Observation and prediction of harmful algal blooms

41 Pages·2008·9.2 MB·English

1
Observation and prediction ofharmful
algal blooms
J.J. Cullen
I.I INTRODUCTION
Phytoplankton are the principal source offood for life in the sea, and the dynamics
ofphytoplankwn communitiesare centrally important to the structureand function
ofpelagic ecosystems. Life forms ofphyroplankwn have evolved to exploit different
regimesofturbulenceandhencenutrients(Margalef, 1978) hutbytheirnature,plank
IOnic food websare struClured by grazing and otherbiological interactions (Ki0rhoe,
1993; Smeracek, 1998). Consequently, physical processes determine the StruCture of
pelagic ecosystems, directly by [heir influence on the growth ofphytoplankton and
indirectlybyaffecting food-web interactions (Cullen etaI., 2002). Phytoplanktondy
namics are particularlyvariable in coastal ecosystems because physical, chemical and
biological influences ate forced in complex ways; flows ofwater are constrained by
coastlines and shallow, highly variable bathymetry; nutrients are supplied from ter
restrial andbenthicsourcesaswell as from deeperwateroffshore; andbenthicand in
tertidal communitiesactively interacrwith (hose in (hewatercolumn. The increasing
concentrationofhumanactivitiesin(hecoastal region(NichollsandSmall,2002)and
the multifaceted importance ofcoastal ecosystems to the sustainability ofthe planet
(Costanza etaI., 1997) ensure the prominenceofbiological variability in coastal eco
systemsasanenvironmental concern.
Transient proliferations of phytoplankton, referted to as blooms, are common
and natural in coastal environments. In pelagicsystems, such outbreaks are the prin
cipal means bywhich Rowsofmanerand energyescape the tightlycoupled microbial
loop to feed higher trophic levels and exportorganic maner to deeperwatersand the
bottom assinkingparticles (Michaelsand Silver, 1988; Legendreand Lc Fevre, 1989;
Ki0rboe, 1993).Algal bloomsarcrhusintegral to planktonicecosystem dynamicsand
biogeochemical cycles. However, some phyroplankton blooms in coastal or brackish
watersareperceivedasharmful.Theycancausemassivefish kills,contaminateseafood
with toxins, and alterecosystems in ways thathumans do not like. Thesearcharmful
algal blooms (HABs), ageneric term thatglossesoverthatfacts that notall HABspe
ciesareclassified asalgae and somespecies causeharmful effectswhen present in low
cell densities, norhing like a bloom (Smayda, 1997b). Arhorough review ofrhe HAB
problemwasrecentlypreparedaspartoftheSciencePlan for an imernational research
programmeon HABs (GEOHAB, 2001). As thischaptershares manyobjectiveswith
theGEOHAB Science Plan,some material from thatdocument is repeated here,with
added emphasis on observation and prediction ofHABs in the context ofreal-time
observation systems.
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Figure 1.1
Mapsofrep(.medoccurrencesofHABtoxicitydemonstratemanyaspects
oftheHABproblem.Topleft:globaldistribUlionsofreponedJnralyrk
shdlfishpoisoning(PSP)ariddi:mheticshellfishpoisoning(DSP)events
showthatharmfuleffectsarcwidespread.R~ionaJ patternsaretosome
extentrelated10thepresenceofsystemsformonitoringandreponing;
absenceofreportsdoesnotnecessarilymeanthatHABshavenotoccurred
(USNadonalOfficeforMarineBioloxinsandHarmfulAlgal Blooms).
Otherpanels:OccurrencesofPSP, DSPandamnesicshellfishpoisoning
(ASP) inICESCOUntrieslfrom 1993-2002showthatsomelocationsare
affectedbyseveraltypesofHAB,whereascertainIypesoftoxicityarc:
morerestricted. Noneofthesemapscanshowthediversityofspeciesthar
generatetheseeffeCls(fable LJ).TheCoastalModuleoftheGlobalOcean
ObservingSystem (lOc.2003)isbeingestablishedtofacilitatemOte
effcctivemonitoringofHABsandOIherphenomenaincoastalenvironmenlS
worldwide,solemporalandspatialpanernscanberesolved.andexplained.'
Sourct: HarmfulAlgaeEvenl Dala Base(HAEDAT),©IFREMER.
'ICES (International Council for the E~ploration ofthe Sea) membercountries arc Belgium, Can:KIa.
Denmark, Estonia, Finland, France,Germany, ledand, Ireland, Latvia, Nerherlands, Norway, Poland,
Porcugal,Russian Federation.Spain,Sweden,UnitedKingdomand UnitedS'ates.
Jh.tp:/lwww.ifremer.frlenvlitldocumen.alionldossie.slciem/aciem-c!.h,m
2
ObSl:rvarionandprl:dil:.:tionofharmfulalgal blooms
l.1.1 DiversityofHABs
ThegreatdiversityofHABspeciesandeffects(Table 1.1,Figure1.1) precludeseffective
generalization. Still, harmful algaearecommonlyclassified in twOgroups:
• toxin producers, which can contaminate seafood, kill fish, orcause health prob
lems in humansthrough directexposure tothetoxins;
high-biomass producers, which can kill or damage marine life after reaching
denseconcentrations, for examplebycausinganoxia aftercollapseofabloom or
bychronicallyshadingbenthicvegetation.
Bloomsofhigh-biomass producers also affect tourism and recreation bydiscolouring
coastal waters and generating noxious foams, slimesor odours. Even this broad clas
sificationofharmfulalgaeisnotexclusive: several HABspeciesthataretoxicalsoform
denseblooms. To compromisegeneralization further, closely relatedspeciescanoccur
in highconcentrations in some regions butnotothers. Forexample,A!~xdndriumdis
colourswaterin the GulfofStLawrencebutgenerallyforms only low-densityblooms
in theGulfofMaine, although itcontaminatesshellfish ineach environment (Ander
son, 1997;Weiseet al., 2002). Otherspecies exhibit variable toxicity, for reasons that
are as yet unresolved (e.g. Graneli et al., 1993~ Scholin eta!" 2007- Chapter II this
volume). Formal definitions ofalgal blooms are thusneitherpractical norparticularly
helpful (Smayda, 1997b);in thischapter, bloom issynonymouswith an increaseinthe
abundance ofa phytoplankton species above a background concentration, in either
spaceor time.
Considered broadly, HABs are agrab-bag ofphenomena with little in common
except for effects that humans perceive as being harmful. Due to their diversity, no
TAStE 1.1 Somedeleteriouseffectscausedbyharmfulalgaeincoastaland brackishwaters
Humanh~alrh
Paralyticshellfish poisoning(PSP) Dinoflagellatl:S AI~xandr;umspp" Pyrodinium
hahammuvar. compr~ssum,
Gymnodiniumcaunatum
Cyanobacteria Anahamacircinalil
Diatrhericshellfishpoisoning Dinoflagellatl:s Dinophysuspp., Prorountrumspp.
(DSP)
Neurotoxicshellfishpoisoning Dinoflagellates Karmia hr~vis
INSP)
Amnesicshellfish poisoning DialOms Puudo-nitzschiaspp.
(ASP)
Anspiracidshl:llfishpoisoning Dinoflagellate Protop~ridinium aassipa'
(AZP)
Ciguatl:rafish poisoning(CFP) Dinoflagellates GamhiadilcustOX;CUI
Rl:spiratoryproblemsandskin DinoflagellateS Karmia hr~vil, Pfi~suriapilcicida
irritation, nl:urologicaleffo:cts
Cyanobactl:ria NodulariaIpumigma
Hepatotoxicity Cyanobacteria Microcystisa~ruginosa, Nodularia
spumigma
(Continud)
3
Real-dmeCoa51al ObservingSysrems
TABLE I I (continu~d)
Examplesofcausaliveorganisms
Nalumland(l<llUr~dmarin~rnOllrUI
Hemolytic, hepatotoxic, Dinoflagellates Gymnodiniumspp.. Cochlodinium
osmoregulatorydTectsandother polykrikoid~s.Hfurocapsa
unspeci~edtoxicity circularilquama, Pjifstaiapiscicida.
Gonyaulaxspp.
RaphidophYles Httaosigmaakalhiwo, Charront!!a
spp.,Fibrocapsajaponiea
Prymncsiophytcs Chrysochromlliinaspp., Phatoryltis
poucherii, Prymnrsiumspp.
Cyanobacleria MicrorysriIaauginosa, Nodulariaspp.
Negativeeffecrson fuding Pel3gophyres Aurrotoccusanophagrffirrns
behaviour
Hypoxia,anoxia Dinoflagellates Prorountrummicans, Crratiumfirrca
Mechanicaldamage DialOms Chatfocrrosspp.
Gillcloggingand necrosis Prymnesiophyles Phatorysrisspp.
TourismandrurtillionalaclivitifS
Productionoffoam, mucilage, Dinoflagellates Noetilucascinri!!ans. Prorocrntrumspp.
discoloration, repellentodour
Prymnesiophyles Phaeorystisspp.
Diatoms Cylindr(Jfhtcadosrrrium
CY3nobacieria Nodulariaspumigena.Aphanizomrnon
fios-aquat, Mierorystisatruginosa,
Lyngbyaspp.
Marinftcosystfmimpacts
Hypoxia,anoxia Dinoflagellates Noetilucascintillans, Httrrocapsa
triquerra
Diatoms SkfltlOntmacosMtum
Prymnesiophyle!i Pharoryslisspp.
Negativeeffecrson feeding Pe!agophytes AllrtococcusIfnophagtffir~ns,
behaviourand reductionofwater AurroumbralagunrnsiI
clarily
Dinoflagellates Prorountrumminimum
Toxiciry10 marineorg3llisms, Dinoflagellates Kareniabrrvis,A/rxandrillmspp.
includinginvenebrares, fish, Diatoms Psrudo-nitzschiaI1mtralis
mammalsandbirds
Sourees: Zingoneand Enevoldscn (2000) asmodified byGEOHAB(2001),with further
modifications.
'Recentlydescribed byJamesetal. (2003).
one cause for HABs can be found, and no single sUategy for deteerion or prediction
willsuffice.Themajorehallengefor understandingHABs[0suPPOrtmanagementand
mitigarion is to describe, for each species, wh3t conditions promore its developmem
insteadof(or in concenwith) orher phytoplankton (GEOHAB, 2001).
4
Observationandpredictionofharmfulalgalblooms
1.1.2 Harmfulalgaeandenvironmentalvariability
ItisaxiomaticthatHABspecieshaveadaptedto manyniches (suitesofecologicalfac
tors that determine their distributions and activities), and that the matches between
the adaptations ofharmful species and oceanographicvariabilityare good enough to
ensuretheirsurvivalfromyeartoyear,andtheirproliferationwhenconditionsarecon
ducive.ThegreatdiversityofHABs- with respect to taxonomy, region, hydrographic
regimeandharmfuleffects- reflectsinafundamentalwaythatenvironmentalforcing
hasselectedforawidevarietyofharmfulalgae,ontimescalesfromevolutionarytodays
and spatial scales from ocean basins to bays. For each harmful species, the challenge
is to unlockits secrets: whydoes it bloom orexert its harmful effects in onesituation
and not in another? The answers lie in detailed information about the distributions
andactivitiesofphytoplanktonspeciesin relationto theoceanographicandecological
processesthatinfluencethem.Thisshouldbecomplementedwithexperimentalresults
describinghoweachspeciesresponds to theseenvironmentalfactors, andwithmodels
- eitherconceptualormechanistic- thatdescribetheprincipalcontrolsonpopulation
dynamicsofthetargetspecies in relation to thephytoplanktoncommunity.
Even though it is an enormously daunting task to resolve the complex interac
tionsthatdeterminethepopulationdynamicsofaphytoplanktonspeciesincoastalor
estuarinewaters, significant progress has been made through the careful work ofin
sightful researchers. ThecontributionsofRamon Margalef(e.g. Margalet 1978;Mar
galefet aI., 1979) are widely regarded as a seminal influence. Considering life forms
ofphytoplankton, that is theirgross morphological and physiological traits, Margalef
andcolleaguesdescribedhowfunctionalgroupsofphytoplankton (withrepresentative
species identified) could be plottedagainst axes representing nutrient availabilityand
the intensity ofturbulence (Margalet 1978). The typical seasonal succession ofphy
toplankton, from fast-growing diatoms to motile dinoflagellates, corresponds to the
temporal transitionfrom awell-mixed, nutrient-richwatercolumn duringwinterto a
nutrient-poor,stratifiedenvironmentlaterintheyear.Thismodelwaslatermodifiedto
includea'red-tidesequence', atrajectoryparallelto thetypicalsuccession, butinenvi
ronmentswith higherlevelsofnutrients (Figure 1.2). Usingasimilarapproach, Colin
Reynolds and colleagues (reviewed briefly by Reynolds and Smayda, 1998; revisited
bySmayda,2002) developed'habitatmatrices' thatrelatevariabilityofphytoplankton
species composition in lakes to several axes ofvariation, still dominated by nutrients
andturbulence.
Summarizing efforts to describe variability in phytoplankton communities,
Reynolds (2002) concluded that the dynamics of individual species are unpredict
able, except on the scale ofdays, and only then ifbased on full knowledge ofinitial
distributions. However, he felt that at a higher lever ofgenerality (Le. for functional
groupsortrait-basedassociationsofphytoplankton), responsesofphytoplanktoncom
munitiestoenvironmentalconditionswouldbeincreasinglypredictable.Akeylinkto
predictabilityis thatvariabilityincommunitycomposition is'explicablein retrospect'
(Reynolds,2002).Thatis, theconfidencewecanhaveinpredictionsofphytoplankton
communitiesunderfuturescenariosdependslargelyonhowwell thesameconceptual
modelsexplainhistoricalvariabilityofphytoplankton.
As the ecological implications ofmorphological (Karp-Boss et aI., 1996), physi
ological and behavioural (Cullen and MacIntyre, 1998) adaptations ofharmful algae
become better understood, new and more powerful definitions offunctional groups
will emerge, guiding how phytoplankton species should be classified, and which
5
Real-timeCoastalObservingSystems
Mucilage-producing
cells
red-tidesequence
\
main
succession
sequence
)
spring
Void /
winter
~
Nutrientsx turbulence=productionpotential
Figure 1.2
The'Mandala' redrawnfromMargalefetal. (1979).Thisdiagram, rich
with informationonphytoplanktonsuccession, isextremelyusefulfor
developinggeneralizationsaboutrelationshipsbetweenlifeformsof
phytoplanktonand hydrographicconditions, particularlyduringseasonal
succession.The'red-tidesequence' (developmentofahigh-biomassHAB)
canbeviewedasrelatedtoelevatednutrients,independentofchangesin
turbulenceregime.Thisconceptualmodelitisnotdirectlyapplicableto
real-tiIneobservationandpredictionofHABs.Nonetheless,Margalef's
frameworkisacornerstoneofphytoplanktonecologyand itshould
supporttherootsofanymodelofseasonalphytoplanktoncommunity
dynamicsasinfluencedbylocalconditions.
environmental factors must be considered, when trying to predict their dynamics.
These more detailed classifications should lead to improvements in predicting the
probabilityofHAB occurrence for aparticular location and time, given measured or
modelledscenarios ofphysical and chemicalconditions. Moving beyondprobabilities
ofoccurrence,thedynamicsofHABsmaybepredictableoverthecourseofdays,given
initial data on species distributions from coastal observation systems (Johnsen et aI.,
1997;StumpfetaI., 2003). Developmentandtestingofanypredictivemodelwill thus
require effective systems for observing the distributions ofphytoplankton, including
HABs, in thecontextofcoastalecosystem dynamics.
1.1.3 Observationandmodellinginthe'olddays'
ObservationsofHABdynamicshaveseldombeenadequatetodescribethethreestages
ofan event: development, maintenanceand decline (TesterandSteidinger, 1997).So,
muchofwhatiskno\vnabout HABscomesfrom carefulanalysisoflimiteddata,with
much relianceon inference. Comprehensiveobservationsofevents precedingabloom
are particularly rare, because the unpredictability that justifies research on harmful
algae also precludes the schedulingofcruises to coincidewith HABs. The unpredict
ablenatureofHABshas ledto facetious acceptanceofthemaxim thatthebestwayto
preventthem is to scheduleamajor research programme tostudythem.
6
Observationandpredictionofharmfulalgalblooms
Some ofthe most effective studies ofHABs and other phytoplankton blooms in
coastalandestuarinewaterscomefrom regionswhereconditionsaresimilaryeartoyear,
andsustainedobservationshaveshown relationships between thepopulationdynamics
ofphytoplankton species and hydrographic conditions. Several are described in this
volume.AstudybyTylerandSeliger(1978, 1981)isanexcellentexampleofobservation
and modelling in the days before autonomous observation systems and three-dimen
sionalcoupledmodels. Theapproach theyusedis justas appropriate todayas itwas 25
years ago. The best available sampling techniques were used to characterize the distri
butions ofthe target species (Prorocentrum minimum, formerly Prorocentrum mariae
lebouriae) in relation to light and hydrographic conditions. In turn, the physiological
andbehaviouralresponsesofthesemotilealgaeto thesameenvironmentalfactorswere
characterized experimentally. Byconsidering the interaction ofphysiology and behav
iourofthephytoplanktonwith theverticalstructureofthewatercolumn andseasonal
transportbyestuarinecirculation,selectionforthetargetspecieswasexplainedandthe
generalfeaturesofpopulationdynamicspredicted.Allthiswasdonewithlimitedinfor
mation. Still, it took 128 ship days during40 cruises over two years (plus hundreds of
hourscountingcellsunderamicroscope) toacquirethedatafordescribingthedynamics
ofProrocentruminChesapeakeBay(TylerandSeliger, 1978).Subsequently,muchmore
has been learned about the dynamics ofProrocentrum minimum in Chesapeake Bay
andelsewhere(HeiletaI., 2005), butthevalidityofTylerandSeliger'smultifacetedap
proach (observations,experimentation,modelling,validation) has notdiminished.
Manyofthe old limitations on coastal ecological research are vanishing. As de
scribedin thisvolume, advances inobservation technologyand modelling, supported
bygreatlyenhancedcapabilitiesforcommunicationsandcomputing,aretransforming
the natureofecological investigation from alabour-intensiveeffort to collectprecious
data,interpretedlargelythroughinference,toaprocessinwhichunprecedentedquan
tities ofdata and model output must be managed effectively to yield useful informa
tion. Still, it is essential to remember that the fundamental principles ofthe research
will not change. Species must be identified and their physiological, behavioural and
ecological interactions must be considered in the context ofoceanographic processes
tounderstandanddescribethepopulationdynamicsofharmfulspeciesasmembersof
phytoplanktoncommunities. Insightsfrom the'olddays'ofthetwentiethcenturywill
certainlyhelp toguideHAB researchwhenthewidespreadavailabilityofobservations
threatens to mask the fundamental need for focused questions about controls on the
populationdynamicsofthecausativespecies.
1.2 DETECTION AND PREDICTION FOR MONITORING
AND MANAGEMENT OF HABs
TheHABproblemisrichwith unansweredquestionsthatwilloccupyscientistsforde
cades. Moreimmediately, itrepresents threatstocoastalecosystemsandactivities,and
these must be dealtwith now (Malone, 2007 - Chapter 14 this volume). Authorities
responsible for environmental protection, economic development and public health
must develop and implement plans for the monitoring and management of HABs
(AndersonetaI., 2001;Andersen etaI., 2003).These plans may include:
• Strategies for monitoring coastal waters for detection ofHABs, including their
effects,withan aim to developearlywarningsystems.
• Developmentofamodellingsystem forshort-termforecasts ofHAB movements.
7
Real-timeCoastalObservingSystems
Integrationofobservations,forecastsandcommunicationsintoanactionplanfor
rapidresponsetoHABevents, includingcriteriaforinitiatingstrategicsampling,
beachclosures,shellfish bansandcommunication to thepublic.
Mitigationstrategies, from directactions to neutralize blooms orminimize their
effects (e.g. application ofclay or movement offish cages) to long-term nutrient
managementplansorbanson ballastwaterdischarges.
Aprogrammeofresearch to predictthelikelihoodofHABs, Le. changes in their
frequencyorimpacts, in response to humanactivitiesorclimatechange.
Integration ofmonitoring, predictions and communications insupportofpolicy
decisions.
All aspects ofmonitoring and management require the means to detect, and broad
ly to predict, the occurrences and impacts ofHABs on scales from days to decades.
These challenges can only be met through fundamental scientific research, but there
isnobenefitinconductingthis research independentlyfrom ongoingmonitoringpro
grammes.
In some jurisdictions, and in the minds ofmany scientists, basic research has
beenconsideredto bedistinctfrom routineactivitiessuchasmonitoring.Asaconse
quence, monitoringactivitiesmaybespecificallyexcludedfrom fundingprogrammes
for research and, in turn, sustained sampling programmes established for research
are regularly threatened with cuts or termination, even though they have proved
invaluablefordescribinglong-term trends inecosystems (e.g.Tont, 1976; Roemmich
and McGowan, 1995; Fromentin and Planque, 1996; Karl et aI., 2001). With the
advent ofreal-time coastal observation systems with capabilities for environmental
forecasts, the goals ofscientists and coastal managers are aligning, as are the means
for attaining these goals: real-time detection ofHABs is essential for earlywarning;
prediction of dynamics with forecast models supports rapid response and mitiga
tion; and sustained observations (equivalent to monitoring) are required to develop
and validate the long-range models ofHAB probabilities needed to develop coastal
managementstrategies.
Theconclusion is that the research topics ofreal-timedetection, sustainedobser
vations and quantitative prediction ofHABs 111ust be integrated with the operational
requirements for monitoring and management. To justify the large investment in re
search and infrastructure, results from real-tinle coastal observation systems must be
accessible, understandable and useful to abroad range ofuser groups. Research must
be more closely coordinated with operational oceanography (Chapter 14), which will
havetochangewithtimetoserveawiderangeofusers.Thiswill requirerestructuring,
not only in the way coastal research is organized, but also in the wayscientists com
municate their results- ahealthychallenge.
1.3 CLASSlFlCATION OF HABs FOR OBSERVATION
SYSTEMS
No observingsystem can provide the oceanographic ideal ofcontinuous andsynoptic
measurementsofphysical, chernical and biological propertiesand processes, so efforts
mustbe made to match observationsandtheirscales to the HAB phenomenaofinter
est (Franks and Keafer, 2003; Srnayda, 2003; Chang and Dickey, 2007 - Chapter 2
this volume). This is no easy task, however. The range ofscales for HABs is inlnlense
(Hallegraeff, 2003). Some may be confined to inlets (Seliger et aI., 1970), others are
8
Observationandpredictionofharmfulalgalblooms
observedoverlargeexpanses (KahruetaI., 1994). Bloomsmaycomeandgooverdays
toweeks, often terminated bywind eventswhich may transport the algae offshore or
cause direct mortality from turbulence (but see Smayda, 2002). Other blooms can
persistformonths,forexampleKarenia brevisbloomsoffFlorida(TesterandSteiding
er, 1997), or even years (Aureoumbra lagunensis in LagunaMadre, Texas; DeYoe and
Suttle, 1994).Manyarefound closeto thesurface, notsurprisinglyas discolorationof
thewater draws attention. Some ofthese can be traced to subsurface layers, brought
near the surface by physical transport, including frontal processes (McMahon et aI.,
1998) andvertical mixing (see Section 1.5.1). Othersurface blooms develop through
verticalmigrationofphytoplankton (Oliver, 1994;Kamykowski, 1995).Acoarseclas
sification ofHABs can be useful as an initial guide to identify relevant scales and
appropriate observation strategies for local or regional observation programmes. The
classificationissummarizedhere,withsuggestionsforobservationstrategies.
1.3.1 WidespreadHABs
Generally, aharmfulalgalbloommusthavewidespreadeffectstoattracttheattention
ofthe scientificcommunityand the general public. Discolouredwater, dead fish and
noxious foam, scums or aerosols over hundreds ofkilometres ofcoastline make the
news, andwithgood reason. The blooms areextensive and the algae areeasilyidenti
fied as the cause ofharm. The features that make such blooms noteworthy are also
usefulforclassificationandthedesign ofstrategiesfor earlywarning, monitoringand
prediction: relativelylarge extent; persistence; and, often but not always, dominance
ofthe phytoplankton byonespecies. Three somewhat idealizedcategorieswithin this
looseclassificationcan beproposed.
1.3.1.1 Extensive,progressivecoastalblooms
Some ofthe most challenging oceanographic and ecological questions are raised by
extensive blooms, nearlymonospecificandoften toxic, that appearinacoastalwaters
andprogressalongtheshoreline,leavingatrailofshellfishclosures, ravagedfish farms
or spoiled beaches. Some examples include blooms ofKarenia brevis in the Gulfof
Mexico (Tester and Steidinger, 1997; Stumpfet aI., 2003), Karenia mikimotoi (for
merly Gyrodinium aureolum or Gymnodinium mikimotoi) in northern European shelf
waters (e.g. Holligan, 1979; DahlandTangen, 1993; Gentien, 1998), the toxicbloom
of Chrysochromulinapolylepis in Scandinavian waters in 1988 (Graneli et aI., 1993;
Gj0s~teretaI., 2000), bloomsofHeterosigmaintheStraitofGeorgiaandadjacentwa
tersin Canada(TaylorandHaigh, 1993), andthedramaticbloomofKareniadigitata
in HongKongwaters inApril 1998 (LeeetaI., 2007- Chapter 18thisvolume).
Hypothesesaboutbloomdynamicsfocus ontheprocessesofinitiation, transport
andinteractionsofpopulationswithsurfacecirculation (TesterandSteidinger, 1997).
Assessmentofimpacts requires information on transmission ofharm (e.g. decaylead
ingtoanoxia, directcontact, toxintransferthrough ingestion, productionofaerosols),
how these processes relate to the distributions ofharmful algae, and environmental
influencesontheproductionofnoxiouseffects.
Many environmental properties must be measured for effective earlywarning,
monitoringandprediction.Whenconditionspermit,remotesensingofoceancolour
and sometimes sea surface temperature from satellites and aircraft can provide key
information on distributions and transport (Stumpf et aI., 2003; Ruddick et aI.,
2007 - Chapter 9 this volume), especially when supplemented by observation
9
Real-timeCoastalObservingSystems
networks that include direct sampling (Johnsen et al., 1997; Tangen, 1997). Even
jfsurface distributions ofdeveloped blooms are resolved with remote sensing, early
stages andsubsurfacedistributions mustbedescribed byothermeans. In particular,
vertical distributions of phytoplankton should be well resolved because the inter
action ofswimming, sinking or floating with frontal features (Franks, 1997), ag
gregation ofseed populations in subsurface layers near the pycnocline (McMahon
et al., 1998), and changes ofbehaviour in mixed waters landward ofa front (Dahl
and Tangen, 1993; Gentien, 1998), possibly associated with nutrition (Cullen and
MacIntyre, 1998), all may be importantin initiation, maintenance and transportof
extensive, progressive, coastal blooms. Consequently, for early warning and moni
toring, observation systems must resolve vertical distributions ofphytoplankton in
relationto temperature,salinityandcurrents,andtheymusthavethemeansto iden
tify target species in situ. Nutrient availability can influence toxicity (Bates, 1998;
Cembella, 1998) and depletion ofnutrients can terminate a bloom. So, for effective
monitoringand modelling, the nutrient regimeshould also beassessed.
Progressive coastal blooms move with coastal currents and can appear or disap
pear on the timescale ofdays. Effective monitoring thus requires nearly continuous
measurements, and mitigation responses (suchas the movementofaquaculturecages)
requirecommunicationsinnearrealtime(Tangen, 1997).Strategiesfor management,
suchascontrolsoncoastalnutrientloadingorsiteselectionforaquaculture,dependon
longtimeseriesofobservations to determinetherelationshipsbetweenenvironmental
variability, humaninfluences,bloomoccurrencesandtheirimpacts.Sustaineddeploy
mentofreal-timeobservationsystemsis thusideallysuitedforobservationandpredic
tionofextensive, progressivecoastalblooms.
1.3.1.2 Extensivebloomsinopenwaters
It has long been recognized that phytoplankton blooms in open waters are part ofthe
natural ecologyofthe oceans. The phenomenologyofsome, such as the vernal diatom
bloomintemperatewaters(Sverdrup, 1953),surfaceaggregationofTrichodesmiumduring
calmperiods(Caponeetal., 1998),Phaeocystisanddiatombloomsassociatedwithreced
ingiceedges(Lancelotetal., 1998)andthegreenwatersofupwellingsystems(Barberand
Smith, 1981) are fairly well understood. The root causes ofdramatic expanses ofmilky
water from coccolithophores are open to informed speculation (e.g. Olson and Strom,
2002) andexaminationthroughnumericalmodelling(Mericoetal., 2004).Satelliteim
agery4revealsmanyotherbloomsinopenwatersthatwillremaincuriositiesuntiltheyare
studiedfurther. Inthecontextofthischapter,interestisfocusedonharmfulorpotentially
harmfulbloomsthatoccurinopenwatersinsemi-enclosedseasornearcoasts,wherethey
can influencecoastal ecosystems and be affected by terrestrial inputs offresh waterand
nutrients.TheBaltic,NorthSeaandBohai(China)areexemplary. Itserveslittlepurpose
toapplythisclassificationstrictly;extensivebloomsinopenwatersaregroupedsothepo
tential forcingfunctions- climatechangeandnutrientsources- can bediscussedalong
withstrategiesforobservingandpredictingecologicalresponsestotheseinfluences.
OpenwaterHABscancauseproblemswhentheyimpingeonthecoast,delivering
scums, foams ortoxicity. Forexample,summerbloomsofnitrogen-fixingcyanobacte
riaintheBalticSeaarecommon (Sellner, 1997).ThehepatotoxicNodulariaspumigena
is conspicuous; during the latter stages ofa bloom, filaments form highly visible ag
gregates at thesurface that can be detected from space (Kahru etal., 1994). Nitrogen
4Forexample:http://visibleearthnasa.gov/
10