Hypomesus Pretiosus) to Environmental

THE PHYSIOLOGICAL RESPONSES OF SURF SMELT

(HYPOMESUS PRETIOSUS) TO ENVIRONMENTAL

STRESS IN PUGET SOUND, WA, USA

A Thesis Presented to the Faculty of California State University, Stanislaus

In Partial Fulfillment of the Requirements for the Degree of Master of Science in Ecology and Sustainability

By Leah Marie Mellinger August 2018 CERTIFICATION OF APPROVAL

THE PHYSIOLOGICAL RESPONSES OF SURF SMELT (HYPOMESUS

PRETIOSUS) TO ENVIRONMENTAL STRESS IN

PUGET SOUND, WA, USA

by Leah Marie Mellinger

Signed Certification of Approval page is on file with the University Library.

Dr. Brian Sardella Date Professor of Biological Sciences

Dr. Scott Hamilton Date Professor of Ichthyology

I dedicate this thesis to my little family. I am so grateful for their support.

Without my husband, Craig Mellinger, helping with my daughter, holding down the fort when I went on my research trips and being my rock, this thesis would not have been possible. Also, the additional support of my mother, Lisa Andreae Holmes, helping with watching my daughter and animals as well as helping editing was so crucial to my success. Thank you, I love you and couldn’t have done this without you.

iv ACKNOWLEDGEMENTS

This thesis was a huge undertaking and was only made possible by all those involved. Thank you to Dr. Brian Sardella for being my advisor and supporting my ambitious research ideas. Thanks to Dr. Scott Hamilton for being my committee member as well as helping set up my flow through system at Moss Landing Marine

Lab. Thank you to Dr. Jeffrey Scales for joining my committee at the last minute and helping with the statistics and image J along with Dr. Kenneth Schoenly. Thanks to Dr.

Matt Cover and Dr. Terry Jones for additional assistance with the microtome and microscope camera. Thank you to the BRC committee for my funding and Dr. Dietmar

Kueltz and Leslie Oberholtzer for use of the UC Davis Histology Lab. Also, I thank

Jerry Mayberry of Elk Grove Milling, Inc. for donating barrels for my flow through system at Moss Landing Marine Lab. A special thank you to all those involved in my

Puget Sound and California collections, specifically: Karl Menard of the UC Davis

Bodega Bay Marine Lab, Anna Kagely, Kurt Fresh, Joshua Chamberlin, and Jason Hall of NOAA NWFSC Seattle, WA, Phil Dionne and James Losee of Washington

Department of Fish and Wildlife, Walker Duval, Tom Freidrich, Tim Wilson, and

Sayre Hodgson of the Nisqually Tribe, Maxim Lundquist and Todd Zackey of the

Tulalip Tribe, and Davey’s Live Bait.

v TABLE OF CONTENTS PAGE

Dedication ...... iv

Acknowledgements ...... v

List of Tables ...... vii

List of Figures ...... viii

Abstract ...... ix

Introduction ...... 1

Objective and Hypothesis ...... 9

Methods...... 11

Collection Sites ...... 11 Assessment of Regional Water Quality ...... 13 Fish Sampling Protocol ...... 14 Tissue Microarray Construction ...... 14 Quantification of Representative Stress Proteins ...... 15 Statistical Analysis ...... 17

Results ...... 18

Regional Water Quality ...... 18 Marine Parasite Prevalence and Intensity ...... 18 Stress Protein Abundance based on Location and Season ...... 22 Stress Protein Abundance in the Brain based on Temperature, Salinity and Parasite Intensity ...... 29 Stress Protein Abundance in the Heart based on Temperature, Salinity and Parasite Intensity ...... 31 Stress Protein Abundance in the Liver based on Temperature, Salinity and Parasite Intensity ...... 33

Discussion ...... 37

Conclusions ...... 42

Future Directions ...... 44 vi References ...... 46

Appendices

A. T-Test: Caspase 3, Glutathione S-Transferase, Heat Shock Protein 70, and Ubiquitin Florescence in the Brain and Parasite Intensity ...... 57 B. T-Test: Caspase 3, Glutathione S-Transferase, Heat Shock Protein 70, and Ubiquitin Florescence in the Heart and Parasite Intensity ...... 59 C. T-Test: Caspase 3, Glutathione S-Transferase, Heat Shock Protein 70, and Ubiquitin Florescence in the Liver and Parasite Intensity ...... 61 D. Stepwise Backwards Linear Regression of Caspase 3 Florescence in the Liver ...... 63 E. Stepwise Backwards Linear Regression of Glutathione S-Transferase Florescence in the Brain ...... 65 F. Stepwise Backwards Linear Regression of Glutathione S-Transferase Florescence in the Heart ...... 67 G. Stepwise Backwards Linear Regression of Heat Shock Protein 70 Florescence in the Liver ...... 69 H. Stepwise Backwards Linear Regression of Ubiquitin Florescence in the Liver ...... 71

vii LIST OF TABLES

TABLE PAGE

1. Temperature and Salinity Data ...... 18

2. Two Way ANOVA Table GST in the Brain versus Season and Location ...... 24

3. Two Way ANOVA Table HSP70 in the Brain versus Season and Location ... 24

4. Two Way ANOVA Table GST in the Heart versus Season and Location ...... 26

5. Two Way ANOVA Table Caspase 3 in the Liver versus Season and Location 28

6. Two Way ANOVA Table HSP70 in the Liver versus Season and Location .... 28

7. Two Way ANOVA Table Ubiquitin in the Brain versus Season and Location 28

viii LIST OF FIGURES

FIGURE PAGE

1. North Puget Sound, Washington Historical Water Quality Data ...... 6

2. South Puget Sound, Washington Historical Water Quality Data ...... 7

3. Wild Surf Smelt Collection Sites ...... 12

4. Summer and Winter Parasite Prevalence ...... 20

5. Parasite Intensity versus Temperature and Salinity ...... 21

6. Mean Brain Abundances versus Location and Season ...... 23

7. Mean Heart Abundances versus Location and Season ...... 25

8. Mean Liver Abundances versus Location and Season ...... 27

9. The Effect of Temperature on Raw and Mean Brain GST Abundance ...... 30

10. The Effect of Temperature on Raw and Mean Heart GST Abundance ...... 32

11. The Effect of Temperature on Raw and Mean Liver Caspase 3 Abundance .... 34

12. The Effect of Temperature on Raw and Mean Liver HSP70 Abundance ...... 35

13. The Effect of Temperature on Raw and Mean Liver Ubiquitin Abundance .... 36

ix ABSTRACT

Drastic environmental changes can severely affect a fish’s metabolism, growth, reproduction and overall survivability. There is a concern that the synergy of multiple stressors, like the threat of climate change, will lead to population collapses of environmentally sensitive species such as forage fish. Forage fish are vital to ocean health and stability; they serve as prey and transfer energy to higher trophic levels, and are targeted globally as some of the most economically important fisheries. The surf smelt (Hypomesus pretiosus) is a crucial forage fish in the Puget Sound, WA as it is one of three forage fishes in the Sound and is a critical food source for native predatory fish, birds and mammals. Four stress related proteins (Caspase 3, HSP 70,

GST and ubiquitin) were used to assess overall population health of surf smelt in the

Puget Sound. GST, HSP70 and ubiquitin abundance increased with collection site temperature (p≤0.001). Caspase 3 abundance increased with collection site salinity

(p<0.001). There was previously undescribed presence of marine parasitic isopods embedded in the gills of the surf smelt as well. It can be concluded that the Puget

Sound is potentially a stressful environment for surf smelt. As abiotic variability continues to increase in the wake of climate change, it will become of greater importance to continually monitor the population health forage fishes such as the surf smelt at the sublethal level, when there is still potentially a chance for recovery.

x INTRODUCTION

Teleost fishes have an intimate exposure to the aquatic environment due to their high surface area gills characterized by an expansive epithelial surface in direct contact with the water (Robertson & Hazel 1999). Drastic environmental changes can severely affect a fish’s metabolism, growth, reproduction and overall survivability. One major drastic change currently threatening teleost fishes is global climate change

(GCC). The effects of GCC have been, and are continuing to be, detrimental to ocean wellness. Global ocean temperatures are measured by thermosteric sea level rise and more accurately by ocean heat content (Abraham et al. 2013). There is an overall trend of increasing heat content of the world ocean from -5 (1022 Joules) in 1950 to around

15(1022 Joules) in 2012; a 400 percent increase (Levitus et al. 2012; Abraham et al.

2013). Ocean salinity has also been adversely affected by GCC. Globally, over a 50- year survey (1950-2000), it has been observed that salty regions are increasing in salinity and brackish-fresh water regions are decreasing in salinity (Durack et. al.

2012). Variation in salinity can be acutely affect inlets such as the Salish Sea in the

Pacific Northwest and can have severe effects on ecological fitness of individual species through physiological, behavioral or immunological effects (Connon et al.

2009). There is a concern that the synergy of multiple stressors, like the threat of climate change, will lead to population collapses of environmentally sensitive species such as forage fish (Enticknap et al. 2011).

1 2

The Puget Sound, in Washington, USA, is a part of the greater Salish Sea which straddles the United States and Canadian border (Quinn 2010). The Salish Sea ecosystem is a fjord with flooded glacial valleys and is classified as a system of estuaries, fed by highly seasonal freshwater streams and rivers from the surrounding basins (Quinn 2010). Water depth increases drastically and rapidly moving away from the shoreline. Mean water depth is 62 m, with a maximum of 370 m (Burns, 1985), and it takes approximately five months to completely turnover Puget Sound water with open ocean water (Quinn 2010). The Puget Sound has highly varied soil composition, geomorphological characteristics, climate conditions, and it supports high levels of biodiversity (Quinn 2010).

As with other Pacific coastal ecosystems, the Puget Sound is in decline, likely in response to a growing human population and its related disturbances (Thom 2001).

The human population in the Puget Sound region has increased from approximately

1.29 million people in 1950 to near 4.22 million in 2005 and is expected to reach 5.36 million by 2025 (Quinn 2010). Tidal marsh and estuarine ecosystems have declined by

80% over the last 150 years through processes of diking and draining (Quinn 2010).

There has been extensive shoreline armoring to protect the developing coastal cities in the Puget Sound. Human shoreline modifications such as these are intended to dissipate wave energy, maintain navigation channels, and control shoreline erosion and commonly include bulkheads, revetments, seawalls, groins (also known as jetties), ramps, tide gates, sewer outfall, beach nourishment, and biotechnical approaches

(Thom 2001). While generally perceived as positive modifications for coastal urban 3 areas, shoreline modifications can have devastating impacts on local ecosystems.

Habitats that produce prey may be lost or degraded, access to prey resources blocked, the local climate (e.g. temperature and salinity) can be drastically altered, and spawning habitat of forage fish, such as surf smelt (Hypomesus pretiosus), can be degraded by massive shifts in erosion and sedimentation regimes (Thom 2001).

Forage fish are vital to ocean health and stability; they serve as prey and transfer energy to higher trophic levels, and are targeted globally as some of the most economically important fisheries. A forage fish is any species that significantly contributes to ecosystem function and resilience due to its role as prey (Enticknap et al.

2011). The Pacific Fisheries Management Council reported that 19 marine mammal,

33 marine bird, and 40 marine fish species rely on forage fish species as a vital energy source on the United States West Coast (PFMC 1998). When forage fish populations become depleted, marine predators are forced to consume less optimal food sources or relocate, which adversely affects the predator demography and population (Enticknap et al. 2011). Population collapse of forage fish may, at the very least, mean decreased fisheries revenues, or at the worst, lead to valued species extinctions (Shelton et al.

2014). Regular monitoring of forage fish population health may help predict, and potentially prevent, disastrous collapses especially in delicate ecosystems such as the

Puget Sound in the greater Salish Sea.

The surf smelt (Hypomesus pretiosus) is a crucial forage fish in the Puget

Sound, WA as it is one of three forage fishes in the Sound serving as a critical food source for native predatory fish, birds and mammals (Pentilla 2007). This species is 4 one of 31 species and 11 genera in the family Osmeridae of teleost fishes. Surf smelt’s range is wide; they can be found from the northernmost area of Prince William Sound,

Alaska to the southernmost point of Long Beach, California (Penttila 2007). This species is a schooling, pelagic, small, cycloid scaled, soft-rayed fish with an adipose fin (Therriault et al. 2002). Average length of an adult surf smelt is around 13.5 cm

(Hart & McHugh 1944) and the maximum length was recorded at 30.5 cm (Hart 1973).

Fish scale length is used to determine age as early as one year to the end of their life cycle at five years (Penttila 1978). Surf smelt spawn intertidally most months of the year, usually during high and ebbing tide, and under a variety of conditions (Hart &

McHugh 1944; Hart 1973). Because of this, it appears that this species is relatively eurythermal and euryhaline (Pentilla 2001; Quinn et al. 2012); however, no studies have been conducted to state this explicitly. The life history of this species range is unknown past the spawning grounds, and an excellent opportunity exists for further investigation.

There are several barriers to surf smelt management strategies, stemming mainly from a paucity of knowledge regarding its life history and physiological capability. National Oceanic and Atmospheric Administration (NOAA) has listed this species as having “inadequate early-life-history information available” (Matarese et al.

1989) with existing knowledge being limited and general. Increasing the available knowledge on the health of surf smelt populations will assist management efforts in the

Puget Sound, and help prevent the degradation and/or collapse of its population. 5

Typically, surf smelt spawn sporadically throughout the year, with the peak of spawning frequency between June and October (Loosanoff 1938). The Puget Sound populations have deviated from this historic spawning pattern, with the height of spawning in northern Sound occurring during the summer months of June-September and the southern Sound in winter months of December-February (Penttila 2001).

Regional water quality data from 1996 to 2016 depict clear, seasonal oscillations of temperature and salinity (Figure 1 and 2). These oscillations explain the differences in water quality between winter and summer seasons. What remains unknown is how the adults of Puget Sound populations cope with chronic stresses of varying ranges of changing water quality and whether there is any differentiation in stress between the

South and North Sound populations that could be causing the deviation in spawning times. be causing the deviation in spawning times. 6

Figure 1. North Puget Sound, Washington historical water quality data taken by anchored buoys from 1990-2016 (Washington Department of Ecology). 7

Figure 2. South Puget Sound, Washington historical water quality data taken by anchored buoys from 1996-2016 (Washington Department of Ecology). 8

Variations of salinity and/or temperature can result in physiological stress and prevent the maintenance of osmotic homeostasis. Fishes maintain their internal milieu at approximately one-third the salinity of sea water (SW) which makes marine fishes hypoosmotic as compared to their environment, so they lose water to the environment through diffusion across their gills (Sardella et al. 2008). To offset this, marine fishes drink SW and absorb ions and water across the intestinal epithelium (Evans 2010). The resulting ion load from the sea water intake is excreted by gill ionocytes, driven by the activity of sodium-potassium ATPase (Evans 2010). In the kidney, water loss is kept to a minimum by excreting small volumes of urine. When a fish loses osmotic balance, it can be detrimental to cellular and biochemical functions and will eventually threaten survival. An increase in temperature can result in negative effects at the cellular and whole organism level directly through protein and membrane disruption, but also via disruptions to osmoregulatory function. Increased temperature elevates metabolism via

Q10 effects, and subsequently increases oxygen consumption, and the resulting increases in ventilation and gill perfusion promotes further water loss due to enhanced osmotic gradient, the so-called osmorespiratory compromise (Sardella & Brauner

2008; Glover et al. 2012). Therefore, if temperature and salinity deviate from normal ranges fishes are subjected to physiological stresses that inevitability will decrease survivability.

The physiological responses to thermal and osmotic stress can be measured by quantifying the abundance of indicator proteins expressed within representative tissues.

The quantification of so-called stress proteins can provide insight to the wellbeing of 9 individual fish and thus may estimate overall population health. Heat shock protein 70

(HSP 70) and ubiquitin are well-documented indicators of cellular stress and indicate cellular proteins damage at various stages (Sardella et al. 2009; Wendelaaar & van der

Meij 1989). Caspase 3 is an enzyme within the apoptosis cascade (programmed cell death), and has been shown to increase during salinity stress due to higher cell turnover

(Sardella et al. 2004; Veal et al. 2002; Wendelaaar & van der Meij 1989). Lastly, glutathione s-transferase (GST) can serve as an indicator of metabolic/oxidative stress, and its expression results mainly in response to the generation of free radicals following incomplete oxygen reduction in the mitochondria (Hellou et al. 2012). The relative levels of expression of these proteins may therefore yield information on the stress being experienced in the surf smelt populations of the Puget Sound.

Objective and Hypothesis

The overall goal of this thesis was to assess if climate change variables such as variable temperature and salinity are affecting forage fish population health, as assessed by the abundance of stress proteins within representative tissues. This thesis addressed how adult surf smelt are coping with shifting water quality regimes in the Puget Sound caused by GCC, and to potentially gain insight into their change in spawning times.

The goal was the quantification of physiological stress level using the abundance of known stress related proteins expressed within representative tissues taken from Puget

Sound surf smelt. Wild smelt were captured from locations in the South, Central and

North Puget Sound that are associated with their presence. The hypothesis is that an 10 increase in environmental stresses will result in measurable upregulation of the four stress related proteins in representative tissues in the wild caught surf smelt. METHODS

Collection Sites

This study is the product of partnerships with NOAA’s Northwest Fisheries

Science Center’s (NWFSC) Estuarine and Oceanic Ecology Department, Washington

Department of Fish and Wildlife (WDFW), Tulalip tribal biologists, and Nisqually tribal fishery biologists for the collection of the wild surf smelt in Puget Sound,

Washington. The collections in Washington state were under the umbrella of the

NWFSC’s permits as well as utilizing their equipment. A total of 96 smelt (12 smelt from each site) were collected from sites within Puget Sound: specifically, in the South and North Puget Sound including the Strait of Juan de Fuca. In the summer of 2017, fish and water quality data were collected at the Elwa River mouth, Skagit Bay,

Camano Island, and the Nisqually Reach (Figure 3). In the winter of 2017-2018, fish and water quality data were collected at Potlatch Beach, East Everett Waterway,

Steamboat Island, and Hope Island (Figure 3). All fish were collected using a beach seine method.

Figure 3. Sites where wild surf smelt were captured. Yellow pins signify summer collections and pink pins signify winter collections (Google Earth).

The beach seine method utilizes a large fine mesh net with a lead line on the bottom side of the net, floats on the top side, and a large purse in the middle that all items and organisms caught will be funneled into. An individual is dropped off on the beach by a small boat with one side of the net and holds the net end steady at the location. The boat then circles the nearshore area being sampled while dropping the net as it goes. Another individual is then dropped off farther down the beach with the other end of the net. Both individuals on the beach will then slowly walk towards one another

13 while pulling in the net. The net is pulled almost all the way on shore except the purse.

The purse is then sampled to retrieve the organisms caught by the seine. All collection and sampling methods were approved prior to investigation by the CSU Stanislaus

Institutional Animal Care and Use Committee (IACUC; Protocol ##005-1617).

Assessment of Regional Water Quality

Collection site anthropogenic characterization and water quality data were taken at every site that surf smelt was caught. Collection site characterization was comprised of three categories: pristine, residential, and commercial. Characterization of each site was qualified by level of human disturbance. Human disturbance is defined as the relative level of shoreline armoring and the type of human buildings on the shoreline (Lucrezi et al. 2009). Pristine was determined as sites that had no human disturbance (i.e. no shoreline armoring and no human buildings). Residential was determined as sites that had low-medium human disturbance (i.e. shoreline lightly armored with logs, rocks, etc. and residential buildings). Commercial was determined as sites that had high human disturbance (i.e. shoreline highly armored with cement and bulkheads with commercial buildings). The water quality data that were taken included temperature and salinity using a YSI meter (YSI 6050000 Proplus

Multiparameter Meter), a refractometer (Brix Refractometer #RF16), and a mercury thermometer (General Lab Thermometer, Mercury-Filled range: -20 to 110 °C). Two measurements were taken at each collection site.

Fish Sampling Protocol

Twelve surf smelt were collected from eight sites (a total of 96 fish) within

Puget Sound: South to North Puget Sound including the Strait of Juan de Fuca. The n=12 was chosen using a statistical power analysis (G*Power 3.1.9.2) for a one-way

Analysis of Variance (ANOVA) that rendered a confidence of 95% with an estimated large effect expected as demonstrated by Mayr et al. 2007. Liver, brain and heart tissues were harvested from the total of 96 fish collected in the Puget Sound within 30 minutes of the capture event. This was to minimize any stress related hormones from accumulating in the tissues of interest due to the capture event.

After capturing and sacrificing the surf smelt, the first cut was made to the spinal cord to sever all nerves. Next, a shallow cut was made to the dorsal side right above the gill to the nose to expose the brain and it was removed. The last incision is to the ventral side from the vent to under the chin. This incision exposes the internal organs and the heart and then liver was retrieved. The tissues were then fixed in

Formalin (10% formaldehyde) instantly after harvest.

Tissue Microarray Construction

Fixed tissues were embedded in paraffin using a Tissue Tek vacuum infiltration processor (Sakura Finetek, Torrance, CA, USA). Paraffin blocks were constructed with a Tissue Tek tissue embedding center (Sakura Finetek, Torrance, CA, USA). One millimeter wax cores were removed from an empty paraffin block using an MTA-1 tissue microarrayer (Beecher Instruments, Sun Prairie WI, USA), and filled with cores taken from embedded liver, heart, and brain tissue. Tissue cores from all treatment

15 groups were inserted into single representative recipient blocks, which was then be sectioned at 4 µm using a Bromma 2218 Historange microtome (LKB, Uppsala,

Sweden). Sections were floated on to a poly-lysine coated glass microscope slide, and dried overnight at 44 ºC.

Quantification of Representative Stress Proteins

Quantification of HSP 70, ubiquitin, caspase 3, and glutathione transferase was completed by staining the TMA of each tissue block with the corresponding fluorescent antibody stain. The TMA method streamlined the analysis process, allowing for simultaneous analysis of a large numbers of specimens, and create experimental uniformity (Sardella et al. 2008; Jawhar 2009). Each tissue sample is treated in an identical manner and microarrays are amenable to a wide range of techniques, including histochemical stains and immunologic stains with either chromogenic or fluorescent visualization (Sardella et al. 2004; Sardella et al. 2008; Jawhar 2009).

For antibody staining, sections were blocked with phosphate-buffered saline

(PBS) containing 1% bovine serum albumin (PBA) with 2% normal goat serum (HSP

70: normal mouse serum) for 30 min, followed by 60 min of incubation in PBA containing the various primary antibodies (Lima and Kultz 2004). The primary anti- mouse antibody for HSP 70 was purchased from Sigma-Aldrich

(www.sigmaaldrich.com). The primary goat antibody for Caspase was purchased from

Novus biologicals (www.novusbio.com) and the primary goat antibody for catalase was purchased from Abcam (www.abcam.com). The primary goat antibodies for GST and ubiquitin and the secondary anti-goat and anti-mouse antibody Alexa Fluor® 488

16 were purchased from Thermofisher (www.thermofisher.com). Specific staining was carried out per the protocol for each individual antibody as provided by the manufacturer. The appropriate secondary antibody, covalently bound to Alexa 688

(Molecular Probes, Eugene, OR, USA), was selected and diluted 1:500 with PBA for secondary fluorescent staining. After 60 minutes of incubation in secondary antibody, slides were rinsed with PBS and counterstained for 30 min with the nuclear stain propidium iodide diluted 1:500 with distilled water (Lima and Kültz 2004). The slides were then rinsed again in PBS and the slide was mounted with 20 microliters of anti- fading reagent (ProLong® Gold Antifade Mountant purchased from Thermofisher), covered with a cover slip and sealed with clear nail polish. The slides were kept in the dark to minimize fade.

A florescent microscope (Olympus IX71 Microscope with DP71 Camera) and

ImageJ were used to quantify protein abundance. The wave lengths used were FITC

(excitation at 480/40 and emission at 535/50) for Alexa Fluor® 488 and TRITC

(excitation at 535/50 and emission at 610/75) for the propidium iodide counter stain.

The pictures taken by the DP71 camera were then analyzed using ImageJ software. In the images, Alexa 488 appears green and in the contrast stain the cell nuclei appear red.

ImageJ was used to quantify the integrated density and that number was used to quantify the Corrected Total Cell Fluorescence (CTCF). The CTCF of each stained protein from an individual fish is then further analyzed.

Statistical Analysis

The effects of location and season, on stress protein abundance (relative tissue fluorescence quantified by the CTCF) were analyzed by using a two-way ANOVA with

R software (R Software Development Team 2013). Mean abundances of the proteins were graphed with 95% confidence intervals.

Statistical analyses were also conducted on unexpected parasite data. Parasite prevalence data correlated to collection season and location was graphed. Then the parasite prevalence and stress protein abundance data were analyzed using an

Independent Samples T-Test in SPSS version 24 (IBM corp. 2016). Mean stress protein abundance data and parasite prevalence were then graphed with 95% confidence intervals. Finally, the parasite intensity data was correlated to temperature and salinity; the correlation was graphed.

The temperature, salinity, parasite intensity, and the stress protein abundance data were analyzed first by using backwards multiple linear regression (BMLR) procedure (Sokal and Rohlf 1981) in SySTAT version 4.0 (Systat Software, Inc.). The procedure involved the stepwise removal of individual variables that had the smallest partial correlation (r) and was done until the F-ratios and partial regression coefficients of the remaining independent variables were statistically significant (Cohen et al.

1994). This was done to find significant independent variables (temperature, salinity and parasite intensity) that predict the dependent variable (relative stress protein fluorescence). For all statistical analyses, an α-value of 0.05 was used.

RESULTS

Regional Water Quality

Temperature and salinity data were recorded at the time and location of smelt collection (Table 1). Temperature was higher in the summer in both North and South

Sound (Table 1). The salinities were lower in the winter and were especially low in the

North Sound (Table 1). Stress protein abundance was not dictated by the collection site characterization of pristine, residential and commercial (p>0.05).

Table 1

Temperature and salinity data for smelt collection sites arranged by season and location; means are presented ±SEM.

Mean Temperature Mean Salinity ±SE Location Season ±SE (g/kg) (°C) Winter 7.35±0.18 2.49±1.84 North Sound Summer 12.6±1.4 22.49±4.47 Winter 7.78 25.5 South Sound Summer 12 27

Marine Parasite Prevalence and Intensity

During collections and dissections of wild surf smelt, several internal isopod parasites were found embedded in the gills. The South Sound collections had 25% of fish infected with parasites (Figure 4). Zero percent of the fish collected in the North

Sound were found with parasites. Four percent of the fish collected at both Everett

Waterway and Potlatch Beach in the north Sound had internal parasites during the winter (Figure 4). The south Sound collections had 75% of fish infected with parasites at Hope Island and 50% of fish infected with parasites at Steamboat Island. Parasite intensity was neither correlated to temperature or to salinity despite the one outlier parasite intensity point of 28 (Figure 5). Also, parasite intensity did not affect stress related protein abundance in the representative tissues.

Figure 4. A) Summer and B) winter parasite prevalence data. Yellow bars denote South Sound collection sites and pink bars denote North Sound sites.

Figure 5. Parasite intensity data versus collection site A) temperature and B) salinity. Dashed line represents regression trendline based on data.

Stress Protein Abundance based on Location and Season

Abundance of each protein was measured as relative fluorescent units (RFU) on tissue microarrays using fluorescent antibody staining, although there was a high degree of variability within the sample groups. Within the brain tissue, fluorescence was detected in all tissues (Figure 6). GST abundance in the brain was higher in the summer (p=0.00014; Table 2, Figure 6C). HSP 70 abundance in the brain was higher in the summer season as well (p=0.026; Table 3, Figure 4A). No other stress related protein abundance was affected by season or location in the brain.

Figure 6. Mean brain abundances of A) HSP 70, B) Caspase 3, C) GST, and D) ubiquitin as assessed by relative fluorescence on tissue microarray. Colors denote seasonal differences and error bars represent 95% confidence intervals. * denotes statistical difference as measured by 95% CI (p<0.05).

Table 2

Two way ANOVA table GST in the brain versus season and location.

Table 3

Two way ANOVA table HSP70 in the brain versus season and location.

There was fluorescence detected for all proteins in the heart tissue, and again there was a great degree of variability (Figure 7). Significant differences in the abundance of GST were observed in both the north and south populations, with summer having a greater abundance relative to winter (p=0.00013; Table 4, Figure 7C). No other proteins were affected in the heart.

Figure 7. Mean heart abundances of A) HSP 70, B) Caspase 3, C) GST, and D) ubiquitin as assessed by relative fluorescence on tissue microarray. Colors denote seasonal differences and error bars represent 95% confidence intervals. * denotes statistical difference as measured by 95% CI (p<0.05).

Table 4

Two way ANOVA table GST in the heart versus season and location.

Finally, within liver tissue there was again detection of all proteins with variability (Figure 8). Caspase 3 abundance was higher in the South Sound; however, there was a larger difference in abundance between summer and winter collection in the North Sound (p=0.014; Table 5, Figure 8B). HSP 70 abundance was greater in summer and there was a greater difference in abundance in the North Sound between summer and winter collections (p=0.018; Table 6, Figure 8A). Finally, ubiquitin levels in the liver were higher in the summer season collection season (p=0.02; Table 7) as well as specifically higher in the North Sound during the summer (p=0.0051; Table 7,

Figure 8D).

Figure 8. Mean liver abundances of A) HSP 70, B) Caspase 3, C) GST, and D) ubiquitin as assessed by relative fluorescence on tissue microarray. Colors denote seasonal differences and error bars represent 95% confidence intervals. * denotes statistical difference as measured by 95% CI (p<0.05).

Table 5

Two way ANOVA table caspase 3 in the liver versus season and location.

Table 6

Two way ANOVA table HSP70 in the liver versus season and location.

Table 7

Two way ANOVA table ubiquitin in the liver versus season and location.

Stress Protein Abundance in the Brain based on Temperature, Salinity and Parasite Intensity

GST was the only protein abundance that had a significant relationship with and abiotic variable in the brain (Appendix E). GST levels were higher with increasing temperature (p<0.001; Figure 9). Salinity and parasite intensity did not affect stress protein abundance in the brain.

Figure 9. The effect of temperature on raw and mean brain GST abundance. Error bars represent 95% confidence intervals. Dashed line represents regression trendline based on data.

Stress Protein Abundance in the Heart based on Temperature, Salinity and Parasite Intensity

GST was the only protein abundance in heart that had a significant relationship the independent variables (Appendix F). GST abundance increased with temperature

(p<0.001; Figure 10). Salinity and parasite intensity were insignificant variables when related to stress protein abundance in the heart.

Figure 10. The effect of temperature on raw and mean heart GST abundance. Error bars represent 95% confidence intervals. Dashed line represents regression trendline based on data.

Stress Protein Abundance in the Liver based on Temperature, Salinity and Parasite Intensity

Caspase 3, HSP70 and ubiquitin abundancies all had significant relationships with independent variables in the liver (Appendix D, G and H). HSP70 abundance increased with temperature (p<0.001; Figure 12) and ubiquitin’s abundance also was higher with increasing temperature (p=0.001; Figure 13). Salinity and parasite intensity did not affect HSP70 and ubiquitin abundance in the liver. Caspase 3 abundance was higher with increasing salinity (p<0.001; Figure 11). Temperature and parasite intensity were insignificant variables when related to caspase 3 abundance in the liver.

Figure 11. The effect of salinity on raw and mean liver caspase 3 abundance. Error bars represent 95% confidence intervals. Dashed line represents regression trendline based on data.

Figure 12. The effect of temperature on raw and mean liver HSP70 abundance. Error bars represent 95% confidence intervals. Dashed line represents regression trendline based on data.

Figure 13. The effect of temperature on raw and mean liver ubiquitin abundance. Error bars represent 95% confidence intervals. Dashed line represents regression trendline based on data.

DISCUSSION

Fish are ectotherms and can be easily stressed when environmental temperature is out of the range of specific physiological tolerances (Basu et al. 2002). There is a historical trend showing substantial temperature variation between summer and winter seasons in both North and South Sound (Figures 1 and 2). This trend was also observed in this study (Table 1); where a 4-5°C difference between winter and summer during both the North and South Sound collections, was observed. Stress protein levels were increased during the summer collection season, which had higher temperatures and salinities (Tables 1 and 3-7; Figures 4-6). There was also a large deviation in salinity from winter to summer, within the North Sound which could have synergized osmotic stress with thermal stress and deleteriously affected the surf smelt. Salinity in the nearshore environment in North Sound was near freshwater levels. Season or location are not directly causing the abundance of stress related proteins. Locations and seasons have specific climate regimes associated with them which includes temperature, salinity, etc. Other independent variables (temperature, salinity, etc.) associated with that season and/or location are the direct cause of stress protein abundance.

Temperature increase caused a subsequent increase of GST abundance in the brain and heart, HSP70 abundance in the liver and ubiquitin abundance in the liver.

The abundance of caspase 3 in the liver increased as salinity increased; however, it wasn’t a significant variable in other protein abundancies. This may potentially be the

first evidence that surf smelt are more euryhaline than expected as well as caspase 3 being upregulated in the liver associated with salinity stress is a discovery of a novel relationship.

Ubiquitin, HSP70, Caspase 3, and GST play vital roles in the physiological responses to environmental stress in cells. Specifically, the upregulation of HSP70 and

Ubiquitin have been associated with a thermal stress response (Sardella et al. 2008;

Todgham et al. 2007). Thermal stress is defined as any temperature (hot or cold) that is out of the range of physiological tolerance and interferes with protein homeostasis

(Todgham et al. 2007). Proteins must exert a balance between stability and flexibility to function properly, and thermal extremes can affect both (Somero 2002). At high temperature, flexibility can increase to the point of destabilization and even become denatured, misfolded or damaged. The aggregation of damaged proteins is detrimental to proper cell function and viability (Sherman and Goldberg 2001).

There are two common solutions for damaged protein aggregation, fix the damaged protein or eliminate it before the formation of protein aggregates can occur, which would result in further by cellular damage. Refolding back into their functioning state is accomplished by molecular chaperones such as those in the HSP70 family (Basu et al. 2002; Pratt 1993). HSP70 is critical in cellular stress recovery, specifically related to temperature and salinity stress (Todgham et al. 2005). Therefore, the increased abundance of HSP70 in surf smelt tissues potentially indicates the need for protein and cellular recovery.

If a protein is unfolded beyond possible repair, there is an increased activity in the ubiquitin-proteasome pathway (Wickner et al. 1999). Ubiquitin is upregulated to tag misfolded and damaged proteins for degradation by the 26S proteasome complex

(Todgham et al. 2007; Goldberg 2003). The of positive relationship of ubiquitin abundance in the with temperature potentially indicates an accumulation of misfolded and damaged proteins resulting from environmental stress.

Previous work has associated GST with oxidative stress and observed upregulation when free radicals are present. Oxidative stress can be driven by thermal stress as well. Moreover, oxidative enzyme abundance positively correlates with temperature in estuarine fish (Madeira et al. 2013). It has been shown that oxidative biomarkers, such as GST, are highly sensitive to temperature and that there is a relationship between thermal stress and oxidative stress (Madeira et al. 2013). GST abundance in the surf smelt tissues with the increase of temperature indicates an increased need for detoxification.

Osmotic stress has been linked previously with an increase in apoptosis

(programmed cell death) due to elevated levels of oxidative stress (Cheng et al. 2018).

Caspase 3, a common indicator of apoptosis, has been shown to upregulate under osmotic stress (Cheng et al. 2015). Caspase 3 has thusly been suggested to be an important component of osmotic stress-induced apoptosis in fish (Cheng et al. 2018).

Caspase 3 is activated after caspase 9 to signal cell apoptosis after the introduction of high temperature and osmotic stress and is strongly associated with HSP70 (Wang and

Lenardo 2000; Cheng et al. 2015; Cheng et al. 2018). The increase of caspase 3 in the

liver of surf smelt with salinity indicates an increase in cellular apoptosis due to osmotic stress. Also, its significant abundance in the liver with significant abundance of HSP70 signals that the fish were experiencing osmotic and temperature stress.

The presence of parasitic isopods attached to the surf smelt gills was completely unexpected. Out of 96 fish collected, a total of 36 parasites were found and parasite prevalence was higher in South Sound than in North Sound during both collection seasons (Figure 7 and 8). These parasites were hooked into the gill and quickly sprung out at the moment of fish sacrifice. During the summer collections, the infected surf smelt had one heavily gravid female within the gill that nearly completely covered the gills underneath the gill plate. During the winter collections, most infected surf smelt had a breeding pair attached to their gills. The isopod parasites were identified as

Lironeca desterroensis (Bhaduri pers. comm. 2018; Moser pers. comm. 2018; Thatcher et al. 2003). This species is known to parasitize fish off the South American coast

(Thatcher et al. 2003). Its presence in Puget Sound, Washington is unknown, and the parasitism of surf smelt has never been described in literature.

In all tissues, stress protein abundancies increased with an increase in temperature. GST abundance increased with an increase in temperature in both brain and heart tissues. This indicates an increase in oxidative stress in heart and brain brought on by thermal stress (Madeira et al. 2013). The liver tissue was the only tissue that showed an increase caspase 3 abundance with the increase in salinity which indicates higher rates of apoptosis with increased salinity (Wang and Lenardo 2000;

Cheng et al. 2015; Cheng et al. 2018). HSP70 and ubiquitin increased abundancies with

the increased temperature indicates that the liver is primarily under thermal stress. This means that there is an increase in protein denaturation, misfolded and damaged proteins, and an increase damaged protein aggregation in the liver (Sardella et al. 2009;

Todgham et al. 2005; Somero 2002; Basu et al. 2002; Pratt 1993).

Despite the fact that significant relationships were found between protein abundances and abiotic and biotic variables, there was a high degree of variation as represented by low R² values. This was potentially due to the high amount of variability in the sampling and analysis, despite all efforts to minimize it. The first introduction of variability is the small number of samples from each location during each season.

Adding more samples in the South Sound and in the summer collection season especially would decrease the variability. Secondly, autofluorescence brought on by the length of formalin fixation added variability by increasing the variable amounts of overall background fluorescence in the collected tissues. In order to correct this for the future, samples should only be left fixed in formalin for a uniform short length of time.

Finally, there is possible user error and bias in the ImageJ quantifications by manually highlighting the tissues for quantification CONCLUSIONS

In conclusion, there was an upregulation of stress related proteins resulting from increased temperatures within the Puget Sound surf smelt. Additionally, there was an increase of caspase 3 with salinity. The relationship seen between thermal and osmotic stress and the upregulation of caspase 3, GST, HSP70 and ubiquitin in the surf smelt are supported by multiple studies describing the relationships between abiotic stressors and the upregulation of stress related proteins in the literature. There was the unexpected introduction of a biotic stressor (the isopod gill parasites), but the parasite prevalence wasn’t related to the specific stress protein abundance. Considering that there were increasing abundancies of stress related proteins found in all the tissues with increasing thermal and osmotic stress, it can be concluded that the Puget Sound is potentially a stressful environment for surf smelt. The Puget Sound is specifically stressful for surf smelt in the summer. However, this thesis’ findings were unable to correlate the deviation in spawning frequencies with the environmental stress surf smelt are experiencing.

The stress related proteins used in this thesis can serve as bioindicators of sublethal stress in order to assess the population health of surf smelt. As abiotic variability continues to increase in the wake of climate change, it will become of greater importance to continually monitor the population health forage fishes such as the surf smelt at the sublethal level, when there is still potentially a chance for recovery. This

42 43

initial assessment was essentially an early warning and has allowed time for intervention before a population crash occurs.

FUTURE DIRECTIONS

GST abundance had a significant and positive relationship to increasing temperatures in two out of three tissues. This finding would make GST a prime candidate as a bioindicator of thermal stress for fisheries management use in assessing overall surf smelt population health. This could be further developed by a direct cause and effect study. Similarly, the liver may potentially be a tissue of importance in assessing population stress levels since HSP70, ubiquitin and caspase 3 abundance had a significant positive relationship with thermal and osmotic stressors. Future studies should focus on validating the use of liver over other tissues for its utility in assessing physiological stress. Also, there is an opportunity to further investigate the previously undocumented parasitic relationship between the isopods found in the gill and the surf smelt. Since the upregulation of osmotic, thermal and oxidative related proteins were not shown to increase with parasite presence and intensity, further investigation can look at the relationship between the parasites and hypoxemic stress and those related protein abundancies.

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APPENDIX A

T-TEST: CASPASE 3, GLUTATHIONE S-TRANSFERASE, HEAT SHOCK PROTEIN 70, AND UBIQUITIN FLORESCENCE IN THE BRAIN AND PARASITE INTENSITY

APPENDIX B

T-TEST: CASPASE 3, GLUTATHIONE S-TRANSFERASE, HEAT SHOCK PROTEIN 70, AND UBIQUITIN FLORESCENCE IN THE HEART AND PARASITE INTENSITY 60 61

APPENDIX C

T-TEST: CASPASE 3, GLUTATHIONE S-TRANSFERASE, HEAT SHOCK PROTEIN 70, AND UBIQUITIN FLORESCENCE IN THE LIVER AND PARASITE INTENSITY 62 63

APPENDIX D

STEPWISE BACKWARDS LINEAR REGRESSION OF CASPASE 3 FLORESCENCE IN THE LIVER

Backward Stepwise Regression:

Dependent Variable: Caspase 3 in Liver

F-to-Enter: 4.000 P = 0.048

F-to-Remove: 3.900 P = 0.051

Model 1 Step 0: Standard Error of Estimate = 36213761.456

Analysis of Variance: Group DF SS MS F P Regression 3 1.270E+016 4.235E+015 3.229 0.026 Residual 92 1.207E+017 1.311E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 23721813.590 15278826.004 Temperature 1937557.159 0.142 1538077.857 1.587 0.211 Salinity 839889.741 0.233 400432.036 4.399 0.039 PP -1889654.868 -0.0375 5565364.993 0.115 0.735

Variables not in Model Group F-to-Enter P

Model 2 Step 1: PP Removed R = 0.307 Rsqr = 0.094 Adj Rsqr = 0.075 Standard Error of Estimate = 36041098.163

Analysis of Variance: Group DF SS MS F P Regression 2 1.255E+016 6.277E+015 4.832 0.010 Residual 93 1.208E+017 1.299E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 21856357.532 14188863.348 Temperature 2140747.832 0.157 1410129.210 2.305 0.132 Salinity 793238.660 0.220 374329.355 4.491 0.037 64

Variables not in Model Group F-to-Enter P PP 0.115 0.735

Model 3 Step 2: Temperature Removed R = 0.268 Rsqr = 0.072 Adj Rsqr = 0.062 Standard Error of Estimate = 36290356.408

Analysis of Variance: Group DF SS MS F P Regression 1 9.559E+015 9.559E+015 7.259 0.008 Residual 94 1.238E+017 1.317E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 40011509.316 7688725.135 Salinity 966900.780 0.268 358887.169 7.259 0.008

Variables not in Model Group F-to-Enter P Temperature 2.305 0.132 PP 0.806 0.372

Summary Table Step # Vars. Entered Vars. Removed R RSqr Delta RSqr Vars in Model

1 PP 0.307 0.0941 0.0941 2 2 Temperature 0.268 0.0717 -0.0224 1

The dependent variable Casp Liver can be predicted from a linear combination of the independent variables: P Salinity 0.008

The following variables did not significantly add to the ability of the equation to predict Casp Liver and were not included in the final equation: Temperature PP

Normality Test (Shapiro-Wilk) Failed (P = <0.001)

Constant Variance Test (Spearman Rank Correlation): Failed (P = 0.028)

Power of performed test with alpha = 0.050: 0.754 65

APPENDIX E

STEPWISE BACKWARDS LINEAR REGRESSION OF GLUTATHIONE S- TRANSFERASE FLORESCENCE IN THE BRAIN

Backward Stepwise Regression:

Dependent Variable: GST in Brain

F-to-Enter: 4.000 P = 0.048

F-to-Remove: 3.900 P = 0.051

Model 1 Step 0: Standard Error of Estimate = 66880217.433

Analysis of Variance: Group DF SS MS F P Regression 3 8.306E+016 2.769E+016 6.189 <0.001 Residual 92 4.115E+017 4.473E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 3726364.683 28217207.057 Temperature 9592800.339 0.366 2840549.486 11.405 0.001 Salinity -280460.673 -0.0403 739524.992 0.144 0.705 PP -11431711.556 -0.118 10278214.851 1.237 0.269

Variables not in Model Group F-to-Enter P

Model 2 Step 1: Salinity Removed R = 0.408 Rsqr = 0.167 Adj Rsqr = 0.149 Standard Error of Estimate = 66571650.316

Analysis of Variance: Group DF SS MS F P Regression 2 8.241E+016 4.121E+016 9.298 <0.001 Residual 93 4.122E+017 4.432E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 3250803.611 28059272.232 Temperature 9164131.580 0.350 2593948.820 12.481 <0.001 PP -12769171.740 -0.132 9609704.436 1.766 0.187 66

Variables not in Model Group F-to-Enter P Salinity 0.144 0.705

Model 3 Step 2: PP Removed R = 0.388 Rsqr = 0.151 Adj Rsqr = 0.142 Standard Error of Estimate = 66842221.712

Analysis of Variance: Group DF SS MS F P Regression 1 7.459E+016 7.459E+016 16.694 <0.001 Residual 94 4.200E+017 4.468E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -11642811.174 25827930.183 Temperature 10174270.317 0.388 2490133.308 16.694 <0.001

Variables not in Model Group F-to-Enter P Salinity 0.655 0.421 PP 1.766 0.187

Summary Table Step # Vars. Entered Vars. Removed R RSqr Delta RSqr Vars in Model

1 Salinity 0.408 0.167 0.167 2 2 PP 0.388 0.151 -0.0158 1

The dependent variable GST Brain can be predicted from a linear combination of the independent variables: P Temperature <0.001

The following variables did not significantly add to the ability of the equation to predict GST Brain and were not included in the final equation: Salinity PP

Normality Test (Shapiro-Wilk) Failed (P = <0.001)

Constant Variance Test (Spearman Rank Correlation): Failed (P = <0.001)

Power of performed test with alpha = 0.050: 0.977 67

APPENDIX F

STEPWISE BACKWARDS LINEAR REGRESSION OF GLUTATHIONE S- TRANSFERASE FLORESCENCE IN THE HEART

Backward Stepwise Regression:

Dependent Variable: GST in Heart

F-to-Enter: 4.000 P = 0.048

F-to-Remove: 3.900 P = 0.051

Model 1 Step 0: Standard Error of Estimate = 52331612.736

Analysis of Variance: Group DF SS MS F P Regression 3 4.159E+016 1.386E+016 5.062 0.003 Residual 92 2.520E+017 2.739E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 10025879.914 22079054.298 Temperature 5320063.649 0.264 2222638.343 5.729 0.019 Salinity 621244.069 0.116 578654.451 1.153 0.286 PP -11612920.090 -0.155 8042371.569 2.085 0.152

Variables not in Model Group F-to-Enter P

Model 2 Step 1: Salinity Removed R = 0.362 Rsqr = 0.131 Adj Rsqr = 0.112 Standard Error of Estimate = 52374535.016

Analysis of Variance: Group DF SS MS F P Regression 2 3.843E+016 1.922E+016 7.006 0.001 Residual 93 2.551E+017 2.743E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 11079287.839 22075332.805 Temperature 6269601.141 0.311 2040761.535 9.438 0.003 PP -8650332.862 -0.116 7560332.350 1.309 0.255 68

Variables not in Model Group F-to-Enter P Salinity 1.153 0.286 Model 3 Step 2: PP Removed R = 0.345 Rsqr = 0.119 Adj Rsqr = 0.109 Standard Error of Estimate = 52460585.135

Analysis of Variance: Group DF SS MS F P Regression 1 3.484E+016 3.484E+016 12.660 <0.001 Residual 94 2.587E+017 2.752E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant 989774.750 20270845.216 Temperature 6953908.352 0.345 1954361.286 12.660 <0.001

Variables not in Model Group F-to-Enter P Salinity 0.374 0.542 PP 1.309 0.255

Summary Table Step # Vars. Entered Vars. Removed R RSqr Delta RSqr Vars in Model

1 Salinity 0.362 0.131 0.131 2 2 PP 0.345 0.119 -0.0122 1

The dependent variable GST Heart can be predicted from a linear combination of the independent variables: P Temperature <0.001

The following variables did not significantly add to the ability of the equation to predict GST Heart and were not included in the final equation: Salinity PP

Normality Test (Shapiro-Wilk) Failed (P = <0.001)

Constant Variance Test (Spearman Rank Correlation): Passed (P = 0.067)

Power of performed test with alpha = 0.050: 0.934 69

APPENDIX G

STEPWISE BACKWARDS LINEAR REGRESSION OF HEAT SHOCK PROTEIN 70 FLORESCENCE IN THE LIVER

Backward Stepwise Regression:

Dependent Variable: HSP 70 in Liver

F-to-Enter: 4.000 P = 0.048

F-to-Remove: 3.900 P = 0.051

Model 1 Step 0: Standard Error of Estimate = 53126322.028

Analysis of Variance:

Group DF SS MS F P Regression 3 5.942E+016 1.981E+016 7.018 <0.001 Residual 92 2.597E+017 2.822E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -24072953.424 22414347.416 Temperature 10318643.153 0.490 2256391.389 20.913 <0.001 Salinity -875052.981 -0.157 587441.913 2.219 0.140 PP 14707387.811 0.189 8164503.242 3.245 0.075

Variables not in Model Group F-to-Enter P

Model 2 Step 1: Salinity Removed R = 0.408 Rsqr = 0.167 Adj Rsqr = 0.149 Standard Error of Estimate = 53473339.085

Analysis of Variance: Group DF SS MS F P Regression 2 5.316E+016 2.658E+016 9.296 <0.001 Residual 93 2.659E+017 2.859E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -25556730.558 22538467.523 Temperature 8981172.660 0.427 2083576.179 18.580 <0.001 PP 10534437.254 0.135 7718946.149 1.863 0.176 70

Variables not in Model Group F-to-Enter P Salinity 2.219 0.140 Model 3 Step 2: PP Removed R = 0.387 Rsqr = 0.150 Adj Rsqr = 0.141 Standard Error of Estimate = 53718114.675

Analysis of Variance: Group DF SS MS F P Regression 1 4.783E+016 4.783E+016 16.577 <0.001 Residual 94 2.712E+017 2.886E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -13269649.326 20756756.432 Temperature 8147818.441 0.387 2001209.164 16.577 <0.001

Variables not in Model Group F-to-Enter P Salinity 0.841 0.362 PP 1.863 0.176

Summary Table Step # Vars. Entered Vars. Removed R RSqr Delta RSqr Vars in Model

1 Salinity 0.408 0.167 0.167 2 2 PP 0.387 0.150 -0.0167 1

The dependent variable HSP Liver can be predicted from a linear combination of the independent variables: P Temperature <0.001

The following variables did not significantly add to the ability of the equation to predict HSP Liver and were not included in the final equation: Salinity PP

Normality Test (Shapiro-Wilk) Failed (P = <0.001)

Constant Variance Test (Spearman Rank Correlation): Failed (P = 0.006)

Power of performed test with alpha = 0.050: 0.976 71

APPENDIX H

STEPWISE BACKWARDS LINEAR REGRESSION OF UBIQUITIN FLORESCENCE IN THE LIVER

Backward Stepwise Regression:

Dependent Variable: Ubiquitin in Liver

F-to-Enter: 4.000 P = 0.048

F-to-Remove: 3.900 P = 0.051

Model 1 Step 0: Standard Error of Estimate = 68624948.084

Analysis of Variance: Group DF SS MS F P Regression 3 6.106E+016 2.035E+016 4.322 0.007 Residual 92 4.333E+017 4.709E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -15728731.187 28953320.483 Temperature 8390443.387 0.320 2914652.023 8.287 0.005 Salinity 636299.577 0.0915 758817.273 0.703 0.404 PP 7365642.586 0.0759 10546346.702 0.488 0.487

Variables not in Model Group F-to-Enter P

Model 2 Step 1: PP Removed R = 0.345 Rsqr = 0.119 Adj Rsqr = 0.100 Standard Error of Estimate = 68435700.070

Analysis of Variance: Group DF SS MS F P Regression 2 5.876E+016 2.938E+016 6.273 0.003 Residual 93 4.356E+017 4.683E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -8457412.590 26942153.427 Temperature 7598431.097 0.290 2677587.105 8.053 0.006 Salinity 818139.759 0.118 710785.542 1.325 0.253 72

Variables not in Model Group F-to-Enter P PP 0.488 0.487

Model 3 Step 2: Salinity Removed R = 0.326 Rsqr = 0.106 Adj Rsqr = 0.097 Standard Error of Estimate = 68553862.198

Analysis of Variance: Group DF SS MS F P Regression 1 5.255E+016 5.255E+016 11.182 0.001 Residual 94 4.418E+017 4.700E+015

Variables in Model Group Coef. Std. Coeff. Std. Error F-to-Remove P Constant -2519486.341 26489310.518 Temperature 8540271.093 0.326 2553898.588 11.182 0.001

Variables not in Model Group F-to-Enter P Salinity 1.325 0.253 PP 1.106 0.296

Summary Table Step # Vars. Entered Vars. Removed R RSqr Delta RSqr Vars in Model

1 PP 0.345 0.119 0.119 2 2 Salinity 0.326 0.106 -0.0126 1

The dependent variable Ub Liver can be predicted from a linear combination of the independent variables: P Temperature 0.001

The following variables did not significantly add to the ability of the equation to predict Ub Liver and were not included in the final equation: Salinity PP

Normality Test (Shapiro-Wilk) Failed (P = <0.001)

Constant Variance Test (Spearman Rank Correlation): Failed (P = <0.001)

Power of performed test with alpha = 0.050: 0.904