During the assessment period cyanobacteria blooms were most intensive in 1999 and 2000. In the year 2003 the index was about the average. In the consecutive years the bloom intensity showed large variation.
In 2003, index shows a minor increase of toxic Nodularia spumigena, while Aphanizomenon flos-aquae stays at the same level as previous two years.
Results and assessment
Relevance of the indicator for describing developments in the environment
The cyanobacteria bloom index integrates the two main bloom forming and nitrogen fixing cyanobacteria A. flos-aquae and N. spumigena bloom intensity during the whole growth season in the Baltic Sea. It allows interpretation of cyanobacteria bloom intensity trends over the years. Index for the toxic species N. spumigena shows separately the intensity of the blooms of that species. Blooms of cyanobacteria are associated with high concentrations of phosphate in relation to inorganic nitrogen. In addition, weather conditions, especially water temperature and wind regime have an effect on bloom formation.
Policy relevance and policy references
Initiatives to reduce loads of phosphate and nitrogen to water are key to reducing cyanobacterial biomass. In addition, initiatives to decelerate global warming ought to be strengthened in order to reduce cyanobacteria bloom intensity and duration. There exist no target values for these indices but the means allow judgement of the bloom intensity of a particular year.
In period from 1997 to 2003, the cyanobacteria bloom index varied from 550 of 1998 to 900 of 1999 (Figure 1A). The index was above the mean in 1999 and 2000 and clearly less than the mean in 1997, 1998 and 2002. Large variability is inherent to cyanobacteria bloom intensities in the Baltic Sea as shown by the difference of the indices of the years 1998 and 1999. The development of the toxic N. spumigena index follows the same patterns as that of cyanobacteria bloom index. In average, slightly more than one third of the blooms consist of toxic N. spumigena (Figure 1B).
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Rantajärvi, E., Olsonen, R., Hällfors, S., Leppänen, J.-M. & Raateoja, M. 1998: Effects of sampling frequency on detection of natural variability in phytoplankton: unattended high frequency measurements on board ferries in the Baltic Sea. – ICES Journal of Marine Science 55(4):697-704.
Ruokanen L., Kaitala S. Fleming V., Maunula P., 2003: Algaline: joint operational unattended phytolankton monitoring system in the Baltic Sea. In: Dahlin, H., Flemming N.C., Nittis K., and Petersson S.E. (eds.); Building the European capacity in Operational Ocenography. Elsevier Ocenography Series 69: 519-522.
Sivonen K., Kononen K., Carmichael W.W., Dahlem A.M., Rinehart K.L., Kiviranta J. & Niemelä S.I. 1989. Occurrence of the hepatotoxic cyanobacterium Nodularia spumigena in the Baltic Sea and structure of the toxin. Appl. Environ. Microbiol. 55:1990-1995.
1. Source: Finnish Institute of Marine Research, contact person Vivi Fleming.
2. Description of data: Original unit of measure is obtained by microscopical observation of samples and by ranking them semiquantitatively in ranks from 1 to 5. Original purpose of the data was phytoplankton monitoring of FIMR Alg@line project.
3. Geographical coverage: Gulf of Finland, Archopelago and Åland Seas and the Baltic Sea Proper along the ferry route between Helsinki and Travemünde (Figure 3). For each crossing, eight samples are microscopically analysed.
Figure 2. Sampling points along the ferry route between Helsinki and Travemünde.
4. Temporal coverage: 1997-2003.
5. Methodology and frequency of data collection: Automated flow-through sampling system on merchant ships, sampling depth ca. 5 m. Samples for microscopical analyses of cyanobacteria are collected at eight of the 24 sampling points (Figure 3) during the period from February/March to October/November.
6. Methodology of data manipulation: The indices are calculated by integrating the area under the local regression curve representing the occurrence of cyanobacteria (A. flos-aquae and N. spumigena) or toxic N. spumigena alone between days 100 and 300 of the particular year (Figure 1 B). Over the years the means of the indices are used as a reference point.
7. Strength and weakness (at data level): The indices are based on large amount of data, which increase reliability of the indices. The indices can be applied for the whole Baltic Sea as well as for local areas, and for semiquantitative as well as biomass data.
8. Reliability, accuracy, robustness, uncertainty (at data level): Semiquantitative ranking is based on expert judgement, which allows for a certain amount of uncertainty due to possible differences in subjective judgement of individual plankton counters. Nevertheless, the present data have been produced by a single counter.
9. Further work required (for data level and indicator level):