EDINBURGH NAPIER UNIVERSITY

PROJECT REPORT: 2016

MSC WILDLIFE BIOLOGY AND CONSERVATION

David Bryson

40179101

The Effects of Anthropogenic Noise on Aspects of

Physiology and Behavioural Ecology in a Model Marine

Species Mytilus Edulis

Introduction

Human activity has greatly altered the natural acoustic background of the world, in both terrestrial

and aquatic environments. Organisms now face disturbance from the noise created by

anthropogenic activity alongside other damaging but more well-known aspects of human behaviour,

such as habitat degradation, chemical pollution, and habitat fragmentation (Goines & Hagler, 2007).

A lack of current understanding of harmful effects of anthropogenic noise makes implementing

appropriate legislation impossible; however with increasing human population and need for

expansion, anthropogenic noise pollution is a problem that is only going to get worse in both the

terrestrial environment and the aquatic- therefore gathering as much data as possible to help make

informed decisions in legislation is key.

The EU Marine Strategy Framework Directive (MSFD) is a framework formally introduced in 2008

which aims to manage human activities and promote sustainable use of marine goods and services.

The overall objective is to achieve a “Good Environmental Status” in all European waters by 2020.

“Good Environmental Status” (or GES) is defined by the Marine Directive as “The environmental

status of marine waters where these provide ecologically diverse and dynamic oceans and seas

which are clean, healthy and productive”. (European Commission, 2008). The MSFD became part of

UK legislation in June 2010. For UK waters to achieve GES 11 separate criteria have to be met:

1. Maintenance of biological diversity.

2. Exotic species introduced by humans do not significantly alter the natural marine

environment.

3. Populations of fish and shellfish currently exploited by humans are maintained at safe levels

with demographics indicative of a healthy population for each species.

4. All elements of food webs in marine ecosystems are maintained at normal abundance and

diversity, and are at levels that ensure long-term longevity.

5. Human-introduced eutrophication is minimised, in particular the negative effects associated

with eutrophication including loss of abundance and diversity.

6. Benthic ecosystems are not significantly altered by human activities.

7. Altering the hydrology of EU marine systems does not significantly negatively affect marine

ecosystems.

8. Organic and inorganic contaminants do not reach levels at which they become harmful

pollutants to marine species.

9. Contaminants in marine species consumed by humans do not reach levels at which they

pose any health risks to humans.

10. Litter, organic waste, and other forms of human waste do not cause harm to coastal and

marine environments.

11. Introduction of extra energy into the marine environment, in particular underwater noise

produced by human activities, does not adversely affect the marine environment.

Of all of these criteria, the final criteria is the least documented within literature and least

understood- necessitating the need for research into the harmful effects noise pollution can have on

the marine environment. Will appropriate knowledge and evidence, proper legislation can be

implemented to protect marine species and promote sustainable use of marine goods. Research into

noise pollution would also be of benefit to terrestrial environments, but would be of particular

benefit to aquaculture- the UK, in particular Scotland, is one of the largest producers of farmed fish

and shellfish in the world (Bostock et al., 2010). All along the west coast of Scotland there are many

fish and shellfish farms, and any research which benefits production will be of use to these farms.

Noise pollution can loosely be defined as any anthropogenically-produced sound which differs

significantly in its attributes from background acoustic noise and is at sufficient levels that it

negatively affects the physiology and behaviour of organisms subject to it- this can range from

human activity such as traffic, to the arrival of an invasive, particularly vocal species, like many

songbirds due to human activity (Kumschick & Nentwig, 2010). These negative effects can manifest

themselves in many different ways- oxidative stress, and reduced fecundity for example (Demirel et

al., 2009, Schroeder et al., 2012). Noise pollution is most commonly associated with humans in large

cities and patients in hospitals- where constant exposure to noise can lead to adverse effects on

human health, such as hampering recovery times for patients in hospitals (Stansfeld & Matheson,

2003). Within the natural environment, anthropogenically-produced sound can be severely

detrimental to many species. For example, Francis et al. (2009) found that noise pollution by human

activity severely negatively affected bird nest distributions throughout woodlands in the state of

New Mexico, USA. Anthropogenically-produced sound has consistently been shown to alter

behavioural ecology in birds (Francis et al., 2011, Patricelli & Blickley, 2006).

While chemical pollution in nature is well documented in scientific literature, and is often at the

forefront of popular science (for example major oil spills such as the recent major spill in the Gulf of

Mexico in April 2010), noise pollution is by comparison poorly documented, and rarely discussed by

the media. The vast array of anthropogenically-produced sound varies enormously, from engine

noise to controlled demolitions. This leaves a wide spectrum of sound waves, intensities and

volumes which can have an array of effects on different species, in a similar way that the vast array

of chemicals used in anthropogenic processes have differing effects on different organisms. Within

human biology, research has been dedicated to noise pollution in cities and hospitals (Topf, 2000,

Zannin et al., 2002), but if we were to compare the vast array of data on human responses to noise

pollution and compare it to the quantity of data currently available on noise pollution effects on

other taxa, and compare this to current understandings of the varying effects of the vast array of

anthropogenically-produced chemical pollutants, there is a clear gap in knowledge and

understanding. To understand the threats that marine life faces to noise pollution, understanding of

how sound movement differs in water to air is first required.

Sound travels much faster in water than through air- the speed of sound through gases at a

temperature of 200c at approximately 1,230 km/h, or approximately 340 metres per second. This can

be calculated using the following formula (Cramer, 1993):

Vsound= √(γRT)/M

Where:

γ is the adiabatic constant (the heat capacity of the particular gas, in the case of air this is equal to

1.4).

R is the universal gas constant, equal to 8.314 J mol-1 K-1.

T is temperature.

M is the molecular mass of the gas- for dry air this is 28.95g/mol.

As water is a denser medium than air, molecules are closer together. As a result sound moves much

faster through the denser medium, and sound travels even faster through solid objects. The speed of

sound through seawater is dependent on the depth of the water at which measurement is taken

(pressure increases with depth, and with increasing pressure molecules are closer together

(Mackenzie, 1981)), and the temperature of the water (with increasing temperatures, molecules

move faster due to the fact that they now contain more energy, and therefore interact with each

other more frequently causing sound waves to travel through the water faster). Because

temperature and pressure are not uniform throughout the oceans, the speed at which sound travels

through the oceans can vary greatly (Mackenzie, 1981). To determine the speed at which sound can

travel through water, the following equation was used:

C(D,S,T)=

1448.96 + 4.591T – 5.304 x 10-2T2 + 2.374 x 10-4T-3 + 1.304 (S-35) + 1.630 x 10-2D + 1.675 x 10-7D2 +

1.025 x 10-2T(S – 35) – 7.139 x 10-13 TD3

Where:

T= temperature (0c)

S= salinity (ppt)

D= depth (m)

Mackenzie’s equation can be used to measure the speed of sound within temperatures of 2 – 300c,

salinities of 25 – 40 parts per thousand, and between depths of 0 – 8000m. This equation allows for

a vast array of experimental noise exposure treatments to be undertaken, capable of mimicking the

vast majority of the marine environment. At standard seawater salinity of 35 ppm, a depth of 0m,

and a temperature of 50c sound travels at a speed of 1496.14m/s, or 5386.1 km/h. Sound travels

faster through seawater than freshwater- as it is a denser liquid than freshwater (Rogers & Cox,

1988). Sound travels in longitudinal waves of pressure which travel through matter- without matter

there is no sound. Every sound wave has a frequency- measured in Hertz. The higher the frequency,

the higher in pitch the sound. The strength of a sound wave determines its amplitude- how loud the

noise is.

Within the marine environment, noise pollution primarily comes in the form of activities such as

shipping lanes, which produce low frequency noise (10 – 500 Hertz) in the water column. Cargo ships

are typically very large vessels-some of the largest ever created, which subsuquently require very

large engines- as a result the noise generated by them is significant. For example, the Wärtsilä-Sulzer

RTA96-C diesel engine produced in Finland, the largest in production, weighs over 2,000 tonnes and

can produce 107,390 hp. An engine of this size can easily produce sound of 130dB and higher. Within

an island country such as the United Kingdom, where shipping is one of the main ways in which

cargo and people are transported (alongside fishing in the rich waters of the North Sea and the great

number of recreational vessels), there is potentially a very large source of noise pollution from ship

engines alone, without even mentioning oil rigs in the North Sea and pile-driving.

Research into noise pollution on terrestrial environments is well-documented, particularly birds

(Rheindt, 2003, Francis et al., 2009). The effects of noise pollution on birds has also shown to affect

ecosystem services they provide, such as increasing pollination by hummingbirds but negatively

affecting seed dispersal (Francis & Kleist, 2012). Within aquatic environments, significant research

has been dedicated to higher organisms such as whales and dolphins (Rossi-Santos, 2014, Knight,

2013), and noise pollution is increasing in recognition by governmental bodies (Dolman & Jasny,

2015, McCarthy, 2007). In fish, noise pollution can disrupt communication between fish (Holt &

Johnston, 2015), and can elicit stress responses as has been performed ex-situ using boat engine

noise (Celi et al., 2016). Information like this is useful to the aquaculture industry, where all potential

sources of stress which will lower meat quality in fish and bivalves need to be addressed to maximise

profits, and adhere to animal welfare laws.

By comparison, relatively little research has been dedicated to marine invertebrates- this can be

partly attributed to the enormous diversity of invertebrates within marine environments. Over

170,000 species have been described, however current estimates predict that there could be

anything from 700,000 to 1,000,000 eukaryotic species within the marine environment (Appeltans et

al., 2012). The limited research available on noise pollution effects on marine invertebrates is also

partly due to the current limited understanding of how marine invertebrates perceive and interpret

sound (Budelmann, 2006). As previously mentioned, sound through water causes particle motion- it

is this that many marine invertebrates interpret as sound through the use of special hairs designed

to detect particle motion, as is the case in many crustaceans such as shrimp and crabs (Lovell et al.,

2005).

Filiciotto et al. (2014) investigated behavioural and biochemical responses indicative of stress in

Palinurus elephas, a species of spiny lobster, in response to boat noise pollution ex-situ. The lobsters

were exposed to random sequences of boat engine recordings and their locomotory behaviour

recorded for analysis. Their hemolymph was also analysed to determine changes in biochemistry and

using chemical bioindicators located within the hemolymph, it was determined that the effect of

noise pollution was increasing stress levels within the lobsters through increased movement, and an

increase of glucose and total proteins within the hemolymph. Wale et al. (2013) examined the role

anthropogenic noise exposure had on the behaviour of the Common Shore Crab Carcinus maenas-

repeated noise exposure negatively affected the crabs ability to forage and elicit antipredatory

behaviour (in the form of retreating to shelter). Additionally, Wale et al. (2013) examined

physiological responses to noise exposure in C. maenas in another study. Greater metabolic rates

were noticed in crabs exposed to ship noise playback, which could be an indicator of stress due to

noise. This was more pronounced in larger, heavier crabs than smaller crabs, indicating that this

could be size-dependent.

The target species used within this investigation, Mytilus edulis, is commonly known as the Blue

Mussel and is very common along coastal regions of the UK. For example, in the town of

Musselburgh where the specimens for this investigation were sourced from, at low tide huge beds of

mussels containing likely hundreds of thousands of specimens can be seen. M. edulis is part of the

Bivalvia class within the Mollusca phylum, which comprises of over 9,000 species (McMahon &

Bogan, 1991). The blue mussel can be found across the northern hemisphere, as far north as Iceland,

and as far south as western Africa. M. edulis has been extensively studied, and is considered a model

marine species for this reason. This thesis aims to contribute to growing currently unpublished data

investigating other negative effects of noise exposure on M. edulis, such as oxidative stress. M.

edulis is capable of tolerating a variety of factors, and is adept at surviving within intertidal zones.

Adult M. edulis generally reach shell lengths of up to 80mm- in optimal conditions this can be

reached within only a few years, but in less favourable conditions such as the high intertidal zone

where individuals may only be submerged for a few hours a day growth can take up to 20 years

(Seed & Suchanek, 1992). They generally favour depths of less than 10m, and form dense

aggregates, which can be seen when placed into a lab environment (they actively group together to

form beds). When viewing mussel beds ex-situ, vast fields of beds containing many thousands of

individuals can be seen. To aggregate together in these clusters, they use a generic foot that all

members of the Mollusca phylum have to move themselves, and use a material called byssus, a

bundle of filaments (which resembles silk threads), produced in a gland near the foot, to anchor

themselves in place, to rocks and other mussels. Byssal threads have a secondary role- it is also used

to ward off predators, such as crabs and predatory gastropods (Leonard et al., 1999). M. edulis is a

filter feeder and will feed on a variety of organisms, including many species of algae, making it a

relatively easy animal to care for within a laboratory environment. They will also filter detritus from

the water, and generally require fairly low concentrations of algae in the water to open their shells

and begin filter feeding (Riisgard et al., 1981).

Due to the amount of research that has been dedicated to M. edulis, partially due to its relative

abundance and hardiness, M. edulis is considered a model marine species (Wootton et al., 2003). M.

edulis is also an economically important species to Scotland and the rest of the UK- it is commonly

farmed along the west coast of Scotland, where the water quality is very high and free of harmful

pollutants, in addition to having many sheltered enclaves which protect from the notoriously

changeable Scottish weather. In 2008, mussel production in farms from Scotland was estimated at

5,800 tonnes, and this is predicted to continue to increase over the coming years as mussels become

a more popular food choice in Scotland (Scott et al., 2010, Hemroth et al., 2002). In addition to its

economic value, M. edulis is an important source of food for a variety of organisms. At its larval

stage, where it is planktonic, it is preyed upon by juvenile fish and jellyfish. When it reaches

adulthood, the mussel is often preyed upon by sea stars and members of the Laridae family (gulls) in

birds. Smaller mussels are also preyed upon by Nucella lapillus, the dog whelk (Petraitis, 1987).

As previously mentioned, M. edulis is a filter feeder. It feeds by drawing in water through a siphon,

where it is then pulled into the branchial chamber, and labial palps isolate and push the food into

the mouth of the organism and digestion begins. Waste water is then pushed through another

siphon, known as the excurrent siphon (Dral, 1967). The matter is digested, and matter which cannot

be used as food such as sand particles and other detritus is covered in mucus and passed through

the digestive system, and leaves in the form of pseudofaeces (Foster-Smith, 1975). M. edulis uptakes

oxygen through a similar method- water is passed through the gills, and oxygen removed while

waste products added and filtered back out the gill. As M. edulis is often present in intertidal zones,

it only respires when submerged, and valves remain closed when the tide goes out and they are

exposed to air. M. edulis is not only a model marine species but also an important member of

intertidal ecosystems. It is an opportunistic species, which will colonise new areas rapidly, and

providing optimal conditions (such as spending most of the day submerged, and ideal temperatures)

will quickly reach sexual maturity and reproduce (Suchanek, 1978). In intertidal flats, they will often

be the dominant species owing partially to their hardiness and quick reproductive cycle (Petraitis,

1995).

Despite its status as a model marine species however, very little research has been undertaken to

investigate noise pollution effects in M. edulis. The only research into noise exposure effects on M.

edulis has shown generally negative effects on the animals. Roberts et al (2015) investigated the

sensitivity of M. edulis to substrate-borne vibrations, by creating exposure to vibrations in-situ. A

range of vibrations through the water at varying intensities from 5 Hz to 410 Hz, mimicking pile-

driving activities within the seabed were used and valve closure used as an indicator of negative

behavioural response to vibration, with thresholds for negative behaviour in M. edulis found to

range from 0.06 to 0.55 m s-2 acceleration. The scientists involved concluded that repeated exposure

to high-intensity substrate-borne vibrations caused by anthropogenic noise was likely to negatively

affect the overall fitness of individual mussels and the beds that they form, by interrupting their

normal daily valve movement. This in turn could have knock-on effects to other organisms, as M.

edulis are an important food source for many marine organisms and seabirds (Petraitis, 1987). De

Soto et al. (2013) examined noise exposure on a similar species belonging to the bivalve class, Pecten

novaezealandiae, also known as the New Zealand Scallop, in its larval form. Their research found

that noise exposure at larval stages caused body malformations, and also slowed down the rate of

development, leaving larvae even more vulnerable to predators such as fish. Based on their similar

morphology it could be inferred that similar effects are likely to be seen in M. edulis, however

dedicated research will confirm this.

As an indicator of behavioural response to a stimulus, feeding rate is useful (Nielsen, 1999, Ghaffar

et al., 2011). An animal that is not feeding while being exposed to a potentially harmful stimuli (in

this instance noise exposure) is clearly showing it is being subjected to stressful conditions. Over an

extended period of time this is likely to negatively impact the animal’s fitness and likelihood of

survival. Alongside behavioural responses, changes in respiratory rate in response to harmful stimuli

can also prove as useful indicators of a physiological response- In the instance of M. edulis this could

appear in the form of reduced oxygen intake due to closing of valves to protect from noise exposure.

The overall aim of this investigation is to add to the growing data highlighting the negative effects

anthropogenic noise exposure is having on a model marine invertebrate- in the hopes that it may

create awareness of noise pollution effects on smaller species lower in the food chain, and to

prompt change in legislation to protect not only larger marine animals such as cetaceans, which

have been well-documented in terms of noise pollution effects, but the keystone species close to the

base of the marine food webs.

Currently, additional work is being performed at Edinburgh Napier University on the effects of

anthropogenic noise exposure on M. edulis. Noise exposure has shown to compromise DNA

structure at singular cell level in mussels, and thiobarbituric acid-reactive substances (TBARS) were

found to have formed in individual cells within the hemolymph (the mollusca equivalent of blood)

and gills as a by-product of lipid peroxidation, hinting that noise playback was causing oxidative

stress in mussels subjected to treatment. Further oxidative stress experiments, such as O2- levels in

cells, Reactive Oxygen Species (ROS) (reactive chemicals containing oxygen that have been subjected

to measurement or a particular chemical process (Bayr, 2005)), and Leptoperoxidase production

however were found to be inconclusive thus far. Further experimental work is planned to be

undertaken within the near future- investigating the effect of noise exposure on frequency of valve

movement, which is an indicator of feeding and respiration (Kramer et al., 1989), and effect length

and variability over longer periods of time.

While the overall aim of current anthropogenic noise exposure research on M. edulis is to determine

all aspects of harmful effects noise pollution can have on this model marine invertebrate species,

this thesis itself aims to investigate whether acute noise exposure affects an aspect of physiology

and an aspect of behavioural ecology in M. edulis- does acute noise exposure affect algal clearance

rate? Similarly, does acute noise exposure also impact the respiratory rate of M. edulis? For the algal

clearance rate experiment, the null hypothesis is that there will be no significant change in feeding

rate between mussels exposed to noise exposure against mussels not exposed to noise exposure,

with the alternative hypothesis stating that there will be a significant negative change in feeding rate

in mussels subjected to acute noise exposure. In the respiratory rate experiment, the null hypothesis

is that no significant change in oxygen in consumption will occur between mussels subjected to noise

exposure and those not exposed to noise, while the alternative hypothesis is that there will be a

significant negative decrease in oxygen consumption in mussels subjected to acute noise exposure

against those not subjected to noise exposure. The methodology used to investigate these

hypotheses are as follows:

Materials and Methods

Algal Clearance Experiment

All experiments were performed within the Sighthill campus of Edinburgh Napier University. Each

tank used had a capacity of 138.24l and was filled with approximately 116.64l of seawater.

Temperature was kept at a constant of 120c, and salinity at a constant of 35ppt, meaning through

the water sound could travel at a speed of 1496.12m/s using Mackenzie’s equation (pressure was

assumed not to have changed). All other water qualities such as nitrites, silicates, and magnesium

were regularly monitored and found to consistently be within normal bounds for seawater (the

water qualities can be found within the appendix of this paper). The M. edulis used in this

investigation were all adults, obtained from the town of Musselburgh, East Lothian, on the morning

of 20th May 2016 during low tide (approximately 7.30am).

Figure 1: Image of the approximate area where the mussels used in this experiment were collected from. The areas in black

are mussel beds. Taken from Google Maps on 16/07/16.

In order to reduce the variability of feeding rates among individual mussels, similarly sized mussels

were collected- this also helped to account for any differences in feeding rates due to age of the

organism. In total, 350 mussels were collected with 250 of these specimens being used for

experimentation. After being thoroughly cleaned by removing any endoparasites, in particular

barnacles, and immersion through clean freshwater to remove any trace contaminants they were

left to acclimatise to a lab environment for two weeks in a large aerated tank with constant flow-

through of sterile and filtered seawater, at the previously mentioned temperature of 120c and

salinity of 35ppt. They were fed with dead Tetraselmis suecica, bought in high concentrate liquid

form (1.5 billion cells per ml) from www.ZMsystems.co.uk , every 24 hours. Tetraselmis suecica is a

marine green algae, commonly used as food-stock within aquaculture, with a length of up to 10ųm

and a width of up to 14ųm. It is easily visible under a light microscope and mostly contains

Chlorophyll-A, meaning it can be used in spectrophotometry at suitably high concentrations.

Figure 2: Microscope image of Tetraselmis suecica within a water sample. Obtained from www.youtube.com on 20/07/16.

Previous experiments involving feeding within M. edulis and closely-related species have utilised T.

suecicca as a food source (Nielsen & Strømgren, 1991, Wong & Levinton, 2004, Vasconcelos, 1995),

validating their suitability for this investigation. Dead T. suecica were used, in order to prevent

changes in algal concentration in the water due to algae reproducing, which could bias results.

Preliminary testing was performed to determine how long noise exposure should run for,

appropriate algal concentrations within the water, and appropriate quantification. 25 mussels were

acclimated in a small tank containing 10l of water for 24 hours before the water was inoculated with

Tetraselmis to make the cell concentration within the water 3000 cells/ml. 3 water samples were

taken every hour and all mussels monitored to determine when their valves closed (an indicator of

when they stopped feeding). After 4 hours, all mussels had closed their valves and subsequently

stopped feeding. The results of this preliminary testing can be found in the results section. 3000

cells/ml was utilised due to recommendations from Riisgȧrd et al. (1981), who concluded that algal

clearance rate in Mytilus edulis is optimal at cell concentrations between 1500-3000 cells/ml In

addition, spectrophotometry was run to determine if this would be a suitable method of

quantification- however the absorbance rates produced by spectrophotometry at a range of

concentrations from 300 cells/ml up to 30000 cells/ml were found to be too low for statistical

analysis. At such low absorbance rates variation due to marks on the cuvettes used could not be

ruled out, therefore this method was abandoned. In addition, mussels exposed to concentrations as

high as 30000 cells/ml were found to not consume enough cells within 3 hours to produce tangible

differences in absorbency rates.

Following acclimation, the mussels were divided into 10 groups of 25, with 5 groups being subject to

noise exposure treatment and the other 5 groups used in a control experiment (n=125 for each

treatment). They were starved 48 hours prior to treatment, and 24 hours prior to treatment were

moved to test tanks. The mussels were placed on platforms made of old crab cages (to allow any

pseudofaeces to fall to the tank floor) within a small tank with a volume of 10.8l. This small tank was

then placed on a platform 5cm tall within the large tank.

The test tank was inoculated with T. suecica at T0 to make the cell concentration within the water

3000 cells/ml (as Riisgȧrd et al. (1981) suggested in their paper regarding optimal cell density for

mussel feeding rates). Water samples were taken at the beginning of the experiment (T=0), halfway

through (T=1.5), and at the end of the experiment (T=3). 5 samples of 1ml were taken at each time,

following vigorous stirring of the water for 10 second using a glass rod to spread out the algae within

the water as best as possible. The mussels were subjected to 3 hours of continuous underwater

recordings of boat engines simulating a busy harbour, randomly sequenced, at a maximum volume

of 135-140 dB (for comparison, this is above the human pain threshold for noise, which is around

125dB (Camp et al., 1962)). Sound was supplied through speakers located to the left side of the large

tank (see figure 3), and ship noise was played through an mp3 player containing a six-hour long

sequence of boats leaving and arriving into a busy harbour. Simultaneously, a control experiment

was set up with the exact same parameters as the noise treatment experiment but without any

noise playback.

Figure 3: Image taken on 21/06/16 of the M. edulis clearance rate experiment. The control experiment was set up

identically to this.

Following the experiment, each individual mussel was dried using paper towels, and its length and

weight noted. These specimens were then returned to a tank and later boiled to serve as feed for

other organisms in the laboratory, as they came from a contaminated site, and were unfit to be

returned to Musselburgh as per university regulations. The weights and lengths of each mussel in

each experiment were compared and no statistically significant difference could be found between

the noise and control mussels in each day (see the appendix for all statistical tests performed on

weight and length).

Each individual 1ml sample was transferred on to a glass Sedgewick-Rafter counting cell, which

contains a grid 50mm x 20mm (figure 4).

Figure 4: Image of a Sedgewick-Rafter counting cell used in this investigation.

Each square within the grid was assigned an x and y co-ordinate, with the bottom-left square being

co-ordinates x1 y1, and using a random number generator 5 random co-ordinates were selected for

analysis within each sample. The counting cell was viewed under an optical microscope and images

were taken using CellSens, a microscope imaging software program, for later analysis. To further

reduce bias during data analysis, each image was assigned a random 6-digit number so that each

image was analysed without knowledge of what treatment the water came from or at what time.

However, during data analysis, it became apparent that it was very easy to differentiate between

samples and instantly tell where they had come from, so this concept was later abandoned. 750

images were taken in total. As the algae did not separate out uniformly through the liquid medium

on the Sedgewick-Rafter Counting Cell, images which had particularly high numbers of algae (>200)

in them were treated as outliers within the dataset and were not included in analysis.

Respiration Experiment

100 mussels were collected from Musselburgh at 9am (low tide) on 20th June 2016, and left to

acclimate in the Edinburgh Napier University aqualab laboratory setting for two weeks identical to

the conditions the mussels from the algal clearance experiment experienced (120c, salinity of 35ppt,

all other water qualities such as magnesium, silicates, etc. within normal seawater boundaries). As

with the algal clearance experiment, they were thoroughly cleaned of all epibionts and washed in

clean freshwater before being transferred to a seawater tank. They were fed on T. suecica daily, and

prior to experimentation were starved for 24 hours in order to prevent increased respiration rate

due to extra oxygen demand for digestion becoming a factor.

Mussels were tested individually, in watertight respiration chambers with a capacity of 200ml

topped with filtered, sterile seawater. The respiration chambers were kept in a large tank containing

approximately 116.24l of sterile seawater, which was isolated from the rest of the system for the

purpose of this experiment (to prevent small organisms such as bacteria entering the system and

tampering with oxygen consumption rates). Respiratory rate was measured by monitoring changes

in oxygen saturation within the water in the chamber using an oxygen sensor connected to the

computer program Presens 3, through the use of a Fibox 3 optical oxygen meter. Once Presens 3 was

hooked up to the respiration chamber with the mussel inside playback began for 2 hours. The same

ship noise playback equipment used in the algal clearance experiment was used in this experiment

for consistency. Presens 3 took measurements of oxygen saturation within the water every second

for the duration of the experiment, and measurements at T0, T1 and T2 were noted for each for

later analysis. And all mussels were measured and weighed following treatment.

The results of both experiments are as follows:

Results

Algal Clearance Experiment

Figure 5: Boxplot showing the mean, interquartile ranges and 95% confidence intervals for each experiment at time T0.

Figure 5 shows the average number of cells counted in each experiment for each treatment. A

general linear model to compare both experiments was performed and at the beginning of the

experiment as predicted there was found to be no significant difference between experiments

(F=0.716, Df 1, 8, P=0.422), and data was normally distributed (p=0.089). It was observed at the

beginning of playback in the noise treatment tank in all experiments mussels would immediately

retreat into their shells upon hearing the ship noise playback, while the control mussels would start

to feed on the recently-added algae to the water. The mussels were regularly monitored but not

interfered with, and at T1.5, 5 1ml samples were taken again:

Control Noise

30

35

40

45

50

Average Cell Count in Each Experiment at Time T0

Treatment

Ave

rag

e C

ell C

ou

nt

Figure 6: Boxplot showing the mean, inter-quartile ranges and 95% confidence intervals for the averages of all samples

collected at T1.5.

At T1.5, it can be observed that the overall mean algal count has dropped between T0 and T1.5 in

the control experiment, while the noise experiment has only dropped slightly, meaning mussels are

eating but at a much reduced rate, or only a select number of the 25 mussels being subjected to

noise were choosing to feed. Visual inspection confirmed that many mussels in the noise treatment

still had their shells closed at T1.5. Although there is a difference between the control experiments

and the noise experiments at t1.5, it is not significant (F=1,393, df= 1,8, P=0.272). Again data was

found to be normally distributed (p=0.272).

Control Noise

15

20

25

30

35

40

Average Cell Count in Each Experiment at Time T1.5

Treatment

Ave

rag

e C

ell C

ou

nt

Figure 7: Boxplot showing the mean, interquartile ranges and 95% confidence intervals between the average cell count in

both the noise and control treatments at T3, the end of the experiment. The circles denote outliers.

As can be seen in figure 7, there is a clear difference between the cell count in the control treatment

and the noise treatment experiments at T3. Statistical analysis shows that there is a significant

difference between the control and noise treatment experiments at T3 (F=7.929, df, 1,8, p=0.023).

Data was normally distributed again (p=0.432). As a result, the null hypothesis that there is no

significant difference in feeding rates between mussels exposed to ship noise playback and mussels

not exposed to ship noise playback after 3 hours of experimentation can be rejected, and the

alternate hypothesis that there is indeed a significant difference after 3 hours can be accepted.

When examining each experiment independently of each other a significant change can be seen over

3 hours within the control experiment:

Control Noise

51

01

52

02

53

03

54

0

Average Cell Count in Each Experiment at Time T3

Treatment

Ave

rag

e C

ell C

ou

nt

Figure 8: Average algal cell count observed change over time in the control experiment. The circle at T3 denotes an outlier.

Within the control experiment, there was found to be a significant change in algal cell count over 3

hours (F=28.884, df= 1,13, p=<0.001). A Tukey’s test of pairwise differences was performed, and

found that while there was a significant difference between times T0 and T3 (p=<0.001) and from

T1.5 to T3 (p=0.034), no significant difference was found between times T0 and T1.5 (p=0.093).

Within the Noise exposure experiments the results were different:

0 1.5 3

10

20

30

40

50

Control

Time

Me

an

alg

al co

un

t

Figure 9: Average algal count change observed over time in the noise experiments. The circle denotes an outlier in the

dataset.

Within the noise experiments, no significant change in algal count was found over time (F=2.667,

df=2,12, p=0.110). A Tukey’s test of pairwise differences revealed no significant pairwise differences

between any of the three counts taken.

From the above data, it can be concluded with a significant amount of confidence that noise

exposure at 135dB negatively affects feeding rates within M. edulis when compared against a

control experiment lacking noise exposure.

Respiration Experiment

0 1.5 3

25

30

35

40

45

Noise

Time (hours)

Me

an

Alg

al C

ou

nt

Discussion

Algal Clearance Experiment

The result show that acute noise exposure clearly impacts feeding behaviour in M. edulis. Numerous

obstacles had to be overcome to create this experiment- the first being a testable methodology free

of bias. Initially, a flow cytometer was planned to be used- which can count the number of

microalgae like Tetraselmis within a water sample following proper calibration (Stauber et al., 2002).

However, due to scheduling conflicts, a flow cytometer could not be used, and another method had

to be sought. As previously mentioned in the materials and methods section spectrophotometry was

considered, but by following the recommendations of Riisgard et al. (1981), who suggest using cell

concentrations of no greater than 3000 cells/ml, this proved to be too low for accurate

spectrophotometry readings. At such low levels of absorbance it would be impossible to determine

whether absorbance rates quantified by the spectrophotometer were coming from the presence of

chlorophyll A in the water sample (which were in turn coming from Tetraselmis in the water sample),

or whether absorbance rates were coming from other sources such as any remaining detritus and

debris still in the water, or marks and scratches on the cuvettes. If cell concentration within the

water were to be increased, not enough would be consumed by 25 mussels. After consulting existing

literature, using a Sedgewick-Rafter Counting Cell proved to be a time-consuming but more robust

method, which when combined with random number generators to take sub-samples within

samples allowed for minimum bias. Another significant advantage of this particular method is that it

is a low-cost, albeit time consuming method, which does not require specialist knowledge beyond

basic microscopy. It is hoped that this method can be further developed from here, and awareness

of its advantages made known, particularly to institutions who may not have the necessary funds to

purchase an expensive machine like a flow cytometer which may cost many thousands of pounds.

This method also does not require specialist training to use, so can be easily taught to volunteer

para-biologists as a way of further cutting costs.

As cell counting proved to be a very time-consuming activity, it proved necessary to take images

using CellSens for later analysis. This has a significant advantage attached to it- it meant more time

and care could be taken when counting, reducing the likelihood of miscounting due to time

pressure.

One interesting thing to notice is that at T1.5 there is no significant difference in algal counts

between the noise and control experiments (figure 6). While this is to be expected with the noise

experiment, where the mussels are not feeding and remaining shut due to acute noise, it raises

interesting questions as to why the control experiment subjects do not feed as much as expected

(figure 8). Further investigation into why mussels did not feed as much as expected in the control

experiments is required- it simply could be due to variation, or may be due to the presence of

humans- the answer is not currently clear.

The results correlate with previous literature- that noise exposure is overall detrimental to M. edulis,

both at physiological and behavioural levels. However, upon receiving the results of the algal

clearance experiment, this immediately creates more questions than it answers. While the results

clearly show that acute noise exposure over a short period of 3 hours negatively effects feeding

rates in M. edulis, how does this look over a longer space of time? For example, over days or even

weeks, as many mussels living in harbours experience? Do M. edulis eventually become acclimated

to loud noise exposure from anthropogenic sources? And if so, how long is this acclimation period?

In addition, the very high level of noise (minimum of 135 dB) used in the algal clearance experiment

is unlikely to be completely representative of the natural environment. Distance, type of engine, and

frequency of ship movement are all factors which will differ from harbour to harbour and the

position of the mussel on the seabed. Therefore this raises another question- do differing intensities

of noise exposure produce variable rates of feeding in M. edulis? At what point does anthropogenic

noise exposure cease to be a harmful stimuli to mussels? While this study shows that there is

significant difference in feeding rates between acute noise exposure and control experiments,

further investigation into noise intensity and duration should be undertaken to determine long-term

and intermediate effects on mussel behaviour. In addition, while this study has shown the harmful

effects of noise, it has shown no indication of the effects the particle motion caused by sound may

have. Setting up a similar experiment with the use of a device such as a Shaker table to mimic sound

pressure waves would help to determine what effects the pressure waves themselves may have. A

Shaker table is capable of mimicking sound pressure waves consistently- unlike the ship noise

playback recording used in this experiment, which may have shown variation due to different types

of engines with different sound frequencies, resulting in different strengths of pressure waves.

Similarly, this investigation and other work undertaken at Edinburgh Napier University has shown

the effects that anthropogenic noise can have on mussels which have reached maturity. However, is

this similarly the case in juveniles? And how does noise pollution influence development? M. edulis

undergoes metamorphic change over its life cycle- upon hatching it becomes a trochophore larva (a

free-swimming planktonic larva with cilia to aid locomotion). From here it metamorphasises into a

veliger larva, where it is still-free swimming but is developing a shell, before finally becoming mostly

sessile as an adult and forming beds with other members of its species in shallow waters (Suchanek,

1981). Further investigation to determine whether feeding rates are altered at juvenile stages who

are present in the water column could further the argument for protective legislation for bivalves if

similar results are found. As previously mentioned in the introduction section of this investigation,

De Soto et al. (2013) found that noise exposure had damaging effects on development in another

member of the bivalve class, Pecten novaezealandiae. By adapting the methodology they followed,

effects of noise on development of M. edulis alongside feeding rates could be determined.

Another factor to consider is that the mussels sourced for this investigation came from a

contaminated site- Musselburgh. The area has numerous contaminants, such as petro-chemicals and

mercury (McLusky & Martins, 1998). Mercury in particular is harmful to M. edulis (Géret et al., 2002)

and can easily bioaccumulate in animals higher in the food chain such as seabirds and fish (Monteiro

et al., 1996). The mussels used in this investigation were not tested in any way to determine how

much mercury was in their hemolymphs- therefore it is unknown if it had any effect on feeding and

respiratory rates. Further investigation involving mussels obtained from a non-contaminated site

(such as the west coast of Scotland) and then comparison involving noise against potentially

contaminated specimens would help identify if contaminants have had any effect on susceptibility to

noise exposure in M. edulis.

It is hoped that the data provided by this investigation, alongside the other work performed at

Edinburgh Napier University will be used to prompt change in Scottish legislation. A well-known

model species like M. edulis will hopefully garner attention, as the results obtained from current

investigation will likely be of interest to those invested in the growing mussel farming industry in

Scotland.

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