Introduction
Human influence and climate change transform terrestrial and marine
ecosystems worldwide at unprecedented rates .
Coastal marine systems experience anthropogenic pressure as well as indirect
changes in climatic conditions, which affect the marine food web
. Ecosystem responses to these changes
are difficult to relate to unique causes . Experiments designed to support biogeochemical model
scenarios e.g. help to
disentangle observed trends. However, the predictive capabilities of
biogeochemical models e.g
remain dependent on calibration against long and consistent multi-variable
time series.
Phytoplankton bloom intensity and timing (bloom phenology) are indicators of
ecosystem health at the base of the food web
(e.g. ; ; ). Phenological studies are
increasingly used to inspect regional ecosystem response to nutrient
reduction efforts and
changing climatic conditions . The Baltic Sea is
a coastal ecosystem affected by eutrophication , which
intensifies naturally occurring spring and summer bloom . The Helsinki Commission formulated a nutrient reduction scheme
aimed at improving ecosystem health in 1992 , which came
into force in 2000. Monitoring of key ecosystem health indicators is
implemented in the national monitoring programmes of HELCOM contracting
parties. These programmes include traditional dedicated sampling campaigns at
sea, and increasingly, the use of highly resolving observation platforms.
Ships of opportunity (typically cargo ships or passenger ferries) offer a
largely weather-independent, reliable, and cost-effective platform for the
collection of high frequency in situ observations . Phytoplankton pigment fluorometers are included in most of
these ferryboxes. In the Baltic sea, such systems have recorded phytoplankton
blooms on the route from Helsinki to Travemünde (and vice versa) since 1992
. On this route, ferryboxes have collected over 9.5
million chlorophyll a pigment fluorescence observations from 1926
transects, with a median revisit time of under two days in the last 15 years
(2000–2014). Ship-based observations from merchant vessels provide continuity
in monitoring, which is particularly important in seasons when other
observation systems are less reliable. In spring, satellite observations are
rare due to high average cloud cover, while high costs of dedicated research
cruises and coastal laboratories limit their spatio-temporal coverage.
Ferrybox observations are therefore the primary source of observations to
study spring bloom dynamics in this region.
Phytoplankton abundance and succession in the Baltic Sea is controlled by
nutrient and light availability
, mixing status
, temperature , ice cover
, and salinity
. In addition, the quantum yield of
fluorescence is influenced by solar irradiance
, species
composition, and physiology . Hence, interpretation of
unattended pigment fluorescence measurements in terms of phytoplankton
biomass presents a number of challenges . Firstly,
phytoplankton distribution exhibits high spatial and temporal variability,
while ferryboxes measure pigment fluorescence at fixed depth
. Therefore, stratified conditions may not be well
represented in the data . Secondly, in a typical ferrybox
setup, fluorescence yield is at best determined as a daily regional average,
which disregards variability on smaller spatio-temporal scales. Despite these
challenges, demonstrated that ferrybox observations in
the Baltic Sea can be used to derive bloom timing and intensity for
biomass-rich sea areas. They report a slightly negative trend in bloom
initiation in the northern Baltic Proper and the Gulf of Finland for the
period 1992–2004. Recent studies also reported shifts in phytoplankton spring
bloom biomass or species composition
e.g.. reported
that the timing of cyanobacterial surface accumulations has advanced
approximately 20 days from 1979 to 2013. However, information about shifts in
Baltic Sea spring bloom timing is still lacking.
Choosing an adequate bloom metric is not trivial, as no clear guidelines exist
that conclusively support one metric over others. Bloom metrics for both
remotely sensed and in situ sampled time series are commonly divided into
three groups: (1) fixed or variable concentration threshold metrics
, (2) growth-rate-based
metrics , and (3) distribution-based metrics
. Threshold-based and
growth-rate-based metrics typically require data preprocessing (e.g.
interpolation and smoothing) to mitigate the impact of gaps, noise,
outliers, and multi-modal bloom distributions on the derived bloom phenology
. Distribution-based metrics fit an
analytical expression to observations using fitting routines designed to cope
with imperfections in the input data while optimally preserving natural
variability. Distribution-based bloom metrics are considered more robust than
threshold-based or growth-rate-based metrics, in the presence of complex,
multi-modal bloom observations . Interpretation based on
several, conceptually different bloom metrics can be used to obtain
uncertainty estimates . It also allows long-term
trends in bloom phenology to be screened for. The latter is because threshold-based metrics are
biased by long-term bloom intensity trends, whereas growth-rate-based and
distribution-based metrics are not. Figure illustrates
how a gradual decline (negative trend) in bloom peak concentration causes any
metric based on fixed thresholds (e.g. derived from climatology or
expert judgement) to introduce an artificial negative trend in bloom
duration. In contrast, metrics based on growth rate, distribution, or
annually derived thresholds yield a single bloom duration in this example
because bloom intensity does not influence these metrics.
The aims of this study are twofold: (1) to report long-term trends for Baltic
Sea spring bloom intensity and timing, and (2) to attribute these trends to
changes in environmental conditions. To meet these objectives, we describe a
methodology to derive quality-controlled time series of
chlorophyll a concentrations from observations collected under the
Baltic Sea Alg@line program over a period of 15 years (2000–2014).
Uncertainties arising from variability in the phytoplankton pigment
fluorescence yield are estimated. Bloom phenology parameters, derived from
threshold- and distribution-based bloom metrics, are explored for long-term
trends. Inter-annual variability of bloom phenology parameters are attributed
to nutrient availability and meteorological conditions (temperature,
radiation level, wind speed), which might help to relate long-term trends to
unique causes. Finally, we summarize how these results contribute to the
discussion on recent changes in the Baltic Sea, and the monitoring practices
that need to be in place to detect such changes.
Illustration of threshold-based bloom metric behaviour when applied to a
data set with a negative peak concentration trend.
Materials and methods
Alg@line data
In situ data in this study were collected until 2009 by the Finnish Institute
of Marine Research, and by the Finnish Environment Institute (SYKE) from 2009
onwards, within the Alg@line network of Baltic Sea ferryboxes. Here we
consider systems installed on two cargo vessels, M/S Finnpartner
(2000–2006) and M/S Finnmaid (2007–2014), which served between
Travemünde (Germany) and Helsinki (Finland) as depicted in Fig. . Three routes were sailed during the study period.
Depending on weather conditions, the passage between Gotland and the mainland
of Sweden (39 % of all transects) was favoured over the direct route east of
Gotland (52 %), while the route with a lay-over in Gdansk (Poland) was only
occasionally served during 2009 to 2012 (7 %). Several transects (2 %) were
sailed for refuelling or maintenance in other ports and not used for this
study.
Details on the instrumentation of the Alg@line ferrybox systems can be found
in , , , and . In
summary, the systems record in vivo fluorescence of chlorophyll a
(Chl a), salinity, and temperature throughout the studied period (2000–2014).
Turbidity and (in summer) phycocyanin pigment fluorescence were recorded from
2005 onwards and are not used here. At cruising speed (20–23 knots) the
sampling interval of 20 s resulted in a nominal spatial resolution of 200 m.
Transect of M/S Finnmaid and M/S Finnpartner through the Baltic Sea from Helsinki
(Finland) to Travemünde (Germany; vice versa). The following sea areas are considered in
this study: the western Gulf of Finland (gof: > 59.5∘ N latitude, along transect), the
northern Baltic Proper (nbp: 58.4–59.5∘ N latitude, along transect),
the western and eastern Gotland basins (got: 56.2–58.4∘ N latitude, along transect),
the southern Baltic Proper (sbp: 54.5–56.2∘ N latitude, along transect) and the Bay
of Mecklenburg (bom: < 54.5∘ N latitude, along transect). Depending on weather conditions, the
north or south of Gotland routes were sailed.
Quality control flags were derived from (1) sensor reading thresholds on
speed, flow rate, hull, and sampled water temperature, and (2) data
variability, expressed as lower and upper bounds for standard deviation
between neighbouring measurements, as described below. Measurements at low
(< 5 knots) or zero ship speed are typically collected in the harbour and were
omitted. Erroneous records, e.g. caused by instrument communication errors,
were removed using a moving window mean filter. A window length of 25
observations (approximately 8.3 min) was used for records of ship speed, and
a window length of 100 observations (33.3 min) was used for flow rate and
temperature records. Low flow rates can indicate blocked passages, pump
failure, or leaks. Flow meter readings were available for approximately
one-third of all records. A proxy for flow disruption is the difference in
ship-hull temperature and in-line temperature. Flow rates < 0.3 L min-1 or a temperature difference > 2 ∘C were used to
flag records as suspect. Instrument failure, communication or digitizing
errors may lead to “stuck” values, which were detected by calculating
standard deviation in a moving window of 100 samples. Observations
corresponding to low standard deviation (σ<1e-4) of Chl a
fluorescence measurements or GPS-derived latitude were omitted. GPS-derived
latitude was additionally filtered for exceptionally high short-term
variability (σ>0.5, window size 50 samples), caused by poor satellite
reception or serial communication errors. Table provides an
overview of the applied quality control flags.
Quality control flag definitions and statistics. Observations were omitted if any of the flags exceeded
the respective threshold. Absolute temperature difference is measured between the water intake and the flow-through
sensors. Availability and rejection rates were calculated relative to the total number of observations. SD
denotes standard deviation.
Sign
Threshold
Availability
Rejection rate
(%)
(%)
Speed (knots)
<
5
100
1.33
Flow (L min-1)
<
0.3
35.95
1.38
Abs. temp. diff. (∘C)
>
2
67.17
2.12
SD latitude (∘)
<,>
1e-4, 0.5
100
0.96
SD Chl a fl. (mg m-3)
<
1e-4
87.65
0.75
All
4.55
Chl a fluorescence data were corrected for sensor drift and discontinuities by
transect-wise normalization (division by transect mean). This was necessary
to account for changes in instrumentation, signal contamination due to
bio-fouling, trapped bubbles and particles, and changes in sensor sensitivity
due to deterioration or manual adjustments. Laboratory analysis results of
bottle samples are typically available from every sixth transect, with up
to 24 samples collected by automated, refrigerated water samplers (Teledyne
Isco). Laboratory analyses included inorganic nutrient concentrations
(nitrate+nitrite, phosphate and silicate), Chl a concentration, and
occasionally inverted light microscopy counts of phytoplankton species.
Laboratory Chl a concentration results were used to convert
transect-normalized Chl a fluorescence to units of Chl a concentration (in
mg m-3). First, a linear (generalized least squares) regression fit of
normalized Chl a fluorescence against corresponding Chl a lab measurements was
carried out for each sampled transect. If the regression failed (R2<0.3
or p>1), a moving window regression was carried out (window length 10
samples), and the subset with the highest R2 was used to determine the
correction factor. The threshold for R2 was determined manually based on
the distribution of R2, while p>1 indicated numerical instabilities
during the fitting procedure. Each transect without corresponding bottle
samples was corrected by individually applying the regression parameters of
the two neighbouring sampled transects. These two solutions were then
interpolated linearly, weighted by their temporal distance to the respective
transect. Negative concentration values occasionally occurred for weak
fluorescence signals, and were set to zero.
The diurnal variability of the fluorescence signal was estimated from
quality-controlled observations in all seasons. First, these observations
were divided by their respective transect mean to remove biomass-driven
first-order variability in the fluorescence signal. Then, diurnal cycles were
derived by dividing these observations into hourly bins and sun elevation
angle ranges (0.1 rad bins).
Meteorological data
Photosynthetically active radiation (par), sea surface temperature
(sst), and wind speed (wind) were derived from the ECMWF (European Centre
for Medium-Range Weather Forecasts) ERA-Interim reanalysis data set . The spatial resolution of
the model is constrained by the underlying atmospheric model, which is stored
on a spatial T255 grid corresponding to approximately 79 km cell size when
projected to a reduced Gaussian grid. Four values per day were retrieved for
each parameter and the entire Baltic Sea. Parameter values for each Alg@line
observation were extracted using spatio-temporal spline interpolation of
third order. The first-order seasonal signal (e.g. rising par and
sst in spring) was removed from the observations by subtracting
multi-year (2000–2014) daily sea area averages, approximated by second-order
polynomials. The seasonally detrended parameters were then averaged over the
bloom period and are further referred to as par, sst, and
wind.
Nutrient concentration and depletion timing
A single term for nutrient availability was adopted from ,
calculated as nut = (NO3+NO2)×PO4×SiO43, where NO3+NO2, PO4, and SiO4 are the
concentrations of nitrite+nitrate, phosphate, and silicate, respectively.
These concentrations were derived from laboratory analysis of bottle samples
that were regularly collected along the transect (further detail in Sect. ). nut was spatially binned for each investigated
sea area and resampled to daily averages and consecutively smoothed with a
21-day centred-running-mean filter. This treatment resembles the processing
of Alg@aline observations (see Sect. ) to enable
consistent interpretation of the joint data set. Nutrient concentrations and
depletion timing are described using the following metrics. The nutrient
concentration prior to bloom start (nut-peakvalue) was defined as
the yearly maximum nutrient concentration (day of year between 31 and 160).
The day of year when the nutrient concentrations equalled 100, 50, and
25 % of their peak values are referred to as nut-peakday,
nut-deplday-50, and nut-deplday-25. The day and value of
the lowest nutrient concentration index are referred to as
nut-minday and nut-minvalue. The rate of nutrient depletion
between 75 and 25 % of the peak value (nut-slope) was determined
through linear regression.
Description and acronyms of bloom phenology, nutrient, and meteorological parameters
that were used in the trend and multi-variate analysis.
Parameter
Unit
Description
bloomidx
mg day m-3
Integrated chlorophyll a concentration during bloom
concavg
mg m-3
Average (mean) chlorophyll a concentration during bloom
peakheight
mg m-3
Highest chlorophyll a concentration during bloom
startday
Julian Day
Bloom start day
peakday
Julian Day
Bloom peak day
endday
Julian Day
Bloom end day
nut-minvalue
µmol L-1
Nutrient concentration at end of bloom
nut-minday-50
Julian Day
Day when nutrients equalled 50 % of nut-peakvalue
nut-peakvalue
µmol L-1
Pre-bloom (wintertime) nutrient concentration
nut-peakday
Julian Day
Day of nut-peakvalue
nut-deplay-25
Julian Day
Day when nutrient concentration equalled 25 % of nut-peakvalue
nut-deplay-50
Julian Day
Day when nutrients concentration equalled 50 % of nut-peakvalue
nut-slope
µmol L-1 day-1
Rate of nutrient depletion between 75 and 25 % of nut-peakvalue
par
W m-2 day-1
Average (seasonally detrended) photosynthetically active radiation level
sst
∘C
Average (seasonally detrended) sea surface temperature
wind
m s-1
Average (seasonally detrended) wind speed
Extraction of bloom timing and intensity
Extraction of bloom timing and intensity was
carried out for five Baltic Sea areas, where each area follows definitions of
the HELCOM Combine program . Figure
illustrates the location of the areas: the western Gulf of Finland
(gof: > 59.5 ∘N latitude, along-transect), the northern
Baltic Proper (nbp: 58.4–59.5 ∘N latitude,
along-transect), the combined western and eastern Gotland basins
(got: 56.2-58.4 ∘N latitude, along-transect), the southern
Baltic Proper (sbp: 54.5–56.2 ∘N latitude,
along-transect), and the Bay of Mecklenburg (bom: < 54.5 ∘N latitude, along-transect). For the got and sbp
areas, only routes that passed by Gotland were selected, whereas routes via
Gdansk were excluded. This is because the route through Gdansk was sailed
only from 2009 to 2012. If not otherwise stated, all further steps are
carried out individually for each of these areas and for day of year between
31 (31 January) and 160 (9 June). The ship-of-opportunity (Alg@line)
measurements typically commenced in the second half of January, which is why
31 January was chosen as the start of our analysis. The end date was chosen
such that it covers all spring bloom events in all basins but excludes summer
bloom.
Alg@line Chl a concentrations (see Sect. ) were resampled
to daily sea area averages, using linear interpolation, and subsequently
smoothed with a 21-day centred-running-mean filter
e.g. to fill in gaps and reduce
short-term variability. We derive several metrics, all of which have in
common that the bloom peak concentration (peakheight, see Table for explanations of acronyms) and timing (peakday)
are defined as the maximum Chl a value at the corresponding day-of-year,
respectively. Two threshold-based metrics and one distribution-fit-based
metric were calculated.
Chl a concentration exceeding a fixed threshold of 5 mg m-3 was
defined as bloom by , further referred to as
const5. A 21-day centred-running-mean filter was used to keep
results comparable to the other metrics considered, whereas
used a 7-day centred-running-median filter.
proposed a spatially variable-threshold metric based on
the 5 % above median concentration, but reported small quantitative
differences for thresholds between 1 and 30 % above median. Their threshold
is based on the complete annual cycle, while here only the spring bloom
period from day-of-year 31 to 160 is considered. We refer to this metric as
median5.
Distributions proposed to describe bloom phenology include
shifted-Gaussian , gamma , and Weibull
distributions . The shifted Gaussian is symmetric in
shape, whereas gamma distributions allow for different slopes of bloom rise
and decline. In addition, Weibull functions recognize non-zero offsets before
and after the bloom phase. The latter has proven essential to obtain a good
fit for the transition phase between spring and summer bloom with the data set
analysed here. A modified Weibull function, as proposed by
, was fitted non-linearly to the preprocessed and scaled
(to a range of 0–1) Chl a concentrations. The bloom initiation and end
are defined as the 10th and 90th percentiles before and after the
bloom peak, respectively. This metric is further referred to as
weibull.
For each metric, bloom initiation, peak, and end dates (startday,
peakday, and endday) were extracted from the data set.
Based on these dates, bloom duration (duration), concentration
average (concavg), and the sum of daily Chl a concentrations
(bloomidx) were calculated. The latter was proposed by
to characterize bloom intensity. We assumed the bloom to
have started prior to Alg@line service commencement if the first data point
had already satisfied the bloom criterion for a given metric. Such cases were
identified for 30 out of 225 combinations of sea region, year, and bloom
metric (nine times for bloom metric const5, sixteen times for
median5, and five times for weibull). Corresponding bloom
start days were replaced by the median value for the region over the 15 years
studied in all subsequent calculations.
Diurnal variability in the chlorophyll a fluorescence yield: (a) normalized
(division by transect-mean) chlorophyll a fluorescence observations plotted against
time of day, and (b) sun elevation angle. The analysis was carried out on three subsets: winter
(November–February), summer (May–August), and transition periods (March, April, September,
October),
using all ferrybox observations along the routes shown in Fig. .
Principal component analysis
Principal component analysis (PCA) was carried out to attribute seasonally
detrended meteorological conditions (sst, par,
wind) and nutrient concentrations (nut-peakvalue,
nut-minvalue) to the inter-annual variability in bloom intensity
(bloomidx, concavg, peakheight) and timing
(startday and peakday, duration). Outliers were
defined for each parameter as departure by more than 3 standard deviations
from the parameter mean, and replaced with the region median. Z score
normalization (subtraction of mean, division by standard deviation) was
carried out on a per-region basis. Region-equalized, zero-mean, and
unit-variance data were then subjected to the PCA function in the Python
framework scikit-learn .
Bloom timing and intensity for each investigated sea area
(Fig. ) and for all applied bloom metrics. SD denotes standard deviation.
Sea area
bom
sbp
got
nbp
gof
Parameter
Metric
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
startday (Julian Day)
const5
68
9.3
87
13.6
95
8
89
5.6
81
8.4
median5
65
9.4
73
7.3
83
9.4
86
4.4
81
8.5
weibull
64
12.7
73
7.2
84
6
87
4.1
89
4.7
peakday (Julian Day)
all metrics
75
14.7
92
14.9
106
7.4
108
4.4
112
4.7
endday (Julian Day)
const5
95
15.2
102
12.5
118
10.7
130
5.2
143
5.6
median5
107
20.3
115
13.1
133
7.1
142
3.3
143
5.2
weibull
94
18.4
116
15.4
128
9
126
5.6
132
5.7
duration (day)
const5
35
12.5
16
12.4
23
13.6
41
6.4
62
11.7
median5
54
18.9
46
15.4
47
8.8
54
4.4
62
12.3
weibull
36
10.8
43
16.4
44
10.9
40
6.1
43
6.7
bloomidx (mg day m-3)
const5
283
167.6
98.4
77.1
162.4
114.2
352.4
84.9
691.6
157.6
median5
334.3
135.1
197
92.6
224.5
77.2
386.9
72.9
694
165.9
weibull
356
178.5
196.7
74.1
232.9
64.1
340.1
62.1
673.7
175.9
concavg (mg m-3)
const5
7.3
2.1
5.3
0.9
6.2
1.3
8.4
1.6
11.7
2.2
median5
6
1.2
4.1
0.7
4.6
1.1
7
1.1
11.7
2.3
weibull
9.9
4.3
4.6
1.1
5.5
1.7
8.5
2.2
13.6
3.3
peakheight (mg m-3)
all metrics
12.3
5.2
6.1
1.7
7.2
2.3
11.3
2.9
20.2
5.7
Results
Quality-controlled chlorophyll a concentration time series
The Alg@line ferrybox systems collected over 9.5×106 observations
between 2000 and 2014, of which 3.8×106 observations were sampled
during spring (day-of-year 31 to 160). Availability and rejection rates for
each quality control parameter are listed in Table . In total,
quality control procedures removed 4.55 % of all observations.
Determination of the fluorescence yield was supported by an “adaptive
regression” method. Where necessary (R2<0.3 or p>1), it selected the
subset of bottle-sampled and laboratory-analysed Chl a concentrations that
yielded the best linear fit to Chl a fluorescence observations for a given
transect. This procedure allowed 318 (98 %) out of 324
transects for which bottle samples were collected to be successfully fit. Only 266 (82 %) transects
could have been used (R2>=0.3 and p≪1) without applying this
technique.
Figure a shows normalized fluorescence
observations as a function of sampling time-of-day. Results are presented
separately for summer (May to August), winter (November to February), and the
transition periods (autumn, spring). Diurnal variability was most pronounced
in summer, when the fluorescence signal varied on average 50 % over the
course of a day. In winter and during the transition periods (spring, autumn)
a diurnal variability of 35 and 38 %, respectively, was contained in the
fluorescence signals. This seasonal effect is likely caused by variations in
average irradiance intensity, which are modulated primarily by sun elevation,
but also by atmospheric conditions (e.g. cloud cover, aerosol optical
thickness) and optical properties of the water body (e.g. ice cover,
attenuation). Figure b depicts normalized
fluorescence as a function of solar elevation. In this representation,
seasonal differences in diurnal variability are essentially absent and the
correspondence between solar elevation and average fluorescence response was
approximately linear for daytime observations.
Bloom timing (bloom start, peak, and end day) for each sea area along the routes in
Fig. , averaged over the period 2000 to 2014, and for all applied bloom metrics. Whiskers
indicate standard deviations over the 15-year study period. The bloom peak day is independent of the
chosen metric and plotted separately. The sea areas are ordered by latitude, from south to north.
Bloom intensity and timing
Blooms generally developed first in the south and progressed towards the
north (see Fig. and Table ). Bloom peak timing (not influenced by choice of metric)
followed this pattern, as did metric-dependent bloom start and end dates. The
fixed-threshold bloom metric const5 suggested longer blooms in
high-biomass sea areas like the gof, compared to low-biomass areas
such as the sbs. The spatially variable-threshold metric
median5 applies area-specific bloom thresholds (nbp: 3.52, gof: 4.95, got: 2.51,
sbs: 2.62, bom: 4.02 mg m-3) and resulted in approximately stable bloom duration in all sea
areas. The weibull metric, which is not sensitive to absolute bloom
intensity, also resulted in comparable bloom durations for all sea areas. The
year-to-year variability of start, peak, and end days generally increased
towards the south for all metrics.
(a) Concentration average and (b) bloom intensity index for each sea area along the
routes in Fig. , averaged over the years 2000 to 2014, and for all applied bloom
metrics. Whiskers indicate standard deviations over the 15-year study period. The sea areas are ordered
by latitude. The metric-independent bloom peak concentration is added in both plots for visual comparison.
(a) Decadal trend of average (concavg) and (b) peak (peakheight) chlorophyll a
concentration during bloom conditions, derived from the Weibull-distribution metric. Concentrations were normalized
prior to regression (subtraction of area-average concentration). Dashed lines indicate the trend line (bold) and its
confidence intervals (5 %, small dashes). SE denotes standard error; RMSE denotes root mean square error.
Spring bloom intensity was described by three parameters: the
metric-independent bloom peak concentration (peakheight), the Chl a
concentration average during bloom conditions (concavg), and the sum
of daily Chl a concentrations over the bloom period (bloomidx).
Similar patterns were observed for all these parameters and bloom metrics, as
illustrated in Fig. . The highest bloom
intensity was found in the gof and nbp, followed by the
bom. Low-intensity blooms were observed in the sbp and the
got. Variability was generally proportional to bloom intensity,
highest in the high-biomass and coastal gof and bom.
Variability in bloomidx was comparable to that in
peakheight, while concavg was considerably more stable. All
calculated bloom phenology parameters can be found in the Supplement.
Trends
Figure shows normalized (subtraction of area-average
concentration) concavg and peakheight for all sea areas
combined, as a function of bloom year. peakheight is independent of
bloom metric and shows a highly significant (R2=0.12, p≪0.01)
negative trend of -0.30±0.10 mg m-3 yr-1. concavg is
dependent on bloom start and end days and was therefore calculated for all
applied metrics. Statistically significant, negative trends resulted from all
metrics: -0.12±0.04 for const5 (R2=0.11, p≪0.01), -0.11±0.05 for
median5 (R2=0.12, p<0.05), and -0.22±0.07 mg m-3 yr-1 for weibull (R2=0.11, p≪0.01).
No significant trends were found for bloomidx, startday,
and peakday with any of the applied metrics, while endday
showed weakly correlated but statistically significant (R2=0.06, 0.08,
p<0.05) positive trends for const5 and weibull with slopes
0.6 to 0.7±0.3 day yr-1, respectively.
Bloom duration resulting from the weibull metric stands out in the
result set with a positive trend of 1.04±0.20 day yr-1 (R2=0.28,
p≪0.01, Fig. ). No significant trend in bloom
duration was found for any fixed- or variable-threshold metric.
Peak nutrient concentrations showed no significant trend, in contrast to
post-bloom nutrient concentrations with a highly significant, negative trend
-0.020±0.004µmol L-1 yr-1 (R2=0.23, p≪0.01). Peak
nutrient concentration timing shifted to earlier dates (-0.7±0.3 day yr-1, R2=0.06, p<0.05), while the 25 % of peak value was
reached progressively later (0.67±0.31 day yr-1, R2=0.06,
p<0.05). No significant trends were found for the nutrient depletion slope, 50 % of peak value timing, or the day of minimal nutrient concentrations.
Inter-annual variability
Pre-bloom nutrient concentrations were positively correlated to bloom peak
height (no normalization, R2=0.39, p≪0.01) and concentration average
(no normalization, R2= 0.37–0.57, p≪0.01, depending on metric). After
applying area-wise mean and variance (z score) normalization, however, a
negative correlation was found for peakheight (R2=0.11, p≪0.01, metric-independent) and concavg (R2=0.12, 0.11, p≪0.01
for const5 and weibull, respectively).
The timing of nutrient depletion, specifically nut-deplday-50, was
positively correlated to the bloom peak day (R2=0.47, p≪0.01), and to
bloom-averaged, detrended par-levels (R2= 0.14–0.29, p≪0.01).
Average wind speed and par were negatively correlated during bloom
conditions (R2= 0.10–0.23, p≪0.01). The bloom timing parameters
(startday, peakday, endday) were weakly but
statistically significantly intercorrelated (results not shown).
PCA scores and loadings of the first three principal components (PCs) are
shown as biplots in Fig. . The first PC is dominated by
negative correlations to bloom intensity parameters (peakheight,
concavg, bloomidx). This component is positively correlated
to pre-bloom nutrient concentration (nut-peakvalue) and bloom
duration, illustrating that bloom intensity is driven by pre-bloom nutrient
availability. The second PC is linked to bloom timing, with strong positive
correlations to startday and peakday. Correlations to
par (positive), sst (positive), and wind
(negative) suggest that weather conditions affect bloom timing. Bloom
duration is positively correlated to the third PC, as well as to
bloomidx. Additional negative correlations to nut-minvalue
and wind, as well as a positive correlation to par, suggest
a link between favourable meteorological conditions (low wind-mixing, high
light level) and efficient nutrient depletion.
Decadal trend of bloom duration, calculated with the Weibull-distribution metric.
Durations were normalized prior to regression (subtraction of area-average duration). Dashed lines indicate the
trend line (bold) and its confidence intervals (5 %, small dashes). SE denotes standard error; RMSE denotes root mean square error.
Discussion
Trends in spring bloom phenology can be interpreted as responses to nutrient
reduction as well as to slowly acting environmental processes, such as
climate change. To disentangle or even quantify these trends, suitable
observation platforms and subsequent analytical approaches must be chosen. We
present evidence that fundamental challenges of ferrybox observations can be
overcome to yield an internally consistent data source. Subsequently, the
behaviour of commonly used bloom metrics in the presence of decadal trends can be
scrutinized in the context of previously reported system knowledge. Finally,
we attempt to disentangle the effects of nutrient availability and
meteorological conditions on inter-annual variability in bloom phenology.
Automated processing of ferrybox observations
Thresholds for speed, flow rate, and data variability were iteratively
adjusted to the data set and may not be applicable to other ferrybox
implementations. Particularly flow rate, derived from differences in line and
hull temperature, will likely require tuning to each ferrybox installation.
However, here we analysed data from two ferrybox installations, which could
be treated with the same set of thresholds. Transect-wise normalization of
the quality-controlled fluorescence data was adequate to consistently
interpret observations collected by different generations of instrumentation.
However, this approach crucially depends on continuous temporal coverage of
reference measurements for calibration to Chl a concentrations. Adaptive
regression analysis improved the handling of statistical outliers which would
otherwise hamper determination of fluorescence yield, while transects for
which no bottle samples are available were corrected with an interpolated
fluorescence yield derived from the closest bottle-sampled transects. The
present procedure allows for automated and reproducible processing, which is
an improvement over manual quality control. Applying the proposed
interpolated fluorescence yield helps in reprocessing and long-term data
analysis of ferrybox fluorescence observations to better represent natural
variability.
Variability in fluorescence yield
Diurnal fluorescence patterns showed low seasonal dependence after accounting
for solar elevation. Unsurprisingly, light intensity is the predominant
factor in Baltic Sea phytoplankton fluorescence yield variability. Other
seasonal differences in fluorescence response can be attributed to typically
higher cloud cover in winter compared to summer and spring/autumn, which was
not accounted for in our analysis. The seasonal cycle of species composition,
from dinoflagelate- and diatom-dominated spring communities
to cyanobacterial summer bloom , influenced fluorescence
yield considerably less than diel cycles.
The diurnal variability in fluorescence response of 50 % during an average
summer day is within the range of earlier findings, e.g. 66 % (±33 %)
for near-surface observations in upwelled waters of the equatorial Pacific
reported by or 30 % for near-surface seaglider
observations in northeast Pacific waters off the Washington coast, United
States ; although differences in normalization impede direct
comparison. The sampling depth of 5 m for Alg@line systems and the high
attenuation of the Baltic Sea in comparison to clear Pacific Ocean waters are
likely to dampen the observed diurnal variability.
In this study, fluorescence observations during spring, when diurnal
variability reached on average 38 %, were binned for five large Baltic Sea
areas. At a typical cruising speed of approximately 23 knots, each sea area is
sampled for at least several hours. This limits the influence of diurnal
variability in fluorescence yield along a transect on derived Chl a
concentration, which is therefore of lesser relevance for the present study.
However, if fluorescence measurements were to be quantitatively evaluated at
a higher spatial resolution, locally varying fluorescence yield should be
accounted for. Analysis of signal coherence offers an
alternative to quantitative interpretation of fluorescence observations and
can be used to qualitatively detect cyanobacterial surface bloom. If light
history is known, e.g. from a dedicated irradiance sensor, a correction of
diurnal fluorescence yield variability might be possible, and further research
in this direction is recommended.
Spring bloom timing and intensity
The presented bloom phenology expands the time series presented by
and is in good agreement for the overlapping period (2000–2004) when comparing the const5 metric results. Remaining
differences are likely due to quality-control and preprocessing procedures
on the fluorescence records. The authors reported for gof,
nbp, and the Arkona Sea that bloom typically started in the south
and ended in the north, while bloom intensity increased towards the north.
These observations are confirmed here. Sea areas not covered in
, e.g the high-biomass bom and low-biomass
sbp and got, followed the reported south–north trend in
bloom development. Present results also support and expand the findings of
, who showed, with simulations and monitoring data from
1994–1996 for the western Baltic Sea, that surface heating in early spring
needs to overcome the temperature of maximum density to repress convective
mixing and allow spring bloom to emerge. The temperature of maximum density
increases with decreasing salinity; therefore convective mixing is sustained
longer in less saline northern Baltic Sea waters when spring temperature is
on the rise. At the same time, incident solar radiation increases slower in
the north due to lower solar elevation.
Principal component analysis biplots: arrows indicate correlation of a parameter with the principal
components (bottom and left axes, percentages refer to the variability explained by the principal component),
and black dots indicate scores of individual observations (top and right axes) on the principal components
(a component 1 and 2, b component 2 and 3).
Trends
Inter-annual variability in coastal systems exceeds long-term trends by orders
of magnitude . Consequently, trends were observed at
relatively low coefficients of correlation. The importance of appropriate
data preprocessing and gap handling e.g and
choice of metric has been demonstrated in literature and
is further emphasized by the present analysis. Robustness of the reported
decadal trends is documented by high statistical significance levels
(p≪0.01, Figs. and ), which were
supported by spatially binning phenology parameters from all examined Baltic
Sea areas. Similar trends were observed earlier for individual Baltic Sea
areas, however, usually outside 95 % confidence intervals
e.g.
reported stable or increasing Chl a concentrations for the
period 2007–2011 in several Baltic Sea areas despite signs of declining
nutrient concentrations. More recently, eutrophication trend reversal and
oligotrophication processes were reported by , based on
analysis of 112 years of consolidated Baltic Sea observations. Both reports
considered surface-layer Chl a concentration in summer as one of the direct
indicators of eutrophication, but did not include spring bloom in their
assessment. The time series for 2000–2014 that we present here fills this
gap: a negative trend in bloom intensity was also found for spring bloom,
providing further evidence for their hypothesis of gradual nutrient load
reduction.
Thresholds of const5 and median5 are fixed for the whole
time series. The observed negative trend in peak concentration was expected
to introduce an artificial negative trend in bloom duration because an
increasingly higher percentile of the distribution is seen below the bloom
threshold (Fig. ). Contrary to this expected behaviour,
however, const5 and median5 revealed no significant trends
in bloom duration. This indicates that the anticipated negative trend in
bloom duration was countered by a positive trend, e.g. in bloom intensity.
The Weibull metric is based on concentration distribution ratios that are
calculated individually for each bloom. Therefore, Weibull-metric results for
bloom duration are not sensitive to long-term trends in peak concentration.
Weibull-distribution metrics confirmed a highly significant, positive trend
in bloom duration. These two sets of results corroborate the conclusion that
spring blooms in the Baltic Sea have become longer, while Chl a peak and
average concentration levels have declined.
This “flattening” of the concentration distribution is supported by the
absence of a trend in time-integrated biomass bloomidx and by shifts
in nutrient concentration timing (earlier nutrient peak concentration, later
25 % of peak value day). These results indicate that annually generated
spring bloom biomass has not changed significantly over the study period, in
contrast to bloom timing. found a similar development for
cyanobacterial summer surface bloom, and reported decadal oscillations, yet
no long-term trend, of surface area covered by cyanobacteria in the period
1979–2013. In the same period, summer bloom initiation moved to earlier dates
by -0.6 day yr-1. These results suggest that the gap has decreased
between dinoflagelate- and diatom-dominated spring bloom and cyanobacterial
summer bloom. Due to the shorter period covered here as compared to the time
series presented by , it cannot be ruled out that the spring
bloom trends are caused by decadal oscillation. Moreover, Alg@line nutrient
records often did not commence sufficiently early in the season to record
bloom onset. Trends in bloom start and nutrient peak timing can therefore not
be derived at the same accuracy and precision as the other phenological
parameters. In future, additional data and longer time series may revise this
analysis. To this end, nutrient metrics derived in this work are provided in
the Appendix.
Our findings emphasize that bloom timing is an essential indicator to monitor
marine ecosystem dynamics, and thus eutrophication status. Observations at
high temporal resolution and choice of bloom metrics are crucial to derive
bloom timing trends. Eutrophication status assessment frameworks such as
HEAT3.0 may be adapted to embrace available
high-frequency data sources to include bloom timing in their analysis. The
present results may also prove useful in the calibration and validation of
ecosystem models of the Baltic Sea.
Environmental forcing
Gradually decreasing nutrient concentrations , as well as rising average air and sea-surface temperatures
have been reported for recent years,
corresponding to a combination of nutrient reduction efforts and global
climate change. Several scenarios for future change are plausible
but extrapolation of the present results to climate
scenarios is beyond the scope of this study. We nevertheless make an attempt
to attribute the observed bloom phenology shifts to reported changes in
environmental drivers.
Wintertime nutrient concentration and bloom intensity were positively
correlated if no spatial normalization was applied. This supports the
paradigm that the first-order driver of bloom intensity is nutrient
availability. Lacking alternative explanations, we attribute the reported
negative trend in bloom peak concentration to declining nutrient
concentrations. First-order spatial trends in bloom intensity and timing can
be removed by an area-wise z score normalization, which effectively
constrains the analysis to inter-annual variability. After this
normalization, both regression and PCA resulted in negative correlation between wintertime
nutrient concentration and bloom intensity. This negative feedback can be
understood as a subtle interaction between meteorological forcing and
nutrient supply: strong wind-forced mixing can cause upwelling of deep,
nutrient-rich waters to surface layers. Wind speed, however, was found to be
negatively correlated to the prevalent light level, as well as to bloom
duration and bloom index. Therefore, in years when additional nutrients are
available due to strong wind forced mixing, low-light regimes that can slow
down bloom development are also likely to prevail.
Bloom duration primarily co-varied with weather conditions; e.g. high
irradiance levels and low wind speeds were frequently observed for
long-lasting blooms (and vice versa). Although the same pattern was observed
for bloom timing, no trend was found for bloom start and peak day.
Increasingly favourable meteorological conditions in late bloom phases are
thus a likely driver for the observed increase in bloom duration. Similar
weather-driven modulations of bloom timing were reported earlier
for spring, and especially
cyanobacterial summer bloom
.