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: Bengt.Karlson@smhi.se

Photos by Höglander, Karlson, Kuylenstierna,Skjevik and others from http://nordicmicroalgae.org

The pelagic food web

2 From a Danish report?

Microplankton Nanoplankton Picoplankton

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 §  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

http://www.bdbiosciences.com/

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http://www.cytobuoy.com http://www.fluidimaging.com/

http://www.mclanelabs.com

Automated analyses of plankton Quantitative Biomass may be estimated Non imaging: Best for pico- and nanoplankton Also for pelagic bacteria Imaging Flow Cytometers Many species can be automatically identified Traing using local populations is necessare For cells up to a few hundred µm

Cell volumes from imaging flow cytometry

Moberg & Sosik 2012

Imaging Flow Cytobot - Sosik & Olson 2007

Images courtesy of Don Anderson, Woods Hole Oceanographic Institute

Cell counts vs. biovolume Station N14 Falkenberg, the Kattegat Data based on Utermöhl method

Diatoms

Unidentified org.

Dinoflagellates

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

0

100

nov feb jun sep dec apr

Cell abundance millions per Litre

0

0,2

nov feb jun sep dec apr

Biovolume mm3 L-1

0

50

nov feb jun sep dec apr

Biomass C mg per Litre

Data from station Danafjord in the Kattegat The phytoplankton campaign year 2011

The missing plots §  Data from phytoplankton campaign year

2011 Kattegat-Skagerrak §  Comparison of:

§  Biovolume vs. Biomass in C §  Diatoms are overestimated if

biovolume is used §  Cell numbers with and without

autotrophic picoplankton §  Biovolume with and without autotrophic

picoplankton §  Biomass in C with and without

autotrophic picoplankton

The Continuous Plankton Recorder

§  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

§  Negative notes §  Data is semi-quantitative §  Selective for large, robust organisms

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Phytoplankton Colour Index (PCI) Code 0 = No Colour Code 1 = Very Pale Green Code 2 = Pale Green Code 6.5 = Green

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

Baltic Sea, 24 July 2014, NASA-MODIS. processed by SMHI

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

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 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

Bloom of cyanobacteria 254July 2014 in the archipelago of Östergötland, photo by the Swedish Coast Guard, Air Patrol

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