1 / 22

Heidi M. Sosik Hui Feng

Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf. In Situ Time Series for Validation and Exploration of Remote Sensing Algorithms. Woods Hole Oceanographic Institution . University of New Hampshire. Heidi M. Sosik Hui Feng.

halona
Download Presentation

Heidi M. Sosik Hui Feng

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf In Situ Time Series for Validation and Exploration of Remote Sensing Algorithms Woods Hole Oceanographic Institution University of New Hampshire Heidi M. SosikHuiFeng

  2. Project Overview Goal: Use unique time series to evaluate algorithms that extend MODIS ocean color data beyond chlorophyll to functional type or size-class-dependent phytoplankton retrievals Approach: End-to-end time series observations, with step-by-step algorithm evaluation and error analysis single cells  phytoplankton community  bulk water optical properties  sea surface optical properties (air and water)  MODIS optical properties Martha’s Vineyard Coastal Observatory Tower mounted AERONET-OC Submersible Imaging Flow Cytometry MODIS products

  3. Approach Phytoplankton Observations Single cells to communities Biomass, size- and taxon-resolved Phytoplankton Algorithms Absorption spectral shape  size structure Diagnostic pigments  size structure Diagnostic pigments  taxonomic structure

  4. Variability in community structure . m m Diatoms Cyano- bacteria m m m m m m m m

  5. Pigment-based retrieval of taxonomic groups Diatoms In situ FCM “CHEMTAX” Mackey et al. 1996 Total Chl a = diatom Chl a + dinoflagellateChl a + cyanobacteria Chl a + … with partitioning according to accessory pigment ratios

  6. Pigment-based retrieval of taxonomic groups Diatoms Diatoms (mg m3)

  7. Pigment-based retrieval of taxonomic groups Diatoms Dinoflagellates Cyanobacteria ~1 mm cells 10 mm

  8. Pigment-based retrieval of taxonomic groups Diatoms Dinoflagellates Cyanobacteria Chl or Carbon (mg m3) ~1 mm cells 10 mm

  9. Diagnostic pigment retrieval from Rrs Pan et al. 2010 band ratio algorithms AERONET-OC SeaPRISM, Rrs(l) Chl a Fucoxanthin Discrete samples HPLC pigment analysis Zeaxanthin Peridinin

  10. Pigment-based retrieval of taxonomic groups Diatoms Dinoflagellates Cyanobacteria Chl or Carbon (mg m3) ~1 mm cells 10 mm

  11. Remote sensing retrieval of taxonomic groups Diatoms Dinoflagellates Cyanobacteria Chl or Carbon (mg m3) AERONET-OC SeaPRISM, Rrs(l)  Loss of seasonal resolution Following: Pan et al. 2010 band ratio algorithms Pan et al. 2011 CHEMTAX application

  12. Remote sensing retrieval of taxonomic groups Diatoms Dinoflagellates Cyanobacteria Fraction of Chl a Relative contribution to total Chl a AERONET-OC SeaPRISM, Rrs(l)  Loss of seasonal resolution Following: Pan et al. 2010 band ratio algorithms Pan et al. 2011 CHEMTAX application

  13. Remote sensing retrieval of taxonomic groups Diatoms Dinoflagellates Cyanobacteria Fraction of Chl a Fraction of Chl a

  14. Ecosystem characterization . Decadal increase in pico-cyanobacteria at MVCO

  15. Ecosystem characterization . Peacock et al. 2014 50 mm

  16. Ecosystem characterization . Peacock et al. 2014 Interannual fluctuations in diatoms  related to parasite infection  linked to temperature

  17. Looking forward on PFT characterization Time series observations single cells  phytoplankton community  bulk water optical properties  sea surface optical properties (air and water)  MODIS optical properties Local detail  Trends and patterns of change  Regional to basin scales Martha’s Vineyard Coastal Observatory Tower mounted AERONET-OC MODIS products Combined in situ & satellite observations Submersible Imaging Flow Cytometry

  18. http://ifcb-data.whoi.edu/ Open data access Standard formats Processing pipelines End-to-end provenance

  19. Ecosystem characterization Cyanobacterium Diatoms Taxa with positive response to warmer winters Taxa with negative response to warmer winters Interannual variability – taxon specific Seasonally adjusted Biomass anomalies vs Temperature anomalies

  20. Observing Phytoplankton at MVCO Martha’s Vineyard Coastal Observatory (MVCO) Cabled site with power and two-way communications Picoplankton Microplankton Automated features for extended deployment (>6 months) Enumeration, identification, and cell sizing Thousands of individual cells every hour FlowCytobot Imaging FlowCytobot Laser-based flow cytometry Fluorescence and light scattering Flow cytometry with video imaging Olson & Sosik 2007 Olson et al. 2003

  21. Single Cells to Biomass Picoplankton Cell volume (mm3) FlowCytobot Menden-Deuer and Lessard 2000 Light scattering Volume from laser scattering Olson et al. 2003 Microplankton Imaging FlowCytobot Volume from image analysis new “distance map” approach Sosik and Olson 2007 Moberg & Sosik 2012

More Related