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THE USEFULNESS OF LONG TERM DATASETS FROM OIL
SPILL POLLUTION MONITORING IN DETECTING CLIMATE
CHANGE IMPACTS.
By
Eugenia Metzaki
Submitted as part assessment for the
degree of Master of Science
In
Climate Change Impacts and Mitigation
University of Heriot-Watt, Edinburgh
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CLIMATE CHANGE, IMPACTS AND MITIGATION
SCHOOL OF LIFE SCIENCES
Project Title: THE USEFULNESS OF LONG TERM
DATASETS FROM OIL SPILL POLLUTION
MONITORING IN DETECTING CLIMATE CHANGE
IMPACTS.
I, EUGENIA METZAKI, confirm that this work submitted for
assessment is my own and is expressed in my own words. Any uses
made within it of works of other authors in any form (ideas, equations,
figures, text, tables, programmes etc.) are properly acknowledged at the
point of their use. A full list of the references employed is included.
Signed: ………………………………………………………..
Date:……………………………………………………………
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ACKNOWLEDGMENTS
The author wishes to express sincere appreciation to Professor James M. Mair for
his guidance in the preparation of this manuscript. In addition, special thanks
should go to Iain Matheson and Jonathan Hunt, of Fugro ERT, for the
provisioning of the historical data, and to Wendy McGonical, of Fugro ERT, for
her assistance with periodical needs of information.
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CLIMATE CHANGE, IMPACTS AND MITIGATION
ABSTRACT
This dissertation presents the results of an analysis of long-term monitoring data
from the Sullom Voe terminal area collected over the years 1979-2011, carried
out by Fugro ERT that kindly provided part of the required data.
The data included population counts and indices for the top five taxa for four
stations in the Shetland Islands area. These stations, identified as C, D, E and K,
were around the Sullom Voe terminal and were the focus of all sampling
proceedings.
It was investigated herein, as to whether any meteorological variables could have
influenced the population, but the results were not conclusive. The variables
examined were seas surface temperatures and salinity measurements of the
Sullom Voe area.
The analysis conducted identified certain areas of improvement for this kind of
assessment. Namely the cross-comparison of the data from other sampling
stations in the area, and also the introduction and analysis of additional
meteorological factors, e.g. precipitation, atmospheric pressure, wave heights,
wind direction, etc., which were outside the scope of this enquiry.
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TABLE OF CONTENTS
1 Introduction ................................................................................................9
1.1 General Approach ...............................................................................9
1.2 Climate in the Area............................................................................14
1.3 Long term Environmental monitoring ...............................................16
1.4 Historical Review ..............................................................................17
1.5 The Sullom Voe monitoring programme ...........................................19
1.6 Visualisation of satellite data with GIS software .................................21
2 Data Analysis .............................................................................................23
2.1 Introduction......................................................................................23
2.2 Methodology .....................................................................................26
2.2.1 Diversity indices ...........................................................................................27
2.2.2 Rarefaction Technique ..................................................................................31
2.2.3 Subsampling method ....................................................................................31
2.3 Results ..............................................................................................32
2.4 Discussion ........................................................................................44
3 Environmental Factors...............................................................................50
3.1 Introduction......................................................................................50
3.2 Methods............................................................................................53
3.3 Results ..............................................................................................56
3.4 Discussion ........................................................................................60
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4 Conclusions ...............................................................................................63
5 Appendix I.................................................................................................68
6 Appendix II ...............................................................................................76
7 Bibliography ..............................................................................................92
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LIST OF FIGURES
Figure 1: The location of the Sullom Voe terminal in Shetland islands, Scotland.
(Fugro ERT, 2010)...................................................................................................12
Figure 2: Macrobenthos sample stations in relation to the full station grid and the
effluent discharge site. Stations C, D, E, and F shown to the North were
used in this report. (Fugro ERT, 2010) .....................................................................25
Figure 3: Station C - Prionospio fallax population for the years 1979-2010 ............................33
Figure 4: Station C - Thyasira flexuosa population for the years 1979-2010 ...........................34
Figure 5: Station C - Amphiura filiformis population for the years 1979-2010........................34
Figure 6: Station C Pholoe spp population for the years 1979-2010 .......................................35
Figure 7: Station C - Urothoe elegans population for the years 1979-2010 ............................35
Figure 8: Station D - Top taxa populations for the years 1979-2010 ......................................37
Figure 9: Station E - Top taxa populations for the years 1979-2010 .......................................38
Figure 10: Station K - Top taxa populations for the years 1979-2010.....................................39
Figure 11: Hurlbert's rarefaction curves for stations C, D, E, and K, Sullom Voe
survey, May 2010. (Fugro ERT, 2010) ......................................................................43
Figure 12: Top taxa species count for station C for the years 1979-2010................................45
Figure 13: Top taxa species count for station D for the years 1979-2010 ...............................46
Figure 14: Top taxa species count for station E for the years 1979-2010................................47
Figure 15: Top taxa species count for station K for the years 1979-2010 ...............................48
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Figure 16: These maps show air pressure patterns on November 7, 2010 (left),
when the Arctic Oscillation was strongly positive, and on December 18
(right), when it was strongly negative. These phases are the result of the
whole atmosphere periodically shifting its weight back and forth between
the Arctic and the mid-latitudes of the Atlantic and Pacific Ocean, like
water sloshing back and forth in a bowl. (Maps by Ned Gardiner and
Hunter Allen, based on Global Forecast System data from the National
Centers for Environmental Prediction.) ...................................................................51
Figure 17: Winter (December through March) index of the NAO based on the
difference of normalised pressures between Lisbon, Portugal, and
Stykkisholmur, celand, from 1864 through 1994. The heavy olid line
represents the meridional pressure gradient smoothed with a low-pass
filter with seven weights (1, 3, 5, 6, 5, 3, and 1) to remove fluctuations
with periods less than 4 years. Data and plot from (Hurrell, 1995). ............................52
Figure 18: Distribution of rainfall across Scotland showing the marked contrast in
precipitation regimes between east and west. As a general rule, Shetland
experiences higher than average rainfall. (Courtesy of UK Meteorological
Office).....................................................................................................................53
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Figure 19: Areas near the Shetlands from which SST is analysed. Main area of
data collection was between 5° E and 0° longitude and 60° N to 65° N
latitude. Note that negative values indicate Westernly longitudes. ..............................55
Figure 20: Temperature measurements in degrees Celsius, for the Shetlands’ area.
The blue line indicates the raw data; the red line shows the 2-year running
mean filtered data, and the black line is the linear trend-line of the filtered
data. Data from (ICES Oceanographic Database) .....................................................58
Figure 21: Salinity measurements in psu, for the Shetland’s area. The blue line
indicates the raw data; the red line shows the 2-year running mean filtered
data, and the black line is the linear trend-line of the filtered data. Data
from (ICES Oceanographic Database). ....................................................................60
Figure 22: Station C - Prionospio fallax population for the years 1979-2010 ..........................68
Figure 23: Station C – Thyasira flexuosa population for the years 1979-2010 .........................68
Figure 24: Station C – Amphiura filiformis population for the years 1979-2010 .....................69
Figure 25: Station C - Pholoe spp population for the years 1979-2010 ...................................69
Figure 26: Station C – Urothoe elegans population for the years 1979-2010 .........................69
Figure 27: Station D – OLIGOCHAETA spp population for the years 1979-
2010 ........................................................................................................................70
Figure 28: Station D – Spio armata population for the years 1979-2010.................................70
Figure 29: Station D – Pholoe spp population for the years 1979-2010..................................71
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Figure 30: Station D – Urothoe elegans population for the years 1979-2010 ..........................71
Figure 31: Station E – Corophium crassicorne population for the years 1979-2010................72
Figure 32: Station E – Bathyporiea elegans population for the years 1979-2010 .....................72
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1 INTRODUCTION
1 . 1 G e n e r a l A p p r o a c h
It is a fact that our society depends heavily on oil production. Most oil producing
governments allocate a large portion of their budget in oil exploration and
drilling. Oil pollution monitoring is crucial nowadays when we can clearly see the
effects on the environment.
Pollution monitoring has unfortunately now become a familiar concept and it has
become even more ubiquitous the last years following the large disaster in the
Gulf of Mexico at the BP oilrig. But it was not until a few decades ago, that the
increasing pollution of the environment became serious a public concern.
Currently the conservation of the environment is a really big issue in the agendas
of the countries, trying to face all these harmful consequences from the change of
the climate. The concentration of carbon dioxide in the atmosphere continues to
rise day by day. The last measurement at Mauna Loa Observatory in Hawaii
indicates that the atmospheric concentrations of carbon dioxide in May 2011 was
394,16 ppm which are much higher than concentrations before the industrial
revolution (280 ppm) (Gammon et al., 1982). During the last century, the earth’s
average surface temperature has risen by about 0.7 C according to the expert
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panel appointed to investigate climate change, the Intergovernmental Panel on
Climate Change (IPCC) (Richard, et al., 2007)
This report will be investigating the data sets from the last 30 years of macro-
benthic taxa populations in the North Sea oil fields, specifically for the oil
platforms of Sullom Voe in the Shetland Islands (see Figure 1). The Sullom Voe
Oil Terminal is located on Calback Ness in the Delting district of the Shetland
Mainland. It is one of the largest oil and liquefied gas terminals in Europe. It
occupies a 400-ha (1000-acre) site on the eastern shore of Sullom Voe, 29 miles
(46 km) north of Lerwick. The terminal supplied by Brent and Ninian pipelines,
provides oil stabilisation and storage, gas separation and liquefaction and tanker
loading facilities. This platform has its particular history and correlations will be
investigated between the monitoring data and the climate changes that have
occurred in the North Sea and in the Artic environment for the last 30 years.
The terminal is working from 1978 and only some months after his opening there
was an oil spillage the tanker Esso Bernicia collided with one of the jetties and
discharged 1400 tonnes of oil which caused much pollution of the surrounding
waters.1
1 From http://www.scottish-places.info/features/featurefirst1417.html
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Due to the importance of the installation and the volume of carbohydrates being
moved, it is essential for the natural habitat that a strict monitoring regime be
applied.
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Figure 1: The location of the Sullom Voe terminal in Shetland Islands, Scotland. (Fugro ERT, 2010)
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There are many companies that have taken the responsibility to do the above
monitoring. This report has been conducted in partnership with Fugro ERT2.
One other independent group is the SOTEAG – the Shetland Oil Terminal
Environmental Advisory Group – who with the collaboration of the University
of Aberdeen carried out surveys for the wellbeing and preservation of the natural
beauty of the Shetland Islands. (SOTEAG in Shetland).
The local authority Works Licensing Conditions and the Secretary of state for
Scotland’s Exemption Certificate of the prevention of oil pollution Act 1971,
require that Sullom Voe terminal operators undertake biological and chemical
monitoring around the discharge site. Furthermore, The European Water
Framework Directive (Directive 2000/60/EC) is a binding legislation for all
actions taken in the Sullom Voe area. This Directive addresses EU surface waters,
including coastal waters, as well as groundwater. By 2015, Member States are to
achieve "good water status", a term that incorporates both chemical parameters
(i.e. low pollution levels) as well as ecological ones (healthy ecosystems).
(European Comission Environment, 2010).
In addition to the above constraining policies there were two more that need to
be taken into account for the Sullom Voe terminal. The first is the Orkney
County Council Act 1974 that introduced new statutory powers for the local
2 http://www.ert.co.uk/
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council authority. The second is a general environmental awareness, in particular
the interest of the ornithine taxa as presented by the Royal Society for the
Protection of Birds (Johnston, 1981).
1 . 2 C l i m a t e i n t h e A r e a
The climate in the North Sea has changed significantly the last 100 years,
primarily due to climate oscillations in the North Atlantic. These oscillations
produce water influx variability within the North Sea that influences its local
climatic conditions. The North Atlantic Oscillation (NAO) affects the cloud
coverage and precipitation in the area of the British Isles and the North Sea. An
additional side effect is the substantial increase of the wind force magnitude over
the last 50 years (OSPAR Commission , 2000). This effect also introduces seas
surface turbulence in the oceans, thus reducing the light that penetrates the lower
depths. The latter is one of the significant factors in micro-fauna reproduction
and a stern guide to its population growth or decline.
There seems to be present an evident correlation between sea surface
temperature in the North Sea and the global temperature increase of approx.
0.6°C in the last 100 years (OSPAR Commission , 2000). In addition, for the
North East Atlantic, a surface air temperature increase of about 1.5°C, a sea level
rise of about 0.5m and a general increase in storminess and rainfall are predicted
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by the year 2100 (OSPAR Commission , 2000). It is however difficult to
distinguish between anthropogenic factors to the climatic change of the marine
environment and the long-term effect of the NAO in the North Sea area.
The main way to try and separate the two is via long term monitoring of the area
in scrutiny. This long term monitoring will help us interpret the changes in the
North Sea and find the best solutions for future challenges in this important area
of concern. More details concerning the analysis of environmental data will be
elaborated in the Data Analysis section.
During the literature review process two questions arose concerning monitoring,
that might be useful to address. The first is when should the monitoring stop?
Monitoring can be really expensive in terms of time, equipment and staff and can
appear to be totally open-ended; to be in fact the sort of continuing project which
managers hate and which institutional systems are hostile. Sometimes in the light
of scientific results, it might well be argued that it can stop now. But
unfortunately we cannot have a specific answer for this question and the
environmental monitoring should be continued until there is clear conclusion
either to the usefulness of non-necessity of the monitoring. Another question is
how can the natural fluctuations be separated from the human activities in order
for the interpretation of the data to be valuable? It became apparent that the
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technology surrounding environmental monitoring is continually advancing and
the simulation and forecasting models are getting more complex and efficient.
1 . 3 L o n g t e r m E n v i r o n m e n t a l m o n i t o r i n g
The role of long term environmental monitoring is essential in the North Sea. It
is not only crucial for the wellbeing of the organisms, but also for evaluating the
toxicity levels of the organisms and trying to intervene when the toxic levels are
reaching critical levels. There are a lot of techniques than can be used in order to
detect the environmental effects from the oil marine pollution and most of them
focusing in chemical monitoring, but it is the biological effects of oil accidentally
released in the North Sea that we must pay special attention. For this reasons the
biological indices that are analysed Chapter 2 will be valuable in a biological
monitoring programme.
There has been an on-going survey since 1979 around the Sullom Voe terminal in
the Shetland Islands. The evaluation of the collected data should provide us with
an insight into the possible environmental effects of the terminal in the are, and
prove a useful paradigm for similar oil platforms and for designing similar surveys
in the near future.
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1 . 4 H i s t o r i c a l R e v i e w
Environmental Monitoring programmes up until the 1960s tried to interpret the
collected data and their biological variables in terms of natural events and cycles.
But after the 1960’s marine pollution marked an increase and there was an
additional difficulty in interpreting the data and separating the natural fluctuations
from the pollution impacts (McIntyre, 1995). Nowadays there is an additional
variable in the form of the current climatic change which was not apparent in the
previous years. The anthropogenic effects in the environment are really crucial
and only in the last years this has become more apparent. Today the monitoring
surveys have another objective; to help identify the individual responsibilities of
the oil and gas companies in order to help the latter mitigate their corporate
carbon footprint.
However the Minamata disease episode in Japan drew attention to metals in
coastal waters in the 1950’s, the eggshell thinning in the Californian Brown
Pelican drew attention; highlighting pesticides in the 1960’s, while the wreck of
the Torrey Canyon in 1967 directed interest in oil. But to go much further the
first form of environmental monitoring took place when the Council for the
Exploitation of the Sea, set up in Copenhagen in 1902, declared an intention to
“conduct quarterly cruises in order to systematically collect information on
general hydrographic regimes over a wide area” (McIntyre, 1995). The first data
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was in the form of salinity, temperature and plankton observations. The marine
environmental modelling is very challenging due to the many influences from
many factors of the marine environment. For instance one long term programme
in the English Channel, trying to gather data and analyse the ecosystem of this
area. The hydrographic front had changed positions and the samples were
collected from different water masses. So the final conclusion was difficult to
interpret. (Southward, 1980)
One of the most popular monitoring programmes since the 1930s, which is
continuing until this day, is the Continuous Plankton Recorder Survey. This
survey has been using commercial vessels in order to conduct field measurements
of plankton organisms across ship’s routes. While this method has its
methodological drawbacks, it has however provided a constant stream of
information for the plankton population for the last 80 years. (SAHFOS )
The biological monitoring is the easiest way to look at organisms as accumulators
of pollutants. This type of monitoring reaches the highest international
expression since 1975 with Goldberg and is commonly known as “mussel
watch”. This technique was based on the ability of sessile filter-feeding molluscs,
like Mytilus, to accumulate some contaminants from the water. This technique is
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used widely all over the world nowadays, with one good example being the
NOAA Mussel Watch Program. 3
The method that has produced the most relevant results is the monitoring of
macro-benthic organisms. It has been established that the variation in the
zoobenthic organisms living in the sediments can be directly correlated to the
existence and abundance of carbohydrates in those same sediments (Davies &
Kingston, 1992). The Stirling University similarly conducted an additional study
for BP, on the effects of the benthic infauna of the refinery and chemical works
effluents (McLusky & McCrory, 1989). Until the 1990s scientists didn’t focus on
these environmental factors, which they are really significant for the productivity
of the populations, therefore now is the effort to gather data from previous
monitoring programmes, e.g. meteorological, oceanic and biological data, and
make correlations between all these factors.
1 . 5 T h e S u l l o m V o e m o n i t o r i n g
p r o g r a m m e
The Sullom Voe terminal has had an on-going environmental monitoring survey
for the last 30 years. It is currently one of the longest environmental monitoring
programmes in Europe. The monitoring programme is really valuable for the
3 More information at: http://ccma.nos.noaa.gov/about/coast/nsandt/musselwatch.aspx
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particular site because of all the data gathered in the last 30 years we will have the
ability to make useful correlations between the micro-fauna and the pollution
levels and thus have a better understanding of the oceans.
The monitoring program has one a big issue as mentioned previously, and that is
how we can separate the natural fluctuations of the flora environment from the
effects of pollution.
According to the Scottish Department of Agriculture, Fisheries and Rural
Statistics, conducted studies in the Shetland area have shown that the gastropod’s
Dogwhelk (Nucella lapillus) population had a sharp decline due to the high
exposure to TBT, a chemical compound used in ships (McIntyre, 1995). In 1991
the Dogwhelk were completely absent from the terminal area and effects could
be shown in the outer area of the Shetlands in Yell Sound (McIntyre, 1995).
Furthermore from the ornithological surveys conducted, there is a big decline
evident in certain birds’ populations. Scotland's breeding seabird populations are
internationally important, encompassing half of the world's great skuas and North
Atlantic gannets, over one third of Europe's Manx shearwaters and at least 10%
of the European breeding populations of ten other species (Lloyd, Tasker, &
Partridge, 1991). The trends showed in these reports clearly show that 11 of 18
increased in the breeding population and four showed a sharp decline.
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The previous are peripheral environmental concerns surrounding the Sullom Voe
Area, regarding different forms of organisms that are prevailing in the Sullom
Voe terminal area. They are referenced here solely as an indication of the multi -
faceted on-going environmental monitoring. The data and the focus of this
report lies solely on the macro-benthic organisms as they are presented in
Chapter 2.
1 . 6 V i s u a l i s a t i o n o f s a t e l l i t e d a t a w i t h
G I S s o f t w a r e
Recently there has been a new useful tool developed in order to monitor the
marine pollution from intentional and unintentional oil spills. It is a Web-based
GIS. IT has become necessary for scientists to try to find new ways in order for
their oil spill analysis to be more effective, useful and quicker for the ecosystem
and the human health consequences. GIS monitoring has earn the appreciation
of many scientists in the last five years, where (Kulawiak, Prospathopoulos,
Perivoliotis, Luba, Kioroglou, & Stepnowski, 2010) have effectively used it to
simulate oil spill migration via a web-based GIS interface. It has become a very
sensitive and effective way to monitor the oceans due to the multi-sensor
observation of the satellites. The (United Arab Emirates University, 2010)
published in July 2010 a report that utilised space-borne Synthetic Aperture Radar
technology in order to identify intentional vessel oil spills near tanker routes and
unintentional seepage from seabed oil structures and oil refinery installations.
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GIS data, with is immediate visual information are a source of invaluable
information regarding oil spill identification as well as a form of monitoring their
migration and deposition to shores.
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2 DATA ANALYSIS
2 . 1 I n t r o d u c t i o n
The macrofauna is defined here as those animals living in or on the seabed that
are retained when sediment is washed on a 0.5mm mesh sieve. Most of the
animals ate infaunal i.e. they live in the sediment, burrowing through it or
forming tubes within it. Since the sediment provides support and protection, as
well as a food source for many species, members of the infauna are particularly
vulnerable to changes in its chemical, physical and biological nature.
In faunal animals are largely sedentary and unable to avoid unfavourable
conditions. Each species responds differently to changes in its environment, so
the species composition are relative abundance in a particular location reflects the
conditions here, both current and historical. The recognition that disturbance and
contaminant inputs may alter sediment characteristics, together with the relative
ease of obtaining quantitative samples from specific locations, had led to the
widespread use of benthic macrofaunal communities in monitoring the impact of
disturbances to the marine environment.
Any effects from the continuous discharge of oil platforms in this particular area
must be studied through a long term monitoring surveys. In my study I will
analyse the data from the last 30 years and how the number of species have
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changed through these years. Correlations between measured contaminant levels
and changes in community structure a really difficult to interpret because there
are many factors that can influence their behaviour. But with the long term
monitoring we can see the trends of the species and the natural variations in
macrofaunal communities. (Fugro ERT, 2010)
The data analysed was provided by collaboration with Fugro ERT Ltd based in
Edinburgh. Out of eleven sampling stations, data was provided for four stations,
identified hereon as Stations C, D, E, and K (see Figure 2). Their geographical
positions are given in Table 1.
Table 1: Positions derived from Stanes Moor GPS, set to WGS84 datum. (Fugro ERT, 2010)
Station
no.
Latitude Longitude Approximate
water depth
Number of
macrobenthos
replicate
grabs
Deg Dec
min
Deg Dec min
C 60 29.700N 01 16.845W 52 5
D 60 29.460N 01 15.300W 20 5
E 60 29.505N 01 15.737W 17 5
K 60 29.486N 01 16.431W 32 5
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Figure 2: Macrobenthos sample stations in relation to the full station grid and the effluent discharge site.
Stations C, D, E, and F shown to the North were used in this report. (Fugro ERT, 2010)
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Another important facet of an ecosystem is it diversity. The data presented in the
Data Analysis section will present certain diversity indices. Diversity of an
ecosystem is not easily defined. It is best understood however by two aspects that
attempts to measure it; the richness and the evenness of the system. The richness
of an ecosystem is broadly speaking the number of species measured. The
population count that is presented in the Results section can only show the
abundance of a population of taxa. The combination of the population count and
the number of species present will provide the magnitude of the ecosystem.
However the underlying issue is how is that total population distributed amongst
the taxa. Having a sample of 100 species with 10000 number of population is not
a diverse ecosystem if the top 2% makes up for 90% of the total population
count. A measurement of the distribution of the population can be given by
different indices. There are three diversity and two evenness indices presented in
this paper. Their particular explanation can be found in the Methodology section
below.
2 . 2 M e t h o d o l o g y
On return to the laboratory, samples were further washed in a 0.5 mm mesh and
the preservative changed to phenoxetol (2%). the vital stain Rose Bengal was also
added to the samples at this stage to facilitate the sorting process. The animals
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were then separated by hand from the remaining sediment in white trays, and
sorted into four major faunal groups (annelids, crustaceans, mollusc and other
phyla), due to the high volume of sediment and infaunal material in these
samples, a subsampling procedure was employed so that one quarter of the total
sediment volume was worked up. This was done in order to keep the analytical
time within acceptable limits and was achieved by splitting the sample in a white
tray marked out in quarters and randomly selecting one of the subsamples.
The animals in each faunal group were then identified and enumerated by
specialist taxonomists. Identification was to species level where possible. A few
specimens, due to their immaturity, to damage incurred during processing or to
inadequacy of taxonomic literature for the group, could not be identified to
species and were identified at higher taxonomic levels as appropriate. After
identification, samples were stored in methanol solution (approximately 70%).
Species abundances were entered into spreadsheet file and sorted into taxonomic
order using ERT’s coding system. The nomenclature conforms largely to that
suggested by (Howson & Picton, 1997). (Fugro ERT, 2010)
2.2.1 Diversity indices
A diversity index is a statistical property used to define the distribution of a
population across its participating members of different type. The three diversity
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indices used in this paper are: Simpson’s, Brillouin’s, and Shannon-Wiener’s
index.
The Simpson index is a statistical property from probability theory. It denotes the
total probability that two consecutive samples of microfauna will belong to
different taxa. The index is calculated with the following formula:
∑
where S is the population size and is the probability of the ith sample belonging
to a certain taxa. The squared probability will denote the probability of to
consecutively sampled specimens belong to the same taxa.
The Shannon-Wiener (SW) index was first used in information theory to define
the amount of entropy in a distribution, assuming the taxa as information bits
and their proportional populations as the probability. The larger the value of the
index, the larger the entropy of the system, the more amalgamated the
population. The index values range, but not exclusively, to values from 1.5 (low
taxa richness and evenness) up to 3.5 (high taxa richness and evenness). The
index is calculated with the following formula:
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∑
where S is the population size and is the probability of the ith taxa. A greater
number of species increases the sums, hence increasing the index, as does more
even distribution of sampled objects amongst taxa. Theoretically the SW index
should only be used on random samples in which the total number of species is
known. However that’s not always attainable, hence this index is used in
collaboration with alternate indices.
The Brillouin indexis used in place of SW when diversity of non-random samples
is being estimated from a known population. Since most of the sampling of
sediment microfauna is being collected randomly this index is not always the best
fitting. The index is calculated with the following formula:
where N is total number of the population and is the population of each of
the S number of species. The problem with this formula is that after a certain
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population magnitude, the factorials become challenging to calculate even with
modern computers.
The two additional evenness indices are Pielou’s and Heip’s. The first one gives a
ration of SW index over the maximum SW index possible. The index is calculated
with the following formula:
where H’ is the SW index mentioned above and is the maximum index
based on the population of the sample S that can be deduced to .
Heip’s index is a modification of the Pielou index which essentially the
antilogarithm of H’, divided by the antilogarithm of . The denominator is
adjusted for extreme values by subtracting 1, making the formula:
where is the antilogarithm of .
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2.2.2 Rarefaction Technique
Rarefaction is a technique utilised to compare species abundance between
different samples from different sizes and location of sampling. Rarefaction
allows the calculation of species richness for a given sample and the plotting of
rarefaction plots. Rarefaction works by multiple re-sampling of the total number
of available samples and then plotting the average number per taxa found, “Thus
rarefaction generates the expected number of species in a small collection of n
individuals (or n samples) drawn at random from the large pool of N samples”
(Gotelli, Colwell, & Colwel, 2001).
According to (Siegel, 2006) the formula to calculate the rarefaction curves is:
[ ] (
)
∑(
)
K is the total number of species of different taxa,
N is the total number of organisms in the sample,
is the total number of organisms in the i th sample, (i=1…K), and
is the number of species still found in the subsample of n organisms.
2.2.3 Subsampling method
Since 1995 all samples were subject to a subsampling procedure. As argued by
(Carey & Keough, 2002) subsampling a sample set, in this case samples of
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0.125m2 were subsampled to 0.05 m2, result in a procedure that is less time-
consuming and expensive while at the same time retaining the statistical
properties of the overall sample.
2 . 3 R e s u l t s
The data was analysed and plotted using Microsoft’s Excel 2011. The data that
was provided were the top four to five taxa of each year for each station. Density
counts and cumulative probabilities of each species were also provided, but in the
absence of the total population measurements only the taxa population density
was evaluated. The measurements indicate organism counts per 0.5m2.
A list of the top percentage of taxa is given in Table 4 in Appendix II for the
years 1979 to 2010. From the above table the time-series shown in Figure 12
were derived.
To provide a quick insight in the time dependent behaviour of the top taxa in
station C the use of Sparklines was utilised in order to convey quickly the change
in the observations. The following five figures demonstrate the time-series of
population for each of the five top species in population. The data spans from
1979 to 2010, and is followed by a win/loss Sparkline that indicates the increasing
or decreasing movement of the population from year to year. The right side of
the plots describes the maximum values throughout the years (in green colour),
the minimum values found (in red colour), and the overall linear trend of the
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population (green upwards arrow shows a positive slope for the polynomial, the
yellow line shows a neutral slope, and the red downwards arrow shows a negative
slope). The figures (Figure 12 to Figure 15) show overlaid plots of all the top taxa
per station. More detailed individual plots are presented in Appendix I.
Prionospio fallax
Year 1979 2010
2669
Count/MINMAX
0
Change/LinearTrend
Figure 3: Station C - Prionospio fallax population for the years 1979-2010
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Figure 4: Station C - Thyasira flexuosa population for the years 1979-2010
Figure 5: Station C - Amphiura filiformis population for the years 1979-2010
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Figure 6: Station C Pholoe spp population for the years 1979-2010
Figure 7: Station C - Urothoe elegans population for the years 1979-2010
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Similar plots below, are presenting the appropriate top ranking taxa in the
following figures Figure 8, Figure 9, and Figure 10 for stations D, E, and K
respectively.
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Figure 8: Station D - Top taxa populations for the years 1979-2010
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Figure 9: Station E - Top taxa populations for the years 1979-2010
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Figure 10: Station K - Top taxa populations for the years 1979-2010
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The Sparklines above were used because of their direct convey of information
regarding the behaviour of the time series in question. The overall trend of the
long-term observations because ease to digest and draw conclusions. High leaks
and low troughs are more easily identified and their corresponding maximum and
minimum values are represented to the right of each Sparkline.
More interesting comparisons can be drawn from them, e.g. the sudden increase
in the top three of four taxa at Station K in 1983 (see Figure 10 and Figure 15).
This will make that year more pertinent to correlate against meteorological data
of the area around Station K, later on.
Fugro ERT also provided community statistics for the years 1978 to 2007 and
2010. The tables Table 2, and Table 3 below present the populations size and
species magnitude and their corresponding diversity and evenness indices for all
the above mentioned years.
The abundance of Thyasira flexuosa at station C in 1998 was the highest
recorded in 19 years, then went up until 2004, where subsequently the number
dropped to the lowest level observed. Numbers have been increased in 2007 but
again decreased in 2010. The overall trend of the time-series shows a positive
slope for the linear fitted line (see Figure 4). There was also a spike in the
population of Prionospio fallax for the duration of 1990 to 1995 with a concurrent
decrease on the population of the rest of the top taxa. Overall the linear trends
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on the top three taxa seem to be increasing while the last two top taxa show
declining trends [see Figure 22 to Figure 26 in Appendix I]. These series are also
coupled with high diversity and evenness indices [see Table 2].
Table 2: Ranges of community statistics, Sullom Voe spring surveys, 1978 to 2007 (Fugro ERT, 2010)
Station
Numbers Diversity indices Evenness
Individuals Taxa Simpson’s Brillouin’s
Shannon-
Wiener’s
Pielou’s Heip’s
C (0.5m2) 1,066 to 6,061 97 to 215 0.75 to 0.98 2.66 to 4.26 3.91 to 6.31 0.54 to -0.84 0.09 to -0.42
* 101 to 129 * 0.94 to 0.97 * 3.74 to 5.55 * 5.20 to 5.82 * 0.78 to -0.83 * 0.35 to -0.43
D (0.5m2) 991 to 4,652 86 to 207 0.74 to 0.98 2.64 to 3.99 3.91 to 6.05 0.52 to -0.86 0.08 to -0.49
*90 to 135 *0.92 to 0.96 *3.49 to 5.23 *4.77 to 5.49 *0.71 to -0.80 *0.26 to -0.39
E (0.5m2) 520 to 3,571 76 to 146 0.79 to 0.96 2.20 to 3.58 3.24 to 5.47 0.51 to -0.82 0.11 to -0.43
*53 to 72 *0.90 to 0.97 *2.86 to 4.66 *4.40 to 5.31 *0.75 to -0.93 *0.36 to -0.74
K (0.5m2) 1,056 to 5,903 118 to 205 0.94 to 0.98 3.54 to 4.19 5.19 to 6.21 0.68 to -0.84 0.18 to -0.46
*93 to 121 *0.95 to 0.97 *3.64 to 5.29 *5.38 to 5.78 *0.79 to -0.88 *0.36 to -0.59
* 1995, 1998, 2001, 2004, and 2007 data, for sample area of 0.125 m2
Station D’s top taxa population is also skewed from the volatile population count
of Spio armata. The high abundance in 1997 and 2010 increase the unevenness of
the population, which is evident by the lowest diversity and evenness indices of
all the stations for 2010 [see Table 3]. All the taxa at Station D show a declining
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trend except for Prionospio fallax whose trend is falsely skewed from the two
outliers in 1997 and 2010.
Station E exhibited an overall declining taxa population in all the top four taxa.
However the diversity and evenness indices were quite high [see Table 3],
producing a evenly mixed total population, despite the population booms of
Urothoe elegans in 1985 and 2001. As shown in Figure 9 the entire top four taxa
have a declining population and overall lowest total populations count for the top
taxa amongst all stations.
Table 3: Community statistics, Sullom Voe spring surveys, May 2010 (Fugro ERT, 2010)
Station K showed evidence of a diverse steady population. The diversity and
evenness indices were the highest of all stations in this data set [see Table 3].
From the top four taxa in the samples, three of them exhibit stable of increasing
populations, except Pholoe spp which recorded a declining trend.
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The rarefaction curves plotted for the stations C, D, E, and K [see Figure 11]
show the taxa found for different number of individual organisms. The curves all
rise quickly from the left side, as the most abundant taxa are identified, and
generally plateau to the right side, as most of the existing taxa have been
identified so far. What is also evident is the smaller population count from
stations D and E as mentioned previously.
Figure 11: Hurlbert's rarefaction curves for stations C, D, E, and K, Sullom Voe survey, May 2010. (Fugro
ERT, 2010)
0
20
40
60
80
100
120
140
0 200 400 600 800 1000 1200 1400
Taxa
Individuals
Rarefaction Curves
C
D
E
K
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2 . 4 D i s c u s s i o n
Unfortunately due to the partial view of the available Sullom Voe monitoring
stations, no safe or conclusive outlooks can be derived. However there are some
interesting observations that can be made. Station C exhibits a flurry of
population for the Prionospio fallax, which is a genera of polychaete worms; it is
characteristic of muddy mixed sediments and favoured by nutrient rich
conditions, is one of the most numerically dominant species present in benthic
samples (Turner & Tibbetts, 2008).
The data for the provided stations, showed the populations for different portions
of the total sample. However all the stations provided population counts for a
range of cumulative percentages between 30-70% of the total sample. With this
in mind it can be argued that the changes reported in the results section above,
are in reference to the most common and abundant taxa in the sample.
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Figure 12: Top taxa species count for station C for the years 1979-2010
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1995
1998
2001
2004
2007
2010
Prionospio fallax 21 0 90 57 63 78 332 311 268 309 200 362 2669 1797 236 332 664 608 632 764
Thyasira flexuosa 141 114 98 76 80 180 116 213 247 155 183 51 130 151 168 408 336 8 256 60
Amphiura filiformis 1 3 2 7 6 14 9 13 5 16 2 5 6 20 4 0 4 4 4 20
Pholoe spp 47 124 195 144 368 200 231 210 159 208 89 44 36 78 92 100 64 19 24 16
Urothoe elegans 10 0 0 1 26 9 3 1 4 2 2 2 0 1 4 0 0 0 0 0
0
100
200
300
400
500
600
700
800
900
1000
Po
pu
lati
on
Station C 1979-2010
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Figure 13: Top taxa species count for station D for the years 1979-2010
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1995 1998 2001 2004 2007 2010
OLIGOCHAETA spp 775 207 242 306 135 15 318 242 165 140 221 0 279 38 352 704 564 624 12 68
Spio armata 132 54 289 44 0 77 275 162 1833 6 0 48 9 5 208 244 20 4 0 1720
Pholoe spp 30 18 48 88 3 113 79 21 39 15 26 10 13 16 36 56 16 7 16 0
Urothoe elegans 93 73 201 316 310 429 20 109 166 140 60 46 53 28 228 572 52 57 64 72
0
100
200
300
400
500
600
700
800
900
1000
Po
pu
lati
on
Station D
1979-2010
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Figure 14: Top taxa species count for station E for the years 1979-2010
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1995
1998
2001
2004
2007
2010
Corophium crassicorne 149 117 154 33 33 91 89 91 55 15 0 0 1 0 0 12 20 4 8 20
Bathyporeia elegans 106 10 0 28 50 219 175 118 115 96 26 73 81 26 0 88 108 76 20 24
Pholoe spp 4 16 58 9 6 11 7 6 4 4 3 1 3 1 0 0 12 0 0 32
Urothoe elegans 4 187 186 175 176 362 450 381 358 230 107 181 68 112 140 64 444 27 16 52
0
100
200
300
400
500
600
700
800
900
1000
Po
pu
lati
on
Station E 1979-2010
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Figure 15: Top taxa species count for station K for the years 1979-2010
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1995 1998 2001 2004 2007 2010
Apistobranchus 11 0 73 0 899 316 162 208 134 341 353 485 194 298 128 224 24 268 84 400
Paradoneis lyra 168 0 273 73 509 280 141 120 75 209 67 60 36 74 232 216 244 264 52 400
Pholoe spp 59 0 91 29 199 95 57 36 57 63 28 30 13 44 36 100 52 16 4 32
Urothoe elegans 137 0 116 318 302 398 209 141 188 119 149 68 94 113 120 708 572 21 144 4
0
100
200
300
400
500
600
700
800
900
1000
Po
pu
lati
on
Station K
1979-2010
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3 ENVIRONMENTAL FACTORS
3 . 1 I n t r o d u c t i o n
Sea surface temperature (SST) is one of the key factors in the distribution and
abundance of marine animal life. Its fluxuation holds a direct impact upon the
population and diversity of macrofauna organisms in the Shetlands area.
The SST in the Shetland area is influenced both by the heat flux exchange with
the air masses, mostly attributed to the North Atlantic Oscillation (NAO) as well
as the cold water currents inflowing from the North poles as well as warm water
currents from the North Atlantic, transferring heat and salt into the North Sea
area.
“The NAO is measured as the monthly difference in surface air pressure between
Iceland and the Azores-Gibraltar area “ (Hurrell, 1995). Two extreme states of
the NAO exist. A positive and a negative state, exhibited during the summer and
winter months respectively [see Figure 16].
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Figure 16: These maps show air pressure patterns on November 7, 2010 (left), when the Arctic Oscillation
was strongly positive, and on December 18 (right), when it was strongly negative. These phases are the
result of the whole atmosphere periodically shifting its weight back and forth between the Arctic and the
mid-latitudes of the Atlantic and Pacific Ocean, like water sloshing back and forth in a bowl. (Maps by
Ned Gardiner and Hunter Allen, based on Global Forecast System data from the National Centers for
Environmental Prediction.) 4
4 Image from http://www.climatewatch.noaa.gov/article/2011/long-distance-relationships-the-arctic-and-
north-atlantic-oscillations, accessed on August 22nd 2011.
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Figure 17: Winter (December through March) index of the NAO based on the difference of normalised
pressures between Lisbon, Portugal, and Stykkisholmur, celand, from 1864 through 1994. The heavy olid
line represents the meridional pressure gradient smoothed with a low -pass filter with seven weights (1, 3,
5, 6, 5, 3, and 1) to remove fluctuations with periods less than 4 years. Data and plot from (Hurrell, 1995).
The NAO phenomenon is largely an atmospheric modus and is considered one
of the most important manifestations of climate influencer in the Northern
humid climates. The phenomenon is closely related to the Arctic Oscillation but
should not be confused with the Atlantic Multidecadal Oscillation.
The winter months in Scotland result in numerous frontal cyclones, the
occurrence of gale force winds and high precipitation [see Figure 18]. In
combination with the NAO phenomenon, it has a substantial impact on the
salinity and SST of the North Sea basin.
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Figure 18: Distribution of rainfall across Scotland showing the marked contrast in precipitation regimes
between east and west. As a general rule, Shetland experiences higher than average rainfall. (Courtesy of
UK Meteorological Office)
There was an abundance of data available for marine environmental indicators
for the last 100 years. Data was accessed from the ICES Oceanographic database
for the Shetlands area. Long-term trends were identified, plotted and correlated in
order to assess their impact on the marine life.
3 . 2 M e t h o d s
SST data was accessed and downloaded from the (ICES Oceanographic
Database). The surface measurements were conducted at 10m depth using CTD’s
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(conductivity, temperature, and depth instruments), sampling bottles,
underway/pump systems, and oceanographic moorings.
These data is arranged as a statistical rectangle of area (rectangles are 1° of
longitude by 30’ of latitude; approximately 30 × 30 nautical miles) . The data
downloaded belonged to the identified rectangle no. 353062 between 5° E and 0°
longitude and 60° N to 65° N latitude.
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Figure 19: Areas near the Shetlands from which SST is analysed. Main area of data collection was between
5° E and 0° longitude and 60° N to 65° N latitude. Note that negative values indicate Westernly
longitudes.
The data was imported into Microsoft’s Excel and anomalies of outliers were
smoothed with a moving 2-year average filter constructed in Excel. The data was
plotted and presented below.
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3 . 3 R e s u l t s
There were approx. 355000 temperature and salinity records for the rectangle
selected from the (ICES Oceanographic Database). The data records involved
multiple locations and multiple records sampled within a day of surveying. In
order to produce meaningful results, the outliers were smoothed using a 2-year
running mean filter in Microsoft’s Excel program. Figure 20 shows the
temperature variation from 1979 to 2011, and the red line plots the running
average filtering. The linear trend-line is also plotted for the filtered data to show
the general trend of the time-series. Aside form the seasonal variations there
seems to be an increasing temperature trend for the last 30 years.
Figure 21 shows the salinity variation for the same period from 1979 to 2011.
The red line shows the 2-year moving average filtering and the black line plots the
trend-line of the filtered data, as in the temperature plot.
The benefit of the 2-year moving average filter is that it allows for most of the
outlier to be disregarded, zero or N/A values as well as unrealistic high/low
values, and to focus on the overall trend of both the salinity and temperature
data.
There seems to be a marked temperature increase after 1995. Both the winter and
summer temperatures tend to oscillate between further extremes, providing
colder SSTs for the winter and warmer SSTs for the summer months. This is
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visibly apparent in Figure 20 for the years after 1995. Before that year SSTs seem
to conglomerate closer to the average of 10.2°C but after that there are seasonal
variations as well as changes in average temperature.
The data indicates changes in temperature for different seasonal months. As an
indication, the winter months between 1999 and 2000 marked an increase of
almost 2°C when their summer months were relatively similar. 2007 and 2008
had winters that were cold as average, but form then on year 2009-2011 produced
temperatures that were markedly warmer than all times since 1979.
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Figure 20: Temperature measurements in degrees Celsius, for the Shetlands’ area. The blue line indicates
the raw data; the red line shows the 2-year running mean filtered data, and the black line is the linear
trend-line of the filtered data. Data from (ICES Oceanographic Database)
What the trend-line indicates is an overall increase in mean temperature of
~0.7°C. Especially since after 2007 it seems to do so with rapid oscillations to
extreme points of high/low temperature. The highest temperature was recorded
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in the summer of 2004 and the lowest at winter of 2007 with 14.3°C and 7°C
respectively.
One more variable that affects the characteristic of the marine environment is the
seawater salinity. It can be a direct indicator of marine climatic change, as it may
affect marine ecosystems and organisms and the ocean circulation (via its effect
on seawater density). In the coasts of UK salinity may be influenced by oceanic
water influx, precipitation and rainfall and fresh water affluent from rivers. The
latter will be of little consequence around the Shetlands. As with temperature
data, salinity data was available from the (ICES Oceanographic Database).
Similarly with the temperature data, the salinity data was processed in Excel
where empty values were replaced with the N/A symbol. After that, the raw
salinity data was filtered with a 7-year moving average, since the salinity data had
less variance than the temperature (the Standard Deviation for the temperature
data was 6.3 while for the salinity was 0.76). This allowed for the emergence of a
periodical trend in the form of a 7 th degree fitted polynomial seen in Figure 21.
The linear trend showed an almost flat increase in overall salinity (less than 0.05
psu) but revealed a low salinity around the year 1995 and 2008 and a high salinity
around the years 2005 and 2009.
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Figure 21: Salinity measurements in psu, for the Shetland’s area. The blue line indicates the raw data; the
red line shows the 2-year running mean filtered data, and the black line is the linear trend-line of the
filtered data. Data from (ICES Oceanographic Database).
3 . 4 D i s c u s s i o n
It is clearly apparent that there has been an SST increase the last 30 years in the
Shetland area. There has not been a continuous incline however, rather than three
periods of different behaviour. While the years 2002 to 2004, and 2008 to 2011
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were marked by high summer SSTs, there was an antipodal behaviour in the years
2005 to 2007, which marked low summer SSTs.
What is also noteworthy is that the SST increase was greater for the winter
months, which are also consistent with (Turrel, 2006). The most sharp increase in
SST was recorded since 1985, with rises between 0.2 and 0.6 °C (Huthnance,
2010).
Mostly likely this is linked to the influx of fresh water in the North Sea basin due
to the NAO phenomenon, presented previously.
The influence of the NAO phenomenon is acutely evident in the salinity data.
The salinity data exhibits an overall slight but visible increasing trend. However
the most interesting aspect is the periodicity of the peaks and troughs. The data
follows an 8-year cycle for the past 30 years. The trough of 1999 is followed by
another trough in 2007 [see Figure 21]. This correlation of the salinity in the
central North Sea with the NAO has also been shown by (Becker & Pauly, 1996).
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4 CONCLUSIONS
The analysis of the meteorological data revealed both long and short-term
changes and variations. Although it was identified that both the SST and the
seawater salinity were correlated with the NAO phenomenon and its 8-year
periodicity, there was a clear inclining trend in the SST data for the last 30 years.
There seems to be an evident increase in the summer temperatures followed by
quite increased winter temperatures following the period immediately after 1995.
That is the period were the mean temperature seems to increase slightly but with
a large standard deviation. The summer temperatures in the years 2008 to 2011
were amongst the highest recorded with the 2010 winter being evidently milder.
The salinity data also exhibited a slight overall increase, however the variations
between peaks and troughs were much more interesting than the linear trend.
There does seem to be a correlation of linear trends between salinity and SST
over the last 30 years, with both marking an apparent increase in their trends.
The environmental data were collected over an area of wider coverage. Dissecting
the data even further could perform more accurate analysis. For future work, the
rectangle of the area could be partitioned in smaller rectangles that engulfed the
Sullom Voe area more tightly. Additionally the SST and salinity time-series could
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be evaluated as two different series of summer and winter observations. Both of
these parameters were outside the scope of the present report.
One more important aspect that was not assessed was any correlation of taxa
populations with regard to spill accidents in the oil installations in the area. The
reason for this omission was that most of these accident go unreported and those
that are reported do not provide enough information in order to make any spatial
analysis of the distribution of the pollution in order to compare populations of
that area for variations. This problem could be mitigated with the recent use of
satellite imaging in synergy with GIS software. The migration of the spill could
more easily be monitored and more meaningful assumptions be made. However
this process in not a panacea as the recent BP spill in the Gulf of Mexico has
shown. What has become apparent now is that given the large depth of the spill
most of the escaped oil has remained trapped in a large depth between different
thermocline layers. This would annul the use of any satellite imaging for spill
monitoring. Sampling methods for the entire water column would be necessary in
this case.
The analysis form the micro-benthic data provided by ERT Fugro also showed
variations in the populations of different organisms. The rate of change was
irregular but the diversity and evenness indices showed abundant and dispersed
populations. There were however examples of large variations in the populations
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of taxa that flourished one year but rapidly declined the next. No clear
correlations to meteorological data were drawn though. The limitation of the
amount of data from more stations in the Sullom Voe area inhibited the attempt
to draw conclusions for a wider area than the four stations, C, D, E, and K. Two
pairs of the stations, C with K and E with D were conglomerated so the data
were not sufficient for a wide area assessment.
The analysis of the benthic data was further complicated by a change in the
sampling methodology in 1993 with the introduction of sub-sampling. This
process introduces some uncertainty when comparing populations pre and post-
1993. However the process was adopted with the premise that the two sampling
methodologies were statistically equivalent.
A further complication, one that obscured the cross-comparison between taxa
was the amendment of the taxa phylum name, e.g. OLIGOCHAETA spp in the
2001 and 2004 data became Grania spp for the 1995 and 1998 data which was
OLIGOCHAETA Type 1 for the previous years. This process hinders multi -
decadal comparisons of population, without being inhibiting, though.
No clear connection was established between the environmental factors in the
area and the population of the taxa. There are however several caveats with this
statement. Primarily the environmental variables that were assessed are in reality a
subset of all the available factors that could be assessed. Variables like
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precipitation, wave heights, wind direction and force, and atmospheric pressure
could also be used to complete the weather model of the area. Additionally the
SST and salinity data that was analysed was sampled from the 10m depth of the
sea surface. There is no way of knowing with the present data what the seabed
temperature variation was and how that affected the micro-benthic taxa. The
assumption taken here with the surface temperature data is that the variations in
the surface are propagated to the seabed.
Overall this research highlighted the need for more data to be collected and
analysed for the region surrounding the Sullom Voe installation. More accurate
and frequent data should be collected from a multitude of sources.
Environmental data should be logged for the wider area and if possible seabed
data should be harvested instead.
Another important issue that has arisen from this work is that there are far too
many differences in sampling and statistical methodology between different
producers of environmental reports. Something like that could have awkward
implications when there are blunt mismatches between reports of the same
monitoring areas. Common ground in the gathering, recognition and statistical
analysis should be agreed form all participating parties. The frequency of the
sampling within a year is also an issue to be resolved. Perhaps the selection of a
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certain time within a year could skew the results in favour of certain populations,
depending on their reproductive cycles.
Finally the matter of data openness needs to be addressed. The most productive
action by an individual conducting a monitoring survey would be to publicise the
end result, as well as the full data, to an open forum for peer-review. Monitoring
data should be shared, in order to avoid incorrect assessment methods or work
duplication to be propagated.
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CLIMATE CHANGE, IMPACTS AND MITIGATION
5 APPENDIX I
Top five taxa for Station C along with linear trend lines
Figure 22: Station C - Prionospio fallax population for the years 1979-2010
Figure 23: Station C – Thyasira flexuosa population for the years 1979-2010
0
500
1000
1500
2000
2500
3000
Prionospio fallax Linear (Prionospio fallax)
0
100
200
300
400
500
Thyasira flexuosa Linear (Thyasira flexuosa)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Figure 24: Station C – Amphiura filiformis population for the years 1979-2010
Figure 25: Station C - Pholoe spp population for the years 1979-2010
Figure 26: Station C – Urothoe elegans population for the years 1979-2010
0
5
10
15
20
25
Amphiura filiformis Linear (Amphiura filiformis)
0
100
200
300
400
Pholoe spp Linear (Pholoe spp)
-10
0
10
20
30
Urothoe elegans Linear (Urothoe elegans)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Top four taxa for Station D along with linear trend lines
Figure 27: Station D – OLIGOCHAETA spp population for the years 1979-2010
Figure 28: Station D – Spio armata population for the years 1979-2010
0
200
400
600
800
1000
OLIGOCHAETA spp Linear (OLIGOCHAETA spp)
0
500
1000
1500
2000
Spio armata Linear (Spio armata)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Figure 29: Station D – Pholoe spp population for the years 1979-2010
Figure 30: Station D – Urothoe elegans population for the years 1979-2010
0
20
40
60
80
100
120
Pholoe spp Linear (Pholoe spp)
0
100
200
300
400
500
600
700
Urothoe elegans Linear (Urothoe elegans)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Top four taxa for Station E along with linear trend lines
Figure 31: Station E – Corophium crassicorne population for the years 1979-2010
Figure 32: Station E – Bathyporiea elegans population for the years 1979-2010
-50
0
50
100
150
200
Corophium crassicorne Linear (Corophium crassicorne)
0
50
100
150
200
250
Bathyporeia elegans Linear (Bathyporeia elegans)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Figure 33: Station E –Pholoe spp population for the years 1979-2010
Figure 34: Station E – Urothoe elegans population for the years 1979-2010
0
10
20
30
40
50
60
70
Pholoe spp Linear (Pholoe spp)
0
100
200
300
400
500
Urothoe elegans Linear (Urothoe elegans)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Top four taxa for Station K along with linear trend lines
Figure 35: Station K – Aristobranchus population for the years 1979-2010
Figure 36: Station K – Paradoneis lyra population for the years 1979-2010
0
500
1000
Apistobranchus Linear (Apistobranchus )
0
100
200
300
400
500
600
Paradoneis lyra Linear (Paradoneis lyra)
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Figure 37: Station K – Pholoe spp population for the years 1979-2010
Figure 38: Station K – Urothoe elegans population for the years 1979-2010
0
50
100
150
200
250
Pholoe spp Linear (Pholoe spp)
0
200
400
600
800
Urothoe elegans Linear (Urothoe elegans)
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6 APPENDIX II
Table 4: Variation in the abundance of selected taxa from stations C, D, E and K, Sullom Voe spring surveys, 1979 - 2007
Station C
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1995 1998 2001 2004 2007 2010
Prionospio fallax 21 0 90 57 63 78 332 311 268 309 200 362 2669 1797 236 332 664 608 632 764
Thyasira flexuosa 141 114 98 76 80 180 116 213 247 155 183 51 130 151 168 408 336 8 256 60
Amphiura filiformis 1 3 2 7 6 14 9 13 5 16 2 5 6 20 4 0 4 4 4 20
Pholoe spp 47 124 195 144 368 200 231 210 159 208 89 44 36 78 92 100 64 19 24 16
Urothoe elegans 10 0 0 1 26 9 3 1 4 2 2 2 0 1 4 0 0 0 0 0
Station D
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1995 1998 2001 2004 2007 2010
OLIGOCHAETA spp 775 207 242 306 135 15 318 242 165 140 221 0 279 38 352 704 564 624 12 68
Spio armata 132 54 289 44 0 77 275 162 1833 6 0 48 9 5 208 244 20 4 0 1720
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Pholoe spp 30 18 48 88 3 113 79 21 39 15 26 10 13 16 36 56 16 7 16 0
Urothoe elegans 93 73 201 316 310 429 20 109 166 140 60 46 53 28 228 572 52 57 64 72
Station E
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1995 1998 2001 2004 2007 2010
Corophium crassicorne 149 117 154 33 33 91 89 91 55 15 0 0 1 0 0 12 20 4 8 20
Bathyporeia elegans 106 10 0 28 50 219 175 118 115 96 26 73 81 26 0 88 108 76 20 24
Pholoe spp 4 16 58 9 6 11 7 6 4 4 3 1 3 1 0 0 12 0 0 32
Urothoe elegans 4 187 186 175 176 362 450 381 358 230 107 181 68 112 140 64 444 27 16 52
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Station K
1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1995 1998 2001 2004 2007 2010
Apistobranchus 11 0 73 0 899 316 162 208 134 341 353 485 194 298 128 224 24 268 84 400
Paradoneis lyra 168 0 273 73 509 280 141 120 75 209 67 60 36 74 232 216 244 264 52 400
Pholoe spp 59 0 91 29 199 95 57 36 57 63 28 30 13 44 36 100 52 16 4 32
Urothoe elegans 137 0 116 318 302 398 209 141 188 119 149 68 94 113 120 708 572 21 144 4
sample size for all stations for all years = 0.5m2
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Table 5: A comparison of the top five ranking species with density and cumulative percentage abundance, at stations C, D, E and K over 25 years of monitoring, Sullom
Voe spring surveys, 1979 to 2010
Yea
r Station C
No/0.5
m2
Cum
% Station D
No/0.5
m2
Cum
% Station E
No/0.5
m2
Cum
% Station K
No/0.
5 m2
Cum
%
197
9
Thyas ira
flexuosa
141 10.2 O LIGOCHA ETA
Type 1
779 27 O LIGOCHAETA
Type 1
318 31.7 Paradoneis lyra 168 11.8
Paradoneis lyra 140 20.4 Pomatoceros
triqueter
286 36.9 Corophium
crass icorne
149 46.5 Urothoe elegans 137 21.5
C YPRIDINIDA E
sp A
113 28.6 Spirorbis
spirillum
249 45.5 Bathyporeia
elegans
103 56.8 Exogone hebes 88 27.6
Exogone hebes 93 35.4 Spio sp B 132 50.1 Exogone hebes 46 61.3 O LIGOC HA ETA
Type 1
69 32.5
Pholoe inornata 47 38.8 Urothoe elegans 93 53.3 Exogone
naidina
42 65.5 Mediomastus
fragilis
66 37.1
198
0
Pholoe inornata 124 12.3 O LIGOCHA ETA
Type 1
207 20.9 O LIGOCHAETA
Type 1
504 36.6 No data
Thyas ira
flexuosa
114 23.6 Glycera lapidum 121 33.1 Urothoe
elegans
187 50.2
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Lucinoma
borealis
53 28.9 Urothoe elegans 73 40.5 Corophium
crass icorne
117 58.7
Paradoneis lyra 52 34.1 Spio sp B 54 45.9 Pomatoceros
triqueter
62 63.2
Owenia
fus iformis
31 37.2 Urothoe marina 54 51.4 Phoxocephalus
holbolli
49 66.7
198
1
Labidoplax
buski
234 7 .1 Pomatoceros
triqueter
659 23.4 Spio sp B 191 11.5 Paradoneis lyra 273 11.1
Polydora spp 232 14.1 Spio sp B 289 33.7 Urothoe
elegans
186 22.6 Polydora spp 193 19
Pholoe inornata 195 20 O LIGOCHA ETA
Type 1
242 42.2 O LIGOCHAETA
Type 1
179 33.4 Spio sp B 121 23.9
Leptocheirus
pectinatus
117 23.6 Urothoe elegans 201 49.9 Corophium
crass icorne
154 42.7 Urothoe elegans 116 28.6
Phoronis
muelleri
110 26.9 Exogone hebes 79 52.2 Exogone Hebes 134 50.7 Prinospio
cirrifera
114 33.2
198
2
Pholoe inornata 114 4 .8 Urothoe elegans 316 11.1 Exogone hebes 288 18.5 Urothoe elegans 318 14
Labidoplax
buski
128 9 .1 O LIGOCHA ETA
Type 1
286 21.1 O LIGOCHAETA
Type 1
280 36.5 Scoloplos
armiger
150 20.6
Polydora flava 101 12.5 Exogone hebes 232 29.2 Urothoe 175 47.7 Polydora caeca 99 25
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CLIMATE CHANGE, IMPACTS AND MITIGATION
elegans
Ampelis ca
tenuicornis
101 15.9 Polydora caeca 168 35.1 Phoxocephalus
holbolli
111 54.8 Tanaops is
graciloides
98 28.3
Chone sp B 99 19.2 Dodecaceria sp 166 40.9 Aonides
paucibranchiata
74 59.6 Polydora flava 74 32.6
198
3
C YPRIDINIDA E
sp A
500 8 .7 Urothoe elegans 310 20.6 Urothoe
elegans
176 16 Apis tobranchus
tullbergi
899 15.2
Pholoe inornata 408 15.9 Bathyporeia
elegans
267 38.3 C YPRIDINIDA E
sp A
143 28.9 C YPRIDINIDA E
sp A
765 28.2
Paradoneis lyra 329 21.6 O LIGOCHA ETA
Type 1
132 47.1 O LIGOCHAETA
Type 1
123 40.1 Paradoneis lyra 509 36.8
Myriochele
heeri
239 25.8 Spiophanes
bombyx
87 52.9 Exogone hebes 76 47 Urothoe elegans 302 41.9
Polydora
quadrilobata
204 29.4 Exogone naidina 72 57.6 Bathyporeia
elegans
50 51.5 Tanaops is
graciloides
273 46.6
198
4
Pholoe inornata 200 6 .5 Urothoe elegans 429 16.4 Urothoe
elegans
362 24.5 Urothoe elegans 398 12.4
Thyas ira
flexuosa
188 12.3 Dendrodoa
grossularia
207 24.4 Bathyporeia
elegans
219 39.3 Apis tobranchus
tullbergi
317 22.3
C YPRIDINIDA E 141 16.8 Metopa ?bruzelii 160 30.5 Spio sp A 200 52.9 C YPRIDINIDA E 292 31.4
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CLIMATE CHANGE, IMPACTS AND MITIGATION
sp A sp A
Myriochele
heeri
117 20.6 Pholoe inornata 113 34.8 Corophium
crass icorne
91 59 Paradoneis lyra 281 40.2
Paradoneis lyra 110 24.1 Pomatoceros
triqueter
104 38.3 Periculodes
longimanus
78 64.3 Chone
collaris /filicaudat
a
102 43.4
198
5
Prionospio
malmgreni
332 7 .5 Pomatoceros
triqueter
407 11.5 Urothoe
elegans
450 25.4 Urothoe elegans 209 8 .4
Pholoe inornata 231 12.8 Spio sp B 275 19.3 Exogone
naidina
197 36.5 Tanaops is
graciloides
199 16.3
Exogone hebes 170 16.7 O LIGOCHA ETA
Type 1
264 26.8 Bathyporeia
elegans
175 46.4 Exogone naidina 164 22.9
Myriochele
oculata
167 20.4 Dendrodoa
grossularia
189 32.1 O LIGOCHAETA
Type 1
147 54.7 Apis tobranchus
tullbergi
162 29.4
Mysella
bidentata
151 23.9 Prionospio
cirrifera
144 36.2 Corophium
crass icorne
89 59.7 Paradoneis lyra 141 35
198
6
Prionospio
malmgreni
311 7 .5 O LIGOCHA ETA
Type 1
241 12.1 Urothoe
elegans
381 24.6 Apis tobranchus
tullbergi
208 11.3
Thyas ira
flexuosa
213 12.5 Spio sp B 162 20.2 Exogone
naidina
156 34.7 Urothoe elegans 142 19.1
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Pholoe inornata 210 17.6 Prionospio spp
juv
144 27.5 Bathyporeia
elegans
118 42.3 Paradoneis lyra 120 25.6
Exogone
naidina
162 21.4 Urothoe elegans 109 32.9 O LIGOCHAETA
Type 1
107 49.2 Prionospio spp
juv
83 30.1
Myriochele
oculata
161 25.3 Exogone hebes 108 38.3 Corophium
crass icorne
91 55.1 Exogone hebes 58 33.3
*
OLIGOCHAETA spp (2001 and 2004 data) = Grania spp (1995 and 1998 data) = OLIGOCHAETA Type 1 of
previous years .
*
*
Spio armata (1995 data) is equivalent to Spio sp B of previous
years .
*
**
Prionospio fallax (2001 onwards data) = Prionospio malmgreni of
previous years .
*
*** Prionospio banyulens is (2001 onwards data) = Prionospio ockelmanni of previous years .
Continued
Yea
r Station C
No/0.5
m2
Cum
% Station D
No/0.5
m2
Cum
% Station E
No/0.5
m2
Cum
% Station K
No/0.
5 m2
Cum
%
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CLIMATE CHANGE, IMPACTS AND MITIGATION
198
7
Prionospio
malmgreni
258 7 .5 Spio sp B 1,833 49.6 Urothoe
elegans
358 22.7 Urothoe elegans 188 6 .6
Thyas ira
flexuosa
247 14.4 Dendrodoa
grossularia
253 46.5 Spio sp B 304 41.9 Scoloplos
armiger
135 11.3
Mysella
bidentata
240 21.1 Urothoe elegans 166 61 Bathyporeia
elegans
115 49.2 Apis tobranchus
tullbergi
134 16.1
Pholoe inornata 159 25.6 O LIGOCHA ETA
Type 1
161 65.3 O LIGOCHAETA
Type 2
90 54.9 Exogone hebes 125 20.4
Leptocheirus
pectinatus
157 30 Pomatoceros
triqueter
68 67.2 Exogone
naidina
63 58.9 Prionospio spp
juv
102 24
198
8
Leptocheirus
pectinatus
321 7 .7 Urothoe elegans 140 11.2 Urothoe
elegans
230 23.6 Apis tobranchus
tullbergi
341 11.1
Prionospio
malmgreni
309 15.2 O LIGOCHA ETA
Type 1
137 22.1 Bathyporeia
elegans
96 33.4 Paradoneis lyra 209 17.9
Pholoe inornata 208 20.2 Prionospio spp
juv
106 30.6 O LIGOCHAETA
Type 1
84 42 Polydora
quadrilobata
148 22.7
C YPRIDINIDA E
sp A
196 24.9 Glycera lapidum 76 36.7 Exogone hebes 44 46.6 Urothoe elegans 119 26.6
Rhodine
gracilior
177 29.2 Exogone hebes 57 41.2 A O RIDA E sp 44 51.1 C YPRIDINIDA E
sp A
117 30.4
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CLIMATE CHANGE, IMPACTS AND MITIGATION
198
9
Leptocheirus
pectinatus
211 5 .89 Prionospio
ockelmanni
204 11.54 Urothoe
elegans
107 12.95 Apis tobranchus
tullbergi
353 11
Prionospio
malgmreni
200 11.48 O LIGOCHA ETA
Type 1
200 22.86 Scoloplos
armiger
89 23.73 Exogone hebes 179 16.5
7
Thyas ira
flexuosa
183 16.6 Glycera lapidum 145 31.07 Spiophanes
bombyx
41 28.69 Eurothoe
elegans
149 21.2
1
Exogone hebes 170 21.35 Polycirrus spp
indet
73 35.2 A O RIDA E sp 31 32.44 Tanaops is
graciloides
149 25.8
6
Rhodine
gracilior
170 26.1 Mediomastus
fragilis
66 38.94 Cochlodesma
praetenue
31 36.2 Exogone naidina 125 29.7
5
199
0
Prionospio
malmgreni
362 11.03 Prionospio
malmgreni
93 9 .33 Urothoe
elegans
181 15.52 Apis tobranchus
tullbergi
606 19.2
6
Prionospio
ockelmanni
263 19.05 Glycera lapidum 71 16.45 Scoloplos
armiger
93 23.5 Prionospio
ockelmanni
214 26.0
5
Exogone hebes 151 23.65 Prionospio spp
juv
58 22.27 Bathyporeia
elegans
73 29.76 Exogone hebes 171 31.4
9
Leptocheirus
pectinatus
144 28.04 Spio sp B 48 27.08 O LIGOCHAETA
Type 1
72 35.94 Prionospio
malmgreni
108 34.9
1
Paradoneis lyra 105 31.24 Prionospio
cirrifera
47 31.8 Exogone
naidina
65 41.51 Tanaops is
graciloides
88 37.6
9
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CLIMATE CHANGE, IMPACTS AND MITIGATION
199
1
Prionospio
malmgreni
2,669 48.95 Prionospio
ockelmanni
443 23.4 Aricidea minuta 119 11.49 Prionospio
malmgreni
227 10.8
1
Prionops io
ockelmanni
370 55.73 O LIGOCHA ETA
Type 1
262 37.24 Bathyporeia
elegans
81 19.31 Apis tobranchus
tullbergi
194 20.0
5
Exogone hebes 173 58.9 Glycera lapidum 150 45.17 Cochlodesma
praetenue
76 26.64 Prionospio
ockelmanni
117 25.6
2
Thyas ira
flexuosa
130 61.29 Pomatoceros
triqueter
57 48.18 Urothoe
elegans
68 33.21 Aricidea minuta 102 30.4
8
Rhodine
gracilior
127 63.62 Caulleriella
zetlandica
53 51 Scoloplos
armiger
63 39.29 O LIGOC HA ETA
Type 2
94 34.9
5
199
2
Prionospio
malmgreni
1,797 30.33 Prionospio
ockelmanni
65 6 .47 Urothoe
elegans
112 15.82 Prionospio
ockelmanni
383 11.6
4
Exogene hebes 338 36.04 C YPRIDINIDA E
sp A
63 12.74 O LIGOCHAETA
Type 1
39 21.33 Apis tobranchus
tullbergi
298 20.6
9
Prionospio
ockelmanni
285 40.85 Pomatoceros
triqueter
48 17.51 Exogone hebes 33 25.99 Prionospio
malmgreni
214 27.2
Rhodine
gracilior
161 43.57 Prionospio
cirrifera
42 21.69 Spio sp A 30 30.23 Jasmineira
caudata
128 31.0
9
Chaetozone
setosa
152 46.13 Leptochiton
asellus
40 25.67 Capitella spp 28 34.18 Urothoe elegans 113 34.5
2
Page 89
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CLIMATE CHANGE, IMPACTS AND MITIGATION
199
5
Prionospio
ockelmanni
280 8 .06 Grania spp* 320 12.56 Grania spp* 140 13.01 Paradoneis lyra 232 9 .51
Paradoneis lyra 252 15.3 Urothoe elegans 228 21.51 Urothoe
elegans
140 26.02 Apis tobranchus
tullbergi
128 14.7
5
Prionospio
malmgreni
236 22.09 Spio armata** 208 29.67 Spio armata** 124 37.55 Urothoe elegans 120 19.6
7
Exogone hebes 184 27.39 Prionospio
ockelmanni
136 35.01 Glycera lapidum 60 43.12 Prionospio
ockelmanni
112 24.2
6
Ampelisca
tenuicornis
176 32.45 Glycera lapidum 128 40.03 Scoloplos
armiger
52 47.96 C YPRIDINA E sp
A
108 28.6
9
199
8
Thyas ira
flexuosa
408 9 .71 Grania spp 616 13.24 Grania spp 132 11.07 Urothoe elegans 708 14.6
3
Prionospio
malmgreni
332 17.6 Urothoe elegans 572 25.54 Bathyporeia
elegans
88 18.46 C YPRIDINA DA E
sp A
436 23.6
4
Exogone hebes 204 22.45 Prionospio
ockelmanni
356 33.19 Scoloplos
armiger
72 24.5 Apis tobranchus
tullbergi
224 28.2
6
Leptocheirus
pectinatus
152 26.07 Glycera lapidum 268 38.95 Urothoe
elegans
64 29.87 Exogone hebes 216 32.7
3
cf
Hemicytherura
140 29.4 Leptocheirus
hirsutimanus
240 44.11 Cochlodesma
praetenue
64 35.23 Paradoneis lyra 216 37.1
9
Page 90
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CLIMATE CHANGE, IMPACTS AND MITIGATION
sp A
*
OLIGOCHAETA spp (2001 and 2004 data) = Grania spp (1995 and 1998 data) = OLIGOCHAETA Type 1 of
previous years .
*
*
Spio armata (1995 data) is equivalent to Spio sp B of previous
years .
*
**
Prionospio fallax (2001 onwards data) = Prionospio malmgreni of
previous years .
*
*** Prionospio banyulens is (2001 onwards data) = Prionospio ockelmanni of previous years .
Continued
Year Station C
No/0.5
m2
Cum
% Station D
No/0.5
m2
Cum
% Station E
No/0.5
m2
Cum
% Station K
No/0.5
m2
Cum
%
2001 Prionospio
fallax*** 664
14.12 O LIGOC HA ETA
spp* 564
15.48 Urothoe elegans 444 25.23 Urothoe elegans
572
14.05
Thyas ira flexuosa
336
21.26 Prionospio
banyulensis**** 304
23.82 O LIGOCHA ETA
spp*
224 37.95 Prionospio
banyulensis**** 312
21.71
Exogone hebes 264 26.87 Glycera lapidum 268 31.17 Exogone naidina 128 45.23 O LIGO C HA ETA 304 29.17
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CLIMATE CHANGE, IMPACTS AND MITIGATION
spp*
Galathowenia
oculata 228
31.72 Leptocheirus
hirsutimanus 264
38.42 Bathyporeia spp 108 51.36 Paradoneis lyra
244
35.17
Exogone naidina 184 35.63 Exogone hebes 168 43.03 Aricidea minuta 68 55.23 Chone filicaudata 180 39.59
2004 Prionospio
fallax***
608 13.01 O LIGOC HA ETA
spp* 624
17.31 Urothoe elegans
108
9 .96 Prionospio
banyulensis**** 432
11.8
Ampelisca
tenuicornis
588 25.6 Prionospio
banyulensis**** 532
32.08 Exogone naidina
88
18.08 Jasmineira
caudata 372
21.97
Jasmineira
caudata
276 31.51 Glycera lapidum
292
40.18 Bathyporeia spp
76
25.09 Apis tobranchus
tullbergi 268
29.29
Leptocheirus
pectinatus
228 36.39 Leptocheirus
hirsutimanus 276
47.84 Cochlodesma
praetenue 68
31.37 Paradoneis lyra
264
36.5
Phoronis muelleri 220 41.1 Jasmineira
caudata 260
55.05 O LIGOCHA ETA
spp* 64
37.27 Exogone naidina
136
40.22
2007 Prionospio
fallax*** 632 19.73
Pomatoceros
triqueter 384 22.38
Perioculodes
longimanus 44 8 .46 Urothoe elegans 144 9 .38
Thyas ira flexuosa 256 27.72
Prionospio
banyulensis**** 108 28.67
Scoloplos
armiger 32 14.62
Jasmineira
caudata 96 15.63
Lumbrineris
gracilis 220 34.58
Galathea
intermedia 80 33.33
Cochlodesma
praetenue 28 20
Apis tobranchus
tullbergi 84 21.09
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CLIMATE CHANGE, IMPACTS AND MITIGATION
Paradoneis lyra 168 39.83 Maera othonis 68 37.3
Spiophanes
bombyx 24 24.62
Prionospio
banyulens is 76 26.04
Phoronis spp 160 44.82 Urothoe elegans 64 41.03
Echinocyamus
pus illus 24 29.23
Polycirrus
norvegicus 60 29.95
2010 Prionospio
fallax*** 764 15.43 Spio armata 1,720 48.53 Spio armata 92 14.11
Jasmineira
caudata 444 9 .69
Exogone
verugera 424 23.99
Pomatoceros
triqueter 116 51.81
Perioculodes
longimanus 56 22.7
Apis tobranchus
tullbergi 400 18.41
Apis tobranchus
tullbergi 272 29.48
Jasmineira
caudata 108 54.85 Urothoe marina 44 29.45 Paradoneis lyra 400 27.14
Jasmineira
caudata 256 34.65
Leptocheirus
hirsutimanus 100 57.67 Spio decorata 32 34.36 Exogone verugera 252 32.64
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