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Zooplankton trace metal accumulation in the Strait of Georgia : trends, sources and insights Flores Ruiz, Bertha Iselle 2020

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  ZOOPLANKTON TRACE METAL ACCUMULATION IN THE STRAIT OF GEORGIA: TRENDS, SOURCES AND INSIGHTS by  Bertha Iselle Flores Ruiz  B. Sc., The University of British Columbia, 2016  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Oceanography)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   September 2020  © Bertha Iselle Flores Ruiz, 2020  ii  The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, a thesis entitled: Zooplankton trace metal accumulation in the Strait of Georgia: trends, sources and insights submitted by Bertha Iselle Flores Ruiz in partial fulfillment of the requirements for the degree of Master of Science in Oceanography  Examining Committee: Dr. Maria Teresa Maldonado, Professor, Earth, Ocean and Atmospheric Sciences, UBC Supervisor  Dr. Evgeny Pakhomov, Professor, Earth, Ocean and Atmospheric Sciences, UBC Supervisory Committee Member Dr. Brian Hunt, Assistant Professor, Earth, Ocean and Atmospheric Sciences, UBC Additional Examiner   Additional Supervisory Committee Members: Dr. Paul Schaffer, Associate Professor, Radiology, UBC Supervisory Committee Member iii  Abstract The Strait of Georgia (SoG) is a highly productive, semi-enclosed body of water located in the Northeast Pacific Ocean. However, increasing anthropogenic metal pollution may pose a threat to SoG organisms. To investigate metal accumulation in zooplankton, we sampled a time series station (49°15.00’ N, 123°40.00’ W) four times between 2017 and 2018. Using trace metal techniques, we measured dissolved (Ag, Cu, Cd) and particulate trace metals (P, Al, Ag, Cu, Cd), and zooplankton community composition, size-fractionated trophic position, and carbon and trace metal content (Ag, Ba, Cd, Co, Cr, Cu, Mn, Ni, Fe, and Zn). We also calculated zooplankton metals’ bioaccumulation factors (BAFs; zooplankton the metal content (mg ∙ kg d.w.-1) divided by seawater dissolved metal concentration (mg ∙ L-1)). Zooplankton were found to be mainly omnivores, and dominated by the calanoid copepod Metridia pacifica. The average content of metals in zooplankton (mg ∙ kg d.w.-1) was: Fe >> Zn ≥ Mn ≥ Ba > Cu > Ni > Cd ≥ Cr > Co ≥ Ag. Higher concentrations for Cu, followed by Cd, and Ag were also found in the dissolved and particulate phases in seawater. In contrast, zooplankton BAFs were Ag >> Cu > Cd. The zooplankton content and BAFs of these metals varied seasonally, but no systematic trends could be identified. We hypothesize that Ag BAFs were highest because of high Ag bioavailability in seawater, and its remarkable propensity to bind soft bases, such as thiolates, relative to essential metals (i.e., Cu, Zn). The results of an applied bio-energetic kinetic model indicate that the majority of Ag, Cu, and Cd assimilated by SoG zooplankton is derived from non-lithogenic particles. Current total concentrations of Cu, Ag, and Cd in SoG do not exceed long-term chronic concentrations reported in the BC Water Quality Guidelines. However, several Ag and Cd zooplankton content measurements surpassed the toxicity threshold reported to hinder reproduction. Based on the tendency of Ag and Cd to scavenge to particles in oxic and anoxic environments, respectively, these metals could pose a threat to the wellbeing of SoG zooplankton and should be carefully monitored.     iv  Lay Summary Marine zooplankton require small concentrations of micronutrients (e.g., iron, copper, and zinc) for their metabolisms, but higher concentrations of these and other non-essential metals (e.g., silver and cadmium) can be toxic. Human activities in the Strait of Georgia (SoG) are increasing, and likely so is metal pollution.  In this thesis, a variety of parameters from the SoG were measured over four seasons to determine the accumulation and assimilation pathways of trace metals in zooplankton, and whether zooplankton metal content is increasing to levels above toxic thresholds that could impact zooplankton health, numbers, and/or food availability for other organisms in the SoG. This thesis provides the first insights into the state of zooplankton metal content in the SoG. It contains complementary data to investigate trace metal accumulation in zooplankton and provides field-based evidence for the use of a biokinetic model to predict zooplankton trace metal content in coastal Northeastern Pacific waters.  v  Preface Chapter 2 Zooplankton sampling was done aboard the CCGH Siyay, CCGH Moytel, and the CCGS Vector. The various zooplankton net tows and water samples were taken between Chris Payne, Cheng Kuang, Yuanji Sun, Maureen Soon, L. Waugh, and I. Zooplankton size fractionation and subsampling for all the different samples (community composition, trophic position, trace metal content, etc.) was mostly done by me, with the help of Y. Sun, C. Kuang, and L. Waugh. Dissolved and particulate metals were mainly sampled by C. Kuang, and M. Soon.  The zooplankton taxonomic analysis (i.e., identification and abundance), and dry biomass estimates were done by Moira Galbraith from the Institute of Ocean Sciences (IOS; Sidney, B.C). Biodiversity calculations were done by me. For the zooplankton 15NAA analysis, trace metal content, and carbon content I rinsed the zooplankton samples with Milli-Q, and freeze-dried  (with the help of M. Soon), and pulverized them until they were homogeneous. Jian Guo weighed, packed, and sent samples for 15NAA analysis and carbon content to the UC Davis Stable Isotope Facility for measuring. Using the 15NAA data I calculated the trophic position of the zooplankton samples. I implemented the digestion protocol designed by Maria Maldonado, Roger Francois and M. Soon. I also pre-weighed, digested and diluted all the zooplankton trace metal samples. Supervised by Vivian Lai, I measured  samples in PCIGR’s Element 2 HR-ICP-MS. All trace metal data analyses, and isotopic and sea salt corrections were done by me. The preliminary sea salt corrections and dilutions were calculated by R. Francois based on test zooplankton measurements done by Eugenie Jacobsen.  All dissolved and particulate metal analytical measurements, data calculations, corrections and analyses were done by C. Kuang, except for the Cu data, and all data calculations for July 2018, which were done by me. Using the dissolved and particulate data, I calculated the zooplankton bioaccumulation factors. I did all statistical analyses presented in this thesis.    vi  Chapter 4 The biokinetic model used in this thesis is based on the one by Wang and Fisher (1998).  The lithogenic and non-lithogenic particulate calculations are based on those done by C. Kuang in Kuang (2019). The conversion of non-lithogenic particulate content to biogenic content was suggested and confirmed by M. Maldonado. Finally, I coded, plotted and created all figures and tables shown in this thesis, except for Appendices A11 and A12. The figure in Appendix A-12 was taken from Whitfield and Turner (1987; Figure 17.3). The figure in Appendix A-13 was taken from Nieboer et al. (1999; Figure 1). Permission to republish these figures has been granted by the publisher (Order number: 1060935 and 1060535, respectively).The thesis was written by me with valuable discussion, feedback and edits from M. Maldonado. Thesis edit suggestions and feedback were also provided by B. Hunt, P. Schaffer, E. Pakhomov, R. Francois and C. Kuang.   vii  Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................ xviii List of Figures ............................................................................................................................. xxi List of Symbols ......................................................................................................................... xxxi List of Abbreviations ...............................................................................................................xxxv Acknowledgements .............................................................................................................. xxxviii Dedication ............................................................................................................................... xxxix Chapter 1: Introduction ................................................................................................................1 1.1 The Georgia Strait Ambient Monitoring Program .......................................................... 1 1.2 Geographical and oceanographic setting ........................................................................ 1 1.2.1 Physical and chemical characteristics of the SoG....................................................... 3 1.2.2 Biological characteristics of the SoG .......................................................................... 4 1.3 Zooplankton community in the SoG ............................................................................... 5 1.4 Metals and metal uptake in zooplankton ........................................................................ 6 1.5 Purpose of studying metals in SoG zooplankton ............................................................ 8 Chapter 2: Methods .....................................................................................................................12 2.1 Field sampling and analytical measurements ............................................................... 12 2.1.1 Sampling station........................................................................................................ 12 2.1.2 General zooplankton sampling ................................................................................. 12 viii  2.1.3 Zooplankton community composition, abundance, and biomass ............................. 13 2.1.4 Zooplankton trace metal content ............................................................................... 14 2.1.5 Zooplankton total carbon content ............................................................................. 16 2.1.6 Zooplankton trophic position .................................................................................... 17 2.1.7 Dissolved and particulate trace metal concentrations ............................................... 19 2.2 Calculations................................................................................................................... 19 2.2.1 Zooplankton diversity indices ................................................................................... 19 2.2.2 Zooplankton bioaccumulation factors....................................................................... 20 2.2.3 Statistical analysis ..................................................................................................... 21 2.2.3.1 Metal content as a function of size fraction ...................................................... 21 2.2.3.2 Two-Way ANOVA ........................................................................................... 21 Chapter 3: Results........................................................................................................................22 3.1 Zooplankton community composition .......................................................................... 22 3.1.1 Abundance ................................................................................................................ 24 3.1.2 Dry weight biomass estimates .................................................................................. 24 3.2 Zooplankton trophic position ........................................................................................ 27 3.3 General zooplankton trace metal content ...................................................................... 29 3.3.1 Trends between trace metals ..................................................................................... 29 3.3.2 Manganese and Chromium ....................................................................................... 33 3.3.3 Zinc and Nickel ......................................................................................................... 33 3.3.4 Cobalt ........................................................................................................................ 33 3.3.5 Copper and Silver ..................................................................................................... 33 3.3.6 Cadmium ................................................................................................................... 34 ix  3.3.7 Carbon-normalized metal content ............................................................................. 34 3.4 Zooplankton trace metal content for metals of primary interest ................................... 34 3.4.1 The C-normalized Ag and Cu content of zooplankton ............................................. 35 3.4.2 The C-normalized Cd content of zooplankton .......................................................... 41 3.5 Carbon-normalized bioaccumulation factors ................................................................ 43 3.5.1 Ag and Cu bioaccumulation factors .......................................................................... 43 3.5.2 Cd bioaccumulation factors ...................................................................................... 49 Chapter 4: Discussion ..................................................................................................................51 4.1 Zooplankton community composition .......................................................................... 51 4.1.1 Zooplankton diversity components ........................................................................... 51 4.1.2 Biomass and abundance ............................................................................................ 51 4.1.2.1 Zooplankton biomass ........................................................................................ 52 4.1.2.2 Abundance and assemblage .............................................................................. 53 4.2 Zooplankton trophic position ........................................................................................ 56 4.3 Metal uptake.................................................................................................................. 59 4.3.1 Zooplankton metal content ....................................................................................... 59 4.3.1.1 Trace metals in size-fractionated SoG zooplankton compared to those from other studies ...................................................................................................................... 59 4.3.1.2 The relative bioactive trace metal concentrations in SoG zooplankton ............ 64 4.3.1.3 General zooplankton trends in trace metal content: similarities among certain trace metals ....................................................................................................................... 71 4.3.1.4 General trends in Ag, Cd and Cu content in zooplankton: temporal variations and the effect of size ......................................................................................................... 74 x  4.3.2 Zooplankton elimination rates of Ag, Cu and Cd ..................................................... 79 4.3.3 Modeling zooplankton metal content........................................................................ 80 4.3.4 Contrasting zooplankton bioaccumulation factors for Ag, Cu and Cd ..................... 85 4.4 SoG Zooplankton metal content relative to the British Columbia Water Quality Guidelines for Marine and Estuarine Aquatic Life ................................................................... 87 4.4.1 Cu .............................................................................................................................. 87 4.4.2 Ag .............................................................................................................................. 88 4.4.3 Cd .............................................................................................................................. 89 4.4.4 The effect of anthropogenic particles discharged by the WWTP on metal bioavailability to SoG zooplankton ...................................................................................... 90 Chapter 5: Conclusion .................................................................................................................91 5.1 Analysis, integration, and implications of the research findings .................................. 91 5.2 Overall significance and contribution of this research ................................................. 94 5.3 Strengths and limitations of this research ..................................................................... 96 5.4 Applications of research findings ................................................................................. 98 5.5 Future directions of this research .................................................................................. 98 References ...................................................................................................................................101 Appendices ..................................................................................................................................116 Appendix A ............................................................................................................................. 116 A.1 Fraction of net tows used for community composition and trace metal measurements for each month sampled. Only four Bongo net tows were performed in June 2018. Samples for trace metal content measurements were later homogenized and also subsampled for trophic position and total carbon content. ........................................................................... 116 xi  A.2 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated December 2017 sample. All feeding type information except (*,#,&) was taken from Mackas et al. (2013). Information on (*) was taken from Tommasi et al. (2013), (#) from El-Sabaawi et al. (2010), and (&) from Choy et al. (2017). ................. 117 A.3 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated April 2018 sample. All feeding type information except (*,+) was taken from Mackas et al. (2013). Information on (*) was taken from Tommasi et al. (2013), (+) Macdonald et al. (2010). ................................................................................... 118 A.4 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated June 2018 sample. All feeding type information except (*,&,!) was taken from Mackas et al. (2013).  Information on (*) was taken from Tommasi et al. (2013), (&) from Choy et al. (2017), and (!) from Jagger et al. (1988). .............................. 119 A.5 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated August 2018 sample. All feeding type information except (*,#,&) was taken from Mackas et al. (2013).  Information on (*) was taken from Tommasi et al. (2013), (#) from El-Sabaawi et al. (2010), and (&) from Choy et al. (2017). ............. 120 A.6 Average zooplankton iron content (mg Fe ∙ kg d.w.-1) measured throughout the time series at Station S4-1.5. Errors indicate one standard deviation from the mean.  Ranges are shown in parenthesis below averages. Median value is shown in italics............................ 121 A.7 Average or sole metal content (mg ∙ kg d.w.-1) for various zooplankton taxa from different geographical regions. Measurement ranges are shown in parentheses. Errors represent one standard deviation from the mean. ............................................................... 122 xii  A.8 Zooplankton Ag, Cu and Cd bioaccumulation factors (BAFs; L ∙ kg d.w. -1) calculated for samples taken between 2017 and 2018 at the time series Station S4-1.5. BAFs were calculated with the following formula: BAF= [Mezoop]/[Meseawater]. Where Mezoop is the zooplankton metal content (mg Me ∙ kg d.w. -1) and Meseawater is the dissolved metal concentration (mg Me ∙  L-1).  Ranges and average dissolved Ag, Cu and Cd concentrations in the water column taken from Kuang (2019) are shown. For dissolved metal concentrations, it was assumed that 1 kg = 1L.  Errors indicate one standard deviation from the mean. Ranges are shown below averages in parenthesis. ............................................. 129 A.9 Dissolved Cu, Ag and Cd concentrations at the time series Station S4-1.5. Dissolved Ag and Cd data from December 2017 and April 2018 were taken from Kuang (2019). Analytical errors shown are based on one standard deviation. It has been assumed that 1 kg seawater = 1L seawater. ...................................................................................................... 130 A.10 Particulate Al, P and Ba content at the time series Station S4-1.5. Particulate Al and P data from December 2017 and April 2018 were taken from Kuang (2019). Analytical errors shown are based on one standard deviation. ............................................................. 132 A.11 Particulate Cu, Ag and Cd content at the time series Station S4-1.5. Particulate Cd and Ag data from December 2017 and April 2018 were taken from Kuang (2019). Analytical errors shown are based on one standard deviation. ........................................... 133 A.12 Figure taken from Whitfield and Turner (1987). Complexation field diagram for elements in seawater. The elements have been primarily divided by increasing ionization potential (groups I to V), and by increasing covalent interactions (groups (a), (a)’, (b)’, and (b)). The elements have also been divided by their complexation in seawater: elements in A xiii  represent those forming weak complexes, B elements are found in chloride complexes, C are strongly hydrolyzed elements, and D are fully hydrolyzed elements. .......................... 134 A.13 Figure taken from Nieboer et al. (1999). Metal ions (and two metalloids) divided into three groups: class A, borderline, and class B. Metal ions are plotted by their ability to form ionic bonds (ionic index) against their ability to form covalent bonds (covalent index). Xm is the Pauling electronegativity, z the ionic charge and r is the ionic radius of the metal species. The covalent index (Xm)2r shows the ability of a metal ion to form covalent over ionic bonds. On the other hands, the ionic index z2 ∙ r-1 measures the ionic interactions of the metal ion.............................................................................................................................. 135 A.14 Monthly and total averages of various particulate metals sampled throughout the time series at Station S4-1.5. The lithogenic fraction was calculated by multiplying the total Al sampled by the Me: Al molar crustal ratios:  7.58 x10-3 for P, 1.56x10-7 for Ag, 1.32 x10-4 for Cu, and 2.93 x10-7 for Cd. Crustal ratios were taken from Taylor and McLennan (1995).  The non-lithogenic fraction was calculated by subtracting the lithogenic fraction from the total Me content. ................................................................................................... 136 A.15 Input values for the kinetic model shown in Wang and Fisher (1998). ku is the uptake rate constant from the dissolved phase (L∙ g--1∙d-1), kew is the efflux rate constant from the dissolved phase (d-1), g is the growth rate constant for copepods (d-1), AE is the Assimilation Efficiency, and IR the copepod ingestion rate. Unless, noted, constants were taken from Wang and Fisher (1998). Values with (*) were taken from Chang and Reinfelder (2002). Cw are the average dissolved metal concentrations (µg Me ∙ L-1) sampled at Station S4-1.5.   Average Assimilation Efficiencies were calculated using the assimilation efficiency values shown on Table 11. Cf are the particulate metal content per phytoplankton xiv  dry weight. Css,w  is the total calculated metal taken up by copepods from the dissolved  phase. Css,f  is the total calculated metal taken up by copepods from the food (dietary phase). Rw and Rf are the respective percentages from the total metal intake from the dissolved and dietary phases, respectively. Css is the total calculated metal content in copepods under steady-state conditions. Particulate metal concentrations from the particulate metal samples were normalized using the average non-lithogenic Me:biogenic P ratios per respective month and normalized to phytoplankton dry weight assuming the Redfield ratio (106 C: 1 P) and a 35% carbon weight per phytoplankton dry weight (Janik et al. 1981). Molar masses used: C (12.0107), Ag (107.8286), Cu (63.546), and Cd (112.411). Note that 1 µg ∙ g-1= 1 mg ∙ kg d.w-1. ...................................................................................................................... 137 A.16 Zooplankton Ag bioaccumulation factors (L∙mol C-1) calculate using dissolved, particulate, total, and non-lithogenic Ag values. Zooplankton trace metal content was divided by either the average surface (0-50 m) or the average full-profile. The ratio between BAFs calculated using full-depth profile concentrations and surface concentrations is also denoted for each month. ...................................................................................................... 139 A.17 Zooplankton Cu bioaccumulation factors (L∙mol C-1) calculate using dissolved, particulate, total, and non-lithogenic Cu values. Zooplankton trace metal content was divided by either the average surface (0-50 m) or the average full-profile. The ratio between BAFs calculated using full-depth profile concentrations and surface concentrations is also denoted for each month. ...................................................................................................... 140 A.18 Zooplankton Cd bioaccumulation factors (L∙mol C-1) calculate using dissolved, particulate, total, and non-lithogenic Cd values. Zooplankton trace metal content was divided by either the average surface (0-50 m) or the average full-profile. The ratio between xv  BAFs calculated using full-depth profile concentrations and surface concentrations is also denoted for each month. ...................................................................................................... 141 Appendix B Reference Materials and statistical results ......................................................... 142 B.1 Zooplankton size fraction (µm) vs trophic position. Linear regression model for all data measured and per month. P-value <0.05 are bolded. .................................................. 142 B.2 Two-Way ANOVA results for zooplankton size fraction (µm) vs trophic position. P-value <0.05 are bolded. ....................................................................................................... 142 B.3 Trace metal content of the reference materials DOLT-5 (Dogfish Liver Certified Reference Material) and DORM 4 (Fish Protein Certified Reference Material). Errors shown are one standard deviation. ...................................................................................... 143 B.4 Major ion content of the reference materials DOLT-5 (Dogfish Liver Certified Reference Material) and DORM 4 (Fish Protein Certified Reference Material). Errors shown are one standard deviation from the mean. .............................................................. 144 B.5 Average instrumental and procedural blanks for the elements studied in this thesis. Blanks measured on Dec 5th and 6th, 2018 were digested along with the December 2017 zooplankton samples. Blanks measured on January 23rd and 24th, 2019 were digested along with the April, June and August 2018 zooplankton samples. Sample numbers for the instrumental and procedural blanks are shown in italics. The blank averages for Zn, Cu, Ni, Ba, and Fe are the averages for the various isotopes measured for each element. Errors shown are one standard deviation from the average. .......................................................... 145 B.6 Zooplankton size fraction (µm) vs metal content (mg ∙ kg d.w. -1). Linear regression model for all data measured. P-value <0.05 are bolded. ..................................................... 146 xvi  B.7 Zooplankton size fraction (µm) vs metal content (mg ∙ kg d.w.-1). Monthly linear regression model. P-value <0.05 are bolded. ...................................................................... 147 B.8 Two-Way ANOVA results for zooplankton size fraction (µm) vs. metal content (mg ∙ kg d.w.-1). P-value <0.05 are bolded. ................................................................................ 148 B.9 Zooplankton size fraction (µm) vs C-normalized metal content (µmol Me ∙ mol C-1). Linear regression model for all data measured. P-value <0.05 are bolded. ........................ 148 B.10 Zooplankton size fraction (µm) vs C-normalized metal content (µmol Me ∙ mol C-1). Monthly linear regression model. P-value <0.05 are bolded. ............................................. 149 B.11 Two-Way ANOVA results for zooplankton size fraction (µm) and month vs C-normalized metal content (µmol Me ∙ mol C-1). P-value <0.05 are bolded. ....................... 149 B.12 Zooplankton size fraction (µm) vs Me BAF (L ∙ kg d.w.-1). Linear regression model for all data measured. P-value <0.05 are bolded. ................................................................ 150 B.13 Zooplankton size fraction (µm) vs Me BAF (L ∙ kg d.w.-1). Monthly linear regression model. P-value <0.05 are bolded. ...................................................................... 150 B.14 Two-Way ANOVA results for zooplankton size fraction (µm) and month vs Me BAF (L ∙ kg d.w.-1). P-value <0.05 are bolded. ................................................................... 151 B.15 Zooplankton size fraction (µm) vs Me BAF (L ∙ mmol C-1). Linear regression model for all data measure. P-value <0.05 are bolded. .................................................................. 151 B.16 Zooplankton size fraction (µm) vs Me BAF (L ∙ mmol C-1). Monthly linear regression model. P-value <0.05 are bolded. ...................................................................... 152 B.17 Two-Way ANOVA results for zooplankton size fraction (µm) and month vs Me BAF (L ∙ mmol C-1). P-value <0.05 are bolded. ................................................................. 153 xvii  B.18 Correlation results between dissolved and particulate Ag, Cu and Cd measurements at Station S4-1.5. Calculations were done for full profile values and values for measurements between 0 and 50 m depth. Strong correlations (>0.6) have been italicized. 153 B.19 Correlation results between dissolved and non-lithogenic Ag, Cu and Cd particle measurements at Station S4-1.5. Calculations were done for full profile values and values for measurements between 0 and 50 m depth. Non-lithogenic metal calculations are described on Table A-14. Strong correlations (>0.6) have been italicized. ........................ 154 B.20 Correlation results between dissolved and lithogenic Ag, Cu and Cd particle measurements at Station S4-1.5. Calculations were done for full profile values and values for measurements between 0 and 50 m depth. Lithogenic metal calculations are described on Table A-14. Strong correlations (>0.6) have been italicized. ........................................ 154 Appendix C Dissolved Cu uptake experiments, using radioactive Cu, in in the Strait of Georgia .................................................................................................................................... 155 C.1 Introduction ............................................................................................................. 155 C.2 Methods................................................................................................................... 155 C.3 Results and Discussion ........................................................................................... 160 C.4 Conclusion .............................................................................................................. 163  xviii  List of Tables Table 1. Essential trace metals and their functions.  The (*) refers to Blindauer (2012), and the (^) to Eisler (2010). All other data are referenced in the text. ........................................................ 7 Table 2. Total zooplankton abundance, biomass (dry weight), carbon content, richness and diversity indices for the samples collected throughout the time series at Station S4-1.5. Biomass measurements are estimates based on averages for set species at set sizes (Galbraith, pers. comm.). ......................................................................................................................................... 22 Table 3. Trophic position ranges of each zooplankton size-fraction collected throughout the time series at Station S4-1.5. Trophic position was calculated from Chikaraishi et al. (2009) using the formula TP=[(δ 15NGlu - δ 15NPhe + β)/TDF]+1 where β is the difference between δ 15NGlu  and δ 15NPhe  in the primary producers -3.4 ± 0.9 ‰ (Chikaraishi et al., 2009), and TDF is the trophic discrimination factor calculated from the difference  in 15N enrichment between  δ 15NGlu  in comparison to δ 15NPhe  per trophic step 7.6 ± 1.2‰ (Chikaraishi et al., 2009). The trophic position error corresponds to one standard deviation from the mean. .......................................... 27 Table 4. Average zooplankton trace metal content (mg Me ∙ kg d.w. -1) measured throughout the time series at Station S4-1.5. Errors indicate one standard deviation from the mean. Ranges are shown in parenthesis below averages. .......................................................................................... 30 Table 5. Monthly averages of the zooplankton trace metal content, normalized to carbon (µmol Me ∙ mol C-1), throughout the time series at Station S4-1.5. Trace metal values were normalized to C by dividing metal content (µmol Me ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1).  Errors indicate one standard deviation from the mean. Ranges are shown in parenthesis below the averages. .................................................................................................... 35 xix  Table 6. Carbon-normalized Ag, Cu and Cd bioaccumulation factors (BAFs; L ∙ mol C -1) for each zooplankton size fraction collected in 2017 and 2018 at the time series Station S4-1.5. Ranges in  parentheses for BAFs are for the minimum and maximum values in the size fractions) BAF= [Mezoop]/[Meseawater], where Mezoop is the zooplankton metal content in µmol Me ∙ mol C-1 and Meseawater is the dissolved metal concentration in µmol Me ∙ L-1.  Ranges and average dissolved Ag, Cu and Cd concentrations in the water column taken from Kuang (2019) are shown. For dissolved metal concentrations (mol Me ∙ L-1), it was assumed that 1 kg = 1L.  Errors indicate one standard deviation from the mean. Ranges are shown below averages in parenthesis........................................................................................................................................................ 44 Table 7. Average plankton metal content from various studies (mg Me ∙ kg d.w-1). Size fractions sampled and averaged are noted. Measurement ranges are shown in parentheses. Errors represent one standard deviation from the mean. Highest values have been bolded. .................................. 62 Table 8. Average dissolved and particulate Ag, Cu and Cd from Station S4-1.5. Both dissolved and particulate metal values were averaged from all data available. Total metal content was calculated by averaging all depths where particulate and dissolved metal data are available). Measurement ranges are shown in parentheses and median values are italicized. Errors represent one standard deviation from the mean. LC50 is the concentration of a pollutant at which acute toxicity is experienced by 50% of the organisms (Hook & Fisher, 2001b). ................................. 65 Table 9. Median plankton metal content (mg Me ∙ kg d.w-1) measured throughout the time series at Station S4-1.5.  Measurement ranges are shown in parentheses. ............................................. 69 Table 10. Plankton metal content normalized to carbon. All information except zooplankton metal content is from Posacka (2017). .......................................................................................... 70 xx  Table 11. Average bioaccumulation factors (L ∙ kgd.w-1), assimilation efficiency and absorption efficiency, plus other values found in the literature. BAFs were calculated by dividing zooplankton metal content by the average dissolved metal concentration from its respective month. Mean and median (in italics) values were calculated from monthly BAF calculations.  Assimilation efficiency (AE) is the fraction of metal in food consumed that an organism is able to assimilate to its tissues (Wang & Fisher, 1996; Hook & Fisher, 2001a).  Measurement ranges are shown in parentheses. Errors represent one standard deviation from the mean. .................... 83 Table 12. Calculated and measured average metal content in zooplankton. The calculated values were a result from the Wang and Fisher’s (1998) kinetic model. The average metal content for “All data” averaged all zooplankton metal measurements, including those from August. For model inputs, please refer to Appendix A-15. .............................................................................. 84 Table 13. Calculated zooplankton trace metal content under steady-state conditions (Css), total calculated metal taken up by copepods from the food (Css,f), and total calculated metal taken up by copepods from the dissolved phase (Css,w). The respective fractions from the final total metal content in zooplankton are shown in italics. ................................................................................. 84 xxi  List of Figures Figure 1. Map of the Salish Sea.  Station S4-1.5 (49° 15.00’ N, 123° 40.00’ W; red star) in the Southern Strait of Georgia was our time series station. This station was sampled four times between December 2017 and August 2018. The bathymetry of the Strait of Georgia is shown in grey tones (i.e., black, deepest; white, shallowest). ........................................................................ 2 Figure 2. a) Zooplankton group abundance (individuals ∙ m-3) and % contribution to total number of individuals (b) measured at the time series Station S4-1.5. Each shade represents a different zooplankton group. Note that amphipods, cirripedia, copepods, decapods, euphausiids, ostracods and cladocera are all crustacean zooplankton.  Less abundant zooplankton species were grouped together as ‘Gelatinous zooplankton’ (i.e., for chaetognaths, siphonophores and ctenophores) and ‘Other zooplankton’ (i.e., for polychaetes, pteropods and bryozoans). Fish includes both fish eggs and larvae. Labels shown are for the zooplankton groups that contribute more than 5% to total number of individuals in that  sample. .................................................................................. 23 Figure 3. a) Zooplankton dry weight biomass (g d.w. ∙ m-2) of each zooplankton group biomass b) measured at the time series Station S4-1.5. Each shade represents a different zooplankton group. Note that amphipods, cirripedia, copepods, decapods, euphausiids, ostracods and cladocera are all crustacean zooplankton. Less abundant zooplankton species were grouped together as ‘Gelatinous zooplankton’ (i.e., for chaetognaths, siphonophores and ctenophores) and ‘Other zooplankton’ (i.e., for polychaetes, pteropods and bryozoans). Fish includes both fish eggs and larvae. Labels shown are for the zooplankton groups that contribute more than 3 g d.w. ∙ m-2 (a) or  5% (b) to total number of individuals in that  sample. .............................................. 26 Figure 4. a) Zooplankton trophic position as a function of zooplankton size-fraction for samples collected in 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) xxii  and August ( ).The slope for the linear regression model is 6.23 x 10-5 µm-1. b) Zooplankton trophic position as a function of zooplankton size-fraction in December, April, June and August at the time series Station S4-1.5. Individual replicates are graphed (missing replicates: December 250 µm; and April 2000 µm and 4000 µm). The slopes of the linear regression models are: December, 1.93 x 10-4 µm-1; April, -9.70 x 10-4 µm-1; June, -5.78 x 10-4 µm-1; August, 2.15 x 10-5 µm-1. Error bars shown represent errors propagated from both the measurements and formula used to calculate values (See Table 3). ......................................................................................... 28 Figure 5. Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in December 2017 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements. .......................................................................................................... 31 Figure 6. Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in April 2018 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements. ............................................................................................................................... 31 Figure 7. Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in June 2018 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements. ............................................................................................................................... 32 Figure 8.  Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in August 2018 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements. .......................................................................................................... 32 Figure 9. Carbon-normalized zooplankton Ag content as a function of zooplankton size-fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ). The slope for the linear regression model is 3.48 x10-5 µmol Ag ∙ mol C-1∙ µm-1. Error bars shown correspond to the analytical error in the zooplankton measurements. xxiii  Silver values were normalized to C by dividing metal content (mol Ag ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). ......................................................................... 37 Figure 10. Carbon-normalized zooplankton Cu content as a function of zooplankton size-fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ). The slope for the linear regression model is 3.40x10-3 µmol Cu ∙ mol C-1∙ µm-1.  Error bars shown correspond to the analytical error in the zooplankton measurements. Copper values were normalized to C by dividing metal content (mol Cu ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). ......................................................................... 38 Figure 11. Carbon-normalized particulate and zooplankton Ag content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate values (green, left Y-axis) shown are the full-depth average measurements per month sampled. Zooplankton metal content (black, right Y-axis) is shown as a function of zooplankton size-fraction. Error bars shown correspond to 1 standard deviation in particles and the analytical error in the zooplankton measurements. Particulate Ag values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pAg is associated to biology and follows the Redfield ratio. Zooplankton Ag values in zooplankton were normalized to C by dividing metal content (mol Ag ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). The monthly linear regression model calculated exclusively for zooplankton Ag content are as follows: December slope = 1.15x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.67 and R2 = 0.07. In April, slope = 9.25x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.01 and R2 = 0.93. In June, slope = 1.01x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.19 and R2 = 0.49. In August, slope = 2.51x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.11 and R2 = 0.62. N/A: not available. No particulate samples were taken for August. .......................................................................................................................................... 39 xxiv  Figure 12. Carbon-normalized particulate and zooplankton Cu content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate values (green, left Y-axis) shown are the full-depth average measurements per month sampled. Zooplankton metal content (black, right Y-axis) is shown as a function of zooplankton size-fraction. Error bars shown correspond to 1 standard deviation in particles and the analytical error in the zooplankton measurements. Particulate Cu values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCu is associated to biology and follows the Redfield ratio. Zooplankton Cu values were normalized to C by dividing metal content (mol Cu ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). The monthly linear regression model calculated exclusively for zooplankton Cu content are as follows: December slope = 2.11x10-3 µmol Cu ∙ mol C-1∙ µm-1, p = 0.35 and R2 = 0.29. In April, slope = 7.42x10-3 µmol Cu ∙ mol C-1∙ µm-1, p = 0.03 and R2 = 0.85. In June, slope = 2.78 x10-4 µmol Cu ∙ mol C-1∙ µm-1, p = 0.81 and R2 = 0.02. In August, slope = 3.84 x10-3 µmol Cu ∙ mol C-1∙ µm-1, p = 0.03 and R2 = 0.82. N/A: not available. No particulate samples were taken for August. ..................... 40 Figure 13. Carbon-normalized zooplankton Cd content as a function of zooplankton size-fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ).The slope for the linear regression model is 9.60x10-5 µmol Cd ∙ mol C-1∙ µm-1.  Error bars shown correspond to the analytical error in the zooplankton measurements. Cadmium values were normalized to C by dividing metal content (mol Cd ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). ......................................................................... 41 Figure 14. Carbon-normalized particulate and zooplankton Cd content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate values (green, left Y-axis) shown are the full-depth average measurements per month sampled. Zooplankton metal content (black, right Y-xxv  axis) is shown as a function of zooplankton size-fraction. Error bars shown correspond to 1 standard deviation in particles and the analytical error in the zooplankton measurements. Particulate Cd values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCd is associated to biology and follows the Redfield ratio. Zooplankton Cd values were normalized to C by dividing metal content (mol Cd ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). The monthly linear regression model calculated exclusively for zooplankton Cd content are as follows: December slope = 2.23x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.13 and R2 = 0.59. In April, slope = 2.11x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.06 and R2 = 0.75. In June, slope = -1.64x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.49 and R2 = 0.17. In August, slope = 1.14x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.39 and R2 = 0.25. N/A: not available. No particulate samples were taken for August. ........................... 42 Figure 15. Carbon-normalized zooplankton Ag bioaccumulation factors (BAFs) per size fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ). The slope for the linear regression model is 40 x 10-4 L ∙ mmol C-1∙ µm-1.  Error bars represent the analytical error propagation for both the zooplankton and dissolved metal measurements. BAFs were calculated as indicated in the legend of Table 6. The average dissolved Ag concentrations used to calculate the BAFs are: 9.40 ± 1.52 pM for December, 9.53 ± 1.60 pM for April, 7.53 ± 1.70 pM for June, and 6.84 ± 1.51 pM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. ............................................ 45 Figure 16. Carbon-normalized zooplankton Cu bioaccumulation factors (BAFs) per size fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ). The slope for the linear regression model is 7.06x10-4 L ∙ mmol C-1∙ µm-1. Error bars represent the analytical error propagation for both the zooplankton and xxvi  dissolved metal measurements. BAFs were calculated as indicated in the legend of Table 6. The average dissolved Cu concentrations used to calculate the BAFs are: 4.66 ± 1.37 nM for December, 5.72 ± 1.74 nM for April, 5.07 ± 2.14 nM for June, and 3.78 ± 0.37 nM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. ............................................ 46 Figure 17. Carbon-normalized particulate and zooplankton Ag bioaccumulation factors (BAFs) content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate BAFs (green, left Y-axis) shown are calculated from the full-depth average particulate and dissolved Ag profiles measured per month sampled. Zooplankton metal content (black, right Y-axis) is shown per size-fraction. Error bars represent 1 standard deviation in particles and the analytical error propagation for both the zooplankton and dissolved metal measurements.  BAFs were calculated as indicated in the legend of Table 6. Particulate Ag values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pAg is associated to biology and follows the Redfield ratio.  Zooplankton Ag values were normalized to C by dividing metal content (mol Ag ∙ kg d.w. -1) by carbon biomass in each size fraction (mol C ∙ kg d.w. -1). The monthly linear regression model calculated exclusively for zooplankton Ag BAFs are as follows:  December slope =1.22x10-3 L ∙ mmol C-1∙ µm-1, p = 0.67 and R2 =0.07. In April, slope = 9.7x10-3 L ∙ mmol C-1∙ µm-1, p = 0.01 and R2 =0.93. In June, slope = 1.34 x10-3 L ∙ mmol C-1∙ µm-1, p = 0.19 and R2 = 0.49. In August, slope = 3.67x10-3 L ∙ mmol C-1∙ µm-1, p = 0.11 and R2 = 0.62. The average dissolved Ag concentrations used to calculate the BAFs are: 9.40 ± 1.52 pM for December, 9.53 ± 1.60 pM for April, 7.53 ± 1.70 pM for June, and 6.84 ± 1.51 pM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. N/A: not available. No particulate samples were taken for August. ............................................................ 47 xxvii  Figure 18. Carbon-normalized particulate and zooplankton Cu bioaccumulation factors (BAFs) content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate BAFs (green, left Y-axis) shown are calculated from the full-depth average particulate and dissolved Cu profiles measured per month sampled. Zooplankton metal content (black, right Y-axis) is shown per size-fraction. Error bars represent 1 standard deviation in particles and the analytical error propagation for both the zooplankton and dissolved metal measurements.  BAFs were calculated as indicated in the legend of Table 6. Particulate Cu values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCu is associated to biology and follows the Redfield ratio.  Zooplankton Cu values were normalized to C by dividing metal content (mol Cu ∙ kg d.w. -1) by carbon biomass in each size fraction (mol C ∙ kg d.w. -1). The monthly linear regression model calculated exclusively for zooplankton Cu BAFs are as follows: December slope = 4.54x10-4 L ∙ mmol C-1∙ µm-1, p = 0.35 and R2 = 0.29. In April, slope = 1.30x10-3 L ∙ mmol C-1∙ µm-1, p = 0.03 and R2 =0.85. In June, slope =5.49 x10-5 L ∙ mmol C-1∙ µm-1, p = 0.81 and R2 = 0.02. In August, slope = 1.02 x10-3 L ∙ mmol C-1∙ µm-1, p = 0.03 and R2 =0.82. The average dissolved Cu concentrations used to calculate the BAFs are: 4.66 ± 1.37 nM for December, 5.72 ± 1.74 nM for April, 5.07 ± 2.14 nM for June, and 3.78 ± 0.37 nM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L.  N/A: not available. No particulate samples were taken for August. ............................................................ 48 Figure 19. Carbon-normalized zooplankton Cd bioaccumulation factors (BAFs) per size fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ). The slope for the linear regression model is 1.42x10-4 L ∙ mmol C-1∙ µm-1.  Error bars represent the analytical error propagation for both the zooplankton and dissolved metal measurements. BAFs were calculated as indicated in the legend of Table 6. The xxviii  average dissolved Cu concentrations used to calculate the BAFs are: 639.58 ± 44.43 pM  for December, 628.83  ±  39.28 pM  for April, 550.58  ±  148.39  pM  for June, and 634.50 ±  71.42  pM  or August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. .................. 49 Figure 20. Carbon-normalized particulate and zooplankton Cd bioaccumulation factors (BAFs) content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate BAFs (green, left Y-axis) shown are calculated from the full-depth average particulate and dissolved Cd profiles measured per month sampled. Zooplankton metal content (black, right Y-axis) is shown per size-fraction. Error bars represent 1 standard deviation in particles and the analytical error propagation for both the zooplankton and dissolved metal measurements.  BAFs were calculated as indicated in the legend of Table 6.  Particulate Cd values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCd is associated to biology and follows the Redfield ratio.  Zooplankton Cd values were normalized to C by dividing metal content (mol Cd ∙ kg d.w. -1) by carbon biomass in each size fraction (mol C ∙ kg d.w. -1).The monthly linear regression model calculated exclusively for zooplankton Cd BAFs are as follows: December slope = 3.49x10-4 L ∙ mmol C-1∙ µm-1, p = 0.13 and R2 =0.59. In April, slope = 3.36x10-4 L ∙ mmol C-1∙ µm-1, p = 0.06 and R2 = 0.75. In June, slope = -2.97x10-4 L ∙ mmol C-1∙ µm-1, p = 0.49 and R2 = 0.17. In August, slope = 1.79x10-4 L ∙ mmol C-1∙ µm-1, p = 0.39 and R2 = 0.25. The average dissolved Cd concentrations used to calculate the BAFs are: 639.58 ± 44.43 pM  for December, 628.83  ±  39.28 pM  for April, 550.58  ±  148.39  pM  for June, and 634.50 ±  71.42  pM  or August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. N/A: not available. No particulate samples were taken for August. .................... 50 Figure 21. Particulate and dissolved Ag depth profiles measured during December 2017, April 2018 and June 2018 at Station S4-1.5 (a-c). Particulate and dissolved monthly averages are xxix  shown in black and red lines, respectively. Profile of Ag associated with lithogenic and non-lithogenic sources and average non-lithogenic and lithogenic ratios shown (d-f). Non-lithogenic monthly averages shown by the cyan line. Profile of Ag associated with non-lithogenic sources normalized to biogenic P (g-i). Lithogenic Ag was calculated by using the Ag:Al molar crustal ratio from Taylor and McLennan (1995; 2x10-7). Non-lithogenic sources were calculated by subtracting lithogenic Ag from the total pAg measured. Shaded area shows range of ratios in particles from studies published by Martin and Knauer (1973), as well as Martin et al. (1983). Error bars represent one standard deviation.  Ag, Al and P were measured in all the samples. ... 68 Figure 22.  Particulate and dissolved Cu depth profiles measured during December 2017, April 2018 and June 2018 at Station S4-1.5(a-c). Particulate and dissolved monthly averages are shown in black and red lines, respectively. Profile of Cu associated with lithogenic and non-lithogenic sources and average non-lithogenic and lithogenic ratios shown (d-f). Non-lithogenic monthly averages shown by the cyan line. Profile of Cu associated with non-lithogenic sources normalized to biogenic P (g-i). Lithogenic Cu was calculated by using the Cu:Al molar crustal ratio  from Taylor and McLennan (1995; 1x10-4). Non-lithogenic sources were calculated like in Figure 21. Shaded area shows range of ratios in phytoplankton from studies by Guo et al. (2012), as well as Twining and Baines (2013). Error bars represent one standard deviation.  Cu, Al and P were measured in all the samples. ................................................................................................. 77 Figure 23. Particulate and dissolved Cd depth profiles measured during December 2017, April 2018 and June 2018 at Station S4-1.5 (a-c). Particulate and dissolved monthly averages are shown in black and red lines, respectively. Profile of Cd associated with lithogenic and non-lithogenic sources and average non-lithogenic and lithogenic ratios shown (d-f). Non-lithogenic monthly averages shown by the cyan line. Profile of Cd associated with non-lithogenic sources xxx  normalized to biogenic P (g-i). Lithogenic Cd was calculated by using the Cd:Al molar crustal ratio  from Taylor and McLennan (1995; 3x10-7). Non-lithogenic sources were calculated like in Figure 21. Shaded area shows range of ratios in phytoplankton from studies by Lane et al. (2009), as well as Twining and Baines (2013). Error bars represent one standard deviation.  Cd, Al and P were measured in all the samples................................................................................... 78  xxxi  List of Symbols Al: Aluminum Ag: Silver AgCl: Silver chloride AgCl2-: Silver dichloride AgCl32-: Silver trichloride Ba: Barium Br: Bromine C: Carbon Cd: Cadmium CdCl+: Cadmium chloride CdCl2: Cadmium dichloride CdS: Cadmium sulfide  Cl: Chlorine Co: Cobalt CO2: Carbon dioxide CO32-: Carbonate Cr: Chromium CrO42-: Chromate  xxxii  Cu: Copper CuO: Copper oxide DOLT-5: Dogfish Liver Certified Reference Material for Trace Metals and other Constituents DORM-4: Fish Protein Certified Reference Material for Trace Metals F: Fluorine Fe: Iron HCl: Hydrochloric acid HCrO42-: Bichromate Hg: Mercury HS: Bisulfide In: Indium L: Liter M: Molar Mg: Magnesium Mn: Manganese n: sample size N: Nitrogen N2: Nitrogen gas Na: Sodium  xxxiii  Ni: Nickel NiO: Nickel oxide O: Oxygen p: P-value P: Phosphorous Pb: Lead r: ionic radius of an ion (as defined by Nieboer et al. 1999) ri:  ionic radius of an ion (as defined by (Whitfield and Turner 1983) R2: R-squared  S: Sulfur SO42-: Sulfate TMC: trace metal clean Xm: electronegativity of an ion (as defined by Nieboer et al. 1999) Zi:  cation charge (as defined by Whitfield and Turner 1983) Zi2/ri: Electrostatic index (as defined by Whitfield and Turner 1983) Zn: Zinc 15N: stable nitrogen isotope with a total of 15 protons (7) and neutrons (8) (Xm)2r: Covalent index of an ion (as defined by Nieboer et al. 1999)  [Cd’]: inorganic Cd  xxxiv  β: difference between δ 15NGlu  and δ 15NPhe  in marine primary producers δ15N: ratio between 15N and 14N content in a sample     xxxv  List of Abbreviations ACS: American Chemical Society Ala: Alanine ANOVA: Analysis of Variance BAF: Bioaccumulation Factor  BCF: Bioconcentration Factor  BC: British Columbia BC WQGs: British Columbia Water Quality Guidelines CCGH: Canadian Coast Guard Hovercraft CCGS: Canadian Coast Guard Ship CF: Complexation Field CRD: Costa Rica Dome dAg: Dissolved silver dCd: Dissolved cadmium dCu: Dissolved copper dMe: Dissolved metal DNA: Deoxyribonucleic Acid DOC: Dissolved Organic Carbon EA-IRMS:  Elemental Analyzer Isotope Ratio Mass Spectrometry  EOAS: Earth, Ocean and Atmospheric Sciences  xxxvi  FIA-CL Flow Injection Analysis with Chemiluminescence detection  GC/C/IRMS: Gas Chromatography Combustion Isotope Ratio Mass Spectrometry   Glu: Glutamic acid ICP-MS: Inductively Coupled Plasma Mass Spectrometry IsoSiM: Isotopes for Science and Medicine IOS: Institute of Ocean Sciences NACME: N-acetyl Amino Acid Methyl Esters NCSI-AA: Nitrogen Compound-Specific Isotope Analysis of Amino acids NE Pacific: Northeast Pacific NRAMP: Natural Resistance-Associated Macrophage Protein NSERC CREATE:  Natural Sciences and Engineering Research Council of Canada -Collaborative Research and Training Experience  pAl: Particulate aluminum pAg: Particulate silver pBa: Particulate barium PBDE: Polybrominated diphenyl ether PCB: Polychlorinated biphenyl pCd: Particulate cadmium PCIGR: Pacific Centre for Isotopic and Geochemical Research pCu: Particulate copper PET: Polyethylene terephthalate PFA: Perfluoroalcoxy alkane  xxxvii  Phe: Phenylalanine pMe: Particulate metal POM: Particulate organic matter pP: Particulate phosphorous SoG: Strait of Georgia TAG: Tryacylglycerols TAM: Trophically available metal TDF: Trophic Discrimination Factor TMF: Trophic magnification factor TP: Trophic Position TRIUMF: Tri-university Meson Facility UBC: University of British Columbia UC: University of California USA: United States of America WE: Wax Esters WWTP: Wastewater Treatment Plant      xxxviii  Acknowledgements I offer my enduring gratitude to my supervisor, Dr. Maria Teresa Maldonado. Thank you for your time, immense patience, help, and support throughout the years. I started as an eager, clueless undergrad volunteer, but with your guidance I have made it all the way to where I am now. You are and have always been a great role model.  I would like to thank Metro Vancouver and the IsoSiM NSERC CREATE program.  Without their financial support, my graduate studies would not have happened. In addition, I would like to thank IsoSiM for their personal development classes and seminars. I learned a lot from them.  Thank you to my fellow Metro Vancouver team members and peers, especially to Dr. Pawlowicz, Dr. Francois, Cheng Kuang, Maureen Soon, Yuanji Sun, Jian Guo, and Sam Stevens for their help and enjoyable company during our field sampling trips. I want to give a big thank you as well to Cheng Kuang for all her amazing support throughout these past few years. You’re such a kind, smart person and I am lucky to have you as a friend and fellow oceanographer. Those midnight experiments would not have been the same without you.  My appreciation and gratitude also goes to Chris Payne and Lora Pakhomova for all their help and wisdom while field sampling. Thank you to the captain and crew of the CCGH Siyay and Moytel, and those from the CCGS Vector as well. Thank you as well to the undergraduate students and lab volunteers that helped me throughout the years (special shout out to Jack Anthony and Lori Waugh).   Finally, I want to give a warm thank you to my loved ones for all their unconditional support and encouragement. I appreciated all of it greatly, and especially during the ongoing COVID-19 pandemic. To my parents, grandparents, and siblings: you may have not completely understood what I was doing, but you were cheering me throughout it all, thank you. Also, thank you mom for teaching me to love learning. Thanks to my chosen family, especially to Alejandro Dounce, Maka Villaseñor, Margoe Hospers, and Luciana Silvestre Fernandes. Thank you for lending me an ear to rant onto, for cheering me on, for supporting me mentally and emotionally during tough times, and  for helping me make great memories outside of graduate school.    xxxix  Dedication        A mí.   Esta tesis ha sido toda una aventura y estoy muy orgullosa de lo que he aprendido y logrado durante el trayecto. ¡Sí se pudo! 1  Chapter 1: Introduction 1.1 The Georgia Strait Ambient Monitoring Program As part of the Georgia Strait Ambient Monitoring Program, the Contaminant Dispersion and Removal in the Strait of Georgia (SoG) project was launched in 2013 as a joint project between Metro Vancouver and a select number of oceanography laboratories in the Department of Earth, Ocean and Atmospheric Sciences (EOAS) at the University of British Columbia (UBC) - Vancouver campus. The purpose of this ongoing project is to measure, analyze, and determine the spatial and temporal variability of a series of physical (e.g., water circulation of different water masses within the SoG, the dispersion of particles within the Strait, etc.), chemical (e.g., the biogeochemical cycling of metals in SoG) and biological (e.g., primary productivity, accumulation of pollutants) parameters in the Strait of Georgia, upon which liquid waste is discharged (Pawlowicz & Francois, 2016). In addition, the program aims to understand the fate of contaminants in the SoG based on their interactions with these physical, chemical and biological parameters. In the first three years, this project was focused on organic pollutants (polychlorinated biphenyl, PCB; and polybrominated diphenyl ethers, PBDE). In 2016, the scope of the project was expanded to include three potential metal pollutants―Cd, Cu, and Ag―in order to study their bioaccumulation in zooplankton and their impact on the SoG food web (Pawlowicz, Francois, & Maldonado, 2017). The work within this thesis is aimed at determining whether Cu, Ag, and Cd concentrations in SoG waters are high enough to: 1) result in the bioaccumulation of these metals in the pelagic food web, and 2) impact the food web of the SoG at any biological or ecological scale. 1.2 Geographical and oceanographic setting The SoG is a semi-enclosed body of water located between Vancouver Island and continental British Columbia, Canada (Figure 1). The SoG, Puget Sound and Juan de Fuca Strait constitute the Salish Sea (Wadewitz, 2012). The SoG is connected to the Northeast Pacific Ocean through Johnstone Strait in the north, and through Juan de Fuca Strait in the south. The southern basin of  2  the SoG – designated to start below Texada Island and end at Haro Strait– has the largest surface area in the SoG and contains the largest volume of water in the Strait (Pawlowicz, Riche, & Halverson, 2007).  It is in this region where the deepest (>400 m) parts of the Georgia basin can be found (Pawlowicz et al., 2007). The southern basin of the SoG is also where the Fraser River drains into the Salish Sea.  Figure 1. Map of the Salish Sea.  Station S4-1.5 (49° 15.00’ N, 123° 40.00’ W; red star) in the Southern Strait of Georgia was our time series station. This station was sampled four times between December 2017 and August 2018. The bathymetry of the Strait of Georgia is shown in grey tones (i.e., black, deepest; white, shallowest).   3  1.2.1 Physical and chemical characteristics of the SoG Incoming water into the SoG mostly originates off the continental shelf west of Vancouver Island, and from rivers- mostly from the Fraser River (Masson, 2006; Pawlowicz et al., 2007). The shelf water entering the Strait comes from  a depth of about 100 and 200 m in the Northeast Pacific Ocean. This upwelled water enters Juan de Fuca Strait in pulses throughout the spring, mixes with SoG waters in Haro Strait and finally enters the SoG basin (Masson, 2006; Pawlowicz et al., 2007). The water imput from the Fraser River into the SoG is the main driver of the Strait’s estuarine circulation (Pawlowicz et al., 2007).   The Fraser River is the most important source of freshwater to the SoG, accounting for more than 70% (1.15 x 1011 m3 ∙ yr-1) of the freshwater input into the Strait (Johannessen, Macdonald, & Paton, 2003). Most of the remaining freshwater comes from other natural sources such as surface runoff, precipitation and groundwater (4.56 x 1010 m3 ∙ yr-1; Johannessen et al., 2003). The Fraser River is also the main source of particles into the Strait (63%; Johannessen et al., 2003).  A fraction of the particles in the river come from discharged effluents of anthropogenic activities, such as industry, agriculture and treated municipal effluent (Johannessen et al., 2003). There are three distinct layers of water in the Strait: a surface layer (0 to 50 m), an intermediate layer (50 – 200 m), and a deep water layer (>200m; Pawlowicz et al., 2007). The surface layer includes the incoming water from the Fraser River and the shallow portion of the water column. Water in this layer is generally warmer and fresher  than in the other layers due to its interactions with the atmosphere and the Fraser plume (Pawlowicz et al., 2007). The residence time of this water ranges from days to weeks. The intermediate layer waters come from Haro Strait and move to the SoG via horizontal advection. Temperature in this layer has a lagged seasonal cycle to the surface layer. Cold surface waters in February mix with Haro Strait waters (i.e., colder, denser waters from the Pacific) and sinks while moving to the SoG (Pawlowicz et al., 2007). Water in this layer has a residence time of months (~160 days). Finally, the deep water layer has its own seasonal cycle, highlighted by a series of  incoming Pacific water pulses during the spring in which SoG surface and deep Juan de Fuca waters mix and deliver colder and more oxygenated waters to this layer. This water renewal (i.e., delivery of oxygenated waters into this layer) happens once a year and upwells some water to the intermediate layer (Pawlowicz et al., 2007).  4  In summer, dense, warm waters with high salinity sink at depth (Pawlowicz et al., 2007).  During the rest of the year, deep water decreases in density as it diffuses with the intermediate layer (for a representation of the various water layers and their residence times, please refer to Pawlowicz et al., 2007). Incoming waters to the SoG are nutrient-rich, low in oxygen and dissolved organic carbon (Pawlowicz et al., 2007; Johannessen, Potentier, Wright, Masson, & Macdonald., 2008). Mackas and Harrison (1997) report that the southern SoG summer nitrate concentrations can be limiting to primary production. Because of water mixing in Haro Strait, the water column in the SoG is well oxygenated (Johannessen & McCarter, 2010). To this day, no anoxic waters have been reported at depth in the SoG (Johannessen & McCarter, 2010). Once inside the SoG, organic carbon is mostly in the dissolved phase and it is highest at the euphotic zone due to riverine input and net primary production (Johannessen et al., 2008). 1.2.2 Biological characteristics of the SoG The SoG is a rich ecosystem. The combination of optimal light availability at this latitude and nutrient-rich water inputs from upwelled Pacific Ocean water or from terrestrial sources makes this temperate coastal environment high in net primary production (i.e., net amount of carbon fixed or oxygen produced by primary producers after accounting for phytoplankton respiration; Field, Behrenfeld, Randerson, & Falkowski, 1998;  Harrison, Fulton, Taylor, & Parsons, 1983).  Because of its location, the SoG experiences strong seasonality. In spring, the combination of higher light levels, high nutrient concentrations, low tidal mixing, and constant freshwater discharge from the Fraser River  promotes stratification in the water column, which in turn provides stability in surface waters, promoting phytoplankton growth and leading to a large phytoplankton bloom (Yin, Harrison, Goldblatt, St. John, & Beamish, 1997). In summer, surface water temperature and riverine freshwater input increases, creating strong stratification in the water column and decreasing phytoplankton abundance due to low nutrient availability at the surface (Harrison et al., 1983). However, wind-induced mixing between the stratified water at the surface and the deeper waters that is still rich in nutrients, leads to small, sporadic blooms throughout this season. As temperatures and irradiance levels begin to lower in the fall, surface  5  waters cool down and mix deeper waters with higher nutrients, leading to a medium-sized autumn phytoplankton bloom. Finally, phytoplankton biomass is lowest during winter because of low-light availability. Because of the strong seasonality in SoG productivity, studies researching biological patterns must be able to sample seasonally in order to capture these changes. The zooplankton community SoG is thought to have a bottom-up trophic control (Mackas et al., 2013). This means that the variability in physical parameters and primary productivity are the main drivers in changes to the zooplankton population (Mackas et al., 2013). Zooplankton are secondary producers and are important for the diet of a variety of animals such as marine birds, larval fish, juvenile salmon and planktivorous fish such as Pacific hake and Pacific herring (Pauly, Christensen, & Haggan,1996 cited in Johannessen & McCarter, 2010; Beamish & Macfarlane, 1999). Finally, the diet of marine mammals such as harbor seals and the southern SoG resident killer whales, is comprised of organisms that feed on zooplankton (Johannessen & McCarter, 2010). 1.3 Zooplankton community in the SoG Zooplankton are aquatic organisms that cannot swim against strong water currents. Organisms within this group are diverse in terms of sizes, physiologies, trophic levels, feeding strategies and taxa (Steinberg & Landry, 2017). Not only are they considered to be the link between primary producers and higher trophic level organisms, but they also play an important role in elemental recycling and removal in the ocean (Steinberg & Landry, 2017; Hook & Fisher, 2001b).  The zooplankton community composition and production in the SoG varies within seasons (Harrison et al., 1983; Mackas et al., 2013). According to previous records spanning decades, zooplankton biomass in this region ranges between 4 and 12 g dry weight ∙ m-2, which indicates that the SoG marine ecosystem is highly productive (Mackas et al., 2013). Harrison et al. (1983) also reported that zooplankton production is highest during spring, and 10% lower throughout the rest of the year. As for community composition, crustaceans− mostly represented by euphausiids, amphipods and medium-large copepods−dominate the zooplankton biomass (Mackas et al. 2013). Gelatinous organisms, such as chaetognaths, also make an important contribution but to a lesser extent (Mackas et al., 2013). Zooplankton taxa in the SoG are  6  generally known to have a variety of diets and to belong to different trophic positions (e.g., herbivorous copepod E. bungii, carnivorous chaetognath T. septentrionalis, detritivorous ostracod D. elegans, and omnivorous copepod M. pacifica; Mackas et al., 2013;Tommasi, Routledge, Hunt, & Pakhomov, 2013).  1.4 Metals and metal uptake in zooplankton Zooplankton require certain metals (e.g., Fe, Mn, Cu, Co, Zn) in small quantities in order to execute a wide range of metabolic processes (Table 1). Even if some metals are essential for zooplankton metabolism, these and non-essential metals can be toxic if the concentrations in the environment are too high. Indeed, it has been shown that Cu (i.e., an essential metal), Ag and Hg (i.e., non-essential metals) can negatively impact the reproductive capabilities of calanoid copepods, such as decreasing their egg production and hatching rate (Hook & Fisher, 2001b; Bielmyer, Grosell, & Brix, 2006).  Zooplankton take up metals via two different routes: through water (dissolved uptake) or through food (dietary or trophic exposure; Wang, 2002; Wang, 2011).  Metal toxicity can occur due to excessive metal incorporation in an organism, beyond the necessary requirements for growth (e.g., Chang & Reinfelder, 2000 and 2002). The toxicity of a metal on zooplankton not only depends on its bioavailability in the environment but also on the intake route through which it was accumulated (Hook & Fisher, 2001a and b). For example, Hook and Fisher (2001b) reported reproductive issues in copepods exposed to phytoplankton grown at high Cd concentrations. However, when exposed to this same concentration of Cd in the dissolved phase, no negative impact was observed. Additionally, metal concentrations and chemical complexation (i.e., inorganic vs. organic metal complexation), salinity and levels of dissolved organic carbon (DOC) are among some of the factors that can determine how toxic or bioavailable a metal is to an organism (Wang, 2002; Hook & Fisher, 2001b citing Campbell, 1995, and Sunda & Huntsman, 1998).    7  Table 1. Essential trace metals and their functions.  The (*) refers to Blindauer (2012), and the (^) to Eisler (2010). All other data are referenced in the text. Trace element Biological role Fe Respiratory electron transport: Fe-containing cytochromes. Electron transfer.* Redox reaction catalyst.* Zn DNA repair. Calcification (involved in carbonic anhydrase)^ Cu Oxygen transport: Found in hemocyanin. Respiratory electron transport: Cu-containing cytochromes. Electron transfer.* Redox reaction catalyst.* Mn Activator of acetyl-CoA carboxylase. Co-factor of Mn-superoxide. Helps with water-splitting processes.* Co Vitamin B12 (cobalamine) Catalyst of radical reactions.* Involved in growth. ^  Zooplankton have the ability to prevent metal toxicity or metal accumulation in a variety of ways, which in turn can affect whether organisms in higher trophic levels are exposed to toxic metals from their prey (i.e., the zooplankton). The content and regulation of metal in zooplankton depends on how the organism sequesters or excretes metals. Chemical factors in the seawater such as metal concentrations and metal complexation, as well as biological factors like metal detoxification pathways, diet, ingestion, and metal efflux rates can also influence metal content in zooplankton (Wang, 2002). Additionally, the level of trophically available metal can vary between marine species depending on the strength of their respective digestive system (Rainbow, Luoma, & Wang, 2011). If an organism cannot remove contaminants from its body above a certain threshold, the contaminant(s) can lead to chronic or lethal toxicity.  8  When organisms cannot get rid of metal pollutants (i.e., toxic metals such as Ag or Cd, or essential metals, such as Cu, that are above their required bioessential threshold), excessive bioaccumulation can occur. Bioaccumulation is the net concentration of a chemical or chemicals taken up by an organism from its environment (Spacie, McCarty, & Rand, 1995). This uptake can occur via one or all the exposure routes an organism may have (i.e., water intake through gills or mouth, dietary or trophic exposure, or absorption) and in any biological (i.e., in their prey), physical (i.e., in dissolved or particulate phase) or chemical form (i.e., in organic or inorganic metal complexes; Spacie et al., 1995; Arnot & Gobas, 2006). If the bioaccumulation of a chemical increases in organisms at successively higher trophic levels in a food chain or web, then biomagnification is said to occur (Rand, Wells, & McCarty, 1995). 1.5 Purpose of studying metals in SoG zooplankton The physical, chemical, and biological characteristics of the SoG make it a very important region for the people living in BC. Many of the province’s industries rely directly on this body of water (e.g., aquaculture, fisheries, tourism, maritime trade, etc.). In addition, water discharge into the Strait includes waste from urban runoff, mining, aquaculture, pulp mills, and five waste water treatments plants (WWTPs) in Vancouver’s metropolitan area (two primary treatment plants and three secondary treatment plants) plus three WWTPs in Victoria (screened waste) and other small cities (Metro Vancouver, 2019b; Johannessen, Macdonald, Burd, van Roodselaar, & Bertold, 2015).  This liquid waste can contain metals, pharmaceuticals, organic pollutants, nutrients, microplastics and pathogens (Johannessen et al., 2015; Desforges, Galbraith, & Ross, 2015). These contaminants are of concern for marine life in the SoG (Johannessen & McCarter, 2010; Desforges, Galbraith, Dangerfield, & Ross, 2014). Unfortunately, negative anthropogenic contaminant impacts have already been observed on some Salish Sea organisms. Microplastics have been found in higher concentrations both in seawater and in copepods and euphausiids in the SoG than off the west coast of Vancouver Island (Desforges et al., 2014; Desforges et al., 2015). In addition, high nutrient and trace metal loading into the SoG can promote harmful algal blooms that affect both marine life and humans (Macdonald, Morton, & Johannessen, 2003). Finally, high levels of persistent organic pollutants, such as flame retardants (e.g., PBDEs and PCBs) have been found in harbor seals and killer  9  whales in Puget Sound and the SoG (e.g., Cullon, Jeffries, and Ross, 2005, and Ross, Ellis, Ikonomou, Barrett-Lennard, and Addison, 2000).   In addition to Cd, Ag and Cu are metals of concern in SoG. As previously mentioned, Cd and Ag are potentially toxic to marine organisms (e.g., Hook and Fisher, 2001a; Luoma, 2008). Indeed, both metals have been shown to negatively impact, for example, the development of copepods (see Hook and Fisher 2001a and b).  Both metals are also known to be naturally-high in the North Pacific waters (Bruland, Orians, & Cowen, 1994; Bruland & Lohan, 2003). Indeed, dissolved Ag and Cd concentrations in the North Pacific (~5-45 pM, and ~2.8-900 pM, respectively) are nine times higher than Ag and three times higher than Cd in the North Atlantic due to the thermohaline circulation of the global ocean (Bruland et al., 1994; Bruland & Lohan, 2003). Cadmium is a non-essential metal (e.g., the exception includes diatoms) to most marine organisms, such as oysters and zooplankton (e.g., Hook and Fisher, 2001b, and Lekhi et al., 2008). However, Cd content in Pacific oysters (Crassostrea gigas) farmed in British Columbia has been reported to exceed the maximum Cd content allowed by international markets (>1 µg ∙ g-1; Kruzynski, 2004). Some of the sources of this metal to C. gigas are suggested to be riverine input and the naturally-high Cd Pacific input waters into SoG (Kruzynski, 2004; Lekhi et al., 2008).  Silver was also used heavily in the photographic industry in the last century, and a decline in its use and content in liquid waste was observed in the 1990’s (Smith & Flegal, 1993; Luoma, 2008). Indeed, dissolved Ag concentrations in the North East Pacific have been decreasing due to changes in manufacturing processes and improved waste water treatment, among others (Flegal et al., 2007). For example, the mean seasonal dAg concentration in San Francisco Bay during 1991 was of 167 pM, while in 2005 it was of 74 pM (Flegal et al., 2007). Nowadays, Ag is used as a bactericide, in the form of silver nanoparticles, in clothing, water purification, and medical industries (Luoma, 2008).  Unfortunately, even if dissolved Ag concentrations have decreased throughout the decades, usage and discharge of silver nanoparticles is on the rise (Luoma, 2008). In addition, the behaviour of nanoparticle silver in seawater is still poorly understood, and could  10  potentially increase Ag toxicity (Luoma, 2008). Thus, measuring and monitoring Ag and Ag toxicity in aquatic organisms is still extremely relevant. In comparison to Ag and Cd, Cu, a known essential micro-nutrient, can also be toxic at high concentrations (Eisler, 2010). For example, in zooplankton, Cu is mainly involved in respiration within hemocyanin, an oxygen transport protein. However, Cu at high concentrations can also impact the reproductive ability and survival of marine organisms (e.g., coastal copepods and salmonids; Bielmyer et al., 2006; Sunda, Tester, & Huntsman, 1987; Van Genderen, Dishman, Arnold, Gorsuch, & Call, 2016). Dissolved Cu levels in the southern SoG had previously been reported to range between 3 and 78 nM (Thomas, 1975). And, although Cu toxicity studies are usually performed with nM, µM or mM Cu concentrations (e.g., Pinho, Pedroso, Rodrigues, de Souza, and Bianchini, 2007, Ng and Wang, 2007, Chang and Reinfelder, 2002, etc.), the current maximum total Cu concentration allowed by the British Columbia Water Quality Guidelines is almost half of the maximum concentration reported by Thomas (1975; Government of British Columbia- Ministry of Environment & Climate Change Strategy, 2019). Thus, Cu concentrations in the Strait should be carefully monitored. Yet, the study of metal accumulation and biomagnification in the SoG pelagic food web has been limited to Hg, Pb, and Cd in the past (Ross, 2014).  The most recent Canadian census (2016) reported a combined population of ~2.8 million people in Vancouver and Victoria and their respective metropolitan areas (Statistics Canada, 2020). As this region’s population grows, so will anthropogenic activities and related wastewater services required. Given a) the naturally-high Cd concentrations in the region, and their impact in the aquaculture industry (Kruzynski, 2004), b) the increase in nanosilver use, and the extreme toxicity of Ag for marine organisms (Luoma, 2008), and c) the high Cu concentrations in the SoG in the 1970’s (Thomas, 1975), any new additions of these three metals to the SoG could be detrimental.  Thus, the purpose of this thesis is to provide the first measurements of trace metal content of lower trophic level organisms from the SoG. Furthermore, we aim to provide a more comprehensive understanding of the seasonal trends in zooplankton biomass and community composition, their trophic position, metal content and possible metal bioaccumulation. In  11  addition, this research will provide insight onto the seasonal dynamics between some of the biological (e.g., bioaccumulation) and chemical (e.g., metal cycling) components influencing trace metal levels in organisms in lower trophic levels inhabiting the SoG. In turn, these parameters will help us determine the current state of zooplankton health in comparison to government guidelines and other regions of the world.  12  Chapter 2: Methods 2.1 Field sampling and analytical measurements 2.1.1  Sampling station The sampling station S4-1.5 (49° 15.00’ N, 123° 40.00’ W; Figure 1) is located in the southern SoG. It was chosen as a time series station because of its depth (380 m; one the deepest regions in the Georgia Basin, Pawlowicz et al., 2007), location away from terrestrial input, and low sediment load from the Fraser River plume (Pawlowicz, Di Constanzo, Halverson, Devred, & Johannessen, 2017). In addition, water at S4-1.5 is well mixed and away from the Iona wastewater outfall and its plume (Maldonado, pers. comm.). Finally, S4-1.5 is close to station S4-1 (also called Station DFO1), which is a traditional time series station for oceanographic measurements (El-Sabaawi et al., 2010). The S4-1.5 station has surface water conditions which are typical of the central SoG (El-Sabaawi et al., 2010), and is in the vicinity of central SoG stations sampled in previous zooplankton studies (e.g., El-Sabaawi et al., 2009a,,and Mackas et al., 2013).  2.1.2 General zooplankton sampling Zooplankton samples were taken during the December 2017, April 2018, June 2018 (aboard the CCGH Siyay and Moytel), and August 2018 cruises (aboard the CCGS Vector) following a modified version of Hakai (2015).  In essence, to sample for zooplankton, three whole-water column, vertical Bongo net (250 µm mesh, 0.5 m mouth diameter, with an attached mechanical flowmeter from General Oceanics, model 2030RC) tows were performed using an AmSteel®-Blue synthetic line and spherical weights at a maximum speed of 1 m ∙ s-1. The weights were plastic-coated to prevent metal contamination. After each tow, the nets were rinsed with seawater―starting at the mouth of the nets and rinsing downwards―to concentrate the sample in the cod-end. The three net tows were first combined and then split several times using a Motodo plankton splitter. Each portion of the total combined sample was meant to be used for a different measurement, including community composition, trophic position, biomass, and trace metals.  It was later decided that material for trophic position and total carbon content measurements would  13  be subsampled from the size-fractionated samples meant exclusively for trace metal content determination. This was done to allow direct comparisons among trophic position, total carbon and trace metal content for the samples. Due to logistical vessel availability, all sampling occurred during the daytime. 2.1.3 Zooplankton community composition, abundance, and biomass The portion of the total sample taken for zooplankton community composition varied between months (Table A-1). Each portion gathered for community composition was filtered through a 250 µm mesh sieve to remove excess seawater. It was then placed in a 250 mL PET jar, filled with filtered seawater (47 mm glass microfiber filter grade F (GF/F) with ~ 0.7 µm porosity), and preserved with 25 mL of 100% buffered formalin (ACS grade; 10% final concentration in jar). Samples were stored in the dark at room temperature until further analysis. At the end of the time series, samples were sent to the Institute of Ocean Sciences (IOS; Sidney, B.C.) for taxonomic analysis. Organisms were identified to species level with size and developmental stage whenever possible. The abundance and dry biomass estimates were also performed at IOS. Densities were calculated by dividing the number of individuals per taxon by the volume filtered by the nets and expressed as individuals per cubic meter (individuals ∙ m-3). On occasions when the flowmeters failed due to technical difficulties, the tow depth (normally 375 m) was used to estimate the volume of seawater filtered. Because only a portion of the total zooplankton was used, volume filtered was calculated using the following formula: Volume (m3) =  𝜋𝜋 ×  𝑟𝑟2 × ℎ × 6 × 𝑝𝑝 Where r is the radius of the net used (m), h is the depth of tow (m), 6 were the total number of tows performed, and p was the fraction of the total tows used for the community composition sampled (%). Dry biomass estimates were calculated by grouping the individuals identified by taxa and size range or developmental stage (units: individuals ∙ m-3). Arthropods were sorted by order, while the rest of the organisms were sorted by phylum. Then, abundance of the now grouped organisms was multiplied by the average dry weight for set species at the set sizes.  14  Finally, the biomass estimates were multiplied by the tow depth to calculate g dry weight (DW) ∙ m-2.  2.1.4 Zooplankton trace metal content After allocating the combined zooplankton, from the three net tows to different samples, the remaining live zooplankton, subsampled for trace metal analysis, were placed in a clean, dust-free plastic bucket filled with filtered seawater for two hours (filtered with a 47 mm glass microfiber filter grade F (GF/F) with ~0.7 µm porosity filter).  This was done in order to purge the zooplankton’s guts before sample collection. Two hours were chosen as a conservative estimate for gut passage time of food based on observations by Reinfelder and Fisher (1994) of non-copepod zooplankton (1 hour gut passage time of radiolabelled food), as well as the time at which > 90% of the initial gut content in copepod Temora longicornis was observed to be eliminated at a temperature similar to that of the SoG water temperature (1.6 hrs; annual full-profile temperature range ~8-15°C; Dam & Peterson, 1988; Kuang, 2019). We acknowledge that because all zooplankton were kept in the same bucket, predation could have occurred during the two hours. This predation could have led to higher trophic position determinations. After purging, the zooplankton were divided into five size-fractions using plastic sieves: 250 µm, 500 µm, 1000 µm, 2000 µm, and 4000 µm. Then, each size fraction was gently rinsed with double-deionized water to remove salts, and placed into trace metal clean cryovials using trace metal clean reinforced nylon disposable forceps (TWD Scientific LLC). Finally, the vials were flash-frozen in liquid nitrogen and stored at -20 °C until further sample preparation.  Once all time-series samples were collected, they were freeze-dried and then pulverized with a PFA mortar and pestle to obtain a homogeneous powder. The mortar and pestle were cleaned with Optima™-level methanol (Fisher Scientific), and then rinsed once with 10% HCl (Trace Metal™ grade, Fisher Scientific, 34-37% w/w) and three times with double-deionized water. After pulverizing each sample, approximately 100 mg of sample was weighed in a pre-weighed trace metal clean PFA vial to determine the starting dry weight of the sample. The PFA vials used were either 15 mL or 7 mL. The 7 mL ones were preferred due to the lower thickness of the vials, the ability to reduce static inside the vial and the capacity to fit more samples on the  15  heating plates during the digestions. Samples were digested in a total of three batches. In the first two batches, samples were digested in 15 mL PFA vials, and in the last batch in 7 mL PFA vials.  To digest the zooplankton samples, 2 mL of nitric acid (double-distilled, 70% w/w) were added to each PFA vial and left overnight on a hot plate at ~180oC. The nitric acid was then evaporated at 225oC and 1 mL of fresh nitric acid and 50 µL of hydrofluoric acid (ARISTAR® ULTRA, BDH Analytical Chemicals, 47-51% w/w) were added to further digest the organic matter. The samples were left once again overnight on a hot plate at 150oC. The next day, 2 mL of perchloric acid (ARISTAR® ULTRA, BDH Analytical Chemicals, 65-71% w/w) was added to oxidize the remaining organic matter. Once the perchloric acid had been added, the hot plate was set to 240oC to evaporate the nitric and hydrofluoric acids and to reflux the perchloric acid. The samples were refluxed constantly for the next two days, only lowering the temperature of the plate to 150oC at night. The following day, the temperature was once again set to 240oC and the perchloric acid was allowed to evaporate. Once the perchloric acid evaporated completely, the samples were washed for a minimum of three times with 1 mL of nitric acid and dried in between additions and after the last one.  Finally, the samples were re-dissolved in a 1 or 2 mL 1% nitric acid, 10 ppb In solution. The 10 ppb In stock was provided by the Pacific Centre for Isotopic and Geochemical Research (PCIGR). The re-dissolved solution was then subsampled and diluted forty times to measure: Ag, Ba, Cd, Co, Cr, Cu, Mn, Ni, and Zn. A subsequent dilution of ten thousand times was done to measure: Na, Mg, Al, P, and Fe. For every digestion, three reagent blanks, at least three procedural blanks and 100 mg of two different reference materials (DOLT-5, Dogfish Liver Certified Reference Material for Trace Metals and other Constituents, and DORM-4, Fish Protein Certified Reference Material for Trace Metals) were also digested (see Appendix B). All digested samples and blanks were measured using a Thermo Finnigan ELEMENT 2™ high resolution single collector ICP-MS at the PCIGR at UBC, Vancouver. All materials and reagents used were handled using trace metal clean techniques. The data were first corrected for any important polyatomic interferences that would over or underestimate metal isotopic measurements in the ICP-MS and then converted to mg ∙ kg dry weight-1. Major ion (i.e., Mg, Al, and P) measurements were corrected for sea salt to subtract any ion content coming from sea salt  16  and measure major ion content in zooplankton accurately. This correction involved calculating the ratio (Fsea-salt) between the content of Na in the zooplankton sample and in seawater: Fsea-salt = (Nasample -1.97%)/ (30.8% - 1.97%) Where Nasample is the percentage of sodium in the zooplankton sample measured, 1.97 is the percentage of Na in calanoid copepod N. plumchrus reported by Masuzawa, Koyama, and Terazaki (1988) and assumed to be a similar percentage to that  in our zooplankton, and ~30.8% is the fraction of sodium in sea salt (Martin & Whitfield, 1983; Pawlowicz, 2013). If any Fsea-salt values were negative, they were changed to 0. Once this ratio was calculated, the percentage of each major ion in zooplankton (Mzoop) was calculated using: Mzoop = [Msample – (Msea-salt ∙ Fsea-salt)]/ (1 - Fsea-salt) Where Msample is the percentage of the major ion in the sample, and Msea-salt is the percentage of major ion in seawater (e.g., 3.6% for Mg; Martin & Whitfield, 1983; Pawlowicz, 2013). Finally, to calculate the major ion content in zooplankton (Mzoop-total), the total sample weight digested (mg) was multiplied by Mzoop and divided by a hundred: Mzoop-total = (Mzoop ∙ sample weight)/ 100 No salt corrections were done in elements with negligible concentrations in seawater (i.e., Ag, Ba, Cd, Co, Cr, Cu, Fe, Mn, Ni, P, and Zn).  2.1.5 Zooplankton total carbon content After enough homogenized and dried size-fractionated zooplankton sample was allocated towards trace metal analysis, we subsampled 0.7 to 2 mg for total carbon and nitrogen analysis. Each subsample was weighed, packed in a tin capsule and sent for carbon and nitrogen analysis in an elemental analyzer interfaced to a continuous flow isotope ratio mass spectrometer (EA-IRMS) at the UC Davis Stable Isotope Facility in California, USA.  To measure C content, the samples were first combusted at a 1000°C temperature. The oxides were then removed in a reduction reactor. This process is then followed by removing water from  17  the sample and then separating the N2 and CO2 gases using a gas chromatography column. These gases were then measured by mass spectrometry (UC Davis Stable Isotope Facility, n.d.). A variety of reference materials (i.e., glutamic acid, alfalfa flour, nylon6, bovine liver and enriched alanine) were measured along with the sample to determine accuracy and for quality control.   2.1.6 Zooplankton trophic position After enough homogenized, and dried size-fractionated zooplankton sample was allocated towards trace metal analysis, we subsampled 50 to 100 mg for stable nitrogen isotope analysis. Each subsample was weighed, packed in a clear glass threaded vial (1.8 or 3.7 mL) and sent for nitrogen compound-specific isotope analysis of amino acids (NCSIA-AA) at the UC Davis Stable Isotope Facility in California (USA), using gas chromatography/combustion/isotope ratio mass spectrometry (GC/C/IRMS) by esterification-acetylation (N-acetyl amino acid methyl esters; NACME; Corr, Berstan, & Evershed, 2007).  Due to lack of material, trophic position data for some size fractions (December 250-500 µm, and April 2000 – 4000 µm and >4000 µm) were not generated. In essence, the method entails these steps and for further details, please refer to Corr et al. (2007). To measure the amino acid compounds in our samples, the proteins in the sample were first broken down to individual amino acids using acid hydrolysis. Acid hydrolysates were induced by adding 6 M HCl to the pre-weighed, previously-dried, homogenized samples. Then, the samples were flushed with nitrogen gas and heated to 170°C for 70 minutes. After cooling down, 200 µL of 6:5 volume by volume ratio of heptane to chloroform was added and the resulting lipophilic layer removed (UC Davis Stable Isotope Facility, n.d.).  Once the acid hydrolysis step was completed, the product was mixed with an internal reference solution and dried using N2 gas. Then, samples were mixed with 1.85 M acidified isopropanol and heated to 100°C for an hour. Any excess reagent left in the sample was removed by evaporation at 40°C using nitrogen gas. This evaporation is then followed by a chloroform rinse and another evaporation step. Samples are then acetylated with acetic anhydride, acetone and trimethylamine at 60°C. Once again any excess of reagents is evaporated with nitrogen gas. The aqueous phase of the sample is removed from the rest of the sample by adding ethyl acetate and a  18  saline solution to the sample, followed by vortexing. The ethyl acetate used to separate the sample solution is removed with N2 gas. After removing any water left in the sample, a small volume of ethyl acetate is used to transfer the NACMEs to the gas chromatographer and start the measuring process (UC Davis Stable Isotope Facility, n.d.). The methodology and data normalization procedures used to measure 15NAA followed those by Corr et al. (2007) and Yarnes and Herzage (2017). Samples were analyzed in duplicate. If the average 15NAA of a sample was higher or lower than 1‰ (i.e., one per-mille) of the expected measurements error, a triplicate measure was taken.  The instrument used for analysis was a Trace Ultra GC gas chromatograph using Dowex 50WX8 SCX resin (Thermo Electron Corp., Milan, Italy) coupled to a Thermo Delta V Plus isotope-ratio mass spectrometer through a GC IsoLink interface (Thermo Electron Corp., Bremen, Germany). The combustion temperature was 1000°C and a Ni/NiO/CuO catalyst was used. Once the 15NAA values were measured, the zooplankton trophic positions were calculated using the formula from Chikaraishi, Ogawa, and Ohkouchi (2010):  TP = [(δ 15NGlu - δ 15NPhe+ β)/TDF] +1 Where β is the difference between δ 15NGlu and δ 15NPhe in marine primary producers -3.4 ± 0.9 ‰ and TDF is the trophic discrimination factor calculated from the difference in 15N enrichment between δ 15NGlu in comparison to δ 15NPhe per trophic step 7.6 ± 1.2‰ (Chikaraishi et al., 2010).  The δ 15NGlu and δ 15NPhe values were chosen to calculate trophic position because of the marked difference in 15N values between both amino acids. Glutamic acid is enriched in 15N in comparison to phenylalanine because of certain metabolic processes:  glutamic acid’s nitrogen bond is cleaved during transamination and then preferentially bonded with 15N. In comparison, the major metabolic pathway for phenylalanine does not interact with its nitrogen bond (Chikaraishi, Kashiyama, Ogawa, Kitazato, & Ohkouchi, 2007; Chikaraishi et al., 2010). Thus, the δ 15N signature in phenylalanine will remain somewhat constant regardless of trophic level, but glutamic acid’s δ 15N signature will increase with increasing trophic position. Other amino acids also go through transamination (e.g., alanine, leucine and isoleucine); however, the  19  variability in measurements for glutamic acid was found to be lower than for the others (Chikaraishi et al., 2010). Although the amino acid methionine does not go through transamination either, phenylalanine is still preferred because of its higher abundance (Chikaraishi et al., 2010). 2.1.7 Dissolved and particulate trace metal concentrations Dissolved and particulate trace metal concentrations at Station S4-1.5 were sampled on the same dates as the zooplankton samples described above. A twelve-depth profile (~ 0, 5, 10, 20, 30, 50, 75, 100, 150, 200, 250 and 300 m; Tables A-9 and A-10) was sampled for dissolved trace metals throughout the time series and for particulate metals in December and April. Only nine depths were sampled in June for particulate trace metals (0, 10, 20, 30, 50, 150, 200, 250, and 300 m), and no samples were collected for particulate metal concentrations in August. For more information on sampling and analytical measurements, see Kuang (2019). 2.2 Calculations 2.2.1 Zooplankton diversity indices Three different diversity indices were calculated: species richness, species evenness and the Gini-Simpson diversity index. Species richness (S) was calculated as the number of species identified in each of the community composition samples (n = 4, one per month sampled; Morris et al., 2014). Zooplankton organisms that were only identified to genus level were treated as a species by themselves.  Then, Gini-Simpson diversity index (D1) was calculated using: D1 = 1-∑(𝑛𝑛𝑁𝑁)2 Where n is the number of individuals in each species, and N  the total abundance of individuals in each species. This index represents the probability that two organisms taken at random belong to different species (Morris et al., 2014).    20  Finally, species evenness (E) was calculated by first calculating Simpson’s dominance index (D2):  D2 = 1∑(𝑛𝑛𝑁𝑁)2 And then dividing it by species richness (S): E = 𝐷𝐷2𝑆𝑆 Species evenness (E) values represent the spread of organisms among species. A low value, closer to zero, suggests that most organisms in a sample belong to a few species, whereas a value closer to one suggests that organisms in a sample a well distributed amongst the species sampled (Morris et al., 2014). 2.2.2 Zooplankton bioaccumulation factors Bioaccumulation factors (BAF) for each size fraction were calculated by dividing the zooplankton metal content per size fraction (Czoop; mols ∙ kg d.w. -1) by the water column averaged dissolved metal concentrations measured during the corresponding date of zooplankton sampling (Cwater; mols ∙ L-1). Dissolved metal concentrations for Ag, Cu and Cd were provided by Kuang (2019).  BAF = Czoop/Cwater In this study, metal bioaccumulation, and not bioconcentration factor, was calculated for the zooplankton. This is because metal bioconcentration factor (BCF) denotes the steady-state incorporation of a metal in an organism from the dissolved phase. Therefore, this factor can only be measured under laboratory conditions, where dietary metal exposure can be prevented (Arnot & Gobas, 2006). While bioconcentration reflects the dissolved metal absorption via respiration and/or its adsorption onto outer surfaces, bioaccumulation reflects the absorption and adsorption of a particulate metal, from both the dissolved and particulate fraction (Arnot & Gobas, 2006). Thus, metal bioaccumulation factor takes into account all seawater fractions of a given metal, as well as all the uptake routes and removal processes in an organism. Therefore, BAF ultimately  21  reflects the steady-state accumulation of a metal in an organism (Arnot & Gobas, 2006). Although total seawater metal concentrations (i.e., dissolved plus particulate) are sometimes used to calculate BAF, dissolved metals concentrations are most commonly used (e.g., Chouvelon et al., 2019, Fisher et al., 2000, and Fang, Hsiao, and Nan, 2014). In our study, we used only the dissolved metals concentrations because they are much higher (between 10 and 26 times higher) than the particulate phase (Kuang, 2019). Thus, bioaccumulation factors in this study are calculated as the ratio of metal content in an organism over the dissolved metal concentration in seawater (Arnot & Gobas, 2006).   2.2.3 Statistical analysis 2.2.3.1 Metal content as a function of size fraction Linear regression analysis was conducted for zooplankton trophic position, zooplankton trace metal content and zooplankton bioaccumulation factors. These dependent variables were modelled against zooplankton size fraction. Each variable was modelled in two different ways: a) with all available data in order to elucidate general trends with increasing size fraction, and b) with monthly data to examine seasonal trends. The intercept, slope, p-value of the slope and R2 for each model can be found in Appendix B.  2.2.3.2 Two-Way ANOVA A Two-Way ANOVA was conducted for zooplankton trace metal content and bioaccumulation factors. The categorical independent values compared were zooplankton size fractions (n = 5) and month sampled (n = 4). If the results were statistically significant, a Tukey HSD test was run to find the groups whose means differed from each other. The results for each of the models can be found in Appendix B.   22  Chapter 3: Results 3.1 Zooplankton community composition Zooplankton abundance, biomass and species richness varied throughout the time series. Abundance was highest in June (1706.36 individuals ∙ m-3) and lowest in December (220.22 individuals ∙ m-3; Table 2; Figure 2a). The same was true for biomass (Table 2; Figure 3a). Species richness ranged from 45 species in December to 61 in April (Table 2). Diversity, species evenness and carbon content of the various size fractions did not vary much.  Table 2. Total zooplankton abundance, biomass (dry weight), carbon content, richness and diversity indices for the samples collected throughout the time series at Station S4-1.5. Biomass measurements are estimates based on averages for set species at set sizes (Galbraith, pers. comm.).   Sampling date Size fraction (µm) December 2017 April  2018 June  2018 August  2018  Abundance (individuals ∙ m-3) n.a.  220.22  559.46  1706.36  723.45  Biomass  (g d.w.∙ m-2) n.a. 6.19 10.49 37.97 16.93       Carbon content (g C.∙ kg d.w-1) Total 2156.26 2010.45 2031.86 2110.8 250 433.82 424.53 382.60 431.25 500 544.20 383.46 401.99 494.22 1000 496.20 444.90 482.48 472.71 2000 377.57 397.32 402.65 376.02 4000 304.47 360.24 362.14 336.60  Species richness    45  61  51  50  Species evenness    0.024  0.019  0.022  0.022  Gini-Simpson diversity index    0.91  0.88  0.88  0.92   23   Figure 2. a) Zooplankton group abundance (individuals ∙ m-3) and % contribution to total number of individuals (b) measured at the time series Station S4-1.5. Each shade represents a different zooplankton group. Note that amphipods, cirripedia, copepods, decapods, euphausiids, ostracods and cladocera are all crustacean zooplankton.  Less abundant zooplankton species were grouped together as ‘Gelatinous zooplankton’ (i.e., for chaetognaths, siphonophores and ctenophores) and ‘Other zooplankton’ (i.e., for polychaetes, pteropods and bryozoans). Fish includes both fish eggs and larvae. Labels shown are for the zooplankton groups that contribute more than 5% to total number of individuals in that  sample.   24  3.1.1 Abundance Copepods were the most abundant organisms, accounting for 64% to 89% of all individuals in each sample (Figure 2b) while the second most abundant group varied among seasons. In December, copepods accounted for 76.6% of all organisms, while as a group, pteropods, polychaetes and bryozoans (named “other zooplankton” in Figures 2 and 3) accounted for 15.1%. In April, euphausiids were the second most abundant organisms (20.1%), followed by the pteropods, polychaetes and bryozoans group (7.8%). In June, copepods (77.8%) and cladocera (12.1%) made up most of the sample. In August, copepods dominated (88.8%), followed by ostracods (6.1%). Other zooplankton groups were present in all samples, but they accounted for less than 1% of the total number of individuals. Among copepods, calanoid copepods (Calanoida order) were most prominent, representing between 47 and 64% of the total individuals in the month sampled. The most dominant species varied among seasons (Tables A-2 to A-5). In December, Oithona atlantica (stages IV-VI; Cyclopoida order) and Metridia pacifica (stages V-VI; Calanoida order) were the most abundant, comprising 18 and 14% of the total sample, respectively. In April, Euphausiidae spp. eggs and larvae comprised 20% of the total sample, while M. pacifica (stages V-VI; Calanoida order) made up 20% and Eucalanus spp. (stages I-III; Calanoida order) 14%.  In June, Paracalanus indicus accounted for 27% (Calanoida order), Podon spp. for 12% and M. pacifica (stages IV-VI; Calanoida order) for 11% of the total number of individuals in the sample. Finally, in August, M. pacifica (stages IV-VI; Calanoida order) comprised 17% of the sample, Pseudocalanus newmani (stages V-VI; Calanoida order) for 14%, and Oithona similis (stages V –VI; Cyclopoida order) and Oithona atlantica (stages IV-VI; Cyclopoida order) 11 and 10%, respectively. 3.1.2 Dry weight biomass estimates Dry weight biomass ranged between 6-38 g d.w. ∙ m-2 (Table 2, Figure 3a). Copepods made up between 35-55% of total biomass per month sampled. Most of the biomass estimated belonged to a combination of the following zooplankton groups: amphipods, copepods, euphausiids, ostracods, gelatinous zooplankton and other zooplankton (pteropods, polychaetes and bryozoans)  25  (Figure 3b). Only in June did the gelatinous zooplankton and euphausiid groups comprise more than 3 g d.w. ∙ m-2 each. In addition, amphipods were an important group in both the June and August biomass estimates (Figure 3b).      26   Figure 3. a) Zooplankton dry weight biomass (g d.w. ∙ m-2) of each zooplankton group biomass b) measured at the time series Station S4-1.5. Each shade represents a different zooplankton group. Note that amphipods, cirripedia, copepods, decapods, euphausiids, ostracods and cladocera are all crustacean zooplankton. Less abundant zooplankton species were grouped together as ‘Gelatinous zooplankton’ (i.e., for chaetognaths, siphonophores and ctenophores) and ‘Other zooplankton’ (i.e., for polychaetes, pteropods and bryozoans). Fish includes both fish eggs and larvae. Labels shown are for the zooplankton groups that contribute more than 3 g d.w. ∙ m-2 (a) or  5% (b) to total number of individuals in that  sample.  27  3.2 Zooplankton trophic position Zooplankton trophic position in our size-fractionated samples ranged between 1.89 and 2.94.  The combined data (i.e., samples from all months) showed that trophic position increased significantly with increasing size-fraction (Table 3; Figure 4a; linear regression model p < 0.05). The averages for all seasons combined showed that the > 4000 µm size-fraction had the highest mean trophic position (2.41 ± 0.44) and the smallest size-fraction (250-500 µm) had the lowest (2.17 ± 0.11; Table 3). On average, the December samples had the highest trophic position (2.53 ± 0.28), and April had the lowest (2.14 ± 0.09; Table 3).   Table 3. Trophic position ranges of each zooplankton size-fraction collected throughout the time series at Station S4-1.5. Trophic position was calculated from Chikaraishi et al. (2009) using the formula TP=[(δ 15NGlu - δ 15NPhe + β)/TDF]+1 where β is the difference between δ 15NGlu  and δ 15NPhe  in the primary producers -3.4 ± 0.9 ‰ (Chikaraishi et al., 2009), and TDF is the trophic discrimination factor calculated from the difference  in 15N enrichment between  δ 15NGlu  in comparison to δ 15NPhe  per trophic step 7.6 ± 1.2‰ (Chikaraishi et al., 2009). The trophic position error corresponds to one standard deviation from the mean. Size fraction December April June August Fraction mean  250 µm - 500 µm  ̶   2.16-2.30  2.01-2.17  2.15-2.34  2.17 ± 0.11 500 µm - 1000 µm  2.32-2.36 1.95-2.17 2.28-2.42 2.20-2.29 2.24 ± 0.14 1000 µm - 2000 µm  2.22-2.35 2.08-2.16 2.12-2.31 2.21-2.23 2.20 ± 0.09 2000 µm - 4000 µm  2.61-2.71 ̶ 2.04-2.14 2.21-2.35 2.34 ± 0.27 > 4000 µm 2.94 ̶ 1.89-2.07 2.31-2.33 2.41 ± 0.44 Seasonal mean 2.53 ± 0.28 2.14 ± 0.09 2.15 ± 0.18 2.26 ± 0.07     28   Figure 4. a) Zooplankton trophic position as a function of zooplankton size-fraction for samples collected in 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June ( ) and August ( ).The slope for the linear regression model is 6.23 x 10-5 µm-1. b) Zooplankton trophic position as a function of zooplankton size-fraction in December, April, June and August at the time series Station S4-1.5. Individual replicates are graphed (missing replicates: December 250 µm; and April 2000 µm and 4000 µm). The slopes of the linear regression models are: December, 1.93 x 10-4 µm-1; April, -9.70 x 10-4 µm-1; June, -5.78 x 10-4 µm-1; August, 2.15 x 10-5 µm-1. Error bars shown represent errors propagated from both the measurements and formula used to calculate values (See Table 3).    29  Examining significant correlations between trophic position and zooplankton size fractions for each month revealed a significant positive correlation between trophic position and zooplankton size only for December (linear regression model, p < 0.01; slope = 1.93 x 10-4 µm-1; Figure 4b). Although we analyzed the variance between months sampled and size fractions sampled, we found that trophic position only varied between months (Two-Way ANOVA, p <0.01). From all the months sampled, the December trophic position values were statistically higher than those for the rest of the year (Tukey HSD test, p <0.05). 3.3 General zooplankton trace metal content Zooplankton trace metal content varied among metals. The maximum metal content measured was for Mn (380.81 ± 8.69 mg ∙ kg-d.w. 1) in the June 250 µm size-fraction, whereas the lowest (below detection limit) was for Cr in the August 4000 µm size-fraction (Figures 5 to 8). On average, trace metal contents spanned four orders of magnitude, and followed this order: Zn > Mn > Cu > Ba > Ni > Cd > Cr > Co > Ag (mg metal ∙ kg d.w. -1; Table 4). On average, Ag and Cu had the highest values in April, Cd in December, and the rest of the metals (Zn, Mn, Ba, Ni, Cr and Co) in June (Figures 5 to 8). The months when we measured the lowest metal content were more variable: the majority of metals were lowest in August (Mn, Ba, Cd, Cr and Co), Ag and Cu were lowest in June, Ni was lowest in April, and Zn in December.   3.3.1 Trends between trace metals Upon closer examination, certain metals exhibited similar trends in regards to their content in zooplankton. For example, the trends of zooplankton metal content are similar for a) Cr, Mn and Ba, b) Ni and Zn, and c) Cu and Ag (from Figures 5 to 8). Cobalt sometimes behaved like Cr, Mn and Ba (e.g., April and June), and other times like Ni and Zn (e.g., December, and August). Cadmium content in zooplankton did not mimic any other trace metal trends. Below we present some of these trace metal trends.     30  Table 4. Average zooplankton trace metal content (mg Me ∙ kg d.w. -1) measured throughout the time series at Station S4-1.5. Errors indicate one standard deviation from the mean. Ranges are shown in parenthesis below averages.    December April June August Average Ag  0.31 ± 0.22 (0.15-0.67)   0.41 ± 0.47 (0.08-1.20)   0.18 ± 0.06 (0.11-0.28)   0.22 ± 0.14 (0.09-0.42)   0.28 ± 0.26 (0.08-1.20)  Ba  8.9 ± 8.43 (2.56-23.68)   11.16 ± 7.49 (4.29-21.16)   34.9 ± 34.88 (12.73-96)   7.42 ± 5.63 (2.89-16.49)   15.60  ± 20.55 (2.56-96)  Cd  3.49 ± 1.49 (1.52-5.56)   2.84 ± 1.24 (1.34-4.22)   3.19 ± 2.12 (1.48-6.86)   1.93 ± 1.10 (0.93-3.66)   2.86 ± 1.53 (0.93-6.86)  Co  0.53 ± 0.47 (0.14-1.11)   0.54 ± 0.19 (0.28-0.78)   1.41 ± 1.16 (0.52-3.32)   0.39 ± 0.05 (0.19-0.73)   0.71 ± 0.72 (0.14-3.32)  Cu  18.95 ± 10.51 (9.60-36.57)   30.92 ± 22.67 (12.44-68.01)   14.5 ± 4.93 (8.83-20.82)   15.45 ± 10.03 (6.69-26.57)   19.95 ± 14.24 (6.69-68.01)  Cr  0.7 ± 0.67 (0.24-1.81)   1.58 ± 1.28 (0.18-3.14)   3.97 ± 4.48 (1.00-11.80)   0.37 ± 0.51 (0-1.24)   1.65 ± 2.61 (0-11.80)  Mn  23.17 ± 18.91 (9.73-53.75)   75.04 ± 58.96 (14.38-154)   137.05 ± 2.31 (46.60-380.81)   12.53 ± 9.75 (4.54-28.49)   61.95 ± 86.7 (4.54-380.81)  Ni  4.94 ± 4.91 (0.68-12.30)   3.76 ± 1.64 (1.65-5.24)   7.62 ± 4.83 (2.34-13.94)   4.77 ± 5.17 (0.44-13.39)   5.27 ± 4.28 (0.44-13.94)  Zn  126.07 ± 83.17 (37.84-213.75)   125.96 ± 60.77 (53.43-187.37)   146.82 ± 71.19 (62.03-225.82)   135.25 ± 102.67 (30.31-278.17)   133.52 ± 74.81 (30.31-278.17)    31   Figure 5. Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in December 2017 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements.   Figure 6. Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in April 2018 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements.   32   Figure 7. Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in June 2018 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements.   Figure 8.  Zooplankton metal content (mg ∙ kg dry weight-1) for samples collected in August 2018 at our time series Station S4-1.5. Error bars correspond to the analytical error in the zooplankton measurements.   33  3.3.2 Manganese and Chromium  Cr and Mn were higher in the smaller zooplankton size fractions, and exhibited identical zooplankton size and temporal trends. On average, the highest metal content for both metals occurred in June and the lowest in August (Table 4). Furthermore, the Cr and Mn content in zooplankton did not change significantly with size fraction (linear regression model, p > 0.05 for both metals; Table B-6).  3.3.3 Zinc and Nickel  Throughout the time series, Ni and Zn levels were lowest for the larger zooplankton size fraction, and in general, the 500 µm fraction had the highest metal content. The highest average concentrations for both metals occurred in June. The lowest average Ni and Zn contents were observed in April and December, respectively (Table 4). Additionally, both Ni and Zn contents were found to decrease significantly with increasing size fraction (linear regression model; for Ni, p < 0.001; for Zn, p < 0.01; Table B-6).  3.3.4 Cobalt  Interestingly, Co content in zooplankton sometimes was similar to that of Cr, Mn and Ba in April and June, and similar to Ni and Zn in December and August. Its lowest average content was measured in August and its highest content in June (Table 4). When data from all months were plotted together, Co content was found to decrease significantly with increasing size fraction (linear regression model; p < 0.05; Table B-6), as seen in Ni and Zn. 3.3.5 Copper and Silver  The highest zooplankton Cu and Ag contents were found in the larger size fractions.  On average, April had the highest Ag and Cu content, whereas June had the lowest (Table 4).  The metal contents of both metals significantly increased with increasing size-fraction (linear regression model, p < 0.05 for both metals; Table B-6).   34  3.3.6 Cadmium  Cadmium tended to behave differently from all other metals. The highest average Cd content was found in December and the lowest in August (Table 4). The larger zooplankton size fractions had the highest Cd content in December, April, and August, while in June, the highest content was found in the smallest size fraction. Cd content did not vary significantly with size fraction (linear regression model, p > 0.05; Table B-6).  3.3.7 Carbon-normalized metal content The ranking of the average zooplankton trace metal content normalized to carbon (Me:C; mol Me ∙ mol C-1; Table 5) was similar to that for the Me normalized to dry weight biomass (mg metal ∙ kg d.w. -1; Table 4), such that the average Me:C contents were Zn > Mn > Cu> Ba > Ni > Cr > Cd > Co > Ag (Table 5). The exception were Cd and Cr, for which the Cd content normalized to dry weight biomass was higher than that of Cr (Cd > Cr). Furthermore, the highest Me:C was observed for Zn in the August 500 µm size fraction (10.34 x 10-5 mol Zn ∙ mol C -1), while the lowest was measured for Cr in the August 4000 µm size fraction (below detection limit). The zooplankton Me:C values followed the same trends described for the Me content normalized to dry weight. For example, Ag:C and Cu:C were highest in April and lowest in June, while Cd:C was highest in December and lowest in August. The contents of Mn, Ba, Cr and Co, normalized to C, were highest in June and lowest in August. The Ni:C was highest in June and lowest in April. The Zn was highest in June and lowest in December (Table 5).  3.4 Zooplankton trace metal content for metals of primary interest  Our larger SoG research project focuses on elucidating the biogeochemical cycles of Ag, Cu and Cd in the SoG, thus the rest of the chapter will elaborate on the seasonal and size-fraction trends observed for Ag, Cu and Cd. Given that the same trends were found for the zooplankton trace metal content normalized to dry weight or to carbon biomass, we will only discuss the C-normalized trace metal contents here.   35  Table 5. Monthly averages of the zooplankton trace metal content, normalized to carbon (µmol Me ∙ mol C-1), throughout the time series at Station S4-1.5. Trace metal values were normalized to C by dividing metal content (µmol Me ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1).  Errors indicate one standard deviation from the mean. Ranges are shown in parenthesis below the averages.  December April June August Average Ag  0.09 ± 0.07 (0.03-0.2)  0.12 ± 0.15 (0.02-0.37)  0.05 ± 0.02 (0.03-0.08)  0.06 ± 0.05 (0.02-0.12)  0.08 ± 0.08 (0.02-0.37) Ba  1.8 ± 1.7 (0.45-4.8)  2.5 ± 1.7 (0.84-4.4)  7.7 ± 8.1 (2.8-21.9)  1.5 ± 1.2 (0.55-3.3)  3.4 ± 4.7 (0.45-21.9) Cd  0.92 ± 0.44 (0.3-1.4)  0.77 ± 0.37 (0.37-1.2)  0.87 ± 0.61 (0.33-1.9)  0.52 ± 0.34 (0.2-1)  0.77 ± 0.45 (0.2-1.9) Co  0.49 ± 0.39 (0.15-0.95)  0.57 ± 0.17 (0.3-0.75)  1.5 ± 1.3 (0.61-3.7)  0.38 ± 0.18 (0.24-0.63)  0.74 ± 0.79 (0.15-3.7) Cu  9.1 ± 6 (3.3-18.3)  15.1 ± 12.3 (5.3-35.7)  7 ± 2.8 (3.5-10.3)  7.6 ± 5.9 (2.7-14.7)  9.7 ± 7.7 (2.7-35.7) Cr  0.37 ± 0.34 (0.13-0.96)  0.88 ± 0.68 (0.11-1.7)  2.3 ± 2.7 (0.63-7.1)  0.19 ± 0.27 (-0.03-0.66)  0.94 ± 1.6 (-0.03-7.1) Ni  2.1 ± 1.9 (0.45-4.6)  1.9 ± 0.79 (0.85-2.8)  3.8 ± 2.5 (1.3-7.1)  2.1 ± 2.1 (0.27-5.5)  2.5 ± 1.9 (0.27-7.1) Mn  11.7 ± 9.1 (4.4-27.1)  39.6 ± 28.9 (7.9-75.7)  75.9 ± 81.1 (28.1-218)  6.3 ± 4.8 (2.9-14.4)  33.4 ± 48.8 (2.9-218) Zn  50.5 ± 28.8 (22.8-88.1)  56.4 ± 24.8 (27.2-77.4)  66.8 ± 33.9 (31.5-103)  55.1 ± 37.3 (16.5-103)  57.2 ± 29.6 (16.5-103)   3.4.1 The C-normalized Ag and Cu content of zooplankton Silver content measured throughout the times series ranged between 0.02 and 0.37 µmol Ag ∙ mol C-1, whereas Cu content ranged between 2.68 and 35.68 µmol Cu ∙ mol C-1 (Table 5). When all the data were combined, the zooplankton metal content was significantly and positively  36  correlated with the organism’s size (linear regression model; both Ag and Cu, p <0.01; Figure 9 and 10, respectively). However, the overall statistically-significant relationship in Ag and Cu  content and  zooplankton size fraction  was  due to the linear trend observed for the months of April and August (only for Cu; Figures 9 and 10; Table B-10).  Interestingly, even though Cu:C values were about 100 times higher than Ag:C values, the metals shared similar seasonal and zooplankton size-related trends. For example, for both metals, the Me:C were highest in the 4000 µm size fraction in April. Both metals had the lowest Me:C in the 1000 µm fraction, but while Ag:C was lowest in April, Cu:C was lowest in August (Figures 11 and 12; Table 5). A Two-Way ANOVA for zooplankton metal content between months sampled and size fractions measured was conducted for each metal separately (Table B-11). The results for Ag:C content were not found to be statistically significant from each other (p >0.05); these results were confirmed by the Tukey HSD test. However, the difference in Cu:C content between size fractions was found to be statistically significant at a 95% confidence level, and was driven by the difference in Cu:C content between the 1000 and 4000 µm fractions (Figure 10).  We observed some interesting and significant seasonal trends for the Ag and Cu content (normalized to C) of zooplankton. For example, the Ag:C content in zooplankton only increased significantly with size fraction in April (linear regression, p <0.05, R2 = 0.93; Figure 11). In contrast, the zooplankton Cu:C content increased significantly with size fraction, both in April and August ( p <0.05, R2= 0.85 and 0.82, respectively; Figure 12).   37   Figure 9. Carbon-normalized zooplankton Ag content as a function of zooplankton size-fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June () and August ( ). The slope for the linear regression model is 3.48 x10-5 µmol Ag ∙ mol C-1∙ µm-1. Error bars shown correspond to the analytical error in the zooplankton measurements. Silver values were normalized to C by dividing metal content (mol Ag ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1).      38   Figure 10. Carbon-normalized zooplankton Cu content as a function of zooplankton size-fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June () and August ( ). The slope for the linear regression model is 3.40x10-3 µmol Cu ∙ mol C-1∙ µm-1.  Error bars shown correspond to the analytical error in the zooplankton measurements. Copper values were normalized to C by dividing metal content (mol Cu ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1).      39    Figure 11. Carbon-normalized particulate and zooplankton Ag content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate values (green, left Y-axis) shown are the full-depth average measurements per month sampled. Zooplankton metal content (black, right Y-axis) is shown as a function of zooplankton size-fraction. Error bars shown correspond to 1 standard deviation in particles and the analytical error in the zooplankton measurements. Particulate Ag values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pAg is associated to biology and follows the Redfield ratio. Zooplankton Ag values in zooplankton were normalized to C by dividing metal content (mol Ag ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). The monthly linear regression model calculated exclusively for zooplankton Ag content are as follows: December slope = 1.15x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.67 and R2 = 0.07. In April, slope = 9.25x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.01 and R2 = 0.93. In June, slope = 1.01x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.19 and R2 = 0.49. In August, slope = 2.51x10-5 µmol Ag ∙ mol C-1∙ µm-1, p = 0.11 and R2 = 0.62. N/A: not available. No particulate samples were taken for August.  40    Figure 12. Carbon-normalized particulate and zooplankton Cu content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate values (green, left Y-axis) shown are the full-depth average measurements per month sampled. Zooplankton metal content (black, right Y-axis) is shown as a function of zooplankton size-fraction. Error bars shown correspond to 1 standard deviation in particles and the analytical error in the zooplankton measurements. Particulate Cu values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCu is associated to biology and follows the Redfield ratio. Zooplankton Cu values were normalized to C by dividing metal content (mol Cu ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). The monthly linear regression model calculated exclusively for zooplankton Cu content are as follows: December slope = 2.11x10-3 µmol Cu ∙ mol C-1∙ µm-1, p = 0.35 and R2 = 0.29. In April, slope = 7.42x10-3 µmol Cu ∙ mol C-1∙ µm-1, p = 0.03 and R2 = 0.85. In June, slope = 2.78 x10-4 µmol Cu ∙ mol C-1∙ µm-1, p = 0.81 and R2 = 0.02. In August, slope = 3.84 x10-3 µmol Cu ∙ mol C-1∙ µm-1, p = 0.03 and R2 = 0.82. N/A: not available. No particulate samples were taken for August. 41  3.4.2 The C-normalized Cd content of zooplankton Cd:C content in zooplankton ranged between 0.2-1.92 µmol Cd ∙ mol C-1 (Table 5).  The highest Cd:C was measured in the June 250 µm size fraction and the lowest in the August 500 µm fraction (Figures 13 and 14). Unlike with other metals, the zooplankton size fraction with the highest Cd:C varied seasonally. For example, in December, April and August the larger zooplankton size fractions had higher Cd content. However, in June the highest Cd content was measured in the smallest zooplankton size fraction (Figure 14).  Although C-normalized Cd content, in general, had a positive, increasing trend with increasing size fraction, this trend was not statistically significant for all the data analyzed together nor for month-specific analysis (linear regression model, p >0.05 for all analyses; Figures 13 and 14). These results were also supported with a two-Way ANOVA; no significant difference was found in zooplankton Cd:C content among months nor size fractions (p >0.05; Table B-11).  Figure 13. Carbon-normalized zooplankton Cd content as a function of zooplankton size-fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June () and August ( ).The slope for the linear regression model is 9.60x10-5 µmol Cd ∙ mol C-1∙ µm-1.  Error bars shown correspond to the analytical error in the zooplankton measurements. Cadmium values were normalized to C by dividing metal content (mol Cd ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1).  42   Figure 14. Carbon-normalized particulate and zooplankton Cd content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate values (green, left Y-axis) shown are the full-depth average measurements per month sampled. Zooplankton metal content (black, right Y-axis) is shown as a function of zooplankton size-fraction. Error bars shown correspond to 1 standard deviation in particles and the analytical error in the zooplankton measurements. Particulate Cd values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCd is associated to biology and follows the Redfield ratio. Zooplankton Cd values were normalized to C by dividing metal content (mol Cd ∙ kg d.w.-1) by carbon biomass in each size fraction (mol C ∙ kg d.w.-1). The monthly linear regression model calculated exclusively for zooplankton Cd content are as follows: December slope = 2.23x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.13 and R2 = 0.59. In April, slope = 2.11x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.06 and R2 = 0.75. In June, slope = -1.64x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.49 and R2 = 0.17. In August, slope = 1.14x10-4 µmol Cd ∙ mol C-1∙ µm-1, p = 0.39 and R2 = 0.25. N/A: not available. No particulate samples were taken for August.  43  3.5 Carbon-normalized bioaccumulation factors  3.5.1 Ag and Cu bioaccumulation factors The Ag and Cu bioaccumulation factors (the net uptake of a pollutant(s) by an organism from sources found in their marine environment; Neff, 2002; Spacie et al., 1995) followed a similar trend to the one seen in their carbon-normalized counterparts. As with Cu:C and Ag:C, both mean BAFs were highest in April and lowest in June (Table 6), although the average dissolved Cu and Ag concentrations were highest in April but lowest in August. Overall, Ag and Cu BAFs significantly increase with increasing zooplankton size fraction (p-values <0.01; Figures 15 and 16). However, this significant trend is driven by the statistically-significant linear regression observed in April for both Ag and Cu, and in August for only Cu (Table B-16).  The Two-Way ANOVA for Ag:C found that differences in BAFs between size fractions were statistically significant (p <0.05; Table B-17); however, results for the Tukey HSD test did not confirm this (p >0.05 for all analyses).  In contrast, Cu:C BAFs among size fractions were significantly different (p <0.05, Two-Way ANOVA), and the Tukey HSD test further showed that the Cu:C BAFs of the 500 and 1000 µm size fractions were statistically-different from the 4000 µm size fraction (p <0.05).  The Ag and Cu bioaccumulation factors as a function of zooplankton size-fraction exhibited seasonal trends. In April, the Ag and Cu BAFs, were positively, and strongly correlated with zooplankton size fraction (R2 = 0.93, and 0.85, respectively; Figures 17 and 18). In addition, in August Cu BAFs significantly increased with zooplankton size fraction (p <0.05, R2 = 0.82). Although dissolved Cu concentrations (3.2-9.7 nM) were about 1,000 times higher than dissolved Ag concentrations (3.4-11.9 pM), the Ag BAFs were 4.65 ± 0.38 times higher than Cu BAFs (Table 6). This finding was also supported by the difference in slope (by 5.24 times) for the Ag and Cu BAFs as a function of zooplankton size-fractions (Figure 15, slope 40 x 10-4 L ∙ mmol C-1∙ µm-1; Figure 16 and  7.06 x 10-4 L ∙ mmol C-1∙ µm-1, respectively). It is important to mention that this difference in slope for both Ag and Cu BAFs  was driven  by the results from April and August (Cu only).  44  Table 6. Carbon-normalized Ag, Cu and Cd bioaccumulation factors (BAFs; L ∙ mol C -1) for each zooplankton size fraction collected in 2017 and 2018 at the time series Station S4-1.5. Ranges in  parentheses for BAFs are for the minimum and maximum values in the size fractions) BAF= [Mezoop]/[Meseawater], where Mezoop is the zooplankton metal content in µmol Me ∙ mol C-1 and Meseawater is the dissolved metal concentration in µmol Me ∙ L-1.  Ranges and average dissolved Ag, Cu and Cd concentrations in the water column taken from Kuang (2019) are shown. For dissolved metal concentrations (mol Me ∙ L-1), it was assumed that 1 kg = 1L.  Errors indicate one standard deviation from the mean. Ranges are shown below averages in parenthesis.  December April June August Average Ag Bioaccumulation factors (L ∙ mol C-1) x103  9.13 ± 7.16 (3.66-21.16)   12.82 ± 15.32 (2.15-38.88)   6.76 ± 2.91 (3.41-11.24)   9.42 ± 7.08 (2.95-18.13)   9.53 ± 8.80 (2.15-38.88)  Dissolved Ag concentration (mol∙ L -1) x10-12  9.40 ± 1.52 (7.1-11.9)   9.53 ± 1.6 (6.7-11.9)   7.53 ± 1.7 (5.5-10.0)   6.84 ± 1.51 (3.4-9.1)   8.33 ± 1.93 (3.4-11.9)  Cu Bioaccumulation factors (L ∙ mol C-1) x103  1.95 ± 1.29 (0.72-3.93)   2.64 ± 2.15 (0.92-6.24)   1.37 ± 0.56 (0.68-2.03)   1.84 ± 1.71 (0.53-3.89)   1.95 ± 1.49 (0.53-6.24)  Dissolved Cu concentration (mol∙ L -1) x10-9  4.66 ± 1.37 (3.49-7.74)   5.72 ± 1.74 (3.84-8.49)   5.07 ± 2.14 (3.68-9.66)   3.78 ± 0.37 (3.21-4.57)   4.81 ± 1.66 (3.21-9.66)  Cd Bioaccumulation factors (L ∙ mol C-1) x103  1.44 ± 0.69 (0.47-2.27)   1.22 ± 0.59 (0.59-1.9)   1.57 ± 1.11 (0.59-3.48)   0.82 ± 0.54 (0.32-1.64)   1.26 ± 0.76 (0.32-3.48)  Dissolved Cd concentration (mol∙ L -1) x10-12  639.58 ± 44.43 (550-681)   628.83 ± 39.28 (546-666)   550.58 ± 148.39 (269-660)   634.50 ± 71.42 (463-717)   613.38 ± 92.34 (269-717)     45   Figure 15. Carbon-normalized zooplankton Ag bioaccumulation factors (BAFs) per size fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June () and August ( ). The slope for the linear regression model is 40 x 10-4 L ∙ mmol C-1∙ µm-1.  Error bars represent the analytical error propagation for both the zooplankton and dissolved metal measurements. BAFs were calculated as indicated in the legend of Table 6. The average dissolved Ag concentrations used to calculate the BAFs are: 9.40 ± 1.52 pM for December, 9.53 ± 1.60 pM for April, 7.53 ± 1.70 pM for June, and 6.84 ± 1.51 pM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L.         46   Figure 16. Carbon-normalized zooplankton Cu bioaccumulation factors (BAFs) per size fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June () and August ( ). The slope for the linear regression model is 7.06x10-4 L ∙ mmol C-1∙ µm-1. Error bars represent the analytical error propagation for both the zooplankton and dissolved metal measurements. BAFs were calculated as indicated in the legend of Table 6. The average dissolved Cu concentrations used to calculate the BAFs are: 4.66 ± 1.37 nM for December, 5.72 ± 1.74 nM for April, 5.07 ± 2.14 nM for June, and 3.78 ± 0.37 nM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L.  47   Figure 17. Carbon-normalized particulate and zooplankton Ag bioaccumulation factors (BAFs) content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate BAFs (green, left Y-axis) shown are calculated from the full-depth average particulate and dissolved Ag profiles measured per month sampled. Zooplankton metal content (black, right Y-axis) is shown per size-fraction. Error bars represent 1 standard deviation in particles and the analytical error propagation for both the zooplankton and dissolved metal measurements.  BAFs were calculated as indicated in the legend of Table 6. Particulate Ag values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pAg is associated to biology and follows the Redfield ratio.  Zooplankton Ag values were normalized to C by dividing metal content (mol Ag ∙ kg d.w. -1) by carbon biomass in each size fraction (mol C ∙ kg d.w. -1). The monthly linear regression model calculated exclusively for zooplankton Ag BAFs are as follows:  December slope =1.22x10-3 L ∙ mmol C-1∙ µm-1, p = 0.67 and R2 =0.07. In April, slope = 9.7x10-3 L ∙ mmol C-1∙ µm-1, p = 0.01 and R2 =0.93. In June, slope = 1.34 x10-3 L ∙ mmol C-1∙ µm-1, p = 0.19 and R2 = 0.49. In August, slope = 3.67x10-3 L ∙ mmol C-1∙ µm-1, p = 0.11 and R2 = 0.62. The average dissolved Ag concentrations used to calculate the BAFs are: 9.40 ± 1.52 pM for December, 9.53 ± 1.60 pM for April, 7.53 ± 1.70 pM for June, and 6.84 ± 1.51 pM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. N/A: not available. No particulate samples were taken for August.   48   Figure 18. Carbon-normalized particulate and zooplankton Cu bioaccumulation factors (BAFs) content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate BAFs (green, left Y-axis) shown are calculated from the full-depth average particulate and dissolved Cu profiles measured per month sampled. Zooplankton metal content (black, right Y-axis) is shown per size-fraction. Error bars represent 1 standard deviation in particles and the analytical error propagation for both the zooplankton and dissolved metal measurements.  BAFs were calculated as indicated in the legend of Table 6. Particulate Cu values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCu is associated to biology and follows the Redfield ratio.  Zooplankton Cu values were normalized to C by dividing metal content (mol Cu ∙ kg d.w. -1) by carbon biomass in each size fraction (mol C ∙ kg d.w. -1). The monthly linear regression model calculated exclusively for zooplankton Cu BAFs are as follows: December slope = 4.54x10-4 L ∙ mmol C-1∙ µm-1, p = 0.35 and R2 = 0.29. In April, slope = 1.30x10-3 L ∙ mmol C-1∙ µm-1, p = 0.03 and R2 =0.85. In June, slope =5.49 x10-5 L ∙ mmol C-1∙ µm-1, p = 0.81 and R2 = 0.02. In August, slope = 1.02 x10-3 L ∙ mmol C-1∙ µm-1, p = 0.03 and R2 =0.82. The average dissolved Cu concentrations used to calculate the BAFs are: 4.66 ± 1.37 nM for December, 5.72 ± 1.74 nM for April, 5.07 ± 2.14 nM for June, and 3.78 ± 0.37 nM for August. For dissolved metal concentrations, it was assumed that 1 kg = 1L.  N/A: not available. No particulate samples were taken for August.     49  3.5.2 Cd bioaccumulation factors Cadmium bioaccumulation factors were, on average, highest in June and lowest in August (Table 6). Based on a Two-Way ANOVA, the Cd BAFs calculated were not significantly different among months or size fractions (p >0.05). These results were confirmed by the Tukey HSD test (Figures 19 and 20). Interestingly, the slope of the linear regression models for the June data is negative; although not statistically-significant (Figure 20).   Figure 19. Carbon-normalized zooplankton Cd bioaccumulation factors (BAFs) per size fraction measured between 2017 and 2018 at the time series Station S4-1.5: December ( ), April ( ), June () and August ( ). The slope for the linear regression model is 1.42x10-4 L ∙ mmol C-1∙ µm-1.  Error bars represent the analytical error propagation for both the zooplankton and dissolved metal measurements. BAFs were calculated as indicated in the legend of Table 6. The average dissolved Cu concentrations used to calculate the BAFs are: 639.58 ± 44.43 pM  for December, 628.83  ±  39.28 pM  for April, 550.58  ±  148.39  pM  for June, and 634.50 ±  71.42  pM  or August. For dissolved metal concentrations, it was assumed that 1 kg = 1L.       50   Figure 20. Carbon-normalized particulate and zooplankton Cd bioaccumulation factors (BAFs) content measured between 2017 and 2018 at the time series Station S4-1.5. Particulate BAFs (green, left Y-axis) shown are calculated from the full-depth average particulate and dissolved Cd profiles measured per month sampled. Zooplankton metal content (black, right Y-axis) is shown per size-fraction. Error bars represent 1 standard deviation in particles and the analytical error propagation for both the zooplankton and dissolved metal measurements.  BAFs were calculated as indicated in the legend of Table 6.  Particulate Cd values were first normalized to measured particulate phosphorous and then normalized to carbon.  It was assumed that all pCd is associated to biology and follows the Redfield ratio.  Zooplankton Cd values were normalized to C by dividing metal content (mol Cd ∙ kg d.w. -1) by carbon biomass in each size fraction (mol C ∙ kg d.w. -1).The monthly linear regression model calculated exclusively for zooplankton Cd BAFs are as follows: December slope = 3.49x10-4 L ∙ mmol C-1∙ µm-1, p = 0.13 and R2 =0.59. In April, slope = 3.36x10-4 L ∙ mmol C-1∙ µm-1, p = 0.06 and R2 = 0.75. In June, slope = -2.97x10-4 L ∙ mmol C-1∙ µm-1, p = 0.49 and R2 = 0.17. In August, slope = 1.79x10-4 L ∙ mmol C-1∙ µm-1, p = 0.39 and R2 = 0.25. The average dissolved Cd concentrations used to calculate the BAFs are: 639.58 ± 44.43 pM  for December, 628.83  ±  39.28 pM  for April, 550.58  ±  148.39  pM  for June, and 634.50 ±  71.42  pM  or August. For dissolved metal concentrations, it was assumed that 1 kg = 1L. N/A: not available. No particulate samples were taken for August.  51  Chapter 4: Discussion 4.1 Zooplankton community composition 4.1.1 Zooplankton diversity components Of all the Canadian marine regions, the Canadian Pacific has the highest number of species of zooplankton recorded, (Archambault et al., 2010). And, although there are more than 240 holozooplankton species in the SoG our samples only included between 18 and 25% of them (Mackas et al., 2013). This could be due to the patchiness in zooplankton distribution (Mackas et al., 2013; Mackas & Tsuda, 1999), net avoidance, or the fact that even though a high numbers of species might live in the area, they are not necessarily present at the same time.  In fact, although the Northeast Pacific ocean is home to a wide variety of species, most of the organisms belong to a few groups (Archambault et al., 2010). This holds true for the SoG as well (Mackas et al., 2013). Species evenness is a measure between 0 and 1 of how well distributed the organisms found in an ecosystem are between the number of species present (Morris et al., 2014). Zero reflects an ecosystem where most organisms belong to a single species, while a value of 1 reflects an ecosystem where most organisms belong to different species.  The species evenness in our samples (< 0.1; Table 2) suggests that most of the organisms in the samples belong to a small number of species. Indeed, Mackas et al. (2013) noted that species evenness values were lower for individual samples compared to values calculated for annual and seasonal species evenness.  At a larger scale Archambault et al. (2010) noted that about 40% of zooplankton species in the Canadian Pacific were calanoid copepods. In comparison, in the Northern hemisphere, marine copepods are known to be most diverse at tropical latitudes around 20 °N (Rombouts et al., 2009). Then, at latitudes of over 40 °N copepod diversity diminishes quickly (Rombouts et al., 2009). Thus, copepod diversity is not as high in our region of interest as it is closer to the tropics..   4.1.2 Biomass and abundance Zooplankton biomass and abundance in temperate ecosystems such as the SoG have strong seasonal variability (Harrison et al., 1983; Mackas, 1992; Mackas et al., 2013).  Zooplankton  52  biomass in the SoG increases in spring, is highest midsummer, starts decreasing during late summer and is lowest in the winter (Mackas 1992; Mackas et al., 2013). This trend can be seen both in abundance (individuals ∙ m-3) and in biomass (g d.w. ∙ m-2) throughout our time series (Figures 2 and 3; Table 2). December had the lowest zooplankton abundance and biomass. Both parameters increased in April and reached their highest values during June before starting to decrease in August.  4.1.2.1 Zooplankton biomass  In a time series study spanning over 20 years researching the SoG zooplankton community composition, Mackas et al. (2013) found that the crustacean taxa have the highest biomass in the depth-integrated samples. More specifically, calanoid copepods, and juvenile and adult euphausiids are the zooplankton groups comprising on average more than half of total biomass sampled in their study. Other groups that also contributed significantly to dry weight biomass in the area were: amphipods and ostracods (both crustaceans), gelatinous zooplankton, and pteropods and polychaetes (shown in our results as part of the “other zooplankton” group; Mackas et al., 2013). On average the same groups mentioned in Mackas et al. (2013) contributed most of the total dry weight biomass in our samples and followed a similar quantitative order (Figure 3). Some differences were observed, including higher biomass for amphipod than euphausiids in our study. To our knowledge, this has not been previously reported. However, these differences can probably be explained by patchiness of zooplankton populations (especially euphausiids patchiness due to their mobility), small sample size as well as spatial variability (Mackas et al., 2013).  Dry weight biomass average estimates for the entire SoG range between a mean minimum of 4-5 g ∙ m-2 in winter and a maximum of 8-12 g ∙ m-2 in late summer (Mackas et al., 2013). There is great variability in our biomass estimates (6.19- 37.97 g d.w ∙ m-2, average 17.9 ± 14.09 g ∙ m-2; Table 2). However, they are within the range observed in Mackas et al. (2013) and the monthly total biomass ranges observed by Boldt et al. (2019) during 2018. In addition, they are also within the range for a sampling station close to ours (Station GEO1 (49.25 °N, 123.75 °W) sampled 90 times throughout 1996-2010: 1.21-74.7 g d.w ∙ m-2 in Mackas et al., 2013, supplementary material).   53  The seasonal cycle for zooplankton community composition in the SoG first described in Harrison et al. (1983) and later updated by Mackas et al. (2013) shows that during winter, the most dominant zooplankton biomass taxa are euphausiids (early winter), and large (3-5 mm) and medium  (1-3 mm) copepods. During spring, medium and large copepods remain dominant. This trend continues during summer, with an increase in euphausiids, ctenophores and medusae. Finally in autumn, the groups that were dominant during spring and summer start to decrease (Mackas et al., 2013). The biomass distribution in our samples was similar to the observations described above (Figure 3b). As observed in Mackas et al. (2013) medium-sized calanoid copepods (1-3 mm) comprised the majority of the biomass measured per month. In addition, large and juvenile euphausiids comprised less than 4% of (in comparison to the ~61% reported for November-December in Mackas et al., 2013) the total winter biomass and there were almost no larval fish observed throughout our time series (Figure 3b). Amphipod and euphausiid groups did increase from spring to summer. Both the euphausiids and the amphipod groups, together with the copepod group, became the most dominant groups in our samples during June and August, which agrees with the observations by Boldt et al. (2019) for the central SoG. The shifts in zooplankton composition we observed, from large copepods to medium-sized copepods, will be discussed below.  4.1.2.2 Abundance and assemblage  The crustacean taxa dominated our samples. From these, the copepod group was the most abundant throughout the time series, always comprising more than half of the individuals in a sample (Figure 2). Indeed, copepods are known to be abundant in the region (e.g., Harrison et al., 1983; Harrison, Whitney, Tsuda, Saito, and Tadokoro, 2004, Boldt et al. 2019, and Archambault et al., 2010). Copepod abundance in our samples comprised a higher percentage of the total zooplankton abundance than the percent of copepod biomass in the total zooplankton biomass (Figures 2 and 3).  Tommasi et al. (2013) observed a similar abundance pattern in Rivers Inlet, a fjord located on the central coast of British Columbia.  There, they found a stark difference between the winter- 54  spring and spring-summer zooplankton communities. With the exception of copepods, the abundance and biomass of most zooplankton taxa in our samples was low during winter. During spring and summer, euphausiids, cladocera, amphipods, polychaetes and gelatinous zooplankton increased in abundance.  From the copepods identified, the calanoid copepod Metridia pacifica was consistently one of the dominant species sampled throughout the time series (Tables A-2 to A-5). Unlike in previous literature (e.g., Mackas and Tsuda, 1999, Mackas et al., 2013, El-Sabaawi, Sastri, Dower, and Mazumder, 2010, etc.), the calanoid copepod Neocalanus plumchrus had low abundance and biomass in our samples. The lower abundance of N. plumchrus in the SoG has also been  observed  by Boldt et al. (2019) and Mahara et al. (2019).  Metridia pacifica is an omnivorous calanoid copepod that inhabits the mid to subarctic Pacific Ocean (Mackas et al., 2013). Unlike the 1-year life cycled N. plumchrus, M. pacifica feeds continuously throughout the year and is known to perform diel vertical migration (El-Sabaawi et al., 2010; Batchelder, 1985). Its life cycle is between 3-4 months, and thus several generations can spawn throughout the year (El-Sabaawi et al., 2010; Batchelder, 1985).  For the past 50 years, Neocalanus plumchrus has been the most dominant copepod species in the SoG. N. plumchrus is a large-sized, primarily herbivorous calanoid copepod usually found in the Subarctic Pacific (Mackas et al., 2013; El-Sabaawi, Dower, Kainz, & Mazumder, 2009). Adults overwinter and reproduce at depth. In early spring, juveniles (copepodite stage I) appear in the surface layer and they feed and mature at shallow depths. Finally, during early summer, copepodite stage V N. plumchrus descend to depths of about 350 m and below to become adults and continue their life cycle (Fulton 1973; Harrison et al., 1983).  The decline and collapse of the N. plumchrus population in the SoG in the early 2000s has been hypothesized to be caused by a shift in their diet from flagellates to diatoms (El-Sabaawi, Dower, Kainz, & Mazumder, 2009a). The increase in the abundance of diatoms, which are less nutritious than flagellates, has impacted younger copepodite stages of N. plumchrus, leading to lower survival and a decrease in the N. plumchrus population (El-Sabaawi et al., 2009a).  An additional hypothesis is the timing mismatch between the spring phytoplankton bloom and the development of N. plumchrus (Mackas et al., 2013). This mismatch results from the migration of N. plumchrus  55  to the surface before the phytoplankton bloom starts. In addition, these copepods have also started overwintering earlier in the year. These issues can affect the growth and survival of N. plumchrus (Mackas et al., 2013).  Lower N. plumchrus populations could have a potential effect on higher trophic levels.  If the same organisms preying on N. plumchrus shift or complement their diet with M. pacifica, they will be decreasing their caloric intake given the difference in energetic value of lipid storage in N. plumchrus and M. pacifica. Albers et al. (1996) studied the composition of different types of lipid storage in polar copepods and found that on average, wax esters (WE) have a higher calorific value than tryacylglycerols (TAG). Wax esters are used as a long-term energy reserve while TAG are a short-term energy resource (Lee, Hagen, & Kattner, 2006). Kotani (2006) observed that copepods with an oil sac, such as N. plumchrus, stored lipids in it as WE, while those that did not perform diapause, such as M. pacifica, stored lipids as TAG. So, most of the lipid reserves that N. plumchrus produces and accumulates while feeding during the spring bloom are stored as WE. N. plumchrus will not only use the energy store in its oil sac while fasting, but will also use it to reproduce (Lee et al., 2006).  N. plumchrus is an important energy source for the larval stages of Pacific hake and Pacific herring, and for juvenile salmon exiting the Strait (Beamish & MacFarlane, 1999; Mackas et al., 2013). Thus, a decrease in abundance and biomass of N. plumchrus could potentially lead to a diet with lower nutritional value. This could then negatively impact the survival of its predator population (Bornhold, 2000). The low N. plumchrus biomass in our samples could be potentially due to a combination of the uneven distribution (‘patchiness’) of the species in the SoG or net avoidance during our daytime sampling. Additionally, according to Harrison et al. (1983) there is relatively low abundance of N. plumchrus in the region of the Strait south of the Fraser River due to shallow (<200 m) depths. Given that our southern SoG station depth is ~375 m, a shallow depth is unlikely to explain the low abundance of N. plumchrus in our samples.   56  4.2 Zooplankton trophic position Trophic position is usually represented in a numerical scale ranging from 1 to 5 where each integer represents a specific feeding behaviour. In theory, a trophic position of 1 represents primary producers, 2 represents herbivores, 3 are carnivores, and 4 and 5 are tertiary predators, with the top predator in the chain having the highest number below or equal to 5 (Chikaraishi et al., 2010).  Omnivory is often described as any value higher than a trophic position equal to 2 and multichannel omnivory –omnivores that eat organisms from various trophic levels– is common (Chikaraishi et al., 2010). In practice, non-integer values are more-than-often recorded (e.g., Chikaraishi et al., 2014, Lorrain et al., 2015, and Hannides, Popp, Landry, and Graham, 2009, etc.).  Using Chikaraishi et al.’s (2010) equation, we determined that the trophic position in our zooplankton size-fractionated samples ranged between 1.89 and 2.94 (Table 3; Figure 4a). Certain studies (e.g., Chikaraishi et al., 2014 and Nielsen et al., 2015) have found that the widely-used trophic discrimination factor (TDF) of 7.6 ‰ (the difference in 15N between glutamic acid and phenylalanine between trophic levels) is adequate for marine organisms with a trophic position equal to or below 3. However, Décima et al. (2013) suggest that it can underestimate the trophic position of certain organisms, including zooplankton, especially if they are protistan consumers.  We calculated trophic positions by subtracting δ15Nphe to δ15Nglu values in our size-fractionated zooplankton. However, the SoG mesozooplankton are ultimately supported by mainly diatoms in the spring and mainly mixed flagellates in the summer (Costalago et al., 2020). Therefore, protistan consumption is expected. However, the trophic position of the zooplankton in our samples may have been underestimated given that our trophic position calculations compared δ15Nglu relative to δ15Nphe values. This underestimation could mask the trophic contribution of these smaller protistans (i.e., flagellates) to the food web. Protists are known to have fast turnover rates, and thus enriched 15N values (Hannides et al., 2009). In addition, both Hannides et al. (2009) and Décima et al. (2013) have observed the largest difference in nitrogen enrichment between these alanine (trophic amino acid) and phenylalanine (source amino acid),  57  and recommend using δ15Nala (alanine) relative to δ15Nphe when calculating trophic position for zooplankton feeding on protistans.  As mentioned in the Methods section, trophic position values were measured on homogeneous, size-fractionated samples. Thus, the trophic position for each sample represents the average trophic position of the organisms present in those samples. From the data gathered from all size fractions measured throughout the seasons, we observe that trophic position increases with increasing zooplankton size fraction (Table 3; Figure 4a). This observation agrees with the literature reviewed by Boyce et al. (2015): in different aquatic ecosystems, it has been observed that body size influences feeding interactions between a potential predator and prey. In our time series, the trophic position values in December were significantly higher than those for the other months (Table 3 and Appendix B-2). These observations could be due to copepod’s opportunistic feeding behavior during phytoplankton bloom conditions. As previously mentioned, medium-sized copepods, such as M. pacifica, comprised the majority of the biomass estimates in the samples. During spring when primary productivity in the SoG is highest, omnivorous zooplankton, such as M. pacifica have a more herbivorous diet (El-Sabaawi et al., 2010; Tommasi et al., 2013). In contrast, when primary production is low they tend to be more carnivorous (El-Sabaawi et al., 2010). Indeed, this species has been known to be carnivorous during winter and herbivorous during summer (Tommasi et al., 2013). In our data, December was the month with the clearest increase in trophic position with increasing size fraction (Figure 4b). This was probably due to lower phytoplankton abundance, and thus an increase in predation in larger size fractions. Trophic positions in December could better reflect the intrinsic trophic position of SoG zooplankton, when opportunistic feeding on phytoplankton might not be an option.   Previous studies such as El-Sabaawi et al. (2009b) measured the trophic position of four SoG copepod species: carnivorous Euchaeta elongata (trophic position 1.2-3.4), omnivorous Neocalanus plumchrus and Calanus marshallae (trophic position 1.51-2.57), and  herbivorous Eucalanus bungii (trophic position 1.22-1.52), and found that even with changes in copepod diets (e.g., more herbivorous during spring, more carnivorous during the rest of the year), due to the temporal and spatial variability of phytoplankton, their trophic position did not vary  58  considerably or consistently. This was also observed by El-Sabaawi et al. (2010) also in the SoG, and in the lab by Aberle and Malzahn (2007). In the Celtic Sea (~48-51°N), Giering et al. (2019) found larger variability in trophic position in small and large mesozooplankton in samples close to the coast and near the shelf edge. However, they argue that this variability is probably due to diet based on geography (some sites had more herbivores and other relied more heavily on the microbial loop) and seasonality. Given the large amount of zooplankton biomass required to measure all the parameters determined in this thesis, trophic position in our samples were measured by size fractions and not by species. Thus, it is difficult to identify if the changes we observed are due to changes in community composition or in dietary preference.  However, it is important to mention that while the four studies mentioned above used bulk δ15N to estimate trophic position, we used compound-specific δ15N (NCSIA-AA). Published literature has shown that bulk δ15N estimates exhibit greater variability than compound-specific δ15N estimates (e.g., Bowes and Thorp, 2015, Hannides et al., 2009, and Blanke et al., 2016). Although trophic position estimates using bulk δ15N tissue analysis are cheaper and faster, trophic position estimates using amino acid-specific are more accurate and do not require added samples to identify the food sources of the organisms studied (Bowes & Thorp, 2015).  For example, some of the trophic position values for the four different SoG copepods in El-Sabaawi et al. (2009b) are most likely underestimating the real trophic position of these organisms. Trophic position values below 2 would suggest that these copepods are either autotrophs or mixotrophs. These underestimations are likely due to the usage of particulate organic matter (POM) as a baseline for stable isotope signatures. The POM used by El-Sabaawi et al. (2009b) was sampled at the chlorophyll maximum, and an attempt to remove both detritus and mesozooplankton was undertaken. However, phytoplankton 15N values can be highly variable due to their fast growth rates and their rapid physiological response to changing environmental conditions (e.g., phytoplankton grown in nutrient-limiting conditions have a higher δ15N; Aberle & Malzahn, 2007). In addition, El-Sabaawi et al. (2009a) speculated that for one of the years sampled, phytoplankton could have been nutrient limited, thus potentially increasing the δ15N values for the POM sampled. Therefore, the δ15N POM baseline used by El- 59  Sabaawi et al. (2009b) for their trophic position calculations could have been higher than anticipated, thus leading to an underestimation for the zooplankton trophic position estimates. Given the variability of trophic positions we observed between months (Figure 4b) and the aforementioned trend of increasing trophic position with increasing size fraction (Figure 4a), we have analyzed zooplankton metal content and bioaccumulation, trophic transfer and trophic magnification factors against zooplankton size fraction.   4.3 Metal uptake Zooplankton metal content varies significantly among taxa (e.g., Eisler, 2010), and is affected by biological factors (e.g., intake pathway and rates, efflux rates, and age/size), chemical factors (e.g., dissolved and particulate metal concentrations), and physical factors (e.g., temperature and salinity) (e.g., Wang, 2002, Cheung and Wang, 2008, Hamanaka and Tsujita, 1981, Croteau, Luoma, and Stewart, 2005, and Eisler, 2010).  Given that our samples mostly consisted of crustaceans, this discussion will focus on this zooplankton group.  4.3.1 Zooplankton metal content 4.3.1.1 Trace metals in size-fractionated SoG zooplankton compared to those from other studies The trace elements we measured in zooplankton include Mn, Fe, Cr, Ba, Zn, Ni, Co, Cu, Ag and Cd. Of these, five (Cu, Fe, Zn, Co, and Mn) are considered essential for zooplankton (Table 1). Metalloproteins have important biological functions in crustaceans, including oxygen transport (Cu; in hemocyanin), respiratory electron transport (Fe-, and Cu-containing cytochromes), gene regulation, translation and deoxyribonucleic acid (DNA) repair (Zn fingers and DNA repair proteins), vitamin B12 (Co-cobalamine), as well as calcification (Zn-containing carbonic anhydrase). Mn is also an important trace element acting as an activator of acetyl-CoA carboxylase (in the fatty acid formation in the endoplasmic reticulum), pyruvate carboxylase (in the formation oxaloacetate in the mitochondria), glycylglycine dipeptidase (in the degradation of denatured intracellular proteins), as well as a co-factor of Mn-superoxide dismutase (facilitating the production of dioxygen in the mitochondria; Cotzias, 1958; da Silva & Williams, 1991).  60  Chromium has been shown to be involved in carbohydrate, fat and lipid metabolism in animals, such as rats and humans (Cr in chromodulin, reviewed by Pechova and Pavlata, 2007), but whether Cr is essential for zooplankton remains to be determined. Similarly, even though Ni has been suggested to be involved in nitrogen metabolism in some marine invertebrates (Ni in urease; Hausinger, 1994), whether it is a required trace element for marine copepods is unknown. Non-essential, and often, toxic trace metals for zooplankton include Ba, Cd, and Ag. However, the content of trace metals in zooplankton is not only determined by their physiological functions, but also by the concentrations of trace metals in their environment (i.e., dissolved and particulate trace elements).  The concentrations of trace metals in the zooplankton we collected from SoG were not elevated, in accordance with the non-toxic levels of dissolved and particulate Ag, Cu, and Cd we measured at the same sampling station. In general, Zn and Mn contents (averaging 133.5 ± 74.8 and 62 ± 86.7 mg Me ∙ kg d.w. -1, respectively; Table 4) were the highest and Co and Ag contents were the lowest (0.7 ± 0.7 and 0.3 ± 0.3 mg Me ∙ kg d.w. -1; respectively; Table 4). Indeed, the concentrations of trace elements in the SoG zooplankton were comparable to those published in an extensive, global compilation of trace metals in crustaceans by Eisler (2010; Table 7).  The exceptions were Cr, which was found at lower levels in SoG zooplankton, and Mn, Ba, Co and Ag, which were higher than those in other published studies, including a study near a sewage outfall in Taiwan (Fang, Hwang, Hsiao, & Chen, 2006; Table 7).   The low Cr content in zooplankton may be explained by the biogeochemical cycle of Cr in the SoG. Chromium concentrations in the SoG are of 1.5-1.8 nM (Davidson, pers. comm.) and are strongly affected by its oxidation state. In oxygenated water, Cr(VI) is soluble, constituting >70% of total Cr, and present as chromate (CrO4−2) and bichromate (HCrO4−), the thermodynamically stable oxyanions (e.g., Achterberg and van den Berg, 1997, and Sirinawin, Turner, and Westerlund, 2000).  Riverine input is the main source of Cr to the ocean, mostly in the form of Cr(VI) (Cranston & Murray, 1980, and Jeandel & Minster, 1987 cited in Scheiderich, Amini, Holmden, & Francois., 2015). However, in oxygen minimum zones (e.g., as that in the NE Pacific Ocean), as well as in productive, oxygenated surface waters (e.g., as in the SoG), reductive removal decreases the concentrations of dissolved Cr (Scheiderich et al., 2015). This  61  process is mediated by the reduction of Cr(VI) to Cr(III)―by electron donors, such as organic matter or photo-produced Fe(II) in surface waters―and the subsequent scavenging of Cr(III) onto sinking particles in seawater. Marine crustaceans are very sensitive to Cr(VI) (Migliore & de Nicola Giudici, 1990), and for example, barnacles bioaccumulate less Cr(III) than other higher valence Cr ions (Van Weerelt, Pfeiffer, & Fiszman,1984 cited in Eisler, 2010).  Thus, the low Cr content in SoG zooplankton might be linked to low Cr(III) in the productive waters of SoG waters.  The Mn content in SoG zooplankton was higher than those reported in other studies (Table 7), and may reflect the high dissolved Mn concentrations in SoG (~10-200 nM; de Mora, 1983) relative to the open ocean (0.08–5 nM; reviewed by Bruland and Lohan, 2003), as shown for other temperate estuaries (e.g., Aller and Benninger, 1981, and Roitz, Flegal, and Bruland, 2002). Important sources of dissolved Mn to the SoG include the Fraser River in surface waters (10-150 nM; de Mora, 1983) and reductive remobilization of Mn from the sediments at depth, whereby redox cycling in and above organic rich sediments increases dissolved and particulate Mn near-bottom waters (up to ~ 180 nM of pMn and dMn; up to 20 m above the sediments; de Mora, 1983). The Mn redox cycling in these sediments involves the reduction of Mn(IV) oxides to soluble Mn(II) within the sediment, the diffusion of Mn(II) into the overlaying water, the oxidation of Mn(II) to Mn(IV) once in contact with oxygenated seawater, and the precipitation of Mn(IV) oxides  (Lam & Bishop, 2008; Lam, Ohnemus, & Auro, 2015; Ohnemus & Lam, 2015; Hansel, 2017; Lam et al., 2018). Therefore, concentrations of dissolved and particulate Mn are enriched in the surface and the bottom waters of SoG, where zooplankton are likely to spend the night and day, respectively. Though crustaceans primarily internalize Mn from dietary exposure (Eisler, 2010), SoG zooplankton likely acquire high Mn directly from seawater (as dMn) and/or its prey (as pMn). Furthermore, the behaviour of Co often shows similarities to that of Mn (Kremling & Hydes, 1988; Tappin, Millward, Statham, Burton, & Morris, 1995), possibly because of the oxidation of Co(II) to Co(III) by Mn(IV) (Hem, 1980). Thus, benthic inputs of Co have also been observed in highly productive coastal waters, such as those in the North Sea (Tappin et al., 1995), and likely in SoG, and might be responsible for high Co in SoG zooplankton.  62  Table 7. Average plankton metal content from various studies (mg Me ∙ kg d.w-1). Size fractions sampled and averaged are noted. Measurement ranges are shown in parentheses. Errors represent one standard deviation from the mean. Highest values have been bolded. Zn Mn Ba Cu Ni Cd Cr Co Ag Studies 133 ± 74.8 (30.3-278) 62 ± 86.7 (4.5-381)  15.6  ± 20.5 (2.6-96) 20 ± 14.2 (6.7-68) 5.3 ± 4.3 (0.4-13.9) 2.9 ± 1.5 (0.9-6.9) 1.7 ± 2.6 (0*-11.8) 0.7 ± 0.7 (0.1-3.3) 0.3 ± 0.3 (0.1-1.2) This study NE Pacific (central Strait of Georgia). 116 ± 33 (61-160) n.d. n.d. n.d. n.d. 6.6 ± 4.2 (2.4-14.6) n.d. n.d. n.d. Hamanaka and Tsujita,(1981) Subarctic NW Pacific Mixed copepod samples. (50-260) (2.9-7) (14-97) (6.2-58.4) (5-13) (1.9-3.5) n.d. n.d. (0.1-0.3) Martin and Knauer (1973) East Pacific Ocean (Hawaii-Monterey Bay transect). Zooplankton samples (>360 µm) 30-100 m depth sampled. Possibly-contaminated samples were not considered. 80.7 ± 22.7 (48-119) 5.6 ± 0.8 (5-7) n.d. 20.3 ± 13.1 (10-47) 4 ± 2.1 (1-8) n.d. 11.2 ± 9.5 (3-29) 1.2 ± 0.5 (0.5-1.8) n.d. Ho et al. (2007) South China Sea. >150 µm size fraction sampled at various depths (<60 m) at 3 different stations. (13-119) (2.7-39.1) (2.2-30.3) (2.7-35.9) (3.8-38.7) (0.2-1.5) (5-81) (0.2-1) (0.03-0.2) Fang et al. (2014) Coastal waters in NE Taiwan. >330 µm size fraction sampled Ranges shown for 4 copepod species. 137 ± 47.9 (41-218) n.d. n.d. 10.5 ± 5.6 (3.3-25) 3.4 ± 1.6 (1-6.9) 0.7 ± 0.3 (0.2-1.2) n.d. 0.5 ± 0.3 (0.2-1.1) 0.1 ± 0.03 (0.1-0.2) Chouvelon et al. (2019). NW Mediterranean Sea. Size fractions averaged: 200-500, 500-1000, 1000-2000, >2000 µm. (323-474) n.d. n.d. (8.4-21.5) (2.8-5) (0.9-1.9) n.d. n.d. n.d. Zauke et al. (1996) North Sea and German Bight Mixed copepods (>300 µm) 30 m to surface.  63  Table 7. Continued Zn Mn Ba Cu Ni Cd Cr Co Ag Studies 1,270 4.4 17 107 10.7 6.3 16.5 2.14 n.d. Eisler (2010). Maximum content in copepods reviewed.  (132-38,991) (5.5-80.8) n.d. (14-160) n.d. (0.23-1.8) (16.5-195) n.d. n.d. Fang et al. (2006). NW coastal Taiwan near sewage outfall. 3 different copepod species sampled. (>333 µm) *Below detection limit, set to 0.    n.d., not determined.            64  In general, the content of Ba in SoG zooplankton was < 30 mg ∙ kg d.w. -1 (Figures 5, 6, and 8), similar to other studies (Table 7). However, during June, the Ba content of the smallest zooplankton (250-500 µm) was relatively high (~ 100 mg ∙ kg d.w. -1; Figure 7). The high Ba in the small zooplankton in June might be due to high concentrations of barite released from the remineralization of the spring phytoplankton blooms (Stecher & Kogut, 1999; Ganeshram, Francois, Commeau, & Brown-Leger, 2003). Indeed, in June we measured the highest concentration of pBa at depth (2.1 ± 0.04 nM pBa at 300 m in June, vs. 1.02 ± 0.04 and 1.52 ± 0.05 nM at 287 and 145 m, respectively, in April, vs. 0.2 ± 0.48  and 1.0 ± 0.5  nM at 282 and 187 m, respectively, in December; Appendix A-10). It is feasible that these enriched-pBa decaying particles are consumed by these small zooplankton, resulting in their high pBa content. Silver is a trace metal exhibiting nutrient-type like profiles (i.e., low concentrations in surface waters due to biogenic and particle scavenging, and increasing concentrations at depth due to particle dissolution; Bruland  & Lohan, 2003)  in the ocean and significant inter-basin fractionation, with the highest concentrations in the North Pacific (e.g., ranging ~5-65 pM; Zhang, Obata, & Nozaki, 2004). In the SoG, important sources of Ag include the Fraser River and Pacific oceanic water. In addition, dissolved Ag concentrations vary seasonally and spatially (ranging from 3.4 to 12.8 pM in the southern SoG; Kuang, 2019) and are always higher than pAg (~ 10 times higher, except in June: 7X higher; Kuang, 2019; Table 8; Figure 21). The incorporation of dissolved Ag into sinking particles and subsequent desorption in deep waters influences its biogeochemical cycling in the SoG (Kuang, 2019), and might also affect zooplankton bioaccumulation (see below). 4.3.1.2 The relative bioactive trace metal concentrations in SoG zooplankton The average, relative concentrations of trace metals in SoG zooplankton were Zn≥ Mn≥ Ba> Cu> Ni> Cd≥ Cr> Co≥ Ag (Tables 4 and 7; Figures 5 to 8). This decreasing trend in trace metal content was practically identical to that found in zooplankton from the coast of the Gulf of Lion (Northwestern Mediterranean Sea, 43°N), for which Zn> Cu> Ni> Cd> Co> Ag were measured (Chouvelon et al., 2019; Table 7), and from the NE coast of Taiwan (~25°N), for which Zn > Cr >Mn≈ Ba≈Cu≈Ni > Cd≈Co >Ag (Fang et al., 2014; Table7).  65  Table 8. Average dissolved and particulate Ag, Cu and Cd from Station S4-1.5. Both dissolved and particulate metal values were averaged from all data available. Total metal content was calculated by averaging all depths where particulate and dissolved metal data are available). Measurement ranges are shown in parentheses and median values are italicized. Errors represent one standard deviation from the mean. LC50 is the concentration of a pollutant at which acute toxicity is experienced by 50% of the organisms (Hook & Fisher, 2001b).     Ag Cu Cd  [dMe]  Average 8.3 ± 1.9 pM (3.4-11.9) 8.3 4.8 ± 1.7 nM (3.2-9.67) 4.1 613.4 ± 92.3 pM (269-717) 640.5 December 9.4  ± 1.5 pM (7.1-11.9) 9.5 4.7 ± 1.4 nM (3.5-7.7) 4.1 639.6 ± 44.4 pM (550-681) 657.5 April 9.5 ± 1.6 pM (6.7-11.9) 9.5 5.7 ± 1.7 nM (3.8-8.5) 5 628.8 ± 39.3 pM (546-666) 645 June 7.5 ± 1.7 pM (5.5-10) 7.6 5.1 ± 2.1 nM (3.7-9.7) 4.1 550.6 ± 148.4 pM (269-660) 632 August  6.8 ± 1.5 pM (3.4-9.1) 7.2 3.8 ± 0.4 nM (3.2-4.6) 3.9 634.5 ± 71.4 pM (463-717) 638.5 [pMe] Average 0.9 ± 0.6 pM (0.2-2.2) 0.6 0.3 ± 0.2 nM (0.1-1) 0.2 24 ± 9.8 pM (5.3-44.2) 23.7 December 0.8 ± 0.6 pM (0.2-2.2) 0.6 0.2 ± 0.1 nM (0.1-0.5) 0.2 21.6 ± 8.2 pM (9.3-34.7) 21.6 April 0.8 ± 0.6 pM (0.2-1.9) 0.6 0.4 ± 0.2 nM (0.2-1) 0.2 23 ± 11.6 pM (5.3-40.3) 19.1 June 1.1 ±  0.6 pM (0.4-2.2) 1.1 0.3 ± 0.2 nM (0.2-0.6) 0.4 28.6 ± 8.6 pM (18-44.2) 19.4  [total Me]  Average 9.8 ± 1.7 pM  (6.8-14.1) 9.8 5.4 ± 1.9 nM (3.7-10.3) 4.5 638.6 ± 86.1 pM  (290.7-705.9) 667.4 December 10.2 ± 1.8 pM (7.8-14.1) 10.3 4.9 ± 1.5 nM  (3.7-8.2) 4.2 661.1 ± 49.2 pM  (559.3-705.9) 683.2 April 10.3 ± 1.5 pM (8.5-12.5) 9.8 6.2 ± 1.9 nM  (4-9) 5.4 651.9 ± 45 pM  (561.8-699.3) 664.3 June 8.7 ± 1.5 pM (6.8-10.4) 9 5.1 ± 2.1 nM  (3.8-10.3) 4.3 590.8 ± 141.1 pM  (290.7-697.2) 660    66  Table 8. Continued.    Ag Cu Cd  BC Water Quality Guidelines [total Me]   Short-term Acute     27.8 nM① 47 nM① 1.1 nM④  Long-term Chronic     13.9 nM① < 31.5 nM① n.a. LC50  Acartia tonsa 400 nM②   dissolved exposure 1.7 µM③  dissolved exposure 1 µM②  dissolved exposure n.d. 2.1 µM③  dissolved and dietary exposure n.d. n.a., not available. ①Government of British Columbia- Ministry of Environment & Climate Change Strategy, 2019. ②96-h LC50 Hook and Fisher (2001b). ③48-h LC50 at 30 ppt, Pinho et al. (2007). ④Sinclair et al. (2015).                     67  In general, the essential trace metal content of zooplankton was highest for Fe, followed by Zn, and then Cu and/or Mn, and finally Co (Tables 9 and 10). This trend for essential metal contents in zooplankton is reasonable, given the physiological pathways involving Fe, Zn, Cu, Mn, and Co in crustaceans (see previous section). Indeed, zooplankton follow the same general trends of C-normalized essential metal ratios found in archaea, bacterioplankton and natural phytoplankton assemblages (Table 10). These microbes, protists and crustaceans have varying content of one metal or another (e.g., differences in Mn content in eukaryotic phytoplankton vs bacteria and zooplankton), but the similarity in metal content rankings (i.e., which one is highest and lowest, etc.) among life forms is to be expected, given the role of these trace metals in major physiological pathways (e.g., catalysis of multiple reactions, including redox reactions, electron transfer, activation of oxygen, as well as water splitting, etc.; Blindauer, 2012). The levels of non-essential Ni and Cd in crustaceans are about one order of magnitude lower than that of Fe (Table 9), and vary among amphipods, copepods and euphausiids (Appendix A-7). For example, copepods and euphausiids seem to have lower Cd and higher Ni, relative to amphipods (with higher Cd relative to Ni; Appendix A-7), as observed in other studies (e.g., Ritterhoff and Zauke, 1997, Honda, Yamamoto, and Tatsukawa, 1987, and Hennig, Eagle, McQuaid, and Rickett, 1985). Even though diatoms have been shown to have a Cd-carbonic anhydrase that can readily exchange Cd and Zn at its active site (Xu, Feng, Jeffrey, Shi, & Morel, 2008), animals only possess the Zn-containing carbonic anhydrase (Lionetto, Caricato, Giordano, & Schettino, 2016). Thus, the Cd content in zooplankton is likely associated with Cd adsorbed on their surface, or that of the phytoplankton, detritus, or any other organic matter that they ingest.  68   Figure 21. Particulate and dissolved Ag depth profiles measured during December 2017, April 2018 and June 2018 at Station S4-1.5 (a-c). Particulate and dissolved monthly averages are shown in black and red lines, respectively. Profile of Ag associated with lithogenic and non-lithogenic sources and average non-lithogenic and lithogenic ratios shown (d-f). Non-lithogenic monthly averages shown by the cyan line. Profile of Ag associated with non-lithogenic sources normalized to biogenic P (g-i). Lithogenic Ag was calculated by using the Ag:Al molar crustal ratio from Taylor and McLennan (1995; 2x10-7). Non-lithogenic sources were calculated by subtracting lithogenic Ag from the total pAg measured. Shaded area shows range of ratios in particles from studies published by Martin and Knauer (1973), as well as Martin et al. (1983). Error bars represent one standard deviation.  Ag, Al and P were measured in all the samples.   69               Table 9. Median plankton metal content (mg Me ∙ kg d.w-1) measured throughout the time series at Station S4-1.5.  Measurement ranges are shown in parentheses.  Fe Zn Mn Ba Cu Ni Cd Cr Co Ag  494  (62-8447)  122  (30.3-278) 28.9  (4.5-381) 8.9  (2.6-96) 16.1  (6.7-68) 4.1  (0.4-13.9) 2.5  (0.9-6.9) 0.9  (0*-11.8) 0.5  (0.1-3.3) 0.2  (0.1-1.2)               70    Table 10. Plankton metal content normalized to carbon. All information except zooplankton metal content is from Posacka (2017). Group (µmol Me: mol C) Fe:C Zn:C Mn:C Cu:C Co:C Archaea Median 352 148 n.d. 2.25 0.75 Range 177-528 20-276 n.d. 0.2-4.3 0.3-1.2 n 2 2 n.d. 2 2 Heterotrophic bacteria Median 37 15 6.3 1.3 0.28 Range 1.5-154 6-47 0.4-14 0.5-4.7 0.01-0.48 n 29 18 15 20 18 Photosynthetic bacteria Median 40 21 8.7 5 0.29 Range 21-677 6-38 5-46 0.1-13 0.2-1.7 n 9 9 9 13 4 Eukaryotic phytoplankton Median 78 6.4 29 1.7 1.2 Range 6.7-786 0.87-116 7-110 0.04-30 0.05-6 n 25 25 25 67 25 Field Median 43 18 3.5 3.5 1.7 Range 34-70 8.1-29 3-15 1.7-4.9  n 4 4 4 4 1 Field_SXRF (means ± 1 SE) Diatoms 57 ± 9 9 ± 2 2.3 ± 0.9 4.2 ± 2.3 0.42 ± 0.23 Autotrophic flagellate 35 ± 2 13 ± 1 3.2 ± 0.4 25 ± 3 0.92 ± 0.11 Autotrophic picoplankton 33 ± 6 10 ± 3 2.4 ± 0.7 33 ± 15 1.5 ± 0.5 Zooplankton+ Median 220 48 13.6 7.3 0.62  Range 40-4083 16.5-103 2.9-218 2.7-35.7 0.15-3.7  n 20 20 20 20 20 + Sample size reflects the four months sampled (December 2017, April 2018, June 2018 and August 2018) at Station S4-1.5 in the SoG , and the five different size fractions measured per month: 250-500 µm, 500-1000 µm, 1000-2000 µm, 2000-4000 µm and > 4000 µm. n.d.: not determined.  71  4.3.1.3 General zooplankton trends in trace metal content: similarities among certain trace metals One of the most interesting findings in this study is the similarity between certain trace metals (i.e., Mn, and Cr (and Ba); vs. Zn and Ni (and Co); vs. Cu and Ag) in regards to their relative content in different zooplankton size fractions. These patterns are not fortuitous, but are likely related to the chemical periodicity of these elements, as demonstrated for their particle-water partition coefficients in the ocean (Turner, Dickson, & Whitfield, 1980; Whitfield & Turner, 1983), which also ultimately influence the accumulation of trace metals by marine organisms. We thus examined a series of chemical parameters that may affect the behavior of trace elements in seawater, including covalent radius, effective nuclear charge, Pauling thermochemical electronegativity, charge, ionic radius, screening loss parameter, and enthalpy of hydration (Whitfield & Turner, 1983).  Among these, ionic radius, charge, and the d orbital screening loss parameter seem to be the key parameters linking the behavior of these trace metal pairs (Mn/Cr (and often Ba), vs. Zn/Ni/Co vs. Cu/Ag). Furthermore, the oceanic distributions of some of these elemental pairs are often very similar, such as Ag and Cu in the NE Pacific (Martin, Knauer, & Gordon, 1983), Zn and Ni (Middag, de Baar, Bruland, & van Heuven, 2020), etc. Indeed, the chemical speciation of the elements in the ocean and their biological function can be summarized using a diagram or grid whose ordinates are the ionization potential (also called electrostatic index, Z2i/ri;; where Zi = cation charge, ri  =  ionic radius) and a ‘softness’ parameter (also called covalent index, which relates to the electronegativity of that element; Whitfield & Turner, 1983).  In essence, marine particles (e.g., oxides, carbonates, oxyhydroxides, or organic matrices) have hydroxyl sites at the surface, which are likely to interact with cations in seawater. The stability constant of a cation with these sites is proportional to the cation hydrolysis constant. In addition, the intensity of the ion-water interactions is related to the electrostatic energy associated with the encounter, as well as to the covalent contribution to chemical bonding. Thus, based on these principles, Whitfield and Turner (1987) displayed the chemical speciation patterns of elements in seawater―according to their electrostatic energy (or ionization potential) and their covalent interactions―in a complexation field diagram [CF diagram- Appendix A-12 ;  72  i.e., a plot of log (Z2i/ri;;  where Zi = cation charge and ri =  ionic radius) versus covalent character (i.e., covalent contribution to chemical bonding, covalent index)]. This diagram divides the element into 2 main groupings: the (a)-type cations bonding most strongly to hard anions (e.g., F-, OH-, CO32–, and SO42-) via electrostatic interactions, and the (b)-type cations bonding most strongly to soft anions (e.g., Cl-, Br- , and HS-) via covalent interactions (Appendix A-12; Whitfield & Turner, 1987). In addition, the elements were grouped into four sectors, depending on their tendency to be complexed in seawater: Sector A, elements weakly complexed and found mainly as free cations; Sector B, elements dominated by Cl- complexation; Sector C, strongly hydrolyzed elements; Sector D, fully hydrolyzed elements.  All the essential cationic trace elements relevant to our work, are grouped in Sector A {Fe(II), Zn, Cu(II), Mn, and Co} in the CF diagram, and are classified as (a)-type cations, with high binding affinity for N and O (and to a lesser extent to S, so that N>O>S) in biological systems. In addition, the inorganic ligand complexation of these elements in seawater is dominated by carbonate for Cu(II), and by hydroxide for Fe(III); or is free for Fe(II), Mn(II), Ni(II), Co(II) and Zn(II) (Millero, Woosley, DiTrolio, & Waters, 2009).  In contrast, some of the most toxic elements (e.g., Ag(I) and Cd(II), as well as Cu(I)) to marine biota are in Sector B, and are (b)-type cations, which tend to make strong S bonds (and to a lesser extent N and O bonds; so that S>N>O) in organic matrices. In addition, in seawater, chloride dominates the inorganic complexation of these three cations.  More recently, based also on metal ion properties, Nieboer et al. (1999) came up with a similar metal classification system to predict metal-ligand stability and toxicity. As Whitfield and Turner (1987; Appendix A-12), this system is based upon electronegativity (the tendency of a metal atom to become negatively charged) and coordination number (the number of sites at which complexed ligands are attached to a metal ion). To classify metal atoms according to their ability to form covalent bonds, the index (Xm)2r was used, where Xm is the electronegativity and r is the ionic radius of the metal species with the most common coordination number. This index, named covalent index, aims to compare valence orbital energy with ionic energy, thus reflecting the relative ability to form covalent versus ionic bonds.  In addition, an ionic index was used (Z2/r)  73  to represent ionic interactions, where Z is the ion charge and r is the ionic radius. The covalent and ionic interaction indices of 36 metals and two metalloids are shown in Appendix A-13 (Nieboer, Fletcher, & Thomassen, 1999). These ions are also grouped into the three traditional groupings of Class A (‘hard’ acids, ions with a tendency to bind O donor atoms), Class B (‘soft’ acids, ions with a tendency to bind N and S donor atoms), and intermediate metal ions (displaying ambivalent affinity for donor atom types; Hancock, & Martell, 1996). Interestingly, for a fixed ionic index value (or covalent index), toxicity―and stability with most ligands (including membrane transporters, proteins and enzymes)―generally increases with increasing magnitude of the covalent index (or ionic index). Thus, in this graph toxicity and ligand stability increase from bottom to top and from left to right (Appendix A-13). Nieboer et al. (1999) illustrated this principle using Hg(II) and Ca(II). Though Hg(II) and Ca(II) have similar ionic indices, the greater toxicity of Hg(II) is due to its significantly higher covalent index. Similarly, the higher covalent index of Ag relative to Cd and Cu, in our case, may explain the significantly higher Ag(I) bioaccumulation factor in SoG zooplankton. But most importantly, they emphasized that for a fixed value of covalent index (or ionic index) the stability of a metal with most ligands is directly proportional to the metal ionic index (or covalent index). This explains, for example, why Pb(II) functionally displaces Ca(II) in neurons by preventing Ca(II) entry through membrane voltage-sensitive channels (Suszkiw, Toth, Murawsky, & Cooper,. 1984). Based on these principles, we would expect Ag(I) to functionally displace Cu(I) and/or Na(I). Thus, the toxicity of Ag(I) might be mediated by its assimilation as a monovalent cation, through high-affinity Cu transporters―which reduce Cu(II) to Cu(I) at the cell surface, before internalizing Cu(I)―followed by the association of Ag(I) with organic phases inside the cell via strong Ag-S bonds.  Similarly, Cd(II) is also a (b)-type cation, which strongly associates with S-bonds (and with N>O) inside the cell, and has been hypothesized to enter phytoplankton through putative, non-specific divalent metal transporters. These divalent metal transporters, belonging to the natural resistance-associated macrophage protein (NRAMP) family, or the ZRT/IRT-like proteins (ZIP) family, are able to transport Co, Cu, Fe, Mn, Pb, Zn, and Cd. The transporter members of these families differ in their substrate range and specificity. For example, in plants several members of  74  the ZIP family have been identified and transport Fe(II), Mn(II), Cd(II), and/or Zn(II) (for reviews see Guerinot, 2000, and  Hall and Williams, 2003). In contrast, members of the NRAMP family may also mediate transport of Fe(II), Mn(II), and/or Cd(II), but no Zn(II) (for reviews see Hall and Williams, 2003, and Pittman, 2005). The physiological interaction between Fe(II) and Cd(II), as well as Zn(II) and Cd(II), through NRAMP and/or ZIP transporters has been corroborated in diatoms. According to the metal classification system of Nieboer et al. (1999), Cd will easily displace Fe(II), Mn(II), and to a lesser extent Zn(II) (see Appendix A-13) at membrane transporter sites or at a functional group within intracellular proteins or enzymes.   Thus, Cd(II) and Ag(I) are particularly toxic to the marine biota, because transporters for essential elements, such as Fe(II)/Mn(II) and Cu(I), respectively, can’t discriminate between the essential and the toxic metal, and once inside they bind preferentially to S and N sites that are essential for the functioning and conformation of many proteins and enzymes. In contrast, given that Cu is also an essential micronutrient for phytoplankton and zooplankton, the toxicity of Cu(II) is mediated by intracellular excess Cu, which triggers massive oxidative stress (for a review see Solymosi and Bertrand, 2012, and Masmoudi et al., 2013).   4.3.1.4 General trends in Ag, Cd and Cu content in zooplankton: temporal variations and the effect of size Given that this study is part of a five year project investigating the cycling of Ag, Cu, and Cd in SoG, for the remaining of this discussion, we will focused on these 3 metals.  In general, the average Cu content in SoG zooplankton (20 ± 14.2 mg ∙ kg d.w. -1) is higher than that of Cd (2.9 ± 1.5 mg ∙ kg d.w. -1) and that of Ag (0.3 ± 0.3 mg ∙ kg d.w. -1; Tables 4 and 7; Figures 5 to 8), in agreement with the Cu requirement for zooplankton growth, but also with the relative concentrations of dissolved (4.8 ± 1.7 nM, 613.4 ± 92.3 pM, and 8.3 ± 1.9 pM, respectively, Table 8) and particulate Cu, Cd and Ag in SoG (0.3 ± 0.2 nM, 24 ± 9.8 pM, and 0.9 ± 0.6 pM, respectively, Table 8).  However, the Ag, Cd and Cu content of zooplankton did vary with season, but we were unable to identify any systematic trends. In general, a number of zooplankton fractions (i.e., 1000 and 2000 µm zooplankton size fractions; as well as the phytoplankton fraction/ particulate trace elements) had higher trace metal content in December,  75  relative to other months (Figures 11,12, and 14). Other fractions (i.e., 500 µm size fractions) had higher metal content in June. In contrast, for the 4000 µm zooplankton size fraction, the month with the highest metal content varied depending on the metal, for Cu and Ag, it was in April (Figures 11 and 12), but for Cd it was in December (Figure 14). Similarly, the 250 µm size fraction had the highest content for Cu in April, but for Ag in December and for Cd in June (Figures 12, 11, and 14, respectively).   We hypothesized that for metals with no significant river input (e.g., Cd), and thus little temporal variability in SoG (e.g., Figure 23), their content in zooplankton might be higher in winter, when the growth rates are the slowest (i.e., as the metal accumulate in the tissue and is not diluted by new biomass). This was partly supported by the data for the 1000 µm zooplankton size fractions and the phytoplankton fraction (i.e., particulate non-lithogenic metal measurements). In contrast, for metals with a high river input from the Fraser River (e.g., Cu; Pawlowicz, Francois, & Maldonado, 2018; Neff, 2002), we might expect higher content in April and June, when fluvial Cu flux is highest (Figure 22). This was supported by data for the 500 µm size fraction in June and the 250 and 4000 µm in April (Figure 12). Furthermore, we observed a significant increase in zooplankton Ag and Cu content as a function of size fraction in April (p = 0.01 and 0.03, respectively; Figures 11 and 12).  Positive trends in Zn:C ratios with zooplankton size fraction have also been reported by Baines et al. (2015) in the Costa Rica Dome (CRD).  Indeed, similar to our study, the larger size fractions (1000-2000 µm and 2000-5000 µm) exhibited higher metal content than the smaller sizes (200-500 µm and 500-1000 µm; Figures 11 and 12). These positive trends were explained by higher metal demands in larger zooplankton or metal biomagnification (Baines, Chen, Twining, Fisher, & Landry, 2015).  Larger zooplankton in our data set may also have a higher Cu demand. In addition, our larger fractions were dominated by copepods, which accumulate Cu very efficiently (i.e., due to fast Cu assimilation rates and slow removal rates; Table 11), even when Cu is at low concentrations in seawater (Kadiene, Ouddane, Hwang, & Souissi, 2019). Given that Ag is not an essential metal, the higher content in the larger zooplankton size fractions can only be explained by Ag biomagnification.   76  However, why did we observe this significant trend (i.e., positive relationship between metal content and size fraction) in April, and only for Cu and Ag (Figures 11 and 12)? A closer examination of this April trend suggests that it was driven by relatively high Ag and Cu content for the two largest zooplankton size fractions (2000 and 4000 µm; Figures 11 and 12). Interestingly, while omnivores/carnivores dominated these fractions in December, and omnivores/herbivores in June and August (Tables A-2, A-4, and A-5), in April these two zooplankton size fractions were dominated (by ~51% of total biomass; Appendix A-3) by herbivorous calanoid copepod Eucalanus bungii, which efficiently filters protozoans, ranging in size from 7-250 µm in diameter (Turner, 1984). Therefore, these E. bungii copepods in April are most likely ingesting metals associated with particles (both lithogenic and biogenic; Wang, 2002; Neff, 2002) and to a lesser degree with the dissolved phase (Kadiene et al., 2019) in seawater.  Examining the bioavailability of total Ag, Cu and Cd in seawater in the months sampled (April, June and December) revealed that highest average concentrations of total Ag and Cu in the water column (dissolved plus particulate metals) were indeed observed in April (Figures 21 and 22; Table 8), while for Cd there was very little temporal variation, but slightly higher total Cd concentrations were measured in December (Figure 23; Table 8).   77   Figure 22.  Particulate and dissolved Cu depth profiles measured during December 2017, April 2018 and June 2018 at Station S4-1.5(a-c). Particulate and dissolved monthly averages are shown in black and red lines, respectively. Profile of Cu associated with lithogenic and non-lithogenic sources and average non-lithogenic and lithogenic ratios shown (d-f). Non-lithogenic monthly averages shown by the cyan line. Profile of Cu associated with non-lithogenic sources normalized to biogenic P (g-i). Lithogenic Cu was calculated by using the Cu:Al molar crustal ratio  from Taylor and McLennan (1995; 1x10-4). Non-lithogenic sources were calculated like in Figure 21. Shaded area shows range of ratios in phytoplankton from studies by Guo et al. (2012), as well as Twining and Baines (2013). Error bars represent one standard deviation.  Cu, Al and P were measured in all the samples.  78   Figure 23. Particulate and dissolved Cd depth profiles measured during December 2017, April 2018 and June 2018 at Station S4-1.5 (a-c). Particulate and dissolved monthly averages are shown in black and red lines, respectively. Profile of Cd associated with lithogenic and non-lithogenic sources and average non-lithogenic and lithogenic ratios shown (d-f). Non-lithogenic monthly averages shown by the cyan line. Profile of Cd associated with non-lithogenic sources normalized to biogenic P (g-i). Lithogenic Cd was calculated by using the Cd:Al molar crustal ratio  from Taylor and McLennan (1995; 3x10-7). Non-lithogenic sources were calculated like in Figure 21. Shaded area shows range of ratios in phytoplankton from studies by Lane et al. (2009), as well as Twining and Baines (2013). Error bars represent one standard deviation.  Cd, Al and P were measured in all the samples.  79  4.3.2 Zooplankton elimination rates of Ag, Cu and Cd  Even though the concentration and speciation of metals in seawater are strong determinants of metal uptake by zooplankton, once inside, the organisms have the ability to store and/or eliminate the metal using multiple pathways. Ultimately, zooplankton metal content is determined by the balance between uptake and elimination (or influx and efflux rates).  Here we briefly discuss metal elimination by zooplankton. Metals can be excreted by zooplankton through various pathways, and the removal/excretion rates are both, exposure route and metal dependent. For example, Cd excretion in the copepod Temora longicornis occurs via fecal pellets, regardless of whether the organism is feeding, or exposed to the metal in the dissolved or particulate phase (Wang & Fisher, 1998).  In contrast, removal rates of Ag and Cd in copepods are two times faster after dietary exposure than dissolved exposure (Wang & Fisher, 1998; Table 11). Copper removal rates in copepods are similar regardless of the exposure type (Chang & Reinfelder, 2002). But, copepods remove Cu at a rate five times slower than that for Ag or Cd―most likely due to the essential role of Cu in their physiology and their Cu homeostasis (Chang & Reinfelder, 2002 and references therein).  Copper and Cd can also be removed by crustaceans through molting (e.g., Hook and Fisher, 2001b, Eisler, 2010, and  Neff, 2002).  In the case of Cu, crabs break down hemocyanin while molting and remove Cu by binding it to metallothioneins, which are later packed into fecal pellets. When new hemocyanin is being produced, crabs will accumulate Cu from their environment. Some of the Cu can also be stored in the gut during molting and used for hemocyanin synthesis (Engel & Brouwer, 1991, and Scott-Fordsmand & Depledge, 1997 cited in Neff, 2002). The loss of trace metals, mediated by molting, differs among zooplankton taxa. For example, molting is an important Cd removal pathway for euphausiids (Hamanaka &Tsujita, 1981; Hook & Fisher, 2001b), and because they molt more frequently than copepods and amphipods (Hamanaka & Tsujita, 1981), euphausiids are known for accumulating less Cd than copepods or amphipods (Hamanaka & Tsujita, 1981; Appendix A-7).   80  4.3.3 Modeling zooplankton metal content  To further investigate whether the SoG zooplankton are internalizing metals mainly from the dissolved or particulate phase in SoG, we first examined the distribution of metals in the dissolved and particulate phase, as well as the lithogenic and non-lithogenic metal concentrations within the particulate phase (Figures 21 to 23 d,e,f ; Appendix A-14).  In SoG, the concentrations of dissolved Cu, Cd and Ag (4.8 ± 1.7 nM, and 613± 92.3 pM, 8.3 ± 1.9 pM, respectively, Table 8) are greatly in excess of those in the particulate phase (0.3 ± 0.2 nM, 24 ± 9.8 pM, and 0.9 ± 0.6 pM, respectively, Table 8).  On average, ~ 95% of the total Cu, Ag and Cd in seawater is in the dissolved phase (Figures 21 to 23 d,e,f;  Kuang, 2019). In seawater, the particulate metals might be associated with lithogenic or non-lithogenic (which may include biogenic and/or authigenic) particles. Using well established metal ratios in the earth crust (i.e., relative to Al), and in phytoplankton (i.e., relative to P), one can estimate the concentrations of metals within the particulate phase associated with lithogenic and non-lithogenic particles, respectively. However, for Ag, there are no published phytoplankton Ag:P ratios thus, the non-lithogenic component was calculated as the difference between total particulate Ag and the lithogenic component.  Using the approach used by Kuang (2019), we estimated that on average, the non-lithogenic particles account for the vast majority of the particulate metals in seawater (83, 99, and 92% for Cu, Cd and Ag, respectively; Appendix A-14). For Ag, the non-lithogenic pmol Ag : nmol P ratio (Figure 21 g,h,i)  agreed well with Ag:P ratios of particles collected in waters off central Mexico and California  (0.018 to 0.071 pmol Ag: nmol P; Martin el at., 1983; Martin & Knauer, 1973). Furthermore, Kuang (2019) observed a strong correlation between the abundance of marine particles and particulate Ag concentrations in SoG waters. In contrast, the non-lithogenic Cu:P and Cd:P ratios were much higher than published phytoplankton Cu:P and Cd:P (Figures 22 and 23 g,h,i). The high Cd:P might be due to the formation of authigenic CdS in particles within anoxic micro-environments in particles in the water column of SoG  (Kuang, 2019). In contrast, the high Cu:P ratio might have resulted from the adsorption of Cu onto the surface of particles, as observed for Ag (Kuang, 2019).  81  With these dissolved and particulate metal concentration data, we used the bio-energetic kinetic model for marine organisms first described in Thomann (1981) and developed for copepods by Wang and Fisher (1998) to determine whether the SoG zooplankton are internalizing metals mainly from the dissolved or particulate phase. This model takes into account assimilation efficiencies of a specific metal (e.g., Ag, Cu and Cd) from ingested algal food, as well as metal uptake rate constants for the dissolved metal phase, and efflux rate constants following uptake from food and water for that specific metal (Table 11). In addition, given that the particulate metals were vastly dominated by non-lithogenic particles (Appendix A-14; Figures 21 to 23, d,e,f ), we assumed that the SoG zooplankton were only feeding on non-lithogenic particles. Our calculated estimates of the total content of Ag, Cu and Cd expected in copepods (mg Me ∙ kg d.w.-1), given the dissolved and particulate concentrations of these metals in the SoG, were relatively close to the metal content we measured in the zooplankton samples (Table 12). For example, for Ag, the average calculated metal content in copepods was only ~ 2 times lower than the average measured ratio in the zooplankton (Table 12). This suggests that the model of Wang and Fisher (1998) also used in Chang and Reinfelder (2002) to estimate Ag content in copepods is remarkably accurate.  For Cd and Cu, the average calculated estimate was ~ 10 times higher than the measured metal content in copepods (Table 12).  The lower Cd and Cu content we measured in SoG zooplankton might be explained by lower (i.e., dissolved metal uptake rate constant or assimilation efficiencies; Table 11; Appendix A-15), or higher Cu and Cd parameters (i.e., removal rate constants) for SoG copepods, compared to the those in previous laboratory studies (Table 10). In some dissolved Cu uptake experiments  with 64Cu realized in 2017 using SoG water and SoG calanoid copepods (in their majority M. pacifica), the average uptake rate measured  was of 4.7 L ∙ g d.w.-1 ∙ d-1 under environmentally plausible dCu concentrations (see Appendix C). This uptake rate is similar to that calculated by Chang and Reinfelder (2002; 5.1 L ∙ g d.w.-1 ∙ d-1). Future studies should determine these parameters in more zooplankton collected in SoG, using radiotracers.  Furthermore, and in agreement with other studies (Chang & Reinfelder, 2002; Hook & Fisher, 2001a and b), our calculations indicated that the vast majority of Cd, Cu and Ag assimilated by SoG zooplankton is derived from the particulate fraction and not from the dissolved fraction (Cd,  82  98-99%; Cu 92-96%, and Ag, 64-87% of the total ingested metal is from the particulate fraction; Table 13; Appendix A-15). And although significant temporal variability was observed in the contribution of particulate Ag to the total Ag intake by SoG zooplankton, little or moderate variability was observed for Cd or Cu, respectively.  According to our calculations, dissolved Cu uptake by zooplankton was almost negligible, while particulate Cu accounted for 92-99 % of the total intake (Table 13). The temporal variability is low, but the role of particulate Cu is most pronounced in December, probably due to the high Cu:P ratio of the non-lithogenic particles at this time of the year (2 times higher than that in June; and more than 10 times higher than published phytoplankton Cu:P ratios; Appendix A-14).Given the low phytoplankton productivity in December, the ratio of non-lithogenic to lithogenic particulate Cu was also lowest during this month. The content of Cd in zooplankton was largely determined by the particulate Cd intake, which accounted for 99- 100% of the total intake (Table 13). Furthermore, since 99% of the particulate Cd is associated with non-lithogenic particles, which are enriched in Cd relative to published phytoplankton Cd ratios, copepods seem to efficiently ingest high Cd associated with non-lithogenic particles.     The content of Ag in zooplankton was controlled more by pAg than dAg. Particulate Ag was more important in the winter (accounting for 87%) relative to spring (accounting for 64%; Table 13). Thus, in contrast to Cu and Cd, dissolved Ag concentrations may have an impact on the Ag content in zooplankton. So, dAg concentrations in SoG should be carefully monitored.  As with Cu, the high Ag content relative to biogenic P of the non-lithogenic faction in winter (~3 times higher than in spring; Appendix A-14) seems to account for the large contribution of particulate Ag in the December intake by zooplankton. As expected, there were more non-lithogenic Ag particles during April, likely due to the phytoplankton bloom.   83  Table 11. Average bioaccumulation factors (L ∙ kgd.w-1), assimilation efficiency and absorption efficiency, plus other values found in the literature. BAFs were calculated by dividing zooplankton metal content by the average dissolved metal concentration from its respective month. Mean and median (in italics) values were calculated from monthly BAF calculations.  Assimilation efficiency (AE) is the fraction of metal in food consumed that an organism is able to assimilate to its tissues (Wang & Fisher, 1996; Hook & Fisher, 2001a).  Measurement ranges are shown in parentheses. Errors represent one standard deviation from the mean.  Ag Cu Cd References Zooplankton BAFs (L ∙ kg-1 d.w.) x104 30.78 ± 25.8 19.69 (7.96-116.62) 6.46 ± 4.14 4.99 (2.74-18.7) 4.19 ± 2.34 3.75 (1.30-11.09) This study. NE Pacific (central Strait of Georgia). Mixed zooplankton samples. n.d. 5.9 ± 3.1 (1.9-10.4) 5.8 ± 1.9 (2.2-8.7) Chouvelon et al. (2019). NW Mediterranean Sea. Size fractions averaged: 200-500, 500-1000, 1000-2000, >2000 µm. Mixed zooplankton samples. 13 n.d. 31 Fisher et al. (2000). Mixed copepod samples. Monaco coast. n.d. (12.7-699) (1.5-53.4) Fang et al. (2006). NW coastal Taiwan. BAF ranges calculated using dissolved concentration ranges found in the outfall. Mixed copepod samples. (0.8-4.2) (0.9-13) (0.1-1.3) Fang et al.(2014). NE coastal Taiwan. BAF ranges calculated using max dissolved concentration ranges from Bruland and Lohan (2003). Mixed copepod samples.  Assimilation efficiency (%) 8-19 n.d. 33-53 Wang and Fisher (1998) 14 n.d. 62 Hook and Fisher 2001b (Cd), Hook and Fisher 2001a (Ag) 17.4 n.d. 30.4 Reinfelder and Fisher (1991)  n.d. 40-50 n.d. Chang and Reinfelder (2000) 14.6 45 44.6 Average used in kinetic  model (based on Wang & Fisher, 1998) Removal or efflux rates (d-1) Food phase: 0.294 Dissolved phase: 0.173 n.d. Food phase: 0.297 Dissolved phase: 0.108 Wang and Fisher (1998)     n.d. 0.06-0.08 (combined dissolved and food phases) n.d. Chang and Reinfelder  (2002)    n.d., not determined. 84  Table 12. Calculated and measured average metal content in zooplankton. The calculated values were a result from the Wang and Fisher’s (1998) kinetic model. The average metal content for “All data” averaged all zooplankton metal measurements, including those from August. For model inputs, please refer to Appendix A-15.  Month Metal Calculated (mg Me ∙ kg d.w.-1) Measured (mg Me ∙ kg d.w.-1) December 2017 Ag 0.3 0.31 ± 0.22 Cd 33.26 3.49 ± 1.49 Cu 227.41 18.95 ± 10.51 April 2018 Ag 0.11 0.41 ± 0.47 Cd 13.28 2.84 ± 1.24 Cu 180.22 30.92 ± 22.67 June 2018 Ag 0.15 0.18 ± 0.06 Cd 16.68 3.19 ± 2.12 Cu 120.31 14.5 ± 4.93 All data Ag 0.19 0.28 ± 0.26 Cd 21.48 2.86 ± 1.53 Cu 180.36 19.95 ± 14.24  Table 13. Calculated zooplankton trace metal content under steady-state conditions (Css), total calculated metal taken up by copepods from the food (Css,f), and total calculated metal taken up by copepods from the dissolved phase (Css,w). The respective fractions from the final total metal content in zooplankton are shown in italics. Metal Month Css  (mg Me ∙ kg d.w.-1) Css,f   (mg Me ∙ kg d.w.-1) Css,w (mg Me ∙ kg d.w.-1) Ag December 0.30 0.26 0.04 87 % 13 % April 0.11 0.07 0.04 64 % 36 % June 0.15 0.12 0.03 79 % 21 % All data 0.190 0.15 0.04 81 % 19 % Cd December 33.26 33.01 0.25 99% 1 % April 13.28 13.04 0.25 98 % 2 % June 16.68 16.47 0.22 99 % 1 % All data 21.48 21.24 0.24 99 % 1 % Cu December 227.41 218.54 8.88 96 % 4 % April 180.22 169.31 10.91 94 % 6% June 120.31 110.65 9.67 92 % 8 % All data 180.36 171.21 9.15 95 % 5 %  85  4.3.4 Contrasting zooplankton bioaccumulation factors for Ag, Cu and Cd  In this study, we calculated the bioaccumulation factor (BAF; Arnot & Gobas, 2006) of Ag, Cu and Cd in zooplankton by dividing the metal content in the different zooplankton size-fractions (mg ∙ kg d.w.-1) by the concentration of dissolved metal in seawater (mg ∙ L-1) in the respective time of sampling (Table 11; Appendix A-8).  The SoG zooplankton BAF were highest for Ag, followed by Cu and Cd (Table 11). These findings are in agreement with other toxicology studies (Wang & Fisher, 1998; Chang & Reinfelder, 2002), that report metal accumulation factors from the dissolved phase into zooplankton such that BAF for Ag >>Cu> Zn>Cd>Co>>Se. Moreover, the bioaccumulation factors for Cu in our samples (ranging from 2.7 to 18.7 x 104 L∙ kg d.w.-1; Table 11) are also comparable to those in Chouvelon et al. (2019; 1.9 - 10.4 x 104 L∙ kg d.w.-1), as well as other studies (Table 11 ). The exception is a study near a sewage outfall in Taiwan, which recorded Cu BAF as high as 699 x 104 L∙ kg d.w.-1. Generally speaking, Cu bioaccumulation factors in our samples increased with increasing zooplankton size fraction (Figure 16), in contrast the findings of Chouvelon et al. (2019), where a negative trend was observed, especially in the spring and summer. Field-based accumulation factors of Ag for zooplankton are rare. The Taiwanese sewage outfall study reported Ag BAFs ranging between 0.8 to 4.2 x 104 L∙ kg d.w.-1, while we calculated Ag BAF almost 30 times higher (Table 11).  Indeed, in our study the BAFs of Ag were 5 to 7 times higher than those for Cu and Cd. This strikingly contrasts the trends in metal content discussed above where the average Cu content was an order of magnitude higher (20 mg ∙ kg d.w.-1) than that of Cd (2.9 mg ∙ kg d.w.-1), and ~ two orders of magnitude higher than that of Ag (0.3  mg ∙ kg d.w.-1; Tables 4 and 7).  So, why is the bioaccumulation of Ag in zooplankton significantly higher than that of Cd and Cu? We hypothesize here that the unique speciation of dissolved Ag, dominated by inorganic complexation, results in high levels of Ag in phytoplankton. In addition, Ag is readily adsorbed onto suspended biogenic and non-biogenic particles. Indeed, Kuang (2019) observed that at surface (0-30 m) particulate Ag is incorporated to both lithogenic and particulate organic matter while it is mostly bound to lithogenic particles at depths below 30 m. These processes at surface  86  would result in high Ag bioaccumulation in zooplankton, as they efficiently consumed these enriched Ag particles. The speciation of dissolved Ag in seawater is dominated by chloro-complexes (e.g., AgCl, AgCl2-and AgCl32-) with negligible organic complexation (Miller & Bruland, 1995). Thus, the vast majority of dissolved Ag in seawater is inorganic, and thus readily bioavailable.  In stark contrast to dissolved Ag speciation, dissolved Cu speciation in the ocean and estuaries is dominated by strong organic ligands which normally complex more than 99% of the dissolved Cu (e.g., Buck and Bruland, 2005). In the SoG specifically, strong organic ligands bind 99.98% of the dissolved Cu (Waugh, 2020). This complexation significantly buffers the free dissolved Cu concentrations in the SoG to 10-13.22 M, well below toxicity threshold for phytoplankton (10-12 M; Brand, Sunda, & Guillard, 1986), and probably also for higher trophic levels (Waugh, 2020).  Metal complexation has been known to lower the toxicity Cu to phytoplankton (e.g., Buck, Ross, Flegal, and Bruland, 2006). Though only a few studies have investigated organic complexation of Cd in the ocean, the chemical speciation of dissolved Cd in seawater is largely controlled by complexation with uncharacterized strong organic ligands and to a lesser extent the inorganic chloride ions (Morel & Hering, 1993).  Relevant to this SoG study, a previous investigation reported that in the North Pacific, ~ 70% of dissolved Cd in surface waters (<175 m) was complexed by strong organic ligands (Bruland, 1992). In contrast, in the subsurface and deep water the speciation of dissolved Cd was dominated by chloro complexes, primarily CdCl+ and CdCl2 (Bruland, 1992; Morel & Hering, 1993). Organic complexation in surface waters reduces inorganic Cd ([Cd’]) to extremely low levels―ranging from 0.6–1.8 pM in the North Pacific―which are not toxic to marine phytoplankton.  Thus, the strong and moderate organic complexation of dissolved Cu and Cd, respectively, in seawater, which is practically lacking for Ag, might be responsible for the lower zooplankton BAFs for Cu and Cd than Ag, even though the concentrations of dissolved Cu and Cd in SoG are ~ 3 and 2 orders of magnitude higher than those of Ag. Indeed, organically-complexed metals are less toxic to marine zooplankton (Eisler, 2010).  87  In addition, Ag(I) is a soft metal, such as Cu(I), Zn(II), and Cu(II). But in contrast to Cu, Ag(I) is not essential, nor redox-active. Silver has also a higher covalent index (Nieboer et al., 1999) and thus a higher propensity to bind soft bases, such as thiolates, than that of Cu(I), Cd(II), Zn(II), and Cu(II). Thus, Ag can efficiently replace native metals in key metalloproteins, such as intracellular metallothionein and plasmatic ceruloplasmin (Liu et al., 2017). Therefore, the toxicity of Ag towards all living organisms mainly arises from the internalization of Ag via Na or Cu transporting pathways (i.e., CTR1 CTR2, for a review see Eckhardt et al., 2013), and the Ag displacement /  inactivation of vital Cu and Zn containing metalloproteins―many of which are meant to be redox active (Stillman, 1999). Metallothioneins are low-molecular weight, cysteine-rich proteins with the ability to bind mono and divalent metal ions with a d10 electron configuration, especially Cu(I) and Zn(II), in the form of metal-thiolate clusters. Metallothioneins are the major intracellular soft metal buffering protein detoxifying essential metals when in excess (i.e., Zn or Cu) or toxic metals (i.e., Cd or Ag). Thus, Ag would readily accumulate inside cells because of its remarkable affinity to bind cysteine rich proteins, including metallothioneins (Dong, Wagner, & Russell, 2018; Dong, Shirzadeh, Fan, Laganowsky, & Russell, 2020; Ruta et al., 2018).   4.4 SoG Zooplankton metal content relative to the British Columbia Water Quality Guidelines for Marine and Estuarine Aquatic Life  Current total concentrations of Cu (averaging 5.4 ± 1.9 nM), Ag (9.8 ± 1.7 pM) and Cd (0.64 ± 0.09 nM) at Station S4-1.5 (Table 8) do not exceed the long-term chronic concentrations values reported in the 2019 British Columbia Water Quality Guidelines (BC WQGs; Government of British Columbia- Ministry of Environment & Climate Change Strategy; 31.5, 13.9 and 1.1 nM, respectively, Table 8). However, constant surveillance of the metal content in the most sensitive groups of aquatic organisms- phytoplankton and zooplankton- is advised.  4.4.1 Cu The BC WQGs for marine and estuarine aquatic life suggest a maximum concentration of 47 nM total Cu (3 µg ∙ L-1) and a maximum 30-day average of 31.5 nM total Cu (≤ 2 µg ∙ L-1; Table 8; Government of British Columbia- Ministry of Environment & Climate Change Strategy, 2019).  88  The lethal concentration for the model calanoid copepod Acartia tonsa has been reported to be 1.7 µM for dissolved Cu, and 2.1 µM Cu for a combined exposure through water and diet (48-h LC50 at 30 ppt; Pinho et al., 2007; Table 8). Therefore, the current total, average Cu concentration in SoG (5.4 nM, particulate + dissolved Cu concentrations; Table 8) is 315 times lower than the dissolved lethal concentration and 6 times lower than the 30-day average concentrations for the BC water quality guidelines.  4.4.2 Ag The applicable BC WQGs for marine waters, open coast and estuaries suggest a  maximum concentration for total Ag of 27.8 nM (3 µg ∙ L-1) and a maximum 30-day average below 13.9 nM (1.5 µg ∙ L-1;  Table 8; Government of British Columbia- Ministry of Environment & Climate Change Strategy, 2019). These guidelines are well below the lethal Ag concentration (LC50) for calanoid copepods Acartia spp. (0.4 µM dAg; Hook & Fisher, 2001b). Furthermore, the current total, average Ag concentrations in SoG (9.8 pM, particulate + dissolved Ag concentrations, Table 8) is 40,820 times lower than the lethal concentrations, and 1,418 times lower than the maximum 30-day average stated by the BC water quality guidelines.   Investigating copepods, Hook and Fisher (2001a) did not observe toxicity in Acartia spp. at a dissolved Ag concentration of 5 nM. However, the egg production was hindered in 50% of the animals consuming phytoplankton grown in 1 nM Ag media (PC50). Under these conditions, the total Ag content of the copepods (included that adsorbed on the exoskeleton) was 0.46 mg ∙ kg d.w.-1 (Hook & Fisher, 2001a). Surprisingly, the Ag content of the 2000-4000 µm zooplankton in our samples (December 0.67, April 0.49 and August 0.42 mg ∙ d.w.-1; Figure 11; Table 4) was consistently over or close to this whole-body Ag content of model Acartia (0.46 mg ∙ kg d.w.-1; Hook & Fisher, 2001a). Furthermore, our April sample, for the > 4000 µm size zooplankton almost tripled this threshold (1.2 mg ∙ d.w.-1; Figure 11).  Although the total seawater Ag concentrations in SoG is ~100 times lower than 1 nM, we are still observing high Ag content in the zooplankton, thus confirming the high bioavailability of Ag in seawater and its tendency to bioaccumulate.    89  4.4.3 Cd The BC Working Water Quality Guidelines for Cd in marine waters recommend a maximum of 1.1 nM total Cd (Government of British Columbia- Ministry of Environment & Climate Change Strategy, 2019; Table 8). This concentration is about a thousand times lower than the lethal concentration of dissolved Cd reported for A. tonsa (1 µM; Hook & Fisher, 2001b; Table 8). However, crustaceans are very sensitive to Cd toxicity (Eisler, 2010). They can indeed take up dissolved Cd by multiple pathways, including adsorption, absorption to organs (i.e., gills), filtration and oral intake (Robinson, Baird, & Wrona, 2003; Kadiene et al., 2019). But, in the majority of cases, Cd exposure to crustaceans, molluscs and annelid worms induces the production of metallothioneins or metallothionein-like proteins (Amiard, Amiard-Triquet, Barka, Pellerin, & Rainbow, 2006) which bind Cd and prevent its toxicity.    Hook and Fisher (2001b) studied Cd toxicity on copepods. They determined that the dissolved Cd taken up by copepods was higher in their exoskeleton (46%) than in their internal organs (40%). They also found that Cd levels in the water at which copepods were exposed to (≤10 nM) did not induce sub-lethal effects on the experimental organisms. Yet, copepod egg production decreased by half in organisms consuming phytoplankton cultured in 5 nM dissolved Cd media with a final Cd content of 7.2 mg ∙ kg d.w.-1 in phytoplankton (64 nmol Cd ∙ g d.w. phyto-1; Hook & Fisher, 2001b). Whole-body Cd content for zooplankton experiencing toxicity was equal or higher than 4.7 mg Cd mg ∙ kg d.w.-1 (42 nmol Cd ∙ g d.w.-1; Hook & Fisher, 2001b). This is relevant to our findings, because the Cd levels measured in at least two of our samples (in December 1000 µm and June 250 µm fractions; Figure 14) are above this threshold (5.56 and 6.86 mg Cd mg ∙ kg d.w.-1, respectively). In addition, the April 2000 µm and 4000 µm fractions are close to this level (4.22 and 4.03 mg Cd mg ∙ kg d.w.-1, respectively; Figure 14). Although Hook and Fisher (2001b) noted that 5% of the Cd taken up by dietary exposure was easily removed, dietary Cd content could potentially pose a threat to the wellbeing and productivity of SoG zooplankton.   90  4.4.4 The effect of anthropogenic particles discharged by the WWTP on metal bioavailability to SoG zooplankton  The Iona Island wastewater treatment plant (Iona WWTP) treats almost half of the total wastewater produced in the greater Vancouver area (Metro Vancouver, 2019b). Once treated, this wastewater is released into the SoG (Metro Vancouver, 2019a). Yet, this outfall is not a significant source of dissolved or particulate Cu, Ag, and Cd into the Strait (Kuang, 2019; Pawlowicz et al., 2017). But the Iona WWTP is a significant source of anthropogenic, organic-rich particles into the SoG (Johannessen et al., 2003). In contrast, the Fraser River is the largest source of particles to the SoG, but these particles are mainly of lithogenic origin (Johannessen et al., 2005). Particles have highly metal-reactive groups at the surface that may promote the precipitation and /or adsorption of metals from the dissolved phase (Bianchi, Weber, Kiko, & Deutsch, 2018). This particle metal enrichment process is particularly significant in seawater for metals, such as Ag and Cu, which are very particle reactive in oxygenated seawater (Whitfield & Turner, 1987). In contrast, Cd is not particle reactive in oxic, surface waters. But in anoxic environments, Cd readily precipitates with S to form insoluble CdS complexes (Janssen et al., 2014).  Indeed, a recent study, investigating dissolved and particulate Cd profiles in SoG, reported an unexpected enrichment of Cd in particles, and hypothesized that anoxic microenvironments inside organic-rich particles released by the Iona outfall may promote the precipitation of Cd, forming insoluble CdS complexes (Kuang, 2019). Thus, it is possible that particles released by the Iona WWTP promote the adsorption of dissolved Ag and Cu onto lithogenic and non-lithogenic particles, as well as the authigenic formation of insoluble CdS complexes inside the particles. These Ag, Cu and Cd enriched particles can be ultimately consumed by plankton, resulting in the bioaccumulation of Ag, Cu and Cd by zooplankton and the potentially transfer and biomagnification of these elements in upper trophic level organisms. 91  Chapter 5: Conclusion 5.1 Analysis, integration, and implications of the research findings   As previously reported, the majority of the zooplankton in the SoG are crustaceans, and the dominant lower taxa are calanoid copepods (e.g., Archambault et al., 2010, and Mackas et al., 2013). Zooplankton biomass and abundance have a strong seasonal variability, with the highest values recorded in June (summer: 37.97 g d.w.∙ m-2 and 1706 individuals ∙ m-3, respectively) and the lowest in December (winter: 6.19 g d.w.∙ m-2 and 220 individuals ∙ m-3, respectively). In general, the depth-integrated dry weight biomass estimates for our time series had great variability (6.19- 37.97 g d.w ∙ m-2). However, the estimates were within the range observed in the decades-long time series by Mackas et al. (2013). In addition, the seasonal cycle for our zooplankton biomass estimates (g d.w.∙ m-2) agrees with the one first described by Harrison et al. (1983) and later updated in Mackas et al. (2013). As seen by Mackas et al. (2013), medium-size calanoid copepods (1-3 mm) made up the majority of the biomass (g d.w.∙ m-2). The amphipods biomass in our samples was also higher than that for euphausiids, possibly due to our small sample size and large spatial variability of the latter (Mackas et al., 2013). As for zooplankton abundance (individuals ∙ m-3), the calanoid copepod Metridia pacifica was the dominant species in our samples. Unlike in previous studies like Mackas et al. (2013) calanoid copepod N. plumchrus was in low abundance . This could merely be due sampling patchiness, bias against strong swimmers and/or visual predators sampled during our day time sampling cruises (Mackas et al., 2013; Harrison et al., 1983). But it could also be due to a decline of N. plumchrus populations, resulting from increasing surface temperatures in SoG that are causing a mismatch between the timing of the phytoplankton bloom and the N. plumchrus feeding season (see Mackas et al., 2013).  The trophic position of the homogeneous size-fractionated SoG zooplankton samples varied between 1.89 and 2.94, and we observed an increase in trophic position as a function of zooplankton size fraction. A trophic position between 2 and 3 is known to represent omnivory (Chikaraishi et al., 2010). Indeed, many zooplankton, especially copepods like M. pacifica, can be opportunistic feeders (see El-Sabaawi et al., 2009, El-Sabaawi et al., 2010, or Landry, 1981). Thus, zooplankton may have a more herbivorous diet when primary production is high (spring in  92  the SoG), and shift to carnivory when primary productivity is low (winter in the SoG) (Tommasi et al., 2013). This may explain why we see a clear positive trend between trophic position and size fraction in December (i.e., more carnivorous diet in larger size fractions), a weak negative trend in June (i.e., more herbivorous diet in larger size fractions), and about the same trophic position in all size fractions in August (i.e., mixture of diets in all size fractions). The variations in metal content in zooplankton size fractions were metal-dependent. On average Zn, Fe and Mn contents were highest in zooplankton (133.5 ± 74.8, 1125 ± 2169, and 62 ± 86.7 mg Me ∙ kg d.w. -1, respectively), while Co and Ag were lowest. Indeed, our zooplankton trace metal content data spanned four orders of magnitude, and followed the following order: Fe >> Zn ≥ Mn ≥ Ba > Cu > Ni > Cd ≥ Cr > Co ≥ Ag (mg metal ∙ kg d.w. -1). A similar pattern was found for C- normalized values. As expected, most of the essential metals (e.g., Fe, Zn, Mn, Cu, and Co) were found in higher concentrations in zooplankton than non-essential metals (e.g., Ag and Cd). Our zooplankton trace metal content data are comparable to published studies conducted in other regions of the world (e.g., Chouvelon et al., 2019, Fisher et al., 2000, etc.). Nevertheless, the Cr content in SoG zooplankton was lower, and the contents of Mn, Ba, Co and Ag were higher than those reported in other studies (e.g., Ho et al., 2007, and Fang et al., 2014), even a study near a sewage outfall in Taiwan (Fang et al., 2006). These discrepancies were hypothesized to be associated with the specific biogeochemical cycle of these elements (i.e., Cr, Co, Ba vs. Ag) in this region, such as, high inputs from the Fraser River (i.e., for Mn) and from the Northeast Pacific input waters into SoG (i.e., for Cd). We also found that similar seasonal trends existed between the following elements and their content in different zooplankton size fractions: Cr and Mn; Ni and Zn; as well as Cu and Ag. The parallel trends for these paired metals were hypothesized to be due to the similarity in their chemical properties, such as ionic charge and radius (Nieboer et al., 1999; Whitfield & Turner, 1987). Furthermore, the interactions between the covalent and ionic indexes among trace metals were able to explain the trends in the content of essential (i.e., Mn, Zn, and Cu) and non-essential trace metals (i.e., Cr, Ni, and Ag) in zooplankton, as well as their bioaccumulation factors.  In addition, for the three metals of interest, higher concentrations for Cu, followed by Cd, and then by Ag were measured not only  93  in SoG zooplankton, but also in the dissolved and particulate SoG seawater phases. Overall, the Ag and Cu content increased significantly as a function of zooplankton size fraction. Indeed, zooplankton metal content was a direct function of size in the April (for both Ag and Cu) and August (for Cu only). In contrast, although a positive trend between Cd content and zooplankton size was also observed for Cd, this trend was not statistically significant.  It is possible that the general increase of Cu content as a function of size fraction is due to higher Cu requirements in the larger zooplankton, or to Cu and Ag biomagnification in SoG plankton (Baines et al., 2015). But the increasing Ag and Cu trends (i.e., metal content as a function of size fraction) in April seemed to be driven by significant higher Ag and Cu content in the > 2000 µm zooplankton, which mainly consisted of herbivorous calanoid copepod Eucalanus spp. These copepods are filter feeders (El-Sabaawi et al., 2009), efficiently ingesting phytoplankton during the spring bloom, which will be dominated by organic particles enriched with adsorbed Cu and Ag.   In order to determine the relative importance of metal uptake from the dissolved or dietary (particulate) phase, we used Wang and Fisher’s (1998) model to calculate metal intake from both pathways. The results of this model suggest that zooplankton metal content is assimilated primarily from food (64-87% for Ag. 92-96% for Cu, and 98-99% for Cd). Indeed, Chang and Reinfelder (2002) have also observed that at the natural Cu concentrations observed in the SoG, particulate metal concentrations will have the highest influence in the amount of metal assimilated by zooplankton. Silver bioaccumulation factors were on average highest (30.8 x 104 L∙ kg d.w. -1), followed by Cu and Cd BAFs (6.46 and 4.19 x 104 L∙ kg d.w. -1, respectively). Similarly to metal content, Cu and Ag BAFs increased significantly as a function of zooplankton size fraction, while Cd BAFs did not. The Ag BAFs in our study were higher than those in other studies, while BAFs from Cu and Cd were comparable (e.g., Fang et al., 2014, Fisher et al., 2000, and Chouvelon et al., 2019).  We believe that the speciation of Ag in seawater, in contrast to that of Cu and Cd, is largely responsible for the high BAFs for Ag, relative to those of Cu and Cd. Unlike Cd and Cu, most dissolved Ag in seawater is not bound to organic ligands (Miller & Bruland, 1995), making  94  dissolved Ag readily bioavailable. In addition, Ag, a non-essential element (Luoma, 2008), is bioaccumulated because cellular metal transporters at the cell surface can’t discriminate between essential and non-essential ions, in this case between Cu(I) and Ag (I) (Eckhardt et al., 2013). Furthermore, once inside the cell, the tendency of Ag(I) to form the most stable ligand complexes with S and N functional groups prevents the binding of essential metal in crucial metalloproteins and enzymes (Whitfield & Turner, 1987; Liu et al., 2017; Ruta et al., 2018). The current average total concentrations for Cu (5.4 ± 1.9 nM), Ag (9.8 ± 1.7 pM), and Cd (0.64 ± 0.09 nM) at our time series station in the southern SoG are below the maximum allowed values stated by the current British Columbia Water Quality Guidelines for aquatic life (31.5, 13.9 and 1.1 nM, respectively; Government of British Columbia- Ministry of Environment & Climate Change Strategy, 2019; Sinclair et al., 2015). However, both the content of Ag and Cd in the larger zooplankton size fractions were close to model copepod metal content where toxicity studies have found a significant decrease in egg production (e.g., Hook and Fisher, 2001a and b). Thus, close monitoring of dissolved, particulate and zooplankton Ag and Cd content is recommended.  5.2 Overall significance and contribution of this research This thesis is a comprehensive study with an incredible array of complementary measurements that can be used to investigate what controls the accumulation of trace metal in natural zooplankton populations in the SoG. We have seasonal zooplankton biomass and community composition, size-fractionated trophic position, metal content, and carbon content measurements, as well as Ag, Cd, and Cu bioaccumulation factors. Our study also reports high-resolution depth profiles for dissolved and particulate Cu concentrations in the SoG, and is complemented with SoG concentrations of dissolved and particulate (lithogenic, biogenic, and non-lithogenic) P, Al, Ba, Ag and Cd reported by Kuang (2019). It also adds new particulate Ag and Cd data, from June 2018, which were not reported in Kuang (2019). The research presented in this thesis provides the first insights into the state of zooplankton metal content in the southern SoG, and can be used as a baseline for future studies of SoG pelagic ecosystem. In addition, the data for multiple zooplankton and seawater parameters reported here contributes to a wealth of data for coastal areas worldwide.   95  In this study, we measured, for the first time, the content of ten trace metals (Mn, Fe, Cr, Ba, Zn, Ni, Co, Cu, Ag and Cd) in SoG zooplankton, including five essential metals (i.e., Zn, Mn, Fe, Co, and Cu), and a series of potentially toxic metals (i.e., Cu, Ni, Cd, Cr, and Ag). The content of trace metals in SoG zooplankton followed a trend, with higher content for some key essential metals (i.e., Fe >> Zn ≥ Mn ≥ Ba > Cu > Ni > Cd ≥ Cr > Co ≥ Ag). We highlighted and attempted to explain why the content of some metals were in excess (i.e., Mn, Ba, Co, and Ag) or in deficit (i.e., Cr) in SoG zooplankton, relative to those in other worldwide zooplankton. We also found interesting seasonal trends in zooplankton metal content for certain element pairings (e.g., Ag and Cu; Ni and Zn; Mn and Cr). Overall, Ag and Cu content increased significantly as a function of zooplankton size fraction, while no significant trend was observed for Cd. The interactions between the covalent and ionic indexes among trace metals were able to explain a) the elemental pairings, b) trends in the zooplankton content of essential (i.e., Mn, Zn, and Cu) and non-essential trace metals (i.e., Cr, Ni, and Ag), and c) their bioaccumulation factors (Ag BAF >> Cu BAF > Cd BAF).  However, the content of all the metals we measured in SoG zooplankton were never approaching toxicity levels reported in previous studies (Hook & Fisher, 2001a and b). This is not surprising considering the non-toxic levels of dissolved and particulate Cu, Cd, and Ag, for example, in SoG waters reported here and in Kuang (2019).  Nevertheless, the content of Ag and Cd in the larger SoG zooplankton were, in a few instances, close to the metal content of model copepods that decrease egg production in laboratory toxicity experiments (e.g., Hook and Fisher, 2001a and b). Thus, close monitoring of dissolved, particulate and zooplankton Ag and Cd content is recommended.  We also used our field-based data to evaluate the accuracy of the laboratory-based kinetic model, developed by Wang and Fisher (1998), to estimate the content of potentially toxic metals in zooplankton, based on measurements of dissolved and particulate metal concentrations in seawater, as well as a variety of zooplankton physiological parameters (e.g., metal assimilation efficiencies, efflux rates, etc.). Our estimates suggest that the Wang and Fisher kinetic model (1998) can predict reasonably well the Ag content of SoG zooplankton populations, but the model needs to be improved for proper predictions for Cd and Cu zooplankton content.   96  5.3 Strengths and limitations of this research The samples (i.e., dissolved, particulate and zooplankton parameters) for this research project were collected multiple times a year at a single station, located in the Southern SoG, away from the Fraser River freshet and from runoff from other natural sources. In most instances, trace metal clean techniques were used. Thus, this research has seasonal resolution in the Southern SoG, where most of the water volume of the Strait is located (Pawlowicz et al., 2007). This study provides a comprehensive data set that can be used in a variety of fields (e.g., taxonomy, ecology, toxicology) or to better understand the interactions between physical, chemical and biological processes in the SoG ecosystem. Two limitations are apparent from this research: 1) the zooplankton sampling resolution, and 2) the accuracy and relevance of some of the parameters used in the kinetic model of Wang and Fisher (1998). First, to measure a wide variety of zooplankton parameters, we had to sample large quantities of zooplankton biomass, which entailed performing three vertical net tows in each sampling date. This time consuming sampling activity prevented us from collecting additional biological replicates, as three more net tows would have been required. In addition, though ideally, we would have liked to collect enough biomass to determine metal content in single zooplankton species, this was not practical.  Therefore, we determined trace metal content in homogenized zooplankton for a given size-fraction. This prevented us from potentially measuring trophic position, metal content and bioaccumulation factors in a variety of specific zooplankton species. In addition, during some sampling dates we did not have enough zooplankton biomass to determine trophic position in certain size fractions. These missing samples may have skewed the overall seasonal regressions between trophic position and size fractions.  Finally, due to our full-water column vertical net tows, overwintering zooplankton such as N. plumchrus were also sampled. This species lives off the reserves they acquire before going to depth to overwinter (Lee et al., 2006), potentially influencing the trace metal content of our samples. Thus, high variability in our SoG zooplankton trace metal content measurements is expected.  Second, although the Wang and Fisher (1998) kinetic model estimates for zooplankton Ag content were only off by two-fold, the estimates for Cd and Cu content were off by one order of  97  magnitude (i.e., the estimates were higher than the metal content we measured in SoG zooplankton). This discrepancy could be due to: 1) the model’s lack of consideration of organic metal complexation in seawater, and 2) the assumption that the SoG zooplankton have similar physiological parameters for metal assimilation and homeostasis as those determined experimentally for the copepods Acartia and Temora  in studies like Wang and Fisher (1998) and Chang and Reinfelder (2002) (i.e., metal assimilation efficiency from food, metal uptake rate constant from the dissolved phase, metal efflux rates from the dissolved and dietary phases, ingestion rate and growth rate).  Indeed, the kinetic model fails to take into account that unlike Ag, Cd and Cu are mostly bound to strong organic ligands (>70% and >99%, respectively) which decreases the bioavailability of these metals (Bruland, 1992; Buck et al., 2006). This might explain why the Ag content estimates from the model are closer to the actual zooplankton measurements. In addition, the constants for zooplankton metal influx, efflux and assimilation, as well as ingestion and growth rate were measured in only two zooplankton species (e.g., for calanoid copepods Temora longicornis in Wang and Fisher 1998’s Cd and Ag study, and Acartia tonsa in Chang and Reinfelder 2002’s Cu study) under laboratory settings. This assumption contradicts the observations that metal content is species-specific. In addition, the ingestion rate used in the model is set for the maximum rate determined for copepods (Wang & Fisher, 1998), and the growth rate was specifically for T. longicornis. Therefore, it is likely that growth and ingestion rates vary widely among zooplankton taxa given their different life cycles and metabolisms and cannot be applied to all zooplankton taxa. Finally, the metal bioconcentration factors in the phytoplankton fed to the copepods are for single-species of diatom cultured exponentially (e.g., Chang and Reinfelder, 2000). These parameters are good estimates for laboratory studies but may not be realistic for the conditions experienced by phytoplankton and zooplankton in the field. For example, we specifically assumed that zooplankton were only consuming non-lithogenic particulate metals. This assumption would work well if the vast majority of particles were non-lithogenic, and these non-lithogenic particles were dominated by phytoplankton, thus providing the only source of food and dietary metal to zooplankton. However, that is unlikely, as the zooplankton are probably non-discriminating between lithogenic and non-lithogenic particles, and as we have seen for Cd, the non-lithogenic fraction is dominated by authigenic  98  particles (Kuang, 2019). However, if zooplankton were consuming both lithogenic and non-lithogenic particulate metals, their estimate should be even higher, and thus deviate even more from our low measurements. So, in essence, the only way to reconcile our low measurements for Cd and Cu content with the higher estimates using the Wang and Fisher model is to assume that either seawater Cd and Cu are less bioavailable in the field than in the lab or that the SoG zooplankton are efficiently effluxing the metals they internalize.  Despite these drawbacks, it is surprising how good the estimates of the model are, given the assumptions made. 5.4 Applications of research findings This research provides a baseline for zooplankton trace metal content and bioaccumulation factors in the SoG taking into account temporal variability. Our data can also be used to assess the general zooplankton health- in relation to dissolved, particulate and the own organisms’ trace metal content. In addition, our findings can help determine if the environmental guidelines for various trace metals in the province of British Columbia are being followed.  Our zooplankton trace metal measurements match up reasonably well―especially for Ag, though less so for Cd and Cu―with those estimated by a zooplankton kinetic model that only requires dissolved and particulate metal concentrations. Thus, studies interested in a rough estimate of zooplankton metal content with a time constraint, small budget, or lack of analytical facilities could consider using Wang and Fisher’s (1998) model instead of spending resources on actual zooplankton trace metal analysis. 5.5 Future directions of this research Some future analysis of this thesis data could include calculating the bioaccumulation factors―as done here for Cd, Cu and Ag―for the rest of the trace metals sampled (i.e., Fe, Mn, Ni, Co, Cr, Zn and Ba). In addition, it would be useful to compare the spatial variability of dissolved, particulate and zooplankton trace metal content, as well zooplankton BAFs in SoG. This would provide a better understanding of how zooplankton trace metal content and accumulation factors vary temporally and spatially in SoG. While sampling for zooplankton in the field, size fractionation or species-specific sampling should occur immediately after the net  99  tows are collected. This will prevent predation among various sized zooplankton while they are kept in filtered seawater for a couple of hours in order to purge their gut (see section 2.1.4). This will ensure that trophic position determinations are not overestimated. The range in trophic position in the different SoG zooplankton size fractions was very small. Therefore, we were unable to calculate trophic magnification factors (TMF), as TMF are calculated from the correlation between the log of pollutant content in various food web organisms against their respective trophic position (e.g., Kelly, Ikonomou, Blair, and Gobas, 2008 for PBDEs). Trophic magnification factors are used to determine whether a metal is biomagnified (i.e., increase in metal accumulation between trophic positions) or biodiluted (i.e., decrease in metal accumulation between one trophic position to the next) across the food web (Arnot & Gobas, 2006). Future work to determine metal trophic magnification factors should include seasonal and spatial sampling for the same parameters that were measured in this thesis but focus specifically on resident SoG organisms with known predator-prey relations (e.g., E. pacifica-> Merluccius productus (Pacific hake) -> Phoca vitulina (Harbour seal)).  In order to better understand zooplankton trophic dynamics and pathways, as well as their relation to trace metal content, I recommend measuring trophic position, carbon content, and  trace metal content of a series of specific species of zooplankton that are ecologically relevant in SoG. The sampling should include enough individuals per species or genus to reach the necessary biomass for all measurements, and following the same sampling procedure for at least one known predator and one known prey. These studies could include additional sampling for organisms involved in the microbial loop in order to study the effects of the microbial food web on both zooplankton trophic position and metal content. Indeed, comparing nitrogen enrichment values between glutamic acid and phenylalanine, alanine and phenylalanine, and among phenylalanine values might shed more light into the trophic pathways involved in SoG zooplankton dynamics.  The variability on the bioaccumulation factors for Ag, Cd and Cu depending on whether they are calculated using average full-depth profile of dissolved metal concentrations vs. using only the average surface dissolved concentrations (0-50 m) should be explored in detail. Our preliminary results to address this issue indicate that the BAFs calculated using both approaches are very  100  similar, except in June, when large differences in dissolved metal concentrations are observed between the top 50 m and the rest of the water column (Appendices A-16 to A-18). In retrospect, calculating BAFs using total or non-lithogenic metals from the surface layer (0-50 m) could provide more accurate values given the results of the bio-kinetic model (i.e., particle ingestion is the most important pathway for zooplankton trace metal accumulation).  However, it should also be noted that the correlation between dissolved and particulate metals vary temporally and between metals (Appendices B-18 to B-20), which could overestimate or underestimate model results for different metals at different points in time. Future radiotracer laboratory studies should also focus on determining trace metal content, as well as trace metal uptake and efflux rate constants, for both dissolved and particulate phases, in a variety of zooplankton taxa (e.g., uptake of dissolved and particulate metals, as well as a pulse-chase feeding experiment). In particular, a pulse-chase feeding experiment would be ideal to study metal biomagnification. In a pulse-chase feeding experiment, an evenly-radiolabeled prey is fed to a predator.  The predator’s metal assimilation and efflux rates are then measured for a determined period of time (for more experimental considerations, refer to Wang, 2002, Wang 2011, and Wang, 2013). In this manner, the relationship between zooplankton taxa, trophic pathway and metal assimilation could be explored further. Studies using radiotracers should also determine zooplankton a) growth and ingestion rates, b) where the metals are assimilated in their bodies, and c) the metals within their possible prey (i.e., phytoplankton, detritus or other zooplankton).  Based on a recent article by Rainbow et al. (2011), another area to focus on could be on the study of trophically available metals (TAMs) to marine organisms. TAMs can vary between prey, predator, and between metals (Rainbow et al., 2011).  Therefore, knowing what metals are available to organisms of distinct trophic position in a food web could also provide insights into metal biomagnification and bioaccumulation.    101  References Aberle, N., & Malzahn, A. M. (2007). Interspecific and nutrient-dependent variations in stable isotope fractionation: Experimental studies simulating pelagic multitrophic systems. Oecologia, 154(2), 291-303. Achterberg, E. P., & Van Den Berg, Constant MG. (1997). 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Month sampled Fraction of total tow used for community composition values (%) Fraction of total tow used for trace metal content (%) December 2017 12.5 50 April 2018 12.5 50 June 2018 7.81 50 August 2018 12.5 50       117  A.2 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated December 2017 sample. All feeding type information except (*,#,&) was taken from Mackas et al. (2013). Information on (*) was taken from Tommasi et al. (2013), (#) from El-Sabaawi et al. (2010), and (&) from Choy et al. (2017). Size fraction (µm) Dominant groups Species and % abundance in size fraction Feeding type Dominant feeding type(s) 250-500 Gymnolaemate Bryozoa *sp. cyphonautes (100) Omnivore* Omnivore 500-1000 Calanoid copepods Scolecithricella minor (8.6) Detritivore Omnivore Aetideus divergens (14) Omnivore# Microcalanus pygmaeus (15) Herbivore/Omnivore* Cyclopoid copepods Oithona similis (21.5) Omnivore Oithona atlantica (7.5) Omnivore Corycaeus anglicus (22.6) Carnivore 1000-2000 Calanoid copepods Aetideus divergens (7.2) Omnivore# Omnivore Pseudocalanus minutus (17.5) Herbivore Metridia pacifica (7.2) Omnivore Calanus pacificus (16.3) Hervibore/Omnivore Cyclopoid copepods Oithona atlantica (37.9) Omnivore Pteropods Limacina helicina (8.4) Herbivore 2000-4000 Calanoid copepods Metridia pacifica (54.8) Omnivore Omnivore Ostracods Discoconchoecia elegans (32.6) Detritivore >4000 Calanoid copepods Eucalanus bungii (14.1) Herbivore Carnivore Polychaetes Tomopteris septentrionalis (58.3) Carnivore Siphonophores Nanomia bijuga  nectophore  (7.1) Carnivore&  118  A.3 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated April 2018 sample. All feeding type information except (*,+) was taken from Mackas et al. (2013). Information on (*) was taken from Tommasi et al. (2013), (+) Macdonald et al. (2010). Size fraction (µm) Dominant groups Species and % abundance in size fraction Feeding type Dominant feeding type(s) 250-500 Calanoid copepods Metridia sp. (29.4) Omnivore Omnivore Euphausiids Euphasiidae sp. eggs (23.5) - Euphasiidae sp. nauplii (29.4) Herbivore Gymnolaemate Bryozoa *sp. cyphonautes (15.7) Omnivore* 500-1000 Calanoid copepods Calanus *sp. (6.4) Herbivore/Omnivore Herbivore/Omnivore Scolecithricella minor (5.3) Detritivore Eucalanus *sp. nauplii (16) Herbivore Metridia sp. (8.5) Omnivore Microcalanus pygmaeus (17) Herbivore/Omnivore* Cirripedia Cirripedia *sp. nauplii (12.8) Omnivore+ Cyclopoid copepods Oithona similis (8.5) Omnivore 1000-2000 Calanoid copepods Metridia pacifica (31) Omnivore Omnivore Eucalanus sp. (13.5) Herbivore Cyclopoid copepods Oithona atlantica (9.2) Omnivore Euphausiids Euphausiidae *sp. protozoea (29.3) Herbivore 2000-4000 Calanoid copepods Metridia pacifica (23.4) Omnivore Herbivore Eucalanus sp. (34.6) Herbivore Ostracods Discoconchoecia elegans (14.3) Detritivore Polychaetes Polynoidae *sp. larvae (5.1) Omnivore+ Spionidae *sp. larvae (9.2) Omnivore+ >4000 Amphipods Scina borealis (8.4) Carnivore Herbivore Calanoid copepods Eucalanus bungii (49) Herbivore Larvacean Oikopleura dioica (9.6) Herbivore Decapods Brachyura *sp. zoea (9.6) Omnivore+ Polychaetes Tomopteris septentrionalis (6) Carnivore  119  A.4 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated June 2018 sample. All feeding type information except (*,&,!) was taken from Mackas et al. (2013).  Information on (*) was taken from Tommasi et al. (2013), (&) from Choy et al. (2017), and (!) from Jagger et al. (1988). Size fraction (µm) Dominant groups Species and % abundance in size fraction Feeding type Dominant feeding type(s) 250-500 Calanoid copepods Metridia spp (72.73) Omnivore Omnivore  Gymnolaemate Bryozoa spp. cyphonautes (27.27) Omnivore* 500-1000  Calanoid copepods Paracalanus indicus (50.71) Herbivore Herbivore Cladocera Podon spp. (22.50) Herbivore! Cyclopoid copepods Oithona similis (5.71) Omnivore 1000-2000 Calanoid copepods  Pseudocalanus minutus (26.85) Herbivore Omnivore Metridia pacifica (24.83) Omnivore Cyclopoid copepods Oithona atlantica (30.20) Omnivore 2000-4000  Calanoid copepods Metridia pacifica  (37.33) Omnivore Omnivore Calanus marshallae  (8.89) Herbivore/Omnivore Ostracod Discoconchoecia elegans  (32) Detritivore Polychaetes Tomopteris *sp. (8.89) Carnivore >4000 Calanoid copepods Eucalanus bungii (41.9) Herbivore Herbivore Euphausiids Euphausia pacifica (11.1) Herbivore Polychaetes Tomopteris septentrionalis  (5.5) Carnivore Siphonophores Nanomia bijuga nectophore (25.3) Carnivore&   120  A.5 Community composition and feeding type of the most abundant species (≥ 5%) found in the size-fractionated August 2018 sample. All feeding type information except (*,#,&) was taken from Mackas et al. (2013).  Information on (*) was taken from Tommasi et al. (2013), (#) from El-Sabaawi et al. (2010), and (&) from Choy et al. (2017). Size fraction (µm) Dominant groups Species and % abundance in size fraction Feeding type Dominant feeding type(s) 250-500 Calanoid copepods Metridia pacifica (62.5) Omnivore Omnivore  Cyclopoid copepods Corycaeus anglicus (12.5) Carnivore Euphausiids Euphausiidae spp. eggs (12.5) - Gymnolaemate Bryozoa spp. cyphonautes (12.5) Omnivore* 500-1000 Calanoid copepods Pseudocalanus newmani (29.35) Herbivore Herbivore & Omnivore  Microcalanus pygmaeus (12.5) Herbivore/Omnivore* Paracalanus indicus (7.07) Herbivore Cyclopoid copepods Oithona similis (23.91) Omnivore Corycaeus anglicus (9.78) Carnivore 1000-2000 Calanoid copepods Metridia pacifica (26.36) Omnivore Omnivore  Pseudocalanus minutus (16.36) Herbivore Aetideus divergens (7.27) Omnivore# Calanus pacificus (6.36) Herbivore/Omnivore Cyclopoid copepods Oithona atlantica (28.18) Omnivore 2000-4000 Amphipods Cyphocaris challengeri (5.25) Carnivore Omnivore  Calanoid copepods Metridia pacifica (48.6) Omnivore Calanus marshallae (7.88) Herbivore/Omnivore Ostracods Discoconchoecia elegans (30.21) Detritivore >4000 Amphipods Themisto pacifica (7.38) Carnivore Carnivore Calanoid copepods Eucalanus bungii (36.88) Herbivore Paraeuchaeta elongata (7.38) Carnivore* Chaetognaths Parasagitta elegans (12.74) Carnivore Pteropods Clione limacine (6.71) Carnivore* Siphonophores Nanomia bijuga nectophore  (14.08) Carnivore&  121  A.6 Average zooplankton iron content (mg Fe ∙ kg d.w.-1) measured throughout the time series at Station S4-1.5. Errors indicate one standard deviation from the mean.  Ranges are shown in parenthesis below averages. Median value is shown in italics.    December April June August All data Fe  349 ± 317  (99-838) 158   2041 ± 3597  (117-8447) 577   1921 ± 2366 (537-6111) 837  188 ± 171  (62-483) 106 1125 ± 2169 (62-8447) 494           122  A.7 Average or sole metal content (mg ∙ kg d.w.-1) for various zooplankton taxa from different geographical regions. Measurement ranges are shown in parentheses. Errors represent one standard deviation from the mean.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Amphipods 81 2.9 n.d. n.d. n.d. n.d. 1.4 0.1 1.6 Masuzawa et al. (1988). Sea of Japan. >1000 µm. Parathemisto japonica. (86-92) n.d. n.d. (21.8-23.5) (3-3.6) (27.7-28.2) n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Themisto abyssorum. 61 n.d. n.d. (23-26.2) (1.6-1.9) (23.5-33.8) n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Themisto libellula. (427-1,495) 1 n.d. (26-135) n.d. (7-18) n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Cyllopus magellanicus. 236 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. Hennig et al.(1985). Antarctica. Eusirus antarcticus. 1657 15 n.d. 140 n.d. 231 n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Hyperiella dilatata. 306 n.d. n.d. 76 n.d. n.d. n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Orchomene plebs. (181-1,121) n.d. n.d. (12-85) n.d. n.d. n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Vibilia antárctica. (282-1,996) (1-15) n.d. (20-875) 3 (8-118) n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Themisto gaudichaudii. 617 (61-1,996) 282 7 (1-15) 2.9 n.d.   122 (12-875) 26.1 2.6 (1.6-3.6) 3 55 (7-231) 27.7 1.4   0.1   1.6   Average Range Median   123  A.7. Continued.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Chaetognaths 188 2.7 n.d. n.d. n.d. n.d. 3.4 0.1 0.4 Masuzawa et al. (1988). Sea of Japan. >1000 µm. Sagitta elegans 69 n.d. n.d. 3.2 1 1.1 n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Eukrohnia hamata. (96-801) n.d. n.d. 175 n.d. n.d. n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Sagitta gazellae. (172-593) (2-7) n.d. (34-118) 1 (8-17) n.d. n.d. n.d. Hennig et al. (1985). Antarctica. Eukronia hamata. 319.8 (69-801) 180 3.9 (2-7) 2.7 n.d.   82.6 (3.2-118) 76 1   8.7 (1.1-17) 8 3.4   0.1   0.4   Average Range Median Copepods 47 2.3 n.d. n.d. n.d. n.d. 0.3 0.04 0.1 Masuzawa et al. (1988). Sea of Japan. >1000 µm. Calanus plumchrus. (86-93) n.d. n.d. (3.8-4.5) (2.6-3) (0.3-0.3) n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Calanus finmarchicus. 79 n.d. n.d. 4 4.3 0.6 n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Calanus glacialis. (88-104) n.d. n.d. (4.4-5.6) (9.7-11.4) (0.7-0.8) n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Calanus hyperboreus.  124  A.7. Continued.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Copepods continued 225 n.d. n.d. 4.5 3.9 0.2 n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Euchaeta barbata. 157 n.d. n.d. 4.7 2.1 0.1 n.d. n.d. n.d. Ritterhoff and Zauke (1997) 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Euchaeta glacialis. 172 n.d. n.d. 4.5 3 0.1 n.d. n.d. n.d. Ritterhoff and Zauke (1997) 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Euchaeta norvegica. (351-389) n.d. n.d. (5.9-7.5) (15.6-19.7) (0.6-0.7) n.d. n.d. n.d. Ritterhoff and Zauke (1997) 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Metridia longa. 100 5.83 n.d. 12.5 1.42 1.25 n.d. n.d. n.d. Honda et al.(1987). Antarctica. Surface.  No species provided. 157.6 (47-389) 102 4.1 (2.3-5.8) 4.1 n.d.   5.6 (3.8-12.5) 4.5 7 (1.4-19.7) 3.9 0.5 (0.1-1.3) 0.6 0.3   0.04   0.1   Average Range Median    125  A.7. Continued.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Mixed copepod samples 116 ± 33 (61-160) n.d. n.d. n.d. n.d. 6.6 ± 4.2 (2.4-14.6) n.d. n.d. n.d. Hamanaka and Tsujita (1981) Subarctic NW Pacific Mixed copepod samples. (13-119) (2.7-39.1) (2.2-30.3) (2.7-35.9) (3.8-38.7) (0.2-1.5) (5-81) (0.2-1) (0.03-0.2) Fang et al. (2014) Coastal waters in NE Taiwan. >330 µm size fraction sampled Ranges shown for 4 copepod species. 103 (26.5-160) 29.7 (9-86) n.d. 13.4 (2.7-61) 4.6 (2.8-8) 3 (0.9-5.3) 5.6 (1.8-10.1) n.d. n.d. Horowitz and Presley (1977) Gulf of Mexico. 250 µm mesh. Various depths sampled: surface to 50-150 m. (323-474) n.d. n.d. (8.4-21.5) (2.8-5) (0.9-1.9) n.d. n.d. n.d. Zauke et al. (1996) North Sea and German Bight Mixed copepods (>300 µm) 30 m to surface. 152 n.d. n.d. n.d. n.d. 1.4 n.d. 0.8 0.3 Fisher et al. (2000) Mediterranean Sea. Mixed copepods between (800-1500 µm). Surface tow. 165 (13-474) 152 34.2 (2.7-86) 24.1 16.3 (2.2-30.3) 16.3 22 (2.7-61) 15 10.2 (2.8-38.7) 4.4 3.2 (0.2-14.6) 1.5 24.5 (1.8-81) 7.6 0.7 (0.2-1) 0.8 0.7 (0.03-0.3) 0.2 Average Range Median Decapods (37-52) n.d. n.d. (12.4-15.6) (1.9-1.4) (9.2-6.7) n.d. n.d. n.d. Ritterhoff and Zauke (1997. 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Hymenodora glacialis.   126  A.7. Continued.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Euphausiids n.d. n.d. n.d. 46 n.d. 2.4 0.2 n.d. n.d. Sydeman and Jarman (1998) Central California. Euphausia pacifica and Thysanoesa spinifera. 175 1.8 n.d. n.d. n.d. n.d. 0.9 0.1 0.2 Masuzawa et al. (1988) Sea of Japan. >1000 µm. Thysanoessa longipes. 42 n.d. n.d. 35.2 2.6 0.4 n.d. n.d. n.d. Ritterhoff and Zauke (1997) 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Meganyctiphanes norvegica. 78 n.d. n.d. 38.5 1.1 0.1 n.d. n.d. n.d. Ritterhoff and Zauke (1997). 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Thysanoessa inermis. (310-694) (3-10) n.d. (17-168) n.d. (7-50) n.d. n.d. n.d. Hennig et al. (1985) Antarctica. Euphausia  triacantha. 44.5 (26.3-64.1) 3.27 (1.3-5.9) n.d. 54.1 (6.7-102) 1.87 (0.4-6.1) 1.9 (0.3-8.4) n.d. n.d. n.d. Honda et al. (1987) Antarctica. Surface.  Euphausia superba. 198.5 (26.3-694) 78 4.4 (1.8-10) 3 n.d.    59.1 6.7-168 38.5 2.6 (0.4-6.1) 1.9 9.8 (0.1-50) 2.4 0.6 (0.2-0.9) 0.6 0.1   0.2   Average Range Median Fish larvae (230-466) (2-16) n.d. (30-38) n.d. (11-27) n.d. n.d. n.d. Hennig et al. (1985) Fish larvae.      127  A.7. Continued.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Ostracods (337-358) n.d. n.d. (43-50.7) (65.8-85.9) (1.2-1.5) n.d. n.d. n.d. Ritterhoff and Zauke (1997) 200 and 300 µm mesh size. Fram Strait and Greenland Sea. Surface to 1500 m tow. Adult Conchoecia borealis. Tunicates 431 1 n.d. 34 n.d. 3 n.d. n.d. n.d. Hennig et al. (1985) Antarctica. Salpa máxima. (20-250) (1-2) n.d. (2-27) n.d. (0.2-3) n.d. n.d. n.d. Hennig et al. (1985) Antarctica. Salpa thompsoni. 233.7 (20-431) 250 1.3 (1-2) 1 n.d.   21 (2-34) 27 n.d.   2.1 (0.2-3) 3 n.d.   n.d.   n.d.   Average Range Median Mixed zooplankton samples 133 ± 74.8 (30.3-278) 62 ± 86.7 (4.5-381)  15.6  ± 20.5 (2.6-96) 20 ± 14.2 (6.7-68) 5.3 ± 4.3 (0.4-13.9) 2.9 ± 1.5 (0.9-6.9) 1.7 ± 2.6 (0*-11.8) 0.7 ± 0.7 (0.1-3.3) 0.3 ± 0.3 (0.1-1.2) This study. NE Pacific (central Strait of Georgia). (50-260) (2.9-7) (14-97) (6.2-58.4) (5-13) (1.9-3.5) n.d. n.d. (0.1-0.3) Martin and Knauer, (1973) East Pacific Ocean (Hawaii-Monterey Bay transect). Zooplankton samples (>360 µm) 30-100 m depth sampled. Possibly-contaminated samples were not considered. 167 7.3 n.d. n.d. n.d. n.d. 4.7 1 0.6 Masuzawa et al. (1988) Sea of Japan. >1000 µm.        128  A.7. Continued.  Zn Mn Ba Cu Ni Cd Cr Co Ag References and species Mixed zooplankton samples continued 80.7 ± 22.7 (48-119) 5.6 ± 0.8 (5-7) n.d. 20.3 ± 13.1 (10-47) 4 ± 2.1 (1-8) n.d. 11.2 ± 9.5 (3-29) 1.2 ± 0.5 (0.5-1.8) n.d. Ho et al. (2007) South China Sea. >150 µm size fraction sampled at various depths (<60 m) at 3 different stations. 32.6 (22.9-51.7) n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. Baines et al. (2015) Costa Rica Dome. Average of all size fractions (200-5000 µm). 137 ± 47.9 (41-218) n.d. n.d. 10.5 ± 5.6 (3.3 -25) 3.4 ± 1.6 (1-6.9) 0.7 ± 0.3 (0.2-1.2) n.d. 0.5 ± 0.3 (0.2-1.1) 0.1 ± 0.03 (0.1-0.2) Chouvelon et al. (2019). NW Mediterranean Sea. Size fractions averaged: 200-500, 500-1000, 1000-2000, >2000 µm. 116.9 (22.9-278) 51.7 59.2 (2.9-381) 7 52.4 (2.6-96) 55 28.1 (3.3-68) 17.5 6.2 (0.4-13.9) 6 2.4 (0.2-6.9) 1.6 9.7 (0-29) 4.7 1.1 (0.1-1.8) 1 0.4 (0.1-1.2) 0.2 Average Range Median *Below detection limit, set to 0.    n.d., not determined.  129  A.8  Zooplankton Ag, Cu and Cd bioaccumulation factors (BAFs; L ∙ kg d.w. -1) calculated for samples taken between 2017 and 2018 at the time series Station S4-1.5. BAFs were calculated with the following formula: BAF= [Mezoop]/[Meseawater]. Where Mezoop is the zooplankton metal content (mg Me ∙ kg d.w. -1) and Meseawater is the dissolved metal concentration (mg Me ∙  L-1).  Ranges and average dissolved Ag, Cu and Cd concentrations in the water column taken from Kuang (2019) are shown. For dissolved metal concentrations, it was assumed that 1 kg = 1L.  Errors indicate one standard deviation from the mean. Ranges are shown below averages in parenthesis.    December April June August Average Ag  Bioaccumulation factors (L ∙ kg d.w -1) x104  30.33 ± 21.40 (15.15-66.51)   40.29 ± 45.38 (7.96-116.62)   22.15 ± 7.74 (13.68-33.88)   30.36 ± 19.44 (11.61-56.75)   30.78 ± 25.8 (7.96-116.62)  Dissolved Ag concentration (mol∙ L -1) x10-12  9.40 ± 1.52 (7.1-11.9)   9.53 ± 1.6 (6.7-11.9)   7.53 ± 1.7 (5.5-10.0)   6.84 ± 1.51 (3.4-9.1)   8.33 ± 1.93 (3.4-11.9)  Cu Bioaccumulation factors (L ∙ kg d.w -1) x104  6.4 ± 3.55 (3.24-12.36)   8.5 ± 6.23 (3.42-18.7)   4.50 ± 1.53 (2.74-6.47)   6.43 ± 4.18 (2.79-11.06)   6.46 ± 4.14 (2.74-18.7)  Dissolved Cu concentration (mol∙ L -1) x10-9  4.66 ± 1.37 (3.49-7.74)   5.72 ± 1.74 (3.84-8.49)   5.07 ± 2.14 (3.68-9.66)   3.78 ± 0.37 (3.21-4.57)   4.81 ± 1.66 (3.21-9.66)  Cd Bioaccumulation factors (L ∙ kg d.w -1) x104  4.86 ± 2.08 (2.12-7.73)   4.02 ± 1.75 (1.89-5.96)   5.16 ± 3.42 (2.39-11.09)   2.71 ± 1.54 (1.30-5.13)   4.19 ± 2.34 (1.30-11.09)  Dissolved Cd concentration (mol∙ L -1) x10-12  639.58 ± 44.43 (550-681)   628.83 ± 39.28 (546-666)   550.58 ± 148.39 (269-660)   634.50 ± 71.42 (463-717)   613.38 ± 92.34 (269-717)    130  A.9 Dissolved Cu, Ag and Cd concentrations at the time series Station S4-1.5. Dissolved Ag and Cd data from December 2017 and April 2018 were taken from Kuang (2019). Analytical errors shown are based on one standard deviation. It has been assumed that 1 kg seawater = 1L seawater. Month/Year Depth (m) dCu (nM) dCu error (nM) dAg (pM) dAg error (pM) dCd (pM) dCd error (pM) December 2017 0 7.7 0.05 11.9 0.5 550 12 4 5.7 0.03 9.7 0.1 573 11 9 5.1 0.05 9.4 0.5 590 11 18 4.3 0.04 11.1 0.5 635 11 28 4.2 0.04 8.7 0.4 640 11 46 4.0 0.04 9.6 0.5 658 12 72 6.6 0.07 11.3 0.5 657 10 93 3.9 0.06 8.1 0.5 673 11 142 3.8 0.06 7.1 0.6 676 11 187 3.6 0.06 7.7 0.5 681 10 233 3.5 0.06 8.1 0.5 672 10 282 3.5 0.05 10.1 0.2 670 10 April 2018 0 4.6 0.13 6.7 0.4 546 5 4 8.0 0.15 10.3 0.5 573 5 9 7.6 0.15 7.6 0.4 584 6 19 8.5 0.15 8.6 0.5 647 6 28 7.4 0.15 9.6 0.4 654 6 47 4.7 0.13 9.4 0.4 632 7 75 4.4 0.13 9.4 0.6 631 6 97 3.8 0.13 9.5 0.5 643 6 145 3.9 0.13 8.3 0.4 663 6 192 6.4 0.14 11.3 0.6 666 6 239 5.2 0.13 11.9 0.6 649 6 287 4.1 0.13 11.7 0.5 658 6         131  A.9. Continued Month/Year Depth (m) dCu (nM) dCu error (nM) dAg (pM) dAg error (pM) dCd (pM) dCd error (pM) June 2018 0 9.7 0.15 7.3 0.4 269 4 4 9.0 0.13 5.5 0.2 275 4 9 6.5 0.10 5.6 0.1 396 2 19 4.1 0.13 5.7 0.3 569 6 28 4.4 0.13 5.6 0.3 596 6 47 3.7 0.13 6.9 0.5 636 6 79 4.2 0.13 7.9 0.4 641 6 102 3.7 0.13 8.2 0.1 631 5 150 3.9 0.13 8.5 0.5 633 6 198 3.8 0.13 10 0.5 648 6 243 3.8 0.13 9.9 0.6 653 6 290 4.0 0.13 9.3 0.5 660 6 August 2018 0 4.6 0.15 5.7 0.3 463 15 5 3.9 0.14 3.4 0.2 538 16 10 4.1 0.14 5.7 0.3 627 18 20 3.9 0.11 6.3 0.3 628 4 30 3.9 0.11 7.2 0.3 628 4 50 3.9 0.11 7.1 0.2 626 4 75 4.0 0.11 6.5 0.3 649 4 100 3.5 0.22 7.2 0.3 676 15 150 3.6 0.22 8 0.3 681 15 200 3.4 0.21 8.7 0.1 690 15 250 3.5 0.22 9.1 0.4 691 15 330 3.2 0.21 7.2 0.3 717 16         132  A.10 Particulate Al, P and Ba content at the time series Station S4-1.5. Particulate Al and P data from December 2017 and April 2018 were taken from Kuang (2019). Analytical errors shown are based on one standard deviation.  Month/Year Depth (m) pAl (nM) pAl error (nM) pP (nM) pP error (nM) pBa (nM) pBa error (nM) December 2017 0 792 45 32 3 1.0 0.7 4 914 23 29 2 1.2 0.6 9 753 35 25 2 1.0 0.6 18 291 16 17 2 0.2 0.5 28 151 6 11 1 0.02 0.5 46 37 4 4 1 -0.1 0.6 72 76 4 11 1 0.02 0.5 93 151 8 9 1 0.1 0.5 142 443 25 14 1 0.5 0.5 187 881 37 25 2 1.02 0.5 233 528 26 20 1 0.6 0.5 282 211 8 12 1 0.2 0.5 April 2018 0 352 9 191 6 2.67 0.61 4 315 19 188 7 1.28 2.17 9 53 4 111 3 0.64 0.55 19 13 4 40 3 0.37 0.62 28 108 9 28 2 0.24 0.51 47 134 5 17 2 0.11 0.55 75 159 5 19 1 0.41 0.05 97 218 3 19 1 0.37 0.01 145 1115 33 41 2 1.52 0.05 192 920 21 41 2 1.44 0.04 239 807 24 31 1 1.32 0.05 287 611 14 30 2 1.02 0.04 June 2018 0 608 32 153 8 1.1 0.07 10 127 2 69 3 0.3 0.03 20 30 2 34 1 0.1 0.04 30 26 1 34 1 0.1 0.02 50 31 2 43 2 0.1 0.01 150 357 2 21 0 0.7 0.01 200 215 6 15 2 0.4 0.01 250 247 3 17 0 0.5 0.05 300 1156 6 46 1 2.1 0.04       133  A.11  Particulate Cu, Ag and Cd content at the time series Station S4-1.5. Particulate Cd and Ag data from December 2017 and April 2018 were taken from Kuang (2019). Analytical errors shown are based on one standard deviation.  Month/Year Depth (m) pCu (pM) pCu error (pM) pAg (pM) pAg error (pM) pCd (pM) pCd error (pM) December 2017 0 472.7 48.5 2.22 0.07 9.3 0.2 4 421.9 3.0 1.71 0.04 14.9 0.4 9 352.5 41.8 1.55 0.06 17.3 0.5 18 174.5 12.9 0.61 0.06 20.7 1.4 28 139.5 10.9 0.38 0.03 26.2 0.7 46 113.9 11.2 0.47 0.05 34.7 2.1 72 187.4 16.9 0.23 0.03 25.6 0.8 93 102.8 3.7 0.39 0.03 10.8 0.2 142 205.6 4.6 0.7 0.05 17 0.9 187 273.1 8.7 0.89 0.09 22.4 1.9 233 186.0 19.5 0.6 0.04 33.9 1.4 282 116.1 7.7 0.43 0.04 25.9 2.1 April 2018 0 276.7 11.9 1.88 0.13 15.8 1 4 990.5 127.9 1.7 0.21 20.6 1.9 9 631.4 37.2 0.88 0.04 7.3 0.3 19 443.0 28.3 0.35 0.03 5.3 0.1 28 366.5 16.9 0.37 0.03 17.6 0.2 47 201.1 12.7 0.35 0.03 35.4 0.9 75 259.4 4.1 0.23 0.02 29.5 0.2 97 153.1 2.7 0.32 0.03 18.2 0.2 145 426.2 18.7 1.33 0.03 16.8 0.3 192 641.1 34.6 0.67 0.03 33.3 0.4 239 571.8 11.2 0.58 0.04 36.3 0.8 287 366.0 4.7 0.61 0.02 40.3 1.4 June 2018 0 649.4 34.7 2.19 0.12 21.7 0.2 10 217.4 9.8 1.73 0.10 24.4 1.0 20 173.1 3.7 1.17 0.09 18.0 1.0 30 171.9 3.3 1.15 0.05 34.5 1.3 50 155.5 2.0 0.87 0.06 38.4 1.4 150 207.7 4.8 0.51 0.03 27.0 0.9 200 189.8 8.6 0.42 0.02 25.6 1.0 250 200.8 5.8 0.39 0.03 44.2 1.0 300 418.0 9.2 1.07 0.02 23.7 0.4        134  A.12 Figure taken from Whitfield and Turner (1987). Complexation field diagram for elements in seawater. The elements have been primarily divided by increasing ionization potential (groups I to V), and by increasing covalent interactions (groups (a), (a)’, (b)’, and (b)). The elements have also been divided by their complexation in seawater: elements in A represent those forming weak complexes, B elements are found in chloride complexes, C are strongly hydrolyzed elements, and D are fully hydrolyzed elements.             135  A.13 Figure taken from Nieboer et al. (1999). Metal ions (and two metalloids) divided into three groups: class A, borderline, and class B. Metal ions are plotted by their ability to form ionic bonds (ionic index) against their ability to form covalent bonds (covalent index). Xm is the Pauling electronegativity, z the ionic charge and r is the ionic radius of the metal species. The covalent index (Xm)2r shows the ability of a metal ion to form covalent over ionic bonds. On the other hands, the ionic index z2 ∙ r-1 measures the ionic interactions of the metal ion.        136  A.14 Monthly and total averages of various particulate metals sampled throughout the time series at Station S4-1.5. The lithogenic fraction was calculated by multiplying the total Al sampled by the Me: Al molar crustal ratios:  7.58 x10-3 for P, 1.56x10-7 for Ag, 1.32 x10-4 for Cu, and 2.93 x10-7 for Cd. Crustal ratios were taken from Taylor and McLennan (1995).  The non-lithogenic fraction was calculated by subtracting the lithogenic fraction from the total Me content.   Particulate metal December April June Average of all data Al Total (nM) 435.67 400.42 310.87 388.81 P Total (nM) 17.42 63 47.9 42.31 Lithogenic fraction (nM) 3.30 3.04 2.36 2.95 Biogenic fraction (nM) 14.11 59.96 45.54 39.36 Ag Total (pM) 0.85 0.77 1.06 0.88 Lithogenic fraction (pM) 0.07 0.06 0.05 0.06 Non-lithogenic fraction (pM) 0.78 0.71 1.01 0.82 Non-lithogenic Ag: lithogenic Ag 17.98 34.82 95.08 45.13 Non-lithogenic Ag/Biogenic P (pmol/nmol) 0.06 0.02 0.03 0.03 Cu Total (pM) 228.83 443.90 264.84 316.86 Lithogenic fraction (pM) 57.52 52.87 41.04 51.33 Non-lithogenic fraction (pM) 171.31 391.04 223.79 265.52 Non-lithogenic Cu: lithogenic Cu 5.91 36.58 18.20 20.41 Non-lithogenic Cu/Biogenic P (pmol/nmol) 13.11 10.15 6.64 10.27 Cd Total (pM) 21.56 23.03 28.61 24.02 Lithogenic fraction (pM) 0.13 0.12 0.09 0.11 Non-lithogenic fraction (pM) 21.43 22.92 28.52 23.90 Non-lithogenic Cd: lithogenic Cd 538.08 430.26 1439.74 744.78 Non-lithogenic Cd/Biogenic P (pmol/nmol) 2.21 0.87 1.10 1.42   137  A.15 Input values for the kinetic model shown in Wang and Fisher (1998). ku is the uptake rate constant from the dissolved phase (L∙ g--1∙d-1), kew is the efflux rate constant from the dissolved phase (d-1), g is the growth rate constant for copepods (d-1), AE is the Assimilation Efficiency, and IR the copepod ingestion rate. Unless, noted, constants were taken from Wang and Fisher (1998). Values with (*) were taken from Chang and Reinfelder (2002). Cw are the average dissolved metal concentrations (µg Me ∙ L-1) sampled at Station S4-1.5.   Average Assimilation Efficiencies were calculated using the assimilation efficiency values shown on Table 11. Cf are the particulate metal content per phytoplankton dry weight. Css,w  is the total calculated metal taken up by copepods from the dissolved  phase. Css,f  is the total calculated metal taken up by copepods from the food (dietary phase). Rw and Rf are the respective percentages from the total metal intake from the dissolved and dietary phases, respectively. Css is the total calculated metal content in copepods under steady-state conditions. Particulate metal concentrations from the particulate metal samples were normalized using the average non-lithogenic Me:biogenic P ratios per respective month and normalized to phytoplankton dry weight assuming the Redfield ratio (106 C: 1 P) and a 35% carbon weight per phytoplankton dry weight (Janik et al. 1981). Molar masses used: C (12.0107), Ag (107.8286), Cu (63.546), and Cd (112.411). Note that 1 µg ∙ g-1= 1 mg ∙ kg d.w-1.  Dissolved phase Food phase Ag ku (L∙ g-1∙d-1) Cw (µg Ag ∙ L-1) kew (d-1) g (d-1) Cf (µg Ag ∙ g d.w. phyto-1) Average AE (%) IR (d-1) kef (d-1) 10.42 December 1.01x10-3 0.173 0.09 December 1.64 14.6 0.42 0.294 April 1.03x10-3 April 0.46 June 8.12x10-4 June 0.76 All data 8.95 x10-4 All data 0.97  Css,w (µg Ag ∙ g-1) Rw (%)  Css,f (µg Ag ∙ g-1) Rf (%)  Css (mg Ag ∙ kg d.w-1)  December 0.04 13.30  December 0.26 86.7  0.302  April 0.04 35.63  April 0.07 64.37  0.114  June 0.03 21.05  June 0.12 78.95  0.153  All data 0.04 18.64  All data 0.15 81.36  0.190     138  A.15. Continued. Dissolved phase Food phase Cu ku (L∙ g-1∙d-1) Cw (µg Cu ∙ L-1) kew (d-1) g (d-1) Cf (µg Cu ∙ g d.w. phyto-1) Average AE (%) IR (d-1) kef (d-1) 5.1* December 2.96x10-1 0.08* 0.09 December 228.94 45* 0.42 0.108* April 3.64x10-1 April 177.37 June 3.22x10-1 June 115.92 All data 3.05x10-1 All data 179.36  Css,w (µg Cu ∙ g-1) Rw (%)  Css,f (µg Cu ∙ g-1) Rf (%)  Css (mg Cu ∙ kg d.w-1)  December 8.88 3.90  December 218.54 96.1  227.41  April 10.91 6.05  April 169.31 93.95  180.22  June 9.67 8.03  June 110.65 91.97  120.31  All data 9.15 5.07  All data 171.21 94.93  180.36 Dissolved phase Food phase Cd ku (L∙ g-1∙d-1) Cw (µg Cd ∙ L-1) kew (d-1) g (d-1) Cf (µg Cd ∙ g d.w. phyto-1) Average AE (%) IR (d-1) kef (d-1) 0.69 December 7.19x10-2 0.108 0.09 December 68.20 44.6 0.42 0.297 April 7.07x10-2 April 26.94 June 6.19x10-2 June 34.02 All data 6.9x10-2 All data 43.87  Css,w (µg Cd ∙ g-1) Rw (%)  Css,f (µg Cd ∙ g-1) Rf (%)  Css (mg Cd ∙ kg d.w-1)  December 0.25 0.75  December 33.01 99.25  33.26  April 0.25 1.85  April 13.04 98.15  13.28  June 0.22 1.29  June 16.47 98.71  16.68  All data 0.24 1.12  All data 21.24 98.88  21.48     139  A.16 Zooplankton Ag bioaccumulation factors (L∙mol C-1) calculate using dissolved, particulate, total, and non-lithogenic Ag values. Zooplankton trace metal content was divided by either the average surface (0-50 m) or the average full-profile. The ratio between BAFs calculated using full-depth profile concentrations and surface concentrations is also denoted for each month.  December April June Size fraction (μm)  250 500 1000 2000 4000 250 500 1000 2000 4000 250 500 1000 2000 4000 Dissolved Me 0-50 m  BAFs (L/mol C)        8,517      3,459      3,422       19,756      7,466      4,750      4,797      2,353       15,735       42,568      2,194      2,762      1,246      2,051      4,109  Full profile BAFs (L/mol C)        9,121      3,704      3,665       21,157      7,995      4,339      4,381      2,150       14,372       38,881      5,912      7,444      3,356      5,527    11,073  Full : Surface 1.07 0.91 2.69 Total Me 0-50 m  BAFs (L/mol C)        7,640      3,102      3,069       17,720      6,696      4,295      4,337      2,128       14,228       38,490    31,726    39,945    18,011    29,656    59,418  Full profile BAFs (L/mol C)        8,366      3,398      3,361       19,406      7,333      4,013      4,053      1,988       13,294       35,964      5,194      6,540      2,949      4,855      9,728  Full : Surface 1.10 0.93 0.16 Particulate Me 0-50 m  BAFs (L/mol C)      74,128    30,103    29,782     171,942    64,974    44,840    45,279    22,215     148,531     401,815      7,266      9,148      4,125      6,792    13,608  Full profile BAFs (L/mol C)    101,070    41,044    40,606     234,436    88,590    53,498    54,022    26,505     177,212     479,404    42,770    53,849    24,281    39,979    80,101  Full : Surface 1.36 1.19 5.89 Non-lithogenic Me 0-50 m  BAFs (L/mol C)      79,354    32,225    31,881     184,064    69,555    46,104    46,556    22,842     152,720     413,146      5,912      7,444      3,356      5,526    11,073  Full profile BAFs (L/mol C)    109,846    44,608    44,132     254,790    96,282    58,190    58,760    28,830     192,754     521,448    44,821    56,432    25,446    41,897    83,943  Full : Surface 1.38 1.26 7.58  140  A.17 Zooplankton Cu bioaccumulation factors (L∙mol C-1) calculate using dissolved, particulate, total, and non-lithogenic Cu values. Zooplankton trace metal content was divided by either the average surface (0-50 m) or the average full-profile. The ratio between BAFs calculated using full-depth profile concentrations and surface concentrations is also denoted for each month.  December April June Size fraction (μm)  250 500 1000 2000 4000 250 500 1000 2000 4000 250 500 1000 2000 4000 Dissolved Me 0-50 m  BAFs (L/mol C) 1,512 645 917 3,545 2,188 1,798 944 776 2,346 5,238 20 13 7 10 18 Full profile BAFs (L/mol C) 1,677 716 1,016 3,931 2,426 2,141 1,124 924 2,793 6,235 2,106 1,386 708 1,029 1,897 Full : Surface 1.11 1.19 106.57 Total Me 0-50 m  BAFs (L/mol C) 1,434 612 870 3,363 2,076 1,679 881 724 2,190 4,890 38 25 13 18 34 Full profile BAFs (L/mol C) 1,598 682 969 3,747 2,313 1,987 1,043 857 2,592 5,786 1,997 1,315 672 976 1,800 Full : Surface 1.11 1.18 53.10 Particulate Me 0-50 m  BAFs (L/mol C) 27,968 11,938 16,953 65,570 40,476 25,268 13,261 10,903 32,963 73,591 1,810,914 1,191,910 609,292 884,770 1,631,855 Full profile BAFs (L/mol C) 34,123 14,565 20,684 80,000 49,384 27,600 14,484 11,910 36,005 80,382 38,840 25,564 13,068 18,976 34,999 Full : Surface 1.22 1.09 0.02 Non-lithogenic Me 0-50 m  BAFs (L/mol C) 36,396 15,535 22,062 85,330 52,673 26,438 13,874 11,408 34,489 76,998 1,727,740 1,137,166 581,307 844,132 1,556,904 Full profile BAFs (L/mol C) 45,581 19,455 27,630 106,862 65,965 31,332 16,443 13,520 40,873 91,250 45,963 30,252 15,464 22,456 41,418 Full : Surface 1.25 1.19 0.03    141  A.18 Zooplankton Cd bioaccumulation factors (L∙mol C-1) calculate using dissolved, particulate, total, and non-lithogenic Cd values. Zooplankton trace metal content was divided by either the average surface (0-50 m) or the average full-profile. The ratio between BAFs calculated using full-depth profile concentrations and surface concentrations is also denoted for each month.  December April June Size fraction (μm)  250 500 1000 2000 4000 250 500 1000 2000 4000 250 500 1000 2000 4000 Dissolved Me 0-50 m  BAFs (L/mol C) 1,179 492 1,968 1,560 2,386 1,000 615 881 1,871 1,971 250,670 91,820 42,824 98,377 82,581 Full profile BAFs (L/mol C) 1,120 468 1,870 1,482 2,267 964 592 849 1,803 1,900 3,408 1,248 582 1,338 1,123 Full : Surface 0.95 0.96 0.01 Total Me 0-50 m  BAFs (L/mol C) 1,141 476 1,904 1,509 2,308 973 598 857 1,820 1,917 69,959 25,626 11,952 27,456 23,047 Full profile BAFs (L/mol C) 1,084 453 1,809 1,434 2,193 930 572 819 1,739 1,832 3,243 1,188 554 1,273 1,068 Full : Surface 0.95 0.96 0.05 Particulate Me 0-50 m  BAFs (L/mol C) 34,920 14,586 58,298 46,195 70,669 35,645 21,916 31,393 66,695 70,265 3,885 1,423 664 1,525 1,280 Full profile BAFs (L/mol C) 33,233 13,881 55,481 43,963 67,254 26,308 16,175 23,170 49,225 51,860 66,974 24,533 11,442 26,284 22,064 Full : Surface 0.95 0.74 17.24 Non-lithogenic Me 0-50 m  BAFs (L/mol C) 35,166 14,688 58,707 46,520 71,165 35,745 21,977 31,481 66,882 70,462 3,681 1,348 629 1,445 1,213 Full profile BAFs (L/mol C) 33,431 13,963 55,811 44,225 67,654 26,443 16,258 23,289 49,476 52,125 67,188 24,611 11,478 26,368 22,134 Full : Surface 0.95 0.74 18.25     142  Appendix B  Reference Materials and statistical results B.1 Zooplankton size fraction (µm) vs trophic position. Linear regression model for all data measured and per month. P-value <0.05 are bolded.   Month Intercept Slope p-value slope R2 of model December 2.1833 1.9324 x10-4 8.551 x10-5 0.9032 April 2.1996 -9.7011 x10-5 0.299 0.1189 June 2.2267 -5.7817 x10-5 0.0836 0.2475 August 2.2287 2.15 x10-5 0.1951 0.1999 All data 2.174 6.2307 x10-5 0.0195 0.126    B.2 Two-Way ANOVA results for zooplankton size fraction (µm) vs trophic position. P-value <0.05 are bolded.  Source Sum Sq. d.f. Mean Sq. F Prob>F Notes Month 0.77512 3 0.25837 9.76 0.0001 Tukey HSD p-value 0.0005 between December and April, p-value 0.0001 between December and June, and p-value 0.0059 between December and August. Size Fraction 0.11994 4 0.02998 1.13 0.3569 Error 0.92614 35 0.02646   Total 1.99103                     143  B.3 Trace metal content of the reference materials DOLT-5 (Dogfish Liver Certified Reference Material) and DORM 4 (Fish Protein Certified Reference Material). Errors shown are one standard deviation.   (mg ∙ kg-1 d.w.) Ag Cd Cr Mn Co Ni Cu Zn Ba  DOLT-5 Reference Material   2.05 ± 0.08 14.5 ± 0.6 2.35 ± 0.58 8.91 ± 0.7 0.27 ± 0.03 1.71 ± 0.56 35 ± 2.4 105.3 ± 5.4 N/A   DOLT-5 measurements1  Average 1.85 ± 0.05 12.17 ± 0.56 2.02 ± 0.19 8.21 ± 0.5 0.32 ± 0.06 1.42 ± 0.12 31.91 ± 1.93 94.84 ± 4.03 0.22 ± 0.06 Median 1.86 12.48 2.01 8.42 0.36 1.43 33.15 96.81 0.19  DORM-4 Reference Material2   0.03 ± 0.01 0.3 ± 0.02 1.87 ± 0.18 3.17 ± 0.26 0.25 1.34 ± 0.14 15.7 ± 0.46 51.6 ± 2.8 N/A  Our DORM-4 measurements  Average 0.01 ± 0.03 0.21 ± 0.02 1.88 ± 0.13 3.65 ± 1.38 0.34 ± 0.06 1.14 ± 0.07 14.05 ± 0.92 44.66 ± 2.6 5.05 ± 0.28 Median 0.02 0.21 1.9 3.19 0.36 1.17 14.16 44.08 5.07 1. n= 5.  2. n= 4. N/A: Not available.   144  B.4 Major ion content of the reference materials DOLT-5 (Dogfish Liver Certified Reference Material) and DORM 4 (Fish Protein Certified Reference Material). Errors shown are one standard deviation from the mean.   (mg ∙ kg-1 d.w.) Na Mg Al P Fe DOLT-5 Reference Material   9,900 ± 1,600  940 ± 100   32 ± 4  11,500  1,070 ± 80   DOLT-5 measurements1  Average 8,380 ± 319  656 ± 119  0*  9,706 ± 491  879 ± 44 Median 8,400 591 0* 9800 885 DORM-4 Reference Material2   14,000 ± 2,400  910 ± 80  1,280 ± 340  8,000  343 ± 20 DORM-4 measurements  Average 11,397 ± 750  566 ± 70  1,170 ± 72  6,357 ± 310  280 ± 6  Median 11,507 552 1,181 6,475 280 1. n= 5.         2. n= 4.      *= below detection limit.                          145  B.5 Average instrumental and procedural blanks for the elements studied in this thesis. Blanks measured on Dec 5th and 6th, 2018 were digested along with the December 2017 zooplankton samples. Blanks measured on January 23rd and 24th, 2019 were digested along with the April, June and August 2018 zooplankton samples. Sample numbers for the instrumental and procedural blanks are shown in italics. The blank averages for Zn, Cu, Ni, Ba, and Fe are the averages for the various isotopes measured for each element. Errors shown are one standard deviation from the average.   Instrumental blank (cps) Procedural blank (ng)  Dec 6th, 2018 Jan 24th, 2019 Dec 6th, 2018 Jan 24th, 2019 Sample size 6 8 5 7 Ag (8.41 ± 2.89) * 10-5 (5.37 ± 0.83) * 10-5 + + Ba (1.09 ± 0.34) * 10-4 (1.89 ± 0.62) * 10-4 0.53 ± 0.05 3.1 ± 0.01 Cd (5.36 ± 2.85) * 10-5 (2.98 ± 1.87) * 10-5 0.13 ± 0.19 + Co (4.03 ± 1.3) * 10-4 (1.65 ± 0.27) * 10-4 0.35 ± 0.19 0.68 ± 0.2 Cr (4.54 ± 5.02) * 10-4 (2.91 ± 3.25) * 10-4 54.25 ± 0.64 61.89 ± 1 Cu (2.75 ± 1.67) * 10-4 (2.13 ± 1.24) * 10-4 0.48 ± 0.04 0.65 ± 0.05 Mn (5.54 ± 0.16) * 10-3 (3.58 ± 0.19) * 10-3 0.3 ± 0.21 0.91 ± 0.17 Ni (1.44 ± 1.85) * 10-3 (7.34 ± 9.56) * 10-4 1.09 ± 0.02 0.71 ± 0.06 Zn (1.94 ± 0.53) * 10-3 (6.16 ± 1.59) * 10-3 8.48 ± 0.06 22.67 ± 0.05  Dec 5th, 2018 Jan 23rd, 2019 Dec 5th, 2018 Jan 23rd, 2019 Sample size 7 9 5 7 Al (74.14 ± 0.75) * 10-3 (106.27 ± 0.89) * 10-3 9.2 ± 0.31 172.93 ± 1.24 Fe (5.03 ± 6.77) * 10-3 (6.37 ± 0.86) * 10-3 4.55 ± 0.18 8.68 ± 0.59 P (3.49 ± 0.57) * 10-4 (4.06 ± 0.41) * 10-4 40.79 ± 0.72 2.66 ± 1.67 Mg (40.84 ± 0.84) * 10-4 (39.78 ± 0.77) * 10-4 54.72 ± 0.95 18.17 ± 0.84 Na (22.26 ± 0.21) * 10-2 (21.41 ± 0.19) * 10-2 546.31 ± 6 135.32 ± 2.4 + = below detection limit.          146  B.6 Zooplankton size fraction (µm) vs metal content (mg ∙ kg d.w. -1). Linear regression model for all data measured. P-value <0.05 are bolded.  Metal Intercept Slope p-value slope R2 of model Ag 0.1251 1.0084 x 10-4 0.0148 0.2877 Cu 11.6527 0.0054 0.0172 0.2769 Cd 2.6287 1.5218 x 10-4 0.5594 0.0193 Cr 2.6517 -6.4196 x 10-4 0.1368 0.1187 Mn 91.2413 -0.0189 0.1910 0.093 Co 1.0655 -2.264 x 10-4 0.0528 0.1927 Ni 8.4349 -0.002 0.0013 0.4441 Zn 202.8701 -0.0447 4.243 x 10-6 0.7002 Ba 22.6520 -0.0046 0.1835 00961                            147  B.7 Zooplankton size fraction (µm) vs metal content (mg ∙ kg d.w.-1). Monthly linear regression model. P-value <0.05 are bolded.   Metal Month Intercept Slope p-value slope R2 of model Ag December 0.2893 1.1775 x 10-5 0.8947 0.0068 April -0.0451 2.9619 x 10-4 0.0066 0.9382 June 0.1359 2.8439 x 10-5 0.1976 0.4756 August 0.1203 6.6968 x 10-5 0.1775 0.5066 Cu December 15.4715 0.0022 0.5935 0.1057 April 10.0186 0.0135 0.0334 0.8231 June 14.2904 1.3797 x 10-4 0.9456 0.0018 August 6.8304 0.0056 0.0717 0.7137 Cd December 2.9628 3.4305 x 10-4 0.5631 0.1228 April 1.8407 6.4605 x 10-4 0.1074 0.6331 June 4.1891 -6.4395 x 10-4 0.4316 0.2149 August 1.5222 2.6355 x 10-4 0.5452 0.1336 Cr December 1.1053 -2.6189 x 10-4 0.2853 0.3594 April 1.943 -2.3432 x 10-4 0.6483 0.0784 June 6.8265 -0.0018 0.257 0.3939 August 0.7319 -2.2568 x 10-4 0.1992 0.4733 Mn December 34.7635 -0.0075 0.2819 0.3634 April 85.259 -0.0066 0.7839 0.0291 June 225.3681 -0.0570 0.2636 0.3856 August 19.5747 -0.0045 0.1786 0.5049 Co December 0.87 -2.2252 x 10-4 0.1712 0.5167 April 0.5914 -3.5774 x 10-5 0.6394 0.0825 June 2.2445 -5.3925 x 10-4 0.1796 0.5033 August 0.5562 -1.0805 x 10-4 0.1645 0.5278 Ni December 8.5362 -0.0023 0.1707 0.5175 April 5.0851 -8.5585 x 10-4 0.1066 0.6349 June 11.8385 -0.0027 0.0629 0.7360 August 8.2797 -0.0023 0.2175 0.4467 Zn December 199.9952 -0.0477 0.0524 0.7646 April 179.2083 -0.0344 0.0602 0.7431 June 211.3704 -0.0416 0.0419 0.7957 August 220.9066 -0.0553 0.0886 0.6736 Ba December 12.9244 -0.0026 0.4243 0.221 April 10.5267 4.0934 x 10-4 0.8940 0.0069 June 56.6799 -0.0141 0.2704 0.3773 August 10.4772 -0.002 0.3541 0.285     148    B.8 Two-Way ANOVA results for zooplankton size fraction (µm) vs. metal content (mg ∙ kg d.w.-1). P-value <0.05 are bolded.   Metal Source Sum Sq. d.f. Mean Sq. F Prob>F Notes Ag Month 0.15912 3 0.05304 0.96 0.4448 None. Size Fraction 0.49029 4 0.12257 2.21 0.1293 Error 0.66574 12 0.05548   Total 1.31515 19    Cu Month 856.77 3 285.589 2.29 0.1304 None. Size Fraction 1500.12 4 375.029 3.01 0.0621 Error 1496.77 12 124.731   Total 3853.65 19    Cd Month 6.8798 3 2.29328 1 0.4245 None. Size Fraction 10.4198 4 2.60495 1.14 0.3837 Error 27.4025 12 2.28354   Total 44.7022 19      B.9 Zooplankton size fraction (µm) vs C-normalized metal content (µmol Me ∙ mol C-1). Linear regression model for all data measured. P-value <0.05 are bolded.   Metal Intercept Slope p-value slope R2 of model Ag 0.0269 3.4781 x10-5 0.0061 0.3486 Cu 4.2461 0.0034 0.0044 0.3705 Cd 0.6208 9.604 x10-5 0.1957 0.0912             149  B.10  Zooplankton size fraction (µm) vs C-normalized metal content (µmol Me ∙ mol C-1). Monthly linear regression model. P-value <0.05 are bolded.   Metal Month Intercept Slope p-value slope R2 of model Ag December 0.0680 1.1468 x10-5 0.6731 0.0674 April -0.0212 9.2487 x10-5 0.0073 0.9342 June 0.0353 1.0083 x10-5 0.1864 0.4927 August 0.0256 2.5085 x10-5 0.1123 0.6231 Cu December 5.8177 0.0021 0.3494 0.2897 April 3.6221 0.0074 0.0263 0.8483 June 6.5313 2.7813 x10-4 0.8110 0.0222 August 1.0134 0.0038 0.0343 0.8201 Cd December 0.5760 2.2315 x10-4 0.1291 0.5903 April 0.4407 2.1121 x10-4 0.0577 0.7499 June 1.1196 -1.6378 x10-4 0.4937 0.1676 August 0.3469 1.1358 x10-4 0.3859 0.2547  B.11 Two-Way ANOVA results for zooplankton size fraction (µm) and month vs C-normalized metal content (µmol Me ∙ mol C-1). P-value <0.05 are bolded.  Metal Source Sum Sq. d.f. Mean Sq. F Prob>F Notes Ag Month 1.4473 x10-14 3 4.8244 x10-15 0.97 0.439 None. Size Fraction 5.4891 x10-14 4 1.3723 x10-14 2.76 0.0775 Error 5.9709 x10-14 12 4.97572x10-15   Total 1.2907 x10-13 19    Cu Month 2.2327 x10-10 3 7.4422 x10-11 2.23 0.1372 Tukey HSD p-value: 0.0376 between 1000 µm and 4000 µm size fractions.  Size Fraction 5.4711 x10-10 4 1.3678 x10-10 4.1 0.0254 Error 4.0039 x10-10 12 3.3366 x10-11   Total 1.1708 x10-9 19    Cd Month 4.6644 x10-13 3 1.5548 x10-13 0.88 0.481 None. Size Fraction 1.1656 x10-12 4 2.9139 x10-13 1.64 0.2278 Error 2.1315 x10-12 12 1.7762 x10-13   Total 3.7635 x10-12 19       150   B.12 Zooplankton size fraction (µm) vs Me BAF (L ∙ kg d.w.-1). Linear regression model for all data measured. P-value <0.05 are bolded.  Metal Intercept Slope p-value slope R2 of model Ag 1.429 x105 106.409 0.0077 0.3331 Cu 3.816 x104 17.057 0.0077 0.3330 Cd 3.907 x104 1.8004 0.6512 0.0116        B.13  Zooplankton size fraction (µm) vs Me BAF (L ∙ kg d.w.-1). Monthly linear regression model. P-value <0.05 are bolded.  Metal Month Intercept Slope p-value slope R2 of model Ag December 2.8532 x105 11.6132 0.8947 0.0068 April -4.3942 x104 288.2817 0.0066 0.9382 June 1.6726 x105 34.9974 0.1976 0.4756 August 1.6296 x105 90.7431 0.1775 0.5066 Cu December 5.228 x104 7.5726 0.5935 0.1057 April 2.755 x104 37.0869 0.0334 0.8231 June 4.4384 x104 0.4285 0.9456 0.0018 August 2.8433 x104 23.1388 0.0717 0.7137 Cd December 4.1209 x104 4.7715 0.5631 0.1228 April 2.6040 x104 9.1394 0.1074 0.6331 June 6.7684 x104 -10.4045 0.4316 0.2149 August 2.1342 x104 3.6951 0.5452 0.1336       151  B.14 Two-Way ANOVA results for zooplankton size fraction (µm) and month vs Me BAF (L ∙ kg d.w.-1). P-value <0.05 are bolded.  Metal Source Sum Sq. d.f. Mean Sq. F Prob>F Notes Ag Month 8.2635 x1010 3 2.7545 x1010 0.53 0.6724 None. Size Fraction 5.5412 x1011 4 1.3853 x1011 2.65 0.0857 Error 6.2789 x1011 12 5.232 x1010   Total 1.26467x1012 19    Cu Month 4.00151 x109 3 1.33384 x109 1.23 0.3404 Tukey HSD and Bonferroni tests results not statistically significant. Size Fraction 1.5525 x1010 4 3.88132 x109 3.59 0.038 Error 1.2976 x1010 12 1.08135 x109   Total 3.250 x1010 19    Cd Month 1.8052 x109 3 6.01716 x108 1.14 0.3712 None. Size Fraction 2.2697 x109 4 5.67431 x108 1.08 0.4099 Error 6.3163 x109 12 5.26355 x108   Total 1.0391 x1010 19      B.15 Zooplankton size fraction (µm) vs Me BAF (L ∙ mmol C-1). Linear regression model for all data measure. P-value <0.05 are bolded.  Metal Intercept Slope p-value slope R2 of model Ag 3.3589 0.004 0.0027 0.4011 Cu 0.8598 7.0569 x10-4 0.0014 0.4393 Cd 1.0454 1.416 x10-4 0.2677 0.0678         152  B.16 Zooplankton size fraction (µm) vs Me BAF (L ∙ mmol C-1). Monthly linear regression model. P-value <0.05 are bolded.  Metal Month Intercept Slope p-value slope R2 of model Ag December 7.2375 0.0012 0.6731 0.0674 April -2.2258 0.0097 0.0073 0.9342 June 4.6860 0.0013 0.1864 0.4927 August 3.7378 0.0037 0.1123 0.6231 Cu December 1.2492 4.5416 x10-4 0.3494 0.2897 April 0.6329 0.0013 0.0263 0.8483 June 1.289 5.4893 x10-5 0.811 0.0222 August 0.2681 0.001 0.0343 0.8201 Cd December 0.9006 3.489 x10-4 0.1291 0.5903 April 0.7009 3.3588 x10-4 0.0577 0.7499 June 2.0335 -2.9746 x10-4 0.4937 0.1676 August 0.5467 1.79 x10-4 0.3859 0.2547                        153  B.17 Two-Way ANOVA results for zooplankton size fraction (µm) and month vs Me BAF (L ∙ mmol C-1). P-value <0.05 are bolded.  Metal Source Sum Sq. d.f. Mean Sq. F Prob>F Notes Ag Month 9.3484 x107 3 3.1161 x107 0.57 0.6424 Tukey HSD and Bonferroni tests results not statistically significant. Size Fraction 7.2775 x108 4 1.8194 x108 3.36 0.0461 Error 6.5053 x108 12 5.4211 x107   Total 1.4718 x109 19    Cu Month 4.1177 x106 3 1.3726 x106 1.13 0.3758 Tukey HSD: Statistical difference between then 500 and 4000 µm size fractions (p-value 0.033) and between then 1000 and 4000 µm size fractions (p-value 0.0271). Size Fraction 2.348 x107 4 5.8695 x106 4.83 0.0148 Error 1.4571 x107 12 1.2142 x106   Total 4.2166 x107 19    Cd Month 1.6094 x106 3 536466.9 1.03 0.4154 None. Size Fraction 3.1258 x106 4 781443.9 1.5 0.2646 Error 6.2704 x106 12 522535.2   Total 1.1006 x107 19      B.18 Correlation results between dissolved and particulate Ag, Cu and Cd measurements at Station S4-1.5. Calculations were done for full profile values and values for measurements between 0 and 50 m depth. Strong correlations (>0.6) have been italicized. Month sampled Depths correlated Ag Cu Cd December 0-50 m 0.50 0.90 0.94 Full profile 0.31 0.70 0.53 April 0-50 m -0.31 0.66 0.08 Full profile -0.38 0.64 0.38 June 0-50 m 0.32 0.94 0.58 Full profile -0.62 0.79 0.44 All months 0-50 m -0.09 0.80 0.11 Full profile -0.27 0.71 0.19  154  B.19 Correlation results between dissolved and non-lithogenic Ag, Cu and Cd particle measurements at Station S4-1.5. Calculations were done for full profile values and values for measurements between 0 and 50 m depth. Non-lithogenic metal calculations are described on Table A-14. Strong correlations (>0.6) have been italicized. Month sampled Depths correlated Ag Cu Cd December 0-50 m 0.51 0.94 0.94 Full profile 0.34 0.84 0.53 April 0-50 m -0.31 0.70 0.08 Full profile -0.43 0.75 0.38 June 0-50 m 0.29 0.94 0.59 Full profile -0.67 0.90 0.44 All months 0-50 m -0.13 0.79 0.11 Full profile -0.30 0.77 0.19      B.20 Correlation results between dissolved and lithogenic Ag, Cu and Cd particle measurements at Station S4-1.5. Calculations were done for full profile values and values for measurements between 0 and 50 m depth. Lithogenic metal calculations are described on Table A-14. Strong correlations (>0.6) have been italicized. Month sampled Depths correlated Ag Cu Cd December 0-50 m 0.31 0.77 -0.95 Full profile -0.08 0.28 -0.54 April 0-50 m -0.07 -0.47 -0.76 Full profile 0.40 -0.42 0.40 June 0-50 m 0.69 0.96 -0.89 Full profile 0.49 0.21 -0.04 All months 0-50 m 0.46 -0.35 0.26 Full profile 0.31 -0.02 0.01       155  Appendix C  Dissolved Cu uptake experiments, using radioactive Cu, in in the Strait of Georgia Some content in this appendix has also been mentioned in Pawlowicz et al. (2018), and presented by me both at the Salish Sea Ecosystem Conference 2018 and the TRIUMF LSPEC 2018 (July 26, 2018) meeting.  C.1 Introduction The usage of radioisotopes as tracers to study metal uptake in aquatic organisms is a common practice (Wang & Fisher, 1998; Fisher, Nolan, & Fowler, 1991; for a review see Wang, 2002).  However, Cu radiotracer uptake experiments in marine organisms are extremely uncommon, due to the particularly short half-life of the two Cu radioisotopes (64Cu, λ = 12.7 hours; 67Cu, λ = 61.83 hours), and the low availability of commercially produced Cu radionuclides (Smith, Bowers, & Ehst, 2012). Additionally, depending on the reaction used to produce the longer-lived 67Cu, issues of contamination with the production of Ni, Co, Mn, Cr and 64Cu can decrease its purity (Smith et al., 2012).  In recent years, the Life Science program at TRIUMF (Tri-University Meson Facility) has kindly produced and purified, for our use, the carrier-free 64Cu, via the 64Ni(p,n)64Cu reaction. Thus, we have had the unique opportunity to determine the dissolved Cu (dCu) uptake rates in Strait of Georgia zooplankton using 64Cu as a radiotracer.  C.2 Methods We conducted three dissolved Cu uptake experiments during 2017: August 19th, September 16th, and November 29th.   Zooplankton sampling We collected zooplankton from the SoG using vertical net tows, following the procedures described in Chapter 2. The zooplankton were then sorted by size, using a series of different sized sieves. Each sorted zooplankton size fraction was placed in a different bucket filled with filtered (GF/F) seawater collected from 30 or 50 m depth. We surrounded the buckets with ice packs and kept them in the dark during transport to UBC.   156  Back at the UBC laboratory, we subsampled and checked all the zooplankton size fractions under a binocular microscope. Once we confirmed that there were enough calanoid copepods in the 1000-2000 µm size fraction, we collected at least 240 organisms. We placed 20 copepods per bottle in a series of 250 mL trace metal clean PC bottles, filled with filtered seawater (GF/F) from the same station and date of sampling. In order to evacuate and starved the copepods, the bottles with the live copepods were kept open, bubbled and covered by a dark plastic bag, and were placed in a 12°C incubation room overnight. Later analysis of a copepod sample preserved in formalin confirmed the experimental organisms used were Metridia pacifica copepodite stages IV and V. Radioisotope production The 64Cu used in the dissolved uptake experiments was produced via the 64Ni(p,n)64Cu reaction at the TRIUMF facilities (Vancouver, B.C.) and gifted to our laboratory. The methodology to produce this radiotracer is based on Zeisler et al. (2003).  First, the enriched 64Ni target is prepared by electroplating 64Ni onto a rhodium disk. Then, the target is irradiated for one hour at a 10 µA proton current in the TR13 cyclotron. Once the disk is recovered, the 64Cu and 64Ni are dissolved using 6N HCl. The isotopes are then separated using a 2.5 mL anion exchange column with AG1-X8 resin (BioRad) where 64Cu is retained in the resin and 64Ni is eluted with HCl. The eluted 64NiCl2 can then be reused to produce more Cu radionuclide. Finally, the purified 64Cu is eluted with ultrapure water and is ready to be shipped.   Table C1. Date and activity of 64Cu produced at TRIUMF and sent to our EOAS laboratory for the dissolved Cu uptake experiments.  Date Activity received (GBq) Volume received (mL) August 18th, 2017 0.37 ~0.8 September 14th, 2017 0.2627 <2 November 29th, 2017 0.3145 <2  Experimental solutions Three experimental solutions and one control solution were prepared in triplicate at least two days before the uptake experiments. Using filtered (GF/F, <0.7 µm porosity) seawater from the  157  Salish Sea, we prepared three dissolved Cu treatments (10 nM, 30 nM and 50 nM), and a control (0 nM addition; Table C2). During the August and September experiments, we used shallow (<50 m) water from Juan de Fuca Strait (sampled July 2017, stored at 4°C in the dark until its use). For the November experiments, we used 28 m depth seawater (sampled on November 27, 2017) from Station S4-1.5 in the SoG. Once the seawater (<80 mL; weighed) was dispensed into each of the trace metal clean experimental bottles (250 mL PC), we made the additions of CuCl2. The additions were prepared using a 6.99 µM secondary stock of CuCl2 (>99.995%; Sigma-Aldrich), as well as an analytical balance, and a pre-calibrated pipette.  Finally, we added enough water to get a final volume in each bottle of 100 mL. After the additions, the bottles were gently shaken, double-bagged, and stored at the same temperature as the holding bottles for zooplankton.  Table C2. Copper concentrations in the experimental bottles for the dissolved Cu uptake experiments. It was previously thought that background Cu concentrations in the SoG would range between 10 and 20 nM (see Pawlowicz et al., 2018), thus the Cu additions for the experiments were designed as 10, 30 and 50 nM. Due to instrumental restrictions, the background, natural in situ Cu concentration in the experimental seawater was never determined. However, based on measurements done in 2018, the average Juan de Fuca shallow water was 2.4 nM Cu, and the 28 m depth dissolved Cu concentration measured on December 4, 2018 was of 4.2 nM.    Estimated total dissolved Cu [dCu] (nM) Cu additions (nM) August  September November  0 2.4 2.4 4.2 2.4 2.4 4.2 2.4 2.4 4.2  9.5 11.6 13.3 10 9.9 11.9 13.3  9.4 12.1 13.3  23.2 30.3 31.5 30 23.2 29.7 31.5  23.3 30.3 31.5 50 37.3 48.5 49.3 37.2 48.9 49.3 37.5 48.6 49.3   158  On the day before the experiment (~12 hours), a known volume of ultrapure water (Milli-Q) was added to the 64Cu vial received from TRIUMF. Then, a subsample of 20 µL was measured in a PerkinElmer Wallac 1480 Wizard Gamma Counter (2 min counting in 64Cu dynamic setting; counting efficiency of 0.06) to calculate the activity received. We calculated an adequate activity of 64Cu to add to the experimental bottles in order to be able to measure 64Cu in seawater and zooplankton at the end of the uptake experiments. The 64Cu spiked experimental bottles (i.e., natural seawater with the cold CuCl2 and 64Cu additions, but without copepods) were then left undisturbed overnight to equilibrate the 64Cu and cold Cu addition with the in situ ligands in the SoG seawater. Until the start of the uptake experiments, the bottles were kept shielded, and at the same temperature as the zooplankton.  Experimental procedure The day after the 64Cu spike, one mL was subsampled from each bottle to measure the initial seawater 64Cu activity. We then filtered down the zooplankton from the holding bottles, previously kept at 12°C, and transferred them to the 64Cu uptake experimental bottles. Once the copepods were inside these bottles, they were placed back into the 12°C incubator in the dark. After four hours, the zooplankton were filtered through a trace metal clean funnel covered with a 250 µm mesh. Then, the zooplankton were rinsed quickly with filtered seawater (10 mL) from the same depth and sampling station as the experimental water. Once rinsed, the zooplankton and the mesh were placed inside a gamma counter vial. One mL of the experimental and the rinsing water mixture was subsampled to measure the final activity in the sample water. After subsampling, the remaining sample was acidified behind a shielded laminar flow hood with 100 µL of concentrated HCl (Seastar Chemicals). This was done to preserve the Cu in solution. After acidification, the solutions were double-bagged and left to decay to later determine the actual dissolved Cu concentrations in each treatment bottle.  Calculations All the 64Cu data were corrected for radioactive decay, including the activity in the experimental bottles, as well as in the copepods, using the radioactive decay equation:  159  A=A0 ∙ e(- λ∙t) where A (cpm) is the decay-corrected activity, A0 (cpm) is the measured activity, λ is the half-life of  64Cu (12.7 hours), and t (hours) is the elapsed time. To calculate the dissolved Cu uptake rate by copepods, we first calculated the specific activity in the different uptake experimental bottles (i.e., activity of 64Cu per total concentration of dissolved Cu) using the measured activity and the estimated total dissolved Cu concentration (background Cu + cold CuCl2 addition): Specific activity (cpm ∙ mol Cu-1) = activity in experimental bottle (cpm ∙ L-1) / total concentration of Cu in experimental bottle (mol Cu ∙ L-1) In addition, the decay-corrected cpm in the copepod samples (i.e., vials with 20 copepods) were divided by the number of copepods in the vial (n = 20), in order to calculate the cpm in a single copepod. Afterwards, the mols of Cu internalized by the copepods were calculated by dividing the cpm in a copepod by the specific activity of 64Cu in that experimental bottle: Internalized Cu (mol Cu ∙ copepod-1) = A (cpm ∙ copepod-1) / specific activity (cpm ∙ mol Cu-1) The Cu uptake rates were then calculated by dividing mols of Cu internalized by the copepod by the time the copepods spent in the experimental solutions:  Cu uptake rate (mol Cu ∙ copepod-1∙ hour-1) = Total Cu uptake (mol Cu ∙ copepod-1) /time in experimental solution (hours) To estimate copepod’s dry weight (DW in mg) from length (mm), we used the equation of Kwong (2016)’s:   log10DW=2.486*log10(total copepod length) - 2.021 and assumed that our copepods were adult M. pacifica measuring 2.89 mm in length (Pomerleau et al., 2015). With these d.w. estimates, we normalized the Cu uptake rates to copepod dry weight (i.e., mol Cu ∙ mg copepod d.w.-1∙ h-1). Finally, we calculated the dissolved Cu uptake rate  160  constant for each treatment and control bottle by dividing these copepods Cu uptake rates by the concentration of dissolved Cu in the bottle:   Cu uptake rate constant by copepods (L ∙ mg-1∙ h-1) = Cu uptake rate (mol Cu ∙ mg-1∙ h-1)/ concentration of Cu in bottle (mol Cu∙ L-1) C.3 Results and Discussion Unfortunately, the final dissolved Cu concentrations in the experimental solutions were not measured due to some technical difficulties and detection limit issues with the flow injection analysis with chemiluminescence detection (FIA-CL) instrument. Thus, we estimated the concentration of dissolved Cu in the natural, in-situ seawater in two ways. For the Juan de Fuca shallow seawater (< 50 m depth), we calculated the average dCu concentration determined by Kuang (2019) for seawater collected between 0 and 50 m in the Juan de Fuca Strait in August 2018. For the November experiment, we estimated the dissolved Cu concentration based on measurements by C. Kuang for seawater sampled five days later (December 4th, 2019) at the same depth (~30 m). The uptake rate of dissolved Cu by calanoid copepods in the September and November experiments appears to be a linear function of the dissolved Cu concentrations in seawater.  Although the August and September experiments were done with water from the same station, date, depth and container, the uptake rate constants (averages: 2.25 and 6.5 L ∙ g d.w.-1∙d-1, respectively), and uptake rate slopes (1.26 * 10-5 and 3.46 * 10-5  pmol Cu ∙ copepod-1 ∙ hour-1 ∙ pM-1, respectively) were different (Figure C1, Table C3). In comparison, the September and November (4.7 L ∙ g d.w.-1∙d-1) uptake rate constant and slopes (2.62 * 10-5 pmol Cu ∙ copepod-1 ∙ hour-1 ∙ pM-1 for November) were similar. Additionally, the linear regression fit for the September and November were higher than in August. It is unclear why the copepods Cu uptake rates in August are slower.  However, a few explanations could involve sampling an entirely different species or the life stage of the collected copepods.    161   Figure C1. Dissolved Cu uptake rates by calanoid copepods at various Cu concentrations. The August (white) and September (grey) 2017 experiments were performed using Juan de Fuca water (< 50 m depth). The November (black) 2017 experiment was performed using SoG water (28 m). All triplicates are shown. The average size of the copepods was approximately 2.89 mm (Pomerleau, Sastri, & Beisner, 2015).The linear regression lines are shown. The slope and p-value of the linear regressions for the three experiments are: August (slope = 1.26 * 10-5 pmol Cu ∙ copepod-1 ∙ hour-1 ∙ pM-1, p-value 1.03*10-3), September (slope = 3.46 * 10-5 pmol Cu ∙ copepod-1 ∙ hour-1 ∙ pM-1, p-value 1.07*10-9).  November (slope = 2.62 * 10-5 pmol Cu ∙ copepod-1 ∙ hour-1 ∙ pM-1, p-value 1.40*10-9).                  162  Table C3.  Dissolved Cu concentrations in each treatment after CuCl2 solution additions, and Cu uptake rates calculated during the experiments in August 2017, September 2017 and November 2017. The copepod dry weight used for the calculations (0.133 mg d.w.) was estimated using Kwong (2016)’s copepod formula. The August and September (black) 2017 experiments were performed using Juan de Fuca water (< 50 m depth). The November 2017 experiment was performed using SoG water (28 m). Errors shown are one standard deviation from the mean.  [dCu] (nM) Cu uptake rate (mol Cu ∙ copepod-1∙ hr-1) Uptake rate  (L ∙ g d.w.-1∙d-1) Average uptake rate  (L ∙ g d.w.-1∙d-1) August 2017 2.4 1.5E-14 1.16 2.25 ± 0.66 2.4 2.5E-14 1.94 2.4 2.1E-14 1.60 9.5 1.5E-13 2.93 9.9 1.5E-13 2.66 9.4 1.4E-13 2.74 23.2 2.6E-13 2.06 23.2 3.5E-13 2.68 23.3 3.1E-13 2.37 37.3 6.4E-13 3.08 37.2 5.4E-13 2.60 37.5 2.5E-13 1.18 September 2017 2.4 7.6E-14 5.76 6.50 ± 1.06 2.4 7.4E-14 5.60 2.4 5.8E-14 4.40 11.6 4.5E-13 6.89 11.9 5.0E-13 7.57 12.1 5.6E-13 8.36 30.3 1.2E-12 7.22 29.7 1.1E-12 6.37 30.3 1.2E-12 7.12 48.5 1.8E-12 6.52 48.9 1.8E-12 6.57 48.6 1.5E-12 5.60 November 2017 4.2 1.0E-13 4.34 4.70 ± 0.43 4.2 1.0E-13 4.32 4.2 1.1E-13 4.73 13.3 3.3E-13 4.46 13.3 3.2E-13 4.37 13.3 4.0E-13 5.49 31.5 7.8E-13 4.44 31.5 9.4E-13 5.36 31.5 8.4E-13 4.80 49.3 1.1E-12 4.12 49.3 1.3E-12 4.85 49.3 1.4E-12 5.07  163  The experiments performed were done at environmentally-relevant dissolved Cu concentrations. Thus, these concentrations were lower than those used in previous toxico-kinetic Cu experiments (e.g., 1.73 µM dissolved Cu in Pinho et al., 2007, and <0.3 mM total Cu in Chang and Reinfelder, 2002). Interestingly, the average calculated uptake rate constants for the September and November experiments (4.7 L ∙ g d.w.-1∙d-1) are similar to the one calculated by Chang and Reinfelder (2002) for coastal calanoid copepod Acartia tonsa (5.1 L∙ g d.w.-1∙d-1). Based on the general trend observed in the three different experiments, we suggest that SoG copepods are unlikely to reach saturation when exposed to higher- but plausible- concentrations of dissolved Cu in the SoG.  C.4 Conclusion We successfully measured the dissolved Cu uptake rates and Cu uptake rate constants on SoG copepods using 64Cu as a radiotracer. Although it is a shorter-lived radioisotope, we have proven that 64Cu is a useful tool to measure dissolved Cu uptake rates in short experiments (~4 hours).  Thus, this radiotracer can also be used to measure dietary Cu uptake rates, and Cu efflux rates from both the dietary and dissolved phases for other small marine organisms.  In the past, published studies investigating Cu toxicity, Cu uptake, or the effects of metal interactions on Cu accumulation have either used proxies to measure Cu (e.g., Ng and Wang, 2007) or complex and lengthy procedures (e.g., cell and tissue level biomarkers, and Cu and Fe coprecipitation; Rementeria et al., 2016; Bielmyer et al., 2006). The 64Cu radiotracer can be used as a simpler tool, providing real-time measurements for Cu exposure experiments. The data on Cu uptake parameters (i.e., assimilation efficiency, and dietary and dissolved efflux rates) for model aquatic organisms (e.g., mussels, oysters, copepods, etc.) are currently scarce. However, future research using Cu radionuclides could expand our knowledge of Cu content assimilation and efflux in marine organisms. To do this, future studies should include: 1) pulse feeding (radiolabeled food) experiments in zooplankton and fish to determine their Cu assimilation efficiencies, dietary uptake rates, and dissolved and dietary efflux rates, 2) the quantification and localization of specific cells and organs involved in dissolved Cu assimilation and excretion, and 3) determining the trophically-available Cu in a prey for different predators. 

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