Long-term Field Observations on Seasonality in Chlorophyll-a Concentrations in a Shallow Coastal Marine Ecosystem, the Wadden Sea
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- Philippart, C.J.M., van Iperen, J.M., Cadée, G.C. et al. Estuaries and Coasts (2010) 33: 286. doi:10.1007/s12237-009-9236-y
Analyses of long-term field observations (1974–2007) on chlorophyll-a concentrations in the western Wadden Sea showed no long-term trends in the timing of the wax and wane of phytoplankton spring blooms. There is weak evidence, however, that the height of the autumn bloom has decreased since the early 1990s. This fading of the autumn bloom may have had consequences for the carbon transfer to higher trophic levels, currently hampering primary consumer species that mostly rely on food supply during late summer. Current and other findings suggest a shortening of the growing season due to the fading of the autumn bloom in the Wadden Sea and a lengthening of the growing season due to an advancement of the spring bloom in the North Sea. These regionally different changes in seasonality may have contributed to the coinciding decrease in bivalve filtering capacity in the western Wadden Sea and the large-scale offshore shift of juvenile plaice from the Wadden Sea to the adjacent North Sea.
KeywordsLong-term dynamicsPhytoplanktonChlorophyll-aPhenologyCoastal marine ecosystems
In temperate marine waters that get stratified during summer, time series of phytoplankton biomass show recurring spring bloom events to begin anytime from late winter to early spring (Townsend et al. 1994). Each bloom is characterized by a period of rapid algae growth followed by a crash in population numbers, after which there is a period where phytoplankton numbers remain minimal. Hereafter, phytoplankton may bloom again in late summer before numbers get low for a longer period during the cold and dark winter. Shallow non-stratified coastal waters, such as estuaries, lagoons, and bays, however, are very diverse systems, and the relative importance and interplay of the various factors that can potentially affect bloom initiation and development show much more variation. For example, such waters may be characterized by series of pulses of growth during spring and summer (e.g., Colebrook and Robinson 1965; Riley 1967; Cadée 1986a). Typical patterns of phytoplankton variability over years include episodic blooms, cyclical blooms, seasonal shifts, long-term trends, stochastic blooms, and novel appearances (Smayda 1998).
The underlying mechanisms that control the timing of onset and duration of the spring bloom can be very different between areas and between years (Townsend et al. 1994; Cloern 1996; Smayda 1998), depending on the interplay of multiple factors, such as day length, light conditions, nutrient availability, and grazing (Iriogen et al. 2005). Although observed interannual variation in timing of onset and duration of blooms is more often attributed to external forcing such as weather conditions and nutrient concentrations than internal dynamics (e.g., Smayda 1998), modeling exercises suggest that such year-to-year variations can be also be fully explained by multispecies interactions (Dakos et al. 2009).
The timing of onset and duration of blooms is particularly important with respect to food web dynamics in these shallow marine systems. Consumption of the bloom by planktonic heterotrophs and filter-feeding bivalves is expected to be much reduced when the bloom occurs in relatively cold waters, e.g., when phytoplankton blooms relatively early in the year (Prins et al. 1994; Townsend et al. 1994). For invertebrates with a complex lifecycle, optimizing the time of spawning is necessary to match reproduction with the most optimal environmental conditions for the first vulnerable life stages (i.e., the settlement-timing hypothesis by Todd and Doyle 1981). In temperate waters, many larvae of marine macrozoobenthos species are planktotrophic, i.e., their food stores are not sufficient to survive the pelagic phase without ingesting exogenous food (Thorson 1950). Although there is still much debate on whether growth and development of invertebrate larvae are food-limited under natural conditions (e.g., Strathmann 1996), there may be a causal relationship between the timing of the planktonic food supply and subsequent bivalve recruitment success (Philippart et al. 2003).
The present paper deals with the long-term dynamics in seasonality of phytoplankton in the western Wadden Sea. Data analysis is based on a long-term dataset originating from one station near the Marsdiep tidal inlet over a study period of 34 years (1974–2007). Previous analyses of parts of this dataset revealed that the phytoplankton community changed drastically both around 1978 and again around 1988 and that it was relatively stable in-between and hereafter (Philippart et al. 2000). The major changes in phytoplankton community structure coincided with changes in absolute and relative nutrient concentrations (Philippart et al. 2000) and were followed by changes in community structures of macrozoobenthos and estuarine birds (Beukema et al. 2002; Philippart et al. 2007). In this paper, we focus on the between-year variation in the timing of the wax and wane of phytoplankton blooms as derived from chlorophyll-a measurements.
Materials and Methods
Long-Term Field Observations
Chlorophyll-a concentrations were assessed from additional 0.5 to 1 l water samples, filtered over MgCO3-coated filters (Cadée and Hegeman 2002). The concentrations in previous publications were calculated from the difference in spectrophotometric measurements of extinctions at 666 nm before and after acidification of the samples to correct for the extinction by chlorophyll degradation products (phaeopigments) in coastal waters at this wavelength (Lorenzen 1967). Serious doubts on the applicability of acidification to correct for the extinction by phaeopigments (Stich and Brinker 2005) motivated us to use the original data of non-acidified values (total chlorophyll-a) in the present paper. On average, this back-calculation resulted in higher chlorophyll-a concentrations (n = 1,344, r2 = 0.98, CHLnon-acidified = 1.174 CHLacidified).
Changes in seasonality of blooms were examined by means of two approaches, viz. a first approach in which the data of each year were analyzed separately for the timing of the spring bloom by means of determination of maximum and minimum daily changes in chlorophyll-a concentrations and a second approach at which the full dataset was examined which allowed for multiple blooms and possible auto-correlation structures.
The onset of the spring bloom was defined as the date at which the maximum daily increase in the interpolated chlorophyll-a concentrations occurred, for the timing of the breakdown of this bloom we used the date for which the minimum value of the maximum daily increase in chlorophyll-a concentrations was found (Fig. 2b). To determine a more biologically realistic determination of the onset of the bloom, it would have been better to calculate specific net growth rates instead of daily increases. If we had used these growth rates, however, we could not have determined the end of the spring bloom as the minimum value of this parameter (Fig 2c). Results based upon daily changes should, therefore, be considered as consistent but rough approximations of the timing of the spring bloom.
The term µs is the expected chlorophyll-a concentration at time s. The covariate Day is coded as the day of the year that sampling took place and has values between 1 (in 2004) and 364 (in 1993 and 1996). Year has values from 1974 to 2007. The term α is an intercept. Model 2 contains two smoothers; the long-term trend f2, and the seasonal effect is modeled with f1. This model assumes that the seasonal effect is the same in each year. Model 1, on the other hand, allows for a change of the seasonal effect over the years.
The models were compared using the Akaike's information criterion (AIC), a tool to measure the goodness-of-fit of an estimated statistical model and model complexity: if competing models are ranked according to their AIC, the one having the lowest AIC is the best. Calculations were carried out in the mgcv (Wood 2006) package from the software R (R Development Core Team 2008).
Generalized additive model analysis of the day numbers at which maximum and minimum daily change occurred, indicating the respective wax and wane of the spring bloom, as derived from interpolation of long-term field observations of chlorophyll-a concentrations in the Marsdiep tidal inlet between 1974 and 2007
Timing of spring bloom
Estimated AICs for various generalized additive models, with one and two dimensions and a number of correlation structures, of long-term field observations of chlorophyll-a concentrations in the Marsdiep tidal inlet between 1974 and 2007
No. of dimensions
f1(Day) + f2(Year)
f1(Day) + f2(Year)
f1(Day) + f2(Year)
f1(Day) + f2(Year)
f1(Day) + f2(Year)
Rational quadratic correlation
f1(Day) + f2(Year)
Rational quadratic correlation
For all models that do include correlation structures, models with one 2-dimensional smoother have lower values for AIC than those with two 1-dimensional smoothers (Table 2). This result implies that the seasonal effect was not the same in each year but has changed between 1974 and 2007.
Generalized additive mixed model analysis (n = 1,370, r2 = 0.37) with a rational quadratic correlation structure of long-term field observations of chlorophyll-a concentrations in the Marsdiep tidal inlet between 1974 and 2007
The limits of the dataset should be taken into account when interpreting the results of the analyses. First, the datasets of some years have fewer values than others. Second, even with a frequency up to 60 measurements a year, the precise timing of the onset of a bloom is very difficult to determine because wax and wane of phytoplankton may take place at a much shorter time scales, viz. days instead of weeks (e.g., Cadée 1986b). The resulting resolution in the dynamics implies that our findings should only be considered as an indication of actual phytoplankton dynamics (Rolinski et al. 2007).
For the western Wadden Sea, the timing of wax and wane of the phytoplankton spring bloom varied strongly but did not show mean long-term advancements or delays, while the autumn bloom appeared to have weakened between 1974 and 2007 (this paper). Our findings on timing of the wax of the spring bloom are in agreement with observations for the eastern (Wiltshire et al. 2008) and central North Sea (Edwards and Richardson 2004) where the timing of the onset of the bloom was fairly constant during the last decades. There is a suggestion, however, of an advancement of the start and peak of the phytoplankton spring bloom by 1 month along the Dutch coastline in the southeastern North Sea (Gieskes et al. 2007; Baretta-Bekker et al. 2009) since the late 1990s.
The fading of the autumn bloom is in agreement with observations in the northern Wadden Sea where chlorophyll-a concentrations in summer (May–Sept) had decreased since the mid-1990s (Loebl et al. 2008, van Beusekom et al. 2009). This decrease was explained by a combination of an enhanced grazing pressure by benthic filter feeder activity and mesozooplankton due to an increase in water temperatures and a decrease of the riverine nitrogen supply (van Beusekom et al. 2009). In the southwestern part of the Wadden Sea, however, the summed filtering capacity of the major benthic grazers declined during the last decades (Philippart et al. 2007). Unfortunately, we do not have long-term field observations on grazing pressure by zooplankton in this area. Assuming that zooplankton grazing did not increase since the late 1990s, the fading of the autumn bloom could be reflecting the decline of the summer productivity of the phytoplankton as a result of the decrease in riverine nutrient inputs into the western Wadden Sea.
These findings point to regional differences in long-term trends in seasonality, viz. a shortening of the growing season due to the fading of the autumn bloom in the Wadden Sea and a lengthening of the growing season due to an advancement of the spring bloom in the open North Sea. If changes in chlorophyll-a concentrations reflect changes in primary productivity, the disappearance of the relatively high chlorophyll-a concentrations in late summer in the Wadden Sea may have resulted in decreased growth rates in primary consumers and have been restrictive to species that depended on food supply during this season (e.g., to build up reserves to reproduce or to survive low food conditions in winter). These changes in seasonality in the phytoplankton blooms may have contributed to the coincident shift in bivalve species composition, the decrease in bivalve filtering capacity since the late 1980s (Philippart et al. 2007), and the large-scale offshore shift of juvenile plaice from the Wadden Sea to the adjacent North Sea (Pastoors et al. 2000, Grift et al. 2004).
We thank Jan Hegeman for his help in collecting long-term field data, Thalia Watmough for her help in preparing the map, and Jan Beukema, Justus van Beusekom, Corina Brussaard, Louis Peperzak, and three anonymous referees for their comments on previous versions of the manuscript.
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