Monitoring Strategies For Phytoplankton in The Baltic Sea Coastal Waters


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Phytoplankton monitoring in the Baltic Sea is to a large extent harmonised through the HELCOM COMBINE protocol. This ensures that the methods of sampling and analysis are quite similar and that data should be relatively comparable. There are differences in the spatial and temporal coverage of samples taken within the different monitoring programs. Moreover, within the national monitoring programs there can be large variations in the number samples taken at different stations, between years and during the year. Most monitoring stations are sampled more frequently during summer. Although the chlorophyll a and species-specific phytoplankton biomass has been measured routinely and by standard methods since the early 1970s, most national monitoring programs have had a reasonable monitoring effort after about 1990 only. New methods for collecting data, such as ships-of-opportunity and remote sensing, provide additional information to the traditional shipboard sampling and other new emerging technologies may provide alternative means for monitoring phytoplankton.

We investigated the variation in phytoplankton biomass on the basis of the CHARM phytoplankton database and proposed a statistical method to improve the precision of biomass indicators. The precision of the annual phytoplankton biomass can be greatly improved by taking the seasonal variation into account, but describing the correlation structure in data contributes to improved precision as well. This latter method attempts to separate variations in phytoplankton biomass into systematic and random variations, thereby obtaining more correct estimates of the residual variance.

Consequently, the number of observations required to obtain a given precision could almost be reduced by 50%, simply by interpreting data from another perspective. Nevertheless, variations in the phytoplankton biomass are still substantial and it may not be realistic to expect precisions below 30% from biweekly to monthly sampling. However, it is possible that improved modelling of the variations by including covariables may reduce the residual variance even further, improve the precision and thereby reduce the monitoring requirements, but this will require more detailed analysis that are outside the scope of the present work.

Sampling several monitoring stations will increase the number of observations used to characterise given water bodies and consequently improve the precision. However, if monitoring stations are located too close to each other there is a risk of information redundancy. Our analysis of spatial correlation from the Gulf of Finland and the Curonian Lagoon suggests that distances between stations should not be less than 5 km for more enclosed areas such as bays, lagoons, and estuaries, and approximately above 15 km for open waters. Distances above 10 km for coastal areas may prove reasonable.

Monitoring within the Water Framework Directive (WFD) aims at classification on an Ecological Quality Ration (EQR) scale, although classification based on uncertain information has not yet been operationally considered in the Common Implementation Strategy (CIS). Classification of phytoplankton biomass on an EQR scale will most likely require a precision less than 10% to obtain confidence intervals within a single classification level. Otherwise, it will be difficult to obtain a distinctive univocal classification. The concept of uncertainty for classifications needs to be stressed and forwarded to the working groups under CIS.

More work will still be needed to identify robust indicators for the structural changes of the phytoplankton community due to nutrient loading (and eventually also other) pressures. While such phytoplankton classification metrics are still under development, some phytoplankton parameters could be suitable to be used in the identification of the areas in risk of failing the environmental objectives (Article 5 of the WFD). However, it is important to conduct a similar analysis of variability and precision for the indicators of other biological quality elements for prioritisation of the monitoring efforts.

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