Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf Heidi M Sosik Hui Feng In Situ Time Series for Validation and Exploration of Remote Sensing Algorithms ID: 264417
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Slide1
Seasonal to Interannual Variability in Phytoplankton Biomass and Diversity on the New England Shelf
Heidi M.
Sosik Hui Feng
In Situ Time Series for Validation and
Exploration
of
Remote Sensing Algorithms
Woods Hole
Oceanographic
Institution
University of
New HampshireSlide2
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 retrievalsApproach:
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
MODIS products
Submersible Imaging
Flow
CytometrySlide3
ApproachPhytoplankton ObservationsSingle cells to communitiesBiomass, size- and taxon-resolvedPhytoplankton AlgorithmsAbsorption spectral shape size structureDiagnostic pigments size structure
Diagnostic pigments taxonomic structureSlide4
m
mm
m
mVariability in community structure
Diatoms
Cyano
-bacteria
.
m
m
m
m
mSlide5
Pigment-based retrieval of taxonomic groupsDiatoms
“CHEMTAX”
In situ FCMTotal Chl a
= diatom Chl a + dinoflagellate Chl a + cyanobacteria Chl a + …
with partitioning according to accessory pigment ratios
Mackey et al. 1996Slide6
Pigment-based retrieval of taxonomic groupsDiatoms
Diatoms (mg m
3
)Slide7
Pigment-based retrieval of taxonomic groups
Diatoms
10
m
m
Dinoflagellates
Cyanobacteria
~1
m
m cellsSlide8
Pigment-based retrieval of taxonomic groupsDiatoms
10
m
m
Dinoflagellates
Cyanobacteria
~1
m
m cells
Chl
or Carbon (mg m
3
)Slide9
Diagnostic pigment retrieval from
Rrs
Pan et al. 2010 band ratio algorithms
AERONET-OC
SeaPRISM
,
R
rs
(
l
)
Discrete samples
HPLC pigment analysis
Chl
a
Fucoxanthin
Peridinin
ZeaxanthinSlide10
Pigment-based retrieval of taxonomic groups
Diatoms
10
m
m
Dinoflagellates
Cyanobacteria
~1
m
m cells
Chl
or Carbon (mg m
3
)Slide11
Remote sensing retrieval of taxonomic groupsDiatomsDinoflagellates
Cyanobacteria
AERONET-OC
SeaPRISM
,
R
rs
(
l
)
Following:
Pan et al. 2010 band ratio algorithms Pan et al. 2011 CHEMTAX application Loss of seasonal resolution
Chl or Carbon (mg m3)Slide12
Remote sensing retrieval of taxonomic groupsDiatomsDinoflagellates
Cyanobacteria
Fraction of Chl a
AERONET-OC
SeaPRISM
,
R
rs
(
l
)
Relative contribution to total Chl a
Loss of seasonal resolutionFollowing: Pan et al. 2010 band ratio algorithms Pan et al. 2011 CHEMTAX applicationSlide13
Remote sensing
retrieval of taxonomic groupsDiatoms
DinoflagellatesCyanobacteria
Fraction of Chl
aFraction of Chl
aSlide14
Ecosystem characterizationDecadal increase in pico-cyanobacteria at MVCO
.Slide15
Ecosystem characterizationPeacock et al. 2014
.
50
m
mSlide16
Ecosystem characterizationInterannual fluctuations in diatoms related to parasite infection linked to temperature
.
Peacock et al.
2014Slide17
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
Martha’s Vineyard Coastal Observatory
Tower mounted
AERONET-OC
MODIS products
Submersible Imaging
Flow
Cytometry
Local detail
Trends
and patterns of
change
Regional to basin scales
Combined
in situ & satellite
observationsSlide18Slide19
http://ifcb-data.whoi.edu/Open data accessStandard formatsProcessing pipelines End-to-end provenanceSlide20
Ecosystem characterizationTaxa with positive response to warmer wintersTaxa with negative response to warmer winters
Interannual variability – taxon specificSeasonally adjusted Biomass anomalies vs Temperature anomalies
Cyanobacterium
DiatomsSlide21
FlowCytobot
Imaging
FlowCytobot
Observing Phytoplankton at
MVCO
Martha’s Vineyard Coastal Observatory (MVCO)
Cabled site with power and two-way communications
Microplankton
Picoplankton
Laser-based flow
cytometry
Fluorescence and light scattering
F
low
cytometry
with video imaging
Automated features for extended
deployment (>6 months)Enumeration, identification,
and cell sizing Thousands of individual cells every hour
Olson et al. 2003
Olson &
Sosik
2007Slide22
Single Cells to Biomass
FlowCytobot
Picoplankton
Imaging
FlowCytobot
Microplankton
Light scattering
Cell volume (
m
m
3)
Sosik
and Olson 2007Moberg & Sosik 2012
Olson et al. 2003Volume from laser scattering
Volume from image analysis new “distance map” approach
Menden-
Deuer
and
Lessard
2000