Identifying cardinal dates in phytoplankton time series to enable the analysis of long-term trends
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Phenology and seasonal succession in aquatic ecosystems are strongly dependent on physical factors. In order to promote investigations into this coupling, methods of characterising annual time series of phytoplankton were derived and applied to a 31-year data set from Saidenbach Reservoir (Saxony, Germany). Field data are often scarce and irregularly sampled, particularly in the transition period from winter to spring, so reliable methods of determining cardinal dates in the time series are necessary. The proposed methods were used to determine the beginning, maximum and end of the spring mass development of phytoplankton by estimating the inflexion points (A), fitting a Weibull-type function (B) and fitting linear segments to the logarithmic values (C). For the data set from Saidenbach Reservoir, all three methods proved to be relevant to the analysis of long-term trends. Differences between the maxima determined by the different methods seemed small, but there were deviations when the maximum was related to physical factors such as ice-out. The Weibull-type fit gave the most reliable and comprehensible results and is recommended for trend analyses. For all methods, long-term analysis of the duration of the spring mass development and the duration of the spring full circulation revealed a period of consistently low values (1975–1990) followed by a period of higher values (1990–2005). These periods were also identified for the date of ice-out, although in this case there was a period of high values followed by a period of low values. A sensitivity analysis that compared results from subsampled time series with increasing time intervals indicated that a minimum of one sample every three weeks is needed to obtain reliable results.
KeywordsSpring mass development Climate change Saidenbach Reservoir Limnology Physical–biological coupling Peak detection
This study is supported by the German Research Foundation (DFG) under grant (PA 1202/1) of priority programme 1162 AQUASHIFT. The authors are grateful to Karsten Rinke and Stephan Hülsmann for fruitful discussions, Otto Richter for bringing up the Weibull function, and two anonymous referees for their detailed, persevering and constructive suggestions and their encouragement of the sensitivity analysis.
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