive to particle size distribution  Can be obtained from in situ from sensors on various platforms, or from ocean color inversion models ICESOCC | SIO | 22 Sep 2014 9 Bricaud et al. 2004 Kostadinov et al. 2009 Specific absorption spectra of major phytoplankton pigments Model simulations of particle backscattering shape in relation to particle size distribution

10. IOPs as community indicators  Recent work in Arctic suggests that IOPs can discriminate particle assemblages  7 planktonic assemblages identified in Chukchi and Beaufort Seas, each with distinct biogeochemical characteristics ICESOCC | SIO | 22 Sep 2014 10 Input to hierarchical cluster analysis 216 normalized spectra of b bp ( ):a p ( ) Ward’s linkage distance Dendrogram obtained from HCA of b bp ( ):a p ( ) Neukermans et al. 2014 Ocean Sciences Meeting Mineral Mix Pico+Detritus-1 MicroPhyto-1 MicroPhyto-2 Pico+Nano Phyto Detritus-2 Beaufort Sea Alaska Canada Mackenzie R. Particle backscattering to absorption (dimensionless)

11. Approach applicable to S. Ocean?  Phytoplankton are generally dominant contributors to nonwater absorption in SO ICESOCC | SIO | 22 Sep 2014 11 CDOM Phytoplankton Nonalgal particles Phytoplankton CDOM Relative contributions to a nw (440) S. OceanArctic Reynolds et al., IOCCG in press Chl [mg m -3 ] b bp (555) [m -1 ] Reynolds et al. 2001  Regional variability in particle size distribution and backscattering has been observed

12. Towards community-based approach  Optics-derived community indicators can be used to  assess community distributions  improve NPP and NCP estimates  validate or assimilate into numerical models  monitor changes in SO environment and biodiversity ICESOCC | SIO | 22 Sep 2014 12 Ward et al. 2012 Satellite-derivedModeled

13. Outstanding questions  What communities can we identify optically in the S. Ocean?  Do current approaches work in the SO?  How do optical communities relate to other indicators of community structure? (genomics, pigments, size structure)  Consistent with environmental patterns? (macro and micronutrients, light and mixing, grazing pressure)  Can we associate these communities with specific biogeochemical behavior?  Do these community distributions change over time and space?  Are there any trends in planktonic community distributions?  What are the potential implications? ICESOCC | SIO | 22 Sep 2014 13

14. Acknowledgements  Collaborators  M. Babin, G. Mitchell, G. Neukermans, J. Piera, D. Stramski, E. Torrecilla, J. Uitz  NASA Programs  Ocean Biology and Biogeochemistry  Biodiversity and Ecological Forecasting  Cryospheric Sciences ICESOCC | SIO | 22 Sep 2014 14