Long-term trends and short-term variability of water ... - Springer Link

Environ Monit Assess (2007) 127:503–521 DOI 10.1007/s10661-006-9298-2

ORIGINAL ARTICLE

Long-term trends and short-term variability of water quality in Skive Fjord, Denmark – nutrient load and mussels are the primary pressures and drivers that influence water quality F. Møhlenberg · S. Petersen · A. H. Petersen · C. Gameiro

Received: 15 December 2005 / Accepted: 8 May 2006 / Published online: 21 October 2006 C Springer Science + Business Media B.V. 2006

Abstract Nineteen years of monitoring data from the eutrophic Skive Fjord, Denmark were examined for linkages to external pressures and drivers, including nutrient inputs, meteorology and stocks of blue mussels. Linkages were examined by: 1) time-series analysis to document effects of nutrient reduction programs, 2) Pearson Rank correlations, 3) multivariate statistical analysis (PLS) to identify water quality variables with high predictability and their linkages to pressures, and 4) regression analysis to quantify relationships between pressures and water quality. Freshwater input, nitrogen load and phosphorus load showed decreasing trends through the period 1984–2002. The load reductions were only partially translated into trends in water quality: phosphorus decreased in most seasons, while total nitrogen decreased during winter and spring only. Phosphorus concentration had the highest predictability (explained by seasonal temperature variation) folF. Møhlenberg () DHI – Institute for water and environment, Hørsholm, Denmark e-mail: [email protected] S. Petersen Søren Petersen Consult, Frederikssund, Denmark A. H. Petersen Anders Højg˚ard Petersen, Frederiksberg, Denmark C. Gameiro Instituto de Oceanografia, Faculdade de Ciências da Universidade de Lisboa, Lisboa, Portugal

lowed by transparency, silicate, tot-N, chlorophyll-a, primary productivity, phytoplankton diversity and phytoplankton turnover. The variation in pressures other than nutrient input confounded the relations between loads and water quality. High biomass of mussels led to reduced chlorophyll-a and increased transparency, while short-term variability in water column mixing led to changes in chlorophyll-a due to nutrient entrainment and coupling to benthic mussels. Keywords Water quality . Nutrient load . Water Framework Directive . Meteorological forcing . Mussels . Benthic grazing . Time trends . PLS statistics . Predictive models

1 Introduction Over decades, increasing anthropogenic inputs of nutrients have led to deterioration of water quality in estuaries. This is evident in high phytoplankton production and biomass, reduction in depth penetration of benthic plants and decreased concentration of dissolved oxygen in bottom water, with potential impacts on macrofauna (Boesch, 2002, Kemp et al., 2005). In Denmark most receiving estuaries are small and shallow with a limited horizontal and vertical exchange and as the surrounding watersheds support extensive agricultural and urban development, eutrophication is pronounced in many estuaries (Conley et al., 2000). Due to countrywide efforts to reduce nutrient inputs to surface Springer

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waters, phosphorus emission to estuaries has been reduced gradually, reaching ca. 30% of the mid 1980’s load by 1996 (Conley et al., 2000). However, because diffuse sources dominate nitrogen input and N-load is tied to precipitation and land run-off, the temporal variation in N-load is less predictable which makes it difficult to detect trends in eutrophication (Ærtebjerg et al., 2002). On the other hand, natural yearly and short-term variation of diffuse inputs do provide opportunities to quantify relations between loads and responses in estuaries. These relations can then be used for management purposes to extrapolate effects of future nutrient reductions. Numerous factors influence the water quality of an estuarine system, including nutrient loadings from point and diffuse sources, freshwater run-off and meteorological forcing. Together with system characteristics such as tidal amplitude and morphometry external forcings interact on various time scales from daily to annual to determine stratification, retention time, exchange with adjacent waters, light attenuation and concentrations of nutrients, phytoplankton, benthic plants and fauna amongst others. In contrast to deep estuaries that resemble lakes in their ‘Vollenweider’ type response to nutrient loads (Vollenweider, 1976) responses of shallow estuaries often are non-linear and highly variable due to a different degree of coupling between the pelagic and benthic environment (Nixon et al., 2001). Being shallow and eutrophic most Danish estuaries are dominated by benthic filtering organisms such as mussels that under wind driven mixing conditions can exert large grazing pressures on phytoplankton (Møhlenberg, 1995; Møhlenberg, 1999a). Therefore, when analysing variation in water quality long-term data are essential to resolve different scales of variability, and care must be taken to quantify all sources of significant pressures and drivers including nutrient load, meteorology and benthic grazing. Previously, oxygen conditions in bottom waters of Skive Fjord, an eutrophic estuary located in the Limfjord belt in Jutland, Denmark have been shown to couple tightly to N-load after the influence of meteorological forcing was ‘filtered out’ (Møhlenberg, 1999b). This study extends the analysis and examines the influence of nutrient load, meteorology and biological interactions (i.e. benthic grazing control) on other water quality parameters in Skive Fjord such as concentrations of nutrients, chlorophyll-a and Secchi depth. To this end four different approaches were applied: Springer

Environ Monit Assess (2007) 127:503–521

1) time-series analysis of nutrient concentrations and chlorophyll to document effects of the nutrient reduction program initiated in 1988, 2) Pearson Rank correlations, 3) multivariate statistical analysis to identify water quality variables that relates to external forcings, and 4) multiple regression analysis to quantify relationships between nutrient inputs and water quality. Such analyses are important preconditions for a successful implementation of the Water Framework Directive. Turbulent mixing is important for key ecological processes, including phytoplankton growth, gas exchange across the air-sea interface and exchanges of nutrients and biological components between benthic compartment and the water column. The processes will lag differently to changes in mixing intensity according to variations in initial conditions, biomass, and generation times. To these ends, we examined the time scale of estuarine responses to changes in nutrient loads and meteorological forcing.

2 Study Site The Skive Fjord/Lovns Bredning estuary is located in the North-western part of Jutland and empties into the southern part of Løgstør Bredning, which constitutes the largest basin of the Limfjord (Figure 1). Despite its name the Limfjord is actually a belt communicating with the North Sea in the West and the Kattegat in East. Skive Fjord is a micro-tidal (average amplitude 0.15 m) and partially mixed estuary with stratification occurring at high run-off events, low winds and/or high insolations (Møhlenberg, 1999a). The area of Skive Fjord/Lovns Bredning is 105 km2 and the average depth is 5 m. The catchment area is 2315 km2 including the catchment area of the dammed reservoir Hjarbæk Fjord (Figure 1). The total freshwater input averages 0.95 km3 y−1 . All major freshwater inputs are gauged and monitored regularly (weekly – bi-weekly) for flows and concentration of nutrients. Point nutrient sources include outlets from sewer treatment plants (with chemical removal of phosphorus and nitrogen since 1988) and fish farms located in the larger streams. The dominating non-point sources include various activities related to agriculture and pig farming that is very intensive in the catchment area (Conley et al., 2000). The estuary is ranked 10th in area weighted nitrogen load, 7th in concentration of tot-N, 9th in concentration of tot-P and 3rd in chlorophyll-a concentration among


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Fig. 1 Map of Skive Fjord/Lovns showing location of intensive monitoring station (St. 27-1), supplementary station (St. 27-2) indicated by arrows, and positions of mussel samplings (• =

assessment dredging; = frame samples). The Hjarbæk reservoir (hatched area) contributes to run-off and nutrient load (see text).

28 Danish estuaries (Kaas et al., 1996). Other anthropogenic influences include mussel dredging averaging 3500 tons per year, which amounts to about 20% of the standing stock of 10–20000 ton (Dolmer et al., 1999). The traditional fishery has become marginal since the 1960’s due to low fish stocks. The ecosystem has changed markedly during the last century. In the beginning of the 20th century eelgrass covered large areas of the Limfjord extending down to 6–7 m depth (Ostenfeld, 1908) and benthic fauna was dominated by brittle stars and other deposit feeders (Blegvad, 1951). Starting in the 1920’s

and continuing to increase through the 1940’s Mytilus edulis now totally dominate the benthic fauna constituting approximately 90% of the benthic biomass (Limfjordsoverv˚agningen, 2001).

3 Data Material 3.1 Freshwater inflow and nutrient loads Data for freshwater inflow and nutrient load (tot-N and tot-P) were obtained from the monitoring program Springer

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carried out by the local authorities. Nutrient discharges from all major point sources and larger streams have been accounted for, which together constitute 70–80% of the total loads. Diffuse loads from unmeasured catchment areas were calculated from discharge-weighted nutrient concentration adopted from nearby monitored catchments. Details are given by Windolf (1996). The contribution of nutrient loads from Hjarbæk reservoir was reduced by 50% due to estimated retention and denitrification and loads were further delayed by 1 month before entering the estuary (Jensen and Holmer, 1994). The nutrient loads were calculated with a resolution of one month or half a month to match the resolution in water quality variables (see below). To examine the time scale of estuarine responses to changes in nutrient loads inputs were calculated with ‘memories’ varying between 1 and 24 months according to: N(P, Q)LdSM (t) = N(P, Q)LdS(t − dt) + (N(P, Q)LdMonthS − N(P, Q)LdS/M)∗ dt,

(1)

where N(P,Q)LdSM (t) represents the load of nitrogen (or phosphorus or freshwater) to Skive Fjord/Lovns with the memory M (1–24 month), (N(P,Q)LdMonthS is the monthly load with N(P,Q) and N(P,Q)LdS/M is the load divided by the ‘memory’, M (1–24 month). Thus, a set of water quality variables (e.g. monthly averages or values from a specific date) can be related to inputs of nutrient and freshwater during the preceding 1–24 months, but with most importance of recent months’ loads and a gradual decrease of importance going back in time. 3.2 Meteorological forcing Meteorological variables, wind speed at 10 m above ground (m s−1 ), solar radiation (W m−2 ) and photosynthetic available radiation (PAR) were obtained from a nearby meteorological station. It was assumed that all wind directions contributed equally to wind stress, because of the location of the sampling station in the middle of the estuary. Hourly values were averaged from noon to noon on consecutive days to match the sampling of water quality parameters that usually occurred around noon. The energy balance of the water column was evaluated by accounting the physical energy input Springer

to stratify and to mix the water (Simpson and Hunter, 1974; Mann and Lazier, 1996). The energy in the wind which is available to increase the thickness of the surface mixing layer was calculated as: √ EK ≈ −C∗d ρa ρ a /ρ ∗ W310

(2)

where ρ a is the density of air (1.25 kg m−3 ), ρ the density of water, Wl0 the wind speed at 10 m above the surface and Cd is a friction coefficient varying between 0.8∗ 10−3 (W10 < 5 m s−1 ) and 2.6∗ 10−3 (W10 > 10 m s−1 ) (Pond and Pickard, 1983). In the interval 5–10 m s−1 Cd is assumed to increase linearly with W10 . The potential energy generated by solar insolation (in case of no mixing) was calculated as: EP = g∗ h ∗ α ∗ Q/2c

(3)

where g is the gravitational acceleration ( = 9.82 m s−2 ), α the thermal expansion coefficient of water (= 1.6 ∗ 10−4 ◦ C−l at 15◦ C), c the specific heat of water (= 4.1 kJ kg−1 ), Q the net gain of heat (W m−2 ) from solar radiation into a layer of thickness h (Mann and Lazier 1991). After inspecting more than 100 CTD profiles from stratified periods, h was fixed at 3.6 m. The balance between mixing (EK ) and stabilising (EP ) energy input (i.e. B = EK + EP ) was then calculated at various resolutions (24 h to 7 days prior to sampling dates in the estuary) and intervals, e.g. average energy balance 3 to 7 days prior to sampling. 3.3 Water quality Water quality parameters have been monitored in Skive Fjord at a fixed station (St. 27-1; E: 9◦ 4.55; N: 56◦ 37,25; depth: 4.8 m) since the early 1980’s using standardised methods, including CTD and dissolved oxygen (DO) profiling, discrete water sampling at 1-2 depths (according to water column structure), analysis of chlorophyll-a, nutrients (NO3 − , NO2 − , NH4 + , tot-N, PO4 3− , tot-P, SiO2 ), DO, Secchi depth, primary productivity, phyto- and zooplankton collected by vertical hauls and benthos using core samplers or manually by scuba diver. Additional sampling (CTD and fluorescence only) has been carried out during summer at a shallower (depth 3.9 m) station (St. 27-2) located 4.2 km south of St. 27-1 (Figure 1). We compared surface chlorophyll-a calculated from in situ fluorescence between the two stations to examine how conditions


at St. 27-1 reflected estuary wide conditions. The annual sampling intensity for pelagic parameters has increased from monthly until 1993, bi-weekly during the period 1993–1996, 3 times per months in 1997 and about weekly thereafter. Water column stratification at individual sampling occasions was calculated as the difference in Sigma-t between bottom (4.5–5 m) and surface water according to the UNESCO Equation (Fofonoff and Millard, 1983). Concentration of inorganic nitrogen species (NO3 − , NO2 − , NH4 + ) was summed and represented as DIN. Phytoplankton species enumeration and estimation of biovolume were carried out using an inverted microscope on settled subsamples from depth-integrated samples. Biovolumes were calculated using geometric equations for the shape of the algae (Olrik, 1991) and specific cell carbon content was based on Edler (1979).

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Fig. 2 Temporal variation in mussel biomass estimated from core, dredge and frame samples. Additional information on population death was obtained after severe anoxia in 1994 and 1997 using video recordings and diver observations. Line represents a combination of interpolation between data and a simple growth model (temperature dependent) developed based on monthly sampling in a nearby area (Møhlenberg unpublished).

6 : 3 : 1). In addition, a biomass reduction of 0.1% d−1 during December-February was imposed due to an assumed respiration loss (Figure 2).

3.4 Benthic filter-feeders 4 Statistical analysis Blue mussels (Mytilus edulis) dominate the benthos in Skive Fjord contributing on average 90% of the total benthic biomass and with a capacity to clear the water column 2–5 times per day (Møhlenberg, 1999a). Temporal variation in mussel biomass was estimated from 4 different sources: 1) 1989–1997 ten core samples taken yearly at St. 27-1, 2) 1993–2001 eight to twelve dredge samples distributed evenly across the estuary and each covering approx. 100 m2 (Dolmer et al., 1999), 3) 1998–2002 twenty five depth-distributed 0.25 m2 frame samples combined with diver observations of mussel coverage along transects , 4) diver observation and video recording in connection with hypoxia events (Figure 2). Sampling was carried out in April/May (all years) and September (core samples in the period 1995–1999, only). Within the study period, variation in mussel biomass is mainly driven by stochastic recruitment, commercial dredging and occasional anoxia events (especially in 1988, 1994, 1997) that can kill major parts of the population. Therefore, mussel biomass as a surrogate for filtration capacity was treated as a forcing variable along with nutrient load and meteorology. The temporal variation in mussel biomass was calculated by a combination of interpolation between sample values and a simple growth model. After 1993 when more than one sampling method was used within the same year, biomass values were weighted roughly reflecting the area sampled on each occasion (i.e. dredging/video surveys : frame sampling : core sampling =

4.1 Time series analysis This analysis covered the period 1984–2002 using monthly averaged values for DIN, tot-N, PO4 , tot-P, chlorophyll-a and inputs of freshwater, nitrogen and phosphorus as external forcings. Temporal trends were examined with the non-parametric, seasonal Kendall’s τ which test for long-term trends and difference in trends between months or seasons (Hirsch et al., 1982). Data were deseasonalised by subtracting the average value calculated from the 19 years’ series from each individual year’s monthly mean. The resulting residuals were then tested for long-term trends with Kendall τ using whole year data or data grouped into seasons. The similarities of the phytoplankton communities between years were assessed with the BrayCurtis similarity index followed by nonparametric multidimensional scaling (MDS) (Warwick and Clarke, 2001). Briefly, standardized Bray-Curtis similarity values were calculated based on species biomass averaged from June through August after omitting rare taxa (i.e. taxa occurring in one or two years only). The similarity matrix was based on 42 different taxa. 4.2 Pearson rank correlations Like most water quality data, nutrient input to, and concentrations of nutrients and chlorophyll-a in Skive Springer

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Fjord deviate from normal distributions. They exhibit seasonality and long-term trends and, moreover, are serially correlated. Except for chlorophyll-a and DIN, none of the variables or their transformations (e.g. log(value + 1) were normally distributed when analysing whole year data. However, analysing data by season (winter: December–February; spring: April– May; summer: June–August or autumn: October– November) most inputs and estuarine concentrations were normally distributed after log-transformation. Noticeable exceptions included freshwater discharge with memories above 7 months, nutrient loads with memories above 6 months and concentration of PO4 and tot-P during summer (Kolmogorov-Smirnov or χ 2 ; p < 0.05). March data were omitted in the analysis because in some years with a late production start March represented winter conditions and in other years with an early production start March belonged to spring. September representing another (variable) breakpoint in the seasonal cycle was likewise omitted in the analysis. Variables were log-transformed and deseasonalised and those showing significant long-term trends (p < 0.05) from the seasonal Kendall τ analysis were detrended using predictive linear regression. Most data were serially correlated, e.g. nutrient load or concentration of nutrients for a particular month was correlated to the values of the previous and following month. To make values truly independent, the autoregressive integrated moving average (ARIMA, Hoff, 1983) procedure was used to calculate the residual between the predicted value from the ARIMA model and the deseasonalised and detrended value. Hence, data for correlation analysis ended up being normally distributed monthly values (when seasonally segregated), without seasonal and long-term trends and independent of the preceding measurements. To correct for Type I errors a modified Bonferroni correction was applied taking account of the average correlation in 11 related tests (i.e. encompassing 2– 12 month load memory) between variable pairs, e.g. N-load2−12month memory vs. DIN in a season (Uitenbroek, 1997). 4.3 Multivariate analysis using PLS Water quality as represented by a suite of variables including concentration of nutrients, algal biomass and physical properties such as transparency of water alSpringer


ways is determined by several factors and processes, and moreover individual water quality variables often are interrelated. Therefore, to reveal relationships analysis must be carried out including all potential and available information characterising the system or a sampling occasion. Only multivariate techniques fulfil these requirements. This analysis covered the period 1989–2002 and included a suite of explanatory variables: N- and Ploads at fortnightly resolution (with memories varying from 1–15 months), water column energy balances (i.e. mixing or stratified) prior to sampling calculated from wind and insolation, PAR, water column stratification at sampling, and mussel biomass. Lack of reliable estimates of mussel biomass prior to 1989 prevented inclusion of data before that year. Water quality parameters were represented by the concentration of nutrients, chlorophyll-a, primary productivity (based on 14 C incubations), phytoplankton diversity and Secchi depth from every sampling occasion. Briefly, a suite of diversity and evenness indices was calculated based on species biomass. Generally, three indices (Margalef’s, Gleason’s and Menhinick’s indices) had the highest predictability (R2 ) which is partly in accordance with previous studies (Danilov and Ekelund, 2001; Kitsiou and Karydis, 2000). Because of a slightly higher R2 the Margalef’s index is used in this study for illustrating the variability of phytoplankton diversity. Samples of water quality parameters taken in the first half of a month were matched by corresponding nutrient loads, e.g. a sample from the 10th of July was matched by nutrient loads calculated for periods 15 February–15 July (i.e. 6 months memory) and so on. We used the multivariate statistics Partial Least Squares of latent structures (PLS) to describe the relationship between two sets of variables, X: external forcings and Y: water quality parameters. Prior to analysis all parameter were log transformed to ensure normal distributed values for the seasonal period chosen (mid May–mid September, see below). PLS starts with a PCA on one data set representing external forcings (X) and then decomposes the X matrix guided by the structure in Y. Generally, the systematic variations in the data are found through projection and modelling of variance and covariance structure of the data matrices. The analysis was carried out using the SIMPLS algorithm (de Jong, 1993) of the program SIMCA-P 8.1 focussing on identifying periods with homogeneous forcing data (PCA-analysis), quantifying the importance of


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Fig. 3 Temporal variation in nutrient loads and concentrations in Skive Fjord: a) N-load (full line) and P-load (stipulated line); b) concentration of monthly averaged tot-N (full line), tot-P (stipulated line).

individual external forcings for explaining variations in water quality parameters and pinpointing water quality variables that exhibit a high predictability. Additionally, PLS was used to identify outliers before multiple regression analysis.

during summer had a significant non-symmetric following a log-normal distribution, i.e. right skewed. 5 Results 5.1 Nutrient load

4.3.1 Multiple regressions The analysis was based on the same data as the multivariate analysis but focused on water quality parameters that varied predictably according to PLS analysis. The analysis aimed at producing simple regressions that easily can be interpreted and used in practical applications such as predicting the effects of reduced nutrient load. To that end values were not log-transformed and despite that data were monthly grouped approximately 25% of explanatory variables and 20% of water quality variables deviated from normal distributions. Inspecting these variables for symmetry only chlorophyll-a

N-load showed a strong seasonal variation (Figure 3, Table 1). Loads were low during dry (summer) months and high during wet (winter) months. The seasonal variation in P-load was less pronounced and in contrast to nitrogen the P-load showed an abrupt decrease after 1988–1989 when tertiary treatment of sewage was being implemented (Figure 3a). N-load in particular tracked the freshwater run-off reflecting the importance of diffuse sources (not shown). The reduction in loads (run-off weighted) after tertiary sewage treatment was much higher for phosphorus (66%) than for nitrogen (15%). In accordance, the molar N:P ratio in loads has Springer

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Table 1 Average of monthly means (SD) of freshwater inflow (Q), nutrient loads, nutrients and chlorophyll-a in Skive Fjord. Data from 1984–2002; n = 15–19. ton km−2 md−1

January February March April May June July August September October November December

1

mg l−1

Q1

N-load

P-load

tot-N

DIN

tot-P

PO4 3−

μg l−1 Chl-a

954.7 (265.0) 961.2 (222.7) 937.4 (210.5) 821.8 (166.8) 695.8 (131.3) 610.6 (107.9) 567.1 (103.9) 573.3 (115.1) 643.0 (146.6) 745.1 (138.6) 799.8 (172.7) 858.2 (187.4)

3.995 (1.193) 3.975 (1.138) 3.898 (0.967) 3.309 (1.033) 2.816 (0.984) 2.113 (0.747) 1.941 (0.691) 1.818 (0.621) 2.010 (0.632) 2.459 (0.637) 2.699 (0.679) 3.193 (0.840)

0.1875 (0.1279) 0.1853 (0.1311) 0.1966 (0.1341) 0.1672 (0.1147) 0.1444 (0.1072) 0.1329 (0.1117) 0.1295 (0.1140) 0.1240 (0.1101) 0.1263 (0.1033) 0.1396 (0.1045) 0.1495 (0.1095) 0.1624 (0.1088)

1.630 (0.493) 1.724 (0.455) 1.662 (0.369) 1.566 (0.370) 1.186 (0.231) 0.928 (0.290) 0.977 (0.315) 0.912 (0.221) 0.905 (0.220) 0.976 (0.234) 1.273 (0.388) 1.248 (0.303)

0.925 (0.346) 1.032 (0.345) 1.004 (0.238) 0.822 (0.238) 0.403 (0.189) 0.109 (0.112) 0.118 (0.135) 0.101 (0.102) 0.143 (0.096) 0.263 (0.157) 0.552 (0.243) 0.636 (0.179)

0.0607 (0.0140) 0.0542 (0.0227) 0.0409 (0.0177) 0.0414 (0.0268) 0.0398 (0.0149) 0.0802 (0.0495) 0.1305 (0.0561) 0.1826 (0.0731) 0.1692 (0.0669) 0.1085 (0.0325) 0.0789 (0.0185) 0.0669 (0.0157)

0.0387 (0.0159) 0.0303 (0.0171) 0.0166 (0.0163) 0.0051 (0.0021) 0.0071 (0.0094) 0.0314 (0.0604) 0.0765 (0.0563) 0.1118 (0.0587) 0.1029 (0.0716) 0.0662 (0.0324) 0.0530 (0.0217) 0.0463 (0.0169)

5.30 (7.66) 6.83 (8.34) 10.56 (12.93) 17.93 (24.36) 14.77 (11.77) 18.71 (9.58) 15.84 (7.77) 22.18 (23.68) 23.00 (30.73) 10.69 (6.46) 6.52 (5.28) 2.96 (2.59)

unit: 1000 m−3 km−2 mo−1

increased from 25 to a high 75 after 1990, suggesting that the production has become even more P-limited.

poral variation of inorganic nutrients generally mimicked the concentrations of total nutrients (not shown).

5.2 Representativness of monitoring station Based on the almost synoptic data pairs of in situ fluorescence the average chlorophyll-a concentration did not differ between stations (average ± SD; St. 27-1: 12.7 ±7.8 μg l−1 ; St. 27-2: 12.0 ±7.3 μg l−1 ), while the individual data pairs on average differed by 28%. Based on a linear regression analysis the concentration of chlorophyll-a at St. 27-1 explained 63% of the variation at St. 27-2 (Figure 4). 5.3 Seasonal variation in water quality Concentration of tot-N closely tracked the load with high concentration during the winter run-off, while concentration of tot-P peaked during summer when external loading was low (Figure 3b, Table 1). TemSpringer

Fig. 4 Relationship between chlorophyll-a measured at intensive monitoring station (St. 27-1) and ‘synoptic’ chlorophyll-a at St. 27-2 located 4.2 km south of St. 27-1. Regression line, regression equation and 75% prediction bands are shown.


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Fig. 5 Temporal variation of monthly averaged chlorophyll-a concentration at station St. 27-1.

Concentration of chlorophyll-a varied seasonally and between years. In years with extended ice cover (longer than one month, i.e. 1985, 1986, 1987, 1996) the seasonal variation was bimodal with peaks recorded early spring under the ice or at ice break followed by a second peak during summer (Figure 5). In warm winter years chlorophyll-a variation usually was uni-modal even though exceptions were recorded (e.g. 1988 and 1997).

5.4 Temporal trends Freshwater input, N-load and especially P-load showed strong negative (i.e. decreasing) trends through the period (Table 2). Except for freshwater input during spring the negative trends occurred in all seasons. However, the decrease of phosphorus was not uniform through time. The highest loads occurred prior to implementation of tertiary sewer treatment in 1988. After the initial sharp decrease P-load has levelled out. Among water quality parameters concentration of totP and PO4 decreased in all seasons except during autumn, while the concentration of tot-N, but not DIN, decreased during winter and spring (Figure 6). Negative trend in concentration of chlorophyll-a was observed during spring, only. The MDS analysis of summer phytoplankton communities showed one distinct group consisting of 7 years (1987–88, 1990, 1993, 1996, 1998, 2000) (Figure 7). With one exception (1988: 17.05◦ C), these years were characterized by water temperatures below average (17.0◦ C), wind energy higher than average

Fig. 6 Temporal trends of tot-N (a), tot-P (b) and chlorophyll-a (c) concentrations calculated for all months. Values are monthly deseasonalised residuals (see text). Lines show long-term trends that were removed from residuals prior to correlation analysis. Springer

512 Table 2 Temporal trends in freshwater input (Q), nutrient loads, nutrients and chlorophyll-a in Skive Fjord. P-value of trends and Kendall Tau (in brackets) calculated for all months (n = 204–226) and seasons1 .


Q (1 mo.) Q (6 mo.) Q (8 mo.) N-load (1 mo.) N-load (6 mo.) N-load (8 mo.) P-load (1 mo.) P-load (6 mo.) P-load (8 mo.) Tot-N

1

winter (December-February, n = 49–56), spring (April-May, n = 37–40), summer (June-August, n = 53–60), autumn (October-November, n = 33–40) 2 ns = trends not significant.

DIN Tot-P PO4 3− Chl-a

All months

Winter

Spring

Summer

Autumn

1.79E-14 (−0.372) 3.05E-13 (−0.354) 1.64E-13 (−0.358) 3.58E-19 (−0.434) 3.84E-30 (−0.554) 8.80E-28 (−0.530) < 1.00E-35 (−0.693) < 1.00E-35 (−0.777) < 1.00E-35 (−0.805) 3.88E-04 (−0.180) ns2 3.68E-08 (−0.276) 3.46E-06 (−0.233) ns2

5.96E-04 (−0.342) 2.11E-04 (−0.370) 2.80E-04 (−0.362) 3.44E-05 (−0.413) 1.69E-09 (−0.601) 9.43E-09 (−0.573) 1.39E-14 (−0.768) 9.18E-17 (−0.829) 6.75E-17 (−0.833) 2.20E-02 (−0.249) ns2 9.47E-07 (−0.525) 3.18E-08 (−0.593) ns2

1.10E-03 (−0.406) ns2

1.02E-06 (−0.487) 5.51E-05 (−0.402) 1.58E-04 (−0.377) 1.35E-06 (−0.482) 6.41E-08 (−0.539) 2.82E-08 (−0.554) 2.30E-11 (−0.667) 4.30E-14 (−0.753) 1.74E-15 (−0.794) ns2

4.58E-03 (−0.353) 9.78E-04 (−0.410) 6.84E-04 (−0.422) 3.70E-04 (−0.443) 2.42E-06 (−0.586) 6.28E-06 (−0.562) 9.24E-08 (−0.664) 1.03E-08 (−0.712) 3.14E-09 (−0.736) ns2

ns2 3.78E-03 (−0.292) 1.77E-02 (−0.239) ns2

ns2 ns2

and the community consistently was dominated by the diatom Skeletonema costatum that on average constituted 65% of the total biomass. In contrast, S. costatum on average contributed with 14% in the other years. In the years with summer temperature above long-term average the community was dominated by the dinoflagellates Gymnodinium sanguineum (1989), Prorocentrum minimum (1992), G. sanguineum/ P. minimum (1995), the diatoms Cerataulina pelagica (1991, 1994, 1997) and Rhizosolenia fragilissima (2002). In 2001 at meteorological conditions close to long-term average the community was dominated by Pseudonitzschia delicatissima (54%) and S. costatum (27%). The community composition was unrelated to phytoplankton biomass, e.g. in the two years with the highest biomass recorded (1987–88) and in 1993 when biomass were lowest the community was dominated by Skeletonema costatum (55–78% of total biomass) and

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ns2 5.62E-03 (−0.344) 5.77E-05 (−0.500) 7.61E-05 (−0.492) 1.33E-07 (−0.656) 6.11E-11 (−0.813) 1.49E-11 (−0.839) 4.05E-03 (−0.364) ns2 5.13E-03 (−0.354) 8.86E-03 (−0.331) 1.00E-03 (−0.416)

ns2 ns2

both low and high biomasses occurred during warm summers (Figure 7). 5.5 Correlations The correlations between monthly variations in freshwater input, nutrient load and concentrations in Skive Fjord varied throughout the year. Hence, data were analysed by season, defining December-February as winter, April-May as spring and October-November as autumn. Concentration of DIN correlated weakly to Nload during winter and summer but strongly to load during spring (Table 3). Concentration of phosphorus was weakly correlated to loads during winter (tot-P only) and spring. During winter, run-off had a negative effect on chlorophyll-a concentration. In spring, chlorophylla correlated strongly to P-load but not to N-load (Table 3). The situation changed in summer as only N-load but not P-load correlated with chlorophyll-a.


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5.6 Multivariate analysis

Fig. 7 Two-dimensional ordination of 16 years of phytoplankton species biomass grouped by standardised Bray-Curtis similarities. Numbers denote years and area of circles indicates the average phytoplankton biomass during summer. The stress coefficient of 0.14 indicates that the projection of the multidimensional space into two dimensions was satisfactory. The years encircled had summer temperatures below long-term average and biomass was dominated by Skeletonema costatum.

Table 3 Seasonal Pearson Rank correlation coefficients for linear correlation between monthly whitened residuals1 of nutrients and chlorophyll-a and whitened residuals of forcings: Q (run-off), N-load and P-load. Correlation coefficients shown for explonatory variables calculated with 2 and 6 months memory. Significance levels corrected for Type I errors using a modified Bonferroni correction of α (see text).

1

Prior to analyses values were seasonally segregated, log-transformed, deseasonalised and detrended in case they showed long term trends. ∗ significant at p < 0.05; ∗∗ at p < 0.01

Initially we used PCA analysis to identify periods and seasons with homogeneous forcing variables. For each year 2 PLS components (i.e. principal axis) were sufficient to explain more than 80% of the total variation in data, hence additional PLS components were not developed. Focusing on the summer values (16 May–10 September) for the period 1989–2002 the variation in water quality parameters was sought explained by the variation in forcing parameters using PLS analysis. After initial runs, the number of nutrient load parameters was reduced from 36 to 6 (i.e. N- and P-loads calculated with 1, 6 or 15 months memory) without any change in final results. Six outliers were identified and subsequently removed using the Hotelling procedure of the SIMCA program (e.g. Chatfield, 1980). In the PLS analysis 19% of the overall variance (including forcings and water quality variables) was loaded onto the first component (Figure 8a). The most important explanatory variables were nutrient inputs receiving loadings between 0.33 and 0.4 followed by mussel biomass

Season/memory

Forcing

Tot-N

DIN

Tot-P

PO4 3−

Chl-a

Winter 2 mo. Memory Winter 6 mo. Memory Spring 2 mo. Memory Spring 6 mo. Memory Summer 2 mo. Memory Summer 6 mo. Memory Autumn 2 mo. Memory Autumn 6 mo. Memory

Q N-load P-load Q N-load P-Load Q N-load P-load Q N-load P-load Q N-load P-load Q N-load P-Load Q N-load P-load Q N-load P-load

0.216 0.280 −0.042 0.269 0.331 −0.233 0.628∗∗ 0.706∗∗ 0.342 0.737∗∗ 0.744∗∗ 0.490∗ 0.289 0.306 0.030 0.567∗ 0.592∗ 0.016 −0.088 −0.009 −0.010 0.041 0.021 −0.069

0.336 0.381∗ 0.204 0.444∗ 0.470∗ −0.004 0.573∗∗ 0.661∗∗ 0.272 0.664∗∗ 0.594∗∗ 0.170 0.363∗ 0.431∗ 0.312 0.426∗ 0.509∗ 0.338 −0.162 −0.108 −0.099 −0.048 −0.116 −0.233

0.064 0.018 0.166 0.059 0.157 0.393∗ −0.044 −0.113 0.380∗ 0.082 0.208 0.473∗ 0.084 0.160 0.041 0.217 0.255 0.068 −0.206 −0.087 −0.009 −0.145 −0.132 −0.024

0.202 0.122 0.115 0.107 0.208 0.189 −0.162 −0.224 0.226 0.191 0.273 0.554∗ 0.086 0.119 0.008 0.231 0.244 0.053 −0.055 −0.137 0.116 −0.197 −0.189 −0.034

−0.565∗∗ −0.586∗∗ 0.080 −0.518∗∗ −0.542∗∗ −0.078 −0.207 0.189 0.463∗ 0.180 0.376 0.616∗∗ 0.314 0.339 0.112 0.397∗ 0.443∗ 0.107 −0.166 −0.099 −0.122 −0.237 −0.203 −0.174

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Fig. 8 Weights (loadings) of forcing and water quality variables contributing to the first (a) and second (b) PLS components. Abbreviations: PAR 48 h, PAR 24 h = photosynthetic available radiation averaged over 48 and 24 hours prior to sampling; Energy 24 h, Energy 48 h, Energy day 3–6 = Energy balance of water column averaged over 24 hours, 48 hours and 3–6 days prior to sampling; −Sigma-T: difference in water density be-

tween bottom (5 m) and surface (1 m) water at time of sampling; N(P)-Load (1-mo. — 15 mo.) = N- (P-) load calculated with 1, 6 and 15 month memories (see text); Pmax = light-saturated primary production; Pmax/Chla = light-saturated primary production normalised to chlorophyll-a. The numeric value and sign of loadings indicate if and how forcing and water quality variables are correlated.

(loading — 0.15). In the first PLS component N-load (and P-load) correlated positively to concentration of nitrogen (DIN and tot-N) and chlorophyll-a but negatively to phytoplankton diversity and concentration of silicate (Figure 8a). Less pronounced was a negative correlation between mussel biomass and chlorophylla. The second PLS component, which accounted for 10% of the overall variance, received high loadings for temperature (0.7), mussel biomass (−0.48) and water column energy status calculated 3–6 days prior to sampling (Figure 8b). Phosphorus, chlorophyll-a and primary production correlated positively to tempera-

ture but negatively to mussel biomass. Secchi depth correlated positively to mussel biomass and negatively to temperature, chlorophyll-a and primary production. Overall, the variation in temperature was the most important explanatory variable for the variation in water quality parameters followed by variations in mussel biomass, nutrient loads, water column energy balance and density stratification (Figure 9). The predictability of water quality variables was highest for phosphorus (R2 = 0.53–0.58) and decreased gradually through Secchi depth, concentration of silicate, tot-N, phytoplankton diversity and chlorophyll-a (R2 = 0.24) (Figure 10).

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Fig. 9 Relative importance of forcing variables in explaining variation in water quality (see legend to Figure 7 for abbreviations).

A high predictability of phosphorus concentration was primarily driven by temperature. Light saturated primary production had a slightly lower predictability than chlorophyll-a, while the variation of chlorophyll-a normalised primary production as a proxy for algal growth rate could not be explained by the PLS model. 5.7 Multiple regression Based on the results of PLS analyses, we selected nutrient loads (with 1–24 months memory), mussel biomass and water column energy balance as the explanatory variables and nutrients, Secchi depth, phytoplankton diversity, chlorophyll-a and primary production as predictable water quality variables. The analyses were run monthly, which removed most of temperature influence (except in August) and reduced biases due to serial correlation of variables. Overall, the developed models could explain between 28 and 57% of the variation in nitrogen concentration and between 32 and 59% of the

Fig. 10 Predictability of water quality variables, R2 ( = coefficient of determination), Q2 values (= predicted variance).

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variation in chlorophyll-a from June through August (Table 4). N-load was the most influential forcing affecting the concentration of nitrogen in April through August, chlorophyll-a from June through August and primary production in June–July (Table 4). Phytoplankton diversity was negatively affected by longterm P-load during July and August. During summer high mussel biomass lead to increases in water transparency (Secchi depth), reductions in chlorophyll-a and primary production, which are in accordance with the PLS analysis. Concentration of nitrogen during summer was negatively influenced by P-load during the previous 7–9 months. In August and September (not shown) concentration of phosphorus was positively related to N-load calculated with 9–11 month memory presumably due to coupling between N-load and oxygen content in bottom water. Thus, via increased production and sedimentation high N-loads lead to increase in oxygen demands of sediments, which promote the release of phosphorus from sediments. The model R2 varied with nutrient load memory with a maximum attained between 4 months memory (DIN in April-June) and 17 month memory (phytoplankton diversity in July) (Table 4). In all regression models a local maximum in R2 could be identified (Figure 11). The load memory at maximum R2 increased from April to August (e.g. Tot-N: 6 months → 10 months) reflecting the importance of winter run-off for the water quality parameters in the following spring and summer. The regression models shown in Table 4 were developed using all available data from within a month. Hence, with up to 3–4 measurements from the same month (especially in later years) biases due to serially correlated data could be important. Therefore, we analysed the robustness of regressions by bootstrapping, i.e. randomly selecting data sets including only one set from a particular month and subsequently carried out 25 multiple regression analysis. Predictor variables were regarded as robust if they proved significant in more than 20 individual regression analyses. Robust predictors included N-load explaining the variation in concentration of nitrogen during April through July and phosphorus concentration in August–September, and P-load explaining the variation in nitrogen concentration during June-July. Variation in mussel biomass was robust in explaining the variation of chlorophyll-a in July-August and the variation of Secchi depth and primary production in August. Springer

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Environ Monit Assess (2007) 127:503–521 Table 4 Coefficients of multiple regressions between forcings (nutrient load, mussel biomass, temperature, water column energy balances) and water quality variables (nutrient concentrations, Secchi

April May June

July

August

Tot-N DIN Tot-N DIN Tot-N DIN Secchi Chl-a PPmax Tot-N Tot-P PO4 3− Secchi Chl-a Divers3 PPmax Tot-N Tot-P PO4 3− Secchi Chl-a Divers3 PPmax

depth, chlorophyll-a, phytoplankton diversity and lightsaturated primary production). Input data are raw data. Only significant (p < 0.05) relations are shown.

N-load

P-load

Mussel

Temp

Energy24 h

Energy3-6d

R2

54 (6 mo)1,2 69 (4 mo)1,2 47 (6 mo)1,2 43 (4 mo)1,2 77 (7 mo)1,2 85 (4 mo)1,2 −0.09 (8 mo)1 1.5 (7 mo)1,2 6.1 (8 mo)1,2 54 (9 mo)1,2 6.0 (10 mo)1 0.58 (9 mo)1 3.3 (10 mo)1 46 (10 mo)1 7.9 (10 mo)1,2 6.9 (10 mo) 1 1.5 (11 mo)1,2 -

−490 (7 mo)1,2 −567 (4 mo)1,2 −313 (9 mo)1,2 −1.56 (17 mo)1 −444 (10 mo)1 −2.82 (16 mo)1 -

0.02 −0.142 −0.95 0.022 −0.432 0.16 −2.62

2.07 1.02 2.692 1.602 −0.22 2.502 −0.76 -

41 652 240

192 166 −24 290 200 4.6 −38 -

0.29 0.60 0.53 0.36 0.43 0.43 0.23 0.32 0.32 0.57 0.23 0.30 0.18 0.37 0.19 0.28 0.28 0.33 0.36 0.47 0.59 0.43 0.32

1

Values in brackets indicate load memory with the highest correlation coefficient. Values in italic denote that predictor variables are robust to removal of potential serially correlated values using bootstrapping (see text) 3 Margalef diversity of phytoplankton community, (S-1)/ln( C biomass); S = number of species. - = predictor not significant (p > 0.05). 2

6 Discussion 6.1 Temporal variation

Fig. 11 Coefficient of determination, R2 in multiple regression model for chlorophyll-a in August as a function of ‘memory’ of N-load (in months). Open squares: N-load not significant in model; filled squares: N-load significant.

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The trend analysis showed that P-load in particular but also N-load have decreased in Skive Fjord during the period 1984–2002. Reduction in P-load was primarily due to construction of tertiary sewer treatment plants during 1988–1989 that has reduced point sources by approximately 95%. N-load is dominated by non-point sources attributed to agricultural activities and the reduction due to efficient sewer treatment has only reduced the total nitrogen load by less than 5%. The observed reduction in N-load during the period 1984–2002 was mainly caused by reduced runoff that occurred in all seasons except spring. Reductions in nutrient loads were translated into decreasing


trends in phosphorus concentrations during winter, spring and summer and decreases in tot-N during winter and spring. However, the trends in concentrations were much less significant than trends in loads. Despite major reductions in nutrient loads, chlorophyll-a concentration showed a negative trend during spring only. Phytoplankton composition did not show consistent changes during the study period and moreover was unrelated to nutrient load reductions. Instead, temperature or associated conditions such as light availability and mixing intensity were important for dominance patterns. In windy and ‘cold’ summers Skeletonema costatum dominated the community, which is consistent with the life-form strategy of small chainforming diatoms being favoured by mixing conditions (Margalef, 1978). In calm, warm summers either dinoflagellates or warm water diatoms such as Cerataulina pelagica dominated the community. C. pelagica does not possess morphological structures to counteract sedimentation (Round et al., 1990), hence we would not expect this species to dominate during calm periods. Dominance of C. pelagica has been observed during relaxation of upwelling events, when the species typically replaces S. costatum that dominates in early phases of upwelling (Tilstone et al., 2000). Thus, there is some evidence that C. pelagica has a competitive advantage over S. costatum during periods with low mixing. Based on long time series of phytoplankton samples and sediment records of diatoms and diagnostic pigments there is amble evidence that eutrophication is associated with changes in composition of phytoplankton community (e.g. Clarke et al., 2003, Mortensen et al., 2004, Marshall et al., 2005). As we could not demonstrate couplings between species composition and nutrient loads we conclude that the variation in nutrient input was too limited, or that system hysteresis, e.g. due to lack of resting cells and internal nutrient loading from sediments, was responsible. 6.2 Correlations The correlation analyses revealed further insights to causality between loads and concentrations in Skive Fjord. During winter, the concentration of chlorophylla was negatively correlated with run-off. High run-off correlates to westerly winds and heavy cloud cover, hence low chlorophyll-a probably is caused by light limitation due to low insolation and high light attenuation caused by resuspension. During spring,

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the concentration of chlorophyll-a correlated strongly to P-load rather than to N-load supporting the general perception of phosphorus limitation during spring in Danish estuaries (Conley et al., 2000). Such limitation is further reinforced by the major reduction in P-load because there are strong indications that the period in spring with limiting DIP concentrations (i.e. < 0.1 μM) has increased in several estuaries in Denmark since 1990 (Ærtebjerg et al., 2002). Despite load reductions, the sediment content of labile phosphorus is still sufficient to fuel production during summer when nitrogen becomes the limiting nutrient. The crucial role of nitrogen is supported by the correlation between chlorophyll-a concentration and N-load in this analysis but also supported by a very low DIN:DIP ratio (molar ratio less than 1) during summer in Skive Fjord. During autumn, heterotrophic processes dominate over autotrophic due to low light availability and nutrient concentrations in the estuary became independent of external inputs because internal loads dominated. 6.3 Multivariate analysis The PLS analysis clearly demonstrates the capability of multivariate techniques to identify both forcings that influence water quality and different variables of water quality that respond predictably to variation in forcings. The overall advantage of PLS and other multivariate techniques is that the complexity of a data set, consisting of several variables, often can be reduced to a much lower number of dimensions by which the essential relationships between forcing and dependent variables can be elucidated. Being a multivariate technique PLS can include variables that are either true independent (e.g. nutrient input and primary production) or inter-related (e.g. load of N and P). PLS analysis is used extensively within spectroscopy and has been used previously within QSAR (quantitative structureactivity relationships) to compare the toxic effects of different substances on various test organisms (i.e. the Y matrix) with the physico-chemical characteristics of the substances (i.e. the X matrix) (Eriksson et al., 1995). Despite the feasibility, PLS has rarely been applied in water quality analysis (see however, Aguilera et al., 2000; Smolders et al., 2004; Aulinger et al., 2004). Besides temperature the biomass of mussels, Nload and the energy status of the water column 3– 6 days prior to samplings were the most important predictors for concentration of phosphorus, Secchi Springer

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depth, concentration of silicate, tot-N, chlorophyll-a and the light saturated primary production. The major influence of temperature was primarily caused by the effect on phosphorus concentrations in July and August when sediment was deoxygenated and phosphorus efflux was high (Klump and Martens, 1981; Møhlenberg and Jürgensen, 1994). The second most influential predictor was biomass of mussels that at high values increased transparency and reduced concentration of chlorophyll-a and the light saturated primary production. The importance of mussels in mitigating effects of eutrophication is due to their ability to filter large volumes of water and clear particles, e.g. plankton algae from the water column (Jørgensen, 1990). If their population is large compared to the depth of the estuary, the theoretical daily clearance capacity may exceed the total volume of the estuary (Møhlenberg, 1999a). In accordance, numerous studies in coastal areas and estuaries have demonstrated inverse relationships between biomass of mussels or other benthic filter feeders and concentration of phytoplankton (Asmus and Asmus, 1991; Petersen and Riisg˚ard, 1992; Prins et al., 1996; Cloern, 1991; Cloern, 1996; Kaas et al., 1996; Møhlenberg, 1999a). N-load was the second most important of the external forcings that influenced the concentration of nitrogen, chlorophyll-a, Secchi depth and primary production in Skive Fjord during summer and the PLS analysis thus confirmed results of the correlation analysis of a nitrogen limited system during summer. The negative relation between N-load and the concentration of silicate was unexpected because run-off is the main external source as for nitrogen (Conley et al., 2000) and no major alterations in catchment area or rivers have occurred within the period analysed (1989–2002) that could have influenced the retention of silicate (Ittekkot, 2000). In Skive Fjord diatoms account for approximately 60% of the phytoplankton biomass and they are the major players in the dynamics of silicate reducing the concentration of DSi during blooms. By sedimentation and benthic filtration biogenic silicate is transferred to sediments along with particulate organic nitrogen where it is mineralised. However, the inverse relation between N-load or DIN and DSi suggests an uncoupling of processes in sediments probably due to a slow dissolution of diatom frustules and a slower mineralisation of BSi compared to PON. In Skive Fjord potentially limiting concentrations (< 2 μM; Egge and Aksnes, 1992) occurs only rarely (i.e. in less than 6% Springer


of sampling occasions during the productive period) and production on a yearly scale surely is not limited by silicate. As there has been no long-term trend in silicate concentration in Skive Fjord the slow mineralisation affects the biogeochemical cycle on seasonal scales rather than inter-annually. The physical energy status calculated from wind stress and solar insolation indicates whether the water column is stratified (positive values) or fully mixed (negative values). The second PLS component showed a negative relation between energy status calculated 3– 6 days prior to sampling and chlorophyll-a but a positive relation to Secchi depth. Thus accumulation of chlorophyll-a is governed by a high intensity of vertical mixing 3–6 days before sampling and the positive effect on chlorophyll-a concentration is further reinforced if a mixing period is followed by a period of stratification immediately prior to sampling, i.e. positive values of Energy 24 h in Figure 8b. Water column mixing will supply nutrients from bottom to surface waters accelerating production and a stratified period following will prevent accumulated algae being filtered by mussels (Møhlenberg, 1995). 6.4 Multiple regression The objective of the multiple regressions was to describe the relationships between water quality variables and independent variables partly under human control. Ideally, such relationships can be used to predict water quality after imposing changes (i.e. reductions) in nutrient loads or intensity of mussel dredging provided that relationships are linear or account is taken if relations are non-linear. When observed on a decadal scale there is ample of evidence that estuarine responses to nutrient enrichment often are non-linear due to structural shifts such as loss of benthic primary producers with implications for sediment stabilization and nutrient recycling between sediments and water column (Kemp et al., 2005). Based on yearly monitoring neither max depth penetration (2–2.5 m) nor %-coverage of eelgrass has changed since 1989 in Skive Fjord, and the seagrass community must be regarded as marginal covering few percentage of the area. Therefore, structural shifts took place prior to 1989 (presumably during 1950–1960) and non-linear “dose-response” relationships probably are not present in the data analysed. In general, the variability in water quality parameters explained by the multiple regression models was huge


ranging from 0% (e.g. tot-P and PO4 April through July, chlorophyll-a during spring) to more than 50% (e.g. nitrogen during spring, chlorophyll-a in August). This implies that different external and internal processes not included in the models accounted for most of the variability of water quality. Also horizontal heterogeneity in water quality parameters is presumably important for the unexplained variability as shown by the 28% average difference in fluorescence between stations located ca. 4 km apart. While the results from the regression analyses generally agreed with the PLS analyses the regressions failed to demonstrate the correlation between P-load and chlorophyll-a concentration during spring, which presumably was due to differences in periods analysed. The Pearson Rank correlation analysis included 5– 6 year’s prior to efficient sewer treatment when P-load was high, while the PLS and regression analyses were confined to a period showing only minor variation in P-load. Despite a low yearly variation, P-load significantly influenced the concentration of nitrogen during summer with high P-loads leading to lower concentrations of nitrogen and vice versa. Hence, at low P-loads nitrogen is not exhausted by algal growth during spring and a larger fraction is retained in the water column making nitrogen more liable to loss by physical export. Analogously, but at a larger scale, the export of bioavailable nitrogen increased from the coastal zone in Himmerfjärden after phosphorus removal from the wastewater of Stockholm (Elmgren and Larsson, 1997). In effect, algal blooms are shifted further downstream of the estuary where phosphorus is in excess (Elmgren and Larsson, 1997; Paerl et al., 2004). In Skive Fjord the reduction in P-load was manifested in a seasonal shift in algal production: production became limited by a low availability of phosphorus in spring and during summer when phosphorus is released from sediments ‘unused’ pelagic nitrogen can fuel production. This is probably the reason for lack of temporal trends in summer chlorophyll-a, despite an almost 30% reduction in N-load. During seasons the coupling between nutrient loads and concentrations varied with residence time and autotrophic activity with a progressing increase in load memories from spring through summer (Table 4). The average freshwater residence time (i.e. input/volume) is 6 month. Because of tidal exchange and wind forced circulation the residence time is markedly shorter, ranging from one month during winter and

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increasing to 2–3 months during summer (Limfjordsoverv˚agningen, 2001). The increase in nutrient load memory is thus likely a result of decreasing ‘dilution rate’ during the dryer summer months and temporary ‘trapping’ of nutrients in algae, macro fauna and sediments during the productive seasons. As shown in a previous study, the memory of N-load that correlated best to oxygen demand in bottom water during summer was estimated to 9–10 months in Skive Fjord and notably larger than the closest coupling between N-load and concentration of DIN and tot-N in the estuary (Møhlenberg, 1999b). Hence, our perception of the sequence of eutrophication events in the estuary (i.e. Nutrient-load → algal biomass → sedimentation → oxygen demand of sediments) is also reflected in the increases in load memories. Oxygen demand of sediments in Skive Fjord has previously been shown to be coupled tightly to N-load (Møhlenberg, 1999b) and this relation surely mediates the coupling between N-load and concentration of totP observed in August. Therefore, the present high Nloads will tend to drive phosphorus out of deoxygenated sediments during late summer and in the long run turn the system towards P limitation for a greater part of the year as long the P-loads are as low as present. Ultimately, the point in time when the estuary becomes P-limited depends on the size of the labile phosphorus pool in the sediments and the horizontal transport during summer. The evidence obtained by the different statistical analyses was unequivocal as the correlation analysis failed to demonstrate significant linkage between N-P loads and phosphorus concentrations during summer (despite using load memories up to 24 months), but the trend analysis showed a long-term decrease in both tot-P and PO4 3− . Combinations of a huge P-buffer in sediments and year-to-year variation in meteorological conditions that affect oxygen conditions will tend to mask linkages between loads and concentrations at scales less than 2–3 years. The importance of sediment sources of phosphorus can be seen by the increase in Tot-P during the period June — August that integrated over the whole estuary on average (19902002) constituted 63% of the annual external load. Because advection and sedimentation is not accounted for the P increase represents a very conservative measure of sediment net release. In July and August mussel biomass was a robust predictor of chlorophyll-a concentration. This period was characterised by a large year-to-year variation Springer

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in mussel biomass primarily determined by duration of stratification and low oxygen concentration. During the windy summers, e.g. 1993, 1996 and 1999 mussel biomass exceeded 50 g flesh wt. m−2 , while most mussels were killed in August 1994 and 1997 due to week-long anoxia (Figure 2). The role of filterfeeding benthos in modifying the effects of eutrophication was also demonstrated in a comparative study of 15 estuaries, where the inclusion of top-down control by filter feeders markedly improved the relationship between nutrient and chlorophyll (Meeuwig et al., 1998). In seasons outside late summer, the variation in mussel biomass between years was very limited in Skive Fjord and accordingly the coupling between mussels and water quality parameters could not be teased out. If we accept linearity between nutrient loads and ecosystem responses we may use the regressions in Table 4 to predict effects of nutrient reductions or changes in mussel biomass. However, if load reductions continue and the internal labile phosphorus pool becomes ‘washed out’ at some point we must expect a recovery from eutrophication effects. An important process will be colonization by benthic primary producers such as eelgrass which will reinforce the ‘restoration’ process (Kemp et al., 2005). During this phase simple linear relations between loads and water quality variables becomes invalid due to complex interactions and feedforward processes. Unfortunately, there are only few examples of restoration from eutrophication effects in estuaries which make predictions of trajectories and pace very difficult. Acknowledgements This study was sponsored by the EU Commission (contracts: FAIR CT98-4201; REBECCA SSP1-CT2003-502158, and by the counties of Nordjylland, Ringkøbing and Viborg. C. Gameiro was funded by Marie Curie (contract: HPMT-CT-2001-00265-11) and by a Ph.D. grant from FCT (POCI-2010/BD/13988/2003). The authors thank Peter Blanner, Svend-Aage Bentsen, Else-Marie Platz, Bent Jensen, Jens Würgler Hansen and Per Dolmer for providing data and for stimulating discussions.

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