Real time observations of coastal algal blooms by an early warning system

Estuarine, Coastal and Shelf Science 65 (2005) 172e190 www.elsevier.com/locate/ECSS

Real time observations of coastal algal blooms by an early warning system J.H.W. Lee a,*, I.J. Hodgkiss b, K.T.M. Wong a, I.H.Y. Lam b b

a Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China Department of Ecology & Biodiversity, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China

Received 7 January 2005; accepted 3 June 2005 Available online 2 August 2005

Abstract In eutrophic sub-tropical coastal waters around Hong Kong, phytoplankton or unicellular microalgae can grow rapidly to very high concentrations under favourable environmental conditions. These harmful algal blooms (HABs) have led to massive fish kills, hypoxia, and beach closures. However, to date the causality and mechanism of coastal algal blooms are still poorly understood. A remotely controlled autonomous real time field monitoring system has been developed to continuously track the changes in chlorophyll fluorescence, dissolved oxygen and other hydro-meteorological variables at two representative mariculture zones. The system can give an alarm when a bloom is detected, so that timely manual water quality sampling can be carried out to supplement the telemetric data. During 2000e2003, the system has successfully tracked 19 algal blooms. In the shallow weakly flushed coastal water (depth 7e10 m, tidal current 5e19 cm sÿ1), the bloom is short-lived, typically lasting a few days to over a week, with chlorophyll and DO concentrations in the range of 20e40 mg mÿ3 and 2e15 g mÿ3, respectively. It is found that: (1) the chlorophyll concentration is strongly correlated with its past values in the previous week, suggesting an auto-regressive type of algal dynamics; (2) the dissolved oxygen can reach highly super-saturated levels (12 g mÿ3) during a diatom bloom, and decreases to below 4 g mÿ3 at the tail of the growth phase; (3) in contrast, a dinoflagellate bloom is characterized by a much more pronounced vertical structure. Diel vertical migration and aggregation to dense layers are clearly observed. Significant dissolved oxygen consumption is associated with the migration, resulting in DO drops by as much as 6 g mÿ3 during the bloom; (4) the predominance of diatoms and dinoflagellates at the two sites can be explained in terms of the different hydrographic and nutrient conditions (the N:P ratio). Net algal growth rate, sinking and swimming velocities are derived from the in situ bloom data. The 4-year high frequency data set provides a basis for development of models for forecast of harmful algal blooms. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: red tide; harmful algal blooms; field observation; diatoms; dinoflagellates; species selection; dissolved oxygen; early warning system

1. Introduction Of the total number of marine phytoplankton species (w5000), only some 300 or so are known to occur in numbers high enough to be visible in seawater (Sournia et al., 1991). About 40 or 50 of these species produce

* Corresponding author. E-mail address: [email protected] (J.H.W. Lee). 0272-7714/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecss.2005.06.005

toxins that can affect natural marine populations of plants and animals, as well as human beings (Hallegraeff et al., 1995). Phytoplankton blooms can also lead to hypoxia and fish kills when they collapse. These socalled HABs (harmful algal blooms) have become the subject of increased research over the past decade, based on the recognition that such blooms are becoming more frequent, more extensive, and more severe on a worldwide basis (Hallegraeff, 1993; Anderson, 1994). Two points need emphasis here: first, that there are many

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harmless algal blooms, where increased production can lead to increased productivity of the entire food web which they support; and second, that not all HABs are caused by microalgae but also by cyanobacteria, and protozoan agents. In general, the term HAB is widely used to describe any bloom that is harmful or which has potential harmful effects. As in many marine systems, most algal blooms in Hong Kong were caused by diatoms (Bacillophyceae) and dinoflagellates (Dinophyceae). Blooms of these two classes of algae are distinctive in many aspects such as distribution, seasonality, structure of the bloom and their impact on mariculture (most harmful algal species are dinoflagellates). Due to the influence of the Pearl River (Fig. 1), the west water region of Hong Kong has estuarine characteristics, whereas the east region is more significantly affected by the oceanic currents (Watts, 1973; Morton, 1980). With an average summer flow of 20,000 m3 sÿ1, the Pearl River provides a significant

input of freshwater, sediment and nutrients into Hong Kong’s coastal waters during the wet season (Yin et al., 2004b). However, red tides occur less frequently in the Pearl River estuary than other regions in Hong Kong (Yin, 2003). In general, diatoms are dominant in the western waters but dinoflagellates are more active in the eastern oceanic zone. In addition, a seasonal trend in bloom occurrence has been observed, where diatom blooms most often occur in the summer while dinoflagellate blooms most often occur in the spring (Environmental Protection Department, 2003). In spring 1998, a massive algal bloom caused by the dinoflagellate, Karenia digitatum (Yang et al., 2000), impacted all northeast to southern Hong Kong waters wiping out over 80% of the mariculture fish stock in Hong Kong (Anderson et al., 1999). HABs (or more frequently called red tides due to their predominant reddish colouration) have been noted in Hong Kong since the early 1970s (Morton and Twentyman, 1971)

Mirs Bay (Dapeng Bay)

Kat O O Pui Tong

114°0’0”E

4

Tui Min 3 Chau

Mainland China

Crescent Island

Kat O Pearl River Estuary

Mirs Bay Tolo Harbour

Hong Kong 22°21’0”N

22°21’0”N

N

East Lamma Channel Luk Chau Wan 2 1

Lamma Island

10 km

114°0’0”E

Lamma Island

Fig. 1. Location map of Hong Kong and the two field monitoring stations (1 e Lamma Island station, 2 e Lamma Island outer sampling point, 3 e Kat O station, 4 e Kat O outer sampling point).

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and they have been studied for the past 30 years. Ho and Hodgkiss (1991) and Hodgkiss et al. (2004) review much of this work. However, most of the previous studies relied on manual water sampling at regular (biweekly or monthly) intervals or short term ship cruises (e.g. Yin et al., 2004a). Limited data on algal dynamics and associated hydrographic conditions were available. Although the use of remote-sensing satellite images can improve the understanding on spatial distribution of water quality (Yin et al., 2004b), its applicability to the study of algal dynamics is limited in several ways. First, many red tides in Hong Kong occur in spring, on cloudy days when no satellite image is available. Moreover, satellite images cannot provide information on the vertical structure which is shown to be vital for understanding algal dynamics (Lee, 1993; see also Section 3.4). Third, simultaneous data required for ground truthing remote-sensing images are often not available; typically very few quantitative conclusions can be made even after sophisticated image analysis and statistical modelling (e.g. Chen et al., 2004). Therefore, an effective alternative is necessary. Since we are far from being able to explain and predict when, where and why HABs, such as the one resulting in massive fish kills in 1998 occurred, and since HABs are a complex problem involving inter alia biology, environmental factors of light and nutrient availability, hydrodynamic and mass transport, water quality issues including dissolved oxygen depletion, toxin release and other impacts due to the bloom, an interdisciplinary approach is necessary. To this end an interdisciplinary research project on the monitoring, modelling and prediction of algal blooms was carried out. A remotely controlled autonomous real time field monitoring system has been developed to monitor the long term as well as short term algal dynamics and related hydrographic conditions in two representative mariculture zones, alongside a manual water sampling programme. As well as being able to detect and give immediate warnings of blooms, the data collected were interpreted with biological knowledge integrated with hydrodynamic modelling in order to develop modelling systems to predict bloom occurrences. As far as we are aware, this is the first high frequency data set with algal and dissolved oxygen dynamics as well as key water quality and hydrographic parameters.

2. Methods and materials 2.1. Location Two mariculture zones are chosen as representatives for the oceanic and estuarine zones in this field monitoring study. They are O Pui Tong at Kat O, on the northeast coast of Hong Kong, and Luk Chau Wan

at Lamma Island, to the south of Hong Kong (Fig. 1). Another important reason for the choice of study area is that, according to the fishermen’s observations, the massive algal bloom in 1998 appeared first in Kat O regions and seemed to be transported south down to Lamma Island where the major fish kill occurred. By studying the changes in water quality and hydrometeorological features at the two field stations, we hope to obtain more clues on the dynamics of algal blooms in the sub-tropical coastal water e in particular the integration of physical and biological processes in bloom description and prediction. 2.2. The telemetry field monitoring system The monitoring programme in this project involved two parts: the real time online telemetry monitoring and manual biweekly water sampling. The real time telemetry system was aimed at providing online continuous (time interval in the order of an hour) meteorological, hydrographic and water quality information (including chlorophyll data), and the water samples were used to both supplement the above water quality information (particularly in terms of algal dynamics) as well as to provide a cross check on some of the important parameters. Routine maintenance of the telemetry equipment was also carried out during the manual water sampling. The telemetry system was designed for continuous monitoring of dissolved oxygen (DO) and algal dynamics (Lee H.S. et al., 1991; Lee and Lee, 1995; Wong, 2004). In particular, the newly established system is capable of keeping track of vertical algal dynamics during a bloom and giving an alarm whenever a bloom is detected. The system is mounted on a refurbished fish raft which also serves as working platform. A Campbell Scientific CR23X micrologger serves as the central processing unit of the system. It controls the operation of the peripheral sensors and logs the data obtained from these sensors. Telecommunication is established between the micrologger and the laboratory computer through a micrologger modem and a phone modem connected to the laboratory’s computer (Fig. 2). Data are retrieved at the laboratory automatically every morning or whenever an algal bloom is detected. Three types of sampling strategies were used for the various parameters: hourly average, 1-min average in a 2-h interval, and vertical profile in a 6-h interval. Hourly data were obtained for physical parameters including global solar and photosynthetically available radiation (PAR), wind speed and direction (3 m above sea surface), air and water temperature (at three depths: surface, middle and bottom), and the tidal elevation and surface and bottom currents. Table 1 gives a summary of the instruments used for the measurement of the water quality and hydro-meteorological parameters.

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J.H.W. Lee et al. / Estuarine, Coastal and Shelf Science 65 (2005) 172e190 Data Retrieval, Analysis & Storage

Personal Computer Modem

LABORATORY

Data Transmission

Telephone line FIELD Control

Modem Measurement

Data Collection

Micrologger FLUOROMETER

DO METER

PYRANOMETER

THERMISTORS

ANEMOMETER

Relay box Pump

ACOUSTIC DOPPLER CURRENT METER

MULTIPROBE & PAR SENSORS

Magnetic valves

CHLOROPHYLL

DISSOLVED OXYGEN

WATER TEMPERATURE

SOLAR RADIATION

WIND

TIDAL LEVEL & CURRENT

WATER PROFILE

Fig. 2. Schematic diagram of telemetry monitoring system.

Bihourly data were obtained for water quality parameters (dissolved oxygen and chlorophyll fluorescence) through pumping. Measurements were taken at 1 m, 3 m, 5 m (surface, middle, bottom) for the 7 m deep Kat O station and 1 m, 4 m, 7 m for the 9 m deep Lamma Island station. Water was pumped aboard from each of the three levels in turn by a peristaltic pump to a Chelsea Minitracker IIC fluorometer measuring cell and also to a DO measuring cell fitted with a Yellow Spring Instruments (YSI) 58 DO probe. The sequence of pumping was controlled by the micrologger by means of

relay control over magnetic valves. Water pumped aboard then flowed through the dissolved oxygen and chlorophyll measurement cells (Fig. 2). Water temperature was measured in situ at the same depths by YSI 44018 thermilinear thermistors suspended at the corresponding locations. The system was designed to give accurate measurements of dissolved oxygen, to about 0.2 g mÿ3 (Lee and Lee, 1995; Wong, 2004). Every 6 h, a vertical profile was measured to obtain the vertical structure of salinity, temperature (Conductivitye TemperatureeDepth profile), pH, dissolved oxygen,

Table 1 Summary of parameters measured by real time continuous monitoring system Parameter

Measurement level

Measurement interval

Instrument name

Wind speed & direction Air temperature Global solar radiation Photosynthetically available radiation Tidal level and current

3 m above sea surface Sea level Sea level Water surface vertical profiling Water surface & bottom

1h 1h 1h 1 h once at noon 1h

Water temperature

Surface, middle, bottom

1h

Vertical profile Surface, middle, bottom Vertical profile Surface, middle, bottom Vertical profile Vertical profile Vertical profile

6h 2h 6h 2h 6h 6h 6h

CSI R.M. Young wind monitor CSI CR23X micrologger Kipp & Zonen CM6B Pyranometer LICOR LI-192SA underwater quantum sensors Sontek Argonaut XR Acoustic Doppler Current Meter YSI thermistor with 44302 thermilinear network YSI 6920 sonde YSI 58 DO meter YSI 6920 sonde Chelsea Minitracker IIC fluorometer YSI 6920 sonde YSI 6920 sonde YSI 6920 sonde

Dissolved oxygen Chlorophyll fluorescence Salinity pH

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chlorophyll fluorescence and photosynthetically available radiation (PAR). The four measurements were made at dawn, noon, dusk and mid-night every day. The PAR profile measured at noon was used to calculate the light attenuation coefficient for that day. During bloom events, the profiling interval could be increased to 2 h to cope with the fast changing algal dynamics. Ten minutes are required to complete each measurement cycle. Data collected by the instruments were stored in the CR23X micrologger and transmitted back to the laboratory every day.

out using the descriptions of Yamaji (1984), Fukuyo et al. (1990), Tomas (1997). For the 1 L water quality sample, 500 mL was used for standard suspended solids analysis and the other 500 mL for spectrophotometric chlorophyll-a measurement (Clesceri et al., 1999). Sample pretreatment and extraction were performed on site and the samples were preserved in an ice box for subsequent laboratory analysis. Filtrates from chlorophyll pretreatment were collected for nutrient analysis (nitrogen, phosphate and silicate) using a ChemLab continuous flow analyzer and standard methods (Strickland and Parsons, 1984).

2.3. Manual water sampling programme 2.4. Bloom alarm and special field monitoring Alongside the telemetry monitoring, water samples were collected at the field stations as well as at an outer point of each bay every two weeks (Fig. 1). Data collected from the outer stations were used to help give the boundary information for mathematical models. Parameters measured in the routine water sampling included: nutrient concentrations (ammonia, nitrite and nitrate, phosphate, silicate), phytoplankton counts (cell number and species composition), zooplankton number, chlorophyll-a, suspended solids and secchi depth. Moreover, a vertical profile of salinity, temperature, chlorophyll fluorescence, dissolved oxygen and pH was measured with a Yellow Spring Instrument (YSI) 6920 multi-parameter sonde. During the field survey, weather conditions, sea colour and other special observations were also noted. Water samples were taken at the same depths as the pumping levels of the telemetry system. Three litres of water was collected for each depth with a Nansen type water sampler. Two litres of the water sample was used for phytoplankton analysis and the remaining 1 L was used for water quality analysis, including suspended solids, chlorophyll-a and nutrients. The 2 L of water from each level was concentrated with a special handmade tube-shaped sieve of mesh size 20 mm. These concentrated samples were washed off the sieve and collected in 100-mL polyethylene vials, preserved with 3 mL Lugol’s solution and taken back to the laboratory for microscopic examination. After overnight settling at 4  C the supernatant was siphoned off and the volume adjusted to either 5 or 10 mL (depending on the concentration of cells collected). A 1-mL aliquot from each thoroughly mixed sample was then pipetted into a Sedgwick Rafter counting chamber and allowed for settling for 10e20 min before examination under an Olympus IX 50 inverted microscope at 100e400 times magnification. Total zooplankton numbers were counted and expressed as number per litre of the original sample. The algal species in each sample were identified and the total numbers of cells in each species expressed as the number of cells per litre of original water sample. Identification of microalgae was carried

The major advancement of the present system is its capability to measure real time in vivo chlorophyll concentrations and produce an algal bloom warning to the laboratory when a possible algal bloom is detected. Indeed, the key objective of the real time continuous observing system is to be able to detect the onset of algal blooms, so that timely water quality sampling surveys can be carried out (e.g. Lee and Lee, 1995), since the manual sampling can be more effective and focus on the parameters that cannot be measured reliably by telemetry (e.g. nutrient parameters). This objective has been achieved by applying the in vivo fluorometric chlorophyll method (Lorenzen, 1966) with a Chelsea Minitracker IIC fluorometer. Although in vivo chlorophyll fluorescence is an indirect method used for chlorophyll measurement which can be affected by the light history of the cells and nutrient availability (Cullen, 1982; Kolber and Falkowski, 1993), such dependence on secondary factors can also be viewed as a measurement of the physiological status of algal cells (Cullen et al., 1997). In general, the chlorophyll fluorescence can be viewed as representative of algal biomass and, in that case, higher fluorescence readings should represent higher algal biomass. Since chlorophyll-a is commonly used as a measure of algal biomass, it is still necessary to translate the fluorescence measurement into a corresponding chlorophyll-a (Chl-a) concentration. The Chelsea Minitracker IIC fluorometer was calibrated against laboratory cultures of different algal species as well as field samples. Samples of different concentration were fed into the measurement flow cell of the fluorometer to obtain instrument reading, and extracted immediately for spectrophotometric analysis of chlorophyll-a (Clesceri et al., 1999). A calibration curve can then be obtained by comparing the instrument reading and the chlorophylla concentration (Wong, 2004). The calibration factor was around 100 mV per mg mÿ3 chlorophyll-a. With a common blooming level of 10e30 mg mÿ3 for Hong Kong waters, a value of 1500 mV was chosen as the

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algal bloom alarm level in the telemetry system. Once this level was exceeded, the CR23X micrologger sends an algal bloom alarm and transmits the data back to the laboratory. After inspection of the data, a phone call would be made to fishermen or government staff in the area so as to identify the actual field conditions. Once a possible bloom was identified, a field trip was immediately made to take the relevant water samples. During an algal bloom, the water quality can change drastically within 24 h, and therefore, if necessary, a 24-h field survey would be done to track the dynamic changes within a diel cycle. Sampling procedures for the 24-h field surveys were similar to those for routine sampling, except that the sampling frequency was increased to once every 3e4 h. Moreover, the causative species were immediately identified in the field. At the same time, the sampling frequency for the vertical profile was increased to once every 2 h to cope with dynamic changes during the bloom.

3. Results 3.1. General field observations The telemetry system has been successfully put into operation for over 4 years since 2000. Figs. 3 and 4 show key physical, chemical and biological data for the Kat O and Lamma Island monitoring stations from 2000 to 2003. Over the 4-year period, the chlorophyll data show clearly that Kat O, a bay relatively free from pollution, is frequented by algal blooms (Fig. 3 and Table 2), and that the phytoplankton dynamics is very rapid e a bloom can form and subside in the order of several days to over a week. The highly nonlinear algal dynamics is accompanied by corresponding changes in secchi depth and dissolved oxygen. In particular, it is observed that the vertical differential in dissolved oxygen can be significant during algal blooms. Fig. 3 also shows the daily variation of solar radiation and water temperature during the same period with a seasonal pattern. However, the algal dynamics is highly complex and does not appear to follow any clear seasonal pattern. The biweekly nutrient measurements reveal total inorganic nitrogen (TIN) and phosphate levels in the order of 100 and 20 mg mÿ3, respectively. It is worth noting that the N:P (atomic) ratio at Kat O often dropped below 16. For Lamma Island, the same complex algal, DO, and nutrient dynamics can be observed (Fig. 4). In general, there are less blooms at Lamma Island where the water transparency is lower (Fig. 4 and Table 2). In general, the nutrient concentration and N:P ratios are higher at Lamma Island, where water quality is notably worse than Kat O due to proximity to pollution sources. Dissolved oxygen in Kat O was usually maintained at a level around 7 g mÿ3 while

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the normal level at Lamma Island was around 5 g mÿ3. The secchi depth at Lamma Island was usually very small (around 2e3 m), as compared to values of around 4e5 m at Kat O. During days of fine weather, sometimes, even the 7 m sea bottom at Kat O could be seen. The species composition in Kat O is also significantly different from that in Lamma Island. From the ecological study between February 2000 and February 2002 (Table 3), 129 algal species were identified in Lamma Island. Of which, approximately 68% (88 species) were diatoms (Bacillariophyceae) and 28% were dinoflagellates (Dinophyceae). The remaining 4% belonged to the Chrysophyceae, Cyanobacteria and Raphidophyceae. In Kat O, a total of 152 species were identified, of which only 55% (84 species) were diatoms. Thirty-eight percentage dinoflagellates composed of 38% algal species. The remaining 7% included Chrysophyceae, Cyanobacteria, Raphidophyceae, Euglenophyceae and Prymnesiophyceae. In other words, while the diatoms were overall dominants at both sites, there was greater diversity of species at Kat O, in particular the dinoflagellate species. Of the total species identified, 109 species (69 diatoms, 35 dinoflagellates and 5 from the other groups) were common to both stations. In addition to the greater number of species and groups represented, the total microalgal biomass was also higher at Kat O (Fig. 3). A summary of the cell density for each species dominant in the samples at Lamma Island and Kat O between February 2000 and February 2002 can be found in Lam (2002).

3.2. Statistical summary With a view of studying the inter-relationship between algal biomass and related environmental and water quality parameters, a correlation analysis of daily values of chlorophyll and a large number of relevant variables has been performed for the period 2000e2001, during which most of the blooms occurred. Table 4 shows the correlation coefficient between the chlorophyll concentration at time (t C 1) and DO, temperature, global solar radiation, wind speed, TIN and chlorophyll itself at different lag times up to a week earlier. Several interesting observations can be made from this statistical summary at Kat O: for the whole data set, (1) the chlorophyll concentration is very highly correlated with past chlorophyll values in the previous week, suggesting the blooms are generated by local populations in the weakly flushed bay. The correlation coefficient with chlorophyll of the week before (not shown) is much weaker, dropping to 0.051 for (t ÿ 14), suggesting the short duration of the algal blooms. This is consistent with a recent study of neural network modelling of algal blooms in Tolo Harbour using biweekly chlorophyll data (Lee et al., 2003), and also

178

Middle

Bottom

Surface

4100

30

3100

20

2100

10

1100

0 Jan00

Chelsea (mV)

YSI Chl-a (mg m-3)

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100 Apr00

Jul00

Oct00

Jan01

Apr01

Jul01

Oct01

Jan02

Apr02

Jul02

Oct02

Bottom

14

Jan03

Apr03

Middle

Jul03

Oct03

Surface

DO (g m-3)

12 10 8 6 4 2 Jan00

Apr00

Jul00

Oct00

Jan01

Apr01

Jul01

Oct01

Jan02

Apr02

Jul02

Oct02

Jan03

Apr03

Jul03

Oct03

Apr00

Jul00

Oct00

Jan01

Apr01

Jul01

Oct01

Jan02

Apr02

Jul02

Oct02

Jan03

Apr03

Jul03

Oct03

Jan01

Apr01

Jul01

Oct01

Jan02

Apr02

Jul02

Oct02

Jan03

Apr03

Jul03

Oct03

Oct01

Jan02

Apr02

Jul02

Oct02

Jan03

Apr03

Jul03

Oct03

Secchi Depth (m)

10.0 8.0 6.0 4.0 2.0 0.0 Jan00

Solar Radiation (MJ hr -1)

4

Global

PAR

3 2 1 0 Jan00

Apr00

Jul00

Air Middle

35

Temperature (°C)

Oct00

Bottom Surface

30 25 20 15 10 Jan00

Apr00

Jul00

Oct00

Jan01

Apr01

Jul01

Fig. 3. Measured time variation of chlorophyll, dissolved oxygen, nutrients and environmental variables at Kat O field station (2000e2003).

direct visual observation; (2) the algal dynamics is positively correlated with temperature and negatively correlated with wind speed; (3) the chlorophyll is only weakly correlated with nutrients. If the correlation analysis is performed only for events with peak algal concentration higher than 100,000 cells/L, the picture is clearer. As DO is intimately related to the photosynthetic production and algal respiration, it is significantly correlated with algal biomass. In particular, it is seen

that chlorophyll is negatively correlated with DO for diatoms and positively correlated with DO for dinoflagellates. 3.3. Algal blooms occurring at the field stations Since the operation of the telemetry monitoring system in 2000, 19 algal blooms has been observed and tracked at the field monitoring stations (Table 2).

179

Wind Speed (ms-1)

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8 6 4 2 0 Jan00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02

Jul02

Bottom

TIN (mg m-3)

400

Jul03

Oct03

Middle

Surface

300 200 100 0 Jan00 Apr00

Jul00 Oct00 Jan01 Apr01

Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03

60

PO4 (mg m-3)

Oct02 Jan03 Apr03

Bottom

Middle

Surface

40

20

0 Jan00 Apr00

40

Jul00

Oct00 Jan01 Apr01

Jul01

Oct01 Jan02 Apr02

Jul02

Oct02 Jan03 Apr03

Jul03

Oct03 (by atom)

(by mass)

Bottom

Middle

Surface

N:P ratio

80 30

60

20

40

10

20

0 Jan00 Apr00 Jul00 10000000 1000000

0 Oct00 Jan01 Apr01

Jul01

Oct01 Jan02 Apr02

Jul02 Oct02 Jan03 Apr03 Jul03

Oct03

Diatoms Dinoflagellates Others

cells L

-1

100000 10000 1000 100 10 1 Jan00 Apr00 Jun00 Oct00 Jan01 Mar01 Jun01 Oct01 Jan02 Apr02

Jul02

Oct02 Jan03 Apr03

Jul03

Oct03

Fig. 3 (continued ).

Although the water quality is generally better, most of these algal blooms (12 out of 19) were observed in Kat O. Among these 19 blooms, 5 were caused by Noctiluca scintillans or other species without chlorophyll content. These species are usually harmless and are not detectable by the telemetry system. The remaining 14 events were mostly caused by diatoms and dinoflagellates. It

can be seen from Table 2 that blooms that occurred at Kat O were usually caused by dinoflagellates and occurred throughout almost the entire year, while blooms at Lamma Island were usually caused by diatoms during the summer. Further details of the weather and site conditions for the observed blooms can be found in Wong (2004).

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DO (g m-3)

35 30 25 20 15 10 5 0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02

4100 3600 3100 2600 2100 1600 1100 600 100

Bottom Middle Surface

Jul02 Oct02 Jan03

Apr03

Jul03

Chelsea (mV)

Chl-a (mg m-3)

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Oct03

14

Bottom

12

Middle Surface

10 8 6 4 2 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03

Secchi Depth (m)

8.0 6.0 4.0 2.0

Solar Radiation (MJ hr-1)

0.0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03

4

Global PAR

3 2 1 0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03

Apr03

Jul03

Oct03

Temperature (°C)

35 30 25 20 15

Air

Bottom

Middle

Surface

10 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03

Fig. 4. Measured time variation of chlorophyll, dissolved oxygen, nutrients and environmental variables at Lamma Island field station (2000e2003).

It should be noted that the mariculture zones in Hong Kong are typically located in well sheltered, weakly flushed coastal bays. For example, the flushing time at Kat O has been determined by three-dimensional models to be around 40 days (Choi and Lee, 2004; Wong, 2004). Around the time of the occurrence of the blooms observed at Kat O and Lamma Island (Table 2),

red tides were also reported at other similar well sheltered coastal waters (Wong, 2004). 3.4. Case studies for algal blooms The bloom that occurred in Lamma Island during 10e11 August 2000 and 18e24 August 2000 and that

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Wind Speed (ms-1)

8 6 4 2 0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03

TIN (mg m-3)

400 300 200 100 Bottom

Middle

Surface

0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03

PO4 (mg m-3)

60

Bottom

Middle

Surface

40

20

0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03 (by atom)

N:P ratio

(by mass) 40

Bottom

Middle

Surface

80

30

60

20

40

10

20

0 Feb00 Apr00 Jul00 Oct00 Jan01 Apr01 Jul01 Oct01 Jan02 Apr02 Jul02 Oct02 Jan03 Apr03 Jul03 Oct03 10000000

cells L-1

1000000

0

Diatoms Dinoflagellates Others

100000 10000 1000 100 10 1 Feb00 May00 Sep00 Dec00 Mar01 May01 Aug01 Nov01 Feb02 May02 Aug02 Oct02 Jan03 Apr03 Jul03 Oct03

Fig. 4 (continued ).

occurred in Kat O during 21 Marche25 March 2001 was chosen for representative case studies for diatom and dinoflagellate blooms, respectively. 3.4.1. Diatom bloom at Lamma Island This algal bloom was caused by a mixture of diatom species (Dactyliosolen fragilissimus, Chaetoceros spp.,

Leptocylindrus danicus and Skeletonema costatum). Algal concentration during this bloom reached 1,000,000 cells/L and caused muddy yellow sea discoloration. Fig. 5 shows the variations in chlorophyll, DO, wind speed, temperature, solar radiation and nutrients during this bloom. The bloom initiated on 10 August but dissipated soon the following day. It occurred again

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Table 2 Algal blooms observed at Kat O and Lamma Island, Hong Kong (2000e2003) and the growth rate, sinking speed (for diatoms) and migration speed (for dinoflagellates) obtained during these blooms. Literature values for similar species obtained from Smayda (1970), Jorgensen (1979), Cullen and Horrigan (1981), Heaney and Eppley (1981), Levandowsky and Kaneta (1987), Kamykowski et al. (1992), Watanabe et al. (1995) and Yamamoto and Okai (2000) Date

10 Dec 99e14 Dec 99, 28 Dec 99e01 Jan 00 10 Feb 00e17 Feb 00 19 Feb 00e21 Feb 00 16 Mar 00e18 Mar 00 03 May 00e08 May 00 10 Aug 00e11 Aug 00, 18 Aug 00e24 Aug 00 18 Aug 00 01 Oct 00e04 Oct 00 17 Dec 00e20 Dec 00, 27 Dec 00e04 Jan 01, 07 Jan 01e11 Jan 01, 19 Jan 01 21 Mar 01e25 Mar 01 14 Apr 01e23 Apr 01, 29 Apr 01e02 May 01 16 Jun 01e20 Jun 01 17 Jul 01 09 Jan 02 22 Mar 02 06 Jun 02e07 Jun 02 20 Jun 02e26 Jun 02, 01 Jul 02e06 Jul 02 24 Jul 02 12 Aug 03 a b

Location a

Dominating species

Algal growth rate (dayÿ1)

Sinking/migration speed (m hÿ1)

This study

Literature

This study

Literature

Kat O

Mesodinium rubrum

e

0.4e0.7

e

e

Lammaa Kat Oa Kat Oa Kat Oa Lamma

Noctiluca scintillans Mixed species Akashiwo sanguinea Prorocentrum sigmoides Mixed diatoms

e 1.7 0.9 0.4 3.4

e e 0.2e0.4 0.3e1.4 e

e 1.0 0.4e1.0 0.5e1.0 0.03e0.1

e e 1.1 0.4e0.9 e

Kat O Kat Oa Kat Oa

Hermesinum adriaticum Scrippsiella trochoidea Noctiluca scintillans

e e e

e e e

e e e

e 0.6 e

Kat Oa Kat Oa

Gonyaulax polygramma Dictyocha speculum & Chattonella ovata Thalassiosira subtilis Karenia mikimotoi Noctiluca scintillans Noctiluca scintillans Gyrodinium instriatum Skeletonema costatum

0.6 0.8

0.2 0.7

3.5 e e e e 3.1

9.2 hb e e e e 1.6e3.0

0.03e0.05 e e e e 0.03e0.05

0.02e0.09 e e e e 0.01e0.06

3.0 2.4

17 hb e

0.03 0.01e0.02

0.02e0.04 0.01e0.02

Lammaa Kat O Kat Oa Kat O Lammaa Lammaa Lammaa Lamma

Chaetoceros spp. Pseudo-nitszchia pseudo-delicatissima

1.0e2.0 0.7e1.0

1.8 0.8

Red tides also reported at other sheltered Hong Kong coastal waters. Generation time.

on 18 August until 24 August when Typhoon Bilis came with strong easterly wind. The weather was fine with continuous sunny days before the bloom. There was a gradual increase in water temperature due to continuous sunshine and some stratification (vertical temperature of 2  C and salinity differential of 3 ppt) was observed. Compared to a long term mean value of 84 mg mÿ3, the total inorganic nitrogen concentration was high, around 300 mg mÿ3 during the onset of the bloom. Nutrient depletion was clearly seen during the bloom. The high nutrient concentration can be attributed to the Pearl River runoff in the wet season. According to local fishermen, water quality can change dramatically during this period e known as the ‘‘west water’’ effect since it comes from the west. Variation in dissolved oxygen and chlorophyll can be observed due to photosynthetic production during the day and respiration at night (Fig. 6). For this diatom bloom, it can be observed that the Chlorophyll-a was uniformly high (20e40 mg mÿ3) over the entire depth, but with higher values at the surface. Dissolved oxygen (DO) was super-saturated during the day (noon), but the DO concentration of the entire column decreased by

3e4 g mÿ3 at night due to algal respiration. This is consistent with previous field and modelling studies of blooms at Yung Shue Au in Tolo Harbour (Lee, 1993). In general, both the chlorophyll and DO varied gradually from the surface to bottom. During the bloom, the dissolved oxygen concentration increased to as much as 12 g mÿ3. However, by the end of the Table 3 Relative abundance of species and species groups at Lamma Island and Kat O (from Feb 2000 to Feb 2002) Group

Bacillariophyceae (diatoms) Dinophyceae (dinoflagellates) Chrysophyceae Cyanobacteria Euglenophyceae Prymnesiophyceae Raphidophyceae Total

Number of species

%Abundance

Lamma

Lamma

Kat O

88

Kat O 84

68.2

55.2

36

58

27.9

38.1

3 1

2.3 0.8

1

3 1 1 1 4

2.0 1.7 0.7 0.7 2.6

129

152

0.8

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Table 4 Correlation between Chlorophyll at time t C 1 and other variables at lagged times (a) 2000e2001 whole data set, (b) 2000e2001 diatom events (cell count O 100,000 cells/L), (c) 2000e2001 dinoflagellate events (cell count O 100,000 cells/L) Variable

Time (days) tÿ1

tÿ2

tÿ3

tÿ4

tÿ5

tÿ6

tÿ7

0.63 0.03 0.08 0.06 ÿ0.08 ÿ0.01

0.50 ÿ0.01 0.07 0.07 ÿ0.08 ÿ0.03

0.38 ÿ0.03 0.07 0.05 ÿ0.06 ÿ0.04

0.30 ÿ0.04 0.06 0.01 ÿ0.05 ÿ0.06

0.24 ÿ0.03 0.06 ÿ0.02 ÿ0.06 ÿ0.07

0.19 ÿ0.04 0.05 ÿ0.07 ÿ0.08 ÿ0.08

0.15 ÿ0.06 0.04 ÿ0.08 ÿ0.10 ÿ0.08

(b) 2000e2001 diatom events (cell count O 100,000 cells/L) Chl 0.85 0.73 0.70 DO ÿ0.17 ÿ0.26 ÿ0.17 Temp 0.34 0.34 0.33 SR 0.11 ÿ0.18 ÿ0.06 WS ÿ0.19 0.00 ÿ0.19 TIN 0.04 0.01 0.01

0.58 ÿ0.07 0.34 ÿ0.12 ÿ0.14 0.02

0.48 ÿ0.14 0.32 ÿ0.04 ÿ0.21 0.03

0.41 0.01 0.33 ÿ0.05 ÿ0.23 0.04

0.30 0.03 0.33 0.17 ÿ0.19 0.01

0.19 ÿ0.03 0.31 ÿ0.07 ÿ0.27 0.00

(c) 2000e2001 dinoflagellate events (cell count O 100,000 cells/L) Chl 0.60 0.52 0.39 DO 0.33 0.21 0.08 Temp 0.14 0.13 0.10 SR 0.19 0.26 0.33 WS ÿ0.24 ÿ0.24 ÿ0.18 TIN 0.09 0.07 0.02

0.24 ÿ0.11 0.08 0.31 ÿ0.10 ÿ0.01

0.18 ÿ0.19 0.07 0.20 ÿ0.06 ÿ0.09

0.14 ÿ0.23 0.06 0.14 ÿ0.03 ÿ0.14

0.05 ÿ0.25 0.05 0.07 ÿ0.06 ÿ0.21

ÿ0.07 ÿ0.31 0.04 0.07 ÿ0.19 ÿ0.21

t (a) 2000e2001 whole data set Chl 0.78 DO 0.05 Temp 0.08 SR 0.02 WS ÿ0.08 TIN 0.00

bloom, the DO level had decreased to around 4 g mÿ3. Such decreases in dissolved oxygen were shown to be caused by the decay of settled dead algae (Lee et al., 1991a,b; Lee and Lee, 1995). 3.4.2. Dinoflagellate bloom at Kat O This bloom was caused by Gonyaulax polygramma, which is one of the most common bloom causing species in Hong Kong. Although G. polygramma is non-toxic, it can cause fish kills and mariculture loss due to oxygen depletion. The variations in the key water quality and associated parameters during the bloom in March 2001 at Kat O are shown in Fig. 7. During the bloom, the weather was sunny to cloudy and there were weak to moderate east winds. Vertical salinity and temperature differentials of 0.5 ppt and 1.2  C were measured. By 19 March, high chlorophyll concentration was recorded at mid-depth which suggests that the Gonyaulax polygramma biomass was already aggregating in a subsurface layer and preparing to bloom. At the same time, red patches of Noctiluca scintillans were also observed in the bay. On 21 March a high subsurface (middle and bottom) chlorophyll concentration was recorded, and on 22 March a bloom was initiated in the afternoon around 15:00. The patch first occurred in the area just in front of Tui Min Chau (Fig. 1). Samples collected from the patch had chlorophyll-a concentration of up to 90 mg mÿ3. The patch migrated downwards and disappeared an hour

later. By that time, tremendous dissolved oxygen depletion was also recorded (from an original DO concentration of around 9 g mÿ3 it dropped to almost 4 g mÿ3 at the bottom level). The bloom lasted until March 25 when the weather deteriorated with heavy rainfall (rainfall intensity up to 30 mm hÿ1). Vertical migration was observed clearly during this bloom. Fig. 8 shows the vertical dissolved oxygen (DO) and chlorophyll profiles on 24 March 2001. It can be observed that the patch formed a dense thin layer ascending before sunrise and descending before sunset. At the mean time, a tremendous decrease in dissolved oxygen was also observed. It can be seen that the DO concentration was around 9e10 g mÿ3 at around mid-night but dropped significantly (by as much as 6 g mÿ3) during 02:00e06:00. This significant DO decrease coincided with the ascent phase of vertical migration. A similar dissolved oxygen pattern was observed in the bloom of Prorocentrum sigmoides in May 2000 at Kat O (Table 2). Based on previous oxygen budget studies (Lee et al., 1991a), it can be shown that the observed DO can hardly be accounted for by sediment oxygen demand (SOD) due to bloom collapse (especially with the drop occurring during the bloom). Moreover, the depletion appears only in the dark period and when the dinoflagellate is migrating. This is consistent with results from the field experiment carried out by Moshkina (1961) who observed that under certain active status, the production and respiration rate of dinoflagellates can increase tremendously,

184

Chl-a (mg m-3)

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Bottom

25

Middle Surface

20 15 10 5

DO (g m-3)

0 5-Aug

Wind Speed (m s-1) Temperature (°C)

15-Aug

20-Aug

25-Aug

10-Aug

15-Aug

20-Aug

25-Aug

10-Aug

15-Aug

20-Aug

25-Aug

10-Aug

15-Aug

20-Aug

25-Aug

10-Aug

15-Aug

20-Aug

25-Aug

Bottom Middle Surface

10

5

0 5-Aug 10 8 6 4 2 0 5-Aug Bottom Middle Surface Air

35 30 25 20 5-Aug

Solar Radiation (MJ m-2)

10-Aug

4 3 2 1 0 5-Aug

10000000.0 1000000.0

TIN (mg m-3) Cell count (cells L-1) Diatoms Dinoflagellates 400 Others Total 300

100000.0

200

10000.0

100

1000.0 8/7

S (1m) 50 M (4m) B (7m)

8/12

8/17

8/22

0 8/4

S (1m) M (4m) B (7m)

Phosphate (mg m-3)

25

8/9

8/14

8/19

8/24

0 8/4

8/9

8/14

8/19

8/24

Fig. 5. Algal and dissolved oxygen dynamics observed during the mixed diatoms bloom (Lamma Island) in August 2000.

and the respiration rate can be as high as 60% of their production rate. It is observed that for the dinoflagellate blooms, oxygen depletion occurs at the time of the bloom due to vertical migration. By the time the bloom terminates and migratory behaviour ceases, dissolved oxygen concentration returns to normal.

4. Discussion 4.1. Advancement through telemetry monitoring Harmful algal blooms (HABs) are one of the most important issues in coastal water quality management. The problem is highly dynamic, involving microalgal

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Depth (m)

06:00

12:00

18:00

24:00

0

0

0

0

1 2

1 2

1 2

1 2

3 4

3 4

3 4

3 4

5 6

5 6

5 6

5 6

7 8

7 8

7 8

7 8

0

10

20

0

30

10

20

0

30

10

20

0

30

10

20

30

Depth (m)

Chl-a (mg m-3) 0 1

0 1

0 1

0 1

2 3

2 3

2 3

4 5 6

4 5 6

2 3 4 5

7 8

7 8 2 4 6 8 10 12

4 5 6

6 7 8 2 4 6 8 10 12

7 8 2 4 6 8 10 12

2 4 6 8 10 12

Dissolved Oxygen (mg/L) Fig. 6. Chlorophyll and dissolved oxygen profiles observed during the mixed diatoms bloom (Lamma Island) on 21 August 2000.

dynamics, hydrodynamic processes and water quality aspects such as nutrient supply, dissolved oxygen and other environmental parameters. The limitations in studies of marine microalgal dynamics include ignorance of successive temporale spatial variations in community structure as well as changes in environmental variables being missed during discrete and periodic water sampling. As a result, non in situ data has often been involved in the interpretation of field results. On the other hand, our real time, in situ, continuous monitoring telemetric system is able to follow the long and short term changes in algal biomass (by chlorophyll fluorescence), dissolved oxygen and other important hydro-meteorological variables. The biweekly water sampling programme also provided information on algal diversity and relative species biomass, so that this could be correlated with the physical, chemical and biological data. These high frequency data together with the field sampling have significantly enhanced our understanding of short term algal and dissolved oxygen dynamics in several ways. 4.2. Factors affecting species dominance From the field data, it has been observed that the general algal composition, algal bloom causative species and the season for algal bloom formation at the two field monitoring stations are significantly different. These differences may be explained as follows. First, due to the

local hydro-geography, wind speeds and tidal currents were always stronger in Lamma Island. Three-dimensional hydrodynamic calculations and field measurements at Kat O and Lamma Island have also been performed (Wong, 2004); tidal currents at the sheltered Kat O site are relatively weak, in the order of 1e5 cm sÿ1, as compared to 15e20 cm sÿ1 at Lamma Island. Typical wind speeds prior to blooms were 2 m sÿ1 and 3 m sÿ1 at the two sites, respectively. Therefore, water turbulence was stronger in Lamma Island. Vertical turbulent mixing could have facilitated the suspension of diatom cells in the photic zone and the supply of nutrient from sediment. Turbulent waters on the other hand could affect the depth regulation or even have an inhibitory effect on the growth of dinoflagellates (Gibson and Thomas, 1995). Using a mathematical model, Wong and Lee (2004) have shown that the effect of turbulent mixing is important in selection of bloom formation species at these two weakly flushed locations. The first order theory considers the algal dynamics that occurs in a vertical water column, and incorporates the essential processes of algal growth and settling, vertical mixing, light penetration, nutrient availability and species competition for nutrients. Based on only simple and readily available field measurements as input parameters, the model predicts the likelihood of an algal bloom as a function of hydro-meteorological conditions and nutrient availability. Moreover, with a nutrient competition consideration, the type of bloom (caused by motile

186

Bottom Middle Surface

Reading (mV)

4100 3100

30

2100

20

1100

10

100 16-Mar

19-Mar

22-Mar

25-Mar

28-Mar

0

Bottom Middle Surface

12 10

DO (g m-3)

Chl-a (mg m-3)

J.H.W. Lee et al. / Estuarine, Coastal and Shelf Science 65 (2005) 172e190

8 6 4 2

Wind Speed (m s-1)

0 16-Mar

19-Mar

25-Mar

28-Mar

8 6 4 2 0 16-Mar

19-Mar

30

Temperature (°C)

22-Mar

22-Mar

25-Mar

28-Mar

Surface Middle Bottom Air

25 20

15 16-Mar

19-Mar

22-Mar

25-Mar

28-Mar

22-Mar

25-Mar

28-Mar

Solar-Radiation (MJ m-2)

4 3 2 1 0 16-Mar

19-Mar -1

10000000.0 1000000.0

Diatoms Dinoflagellates Others Total

Cell count (cells L )

400

S (1m) 50

TIN (mg m-3)

M (3m)

300 100000.0

1000.0 3/13

B (5m)

25

200

10000.0

S (1m) M (3m) B (5m)

-3 Phosphate (mg m )

100 3/18

3/23

3/28

4/2

0 3/13

3/18

3/23

3/28

0 4/2 3/13

3/18

3/23

3/28

4/2

Fig. 7. Algal and dissolved oxygen dynamics observed during the Gonyaulax polygramma bloom (Kat O) in March 2001.

or non-motile species) can be classified. The model requires as input: (1) simple and readily available field measurements of water column transparency, nutrient concentration, and representative maximum algal growth rate of the motile and non-motile species; and

(2) estimate of vertical mixing as a function of tidal range, wind speed, and density stratification. Essentially, the model reveals that vertical turbulent diffusivity is a key controlling factor in the occurrence of algal blooms. Given a minimum threshold level for nutrient

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04:00

Depth (m)

02:00

06:00

08:00

10:00

12:00

0

0

0

0

0

0

1

1

1

1

1

1

2

2

2

2

2

2

3

3

3

3

3

3

4

4

4

4

4

4

5

5

5

5

5

5

6

6

6

6

6

6

7

7

7

7

7

0 10 20 30 40

0 10 20 30 40

0 10 20 30 40

0 10 20 30 40

7

0 10 20 30 40

0 10 20 30 40

Depth (m)

Chl-a (mg m-3) 0

0

0

0

0

0

1

1

1

1

1

1

2

2

2

2

2

2

3

3

3

3

3

3

4

4

4

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4

4

5

5

5

5

5

5

6

6

6

6

6

6

7

7

7

7

7

2 4 6 8 10 12 Dissolved Oxygen

2 4 6 8 10 12

2 4 6 8 10 12

2 4 6 8 10 12

7

2 4 6 8 10 12

2 4 6 8 10 12

-3

(g m ) 14:00

Depth (m)

0

0

16:00

0

18:00

0

20:00

0

22:00

0

1

1

1

1

1

1

2

2

2

2

2

2

3

3

3

3

3

3

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4

4

4

4

4

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5

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6

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7

0 10 20 30 40

0 10 20 30 40

0 10 20 30 40

0 10 20 30 40

24:00

7

0 10 20 30 40

0 10 20 30 40

Depth (m)

Chl-a (mg m-3) 0

0

0

0

0

0

1

1

1

1

1

1

2

2

2

2

3

3

2

2

3

3

3

3

4

4

4

4

4

4

5

5

5

5

5

5

6

6

6

6

6

6

7

7

7

7

7

2 4 6 8 10 12

2 4 6 8 10 12

2 4 6 8 10 12

2 4 6 8 10 12

7

2 4 6 8 10 12

2 4 6 8 10 12

Dissolved Oxygen

(g m-3) Fig. 8. Chlorophyll and dissolved oxygen profile observed during the Gonyaulax polygramma bloom (Kat O) on 24 March 2001.

availability, the vertical turbulent mixing has to be below a threshold level for blooms to develop. Further, for very calm waters, dinoflagellates would be able to out-compete diatoms by virtue of its ability to obtain nutrients through vertical migration. The model has been validated against field observations. Further details of the red tide forecast model can be found elsewhere (Wong, 2004; Wong and Lee, 2004).

In addition to hydrodynamic effects, the N:P ratio difference between these two stations is believed to also have caused certain species selection effects. By means of bioassay experiments, Ho and Hodgkiss (1993) demonstrated that dinoflagellates preferred a low N:P ratio environment (4e22 in atomic ratio). They further showed that the optimal N:P ratio for the diatom, Skeletonema costatum, was 15e30 while that for the

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dinoflagellate, Prorocentrum triestinum, was 5e15. Hodgkiss and Ho (1997) and Hodgkiss et al. (2004) have shown that, when the minimum TIN concentration is above 100 mg mÿ3 and the minimum dissolved inorganic phosphate (DIP) is above 20 mg mÿ3 (the socalled limiting values), such variation in N:P ratio plays a significant role in controlling species dominance. This coincides with our N:P ratio measurements in these two locations (Figs. 6 and 7). While dinoflagellate dominated blooms were more common in Kat O (Table 2), the N:P ratio was generally lower there, in the range of 5e15 for more than 60% of the observations. Nutrient supply also appears to play an important role in the algal dynamics in Kat O, where a close resemblance is observed between the nutrient and chlorophyll measurements. By the end of summer 2001, as a result of the Tolo Nutrient Export Scheme (nutrient diversion away from Tolo Harbour, see Fig. 1), there had been a reduction in the nutrient loading into the surrounding waters of Kat O. Correspondingly, the number of bloom occurrences and the chlorophyll level showed a significant reduction. In the case of Lamma Island, nutrient concentration is usually very high and it does not appear to be significant in limiting or promoting algal blooms. Instead, turbulent mixing appears to be a more important factor in governing bloom formation. These observations have been further confirmed by the red tide prediction model (Wong et al., submitted for publication). This suggests that the stratification caused by the fresh water input (rather than the nutrient input) by the Pearl River is the main cause of the higher frequency of algal blooms in summer at Lamma Island. From the results, it can also be observed that the southern waters seem not to be a suitable habitat for dinoflagellates and that the blooms occurring at Kat O and Lamma are unrelated in terms of both causative species and timing (Table 2). This means it is unlikely to have the two stations attaining favourable condition for bloom initialisation of the same species at similar time. Therefore, it is unlikely that the massive algal bloom caused by Karenia digitatum in 1998 developed locally in the Lamma Island waters. Instead, the bloom in 1998 was most likely transported from north to south. This algal bloom transport pattern has recently been confirmed by three-dimensional hydrodynamic modelling (Lee and Qu, 2004). 4.3. Dissolved oxygen as signature of dinoflagellate blooms During the algal blooms it has also been observed that the vertical structure of chlorophyll-a and dissolved oxygen are distinctive signatures for bloom formation by diatoms versus dinoflagellates. During dinoflagellate blooms, vertical migration of algal cells and their

aggregation to a dense thin layer have been clearly observed. Moreover, due to their high respiratory consumption during vertical migration, tremendous depletion of dissolved oxygen was observed during the bloom. The positive correlation of chlorophyll with DO (Table 4) may reflect the higher carbon to chlorophyll ratios of dinoflagellates and hence higher production (Lee et al., 1991b). For diatoms, which are unable to swim in this way, the vertical gradient of algal concentration is much less pronounced. Dissolved oxygen concentration is always high during the bloom; oxygen depletion takes place at the time when the bloom collapses and dead algal cells begin to decay. Fig. 5 shows clearly that the dissolved oxygen starts to drop towards the end of the growth phase, when the chlorophyll measurements still indicate a high level. The negative correlation of chlorophyll with DO for diatoms (Table 4b) may suggest the consumption of DO by the decay of algal biomass (Lee et al., 1991b). 4.4. Estimation of algal dynamics parameters This high frequency continuous monitoring data have provided valuable information on algal dynamics. From the in situ data, useful parameters including algal growth rate, sinking velocity (diatoms), and migratory speed (dinoflagellates) can be estimated for the causative species. The net growth rate can be inferred from the changes in algal biomass during the logarithmic growth phase. The sinking velocity (for diatoms) can be estimated from the rate of change in centre of gravity during the dark period. The vertical migration speed (of dinoflagellates) is estimated directly from the speed of advance of the concentrated thin layer. Table 2 summarizes the net growth rates, sinking and swimming velocities inferred from the blooms observed at Kat O and Lamma Island. These values are generally comparable with literature values; in particular, the significant migration speeds of 0.4e2.0 m/h for dinoflagellates should be noted. This provided a basis for algal bloom modelling (Wong, 2004; Wong and Lee, 2004). The reader is referred to Wong (2004) for further details on the parameter estimation.

5. Concluding remarks Real time field observations of coastal algal blooms have been made by a telemetric early warning system. The study has resulted in hitherto unavailable detailed knowledge of algal dynamics, and has advanced our understanding of algal blooms in sub-tropical coastal waters. As far as we are aware, this is the first time hydro-biological variables are measured during a bloom and interpreted in an integrated manner. From the data, the importance of water turbulence and nutrients on

J.H.W. Lee et al. / Estuarine, Coastal and Shelf Science 65 (2005) 172e190

species selection and bloom control can be clearly seen. This has greatly facilitated the development of various bloom prediction models. The high frequency data have opened the possibility of using advanced data-assimilation methods to study algal blooms. This study has also paved the way for the development of an early warning system integrated with a NOWCAST prediction model running online. This type of system would aid in early planning for mitigation measures before the bloom has impacts on mariculture and other amenities. A robust nutrient autoanalyzer (at least for nitrate) could also be integrated into the telemetry system to further enhance our understanding of nutrient dynamics (Lam et al., 2004). Laboratory proteomic work accompanying this project has revealed how algal species can be identified and how toxic species can be distinguished in a relatively short time (Chan et al., 2004, 2005). This could ease one of the major remaining limitations of the current system because it relies on species identification by standard macroscopic and electronic microscopic examination, both involving extremely time consuming and highly technical work. Current proteomic work is centered on antibodies, which would allow the enumeration and separation of specific species by antibody based probes and immunofluorescence would allow telemetric transmission of confirmatory results for toxic species, thereby also speeding up the detection of confirmed toxic species. Identification of species and confirmation of their toxic nature could well be a significant development of the system. The ‘‘harvesting’’ of species together with their toxins (or indeed any other biologically active materials) from the probes is also a possibility, and application of such methods could well be a very important offshoot of this study.

Acknowledgements The work reported herein was supported by a Hong Kong Research Grants Council (RGC) group research project (HKU 2/98C and 1/02C), and partially by a grant from the University Grants Committee of the Hong Kong Special Administrative Region, China (Project No. AoE/P-04/04). The assistance of the Hong Kong Agriculture, Fisheries and Conservation Department (AFCD) in the field work and nutrient measurement is gratefully acknowledged. The many discussions with Dr Patsy Wong have been most helpful. The assistance of Ms G. Fernando in the data analysis is appreciated.

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