Baltic GIG – PHYTOPLANKTON
Annex 1
2. Overview of Methods to be intercalibrated
2.1.Methods and required BQE parameters
In the table below it has to be indicated if all relevant parameters indicative of the biological quality element are covered (see Table 1 in the IC Guidance). A combination rule to combine parameter assessment into BQE assessment has to be defined. If parameters are missing, Member States need to demonstrate that the method is sufficiently indicative of the status of the QE as a whole.
MemberState / Full BQE method / Taxonomic composition / Abundance (or cover) / Frequency and intensityof algal blooms / Biomass / Combination rule of metrics
Germany / No, for
Western part
Yes for Eeastern part / biovolume of Cyanophytes (only in the eastern part) biovolume of Chlorophytes (only in the eastern part); / Chlorophyll a (μg/l) -
total biomass (biovolume [mm3/L]) / Weighted average if taxonomic composition can be included; otherwise the average of total biovolume and chlorophyll a
Estonia / No / Median** chlorophyll a conc. - Total median wet weight autotrophic biomass (including autotrophic ciliate Mesodinium rubrum) mg/l
(months VI-IX) / Average of
chl a and biovolume
Finland / No / Mean chlorophyll a – total biomass (mg/l) (months VII-IX) / Total biomass is not yet officially accepted as a national classification metrics. Combination rule will be average of the EQR values of chl and biovolume.
Latvia / No / Still under development and not included into assessment system / Mean chlorophyll a concentration – biovolume (mg/m3, month VI-IX) / Average of
chl a and biovolume
Lithuania / No / Still under development and not included into assessment system / Mean Chlorophyll a – total biomass (mg/l) (months VI-IX) / No official rules for combination. Average of chlorophyll a and total biovolume is considered
Poland / No / Chlorophyll a (mean conc. of summer months (VI-IX) - total biomass, mean of summer months (VI-IX) / Mean of chlorophyll a and biomass
Sweden / No / Biomass of autotrophic and mixotrophic phytoplankton expressed as:
1. Chlorophyll a concentration (µg/L) and
2. Total biovolume (mm3/L) (if available)
June-August (mean) from at least 3 years from the latest 6-year period / Weighted classification
average (see decscription annex 1). *
As biovolume and chlorophyll data is available, they should be cofactored into one standardised status classification for phytoplankton. If there is no data for any of these parameters, the classification is based on the remaining
Denmark / No / Under development
See text below / x / Summer (May-September) mean Chlorophyll a concentration or 90th percentile of Chl-a conc. from March through September / No combination
* Sweden: From report “Assessment criteria for coastal and transitional waters”:Cofactoring of EQRs for biovolume and chlorophyll a see annex 1
**Estonia: The use of the mean chlorophyll-a concentration and total biomass is somewhat criticized, and suggested that median values would be used instead. Rationale behind is the high variability of phytoplankton and deviations from normal distribution. For example, bloom-like events may influence too much to the assessment value. Furthermore, the bloom periods are usually short-term (some days to some weeks) and can be easily missed by regular monitoring. Taking into account the total length of season (3-4 months), short-term maxima will attain disproportionate importance in calculations.
In this case all Member States have to explain why abundance and/or frequency and intensity of algal blooms have not been included in their assessment method and the efforts they made to investigate the usefulness of the parameters!!! All Member States Germany have to explain why taxonomic composition is not included in their assessment method.
Germany: Sagert et al. (2008) found a dependability of phytoplankton community composition on nitrogen concentrations in a salinity range from 5 to 10 psu: Strong correlations were found between mean total nitrogen (TN) concentrations (summer period) and mean summer biovolume of Cyanophyceae, Chlorophyceae and Cryptophyceae.
These results were tried to be adapted to waterbodies with higher salinities located in the western part of the German Baltic coastal waters. For this reason a dataset from 11 stations in the Kiel Bight from 2006 to 2010 was used covering a salinity range from 10 to 20 psu. Only samples from the summer period (May-September) were considered in the analysis.
For the biovolume of diatoms, Cryptophyceae and Euglenophyceae only weak correlations to summer TN values could be found. Multiple analyses showed that only for the last two groups nitrogen is the most important factor influencing their biovolume. The strongest effect was found for Euglenophyceae with R2 = 0.23. This phytoplankton group only accounts for a very small proportion of total phytoplankton biomass (2%) and is thus not suitable as indicator for eutrophication.
The use of the maximal concentrations of dissolved nitrogen in winter as indicator for eutrophication produced better results. The best correlation was found between winter DIN and Cryptophyceae (R2 = 0.35), but the effect of the nitrogen concentration on the biovolume of this group is nevertheless much weaker than found for Cyanophyceae and Chlorophyceae in regions with lower salinity. Therefore these groups are not considered as suitable indicators for eutrophication.
Lithuania - Phytoplankton species abundance is a difficult indicator to assess from monitoring data, as the number of species recognised in a sample highly depends on the taxonomical skills of the person analysing the sample. Moreover, the taxonomy of phytoplankton is constantly developing and the awareness of new types of species is increasing. These factors will impact the use and reliability of species abundance and diversity in the classification of coastal waters. At least, robust and unbiased indicators of the structural changes of phytoplankton communities need to be developed before phytoplankton species composition can be applied for classification of coastal waters of the Baltic Sea."(Carstensen, J., A.-S. Heiskanen, P. Kauppila, T. Neumann, G. Schernewski and Gromisz S. (2005). Developing reference conditions for phytoplankton in the Baltic coastal waters. Part II: Examples of reference conditions developed from the Baltic Sea. Ispra: Inst. Environ. and Sustainability. 35 S. (Technical report/ EU Joint Research Centre; EU 21582/EN/2)). Explanations why phytoplankton species abundance, algal blooms are not good indicators are clarified below (see Latvia, Estonia and Finland).
Latvia – the index of algal blooms is not included in the assessment method due to the lack of data. The taxonomic composition has been considered for inclusion but in later data analysis no meaningful relations between pressure indicators and the taxonomic structure were found. Thus, further development of this parameter is necessary, probably addressing the species level not just taxonomic groups. Species, proposed by Jaanus et al. 2009 (see Estonia comments) have been tested and not been useful for Latvian coastal waters.
Sweden - The Swedish assessment system for coastal phytoplankton is only based on biomass measured as Biovolume and Chlorophyll a (3-years-mean for period June-August). A high sampling frequency both in time and space is required to develop an assessment system based on taxonomic composition, abundance/cover and frequency and intensity of algal blooms. Such data were not available when the Swedish assessment system for phytoplankton was adopted in 2006.
Taxonomic composition was tested in the few gradients where also pressure data were available. Due to limited data it was difficult to get any clear correlation between composition and pressure data. The studies were also obstructed due to the difficulty to separate anthropogenic pressure from natural gradients as salinity gradients. The spatial and temporal resolution was not high enough to get information about phytoplankton abundance/cover and algal blooms, so these parameters could not be tested. Satellite images are used to monitor surface accumulations of diazotrophic cyanobacteria during summertime. This has worked for the open sea areas but has been less successful in coastal areas. The algorithms for coastal areas are being improved and satellite images may in the future be a complement to follow surface accumulating species. Algal blooms starting in open sea areas that drift into coastal areas (as blooms of the cyanobacteria Nodularia spumigena) can though make it difficult to use the bloom criteria in some coastal areas.
Taxonomic composition, as groups, has quickly been tested within the 2nd Intercalibration round within the Baltic GIG based on a common metric. This did though not give any clear answers.
A five-year Swedish project (WATERS - Waterbody Assessment Tools for Ecological Reference conditions and status in Sweden) started in March 2011 to further explore the possibility of using taxonomic composition in the assessment system. This will be based on data collected within the last 5 years, old data and possibly new samples collected in pressure gradients. The Swedish monitoring program will though not provide enough data to evaluate the parameters abundance/cover or algal blooms.
Estonia - Cell abundance in a phytoplankton sample depends greatly on counting strategy. Different counting units can be used for the same taxa (e.g. cells and colonies for some cyanobacteria and green algae, cells and 100 µm filaments for filamentous cyanobacteria etc). It makes the counting results highly variable (up to 2 orders of magnitude). Secondly, abundance/biomass ratio greatly depends on the physiological or developmental state of the cell (e.g. how to count dividing cells?). Thirdly, the number of cells counted depends on the skills of analyst, microscopes used and program requirements (to include picoplankton or not?). Sometimes it is difficult to discriminate between autotrophic and heterotrophic cells and bacteria (epifluorescence technique is needed).
The lack of useful taxonomy-based evaluation systems for Baltic brackish coastal areas is probably caused by the high temporal and spatial variability of hydrological and geochemical parameters. If we choose single indicator taxa, it should be taken into account that most of phytoplankton species appear in moderate or big numbers only for a relatively short period (weeks to 1-2 months). In case of sparse sampling during the assessment season, some potential indicator species can just be eluded or recorded in very low numbers (with biomasses near 0). The sensitive species are rare in abundance in comparison with the omnipotent species and are therefore less suited from the statistical point of view. The species suggested as reliable eutrophication indicators (Jaanus et al. 2009. Potential phytoplankton indicator species for monitoring Baltic coastal waters in the summer period. Hydrobiologia 629: 157-168.) – oscillatorialean cyanobacteria and the diatoms Cyclotella choctawhatcheeana and Cylindrotheca closterium have their maxima toward the end of July or in August-September. For example, C. choctawhatcheeana is among the predominant species in the NE Gulf of Riga in August-September and C. closterium has recurrently formed summer blooms in a eutrophic bay in West-Estonian coastal waters. For these two species, a preliminary assessment system has been developed and testing against available metrics (chl a and total biomass) has given satisfactory results. The indicator taxa could be included in a multimetric indice, where each attribute is calculated from the number of times that the sub-metric exceeds the threshold as a proportion of the total number of sampling times and calculated as a 5-6 year mean.
The oscillatorean cyanobacterium Planktothrix agardhii has been proposed as one more potential eutrophication indicator species in the northern part of the Baltic Sea, since it responds positively to increased TN levels (Carstensen & Heiskanen, 2007. Phytoplankton responses to nutrient status: application of a screening method to the northern Baltic Sea. Marine Ecology Progress Series 336: 29-42). This species has been found during summer throughout the 20th century, when temperature conditions were stable until the 1990s in the coastal waters surrounding the cities of Stockholm and Helsinki (Johansson & Wallström 2001. Urban impact in the history of water quality in the Stockholm Archipelago. Ambio 30: 277–281; Finni et al. 2001. The history of cyanobacterial blooms in the Baltic Sea. Ambio 30: 172–178), but also in Kuressaare Bay (Trei & Piirsoo, 1996. Short-term effect of the sewage treatment plant on the phytoplankton in Kuressaare Bay. Proceedings of the Estonian Academy of Science, Ecology 6: 154–166). These authors attributed the decrease in total biomass and change in phytoplankton dominance from P. agardhii to a more species-rich community to an effective reduction in nutrient load. And vice versa, P. agardhii has replaced Aphanizomenon flos-aquae as the most abundant cyanobacterium during the late 1980s in Neva Bay and during the 2000s in the Curonian Lagoon (Basova & Lange, 1998. Trends in late summer phytoplankton in the Neva Bay and eastern Gulf of Finland during 1978 to 1990. Memoranda Societatis pro Fauna et Flora Fennica 74 (1); Jaanus et al., 2011. Changes in phytoplankton communities along a north-south gradient in the Baltic Sea between 1990 and 2008. Boreal Environment Research 16 (Suppl. A): 191-208.). Both localities have suffered from gradual deterioration in environmental quality during the last few decades.
Finland - Explanations why phytoplankton species abundances are not a good indicator are already clarified above (see Lithuania and Estonia). The spring bloom intensity index, developed by Fleming and Kaitala (2006) for the status assessments of HELCOM, is based on high-frequency monitoring data of chlorophyll a (HELCOM 2009). The index is under further development to be possibly used in MSD assessments. Until now the spring bloom index lacks of the definition of reference conditions, of which reason it does not fulfill the requirements of the WFD. A statistical approach to define algal blooms based on chlorophyll a (Carstensen et al. 2006) was tested during the CHARM project using the intensive monitoring data of Finnish coastal waters. This approach requires at least biweekly sampling data (Carstensen et al. 2006).
Phytoplankton total biomass will be included in Finnish classification system, not only to describe other aspects of biomass than chlorophyll a, but also as a kind of proxy to phytoplankton composition. Biomasses of different phytoplankton groups, their ratios and chosen phytoplankton species were tested using Finnish data of the period 1990-2010 during the Baltic GIG work. The links of phytoplankton species and groups with nutrient concentrations were weak (R2 <0.2). The results is in accordance with the study by Olli et al. (2011), where eutrophication-related parameters (total and mineral nutrients) revealed low association with the phytoplankton community composition in all Baltic Sea sub-basins (R2 <0.2). Even so, there are clear evidences that changes in eutrophication level affect phytoplankton community structure. For example, in Helsinki sea area the cyanobacterial species like Anabaenopsis spp., Planktothrix agardhii and Oscillatoriales (narrow filaments) showed positive correlation with total nitrogen, phosphate phosphorus and total phosphorus (Pellikka et al. 2007). Additionally, in the northern and eastern Baltic Sea, phytoplankton composition has changed along with proceeding eutrophication (e.g. Kauppila and Lepistö 2001, Suikkanen et al. 2007).
Denmark - In Denmark the use of biovolume as a measure of phytoplankton biomass and the use of composition of phytoplankton in classification has been examined. Total biovolume of phytoplankton correlated with TN when including all stations with sufficient phytoplankton data (Figure 1).
Fig. 1. Relationship between total phytoplankton biovolume and TN based on data since 1988 from 21 Danish sites.Based on this relationship and a combination of hind-casted estimates of N-inputs to Danish and expert judgement of the corresponding ecological status during different time periods, WFD compliant class boundaries were established for biovolume at these stations (Table 1). Reference conditions and boundary values were calculated from the TN-biovolume regression using the corresponding reference and boundary values of TN as input (Carstensen et al. 2008). Standard errors for these estimates were found by Monte Carlo simulation taking variations in the estimated model as well as uncertainty of the TN reference condition and boundary values into account. A similar classification scheme for phytoplankton carbon biomass is under development.
The relationship between individual phytoplankton classes and TN was also examined. A few classes correlated with TN but at present the use of taxonomic composition is not fully developed.
Table 1. Suggested reference conditions and boundary values for summer (May-September) phytoplankton biovolume (mm3/L) computed from corresponding values of TN concentrations (Carstensen et al. 2008). Boundary values between good and moderate status are highlighted.
Ref. cond. / H/G / G/M / M/P / P/BHevring Bugt / 0.69 (±0.05) / 0.73 (±0.06) / 0.81 (±0.08) / 0.89 (±0.11) / 0.94 (±0.13)
Horsens Fjord / 0.80 (±0.05) / 0.93 (±0.06) / 1.16 (±0.07) / 1.41 (±0.10) / 1.55 (±0.11)
Køge Bugt / 0.69 (±0.05) / 0.73 (±0.05) / 0.79 (±0.07) / 0.87 (±0.08) / 0.92 (±0.09)
Lillebælt / 0.69 (±0.05) / 0.72 (±0.05) / 0.79 (±0.06) / 0.86 (±0.07) / 0.91 (±0.07)
Løgstør Bredning / 0.91 (±0.05) / 1.12 (±0.06) / 1.49 (±0.09) / 1.86 (±0.13) / 2.08 (±0.16)
Nissum Bredning / 0.83 (±0.05) / 0.98 (±0.06) / 1.24 (±0.08) / 1.51 (±0.10) / 1.67 (±0.13)
Nissum Fjord / 1.25 (±0.06) / 1.69 (±0.10) / 2.42 (±0.19) / 3.14 (±0.32) / 3.56 (±0.39)
Nordlige Kattegat / 0.69 (±0.05) / 0.72 (±0.06) / 0.78 (±0.07) / 0.85 (±0.09) / 0.90 (±0.11)
Odense Fjord Ydre / 0.92 (±0.05) / 1.14 (±0.06) / 1.51 (±0.09) / 1.89 (±0.13) / 2.12 (±0.16)
Præstø Fjord / 0.86 (±0.06) / 1.03 (±0.08) / 1.33 (±0.12) / 1.64 (±0.17) / 1.82 (±0.20)
Roskilde Fjord / 0.90 (±0.05) / 1.11 (±0.06) / 1.46 (±0.08) / 1.83 (±0.12) / 2.04 (±0.14)
Skive Fjord/Lovns Bredning / 0.98 (±0.05) / 1.23 (±0.06) / 1.67 (±0.09) / 2.12 (±0.15) / 2.38 (±0.18)
Sydfynske Øhav / 0.71 (±0.05) / 0.76 (±0.06) / 0.87 (±0.07) / 0.97 (±0.08) / 1.04 (±0.09)
Vadehav indre / 0.86 (±0.05) / 1.04 (±0.06) / 1.35 (±0.07) / 1.67 (±0.10) / 1.85 (±0.13)
Vadehav ydre / 0.82 (±0.06) / 1.14 (±0.06) / 1.66 (±0.09) / 2.17 (±0.15) / 2.46 (±0.20)
Vejle Fjord / 0.75 (±0.05) / 0.84 (±0.06) / 1.00 (±0.07) / 1.17 (±0.09) / 1.27 (±0.10)
Århus Bugt / 0.67 (±0.05) / 0.70 (±0.05) / 0.74 (±0.06) / 0.79 (±0.07) / 0.81 (±0.08)
Øresund Nord / 0.68 (±0.05) / 0.71 (±0.05) / 0.76 (±0.05) / 0.82 (±0.06) / 0.86 (±0.06)
Carstensen, J., Krause-Jensen, D., Dahl, K. & Henriksen, P. 2008: Macroalgae and phytoplankton as indicators of ecological status of Danish coastal waters. National Environmental Research Institute, University of Aarhus. 90 pp. - NERI Technical Report
No. 683. Available at:
Carstensen et al. (2006) proposed a definition for identification of blooms and used this definition to investigate the underlying mechanisms of summer blooms and their link to nutrient enrichment in Danish estauaries. Blooms were defined as chlorophyll a observations deviating significantly from a normal seasonal cycle; the frequency and magnitude of these deviating observations characterized bloom frequency and intensity. The definition was applied to a large monitoring data set from five estuaries in Denmark with at least biweekly sampling. Four mechanisms with links to nutrient enrichment were identified as sources of summer blooms: (1) advection from biomass-rich inner estuary, (2) resuspension of nutrients and algae from sediments, (3) nutrient releases from sediments during hypoxic conditions, and (4) decoupling of benthic grazers. Bloom frequency and intensity decreased from 1989 to 2004, corresponding to decreases in nutrient inputs and concentrations, but only bloom frequency could be directly linked to the actual total nitrogen concentrations, whereas bloom intensities depended on site-specific features, particularly a threshold response for stations exposed to hypoxia. Bloom frequency has increased over longer timescales in response to nutrient enrichment.
The identified relation with TN suggested that bloom frequency may be used as an ecological indicator in relation to eutrophication, but the complexity of bloom mechanisms, evident by the large variation around the regression line, questions if bloom frequency is also a precise indicator useful for assessing ecological status. Inclusion of site-specific features combined with data on driving forces, typically wind, may reduce the random variation, at the cost of indicator generality. Thus, the frequency of summer blooms in Danish estuaries is most likely higher today than under pristine conditions, but it will require large amounts of data and large changes in nutrient conditions to document significant changes.