Environmental risk methodology MIRA

DEVELOPMENT OF METHODOLOGY FOR CALCULATIONS OF
ENVIRONMENTAL RISK FOR THE MARGINAL ICE ZONE - A JOINT
PROJECT BETWEEN AKVAPLAN-NIVA AND DNV GL
Report
Norsk olje og gass
Statoil Petroleum AS
RWE Dea Norge AS
Total E&P Norge AS
GDF SUEZ E&P Norge AS
Report No.: 2014-0545, Rev00.
Document No.: 18L9BD0-6
Date: 2014-05-23
Table of contents
1
EXECUTIVE SUMMARY ..................................................................................................... 3
2
INTRODUCTION .............................................................................................................. 4
3
OIL DRIFT MODELLING IN THE MARGINAL ICE-ZONE (MIZ) ................................................. 5
3.1
Definition of the MIZ for environmental risk assessments
5
3.2
Oil and ice interactions
8
3.3
SINTEFs OSCAR model
3.4
Static ice concentration grids
10
3.5
Dynamic ice concentration grids
14
4
EFFECT AND DAMAGE KEYS ........................................................................................... 17
4.1
Expert working group
17
4.2
Issues relevant for effects of oil in ice
17
4.3
Current MIRA Model structure
21
4.4
Existing vulnerability values and effect keys in MIRA
23
4.5
Existing Damage keys
25
4.6
Proposed Application of the Existing Methodology to MIZ
26
4.7
Proposed Vulnerability values
28
4.8
Proposed Effect Keys
33
4.9
Proposed Damage keys
33
5
REVIEW OF DATASETS ON ENVIRONMENTAL RESOURCES ................................................. 34
5.1
Identification of relevant environmental resources
34
5.2
Relevant datasets
34
5.3
Recommendations for handling data with different resolution and quality
37
6
CONCLUSIONS AND RECOMMENDATIONS ........................................................................ 38
7
REFERENCES ................................................................................................................ 41
9
APPENDIX A .............................................................................................................................. 45
APPENDIX B ............................................................................................................................. 46
1
EXECUTIVE SUMMARY
The Norwegian Oil and Gas Association (NOROG) has identified, on behalf of the
operators on the Norwegian Continental Shelf, the need for a standardised approach for
calculating environmental risk for the marginal ice zone (MIZ), also referred to as the iceedge. Scope of Work for the project was to identify and develop an approach within the
current MIRA method (OLF 2007) that can be applied already from this year.
The project covers areas of the MIZ in the Barents Sea that could be affected by oil spills
from current oil and gas activities on the Norwegian Continental Shelf. For these
activities, oil from an accidental release will have drifted on the sea surface for several
days after the release before the oil reaches the MIZ, a situation very different from
release of oil in ice infested waters, i.e. in terms of toxicity over time. The proposed
methodology does not cover spills of oil directly in or underneath ice or exposure
scenarios resulting from oil that has been frozen into the ice.
There is a range of definitions of the MIZ in terms of ice concentrations. For the purpose
of performing environmental risk assessments, we recommend to use 10-30 % ice
concentration as a definition of MIZ. The vulnerability assessments that form the basis
for the proposed method is based on exposure scenarios in these ice concentrations.
However, the proposed methodology is based on modelling of oil in all ice concentrations
and there is no need within the proposed methodology to define the boundaries of the
MIZ to specific ice concentrations in that sense.
The present version of SINTEFs OSCAR model (v. 6.5) takes the ice-coverage as an
adjusting parameter into the calculations and seems to provide a suitable basis for shortterm prediction of oil spill trajectories. As there is still limited implementation of oil in ice
interactions in OSCAR and other models, caution should be made when trying to model
longer periods (weeks) of oil drift within ice concentrations above 30 %.
Expert consultations and workshops were carried out with the Norwegian Institute for
Nature Research, the Norwegian Polar Research Institute and the Norwegian Institute for
Marine Research. From these sessions, it was concluded that existing effect keys and
damage keys could be applied for environmental risk assessments in the MIZ.
For some seabird species and most marine mammal species, higher vulnerabilities
leading to application of stricter effect keys was recommended. For sympagic species,
expert consultations identified a set of LC50 values that could be applied in a similar
manner as currently implemented for sensitive life stages of fish. The presence of
breeding seabirds in the colonies before start of breeding is an issue that would need to
be addressed in data sets applied in assessments.
Relevant sources for species distribution in the Barents Sea MIZ have been evaluated and
recommendations for changes in the datasets to provide a more realistic picture of the
MIZ have been put forward. Variation both in the quality and resolution of the datasets
should be expressed as increased uncertainty. Sensitivity assessments should be
considered when using data containing semi-quantitative to low resolution.
Some of the work carried out may also have relevance for a future development of MIRA
in the direction of more continuous functions describing the relationship between oil
and.lethality, as well as on a future development of the ERA Acute method. The adjusted
vulnerability indices, and associated damage keys have applicability in this context.
3
2
INTRODUCTION
Oil and gas exploration is moving north, causing a potential for accidental oil spills in
areas of seasonal sea ice. The boundary between ice and water is often referred to as
either the ice –edge or the marginal ice zone (MIZ). In this report we will refer to this
area as the MIZ.
Several operators will be drilling exploration wells in the Barents Sea this year, with the
potential to influence sea ice given a spill. This has previously been a less relevant issue,
and the MIZ is not included in the existing method for environmental risk analysis (ERA)
used at the Norwegian Continental Shelf.
The management plan for the Barents Sea and the sea area outside Lofoten states that
there is a need for a general methodological development of environmental risk analysis.
(Ministry of Climate and Environment 2011). In addition, The Norwegian Oil and Gas
Association (NOROG) has identified, on behalf of the operators on the Norwegian
Continental Shelf, the need for a standardised approach for calculating environmental
risk for the MIZ. Development towards more science based approaches is needed.
The methodology currently in use is the MIRA environmental risk analysis method (OLF,
2007). In MIRA, the environmental risk to four groups of vulnerable resource (VØKs),
seabirds, marine mammals, seashore and fish stocks is calculated based on vulnerability.
The individual vulnerability is linked to effect- and damage keys.
NOROG has initiated a phase I project to develop a methodology for calculations of
environmental risk in the MIZ that can be applied already from this year. The Scope of
Work for phase I was to identify and develop an approach within the current MIRA
method covering the following key aspects; definition of what percentage of ice is
representing the MIZ for environmental risk assessments (ERAs), oil drift modelling in ice,
modification of existing effect and damage keys according to exposure scenarios in ice
infested water and an evaluation of available species distribution data.
Given the time frame of the project, a combination of literature search and expert
meetings/consultations were applied. Consultations were made with experts within
relevant fields:
•
Seabirds: Norwegian Institute for Nature Research (NINA), and the Norwegian
Polar Research Institute (NP).
•
Marine mammals: Norwegian Polar Research Institute (NP) and Institute of Marine
Research (IMR).
•
Sympagic flora and fauna: UiT - The Arctic University of Norway (UiT) and
Akvaplan-niva.
•
Oil drift modelling: Sintef and met.no
The project is defined to cover the areas of the MIZ that could be affected by oil spills
from current oil and gas activities on the Norwegian Continental Shelf in the Barents Sea.
For these activities, oil from an accidental release will have drifted on the sea surface for
several days, a situation very different from release of oil in ice infested waters, i.e. in
terms of toxicity over time. The proposed methodology does not cover spills of oil directly
in or underneath ice or exposure scenarios resulting from oil that has been frozen into
the ice.
4
3
OIL DRIFT MODELLING IN THE MARGINAL ICE-ZONE (MIZ)
3.1 Definition of the MIZ for environmental risk assessments
3.1.1 Definition of MIZ
The MIZ refers to the area where open ocean processes alter significantly the dynamical
properties of the sea ice cover (http://msc.nersc.no/?q=MIZ). The area is highly variable
and subject to rapid changes. As illustrated in Figure 1, variations in ice concentration in
the MIZ result from wind and currents, growth and melt and can vary on a daily, monthly,
seasonal or yearly basis. Compacted edges are clearly defined and result from wind and
currents moving toward the pack. Diffuse edges are poorly defined and usually
associated with wind and currents moving away from the pack. The shift from ice
covered to open water gives rise to unique processes including water mass formation,
oceanic upwelling and eddy formation which again give rise to increased biological
productivity in the water at times of year where light conditions are favourable.
Figure 1. Processes that dynamically modify ice-thickness distribution. (AMAP
2011)
There is a range of definitions of the MIZ in terms of ice concentrations. Examples are
15-30 %, 15-40 % and 10-30 %. Some sources cite 15 % ice concentration as the start
of
the
MIZ,
and
extend
the
zone
to
higher
ice
concentrations
(http://seaiceatlas.snap.uaf.edu/glossary).
5
Parameters that are relevant to consider when deciding appropriate MIZ-definitions for
ERAs include; accessibility, resolution and robustness of ice data and biological activity
related to the MIZ The feasibility of mechanical recovery in icy waters is also affecting the
environmental risk picture of an oil spill in the MIZ, but since oil spill preparedness is not
within the Scope of Work for this project this factor has not been emphasized.
Because the proposed methodology is based on modelling of oil in all ice concentrations
there is no need to define the boundaries of the MIZ to specific ice concentrations for
modelling purposes. The recommendation to use 10-30 % ice concentration for the MIZ
for ERAs is hence mainly based on oil exposure scenarios resulting from the presence of
ice. Compared to the widely used definitions of the MIZ, our definition of 10% as the
start of the MIZ can be considered moderately conservative. Information about the
species preferred ice-concentrations is important for making predictions about their
distribution-patterns. Such information is available for a few species and ecosystem
components. However, knowledge is in general is scarce in this matter supporting the
slight conservative choice of the 10-30% definition of the MIZ.
According to met.no ice concentrations below 10 % are defined as open water and the
regular MIRA method should be applied. Above 10 % ice coverage, the presence of ice
will change the exposure scenarios and the proposed MIRA-MIZ should be applied.
Higher ice-concentrations than 30 % are not within the scope of this project.
3.1.2 The Barents Sea MIZ
Sea ice is an important component of the Barents Sea climate system, influenced by both
atmospheric factors and ocean currents. The north-western Barents Sea is influenced by
a cold arctic current in a south-easterly direction extending south to Bear Island, whereas
the south-western and eastern Barents Sea is influenced by the warm current coming
from the Norwegian Sea. As a result the south-western Barents Sea is ice free yearround. The ice cover in the Barents Sea has a strong seasonal variability, with the largest
sea-ice extent in March or April. In May the ice begins to recede and in September, the
entire Barents Sea is usually more or less ice free. The sea-ice extent in the area is also
characterized by a large inter-annual variability. The decline in sea-ice concentration and
sea-ice cover in the Arctic in general is well documented and the most pronounced
effects
have
been
observed
for
the
Barents
Sea
(http://geodata.npolar.no/barentsportal/).
There are many sources for sea-ice data providing daily information, statistical maps and
forecasts of ice distribution and concentration for the Barents Sea. In this project we
have used ice data from the Norwegian Meteorological Institute (met.no). Ice data from
met.no is of high quality and represent the best available data set based on satellite
imagery for this area (Figure 2). Standard maps present the following concentration
intervals: <10 % (defined as open water), 10-40 % (defined as very open drift ice), 4070 % (defined as open drift ice), 70-90 % (defined as close drift ice), 90-100% (very
close drift ice). Met.no may provide data sets with 10 % increments in ice concentration,
which is the resolution in the underlying satellite imagery.
6
Figure 2. Ice distribution 30.03.2011. Data from met.no.
The location of the MIZ in the Barents Sea is subject to rapid changes and can also be
translocated 100-200 km north- and eastwards if the mean rather than the maximum ice
coverage (during the past decade) is considered. This is due to the rather extreme ice
coverage in early 2003 when sea ice reached south of Bear Island and stretched well into
the central bank between Svalbard and Novaya Zemlya. To illustrate the dynamics of the
Barents Sea sea ice system ice maps from the same dates, three consecutive years and
days (all from met.no) is presented in APPENDIX A.
3.1.3 Biological resources
Melting of sea ice in spring, in combination with increasing light, induce a strong
seasonality of primary production following the northward retreat of the sea ice in the
Barents Sea. These highly productive waters provide food for fish, seabirds and marine
mammals. Spring blooms are also important for bottom communities (benthos) as much
of the organic matter produced during this period sink to the bottom and become an
important source of energy for the benthic compartment. A simplified illustration of the
Barents Sea food web is given in Figure 3 A more detailed description of the MIZecosystem is described in e.g. Olsson (1999) and a detailed description of the species
covered in the current methodology is given in Chapter 4.
7
Figure 3. Barents Sea simplified food web (AMAP 2007).
3.2 Oil and ice interactions
There are many ways that oil and ice can interact: the oil can be on the surface of the ice
or absorbed in the snow, encapsulated by the ice, trapped within cracks in a broken ice
field or in open water regions between ice floes, and it can get trapped under the ice by
keels that extend into the water (Figure 4), (Drozdowski et al. 2011).
When oil and ice interact, some fraction of the oil will move with the sea ice, which does
not necessarily follow the ocean currents. In cases where the oil is trapped between large
ice floes, the oil is generally contained quite well and follows the ice. The presence of ice
can also shelter the oil from wind and wave action, subsequently slowing down spreading
and weathering of the oil. However, during the spring melt, any oil stored within the
rubble field at the land fast ice edge will either be released into the ocean or move with
the ice edge as it retreats toward the coast (Drozdowski et al. 2011).
Oil can get trapped in ice in three different ways:
1) Oil can become frozen in a solid ice body and remain there until the ice melts. The
water beneath an ice sheet will continue to freeze in the early part of the ice season. Oil
under an ice sheet or pack ice will be completely encapsulated within 18 to 72 hours,
depending on the time of year (Dickins 1981, Buist 1983b, Buist 1983a)
2) Oil can be trapped between ice fragments in broken ice fields. Studies have shown
that the oil will generally drift with the ice for ice concentrations greater than 30%. For
smaller concentrations, the oil will behave as it does in open water (Drozdowski et al.
2011).
3) Oil can get trapped on the under-ice surface in small cavities. This oil can become
encapsulated, but not necessarily (Drozdowski et al. 2011).
8
The oil movement in icy waters is highly dependent on the ice coverage. Several studies
have been performed and different approaches to explain the movement have been
suggested. In general, it is assumed that up to 30% of ice coverage, the effect on oil
movement is negligible. For ice coverage between 30 % and 80 %, explanations such as
Ekman veering, causing advection deflected to 15 degrees to the right of the wind
direction has been proposed. For ice coverage above 80%, the oil is assumed to be
trapped under the ice and move with it (Khelifa 2010).
Figure 4: Interactions between oil and ice related to spill trajectory modelling
(Drozdowski et al. 2011)
3.3 SINTEFs OSCAR model
The present version of SINTEFs OSCAR model (v. 6.5) takes the ice-coverage as an
adjusting parameter into the calculations. The fractional ice cover/ice concentration can
be provided as grids similar to current, wind or habitat data. The ice cover affects
weathering, spreading, evaporation of surface oil, as well as drifting of oil with ice. In a
more OSCAR technical description:
•
•
•
dissolution: dissolution in water column reduced roughly linear with ice coverage
percentage
evaporation: evaporation is reduced proportional to ice coverage percentage
entrainment: no entrainment if more than 30% ice coverage (= no effect of wind)
9
•
spreading: if > 30% coverage, use spread ice instead of other algorithm
(decreases spreading)
The drift rate has been modified by the Froude number (on the basis of flow velocity and
block thickness) and for the Coreolis’ effect (due to earth rotation; 35 degrees rotation
CCW for 1 m thick ice) ice in the OSCAR model (SINTEF 2012)
Conclusions from a field trial in ice in 2011 (Faksness et al. 2011), indicated that the
evaporative loss in high ice concentrations were underestimated in the model.
For the purpose of this report, two approaches of oil in ice modelling has been tested.
First, a uniform ice concentration grid was established with different static ice coverage
fractions (from 0 to 90%). A single simulation was modelled with the different ice
concentrations to show trajectory and mass balance. Two oil types were modelled (Norne
oil and Skrugard oil). Secondly, modelling was performed with dynamic ice concentration
data (daily values) for a one month period (April 2006).
A surface spill rate of 4000m3/d for 10 days was modelled with 10 days following time
after release end. The release location was in the Barents Sea.
3.4 Static ice concentration grids
Figure 5 to Figure 8 shows the result of the spill simulation with varying static ice
concentrations (note that not all ice concentrations were modelled in Figure 6 and Figure
7). There is a clear shift in spill trajectory and spread when ice concentration is above
30 %. Below 30 % ice concentration, oil drift and spread is as for open water. The oil
preserves in the ice and evaporation and down mixing will be reduced leading to more oil
at the surface due to less influence by the wind compared to simulations without ice. No
particular change in trajectory is noticed from 40 to 90 % ice concentration, neither for
Norne nor for Skrugard oil.
10
Figure 5 Modelled ice drift with Norne oil under varying static iceconcentrations. Snapshots are taken after 4, 10 and 20 days.
Figure 6 Modelled ice drift with Skrugard oil under varying static iceconcentrations. Snapshots are taken after 4, 10 and 20 days.
11
Figure 7 The maximal total hydrocarbon concentration (ppm) in the upper 100
meters corresponding to Figure 6.
Figure 8 North-south vertical cross section of maximal total hydrocarbon
concentration corresponding to Figure 7, after 4 days release, 20 % ice
concentration (top) and 90 % ice concentration (bottom)
When applying a static ice-concentration grid to the north of the spill location, similar
differences in trajectory is noted as above for the part of the slick that enters the ice. Oil
will spread much less, be thicker and the weathering process will slow considerably down
in ice (Figure 9 and Figure 10).
12
Figure 9 Modelled ice drift with Norne oil under varying static iceconcentrations (above dashed line). Snapshots are taken after 4, 10 and 20
days.
Figure 10. Modelled ice drift with Skrugard oil under varying static iceconcentrations (above dashed line). Snapshots are taken after 4, 10 and 20
days
13
3.5 Dynamic ice concentration grids
Figure 11 to Figure 14 shows the result of a spill simulation at different time steps with
and without dynamic ice concentrations at a location in the Barents Sea from a period in
April 2005. The release location is mostly in the ice and partly in open water due to daily
variation of the ice zone. As for the static ice concentration there is a clear shift in spill
trajectory and spread when oil is encountering the ice concentration above 30 %. Below
30 % ice concentration, oil drift and spread is as for open water. The oil preserved in the
ice and the mass balance shows reduced evaporation and down mixing, and more oil at
the surface due to less influence by the wind when comparing with the simulation without
ice. Note that from day 15 of the release (Figure 13) to day 20 (Figure 14) more
emulsion is found to be at the surface in the open water. This is due to resurfacing of oil
during periods with calmer weather (reduced wind and wave activity) following periods of
strong winds.
Figure 11 Emulsion thickness at the surface after 5 days release of Skrugard oil under
varying dynamic ice-concentrations to the left and without influence of ice to the right
Figure 12 Emulsion thickness at the surface after release end 10 days of Skrugard oil
under varying dynamic ice-concentrations to the left and without influence of ice to the
right.
14
Figure 13 Emulsion thickness at the surface after 15 days release of Skrugard
oil under varying dynamic ice-concentrations to the left and without influence of
ice to the right
Figure 14 Emulsion thickness at the surface after simulation end 20 days of
Skrugard oil under varying dynamic ice-concentrations to the left and without
influence of ice to the right
3.5.1 Recommended ice and metocean-data
Data on daily sea ice concentrations and several other ice-related parameters are publicly
available from the Nordic Seas 4 km numerical ocean hindcast archive(SVIM) at
ftp://ftp.met.no/projects/SVIM-public/SVIMresults/. The hindcast archive also includes
4x4 km current data and has data from 1958 to 2011 (Lien et. al. 2013). A plot of ice
concentration from the archive is given in Figure 15 below.
15
Figure 15 Average sea ice concentrations from 28.01.2011 taken from the
SVIMs 4x4 km data archive (Source met.no/IMR 2013)
Today the metocean files from the SVIM archive cannot be read directly into OSCAR.
Norwegian Meteorological Institute has generated a workaround to “regrid” the data to
OSCAR NetCdf format. The regridding of the daily sea ice concentration data is done with
the
software
Fimex.
This
software
is
open
source
and
available
through https://wiki.met.no/fimex/start.
The sea ice data are originally provided in a Lambert-Asimuthal projection, with 25km
resolution. The sea ice data is regridded to a isolat-isolon projection (WGS84 datum) with
0.1degree resolution, using nearest neighbour interpolation.
Note that SINTEF for the moment is implementing a new NetCdf import routine in OSCAR
(v6.6) for reading SVIM metocean NetCdf files (for this purpose current and ice
concentration) directly.
The wind data (from met.no) used in this project has a horizontal resolution 20 km and a
time resolution every 3 hours. STATOIL is the owner of a wind data set that correspond
to the current data set in the SVIM archive, and hopefully in the near future this wind
data set can be available for oil drift modelling in Norwegian waters.
16
4
EFFECT AND DAMAGE KEYS
4.1 Expert working group
Given the time frame of the project, a combination of literature searches and expert
meetings/consultations were applied for the identification of relevant species/ecosystem
factors to be included in risk assessments of oil spills in the MIZ.
Consultations were made with experts within relevant fields:
•
Seabirds: Geir Helge Systad, Norwegian Institute for Nature Research (NINA), and
the Hallvard Strøm, Norwegian Polar Research Institute (NP).
•
Marine mammals: Christian Lydersen, Norwegian Polar Research Institute (NP),
Anne Kristine Friele, Institute of Marine Research (IMR) and Martin Biuw,
Akvaplan-niva.
•
Sympagic flora and fauna: –Jørgen Berge, The Arctic University of Norway (UiT)
and Øystein Varpe, Akvaplan-niva.
Two meetings were held with NP and IMR and one with NINA and NP in the Akvaplanniva offices in Tromsø. Literature search and reviews were made on the basis of
information provided by the participants of the consultations.
Summaries/minutes of each meeting was prepared by Akvaplan-niva and distributed to
all participants. These are in Norwegian, and presented in APPENDIX B in this report.
In an early phase of the project, it was identified that the characteristics of populations
and colonies present in the MIZ may be different than those along the coast of mainland
Norway and in the Southern Barents Sea. On this basis, the topic was raised and
discussed in the expert consultations.
4.2 Issues relevant for effects of oil in ice
In this project it was generally agreed to use the most conservative value where there
are limited data or knowledge gaps, as is customary in risk assessments. Monthly overall
vulnerability values were suggested as a starting point for verification by the expert
group.
The properties of oil in cold water and in the MIZ differ from warmer water in all physical
aspects. Evaporation and degradation is slowed down in colder water and photo-oxidation
is reduced when there is less light. Oil is therefore expected to stay undegraded for a
longer period compared to in more temperate areas. The following key points have been
used as background information for the evaluation of effect and damage keys in this
project.
•
Toxicity of lighter aromatic crude components may be relevant due to lower
evaporation.
•
Narcotic properties of lighter aliphatic HC components may be relevant due to
lower temperatures and thereby lower/slower evaporation.
•
Irritation effects on respiratory organs and ophthalmic irritation may be relevant.
17
•
Lighter, less degraded oil/emulsion may have different properties relating to
whether oil can enter the airways of cetaceans than in the tropics/temperate
zones.
•
Less light in the winter is expected to lead to lower rates of photo-oxidation, a
step in the degradation of certain oil hydrocarbons, the process of photo-oxidation
leads to a more toxic degradation intermediate, before further degradation. More
light in summer will lead to a higher photo-oxidation than in lower latitudes,
modified by reduced light due to the prevalence of fog.
Most of the above issues were addressed in the Norwegian Environmental Agency project
on environmental values (“Miljøverdiprosjektet”) (Spikkerud et al., 2013), while some
additional issues were identified from new literature and from the expert consultations.
4.2.1 Oil per unit sea surface area
As ice concentration increases, the area available for spreading of oil decreases. At the
same time, the spreading behaviour changes drastically in the presence of ice. The
process is slowed by cold water and the formation of a wax layer. The presence of ice
further limits spreading by herding of oil. As the current effect keys imply a relationship
between the mass of oil per grid cell area, it could be argued that increasing ice cover
might lead to increasing concentration of the oil between the floes in addition to
decreased availability of unexposed areas. On the other hand, the total area covered by
oil could be reduced in ice covered water. The modelling in OSCAR with static ice
concentrations of 40 % indicates a reduction of 90 % in the spreading of oil compared to
open water. Further work is needed to evaluate the exposure mechanisms in the MIZ
and at higher ice concentrations. It is therefore proposed to keep the oil mass categories
unaltered at present and rather evaluate exposure related to behaviour in the individual
vulnerability assessments combined with the results of oil-in-ice modelling.
4.2.2 Temperature
Temperature and ice significantly changes the weathering of oil (Payne et al., 1991). The
physical weathering of evaporation, dissolution, dispersion of oil droplets and water-in-oil
emulsification occur, but temperature and ice can alter the relative importance of these
processes. Evaporation decreases at low temperature and presence of ice, often leading
to a higher dissolution of light components in water. The viscosity generally increases at
low temperatures. The photochemical breakdown is reduced as oil is covered by ice and
microbial degradation is low during winter. A thorough review of fate and effect of oil in
the arctic marine environment is found in Lee et al. (2011).
It was also discussed whether lower sea temperatures of the MIZ and Barents Sea would
lead to significantly faster death by loss of insulation properties for sea birds. Seals that
are associated with ice generally have a thick layer of blubber and are thus not equally
susceptible to loss of thermo-insulating properties of fur. The exception to this are the
seal pups, which are dependent on fur for insulation, the populations of seals may
therefore be considered to be more vulnerable in the breeding period when the pups are
dependent on fur for thermo-insulation.
For sea birds it was considered that a faster death rate than in more temperate areas
may not be significant as long as the time frame of an acute oil spill is months, one
18
considers all exposed sea birds to be killed as described in vulnerability indices and effect
keys. However, this may need more detailed discussion, as it could be expected that
there is a lower threshold value for a lethal effect of loss of thermo-insulating properties
of plumage for sea birds in lower ambient temperatures. Øritsland et al. (1981) carried
out experiments on polar bears which showed that polar bears experience increased
metabolic stress after oil contamination and did not actively avoid oil. For sea birds, more
work is needed to evaluate whether a lower threshold value for lethal effect is relevant
for the MIZ.
4.2.3 Exposure
Low temperatures and strong seasonality in presence of sea ice and food availability have
favoured development of physiological adaptations for living in the Arctic. Among a range
of different adaptations are that Arctic marine organisms are dependent on lipid as
energy storage (Falk-Petersen et al., 2007). Organisms are exposed to oil or petroleum
products by three main routes; direct contact, ingestion and inhalation. The uptake of
hydrocarbons occurs through skin, respiratory membranes or the gut. As most
hydrocarbons from oil are lipophilic, oil components will be soluble and stored in the
lipids. As the lipids stores cycle through the year, relationships between lipid content and
toxicity might vary with season.
For animals that are present within the MIZ for foraging or resting it is expected that the
exposure may also increase due to the fact that the ice concentrates surface oil, and the
animals tend to use this zone more frequently, entering and exiting the water where oil
may be concentrated, thus increasing the probability of being exposed to oil as the MIZ is
a preferred habitat for many species. However, quantifying or verifying this assumption
needs more work.
4.2.4 Toxicity and Other Harmful Effects
4.2.4.1
Toxic effects
Oil is a mixture of organic compounds with different physiological, chemical and
toxicological properties. These compounds can affect the organism in different ways and
the toxicity will vary depending on the composition of the oil, the clean-up technology
(dispersant, herders or only mechanical – if any), the exposure, the time and duration of
exposure, the exposure way (breathing, eating, dissolution over gills, skin or feather)
and the biology of the organism.
Weathering changes the relative composition of oils and their relative toxicity (Neff et al.,
2000). The most toxic compounds of fresh oil are monocyclic aromatic hydrocarbons
(MAHs), but with weathering the proportion of lighter compounds decrease and the
contribution of PAHs to the toxicity increase (Neff et al., 2000). As mentioned in the
section on temperature (section 4.2.2) the weathering rate is lowered in colder
temperatures, it is therefore expected that for some locations of oil spills, toxic effects of
less degraded crude oil composition may be more relevant from offshore spills in the
Arctic than is considered to be the case in temperate waters.
The current project focuses on the mentioned ice fauna in the water column, seabirds
and marine mammals. On a general basis the exposure routes for oil and oil components
are different for these three groups. The ice fauna in the water column will mainly be
19
exposed through water-soluble components through the body surface membranes, gills
or the gut, and the following toxic effects. For these effects, some quantifiable
relationships between oil amounts/concentrations (dose) and lethality (response) exist.
On a longer scale than the acute phase during the actual spill and clean-up, sea birds and
mammals may be exposed through the food chain. Øritsland et al. (1981) found that
polar bears did not seem to avoid oil, and even tried to clean their coat by licking it.
Scavengers such as polar bears and polar sea gull species may be especially vulnerable
to exposure through oiled carcasses, including oiling of fur, oral ingestion etc.
Seals and whales have potential for exposure to oil through direct contact with oil in the
water column, through ingestion of water, sediments and contaminated food. As the
mammals breathe air immediately above the sea surface they can be exposed to volatile
and aerosolized petroleum-associated compounds through inhalation. Comparison of
common bottlenose dolphins (Tursiops truncatus) from an affected area and a clean area
after the Deep Water Horizon accident showed that dolphins from the contaminated site
were 5 times more likely to have lung disease (Schwacke et al., 2013). The medical
conclusion was that 17 % of the dolphins were in such a bad condition that they were not
expected to survive (Schwacke et al., 2013).
4.2.4.2
Non-toxic harmful effects
Sea birds
The main acute phase hazard for seabirds in an oil spill is contamination of plumage and
thereby loss of thermoregulatory and flotation properties, resulting in death by drowning
or hypothermia.
Several studies have been carried out to try to quantify the relationship between the size
of crude oil spills and the effects in population losses, based on real incidents. The
variability between the presence of resources, crude oil types etc. and also the effects
registered vary, also with respect to how much effort has been put into post-spill
research. In one example of such studies and literature reviews, the mortality of seabirds
was evaluated for 45 different oil spills (Burger, 1993), but only a weak relationship
between spill volume and numbers of seabirds killed was found. Thorough Norwegian
working group reviews were carried out by Moe et al. 1993 and Sørgård et al. 1995
leading up to the effect keys that are in use in MIRA today, although the quantified
relationship is scientifically weak, the collective efforts of these experts has been
evaluated to be the best available for the MIRA method.
Sea birds that encounter oil and lose insulating and flotation properties of plumage will
die at sea and often sink, thereby making accurate assessment of population losses after
an oil spill impossible; losses are assumed to be under-estimated. It may be argued that
the physiological sensitivity to loss of insulation as described above will be higher in
colder water, but quantification of such a relationship is expected to have to be based on
expert judgement (see 4.2.2).
Cetaceans
Cetaceans might come in contact with oil as they spend considerable time near the
surface, frequently breaking the surface to breathe. Contact with viscous oils can lead to
long-term coating of the body surface and may interfere with swimming ability. It has
20
been suggested that rough body surfaces may cause adherence of more oil than on
smoother body surfaces; however, later studies of cetaceans after the Deep water
Horizon incident suggest that also species with smoother skin may be susceptible to oil
contamination (Schwacke et al. 2013). The filtering capabilities by baleen whales might
be influenced and especially for those species that skim the surface layers of the sea
when foraging. It can be expected and has been suggested that oil may contaminate the
baleens of whales feeding by skimming at the surface. This factor was considered by the
working group to be especially relevant for bowhead whales that feed by skimming the
surface. Also, species that break the surface when feeding (e.g. humpback whales) may
also be susceptible to baleen contamination and oral ingestion.
High concentrations of environmental contaminants in higher trophic levels in the Arctic
is well documented, and leads to increased physiological stress and sensitivity to
additional contamination, in particular for toothed whales (Andersen et al., 2006;
Wolkers et al., 2006).
It is difficult to estimate death rates of marine mammals as a consequence of an oil spill
as carcasses may sink, and thus cannot be counted. Analyses of cetacean data from the
Deep Water Horizon accident showed that the direct death of cetaceans might be as
much as 50 times higher than the reported number of observed carcasses (Williams et al.,
2011).
Further, it seems cetaceans are able to detect the presence of oil but do not necessarily
avoid it (Geraci et al., 1988).
Polar bears
The polar bear (Ursus maritimus) is the only marine mammalian species in the Arctic that
depends on the fur for insulation. Oil on fur reduces the insulation efficiency and results
in an increased metabolic cost (Hurst and Øritsland, 1982; Hurst et al., 1991). See
section 4.2.2.
Seals
Seals in the Arctic are as mentioned in section 4.2.2 less dependent on the insulating
properties of their fur, but adult females may contaminate pups during the lactation
period. An increased mortality of pups may be expected.
4.3 Current MIRA Model structure
The MIRA model (Sørgård et al., 1995; Jødestøl, 1995; OLF, 2007) is currently used as
the industry standard ERA assessment method on the Norwegian Continental Shelf. The
method uses oil drift simulation results of oil amounts in a grid cells and species'
vulnerability values to calculate expected percentage of a resource population lost in that
grid cell using so-called effect keys. Based on the expected population loss, the next step
is to use the damage key to predict a probability distribution of expected restitution times
for the population to be restored to its original population size. The environmental risk is
presented as a percentage of the risk acceptance criteria, for each damage category
(described in more detail below). The acceptance criteria are mandatory by Authority
21
Regulation. Each Operating company has to decide on an accepted frequency of
damage/impact within each damage category for a given activity type.
The environmental risk method is currently applicable for grid based calculations of
mortality of natural resources in the water column, on the sea surface or on the shoreline.
The three factors involved are described in more detail below, and current vulnerability
values, effect keys and damage keys are presented.
4.3.1 Vulnerability
Each resource is assigned a vulnerability value on a monthly basis, depending on the
resource's physiological sensitivity and behavioural factors that make it more or less
vulnerable to the toxic or otherwise harmful effects of oil. Current tables of vulnerability
values sometimes give a value of “0” denoting that that either the resource is not present
in the given month, or that exposure to a marine oil spill is highly unlikely. Hence, a
vulnerability of zero implies zero risk to this resource. Currently used in the MIRA method,
vulnerability values range from 1 (lowest) to 3 (highest). The vulnerability values may be
given based on factors such as individual physiological sensitivity to oil contamination
(either to the toxic effects of oil or a high susceptibility to the clogging/mechanical
damage caused by the physical properties of oil emulsions, e.g. loss of thermo-insulating
and flotation properties of plumage or fur), but may also be given due to population
vulnerability-deciding factors such as rarity or behavioural aspects causing the animal to
be more or less likely to encounter oil and become contaminated, or the population to
recover slowly. All factors that contribute to estimation of the number of individuals killed
by a given oil amount are reflected in the vulnerability value ascribed to the species, as
this decides which column is used in the effect key table.
The individual vulnerability decides the expected mortality related to mass of oil (effect
keys). In the original MIRA documentation (Jødestøl et al. 2001), references are given to
methodology for deciding individual vulnerability to seabirds (Sørgård et al, 1995) and
marine mammals (Jødestøl et al 2000), and it is also stated in Jødestøl et al. (2001) that
the population's vulnerability is an expression of the potential restitution of the
population, which can be determined on basis of population biology and status. The
inclusion of population-related vulnerability factors is emphasised also in the updated
MIRA method where it is stated that a population which is declining can have a higher
vulnerability (OLF, 2007). Poor restitution ability in declining species is otherwise possible
to reflect by using damage keys with longer restitution times for the same population
loss.
Based on the individual vulnerability value, the effect keys will decide the loss of
population within a grid cell (see 4.3.2). In this calculation data sets containing grid-bygrid population densities are combined with modelled oil volumes to calculate the fraction
of a population that may be lost. This means that a grid cell with a higher population
fraction present will be a more "sensitive" cell than one with a lower population fraction.
However, other vulnerability-increasing factors such as the tendency of the species to
aggregate in flocks, and thus increasing the susceptibility to kill more individuals with
lower amounts of oil, can be incorporated in the vulnerability value of the species, as this
may not be reflected in population density data sets. The (limited) experience concerning
population losses following actual spills is incorporated into the effect keys (see below).
Foraging habits and characteristics also determine vulnerability (see section 4.4).
22
There is also an option to ascribe vulnerability to a habitat, which is frequently used for
shoreline habitats of varying vulnerability towards oil spills.
Discussions within the project group have identified a need for definition and specification
of terms and factors in the model documentation, to be followed up in subsequent work.
4.3.2 Effect keys
Depending on the individual vulnerability described above, "Effect keys" define the
relationship between mass of oil in a grid cell and the mortality of the resources present
within that cell. The effect keys are related to vulnerability values 1 through 3, and to the
resource group in question.
The expected population losses for each vulnerability group currently presented for sea
birds and marine mammals in the MIRA documentation (OLF 2007) are based on the
conclusions of several expert working groups' literature reviews of effects of oil spills on
fauna, leading up to the original MIRA method documentation (Jødestøl et al. 1996) and
later updates (OLF 2007). The scientific data of population losses following spills are
sparse, and the spatial and temporal variation in the presence of the resources during
the time of the spill leads to a great variation in population loss between spills. These
working groups reviewed the scientific literature and reports from oil spills and arrived at
the effect keys currently used today, which have been reviewed briefly by the expert
group in the current project. There are different effect keys for sea birds and marine
mammals. Several references for the current effect keys are given in (OLF2007).
4.3.3 Damage keys
Damage keys define how a given population loss is considered in terms of environmental
damage (as expected restitution times given in intervals of numbers of years),
categorised in damage categories denoted “Minor (< 1 year restitution time)”, “Moderate”
(1-3 years restitution time), “Considerable” (3-10 years) and “Serious” (>10 years). The
damage key gives a distribution of probabilities between these categories.
There are ongoing discussions on alternative endpoints to describe environmental
damage, including using altered time to extinction, which is considered a possible
relevant candidate endpoint for species with a declining population (Norwegian oil and
gas workshop, 05.02.14).
In the current project, modifications of damage keys were discussed in relation to the
population status in northern areas for species identified in the consultations with experts.
4.4 Existing vulnerability values and effect keys in MIRA
4.4.1 Seabirds
Seabirds cannot be treated as one uniform group. Different species use the sea in
different ways for foraging and rest. The seabirds can for this purpose roughly be divided
into two groups, divers and surface feeders. Divers use the sea surface for recovering
from dives, for rest and digestion. Apart from large scale migrations and breeding, the
divers stay on the sea surface for a large part of their life. All pelagic and coastal diving
species typically have a vulnerability value of 3 in all months. Surface feeders either
23
swim on the water surface when feeding (dabbling), they fly over the surface and plunge
dive for catching food or they pick the food items from the surface after hovering over.
The surface feeders often rest on land or on ice. All seabirds have a high probability of
being exposed to oil emulsion on the surface, but different adaptations for feeding and
rest imply that the risk of being exposed to oil varies between the species. The
behavioural factors of the surface feeders generally lead to lower vulnerabilities in some
or all months. The breeding period is generally considered to be a more vulnerable period
for the birds. The currently used monthly vulnerability values for the most relevant
seabirds are given in Table 1
Table 1. Monthly vulnerability values for the most relevant seabird species.
Species
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Okt
Nov
Des
Razorbill
3
3
3
3
3
3
3
3
3
3
3
3
Little auk
3
3
3
3
3
3
3
3
3
3
3
3
Fulmar
2
2
2
2
2
2
2
2
2
2
2
2
Barnacle goose
0
0
0
0
1
2
2
2
2
1
1
0
Great northern diver
3
3
3
3
3
3
3
3
3
3
3
3
Ivory gull
2
2
2
2
1
1
1
2
2
2
2
2
Black legged kittiwake
2
2
2
2
2
2
2
2
2
2
2
2
Common guillemot
3
3
3
3
3
3
3
3
3
3
3
3
Puffin
3
3
3
3
3
3
3
3
3
3
3
3
Pomarine skua
0
0
0
2
2
2
2
2
2
2
2
0
Brünnich's guillemot
3
3
3
3
3
3
3
3
3
3
3
3
Glaucous gull
1
1
1
1
2
2
2
2
1
1
1
1
Grey phalarope
1
1
1
1
1
1
1
1
1
1
1
1
King eider
3
3
3
3
3
3
3
3
3
3
3
3
Arctic tern
0
0
0
0
2
2
2
2
1
0
0
0
Sabine's gull
1
1
1
1
2
2
2
2
1
1
1
1
Steller eider
3
3
3
3
3
3
3
3
3
3
3
3
Great black-backed gull
1
1
1
2
2
2
2
2
1
1
1
1
Black guillemot
3
3
3
3
3
3
3
3
3
3
3
3
Greater White-fronted
Goose
1
1
1
2
2
2
2
1
1
1
1
1
The existing MIRA effect key (Table 2) for sea birds is developed for areas without sea ice.
Table 2: The current MIRA effect key for seabirds.
Effect key – acute mortality
Individual vulnerability of seabirds
(% mortality of a population)
Mass of oil in a 10x10 km grid cell
S1
S2
S3
1-100 tonnes
5
10
20
100-500 tonnes
10
20
40
500-1000 tonnes
20
40
60
≥ 1000 tonnes
40
60
80
24
4.4.2 Marine mammals
The current knowledge of the larger marine mammal migration patterns, habitat
requirements, abundance and distributions is deficient. Observations and scientific work
are mainly carried out during summer; many species have a global distribution and
migrations and are generally elusive. Several species have also been hunted to the verge
of extinction, and the recovery rate is not well known. However, there are in general
more observations as a result of increased traffic and the scientific work has expanded in
the last years, partly as a result of using modern equipment as for example listening
buoys for whales and tracking devices.
Marine mammals in the Arctic belong to three groups, the seals, whales and the polar
bear. The whales belong to the two systematic main groups of baleen whales and toothed
whales. This reflects the feeding habits and anatomy, where baleen whales eat
zooplankton and small fish, while the toothed whales in these parts of the Arctic
predominantly eat fish.
The most common species of seals in the Barents Sea and around Svalbard archipelago
are harp seals, hooded seals, ringed seals, bearded seals, harbour seals and walruses.
Common high Arctic whales are narwhal, white whale (beluga) and the bowhead whale.
In addition, fin whales, minke whales and killer whales are regular at least part of the
year, while blue whales and sei whales are rare visitors.
Currently used vulnerability values for some marine mammals are given in Table 3 and
effect keys are given in Table 4. The MIRA method has focused on seals and otters.
Whales were evaluated for the MIRA analysis (Jødestøl et al., 2000) but later not
included because whales are not particularly common along the Norwegian coast.
Table 3. Monthly vulnerability values for marine mammals.
Species
Grey seal
Harbour seal
Whales
Polar bear
Jan
0
0
1
3
Feb
1
0
1
3
Mar
1
0
1
3
Apr
0
0
1
3
May
0
0
1
3
Jun
0
3
1
3
Jul
0
3
1
3
Aug
0
1
1
3
Sep
3
0
1
3
Oct
3
0
1
3
Nov
3
0
1
3
Des
3
0
1
3
Table 4. Current effect keys for marine mammals.
Effect key – acute
mortality
Individual vulnerability of marine mammals
(% mortality of a population)
Mass of oil in a
10x10 km grid cell
S1
S2
S3
1-100 tonnes
5
15
20
100-500 tonnes
10
20
35
500-1000 tonnes
15
30
50
≥ 1000 tonnes
20
40
65
4.5 Existing Damage keys
For seabirds and marine mammals, acute population reduction is ranked in severity
according to the duration of impact given as a probability distribution between defined
25
restitution times (see section 4.3.3), as shown in Table 5. The damage key is only
established for populations with low growth potential (S3) and was initially designed to
reflect the (low) recovery potential of auks, although commonly used for all seabirds. The
population models used for marine mammals in MIRA are based on growth rates in the
range 2-6 % per year and ended up in the similar damage key as for seabirds with low
recovery potential.
As an example, for a population reduction of 6 %, the frequency of damage is distributed
with 25 % in impact category Minor, 50 % in Moderate, and 25 % in Significant. There
also exists a more conservative damage key that estimates longer restitution times,
which has been used for seabird species with a declining or small population.
Table 5: Current damage key for seabirds and marine mammals.
Damage key,
population
Seabirds/Marine
mammals
Damage category (restitution time in years)
(Proabability in per cent)
Acute reduction in
population (%)
Minor
Moderate
Significant
Serious
<1 yr
1-3 yr
3-10 yr
>10 yr
1-5
50
50
5-10
25
50
25
25
50
25
50
50
10-20
20-30
>30
100
4.6 Proposed Application of the Existing Methodology to MIZ
4.6.1 Previous Work Used in the evaluations
For this project, the currently used vulnerability indices and effect keys were submitted
to the NP/IMR expert group for evaluation with respect to validity for species that are
present in the MIZ either seasonally or all-year.
In a project for the Directorate of Nature Management (now Norwegian Environmental
Agency), areas of marine environmental value were determined for Norwegian waters
and Svalbard (http://havmiljø.no/) (“Miljøverdiprosjektet”). The different working groups
under this main project also evaluated vulnerability towards certain environmental
stressors, acute oil spills being one of them. The vulnerability value in that project
covered both individual vulnerability (as used in the effect keys in MIRA) and population
vulnerability (as used in damage keys in MIRA).
The working group on marine mammals evaluated the vulnerability of species with
respect to individuals) based on a common evaluation method, and recommended values
for each species. Especially the Arctic species were thoroughly discussed in this working
group, and the arguments for the vulnerability indexes are summarised and discussed in
Spikkerud et al. (2013). This work was submitted to the working group for marine
mammals for evaluation within the context of MIRA and the MIZ.
26
The approach for the current project was to assess the existing effect and damage keys
for application in the MIZ. One expert meeting with NINA and NP was held on the 23rd of
January 2014 to discuss and evaluate the need for adjustments. The experts' opinion is
still that calculation of sea bird mortality from an oil spill is difficult as the scientific
correlation between spill size and percentage of killed birds is weak as previously
mentioned. The conclusion is therefore to maintain existing effect keys developed for
non-ice-infested waters in previous expert working groups while adjusting vulnerability
and presence of resources where relevant for the MIZ. Current vulnerability values of
selected seabird species are given in Table 1).
Having access to datasets of spatial distributions of resources of a good quality are
equally important in calculating population losses. For seabirds, the spatial distributions
are covered by the SEAPOP project database and are available for MIRA. However, new
technology has made it possible to track seabird movements during the year, and this
new knowledge of distribution outside the breeding season (Frederiksen et al., 2011; Gilg
et al., 2010; Gilg et al., 2013) has resulted in preliminary delineation of presence for the
species Ivory Gull and King Eider.
4.6.2 Adaptation of MIRA for Sympagic (Ice-Associated) fauna
The ice-associated (sympagic) ecosystem includes the animals that live at least
temporarily in or on the submerged parts of the sea ice (Lønne and Gulliksen, 1991).
Four amphipod species are commonly associated with Arctic drifting sea ice, the
Gammarus wilkitzkii, Apherusa glacialis, Onisimus nanseni and O. glacialis, although
Gammaracanthus loricatus might also be found (Hop and Pavlova, 2008; Lønne and
Gulliksen, 1991). In the current project five ice-associated species were selected as
representative species of the MIZ. These were chosen based on two ice-ecology experts'
assessment of the representativeness (Table 6).
Table 6: Species chosen as representative for the MIZ
Species
Apherusa glacialis
Gammarus wilkitzkii
Gammaracanthus loricatus
Polar cod (Boreogadus saida)
Calanus glacialis
Suggested by
Øystein Varpe
Øystein Varpe
Jørgen Berge
Why
Abundant in MIZ
Abundant in MIZ
Indicator species of ice from east
Abundant in MIZ
Endemic to arctic waters.
A literature review and cross checking among experts on oil related effects were used as
approach for reviewing the LC50 values of ice-associated species.
LC50 values for the sea-ice amphipod Gammarus wilkitzkii (Camus and Olsen,
2008;Hatlen et al., 2009;Olsen et al., 2008) and Gammaracanthus loricatus (Carls and
Korn, 1985), the pelagic copepod Calanus glacialis (Gardiner et al., 2013;Hansen et al.,
2013;Hansen et al., 2011) and the polar cod fish (Boreogadus saida) (Carls and Korn,
1985; Gardiner et al., 2013;Olsen et al., 2011) reported in literature is present in Table 7
and based on these are new LC50 values for MIRA suggested in Table 10.
27
Table 7: LC50 values for ice-associated faunal species.
Species
Order
Region
Habitat
Chemical
(Class)
LC50
(96 h)
mg/l
Reference
(Hatlen et
al., 2009)
(Olsen et
al., 2011)
(Carls and
Korn,
1985)
(Carls and
Korn,
1985)
(Hansen et
al., 2011)
Apherusa glacialis
Amphipoda
Arctic
Sea ice
Gammarus
wilkitzkii 2)
Gammarus
wilkitzkii 3)
Gammaracanthus
loricatus 4)
Amphipoda
Arctic
Sea ice
WSF oil
5)
Amphipoda
Arctic
Sea ice
Naphthalene
1.34
Amphipoda
Arctic
Sea ice
WSF crude
oil
>1.7
Gammaracanthus
Loricatus
Amphipoda
Arctic
Sea ice
Napthalene
2.3
Calanus glacialis
Copepoda
Arctic
Pelagic
1.0
Calanus glacialis
Copepoda
Arctic
Pelagic
THC
weathered
crude oil
WSF marine
diesel
Naphthalene
1)
6)
0.9 – 1.4
(Hansen et
al., 2013)
Calanus glacialis
Copepoda
Arctic
Pelagic
0.05
(Gardiner
et al.,
2013)
Boreogadus saida
Gadiformes
Arctic
Sea ice
WSF crude
1.6
(Carls and
oil
Korn,
1985)
Boreogadus saida
Gadiformes
Arctic
Sea ice
2-methyl
0.8
(Olsen et
naphthalene
al., 2011)
Boreogadus saida
Gadiformes
Arctic
Sea ice
Naphthalene
0.031
(Gardiner
et al.,
2013)
1)
Suggested by Marine ecologist Øystein Varpe as an important species in the autochthonous
ice macrofauna in Arctic drifting sea ice (Hop and Pavlova, 2008;Lønne and Gulliksen,
1991). Oil effect studies of this species are lacking.
2)
Suggested by Marine ecologist Øystein Varpe as an important species in the autochthonous
ice macrofauna in Arctic drifting sea ice (Hop and Pavlova, 2008;Lønne and Gulliksen,
1991).
3)
Gammarus sp (Olsen et al., 2011).
4)
Suggested by professor Jørgen Berge as an important indicator species for ice formed in
eastern part of the Barents Sea and Sea area east of the Barents Sea, particularly
freshwater influenced Sea ice.
5)
Only non-lethal effects (Hatlen et al., 2009).
6)
"Using sigmoid curve fitting, calculated LC50 for 96 h for C. finmarchicus and C. glacialis
were 0.817 (0.784–0.853) and 1.037 (0.883–1.217) mg THC/L, respectively (Hansen et al.,
2011).
4.7 Proposed Vulnerability values
For the expert group evaluations on marine mammals, the existing vulnerability values
were presented along with the newer values suggested by the marine mammal working
group in the Norwegian Environmental Agency project on environmental values
(“Miljøverdiprosjektet”) (Spikkerud et al, 2013). In the latter project, High Arctic and MIZ
issues were an important theme for the discussions of factors leading to the setting of
specific values. It should be noted that this lead to higher vulnerabilities in general for
most seals and cetaceans, believed to better reflect current knowledge about marine
mammals and oil spills, and increased vulnerability due to other environmental stressors
28
in the Arctic. Whether this vulnerability should be adopted for temperate areas should be
the subject of further discussions.
The current notation in some tables of "0" to indicate that either the resource is not
present in the given month or that exposure to a marine oil spill is highly unlikely should
be revised, as presence or no presence is better reflected in data sets with monthly
population densities, where a grid cell with no presence in a month will have a population
of 0 in that month.
4.7.1 Seabirds
During the expert consultations (see 4.1), the list of relevant species was modified, and
for some species, the individual vulnerability values were changed, resulting in
application of different effect keys. A detailed overview of changes may be obtained by
comparing the original table presented in the previous section with the table given below,
but the key changes are:
- Glaucous Gull has been designated an increased vulnerability due its relation to
ice and to a higher probability for exposure to oil on water and on ice floes.
Additional factors include being a scavenger, and increased availability of oil
contaminated prey in the MIZ. Outside the MIZ and areas of higher ice
concentrations, this species have a similar vulnerability to the other gull species.
- Sabine´s Gull has been designated a higher vulnerability due to the same issues
as for Glaucous Gull. In addition, this species is sensitive to disturbance during
breeding, and have very few nesting sites.
- Ivory Gull is associated with ice throughout its life cycle, and rarely encountered
in areas without ice. Similar issues as for Glaucous Gull are also applicable to
Ivory Gull, and it is also a scavenger, a.o. feeding on scraps from feeding polar
bears.
- Black-legged Kittiwake was originally designated a vulnerability of 2, as a surface
feeding seabird. On a general basis, it is assessed that the vulnerability should be
increased, as the species is restricted in dietary requirements, and thus sensitive
to availability of suitable prey species. Outside the MIZ and on mainland Norway,
more conservative damage keys are relevant, while not in the MIZ and high Arctic.
It should also be noted that the tendency to aggregate is higher in the MIZ than in
other areas.
29
Table 8: The proposed new individual vulnerability for seabirds in the MIZ. Bjørnøya breeding birds are marked by colour green. Species
where vulnerability is changed are indicated by *.
English name
Scientific name
Jan Feb Mar Apr Mai Jun Jul Aug Sep Oct Nov Des Red list
Red list IUCN
Svalbard Norway
Razorbill
Little auk
Fulmar
Barnacle goose
Great northern diver
Ivory gull*
Alca torda
Alle alle
Fulmarus glacialis
Branta leucopsis
Gavia immer
Pagophila eburnea
3
3
2
0
3
3
3
3
2
0
3
3
3
3
2
0
3
3
3
3
2
0
3
3
3
3
2
1
3
3
3
3
2
2
3
3
3
3
2
2
3
3
3
3
2
2
3
3
3
3
2
2
3
3
3
3
2
1
3
3
3
3
2
1
3
3
3
3
2
0
3
3
EN
Black legged kittiwake*
Common guillemot
Puffin
Pomarine skua
Brünnich's guillemot
Glaucous gull*
Grey phalarope
King eider
Arctic tern
Sabine’s gull*
Steller eider
Great black-backed gull
Black guillemot
Greater White-fronted Goose
Common eider
Pink-footed goose
Brent goose
Rissa tridactyla
Uria aalge
Fratercula arctica
Stercorarius pomarinus
Uria lomvia
Larus hyperboreus
Phalaropus fulicarius
Somateria spectabilis
Sterna paradisaea
Xema sabini
Polysticta stelleri
Larus marinus
Cepphus grylle
Anser albifrons
Somateria mollissima
Anser brachyrhynchus
Branta bernicla
3
3
3
0
3
2
0
3
0
2
3
1
3
1
3
0
0
3
3
3
0
3
2
0
3
0
2
3
1
3
1
3
0
0
3
3
3
0
3
2
0
3
0
2
3
1
3
1
3
0
0
3
3
3
2
3
2
0
3
0
2
3
2
3
2
3
0
0
3
3
3
2
3
3
1
3
2
3
3
2
3
2
3
1
2
3
3
3
2
3
3
1
3
2
3
3
2
3
2
3
1
3
3
3
3
2
3
3
1
3
2
3
3
2
3
2
3
1
3
3
3
3
2
3
3
1
3
2
3
3
2
3
1
3
1
3
3
3
3
2
3
2
0
3
1
2
3
1
3
1
3
1
3
3
3
3
2
3
2
0
3
0
2
3
1
3
1
3
1
2
3
3
3
2
3
2
0
3
0
2
3
1
3
1
3
1
2
3
3
3
0
3
2
0
3
0
2
3
1
3
0
3
0
0
NT
VU
EN
CR
VU
NT
NT
NT
NT
VU
30
VU
NT
VU
EN
VU
VU
NT
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Near
Threatened
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Vulnerable
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
Least Concern
4.7.2 Marine mammals
The expert evaluation for sea mammals is a product of two meetings with mammal experts
from the Norwegian Polar Institute and the Institute of Marine Research (see 4.1), and
preliminary values are presented in Table 9.
Several aspects of marine mammal ecology, distribution and red list status were taken into
account as vulnerability was evaluated.
Also, the early results coming out of environmental follow-up studies after the Macondo
incident have been included in the evaluation, as these are the most comprehensive study of
the effects of oil spills on cetaceans. Important aspects include;
•
•
•
•
•
•
Distribution and behaviour.
Physiological aspects of oiled skin and fur, in terms of insulation and ingestion (polar
bear)
Feeding ecology and other behavioural issues in relation to probability of encountering
oil, especially for the baleen whales
The mortality of cetaceans may be as much as 50 times higher than the reported
number of observed carcasses from the Deep Water Horizon incident. (Williams et al.,
2011).
The higher environmental stress load of environmental contaminants in the higher
trophic levels of the Arctic. In particular relevant for toothed whales.
The exposure of volatile and aerosolized petroleum-associated compounds through
inhalation (Schwacke et al., 2013). This was considered to be relevant also for colder
waters, where evaporation of the lighter components in crude oil is expected to be
slower than in temperate and tropical ambient temperatures.
The Harbour seal of Svalbard is endemic and limited to a small population in the western part
of Svalbard. The higher vulnerability value assigned was deemed appropriate by the expert
group and is applicable for the Svalbard population only.
The bowhead whale has been assigned a tentative individual vulnerability index of 3.They are
considered to be more sensitive than other whales as they skim the upper water layers and
near the surface when foraging, thus supporting this high vulnerability ( Geraci and Staubin,
1980;Reeves et al., 2014). In addition, the species has high population vulnerability as a
population as it is critically endangered in the European part of the Arctic (Kålås, 2010) and
restitution may be slower/is uncertain.
In addition to the harbour seal, walrus and the bowhead whale, several species have been
assigned a vulnerability index of 3. These values are provided by experts and are based on
best available scientific knowledge ( Freitas et al., 2008; Freitas et al., 2009; Lydersen et al.,
2008; Lydersen et al., 2014; Reeves et al., 2014). The change for walrus is based a.o. on
work reported in Freitas et al., (2009), and Lydersen et al., (2008).
31
Table 9: The expert evaluation of vulnerability for sea mammals related to
the Arctic. Species with changed vulnerability are indicated by *.
Jan Feb Mar Apr May Jun Jul Aug Sep
Polar bear
3
3
3
3
3
3
3
3
3
Harbour seal*
2
2
2
2
2
3
3
3
2
Bearded seal
2
2
2
2
3
3
2
2
2
Hooded seal
2
2
3
3
3
3
3
2
2
Harp seals
2
3
3
3
3
2
2
2
2
East Ice pop.
Harp seals West Ice pop. 2
2
3
3
3
3
2
2
2
Ringed seal
2
2
3
3
2
3
3
2
2
Walrus
3
3
3
3
3
3
3
3
3
Beluga Whale*
Narwhal*
Sperm whale*
Orca*
Humpback whale*
Blue whale
Minke whale*
Fin whale
Bowhead whale*
2
2
2
2
2
2
2
2
1
2
1
3
2
2
1
2
1
3
2
2
1
2
1
3
2
2
1
2
2
1
2
1
3
2
2
1
2
2
1
2
1
3
2
2
1
2
2
1
2
1
3
2
2
1
2
2
1
2
1
3
2
2
1
2
2
1
2
1
3
ice-filled water of
2
2
1
2
2
1
2
1
3
Oct
3
2
2
2
2
Nov
3
2
2
2
2
Des
3
2
2
2
2
2
2
3
2
2
3
2
2
3
2
2
1
2
2
1
2
1
3
2
2
2
2
2
2
1
2
1
3
2
2
1
2
1
3
4.7.3 Sympagic fauna
The suggested LC50 are presented in Table 10. The different use of compounds and the large
difference in results indicate that further data should be obtained through future studies
before introducing a threshold toxicity value in the analyses.
Table 10: LC50 values for ice-associated species in the water column.
Species
Order
Region
Habitat
Chemical
LC50
(Class)
(96 h)
ppb
Apherusa glacialis
Amphipoda
Arctic
Sea ice
-
Reference
-
1)
Gammarus
wilkitzkii
Gammaracanthus
Loricatus
Amphipoda
Arctic
Sea ice
Naphthalene
1300
Amphipoda
Arctic
Sea ice
Napthalene
2300
Calanus glacialis
Copepoda
Arctic
Pelagic
THC
1.04
Calanus glacialis
Copepoda
Arctic
Pelagic
Naphthalene
Boreogadus saida
Gadiformes
Arctic
Sea ice
WSF crude
oil
1600
Boreogadus saida
Gadiformes
Arctic
Sea ice
800
Boreogadus saida
Gadiformes
Arctic
Sea ice
2-methyl
naphthalene
Naphthalene
1)
2)
50
31
(Olsen et
al., 2011)
(Carls and
Korn,
1985)
(Hansen et
al., 2011)
(Gardiner
et al.,
2013)
(Carls and
Korn,
1985)
(Olsen et
al., 2011)
(Gardiner
et al.,
2013)
No LC50 found
"Using sigmoid curve fitting, calculated LC50 for 96 h for C. finmarchicus and C. glacialis were 0.817
(0.784–0.853) and 1.037 (0.883–1.217) μg THC/l, respectively (Hansen et al., 2011).
32
4.8 Proposed Effect Keys
The existing effect keys have been assessed as applicable also for the MIZ. For some species,
stricter effect keys are recommended, reflecting changes in vulnerability.
For higher ice concentrations, modified effect keys may be relevant. This may be addressed
either through more conservative relationships between mass of oil and mortality at increasing
ice concentrations, if further assessments of the validity of oil drift spreading at higher ice
concentrations conclude this to be relevant.
4.9 Proposed Damage keys
Black legged kittiwake and Common Guillemot populations are declining on large parts of
mainland Norway, while their populations are stable or even increasing in Northern areas,
including Bjørnøya and on Svalbard. The “standard” damage is thus considered appropriate for
these species in the MIZ, whereas more conservative damage keys may be appropriate for
areas south of the MIZ and along mainland Norway.
The damage key used by DNV GL for declining species is presented in Table 11, and is
proposed to be used for bowhead whales, based on expert workshops and consultations. This
leads to a more conservative estimation of the severity of a population loss calculated by the
effect key. The damage key could be considered interim, and to be addressed in future
development of the MIRA method.
Table 11: Proposed damage key for bowhead whale.
Damage key,
population
Seabirds/Marine
mammals
Damage category (restitution time in years)
(Proabability in per cent)
Acute reduction in
population (%)
Minor
Moderate
Significant
Serious
<1 yr
1-3 yr
3-10 yr
>10 yr
1-5
40
50
10
5-10
10
50
30
10
10
50
40
20
80
10-20
20-30
>30
100
33
5
REVIEW OF DATASETS ON ENVIRONMENTAL RESOURCES
5.1 Identification of relevant environmental resources
Relevant environmental resources to be included in environmental risk assessments for the
MIZ were identified by a combination of literature searches and expert meetings/consultations
as described under Chapter 4. Based on this information a review of relevant sources of
species distribution data was performed. The quality, availability and resolution of the
datasets have also been evaluated and recommendations for handling data with a different
level of detail and resolution have been proposed.
5.2 Relevant datasets
Description of relevant datasets/ data sources to be used in environmental risk assessment
including the MIZ:
SEAPOP
The majority of the seabirds in Norway and Svalbard have been mapped through the
SEAPOP program (WWW.SEAPOP.no). SEAPOP is collecting data from databases run by the
Norwegian Polar institute and NINA. Both institutions are responsible for updating and
quality of the available information. Distributions of coastal and pelagic seabirds are
mapped in different processes.
Data for pelagic seabirds is collected with standard methods for line transects (Tasker et al.
1984). The distribution of birds is then used to estimate the diffusion and density through
the Gam-model, giving an estimated number per 10 km2. Simulations of population
distributions are available for each species in three seasons: Summer (April-July), autumn
(August-October) winter (November-March). Easily sighted species have a tendency to
follow the boat (e.g. Gulls and Fulmar) and they are probably over estimated, while smaller,
less visible and diving species (e.g. Auks) are under estimated. This behaviour infers some
degree of uncertainty in the data.
There are three limitations of concern related to the pelagic data from SEAPOP in relation to
the evaluation of environmental risk of oil spill in the MIZ. The first is that the northern part
of the Barents Sea isn’t covered by the modelled pelagic data set (above 65 °N). When the
MIZ tend to be located further north there is a lack of coverage. Another limiting factor is
that the MIZ is not reflected in the model used to predict the distribution (GAM-model).
This could result in an underestimated numbers of birds present in the area at times with
favourable foraging conditions such as during spring bloom. More knowledge about the
distribution of birds in relation to the MIZ could be obtained by studying the line transects
data from different years and making overlaps with ice-data and calculate the distance to
the MIZ for each of the data points (Fauchald, pers com). The final concern is that the
pelagic datasets normally are provided as the “average probability of presence” over a
period of 3 months. As the MIZ is very dynamic, this needs to be better reflected in the
input data for presence of seabirds.
Data for coastal seabirds is based on observations from boat or plane. The sizes of hatching
colonies are documented through repeated counts during the summer periods. The
reported number is based on the numbers of hatching couples. By the end of the hatching
34
season, the newly hatched birds that are ready to fly are not taken into account and
population sizes are to some degree underestimated. The datasets provide a basis for
estimating the potential abundance of birds foraging at MIZ in vicinity to the colony.
New information about near shore populations from SEAPOP need to be adjusted for earlier
arrival in the breeding colonies Bjørnøya and Svalbard.
SEATRACK
While the basis for the SEAPOP data is (and limited to) human observations, the SEATRACK
project aim at mapping winter distribution and identifying migration patterns and wintering
grounds of Norwegian and Russian seabird populations using Geolocators or Global location
sensing (GLS) loggers (Strøm 2013). The project intends to fill gaps in knowledge about
the movement of seabirds outside the breeding season and the first results will be available
fall 2015. SEATRACK will deliver data in a grid-format with information about population
distribution and their affiliation. These data will provide valuable information about the
different species affiliation with the MIZ both within and outside breeding season. Species
that will be included in the study relevant for the MIZ are common murre, atlantic puffin,
thick-billed murre, little auk, black–legged kittiwake, northern fulmar, glaucous gull,
herring gull, lesser black-backed gull, common eider and common shag.
Satellite tracking data
New data on the post-breeding movements of Ivory Gull has resulted from satellite studies
(Gilg et al. 2010). Ivory gull is not included in the SEATRAK-program. Since tracking based
on satellites generates a different type of data than the GLS-loggers (Hallvard Strøm
personal communication) additional work is required to make them compatible with the
SEATRACK/SEAPOP and include information about population distribution and their
affiliations.
Havmiljø.no
Havmiljø.no is a map-based portal presenting marine areas with high environmental values
along the coast of Norway and Svalbard. A marine area has a high environmental value
when it is important for preserving the diversity, productivity and special functions of the
ecosystem. In addition to environmental values, the web site also provide information
about vulnerability relative to acute oil pollution in addition to tables showing the
vulnerability of various animal species to defined pressures such as noise. The evaluations
are presented as 10X10 km grid evaluation for specific species/group / habitats in a
monthly resolution. Information about uncertainties in data and analyses is given in
separate maps. The portal is developed in collaboration between the Norwegian
Environment Agency, Norwegian Institute for Nature Research, Institute of Marine Research,
The Norwegian Polar institute, NGU (Norges Geologiske Undersøkelser) and DNV GL.
An important part of this project has been to develop species distribution data and
especially relevant (partly lacking from other data sources) for risk assessments in the MIZ,
is the updated distribution data for marine mammals at Svalbard. The work was carried out
in collaboration with the Norwegian Polar Institute and Institute of Marine Research and
coordinated by Akvaplan-niva as part of the project “ Miljøverdi – og sårbarhet for marine
35
arter og leveområder – Harmonisering av Verdi- og sårbarhetsverdier for marine pattedyr
(Spikkerud 2013). All processed data from Havmiljø.no is publicly available as 10X10Km
grid cells. The raw data is also publicly available, but not all of the input material exist in a
map format and would require some processing.
Arctic Marine Shipping Assessment (AMSA) II c
The Arctic Council`s 2009 Arctic Marine Shipping Assessment (AMSA) put forward a
number of recommendations to guide future Arctic marine activity. The recommendation
IIC included the need for identification of areas of heightened ecological significance in light
of changing climate conditions and increasing use of Arctic waters by several industries.
The assessment was performed by AMAP, CAFF and SWD and published in September 2013
(AMAP/CAFF/SDWG 2013).
The report contains pan-Arctic species distribution data, in addition to information about
the different ecological uses of the areas for marine mammals, fish, birds and benthos.
Data is available as shape files from the Institute of Marine Research. The data have a
variable level of details and do generally not contain information about populations
distribution like some of the other dataset mentioned above. However, they represent the
best available information regarding distribution of species on the Russian side of the
Barents Sea in areas that could potentially be within the MIZ influence area of an oil spill on
the Norwegian continental shelf in the Barents Sea like the Pechora and western Novaya
Zemlja. Of special interest would be the data on wintering areas for marine mammals,
especially walrus in the Pechora. As was shown in a study for the Ministry of Oil and Energy
(DNV 2012) there is however a low probability that oil from activity in the Norwegian part
of the Barents Sea would reach sea ice close to the Russian border. But, in the case of the
combination of long duration of a spill and extensive ice coverage, the possibility that oil
could reach MIZ on the Russian side of the Barents Sea cannot be excluded.
Specific distribution data on ice-associated fauna and planktonic species like copepods and
phytoplankton does not exist. The association of planktonic blooms with the MIZ has proven to
be challenging as they are very dynamic in nature and not necessarily concentrate close to the
ice (Perette et al 2011, Science Daily 2011). These species will not be addressed in this phase
of the project. Ice –associated fauna will be given an equal distribution in the MIZ as
described below.
The species identified above are to a variable degree dependent and associated with the MIZ.
Some reside in ice infested waters all year, like the Ivory Gull and the Ringed Seal, whereas
others only spend parts of the year/life history in vicinity to ice like the Glaucous Gull and the
Black-legged Kittiwake. When in the ice, different species also have different preferred iceconcentrations according to different ecological activities (e.g.
breeding, feeding and
moulting). This is to a variable degree reflected in the datasets. For ice related species, data
on ice distribution may be used to represent the species.
36
5.3 Recommendations for handling data with different
resolution and quality
All the above datasets originate from renowned institutions with updated knowledge within
their areas of expertise. But, for some environmental resources, periods or geographical areas
there is a lack of knowledge on presence and/or vulnerability. Thus, there are huge variations
in both quality and resolution in the relevant species distribution datasets. This will influence
the level of details and uncertainties in the results of environmental risk assessments where
the data is used.
We propose the following approaches depending on quality and resolution:
1) For datasets containing quantitative descriptions, quantitative assessments can be
made similar to the assessments currently performed in ERAs for e.g. the Norwegian
Sea, according to the MIRA method.
2) For datasets containing information based on semi-quantitative descriptions, semiquantitative assessments should be made. A quantitative approach can be taken.
Density distribution within an area should then be processed and distributed on the
standard 10 x 10 km grid, calculating fraction of area for each grid cell for each month
the resources is present. In the absence of density distribution information, an even
distribution within the area (expressed as 10X10 km grids) should be assumed
3) For datasets containing low resolution, simple overlaps between occurrence of species
and oil drift should be made. This results needs to be integrated with the complete
results of the MIRA, taking into account the increased uncertainty.
Sensitivity assessments should be considered when using data containing semi-quantitative to
low resolution.
37
6
CONCLUSIONS AND RECOMMENDATIONS
Based on the work carried out within the project, as well as input from external experts and
findings within the scientific literature, we have arrived at a joint recommendation for
performing environmental risk assessments for activities within the opened areas of the
Norwegian Continental Shelf (NCS) where accidental discharges of oil may affect the MIZ. The
methodology is ready to be applied.
The approach cover modelling of oil in ice, recommends a definition of the MIZ for ERAs
corresponding to 10-30 % ice and is within the framework of MIRA, the existing and most
adopted approach to damage based environmental risk assessments on the NCS. In addition,
we have made recommendations for how to include spatial data where the distribution of
biological resources within the area is not known.
Our recommendations are as follows:
Definition of the MIZ
There is a range of definitions of the MIZ in terms of ice concentrations. Based on oil exposure
scenarios and changes in species vulnerability resulting from the presence of ice we
recommend to use 10-30 % ice concentration when performing ERAs for the MIZ. This is a
moderately conservative selection from the different options for defining the MIZ.
Since the proposed methodology is based on modelling of oil in all ice concentrations there is
no need in the proposed methodology to define the boundaries of the MIZ to specific ice
concentrations. In order to make good predictions, data of ice concentrations should match, in
time, the species distribution datasets.
Oil in ice modelling
In order to be more specific in oil spill risk calculations in the MIZ, ice concentrations needs to
be included in the oil drift modelling. The present version of SINTEFs OSCAR model (v. 6.5)
can take the ice-coverage as an adjusting parameter into the calculations. The fractional ice
cover/ice concentration can be provided as grids similar to current, wind or habitat data. The
ice cover affects weathering, spreading, evaporation of surface oil, as well as drifting of oil
with ice. As the MIZ is very dynamic and changes in location occur over days, it is
recommended to include daily ice coverage values into the modelling. Data on daily sea ice
concentrations and several other ice-related parameters are publicly available from the Nordic
Seas 4 km numerical ocean hindcast archive (SVIM) at ftp://ftp.met.no/projects/SVIMpublic/SVIMresults/ . The hindcast archive also includes 4x4 km current data and has data
from 1958 to 2011 and preferably the data on current and ice should be derived from the
same source when applied in the model. Preferably this complete archive (or at least the
latest 15 years of data) should be converted to OSCAR compatible .hyd files and be utilized in
the modelling.
It is further recommended to await the new version of OSCAR (v.6.6, available in Q2 2014),
as the 6.5 version has some issues in exporting water column concentrations.
In a summary review of oil in ice modelling, Drozdowski et al. (2010) concludes that the
existing commercial oil spill models (incl. OSCAR) seem to provide a suitable basis for shortterm prediction of oil spill trajectories. As there is still limited implementation of oil in ice
38
interactions in OSCAR and other models, caution should be made when trying to model longer
periods (weeks) of oil drift within ice concentrations above 30 %. The model is not currently
capable of predicting with sufficient accuracy the weathering and drift of oil frozen in ice or
trapped beneath the ice. The interactions between ice and oil take place at the scale of cracks
in the ice,floes, leads, ridges, etc. These typically have scales of 1 m to 100 m and until high
resolution ice models exist that support implementation of such interactions, simpler
approaches are favoured.
Vulnerability, effect keys and damage keys
There are no major changes in the overall model structure, and to a large extent existing data
can be applied with no or minor modifications. For quantitative assessments within the
existing damage based MIRA, changes and modifications required are given below.
•
Seabirds:
o
•
Marine mammals
o
•
Revised vulnerability values need to be applied as described in previous sections
As for seabirds, where distribution of the population within an area is not
identified, an even distribution and sensitivity assessments may be applied.
Sympagic fauna
o
LC50 values for these species may be applied in a similar way as for fish eggs
and larvae, i.e. a step 1 approach according to (OLF, 2007b).
o
As sympagic species are closely associated with the MIZ, data sets of statistical
ice distribution may be used to derive a data set for the species.
When performing environmental risk assessments for activities where the MIZ is within the
expected affected area, effect keys as defined by the proposed vulnerability values should be
applied. Except for the Bowhead whale, the damage key given in the MIRA method are
deemed applicable, and for this species, a more conservative damage key is suggested..
With regards to presence of ice in the MIZ, adjusted oil mass intervals in the effect keys needs
further evaluation and documentation. Whether or not oil drift modelling is undertaken with
ice distribution data included is an issue that would need to be considered for individual
activities, and is discussed elsewhere.
Datasets on environmental resources
Relevant sources for species distribution in the Barents Sea MIZ are SEAPOP, SEATRACK (and
other seabird satellite tracking data), havmiljø.no and AMSAIIC. SEAPOP and SEATRACK
contain information about birds only, whereas havmiljø.no and AMSA IIC contain information
about benthos, fish and marine mammals in addition to seabirds. Havmiljø.no is covering
Norwegian waters, whereas AMSAIIC contain pan-Arctic information. Havmiljø.no is providing
important updated species distribution data of marine mammals around Svalbard and AMSA
39
IIC complements the other sources covering the northern Barents Sea in addition to species
associated with sea ice in Russian waters in the east of the Barents Sea.
Recommended adaptions necessary for the datasets to provide a more realistic picture of the
species distribution in the MIZ:
•
The SEAPOP-data must be remodelled taking the MIZ-into account
•
New data on marine mammals around Svalbard from Havmiljø.no require processing to
obtain more defined species distribution patterns.
•
For ice related species where no distribution data are available (e.g. Ivory Gull), data
sets on ice distribution may be used to derive data sets to represent the species.
All datasets originate from renowned institutions with updated knowledge within their area of
expertise. But, for some environmental resources, periods or geographical areas there is a
lack of knowledge. Based on variation both in the quality and resolution in the datasets three
approaches have been suggested. The results should be integrated with the complete results
of the MIRA at the same time expressing uncertainty. Sensitivity assessments should be
considered applied when analyses is based on data with low resolution.
This report presents the results of the work carried out under phase 1 of the project, where
the objective described in the Scope of Work was to arrive at an approach to ERA that could
be implemented immediately in assessments for activities on the NCS. This objective is met
by identifying data sets applicable in oil drift modelling, resource data sets and
recommendations for their use in the assessment, and vulnerabilities and effect keys that are
recommended by the leading scientific institutes. These aspects can as of April 2014 be
included in assessments using the MIRA method.
There are also ongoing projects and discussions regarding alternative approaches to
environmental risk assessments, including ERA acute, as well as a potential development and
revision of the MIRA method. To a large extent, the findings within the current project has
value also for these processes.
40
7
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habitat selection in male Atlantic walruses. Marine Ecology Progress Series 2009; 375: 247-261.
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Chastel, J. Moreau, R. A. Phillips, T. Oudman, E. M. Biersma, A. A. Fenstad, J. Lang & L. Bollache
(2013) Trans-Equatorial Migration Routes, Staging Sites and Wintering Areas of a High-Arctic
Avian Predator: The Long-tailed Skua (Stercorarius longicaudus). Plos One, 8, e64614.
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Lien, V.S., Y. Gusdal, J. Albretsen, A.Melsom & F.B Vikebø (2013) Evaluation of a Nordic Seas 4 km
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Lydersen, C., P. Assmy, S. Falk-Petersen, J. Kohler, K. M. Kovacs, M. Reigstad, H. Steen, H. Strøm, A.
Sundfjord, Ø. Varpe, W. Walczowski, J. M. Weslawski & M. Zajaczkowski (2014) The importance
of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway. Journal of Marine
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oljeutslipp. DNV rapport 2007-2075
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215-216.
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APPENDIX A
45
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
© www.akvaplan.niva.no
APPENDIX B
46
Akvaplan-niva AS
Rådgivning og forskning
innen miljø og akvakultur
Org.nr: NO 937 375 158 MVA
www.akvaplan.niva.no
Norge – Island – Frankrike – Russland – Spania
Tromsø-kontoret (svaradresse)
Framsenteret
9296 Tromsø
Tlf: 77 75 03 00
Fax: 77 75 03 01
E-post: tromso@akvaplan.niva.no
Skrevet av: Kjetil Sagerup
Direkte tlf: 77750334
E-post: kjetil.sagerup@akvaplan.niva.no
Intern oppsummering
Til:
Deltakere: Marte Rusten (DNV), Cathrine Spikkerud (Apn), Geir Morten Skeie (Apn), Anne Kristine
(Stine) Frie (IMR), Christian Lydersen (NP), Martin Biuw (Apn) og Kjetil Sagerup (Apn)
Dato:
30.01.2014
Møte:
MIRA iskant, marine pattedyr
Møtedato:
24.01.2014
Ref: 430.6783/KSA
Antall sider: 3
Møtested: Tromsø
Geir innledet om bakgrunnen for møtet. Dette møtet er en første fase i et prosjekt som skal se på
mulighetene for å bedre Metode for Miljørettet Risikoanalyse (MIRA) i iskantsystemer. Prosjektet er
delt mellom DNV og Apn. DNV har ansvaret for datasett, iskantdefinisjon og drift av olje, mens
Akvaplan-niva har ansvaret for å evaluere om dagens effekt- og skadenøkler bør justeres for iskanten.
Prosjektet er betalt av Norsk olje og gass (NO&G). Operatørene ønsker en felles tilnærming til
problemstillingen. Et viktig poeng med prosjektet er at resultatet implementeres hos alle som bruker
MIRA.
Prosjektperioden er frem til sommeren 2014. Utkast til rapport skal leveres NO&G før påske, så skal
den sirkuleres av NO&G, før tilbakemeldingene innarbeides i endelig rapport før sommeren.
Dette prosjektet er del 1: hastefase hvor tilgjengelig informasjon skal foreslå forbedringer på dagens
MIRA. Del to kommer i etterkant og dette kan ha utviklingsmoduler til problemstillinger som tas opp i
del 1.
Geir orienterte videre kort om hvordan MIRA analysen fungerte og at risikovurdering er noe som er
hjemlet i forurensingsloven. Fokus i gjennomgangen ble satt på skade og effektnøkklene siden dette
er tema for møtet.
Dagens tema:
•
Spørsmål om små utslipp som for eksempel ved produksjon. Ledningsbrudd og andre
uhellsutslipp og oljeinnhold i produsert vann.
o Vi har i dag ikke gode metoder for å behandle effektene av disse utslippene. F.eks har
MIRA og flere andre metoder en begrensning i at 1 % bestandstap per definisjon ikke er
en effekt.
o Oljeinnhold i produsert vann er behandlet i utslippstillatelsen for feltet og skal ikke gi
Akvaplan-niva AS – Tromsø
30.01.2014 - ref: 430.6783/KSA - Side 1 av 3
effekter siden fortynningen i sjøen er stor.
Det er imidlertid andre prosjekter i gang som jobber med disse problemstillingene, for
eksempel ERA-acute.
Dette prosjektet vil følge Meteorologisk institutt sin definisjon av iskant som er 10-30 % isdekke.
o Det ble påpekt at det vil være tider da iskanten er strukket spredt og slik sett ha lavere
% isdekke til at iskanten er presset sammen og har stort % vis isdekke. Vindretning
påvirker dekningsgrad og utbredelse.
Effekt- og skadenøkkler:
o Er det faktorer som tilsier at olje vil ha andre effekter i iskant i forhold til åpent hav?
o Ja – På grunn av lave temperaturer vil fordampning og forvitring være lavere.
o Dette betyr at flyktige stoffer vil ha lengre oppholdstid og kunne gi effekter på øye og
lunger.
Noen konkrete eksempler:
o Isbjørn. Termoregulatoriske og gifteffekter. Isbjørnen svømmer en del, kanskje mer enn
vi tidligere trodde. Observasjoner av isbjørn og olje viser at isbjørnen spiser/slikker på
olje. Det er derfor stor sannsynlighet for at isbjørn i iskanten vil få olje på seg og i seg
ved tilstedeværelse av olje.
o Grønlandshval. Skimmer vann gjennom barder i overflaten. Vil derfor være mer utsatt
for olje enn hvaler som spiser dypere. (Den har også forholdsvis tette barder).
Akutte effekter:
o Død
o Lavere reproduksjon
o Lavere nedbrytbarhet
o Giftige flyktige komponenter damper senere. Hvor lenge varer de lettflyktige? Finnes
det data? Rapporter fra Exxon Valdez at sel var påvirket av flyktige oljekomponenter –
narkotisk effekt.
o Større mengde olje pr. arealenhet siden is opptar plass.
Biologiske parameter:
o Flokking og andre økologiske parametre.
o Pels (kun isbjørn) eller hud.
o R, K strategi. De fleste pattedyr har lav reproduksjon K.
Sårbarhet vurderes i MIRA som: (side 72 i MIRA veiledning, OLF 2007).
o Populasjons- (bestands-) biologiske egenskaper
o Fertilitet
o Alder ved kjønnsmodning
o Dødelighet på ulike alderstrinn
o Vekstkurve (hovedsakelig fisk; men også aktuelt i fb. med beskatning av sjøfugl og
sjøpattedyr)
o Levealder
o Tetthetsavhengige responser i bestanden.
o I forbindelse med AKUP Barentshavet Nord ble det utviklet en sårbarhets/risikomodell
for sjøpattedyr (Jødestøl/Ugland et al., 1995), med følgende sårbarhetskriterier på
individnivå: Eksponering: adferd ved næringssøk, sjømobilitet, evne til unngåelse
Følsomhet: Toksisitet, hindring av mobilitet, kondisjon, termoregulering, mattilgang,
evne til restitusjon.
Helårige analyser. Det er viktig å ta hensyn til sesong. Sårbarhet setter pr mnd.
Sannsynlighet for å treffe olje.
Spesielle bestander.
Risiko for eksponering er det vi kanskje kan gjøre best ut i fra vurderinger og dagens kunnskap.
Kunnskaper om arter:
o Klappmyss. Det finnes noe data som kan reanalyseres.
o
•
•
•
•
•
•
•
•
•
•
•
Akvaplan-niva AS - Tromsø
30.01.2014 - ref: 430.6783/KSA - Side 2 av 3
o
o
o
o
o
o
o
o
Grønlandshval. Ny artikkel (Reeves et al. 2014) om utbredelse og risiko ved
klimaendringer (Lydersen, Kovacs medforfattere).
Steinkobbe, ny artikkel kommer nå
Grønlandssel og klappmyss beiter underisen.
Ringsel beiter også under isen hele året. Polartorsk 0-gruppe og amfipoder.
Andre arter, artikkel (Lydersen et al. 2014) om bruk av brefronter + fastis knyttet til
fjorder.
Hele Barentshavet er grunt slik at mange marine pattedyr kan dykke til bunnen i hele
havet.
Hval har generelt dårlige evner til å metabolisere miljøgifter. Tannhval har spesielt lav
metaboliseringsevne.
Vi lager en liste over arter (dvs: Marte tar ut en liste fra MIRA). Kan vi få til en vurdering
for justering av effekt og skadenøklene?
 Rødliste
 Små bestander
 Bestandsnedgang
 Datatilgang
Aksjonsliste på bakgrunn av dette møtet og sjøfuglmøtet 23.01:
o
Marte: liste over eksisterende datasett
o
Marte: månedsvis sårbarhet for disse artene (Cathrine og Geir).
o
Geir: lager oversikt over gjeldende effekt- og skadenøkler.
o
Geir: Følgedokument for utsendelse til Norsk Polarinstitutt/HI.
o
Kjetil: sett opp en liste over de artene hvor det er kommet innspill fra disse møtene.
o
Kjetil: Følg opp Hallvard for mulig kartfesting for fase I og mulig oppfølging til fase II.
o
Kjetil: Møtenotater over til Geir – Geir videresender etter gjennomlesning.
o
Kjetil: Les SAGIS rapporten om det er noe av effekt/skade som skal trekkes med.
o
Geir: Les Jødestøl 2000-rapport om det er noe av effekt/skade som skal trekkes med i
prosjektet.
o
Kjetil: Snakk med Marianne/Jasmine om de mest sensitive artene.
o
Kjetil: Etter artsliste - Hvilke bestander trengs å behandles spesielt (nedadgående/rødliste).
o
Kjetil: Ta opp diskusjon med Jørgen Berge etter 7. februar.
o
Kjetil: Noen arter kommer tidligere til området enn selve hekkingen - Lag liste. Dette må i
resultatet fra fase I reflekteres i datasettene. Sårbarhet i perioden eks februar-mars. (Fase II, eller
senere: SEATRACK).
o
Dato for møte med sjøpattedyrfolkene + sympagic fauna den 20. februar. Starter med lunch
kl. 11.30.
Referent: Kjetil Sagerup
Reference List
Lydersen, C., Assmy, P., Falk-Petersen, S., Kohler, J., Kovacs, K. M., Reigstad, M., Steen, H., Strøm,
H., Sundfjord, A., Varpe, Ø., Walczowski, W., Weslawski, J. M., and Zajaczkowski, M. 2014
The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway.
Journal of Marine Systems 129, 452-471.
Reeves, R. R., Ewins, P. J., Agbayani, S., Heide-Jørgensen, M. P., Kovacs, K. M., Lydersen, C.,
Suydam, R., Elliott, W., Polet, G., van Dijk, Y., and Blijleven, R. 2014 Distribution of endemic
cetaceans in relation to hydrocarbon development and commercial shipping in a warming
Arctic. Marine Policy 44, 375-389.
Akvaplan-niva AS - Tromsø
30.01.2014 - ref: 430.6783/KSA - Side 3 av 3
Akvaplan-niva AS
Rådgivning og forskning
innen miljø og akvakultur
Org.nr: NO 937 375 158 MVA
www.akvaplan.niva.no
Norge – Island – Frankrike – Russland – Spania
Tromsø-kontoret (svaradresse)
Framsenteret
9296 Tromsø
Tlf: 77 75 03 00
Fax: 77 75 03 01
E-post: tromso@akvaplan.niva.no
Skrevet av: Kjetil Sagerup
Direkte tlf: 77750334
E-post: kjetil.sagerup@akvaplan.niva.no
Intern oppsummering
Til:
Deltakere: Hallvard Strøm (NP), Geir H. Systad (NINA), Marte Rusten (DNV), Cathrine Spikkerud
(Apn), Geir M. Skeie (Apn), Kjetil Sagerup (Apn)
Dato:
30.01.2014
Møte:
MIRA iskant, sjøfugl
Møtedato:
23.01.2014
Ref: 430.6783/KSA
Antall sider: 2
Møtested: Tromsø
Geir M. innledet om bakgrunnen for møtet. Dette møtet er en første fase i et prosjekt som skal se på
mulighetene for å bedre Metode for Miljørettet Risikoanalyse (MIRA) i iskantsystemer. Prosjektet er
delt mellom DNV og Apn. DNV har ansvaret for datasett, iskantdefinisjon og drift av olje, mens
Akvaplan-niva har ansvaret for å evaluere om dagens effekt- og skadenøkler bør justeres for iskanten.
Prosjektet er betalt av Norsk olje og gass (NO&G). Operatørene ønsker en felles tilnærming til
problemstillingen. Et viktig poeng med prosjektet er at resultatet implementeres hos alle som bruker
MIRA.
Prosjektperioden er frem til sommeren 2014. Utkast til rapport skal leveres NO&G før påske, så skal
den sirkuleres av NO&G, før tilbakemeldingene innarbeides i endelig rapport før sommeren.
Dette prosjektet er del 1: hastefase hvor tilgjengelig informasjon skal foreslå forbedringer på dagens
MIRA. Del to kommer i etterkant og dette kan ha utviklingsmoduler til problemstillinger som tas opp i
del 1.
Dagens tema:
•
•
•
Hvordan bruke og tilrettelegge nye datasett?
Finnes det nye data?
o Datasett fra sjøfugl i åpent hav kan reanalyseres med tanke på iskant. Imidlertid er det
noe manko på data fra selve iskanten siden toktene går i retning mot iskant, men aldri
har hatt fokus på selve iskanten (Per Fauchald – fase II).
Norsk Polarinstitutt har noen gamle datasett i forhold til iskant, men vi vet at disse var vanskelig
å få ut noe systematisk resultat fra datasettene. Systemet iskant og sjøfugls tilstedeværelse er
svært fleksible og det var ikke mulig å finne systemer/forklaringsvariabler i disse dataene.
Datasettene er nå også over 20 år gamle.
Akvaplan-niva AS – Tromsø
30.01.2014 - ref: 430.6783/KSA - Side 1 av 2
•
•
•
•
•
•
•
•
Det er mulig at vi må ha en tilnærming over større skala. For eksempel; hvilke arter er relatert til
is og til hvilke tider av året?
o Dette begynner vi å få en viss oversikt over takket være nye data fra logger- og satellitttelemetri-data. Eksempel fra artene:
 Krykkje, polarlomvi, ismåke, alkekonge.
 Teist er en art som bruker fastiskanten nær land, men også iskanten i
Barentshavet.
 Ærfugl er også knyttet til fastiskanten deler av året.
 Praktærfugl har tilknytning til bankene nord for Bjørnøya om vinteren. For
denne arten er det slik at området med <50 meters dyp er viktig, og ikke is. Isen
kan ved høy tetthet være et praktisk problem. Derfor er det for denne arten
beiteområdet som er viktig. Praktærfugl må dykke til bunnen for å spise.
o Vi må derfor skille mellom arter som bruker pelagiske iskantsystemer og kystnære
fastissystemer.
o I dette prosjektet er det det pelagiske iskantsystemet som er mest interessant.
o Ismåke er den arten som er tilknyttet iskant hele året og hele livet. Dette er den eneste
arten bare unntaksvis oppholder seg i områder uten is. (så langt vi kjenner til).
o For alle andre arter er det en enorm dynamikk i forhold til bruk av iskantområdene.
o Bruken av iskant er tilsynelatende forbundet med mattilgang. Det er derfor viktig å
knytte tilstedeværelsen opp mot produktivitet (NINA, Per Fauchald).
Effektnøkkel: Giftighet i forbindelse med lave temperaturer?
Det er mange faktorer som styrer effektnøkkelen:
o Treffsannsynlighet.
o Opp-konsentrasjon av fugl i iskant i forhold til åpent hav.
o En viktig parameter er eksponering. Hvor lenge oljen er tilstede og tilgjengelig.
Det vi vil ende opp med er en justering av %-vise effekter.
Geografisk tetthetsdynamikk. Utfordringene ligger i å få inn tid og geografi i forhold til tetthet.
Eksempel Bjørnøyafugl på våren.
Ved å bruke best tilgjengelig kunnskap kan vi sette noen ny kunnskap på kartet, eks;
o Krykkje etter hekkesesong (aug-? øst av Svalbard)
o Praktærfugl vinterområde (nord av Bjørnøya på banker grunnere enn 50-60m)
o Alkekonge?
o Andre?
Bjørnøya hekkefugl kommer generelt inn mot øya i april. Kan konsentreres opp på grunn av
iskant. Et unntak er havhest som kommer tidligere.
Generelle bestandsstatuser / overlevelse. Som på fastlandet, men det er andre negative faktorer
som virker. Eks på arter i tilbakegang; polarmåke, polarlomvi, alkekonge.
Konklusjoner / aksjonsliste:
1
Tilstedeværelse av flere arter i koloniene før hekking (f.o.m. mars) dette må hensyntas i analysene
ved at datasettene (spesielt for Bjørnøya) reflekterer dette.
2
NP vil gi en tilbakemelding på muligheten for kartfesting av nyoppdagede områder i løpet av
prosjektperioden.
3
Artene Ismåke, teist, polarmåke og polarlomvi er arter som er spesielt assosiert med iskanten.
4
Behandling av arter med lave bestander og nedadgående bestandsutvikling tas opp i
sjøfuglworkshop 05.02.14
Referent: Kjetil Sagerup
Akvaplan-niva AS - Tromsø
30.01.2014 - ref: 430.6783/KSA - Side 2 av 2
The Marginal Ice Zone
Effect from oil and damage classification
Basis for discussions 23.01 & 24.01.2014
www. akvaplan.niva.no
www.senseweb.no
Objectives
• Discuss relevant issues
• Modify based on participant knowledge
• Identify sources for literature review and followup postmeeting work
• Agree on further process and review/QA on suggested
modifications
• Discuss how to apply existing data sets
• Identify new data sets/needs for improvements within project
period.
www. akvaplan.niva.no
www.senseweb.no
Issues for discussion
• Will a given amount of oil result in higher mortality in the
Marginal Ice Zone and in higher ice concentrations?
– Water and air temperature
– Longer period of toxicity due to slower weathering
– More oil per surface area as ice concentration increases
• Should a given reduction in population size due to mortality be
considered more or less «serious» than in open waters?
– Colony related issues – feeding range?
• Which species are candidates for representing the MIZ
ecosystem/habitat
– Additional species related to what we have of available data sets?
– Refining existing data sets for MIZ (Seabirds?)
www. akvaplan.niva.no
www.senseweb.no
Low surface water temperature
www. akvaplan.niva.no
www.senseweb.no
Surface area
www. akvaplan.niva.no
www.senseweb.no
Current effect keys in MIRA
www. akvaplan.niva.no
www.senseweb.no
Current damage keys in MIRA
www. akvaplan.niva.no
www.senseweb.no
The Marginal Ice Zone
Effect and damage keys in MIRA
Followup of discussions 23.01 & 24.01.2014
www. akvaplan.niva.no
www.senseweb.no
Issues for review
• Will a given amount of oil result in higher mortality in the
Marginal Ice Zone and in higher ice concentrations?
– Water and air temperature
– Longer period of toxicity due to slower weathering
– More oil per surface area as ice concentration increases
• Should a given reduction in population size due to mortality be
considered more or less «serious» than in open waters?
– Colony related issues – feeding range?
• Which species are candidates for representing the MIZ
ecosystem/habitat
www. akvaplan.niva.no
www.senseweb.no
Basic steps in MIRA
• Assign vulnerability for each species/habitat on a
monthly basis
– Current range 1 to 4
• Based on resource type (seabirds, marine mammals,
shoreline), and vulnerability, apply effect keys
– Relationship between mass of oil and mortality calculated for
each grid cell affected by one oil drift simulation (start date,
duration of release and subsequent drift of oil)
– Resulting a population loss in % for each simulation
• Based on the population loss and vulnerability, apply
damage keys to assign damage categories.
– Frequencies within damage categories express environmental
risk.
www. akvaplan.niva.no
www.senseweb.no
Current effect keys in MIRA
www. akvaplan.niva.no
www.senseweb.no
Current damage keys in MIRA - 1
www. akvaplan.niva.no
www.senseweb.no
Current damage keys in MIRA - 2
• Vulnerability 4.
www. akvaplan.niva.no
www.senseweb.no
Relevante referanser(antall, atferd i forhold til is, habitat preferanser o.a.)
Bearded seals:
Gjertz, I.; Kovacs, K. M., Lydersen, C. and Wiig, Ø. 2000. Movements and diving of bearded
seal (Erignathus barbatus) mother and pups during lactation and post-weaning. Polar Biol. 23:
559-566.
Kovacs, K. M., Lydersen, C. and Gjertz, I. 1996. Birth site characteristics and prenatal
molting in bearded seals (Erignathus barbatus). J. Mammal. 77: 1085-1091.
Krafft, B. A., Lydersen, C., Kovacs, K. M., Gjertz, I. and Haug, T. 2000. Diving behaviour of
lactating bearded seals (Erignathus barbatus) in the Svalbard area. Can. J. Zool. 78: 14081418.
Lydersen, C., Hammill, M. O. and Kovacs, K. M. 1994. Diving activity in nursing bearded
seal (Erignathus barbatus) pups. Can J. Zool. 72: 96-103.
Lydersen, C., Kovacs, K. M., Ries, S. and Knauth, M. 2002. Precocial diving and patent
foramen ovale in bearded seal (Erignathus barbatus) pups. J. Comp. Physiol. B 172: 713-717.
Bowhead whales:
Lydersen, C., Freitas, C., Wiig, Ø., Bachmann, L., Heide-Jørgensen, M. P., Swift,
R. and Kovacs, K. M. 2012. Lost highway not forgotten: Satellite tracking of a bowhead
whale (Balaena mysticetus) from the critically endangered Spitsbergen stock. Arctic 65: 7686.
Moore, S. E., Stafford, K. M., Melling, H., Berchok, C., Wiig, Ø., Kovacs, K. M., Lydersen,
C. and Richter-Menge, J. 2012. Comparing marine mammal acoustic habitats in Atlantic and
Pacific sectors of the High Arctic: year-long records from Fram Strait and the Chukchi
Plateau. Polar Biol. 35: 475-480.
Stafford, K. M., Moore, S. E., Berchok, C. L., Wiig, Ø., Lydersen, C., Hansen, E., Kalmbach,
D. and Kovacs, K. M. 2012. Spitsbergen’s endangered bowhead whales sing through the polar
night. Endang. Spec. Res. 18: 95-103.
Wiig, Ø., Bachmann, L., Øien, N., Kovacs, K. M. and Lydersen, C. 2010. Observations of
bowhead whales (Balaena mysticetus) in the Svalbard area 1940-2009. Polar Biol. 33: 979984.
Climate change general:
Kovacs, K. M. and Lydersen, C. 2008. Climate change impacts on seals and whales in the
North Atlantic Arctic and adjacent shelf waters. Sci. Progr. 91: 117-150.
Kovacs, K. M., Lydersen, C., Overland, J. E. and Moore, S. E. 2011. Impacts of changing seaice conditions on Arctic marine mammals. Mar. Biodiv. 41: 181-194.
Kovacs, K.M., Aguilar, A., Aurioles, D., Burkanov, V., Campagna, C., Gales, N., Gelatt, T.,
Goldsworthy, S., Goodman, S.J., Hofmeyr, G.J.G., Härkönen, T., Lowry, L., Lydersen, C.,
Schipper, J., Sipilä, T., Southwell, C., Stuart, S., Thompson, D. and Trillmich, F. 2012.
Global threats to pinnipeds. Mar. Mammal Sci. 28: 414-536.
Harbour seals:
Gjertz, I., Lydersen, C. and Wiig, Ø. 2001. Distribution and diving of harbour seals (Phoca
vitulina) in Svalbard. Polar Biol. 24: 209-214.
Hamilton, C. D., Lydersen, C., Ims, R. A. and Kovacs, K. M. 2014. Haul-out behaviour of the
world's northernmost population of harbour seals (Phoca vitulina) throughout the year. PLoS
ONE 9(1): e86055. 15 pp.
Lydersen, C. and Kovacs, K. M. 2005. Growth and population parameters of the world’s
northernmost harbour seals Phoca vitulina residing in Svalbard, Norway. Polar Biol. 28: 156163.
Lydersen, C. and Kovacs, K. M. 2010. Status and biology of harbour seals (Phoca vitulina) in
Svalbard. NAMMCO Sci. Publ. 8: 47-60.
Merkel, B., Lydersen, C., Yoccoz, N. G. and Kovacs, K. M. 2013. The world's northernmost
harbour seal population - how many are there? PLoS ONE 8(7): e67576. 11pp.
Reder, S., Lydersen, C., Arnold, W. and Kovacs, K. M. 2003. Haul-out behaviour of high
Arctic harbour seals (Phoca vitulina vitulina) in Svalbard, Norway. Polar Biol. 27: 6-16.
Health:
Jensen, S. K., Aars, J., Lydersen, C., Kovacs, K. M. and Åsbakk, K. 2010. The prevalence of
Toxoplasma gondii in polar bears and their marine mammal prey: evidence for a marine
transmission pathway? Polar Biol. 33: 599-606.
Roth, S. J., Tischer, B. K., Kovacs, K. M., Lydersen, C., Osterrieder, N. and Tryland, M. 2013.
Phocine herpesvirus 1 (PhHV-1) in harbor seals from Svalbard, Norway. Vet. Microbiol. 164:
286-292.
Tryland, M., Thoresen, S. I., Kovacs, K. M. and Lydersen, C. 2006. Serum chemistry values
of free-ranging white whales (Delphinapterus leucas) in Svalbard. Vet. Clin. Pathol. 35: 199203.
Tryland, M., Krafft, B. A., Lydersen, C., Kovacs, K. M. and Thoresen, S. I. 2006. Serum
chemistry values for free-ranging ringed seals (Pusa hispida) in Svalbard. Vet. Clin. Pathol.
35: 405-412.
Tryland, M., Lydersen, C., Kovacs, K. M. and Thoresen, S. I. 2009. Serum chemistry
reference values in free-ranging north Atlantic male walruses (Odobenus rosmarus rosmarus)
from the Svalbard archipelago. Vet. Clin. Pathol. 38: 501-506.
Narwhals:
Lydersen, C., Martin, T., Gjertz, I. and Kovacs, K. M. 2007. Satellite tracking and diving
behaviour of sub-adult narwhals (Monodon monoceros) in Svalbard, Norway. Polar Biol. 30:
437-442.
Wolkers, H., Lydersen, C., Kovacs, K. M., Burkow, I. and Bavel, B. van. 2006. Accumulation,
metabolism, and food-chain transfer of chlorinated and brominated contaminants in subadult
white whales (Delphinapterus leucas) and narwhals (Monodon monoceros) from Svalbard,
Norway. Arch. Environ. Contam. Toxicol. 50: 69-78.
Polar bears:
Aars, J., Marques, T. A., Buckland, S. T., Andersen, M., Belikov, S., Boltunov, A. & Wiig. Ø.
2009. Estimating the Barents Sea polar bear subpopulation size. Mar. mammal Sci. 25: 35-52.
Durner, G. M., Douglas, D. C., Nielson, R. M., Amstrup, S. C., McDonald, T. L., Stirling, I.,
Mauritzen, M., Born, E. W., Wiig, Ø., DeWeaver, E., Serreze, M. C., Belikov, S. E., Holland,
M. M., Maslanik, J., Aars, J., Bailey, D. A. & Derocher, A. E. 2009. predicting 21st-century
polar bear habitat distribution from global climate models. Ecol. Monogr. 79: 25-58.
Freitas, C., Kovacs, K.M., Andersen, M., Aars, J., Sandven, S., Skern-Mauritzen, M., Pavlova,
O. & Lydersen, C. 2012. Importance of fast ice and glacier fronts for female polar bears and
their cubs during spring in Svalbard, Norway. Mar. Ecol. Progr. Ser. 447: 289-304.
Mauritzen, M., Derocher, A. E., Wiig, Ø., Belikov, S. E., Boltunov, A. N., Hansen, E. &
Garner, G. W. 2002. Using satellite telemetry to define spatial population structure in polar
bears in the Norwegian and western Russian Arctic. J. Appl. Ecol. 39: 79-90-Ringed seals:
Carlens, H., Lydersen, C., Krafft, B. A. and Kovacs, K. M. 2006. Spring haul-out behavior of
ringed seals (Pusa hispida) in Kongsfjorden, Svalbard. Mar. Mammal Sci. 22: 379-393.
Freitas, C., Kovacs, K. M., Ims, R. A., Fedak, M. A. and Lydersen C. 2008. Ringed seal postmoulting movement tactics and habitat selection. Oecologia 155: 193-204.
Freitas, C., Kovacs, K. M., Ims, R. A. and Lydersen, C. 2008. Predicting habitat use by ringed
seals (Phoca hispida) in a warming Arctic. Ecol. Model. 217: 19-32.
Gjertz, I.; Kovacs, K. M., Lydersen, C. and Wiig, Ø. 2000. Movements and diving of adult
ringed seals (Phoca hispida) in Svalbard. Polar Biol. 23: 651-656.
Krafft, B. A., Kovacs, K. M., Ergon, T., Andersen, M., Aars, J., Haug, T. and Lydersen, C.
2006. Abundance of ringed seals (Pusa hispida) in the fjords of Spitsbergen, Svalbard, during
the peak molting period. Mar. Mammal Sci. 22: 394-412.
Krafft, B. A., Kovacs, K. M. and Lydersen, C. 2007. Distribution of sex and age groups of
ringed seals (Pusa hispida) in the fast-ice breeding habitat of Kongsfjorden, Svalbard. Mar.
Ecol. Progr. Ser. 335: 199-206.
Smith, T. G. and Lydersen, C. 1991. Availability of suitable land-fast ice and predation as
factors limiting ringed seal populations, Phoca hispida, in Svalbard. Polar Res. 10: 585-594.
Walruses:
Freitas, C., Kovacs, K. M., Ims, R. A., Fedak, M. A. and Lydersen, C. 2009. Deep into the ice:
over-wintering and habitat selection in male Atlantic walruses. Mar. Ecol. Progr. Ser. 375:
247-261.
Lydersen, C., Aars, J. and Kovacs, K. M. 2008. Estimating the number of walruses in
Svalbard from aerial surveys and behavioural data from satellite telemetry. Arctic 61: 119128.
Lydersen, C., Chernook, V. I., Glazov, D. M., Trukhanova, I. S. and Kovacs, K. M. 2012.
Aerial survey of Atlantic walruses (Odobenus rosmarus rosmarus) in the Pechora Sea, August
2011. Polar Biol. 35: 1555-1562
White whales: (tar med noe økotoks her siden nivåene er så høye)
Andersen, G., Kovacs, K. M., Lydersen, C., Skaare, J. U., Gjertz, I. and Jenssen, B. M. 2001.
Concentrations and patterns of organochlorine contaminants in white whales (Delphinapterus
leucas) from Svalbard, Norway. Sci. Total Environ. 264: 267-281.
Andersen, G., Foreid, S., Skaare, J. U., Jenssen, B. M., Lydersen, C. and Kovacs, K. M.. 2006.
Levels of toxaphene congeners in white whales (Delphinapterus leucas) from Svalbard,
Norway. Sci. Total Environ. 357: 128-137.
Lydersen, C., Martin, A. R., Kovacs, K. M. and Gjertz, I. 2001. Summer and autumn
movements of white whales (Delphinapterus leucas) in Svalbard, Norway. Mar. Ecol. Progr.
Ser. 219: 265-274.
Lydersen, C., Nøst, O. A., Lovell, P., McConnell, B. J., Gammelsrød, T., Hunter, C., Fedak,
M. A. and Kovacs, K. M. 2002. Salinity and temperature structure of a freezing Arctic fjord monitored by white whales (Delphinapterus leucas). Geophys. Res. Lett. 29: art. no. 2119,
doi: 10.1029/2002GL015462. 4 pp.
Villanger, G. D., Lydersen, C., Kovacs, K. M.. Lie, E., Skaare, J. U. and Jenssen, B. M. 2011.
Disruptive effects of persistent organohalogen contaminants on thyroid function in white
whales (Delphinapterus leucas) from Svalbard. Sci. Total Environ. 409: 2511-2524
Wolkers, H., Bavel, B. van, Derocher, A. E., Wiig, Ø., Kovacs, K. M., Lydersen, C. and
Lindström, G. 2004. Congener-specific accumulation and food chain transfer of
polybrominated diphenyl ethers in two Arctic food chains. Environ. Sci. Technol. 38: 16671674.
Wolkers, H., Lydersen, C., Kovacs, K. M., Burkow, I. and Bavel, B. van. 2006. Accumulation,
metabolism, and food-chain transfer of chlorinated and brominated contaminants in subadult
white whales (Delphinapterus leucas) and narwhals (Monodon monoceros) from Svalbard,
Norway. Arch. Environ. Contam. Toxicol. 50: 69-78.
Marine mammals
The expert evaluation for sea mammals is a product of two meetings with mammal
experts from the Norwegian Polar Institute and the Institute of Marine Research, and
preliminary values are presented in Table 9.
Several aspects of marine mammal ecology, distribution and red list status were taken
into account as vulnerability was evaluated.
Also, the early results coming out of environmental followup studies after the Macondo
incident have been included in the evaluation, as these are the most comprehensive
study of the effects of oil spills on cetaceans. Important aspects include;
•
•
•
•
•
•
Distribution and behaviour.
Physiological aspects of oiled skin and fur (polar bear)
Feeding ecology and other behavioural issues in relation to probability of
encountering oil, especially for the baleen whales
The mortality of cetaceans may be as much as 50 times higher than the reported
number of observed carcasses from the Deep Water Horizon incident. (Williams et
al., 2011).
The higher environmental stress load of environmental contaminants in the higher
trophic levels of the Arctic. In particular relevant for toothed whales (Andersen et
al., 2006;Wolkers et al., 2006)
The exposure of volatile and aerosolized petroleum-associated compounds
through inhalation (Schwacke et al., 2013). This was considered to be relevant
also for colder waters, where evaporation of the lighter components in crude oil is
expected to be slower than in temperate and tropical ambient temperatures.
The Harbour seal of Svalbard is endemic and limited to a small population in the western
part of Svalbard. The higher vulnerability value assigned was deemed appropriate by the
expert group and is applicable for the Svalbard population only.
The bowhead whale has been assigned a tentative individual vulnerability index of 3.They
are considered to be more sensitive than other whales as they skim the upper water
layers and near the surface when foraging supporting this high vulnerability {Hold refs}
(Geraci and Staubin, 1980;Reeves et al., 2014). In addition, the species has high
population vulnerability as a population as it is critically endangered in the European part
of the Arctic (Kålås, 2010) and restitution may be slower/is uncertain.
In addition to the harbour seal and the bowhead whale, several species have been
assigned a vulnerability index of 3. These values are provided by experts and are based
on best available scientific knowledge {Hold refs} (Freitas et al., 2008;Freitas et al.,
2009;Lydersen et al., 2008;Lydersen et al., 2014;Reeves et al., 2014).
Table 9: The expert evaluation of vulnerability for sea mammals related to ice-filled water of the Arctic.
Geir: hvalross er referansene (Freitas et al., 2009;Lydersen et al., 2008) gitt I setningen over.
4.7.3 Sympagic fauna
References
Andersen G, Foreld S, Skaare JU, Jenssen BM, Lydersen C, Kovacs KM. Levels of toxaphene congeners
in white whales (Delphinapterus leucas) from Svalbard, Norway. Sci Total Environ 2006; 357: 128137.
Freitas C, Kovacs KM, Ims RA, Fedak MA, Lydersen C. Ringed seal post-moulting movement tactics
and habitat selection. Oecologia 2008; 155: 193-204.
Freitas C, Kovacs KM, Ims RA, Fedak MA, Lydersen C. Deep into the ice: over-wintering and habitat
selection in male Atlantic walruses. Marine Ecology Progress Series 2009; 375: 247-261.
Geraci JR, Staubin DJ. Offshore Petroleum Resource Development and Marine Mammals - A Review
and Research Recommendations. Marine Fisheries Review 1980; 42: 1-12.
Lydersen C, Aars J, Kovacs KM. Estimating the number of walruses in Svalbard from aerial surveys and
behavioural data from satellite telemetry. Arctic 2008; 61: 119-128.
Lydersen C, Assmy P, Falk-Petersen S, Kohler J, Kovacs KM, Reigstad M, Steen H, Strøm H, Sundfjord
A, Varpe Ø, Walczowski W, Weslawski JM, Zajaczkowski M. The importance of tidewater glaciers for
marine mammals and seabirds in Svalbard, Norway. Journal of Marine Systems 2014; 129: 452-471.
Reeves RR, Ewins PJ, Agbayani S, Heide-Jørgensen MP, Kovacs KM, Lydersen C, Suydam R, Elliott W,
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