How do we best estimate phytoplankton biomass?

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1 How do we best estimate phytoplankton biomass? Bengt Karlson 1, Agneta Andersson 2, Siv Huseby 3, Helena Höglander 4, Marie Johansen 1, Chatarina Karlsson 3, Joanna Paczkowska 2, Ann-Turi Skjevik 1, Patrik Strömberg 1 and Jakob Walve 4 1 Swedish Meteorological and Hydrological Institute, Oceanographic unit Sven Källfelts gata 15, Västra Frölunda, Sweden 2 Dept. of Ecology and Environmental Science, Umeå University, SE Umeå Sweden 3 Umeå Marine Sciences Centre, Umeå University, Norrbyn, SE Hörnefors, Sweden 4 Department of Ecology, Environment and Plant Sciences, Stockholm University, Sweden Bengt.Karlson@smhi.se Photos by Höglander, Karlson, Kuylenstierna,Skjevik and others from

2 The pelagic food web Microplankton Nanoplankton Picoplankton From a Danish report? 2

3 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: Coastal waters Biological elements Composition, abundance and biomass of phytoplankton

4 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 Direct effects of nutrient enrichment Chlorophyll concentration in the water column Water transparency related to increase in suspended algae, where relevant Species shift in floristic composition such as diatom to flagellate ratio bloom events of nuisance/toxic algal blooms

5 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

6 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 K d from in situ light data K d from satellite remote sensing Particulate carbon and nitrogen (POC and PON)

7 Cell counts and cell volume measurements Cell counts and cell volume measurements Microscopy Utermöhl method Cell volumes from HELCOM-PEG Olenina et al with updates Fluorescence microscopy of autotrophic picoplankton Data processing using free software: Plankton Toolbox available at (product of Swedish Lifewatch) Included in proposed new OSPAR monitoring guidelines partly based on CEN.

8 Flow Cytometry 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

9 Cell volumes from imaging flow cytometry Moberg & Sosik 2012 Images courtesy of Don Anderson, Woods Hole Oceanographic Institute Imaging Flow Cytobot - Sosik & Olson 2007

10 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

11 Autotropic picoplankton is a major component of phytoplankton biomass Paczkowska J, Rowe OF, Schlüter L, Legrand C, Karlson B, Andersson A (unpublished)

12 Picoplankton at a coastal station in the Baltic Sea Sata from 2008 Station B1 near Askö Sefbom master thesis

13 Biovolume vs. Biomass in carbon units 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 100 0,2 50 Cell abundance millions per Litre 0 nov feb jun sep dec apr Biovolume mm 3 L -1 0 nov feb jun sep dec apr Biomass C mg per Litre 0 nov feb jun sep dec apr Data from station Danafjord in the Kattegat The phytoplankton campaign year 2011

14 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 Phytoplankton Colour Index (PCI) Code 0 = No Colour Code 1 = Very Pale Green Code 2 = Pale Green Code 6.5 = Green 14

15 Chlorophyll a Some methods Chlorophyll a, lab. analyses K d Ocean colour - reflectance Chlorophyll in situ fluorescence 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

16 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)

17 Chlorophyll a vs. Biovolume

18 Chlorophyll fluorescence Laeso buoy, the Kattegat 2003 Karlson unpublished

19 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

20 Near surface condtions in 2013 results from a Ferrybox system 20

21 Ferrybox Chl. fluorescence vs. Chl. a Water samples collected between Lübeck and Gothenburg

22 Combining Ferrybox and buoy data Timeseries of Chl. a fluorescence from the buoy (open circle, 1 m) and Ferrybox data (closed circle, 4m) from Unpublished Sørensen and Karlson

23 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

24 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

25 Chlorophyll a based on satellite data Correlation between sea truth data Wozniak et al Best R

26 Ocean colour based chl. a in the Himmerfjärd Harvey et al. 2015

27 Water transparency Secchi depth K d from in situ light data K d from satellite remote sensing Major water constituents that influence water transparency Phytoplankton Inorganic material suspended sediments Coloured Dissolved Organic Matter K d 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

28 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 K d can be uesd to estimate chl. A it integrates over depth. Secchi depth should not be used to estimate phytoplankton biomass

29 Thank you for listening Welcome to the Scientific Symposium on Harmful Algal Blooms and Climate Change, Gothenburg, Sweden May 2015 Summary for stakeholder on Friday 22 May Bloom of cyanobacteria 254July 2014 in the archipelago of Östergötland, photo by the Swedish Coast Guard, Air Patrol

Bio-optical monitoring of coastal Baltic Sea waters from research to applications

Bio-optical monitoring of coastal Baltic Sea waters from research to applications Susanne Kratzer Department of Systems Ecology, SU Suse@ecology.su.se Gerald Moore Petra Philipson Christian Vinterhav Therese