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MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser
Vol. 339: 283299, 2007 Published June 6
INTRODUCTION
Although fish eggs and larvae may suffer mortalitythrough oil
spills, there are few reported cases inwhich oil spills have
conclusively had a significant im-pact on fish stocks (IPIECA
1997). However, this doesnot mean that fish stocks cannot be
significantly af-fected by oil spills. Most data on large oil
spills derivesfrom temperate and subtropical environments,
where
biological productivity and oil degradation rates arehigh,
ecosystems relatively complex, and fish stocksoften spawn over a
longer period of the year or evenyear-round. A significant
proportion of the unexploitedoil reserves (approx. 25%; see USGS
2000) are, how-ever, found in the polar regions, and oil companies
arepresently turning towards these regions to developnew oil
fields. In these areas physical and biological oildegradation is
likely to be slower than in more temper-
Inter-Research 2007 www.int-res.com*Corresponding author. Email:
[email protected]
REVIEW
Fish and oil in the LofotenBarents Sea system:synoptic review of
the effect of oil spills on fish
populations
Dag . Hjermann1, Arne Melsom2, Gjert E. Dingsr1, Jol M. Durant1,
Anne Maria Eikeset1, Lars Petter Red2, 3, Geir Ottersen4, 5, Geir
Storvik1, 6,
Nils Chr. Stenseth1, 7,*
1Centre for Ecological and Evolutionary Synthesis (CEES),
Department of Biology, University of Oslo, PO Box 1066
Blindern,0316 Oslo, Norway
2Norwegian Meteorological Institute, PO Box 43 Blindern, 0313
Oslo, Norway3Department of Geosciences, Section Meteorology and
Oceanography, University of Oslo, PO Box 1022 Blindern,
0315 Oslo, Norway4Institute of Marine Research, Gaustadallen 21,
0349 Oslo, Norway
5Bjerknes Centre for Climate Research/GEOS, University of
Bergen, Allgaten 55, 5007 Bergen, Norway6Department of Mathematics,
University of Oslo, PO Box 1053 Blindern, 0316 Oslo, Norway
7Institute of Marine Research, Fldevigen Marine Research
Station, 4817 His, Norway
ABSTRACT: The Lofoten-Barents Sea area, which contains some of
the most valuable fish stocks ofthe Atlantic Ocean, is being
considered for offshore oil production. We review the effects of
ahypothetical oil spill on fishes in this area, with a focus on
effects on the egg and larval stage of the3 dominating fish stocks:
NE Arctic cod Gadus morhua, Barents Sea capelin Mallotus villosus,
andNorwegian spring-spawning herring Clupea harengus. In
particular, we emphasise that the long-term population impact of an
oil spill depends on ecological and oceanographic factors, some
ofwhich have been poorly explored. Among these are (1) effects of
the physical state of the ocean, espe-cially mesoscale circulation
features, on the advection of oil and fish larvae, (2) effects of
the spatialdistribution of spawners, (3) effects of harvesting on
stock structure and length of the spawning sea-son, (4) effects of
natural mortality and species interactions subsequent to an oil
spill, and (5) chronicsublethal effects from persistent oil
residues.
KEY WORDS: Advection Fishes Pollution Petroleum
Vulnerability
Resale or republication not permitted without written consent of
the publisher
Hadi Abaza
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Mar Ecol Prog Ser 339: 283299, 2007
ate regions, due to low temperatures(Garrett et al. 2003,
Brakstad et al.2004). Moreover, polar ecosystems arefairly simple
(Hamre 1994, Hillebrand2004) and thus possibly more vulnera-ble to
changes in the abundance of thefew key species (Paine 1980). One
ex-ample is that the collapse of the BarentsSea capelin stock in
the 1980s signifi-cantly affected several trophic levels in-cluding
the capelins prey, zooplankton(Dalpadado et al. 2001), predators
suchas cod (Hjermann et al. 2004a) and harpseals (Haug &
Nilssen 1995), and alter-native prey of its predators such asshrimp
(Worm & Myers 2003). Perhapsmost importantly, fish stocks such
ascod and herring are close to their cli-matic limits in these
habitats, and dueto these environmental constraints theytend to
have short, intensive spawningseasons and localised spawning
areas.As a result, their eggs and larvaethelife stages most
susceptible to oil expo-suremay be relatively concentratedalong a
narrow advection route. Thesestocks are among the economically
andecologically most important fish stocksof the Norwegian and
Barents Seas. For more than amillennium, this ecosystem (Fig. 1)
has been one of themain food chambers for Europe and together
withfarmed salmon is the main reason why Norway is theworlds third
largest exporter of fishes, measured byexport value. Norway is also
the worlds third largestexporter of oil. Exploitation of oil and
gas will shortlybegin in the LofotenBarents Sea area (later
denotedLBS; see also Fig. 1), with a gas project (Snhvit) start-ing
production in 2007, and a nearby oil field (Goliat)to be developed
soon. In 2006, it was decided thatsome of the most controversial
areas from a fisheriesviewpoint (Aglen et al. 2005) will not be
opened for oilexploration until 2010 (Anonymous 2006).
However,these areas are also those that are most likely to con-tain
large oil and gas reserves (Anonymous 2006) andtheir opening will
continue to be advocated by the oilindustry. In addition, a
substantial number of tankerssail along the Norwegian coast,
carrying Siberian oilfrom Russian oil terminals. Risk analyses
indicate thatRussian export traffic constitutes a higher risk for
oilaccidents than LBS oil production (Blom-Jensen &Dervo 2003),
especially as new oil terminals are beingbuilt on the coast of NW
Russia. It has been esti-mated that this traffic may increase
strongly, from12 million metric t of oil in 2004 (almost 1 tanker
d1) to50 to 150 million metric t in the next decade (Bam-
bulyak & Frantzen 2005). Although the issues of thisreview
are also relevant for tanker spills, we have con-centrated on
spills from oil installations such as drillingplatforms. However,
much of the data we review stemfrom ship accidents.
Herein, we review available information on the im-pact of
possible oil spills in the LBS on 3 fish stocks ofmajor economic
importance: the North-east Arctic(NEA) cod, the Barents Sea (BS)
capelin, and the Nor-wegian spring-spawning (NSS) herring (Hamre
1994).The most important spawning grounds of the former 2stocks are
inside the LBS; the latter spawns mainlysouth of LBS but its larvae
are advected into the LBSwith the Norwegian Coastal Current (NCC;
Fig. 1). Inaddition, the 3 stocks interact strongly with each
otherthrough predatorprey relationships (Bogstad et al.2000, Hamre
2003, Hjermann et al. 2004a,b, 2007).
In Norway, an environmental impact assessment(EIA) has to be
performed before a new area is openedto oil explorations. Part of
such an EIA is an assessmentof the potential impact of an oil
spill. The EIA for oildevelopment in the LBS region was prepared in
2003(Anonymous 2003), and included a report on possibleeffects of
accidental oil spills on life in the watercolumn (Johansen et al.
2003). With this report as astarting point, we review some factors
that affect theimpact of an oil spill on fishes in the LBS area.
Rather
284
Fig. 1. LofotenBarents Sea ecological system with spawning
locations andadvection routes of eggs and larvae of 3 fish stocks;
red: North-east Arctic cod;purple: Norwegian spring-spawning
herring; green: capelin (Aglen et al. 2005).Dotted line: maximum
extension of the 3 species in the Barents Sea in the firstsummer
following spawning. Light blue: continental shelf (
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Hjermann et al.: Fish and oil in the LofotenBarents Sea
system
than providing a comprehensive review of the vastliterature on
the toxicological effects of oil, we wish toemphasise some
oceanographic and ecological aspects(Fig. 2) that are often
considered only superficially inEIAs of oil spills, and which need
to be better under-stood in order to obtain a clear picture of the
conse-quences of oil exploitation on the marine resources inthis
region (Fig. 3).
ENVIRONMENTAL IMPACT ASSESSMENT (EIA)
The oil spill section of the EIAs conducted beforeopening oil
exploration fields includes the develop-ment of possible oil
trajectories and concentrations of
oil in the water column which, combined with distribu-tion maps
of sensitive organisms, is used as a basis forassessing likely
ecological impact scenarios. Regionsthat may be affected by oil
pollution are determined bysimulated trajectories and weathering of
the oil, andusually defined as regions where the probability of
animpact from an accidental spill exceeds 5%. Tradition-ally, drift
trajectories have been computed using anempirical relation between
surface winds and theocean current with which the oil spill is
advected(Martinsen 1982). In more recent work, the drift
trajec-tories have been computed from the flow fields ofocean
circulation models (Martinsen et al. 1994), tak-ing the full
3-dimensionality of the flow field andhydrology into account
(Wettre et al. 2001). Due to the
steady expansion of available com-puter resources, such
simulations arenow performed with a grid mesh thatresolves features
such as filaments andeddies. In Norwegian waters, this re-quires a
grid mesh with a resolution of5 km. Such a simulation was usedfor
production of drift trajectories inthe most extensive examination
yet ofpotential oil spills on the ecology of theLBS region
(Johansen et al. 2003).
The drift trajectories are used to sim-ulate drift of both
hydrocarbons andfish eggs/larvae, and thereby calculatethe exposure
of eggs or larvae to hydro-carbons (Johansen et al. 2003). This
canthen be combined with results from lab-oratory experiments on
the toxicologi-cal effects of hydrocarbons to calculatethe
proportion of the stock that is sub-ject to exposure above some set
level.However, several aspects are often dis-regarded. Firstly, the
ecosystem is oftenviewed in a static way; for instance, it
isfrequently assumed that the proportionof fish spawning at each
spawning loca-tion is the same between years, while inreality, this
varies substantially. Thevariation between spawning locationsmay be
caused both by climate (pre-vailing oceanographic conditions)
aswell as by the age/size structure of thespawning stock. In cod
and herring,which spawn in the coastal current,spawning results in
a train of larvae,whose length depends on the spawningtime (which
varies for biological rea-sons) and whose route depends onweather
conditions. Secondly, it is as-sumed that all larvae are of
equal
285
Movementof oil
Movement ofeggs/larvae
Egg/larvaeexposure
Oil toxicity
Spawnerpopulationstructure
Oil weatheringand mixing
Egg/larvaemortality
Wheremortalityoccurs
Survival ofremainingpopulation
Otherspecies
Populationsize andstructure
Spawninglocation and
period
Fisheries
Climate
OIL SPILL
Fisheries
Days YearsTime after oil spill
Fig. 3. Oil spill sites used in the simulations of the ULB 7-c
spill (Johansen et al. 2003)
Fig. 2. Factors that affect the long-term impact of oil spills.
Theexisting Environmental Impact Assessment (EIA) focuses
oncomputing movement of oil and eggs/larvae, calculating
theexposure of eggs/larvae to oil, and estimating the
consequentmortality. However, the consequences of an oil spill are
also pro-foundly affected by other factors (see What affects the
impact
of an oil spill on a fish population?)
Hadi Abaza
Hadi Abaza
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Mar Ecol Prog Ser 339: 283299, 2007
worth, i.e. that, in the absence of an oil spill, all larvaehave
an equal chance of survival to fishable size ormaturity. However,
natural mortality after the larvalstage varies substantially both
spatially and amongyears (Ciannelli et al. 2007). Spatial variation
is impor-tant since an oil spill kills larvae in a specific
area.Thus, the impact of a spill on a population will
vary,depending on whether the larvae killed were thosethat would
otherwise have had the best chance ofreaching fishable size, or
whether they were destinedto have been transported to an area in
which theywould have a very low chance of survival.
Year-to-yearvariation in natural survivalwhich may depend onthe
state of the ecosystem (abundance of prey, preda-tors, and
competitors)will affect the populationslong-term response to oil
spill mortality. Finally, wewill briefly consider the possibilities
of chronic effectsof those oil components with long persistence
times.Such chronic effects are largely disregarded in thepresent
EIA.
THE LOFOTENBARENTS SEA PELAGICECOSYSTEM
Oceanography
The LBS region consists of the Barents Sea, an openarcto-boreal
shelf-sea with an average depth of about230 m (Zenkevitch 1963),
and the narrow continentalshelf along the Norwegian coast downto
Lofoten (Fig. 1). The ocean circula-tion is dominated by the NCC
andthe Norwegian Atlantic Current (NAC)(Helland-Hansen & Nansen
1909,Stre & Mork 1981, Orvik & Niiler2002). The NCC is
associated withlow-saline coastal water masses. Thesewater masses
originate in the Skager-rak and are the result of mixing
withfreshwaterprimarily from rivers dis-charging into the Baltic
Sea, but alsointo the North Sea from the UK, conti-nental Europe
and Scandinavia (Al-bretsen & Red 2006). The NCC passesthe
southern tip of Norway as it exitsfrom the Skagerrak, and
continuesalong the Norwegian coastal shelf tothe Barents Sea. The
NAC on the otherhand is characterised by water massesof Atlantic
origin that flow into theNordic Seas across the IcelandFaeroeRidge
and through the FaeroeShet-land Channel (sterhus & Hansen2000).
This inflow is easily recognisable
in satellite images (Fig. 4) as a warm-water mass in-truding
into the Nordic Seas as far north and east asthe Barents Sea. As
shown in Fig. 4, the inflow is sepa-rated by 2 fronts, an easterly
front separating it fromthe NCC, and a westerly front separating it
from thepolar water masses on the broad Greenland shelf
andeastwards. At the entrance to the Barents Sea theNAC divides
into 2 branches (Fig. 4), one entering theBarents Sea, and one
continuing northwards towardsSpitsbergen (Svalbard). Other major
characteristics ofthe ocean circulation in the area are the
presence ofstrong tides and (not least) many mesoscale
structuressuch as filaments, meanders and eddies. Eddies maydiverge
from the front and influence circulation andhence residence time.
Such mesoscale features onscales of 5 to 10 km are prominent in the
Barents Sea(Fig. 5). Note, for instance, the strong current
filamentor jet-like structure along the Norwegian coast presentin
both the satellite image (Fig. 5a) and the model fore-cast in Fig.
5 and how, in general, the dark areas in thesatellite image
correspond to the areas of strong cur-rents in the model forecast.
This reflects the fact thatareas of strong currents are areas of
strong verticalmixing which tends to reduce the backscatter signal
tothe satellite. Due to the relatively warm Atlantic watermasses of
the NAC, sea temperatures in the LBS areaare higher than in other
regions at similar latitudes. Asa result, the southern part of the
Barents Sea staysice-free even in the most severe winters.
Year-to-yearvariability in temperature south of the oceanic
Polar
286
Fig. 4. Sea surface temperature (SST) and sea-ice extent in the
northern NorthAtlantic and Nordic Seas Ocean and Sea Ice Satellite
Application (OSI-SAF)product (http://saf.met.no); 7 day satellite
imagery composite, centered on April11, 2004. White: sea ice; gray:
cloud masked (contaminated) areas. Latitude-longitude grid on land
masses at 10 intervals. NAC: warm-water tongue of
Norwegian Atlantic Current
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Hjermann et al.: Fish and oil in the LofotenBarents Sea
system
front is strongly influenced by the inflow of Atlanticwater
(Loeng 1991, Ingvaldsen et al. 2003). This vari-ability appears to
be mainly wind-driven and hencelinked to the North Atlantic
Oscillation (NAO) (Hurrellet al. 2003). The correlation between the
NAO and theinflow and temperature of the Barents Sea variesgreatly
with time, being most pronounced early in thelast century and over
the last 3 to 4 decades (Dicksonet al. 2000, Ottersen et al.
2001).
Ecology of the focal fish stocks: NEA cod, NSSherring and BS
capelin
These fish stocks all spawn along the Norwegiancoast in winter
to spring (Fig. 1). The NSS herring havethe southernmost spawning
distribution, with spawn-ing sites spread from around 58 to 69 N,
but espe-cially between 62 and 64 N (Aglen et al. 2005). Theyspawn
on the bottom, and their larvae drift towards theBarents Sea with
the NCC current. The herring stay inthe Barents Sea until they have
grown to a size of20 cm (typically at the age of 3 yr) and then
migrate tothe Norwegian Sea, where they remain for the rest oftheir
lifespan. Spawning NEA cod are distributed fur-ther north, with
spawning sites at 62 to 71 N, but withthe spawning sites at Lofoten
and the Rst bank(around 68 N) being the most important (Aglen et
al.2005). Cod spawn pelagically, and their eggs hatchinto larvae as
they drift northwards. The cod juvenilesreach the Barents Sea in
summer, and then shift from a
pelagic to a demersal life style in September to Octo-ber; they
remain in the Barents Sea for the rest of theirlife cycle except
for spawning migrations after matura-tion at an age of 6 to 8 yr.
Of the 3 species, BS capelinspawn furthest north (north of 70 N),
close to the coaston the southern fringe of the Barents Sea, at
depths of10 to 60 m. They spawn on sandy/gravel bottoms,attaching
their eggs to gravel on the bottom. The eggshatch after 3 to 8 wk
and the larvae migrate upwards tothe upper water layers, in which
they are advectednorthward into the Barents Sea during summer
andautumn (Gjster 1998, Aglen et al. 2005).
Economic and ecological importance of the fishstocks
Economically, the NEA cod is by far the most impor-tant of the 3
stocks. Dried cod originating from thecods spawning grounds at
Lofoten has been one ofNorways largest export items for about 1000
yr. Atpresent, most of the other large stocks of Atlantic codhave
collapsed, with the NEA stock the largest remain-ing, accounting
for about 50% of the total annual codcatch (2004: ca. 600000 metric
t of NEA cod includingunreported landings; ICES 2005). Second in
economicimportance is the worlds largest herring stock, theNSS
herring, which supports an annual fishery of up to1.2 million
metric t (one of the largest fisheries in theNorth Atlantic). The
BS capelin stock is also the worldslargest stock of this species
(catch up to 2.9 million
287
Russia
Bare
nts S
ea
Bear Island
NC
72N
30E
25Eb.
NAC
Fig. 5. (a) Barents Sea as seen by the Moderate Resolution
Imaging Spectroradiometer (MODIS), captured by the Aqua satellite
on July 19,2003 (spatial resolution: 250 m). The bright turquoise
areas are blooms of phytoplankton, probably the coccolithophore
Emiliania huxleyi.NC: North Cape. (b) An ocean model forecast
showing the sea surface current at practically the same time (July
24, 2003), using the forecastsystem of the Norwegian Meteorological
Institute. Arrow length is proportional to current strength, which
is also indicated by the colors fromlight blue (weak) to yellow and
red (strong). The latitudelongitude grid has a resolution of 1.
NAC: the Norwegian Atlantic current
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Mar Ecol Prog Ser 339: 283299, 2007
metric t yr1. However, its economic importance ispartly indirect
in that: (1) it comprises the most impor-tant prey item for the cod
(Kjesbu et al. 1998, Marshallet al. 1999); (2) both capelin and
herring are importantsources of fishmeal and fish oil, and thereby
contribute(as fish food) to the huge Norwegian aquaculture sec-tor
(Norwegian farmed Atlantic salmon, 570 000 t yr1,accounts for
almost half of the worlds Atlantic salmonproduction).
In the context of biodiversity, these 3 fish stocks arevitally
important as prey for large populations of sea-birds (20 million in
the summer) and large populationsof seals and whales. They also
play an important role inenergy and nutrient flow. For instance,
capelin is theonly species able to effectively exploit the rich
plank-ton bloom along the ice edge (Gjster & Loeng 1987,Hassel
et al. 1991, Gjster et al. 2002). It effectivelytransports a
substantial amount of energy from theremote central and northern
Barents Sea to coastalareas, where it becomes easily available to
piscivorousfishes, seabirds, mammals and fisheries restricted tothe
southern parts of the Barents Sea. In contrast,much of the biomass
accumulated by the herringmoves out of the Barents Sea when the
3-yr old herringreturns to the Norwegian Sea.
This review does not deal with other fish stocks inthe LBS, such
as the coastal cod stocks, haddockMelanogrammus aeglefinus, saithe
Pollachius virens,2 species of redfish Sebastes spp., and
Greenlandhalibut Reinhardtius hippoglossoides, although theyare (or
have been) important both economically andecologically.
EFFECTS OF OIL SPILLS ON FISHES
When assesing the effects of oil exploration, the EIAsconsider
issues such as the amount of stranded oil, thelength of the
affected coastline, and the regional prob-ability of an impact in
the case of an accidental oil spill.In impact studies for the LBA
area, various approacheshave been applied to examine transport of
oil after anaccidental oil spill and its impact on fishes. The
oceancurrents that advect oil were initially derived frommonthly
climatologies and empirical relations betweencurrents and winds for
the 35 yr period 1955 to 1990(Rudberg 2003a).
Results from 3-dimensional hydrodynamic ocean cir-culation
models were used in the EIA on fish stocks(Johansen et al. 2003).
Ocean circulation simulationswere performed with 2 different
models. The durationof both simulations was limited to a single
model yearbecause of the intensive use of computer
resourcesrequired. A grid mesh with a horizontal resolution of4 km
was applied in the simulations, and tides were
included in both cases. At this resolution, the resultsreveal
the presence of ocean circulation features suchas meanders and
ocean eddies in addition to largerscale circulation structures. The
simulation of egg andlarvae advection was based on model results
for cur-rents for the relevant year. Oil was assumed to bespilled
from 1 of 6 possible locations in the area (Fig. 3).From the
simulations, Johansen et al. (2003) calculatedhow large a fraction
of the population would beexposed to 90 ppb of the water-soluble
fraction of oil(or 8000 ppb hours; see same section below).
An oil spill from the surface from either a platform ora ship
will form a plume on the surface downstreamfrom the source.
Although oil (density 0.79 to 1.00 103 kg m3) is lighter than
seawater (density 1.03 103 kg m3) some of it will enter the water
column be-low the slick by dispersion through wave action and
byvertical mixing and chemical dissolution. The extent ofdispersion
depends on the composition of the oil aswell as on the weather
(wave energy). Dissolution is aless important pathway, since the
most soluble sub-stances are light aromatics (e.g. benzene,
toluene),which are the first to be lost through evaporation(ITOPF
2002). Oil persistence depends on many fac-tors, especially density
(ITOPF 2002). The oil of theLBS area is of medium density, 0.86 to
0.91 103 kg m3
or American Petroleum Institute (API) gravity 24 to 33(Rudberg
2003b), i.e. with a persistence in the order ofmonths (National
Research Council 1985). The oil insome areas has a high wax content
(up to 13%) and isthus fairly viscous (1.968 Pa s) (Rudberg 2003b).
Atlow temperatures, natural breakdown of such oil isslow and cannot
be effectively combated with disper-sants (ITOPF 2002). The oil on
board Russian exporttankers varies in quality, but the Russian
Export BlendCrude Oil (REBCO, or Urals) typically has a density
ofaround 0.87 103kg m3.
We focus on the long-term population consequencesof oil
impacting early stages of the fishes, i.e. eggs andlarvae (Fig. 2)
for 2 reasons: (1) In laboratory studies,adult fishes were able to
detect petroleum at very lowconcentrations (Hellstrm & Dving
1983, Dauble et al.1985, Beitinger 1990, Farr et al. 1995); (2)
large num-bers of dead fishes have seldom been reported after
oilspills. Thus, juvenile and adult fish appear to be capa-ble of
avoiding water with high hydrocarbon concen-trations. One notable
exception was the oil spill follow-ing the Amoco Cadiz ship
accident, which killedlarge numbers of fishes, including a locally
high pro-portion of 1 yr-old fishes of the commercially
importantplaice and sole. This was probably due to massiveamounts
of emulsified oil in the shallow waters wherethe spill occurred
(IPIECA 1997). Oil at very low con-centrations (ppb levels) can
also taint adult fishes (i.e.impart unpleasant odours and flavour
to their flesh).
288
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Hjermann et al.: Fish and oil in the LofotenBarents Sea
system
Fishes can become tainted directly from the water orfrom
sediments (via absorption through the gills andskin), or through
contaminated prey species (IPIECA1997). The slightest suspicion of
tainting or that fishesmay constitute a human health hazard can
renderthem unmarketable for extended periods (Birtwell
&McAllister 2002). For this reason, fishing is sometimesbanned
in the area of an oil spill. Also, oil-pollutedsediments may
negatively affect burrowing fishes suchas flatfishes or sandlance
in several ways (see Birtwell& McAllister 2002 and references
therein). However,the focal species of the present review are not
closelylinked to the seabed.
In contrast to juveniles and adults, fish eggs and lar-vae are
planktonic and thus unable to escape from pol-luted water; they are
exposed to any toxic compoundsthe water may contain. The toxicity
of oil for fish eggsand larvae has been reviewed, for example, by
Clark(2001). Toxicological mechanisms are complex, ascrude oil is a
mixture of different hydrocarbons andother organic and inorganic
substances, and its compo-sition varies greatly between oil types.
In addition, theperseverance and toxicity of oil are affected by
severalother factors such as weathering, dispersion and
emul-sification, which again depend on external factors suchas
weather conditions. Toxicological laboratory studieson animals
typically use oil itself (fresh or weathered),the light aromatics
benzene, toluene, ethylbenzene,and xylene (the BTEX components,
which make up 80to 90% of the water-soluble fraction), or the
polycyclicaromatic hydrocarbons (PAHs). The toxicity of fresh oilis
correlated to its PAH content, which therefore hasbeen thought to
be the primary toxin of oil. However,toxicity and PAH are not
correlated in weathered oils(Barron et al. 1999). In addition,
toxicity test proce-dures typically use 25C water and warm-water
organ-isms, which may be more robust than cold-waterorganisms
(Perkins et al. 2005). Relatively few studieshave been performed on
eggs and larvae of the organ-isms reviewed in the present paper.
Booman et al.(1995) tested the susceptibility of NEA cod larvae
toBTEX, and found them to be relatively susceptiblecompared to
those laboratory organisms more com-monly used in toxicity tests.
For instance, yolk-sac lar-vae of NEA cod showed reduced oxygen
uptake whenexposed for 24 h to 2080 ppb of the
water-solublefraction of oil. However, Booman et al. (1995) found
noeffects on growth or first feeding success. Toxicologicalstudies
have also traditionally been conducted in theabsence of UV light,
but the toxicity of PAH andweathered oil has been found to increase
with a factorof 2 to >1000 in the presence of sunlight (Barron
et al.2003). The use of chemical dispersants to avoid stran-ded oil
on the beaches will in general increase thetoxic effects of oil on
fish larvae, at least temporarily
(Couillard et al. 2005). In the EIA for the LBS, Johansenet al.
(2003) found the lowest LD50 (concentration lead-ing to 50%
mortality) to be 900 ppb among 93 studiesof planktonic organisms
(the studies used differenttypes of oil products and either
invertebrates, fishes oralgae). Using a security factor of 10, they
set thepredicted no-effect concentration (PNEC) at 90 ppb(g l1)
hydrocarbons. Since most studies used a 96 hexposure, this is
equivalent to approximately 8000(90 96) ppb h.
A critical issue that has arisen in recent years, in par-ticular
following the Exxon Valdez accident, is theeffect of sublethal
damage to eggs and larvae. Al-though acute lethality tests are
useful for generatingguidelines to protect against physiological
death (i.e.mortality), they ignore ecological death, i.e.
pollutiondamage that renders fishes unable to function in
anecological context, even if they are not visibly harmed(Scott
& Sloman 2004). PAHs cause a range of abnor-malities such as
morphological deformities and cyto-genetic abnormalities (Hose et
al. 1996) and embryoniccardiac dysfunction (Incardona et al. 2004).
Moresubtle, but possibly serious effects include lastingdisruption
of complex behaviour, such as predatoravoidance, reproductive and
social behaviour (Scott &Sloman 2004). For fish eggs that
develop in intertidalsediments, such as Pacific herring and pink
salmon,such effects can result from PAH concentrations as lowas 0.4
ppb (Carls et al. 1999). It has also been shownthat weathered crude
oil can cause immunosuppres-sion and expression of a viral disease,
viral hemor-rhagic septicemia virus (VHSV). The 1989 Pacific
her-ring year-class, which was exposed to Exxon Valdezoil at the
egg stage, displayed a high incidence ofVHSV as well as an
extremely low survival until theage of spawning. This caused a
dramatic collapse ofthe stock in 1993. Carls et al. (2002) argued
that theExxon Valdez oil spill might, at least partly, havecaused
this collapse, although this could not be con-clusively
demonstrated.
WHAT AFFECTS THE IMPACT OF AN OIL SPILLON A FISH POPULATION?
The effect on a fish population of an oil spill in anarea with
fish larvae depends to a great extent onoceanographic and
ecological conditions. The extent ofthe spill, the weather
conditions at the moment ofimpact, the time of year and several
ecological aspectsall influence the extent of the impact on the
year-classaffected. Below, we discuss some of these aspects.While
the current EIA is in general scientifically sound,there are some
topics which it deals with fairly super-ficially.
289
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Physical state of the ocean, especiallymesoscale circulation
The oceanic conditions at the time ofthe spill and immediately
afterwardsaffect the advection of both oil andspawning products.
The amount of oilthat is dissolved and dispersed fromthe spill site
into the water columndepends on the weather, ocean condi-tions, and
location and depth of thespill (spills at the sea bottom give
riseto different hydrocarbon concentra-tions than surface spills).
Almost allthe existing oil pollution impact stud-ies for Norwegian
waters have beenbased on empirical relations betweenhistorical
winds and ocean currents.The wind strength in the LBS area canbe
characterised by the North AtlanticOscillation (NAO) index
(Ottersen etal. 2001, Stenseth et al. 2002, Hurrellet al. 2003,
Stenseth et al. 2003). Thisindex is a measure of the strength ofthe
North Atlantic westerlies, whichhave a significant impact on
thestrength and position of the NAC (Or-vik et al. 2001). However,
the weatherin this region is not solely determinedby the NAO, which
is why an ap-proach using historical winds has beenconsidered
preferable to simply de-ducing the impact from the NAO. Withthis
approach, ocean circulation fea-tures will be on the same scale as
theatmospheric scale, with possible modi-fications for coastal
areas. However,because of the different properties ofair and water,
oceanic scales aresmaller than atmospheric scales by afactor of
1/10 to 1/100 (Charney &Flierl 1981).
Smaller scale oceanic features com-prise eddies, filaments and
meanders(mesoscale features) (Fig. 5). Thesemesoscale features
influence how oilspills affect biological activity in theocean.
Generally, the water mass of aneddy differs from its surroundings.
Aneddy traps particles and dissolved ma-terial (e.g. fish eggs and
hydrocarbons,if present) until the eddy dissipates oris dispersed.
In order to include meso-scale effects, the results of
hydrody-namic ocean-circulation models have
290
a
b
MainlandMainland
Coastalcurrent
spawning(April)
larvae(June)
Age 1(Februaryfollowing year)0-group
(September)
Widespreadspawning
Long spawningperiod
Naturalmortalityhigh inoceanicareas
c
MMainland
Coastalcurrent
spawning(April)
Mostspawning in
oceanic areas
Short spawningperiod
No spatialvariation in
naturalmortality
larvae(June)
Age 1(Februaryfollowing year)
0-group(September)
Fig. 6. Biological and oceanographic factors may determine how
an oil spill af-fects mortality in cod larvae, and thus the
North-east Arctic (NEA) cod stock.(a) Red: spawning areas; green:
position of the eggs and larvae in April andMay; blue arrow:
movement of fish eggs and larvae with currents (NCC andNAC). (b,c)
Location of a year-class of NEA cod at 4 stages: eggs at the
mainspawning grounds of Lofoten, larvae drifting along the coast in
the NorwegianCoastal Current (NCC), 0-group fish in the Barents
Sea, and 1 yr olds at roughlythe same location as the 0-group. An
oil spill in the oceanic part of the NCC killspart of the larval
population (shaded area), with small impact in (b) and large
im-pact in (c). (b) Cod spawning occurs throughout coastal and
oceanic sites over anextended period, resulting in a long train of
cod larvae; the oil spill only killslarvae that would have had a
small chance of surviving the first winter. (c) Codspawn mostly in
the oceanic areas and for a short period, resulting in a
moreconcentrated distribution of larvae, and the larvae killed by
oil would have hadan average (or higher) chance of future survival.
Oceanography determines thedegree of mixing (i.e. spatial
distribution from eggs to larvae to the 0-group stage)
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recently been used in impact studies. As mentioned inan earlier
subsection, results from 2 such simulationswere used in the EIA for
the LBS area (Johansen et al.2003). A horizontal resolution of 4 km
allows formesoscale features to be represented in such sim-ulations
(exemplified in Fig. 5). Nevertheless, the useof such results for
calculation of mesoscale statistics isquestionable for 2 reasons:
(1) the use of a singlemodel year in each simulation means that
this doesnot reflect year-to-year variability in the
larger-scaleocean circulation associated with (e.g.) the NAOindex,
and its impact on mesoscale structures. Large-scale ocean
circulation provides the energy formesoscale motion since energy
arising from large-scale stratification and momentum is converted
intomesoscale energy (e.g. Fossum 2006, Fossum & Red2006). (2)
A single realization for each period meansthat the small-scale
variability associated with thestochastic nature of flow
instability is not included. Inorder to describe the statistics
associated with meso-scale variability, a proper ensemble of
simulations (i.e.a set of realizations of the system) must be
computed,
similar to the manner in which medium-range weatherforecasts are
routinely produced.
Distribution of spawning sites
The only way in which fishes can decide where tobe at the egg
and larval stages is by the parents' choiceof spawning time and
site (Fig. 7), and by the densityof the eggs (which determine their
average depth).For each of the 3 species, the proportion of
individualsspawning at each spawning location varies substan-tially
between years. Herring spawning sites canchange dramatically both
from one year to the nextand on multidecadal time scales. The
spawning pat-tern of cod is more stable, but there is still
considerablevariation in the distribution of spawning cod amongthe
main areas of Lofoten (Vestfjorden and Yttersida;Fig. 7, Table 1).
Spawning capelin have an easterly dis-tribution (28 to 33 E) in
some years and a westerlydistribution (18 to 31 E) in others
(Gjster et al.1998). Changes in temperature, stock size or mean
age
of the spawning stock, and changes in eggpredation from herring
are possible explana-tions for the observed patterns. However,there
is no simple relationship between (e.g.)stock size and climatic
conditions.
Year-to-year variability in the spawningarea may be correlated
with the severity ofthe impact from an oil spill, since
certainocean circulation regimes (correlated withweather patterns
such as NAO) may be asso-ciated with both anomalous hydrocarbon
con-
291
2003
Yttersida
Vestf
jorde
n
70
69
68
67
10 12 14 16 18
2002 2004
Fig. 7. Gadus morhua. Spatial distribution of spawning
North-east Arctic (NEA) cod in 2002 to 2004 in the coastal area
Vest-fjorden and the more oceanic area of Yttersida (indicated on
the left map: Salthaug 2002, Mehl & Nedreaas 2003, Mehl
2004).Shading indicates the strength of acoustic registrations of
cod (darker shading = high density of cod spawners). See Table
1
for additional data
Cod stock Cod standing Cod first-time Capelinin Vestfjorden
stock biomass spawners biomass
(%) (103 metric t) (%) (103 metric t)
2002 36 158 57 35002003 21 263 83 21002004 4 286 38 700
Table 1. Gadus morhua and Mallotus villosus. Stock data for the
spawning periods shown in Fig. 7
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Mar Ecol Prog Ser 339: 283299, 2007
tents from an oil spill, and with a shift in the preferen-tial
spawning site. A change in spawning area or timeof spawning will
thus affect the drift pattern of eggsand larvae which, in turn,
will affect the probabilityof contact with oil from an oil
spill.
Length of the spawning season as an ecological andevolutionary
consequence of harvesting
To fully understand external influences such asfishing pressure
and perturbations such as oil spills,it is important to study their
impact on the life-history traits of the relevant population.
Decrease ofgenetic variation may lead to a population that ismore
susceptible to environmental disturbances suchas oil spills by
reducing its ability to adapt andrespond to stress. A growing body
of evidence sug-gests that high fishing pressure and
size-selectiveharvesting may result in fisheries-induced
evolutionof some life-history traits (Heino & God
2002),including age and size at maturation (Heino et al.2002a,b,
Barot et al. 2004, Ernande et al. 2004, Olsenet al. 2004). In the
case of NEA cod, the decrease inage and size at maturation observed
since 1950 hasbeen suggested to be not only a plastic but also
agenetic response to a high fishing pressure, as wellas changes in
the fisheries size selectivity (Heino etal. 2002b, Eikeset et al.
2005). Other life-history traitsmay also be affected by
fisheries-induced changes;
e.g. individual growth rate (Conover & Munch 2002)and
reproductive investment (Reznick et al. 1996,Reznick &
Ghalambor 2005, Rijnsdorp et al. 2005).Therefore, it is important
to take fishery-inducedevolution into account when assessing the
sensitivityof fish populations to oil spills. In the case of cod,it
has also been shown that compared to first-timespawners, older
individuals that have spawned be-fore (repeat spawners) arrive
earlier on the spawn-ing grounds, spawn over a longer time span,
produc-ing several times more eggs during the spawningseason and
over a wider range of vertical distribution(Kjesbu et al. 1992,
1996, Marshall et al. 1998). As aresult, eggs and larvae of repeat
spawners spreadout much more horizontally, vertically and
tempo-rally, the plume of eggs and larvae downstreamfrom the
spawning site is longer, and therefore thepopulation will be less
susceptible to oil spills(Fig. 6). Since the 1950s, the proportion
of older,larger cod has decreased dramatically. While 90% ofthe
spawning stock biomass originated from fish ofage 10 yr and older
in 1947, this value was 2.5% in2002 (Ottersen et al. 2006).
Therefore, cod eggs andlarvae have become increasingly concentrated
inspace and time, and thus the consequences of an oilaccident may
be more severe. Physical conditionsalso influence the distribution
and advection of eggsand larvae (e.g. the less wind, the more
larvae willremain high in the water column, where concentra-tions
of hydrocarbons are highest).
Natural mortality and species inter-actions subsequent to oil
spill mortality
In the absence of human interference,there is enormous natural
mortality ofmany fish species from the larval stage tothe time when
they recruit to the fish-eries. For instance, for NEA cod,
survivalis in the order of 106 from eggs to age 3yr (i.e. ~1 out of
1000 000 eggs survives).Still, this mortality appears to vary a
gooddeal between years (in the order of ~10for NEA cod) and between
areas. Thesurvival of NEA cod from age 5 mo to age11 mo appears to
vary substantially spa-tially as well, being higher in the
easternBarents Sea (Ciannelli et al. 2007; seealso present Fig. 8).
One possible reasonis that small cod may be protected
fromcannibalism in colder waters (Ciannelli etal. 2007). Since an
oil spill kills larvae in alimited area, this will influence the
de-gree to which the population is affected
292
Fig. 8. Gadus morhua. Spatial patterns of North-east Arctic cod
survival fromage 5 mo (September) to age 11 mo (February), during
the period from 1980 to2004, as estimated by an additive GAM model
including geographic co-ordinates (latitude and longitude), bottom
depth, fish length and wintertemperature as covariates. Circles are
proportional to the relative survival (i.e.larger circles show
better survival). Circles of different sizes within the samegrid
location reflect the interannual variability of the survival metric
(from
Ciannelli et al. 2007)
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by the oil spill. For instance, assuming that an oil spillkills
20% of the fish larvae, and that these 20% werelarvae that would
have been advected to ecologicallyunfavourable areas and thus have
suffered 100% mor-tality, then the real effect of the oil spill
would be closeto zero when the cohort reaches fishable age (e.g.
age 3yr). On the other hand, if the 20% larvae killed werethose
that would have been advected to favourable ar-eas and thus have
had the best chance of survival, thenthe final effect would be more
than 20%. Therefore, anunderstanding of the spatial variability in
survival andits causes is crucial (Fig. 6). The fact that an oil
spilldoes not affect all eggs or larvae equally can affect
theconclusions about the effects of an oil spill. For
instance,according to Johansen et al. (2003: their Fig. 7.8), an
oilspill from the Troms I petroleum region will tend toaffect/kill
cod larvae in the eastern part of their distrib-ution area.
Assuming that these will also have aneasterly position at the
0-group stage, these larvae arethose with the best chance of
survival from the 0-groupstage to age 1 yr (Ciannelli et al. 2007;
Fig. 8).
Even if the primary effect of an oil spill was to reducea single
year-class of a single species, ecological inter-actions would lead
to secondary effects arising frompredation, competition and
cannibalism (Fig. 9). Sucheffects could be either intraspecific
(e.g. changedgrowth and life-time among the surviving portion ofthe
year-class as well as other year-classes) or interspe-cific (i.e.
changes in temporal distribution, growth andsurvival among the
prey, predators and competitors ofthe species affected by oil).
Intraspecific secondaryeffects can reduce or increase the primary
effect,(compensation or depensation, respectively). Compen-sation
is expected if the stock is bottom-up controlled(i.e. if prey is
the limiting factor). For instance, if 15%
of the cod larvae were killed by oil (pri-mary effect), reduced
competitionamong the survivors might partly com-pensate for the
mortality, reducing theyear-class by 15% at age 3 yr as aresult of
the oil spill. Whether compen-sation or depensation occurs may
welldepend on the ecological and/or cli-matic state of the
ecosystem. In the caseof cod, a mechanism for such shiftscould be
cannibalism, which is moreprevalent when the capelin stock is
small. Thus, the early survival of cod may be bottom-up
controlled when capelin is abundant and top-downcontrolled when
capelin is scarce.
Interspecific secondary effects are highly likely in theLBS
system since the 3 species are strongly interlinked(e.g. Dingsr et
al. 2007, Hjermann et al. 2007): (1)Capelin is a key food source
for cod, and thus highcapelin biomass has a significant positive
effect on cod,reducing cannibalism, and increasing
reproductiveoutput of the latter (Marshall et al. 1999). Likewise,
codpredation has a significantly negative effect on capelinsurvival
(Hjermann et al. 2004c). (2) Capelin recruit-ment is heavily
affected by predation by juvenileherring on capelin larvae; the
capelin population cancollapse (decrease by >95%) following
years withabundant juvenile herring (Gjster & Bogstad
1998).Because of their intimate interdependence, all 3 stocksare
likely to be affected should only one of them be di-rectly exposed
to pollution. The extent and duration ofsuch cascading effects
depends on the structure of theecosystem and the strength of
interactions, which tosome degree is known for at least the Barents
Seaecosystem (Tjelmeland & Bogstad 1998, Bogstad etal. 2000).
Also, if an oil spill directly disrupts thetemporal distribution of
only one trophic level, othertrophic levels may become temporally
mismatched,thus weakening trophic links the essence of
thematch-mismatch hypothesis (Hjort 1914, Cushing 1990,Durant et
al. 2007).
Chronic sublethal effects of persistent oil residues
The immediate effects of oil spills are reflected inacute
mortality of sea birds and oceanic mammals
293
Fig. 9. Gadus morhua, Mallotus villosus and Clupea harengus.
Trophicrelationships between the main components of the food web,
including cod
cannibalism
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through oil slicks. However, the Exxon Valdez oilspill has shown
that the mortality immediately follow-ing an oil spill can be less
damaging than long-term(e.g. a decade) chronic exposure to
persistent oilresidues in the environment (Peterson et al.
2003).Many of the populations that suffered heavy short-term
mortality in the 1989 Exxon Valdez spill haverecovered, but not all
some populations are stillrecovering slowly and some not at all. 15
years afterthe accident, significant amounts of Exxon Valdez
oilremain in intertidal and subtidal sediments and belowmussel
beds. Thus, species such as sea otters andharlequin ducks that prey
on benthic organisms, stillexhibit increased levels of the
detoxification enzymeCYP1A, and suffer higher mortality in the
areaimpacted by the spill (Peterson et al. 2003). The ele-vated
level of toxicity also impacts those fishes whoseeggs develop in
intertidal habitats. One example isthat among pink salmon exposed
to
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Hjermann et al.: Fish and oil in the LofotenBarents Sea
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may be gained by direct observation, some (in manycases
considerable) uncertainties will always remain.While such
uncertainties can be integrated in theMonte Carlo procedure, this
source of uncertainty istypically ignored.
KNOWLEDGE GAPS AND RESEARCH NEEDS
The biological effects of oil spills are generally ex-tremely
hard to predict. Their impact is affected by somany parameters that
the effects of no 2 oil spills arethe same. The amount of oil
spilled is hardly correlatedwith the ecological impact of the
spill. Measured in theamount of oil spilled, the Exxon Valdez oil
spill ranksonly as no. 40 among oil spills that occurred
between1967 and 1994, but cost Exxon 3.5 billion dollars indirect
expenses (ExxonMobil 2004). In comparison,15 times as much oil was
spilled in the IXTOC I blow-out in the Mexican Gulf in 1979, and
6500 times asmuch in the Persian Gulf in 1991 (Paine et al. 1996).
Anoil slick that was barely visible killed 10 000 seabirds ina
fiord close to Vard, Northern Norway in 1979 (Bam-bulyak &
Frantzen 2005). Thus, ecological impact isobviously determined by
other factors in addition tospill size, such as location (closeness
to shoreline), timeof year, atmospheric as well as oceanic
conditions atthe time of the spill, and type of oil. However, as
wehave attempted to show herein, the effects of an oilspill also
depend in more subtle ways on its inter-actions with prevailing
oceanographic and ecologicalconditions. Thus, assessing the
potential effects ofoil spills demands careful consideration of
both thephysical and the biological processes involved.
With regard to physical processes, drift trajectories,and hence
the region of influence of a potential acci-dental oil spill, are
traditionally computed using anempirical relationship between
surface winds and theocean current with which the oil spill is
advected.Thus, based on wind data from an atmospheric circula-tion
analysis, drift trajectories are completely deter-ministic. In one
sense, the ocean is subservient to theatmosphere: In a hypothetical
case of 2 years withidentical atmospheric forcing, the ocean
circulation,and drift trajectories, becomes identical. However,
asdemonstrated by the results of recent ocean circulationstudies
using an ensemble of realizations, the relation-ship between ocean
circulation and atmospheric forc-ing is not deterministic (e.g.
Metzger & Hurlburt 2001,Melsom et al. 2003). In a study with a
mesh configura-tion (i.e. grid size and map projection) similar to
thesimulations of Johansen et al. (2003), Melsom (2005)found that
in frontal regions, about 13 of the variabilitycan be attributed to
the non-deterministic variabilityassociated with flow instabilities
resulting in meso-
scale features such as eddies, filaments and meanders(e.g.
Fossum 2006, Fossum & Red 2006). This is in factan
underestimate, since the variance in the probabilitydistributions
for (e.g.) salinity and temperature wasunderestimated in Melsoms
(2005) study.
In order to fill the gaps on physical aspects in ourknowledge of
how oceanic transport of oil will affect itsimpact on marine life,
we must address the 2 issues notyet appropriately analyzed in the
existing impactstudies of the LBS area: (1) How does large-scale
vari-ability affect mesoscale circulation and thereby
thevariability of transport pathways, and (2) what is
theprobability distribution of transport pathways asso-ciated with
non-deterministic variability? In order toresolve question (1), a
hydrodynamic model must beapplied to a time period that spans
various atmosphericforcing regimes in a manner that reproduces
meso-scale features. Question (2) is resolved by an
ensemblesimulation, whereby the results have a probability
dis-tribution that is similar to the observed distribution.
Both approaches are computationally demanding.The fact that the
hydrodynamic model of Johansen etal. (2003) was limited to two 1 yr
simulations was prob-ably due to the limits of the computer
resources avail-able at that time. This situation is changing.
Moreover,an ocean circulation ensemble that exhibits a
properprobability distribution in the context of
mesoscalevariability is yet to be defined, and constitutes a
scien-tific challenge, since the mesoscale circulation is
notresolved by synoptic observations in space and time.We suggest
that inclusion of continuously perturbedatmospheric forcing fields
will yield more realisticprobability distributions than the
approach using ini-tial state perturbations of decadal simulations
thatwere performed by Melsom (2005).
With regard to ecological aspects, we have pointedto some
unknowns for these 3 species. This may seemparadoxical, since cod
and herring have been inthe focus of Norwegian fishery science for
more than100 years. However, even for NEA cod, one of the
best-studied fish stocks in the world, there are gaps in
ourknowledge that significantly hamper our ability topredict the
effects of an oil spill. This is illustrated bythe Exxon Valdez
spill, for which it has proved diffi-cult to assess the oil spill
damage and the recoverytime even in the case of populations that
had been verywell-studied prior to the accident, such as the
killerwhale Orcinus orca and pink salmon Oncorhynchusgorbuscha.
We believe, however, that existing predictions forpossible oil
spill impacts can be improved by usingavailable data. Typically,
the expected impact of an oilspill is given as a function of
approximate location (e.g.distance from the coast) and time of year
(month).Oceanographic models should be able to provide more
295
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Mar Ecol Prog Ser 339: 283299, 2007
detailed guidance on the risks associated with spills
atdifferent locations. Also, as detailed above, the impactwill
depend on the ecosystems ecological state (abun-dance of key
species, such as capelin in the case ofLBS) as well as its climatic
state (e.g. high or low NAOyears). These variables can affect both
the location/distribution of eggs/larvae, the drift patterns of
botheggs/larvae and oil, as well as the consequences tofish
populations (including effects of ecosystem inter-actions) of
oil-caused mortality at the larval stage. Withregard to possible
cascade effects of oil spills causinglarval mortality, oil spills
comprise but one kind ofpulse perturbation (e.g. Chan et al. 2003),
and thusother kinds of perturbations (i.e. climatic and eco-logical
conditions causing mortality in specific lifestages) can provide
insight into such cascade effects.Ecological/genetic models can
predict the combinedeffects of fisheries and oil on fish dynamics,
i.e. howfisheries affect susceptibility of fish stocks to oil
spillsand vice versa.
Even in a pristine state, the Barents SeaNorwegianSea ecosystem
may not be characterised by balanceand stability. For instance,
recruitment (i.e. larval mor-tality) of the herring, a key species
in this system, isextremely variable from year to year, in part
because ofvariations in the oceanic climate (Fiksen & Slotte
2002).Hence, the ecosystem is inherently stochastic: Evenwith a
perfect knowledge of the system and the lethaland non-lethal
effects of an oil spill, we could not pre-dict its effects on the
ecosystem for more than a fewyears ahead. Further, our knowledge on
ecosystemprocesses in this system is imperfect. Since no
largedegree of certainty can be achieved in any conclusionsabout
long-term effects, at best we can attempt, bymodelling, to attain a
quantitative indication of thepossible outcomes of oil spills in
the ecosystem context.Qualitatively, we can assess at which places
and timesan oil spill may be expected to have the most signifi-cant
long-term effects, and under which climatic re-gimes the effect of
spills will be most harmful.
Acknowledgements. We thank the following NorwegianResearch
Council programmes: Havet og Kysten for sup-porting the LEO
(Long-term Effects of Oil accidents) project,and Polar Climate
Research for supporting the ECOBE(Effects of the North Atlantic
Variability on the Barents SeaEcosystem) project. We also thank
VISTA (www.vista.no) forfunding D..H, and Hydro for financial
support. Commentsprovided by 4 reviewers on an earlier version of
the paper aregreatly appreciated.
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Editorial responsibility: Howard Browman (Associate
Editor-in-Chief), Storeb, Norway
Submitted: June 11, 2006; Accepted: October 10, 2006Proofs
received from author(s): May 3, 2007