Dancing with the Tides Figure 3. Wavelet analysis of the tidal data. (A) Time series of water level measured at station Sheerness in 2004, with the corresponding (B) wavelet power spectrum and (C) global wavelet power spectrum. (D) Time series of tidal range, with the corresponding (E) wavelet power spectrum and (F) global wavelet power spectrum. The wavelet power spectra in (B) and (E) are presented as contour plots, where the y-axis plots the periodicities in the time series, and the x-axis plots how these periodicities change over time. Color coding indicates the wavelet power, ranging from low power in blue to high power in red. Red areas surrounded by black contour lines enclose significant regions in the wavelet power spectra (i.e., regions of .95% confidence that the local wavelet power exceeds red noise). Shaded areas on the left-hand and right-hand side of the two thick black lines in (E) represent the cone of influence, where edge effects might distort the signal. Results in the cone of influence are therefore excluded from further analysis. Panels (C) and (F) show global wavelet power spectra of the time series (solid lines). Peaks exceeding the 95% confidence level of the corresponding red noise spectra (dashed lines) are significant. doi:10.1371/journal.pone.0049319.g003 years the monitoring data contained one or more larger gaps due to fouling of the sensors, such that we could not use those years to reliably assess the full sequence of events during the spring bloom. Discussion Fluctuations Driven by the Semidiurnal Tidal Cycle Our results show that short-term fluctuations of coastal phytoplankton were dominated by periodicities of 6 hours 12 min, 12 hours 25 min, and to a lesser extent 24 hours, reflecting the typical periodicities in tidal current speed, horizontal displacement of tidal waters, and the day-night cycle, respectively. Likewise, SPM also fluctuated at 6-hour and 12-hour periodicities, in phase with the chlorophyll fluctuations. One possible explana- tion for the in-phase fluctuations of chlorophyll and SPM could be that SPM is largely composed of phytoplankton. We therefore estimated the relative contribution of phytoplankton to SPM. Assuming a phytoplankton biomass to carbon ratio of 2.5 [37] and a carbon to chlorophyll ratio of 40 (for marine diatoms in nutrient- rich temperate waters; [38]), then 1 mg m23 of chlorophyll should contribute 100 mg m23 of SPM. In view of the measured concentrations of chlorophyll and SPM in our time series, this implies that phytoplankton comprised less than 5% of the SPM concentration throughout the entire year, except during the spring bloom when phytoplankton increased to ,20% of total SPM. However, during the spring bloom the phytoplankton dynamics diverged from the SPM dynamics, as the decline in SPM concentration was accompanied by a rise in chlorophyll concen- tration. For these reasons, the in-phase fluctuations of chlorophyll and SPM cannot be explained by the assumption that SPM consisted mostly of phytoplankton particles. PLOS ONE | www.plosone.org 6 November 2012 | Volume 7 | Issue 11 | e49319