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1 Stimulated bioluminescence as an early indicator of bloom development of the toxic dinoflagellate Alexandrium ostenfeldii Anniina H. Le Tortorec, Päivi Hakanen, Anke Kremp, John Olsson, Sanna Suikkanen and Stefan G.H. Simis Finnish Environment Institute (SYKE), Marine Research Centre, P.O. Box 140, FI-00251 Helsinki, Finland. Corresponding author: [email protected] ABSTRACT Daily patterns of stimulated bioluminescence were recorded throughout an Alexandrium ostenfeldii bloom at a remote shallow site in the Finnish Archipelago Sea. Transect measurements showed highly localized bioluminescence prior to the bloom peak. High bioluminescence, corresponding to peak dinoflagellate biomass, lasted 15 days in mid-August. Large day-to-day variation of bioluminescence intensity was observed throughout the bloom. Bioluminescence intensified after sunset, peaked around midnight and continued during the early morning. Night-time chlorophyll-a (Chl-a) fluorescence was significantly correlated with bioluminescence intensity during the bloom. The relation between Chl-a fluorescence and bioluminescence was indicative of the presence of A. ostenfeldii, confirmed by infrequent cell counts. Low concentrations of cellular paralytic shellfish poisoning (PSP) toxins were detected throughout the sampling period while higher toxin concentrations were limited to the period where bioluminescence intensities were high and bioluminescence could be easily detected by eye. These results suggest that, under special conditions as described here, autonomous optical measurements may be useful to predict the onset of toxic dinoflagellate bloom, although the highly localized distribution may require advance knowledge of potential bloom locations. Key words: Alexandrium ostenfeldii, bioluminescence, harmful algal blooms, optical detection, paralytic shellfish poisoning toxins

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Page 1: Stimulated bioluminescence as an early indicator of bloom ......Dinoflagellate bioluminescence is considered a defence mechanism against grazing, possibly alerting the predator's predators

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Stimulated bioluminescence as an early indicator of bloom development of the

toxic dinoflagellate Alexandrium ostenfeldii

Anniina H. Le Tortorec, Päivi Hakanen, Anke Kremp, John Olsson, Sanna Suikkanen and Stefan

G.H. Simis

Finnish Environment Institute (SYKE), Marine Research Centre, P.O. Box 140, FI-00251 Helsinki,

Finland.

Corresponding author: [email protected]

ABSTRACT

Daily patterns of stimulated bioluminescence were recorded throughout an Alexandrium ostenfeldii

bloom at a remote shallow site in the Finnish Archipelago Sea. Transect measurements showed highly

localized bioluminescence prior to the bloom peak. High bioluminescence, corresponding to peak

dinoflagellate biomass, lasted 15 days in mid-August. Large day-to-day variation of bioluminescence

intensity was observed throughout the bloom. Bioluminescence intensified after sunset, peaked

around midnight and continued during the early morning. Night-time chlorophyll-a (Chl-a)

fluorescence was significantly correlated with bioluminescence intensity during the bloom. The

relation between Chl-a fluorescence and bioluminescence was indicative of the presence of A.

ostenfeldii, confirmed by infrequent cell counts. Low concentrations of cellular paralytic shellfish

poisoning (PSP) toxins were detected throughout the sampling period while higher toxin

concentrations were limited to the period where bioluminescence intensities were high and

bioluminescence could be easily detected by eye. These results suggest that, under special conditions

as described here, autonomous optical measurements may be useful to predict the onset of toxic

dinoflagellate bloom, although the highly localized distribution may require advance knowledge of

potential bloom locations.

Key words: Alexandrium ostenfeldii, bioluminescence, harmful algal blooms, optical detection,

paralytic shellfish poisoning toxins

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INTRODUCTION

Harmful phytoplankton blooms increasingly threaten ecosystem health in estuaries and coastal waters

worldwide (Hallegraeff, 2010; Anderson et al., 2012). The main groups forming harmful blooms

include cyanobacteria, diatoms, haptophytes, raphidophytes and dinoflagellates (Hallegraeff et al.,

2003). Dinoflagellates account for an estimated 75% of the species that form harmful algal blooms

(Smayda, 1997). A wide range of dinoflagellate species produce toxic substances that can affect

higher trophic levels (e.g. fish and waterfowl) and accumulate in benthic and littoral food webs

(Hallegraeff, 2010; Anderson et al., 2012). Blooms of dinoflagellates in coastal waters can cause

paralytic and diarrheic shellfish poisoning (PSP, DSP) with associated losses to aquacultures and the

environmental and recreational value of affected areas (Hallegraeff et al., 2003; Hallegraeff, 2010;

Anderson et al., 2012).

Bioluminescence is a night-time optical phenomenon where cells emit flashes of light induced by

pressure on the cell wall (Van den Hoek et al., 1995). A large number of harmful dinoflagellate taxa

luminesce (Van den Hoek et al., 1995), and virtually all bioluminescence in surface waters of marine

environments originates from dinoflagellates (Swift et al., 1995; Marcinko et al., 2013a).

Dinoflagellate bioluminescence is considered a defence mechanism against grazing, possibly alerting

the predator's predators (e.g. Abrahams & Townsend, 1993), thus suggesting the potential to alter the

marine food web in favour of dinoflagellate presence. Bioluminescence has been mainly studied in

the open ocean (e.g. Marra, 1995) and in the coastal zone (Herren et al., 2005; Moline et al., 2007;

Moline et al., 2009), whereas few studies have focused on bioluminescent blooms in near-shore

waters (Kim et al., 2006) where animal and human populations are particularly vulnerable.

While many bioluminescent dinoflagellate species occur in the Kattegat-Skagerrak area of the Baltic

Sea, only few species are known to occur in the central and northern parts of the Baltic Sea (Hällfors,

2004). Protoceratium reticulatum (Claparède & Lachmann) Bütschli is generally found in low

abundances throughout the open Baltic Sea (Hällfors, 2004; Mertens et al., 2012). Lingulodinium

polyedrum (Stein) Dodge is encountered in the Southern Baltic Sea (Hällfors, 2004). In recent years,

Alexandrium ostenfeldii (Paulsen) Balech and Tangen has been increasingly observed in shallow

waters and bays of the Baltic Sea up to the Archipelago Sea, with reports from Åland, the Gulf of

Gdansk, Gotland and Kalmar (Larsson et al., 1998; Hajdu et al., 2006; Kremp et al., 2009; Hakanen

et al., 2012). Sampling in recent years in the Åland archipelago (located between Finland and Sweden,

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Fig. 1) suggests that A. ostenfeldii is the only bioluminescent dinoflagellate species found in this area

(Kremp et al., 2009; Hakanen et al., 2012).

A. ostenfeldii is found in temperate waters worldwide (e.g Moestrup and Hansen, 1988; Mackenzie

et al., 1996; John et al., 2003; Gribble et al., 2005). In oceans it normally occurs at low densities

(Moestrup and Hansen, 1988; John et al., 2003; Gribble et al., 2005) but in the coastal areas of the

Baltic Sea recurrent dense blooms are observed (Larsson et al., 1998; Hajdu et al., 2006; Kremp et

al., 2009; Hakanen et al., 2012). Summer blooms with densities up to 106 cells L-1 have formed in

the Åland archipelago at least in the last decade (Kremp et al., 2009; Hakanen et al., 2012). These

blooms pose a potential threat to the ecosystem due to the observed production of PSP toxins (Kremp

et al., 2009; Hakanen et al., 2012). PSP effects on humans have thus far not been considered, probably

because a commercial shellfish industry is absent in the Baltic Sea. Laboratory studies have, however,

shown that A. ostenfeldii toxins affect zooplankton grazing behaviour (Sopanen et al., 2011), and

shellfish and small fish collected from the bloom area contained concentrations of PSP toxins that

exceed the European Commission safety limits of 80 µg 100g-1 (O. Setälä, Helsinki, personal

communication).

A clear need for an early warning of dinoflagellate presence exists in the coastal areas of the Baltic

Sea in summer. Due to the complex coastline with many isolated and remote bays, this demand cannot

be met using traditional sampling and analysis of water samples. The recurrent dense toxic blooms

of A. ostenfeldii in the Åland archipelago provide a case to develop an autonomous monitoring and

early warning strategy based on bioluminescence. The environmental monitoring potential of

bioluminescence as an early indicator of the presence of toxic dinoflagellate bloom has thus far

received little attention. Relations between dinoflagellate toxicity and bioluminescence dynamics are

also not known. In this study, we investigate spatiotemporal patterns of bioluminescence produced

during different stages of an A. ostenfeldii bloom, in an area that due to its high latitude (60° N)

experiences short nights in summer, which may limit the light-suppressed production of

bioluminescence. The high-resolution in situ dynamics of bioluminescence and the biomass threshold

at which bioluminescence and toxicity are evident are addressed in this study, in order to define an

optimal monitoring strategy.

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METHOD

Study area

The study site is situated in a remote area in the Åland archipelago between Finland and Sweden (Fig.

1A). Salinity ranges from 6 to 7 psu, and surface water temperature may reach up to 24 °C in isolated

bays. Summer day length at this latitude (60° N) reaches up to 19 hours. In the middle of July the sun

typically rises around 04:40 and sets at 22:45, and in the beginning of September the sun rises around

06:35 and sets at 20:45. A recurrent A. ostenfeldii bloom site is located in a sound in the northern part

of the Föglö archipelago (60° 5' 35" N, 20° 32' 12" E, Fig. 1C). The 3-km long sound is shallow

(water depth < 3 m) and sheltered with limited water exchange. A detailed description of the study

area is given in Kremp et al. (Kremp et al., 2009).

Semi-continuous measurements

A sensitive submersible light sensor (GlowTracka, Chelsea Technologies Group, West Molesey,

UK), a temperature logger (HOBO U22 Water Temp Pro v2), and a chlorophyll a (Chl-a) fluoroprobe

(SCUFA, Turner Designs, Sunnyvale, CA, USA) were mounted on a submerged frame and installed

on-site on 14 July 2011. After minor changes to resolve light contamination, daily patterns of

bioluminescence and Chl-a fluorescence were recorded continuously from 27 July to 7 September

2011, covering the start, peak, and decline of an A. ostenfeldii bloom. The sensor frame was moored

approximately 10 meters from the shore at 0.5 - 1.0 m depth (station A, Fig. 1) where water depth

was approximately 1.5 m. Water was pumped through the sensor chamber of the GlowTracka at 6 L

min-1 using a submersible pump (SBE 5P, Sea-bird Electronics, Bellevue, WA, USA) mounted

directly in front of the sensor chamber. A 1-mm mesh was positioned before the water intake, using

a funnel to widen the intake area. The mesh was required to keep macrophytes away from the sensor

chamber, and the wide area over the funnel kept the water flow through the mesh sufficiently low to

prevent stimulation of bioluminescence before it reached the pump. Black tubing and masking tape

were used to shield the sensor from ambient light. The sensors were connected to a data logger (DI-

710, DATAQ, Akron, OH, USA) on the shore and powered from a solar panel. A microcontroller

(Arduino Uno) was used to power the system for one minute at 10-min intervals. The data logger

recorded the light sensor signal at 15 Hz. This combination of sensor and data recording frequency

was not sufficient to analyse flash patterns from individual cells, but allowed in situ data storage over

a long observation period. The noise floor of the instrument was 0.05 ± 0.006 nW cm-2. Because of

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the low noise floor of the instrument, no threshold was used to define the presence of a

bioluminescence signal. The raw bioluminescence and fluorescence data were averaged over each

recorded 1-min period to generate the results shown in this paper. Chl-a fluorescence values were

converted to pigment concentrations using a laboratory calibration. The factory calibration of the light

meter was used to convert the response of the photodiode to corresponding light intensity at 560 nm.

The sensor surfaces were cleaned weekly to avoid biofouling.

Spatial distribution of bioluminescence

The time-series of bioluminescence measurements at station A was interrupted for two nights to

assess the spatial distribution of bioluminescence in the study area, from 8 to 11 August 2011. A 7-

km transect (Fig. 1) was covered with a motorboat four times each night: between 21:00 - 23:00 h,

01:00 - 03:00 h, 05:00 - 07:00 h, and 09:00 - 11:00 h. The sensor frame was lowered to a depth of 0.5

- 1.0 m, every 100 m to record for approximately 2 minutes. Water samples for phytoplankton counts

were collected at a depth of 0.5 m at 10 points along the same transect line on the day following the

last bioluminescence measurements.

Sample preparation and analysis

Toxin samples were collected every second week between mid-July and the end of September, by

sequentially filtering 10 L of seawater from 0.5 m depth through 75-µm and 25-µm sieves. The 25 -

75 µm fraction was retained and washed into a 50 mL Falcon tube. The concentrated sample was

filtered onto a Whatman GF/F glass fiber filter (Ø 25 mm), transported in liquid nitrogen, and stored

at -80 °C until further analysis. The samples were freeze-dried before extraction. The extraction

procedure and the protocol for high-performance liquid chromatography with fluorescence detection

(HPLC-FD) used to quantify PSP toxin concentrations are described in detail in Hakanen et al.

(Hakanen et al., 2012).

Samples for phytoplankton analyses were fixed with Lugol's solution and counted following the

Utermöhl inverted microscope method (HELCOM, 2008). Depending on cell density, sample

volumes of 3 - 50 mL were left to settle in phytoplankton counting chambers for 3 - 24 h.

Phytoplankton enumerations were performed at 200× and 400× magnifications using a Leica

DM13000B inverted microscope. At least 500 cells were counted. The identity of A. ostenfeldii cells

against other thecate dinoflagellates was confirmed with epifluorescence microscopy. Cells were

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stained with 1 mg mL-1 solution of Fluorescent Brightener 28 (Sigma-Aldrich) to see thecal plate

tabulation and confirm identity (Friz and Tiemer, 1985).

Statistical analyses

Linear least-squares regression analysis was used to assess the relations between A. ostenfeldii cell

density, bioluminescence intensity, and toxicity, and between Chl-a concentration and

bioluminescence intensity in the semi-continuous monitoring study. The same method was used to

relate cell density to bioluminescence intensity in the transect study. Results were considered

statistically significant if the p-value was below 0.05.

RESULTS

Semi-continuous measurements

Stimulated bioluminescence was first detected during the night of 30 July, then again from 6 August

lasting until the end of the measuring period on 7 September. Intense (> 2 nW cm-2) bioluminescence

was recorded from mid-August and lasted approximately 15 days, with a maximum intensity (1–min

average) of 14.46 nW cm-2 on 26 August. Fig. 2 describes the recorded bioluminescence at 10-minute

intervals for the whole study period. The bioluminescence intensity was used to arbitrarily divide the

data set into four time periods. The first period (27 July - 6 August) was characterized by very low

bioluminescence (mean ± standard deviation (std): 0.07 ± 0.04 nW cm-2). The second period (7 - 16

August) was associated with intermediate bioluminescence levels (0.54 ± 0.46 nW cm-2), which were

clearly visible to the naked eye. The strongest bioluminescence (2.09 ± 2.05 nW cm-2) was observed

in period 3 (17 August – 1 September). During the last period (2 - 7 September) bioluminescence

returned to low intensities, although the signal intensity exceeded the pre-bloom situation up until the

end of the observation period (0.10 ± 0.03 nW cm-2).

Large variations in the day-to-night intensity of bioluminescence were observed throughout the study

period (Fig. 2). Most noticeable is that during the peak of the bloom (period 3), bioluminescence did

not completely disappear during the day. The ratio of night-to-day bioluminescence was highly

variable with mean ± standard deviation of 3.27 ± 2.53 for period 1, 19.69 ± 11.63 for period 2, 32.90

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± 22.20 for period 3, and 1.73 ± 0.68 for period 4. Here, night-time bioluminescence was calculated

from sunset to sunrise. Day-time bioluminescence (sunrise to sunset) was close to the instrument

noise level only during the first period.

Daily rhythms of bioluminescence are visualized in Fig. 3 following standardization of the signal in

24-h periods, by first subtracting the 24-h minimum value and subsequently normalizing to the 24-h

peak value. The four periods are presented separately and each plot is centered on sunset. A sinusoid

rhythm, rising shortly after sunset and decreasing from morning to late afternoon was already visible

in the first period, despite the weak amplitude of the signal (Fig. 3A). The fluctuations between day

and night-time bioluminescence became more pronounced during some nights in the second period

(Fig. 3B), indicating the transition to the bloom. In period 3, the period of highest bioluminescence,

a clear pattern was observed, with bioluminescence rising sharply after sunset, peaking around

midnight, and declining in the morning (Fig. 3C). In period 4 the weak sinusoid pattern of the first

period was again seen during some nights, whereas isolated peaks (some during the day) during other

24-h periods, associated with low nightly mean bioluminescence, suggest that the population of

bioluminescent cells was too sparse to discern any remaining rhythm (Fig. 3D).

To assess whether night-time stimulated bioluminescence corresponded to A. ostenfeldii abundance,

toxicity, and the abiotic environment, the results recorded with the autonomous in situ instruments

and laboratory analyses (cell counts and PSP toxin concentrations) are plotted on the same time scale

in Fig. 4. Despite the small number of water samples that could be brought to the lab from the remote

site, night-time bioluminescence (Fig. 4A) and cell density (Fig. 4D) show comparable temporal

trends for which statistical analysis is given below. Chl-a concentration fluctuated throughout the

study period with a mean and standard deviation of 10.66 ± 3.12 mg m-3. The night-time Chl-a

concentrations (Fig. 4B) were highest in periods 2 and 3 concurrently with the highest night-time

bioluminescence intensities (Fig. 4A) and A. ostenfeldii cell densities (Fig 4D).

Mean daily water temperatures were > 22 °C from 19 July until the start of period 2 and > 19 °C in

period 3, the height of the bloom. Decreasing water temperatures to < 19 °C coincided with a decrease

in the intensity of bioluminescence (Fig. 4C). Changes in water temperature corresponded closely to

the visual separation of bioluminescence intensities between periods 1-4. These high-resolution

observations support the idea that high temperatures promote A. ostenfeldii blooms (Larsson et al.,

1998; Hajdu et al., 2006; Kremp et al., 2009; Hakanen et al. 2012).

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A. ostenfeldii cells were present in the water column throughout the study period but high abundances

were only reached in late August, with a maximum of 3.08 × 105 cells L-1 on 22 August (Fig. 4D).

Despite the low number of concurrent samples (n = 7), there was a significant positive relation

between night-time bioluminescence and the number of A. ostenfeldii cells (BL = 1.63×10-5 cells +

0.52, R2 = 0.66, n = 7, p = 0.03). The distribution of corresponding bioluminescence and cell number

data did not allow accurate determination of a cell density threshold for bioluminescence detection.

Low concentrations of cellular PSP toxins were detected throughout the sampling period while higher

toxin concentrations (> 0.28 mg m-3) were limited to the period when measured bioluminescence was

> 1 nW cm-2 at night (Fig. 4E), and could be easily detected by eye. PSP toxins gonyautoxins 2 and

3 (GTX2/GTX3) and saxitoxin (STX) were found. The highest total cellular PSP toxin concentrations

were 0.31 and 0.28 mg m-3 on 10 and 26 August, respectively. With only four concurrent samples no

relation between toxicity and night-time bioluminescence could be established. Six concurrent

samples were available to relate toxicity and A. ostenfeldii cell number. This relation was positive

and significant (toxins = 5.43×10-6cells + 0.02, R2 = 0.89, n = 6, p = 0.005).

The results for regression analysis between all night-time (sunset+1h - sunrise-1h) observations of

bioluminescence and Chl-a fluorescence are summarized in Table I, as well as the correlation between

nightly mean bioluminescence and nightly mean Chl-a fluorescence. Nightly mean values gave

stronger regression results when analysed over the whole study period, and during period 3,

suggesting that short-term variability between the sensor signals (measured from slightly different

positions in the sensor frame) was significant. Regression results obtained during periods 2 and 4

were only significant when all night-time observations were considered. The correlation between

night-time bioluminescence intensity and Chl-a concentration varied markedly between the four

designated periods (Table I, Fig. 5). No meaningful correlation was defined during period 1 when

bioluminescence did not significantly depart from the noise level. In period 2 a weak negative trend

was observed (Fig. 5B), possibly suggesting a succession from higher biomass non-bioluminescent

cells to low abundances of A. ostenfeldii. The mean bioluminescence intensity (0.54 nW cm-2) rose

nearly 7-fold compared to period 1, suggesting that infrequent flashes or bioluminescent glow were

present. In period 3, regression statistics show a significant positive relation between bioluminescence

and Chl-a (Fig. 5C). If we assume the bloom to be dominated by A. ostenfeldii during this period,

extrapolation of this trend implies a minimum biomass threshold for bioluminescence detection at

5.52 mg Chl-a m-3. In period 4, bioluminescence returned to weak intensities, although regression

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analysis indicates a stronger relation to Chl-a fluorescence (Fig. 5D), suggesting that bioluminescent

cells were predominant even though biomass was declining. The minimum biomass threshold to

detect bioluminescence in this period can be extrapolated from the regression analysis to 1.50 mg

Chl-a m-3.

Spatial distribution

Transect measurements taken along the sound during two nights in the second period showed highly

localized bioluminescence prior to the bloom peak. Fig. 6 describes the mean bioluminescence values

recorded during two-minute intervals at each site. Bioluminescence was concentrated around the

permanent mooring (station A) in the inner areas of the sound and sharply decreased towards the open

sea until it was indistinguishable from instrument noise. The brightest bioluminescence was 1.89 nW

cm-2, easily visible to the naked eye. The horizontal distribution of bioluminescence and A. ostenfeldii

cells was similar (Fig. 6) with a positive correlation between bioluminescence intensity and the A.

ostenfeldii abundance in the geographically closest water sample taken on the next day (BL =

8.41×10-6 + 0.08, R2 = 0.76, n = 10, p = 0.001). On 26 August, during the bloom peak, visible

bioluminescence was observed to extend until the open sea on the west side of the sound. Transect

measurements are however not available for that time.

DISCUSSION

The aim of this study was to assess the environmental monitoring potential of bioluminescence as an

early indicator of the presence of a toxic dinoflagellate bloom. Our results show that bioluminescence

is already detectable when A. ostenfeldii is present at low cell concentrations in the water column.

Two simultaneous patterns explained most of the variation in the recorded bioluminescence. The

widely observed circadian rhythm of bioluminescence was the primary source of variation in the

recorded signal. The second source of variability in the bioluminescence signal was the seasonal

change in the intensity of bioluminescence driven by phytoplankton succession and fluctuations in

phytoplankton biomass. The transect study showed that bioluminescence remained highly localized

until mere days before the bloom peak. High toxin concentrations occurred only at intermediate and

high bioluminescence intensities, although these observations were sparse. Various aspects of bloom

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dynamics, acting on different time and spatial scales, must therefore be considered before a

monitoring strategy can be implemented.

The diurnal variation in bioluminescence in periods 2 and 3 (Fig. 3B-C) has been repeatedly observed

in nature (e.g. Utyushev et al., 1999; Geistdoerfer and Cussatlegras, 2001; Marcinko et al., 2013b)

and in cultured cells of single species (e.g. Fritz et al., 1990; Knaust et al., 1998). Bioluminescence

increased sharply after sunset and peaked around midnight. Bioluminescence continued into the early

morning and decreased thereafter, supporting the idea that the production of substrate for the

bioluminescent reaction is limited by light. The ratio between night and day intensity observed in this

study (max 32.90 ± 22.20 during the period of high bioluminescence) is similar to that found in other

natural populations, e.g. a maximum ratio of 23 in Marcinko et al. (Marcinko et al., 2013b), but lower

than reported in the literature for cultured dinoflagellates with ratios of 200 for Pyrodinium

bahamense and Gonyaulax polyedra and up to 950 for Pyrocystis lunula (Biggley et al., 1969).

However, results obtained from cultured strains are difficult to compare to results from the field,

where changes in the light field are gradual and illumination conditions (cloud cover) can be variable.

The seasonal change in the intensity of bioluminescence was likely governed by fluctuations in the

biomass of A. ostenfeldii at the study site. Biomass fluctuations are typically caused by currents,

population growth and decline as well as life cycle transformations (Smayda, 1997; Wyatt and

Jenkinson, 1997). Natural currents and currents produced by ships passing either side of the sound

might influence local A. ostenfeldii biomass fluctuations. The most likely reason for the seasonal

change in the intensity of bioluminescence is the natural population dynamics: A. ostenfeldii has in

the years prior to this study repeatedly peaked after mid-July and declined at the end of August. Peaks

were always short-lived, and outside the peak period the species persisted in the water column at

modest or low numbers (Hakanen et al., 2012). Worldwide blooms of Alexandrium are generally

described as short-lived (Wyatt and Jenkinson, 1997). It has been suggested that in the Baltic Sea

warm water temperatures (around 20 °C) could be the most important single environmental factor

determining whether A. ostenfeldii blooms or not (Larsson et al., 1998; Hajdu et al., 2006; Kremp et

al., 2009; Hakanen et al., 2012). In this study the mean daily water temperatures remained over 19

°C until the end of August, when the period of high bioluminescence ended. Subsequently,

temperature and bioluminescence both decreased steadily. This supports the idea that warm water

promotes persistence of the bloom. Like many seasonal Alexandrium species, A. ostenfeldii produces

resting cysts during its life cycle, which serve as survival stages through periods of unfavourable

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conditions (Hakanen et al., 2012). The drop in temperature during early autumn could induce cyst

formation, thereby accelerating population decline.

A. ostenfeldii cell density and night-time bioluminescence intensity were positively related,

supporting the hypothesis that bioluminescence could be used to follow bloom development. In turn,

the number of A. ostenfeldii cells was positively related with toxicity, in line with Hakanen et al.

(Hakanen et al., 2012) who reported that PSP toxin dynamics corresponded to A. ostenfeldii

abundance. Thus, while simultaneous toxin and bioluminescence data were too scarce to develop a

predictive model, bioluminescence and toxicity were indirectly related in our data set.

The relationship between night-time bioluminescence intensity and Chl-a concentration was strong

during the bloom period in the middle of August. Extrapolation of this relationship yields a lower

limit of bioluminescence detection at 5.52 mg Chl-a m-3 while the bloom reached biomass in the order

of 10 - 20 mg Chl-a m-3. 60% of the variability in mean nightly bioluminescence could be attributed

to changes in mean nightly Chl-a (dominated by A. ostenfeldii biomass). A significant but slightly

weaker relationship was found over the whole study period explaining 48% of variability in mean

nightly data. The weak negative trends between night-time bioluminescence intensity and Chl-a

concentration until the middle of August were likely due to other species than A. ostenfeldii

dominating the phytoplankton community and contributing to Chl-a fluorescence. Hakanen et al.

(Hakanen et al., 2012) have shown that in the Föglö study area A. ostenfeldii does not form

monospecific blooms but is part of a diverse phytoplankton community. During the abundance peaks

in 2009, A. ostenfeldii contributed > 60% of the phytoplankton biomass in July and around 30% in

August (Hakanen et al., 2012). At the start of the period 1 in the current study, filamentous

cyanobacteria were visibly dominant but later disappeared. Kim et al. (Kim et al., 2006) noted similar

trends in Chl-a concentration and bioluminescence intensity, related to increased dinoflagellate

abundance. The positive, albeit weak, relation between night-time bioluminescence intensity and Chl-

a in September, when bioluminescence levels were decreasing, may be explained by a decrease of

the total phytoplankton biomass towards autumn. Bioluminescent cells may nevertheless have been

relatively more abundant, judging from the lower detection threshold of bioluminescence

corresponding to Chl-a concentrations of 1.50 mg Chl-a m-3. The weak correlations in periods 2 and

4 are not seen to be significant within the mean nightly data, likely due to low sample size. In

summary, these results suggest that the combined use of Chl-a fluorescence and bioluminescence can

help to determine the development of dinoflagellates in the phytoplankton community.

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The transect measurements revealed that bioluminescence was highly localized prior to the bloom

peak. It was strongest in the inner areas of the sound and decreased towards the open sea. This aspect

is interesting as it suggests that the bloom spread out from a single location, supporting earlier

findings that the distribution of A. ostenfeldii is highly localized in the Baltic Sea (Larsson et al.,

1998; Hajdu et al., 2006; Hakanen et al., 2012). On 26 August bioluminescence was visible up to the

open sea in the west end of the sound and the total area where the signal was visible spanned

approximately three kilometres. It is not well known what factors determine the distribution of A.

ostenfeldii but benthic resting cysts could play an important role in population establishment, as it

has been shown that cyst beds are a key feature of many Alexandrium blooms (McGillicuddy et al.,

2003, 2005; Stock et al., 2005).

In support of bioluminescence measurements to monitor the development of dinoflagellate blooms in

shallow, near-coastal systems, it is safe to state that the measurement system used in this study was

sufficiently sensitive for early detection of dinoflagellate blooms, since bioluminescence could be

measured in periods where dinoflagellates were not yet common. However, the diurnal rhythm of

bioluminescence and localization of blooms in their early stages of establishment should be taken

into account when planning environmental monitoring of this phenomenon. We were able to measure

bioluminescence during the whole summer night (between 7.5 - 9.5 hours in periods 2 and 3) and low

levels of bioluminescence could also be detected during some parts of the day. Because the bloom

area was strongly localised at first, monitoring sites should be carefully selected. We therefore

recommend targeted monitoring in areas where blooms are frequently observed or where risks are

high, e.g. close to human settlements or fish farms. Biofouling did not pose a problem. The system in

this study was cleaned weekly as a precaution, but there was no evidence of significant build-up of

materials in the sensor chamber. This may be attributed to the absence of sunlight in the chamber,

and the use of a pump with a mesh.

In conclusion, this study shows that it is possible to relate bioluminescence to the succession of

phytoplankton community towards a dinoflagellate bloom using automated measurements. Further,

the dynamics of in situ bioluminescence correspond to cell numbers of A. ostenfeldii. Toxicity was

positively related to abundance of A. ostenfeldii, as shown in more detail in previous studies. Future

work should improve the estimates of toxicity predictions from bioluminescence and Chl-a

fluorescence. This could be done by further concurrent field measurements with increased sampling

for toxicity, or alternatively through laboratory experiments. Sampling blooms at remote locations

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remains a major challenge, and the autonomous optical measurements may be used to direct sampling

efforts.

ACKNOWLEDGEMENTS

The authors wish to thank Johan and Helene Franzén for their help and hospitality during field work,

Hanna Kauko and Mira Stenman for helping with data collection, and Johanna Oja for assistance in

the lab. The constructive comments provided by two anonymous reviewers are gratefully

acknowledged.

FUNDING

This work was supported by the Academy of Finland [grant number 128833 to A.K. and S.S., 132409

to A.H.L. and S.G.H.S.]; EC/IAPP project WaterS [grant number 251527 to J.O.] and the Finnish

Cultural Foundation [grant to P.H.].

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TABLES

Table I. Regression results for night-time bioluminescence and night-time Chl-a. In the first columns,

all night-time samples for the specified period are included. The last columns include only nightly

mean values in the analysis. Results for period 1 are not shown because the bioluminescence signal

did not significantly depart from the noise level of the instrument during that period. (r.c. = regression

coefficient, i = model intercept).

Period All night-time samples Nightly mean values r.c. i R2 n p-value r.c. i R2 n p-value

All 0.45 -3.48 0.37 1581 < 0.0001 0.52 -4.40 0.48 40 < 0.0001 2 -0.02 0.76 0.03 276 0.007 -0.03 0.96 0.21 8 0.25 3 0.31 -1.71 0.22 2145 < 0.0001 0.74 -6.37 0.60 16 0.0004 4 0.02 -0.03 0.21 280 < 0.0001 0.02 -0.07 0.37 5 0.27

Statistically significant results (p < 0.05) are shown in bold.

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FIGURES

Fig. 1. (A) The Finnish Archipelago Sea in the northern Baltic Sea showing the location of the Åland

islands (black box). (B) The Föglö islands and the area surrounding the study site (black box). (C)

Detail of the islands around the study site showing the sampled transect (drawn line) and the location

of station A (black marker) where semi-continuous measurements took place during the study period.

The transect corresponds to a distance of approximately 7 km.

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Fig. 2. Intensity of bioluminescence, measured at 10-min intervals.

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Fig. 3. Daily rhythm of standardized bioluminescence (lowest intensity subtracted, followed by

normalization to peak intensity) per 24-h period. Each plot is centered on sunset. Thick drawn lines

are the average dynamics observed over the whole period (all curves in graph). The dark period is

indicated (hatched) at the top of each figure. The hatched pattern is divided into two parts, the first

part indicating the length of the shortest night and the second part indicating the length of the longest

night during the period in question. (A) Period 1 (27 Jul - 6 Aug), (B) period 2 (7 - 16 Aug), (C)

period 3 (17 Aug – 1 Sep) and (D) period 4 (2 - 7 Sep).

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Fig. 4. Dynamics of (A) night-time bioluminescence (nightly mean, standard deviation as error bars),

(B) night-time chlorophyll a (nightly mean, standard deviation as error bars), (C) water temperature,

(D) A. ostenfeldii cell abundance (one replicate per sampling) and (E) total cellular PSP toxin

concentration (mean of two replicates) (* = traces of GTX toxins found on 29 September) at station

A from 11 July to 29 September.

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Fig. 5. Correlation between night-time (one hour after sunset until one hour before sunrise)

bioluminescence and night-time chlorophyll a concentration for (A) the whole data set, (B) period 2

(7 - 16 Aug), (C) period 3 (17 Aug – 1 Sep) and (D) period 4 (2 - 7 Sep). Correlation for period 1 is

not shown because the bioluminescence signal did not significantly depart from the noise level of the

instrument during this period.

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Fig. 6. Horizontal distribution of bioluminescence (circles) and A. ostenfeldii cell abundance (line

with squares) measured along the transect line. 0 m represents station A.