The Response of Estuarine Bivalve Austrovenus Stutchburyi to Hypoxic Multiple-Stressor Environments

The response of estuarine bivalve Austrovenus stutchburyi to hypoxic multiple-stressor environments

Nichola Helen Salmond

A thesis submitted for the degree of Master of Science (Marine Science) University of Otago Dunedin June 2020

Abstract Eutrophication-induced hypoxia is amongst the most widespread anthropogenically deleterious environmental issue occurring globally in coastal marine environments. Along with habitat loss, overfishing and harmful algal blooms, hypoxia contributes to a suite of major stressors on marine organisms. These cause stress to aerobic organisms, changes to biogeochemical processes and loss of biodiversity and ecosystem function, particularly in estuaries.

The response of a species to a single stressor is often very different when compared to exposure in an environment with multiple stressors. The focus of the current thesis is on the sublethal and lethal effects of hypoxia, warming and nutrient enrichment on the estuarine bivalve Austrovenus stutchburyi. Hypoxia and warming rarely occur in isolation as stressors that shape a species or community response. Midday low tides have been identified as a driver of mortality events for bivalves globally as a combined result of thermal stress and desiccation. Laboratory experiments were used to test the effects of simulated midday low tides and chronic NH3 exposure on activity, respiration and survival of A. stutchburyi experiencing oxygen depletion.

A long-term decline in oxygen solubility associated with warming was identified for Coastal Otago. Monitoring of bottom-water dissolved oxygen (DO) in a shallow microtidal estuary on the South East Coast of New Zealand was undertaken to bridge observational and experimental work. This monitoring revealed diel- and seasonal DO cycling. Modelling indicated spatial variability within Waitati Inlet. Land-based influence from freshwater and tidal forcing was driving low DO in Waitati channel, while PAR and wind speed were key drivers of biological respiration and wind-induced mixing in Warrington channel which has Ulva spp., present in it. Daily DO minima occurred primarily early morning at low tide as a likely consequence of respiration by primary producers and the tidal ebb carrying water depleted of oxygen.

Siphon activity and respiration rates were enhanced under hypoxia (20% saturation) compared to normoxia in 22 °C and 25 °C water conditions, while survivorship decreased with exposure to increasingly stressful environments. Experimental findings indicated the likely existence of a positive feedback loop whereby A. stutchburyi, stressed by low oxygen, produce more ammonia waste, increasing risk of mortality and subsequent NH3 concentrations. A lethal emersion thermal threshold was surpassed at 33 °C in 20% and 100% DO, suggesting extreme thermal events such as heatwaves could have substantial detrimental implications for bivalve populations and subsequent ecosystem functions.

The findings of the present thesis are relevant to understanding the likely biological consequences of observed patterns of warming in the coastal marine environment and the decline in oxygen solubility observed in Otago. The increased biological oxygen demand due to warming may lead to more frequent hypoxic events and degradation of estuarine environments.

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Table of Contents

Abstract ...... ii

Table of Contents ...... iv

Acknowledgments...... vii

List of Figures ...... viii

List of Tables ...... xi

Chapter One : General Introduction ...... 1

General Introduction ...... 2

Drivers of Oxygen Depletion in Estuaries ...... 3

Seasonality and Climate ...... 3

Nutrients and Eutrophication ...... 6

Temperature and the Physiological Implications ...... 7

Response to Hypoxia ...... 7

Repercussion of Hypoxia ...... 8

Study Species: Austrovenus stutchburyi ...... 9

The Main Issue ...... 10

Objectives ...... 11

Overview ...... 11

Chapter Two : Spatial and temporal variability of dissolved oxygen in two channels of Waitati Inlet, Otago ...... 13

Introduction ...... 14

Methods...... 16

Study Area ...... 16

Theoretical Oxygen Saturation ...... 18

PML SST and DO Anomaly ...... 19

Bottom Dissolved Oxygen Time Series ...... 19

Waitati Inlet in situ DO Deviations ...... 21

Candidate Explanatory Variable Selection ...... 21

Data Analysis ...... 22

Results ...... 23

PML SST and DO Anomaly ...... 23

Variability in Waitati Inlet Bottom Water Dissolved Oxygen ...... 25

Theoretical vs. Actual Timeseries and Temporal Cycling...... 34

Discussion ...... 35

PML SST and DO Anomaly ...... 35

Variability in Bottom Water Dissolved Oxygen ...... 35

Chapter Three : The interaction of temperature and hypoxia on Austrovenus stutchburyi and influence of chronic ammonia dosing ...... 42

Introduction ...... 43

Nitrogen Cycle ...... 43

Ammonia...... 45

Benthic Bivalves and their Ecosystem Services ...... 46

Study species ...... 47

Materials and Methods:...... 49

Collection and Acclimation Protocol ...... 49

Experimental setup...... 49

Survival ...... 50

Siphons ...... 51

Respiration ...... 52

Statistical Analysis ...... 53

Results ...... 54

Effects of DO and Temperature ...... 54

Ammonia...... 56

Discussion ...... 59

Effects of DO and Temperature ...... 59

Ammonia...... 60

Chapter Four : When does a challenge become a death sentence? Understanding the response of Austrovenus stutchburyi to extreme conditions under oxygen depletion ...... 64

Introduction ...... 65

Materials and Methods ...... 70

Experimental Setup ...... 70

Low Tide Exposure ...... 72

Respiration ...... 73

Statistical Analysis...... 74

Results ...... 75

Siphons ...... 75

Respiration ...... 75

Survivorship ...... 76

Discussion ...... 79

Chapter Five : General Discussion ...... 85

Study Aims...... 86

Main findings ...... 87

Ecological Importance, Implications, and the Wider Context ...... 90

Future Research ...... 92

Conclusion ...... 92

References ...... 94

Appendix ...... 126

Acknowledgments Thank you, Professor Steve Wing, for sharing your knowledge, providing guidance, and offering support. Your input has been invaluable throughout this journey, and without it, this thesis would not be as it is today. Thank you to my second supervisor, Dr Marc Schallenberg for the advice, support and for showing me the value of engaging with communities to make a positive difference for everyone.

To all the staff at Portobello Marine Laboratory and especially Dr. Doug Mackie, Linda Groenewegen, Dave Wilson and Reuben Pooley, I cannot thank you all enough for helping to bring the experiments to fruition, suggesting alternative solutions, offering advice and making equipment; you are the backbone to many projects which come out of Marine. Thank you, Doug, for allowing me access to the Portobello SST timeseries, for letting me to knock on your office door whenever I needed help and for giving me opportunities to extend myself; you really are an unofficial supervisor. Furthermore, students and staff are indebted to Dr. Elizabeth (Betty) Bathman for her insight in establishing the Portobello SST data set in 1953.

Thank you to John Campbell for helping with skippering the RV Tūhura during my field deployments into Waitati Inlet; you were supportive, whether it was a lovely sunny day or a howling southerly. Thank you also to Sorrel O’Connell-Milne for taking me under your wing during the early stages of my MSc. I appreciated the chats, the help with field work and the enthusiasm you brought. To my fellow students, Leo Durante, Tim Howarth, Rebecca McMullin, Brad Lamont and Jacquetta Udy, many thanks for your help and strength with the deployments; without you all I could not have managed. A special thank you to Sean Heseltine for your patience, support and friendship you have given me throughout this journey. Whether it was clarification on boat work, technical problems or if I just needed to chat, you would always listen and provide a laugh which I appreciate immensely.

Tim Jowett, thank you for your help and advice with the statistics it was invaluable. To my other fellow students and friends, thanks for providing laughs, advice, distractions and support in the good and tough times.

Finally, to my parents, Helen and Malcolm, you shared your love for nature and the outdoors with me; you have been the greatest support and encouragement of all. You have shown me how strong I am and have provided me with opportunities to develop the skills and tenacity which has allowed me to get to where I am now. Thank you.

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List of Figures

Figure 1.1. Positive feedback loop which can be initiated from anywhere within the loop and cause extended bottom hypoxia, altering infauna and biogeochemical processes. Green boxes indicate exogenous drivers. Dissolved organic carbon (DOC), nutrient flux, atmospheric reactive nitrogen

(Nr) and warming temperature. Arrows indicate direction of expected change. Diagram reproduced from Jager et al. (2018)...... 4 Figure 1.2. Possible effects of climate change and development of hypoxia in coastal waters. Figure based on Justić et al. (2007) ...... 5 Figure 2.1. Left: Location of deployment sites in Waitati Inlet, New Zealand. Yellow and blue markers represent Waitati and Warrington channel deployment sites respectively. Right: Location of Portobello sea surface temperature and theoretical dissolved oxygen datasets in the Otago Harbour in relation to Waitati Inlet...... 17 Figure 2.2. Average, minimum and maximum theoretical A) oxygen concentration (mg L-1) B) oxygen anomaly (mg L-1) value based on the 65-year (1953-2018) Portobello sea surface temperature time-series. Theoretical dissolved oxygen derived from Sverdrup et al. (1942) and Richards (1957)...... 24 Figure 2.3. Observed (blue) and theoretical (orange) dissolved oxygen (mg L-1) concentration for May 2014 to November 2018 Portobello TDO timeseries. Measurements recorded at 30 minute intervals. Gap in 2017 data represents period of missing data...... 24 Figure 2.4. Frequency distribution of theoretical dissolved oxygen (DO mg L-1) anomaly between 1st December to 28th February as deviations away from the daily DO mean of the 65-year Portobello SST timeseries. 1963/64 represents a cold summer while 2017/18 represents a marine heatwave. Positive and negative values represent an increase and decrease in measured oxygen saturation, respectively...... 25 Figure 2.5. Mean dissolved oxygen (mg L-1) values throughout each deployment. Vertical bars represent the minimum and maximum values recorded for A) Waitati channel and B) Warrington channel. Deployments not connected by the same letter are significantly different...... 27 Figure 2.6 Time series of A) dissolved oxygen (mg L-1), B) water temperature (°C), C) water depth (m) in the bottom waters of Waitati channel, Waitati Inlet and corresponding D) PAR (photosynthetically active radiation; μmol m-2 s-1) and E) wind speed (ms-1) taken from Department of Physics, University of Otago during January 2019. Shaded areas represent sunset to sunrise...... 29 Figure 2.7. Daily variability in environmental drivers across all deployment days for Waitati channel (summer to late winter). Grey line represents the mean value of associated environmental

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drivers. A) Oxygen concentrations (mg L-1), B) water temperature (°C), C) salinity (psu), D) photosynthetically active radiation 3 hour lag (PAR; μmol m-2 s-1) and E) wind speed (ms-1) ..... 30 Figure 2.8. Daily variability in environmental drivers across all deployment days for Warrington channel (summer to late autumn). Grey line represents the mean value of associated environmental drivers. A) Oxygen concentration (mg L-1), B) water temperature (°C), C) salinity (psu), D) photosynthetically active radiation 3 hour lag (PAR; μmol m-2 s-1) and E) wind speed (ms- 1) ...... 31 Figure 2.9. Regression model from top to bottom of daily mean salinity (psu; R2 = 0.44, p = 0.0004), daily 90th quantile of PAR (μmol m-2 s-1; R2 = 0.06, p = 0.23) and daily 75th quantile of wind speed (ms-1; R2 = 0.03, p = 0.34) against daily dissolved oxygen 10th quantile (mg L-1) for Waitati channel. Green line represents mean value, while red line and shading represent linear fit and 95% CI. n = 24 ...... 32 Figure 2.10. Regression model from top to bottom of daily mean salinity (psu; R2 = 0.03, p = 0.45), daily 75th quantile of PAR (μmol m-2 s-1; R2 = 0.02, p = 0.49) and daily 75th quantile of wind speed (ms-1; R2 = 0.004, p = 0.78) against daily dissolved oxygen 10th quantile (mg L-1) for Warrington channel. Green line represents mean value, while red line and shading represent linear fit and 95% CI. n = 19 ...... 33 Figure 2.11. Percent deviation of actual dissolved oxygenaway from the theoretical oxygen saturation (mg L-1) within Waitati channel, Waitati Inlet, NZ in A) summer (15 - 21 Janurary 2019) and B) autumn (16 - 22 May 2019). Calculation derived from Richards (1957). 1-hour mean PAR represented by the orange line. Time of day represents 12-noon and midnight for each day of the 6-day deployments...... 34 Figure 3.1. Experimental design for reservoir setup for hypoxic laboratory experiments. From the

water outflow, seawater was pumped into the corresponding experimental tank. N2 bubbling

within tank for the 100% saturation is connected to an airline rather than N2 cylinder ...... 50 Figure 3.2. Inflexion points representing the time (hours) Austrovenus stutchburyi were respiring between the opening and closing of the two valves...... 53 Figure 3.3. Average percent siphon activity (±1 SE) of Austrovenus stutchburyi exposed to 10, 20 and 100% dissolved oxygen saturations at 22 °C or 25 °C water temperatures n = 6. Levels not connected by the same letter are significantly different α = 0.05 ...... 54 Figure 3.4. Response of Austrovenus stutchburyi exposed to 22 °C and 25 °C water temperatures under hypoxia (light grey) and normoxia (dark grey) for A) average percentage active siphons(±1 SE)

-1 -1 -1 (n=12) and B) respiration rate (mg O2 L g(AFDW) h ) (L-R: n = 9, 15, 10 and 12). Whiskers indicate maximum and minimum values with dots indicating outliers. Box limits represent 25th

and 75th percentiles and the internal white lines are median values. Levels not connected by the same letter are significantly different α = 0.05 ...... 55 Figure 3.5. Austrovenus stutchburyi survival probability (%). Crosses represent removal of a predetermined censored individuals. Shaded areas represent 95% confidence intervals. n = 6...... 56

-1 Figure 3.6. Peak ammonia concentration (NH3 µg L ) in either hypoxia and normoxia under ambient (dark grey) or dosed (light grey) treatments each containing 15 Austrovenus. Whiskers indicate maximum and minimum values. Box limits represent 25th and 75th percentiles and white solid lines within boxes are median values. Extreme values from the HyD (967, 316, 222 and 189µg

-1 NH3 L ) were excluded graphically due to skewing of y-axis. n = 36. Levels not connected by the same letter are significantly different α = 0.05 ...... 57

Figure 3.7. Spiked NH3 treatment response of Austrovenus stutchburyi to A) average daily active

-1 -1 -1 siphons (%) (±1 SE) over days 1-23 (n = 3) and B) respiration rate (mg O2 L g(AFDW) h ) under 22 oC hypoxia (n = 8) and normoxia (n = 6). Whiskers indicate maximum and minimum values. Box limits represent 25th and 75th percentiles and white solid lines within boxes are median values. Levels not connected by the same letter are significantly different α = 0.05 ...... 58 Figure 3.8. Mean survivorship probability of Austrovenus stutchburyi exposed to 20% or 100% oxygen

and dosed or ambient NH3. Crosses indicate the removal of censored individuals. Shaded areas represent 95% confidence intervals. n = 3 ...... 58 Figure 4.1. Experimental emersion of A. stutchburyi to midday low tides ...... 72 Figure 4.2. Inflexion points indicating the start and end point of A. stutchburyi respiring ...... 72 Figure 4.3. Interaction effects on the average daily siphon activity (%) (A-C) and mean respiration rate

-1 -1 -1 (mg O2 L g(AFDW) h (D-F) of Austrovenus stutchburyi under multiple stressor conditions in either hypoxic (light grey) or normoxic (dark grey) conditions. A-C error bars represent ±1 SE. D- F box plots represent 25th and 75th quartiles, medium, minimum and maximum values. Levels not connected by the same letter are significantly different...... 77 Figure 4.4. Mean survivorship of Austrovenus stutchburyi under hypoxic or normoxic conditions when exposed to different challenges. Small (22 °C water, 25 °C emersion), medium (25 °C water, 25 °C emersion) and large (25 °C water, 33 °C emersion) (n = 3). Shading represents 95% CI through time. Pluses (+) on lines indicate time of removal of predetermined censored individual...... 78

List of Tables

Table 2.1. Date, location, and data loggers used in the deployments within Waitati Inlet. RBR CT & CTD measured conductivity, temperature and depth, Turner Designs® C3 submersible fluorometer was used to measure chlorophyll a and D-Opto to measure dissolved oxygen...... 21 Table 2.2. Breakdown of the actual dissolved oxygen saturations (%) within Waitati Inlet, Otago, classified into four categories based on Wenner et al. (2004) and ANZECC/ARMCANZ (2000) guidelines. Values represent percentage of observations within each category seasonally and spatially. Warrington winter excluded due to significant DO drift. Waitati spring measurements performed with D-Opto dissolved oxygen meter...... 26 Table 2.3. Relative explanatory power of environmental indicators for dissolved oxygen (mg L-1) in models observing differences in either Waitati (top) or Warrington (bottom) channels corrected Akaike’s information criterion (AICc) model selection criteria. K: no. of model parameters; RSS:

residual sum of squares; Δi: difference in model AICc from minimum AICc; wi: model AICc weight. Salinity: daily mean salinity (psu), photosynthetically active radiation 3hr lag: PAR-3hr and wind: wind speed (ms-1). 75th and 90th represent the daily quantiles...... 28 Table 3.1. Survivorship risk ratios for Austrovenus stutchburyi between oxygen treatments (100, 20 and 10 % saturation) with upper and lower 95% confidence intervals. Value greater than one indicates elevated risk. Reciprocal ratio (RR) is the inverse. All differences are statistically significant (p < 0.0001) ...... 55 Table 4.1. Experimental treatments for models I-III with associated experimental duration (days),

mean days to onset of mortality (±1 SE) and median LT50 (CI 95%) for Austrovenus stutchburyi. 25+ and 36+ represents no Austrovenus died within the given time frame. 35.0 (N/A) indicates

only one individual died. Treatments which did not reach 50% mortality (LT50) are represented by N/A. Levels not connected by the same letter are significantly different...... 72 Table 4.2. Chi-squared values from Kaplan-Meier survival analysis comparison of stressors oxygen and emersion temperature against other treatments for Austrovenus stutchburyi held at 25 °C water. Overall 33 °C vs 25 °C Exposure at 25 °C water temperature; Chisq = 171 on 3df, p < 2e-16 ...... 78

Chapter One: General Introduction

1 | GENERAL INTRODUCTION General Introduction The ecological importance of oxygen concentration in many ecosystems cannot be overstated. Oxygen is an element essential for regulation of physiological processes at cellular and organismal level and for regulation of global cycles of carbon and major nutrients (Breitburg et al. 2018). Along with oxygen, temperature, pH, and net primary productivity (NPP) are fundamental in controlling marine ecosystems, with any change capable of causing ecosystem alteration (Fischlin et al. 2007). Deoxygenation of marine environments has occurred since at least the 20th century (Diaz and Rosenberg 2008, Keeling et al. 2010), and it is projected to accelerate with climate change induced warming (Bopp et al. 2013). Traditionally defined as -1 -1 water falling below 2 mg O2 L (ca. 1.4 ml O2 L , approx. 30% saturation) (Diaz and Rosenberg 1995, Diaz 2001), the critical oxygen threshold, referred to as hypoxia, is quickly becoming a common global occurrence in the open ocean and coastal zones (Diaz and Rosenberg 2008, Breitburg et al. 2018), having increased at an exponential rate in the past 60 years. Hypoxic events are now considered a major threat to shallow coastal systems worldwide, rating above overfishing, habitat loss and harmful algal blooms as the most deleterious anthropogenic influence in some regions (Diaz 2001, Vaquer-Sunyer and Duarte 2008).

Long-term scientific observations of systems such as the Adriatic Sea, where historically there was little evidence of hypoxia, now have regular hypoxic events (Justić 1987, Nerlovic et al. 2011 and references within). Worldwide, near-bottom water hypoxia is estimated to cover 245,000km2 (Diaz and Rosenberg 2008). Similarly, an increase in organic matter (OM) production in areas that have historically experienced hypoxia, have shown an increase in the severity, expansion and duration of hypoxia such as in Chesapeake Bay and Baltic Sea where the Baltic hypoxic area is more than 6-fold greater than pre 1950 at >60,000 km2 (Larsson et al. 1985, Hagy et al. 2004, Conley et al. 2009, Carstensen et al. 2014, Hansson et al. 2019). The increased observations of hypoxic events are not simply an artefact of increased research effort as the significance of the phenomenon is understood, but a true increase in severity and extent of the process (Altieri and Diaz 2019).

Marine productivity, biodiversity and biogeochemical cycling are all influenced by deoxygenation (Rabalais et al. 2014, Breitburg et al. 2018). Implications of changes in the marine environment can be recognised in ecosystem services such as fisheries changing and range shifts as well as alterations in food webs (Diaz 2001, Sumaila et al. 2011), this highlighting the serious nature of the hypoxia phenomenon. Subsequently, deoxygenation has gained attention with reporting and research into low oxygen events having increased since the

2 |GENERAL INTRODUCTION 1950/60’s (Barlow et al. 1963, Gilbert et al. 2010, Keeling et al. 2010), where we have seen a 2% decline in oceanic oxygen content (Schmidtko et al. 2017).

DO concentrations have been found to be declining in coastal zones faster than open oceans, attributed primarily to eutrophication in areas that receive riverine inputs and sediments carrying nutrients (Gilbert et al. 2010). Along with this, the growth rate of coastal hypoxia was reported by Vaquer-Sunyer and Duarte (2008) as exponential, increasing at a rate of 5.5% per year. Natural processes can also lead to hypoxia and anoxia (≤0.5 mg L-1) such as oxygen minimum zones (OMZ’s) in ocean basins, which can persist for years. More commonly however, coastal hypoxia is a result of anthropogenic activities linked to coastal eutrophication (Rabalais et al. 2014).

Oxygen can be measured in a multitude of ways and reflect differences based on the solubility which varies with temperature, salinity and pressure. The absolute measurement for dissolved oxygen is in mg L-1 and specifies how much DO the water contains, Percent air saturation (% saturation, % a.s.) comparatively is a relative measure of DO and is dependent on the three variables listed above. As a relative measure, two 100% air saturation values may have different conditions require different amounts of DO to reach 100% saturation.

Drivers of Oxygen Depletion in Estuaries Seasonality and Climate Climate change is recognised as a significant influence on coastal and estuarine hypoxia (Breitburg et al. 2018). Alteration to ocean currents, precipitation patterns, sedimentation, biogeochemical cycling, wind-induced mixing, residence time, atmospheric deposition, stratification, irradiance and temperature all have consequences on regulation and supply of oxygen (Figure 1.1) (Levin et al. 2009, Tyler et al. 2009, Llebot et al. 2014).

The characteristics of a water body influence the processes that can result in deoxygenation (Kemp et al. 2009). Upwelling alongside density or thermal driven stratification are common drivers of deoxygenation within deeper water bodies. Due to the nature of shallow temperate estuaries, many experience high spatio-temporal variability, in part owing to their existence at the terrestrial-marine interface (Helmuth et al. 2006b) and therefore, experience stressors and drivers associated with both environments (Pesce et al. 2018, Ayyam et al. 2019). The influence of climatic conditions on oxygen saturation including air temperature, wind, photosynthetically active radiation (PAR) and precipitation are greater in shallow waters which have a low buffering capacity (Madeira et al. 2012, Llebot et al. 2014). Consequently, shallow tidal

3 |GENERAL INTRODUCTION estuaries are more likely to have diel cycling and seasonal patterns of DO (Tyler et al. 2009, Sawabini et al. 2015). This variability occurs through the production and consumption of oxygen and fluctuates with the degree of vertical mixing, water depth, light penetration depth and biomass of algae and macrofauna (Figure 1.1) (Troup et al. 2017, Breitburg et al. 2018, Miyamoto et al. 2019)

The hydrology of an estuarine system influences the dominant form of primary production. Resident phytoplankton populations generally dominant poorly flushed systems which have longer residence times (Swaney et al. 2008, Paerl et al. 2014). Comparatively, a higher flushing rate and low residence time limits the establishment of phytoplankton populations which often reduce irradiance to the bottom waters, instead allowing macroalgal dominance (Valiela et al. 1997, Baird et al. 2004, Plew et al. 2020).

atmospheric reactive nitrogen (Nr) and warming temperature. Arrows indicate direction of expected change. Diagram reproduced from Jager et al. (2018).

4 |GENERAL INTRODUCTION Winter in temperate regions, including New Zealand, is often associated with elevated precipitation, lower irradiance and cooler temperatures. Under the Intergovernmental Panel on Climate Change (IPCC), modelling of changes to rates of precipitation in New Zealand vary by region, with some areas predicted to receive heavier more extreme rainfall events (Ministry for the Environment 2018), leading to increased land erosion and consequent higher export of nutrients, OM and fine sediments into estuaries and coastal zones.

Deposition of nutrients, OM and sediment into estuarine and coastal systems in winter and subsequent increasing temperatures and irradiance during warmer months, produces an environment with suitable growing conditions, that can stimulate excessive primary production in spring and summer (Figure 1.2) (Paerl et al. 1998, Martins et al. 2001, Bright et al. 2019). Eutrophic events can also occur during early autumn, when higher rainfall occurs and good abiotic growing conditions still exist, stimulating further bloom events (Wilding et al. 2012). During the warmer months, the algal biomass tends to be greatest (Carstensen et al. 2007, Miyamoto et al. 2017). The switch between primary producers photosynthesizing during the day and respiring at night, can cause diel DO cycling (D'Avanzo and Kremer 1994). Net DO consumption occurs at night, due to algal and community respiration, biogeochemical processes and decomposition. In many systems, this leads to daily bottom water DO minima occurring around sunrise before algae begin to photosynthesize again (e.g. D'Avanzo and Kremer 1994, D'Avanzo et al. 1996, Tyler et al. 2009, Wilding et al. 2012).

Greenhouse Effects

Increased temperature Increased freshwater input

Increased nutrient flux Increased stratification

Nutrient enrichment Enhanced productivity

Sediment Hypoxia

Figure 1.2. Possible effects of climate change and development of hypoxia in coastal waters. Figure based on Justić et al. (2007)

5 |GENERAL INTRODUCTION Furthermore, benthic microalgae (microphytobenthos, MPB) can play a critical role in the oxygen dynamics of shallow estuaries, being the dominant primary producer in some system contributing up to 50% (Underwood and Kromkamp 1999). Quantifying the annual contribution of benthic microalgae in estuaries and shallow system is difficult due to spatial and temporal heterogeneity as well as the need to incorporate into the calculations tidal dynamics, seasonal and daily changes in irradiance strength, temperature, emersion effects and species composition (Underwood and Kromkamp 1999).

Nutrients and Eutrophication The increase in anthropogenic activity, linked to a growing human population, has led to an imbalance in the nitrogen cycle (Diaz 2001, Levin et al. 2015) and loss of reactive nitrogen

(Nr) to the environment (Erisman et al. 2013). Nutrients primarily enter watersheds and coastal zones in areas surrounding urbanisation and agriculture (Howarth et al. 1988, Howarth 2008). The shift to industrial scale nitrogen fertilizer use in the 1960s’ is a key contributor to the export nutrients into coastal zones (Levin et al. 2015, Bright et al. 2019), stimulating primary production and possible blooms (Vitousek et al. 1997, Howarth 2008). Eutrophication exacerbates diel cycling and the rate of DO depletion in shallow aquatic environments (Miyamoto et al. 2019). Post bloom, the decaying particulate organic matter (POM) sinks to the benthos where bacterial degradation occurs, depleting the DO (Gray et al. 2002, Diaz and Rosenberg 2008, Coffin et al. 2017). Macroalgal mats and dense phytoplankton blooms can smoother the sediment and infauna, restricting water flow and oxygen into the sediment (Norkko and Bonsdorff 1996, Franz and Friedman 2002). If the biological oxygen demand (BOD) exceeds the DO supply, hypoxia can result, causing mortality to fauna, which in turn provides further OM to fuel the microbial loop, increasing the severity of hypoxia, thus causing a positive feedback (Cloern 2001, Pinckney et al. 2001, Levin et al. 2009, Altieri and Diaz 2019).

Positive feedback loops also exist in sediment resulting from eutrophication-induced hypoxia. Limited oxygen exchange and reduced oxygen gradients in the bottom water and across the sediment-water interface (SWI) can alter biogeochemical processes resulting in shifts in fluxes of nutrients and reduced compounds, including ammonia and hydrogen sulphide into the water column (Breitburg 2002, Middelburg and Levin 2009). This positive feedback loop induces further stress on infauna. Given Austrovenus is a sediment dwelling species, the oxygen conditions in the sediment are likely to be highly influential on the species.

6 |GENERAL INTRODUCTION Temperature and the Physiological Implications Eutrophication-induced hypoxia and low oxygen events are expected to be exacerbated by warming (Breitburg et al. 2018). Oxygen solubility is driven by temperature in aquatic environments (Weiss 1970) and temperature is fundamental to the rate at which biochemical reactions and metabolic processes occur (Brown et al. 2004, Veraart et al. 2011, Mahaffey et al. 2020). Under warming, these processes are expected to increase. Respiration (R) is promoted over photosynthesis (P), thus BOD becomes greater than the DO available (Brown et al. 2004) as the P:R ratio decreases, requiring an organism to induce a multiple stressor response to warming and oxygen depletion (Mahaffey et al. 2020). This shift in BOD due to warming has been modelled for a north-eastern Atlantic estuary, whereby a theoretical 4 °C increase in summer water temperature will increase NPP by 20% and heterotrophic metabolism by 43%, thus resulting in greater probability of hypoxia occurring (Harris et al. 2006). Furthermore, respiration-induced acidification is an artefact of elevated BOD under warming and hypoxia (Melzner et al. 2013, Laurent et al. 2018).

Response to Hypoxia When measuring and discussing oxygen stress, the effect and degree of degradation and mortality is dependent on the intensity and duration of exposure below a threshold (Diaz and Rosenberg 2008, Vaquer-Sunyer and Duarte 2011, Jager et al. 2018). The tolerance to hypoxia is highly species-specific, each utilizing a range of response strategies, given their life history, thermal tolerance and mechanisms for withstanding or adapting to unfavourable conditions (Gray et al. 2002, Troup et al. 2017). Responses to hypoxia range from cellular to ecosystem scales. Maintaining oxygen delivery is the first phase when an individual is subjected to low oxygen or hypoxia (Wu 2002). This can be achieved through increased respiration rates, red blood cell counts and/or binding capacity of haemoglobin (Randall 1982, Wu 2002). Motile species tend to be less tolerant to hypoxia, however, their primary avoidance mechanism is shifting to higher DO environments (Bell and Eggleston 2005).

Some fishes employ aquatic surface breathing in hypoxic conditions (Domenici et al. 2007). Benthic species, including bivalves that may have limited ability to flee from low DO, employ other maintenance strategies, for instance, migrating to shallower sediment depths (Sparks and Strayer 1998, Marsden and Bressington 2009) and/or extending siphons further into the water column to reach higher DO environments (Jorgensen 1980, Nilsson and Rosenberg 1994, Sparks and Strayer 1998).

7 |GENERAL INTRODUCTION When maintaining oxygen delivery is unachievable and critical oxygen concentrations are reached, energy conservation is employed through metabolic depression, downregulation of protein synthesis and particular regulatory enzymes and reduced locomotion (Carroll and Wells 1995, Sobral and Widdows 1997, Wu 2002 and references within). Short-term, a reduction in oxygen demand by suppression of aerobic metabolism, cessation of ventilation and upregulation of anaerobic metabolism by the hypoxic-inducible factor (HIF-1) can allow an organism to withstand episodic hypoxic episodes (Sobral and Widdows 1997, Wu 2002, Storey 2004). Sealing shut the valves to reduce metabolic expenditure or increase metabolic efficiency, are energy saving mechanisms which are commonly employed by bivalves (Sobral and Widdows 1997, Somero 2002). In addition to strategies in response to hypoxia, prolonged or repeated exposure can alter feeding, growth, fitness, reproduction and survival (Wu 2002, Long et al. 2008, Levin et al. 2009).

Significant mortality and mass mortality events (MMEs) as a result of being unable to withstand the hypoxia or the consequences of hypoxia, are evident in marine benthic macrofauna (e.g. Diaz and Rosenberg 1995; Table 1). However, pinpointing the exact cause of many MMEs and whether they are natural or anthropogenic is often hard to distinguish (Callaway et al. 2013, Burdon et al. 2014). The magnitude of metabolic responses to declining DO conditions has been shown to be influenced by food availability, glycogen storage, phase of reproductive cycle, body size as well as abiotic factors including the intensity and duration of exposure (e.g. Sobral and Widdows 1997 and references within, De Luca-Abbott 2001).

Repercussion of Hypoxia Stress due to unfavourable conditions, including hypoxia and elevated temperatures, can change the aerobic scope and energy budget of organisms, thereby influencing growth, reproduction, energy reserves, susceptibility to parasitic infection and disease, locomotory activity and survivorship (Diaz and Rosenberg 1995, Domenici et al. 2007, Kooijman and Kooijman 2010, Long et al. 2014, Breitburg et al. 2018, Yoann et al. 2019). The notion that the majority of sedentary intertidal bivalves live at or close to their thermal tolerance (Madeira et al. 2012) and are unable to shift distribution, means they are more susceptible to the effects of climate change and therefore, they have formed the dominant research clade (Bayne 2017).

The loss of a species or significant mortality event can have serious ramifications as has been observed in the Dutch Wadden Sea, where overharvesting and recruitment failure resulted in a crash in bivalve populations and change in trophodynamics and energy flow (Beukema and

8 |GENERAL INTRODUCTION Cadée 1996). A loss of species richness, biodiversity, changes in functional groups and efficiency of ecosystem services are all consequences of hypoxia and mortality events (Wu 2002, Levin et al. 2009). The recovery time of a system or population is dependent on the duration and intensity of hypoxia. Defaunation is possible under hypoxia which lasts seasonally or greater. The recovery time of a system from such an event is in part dependent on the life history and reproductive strategy of species as well as other pressures such as fishing (Beukema and Cadée 1996, Tricklebank et al. 2020).

The spatial and temporal magnitude of the disturbance relates to how widely the effects are felt within and across systems (Magni et al. 2000, Buckley and Huey 2016). Climate change, extreme temperatures and heatwaves are likely to cause widescale disturbances while localized disturbances, such as small-scale mats of macroalgae, would have localized effects (Callaway et al. 2013). The disturbance history, such as previous eutrophication and hypoxia, is likely to alter the resilience of a system or area and is influenced by the biodiversity and functional redundancy that exists (Cardoso et al. 2004, Lohrer et al. 2010, Norkko et al. 2015).

Study Species: Austrovenus stutchburyi Shallow burrowing suspension feeder, Austrovenus stutchburyi (Wood, 1828) also known as the New Zealand cockle, Tuaki and Taungi by Māori (hereafter Austrovenus) is a key species in estuaries and sheltered sand and mudflats throughout New Zealand living in the top 1 – 3cm of sediment (Whitlatch et al. 1997) at densities up to 3000 m-2 (Stephenson and Chanley 1979). Austrovenus are found between the lowest high neap tide line and lowest part of the shore and some remain predominantly submerged below the low tide mark (Morton and Miller 1973, Marsden 2004). Austrovenus are an important link between primary producers and consumers, including wading birds such as oystercatchers. Commercial, recreational and customary harvests exist for Austrovenus throughout New Zealand where it is regarded by Māori as a taonga (treasure) species. Overharvesting, weather events, pollution, oxygen depletion and mortality events can occur, altering the functioning and productivity within an ecosystem (Marsden and Bressington 2009, Lohrer et al. 2012, Karlson et al. 2016, Karlson et al. 2020). Austrovenus contributes to top-down and bottom-up processes as a benthic-pelagic coupler and plays an important role in controlling phytoplankton populations by filter feeding, which in turn influences biogeochemical cycling as they bulldoze the surficial sediments and excrete waste (Stephenson and Chanley 1979, Thrush et al. 2006, Ehrnsten et al. 2019). Excretion of faeces and pseudofaeces directly on to the sediment, supports primary production (pelagic and

9 |GENERAL INTRODUCTION microphytobenthos) by recycling of nutrients and enhancing bioturbation (Sandwell et al. 2009), thereby creating a positive feedback and further primary production.

Austrovenus uses their muscular foot to move through the sediment. During immersion, Austrovenus valves gape and extends its inhalant and exhalant siphon into the water column to respire and filter feed. At low tide when exposed to aerial conditions, Austrovenus can bury themselves into the sediment, ceasing water exchange and aerobic respiration until re- immersion. A correlation between ecological niche and anaerobic strategy of different metabolic rates and pathways identified by Carroll and Wells (1995) helps to explain why Austrovenus, and other functionally and ecologically similar estuarine ( and low energy environments) bivalves (e.g. Wegeberg and Jensen 1999), can withstand short-term environmental hypoxia. Under environmental hypoxia, Austrovenus utilizes the pyruvate reductase enzyme strombine dehydrogenase (SDH) pathway and has lower energy demands relative to bivalves that inhibit high energy environments such as surf zone which fare better in under functional anaerobiosis induced as they burrow, but not environmental hypoxia (Carroll and Wells 1995).

The Main Issue Intensive filter feeding by bivalves such as cockles, oysters and mussels can regulate control POM, dissolved OM and nutrients within coastal systems, reducing the risk of eutrophication (Smaal and Prins 1993, Newell 2004). It is important to maintain the balance between sufficient POM in the water for filter feeders to consume and utilize for growth and reproduction and having excessive quantities of POM, that can cause habitat degradation and mortality (Carmichael et al. 2012). Overfishing and habitat degradation are major threats to exploited coastal areas. The loss of key filter feeders, including oysters, has been observed in New York Harbour and Chesapeake Bay, resulting in the loss of filtration capacity and nutrient cycling (Kirby 2004, Zu Ermgassen et al. 2013, McFarland and Hare 2018), which can cause positive feedbacks for hypoxia and anoxia.

Within Waitati Inlet, Otago, New Zealand, the focal region of this research and Austrovenus collection site, the Inlet is considered a potential sink of organic carbon, as a consequence of filter feeding bivalves, primarily Austrovenus (Smith et al. 2010, O'Connell-Milne et al. forthcoming). Within the Inlet Austrovenus has a total biomass of 14161.7 (± 1081.8) tonnes (Miller and Black 2019), with the filtration capacity of the population estimated at 350,000,000L per tidal cycle (Kainamu 2011). Since the beginning of commercial harvesting

10 |GENERAL INTRODUCTION in 1986, Austrovenus biomass has declined by 55% (Irwin 2004) within the commercial beds in Waitati Inlet, with annual landings reaching 960 tonnes (Miller and Black 2019). The deposit feeding bivalve Macomona liliana is also a common infaunal species within the Inlet (O'Connell-Milne et al. 2016a). Hypoxia-induced changes in biodiversity and habitat quality have been observed in estuaries worldwide (e.g. Newell 1988, Eby and Crowder 2002, Hagy et al. 2004). One such change is the shift from suspension to subsurface-deposit feeding bivalves or absence of bivalves from a population entirely (Wu 2002, Green et al. 2014).

While Waitati Inlet has a high tidal flushing potential (Robertson et al. 2016b), Ulva spp., blooms establish most years, however, in 2017/18 a bloom was observed to persist throughout winter (Chai et al. 2020). It is due to excessive buildup of rotting OM that these blooms in the intertidal zone and bottom of channels have the potential to enter hypoxia, which can influence the infauna, water quality and biogeochemical cycling (Song et al. 2020). Within tidally dominated shallow estuaries such as Waitati Inlet, hypoxic conditions tend to exist on short time-scales (hours - days) (D'Avanzo and Kremer 1994, Tyler et al. 2009), however, future conditions of nutrient loading, temperature, precipitation, irradiance and wind, reduced oxygen solubility and increased respiration have the ability to alter this pattern (Schmidtko et al. 2017). Most bivalves are considered tolerant to extended duration of hypoxic events (Sobral and Widdows 1997). It is therefore important to understand how a key estuarine species, such as Austrovenus, will respond to chronic hypoxia and interacting stressors and where thresholds may exist under different oxygen conditions.

Objectives The overall aim was to investigate the impact of hypoxia on the filter feeding bivalve Austrovenus. As most fish and marine species respond to low oxygen below 30% saturation -1 (2.0mg O2 L ), and estuarine bivalves are routinely exposed during low tide, the objectives of the present experiments were to investigate the response of Austrovenus to chronic hypoxia (20% saturation) across different future environmental condition, multiple stressor scenarios.

Overview The current study is presented in five chapters consisting of a general introduction (the current chapter), three data chapters and a general discussion.

Chapter Two examines how dissolved oxygen in Waitati Inlet, Otago, varies spatially and temporally between two estuarine channels. Furthermore, modelling is used to determine which exogenous or endogenous parameters likely account for the variability observed. The analysis

11 |GENERAL INTRODUCTION provides insight into how a shallow intertidal-dominated estuarine system responds to different environmental conditions in terms of oxygen concentration.

The second and third data chapters (Chapter Three and Four) focus on laboratory experiments, investigating how adult New Zealand cockles (Austrovenus stutchburyi) respond to different stressor events and intensities of the associated stressors. Chapter Three investigates the impact of low oxygen on survival of Austrovenus and how survival changes under warming conditions. Additionally, it investigates how chronically dosing the experimental systems with ammonia may contribute to changes in respiration rates and survival.

Chapter Four follows on from the results of Chapter Three and focuses on quantifying respiratory responses and survival of Austrovenus under different intensities of environmental stress. This was assessed by simulating in situ conditions and subjecting Austrovenus to tidal emersion at high aerial temperatures, before re-immersion to see how they may respond to concurrent low oxygen and warming, simulating eutrophic conditions which could occur and climate change and heatwaves.

Chapter 5 discusses the key results from each data chapter and how they fit together to provide a general synthesis of the findings and ramifications of this work for the future.

12 |GENERAL INTRODUCTION

Chapter Two: Spatial and temporal variability of dissolved oxygen in two channels of Waitati Inlet, Otago

13 | C H A P T E R 2 Introduction Dissolved oxygen (DO) is the most important ecological variable that has experienced drastic changes over a short time period in coastal systems (Diaz and Rosenberg 1995, Diaz 2001). Globally, the area of coastal hypoxia is increasing exponentially at a rate of 5.5% per year (Vaquer-Sunyer and Duarte 2008). Attributed as the primary mechanism of hypoxia and low oxygen, eutrophication is caused by excessive nutrient availability (Gilbert et al. 2010) from primarily allochthonous systems (Kennish 2002).

Reduced growth, development and fecundity of many marine organisms and higher mortality are all consequences of reduced DO. The ramifications of hypoxia however, can be more extensive, altering trophic and food web structures, biogeochemical cycling and negatively impacting commercial fisheries and the extensive services we receive from the coastal ocean (Diaz 2001, Breitburg 2002, Kennish 2002, Levin et al. 2009).

Hypoxia occurs on different time scales including annual, seasonal, episodic and diel time periods. Seasonal hypoxia is more common in deeper (>10 m) stratified waters with strong pycnoclines such as the mainstem of Chesapeake Bay (Officer et al. 1984, Davanzo and Kremer 1994, D'Avanzo et al. 1996, Testa et al. 2014). A weak unstable pycnocline from reduced mixing and increased stratification can result in episodic hypoxia, which persists for days to weeks (Turner et al. 1987). Diel cycling has been recognized for decades (Nixon and Oviatt 1973, Hackney et al. 1976) and occurs in the photic zone as the result of production of oxygen during daylight by primary producers and depletion of DO at night due to autotrophic and heterotrophic respiration (Day et al. 1989, D’Avanzo and Kremer 1994, Levin et al. 2009, Tyler et al. 2009, Sawabini et al. 2015). Consequently, daily cycles of DO are predictable, with minima primarily occurring near sunrise following night-time respiration while DO maxima often occur in the late afternoon/early evening following hours of intense photosynthesis; however, the intensity of cyclical events varies substantially (Sanford et al. 1990, D’Avanzo and Kremer 1994, Tyler et al. 2009, Sawabini et al. 2015, Miyamoto et al. 2019). In these systems, biomass of primary producers and intensity of photosynthetically active radiation are the primary drivers of low oxygen events.

In temperate regions, diel DO cycling is more pronounced during summer when metabolic rates of photosynthesis and respiration are relatively high (Davanzo and Kremer 1994, Sawabini et al. 2015) as result of warmer temperatures and long daylength. Within estuarine environments, the DO concentration can be broadly classified into four categories: oxygenated

14 |CHAPTER 2 -1 -1 -1 water (>5mg O2 L ), moderate hypoxia (2–5mg O2 L ), severe hypoxia (2–0.5mg O2 L ) and -1 anoxia (<0.5mg O2 L ) (US EPA 1998, Gibson and Bowman 2000). Fauna exposed to severe hypoxia for extended durations (1 – 4 days) may exhibit altered behaviour and functioning, decreased growth and reproduction, increased susceptibility to toxins and disease and even death (Gibson and Bowman 2000, Diaz and Rosenberg 2011, Breitburg et al. 2015). Wherever seasonal and episodic hypoxia is observed (they are globally widespread), its duration and intensity varies based on estuary flushing time, freshwater input, nutrient loading, tidal- and wind-driven mixing and abundance of filter feeding bivalves (Diaz and Rosenberg 2008, Tyler et al. 2009, Hitchcock et al. 2014, Justić et al. 2017, Breitburg et al. 2018).

Photosynthetically active radiation (PAR) is a key exogenous regional factor which influences the DO balance in estuarine systems (Sanford et al. 1990, Tyler et al. 2009, Sawabini et al. 2015) through increasing organic matter (OM) production and biomass. Other variables also determine the magnitude of diel hypoxia: time of day, tidal stage, precipitation, proximity to nutrient source, oceanic exchange, winds, temperature and cloud cover (D’Avanzo and Kremer 1994, Boynton et al. 1996, D'Avanzo et al. 1996, Tyler et al. 2009). Within diel-cycles, DO -1 -1 concentrations can vary from anoxic (<0.5 mg O2 L ) to supersaturated (>15mg O2 L ). This can occur if there is a sufficient biomass of algae associated with particular meteorological conditions (D’Avanzo and Kremer 1994), such as high PAR followed by several days of low irradiance and winds, leading to lower DO maxima and minima (Sawabini et al. 2015).

Gilbert et al. (2010) described time series data taken over a shorter time period often results in inflation of true trends in oxygen variability. To understand if episodic or diel cycling of hypoxia exists in an estuary or water body, high-frequency monitoring is required (Wenner et al. 2004). This, however, can become costly, requiring multiple monitoring instruments. Monitoring programs, such as the one described in O’Boyle et al. (2009), do not permit determination of diel-cycling but instead give a broadscale indication of water quality, with monthly measurements made at 533 stations around Ireland between 2003 and 2007. Alternatively, variation in water quality, from 55 sites across the USA measured at a high- frequency (every 30 minutes), was analysed to characterize temporal and spatial variability in water quality (Wenner et al. 2004). Finally, highlighting the difference in spatial scales of monitoring and sampling, Tyler et al. (2009) measured water quality every 15 minutes at sites within the Delaware Bay, USA, a coastal lagoon estuary. These studies highlight the trade-offs that exist when developing sampling programmes to identify drivers of DO variability focussed on resolving temporal and spatial variability in hypoxic conditions.

15 |CHAPTER 2 Previous studies have focused on estuaries with longer flushing times and resulting in high retention time of water, that allow for establishment of phytoplankton populations as the dominant primary producers (Cloern 1996, Tyler et al. 2009, Sawabini et al. 2015). An Estuary Trophic Index (ETI) was developed to classify New Zealand estuarine environments based on susceptibility to eutrophication (Robertson et al. 2016b). The ETI first classifies estuaries into one of four morphological and hydrodynamic categories (Hume et al. 2016): shallow intertidal dominated estuaries (SIDEs); shallow, short residence time tidal river and tidal river with adjourning lagoon estuaries (SSRTREs); deeper subtidal dominated, longer residence time estuaries (DSDEs); and intermittently closed/open lake and lagoon estuaries (ICOLLs) (Robertson et al. 2016a, Robertson et al. 2016b). Eutrophication indicators in SIDES include macroalgal growth rather than pelagic phytoplankton blooms due to short hydraulic residence times (or alternatively, high flushing rates), that are often less than one day (Sutula et al. 2014, Robertson et al. 2016a, Plew et al. 2020).

Throughout New Zealand, many catchments have excess nitrogen and phosphorous loading (Lawton and Conroy 2019) resulting from catchment runoff and consequent nutrient loading into receiving estuarine environments (Heggie and Savage 2009), which become more susceptible to eutrophication and excess algal growth (e.g. Schallenberg et al. 2017).

The aim of the present study was to determine how bottom-water DO concentrations in two subchannels in Waitati Inlet, Otago, varied on diel and seasonal scales and to investigate the likely environmental drivers of the observed DO variability. Two channels in the estuary located ~700m apart were monitored to compare the spatial variability between the two main water masses within the estuary. The research considers possible local drivers on oxygen variability, such as the occasional high densities of macroalgae, Ulva spp., in the inlet.

Methods Study Area Waitati Inlet is a temperate microtidal estuary located on the South-east coast of the South Island, New Zealand, north of the Otago Peninsula (Figure 2.1). Waitati Inlet is a SIDE (Robertson 2018) that experiences semidiurnal tides with an average tidal range of 1.63m. It has an intertidal area spanning 5.2km2 (86%), excluding saltmarshes (Hume et al. 2016, Robertson 2018) and a mean depth of 1.2m at spring high tide. Channels, however, can reach depths of 4m at the locations where the sampling equipment was deployed. Waitati Inlet is connected to the sea through a single narrow channel between Warrington sand spit and

16 |CHAPTER 2 Doctor’s Point beach, with strong tidal currents at the entrance (Heath 1976). Inside of the main channel lies Rabbit Island, which separates the main channel into two, the northern and southern channels in the system. The northern channel runs north-south parallel to Warrington sand spit, while the channel in the southern part runs east-west as the dominant primary channel between Doctors Point and Rabbit Island and receives freshwater inputs from the Waitati River (Figure 2.1). The channels are referred to here as Warrington channel and Waitati channel, respectively. These two channels were chosen due to their close proximity and the idea that Waitati Inlet acts as two separate systems on the ebb tide, with two different freshwater inputs draining into the Inlet on the southwest and northwest corners at Waitati River and Carey’s Creek with estimated mean annual flow rates of 0.6 m3 s-1 and 0.3 m3s-1 respectively (Otago Regional Council 2009). Water quality indexes (ammoniacal-N, DO, dissolved reactive phosphorus (DRP), nitrite/nitrate-nitrogen and turbidity) measured between July 2007 and March 2008 indicated the median values were below the ANZECC/ARMCANZ (2000) guidelines for Carey’s Creek and Waitati River except for DRP in the later (Otago Regional Council 2008). Together these freshwater inputs drain a 91.0km2 catchment (Robertson et al. 2016b) comprising low density housing, farmland and regenerative forests resulting in low nitrogen (N) loading (4.9 mg N m2 d-1) into the inlet (Robertson 2018).

Carey’s Creek

Warrington sand spit

Rabbit Island Doctors Point

Waitati River

Figure 2.1. Left: Location of deployment sites in Waitati Inlet, New Zealand. Yellow and blue markers represent Waitati and Warrington channel deployment sites respectively. Right: Location of Portobello sea surface temperature and theoretical dissolved oxygen datasets in the Otago Harbour in relation to Waitati Inlet.

17 |CHAPTER 2 Waitati Inlet has a high flushing potential, therefore, nutrients are more likely to be flushed out to sea rather than retained within the estuary (Robertson et al. 2016b). High flushing indicates dense phytoplankton populations are unlikely to be sustained (Valiela et al. 1997, Plew et al. 2020), while opportunistic macroalgae are likely to be the dominant primary producer within Waitati Inlet, as indicated from remote sensing (Chai et al. 2020). Substantial Ulva spp. blooms have been observed in the inlet in the past several years, with a bloom persisting throughout winter in 2017/18 (Chai et al. 2020). A wastewater treatment plant situated on Warrington sand - + spit, contributing to N loading within the inlet (26.5% NO3 , 11.5% TN and 0.4% NH4 ), has no observed effects on localised sediment anoxia, as determined by Stewart (2014) but may well affect the well mixed system as a whole. Waitati Inlet has high densities of the filter feeding bivalve Austrovenus stutchburyi (up to 800 ind. m-2) and deposit feeding bivalve Macomona liliana (O'Connell-Milne et al. 2016a). The A. stutchburyi population in Waitati Inlet is subject to large recreational and customary harvest of unknown biomass, as well as commercial harvests of up to 906 tonnes per annum (Miller and Black 2019).

Theoretical Oxygen Saturation The theoretical daily maximum dissolved oxygen concentration (mg L-1) was calculated from temperature measurements using the following two formulae derived from Sverdrup et al. (1942) and Richards (1957) for chlorinity and DO respectively. Whereby;

• t = temperature (°C) • Cl = Chlorinity (parts per mil) • Salinity = Standard seawater (35 psu), where actual salinity value not measured

(푆푎푙𝑖푛𝑖푡푦−0.03) 퐶푙 = Eq. 1 1.805

−1 2 3 푚푙 푂2 퐿 = 10.291 − 0.2809푡 + 0.006009푡 − 0.0000632푡 − 퐶푙(0.1161 − 0.003922푡 + 0.0000631푡2) Eq. 2

Further conversion from mL L-1 to mg L-1 was done using the ratio of molar volume at standard temperature and pressure (STP) to the molar weight of oxygen:

푚퐿 퐿−1 푚𝑔 퐿−1 = Eq. 3 0.69976

These theoretical oxygen measurements are based on saturation at a given temperature and do not account for biological productive and consumptive processes (Rabalais et al. 2014) or non-

18 |CHAPTER 2 biological factors including winds, tides, solar radiation and precipitation (Sawabini et al. 2015, Nelson et al. 2017) which all influence oxygen concentrations.

PML SST and DO Anomaly Austral summer periods (1st December - 28th February) were extracted from the 65-year sea surface temperature (SST; 1953 - 2018) time-series from Portobello Marine Laboratory, Otago (hereafter Portobello) (Figure 2.1). Equations 1 to 3 were applied to temperature measurements taken daily at 0900 hours to determine the maximum theoretical oxygen saturation. A baseline was derived from the average daily maximum DO across the 65-years.

A 31-day moving average was performed on daily theoretical maximum DO measurements to derive a daily DO anomaly based on oxygen saturation values. The 65-year mean anomaly was used to identify long term seasonal trends in the oxygen saturation values. Furthermore, DO anomalies for both temperature extremes of a cold and hot summer were used to determine the frequency and distribution of these anomalies, thereby, understanding thermal extremes and implications for oxygen saturation. Shears and Bowen (2017) identified the early 1960’s as a decade with persistent thermally cold anomalies. Calculating anomalous cold events during summers of the early 1960’s, demonstrated that the summer of 1963/64 had the most numerous and extreme cold temperatures. The anomalies of 1963/64 were compared with those in the summer of 2017/18 when Otago and New Zealand experienced an unprecedented marine heatwave (Salinger et al. 2019). Anomalies were rounded to the nearest 0.05 mg L-1 and displayed as a frequency distribution.

High-frequency temperature and oxygen data obtained using a D-Opto oxygen meter every 30 minutes at 1.5 – 2m water depth on the pier at Portobello, were used to calculate the theoretical DO based on the temperature (referred to hereafter as TDO data). A comparison of the actual versus theoretical DO was performed. Data were filtered to remove outlying values that could have been influenced by local perturbations such as vessel movements. Due to both the 65- year temperature and 4-year TDO timeseries being void of salinity measurements, theoretical oxygen saturation was analysed under the assumption of a salinity of 35 psu for Portobello.

Bottom Dissolved Oxygen Time Series Data for the current study in Waitati Inlet was collected from January to December 2019 to determine diel and seasonal variation. Near bottom-water DO (% and mg L-1), temperature (°C), depth (m), salinity (psu), pH and chl a (μg L-1) were measured using a Yellow Springs Instrument Company (YSI™) EXO3 multi-parameter Sonde. A second multiparameter Sonde,

19 |CHAPTER 2 YSI™ 6600V2, was used to measure dissolved oxygen, temperature (°C), pH and salinity (psu) during paired deployments. Sondes were deployed on steel cross arm horizontal frames with sensors positioned approximately 0.15 – 0.4 m above the sediment, taking measurements every 2 minutes (Table 2.1). On each frame there was also a XR-420 model RBR data logger to measure salinity, temperature, and depth. A Turner Designs®, C3 submersible fluorometer, complete with wipers and shade cap was also deployed in both channels in January, March, and May to infer Chl a concentration (fluorescence at 635nm and 850nm). A D-Opto dissolved oxygen sensor was used in December 2019 also (Table 2.1). Each frame was deployed into either the Warrington or Waitati channel. Water depth reported was the distance from the instrument sensor to the water surface.

The day prior to deployment, the Sondes were calibrated for salinity, dissolved oxygen (100% air saturation) and pH. Sondes and RBR were deployed for 5 – 10 days to capture the spring tide, midday low tide periods and before instruments required re-calibration. The length of data recorded within a deployment period varied due to variations in battery life among the instruments and dates.

Measurements of local environmental variables were collected by the Department of Physics, University of Otago (45°52ˈS, 170°31ˈE) 45 m above MSL, 16 km away from the field sites. Photosynthetically active radiation (PAR; μmol m-2 s-1, Li-Cor LI190SB Quantum Sensor) was chosen as a key variable as it encompasses the portion of the visible light spectrum (400 - 700 nm) used in photosynthesis. Measurements for PAR and other meteorological variables were recorded at 5-minute intervals, with further 1-hour averages taken and applied to the corresponding 2-minute interval in-situ measurements; this was done to avoid loss of fine scale DO variability. Meteorological measurements were unable to be obtained for the December deployment due to the station being not operational.

Data from each deployment was reviewed to identify any anomalous data points or evidence of sand burial. Any such data points were excluded from analysis along with all other variables associated with that time stamp. This was performed on temperature, DO, salinity, tidal height and pH. Day and night-time were determined using sunrise and sunset times for Dunedin (www.timeanddate.com/sun/new-zealand/dunedin) for each deployment. Sampling was timed around the spring tides. The reasoning behind selecting spring tides was due to there being lower low tides and therefore the flux of heat would be more obvious in a shallower body of water during warmer months (Orton et al. 2010).

20 |CHAPTER 2 Waitati Inlet in situ DO Deviations Percentage deviation of the measured DO concentration from the maximum theoretical DO concentration was calculated using equations 1 – 4 and substituting in the measured temperature (t) and salinity value for each 2-minute reading. A positive percent deviation indicates DO was higher than expected under the specified water temperature and salinity conditions while a negative value indicates less DO is present than is theoretically expected.

푀푒푎푠푢푟푒푑 퐷푂−푇ℎ푒표푟푒푡𝑖푐푎푙 퐷푂 % 퐷푒푣𝑖푎푡𝑖표푛 = ( ) × 100 Eq. 4 푇ℎ푒표푟푒푡𝑖푐푎푙 퐷푂

Deployment period Location Datalogger type Jan, March Waitati YSI EXO3 sonde, XR420 CT RBR, C3 submersible fluorometer Warrington YSI 6600V2 sonde, XR-420-CTD RBR, C3 submersible fluorometer May, June, August- Waitati YSI EXO3 sonde, September XR420 CT RBR, Warrington YSI 6600V2 sonde, XR-420-CTD RBR December Waitati YSI 6600V2 sonde, XR-420-CTD RBR, D-OPTO logger Warrington YSI EXO3 sonde, XR420 CT RBR Candidate Explanatory Variable Selection The data used in this analysis consisted of a subset of the field and meteorological data available. Environmental variables selected are known to influence DO in estuaries, based on previously published studies. These included water temperature, tidal height, chlorophyll a, time of day, PAR and wind speed (e.g. Sanford et al. 1990, Tyler et al. 2009, Sawabini et al. 2015). pH was excluded from the model due to co-variance with DO over a diel-cycle (Zang

21 |CHAPTER 2 et al. 2011, Breitburg et al. 2015) as CO2 accumulated as a result of respiration and drawdown of oxygen (Levin et al. 2009, O'Boyle et al. 2015).

Water temperature, insolation and tidal dynamics were the three most important predictors based on analysis of estuarine hypoxia identified by Tyler et al. (2009) while wind speed and precipitation were also notable. Nelson et al. (2017) and Beck et al. (2015) highlighted that tidal height was a significant explanatory variable of DO. The daily DO minima and maxima were identified in many papers as predominately occurring near sunrise and late afternoon/early evening, respectively.

Data Analysis The correlation of PAR and DO was analysed using general linear models at various PAR lag times ranging from 0 – 24h at 3h intervals. Previous studies of shallow estuarine systems identified significant time lags between PAR and photosynthetic rates, ranging from 6 – 12h in well flushed estuaries (Sanford et al. 1990), to the preceding days total PAR for estuaries with longer residence times; days to weeks (Tyler et al. 2009) and even the prior 3-day average irradiance in semi enclosed estuaries (D’Avanzo and Kremer 1994). Visualising the residual sums of squares (RSS) and r2 values for the bottom half of the tide data, a 3h lag was identified as the most suitable value to represent the effects of PAR on photosynthesis-respiration for the current study.

Analysis of DO was performed on the DO value representing the bottom 10% quantile for each day of the deployment. Associated mean daily salinity values were attained for each day. Analysis of daily quantiles of variables identified as proxies selected to differentiate between mechanisms that influence DO variability, included PAR-3hr lag and wind speed at 75th and 90th percentiles due to high PAR driving photosynthesis-respiration cycles and high winds driving wind-induced mixing. Salinity was used as a proxy for identifying catchment freshwater input and tidal flushing of the system, both noted to differ between channels. Temperature directly influences the oxygen solubility (Weiss 1970) through solar heating and seasonal cycling. Consequently, it was excluded from the AIC analysis to allow other mechanisms driving oxygen such as wind, PAR or salinity to be identified.

General linear models were used to test different explanatory variables including PAR-3hr, mean daily temperature and salinity, 75th and 90th quantiles of daily wind speed on the daily 10th quantile of DO measurements. Model selection was performed on a reduced model, ranking variables based on how well they explained the DO variability (r2). An Akaike’s

22 |CHAPTER 2 Information Criterion (AIC) was used to assess the best set of models based on the top three variables obtained from the r2 values. This was done to evaluate and describe how the explanatory power of the variables explained above, account for variability in DO within Waitati Inlet.

The number of deployment days associated with Waitati and Warrington channel were 24 and 19 respectively due to part days where PAR values were not representative (<100 μmol m-2 s- 1). December deployments were not incorporated into the AIC modelling due to data from the weather station not being available, while instrument failure during August-September excluded the deployment from Warrington channel. All analysis was performed in JMP Pro 11 using linear models.

Within Warrington and Waitati channels individually, a single factor ANOVA was conducted on DO measurements between deployments (Deployment, fixed factor, 4 or 5 levels respectively). A post-hoc Students t-test was used to test for differences between deployment months. A breakdown of the percentage of total observations for supersaturated (>120%), saturated (90-120%), depleted (80-90%) and low (<80%) DO water was done for each deployment based on Wenner et al. (2004) and ANZECC/ARMCANZ (2000). This was performed on data spanning the entire tidal cycle, enabling seasonal and spatial variability to be observed.

Results PML SST and DO Anomaly A seasonal trend in theoretical DO was present at Portobello (Figure 2.2a) with mean DO range from 8.1 – 9.8 mg L-1. The highest DO was experienced in winter when temperatures were lower and the anomaly was positive (Figure 2.2b). Analysis of the variability in TDO data indicated DO in Otago Harbour was steadily above the theoretical oxygen saturation between 2014 and 2018 (Figure 2.3). The distribution of DO anomalies for two extreme summers were normally distributed but offset in opposing directions in relation to the 65-year average (Figure 2.4). During 2017/18, 90.0% of the days between 1st December and 28th February were the same or below the average daily theoretical DO. In contrast, 82.2% of days during 1963/64 were the same as or greater than the long-term maximum DO mean.

23 |CHAPTER 2 A

Figure 2.2. Average, minimum and maximum theoretical A) oxygen concentration (mg L-1) B) oxygen anomaly (mg L-1) value based on the 65-year (1953-2018) Portobello sea surface temperature time-series. Theoretical dissolved oxygen derived from Sverdrup et al. (1942) and Richards (1957).

Figure 2.3. Observed (blue) and theoretical (orange) dissolved oxygen (mg L-1) concentration for May 2014 to November 2018 Portobello TDO timeseries. Measurements

recorded at 30 minute intervals. Gap in 2017 data represents period of missing data.

24 |CHAPTER 2 Figure 2.4. Frequency distribution of theoretical dissolved oxygen (DO mg L-1) anomaly between 1st December to 28th February as deviations away from the daily DO mean of the 65-year Portobello SST timeseries. 1963/64 represents a cold summer while 2017/18 represents a marine heatwave. Positive and negative values represent an increase and decrease in measured oxygen saturation, respectively.

Variability in Waitati Inlet Bottom Water Dissolved Oxygen Significant differences in DO were present between all Waitati and Warrington channel deployments (p < 0.05) (Figure 2.5). In addition, mean DO values demonstrated variation between the two channels. The channels had different DO regimes, with Waitati channel experiencing the second lowest mean DO value of 7.99 mg L-1 in summer, while Warrington channel had a corresponding DO of 9.34 mg L-1. This held true for spring, whereby Warrington channel and Waitati channel had the highest (10.05 mg L-1) and lowest (7.87 mg L-1) DO concentrations, respectively (Figure 2.5).

Overall, bottom waters of the channels in Waitati Inlet are in an oxygen saturated state throughout the year. During summer the Warrington channel was supersaturated for 56.2% of the duration of the deployment, while Waitati channel experienced supersaturation less than 4.5% of the time (Table 2.2). Comparatively, in late summer both channels had similar regimes. Deployments in autumn and spring for both channels spent little to no time in a supersaturated state, with Warrington channel spending 53% of the time in a depleted state in autumn. This was the greatest percentage of time of all deployments spent between 80 – 90% DO, however, Figure 2.5b shows this deployment also had the highest minimum DO values. The latter also

25 |CHAPTER 2 held true for measurements taken in Waitati channel in autumn which experienced little DO variability (Figure 2.5a), maintaining a saturated state 99.9% of the time, while reaching minimum of 0 mg L-1 at low tide across three consecutive tides in late summer (Appendix 2).

Table 2.2. Breakdown of the actual dissolved oxygen saturations (%) within Waitati Inlet, Otago, classified into four categories based on Wenner et al. (2004) and ANZECC/ARMCANZ (2000) guidelines. Values represent percentage of observations within each category seasonally and spatially. Warrington winter excluded due to significant DO drift. Waitati spring measurements performed with D-Opto dissolved oxygen meter.

Percent of total observations Supersaturated Saturated Depleted Low Channel-season (>120%) (90–120%) (80–90%) (<80%) Waitati summer 4.49 79.49 14.59 1.44 Waitati late-summer 5.03 72.75 12.62 9.61 Waitati autumn 0 99.93 0.07 0 Waitati winter 5.6 86.88 7.53 0 Waitati spring 0 87.68 2.8 9.52 Warrington summer 56.21 38.04 4.06 1.69 Warrington late-summer 8.82 73.38 11.51 6.29 Warrington autumn 1.71 43.2 52.99 2.1 Warrington spring 0 77.24 16.89 5.87

Diel DO cycles were evident within Waitati Inlet (Figure 2.6, Figure 2.11). General linear models indicate PAR-3hr in Waitati and Warrington summer deployments accounted for 21.3% and 35.1% of the variability in DO respectively (not shown). Daily DO minima and maxima both occurred near/on the bottom of the tidal cycle, prior to the flood tide, when water temperature fluctuated between 14 – 22 °C during the summer deployment (Figure 2.6).

Within Waitati and Warrington channels, Figure 2.7 and Figure 2.8 depict oxygen variability during the deployments in summer and late-summer, whereby DO was less than the general mean more often than during deployments in late-autumn and winter. The observed pattern is consistent with a dramatic shift in water temperature from 14 – 23 °C to 7 – 12 °C in winter and declines in salinity associated with increased freshwater runoff. Similar trends were present in Warrington channel, with stepwise patterns in water temperature observed between the three

26 |CHAPTER 2 deployments from summer, late-summer to late autumn. High salinity was present in summer, while late summer and autumn had salinity values below 34 psu. Higher PAR can be identified in summer months and decreased throughout the deployments (Figure 2.7, Figure 2.8). No obvious temporal trends were identified for magnitude of wind speed.

A B d d e c a c b b a

Figure 2.5. Mean dissolved oxygen (mg L-1) values throughout each deployment. Vertical bars represent the minimum and maximum values recorded for A) Waitati channel and B) Warrington channel. Deployments not connected by the same letter are significantly different.

Results from the general linear models demonstrate that water TEMPERATURE is the dominant driver of oxygen in Waitati channel, explaining 73.7% of the variation in the bottom 10th quantile of DO (F1,22 = 61.79, p < 0.0001), with increasing TEMPERATURE and SALINITY reducing DO (Figure 2.9).

Excluding water TEMPERATURE from the AIC for Waitati channel due to the direct relationship between DO and temperature (Weiss 1970), SALINITY explained the most variability at 44.4% as a single driver with a factor weighting of 0.99. This was followed by PAR-3hr 90th*Wind 75th which accounted for 0.04% of the variation (Table 2.3). Results from the AIC analysis demonstrated a combined model of the daily 75th quantiles of PAR and WIND SPEED together had the greatest explanatory power of 41.7%. Salinity*PAR 75th was the second-best predictor accounting for 23.9% of the variability (Table 2.3). Overall, the daily PAR has the greatest contribution to low DO in Warrington channel, with a factor weight of 0.93, followed by the 75th quantile of daily wind speed at 0.87 (Table 2.3). In both channels, PAR and WIND SPEED accounted for ≤ 7% of the variability in DO as single drivers (Figure 2.9, Figure 2.10).

27 |CHAPTER 2 Table 2.3. Relative explanatory power of environmental indicators for dissolved oxygen (mg L-1) in models observing differences in either Waitati (top) or Warrington (bottom) channels corrected Akaike’s information criterion (AICc) model selection criteria. K: no. of model parameters; RSS: residual sum of squares; Δi: difference in model AICc from minimum AICc; wi: model AICc weight. Salinity: daily mean salinity (psu), photosynthetically active radiation 3hr lag: PAR-3hr and wind: wind speed (ms-1). 75th and 90th represent the daily quantiles.

2 Waitati channel K RSS r Aicc Di Wi Factor weight PAR-90th*Wind-75th*Salinity 5 17.3 0.107 5.5 0.0 0.0002 Salinity*Wind-75th 4 19.4 0.000 5.0 -0.5 0.0002 Salinity*PAR-90th 4 19.4 0.002 5.0 -0.5 0.0002 PAR-90th*Wind-75th 4 19.3 0.004 4.9 -0.6 0.0002 Wind-75th 3 18.6 0.040 1.1 -4.4 0.0014 0.002 PAR-90th 3 18.2 0.064 0.5 -5.0 0.0019 0.004 Salinity 3 10.8 0.445 -12.0 -17.5 0.9959 0.998

2 Warrington channel k RSS r Aicc Di Wi Factor weight PAR-75th*Wind-75th *Salinity 5 16.2 0.000 11.6 0.0 0.0008 Salinity*Wind-75th 4 14.8 0.085 6.1 -5.4 0.011 Wind-75th 3 16.1 0.005 4.5 -7.1 0.026 0.87 PAR-75th 3 15.7 0.028 4.0 -7.6 0.032 0.93 Salinity 3 15.7 0.033 3.9 -7.6 0.034 0.11 Salinity*PAR-75th 4 12.3 0.239 2.6 -8.9 0.066 PAR-75th*Wind-75th 4 9.4 0.417 -2.4 -14.0 0.828

28 |CHAPTER 2 A

Figure 2.6 Time series of A) dissolved oxygen (mg L-1), B) water temperature (°C), C) water depth (m) in the bottom waters of Waitati channel, Waitati Inlet and corresponding D) PAR (photosynthetically active radiation; μmol m-2 s-1) and E) wind speed (ms-1) taken from Department of Physics, University of Otago during January 2019. Shaded areas represent sunset to sunrise.

29 |CHAPTER 2 A

Figure 2.7. Daily variability in environmental drivers across all deployment days for Waitati channel (summer to late winter). Grey line represents the mean value of associated environmental drivers. A) Oxygen concentrations (mg L-1), B) water temperature (°C), C) salinity (psu), D) photosynthetically active radiation 3 hour lag (PAR; μmol m-2 s-1) and E) wind speed (ms-1)

30 |CHAPTER 2 A

Figure 2.8. Daily variability in environmental drivers across all deployment days for Warrington channel (summer to late autumn). Grey line represents the mean value of associated environmental drivers. A) Oxygen concentration (mg L-1), B) water temperature (°C), C) salinity (psu), D) photosynthetically active radiation 3 hour lag (PAR; μmol m-2 s-1) and E) wind speed (ms-1)

31 |CHAPTER 2

Figure 2.9. Regression model from top to bottom of daily mean salinity (psu; R2 = 0.44, p = 0.0004), daily 90th quantile of PAR (μmol m-2 s-1; R2 = 0.06, p = 0.23) and daily 75th quantile of wind speed (ms-1; R2 = 0.03, p = 0.34) against daily dissolved oxygen 10th quantile (mg L-1) for Waitati channel. Green line represents mean value, while red line and shading represent linear fit and 95% CI. n = 24

32 |CHAPTER 2

Figure 2.10. Regression model from top to bottom of daily mean salinity (psu; R2 = 0.03, p = 0.45), daily 75th quantile of PAR (μmol m-2 s-1; R2 = 0.02, p = 0.49) and daily 75th quantile of wind speed (ms-1; R2 = 0.004, p = 0.78) against daily dissolved oxygen 10th quantile (mg L-1) for Warrington channel. Green line represents mean value, while red line and shading represent linear fit and 95% CI. n = 19

33 |CHAPTER 2 Theoretical vs. Actual Timeseries and Temporal Cycling The DO deviation varied, displaying both daily and seasonal effects. Summer and late summer -1 showed a 10 – 20% (up to ±2 mg O2 L ) drawdown of DO in Waitati, while autumn had less than 10% variability (Figure 2.11, Appendix 3). Similar patterns in seasonality were identified in Warrington channel, however, fluctuations were greater in summer reaching +40% (up to 4 -1 mg O2 L ) following periods of high PAR (Appendix 4). In late summer, both channels experienced similar diel patterns. There were distinct differences in oxygen concentrations between the channels in spring, with Warrington DO predominantly below the theoretical saturation in contrast to Waitati channel, which had higher DO concentrations than expected (Appendix 3, Appendix 4). From Figure 2.11, Appendix 3 and Appendix 4, the DO deviation reduced as the intensity of PAR diminished during winter, where PAR intensity can be less than half that observed in summer, visually supporting the AIC models that indicate that PAR- 3hr lag is a key driver of oxygen variability.

Figure 2.11. Percent deviation of actual dissolved oxygenaway from the theoretical oxygen saturation (mg L-1) within Waitati channel, Waitati Inlet, NZ in A) summer (15 - 21 Janurary 2019) and B) autumn (16 - 22 May 2019). Calculation derived from Richards (1957). 1-hour mean PAR represented by the orange line. Time of day represents 12-noon and midnight for each day of the 6-day deployments.

34 |CHAPTER 2 Discussion PML SST and DO Anomaly Within shallow estuaries and intertidal zones, water temperatures can deviate significantly from the SST due to thermal heating and the influence of atmospheric conditions, resulting in -1 periodic hypoxia (Helmuth et al. 2002). An observed 0.15 mg O2 L decrease in theoretical oxygen saturation between 1953–2017 (Appendix 1) at Portobello, in conjunction with observed warming by 0.10 °C decade-1 (1953–2016) or 0.147 °C decade-1 (1982–2016) (Shears and Bowen 2017), highlights the issue of warming oceans and consequent deoxygenation of coastal waters. Furthermore, this difference in length of timeseries analysed in Shears and Bowen (2017) highlights that the duration of monitoring influences the magnitude of conclusions that can be drawn (Gilbert et al. 2010). The Portobello TDO data demonstrates that measured DO values are above the theoretical values consistently, highlighting that temperature is not the only variable that influences DO variability. Mechanisms likely to cause the variation include winds, tides, solar radiation and precipitation (Sawabini et al. 2015, Nelson et al. 2017). This theoretical method is used to detect possible long-term trends in DO including seasonal variability, however, it falls short of describing the biological processes that likely dominate variability in oxygen concentration.

In 2017/18 and 2018/19, New Zealand and the Tasman Sea experienced multiple heatwave events (Salinger et al. 2019, Salinger et al. 2020). Maximum SST recorded during the 2017/18 heatwave by the Portobello time series was 21.3 °C (not shown) while Waitati Inlet experienced 24-hour mean December 2017 summer water temperatures of 17 °C to 20 °C with spikes of up to 26 °C (Southern Clams Ltd 2020). The combination of elevated water temperatures, midday and night time low tides and algal blooms can result in strong diel and seasonal low oxygen events (Helmuth et al. 2002). As such, during periods of warming, decreased oxygen solubility and increased oxygen consumption limits energy acquisition (Sokolova 2013, Breitburg et al. 2018). Ito et al. (2017) demonstrated the same trend by showing that, since the 1980s, a strong negative relationship between oxygen saturation and ocean heat content has occurred.

Variability in Bottom Water Dissolved Oxygen Within the current study, there was clear evidence of diel and seasonal cycling of oxygen concentrations in Waitati Inlet, Otago. Seasonality in oxygen concentrations are understood to be underpinned by biological activity during warmer months and hydrologic variables in late autumn and winter (Kemp and Boynton 1980, Orton et al. 2010, Nelson et al. 2017).

35 |CHAPTER 2 Spatial and temporal variability was analysed in the current study to identify factors that covary with DO, as well as likely mechanisms driving variability in oxygen concentrations. Spatial variation between Warrington and Waitati channels appears to exist, based on key explanatory variables produced in the GLM models. Here the channels are likely subject to different primary drivers of oxygen variability, with the Waitati channel more closely linked to catchment runoff and the Warrington channel more influenced by the effects of PAR on the photosynthesis-respiration cycles of primary producers.

Throughout the deployments, the Inlet’s water temperature exhibited tidal and seasonal signals. In summer PAR reached ~2000 µmol m-2 s-1 while autumn and winter received less than 900 µmol m-2 s-1 which is associated with day length and temperature, thus resulting in inter- seasonal variability in primary productivity from macroalgae, phytoplankton and MPB (Murrell et al. 2007). Due to reduced day length and cooler temperatures, the rate of photosynthetic activity and biomass in autumn and winter lead to less supersaturation events similar to Wenner et al. (2004). Respiration was considered to be the primary biological mechanism of DO drawdown driven by PAR, due to the reduced algal biomass and increase oxygen solubility resulting from colder waters, this ultimately lead to less deviation away from the theoretical DO in winter.

Looking exclusively at the summer deployments, PAR accounted up to 35.1% of the DO variability. However, when looking across the deployments at daily values, temperature is the primary predictor variable of low DO in Waitati channel based on a GLM, but less so in Warrington channel (data not shown). Water temperature influences the rate of biological activity (Brown et al. 2004). Consequently, in summer when there was more primary productivity, with greater deviations away from the theoretical oxygen value, due to increased photosynthesis and respiration during the day and night, respectively. Water temperature can be driven by seasonal and tidal cycles. Given many diel oxygen minima events in Waitati Inlet and estuaries globally occur near sunrise (e.g. Tyler et al. 2009, Sawabini et al. 2015), following night-time respiration when PAR is zero, PAR can be used as a proxy for the mechanism driving photosynthesis rather than warming water temperatures. Analysis was conducted on daily average water temperatures that exclude resolution at the tidal cycle level and instead reflect seasonality. This was observed in the seasonality of the Portobello SST and across deployment days in Waitati Inlet, thus, it was excluded from the AICc analysis.

36 |CHAPTER 2 Comparisons between the two channels supports the theory that the two channels and sides of Waitati Inlet are subject to different environmental drivers of DO variability. Within Waitati channel, along with temperature which co-varies with salinity, salinity is shown to be the dominant driver, with freshwater from the Waitati River having a strong influence over the DO, with lower salinity resulting in higher DO as freshwater holds more oxygen (Richards 1957). Comparatively, Warrington channel has a mixture of variables driving DO, with less land-based influence (i.e. salinity and freshwater from Carey’s Creek). Instead, PAR and wind speed were important mechanisms likely driving variability in low DO in Warrington channel through increased photosynthetic activity and wind-induced mixing. As well as the strong influence of salinity in Waitati channel, processes such as tidal forcing may be driving the variability also due to dominance of salinity and temperature. This pattern for Waitati channel is supported by O'Connell-Milne et al. (forthcoming) who identified little along-channel vertical structure in water velocity profiles in the main channel of Waitati Inlet, which lies directly in front of Waitati channel. This suggests strong tidal forcing exists in Waitati channel, which is likely a key mechanism driving DO variability.

The prevailing wind directions around Waitati Inlet is from the north/northeast and south/southeast (Single and Benn 2007). This would suggest Waitati channel is exposed to both prevailing wind directions, causing greater wind-induced mixing compared to Warrington channel. However, the freshwater input and variability in salinity is more significant. Within both channels, wind speed accounts for 4 – 5% of the variability. Modelled with PAR however, for Warrington channel, the two mechanisms account for 41.7% of the variability in DO, suggesting coupling between mechanisms driving wind-induced mixing and photosynthesis.

Based on visual observations made during each deployment and retrieval of monitoring frames from Warrington channel, there was higher abundances of the seasonal macroalgae Ulva spp., observed from the RV Tūhura in the channel even during winter, with the ropes and frames commonly having aggregations of Ulva spp. on them, which was not as evident in Waitati channel. Miyamoto et al. (2019) demonstrated the presence of macroalgae and warming temperatures amplified diel cycling in DO and caused a synergistic increases in the intensity of diel cycles of hypoxia, whereas in the absence of macroalgae, warming temperatures did not cause the same amplification in cycling of hypoxia. This study, however, did not investigate the influence of MPB in either channel which can also alter oxygen dynamics in intertidal estuaries, contributing to the dawn oxygen minima (Sandwell et al. 2009). Possible reduced tidal forcing due to Warrington channel being positioned perpendicular to the Inlet entrance

37 |CHAPTER 2 and having less freshwater input, the channel may experience lower water velocities than Waitati channel, thereby permitting Ulva spp., to settle on the benthos rather than being dislodged. This abundance of macroalgae in Warrington channel, supports PAR being the best predictor of low DO due to its influences photosynthesis and respiration which are the key biological mechanisms driving observed DO variability.

Daytime cloud cover can suppress the rate of photosynthesis, therefore, the magnitude of afternoon DO maxima can be reduced. Consequently, the DO minima during the night or early morning can be lower and exist for longer (Tyler et al. 2009). Similar results highlighting the importance of PAR were observed by Sawabini et al. (2015) where up to 87% of the variation in DO depletion could be explained by the intensity of PAR. High irradiance (PAR > 400 µmol m-2 s-1) resulted in high DO and a lower incidence of low DO events as more draw down was necessary to achieve low DO concentrations. Comparatively, low irradiance (PAR < 400 µmol m-2 s-1) led to a greater proportion of low DO events during times of sufficient biomass of primary producers (D’Avanzo and Kremer 1994, Sawabini et al. 2015).

The results from Sawabini et al. (2015) differed to those of the current study when observing DO through time during the summer Waitati channel deployment. Here low PAR on the 18th and 19th January 2019 (average PAR 247 µmol m-2 s-1 and 172.51 µmol m-2 s-1 respectively) was followed by very little variation in subsequent observed oxygen values (Figure 2.6). A key difference between these systems described previously and Waitati Inlet, is the estuary location. Many estuaries with hypoxic zones exist adjacent to the Gulf Stream which carries warm water poleward up the east coast of USA (e.g. Officer et al. 1984, Sanford et al. 1990, D’Avanzo and Kremer 1994, D'Avanzo et al. 1996, Wenner et al. 2004, Tyler et al. 2009, Sawabini et al. 2015). This is dissimilar to Waitati Inlet which boarders the cold Southland Current and inshore neritic waters (Sutton 2003). Further to geographic and oceanographic differences, the reduction in DO variability from the 19th to 21st January in Waitati channel are likely due to the arrival of a cold, low-pressure weather system which brought stronger more consistent winds and cooler temperatures, which likely maintained wind-driven mixing and DO levels. This is similar to the situation observed in Waquoit Bay in association with unusually high winds, low light and cooler water (D’Avanzo and Kremer 1994). Similar in winter, less biological activity, cooler temperatures and increased seasonal rainfall resulted in less DO variability compared to warmer months with higher biological activity. In winter, the importance of hydrological and climatic variables often increases relative to climatic and biotic

38 |CHAPTER 2 descriptors of DO variability (Orton et al. 2010, Nelson et al. 2017) such as freshwater input and water velocity (Orton et al. 2010, Yoon and Kasai 2017).

Within the current study, deployments were chosen based on sampling spring tides when low tide occurred early morning and evening. PAR was the largest measured influence on oxygen content in Warrington channel through photosynthesis and biological respiration (D’Avanzo and Kremer 1994) while Waitati channel was likely dominated by tidal forcing. Greater drawdown of DO, and consequent oxygen minima, often occurred when low tide occurred near dawn when there was insufficient irradiance for photosynthetic activity, as was also observed to be the pattern reported in Sawabini et al. (2015).

Compared to Warrington channel, where aggregations of Ulva spp., were identified and commonly pulled up on the ropes and equipment, the observed high water flow rates through Waitati channel would likely dislodge Ulva spp., or limit extensive mats from forming. This was suspected due to limited detection of macroalgae and presence of sand waves in late summer using a side scan sonar (Lowrance HDS7) at the bottom of the Waitati channel. Furthermore, shoreline ropes were commonly buried in sand, while based on raw data and difficultly in retrieving the frames and instruments from the channel, they were was also presumed to have been buried in shifting sands at some point. These observations further support the idea that tidal forcing and land-based runoff from Waitati River catchment are the dominant mechanisms driving DO in the Waitati channel.

Tidal height has previously been identified as being important in diel cycling of DO (Beck et al. 2015, Nelson et al. 2017). Oxygen minima and maxima often occur on the ebb tide near the bottom of the tide, as the flood tide carries saturated water into the inlet (Nelson et al. 2017), which is clearly seen in the late-summer Waitati deployment where DO reached anoxic conditions temporally. The observed relationship between tidal height and DO indicates that the flood tide replenishes oxygenated water in the Inlet, which is subsequently depleted by estuarine organisms and algae such as MPB and Ulva spp., which is present in Warrington channel and carried out of the inlet on the ebb tide.

In the late-summer Waitati channel deployment during three consecutive low tides, DO dropped from 9 mg L-1 to 0 mg L-1 on the ebb tide along with salinity, prior to rapidly increasing again with the flood tide. The true reason for these declines is unknown, however, it is possible on the ebb tide, advection of low oxygen porewater into the channel from the channel banks known as “body” circulation may have occurred lowering the DO (Billerbeck et al. 2006,

39 |CHAPTER 2 Schutte et al. 2019). Alternative explanations include macroalgae may have become stuck on the monitoring frame, which consequently led to depletion before the flood tide dispersed the algae, allowing oxygen and salinity to increase or the observation is simply a depletion of oxygen from the waters as the tide ebbed.

From the present study, Waitati Inlet appears to maintain a healthy condition, aligning with ANZECC/ARMCANZ (2000) and Gibson and Bowman (2000) guidelines for DO of 80 – 120% saturation (>5 – 6mg L-1) and conclusions drawn by Robertson (2018). The present study indicates that the two channels and sides of Waitati Inlet have different environmental mechanisms driving the DO variability. These data support the idea that the two separate channels in the inlet during the tidal ebb are subject to different DO variability. In this case there was a strong land and tidal based influence on DO in Waitati channel, while variability in DO in Warrington channel was likely dominated by the photosynthesis-respiration cycle of algae in the channel. Robertson (2018) identified Waitati Inlet as having low to moderate susceptibility to nutrient enrichment which causes accentuated DO fluctuations, however, the inlet’s high flushing rate may help to limit episodic and diel hypoxic events as seen in late- summer, that are observed in other estuaries (Tyler et al. 2009). Waitati Inlet DO measurements indicated daily variability of 2 mg L-1 to 4mg L-1. However, it is important to note, all deployments were carried out around the spring tides when there was a larger tidal prism and faster flood and ebb tides. Times of deployments were kept as similar as possible coinciding with the spring tides to avoid confounding variability of measurements around neap tides. Spring tides compared to neap tides have more mixing and greater exchange with the coastal ocean. Consequently, it is theorized that lower oxygen minima would exist during neap tides when there is less tidal exchange and mixing. However, this study was interested in looking at DO variability in shallower waters which occur during the spring tides rather.

The purpose of the current study was to identify how environmental parameters covaried with DO variability. In agreement with previous observations, PAR was an important driver of DO along with wind speed, temperature and salinity, time of day and tidal height. These variables can directly and/or indirectly influence DO by serving as proxies for either biotic, climatic, or hydrologic mechanisms for oxygen depletion or replenishment in the estuary. The current study focused on recognising if differences in DO between Waitati and Warrington channels existed. More extensive monitoring studies in conjunction with measurements of benthic oxygen flux on different time scales, from diel to seasonal, would allow for a greater understanding of the contribution of PAR to the system and other mechanisms that may drive fine scale patterns in

40 |CHAPTER 2 oxygen variability within channels. Moreover, incorporating multiple estuarine systems would enable differentiation between biological and non-biological (tidal forcing, winds and precipitation) drivers of DO, which would allow spatial scale to be incorporated into the modelling, similar to the Massachusetts Estuaries Project (Sawabini et al. 2015).

Understanding near bottom-water variability gives insight into processes occurring within the Waitati Inlet channel at 1 to 4 m depth, at various tidal stages. Variability in the near bottom- -1 water reached maximum fluctuations of around 20 – 50% (1.5 – 4 mg O2 L ) from the theoretical DO value, which have the possibility to induce short term responses to oxygen depletion in fauna and infauna. Within channels such as Warrington channel, where extensive quantities of Ulva spp., were observed year-round, as this macroalgae decomposes, microbial decomposition would be expected to consume DO within sediments, triggering a possible stress response from infauna living beneath the rotting Ulva spp., temporally altering the biogeochemical processes at the sediment-water interface (Neubacher et al. 2011). This scenario is further investigated in Chapter Three using experimental approaches.

The Portobello 65-year SST term data sets demonstrates a long-term decrease in estimated -1 oxygen saturation of 0.15 mg O2 L . Portobello SST highlights in an “extreme” summer associated with a heatwave event, that the theoretical saturation point can decline by 0.75 mg -1 O2 L . In the future the oxygen solubility of salt water is expected to decline in many areas. This is demonstrated in the reduction of the maximum theoretical DO saturation for Portobello and the expectation of warming through climate change and increased intensity and occurrence of extreme heatwave events. Additionally, algal blooms can cause oxygen depletion. Consequently, Chapter Four investigates how key estuarine fauna, already stressed from oxygen depletion, respond to differing environmental challenges, including midday low tide conditions associated with high PAR and heatwaves.

41 |CHAPTER 2

Chapter Three: The interaction of temperature and hypoxia on Austrovenus stutchburyi and influence of chronic ammonia dosing

Chapter Three and Four have share several similarities within the introduction and methods sections due to the use of the same principle experimental design. Methods have been repeated as the two following chapters are planned to be developed into manuscripts and submitted to the Journal of Experimental Marine Biology and Ecology.

42 | C H A P T E R 3 Introduction Estuaries are high stress environments, influenced by biotic and abiotic stressors including climatic and tidal regimes. As semi-enclosed water bodies, estuaries are repositories for contaminants and nutrients (Ridgway and Shimmield 2002), for example, they have historically been loaded with heavy metals and increasingly with excess nutrients and sediments. A three-fold increase in the human population since the 1950’s (United Nations Department of Economic and Social Affairs Population Division 2017), has led to high pressure on natural resources, urban development and food production. To sustain demand for crops, New Zealand’s usage of nitrogenous fertilizer increased 60-fold between 1961 and 2001 (MacLeod and Moller 2006), while since the 1950’s, global fertilizer usage has increased 10- fold (Breitburg et al. 2018). Increased fertiliser usage has resulted in excessive nitrogen (N) and phosphorus (P) loading into aquatic ecosystems, promoting eutrophication and ecosystem degradation (Howarth et al. 1988, Diaz and Rosenberg 1995, Nixon 1995, Cloern 2001, Morrissey et al. 2003). Oxygen deficiency in the open ocean and coastal zones has increased in intensity and duration in recent years (Diaz and Rosenberg 2008, Rabalais et al. 2014, Breitburg et al. 2018), with coastal hypoxia primarily attributed to eutrophication (Gilbert et al. 2010) through delivery of terrestrial and riverine organic matter (OM) (Levin et al. 2009, Middelburg and Levin 2009). The resulting environmental changes pose major threats to biodiversity and water quality, through increased susceptibility of eutrophication and shifts in biogeochemical pathways, as well as acidification (Nixon 1995, Worm et al. 2006, Howarth et al. 2011, Jager et al. 2018). Low bottom-water oxygen can cause shifts in biogeochemical pathways, notably in the benthos that can be detrimental to organisms, with higher fluxes of compounds including sulphides, that may further exacerbate mortality risk (Wu 2002, Middelburg and Levin 2009, Lee et al. 2019).

Temperature is fundamental to the rate that biochemical reactions and metabolic processes occur, with exponential increases in metabolic rates of some organisms observed with warming (Brown et al. 2004, Veraart et al. 2011, Mahaffey et al. 2020). For example, under a warming regime, higher rates of respiration will occur compared to photosynthetic activity, due to differences in activation energies (Harris et al. 2006), thus, elevating the biological oxygen demand (BOD) above the available oxygen (Brown et al. 2004).

Nitrogen Cycle Biogeochemical cycling in soft sediment estuarine environments is highly complex, with different N transformation pathways occurring concurrently (Liu et al. 2019) that are driven

43 | CHAPTER 3 primarily by vertical redox gradients (Berner 1981, Kuwae et al. 2003, Gerwing et al. 2015). - Repeatedly described as nitrogen sinks, estuaries have high rates of denitrification (NO3 → - NO2 → NO → N2O → N2) and to a lesser extent anaerobic ammonium oxidization (anammox). - + Denitrification utilizes nitrate (NO3 ) and nitrite (NO2) produced by nitrification (PON → NH4 - - → NO2 → NO3 ) and anammox and fixes it as biologically unavailable N2, removing it from the system (Seitzinger 1988, Howarth et al. 2011, Liu et al. 2019), consequently helping to control N over-enrichment and eutrophication (Seitzinger 1988).

Dissimilatory nitrate reduction to ammonium (DNRA) competes with denitrification and + - - anammox for NO3, recycling it into biologically available ammonium (NH4 ) (NO3 → NO2 → + NH4 ) resulting in net retention of sediment N (Anderson et al. 2003, Welsh 2003, Murphy et al. 2016, Robertson et al. 2016c). This can lead to eutrophication and detrimental ecosystem effects.

Rates of denitrification and DNRA in coastal zones are temperature dependent (Seitzinger 1988, Veraart et al. 2011, Crawshaw et al. 2019a). Environmental and seasonal characteristics influence the favoured benthic N process in estuaries and coastal systems (Smyth et al. 2013, Smyth et al. 2018) which are tightly associated with OM loading, temperature, DO, salinity and nutrients (Liu et al. 2019 and references within). DNRA is favoured under reducing - conditions caused by high OM, low NO3 , increased sulfides and anoxia, while denitrification becomes inhibited under these same conditions (Pina-Ochoa and Alvarez-Cobelas 2006, Conley et al. 2009, Murphy et al. 2016, Robertson et al. 2016c). Consequently, climate change and associated warming could alter the relative contribution of DNRA and denitrification, owing to increased coastal eutrophication (Gilbert et al. 2010, Breitburg et al. 2018), reduced oxygen solubility (Weiss 1970) and the coupling of nitrification and denitrification processes (Conley et al. 2009).

+ Favouring of DNRA and reduced nitrification, results from a greater of efflux NH4 into the + overlying water column (Berner 1980, Welsh 2003, Middelburg and Levin 2009). NH4 is preferentially assimilated into the tissues of photosynthetic organisms, enhancing primary production and OM biomass, contributing to eutrophication (Lohrer et al. 2004, Lohrer et al. 2010, Ale et al. 2011, Robertson and Savage 2018). Large quantities of organic carbon under - low water column NO3 concentrations further limits denitrification and lowers DO during microbial decomposition. This creates a positive feedback associated with high OM and elevated concentrations of total ammoniacal-nitrogen (Wu 2002, Welsh 2003).

44 | CHAPTER 3 Ammonia + Ammoniacal-nitrogen (NH3-N) is comprised of ionised NH4 and unionised ammonia (NH3).

NH3-N primarily enters waterways and coastal areas through point source discharges, including sewage and farming effluent. Naturally present in the sediment, porewater and overlying water, at higher concentrations NH3 is toxic to aquatic life. At 20 °C, pH 8 and 30 ‰ salinity seawater,

NH3 accounts for 3.8% of the total ammonia (ANZECC/ARMCANZ 2000); however, the relative fraction of NH3 is governed by water temperature and pH (Bower and Bidwell 1978).

Most biological membranes are permeable to NH3, therefore, maintaining ionic balance by avoiding ammonia burden is important to ensure normal physiological and behavioural responses and survival (Eddy 2005). Ammonia stress on benthic estuarine species can be high due to fluctuations in abiotic and biotic variables including salinity, temperature, pH and proximity to point source discharge areas.

During events such as eutrophication, marine heat waves (MHW) and summer time midday low tides, where mass mortality events of bivalves and aquatic organisms have been documented (Tsuchiya 1983, Desprez et al. 1992, Harley 2008, Ilarri et al. 2011, Jones et al.

2017), replenishment of water in estuaries is extremely important for limiting NH3 and sulfide accumulation as water quality is degraded by the decay of biological soft tissue (Berner 1980, de Zwaan et al. 2002, Cherry et al. 2005). In an artificial stream simulation Cherry et al. (2005) demonstrated that restricting water flow to high density bivalve populations can cause mass -1 die-offs whereby NH3 concentrations reached over 5 mg L . This NH3 peak coincided with hypoxia (<1 mg L-1) before flow was resumed, increasing DO and NH3 dropped to 0.41 -1 mg L . This indicates the interaction of low DO and NH3 production may be critical drivers influencing lethal and sublethal responses of clam Corbicula fluminea and other bivalves.

The toxicity of NH3 to aquatic organisms means guidelines are necessary to avoid sublethal and lethal effects of acute or chronic exposures. Ammonia guidelines are often based on datasets comprised of LC50’s for acute endpoints with no observed effects concentration (NOEC) for chronic exposures (Batley and Simpson 2009). Data are typically presented as total nitrogen + (NH3-N) values rather than NH3 or NH4 as the latter is also considered toxic to organisms at high levels (US EPA 1989). Compared to riverine systems, estuaries in New Zealand have minimal ammonia toxicity guidelines in place. Under ANZECC/ARMCANZ (2000), Australia and New Zealand have a 95% species protection concentration (PC) trigger value of 35 µg NH3

-1 -1 L or 910 µg NH3-N L (at 20 °C and pH 8), however, no estuarine systems in New Zealand were used in generating these values. The US EPA (1989) have the chronic criterion at 35 µg

45 | CHAPTER 3 -1 -1 NH3 L or 760 µg NH3-N L (at 20 °C and pH 8). More recently Batley and Simpson (2009)

-1 derived a new PC95 and PC90 trigger values of 460 and 750 µg NH3-N L for slight to moderately disturbed systems, representing low risk of acute or chronic toxic effects (PC95) and low to moderate risk of toxic effects, with sensitive species at risk of chronic toxicity (PC90) respectively.

DO is a critical component of estuaries, it is not only a major stressor influencing an organism’s physiology and sediment biogeochemistry, but also affects the sediment-water interface (SWI) fluxes (Middelburg and Levin 2009) which in turn can have adverse effects on aquatic organisms. A decline in bottom-water DO, thins the oxic sediment layer, promoting the + formation of reduced compounds and metals (H2S, NH4 , Mn(II), Fe(II)) which flux into the water from hypoxic and reduced sediments (Breitburg 2002, Middelburg and Levin 2009).

The gradients of DO across the SWI are driven by the bottom water DO and sediment grain size (Serpetti et al. 2016) and influence the rate of sediment oxygen consumption, aerobic respiration and re-oxidation reactions in surficial sediments (Middelburg and Levin 2009, Crawshaw et al. 2019a). Along with hydrological processes, macrofaunal bioturbation incorporates oxygen into the sediment, enhancing nitrification rates (Crawshaw et al. 2019b). Under reduced bottom-water DO (< 3.5 mg L-1), the sediment oxygen penetration depth can shallow as infauna move closer to the sediment surface to reach higher oxygen environments (Marsden and Bressington 2009, Sturdivant et al. 2012), thus further diminishing sediment + oxygen, stimulating DNRA and the increased efflux of NH4 and ammonia under degraded + anoxic N and OM enriched sediments (Berner 1980). In conjunction with NH4 flux, most aquatic invertebrates, including clams such as Donax serra and D. sordidus are ammonotelic, excreting labile OM and nitrogenous waste primary as ammonia and amino acids into the surrounding environment (Cockcroft 1990, Wright 1995).

Benthic Bivalves and their Ecosystem Services Suspension feeding bivalves are a key species in estuaries, fostering benthic-pelagic coupling, exerting top-down control on phytoplankton, bottom-up control on nutrient regeneration and OM mineralization. They help with sediment stability and enhance nutrient fluxes at the SWI through bioturbation, bio-irrigation and deposition of faeces and pseudofaeces (Smaal and Prins 1993, Newell et al. 2002, Welsh 2003, Newell 2004, Sandwell et al. 2009). Furthermore, bivalves are often the intermediary between primary producers and mobile consumers, providing a food source for higher trophic organisms (Karlson et al. 2020). Higher densities of

46 | CHAPTER 3 + benthic bivalves and infauna can enhance NH4 efflux and support higher rates of primary productivity through nitrogen regeneration (Smaal and Prins 1993, Biles et al. 2002, Newell + 2004, Sandwell et al. 2009), with fluxes (including NH4 ) greatest in summer (Dame et al. 1992). This increases the amount of seston available for suspension feeders, leading to a positive feedback cycle. Under increasing stress, contaminants and heavy metals can degrade and decouple estuarine processes, causing a cascade of ecosystem functioning issues (Lohrer + et al. 2011) including weakening the benthic primary production NH4 uptake interaction and + also the macrofaunal biomass, total benthic oxygen consumption with NH4 efflux (Lohrer et al. 2011, Lohrer et al. 2012).

Additional stress to benthic communities from increased OM loading can come in the form of eutrophication, whereby decomposition of OM reduces DO and increases NH3 (Wu 2002).

Consequently, a synergistic effect of simultaneous exposure to NH3 and low DO could heighten the sublethal and lethal stress response of aquatic organisms, as seen in freshwater species including rainbow trout (Magaud et al. 1997). Within fish, stressed individuals have been shown to be more sensitive to external ammonia concentration than non-stressed counterparts (Randall and Tsui 2002). Changes to DO conditions in the bottom-waters or at the SWI, influence the DO uptake and penetration depth in the sediment. Therefore, multiple stressors may be at play, that can decouple estuarine processes and stress macrofauna causing shifts in benthic communities and resilience or further decouple processes (Lohrer et al. 2011, Lohrer et al. 2012).

Study species Venerid bivalve Austrovenus stutchburyi (Wood, 1828) known as the New Zealand cockle or Tuaki/Tuangi (hereafter Austrovenus) is a critical suspension feeder in estuarine environments. Due to their short siphons, Austrovenus are found predominately within the top 1 - 3 cm of sediment (Whitlatch et al. 1997) throughout New Zealand’s harbours and estuaries (Morton and Miller 1973, Marsden 2004). Austrovenus help control phytoplankton populations and resuspended MPB through filtering vast quantities of seawater (Stephenson and Chanley 1979, Dame 1993, Kainamu 2011, Zu Ermgassen et al. 2013), thereby influencing biogeochemical cycling in estuarine environments through bioturbation and excretion (Thrush et al. 2006, Sandwell et al. 2009).

Being sedentary requires infaunal bivalves to evolve mechanisms to withstand adverse environmental conditions, including the consequences of eutrophication, such as hypoxia and

47 | CHAPTER 3 elevated nutrient concentrations. Behavioural responses to DO depletion for clams includes shallowing sediment burrowing depths (Marsden and Bressington 2009, Lewis and DeWitt 2017), crawling away (Mouritsen 2004), extending their siphons further into the water column to reach high oxygen environments (Jorgensen 1980), lowering metabolic rate and inducing short-term anaerobic energy production to increase their chance of survival (Carroll and Wells 1995, Long et al. 2008).

Temperature, DO and NH3 are all direct stressors on biota (ANZECC/ARMCANZ 2000). However, temperature and DO are also indirect stressors, altering physiological performance and the biogeochemistry respectively. Meta-analysis by Vaquer-Sunyer and Duarte (2011) found molluscs, compared to fish and crustaceans, were more resilient to warming, with the -1 LC50 threshold for hypoxia increase by 1.42 ± 0.36 % O2 saturation °C and decreased survival -1 -1 time by 41.90 ± 5.46 h °C compared to crustaceans LT50 of 2.40 ± 0.36 % O2 saturation °C . These results highlight bivalves may be more tolerant to low oxygen conditions than other more sensitive species. To the best of one’s knowledge at the time of the present study, laboratory experiments investigating the repercussions of multiple-stressor events involving chronic NH3 exposure and hypoxia have not been conducted previously on Austrovenus.

Commercial and recreational fisheries exist for adult Austrovenus as well as customary harvesting by Māori. Through overharvesting, adverse weather events such as MHW and floods, pollution and DO depletion, mortality events can occur, altering the function and productivity within a system (Marsden and Bressington 2009, Lohrer et al. 2012, Karlson et al. 2016). The ecological, cultural and economic value of Austrovenus makes it important to research the effects the species may face in the future as a consequence of climate change and eutrophication (Howarth et al. 2011, Hobday et al. 2016, Ministry for the Environment 2018).

The current study had two broad aims: 1) to determine if a synergistic effect is present between warming waters and chronic low DO exposure and 2) if there is an effect of long-term chronic ammonia exposure on Austrovenus when subjected to hypoxia. It was hypothesized within each multiple stressor experiment that a synergistic effect of the stressors would be present. It was predicted under warming temperatures, sublethal and lethal responses would be intensified when comparing the same DO treatment. Furthermore, the stressor response of Austrovenus would be greater when exposed to chronic NH3 and hypoxia simultaneously than by either stressor individually. Additionally, it was expected the overall NH3 concentration across

48 | CHAPTER 3 treatments would increase based on how challenging each treatment was, with highly stressed

Austrovenus producing more NH3.

Accordingly, assessment of 1) siphon activity 2) respiration rates 3) survival were made on

Austrovenus subjected to low oxygen and multiple stressor events and 4) mean NH3 concentration across treatments. It was predicted that lower DO and warmer water temperature would cause greater stress, negatively impacting non-lethal and lethal response of Austrovenus.

Materials and Methods: Collection and Acclimation Protocol Austrovenus were collected from the mid and low intertidal zones at Doctors Point, Waitati Inlet, Otago, (45°44’S, 170°35’E) between June and September 2019 and transported to Portobello Marine Laboratory (PML). Individuals were maintained in flow-through 200 L tanks without sediment containing 10 μm sand-filtered seawater, fully aerated for seven to 14 days. Water temperature was increased daily at an average rate of 1 °C from autumn/winter seawater temperatures of between 6 °C –11 °C to 22 °C and 25 °C respective of the experiment. Biofilm and epibionts on the Austrovenus were removed by scrubbing the shell to mitigate biological activity which could confound laboratory experiments. Active supplementary feeding was withheld during acclimation and experiments. Standardisation of food availability helped to ensure metabolic rates were as close to their basal rates as possible (Brown et al. 2004, Peck et al. 2008, Aguirre-Velarde et al. 2016) for measuring respiration rate, thus helping to isolate effects of the primary stressors and enhanced metabolism due to digestion.

Experimental setup Experiments were conducted in modified 5 L sealed plastic tanks with a mean (±SE) seawater tank flowrate of 4.17 ± 0.1 L h-1 within a temperature-controlled room with a 12h light-dark photoperiod. Water as deoxygenated in two 200 L seawater reservoirs to 10 ± 3% or 20 ± 3% saturation using an IKS AquaStar Oxygen Module and IKS control panel that enabled automatic injection of N2. Normoxic seawater (≥100% saturation) was continuously aerated in a separate 200 L reservoir. Each reservoirs water temperature was maintained at 22 °C or 25 °C for respective experiments, each using two aquarium heaters (Thermo Eheim thermo-control 300W) and a fine-tuned thermostat. Water levels were regulated with a ball-cock float system and recirculated within the reservoir using water pumps (Hailea HX6530) for complete mixing of heated deoxygenated water (Figure 3.1).

49 | CHAPTER 3 A flow-through system from the reservoirs to experimental tanks was developed to mitigate the effects of excretion, sulfide accumulation and large shifts in tank pH (de Zwaan et al. 2002). Daily measurements of water temperature and DO saturation (%) (YSI™ ProODO Optical Dissolved Oxygen meter; resolution 0.1% air saturation (Yellow Springs, Ohio, USA; YSI (2020)) were taken from the top 3 cm of the tank. Furthermore a visual count was made of protruding Austrovenus siphons, as a proxy of stress and activity (Sparks and Strayer 1998) (detailed in section 3.2.4). Dead individuals were removed during daily checks and shell lengths recorded. After daily measurements, steps to mitigate waste product accumulation were taken; existing water was discarded and surfaces in each experimental tank were wiped down to remove surface biofouling.

Figure 3.1. Experimental design for reservoir setup for hypoxic laboratory experiments.

From the water outflow, seawater was pumped into the corresponding experimental tank. N2

bubbling within tank for the 100% saturation is connected to an airline rather than N2 cylinder

Survival 3.2.3.1 Effects of DO and temperature An initial comparison of how increasing water TEMPERATURE from 22 °C to 25 °C effected response variables of Austrovenus was conducted at 10, 20 and 100% DO (OXYGEN) using the experimental design described in section 3.2.2. Three treatments with six replicates each were set up at 22 °C and 25 °C each containing 20 Austrovenus.

3.2.3.2 Chronic ammonia exposure After acclimation, three replicates containing 15 Austrovenus were assigned to one of four

AMMONIA and OXYGEN treatments for 45 days: normoxic spiked with NH3 (OxD), hypoxic

50 | CHAPTER 3 spiked with NH3 (HyD), normoxic with no added NH3 (OxA) and hypoxic with no added NH3

(HyA) whereby Ox = 100% DO (Normoxic) , Hy = 20% DO (Hypoxic), A = ambient NH3 -1 existing in the water and D = dosed to target NH3 concentration of 40 µg L above ambient. When spiking treatments, Austrovenus experienced chronic ammonia exposure. The ammonia concentration was based on NH3 concentrations from pilot studies (unpublished) investigating effects of hypoxia and anoxia on survivorship and also environmental ammonia guidelines (US EPA 1989, ANZECC/ARMCANZ 2000, Batley and Simpson 2009). Furthermore, measurements by O'Connell-Milne et al. (forthcoming) in nearby Waitati Inlet, Otago, (the location of Austrovenus collection) indicated mean water column NH3 concentration of 25 µg L-1. Given porewater tends to have higher nutrient concentrations than the overlying water (Robertson and Savage 2018) and assuming a gradient in nutrient concentration between the porewater, SWI and through the water column, NH3 concentrations at the SWI would be greater than measured in O'Connell-Milne et al. (forthcoming) for water column concentrations. The use of 40 µg L-1 above ambient therefore, aims to reflect these environmental conditions as a chronic stressor. It is acknowledged however, there is a mismatch in the stressor with concentrations of NH3 and other nutrients changing based on the phase of the tidal cycle and the fact Austrovenus within this experiment were constantly submerged.

A stock solution for dosing treatments was prepared from NH4Cl and de-ionised water at a -1 concentration of 250 µg NH3 L . The stock solution was diluted at a 1:200 ratio using reverse -1 osmosis water and pumped as working solution into treatment tanks at 40 µg L unionised NH3 using peristalsis (Masterflex L/S 16), at a flow rate of 1.76 ± 0.01 mL min-1. New working solution was made daily. Every other day (Day 2 – 32), water samples were taken and immediately filtered (Whatman Grade GF/C glass 25mm microfibre filter) and frozen at -20

°C prior to rinsing tanks. Peak NH3 concentrations were determined by flow injection analysis (FIA; Lachat QuikChem® 8500 Series 2 Auto Analyzer under QuikChem® Method 31-1076- 1-B) with a detection limit of 1 µg L-1 at Portobello Marine Laboratory.

Siphons Siphon and gaping activity have previously been identified in bivalves as behavioural responses to temperature (Rodland et al. 2009), varying algal concentrations along with normal biological response to environmental conditions and endogenous cycles (Beentjes and Williams 1986). In this study, visual daily siphons counts were used as a proxy for stress, with increasing stress on individuals being reflected in spending a greater proportion of time with siphons extended (Rodland et al. 2009). As individuals died throughout the experiments, the

51 | CHAPTER 3 contribution of each surviving individual’s siphon activity observation increased due to reduced sample population. Repeated daily siphon activity counts therefore lacked temporal independence. Consequently, siphon activity was reported as the mean of all daily observations across the first half of each experiment when most individuals had not yet died but were still subjected to the stressors. Siphon activity data was analysed post-hoc of the experiments.

Respiration The current study focused on the respiration during Austrovenus’s recovery and reoxygenation phase when they are exposed to oxygenated water on the flood tide. Respiration rate measurements were carried out in seven 0.24 L acrylic closed respiratory chambers. Six chambers contained a singular Austrovenus while the seventh (control) contained only seawater to account for bacterial respiration and temperature flux. Five Austrovenus per tank were labelled (1 – 5) prior to the experiment to avoid selection bias. If a selected individual died prior to the associated respiratory measurement, the next alive individual in numerical order was used. A magnetic stirrer plate and flea ensured continuous mixing of the water during the incubation, while a mesh divider ensured the individual was not disturbed by the magnetic flea. -1 DO (measured as % a.s and later converted to mg O2 L using a PreSens conversion table) was recorded at 30-minute intervals for the incubation duration, using a non-invasive fibre optic oxygen sensor and sensor spots (PreSens OXY-1 SMA meter and PreSens Oxygen-Sensitive Spots (SP-PSt3-NAU-D3-YOP)) and PreSens Measurement Studio 2 software.

Per treatment, the respiration rate was measured on 9 – 15 Austrovenus during the experiment. All soft tissue was dissected from each Austrovenus and placed in pre-weighed crucibles and dried at 60 °C for a minimum of four days to obtain the tissue dry weight (DW) (g). Ash-free dry weight (AFDW) (g) was determined as the weight change from the dried sample based on the lost-on ignition approach following exposure to 450 °C for four hours.

The unadjusted respiration rate for each chamber was calculated (eq. 1) whereby T was the duration of time (h) when an individual began and finished respiring to the closest measurement or for the duration of incubation for control, determined by visualising inflexion points per -1 measurement (Figure 3.2), D as the change in oxygen concentration (mg O2 L ) in the chamber for the same time period as T and V as the volume of water (L) in each chamber (0.24 L).

∆퐷 푂푥푦𝑔푒푛 푐표푛푠푢푚푝푡𝑖표푛 푟푎푡푒 (푚𝑔 푂 퐿−1 ℎ−1) = ( ) 푉 eq.1 2 ∆푇 Respiration rates were adjusted for and standardized using the AFDW and calculated as:

52 | CHAPTER 3 푂 퐶푅 − 푂 퐶푅 푅푒푠푝𝑖푟푎푡𝑖표푛 (푚𝑔 푂 𝑔(퐴퐹퐷푊)−1 ℎ−1) = 2 푐ℎ푎푚푏 2 푐표푛푡 eq.2 2 퐴퐹퐷푊

푂2퐶푅푐ℎ푎푚푏 and 푂2퐶푅푐표푛푡 the oxygen consumption rate in the chamber with Austrovenus and −1

control chamber respectively (푚𝑔 푂2 ℎ ).

) 1

- 10

6 Start 4 Finish

Dissolved Oxygen L Oxygen Dissolved (mg 0 0 1 2 3 4 5 6 7 Time (h) Figure 3.2. Inflexion points representing the time (hours) Austrovenus stutchburyi were respiring between the opening and closing of the two valves.

Statistical Analysis 3.2.6.1 Ammonia Tank Concentration, Siphon Activity & Respiration

DO and NH3 concentrations were tested on Log10 NH3 values to meet assumptions of normal distribution (Shapiro-Wilk W test (p > 0.05)). Outlying data points of the extreme treatment 1 -1 -1 -1 2 (HyD) were removed . Respiration values less than 0.2 mg O2 L g(AFDW) h and/or r value less than 0.85 were withheld from the analysis. Analysis for statistical differences in NH3 concentration, siphon activity and respiration rates were performed in JMP Pro 11.0 using two- way ANOVAs with Student t-test. All statistical tests were applied at the 5% confidence level.

3.2.6.2 Survivorship Time to onset of mortality was compared across all treatments using a single factor ANOVA (α = 0.05). The death of an individual was considered an event, while individuals removed for respiration measurements were censored. A proportional hazards model incorporating censored data in JMP 11.0 was used for Austrovenus survival at 10, 20 and 100% saturation. Survivorship analysis comparing OXYGEN and AMMONIA was carried out in RStudio 1.0.143. A semi-parametric Cox proportional hazards model (coxph) and Efron Approximation (Borucka 2014) within the “Survival” package were used to calculate the hazard ratios and 95% confidence intervals (95% CI).

1 -1 Extreme NH3 concentrations from HyD include 967.73, 316.03, 222.26 and 189.49 µg NH3 L

53 | CHAPTER 3 Results Effects of DO and Temperature Comparing three different DO levels, OXYGEN saturation (F = 83.51, p < 0.001), TEMPERATURE (F = 22.17, p < 0.001) and the whole model (F = 10.66, p = 0.0003) influenced the average daily siphon activity of Austrovenus. At 22 °C, siphon activity increased with reducing oxygen saturation, while at 25 °C activity was greatest at 20% saturation (Figure 3.3). Increasing temperature reduced siphon activity at 10% DO, while 20% and 100% DO treatments showed no difference.

Austrovenus siphon activity (%) was greater under warmer hypoxic water compared to 22 °C

(Figure 3.4a), with differences resolved between each stressor (FTEMPERATURE = 24.52, p <

0.0001, FOXYGEN = 589.15, p < 0.0001) and the interaction (F = 11.33, p = 0.0008). Differences in respiration rate were identified when comparing OXYGEN, with hypoxia increasing this rate (F= 6.45, p = 0.014) (Figure 3.4b). Differences in respiration were unable to be resolved for TEMPERATURE (F = 1.71, p = 0.19) or TEMPERATURE*OXYGEN (F = 0.0004, p = 0.98).

a ab bc c

d d

Figure 3.3. Average percent siphon activity (±1 SE) of Austrovenus stutchburyi exposed to 10, 20 and 100% dissolved oxygen saturations at 22 °C or 25 °C water temperatures n = 6. Levels not connected by the same letter are significantly different α = 0.05

54 | CHAPTER 3 Survivorship decreased with reducing OXYGEN (Chisq < 0.0001). The risk of mortality at 10% saturation was 12.7 and 507.2 times more likely than at 20% and 100% DO respectively when observing OXYGEN in isolation. Mortality risk increased by 39.9 times when in 20% DO compared to 100% (Table 3.1). A synergistic effect of TEMPERATURE*OXYGEN (F = 19.67, p < 0.0001) on survivorship is evident, with reduced survivorship under hypoxia and warming waters when compared to the influence of warming alone (F = 0.004, p = 0.94) (Figure 3.5).

Table 3.1. Survivorship risk ratios for Austrovenus stutchburyi between oxygen treatments (100, 20 and 10 % saturation) with upper and lower 95% confidence intervals. Value greater than one indicates elevated risk. Reciprocal ratio (RR) is the inverse. All differences are statistically significant (p < 0.0001)

Level 1 Level 2 Risk Ratio (95% CI) Reciprocal ratio 10% 20% 12.70 (8.75 - 18.84) 0.078

10% 100% 507.2 (112.9- 8938.7) 0.002

20% 100% 39.94 (8.66-708.90) 0.025

A a B ab

b ab b a

c c

Figure 3.4. Response of Austrovenus stutchburyi exposed to 22 °C and 25 °C water temperatures under hypoxia (light grey) and normoxia (dark grey) for A) average percentage -1 -1 -1 active siphons(±1 SE) (n=12) and B) respiration rate (mg O2 L g(AFDW) h ) (L-R: n = 9, 15, 10 and 12). Whiskers indicate maximum and minimum values with dots indicating outliers. Box limits represent 25th and 75th percentiles and the internal white lines are median values. Levels not connected by the same letter are significantly different α = 0.05

55 | CHAPTER 3 Figure 3.5. Austrovenus stutchburyi survival probability (%). Crosses represent removal of a predetermined censored individuals. Shaded areas represent 95% confidence intervals. n = 6.

Ammonia

Dosed hypoxic tanks had the highest concentration of NH3 compared to the other treatments, that were all significantly different log10NH3 to each other (Figure 3.6). Dosed AMMONIA treatments had higher concentrations than ambient (F = 16.81, p < 0.001) while hypoxic treatments greater than normoxic treatments (F = 120.14, p < 0001). However, no significant interaction between the two stressors was resolved (F = 0.81, p = 0.36), suggesting the stressors acted independently on NH3 concentration.

Siphon activity was higher in hypoxia-dosed treatments with a significant AMMONIA*

OXYGEN interaction (F = 5.96, p = 0.01) than stressors alone (FNH3 = 11.70, p = 0.0007;

FOXYGEN = 388.79, p < 0.0001). Under normoxia, activity did not vary (data not shown), but remained significantly less than hypoxic treatments (Figure 3.7a). AMMONIA (F = 4.88, p = 0.03) and OXYGEN (F = 8.25, p = 0.007) influenced respiration rates as single stressors, however no difference was observed when comparing the AMMONIA*OXYGEN (F = 0.16, p = 0.68) as illustrated in Figure 3.7b between dosed treatments. Only the rate for HyA (1.329 ± -1 -1 -1 0.070 mg O2 L g(AFDW) h ) was significantly higher than normoxic treatments. The AMMONIA*OXYGEN interaction was unable to be resolved using a Coxph model due to limitations of the data set iterations. However, individually both stressors reduced survivorship, with AMMONIA dosing increasing risk mortality 2.8-fold times (95% CI 1.4-5.68 p = 0.003)

56 | CHAPTER 3 while a 49.6-fold higher risk of mortality was associated with hypoxia exposure (95% CI 6.8- 363.2, p = 0.0001) (Figure 3.8) whereby majority of deaths occurred in hypoxic treatments.

HyD was the only treatment to reach the LT50 at day 41 within the study (Figure 3.8). No individuals in the control (OxA) died.

b a

c d

57 | CHAPTER 3 a A B a

Figure 3.7. Spiked NH3 treatment response of Austrovenus stutchburyi to A) average daily -1 active siphons (%) (±1 SE) over days 1-23 (n = 3) and B) respiration rate (mg O2 L g(AFDW)-1 h-1) under 22 oC hypoxia (n = 8) and normoxia (n = 6). Whiskers indicate maximum and minimum values. Box limits represent 25th and 75th percentiles and white solid lines within boxes are median values. Levels not connected by the same letter are significantly different α = 0.05

Figure 3.8. Mean survivorship probability of Austrovenus stutchburyi exposed to 20% or

100% oxygen and dosed or ambient NH3. Crosses indicate the removal of censored individuals. Shaded areas represent 95% confidence intervals. n = 3

58 | CHAPTER 3 Discussion Effects of DO and Temperature Throughout the current study, increasing water temperature from 22 °C to 25 °C was shown to influence the siphon activity of Austrovenus. Comparatively, temperature did not influence the respiration or survival rates as a single stressor. Instead, when simultaneously exposed to low DO and 25 °C water, the survival probability was lower than expected from warming or hypoxia alone, indictive of a synergistic response and narrowing of the thermal niche. This synergism was also demonstrated within elevated siphon activity, which is a normal physiological behaviour. Under adequate DO conditions Austrovenus will rhythmically extend its siphon into the water to respire and feed based on tidal phase (Beentjes and Williams 1986). The efficiency of obtaining DO from the deficient seawater implies individuals needed to respire for longer periods of time to maintain oxygen delivery (Wu 2002).

Below a critical oxygen level (PO2), an organism loses the ability to oxyregulate and therefore, shifts to oxyconformity, whereby the metabolism switches from aerobic to anaerobic (Pörtner 2010). Intertidal species often live close to their upper thermal tolerance (Somero 2002,

Hofmann and Todgham 2010). Further warming alters the PO2 of an organism, causing the

PO2 to increase and thermal tolerance to narrow (Pörtner and Knust 2007), in turn influencing growth, the accumulation of intermediary metabolic products and survival (Wu 2002, Pörtner and Knust 2007). This change is visible in survival of Austrovenus, where progressing from 22

°C 10% DO to 25 °C 20% DO conditions increased the rate of mortality and reduced the LT50 from 14 to 10 days. These results reflect the oxygen- and capacity-limited thermal tolerance (OCLTT) concept by Pörtner (2010) that describes the relationship between aerobic scope, oxygen supply and organismal fitness, whereby at some threshold there is a narrowing of thermal niche, resulting from an imbalance in DO supply and demand. Similar to the results of the current study, king scallop Pecten maximus showed an increase in PO2 from 18.3% DO at 10 °C to 36.1% at 25 °C (Artigaud et al. 2014). Likewise the concurrent exposure to hypoxia and hypercapnia at 22.5 °C water compared to 25 °C warming alone, reduces the LT50 of P. maximus (Götze et al. 2020), also indicating a narrowing of thermal niche and synergistic response to multiple stressors.

Not only was siphon activity elevated within the current study, but visual observations demonstrated that hypoxic stressed Austrovenus extended their siphons further up into the water column (pers. obsv). This response has been documented in the wild whereby stressed benthic bivalves move to the sediment surface and extend their siphons to overcome the SWI

59 | CHAPTER 3 to find higher oxygen environments (Jorgensen 1980, Nilsson and Rosenberg 1994, Sobral and Widdows 1997). Emergence from the sediment and increased time spent siphoning water under hypoxic conditions increases the risk of predation and siphon- and foot cropping by demersal fish, predatory whelks and birds (Whitlatch et al. 1997, Mouritsen and Poulin 2003).

Water exchange at the SWI during a macroalgal bloom can become limited and result in hypoxia in the sediment, porewater and overlying water (Franz and Friedman 2002). Penetration of DO into the sediment is therefore reduced, causing the emergence of infauna to the sediment surface or into the water column (Norkko and Bonsdorff 1996, Franz and Friedman 2002, Marsden and Bressington 2009), as documented for Austrovenus (Marsden and Bressington 2009) and the intertidal cockle Clinocardium nuttallii (Lewis and DeWitt 2017). Furthermore, macroalgal blooms are suspected to be more detrimental to short-siphoned bivalves such as Mya arenaria, that are unable to penetrate through the algal mat to higher DO environments compared to Macomona balthica. Consequently, these short siphoned bivalves face an increased risk of dying under macroalgal mats (Thiel et al. 1998). Similar to M. arenaria, Austrovenus may experience increased mortality risk under eutrophic bloom conditions.

Ammonia The reduced burial depth and surfacing behaviour, hypoxic water and possible warming at the SWI, due to the lack of water exchange for Austrovenus (Marsden and Bressington 2009), leads to the hypothesis that individuals already stressed from hypoxia and warming may be exposed + to higher ammonia concentrations. This is due to increased efflux of NH4 and NH3 into the overlying water resulting in the shift from denitrification to DNRA, shallowing of the RPD depth and reducing sediments, resulting in a multiple stressor environment. Thus, the second part of the current study focused on the influence of chronic NH3 exposure on Austrovenus.

Peak NH3 concentrations taken across the first 32 days of the 45-day experiment revealed that the NH3 concentration increased with treatment intensity, whereby the highest concentration was in HyD treatment followed by HyA, OxD and OxA. NH3 did not influence respiration rates within DO treatments. Although not statistically significant, a pattern in respiration rate was visible across the treatments with ambient NH3 treatments experiencing higher rates than dosed treatments when compared with those within hypoxic and normoxic treatments (HyA > HyD

> OxA > OxD). This suggests that if more replicates were used, an elevation in seawater NH3 may have shown detrimental consequences on the respiration rates of Austrovenus when

60 | CHAPTER 3 combined with the mean concentrations from water samples taken. This observation highlights hypoxia, rather than chronic NH3 spiking, influences the NH3 concentration. From this, it is proposed that stressed individuals produce more NH3 waste products (Cockcroft 1990, Gao et al. 2017) contributing to the death of other individuals in proximity, especially when at high densities (e.g. Cherry et al. 2005). Within fish, stressed individuals have demonstrated greater sensitivity to external ammonia than non-stressed counterparts (Randall and Tsui 2002). Responses to chronic exogenous ammonia exposure can include downregulation of ammonia production and increased ammonia excretion (Gao et al. 2017) which may be what is observed in the current study. Temperature, emersion and nutritional status can influence the rate of ammonia excretion (Cockcroft 1990). For example, Cockcroft (1990) demonstrated a temperature increase from 15 °C to 25 °C increased the ammonia excretion rate shown by the

Q10 values of 2.2 and 2.3 for D. serra and D. sordidus respectively.

The survival of the Austrovenus was reduced under hypoxia and chronic NH3 individually with HyD and HyA having a ~60% and ~30% reduction in survival after 45 days respectively. Comparatively, under normoxic treatments, all individuals except one (OxD day 32) survived, even when dosed with NH3. This outcome may reflect the difference in NH3 concentration between treatments, specifically observing a 46.9% concentration increase HyA compared to OxD, further supporting the hypothesis of hypoxia being the primary driver of stress and increased NH3 excretion. Peak NH3 levels however, coincided with days where deaths occurred in associated tanks, leading to the proposition that the origin of at least some ammonia was from decaying tissues. Consequently, the flushing rates and dilution efficiency of systems and estuaries are important components to consider when a population of bivalves is stressed or a die off event occurs, stimulating NH3 production which could have ramifications for other fauna assemblages and water quality (Cherry et al. 2005).

Initial concentrations from stock solution used in the current study aligned with guidelines for chronic NH3 exposure from US EPA (1989), Batley and Simpson (2009) and ANZECC/ARMCANZ (2000), however, treatments, as explained previously, did not remain at these concentration. While at low exogenous NH3, Austrovenus may successfully employ

NH3 detoxification mechanisms such as glutamine synthesis similar to the marsh clam

Polymesoda expansa (Hiong et al. 2004). These observations may help explain why NH3 concentrations in OxD are lower than HyA whereby Austrovenus are less stressed and thus able to employ detoxifying mechanisms.

61 | CHAPTER 3 Weight and size are strongly correlated in bivalves. Furthermore, body mass influences the ammonia excretion rate (Cockcroft 1990). Prior to the beginning of the current experiments, 15 and 20 Austrovenus respective of the experiments were randomly selected per tank with the aim of replicates having similar wet weights of Austrovenus (µ = 361.7g ± 4.7%). Some Austrovenus were swapped to reduce weight discrepancy; however, this was done on weight rather than size exclusively. Consequently, some individuals were larger than others with size ranging from 33 – 47mm (µ = 37.97 ± 0.17 (SE)), which may have influenced NH3 excretion slightly.

The concentration of nutrients in sediment porewater often occurs at orders of magnitude greater than the overlying waters (Robertson and Savage 2018). The current study, as a chronic water-only ammonia exposure experiment, rather than a whole-sediment toxicity test, allowed for responses of Austrovenus to be more confidently identified to the specific stressors of bottom-water hypoxia and NH3. Vertical pH gradients exist across the SWI and within the + sediment which can shift the equilibrium between NH4 and NH3 ions, increasing the variables within the study (Newton and Bartsch 2007). Batley and Simpson (2009) argue that toxic NH3 exposure for many benthic species is primarily from filtration of the overlying water rather than sediment porewater. In addition, as a short-siphoned filter feeding bivalve, Austrovenus live near or on the sediment surface feeding on particulates from the water column. When they are not feeding, they remain in the sediment with their valves sealed closed, avoiding the external environment and porewater which have higher NH3 concentrations. Therefore, a water-only experiment was justified.

Experiments carried out within the current study focused on larger adult populations of Austrovenus rather than planktonic larval stages or juveniles (5 – 18 mm). Therefore, the current study did not account for vulnerabilities of younger cohorts and the potential implications for increased susceptibility to NH3 exposure. For example, M. edulis larvae are two times more sensitive to unionised ammonia than juveniles (e.g. Kennedy et al. 2017). Due to its important ecological and cultural significance, further work to better understand tolerances and thresholds of Austrovenus at each life stage is necessary to understand implications for the species.

Eutrophication and warming of coastal seas and estuaries are consequences of climate change and anthropogenic influence. Within this study, it has been demonstrated Austrovenus are likely to respond to hypoxia and warming water negatively, with increased stress and reduced

62 | CHAPTER 3 survivorship. A consequence of estuarine hypoxia is a shift in nutrient cycling towards increased flux of DNRA, contributing to higher concentrations of ammonia in bottom waters (Song et al. 2020). These higher rates of ammonia are shown to reduce the survivorship of Austrovenus. Consequently, stressed individuals were more susceptible to elevations in + environmental NH4 , therefore, during an algal bloom, high flushing during the subsequent + decomposition phase is important to limit NH4 and NH3 accumulation which has detrimental influence on survival of Austrovenus and possibly other benthic bivalves. The temperature interaction with ammonia was outside the scope of this study however, combining these experiments observing the effect of increasing temperature and dosing with ammonia, there is a real risk for the viability of Austrovenus populations in New Zealand in the future when combined with low oxygen. The implications of a decline in Austrovenus populations includes the loss of ecosystem services including filtration of water, benthic-pelagic coupling and contribution to biogeochemical cycling. The loss or reduction in one or all these services can result in the degradation of coastal environments.

63 | CHAPTER 3

Chapter Four: When does a challenge become a death sentence? Understanding the response of Austrovenus stutchburyi to extreme conditions under oxygen depletion

64 | C H A P T E R 4 Introduction Understanding plasticity in a species’ environmental niche is necessary to predict likely responses to projected changes in the oceans. Shifts in environmental conditions are already occurring in response to climate change as illustrated by warming oceans (Sorte et al. 2010a), enhancement of ocean stratification and increases in the occurrence and intensity of extreme events (Ummenhofer and Meehl 2017) such as marine heat waves (MHWs) (Oliver et al. 2017, Frölicher et al. 2018, Oliver et al. 2018, Pansch et al. 2018, Salinger et al. 2019, Salinger et al. 2020). One of the biological responses to warming has been marked shifts in species distributions (Poloczanska et al. 2013). Furthermore, as the water warms there is a decline in oxygen saturation (Weiss 1970), consequently, the manifestation of hypoxic events throughout the oceans, coasts and estuaries continues to increase (Schmidtko et al. 2017, Breitburg et al. 2018, Altieri and Diaz 2019). Organisms associated with the intertidal zone are subjected to high variability in abiotic and biotic conditions including temperature (Steckbauer et al. 2011), salinity (Tallis et al. 2004, Rutger and Wing 2006, Wing and McLeod 2007, McLeod and Wing 2008), oxygen depletion (Rosenberg et al. 1991, Diaz and Rosenberg 1995, Yamada et al. 2016), food availability (Norkko et al. 2005) and desiccation (Somero 2002, Helmuth et al. 2005). Consequentially many species have evolved specific mechanisms to survive under rapidly changing conditions. Nevertheless, exceeding critical oxygen levels (PO2) and thermal tolerance thresholds can result in large scale mortality events (Desprez et al. 1992, Harley 2008, Jones et al. 2017, Callahan 2019, Graham-McLay 2020).

Hypoxia is a major stressor for many marine organisms, as dissolved oxygen (DO) is essential for aerobic respiration and metabolism. In adequate DO environments, marine organisms do not appear to be sensitive to DO fluctuations until a threshold or hypoxic levels is reached (Vaquer-Sunyer and Duarte 2008). Below the species-specific threshold, DO deficiency affects metabolic rates, behaviour, growth, reproduction and survival (Wu 2002). An organism’s response to oxygen depletion determines whether it is classified as an oxyconformer or oxyregulator (Herreid 1980, Cobbs and Alexander 2018). Oxyregulators consume DO at rates that are independent of ambient levels; when they reach their PO2 they shift to conforming to the environment or induce metabolic depression. Oxyconformers are subject to proportional decreases in DO consumption as concentrations decline.

-1 The critical hypoxia threshold of 2.0 mg O2 L defined in Diaz and Rosenberg (1995) is used as a general threshold for survival of metazoans, however, taxa specific thresholds (Vaquer- Sunyer and Duarte 2008) and intraspecific thresholds exist and often relate to the population

65 | CHAPTER 4 size structure (Bayne 1971, Taylor and Brand 1975, Pan et al. 2016). DO concentration can be linked to the fitness of an organism, the population and ecological role in the ecosystem (Pörtner 2010). Ramifications of DO depletion events may therefore be disproportionate, whereby selected size cohorts experience mortality, altering population structures (Diaz and Rosenberg 1995, Callaway et al. 2013, Tricklebank et al. 2020).

Recently New Zealand has experienced sequential heatwaves in the summers of 2017/18 and 2018/19 (Salinger et al. 2019, Salinger et al. 2020). A coupled ocean-atmospheric heatwave that stalled over the Tasman Sea and New Zealand in the austral summer (DJF 2017/2018) caused terrestrial and marine temperatures to increase 2 °C or greater, up to 6 °C compared to the 1981 – 2010 averages (Salinger et al. 2019). Effects transpiring in the marine environment from the 2017/18 heatwave, reported in Salinger et al. (2019), include a decline in the giant kelp Macrocystis pyrifera in southern New Zealand, mortality of farmed salmon Oncorhynchus tshawytscha and the onset of snapper Pagrus auratus spawning six weeks earlier than normal. Since 1925, the occurrence of annual MHW days has increased by 54% globally, with a 34% and 17% increase in MHW frequency and duration respectively (Oliver et al. 2018). This increase in heatwaves and associated climate change can cause mass mortality events (MMEs) as organisms experience stressors such as temperatures beyond their thermal tolerance and associated lowered oxygen availability.

MMEs in the marine environment have been documented for decades (Johnson 1966, Tsuchiya 1983, Desprez et al. 1992). However, the magnitude of MMEs and the quantity of events published in scientific literature has increased; many others are only anecdotally reported (Burdon et al. 2014, Fey et al. 2015, Soon and Zheng 2019). The primary cause of literature reported MMEs is disease outbreak and contributing synergistic effects of multiple stressors on disease susceptibility. Distinguishing single and multiple stressor events is often difficult (Ilarri et al. 2011, Burdon et al. 2014) however, changes in singular climatic conditions including oxygen or thermal stress, starvation or weather, accounted for 24.7% of reported MMEs (Fey et al. 2015). A metanalysis by Fey et al. (2015), indicated the main factors of multiple stressor MMEs were in response to toxicity and biotoxicity combined with oxygen stress respectively. Distinguishing thermal stress from oxygen stress in the natural environment is complex compared to controlled laboratory conditions due to the negative relationship between the coupled stressors. Oxygen and thermal stress are especially prevalent on the soft sandy/muddy intertidal areas and the rocky shores which are both extensively studied model systems (Petes et al. 2007).

66 | CHAPTER 4 The rocky shore has provided a natural laboratory for evaluating an organism’s environmental thresholds and upper distributional limits due to the gradients of thermal stress and desiccation occurring across small spatial scales (Connell 1972, Petes et al. 2007). For example, in Bodega Bay, northern California, various MMEs have been documented on the rocky shore since the 1960’s for the Californian mussel Mytilus californianus (Harley 2008) and limpet Lottia scabra (Sutherland 1970). Unseasonably warm spring conditions in 2004, coinciding with daytime low tides, caused patches of L. scabra to perish. The following month widespread mortality of M. californianus occurred under similar conditions (Harley 2008). The occurrence of midday low tides, high aerial temperatures, substratum aspect and small scale patch dynamics lead to heat stress being hypothesized as the dominant predictor for sublethal and lethal responses of individuals on the rocky shore (Tsuchiya 1983, Helmuth and Hofmann 2001, Helmuth et al. 2006a, Harley 2008). This theory is further supported by the mass mortality of M. californianus in Bodega Bay in June 2019, when a reported record-breaking heatwave caused individuals to reach temperatures of over 37 °C at low tide, resulting in the largest MME in Bodega Bay in 15 years, with tens of 1000’s of mussels being cooked in their shells (Cagle 2019).

The stressors that resulted in MMEs observed in northern California are comparable to those in populations not only on the rocky shores but also in intertidal environments. Documented MMEs in the literature include large die offs of echinoids (Glynn 1968), blue mussels Mytilus edulis (Tsuchiya 1983), benthic bivalves and polychaetes (Desprez et al. 1992), clams Corbicula fluminea (Ilarri et al. 2011) the common cockle Cerastoderma edule (Desprez et al. 1992) and New Zealand’s estuarine clams Paphies australis and Austrovenus stutchburyi (Bingham 2009, Ministry for Primary Industaries 2015, Tricklebank et al. 2020).

Unlike the rocky shore, where there is often a large degree of substratum heterogeneity and consequentially individuals can be exposed to shade or sun, the sandy/muddy shore is primarily flat, providing little substratum aspect. The benthic surface homogeneity limits epifaunal protection from harsh conditions, including sunlight which causes desiccation. Intertidal infauna, including bivalves, periodically experience oxygen depletion during low tide when they are isolated from the water column. Accordingly, bivalves have evolved mechanisms to avoid or withstand periodic hypoxia (Diaz and Rosenberg 1995) including closure of valves and suppression of metabolic rates (Haider et al. 2020). The manifestation of coastal eutrophication and climate change enhances stress on a highly variable environment within which an organism must survive.

67 | CHAPTER 4 High variability and patch dynamics in the Bay of Somme, France, were experienced during summertime MMEs of benthic macrofauna including polychaetes and the cockle C. edule in 1982, 1983 and 1989 as a direct result of hypoxia/anoxia. Behavioural responses to hypoxia included bivalves altering their position on the benthos and extending their siphons further into the water column (Desprez et al. 1992) to reach higher oxygen environments. The circumstances under which the three Bay of Somme MMEs occurred were similar; high aerial temperatures exceeding maximum summer temperatures by 2 °C, 4 °C and 6.5 °C were recorded leading up to and during the 1982, 1983 and 1989 events respectively. Additionally, phytoplankton blooms due to high nutrient availability were identified in the 1989 event as a key contributor and these are thought to have also contributed to the prior MMEs. The collective effects of morning neap tides in conjunction with limited mixing, diel oxygen cycling, and high aerial temperatures warming shallow pools and microbial decomposition all contributed to reduced oxygen availability for benthic macrofauna, resulting in extreme stress and mortality (Desprez et al. 1992).

A high parasitic infection and increased disease susceptibility of the New Zealand cockle Austrovenus stutchburyi, was attributed to high aerial temperatures occurring with summer time midday low tides, resulting in a MME in Whangateau Harbour, New Zealand in February 2009 (Bingham 2009, Tricklebank et al. 2020) whereby 60% of the total population (Jones et al. 2017) and 80 – 84% of large individuals (≥30mm) died (Ministry for Primary Industaries 2015). Two further smaller mortality events were observed also in Whangateau Harbour in 2014 and 2018 (Tricklebank et al. 2020). An experimental study by Wegeberg and Jensen (1999) on the interaction between parasitic loading and DO depletion in C. edule and a study by Nam et al. (2018) indicating a lowered thermal stress tolerance and immune response following high parasitic loading thereby leading to large mortality events, both corroborate findings by Jones et al. (2017) and Bingham (2009) for A. stutchburyi. These studies highlight the importance of improving knowledge of multiple stressor interactions and the possibility of stress becoming fatal under bioenergetically demanding conditions (Sokolova 2013).

Observational approaches similar to Desprez et al. (1992) and follow up investigations such as those by Jones et al. (2017) and Tricklebank et al. (2020) are insightful and important for recognising conditions detrimental to a species or ecosystem and demonstrate how the fitness of an individual or population can be altered when multiple stressors are exerted. However, the effect of each stressor and the relative interaction between stressors cannot easily be measured with observational studies; laboratory-based experiments and isolating of stressors is necessary

68 | CHAPTER 4 for a higher level of understanding into the non-lethal and lethal responses to environmental change involving multiple stressors (Jansson et al. 2015).

Austrovenus stutchburyi (Bivalvia: Veneridae) known as the New Zealand cockle or Tuaki/Tuangi (hereafter Austrovenus), is a key suspension feeding bivalve, endemic to New Zealand. Distributed throughout sheltered sand and mudflats (Marsden 2004) and reaching densities of up to 3000 individual m-2 (Stephenson and Chanley 1979), they are a benthic- pelagic coupler, contributing to both top-down and bottom-up processes (Powell 1979, Marsden 2004). They help control phytoplankton populations through filtering vast quantities of seawater (Stephenson and Chanley 1979, Dame 1993, Zu Ermgassen et al. 2013) thereby influencing biogeochemical cycling in estuarine environments (Thrush et al. 2006, Sandwell et al. 2009, Karlson et al. 2020).

Austrovenus are harvested for commercial and recreational purposes as well as customary fisheries by Māori. Mortality events as a consequence of overharvesting, adverse weather events, pollution and oxygen depletion, can alter the functioning and productivity within a system (Hartill et al. 2005, Marsden and Bressington 2009, Lohrer et al. 2012, Karlson et al. 2016). Many sedentary benthic species including Austrovenus are unable to avoid unfavourable environmental conditions such as hypoxia but have been observed to alter behavioural responses to low oxygen events. These include reduced sediment burrowing depths in an attempt to maintain themselves in an oxygenated environment (Marsden and Bressington 2009), lowered metabolic rate and induced short-term anaerobic metabolism to increase their chances of survival (Carroll and Wells 1995, Long et al. 2008).

New Zealand experiences semi-diurnal tides, resulting in estuarine bivalves being rhythmically immersed and emersed to aerial conditions for 6 to 12 hours daily. Climate models for New Zealand predict temperatures to increase by 0.7 – 4.6 °C by 2090 (Ministry for the Environment 2018). Along with the enhanced probability of increased temperatures there will also be increases in the duration and intensity of MHW events as a result of climate change (Hobday et al. 2016, Frölicher et al. 2018, Frölicher and Laufkötter 2018, Oliver et al. 2018, Salinger et al. 2019, Salinger et al. 2020). Consequently, thermally driven MMEs which have rarely been documented in the past are expected to increase (Harley 2008). This has the potential to further alter highly variable environments with low thermal inertia which may instigate shifts in ecosystem equilibrium and a species’ ability to respond to stressful events (Madeira et al. 2012). Therefore, the aim of the current research deviates from Chapter Three, to investigate

69 | CHAPTER 4 the exposure thresholds and tolerances of adult Austrovenus exposed to multiple stressor scenarios under repeated stress (low tide emersion) conditions including chronic pre- conditioned low oxygen and elevated water temperatures along with increased intensity of heat and desiccation exposure during simulated summertime low-tide conditions.

Accordingly the present study assessed: 1) siphon activity, 2) respiration rates and 3) survival of Austrovenus subjected to multiple stressors and subsequent heat exposure challenges. It was predicted that lower DO and warmer waters would cause stress to Austrovenus and negatively impact survival.

Materials and Methods Austrovenus were collected within the mid and low intertidal zones at Doctors Point, Waitati Inlet, Otago (45°52ˈS, 170°31ˈE), between June and September 2019 and transported to Portobello Marine Laboratory (PML). Individuals were maintained in flow-through 200 L tanks without sediment, containing 10 μm sand-filtered seawater fully aerated for seven to 14 days. Water temperature was increased daily at an average rate of 1 °C from ambient winter seawater temperatures of 6 – 11 °C to 22 °C and 25 °C respective of the experiment. Biofilm and epibionts living on the Austrovenus were removed by scrubbing the shell to mitigate biological activity which may confound laboratory experiments. Active supplementary feeding was withheld during acclimation and experiments. Standardisation of food availability helped to ensure metabolic rates were as close to their basal rates as possible (Brown et al. 2004, Peck et al. 2008, Aguirre-Velarde et al. 2016) for measuring respiration rate, thus helping to isolate effects of the primary stressors and increased metabolism due to digestion.

Experimental Setup Experiments were conducted in modified 5 L sealed plastic tanks with a mean (± SE) seawater tank flowrate of 4.17 ± 0.1 L h-1. Four treatments were assigned systematically, with three replicates of each containing 15 or 17 Austrovenus. Water was deoxygenated in 200 L seawater reservoirs to 20 ± 3% saturation (hypoxic) using an IKS AquaStar Oxygen Module and IKS control panel that enables automatic injection of nitrogen gas (N2). A control reservoir (normoxic treatment) was continuously aerated to maintain ≥100 % saturation. Each reservoirs water temperature was maintained at 22 °C or 25 °C for respective experiments using two aquarium heaters (Thermo Eheim thermocontrol 300Watt) and a fine-tuned thermostat. Water levels were regulated with a ball-cock float system and heated deoxygenated water was recirculated within the reservoir using water pumps (Hailea HX6530).

70 | CHAPTER 4 A flow-through system from the reservoirs to experimental tanks was developed to mitigate the effects of excretion and sulfide accumulation and dramatic shifts in tank pH (de Zwaan et al. 2002). Daily measurements of water temperature and DO saturation (YSI ProODO Optical Dissolved Oxygen meter; resolution 0.1% a.s (Yellow Springs, Ohio, USA; YSI (2020)) were taken from the top 3 cm of the tank. Furthermore, a visual count was made of protruding Austrovenus siphons, as a proxy for stress and activity (Sparks and Strayer 1998). Dead individuals were removed daily with total shell lengths measured. After daily measurements, steps to mitigate waste product accumulation were taken; existing water was discarded and the surfaces in each experimental tank were wiped down to remove surface biofouling.

Three different challenges (Table 4.1; models I-III) were created with response variables siphon activity, respiration rate and survivorship of Austrovenus to determine the effect and interaction between OXYGEN (hypoxia (20% DO) and normoxic (100% DO)) with:

i. WATER TEMPERATURE (22 °C vs. 25 °C) given emersion at 25 °C (model I) ii. Intensity of EMERSION (25 °C vs. 33 °C) given 25 °C water temperature when immersed (model II) iii. EXPOSURE (emersion vs. immersion) at 25 °C given 25 °C water temperature when immersed (model III)

33 °C low tide exposure was selected due to air temperatures within Waiati Inlet during summer having been observed regularly surpassing 30 °C (Southern Clams Ltd 2020). Furthermore, expected aerial warming of up to 4.6 °C by 2090 due to climate change (Ministry for the Environment 2018) ensures 33 °C is realistic of future conditions Austrovenus may experience within the intertidal zone in Otago.

Water temperatures during the DJF 2017/18 MHW within Waitati Inlet, Otago, reached mid 20 °C’s, with prominent temperature peaks occurring during low tide, with mean 24-hour water temperatures in February 2018 also reaching 20 °C (Southern Clams Ltd 2020). Furthermore, sea surface temperatures at PML during the same DJF heatwave event reached a maximum mean intensity of 5.7 °C above the climatological value (Salinger et al. 2019). Consequently, in association with warming waters from climate change, heatwaves such as those experienced in 2017/18 and 2018/19 (Salinger et al. 2019, Salinger et al. 2020) have the potential to cause extensive warming of waters beyond 22 °C and 25 °C for extended periods of time within estuaries.

71 | CHAPTER 4 Table 4.1. Experimental treatments for models I-III with associated experimental duration

(days), mean days to onset of mortality (±1 SE) and median LT50 (CI 95%) for Austrovenus stutchburyi. 25+ and 36+ represents no Austrovenus died within the given time frame. 35.0

(N/A) indicates only one individual died. Treatments which did not reach 50% mortality (LT50) are represented by N/A. Levels not connected by the same letter are significantly different.

Exposure Experimental Water Exposure Days to onset LT (median Model Oxygen 50 condition Duration (°C) (°C) (Mean (SE)) (95% CI))

I Hypoxic Emersed 25 22 25 15.0 (2)b N/A

I Normoxic Emersed 25 22 25 25+ N/A

I II III Hypoxic Emersed 36 25 25 13.3 (0.88)b 18 (17-23)

I II III Normoxic Emersed 36 25 25 35.0 (N/A)c N/A

II Hypoxic Emersed 16 25 33 3.6 (0.33)a 6 (5-8)

II Normoxic Emersed 16 25 33 4.0 (0.57)a 9 (5-N/A)

III Hypoxic Immersed 36 25 25 12.5 (1.55)b 18 (16-19)

III Normoxic Immersed 36 25 25 36+ N/A

Low Tide Exposure Individuals in emersion treatments were removed from the tanks and placed on a 3 cm layer of sediment under heat lamps (4 Philips Flood 240V 120W 3C lamps) for 4 – 6 hours (Figure 4.1) before being re-immersed in their respective tanks. This treatment aimed to mimic exposure to summertime aerial conditions and the thermal capacity of sediment. Three temperature loggers (Onset, HOBO® Pendant) were used to determine emersion temperature.

) 10

1 - 8 6 Start 4 Finish 2 0 Dissolved Oxygen L (mg Oxygen Dissolved 0 1 2 3 4 5 6 7 Time (h) Figure 4.1. Experimental emersion of A. Figure 4.2. Inflexion points indicating the stutchburyi to midday low tides start and end point of A. stutchburyi respiring

72 | CHAPTER 4 Respiration The current study focused on the respiration during the recovery and reoxygenation (normoxia) phase for Austrovenus, simulating a flood tide carrying oxygenated water into an estuary. Respiration rates were carried out in seven (0.24 L) acrylic closed respiratory chambers. Six chambers contained a singular Austrovenus while the seventh was a control chamber, only containing seawater to account for bacterial respiration and temperature flux. A magnetic stirrer ensured continuous mixing of the water during the incubation, while a mesh divider ensured the individual was not disturbed by the magnetic flea. Oxygen concentration (measured as % -1 a.s and later converted to mg O2 L using a PreSens conversion table) was recorded at 30 minute intervals for the duration of the incubation using a non-invasive fibre optic oxygen sensor and sensor spots (PreSens OXY-1 SMA meter and PreSens Oxygen-Sensitive Spots (SP-PSt3-NAU-D3-YOP)) and PreSens Measurement Studio 2 software.

Per treatment, the respiration rate was measured on 6 – 17 individuals during the experiment. Whole wet weight (g) and total shell length (mm) were taken prior to dissection of each specimen. All tissue was removed from the shell and placed into pre-weighed crucibles and dried at 60 °C for a minimum of four days to obtain the tissue dry weight (DW) (g). Ash-free dry weight (AFDW) (g) was determined as the weight change from the DW based on the lost- on ignition approach following exposure to 450 °C for four hours.

Unadjusted respiration rate for each chamber was calculated (eq. 1) whereby D is the change -1 in DO (mg O2 L ) for the period an individual was respiring or the duration of incubation for control chamber, T as the duration of time (h) between when an individual began and finished respiring to the closest measurement or the duration of incubation for control chamber and V as the volume of water (L) in each chamber (0.24 L). The time used to calculate the respiration rate was determined based on observations of Austrovenus valves opening and closing and how these coincided with inflexion points of the relationship (Figure 4.2).

∆퐷 푂푥푦𝑔푒푛 푐표푛푠푢푚푝푡𝑖표푛 푟푎푡푒 (푚𝑔 푂 퐿−1 ℎ−1) = ( ) 푉 eq.1 2 ∆푇

Respiration rates were adjusted for and standardized using the AFDW and calculated as:

푂 퐶푅 − 푂 퐶푅 푅푒푠푝𝑖푟푎푡𝑖표푛 (푚𝑔 푂 ℎ−1𝑔(퐴퐹퐷푊)−1) = 2 푐ℎ푎푚푏 2 푐표푛푡 eq.2 2 퐴퐹퐷푊

푂2퐶푅푐ℎ푎푚푏 and 푂2퐶푅푐표푛푡 the oxygen consumption rate in the chamber with Austrovenus and −1 control chamber respectively (푚𝑔 푂2 ℎ ).

73 | CHAPTER 4 Statistical Analysis. 4.2.4.1Siphon Activity & Respiration Siphon and gaping activity have previously been identified in bivalves as behavioural responses to temperature (Rodland et al. 2009), varying algal concentrations along with normal biological response to environmental conditions and endogenous cycles (Beentjes and Williams 1986). In this study, visual daily siphons counts were used as a proxy for stress, with increasing stress on individuals being reflected in spending a greater proportion of time with siphons extended (Rodland et al. 2009). As individuals died throughout the experiment, the weighting of each surviving individuals siphon observation increased due to a reduced sample population. Repeated daily siphon counts lacked temporal independence. Consequently, siphon activity was reported as the mean of all observations across only the first half of each experiment when most individuals had not yet died but were still subjected to the stressors. -1 -1 -1 2 Respiration values less than 0.2 mg O2 L g(AFDW) h and/or r value less than 0.85 were withheld from the analysis. Statistical analysis on siphon activity and respiration rates was performed in JMP Pro 11 using two-way ANOVAs with post-hoc student t-test with significance detected at α = 0.05.

4.2.4.2 Survivorship Time to onset of mortality was compared across all treatments using a single factor ANOVA (α = 0.05) in JMP Pro 11. Survival analysis involved analysing time to event (survival) data of Austrovenus. The death of an individual is considered an event, while removal of living individuals for respiration measurements during the study were not. Survivorship analysis was performed in RStudio 1.0.143 using the package Survival for all survival modelling. R function Coxph was used to fit the semi-parametric Cox proportional hazards (Coxph) regression model with the Efron approximation to account for the discrete daily measurements of survival (Borucka 2014). Coxph was used for modelling 22 °C and 25 °C and immersion-emersion experiments. The hazard ratio calculated in the Coxph and presented here, is defined as the risk of outcome in one group over the risk of the outcome in another group at a given time. Where significances were not detected or variables could not converge, type II ANOVAs were used to obtain a likelihood ratio test score and α of 0.05.

A non-parametric Kaplan-Meier test was used to estimate the logrank values for comparing time to event curves (Kaplan and Meier 1958) using the R functions survfit and survdiff in the

25 °C versus 33 °C EMERSION challenge due to failed Coxph model diagnostics. An LT50 was

74 | CHAPTER 4 calculated for treatments where 50% mortality was reached using the Kaplan-Meier survfit function. 95% confidence intervals were used to test for significance.

Results Siphons Across the three models (I-III), OXYGEN was consistently a significant predictor (p < 0.0001), with siphon activity (%) at least 20% higher in each hypoxia model compared to oxygenated conditions (Figure 4.3a-c). Significantly higher activity for Austrovenus exposed to 25 °C hypoxia indicated a synergistic effect of WATER TEMPERATURE when compared to siphon activity exposed to hypoxic conditions at 22 °C (F = 7.57, p = 0.006), while temperature alone did not influence siphon activity when comparing non-stress (normoxic) individuals (Figure 4.3a; model I). Both stressors were also highly significant predictors of siphon activity as main effects (p < 0.001).

Within model II, along with OXYGEN, EMERSION (F = 4.88, p < 0.05) and the interaction (F = 15.70, p < 0.0001), were predictors of siphon activity of Austrovenus when held in 25 °C water (Figure 4.3b). Siphon activity in hypoxic treatments reduced from 65.7% to 45.9% when comparing 25 °C and 33 °C EMERSION, respectively. Figure 4.3b shows similar patters to model I, whereby no difference was able to be resolved in activity between temperatures when Austrovenus were in normoxic conditions.

The form of EXPOSURE did not influence siphon activity (F = 1.74, p = 0.18) when compared within oxygenated treatment alone (model III). Instead OXYGEN was the key predictor of siphon activity (F = 1083.69, p < 0.0001), causing an interaction with EXPOSURE (F = 15.70, p < 0.0001), with greater siphon activity in immersed individuals than those exposed to low tide conditions (Figure 4.3c).

Respiration The highest respiration rates were obtained from Austrovenus exposed to hypoxia (F = 12.30, -1 -1 -1 p = 0.001) in either 22 °C or 25 °C water (1.5177 ± 0.1256 mg O2 L g(AFDW) h and 1.5330 -1 -1 -1 ± 0.1473 mg O2 L g(AFDW) h respectively) compared to oxygenated conditions. WATER TEMPERATURE and the interaction with OXYGEN had no effect on respiration (p = 0.95 and p = 0.96 respectively) (Figure 4.3d).

Effect tests identified neither OXYGEN (F = 3.30, p = 0.079) nor EMERSION (F = 1.75, p = 0.19) influenced the respiration rates as main effects for model II. A statistical difference within

75 | CHAPTER 4 the treatments was identified between 25 °C hypoxic and oxygenated treatments, but any difference was unable to be resolved this was not significantly different to hypoxic 33 °C emersion (p > 0.05) (Figure 4.3e).

Under hypoxic conditions, Austrovenus is estimated to consume oxygen at a rate of 0.2034 mg -1 -1 -1 O2 L g(AFDW) h faster than individuals in normoxic conditions (F = 6.71, p = 0.013) in model III. The effect of EXPOSURE condition had on respiration rates was unable to be resolved, however, the p-value was nearly significant (F = 3.22, p = 0.08) (Figure 4.3f). Similar to the prior two models for respiration, no differences were observed between Austrovenus held in normoxia and the other challenge (Figure 4.3d-f).

Survivorship All survivorship models indicated hypoxia was a primary stressor that reduced Austrovenus survivorship (Figure 4.4). Under 33 °C emersion, the onset of mortality was reduced by at least 8 days compared to the other treatments (Table 4.1) and under hypoxia, mortality occurred at a greater rate (Chisq = 12.2, p = 0.005) compared to in normoxia. Individual Kaplan-Meier logrank tests done on EMERSION showed highly significant differences in survival curves when subset by any of the stressor levels on the respective variable (Table 4.2). Excluding 33 °C emersion, the onset to mortality was insignificant among hypoxic treatments. However, hypoxia initiated mortality occurred earlier than in oxygenated conditions where all except for one individual survived (Table 4.1). Within model I, warming seawater 3 °C from 22 °C to 25 °C increased mortality risk by 15.98 times (95% CI 5.58 - 45.67, p < 0.0001), while exposure to hypoxic conditions increased the risk of mortality 133.09 times (95% CI 17.40 - 1017.90, p < 0.0001) compared to oxygenated conditions. Thus, highlighting hypoxia as the greater predictor of mortality. The OXYGEN*WATER TEMPERATURE interaction was unable to be tested based on the hazard ratio, however, a type II ANOVA indicated no interaction was present (LR = 0.064, p = 0.80).

OXYGEN was the only predictor of survival, with hypoxic individuals 333.91 (95% CI 43.86 - 2542.21, p < 0.0001) times more likely to die than individuals in oxygenated treatments when comparing EXPOSURE (model II). Differences in survival were unable to be resolved for EXPOSURE as a single stressor (LR = 2.04, p = 0.15) or when interacting with OXYGEN (LR = 1.79, p = 0.18). The onset to mortality under 25 °C compared to 22 °C hypoxic water was the same, however, at 25 °C the LT50 was reached on day 18, while 22 °C did not reach LT50 over the course of the experiment (Table 4.1,Figure 4.4). The rate of mortality increased and

76 | CHAPTER 4 overall survival decreased when exposed at 33 °C (p < 0.0001) (Table 4.1) further interacting with OXYGEN (LR = 21.43, p < 0.0001).

A D a a

a b

b b

c c

B E a a

b ab b b

c c

a C F a b

b b

c d

Figure 4.3. Interaction effects on the average daily siphon activity (%) (A-C) and mean -1 -1 -1 respiration rate (mg O2 L g(AFDW) h (D-F) of Austrovenus stutchburyi under multiple stressor conditions in either hypoxic (light grey) or normoxic (dark grey) conditions. A-C error bars represent ±1 SE. D-F box plots represent 25th and 75th quartiles, medium, minimum and maximum values. Levels not connected by the same letter are significantly different.

77 | CHAPTER 4 Figure 4.4. Mean survivorship of Austrovenus stutchburyi under hypoxic or normoxic conditions when exposed to different challenges. Small (22 °C water, 25 °C emersion), medium (25 °C water, 25 °C emersion) and large (25 °C water, 33 °C emersion) (n = 3). Shading represents 95% CI through time. Pluses (+) on lines indicate time of removal of predetermined censored individual.

Table 4.2. Chi-squared values from Kaplan-Meier survival analysis comparison of stressors oxygen and emersion temperature against other treatments for Austrovenus stutchburyi held at 25 °C water. Overall 33 °C vs 25 °C Exposure at 25 °C water temperature; Chisq = 171 on 3df, p < 2e-16

Oxygen Emersion

Hypoxic Normoxic 25 °C 33 °C

Oxygen 76.9, p < 2e-16 *** 12.2, p = 5e-4 ** Variable Emersion 101, p < 2e-16 *** 35.5, p = 3e-9***

78 | CHAPTER 4 Discussion The present study was designed to determine the influence of hypoxia on Austrovenus in combination with the warming conditions that are predicted for southern New Zealand. The results demonstrate that under persistent hypoxia (~20% at 22 °C to 25 °C), Austrovenus displays behavioural changes including heightened siphon activity and enhanced respiration rates. The time until onset of mortality and LT50 both shortened with increasing challenge stress as WATER TEMPERATURE increased from 22 °C to 25 °C and EMERSION challenge from 25 °C to 33 °C.

These results indicated that the intensity of the chronic stressors exerted on Austrovenus were important components determining the survival probability during an extreme event or challenge. During low tide emersion at 33 °C, conditions were stressful enough that some Austrovenus were observed perishing on the sediment while others were highly stressed, indicated by foot protrusion, valve gaping and expulsion of internal fluids. Similar sublethal behavioural responses have been observed by Sparks and Strayer (1998) in the juvenile freshwater mussel Elliptio complanata under persistent 2, 4 and 8 mg L-1 DO while Strahl et al. (2011) found that for the bivalve Arctica islandica siphons remained consistently open under hypoxia and anoxia compared to individuals under normoxic conditions which would alternate between open and closed.

Mortality following 33 °C EMERSION demonstrated that Austrovenus may have surpassed their upper limit thermal threshold following extreme aerial and sediment temperatures and 25 °C water conditions. The observed pattern likely represents a thermal limit given that many intertidal marine organisms already live near or at their upper physiological thermal tolerance (Somero 2002, Madeira et al. 2012). European cockles have upper thermal limits similar to the 33 °C EMERSION challenge used in the current study, for example upper thermal limits of 29 °C to 31 °C exist for C. edule, and C. tuberculatum and 33 °C to 35 °C for C. glaucum (Ansell et al. 1981). As coastal sea surface temperature (SST) around south-eastern New Zealand warm at a rate of at least 0.10 °C per decade (Shears and Bowen 2017) and MHWs become more prominent (Schlegel et al. 2017, Frölicher et al. 2018, Oliver et al. 2018, Salinger et al. 2019, Salinger et al. 2020), the risk of intertidal organisms reaching their upper limit thermal tolerance is expected to increase (Madeira et al. 2012, Carneiro et al. 2020). Accordingly, an increased occurrence of die-offs and MMEs is expected, altering species distributions, ranges and community structure (Sorte et al. 2010a, Sorte et al. 2010b, Madeira et al. 2012, Tricklebank et al. 2020). For example, a decade worth of MHWs is thought to be the

79 | CHAPTER 4 mechanism behind a population decline of the subtropical bivalve Anomalocardia flexuosa (Carneiro et al. 2020).

MMEs of wild populations of mussels in New Zealand have arisen following similar heat stress to Austrovenus under experimental 33 °C emersion conditions, supporting these findings that Austrovenus and intertidal bivalves are susceptible to extreme heat stress. For example three days of aerial temperatures averaging 31 °C (max 36.9 °C, highest weekly average 23 °C 2001- 2004) during mid-January 2005 in Christchurch, New Zealand, saw 35.4% and 3.4% of the local population of mussels Perna canaliculus and M. galloprovincialis respectively perish due to heat stress (Petes et al. 2007). Similarly, low spring tides and hot dry aerial temperatures were speculated to have triggered a P. canaliculus MME in north-western New Zealand in mid- February 2020 killing half a million individuals (Graham-McLay 2020). Recurrent moderate heat stress during both these events rather than a single extreme heat event is a probable mechanism for the mass die-offs as Seuront et al. (2019) showed experimentally, with reduced survivorship and LT50 on blue mussel M. edulis.

The effect of hypoxia on the hazard ratio varies based on the model. Both hazard ratios indicate that hypoxia negatively affects Austrovenus’ survival probability, being reduced by 133 or 333 times respectively when comparing WATER TEMPERATURE and OXYGEN or EMERSION and OXYGEN. Increased siphon activity overall supports this stressor response as warmer water enhances stress on Austrovenus. This is likely due to a reduction in oxygen solubility (Weiss 1970) and increased rates of biochemical metabolic processes (Brown et al. 2004, Veraart et al. 2011, Mahaffey et al. 2020), thereby Austrovenus requires more oxygen. Already below the general specified DO tolerance, the multi-stressor interaction of elevated temperature and hypoxia on increased siphon activity was not reflected statistically in the respiratory response. Individuals previously exposed to hypoxia showed respiration rates ~50% higher than individuals in normoxic conditions. These findings of increased activity and differences in respiration rates are likely due to the reduction in absolute volume of oxygen in the surrounding environment during the study and the individual needing to recover or enter back into an aerobic state when initially put in higher DO environments during the respiration experiments. Furthermore, intertidal bivalves including Austrovenus, are rhythmically being immersed and emersed by the tide and employ mechanisms to withstand these conditions: sealing their valves closed, entering into an anaerobic state, slowing their metabolism or ceasing activity for the duration of the tidal cycle.

80 | CHAPTER 4 Svendsen et al. (2012) demonstrated metabolic recovery times for rainbow trout Oncorhynchus mykiss following exposure to oxygen below their PO2 would take longer when returned to higher oxygenated environments. A similar principle may be able to be applied to invertebrates including bivalves, whereby they require more time filtering the water to obtain the necessary amount of oxygen required and during recovery to maintain basal metabolic activity. An organism’s response to reoxygenation following low DO is important to understanding the mechanisms of recovery and thresholds for an individual’s or a population’s tolerance to hypoxia. This tolerance can vary depending on proximity to their upper thermal limit (Madeira et al. 2012, Deutsch et al. 2015).

The present study demonstrated that respiration was greater during the recovery phase for individuals exposed to hypoxic rather than oxygenated conditions. The observed pattern differs from some previous studies on respiration in bivalves (Le Moullac et al. 2007, Anestis et al. 2010, Dong et al. 2011, Strahl et al. 2011). The observed difference in rates can be attributed to differences in study design and focus whereby prior research measured respiration in treatment conditions (i.e. hypoxia) to observe the physiological response. In contrast, the current study focused on the recovery and reoxygenation phase, therefore rates are not directly comparable with studies by Artigaud et al. (2014), Aguirre-Velarde et al. (2016) and Sobral and Widdows (1997) due to differences in study design and focus on resolving responses at the stage of recovery from long term (days-weeks) hypoxia. Although the present study subjected individuals to continuous hypoxic or normoxic conditions when immersed, water within the respiratory chambers was initially oxygenated and depleted through time as individuals consumed the oxygen. These conditions simulated an incoming tide which carried oxygenated water that became depleted in oxygen over time.

Often studies of physiological response under hypoxia or warming stress identify metabolic depression as a key response for many marine invertebrates (Anestis et al. 2007, Le Moullac et al. 2007, Jansson et al. 2015). Metabolic depression is a mechanism employed to withstand warming and/or low oxygen environments. The effect is achieved by reducing respiration and metabolic rates to conserve energy and reduce accumulation of toxic end products including ammonia and increased ammonia excretion (Wu 2002, Sokolova et al. 2012, Haider et al.

2020). Declines in oxygen concentrations below the PO2 for Crassostrea gigas (Le Moullac et al. 2007) and M. edulis (Artigaud et al. 2014, Tang and Riisgard 2018) resulted in rapidly reduced respiration rates whereby normal aerobic metabolism and oxyregulation were no longer able to be maintained. This triggers a shift to anaerobic metabolism with organisms

81 | CHAPTER 4 conforming to the surrounding environmental oxygen conditions. Metabolic depression may have occurred during the present study. Hypoxic Austrovenus emersed at 33 °C appeared to have reduced respiration rates compared to their counterparts emersed at 25 °C, however, individuals in the 33 °C EMERSION treatment died before sufficient respiration measurements could be taken, resulting in a reduction in statistical power.

The aim of the current research was not to determine the PO2 of Austrovenus, but to observe how future multi-stressor conditions will likely influence their survival when experiencing extreme events. By examining extreme environmental conditions existing under varying time scales, such as a macroalgal bloom or heat wave, rather than future mean conditions, the current research focussed on multiple stressor challenges to Austrovenus survival consistent with predictions of the future environmental estuarine conditions. For example, temperatures used within the present study align with future extreme conditions or MHW events for New Zealand. Using the definition of a MHW from Hobday et al. (2016), Salinger et al. (2019) identified that the 2017/18 MHW lasted 147 days in the Tasman Sea east of New Zealand. During this unprecedented event, four MHWs were identified in Otago, whereby the Portobello Marine Laboratory SST anomaly reached maximum mean intensity of 5.7 °C and mean intensity of 3.4 °C between 22 November and 20 December 2017. Extreme heatwave events such as the one observed in Coastal Otago in 2017/18 demonstrate that shallow bodies of water such as estuaries are particularly susceptible to extreme temperature fluctuations. For example, within Waitati Inlet, Otago, during the DJF 2017/18 MHW, water temperatures reached mid 20’s °C, with prominent peak temperatures occurring during low tide (Southern Clams Ltd 2020).

During periods of low tide, intertidal organisms can become stranded in tidal pools that can heat up and deoxygenate or be exposed to aerial conditions and desiccation. The density and rate of respiration within an area can influence the oxygen availability. This issue is expected to be exacerbated under climate change which will alter suitable marine habitats and tighten metabolic constraints on many species (Deutsch et al. 2015). For example Nam et al. (2018) demonstrated that sediment and water thermal stress can influence a negative metabolic response in the Manila clam Ruditapes philippinarum. High parasitic burden causes further stress, ultimately resulting in multiple stressors interacting on survival of R. philippinarum which have been attributed to the reported MMEs. Similarly Wegeberg and Jensen (1999) identified a reduced survival tolerance for C. edule under experimental hypoxia (0.4 – 0.5 mg -1 O2 L ) when also stressed by infection of the trematode Himasthla.

82 | CHAPTER 4 The potential survival times estimated in the present study and other experimental systems, either static or flow through, are proposed to be underestimated relative to natural conditions (de Zwaan et al. 2002). Acceleration of mortality resulting from the mortality of one or more individuals can cause a proliferation of bacteria and pathogens in a natural system, causing tissue damage and further stress to surviving animals (de Zwaan et al. 2002). This effect is consistent with the observation that antibiotic use in incubations of bivalves has shown to increase survival duration (de Zwaan et al. 2002). The current study refrained from using antibiotics, instead opting for a flow through system and daily tank cleaning. Although this may have underestimated the survival duration, the practice gives a conservative platform for starting to understand the survival probability of Austrovenus populations to these stressors. Furthermore, the use of antibiotics does not reflect estuarine conditions, whereby pathogens exist but often not at either extreme concentrations or for the duration of time assumed in laboratory experiments.

Under eutrophication and MMEs, the proliferation of bacteria and pathogens would be observed in environments with poor water quality (de Zwaan and Babarro 2001, de Zwaan et al. 2002). Personal observations and other studies demonstrated shell blackening and a sulfurous smell under low DO conditions. Such observations are indicative of the presence and accumulation of sulphide by sulfate reducing bacteria and other microbes which occur when individuals remain within a system where other organisms have died, subsequently reducing the water quality and survival probability of remaining individuals (de Zwaan et al. 2001, de Zwaan et al. 2002).

The tight coupling between oxygen saturation and warming waters (Weiss 1970) results in multiple stressors occurring simultaneously under climate change, that has significant effects on estuarine species. Rarely do stressors such as temperature and DO occur in isolation. The present study provides key findings about the responses of Austrovenus to multiple stressor environments, focusing on future climatic conditions and degradation of estuarine health through macroalgal blooms causing oxygen depletion. It demonstrated that Austrovenus responds differently to single and multiple stressors. Repeated exposure to 33 °C emersion followed by re-immersion in warm 25 °C waters resulted in a synergistic effect of multiple stressors on survivorship and siphon activity. This extreme low tide emersion scenario, regardless of oxygen conditions, showed a significant reduction in survival probability compared to immersion and emersion at 25 °C when previously exposed to hypoxia. The observed effect highlights a possible lethal thermal tolerance for Austrovenus. These results

83 | CHAPTER 4 highlight the importance of understanding how Austrovenus and other species respond to multiple stressor environments and extreme events and how preconditioning under stressful conditions lessens the probability of surviving an extreme event.

The study also compared the physiological responses of individuals when they remained continuously submerged in seawater and when either immersed or exposed to a challenge during low tide conditions. Although no differences in survivorship were found when comparing 25 °C immersion and emersion, like many intertidal species, Austrovenus experiences emersion periodically with the flood and ebb of the tide. Furthermore, 25 °C emersion is realistic during hot summer days for populations within Waitati Inlet, Otago. Experimental emersion at 25 °C may have not been hot enough to stress individuals to determine a difference in survival, but 33 °C was lethal. The observed result is reassuring for the future of Austrovenus populations in Coastal Otago whereby 25 °C days are expected to be more common than emersion at 33 °C, therefore Austrovenus may be capable of withstanding warmer water in conjunction with higher aerial temperatures in the future. Further research examining the effects of temperature and DO on survival and respiration rates at finer scales, as in other studies previously mentioned, would allow more direct comparison between Austrovenus and other estuarine bivalves.

84 | CHAPTER 4

Chapter Five: General Discussion

85 | GENERAL DISCUSSION Study Aims The objective of the present thesis was to investigate and quantify how low oxygen events in estuaries, in conjunction with warming and nutrient pollution in coastal waters, may influence Austrovenus stutchburyi (hereafter Austrovenus), a critical species in New Zealand estuaries. The response of organisms and ecosystem functioning to single stressors is often highly different to the response to multiple stressors.

Globally, mass mortality events on the intertidal zone have often occurred as a result of interacting multiple stressors including thermal stress, tidal phase interacting with midday heat stress, desiccation, oxygen depletion, parasitic burden and disease (Helmuth et al. 2006a, Petes et al. 2007, Harley 2008, Seuront et al. 2019). Quantifying organismal thresholds to single or coupled multiple stressors during or post mortality events in situ is difficult, even when systems are under study before and during a mortality event. Within New Zealand, mortality events of bivalves have been documented in Whangateau Harbour (Jones et al. 2017) and Banks Peninsula (Petes et al. 2007). The use of laboratory-based experiments allows for manipulation of environmental conditions to help pinpoint stressors and thresholds contributing to the observed population level responses in the field. Consequently, to better understand how hypoxia may affect intertidal benthic species, a series of laboratory experiments were established. Austrovenus were used due to their importance in ecosystem functioning. A meta- analysis indicated molluscs are more tolerant to low DO stress than other marine organisms such as crustaceans (Vaquer-Sunyer and Duarte 2011). It was therefore hypothesized that if Austrovenus were to negatively respond to a hypoxic multiple stressor event, other more sensitive estuarine taxa would likely be more detrimentally affected. Therefore, finding where stressor interactions and thresholds exist for Austrovenus provides a key measure of stress on a critical species in the estuarine ecosystem.

Estuaries are difficult to model and monitor due to their positioning between terrestrial and marine environments and high levels of physical variability (Scanes et al. 2020). Waitati Inlet is important to tangata whenua and is of economic importance to a local commercial fishing operation that harvests up to 906 tonnes of Austrovenus annually. The idea of monitoring DO in Waitati Inlet came about through observations that the Inlet was experiencing large quantities of overwintering Ulva spp., during 2017/2018, which may have important implications for oxygen variability. Further, observations that the Inlet divides into two separate channels on the ebb tide led to questions about whether there would be differences in DO variability between channels, and perhaps difference in the environmental forcing of DO.

86 | GENERAL DISCUSSION The close proximity of the system for field deployments and Austrovenus collections for laboratory studies provided an excellent opportunity to complete the present study.

Sequentially moving through the chapters of the present thesis, the focus shifts from observing declines in theoretical oxygen saturation in the Otago SST time series (1952-2018) to determining drivers and cycling of bottom-water DO profiles in two contrasting channels in Waitati Inlet, Otago, (Chapter Two). From there we applied the multiple stressors: warming, reduced oxygen solubility, increased low oxygen events and nutrient loading in experiments to test how the physiological responses of fully submerged Austrovenus changed during chronic low oxygen in combination with varying temperature and chronic ammonia conditions (Chapter Three). It is often not the average conditions but extreme events such as heat waves or summertime midday low tides that cause thermal and desiccation stress that trigger an organism’s response (Gaines and Denny 1993). Accordingly next we focussed on reproducing environmental conditions that approximated extreme events that have been observed to cause mass mortality in bivalve populations. Primarily inhabiting the intertidal zone, Austrovenus are periodically exposed to heat stress and desiccation at low tide. Accordingly, Chapter Four seeks to understand how prior exposure to chronic hypoxia influences the ability of Austrovenus to survive thermal challenges simulating midday low tide conditions, which have proven lethal for bivalves in a range of systems (Petes et al. 2007, Jones et al. 2017, Cagle 2019, Graham- McLay 2020, Tricklebank et al. 2020).

Main findings Chapter Two: Results of field observation reported in Chapter Two reveal how DO within Waitati Inlet varied temporally and spatially. The observation that the two sides of Waitati Inlet act as separate systems on the ebb tide is supported when comparisons of the likely environmental mechanisms driving DO variability between Waitati and Warrington channel are made. Salinity was the dominant predictor of low DO in Waitati channel, suggesting that the land-based influence of freshwater from Waitati River and strong tidal forcing due to the proximity to the mouth of the inlet were primarily responsible for DO variability in the channel. In contrast, a combined model of daily 75th quantiles of PAR and wind speed had the greatest explanatory power (41.7%) for low DO in Warrington channel. Given PAR supports the photosynthesis-respiration cycle of primary producers and given that there was an abundance of macroalgae Ulva spp., in Warrington channel, we concluded that DO variability in Warrington channel was primarily driven by biological processes.

87 | GENERAL DISCUSSION Early morning DO minima were common in both channels of the estuary and occurred near sunrise while maxima occurred in the late afternoon. The influence of PAR on photosynthetic activity and subsequent respiration by primary producers likely influenced variability in DO concentrations, which corroborates studies from other shallow estuaries (e.g. Sanford et al. 1990, Tyler et al. 2009, Sawabini et al. 2015). Fluctuations in DO were greatest in summer with more supersaturation and low oxygen events than in cooler months when DO remained saturated more regularly. The observed pattern was likely due to summer having warmer temperatures, higher PAR and fewer storms/wind induced mixing events, each of which would favour proliferation of algae. The subsequent high algal biomass resulted in a strong influence on diel DO cycling through photosynthesis and respiration in both macro and microalgae populations.

Although not measured in the current study, differences in flow rates between the two channels are likely to have contributed to the observed differences in DO. For example, Waitati channel is directly in front of the main channel entrance, while Warrington channel is positioned perpendicular to the entrance, possibly slowing the water velocity enough to allow the macroalgae Ulva spp., to settle in the bottom of the channel relatively undisturbed where it can respond to PAR.

Chapter Three and Four: Experiments conducted in Chapter Three and Four were based on the hypothesis that Austrovenus experiencing stress from hypoxia, as well as warming and elevated ammonia concentrations or high aerial temperatures, would experience greater stress- induced physiological responses when exposed to a multiple stressor environment than it would when exposed to individual stressors. Exposure to extreme conditions, simulated in the current study, by midday low tides in conjunction with 20% and 100% DO, illustrated the effects of increasing intensity of both interacting and single stressors being key to how Austrovenus responded.

The response of Austrovenus to oxygen and temperature as single stressors was significantly smaller than the observed response to the interaction among multiple stressors. Chronic low oxygen caused elevated stress to Austrovenus as the factor oxygen was significant in all models. Siphon activity in all experiments was consistently higher in hypoxic than normoxic treatments, likely due to the need for Austrovenus to respire for longer to gain the same amount of DO as during normoxic treatments. Key findings from Chapter Four support those reported in Chapter

88 | GENERAL DISCUSSION Three, highlighting hypoxia as a key stressor for Austrovenus increasing their respiration rate under conditions simulating a reoxygenation event such as a flood tide.

Thermal stress alone, highlighted by the 100% DO treatment, did not decrease survival over 36 days. However, within a multi-stressor environment with increasing water temperature from 22 °C to 25 °C, Austrovenus survival decreased at a substantially faster rate at the 10% and 20% DO indicating an interaction between temperature and oxygen depletion. It should be noted here that the two factors are coupled in nature, with oxygen saturation being strongly temperature dependent.

By removing the extreme thermal stress and instead subjecting Austrovenus to chronically high

NH3 concentrations in combination with low oxygen conditions, the interaction between NH3 concentration and hypoxia was tested. As single stressors, ammonia and oxygen influenced all measured responses. A synergistic interaction was identified in the siphon response with higher activity in hypoxic treatments, with the dosed treatment extending their siphons 54.4% of the time, compared to 41.6% of the time for hypoxic ambient treatments and 4 – 7% for normoxic treatments.

Experimental findings indicated that oxygen had a larger influence on the NH3 concentration than ammonia dosing, with hypoxic treatments experiencing higher NH3 concentrations. The observed pattern supports the hypothesis that more stressed individuals produce more ammonia. Moreover, peak ammonia concentrations coincided with days with high mortality in the associated tanks. This pattern is consistent with the notion that decaying tissue may have had an additive influence on the experimental results. In this experiment the risk of mortality in hypoxic treatments was 49.66 times more likely compared with 2.83 times when exposed to chronic ammonia at 100% DO. No interaction between the two stressors was able to be statistically resolved due to model limitations, however, an interaction appeared to be present.

Moving from a small (22 °C water, 25 °C emersion), to medium (25 °C water and emersion) to finally a large challenge (25 °C water, 33 °C emersion), there is a clear trend in survival probability decreasing with increased preconditioning with stress. Overall, Austrovenus survivorship during challenges simulating a midday low tide was significantly reduced under chronic hypoxia exposure compared to oxygenated conditions. These results indicated that preconditioning of Austrovenus by hypoxic stress made it less likely that they would survive an extreme event. Stress induced from extreme 33 °C emersion depicted a scenario whereby regardless of the oxygen concentration, some mortality occurred at a rate not represented in

89 | GENERAL DISCUSSION other experimental conditions. The observed response highlighted that a thermal threshold was reached at 33 °C, whereby even when in oxygenated water at 25 °C, Austrovenus began perishing within a day. This emphasized the importance of including heat stress during emersion when considering resilience and stress on bivalve populations under future extreme conditions resulting from climate change and increased frequency of heatwaves.

Ecological Importance, Implications, and the Wider Context Conclusions that can be drawn from the current thesis include Waitati Inlet has predominantly good quality bottom-water DO in accordance with ANZECC/ARMCANZ (2000) guidelines, with the two channels likely having different environmental mechanisms driving DO variability. In both channels most of the oxygen depletion occurred during low tide slack water as a likely consequence of respiration by primary producers and the ebb tide carrying water, likely including extruded porewater, depleted in DO.

The high flushing rates present in Waitati Inlet may not only help maintain high DO concentration but assist in limiting accumulation of nutrients and decaying material that may exist during an algal bloom or mortality event as demonstrated in Chapter Three and Four.

These experimental observations are ecologically important. High concentrations of NH3 are toxic to many fauna in estuaries, including bivalves such as Austrovenus. The experiments reported in the current study demonstrate hypoxia, as a single stressor, is sufficient to cause stress and mortality, however, having a multiple stressor environment has been demonstrated to strongly exacerbate these responses by Austrovenus, setting up conditions for a mass mortality event.

Conclusions drawn here suggest that elevated NH3 concentrations and emersion temperatures increased the rate of mortality when Austrovenus were already stressed by low oxygen conditions. The observation that Austrovenus mortality adds to ammonia concentrations indicates that a positive feedback loop exists during mortality events linked to tissue decay. This observation highlights the fact that the tidal flushing rate of a system is important to minimize the combined effects demonstrated in the current studies.

Using the Whangateau Harbour 2009 Austrovenus MME as a case study, parallels can be drawn between the Whangateau Harbour and Waitati Inlet in terms of the hydrosystem (Appendix 5) (Hume et al. 2016). Both systems are considered permanently open tidal lagoons, with near complete tidal flushing (Kelly 2009, Townsend et al. 2010, Robertson 2018) which is one of the most abundant geomorphic classes in New Zealand (Hume et al. 2016). When combining

90 | GENERAL DISCUSSION the results from the preceding three chapters, the implications of a potential scenario whereby unusually high summer air temperatures, light/no winds coupled with spring low tides, may result in Austrovenus populations becoming stressed to the point of initiating a mortality event. In light of the positive feedback between ammonia and mortality, such an event may be similar to the mortality event in Whangateau Harbour where there was a 63% reduction the local population of Austrovenus (Bingham 2009, Jones et al. 2017, Tricklebank et al. 2020).

Commercial harvesting of Austrovenus has been shown to increase the density of echinostome parasites in their tissues (O'Connell-Milne et al. 2016a). An increased parasitic burden is known to reduce growth, body condition and foot length as a result of energy required by the parasites (O'Connell-Milne et al. 2016b). Parasitic burden also results in Austrovenus becoming unburied and fully exposed to heat stressors (O'Connell-Milne et al. 2016b). Parasitic infection was an associated stressor along with midday low tides that likely led to the Austrovenus mortality event in Whangateau Harbour (Bingham 2009, Jones et al. 2017). Furthermore, experimental manipulations of parasitic loading and hypoxia on the functionally similar clam Cerastoderma edule, support this premise of infected clams having reduced survival when stressed by hypoxia (Wegeberg and Jensen 1999). Evidence from Chapter Four corroborates these findings to suggest thermal stress from hot aerial conditions, with or without hypoxia, can increase mortality risk to Austrovenus populations in Waitati Inlet and Coastal Otago, given elevated parasitic burden from harvesting (e.g. O'Connell-Milne et al. 2016a).

As the current thesis provides a framework for understanding how adult Austrovenus may respond to multiple environmental stressors, there remains a need to understand how different life history phases, including larvae and new recruits, respond to different stressors in the context of environmental change (e.g. Kennedy et al. 2017). The recent observation that small and large Austrovenus influence ecosystem functions such as denitrification differently highlight these considerations (Thomas et al. 2020). Together the results of the present thesis highlight how combinations of various multiple stressors that are forced by different climatic, physical and biological parameters, combine to influence survival of Austrovenus under oxygen depletion events. Hypoxia occurs on different spatial and temporal scales and rarely occurs in isolation as an environmental stressor, highlighting the critical need to consider its effects on bivalve populations in a multiple stressor context.

91 | GENERAL DISCUSSION Future Research Multi-year sampling would allow for greater resolution of the spatiotemporal variability of dissolved oxygen and other key physiochemical parameters, including temperature and salinity in Waitati Inlet. Incorporating water velocity, precipitation and freshwater input as well as sources of nutrients into models for both sides of the inlet, would allow for a greater understanding of the physical and biological drivers of spatial and temporal variability in DO.

In Chapter Four, an upper thermal threshold for emersion was identified at 33 °C for survival of Austrovenus. This limit may be lower than 33 °C and further research may determine the exact thermal limit and how duration of exposure interacts with magnitude of stress. Additionally, the thermal limit may differ among size cohorts within a population and under various stressor combinations (Thomas et al. 2020). Consequently, focussed laboratory research using higher resolution temperature increments is required to understand the thermal tolerance of Austrovenus to emersion at high aerial temperatures among different size cohorts, and how different ecotypes may respond to stressors (McLeod and Wing 2008).

To understand how these laboratory studies translate into survival in the intertidal zone, additional mesocosm field experiments are required. In this case, field manipulations of algal biomass and Austrovenus size and density could be conducted to replicate different macroalgal bloom intensities and natural diel cycling and subsequent local hotspots of oxygen depletion due to decomposition in patches. Comparisons with control plots of Austrovenus survival, sediment burial depth and RPD depth and ammonia concentrations responses of Austrovenus in situ, would provide a real-world test of the results from laboratory experiments.

Conclusion All of the challenges faced by Austrovenus in the current thesis were based on prediction of future extreme environmental conditions. Under current conditions Waitati Inlet does not consistently reach an average of 22 °C or 25 °C water temperature in the summer. Nevertheless, ephemeral events of water temperatures reaching mid 20s °C were noted during the time of the 2017/18 heatwave for the Inlet. Similarly, average ammonia concentrations are typically in the 0.25 mg L-1 range, but sharp increases in concentration can be seen during storm events. These patterns reinforce the idea that we must consider the distribution, duration and probability of extreme events when we seek to understand the effects of multiple stressors on Austrovenus (e.g. Gaines and Denny 1993, Rutger and Wing 2006).

92 | GENERAL DISCUSSION Together these chapters highlight how combinations of different multiple stressors will likely impact adult populations of Austrovenus. Hypoxia is a significant driver of stress among Austrovenus populations and when combined with warming waters and increases in ammonia concentrations, a serious threat exists to ecosystem functioning, food-webs and overall health of estuaries and harbours.

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6x1

used for

. . y =

were were

)

rature for Otago for rature

–

(

0.15mg O

18. 18. Equation

) based on sea surface tempe surface sea on based )

1953 1953 to 20

from

theoretical theoretical saturation of

all all data to show change in DO

applied applied

r r trend line

collected from Portobello Marine Laboratory, Otago

Linea

. .

= 0.0054 =

Daily theoretical maximum dissolved oxygen concentration (mg L (mg concentration oxygen dissolved maximum theoretical Daily

. .

are

+ 9.0826 9.0826 +

Appendix Harbour, Dunedin conversion 06

126 | APPENDIX Appendix 2. Dissolved oxygen (mg L-1) and tidal height (m) for Waitati channel, Waitati Inlet, Otago, in late summer 2019. Tick marks represent 12noon daily

10 4

)

1 - 8 3

6 2

4 Tidal Height (m) 1 Dissolved Oxygen Dissolved Oxygen L (mg 2 DO Tidal Height 0 0 13/03/2019 14/03/2019 15/03/2019 16/03/2019 17/03/2019 18/03/2019 19/03/2019 20/03/2019 Date

127 | APPENDIX Appendix 3. Percent deviation of observed oxygen away from theoretical oxygen saturation (mg L-1) (black line) within Waitati channel, Waitati Inlet, Otago, NZ. Calculation derived from Richards (1957) based on water temperature and salinity. 1-hour mean PAR represented by orange lines. Time dates represent 12-noon and midnight for each day.

70 2000

50 )

1 -

1500 s 2

30 - 10 1000

-10 (μmol m (μmol Deviation -30 500

-50 PAR Summer Oxygen % Oxygen Summer -70 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 70 2000

50 )

1 -

1500 s 2

30 - 10 1000

-10 (μmol m (μmol

Deviation Deviation -30 500

-50 PAR

Late summer oxygen % summer Late -70 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 70 2000

50 )

1 -

1500 s 2

30 - 10 1000

-10

(μmol m (μmol Deviation

-30 500 Autumn Oxygen % Autumn Oxygen -50 PAR -70 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00

70 2000

50 )

1 -

1500 s

30 2 - 10 1000

-10

(μmol m (μmol Deviation

-30 500 Winter Oxygen % Oxygen Winter -50 PAR -70 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 70 50 30 10

-10 Deviation

-30 Spring Oxygen % Oxygen Spring -50 -70 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00

128 | APPENDIX Appendix 4. Percent deviation of observed dissolved oxygen away from theoretical oxygen saturation (mg L-1) (black line) within Warrington Channel, Waitati Inlet, Otago, NZ in summer (January), late-summer (March) and) autumn (May) and spring (December) 2019. Calculation derived from Richards (1957) Richards (1957) based on water temperature and salinity. 1-hour mean PAR represented by orange line. Time dates represent 12-noon and midnight for each day of the 6-day deployments.

60 2000

40 )

1 -

1500 s 2 20 - 0 1000

-20 m (μmol 500 -40 PAR

Summer Oxygen % Deviation Oxygen Summer -60 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00

60 2000

40 )

1 -

1500 s 2 20 -

0 1000 (μmol m (μmol Deviation -20 500

-40 PAR Late summer Oxygen % Oxygen summer Late -60 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00

60 2000

40 )

1 -

1500 s 2 20 -

% % Deviation 0 1000

-20 m (μmol

Oxygen 500

-40 PAR

Autumn -60 0 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00

60 40 20 0 -20 -40

-60 Spring Oxygen Deviation % Oxygen Spring 12:00 0:00 12:00 0:00 12:00 0:00 12:00 0:00 12:00

129 | APPENDIX

)

River River

95773

130457

inflow per per inflow cycle tidal

)

Hume et al. (2016) al. et Hume

91 42

area (km area

Catchment Catchment

)

ism

tidal tidal (m

Spring Spring

5787209 9491105

1.6 2.2

(m)

Spring Spring

tidal range range tidal

)

7559191

tide (m tide

11662589

Volume at at Volume

spring high high spring

1 2

(m)

at spring spring at

high tide tide high

Mean depth depth Mean

ween Waitati Inlet and Whangateau Harbour based on based Harbour Whangateau and WaitatiInlet ween

86 85

ions bet ions

area)

high tide tide high

Intertidal Intertidal

area (% of of (% area

)

ring ring

at sp at

6230597 7460453

high tide tide high

Surface area area Surface

system classificat system

ine

405

(m)

0397

2 32

of hydro of

Shorel perimeter

(m)

162 444

mouth mouth

Width of of Width

. Comparision Comparision .

Inlet

Waitati Waitati

Harbour

Whangateau Whangateau Appendix

130 | APPENDIX