The physiological and ecological effects of

temperature and oxygen on an estuarine fish

Nathan Beerkens, BSc

Thesis for the Honours Degree in Conservation and Wildlife Biology

School of Veterinary and Life Sciences, Murdoch University

2016

Declaration

I declare this thesis is my own account of my research and contains as its main content

work which has not been previously submitted for a degree at any tertiary education

institution.

Nathan Beerkens

iii

Abstract

Temperature and oxygen are the primary abiotic variables controlling and limiting the

metabolic capacity of fishes. This has been attributed to the strong influence of each

variable on aerobic scope; the capacity of organisms to distribute energy across

physiological functions. This study incorporates laboratory quantification of aerobic

scope with field acoustic accelerometry to determine the relative importance of both

temperature and oxygen to an estuarine teleost, the black bream (Acanthopagrus

butcheri). In respirometry experiments, A. butcheri were found to display remarkably

high thermal tolerance, maintaining stable aerobic scope across a 9°C thermal window.

However, their aerobic scope was heavily reduced with reductions in oxygen

availability, reaching a critical oxygen level at ~30% DO. The ecological importance of

this was quantified in wild fishes as, whilst temperature displayed little effect on

movement dynamics, the presence of hypoxia resulted in significant habitat

compression, with bream restricted to shallow, oxygenated microhabitats. Under such

compression, bream are likely to be at increased risk of predation and the negative

effects of increased density, including competition and disease. These results provide

empirical evidence for the hypothesis that hypoxia is a key driver of A. butcheri growth

rates within the Swan River Estuary. It also highlights the population’s vulnerability to

hypoxic episodes, which are expected to increase in both extent and frequency as a

result of anthropogenically-enhanced eutrophication and climate change.

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Contents

Abstract ..................................................................................................................................... iii

Acknowledgements .................................................................................................................... v

1 Introduction ........................................................................................................................... 1

1.1 Metabolism and respiration ............................................................................................ 2

1.2 Challenges of respiration in fish ...................................................................................... 4

1.4 Thermal tolerance in fishes ............................................................................................. 5

1.5 Oxygen tolerance in fishes .............................................................................................. 6

1.6 Hypoxia – cause and effect ............................................................................................. 8

1.7 Quantifying responses to temperature and hypoxia ...................................................... 8

1.8 Aerobic scope ................................................................................................................ 11

1.9 Limiting oxygen level ..................................................................................................... 15

1.10 Measuring metabolism - laboratory ........................................................................... 17

1.11 Measuring metabolism – field .................................................................................... 19

1.12 Study site and species ................................................................................................. 21

2 Methods ............................................................................................................................... 23

2.1 Respirometry ................................................................................................................. 23

2.2 Acoustic telemetry ........................................................................................................ 29

2.3 Data analysis ................................................................................................................. 34

3 Results .................................................................................................................................. 36

3.1 Respirometry ................................................................................................................. 36

3.2 Acoustic telemetry ........................................................................................................ 41

4 Discussion ............................................................................................................................. 53

5 Conclusion ............................................................................................................................ 65

References .............................................................................................................................. 67

v

Acknowledgements

Firstly, I would like to thank Recfishwest, the Department of Parks and Wildlife

(DPaW) and Murdoch University for providing funding for this project and to the

Australian Centre for Applied Aquaculture Research for the use of their facilities.

The respirometry experiments would not have been possible without the help of many

people. Thanks to Gavin Partridge and the ACAAR staff for their technical assistance

and support, and to my army of volunteers; Jenna Hounslow, Cameron McVeigh,

Michael Beerkens, Veronica Beerkens, Rachel Rintoul, Siew Mee Bong, Le Ma, Emily

Hamley, Emily Lawlor, Aline Gibson Vega, Brooke Richards, Brendan Loh, Jeremy

Beerkens, Samuel Beerkens, Stephen Beerkens, Aaron Haji Ali, Hannah Ashe and Tash

Harrison.

I wish to thank the members of Murdoch University’s Centre for Fish and Fisheries

Research, several of whom have already been named, for the support and advice they

have provided throughout the year. Thanks especially to Jake Watsham, Karissa Lear,

Daniel Yeoh, Alan Lymbery, Tom Ryan, Jeff Whitty, Alan Cottingham and David

Morgan. Thanks also to DPaW’s Alex Hams and Steeg Hoeksma for their assistance in

the field and for providing access to DPaW’s environmental dataset, and thanks to all

the recreational fishers who let us tag and release their prize catches.

Thanks to my family and friends for putting up with my fish ramblings and thanks to

my supervisors, Adrian Gleiss and Stephen Beatty for all the time they have given and

support they have shown throughout the year. Thanks for making me bream with

excitement.

This research was conducted under Murdoch University’s Animal Ethics Permit

RW2800/15 and Department of Fisheries Exemption 2700.

1

1 Introduction

There are approximately 20,000 species of teleost (i.e, bony fish) globally, all of which

are aquatic, with a few exceptional species capable of surviving out of the water or

aestivating underground for extended periods of time, with the proviso that they remain

adequately moist (Delaney et al., 1974, Wootton, 2012). Depending upon habitat

connectivity and life-stage, many teleosts can display a high dispersal capacity, often

allowing individuals and populations to migrate and perform range shifts over relatively

short timeframes (Kramer and Chapman, 1999, Perry et al., 2005, Chen et al., 2011).

They are also known to display intelligence and participate in many synergistic

activities, both within and between species (Bshary and Schäffer, 2002, Bertrand et al.,

2006, Brown, 2015).

Teleosts are heavily reliant upon their aquatic habitat for survival, with each species

displaying unique thermal, oxygen and salinity requirements (Somero and DeVries,

1967, Uchida et al., 2000, Pörtner, 2001). If these requirements are not met, the

performance of individuals may be reduced, and survival potentially compromised

(Williams and Williams, 1991, Pörtner and Farrell, 2008). This has resulted in a recent

flurry of research into the adaptive capacity of fishes to respond to ongoing and severe

anthropogenically-based changes to their aquatic habitats (Pörtner and Farrell, 2008,

Claireaux and Chabot, 2016, Sandblom et al., 2016). This knowledge is critical from a

conservation perspective, with freshwater fishes (which represent ~41% of all fishes)

among the most threatened groups of vertebrates, globally (Liermann et al., 2012).

Moreover, teleosts are also heavily exploited by humans as a resource for both food and

sport (Aguado et al., 2016, Worm, 2016, Young et al., 2016). As such, predicting the

effects that changes to the abiotic environment will have on fish populations and

ecosystems is imperative in the creation of sustainable fisheries management protocols

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and developing effective conservation strategies (Myers and Worm, 2003, Cheung et

al., 2016, Young et al., 2016).

Conservation physiology is an increasingly popular field and involves the quantification

of the effect of the abiotic environment on aquatic species (Chabot et al., 2016b). It is

well-established that the external environmental variables such as temperature and

oxygen play a critical role in determining the metabolic outputs of organisms,

particularly in ectotherms such as fishes (Fry, 1947, Fry, 1971, Enders and Boisclair,

2016). As a result, conservation physiologists have attempted to characterise

metabolism as a causal mechanism influencing the capacity of fishes to respond to

many environmental challenges, including climate change, contamination, and hypoxia

(Chabot et al., 2016b). This thesis will focus on the effects of temperature and oxygen

on metabolism in a heavily exploited estuarine fish species, and the effects that this has

on its movement dynamics.

1.1 Metabolism and respiration

From the simplest microorganism to the largest whale, sustaining life requires the

ability to convert chemical potential energy into a usable form, namely adenosine

triphosphate (ATP), which may occur in the presence or absence of oxygen (Brown et

al., 2004, Chabot et al., 2016b). Aerobic respiration (oxygen present), accounts for the

vast majority of a higher organism's total energy budget, with anaerobic respiration

(oxygen absent) only utilised during short bursts of intensive activity, due to a

combination of its inefficiency and potentially hazardous lactate by-products (Goolish,

1991, Jobling, 1993). In order to achieve aerobic respiration, organisms must have the

capacity to transport oxygen from the external environment into their mitochondria,

where the metabolic reactions take place. This requires cohesion between the

individual’s structural, physiological and behavioural attributes, and means that

malfunction in any one of these systems may have substantial effects on the organism’s

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fitness, by reducing growth rates and body condition. In its most extreme, such

malfunctions may also prove fatal.

Respiratory organ structure is well established and known to be largely based on

phylogeny and the ancestral environment. Whilst many forms exist, ranging from skin

to lungs, gills and insect trachea, all require the same fundamental properties; a large,

wet, and thin semi-permeable surface area for aqueous dissolved oxygen (DO) to be

exchanged through, and the capacity to pass oxygen over it, whether passively or

through active ventilation (Graham, 1990, Maina, 2002). The efficiency of these

structures in uptaking oxygen and removing CO2 depends on their access to the

circulatory system (Graham, 1990). For example, whilst solely relying upon direct

cutaneous respiration is effective only when the cellular masses involved are less than

1mm in diameter, by vascularising the skin surface, larger animals such as teleost fishes

and amphibians are also capable of effective cutaneous respiration (Krogh, 1904, Kirsch

and Nonnotte, 1977, Nonnotte, 1981). This can be very efficient, in some cases capable

of supplying all of an animal's oxygen requirements, and is possible due to

proliferations of blood vessels within the skin of the animal, which run countercurrent

to the flow of water, maximising oxygen uptake (Nonnotte and Kirsch, 1978, Liem,

1981, Bridges, 1988, Feder et al., 1988). However, the importance of cutaneous

respiration to a fish’s net oxygen uptake changes throughout its lifetime, tending to be

of much greater importance to larval and juvenile fish, than adults (Hughes and Al-

Kadhomiy, 1988, Rombough, 1998). With increasing size, fishes become increasingly

reliant upon gills for gas exchange (Rombough and Moroz, 1990). Gills also take

advantage of countercurrent flows, and scale with mass, ensuring that fishes maintain

the capacity to extract adequate quantities of oxygen throughout their lifetime (Muir and

Hughes, 1969, Hughes, 1984). Additionally, they contain oxygen receptors and have

been observed remodelling in response to changes in both temperature and dissolved

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oxygen saturation, indicating that they are highly plastic and adaptable structures

(Daxboeck and Holeton, 1978, Sundin et al., 1998, Sollid and Nilsson, 2006).

1.2 Challenges of respiration in fish

Oxygen dissolved in water is present at concentrations generally 20 - 40x below that of

air and diffuses at a 10,000 - 300,000x slower rate (Graham, 1990, Verberk et al.,

2011). As such, gathering oxygen requires much greater energy inputs in water than in

air and can result in respiration taking up much of an aquatic organism’s energetic

output (Thompson and MacDonald, 2006). The amount of oxygen that can be taken up

by a fish is limited by a number of factors, but most critically by temperature and the

saturation of dissolved oxygen (Fry, 1947, Fry, 1971, Claireaux and Chabot, 2016).

Before I begin to describe the effects that these factors have on fish metabolism, I will

firstly establish the relative importance of each.

Temperature is the major factor controlling fish metabolism as, with the exception of a

few large, pelagic species, fish are ectothermic, meaning that their body temperature

equals that of the external environment (Magnuson et al., 1979, Altringham and Block,

1997, Claireaux and Chabot, 2016). Increasing temperatures will result in an increase in

a fish’s overall metabolic rate, and therefore increase the demand for oxygen, regardless

of external oxygen saturation, to a point (Fry, 1947, Fry, 1971, Magnuson et al., 1979).

Environmental oxygen saturation then limits the capacity for a fish to utilise their full

metabolic capacity at any given temperature (Figure 1) (Farrell and Richards, 2009,

Claireaux and Chabot, 2016). With this in mind, I will now explain the effects of each

factor on fish metabolism.

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1.4 Thermal tolerance in fishes

Temperature is arguably the most important environmental influence on fish fitness and

survival, affecting many processes, including metabolism (Fry, 1947), fecundity

(Pörtner, 2001), sex determination (Goto et al., 1999), swimming speed (Johansen and

Jones, 2011) and digestion (Brett and Higgs, 1970). Globally, fish are found in water

temperatures ranging from below freezing to 40°C (Clarke and Johnston, 1999).

However, no species is able to tolerate this full range and as such, each is restricted to a

thermal niche (Wardle, 1980, Magnuson and DeStasio, 1997). This niche may be broad

(eurythermal species) or narrow (stenothermal species), depending upon the

physiological and behavioural attributes of the species (Pörtner et al., 2000).

Eurythermy tends to be prevalent in temperate fishes, and those in variable

environments, which experience significant fluctuations in temperature, on either a daily

or seasonal basis (Verberk et al., 2016a). Conversely, fishes in historically stable

environments, such as polar oceans, may specialise towards stenothermy (Pörtner et al.,

2000, Verberk et al., 2016a).

Regardless of tolerance capacity, as an individual nears its lower thermal limits, it will

experience a range of physiological challenges, including changes in cellular

composition (Hazel, 1984), and reductions in both digestive efficiency (Cho and

Kaushik, 1990) and maximum metabolic rate (Johnston and Dunn, 1987, Verberk et al.,

Figure 1: Effect of temperature on the maximum metabolic rate in a hypothetical ectotherm, at two levels of oxygen availability; high (solid line) and low (dashed line). Adapted from Clark et al., 2013a.

6

2016a). This reduces the individual’s capacity to perform vital behaviours such as

predator avoidance and foraging, and may also induce dormancy, diminishing the

organism’s likelihood of survival (Johnston and Dunn, 1987, Verberk et al., 2016a).

On the other hand, as individuals approach upper thermal limits, enzyme denaturation

begins to occur and anaerobic waste products accumulate in the liver (Fields and

Somero, 1998, van Dijk et al., 1999). This onset of anaerobiosis indicates that

insufficient oxygen is available to complete the aerobic respiration process (van Dijk et

al., 1999). There are two possible causes for this failure; (1) a breakdown of the electron

transport chain within the mitochondria, or (2) an inadequate supply of oxygen to the

body tissues (= hypoxaemia). Of these hypotheses, the latter is believed to be correct, as

mitochondria are known to be capable of functioning at temperatures beyond the

thermal limits of the species they belong to (Hardewig et al., 1999, van Dijk et al.,

1999). This suggests that it is largely limitations and failures of the respiratory and

circulatory systems which determine the maximal thermal bounds of a species (Pörtner

and Knust, 2007, Pörtner and Farrell, 2008).

1.5 Oxygen tolerance in fishes

Without oxygen, fishes cannot survive. As previously mentioned, whilst temperature

determines the potential metabolic output of fishes, the availability of oxygen limits the

capacity for individuals to fully realise this capacity (Claireaux and Chabot, 2016). For

example, the maximum metabolic rate that can be achieved by individual fishes at any

given temperature decreases with decreasing dissolved oxygen saturations (Claireaux

and Chabot, 2016).

It has been difficult to provide a definition of what exactly ‘hypoxia’ is. Traditionally, a

threshold of <2.0 mgO2/L has been employed to define lethal levels of hypoxia in both

ecological and fisheries research (Diaz and Rosenberg, 1995). However, in a review of

872 research articles, Vacquer-Sunyer and Duarte (2008) found that published hypoxia

7

thresholds between species and life-stages ranged from 8.6 mgO2/L to 0.mgO2/L, with a

mean of 2.31 ± 0.1 mgO2/L. Therefore, the one-size-fits-all approach of Diaz and

Rosenberg (1995) fails to adequately represent the effects of low-oxygen environments

across the diversity of aquatic taxa, and taxa-specific thresholds should be followed,

albeit with caution (Vaquer-Sunyer and Duarte, 2008). In this scenario, the lethal

hypoxia-limits of fish would be defined as <1.54 ± 0.07 mgO2/L, over a period of 59.9

± 12.3 hours, with a sublethal threshold of 4.41 ± 0.39 mgO2/L (Vaquer-Sunyer and

Duarte, 2008).

Moreover, as a means of controlling for the effects of temperature and salinity on

oxygen concentration, physiologists tend to measure the oxygen content of water as a

percentage of air saturation (Chabot et al., 2016b, Chabot et al., 2016c). Dissolved

oxygen concentrations can vary markedly with changes in temperature, but may still be

considered equivalent in terms of their comparative effect on fish (Chabot et al., 2016b).

An example scenario is a hypothetical water body constantly at 35ppt salinity and 100%

DO saturation. An increase in water temperature from 10°C to 30°C will result in a DO

concentration reduction from 9.03 mgO2/L to 6.24 mgO2/L, simply as a result of

reductions in oxygen solubility (Weiss, 1970). When using this scale, dissolved oxygen

saturations >80% that of air, are deemed ‘normoxic’, and considered to be optimal to

fish survival (Petersen and Steffensen, 2003, Schurmann and Steffensen, 1994, Snyder

et al., 2016). Below 80% saturation, physiological adjustments begin to take place, to

minimise the risks associated with the lower-oxygen environment (Hughes, 1973, Clark

et al., 2013a), and ‘hypoxia’ is generally recognised as being <30% or <20% DO

(Nilsson and Renshaw, 2004, Vaquer-Sunyer and Duarte, 2008). Below this, 5-10% DO

equates to ‘severe hypoxia’, and finally, 0% DO is indicative of ‘anoxia’ (Nilsson and

Renshaw, 2004, Vaquer-Sunyer and Duarte, 2008).

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1.6 Hypoxia – cause and effect

There are multiple causes of hypoxia in aquatic environments, often acting in

combination, including water column stratification, eutrophication, phytoplankton and

macroalgal blooms, and the microbial breakdown of dissolved organic carbon (Prasad et

al., 2014, Tweedley et al., 2016). Pressure from anthropogenic activities, such as

urbanisation and agriculture, is currently exacerbating these processes worldwide,

especially through the additional input of nutrients such as nitrogen and phosphorous

from fertilisers (Service, 2004, Jenny et al., 2015).

Hypoxia has many negative impacts upon fish, and aquatic ecosystems in general.

These range from reduced growth rates and fecundity to mass mortality events and can

occur at all scales, from small, intertidal rock pools to multiple-kilometre wide oceanic

‘dead zones’ (Service, 2004, Vaquer-Sunyer and Duarte, 2008). However, many

systems are naturally affected by hypoxia, including freshwater, estuarine, coastal and

benthic zones (Nordberg et al., 2001, Thompson and Withers, 2002, Nilsson and

Renshaw, 2004). As such, species may be adapted to withstand some level of hypoxia,

with some, such as the crucian carp (Carassius carassius), able to maintain routine

metabolic rate in 5 - 10% DO (Nilsson and Renshaw, 2004). Additionally, it is possible

for some fishes to acclimate to hypoxic conditions, allowing them to improve the

functionality of multiple physiological characteristics such as oxygen-extracting

capacity (Lomholt and Johansen, 1979) and maximum swimming speed (Fu et al.,

2011). However, even species that do not display significant hypoxia tolerance are

capable of surviving short-term incursions into low-oxygen environments, which may

occur whilst foraging and evading predators (Eby and Crowder, 2002, Eby et al., 2005).

1.7 Quantifying responses to temperature and hypoxia

Quantifying the responses of fishes to changes in temperature and dissolved oxygen is

difficult, especially when attempting to elicit mechanistic, cause-and-effect relationships

9

across organisational levels (Whitfield et al., 2012, Harvey et al., 2014). Ideally, such

experiments would be conducted upon wild fish in their natural habitat, however, in

many cases, this is simply impractical or impossible, due to the inherent variability of

natural environments. As such, many of the consequences of environmental changes,

especially up to the organ-system level of organisation, tend to be assessed in laboratory

conditions, where background interference can be mitigated and controlled (Chabot et

al., 2016b, Chabot et al., 2016c). Unfortunately, this may also provide results which are

not representative of true biological responses in natural conditions (Jutfelt et al., 2014,

Pörtner, 2014). For example, captive animals may not exhibit behavioural traits, which

in the wild, would assist them in countering physiological stressors (Henderson and

Fabrizio, 2014).

Despite their limitations, laboratory experiments are still vital to improving our

fundamental knowledge of fish physiology. Changes in fecundity, digestive capacity,

disease resistance, and swimming speeds due to temperature and hypoxia are all

quantifiable in controlled environments (Qiang et al., 2013, Eliason and Farrell, 2016).

Also quantifiable is the energetic cost that fish must expend to maintain these functions,

which may be measured in a number of different ways (Nelson, 2016, Peck and

Moyano, 2016). A common measurement taken in physiological experiments is the

minimum energetic output required to maintain only basic survival at the cellular, tissue

and organismal levels, which may be taken as either the basal- or standard, metabolic

rate (Glazier, 2015, Chabot et al., 2016b). Whilst both of these measurements have been

taken on fishes (Claireaux et al., 2000, Ruzicka and Gallager, 2006), basal metabolic

rate is no longer considered to be appropriate for use on ectothermic animals, as it takes

into account the energetic expenditure incurred through thermogenesis in homeothermic

organisms (Peck and Moyano, 2016). As such, from this point on, the minimum level of

10

oxygen consumption in a fish, at any given temperature, will be referred to as the

standard metabolic rate (SMR).

All other bodily processes, including digestion, gamete production, and aerobic

exercise, add further expense to the individual's energy budget (Eliason and Farrell,

2016). When measured during regular activity, a fish’s oxygen consumption is termed

its routine metabolic rate (RMR) (Peck and Moyano, 2016). If measured directly after

feeding, the energetic costs of digestion can be elicited (specific dynamic action; SDA)

and, if tested whilst moving at known speeds, the speed-specific active metabolic rate

(AMR) can be determined (Peck and Moyano, 2016). The capacity for any organism to

increase its uptake and transfer of oxygen between tissues is limited, with the upper

bound of energetic output known as the maximum metabolic rate (MMR) (Norin and

Clark, 2016). All of these measurements are useful, and provide insight into the

fundamental drivers of fish performance in different environmental conditions. Of

particular interest to many physiologists is comparing the difference between SMR and

MMR at any given temperature and dissolved oxygen saturation, which enables us to

determine the aerobic scope of the individual, i.e. their capacity to increase energetic

output (Figure 2) (Farrell, 2016, Rosewarne et al., 2016).

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Having a larger aerobic scope increases the capacity to apportion energy across a range

of functions, such as predator avoidance, foraging and reproduction, increasing the

likelihood of both successful reproduction and survival (Farrell, 2016). Aerobic scope

can, therefore, be used as an effective surrogate for fitness in fishes, which, given their

high fecundity and capacity for juvenile dispersal, is often impractical to measure

directly (Pörtner, 2001, Farrell, 2016).

1.8 Aerobic scope

Aerobic scope is not a new parameter to the field of physiology, being first recognised

by F. E. J. Fry in 1947 as the “scope for activity”. However, it is only in the past two

decades that it has seriously taken traction and become a focal point of research

(Claireaux and Chabot, 2016, Farrell, 2016). It’s mechanistic nature and capacity to act

as a fitness surrogate in aquatic organisms has led to the creation of the oxygen- and

capacity-limited thermal tolerance (OCLT/OCLTT) hypothesis (Pörtner and Knust,

2007, Pörtner and Farrell, 2008, Pörtner and Peck, 2010). This hypothesis aims to link

changes in the physiological performance of tissues, as a result of varying temperatures

and oxygen, to changes in organism-level metabolic rates (Pörtner and Knust, 2007,

Pörtner and Peck, 2010, Verberk et al., 2016a, Verberk et al., 2016b, Verberk et al.,

Figure 2: The relationship between temperature, maximum metabolic rate, standard metabolic rate and aerobic scope in a hypothetical ectotherm (Verberk et al., 2016b).

12

2016c). The hypothesis centres around the importance of pejus temperatures (‘pejus’

literally meaning “getting worse”), which are determined as being the temperatures at

which oxygen circulatory capacity becomes limited, both above and below an optimum

temperature (Top) (Pörtner and Knust, 2007, Pörtner and Farrell, 2008). Beyond these

pejus temperatures, aerobic scope continues to decrease until anaerobiosis and finally

enzyme denaturation occur, in a bell-shaped curve representing the organism’s total

thermal window (Figure 3) (Pörtner and Farrell, 2008). Figure 3 also shows how the

thermal window may be reduced by sup-optimal environmental conditions, such as

hypoxia and high levels of CO2 (Pörtner and Farrell, 2008).

13

This hypothesis has greatly renewed interest in calculating the aerobic scope of species,

and has been used and accepted to the point that it is “treated as a confirmed theory”

(Clark et al., 2013a). Clark and colleagues (2013a,b) have challenged this relatively-

unquestioned acceptance, arguing that the optimal temperature of a species is not solely

determined by oxygen transport capability. Instead, they suggest a new hypothesis of

‘multiple performances – multiple optima’ (MPMO), whereby Top and TopAS, the

temperature at which aerobic scope is maximised, are not the same (Clark et al., 2013a).

They argue that different physiological characteristics have distinct optimal

temperatures and that the net Top of an organism is a compromise between these traits

(Figure 4). They also stress that the Top and tolerance distribution of any trait can vary

in both absolute value and relative importance interspecifically and intraspecifically,

with life stage and external ecological and environmental conditions (Clark et al.,

2013a, Jutfelt et al., 2014).

Figure 3: Hypothesised effect of temperature on aerobic scope in fishes in optimal (solid line) and sub-optimal (dashed line) conditions. Adapted from Pörtner & Farrell, 2008.

14

Clark et al. (2013a,b) also argue that caution must be taken by researchers when

extrapolating data beyond the scope of their studies. One of the most appealing aspects

of the OCLTT hypothesis is its direct relevance to the current issue of increasing ocean

temperatures through anthropogenically-induced climate change (Wrona et al., 2006).

Many laboratory studies, using thermal acclimations of weeks, have attempted to

present their results as indicators of performance in future environmental conditions

(Pörtner and Knust, 2007, Pörtner and Farrell, 2008, Pörtner, 2010). Clark et al. (2013a)

stress that such acclimations are incomparable to the long-term acclimations wild fish

would experience through climate-induced, slowly increasing water temperatures, as

evidenced by several recent studies (Norin et al., 2014, Sandblom et al., 2016).

Advocates for the OCLTT hypothesis have argued the pressing need for biologists to

follow an overarching, mechanistic-based framework to understand the impacts of

climate change on ectothermic species across phyla (Farrell, 2013, Pörtner and Giomi,

2013). They also suggest that studies which question the general applicability of the

hypothesis (e.g. Gräns et al., 2014) only do so as they fail to either fully understand the

Figure 4: Hypothetical optimal temperatures of a range of physiological characteristics, including aerobic scope (black line), and the preferred temperature of the organism (blue line), representing a compromise between traits. Adapted from Clark et al., 2013a.

15

concept or maintain the ‘steady state’ conditions required to accurately determine

aerobic scope (Pörtner, 2014, Claireaux and Chabot, 2016). This has led to further

arguments regarding the validity of many of the studies that have been used to support

the OCLTT concept (Jutfelt et al., 2014). Jutfelt et al. (2014) pointed out that many of

the studies referenced by Pörtner and Knust’s pioneering 2007 paper are dated, and

measured aerobic scope through varied and now unconventional methodologies,

weakening their reliability. They also question the capacity of pro-OCLTT studies (e.g.

Eliason et al., 2011) to attain the steady-state conditions called for by Pörtner (2014) to

ensure that the most accurate measurements possible are taken (Jutfelt et al., 2014).

This debate is still far from settled, however, there is consensus that there is a need for

high quality, rigorous and standardised experimenting (such as those of Clark et al.,

2013a) to provide weight towards either the OCLTT or MPMO hypotheses, or provide

ideas for a novel conceptual approach (Clark et al., 2013a, Clark et al., 2013b, Farrell,

2013, Jutfelt et al., 2014, Pörtner, 2014).

1.9 Limiting oxygen level

Regardless of which hypothesis is deemed more valid, the fact remains that the maximal

metabolic rate achievable by a fish decreases with the decreasing availability of oxygen

(Fry, 1947, Pörtner and Farrell, 2008, Claireaux and Chabot, 2016). At any given

temperature, this decline in aerobic scope can be graphically expressed as a limiting

oxygen level curve (Figure 5) (Claireaux and Chabot, 2016). Any initial rate of

metabolic output in normoxic conditions can be maintained in lower oxygen

environments through physiological adjustments until a limiting oxygen level (Fry,

1947, Neill et al., 1994). As oxygen availability falls beneath the limiting oxygen level,

the capacity of individuals to distribute energy across physiological functions falls, until

the environment reaches critical oxygen levels, at which point only the most essential

processes are able to be maintained (Neill et al., 1994, Claireaux and Chabot, 2016).

16

Below this, survival becomes influenced not only by oxygen saturation but also, the

length of time that it spends in these conditions, as individuals become reliant upon

anaerobiosis to supply their metabolic needs (Neill et al., 1994, Claireaux and Chabot,

2016). The duration which individuals can endure below the critical oxygen level

decreases with oxygen availability until the incipient lethal oxygen level, where

survival, even in the very short-term, is fully compromised (Neill et al., 1994, Pörtner

and Farrell, 2008, Claireaux and Chabot, 2016). The oxygen saturations at which each

of these occurs varies among individuals, species, life-stage, acclimation history and the

activities being performed (Claireaux and Chabot, 2016).

Determining the points at which fishes reach these limiting-, critical- and incipient

lethal oxygen levels is difficult and varies between species, but when gathered, can

provide valuable empirical evidence on the capacity of a species to survive both short-

and long-term hypoxic episodes (Claireaux and Chabot, 2016). Many of these

Figure 5: Hypothetical limiting oxygen level (LOL) curve. The solid line indicates the maximum possible metabolic output of an individual at the corresponding oxygen saturation. Two series (a’-b’-c’-d’ and a-b-c-d) are shown, representing the change in metabolic rate with declining ambient oxygen, in two individuals from two initial metabolic outputs (a’, a), to the limiting oxygen levels (b’, b), critical oxygen levels (c’, c) and incipient lethal oxygen levels (d’, d). The critical- and incipient-lethal oxygen levels differ slightly in each series to show inter-individual variation (Claireaux & Chabot, 2016).

17

difficulties relate back to fundamental challenges faced by experimental biologists in

accurately determining metabolic rates in aquatic species, which will now be discussed.

1.10 Measuring metabolism - laboratory

The most effective technique for measuring animal metabolism is through direct

calorimetry, which measures heat loss from the organism (Brett and Groves, 1979). This

methodology is preferred as heat is generated as a by-product of all the metabolic

reactions involved in both the aerobic and anaerobic stages of cellular respiration and as

such, while not infallible, it can account for the vast majority of an animal’s energy

production (Brett and Groves, 1979, Nelson, 2016). However, direct calorimetry is

largely unsuitable for aquatic ectotherms, which tend to exhibit low metabolic rates and

live in a medium of high heat capacity (Brett and Groves, 1979, Nelson, 2016). This

results in a very small signal-to-noise ratio and makes obtaining accurate measurements

of fish heat loss in water both highly problematic and expensive (Nelson, 2016). As

such, this methodology has been largely avoided for organism-level studies and

supplemented by the development of indirect calorimetry (Regan et al., 2013, Nelson,

2016).

Indirect calorimetry measures changes in the environmental concentrations of either the

reactants or products of aerobic respiration, namely oxygen and carbon dioxide (Nelson,

2016, Svendsen et al., 2016a). Of these, CO2 has also been largely avoided by the

aquatic physiology community, as a result of its high solubility in water, and its

tendency to change in concentration with changes in water chemistry (Pfeiffer et al.,

2011, Nelson, 2016). The use of oxygen uptake rate (MO2) as a proxy for metabolic

rate, however, is overwhelmingly prominent and has long been the primary mechanism

for quantifying fish metabolism (Fry, 1947, Chabot et al., 2016a, Chabot et al., 2016c,

Norin and Clark, 2016, Rosewarne et al., 2016). It is not without faults; being an

indirect methodology, it cannot be used to determine the energetic outputs resulting

18

from anaerobic metabolism, which begs questions about its usefulness as a

measurement tool in hypoxic conditions, where anaerobiosis is likely to form a large

portion of the individuals total energy production (Nelson, 2016). In addition, there is

also concern that by ignoring the energetic differences between the substrates being

oxidised and resultant nitrogenous waste products, MO2 is actually incapable of

measuring metabolic rate (Nelson, 2016). As such, MO2 should be seen as a measure of

respiration rate, however, recognition of this is lacking in the vast bulk of the peer-

reviewed literature, and the dominating ‘metabolic rate’ acts as improper surrogate

terminology (Chabot et al., 2016c, Nelson, 2016). This thesis will follow the example

set by Chabot et al. (2016c) and continue to use the term ‘metabolic rate’, for ease of

comparison between studies, whilst stressing the fact that it is indeed respiration rate

that is being measured.

As a proxy for metabolic rate, MO2 is very effective, being both practical to test and

applicable to ecologically-relevant physiological hypotheses in the aquatic realm

(Chabot et al., 2016c, Norin and Clark, 2016). It is measured as the quantity of oxygen

removed from a known volume of water, per kilogram of fish, per hour (mgO2/kg-1

/hr-

1), with techniques varying by species. Two main experimental approaches exist; swim

tunnel respirometry, for species capable of maintaining continuous aerobic swimming,

and static respirometry, for relatively sedentary species, both of which are suitable for

determining the standard- and maximum metabolic rates of fishes (Clark et al., 2013a,

Chabot et al., 2016a, Chabot et al., 2016c, Norin and Clark, 2016).

To maximise comparability between studies with regards to SMR, researchers must aim

to ensure that test subjects are post-absorptive and in a steady state of either aerobic

exercise or rest, depending on the species biology, for the duration of the trial, which

typically lasts for over 24 hours (Chabot et al., 2016c). Alternatively, the methods for

determining MMR are more flexible, and, dependent upon the biology of the target

19

species (Norin and Clark, 2016). The two most common methodologies to achieve

MMR are 1) increasing the swimming speeds required to maintain equilibrium within a

swim tunnel respirometer, or 2) an exhaustive chase followed by recovery in static

respirometers, (Clark et al., 2013a, Norin and Clark, 2016). The use of swim tunnel

respirometry is favoured, where possible, as it allows for the determination of both

MMR and critical swimming speed (Ucrit); the point at which fatigue sets in and the fish

can no longer swim against the current (Norin and Clark, 2016). However, many

species are unwilling to swim continuously against such a current, and in such cases

exhaustive chase protocols are currently the most accepted alternative (Reidy et al.,

1995, Clark et al., 2013a, Norin and Clark, 2016).

1.11 Measuring metabolism – field

Both direct and indirect calorimetry techniques are unsuitable for quantifying metabolic

rate in free-ranging fishes (Metcalfe et al., 2016). As such, alternative proxies have been

developed in recent decades, based upon radio and acoustic biotelemetry (Farrell et al.,

2008, Metcalfe et al., 2016).

Radio and acoustic biotelemetry involve the attachment of electronic tags to fish, either

internally or externally, that transmit individually-coded information to either radio

receivers or submerged hydrophones (Farrell et al., 2008, Grothues, 2009, Kessel et al.,

2014). The information transmissible varies, depending upon the model of electronic tag

fitted, but may include an ID number, location, depth, and/or acceleration (Wilson et al.,

2013, Metcalfe et al., 2016). These techniques have greatly increased our capacity to

understand the spatial and movement dynamics of aquatic species, which is otherwise

largely limited to mark-recapture surveys and underwater observation (Metcalfe et al.,

2016). Acoustic telemetry, in particular, has enabled the development of long-term

movement patterns as once established, hydrophone receivers will passively log and

store any recorded transmissions, enabling researchers to perform data downloads

20

infrequently and still have access to fine-scale information (Grothues, 2009). However,

the effectiveness of these receivers is limited by the range at which they can reliably

receive transmissions, which may be limited by many factors, including the tag’s

transmission-strength, system geomorphology, flow regimes and accumulation of

biological matter on the hydrophone itself (Kessel et al., 2014). However, even when

taking these factors into consideration, most individual hydrophone stations are capable

of receiving data from several hundred metres in any direction, meaning that large tracts

of water may be monitored with a limited number of receivers (Grothues, 2009,

Thorstad et al., 2013). Such a group of receivers is known as an array, within which

stations may be established with either overlapping ranges, to assist with fine-scale

location estimates, or isolated from one another, to focus on larger-scale movements

(Chapman et al., 2005, Heupel et al., 2006).

Acoustic accelerometry takes advantage of acoustic biotelemetry technology and

provides physiologists with a means of estimating energetic costs of activity in free-

swimming fishes (Metcalfe et al., 2016). In addition to being quantifiable and intimately

linked to activity, acceleration has been shown to correlate strongly with MO2, both in

the laboratory (Gleiss et al., 2010) and field (Wilson et al., 2013), making it a good

proxy for estimating metabolic rate in wild aquatic organisms. Acceleration

measurements are calculated frequently, with a mean value transmitted every few

minutes; frequently enough that temporal patterns in movement, such as diel cycles, can

be elicited (Whitney et al., 2007). At fine scales, rapid accelerations allow fish

physiologists to estimate the energetic costs of anaerobism, which play critical short-

term roles in the survival of fish by allowing them to escape predators and capture prey

(Claireaux & Chabot, 2016, Metcalfe et al., 2016).

Movement is one of the four most energetically costly functions of animals, alongside

SMR, temperature-related changes in energy expenditure, and digestion (Metcalfe et al.,

21

2016). As such, it is likely that if a fish’s aerobic scope is reduced, it will prioritise

maintaining SMR over movement (Sokolova, 2013, Farrell, 2016, Metcalfe et al.,

2016). Abnormal reductions in activity, i.e. those not associated with natural diel

patterns, may, therefore, be indicative of a stress-response to suboptimal environmental

conditions, especially of changes in temperature and/or dissolved oxygen concentration

(Metcalfe et al., 2016). Whether or not this is the case can be determined by measuring

abiotic variation at each receiver throughout the study period, in conjunction with

laboratory-based respirometry experiments to quantify the physiological responses of

the target species to such variation in controlled conditions (Murchie et al., 2011).

1.12 Study site and species

The Swan River Estuary is a dynamic system, situated in the heart of Perth; a heavily

urbanised major city. Like many estuaries, it has dynamic environmental characteristics

and exhibits large seasonal fluxes in temperature, as well as widespread hypoxic

episodes and water stratification (Stephens and Imberger, 1996, Tweedley et al., 2016);

making it an ideal system to measure the aerobic scope capacities of estuarine fishes.

The hydrology of the system has been altered dramatically in the past century, with

dredging of the downstream Fremantle Harbour in the early 1900s allowing for far

greater intrusion of oceanic water (Hamilton et al., 2001). This has resulted in a

significant salt wedge region developing within the upper estuary, characterised by

stratification of the water column and significant hypoxic events, which are exacerbated

by high levels of nutrient input from the surrounding urban and agricultural floodplain

(Hamilton et al., 2001). In recent decades, these hypoxic episodes, in combination with

macroalgal blooms, have resulted in several major fish kill events (Zammit et al., 2005,

Smith, 2006), which has prompted estuarine managers to install two artificial

oxygenation plants within the upper estuary, as a means of mitigating the effects that

22

these events have on the native fauna (Hamilton et al., 2001, Tweedley and Hallett,

2014).

One species which has experienced severe mortalities during these extensive fish kill

events is the black bream (Acanthopagrus butcheri Munro 1949), a teleost of the family

Sparidae, native to temperate estuaries across southern Australia (Norriss et al., 2002).

A. butcheri generally complete their life cycle fully within their natal estuary, however,

are capable of venturing into fresher water, and may be flushed into the ocean following

heavy-flow events (Norriss et al., 2002). These flush events do not tend to result in

migration between estuaries and as such, very little genetic exchange between estuaries

has been observed (Farrington et al., 2000). Therefore, for management purposes, each

estuary is considered to contain a distinct population unit (Farrington et al., 2000,

Norriss et al., 2002).

Black bream are relatively large, long-lived, opportunistic omnivores, reaching

>480mm in total length, 2.89kg in weight and >21 years of age in the Swan River

Estuary (Sarre and Potter, 2000, Norriss et al., 2002). Reproduction occurs

predominantly in the months of August-December, in salinity zones of 15 – 20ppt, with

females producing an average of 1,580,000 eggs and capable of spawning multiple

times per season (Sarre and Potter, 1999). The species exhibits highly plastic growth

rates that can vary both between populations and within populations over time (Sarre

and Potter, 2000, Doubleday et al., 2015, Cottingham et al., 2016). The drivers of this

change are also variable, but do not appear linked to salinity, with bream exhibiting a

broad optimal salinity range of 12 – 48ppt and salinity stress not onset until 60ppt (Sarre

and Potter, 2000, Partridge and Jenkins, 2002, Doupe et al., 2005). Over the past two

decades, within the Swan River Estuary, growth rates have varied between years, driven

by either temperature or hypoxia (Sarre and Potter, 2000, Cottingham et al., 2016). This

plasticity in growth determinants, in combination with its fully restricted, estuarine

23

lifecycle, enables A. butcheri to act as an effective indicator species when studying the

effects of environmental variation on estuarine ichthyofauna (Cottingham et al., 2016).

This thesis aims to integrate the laboratory determination of aerobic scope with field-

based acoustic accelerometry to determine the effects of temperature and dissolved

oxygen saturation on the black bream in the Swan River Estuary, Western Australia. I

hypothesise that 1) the aerobic scope of black bream will increase with temperature,

until an upper critical threshold is reached; 2) the aerobic scope of black bream will

decrease with increasing levels of hypoxia, following a limiting oxygen level curve; 3)

movements of wild black bream will correlate with fish attempting to remain in

appropriate water temperatures and normoxia; and 4) the intensity and frequency of

black bream activity will decrease with both decreasing temperatures and increasing

levels of hypoxia.

2 Methods

2.1 Respirometry

2.1.1 Husbandry and acclimation protocols

Sixteen black bream (460-700g, x = 514g) were housed in groups of 3-4 in shaded 280L

flow-through tanks at the Australian Centre for Applied Aquaculture Research,

Fremantle, Western Australia between 1 April and 14 July 2016. During this time, the

fish were fed daily to satiation with EWOS® 2140 6mm pellets (www.ewos.com) and

were exposed to 12 hour:12 hour light:dark cycles.

The fish were obtained from a captive broodstock located at South Metropolitan TAFE,

where they had lived for a minimum of four years. Each bream had originally been

sourced from the Swan River Estuary, Western Australia. They were all microchipped

beneath the dorsal fin for identification and were weighed both at the beginning of the

24

experimental period and after two months. Fish were calmed with Aqui-S®

anaesthesia

prior to weighing.

Water quality in their ‘home tanks’ was maintained at 35ppt salinity, normoxia (>80%

DO) and appropriate acclimation temperature. All fish were acclimated for a minimum

of two weeks prior to experimenting, with temperatures maintained through the regular

re-adjustment of the inflowing water to within ±0.1°C of the desired temperature. The

fish were initially acclimated to either 27 (n = 8) or 30°C (n = 8). During this

experimental period, seven fish died; three as a result of the stress incurred during 30°C

testing, and four as a result of a technical malfunction during a 27°C SMR experiment.

After the conclusion of this experimental series, the remaining fish were randomly

reassigned to new temperatures of 21 (n = 3) and 24°C (n = 6). No deaths occurred

during this second experimental series.

On two occasions, overnight malfunction of ACAAR’s water heating system resulted in

sharp reductions in temperature. In the first situation, fish at 27 and 30°C were affected,

with tank temperatures falling to 20 and 23°C, respectively. The system was fixed by

midday and appropriate temperatures were reset. The acclimation period of all fish was

a)

c)

b)

d)

Figure 6: Home tanks; b). Experiment tank (Photo: Michael Beerkens); c). Respirometry experiment, with four bream sealed in individual chambers within the experiment tank; d) Black bream in a respirometry chamber

25

then extended for a minimum of five days prior to their next experiment. On the second

occasion, approximately a week later, only the acclimation of fishes at 27°C were

affected, with temperatures again falling to 20°C. This issue was fixed in the early

morning, and the fish’s acclimation period was extended for an extra three days.

2.1.2 Experimental setup

Intermittent-flow respirometry was performed on four fishes at a time, in 12L chambers

(40cm x 15cm x 20cm), with an additional 151mL of piping connecting each chamber

to two Eheim universal pumps (www.eheim.com) and a PreSens oxygen dipping probe

(www.presens.de). Chamber sizes were large enough to ensure that all fish could sit

comfortably inside, whilst maintaining a fish mass: water volume ratio of 1:16 – 1:25. It

is important that these ratios are kept below 1:50, to ensure that the time it takes the

oxygen content of the chamber to fall 10% is short (3-6 minutes) (Svendsen et al.,

2016b). The oxygen dipping probe was located in an isolated loop connected to the

chamber and had water continually circulating across it throughout the experiment.

Each chamber-probe setup was submerged in a 960L flow-through reservoir tank, with

new water continually added throughout the experiment to maintain temperature. A

photograph of a running experiment is shown in Figure 6c, and the experimental setup

is graphically presented in Figure 7. Temperature and oxygen measurements were

recorded by a Loligo®

Systems Witrox 4, and MO2 calculated using the Loligo®

Systems AquaRespTM

program (www.loligosystems.com), following the formula:

where MO2 (mgO2 kg-1

hr-1

) is the oxygen consumption rate, K is the rate of decline in

oxygen over each measuring period (kPa h-1

), V is the volume of water contained within

the respirometer, corrected for the volume of the fish, is the solubility of oxygen at the

26

experimental temperature and salinity and M is the body mass of the animal (kg)

(Rosewarne et al., 2016).

2.2.3 Experimental protocols

2.2.3.1 Temperature and oxygen saturations

Respirometry experiments were performed at four temperatures (21, 24, 27 and 30°C)

and three dissolved oxygen saturations (> 80%, 60% and 30% DO). Maximum

metabolic rate (MMR) was determined across all temperatures under both normoxia (>

80% DO) and 60% DO. Due to technical limitations beyond my control, MMR

experiments at 30% DO were conducted only at 21 and 24°C. Standard metabolic rate

(SMR) was determined only in normoxic conditions, and was conducted by extending

the period over which fish were kept in the respirometer following the elicitation of

MMR. It was deemed appropriate to only measure SMR in normoxia, as the minimum

rate of oxygen uptake required to sustain physiological functionality in ectothermic

species is determined by temperature, not oxygen availability (Chabot et al., 2016c). In

normoxic experiments, air was continuously bubbled into the reservoir tank. However,

in order to maintain oxygen saturations at 60% and 30% DO, nitrogen gas was bubbled

into the reservoir at a variable rate, controlled by an OxyGuard® Pacific

(www.oxyguard.dk).

Figure 7: Respirometry setup. Adapted from Rosewarne et al., 2016.

27

2.1.3.2 Fish handling and monitoring procedures

Prior to each experiment, the fish were fasted for 48 hours. Each fish was then

individually caught from their home tanks in dip nets, scanned for identification and

transferred directly to a 110L, 850mm diameter chase tank containing the dissolved

oxygen saturation being tested. All scanning and transfer activities were completed

within 20 seconds. In this chase tank, the fish were then subjected to an exhaustive

chase protocol. This protocol was employed as it was deemed the strategy most likely to

elicit MMR in A. butcheri. Bream were chased until exhaustion (2.5 - 3.25 minutes)

with the handle of a dip net, with occasional light tapping on their posterior lateral

surface to stimulate burst swimming. Once exhausted, and therefore assumed to be

experiencing maximal O2 demand, the fish were caught in the dip net, transferred into

the respirometry chambers and sealed inside (transfer times <10 seconds). The

experiment was then immediately commenced.

Each fish was then monitored every minute for 15 minutes to ensure that they were

maintaining equilibrium and respiring appropriately. During this intensive monitoring

period, no other activities were undertaken to ensure rapid response times if required. In

the event of a fish losing equilibrium or not respiring, it was immediately removed from

its chamber and placed back into its oxygenated home tank. This was generally enough

to revive the bream, however, if the fish did not respond to this, it was quickly

euthanised by immersing in a bucket of concentrated Aqui-S® anaesthetic. Following

this intensive monitoring period, fishes in normoxic conditions were monitored every

five minutes for a further hour, before the reservoir was covered by a shade cloth and

left uninterrupted until the end of the SMR experiment. Fishes at 60 and 30% DO were

monitored every five minutes for the remainder of their experiment.

At 60% and 30% DO, the fish remained inside the chambers for three hours; long

enough to ensure that the maximum MO2 of each fish was elicited. In normoxic

28

conditions, the fish remained inside the chambers for 24 – 32 hours, allowing enough

time for their rate of oxygen consumption to settle back to standard levels. Upon

completion, the fish were removed from the chambers with dip nets and transferred

back into their home tank, with transfer times of <10 seconds.

2.1.3.3 MO2 calculation cycles

The experiments ran on a flush-wait-measure cycle. During flush periods, water from

the reservoir tank was pumped into the chambers to replenish the internal oxygen

saturation and reduce waste product accumulation. Once the flush period ceased, the

chambers were closed off from the outside reservoir. The system then waited, to allow

the water to settle before the measurement period commenced. During this period, the

quantity of oxygen being removed from the chamber by the fish was measured and

regressions were automatically fitted by the AutoResp® program to the slope of the

oxygen decline. The overall strength of the regression fit was provided supplied as a

confidence interval (R2). Once the dissolved oxygen saturation fell 10% in any one

chamber, the flush period was recommenced. The flush-wait-measure cycle was set to

240 – 30 - 120 seconds in all 30°C and 27°C experiments and the 24°C and 21°C 60%

DO experiments. To ensure that the oxygen content of the chambers always returned to

the appropriate post-flush saturation, in the normoxic and 30% DO experiments of 24°C

and 21°C, the flush time was increased, resulting in a flush-wait-measure cycle of 360 -

30 - 120 seconds.

2.1.3.4 Background respiration management

After a maximum of three sequential experiments, or five days, the reservoir was

drained and all chambers and piping were bathed in chlorine, hosed down with fresh

water and left to dry before the next experiment could begin, to avoid microbial build-

up. Such build-up has the capacity to alter the rates background respiration and was

measured either for 30 minutes post-experiment in all chambers or concurrently during

29

an experiment in one randomly-assigned, empty chamber. In all cases, any background

oxygen changes were extremely minimal.

2.2 Acoustic telemetry

During two field days, on the 31 March and 1 May 2016, a total of 25 black bream

(>300mm, x weight 09.5g) were tagged with VEMCO® V13AP-1H passive acoustic

transmitters in the Swan River Estuary, Western Australia. The tags provided activity

and depth information, transmitted separately and randomly every 90 – 150 seconds,

with an estimated life-span of 366 days. Acceleration was measured on three axes (X, Y

and Z; Figure 8), at a rate of five samples per second. Activity (m/s2) was calculated as

the root mean square of acceleration averaged over the time between transmissions,

following the equation:

√

The transmissions were logged by a series of 25 VR2W acoustic receivers

(www.vemco.com) spaced approximately 1km apart within the estuary (Swan-Canning

Acoustic Array; Figure 9). Each receiver is estimated to have a recording range of

approximately 300m (Watsham, 2016). The data was downloaded from the array on the

28th

and 29th

of July, 2016.

Figure 8: V13AP-1H transmitter, displaying the three axes on which acceleration is measured (X, Y and Z) to calculate activity (m/s

2). Image: VEMCO® (www.vemco.com).

30

2.2.1 Surgery protocol

31

On March 31, 2016, 10 bream were captured off Claughton Reserve, Bayswater

31°55’37.7 ”S, 115°5 ’18”E) by either 41m seine net or rod-and-line fishing and

transferred in live-wells to Riverside Gardens, Bayswater (31°55’51.24”S,

115°55’40. 3”E) for tagging. On May 1, 2016, 15 bream, captured by recreational rod-

and-line fishers throughout the estuary, were transported in live-wells to the Bayswater

Sea Scouts shed (31°55’51.93”S, 115°55’5.32”E) for surgery. Prior to surgery, the

bream were housed in 110L water-filled and aerated portable coolers. One at a time, the

Figure 9: Distribution of the Swan-Canning Acoustic Array (SCAA), within the Swan-Canning Estuary, Western Australia, the location of each acoustic receiver marked. The Swan River Estuary extends inland from the ocean, along the upper arm of the overall Swan-Canning Estuary.

32

fish were transferred into another aerated 50L tank, containing 2-5mL Aqui-S®

anaesthetic, where they were left until unresponsive (x = 6.03 minutes). They were then

weighed, measured and secured on an operating table, with a hose flushing anaesthetic-

filled water into their mouth and across their gills. Tag implantation surgery involved

removing a scale and using a scalpel to make a small (approximately 2cm) incision on

the fish’s ventral surface, into the peritoneal cavity. The tag was then gently inserted

into the cavity and the incision closed with two sutures. Finally, an external t-bar tag

was implanted beneath the dorsal fin and the fish was placed into another 110L aerated

33

tank to recover. Once recovered, the fish were released back into the estuary at the site

of surgery. Several photographs of the surgical procedure are presented in Figure 10.

2.2.2 Environmental data collection

Water temperatures and dissolved oxygen saturations were measured weekly by the

Department of Parks and Wildlife and Department of Water at each acoustic receiver

throughout the estuary (www.dpaw.wa.gov.au). At each site, environmental data were

collected at approximately 50cm depth intervals down the height of the water column

using a YSI®

6600 V2 multi-parameter water quality sonde (www.ysi.com).

a) b)

c) d)

e) f)

Figure 10: a). Seine netting in the Swan River Estuary; b). Anaesthesia bath, with hose connecting to operating table (Photo: Nathan Beerkens); c). V13 tag implantation surgery; d). Incision closed with suture; e). Tagged bream release (Photo: Daniel Yeoh); f). Local child participating in a release (Photo: Michael Beerkens)

34

2.3 Data analysis

2.3.1 Respirometry

After each experiment, each fish’s MO2 output dataset was subsetted to only contain

readings with R2 confidence intervals >0.95, to ensure that reliable estimates of MMR

and SMR could be made. MMR was defined as the highest MO2 reading for each

individual fish in each experiment (Norin and Clark, 2016). To calculate SMR, the

dataset was subsetted to only include MO2 values obtained after 10 hours of

experimenting, by which time each fish’s respiration rate had settled, following the

stress of transfer and/or exhaustive chase. Following the guidelines of Chabot et al.

(2016c), SMR was classified as the lowest 20th

percentile of the remaining MO2 values.

As some fishes did not achieve a stable resting state throughout the experiment, their

calculated SMR values are likely to overestimate their true SMR (Clark et al., 2013a,

Pörtner, 2014). These fishes displayed SMR values > two standard deviations away

from the mean of the other measurements in the appropriate temperature and oxygen

group, and subsequently were classified as outliers and excluded from further analysis.

The effects of both temperature and oxygen saturation on SMR, MMR and AS were

then analysed using randomised linear mixed effects models (lmer) from the lme4

library (Bates et al., 2015) in R (R Core Team, 2010), followed by multiple comparison

of means Tukey contrasts using the glht function of the multcomp library (Hothorn et

al., 2008). The temperature coefficient (Q10) was used to calculate the rate at which

MMR, SMR and AS changed with temperature, following the formula:

( )(

)

where T1 and T2 are the lowest and highest temperatures at which the metabolic

function was recorded, and R1 and R2 are the mean values of the functions at T1 and T2

respectively.

35

2.3.2 Acoustic telemetry

Each individual fish and receiver station dataset, comprising raw activity and depth

values, was subsetted to include only days for which estuarine environmental data were

available. The first seven days post-release were also excluded, resulting in a total of 16

measurement days for each individual tagged on March 31, 2016, and 12 measurement

days for individuals tagged on May 1, 2016.

Analysis focussed on the active period of the fish’s circadian cycle, which, from

qualitative visual assessment of the data, appeared to be during the daytime period. The

active period was chosen as this is the time when ecologically-important processes, such

as foraging and predator avoidance, are most likely to occur. To test whether this was

statistically apparent in the data, a representative receiver station, containing a

continually high presence and residency of fish, station SCAA12, was selected. From

this station, two representative days, containing continuous activity readings were

selected, one with hypoxia present, and the other with hypoxia absent (16 May and 11

July, respectively). Recordings were binned by the hour of the day to which they

belonged and a generalised mixed model (GAM), from the mgcv library (Wood, 2006)

in R (R Core Team, 2010) was then fitted to the data. A cyclic smoother (the “cc”

smoother) was implemented to account for the circular nature of hours in the day. This

analysis showed a marked increase in activity during the daytime period, with ‘daytime’

defined as the diurnal period between astronomical twilights (~ 05:50am – 07:20pm).

GAM analysis was appropriate as it allows for non-linearity in the data, which would be

expected to be present in the analysis of circadian patterns (Gleiss et al., 2013, Gleiss et

al., 2016).

The dataset was then subsetted to only include those measurements recorded during the

daytime period. For each period, the mean and maximum depth and activity of each

individual fish, and the receiver(s) at which individual fish were detected, were

36

determined. These were considered to be appropriate response variables as the point-

sampling nature of the environmental data prevented finer-scale analysis of the effects

of temperature and dissolved oxygen from being performed. The mean daily depth of

each fish and the station at which they were residing was then compared to DPaW’s

environmental dataset to estimate the temperature and dissolved oxygen saturation

which each fish was mostly exposed to on each day. The lowest dissolved oxygen

recorded daily at the bream’s location was also considered, to estimate their potential

exposure to hypoxia. A Chi-square test of independence was performed to determine if

the oxygen saturations inhabited by black bream varied from the overall frequency of

oxygen saturations measured by DPaW throughout the study period, with DO

saturations binned into 10% bands.

Generalised additive mixed models (GAMM) were then used to analyse the effects of

temperature and oxygen on activity and depth. This analysis allowed not only for non-

linearity, but for individual fish to be included as random effects, mitigating the risk of

pseudo-replication. For activity GAMMs, all daily mean and maximum activity values

for each fish were included and smoothed by temperature and the oxygen saturation of

their mean depth. For depth GAMMS, all daily mean and maximum depth values for

each fish were included and smoothed by temperature and the lowest oxygen saturation

of the water column. GAMMs were produced in the mgcv library (Wood, 2006) in R (R

Core Team, 2010). Best-fit models were determined with modified Akaike’s

Information Criterion (AICc), with model exhibiting the lowest AICc deemed best.

3 Results

3.1 Respirometry

3.1.1 MMR and SMR

37

MMR readings always occurred near the start of the experiment, generally within two

hours of the cessation of chase protocols, with metabolic rates approaching SMR after

10 hours (Figure 11). After 10 hours, oxygen consumption remained relatively stable,

with slight, sporadic increases indicative of minor bouts of activity. The total number of

individuals from which reliable data could be extracted varied by temperature and

oxygen treatments (Table 1).

Table 1: Number of individual fish for which SMR, MMR and aerobic scope could be determined across all measured temperatures and dissolved oxygen saturations.

Temp

(°C)

SMR MMR Aerobic Scope

Normoxia 60% DO 30% DO Normoxia 60% DO 30% DO

21 2 3 3 3 2 2 2

24 5 6 6 6 5 5 5

27 4 8 3 - 4 3 -

30 5 6 3 - 5 3 -

Oxygen saturation was found to have a significant effect on MMR (F = 81.229, df = 2, p

< 0.001), with MMRs decreasing with decreasing oxygen availability (Figure 12).

Multiple comparison results at 21 and 24°C are presented in Tables 2 and 3. The only

50

100

150

200

250

0 5 10 15 20 25

MO

2

Time (hours)

Figure 11: MO2 traces of two bream throughout a 27°C experiment. MO2 can be seen to maximise immediately following a chase protocol, before settling over time as the fishes reached SMR. The patterns exhibited here were typical of the other fish tested.

38

situations where MMR was not found to differ significantly between oxygen saturations

was between normoxia and 60% DO, at both 21 and 27°C (p > 0.05). At 27 and 30°C,

where MMR at 30% DO was not measured, MMR at 60% DO was significantly higher

than SMR (p < 0.05). At both temperatures that MMR at 30% DO was measured, it was

statistically indistinguishable from SMR (p > 0.05, Table 4).

The metabolic response of fishes, at any given oxygen saturation, varied between

temperatures (Figure 12). MMR in normoxia displayed a shallowly increasing trend

with temperature between 21 and 30°C (Q10 = 1.23), with MMR at 21°C significantly

lower than at both 24 and 30°C (p <0.05). There was a decline at 27°C (Figure 12);

however, this is likely the result of impaired acclimation period. MMR at 60% DO was

significantly higher at 21°C than all other temperatures (p < 0.05). However, the rate of

decrease in MO2 was very low (Q10 = 0.98). MMR at 30% DO displayed a slightly

increasing trend with temperature (Q10 = 1.54), however, this was not significant (p >

0.05). Similarly, despite SMR also displaying an increasing trend with temperature (Q10

= 1.53, Figure 12), it was not found to vary significantly between any temperatures (p >

0.05, Table 4).

0

50

100

150

200

250

300

350

20 22 24 26 28 30

MO

2 (

mgO

2/k

g/h

r)

Temperature (˚C)

Figure 12: Effect of temperature on oxygen consumption. Data presented shows MMR at >80% DO (blue), 60% DO (grey) and 30% DO (yellow), as well as mean SMR (orange). All values are presented as means ± standard deviation.

39

Table 2: Effect of oxygen saturation on MMR at 21°C. Significant differences, as determined by Tukey post-hoc comparison of means results of lmer analysis are displayed.

>80% 60% 30%

80% - **

60% **

30%

p > 0.05 = -; p < 0.05 = *; p < 0.01 = **

Table 3: Effect of oxygen saturation on MMR at 24°C. Significant differences, as determined by Tukey post-hoc comparison of means results of lmer analysis are displayed.

80% 60% 30%

80% ** **

60% **

30%

p > 0.05 = -; p < 0.05 = *; p < 0.01 = **

Table 4: Difference between SMR and MMR at each tested temperature. SMR results are compared to MMR values elicited at each oxygen saturation measured (>80%, 60% and 30% DO). Significant results, as indicated by Tukey multiple comparison of means post-hoc tests, are presented.

MMR

21°C 24°C 27°C 30°C

>80 60 30 >80 60 30 >80 60 >80 60

SMR

21°C ** ** -

24°C ** ** -

27°C ** **

30°C ** **

p > 0.05 = -; p < 0.05 = *; p < 0.01 = **

Table 5: Effect of temperature on SMR. Tukey multiple comparison of means post-hoc tests revealed no significant inter-temperature variation.

21°C 24°C 27°C 30°C

21°C - - -

24°C - -

27°C -

30°C

p > 0.05 = -; p < 0.05 = *; p < 0.01 = **

3.1.2 Aerobic scope

40

AS was unaffected by temperature, with no significant difference observed between

temperatures; at any given oxygen saturation (Figure 13). The Q10 values of AS in

normoxia, 60% DO and 30% DO were 1.08, 0.83 and 0.30, respectively. Overall, AS

was found to be significantly affected by oxygen saturation within temperatures (F =

89.765, df = 2, p < 0.01). At 21, 24 and 30°C, Tukey post-hoc analyses revealed

significant declines between all measured oxygen saturations (p < 0.05; multiple

comparisons tables for 21 and 24°C presented in tables 6 and 7, respectively). However,

at 27°C, AS at normoxia did not vary from that at 60% DO (p > 0.05).

Table 6: Effect of oxygen saturation on aerobic scope at 21°C. Significant differences, as determined by Tukey post-hoc comparison of means results of lmer analysis are displayed.

>80% 60% 30%

80% ** **

60% **

30%

p > 0.05 = -; p < 0.05 = *; p < 0.01 = **

-50

0

50

100

150

200

250

20 22 24 26 28 30

Aero

bic

Scope (

mgO

2/k

g/h

r)

Temperature (˚C)

Figure 13: Effect of temperature on aerobic scope. Data presented show aerobic scope at >80% DO (blue), 60% DO (grey) and 30% DO (yellow).

41

Table 7: Effect of oxygen saturation on aerobic scope at 24°C. Significant differences, as determined by Tukey post-hoc comparison of means are displayed.

80% 60% 30%

80% ** **

60% **

30%

p > 0.05 = -; p < 0.05 = *; p < 0.01 = **

3.2 Acoustic telemetry

Of the 25 black bream tagged during the study, 72% were still transmitting data after

four months. A total of 190,800 activity and 191,188 depth transmissions were recorded

throughout the study period, with an average of 15273 per fish.

Surgical operations lasted a mean time of 6.35 minutes, in addition to the anaesthetising

and recovery periods (x anaesthesia time = 6.03 minutes, x recovery time = 8.98

minutes; Table 8). A. butcheri tagged on March 31, 2016 and May 1, 2016 were

monitored for a total of 116 and 85 days, respectively. Environmental data sampling

was undertaken on a weekly basis, resulting in a total of 16 measurement days for each

individual tagged on March 31, 2016, and 12 measurement days for individuals tagged

on May 1, 2016.

3.2.1 Environmental conditions

Dissolved oxygen saturations were highly stratified within the water column throughout

the study period between April and June, decreasing in frequency and extent with the

onset of winter (Figures 15, 16 and 17). During the same period, water temperature also

0

20

40

60

80

100

10

12

14

16

18

20

22

24

31/Mar/16 10/May/16 19/Jun/16 29/Jul/16

Min

imum

oxygen

satu

ratio

n (%

)

Tem

pera

ture

(°C

)

Time

Figure 14: Change in water temperature (red) and minimum dissolved oxygen saturation (blue) at all stations at which bream were present over the course of the study period. Each dot represents an individual station.

42

fell markedly, from 22.2°C in early April to 10.1°C in early July (Figure 14).

43

Table 8: Summary of 25 acoustic tag surgical implantation procedures, performed on 31 March 2016 and 1 May 2016.

Date Time Fish ID Length (mm) Weight (g) Tagging Time (min:sec) AQUI-S Concentration

Caught Released Fork Total Anaesthesia Surgery Recovery

31-03-16 12:40pm 2:00pm 30 279 302 456 4:10 5:41 5:00 2ml/50L

31-03-16 4.30pm 6:31pm 48 265 302 454 6:00 7:00 6:30 3.5ml/60L

31-03-16 4.30pm 7:00pm 50 284 315 508 5:05 5:50 7:20 3.5ml/60L

31-03-16 4.30pm 7:00pm 52 272 310 467 3:45 9:05 8:00 3.5ml/60L

31-03-16 4.30pm 7:00pm 54 273 306 455 4:00 7:00 6:30 3.5ml/60L

31-03-16 12:40pm 3:43pm 64 311 343 684 6:30 11:30 6:45 3ml/50L

31-03-16 12:40pm 4:51pm 66 291 323 541 4:00 5:45 8:07 3.5ml/60L

31-03-16 12:40pm 4:56pm 68 278 305 465 4:15 4:50 9:30 3.5ml/60L

31-03-16 4.30pm 6:05pm 70 282 310 497 5:33 11:32 11:50 3.5ml/60L

31-03-16 12:40pm 4:40pm 72 318 344 724 6:30 19:50 20:20 3.5ml/60L

01-05-16 7am-2pm 4:57pm 24 325 350 720 6:15 4:35 N/A 5.0ml/50L

01-05-16 7am-2pm 4:56pm 26 290 320 505 6:05 4:25 N/A 5.0ml/50L

01-05-16 7am-2pm 3:17pm 28 320 350 679 5:30 4:40 N/A 4.5ml/50L

01-05-16 7am-2pm 4:24pm 32 300 330 530 9:30 4:25 N/A 5.0ml/50L

01-05-16 7am-2pm 2:47pm 34 345 390 860 14:00 5:50 N/A 4.5ml/50L

01-05-16 7am-2pm 2:25pm 36 320 355 725 6:00 6:05 N/A 3.5ml/50L

01-05-16 7am-2pm 2:57pm 38 290 330 594 6:30 4:20 N/A 4.5ml/50L

01-05-16 7am-2pm 4:00pm 40 310 340 630 6:00 4:25 N/A 4.5ml/50L

01-05-16 7am-2pm 4:34pm 42 290 315 542 6:00 4:20 N/A 5.0ml/50L

01-05-16 7am-2pm 3:37pm 44 300 330 595 4:00 4:25 N/A 4.5ml/50L

01-05-16 7am-2pm 2:54pm 46 350 375 879 6:20 5:05 N/A 4.5ml/50L

01-05-16 7am-2pm 4:11pm 56 290 315 502 6:30 4:55 N/A 4.5ml/50L

01-05-16 7am-2pm 3:52pm 58 290 310 415 5:45 4:30 N/A 4.5ml/50L

01-05-16 7am-2pm 3:15pm 60 345 380 858 6:05 4:21 N/A 4.5ml/50L

01-05-16 7am-2pm 3:28pm 62 345 390 952 6:40 4:40 N/A 4.5ml/50L

44

Figure 15: Weekly vertical plots of the Swan River Estuary, displaying the distribution of dissolved oxygen throughout the water column; 11 April - 9 May, 2016.

45

46

Figure 16: Weekly vertical plots of the Swan River Estuary, displaying the distribution of dissolved oxygen throughout the water column; 16 May – 13 June, 2016.

47

Figure 17: Weekly vertical plots of the Swan River Estuary, displaying the distribution of dissolved oxygen throughout the water column; 20 June – 25 July, 2016.

48

3.2.2 Circadian cycles

Circadian cycles of both activity and depth were apparent through qualitative visual

inspection of the total raw data, with both activity and depth increasing during daytime

hours (Figure 18). GAM analysis of activity measurements, binned and smoothed by

hour of the day, across two days of activity measurements, representative of hypoxic

0

0.5

1

1.5

2

2.5

3

3.5

4

0

0.5

1

1.5

2

2.5

3

3.5

4

16/6 12:00 AM 17/6 12:00 AM 18/6 12:00 AM 19/6 12:00 AM 20/6 12:00 AM

Depth

(m)

Activity

(m/s

2)

Time

Acceleration

Depth

Figure 188: Activity (light blue) and depth (dark blue) of all black bream present at one station (SCAA12) over four days (16-20 June, 2016) representative of the study period. Distinct peaks in both activity and depth can be seen during daytime periods. Conditions were normoxic during this period.

Figure 19: Fitted generalised mixed model of circadian activity patterns in A. butcheri (solid line), with confidence intervals (dashed lines), smoothed by hour of day and accounting for the presence or absence of hypoxia. The GAM fitted to two days of data (16 May and 11 July, 2016) at station SCAA12, which respectively, exhibited hypoxic and normoxic conditions. Results indicate an active period between the hours of 0800 and 1600 hours, with minimum activity occurring around 0130.

49

and normoxic conditions also indicated that activity was maximised during daytime

hours (Figure 19).

3.2.3 Activity

Neither the oxygen saturation of the bream’s mean depth nor temperature, significantly

explained daytime activity patterns (p > 0.05, Table 9). The smoothed GAMMs

highlights these non-significant trends and reveal large confidence intervals, indicating

much unexplained variation within the data (Figure 20).

Figure 19: Generalised additive mixed models for the effects of ambient oxygen and temperature on maximum (a, b) and mean (c, d) activity, with confidence intervals. Neither changes in oxygen nor temperature were found to significantly influence black bream activity.

50

Table 9: Model selection criteria of the fitted GAMMs presented in Figure 20. All models include individual as a random factor. All maximum activity models also included a smoothing factor accounting for differences in the number of daily activity observations. Best-fit model is indicated with italics. Neither mean nor maximum activity showed any significant interaction terms, indicating that activity patterns are not affected by variation in chosen oxygen saturation or temperature.

Model f Intercept AICc ∆AICc p (smoother)

Maximum Activity

~s(Chosen O2) + s(Temperature) 9 2.46 348 7

~s(Temperature) 7 2.46 345 4 0.254

~s(Chosen O2) 7 2.46 345 4 0.314

Mean Activity

~s(Chosen O2) + s(Temperature) 7 0.95 137 3

~s(Temperature) 5 0.94 135 1 0.665

~s(Chosen O2) 5 0.95 134 0 0.09

3.2.4 Depth

Black bream more frequently occupied higher oxygen saturations than were evident

across the range of observed ambient conditions throughout the study period (χ2 =

18.30, df = 10, p < 0.0001; Figure 21). For example, 83% of fish occurrences were at

depths containing normoxic conditions, whilst in contrast, the minimum DO recorded in

sites inhabited by bream was below normoxic 79.9% of the time. Similarly, only one

fish, on one occasion, recorded a daily mean depth containing < 30% DO, despite the

minimum DO being below 30%, 59% of the time.

51

Both maximum and mean daily depth reached by bream increased significantly with

reduced hypoxia (Figure 22 a,c). Figure 23 provides a representative example of the

significant influence of hypoxia on mean depth at a representative station, SCAA12, on

two days, characterised respectively by significant hypoxia and complete normoxia.

0

5

10

15

20

25

30

35

40

0 10 20 30 40 50 60 70 80 90 100 More

Fre

quency (

%)

Oxygen Saturation (%)

Figure 20: Frequency distributions of the total available DO throughout the study period (blue) and the DO the mean daily depths of A. butcheri (orange).

52

Depth was also significantly influenced by temperature, with both maximum and mean

depths significantly decreasing with increased temperature (Figure 22 b, d; Table 8).

Table 10: Model selection criteria of the fitted GAMMs presented in Figure 22. Best-fit model is indicated with italics, and significant interaction terms indicated in bold type.

Model df Intercept AICc ∆AICc p (smoother)

Maximum Depth

~s(Lowest O2) +

s(Temperature)

7 1.19 445 1

~s(Temperature) 5 1.18 444 0 <0.001

~s(Lowest O2) 5 1.19 445 1 <0.01

Mean Depth

~s(Lowest O2) +

s(Temperature)

7 0.37 204 0

~s(Temperature) 5 0.36 207 3 0.001

~s(Lowest O2) 5 0.37 211 7 0.006

Figure 21: Generalised additive mixed models for the effects of minimum oxygen and temperature on maximum (a, b) and mean (c, d) depth. Both mean and maximum depth increased significantly with increases in the minimum O2 saturation of the water column. Depth is also shown to have significantly decreased with increasing water temperature.

53

4 Discussion

4.1 Effect of temperature on aerobic scope

This study revealed no effect of temperature on the aerobic scope of A. butcheri

between 21 and 30°C, with the Q10 values exhibited by AS, SMR and MMR far below

what is expected of an ectothermic organism. Typically, the rate of energetic

consumption would be expected to increase two to three fold with a 10°C rise in

temperature (Brett and Groves, 1979), however, the highest rate of change elicited in A.

butcheri was a mere 1.54. Such a broad and stable thermal tolerance window response

is unusual among fishes and is indicative of an exceptionally eurythermal species

(Farrell, 2016). Unfortunately, it also made it impossible to quantify its pejus

temperatures using the temperature range it was tested under, and, therefore, it is

uncertain whether the observed AS represents the maximum AS of the species.

However, this may be speculated within the context of the two leading thermal

metabolism frameworks, the OCLTT and MPMO hypotheses, taking into account the

ecological relevance of the experimental temperatures. For context, experiments were

conducted in the mid - upper range of ecological relevance in A. butcheri, which may be

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

0 6 12 18 24

Depth

(m

)

Time of Day (24 Hour Time)

Figure 22: Mean hourly depth of all tagged bream at one receiver station (SCAA12) across two representative days, one which was characterised by significant hypoxia (red; 16 May 2016) and another where the entire water column was normoxic (blue; 11 July 2016).

54

expected to encounter a temperature range of ~10 – 30°C annually in the Swan River

Estuary (Stephens and Imberger, 1996). Additionally, it has been found to exhibit an

upper lethal threshold of ~33 - 34°C (Weng, 1971).

Following the OCLTT hypothesis, it would be expected that AS would follow a roughly

bell-shaped curve, maximising at the fishes’ optimal thermal window and reducing to

upper and lower critical thresholds (Pörtner and Knust, 2007, Pörtner and Farrell, 2008).

As such, the stable tolerance window observed between 21 and 30°C may be taken as

representing the maximum AS of the species, which could potentially extend further in

either direction (Eliason et al., 2011, Farrell, 2016). However, the OCLTT hypothesis

places critical importance on the role of AS in determining a fish’s upper lethal bounds

(Pörtner and Knust, 2007, Pörtner and Farrell, 2008), and as such, between 30°C and

their upper thermal threshold of 33°C, aerobic scope would be expected to fall

dramatically (Pörtner and Farrell, 2008, Farrell, 2016). Whilst this is theoretically

possible through a rapid increase in SMR, it is unlikely, as SMR has displayed no

significant increase between 21 and 30°C. Additionally, this would result in a left-

skewed distribution of aerobic scope, in stark contrast to the bell-shaped curves

predicted by OCLTT (Pörtner and Knust, 2007, Pörtner and Farrell, 2008). More

plausible is the hypothesis that AS in A. butcheri is maximised close to the species’

upper lethal thermal threshold, thereby not following the expectations of OCLTT (Fry,

1947, Clark et al., 2013a, Norin et al., 2014). This phenomenon has been documented in

freshwater (Fry, 1947), estuarine (Norin et al., 2014) and oceanic (Gräns et al., 2014)

species, and fits the criteria of Clark et al.’s (2013) MPMO hypothesis, whereby the

optimal temperature for aerobic scope is independent of whole-organism optimal

temperature. However, MPMO has also been founded upon the hypothesis that AS will

continually increase with increasing temperature (Clark et al., 2013a), which was not

found to be the case in this study.

55

Although it is rare that a fish exhibits a broad and relatively stable optimum for aerobic

scope, it is not without precedence (Eliason et al., 2011, Eliason and Farrell, 2016).

Eliason et al. (2011) discovered that the Chilko population of sockeye salmon

(Oncorhynchus nerka) in the Fraser River, Canada, displayed high (> 90% maximum)

AS consistently across an ~10°C range of temperature. In fact, the fish’s optimal

thermal window was so broad that the authors considered that it may act as a

“superfish” if faced with a warming environment (Eliason et al., 2011). On either side

beyond this broad thermal optimum, the AS of Chilko sockeye salmon reduced in

accordance with the OCLTT hypothesis (Eliason et al., 2011). Our results are very

similar to those elicited by that study and whilst it may be unlikely that AS in A.

butcheri will reduce above 30°C following the OCLTT hypothesis, it is not

unreasonable to believe that such a broad thermal optimum is also possible to exist

under the MPMO framework.

4.2 Effect of oxygen on aerobic scope

Whilst temperature had no discernible effect on metabolic output, the effect of oxygen

saturation was clear and marked, with MMR, and subsequently aerobic scope, falling at

all temperatures with decreasing oxygen saturation. This is consistent with the

expectations of both the OCLTT and MPMO hypotheses, and the many empirical

studies upon which they are based (Pörtner and Knust, 2007, Pörtner and Farrell, 2008,

Clark et al., 2013a, Claireaux and Chabot, 2016, Farrell, 2016).

The reduction in MMR with reducing oxygen fitted well within the limiting oxygen

level framework (Figure 24) (Fry, 1947, Fry, 1971, Claireaux and Chabot, 2016). At all

temperatures, AS was maximised in normoxia, with fishes at 24 and 30°C also

exhibiting maximum AS at 60% DO. However, at 21 and 27°C, MMR at 60% DO was

significantly lower than in normoxia, suggesting that in these individuals, the limiting

oxygen level had been reached. Limiting oxygen levels are characterised as the oxygen

56

saturations below which physiological adjustments alone are incapable of maintaining

maximal aerobic scope, and as such represent the point at which oxygen stress begins to

impact upon the metabolic capacity of fish (Claireaux and Chabot, 2016). Such

physiological adjustments may include increased gill ventilation frequencies, and

perfusion of the gills and other vital organs (Claireaux and Chabot, 2016). Below an

organism’s limiting oxygen level, metabolic rate becomes oxygen dependent and, in

theory, should continually reduce with reducing oxygen availability (Claireaux and

Chabot, 2016). This pattern was confirmed in A. butcheri, with AS at 30% DO

significantly lower than at 60% DO. Interestingly, the fish reached their critical oxygen

limit, the minimum saturation required to maintain SMR to the exclusion of all other

energetic functions, at 30% DO. Indeed, several individuals at 24°C displayed negative

aerobic scope at 30% DO (Figure 13), indicating that they had already passed their

critical oxygen threshold and were unable to reach SMR. Instead, those individuals

were reliant upon anaerobic ATP production to maintain short-term survival (Claireaux

and Chabot, 2016). This signifies a low tolerance to hypoxia and is likely explained by

the relatively-low historic frequency of hypoxia in their natal Swan River Estuary. Prior

to river mouth dredging, salt wedge extension, and anthropogenically-enhanced

eutrophication, hypoxia is likely to have exerted less evolutionary pressure on black

bream than it has over the past century (Cottingham et al., 2016). This is a short time

span in an evolutionary sense, over which it is unlikely that population-level

improvements in hypoxia tolerance will occur, especially in a long-lived species such as

black bream (Rijnsdorp et al., 2009). Such hypoxia tolerance is possible and exhibited

by many estuarine and freshwater teleosts which have adapted over long periods of time

(Chapman et al., 2002, Nilsson and Renshaw, 2004, Collins et al., 2013).

4.3 Aerobic scope and growth

57

Aerobic scope represents the net energy available for apportioning between the

physiological functions of an organism, above and beyond the energy required to simply

sustain survival (Farrell, 2016). As such, the declines in aerobic scope observed in

response to declining oxygen availability in A. butcheri are likely to translate into

reductions in both physiological performance and growth capacity (Clark et al., 2013b,

Pörtner and Giomi, 2013, Farrell, 2016). My results indicate that the energetic capacity

for growth in A. butcheri is reduced below ~60% DO, and falls to zero at 30% DO. This

provides empirical support for Cottingham et al.’s (201 ) hypothesis that an increase in

the extent of hypoxia within the Swan River Estuary has driven recent declines in wild

A. butcheri growth rates. Additionally, the fact that this study has found no influence of

temperature on the aerobic scope of A. butcheri also provides a physiological basis for

their hypothesis that temperature is not always a key driver of growth capacity in the

Swan River Estuary population (Cottingham et al., 2016). This is unexpected in an

ectothermic species, which should, under the metabolic theory of ecology, display an

increase in growth rates with increasing temperatures (Brown et al., 2004). However,

this study is not isolated, with a negative correlation between growth and temperature

having also been observed in Tasmanian populations of A. butcheri (Doubleday et al.,

2015). Together, these studies provide evidence that declines in aerobic scope due to

non-thermal parameters, such as oxygen, have the capacity to override the expected

positive relationship between temperature and growth in ectothermic organisms

(Doubleday et al., 2015, Cottingham et al., 2016).

58

4.4 Circadian rhythms

This is the first study to quantify activity patterns in A. butcheri directly using activity

transmitters. A. butcheri in the Swan River Estuary were found to display distinct

circadian rhythms, with an active period concentrated during daylight hours, followed

by reduced activity at night. This was also synchronised to diel vertical movements

(DVM) throughout the water column, with fish spending daytime in relatively deep

water, and ascending into the shallows at night. Circadian rhythms are common

amongst organisms, and have previously been documented in other sparid species

(Hartill et al., 2004, Payne et al., 2013) and eastern populations of A. butcheri (Hindell

et al., 2008, Sakabe and Lyle, 2010), making it unsurprising that such patterns were

elicited by A. butcheri in the Swan River Estuary. However, the timing of hypothesised

peak activity in A. butcheri has varied between previous studies, with Sakabe and Lyle

(2010) observing diurnal peaks in activity and Hindell et al. (2007, 2008) hypothesising

that A. butcheri actively forage at night. Both of those studies were based upon acoustic

tracking records, which only recorded the location of individuals. Our findings suggest

50

100

150

200

250

300

20 30 40 50 60 70 80 90 100

MO

2 (

mgO

2/k

g/h

r)

Oxygen saturation (%DO)

Figure 23: Limiting-oxygen-level (LOL) curve, of the change in MO2 with reducing oxygen availability at 24C (orange) and 21C (blue))) ±standard deviation. Dashed trend lines indicate changes in MMR, whilst the lower solid lines indicate temperature-dependent SMR.

59

that the diurnal period is used for active foraging at depth by A. butcheri in the Swan

River Estuary, followed by inactive rest in shallow water at night, likely in crags and

snags lining the bank (Watsham, 2016). This provides multiple ecological benefits to A.

butcheri. Firstly, as visual predators, foraging during daylight hours is likely to increase

their likelihood of hunting success, and secondly, by foraging in comparatively deep

habitats, A. butcheri are able to minimise the risk of predation imposed by avian

predators such as pelicans and cormorants. However, this is not to say that the

hypothesis of nocturnal activity in A. butcheri in the Gippsland Lakes, Victoria is

incorrect (Hindell, 2007, Hindell et al., 2008). Although not observed in my study, the

diel activity rhythms in a closely related estuarine sparid, Acanthopagrus australis have

recently been shown to switch from diurnal to nocturnal, in response to rainfall (Payne

et al., 2013). As such, it stands to reason that, should trade-offs between predation risk

and foraging efficiency favour nocturnal activity, A. butcheri populations may also

display the capacity to perform diel reversals. Moreover, the current study was only

conducted over a relatively short time frame and the additional data that will be

gathered over the life of the current tags (i.e. an entire year) may reveal temporal

variations in the circadian rhythms of the species that were elucidated here.

4.5 Hypoxia

One of the most striking results of this study was the effect of hypoxia on the diel

vertical movements of A. butcheri. Throughout the monitoring period, black bream

actively avoided hypoxic statra of the water column, concentrating in normoxic

microhabitats near the water surface. Then, as the benthic hypoxia subsided with the

onset of winter, the bream extended their DVMs into deeper, previously inaccessible

habitats. Such nearshore shoaling behaviour in response to hypoxia was also observed

by Cottingham et al. (2014) and provides a textbook example of hypoxia-based habitat

compression (Eby and Crowder, 2002, Prince and Goodyear, 2006, Stramma et al.,

60

2012). This behaviour allows wild A. butcheri to avoid the deleterious effects that

hypoxia imposes on their aerobic scope, and ensures maximum functionality of their

physiological systems. However, it also exposes the bream to increased risks of

predation from avian predators and recreational fishing, as well as the risk of negative

density-dependent processes such as increased competition for food and shelter and

increased prevalence of disease and parasitism (Hixon and Carr, 1997, Rose et al., 2001,

Cottingham et al., 2014). Such density-dependent factors have previously been

implicated in reducing the growth rate of A. butcheri in the Swan River Estuary

(Cottingham et al., 2014, Cottingham et al., 2016). This provides quantitative evidence

that the reductions in aerobic scope associated with hypoxia in the laboratory directly

impacts upon the ecology of A. butcheri in its estuarine habitat.

There was also a significant effect of temperature on DVM, with black bream entering

deeper waters as the temperatures they were exposed to fell. However, this is likely to

be the result of covariance in the environmental data, with lower temperatures

simultaneously correlating with a reduced prevalence of benthic hypoxia, simply due to

seasonal change from autumn to winter and the fact that the dissolution of oxygen in

water increases with lowered temperatures. Given the minimal influence exhibited by

temperature on aerobic scope and the high influence of oxygen saturation, and in

conjunction with Cottingham et al.’s (2014, 201 ) correlations between growth rate and

hypoxia, it is parsimonious to consider that it was reductions in benthic hypoxia, not

reductions in water temperature, which permitted deeper DVMs by A. butcheri.

4.6 Activity

Aerobic scope is also predicted to have a significant influence on activity, by

determining the amount of energy allocable to movement (Gleiss et al., 2010, Metcalfe

et al., 2016). As such, it was hypothesised that the activity of wild bream would increase

with both temperature and oxygen availability. However, neither variable displayed any

61

significant correlation with activity. In regards to temperature, the bream maintained

stable activity throughout the study period, despite temperatures falling from 22 to

10°C. This is likely to once again be a product of the black bream’s remarkable

eurythermy and provides evidence that the aerobic scope of A. butcheri is unlikely to

significantly decline with temperature beneath those temperatures quantified in this

experiment.

Given the intolerance of A. butcheri to hypoxia, it was expected that reductions in

oxygen saturation would significantly reduce the activity rates of wild bream. That no

effect was found in this study is very likely to have been caused by the bream’s

predisposition to avoid hypoxic waters. Throughout the study period, only one fish, on

one day, was situated within hypoxic strata, whilst on almost 80% of occasions, the

daily mean depth of individual fish was in normoxic waters. As such, the vast majority

of daily measurements were representative of fish in microhabitats of >70% DO, which,

being higher than the limiting oxygen level of A. butcheri, was unlikely to have any

impact on energetic processes such as swimming performance (Claireaux and Chabot,

2016). Visually, activity does appear to decline drastically below 50% DO (Figure 20c),

however, the low sample sizes inhibit confident analysis. Therefore, given the high

reductions in aerobic scope associated with hypoxia, it is hypothesised that with a

greater distribution of sample sizes, hypoxic conditions will be found to significantly

reduce the activity patterns of wild A. butcheri.

4.7 Management implications

This study indicated that an increase in the extent, frequency and/or intensity of hypoxic

episodes is likely to seriously impact upon A. butcheri populations in the Swan River

Estuary. These impacts will largely be the result of habitat compression into shallow

surface waters, and the negative density-dependent effects which this may exert upon

growth rates as suggested by Cottingham et al. (2014, 2016). The fish are likely to

62

receive significant benefit from the two artificial oxygenation plants installed within the

upper Swan River Estuary, which provide normoxic refuges during periods of hypoxia

(Tweedley and Hallett, 2014), and would help maintain the growth capacity in the

species. As such, it is recommended that these oxygenation plants continue to be

operated and the prevalence of hypoxia continue to be closely monitored throughout the

Swan River Estuary. However, these plants are a bandage solution, and for sustainable

management into the future, it is imperative to work towards reducing the excessive

nutrient inputs currently entering the Swan River Estuary from surrounding urban and

agricultural land use (Kristiana et al., 2012, Adolf et al., 2015). These inputs, especially

nitrogen and phosphorous, greatly increase the risk of eutrophication and phytoplankton

blooms and are a leading cause of fish kill events within the estuary (Kristiana et al.,

2012). They are also likely to exacerbate the effects of climate change which, through

reducing stream flows, is predicted to increase the extent and duration of water column

stratification, leading to further expansion of benthic hypoxia within the system

(Davies, 2010). Therefore, it is imperative that work continues to reduce the influx of

nitrogen and phosphorous into the Swan River Estuary system. This will provide a

longer-term, sustainable improvement in both the health of the estuary ecosystem and

the viability of its estuarine fish populations.

4.8 Limitations and future recommendations

There were several limitations to this study and there is great potential to increase the

breadth of the data. Firstly, in regards to the respirometry experiments, further testing

both above- and below the temperatures measured here, are highly recommended, to

allow for the determination of pejus and critical temperatures in A. butcheri and provide

a more concrete assessment of whether the physiological responses of the fish to

temperature follows the expectations of the OCLTT or MPMO hypotheses. Further

63

testing at the temperatures already quantified in this study may also be considered, to

increase the statistical power of analyses.

There was also evidence observed of potential individual behavioural syndromes in A.

butcheri. These included propensity to perform active movements, as well as consistent

differences in the depth at which individuals typically chose to occupy within their

home tank. All fishes were healthy, however, some were continually observed to rest on

the floor of the tank, whilst others rarely stopped moving through the midwater. The

highly active fishes were less inclined to settle within the respirometry chambers and

occasionally did not settle to reach SMR throughout the experiments. Overall, the static

respirometry setup was appropriate for most bream, as they were unlikely to swim

against the flume of a swim tunnel. However, future respirometry experiments should

consider using a combination of both static and swim tunnel respirometers, depending

upon the behavioural patterns of individual fish. Additionally, it would be interesting to

test whether such behavioural syndromes drive patterns of activity and movement in

wild bream as this has rarely been quantified in fishes (Dingemanse et al., 2007, Conrad

et al., 2011).

With regards to acoustic telemetry, whilst this study was effective at quantifying the

effects of temperature and oxygen on patterns of activity and depth, it covered only a

small portion of the annual variation which A. butcheri would typically experience in

the Swan River Estuary. As the movement dynamics of the species has previously been

found to vary between seasons (Hindell, 2007, Hindell et al., 2008, Sakabe and Lyle,

2010) and given the tag battery life is ~one year, this should continue to be monitored to

allow seasonal variation in circadian rhythms, activity, and DVMs to be determined.

Furthermore, this study has focussed solely on mature fish and, as such, the

physiological impacts of temperature and oxygen on juvenile and larvae bream remain

64

unknown. Quantification of this is recommended to allow for a better understanding of

how the thermal and hypoxic tolerances of A. butcheri are influenced by ontogeny.

One of the largest limitations of the study was the limited access to environmental data

in the Swan River Estuary. The weekly point sampling nature of the data collection not

only limits the activity and depth analyses to means and maxima on sampling days but

also, due to inconsistent timing of sampling, may bias the observed environmental

readings. For example, the intensity and vertical distribution of both temperature and

hypoxia are known to cycle in estuaries on a diel basis (Beck and Bruland, 2000, Tyler

et al., 2009). Therefore, in theory, by collecting the environmental samples of each

station at different times of the day, the observed temperature and hypoxia values may

be a product of this time of day and not comparable between stations. However, the

overall risk of this was deemed to be low, as temperature variation between stations on

any given day was small and most bream remained well within the upper estuary for the

duration of the study period, where the influence of tidal movements is dampened

(Watsham, 2016). In saying this, the issue may be resolved through the placing of data

loggers at each station to provide a continuous environmental dataset. Diel cycling of

hypoxia has previously been shown to influence the movement patterns of estuarine fish

(Tyler and Targett, 2007, Brady et al., 2009), and, given the propensity of A. butcheri to

both avoid hypoxia and perform DVMs, research into whether diel cycling of hypoxia

in the Swan River Estuary influences the activity and depth of the species is both well-

justified and recommended.

Caution should be taken if attempting to extrapolate these results into A. butcheri

populations beyond the Swan River Estuary. This is exemplified by studies on Canada’s

sockeye salmon (O. nerka), where the aerobic scope and thermal tolerances of

individuals not only varied between rivers but also between populations within rivers

(Farrell et al., 2008, Eliason et al., 2011, Eliason and Farrell, 2016). This was attributed

65

to a combination of local variation in water temperatures and a lack of genetic

migration, resulting in each population becoming isolated and adopting independent

evolutionary trajectories (Eliason and Farrell, 2016). This situation is mirrored

strikingly by A. butcheri, with each estuarine population essentially isolated from one

another, and known to display significant variation in critical ecological parameters

such as growth rate, fecundity, and size at maturity (Sarre and Potter, 1999, Sarre and

Potter, 2000, Doubleday et al., 2015). As such, it is also likely that the relative

importance of hypoxia and temperature to aerobic scope also varies between estuaries,

with the only reliable method of confirming this being additional experiments upon

individuals from a range of populations. Such experiments would also permit

quantification of the adaptive isolation of populations, which may be important for

determining source populations for future restocking programs.

5 Conclusion

Temperature and oxygen availability are fundamental environmental variables

controlling and limiting the metabolic capacity of fishes and their capacity to distribute

energetic output across functions. By intersecting physiological and ecological

methodologies, the mechanistic drivers of activity and microhabitat use in an estuarine

teleost were able to be elicited. This study revealed remarkable thermal tolerances in A.

butcheri, as well as a low tolerance to hypoxia and has provided empirical evidence for

the hypothesis that the frequency and extent of hypoxia is a key driver of A. butcheri

growth rates within the Swan River Estuary. Given that A. butcheri are heavily targeted

by recreational fishers within the estuary and that the frequency of hypoxic episodes has

increased in recent decades, the maintenance of current artificial oxygenation programs

in the upper estuary is seen as an appropriate short-term strategy for managing and

conserving this population. In the long-term, significant effort must be directed towards

reducing nutrient inputs into the system, which, in combination with a drying climate,

66

are likely to increase the frequency, extent, and intensity of hypoxic episodes within this

dynamic estuarine system.

67

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