Table 1. Main characteristics of the mooring station Warp. Variable Latitude – Longitude Monitoring years Water depth (m) Tidal range (m)* Salinity (-) SPM (mg L21) Temperature (uC) Value 51.31 N - 1.02 E 2001– present 15 4.3 33.7 (w) –34.3 (s) 33.6 (w) –17.3 (s) 6.1 (w) –18.6 (s) Winter-averaged values (w) of salinity, SPM and temperature are based on mooring data of January – February; summer-averaged values (s) on July – August. *measured at the nearby station Sheerness. doi:10.1371/journal.pone.0049319.t001 the samples were measured by filtering known sample volumes through pre-weighed 0.4 mm polycarbonate filters and subsequent rinsing with 2650 mL ultrapure water. Filters were then dried in a desiccator at room temperature and weighed until filter weight remained constant. Calibration curves of in situ Seapoint turbidity measurements versus SPM concentrations in the water samples had a R2 of 0.72 when averaged over all monthly samples. The concentration of total oxidisable nitrogen (hereafter referred to as nitrate) was measured with a NAS-3X nutrient analyzer (EnviroTech). Salinity and temperature were measured using an FSI CT sensor (Falmouth Scientific Inc.). Salinity measurements were calibrated using a Guildline 8400B salinom- eter, which had been standardized with IAPSO standard seawater. Downwelling PAR was measured at 1 and 2 m depth using two LiCor (LI-192) underwater quantum sensors (LiCor Biosciences). The SmartBuoy measurements went through a Quality Assurance protocol within the SmartBuoy Data Management System, checking all data manually for possible sensor malfunction and biofouling. For instance, fluorescence measurements directly before each monthly service were compared with those directly after monthly service to identify signatures of biofouling. Data that looked suspect were flagged and not used in the analyses. Phytoplankton species composition at the mooring station was determined on an approximately monthly basis. Water samples (150 mL) taken at 1 m depth were preserved with 2.5 mL acidified Lugol’s solution, and analyzed by inverted microscope after 12 hours of settling in a 25-mL glass chamber [36]. Tidal data were obtained from a tide gauge at the nearby coastal station Sheerness (Fig. 1). Inspection of co-tidal lines of the amphidromic system of the North Sea indicated that the tidal wave arrives almost simultaneously at both station Warp and station Sheerness. The tide gauge measured sea water levels at 10 min intervals. The tidal range was calculated as the difference between the maximum and minimum water level of each day. When tidal data from station Sheerness were missing, we resorted to tidal data measured at platform K13A in the central North Sea to approximate the spring-neap cycle at station Sheerness. Data Preprocessing Because the sampling interval of the automated measurements varied between instruments and between years, we calculated hourly averages to obtain a more uniform dataset. We also calculated daily averages of all variables, where the chlorophyll data were confined to measurements made in the dark (i.e., when observed PAR at 1 m depth was ,1 mmol quanta m22 s21), to Dancing with the Tides remove possible effects of non-photochemical quenching of chlorophyll fluorescence. We analyzed the hourly averaged and daily averaged time series separately to investigate variability at different time scales. To analyze periodicities in the time series, we preprocessed the hourly averaged and daily averaged data of chlorophyll concen- tration, SPM concentration, salinity and temperature following a three-step procedure. First, we log-transformed the data using the natural logarithm. Log transformation reduces the skewness of the data sets, and suppresses the impact of isolated large peaks. Second, we smoothed the log-transformed data using a 3-point moving average filter (i.e., 3-hour and 3-day moving averages for the hourly and daily time series, respectively) to reduce the effect of small-scale variability caused by e.g. measurement noise, inter- mittent turbulence or local patchiness. We used these small intervals for the moving average filter to avoid smoothing out relevant periodicities of the tidal cycle. Third, we calculated the rates of change from the difference between the data values at time t and time t-Dt, where Dt represents a single time step of one hour or one day. Because we had log-transformed the data, this procedure effectively resulted in a time series of the relative rates of change (‘relative growth rates’) of the measured variables, since. d ln C dt ~ 1 dC C dt We analyzed the rates of change of the data instead of their concentrations, because concentrations are the result of growth and decay processes during the preceding period whereas instantaneous rates of changes better reflect the conditions at the time of measurement. Wavelet Analysis We used wavelet analysis [27,28] to identify dominant periodicities in the time series. Wavelet analysis makes use of local periodic functions, known as wavelets. By decomposing the fluctuations of time series into a series of local wavelets (expressed as local wavelet power spectra), one can analyze both the frequency (periodicity) and the timing of the fluctuations. The global wavelet spectrum is the average of all these local wavelet power spectra and is comparable to the power spectrum of traditional spectral analysis. Different wavelet shapes (mother wavelets) are available for wavelet analysis. We have used the Morlet wavelet, which provides a good balance between time and frequency localization [29]. We applied wavelet coherence, an extension of wavelet analysis, to investigate the ‘coherence’ of two time series [28,29]. Wavelet coherence enables detection of similar periodicities in the fluctuations of two time series and estimates their phase differences. The statistical significance of periodicities revealed by wavelet analysis was tested by comparing the wavelet power spectrum of our time series against the 95% confidence level of wavelet power spectra generated by red noise, using an autoregressive AR1 model with the same autocorrelation coefficient as our time series [27,29]. Likewise, we tested whether peaks in the wavelet coherence spectra of two time series were significantly different from the wavelet coherence spectra generated by two red-noise processes with the same autocorrelation coefficients as the two time series. The Matlab scripts for wavelet analysis and wavelet coherence analysis, including significance tests, were developed by Torrence and Compo [27] and Grinsted et al. [29] (available at http://www.pol.ac.uk/home/research/waveletcoherence/). PLOS ONE | www.plosone.org 3 November 2012 | Volume 7 | Issue 11 | e49319