Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Sunthareswaran, Dilan
Master of Science - MSc
University of British Columbia
Marine food webs in coastal regions are among the most productive and culturally significant globally, yet their position at the land-sea interface makes them vulnerable to terrestrial disturbances. Terrestrial inputs of freshwater, organic matter, and nutrients heavily shape the structure and productivity of coastal marine food webs. Urbanization introduces novel organic materials to coastal ecosystems including stormwater runoff and wastewater discharge. Concurrently, altered nutrient dynamics can influence marine primary and microbial productivity. Through these changes, urbanization can shift the quantity and quality of the organic matter pool available for marine primary consumers. Such changes have implications for food web nutritional health and trophic interactions. To investigate the fate and implications of organic matter types in urban-based coastal food webs, I assessed the assimilation of organic matter and subsequent effects on nutritional health in suspension feeding mussels. Mussels were collected monthly for one year from eight sites around Burrard Inlet. Two approaches were applied to estimate uptake of marine (phytoplankton and macrophyte) and urban-derived (rivers, stormwater, and wastewater) organic matter types: 1) deriving fatty acid biomarkers for each organic matter type, and 2) applying a Bayesian mixing model combining fatty acid and stable isotope tracers. Both approaches yielded similar results (average difference = 4.14 %), giving confidence in the estimates. Phytoplankton were always dominant contributors to mussel diets (~ 50 - 70 %), underscoring the sustained high primary production despite urban pressures. Urban-derived contributions were mostly moderate (~ 15 - 20 %) and highest in an urban centre site (Shipyards) and during the wet season (October - February) when phytoplankton production was low and urban material flux was expected to be high. Nutritional variation in mussels was associated with shifts in organic matter contributions. Urban-derived inputs were primarily associated with saturated fatty acids, which are insufficient to support physiological demands for marine consumers. Coastal regions will face the continued rise of urbanization coupled with anthropogenically driven climate change, further disrupting terrestrial inputs and marine food webs. Ultimately, this study outlined a generalizable framework for examining the impact of these anthropogenic influences on coastal food webs, linking urban pressures to consumer health.
FROM POOP TO MARINE SOUP: DECONSTRUCTING THE DIET AND HEALTH OF MUSSELS IN A HIGH RAINFALL COASTAL REGION by Dilan Sunthareswaran B.Sc., University of British Columbia, 2022 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) October 2025 © Dilan Sunthareswaran, 2025ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: From poop to marine soup: deconstructing the diet and health of mussels in a high rainfall coastal region submitted by Dilan Sunthareswaran in partial fulfilment of the requirements for the degree of Master of Science in Oceanography Examining Committee: Brian Hunt, Associate Professor, Department of Earth, Ocean and Atmospheric Sciences, UBC Supervisor Chris Harley, Professor, Department of Zoology and Institute for the Oceans and Fisheries, UBC Supervisory Committee Member Kate Menzies, Natural Resources Planning Manager, Tsleil-Waututh Nation Supervisory Committee Member Hal Bradbury, Assistant Professor, Department of Earth, Ocean and Atmospheric Sciences, UBC Additional Examiner iii Abstract Marine food webs in coastal regions are among the most productive and culturally significant globally, yet their position at the land-sea interface makes them vulnerable to terrestrial disturbances. Terrestrial inputs of freshwater, organic matter, and nutrients heavily shape the structure and productivity of coastal marine food webs. Urbanization introduces novel organic materials to coastal ecosystems including stormwater runoff and wastewater discharge. Concurrently, altered nutrient dynamics can influence marine primary and microbial productivity. Through these changes, urbanization can shift the quantity and quality of the organic matter pool available for marine primary consumers. Such changes have implications for food web nutritional health and trophic interactions. To investigate the fate and implications of organic matter types in urban-based coastal food webs, I assessed the assimilation of organic matter and subsequent effects on nutritional health in suspension feeding mussels. Mussels were collected monthly for one year from eight sites around Burrard Inlet. Two approaches were applied to estimate uptake of marine (phytoplankton and macrophyte) and urban-derived (rivers, stormwater, and wastewater) organic matter types: 1) deriving fatty acid biomarkers for each organic matter type, and 2) applying a Bayesian mixing model combining fatty acid and stable isotope tracers. Both approaches yielded similar results (average difference = 4.14 %), giving confidence in the estimates. Phytoplankton were always dominant contributors to mussel diets (~ 50 - 70 %), underscoring the sustained high primary production despite urban pressures. Urban-derived contributions were mostly moderate (~ 15 - 20 %) and highest in an urban centre site (Shipyards) and during the wet season (October - February) when phytoplankton production was low and urban material flux was expected to be high. Nutritional variation in mussels was associated with shifts in organic matter contributions. Urban-derived inputs were primarily iv associated with saturated fatty acids, which are insufficient to support physiological demands for marine consumers. Coastal regions will face the continued rise of urbanization coupled with anthropogenically driven climate change, further disrupting terrestrial inputs and marine food webs. Ultimately, this study outlined a generalizable framework for examining the impact of these anthropogenic influences on coastal food webs, linking urban pressures to consumer health. v Lay Summary Coastal marine food webs are culturally and ecologically important around the world. They are uniquely located between the land and ocean, making them dependent on both environments for food and nutrients. Urbanization can profoundly impact these food sources through land-use change, and this can influence the stability and health of the marine environment. Using mussels in Burrard Inlet, I investigated how urbanization effects the consumption of different food types and the subsequent impacts to their health. I found that marine based food was always the most important source, but food from urban land was detected in moderate levels. Ultimately, this study developed a generalizable framework for detecting urban effects to marine food webs. This study builds on an increasingly critical area of research amid the continued rise of urbanization which will further disrupt marine food webs.vi Preface This thesis is ultimately based on the sampling program of the first stage of the Urban Oceans Project, the subject of a collaboration between the Pelagic Ecosystems Laboratory, Tsleil-Waututh Nation, and Metro Vancouver. None of the text of the thesis is taken directly from previously published or collaborative articles. For Chapter 2, S. Lye and I primarily led the study design, field work, and lab work. I led the collection, lab work, and analysis for all mussel and fatty acid data. Additionally, I led all analysis, writing, and creation of figures and tables in this thesis. S. Lye collected and performed the analysis on all particulate organic matter samples for stable isotope data along with all macronutrient and chlorophyll data. A version of the data for macronutrients, chlorophyll, and particulate organic matter for both fatty acids and stable isotopes were included in Lye. (2025) and Lye et al. (in prep). Co-authors and Anna McLaskey (Research Associate, Institute for Oceans and Fisheries, UBC) contributed to study design and provided analytical support. This Chapter is being prepared for submission for peer review. Brian Hunt provided supervision, analytical support, and editing to each chapter. Generative AI was not used in the conception, execution, or drafting of this thesis.vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................. xi List of Figures ............................................................................................................................. xiii List of Symbols ............................................................................................................................ xv List of Abbreviations .................................................................................................................. xvi Acknowledgements .................................................................................................................. xviii Chapter 1: Introduction ............................................................................................................... 1 1.1 Coastal regions and land-sea relationship ....................................................................... 1 1.2 Urbanization and its impact on land-sea relationship ..................................................... 2 1.3 Knowledge gap of urbanization impacts to organic matter uptake ................................. 4 1.4 Mussels as bioindicators ................................................................................................. 4 1.5 Tracing land-derived materials ....................................................................................... 5 1.6 Burrard Inlet and Metro Vancouver (study region) ......................................................... 6 1.7 Thesis objectives ............................................................................................................. 7 viii Chapter 2: From poop to marine soup: deconstructing the diet and health of mussels in a high rainfall coastal region ........................................................................................................... 8 2.1 Introduction ........................................................................................................................... 8 2.2 Methods............................................................................................................................... 14 2.2.1 Study area ..................................................................................................................... 14 2.2.2 Site classification ......................................................................................................... 17 2.2.3 Field sampling .............................................................................................................. 18 2.2.4 Laboratory processing .................................................................................................. 22 2.2.4.1 Chlorophyll a ............................................................................................................ 22 2.2.4.2 Macronutrients .......................................................................................................... 22 2.2.4.3 Mussels ..................................................................................................................... 22 2.2.4.4 Macrophytes .............................................................................................................. 23 2.2.4.5 POM FA filtration ..................................................................................................... 23 2.2.4.6 POM SI filtration ...................................................................................................... 23 2.2.5 Data analysis ................................................................................................................ 25 2.3 Results ................................................................................................................................. 32 2.3.1 Overview of environmental conditions ........................................................................ 32 2.3.2 Biochemical (FA & SI) variation in mussels within Metro Vancouver ....................... 34 2.3.3 Biochemical (FA & SI) variation in organic matter types ........................................... 39 2.3.4 Indicator analysis: derivation of FA biomarkers for organic matter types ................... 41 2.3.5 Mussel dietary estimates: FA biomarker approach ...................................................... 43 2.3.6 Mussel dietary estimates: combined FA-SI mixing model approach .......................... 46 2.3.7 Mixing model vs. biomarker estimates ........................................................................ 51 ix 2.3.8 Nutritional analysis of mussels .................................................................................... 51 2.3.9 Relationships among biochemical metrics and with environmental data .................... 56 2.4 Discussion ........................................................................................................................... 57 2.4.1 Silicate measurements in Burrard Inlet ........................................................................ 57 2.4.2 Spatial and seasonal variation in mussel fatty acid and stable isotope tracers ............ 58 2.4.3 Delineation of organic matter sources ......................................................................... 60 2.4.4 Assessment of FA biomarkers from indicator analysis ................................................ 62 2.4.5 Dietary estimates .......................................................................................................... 63 2.4.6 Biomarker vs. mixing model approach ........................................................................ 66 2.4.7 Comparison of mussel diets with marine POM ........................................................... 67 2.4.8 Spatial and temporal variation in mussel nutrition ...................................................... 69 2.4.9 Implications of dietary estimates to mussel health ...................................................... 70 2.4.10 Limitations and future directions ............................................................................... 72 2.5 Conclusions ......................................................................................................................... 74 Chapter 3: Conclusion ................................................................................................................ 75 3.1 Urban effects to food webs: knowledge gaps ..................................................................... 75 3.2 Local context ....................................................................................................................... 75 3.3 Global context ..................................................................................................................... 76 3.4 Future directions ................................................................................................................. 77 3.5 Final remarks ...................................................................................................................... 78 Bibliography ................................................................................................................................ 80 x Appendices ................................................................................................................................. 127 Appendix A: Supplemental Tables, Figures, and Other Graphics .......................................... 127 xi List of Tables Table 1. Mussel collection sites with impervious cover percentage, land-use, shore type and averaged temperature and salinity (± standard deviation) separated by season (dry = June to September, wet = October to February, wet bloom = March to May) .......................................... 16 Table 2. Fatty acid biomarkers for organic matter types derived from indicator analysis ............ 42 Table 3. Tracers, mean calibration coefficients (CCs, for fatty acids), and trophic enrichment factors (TEFs, for stable isotopes) used in the mixing model ....................................................... 48 Table A1. Sample size (N) for each sample type separated by biotracer .................................... 128 Table A2. Environmental conditions, total Chl a concentrations, and size fractionated Chl a proportions at four mussel sites from April 2023 to March 2024 ............................................... 129 Table A3. Permutational multivariate analysis of variance (PERMANOVA) of mussel fatty acids and stable isotopes comparing sites and months and organic matter fatty acids and stable isotopes comparing types .......................................................................................................................... 131 Table A4. Mean ± standard deviation of percent composition of abundant (>0.5%) fatty acids (FA) and stable isotope (SI) composition (δ¹³C and δ¹⁵N) in organic matter types .................... 133 Table A5. Two-way analysis of variance (ANOVA) of mussel total fatty acids (TFA; mg g-1), essential fatty acids (EFA; mg g-1), ratio between docosahexaenoic acid and eicosapentaenoic acid (DHA:EPA), and percent composition of fatty acid biomarkers (phytoplankton, urban, macrophyte) comparing sites and months ................................................................................... 134 Table A6. Mean ± standard deviation of percent composition of fatty acid biomarkers (phytoplankton, urban, macrophyte), total fatty acid (TFA; mg/g), essential fatty acid (EFA; mg/g), and ratio between docosahexaenoic acid and eicosapentaenoic acid (DHA:EPA) in mussels ........................................................................................................................................ 135 xii Table A7. Regression parameters (b, m, R2) and mean calibration coefficients (CCs) between consumer and diet proportions in bivalve feeding studies (N = number of studies) .................. 140 Table A8. MixSIAR results of source contributions ................................................................... 141 Table A9. Fatty acid biomarkers from our study (pooled and individual organic matter groups) compared to marine bivalve studies (literature marker) ............................................................. 143 xiii List of Figures Figure 1. Map of all sites for mussel and organic matter (marine, river, macrophyte and stormwater) collection .................................................................................................................. 15 Figure 2. Temperature (°C), salinity (ppt), total Chl a concentrations, and size fractionated Chl a proportions at four mussel sites from April 2023 to March 2024 ................................................. 33 Figure 3. NMDS ordination of percent composition of abundant (>0.5%) fatty acids (A and B; stress = 0.13) and stable isotope biplot of δ15N and δ13C (C and D) in mussels coloured by site (A and C) and month (B and D) ......................................................................................................... 37 Figure 4. NMDS of percent composition of abundant (>0.5%) fatty acids (A; Stress = 0.11) and δ¹³C ‰ and δ¹⁵N ‰ biplot (B) in organic matter coloured by type .............................................. 40 Figure 5. Fatty acid (FA) biomarkers of organic matter sources in mussels per month ............... 44 Figure 6. NMDS of fatty acid and stable isotope tracers used in the mixing model for mussel (circles) and organic matter types (squares) ................................................................................. 47 Figure 7. Mixing model results of mean contribution of organic matter (%) to mussels, separated by site and season .......................................................................................................................... 50 Figure 8. Monthly concentrations at each site of mussel total fatty acids (FA; mg g-1) and essential fatty acids (EFA; mg g-1), and ratios of docosahexaenoic acid (DHA) to eicosapentaenoic acid (EPA) and carbon (C) to nitrogen (N), for April 2023 to March 2024 ..... 53 Figure 9. Spearman’s correlation coefficients between mussel fatty acid and stable isotope values. ........................................................................................................................................... 55 Figure A1. ArcGIS screenshot of Metro Vancouver’s land cover classification data (Metro Vancouver, 2023) with 1 km radial buffer (blue circles) used to estimate impervious cover of each mussel site (yellow dots). ................................................................................................... 127 xiv Figure A2. Non-metric multidimensional Scaling (NMDS) ordination of percent composition of abundant (>0.5%) fatty acids in mussels over 12 months from April 2023 to March 2024 ....... 132 Figure A3. δ¹³C and δ¹⁵N biplot (A) and non-metric multidimensional Scaling (NMDS) ordination of percent composition of fatty acid tracers (B) used for mixing model in mussels and organic matter ............................................................................................................................. 139 Figure A4. Average difference of contribution estimates (%) for organic matter sources between MixSIAR and biomarker analysis. .............................................................................................. 147 Figure A5. Spearman’s correlation coefficients between mussel fatty acid (FA) or stable isotope (SI) values and salinity (ppt), temperature (°C), macronutrients (phosphate, silicate, nitrate; μmol L-1), total chlorophyll (total Chl a; μg L-1), Chl a size classes (micro, nano, pico; % of total Chl a), or particulate organic matter (POM) FA and SI values .......................................................... 149 Figure A6. Monthly size at each site of mussel soft tissue wet weight (mg; digestive gland removed) and shell length (cm) for April 2023 to March 2024 .................................................. 150 xv List of Symbols α alpha ~ approximately * asterisk ° degrees °C degrees Celsius δ delta = equals > greater than < less than μ micro % percent ‰ per mill ± plus or minus xvi List of Abbreviations ANOVA analysis of variance ARA arachidonic acid b y-intercept (regression coefficient) BC British Columbia C carbon CC calibration coefficient Chl a chlorophyll a CSO combined sewer overflow Df degrees of freedom DHA docosahexaenoic acid DOM dissolved organic matter EPA eicosapentaenoic acid km kilometre FA fatty acid F F-statistic GF/F glass fiber filter L litre m metres (distance) or slope (regression coefficient) mg milligram mg g-1 milligram per gram mL millilitre xvii MixSIAR R package to create and run Bayesian mixing models N sample size or nitrogen ND no data NE Northeast NMDS non-metric multidimensional scaling OM organic matter PERMANOVA permutational multivariate analysis of variance POM particulate organic matter ppt parts per thousand PUFA polyunsaturated fatty acid R2 coefficient of determination SD standard deviation SFA saturated fatty acid SOG Strait of Georgia SS sum of squares TFA total fatty acids μm micrometre µmol L-1 micromole per litre WWTP wastewater treatment plant YSI Yellow Springs Instruments xviii Acknowledgements I am privileged to have lived, studied, and worked on the traditional, ancestral, and unceded Səl̓ílwətaʔ/Selilwitulh (Tsleil-Waututh), xwməθkwəy̓əm (Musqueam), and Skwxwú7mesh (Squamish) Nations. These Nations have lived and taken care of these lands and waters since time out of mind. To my supervisory committee, Chris Harley and Kate Menzies, thank you both for your guidance, interest, feedback and patience throughout the project; the outcomes of this project were achieved thanks to your support. I would like to thank my funders at UBC, NSERC and Metro Vancouver, for supporting my research project. In addition, I thank the Tsleil-Waututh Nation for supporting the Urban Oceans project as a whole and specifically with my research. I learned so much about Burrard Inlet and its history through meetings with Tsleil-Waututh Nation representatives and expeditions with their field crew. I witnessed first-hand the strength and benefit of scientific collaboration with the ancestral stewards of these lands and I hope this project can directly support efforts in restoring Burrard Inlet. To my supervisor, Brian Hunt, I am endlessly grateful for your support and guidance for this research project and beyond. I went from a 2nd year undergrad student sitting at the back of your classroom to completing this research project under your supervision. Your kindness and trust helped kickstart my research career and allowed me to pursue a passion of mine. My academic achievements and pride as a researcher are thanks to you, Brian. I am also incredibly thankful for the Pelagic Ecosystems Lab for their support ever since I joined the group. I appreciate their strong sense of community as I received constant support and was also challenged to learn more every day. Particularly, to Anna McLaskey, thank you for xix your positivity and advice, it grew my love for fatty acids and set a high standard for what a great mentor should be. Above all, thank you to my bestie, Sadie Lye. We started this Masters together and I am so thankful to have worked alongside someone so caring, attentive, and of course, such an amazing friend. From the jorts guy to filtering till 3 AM, I will always cherish our experiences during this project. To my amazing parents, thank you for the financial and moral support throughout all my schooling. I am deeply thankful for the strength and sacrifices required to immigrate from a war-torn country to ensure a better life for our family. I hope this Masters project stands as a testament that your efforts were worthwhile. To my siblings, Dinesh, Dilaxan, Dilisan, Raxana, and Vishakan, I am endlessly thankful for your support and love; I am lucky to see you all as my best friends. Finally, thank you to my partner, Lauren, as a reason to smile every day, remain positive, and take care of myself; I appreciate your unwavering support through the most difficult periods of this degree. 1 Chapter 1: Introduction 1.1 Coastal regions and land-sea relationship Coastal regions are among the most productive and highly valued marine environments globally due to their ecological richness, economic value, and cultural importance. Eighty percent of countries in the world have a coastline, and nearly half of the world’s population live within 100 km of a coastline (Martínez et al., 2007). Coastal regions support up to 30 % of marine primary production despite coastal waters covering only 10 % of the global ocean area (Hickman et al., 2012). This disproportionately high production yields some of the most biodiverse ecosystems in the world, supporting ecologically and culturally valuable species (Seitz et al., 2014; Tittensor et al., 2010), and a wide array of ecosystem services (Costanza et al., 1997; Martínez et al., 2007). For many Indigenous communities, shorelines and nearshore waters represent sacred spaces that sustain ancestral knowledge and spiritual ceremonies across generations (Lilley et al., 2017; Taft et al., 2021). These communities have lived in and along coastal regions since time out of mind and still manage them today. Marine food webs in coastal regions are often highly productive and biodiverse. Phytoplankton and macrophytes primarily drive primary production (Cloern et al., 2014; Mann, 1973) which is likely limited by nitrogen in coastal regions (Strom et al., 2006; Thom & Albright, 1990). These autotrophs fuel primary consumers like zooplankton, bivalves, and grazers, while upper trophic level species can include humans, crustaceans, birds, and marine mammals. Diverse biological communities are sustained in various nearshore habitats found in coastal regions, such as beaches (Bergamino et al., 2011), rocky shores (Kéfi et al., 2015), mudflats (Marley et al., 2019), and kelp forests (Zuercher & Galloway, 2019). The structure and 2 complexity of nearshore habitats can influence species composition and trophic interactions within marine food webs (Jinks et al., 2019; Nauta et al., 2023). In addition, the nutritional quality of prey can shape the overall health and reproductive success of consumers (Fast, 2024; McLaskey et al., 2022). Importantly, terrestrial inputs also have wide ranging effects on coastal food webs, supporting production at multiple trophic levels and shaping food web structure and function (Hunt et al., 2024; Xenopoulos et al., 2017). Coastal regions are at the interface between land and sea and this connection to the land is a key factor in the productivity, health, structure, and stability of coastal marine ecosystems (Hunt et al., 2024). Functioning coastal marine ecosystems provide many ecosystem services to the land, including marine-derived oxygen production, food provision, and nutrient delivery (Barbier et al., 2011; Fu et al., 2015). Nearshore habitats like tidal marshes and eel grass beds also dissipate incoming wave energy and trap sediments, protecting shorelines (Mullarney et al., 2017; Rowland et al., 2025) and reducing erosion (Walter et al., 2020), respectively. In the seaward direction, the relationship between land and ocean is largely driven by inputs of freshwater and its associated materials from varied sources. For instance, large rivers can annually discharge over 100 km3 of freshwater into the ocean (Meybeck & Ragu, 2012), influencing nearshore salinity levels and delivering organic matter and nutrients to nearshore waters (Hickey et al., 2010). Given the importance of the land-sea relationship in coastal regions, modifications in either terrestrial or oceanic environments can inevitably lead to changes in the other. 1.2 Urbanization and its impact on land-sea relationship Urbanization involves the conversion of natural and rural lands to developed areas in response to rising human populations, influencing coastal ecosystems around the world. 3 Shoreline modifications, such as seawalls, are used to protect cities from erosion while benefiting aesthetic and recreational development. These structures damage natural habitats for marine species and often develop less-diverse biological communities (Bulleri & Chapman, 2010; Taft et al., 2021). Many other threats to coastal food webs are directly associated with urbanization, including, but not limited to, noise pollution, introduction of invasive species, sea surface temperature rise, and eutrophication (Freeman et al., 2019). These stressors have consequences for the stability, diversity, and productivity of coastal ecosystems. In addition, urbanization disrupts the land-sea relationship by modifying the nature of terrestrial inputs before entering the marine environment. For instance, characteristic features of urbanization, such as channel straightening and expansion of impervious surfaces, influence the volume and timing of freshwater flows and the materials that they transport (Sutfin et al., 2016; T. Zhou & Endreny, 2020). Urban areas frequently discharge large volumes of stormwater and wastewater effluent (Jones et al., 2021; Levin et al., 2020) containing bioavailable nutrients (Krogh et al., 2018) and organic materials (Quéméneur & Marty, 1994), representing a novel input to coastal waters. Urban derived inputs can reduce ecosystem resilience and shift food web structure. These inputs can degrade water quality mostly due to increased sediment loads (Brand et al., 2021) and chemical pollution (Brown & Takada, 2017; Saxena, 2025). Poor water quality has been shown to drive reproductive impairment (Whalan et al., 2007; Wilson et al., 2005) and recruitment failure (Breitburg et al., 2009; Nilsson et al., 2004) in various coastal fishes and invertebrates. In addition, contaminants can bioaccumulate during trophic transfer, threatening the health of upper trophic level species (Pastor et al., 1996; Reis et al., 2011). Nutrient loading from urban-derived inputs can induce localized eutrophication (Freeman et al., 2019; Howarth & Marino, 2006), 4 which has been linked to anoxic conditions (Rosenberg & Loo, 1988), lower phytoplankton production (Cambridge et al., 1986; Savage et al., 2010), growth of toxic algae (Paerl & Otten, 2013) and shifts in community composition for both phytoplankton (Elser et al., 2007) and benthic invertebrates (Blumenshine et al., 1997). Organic material from urban-derived sources is also known to significantly contribute to coastal waters (Lye, 2025). Therefore, urbanization restructures the dietary regime available for primary consumers. 1.3 Knowledge gap of urbanization impacts to organic matter uptake Food webs within or adjacent to urban development face a wide range of modified or novel inputs. However, how primary consumers utilize these different inputs and the implication to their health remains unknown. The effects of urbanization can also interact with the seasonal cycles of different inputs, such as precipitation patterns that dictate freshwater runoff. Research needs to therefore consider temporal variability of urban effects to food webs. Finally, a generalized approach to measure urban-derived organic matter uptake by primary consumers is needed. Addressing these concerns requires: 1) effective bioindicators of organic matter uptake into marine food webs 2) biochemical tracers that can be used to delineate organic matter types and assess nutritional health 3) a case study of an urbanized environment with strong land-ocean connections 1.4 Mussels as bioindicators Suspension feeding organisms serve as primary entry points for terrestrial and other organic matter into the food web, and as such can act as bioindicators for key processes in coastal environments (Azizi et al., 2018; Fokina & Chesnokova, 2024; Signa et al., 2015). Mussels are 5 sessile organisms that often form dense intertidal beds in coastal environments. They filter large quantities of water at efficient rates which allow them to influence nutrient cycling, phytoplankton production and water clarity (Newell, 2004; Petersen et al., 2014; Timmermann et al., 2019). Mussels serve as a direct food source and promote benthic-pelagic coupling by transferring suspended particulate organic matter (SPOM) from the water column to the sediments (Alonso-Pérez et al., 2010; Hatcher et al., 1994). Their wide range of ecosystem services suggest that anthropogenic-related impacts on bivalve filter-feeding communities can have cascading effects on the surrounding food web. Mussels also serve as effective proxies for culturally important species like clams, as they both inhabit similar benthic environments and consume POM through suspension feeding. Clams are an important traditional food source for many First Nations communities. Due to cumulative anthropogenic effects in the inlet, including contaminant loading and harmful algal blooms, First Nations communities have lost their right to harvest healthy and abundant shellfish. 1.5 Tracing land-derived materials Urban effects on the marine environment can be identified by tracing the fate of land-derived materials. Contaminant analysis often capture these signals as they are delivered through urban-derived runoff to receiving waters with adverse effects to water quality and species health (Li et al., 2025; Parker et al., 2010). Similarly, persistent inorganic nutrient loading originates from urban discharge, leading to localized eutrophication and phytoplankton blooms (Howarth & Marino, 2006; Paerl & Otten, 2013). Urban-derived runoff also carries organic matter (Levin et al., 2020). Biochemical analysis, such as stable isotopes and fatty acids, have shown to delineate various types of organic matter in the marine environment (Lye et al., in prep; St. Pierre et al., 2022) and in organism tissues (Both et al., 2020; F. Guo et al., 2020). Therefore, biochemical 6 tools can be applied to organic matter sources and consumer tissues to enable direct tracing of urban-derived inputs into the food web. 1.6 Burrard Inlet and Metro Vancouver (study region) səl̓ilw̓ət, now also known as Burrard Inlet, is a coastal inlet in British Columbia, Canada, situated on the unceded, ancestral, and traditional territory of the Coast Salish Peoples, including the territories of the Səl̓ílwətaʔ/Selilwitulh (Tsleil-Waututh), xwməθkwəy̓əm (Musqueam), Skwxwú7mesh (Squamish), and Stó:lō Nations. Before European contact, the inlet was a thriving environment that supported thousands of First Nations peoples' culture, economies, and ways of life. During this time, salmon, herring, and clams were vital food sources, and through these and other species, the marine environment supplied over 90% of the Tsleil-Waututh communities' diet (Efford et al., 2025). Since the 1800s, urban and industrial expansion has modified the land surrounding Burrard Inlet. Impacts of colonial development have resulted in deterioration of the ecosystem well beyond what is allowable under Tsleil-Waututh Nation law, including the loss of shellfish harvesting due to contamination and decline of salmon and herring populations (Taft et al., 2021). These modifications ultimately disrupt societal, cultural, and ecological relationships with the inlet. Today, Metro Vancouver, the regional district that has developed around Burrard Inlet, spans 21 municipalities and has a population over 3 million (Metro Vancouver, n.d.; Statistics Canada, 2023). Burrard Inlet receives substantial urban runoff from Metro Vancouver, facilitated by the inlet's 407 active and historic stormwater outfalls and 24 active combined stormwater-sewer outfalls (Rao, 2022). With the support of First Nations stewardship and environmental protection initiatives, there is ongoing work to heal Burrard Inlet and restore conditions that support productive food webs. 7 Metro Vancouver and Burrard Inlet represent an ideal case study region to investigate the impacts of urbanization on coastal environments. As a high rainfall region (>1400 mm average annual precipitation in Vancouver) with substantial urban-derived runoff, this region has strong land-ocean connections that influence the health and stability of marine food webs (Environment and Climate Change Canada, 2025). Due to climate change, winter precipitation and summer droughts are expected to increase in the North Pacific Coastal region, where Metro Vancouver is situated (Hunt et al., 2024). These effects are coupled with the continued rise of urbanization, with Metro Vancouver's population projected to be over 4 million by 2050 (Metro Vancouver, 2024b). Therefore, anthropogenic effects on the land-sea relationship may be amplified in the future. To monitor and mitigate these cumulative stressors, ongoing efforts have been launched by First Nations, municipal authorities, and provincial agencies. This includes the Burrard Inlet Action Plan, initiated by the Tsleil-Waututh Nation, a strategic plan towards restoring the health of Burrard Inlet (Lilley et al., 2017). The objectives include mitigating anthropogenic pressures and improving water quality such that Tsleil-Waututh communities can reclaim their ancestral and constitutionally protected right to sustainably and safely harvest healthy and wild marine food. 1.7 Thesis objectives The overarching aim of this thesis was to determine the fate and implications of organic matter types on food webs in an urban setting. Specifically, I quantified the contribution of organic matter types to a sentinel species (mussels) and assessed their nutritional health. In addressing these aims, I developed a generalizable framework for examining anthropogenic influences on coastal marine food webs. 8 Chapter 2: From poop to marine soup: deconstructing the diet and health of mussels in a high rainfall coastal region 2.1 Introduction Coastal marine food webs are heavily influenced by terrestrial inputs of organic matter and nutrients, including supporting production at multiple trophic levels and shaping food web structure and function (Hunt et al., 2024; Xenopoulos et al., 2017). Urbanization modifies the nature, timing, magnitude, and fate of these terrestrial inputs (i.e., urban-derived inputs) through land-use change (Boëchat et al., 2014; Lambert et al., 2017), increased stormwater runoff , and continuous wastewater discharge (Mudge & Duce, 2005). These changes can disrupt ecological and cultural land-sea connections in nearshore food webs (Hunt et al., 2024). While organic matter types contributing to coastal marine suspension feeders has been investigated (e.g., Both et al., 2020; Mathieu-Resuge et al., 2019), there has been limited research on how this uptake is modified by urbanization (Puccinelli et al., 2016; Schlieman et al., 2022). Terrestrial organic matter inputs have wide ranging effects on coastal food webs. Terrestrially derived dissolved organic matter (DOM) and particulate organic matter (POM), together with its associated nutrients, fuel microbial bacterial activity and phytoplankton growth (Vargas et al., 2012). Locations and periods of substantial terrestrial flux (e.g., spring freshet) can become hotspots of microbial production (Herfort et al., 2017), and in some cases can reshape food web structure by favouring heterotrophic activity over autotrophic production (Andersson et al., 2018; Wikner & Andersson, 2012). Terrestrial organic matter can also contribute directly to metazoan primary consumer diets, varying with proximity to freshwater sources, seasonal hydrological cycles, and availability of other resources (Jiao et al., 2024; Kasai & Nakata, 2005; Rumolo et al., 2017). The nutritional quality of terrestrial material is highly 9 variable but often has low concentrations of important nutrients, such as essential fatty acids, and low nitrogen content, critical for consumer growth and survival (Cummings et al., 2009; Harfmann et al., 2019). Terrestrial organic matter consumption can drive nutrient recycling and also regulate oxygen levels, contributing to primary production and shaping habitat conditions, respectively (Conway-Cranos et al., 2015; Vargas et al., 2012). Overall, terrestrial organic matter inputs have a critical influence on coastal food webs that depends on its quantity, quality, and timing. Urbanization alters organic matter inputs to coastal marine ecosystems in several ways. Urbanization can influence the flow regime of freshwater and its associated materials through reduced water infiltration on impervious surfaces (Strohbach et al., 2019), accelerated runoff in modified channels (Fellman et al., 2021), and the intensification of the seasonal hydrological cycle (Hunt et al., 2024; Kim et al., 2025). During rainfall events, stormwater can deliver concentrated pulses of labile OM to the ocean influenced by both natural (e.g., leaf litter) and non-natural (e.g., road dust) materials (Kaushal & Belt, 2012; Levin et al., 2020). Urban areas also discharge large volumes of untreated and / or treated wastewater effluent (Jones et al., 2021) that includes high concentrations of bioavailable nutrients and organic matter (Krogh et al., 2018; Quéméneur & Marty, 1994). Urbanization can also impact the contribution of marine origin organic matter pools to coastal food webs. Anthropogenically-driven elevated terrestrial inputs can lower overall primary production in nearshore waters through intensified turbidity and stratification that reduce light penetration and vertical nutrient exchange (McKinnell et al., 2014; Thomson et al., 2012). However, urban-derived inputs can also augment nutrient loading that can induce marine eutrophication, leading to increased phytoplankton biomass and shifts in community composition 10 (Howarth & Marino, 2006; Paerl & Otten, 2013). While macrophyte growth may improve with eutrophication (Dillon & Chanton, 2008), it can also be reduced due to shading from phytoplankton and negatively impacted by other urban-related stressors including habitat loss from dredging (Dethier et al., 2016; Scherner et al., 2013). Through a combination of novel (e.g., wastewater) and amplified (e.g., stormwater, rivers) inputs, and impacts on phytoplankton and macrophyte production, urbanization can therefore shift the organic matter pool available for coastal marine suspension feeders. For example, eutrophication through the input of anthropogenically-associated nutrients has been suggested to correspond with elevated proportions of polyunsaturated fatty acids in mussels, reflecting intensified phytoplankton productivity (Puccinelli et al., 2016). Reductions in benthic macrophyte biomass due to urban stressors were found to be reflected in an inverse relationship between urban land cover and macrophyte contribution to mussel tissues (Schlieman et al., 2022). Watershed nitrogen loading from anthropogenic inputs including salmon farms (Redmond et al., 2010; Schlieman et al., 2022), agricultural runoff (C. Anderson & Cabana, 2005; Fertig et al., 2010), and wastewater discharge (R. Carmichael et al., 2008; J. Tucker et al., 1999) have also been found to be reflected in consumer tissues. Anthropogenically-driven shifts in the dietary regime of primary consumers carry implications for nutritional quality, contaminant exposure and trophic interactions in nearshore food webs. Phytoplankton supply essential fatty acids that support consumer growth and reproductive success that are not found in other sources (Glencross, 2009; Parrish, 2009). Urbanization driven changes in phytoplankton production and species composition therefore have implications for the provision of these nutrients (McLaskey et al., 2022). While organic matter in rivers, stormwater, and wastewater can have higher concentrations of macronutrients 11 and fatty acids than the marine environment (Lye et al., in prep), the fate of these urban-derived organic matter types in the marine food web remains unresolved. Investigating this knowledge gap will elucidate the associated effects on food web production and structure, and also has implications for the delivery of associated contaminants (e.g., pharmaceuticals, trace metals, 6PPDE-quinone) that can accumulate in suspension feeder tissues, disrupt enzyme activity, and reduce filtration efficiency (Li et al., 2025; Renault, 2015). These combined nutritional and toxicological stresses can cascade through predator-prey interactions, altering trophic transfer efficiency, consumer body condition, and the overall stability of nearshore food webs, including humans (M. Brett & Müller-Navarra, 1997; Fleeger et al., 2003; Layman et al., 2012). Therefore, it is critical to understand how the wide range of organic matter types are used in nearshore food webs under varying urban stressors. Accurately resolving this requires analytical frameworks capable of both partitioning multiple organic matter sources and measuring subsequent food web transfer and implications. Fatty acids (FAs) are an effective biochemical tool for distinguishing organic matter types and tracing dietary uptake across spatial and temporal scales. Distinct processes impart unique FA signatures on each organic matter type. For instance, the FA 16:1n-7 (i.e., palmitoleic acid) is exclusively produced by phytoplankton and leaves a strong signal in consumer lipids (Dalsgaard et al., 2003). The FA 18:1n-9 (i.e., oleic acid), abundant in human feces (Williams et al., 1960), has comprised a large fraction of wastewater lipids (Quéméneur & Marty, 1994). As such, this FA has been used in part to trace wastewater uptake in consumers (Boëchat et al., 2014; Sakdullah & Tsuchiya, 2009). FA profiles integrate organism diets over weeks to months (Redmond et al., 2010), therefore providing a time-integrative measure of organic matter contributions in food webs. Individual FAs, FA groups, and FA ratios, known as FA biomarkers, 12 have been derived to represent uptake of different organic matter types (e.g., Budge et al., 2001; Dalsgaard et al., 2003; Kharlamenko et al., 1995). These biomarkers have been effectively used to resolve contributions of organic matter types to the marine environment (Canuel, 2001; Liénart et al., 2020) and to suspension feeder diets (e.g., Bachok et al., 2009; Both et al., 2020; Guo et al., 2020). However, since FA profiles can shift with environmental and biological factors, including temperature (Shang et al., 2024) and plankton community composition (Galloway & Winder, 2015; McLaskey et al., 2022), FA biomarkers can reflect local conditions. Therefore, accurate dietary uptake estimates depend on FA biomarker derivations that are specific to the study region. In addition, FAs have been used to assess the nutritional dynamics of coastal marine food webs (e.g., Costalago et al., 2020; McLaskey et al., 2022; Schaub et al., 2023). FAs are the main constituent of lipids, and thus, total fatty acid content (TFA) serves as a general proxy for metabolic energy (Sargent et al., 2003). Essential FAs (EFAs) are a unique class of FAs that cannot be synthesized de novo by most primary consumers and therefore must be acquired through their diet (Parrish, 2009). These EFAs include, but are not limited to, 22:6n-3 (DHA), 20:5n-3 (EPA), and 20:4n-6 (ARA). Higher ratios of DHA:EPA are associated with improved growth and stress tolerance in suspension feeders (Hendriks et al., 2003; Knauer & Southgate, 1999; Yuan et al., 2023). Similar to FAs, carbon (δ13C) and nitrogen (δ15N) stable isotopes can resolve organic matter types and their contributions to food webs (e.g., Guest et al., 2010; Lye, 2025; St. Pierre et al., 2022). δ13C and δ15N values reflect unique carbon acquisition pathways and nitrogen sources, respectively (Lerner & Hunt, 2024). Using the SI and FA signatures of organic matter sources, it is possible to estimate their contribution to consumer dietary uptake through Bayesian mixing 13 model analysis (M. T. Brett et al., 2016; Pickett et al., 2024). Combining SIs and FAs in the same model can increase the resolving power of the model when using multiple source inputs (Neubauer & Jensen, 2015; O’Donovan et al., 2018; Pickett et al., 2024). The overarching aim of this study was to investigate the food web fate and implications of organic matter types in an urban setting. Specifically, I determined: 1. Spatial and seasonal variation in assimilation of organic matter types by a model suspension feeder (mussels). 2. The effect of assimilated organic matter on primary consumer nutritional health. In addressing these two aims, I developed a generalizable framework for examining anthropogenic influences on coastal marine food webs that combines fatty acid biomarkers of organic matter types and Bayesian mixing model analysis integrating both fatty acids and stable isotopes. I achieved these objectives by conducting monthly collections of marine mussels (Mytilus spp.), a sentinel suspension feeder representing an entry point of organic matter inputs to food webs. Our collections spanned local sites that ranged in environmental characteristics, including urban pressures. I predicted that phytoplankton contributions to mussel diets would display temporal variability that reflects phytoplankton dynamics, with the highest contributions during the wet bloom season. Temporal and spatial variation of urban-derived contributions to mussel diets were expected to reflect the seasonal hydrological cycle and proportion of impervious coverage, respectively. Amid the rising trend of urbanization and disproportioned effects towards coastal areas (Reimann et al., 2023), our findings link urban pressures to marine consumer health. Our results provide pertinent information towards ongoing monitoring efforts that can translate to meaningful restorative actions (i.e., Burrard Inlet Action Plan and Burrard Inlet Water Quality Objectives; Bjorklund et al., 2024; Lilley et al., 2017). 14 2.2 Methods 2.2.1 Study area This study was completed in səl̓ilw̓ət (Burrard Inlet), a coastal inlet in British Columbia, Canada, situated on the unceded, ancestral, and traditional territory of the Coast Salish Peoples, including the territories of the Səl̓ílwətaʔ/Selilwitulh (Tsleil- Waututh), xwməθkwəy̓əm (Musqueam), Skwxwú7mesh (Squamish), and Stó:lō Nations (Figure 1). Burrard Inlet consists of diverse ecosystems that support a wide range of aquatic species, including intertidal areas for bivalves and rearing habitats for juvenile salmon. The inlet is an estuarine fjord located within the Northeast Pacific coastal temperate rainforest region, influenced by heavy winter rainfall and spring freshets, with cool, wet winters and mild, relatively dry summers (Hunt et al., 2024). Regions with similar physical and climatic regimes include Puget Sound (Moore et al., 2008) and the Chilean Patagonian fjords (Iriarte et al., 2014), highly productive coastal environments with heavy rainfall that drives strong seasonal cycles of salinity, temperature and phytoplankton production. Lower salinity levels in Outer Harbour sites (i.e., Tower Beach, PSEC, Dundarave Beach, Stanley Park; Table 1) reflect the influence of the Fraser River, which discharges considerable volumes of freshwater to Burrard Inlet (Thomson, 1981). 15 Figure 1. Map of all sites for mussel and organic matter (marine, river, macrophyte and stormwater) collection. The name of each mussel site is indicated in white text. Map created using the ggmap package in R (Kahle & Wickham, 2013). Tower Beach Kitsilano Beach False Creek Stanley Park Dundarave Beach PSEC Maplewood Flats Shipyards 16 Table 1. Mussel collection sites with impervious cover percentage, land-use, shore type and averaged temperature and salinity (± standard deviation) separated by season (dry = June to September, wet = October to February, wet bloom = March to May). For methods and definitions regarding impervious cover, land-use and shore type, see 2.2.2 Site classification. Site Impervious Cover (%) Land-use Shore type Season Temperature (°C) Salinity (ppt) Stanley Park 5.95 Municipal Park Rock and gravel Dry 17.72 ± 3.06 21.05 ± 3.86 Wet 8.2 ± 0.73 23.82 ± 2.23 Wet Bloom 11.67 ± 4.45 20.53 ± 9.48 PSEC 23.38 Research facility beach Sand Dry 16.27 ± 1.71 21.3 ± 3.27 Wet 8.74 ± 0.94 23.9 ± 4.66 Wet Bloom 8.42 ± 0.71 18.75 ± 5.94 Maplewood Flats 36.11 Conservation area Gravel Dry 15.97 ± 1.87 25.7 ± 1.53 Wet 8.9 ± 0.9 21.78 ± 3.22 Wet Bloom 9.4 ± 0.33 21.83 ± 3.01 Tower Beach 46.82 University beach Gravel Dry 17.73 ± 2.95 19.62 ± 3.94 Wet 8 ± 0.99 22.72 ± 4.1 Wet Bloom 11.97 ± 3.8 18.9 ± 3.4 Dundarave Beach 51.22 Urban centre beach Rock and sand Dry 16 ± 1.72 20.9 ± 4.24 Wet 8.82 ± 0.77 18.93 ± 4.37 Wet Bloom 9.6 ± 0.44 9.83 ± 9.54 Kitsilano Beach 65.40 Urban centre beach Rock and sand Dry 17.25 ± 2.58 21.23 ± 3.62 Wet 8.45 ± 0.47 25.8 ± 2.91 Wet Bloom 11.3 ± 3.83 20.4 ± 10.73 Shipyards 85.74 Urban centre waterfront Rock Dry 15.62 ± 1.46 24.68 ± 2.91 Wet 9.02 ± 0.65 24.8 ± 3.74 Wet Bloom 8.76 ± 0.33 24.3 ± 4.06 False Creek 89.35 Urban centre beach Rock and sand Dry 17.12 ± 2.76 17.1 ± 4.61 Wet 8.28 ± 0.78 24.32 ± 1.83 Wet Bloom 12.4 ± 4.44 20.43 ± 7.19 Before European contact, Burrard Inlet was a thriving environment that also supported thousands of First Nations peoples’ diets, culture, economies, and ways of life. The inlet spans multiple municipalities sharing approximately 190 km of shoreline within a catchment area of 1288 km2 (Rao et al., 2019). Since the late 1800s, land surrounding the inlet have undergone impacts from colonial development which has impacted the health of Burrard Inlet, including its water quality (Rao, 2022). Land modification around Burrard Inlet reflect historically 17 unregulated and ongoing urban development including shoreline alterations, damming of major rivers, industrialization, and increased urban runoff, facilitated by the inlet's 407 active and historic stormwater outfalls and 24 active combined stormwater-sewer outfalls (Rao, 2022). As a result of colonial development, the inlet has lost over 1000 ha of intertidal and shallow subtidal areas since European contact (Taft et al., 2021). Existing initiatives, like the Burrard Inlet Action Plan and Burrard Inlet Water Quality Objectives, emphasize the importance of ecosystem monitoring that can inform meaningful action towards restoring the health of Burrard Inlet (Bjorklund et al., 2024; Lilley et al., 2017). 2.2.2 Site classification This study sampled sites across the Metro Vancouver shoreline (Figure 1). Mussel sites were classified based on land-use designation and impervious surface coverage, informed by Metro Vancouver open-access land cover data, official land-use designations, and site-specific management context (Table 1). Sites with the land-use classification “urban centre” (False Creek, Shipyards, Kitsilano Beach, Dundarave Beach) are located within designated “urban centres” identified by Metro Vancouver, which are focal areas for growth, transit, and mixed land use (Metro Vancouver, 2011). Maplewood flats was classified as a “conservation area” as it was designated as a conservation area in 1993, managed by the non-profit organization, Wild Bird Trust. PSEC (Pacific Science Enterprise Centre) was classified as a “research facility beach” since the site is connected to a research centre managed by Fisheries and Oceans Canada with restricted access. Tower Beach was classified as “university beach” as the site is adjacent to University of British Columbia. The “shore type” classification refers to the substrate type along the shoreline for each site and was derived from ShoreZone, a standardized coastal habitat mapping system (Howes et al., 1994; ShoreZone, n.d.). 18 Impervious surface coverage at each site was calculated using the 2020 Metro Vancouver Regional Land Cover Classification dataset (Metro Vancouver, 2023), following the approach outlined in the 2020 Regional Tree Canopy Cover and Impervious Surface report (Metro Vancouver, 2024a). The land cover dataset is an open-access raster file with 5 m spatial resolution, classifying land into one of 16 land-cover types using PlanetScope satellite imagery, LiDAR data (wherever available), and additional supporting datasets (Metro Vancouver, 2023). For our study, land-cover data with a 1 km radial buffer around each site was extracted using ArcGIS (Figure A1). Within each buffer, land cover classified as impervious was summed and divided by the total land area excluding water. An impervious weighting scheme was applied to reflect partial imperviousness with certain land types (i.e., soil; Metro Vancouver, 2024). Since the land-cover dataset does not distinguish the “shadow” land-type (i.e., dark pixels with low reflectance values), it was classified as “urban-shadow” for sites located within Metro Vancouver-designated “Urban Centres” (Metro Vancouver, 2011), and as “non-urban shadow” for all other sites. 2.2.3 Field sampling To understand the relative uptake of organic matter into the Burrard Inlet food web, I sampled mussels (representative organic matter consumer) and collected five different sources of organic matter (marine water, rivers, stormwater, wastewater, and macrophytes; see Table A1 for sample numbers). I also conducted environmental measurements at all mussel sites. To ensure specimens were kept alive and to prevent material degradation (e.g., fatty acids, stable isotopes, chlorophyll, macronutrients), samples were stored in a cooler with Techni ice for a maximum of 3 hours until they were transported to the UBC lab. 19 2.2.3.1 Environmental measurements To provide environmental context for interpreting mussel data, surface water temperature and salinity were measured at the nearest marine point to mussel collection using a Pro30 YSI at all mussel sites. To directly compare detailed water column conditions to mussel data, surface marine water was collected at four mussel sites (Stanley Park, Maplewood Flats, Shipyards, False Creek) for chlorophyll, macronutrients (nitrate, phosphate, and silicate), and POM for FA and SI analysis. At Shipyards, marine water was collected from a nearby dock (<100 m from mussel site) due to limited shore access. The four mussel sites with water samples were selected to represent less urban (Stanley Park and Maplewood Flats) and more urban (False Creek and Shipyards) areas with a wide geographical coverage in Metro Vancouver. For chlorophyll, 500 mL of surface marine water was collected into a wide-mouth high-density polyethylene (HDPE) and amber-coated bottle to prevent chlorophyll degradation due to light exposure. For macronutrients, 12 mL of surface marine water was filtered through a 0.45 μm filter into a triple-rinsed falcon tube on site. 2.2.3.2 Mussel collection Blue mussels (Mytilus spp.) were sampled during low tide at 8 sites around Metro Vancouver in the last week of each month from April 2023 to March 2024 (Figure 1). Most mussel samples were likely Mytilus trossulus (Marshall et al., 2021), though the exact Mytilus species was not determined in this study. The October collection was delayed and occurred in the 2nd week of November. Mussels were collected alive at fixed points (maximum tolerance of ~50 m around the selected point) for each site. The sex of the mussel was not determined and mussels varied in size (length = 3.9 ± 0.70 cm; width = 2.15 ± 0.40 cm; mean ± SD). 20 2.2.3.3 Burrard Inlet organic matter types 2.2.3.3.1 Phytoplankton Between March 2023 to 2024, marine water was collected at 19 offshore and 7 nearshore stations in Burrard Inlet (Figure 1). All offshore stations and 3 nearshore stations in False Creek were collected seasonally (March, May, August, November 2023), and the remaining 4 nearshore stations were collected monthly during mussel collection surveys. Marine water was collected at the surface using a Niskin bottle or bucket for offshore and nearshore stations, respectively, for fatty acid and stable isotope analysis of POM. Cubitainers were triple rinsed with the collected water before filled. At each station, surface water temperature and salinity were measured with a Pro30 YSI. 2.2.3.3.2 Macrophytes Macrophytes were collected at 15 sites (including all 8 mussel sites) in April, June, August, and December 2023 (Figure 1). Three different macrophyte groups were collected, red (Pyropia sp. and Mastocarpus sp.), green (Ulva spp.), and brown (Fucus spp., Sargassum muticum, Saccharina latissima, Laminaria spp.) macroalgae. As collected material was limited to what could fit in a standard whirl Pak bag (118 mL), either blade sections, frond segments or whole shoots were sampled. Only living tissues were collected for all samples. 2.2.3.3.3 Rivers Five rivers (Brunette River, Coquitlam River, Capilano River, Lynn River, Seymour River), four creeks (Lawson’s Creek, Mossom Creek, Noon’s Creek, Spanish Creek) and two modified channels (Dundarave Pipe and Stanley Park Culvert) in Metro Vancouver, hereafter referred to as rivers, were sampled in April, June, September, and December 2023. One river, Stanley Park Culvert, recorded no flow in September (Figure 1). The catchment area for all rivers 21 in this study included land that has been modified by urbanization or industrialization. River water was collected using a triple-rinsed bucket and then sampled as described above for marine water for fatty acid and stable isotope analysis of POM, and temperature, conductivity, and salinity. 2.2.3.3.4 Stormwater Stormwater was collected opportunistically at 16 sites around Metro Vancouver during 7 rain events between September 2023 and February 2024 (Figure 1). Stormwater was collected directly off-road, above a storm grate using a plastic dustpan and then sampled as described above for marine water for fatty acid and stable isotope analysis of POM. A Pro30 YSI was used to measure temperature, conductivity, and salinity directly from the water collected in the cubitainer. 2.2.3.3.5 Wastewater One composite influent and one composite effluent sample was collected at each of the five wastewater treatment plants (WWTPs) in Metro Vancouver: Iona, Lulu Island, Lions Gate, Annacis Island, and Northwest Langley. WWTP material was collected into an acid-washed vessel (max 2L total per sample) hourly from 7:00 AM, September 18th, 2024, to 7:00AM, September 19th, 2024. Samples were kept cool throughout the sampling period. Wastewater samples were then transported in a cooler with Techni ice to UBC for processing on September 19th, 2024. 22 2.2.4 Laboratory processing 2.2.4.1 Chlorophyll a For each chlorophyll a (Chl a) sample, 250 mL seawater was sequentially filtered through three pore sizes (20 μm, 3 μm, and Sterlitech GF/F) to generate distinct chlorophyll size fractions: micro-Chl (>20 μm), nano-Chl (3-20 μm), and pico-Chl (0.7-3 μm). To obtain bulk Chl a samples, 250 mL was filtered through a Sterlitech GF/F filter. Following filtration, filters were immediately stored in darkness at -20°C. Chlorophyll a was extracted from the filters with 90 % acetone and concentrations were measured by fluorometry (Trilogy Laboratory Fluorometer) using acidification (Holm-Hansen et al., 1965). 2.2.4.2 Macronutrients Water samples for macronutrient analyses were filtered through 0.45 μm filters, then stored upright at -20˚C until analyzed on a Lachat QuikChem 8500 Series 2 Flow Injection Analysis System, following adapted methodologies to Grasshoff et al. (2009). I followed analysis methods outlined in the literature for silicate (S. Tucker, 2010), phosphate (Knepel & Bogren, 2008), and nitrate plus nitrite combined (hereafter referred to as nitrate; Smith & Bogren, 2003). 2.2.4.3 Mussels In the lab, mussels were cleaned of epibionts and kept in a cooler with ice to keep mussels alive until dissection. The length and width of each mussel was recorded. To ensure the analysis focused on the biochemical signature assimilated into mussel tissues, the digestive gland of each mussel was removed using a scalpel, and the remaining tissue was used for analysis. To ensure enough material for analysis was achieved, mussel tissues of 3 to 5 individuals were 23 pooled as one replicate. A total of three replicates were used for each site and month. The wet weight of mussel soft tissue (digestive gland removed) was recorded for each replicate. Mussel samples were placed in whirl-pak bags and stored in -80℃ . 2.2.4.4 Macrophytes Macrophytes were cleaned of epibionts, rinsed with deionized (DI) water, placed in whirl-pak bags and stored in -80℃. 2.2.4.5 POM FA filtration For fatty acids, up to 5 L of water was filtered through separate pre-weighed and pre-combusted (at 475 °C for 4h) 47 mm Sterlitech GF/F filters. Two filters were combined to represent a single POM FA sample (10 L maximum per sample). Each filter was folded and stored in a tin foil packet at −80˚C until further processing. 2.2.4.6 POM SI filtration For stable isotopes, up to 2 L of water was filtered onto a pre-weighed and pre-combusted (at 475˚C for 4 h) ~ 0.7 µm 25 mm Sterlitech GF/F filter. For each water sample, duplicate filters were taken with one filter acidified with 2 mL 1M HCl to remove particulate inorganic carbon (Lorrain et al., 2003). After 30 seconds, HCl was rinsed off with filtered seawater for marine samples and mili-Q water for river, stormwater, and wastewater samples. Filters were folded and stored at -20˚C until further processing. 2.2.4.7 Fatty acids For mussels and macrophytes, samples were freeze dried in whirl-pak bags, homogenized, and then transferred into Pyrex screw cap tubes. For POM, filters were transferred into Pyrex screw cap tubes and freeze dried. 24 Fatty acids were measured as fatty acid methyl esters (FAMEs) which followed a one-step transesterification method described in McLaskey et al. (2022) and quantified by gas chromatography with flame ionization detection (Scion 436). Briefly, HCl in methanol (catalyst) and nonadecanoic acid (19:0; internal standard) were added to samples. FAMEs were separated with an Agilent DB-23 column (30 m, 0.25 mm, 0.15 μm) with hydrogen carrier gas (1.2 mL min-1). Samples were injected at 250 ˚C; the column temperature started at 50 ˚C held for 1 minute, then increased at a rate of 5 ˚C min-1 until 170 ˚C, then 2 ˚C min-1 until 176 ˚C, and then 8 ˚C min-1 until 220 ˚C for a total run time of 33.5 minutes. Peaks were identified by comparing to commercially available standards (Nu-Chek Prep GLC-37; Matreya PUFA Mix No. 3). Fatty acid concentrations were reported as milligrams of FA per gram of mussel dry weight (referred to as mg g-1 hereafter) and proportions as a percentage of total FA content. Due to freeze dryer failure and C19 contamination, 31 marine POM, 19 freshwater POM, and 22 stormwater POM samples were removed for fatty acids. Two mussel samples (False Creek in December, Dundarave Beach in February) evaporated during FA processing and two other mussel samples (Shipyards in April, PSEC in March) were removed due to peak identification disturbances. All Pyropia spp. samples became highly static following freeze-drying, making them difficult to process for analysis. These macrophyte samples had outlier FA and SI values and were removed from this study. 2.2.4.8 Stable isotopes For mussels and macrophytes, samples were freeze-dried and homogenized in whirl-pak bags and then encapsulated into a tin capsule using forceps. For POM, filters were dried in an oven for 24 hrs. at 50˚C and weighed. Filters were then encapsulated in a crimped tin capsule using a fulcrum press. 25 Encapsulated samples were placed in 96 well trays and sent to the University of Windsor for carbon and nitrogen stable isotope analysis. Samples were analyzed on a Delta V Advantage Mass spectrometer (Thermo) coupled to Costech 4010 Elemental Combustion system and a ConFlo IV gas interface. Stable isotope ratios were presented in δ-notation. The C:N represents the ratio between percent carbon and percent nitrogen. Measurements for carbon (δ¹³C and particulate organic carbon) and nitrogen (δ¹⁵N and C:N) were presented by acidified and non-acidified filters, respectively. One mussel sample was excluded due to low sample weight (Kitsilano Beach in July), and one mussel (Shipyards in March), one influent and three effluent samples were removed due to an invalid carbon percentage (>100%). With mussel mean C:N above 3.5 (4.23 +/- 0.43) and bivalve lipid content generally above 5% of dry mass (Naik et al., 2020; Prato et al., 2009) I applied a lipid correction to δ13C for mussels with the following equation: δ13Ccorrected = δ13Cuncorrected - 3.32 + 0.99 x C:N (Post et al., 2007). No lipid correction was applied to POM samples. 2.2.5 Data analysis All data analysis, including data visualization and statistical tests, were completed in R 4.02 (R Core Team, 2020). The map of sites (Figure 1) was created with the ggmap package (Kahle & Wickham, 2013). 2.2.5.1 Environmental measurements For all mussel sites, temperature and salinity were reported as seasonal averages. Monthly temperature, salinity, chlorophyll and macronutrient data was reported and visualized with a line plot for the four mussel sites where it was collected. In this study, temperature, salinity and conductivity were not reported for any source collections. Silicate concentrations were compared to deep-water (< 50 m) measurements in Strait of Georgia, which represented a 26 natural maximum baseline near Burrard Inlet (Dosser et al., 2021). Surface values exceeding this baseline (~ 50 µmol L-1) were interpreted as silicate inputs from urban-derived runoff (Lye et al., in prep; Tavakoly Sany et al., 2019). 2.2.5.2 Seasonal Separation Data were grouped into wet (October to February), wet bloom (March to May), and dry (June to September) seasons to reflect annual rainfall (Bjorklund et al., 2024) and phytoplankton bloom (Allen & Wolfe, 2013) patterns in BC. The timing of the Fraser River freshet (May – June) was not considered for the seasonal groupings in this study as Lye (2025) identified limited contributions from Fraser River POM to marine POM in Burrard Inlet during the freshet. 2.2.5.3 Phytoplankton endmember For the indicator analysis and mixing model, to ensure marine POM accurately represented phytoplankton as a source and not heavily interfered by a detrital signal, I only used samples with a particulate organic carbon to chlorophyll ratio less than or equal to 75 (Burt et al., 2018). 2.2.5.4 Analysis of mussel FA & SI profile Fatty acids with proportional contributions to total fatty acids of > 0.5% in all mussel samples were used for multivariate analysis of mussel FA profiles. Proportions were arcsine square root-transformed and converted to a Bray–Curtis dissimilarity matrix before visualization by non-metric multidimensional scaling (NMDS) sorted by site and sample month (R package vegan; Oksanen et al., 2020). The influence of site and sample month on mussel FA profiles were tested with a permutational analysis of variance (PERMANOVA; Anderson, 2001). Pairwise comparisons between months and sites were also tested using pairwiseAdonis; Bonferroni-27 corrected p-values were used to determine significance. Mussel carbon and nitrogen isotope data were visualized with a biplot, tested with PERMANOVA for the effect of site and season, followed by pairwise comparisons. 2.2.5.5 Indicator analysis Indicator value analysis (IndVal) was used to identify the fatty acids characteristic of each organic matter source (R package labdsv; Dufrêne & Legendre, 1997). The IndVal quantifies the strength of association between a species (fatty acid) and a group (source). The IndVal is a product of specificity (how exclusively a fatty acid occurs in a source) and fidelity (how consistently a fatty acid occurs within a source), thus higher IndVals indicate a stronger association. For this analysis, only fatty acids with proportions > 0.5% (i.e., “abundant fatty acids”) were selected. Due to uneven sample sizes between macrophytes (N=96) and the sum of water-derived (phytoplankton, river stormwater, and wastewater POM) samples (N=54), abundant fatty acids specifically in water-derived samples (12:0, 16:2n-4, 17:0, 20:0, ant-15:0, iso-15:0) were also included. Influent and effluent and samples were combined as a single source (wastewater). I first conducted a pooled-group analysis, where each fatty acid can be associated with up to three sources (R package indicspecies; Cáceres & Legendre, 2009), followed by an individual-group analysis, where each fatty acid could only be associated with one source. In the pooled-group analysis, I highlighted ecologically meaningful source combinations, specifically, all macrophytes (red, brown, green macroalgae) and urban-derived POM (river, stormwater and wastewater). A holm correction was applied to the IndVal p-values and only significant (p>0.05) IndVals are displayed. 28 I summarized FA biomarkers used to estimate dietary contributions in previous marine bivalve studies (Allan et al., 2010; Bachok et al., 2009; Budge et al., 2001; Ezgeta-Balić et al., 2012; Gaillard et al., 2017; F. Guo et al., 2020; Irisarri et al., 2014; Najdek et al., 2013; Puccinelli et al., 2016; Ricardo et al., 2021; Richoux et al., 2014; Zhao et al., 2013; Zhukova, 2019) to compare with the ones derived from organic matter sources in our study. 2.2.5.6 Estimating source contributions to mussels Two approaches were used to estimate source organic matter contributions to mussel tissues. 2.2.5.6.1 Biomarkers proportions in mussels: To estimate the relative contribution of POM sources to mussels, I measured the percent composition of fatty acids for three biomarker groups (phytoplankton, urban-derived POM and macrophytes) in mussels. The selection of biomarker groups prioritized fatty acids with higher IndVals while ensuring all sources in our study were considered. The biomarker proportions (% of total FA) were visualized with a stacked bar plot and tested with an ANOVA. Biomarker concentrations (mg g-1) were visualized with a line plot. 2.2.5.6.2 Mixing model (MixSIAR) I used a Bayesian mixing model in MixSIAR (Stock et al., 2018) to estimate the relative contributions of seven potential POM sources (phytoplankton, rivers, stormwater, wastewater, and red, brown, and green macroalgae) to mussel diets for each season with a dual-tracer (FA and SI) approach. Effluent and influent wastewater were combined into a single source. Since FA and SI data were combined, only samples with both FA and SI data were included, reducing the overall dataset size (Table A1). Trophic enrichment factors for SIs and calibration coefficients for 29 FAs were applied to source data, and then both source and mussel data were standardized to z-scores (i.e., subtracted by the mean and divided by the standard deviation; Dethier et al., 2013; O’Donovan et al., 2018; Schaub et al., 2023). Standardized data were visualized using NMDS to ensure consumers were sufficiently represented by the sources (Stock & Semmens, 2016a), and PERMANOVA was used to confirm that sources were significantly distinct. Fatty acid tracers were selected following Jardine et al. (2020), prioritizing FAs with a predictable influence on mussel profiles. I used the FA database from Jardine et al. (2020), which contains diet and consumer data from controlled feeding experiments, and added three mussel feeding trials (Eryalçın et al., 2024; Handå et al., 2012; Liénart et al., 2023). To increase sample size and reduce bias related to dietary variability, I filtered feeding experiments at the class level (Bivalvia), rather than family (Mytilidae) or species. For each FA, I calculated the R² between consumer and diet FA proportions (R package nlme; Pinheiro et al., 2025) and determined the calibration coefficient (CC) as the mean ratio between diet and consumer proportions. I retained FAs with an R² > 0.20 and a CC between 0.6 and 2 to exclude FAs with weak explanatory power (Jardine et al., 2020) and reduce uncertainty from extreme modification (Neubauer & Jensen, 2015), respectively. Using this approach, ten FA tracers were selected for the mixing model, including 3 saturated (SFA), 1 omega-3 (n-3), 4 omega-6 (n-6), and 2 monounsaturated (MUFA) FAs. Arachidonic acid (ARA; 20:4n-6) was the only essential fatty acid (EFA) included in the mixing model. Fifteen FAs were removed from analysis due to a low R² (<0.20) or to limit uncertainty from over-modification (calibration coefficient outside 0.6-2.0). I corrected our source FA data for trophic modification using the derived calibration coefficients. This approach differed from Jardine et al. (2020), who used a linear equation derived from the regression parameters to correct the source FA data (%FAconsumer = m * %FAdiet 30 + b). Given the inclusion of stable isotope tracers in our model, the FA data were standardized, making it incompatible with regression equations based on percentage values. Stable isotope data were included to supplement FA tracers, as they are commonly used in bivalve mixing models (Both et al., 2020; Kasai & Nakata, 2005; Koo et al., 2025). I applied trophic enrichment factors of 0.4 ± 1.0 ‰ for δ¹³C and 3.4 ± 1.0 ‰ for δ¹⁵N (Post, 2002). The Bayesian mixing model was set up with site as a fixed factor. To ensure model convergence, I ran separate models for each season. Source data were input as means and any missing values were replaced with near zero values (1x10-5) as MixSIAR cannot have missing or 0 values. Since the source data was transformed to z-scores, trophic enrichment and calibration coefficients were applied before running the model. Thus, discrimination factors for FA tracers were set to zero in the model. I used a process-only error structure with uninformative priors (Stock & Semmens, 2016b). Model convergence was assessed by the Gelman-Rubin (Gelman et al., 2013) and Geweke (Geweke, 1992) diagnostics and reached with the Markov Chain Monte Carlo (MCMC) run length set to "very long" (Stock & Semmens, 2016a). To compare MixSIAR and biomarker estimates, I subtracted their annual average contributions for each source and site (MixSIAR - Biomarker). I ensured estimates were comparable between both approaches by 1) calculating the biomarker's relative percentages across the three source groups (phytoplankton, urban-derived POM, macrophytes) and 2) aggregating the MixSIAR results into the same source groups (i.e., summing river, stormwater, and wastewater into “urban-derived POM” and red, brown, and green macroalgae into “macrophytes”). 31 2.2.5.7 Nutritional analysis Nutritional differences in mussels among sites and months were tested using total (TFA) and essential (EFA) fatty acid concentration (in mg g-1), and the ratio of docosahexaenoic acid to eicosapentaenoic acid (DHA:EPA) as nutritional indicators. EFAs included 20:4n-6 (ARA), 20:5n-3 (EPA), 22:6n-3 (DHA) (Both et al., 2020; Parrish, 2009). The nutrition data were visualized with a line plot and tested with a two-way analysis of variance (ANOVA). Post-hoc pairwise comparisons between sites were tested using TukeyHSD. To provide context of mussel size for interpreting nutrition data, I reported average mussel wet weight (mg; digestive gland removed) and length (cm). 2.2.5.8 Correlation with environmental variables To understand relationships between biochemical parameters in mussels, correlations between mussel fatty acid (TFA, EFA, DHA:EPA, and biomarkers derived from indicator analysis) and stable isotope (δ¹³C, δ¹⁵N, and C:N) metrics were tested using the corr function (R Core Team, 2020). A Spearman’s correlation (α = 0.05; Bonferroni-corrected α = 0.00018) was used because some variables did not pass visual tests for normality. I also tested correlations between mussel fatty acid and stable isotope metrics with environmental variables (salinity, temperature, macronutrients), Chl a size fractions, and POM FA (TFA, EFA, DHA:EPA) and SI (δ¹³C, δ¹⁵N, and C:N) measurements with a Spearman’s correlation (α = 0.05; Bonferroni-corrected α = 0.00037). In this correlation, only sites with corresponding environmental data (Stanley Park, Maplewood Flats, Shipyards, False Creek) were included and mussel replicates were averaged. The correlation matrices were visualized with heatmap (package ComplexHeatmap; Gu et al., 2016). 32 2.3 Results 2.3.1 Overview of environmental conditions Salinity in Stanley Park and False Creek showed a strong seasonal signal (Figure 2), with lower values in May (Stanley Park: 7.9 ppt; False Creek: 10.9 ppt; Table A2) that increased throughout the dry months (~ 15 ppt in June to ~ 25 ppt in September) to a peak in November (Stanley Park: 27.9 ppt; False Creek: 27.3 ppt). In contrast, at Maplewood Flats and Shipyards, salinity was generally > 20 ppt, though there was a decrease in May (~ 19 ppt), October (~ 18 ppt), and January (~ 20 ppt) for both sites. Temperature across all sites displayed clear seasonality, with warmer temperatures (~ 16 to 20 °C) early in the dry season (June and July) and cooler temperatures (~ 7 to 9°C) throughout the wet season (October to February) and most of the wet bloom season (March and April). 33 Figure 2. Temperature (°C), salinity (ppt), total Chl a concentrations, and size fractionated Chl a proportions at four mussel sites from April 2023 to March 2024. Chl a size classes represent filter size with micro (>20 μm), nano (3–20 μm), and pico (0.7–3 μm). Line colours indicate different seasons (dry = June to September, wet = October to February, wet bloom = March to May). Gaps indicate months with no data. 34 Phosphate and nitrate showed similar seasonal trends (Figure 2), with highest concentrations during the wet season (phosphate ~ 1.5 to 2.0 µmol L-1 and nitrate ~ 20 to 30 µmol L-1; Table A2) followed by a rapid decline during the wet bloom season, with concentrations remaining low until (< 10 µmol L-1) July at all sites. Nitrate values were near 0 µmol L-1 in May and June at most sites. Silicate concentrations were always above 10-20 µmol L-1, with lowest values occurring during the wet bloom season. Shipyards remained the most stable among sites, with silicate concentrations ~ 40 to 50 µmol L-1 for most of the year, while False Creek displayed some variability, with peaks in May (59.05 µmol L-1), August (89.51 µmol L-1), and November (64.56 µmol L-1). Total chlorophyll (Chl) a peaked during the wet bloom season for all sites, which corresponded to higher contributions from micro-Chl (Figure 2). Shipyards demonstrated a strong bloom signal in May, with the highest Chl a across all sites and months (16.78 µg L-1; Table A2). The lowest Chl a values (~4 µg L-1) occurred during the wet season for all sites and showed no clear trend of size classes during this period. During the dry season, micro-Chl tended to make the largest contribution to total Chl a (~ 30 to 60 %), but nano (~ 20 to 50 %) and pico (~ 20 to 40 %) contributions were also high. Overall, the contributions from nano and pico-Chl a followed similar trends, including an increase in both size classes from May to June in Stanley Park, Maplewood Flats, and Shipyards, and from April to May in False Creek. 2.3.2 Biochemical (FA & SI) variation in mussels within Metro Vancouver The NMDS ordination of percent composition of mussel fatty acids showed weak patterns with site (Figure 3A) and some separation of samples by sample month (Figure 3B), suggesting that temporal factors were the primary driver of FA variation. PERMANOVA confirmed this trend as the effect of sample month alone (R2 = 0.4) was greater than location 35 alone (R2 = 0.2; Table A3). There was also a significant interaction between sample month and location for mussel FA profiles (R2 = 0.20; Table A3), indicating that location differences varied by month. A majority of the Stanley Park samples clustered distinctly with negative NMDS1 scores, driven predominantly by 20:4n-6. All pairwise comparisons among sites were significant apart from Tower Beach with Kitsilano Beach. Excluding the wet bloom season, all Dundarave and Shipyards samples showed positive NMDS2 scores, mostly driven by 18:3n-3, 18:4n-3, 18:2n-6, and 18:1n-9. Wet bloom samples were distinct from other months in the NMDS (Figure 3B), with positive NMDS1 and negative NMDS2 scores driven by the FAs EPA (20:5n-3), 18:1n-7, and 16:1n-7, while wet and dry months showed considerable overlap. All pairwise comparisons between months were significant apart from January with February. 36 37 Figure 3. NMDS ordination of percent composition of abundant (>0.5%) fatty acids (A and B; stress = 0.13) and stable isotope biplot of δ15N and δ13C (C and D) in mussels coloured by site (A and C) and month (B and D). In B and D, symbol shape indicates season and months are ordered by collection date (April 2023 to March 2024). In A and B, arrows indicate influence and direction of each fatty acid on sample distribution in the NMDS. 38 The NMDS ordination that illustrated monthly shifts of FA profiles for each site (Figure A2) highlighted 1) consistent seasonal patterns across most sites and 2) site groupings for particular months. All sites followed a shared seasonal trajectory, with negative NMDS1 and NMDS2 scores in April, then clustering closely during the subsequent dry and wet months and returning towards their initial positions by March with negative NMDS1 and NMDS2 scores (Figure A2). The close proximity in the NMDS between PSEC and Urban Centre sites suggested similar FA profiles during specific months of the year, including False Creek (April and December), Dundarave (June), and Shipyards (September). In contrast, Stanley Park remained notably distinct from other sites throughout the year, reflecting unique FA profiles. The stable isotope (SI) biplot revealed stronger separation by site (Fig. 3C) than by month (Fig. 3D), in contrast to the NMDS. PERMANOVA confirmed that site (R2 = 0.52) alone explained more variance in δ13C and δ15N than month (R2 = 0.23), although their interaction was still significant (R2= 0.17; Table A3). Maplewood Flats had the most distinct SI ratios among sites, with δ15N and δ13C values generally between 7.5 to 9.0 ‰ and –20 to -18 ‰, respectively. Stanley Park and Shipyards clustered more tightly, similar to the FA NMDS. Unlike FA composition, no strong seasonal patterns emerged in the isotope data. All pairwise comparisons among sites were significantly different, except PSEC and Kitsilano Beach with Dundarave Beach, False Creek and from each other. Among months, January and February were significantly different to all months except each other, and in the case of January, with December. April was significantly different from all months, except March, June, November, and December. May was only significantly different to wet and wet bloom months (October to April). 39 2.3.3 Biochemical (FA & SI) variation in organic matter types The NMDS ordination of FA percent composition for source materials showed a clear separation of samples by type, demonstrating distinct FA profiles (Figure 4A). PERMANOVA results showed that sample type had strong significant effect on FA variation (R2=0.85; Table A3). Macroalgal samples were distinct from phytoplankton, river POM, stormwater POM and wastewater POM. Urban-derived POM groups (rivers, stormwater, wastewater) showed some overlap in the NMDS, driven by the SFAs 12:0, 14:0, 17:0, 18:0, and branched FAs ant-15:0 and iso-15:0, highlighting similar FA profiles. 40 Figure 4. NMDS of percent composition of abundant (>0.5%) fatty acids (A; Stress = 0.11) and δ¹³C ‰ and δ¹⁵N ‰ biplot (B) in organic matter coloured by type. In A, arrows indicate influence and direction of each fatty acid on sample distribution in the NMDS, with longer arrows indicating stronger influence. 41 Sources showed some separation in the stable isotope (SI) biplot of δ¹³C and δ¹⁵N (Figure 4B), with PERMANOVA finding a significant effect of source type on SI variation (R² = 0.87; Table A3), similar to FAs. Pairwise comparisons confirmed significant differences among all sources, except between macrophyte groups (green, red, and brown macroalgae) and between stormwater and river POM. Unlike the FA profiles, macrophytes showed considerable overlap in the SI biplot, with similar δ¹³C (-15.09 to -16.62 ‰) and δ¹⁵N (6.63 to 6.87 ‰) values (Table A4). Similarly, stormwater (δ¹³C = -28.34 ± 0.4 ‰; δ¹⁵N = 2.37 ± 1.52 ‰) and river (δ¹³C = -28.15 ± 1.76 ‰; δ¹⁵N = 2.26 ± 2.13 ‰) POM had overlapping isotopic signatures, consistent with their FA profiles. However, wastewater was isotopically distinct from other urban-derived POM groups (stormwater and river), characterized by lower δ¹³C (-24.29 ± 0.48 ‰) and the lowest δ¹⁵N (-1.37 ± 1.32 ‰) among sources. Phytoplankton exhibited high δ¹⁵N values (5.55 ± 1.78 ‰) similar to macrophytes but differed from macrophytes with higher δ¹³C (-23.32 ± 1.99 ‰), overlapping with wastewater δ¹³C values. 2.3.4 Indicator analysis: derivation of FA biomarkers for organic matter types Indicator value analysis identified significant indicator fatty acids for each organic matter source, which demonstrated potential tracers for each (Table 2). Indicator values increased when all urban-derived and macrophyte type sources were grouped (Table 2). The FA 22:5n-3 was only an indicator for green macroalgae regardless of group-pooling. Indicator value analysis grouped sources that made ecological sense, like urban-derived POM, which was indicated by the SFAs 12:0, 18:0, 20:0, 24:0, and bacterial FA markers (iso-15:0 and ant-15:0). For individual groups, river POM was indicated by short-chain (C15–C18) SFAs, though, 12:0 and 14:0 were indicators for wastewater and phytoplankton, respectively. All 20-carbon SFAs in the indicator analysis were associated with stormwater, whereas most 20-carbon n-3 and n-6 FAs, including the 42 essential fatty acids EPA and ARA, were indicators for red macroalgae. Green and brown macroalgae were exclusively indicated by n-3 and n-6 PUFAs, respectively. 18:1n-7 showed no significant association with any individual source group, indicating it may have limited value in distinguishing among organic matter sources. Table 2. Fatty acid biomarkers for organic matter types derived from indicator analysis. Fatty acids are ordered by indicator value (IndVal) for each source or subgroup. Asterix(s) indicates IndVal significance (* p<0.01, ** p<0.001). Organic Matter Type Organic Matter Subgroup FA Biomarker IndVal Phytoplankton 22:6n-3 0.702** 16:2n-4 0.452** 16:1n-7 0.344** 14:0 0.312** Urban-Derived 24:0 0.933** 12:0 0.929** ant:15:0 0.924** 20:0 0.915** iso:15:0 0.896** 18:0 0.872** River 17:0 0.390** 18:0 0.342* iso:15:0 0.312* 15:0 0.252* 16:0 0.186* Stormwater 24:0 0.524** 20:0 0.406** 22:0 0.386** ant:15:0 0.352** Wastewater 18:1n-9 0.361** 12:0 0.298* Macrophytes 20:3n-6 0.982** 20:4n-6 0.981** 22:5n-3 0.879** Green Macroalgae 22:5n-3 0.772** 18:3n-3 0.620** 18:4n-3 0.481** Red Macroalgae 20:4n-6 0.560** 20:5n-3 0.507** 20:3n-6 0.422** Brown Macroalgae 18:3n-6 0.476** 18:2n-6 0.220** 43 2.3.5 Mussel dietary estimates: FA biomarker approach 2.3.5.1 Proportions Phytoplankton-derived FA proportions were the highest among sources in all sites throughout the year, suggesting that phytoplankton was the mussels’ primary dietary source (Figure 5A). Two-way ANOVA indicated that site, month, and their interaction (site : month) had a significant effect on phytoplankton FA % (Table A5). Among sites, Stanley Park had the lowest phytoplankton FA proportions (annual average = 20.75 ± 2.41 %; Table A6), and was significantly different to all other sites, while PSEC (24.59 ± 1.69 %) and Maplewood Flats (24.61 ± 1.87 %) had the highest, and were significantly different to all sites (except PSEC with Maplewood Flats and Shipyards). Tower Beach, Dundarave Beach, Kitsilano Beach, Shipyards, False Creek showed no significant differences in phytoplankton FA % (except Tower Beach with Shipyards), suggesting similar proportional uptake of phytoplankton. Phytoplankton FAs showed a seasonal trend across sites, with the highest proportions typically occurring within the wet bloom and early dry (June) months. 44 Figure 5. Fatty acid (FA) biomarkers of organic matter sources in mussels per month. Monthly FA proportions (% of total FA; A) and concentrations (mg g-1; B) of biomarkers in mussels over a year. Colours indicate different biomarkers (phytoplankton = purple, macrophyte = green, urban-derived = grey) In B, shape indicates different seasons. FAs used to represent each biomarker are indicated in Table 2. Months are ordered by collection date (April 2023 to March 2024). Values represent averages across mussel replicates (see Table A6 for standard deviations). 45 Macrophyte FA % was the most variable across sites, with significant differences for most pairwise comparisons between sites (Figure 5A). This excludes Kitsilano Beach, Shipyards and False Creek, where no significant differences of macrophyte FA % were shown between each other, indicating similar macrophyte contributions to mussels across sites. Stanley Park had the highest annual average of macrophyte contributions (10.11 ± 2.27 %; Table A6) and was significantly different to all other sites. In contrast, macrophyte contributions were significantly lower at PSEC than all other sites, with the lowest annual average (6.73 ± 1.62 %). Macrophyte contributions were generally lower in all sites during the wet bloom season, indicating a strong temporal effect, irrespective of site. Urban-derived FA % in mussels showed minimal effect of site or month, with generally low values across all locations throughout the year (Figure 5A). Urban derived FAs were lowest at False Creek (5.73 ± 0.95 %) and highest at Stanley Park (6.58 ± 1.35 %), and this was the only significant pairwise comparison of urban-derived FAs between sites (Table A6). 2.3.5.2 Concentrations Organic matter source concentrations (Figure 5B) strongly reflected their proportions (Figure 5A), with phytoplankton exhibiting the highest concentrations among sources at all sites throughout the year, reinforcing its role as the primary dietary source for mussels. Unlike their proportions, concentrations for all three sources generally followed a similar seasonal trend, declining progressively throughout the sampling period (April 2023 to March 2024). Peak concentrations for all sources occurred between April and June across sites. The inverse relationship between phytoplankton and macrophyte proportions was not shown in their concentrations, except at Stanley Park, where lower phytoplankton concentrations corresponded to elevated macrophyte values in December and January. At most sites, urban-derived FAs had 46 higher concentrations than macrophytes from April through the early dry months (June–July) but fell below macrophyte concentrations around August. Stanley Park was the only site where macrophyte concentrations consistently exceeded urban-derived FAs year-round, highlighting macrophytes as a stable dietary source at this site. 2.3.6 Mussel dietary estimates: combined FA-SI mixing model approach The NMDS of mixing model tracers showed mussels clustering tightly and falling mostly within the convex hull of organic matter sources, confirming that our selected sources captured mussel data (Figure 6). Organic matter sources separated clearly in the NMDS, indicating distinct biochemical profiles (FA and SI), confirmed by PERMANOVA (Table A3). Wastewater had the largest error bars among sources, suggesting the most variable biochemical profile. In the SI-only biplot, all macrophyte groups along with stormwater and rivers showed notable overlap, while in the FA-only NMDS, mussel samples were outside the convex hull of organic matter sources (Figure A3). 47 The regression analysis on bivalve feeding trial data revealed ten FA tracers with an R² > 0.20 and a CC between 0.6 and 2 (15:0, 16:0, 16:1n-7, 17:0, 18:1n-9, 18:2n-6, 18:3n-3, 20:3n-6, 20:4n-6, 22:5n-6; Table 3). Fifteen FAs did not meet both the R2 threshold and CC range and thus, were removed from the mixing model analysis (Table A7). Figure 6. NMDS of fatty acid and stable isotope tracers used in the mixing model for mussel (circles) and organic matter types (squares). Squares and error bars indicate mean and uncertainty (± 1 SD), respectively, of NMDS scores for each organic matter type. Calibration coefficients and trophic enrichment factors were applied to source data and all data were standardized with z-scores. Stress values did not reach convergence (100 iterations; stress values ~0.11 to 0.15). 48 Table 3. Tracers, mean calibration coefficients (CCs, for fatty acids), and trophic enrichment factors (TEFs, for stable isotopes) used in the mixing model. R2 indicates the regression value between consumer and diet fatty acid proportions in bivalve feeding studies. NA = Not applicable. Tracer CC / TEF R2 15:0 0.60 0.37 16:0 1.10 0.30 16:1n-7 0.70 0.40 17:0 0.91 0.68 18:1n-9 1.58 0.36 18:2n-6 0.77 0.78 18:3n-3 0.76 0.57 20:3n-6 0.54 0.42 20:4n-6 1.65 0.22 22:5n-6 1.59 0.57 δ13C 0.40 NA δ15N 3.40 NA Mixing‐model results indicated variation among sites and seasons (Figure 7). As with the biomarker analysis, phytoplankton was the dominant organic matter source for mussels at all sites. The highest phytoplankton contribution was at Maplewood Flats (annual average = 79.07 ± 2.89 %), while all other sites ranged from 60 to 70 % (average = 68.13 ± 5.92 %; Table A8). The contribution of phytoplankton was similar during the wet and wet bloom seasons, and highest during the Dry season except at the two northern Outer Harbour sites, PSEC and Dundarave Beach. Macrophyte organic matter contribution averaged 16.5% across all sites and was highest at Stanley Park (21.6 %). The macrophyte contribution was always dominated by red macroalgae (site average = ~14 %). Green macroalgae contribution was higher (site average = ~1.5 %) than brown macroalgae (site average ~0.7 %), and highest at Dundarave Beach during the Wet (4.3 ± 0.7 %) and Dry (4.7 ± 0.9 %) seasons. The contribution of brown macroalgae was mostly below 1%, except at Shipyards during the Wet season (4 ± 2.3 %). The average sum of urban-derived POM (river+stormwater+wastewater) contributions was ~15 % and highest at Shipyards (21.27 49 %) and lowest at Maplewood Flats (6.16 %). Contributions from river organic matter ranged from 3 % to 22% and were generally lowest in the dry season. The average contribution of river organic matter was highest at Shipyards (19.03 ± 2.24 %) and lowest at Maplewood Flats (4.9 ± 1.6 %). The percent contribution of river organic matter was generally higher when phytoplankton was lower, indicating an inverse pattern between the organic matter sources. Combined stormwater and wastewater contributions were estimated to be ~1-2% on average across sites. The highest stormwater and wastewater contribution was 2.9 % (± 2.4) and 1.3 % (± 1.1) during the Wet season at Shipyards and PSEC, respectively. Stanley Park and Tower Beach shared the lowest annual average contributions from stormwater (0.47 %) and wastewater (~ 0.3 %).50 Figure 7. Mixing model results of mean contribution of organic matter (%) to mussels, separated by site and season. Colours indicate different organic matter types. See Table A8 for 95 % confidence intervals for each estimate. 51 2.3.7 Mixing model vs. biomarker estimates The mixing model (MixSIAR) and biomarker OM contribution estimates were mostly similar, with absolute differences ranging from 0.33 to 13.24 % (average = 4.14 ± 3.13 %) across all sources and sites (Figure A4). Phytoplankton contributions were always higher with MixSIAR, except in Shipyards, while macrophyte contributions were always higher using biomarkers. Maplewood Flats showed the highest discrepancies between both approaches (absolute average difference = 8.81 ± 5.17 %), marked by high differences for phytoplankton (13.24 %) and urban-derived POM (-10.06 %). By contrast, both northern Outer Harbour sites (PSEC and Dundarave) showed the most consistent dietary estimates with an average difference of ~ 2 % between MixSIAR and biomarker analysis. 2.3.8 Nutritional analysis of mussels Overall, mussel nutritional metrics (TFA, EFA, DHA:EPA, C:N) showed modest spatial variability, but exhibited clear and consistent seasonal patterns across all sites (Figure 8). PSEC exhibited the highest annual TFA (29.85 ± 8.96 mg g-1) and EFA (11.59 ± 3.33 mg g-1) and was significantly different to all sites except Maplewood Flats and Dundarave Beach (Table A6). In contrast, Stanley Park, Tower Beach, and Kitsilano Beach all averaged the lowest nutritional concentrations (TFA ~ 21 mg g-1; EFA ~ 8 mg g-1) among sites, with significant differences between PSEC, Maplewood Flats and Dundarave. Annual DHA:EPA and C:N showed limited variability across sites (0.98–1.35 and 4.07–4.50, respectively), indicating similar nutritional quality. Only Shipyards maintained a year‐round DHA:EPA above 1, whereas False Creek alone averaged below 1, and both locations differed significantly from all other sites. 52 53 Figure 8. Monthly concentrations at each site of mussel total fatty acids (FA; mg g-1) and essential fatty acids (EFA; mg g-1), and ratios of docosahexaenoic acid (DHA) to eicosapentaenoic acid (EPA) and carbon (C) to nitrogen (N), for April 2023 to March 2024. EFA concentration represents the sum of DHA, EPA, and arachidonic acid (ARA) concentrations. Months are ordered by collection date (April 2023 to March 2024) and line colours indicate season (dry = June to September, wet = October to February, wet bloom = March to May). The x-axis indicates the final month for the wet bloom (May), dry (September), and wet (February) season. Points represent averages across mussel replicates for each month (see Table A6 for standard deviations). 54 Seasonal cycles of nutrition in mussels were pronounced across sites. TFA and EFA exhibited nearly identical seasonal cycles, declining throughout the collection period, with the higher concentrations in the wet-bloom and lower values during the wet season for most sites. TFA also peaked in June for some sites (PSEC, Dundarave, Kitsilano). DHA:EPA and C:N also shared similar seasonal cycles with the lowest ratios during wet bloom months and highest values in the dry season for most sites. DHA:EPA and C:N peaked during the wet season for some sites (PSEC and False Creek). DHA:EPA and C:N were positively correlated, though less strongly than TFA and EFA (Figure 9). For example, False Creek in July had a peak and trough for DHA:EPA and C:N, respectively. Between the wet bloom and dry months, DHA:EPA and C:N varied inversely with TFA and EFA, while all nutritional metrics decreased during the wet season.55 Figure 9. Spearman’s correlation coefficients between mussel fatty acid and stable isotope values. Statistically significant correlations (p<0.05) are only shown and correlations marked with ** indicate p < Bonferroni-corrected α = 0.00018. 56 2.3.9 Relationships among biochemical metrics and with environmental data Urban-derived FA % were negatively correlated with phytoplankton FA % (Figure 9), as months of higher urban contributions generally corresponded to lower phytoplankton values (Figure 5). Macrophyte FA % varied inversely with phytoplankton FA % (Figure 9), with peaks in macrophyte contribution generally corresponding with periods of low phytoplankton-derived FA %, including Stanley Park throughout the year, Maplewood Flats and PSEC from April to July, and False Creek in February (Figure 5). Mussel tissue TFA and EFA correlated positively with percent of phytoplankton FA biomarkers, but negatively with percent macrophyte FA biomarkers, suggesting enhanced mussel nutritional quality when phytoplankton contributions dominated (Figure 9). However, DHA:EPA correlated negatively with TFA and EFA, but positively with percent macrophyte FA biomarkers, suggesting nutritional trade-offs between these source contributions. TFA and EFA in mussels correlated positively with TFA and EFA in organic matter sources, and total chlorophyll, but negatively with nitrate and organic matter source C:N, suggesting that environmental conditions drive variation in mussel nutritional quality (Figure A5). DHA:EPA in mussels correlated negatively with micro-Chl but positively with pico-Chl, indicating phytoplankton composition affected nutritional quality in mussels. Phytoplankton and macrophyte FA biomarkers in mussels showed no significant correlations with any environmental parameters, while urban-derived FA biomarkers correlated positively with temperature and pico-Chl.57 2.4 Discussion Coastal development modifies land—ocean connections in coastal environments, including the volume and timing of freshwater flow and the quantity and quality of material that it transports. Organic matter inputs to coastal food webs underpin their health and production, yet questions remain as to how urbanization disrupts the contributions of different organic matter sources due in part to the methodological challenges in doing so. In this study I applied two approaches, fatty acid biomarker analysis and a Bayesian mixing model combining stable isotopes and fatty acids, to estimate urban-derived material uptake by a model primary consumer and assess their nutritional health. In Burrard Inlet, an urbanized marine environment, mussel uptake of OM was dominated by phytoplankton with moderate contributions from urban-derived OM and macrophytes. These contributions varied with seasonal patterns of primary production, relative availability of other OM sources, and land-use, revealing controls on the flux of OM to the marine food web. Phytoplankton served as the primary source of OM for mussels, underscoring Burrard Inlet's sustained productivity despite urban pressures. Nutritional variation in mussels were associated with shifts in OM contributions, indicating food web implications from anthropogenically driven shifts in OM flux. Below, I discuss our results in the context of other studies in urbanized environments, mechanisms for the variability in uptake and nutritional quality in mussels, and the implications of this variability. Finally, I assess utility of the biomarker and mixing model approach for estimating source contributions, limitations of our study, and future directions. 2.4.1 Silicate measurements in Burrard Inlet For most of the year, Burrard Inlet surface water silicate concentrations exceeded Strait of Georgia deep water measurements (~ 50 µmol L-1; Dosser et al., 2021), likely reflecting 58 additional silicate inputs from urban-derived discharge. Silicate concentrations measured in rivers during this study (114.98 ± 168.91; Lye et al., in prep) were 3 times higher than non-urbanized surface waters in the Central Coast of BC (31.35 ± 15.18; Pacific Salmon Foundation, n.d.). 2.4.2 Spatial and seasonal variation in mussel fatty acid and stable isotope tracers Mussel FA profiles varied more strongly seasonally than spatially, reflecting seasonal shifts in phytoplankton production. Samples from the wet bloom season clustered distinctly, irrespective of site, driven by elevated levels of diatom (16:1n-7; Dalsgaard et al., 2003) and bacterial (18:1n-7; Mayzaud et al., 1989; Najdek et al., 2002) FA markers. This pattern is in agreement with previous studies showing increased diatom and bacterial uptake by mussels during spring blooms (Both et al., 2020; Irisarri et al., 2014; Ventrella et al., 2008). While I did not measure bacteria biomass in this study, micro-phytoplankton (indicative of diatoms) biomass peaked during the wet bloom and diatoms have been shown to drive the seasonal FA cycle of phytoplankton in BC coastal waters (McLaskey et al., 2022). Elevated bacterial uptake may reflect ingestion of phytoplankton particles colonized by heterotrophic bacteria (Xu & Yang, 2007) in addition to increases in cyanobacteria production. Macrophytes and wastewater discharge may explain spatial patterns in mussel FA profiles. In the NMDS, Stanley Park mussels separated from all other sites throughout the year mostly because of higher ARA proportions. As ARA was associated with macrophytes in our study, this suggests elevated macrophyte uptake at Stanley Park, aligning with our dietary estimates (see 2.4.5 Dietary Estimates). At two Urban Centre sites, Dundarave and Shipyards, FA variation was mostly driven by other macrophyte-associated FAs (18:2n-6, 18:3n-3, 18:4n-4), along with 18:1n-9, which was associated with wastewater. Elevated proportions of these FAs 59 have been observed in primary consumers from urban centres (Puccinelli et al., 2016) and downstream of wastewater discharge (Sakdullah & Tsuchiya, 2009). In contrast to fatty acids, mussel stable isotope values varied more strongly among sites than seasons. Similarly, Liénart et al. (2021) identified δ¹³C and δ¹⁵N in Mytilus with clear spatial differences driven by nutrient input, but no consistent short-term temporal patterns. The elevated mussel δ¹³C and δ¹⁵N values at Maplewood Flats may reflect macroalgal uptake. I observed enriched isotopic signatures in macrophytes compared to other organic matter sources. Additionally, our dietary estimates indicated notable macrophyte uptake at Maplewood Flats. Other drivers of high carbon and nitrogen isotopic values include high phytoplankton biomass and eutrophication, which are known to elevate δ¹³C (Oczkowski et al., 2018) and δ¹⁵N (Fukumori et al., 2008; Liénart et al., 2020) values, respectively. Anthropogenic activities can induce localized eutrophication and subsequently, increase δ¹⁵N values in bivalves (R. H. Carmichael et al., 2012; Thibault et al., 2020). In this study, freshwater inputs in the form of stormwater and riverine POM had low δ¹³C values. Thus, the low δ¹³C and high δ¹⁵N values in Shipyards' mussels may reflect terrestrial carbon and nitrogen inputs, respectively, both driven by high impervious surface cover (Levin et al., 2020). Indeed, high freshwater (river and stormwater) organic matter contributions to marine POM was found in the Shipyards’ basin at the time of this study (Outer Harbour; Lye, 2025). Tower Beach was the site closest to the Fraser River mouth, and the low δ¹³C and δ¹⁵N values therefore likely reflect freshwater inputs from this source. Analysis of Fraser River POM during our study period showed low δ¹³C (-27.54 ‰) and δ¹⁵N (1.13 ‰) values (unpublished data), which may lower the isotopic signature in mussels both directly (ingestion of riverine POM) and indirectly (lowering the isotopic baseline in phytoplankton and detritus). 60 2.4.3 Delineation of organic matter sources Understanding the biochemical variation of organic matter sources is necessary to trace their uptake into the food web. Specific FAs or FA groups clearly distinguished phytoplankton, macrophytes (red, brown, and green macroalgae), and urban-derived (river, stormwater, and wastewater) POM. Phytoplankton's distinct FA profile was driven by high proportions of the EFA 22:6n-3 (DHA) and the MUFA 16:1n-7, reflecting biosynthesis of these FAs by flagellates and diatoms, respectively (Budge & Parrish, 1998; Dalsgaard et al., 2003). Macrophytes had high contributions from n-3 and n-6 PUFAs, including the EFAs 20:5n-3 (EPA) and 20:4n-6 (ARA). The FA profiles of macrophytes displayed a clear taxonomic signal, aligning with previous findings in the Northeast (NE) Pacific (Colombo et al., 2006; Dethier et al., 2013; Galloway et al., 2012; Serviere-Zaragoza et al., 2015; Susanto et al., 2019) and likely indicate divergent evolutionary lineages (Kelly & Scheibling, 2012). In this study, red macroalgae were distinct from brown and green macroalgae by their elevated EPA (20:5n-3) and ARA (20:4n-6) content, as previously reported (Galloway et al., 2012; Susanto et al., 2019). As the oldest macroalgal lineage, red macroalgae’s high proportion of C20 PUFAs reflect unique n-3/n-6 desaturation/elongation pathways retained from their marine ancestors (Graeve et al., 2002; Guschina & Harwood, 2006). Additionally, the high concentration of 18:3n-3 and 22:3n-3 in green macroalgae has been documented in the NE Pacific (Colombo et al., 2006; Khotimchenko et al., 2002; Nelson et al., 2002), highlighting FA-synthesis pathways also seen in terrestrial plants (Guschina & Harwood, 2006). Prior to this study, no FA data were available for Metro Vancouver riverine, stormwater, and wastewater POM. Though the FA profiles of urban-derived POM showed some overlap, they were distinct from other organic matter sources, driven by high proportions of SFAs and bacterial 61 FA markers (ant 15:0 and iso 15:0; Budge & Parrish, 1998; El-Sabaawi et al., 2009). High contributions of SFAs and bacterial FA markers were also reported for an urbanized tropical river in Brazil (Boëchat et al., 2014), stormwater in the United States (Eganhouse et al., 1981), and sewage in France (Quéméneur & Marty, 1994), but have also been found in pristine rivers in Chile (Vargas et al., 2011) indicating that urbanization may not be the only driver of these FA markers. In this study, some differences in river, stormwater, and wastewater POM were observed. Among urban-derived POM, rivers showed elevated proportions of long-chain (> 20 carbon atoms) PUFAs, indicative of microalgal production likely absent in stormwater and wastewater. Wastewater exhibited distinctively high contributions of 18:1n-9, consistent with prior findings (Christman, 2020; Mustapha et al., 2017; Quéméneur & Marty, 1994), a prominent FA in cooking oils (Bautista et al., 2009; Segura, 1988) and human feces (Williams et al., 1960). Conversely, stormwater contained higher proportions of 18:3n-3, likely from terrestrial plant material carried via surface runoff (Zhu et al., 2024). Stable isotopes provide an alternative approach to FAs for distinguishing the biochemical profiles of organic matter sources. Compared to FAs, the δ¹³C and δ¹⁵N values of organic matter sources showed less variation, with overlap between macrophyte groups and between stormwater and river POM. The elevated δ¹³C values in macrophytes, intermediate δ¹³C values in phytoplankton, and similarly high δ¹⁵N signature in both sources aligned with previous findings in BC (St. Pierre et al., 2022). Overlapping δ¹³C and δ¹⁵N values across different macrophyte taxa have also been reported in NE Pacific (M. T. Brett et al., 2016) and in a Western Scotland fjord (Bebb et al., 2023), reflecting consistent carbon and nitrogen uptake pathways (Marconi et al., 2011; Raven et al., 2002). River organic matter δ¹³C and δ¹⁵N values in our study aligned with previous findings in BC (Johannessen et al., 2005; Sakamaki & Richardson, 2008; St. Pierre 62 et al., 2022), while stormwater measurements resembled those in Florida (Jani et al., 2020). The similar isotopic signatures of stormwater and river POM suggest shared sources of organic matter (Allison et al., 1998; Kaushal & Belt, 2012; Yang & Lusk, 2018). This similarity can be mostly attributed to leaf litter, as it is common in Vancouver (City of Vancouver, 2025) and known to influence the isotopic signature in both stormwater (Jani et al., 2020) and rivers (Meyer et al., 1998; Zhang et al., 2018). 2.4.4 Assessment of FA biomarkers from indicator analysis Many studies have used fatty acid biomarkers to resolve bivalve diets (e.g., Allan et al., 2010; Bachok et al., 2009; Guo et al., 2020), but the use of specific FAs to represent dietary sources vary considerably. For example, the essential FA 20:5n-3 (EPA), has been used as a dietary tracer for red macroalgae (F. Guo et al., 2020), brown macroalgae (Gaillard et al., 2017), and diatoms (Table A9; Irisarri et al., 2014; Najdek et al., 2013; Puccinelli et al., 2016). In this study, I applied indicator analysis to measured FA profiles of organic matter sources in Metro Vancouver to determine representative FAs specific to the study region. While our identified FA biomarkers generally aligned with previous studies, there were some notable differences. For instance, although 18:4n-3 was an indicator of green macroalgae in our study and elsewhere (Gaillard et al., 2017), it is also frequently used as an indicator of dinoflagellates (Bachok et al., 2009; Gaillard et al., 2017; Irisarri et al., 2014). Similarly, while 16:0 was an indicator of river POM in our study, it is also widely used as a phytoplankton marker (Ezgeta-Balić et al., 2012; Irisarri et al., 2014; Najdek et al., 2013). Some SFAs (12:0, 14:0, 18:0, and 20:0) have limited prior application as indicators of specific dietary sources. Many SFAs are ubiquitous in aquatic environments (Volkman et al., 1981; Wakeham, 1995) and can be synthesized de novo by bivalves (Ruano et al., 2012; Zhukova, 2019), thus complicating source tracing. Our findings 63 also have implications for biomarkers representing urban-derived sources. Rocchetta et al. (2014) used 18:1n-9, 18:1n-7, and 18:2n-6 to represent urban organic matter uptake in a headwater stream. While 18:1n-9 was identified as a wastewater marker in Metro Vancouver, it was also strongly associated with brown macroalgae and stormwater (~ 18 % contribution). Furthermore, 18:2n-6 was associated with brown macroalgae in our study and 18:1n-7 showed no significant associations with any individual organic matter sources. These results suggest that the FA profile of brown macroalgae complicate the tracing of urban derived organic matter into the marine environment using literature biomarkers. The differences between our FA biomarkers and literature markers underscore the need to assess the biochemical profiles of location-specific dietary sources to accurately determine their fate in the local marine environment. 2.4.5 Dietary estimates In this study, phytoplankton were always dominant contributors to mussel diets (~ 50 – 70 %). Urban-derived sources were mostly important during the wet and wet bloom seasons, while spatial differences were only apparent with the mixing model approach. Macrophyte contributions were always dominated by red macroalgae, highest at Stanley Park (~ 25 %) and generally consistent throughout the year for all sites (~ 15 – 20 %). Our biomarker results showed the lowest proportion and concentration of phytoplankton FAs at Stanley Park, the site with the lowest urban intensity. However, chlorophyll levels at Stanley Park were comparable to other sites, except for a lower bloom peak. Both biomarker and mixing model estimates from our study indicated that the reduced contribution of phytoplankton FAs at this site was related to the high contribution of macrophytes to mussels. Stanley Park has considerable macrophyte presence, with over 61 taxa identified (Schenk et al., 2025). Macrophytes have previously been shown to be important contributors to nearshore organic 64 matter pools (Kaehler et al., 2006; Lye, 2025; St. Pierre et al., 2022) and bivalve diets (Allan et al., 2010; Both et al., 2020; Tallis, 2009). The high proportional contribution of red macroalgae in particular to mussel diets (~ 10 - 20 %) was unexpected and may in part be related to their high nutritional quality and biomass. Red macroalgae had EPA and ARA contributions each exceeding 20% while all other sources had less than 10 %. These EFAs were among the dominant FAs in mussels from our study (EPA: ~16%; ARA: 6%), and support reproduction and stress resistance in marine animals (Sargent et al., 2003). Relative to other macroalgal groups, red macroalgae have also shown superior nutritional quality with higher protein concentrations (Garcia-Vaquero & Hayes, 2016) and lower contaminant levels, particularly mercury (Dawczynski et al., 2007). Mytilus trossulus are known to distinguish among particles of varying chemical composition (Levinton et al., 2002), and preferential uptake of the highly-nutritious red macroalgae may drive the high proportions observed in mussels. Additionally, red macroalgae are expected to exhibit considerable biomass levels at our sites. They have showed high species diversity and percent cover relative to other macroalgal types in Burrard Inlet (Campbell et al., 2023; Schenk et al., 2025), though global intertidal and subtidal rates of primary production is higher in brown macroalgae (Pessarrodona et al., 2022). Despite evidence of considerable macrophyte contributions to mussels, I observed a strong negative correlation between the percent of macrophyte FA biomarkers and mussel TFA and EFA concentrations. The annual average proportion of macrophyte FAs in mussels ranged from ~ 7 to 10 % across all sites. Mussels ingest macrophytes primarily as detritus, a form expected to lose much of its labile lipids compared to their parent material (Both et al., 2020). Macrophyte tissues are also rich in structural carbohydrates such as cellulose and lignin, which 65 resist breakdown by consumer enzymes, reducing the efficiency of FA absorption by mussels (M. T. Brett et al., 2017; Mann, 1988). Therefore, substantial contributions from macrophytes may not translate into high lipid supply for mussels. Urban-derived FA biomarkers consistently contributed between 5.73 and 6.58 % in mussel lipids and showed little spatial or temporal variation. Given that mussel diets have been shown to shift among microhabitats (Kon et al., 2007; Schaal et al., 2011), this low variability suggests that the urban FA signal is pervasive enough to override small scale differences among sites or months. In our study, urban-derived FAs in mussels primarily consisted of the saturated fatty acid 18:0, while the other urban-derived FAs (12:0, 20:0, 24:0, ant 15:0, iso 15:0) each accounted for less than 0.5% on average across all samples. Mussels in Stanley Park, the site with the lowest impervious cover, showed the highest annual mean percentage of 18:0 (6.38 ± 1.35 %) among sites, which explains why urban derived FAs were also highest at this site. The predominance of 18:0 likely originates from its wide distribution in aquatic environments (Volkman et al., 1981; Wakeham, 1995) and de novo synthesis by bivalves (Ruano et al., 2012; Zhukova, 2019). Consequently, 18:0 is a suboptimal dietary tracer, as it may have overestimated urban-derived contributions using the biomarker approach. In contrast to the biomarker analysis, the mixing model approach clearly distinguished different urban-derived POM contributions between sites, with the highest proportions at a heavily impacted site (Shipyards) and the lowest at a restoration site (Maplewood Flats). Mussels from the Shipyards site also showed the highest proportions of stormwater POM. The spatial pattern suggests that urban intensity can influence organic matter uptake by mussels. Shipyards, located within a designated Metro Vancouver “urban centre”, has high urban development characterized by extensive impervious surfaces (Metro Vancouver, 2011). Thus, rainfall events 66 facilitate stormwater runoff that mobilizes organic material accumulated on these impervious surfaces (McCabe et al., 2021; Yang & Lusk, 2018), which is subsequently delivered to mussels. Our mixing model results show that mussels assimilate urban-derived organic matter, a form of terrestrially sourced carbon, amid debates over its dietary importance for bivalves (Dubois et al., 2014; Liénart et al., 2025; Malet et al., 2008). Our mixing model results also revealed clear seasonality in contributions from organic matter sources. Phytoplankton contributions were similar in the wet and wet bloom seasons but highest during the dry season, whereas river contributions were highest during the wet and wet bloom seasons and lowest during the dry season. The seasonality of phytoplankton contributions may reflect the tissue turnover rate in mussels (2-3 weeks; Redmond et al., 2010), delaying the phytoplankton signal in consumers from the spring bloom. Additionally, diatom blooms later in the year have been shown to contribute more to zooplankton FAs than the spring bloom on the BC Coast (McLaskey et al., 2024). Terrestrial contributions to hermit crabs were also found to increase with rainfall in a subtropical bay (Gorman et al., 2019). Our results align with expectations that hydrological cycles drive the seasonality of terrestrial subsidies to coastal food webs, controlled by precipitation patterns and the spring freshet in the North Pacific (Delbecq et al., 2024; Lye, 2025; St. Pierre et al., 2022). 2.4.6 Biomarker vs. mixing model approach The FA-biomarker and Bayesian mixing model approaches applied in this study yielded similar estimates of organic matter contributions to mussels (average difference = 4.14 %), giving confidence in the estimates from both methods. The FA-biomarker approach required lower computational power than the Bayesian models and provided higher temporal resolution (monthly) insights. The mixing model had higher data requirements and monthly analysis with 67 MixSIAR inflated parameter uncertainty which prevented model convergence (Stock et al., 2018). However, with sufficient data, MixSIAR distinguished all seven individual sources, whereas the FA-biomarker approach required pooling of sources into three ecologically meaningful groups (phytoplankton, urban-derived POM, macrophytes). Indicator analysis yielded low IndVal scores for some associations between individual sources and FAs (e.g., 16:0 for rivers = 0.186), reflecting weak specificity and/or fidelity (Dufrêne & Legendre, 1997). Given that a clear IndVal threshold for biomarker reliability has not been established, I elected to pool sources into these broader categories for the biomarker analysis. MixSIAR also allowed the integration of stable isotope tracers (i.e., δ¹³C and δ¹⁵N), which contributed to the better source resolving power. Overall, FA biomarkers and MixSIAR complement each other’s limitations, highlighting the strength of coupling both approaches when resolving consumer diets. Researchers should consider these trade-offs against their data availability and objectives when deciding between methods. Nonetheless, both approaches provide accurate estimates for consumer diets. Previous work has demonstrated that gut content has yielded similar results with an FA-based approach using biomarkers (Howell et al., 2003; Muralidharan et al., 2023) and mixing models (Guerrero & Rogers, 2020; Pickett et al., 2024). 2.4.7 Comparison of mussel diets with marine POM Mussel tissue fatty acid and stable isotope profiles did not directly reflect the organic matter composition available in the water column (Lye, 2025). For instance, average phytoplankton contributions were 12 % lower in mussels than in marine POM. In False Creek, freshwater (stormwater + river) contributions to marine POM (~ 2 – 20 %) and mussels (~ 10 – 20 %) were similar while wastewater contributions were higher in marine POM (~ 1 to 26 %) than in mussels (0.4 to 0.9 %). This discrepancy between mussels and marine POM may result 68 from variations in source quality, as wastewater POM showed lower proportions of EFAs (~ 1 %) compared to other sources. In contrast, macrophytes contributed proportionally more to mussel diets (~ 17 to 18 %) than to marine POM (~ 2 to 11 %) and had a higher EFA percentage (~ 3 to 44 %) compared to wastewater POM. Given that Mytilus trossulus can selectively feed based on biochemical composition (Levinton et al., 2002), preferential uptake of higher quality POM may explain these observed differences. It is also important to consider that FA and SI turnover rates for Mytilus range from 2 to 3 weeks (Redmond et al., 2010), integrating dietary inputs over time. In contrast, marine POM measurements represent a snapshot of suspended particles and can thus capture pulses of primary production that elevate phytoplankton contributions (Cloern, 1996). However, the POM source contrbutions in Lye. (2025) were seasonal averages, and so integration periods were not a factor in this study, and selectivity is more likely. The low uptake of wastewater POM poses questions as to the fate of these materials in the marine evironment. The fate of urban-derived organic matter extends beyond mussel tissues and marine POM, including uptake by other primary consumers and accumulation in sediments. Qualitative evidence of urban-derived uptake has been reported in oysters (Fertig et al., 2010; Piola et al., 2006), clams (Canuel et al., 1995; Rocchetta et al., 2014), zooplankton (Ke et al., 2022; Miller et al., 2010), and various fish species (Aguilar et al., 2008; de Carvalho et al., 2019, 2020; Schlacher et al., 2005). Previous studies have identified contributions from sewage (J. Tucker et al., 1999; Xiao & Liu, 2010), stormwater (Badin et al., 2008), and agriculture (Derrien et al., 2018) to marine sediment organic matter pools. Understanding the fate of urban-derived organic matter in coastal environments requires resolving terrestrial-derived organic matter 69 contributions across all potential sinks, suspended POM, sediments, and both benthic and pelagic primary consumers. 2.4.8 Spatial and temporal variation in mussel nutrition The nutritional quality metrics in mussels (TFA, EFA, DHA:EPA, C:N) showed a consistent temporal pattern across all sites. TFA and EFA concentrations peaked during the wet bloom season, remained elevated but variable in the dry season, and declined throughout the wet months. In contrast, DHA:EPA and C:N ratios displayed a delayed peak compared to TFA and EFA, as ratios were highest in the late-dry season. These temporal trends in nutritional quality were closely linked to phytoplankton dynamics. TFA and EFA concentrations in mussels were positively correlated with the percent of phytoplankton-derived FAs and total chlorophyll, while DHA:EPA was correlated negatively with microphytoplankton and positively with picophytoplankton proportions. A similar seasonal pattern in FA nutrition has been reported for phytoplankton in the northern Strait of Georgia, driven by shifts in size class structure (McLaskey et al., 2022). Specifically, larger phytoplankton (i.e., microphytoplankton) were associated with higher TFA and EFA concentrations, while smaller phytoplankton (i.e., picophytoplankton) were linked to elevated DHA:EPA ratios. These findings suggest that phytoplankton size class structure is a key driver of the seasonal variation mussel nutritional quality. Nutritional variation among sites may reflect habitat differences and the associated competitive dynamics. Both sites with the lowest TFA and EFA concentrations (Stanley Park and Tower Beach) are expansive intertidal ecosystems supporting abundant and diverse primary consumers, including clams, crabs, barnacles, and limpets (Amy et al., 2022; Comeau & Roberts-Farina, 2020). Consequently, mussels at these locations likely experience increased 70 competition for organic matter sources, particularly phytoplankton, potentially limiting their uptake of FAs. In contrast, sites with a less healthy and diverse intertidal community such as False Creek and PSEC may have lower competitive pressures and increased availability of organic matter, facilitating higher TFA and EFA accumulation in mussels. Previous studies indicate that bivalve diets are sensitive to microhabitat-level changes (Kon et al., 2007; Schaal et al., 2011), which can subsequently influence their FA nutritional quality (Dalsgaard et al., 2003; Sargent et al., 2003). Additionally, mussel FA concentrations have been suggested to shift with size (Fokina et al., 2020). While mussel length and wet weight did show differences among sites and months (Figure A6), they did not seem to correspond with the variability in FA concentrations. 2.4.9 Implications of dietary estimates to mussel health Our results indicate that mussel nutritional health depends on the relative contributions of organic matter sources. Sites with elevated chlorophyll levels and phytoplankton-derived FA % exhibited higher TFA and EFA concentrations, whereas greater macrophyte-derived FA % corresponded to reduced TFA and EFA levels. TFAs serve as a key source of metabolic energy (Sargent et al., 2003), while EFAs must be obtained from the diet and are required for growth and membrane function (Glencross, 2009; Knauer & Southgate, 1999). Moreover, higher macrophyte-derived FA % was associated with increased DHA:EPA, highlighting a nutritional trade-off with TFA and EFA. Elevated DHA:EPA in bivalves has been linked to improved reproductive success (Hendriks et al., 2003), growth rate (Knauer & Southgate, 1999), and toxin resilience (Yuan et al., 2023). This nutritional variation reflects anthropogenic influences on organic matter flux, including primary productivity and urban-derived sources. Overall, our 71 results demonstrate the role of local environmental gradients in shaping consumer nutritional health. Our indicator analysis revealed FAs associated with urban-derived inputs (12:0, 18:0, 20:0, 24:0, ant-15:0, iso-15:0), which carry nutritional implications for the marine food web. SFAs, primarily 18:0, are sequestered into bivalve tissues as energy reserves that can be later catabolized to support metabolism and growth (Napolitano et al., 1988; Parrish et al., 1994), though long-chained SFAs (e.g., 20:0 and 24:0) are believed to be difficult to catabolize by molluscs (Fokina & Chesnokova, 2024; G. G. Smith et al., 2003). In addition, these reserves alone cannot fulfill all physiological needs without other FA classes, namely EFAs (i.e., DHA, EPA, ARA; Parrish, 2009; Rato et al., 2019). Bivalves have exhibited a limited ability to elongate SFAs into PUFAs and MUFAs that can support membrane fluidity, stress tolerance, and reproductive success (Zhukova, 2019). For instance, experimental feeds enriched in PUFAs have significantly improved survival and settlement success in marine mussels (Liénart et al., 2023; Pettersen et al., 2010). In the perspective of human nutrition, cardiovascular diseases and diabetes have been linked to high SFA intake (Riccardi et al., 2004). The branched FAs, ant-15:0 and iso-15:0, are frequently used as bacterial tracers (e.g., Guo et al., 2020; Irisarri et al., 2014; Ricardo et al., 2021) while insight on their nutritional implications to the marine environment are limited. Collectively, urban-derived FAs may benefit energy stores, but are insufficient to support physiological demands of marine consumers and have nutritional consequences for humans. Urban-derived organic matter uptake by mussels also has implications for contaminant loading. Various contaminants, notably heavy metals and polycyclic aromatic hydrocarbons (PAHs), are frequently associated with urban rivers (Tibbetts et al., 2018; Windsor et al., 2019), stormwater (Parker et al., 2010; Williamson & Morrisey, 2000), and wastewater (Bolong et al., 72 2009). Additionally, the proliferation of impervious surfaces has been associated with increased nearshore accumulation of contaminants through accelerated stormwater flow (Lee et al., 2006; Todd et al., 2019). POM consists of 1) many long non-polar aliphatic chains that can take up PAHs more readily than water (X. Guo et al., 2010; J. L. Zhou et al., 1998) and 2) functional groups (e.g., carboxyl and hydroxyl) that effectively bind to metal ions (Turner et al., 2004). Consequently, sites with elevated uptake of urban-derived POM, such as Shipyards, may show greater contaminant loading in mussel tissues. According to Pollution Tracker, the sediments at Shipyards ranked sixth in contamination among 69 coastal sites in B.C, although mussels were not sampled there (Ocean Wise Conservation Association, n.d.). 2.4.10 Limitations and future directions Urbanization alters the land-sea connection, shifting the organic matter pool available to primary consumers with implications throughout the food web. Multiple research topics arose from this study that require further research to better resolve anthropogenically driven effects on the land-sea interface. As mentioned above (2.4.7 Comparison of mussel diets with marine POM), future work should investigate all sinks of terrestrial organic material to improve insight towards the fate of urban-derived material in coastal areas. Zooplankton may be an unmeasured source for mussels in this study (Both et al., 2020). However, the smallest zooplankton size fraction collected during this study (64 μm) was above the known particle feeding range for blue mussels (2 to 20 μm). Controlled bivalve feeding experiments should be employed to resolve 1) bivalve-specific FA calibration coefficients (CCs) and 2) how urban-derived organic matter FAs are assimilated and modified upon digestion. Uncertainty regarding the selection and modification of FAs in primary consumers remains amongst the largest limitations with FA-based dietary estimates (Jardine et al., 2020; Pickett et al., 2024). 73 Our work demonstrated that mussel diets are dominated by phytoplankton, irrespective of site or month. Therefore, it’s important to detect the urban effect on phytoplankton community composition, productivity, and quality. These elements have shown to shift in response to anthropogenic pressures including urban-derived inputs (Gierach et al., 2017; Stelmakh et al., 2023; Varkey et al., 2018). Amid the profound reliance on phytoplankton to support many coastal primary consumers such as mussels (this study) and zooplankton (McLaskey et al., 2024), it is critical to understand how urbanization impacts phytoplankton dynamics in coastal ecosystems. Finally, our findings demonstrated the need to incorporate seasonality when resolving urban-driven effects to coastal environments. Mussel organic matter uptake and nutritional quality displayed a clear seasonal signal in our study. Seasonal variability has been detected in the biochemical signature of phytoplankton (McLaskey et al., 2022), macrophytes (Serviere-Zaragoza et al., 2015), and rivers (Liénart et al., 2020). In addition, stormwater and wastewater fluxes have shown seasonal variation, driven by factors such as rainfall and snowmelt patterns (Bidlack et al., 2021; Wang et al., 2017), combined sewer overflow (CSO) events (Rao, 2022), and seasonal water use practices (Rathnayaka et al., 2015). However, research on anthropogenic effects to organic matter flux often focus on a singular time period. Therefore, future work should continue to understand how seasonal patterns interact with urban effects to the land-sea connection. This can further support restorative efforts by identifying critical periods of land-based effects to the coastal food web. 74 2.5 Conclusions I presented a framework to trace urban-derived effects to marine food webs. The comparison of two diet estimation approaches (FA biomarkers and FA-SI Bayesian mixing model) revealed their benefits and trade-offs with spatial and temporal resolution, though their results were largely similar. In Burrard Inlet, an urbanized marine environment, mussel uptake of OM was dominated by phytoplankton with moderate contributions from urban-derived OM and macrophytes. These contributions varied with seasonal patterns of primary production, relative availability of other OM sources, and land-use, revealing controls on the flux of OM to the marine food web. Phytoplankton served as the primary source of OM for mussels, underscoring Burrard Inlet's sustained productivity despite urban pressures. Nutritional variation in mussels were associated with shifts in OM contributions, indicating food web implications from anthropogenically driven shifts in OM flux. The frequency and intensity of rainfall events in Burrard Inlet are expected to increase with anthropogenic climate change (Gillett et al., 2022; Radić et al., 2015), potentially amplifying urban-derived OM fluxes and thus, further disrupting food web health and stability. 75 Chapter 3: Conclusion 3.1 Urban effects to food webs: knowledge gaps Urbanization has disrupted the connections between land and ocean, altering natural inputs of organic matter and introducing novel sources. These changes have significant implications for the health and stability of food webs reliant on organic matter inputs (Hunt et al., 2024). Despite this, current research rarely addresses how urbanization influences the assimilation of organic matter by coastal organisms, nor does it sufficiently quantify urban-derived organic matter uptake. This knowledge gap reflects the absence of clearly defined biochemical markers capable of tracing urban-derived inputs into food webs. By combining stable isotopes and fatty acids, our study addresses this limitation, enhancing the ability to detect direct and indirect urban influences on coastal food webs and, more broadly, deepening our understanding of land-sea interactions. 3.2 Local context This study established fatty acid markers that can effectively trace the assimilation of distinct organic matter types into coastal consumers. Given the sensitivity of fatty acid profiles to environmental variation, it is critical to develop locally derived biomarkers to accurately reflect regional conditions. This approach is particularly relevant to Burrard Inlet, where the Tsleil-Waututh Nation seeks to understand the extent to which urban-derived inputs from Metro Vancouver reach nearshore communities (Bjorklund et al., 2024; Lilley et al., 2017). Identifying and quantifying these inputs is an essential step toward addressing concerns related to ecosystem health and food security in the area. 76 The methodological framework developed in this study enables the resolution of dietary source contributions within urbanized coastal ecosystems. Beyond dietary tracing alone, our approach also offers a practical tool for assessing the efficacy of local anthropogenic mitigation measures from a food-web perspective. More specifically, quantifying stormwater-derived organic matter uptake in coastal consumers can directly evaluate the effectiveness of interventions associated with the region's Citywide Integrated Rainwater Management Plan (IRMP), such as bioswales and stormwater gardens (City of Vancouver, n.d.). When paired with ongoing ecosystem monitoring initiatives (e.g., Pollution Tracker and Burrard Inlet Water Quality Objectives), these biological metrics provide resource managers and local communities with concrete feedback for assessing urban-derived runoff controls and coastal management practices (Bjorklund et al., 2024; Ocean Wise Conservation Association, n.d.). 3.3 Global context While anthropogenic land modifications are generally consistent globally (e.g., pollution, impervious surfaces, habitat loss), the oceanographic features of the adjacent marine environments can vary widely, influencing the detection of urban effects in food webs. In our study, phytoplankton always dominated mussel diets despite known urban pressures. This indicates that highly productive ecosystems may mask the effects of anthropogenic inputs, as suggested previously (Bode et al., 2014; Schloemer et al., 2024). These effects may be more noticeable in oligotrophic environments, where primary productivity is low and consumers rely more heavily on terrestrially derived organic matter inputs (Correa & Winemiller, 2018). As a result, urban-derived organic matter inputs can comprise a higher proportion of available dietary sources (Connolly et al., 2009). 77 While our study primarily focused on the direct uptake of urban-derived organic matter by mussels, indirect and cumulative anthropogenic impacts likely play important roles in shaping urban coastal food webs. For instance, persistent nutrient enrichment from urban runoff can shift phytoplankton community composition, altering food quality and potentially influencing the nutritional health and growth rates of suspension feeders like mussels (Cloern, 2001; McLaskey et al., 2022; Suikkanen et al., 2013). Urbanization-driven habitat modifications, including increased sedimentation and contamination, can cumulatively impair physiological processes in marine organisms, leading to reduced reproductive success and declining populations over time (Johnston & Roberts, 2009). Contaminants associated with urban runoff, such as heavy metals and hydrocarbons, may also accumulate within food webs, indirectly compromising consumer health (Chandurvelan et al., 2015; Maar et al., 2018). 3.4 Future directions My study revealed a number of research directions that can further improve our understanding of urban effects to the land-sea interface. The biochemical signature of organic matter types are known to vary seasonally (see 2.4.10 Limitations and future directions). Seasonally resolved collections of organic matter types may therefore provide season-specific biomarkers and increase the temporal discriminatory power in mixing model estimates (Stock & Semmens, 2016b). Other biochemical tools, such as compound specific stable isotopes (CSI), can be applied to further delineate organic matter types and dietary contributions to consumers. For instance, Mathieu-Resuge et al. (2019) found that the dinoflagellate FA markers in some mussels had more negative δ¹³C, indicating this dietary source originated locally rather than offshore. CSI can be applied to various biotracers including fatty acids (Gaillard et al., 2017; Mathieu-Resuge et al., 2019), contaminants (Dempster et al., 1997), and amino acids (Larsen et 78 al., 2013; O’Connell, 2017), though its high cost remains a barrier to widespread use in ecology (Hofstetter et al., 2024; Pickett et al., 2024). Future work should also compare the biochemical profiles between stormwater POM collected directly off-road (like this study) and directly from pipes to understand if off-road measurements are broadly representative of stormwater inputs to the marine environment. Additionally, conducting a paired contaminant-fatty acid analysis can provide insights into how contaminants may influence nutritional health. Future research can also incorporate additional sites farther from sources of urban inputs in Burrard Inlet (e.g., Indian Arm and Indian River) to better understand the spatial influence of urban inputs. More broadly, future research should assess how accurately FA profiles reflect nutritional quality. In a controlled feeding experiment, mussel condition did not improve with a high EFA-diet (Liénart et al., 2023). In addition, the mussel condition index was negatively correlated with total PUFA percent and specifically DHA proportions. This highlights the importance of pairing FA analysis with other physiological biomarkers, like protein content, energy density, condition index (soft tissue:shell ratio) to improve the link between FA profiles and organism health. FA profiles have also shifted in parallel with altered gene expression to accommodate thermal stress in bivalves (Le Luyer et al., 2022), highlighting genetic biomarkers as a complementary tool to FA-based assessments of organism health. 3.5 Final remarks This study contributes to the Urban Oceans Project, a larger initiative led by the Pelagic Ecosystem Lab to understand how urbanization reshapes coastal ocean ecosystems and affects their health. Other ongoing research efforts include determining how urbanization influences phytoplankton and zooplankton community composition and nutritional health. Our results showed that the relative uptake of different organic matter types, including urban-derived 79 sources, varies seasonally and spatially. These findings occurred within a productive, urbanized environment that holds significant value for Indigenous communities, governmental agencies and the general public. 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Trends in Food Science & Technology, 152, 104676. https://doi.org/10.1016/j.tifs.2024.104676 Zhukova, N. V. (2019). Fatty Acids of Marine Mollusks: Impact of Diet, Bacterial Symbiosis and Biosynthetic Potential. Biomolecules, 9(12), Article 12. https://doi.org/10.3390/biom9120857 Zuercher, R., & Galloway, A. W. E. (2019). Coastal marine ecosystem connectivity: Pelagic ocean to kelp forest subsidies. Ecosphere, 10(2), e02602. https://doi.org/10.1002/ecs2.2602 127 Appendices Appendix A: Supplemental Tables, Figures, and Other Graphics Figure 10. ArcGIS screenshot of Metro Vancouver’s land cover classification data (Metro Vancouver, 2023) with 1 km radial buffer (blue circles) used to estimate impervious cover of each mussel site (yellow dots). 128 Table A1. Sample size (N) for each sample type separated by biotracer. FA = fatty acid; SI = stable isotope; FA+SI = FA and SI combined dataset. Sample Type FA SI FA + SI Mussel 284 286 282 Phytoplankton 17 23 12 Green Macroalgae 33 30 28 Red Macroalgae 11 11 10 Brown Macroalgae 52 52 51 River 15 37 15 Stormwater 9 32 9 Wastewater 10 6 6 129 Table A2. Environmental conditions, total Chl a concentrations, and size fractionated Chl a proportions at four mussel sites from April 2023 to March 2024. ND = no data. Location Sample Month Salinity (ppt) Temperature (°C) Phosphate (µmol L-1) Silicate (µmol L-1) Nitrate (µmol L-1) Chl a (µg L-1) Micro-Chl (%) Nano-Chl (%) Pico-Chl (%) Stanley Park April 26.7 8.7 0.76 15.02 5.13 5.04 17.01 20.27 62.72 May 7.9 17.6 0.62 65.45 3.46 5.42 90.45 7.88 1.67 June 15.6 19.4 1.00 24.91 1.36 0.62 50.51 21.62 27.87 July 20.9 16.5 0.90 37.46 1.79 3.47 44.32 18.89 36.79 August 21.7 17.5 0.67 38.43 3.72 2.51 45.73 22.16 32.11 September 26 13.1 1.46 40.87 10.30 4.48 31.76 35.5 32.74 October 24.1 ND 1.83 46.76 21.44 0.84 41.1 22.02 36.88 November 27.9 7.9 2.17 47.61 24.25 2.28 59.82 24.04 16.14 December 22.4 9.2 1.96 50.84 25.13 1.58 79.43 11.49 9.08 January 22.6 8.4 1.73 54.62 24.46 0.69 28.93 16.75 54.32 February 22.1 7.3 2.00 47.80 23.02 0.74 74.16 14.48 11.37 March 27 8.7 0.81 13.09 4.48 1.52 80.14 9.88 9.98 Maplewood Flats April 21.4 9.7 0.36 30.89 3.89 12.08 82.11 15.09 2.8 May 18.6 ND 0.27 18.72 2.22 1.81 51.35 27.7 20.96 June 24.5 17.6 1.33 43.02 5.77 7.11 20.49 41.32 38.19 July 24 17.7 1.44 47.28 7.31 3.99 29.3 23.52 47.19 August 27.2 15.2 1.77 55.56 10.84 4.35 38.31 51.85 9.85 September 27.1 13.4 ND ND ND 3.16 61.88 22.85 15.27 October 16.3 10.2 1.28 36.48 17.44 2.00 29.73 46.82 23.45 November 24.4 8.9 1.56 37.24 19.98 1.22 66.96 23.01 10.03 December 23.3 9.4 1.74 49.99 24.31 0.16 62.47 18.01 19.52 January 20.4 8.3 0.94 51.33 14.16 1.02 49.71 39.01 11.28 February 24.5 7.7 1.53 58.70 19.48 1.52 67.98 21.37 10.65 March 25.5 9.1 1.24 35.10 12.98 2.79 75.05 20.76 4.19 Shipyards April 27.3 8.4 0.86 22.00 10.55 5.27 74.26 20.18 5.56 May 19.4 ND 0.84 42.31 7.29 16.78 76.47 5.37 18.16 June 22.1 16.4 0.62 37.61 3.31 4.36 29.97 34.87 35.16 July 21.8 16.4 1.02 39.27 7.89 4.30 45.28 30.01 24.71 August 26.6 16.5 1.79 40.52 18.49 5.07 27.85 60 12.15 130 Location Sample Month Salinity (ppt) Temperature (°C) Phosphate (µmol L-1) Silicate (µmol L-1) Nitrate (µmol L-1) Chl a (µg L-1) Micro-Chl (%) Nano-Chl (%) Pico-Chl (%) Shipyards September 28.2 13.2 1.84 42.41 18.37 3.14 56.53 21.13 22.34 October 20.5 9.8 1.56 41.03 20.74 1.64 25.61 46.14 28.24 November 28.6 9.2 2.07 46.52 25.24 0.52 54.36 32.75 12.9 December 28 9.5 2.13 48.08 26.15 1.29 42.87 39.1 18.02 January 20.4 8.5 1.54 44.70 20.98 0.12 15 34 51 February 26.5 8.1 1.94 47.75 25.12 0.27 42.73 42.91 14.36 March 27.2 9 1.02 27.20 13.29 3.58 76.54 16.06 7.4 False Creek April 26.1 9.1 0.42 9.52 3.72 8.39 84.04 10.95 5.01 May 10.9 18.3 0.22 59.06 2.16 7.36 19.63 49.13 31.25 June 16.3 20.5 0.67 45.35 1.69 2.68 48.9 27.27 23.83 July 16.4 20.4 0.97 34.47 6.09 5.27 50 37.46 12.54 August 19.4 18.3 1.56 89.51 21.37 1.99 69.5 15.37 15.13 September 22.3 13.3 1.13 61.07 32.49 6.95 68.54 19.52 11.94 October 25.1 ND 1.85 46.36 22.64 3.38 88 6.78 5.22 November 27.3 7.6 2.14 76.08 20.46 0.65 81.57 10.47 7.96 December 22.6 8.5 1.57 64.56 34.45 2.88 69.02 24.28 6.7 January 22.3 9.3 1.61 55.90 31.56 4.22 57.45 37.99 4.56 February 25.3 7.5 1.80 49.84 28.08 1.60 73.09 19.38 7.53 March 24.3 9.8 0.81 19.00 9.44 1.70 81.04 14.18 4.79 131 Table A3. Permutational multivariate analysis of variance (PERMANOVA) of mussel fatty acids and stable isotopes comparing sites and months and organic matter fatty acids and stable isotopes comparing types. Organic matter FA + SI represent a combined fatty acid and stable isotope dataset standardized with z-scores (see Table 3). Df = degrees of freedom; SS = sum of squares. Df SS R2 F p-value Mussel fatty acids Site 7 0.15505 0.19771 26.066 0.001 Month 11 0.31527 0.40200 33.726 0.001 Site : Month 77 0.15417 0.19658 2.356 0.001 Residual 188 0.15976 0.20371 Total 283 0.78425 Mussel stable isotopes Site 7 153.609 0.52060 177.348 0.001 Month 11 69.294 0.23485 50.911 0.001 Site : Month 77 49.145 0.16656 5.158 0.001 Residual 186 23.015 0.07800 Total 281 295.062 Organic matter fatty acids Source 6 8.2516 0.84575 127.94 0.001 Residual 140 1.5049 0.15425 Total 146 9.7566 1 Organic matter stable isotopes Source 6 7279.0 0.86921 203.8 0.001 Residual 184 1095.3 0.13079 Total 190 8374.3 1 Organic matter FA + SI Source 6 0.96665 0.83097 105.7 0.001 Residual 129 0.19663 0.16903 Total 135 1.16328 1 132 Figure 11. Non-metric multidimensional Scaling (NMDS) ordination of percent composition of abundant (>0.5%) fatty acids in mussels over 12 months from April 2023 to March 2024. Each point on the NMDS represents an average of up to 3 replicates. Lines and points are coloured by site and shapes indicate the month. A month from the wet bloom (April, triangle), dry (August, square), and wet (December, filled circle) season are represented with different shapes. Other refers to all months apart from April, August, and December. 133 Table A4. Mean ± standard deviation of percent composition of abundant (>0.5%) fatty acids (FA) and stable isotope (SI) composition (δ¹³C and δ¹⁵N) in organic matter types. N indicates the sample size for each respective tracer. Phytoplankton Green Macroalgae Red Macroalgae Brown Macroalgae River Stormwater Wastewater 14:0 12 ± 5.26 0.76 ± 0.49 4.44 ± 0.83 9.74 ± 2.12 4.64 ± 1.25 3.33 ± 0.44 3.57 ± 0.86 15:0 1.14 ± 0.49 0.15 ± 0.1 0.52 ± 0.19 0.38 ± 0.1 1.27 ± 0.37 0.68 ± 0.19 0.9 ± 0.21 16:0 23.29 ± 6.15 29.73 ± 3.5 31.73 ± 1.56 17.25 ± 2.72 33.27 ± 7.42 23.99 ± 2.37 19.46 ± 10.64 16:1n-7 11.99 ± 6.12 0.91 ± 0.53 2.78 ± 0.56 2.08 ± 0.93 7.49 ± 4.2 3.98 ± 0.83 5.63 ± 2.56 18:0 9.59 ± 5.99 0.43 ± 0.29 1.23 ± 0.73 0.74 ± 0.46 18.87 ± 7.21 11.34 ± 2.84 12.97 ± 6.25 18:1n-7 3.55 ± 2.31 6.06 ± 3.8 1.85 ± 1.8 0.1 ± 0.09 3.42 ± 1.29 3.77 ± 0.65 3.99 ± 1.39 18:1n-9 4.67 ± 2.92 0.99 ± 0.43 6.33 ± 0.98 18.48 ± 7.88 8.33 ± 3.63 18.16 ± 6.79 32.12 ± 9.68 18:2n-6 2.68 ± 1.76 8.03 ± 2.07 1.23 ± 0.2 9.69 ± 1.96 3.89 ± 1.62 10.26 ± 5.28 8.25 ± 2.26 18:3n-3 1.64 ± 1.56 30.56 ± 5.84 0.18 ± 0.33 6.06 ± 1.53 2.37 ± 1.71 6.92 ± 2.69 1.6 ± 0.58 18:3n-6 0.17 ± 0.15 0.83 ± 0.42 0.26 ± 0.3 1.42 ± 1.76 0.01 ± 0.05 0.11 ± 0.12 0.19 ± 0.3 18:4n-3 4.29 ± 3.57 10.9 ± 7.14 0.17 ± 0.25 6.45 ± 4.94 0.66 ± 0.94 0 ± 0 0.19 ± 0.19 20:3n-6 0.03 ± 0.05 0.63 ± 0.35 1.28 ± 0.31 1 ± 0.22 0 ± 0 0.04 ± 0.09 0.05 ± 0.1 20:4n-6 0.25 ± 0.22 1.13 ± 0.44 21.24 ± 4.43 14.15 ± 1.72 0.3 ± 0.3 0.46 ± 0.3 0.37 ± 0.53 20:5n-3 7.9 ± 3.41 2.11 ± 1.04 22.27 ± 4.22 9.22 ± 3.92 1.44 ± 0.83 0.58 ± 0.4 0.43 ± 0.51 22:0 0.57 ± 0.81 1.22 ± 0.35 0.09 ± 0.13 0.14 ± 0.1 1.48 ± 0.73 2.7 ± 1.58 0.8 ± 0.49 22:5n-3 0.15 ± 0.18 3.02 ± 1.42 0.06 ± 0.13 0.01 ± 0.04 0.2 ± 0.51 0 ± 0 0 ± 0 22:6n-3 4.47 ± 2.63 0.11 ± 0.14 0.13 ± 0.18 0.88 ± 0.6 0.55 ± 0.45 0.04 ± 0.13 0.18 ± 0.17 24:0 0.5 ± 0.46 0.09 ± 0.3 0.11 ± 0.13 0.15 ± 0.09 1.81 ± 0.74 3.5 ± 3.32 0.52 ± 0.14 12:0 0.69 ± 0.5 0 ± 0 0 ± 0 0.01 ± 0.03 1.47 ± 0.6 1.44 ± 0.37 1.54 ± 0.3 16:2n-4 1.89 ± 1.54 0.04 ± 0.1 1.25 ± 0.43 0.21 ± 0.12 0.22 ± 0.33 0.08 ± 0.11 0 ± 0 17:0 0.68 ± 0.37 0.04 ± 0.06 0.16 ± 0.13 0.13 ± 0.04 1.67 ± 0.55 1 ± 0.22 0.6 ± 0.15 20:0 0.42 ± 0.3 0.09 ± 0.09 0 ± 0 0.31 ± 0.13 1.37 ± 0.51 2.06 ± 0.94 0.81 ± 0.2 ant:15:0 0.69 ± 0.73 0.01 ± 0.04 0.02 ± 0.05 0 ± 0 1.27 ± 0.76 1.72 ± 0.62 1.18 ± 0.47 iso:15:0 0.96 ± 0.62 0.05 ± 0.23 0 ± 0 0 ± 0 1.6 ± 0.87 1.3 ± 0.77 1.21 ± 0.9 δ¹³C -23.32 ± 1.99 -15.09 ± 2.38 -16.62 ± 2.47 -16.18 ± 2.54 -28.15 ± 1.76 -28.34 ± 0.4 -24.29 ± 0.48 δ¹⁵N 5.55 ± 1.78 6.87 ± 0.6 6.63 ± 0.71 6.85 ± 0.61 2.37 ± 1.52 2.26 ± 2.13 -1.37 ± 1.32 N for FA 17 33 11 52 15 9 10 N for SI 23 30 11 52 37 32 6 134 Table A5. Two-way analysis of variance (ANOVA) of mussel total fatty acids (TFA; mg g-1), essential fatty acids (EFA; mg g-1), ratio between docosahexaenoic acid and eicosapentaenoic acid (DHA:EPA), and percent composition of fatty acid biomarkers (phytoplankton, urban, macrophyte) comparing sites and months. Df = degrees of freedom; SS = sum of squares; F = F-statistic. Df SS F p-value Phytoplankton FAs (%) Site 7 414.96 26.67 3.43E-25 Month 11 135.80 5.55 1.12E-07 Site: Month 77 323.27 1.89 2.60E-04 Residuals 188 417.94 Urban FAs (%) Site 7 17.95 2.74 9.96E-03 Month 11 55.40 5.38 2.11E-07 Site: Month 77 96.32 1.34 5.89E-02 Residuals 188 176.10 Macrophyte FAs (%) Site 7 413.34 67.16 8.34E-48 Month 11 328.72 33.99 5.45E-39 Site: Month 77 205.02 3.03 4.26E-10 Residuals 188 165.30 TFA (mg g-1) Site 7 2317.91 11.60 3.14e-12 Month 11 8699.58 27.71 8.81e-34 Site: Month 77 2994.96 1.36 4.70e-02 Residuals 188 5364.80 EFA (mg g-1) Site 7 330.53 12.57 3.51e-13 Month 11 1317.80 31.88 2.58e-37 Site: Month 77 418.47 1.45 2.29e-02 Residuals 188 706.42 DHA:EPA Site 7 3.05 64.82 8.64e-47 Month 11 13.15 177.74 4.91e-93 Site: Month 77 2.21 4.27 2.96e-16 Residuals 188 1.26 C:N Site 7 5.98 15.86 2.46e-16 Month 11 27.44 46.31 7.14e-48 Site: Month 77 9.24 2.23 5.34e-06 Residuals 190 10.23 135 Table A6. Mean ± standard deviation of percent composition of fatty acid biomarkers (phytoplankton, urban, macrophyte), total fatty acid (TFA; mg/g), essential fatty acid (EFA; mg/g), and ratio between docosahexaenoic acid and eicosapentaenoic acid (DHA:EPA) in mussels. Location Month Phytoplankton FAs (%) Macrophyte FAs (%) Urban FAs (%) TFA (mg/g) EFA (mg/g) DHA:EPA C:N Stanley Park April 21.38 ± 3.38 8.46 ± 1.77 6.04 ± 1.52 34.04 ± 6.53 13.07 ± 1.06 0.77 ± 0.09 3.72 ± 0.1 May 20.68 ± 0.48 9.78 ± 0.11 7.11 ± 0.05 23.92 ± 0.22 9.63 ± 0.11 1.17 ± 0.07 3.97 ± 0.15 June 20.92 ± 0.4 9.43 ± 0.77 7.33 ± 0.79 21.29 ± 1.49 8.45 ± 0.69 1.36 ± 0.07 4.31 ± 0.03 July 21.09 ± 1.53 9.03 ± 2.98 6.99 ± 1.04 24.74 ± 5.28 9.52 ± 1.63 1.39 ± 0.09 4.45 ± 0.21 August 19.3 ± 1.56 12.27 ± 1.22 7.01 ± 0.56 18.82 ± 0.47 7.35 ± 0.22 1.47 ± 0.08 4.27 ± 0.16 September 20.06 ± 0.72 10.83 ± 1.31 8.14 ± 0.84 18.4 ± 7.55 6.81 ± 2.66 1.54 ± 0.09 4.31 ± 0.06 October 19.2 ± 1.63 11.31 ± 1.19 7.34 ± 2.46 20.66 ± 2.23 7.82 ± 0.59 1.39 ± 0.13 4.03 ± 0.14 November 20.56 ± 1.95 9.47 ± 1.33 6.74 ± 1.37 20.89 ± 4.25 8.21 ± 1.78 1.22 ± 0.03 4.05 ± 0.17 December 19.81 ± 1.02 12.65 ± 0.26 6.01 ± 0.34 20.26 ± 1.14 8.2 ± 0.36 1.34 ± 0.09 4.19 ± 0.08 January 18.58 ± 1.37 13.22 ± 0.11 5.69 ± 1.1 16.24 ± 6.09 6.37 ± 2.42 1.35 ± 0.09 3.9 ± 0.13 February 23.47 ± 5.28 7.88 ± 2.57 5.47 ± 1.86 15.38 ± 6.21 5.51 ± 1.52 0.98 ± 0.14 3.73 ± 0.34 March 23.95 ± 1.94 7.02 ± 1.17 5.12 ± 0.84 25.9 ± 12.11 9.65 ± 4.27 0.83 ± 0.09 3.96 ± 0.06 Annual 20.75 ± 2.41 10.11 ± 2.27 6.58 ± 1.35 21.71 ± 6.73 8.38 ± 2.45 1.23 ± 0.25 4.07 ± 0.26 PSEC April 22.78 ± 2.39 4.96 ± 1.26 6.85 ± 1.61 40.5 ± 10.29 16.35 ± 3.86 0.58 ± 0.12 3.74 ± 0.07 May 25.83 ± 1.28 4.18 ± 0.22 6.04 ± 1.12 30.06 ± 4.66 10.78 ± 1.71 0.8 ± 0.1 4.37 ± 0.27 June 27.71 ± 0.33 3.73 ± 0.23 5.52 ± 0.65 46.11 ± 0.82 17.05 ± 0.33 1.16 ± 0.05 4.26 ± 0.26 July 24.7 ± 0.53 4.42 ± 0.29 5.89 ± 1.04 36.94 ± 5.47 13.62 ± 1.87 1.37 ± 0.04 4.15 ± 0.18 August 23.93 ± 0.55 5.95 ± 0.19 6.47 ± 0.47 27.49 ± 1.83 10.55 ± 0.31 1.3 ± 0.03 4.15 ± 0.08 September 23.43 ± 0.66 7.74 ± 1.03 6.82 ± 0.55 20.64 ± 2.09 8.09 ± 1.13 1.54 ± 0.1 4.84 ± 0.34 October 23.78 ± 0.84 6.9 ± 0.73 5.33 ± 0.36 23.72 ± 1.8 9.71 ± 0.87 1.17 ± 0.1 4.89 ± 0.23 November 24.76 ± 1.72 6.17 ± 0.16 6.28 ± 1.49 26.26 ± 3.45 10.64 ± 0.91 1.02 ± 0.06 4.64 ± 0.31 December 24.06 ± 0.66 7.67 ± 0.2 5.34 ± 0.56 23.11 ± 1.92 9.61 ± 0.78 1.11 ± 0.05 4.54 ± 0.1 January 24.65 ± 1.96 6.54 ± 0.43 5.73 ± 1.14 31.7 ± 6.39 12.79 ± 1.77 1.06 ± 0.05 4.25 ± 0.18 February 24.62 ± 2.48 6.49 ± 1.03 5.55 ± 0.58 21.76 ± 10.59 7.95 ± 4.33 1.2 ± 0.17 3.96 ± 0.28 March 24.93 ± 0.33 5.24 ± 0.49 5.92 ± 0.51 29.91 ± 1.01 12.07 ± 1.37 0.8 ± 0.07 3.86 ± 0.07 Annual 24.59 ± 1.69 5.85 ± 1.41 5.98 ± 0.94 29.85 ± 8.96 11.59 ± 3.33 1.1 ± 0.27 4.3 ± 0.4 136 Location Month Phytoplankton FAs (%) Macrophyte FAs (%) Urban FAs (%) TFA (mg/g) EFA (mg/g) DHA:EPA C:N Maplewood Flats April 27.53 ± 0.22 4.54 ± 0.35 4.81 ± 0.24 41.75 ± 0.73 15.7 ± 0.49 0.61 ± 0.03 3.91 ± 0.08 May 24.75 ± 1.73 5.03 ± 0.84 6.97 ± 1.15 27.35 ± 8.5 9.95 ± 2.07 0.84 ± 0.1 4.22 ± 0.31 June 26.39 ± 1.08 4.92 ± 0.47 6.06 ± 1.7 31.6 ± 1.52 13.08 ± 0.13 1.17 ± 0.07 4.22 ± 0.21 July 25.31 ± 1.32 5.33 ± 0.69 6.12 ± 1.1 31.16 ± 6.87 12.34 ± 2.18 1.14 ± 0.04 4.35 ± 0.24 August 23.91 ± 0.75 6.94 ± 0.53 6.33 ± 0.93 25.61 ± 2.36 10.49 ± 1.2 1.13 ± 0.12 4.69 ± 0.17 September 24.26 ± 1.06 7.62 ± 0.52 6.23 ± 0.4 28.9 ± 6.5 11.33 ± 2.82 1.3 ± 0.14 4.55 ± 0.28 October 23.52 ± 0.87 8.3 ± 0.97 6 ± 0.61 22.82 ± 0.58 9.42 ± 0.28 1.31 ± 0.06 4.53 ± 0.55 November 23.25 ± 1.39 8.59 ± 1.23 7.08 ± 1.15 22.52 ± 4.71 9.58 ± 2.38 1.19 ± 0.24 4.16 ± 0.09 December 23.28 ± 2.21 7.64 ± 0.39 7.69 ± 2.21 22.08 ± 0.8 8.73 ± 1.17 1.24 ± 0.08 4.44 ± 0.17 January 24.81 ± 2.72 8.47 ± 2.17 4.89 ± 0.95 20.73 ± 3.94 8.41 ± 1.86 1.14 ± 0.11 3.95 ± 0.29 February 22.85 ± 1.99 7.32 ± 0.28 5.47 ± 1.3 18.1 ± 6.36 7.21 ± 2.49 0.96 ± 0.05 3.64 ± 0.14 March 25.52 ± 1.4 6.08 ± 0.57 5.39 ± 0.64 26.6 ± 10.89 10.33 ± 4.28 0.73 ± 0.07 3.86 ± 0.07 Annual 24.61 ± 1.87 6.73 ± 1.62 6.09 ± 1.28 26.6 ± 7.66 10.55 ± 2.84 1.06 ± 0.24 4.21 ± 0.37 Tower Beach April 22.62 ± 1.91 6.12 ± 0.7 5.83 ± 0.91 33.57 ± 5.78 14.01 ± 1.96 0.67 ± 0.04 3.57 ± 0.1 May 24.05 ± 1.82 5.43 ± 0.59 6.81 ± 0.84 27.75 ± 4.01 11.68 ± 1.18 0.84 ± 0.03 3.86 ± 0.14 June 23.64 ± 1.74 6.93 ± 1.39 5.66 ± 1.05 28.57 ± 6.78 11.03 ± 2.02 1.24 ± 0.06 4.09 ± 0.03 July 20.29 ± 1.29 8.51 ± 0.22 7.7 ± 1.59 19.81 ± 1.57 7.43 ± 0.62 1.32 ± 0.08 4.39 ± 0.35 August 22.07 ± 0.58 8.53 ± 0.26 6.5 ± 0.49 20.96 ± 0.39 8.31 ± 0.16 1.29 ± 0.06 4.42 ± 0.35 September 22.7 ± 0.99 9.19 ± 1.04 5.9 ± 0.9 16.75 ± 5.94 6.56 ± 2.28 1.65 ± 0.14 4.63 ± 0.24 October 20.8 ± 0.05 10.45 ± 0.28 5.37 ± 0.26 18.68 ± 1.05 7.39 ± 0.34 1.28 ± 0.03 4.37 ± 0.17 November 22.65 ± 1.03 8.01 ± 0.61 5.72 ± 0.56 18.31 ± 1.5 7.74 ± 0.8 1.15 ± 0.02 4.23 ± 0.1 December 22.1 ± 0.65 9.74 ± 0.61 5.67 ± 0.49 19.05 ± 2.3 8.02 ± 1.04 1.3 ± 0.06 4.11 ± 0.07 January 21.07 ± 1.02 9.96 ± 1.26 5.36 ± 0.7 13.15 ± 4.61 5.45 ± 2.2 1.17 ± 0.07 3.98 ± 0.1 February 20.4 ± 1.25 9.39 ± 1.31 5.37 ± 0.83 19.59 ± 8.65 7.92 ± 3.47 1.06 ± 0.06 3.54 ± 0.03 March 23.04 ± 0.67 6.36 ± 0.45 5.21 ± 0.62 23.89 ± 9.6 9.38 ± 3.56 0.82 ± 0.05 3.67 ± 0.08 Annual 22.12 ± 1.56 8.22 ± 1.75 5.92 ± 0.99 21.67 ± 7.07 8.74 ± 2.84 1.15 ± 0.27 4.07 ± 0.38 Dundarave Beach April 21.76 ± 2.67 5.68 ± 1.2 7.03 ± 2.34 38.74 ± 8.81 15.7 ± 3.22 0.59 ± 0.12 3.73 ± 0.06 May 22.49 ± 0.99 6.19 ± 0.99 6.99 ± 0.79 32.88 ± 3.41 13.85 ± 1.93 0.89 ± 0.07 4.42 ± 0.11 June 25.88 ± 0.61 4.26 ± 0.72 5.32 ± 0.99 40.95 ± 7.95 14.6 ± 2.21 1.13 ± 0.05 4.55 ± 0.4 137 Location Month Phytoplankton FAs (%) Macrophyte FAs (%) Urban FAs (%) TFA (mg/g) EFA (mg/g) DHA:EPA C:N Dundarave Beach July 21.35 ± 0.85 6.1 ± 0.06 6.78 ± 1 27.76 ± 3.07 9.92 ± 0.88 1.33 ± 0.01 4.37 ± 0.34 August 20.94 ± 0.63 7.37 ± 1.56 6.95 ± 1.35 23.24 ± 5.49 8.27 ± 1.42 1.34 ± 0.13 4.92 ± 0.24 September 23.15 ± 0.18 6.88 ± 0.58 5.3 ± 0.29 29.23 ± 4.38 11.1 ± 1.92 1.49 ± 0.05 5.17 ± 0.44 October 23.18 ± 0.92 7.14 ± 0.12 5.67 ± 0.59 20.88 ± 1.16 8.39 ± 0.56 1.23 ± 0.08 4.81 ± 0.27 November 23.25 ± 0.99 7.25 ± 0.97 5.31 ± 0.27 24.03 ± 2.87 9.17 ± 0.65 1.17 ± 0.14 4.62 ± 0.51 December 23.56 ± 1.27 7.56 ± 0.17 5.12 ± 0.25 18.85 ± 8.65 7.57 ± 3.47 1.24 ± 0.04 4.67 ± 0.1 January 23.2 ± 0.72 7.53 ± 0.5 5.2 ± 0.5 26.41 ± 2.37 10.84 ± 0.56 1.22 ± 0.03 4.12 ± 0.28 February 23.33 ± 2.38 6.67 ± 1.64 5.56 ± 0.92 16.18 ± 5.23 5.39 ± 1.27 1.02 ± 0.09 3.9 ± 0.21 March 22.73 ± 0.42 6.46 ± 0.43 6.14 ± 0.32 23.01 ± 8.11 9.59 ± 3.49 0.77 ± 0.05 3.73 ± 0.05 Annual 22.89 ± 1.64 6.56 ± 1.19 5.97 ± 1.12 26.86 ± 8.88 10.35 ± 3.48 1.12 ± 0.26 4.41 ± 0.52 Kitsilano Beach April 21.46 ± 1.78 7.05 ± 0.38 6.83 ± 0.1 30.89 ± 6.19 12.27 ± 1.82 0.83 ± 0.1 3.71 ± 0.12 May 23.22 ± 1.15 6.43 ± 0.5 7.61 ± 0.96 27.35 ± 2.04 10.9 ± 0.87 1.11 ± 0.02 3.78 ± 0.1 June 24.86 ± 1.38 5.19 ± 0.75 5.75 ± 0.79 37.04 ± 10.16 13.58 ± 3.59 1.19 ± 0.03 4.54 ± 0.03 July 21.78 ± 0.47 7.09 ± 0.44 7.38 ± 0.69 22.69 ± 6.61 8.55 ± 2.91 1.45 ± 0.07 4.8 ± 0.4 August 22.99 ± 0.81 7.46 ± 0.93 6.84 ± 0.18 21.48 ± 4.1 8.33 ± 1.67 1.43 ± 0.16 4.83 ± 0.63 September 21.48 ± 0.46 9.19 ± 0.59 7.44 ± 0.51 17.94 ± 0.9 6.77 ± 0.32 1.67 ± 0.07 4.31 ± 0.18 October 20.71 ± 0.86 8.86 ± 0.95 6.27 ± 0.35 19.13 ± 0.49 7.69 ± 0.22 1.06 ± 0.02 4.33 ± 0.16 November 22.66 ± 1.15 8.04 ± 0.95 5.99 ± 0.37 19.35 ± 0.54 8.12 ± 0.26 1.17 ± 0.04 4.06 ± 0.24 December 23.23 ± 0.23 8.45 ± 0.36 5.42 ± 0.45 19.8 ± 2.26 8.28 ± 0.94 1.36 ± 0.1 4.59 ± 0.02 January 22.71 ± 2.05 7.56 ± 0.62 5.05 ± 1.17 15.98 ± 6.54 6.21 ± 2.86 1.18 ± 0.03 4.18 ± 0.12 February 22.15 ± 0.85 7.54 ± 0.27 5.4 ± 0.7 11.87 ± 0.48 4.43 ± 0.41 1.09 ± 0.04 3.66 ± 0.04 March 22.98 ± 0.95 6.87 ± 0.64 6.22 ± 0.33 11.35 ± 0.33 4.75 ± 0.14 0.8 ± 0.04 4.08 ± 0.41 Annual 22.52 ± 1.42 7.48 ± 1.19 6.35 ± 0.99 21.24 ± 8.21 8.32 ± 3.07 1.2 ± 0.25 4.22 ± 0.44 Shipyards April 25 ± 4.37 7.01 ± 1.93 5.75 ± 2.51 36.17 ± 7.68 14.57 ± 0.89 1.02 ± 0.1 3.76 ± 0.18 May 25.13 ± 0.69 6.75 ± 1.13 6.72 ± 0.26 27.07 ± 7.54 10.74 ± 1.62 1.31 ± 0.1 3.71 ± 0.16 June 24.84 ± 1.25 6.55 ± 0.94 6.36 ± 0.94 29.01 ± 8.19 11.8 ± 2.91 1.38 ± 0.06 4.26 ± 0.35 July 23.71 ± 0.69 6.44 ± 0.51 6.6 ± 0.48 22.99 ± 1.36 9.39 ± 0.4 1.37 ± 0.04 4.3 ± 0.06 August 24.44 ± 1.24 6.01 ± 0.34 5.94 ± 1.18 29.04 ± 3.94 10.5 ± 0.87 1.36 ± 0.06 4.79 ± 0.22 September 23.51 ± 0.98 8.13 ± 0.58 5.8 ± 0.23 22.72 ± 4.04 8.92 ± 1.65 1.56 ± 0.01 4.35 ± 0.15 138 Location Month Phytoplankton FAs (%) Macrophyte FAs (%) Urban FAs (%) TFA (mg/g) EFA (mg/g) DHA:EPA C:N Shipyards October 22.2 ± 3.26 7.72 ± 0.49 6.98 ± 2.17 25.23 ± 4.31 9.42 ± 2.23 1.43 ± 0.14 4.51 ± 0.32 November 24 ± 1.08 7.58 ± 0.71 6.28 ± 0.27 24.47 ± 3.92 10.05 ± 1.86 1.36 ± 0.05 4.36 ± 0.2 December 23.56 ± 0.54 8.35 ± 0.54 6.91 ± 1.25 22.63 ± 1.74 9.32 ± 0.64 1.46 ± 0.06 4.22 ± 0.1 January 22.92 ± 1.08 8.44 ± 0.58 5.49 ± 0.54 20.41 ± 7.7 7.72 ± 3.03 1.41 ± 0.07 3.97 ± 0.12 February 22.19 ± 0.67 8.13 ± 0.52 5.61 ± 0.53 12.71 ± 1.2 4.57 ± 0.4 1.28 ± 0.07 3.86 ± 0.08 March 20.99 ± 0.41 7.96 ± 0.64 5.85 ± 0.6 18.56 ± 7.61 7.25 ± 3.15 1.05 ± 0.03 3.56 ± 0.05 Annual 23.5 ± 1.79 7.43 ± 1.03 6.2 ± 1.01 23.91 ± 7.05 9.38 ± 2.77 1.34 ± 0.16 4.16 ± 0.38 False Creek April 23.16 ± 3.29 4.81 ± 0.93 5.45 ± 1.19 40.39 ± 8.04 15.42 ± 1.14 0.44 ± 0.06 4.11 ± 0.11 May 23.37 ± 0.71 5.23 ± 0.18 6.37 ± 0.78 31.38 ± 2.8 11.53 ± 0.57 0.82 ± 0.04 4.65 ± 0.29 June 22.56 ± 0.87 6.72 ± 0.42 7.26 ± 1.37 28.61 ± 2.54 11.38 ± 0.86 0.88 ± 0.02 4.85 ± 0.42 July 23.63 ± 0.77 7.34 ± 0.9 5.87 ± 0.43 28.7 ± 5.47 10.43 ± 1.55 1.23 ± 0.07 4.41 ± 0.21 August 22.45 ± 1.4 9.47 ± 0.78 6.23 ± 0.52 24.23 ± 2.55 9.82 ± 0.94 1.1 ± 0.04 4.88 ± 0.02 September 21.44 ± 0.73 9.2 ± 0.91 6.22 ± 0.05 17.64 ± 4.22 6.92 ± 1.89 1.11 ± 0.09 4.83 ± 0.25 October 22.08 ± 0.99 8.69 ± 1.64 5.02 ± 0.3 23.63 ± 2.26 9.54 ± 1.17 1.07 ± 0.06 4.96 ± 0.14 November 24.31 ± 0.06 7.53 ± 0.65 4.31 ± 0.23 24.54 ± 3.37 10.18 ± 1.39 0.82 ± 0 5.08 ± 0.01 December 22.88 ± 0.48 7.69 ± 0.54 4.69 ± 0.39 19.63 ± 4.39 7.63 ± 1.8 1.06 ± 0.06 4.61 ± 0.46 January 23.4 ± 0.45 8.12 ± 0.4 5.24 ± 0.04 22.36 ± 2.71 8.61 ± 0.68 1.36 ± 0.09 4.25 ± 0.35 February 18.89 ± 0.40 10.75 ± 1.52 5.86 ± 0.8 15.89 ± 9.41 6.25 ± 3.52 0.99 ± 0.05 3.67 ± 0.02 March 23.31 ± 1.55 7.17 ± 1.42 5.77 ± 0.45 16.36 ± 4.5 6.44 ± 1.78 0.77 ± 0.06 3.88 ± 0.23 Annual 22.58 ± 1.71 7.73 ± 1.86 5.73 ± 0.95 24.44 ± 8.06 9.49 ± 2.9 0.98 ± 0.24 4.5 ± 0.48 139 Figure 12. δ¹³C and δ¹⁵N biplot (A) and non-metric multidimensional Scaling (NMDS) ordination of percent composition of fatty acid tracers (B) used for mixing model in mussels and organic matter. Mussel data are coloured by site and organic matter is coloured by type. Source data were corrected with trophic enrichment factors and calibration coefficients for stable isotope (A) and fatty acid (B) data, respectively (see Table 3). In A, error bars indicate organic matter type uncertainty ± 1 SD. In B, arrows indicate influence and direction of each fatty acid on sample distribution in the NMDS. The NMDS stress values did not reach convergence (100 iterations; stress values ~0.12 to 0.18). 140 Table 4. Regression parameters (b, m, R2) and mean calibration coefficients (CCs) between consumer and diet proportions in bivalve feeding studies (N = number of studies). Fatty acids used in the mixing model, R2 values greater than 0.20, and CCs between 0.6 and 2.0 are all bolded. Fatty Acid b m R2 CC N 14:0 2.87 0.14 0.08 0.62 ± 1.01 46 15:0 0.35 0.25 0.37 0.6 ± 0.24 14 16:0 10.71 0.36 0.30 1.1 ± 0.41 47 16:1n-7 2.69 0.20 0.40 0.7 ± 0.88 35 16:1n-9 0.04 2.32 0.33 2.27 ± 1.58 14 16:2n-4 0.94 -0.11 0.06 0.43 ± 0.34 9 16:3n-4 2.55 0.14 0.03 0.74 ± 0.56 14 17:0 0.07 0.82 0.68 0.91 ± 0.3 8 18:0 5.18 0.23 0.08 4.29 ± 4.21 35 18:1n-7 5.32 -0.29 0.04 3.35 ± 2.26 41 18:1n-9 3.07 0.41 0.36 1.58 ± 2.31 40 18:2n-6 1.14 0.36 0.78 0.77 ± 0.4 45 18:3n-3 1.44 0.27 0.57 0.76 ± 0.76 44 18:3n-6 0.68 0.08 0.02 0.58 ± 0.41 12 18:4n-3 3.24 0.22 0.04 0.88 ± 0.85 39 20:0 0.15 0.04 0.49 0.14 ± 0.15 8 20:1n-9 7.89 -2.99 0.31 5.9 ± 5.91 4 20:2n-6 0.35 0.17 0.09 0.65 ± 0.16 4 20:3n-6 0.17 0.36 0.42 0.54 ± 0.32 6 20:4n-3 0.09 0.94 0.16 1.11 ± 0.68 5 20:4n-6 2.80 0.29 0.22 1.65 ± 1.25 32 20:5n-3 7.16 0.22 0.10 1.63 ± 2.17 45 22:5n-3 -0.07 1.07 0.09 0.96 ± 0.77 5 22:5n-6 -2.53 2.94 0.57 1.59 ± 1.57 14 22:6n-3 7.63 0.66 0.12 2.49 ± 2.12 38 141 Table 5. MixSIAR results of source contributions. Mean ± standard deviation and 95 % credible intervals (CI) of the estimated percent contribution (%) of phytoplankton, macrophytes (green, red, brown macroalgae), river, stormwater, and wastewater organic matter types to the diet of blue mussels. Site Season Phytoplankton Green Macroalgae Red Macroalgae Brown Macroalgae River Stormwater Wastewater Stanley Park Wet 61.6 ± 3.6 2.1 ± 0.6 21.5 ± 1.2 0.5 ± 0.5 13.5 ± 3.7 0.5 ± 0.5 0.3 ± 0.3 Wet Bloom 64.8 ± 3.5 0.6 ± 0.6 18.9 ± 1.7 0.6 ± 0.7 14.2 ± 3.5 0.5 ± 0.7 0.4 ± 0.4 Dry 71.6 ± 3 1.1 ± 0.6 19.3 ± 1.4 0.2 ± 0.3 7 ± 2.9 0.4 ± 0.5 0.3 ± 0.3 Annual 66 ± 5.11 1.27 ± 0.76 19.9 ± 1.4 0.43 ± 0.21 11.57 ± 3.97 0.47 ± 0.06 0.33 ± 0.06 PSEC Wet 66.9 ± 3.3 3.4 ± 0.7 11 ± 0.9 0.7 ± 0.7 15.8 ± 3.6 0.9 ± 0.9 1.3 ± 1.1 Wet Bloom 74.9 ± 3.9 0.4 ± 0.4 9.3 ± 1.4 0.5 ± 0.6 13.1 ± 4.1 0.7 ± 0.9 1.2 ± 1.4 Dry 68.6 ± 3.1 3.5 ± 0.8 9.9 ± 0.9 0.3 ± 0.3 15.9 ± 3.3 1 ± 1.1 0.9 ± 0.9 Annual 70.13 ± 4.21 2.43 ± 1.76 10.07 ± 0.86 0.5 ± 0.2 14.93 ± 1.59 0.87 ± 0.15 1.13 ± 0.21 Maplewood Flats Wet 77.4 ± 2.9 0.7 ± 0.4 14.2 ± 1.3 1.6 ± 1.5 4.9 ± 2.8 0.7 ± 0.8 0.6 ± 0.6 Wet Bloom 77.4 ± 3.8 0.4 ± 0.5 13.8 ± 2.5 0.7 ± 0.9 6.5 ± 3.1 0.6 ± 0.8 0.7 ± 0.9 Dry 82.4 ± 2.5 1.2 ± 0.7 11.3 ± 1.2 0.5 ± 0.7 3.3 ± 2 0.6 ± 0.7 0.6 ± 0.7 Annual 79.07 ± 2.89 0.77 ± 0.4 13.1 ± 1.57 0.93 ± 0.59 4.9 ± 1.6 0.63 ± 0.06 0.63 ± 0.06 Tower Beach Wet 60.2 ± 3.7 1.6 ± 0.6 16.8 ± 1 0.3 ± 0.3 20.4 ± 3.8 0.4 ± 0.4 0.3 ± 0.3 Wet Bloom 65.1 ± 3.9 0.3 ± 0.3 14.5 ± 1.3 0.3 ± 0.3 19.2 ± 3.9 0.3 ± 0.4 0.3 ± 0.3 Dry 69 ± 3.1 1.7 ± 0.7 14.5 ± 1.1 0.3 ± 0.4 13.5 ± 3.1 0.7 ± 0.8 0.3 ± 0.4 Annual 64.77 ± 4.41 1.2 ± 0.78 15.27 ± 1.33 0.3 ± 0 17.7 ± 3.69 0.47 ± 0.21 0.3 ± 0 Dundarave Beach Wet 62.2 ± 3.3 4.3 ± 0.7 12.1 ± 0.9 1 ± 0.6 18.6 ± 3.5 1.1 ± 0.9 0.8 ± 0.7 Wet Bloom 70.9 ± 3.5 0.4 ± 0.4 13 ± 1.5 0.5 ± 0.5 14 ± 3.4 0.5 ± 0.5 0.5 ± 0.5 Dry 67.1 ± 2.9 4.7 ± 0.9 11.3 ± 1.1 0.5 ± 0.5 14.4 ± 3 1.1 ± 1.1 0.8 ± 0.8 Annual 66.73 ± 4.36 3.13 ± 2.38 12.13 ± 0.85 0.67 ± 0.29 15.67 ± 2.55 0.9 ± 0.35 0.7 ± 0.17 Kitsilano Beach Wet 64.4 ± 3.6 1.7 ± 0.7 14.6 ± 1 0.5 ± 0.5 18 ± 3.7 0.5 ± 0.5 0.3 ± 0.3 Wet Bloom 62.5 ± 3.9 0.5 ± 0.5 15.8 ± 1.5 0.3 ± 0.4 20 ± 3.7 0.4 ± 0.5 0.3 ± 0.4 Dry 72.6 ± 3.1 2.5 ± 0.8 13.4 ± 1.2 0.4 ± 0.5 9.8 ± 3 0.7 ± 0.8 0.6 ± 0.6 142 Site Season Phytoplankton Green Macroalgae Red Macroalgae Brown Macroalgae River Stormwater Wastewater Annual 66.5 ± 5.37 1.57 ± 1.01 14.6 ± 1.2 0.4 ± 0.1 15.93 ± 5.4 0.53 ± 0.15 0.4 ± 0.17 Shipyards Wet 61 ± 3.2 1.1 ± 0.5 12.3 ± 1.5 4 ± 2.3 18 ± 3.9 2.9 ± 2.4 0.7 ± 0.7 Wet Bloom 60.9 ± 4.6 0.5 ± 0.5 14.5 ± 1.7 1.2 ± 1.5 21.6 ± 4.9 0.9 ± 1.1 0.5 ± 0.5 Dry 66.9 ± 3.1 1.7 ± 0.7 11.8 ± 1 0.5 ± 0.7 17.5 ± 3.3 1.2 ± 1.3 0.5 ± 0.5 Annual 62.93 ± 3.44 1.1 ± 0.6 12.87 ± 1.44 1.9 ± 1.85 19.03 ± 2.24 1.67 ± 1.08 0.57 ± 0.12 False Creek Wet 63.8 ± 3.5 2.8 ± 0.7 14.4 ± 1.1 0.9 ± 0.8 15.9 ± 3.7 1.1 ± 1.1 0.9 ± 0.9 Wet Bloom 68.9 ± 3.6 0.3 ± 0.4 16.8 ± 1.5 0.3 ± 0.4 12.5 ± 3.6 0.5 ± 0.7 0.6 ± 0.7 Dry 71.6 ± 3 1.3 ± 0.6 16.6 ± 1.3 0.3 ± 0.3 9.2 ± 2.9 0.6 ± 0.6 0.4 ± 0.5 Annual 68.1 ± 3.96 1.47 ± 1.26 15.93 ± 1.33 0.5 ± 0.35 12.53 ± 3.35 0.73 ± 0.32 0.63 ± 0.25 143 Table 6. Fatty acid biomarkers from our study (pooled and individual organic matter groups) compared to marine bivalve studies (literature marker). Pooled group markers used in this study are bolded. ND = No data. Fatty Acid Pooled group Individual group Literature Marker Reference 12:0 River+Stormwater+Wastewater (urban-derived) Wastewater ND ND 14:0 Brown Macroalgae+Phytoplankton+River Phytoplankton ND ND 15:0 Phytoplankton+River+Wastewater River Bacteria (F. Guo et al., 2020; Irisarri et al., 2014; Puccinelli et al., 2016; Ricardo et al., 2021) ant:15:0 River+Stormwater+Wastewater (urban-derived) Stormwater Bacteria (Allan et al., 2010; Bachok et al., 2009; Ezgeta-Balić et al., 2012; Gaillard et al., 2017; F. Guo et al., 2020; Najdek et al., 2013; Puccinelli et al., 2016; Zhao et al., 2013) iso:15:0 River+Stormwater+Wastewater (urban-derived) River Bacteria (Allan et al., 2010; Bachok et al., 2009; Ezgeta-Balić et al., 2012; Gaillard et al., 2017; F. Guo et al., 2020; Najdek et al., 2013; Puccinelli et al., 2016; Zhao et al., 2013) 16:0 Green Macroalgae+Red Macroalgae+River River Dinoflagellate (Ezgeta-Balić et al., 2012; Irisarri et al., 2014; Najdek et al., 2013) Diatom (Ricardo et al., 2021) 16:1n-7 Phytoplankton+River+Wastewater Phytoplankton Diatom (Allan et al., 2010; Gaillard et al., 2017; Irisarri et al., 2014; Puccinelli et al., 2016; Ricardo et al., 2021) Bacteria (Gaillard et al., 2017) 16:2n-4 Brown Macroalgae+Phytoplankton+Red Macroalgae Phytoplankton Diatom (Zhukova, 2019) 144 Fatty Acid Pooled group Individual group Literature Marker Reference 17:0 Phytoplankton+River+Stormwater River Bacteria (F. Guo et al., 2020; Irisarri et al., 2014; Puccinelli et al., 2016; Ricardo et al., 2021) 18:0 River+Stormwater+Wastewater (urban-derived) River ND ND 18:1n-7 Green Macroalgae+Phytoplankton+Wastewater ND Bacteria (Bachok et al., 2009; Ezgeta-Balić et al., 2012; Gaillard et al., 2017; Irisarri et al., 2014; Najdek et al., 2013) 18:1n-9 Brown Macroalgae+Stormwater+Wastewater Wastewater Fish feed (Irisarri et al., 2014) Zooplankton (Ezgeta-Balić et al., 2012; Irisarri et al., 2014; Najdek et al., 2013) Fungal strains (F. Guo et al., 2020) Brown macroalgae (Gaillard et al., 2017; F. Guo et al., 2020) 18:2n-6 Brown Macroalgae+Stormwater+Wastewater Brown macroalgae Brown macroalgae (Gaillard et al., 2017) Green macroalgae (Bachok et al., 2009; F. Guo et al., 2020) Macrophytes (Allan et al., 2010; Richoux et al., 2014) Zooplankton (Ezgeta-Balić et al., 2012; Najdek et al., 2013) Fish feed (Irisarri et al., 2014) Fungal strains (F. Guo et al., 2020) 18:3n-3 Brown Macroalgae+Green Macroalgae+Stormwater Green macroalgae Green macroalgae (Bachok et al., 2009; Gaillard et al., 2017; F. Guo et al., 2020) Macrophytes (Allan et al., 2010; Richoux et al., 2014) 18:3n-6 Brown Macroalgae+Green Macroalgae+Wastewater Brown macroalgae Green macroalgae (Bachok et al., 2009; F. Guo et al., 2020) 18:4n-3 Green macroalgae Green macroalgae (Gaillard et al., 2017) 145 Fatty Acid Pooled group Individual group Literature Marker Reference Brown Macroalgae+Green Macroalgae+Phytoplankton Dinoflagellates (Bachok et al., 2009; Gaillard et al., 2017; Irisarri et al., 2014; Najdek et al., 2013; Puccinelli et al., 2016; Ricardo et al., 2021) 20:0 River+Stormwater+Wastewater (urban-derived) Stormwater ND ND 20:3n-6 Brown Macroalgae+Green Macroalgae+Red Macroalgae (macrophytes) Red macroalgae Brown macroalgae (F. Guo et al., 2020) 20:4n-6 Brown Macroalgae+Green Macroalgae+Red Macroalgae (macrophytes) Red macroalgae Brown macroalgae (Gaillard et al., 2017) Zooplankton (Ezgeta-Balić et al., 2012; Najdek et al., 2013) Fish feed (Irisarri et al., 2014) Microeukaryotes and protozoans (F. Guo et al., 2020) 20:5n-3 Brown Macroalgae+Phytoplankton+Red Macroalgae Red macroalgae Red macroalgae (20:5n-3/20:4n-6 > 10) (F. Guo et al., 2020) Brown macroalgae (Gaillard et al., 2017) Diatoms (Allan et al., 2010; Bachok et al., 2009; Ezgeta-Balić et al., 2012; F. Guo et al., 2020; Irisarri et al., 2014; Najdek et al., 2013; Puccinelli et al., 2016; Ricardo et al., 2021) 22:0 Green Macroalgae+River+Stormwater Stormwater Terrestrial material (Budge et al., 2001) 22:5n-3 Green Macroalgae (macrophyte) Green macroalgae ND ND 22:6n-3 Brown Macroalgae+Phytoplankton+River Phytoplankton Dinoflagellates (Allan et al., 2010; Ezgeta-Balić et al., 2012; F. Guo et al., 2020; Irisarri et al., 2014; Najdek et al., 2013; Puccinelli et al., 2016; Ricardo et al., 2021) 146 Fatty Acid Pooled group Individual group Literature Marker Reference 24:0 River+Stormwater+Wastewater (urban-derived) Stormwater Terrestrial material (Budge et al., 2001) 147 Figure 13. Average difference of contribution estimates (%) for organic matter sources between MixSIAR and biomarker analysis. Positive and negative values indicate estimates were greater for MixSIAR and biomarker analysis, respectively. 148 149 Figure 14. Spearman’s correlation coefficients between mussel fatty acid (FA) or stable isotope (SI) values and salinity (ppt), temperature (°C), macronutrients (phosphate, silicate, nitrate; μmol L-1), total chlorophyll (total Chl a; μg L-1), Chl a size classes (micro, nano, pico; % of total Chl a), or particulate organic matter (POM) FA and SI values. Total fatty acids (TFA) and essential fatty acids (EFA) are in mg g-1 and mg L-1 for mussels and POM, respectively. DHA:EPA and C:N indicate the ratio between docosahexaenoic acid (DHA) to eicosapentaenoic acid (EPA) and carbon (C) to nitrogen (N), respectively. Mussel values were averaged across up to 3 replicates. Statistically significant correlations (p<0.05) are only shown and correlations marked with ** indicate p < Bonferroni-corrected α = 0.00037. 150 Figure 15. Monthly size at each site of mussel soft tissue wet weight (mg; digestive gland removed) and shell length (cm) for April 2023 to March 2024. Months are ordered by collection date (April 2023 to March 2024) and line colours indicate season (dry = June to September, wet = October to February, wet bloom = March to May). The x-axis indicates the final month for the wet bloom (May), dry (September), and wet (February) season. Points represent averages across mussel replicates for each month.
Thesis/Dissertation
eng
Oceanography
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hunt, Brian P. V.
From poop to marine soup : deconstructing the diet and health of mussels in a high rainfall coastal region
Text