Establishing the Optimal Salinity for Rearing Salmon in Recirculating Aquaculture Systems

Home , Brackish water, Salmon run

Establishing the optimal salinity for rearing salmon in recirculating aquaculture systems

Joshua David Emerman

B.S., University of New England, 2010

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology)

THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)

February, 2016

Abstract

Aquaculture of salmon worldwide is a 15.3 billion dollar industry and the majority of fish are produced in net-pen systems in coastal waters. Recently producers have begun investigating the feasibility of moving salmon production onto land and into recirculating aquaculture systems

(RAS). The major downsides to RAS are the startup and operational costs; however the ability to optimize many environmental variables to enhance growth and feed conversion, something impossible to do in net-pen systems, may help defray these otherwise prohibitive costs. Salinity may be the most important of these variables due to the metabolic cost of osmoregulation, which has been estimated to account for 5-50% of routine metabolic rate. Decreased osmoregulatory costs could result in a greater allocation of energy toward growth, thus shortening production times and improving feed conversion efficiency. To establish an optimal salinity for growth in salmon, seven replicate, 15,000 liter RAS were constructed at the University of British

Columbia’s InSEAS research facility. I conducted a preliminary study to validate that each system was able to control water quality parameters and yield similar levels of growth and feed conversion in coho salmon (Oncorhynchus kisutch). I then conducted salinity trials with Atlantic

(Salmo salar) and coho salmon. Fish were grown in five salinities ranging from freshwater to seawater (0, 5, 10, 20, 30 ppt) for approximately five months. Growth rates and feed conversion ratios (FCR) were measured throughout the trial. The fastest growth rate and lowest FCR in coho salmon was at 10 ppt, which is approximately isosmotic to the blood. Growth rate of coho at intermediate salinities was almost double that at 0 or 30 ppt through the first growth period. This trend was not seen during the second coho growth period, possibly due to a size-dependent or density effect. Unexpectedly, salinity had no effect on growth rate and FCR in Atlantic salmon, although growth rates were consistent with those seen in industry. This research will help further

ii move salmon production out of the oceans and onto land, alleviating some of the environmental costs associated with salmon grown in the oceans.

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Preface

I conducted all of the research under the supervision of Drs. Jeffrey G. Richards and Colin J.

Brauner. I wrote all 4 chapters of the thesis and received editorial feedback from Drs. Colin J.

Brauner, Jeffrey G. Richards, and Anthony P. Farrell. Treatment and experimental protocols involving animals were followed according to The University of British Columbia’s Animal

Care Committee, certificate A13-0016.

Table of Contents

Abstract ...... ii Preface ...... iv Table of Contents ...... v List of Tables ...... viii List of Figures ...... ix List of Abbreviations ...... xi Acknowledgements ...... xii Chapter 1: General Introduction ...... 1 1.1 History of Aquaculture ...... 1 1.2 Aquaculture of Salmon and Environmental Impacts ...... 4 1.3 Recirculating Aquaculture ...... 7 1.4 Salinity ...... 9 1.5 Research Goals...... 12 Chapter 2: Validation of a novel RAS research facility: InSEAS ...... 14 2.1 Summary ...... 14 2.2 Introduction ...... 15 2.3 Methods...... 20 2.3.1 Overview ...... 20 2.3.2 Experimental Design ...... 20 2.3.3 Water Parameter Testing & Analysis ...... 22 2.3.4 Calculations...... 23 2.3.4.1 Growth Rates ...... 23 2.3.5 Statistical Analysis ...... 24 2.4 Results ...... 24 2.4.1 Water Parameters ...... 24 2.4.2 Freshwater Growth Trial ...... 25 2.4.3 VAKI Biomass Estimator Validation ...... 29 2.5 Discussion ...... 30

2.5.2 Fish Growth ...... 30 2.5.1 Environmental Parameters ...... 31 2.5.3 Biomass Estimator ...... 32 2.6 Conclusion ...... 33 Chapter 3: The effects of salinity on the growth and feed conversion of coho and Atlantic salmon ...... 34 3.1 Summary ...... 34 3.2 Introduction ...... 35 3.3 Methods...... 38 3.3.1 Overview ...... 38 3.3.2 Experimental Design ...... 40 3.3.3 Water Testing & Analysis ...... 42 3.3.4 Calculations...... 43 3.3.4.1 Growth Rates ...... 43 3.3.4.2 Condition Factor ...... 44 3.3.4.3 Feed Conversion ...... 44 3.3.4.4 Muscle Water Content ...... 45 3.3.5 Statistical Analysis ...... 45 3.4 Results ...... 46 3.4.1 Water Parameters ...... 46 3.4.2 Coho Salmon ...... 48 3.4.3 Atlantic Salmon ...... 55 3.5 Discussion ...... 60 3.5.1 Coho Salmon ...... 60 3.5.2 Atlantic Salmon ...... 65 3.5.3 Water Quality ...... 67 3.6 Conclusion ...... 68 Chapter 4: General Conclusion ...... 70 4.1 Summary ...... 70 4.2 Potential Economic Impact of Results ...... 71 4.3 Study Strengths and Limitations ...... 73

4.4 Future Directions ...... 74 4.6 Issues to Address...... 75 References ...... 77 Appendix ...... 86 Appendix A: Supplementary Figures and Tables ...... 86

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List of Tables

Table 2.1 Acceptable values of unionized ammonia (NH3), nitrite, nitrate and oxygen in freshwater...... 23 Table 2.2 Water parameters for five systems throughout the duration of the freshwater validation growth trial...... 25 Table 3.1 Mean values for all water parameters measured within each treatment system for the duration of the optimal salinity growth trial (May-October)...... 47 Table A.1 Mean monthly values for all water parameters measured within each salinity treatment (Chapter 3) for the duration of the experiment...... 88

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List of Figures

Figure 2.1 Schematic of one InSEAS recirculation aquaculture system, consisting of two 5000 l tanks and associated mechanical and biological components...... 17 Figure 2.2 Overhead schematic of our large experimental room in InSEAS, which houses five of the independent 15,000 L recirculation aquaculture systems and their respective biofilters (BF)...... 18 Figure 2.3 Mean body mass of all coho salmon (n = 338-404) measured during freshwater validation growth trial over the 5-month growth trial...... 26 Figure 2.4 Mean growth rates between all coho (n = 338-404) measured during freshwater validation growth trial over the 5-month growth trial...... 28 Figure 2.5 Initial and final total biomass and stocking density of each tank of coho salmon measured during freshwater validation growth trial over the 5-month growth trial...... 28 Figure 2.6 Estimated (using the VAKI biomass estimator) and measured final masses of coho salmon measured during freshwater validation growth trial over the 5-month growth trial. 29 Figure 3.1 Gantt chart outlining the growth periods within each growth trial. Dates fish were added and removed from trials are denoted by black bars...... 42 Figure 3.2 Mean body mass of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial...... 49 Fig 3.3 Mean growth rates between subsamples of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial...... 51 Fig 3.4 Mean condition factor of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial...... 52 Fig 3.5 Mean economic feed conversion ratio (eFCR) between subsamples of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial...... 53 Fig 3.6 Mean muscle water content (MWC) of muscle tissue samples taken from 15 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities

of 0, 5, 10, 20 or 30 ppt and measured at the second time point of the 5-month growth trial...... 54 Fig 3.7 Mean body mass of 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial (Time point 1)...... 55 Fig 3.8 Mean growth rate between the average initial stocking mass and 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial...... 57 Fig 3.9 Mean condition factor of 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial...... 58 Fig 3.10 Mean economic feed conversion ratio (eFCR) between the average initial stocking mass and subsamples of 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial...... 59 Fig A.1 Mean body mass of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt...... 89 Fig A.2 Mean growth rate of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt between time point 1 and time point 2...... 90 Fig A.3 Mean condition factor of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt...... 91 Fig A.4 Economic feed conversion ratio (eFCR) of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt between time point 1 and time point 2...... 92 Fig A.5 Mean muscle water content (MWC) of muscle tissue samples taken from 15 Atlantic salmon (randomly selected from a group of 74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at the second time point of the 59 day growth trial...... 93

List of Abbreviations

°C degrees Celsius

ANOVA analysis of variance

ATP adenosine triphosphate

ATPase adenosinetriphosphatase

Cl- chloride

eFCR economic feed conversion ratio

FCR feed conversion ratio

HOG head on gutted

K+ potassium

MS-222 tricaine methane sulphonate

MWC muscle water content

Na+ sodium

NKCC sodium-potassium-2 chloride cotransporter

O2 oxygen

ppt parts per thousand

RAS recirculating aquaculture system

RBC red-blood cell

SGR specific growth rate

TGC thermal growth coefficient

Acknowledgements

First and foremost I would like to thank my co-supervisors, Drs. Jeffrey Richards and Colin

Brauner. They have both been amazing mentors and incredibly supportive of me, even when I started my Masters and knew close to nothing about physiology. They have been there to support me during what was undoubtedly the most intensely stressful, yet rewarding period of my life, pushed me when I wanted to quit, and taught me more about science than all that I’ve learned in my previous 25 years. I would also like to thank Dr. Anthony Farrell, for all of his insight and support and for co-authoring the one paper that I read more than any during my degree.

I would also like to thank everyone in both of my labs, Andrew Thompson, Derek Somo,

Cristostomo Gomez, Gigi Lau, Milica Mandic, Matt Regan, Dave Allen, Michelle Ou, Katelyn

Tovey, Till Harter, Phil Morrison, Yuanchang Fang, Mike Sackville, Ryan Shartau, Libby

McMillan, Ellen Jung, and Naomi Pleizier, without all of you I would have never made it through. I would like to give a special thanks to Adam Goulding for all his help with sampling

and troubleshooting. I need to thank my amazing peer mentor, Yvonne Dzal, for always being encouraging and for sharing her beer wisdom. I would like to remember Emily Gallagher, your

smile always lit up the room whenever you entered. You were a great lab mate, and friend. We

all miss you.

Patrick Tamkee, my Asian brother, without your help there is no way on Earth these fish would

have stayed alive as long as they did. The same goes to Victor Chan, the technician who has seen

it all, and for some reason still decided to start a Master’s degree. You are a true glutton for

punishment, I wish you luck. I am grateful for all the help I have gotten over the past three years

xii by many undergraduate volunteers and USRA students; they were integral to the collection of the exorbitant amount of water quality data that I had the joy of analyzing.

I would like to thank my dad and grandfather, Darryl and Samuel Emerman, who took me boating and fishing since before I could walk, they are the root of my obsession with fish for all these years. I have witnessed the negative impacts we humans have had on our oceans within my short lifetime, and my dad and grandfather have always encouraged me to be a part of the movement to fix that. Thanks to my sister, Chelsea Emerman, for her encouragement and commiseration while she completes her degree as well. And lastly, thank you to Elizabeth

Goundie for taking this crazy ride with me. There is no one I would rather have by my side. You have always been there to support, motivate, and push me further than I ever thought I could go, and I am eternally grateful to you for that. I could not have done this without you.

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For my mom

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Chapter 1: General Introduction

The demand for seafood is driving an extremely rapid growth of aquaculture, one of the fastest

growing of all agricultural sectors worldwide. Salmon culture is a very large segment of total

aquaculture production, and is one of the most profitable fish species to grow. In order to meet

increasing demand and alleviate environmental concerns, the industry is attempting to adopt a

new method of culturing these fish, in what is known as recirculating aquaculture systems

(RAS). However, profitability is not as high as the traditional net-pen culture method, due to the high initial investment and operating costs. To make this system more profitable, the industry needs to optimize the environmental parameters for growth and/or feed conversion, however these optimal values are not currently known.

This chapter will focus on the history of aquaculture with emphasis on salmonid culture, its importance in Western Canada, and the traditional methods used to grow these fish. I will describe the advantages and disadvantages of RAS as it relates to the more traditional net-pen culture system and how RAS can be improved upon. I focus mainly on explaining why salinity may be the most logical environmental parameter to optimize and how an optimal value will be determined, which is the focus of my thesis.

1.1 History of Aquaculture

Aquaculture is the farming of aquatic organisms. These can be either plants or animals, both

vertebrates and invertebrates, and the organisms can be grown in fresh, salt, or brackish water.

Organisms can be grown for many different purposes including food, jewelry,

restocking/restoration programs, the aquarium trade, or the pharmaceutical/supplement industry.

Aquaculture has been practiced for thousands of years, but within the last 40 years it has industrialized and has increased rapidly in size to fulfill the increasing demand for seafood around the globe (Robson 2006; Cressey 2009). It is currently the fastest growing food producing industry in the world (Klinger and Naylor 2012).

Native Americans on the west coast of North America were the first peoples in the world to practice aquaculture. There is evidence to suggest that they had been farming clams more than

5,000 years ago. They built stone walls across small coves and bays up to the height of the average low tide line to promote soft sediment accumulation and an optimal beach slope, creating the appropriate substrate for wild clam larvae to settle on (Groesbeck et al. 2014). The density of clams found in these gardens was up to 4 times higher than in adjacent non-walled areas.

The first records of fish keeping were found in China in the writings of Yu The Great from

~2,000 BC (Nash 2010). These were most likely a carp species, but the writings are somewhat unclear. Young fish were captured from the wild and placed into ponds to grow. Rearing fish in ponds this way had a practical purpose of providing a readily available supply of fresh fish at home, and was also viewed as a symbol of status (Nash 2010).

Pond culture methods also came about in Europe not long after China. Stone ponds at the intertidal interface, known as vivarie, date back to as early as 300 B.C. (Columella 1941). These were used to hold fish caught offshore to keep them fresh. This simple pond culture, used in both freshwater and seawater, remained relatively unchanged through antiquity and the middle ages,

up until around 1750 A.D. The rise of modern fishing techniques through this time period kept

the need for aquacultured products to a minimum and therefore development was stymied in

much of the world. Asia was still at the forefront of aquaculture technology, and by beginning of

the renaissance, Asia was already practicing high level breeding and domestication of various

species of fish and shellfish (Nash 2010).

The first scientific paper published on methods to artificially spawn trout was written in 1844

(Gehin and Remy 1851). The first commercial fish hatchery opened in Huningue, France in

1852, and the industry for raising salmonids and other fish in captivity exploded over the next hundred years. By the late 1800’s commercial hatcheries were in operation to rear fish for food and to replenish the wild stocks that were already being overfished through the use of modern industrialized fishing methods. Fertilized eggs from species from every corner of the world were shipped all over and introduced into foreign water bodies for recreational fishing. Rainbow trout

(Oncorhynchus mykiss), a North American native species, were sent to hatcheries in South

America, Europe, New Zealand, and even Japan by the start of the 20th Century (MacCrimmon

1971).

During World War I the development of aquaculture slowed as resources were being diverted toward the war effort. However, during World War II there was a huge increase in the need for inexpensive and plentiful food and consequently, aquaculture technology flourished. Many of the

European Jews that relocated to Israel brought the pond culture methods they knew with them, advancing tilapia culture dramatically (Wohlfarth 1983). During this time period there were huge developments in aquaculture around the world and this began the start of modern aquaculture.

This industry is still rapidly expanding, with an average annual rate of increase of 8.2% since

1980 (FAO 2014), which is much higher than terrestrial plant agriculture (3%) and other domestic livestock (1.7%). In 2012, aquacultured products produced globally (90.4 million tons) had a total value of $144.4 billion USD (FAO 2014). The largest and most valuable sector of aquaculture is food fish production, which accounts for 66.6 million tons, and $137.7 billion

USD. Of the food fish produced, only a small percentage of them are marine fishes (12.6%) however, they account for 22.4% of the total value because they are typically higher value fish.

1.2 Aquaculture of Salmon and Environmental Impacts

Salmon production has the largest market share of all the marine aquacultured fishes at 14% of total seafood consumed worldwide (FAO 2014). In British Columbia, salmon are by far the most important species produced in aquaculture, making up 94% by value of all products raised in the province (Statistics Canada 2014). Salmon farming in Canada began in the early 1970’s off the

B.C. coast with some small experimental Chinook (Oncorhynchus tshawytscha) and coho

(Oncorhynchus kisutch) net pen operations (Novotny 1975). In the early 1980’s farmers began to switch to culturing Atlantic salmon (Salmo salar) domesticated in Norway because they grew faster and could be farmed at higher densities (Nash 2010). From 1981 to 1989 the amount of salmon farmed in B.C. grew from 176 tons (Robson 2006) to 12,000 tons (Statistics Canada

2014). By 2012 that number had increased to 80,000 tons (Statistics Canada 2014); the majority of which was largely from 1989 to 2000, when a moratorium was placed on new net-pen sites.

The total value of aquacultured salmon in Canada increased from $195 million in 1991 to $634 million in 2013; a 300% increase in only twenty-two years (Statistics Canada 2014).

Salmon are anadromous, which means that they are born and live in freshwater as juveniles

before migrating to seawater where they begin growing rapidly and live most of their lives as

adults. They then return to freshwater to spawn. All Pacific salmon return to freshwater, spawn

and then die, a reproductive strategy known as semelparity. Atlantic salmon however, can spawn

multiple times (iteroparity) and return to the ocean between each spawning event. Different

species of salmonids spend different amounts of time in each life stage. Pink salmon

(Oncorhynchus gorbuscha), for example, migrate downstream toward the ocean almost immediately after hatching, while Atlantic salmon may stay in fresh water for up to 8 years depending on the river in which they are born (Groot and Margolis 1991; Klemetsen et al. 2003).

Most farmed salmon are raised in net pen systems in the open ocean. The fish raised in these

farms are typically first raised on land in freshwater tanks until they are large enough to smolt,

which is the physiological process that prepares the salmon for life in seawater at around 12

months (Willoughby 1999). They are then transferred to the net pens, where they are fed until

they reach a typical market size of approximately five kilograms. This process takes an

additional 15-20 months depending on temperature of the surrounding waters (Willoughby

1999).

Net pens are floating, open-top mesh nets, which are typically anchored to the seafloor in semi- sheltered bays or inlets. They allow fish to be raised in natural seawater, flushed by the tidal currents, and experience normal oscillations of daylight, temperature, and salinity among other environmental variables. The advantage of this means of production is that oxygen is replenished

without cost by the tides, and all of the waste is similarly swept away into the ocean and has no

impact on the water quality the fish live in. It also means that no environmental controls are

needed to keep the fish alive and healthy, which would be costly and difficult to maintain. There are disadvantages with this system of production as well. Net pens can cause dead zones in the substrate directly beneath them if too much waste settles to the bottom and eutrophication in the areas surrounding them (Folke et al. 1994). They cause many marine mammal deaths by entanglement and nuisance culls (Nash et al. 2000; Würsig and Gailey 2002). There are also

issues with relying on nature to maintain fish. For example, uncontrollable water temperatures

and hypoxic events caused by upwelling or eutrophication of cyanobacteria can lead to low

performance or even loss of entire nets of fish. Net pen fish can also act as a vector for disease

transfer from farmed to wild stocks (Johansen et al. 2011).

While these systems can have a negative impact on the environment, the same is true of all

agricultural practices. Net-pen aquaculture of salmon has a much lower environmental impact

than beef production and a very similar impact to poultry and pork production (Pelletier et al.

2009; Nijdam et al. 2012). Improved technologies for treatment and mitigation along with more

stringent environmental guidelines have significantly improved the sustainability of this salmon

farming system (Rust et al. 2014). Even though great improvements have been made to reduce

the impact of net-pen salmon culture, there are still many challenges and rearing fish in

recirculating aquaculture systems has been proposed as an alternative.

1.3 Recirculating Aquaculture

With many of these negative environmental impacts being reported in the media, many

individuals and environmental organizations have publically called for the end to open net pen

salmon farms (Holdsworth 2014; Morton 2015; Tuton 2015). The only potentially viable way to

move away from open net pen farming, while maintaining valuable fish production, is by

developing the technologies and knowledge to grow these fish in a closed-containment system.

A few different closed-containment technologies are being researched, but land-based recirculating aquaculture systems (RAS) seem to provide the most logical path forward. They not only address all of the environmental impacts of open net pen farms by moving production onto land, but they also have other added benefits. First, these systems use far less water than other land-based flow through types of systems (Verdegem et al. 2006; Blancheton et al. 2007), all waste produced within the system can be recaptured, and can be processed for use as fertilizers.

These systems can also be placed wherever they are needed, allowing for production of salmon near major marketplaces that may be nowhere near the ocean, potentially opening up many new opportunities for salmon sales. Adding to this, production nearby a market would decrease the need to transport fresh fish long distances, significantly lowering the carbon footprint of the products.

RAS are not without drawbacks. There are large start-up costs, high power consumption required by the pumps and filtration components, and issues with fillet quality stemming from precocious maturation of the fish (Boulet et al. 2010) and off flavor compounds that accumulate in the systems (Schrader and Summerfelt 2010; Houle et al. 2011; Burr et al. 2012). These issues have led to concerns about the commercial viability of RAS. The only way to overcome the economic

issues is by decreasing the costs associated with building and running these systems and/or by increasing the gross profit from fish that are raised in them by growing them more rapidly or efficiently. To tackle the biological issues, research needs to be done to pinpoint the causes and

determine solutions. The unique properties of RAS, however, provide the ability to precisely

control the environmental parameters that the fish are exposed to. By optimizing these

parameters for growth and feed conversion, there is potential to shorten production times, increase profits and possibly increase quality, all while eliminating the negative impacts of the net pen systems for producing the same fish. Finding the optimal environmental parameters in

RAS that provide enhanced growth in salmon, increased feed conversion efficiency, and/or potentially increased quality is crucial to the viability of RAS.

One of the largest expenses in salmon aquaculture is the feed; therefore one way to help improve profitability of RAS is by increasing feed conversion efficiency. In fact, feed costs account for approximately 50% of the total expenditure in salmon production (Guttormsen 2002). This is because salmon, as carnivores, require a very high protein input, approximately 45% of their

total dietary intake (Committee on Animal Nutrition and Board on Agriculture 1993). This is

typically sourced from wild caught forage fish and is a very expensive component in the feed.

The way feed conversion efficiency is typically measured in livestock is as a feed conversion

ratio (FCR), where efficiency is expressed as a ratio of food fed to mass gained with the lower

the number representing higher efficiency. Salmon, being one of the more efficient animals

grown typically have a FCR of between 0.9 and 1.2 when grown in net pen systems (Austreng et

al. 1987; Storebakken and Austreng 1987; Refstie et al. 1998; Cook et al. 2000; Thorarensen and

Farrell 2011). FCR of terrestrial animals is higher with beef at about 10, pork 3, and chicken 1.75

(Tolkamp et al. 2010).

The ability to precisely control many different parameters of the animal’s environment gives

farmers the unique opportunity to optimize each parameter to achieve maximal growth in a

particular fish species. While there have been a few studies investigating manipulation of

environmental parameters on fish growth in RAS, including the effects of substrate and lighting

on growth and stress levels (Volpato and Barreto 2001; Batzina and Karakatsouli 2012), research

in closed containment systems in general and, especially in relation to salmonids, is still very

much unexplored.

1.4 Salinity

Salinity has the potential to have one of the most significant impacts on growth and feed

conversion of all the environmental parameters that can be manipulated in RAS due to the

potential high cost of osmoregulation. The water that salmon live in throughout their lives varies

significantly in salinity, and can be anywhere from freshwater, where salinity can be as low as 0

parts per thousand (ppt) (<0.1 mOsm), up to full seawater at 35 ppt (~1000 mOsm) (Edwards and

Marshall 2012). Since salmon are osmoregulators, they maintain relatively constant blood ion

levels at around 250-300 mOsm, or ~10 ppt (McCormick et al. 1989; Bradley 2009) regardless

of the osmolality of their surroundings. To achieve this they utilize different processes to either take in or excrete ions, depending on whether the fish is hyperosmotic or hyposmotic to its surroundings. The major inorganic ions in the blood that contribute to this osmolality are Na+,

Cl- , and K+ (Edwards and Marshall 2012).

In freshwater, salmon are hyperosmotic to the water around them. This means that they passively

lose salts, as they diffuse from skin and gills down their respective ionic gradients, and fish

passively gain water, through osmosis. In this environment, osmoregulation involves active

uptake of ions from the water across the gill epithelium, driven by Na+/K+-ATPase (NKA) which

consumes ATP. These fish must also excrete large amounts of very dilute urine to remove the

excess water from their systems without losing too much salt.

In saltwater, the opposite occurs because salmon are hyposmotic to the water around them. Ions

are passively gained through diffusion across the gill epithelium and water is passively lost

through osmosis. Fish combat this efflux of water at the gill by drinking seawater. They first

desalinate the ingested water using NKA; 50% of this ion uptake occurs in the esophagus while

the rest occurs elsewhere in the intestinal tract. The desalinated water, which is of lower total

salinity than the body, leads to passive diffusion of water back into the fish (Grosell et al. 2011;

Whittamore 2011).The ions in the water are rapidly transported to the blood (due to the high

density of capillaries in the esophagus), where they are then actively excreted across the gill by a

Cl- co-transporter (NKCC) and again driven by NKA (Edwards and Marshall 2012), although of

a different isoform than when in freshwater, which together pumps Na+ and Cl- out of the gill, into the surrounding environment. The fish also excrete very concentrated urine to remove calcium and magnesium sulfate and bicarbonate, other osmolytes, while losing as little water to urine as possible (Bradley 2009; Whittamore 2011).

The processes involved in osmoregulation are likely energetically costly but estimates have been

notoriously difficult. Estimates of the cost range dramatically from as little as 5% to as much as

50% (Rao 1968; Kirschner 1993; Bœuf and Payan 2001) of resting metabolic rate. Nevertheless, if a fish is raised in an environment which is isosmotic to its blood, it could theoretically eliminate the energetic expenditure associated with osmoregulation. This saved energy could potentially be repurposed in two different ways; it could either result in increased somatic growth, leading to a fish that grew faster, or it could reduce overall energetic costs, leading to a fish that required less energy to grow at the same rate through an increase in feed conversion efficiency, or a combination of the two.

Recent research examined optimal salinity for growth of various species of fish, with equivocal findings. Sea bream (Sparus sarba) raised in RAS at three salinities (7, 15, and 35 ppt) and many levels of dietary protein intake grew fastest in 15 ppt saltwater at every level of dietary protein, potentially due to decreased osmoregulatory costs (Woo and Kelly 1995). Two Atlantic salmon salinity trials have been done, both in post-smolt fish. The first simply looked at fish in freshwater (0 ppt) and fish in seawater (34.5 ppt) and found that freshwater fish grew faster

(Imsland et al. 2011). The second exposed fish to 0, 10, and 30 ppt saltwater at different rations and found that ration affected which salinity fish grew fastest in (McCormick et al. 1989). They found that no difference in growth was detected at a low ration. However at a high ration fish grown in the isosmotic treatment of 10 ppt grew slower than the fish in either 0 or 30 ppt.

Another study was done exploring growth of Abant trout (Salmo abanticus), a closely related freshwater obligate species to Atlantic salmon, at 0, 9, and 18 ppt saltwater (Kocabas et al.

2011). This study found that fish grew fastest in 0 ppt. Lastly, two species of pacific salmonids

were grown in five salinities (0, 4, 8, 12, 16, and 28 ppt) as fry and it was found that fish grew

fastest in 0 ppt fresh water (Morgan and Iwama 1991). The lengths of these previous studies

ranged from 1-5 months and no studies to date have investigated longer term effects of different

salinities on growth from post-smolt through to a marketable size in any salmonid species.

1.5 Research Goals

The goal of this thesis was to determine how salinity affects growth and feed conversion in

Atlantic salmon (Salmo salar) and coho salmon (Oncorhynchus kisutch) in RAS. These species

were chosen because Atlantic salmon are the most widely raised species of salmonid for food

consumption worldwide, and coho are a BC native species that has shown much promise when grown in RAS.

The InSEAS (Initiative for the Study of the Environment and its Aquatic Systems) facility is unique in that it is designed to rigorously test the effect of salinity, as well as other environmental factors, on physiological parameters of salmonids reared at a semi-industrial scale. This allows us to make direct recommendations to industry on the way the unique physiology of these fish can be harnessed to increase productivity and therefore profitability.

A crucial step in reaching the goal of this thesis was to validate that the newly constructed RAS systems (consisting of seven independent 15,000 L recirculating systems) maintain similar environmental variables across each system as well as grow fish consistently when all parameters were held constant. This was the focus of my second chapter and was shown to be the

case. This allowed me to then manipulate one environmental parameter (salinity) within the

setup and have reasonable confidence that this manipulation was the likely cause of any trends seen. My third chapter focuses on defining an optimal salinity for raising both Atlantic and coho salmon in RAS by analyzing how growth and feed conversion are affected by the salinity of the water. My hypothesis is that in isosmotic water fish will have the highest rate of growth and have the lowest feed conversion ratio due to the lowered osmoregulatory cost associated with these conditions.

Through the work that I outlined and future work at InSEAS, my goal is to provide information that will help to decrease the costs associated with growing salmon in RAS to a level that makes land based salmon farming a feasible alternative in the future of seafood production.

Chapter 2: Validation of a novel RAS research facility: InSEAS

2.1 Summary

Land-based recirculating aquaculture systems (RAS) for rearing salmonids have the potential to help supply the world with needed protein while minimizing some of the perceived negative effects of net pen aquaculture. Within RAS, environmental conditions, such as temperature, photoperiod, or salinity can be precisely controlled; however, there are large knowledge gaps regarding the optimal conditions for rearing salmon, which is required to maximize output in order for these highly technical systems to be successful and economically feasible. InSEAS at

The University of British Columbia has been designed to systematically define optimal

conditions for salmon growth in RAS and consists of seven replicate systems, semi-industrial in

scale. This data chapter validates that the novel experimental setup at InSEAS can grow fish

consistently at rates similarly to industry when all environmental parameters are held constant.

Coho salmon were grown for approximately five months in five replicate RAS while all

environmental parameters were kept as consistent as possible. Water parameters were monitored

daily and individual fish mass was taken at the start and termination of the experiment. A new

non-contact method of fish mass estimation was also tested. Throughout the trial, water

parameters did not vary between systems at a level that would affect growth. Fish in each tank

grew an average of 342.2±2.52 g over the 5-month growth trial. There were no significant differences between systems for both initial (P=0.147) and final (P=0.529) masses of the fish.

The average specific growth rate across systems was 0.7±0.01 %·day-1. The non-contact method

of fish mass estimation was found to consistently underestimate growth by 15.6%, and a

correction factor can be used to adjust for this. Further research can now be conducted using

these systems to investigate optimal environmental parameters for growth and welfare of

salmonids.

2.2 Introduction

Aquaculture is the fastest growing sector of food production worldwide (FAO 2014). It

surpassed wild caught fisheries as the primary source of seafood for the first time in 2014, and is

expected to continue to increase dramatically as the world’s demand for high quality animal

protein increases. Farmed salmon is a large percentage of overall aquaculture production,

especially in Canada, where it accounts for 83% of all finfish produced (Statistics Canada 2014).

The vast majority of salmon grown worldwide are grown in net pens usually located in sheltered harbors or bays.

Recently, there has been increased public pressure to address and reduce some of the negative environmental impact of aquaculture on the surrounding area. Net pen salmon culture has been implicated in issues such as eutrophication (Folke et al. 1994; Black et al. 1997) caused by uneaten feed and feces accumulating beneath the pens. Disease transfer to wild fish stocks

(Johansen et al. 2011) are also frequently a topic of concern. Pathogens such as infectious salmon anemia virus (ISAV) or salmon louse (Lepeophtheirus salmonis) can cause localized outbreaks tied to net pen salmon farms. Negative marine mammal interactions (Nash et al. 2000;

Würsig and Gailey 2002) have also been a major source of public concern. Many of these issues have since been mitigated, at least in North America and Europe, by enforcing stricter regulations and improving siting requirements (Rust et al. 2014).

As an alternative means of production, land-based recirculating aquaculture has been proposed

as a viable alternative to traditional net pen farming. Recirculating aquaculture systems (RAS)

are not without their issues, however. The major drawbacks are the high initial investment in

these types of facilities and the smaller profit margins due to increased production costs (Boulet

et al. 2010). Consequently, fish production within RAS needs to be done as efficiently as

possible in order to make it competitive with the other sources of salmon.

RAS provides the unique ability to control all environmental parameters, which can each be fine-

tuned to optimize growth and feed conversion. Defining these optimal parameters can help to

minimize costs by making fish more efficient at food utilization and/or making them grow faster,

shortening the time needed to grow salmon to a harvestable size. Many studies on salmonids to

date have focused on understanding how differing environmental parameters effect survival. The

extreme limits of parameters for health and survival are well documented (see Thorarensen and

Farrell 2011) for a review of these parameters in the context of RAS); however, no studies have

been undertaken to define optimal parameters for growth of salmon in RAS.

InSEAS, (Initiative for the Study of the Environment and its Aquatic Systems) at the University

of British Columbia, was designed to systematically determine optimal environmental

parameters for RAS and how they affect the underlying physiology of fish, which requires

multiple identical systems. InSEAS consists of seven replicate but independent 15,000 L RAS.

Each system is comprised of two 5,000 L blue-walled fiberglass tanks and two 700 L blue- walled fiberglass tanks, which for the purpose of this study only served to increase total water capacity and as an area to add probes and pH buffering agents to the system. The systems are

housed in two experimental rooms; five in one room and two in the other. The water quality

control part of each system consists of three stages of mechanical filtration, two stages of

biological filtration, ultraviolet (UV) sterilization, oxygenation, a degasser, a heat exchanger, and

a pH regulation unit (Fig. 2.1).

Figure 2.1 Schematic of one InSEAS recirculation aquaculture system, consisting of two 5000 l tanks and associated mechanical and biological components. Arrows represent direction of water flow through the system. The thick black bar represents the floor which separates the main room (above) with the basement (below). The two small 700L tanks that are also contained within each system were omitted from this diagram.

Aside from the tanks, each room houses the combination biofilter/degassing units for each system (Fig. 2.2). Systems are designated 1 through 7, and each system has two tanks, A & B.

Fluorescent lighting on automatic timers are used to illuminate the rooms and maintain a

17 constant fixed photoperiod. The systems maintain precise control of temperature, pH, and oxygen levels and all of these parameters can be remotely monitored in real time.

Figure 2.2 Overhead schematic of our large experimental room in InSEAS, which houses five of the independent 15,000L recirculation aquaculture systems and their respective biofilters (BF). Systems are labeled 1 through 5, with each of the replicate 5,000L tanks within each system labeled either A or B. Small unlabeled tanks are 700L, and were not used to house fish during this experiment.

Before InSEAS can be used to define optimal environmental parameters, we must first validate that the systems can maintain precise and constant environmental conditions and grow fish consistently between tanks.

The goal of this data chapter was to determine whether five of the seven independent RAS systems at InSEAS could control crucial water quality parameters and yield similar levels of growth and feed conversion when they were held under identical conditions. Also, since growth can be negatively affected by handling stress, I tested a novel non-invasive means of estimating

fish mass with minimal stress. I chose the five systems in the one large experimental room for

this validation study because the other two systems were not functional at time of testing. Since

it has been shown that variation in parameters such as tank placement (Speare et al. 1995),

lighting (Boeuf and Le Bail 1999), and flow (Ross et al. 1995) can effect growth, a study needs

to be conducted verifying that, given the same parameters, fish will grow at an identical rate in

these systems.

The objectives of this study were to test for the ability of the systems to maintain precise control

+ - - over total ammonia (TAN; NH3 & NH4 ), nitrite (NO2 ), nitrate (NO3 ), pH, temperature (°C),

and oxygen concentrations (mg·L-1) over the course of approximately 6 months; to grow fish in

each of the systems, under identical conditions, and measure growth rate over the 6 month trial;

and to test the effectiveness of two new non-contact devices for the use of estimating fish size

within our systems; the VAKI Biomass Estimator, and the Vicass HD Biomass Estimator. My

goal was to validate that the novel InSEAS system at the UBC can reliably be used to define optimal environmental conditions of salmonids by ensuring that each of the replicates, while independent, can function identically under a given set of conditions. Once this is validated, we can be confident that when systematically altering individual environmental parameters across

RAS systems any variation in growth of the fish within the systems is likely due to the environmental alteration and not a system-specific parameter.

2.3 Methods

2.3.1 Overview

The University of British Columbia’s InSEAS facility (Vancouver, BC, Canada) consists of

seven identical recirculation systems; five housed in one large room and two in a separate

smaller room. My study measured growth of coho salmon from November 2013 to May 2014 to

validate that each of the five systems in the large experimental room can reliably and

consistently grow fish when the RAS are held under identical conditions. An all-female line of

coho salmon were obtained from Target Marine (Sechelt, BC, Canada). Fish were approximately

1 year old and 40 g at time of shipment. Experiments were conducted under UBC Animal Care

Permit # A13-0016.

2.3.2 Experimental Design

A total of 3,862 fish were individually weighed and randomly distributed throughout the 10

experimental tanks (two replicate tanks for each of the five systems). All fish were fasted for two

days prior to handling, to reduce stress and eliminate waste products that may influence

estimates of body mass and negatively affect water quality in the temporary holding tanks used

for estimating biomass. Fish were netted from their stock tank, anesthetized in an aqueous

-1 -1 solution of 0.1 g L Tricaine-S (MS-222) buffered with 0.2 g L sodium bicarbonate (NaHCO3)

and individually weighed. Fish were then transferred to a recovery tank, which sat on a large

floor scale until a batch mass of approximately 10 kg was reached. The batch was then

transferred to one of the experimental tanks at random. This process was repeated until all fish

were weighed, and all tanks had approximately the same final biomass (70-77 kg).

Once fish were distributed to their tanks, feed was gradually increased over a week until fish were fed to approximate satiation. Arvo-Tec automatic feeders (Arvo-Tec Oy, Huutokoski,

Finland) were used throughout the experiment, which provided each tank identical amounts of

feed evenly throughout the day. Satiation was determined by monitoring for uneaten feed in the

radial flow separator on each system. Feed rates were increased until uneaten food was found in

the separator. Feed amounts were then increased incrementally each day, controlled by standard

growth curves programmed into the feeders.

Two days prior to the start of the final sampling, fish were fasted again. This was done both to

halt growth to allow comparison of measured and estimated weights, and also to prepare fish for

measurement at the termination of the experiment. Fish were terminally sampled according to

CCAC guidelines (Canadian Council on Animal Care 2005) between 144 – 151 days after the

start of the trial. They were placed in an aqueous solution of 0.25 g L-1 MS-222 and 0.5 g L-1

NaHCO3 and held until opercular movement was no longer visible. Fish length and mass were

then measured and recorded and all fish were disposed of according to the UBC Hazardous

Waste Management Procedures.

During the experiment, I tested the feasibility of the VAKI Biomass Estimator (VAKI

Aquaculture Systems Ltd., Kópavogur, Iceland) to accurately estimate fish mass within the tank,

without having to remove fish for measurements. The VAKI comprises a base computer unit

connected to a frame that is placed inside the tank that the fish swim though. The frame contains

a grid of infrared emitters and receivers on the inside, which map the body shape and size of fish

that swim through it. It then converts the mapped fish shape/size to body mass based upon pre-

programmed algorithms. Two weeks preceding the termination of the experiment, the Biomass

Estimator was placed into each tank and collected data until, at minimum, the floating average

plotted by the base unit stopped fluctuating, which was usually around 1,500 samples and

generally between 1 and 3 days per tank. We also briefly tested the Vicass HD Biomass

Estimator (AKVA Group, Bryne, Norway). The Vicass HD uses two stereoscopic cameras to

estimate body mass by measuring the length and width of individual fish in sets of images it

takes while in a tank. The camera unit was placed into the tank and the software automatically

took photos at a set interval that were saved to an attached laptop. I then plotted points for

individual fish in each of the sets of stereoscopic photos. The software used these points to

calculate the distance from the camera and length and width of the fish, and used this to estimate

a body mass.

2.3.3 Water Parameter Testing & Analysis

Throughout the experiment water samples were tested daily for total ammonia (TAN; NH3 &

+ - - NH4 ), nitrite (NO2 ), and nitrate (NO3 ). Free ammonia (NH3) was calculated from daily TAN, pH, and temperature readings using the formula described in the Florida department of environmental protection chemistry laboratory methods manual (Patton et al. 2001) and implemented using the “Unionized Ammonia Calculator v1.2” in Microsoft Excel (Ross 2001).

Temperature (°C), pH, and oxygen concentrations (mg L-1) were monitored automatically via

probes in the system, and values were recorded daily. Water chemistry is rarely completely

constant, so a range of values from the literature were obtained from which no effects on growth

22 should be expected in freshwater (Table 2.1). Any variation within these values should have no effect on growth.

Table 2.1 Acceptable values of unionized ammonia (NH3), nitrite, nitrate and oxygen in freshwater. It is assumed that values less than or equal to those that are listed for unionized ammonia (NH3), nitrite, nitrate will have little to no effect on growth. Values between those listed for oxygen should have little to no effect on growth. Parameter Acceptable Levels References -1 NH3 ≤0.012 – 0.025 mg L (Fivelstad et al. 1995; Wedemeyer 1996; Tidwell 2012) - -1 NO2 <0.1 mg L (Wedemeyer 1996) - -1 NO3 <1 - 400 mg L (Wedemeyer 1996; Tidwell 2012) Oxygen 80-100% (Berg et al. 1993; Lygren et al. 2000; Bergheim et al. 2006)

2.3.4 Calculations

2.3.4.1 Growth Rates

Growth rate was calculated from the tank means at each time point in 3 different ways.

Initially growth rate was simply calculated as:

( ) ( ) = 2 1 푀푎푠푠 푔 − 푀푎푠푠 푔 퐺푟표푤푡ℎ 푅푎푡푒 2 1 where and are mean body mass at 푇푖푚푒 and− 푇푖푚푒 , respectively.

2 1 2 1 푀푎푠푠 푀푎푠푠 푇푖푚푒 푇푖푚푒 To linearize exponential growth between each time point, specific growth rate (SGR) was calculated as:

ln ( ) ln ( ) = × 100 2 1 푀푎푠푠 푔 − 푀푎푠푠 푔 푆퐺푅 2 1 where and are mean body 푇푖푚푒mass at− 푇푖푚푒and , respectively.

2 1 2 1 푀푎푠푠 푀푎푠푠 푇푖푚푒 푇푖푚푒

Finally, to standardize for both mass specific growth scaling and the effect of temperature on

growth rate the thermal growth coefficients (TGC) were calculated using the formula:

( ) / ( ) / = × 1000 (°1 3) × ( 1 3 ) 푀푎푠푠2 푔 − 푀푎푠푠1 푔 푇퐺퐶 2 1 푇푒푚푝푒푟푎푡푢푟푒 퐶 푇푖푚푒 − 푇푖푚푒

where is mean temperature between and . and are

1 2 2 1 mean body푇푒푚푝푒푟푎푡푢푟푒 mass at and , respectively. 푇푖푚푒 푇푖푚푒 푀푎푠푠 푀푎푠푠

2 1 푇푖푚푒 푇푖푚푒

2.3.5 Statistical Analysis

Data were analyzed using Sigmaplot 12.0. All data are given as means ± 1 SEM. Significance was set at α = 0.05. Significant differences in water parameters between systems were tested

using Kruskal-Wallis one-way ANOVA on ranks because data were not normally distributed.

Differences in mass at the beginning and end of the experiment were tested using standard

ANOVAs. Tukey post hoc analysis was performed to compare the differences between all five

systems independently.

2.4 Results

2.4.1 Water Parameters

Throughout the trial, ammonia, nitrite, pH, oxygen, and temperature varied significantly between

systems (P < 0.001-0.028). Although there was significant variation in many of the measured

environmental parameters, primarily due to the large number of samples taken, the magnitude of

variation in each of these parameters was very small (Table 2.2), which is unlikely to have a

measurable biological effect. Nitrate did not vary significantly between systems (P=0.912).

None of the parameters deviated outside of the values set in Table 2.1.

Table 2.2 Water parameters for five systems throughout the duration of the freshwater validation growth trial. Between 91 and 143 measurements were made for each parameter in each system, over a total of 144-151 days. Values are mean ± standard error of the mean. P-values are shown for Kruskal-Wallis One Way ANOVA on Ranks. Dunn’s Method for multiple comparisons was used to determine which systems differed. Letters that differ indicate statistically significant differences between systems.

Unionized Nitrite Nitrate Oxygen Temperature System pH Ammonia (µg L-1) (mg L-1) (mg L-1) (mg L-1) (°C) A1 0.2±0.02a 0.00±0.00 3.0±0.6 6.4±0.03ab 9.7±0.05a 9.1±0.05a A2 0.2±0.02ab 0.05±0.10 3.2±0.6 6.4±0.03a 10.5±0.05b 9.5±0.1bc A3 0.4±0.04b 0.13±0.14 2.7±0.4 6.6±0.02c 9.4±0.06c 10.0±0.1bd A4 0.3±0.03ab 0.01±0.02 2.5±0.4 6.5±0.02ab 10.1±0.05d 9.6±0.08bcd A5 0.3±0.02ab 0.02±0.03 2.6±0.4 6.5±0.03b 9.4±0.09ce 9.9±0.2b P-value 0.014 0.025 0.912 <0.001 <0.001 <0.001

2.4.2 Freshwater Growth Trial

Average fish mass in each tank at initial stocking was between 189±2.7 g and 200±3.2 g (Fig.

2.3); however there were no statistically significant differences in mass between tanks (P =

0.105). Accidental mortality, due to a brief system failure, was noted in system 2 and therefore

this system was removed from further analysis. Fish in the remaining 8 tanks grew over the

course of the trial to an average size of 522±9.8g to 546±12.0g There was no significant

difference in mass among these tanks (P = 0.529).

Figure 2.3 Mean body mass of all coho salmon (n = 338-404) measured during freshwater validation growth trial over the 5-month growth trial. Filled circles represent starting mass, open circles represent final mass. Systems are designated 1-5 with replicate tanks indicated by A or B. All points are means ± SEM. System 2 was removed due to an accidental mortality event. No statistical differences were detected between tanks at each time point (P=0.529)

Growth rate of fish was calculated in each tank. While no statistical analysis could be performed, it appears that there was no effect of tank or system on growth rate in coho salmon when expressed as either grams per day (2.19 to 2.42 g·day-1; Fig. 2.4A), SGR (0.65 to 0.72%·day-1;

Fig. 2.4B), or TGC (1.57 to 1.71; Fig 2.4C).

Figure 2.4 Mean growth rates between all coho (n = 338-404) measured during freshwater validation growth trial over the 5-month growth trial. Points represent growth rates between the starting mass and final mass. Panel A is absolute growth rate expressed in grams per day, panel B standardizes for size by representing specific growth rate in percentage per day, panel C standardizes for size and temperature by representing the thermal growth coefficient which does not have units. Statistical analysis could not be performed on these data. For additional information see Fig 2.3 legend.

The mass of all fish were summed to obtain a total biomass for each tank, both at initial stocking and at the termination of the experiment (Fig.2.4). At stocking, biomass ranged from 70.0-77.3 kg per tank representing a density ranging from 14.0-15.4 kg·m-3. At the termination of the

experiment, biomass ranged from 191.7-207.9 kg, representing a density range of 38.6-41.6

kg·m-3.

Figure 2.5 Initial and final total biomass and stocking density of each tank of coho salmon measured during freshwater validation growth trial over the 5-month growth trial. Filled circles represent starting biomass/density, open circles represent final biomass/density. Statistical analysis could not be performed on these data. For additional information see Fig 2.3 legend.

2.4.3 VAKI Biomass Estimator Validation

Average mass estimates collected from the VAKI Biomass Estimator during the final two weeks of the growth period ranged from 412±3.7 g to 472±4.9 g, while measured masses ranged from

509±11.3 g to 567±13.7 g (Fig. 2.5). All estimated masses were significantly lower than measured masses (P= <0.001). The difference between estimated and measured mass was between -37 g and -127 g with an average error between estimated mass and measured mass of -

15.6±4.4%. There was significant variation in estimated mass between tanks (P = <0.001) but not in measured mass between tanks (P = 0.529).

Figure 2.6 Estimated (using the VAKI biomass estimator) and measured final masses of coho salmon measured during freshwater validation growth trial over the 5-month growth trial. Filled

circles represent measured mass (n = 338-404) at the end of the freshwater validation growth trial, open circles represent estimated mass (n=1,064-5,000) taken over the two weeks prior to the end of the growth trial. All points are means ± SEM. Letters that differ indicate statistically significant differences between tanks and/or method. For additional information see Fig. 2.3 legend.

2.5 Discussion

I performed an experiment to validate that each of five recirculating aquaculture systems at

InSEAS could reliably grow coho salmon at the same rate while maintaining constant

environmental parameters. Due to a brief system failure, only 4 systems were included in the

analysis. We found that due in part to the large sample size, significant differences in some

environmental parameters were detected; however all values were within the range of those that

are known to have no effect on growth in freshwater (Table 2.1). The 1°C variation in

temperature between systems did not affect growth. All tanks included in the analysis grew fish

consistently, and at a similar rate to industry. The VAKI Biomass Estimator was found to

consistently underestimate the size of fish.

2.5.2 Fish Growth

Initial and final measured masses were not significantly different between tanks tested. Tanks

within system 2 were excluded due to a system failure, and associated accidental mortality. A pH

regulation unit malfunctioned and briefly, but rapidly, elevated the pH in system 2 by 2 pH units,

causing approximately 45 of 760 fish to die. This likely caused higher growth due to lower

densities after the mortality event. High rearing density has been shown to negatively affect

growth rates in various salmonids, however, there seems to be no strong consensus on what

maximum density fish can be reared at (Thorarensen and Farrell 2011).

Although statistical analysis could not be performed on growth rate, as it was calculated on

single tank means, I believe that there were no differences in measurable growth rates amongst

systems when functioning normally. Growth rates are presented using three different metrics, for

comparison with literature values. TGC is the current industry standard, as it represents growth

rate in a way that is independent of the effects of temperature and size of fish. The TGC’s we

obtained (1.57 to 1.71) are similar and slightly higher than those seen in the industry (1.45; coho

salmon producer, Agassiz, B.C.). This is to be expected, since the industry data is over the entire growth period, and growth rate generally slows as fish get larger. So, I can reliably conclude optimal values determined within the systems can directly inform optimal values in the industry.

In subsequent trials we can therefore reliably conclude that any changes in growth are due to the independent variable and not random variation within the systems.

2.5.1 Environmental Parameters

Throughout the trial we monitored many environmental parameters. Because of the large sample sizes (n=91-143), we were able to detect very small statistically significant differences between values within the systems (Table 2). While the ability to detect these minute differences shows the power of the systems and the experimental design, they do not change over a magnitude that

has biological relevance (Table 1), with the exception of temperature. Any variation in

temperature will always effect growth (Elliott 1982; Handeland et al. 2008). Since fish are

ectothermic, any activity including digestion, locomotion, or somatic growth is directly influenced by the temperature of the environment the organism is in (Brett and Glass 1973;

Handeland et al. 2008). I found variation in temperature of ~1°C between systems, which may

have had a slight effect on growth, but the magnitude of this effect, was not substantial enough to

detect within our trial. Therefore, in later studies, we can assume that variation of temperature

within 1°C should not significantly affect growth.

2.5.3 Biomass Estimator

The VAKI Biomass Estimator underestimated growth on average by 15.6±4.4%. This underestimation is likely due the design of the algorithm which calculated body mass. The system is programmed for Atlantic salmon, and the body shape of coho and Atlantic’s differ slightly. Since this was a fairly consistent underestimation between all tanks, a correction can be applied by increasing the estimated mass by 15.6%. This will allow for accurate estimation of fish mass within the tank, without the need to physically remove and handle them to obtain the information.

Being able to use this system would allow for more regular measurements of growth throughout experiments. It should also lead to improved growth within the tanks, as handling stress has been shown to decrease growth (McCormick et al. 1998). This is due to decreases in food consumption following handling stress, which also coincides with changes in levels of plasma growth hormones in stressed fish (Pickering et al. 1991; McCormick et al. 1998). I had other

issues with the biomass estimator, however, mostly due to the disruption of radial flow within

the tank that the frame caused. It created an area of low current, which allowed fish to rest inside

or directly behind the frame. This caused fewer fish to swim through the system, increasing the time it took to take measurements. It also may have caused a size-related bias to fish swimming

through the system. If this bias is true, the correction factor would not be effective. More studies

need to be done to understand the mechanism by which the system underestimates mass of coho

salmon before it can be reliably used for experiments at InSEAS.

There are other non-contact systems which can be used to measure in-tank mass of salmon, one

being the Vicass HD Biomass Estimator. This system was tested briefly in our facility as well

but the focal point and lighting requirements of the stereoscopic cameras made it incompatible

with our tank design. With the further development of these and other systems, real-time

accurate estimation of biomass and size distributions within tanks should become more

commonplace, leading to enhanced growth rate, by reducing stress, and to better feeding practices by having accurate real time data to minimize feed waste.

2.6 Conclusion

This study provides validation of a novel system that can maintain constant environmental

parameters in replicate recirculating aquaculture systems, at an industrially relevant scale and grow fish at rates similar to those seen in industry. By doing this, we are able to ensure that subsequent experiments conducted within the facility to define optimal values of individual environmental parameters are valid and repeatable. These future experiments will be conducted to better understand how to maximize fish growth by altering environmental parameters. They will provide the industry with important tools to help facilitate the transition from net pen production to land-based RAS production of salmonids.

Chapter 3: The effects of salinity on the growth and feed conversion of coho and Atlantic salmon

3.1 Summary

Net-pen aquaculture has been the standard for salmon production for decades. However, recent negative public perception and potential environmental damage has driven the innovation of a new land-based system of production. Recirculating aquaculture systems (RAS) can be setup anywhere and can grow fish under precisely controlled environmental parameters, while mitigating many of the issues of net-pen production. With this move to shift salmon production onto land, the need exists to optimize all of the environmental parameters for growth and feed conversion in RAS in order to make it a profitable endeavor. However, optimal levels of most environmental parameters for growth or feed conversion are currently unknown. I tested the effect of salinity on growth and feed conversion in UBC’s InSEAS research lab in order to define an optimal salinity. Coho salmon grown in five different salinities (0, 5, 10, 20, and 30 ppt) grew twice as fast at isosmotic salinities around 10 ppt than at either 0 or 30 ppt for the first growth period (from the start until day 59). The lowest eFCR occurred at 10 ppt as well. The trends for coho salmon seen in the first time period were not as strong through the second time period

(from day 59 to day 156) possibly due to a size-dependent or density-dependent effect on growth. Atlantic salmon growth or feed conversion was unaffected by salinity, but they grew at rates similar to industry standards. These data will be useful to the RAS community as they begin to expand the land-based production of salmon.

3.2 Introduction

Farmed seafood consumption has seen the largest per capita growth of any protein sector in the

world in the last few decades (FAO 2002; FAO 2014). Salmon are one of the most widely

consumed fish in today’s marketplace and that demand keeps increasing (Knapp et al. 2007).

Wild-caught fisheries catches plateaued approximately 20 years ago (FAO 2014), thus the only way to meet increasing consumer demand is through an increase in salmon aquaculture. This gap has been filled solely by culture of salmon in net pens until very recently, with a small number of salmon being produced through alternative means.

While raising fish in the traditional net pen systems has been the standard for many years, increasingly strict regulations and negative public perceptions of the practice have limited the net pen industry to grow. Some countries have put periodic moratoriums on new site leases to limit the amount of nets in the oceans (Wagner 2010; Drews 2013), while other countries are calling for strict monitoring of individual sea lice counts on fish to limit potential for pathogen interactions (Hersoug 2015). While the industry as a whole is trying to mitigate the negative impacts on the environment, public perception of net pen produced fish will be hard to change.

The salmon farming industry is in need of a new means of production if it is to satisfy consumers and meet the increasing demand.

Recirculating Aquaculture Systems (RAS) give the industry the ability to produce fish locally to high demand markets, instead of shipping them from around the world, and to alleviate many of the environmental issues that have led to the negativity toward the industry as a whole. However, this new production system is costly and, so far, unproven. The infrastructure often costs tens of

millions of dollars for a large operation, and currently these systems must run continuously for

ten to fifteen years before a profit can be realized (Boulet et al. 2010). The RAS industry can potentially realize profits faster if salmon can be grown more efficiently than in net pen

production.

The unique advantage of production in RAS is the ability to precisely control all of the

environmental parameters under which the fish grow. This is something that is not an option in

the net pens, as the fish are raised in open water, where seasonal and diurnal fluctuations are

constantly occurring. With the ability to control all of the environmental parameters, the option

exists to optimize each parameter for growth and feed conversion efficiency. While there is a

good understanding of what conditions are adequate for growth (see Thorarensen and Farrell,

2011 for a review of biological requirements of post-smolt Atlantic salmon), research into

optimal values of any environmental parameter for growth or feed conversion is largely lacking.

RAS have the ability to control water quality parameters such as temperature, photoperiod,

salinity, levels of nitrogenous compounds, and oxygenation; as well as the rearing conditions

such as density, feed amount or timing, or the velocity of water within the tanks.

Due to the unique life history of salmon, which allows them to survive over a large range of

salinities, as well as the metabolic costs associated with osmoregulation, salinity may have the

most significant effect on growth and/or feed conversion efficiency of the many variables that

can be controlled in RAS. Salmon are anadromous, moving from freshwater where they hatch

and live as juveniles to the ocean to grow into adults, and back to freshwater again to spawn

throughout their life cycle. This life history trait allows a salmon to survive in water ranging

from freshwater (0 ppt) to seawater (35 ppt).

Salmon, however, are still osmoregulators, which means that they maintain a fixed osmolality

within their bodies regardless of the salt content of the water around them (Foskett et al. 1983).

To maintain this salt balance they employ active ion transport using Na+/K+-ATPase (NKA), an enzyme that consumes energy in the form of ATP to function. The cost of osmoregulation has been studied extensively in many fish species. However the literature is very contentious.

Theoretical estimates of 2.5% (in freshwater) and 7.5% (in salt water) of the total standard metabolic rate (Kirschner 1993; Kirschner 1995) are higher and opposite the values of 3.9% and

2.4% (freshwater and salt water respectively) seen in isolated gill preparations (Morgan and

Iwama 1999). These values are considerably lower than those determined in whole animal trials

where osmoregulation has been estimated to comprise between 10-50% of standard metabolic

rate (Rao 1968; Febry and Lutz 1987; Nordlie et al. 1991; Bœuf and Payan 2001; Ern et al.

2014).

If fish are raised in water that is isosmotic to their bodies, they could potentially redirect the

saved energy toward somatic growth and/or lower their standard metabolic rate. This would

allow for faster growth and/or a reduced feed conversion ratio (FCR). Either of these scenarios

would likely increase the profitability of a RAS, making them a more feasible way to produce

salmon. While many studies exist which have studied the effect of salinity on growth of various

fish species (Morgan and Iwama 1991; Woo and Kelly 1995; Bœuf and Payan 2001; da Silva

Rocha et al. 2005; Gústavsson et al. 2009; Kocabas et al. 2011) very few have looked at how

salinity effects growth of post-smolt salmonids (McCormick et al. 1998), and none have looked

at it over many months of continuous and intensive rearing.

The objective of this study was to define an optimal salinity for growth of salmonids by

investigating how salinity effects growth and feed conversion in two commercially important

species of salmonids, coho and Atlantic salmon. Coho and Atlantic salmon were raised in five

different salinities (0, 5, 10, 20 and 30 ppt) and the change in weight and amount fed was

monitored over a period of approximately five months. To ensure that any increase in mass is

due to somatic growth and not intramuscular water retention, muscle water content (MWC) was

also measured. Since salmon maintain their blood osmolality at around one third seawater, I

expected to see the greatest growth and/or lowest FCR at 10 ppt saltwater. These data will be of

value for the industry to model the cost of growing salmonids in various salinities. The data

herein could help further the success of RAS salmon production, which could be an important

milestone in the future of high quality protein production.

3.3 Methods

3.3.1 Overview

The University of British Columbia’s Initiative for the Study of the Environment and its Aquatic

Systems (InSEAS; Vancouver, BC, Canada) consists of seven identical recirculation systems; five housed in one large room and two in a separate smaller room. Our study measured growth of coho and Atlantic salmon from May 2014 to October 2015 in five of the experimental systems set to five different salinities.

Coho salmon were obtained from Target Marine (Sechelt, BC, Canada) while Atlantic salmon

were obtained from Omega Pacific Hatchery (Great Central Lake, Port Alberni, BC, Canada); all

fish were smolted prior to their arrival via photoperiod manipulation. The coho and the first

cohort of Atlantic salmon were approximately 1 year old, and 40 g at time of shipment. These fish were temporarily held in four 5,000L recirculating freshwater holding tanks. A second

cohort of Atlantic salmon was approximately 1 year old but slightly larger, at around 70 g at time

of shipment and held outdoors in two 10,000 L flow-through freshwater tanks. All fish were shipped to the University of British Columbia (UBC) by transport truck.

All fish were raised in one of five 15,000 L recirculating aquaculture systems at the UBCs

InSEAS research lab, which is described in detail in the previous chapter (See section 2.2). But briefly, each system was comprised of two 5,000 L blue-walled fiberglass tanks, one housing coho and the other housing Atlantic salmon, and two 700 L blue-walled fiberglass tanks, the latter of which were not used in this study. Systems were designated 1 through 5, and each system had two tanks, A & B. Each of these systems possessed three stages of mechanical filtration, two stages of biological filtration, ultraviolet (UV) sterilization, oxygenation, intermittent ozonation, a degasser, a heat exchanger, and a pH regulation unit. Each system was maintained identically with the exception of salinity, which was maintained at 0, 5, 10, 20, or 30 ppt salt. Salt water was created by mixing a commercial synthetic seawater mix (Instant Ocean®,

Aquarium Systems Inc., Mentor, OH) into the dechlorinated city tap water within the system.

Aside from tanks, the experimental rooms housed the combination biofilter/degassing units as

well as pH dosing units with barrels containing a concentrated soda-ash (Na2CO3) solution used

to increase pH for each system. Fluorescent lighting on a constant 24 h photoperiod was used to

illuminate the room. The systems were able to monitor and maintain precise control of salinity,

temperature, pH, and oxygen levels and we had the ability to remotely monitor all of these

parameters in real time.

3.3.2 Experimental Design

Initially 200 coho from each of the 4 freshwater holding tanks were lightly anesthetized in an

aqueous solution of 0.1 g L-1 Tricaine-S (MS-222) buffered with 0. 2g L-1 sodium bicarbonate

(NaHCO3) and measured for length and mass. The remaining fish were weighed in batches and

randomly distributed into the five trial tanks with preset salinities of either 0, 5, 10, 20 or 30 ppt.

85 kg of fish were added to each tank, which was approximately 662 fish per tank and an initial

density of 17 kg·m-3. Fish were held in the trials for approximately 6 months on a very low ration

of approximately 0.2% of the total biomass per tank per day using automated feeders (Arvo-Tec

Professional Feeding Control System & T-drum 2000 Feeder, Arvo-Tec, Finland) while the biofilters were allowed to colonize with nitrifying bacteria and begin breaking down ammonia.

Once the biofilters came online and could process the metabolic waste of all the fish feeding to satiation, 100 fish from each coho tank were taken at random, anesthetized as above and measured for length and mass (Time point 1) then returned to their respective trials. Fish were then grown for 59 days before these measurements were repeated (Time point 2). They were fed using automated feeders to approximate satiation throughout the trial, which was determined by

40 periodically monitoring for uneaten feed in the radial flow separator for each system. At the end of the 2-month growth trial all coho were placed back into their trials with the exception of 15 fish which were euthanized for sampling of muscle tissue for muscle water content. After 97 additional days (156 days from the time point 1), a 3rd measurement (Time point 3) was taken of the coho and all fish were again returned to the systems. These fish will continue to be grown and monitored approximately every two months until a size typically seen in the industry at harvest (3-4 kg) is reached, which should be another 6-8 months.

The first cohort of Atlantic salmon became infected by a virulent fungal infection after transfer into the facility. These fish are excluded from this experiment. For a full description of the trial using the first cohort, see appendix A. A second cohort of Atlantic salmon were transported to

UBC and held in two large freshwater flow-through tanks, where they were prophylactically treated with formalin to curtail the fungal outbreak seen in the first cohort. After approximately one month in the flow through holding tanks 200 fish from each of the two tanks were anesthetized as above and measurements of length and mass were taken for an initial estimate of mean population mass. Fish were then weighed out in batches and equally dispersed into five treatment tanks. 40kg of fish were added to each tank, which was approximately 572 fish per tank and a starting density of 8 kg·m-3.

Since we elected to restart the Atlantic salmon trials, the second cohort of Atlantic salmon was added from the outdoor holding tanks to the experimental systems approximately one week after the first cohort was removed. This allowed for adequate time to clean and sanitize the tanks to prevent transfer of fungus from one cohort to another. The biofilters were colonized at this time,

41 and the growth trial began immediately. These fish were fed using automated feeders to approximate satiation, determined the same way as above. They were grown for 96 days before

100 fish from each tank were anesthetized and length and mass measurements were collected

(Time point 1). Fish were returned to their respective tanks to complete the growth trial. These fish were grown for an additional 8-12 months, monitored for growth approximately every two months, until a representative marketable size (4-5kg) was reached. All dates of procedures are outlined in Fig 3.1.

Figure 3.1 Gantt chart outlining the growth periods within each growth trial. Dates fish were added and removed from trials are denoted by black bars. Red bars indicate when fish were being held at low rations and green bars show growth periods which were started and ended by performing measurements of length and mass on fish in each tank.

3.3.3 Water Testing & Analysis

Throughout the experiment water samples were tested daily for total ammonia (TAN; NH3 &

+ - - NH4 ), Nitrite (NO2 ), and Nitrate (NO3 ), and salinity (ppt). Unionized ammonia (NH3) was calculated from daily TAN, pH, salinity, and temperature readings using the formula described in the Florida department of environmental protection chemistry laboratory methods manual

(Patton et al. 2001) and implemented using the “Unionized Ammonia Calculator v1.2” (Ross

2001) in Microsoft Excel.

Temperature (°C), pH, and oxygen concentrations (mg·L-1) were monitored automatically via probes in the system and values were recorded daily. Due to placement of the probes, pH was measured in tank, while temperature and oxygen concentrations were measured in the sump.

Some minor variations are to be expected in water chemistry, so values from the literature were obtained from which no effects on growth should be expected. These values were presented in the previously (Table 2.1). Any variation within these values should have no effect on growth.

3.3.4 Calculations

3.3.4.1 Growth Rates

Growth rate was calculated from the tank means at each time point in 3 different ways.

Initially growth rate was simply calculated as:

( ) ( ) = 2 1 푀푎푠푠 푔 − 푀푎푠푠 푔 퐺푟표푤푡ℎ 푅푎푡푒 2 1 where and are mean body mass at 푇푖푚푒 and− 푇푖푚푒 , respectively.

2 1 2 1 푀푎푠푠 푀푎푠푠 푇푖푚푒 푇푖푚푒

To correct for mass specific growth scaling, specific growth rate (SGR) was calculated as:

ln ( ) ln ( ) = × 100 2 1 푀푎푠푠 푔 − 푀푎푠푠 푔 푆퐺푅 2 1 where and are mean body 푇푖푚푒mass at− 푇푖푚푒and , respectively.

2 1 2 1 푀푎푠푠 푀푎푠푠 푇푖푚푒 푇푖푚푒

Finally, to standardize for both mass specific growth scaling and the effect of temperature on

growth rate the thermal growth coefficients (TGC) were calculated using the formula:

( ) / ( ) / = × 1000 (°1 3) × ( 1 3 ) 푀푎푠푠2 푔 − 푀푎푠푠1 푔 푇퐺퐶 2 1 푇푒푚푝푒푟푎푡푢푟푒 퐶 푇푖푚푒 − 푇푖푚푒

where is mean temperature between and . and are

1 2 2 1 mean body푇푒푚푝푒푟푎푡푢푟푒 mass at and , respectively. 푇푖푚푒 푇푖푚푒 푀푎푠푠 푀푎푠푠

2 1 푇푖푚푒 푇푖푚푒

3.3.4.2 Condition Factor

Condition factor for each individual fish was calculated using the formula:

( ) = (10 ) ( ) 5 푀푎푠푠 푔 퐶표푛푑푖푡푖표푛 퐹푎푐푡표푟 3 퐹표푟푘 퐿푒푛푔푡ℎ 푚푚 3.3.4.3 Feed Conversion

Economic feed conversion ratios (eFCR) for each of the large tanks was measured by using the

formula:

( ) = ( ( ) ( )) 푇표푡푎푙 푀푎푠푠 표푓 퐹푒푒푑 퐹푒푑 푘푔 푒퐹퐶푅 푇표푡푎푙 푇푎푛푘 퐵푖표푚푎푠푠 퐺푎푖푛푒푑 푘푔 − 푇표푡푎푙 푀푎푠푠 표푓 푀표푟푡푎푙푖푡푖푒푠 푘푔

This method calculates the FCR based solely on the total amount of feed that is put into the tank.

This method is not as accurate as traditional measures of FCR because some feed is lost when

feeding that is not accounted for with this method. It does however, accurately allow growers to

estimate how much feed will need to be bought in order to feed their animals until harvest. This

method also accounts for mortalities incurred throughout the duration of the trial. Mass of

individual mortalities was measured whenever possible but if a measurement was missing, the

calculated average mass of fish on that day was used, these mortalities were then summed and subtracted from the total tank biomass gained.

3.3.4.4 Muscle Water Content

Muscle water content (MWC) was determined from fillets harvested from fish as stated above.

Fillets were vacuum sealed and frozen, and then 3 small (approximately 1 cm3) pieces of skin-

free white muscle were cut from the anterior dorsal most section of each of the frozen fillets.

Individual pieces were placed in pre-weighed aluminum weigh boats, weighed, dried in an oven

at 90°C for 4 days, and then weighed again. MWC of each sample was calculated using the

equation:

( ) ( ) = 100 ( ) 푚푎푠푠 표푓 푤푒푡 푠푎푚푝푙푒 푔 − 푚푎푠푠 표푓 푑푟푖푒푑 푠푎푚푝푙푒 푔 푀푊퐶 푥 푚푎푠푠 표푓 푤푒푡 푠푎푚푝푙푒 푔

3.3.5 Statistical Analysis

Data are all presented as means ± standard error when standard error can be calculated. Since growth is represented as the difference between two population means, no calculation of standard error can be obtained. Two-way ANOVA was used to test for statistical differences between

either mass or condition factor and salinity when measured at multiple time periods. One-way

ANOVA was used when testing for statistical differences between mass, eFCR, or condition

45 factor and salinity when there was only one time period. When statistical significance was detected, Tukey Post Hoc analysis was performed to isolate the group or groups that differed from one another.

3.4 Results

3.4.1 Water Parameters

- - TAN, NO2 , NO3 , Oxygen Concentration, pH, salinity, and temperature were measured on a daily basis. NH3 was calculated from TAN, pH, salinity, and temperature according the formula referenced in section 3.2.2. Due to the large sample sizes significant differences were obtained for every parameter (P<0.001) (Table 3.1). For a more detailed description of each parameter, see Appendix A for a monthly breakdown. Values were compared to the upper acceptable levels of water parameters in Table 2.1 and any values that exceed these are indicated in bold.

Table 3.1 Mean values for all water parameters measured within each treatment system for the duration of the optimal salinity growth trial (May-October). Treatments were 0, 5, 10, 20, and 30 ppt salt water. The parameters displayed are total Ammonia, unionized ammonia, nitrite, nitrate, oxygen concentration, pH, salinity, and temperature. Values are means ± SEM. Bold values indicate that they exceed the upper acceptable levels indicated in Table 2.1. P-values are shown for Kruskal-Wallis One-Way ANOVA on Ranks. Dunn’s Method for multiple comparisons was used to determine which salinities differed. Letters that differ indicate statistically significant differences between salinities. Total Unionized Temperature Ammonia (mg Ammonia (µg Nitrite (mg L-1) Nitrate (mg L-1) pH Oxygen (mg L-1) Salinity (ppt) Treatment (°C) L-1) L-1)

0 0.36±0.05a 0.5±0.07a 0.13±0.03a 17.13±3.00a 6.49±0.03a 9.78±0.04a 0.00±0.00a 12.83±0.06a

5 0.49±0.05b 0.7±0.08ac 2.24±0.16b 72.06±5.72bc 6.69±0.03b 10.55±0.04b 4.99±0.06b 12.97±0.06b

10 0.38±0.07ab 0.5±0.03a 0.31±0.02c 47.45±4.29bc 6.69±0.03b 10.48±0.02b 9.52±0.10c 12.43±0.02c

20 3.09±0.33c 2±0.2b 2.22±0.12b 40.25±3.44c 6.46±0.02a 9.93±0.03a 18.86±0.13d 12.89±0.04ab

30 0.35±0.03ab 0.8±0.08bc 2.75±0.13b 58.63±4.25bc 7.17±0.33c 10.36±0.04c 28.94±0.16e 12.43±0.02c P-value P<0.001 P<0.001 P<0.001 P<0.001 P<0.001 P<0.001 P<0.001 P<0.001

3.4.2 Coho Salmon

Salmon were initially placed into salinity trials and held at a low ration for 149 days, after which time fork length and individual body mass were measured for a random subsample of 100 fish

(Time point 1) and no significant difference in body mass between salinities was detected

(P>0.226). Food ration was increased from the very low ration (approximately 0.2 %·day-1) to

approximate satiation ( approximately 1%·day-1) and fish were grown for 59 days before fork length and individual body mass was measured again on random subsample of 100 fish (Time point 2). At time point 2 significant differences in body mass with salinity were detected. Coho grown at 10 ppt were significantly larger than coho grown at 0 and 30 ppt) and fish grown at 5 or

20 ppt had an intermediate body mass. Fish were grown for another 97 days before a final measurement of fork length and individual body mass was taken (Time point 3), at which time fish at 10 ppt were still largest (624.0 g) although not significantly different than at 20 ppt (594.4

g; Tukey Post-Hoc P = 0.390), with average mass decreasing as salinity deviated from 10 ppt

(Fig. 3.2).

Figure 3.2 Mean body mass of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial. Filled circles represent the start of the growth trial (Time point 1), open circles represent 59 days after the start of the trial (Time point 2), and filled triangles represent 156 days after the start of the trial (Time point 3). All points are means ± SEM. Letters that differ indicate statistically significant differences among salinities.

Growth rate of fish was calculated between time points 1-2 and 2-3 in each treatment. While no

statistical analyses could be performed on these data, the growth rate in grams per day of the coho salmon from the first to second time point (59 days) appears to be greatest at 10 ppt and was almost double (2.99 g·day-1) then the growth rate in either 0 or 30 ppt (1.7 g·day-1 and 1.62

g·day-1 respectively; Fig. 3.3A). The increased growth rate at intermediate salinities in grams per

day was also seen from the second to third time point; however the effect of salinity was not as

49 great (Fig. 3.3A). When the growth rate was standardized for body mass, i.e., SGR (Fig. 3.3B), a consistent effect of salinity on growth rate (highest at 10 ppt, 1.31 %·day-1) was observed between the first and second time point, however between the second and third time point the growth rate was lowest at 10 ppt (0.65 %·day-1) and highest at 30 ppt (0.79 %·day-1). Because these values are calculated as the difference between two means, statistical analysis could not be applied. When growth rate is standardized for both temperature and size and viewed as a TGC, the same trend is seen as with the SGR (Fig 3.3C).

Fig 3.3 Mean growth rates between subsamples of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial. Filled circles represent growth rates between time point 1 (133-167g) and time point 2 (230-330g), open circles represent growth rates between the time point 2 and time point 3 (495-624g). Panel A is absolute growth rate expressed in grams per day, panel B standardizes for size by representing specific growth rate in percentage per day, panel C standardizes for size and temperature by representing the thermal growth coefficient which does not have units. Statistical analysis could not be performed on these data. For additional information see Fig 3.2.

Salinity did not have an effect on condition factor except at the first time point at 30ppt, where condition factor was significantly lower than the other salinities (P<0.001; Fig. 3.3). Condition factor increased significantly with time, indicating the fish were gaining mass proportionally faster than length (P<0.001) as expected for satiation feeding.

Fig 3.4 Mean condition factor of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial. Filled circles represent time point 1, open circles represent time point 2, and filled triangles represent Time point 3. All points are means ± SEM. Letters that differ indicate statistically significant differences between salinities. For additional information see Fig 3.2.

Economic feed conversion ratio (eFCR) was measured from one time point to the next, and while

no statistical analysis could be performed on the points, the general trend shows that eFCR is

lowest at intermediate salinities of 5 or 10 ppt (Time point 1-2: 0.72 and 0.70 respectively; Time

point 2-3: 1.08 and 1.08 respectively) and higher as salinity increases or decreases. The one

exception is at 30 ppt from the second to third time point, where eFCR is as low as the

intermediate salinities (1.08) (Fig. 3.5).

Fig 3.5 Mean economic feed conversion ratio (eFCR) between subsamples of 100 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at 3 time points over the 5-month growth trial. Filled circles represent growth rates between time point 1 and time point 2, open circles represent growth rates between the first and second time points. Points are the ratio between total food fed and total biomass gained. Data are corrected for mortalities throughout the trial. Statistical analysis could not be performed on these data. For additional information see Fig 3.2.

Muscle water content was measured for a subset of fish collected at time point two. No significant differences at any salinity were found. (74.06±0.44% to 75.21±0.69%; P=0.459; Fig.

3.6)

Fig 3.6 Mean muscle water content (MWC) of muscle tissue samples taken from 15 coho salmon (randomly selected from a group of approximately 400-800 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at the second time point of the 5-month growth trial (230- 330g). Samples were analyzed in triplicate. All points are means ± SEM. No statistical differences were detected (P=0.459). For additional information see Fig 3.2.

3.4.3 Atlantic Salmon

Atlantic salmon were distributed among the five different salinities at an average mass of

70.58±1.11 g and grown for 96 days, at which point the first tank level measurement was taken.

There was no significant effect of salinity on body mass after 96 days (P=1.000; Fig. 3.7).

Fig 3.7 Mean body mass of 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial (Time point 1). Fish were 70.82g average when added into the trial. All points are means ± SEM. No statistical differences were detected (P=1.000).

Growth rate of fish was calculated in each treatment. While no statistical analysis could be

performed, it appears that salinity had no effect on growth rate in Atlantic salmon when expressed as either g·day-1 (1.79 to 1.84 g·day-1; Fig. 3.8A), SGR (1.29 to 1.31%·day-1; Fig.

3.8B), or TGC (1.69 to 1.73; Fig 3.8C).

Fig 3.8 Mean growth rate between the average initial stocking mass and 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial. Panel A is absolute growth rate expressed in grams per day, panel B standardizes for size by representing specific

57 growth rate in percentage per day, panel C standardizes for size and temperature by representing the thermal growth coefficient which does not have units. Statistical analysis could not be performed on these data. For additional information see Fig 3.7.

Average condition factor was calculated for the subsample of Atlantic salmon measured at the end of this experiment, and slight but significant differences were found (1.05 to 1.11; P<0.001).

However, there was no trend in relation to salinity and the large sample size and the very small range in which the condition factor varied indicates these changes may have limited biological significance (Fig. 3.9).

Fig 3.9 Mean condition factor of 100 Atlantic salmon (randomly selected from a group of approximately 400-550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial. All points are means ± SEM. Letters that differ indicate statistically significant differences between salinities. For additional information see Fig 3.2.

The average eFCR over the growth period varied by approximately 20% among salinity between treatments (0.86-0.71) (Fig 3.10); however statistical analysis could not be applied to these data.

Fig 3.10 Mean economic feed conversion ratio (eFCR) between the average initial stocking mass and subsamples of 100 Atlantic salmon (randomly selected from a group of approximately 400- 550 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured 96 days after being added to the trial. Points are the ratio between total food fed and total biomass gained. Data are corrected for mortalities throughout the trial. Statistical analysis could not be performed on these data. For additional information see Fig 3.2.

3.5 Discussion

This study found a very distinct species-specific effect of salinity on growth and feed conversion.

Coho salmon showed a strong effect of salinity on growth and feed conversion, with 10 ppt

saltwater being an optimal salinity for both. During the first growth period, coho at 10 ppt grew

almost twice as fast as fish at either 0 or 30 ppt. The economic feed conversion ratio was also

lowest at intermediate salinities of 5 and 10 ppt, and increased as salinity increased or decreased.

These results support my hypothesis that fish grown at an isosmotic salinity would show

enhanced growth and increased feed conversion efficiency, although the effect was only seen during the first growth period indicating a possible shift over time. Atlantic salmon, however, showed no effect of salinity on any metric measured.

3.5.1 Coho Salmon

Coho salmon displayed a strong effect of salinity on growth. From the start of the trial until day

59, the fish at 10 ppt salinity grew significantly larger than the fish that grew in either freshwater

(0 ppt) or seawater (30 ppt) during that same time period. This effect was seen even more clearly

with growth rate which was almost double at 10 ppt, than at 0 or 30 ppt throughout the first

growth period (time point 1 to 2). eFCR was also optimal at 10 ppt, at approximately 15% lower

than the fish grown at 0 or 30 ppt. Over the course of the trial, as the coho grew, their condition

factor increased, unaffected by salinity. This means that these fish were gaining mass

proportionally faster than length (i.e. getting fatter) as time progressed, but salinity did not play a

role in the increase in condition factor. This confirms that the fish were being fed close to

satiation.

It appears that coho salmon are able to take advantage of lower osmoregulatory costs at intermediate salinities and allocate more energy towards faster growth, at least during the first growth period. However, what is driving this enhanced growth is unclear. I tested muscle water content (MWC) to ensure that the growth seen was not simply an increase in muscle water, and I found that MWC did not vary significantly with salinity in coho salmon. This means that the increased mass seen in fish grown at intermediate salinities was somatic growth, and not an increase in total water weight. Exposure to different salinities typically does not affect the MWC of fishes that are capable of tolerating those salinities (Tipsmark et al. 2002; Sardella et al. 2004;

Giffard-Mena et al. 2008; Tang et al. 2009).

With the cost of osmoregulation untested for these fish, but likely between 5 and 30% of total metabolic expenditure, it is interesting that I saw a doubling of growth. This would point to the fact that either coho salmon have a very high cost of osmoregulation, at around 50% of total metabolic rate, or that something else related to the salinity is driving this growth.

One possibility might be that fish at isosmotic salinity have higher levels of hunger related hormones, such as orexin, leptin and ghrelin (Le Bail and Bœuf 1997; Volkoff et al. 2009).

Accumulation of hormones could be related to the metabolic clearance rate (MCR) of these hormones. Although how salinity effects MCR of hunger related hormones has never been studied on any fish species, a study was published on how salinity affected MCR of cortisol in coho salmon (Redding et al. 1984) which did find higher clearance rates in seawater-acclimated fish versus freshwater-acclimated fish. They speculated that cortisol is more important in

61 osmoregulatory functions for salmonids in seawater than in freshwater. If this is true, fish in isosmotic water may have even further reduced clearance rates of cortisol and possibly hunger related hormones as well. Lastly, Redding et al. (1984) speculated that the relationship of the compartmental distribution and turnover of water and osmoregulation may be a factor in MCR’s.

Fish in isosmotic water should in theory have the lowest turnover of water, since they are not gaining water passively, nor drinking it to maintain proper osmotic balance.

Another possibility is the relationship between salinity and feed conversion. Coho at isosmotic salinity were more efficiently able to process food into mass, which would have increased the growth rate beyond what is expected due to metabolic savings alone. A study done on fat snook

(Centropomus parallelus), that explored the effects salinity (5, 15, 35 ppt) on growth and digestive enzyme activity, found that at an intermediate salinity of 15 ppt proteinase and amylase enzyme activities were elevated above the other salinities (Tsuzuki et al. 2007). They also found it corresponded to highest growth rates. If digestive enzymes are working optimally at intermediate salinities in coho salmon as well, it would increase feed conversion efficiency and could lead to enhanced growth rates.

Most likely it is a combination of reduced osmoregulatory costs, altered hormonal hunger cues, enhanced digestive enzyme activity, as well as other unknown biological processes at isosmotic salinities which lead to the greatly enhanced growth I saw in this study. While this warrants further research to pinpoint the various processes and mechanisms that produces these effects, the main point to take home is that the growth is substantially higher at an intermediate salinity in coho salmon. This information is important because of the impacts that greatly enhanced

growth could have on the economics of RAS grown coho salmon. If the growth can be doubled,

like I found in this study, the viability of RAS to provide fish at the same cost as net pen systems

and remain profitable is greatly increased. A thorough economic analysis of these data should be

performed to inform the industry of the potential that adding salt to a system might have. A

preliminary analysis was performed and the results discussed below (Section 4.2). These data

may help lead the transition of net pen grown salmon into land based RAS, where producers can

have better control of growing conditions and environmental impacts of production.

The coho salmon were grown for a second growth period, from day 59 to day 156, and displayed

the same pattern of mass gain as they did from day 0 to day 56. Fish at 10 ppt were significantly

larger than at all other salinities except 20 ppt. However, the effect of salinity on growth rate was

not similar during the second growth period. In fact, growth rate, when expressed as SGR, was

lowest at 10 ppt during this period. This may indicate a size-dependent effect of salinity on

growth, or it could be an effect of stocking density in the tanks. The increased density of fish

within the tanks at intermediate salinities could have potentially slowed growth rate. During the

first growth period, density increased from between 15 and 25 kg·m-3 to 21 and 43 kg·m-3, and

this was not readjusted before the start of the second growth period. During the second growth

period, density increased from 21-43 kg·m-3 to between 42-83 kg·m-3. Density dependent effects on growth have been observed in some studies with Atlantic salmon, but not all (Kjartansson et al. 1988; Ellis et al. 2002; Sirakov and Ivancheva 2008; Thorarensen and Farrell 2011). In the studies in which density effects were seen in Atlantic salmon, the density that reduced growth varied, but was always above 50 kg·m-3 (Seymour et al. 1992; Boujard et al. 2002). Densities

above 50 kg·m-3 were experienced by the fish that grew the fastest during the first time period

63 during the second growth period. Since density effects on growth of coho have never been measured in an aquaculture setting, the possibility exists that coho are more density sensitive and the densities seen in this experiment had an effect on growth.

Another possibility is that at these high densities, oxygen supply in the tank was limiting, which would decrease growth (Berg et al. 1993; Wedemeyer 1996; Bergheim et al. 2006). Oxygen was measured at the sump and not directly in the tanks so I am unable to determine if this was the case, but since there was potential for oxygenation where the tank effluent splashed into the sump, this possibility exists. A third and plausible scenario that could lead to decreased growth in the intermediate salinities is an effect of tank size. Concurrent trials were conducted in smaller

0.7 m3 tanks to understand the effect of tank size on growth, and coho grew normally until they reached approximately 300 mm in length, when feeding slowed drastically and growth stopped

(unpublished data). This may be due to a fish to tank size ratio issue, wherein once salmon grow to within a certain proportion of the size of their tank they become stressed and stop feeding, which would cause growth to slow or stop.

There is also the possibility that the changes seen in growth rates from the first to second time period are due to the increasing size of the fish. Since the surface-area-to-volume ratio decreases as the fish get larger, osmoregulatory need should also decrease. Less surface area per unit mass would mean lower rates of diffusion of ions and water, lowering the percentage of total energy expenditure that is needed to maintain homeostasis. If this is true, and the change in the cost of osmoregulation was sizable between coho at different sizes, then this could potentially explain the apparent leveling off of the growth rates at different salinities seen during the second time

64 period. Future studies should attempt to measure the cost of osmoregulation at not only different salinities, but also at different sizes to better understand how the surface-area-to-volume ratio affects osmoregulatory need.

3.5.2 Atlantic Salmon

The effect of salinity on growth rate and feed conversion in coho salmon were not seen in the

Atlantic salmon. They experienced no effect of salinity on growth, feed conversion, or condition factor. While other studies have seen higher growth rates in Atlantic salmon (Thorarensen and

Farrell 2011), the only two other facilities growing Atlantic salmon in North America on a scale at least as large as InSEAS have found identical growth rates to what I measured (UBC InSEAS;

TGC = 1.7, Kuterra Atlantic Salmon; TGC = 1.7, Freshwater Institute; TGC = 1.7). Average growth rates of Atlantic salmon over the approximately 10 generations since domestication have increased 10-15% per generation, meaning these farmed fish are already growing 100-150% faster than their wild counterparts (Gjedrem 2000). This increase in growth must have increased the routine metabolic rate. In comparison to the energy being expended to grow at such a high rate, the potential drop in osmoregulatory energy expenditure at isosmotic salinities may be so small that we are unable to see any noticeable effect on growth. Muscle water content was not measured in the second cohort of Atlantic salmon. Since these fish all grew equally, had similar eFCR’s, and showed no trend of condition factor with salinity there was no basis to perform analysis of MWC.

An experiment was conducted with the first cohort of Atlantic salmon, which produced similar results. Since those fish had been previously treated for fungus, held at a low ration for long

periods, and subjected to high ammonia concentrations I did not include them in this study

(Additional details can be found in Appendix A). It is worth mentioning however, that although

this experiment was not included due to the potential confounding effects of their past life

histories, salinity had no effect on growth except on eFCR (Appendix A: Figs A.1, A.2, A.3, and

A.4), which is contrary to my findings from the second cohort. However, it is unknown if this is due to a size effect, since the size of the first cohort of Atlantic salmon was much larger, or if variation of eFCR was related to one of the undesirable stressors that these fish were subjected to. The continuation of the growth trial with the second cohort will provide a better understanding of how the effect of salinity on eFCR relates to size of Atlantic salmon.

A previous study examined the effect of salinity and ration on growth of post-smolt Atlantic salmon (McCormick et al. 1989). Results demonstrated that fish grown at a low ration showed no effect of salinity on growth, however at a high ration growth in fish at 0 and 30 ppt was greater than at an isosmotic salinity of 10ppt. The feed rates used in this study translate roughly to the low ration in the McCormick et al. (1989) study (i.e. approximately 1%·day-1 for this study

vs. 0.8%·day-1 in their study). Fish used in the McCormick et al. (1989) study were reared under

a natural photoperiod. Seasonality and photoperiod have a very strong effect on growth of

salmonids (Björnsson et al. 1989; Berrill et al. 2003; Guerrero-Tortolero and Bromage 2008), so

growth would likely be varied depending on the time of year. Since my fish were grown on 24 h

daylight, growth patterns would presumably be different. Regardless, the findings from

McCormick et al. (1989) do validate the findings in this thesis.

Interestingly, the eFCR I obtained of 0.7 is lower than what is typically seen in the commercial

production of Atlantic salmon (0.9-1.2) (Austreng et al. 1987; Storebakken and Austreng 1987;

Refstie et al. 1998; Cook et al. 2000; Thorarensen and Farrell 2011), indicating a very efficient

conversion of food to body mass. This is possibly due to the small window of growth I

measured, as FCR’s are typically averaged across the entire growth period of the fish, typically

from smolt to harvest. The continued growth of these fish will be informative to the overall

eFCR of salmon grown in the InSEAS systems.

3.5.3 Water Quality

Water quality was consistently maintained throughout the experiment, although significant

differences between systems did arise. Due to the daily monitoring of all water quality

parameters, the sample sizes were very large, allowing for detection of very minute water quality

- differences. However, most are not relevant on a biological level. NH3, NO3 , pH, oxygen concentrations, and temperature did not vary enough to likely cause any changes in growth.

Nitrite varied more, and some of the high levels were above the threshold where there is the potential to see some effects on growth (Thorarensen and Farrell 2011). At higher salinities biofilters are generally less efficient (Aslan and Simsek 2012) and take longer to become fully colonized by the nitrifying bacteria that grow inside them (Sánchez et al. 2004; Grommen et al.

2005). Because of this, higher than desired nitrite levels (0.1±0.04 mg·L-3 to 4.67±0.33 mg·L-3)

were present for a few months of the trial.

The level at which nitrite becomes toxic is very dependent on pH and ion concentrations in the

water (Perrone and Meade 1977; Russo et al. 1981; Kroupova et al. 2005; Tomasso 2012),

therefore water with high nitrite levels that also has salt in it is much less toxic. The systems in

this study that were exposed to high nitrite levels were at 5, 10, 20, and 30 ppt saltwater, which

will greatly reduce the toxic effects of the nitrite. In coho, water low in Cl- (2.5 mg·L-1) had

>50% mortalities with nitrite levels of only 3.8 mg·L-1(Perrone and Meade 1977). This was

likely due to high nitrate uptake at the gill and the oxidation of hemoglobin to methemoglobin

within the red-blood cell (RBC) which leads to methemoglobinemia, or the inability for the RBC

to bind oxygen properly. Cl- ions in the water from the salt competitively inhibit the uptake of

nitrite at the gill. In high enough concentrations (260 mg·L-1 Cl- or 0.4 ppt saltwater) the uptake

of nitrite at any level conceivably found in aquaculture (≤30 mg·L-1 nitrite) would be prevented

(Perrone and Meade 1977). Since the only systems to experience high nitrite were well above

0.4ppt, it is unlikely to have been a factor affecting growth.

3.6 Conclusion

During the first growth period, coho salmon grew fastest and had the lowest feed conversion at

intermediate salinities around isosmotic, however Atlantic salmon showed no difference in

growth or feed conversion at different salinities. The second growth period of coho salmon

showed an opposite effect, but I believe that other variables may have influenced the results.

Future studies should build on these results and explore growth and FCR over an entire grow-out

cycle to better understand how these factors change with salinity at different life stages. This

study shows that the control of salinity can play an important role in reducing the cost of

growing coho salmon in recirculating aquaculture, but not so clearly for Atlantic salmon. The

economic implications associated with this data are very promising for coho production,

however a more thorough economic analysis should be undertaken to explore this in further

68 detail (see chapter 4). Despite the high initial investment, faster growth rates and lowered FCR should allow RAS to become a more feasible alternative to net pens.

Chapter 4: General Conclusion

4.1 Summary

The overall goal of this thesis was to define an optimal salinity which would allow salmonids to

grow the fastest and use the least amount of food when grown in recirculating aquaculture systems. I first started by testing that the replicate systems at the University of British Columbia

InSEAS research lab could maintain similar environmental parameters and grow fish evenly under these conditions. I validated that the independent systems did indeed grow fish consistently under the same conditions by growing coho salmon in freshwater for five months. I proceeded to then test the hypothesis that salmon grown at an isosmotic salinity would show enhanced growth, possibly due to lowered osmoregulatory related energy expenditure. I found that there are species specific differences in how salmonids grow in different salinities.

Coho salmon showed a very strong growth response to salinity. They grew faster in the salinities that were close to isosmotic. The feed conversion ratio (eFCR) for coho followed the same trend, decreasing as salinity become isosmotic. The difference in growth rates became less distinct as the size of the fish increased, however, perhaps due to an increased stocking density in the tanks that had highest growth, and possibly an associated hypoxia due to higher total biomass in the tanks.

Atlantic salmon showed no effect at all of salinity on growth, and grew at similar rates in all salinities tested. Atlantic salmon had a very low eFCR that did not change with salinity either. It

is my belief that since these fish have been bred for greatly enhanced growth rates, the increased

somatic growth would likely increase their total metabolic expenditure. Assuming that

osmoregulatory costs did not change proportionately, the cost of osmoregulation expressed as a

percentage of metabolic expenditure would be greatly decreased. If growth in isosmotic salinity

lowers their total metabolic expenditure and alters growth rate or eFCR I was not able to detect

the change; likely due to the cost of osmoregulation being a smaller percentage of total metabolic

rate.

My results and future studies at InSEAS could influence the way salmon are reared in RAS and

help to increase efficiency and productivity in these systems. Further work should focus on determining the mechanisms by which salinity affect growth in coho salmon. The difference in metabolic rate between salinities in Atlantic salmon should also be tested. A detailed economic analysis incorporating the cost of salt, as well as the differences in associated infrastructure, should also be undertaken to better realize the economic impacts of growing coho at isosmotic salinity.

4.2 Potential Economic Impact of Results

Since coho salmon growth rate and feed conversion changed markedly with salinity, I

incorporated these data into an economic feasibility model to frame the results in an economic

context. The Department of Fisheries and Oceans published a feasibility study of RAS

production of Atlantic salmon, which included a model to estimate profitability of a production

system given many economic and biological variables (Boulet et al. 2010). This model takes into

account the costs to build a system designed to produce 2,500 MT of salmon per year as well as

71 the operating costs associated with stocking, feeding, and running such a facility. I used the original DFO model but made alterations to produce two scenarios: one that lines up with my results at 0 ppt, since it was the salinity with the lowest growth rate and also the current industry standard, and another for my results at 10 ppt, where we saw the highest growth rate and lowest

FCR in coho salmon. I altered TGC and FCR to match my respective results from the first growth period, set temperature at 13°C, which was the temperature in which I grew these fish, and set a market price of $5.00 per pound (the current wholesale HOG price for RAS produced coho; RAS producer, private communication, 2015) to estimate average yearly income over 20 years. At 0 ppt, a 20-year averaged yearly income is estimated to be $1,313,081, while at 10 ppt the 20-year averaged yearly income is estimated to be $9,348,107; a yearly income seven times greater. It is important to note again that the data was from the first growth period where differences were greatest.

I also ran two conservative models, which account for the fact that the growth rate during the second time period evened out amongst salinities. For these models, I used TGC and FCR for the

0 ppt and 10 ppt treatments for fish up to ~350 g, then switched to the average TGC and FCR seen in the second growth period for the continuation of the grow out. This produced a 20-year averaged yearly net income of $1,195,985 for the 0ppt treatment and $2,694,922 for the 10ppt treatment. This is still a 2.25 times greater profit, even if the fish are only grown at 10 ppt from purchase (75 g) to 350 g then switched to a freshwater for the rest of the growth cycle.

It is important to note that these numbers are all rough estimates, as this model does not take into

account the cost of salt (or seawater) for maintaining 10 ppt saltwater or the system maintenance

involved once salt is incorporated, so it is slightly overestimating the yearly income at this

salinity. Furthermore, I did not alter the model to take into consideration other economic

differences between species, which would potentially affect the outcome, such as harvest size or

smolt price. However, despite the limitations of the model, the large differences in yearly income

between salinities, even when looked at conservatively, shows that growing coho in isosmotic

salinity for part or all of their growth would be far more profitable than growing them wholly in

freshwater. A scenario similar to my conservative model could be ideal for the industry as it

would increase growth during the critical first few months, yet save significantly on the cost of

salt for the remaining approximately 12 months until harvest size. The economic implications of

these analyses might prompt some producers to rethink their business models.

4.3 Study Strengths and Limitations

The InSEAS facility is one of the few facilities in the world designed to rigorously test the effect

of salinity on physiological parameters of salmonids reared at a semi-industrial scale. I believe that the study outlined above will allow us to make direct recommendations to industry on the way the unique physiology of these fish can be harnessed to increase profitability.

No experimental system is without drawbacks however, and one of the major limitations of this study was the lack of replicate systems at each salinity. Unfortunately due to size and space constraints, I could only have one system set to each of my five salinities, which does not allow for accurate discrimination between tank effects and true salinity effects. I tried to alleviate this

73 issue by running the validation study to show that there was no tank effect on growth, and while

I am confident that conditions did not change, the possibility that tank conditions could change over time and lead to erroneous results exists. It also excludes redundancy as a safety factor against human or mechanical error. Ideally, this study would be conducted subsequent times choosing the salinities with the highest and lowest values and running them in duplicate or triplicate to increase the statistical power of the experiment.

4.4 Future Directions

The second chapter of this thesis serves as a validation of the capabilities of the novel InSEAS research facility at the University of British Columbia. The data within this thesis will allow future users of InSEAS to confidently test the effects of a wide variety of environmental parameters on growth and physiological performance of salmonids. The results of my study on salinity and growth will serve as a starting point for understanding how isosmotic salinities effect growth and physiological performance. The data leads to many other questions regarding the clear differences seen in growth performance with salinity between the two species studied.

Quantifying the cost of osmoregulation at different salinities in both coho and Atlantic salmon would be an insightful way to better understand if the effect I saw in coho salmon is driven by the osmoregulatory savings of being in isosmotic water, or if some other factor is the cause. If cost of osmoregulation differed significantly between coho and Atlantic salmon, it could help to explain the species-specific effect we saw.

Understanding how different salinities might affect other physiological properties of salmonids might be very insightful to the industry as well. If the enhanced growth I saw in coho salmon

comes at a cost to aerobic scope, thermal or hypoxia tolerance, or welfare, it might make for a

fish that is too fragile to rear in a production facility where water parameters might fluctuate

significantly. In this case, having a slower growing but hardier fish might be preferable.

However, studies such as these have never been conducted for recirculating aquaculture systems.

4.6 Issues to Address

Aside from understanding how environmental parameters effect growth, the RAS industry has

other hurdles that need to be overcome. One issue with intensive reuse of the water in these RAS

is the build-up of off flavor compounds, geosmin and 2-methylisoborneol (MIB), in the water

and fish tissue. These compounds are produced by bacteria in the system, but accumulate in the

fish tissue leading to muddy flavors in the final product. Currently the only way to rid the

product of off flavors is by depurating, or purging, the fish by placing them in a separate

sanitized system that is devoid of these off flavor producing bacteria for 10 to 15 days (Burr et

al. 2012). While the fish are being depurated they are not fed, which leads to weight loss and therefore lost profits. Testing the effects of salinity on the accumulation of these compounds at

InSEAS would be very informative to the industry.

A second issue is with premature maturation, or grilsing. Salmon grown in RAS have a much higher prevalence of grilsing over fish grown in net pen systems; however the exact cause is unknown. Some evidence seems to suggest that the accumulation of different compounds might be the cause (Madsen et al. 1997; Good et al. 2014); while other evidence points to photoperiod or temperature, environmental components, as a cause for grilsing (Porter et al. 1999; Good et al.

2015). InSEAS has the ability to test all of these hypotheses in replicate, by altering different

75 environmental parameters, installing different filtration mechanisms, or adding synthetic hormones to the water to understand what is causing the prevalence of grilsing in these systems.

The effect of salinity on the incidence of grilsing will be studied as the fish used in this study begin to mature.

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Appendix

Appendix A: Supplementary Figures and Tables

The data collected from the first cohort of Atlantic salmon was included below. Due to the

multiple stressors these fish experienced before being added to the salinity trials, the data

obtained was excluded from the body of the thesis.

After obtaining the Atlantic salmon they became infected by a virulent Saprolegnia sp. infection.

These fish were treated with formalin and malachite green which controlled the spread of the

fungus, but significant losses were incurred. The remaining fish were then transferred to two 5

ppt saltwater tanks to kill of any remaining fungus and held there so they could recover from the

infection.

100 fish from each of the two 5 ppt tanks were lightly anesthetized and measured for length and

mass to calculate an average. These fish were randomly distributed to one of the five salinities

(0, 5, 10, 20, and 30 ppt). The remaining fish in the 5ppt treatment tanks were weighed in batches and 42 kg of fish were randomly distributed into the five treatments, which was approximately 112 fish per tank. Fish were held in the trials for approximately 6 months on a very low ration while we allowed the biofilters to colonize with nitrifying bacteria and begin breaking down ammonia. All of the fish in each tank were then anesthetized and measured for length and mass (Time point 1). Fish were then grown for two months before these measurements were repeated (Time point 2). They were fed to approximate satiation throughout, which was determined by monitoring for uneaten feed in the radial flow separator for each

86 system. At the end of the two month growth trial all Atlantic salmon were euthanized and samples for muscle water content were collected.

Table A.1 Mean monthly values for all water parameters measured within each salinity treatment (Chapter 3) for the duration of the experiment. Treatments were 0, 5, 10, 20, and 30ppt salt water. The parameters displayed are unionized ammonia, nitrite, nitrate, oxygen concentration, pH, salinity, and temperature. Values are means ± SEM. Bold values indicate they exceed the acceptable high levels indicated in Table 2.1.

Salinity Monthly Mean Parameter Treatment December January February March April May June July August September October 0 0.0028±0.0004 0.0006±0.0001 0.0006±0.0002 0.0006±0.0001 0.0004±0.0001 0.0005±0.0001 0.0004±0.0001 0.0009±0.0002 0.0003±0.0001 0.0004±0.0002 0.0000±0.0000 5 0.0339±0.0036 0.0072±0.0016 0.0005±0.0001 0.0004±0.0001 0.0005±0.0001 0.0005±0.0001 0.0004±0.0000 0.0004±0.0001 0.0001±0.0000 0.0002±0.0001 0.0007±0.0006 Unionized Ammonia 10 0.0530±0.0085 0.2498±0.0261 0.1459±0.0189 0.0110±0.0032 0.0004±0.0001 0.0005±0.0001 0.0010±0.0002 0.0004±0.0001 0.0031±0.0006 0.0006±0.0003 0.0004±0.0003 (mg/L) 20 0.0807±0.0378 0.0925±0.0033 0.1160±0.0096 0.1319±0.0104 0.0278±0.0074 0.0005±0.0001 0.0006±0.0000 0.0003±0.0001 0.0393±0.0392 0.0004±0.0002 0.0003±0.0002 30 0.0028±0.0004 0.0006±0.0001 0.0006±0.0002 0.0006±0.0001 0.0004±0.0001 0.0005±0.0001 0.0004±0.0001 0.0009±0.0002 0.0003±0.0001 0.0004±0.0002 0.0000±0.0000 0 0±0 0±0 1±1 0±0 0±0 0.1±0.04 0.09±0.02 0.04±0.02 0.47±0.16 0.05±0.02 0.17±0.08 5 1.8±0.12 1.28±0.13 2.83±1.15 2.13±0.19 18.74±11.42 3±0.29 3.2±0.33 2.03±0.31 0.95±0.12 0.95±0.45 0.5±0 Nitrite (mg/L) 10 2.11±0.14 1.35±0.16 1.83±1.14 0.32±0.05 0.51±0.1 0.45±0.03 0.31±0.05 0.29±0.04 0.18±0.05 0.21±0.03 0.25±0 20 0.04±0.02 1.84±0.33 3.96±0.83 2.33±0.3 1.26±0.14 3±0.29 2.22±0.15 1.54±0.16 1.63±0.11 2.44±0.53 4.67±0.33 30 0±0 0.2±0.08 4.56±1.12 5.36±1.1 4.47±0.21 2.42±0.29 3.29±0.29 2.36±0.2 2.53±0.26 3.25±0.31 3.67±0.33 0 6.52±1.59 17.08±5.68 54±8.69 66.25±12.04 60.53±9.26 9.17±4.16 3.15±0.71 43.75±11.21 41.05±7.92 1.17±0.69 1.67±0.83 5 92.31±7.02 81.81±9.84 87.96±10.29 112.5±11.85 124.74±13.9 46.25±8.72 76±11.37 92.4±11.3 90.53±14.21 56.13±25.01 1.67±0.83 Nitrate (mg/L) 10 64.04±9.84 91.72±9.57 86.16±11.72 105.21±13.2 119.74±14.7 33.33±8.99 49.23±9.68 54.38±6.79 63.68±8.06 41.88±21.58 1.67±0.83 20 1.48±0.56 31.1±5.81 80.79±11.46 105±11.67 95.26±9.98 35.83±7.24 19.26±3.09 71.67±8.33 47.89±5.74 19.5±5.06 20±10.41 30 0±0 2.1±0.75 81.71±11.07 103.75±12.4 120±12.61 33.54±7.13 53.58±9.66 74±6.73 83.33±9.32 60±16.26 23.33±6.01 0 10.92±0.05 9.9±0.41 11.07±0.71 10.3±0.02 13.51±3.28 9.68±0.06 9.94±0.08 9.27±0.11 9.87±0.07 10.16±0.07 9.66±0.13 5 10.59±0.03 10.33±0.02 10.96±0.72 10.13±0.29 10.84±0.02 10.63±0.05 10.43±0.06 10.41±0.07 10.37±0.03 10.62±0.14 11.33±0.07

O2 (mg/L) 10 9.87±0.42 10.5±0.02 11.24±0.73 10.4±0.02 10.56±0.02 10.26±0.03 10.51±0.06 10.44±0.04 10.49±0.06 10.69±0.05 10.5±0.14 20 10.4±0.03 9.42±0.5 10.93±0.72 9.85±0.29 10.14±0.04 9.65±0.07 10.18±0.03 9.82±0.06 10.06±0.06 9.97±0.07 9.94±0.04 30 10.79±0.05 10.53±0.04 11.16±0.71 10.4±0.02 10.35±0.02 10.23±0.02 10.2±0.03 9.98±0.11 10.85±0.08 10.42±0.11 10.71±0.1 0 6.62±0.09 6.99±0.01 7.81±0.83 28.95±22.2 6.91±0.01 6.38±0.06 6.72±0.03 6.44±0.04 6.08±0.08 6.69±0.08 6.93±0.01 5 7.03±0.01 7.06±0.01 7.87±0.83 7.06±0.01 7.02±0.01 6.98±0.02 6.35±0.06 6.76±0.05 6.6±0.06 6.83±0.03 6.5±0.03 pH 10 7.39±0.01 7.03±0.01 7.84±0.83 7.02±0.01 7.03±0.01 6.82±0.04 6.75±0.04 6.8±0.07 6.1±0.03 6.89±0.03 7.03±0.01 20 7.68±0.01 7.92±0.01 8.53±0.77 6.85±0.05 7.05±0.03 6.67±0.06 6.29±0.04 6.58±0.06 6.35±0.02 7±0.4 6.41±0.01 30 7.64±0.02 7.72±0.01 8.56±0.8 7.74±0.01 7.06±0.08 7.1±0.1 7.03±0.01 6.78±0.07 8.11±1.73 6.83±0.07 7.02±0.01 0 0.24±0.03 0.06±0.02 0.02±0.01 0.01±0.01 0±0 0±0 0±0 0±0 0±0 0±0 0±0 5 4.91±0.13 5.01±0.08 5.08±0.04 5.39±0.1 5.09±0.08 4.9±0.04 4.59±0.12 5.13±0.06 5.07±0.07 5.83±0.24 4.89±0.11 Salinity (ppt) 10 9.31±0.05 10.04±0.13 10.6±0.05 10.08±0.05 9.79±0.02 10.35±0.17 9.32±0.12 9.3±0.11 9.47±0.13 9.33±0.18 9.56±0.44 20 18.58±0.22 19.71±0.14 19.36±0.06 19.32±0.06 19.34±0.14 18.81±0.26 18.2±0.24 19.14±0.2 19.38±0.18 19.11±0.36 18.56±0.73 30 31.91±0.31 31.61±0.23 31.2±0.14 31.34±0.1 27.91±0.47 29.41±0.41 30.51±0.6 29.35±0.34 28.93±0.33 29.2±0.58 27.89±0.56 0 12.62±0.05 12.52±0.04 12.83±0.75 12.51±0.04 12.2±0.39 12.53±0.09 12.4±0.04 13.8±0.22 12.9±0.09 12.47±0.09 13.02±0.06 5 13.13±0.05 13.17±0.05 13.72±0.62 13.16±0.04 12.56±0.04 13.19±0.15 13.57±0.2 13.16±0.02 12.85±0.07 12.37±0.05 12.4±0.05 Temperature (°C) 10 12.98±0.04 12.89±0.03 13.49±0.63 12.86±0.03 12.41±0.05 12.43±0.04 12.43±0.05 12.39±0.03 12.48±0.04 12.45±0.04 12.29±0.09 20 13.39±0.03 13.34±0.03 14±0.61 13.55±0.08 12.86±0.06 12.95±0.04 12.88±0.05 13.19±0.1 13.06±0.08 12.46±0.09 12.74±0.23 30 13.15±0.02 12.85±0.1 12.99±0.64 12.52±0.07 12.55±0.03 12.51±0.05 12.44±0.02 12.46±0.03 12.33±0.04 12.42±0.03 12.37±0.06

Fig A.1 Mean body mass of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt. Filled circles represent fish at the start of the growth trial (Time point 1), open circles represent fish 59 days after the start of the trial (Time point 2). All points are means ± SEM. Letters that differ indicate statistically significant differences between salinities.

Fig A.2 Mean growth rate of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt between time point 1 and time point 2. Panel A is absolute growth rate expressed in grams per day, panel B standardizes for size by representing specific growth rate in

percentage per day, panel C standardizes for size and temperature by representing the thermal growth coefficient which does not have units. Statistical analysis could not be performed on these data. For additional information see Fig A.1.

Fig A.3 Mean condition factor of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt. Filled circles represent fish at time point 1, open circles represent fish at time point 2. All points are means ± SEM. Letters that differ indicate statistically significant differences between salinities. For additional information see Fig A.1.

Fig A.4 Economic feed conversion ratio (eFCR) of all Atlantic salmon (n=74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt between time point 1 and time point 2. Points are the ratio between total food fed and total biomass gained. Statistical analysis could not be performed on these data. For additional information see Fig A.1.

Fig A.5 Mean muscle water content (MWC) of muscle tissue samples taken from 15 Atlantic salmon (randomly selected from a group of 74-119 fish per tank) reared at salinities of 0, 5, 10, 20 or 30 ppt and measured at the second time point of the 59 day growth trial. Samples were analyzed in triplicate. All points are means ± SEM. Letters that differ indicate statistically significant differences between salinities. For additional information see Fig A.1.

SUMMARY

Though these fish experienced a severe fungal infection, a brief period in 5 ppt salinity water before being transferred to their salinity trials, and an extended time in less than ideal water quality, the data is consistent with the data from the second cohort of Atlantic salmon (Chapter

3) with the exception of eFCR. No effect of salinity on growth was detected; however, eFCR did appear to be lower at intermediate salinities. This could point to the fact that some digestive

93 enzymes seem to function more efficiently at intermediate salinities (Tsuzuki et al. 2007). MWC was also lower at 5ppt than at 20ppt, however when these fish were sampled it was noted that some were sexually maturing. Sexual maturation in salmonids is known to cause changes in muscle characteristics, including MWC (Bilinski et al. 1984; Aksnes et al. 1986; Acharya

2012)The number of fish sampled that were maturing varied between treatment, and could have been a the source of the deviation in MWC seen.