Download How do we best estimate phytoplankton biomass?

How do we best estimate
phytoplankton biomass?
Bengt Karlson1, Agneta Andersson2, Siv Huseby3, Helena Höglander4, Marie
Johansen1, Chatarina Karlsson3, Joanna Paczkowska2, Ann-Turi Skjevik1, Patrik
Strömberg1 and Jakob Walve4
1Swedish
Meteorological and Hydrological Institute, Oceanographic unit
Sven Källfelts gata 15, 426 71 Västra Frölunda, Sweden
2Dept. of Ecology and Environmental Science, Umeå University, SE-901 87 Umeå Sweden
3Umeå Marine Sciences Centre, Umeå University, Norrbyn, SE-905 71 Hörnefors, Sweden
4Department of Ecology, Environment and Plant Sciences, Stockholm University, Sweden
Email: [email protected]
Photos by Höglander, Karlson, Kuylenstierna,Skjevik and others from http://nordicmicroalgae.org
The pelagic food web
Microplankton
Nanoplankton
Picoplankton
From a Danish report?
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EU Water Framework Directive
- Good Ecological Status
Directive 2000/60/EC of the European Parliament and of the Council
of 23 October 2000
§  The biological quality elements for classification of the ecological status of
coastal waters include phytoplankton:
§  1.1.4. Coastal waters
§  Biological elements
§  Composition, abundance and biomass of phytoplankton
Marine Strategy Framework Directive
Good Environmental Status
Directive 2008/56/EC of the European Parliament and of the Council
of 17 June 2008
Commission decision of 1 September 2010 on criteria and methodological standards on good
environmental status of marine waters (2010/477/EU)
Descriptor 1: Biological diversity, e.g.
Species distribution, population size and population condition
Ecosystem structure - Composition and relative proportions of ecosystem
components (habitats and species)
Descriptor 2: Non-indigenous species
Descriptor 4: Marine food webs
Descriptor 5: Eutrophication, e.g.
5.2. Direct effects of nutrient enrichment
5.2.1 Chlorophyll concentration in the water column
5.2.2. Water transparency related to increase in suspended
algae, where relevant
5.2.4 Species shift in floristic composition such as diatom to flagellate ratio
bloom events of nuisance/toxic algal blooms
Some problems to solve
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Resolving the natural variability at a sufficient level
Designing monitoring programs that are cost efficient
Producing results that are relatively easy to interpret
Including the long term perspective – global change
Methods for estimating phytoplankton biomass
Direct measurements
§  Cell counts, species identification and cell volume measurements
Proxies
§  Genomic barcoding data (e.g. rDNA or rRNA)
§  Estimates of chlorophyll a
§  Water sampling and laboratory analysis
§  Automated measurements using Ferrybox systems, buoys etc.
§  Continuous Plankton Recorder – colour of silk
§  Air borne remote sensing
§  Satellite remote sensing
§  Water transparency
§  Secchi depth
§  Kd from in situ light data
§  Kd from satellite remote sensing
§  Particulate carbon and nitrogen (POC and PON)
Cell counts and cell volume measurements
§  Cell counts and cell volume measurements
§  Microscopy
§  Utermöhl method
§  Cell volumes from HELCOM-PEG Olenina et al. 2006 with updates
§  Fluorescence microscopy of autotrophic picoplankton
§  Data processing using free software: Plankton Toolbox available at
http://nordicmicroalgae.org/tools (product of Swedish Lifewatch)
§  Included in proposed new OSPAR monitoring guidelines partly based
on CEN.
Flow Cytometry
Automated analyses of plankton
Quantitative
Biomass may be estimated
Non imaging:
Best for pico- and nanoplankton
Also for pelagic bacteria
http://www.bdbiosciences.com/
Imaging Flow Cytometers
Many species can be automatically identified
Traing using local populations is necessare
For cells up to a few hundred µm
http://www.cytobuoy.com
http://www.fluidimaging.com/
http://www.mclanelabs.com
Cell volumes from imaging flow cytometry
Moberg & Sosik 2012
Images courtesy of Don Anderson, Woods Hole Oceanographic Institute
Imaging Flow Cytobot - Sosik & Olson 2007
Cell counts
vs. biovolume
Station N14 Falkenberg, the Kattegat
Data based on Utermöhl method
Diatoms
Dinoflagellates
Unidentified org.
Skjevik Ann-Turi, Bäck Örjan, Edler Lars, Hansson Lars Johan,
Johansen Marie & Karlson Bengt 2011
Autotropic picoplankton is a major
component of phytoplankton biomass
Paczkowska J, Rowe OF, Schlüter L, Legrand
C, Karlson B, Andersson A (unpublished)
Picoplankton at a coastal station in the
Baltic Sea
§  Sata from 2008
§  Station B1 near Askö
§  Sefbom master thesis
Biovolume vs. Biomass in carbon units
Cell abundance millions per Litre
100
The missing plots
0
§  Data from phytoplankton campaign year
nov
2011 Kattegat-Skagerrak
§  Comparison of:
§  Biovolume vs. Biomass in C
0,2
§  Diatoms are overestimated if
0
nov
biovolume is used
§  Cell numbers with and without
autotrophic picoplankton
50
§  Biovolume with and without autotrophic
0
picoplankton
nov
§  Biomass in C with and without
autotrophic picoplankton
feb
jun
sep
dec
apr
dec
apr
Biovolume mm3 L-1
feb
jun
sep
Biomass C mg per Litre
feb
jun
Data from station Danafjord in the Kattegat
The phytoplankton campaign year 2011
sep
dec
apr
The Continuous Plankton Recorder
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Operated by SAHFOS
Developed before World War II
Towed behind ships
Plankton collected on silk mesh
Formalin used as preservative
§  Positive notes
§  Integrates over large sea areas
§  Some very useful long time series exist
Phytoplankton Colour Index (PCI)
Code 0 = No Colour
Code 1 = Very Pale Green
Code 2 = Pale Green
Code 6.5 = Green
§  Negative notes
§  Data is semi-quantitative
§  Selective for large, robust organisms
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Chlorophyll a
Some methods
§  Chlorophyll a, lab. analyses
§  Chlorophyll in situ fluorescence
§  Kd - 490
§  Ocean colour - reflectance
Major problem:
Chl. a is not biomass of
phytoplankton, only a proxy
§  Chl. a content is species specific
§  Chl. a content varies due to light
conditions
§  Chl. a content varies due to
nutrient conditions
Test hose vs. Discrete depths
§  Chl. a samples collected using hose 0-10 m
§  Chl. a samples collected at discrete depths ( 0, 5 and 10 m)
Chlorophyll a vs. Biovolume
Chlorophyll fluorescence
Laeso buoy, the Kattegat 2003
Karlson unpublished
Swedish oceanographic buoy network
A network in development
§  Infrastructure funded by the
Swedish Research Council,
SMHI and other partners
§  Partners: Univ. of Gothenburg,
Umeå univ., Stockholm univ.,
Linnaeus univ. and SMHI
Sensors
§  Chl. fluorescence
§  Oxygen
§  Temperature
§  Salinity
§  Currents
§  Waves
§  others to be decided
Positions approximate
Near surface condtions in 2013 –
results from a Ferrybox system
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Ferrybox Chl. fluorescence vs. Chl. a
Water samples
collected between
Lübeck and
Gothenburg
Combining Ferrybox and buoy data
Timeseries of Chl. a fluorescence from the buoy (open circle, 1
m) and Ferrybox data (closed circle, 4m) from 2014.
Unpublished
Sørensen and Karlson
Ocean Color (Reflectance)
Satellites available
§  NASA Aqua/Terra MODIS
§  NASA NPP-Suomi VIIRS
Discontinued
§  ESA EnviSAT
§  NASA SeaWiFS
To be launched
§  ESA Sentinel 3a (autumn 2015)
§  ESA Sentinel 3b (2016?)
Positive notes
§  Good horizontal coverage
§  High correlation to chlorophyll a
in oceanic condtions
§  Good correlation in coastal areas
§  Very useful for detecting surface
scums of cyanobacteria
§  Very useful for detecting
coccolithophorid blooms
Negative notes
§  Cloud cover problematic
§  Shallow water – benthic algae
§  Shallow water – suspended
sediments
§  CDOM – humic substances
Baltic Sea, 24 July 2014, NASA-MODIS.
processed by SMHI
CDOM
Coloured Dissolved Organic
Matter
§  ~ Humic substances
§  ~ Gelbstoff
§  Gradient from the Bothnian
Sea to the Skagerrak
§  Energy rich
§  Affects other optical in situ
data
§  Affects remote sensing data
Chlorophyll a based on satellite data
Correlation between sea truth data
§  Wozniak et al. 2014
§  Best R2 0.478
Ocean colour based chl. a in the Himmerfjärd
Harvey et al. 2015
Water transparency
§  Secchi depth
§  Kd from in situ light data
§  Kd from satellite remote sensing
Major water constituents that influence water transparency
§  Phytoplankton
§  Inorganic material – suspended sediments
§  Coloured Dissolved Organic Matter
Kd based on in situ data at specific wavelengths is very
useful to estimate chl. a.
Secchi depth should not be used as a quantitative
estimate of phytoplankton biomass.
Please listen to talk by Harvey et al. tomorrow
Conclusions
§  Phytoplankton biomass is only one of the mandatory phytoplankton parameters
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in WFD and MSFD
Cell abundance is not phytoplankton biomass
Microscope cell counts and identification of organisms with cell volume estimates
can be used to estimate phytoplankton biomass at the:
§  Species level
§  Class level
§  Total phytoplankton biomass
Analysis of autotrophic picoplankton should be part of the monitoring programs
Imaging flow cytometry can be used to increase sampling frequency
Water sampling and laboratory chlorophyll a analysis is a robust method
Buoys with night time chlorophyll fluorescence should be used to improve
temporal resolution
Ferrybox-systems should be used to increase spatial and temporal resolution
In situ measurements of Kd can be uesd to estimate chl. A – it integrates over
depth.
Secchi depth should not be used to estimate phytoplankton biomass
Thank you for listening
Welcome to the Scientific Symposium on Harmful Algal Blooms and Climate Change, Gothenburg, Sweden 19-22 May 2015
Summary for stakeholder on Friday 22 May 1400-1500
Bloom of cyanobacteria 254July 2014 in the archipelago of Östergötland, photo by the Swedish Coast Guard, Air Patrol