Dissertation Final Eugenia Metzaki

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

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:……………………………………………………………

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.

ii


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.

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

4


4 Conclusions ...............................................................................................63

5 Appendix I.................................................................................................68

6 Appendix II ...............................................................................................76

7 Bibliography ..............................................................................................92

5


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

6


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

7


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

8


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

9


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

10


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

11


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.

12


Figure 1: The location of the Sullom Voe terminal in Shetland Islands, Scotland. (Fugro ERT, 2010)

13


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/

14


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

15


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

16


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.

17


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

18


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

19


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

20


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.

21


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.

22


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.

23


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

24


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

25


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)

26


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

27


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

28


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:

29


∑

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

30


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 .

31


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

32


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

33


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

34


Figure 4: Station C - Thyasira flexuosa population for the years 1979-2010

Figure 5: Station C - Amphiura filiformis population for the years 1979-2010

35


Figure 6: Station C Pholoe spp population for the years 1979-2010

Figure 7: Station C - Urothoe elegans population for the years 1979-2010

36


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.

Figure 8: Station D - Top taxa populations for the years 1979-2010

38


Figure 9: Station E - Top taxa populations for the years 1979-2010

39


Figure 10: Station K - Top taxa populations for the years 1979-2010

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

41


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

42


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.

43


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

44


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.

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

46


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

47


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

48


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

50


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].

51


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.

52


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.

53


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

54


(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.

55


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.

56


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

57


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.

58


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

59


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.

60


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

61


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).

62


63


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

64


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

65


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

66


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

67


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.

68


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)

69


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)

70


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)

71


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


0

100

200

300

400

500

600

700


72


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)

73


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


0

100

200

300

400

500


74


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)

75


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


0

200

400

600

800


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

77


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

78


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

79


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

80


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


Type 1

179 33.4 Spio sp B 121 23.9

Leptocheirus

pectinatus

117 23.6 Urothoe elegans 201 49.9 Corophium

crass icorne


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


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

81


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


Type 1


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


Thyas ira

flexuosa

188 12.3 Dendrodoa

grossularia


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

82


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


elegans

175 46.4 Exogone naidina 164 22.9

Myriochele

oculata

167 20.4 Dendrodoa

grossularia


Type 1


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


tullbergi

208 11.3

Thyas ira

flexuosa

213 12.5 Spio sp B 162 20.2 Exogone

naidina


83


Pholoe inornata 210 17.6 Prionospio spp

juv


elegans


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


*

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

%

84


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


tullbergi

134 16.1

Pholoe inornata 159 25.6 O LIGOCHA ETA

Type 1


Type 2


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


tullbergi

341 11.1

Prionospio

malmgreni


Type 1


elegans


Pholoe inornata 208 20.2 Prionospio spp

juv


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

85


198

9

Leptocheirus

pectinatus

211 5 .89 Prionospio

ockelmanni

204 11.54 Urothoe

elegans


tullbergi

353 11

Prionospio

malgmreni


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


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


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

86


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


Type 1


elegans


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


ockelmanni

383 11.6

4

Exogene hebes 338 36.04 C YPRIDINIDA E

sp A


Type 1


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


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

87


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


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


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


9

88


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*


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

89


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

90


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

92


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Dissertation Final Eugenia Metzaki

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