INTERACTION OF DISEASE AND TEMPERATURE ON THE AEROBIC
SCOPE OF FRESHWATER FISH AND IMPLICATIONS FOR CHANGING
CLIMATES
Emily Lawlor
BSc.
This thesis is presented for the Honours Degree in Conservation and Wildlife
Biology, School of Veterinary and Life Sciences, Murdoch University, 2016.
DECLARATION
I declare that 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.
......
EMILY LAWLOR
ABSTRACT
Climate change is a major threat to both freshwater and marine ecosystems on a global scale. There is evidence that for aquatic pathogens and parasites, increasing water temperatures will favour increasing transmission rates and virulence. Increasing water temperature may stress fish and transiently compromise the immune system, exacerbating the effects of infection. The high energy costs of an upregulated immune response will have consequences on other physiological processes such as growth and reproduction.
In order to study the effects of temperature on pathogenicity, a bioassay to challenge the
Australian native freshwater pygmy perch, Nannoperca vittata with a bacterium
Photobacterium damselae damselae was carried out. The bioassay was carried out at two temperatures; 17°C, thought to be the optimum for fish survival and growth, and
28°C, presumed to be approaching the upper critical limit for the species. Nannoperca vittata was found to be susceptible to infection by P. damselae damselae, the first time that infection has been demonstrated in native Australian freshwater fishes. The effect concentration that caused mortalities in 50% of the population (EC50) was lower at 28°C
(4.82x105 CFU ml-1) than at 17°C (2.81x106 CFU ml-1). Fish mortalities were significantly greater and times to death were significantly shorter at 28°C than at 17°C and this is the first time that this has been demonstrated experimentally for P. damselae damselae.
Following the pathogenicity trial, the aerobic scope of exposed versus non-exposed pygmy perch of both 17°C and 28°C experimental groups was determined in a respirometer. Aerobic scope is defined as the difference between maximum metabolic iii
rate and standard metabolic rate. The aerobic scope was greater in exposed than in unexposed fish at 17°C and conversely, greater in unexposed than in exposed fish at
28°C.. This difference occurred because standard metabolic rate increased with exposure at both temperatures, whereas maximum metabolic rate increased at 17°C, but not at 28°C. The increase in standard metabolic rate at both temperatures was expected as a consequence of an upregulated immune response following exposure to the bacterium. The difference in maximum metabolic rate is hypothesised to be a consequence of a compensatory increase in oxygen carrying capacity, which is counteracted by a persistent immune response at 28 but not at 17°C. This hypothesis requires further testing.
The results from this study suggest, firstly, that fish are less tolerant of infection at higher water temperatures and secondly, that a combination of higher water temperature and increased exposure to pathogens may decrease aerobic scope and therefore fitness.
This study showed that higher temperatures decrease aerobic scope in fish, thus suggesting that rising temperatures with global warming may have the same effect. This is the first study to demonstrate such a response in an Australian native freshwater fish.
Further research in this field is urgently required to enable conservation management plans that address the threats posed to native freshwater fish species through climate change.
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TABLE OF CONTENTS
ABSTRACT ...... iii TABLE OF CONTENTS ...... v ACKNOWLEDGEMENTS ...... vii LIST OF FIGURES ...... viii LIST OF TABLES ...... xi 1. INTRODUCTION ...... 1 1.1 Conservation of freshwater fishes ...... 1 1.1.1 Global freshwater fish diversity ...... 1 1.1.2 Australian freshwater fish diversity ...... 1 1.1.3 Conservation status of freshwater fishes...... 4 1.2 Effects of climate change on freshwater biodiversity ...... 7 1.2.1 Climate change in south-western Australia ...... 9 1.2.2 Effects of climate change on freshwater fishes ...... 11 1.3 Direct effects of increasing temperature on physiological processes...... 11 1.4 Indirect effects of increasing temperature on infectious disease ...... 18 1.4.1 Effect of temperature on pathogen reproductive rate and virulence ...... 19 1.4.2 Effect of temperature on host immunocompetence ...... 21 1.5 Synergistic effects of increased temperature and infectious disease on freshwater fishes ...... 24 1.6 Research aims ...... 26 2. MATERIALS AND METHODS ...... 28 2.1 Collection and maintenance of fish ...... 28 2.2 Production of bacterial challenge materials ...... 28 2.3 Pathogenicity trial ...... 29 2.3.1 Experimental design ...... 29 2.3.2 Data analysis ...... 30 2.4 Respirometry trial ...... 32 2.4.1 Experimental design ...... 32 2.4.2 Measurement of SMR and MMR ...... 33 2.4.3 Data analysis ...... 34 3. RESULTS ...... 37 3.1 Pathogenicity trial ...... 37 v
3.2 Respirometry trial ...... 43 4. DISCUSSION ...... 48 4.1 The effect of temperature on the pathogenicity of Photobacterium damselae damselae to Nannoperca vittata ...... 48 4.2 The effect of temperature and exposure to Photobacterium damselae damselae on the metabolic rate and aerobic scope of Nannoperca vittata ...... 53 4.2.1 The effect of body mass on SMR, MMR and AS ...... 53 4.2.2 The effect of temperature on SMR, MMR and AS ...... 54 4.2.3 The combined effects of temperature and pathogen exposure on SMR, MMR, and AS ...... 57 4.3 Implications for the conservation of freshwater fishes in south-western Australia ...... 61 4.4 Limitations and future research ...... 62 5. CONCLUSION ...... 66 6. REFERENCES ...... 67
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ACKNOWLEDGEMENTS
I wish to take this opportunity to express my gratitude to my three supervisors Alan
Lymbery, Stephen Beatty and Adrian Gleiss, from the Freshwater Fish Group and Fish
Health Unit at Murdoch University, for without their support this thesis would not have been possible. These three lads have helped me through all the bumps in the road and I would like to thank you for your time, generosity, shared knowledge and for just being down to earth people whose doors are always open.
I would also like to thank Ph.D. student Siew Mee Bong for her constant jokes and help for the many hours spent together in the lab.
A sincere thank-you goes to Nicky Buller (head microbiologist at the Department of
Agriculture and Food) for sharing her knowledge of laboratory techniques and help with culturing the bacteria.
Finally, I express my greatest love and gratitude to my parents and friends who have put up with and supported me throughout the entirety of this project.
This study was conducted under the Murdoch University ethics permit (R2881/16;
R2834/16).
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LIST OF FIGURES
Figure 1: The Australian freshwater fish biogeographic provinces.
Figure 2: Australian freshwater fish richness (number of species) by region.
Figure 3: Australian freshwater fish endemism by region.
Figure 4: The six major threat categories and their established or potential interactive
impacts on freshwater biodiversity.
Figure 5: The average number of record hot days in Australia per year for each decade
from 1960 to 2010.
Figure 6: Summary of rainfall recorded from 1st January 1997 to 31st December 2010.
Figure 7: The thermal dependency of standard metabolic rate (SMR) and maximal
metabolic rate (MMR), and the difference between them (aerobic scope).
Figure 8: Hypothetical curves depicting changes in the aerobic scope of fishes with
temperature.
Figure 9: The conceptual performance mismatch between optimum temperature and
fitness (growth rate).
Figure 10: Positive feedback loop caused by an increase in temperature resulting in a
reduced aerobic scope consequently leading to an insufficient immune
system and deterioration of body conditions.
Figure 11: An intermittent-flow respirometer used to calculate oxygen consumption
(MO2) rates.
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Figure 12: The output provided by AutoRespTM showing the decreasing slope in
oxygen concentration over time, used to calculate oxygen consumption rate
(MO2).
Figure 13: The oxygen consumption (MO2) of four individual unexposed fish at 28°C
during a 24 hour period in a respirometer chamber.
Figure 14: Extreme eye haemorrhage in a Nannoperca vittata caused by a
Photobacterium damselae damselae infection.
Figure 15: Ventral lesions in a Nannoperca vittata caused by a Photobacterium
damselae damselae infection.
Figure 16: Polymerase chain reaction (PCR) assay for Photobacterium damselae
damselae from Nannoperca vittata.
Figure 17: The mortality rates of Nannoperca vittata infected with increasing
concentrations of Photobacterium damselae damselae at 17°C and 28°C.
Figure 18: Survival probability of Nannoperca vittata infected with Photobacterium
damselae damselae at 5 different concentrations.
Figure 19: Survival probability of Nannoperca vittata infected with Photobacterium
damselae damselae at 5 different concentrations.
Figure 20: The times to death of Nannoperca vittata infected with increasing
concentrations of Photobacterium damselae damselae at 17°C and 28°C.
Figure 21: Aerobic scope of Nannoperca vittata exposed and not exposed to
Photobacterium damselae damselae at 17°C against body mass.
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Figure 22: Aerobic scope of Nannoperca vittata exposed and not exposed to
Photobacterium damselae damselae 28°C against body mass.
Figure 23: Standard metabolic rates of Nannoperca vittata exposed and not exposed to
Photobacterium damselae damselae at 17°C against body mass.
Figure 24: Standard metabolic rates of Nannoperca vittata exposed and not exposed to
Photobacterium damselae damselae at 28°C against body mass
Figure 25: Maximum metabolic rates of Nannoperca vittata exposed and not exposed
to Photobacterium damselae damselae at 17°C against body mass.
Figure 26: Maximum metabolic rates of Nannoperca vittata exposed and not exposed to
Photobacterium damselae damselae at 28°C.
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LIST OF TABLES
Table 1: The 11 native freshwater species located in the Southwestern Ichthyological
Province and their conservation status.
-1 Table 2: Effect concentrations (EC25, EC50 and EC95 (CFU ml )) of Photobacterium
damselae damselae on Nannoperca vittata at 17°C with upper and lower 95%
confidence intervals.
-1 Table 3: Effect concentrations (EC25, EC50 and EC95 (CFU ml )) of Photobacterium
damselae damselae on Nannoperca vittata at 28°C with upper and lower 95%
confidence intervals.
Table 4: The species of fish from which the bacterial pathogen Photobacterium
damselae damselae has been isolated.
Table 5: The clinical signs exhibited by fish infected with Photobacterium damselae
damselae.
Table 6: The effect of increasing temperature on the aerobic scope of a range of fish
species.
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1. INTRODUCTION
1.1 Conservation of freshwater fishes
1.1.1 Global freshwater fish diversity
Freshwater ecosystems are biodiverse habitats, with at least 126,000 animal and plant species depending on them (Collen et al., 2014). Freshwater ecosystems have high diversity and endemism, most likely because freshwater habitats are fragmented and embedded within the terrestrial landscape, thereby restricting dispersal. The diversity of fishes, in particular, is much greater in freshwater than in marine ecosystems; surface freshwater constitutes only 0.01% of all global water (Vliet et al., 2013; Mantyka-
Pringle et al., 2014), yet it contains more than 40% (over 10,000) of all known fish species (Dudgeon et al., 2006). Collen et al. (2014) found that, while total species richness of freshwater fishes is greatest in the Amazon Basin, China, and the USA, the richness of threatened and restricted-range species is greatest in south and south-east
Asia, the tropical regions of Central and South America and Africa, and eastern and south-western Australia. Due to the high richness of threatened species in those areas, further research would benefit our understanding of the impacts of current and future threatening processes on freshwater fishes.
1.1.2 Australian freshwater fish diversity
Australia contains approximately 256 native freshwater fish species and at least 190
(74%) are endemic (Unmack, 2013). Ten distinct biogeographic provinces have been proposed for the Australian freshwater fish fauna (Figure 1; Unmack, 2001). In general, species richness is greater in eastern and northern provinces, while species endemism is
1 greater in western and southern provinces (Figures 2, 3). The Eastern Province has 30 endemic species (39%) and covers a large area along the eastern coast (Unmack, 2013).
The Southern Tasmanian Province has eight species within its boundaries and all eight are endemic to this region (Unmack, 2001). The Bass Province has only four locally endemic species and all four have narrow ranges (Unmack, 2013). The Murray-Darling
Province contains a blend of central eastern, southern, north-western and north-eastern influences with 12 endemic species (Unmack, 2013). Strong northern, north-eastern and south-eastern influences are seen in the Central Australian Province with 18 endemic species (Unmack, 2013).
Figure 1: The Australian freshwater fish biogeographic provinces. Reproduced from Unmack (2001).
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Western Australia encompasses five of the ten ichthyological provinces in Australia
(South-Western, Pilbara, Kimberley, Palaeo and Northern Provinces). The South-
Western Province has the lowest level of species richness (only 11 species), however, it has the highest proportion of endemic species (~82%), genera and families compared to all other provinces (Unmack, 2013), with nine of these species found nowhere else
(Morgan et al., 2014).
Figure 2: Australian freshwater fish richness (number of species) by region. Reproduced from Unmack (2001).
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Figure 3: Australian freshwater fish endemism by region. Reproduced from Unmack
(2001).
1.1.3 Conservation status of freshwater fishes
Freshwater ecosystems are experiencing greater declines in biodiversity than terrestrial and marine ecosystems, even though they cover less than one percent of the Earth’s surface (Morgan et al., 1998; Vliet et al., 2013; Mantyka-Pringle et al., 2014). Water margin and wetland vertebrates are experiencing major declines, with negative population trends recorded for 92 birds, 72 reptiles, 44 fish and 19 mammal species
(Dudgeon et al., 2006). Extinction rates are also increasing at alarming scales for freshwater biota. Ricciardi & Rasmussen (1999), for example, found that the extinction rates for North American freshwater animals were five times higher than those for terrestrial animals.
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Australia is home to 74 freshwater fish species listed as Threatened, one species listed as Extinct in the Wild, 12 Critically Endangered, 13 Endangered and 21 Vulnerable
(Lintermans, 2013). Western Australia has a total of 33 listed freshwater fish species, with the Southwestern Province having the highest proportion of threatened fishes in the
State (Morgan et al., 2014). The Southwestern province contains 11 native freshwater species and almost all of them have experienced extensive range contractions (Morgan et al., 2014). As a result, one species is at Lower Risk/Near Threatened; two species are
Critically Endangered; one species is Vulnerable and one species is listed as
Endangered (with a further two recently nominated) (Table 1) (Beatty et al., 2014). All of these species appear to be potamodromous (meaning they migrate exclusively within freshwater, usually in order to breed, and complete their life-cycle), which emphasises their reliance on such habitats (Morgan et al., 2014).
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Table 1: The 11 native freshwater species located in the Southwestern Ichthyological
Province and their conservation status.
(1)= International Union of Conservation of Nature and Natural Resources (IUCN); (2)
= Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act 1999);
(3) = Wildlife Conservation Act 1950; (4) = data deficient in IUCN; (5)= Ogston et al.,
(2016) ‘*‘= not listed by Wildlife Conservation Act, 1950; EPBC Act, 1999; IUCN,
2011. Reproduced from Morgan et al. (2014).
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1.2 Effects of climate change on freshwater biodiversity
On a global scale, biodiversity declines in freshwater ecosystems can usually be ascribed to one or more of five interacting threat categories: invasion by exotic species; flow modification; habitat degradation; over-exploitation and water pollution (Dudgeon et al., 2006; Figure 4). These interacting threatening processes are likely to be further exacerbated by climate change, which will have both direct and indirect effects on freshwater biota.
Figure 4: The six major threat categories and their established or potential interactive impacts on freshwater biodiversity. The arrows represent the scale of interactions between different threats; green arrows represent stronger interactions than blue arrows; red arrows indicate that climate change interacts with all five threat categories. Adapted from Dudgeon et al. (2006).
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Climate change is the result of both natural and anthropogenic processes. It occurs when energy other than the natural energy the earth experiences is trapped in the climate system due to increased greenhouse gases such as carbon dioxide (CO2) from burning fossil fuel (Ficke et al., 2007; Hobday & Lough, 2011; Koehn et al., 2011; Lane et al.,
2015). Mean global air temperature is predicted to increase by 1-7°C within the next hundred years (Ficke et al., 2007). The predicted rise in air temperature will be followed by an associated rise in water temperature in both marine and freshwater environments and it is estimated that a 2-3°C increase may result in 20-30% of animal and plant species being at a high risk of extinction (Marcogliese, 2008). Australia, Europe, South
Africa, Southeast Asia and the United States are expected to experience the greatest increases in water temperature due to climate change (Vliet et al., 2013).
As well as increases in air and water temperature, global warming is expected to decrease rainfall and hence runoff in southern latitudes affecting important aquatic habitats such as lakes, rivers and estuaries (Hobday & Lough, 2011; Pittock, 2013).
Stream flow has diminished by more than 30% over the past 50 years across extensive areas of western and southern Africa, south-east Asia, Australia, southern Europe and the Middle East (Milliman et al., 2008; Cañedo-Argüelles et al., 2013). Vliet et al.
(2013) used collective spatial patterns in streamflow and water temperature to project future streamflow changes between 2071 to 2100. Southern Australia, southern Africa, eastern China, Europe and the U.S.A are expected to experience greater than 25% declines in stream flow. Conversely, northern latitudes and tropical regions are predicted to experience greater than 50% increases in seasonal flow (Vliet et al., 2013), increasing the frequency and severity of flooding (Lõhmus & Björklund, 2015). These future projections suggest there will be detrimental effects on the freshwater species
8 which depend on river flow for seasonal cues, organic materials, food and reproduction
(Milliman et al., 2008; Bartolini et al., 2015).
1.2.1 Climate change in south-western Australia
Since the mid 1970’s, south-western Australia has experienced a climatic shift that has resulted in an increase in mean air temperature and recorded hot days (Figure 5) and a reduction in annual rainfall (Figure 6), leading to a reduction in the volume of annual stream flow into dams and reservoirs from 338GL to less than 90GL (Cleugh et al.,
2011; Hobday & Lough, 2011). Global climatic models consistently predict that this warming and drying trend will continue, with an additional 8% reduction in annual rainfall and 25% reduction in annual streamflow volume by 2030 (Silberstein et al.,
2012). Additionally, it is predicted that fresh groundwater levels will retreat by less than
10m in some parts of the south-west by 2030 (CSIRO, 2009). These changes are predicted to have severe impacts on the highly endemic freshwater fishes of the South
Western Australian Province (Morrongiello et al., 2011; Beatty et al., 2014; Ogston et al., 2016).
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Figure 5: The average number of record hot days in Australia per year for each decade from 1960 to 2010. Reproduced from Ash et al., 2011.
Figure 6: Summary of rainfall recorded from 1st January 1997 to 31st December 2010.
Reproduced from Ash et al., 2011.
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1.2.2 Effects of climate change on freshwater fishes
Although climate change may affect freshwater fish species in many ways, for example through increased water temperature, changed hydrological regimes or more extreme weather events, most studies have concentrated on rising water temperature as a driver of change (e.g. Meisner, 1990; Heino et al., 2009; Comte et al., 2013). Increasing water temperature may impact fish populations directly, by affecting physiological or behavioural processes of fishes, or indirectly by affecting food supply, competitive interactions or infectious diseases. The result of these impacts may be local extinction, a shift in geographic range or genetic changes through rapid natural selection (Reist et al.,
2006).
1.3 Direct effects of increasing temperature on physiological processes
Increases in temperature will affect a fish’s metabolism and feed rates, its ability to maintain internal homoeostasis, reproductive success, and growth (Glencross & Felsing,
2006; Ficke et al., 2007; Lafferty, 2009). The oxygen and capacity-limited thermal tolerance (OCLTT) theory provides an explanation of these effects, through the relationship of temperature to standard metabolic rate, maximum metabolic rate and aerobic scope (Pörtner et al., 2004; Pörtner, 2010; Pörtner & Peck, 2010; Jutfelt et al.,
2014).
Standard metabolic rate (SMR) is a measure of the rate at which an animal uses its energy to maintain basic physiological function (Van Dijk et al., 1999; Sloat et al.,
2014). Standard metabolic rate is measured as the oxygen consumption rate in the total
11 absence of voluntary muscular movements and when no digestion processes are taking place (Chabot et al., 2016). Standard metabolic rate can be seen as a variable of limited ecological significance because fish in the wild rarely exhibit such states. However, it is essential in calculating aerobic scope and can be used to explain growth rate, social interactions, lifestyle, etc. (Chabot et al., 2016). Standard metabolic rate varies both between species and between individuals of the same species. It is hypothesised that individuals with a greater SMR exhibit higher foraging demands to fulfil their energetic needs, and can be seen to select cooler temperatures, especially when food sources are low, and have feeding territories with the highest rates of food delivery (Killen, 2014;
Sloat et al., 2014).
Maximum metabolic rate (MMR) is the maximum rate of aerobic metabolism of an animal, i.e. the maximal rate at which oxygen can be consumed and delivered to the tissue mitochondria (Norin & Clark, 2016). Maximum metabolic rate is measured as the oxygen consumption rate during or immediately after exhaustive physical exercise
(Norin & Clark, 2016). A swim-tunnel respirometer can be used to measure MMR; this involves the fish being forced to swim against a high current until exhaustion (Norin &
Clark, 2016). Another way to measure MMR is to manually chase the fish with a blunt instrument until it becomes exhausted (Norin & Clark, 2016). MMR of fishes can be influenced by a number of factors, for example, body mass, temperature, digestion and environmental oxygen concentration (Norin & Clark, 2016). Individuals with a high
MMR often have a correspondingly high SMR; this is due to the greater maintenance costs to organ systems in order to fuel this high metabolic capacity, even at rest, thus increasing the SMR (Norin & Clark, 2016).
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Aerobic scope (AS) is the difference between SMR and MMR (Fry, 1947). Aerobic scope is a measure of the capacity to expend energy beyond that required for basic existence. Energy from this reserve capacity can be potentially used for activities that contribute to survival and is an important physiological variable used to determine thermal performance (Farrell et al., 2009; Campos et al., 2016). Aerobic scope represents the capacity for aerobic metabolism and energy production for important physiologic functions such as growth, activity, reproduction, locomotion, predator avoidance or prey capture. (Killen, 2014; Verberk et al., 2016). In order for an animal to utilise aerobic energy for these vital physiological activities, there must be an “excess” capacity for oxygen delivery with MMR exceeding SMR (Verberk et al., 2016). A deficit in AS can lead to the organisms using anaerobic metabolism which can result in exhaustion and eventual mortality (Farrell et al., 2008).
As temperature increases so does the oxygen demand, thus in ectotherms, SMR increases with temperature ( Munoz et al., 2012; Campos et al., 2016; Norin & Clark,
2016; Verberk et al., 2016). Maximum metabolic rate, on the contrary, does not always increase with temperature and can plateau or even decrease at high temperatures (Fry &
Hart, 1948; Norin & Clark, 2016), causing a bell-shaped curve when displaying the relationship between AS and temperature (Norin & Clark, 2016) (see Fig. 6).
The OCLTT theory is based on the assumption that AS is a measure of fitness, and is maximised at an optimal temperature (Topt), with reduced performance at lower and higher temperatures (Tpejus) due to the limitations of the ventilatory and circulatory systems to deliver oxygen to respiring tissues, and becomes lowest at critical 13
temperatures (Tcrit) (Farrell et al., 2009; Pörtner & Lannig, 2009; Clark et al., 2013;
Norin et al., 2014; Farrell, 2016; Verberk et al., 2016). Therefore, as temperatures reach beyond Tpejus and approach Tcrit, AS can no longer support any increase in energy demands and this can result in death. Fatalities can be postponed through anaerobic metabolism, but with long-term effects on physiological fitness including survival, reproductive success, ability to find food etc. (Pörtner & Peck, 2010; Verberk et al.,
2016). Aerobic scope is a useful physiological metric to measure when investigating the effects of temperature and other physically limiting attributes.
Figure 7: The thermal dependency of standard metabolic rate (SMR) and maximal metabolic rate (MMR), and the difference between them (aerobic scope). Adapted from
Verberk et al. (2016).
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The OCLTT theory has been used to predict the response of fish species to future climate change and associated increases in water temperature. Rummer et al. (2014) examined the thermal ranges at 29, 31, 33 and 34°C for six species (Dascyllus melanurus, Chromis atripectoralis, Pomacentrus moluccensis, Acanthochromis polyacan, Zoramia leptacantha and Cheilodipterus quinquelineatus) and their Topt. All six species had a reduced AS 3°C above current-day temperature. By assessing the aerobic scope of the six species at increasing temperatures, they were able to conclude, based on OCLTT theory, that future increases in water temperature, which decreases AS and thus fitness, could result in population declines. Eliason et al. (2011) investigated mortalities of Oncorhynchus nerka during river migration in relation to elevated water temperatures by applying the OCLTT theory and inferred that mortalities were a result of limitations in AS due to cardiac collapse at high temperatures.
Despite its popularity, the OCLTT theory is somewhat controversial. Clark et al. (2013) argued that AS will continue to increase with increasing temperature until close to Tcrit and then sharply decline, leading to a left-skewed, rather than a bell-shaped performance curve (Figure 8). There is support for this view in the literature. Fry (1947) found that AS of bullhead (Ameiurus nebulosus) was greatest at a temperature of ~37°C and Munday et al. (2009) discovered that the AS of two coral reef fish species
(Ostorhinchus doederleini and O. cyanosoma) decreased as temperature increased, but still remained high at 32°C, where the lethal temperature was 33°. Similarly, Clark et al.
(2011) documented maximum AS at ~21°C for pink salmon (Oncorhynchus gorbuscha), again reaching upper thermal lethal limits for this species. More recently,
Norin et al. (2014) highlighted that AS was greatest at temperatures at the uppermost
15 end of the temperature range (38°C) in barramundi (Lates calcarifer) and that when given the opportunity, this species preferred to be at 29°C.
Whether the curve is bell-shaped or left-skewed, the relationship between AS and temperature is still assumed to result from the failure of MMR to continue increasing at the same rate of SMR, and the theoretical underpinning of the OCLTT theory remains; decreases in AS at higher temperatures will have a detrimental effect on fitness.
However, even this most basic assumption of the theory has recently been questioned.
Clark et al. (2013) developed an alternative explanation, called the multiple- performances multiple optima hypothesis (MPMO). Instead of viewing AS as an overarching physiological process which governs multiple performance attributes such as reproduction, growth and immune function, the MPMO concept assumes different physiological functions have individual optimum temperatures. Gräns et al. (2014), for example, investigated the AS and growth rate of Hippoglossus hippoglossus (Atlantic halibut) with increasing temperatures and found that growth rate was highest at 10-14°C yet AS continued to increase at the two highest temperatures (16, 18°C). This contradicts the OCLTT theory and suggests that AS is greater at higher temperatures but this does not necessarily reflect on fitness (Figure 9). Decreased growth rate when aerobic scope is highest was also reported by Healy & Schulte (2012) in Fundulus heteroclitus macrolepidotus and Holt & Jørgensen (2015) in Gadus morhua. The
OCLTT theory is hypothesised to be applicable across animal taxa, however, it remains unclear if this is indeed a “unifying” model used to comprehend the thermal performance of animals.
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Figure 8: Hypothetical curves depicting changes in the aerobic scope of fishes with temperature, where A assumes that the optimal temperature of the species coincides with maximal aerobic scope and B assumes the optimal temperature is below that which elicits maximal aerobic scope and instead aerobic scope increases until close to the upper critical temperature. Reproduced from Clark et al. (2013).
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Figure 9: The conceptual performance mismatch between optimum temperature and fitness (measured here as growth rate). Aerobic scope increases with temperature up until extreme temperatures (A), but growth rate has its own optimum temperature and then decreases even though aerobic scope continues to increase (B), contradicting the
OCLTT theory. Reproduced from Gräns et al. (2014).
1.4 Indirect effects of increasing temperature on infectious disease
Climate warming is expected to increase the incidence of infectious disease in fishes and other aquatic organisms (Hakalahti et al., 2006). The effect of temperature on disease threats to aquatic organisms is exemplified on a spatial scale, by increased pathogen-related mortality closer to the equator (Lõhmus & Björklund, 2015;
Marcogliese, 2008), and on a temporal scale by increased presence and transmission of aquatic diseases in the warm seasons in temperate regions (Karvonen et al., 2010).
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Increasing water temperature may increase the incidence of infectious disease by increasing the reproductive rate and virulence of pathogens (viruses, bacteria, protozoans, helminth and arthropod parasites), and by decreasing the immunocompetence of hosts (Marcos-López et al., 2010).
1.4.1 Effect of temperature on pathogen reproductive rate and virulence
Pathogens, like their fish hosts, are ectotherms, meaning they display a mean optimum temperature for growth and reproduction (Marcos-López et al., 2010). For example, the bacterium Cytophaga psychrophilia causes disease in salmonid fishes at 5-10°C and
Chondrococcus columnaris at 15-20°C (Snieszko, 1974), suggesting the bacterium have different optimum temperatures just like different fish species. As temperatures increase so do biochemical reactions that disburse energy which increases activity, development, growth and reproduction (Lafferty, 2009). Warming waters generally stimulate greater replication rates and shorter generation times for most pathogens (Marcos-López et al.,
2010; Crozier & Hutchings, 2014). For example, the generation cycle of the crustacean parasite Argulus coregoni, commonly seen on salmonoids, may be doubled at higher temperatures (Hakalahti et al., 2006). Membre et al. (2005) studied the effects of temperature increases ranging from 2-48°C on the growth rates of Salmonella spp.,
Listeria monocytogenes, Clostridium perfringens, Escherichia coli and Bacillus cereus; the growth rate of all bacterial species increased with temperature.
Increases in pathogen replication rates will increase the pathogen population size and thus since population size relates to fitness for prokaryotes, increases the level and
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likelihood of infections (Marcos-López et al., 2010; Schade et al., 2014). For example, in aquaculture systems, outbreaks of the bacterial disease furunculosis (caused by
Aeromonas salmonicida) in farmed salmonids occur at higher temperatures (Ficke et al.,
2007). Increased sea temperature also increases the transmission and severity of withering syndrome, caused by Xenohaliotis californiensis, in red abalone (Haliotis rufescens) in North America (Braid et al., 2005). Camp & Buchwalter (2016), investigated insecticide (imidacloprid) toxicity on an aquatic insect (Isonychia bicolor) at 15, 18, 21 and 24°C and found that time-to-effect was significantly shorter with increasing temperatures. Similarly, Gilad et al. (2003) found the mean time to death of common carp (Cyprinus carpio) infected with koi herpesvirus was lowest at the highest temperature and greater at lower temperatures. Jun et al. (2009) found that the mortality rate of Oplegnathus fasciatus challenged with the iridovirus Sachun increased with temperature, presumably because of increased virus replication rates.
Not only may temperature affect the replication rate and thus abundance and transmission of pathogens, but also their expression of virulence factors. For example
Kimes et al. (2012) investigated the mechanisms underlying the relationship between pathogen virulence (using Vibrio coralliiyticus) and temperature. It was determined that as temperature increased, virulence factors associated with secretion, motility, host degradation and antimicrobial resistance were up-regulated and this upregulation of virulence factors was associated with the phenotypic changes in cytotoxicity, motility, haemolysis and antibiotic resistance (Kimes et al., 2012). Similarly, Zhang et al. (2011), discovered that higher temperatures enhance the toxic effects of microcystin on Danio rerio. Decostere et al. (1999), found that Flavobacterium columnare adhered more
20
efficiently to the gill tissue of common carp under high temperatures, increasing the susceptibility of fish to this bacterium. Likewise, Pulkkinen et al. (2010) discovered that at higher temperatures the freshwater bacterium Flavobacterium columnare degrades the tissues of Salmo salar more rapidly, and especially for more virulent strains.
1.4.2 Effect of temperature on host immunocompetence
The immune system is an extremely important physiological system involved in body maintenance, such as cellular repair and renewal, as well as the resistance to or tolerance of infectious agents (Rauw, 2012). The immune system is energetically expensive (Rauw, 2012). Activating the immune system can reduce an organism’s appetite, resulting in anorexia, and can redirect energy allocations away from other important processes, compromising growth, survival and reproductive success (Barber et al., 2000; Lochmiller & Deerenberg, 2000; Freitak et al., 2007; Ardia et al., 2012;
Catalán et al., 2012; Xu & James, 2012; Van Peteghem & Vanhoucke, 2013). Allen and
Little (2011), for example, stimulated the growth of juvenile Daphnia magna and found that infection rates were abnormally greater when exposed to Pasteuria ramosa, signifying that when energy resources were allocated to growth, the ability to resist infection is impaired. Wang et al. (2012), exposed the scallop Chlamys farreri to the bacterium Vibrio anguillarum and found that exposed hosts showed greater energy consumption than those not exposed, with available energy being diverted away from less critical processes towards the immune system.
21
According to the OCLTT theory, as water temperatures increase and levels reach beyond their optimum range for fishes, AS will decrease, therefore limiting the amount of energy to be partitioned among competing physiological functions (Farrell et al.,
2009; Seth et al., 2013). Decreased AS may, therefore, have an adverse effect on immune function, thereby increasing the incidence and severity of infectious disease
(Mohammed et al., 2016). Schade et al. (2014), for example, found that sticklebacks
(Gasterosteus aculeatus) infected with Vibrio sp. and held at a high water temperature experienced greater mortality rates than infected fish at a lower temperature. Similarly,
Zhang et al. (2011), investigated the effects of temperature on Danio rerio exposed to microcystin and found that the lethal dose decreased with increasing temperatures.
Zheng et al. (2004), also found the lethal dose decreased with increasing temperatures using Paralichthys olivaceus infected with Edwardsiella tarda.
In addition to decreasing the available energy to be allocated to immune function, increasing water temperature may also increase the stress response of fish, which may further impact immunocompetence. An acute stress response to a noxious stimulant or stressor consists of a fast ‘flight or fight’ response initiated by the sympathetic nervous system and a slower glucocorticoid hormone response initiated by the hypothalamic- pituitary-interrenal axis (equivalent to the hypothalamic-pituitary-adrenal axis in mammals) (Dickens et al., 2010). These lead to physiological and behavioural changes which alleviate the acute stressor, with negative feedback suppressing glucocorticoid release once the stressor diminishes (Sapolsky et al., 2000). If the stressor persists, however, the negative feedback signal is disrupted, with prolonged elevation of glucocorticoid levels (Romero, 2004). There is a well-documented link between the
22
chronic elevation of glucocorticoid and reduced immunocompetence, particularly in mammals, but also in many other vertebrates, including fish (Hing et al., 2016) .
Reduced immunocompetence may both increase the intensity and clinical significance of existing pathogens, and increase the susceptibility of the host to infections with new pathogens (Lochmiller, 1996; Lindström et al., 2004). Small and Bilodeau (2005), for example, used a low-water stress event where water flow into the aquaria was stopped and the water was then rapidly drained to eye-level to induce a stress response in catfish, Ictalurus punctatus, and discovered a 2% increase in susceptibility to
Edwardsiella ictaluri. Salas-Leiton et al. (2012) fund that prolonged exposure to dexamethasone, a synthetic glucocorticoid, increased the susceptibility of sole, Solea senegalensis, to pathogens such as Photobacterium damselae damselae as well as reducing the growth rate of the fish.
23
1.5 Synergistic effects of increased temperature and infectious disease on freshwater fishes
A greater intensity of infection and infection with a greater diversity of pathogens is likely to reduce host condition, because of both direct utilisation of host resources by parasites and direction of more host resources into an increased immune response
(Beldomenico & Begon, 2009; Tompkins et al., 2011). There is also evidence that hosts in poor condition are more susceptible to infection by pathogens (Beldomenico &
Begon, 2009; Blanchet et al., 2009). For example, common toads (Bufo bufo) infected with Batrachochytrium dendrobatidis are more likely to die if their body mass prior to infection is low (Garner et al., 2009), and Krist et al. (2004), established that mud snails
(Potamopyrgus antipodarum) in poor condition infected with Microphallus sp. exhibited higher parasite-induced mortality than those in good condition.. Esch &
Hazen (1980), found a significant positive correlation between reduced body condition of largemouth bass (Micropterus salmoides) and the probability of infection with the bacterium Aeromonas hydrophi, causing red-sore disease.
The combined effects of decreased AS at higher temperatures, increased virulence of pathogens and reduced energy available for immune response to infection may therefore produce a positive feedback loop, whereby reduced AS and increased stress compromises the ability of organisms to mount an effective immune response against infection, leading to a deterioration of body condition, which further reduces both AS and immune function (Figure 10).
24
Figure 10: Positive feedback loop caused by an increase in temperature resulting in a reduced aerobic scope consequently leading to an insufficient immune system and deterioration of body condition.
The end result of this positive feedback loop is that the increasing water temperature and increased exposure to infectious disease are likely to act synergistically, rather than additively, on freshwater fishes in a changing climate. That is, the combined effects of increasing water temperature and disease exposure may be greater than we would predict from considering each stressor in isolation. However, this has not yet been confirmed for freshwater fishes. This leads to a number of testable hypotheses:
1) The AS will be reduced as water temperature approaches Tcrit (the OCLTT theory);
2) The AS scope will be reduced when fish are exposed to a bacterial infection, because of diversion of resources into an immune response;
25
3) Bacterial infections at water temperatures above Topt will reduce AS by a greater amount than expected when considering each stressor in isolation because temperature and infection will act in a synergistic fashion.
1.6 Research aims
This study tested these predictions using the southwestern Australian freshwater fish species western pygmy perch, Nannoperca vittata and the bacterium species
Photobacterium damselae damselae.
Nannoperca vittata (western pygmy perch) is a small (<75mm) freshwater fish located in rivers, lakes, and streams from north of Perth, the Moore River (Gingin), to the east of Albany (Philips River) (Morgan et al., 1998). The species was once common throughout the region, however, as habitat destruction, climate change, groundwater extraction, exotic fishes and seasonal drying of permanent water bodies have continued to increase, the range of the species has been reduced (Beatty et al., 2011, 2014;
Hourston et al., 2014). Due to the species experiencing distribution contractions and the fact that they are known to be a hardy fish, easy to manage and breed (Stephens, 2009),
N. vittata provides an ideal model to establish how south-western Australian freshwater fish species will respond to climate change.
Photobacterium damselae subsp. damselae is a motile, Gram negative, autochthonous, facultative anaerobic bacterium belonging to the Vibrionaceae family (MacDonell &
Colwell, 1985; Smith et al., 1991; Fouz et al., 2000; Vaseeharan et al., 2007; Rivas et al., 2013; Hassanzadeh et al., 2015; Yamaki et al., 2015). Photobacterium damselae
26
damselae can grow at temperatures up to 37°C (Rivas et al., 2013). The bacterium has been globally isolated from both various aquatic animals and humans (Fouz et al., 2000;
Xiao et al., 2008; Chiu et al., 2013; Rivas et al., 2013; Hassanzadeh et al., 2015).
Epizootic outbreaks and the transmission of the pathogen through water occurs within fish populations that are experiencing overcrowding or are immunocompromised
(Stephens et al., 2006; Labella et al., 2010). The virulence of this pathogen can be influenced by temperature, for example, as water temperatures increase from 18 to 22-
25°C, epizootic outbreaks of Photobacterium damselae damselae are more likely to occur (Fouz et al., 1992). Associated mortalities and outbreaks of this disease are growing in Australia (N. Buller, pers. comm.) making it important to further explore the response of freshwater fishes to this pathogen in warming waters.
The aims and hypotheses of the study were to:
1. Determine the pathogenicity of P. damselae damselae to N. vittata at
temperatures of 17°C (the presumed optimum for fish survival) and 28°C. It is
hypothesised that the median effect concentration (EC50) of P. damselae
damselae will be lower at the higher temperature.
2. Determine the SMR, MMR and AS of N. vittata that have been exposed to (a
non-lethal concentration of) P. damselae damselae at temperatures of 17°C and
28°C, and compare these metabolic parameters to fish that have not been
exposed, at the same temperatures. It is hypothesised that AS will be reduced at
higher temperatures and following exposure to the bacterium, and that the
reduction of aerobic scope in fish exposed at 28°C will be greater than the
reduction expected by considering each factor in isolation.
27
2. MATERIALS AND METHODS
2.1 Collection and maintenance of fish
Adult N. vittata were sourced from an aquaculture farm in Pickering Brook and transported to Murdoch University in aerated plastic containers. Prior to the experiment fish were kept in 80L filtered, aerated tanks at ambient temperature (approximately
15°C). Water quality parameters (ammonia, nitrite, pH) were checked weekly and 25% water changes were performed every two weeks. Fish were fed daily at 10am with commercial dry fish pellets (New Life Spectrum Thera + A).
2.2 Production of bacterial challenge materials
The original culture of P. damselae damselae used throughout the entirety of this study was isolated from Pagrus auratus (Pink Snapper) from a diagnostic case submitted to the Bacteriology Laboratory, Animal Pathology, Department of Agriculture and Food
Western Australia and stored at -80°C (N. Buller, pers. comm.). Bacterial cultures of the desired concentration for experimental purposes were obtained by the method of Amaro et al. (1995). Briefly, pure growth bacterial colonies, grown on a blood agar plate were scraped off using an inoculation loop, transferred into 1ml of Trypticase soy broth
(TSB) and mixed until evenly dispersed. This culture was then inoculated overnight at
25°C, then diluted to 100 ml volumes and allowed to grow overnight at 25°C in order to establish a count of up to 1×108 colony forming units (CFU ml-1).
Counts were performed with a diluted bacteria sample using the Thoma Chamber (a gridded glass slide chamber with a 1 mm x 1 mm square of 0.2 mm x 0.2 mm squares, which are further subdivided into 0.05 mm x 0.05 mm (Cruickshank, 1975). Bacteria
28 were counted and the total number (N) was used to establish the concentration using the formula: concentration = N x 104 x dilution (Cruickshank, 1975).
Total viable counts were also performed by pipetting 20μl of the bacteria culture into
180μl of saline (1:10 dilution), followed by 1:100 dilutions using 20μl of the 1:10 dilution and adding it to 180μl of saline, and 1:1000 dilutions by adding 20μl of the
1:100 dilution to 180μl of saline. Each dilution was plated on a separate red blood agar which was then inoculated overnight at 25°C. Bacterial concentration was evaluated the next day by visually counting the number of bacterial colonies and multiplying by the dilution times ten. Once the bacterial concentration was known, TSB was used to dilute the culture to the desired concentrations (1×104 CFU ml-1, 1×105 CFU ml-1, 1×106 CFU ml-1 and 1×107 CFU ml-1, see below).
2.3 Pathogenicity trial
2.3.1 Experimental design
Of a total of 180 N. vittata (mean body mass =1.02 ± 0.62g, mean standard length =
40.7 ± 6.8 mm) used for this experiment, ninety fish were gradually acclimatised to
28°C in groups of six fish each in fifteen 7 L heated and aerated tanks. Ninety fish were gradually acclimatised to 17°C in groups of six fish each in fifteen 7 L aerated tanks.
Both groups of fish were acclimatised at increments of 2°C per hour and held at target temperature two weeks prior to start of experiments. Partial water changes of 25% and water quality testing were carried out weekly. Fish were fed once daily at 10 am using a commercial dry fish pellet (New Life Spectrum Thera + A).
29
Following acclimatisation, triplicate groups of six fish each were exposed to one of four bacterial challenge treatments (1×104 CFU ml-1; 1×105 CFU ml-1; 1×106 CFU ml-1;
1×107 CFU ml-1) and diluted TSB as negative control, in a 1.5L bath for six hours. After the challenge, all fish groups were returned to aerated aquariums for 20 days at 17°C or
28°C. Fish were monitored daily over this 20 day period and mortalities were recorded.
Upon observations of any signs of disease (loss of vision, haemorrhages, ulceration) fish were removed and euthanized using an ice bath; euthanized fish were counted as mortalities. After 20 days, all the surviving fish were removed and euthanized. All fish were weighed and measured for total length (tip of the snout to the caudal peduncle) and standard length (tip of the snout to the tip of the tail), and condition was calculated from the residuals of body mass on total length. Fish were then dissected, swabbed and plated on blood agar plates to be incubated overnight at 25°C. Growth from the plates was then read by a mass spectrometer to identify the presence or absence of P. damselae damselae to confirm cause of death. PCR analysis was performed initially to confirm that N. vittata can be infected by P. damselae damselae using the methods described in
Osorio et al. (2000).
2.3.2 Data analysis
Mortality rates were compared among treatments using a generalised linear mixed model (GLMM), implemented with the R package lme4 (Bates et al., 2015) using the R software 3.2.4. The response variable was mortality, considered as a binomial with a logit link function, with fixed effects of temperature, bacterial concentration, and temperature x bacterial concentration, total length as a covariate and a random effect of
30
aquarium. The significance of the fixed effects was estimated by comparing the log- likelihood of models with and without each effect. Times to death in days were compared among treatments by parametric survival fitting, with the same effects as described for the GLMM, using the software JMP 10.0 (SAS Institute Inc., Cary, N.C.).
An exponential distribution was assumed, following univariate analyses of survival at each temperature using the Kaplan-Meier method and visual examination of survival plots.
Logistic regression analysis was undertaken on the proportion of mortalities at each treatment combination to calculate the effect concentrations (EC); EC25, EC50 and EC95 represent the bacterial concentrations at which 25%, 50% or 95% failure (i.e. stress that is predicted to lead to mortality) occurred at the termination of the trial period, respectively. For each variable, the logistic regression curve was fitted by bootstrapping
1000 random samples, and the EC25, EC50 and EC95 values calculated according to a logistic model (see Beatty et al., 2011). The bootstrapping of 1000 random samples also produced upper and lower 95% confidence intervals (CI) of the parameters.
31
2.4 Respirometry trial
2.4.1 Experimental design
A total of 52 N. vittata were used in the respirometry trial, with 26 fish acclimatised to
17°C and 26 fish acclimatised to 28°C as described above. Following acclimatisation,
13 fish acclimated to 28°C and 13 fish acclimated to 17°C were exposed to a bacterial challenge treatment of 1×104 CFU ml-1 (corresponding to the EC25 value; see Results), and 13 fish acclimated to 28°C and 13 fish acclimated to 17°C were exposed to a control treatment of diluted PSB in a 1.5L bath for six hours with two replicates per treatment. After the challenge, all fish groups were returned to aerated aquaria and maintained as previously described for 20 days. Fish were monitored daily over this 20 day period and at any signs of disease (loss of vision, haemorrhages, ulceration), fish were removed and euthanized using an ice bath.
Of the fish that survived the bacterial challenge, eight were selected randomly from each acclimation temperature in the exposed group and eight from each acclimation temperature in the unexposed (control) group, giving a total of 32 fish (mean body mass
=1.9 ± 0.2g, mean standard length =4.6 ± 0.1 cm, mean total length =5.5 ± 0.2 cm) to be used in the respirometry experiment. These fish were held in individual isolation containers in an 180L aquarium at the designated temperature and maintained as previously described. Each fish was then measured for SMR and MMR as described below. Feed was withheld for 48 hours and fish were weighed before the commencement of each experiments.
32
2.4.2 Measurement of SMR and MMR
An intermittent-flow respirometer was used to calculate oxygen consumption (MO2) rates (Figure 11). This consisted of four resting chambers (each 159ml), which were linked to a flush pump and a recirculation pump using vinyl tubing (148 ml), that was submerged in a temperature controlled aquarium (150L) at the set temperature for each experimental treatments. Each chamber set had an individual fibre-optic oxygen sensor
(optode) attached into the recirculation tubing and a single temperature probe measured the temperature of the water entering all four chambers. Probes were connected to a wireless fibre optic instrument (Witrox), sending measurements to the program
AutoRespTM (Loligo Systems; www.loligosystems.com) on a PC console. Background measurements were recorded to recognise any oxygen deviations without experimental fish.
Figure 11: An intermittent-flow respirometer used to calculate oxygen consumption
(MO2) rates. See text for details. Adapted from Rosewarne et al. (2016).
33
To measure SMR, fish were removed from their holding tanks and transferred using a mesh net directly to the resting chamber. When measuring MMR, fish were first transferred to a 1.5L tank in which they were stimulated to exercise with a blunt object until exhaustion, upon which the fish no longer responded to stimulation, and were then immediately removed and transferred to a resting chamber. Fish remained in the resting chambers for a 24 hour period when measuring SMR and a three hour period when measuring MMR. The resting chambers were subjected to alternating automated cycles; a closed cycle (17°C: 1400 seconds, 28°C: 500 seconds), where the fish respired and the oxygen concentration within the chamber declined, and a flush cycle (17°C: 30 seconds,
28°C: 60 seconds), where once the oxygen level reached critical levels (~80%), a pump was activated, flushing the chamber with new oxygenated water. After the allocated time period, fish were removed from their resting chambers and returened to their home tanks to be immediately fed.
2.4.3 Data analysis
Oxygen consumption rate, used to calculate SMR, MMR and therefore AS, was calculated during each measuring phase (see Figure 12) from the slope (K) of the linear regression of the reduced oxygen percentage (kPa) over time (h) according to the equation:
y=KVβM-1
-1 -1 where y (MO2) represents the oxygen consumption rate (mgO2/kg /hr ), K represents the rate of decline (kPah-1) in oxygen percentage over time during a measurement phase,
V represents the volume of the respirometer including pipe volume and is corrected for fish volume (l), β represents the solubility of oxygen in water at the experimental water
34
-1 -1 temperature and salinity (mgO2l kPa ) and M is the body mass of the fish (kg)
(Rosewarne et al., 2016). In this study MO2 values were automatically calculated by
AutoRespTM.
Figure 12: The output provided by AutoRespTM showing the decreasing slope in oxygen concentration over time, used to calculate oxygen consumption rate (MO2). F is during a flush phase, W is during a waiting phase and M is during a measuring phase.
Over the 24 hour period MO2 decreased until SMR was reached (Figure 13). Standard
-1 -1 metabolic rate was calculated by considering the first 10 hours of MO2 (mgO2/kg /h ) values as outliers, due to the time taken for fish to settle after handling; SMR was
2 therefore considered as the lowest MO2 value after 10 hours with a R value (the measure of how close the MO2 values are the regression line, the slope of oxygen consumption) of 0.9 or higher (Chabot et al., 2016). Maximum metabolic rate was considered as the highest MO2 reading during the measuring period after the implementation of exhaustive exercise (Norin & Clark, 2016). Absolute metabolic rates
-1 (mgO2 h ) for SMR and MMR were calculated by multiplying MO2 by the mass of the
35
fish in kilograms. Aerobic scope was calculated for each fish as the difference between absolute values for MMR and SMR.
1200 ) ) 1 - /hr 1
- 1000
800
600
400
200
Oxygen Consumption (mgO2/kg Oxygen Consumption (mgO2/kg 0 Time (24 hours)
Fish 1 Fish 2 Fish 3 Fish 4
Figure 13: The oxygen consumption (MO2) of four individual unexposed fish at 28°C during a 24 hour period in a respirometer chamber.
2.4.3 Data analysis
Standard metabolic rate, MMR, AS and body mass were normalised by log10 transformation. The effects of temperature, exposure to bacterial infection, and the interaction between temperature and exposure on AS, SMR and MMR were analysed using a general linear model, with body mass as a covariate, implemented in R (R
Development Core Team, 2013).
36
3. RESULTS
3.1 Pathogenicity trial
Seventy two fish suffered clinical signs consistent with P. damselae damselae infections
(eye haemorrhages and lesions; Figure 14, 15) during the 20 day observational period, and were euthanized. Positive culture results were obtained from 8.6% of euthanized fish that had been exposed to bacterial infection, but not from any unexposed fish.
Initial PCR assays confirmed species identity of P. damselae damselae in a subset of fish exposed to infection (Figure 16).
Figure 14: Extreme eye haemorrhage in a Nannoperca vittata caused by a
Photobacterium damselae damselae infection.
Figure 15: Ventral lesions in a Nannoperca vittata caused by a Photobacterium damselae damselae infection.
37
Figure 16: Polymerase chain reaction (PCR) assay for Photobacterium damselae damselae from Nannoperca vittata. The lanes of concern to this study are lanes 6 and 7 which come from successfully infected N. vittata, lane 9 is a Photobacterium damselae piscicida-like control, lane 10 is a Photobacterium damselae damselae control, and lane
MW is a molecular weight marker.
Mortality rates increased with increasing bacterial concentration and were greater at
28°C than at 17°C (Figure 17). The GLMM analysis confirmed a significant effect of
2 2 bacterial concentration (χ 8 = 38.2, p < 0.0001) and temperature (χ 8 = 38.2, p <
2 0.0001), but not their interaction (χ 4 = 5.4, p = 0.247), on mortality. Not only were mortality rates increased, but the times for mortalities to occur were also reduced at greater bacterial concentrations and higher temperatures (Figures 18, 19). Parametric
2 survival analysis showed a significant effect of bacterial concentration (χ 4 = 160.1, p <
2 2 0.0001), temperature (χ 1 = 9.9, p = 0.002) and their interaction (χ 4 = 10.6, p < 0.0001)
38 on survival time. The significant interaction arose because the difference in survival times between fish maintained at 17 and 28°C was much more pronounced at lower bacterial concentrations; as bacterial concentrations increased temperature had less of an effect on survival time (Figure 20).
1.00
0.80
0.60 28C Mortality Rate 0.40 17C Mortality Rates 0.20 Mortality Rate Mortality
0.00 10000 100000 1000000 10000000 100000000 -0.20 Bacteria Concentration (CFU ml-1)
Figure 17: The mortality rates of Nannoperca vittata infected with increasing concentrations of Photobacterium damselae damselae at 17°C and 28°C. Bars show standard errors.
39
Figure 18: Survival probability of Nannoperca vittata infected with Photobacterium damselae damselae at 5 different concentrations (104, 105, 106, 107, 0 CFU ml-1) at
17°C.
Figure 19: Survival probability of Nannoperca vittata infected with Photobacterium damselae damselae at 5 different concentrations (104, 105, 106, 107, 0 CFU ml-1) at
28°C.
40
30.00
25.00 28C Times to Death 20.00 17C Times to Death 15.00
10.00
5.00 Times to Death (days) Death to Times 0.00 10000 100000 1000000 10000000 100000000 -5.00 Bacteria Concentration (CFU ml-1)
Figure 20: The times to death of Nannoperca vittata infected with increasing concentrations of Photobacterium damselae damselae at 17°C and 28°C. Bars show standard errors.
The EC25, EC50 and EC95 values for exposure of N. vittata to P. damselae damselae, derived from the bootstrap logistic regression, are shown in Tables 2 and 3.
-1 Table 2: Effect concentrations (EC25, EC50 and EC95 (CFU ml )) of Photobacterium damselae damselae on Nannoperca vittata at 17°C with upper and lower 95% confidence intervals.
EC25 EC50 E95 Median 1.37x106 2.81x106 6.64x106
Low 95% 5.80x105 1.34x106 2.95x106
Upper 95% 2.37x106 3.84x106 8.38x106
41
-1 Table 3: Effect concentrations (EC25, EC50 and EC95 (CFU ml )) of Photobacterium damselae damselae on Nannoperca vittata at 28°C with upper and lower 95% confidence intervals.
EC25 EC50 E95 Median 7.78x104 4.82x105 1.96x106
Low 95% 3.25x103 2.12x105 9.14x105
Upper 95% 2.51x105 1.08x106 4.09x106
42
3.2 Respirometry trial
3.2.1 Aerobic scope
Although there was no significant effect of temperature (F = 0.02; p= 0.88) or exposure to P. damselae damselae (F = 0.08; p=0.78) on AS, there was a significant interaction between temperature and exposure (F =7.15; p=0.02) and a significant effect of body mass (F = 5.63; p = 0.02). Aerobic scope increased with fish body mass. The interaction between temperature and exposure to infection occurred because the differences in AS between exposed and unexposed fish were different at 17 and 28°C. At 17°C, fish had a
-1 higher AS when exposed (Least square mean (LSM) = 0.829 ± 0.089 mg O2 hr ) than
-1 unexposed (0.560 ± 0.086 mg O2 hr ) (Figure 21), whereas at 28°C fish had a higher
-1 - AS when unexposed (0.792 ± 0.085 mg O2 hr ) than exposed (0.572 ± 0.085 mg O2 hr
1) (Figure 22).
)
1 1.6 -
hr 1.4 2 2 1.2
1.0
0.8
0.6
0.4
0.2
0.0 Oxygen Consumption (mgO 0 0.5 1 1.5 2 2.5 3 3.5 Mass (g)
17C Exposed 17C Unexposed
-1 Figure 21: Aerobic scope (mgO2 hr ) of Nannoperca vittata exposed (crosses) and not exposed (circles) to Photobacterium damselae damselae at 17°C against body mass.
43
)
1 1.2 - hr
2 1.0
0.8
0.6
0.4
0.2
0.0 0 1 2 3 4 5 6 Oxygen Consumption (mgO Mass (g)
28C Exposed 28C Unexposed
-1 Figure 22: Aerobic scope (mgO2 hr ) of Nannoperca vittata exposed (crosses) and not exposed (circles) to Photobacterium damselae damselae 28°C against body mass.
3.2.2. Standard metabolic rate
There was a significant effect of temperature (F = 5.33; p = 0.03), exposure to P. damselae damselae (F = 31.42; p < 0.0001), the interaction between temperature and exposure (F = 23.57; p < 0.0001) and body mass (F =24.16; p < 0.0001) on SMR of N. vittata. Standard metabolic rate increased with fish body mass. Averaged over both temperature treatments, SMR was greater in exposed fish than unexposed fish.
Averaged over exposure treatments, SMR was greater at 28°C than at 17°C. The significant interaction between temperature and exposure occurred because the SMR
-1 was much greater in exposed fish (LSM = 0.647 ± 0.055 mg O2 hr ) than in unexposed
-1 fish (0.211 ± 0.053 mg O2 hr ) at 17°C (Figure 23), but was very similar between
-1 exposure treatments at 28°C (0.501 ± 0.054 mg O2 hr in exposed fish compared to
-1 0.509 ± 0.053 mg O2 hr in unexposed fish) (Figure 24).
44
)
1 1.2 - hr
2 1
0.8
0.6
0.4
0.2
0
Oxygen Consumption (mgO 0 0.5 1 1.5 2 2.5 3 3.5 Mass (g)
17C Exposed 17C Unexposed
-1 Figure 23: Standard metabolic rates (mgO2 hr ) of Nannoperca vittata exposed
(crosses) and not exposed (circles) to Photobacterium damselae damselae at 17°C against body mass.
) 1
- 1.0 hr
0.9 2 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0
Oxygen Consumption (mgO 0 1 2 3 4 5 6 Mass (g)
28C Exposed 28C Unexposed Figure 24: Standard metabolic rates (mgO2 hr-1) of Nannoperca vittata exposed
(crosses) and not exposed (circles) to Photobacterium damselae damselae at 28°C against body mass.
45
3.2.3 Maximum metabolic rate
Although there was no effect of temperature on the MMR of N. vittata, (F = 1.50; p =
0.23), there were significant effects of exposure to P. damselae damselae (F = 9.48; p =
0.005), the interaction between temperature and exposure (F = 19.91; p < 0.0001) and body mass (F = 19.96; p < 0.0001). MMR increased with fish body mass. Averaged over temperatures, MMR was greater in exposed fish than unexposed fish. The significant interaction occurred because at 17°C MMR was greater in exposed fish
-1 -1 (LMS = 1.475 ± 0.105 mg O2 hr ) than in unexposed fish (0.771 ± 0.101 mg O2 hr )
(Figure 25), whereas at 28°C MMR was greater in unexposed fish (1.301 ± 0.101 mg O2
-1 -1 hr ) than in exposed fish (1.072 ± 0.104 mg O2 hr ) (Figure 26).
)
1 2.5 - hr
2 2
1.5
1
0.5
0
Oxygen Consumption (mgO 0 0.5 1 1.5 2 2.5 3 3.5
Mass (g)
17C Exposed 17C Unexposed
-1 Figure 25: Maximum metabolic rates (mgO2 hr ) of Nannoperca vittata exposed
(crosses) and not exposed (circles) to Photobacterium damselae damselae at 17°C against body mass.
46
) 2.0 1 -
hr 1.8
2 1.6 1.4
1.2 1.0 0.8 0.6 0.4 0.2 0.0
Oxygen Consumption (mgO 0 1 2 3 4 5 6
Mass (g)
28C Exposed 28C Unexposed
-1 Figure 26: Maximum metabolic rates (mgO2 hr ) of Nannoperca vittata exposed
(crosses) and not exposed (circles) to Photobacterium damselae damselae at 28°C.
47
4. DISCUSSION
This study hypothesised, firstly that the pathogenicity of P. damselae damselae to N. vittata would be greater at higher water temperatures, and secondly, that higher water temperatures and exposure to infection with P. damselae damselae would act synergistically to reduce AS below the level expected from each stressor acting in isolation. The first hypothesis was confirmed, and the second was partially confirmed, although with some potentially interesting inconsistencies.
4.1 The effect of temperature on the pathogenicity of Photobacterium damselae damselae to Nannoperca vittata
The results from this study demonstrated, for the first time, that Nannoperca vittata is susceptible to infection by P. damselae damselae. As far as this study is aware, this is the first demonstration that P. damselae damselae can infect Australian native freshwater fish species. Photobacterium damselae damselae has been isolated from a wide range of fish species throughout the world (Table 4). Photobacterium damselae damselae has also been recently isolated from people; for example, from a 75-year-old man following a fishing incident in Sydney (Akram et al., 2015) and a 64-year-old man following an incident with a sharp-edged keel in Western Australia (Hundenborn et al.,
2013). Stephens et al. (2006) investigated a Western Australian aquaculture facility where Photobacterium damselae damselae had been associated with sporadic problems of mortality in cultured fish and found that stressors influenced the susceptibility of fish.
In Australia, the bacteria seems to reside in the gut of aquacultured barramundi, pink snapper, and yellowtail kingfish, and faeces of cultured grouper, although there is limited information on wild fish, except for yellowtail kingfish where it was isolated from the gut (N. Buller, pers. comm.).
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Table 4: The species of fish from which the bacterial pathogen Photobacterium damselae damselae has been isolated.
Common name Scientific name References
Atlantic pygmy octopus Octopus joubini (Rivas et al., 2013) Australasian snapper/silver Pagrus auratus (Stephens et al., 2006) seabream Barramundi Lates calcarifer (Hassanzadeh et al., 2015) Bigeye trevally Caranx sexfasciatus (Hassanzadeh et al., 2015) Blue mackerel Scomber australasicus (Hassanzadeh et al., 2015) Sandbar shark Carcharhinus plumbeus (Fouz et al., 2000; Khouadja et al., 2014) Bryde’s whale Balaenoptera edeni (Rivas et al., 2013) Cobia Rachvcentron canadum (Hassanzadeh et al., 2015) Common bottlenose dolphin Tursiops truncates (Rivas et al., 2013) Damselfish Chromis punctipinnis (Fouz et al., 2000; Khouadja et al., 2014) European eel Anguilla anguilla (Fouz et al., 2000; Khouadja et al., 2014) European seabass Dicentrarchus labrax ( Rivas et al., 2013; Hassanzadeh et al., 2015) Leatherback sea turtle Dermochelys coriacea (Rivas et al., 2013) Meagre Argyrosomus regius (Labella et al., 2010; Rivas et al., 2015) Ovate pompano Trachinotus ovatus ( Rivas et al., 2013; Hassanzadeh et al., 2015) Rainbow trout Oncorhynchus mykiss ( Rivas et al., 2013; Hassanzadeh et al., 2015) Redbanded seabream Pagrus auriga ( Labella et al., 2010; Rivas et al., 2013; Hassanzadeh et al., 2015) Seabream Sparus aurata (Fouz et al., 2000; Vaseeharan et al., 2007; Rivas et al., 2013; Khouadja et al., 2014;) Short-beaked common Delphinis delphis (Rivas et al., 2013) dolphin Speckled longfin eel Anguilla reinhardtii (Rivas et al., 2013) Tiger prawn Penaeus monodon (Vaseeharan et al., 2007) Turbot Scophthalmus maximus (Fouz et al., 2000; Serracca et al., 2011; Rivas et al., 2013; Khouadja et al., 2014) White seabream Diplodus sargus (Labella et al., 2010; Rivas et al., 2013) Yellowtail Seriola quinqueradiata (Fouz et al., 2000; Rivas et al., 2013; Khouadja et al., 2014; Hassanzadeh et al., 2015)
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Table 5: The clinical signs exhibited by fish infected with Photobacterium damselae damselae.
Clinical signs References Accumulation of blood vessels in abdominal (Hassanzadeh et al., 2015) cavity Bladder filled with bile (Hassanzadeh et al., 2015) Dark pigments on skin (Hassanzadeh et al., 2015) Haemorrhages in mouth, eyes, gills and (Fouz et al., 2000; Stephens et al., 2006; musculature Labella et al., 2010; Khouadja et al., 2014; Hassanzadeh et al., 2015; Rivas et al., 2015) Haemorrhagic liver (Khouadja et al., 2014; Hassanzadeh et al., 2015) Large spleen (Hassanzadeh et al., 2015) Septicaemia (Fouz et al., 2000; Stephens et al., 2006; Labella et al., 2010; Rivas et al., 2013) Skin ulcerative lesions (Fouz et al., 2000; Rivas et al., 2013; Khouadja et al., 2014; Hassanzadeh et al., 2015)
Although not always associated with disease, infections with P. damselae damselae can produce a wide range of clinical signs (Table 5). In the present study, infected N. vittata that exhibited clinical signs such as ulcerative skin lesions and eye haemorrhages, due to the bacteria’s ability to induce haemolysis, were euthanized and treated as mortalities.
As expected, mortality rate increased and the time taken for mortalities to occur decreased with increasing concentration of P. damselae damselae, presumably due to the increasing number of bacterium colony-forming units available to infect the host
(Marcos-López et al., 2010; Schade et al., 2014). At a concentration of 107 CFU ml-1 all exposed fish died.
There has been speculation in the literature as to whether P. damselae damselae is a primary or secondary pathogen. Rivas et al. (2013) and Vaseeharan et al. (2007) both considered P. damselae damselae as a primary pathogen whereas Fouz et al. (2000) 50
considers that different strains can influence whether the pathogen is secondary or primary. In the current study, healthy N. vittata were susceptible to infection with P. damselae damselae, leading to mortalities, strongly suggesting that the strain used is a primary pathogen.
The LD50, LC50 (the injection and submersion concentrations, respectively, of a pathogen that will essentially kill 50% of a host population) and EC50 (the concentration that will induce a response in 50% of the population) are common factors to consider when assessing pathogenicity. Fouz et al. (2000) suggested that strains of P. damselae
8 -1 damselae should be considered virulent when LD50 ≤ 10 CFU ml .This study found the
5 6 -1 EC50 of P. damselae damselae infecting N. vittata to be 4.8x10 -2.8x10 CFU ml , which suggests that this strain was virulent. Labella et al. (2010) found an LD50 concentration of 1x105 CFU ml-1 for P. damselae damselae infecting cultured redbanded seabream (Pagrus auriga). Vaseeharan et al. (2007) found the LD50 for black tiger shrimps (Penaeus monodon) ranged from 2x103 to 5x105 CFU ml-1 and Song et al.
(1993) found it was 1x105 CFU ml-1for the same species. Other pathogens have also been shown to vary depending on the strain. For example, Xiao et al. (2008) discovered the LD50 for Xiphophorus helleri infected with several strains of Edwardsiella tarda was between 2.8x103 and 3.8x105 CFU g-1, depending on the strain and Moyer & Hunnicutt
(2007) discovered the LC50 for Danio rerio infected with Flavobacterium columnare was 1.1x106 CFU ml-1. Therefore, the strain used in the current study is comparable in virulence to others discussed in literature.
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The current study found that fish infected with P. damselae damselae experienced greater mortality rates and shorter times till mortalities at a higher temperature, and is the first to demonstrate this experimentally. There was almost an order of magnitude
6 -1 difference in estimated EC50 at the two temperatures; 2.81 x 10 CFU ml at 17°C and
4.82 x 105 CFU ml-1 at 28°C. These findings support those of Fouz et al. (2000) who suggested, from field data on northern hemisphere fish species (turbot Scophthalmus maximus and eels Anguilla anguilla), that pathogenicity of P. damselae damselae increased at higher water temperatures.
Increased mortality at higher water temperatures may be due to an increased replication rate of the bacterium or increased expression of virulence factors, and/or to the stressful effects of increasing water temperature on immune competence on the fish. All of these effects have been observed in other pathogen/host relationships (Stephens et al., 2006;
Jun et al., 2009; Labella et al., 2010; Zhang et al., 2011; Catalán et al., 2012; Kimes et al., 2012; Hassanzadeh et al., 2015; Camp & Buchwalter, 2016) and have also been used to explain the epizootic outbreaks of P. damselae damselae that occur commonly as temperatures reach 22-25°C (Fouz et al., 2000). At this stage, it is not possible to say definitively which of these factors is responsible for the increased mortality at higher water temperatures seen in the current study and this should be the subject of future research.
There is evidence that stress-induced immunosuppression due to crowding in aquaculture facilities is linked to outbreaks of P. damselae damselae (Stephens et al.,
2006; Labella et al., 2010), but as far as this study is aware there have been no studies 52
on the effect of temperature on either replication rate or expression of virulence factors in the bacterium. The hemolytic ability of P. damselae damselae is one of its virulent factors (Hassanzadeh, Bahador and Baseri-Salehi, 2015) and thus powerful cytolysin and exotoxins produced by this bacterium make up its pathogenicity (Vaseeharan et al.,
2007). It would be of interest to monitor these factors as temperature increases. It would also be beneficial to examine the replication rate of this bacterium by looking at the cell colony forming units either optically or by a total viable count at a range of temperatures.
4.2 The effect of temperature and exposure to Photobacterium damselae damselae on the metabolic rate and aerobic scope of Nannoperca vittata
4.2.1 The effect of body mass on SMR, MMR and AS
As expected, this study found a significant positive relationship between body mass of
N. vittata and all metabolic rate measurements. Metabolic rate is known to be highly influenced by body mass (Rosewarne et al., 2016). For a given species, larger animals typically consume more oxygen per unit time, although less per unit tissue, than smaller animals (Nelson & Chabot, 2011; Chabot et al., 2016). Clarke and Johnston (1999), in a review of the relationship between SMR and body mass in teleost fish, found an intraspecific scaling exponent of 0.79. Auer et al. (2015) found that both SMR and
MMR of Brown trout (Salmo trutta) increased with body mass, as did Careau et al.
(2012) with Eastern chipmunk (Tamias striatus).
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4.2.2 The effect of temperature on SMR, MMR and AS
In fish which were not exposed to bacterial infection in this study, SMR, MMR and AS were all greater at 28°C than at 17°C. Standard metabolic rate is expected to be positively related to temperature in ectotherms; temperature acts as a catalyst for biochemical reactions and so as temperatures increase so do these biochemical reactions, leading to an acceleration in the rate at which energy is supplied to bodily functions, thereby increasing the rate of respiration (Brown et al., 2004; Lafferty, 2009).
According to the OCLTT theory, MMR is also expected to increase with temperature until a certain threshold (Tpejus) is reached, at which point performance is impaired and both MMR and AS will begin to decline. Different interpretations of the OCLTT theory predict either a bell-shaped (Portner & Farrell, 2008) or a left-skewed curve of AS against temperature Clark et al. (2011). The results might suggest that 28°C is below the threshold at which reduced performance is expected to occur for N. vittata or that the two temperatures were below and above the optimal temperature of this species.
Unfortunately, little is known about temperature preferences for N. vittata, or indeed for other freshwater fish species in south-western Australia. The species has, however, been found in waters ranging from 8-35°C (Beatty et al., 2013).
Studies of MMR and AS in other fish species have found a range of responses to increasing temperature (Table 6). Aerobic scope is commonly found to increase with temperature because when it is investigated, a large temperature range is examined.
Conversely, some studies have found AS not to change when examining two temperatures. Lapointe et al. (2014), for example, measured the AS of Morone saxatilis at 20 and 28°C and found no significant difference. This was explained using the
54
OCLTT theory and suggesting the absence of the difference may have been because these two temperatures were below and above the optimal temperature for this species.
It was also suggested in the latter study, that the alternative hypothesis to the OCLTT theory, developed by Clark et al. (2013), could also explain the AS, in that 28°C is close to the upper critical temperature for the species.
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Table 6: The effect of increasing temperature on the aerobic scope of a range of fish species.
Common Name Species Name Temperature Aerobic Scope Reference Range (°C)
Atlantic cod Gadus morhua 2-10 Increased (Claireaux et al., 2000)
Atlantic halibut Hippoglossus 5-18 Increased (Gräns et al., 2014) hippoglossus
Barramundi Lates calcarifer 23-38 Increased (Norin et al., 2014)
Blacktail Dascyllus 29-34 Increased then (Rummer et al., humbug melanurus decreased 2014) beyond 31°C Common Fundulus 5-33 Increased then (Healy & Schulte, killifish heteroclitus decreased 2012) beyond 33°C Five-line Cheilodipterus 29-34 Increased then (Rummer et al., cardinalfish quinquelineatus decreased 2014) beyond 31°C Four-line Ostorhinchus 29-32 Decreased (Munday et al., cardinalfish doederleini 2009)
Lemon damsel Pomacentrus 29-34 Increased then (Rummer et al., moluccensis decreased 2014) beyond 33°C Orange-lined Ostorhinchus 29-32 Decreased (Munday et al., Cardinalfish cyanosoma 2009)
Pink salmon Oncorhynchus 8-28 Increased then (Clark et al., gorbuscha decreased 2011) beyond 11°C Spiny chromis Acanthochromis 29-34 Increased then (Rummer et al., damselfish polyacanthus decreased 2014) beyond 31°C Striped bass Morone saxatilis 20, 28 No change (Lapointe et al., 2014)
Threadfin Zoramia 29-34 Increased then (Rummer et al., cardinalfish leptacantha decreased 2014) beyond 33°C
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4.2.3 The combined effects of temperature and pathogen exposure on SMR, MMR, and
AS
The effect of exposure to P. damselae damselae on metabolic rate parameters of N. vittata was contingent upon the temperature at which exposure occurred, as shown by the significant interaction terms in all analyses. At 17°C, exposure to the bacterium increased SMR, MMR, and AS, while at 28°C, exposure did not alter SMR, but led to a decrease in both MMR and AS.
A number of previous studies, on fish and other organisms, have found an increase in
SMR following exposure to pathogens. Careau et al. (2012), for example, found the resting metabolic rate of the Eastern chipmunk (Tamias striatus) to be greater in those infected with the cuterebrid bot fly (Cuterebra emasculator). Similarly, Catalán et al.
(2012) discovered the SMR to be higher in beetle (Tenebrio molitor) larvae following a lipopolysaccharide (LPS) challenge. Freitak et al. (2003) likewise found SMR to be greater when butterfly (Pieris brassicae) larvae were challenged with a nylon implant.
Ardia et al. (2012) also found the resting metabolic rate to increase in the beetles
Cotinis nitida and Tenebrio molitor, the house cricket Acheta domesticus and the cockroach Periplaneta americana, when inducing an encapsulation response and/or wounding. Ots et al. (2001) similarly found the basal metabolic rate of the great tit
Parus major to be greater when infected with sheep red blood cells. An increase in
SMR after infection is thought to occur because of an activation of the energetically expensive immune system, causing an increase in oxygen intake, even when hosts are physically at rest (Robar et al., 2011). Conversely, Lapointe et al. (2014) infected striped bass Morone saxatilis with mycobacteriosis but found no increase in SMR; this
57 was thought to be due to the regrowth of epidermis which minimized any increases in osmoregulatory burden associated with ulceration.
Although only a limited number of studies have examined the effect of infection or exposure to pathogens on MMR and AS, most of these have found either no change or a decrease in these parameters. For example, Scholnick et al. (2012) found that AS in
Western fence lizards (Sceloporus occidentalis) was lower in individuals who had been infected with Plasmodium mexicanum. This was caused by the reduced MMR which in turn was suggested to be due to the changes in haemoglobin concentrations which are thought to be a main contributing factor for oxygen limitations during MMR. Bruneaux et al. (2016) also reported a reduced AS due to the MMR decreasing in Brown trout
(Salmo trutta) infected with Tetracapsuloides bryosalmonae. Aerobic scope was thought to decrease due to the decreased oxygen transportation capacity of the circulatory system, which facilitated the infection. Similarly, Careau et al. (2012) found the maximum oxygen consumption of the Eastern chipmunk, Tamias striatus, to be lower in individuals who had been infected with the cuterebrid bot fly (Cuterebra emasculator). This was hypothesised to be due to the costly nature of activating an immune system which reduced the overall energy budget.
While the effect on MMR and AS of N. vittata following exposure to P. damselae damselae at 28°C was consistent with these previous studies, the increase in MMR and
AS at 17°C was unexpected. This counter-intuitive response has not previously been recorded. It is possible that the results at 17°C reflect experimental error; sample sizes were small (8 individuals per treatment group) and between-individual variation was 58
substantial (relative standard errors approaching 25% for MMR). However, there was no indication that the results were influenced by outliers, and the extent of the difference in response between the two temperature treatments supports an underlying biological explanation. At this stage, it is only possible to hypothesise about biological reasons for this response at 17°C.
At 17°C, the exposed fish may have compensated for the increased energetic demands of an immune response by increasing oxygen carrying capacity. Exposed fish may have responded by increasing their mitochondrial density, which increases aerobic functional capacity (Pörtner et al., 2004), to a level that more than accounted for the additional energetic costs of the immune response. Another possible explanation could be that the erythrocyte or haemoglobin concentration of exposed fish was increased that resulted in a net increase in respiratory capacity. An increase in oxygen carrying capacity in response to physiological challenge is not without precedence in other animals.
Mammals, for example, can compensate for anaemia by increasing capillary blood supply (Lindbom et al., 1988). It is, therefore, possible that in the current study fish were able to overcome infections quickly at the lower temperature, leading to a rapid downregulation of the immune response (and hence energy demand), while compensatory mechanisms were still in place, thus enhancing MMR and AS. At the higher temperature, however, fish were less able to overcome infections, so the immune system may have remained upregulated, with a high energy demand and therefore, net decrease in MMR and AS. This hypothesis draws some support from a study by
Lapointe et al. (2014) on mycobacteria infection in striped bass Morone saxatilis ), in which MMR and AS were increased (although not significantly so) in moderate
59
infections in normal conditions, but this increase disappeared in heavy infections or under hypoxia. This was assumed to be due to the occurrence of compensatory mechanisms that assist exposed fish to increase circulating erythrocyte concentrations, assisting blood oxygen carrying capacity.
At present, the explanation for the increase in MMR and AS seen in N. vittata exposed to infection with P. damselae damselae at 17°C remains hypothetical. In the future, in could be tested by a direct examination of the proposed compensatory mechanisms, such as measuring haemoglobin and haematocrit levels in fish exposed to infection, and by following the metabolic response of infected fish for a longer period of time, as
MMR would be expected to decline to pre-exposure levels as the hypothesised compensatory mechanisms were downregulated.
Regardless of the reasons behind the increased MMR and AS seen in fish exposed to infection at 17°C, at 28°C, this increase did not occur. It is likely that immune activation was more pronounced at the higher temperature because the pathogenic effects of infection were greater. Activating the immune system is energetically expensive and this is assumed to reduce the ability of exposed fish at 28°C to increase their MMR more so than unexposed fish at this temperature. It is also possible that if 28°C is approaching the critical thermal limit of N. vittata, processes such as protein denaturation could have been occurring (Munoz et al., 2012), and the fish could have prioritised their energy to replenishing the proteins being broken down, leaving them incapable of responding to the immune challenge at the same level as fish at 17°C.
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4.3 Implications for the conservation of freshwater fishes in south-western
Australia
Over most of Australia, annual average temperatures are predicted to increase by 0.4-
2.0°C or higher by 2030 (compared to 1990 data) and by 2070 they are predicted to increase by 1.0-6.0°C (Hughes & Steffen, 2013). Climate change is an increasingly studied subject which, in relation to increasing water temperatures, has been experimentally researched to predict the future changes in aquatic ecosystems (Farrell et al., 2008; Feidantsis et al., 2009; Clark et al., 2011; Healy & Schulte, 2012; Rummer et al., 2014). Host-pathogen relationships and their interaction with rising temperatures have not been as well studied as temperature alone, however, it is has been shown from the limited studies conducted that pathogens will benefit while hosts will be at a disadvantage in the face of climate change (Leef et al., 2007; Careau et al., 2012;
Catalán et al., 2012; Scholnick et al., 2012; Bruneaux et al., 2016)
Based on the results of this study, it can be assumed that disease outbreaks will become more common as water temperatures continue to rise. Pathogen populations can be assumed to increase and fish susceptibility to disease will decrease. As the waters warm, fish will be confronted by multiple stressors which are expected to increase the chances of mortality. Endangered species will be at greater risk of extinction and species that are highly isolated within rivers and creeks with no escape of the rising water temperatures will be at high risk of population decreases. This study emphasises the need for conservation protocols and management plans which will ensure the continued existence of the native freshwater species. Management plans could include period pathogen monitoring and river rehabilitation to reduce fragmentation in order ensure
61
sensitive fish species are able to migrate into cooler waters (Davies, 2010). Increasing riparian vegetation and topography has shown to increase the shade over streams which in turn maintained a lower water temperature (Davies, 2010).
4.4 Limitations and future research
This study focused on the effects of an activated immune system on the aerobic scope of
N. vittata at different temperatures. The conclusion that is drawn from the results are potentially limited by the fact that no immune testing was performed, thus there is no direct evidence as to whether or not immunosuppression had occurred. Future research should consider immune testing when further assessing the effects of disease on the aerobic scope.
Previous studies have investigated the effects of disease and other variables on the immune system by measuring the levels of the bacteriolytic enzyme N-acetylmuramide glycanohydrolase (LY) (Timalata, 2015), while others have examined lysozyme activity, due to its antiviral, anti-inflammatory and antibacterial properties, cytokines, leucocytes, lymphocytes and also using flow-cytometry to assess phagocytes (Salazar-
Lugo et al., 2009; Kiron, 2012; Hing et al., 2016). More recently, gene expression patterns have been analysed in immune-associated genes such as β2 microglobulin
(Freitak et al., 2007; Bradley & Jackson, 2008; Kiron, 2012; Xu & James, 2012; Kokou et al., 2015); a number of studies have used gene expression analysis to determine the effects of both infection and temperature using HSP70 (heat-shock protein 70)
62
(Perezcasanova et al., 2008; Wang et al., 2012; Kokou et al., 2015). These analyses could be used in future research to confirm that immune system activation as a response to infection was driving changes in the aerobic scope as suggested here.
For this study, infection with P. damselae damselae and temperature were used as representative stressors resulting from climate change. Future studies should include stress indicators to develop a more comprehensive understanding on the level of stress endured by fish and how it truly affects aerobic scope and pathogenicity. The primary response to stress in fish is the release of corticosteroids (the central biomarker for physiological stress in fish) such as glucocorticoids from interrenal cells (Gesto et al.,
2015; Romero, 2004; Salas-Leiton et al., 2012; Sapolsky et al., 2000).
Cortisol, a glucocorticoid which regulates metabolic, immune, cardiovascular functions, among others, is most commonly assessed when analysing stress in fish and can be measured by enzyme-linked immunosorbent assay, radioimmunoassay using plasma, directly through the ambient water of the fish and, in small fish, through gill homogenates (cell fragments and constitutes) (Barton et al., 1986; Gabor & Contreras,
2012; Gesto et al., 2015; Hing, Narayan, et al., 2016). RNA sequencing can also be used to determine the effects of temperature stress through measuring the expression of genes involved in metabolic processors and heat shock proteins (Chadwick et al., 2015;
Feidantsis et al., 2009; Jeffries et al., 2016). Measuring alkaline phosphatase levels in the epidermal mucus of Atlantic salmon (Salmo salar) is also another method to measure the level of stress in fish (Ross et al., 2000). Measuring these biomarkers would be beneficial in further research in order to obtain a better understanding of the physiological response of fish to multiple stressors.
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There is the possibility that there were underlying unknown factors that could potentially have affected the results in this study. Fish mass and length were taken into consideration when comparing the metabolic rate between groups of fish, however, gender, age and phenotypic differences were not. Metabolic rate has found to decrease with increasing age (Shimokata & Kuzuya, 1993), for example, Fidhiany & Winckler
(1998) discovered that the freshwater cichlid (Cichlasoma nigrofasciatum) metabolic rate decreases with increasing age from 74 to 403 days old. Gender can influence both the ability to mount an efficient immune response (Lochmiller & Deerenberg, 2000) and oxygen consumption rate (Clark et al., 2011). For example, Braune & Rolff (2001) found that in damselflies (Coenargrion puella) infected with water mite (Arrenurus cuspidator) females and males differ in their response to parasitism. Clark et al. (2011) discovered male pink salmon (Oncorhynchus gorbuscha) have a significantly elevated
SMR compared to female fish and a significantly different AS in that male fish had a greater AS than female fish. Phenotypic differences can also influence the susceptibility to disease. These differences are often assumed to be a consequence of infection, however, pre-existing phenotypic differences, such as growth rate, can potentially influence the infection of fish (Blanchet et al., 2009).
This study used two different temperatures to assess the difference in aerobic scope and pathogenicity (optimum and high). Due to the limited number of temperature exposures, there is no representation of the changes of aerobic scope with temperature using a slope and thus the OCLTT theory cannot be completely assessed (Portner & Knust, 2007).
Future studies should be advised to increase the temperature range beyond that used in
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this study. Lastly, the limited sample sizes used in this study reflected the time it takes to run a SMR cycle. Individual fish vary greatly in AS, SMR and MMR and a larger sample size would increase the power of the analyses and hence the confidence in the results.
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5. CONCLUSION
Climate predictions reveal that freshwater environments are to experience major increases in water temperature in south-western Australia and other parts of the world.
This may have a profound influence on host-pathogen relationships. This study revealed that the bacterial pathogen Photobacterium damselae damselae could potentially put
Nannoperca vittata and other freshwater fish species at risk. At higher water temperatures, pathogenicity of P. damselae damselae increased and aerobic scope of N. vittata exposed to the bacterium decreased, relative to lower water temperatures. The findings from this study predict greater fish mortalities in the face of climate change, placing native freshwater fish species at greater risk of extinction.
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