Definition of Seasonal Phytoplankton Events for Analysis of Long Term Data

Transactions on Ecology and the Environment vol 65, © 2003 WIT Press, www.witpress.com, ISSN 1743-3541 Definition of seasonal phytoplankton events for analysis of long term data from coastal waters of the southern Baltic Sea with respect to the requirements of the European Water Framework Directive T. Rieling, S. Sagert, M. Balmwart, U. Selig & H. Schubert Institute of Aquatic Ecology, University of Rostock, Germany. Abstract The key feature of the European Water Framework Directive (EU-WFD) is to measure water quality with respect to ecological categories, i.e. in terms of the community structure and functioning of natural ecosystems. For the use of phytoplankton data in comparative ecosystem assessment it is important to compare similar seasonal phytoplankton stages since phytoplankton runs through different succession stages throughout the year, all characterised by differences in biomass and taxonomical composition. In this paper calculable seasonal markers are suggested from the analysis of a long term data set indicating important successional stages at different types of coastal waters in the southern Baltic Sea. In order to test the applicability of the temporal markers for water quality assessment, phytoplankton biomass was calculated for the defined seasonal events and compared to annual mean biomass calculations. While the calculation of the annual mean failed to indicate different trophic states, the event based biomass calculation showed clear differences between sampling sites, reflecting the pronounced differences between the stations concerning their nutrient regime. Thus, the proposed phytoplankton event definition is a recommendable tool for the application of biological metrics in evaluating coastal ecosystems with respect to the needs of the EU-WFD. Transactions on Ecology and the Environment vol 65, © 2003 WIT Press, www.witpress.com, ISSN 1743-3541 104 Water Pollution \//I: Modclling, Mca~uringand Prediction 1 Introduction The EU Water Framework Directive (WFD) aims to establish a framework for the protection of inland, transitional and coastal waters with significant emphasis on the ecological status of aquatic ecosystems. The key feature of the directive is to measure quality with respect to ecological categories, i.e. in terms of the community structure and functioning of natural ecosystems. For this purpose the directive demands considering phytobenthos, zoobenthos, fish and phytoplankton for the assessment of the ecological state. Despite a long tradition in ecological studies at the southern Baltic coast [l, 2, 3, 4, 51 no biological evaluation system exists for the indication of the ecological state, so far. This lack is mainly caused by the extreme variability of hydrological and geochemical parameters in the coastal area (salinity, water exchange, resuspension events, nutrient loading fiom the sediment), making it difficult to assign especially specific phytoplankton pattern to certain load conditions. Thus, a typology of coastal waters into distinct ecoregions in the sense of the EU-WFD is necessary for the development of standardised evaluation procedures. A second problem in the biological assessment of ecosystem, again especially for the use of phytoplankton as indicative biocoenoses for ecosystem evaluation, is a high annual variability of phytoplankton biomass and taxonomic composition. Due to high reproduction rates phytoplankton runs through different succession stages throughout the year all characterised by differences in biomass and taxonomical composition [6, 71. Therefore, it is important to define these succession stages for analysis of long term data, to guarantee the analysis of comparable seasonal phytoplankton stages (e. g. spring bloom, summer maximum). Moreover, the specific hydrodynamic features (salinity gradient, dimictic shallow waters) of the Southern Baltic coast make it difficult to apply well known seasonality models for phytoplankton cycles in lakes and marine waters to predict the occurrence of seasonal stages in time and space. Thus, there is a need for the development of reliable procedures to define seasonality in various coastal waters. Potentially, indicators for seasonality can be derived from the analysis of long term data sets. In this work, phytoplankton data fiom 23 coastal stations at the southern Baltic Sea were analysed, to search for specific markers of seasonal stages. 2 Material and methods Altogether 2985 data sets from 23 sampling sites were used for the analysis. Samplings were made from 1988-1999 in irregular intervals of 2-8 weeks by the Landesamt aNaturschutz und Geologie of Mecklenburg-Vorpommern, LUNG (Germany). The sampling area covered a broad spectrum of oligo- to mesohaline sites along the coast of Mecklenburg-Vorpornmern, which represents an almost representative set of sites along the different coastal types of the southern Baltic Sea (Fig. 1). Phytoplankton was sampled from surface waters with a Niskin bottle sampler, fixed with formaldehyde and counted using an inverse Transactions on Ecology and the Environment vol 65, © 2003 WIT Press, www.witpress.com, ISSN 1743-3541 microscope according to UTERMOHL [g]. Phytoplankton biovolume was calculated approximating the cell shape to simple geometrical solids according to ROTT [93. Taxonomic resolution was performed on the species level as far as possible. For the analysis of seasonal patterns single species were aggregated to higher taxonomical classes (Cyanobacteria, Chlorophyta, Cryptophyta, Bacillariophycea, Dinophycae). Due to the high year-to-year shifts of phytoplankton biomass at each station it was not possible to define seasonal events from threshold values of indicative phytoplankton groups. Also the frequently used stacked ordination of the relative proportion of group-specific biomass to total phytoplankton biomass did not show clear seasonal pattern due to high variability and loss of information from less abundant taxa. Though, phytoplankton biomass of a specific taxon was set in relation to total biomass (biomassi * biomass total-'), and the occurrence of a specific seasonal event (eventi) was defined as the situation when the relative portion of a defined taxon exceeded its annual mean value by 1 standard deviation (c)(equation 1). N biomass, biomass, 4 biomasslo,o, + biomassi >( ) G event, equation (1) biomassIolnl bi~mass,,,,~ From this calculation a binary monthly matrix of events over the time of investigation was obtained, indicating a temporal window when the relative proportion of a single group (Cyanobacteria, Chlorophyta, Cryptophyta, Bacillariophycea, Dinophycae) exceeded the average annual contribution of this group. For comparison between sampling sites, site specific event patterns were compared pair wise for occurrence of common events during the time of investigations and related to the number of months sampled at the same time. The calculated similarity matrix served as the basis for cluster analysis using NCSS. For cluster analysis similarity values were transformed to dissimilarities (dissimilarity = 1 - similarity value) and clustered using Complete Linkage procedure to maximize selectivity between the stations. To gather information about the power of the event based analysis to indicate a good ecological state, 5 stations different in their degree of nutrient loading from the river Odra were analysed for their degree of eutrophication. For this purpose, mean phytoplankton biomass at a defined taxonomic seasonal event (diatom event) and the mean biomass over the year were calculated for each station over the entire investigation period. In order to test the applicability of the obtained values for water quality assessment, calculated biomass was compared to a biomass based system of classification suggested by HEINONEN[l01 Transactions on Ecology and the Environment vol 65, © 2003 WIT Press, www.witpress.com, ISSN 1743-3541 106 Water Pollution \//I: Modclling, Mca~uringand Prediction Longitude (E) Figure 1: Sampling area and location of sampling stations. 3 Results The analysis of the occurrence of different seasonal taxonomic events revealed clearly different temporal and spatial patterns at different sampling sites of the Southern Baltic coast. Considering the whole data pool, the number of Cyanobacteria and Chlorphyta events ranged from 0-3 events a-' while the number of Bacillariophycae, Dinophycea and Cryptophycea events ranged from 0-4 events a 'I. No negative correlation was found for the number of missing data values and the number of events at a sampling site, indicating the screening frequency to be high enough to identify the maximum number of events. In order to estimate the spatial heterogeneity between the sampling sites, stations were clustered by their similarity in the distribution of seasonal events in the entire sampling period. Figure 2 shows a dendrogram of the cluster analysis on the basis of transformed percentual similarity between individual station pairs. Setting a threshold value of 0.35, stations divided into three groups with different seasonal event patterns, being well related to their geographical exposition to freshwater influence from the main rivers. Cluster 1 covers sampling sites UW1-UW6 located in the Warnow estuary. Cluster 2 includes stations strongly influenced by the hydrodynamic regime of the river Odra (OB1- 0B4) and the eutrophicated stations S23, S66 and GB 19, located in the transition zone between the inner and the outer coastal waters of the investigation area. The third cluster mainly consists of

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