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leES i992 Mini-SymposiUm on
Ecosystem Modellingas a Tool
to Predict Pollution-Associated
Risks for the Marine Environment
Many Risks, Many Models: ,
Addressing the Variaty of Problems that Polh.ltion Carl Cause
. William Silvert
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Habitat Ecology Division
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Biologieal, Sciences Branch, Scotia-Fundy Region
Bedfor'd Institute of Oceanography
P.O. Box 1006, Dartmouth, N.S.
Canada B2Y 4A2
Abstract
The tenn "Pollution" covers a wide range of effeetS that can interaet with the marine environment in many
different ways and over vaned space and wne scales. It is aImost impossible to develop a sirigle model
th.u addresses all of these effeets and can be used .10. assess bOth long and shon-teim risks 10 all
comPone~ts of amarine ecosystem. Instead it is imponant to construci as many models aS are requifed
10 address all pOSsible majoriri'ipactS arid 10 mtegrate these models in a context which cim be understood
and uSed by managers and mitigation specialistS. This paper deals with several examples of this, rimging
from pollution caused by petroleum exploration on contineritaI Shelves 10 thiit ansing from fish fanning
in estuaries. The ta1k Wut indude a description of adecision suppOrt system being developed to facilitate
the use of multiple mOdels in managing pollution riskS rroin aquacUIture.
Introduction
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The use of rnodeUirig 10 supPen management objectives involves two major charienges. One iS
of course the development of adequate scientific models which provide a reliable and accurate basis for
management decision-making. The other, which at timesseems 10 pose an even grea.ler challenge. is 10
present these modelS 10 managers in wäys which they imderstand and trUst enough to use.
Tbe problem is, not siJriply oile of cOmmunication. Even when managers hllve the scientific
background 10 wade through a detailed and Sophisticated ecological model. they generally will not do So.
Thcre are Several reasons for this. One is time. Since even for a pr.ictising scientist it can take a lang time
to iuiderstand the strUeture and funetioning of a complex model. Another is accouritability - it is not
eriough for the manager tri understaild the mOdel, its use hllS,1o bC justifioo 10 "clieritS" whö may be
suspicious of what appears 10 be a black box approach. But 1have fOUnd that the greatest problem iS
simply lack of attention; managers face many prcssures from above (politicians) and below (clientS) and
fmd it far Wier 10 ignore tlie scientisis than the concemed parties who are chUnoüring for their attention.
Consequently•.scientific input of all kiridS gets only semt attention. and inOdels. which have a mystique
akin to blact magie, are orten ignored.
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In ofder 10 make scientific advlce palatable it must be simplified. As pointed out by Ashby
(1954), managers have 10 deat with a griat deal of variety and must mter ihis down tri a manageable level.
Ir the scientists, who are the ones best qualified to do this filteririg, fall 10 do so, then the managers will.
Thus the challenge is 10 present models in ways that retain ihe essenti3.J. scientific content without the
scientific details that support it
Multiple Models of on Spill Risks
To illustrate the necessity of using a multiplicity of models 10 predict the potential effects
associated with environmental pollution, consider the evaluation of riskS associated with oil spills from
off-shore petroleum exploration. This problem was addressed in eonnection with the development of the
Hibemia oil field (Mobil 1985), and the environmental impact statement is the fesuIt of a substantial .
number of field and modelling swdies. In addition, the Govemmerii of Canada carried out an independent
ecosystem-Ievel study of the riskS involved (Silvert 1985, 1986, 1987).
Focusing specifically on the impacts on marine organisms, several separate modelling approaches
appear in this body of research literature:
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Mortality through physical contaet, such as the loss of seabirds that become soaked in oil by
diving into oil stickS.
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Direet toxicity, the fesu1t of accumulating tOxic hydrocarbons through exposure 10 polluted water
and sediments.
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Indireet tOxicity, the result of eating contarninated food organisms.
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Sublethal effects, Such as lessened ability 10 feed or avoid predators.
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Tainting, which affeets the fishery without necessarily damaging the fish.
Althmigh in principle it would be possible 10 bulld a single model that included bird distributions and oll
slick disPersion, fish distribution and soluble hydrocarbons, and everYthing else, this would be hath
cumbersome and contrary 10 good modelling practice.
What happened in practice was that these erfeets were modelled separately as separate projects,
and some were included in the Environmerital ImpaCt Statement (Mobil 1985) while ethers were reported
separately or not at alle Theextent 10 which these different modelS were then used iri aSsessing the nsks
associated with offshore oil productiori is not a matter ofpublic record, butthe muItipUcity of approaches
and complexity of each model create forbidding task for decision makers. The wegriWon of these
biological risks with other pollution-related factors, such as employment opportunities and 10urism impacts,
is such an enomlous job thai it is haßny surprising that important scientific considerations may be
overlooked.
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Enviromnental Impacts of AqUaculture
For several years the Habitat Ecology Division at the Bedford Institute of Oeeanography has been
developing models of the environmental impacts ef finfish aqulicuIture 10 aid managerS in evaluatlng
Iicense applications. As in the case of oil spill impacts, several Potential effects on different tinie and
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space sCaIes are of conceJTl, and several models havet>eendeveloped in thecourse of this project Thiee
ofihem suffice to provide an exainple ofhow mUltiple mOdelS inay 00 lequired ror impact assessment (for .
further detailS see Silvert 1992).
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A short-~ge model is Ileeded 10 calculate the oxygen levels at each cage site. The SpätiilJ. eitent
of this model need be nO greater than that of the cage, usually about 10 ni or so. Since it OIuy takes a
few mimiteS for fish 10 asphyxiate. "cI)'
time scale is required as well; and only cJitical peJiods
lilre lew stack \vater need be model1ed.
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BenthIc impactS OCcuf on larger time ami space scales. Tbe time seale for irariSfonnation and
äSSimilation of some.of the more refractory comPonents of faeces may be on the ofder of several years,
but the sIi:itial scale is commoilly on the order of teßS or hundreds of meters.
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Thewide-range euirOphlcation ofan emire inlet \lSed foraquaculture lnvolves veiy differeiltscaies.
The time scale is affected by the physical and biologiciü dynamics of the inlet and can re as sniall as
Several days during bloom conditiorls, while the spatial extent is detennined by the sire of the basin.
Models of these thfee effects. are quite distinct The acttiiü mcx1els are constructed by using a
single point source model iri all mree of theni io repr.eSent the oxygen demarid, particulaie outPut, and
nument release of individual fish, and usmg this Point source 10 drive
appropriate transpOrt and
water qUality model (Silvelt tit 1990). The procedure is straightforwaro for a scientisi, but.C1early
cumbersome from the viewpoint of a manager. The idea umi in order 10 evaluate a licenSe äpplication
fequir.eS iunning three mOdelS, tri say nöthirig öf obtaining the neceSs:iry parameter values ror these
models, is unacceptable rrom praetical point of view.
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ModeÜing arid Decision Support Systems
Running several models 10 evaluate the risks poSed by Pollution is riot necessarily difficult, but
it is conrUsmg and time-COnSuming, and it is not reasonable 10 eXJ>ect that non-scientists.
likely 10
aceept the respOIlSibility for such a cOinpIicated tasIe. Fonuriately however, this is the sort of activity that
computers can carry out .
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To demonstrate this, Seveml versions of a prototyPe riecision SuppOrt System (bSS) for
aquaculture regulation wem develoPed and demonstrated to variouS client groups. The DSS is simply an
interaetive. computer progfam tliat beginS by. querying the user for relevänt information on the proposed
si.te - phYsic3J. diJriensiorls, arinual production, etc. These preliminary versionS also query the User about
the wateT depth, current speed, arid bottOID type, btitit would bC heiter toask simply rar the location and
eitraet these physical parameters from a database. The computertheri aciually cames out sunulations with
threc: separate models, evaIuateS the application rrom several pointS of view, arid prepares abrief repoit
with recommeßdations:This approach häs been well-received arid there are tentative plans 10 proceed to
a more feaIistic implementation of this approach tb3t can be field-tested.
In keeping with what was sIDd earlier about presenting the model results in a manner that fs easy
to use arid which clearly condenSes the reSutt8, rather trum coIuusing the user with masses of.cIyptic
technical infonnation,.a graphical user interface iS
as an impcirtant component of this approach.
InStead of having iO enter parameters like depm at
proposed site, or even just latinide and longitude,
the uSer should be abh~ 10 seIeet a SPot with a mouse, and as the mouse movesthe relevant physical
parameters at that point shou1d he continuously displayed. Simihirly, although a detailed prlnted rePolt
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should be avallable for fonnal evaluation and record-keeping, a system of immediate graphical feedback
should be available. This will not only make the systemeasier touse, it will provide geneniI feedbaCk
10 the regulatory process - seeing that
emire area is shadCd in rCd is more infonnative than recalling
that several sites in the region received negative iecommendations.
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It is not riow c1ear whether work will proceed on thC deveiopment of this DSS project. . A1though
it sounds simple, it involves some very major subprojeets which may not be feasible in the immediate
future. Probably the largest of these is the development of a geagraphically indexed data base that can
be
tri provide physical parameters such as depth, current speed, and hottom type at the click a
mouse. Such a database must have modelling eapabilities that Pennit the estimation of values for which
data are not available. The user interface must alSo be clear, with full on-Une help facilities. There are
many teehnical questions to be resolved, such aS whether the objective is iO provide a stand-atone system
that can run. on desktop computers, or whether more powerful worksiations networked to a remote
database might be preferable.
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SUriunary
Modelling the risks associated with environmeniaI pollution requires sophisticateci modelling
approaches and is likely to involve sevei-ai distinct models to cOver an relevant aspecis. The outeome of
such modelling work is likely to be toöcomplex Cor easy assimilation into the inanagement process, arid
scientistS may fmd some of th"eir best ädvice ignored because of the need to simplify an asJiects of a
complex management issue•. One.Possible approach is to use Decision Support Systems as a tool to ease
the interface betweeri scientists and managerS and 10 provide scientific advice in a form that is both
comprehensive and easily assimilated. This is not
task, but it appears 10
a Possible arid a
desirable one.
an easy
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Rererences
Ashby, W. Ross. 1954. An Introduction to Cybernetics. Chripman & Hall, Lendon. ix+295 p.
Mobil Oil Canada, Ud. Hibernia Development Project, Environmental ImPact Statement.
Biophysical Assessment. 496 pp.
Vol. DI.
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Silvert, W. 1985. Report on Grand Banks Modelling Project. 9 May 1985. 52 pp.
Silvert, W. 1986. Report on Grand Banks MOdelling Project. 1 Apri11986. 40 pp.
Silvert, W. 1987. Modelling the Grand Banks: what now'1 CAFSAC Workirig
Silvert, W. 1992. AsseSsing Envirrinmeiltal ItIlpacts
Aquacu1ture 106:000-000 (in press).
of
PaPer 87/150.
Finfish AquacU1ture in
Marine
Silvert, W. L.• P. D. KeiUr, D. C. Gorden Jr. arid D. Duplisea. 1990. ModeUing the feedirig,
metabolism of CuItured salmonids. lCES Report C. M. 1990/F:8 (Sess. 0).
Waters.
growth and
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