{"http:\/\/dx.doi.org\/10.14288\/1.0432346":{"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool":[{"value":"Science, Faculty of","type":"literal","lang":"en"},{"value":"Oceans and Fisheries, Institute for the","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider":[{"value":"DSpace","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeCampus":[{"value":"UBCV","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/creator":[{"value":"McMullen, Karly","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/issued":[{"value":"2023-05-16T18:52:58Z","type":"literal","lang":"en"},{"value":"2023","type":"literal","lang":"en"}],"http:\/\/vivoweb.org\/ontology\/core#relatedDegree":[{"value":"Master of Science - MSc","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeGrantor":[{"value":"University of British Columbia","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/description":[{"value":"Microplastic pollution now threatens some of the world's most iconic locations for biodiversity, including the Gal\u00e1pagos Islands, Ecuador. This thesis highlights the pervasive and harmful nature of plastic pollution in our oceans, which has significant ecotoxicological implications for marine ecosystems, biodiversity, and human health.\r\n\r\nUsing the Gal\u00e1pagos penguin as an indicator species, this study assessed the concentration of microplastics and other anthropogenic particles in surface seawater near Gal\u00e1pagos penguin colonies, penguin prey, zooplankton, and penguin scat, as well as microplastic bioaccumulation and biomagnification potentials in the Gal\u00e1pagos penguin using trophodynamic Ecopath with Ecosim (EwE) ecosystem modeling with the Ecotracer contaminant tracing routine. The study also investigated Ecuadorian mangrove communities' perceptions of plastic pollution's impact on the environment, economy, and human health, providing localized context for effective strategies to tackle this pervasive problem.\r\n\r\nEmpirical evidence collected in the Gal\u00e1pagos in October 2021 revealed an average of 0.54\u00b10.49 particles\/L in surface seawater (< 10 um). This study highlighted that the Gal\u00e1pagos penguin may consume 3,500 to 12,000 anthropogenic particles per day from fish, with 5 of 11 anchovies assessed having 0 to 3 particles per fish, and all 6 mullets and 1 milkfish assessed having 3 to 27 particles per fish (average 7.27). \r\n\r\nThe research concluded that microplastics can bioaccumulate in all predator-prey combinations. Biomagnification, however, was not observed in a scenario where all taxa eliminated 99% of microplastics ingested per day, indicating that the elimination or egestion rate of microplastics is a key factor in determining their biomagnification behaviour.\r\n\r\nSurveys conducted in Puerto Hondo (2019 & 2021) and Isla Santay (2021) found that individuals who lived and worked within the mangroves were more concerned about plastic pollution than those who worked in the city, suggesting \"out of sight, out of mind\" phenomena. The older generation was also more concerned than the younger generation, and in contrast to literature, males appeared to be more concerned. Both communities linked their well-being to their connections with nature, thus further advocating for locally grounded solid waste management policies to reduce marine plastic and uplift and empower the well-being of coastal mangrove communities in Ecuador.","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO":[{"value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/84664?expand=metadata","type":"literal","lang":"en"}],"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note":[{"value":" THE GAL\u00c1PAGOS PENGUIN AS THE \u201cCANARY IN THE COAL MINE\u201d FOR MICROPLASTICS RESEARCH IN THE GAL\u00c1PAGOS MARINE RESERVE & PLASTIC POLLUTION PERCEPTIONS IN ECUADORIAN MANGROVE COMMUNITIES by Karly McMullen M.Sc., The University of British Columbia, 2023 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Oceans and Fisheries) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 \u00a9 Karly McMullen, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The Gal\u00e1pagos Penguin as the \u201cCanary in the Coal Mine\u201d for Microplastics Research in the Gal\u00e1pagos Marine Reserve & Plastic Pollution Perceptions in Ecuadorian Mangrove Communities submitted by Karly McMullen in partial fulfilment of the requirements for the degree of Master of Science in Oceans and Fisheries Examining Committee: Dr. Evgeny Pakhomov, Professor, Institute for the Oceans and Fisheries & Department for the Earth, Ocean & Atmospheric Sciences, UBC Supervisor Dr. Juan Jos\u00e9 Alava, Research Associate, Institute for the Oceans and Fisheries, UBC Co-supervisor Dr. Michelle Tseng, Assistant Professor, Department of Zoology, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Brian Hunt, Associate Professor, Institute for the Oceans and Fisheries, UBC Supervisory Committee Member iii Abstract Microplastic pollution now threatens some of the world's most iconic locations for biodiversity, including the Gal\u00e1pagos Islands, Ecuador. This thesis highlights the pervasive and harmful nature of plastic pollution in our oceans, which has significant ecotoxicological implications for marine ecosystems, biodiversity, and human health. Using the Gal\u00e1pagos penguin as an indicator species, this study assessed the concentration of microplastics and other anthropogenic particles in surface seawater near Gal\u00e1pagos penguin colonies, penguin prey, zooplankton, and penguin scat, as well as microplastic bioaccumulation and biomagnification potentials in the Gal\u00e1pagos penguin using trophodynamic Ecopath with Ecosim (EwE) ecosystem modeling with the Ecotracer contaminant tracing routine. The study also investigated Ecuadorian mangrove communities' perceptions of plastic pollution's impact on the environment, economy, and human health, providing localized context for effective strategies to tackle this pervasive problem. Empirical evidence collected in the Gal\u00e1pagos in October 2021 revealed an average of 0.54\u00b10.49 particles\/L in surface seawater (< 10 \uf06dm). This study highlighted that the Gal\u00e1pagos penguin may consume 3,500 to 12,000 anthropogenic particles per day from fish, with 5 of 11 anchovies assessed having 0 to 3 particles per fish, and all 6 mullets and 1 milkfish assessed having 3 to 27 particles per fish (average 7.27). The research concluded that microplastics can bioaccumulate in all predator-prey combinations. Biomagnification, however, was not observed in a scenario where all taxa eliminated 99% of microplastics ingested per day, indicating that the elimination or egestion rate of microplastics is a key factor in determining their biomagnification behaviour. Surveys conducted in Puerto Hondo (2019 & 2021) and Isla Santay (2021) found that individuals who lived and worked within the mangroves were more concerned about plastic pollution than those who worked in the city, suggesting \"out of sight, out of mind\" phenomena. The older generation was also more concerned than the younger generation, and in contrast to literature, males appeared to be more concerned. Both communities linked their well-being to their connections with nature, thus further advocating for locally grounded solid waste management policies to reduce marine plastic and uplift and empower the well-being of coastal mangrove communities in Ecuador. iv Lay Summary Despite increasing public concern about ocean plastic pollution, plastic production and demand continue to rise. Microplastics, plastics smaller than 5 mm in size, are a relatively recent topic of scientific investigation. They can be found everywhere, from ocean depths to Arctic snow, but little is known about their impact on top predators like charismatic penguins. This study highlights the Gal\u00e1pagos penguin as an indicator species for microplastic pollution in a tropical seabird food web. Microplastics were collected from Gal\u00e1pagos seawater, fish that penguins eat, zooplankton, and penguin fecal matter. This information was used in an ecosystem model to assess how microplastics move through food webs, and whether they accumulate in top predators over time. Lastly, this study explored attitudes towards plastic pollution's impact on the environment, economy, and human health in Ecuadorian mangrove communities, specifically Isla Santay and Puerto Hondo, to provide local context. v Preface Chapter 2 is based on work conducted in the Gal\u00e1pagos National Park under the guidance and permission of Eduardo Espinoza and Harry Reyes, and with Dr. Juan Jos\u00e9 Alava as point of contact and lead. Franklin Gil of the National Park guided us when scouting for penguin scat on Isabela island. Dr. Paola Calle of the Toxicological Research and Environmental Health lab, ESPOL-Polytechnic School in Guayaquil, Ecuador, organized fieldwork permits. Co-supervisor, Dr. Juan Jos\u00e9 Alava coordinated air and maritime transportation and COVID-testing for fieldwork. Dr. Juan Jos\u00e9 Alava and I organized risk assessment documentation and approvals. Omar Alvarado-Cadena of the Toxicological Research and Environmental Health lab assisted in the field, as did Dr. Marynes Montiel Romero and Dr. Felix Morales. Omar collected water quality parameters reported in this chapter. Equipment was lent by Dr. Evgeny Pakhomov and Dr. Brian Hunt. I was responsible for planning the proposal, organizing equipment, preparing for travel, and leading the fieldwork design. Dr. Calle from ESPOL helped to obtain an official research permit (PC-76-21) to collect water, zooplankton, fish species, and penguin scat samples in the Gal\u00e1pagos, as well as permission to export them to Canada from the Gal\u00e1pagos National Park (Ministry of Environment and Water of Ecuador). Additionally, a sanitary guide license for the transportation of samples (0219-ABG-CT-2021) was provided to Dr. Juan Jos\u00e9 Alava by the Gal\u00e1pagos Biosafety and Quarantine Regulation and Control Agency. I organized animal care ethics (protocol A21-0196) with guidance from Dr. Evgeny Pakhomov and Dr. Juan Jos\u00e9 Alava, which was approved by the University of British Columbia Animal Care Committee for the collection of fish and penguin scat. Laboratory work was conducted in the Gal\u00e1pagos National Park lab, the Toxicological Research and Environmental Health lab at ESPOL-Polytechnic School, and at the Marine Zooplankton and Micronekton Laboratory and Pelagic Ecosystem lab in the Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia. At ESPOL, Dr. Juan Jos\u00e9 Alava, Dr. Paola Calle, Omar Alvarado-Cadena and Carlos Sanchez Monserrate assisted in the lab. Dr. Evgeny Pakhomov and Dr. Brian Hunt approved the construction of a cleanroom in their laboratory for the purposes of this study and Dr. Brian Hunt provided funding for a HEPA-filter and cleanroom materials. Sea-going Technician, Chris Payne, and I built the cleanroom where lab work was conducted. Dr. Pakhomov and Dr. Hunt lent equipment to be used in the lab. Raman-spectroscopy was conducted in the Grant Lab, in the Department of Chemistry at the University of British Columbia, under the guidance of Matt Kowal, PhD Candidate. Matt taught me how to use the microscope and laser and troubleshooted issues. I was responsible for the sample processing. R Studio support was provided by Ambre Soszynski and Izzy Morgante, of the Institute for Oceans and Fisheries, University of British Columbia. The map figure was first created by Ambre which I later adjusted slightly. Chapter 3 is based on work I conducted, that was inspired by previous ecosystem modelling work using Ecopath with Ecosim and Ecotracer to track the bioaccumulation behaviour of microplastics in global ocean foodwebs by Dr. Juan Jos\u00e9 Alava for the Nippon Foundation-ocean Litter Project, vi as well as Dr. Villy Christensen\u2019s class \u201cFISH 501 Ecopath with Ecosim modelling\u201d with TA Santiago de la Puente. Additionally, Dr. Diego Ruiz graciously provided the EwE model for the Bolivar Channel he created with Dr. Matthias Wolff. Dr. Ruiz assisted and answered model questions. Chapter 4 was led by Dr. Juan Jos\u00e9 Alava and I at the Ocean Pollution Research Unit (Institute for the Ocena and Fisheries, University of British Columbia), and has been submitted for publication. Surveys were created by the Nippon Foundation-Ocean Nexus Centre team. Surveys were translated by Dr. Juan Jos\u00e9 Alava and I. Madeline Calle helped to contact and provided information for survey work in Isla Santay. I was responsible for collecting the data from the surveys, analyzing results, and writing a report. Dr. Jessica Vandenberg and Dr. Yoshitaka Ota provided valuable contributions and insights. The 2019 survey was conducted by Dr. Juan Jos\u00e9 Alava and colleagues at ESPOL. The 2021 surveys were conducted by Dr. Juan Jos\u00e9 Alava, Dr. Paola Calle, Omar Alvarado-Cadena, Carlos S\u00e1nchez Monserrate, and I. Dr. Ana Tirape provided valuable insights and contributions to the text and figures as well. Roshni Mangar provided training on qualitative research software and radar charts were inspired by her work. Holly Amos provided a summary of the report for the Ocean Nexus Equity & Marine Plastic Pollution Report (2022) \u201cTowards an Equitable Approach to Marine Plastic Pollution.\u201d A gracious and special thanks to the willing participants of the survey in Puerto Hondo and Isla Santay, especially Geovanny Parrales who kindly assisted as the local naturalist guide for the 2021 surveys conducted at Isla Santay. vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ................................................................................................................................. iv Preface ............................................................................................................................................. v Table of Contents .......................................................................................................................... vii List of Tables .................................................................................................................................... x List of Figures ................................................................................................................................ xi List of Abbreviations ..................................................................................................................... xii Acknowledgements ....................................................................................................................... xiii Dedication .................................................................................................................................. xviii Chapter 1: Introduction and Literature Review ............................................................................. 1 1.1 The Plasticene: The Age of Plastics and Impacts on Ocean Wildlife ...................................................... 1 1.2 Microplastics: An Emerging Pollutant of Concern ................................................................................. 2 1.3 Lagging Legislation and Mitigation Efforts Despite Growing Awareness.............................................. 3 1.4 Gaps in Knowledge: Microplastics in Food Webs and Locally Grounded Plastic Perceptions ............. 5 1.5 Thesis Objective and Aims: Microplastics in the Gal\u00e1pagos Penguin and Coastal Community Perceptions .................................................................................................................................................... 6 1.6 Rationale .................................................................................................................................................. 7 1.7 Positionality Statement ............................................................................................................................ 9 Chapter 2: Ecotoxicological assessment of microplastics and anthropogenic particles in the Gal\u00e1pagos Islands and Gal\u00e1pagos penguin food web ................................................................. 10 2.1 Introduction ........................................................................................................................................... 10 2.2 Methods.................................................................................................................................................. 15 2.2.1 Permits ....................................................................................................................................................... 15 2.2.2 Study Area ................................................................................................................................................. 15 2.2.3 Sample collection and Storage .................................................................................................................. 19 2.2.4 Laboratory Processing: Sample Preparation.............................................................................................. 22 2.2.5 Laboratory Processing: Digestion ............................................................................................................. 24 2.2.6 Digestion Limitations ................................................................................................................................ 26 2.2.7 Microplastic identification ......................................................................................................................... 26 2.2.8 Chemical Composition and Size Detection Limits ................................................................................... 28 2.2.9 Quality assurance\/Quality control (QA\/QC) ............................................................................................. 28 2.2.10 Statistical analysis ................................................................................................................................... 30 2.3 Results .................................................................................................................................................... 31 2.3.1 Overall anthropogenic particle screening .................................................................................................. 31 2.3.2 Anthropogenic particles in surface seawater ............................................................................................. 39 2.3.3 Seawater quality parameters ...................................................................................................................... 42 2.3.4 Anthropogenic particles in zooplankton .................................................................................................... 48 2.3.5 Zooplankton density and composition ....................................................................................................... 51 2.3.6 Anthropogenic particles in fish ................................................................................................................. 53 viii 2.3.7 Fish total lengths, weight, and fullness ..................................................................................................... 59 2.3.8 Anthropogenic particles in penguin scat (guano) ...................................................................................... 59 2.3.9 Gal\u00e1pagos penguin\u2019s exposure to anthropogenic particles through diet ................................................... 60 2.4 Discussion .............................................................................................................................................. 65 2.4.1 Anthropogenic particles in surface seawater ............................................................................................. 65 2.4.2 Seawater quality ........................................................................................................................................ 67 2.4.3 Anthropogenic particles in zooplankton .................................................................................................... 67 2.4.4 Zooplankton community ........................................................................................................................... 68 2.4.5 Anthropogenic particles in fish ................................................................................................................. 69 2.4.6 Anthropogenic particles in Gal\u00e1pagos Penguin scat ................................................................................. 71 2.5 Conclusion ............................................................................................................................................. 72 Chapter 3: Modelling microplastic bioaccumulation and biomagnification in the Gal\u00e1pagos penguin ecosystem using Ecopath and Ecosim (EwE) models with Ecotracer ............................ 75 3.1 Introduction ........................................................................................................................................... 75 3.2 Methods.................................................................................................................................................. 80 3.2.1 Ecosystem Modelling Theory .................................................................................................................... 80 3.2.2 Model Descriptions ................................................................................................................................... 80 3.2.3 Modelling Bioaccumulation of Microplastics with EwE Ecotracer Routine ............................................ 85 3.2.4 Model Scenarios ........................................................................................................................................ 89 3.2.5 Bioaccumulation and biomagnification metrics ........................................................................................ 90 3.2.6 Sensitivity Assessment .............................................................................................................................. 92 3.2.7 Model Bias................................................................................................................................................. 92 3.3 Results .................................................................................................................................................... 93 3.3.1 Gal\u00e1pagos Penguin Food Web Model in Ecopath ..................................................................................... 93 3.3.2 Microplastic Bioaccumulation and Biomagnification via Ecotracer ......................................................... 96 3.3.3 Model Sensitivity..................................................................................................................................... 108 3.3.4 Model Bias............................................................................................................................................... 116 3.4 Discussion ............................................................................................................................................ 122 3.5 Conclusion ........................................................................................................................................... 125 Chapter 4: Marine litter and social inequities entangle Ecuadorian mangrove communities: Perceptions of plastic pollution and well-being concerns in Puerto Hondo and Isla Santay, Ecuador ....................................................................................................................................... 126 4.1 Introduction: Call to include coastal community input in marine litter management ........................ 128 4.2 Methods................................................................................................................................................ 130 4.2.1 Location justification and historical context ........................................................................................... 130 4.2.2 Study instrument: Surveys ....................................................................................................................... 134 4.2.3 Data treatment and analysis ..................................................................................................................... 136 4.2.4 Limitations ............................................................................................................................................... 136 4.3 Results and discussion ......................................................................................................................... 136 4.3.1 Preliminary survey insights: Puerto Hondo ............................................................................................. 137 4.3.2 Mangroves and well-being in Puerto Hondo and Santay Island: livelihood, community and seafood security ............................................................................................................................................................. 142 4.3.3 Plastic pollution, waste management, litter in mangroves, governing bodies and solutions .................. 149 4.3.4 Marine litter and mangroves .................................................................................................................... 153 4.3.5 Gender and age considerations ................................................................................................................ 155 4.3.6 Solutions .................................................................................................................................................. 160 4.4 Towards community-led bottom-up frameworks for plastic use and waste management ................... 164 ix 4.5 Conclusions.......................................................................................................................................... 166 4.6 Acknowledgements ............................................................................................................................... 167 Chapter 5: Conclusion - Transdisciplinary Microplastic Thesis for Equitable, Solutions-Oriented Research ...................................................................................................................................... 168 References ................................................................................................................................... 174 Appendix A ................................................................................................................................................. 213 Appendix B ................................................................................................................................................. 220 Appendix C................................................................................................................................................. 221 Appendix D ................................................................................................................................................ 223 x List of Tables Table 2.1: Anthropogenic Particles in Surface Seawater.............................................................. 39 Table 2.2: Water quality parameters by sampling location and island ......................................... 44 Table 2.3: Anthropogenic Particles in Zooplankton ..................................................................... 49 Table 2.4: Average body composition metrics (e.g., total length [TL], weight, fullness index (Hureau, 1970)) ............................................................................................................................. 55 Table 2.5: Microplastic Daily Intake estimation for the Gal\u00e1pagos Penguin from Prey (Fish) Items part of its diet in the Gal\u00e1pagos Islands .............................................................................. 64 Table 3.6: Diet composition matrix for the Gal\u00e1pagos penguin (GP) EwE model ...................... 83 Table 3.7: List of species and functional groups of the Gal\u00e1pagos penguin (GP) EwE model .... 83 Table 3.8: Sources used to determine parameters for basic Ecopath estimates. ........................... 84 Table 3.9: Ecotracer environmental parameters, input data, and respective sources used in the Gal\u00e1pagos Penguin (GP) EwE model ........................................................................................... 88 Table 3.10: Data values of microplastic retention times to calculate the egestion (elimination) rates with reference sources used for the Gal\u00e1pagos Penguin (GP) EwE model.......................... 89 Table 3.11: Modelling scenarios and respective Ecotracer input data for model kinetic parameters. .................................................................................................................................... 90 Table 3.12: Parameter output from the Gal\u00e1pagos penguin EwE food web model ...................... 95 Table 3.13: Linear regression data for the GP EwE model ........................................................ 100 Table 3.14: Bioaccumulation factor (BAF), bioconcentration factor (BCF), and predator-prey biomagnification factors (BMFTL) in the Gal\u00e1pagos penguin food web model. ........................ 101 Table 3.15: Linear regression data for the BCE ......................................................................... 106 Table 3.16: Bioaccumulation factor (BAF), bioconcentration factor (BCF), and predator-prey biomagnification factors (BMFTL) in the BCE web model. ....................................................... 107 Table 3.17: Bioaccumulation factor (BAF), bioconcentration factor (BCF), and predator-prey biomagnification factors (BMFTL) .............................................................................................. 109 Table 3.18: Apparent trophic magnification factors (TMF). ...................................................... 112 xi List of Figures Figure 2.1: (A) Sampling sites for sea surface water sampling and zooplankton......................... 18 Figure 2.2: Conceptual overview of methods ............................................................................... 20 Figure 2.3: Results breakdown (Microplastics, cellulose, unidentified particles) ........................ 33 Figure 2.4: Images of Recovered Microplastics in Marine Biota Samples. ................................. 36 Figure 2.5: Microplastic (A) and Fiber (B) Physical Characteristics. .......................................... 38 Figure 2.6: Map: Anthropogenic Particles in Surface Seawater ................................................... 40 Figure 2.7: Anthropogenic particles in Seawater and Inter-Island Differences............................ 41 Figure 2.8: Anthropogenic Particles Inter-Island and Category ................................................... 42 Figure 2.9: Water Quality Parameters Variability as shown by Principal Component Analysis . 47 Figure 2.10: Microplastics Sizes per Zooplankton Group ............................................................ 50 Figure 2.11: Zooplankton density and composition ..................................................................... 53 Figure 2.12: Anthropogenic Particles in Fish \u2013 Inter-Fish Species Differences........................... 56 Figure 2.13: Relationship of Fish Size and Number of Anthropogenic Particles ......................... 58 Figure 2.14: Anthropogenic Particles Per Gram (g) and Per Individual (#) by Group. ................ 62 Figure 3.15: Energy flow diagram of the Gal\u00e1pagos penguin food web model in EwE. ............. 94 Figure 3.16: Bioaccumulation and biomangification simulations using the EwE Ecotracer routine and GP Model. ............................................................................................................................ 100 Figure 3.17: Bioaccumulation and biomangification simulations using the EwE Ecotracer routine and BCE Model........................................................................................................................... 106 Figure 3.18: Linear regressions between predicted concentrations of microplastics (log-transformed data) and trophic levels ........................................................................................... 115 Figure 3.19: Assessment of the model bias as an average .......................................................... 117 Figure 3.20: Assessment of the model bias plotted over time .................................................... 122 Figure 4.21: Location of Puerto Hondo (2\u00b012'S, 80\u00b01'0W) and Isla Santay (2\u00ba13'S, 79\u00ba51'W). 134 Figure 4.22: Flow-chart diagram illustrating the survey methods .............................................. 135 Figure 4.23: Preliminary Survey Insights: Puerto Hondo in 2019. What is Marine Debris? Where does it accumulate? Who produces it? ........................................................................................ 139 Figure 4.24: Well-being parameters. .......................................................................................... 143 Figure 4.25: Basic needs self-assessment ................................................................................... 144 Figure 4.26: Self-reported seafood consumed ............................................................................ 147 Figure 4.27: \u201cPeri-urban\u201d geographic landscape ........................................................................ 149 Figure 4.28: Marine plastic repurposed and reused .................................................................... 150 Figure 4.29: Self-reported solid waste disposal .......................................................................... 151 Figure 4.30: Radar plots perception of stress caused by marine litter. ....................................... 155 Figure 4.31: Radar plots responses by gender. ........................................................................... 157 Figure 4.32: Regressions showing marine litter impacts on basic needs as a function of respondents\u2019 age.......................................................................................................................... 158 Figure 4.33: Images from Puerto Hondo. \u201cLitter blindness.\u201d ..................................................... 160 Figure 4.34: Suggested solutions ................................................................................................ 163 Figure 4.35: Conceptual framework highlighting the complexities involved in the decision-making process of using plastic items. ....................................................................................... 166 xii List of Abbreviations ABS \u2013 Acrylonitrile Butadiene Styrene ACR \u2013 Poly(methyl methacrylate) BAF \u2013 Bioaccumulation Factor BCE \u2013 Bolivar Channel Ecosystem EwE Model BCF \u2013 Bioconcentration Factor BMF \u2013 Biomagnification Factor CE \u2013 Cellulose EwE \u2013 Ecopath with Ecosim GI \u2013 Gastroinestinoal GMR \u2013 Gal\u00e1pagos Marine Reserve GP \u2013 Gal\u00e1pagos Penguin Food Web EwE Model IOF \u2013 Institute for Oceans and Fisheries MPs \u2013 Microplastics PA \u2013 Polyamide PC \u2013 Polycarbonate PE \u2013 Polyethylene PET \u2013 Polyethylene Terephthalate POPs \u2013 Persistent Organic Pollutants PP \u2013 Polypropylene PS \u2013 Polystyrene PVC \u2013 Polyvinyl Chloride TMF \u2013 Trophic Magnification Factor xiii Acknowledgements \u201cWhat is the most important thing in life? It is people\u2026 it is people\u2026 it is people.\u201d - Kevin Chang, Executive Director of Kua\u2019aina Ulu \u2018Auamo, IMPAC5 2023 (a paraphrased message from Kevin\u2019s friend) To the people of the Gal\u00e1pagos Islands, Puerto Hondo, and Santay Island, I express my most heartfelt gratitude for allowing me to visit your home to conduct research and expand my understanding of threats to the natural world. Without your gracious hospitality and support, this research would not have been possible. Thank you for entrusting me to tell your stories. I want to acknowledge the land where most of this work was completed, now called Vancouver. I\u2019ve been grateful and humbled to learn on the traditional, ancestral, and unceded territory of the x\u02b7m\u0259\u03b8k\u02b7\u0259y\u0313\u0259m (Musqueam), Stz'uminus, S\u2019\u00f3lh T\u00e9m\u00e9xw (St\u00f3:lo\u0304), Skwxw\u00fa7mesh (Squamish), and s\u0259l\u0313ilw\u0313\u0259t (Tsleil-Waututh) nations, who\u2019ve led in conservation and stewardship of this land since time immemorial. Thank you to the Nippon-Foundation-Ocean Litter Project, led by Director Dr. Yoshitaka Ota and Dr. Jessica Vandenberg, and the grant awarded and led by Dr. Juan Jos\u00e9 Alava at the Ocean Pollution Research Unit. This grant provided the funding to make this dream research project come true. I am grateful for the Ocean Litter Project team\u2019s support, guidance, and the framework and plans that inspired the social-psychological chapter. This chapter is a cornerstone piece of this thesis and a crucial reminder of the equity considerations of conducting research outside of one's home country. Thank you, Dr. Evgeny Pakhomov, for saying \"yes\" and giving me a chance in the world of academia. I am very grateful and will cherish the holiday dinners made extra special with year-in-review slideshows, your stories from expeditions, and the instrumental knowledge you shared with me. Never again will I spend hours agonizing over decapod larvae or euphausiids. Sampling and admiring the plankton world at Bamfield was a great way to end my term at IOF. Thank you, Dr. Juan Jos\u00e9 Alava, for choosing me as a master student to conduct marine plastic pollution research and contribute new knowledge to microplastic science in your home country, Ecuador and the Gal\u00e1pagos Islands. Thank you for your hard work in acquiring funding and for ensuring robost, quality science. You provided me with the invaluable opportunity to conduct this research, which is a crucial stepping-stone into the world of ocean conservation. Dr. Brian Hunt, you were a committee-member-extraordinaire. Your help has been integral to the completion of this thesis. I am grateful and truly in awe of the efforts you make to help your students (even when they aren't your direct students). When confronted with obstacles, which felt like often, you were there to provide advice, machinery, lab space, and anything else I needed. I am beyond grateful for your generous help. I would like to express my sincere gratitude to Dr. Hernan Vargas, an honourary committee member for this thesis. Your extensive knowledge of the Gal\u00e1pagos ecosystems and the Gal\u00e1pagos penguin is xiv invaluable to this research. I would also like to extend my appreciation to Dr. Diego Ruiz for granting access to the Bolivar Channel Ecosystem Model and for providing kind support. Lauren, Chris, and Lora, I likewise could not have done this thesis without you. I was constantly in a state of \u201cI need this\u201d, \u201cI need that\u201d and you kindly helped at every stage. There is an analogy of a duck swimming in water - calm on the surface, but with the legs ferociously kicking underwater to drive the motion forward. Your contributions to the research being carried out here is akin to those hard-working legs, propelling the projects forward. Your efforts and adaptability are so appreciated (I had a blast building the cleanroom), and I can't imagine what I would have done without your invaluable support. To my colleagues at ESPOL, Dr. Paola Calle, Dr. Ana Tirape, Omar, and Carlos, thank you for welcoming me into your lab and going above and beyond to make this research a reality. I thoroughly enjoyed working at ESPOL and hope we meet up again soon. Thank you, Eduardo Espinosa, for welcoming me to the Gal\u00e1pagos and for guiding and helping us in our pursuit to conduct research on these precious islands. The unforgettable boat trips were made possible by your expertise and passion for the incredible ecosystems. Sea lions, turtles, sharks - oh my! I will never forget walking past dozens of marine iguanas on our daily trips. Thank you to Harry Reyes for facilitating the expedition, as well. To Salome, thank you for your advice, words of wisdom, and for simply being one of the kindest, most thoughtful, and strong people I know. Your research has always been so inspiring, and it's an honour to know you. P.S. Thank you for the shapefile! To the many colleagues in the Gal\u00e1pagos who helped me along the way, including those at the Gal\u00e1pagos National Park and skippers, thank you for welcoming me to the incredible place you call home. I'd like to give a special thank you to Franklin Gil for your work in completing monthly penguin surveys and for guiding us during that portion of the expedition. Thank you Geovanny Parrales, of Isla Santay National Recreation Area, for touring us around your beautiful island. To Dr. Ed Grant and the Grant Research Group, thank you kindly for allowing me to work with your incredible team and use your laboratory and Raman microscope. Matt, it\u2019s been an honor to learn from you. Teresa, Hooman, and the whole lab, I so enjoyed my time with you all. Thank you for lending a helping hand when needed. To Natalie Mahara, you provided such incredible advice throughout my time at IOF. Thank you for guiding me and paving the way. Without you, this journey would have been so much more challenging. I will forever admire your knack for identifying zooplankton. To Izzy and Ambre, you are deep sea data heroes. Your ability to navigate R is unmatched and I believe I speak for a large group at IOF when I say, \"THANK YOU!\" The time and effort you put into helping others, even when you have a lot on your plate, is incredible, kind, and so appreciated. Thank you for helping me with the data analysis for my project. To Dr. Iria Garcia Lorenzo, thank you for your patience and help as I faced my most daunting task, preparing my first paper. You have incredible wisdom, knowledge, and expertise. You are an incredible xv teacher, and the words you offered me stayed with me throughout the writing of these 200 or so pages. Thank you. To Roshni, thank you for teaching me the latest and greatest in qualitative data visualization software and for our productive working sessions. Thank you for being so kind and supportive in every scenario. To Kristen, thank you for reminding us to look up from our computers and enjoy social activities, and, ironically, for all the work you did at your computer to make these social events possible. You are an angel in this department. To Dana for being a stellar lab-mate and sharing in all of the trials and tribulations of microplastics research with me. It has been amazing to bounce ideas off each other and your insights have been so helpful. It's been a wild ride and I'm grateful to have had you by my side. To the brunch squad, Julia, Polina, and Sarah, thank you for being wonderful people to be around and for reminding me that taking an hour break on the weekend is okay! You are all incredible scientists and make coming to work so fun. (Sarah, I\u2019m writing this up the same day you gave me the beaded penguin earrings and my heart feels so full.) Julia, Julia, Julia. Where do I start? These past years have challenged us, but I truly could not have done it without you. You\u2019ve been a sounding board, you\u2019ve fed me, you\u2019ve made me laugh, you\u2019ve indulged with me, you\u2019ve taught me about marine biology, you\u2019ve edited my work \u2013 the list goes on. Most importantly, you\u2019ve been an incredible friend. We\u2019ve made it so far and I am excited to see where the next chapter leads us! Dr. Colette Wabnitz, I cannot thank you for the number of \" beams\" you\u2019ve sent over the past two years! You are one of the most genuinely kind, caring, and overall amazing human beings I've had the pleasure of meeting. I am grateful to have learned under your guidance. Thank you for being such an important source of comfort and sunshine in IOF and thank you for always being there for students. To the entire IOF squad, Dr. Gabriel Reygondeau, Elsa, Taryn, Adam, Benia, Kate, Tess, Katie, Kat, Alejandro, Vianney, Mel, Julia, Jake, Patrick, Ema, Haley, Jessica, Dr. Andreas Novotny, Dr. Juliano Palacios Abrantes, and everyone at the office: thank you for the laughs, insights, student society meetings, summer beers, and day-to-day catchups at coffee time. These are the moments I will remember most. To my EOAS lab-mates in the Macrozooplankton and Micronekton lab, I\u2019ve learned so much from all of you and am grateful to have spent two years surrounded by such an inspiring crew. Lian, thank you for welcoming me to IOF and reminding me that a learning curve is normal and okay. Alexis and Brooke, you two have such a way of making me laugh, while also being there for emotional support. It's been such a journey and I am grateful to have worked alongside such bright people. Florian, thank you for reminding me to stay on top of things in the lab, and for your patience with me when it slips my mind. Yulia, Joanne, Yuliya, Spencer, and the whole squad, thank you for allowing me to learn from you all. I am grateful that our time overlapped. Thank you to Ashleigh and Jeremy at Ocean Diagnostics Inc. whom I've had the pleasure of working alongside, and who have been incredibly supportive and flexible as I complete my degree. xvi To the incredible people in my \u201clife outside of school,\u201d you have been my saving grace, my light, my reminder of things aside from microplastics and penguins. To my hometown hero, Dr. Nadia Laschuk, thank you for introducing me to this world and being so supportive every step of the way. I look up to you in so many ways and am so grateful for your friendship. To the inspiring, kind, loving women I had the pleasure of living with at Wilfrid Laurier University, Meg, Tor, Cam, Dal, Kelt, Nic: thank you for the unexpected calls, the brief but much-needed visits, and for always being there no matter the distance (whether it be Australia\/U.K., Alberta, Ontario, or B.C.). Being able to sit down and have a coffee or a drink after months apart, as if no time had passed at all, means the world to me. In this group, differences of opinion are embraced, conversations are supportive and encouraging, successes are celebrated, and there is no fear of judgement. I wish for a world with more women like you. To Caroline, Amelia, Iker, Rafe, Daria, Emily, and Santiago, you all have made such a difference in my life. You've always been there to remind me to let go and have fun and have been so patient with the ups and downs of this project. I am truly grateful to have each of you in my life. To the Landry\u2019s, thank you for encouraging my love for the ocean. Every visit and little note online, reminds me how lucky we are to have you all in our lives. To my partner, Jake you came into my life as I was wrapping up this thesis, and boy has it been a journey! Thank you for your patience, for cooking meals for me, for sitting through my working sessions, and for being okay with the fact that every date night involves a little splash of \"I need to work on my thesis.\" Thank you for being an amazing partner and for supporting me in so many ways, including the use of your computer and iPad for months on end. Last, but not least, to my sweet family\u2026 Thank you for your unwavering support and for always putting a smile on my face. Out of all the things to be blessed with in life, I am beyond grateful to be blessed with this family. Mom and Dad, thank you for always answering my calls and for listening to my sometimes-endless stories. I love coming home. Dad, thank you for the meals, the edits, and the advice. Mom, thank you, thank you, thank you for reading this thesis, even without me asking. The amount you do for others is so incredibly kind. Morgan, the inspiration for this master\u2019s came during our travels. You are my rock, my travel buddy, my friend, and soon we will both be armed with master\u2019s in environmental science, ready to save the world! To my brother, Kurtis, and my sister-in-law, Kaity, I think I\u2019ve called you both more in the last two years than I\u2019ve called anyone, ever! Thank you for being so supportive and for being two people I look up to and admire so much. Spending time at your house in the summer have been the highlights of my last two years. To my nephew Jackson, one day you may read this and when you do, please know that our video calls were the highlights of my day. When the stress was tough, photos of you splashing in waves, playing the piano, racing cars (but making sure we were ready first), learning to speak, have left my heart fuller than I could have ever imagined. To my grandmas and late grandpas: Thank you Grandma McMullen, for supporting me, for clipping out newspaper articles about marine scientists, for the conversations about grandpa\u2019s diving adventures and adventurous spirit, the emails, the laughs \u2013 I am so grateful. Grandma Reynolds, thank you for keeping me in your thoughts, for the phone calls and long-distance love, for sending me emails that make me laugh, and for always reminding me that home is the most important place - no matter how far away I am. To my late grandpas for instilling in me a sense of adventure and kindness. To my entire family, near and far, I love you. xvii I am overcome with gratitude as I reflect on the human beings who have brought monumental amounts of light into my life these last few years. I am reminded that no matter what field you are in or where you are in the world, kind people find kind people. The journey has been tough, but these people made it extraordinary. On a personal note: In 2019, while drifting in a canoe in a small mangrove riverine in Donsol, Philippines, I made the decision to apply for a master\u2019s in marine science at the University of British Columbia. It was a serene moment - fireflies lit up the mangrove trees around us and grew brighter at the sound of our canoe motor. My sister's foot dangled in the water, and without notice, bioluminescent plankton began to light up the trail of our path. Earlier that week, we were graced by whale sharks and saw fish leap out of the water and soar over the waves, confusing us as to whether they were terrestrial or aquatic in nature. The ocean had once again dazzled us with its magic. It was in that moment that I made the decision to use my passion for the arts to contribute to a more sustainable future for our oceans. This is just the beginning. With a Bachelor of Arts circa 2012, I knew it would be an uphill battle and it challenged me in ways I did not anticipate. I had the incredible honour to be invited to another country to do research. I experienced the Gal\u00e1pagos, a worldly treasure that I hope others can continue to enjoy for generations to come. I planned fieldwork, learned to model, explored data analytics and coding in R, completed qualitative data surveys, and more. If someone had told me, after completing my Bachelor of Arts, that I would be conducting my master's thesis research on microplastics in the Gal\u00e1pagos within five years, I would not have believed them. This piece of work is a testament to the fact that dreams can come true with hard work and heartfelt enthusiasm. So, without further ado, I present my thesis with the hope that we may foster a healthier and more equitable blue future, driven by our love for seawater. xviii Dedication To the people of Ecuador and the Gal\u00e1pagos, thank you for welcoming me to your home To my family, thank you for your immeasurable support 1 Chapter 1: Introduction and Literature Review \u201cEach year, at least 8 million tonnes of plastics leak into the ocean \u2013 which is equivalent to dumping the contents of one garbage truck into the ocean every minute.\u201d - \u201cThe New Plastics Economy: Rethinking the Future of Plastics,\u201d 2016 1.1 The Plasticene: The Age of Plastics and Impacts on Ocean Wildlife We now live in the \u201cAge of Plastics\u201d, an era recently defined as the \u201cPlasticene\u201d (Haram et al., 2020). Plastics reshaped the material world, transforming how we produce, consume, and dispose of goods. Production has increased dramatically, climbing from around two million tonnes produced globally in 1950 (Crawford & Quinn, 2017; Geyer et al., 2017; Gibb, 2019) to an estimated 367 million tonnes produced in 2020 (PlasticsEurope, 2019; PlasticsEurope, 2021). This lightweight, durable, and inexpensive material is completely ingrained in the many facets of our everyday life and show no significant signs of slowing down. Life without plastic is no longer fathomable, as is life without plastic pollution. With a lack of adequate recycling infrastructure and little progress on a circle economy for plastics, it is no surprise that 10% of plastics produced end up in global oceans (Cole et al., 2011). According to a recent report by the United Nations Environment Programme, 75 to 199 million tonnes of plastics have accumulated in the worlds\u2019 ocean (UNEP, 2021). Despite the undeniable benefits of plastics and the convenience they offer, their tangible anthropogenic footprint and various impacts on different spatial and temporal scales cannot be ignored. The threat of plastics to ocean wildlife first caught the academic eye in the 1960s when Kenyon & Kridler (1969) reported synthetic plastics in stomachs of Laysan Albatross chicks (Phoebastria immutabilis) (Ryan, 2015). The discovery was followed by continued reports of plastic ingestion by seabirds, including Leach\u2019s storm petrel (Oceanodroma leucorhoa) in eastern Canada (Rothstein, 1973) and prions (Pachyptila spp.) in New Zealand (Harper & Fowler, 1987). As research continued throughout the 1970s and 1980s, the perils of plastic pollution became increasingly apparent as evidence of its risks on marine life extended beyond seabirds; an increasing number of reports surfaced recounting turtle, cetacean, and pinniped entanglements or 2 plastic ingestion, often causing injuries or death (Ryan, 2015). Mass media and public attention surrounding the issue of plastic pollution catapulted following a viral video of a plastic straw in an Oliver Ridley Sea turtle (Robinson et al., 2015). The body of research on macroplastics (> 5 mm) continues to grow, with similar narratives of plastic entanglement and ingestion. 1.2 Microplastics: An Emerging Pollutant of Concern While the threat of macroplastic (i.e., large plastics > 5 mm in size) pollution was gaining traction in literature and media, a smaller problem was looming under the surface. Small synthetic fibers (Buchanan, 1971) and plastic pellets (Carpenter & Smith, 1972) began appearing among routine plankton and coastal water samples. Thompson et al. (2004) recognized the threat and coined the term \u2018microplastics\u2019 in 2004. These tiny plastic particles are officially defined as plastic debris smaller than 5 mm in size. They can be intentionally manufactured (primary microplastics) or degraded fragments of macroplastics (secondary microplastics) (Boucher & Billard, 2019). Primary microplastics from pharmaceuticals, abrasives, or pre-production plastic pellets can enter the waterways via wastewater, urban runoff, and drainage systems. Secondary microplastics, typically larger plastic items broken down, can likewise come from wastewater, but more commonly enter the environment through urban runoff and the breakdown of plastic items already in the environment (Browne et al., 2007; Cole et al., 2011). Microplastics have been discovered all over the world (Andrady, 2017), from oceans to estuaries (Lima et al., 2015; Zhao et al., 2015), to freshwater (Sanchez et al., 2014), to mountain glaciers and snow (Pastorino et al., 2021; Zhang et al., 2022) as well as remote Arctic ice (Kanhai et al., 2020; Lusher et al., 2015; Peeken et al., 2018). Transported by ocean currents, wind, and long-range atmospheric fallout (Allen et al., 2021; Allen et al., 2022; Dris et al., 2016), microplastics are highly bioavailable and are easily ingested by various marine animals due to their small size and pervasiveness (Andrady, 2017; Browne et al., 2007; Cole et al., 2011). Subsequently, microplastics have been discovered in a range of seabirds, sea turtles, marine mammals, fishes, and zooplankton (Caron et al., 2018). Appendix A presents a non-exhaustive list of plastic incidents documented in the literature. While not comprehensive, this table offers examples of the various types of plastic incidents that have been reported in the literature. 3 Ecotoxicological impacts of microplastics depend on factors such as particle size, shape, crystallinity, surface chemistry, and polymer and additive composition (Lambert et al., 2017). Studies have found that ingestion of microplastics by small and low trophic level species can result in internal lesions, digestive tract blockages, drowning, diminished predator avoidance, impaired feeding ability or falsified satiation, blockage of enzyme production, reduced growth rates, lowered steroid hormone levels, delayed ovulation and reproductive failure, and also absorption of toxic chemicals (in review Cole et al., 2011; Galgani et al., 2010; Ryan, 2015; Wright et al., 2013). However, it is important to note that some studies have found no significant effects of ingested microplastics on planktonic invertebrates, while others have shown that benthic invertebrates can reject microplastics before digestion, and fish may quickly expel them (Ryan et al., 2015). The widespread presence of microplastics in various aquatic environments and their potential harmful effects on marine organisms highlight the urgent need for more research investigating the various impacts of microplastics on marine species. Concurrently, mitigation strategies are needed to divert plastics from entering waterways, ultimately to protect the biodiversity and health of our oceans. 1.3 Lagging Legislation and Mitigation Efforts Despite Growing Awareness Despite increased attention in recent years on the pervasive and concerning nature of plastics across all societal levels, plastic production continues to soar, massively outpacing efforts to mitigate its environmental and socio-economic impacts (Andrady, 2022). In the legal sphere, plastic pollution was first identified as a marine issue in the 1990s (Laist, 1997) and by 2011, it had gained recognition as one of the three key emerging issues of the global environment by the United Nations Environment Programme (UNEP, 2011). While more recently some legislation has been put in place, such as bans on plastic bags in many countries (\u201cEver More Countries Are Banning Plastic Bags: But the Environmental Impact of Such Measures Is Questionable,\u201d 2019), Canada's ban on six single-use plastics (Environment and Climate Change Canada [ECCC], 2022; Canada\u2019s Canada\u2019s Ban on Certain Harmful Single-Use Plastics, 2022), and local bans on single-use plastics (e.g., plastic bags and polystyrene food containers) in protected areas like the Gal\u00e1pagos Islands (Alava et. al., 2022; Gal\u00e1pagos Government Council, 2021; UNESCO World 4 Heritage Centre, 2019), there is still a long way to go to reduce the influx of plastics into the environment. To address the problem of microplastic pollution, reducing the use of macroplastics like bags and single-use plastics is a key step that can prevent plastics from breaking down into secondary microplastics; however, this is just one solution to a multifaceted issue. Primary microplastics also pose a significant threat to our environment. While some countries, such as Canada, have already banned the use of microbeads in toiletries (Government of Canada, 2018), there are still concerns regarding other primary microplastics including industrial preproduction pellet (nurdle) spills and microfibers from clothing being released into the environment during washing. This highlights the need for further action and innovation to address the root sources of microplastic pollution. Efforts to mitigate the release of plastics and microplastics into the environment, although important, pale in comparison to the rising production rates and demand for plastics (Andrady, 2022; Arp et al., 2021; Bergmann, et al., 2017; Haram et al., 2020; Macleod et al., 2021; Persson et al., 2022; United Nations Environment Programme, 2021). Environmental impacts caused by plastic waste are numerous and are particularly concerning given plastics\u2019 track record of infiltrating food webs, including those that humans rely on. Consuming plastics can affect individual organisms and may alter organism behaviour and biogeochemical processes that are essential for the greater ecosystem's functioning (Galloway et al., 2017; Rochman et al., 2013). More than half of all known plastics contain chemical components classified as hazardous under the United Nations\u2019 Globally Harmonized System of Classification and Labelling of Chemicals (Rochman et al., 2013). In fact, Rochman et al. (2019) argue that microplastics should be considered a \u2018class of contaminants\u2019, similar to other organic pollutants and pesticides. More information and data are needed to prompt extensive action to reduce production of new harmful plastics, as well as to increase efforts to manage plastic waste and prevent it from entering the environment. The widespread impact of plastic pollution worsened during the COVID-19 pandemic. Although the pandemic brought about positive environmental impacts (Khan et al., 2021; Le et al., 2020; Rume & Islam, 2020; Zambrano-Monserrate et al., 2020), it also resulted in escalated demand for 5 single-use plastics due to a rapid implementation of public health measures and mandatory use of personal-protective equipment (PPE) (Alava et al., 2022; Chowdhury et al., 2021; Patr\u00edcio Silva et al., 2021; Shams et al., 2021; Yuan et al., 2021). PPE was frequently disposed of in the environment (Chowdhury et al., 2021) which led to interactions with marine fauna and coastal wildlife. This is well illustrated by the fatality of a Magellanic penguin (Spheniscus magellanicus), after ingesting a plastic facemask (Gallo Neto et al., 2021). While the world has increasingly relied on plastic during the pandemic, we are now at a crucial point to shift away from this material towards suitable and safe alternatives to mitigate its impact on the planet. 1.4 Gaps in Knowledge: Microplastics in Food Webs and Locally Grounded Plastic Perceptions Microplastics are making waves in political and ecological realms, but there are still critical gaps in our understanding of their impacts. Unlike macroplastics, microplastics have been studied for less than two decades, and there is still a need to determine ecotoxicological effects of marine species chronically exposed to microplastics in environmental concentrations (Everaert et al., 2018; Nelms et al., 2019). Likewise, there is a growing need to understand how microplastics affect apex predators and high trophic levels. Research on the effects of microplastics have thus far been limited to small and low trophic level species. While some species have been observed to selectively feed on microplastics or avoid them depending on food availability, the effects and bioaccumulation to higher trophic levels are not well understood (Botterell et al., 2021; Cole et al., 2013; Hays & Cormons, 1974; Nelms et al., 2018; Pan et al., 2021). We know microplastics are everywhere, in all aquatic environments, but uncertainty remains in how they shift and move through food webs and what biogeochemical processes are altered in the meantime. There is also the potential for microplastics to bioaccumulate in humans who consume seafood (Rochman et al., 2015). Modelling studies suggest that human beings may consume between 74,000 and 121,000 microplastics annually, with the consumption rate increasing with the amount of seafood consumed (Cox et al., 2019). More recently, Domenech and Marcos (2021) estimated the global per capita consumption of microplastics and nanoplastics via seafood in the order of 22.04 x 103 plastic particles per year. As of 2022, microplastics were recorded to have breached 6 the human placenta (Ragusa et al., 2021), have been found in human blood (Leslie et al., 2022), human breastmilk (Ragusa et al. 2022), liver (Horvatits et al., 2022), and lung tissues (Amato-Louren\u00e7o et al., 2021), prompting just concern for the impact of microplastics on human beings. Current literature on microplastics often lacks locally grounded information on the use and perception of plastics and their impact on the environment and community health. As the consequences of marine litter vary across different ecosystems and locations, it is critical to include human social-psychological components in environmental assessments of microplastic pollution. In conclusion, there are still significant gaps in terms of the impact of microplastics on ocean animals, especially top trophic level-species and potential for bioaccumulation. It is essential to incorporate locally grounded information and consider the wider context of human health and plastic use to fully comprehend the implications of microplastic pollution. 1.5 Thesis Objective and Aims: Microplastics in the Gal\u00e1pagos Penguin and Coastal Community Perceptions With the aforementioned context in mind, this research project had two objectives (1) to advance knowledge and science on microplastic pollution in the tropical waters surrounding Ecuador\u2019s Gal\u00e1pagos Islands, using the Gal\u00e1pagos penguin as a sentinel and indicator species for seabird exposure to microplastic pollution in the remote, UNESCO World Heritage site, the Gal\u00e1pagos Islands and (2) to locally ground these data by assessing marine plastic pollution perceptions in Ecuadorian Coastal Communities. To achieve these objectives, there were three aims. To advance knowledge of microplastic pollution in the Gal\u00e1pagos Marine Reserve (GMR) using the Gal\u00e1pagos penguin as the \u201ccanary in the coal mine\u201d, I aimed to (A) gather empirical data of microplastics in waters surrounding Gal\u00e1pagos penguin colonies, in the diet items of penguins, and in penguin fecal matter. Furthermore, I sought to (B) explore the movement, bioaccumulation and biomagnification potentials, of microplastics through the Gal\u00e1pagos penguin food web using trophodynamic ecosystem modelling. To provide a locally grounded perspective, I (C) utilized social-psychological methods and surveys to measure local perceptions and attitudes towards plastic pollution in nearby coastal communities in Ecuador. This approach empowers policy recommendations to consider the context of local attitudes towards plastic pollution. It is worth 7 noting that due to logistical limitations, it was not possible to survey Gal\u00e1pagos residents directly. Therefore, instead of surveying Gal\u00e1pagos residents, two coastal communities in continental Ecuador were surveyed. This decision was made based on established relationships between collaborators and communities in the chosen areas. The thesis is organized around the two main objectives, with each objective having its own set of aims and associated research questions: Objective 1: Investigating microplastic pollution in the Gal\u00e1pagos penguin food web and UNESCO World Heritage site. \u2022 Chapter 1: Research Questions o What are the concentration levels of microplastics and other anthropogenic particles in waters near Gal\u00e1pagos penguin colonies? o How prevalent are microplastics and other anthropogenic particles in Gal\u00e1pagos penguin prey (e.g., zooplankton, anchovies, and mullets)? o What level of microplastics and other anthropogenic particles are found in Gal\u00e1pagos Penguin scat? \u2022 Chapter 2: Research Question o To what magnitude can microplastics potentially bioaccumulate and\/or biomagnify in endemic Gal\u00e1pagos seabirds, specifically, the Gal\u00e1pagos Penguin (Spheniscus mendiculus) as a flagship species? Objective 2: Investigating plastic pollution perceptions in Ecuadorian Coastal Mangrove Communities: \u2022 Chapter 3: Research Question o How do Ecuadorian mangrove communities, Puerto Hondo and Santay Island, perceive plastic pollution\u2019s impact on the environment, economy and local human health? 1.6 Rationale In summary, the issue of microplastic pollution in our oceans is a significant problem that needs to be addressed urgently. Research on microplastic pollution in the Gal\u00e1pagos Islands and GMR is critical as we seek to preserve the biodiversity of our oceans. Microplastics have intruded food webs (from zooplankton to top predators) and all aquatic zones. There is evidence to suggest that microplastics pose significant threats to marine biodiversity, ecosystem functioning, and human 8 health. However, microplastic impacts on high tropic level predators and the extent of microplastic pollution in the Gal\u00e1pagos Islands is still relatively unknown. By using the Gal\u00e1pagos penguin as a sentinel and indicator species, this study aimed to fill this knowledge gap and provide insight into the concentration of microplastics in the food web of this remote ecosystem. Additionally, by exploring the attitudes of Ecuadorian coastal communities towards plastic pollution, this study aimed to ground the research in local perceptions and provide a more comprehensive understanding of the problem. The impact of marine litter is not felt equally across the globe, and the threat of microplastics is disproportionately felt by both human beings and ecosystems. The people living in oceanic, remote and continental coast areas, mainly ancestral, Indigenous and marginalized native communities, from developed and developing countries, have common and unique health issues in the face of pervasive ocean pollution by marine plastic and microplastics. The marine plastic pollution problem is exacerbated by lack of equity, well-being and environmental justice due to inequality gaps dismissing equitable interventions and fair access to basic resources (e.g., effective solid waste management, appropriate sanitation and hygiene levels, primary education and public health programs) in the most affected and exposed communities and overburden minorities groups (Bennet et al., 2022; Vandenberg & Ota, 2022). This thesis also seeks to highlight the direct and indirect implications of microplastics on oceanic food webs and how microplastics are disproportionately distributed about the globe. Ultimately, the last chapter (see Chapter 3; and McMullen, 2022) will highlight the in-person survey outcomes of two Ecuadorian coastal communities on marine plastic pollution to explore the human perceptions as to how the threat of microplastics is disproportionately felt, at the human level and ecosystem level (McMullen, 2022). Although it is not common practice, I would be remiss not to include the human-element in this thesis. Empirically based scientific theses can be complemented by locally grounded work to ensure that the findings are relevant and applicable to the communities where the research is conducted and should be considered going forward. This approach allows for a more comprehensive understanding of the problem and can lead to the development of more effective strategies to address the issue of microplastic pollution in our oceans. The results from these investigations provide critical information for the Gal\u00e1pagos National Park authorities, Ecuadorian 9 communities, policymakers of Ecuador\u2019s government, and conservation and environmental groups to develop effective strategies to mitigate plastic pollution in the Gal\u00e1pagos and beyond, thus contributing to the conservation of marine ecosystems and biodiversity. 1.7 Positionality Statement As a female, Canadian researcher, of European descent, I am humbled and honoured to complete microplastics research in stunning Ecuador. I acknowledge that my life experiences and personal biases may influence my interpretation of data. 10 Chapter 2: Ecotoxicological assessment of microplastics and anthropogenic particles in the Gal\u00e1pagos Islands and Gal\u00e1pagos penguin food web \"The Gal\u00e1pagos penguin is a symbol of resilience, defying all odds to exist in a place where few can survive.\" - Anonymous 2.1 Introduction Today the global ocean is threatened by several anthropogenic pollutants and multiple environmental stressors which jeopardize the wealth of marine biodiversity, alter biogeochemical processes, and impair the resilience of ocean and coastal ecosystems with negative consequences for public health and coastal seafood reliant communities (Bennett et al., 2023; Halpern et al., 2008, 2019; Landrigan et al., 2020; Persson et al., 2022). Ocean plastics in particular, have become eco-markers of the anthropogenic footprint, which have implications at a global scale for marine biodiversity and ocean ecosystems (Arp et al., 2021; Bergmann, et al., 2017; Haram et al., 2020; Macleod et al., 2021; United Nations Environment Programme, 2021). 11 An estimated 5 to 13 million metric tons of plastic waste entered the ocean from coastal-based sources in 2010 alone (Jambeck et al., 2015), which is likely only a fraction of the current annual input given the steady rise in plastic production (Jambeck et al., 2015; PlasticsEurope, 2019). Plastics are known to accumulate in coastal regions and remote areas, including the Great Pacific Garbage Patch (Lebreton et al., 2018), the North Atlantic subtropical gyre (Law et al., 2010), the deep sea (Kane et al., 2020; Mountford & Morales Maqueda, 2019), and seamounts in the South West Indian and South Atlantic oceans (Chiba et al., 2018; Pabortsava & Lampitt, 2020; Woodall et al., 2015). Despite their resilience to degradation, plastics can break down and form microplastics (< 5 mm) under certain environmental conditions. In addition, small plastic particles (pellets or nurdles) that are used in plastic production may persist in the oceans for decades (Boucher & Billard, 2019; Thompson et al., 2004). The Gal\u00e1pagos Islands, a UNESCO World Heritage site and marine reserve, are unfortunately threatened by the footprint of global pollution, despite the islands\u2019 relatively remote and protected status. Ecosystems in the Gal\u00e1pagos have battled anthropogenic inputs including persistent organic pollutants (POPs) such as polychlorinated biphenyls (PCBs) and organochlorine pesticides (Alava et al., 2009, 2012; Alava et al., 2011a, 2011b; Riascos-Flores et al., 2021), mercury (Alava & Ross, 2018; Maurice et al., 2021; Mu\u00f1oz-Abril et al., 2022), and marine anthropogenic debris, including ocean macro- and microplastics (Alava et al., 2014, 2022; Alfaro-N\u00fa\u00f1ez et al., 2021; Jones et al., 2021; van Sebille et al., 2019). At the regional level, local sources of chemical and biological pollution threaten water quality, endemic and native species, and fragile ecosystems of the Gal\u00e1pagos Marine Reserve (GMR) and Gal\u00e1pagos National Park (Alava et al., 2014, 2022; Alava & Ross, 2018; Mateus et al., 2019; Riascos-Flores et al., 2021). The effects of microplastics and other emerging pollutants on the Gal\u00e1pagos Islands and its unique marine species remain uncertain. These islands make up one of the last living laboratories and research frontiers to study evolutionary processes. Formed by volcanic activity, the Gal\u00e1pagos Islands are renowned for their unparalleled biodiversity, owed in part to the nutrient rich currents converging on the archipelago. While the Humboldt current brings cool waters and nutrients necessary for the survival of iconic animals like the Gal\u00e1pagos penguin (Spheniscus mendiculus), this current and others coming to 12 the islands can also transport microplastics from mainland South America and nearby fishing zones (Jones et al., 2021; Schofield et al., 2020; van Sebille et al., 2019). Tourism and, to a lesser extent, local activities likewise release microplastics and expose local marine species to microplastics (Alava et al., 2022; Jones et al., 2021). Microplastic concentrations ranging from 0.01 to 0.15 particles\/m3 (with a minimum size of 150 \u00b5m) have been estimated in surrounding international waters around the archipelago (Alfaro-N\u00fa\u00f1ez et al., 2021). Furthermore, plastic particle concentrations of 0.04 to 0.89 particles\/m3 have been detected in various locations across marine-coastal zones of San Crist\u00f3bal Island (Jones et al., 2021). Microplastics have been described as a \u201cserious threat to all marine life\u201d (UNEP, 2021), due to exposure risks and the likelihood and ease of ingestion (Andrady, 2017; Bergmann et al., 2015; Cole et al., 2011; L\u00f3pez-Mart\u00ednez et al., 2021). Ingestion or uptake of microplastics is often the result of feeding preferences (Bergmann et al., 2015; Everaert et al., 2020) and can lead to adverse lethal and sublethal effects depending on various physical characteristics such as surface texture, density, size, and shape, as well as chemical toxicity, including polymer composition and associated additives, dyes, or toxic chemicals (Athey et al., 2022; Hartmann et al., 2017; Thornton Hampton et al., 2022; Galloway et al., 2017; Kirstein et al., 2016; Oberbeckmann et al., 2015; Zettler et al., 2013). Despite growing evidence of adverse impacts at low trophic levels (see Section 1.2), little is known about microplastic trophic transport, bioaccumulation potential, and effects for high trophic level species (Germanov et al., 2018; Nelms et al., 2018; Provencher et al., 2019). Studies suggest microplastics are prone to bioaccumulate, but empirical evidence for bioaccumulation and biomagnification is lacking (Alava, 2020; Covernton et al., 2022; Hamilton et al., 2021; Miller et al., 2020). The Gal\u00e1pagos penguin, an endangered top predator in its ecosystem, has a small population of 1200 mature individuals distributed across the main breeding grounds located on Isabela and Fernandina islands, with some individuals also found on Floreana and Santiago islands, as well as several offshore islets (Boersma et al., 2013; BirdLife International, 2020; Vargas et al., 2005). The species, which belongs to the Spheniscidae family, is a unique and charismatic ocean stakeholder. Reaching around 50 cm (20 inches) in height, it is the smallest in the family, the second smallest penguin species globally, and the only penguin species living north of the equator 13 (Pearson & Beletsky, 2000). Due to its endemic nature, endangered status, and limited foraging range (Steinfurth et al., 2008; Wilson & Wilson, 1990), this penguin species is susceptible not only to the high intensity and increasing frequency of El Ni\u00f1o-Southern Oscillation (ENSO) events (Vargas et al., 2006, 2007), but also emerging marine pollution such as plastic and microplastics (Jones et al., 2021). Jones et al. (2021) assessed the risk of plastic pollution to Gal\u00e1pagos species using a systematic priority scoring method and found a high ingestion risk for the Gal\u00e1pagos penguin, with a score of 18; a score > 10 is considered highly threatened. In sum, the Gal\u00e1pagos penguin, with its unique and charismatic nature, serves as a valuable case study for microplastic pollution in the GMR. Seabirds have long served as indicator species for ocean health and specifically plastic pollution (see Appendix A), with the plastic-filled stomachs of Laysan Albatrosses garnering media and research attention as early as 1969 (Kenyon & Kridler, 1969). With a limited range and vulnerability to changing conditions, penguins make superb indicator species for localized pollution issues (Boersma, 2008). Boersma (2008) makes the case for penguins as sentinels of the marine environment, highlighting their role in revealing changes in ocean productivity and climate variation. No studies have thus far assessed the Gal\u00e1pagos penguin for plastic or microplastic ingestion; however, scat studies in Antarctic and sub-Antarctic species indicate microplastic ingestion is prevalent by penguins of the Southern Hemisphere, namely, ad\u00e9lie (Pygoscelis adeliae), chinstrap (Pygoscelis antarcticus), gentoo (Pygoscelis papua), and King Penguins (Aptenodytes patagonicus) (Bessa et al., 2019; Frag\u00e3o et al., 2021; le Guen et al., 2020). A notable exception includes the emperor penguin (Aptenodytes forsteri) as microplastic ingestion was not documented in this species (Leistenschneider et al., 2022). Preliminary and basic evidence indicates that tropical and subtropical seabirds of the southeastern Pacific are ingesting plastic (Spear et al., 1995). Based on this information as well as the prevalence of microplastics in the seawater surrounding the Gal\u00e1pagos (Alfaro-N\u00fa\u00f1ez et al., 2021; Jones et al., 2021), it is reasonable to hypothesize that the Gal\u00e1pagos penguin is ingesting microplastics through contaminated prey. The penguin\u2019s diet includes various planktivorous, omnivorous, and detritivorous fish (Karpouzi, 2005; Okey et al., 2004; Wilson & Wilson, 1990; Ruiz & Wolff, 2011; Vargas et al., 2006; E. Espinoza pers. Comm., Gal\u00e1pagos National Park, October 2021, 14 unpublished data) known to ingest microplastics (Wootton et al., 2021). Therefore, these resident, endemic tropical penguins are suitable sentinel species that can be considered as \u201cthe canary in the coal mine\u201d for assessing potential bioaccumulation and biomagnification of microplastics in the GMR ecosystems. Additionally, these flightless seabirds live primarily within mangrove zones (Moity et al., 2019), which have recently been highlighted in literature as traps for marine debris resulting in potentially higher microplastic exposures (De et al., 2022; John et al., 2022; Luo et al., 2021). Conserving endangered species, like the Gal\u00e1pagos penguin, relies heavily on understanding the impact of microplastic pollution in the GMR as well as comprehending the potential bioaccumulation of microplastic and identifying the associated risks. This study serves as a baseline assessment of microplastics and anthropogenic particles in coastal and oceanic waters of four main islands in the GMR, as well as in the Gal\u00e1pagos penguin\u2019s food web to assess zooplankton and penguin prey items (i.e., fish) as potential transport biovectors of microplastics to Gal\u00e1pagos seabirds. Research on marco- and microplastic pollution is lacking in the tropics, with only 14% of available literature on ingested plastics by marine species having been conducted in the tropics (Tekman et al., 2022). Whilst adhering to strict contamination control protocols in the field and laboratory, this study aimed to assess the microplastic presence, abundance, and exposure in (1) Gal\u00e1pagos surface seawater surrounding Santa Cruz, Santiago, Floreana, and Isabela islands, (2) zooplankton, (3) anchovies, (4) mullets, and (5) penguin scat. The results provide valuable insights into the extent of microplastic pollution in the coastal waters surrounding the Santa Cruz, Santiago, Floreana, and Isabela islands, and shed light on the potential of microplastics to bioaccumulate and biomagnify in the Gal\u00e1pagos ecosystem. Additionally, the study assessed the amount of microplastics egested by Gal\u00e1pagos penguins, as indicated by analysis of scat samples collected in-situ. This study aimed to contribute novel data to a limited body of literature on microplastic contamination in the vicinity of the Gal\u00e1pagos Islands. It was inspired to provide crucial data to support the development and implementation of effective plastic pollution management and mitigation strategies to preserve the delicate ecosystem of one of the world's most iconic and precious UNESCO sites. 15 2.2 Methods 2.2.1 Permits An official research permit (PC-76-21) for sampling of water, zooplankton, fish species, and penguin scat transportation to Ecuador and export to Canada were granted by the Gal\u00e1pagos National Park (Ministry of Environment and Water of Ecuador). Sanitary guide license for the mobilization of samples (0219-ABG-CT-2021) was provided by the Gal\u00e1pagos Biosafety and Quarantine Regulation and Control Agency. Animal care ethics (protocol A21-0196) was approved by the University of British Columbia Animal Care Committee for the collection of fish and penguin scat. 2.2.2 Study Area The Gal\u00e1pagos Islands are volcanic formations, comprising thirteen large islands and hundreds of small islands, islets, and other rock formations located just under 1,000 km off the northwestern tip of South America (Jackson, 1993). Though the sea floor drops to 2,000 to 3,000 m outside the archipelago, major islands including Santa Cruz, Isabela, Fernandina, Santiago, and San Crit\u00f3bal fall within a 200 m shelf (Jackson, 1993). Three major currents converge on the island, including the cold Humboldt current (Peru Oceanic current conjoined with the Peru Coastal current) from the Southeast Pacific, the warm Panama current from the northeast, and the Cromwell current bringing cold bottom flowing water from central equatorial Pacific to western Gal\u00e1pagos (Constant, 2007). Gal\u00e1pagos penguins reside among the molten rock (lava) and sporadic mangrove patches primarily on Isabela (68% of penguin population) and Fernandina (27% of population) islands, as well as, in smaller groups on Santiago and Bartolom\u00e9 (combined 4% of population) and Floreana (combined 1% of population) (Vargas et al., 2007). Thus, the spatial design for sampling was concentrated within Gal\u00e1pagos penguin colony habitats and the species local distribution along these islands from October 8 to 16, 2021. Seawater, zooplankton, fish samples, and penguin scat were collected near these islands. Sampling sites were categorized as a tourism sites (T), harbours 16 (H), and remote locations sites (R), based on if tourism and recreational activities were conducted there (e.g., beach going), if it was a main boat anchorage location for fishing vessels and tourism boats, or if it was a protected location with no public access, open ocean or a \u201cno take zone\u201d where access was restricted to field scientific research, respectively (see Figure 2.1 for sampling sites). Surface seawater (n=13) and zooplankton (n=14) samples were collected in the vicinity of four islands: Santa Cruz, Santiago and Bartolom\u00e9, Isabela, and Floreana. A higher number of samples were collected at Santa Cruz, one of the most populated islands by humans, to establish a baseline of microplastic in surrounding waters near populated islands. Western Isabela, where the largest population of penguins reside, was not sampled due to remote logistics and limited resources, namely, proximity, permit limitation, and appropriate vessel. Latitude, longitude, maximum depth, surface water temperature, pH, dissolved oxygen, salinity, nitrite, nitrate, phosphate, conductivity, salinity, and turbidity were taken in sea surface water at each sampling site except for NTS-007 and IZ-010 using a Hach HQ40d Portable pH, Conductivity, Dissolved Oxygen, ORP, and ISE Multi-Parameter Meter. 17 A) 18 Figure 2.1: (A) Sampling sites for sea surface water sampling and zooplankton horizontal net tows is coastal waters of four islands: Santa Cruz (n=6 sampling sites), Santiago (n=3), Isabela (n=2), and Floreana (n=2). (B) A closer view of sampling sites for Santa Cruz. Field codes for sampling stations are described throughout results and summarized in Table 2.2. An asterisk indicates variance in sample collection; namely, at NTS-007* only zooplankton were collected, at IP-011* Gal\u00e1pagos penguin scat was collected in addition to surface water and zooplankton, and at LN-005* only water was collected. B) 19 2.2.3 Sample collection and Storage Inflatable boats (e.g., dinghy or zodiac), a \u201cSea Ranger\u201d motorboat, or a small fishing vessel were used as platforms for the field sampling deployment to collect surface seawater and zooplankton. Methods were in part adapted from Mahara et al. (2022). See Figure 2.2 for a simplified method infographic. 20 Figure 2.2: A high-level conceptual overview of methods for field sampling and laboratory methods used to collect abiotic and biotic matrices to digest for detection and isolation of microplastics and anthropogenic particles from water, zooplankton, fish, and guano environmental samples. The filtration system is depicted by an Erlenmeyer flask with a vacuum filtration pump. Horizontal lines indicate the sequence of events in the order of top to bottom. 21 Surface Seawater - Surface seawater samples (< 0.3 m) were collected at 13 stations using a 5 L Niskin bottle manually placed into the water. After rinsing three times with seawater to remove any external contamination, the Niskin bottle was fully submerged into the water at the front of the vessel. The tether to cap the Niskin bottle was released while the bottle was fully submerged in the surface seawater. Water was stored in the Niskin bottle at room temperature (~22\u00b0C) until digested and filtered in the laboratory. Field controls were used during the seawater sampling and are explained in section 2.2.9. Zooplankton - Zooplankton samples were collected using a 3 m ring net with a mouth diameter of 0.5 m and a mesh size of 250 \u03bcm. A total of 13 tows were deployed at different stations: 12 were completed using a ring net deployed from the back of the vessel and towed horizontally for 5 minutes at 2 knots during daylight. At a single station, a vertical tow was carried out during nighttime (5:45 am). Contents were rinsed into the cod end and collected into a glass jar. A General Oceanics flowmeter was positioned in the mouth of the net to calculate volume filtered for community composition metrics. For sample preservation, 25 mL of 95% Ethanol or 37% Formaldehyde solutions were immediately added to each 50 mL sample jar and samples were kept at room temperature (~22\u00b0C) until processing. Field controls were used during the collection of zooplankton samples are explained in section 2.2.9. Fish samples - Limited diet data revealed the Gal\u00e1pagos penguin relies on a mix of small planktivorous fish including sardines (Sardinops sagax), piquitingas (Lile stolifera), and mullets (Mugil sp.) (Mills, 1997; Vargas et al., 2006; Wilson & Wilson, 1990) as well as the endemic fish, salema (Xenocys jessiae) (E. Espinoza pers. comm., Gal\u00e1pagos National Park, October 2021, unpublished data). Diet composition data from the closely related Humboldt Penguin (Spheniscus humboldti) reveal that the Gal\u00e1pagos penguin may well feed on available anchovies (Engraulis ringens), Araucanian herring (Strangomera bentincki), and silverside (Odontesthes regia), with mean lengths between 11 and 29 cm and mean masses 9 and 156 g, as well as, to a lesser extent, cephalopods (Loligo gahi and Dosidiscus gigas) and crustaceans (stomatopods and isopods, spp.) (Herling et al., 2005). Diet composition from relative Magellanic Penguin (Spheniscus magellanicus) demonstrated similar prey spectrum including anchovy (Engraulis anchoita), redfish (Sebastes oculatus), thornfish (Bovichtus argentinus), snailfish (Agonopsis chiloensis), 22 lobster (Munida gregaria), and squid (Fernandez et al., 2019). Based on these data, 11 anchovies (Anchoa naso), 6 mullets (Mugil galapagensis and Mugil cephalus), and 1 milkfish (Chanos chanos) were acquired from fishers in Puerto Ayora, Santa Cruz on October 15, 2021. They were kept in a -20\u00b0C freezer on Santa Cruz until they were transferred on ice to mainland Guayaquil on October 19, 2021, where they were kept in a -80\u00b0C freezer until processing. Penguin Guano - Fecal matter was collected from two Gal\u00e1pagos Penguins (S. mendiculus) near Puerto Villamil, Isabela (0\u00b0 57.9488' S, 90\u00b0 57.6933' W). Under the guidance of a local and experienced Gal\u00e1pagos National Park ranger (F. Gil), penguin guano samples were collected from scouted penguin resting areas with signs of localised defecation sites on the lava shores. Guano was collected from molten lava rock bed, when possible, beneath the penguin cloaca, using a metal tongue depressor. The fecal material was added to a glass jar and capped immediately. Jars were stored at room temperature (~22\u00b0C) until processing. A field control was used during the collection of scats. 2.2.4 Laboratory Processing: Sample Preparation All laboratory processing was done in a specialized cleanroom and laboratory controls were used to identify background contamination. Contamination controls are covered in section 2.5. Zooplankton isolation - Based on a rapid survey of zooplankton community compositions, five taxonomic groups of interest were selected for zooplankton digestions including Copepoda, Euphausiacea (Euphausiids), decapod larvae, Chaetognatha, and fish larvae (ichthyoplankton). Samples from each station were divided in half. The second half was later used for assessing overall community composition data. The half-sample was sieved through a 125 \uf06dm steel sieve, so that only organisms larger than 125 \uf06dm could be selected. This was done on the premise that plastics ingested by smaller organisms would not be detected based on microscopy and spectroscopy limitations. A Fisher Stereomaster\u2122 microscope (WF10x, 0.7-4.5x, Cat. No. 12-562-1) was used to select and inspect up to 100 individuals in each taxonomic group. All individuals were inspected for external microplastics and debris and were placed in glass vials respective to their taxonomic group and sample location. A random selection of 10 individuals 23 from each group were immediately weighed (wet weight) and measured for total length (TL). After being exposed to the air in the cleanroom for 1 to 2 minutes to allow the water to evaporate, each group were visibly dry and were then weighed again (dry weight). A more extensive weighing and measuring scheme is needed for a comprehensive community composition assessment. Zooplankton community composition - The second half-samples of zooplankton were used for community composition analysis. The sample was halved once more and diluted to 250 mL, without sieving. A 5 mL subsample was placed into a Bogorov Counting Chamber and all organisms present in the subsample were then identified by their taxonomic group and counted. This was repeated by incrementally adding 5 mL of the diluted subsample until a total of 250 mL or until the count exceeded 300 individuals, at which point it was deemed sufficient. The number of individuals per cubic meter was calculated using data from the General Oceanics flowmeter (2030 and 2031 Series Mechanical and Electronic Digital Flowmeter Operator Manual, 2018), based on a standard speed rotor constant of 26,873 as outlined below: \ud835\udc37\ud835\udc56\ud835\udc60\ud835\udc61\ud835\udc4e\ud835\udc5b\ud835\udc50\ud835\udc52 = (\ud835\udc36\ud835\udc5c\ud835\udc62\ud835\udc5b\ud835\udc61\ud835\udc53\ud835\udc56\ud835\udc5b\ud835\udc4e\ud835\udc59 \u2212 \ud835\udc36\ud835\udc5c\ud835\udc62\ud835\udc5b\ud835\udc61\ud835\udc56\ud835\udc5b\ud835\udc56\ud835\udc61\ud835\udc56\ud835\udc4e\ud835\udc59) \u2022 26,873999,999 \ud835\udc49\ud835\udc5c\ud835\udc59\ud835\udc62\ud835\udc5a\ud835\udc52 = 3.14159 \u2022 (\ud835\udc41\ud835\udc52\ud835\udc61\ud835\udc40\ud835\udc5c\ud835\udc62\ud835\udc61\u210e\ud835\udc45\ud835\udc4e\ud835\udc51\ud835\udc56\ud835\udc62\ud835\udc60)2 \u2022 \ud835\udc37\ud835\udc56\ud835\udc60\ud835\udc61\ud835\udc4e\ud835\udc5b\ud835\udc50\ud835\udc52 Distance in meters and volume in cubic meters was calculated to determine overall volume of water sampled. Density of individuals per cubic meter was calculated using standard operating procedures for microcrustacean densities (Standard Operating Procedure for Zooplankton Analysis LG403, 2016), i.e., \ud835\udc37 =\ud835\udc41(Pr \u2022 \ud835\udc49) Where density per cubic meter (D) is equal to total number of individuals counted in the subsample (N), divided by the proportion of the subsample to the total sample (Pr) multiplied by the volume filtered in cubic meter (V). 24 Fish dissection - All fish were weighed and measured for TL, before dissection (using a National DC4-456H Digital Stereo Microscope W10x\/20, 1-3x, S\/N S 219052107 for anchovies). Fish were washed to remove external microplastics and a scalpel was used to carefully create an incision from the anal region to the pelvic girdle. The gastrointestinal (GI) tracts, including intestines and stomach, were removed from the individual. The full and empty stomachs were weighed to determine food weight and fullness index (Hureau, 1970). The samples were then rinsed three times with deionized water and placed in separate clean glass jars covered with tinfoil or aluminum caps. The fullness index (Hureau, 1970) was determined by comparing the fish wet weight (g) to the stomach contents wet weight (g). It can be depicted as follows: \ud835\udc39\ud835\udc3c =\ud835\udc36\ud835\udc4a \u2022 100 Where the fullness index (FI) is a ratio of the stomach contents (C) to fish wet weight (W), multiplied by 100 to obtain the percentage. 2.2.5 Laboratory Processing: Digestion Due to variations in equipment availability (e.g., oven or water bath), sample treatment methods were adjusted to balance the available equipment and select the most appropriate techniques for each sample type. Surface seawater - Following field sampling, surface water was poured directly from the Niskin bottle through a 10 \u03bcm 47 mm diameter polycarbonate membrane filter (PCTE, Sterlitech), using a glass funnel and Erlenmeyer flask with a vacuum GAST filtration pump (DOA-P704-AA) to avoid external background contamination by microfibers fallout in the Gal\u00e1pagos National Park lab. The filter was added to a glass jar with 30 mL of a pre-filtered 10% potassium hydroxide (KOH) (21.826 g\/mol) solution (referred to hereafter as 10% KOH), in which the filter was fully 25 submerged. The jar was covered with tinfoil and placed in an oven at 50\u00b0C for 3 days, after which the solution was re-filtered, and the filter was placed in a clean petri-dish. Zooplankton - Glass vials (10- or 20-mL) containing the pre-selected individuals separated by the sample site and the taxonomic group were treated with 5 mL of 10% KOH. The vial was capped, and the jar was left at room temperature (~21\u00b0C) for 6 to 8 weeks, until most of biota was digested. The solution was filtered through a 10 \u03bcm 47 mm polycarbonate filter using a vacuum filtration pump and was placed into a clean Petri dish. Fish GI tracts - All fish GI tracts were digested with 30 to 50 mL of pre-filtered 10% KOH at a 3:1, 10% KOH to sample, ratio. After capping the jar, samples were agitated for 1 minute using a centrifuge (Reax top 5 mm shaking orbit at 1,000 rpm) to aid in the breakdown of biological material. Samples were placed in a 60\u00b0C water bath for 3 weeks. The digested anchovy and milkfish GI samples were filtered through a glass funnel over a 10 \u03bcm 47 mm polycarbonate filter using a vacuum filtration pump. The detritivorous mullets\u2019 GI tract contained substantial sediment; therefore, an additional digestion with 25% pre-filtered hydrogen peroxide (H202) (22.0145 g\/mol) was performed after the first digestion and placed in a 60\u00b0C water bath for an additional 1 week. Directly before filtering, a density separation was performed by adding 30 mL of a pre-filtered sodium chloride (NaCl) to mullet samples and samples were agitated for 1 minute using a centrifuge (Reax top 5 mm shaking orbit at 1,000 rpm). After density separation, the supernatant was filtered through a glass funnel and any remaining sediment was visually inspected for suspected microplastics and other anthropogenic particles. This density separation technique was applied to increase the microplastic recovery rate. All fish GI tract samples underwent additional digestion due to a large amount of biological material remaining on the filters. Filters were added to a glass jar and 30 mL of pre-filtered 30% hydrogen peroxide (H202) (22.81412 g\/mol) was added to submerge the filters fully. Glass jars were covered and placed in a 50\u00b0C oven for 3 days. The solution was filtered through a 10 \u03bcm 47 mm polycarbonate filter using a vacuum filtration pump after incubation. Remaining filters were rinsed three times with Milli-Q over the filtration glass funnel and microscopically (Lusher et al., 26 2017) inspected for microplastics and other anthropogenic particles. The new filter was collected and placed into a clean Petri dish. Penguin guano \u2013 For fecal matter digestion, 30 mL of pre-filtered 10% KOH was added to jars containing penguin guano. Samples were agitated for 1 minute using a Reax top 5 mm shaking orbit at 1,000 rpm and placed in a 60\u00b0C water bath for 4 weeks. A density separation was conducted using 30 mL of pre-filtered NaCl and samples were agitated again for 1 minute to homogenize the solution and sample. After density separation, the supernatant was filtered through a 10 \u03bcm 47 mm polycarbonate filter using a vacuum filtration pump and any remaining material was visually inspected for microplastics and other anthropogenic particles. 2.2.6 Digestion Limitations It must be noted here that fixation and digestion solutions may have impacted polymers and, therefore, results are likely conservative. Note that Nylon 6,6 (PA, Polyamide), polycarbonate (PC), polyethylene terephthalate (PET, polyester), polypropylene (PP), and polystyrene (PS) are only partially resistant to 95% ethanol (Lusher et al., 2017; see supplementary information). Likewise, PA and PS are only partially resistant to 10% formaldehyde (Lusher et al., 2017). In this study, a solution of just under 20% formaldehyde was used to preserve zooplankton samples. In terms of chemical digestions used in this study, PC and PET are not resistant to 10% KOH and PA and PET are not resistant to 30% H202. Nevertheless, these digestion techniques are the least destructive and most widely used in fish and invertebrate digestion studies for microplastic ingestion (Lusher et al., 2017). 10% KOH at 60\u00b0C overnight is the recommended approach (Lusher et al., 2017); therefore, this method was prioritized and adapted when biological material persisted. More research is needed to determine efficient and appropriate digestion techniques. 2.2.7 Microplastic identification Identification of suspected microplastics - Suspected microplastics were identified on filters using a National DC4-456H Digital Stereo Microscope (W10x\/20, 1-3x, S\/N S 219052107) operating with Motic Images Plus 2.0. Colour and morphology (e.g., fiber, fragment, foam) were described 27 (following Andrady, 201; Provencher et al., 2020; Rochman et al., 2019). Each suspected microplastic was photographed and measured (i.e., length and width) using Motic Images Plus 3.0.19.98b (\u00a9 Motic China Group Co, Ltd 2015). Shape, size, colour, and morphology were reported as well as size minimums and detection limits (following Provencher et al., 2020). Chemical identification - Suspected microplastics were analyzed using \u03bc-Raman spectroscopy fitted on a microscope at the Grant Research Group, Department of Chemistry, University of British Columbia laboratory. Forceps were used to isolate suspected microplastics from the polycarbonate membrane filter and transfer it to a clean aluminum disk which was placed under the microscope. A 785 nm laser was focused onto the suspected microplastic using a BX-51 Olympus microscope fitted with a Reichert Plan Achro 40\u00d7\/0.66 NA Infinity Objective. Raman-scattered light entered a spectrometer (Princeton Instruments Acton SP2300) with 600 grooves mm\u22121 grating with 1 cm\u22121 spectral resolution. For the polymer identification of each suspected microplastic, 3 to 5 spectra were used. Spectra was collected in the range 400-2000 cm\u22121 and results were run through a purpose-built Raman library created by M. Kowal and written in Python, based on the dot product (M. Kowal). It included 278 unique spectra from 28 different materials (see Mahara et al., 2022). Each sample spectrum was compared to reference spectra and assigned a \u2018match\u2019 that was accepted when certain thresholds were met, as reported in Mahara et al. (2022). For spectra with low noise to peak ratios or low confidence matches, spectra were also uploaded to OpenSpecy\u2019s library (https:\/\/openanalysis.org\/openspecy\/ by Cowger et al., 2021) and analyzed. If confidence was still low, spectra was visually assessed with Raman experts (M. Kowal) and, in most cases, removed from results. Lastly, in addition to identified plastic polymers, cellulose was included in results alongside confirmed plastics, based on recommendations and logic that naturally derived fibers are also heavily modified (Hartmann et al., 2019) and on the assumption that non-modified natural cellulose particles should digest in chemical treatments. 28 2.2.8 Chemical Composition and Size Detection Limits Chemical composition data is critical in the field of microplastics research, however, there are certain limitations that must be considered. Namely, samples emitting high fluorescence can overwhelm Raman spectra and make it difficult to identify particle chemical composition (Araujo et al., 2018; Lenz et al., 2015; Xu et al., 2019). Fluorescence can cause a very strong background signal that is more intense than the peaks that you would expect to find in the plastics of interest. The location and intensity of the Raman peaks from the plastic are necessary to identity the composition of the material, and if they are obscured, then there is no way to determine the type of material. Sources of fluorescence are common and can be plastic additives, dyes, or degradation during digestion in the organism's GI tract or treatments with chemical digestion (Dong et al., 2020; Lenz et al., 2015). I took a critical approach and only accepted high quality Raman spectrum matches, thus assumptions were minimized to increase the confidence in the material determination. Secondly, it is necessary to establish and report lower size thresholds when reporting microplastics, given that certain size categories of microplastics may not be accounted for depending on collection techniques (Provencher et al., 2020). There are limitations in detecting particularly small particles through Raman spectroscopy. As the particle size decreases, the amount of Raman scattered light decreases proportionally. To compensate for this, a higher magnification lens can be used, but it makes it more challenging to locate the material. Longer exposure times and higher laser power can also be utilized, but these techniques may damage the sample if not carefully controlled. Therefore, it is crucial to strike a balance between signal strength and potential sample damage. These limitations are determined by the capabilities of the system being used. Due to microscopy and spectroscopy limitations in this study, the minimum detection length of microplastics in this study was 19.7 \u00b5m and the smallest width detected was 10.9 \u00b5m. 2.2.9 Quality assurance\/Quality control (QA\/QC) Sample collection QA\/QC - Field blanks were present during each seawater and zooplankton sample collection and microplastics found in blanks matching those observed in samples (i.e., by 29 polymer type and colour) were excluded from results as a blank-correction method. Field blanks, which included a clean open glass jar filled 50% with deionized water, were capped after collection period and were filtered in the laboratory. Clean instruments were treated prior to sampling with three washes of deionized water and three washes of ethanol. Instruments were also rinsed three times with seawater between samples. When possible, cotton clothing was used in the field and water samples were collected from the opposite direction of the surface water current to avoid boat contamination, following best practices for microplastics research, as documented in Provencher et al. (2020). Laboratory analysis QA\/QC - Most sample processing was conducted in an isolated cleanroom, fit with a Mac 10\u00ae IQ Fan Filter unit High Efficiency Particulate Air (HEPA) Filter (99.99% @ 0.3 micron (H13)), at the Marine Zooplankton and Micronekton Laboratory, Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia. The exceptions include fish dissections and digestions, which took place in a small, isolated room with restricted access and airways blocked off with tinfoil at the Toxicological Research and Environmental Health lab, ESPOL-Polytechnic School in Guayaquil, Ecuador. Rigorous protocols were in place to minimize contamination, including laboratory blanks present anytime a sample was open. Laboratory blanks consisted of a clean glass Petri-dish filled to 50% with deionized or Milli-Q water and left open during all sample processing in the laboratory. Blank controls were later filtered in the same manner as true samples. Microplastics found in blanks that corresponded, in polymer type and colour with those in samples, were excluded from results (n=10). An additional 6 microplastics were also removed because they were identified as PC, which was used in the filtering process. A pink cotton lab coat or a yellow Tyvex suit was worn during processing. Workspace walls and lab benches were cleaned three times with Milli-Q or deionized water and three times with ethanol at the start of each session and equipment was cleaned three times with Milli-Q or deionized water and three times with ethanol prior to use and between samples. While filtering seawater, a clean sheet of tinfoil was used to cover the top of the glass funnel to create another layer of protection against airborne contamination. Raman spectroscopy QA\/QC - Spectra that returned low confidence library matches or low signal to noise ratios were visually assessed. Researchers took a precautionary approach and excluded 30 matches missing prominent polymer peaks. Thus, some microplastics polymer composition results are likely underestimated. 2.2.10 Statistical analysis Data treatment and statistical analyses were conducted using \u00a9 2009-2022 RStudio, PBC version 2022.07.2 Build 576. All datasets were tested for normality using the Shapiro-Wilks test. To evaluate the potential influence of surrounding water quality conditions on anthropogenic particle concentrations (particles\/L) at each site, as well as to assess if these conditions influenced each other, a Principal Component Analysis (PCA) was conducted. One-way Analysis of Variance (ANOVA) were performed on normally distributed and homoscedastic datasets confirmed via Barlett tests to assess the relationship between subgroups of data (e.g., site category including tourism, remote, or harbour) and the differences among their means in anthropogenic particle concentrations. In one instance, data normality and homogeneity of variance were not met. Despite this, the one-way ANOVA was used to assess the relationship between anthropogenic particle abundance and site category (i.e., tourist, harbour, or remote) as has been done previously in the field (Mahara et al., 2022), given these limitations. Likewise, it should be noted that the open ocean site between Santa Cruz and Isabela was excluded from the one-way ANOVA comparison of anthropogenic particle abundance between islands, as a one-way ANOVA requires at least two observations per category. The distribution and comparisons of categorical (nominal) variables, lacking a normal distribution, were analyzed by applying Chi-square tests of independence (\u03c72), as a non-parametric approach, to assess relationships between categorical data (e.g., particle shape, colour, and polymer type by island, zooplanktonic group, and fish species). A significance level (\u03b1) of 0.05 was used. For comparison purposes, anthropogenic particle were converted into grams based on Alava (2020) and Everaert et al. (2018). Unit conversion factors were gathered from Lusher (2015). 31 2.3 Results 2.3.1 Overall anthropogenic particle screening The results of this study showed the widespread presence of suspected microplastics and particles derived from natural polymers in the seawater, zooplankton, anchovies, mullets, and milkfish. Across all water and biotic samples analyzed in this study, 334 suspected microplastics were isolated, of which 95 and 29 were identified as plastics and cellulose, respectively (Figure 2.3 A). Overall, an average of 41% of suspected microplastics were successfully identified by polymer type (Figure 2.3 B). As outlined in Section 2.2.8 and 2.2.6, Raman spectra interference due to florescence, dyes, additives, and degradation, as well as size detection limits, led to a probable underestimation of plastics, which is common in microplastic research (Thiele et al., 2021). In seawater, 96 suspected microplastics were found, with 25% of these particles identified as plastics (n=24), 8% as cellulose (n=8), 5% removed due to controls (n=5), and 61% remained unidentified (n=59), as shown in Figure 2.3. A similar trend was observed in zooplankton, where 13% of the 77 suspected microplastics were identified as plastics (n=10), 5% as cellulose (n=4), 8% were removed due to controls (n=6), and 74% remained unidentified (n=57) (Figure 2.3; Figure 2.4 A). Of note, one PP fragment was found in an airborne field control. It was the only PP found in controls and have a very distinct colouring (blue with red dots). Since no other PP fragments like this were recovered from samples, PP were included in the results (see Appendix B). In anchovies, 10% of the 29 suspected microplastics were identified as plastics (n=3), 17% as cellulose (n=5), 3% removed due to controls (n=1), and 69% remained unidentified (n=20) (Figure 2.3; Figure 2.4 B). In mullets, 27% of the 116 suspected microplastics were identified as plastics (n=31), 10% as cellulose (n=12), 3% removed due to controls (n=4), and 59% remained unidentified (n=69) (Figure 2.3; Figure 2.4 C). 32 During the examination of fish caught by fishers around Santa Cruz Island waters, an unexpected milkfish (C. chanos) was found among the similarly sized mullets. The results were particularly striking for the milkfish, where 84% of the 32 suspected microplastics were identified as plastics (n=27) and 16% remained unidentified (n=5). The milkfish exhibited a remarkable abundance and diverse array of colours in terms of suspected microplastics, along with the presence of two compactly coiled fiber bundles (Figure 2.3; Figure 2.4 D; Figure 2.4 E; Figure 2.4 F). See Figure 2.5 for the (A) microplastics and (B) particles derived from natural polymers broken down by polymer type, colour, morphology, and size, all of which are expanded upon in sections to follow. 33 A) B) Figure 2.3: Microplastics and Unidentified Particles in Surface Seawater and Biotic Sample Groups. Composition of detected particles (A: total number; B: proportion) separated by sample group. The breakdown includes particles confirmed as plastics (black) or cellulose (dark grey), suspected particles removed due to microplastics identified in controls and blanks or due to positive PC identification (see text in subsection 2.5.2) (red), and, lastly, those that were unidentified (light grey). Data labels show total particle number in each category. \u201cN\u201d refers to the sample number in each compartment group, the units of which differ between groups. Sample size for surface seawater is measured in particles \/ liters; zooplankton and fish in particles \/ individual; 34 and penguin guano sample size is measured in grams. Penguin scat was collected from two individuals totaling 3.40 grams of scat. 35 A) B) C) D) 36 E) F) Figure 2.4: Images of Recovered Microplastics in Marine Biota Samples. Examples of microplastics that were confirmed by polymer-type and their corresponding Raman spectra are presented, obtained from particles discovered in (A) zooplankton, (B) anchovies, (C) mullets, and (D) milkfish. Additionally, images of fiber bundles from the milkfish are presented (E), with the top photo depicting the bundle prior to unraveling and the bottom photo showing the two bundles side by side. These photos offer a true representation of the specimens used for analysis, including intact gastrointestinal tracts and stomach contents where applicable, and other images to help visualize methods. PS=polystyrene; PET= polyethylene terephthalate; PE=polyethylene; PP=polyprolyene; CE=cellulose. 37 A) Microplastics Polymer Colour Morphology Size B) Particles Derived from Natural Polymers Polymer Colour Morphology Size 38 Figure 2.5: Microplastic (A) and Natural Anthropogenic Fiber (B) Physical Characteristics and Polymer Composition in Surface Seawater and Biotic Sample Groups. Charts are titled according to the characteristic they describe. The first panel includes the polymer breakdown of particles where pe=polyethylene; acr=Poly(methyl methacrylate); pa=polyamide; pvc=polyvinyl chloride; pp=polypropylene; pet=polyethylene terephthalate; ps=polystyrene; abs=acrylonitrile butadiene styrene. Particles derived from natural polymers were all identified as cellulose. The second panels shows the colour breakdown of particles. The third panel shows to morphological breakdown of particles. The fourth panel illustrates the average size (calculated by longest side \u00b5m) in a box plot where horizontal bars indicate median, X indicates mean particle\/L, and the upper and lower edges of the box denote the approximate 1st and 3rd quartiles, respectively. The vertical error bars extend to the lowest and highest data value inside a range of 1.5 times the inter-quartile range, respectively. Points outside the box indicate extreme values. 39 2.3.2 Anthropogenic particles in surface seawater Anthropogenic particles were found in all but two surface seawater samples, with an average of 0.54\u00b10.49 particles\/L or 4.11x10-6\u00b13.72x10-6 g\/L (Table 2.1; Figure 2.6). Plastic polymers were identified primarily as PP and PET, the majority of which were fibers (Figure 2.5). Cellulose fibers were also present. Colours varied but were primarily blue and most sizes fell between 10 \uf06dm to 1000 \uf06dm (Figure 2.5). The sites with the highest concentrations of anthropogenic particles were near Santa Cruz, with the highest being 1.6 particles\/L or 1.26x10-5 g\/L. No anthropogenic particles were found in samples in Las Ninfas (LN-005) on Santa Cruz as well as near Isabela, adjacent to a known penguin colony (IP-011) (Figure 2.6). Table 2.1: Anthropogenic Particles in Surface Seawater. Category is defined as tourism location (T), harbour (H), or remote (R). Stations with an asterisk were only able to be sampled for either water or zooplankton due to logistical issues. P\/L=Particle per liter; MP\/L=microplastics per liter; CE\/L=cellulose per liter. Station Island Category P\/L MP\/L CE\/L TB-001 Santa Cruz T 1.7 1.00 0.67 GR-002 Santa Cruz R 0.8 0.80 0 GR-003 Santa Cruz R 0.8 0.60 0.20 AB-004 Santa Cruz H 1.2 0.80 0.40 LN-005* Santa Cruz T 0 0 0 FB-006 Santa Cruz T 0.2 0.20 0 NTS-007* Santiago R NA NA NA S-P-008 Santiago R 0.8 0.40 0.40 S-BB-009 Santiago T 0.4 0.20 0.20 IZ-010 Open Ocean R 0.4 0.40 0 IP-011 Isabela H 0 0 0 SI-012 Isabela T 0.4 0.40 0 FL1-013 Floreana T 0.2 0.20 0 FL2-014 Floreana R 0.2 0.20 0 Average 0.54 0.40 0.14 Standard Deviation 0.49 0.32 0.22 40 Figure 2.6: Anthropogenic Particles in Surface Seawater from Santa Cruz and Gal\u00e1pagos Penguin habitat waters at Isabela, Santiago, and Floreana. Site particle concentrations (P\/L=particle\/liter) in the Gal\u00e1pagos Islands. The second panel shows a closer version of Santa Cruz for better resolution of sampling sites. Site codes are plotted adjacent to the colour-coded results. See Table 2.2 for detail on sampling sites. Larger, dark blue circles illustrate higher concentrations of particles\/L, whereas smaller, lighter blue circles illustration lower concentrations, or no particles as in IP-011 and LN-005. 41 The morphology of anthropogenic particles from surface seawater was found to be significantly related to the islands where samples were collected (\u03a7\u00b2 = 14.738; DF = 4; p = 0.005; Figure 2.7 A). Likewise, the colour of anthropogenic particles was also found to be significantly related to the islands (\u03a7\u00b2 = 41.856; DF = 20; p = 0.003; Figure 2.7 B), as were the type of polymers (\u03a7\u00b2 = 27.872; DF = 12; p = 0.006; Figure 2.7 C). The most prevalent colour and shape of anthropogenic particles found in the waters of Santa Cruz Island were blue microplastics and fibers. In contrast, white and clear anthropogenic particles were more prevalent in Floreana, Santiago, and Isabela, while only black fibers were discovered in the open ocean between Santa Cruz and Isabela (Figure 2.7). A) B) C) Islands Figure 2.7: Anthropogenic particles in Seawater and Inter-Island Differences. The Chi-squared analysis of microplastics in seawater depicts the proportion of the overall distribution explained by the observed values for microplastic (A) morphologies, (B) colour, and (C) polymer type across different islands. pe=polyethylene; acr=Poly(methyl methacrylate); pa=polyamide; pvc=polyvinyl chloride; pp=polypropylene; pet=polyethylene terephthalate; ps=polystyrene; abs=acrylonitrile butadiene styrene. 42 Although Santa Cruz and Santiago sites had higher anthropogenic particles concentrations per liter compared to other islands (as shown in Figure 2.8 A), there was no significance between the abundance of particles\/L and respective island (one-way ANOVA, SS=0.8059; DF=3; p=0.426; Bartlett\u2019s K2=Inf.; DF=3, p < 2.2x10-16; Shapiro-Wilk, p < 2.2x10-16). There were also no statistically significant differences observed between the abundance of particles\/L and station categories, e.g., tourism, harbor, or remote (one-way ANOVA, SS=5.74; DF=2; p=0.471; Bartlett\u2019s K2=3.13; DF=2, p=0.201; Shapiro-Wilk, p=0.178; Figure 2.8 B). A) B) Figure 2.8: Anthropogenic Particles Inter-Island and Category Box Plot. A box plot (A) showing particle concentrations averaged for each (A) island and (B) site category: \u201cT\u201d - tourism sites, \u201cH\u201d - harbour sites, and \u201cR\u201d - remote sites. Horizontal bars indicate median. X indicates mean particle\/L. The upper and lower edges of the box denote the approximate 1st and 3rd quartiles, respectively. The vertical error bars extend to the lowest and highest data value inside a range of 1.5 times the inter-quartile range, respectively. Points outside the box indicate extreme values. 2.3.3 Seawater quality parameters The seawater parameters are outlined in Table 2.2. Consistent with expected sea surface temperatures (SST) during the cool gar\u00faa season under normal conditions (e.g., lack of ESNO event) (Mateus et al., 2019), SST ranged from 22.32\u00b0C to 25.95\u00b0C. Dissolved oxygen (DO) ranged from 6.97 to 8.56 mg\/L, with slightly higher concentrations observed near more remote islands 43 like Santiago, Isabela, and Floreana. Nitrite were typically around 0.01 mg\/L, with some locations around Santa Cruz increasing to 0.10 or 0.30 mg\/L. Nitrate was low across sites ranging from 0.1 mg\/L to 0.24 mg\/L at LN-005 to TB-001, respectively. Phosphate ranged from 0.21 mg\/L to 0.42 mg\/L at LN-005 and Santiago stations (S-P-008 and S-BB-009), respectively. Ammonia ranged from 0.21 mg\/L to 0.42 mg\/L at LN-005 and Santiago stations (S-P-008 and S-BB-009), respectively. Salinity levels ranged from 33.01 to 35.40 PSU. The turbidity levels ranged from 1.2 NTU to 8.1 NTU. Some areas showed higher values, including 6.4 and 8.1 at GR-002 and TB-001 respectively. The sampling sites were located at depths ranging from 5 m to 90 m, with one sample collected at a depth of 230 m in the open ocean between Santa Cruz and Isabela. 44 Table 2.2: Water quality parameters by sampling location and island in the Gal\u00e1pagos Marine Reserve where seawater and zooplankton samples were collected. The sampling sites are classified into three categories: T for tourism locations, H for harbors, and R for remote \u201cno take\u201d zones. The depths at which zooplankton samples were collected are indicated by the labels S-H for surface horizontal tow or 50-V for vertical tow at 50 meters. Location ID Island Category Sampling Date Sampling Time Latitude Longitude Water Sampled Depth (m) Zooplankton Tow and Depth (m) Temperature \u00b0C Dissolved Oxygen (mg\/L) Nitrite (mg\/L) Nitrate (mg\/L) Phosphate (mg\/L) pH Ammonia (mg\/L) Salinity (PSU) Turbidity (NTU) Maximum Depth (m) TB-001 Santa Cruz T Oct 8 2021 10:50am 0\u00b0 46.006' S 90\u00b0 20.8328' W 0.1 S-H 25.95 6.97 0.01 0.24 0.27 7.22 0.21 35.24 8.1 5 GR-002 Santa Cruz R Oct 8 2021 1:12pm 0\u00b0 41.97' S 90\u00b0 13.2018' W 0.1 S-H 24.35 7.7 0.11 0.21 0.24 7.21 0.2 34.24 6.4 5 GR-003 Santa Cruz R Oct 8 2021 1:45pm 0\u00b0 42.0073' S 90\u00b0 13.4408' W 0.1 S-H 25.2 8.2 0.10 0.21 0.26 7.34 0.19 34.21 3.2 5 AB-004 Santa Cruz H Oct 9 2021 9:28am 0\u00b0 44.976' S 90\u00b0 18.3238' W 0.1 S-H 22.32 7.89 0.10 0.19 0.28 7.35 0.16 34.49 1.2 20 LN-005 Santa Cruz T Oct 9 2021 5:47pm 0\u00b0 44.856 7\u2019 S 90\u00b0 18.9812' W 0.1 NA 23.97 7.77 0.10 0.1 0.21 7.81 0.1 34.14 1.9 5 FB-006 Santa Cruz T Oct 10 2021 11:02am 0\u00b0 45.4922' S 90\u00b0 18.3325' W 0.1 S-H 22.72 6.97 0.30 0.19 0.22 7.01 0.29 33.01 2.1 4 NTS-007 Santiago R Oct 11 2021 5:35am 0\u00b0 22.6657' S 90\u00b0 39.8918' W NA 50-V NA NA NA NA NA NA NA NA NA 50 S-P-008 Santiago R Oct 11 2021 9:27am 0\u00b0 20.8728' S 90\u00b0 40.2837' W 0.1 S-H 23.71 8.56 0.01 0.14 0.42 7.56 0.18 35.4 4.68 15 S-BB-009 Santiago T Oct 11 2021 2:21pm 0\u00b0 17.0703' S 90\u00b0 33.5883' W 0.1 S-H 23.71 8.56 0.01 0.14 0.42 7.56 0.18 35.4 4.68 40 IZ-010 Between Islands R Oct 13 2021 9:28am 0\u00b0 53.3403' S 90\u00b0 36.3405' W 0.1 S-H NA NA NA NA NA NA NA NA NA 230 IP-011 Isabela H Oct 14 2021 11:32am 0\u00b0 57.9488' S 90\u00b0 57.6933' W 0.1 S-H 23.87 8 0.01 0.14 0.38 7.37 0.19 34.39 5.75 5 SI-012 Isabela T Oct 14 2021 12:35am 1\u00b0 3.8642' S 91\u00b0 10.4192' W 0.1 S-H 23.87 8 0.01 0.14 0.38 7.37 0.19 34.39 5.75 90 FL1-013 Floreana T Oct 16 2021 9:09am 1\u00b0 13.7548' S 90\u00b0 27.0855' W 0.1 S-H 23.71 8.23 0.01 0.12 0.22 7.45 0.2 34.22 4.69 15 FL2-014 Floreana R Oct 16 2021 11:30am 1\u00b0 13.2588' S 90\u00b0 25.7593' W 0.1 S-H 23.71 8.23 0.01 0.12 0.22 7.45 0.2 34.22 4.69 55 45 A Principal Component Analysis (PCA) was performed on this dataset. The results of the analysis showed that PC1 accounted for 36.7% of the total variance while PC2 accounted for 27.5% of the variance (Figure 2.9). Sites were colour-coded first by island (Figure 2.9 A) and then by category (Figure 2.9 B), to identify trends between island or site category. The Santa Cruz sites (GR-002, GR-003, AB-004, FB-006; Figure 2.9 A) were found to have higher turbidity, temperature, nitrate, ammonia, nitrite, and average particles per liter compared to other sites sampled. This group of parameters was found to cluster within these sites, indicating these parameters co-vary. On the other hand, phosphate was higher in the Santiago and Isabela sites (Figure 2.9 A). There was no clear clustering when comparing site category (Figure 2.9 B). The proximity of pH, DO, and site depth vectors indicates that these variables co-vary. The same is true for salinity and phosphate, and for turbidity and temperature. Interestingly, the vector representing average particles per water concentrations (particles\/L) was most closely related to temperature, turbidity, and nitrate (Figure 2.6). Higher microplastic concentrations were found in warmer and more turbid waters. It is important to note, Santa Cruz also happens to have warmer, more turbid waters and has a higher population (Figure 2.9 A: Blue). The vector points to the site with the highest microplastic concentrations (TB-001), a tourism site (Figure 2.9 B) in Santa Cruz (Figure 2.9 A). 46 A) 47 B) Figure 2.9: Water Quality Parameters Variability as shown by Principal Component Analysis (PCA). The PCA was performed on water quality data collected from various sample sites. The results showed that PC1 accounted for 36.7% of the total variance, while PC2 accounted for 27.5% of the variance. Sampling sites are labeled as data points. The groups are colour-coded based on (A) the island and (B) the location category (harbour - H, tourism - T, remote - R). In (A), the colour red represents data from Floreana, green represents Isabela, blue represents Santa Cruz, and purple represents Santiago. Santa Cruz data is represented by a blue eclipse. In (B), red represents harbour locations, green represents remote locations, and blue represents tourism sites. 48 2.3.4 Anthropogenic particles in zooplankton The examination of 3372 zooplankton individuals belonging to the taxonomic groups of Copepoda, Euphausiacea, Decapoda larvae, Chaetognatha, and Ichthyoplankton (fish larvae) resulted in the discovery of 14 anthropogenic particles, yielding an average of 0.004 anthropogenic particles per individual zooplankton or an average of 7.38x10-7 g of particles per kilogram (g\/kg) of zooplankton (Table 2.3). The highest number of zooplankton with ingested anthropogenic particles was found at station TB-001 with a total of 5, followed by 3 at station S-BB-009. No microplastics or cellulose were recovered from zooplankton samples at stations AB-004, GR-003, S-P-008, IZ-010, NTS-007, FL2-014, and IP-011 (Table 2.3). No significant relationships were found between zooplankton collection location (e.g., island) and particle colour (\u03a7\u00b2 = 11.210; DF = 12; p = 0. 511), shape (\u03a7\u00b2 = 14.021; DF = 8; p = 0.08), or polymer (\u03a7\u00b2 = 14.95; DF = 8; p = 0.06). 49 Table 2.3: Anthropogenic Particles in Zooplankton. Category is defined as tourism location (T), harbour (H), or remote (R). Stations with an asterisk were only able to be sampled for either water or zooplankton due to logistical issues. MP=microplastics; CE=cellulose; P=particle. Station Island Category Zooplankton Assessed MP CE MP\/ Zooplankton indiv.-1 CE\/ Zooplankton indiv.-1 P\/ Zooplankton indiv.-1 TB-001 Santa Cruz T 402 3 2 0.007 0.005 0.012 GR-002 Santa Cruz R 151 0 1 0.000 0.007 0.007 GR-003 Santa Cruz R 180 0 0 0.000 0.000 0.000 AB-004 Santa Cruz H 265 0 0 0.000 0.000 0.000 LN-005* Santa Cruz T NA NA NA NA NA NA FB-006 Santa Cruz T 369 2 0 0.005 0.000 0.005 NTS-007* Santiago R 233 0 0 0.000 0.000 0.000 S-P-008 Santiago R 352 0 0 0.000 0.000 0.000 S-BB-009 Santiago T 341 2 1 0.006 0.003 0.009 IZ-010 Open Ocean R 306 0 0 0.000 0.000 0.000 IP-011 Isabela H 154 0 0 0.000 0.000 0.000 SI-012 Isabela T 139 2 0 0.014 0.000 0.014 FL1-013 Floreana T 249 1 0 0.004 0.000 0.004 FL2-014 Floreana R 231 0 0 0.000 0.000 0.000 Average 0.0031 0.0012 0.0043 Standard Deviation 0.0045 0.0023 0.0053 50 The majority of the identified polymers were PP and PS, as well as CE, which were mostly composed of fibers, foams, and fragments in approximately equal amounts (Figures 2.5). The predominant microplastic colors were blue and white (Figure 2.5), and most of the microplastics identified for the zooplanktonic group were between 10 \uf06dm and 1500 \uf06dm in size (Figure 2.5). Despite ecological differences in zooplankton taxonomic groups such as organism size and feeding preference, there was no significant relationship between size of the microplastic and zooplankton taxonomic group (one-way ANOVA, SS=594897; DF=3; p=0.924; Bartlett\u2019s K2=3.07; DF=3, p=0.3798; Shapiro-Wilk, p<0.0008751; Figure 2.10), nor was there a significant relationship between zooplankton taxonomic group and microplastic characteristics (colour: \u03a7\u00b2 = 19.9; DF = 12; p = 0. 07, shape: \u03a7\u00b2 = 15.5; DF = 8; p = 0.53, polymer: \u03a7\u00b2 = 10.21; DF = 8; p = 0.25; Figure 2.10). Overall, blue fibers were dominant in microplastics found in Euphausiids, Chaetognaths, and Copepods from Santa Cruz water, while white fragments were mostly found in Decapods and Euphausiids from Floreana and Isabela. This bears striking resemblance to the colour pattern observed in microplastics found at each island (Figure 2.7). Figure 2.10: Microplastics Sizes per Zooplankton Group. Box plots illustrating the differences in sizes between microplastics found in different zooplankton taxonomic groups. Horizontal bars indicate median. X indicates mean particle\/L. The upper and lower edges of the box denote the approximate 1st and 3rd quartiles, respectively. The vertical error bars extend to the lowest and 51 highest data value inside a range of 1.5 times the inter-quartile range, respectively. Points outside the box indicate extreme values. 2.3.5 Zooplankton density and composition Overall zooplankton density ranged from 9 ind.\/m3 at IP-011 to 2139 ind.\/m3 at TB-001 (Figure 2.11 A). Low densities of zooplankton were found in shallow harbour waters as well as off eastern Isabela. The harbour area near Puerto Villamil (Isabela Island) had the lowest zooplankton density (IP-011; 5 m maximum depth). This site is also a known location of penguins. Two stations located in the Puerto Ayora harbour, Santa Cruz, had low densities as well with 179 ind.\/m3 and 221 ind.\/m3 at AB-004 (20 m maximum depth) and FB-006 (4 m maximum depth), respectively. Otherwise, stations in Santa Cruz had high densities of zooplankton. Santiago Island exhibited moderate zooplankton density with an increase noted during the vertical evening tow (NTS-007*; 50 m maximum depth). Moderate densities were also found at the sample site between islands (IP-010; 230 m maximum depth), although a large amount of gelatinous material was present, which may not have been well-preserved. Refer to Table 2.2 for site depths and additional site location information. 52 A) B) 53 Figure 2.11: (A) The estimated total number of individuals per cubic meter at each site. Colours indicate island: Santa Cruz (Blue), Santiago (Grey), Isabela (Yellow), between islands (Red), Floreana (Green). (B) The relative composition of specific taxonomic zooplankton groups by sample site. Copepods were the most abundant taxa group, making up 89% of the total individuals captured across all sample sites (Figure 2.11 B). Other notable taxonomic groups included Decapoda larvae (3.2%), Chaetognatha (2.8%), Euphausiacea (euphausiid larvae, 1.9%), Tunicata (Appendicularia, 1.0%), and Mollusca (juvenile Bivalvia, 0.6%). Although Ichthyoplankton (fish larvae) were present, they only accounted for 0.11% of the total individuals. The composition of zooplankton taxonomic groups varied between different sample sites. Samples from sites near Santa Cruz were overwhelmingly dominated by copepods (e.g., TB-001, GR-002, GR-003), while other areas showed higher taxonomic diversity sites (Figure 2.11 B). For example, at stations FB-006, IZ-010, and FL1-013, Chaetognatha, Euphausiids, and decapod larvae, respectively were proportionally prominent (Figure 2.11 B). 2.3.6 Anthropogenic particles in fish All fish species, but not all individuals, had anthropogenic particles present in the GI tract. The greatest number of anthropogenic particles per fish was found in the GI tract of the individual milkfish (Table 2.4). Anchovies had the lowest number of anthropogenic particles per fish, ranging from 0 to 3 particles per individual (Table 2.4). For anchovies, anthropogenic particles were present in 5 of the 11 individuals examined, resulting 1 anthropogenic particle per 2.2 individual anchovy (Table 2.4). Black fibers were the dominant colour and shape, followed by black fragments. Only PET and cellulose were present (Figure 2.12), with a noteworthy and surprising absence of PP polymers which were prevalent in the waters of Santa Cruz. Anthropogenic particles were present in all mullet GI tracts, with the minimum and maximum amount being 3 and 20 microplastics, respectively (Table 2.3). Mullets had an average 7.27 anthropogenic particles per individual and had higher diversity in microplastic polymer types, shapes and colours compared to other fish groups, though the microplastics found in mullets were 54 primarily fragments (Figure 2.12). Additionally, mullets were the only fish group to contain foam particles. Blue and black microplastics were the dominant colours, but grey, clear, white, and yellow particles were prevalent as well. Mullets contained an array of polymers, primarily CE, PP, and PE, but also ABS, PET, PVC, and PA (Figure 2.12). The individual milkfish contained 27 ingested microplastics (Table 2.3; Figure 2.12). Fibers were the most common shape and black and blue colours dominated the array, mostly PET, including 2 PET fiber bundles (Figure 2.12; Figure 2.4). 55 Table 2.4: Average body composition metrics (e.g., total length [TL], weight, fullness index (Hureau, 1970)) and total anthropogenic particles found as well as the percentage of fish that exhibited ingested particles. All fish were collected from local fishers on Santa Cruz Island on 15 October 2021. MP=Microplastics. CE=Cellulose. Sample Island Sampling date Sample size (n) TL range (cm) Total weight range (g) Fullness Index (%) Male (M)\/ Female (F) Total MP # of Fish with MP Total CE # of Fish with CE % of fish with anthropogenic particles Anchovy Santa Cruz Oct 15 2021 11 9 \u2013 11.7 4.2 \u2013 9.1 0.08 \u2013 1.29 2 F : 9 M 3 2 5 4 45% Mullet Santa Cruz Oct 15 2021 6 26 \u2013 33.5 190 \u2013 450 3.9 \u2013 7.7 4 F : 2 M 31 6 12 5 100% Milkfish Santa Cruz Oct 15 2021 1 32.3 310 6 1 F 27 1 0 NA NA 56 The fish species was significantly related to the particle shape (\u03a7\u00b2 = 12.13; DF = 4; p = 0.016; Figure 2.12 A) as well as polymer type (\u03a7\u00b2 = 56.125; DF = 16; p =2.32 x 10-6; Figure 2.12 B), but not colour (\u03a7\u00b2 = 17.49; DF = 12; p =0.13). Mullets, which feed on detritus, had the highest variety of particles in terms of different characteristics (e.g., colour, polymer, morphology, and known plastic densities). On the other hand, anchovies, which are selective planktivorous feeders, had the least number of particles and limited diversity in terms of shape and polymer. The herbivorous milkfish had a high amount of PET and PE in its GI tract (Figure 2.12). A) B) Figure 2.12: Anthropogenic Particles in Fish \u2013 Inter-Fish Species Differences. The Chi-squared analysis of microplastics in fish depicts the proportion of the overall distribution explained by the observed values for microplastics\u2019 (A) morphologies and (B) polymer type across different islands. There were no significant relationships between numbers of anthropogenic particles and TL, weight, and fullness within the mullet or anchovy groups (Figure 2.13). No relationship was found between the fullness index and the number of anthropogenic particles in the fish. In fact, for mullets, a non-significant negative correlation was observed between fullness and the number of anthropogenic particles (Figure 2.13 B). No discernible pattern was found for anchovies. The total length of fish species versus the number of anthropogenic particles had a positive relationship for both groups, but again it was not significant (p = 0.18 for mullets and p = 0.50 for anchovies; Figure 2.13). Likewise, fish weight and number of anthropogenic particles showed positive trends 57 for both mullets and anchovies (Figure 2.13), but the relationship was not statistically significant (p = 0.35 and p = 0.33). 58 A) B) Figure 2.13: Relationship of Fish Size and Number of Anthropogenic Particles by (A) Anchovies and (B) Mullets and Milkfish. The first panel shows the fullness index; the second shows total length (TL); and the third shows wet weight (g) index all plotted against number of particles (y-axis). The red, dotted lines show the direction of the non-significant trends. 59 2.3.7 Fish total lengths, weight, and fullness The body size and biometric data of anchovies, mullets, and milkfish are reported in Table 2.3. The anchovies (A. naso) averaged 10.7\u00b10.67 cm in TL, indicating they were fully grown adults (Bayliff, 1967). They also averaged 7.7\u00b11.3 g in weight. The majority (81%) were males. Food wet weight contents ranged from 0.0063 to 0.097 g with an average of 0.0536\u00b10.03 g and respective fullness indices (Hureau, 1970) ranged from 0.08% to 1.29% with an average 0.7\u00b10.003% (Table 2.3). Stomach contents were fully digested, and no taxa could be determined from any of the anchovy specimens. Mullets (n=6) and the milkfish (n=1) had an average TL of 30.4\u00b12.7 cm and weight of 331.7\u00b183.5 g, respectively. This indicates most mullets were young adults (e.g., 50% maturity reached at 31 cm in females and 34 cm in males based on (M. cephalus) (Silva & de Silva, 1981). Most of these fish were female (71.4%, including the individual milkfish). Food contents ranged from 12.6 to 30.9 g in weight, with an average of 12.7\u00b16.2 g. The mullet stomachs were full of sediment, whereas the milkfish stomach was full of digested macroalgae. The fullness index (Hureau, 1970) for mullets ranged from 3.85 to 7.73% with an average 5.41\u00b11.46%, and the milkfish had a full index of 5.62% (Table 2.3). 2.3.8 Anthropogenic particles in penguin scat (guano) Despite evidence of anthropogenic particles in diet items of the Gal\u00e1pagos penguins, no microplastics or particles derived from natural polymers were identified in 3.40 g of guano collected from 2 penguins. Given the expected dietary exposure rate via selected fish as prey, it was surprising not to find any microplastics in the scat, despite the small sample size. It is recommended to sample and analyze other fish species that are part of the Gal\u00e1pagos penguin diet in the future to better understand the extent of microplastic ingestion by these seabirds. 60 2.3.9 Gal\u00e1pagos penguin\u2019s exposure to anthropogenic particles through diet In an effort to evaluate the extent of anthropogenic particle exposure of Gal\u00e1pagos penguins through their diet, the minimum daily prey requirements and maximum prey intake, as a function of body weight, were gathered from related literature. Particles per gram of prey were then estimated based on empirical data. The dietary requirements of penguins vary and, though no data exist on the Gal\u00e1pagos penguins\u2019 dietary requirements, close relatives like the Humboldt Penguin (S. humboldti) are estimated to consume a minimum of 9% of their body weight per day (wet weight prey day-1) to account for foraging costs (based on ~ 340 g anchovies day-1; Luna-Jorquera & Culik, 2000) and 14% to meet energy requirements (based on ~ 489 g anchovies day-1 Herling et al., 2005). African penguins (Spheniscus demersus), also related, will eat up to a maximum 30% of their body weight at a time before excretion (based on maximum ~ 812 g found in stomach of S. demersus penguins; Croxall, 1987). Though not assessed in this study, these requirements likely increase during the chick rearing period (Gales & Green, 1990). Data from zooplankton and fish collected in this study were used to estimate anthropogenic particles in penguin prey. Grams of particles per kilogram of zooplankton and fish (g\/kg) are shown in Figure 2.14 A. Average number of particles per individual are shown in Figure 2.14 B. Lastly, log particles per prey (g\/g) and log number of particles per number of prey (#\/#) are illustrated across sample groups in Figure 2.14 C. Zooplankton, having the lowest wet weight, had the highest average particle weight (g) per zooplankton weight (kg). However, when considering number of particles (#) per zooplankton individual (#), the opposite was observed, which is not surprising. In contrast, mullets which weigh much more than individual zooplankton, showed lower particle per wet weight (g\/kg) and higher number of particles per individual (#\/#). Again, this is not surprising since the wet weight of mullets is significantly greater than the mass of the contaminant, anthropogenic particles. For this reason, higher proportions of zooplankton eaten would result in greater microplastic intake (g\/kg), whereas higher proportions of mullet eaten would result in lower microplastic intake. Anchovies 61 fall in between, with a moderate level of microplastics per wet weight (g\/kg) and number of particles per individual (#\/#). See Figure 2.14. It is important to note that TL and weight of the anchovies in this study were consistent and similar to the main prey items of Gal\u00e1pagos penguin\u2019s relative, the Magellanic penguin (S. magellanicus) (Fernandez et al., 2019); however, mullets were slightly larger than the size range of prey items of Gal\u00e1pagos penguin\u2019s relative, the Humboldt penguin (S. humboldti) (Herling et al., 2005). A) B) 62 C) Figure 2.14: Anthropogenic Particles Per Gram (g) and Per Individual (#) by Group. (A-B) Average particle ingestion rates per sample group (A) and anthropogenic particles in g\/kg of organism where the y-axis is presented in log scale (B). Panel (C) shows the log-transformed data for microplastics concentrations in g\/kg by the log wet weight (g) of each organism, revealing clusters in each sample group. Blue circles indicate anchovy data, green is mullet and milkfish, and orange diamonds are zooplankton. I created an estimated diet scenario based on best available diet literature on the Gal\u00e1pagos penguins and Spheniscus relatives (Herling et al., 2005; Mills, 1997; Vargas et al., 2006; Wilson & Wilson, 1990). Assuming that Gal\u00e1pagos penguins eat 60% anchovies, 20% mullets, and 20% zooplankton, and considering minimum daily intake (day-1) to maximum consumption at one time (9% to 30% of body weight, respectively), a Gal\u00e1pagos penguin could ingest a minimum of 3,521 63 particles per day-1 or a maximum of 11,746 per foraging event through contaminated prey items. This is based on a weight of 2 kg per penguin (Steinfurth et al., 2008). The average weight of anthropogenic particles, calculated from the minimum particle size range (i.e., 250 \u00b5m diameter based on Alava, 2020) is 7.57x10-6 g. Based on this, the daily intake of anthropogenic particles from prey could range from 1.16x10-13 to 3.87x10-10 g of particles per kg of (Table 2.5). 64 Table 2.5: Microplastic Daily Intake estimation for the Gal\u00e1pagos Penguin from Prey (Fish) Items part of its diet in the Gal\u00e1pagos Islands Requirement Daily % of body weight intake Average body mass of Gal\u00e1pagos penguin (g) Daily prey requirement (g) Weighted number of anthropogenic particles per grams of prey Daily anthropogenic particle (#) intake from prey items Weighted grams of anthropogenic particles per kg of prey Daily grams of anthropogenic particles intake from kg of prey Low: To account for foraging costs1 0.09 2000 180 19.56 3520.86 6.45x10-10 1.16x10 -13 Medium: to meet energy requirements2 0.14 2000 280 19.56 5476.89 6.45x10-10 1.81x10-10 High: Maximum intake reported for African penguins (S. demersus)3 0.3 2000 600 19.56 11736.2 6.45x10-10 3.87x10-10 1 Luna-Jorquera & Culik, 2000 2 Herling et al., 2005 3 Croxall, 1987 65 2.4 Discussion This is the first baseline assessment investigating microplastics and particles derived from natural polymers in seawater, zooplankton and marine fish, serving as prey of the Gal\u00e1pagos penguins, in several locations around the central region of the Gal\u00e1pagos Archipelago, including coastal and oceanic waters around Santa Cruz, Isabela, Santiago and Floreana islands. Other studies have assessed local microplastic pollution in coastal waters around San Crist\u00f3bal (Jones et al., 2021) and sampling offshore or in international waters around the Gal\u00e1pagos Islands (Alfaro-N\u00fa\u00f1ez et al., 2021); this study narrows the gap on anthropogenic particles ingested by zooplankton and prey items for the Gal\u00e1pagos penguin and provides a baseline for microplastics present in their nearshore waters. To understand the effects of anthropogenic particles, it is critical to first gain insight into their movement through food webs and the level of exposure experienced by endangered and iconic species. 2.4.1 Anthropogenic particles in surface seawater Microplastics within the size range found in this study were comparable to other locations near major cities but less than over remote islands, and there were interesting trends that add to the body of literature on anthropogenic particle levels and behaviours through the water column. The average 0.54\u00b10.49 particles\/L (or 0.40\u00b10.32 microplastics\/L) is lower, but comparable to the average of 0.59\u00b10.04 microplastic particles\/L found in a study using similar methods to assess seawater off the west coast of Canada in the northeastern Pacific (Mahara et al., 2022). However, this concentration is much less than observed at the remote southern Atlantic islands, Ascension and Falkland (Green et al., 2018). Extrapolating the results of this study to microplastics\/km2 or microplastics\/m3, using conversations from Lusher (2015), shows that microplastic abundance was more than three orders of magnitude higher than microplastic hotspots like the Great Pacific Garbage Patch (Lebreton et al., 2018). However, caution must be exercised when comparing these studies as there are significant methodological differences, namely, the use of a net for sampling compared to Niskin bottles used in this study. The average width of anthropogenic particles found in this study was 66 45.7\u00b161.5 \u03bcm, which would easily pass through commonly used 330 \u03bcm mesh size nets (Green et al., 2018) used for microplastics sampling over longer distances. While plankton nets can increase the volume filtered, studies using these nets likely underestimate the abundance of microplastics and anthropogenic particles in seawater (Barrows et al., 2017; Watkins et al., 2021). Thus, it is reasonable to conclude that many previous studies of microplastics in seawater underestimated the amount of smaller microplastics or anthropogenic fibers, as highlighted by this study. Lack of inter-site statistical differences between site category or islands is of particular interest and may illustrate a global pollution source of microplastics and anthropogenic fibers, suggesting these particles can, to a certain degree, become evenly distributed or homogenized around the islands or globally due to transport via currents (Van Sebille et al., 2019; Van Sebille et al., 2020). There are various factors that can cause the movement of plastics, including large-scale ocean currents, tides, onshore Stokes drift, wind forces, and beaching (Olivelli et al., 2020) as well as vertical transport and mixing, which may be occurring at the highly turbid site: TB-001, as well as transport by organisms, which we see through the microplastics ingested by organisms (van Sebille et al., 2020). The most prominent polymer found in seawater in this study was polypropylene (PP), which is consistent with the findings from a nearby island, San Cristobal (Jones et al., 2021). The morphology of PP found in San Cristobal matched PP found in seawater and the milkfish in this study (see Figure 2.4 D and Jones et al., 2021). Fishing gear such as marina and fishing ropes and nets are commonly made of PP, suggesting fishing gear may be a major source of microplastic pollution in the Gal\u00e1pagos Archipelago. This is compounded by the presence of industrial fisheries as well as illegal, unreported, and unregulated (IUU) fishing by foreign fleets (Alava & Paladines, 2017; Alava et al., 2017; Alava et al., 2022; Jacquet et al., 2008; Schiller et al., 2015). Fishing gear has also been identified as a major source of plastic pollution in the North Pacific subtropical Gyre (Lebreton et al., 2022). 67 2.4.2 Seawater quality Concerns were raised about the water quality at certain locations near the city of Puerto Ayora on Santa Cruz Island. The site TB-001 was found to have higher temperatures, low DO, high salinity, and turbidity, and it was also found to have the highest concentration of zooplankton with ingested anthropogenic particles. It likewise had the highest amount of anthropogenic particle concentrations (particles\/L). Results at this site may be due to shallow depths, urban and agricultural runoff (Alava et al., 2014; Mateus et al., 2019) though nitrite levels are low, or tourism impacts. Conclusions cannot be drawn without replicates and more data. Given that TB-001 is a popular swimming location, further data should be gathered. 2.4.3 Anthropogenic particles in zooplankton Ingestion of anthropogenic particles by zooplankton was particularly low, despite examining over 3,000 individuals. Anthropogenic particles in zooplankton in this study (0.0031 microplastic\/indiv-1 or 0.0043 particles\/indiv-1) were one to two orders of magnitudes lower than microplastics of similar sizes ingested by zooplankton in the Northeast Pacific (0.03 to 0.06 microplastics\/zooplankton) (Desforges et al., 2015), Kenya (0.16 to 0.14 microplastics\/zooplankton) (Kosore et al., 2018), and along the Portuguese coast (0.04 to 0.14 microplastics\/zooplankton) (Frias et al., 2014). Interestingly, microplastics in zooplankton were also substantially lower than remote locations, like the Fram Strait in the Arctic where microplastic ingestion by zooplankton ranged from 1 microplastic\/indiv-1 (100%) to 1 microplastic\/102 indiv-1 (1%) (Botterell et al., 2022), compared to this study where 1 anthropogenic particle\/241 indiv-1 (0.4%). The findings were consistent with microplastics ingested by calanoid and chaetognatha from the southern South China Sea (Amin et al., 2020), though notably in the same study, fish larva contained far more ingested plastics than observed here. The polymer types of anthropogenic particles found in zooplankton around the world differed. Zooplankton from the Arctic had higher amounts of polyurethane (PU) (Botterell et al., 2022), whereas this study had predominantly PP and PS. Again, this may support the case that marinas 68 and fishing gear (near and far) may be contributing most to microplastics found near the Gal\u00e1pagos. The microplastics ingested by zooplankton were similar to the microplastics present in the surrounding waters. For instance, blue PP fibers were found in Euphausiids, Chaetognaths, and copepods collected from Santa Cruz and Santiago, which matched the most prevalent microplastics found in the waters of Santa Cruz and Santiago. On the other hand, white microplastics were detected in decapod and Euphausiids from the waters surrounding Floreana and Isabela, which aligned with the colours found in the islands of Isabela and Floreana. However, there were notable polymers missing in the zooplankton. PS was not found in surface seawaters, but was found in zooplankton, whereas PE was found in seawater, but not in zooplankton. This could suggest either that PS is more bioavailable than PE to zooplankton or zooplankton selectively feed on PS and not PE. For the first argument, PS is less dense than PE; it could be the case that PS is more bioavailable than PE to zooplankton preforming diel vertical migration to feed. In terms of selective feeding, being less dense, PS also more likely remains in the photic zone and may have more opportunity to collect microalgae and microbial communities that could attract zooplankton. Microplastics selectively has been established in previous studies, primarily with copepods (Botterell et al., 2021; Cole et al., 2013; Pan et al., 2021). 2.4.4 Zooplankton community Overall, the zooplankton density generally align with high abundance expected in the Gal\u00e1pagos Archipelago (Fern\u00e1ndez-\u00c1lamo & F\u00e4rber-Lorda, 2006). Zooplankton density was lowest near shallower areas in harbours. Lower densities can be expected in shallow waters, but it would be interesting to assess the impact of boat traffic noise disturbance (Weilgart, 2018), agriculture runoff (Alava et al., 2014; Andrade et al., 2022; Golmarvi et al., 2017; Mateus et al., 2019; Riascos-Flores et al., 2021; Sakamoto & Ha, 2008), or pollution, including microplastics, on zooplankton density in harbours. Of note, one penguin colony lives at Isabela\u2019s Puerto Villamil harbour. 69 The taxonomic composition of the zooplankton community in this study is generally similar to previous studies in central and southeastern Gal\u00e1pagos during the cool months (Figueroa, 2021), with the exception of decapod larvae which were found to be more abundant in this study, particularly at stations S-P-008 and FL1-013. Decapod larvae was more abundant than Euphausiids overall; I suggest Euphausiids dominate in western upwelling zones where the cold Cromwell current converge near the Bolivar Channel (Figueroa, 2021), whereas Decapod larvae dominate the coastal areas sheltered from major currents. 2.4.5 Anthropogenic particles in fish To the author\u2019s knowledge, this study provides the first data on microplastics and anthropogenic particles found in anchovies and milkfish near the Gal\u00e1pagos Islands. The results emphasize the pressing need to address the issue of anthropogenic debris and their impact on the environment, marine fauna and coastal wildlife. The majority of the microplastics found were not able to be identified, highlighting the need for further research to understand their composition and origin. Compared to other areas of the world, fish caught in South America have shown lower microplastic loads (Wootton et al., 2021). This study revealed 0.27 microplastics\/anchovy (or 0.0727 anthropogenic particles\/anchovy), 5.17 microplastics\/mullet (or 7.17 anthropogenic particles\/mullet), and 27 microplastics per milkfish. These results are lower than fish from Asia (27.4 to 366 microplastics per individual) and North America (36.7 to 82.6 microplastics per individual) (Wootton et al., 2021), which have more highly populated coastal cities. Ingestion rates for fish were similar to Mahara et al. (2022) in which authors found juvenile herring had 0.089 microplastics per individual. These results underscore the importance of restricting the release of microplastics from coastal cities (van Sebille et al., 2019). Anchovies - Low ingestion rates have been seen for anchovies in the southeastern Pacific where 2% of assessed anchovies contained microplastics (Ory et al., 2018), compared to 18% in this study where 2 out of 11 anchovies had microplastics and 5 out of 11 anchovies had microplastics or anthropogenic particles. Conversely, microplastics were found in 100% of anchovies sampled off the coast of Spain (S\u00e1nchez-Guerrero-Hern\u00e1ndez et al., 2023), in the Mediterranean Sea, which 70 is considered a microplastic hotspot (Lusher, 2015). These findings indicate anchovies in the eastern Pacific are exposed to lower levels of microplastics. This lower level of microplastics found in anchovies may indicate that these fish are still relatively less exposed to microplastics. It may be related to selective feeding especially given that there are much higher densities of zooplankton compared to microplastics (Chavarry et al., 2022 found a ratio of one microplastics per 3399 zooplankton individuals in the Northeastern Pacific). Selectively is evidenced by previous studies having shown that anchovies prefer black and blue coloured microplastics (Renzi et al., 2019), which is supported by these results. Likewise, anchovies at different life stages are likely exposed to different extents. Only adults were assessed in this study, but evidence suggests juveniles may be more at risk to microplastic ingestion (Chavarry et al., 2022), which would have consequences on an ecosystem level (Galloway et al., 2017). Mullets - In this study, microplastics were found in all mullets, which is more than previous studies reporting microplastics in 48.5% of squaretail mullet (Ellochelon vaigiensis) from the south Pacific tropical gyre (Markic et al., 2018), 0% from twelve M. curema off western Mexico (Jonathan et al., 2021), 60% in flathead (M. cephalus) with an average of 4.3 plastics per mullet in South China Sea, near Hong Kong (Cheung et al., 2018), and 18% in red mullets (Mullus barbatus) from the northeast Atlantic and Mediterranean coasts (Bellas et al., 2016). Recently, 67% of 121 fish sampled, including family Mullidae were found to have ingested microplastics in Southwest Taiwan (Su & Lin, 2023). Mullidae had an average 1.73 microplastics per individual, and differences among fish were found to be a result of environmental contaminant concentrations, rather than biological characteristics (e.g., trophic level or size), which is different than other studies finding relationships between size and number of microplastics (Su & Lin, 2023). Milkfish - Studies of microplastics in milkfish are limited to water surrounding Indonesia and have found much lower amounts of microplastics with an average of 3.5 items per individual near South Sulawesi, Indonesia (Amelinda et al., 2021) and 1.3 microplastic per individual near Citarum River (Sembiring et al., 2020). 71 Fish ingestion of microplastics seems to be largely driven by feeding preferences, but there is disagreement between studies as to which feeding preference results in the most microplastic uptake. In review, Wootton et al. (2021) found detritivorous fish consume more compared to omnivores and herbivores. Carnivores appear to contain the least amount of microplastics (Alfaro-N\u00fa\u00f1ez et al., 2021; Wootton et al., 2021), which aligns with anchovy results from this study. On the other hand, mullets, which feed mostly on detritus from marine sediments on the seafloor, have a higher encounter rate with dense plastics and those weighed down by biofouling, thus it is not surprising that they contained PE or high-density polyethylene (HDPE), whereas planktivorous anchovies and herbivorous did not. Mullets in this study also had the highest variety of microplastics in terms of different characteristics (e.g., colour, polymer, morphology). This is not surprising given that they essentially scoop up any material that falls into marine sediment. Milkfish, being herbivores feeding on macroalgae in the marine environment, have a higher encounter rate with microplastics PET and PP fibers commonly found in harbour equipment, such as buoys, ropes, and boat apparatuses, that are often covered by macroalgae. This suggests that herbivorous marine fish in harbors may be more exposed to fibers from the marina environment. This was further supported by Jones et al. (2021) who observed plastic particles embedded with or attached to macroalgae in the intertidal zone of San Cristobal Island. 2.4.6 Anthropogenic particles in Gal\u00e1pagos Penguin scat Our results suggest that Gal\u00e1pagos penguins may be ingesting 1.16x10-13 to 3.87x10-10 g of anthropogenic particles per kg of prey or ~3,500 to ~12,000 microplastics per individual penguin per day or per maximum foraging event, respectively, and yet, no microplastics were found in the limited scat assessed. This finding may be because (1) selected penguins may not have ingested and accumulated microplastics in the portion of gut contents that was excreted as alluded to in Leistenschneider et al. (2022); (2) microplastics may not be readily excreted, indicating a high retention time in the GI tract with a slower elimination rate; (3) the diet composition is not representative. It would be interesting to corroborate these data with a larger sample size of scat and diet assessments. 72 It is important to remember microplastics are a class of contaminants (Rochman et al., 2019), and thus, while certain microplastics types may be egested, others may accumulate in the gut, or pass-through tissue membranes (Abbasi et al., 2018; Alava, 2020; Dong et al., 2023; Horvatits et al., 2022). Most microplastics found in this study were fibers, but fragments seem to be the most prominent type of microplastic found in seabirds in the Pacific and North seas (Collard et al., 2022; Provencher et al., 2018). It may be the case that fibers are more readily eliminated compared to fragments, but more research is needed. As it is not practical or ethical to conduct invasive or lethal sampling of endangered penguins to examine penguin GI tracts, food web bioaccumulation modeling of microplastics could be used as an alternative methodological approach (Alava, 2020) to estimate the amount of microplastics and anthropogenic particles ingested by penguins and to understand why no microplastics were present in the guano analyzed in this study. 2.5 Conclusion These findings highlight the need for further research to support mitigation strategies aimed to reduce the exposure of Gal\u00e1pagos penguins and other marine organisms to microplastics and anthropogenic particles. Conservation efforts to protect this endangered species and the surrounding environment should include measures to reduce the presence of microplastics in their habitat and subsequently their prey items. Efforts to reduce microplastics for the Gal\u00e1pagos penguin will likewise aid in protecting other iconic seabirds in the Gal\u00e1pagos, like the flightless cormorant (Nannopterum harrisi) and blue-footed booby (Sula nebouxii), other species that depend on small pelagic and demersal fish such as Gal\u00e1pagos sea lions (Zalophus wollebaeki), as well as animals that depend on macroalgae, like the marine iguana (Amblyrhynchus cristatus), that may contain microplastics, as discussed in this study. The Gal\u00e1pagos penguin species are still recovering from devastating El Ni\u00f1o events that more than halved the population (Nims et al., 2008; Vargas et al., 2007). They have faced numerous anthropogenic threats including fledgling predation from non-native wild dogs, cats, fishery interactions and bycatch, invasive parasites, toxic metal pollution (Alava et al., 2022; BirdLife 73 International, 2020; Boersma et al., 2020; Jim\u00e9nez-Uzc\u00e1tegui et al., 2019), and now ocean plastics. Seabirds around the world are contending with plastics, and other pollutants like light pollution, oil spills, noise, and other anthropogenic stressors (Gilmour et al., 2023). Boersma et al. (2020) states, \u201cGiven the [Gal\u00e1pagos penguin] population's sensitivity to environmental variability, action should be taken to protect the population even in the absence of complete scientific understanding\u201d. The Gal\u00e1pagos penguin population is decreasing, and, with less than 1,200 mature individuals, the iconic bird is listed as endangered (BirdLife International, 2020). Based on this baseline study, the penguins are taking in thousands of microplastic particles per day based on estimation from microplastics in their prey items. Moreover, the threat of microplastics to the prey items themselves may impact or reduce the number of prey available to the penguin. Microplastic research is still maturing. More research is needed to understand the impacts microplastics may have on high trophic level species like the Gal\u00e1pagos penguin. The ecotoxicology of microplastics is influenced by several factors, including polymer and additive composition (Lambert et al., 2017). Hence, it is crucial to distinguish the characteristics of microplastics to determine their potential risks (Lambert et al., 2017; Provencher et al., 2020; Thornton Hampton et al., 2022). Evidence suggests that PP fibers are more toxic than polypropylene beads commonly used in laboratory studies (Lambert et al., 2017). PP dominated the microplastics found in this study and questions linger on the protracted health risks by this plastic polymer type for native and endemic species of the Gal\u00e1pagos Islands in the long-term. Everaert\u2019s (2020) recent study established an effect threshold of 121,000 microplastics\/m3 (121 microplastics\/L) as the Predicted No-Effect Concentration (PNEC) based on twenty-three species-specific effects thresholds derived from microplastic concentration data. While the current microplastic concentrations in most of the global ocean's surface layer (0 to 5 m) are generally much lower than this predicted PNEC, the data presented here support that smaller microplastics are slipping through the cracks when net tows are used, and the actual amount of microplastics present in surface seawaters may be greater than previously estimated. This study found higher levels of microplastics in seawater than other studies that primarily relied on net tows. Even with 74 past methodological limitations suggesting a current underestimation of smaller microplastics in surface seawater, Everaert's (2020) study revealed that microplastic concentrations in parts of the Mediterranean Sea and Yellow Sea are already exceeding the PNEC and therefore organisms in these locations are already at risk. Likewise, this study only assessed surface seawater. It is currently estimated that ~1% of the plastic particles flowing into the ocean remain in surface waters, while up to 95 to 99% are distributed throughout the seawater column or are buried in bottom sediments (Choy et al., 2019; Eriksen et al., 2014; Kooi et al., 2017; Mountford & Morales Maqueda, 2019; van Sebille et al., 2015, 2020). Known microplastic amounts may be only a fraction of what currently reside in the oceans; thus, it crucial to reduce microplastic releases before the situation worsens. Coupled with a deeper analysis of particle sources, these findings could be used to inform policy making for discharged wastewater, urban runoff, and release of fishing gear in coastal Pacific cities. Mitigation strategies must be done with input from local community members to ensure equity and successful remediation. 75 Chapter 3: Modelling microplastic bioaccumulation and biomagnification in the Gal\u00e1pagos penguin ecosystem using Ecopath and Ecosim (EwE) models with Ecotracer \u201cAll models are wrong, some are useful.\u201d - George E. P. Box 3.1 Introduction In the Plasticene, escalating plastic production is by far outperforming mitigation efforts to reduce plastic consumption, as the abundance and weight of plastic pollution in the environment continues to increase (Borrelle et al., 2020; Cordier & Uehara, 2019). In a model introduced by Lebreton et al. (2019), microplastics were estimated to account for 32% of total plastic waste in the sea, inclusive of 22 to 60 million metric tonnes from coastal cities and 0.29 to 0.80 million metric tonnes from waste released or suspended at sea. These figures continue to increase (Andrady, 2022; Jambeck et al., 2015) and have led to more macro- and microplastic debris in the ocean. Nearly 5.25 to 50 trillion plastic particles, equivalent to 236,000 to 268,940 metric tonnes, are estimated to be floating in the global oceans (Eriksen et al., 2014; van Sebille et al., 2015), where these particles become bioavailable to organisms on the hunt for food (Botterell et al., 2019). Unsurprisingly, microplastics have been documented in an array of marine organisms, most predominantly in fish (Azevedo-Santos et al., 2019; Jovanovi\u0107 et al., 2018) and invertebrates 76 (Ugwu et al., 2021), including zooplankton (Botterell et al., 2019), and to a lesser extent seabirds (Ivar Do Sul & Costa, 2014; K\u00fchn & van Franeker, 2020; Roman et al., 2019; Wilcox et al., 2015), marine mammals (L\u00f3pez-Mart\u00ednez et al., 2021; Lusher et al., 2018; Nelms et al., 2018; Nelms et al., 2019; Zantis et al., 2021), and sea turtles (Lynch, 2018; L\u00f3pez-Mart\u00ednez et al., 2021; Schuyler et al., 2014; Yaghmour et al., 2021) (see Appendix A). Due to their size and pervasiveness, microplastics and microfibers enter biota easily through direct intake via water in gill-ventilating or water respiring organisms (Alava, 2020; Koelmans, 2015; Roch et al., 2020; Set\u00e4l\u00e4 et al., 2014) or inhalation via air in air-breathing animals such as marine mammals and seabirds (Alava, 2020; Amato-Louren\u00e7o et al., 2021), but can also be indirectly ingested via dietary exposure through prey contaminated with microplastics (Alava, 2020; Akhbarizadeh et al., 2019; Farrell & Nelson, 2013; Goswami et al., 2020; Miller et al., 2023; Nelms et al., 2018). The latter may represent a major pathway of microplastic ingestion for top predators (Nelms et al., 2018). Studying microplastics ingestion and bioaccumulation by high trophic level species presents logistical and ethical dilemmas (Alava, 2020; Gouin, 2020), and thus alternative means are needed to assess the ecotoxicological risk of microplastics to high trophic level species. Microplastic ingestion and resulting health conditions are well documented in invertebrates (Anbumani & Kakkar, 2018; Everaert et al., 2020; Bergmann et al., 2015; Cole et al., 2013). Evidence suggests microplastics can cause physical damage to the gut (e.g., lesions), digestive tract blockages, absorption of toxic chemicals leading to negative endocrine impacts such as reduced reproductive fitness, impaired feeding ability or falsified satiation, diminished predator avoidance, and drowning (Bergmann et al., 2015; Cole et al., 2013; Everaert et al., 2020; K\u00fchn et al., 2015; Wright et al., 2013). These sublethal impacts may have cascading effects at a species- or ecosystem-level (Galloway et al., 2017). The effects of microplastics on high trophic level species are more challenging to investigate and isolate. Seabirds are susceptible to the ingestion and accumulation of plastic debris and microplastics (K\u00fchn & van Franeker, 2020; Roman et al., 2019; Wilcox et al., 2015). For example, several studies have documented that Antarctic penguin species seem to be vulnerable to microplastics ingestion (Bessa et al., 2019; Frag\u00e3o et al., 2021; le Guen et al., 2020). Persistent organic pollutants 77 (POPs) that absorb onto plastics have also been found in seabirds, including in the streaked shearwater (Calonectris leucomelas) and short-tailed shearwater (Puffinus tenuirostris) (Ivar Do Sul & Costa, 2014). There is also evidence that high concentrations of polychlorinated biphenyls (PCBs) on microplastics can be transferred to seabird tissues (Ivar Do Sul & Costa, 2014). Microplastics can also act as vectors that transfer pollutants (Hartmann et al., 2017), or can transport and disseminate pathogens or other harmful organisms, including bacteria and invasive species, across marine and coastal ecosystems (Kirstein et al., 2016; Vir\u0161ek et al., 2017). Lastly, depending on the size and shape of microplastics, or smaller nanoplastics (< 1 \uf06dm) (Athey et al., 2022; Thornton Hampton et al., 2022), these particles may cross tissue barriers, making their way into the bloodstream and accumulating in tissues and organs (Dong et al., 2023; Leslie et al., 2022; Ramsperger et al., 2023). Thus, to assess these exposure risks to high trophic level species, it is necessary first to ascertain exposure concentration levels in abiotic and biotic matrices. Bioaccumulation, bioconcentration, and biomagnification are common toxicokinetic and bioaccumulation science concepts often used in ecotoxicological and environmental risk assessments for water soluble (hydrophilic) chemicals (e.g., pesticides, metals, hydrocarbons, and pharmaceuticals, and personal care products) (Chormare & Kumar, 2022), as well as for hydrophobic or lipophilic pollutants such as POPs (Arnot & Gobas, 2006; Gobas et al., 2009; Gobas & Morrison, 2000). Bioaccumulation refers to the increase of a pollutant or microplastics in an organism over time, or the gradual net uptake from all environmental compartments including the surrounding environment and the food items (Alava, 2020; Gobas & Morrison 2000; Goswami et al., 2020; Miller et al., 2023; Provencher et al., 2019). Bioconcentration is a subcategory of bioaccumulation; it refers to the gradual buildup of a contaminant in an organism from the water alone, excluding uptake from prey (Arnot & Gobas, 2006; Miller et al., 2023). Conversely, biomagnification implies an increase of higher contaminant concentration at each trophic level in the food web with apex predators exhibiting the highest concentrations. Namely, the contaminant concentration amplifies through the food web and therefore organisms at higher trophic levels or top predators will have higher contaminant concentrations than organisms at lower trophic levels (Gobas & Morrison, 2000; Kelly et al., 2007). Microplastics biomagnification 78 is currently being investigated to predict whether plastics particle biomagnify in marine food webs (Alava, 2020; Hamilton et al., 2021; Miller et al., 2020, 2023). These concepts have recently been applied to microplastic science (Akhbarizadeh et al., 2019; Alava, 2020; Chormare & Kumar, 2022; Goswami et al., 2020; Miller et al., 2020, 2023). A recent literature review of empirical data supports bioaccumulation of microplastics within trophic levels (Miller et al., 2020). Studies have also established trophic transfer of microplastics in natural-like laboratory settings (Farrell & Nelson, 2013; Nelms et al., 2018); however, while species-specific bioaccumulation is likely to occur in marine species as a function of the elimination rate (Alava, 2020), evidence for biomagnification of microplastics between trophic levels is lacking (Covernton et al., 2022; Miller et al., 2020, 2023). The bioaccumulation and biomagnification potential of microplastics are a cause for concern due to the potential impacts on high trophic level species, and therefore require further research (Miller et al., 2020; Provencher et al., 2019). Ecosystem and bioaccumulation modelling and the application of several ecotoxicological and bioaccumulation metrics can help to determine exposure levels and bioaccumulation potential, without needing to conduct invasive and lethal samplings or dissection of live individual organisms. Within this rationale, food web bioaccumulation modeling is a feasible and useful tool to predict and estimate the bioaccumulation potential and ecotoxicological risks of microplastics to higher trophic level species (Alava, 2020). The remote Gal\u00e1pagos Island food webs present unique and relatively isolated opportunities to study microplastic bioaccumulation and biomagnification. The Gal\u00e1pagos Marine Reserve (GMR) is a UNESCO World Heritage site and described as a \u201cliving laboratory\u201d due to the magnitude of biodiversity and unique island-specific adaptations referenced in Charles Darwin\u2019s seminal theory of natural selection in \u201cOn the Origin of Species\u201d (1859). The islands are world-renowned and yet are still at the mercy of multiple anthropogenic impacts, including climate change, fishing pressure and marine pollution, including oil spills, POPs, current use pesticides, metals (e.g., mercury), and more recently ocean plastics, including microplastic pollution (Alava et al., 2022). Recent studies have highlighted microplastics in the archipelago (Alfaro-N\u00fa\u00f1ez et al., 2021; Jones et al., 2021), 79 but little is known about the trophic transfer and accumulation of microplastics in endemic and endangered marine species in the unique food webs of the islands. Seabirds have been used as indicator species for plastic and microplastic pollution since its introduction into scientific literature (Auman et al., 1994; Bergmann et al., 2015), and continue to be good sentinel species (\u201ccanaries in the coal mine\u201d) to biomonitor microplastic pollution in local areas. The Gal\u00e1pagos Islands are home to famous and charismatic seabirds, one of which is the Gal\u00e1pagos penguin (Spheniscus mendiculus), the only tropical penguin species. This flightless seabird props up on molten rock, feeds on small planktivorous fish, and survives off the nutrient rich, cool waters brought to the islands from the Humboldt current (Boersma, 1998; Steinfurth, 2007; Vargas, 1996; Wilson & Wilson, 1990). Unfortunately, strong El Ni\u00f1o events (El Ni\u00f1o Southern Oscillation, ENSO) intensified by ocean warming in tandem with emerging heat waves have devastated the fragile Gal\u00e1pagos penguin population (Vargas et al., 2006; Wolff et al., 2012), leaving them in an endangered state with an estimated 1200 individuals left (BirdLife International, 2020). As is the case of the Gal\u00e1pagos penguin, seabirds are top predators and key functional species in their ecosystems. They offer a succinct opportunity to model and assess microplastic bioaccumulation and biomagnification in a relatively isolated and simplistic food web. Using the Gal\u00e1pagos penguin as \u201cthe canary in the coal mine\u201d to predict the bioaccumulation and biomagnification potential of microplastics in Gal\u00e1pagos food webs, the aims of this study were to: (1) understand the bioaccumulation behaviour of microplastics in the food web of the Gal\u00e1pagos penguin; (2) predict the biomagnification potential of microplastics in the food web of the Gal\u00e1pagos penguin; and (3) compare these findings to empirical data gathered in the Gal\u00e1pagos. This research component embraced the application of a trophodynamic ecosystem modeling using Ecopath and Ecosim (EwE) models along with the Ecotracer routine. First, a basic EwE model was created to model the Gal\u00e1pagos penguin\u2019s food web. In addition, an advanced model of the Bol\u00edvar Channel where most of the penguin population live (Ruiz & Wolff, 2011) was utilized to compare against the simplistic model. A simulation of microplastic intake was applied using the Ecotracer routine (Christensen & Walters, 2004) to track the potential bioaccumulation of microplastics in the Gal\u00e1pagos penguin food web. Empirical data, gathered in 80 the Gal\u00e1pagos (Chapter 2), was used as uptake and environmental input data, and later was compared against the predicted data to assess model bias. For the purposes of this study, ecosystem and food web bioaccumulation modeling was developed and applied as a suitable approach to simulate bioaccumulation potential and ecotoxicological risks of microplastics to higher trophic level species and endangered species to support risk management and regional policy to combat marine plastic pollution in the Gal\u00e1pagos Islands. 3.2 Methods 3.2.1 Ecosystem Modelling Theory Ecopath with Ecosim (EwE) is a trophodynamic ecosystem modeling tool used to understand how different species in an ecosystem interact with one another, how they respond to changes in the environment, and how human activities might impact the ecosystem (Christensen & Walters, 2004). Ecopath is central to the software suite and provides a mass-balance snapshot of the ecosystem in question, while Ecosim offers a dynamic approach for temporal simulations based on predator-prey interactions using Lotka-Volterra and foraging arena theories (Christensen & Pauly, 1992; Christensen & Walters, 2004, 2005). The EwE model together integrates biotic and abiotic components in an ecosystem by incorporating the principles of mass balance as well as a set of linear equations that describe and track the average flow of mass and energy between functional groups according to a diet composition matrix. Functional groups can be species or groups of species that have similar life-history or characteristics which are combined into biomass pools. The diet composition matrix is used to represent the flow of mass and energy within the ecosystem. It also accounts for energy losses over time through processes such as respiration, emigration, and decomposition (Christensen & Pauly, 1992; Christensen & Walters, 2004, 2005). The core principles and mathematical equations of EwE are described in the user guide, which is accessible at http:\/\/www.ecopath.org (Christensen et al., 2008). 3.2.2 Model Descriptions Two models were used to simulate microplastic movements through the Gal\u00e1pagos penguin food web, namely, (1) a novel and simplistic food web model for the Gal\u00e1pagos penguin based on the 81 species diet; and, (2) a trophic model of the Bolivar Channel Ecosystem (BCE), based on Ruiz and Wolff (2011), which has been vetted through peer-review and includes an advanced snapshot of the BCE, with functional groups including seabirds, a proportion of which are Gal\u00e1pagos penguins (i.e., ~26% of the functional group \u2018pool\u2019). The latter incorporates a wider variety of groups and energy flows for a holistic ecosystem view, while the former isolates the Gal\u00e1pagos penguin food web to closely track microplastic movements within the specific food web. Both models were run with the Ecotracer routine to assess and compare bioaccumulation and biomagnification. The Gal\u00e1pagos Penguin (GP) Food web Model - The Gal\u00e1pagos Penguin (GP) food web model was constructed using EwE 6.6.8 (Christensen et al., 2008). The model was developed using a top-down approach by first analyzing the diet of the Gal\u00e1pagos penguin. Based on limited available dietary data, the Gal\u00e1pagos penguin is suggested to feed primarily on small planktivorous fish, including sardines (Sardinops sagax), piquitingas (Lile stolifera), mullets (Mugil spp.), and salema (Xenocys jessiae) (Mills, 1997; Vargas et al., 2005, 2006; Wilson & Wilson, 1990; E. Espinoza, pers. comm., Gal\u00e1pagos National Park, October 2021, unpublished data). Closely related to the Gal\u00e1pagos penguin, the Humboldt Penguin (Spheniscus humboldti) also feeds on anchovies (Engraulis ringens), Araucanian herring (Strangomera bentincki), and silverside (Odontesthes regia), and to a lesser extent, cephalopods (Loligo gahi and Dosidiscus gigas) and crustaceans (stomatopods and isopods) (Herling et al., 2005). The diet composition of the Magellanic penguin (Spheniscus magellanicus), which is also closely related to the Gal\u00e1pagos penguin, was found to be similar (Fernandez et al., 2019). Assessed penguins primarily ate anchovy (Engraulis anchoita) and thornfish (Bovichtus argentinus) (Fernandez et al., 2019). This information provided a solid foundation for creating the GP model based on plausible diet matrix assumptions. The finalized diet matrix was compared and adjusted based on an existing EwE model available on Ecobase (http:\/\/ecobase.ecopath.org\/) (Coll\u00e9ter et al., 2013; Colleter et al., 2015), the Floreana island rocky reef ecosystem model, which includes the Gal\u00e1pagos penguin in the seabird functional group along with other marine species (Okey et al., 2004), as well as the BCE model (Ruiz & Wolff, 2011). Initially, cephalopods were part of the GP model, however, this species functional group were eventually excluded due to the unavailability of sufficient biomass data and the fact that they were not accounted for in the Floreana and BCE models. Conversely, barracuda 82 (Barracuda pelicano) were added to the GP model because they were recognized as seabird prey in the Floreana and BCE models (Okey et al., 2004; Ruiz & Wolff, 2011). It is reasonable to conclude that juvenile barracuda are likely to be preyed upon, given the preferred size of prey for penguins (Fernandez et al., 2019; Herling et al., 2005). The final diet matrix consisted of ten functional groups as shown in Table 3.6. Species selection and identification was done using scuba diving field guides (Constant, 2007; Humann & DeLoach, 2003) as well as using the BCE model (Ruiz & Wolff, 2011). Diet information for penguin prey and lower trophic level species were accessed through FishBase (https:\/\/www.fishbase.se\/) and SeaLifeBase (https:\/\/www.sealifebase.ca\/) (Froese & Pauly, 2000, 2022; Palomares & Pauly, 2022). Where data was not available, the diet information of the closest related species was used. Functional group biomasses (t\/km2) were calculated based on existing models and reasonable estimations of biomass in g\/m2. Production per biomass (P\/B) was calculated based on the average lifespan of the species (i.e., the inverse of mortality). Gal\u00e1pagos penguins, for example, live up to 15 to 20 years (Rafferty, 2020), therefore a P\/B of 0.07 is reasonable when assuming a mortality of 115 y-1. Consumption per biomass (Q\/B) was obtained from FishBase and SeaLifeBase (Froese & Pauly, 2000, 2022; Palomares & Pauly, 2022). The Q\/B parameter from FishBase was adjusted and recalculated for barracuda and mullets, given the penguins preference for smaller sized prey; therefore, assuming the barracuda and mullet functional groups are comprised of juveniles, the size i.e., max total length (TL), was set to 30 cm for barracuda S. idiates and 25 cm for mullet M. galapagensis, then FishBase calculations were rerun. Ecotrophic efficiency was left to be calculated by the EwE model. The sources of information and input data can be found in Table 3.7 & Table 3.8. After inputting all parameter estimates, the model was found to be unbalanced, therefore parameters were manually adjusted until balance was achieved. The Q\/B was reduced, and the P\/B was increased for fish, so that the P\/Q ratio was near 0.2-0.3 based on EwE best practices (Christensen, 2021; Heymans et al., 2016). The biomass of herbivorous and predatory zooplankton was increased to account for their predation. Given the intentionally limited scope of the model and specific focus on the Gal\u00e1pagos penguin, diet import was reasonably assumed for most functional groups given that not all prey were included (e.g., not all barracuda prey). 83 Table 3.6: Diet composition matrix in the food web of the Gal\u00e1pagos penguin (GP) EwE model Prey\/predator TL 1 2 3 4 5 6 7 1 Gal\u00e1pagos Penguin 3.7 2 Barracuda 3.6 0.05 3 Mullet 2.3 0.05 4 Anchovy, Herring, Sardines, Salema 2.7 0.81 0.6 5 Decapods 2.0 0.05 6 Predatory zooplankton 2.6 0.02 0.1 0.1 0.25 0.01 0.2 7 Herbivorous zooplankton 2 0.02 0.1 0.05 0.2 0.01 0.25 8 Macroalgae 1 0.05 0.3 0.05 9 Microalgae\/phytoplankton 1 0.2 0.2 0.2 0.2 0.6 10 Detritus 1 0.4 0.15 0.2 0.15 0.15 Import 0 0.1 0.2 0.2 0.28 0.2 0.2 Sum 1 1 1 1 1 1 1 Table 3.7: List of species and functional groups of the Gal\u00e1pagos penguin (GP) EwE model based on the existing literature sources. Common name Species name Source Gal\u00e1pagos Penguin Spheniscus mendiculus NA Barracudas Sphyraena idiates (Constant, 2007) Mullet Mugil galapagensis*, cephalus, curema (Constant, 2007; Humann & DeLoach, 2003; Ruiz & Wolff, 2011) Anchovy Engraulidae: Anchoa naso (Constant, 2007) Sardines Sardinops sagax sagax (Constant, 2007) Herrings Clupeidae: Opisthonema berlangai (Constant, 2007) Salema Xenichthys agassizi, Xenocys jessiae* (Humann & DeLoach, 2003; Ruiz & Wolff, 2011) Decapods Panulirus gracilis, panulirus penicillatus, Scyllariidea astori* (Constant, 2007; Ruiz & Wolff, 2011) Predatory Zooplankton Functional group spp. See Ruiz & Wolff (2011) Herbivorous Zooplankton Functional group spp. See Ruiz & Wolff (2011) *The asterisk represents the main species referenced to determine biological parameters. 84 Table 3.8: Sources used to determine parameters for basic Ecopath estimates. Diet Biomass (t\/km2) Q\/B & P\/B Gal\u00e1pagos penguin (Karpouzi, 2005; Okey et al., 2004; Wilson & Wilson, 1990; Ruiz & Wolff, 2011; Vargas et al., 2006; E. Espinoza pers. Comm., Gal\u00e1pagos National Park, October 2021, unpublished data) 0.125 (Ruiz & Wolff, 2011) Q\/B = 60.3 P\/B = 0.067 (Ruiz & Wolff, 2011) Barracuda (Ruiz & Wolff, 2011) 13.06 (Ruiz & Wolff, 2011) Q\/B = 3.9 P\/B = 0.063 Fishbase Life-history: Sphyraena idiates Mullet (Ruiz & Wolff, 2011) 22.6 (Ruiz & Wolff, 2011) Q\/B = 10.9 P\/B = 2.8 Fishbase Life-history: Mugil galapagensis Anchovy, Herring, Sardines, Salema (Grove & Lavenberg, 1997; Muck et al., 1989; Ruiz & Wolff, 2011; Serra & Tsukayama, 1988) 19 (Ruiz & Wolff, 2011) Q\/B = 15 P\/B = 4.6 Fishbase Life-history: Brachygenys jessiae, Sardinops sagax, Anchoa nasus, Opisthonema berlangai Decapods (Ruiz & Wolff, 2011) 14.48 (Ruiz & Wolff, 2011) Q\/B = 11.95 P\/B = 0.687 Sealifebase Scyllarides astori Predatory zooplankton (Ruiz & Wolff, 2011) 15 (Ruiz & Wolff, 2011) Q\/B = 99.13 P\/B =45 (Ruiz & Wolff, 2011) Herbivorous zooplankton (Ruiz & Wolff, 2011) 22 (Ruiz & Wolff, 2011) Q\/B = 200 P\/B = 36 (Ruiz & Wolff, 2011) Macroalgae NA 800.47 NA 85 (Ruiz & Wolff, 2011) Microalgae\/ phytoplankton NA 31.16 (Ruiz & Wolff, 2011) NA Detritus NA 500 (Ruiz & Wolff, 2011) NA Bolivar Channel Ecosystem (BCE) Model - The Bolivar Channel Ecosystem (BCE) model is a more advanced EwE model consisting of thirty functional groups known to inhabit the highly productive, upwelling zone between Isabela and Fernandina islands (Ruiz & Wolff, 2011). The original model was provided by courtesy of the authors to support the assessment of microplastic bioaccumulation in the area. Ruiz & Wolff (2011) estimated biomass from observed sightings and censuses where applicable. The model includes one functional group entitled seabirds, comprising 74% of flightless cormorants (Phalacrocorax harrisi) and 26% Gal\u00e1pagos penguin (S. mendiculus). Input parameters underwent a series of resampling through the Ecoranger resampling routine to select a random set of input values from normal distributions of the input parameters. The model is fully described in Ruiz & Wolff (2011). 3.2.3 Modelling Bioaccumulation of Microplastics with EwE Ecotracer Routine The Ecotracer routine is an EwE module used to track and assess the bioaccumulation potential of pollutants in marine food webs over time (Booth, 2016; Christensen & Walters, 2004; Coombs, 2004). Ecotracer uses Ecosim temporal simulations to predict the flow of contaminants through the food web (Alava et al., 2018; Booth, 2016; Coombs, 2004). The contaminant concentration over time in each functional group is based on the flow rates from Ecosim, as well as decay, elimination, and physical exchange rates. The linear dynamical equation for changes in contaminant concentration over time for a given functional group (pool) or species i is expressed as: 86 \ud835\udc36\ud835\udc56\ud835\udc35\ud835\udc56\ud835\udc51\ud835\udc61= (\ud835\udc36\ud835\udc57 \u2022 \ud835\udc3a\ud835\udc36\ud835\udc56 \u2022\ud835\udc44\ud835\udc57\ud835\udc56\ud835\udc35\ud835\udc57) + (\ud835\udc62\ud835\udc56 \u2022 \ud835\udc35\ud835\udc56 \u2022 \ud835\udc36\ud835\udc5c) + (\ud835\udc50\ud835\udc56 \u2022 \ud835\udc3c\ud835\udc56) \u2013 [(\ud835\udc36\ud835\udc56 \u2022\ud835\udc44\ud835\udc56\ud835\udc57\ud835\udc35\ud835\udc56) + \ud835\udc36\ud835\udc56 \u2022 \ud835\udc40\ud835\udc42\ud835\udc56 + (1 \u2212 \ud835\udc3a\ud835\udc36\ud835\udc56) \u2022 \u2211 \ud835\udc36\ud835\udc57\ud835\udc57 \u2022\ud835\udc44\ud835\udc57\ud835\udc56\ud835\udc35\ud835\udc57 + \ud835\udc52\ud835\udc56 \u2022 \ud835\udc36\ud835\udc56 + \ud835\udc51\ud835\udc56 \u2022 \ud835\udc36\ud835\udc56] The time dynamic changes in contaminant concentration in the biomass (CiBi) of a given functional group or \u2018pool\u2019 (i) can be described by the following components, based on Christensen and Walters (2005): 1. Add uptake from food: \u2022 Cj \u2022 GCi \u2022 \ud835\udc44\ud835\udc57\ud835\udc56\ud835\udc35\ud835\udc57 where Cj is the concentration in food\/prey j, GCi is the proportion of food assimilated by type i organisms, Qji is biomass flow rate from j to i (estimated in Ecopath as Bi \u2022 (Q\/B)i \u2022 DCij, where Bi is the biomass for i, (Q\/B)i is the consumption \/ biomass ratio for i, and DCij is the fraction of food\/prey (i) in the average diet of predator (j)), and Bj is food\/prey j biomass. 2. Add direct uptake from environment: \u2022 ui \u2022 Bi \u2022 Co, where ui is uptake per biomass per time, per unit of environmental concentration, Bi is biomass (i) functional group, and Co=environmental concentration. 3. Add concentration in immigrating organisms: \u2022 ci \u2022 Ii, where ci is contaminant per unit of biomass in immigrating biomass and Ii is biomass of pool i immigrants per time. 4. Subtract predation: \u2022 Ci \u2022 \ud835\udc44\ud835\udc56\ud835\udc57\ud835\udc35\ud835\udc56, where Ci is the contaminant concentration in pool i, Qij is the consumption rate of i organisms by predator j, and Bi is biomass in pool i. 5. Subtract detritus: \u2022 Ci \u2022 MOi + (1-GCi) \u2022 \u2211jCj \u2022 \ud835\udc44\ud835\udc57\ud835\udc56\ud835\udc35\ud835\udc57,, where MOi is non-predation death rate of pool i (per year), GCi is the fraction of food intake assimilated, Qji is the intake rate of type j biomass by type i, and Bj is biomass in pool j. 6. Subtract emigration: 87 \u2022 ei \u2022 Ci, where ei is the emigration rate (per year) and Ci is the contaminant concentration in food\/prey (i). 7. Subtract metabolism: \u2022 di \u2022 Ci, where di is metabolism + decay rate for the material while in pool i. The contaminant concentrations in immigrating (ci \u2022 Ii) and emigrating (ei \u2022 Ci) organisms were considered to be negligible for the purposes of this modeling work to simplify the model and because the Gal\u00e1pagos penguins are endemic and residents of the Gal\u00e1pagos Islands and do not undertake migration. Therefore, the equation was simplified as: \ud835\udc36\ud835\udc56\ud835\udc35\ud835\udc56\ud835\udc51\ud835\udc61= (\ud835\udc36\ud835\udc57 \u2022 \ud835\udc3a\ud835\udc36\ud835\udc56 \u2022\ud835\udc44\ud835\udc57\ud835\udc56\ud835\udc35\ud835\udc57) + (\ud835\udc62\ud835\udc56 \u2022 \ud835\udc35\ud835\udc56 \u2022 \ud835\udc36\ud835\udc5c) \u2013 [(\ud835\udc36\ud835\udc56 \u2022\ud835\udc44\ud835\udc56\ud835\udc57\ud835\udc35\ud835\udc56) + \ud835\udc36\ud835\udc56 \u2022 \ud835\udc40\ud835\udc42\ud835\udc56 + (1 \u2212 \ud835\udc3a\ud835\udc36\ud835\udc56)\u2022 \u2211 \ud835\udc36\ud835\udc57\ud835\udc57 \u2022\ud835\udc44\ud835\udc57\ud835\udc56\ud835\udc35\ud835\udc57 + \ud835\udc51\ud835\udc56 \u2022 \ud835\udc36\ud835\udc56] Hence, the following input parameters were necessary to determine and include: 1. Initial pool concentrations Ci, including environmental concentration Co 2. Direct uptake rate parameters ui as rates per time per biomass per unit Co 3. Metabolism\/decay rates di, including egestion rates Input parameters were gathered from observed data from the field (see Chapter 2), laboratory studies, and existing modelling work outlined in Table 3.9. Microplastic concentrations (particle size size >10 \uf06dm) in the environment were obtained from McMullen et al. (in prep., Chapter 2) and were entered as input data for the initial and inflow environmental concentrations of microplastics in the Ecotracer module for both EwE models. The direct uptake rates were derived from the observed zooplankton ingestion rates of anthropogenic particles, as reported in McMullen et al. (in prep., Chapter 2). This information was utilized as the exclusive source for the microplastic uptake, based on the premise that zooplankton occupy a pivotal position at the bottom of aquatic food webs and therefore represent the initial point of entry for the bioaccumulation of microplastics in the ecosystem (Alava, 2020). Considering the purpose of the study, aimed to 88 evaluate the levels and magnitude of bioaccumulation and biomagnification of microplastics in the Gal\u00e1pagos penguin food web, alternative sources of microplastic exposure such as inhalation of airborne particles or direct uptake from the environment were assumed to be negligible and thus not considered. Microplastic elimination or egestion rates were adopted from the food web bioaccumulation model developed by Alava (2020) or collected from the best available data (See Table 3.10). Briefly, the egestion or elimination rate of microplastic is computed from the retention time (\u03c4) as the elimination or egestion rate is inversely related to the retention or residency time: \u03c4 = 1\/kE; thus, solving for kE: kE = 1\/\u03c4 (Alava, 2020). Finally, an average decay rate was set at ~0.0283 per year (2.825% per year), based on plastic particles decay estimates (ranging 0.65% to 5% per year), accounting for weight loss due to solar radiation and oxygenation, documented by Everaert et al. (2018). Ecotracer was run for a simulation period of 100 years. Table 3.9: Ecotracer environmental parameters, input data, and respective sources used in the Gal\u00e1pagos Penguin (GP) EwE model Ecotracer Parameter Environmental Input Data Source\/Details Initial concentration (t\/km2) 4.11x10-2 7.57x10-6 g\/MP (Alava, 2020) 4.11x10-6 \uf0b1 3.72x10-6 average MP g\/L (McMullen et al., in prep., Chapter 2) Converted to t\/km2 based on Lusher (2015) Base inflow rate (t\/km2\/y) 4.11x10-2 Consistent state scenario based on (Lebreton et al., 2019) Decay Rate (\/year) 0.0283 (Everaert et al., 2018) Direct absorption rate (g\/kg) 7.38x10-2 Based on observed zooplankton ingestion rate (McMullen et al., in prep., Chapter 2) 89 Table 3.10: Data values of microplastic retention times to calculate the egestion (elimination) rates with reference sources used for the Gal\u00e1pagos Penguin (GP) EwE model. Group Conservative Elimination Rate (Short Retention time) Elimination Rate Based on Literature Source Gal\u00e1pagos penguin <24 hours 70 hours (Jackson, 1992; Laugksch & Duffy, 1986) Barracuda <24 hours 49 days (Critchell & Hoogenboom, 2018; Ory et al., 2018) Mullet <24 hours 30 days (Jovanovic et al., 2018) Anchovy, Herring, Sardines, Salema <24 hours 49 days (Critchell & Hoogenboom, 2018; Ohkubo et al., 2022; Ory et al., 2018) Decapods <24 hours 14 days (Murray & Cowie, 2011) Predatory zooplankton <24 hours 7 days (Cole et al., 2013) Herbivorous zooplankton <24 hours 7 days (Cole et al., 2013) 3.2.4 Model Scenarios To explore the microplastic bioaccumulation potential and biomagnification capacity in the food web, the model was run using four different scenarios: (1) A baseline scenario using observed environmental anthropogenic particle concentrations (McMullen et al., in prep., Chapter 2) and egestion rates from literature; (2) a high environmental concentration scenario, which assumed a higher abundance of microplastics in the environment, based on the upper limit of the standard deviation of the aforementioned observed microplastic abundance data; (3) a low environmental concentration scenario, which assumed a lower abundance of microplastics in the environment, based on the lower limit of the standard deviation of the microplastic abundance data; and finally, (4) a 99% egestion rate scenario which assumes all microplastics are excreted in under 24 hours, using the environmental concentration data from baseline scenario (Table 3.11). See Appendix C for additional Ecotracer input data. 90 Table 3.11: Modelling scenarios and respective Ecotracer input data for model kinetic parameters. Scenario Title Initial concentration & base inflow (t\/km2) Decay Rate Direct absorption rate for zooplankton (g\/kg) Elimination rates 1 Baseline 4.11x10-2 0.0283 7.38x10-2 Table 3.10 2 High Environmental Concentration 7.83x10-2 0.0283 7.38x10-2 Table 3.10 3 Low Environmental Concentration 3.9x10-3 0.0283 7.38x10-2 Table 3.10 4 Elimination rates changed to 99% eliminated in 1 day 4.11x10-2 0.0283 7.38x10-2 All groups set to 0.99 3.2.5 Bioaccumulation and biomagnification metrics Bioaccumulation and biomagnification criteria and metrics for microplastics were based on Alava (2020) and (Alava, 2021). Bioaccumulation factor (BAF): The bioaccumulation factor (BAF) was calculated by comparing the amount of microplastics accumulated by aquatic biota to the total concentration of microplastics present in both the aquatic environment and diet (e.g., the predator's prey). A BAF greater than 1 indicates plausible bioaccumulation under steady state (e.g., BAF >1). \ud835\udc35\ud835\udc34\ud835\udc39 =\ud835\udc36\ud835\udc56\ud835\udc36\ud835\udc5c + \ud835\udc36\ud835\udc57 Where Ci is the microplastic concentration in the predator (in units of g\/kg), Co is the concentration in the environment and Cj is the concentration in the diet (prey) in units of g\/kg. Bioconcentration factor (BCF): The bioconcentration factor (BCF) was calculated by comparing the amount of microplastics accumulated by the aquatic organism or biota (in units of g\/kg) to the 91 total concentration of microplastics present in the water or aquatic environment (in units of g\/L). A BCF greater than 1 indicates bioconcentration in the organism (e.g., BCF >1). \ud835\udc35\ud835\udc36\ud835\udc39 =\ud835\udc36\ud835\udc56\ud835\udc36\ud835\udc64 Where Ci is the microplastic concentration in biota (g\/kg) and Cw is the concentration in the water (g\/L). Predator-prey biomagnification factor (BMFTL): The predator-prey biomagnification factor (BMFTL) was calculated by comparing the amount of microplastics accumulated by the predator in units of g\/kg to the total concentration of microplastics (g\/kg) in the prey, divided by the difference in trophic levels. A BMF greater than 1 indicates plausible trophic biomagnification (e.g., BMFTL >1). \ud835\udc35\ud835\udc40\ud835\udc39 =\ud835\udc36\ud835\udc56\/\ud835\udc36\ud835\udc57\ud835\udc47\ud835\udc3f\ud835\udc56 \u2212 \ud835\udc47\ud835\udc3f\ud835\udc57 Where Ci is the microplastic concentration in the predator, Cj is the concentration in prey, TL is the trophic level for the predator (i) and prey (j). Trophic Magnification Factor (TMF): The trophic magnification factor (TMF) is a commonly employed metric in food web analysis that measures the biomagnification of pollutants at various trophic levels (Alava, 2020; Borg\u00e5 et al., 2012; Conder et al., 2012; Walters et al., 2016). The TMF is calculated as the antilog of the regression slope of the linear regression between the logarithmic-transformed concentrations of microplastics (Log MPs) predicted in the GI tract of organisms of the food web and their trophic level, TL (Alava, 2020). The linear equation is as follows. \ud835\udc3f\ud835\udc5c\ud835\udc5410 \ud835\udc40\ud835\udc43 = \ud835\udc4e + \ud835\udc4f\ud835\udc47\ud835\udc3f Where b is the slope, TL is the trophic level, and a is the y-intercept. The TMF can be expressed as the equivalent exponential mathematical terms expressed as TMF =10b, where b is the slope. 92 \ud835\udc47\ud835\udc40\ud835\udc39 = 10\ud835\udc4f The TMF (slope, b) is statistically evaluated using a significance level (\u03b1) of 0.05. A TMF > 1 (b > 0) indicates that the contaminant biomagnifies in the food web. A TMF < 1 (b < 0) indicates trophic dilution of the contaminants, while a TMF=1 (b=0) indicates no change in contaminant concentrations among organisms of a food web (Alava, 2020; Borg\u00e5 et al., 2012). 3.2.6 Sensitivity Assessment The sensitivity of the model was assessed by testing changes in the environmental concentrations and the functional group elimination rates. This was conducted by comparing the outcomes of the model through four of the different scenarios (Table 3.11), including high and low environmental concentrations as well as high (e.g., > 24 hours) and low (e.g., < 24 hours) retention times, equivalent to slow (high retention) or fast (low retention) elimination rates, respectively. The most sensitive parameters were determined by assessing the variance in output data according to changes in environmental concentrations and elimination rate. 3.2.7 Model Bias A model bias (MB) approach was applied to assess the performance of the food web model and corroborate the projections of microplastics under the three abiotic concentrations\u2019 scenarios (scenario 1-3) and one conservative egestion rate scenario (scenario 4). The performance of the model was analyzed in terms of the model bias ratio: \ud835\udc40\ud835\udc35 =\ud835\udc36\ud835\udc35\ud835\udc43,\ud835\udc56\ud835\udc40\ud835\udc43\ud835\udc36\ud835\udc35\ud835\udc42,\ud835\udc56\ud835\udc40\ud835\udc43 where CBP,iMP and CBO,iMP are the model calculated (predicted) and observed microplastic concentrations in species i, respectively. This analysis was done by comparing the projected microplastics concentrations in biota (zooplankton, anchovy, and mullet) yielded by the model simulations to the empirical data measured in free-ranging zooplankton, wild caught anchovy, and 93 mullets in waters of the Gal\u00e1pagos National Park (McMullen et al., in prep., Chapter 2). The microplastic and anthropogenic particle mean concentration for zooplankton, anchovy, and mullet were 7.38x10-7, 7.69x10-10, and 1.48x10-8 g\/kg, respectively (McMullen et al., in prep., see Chapter 2). 3.3 Results 3.3.1 Gal\u00e1pagos Penguin Food Web Model in Ecopath A successfully balanced GP model was constructed with a total of ten functional groups of varying trophic levels (Figure 3.15: Table 3.12). Analysis of the energy flow diagram revealed the highest energy flows from microalgae\/phytoplankton to herbivorous zooplankton, as well as from anchovy, herring, sardines, and salema to barracuda and to the Gal\u00e1pagos penguin. A high biomass of macroalgae and detritus was necessary to maintain the balance of the model, and matched output of the BCE model (Ruiz & Wolff, 2011). Detritus, microalgae\/phytoplankton, and macroalgae made up trophic level 1, followed by herbivorous zooplankton, mullet, predatory zooplankton, and anchovy, herring, sardines, salema as trophic level 2, in order of lowest to highest. Predatory zooplankton had a higher trophic level than mullets, which is expected given mullet\u2019s preference for detritus compared to predatory zooplankton consuming other zooplankton. Likewise, anchovy, herring, sardines, and salema had a similar trophic level compared to predatory zooplankton, which is not surprising given similar food preferences, namely other zooplankton. The Gal\u00e1pagos penguin and barracuda were the high trophic level species included in the model. Ecotrophic efficiencies (EE) were comparable to Ruiz & Wolff (2011) and ranged from 0 to 0.68 where the highest EE resulted in predatory zooplankton, herbivorous zooplankton and microalgae\/phytoplankton. 94 Figure 3.15: Energy flow diagram of the Gal\u00e1pagos penguin food web model in EwE, where circles represent the amount of biomass and labels indicate the respective functional group. Thicker lines between functional groups reflect higher energy flows from one functional group to another and trophic levels are indicated on the y-axis. 95 Table 3.12: Parameter output from the Gal\u00e1pagos penguin EwE food web model, capturing a balanced, static representation of the food web at a moment in time with respective biomass, production \/ biomass (P\/B), consumption \/ biomass (Q\/B), Ecotrophic Efficiency (EE), and production \/ consumption (P\/Q) metrics. Group name Trophic level Habitat area (proportion) Biomass in habitat area (t\/km2) Biomass (t\/km2) Production \/ biomass (\/year) Consumption \/ biomass (\/year) Ecotrophic Efficiency Production \/ consumption (\/year) 1 Gal\u00e1pagos Penguin 3.710 1 0.0125 0.0125 0.067 60.30 0.000 0.001 2 Barracuda 3.636 1 13.06 13.06 0.063 3.9 0.046 0.016 3 Mullet 2.260 1 22.6 22.6 2.8 10.9 0.0006 0.257 4 Anchovy, Herring, Sardines, Salema 2.743 1 19 19 4.6 15 0.386 0.307 5 Decapods 2.035 1 14.5 14.5 0.687 11.95 0.004 0.058 6 Predatory zooplankton 2.578 1 15 15 45 99.1 0.593 0.454 7 Herbivorous zooplankton 2 1 22 22 36 200 0.566 0.18 8 Macroalgae 1 1 800.5 800.5 15.7 0.023 9 Microalgae\/ phytoplankton 1 1 31.2 31.2 146.3 0.675 10 Detritus 1 1 500 500 0.066 96 3.3.2 Microplastic Bioaccumulation and Biomagnification via Ecotracer Gal\u00e1pagos Penguin Food Web Model - Utilizing the baseline Ecotracer scenario for the Gal\u00e1pagos penguin food web model, output data revealed a rapid increase in contaminant concentration (g) per biomass (kg) until around year 5, after which it shifted to a more gradual increase and eventually plateaued, reaching steady state around year 60 (Figure 3.16 A). The microplastic concentration in the Gal\u00e1pagos Penguin plateaued at 7.6x10-5 g\/kg, while the microplastic concentration in their primary prey item, planktivorous fish, reached steady state at 4.93x10-8 g\/kg. The Gal\u00e1pagos penguin was found to have the highest level of microplastics per biomass, followed by barracuda, anchovy, sardine, herring, and salema, and then predatory zooplankton (Figure 3.16 A). In contrast, species or functional groups at lower trophic levels had lower concentrations of microplastics per unit of biomass. The baseline simulation results predicted the plausible bioaccumulation and biomagnification of microplastics in the functional groups of species within this model. The relationship between the logarithmic-transformed concentration of microplastics and the species\u2019 trophic levels shows that the TMF was significantly greater than 1 (i.e., TMF > 1; when the slope is statistically different or greater than zero [(b > 0)] in a positive, significant linear regression). The TMF increase from 7.08 at year 1 to 60.3 at year 100 (Figure 3.16; Table 3.13). By year 100, there was substantial difference between the Gal\u00e1pagos penguin and Barracuda (trophic level 3) compared to lower trophic level groups, as an indication of biomagnification in the model. The regression between the logarithmic microplastic concentration (g\/kg) revealed a slope of 1.79 with a TMF = 61.7 (p < 0.05) further indicating a statistically significant relationship for biomagnification in the model (Figure 3.16 B). The Gal\u00e1pagos penguin had the highest BAF, regardless of what scenario was assessed, while the mullet had the lowest BAF and BCF (Figure 3.16: Table 3.14). The BMF increased with each trophic level and was high (BMFTL=1239 and BMFTL=1796) for the Gal\u00e1pagos penguin with planktivorous fish and mullet being considered as prey, respectively, though it was substantial for all prey items from the Gal\u00e1pagos penguin. The BMFTL was the lowest for planktivorous fish 97 (BMFTL=3.06) as well as for predatory zooplankton with herbivorous zooplankton as prey (BMFTL=3.05). 98 A) 99 B) 100 Figure 3.16: (A) Bioaccumulation simulations using the EwE Ecotracer routine, showing the projections of microplastics (MPs) bioaccumulation in the Gal\u00e1pagos penguins food web under the baseline scenario with seawater initial concentrations = 0.00411 t\/km2. The simulations for the bioaccumulation include the elimination rates based on the literature. For zooplankton, as the key trophic level for the initial uptake of microplastics, the ingestion rate of microplastics and anthropogenic particles by zooplankton (i.e., 7.38x10-7 g\/kg\/day) was used, based on McMullen et al. (in prep., Chapter 2). (B) Linear regressions showing the significant relationship between predicted concentrations of microplastics (log-transformed data) and trophic levels in the GP model at year 100. The antilog of the regression slope was used to determine TMF. Table 3.13: Linear regression data for the GP EwE model with the baseline scenario showing the significant relationship between predicted concentrations of microplastics (log-transformed data) and trophic levels in the GP model at years 1, 25, 50, and 100. The antilog of the regression slope was used to determine TMF. Statistical regression parameters Year 1 Year 25 Year 50 Year 100 Correlation Coefficient (r) 0.87 0.93 0.93 0.92 Coefficient of determination (r2) 75.56% 86% 86% 85% Slope for least square line (b) 0.8499 1.61 1.74 1.79 y-intercept for least square line -10.7613 -11.41 -11.54 -11.65 P-value* 0.011 0.0028 0.0028 0.0032 TMF 7.08 40.7 55.0 61.7 *Statistically significant linear regression 101 Table 3.14: Bioaccumulation factor (BAF), bioconcentration factor (BCF), and predator-prey biomagnification factors (BMFTL) from average microplastic concentration (g\/kg) from selected predator-prey combinations in the Gal\u00e1pagos penguin food web model. Predatory zooplankton with prey set to herbivorous zooplankton Planktivorous fish with prey set to predatory zooplankton Planktivorous fish with prey set to herbivorous zooplankton Mullet with prey set to detritus Gal\u00e1pagos penguin with prey set to planktivorous fish Gal\u00e1pagos penguin with prey set to mullet Gal\u00e1pagos penguin with prey set to decapods BAF 4.36x10-8 4.92x10-8 4.92x10-8 2.26x10-8 5.90x10-5 5.90x10-5 5.90x10-5 BCF 4.36x10-8 4.92x10-8 4.92x10-8 2.26x10-8 5.90x10-5 5.90x10-5 5.90x10-5 BMFTL 3.50 6.84 3.06 11.3 1239 1796 2308 102 Bolivar Channel (BCE) Ecosystem Model - The Bolivar Channel Ecosystem model with the Baseline Ecotracer scenario yielded similar results to the Gal\u00e1pagos Penguin food web model in terms of overall trend, but with lower microplastic concentrations per functional group. Output data once again revealed a rapid increase in contaminant concentration (g) per biomass (kg) until around year 5, and diverging slightly from the GP model, concentrations plateaued earlier, reaching steady state around year 35 as shown in Figure 16A. Microplastic concentrations in the seabirds plateaued at around 5.9x10-7 g\/kg, two orders of magnitude lower than results from the GP model. Microplastic concentrations in the penguins\u2019 primary prey items, planktivorous fish, reached steady states at 6.63x10-8 g\/kg (Figure 3.17 A), which is within the same order of magnitude as yielded in the GP model. The functional groups with the highest concentrations of microplastics per biomass displayed a pattern consistent with the one established in the GP model. The functional groups of interest with elevated microplastic concentrations included seabirds as the highest, followed by barracuda, predatory zooplankton, and finally, small planktivorous reef fish (Figure 3.17 A). Converse to the GP model, predatory zooplankton had slightly higher microplastic concentrations compared to small planktivorous fish and herbivorous zooplankton had higher concentrations of microplastics than both detritivorous species, mullets, and lobsters. Similar to the GP model simulations, BCE simulation resulted in plausible bioaccumulation and biomagnification of microplastics in the functional groups of species in the model. The relationship between the concentration of microplastics and the species\u2019 trophic levels shows that the TMF was once again significantly greater than 1 (slope > 0; Figure 3.17 A). The TMF ranged from 6.17 at year 1 to 16.6 at year 100 (Table 3.15). At year 100, the TMF was lower than estimated by the GP model. Seabirds and barracuda displayed a similar trend, yielding higher bioaccumulation and biomagnification potentials than other functional groups of interest. Of particular interest was the projections for predatory marine mammals and sharks, having the highest biomagnification capacity of all functional groups in the model. Overall, the regression indicated a high biomagnification potential as the logarithmic microplastic concentration (g\/kg) versus the trophic level revealed a statistically significant slope of 1.22, i.e., TMF=16.7 (p < 0.001; Figure 3.17; Table 3.15). 103 As in the GP model, seabirds had the highest BAF and BCF potential, regardless of what prey scenario was assessed, and the mullet once again had the lowest BAF and BCF (Table 3.16). The resulting BMFTL were different than the GP model. Foremost, the planktivorous reef fish revealed BMFTL < 1, indicating lack of biomagnification. The Gal\u00e1pagos penguin revealed positive BMFTL across all prey items, where the highest was once again with prey item lobster. That said, BMFTL was substantially lower than the GP model; the highest BMFTL in the BCE model was a factor of 76 compared to a factor of 2308 in the GP model. 104 A) 105 B) 106 Figure 3.17: (A) Bioaccumulation simulations using the EwE Ecotracer routine, showing the projections of microplastics (MPs) bioaccumulation in the BCE model under the baseline scenario where seawater initial concentrations = 0.00411 t\/km2. The simulations for the bioaccumulation include the elimination rates based on the literature. For zooplankton, as the key trophic level for the initial uptake of microplastics, the ingestion rate of microplastics and anthropogenic particles by zooplankton (i.e., 7.38x10-7 g\/kg\/day) was used, based on McMullen et al. (in prep., Chapter 2). (B) Linear regressions showing the significant relationship between predicted concentrations of microplastics (log-transformed data) and trophic levels in the GP model at year 100. The antilog of the regression slope was used to determine TMF. Table 3.15: Linear regression data for the BCE with the baseline scenario showing the significant relationship between predicted concentrations of microplastics (log-transformed data) and trophic levels in the GP model at years 1, 25, 50, and 100. The antilog of the regression slope was used to determine TMF. Statistical regression parameters Year 1 Year 25 Year 50 Year 100 Correlation Coefficient (r) 0.62 0.79 0.79 0.79 Coefficient of determination (r2) 38.83% 62.65% 61.87% 61.87% Slope for least square line (b) 0.80 1.20 1.22 1.22 y-intercept for least square line -11.1 -10.7 -10.6 -10.6 P-value* 0.0005 8.8x10-7 1.15x10-6 1.148x10-6 TMF 6.17 15.8 16.6 16.6 *Statistically significant linear regression 107 Table 3.16: Bioaccumulation factor (BAF), bioconcentration factor (BCF), and predator-prey biomagnification factors (BMFTL) from average microplastic concentration (g\/kg) from selected predator-prey combinations in the BCE web model. Predatory zooplankton with prey set to herbivorous zooplankton Planktivorous fish with prey set to predatory zooplankton Planktivorous fish with prey set to herbivorous zooplankton Mullet with prey set to detritus Seabirds with prey set to planktivorous fish Seabirds with prey set to mullet Seabirds with prey set to lobster BAF 7.34x10-8 6.62 x10-8 6.62x10-8 1.14x10-8 5.85x10-7 5.85x10-7 5.85x10-7 BCF 7.34x10-8 6.62 x10-8 6.62x10-8 1.14x10-8 5.85x10-7 5.85x10-7 5.85x10-7 BMFTL 4.57 -2.73 -2.73 18.3 8.61 36.9 75.8 108 3.3.3 Model Sensitivity To assess the sensitivity of the model parameters, four different scenarios were evaluated for each model, resulting in a total of eight simulations to examine microplastic bioaccumulation and biomagnification. The simulation outcomes showed that the elimination rate was the most sensitive parameter. This was evidenced by the fact that the results from this scenario displayed the greatest variability in terms of BAF and BCF, as well as smaller ranges in BMFTL across both the GP model and the BCE model, as shown in Table 3.17. In the 99% elimination rate scenario for both Gal\u00e1pagos penguin and the BCE models, the BAF and BCFs were substantially lower, with values that were two to four orders of magnitude lower than those estimated in the high and low abundance scenarios as well as the baseline scenarios (Table 3.17). The BAF and BCF for the high and low abundance simulations were comparable to the baseline scenario for the Gal\u00e1pagos penguin and BCE models. For the GP model using the 99% elimination rate scenario, the BMFTL ranged from 0.02 to 49.6 depending on the predator and prey (Table 3.17). Conversely, the BMFTL had a much broader range (e.g., 3.06 to 2308) across the other three scenarios. When comparing the BMFTL for the Gal\u00e1pagos penguins and planktivorous fish, the baseline scenario resulted in the highest BMFTL (BMFTL=1239). This value was comparable to the high and low abundance scenarios but was substantially higher than the BMFTL of 27.7 as estimated by the 99% elimination scenario for the same species. For the BCE model, the BMFTL ranged from -0.04 to 76.71 in the 99% eliminate rate scenario, which was more comparable to the other three scenarios yielding -2.73 to around 76 BMFTL, depending on the predator prey combination (Table 3.17). Once again, the BMFTL for the Gal\u00e1pagos penguins and planktivorous fish was much higher in the high and low abundance scenarios as well as the baseline scenarios (BMFTL= ~8.5) compared to the 99% elimination rate scenario (BMFTL= 0.10). 109 Table 3.17: Bioaccumulation factor (BAF), bioconcentration factor (BCF), and predator-prey biomagnification factors (BMFTL) from average microplastic concentration (g\/kg) from selected predator-prey combinations in the Gal\u00e1pagos penguin and BCE web model, under four different scenarios including low microplastic abundance in seawater, high microplastic abundance in seawater, 99% elimination rates for all functional groups, and the baseline scenario which includes elimination rates based on available literature. A) Gal\u00e1pagos Penguin (GP) Model Metric Scenario Predatory zooplankton with prey set to herbivorous zooplankton Planktivorous fish with prey set to predatory zooplankton Planktivorous fish with prey set to herbivorous zooplankton Mullet with prey set to detritus Gal\u00e1pagos penguin with prey set to planktivorous fish Gal\u00e1pagos penguin with prey set to mullet Gal\u00e1pagos penguin with prey set to decapods BAF Low microplastic abundance 4.36x10-8 4.92x10-8 4.92x10-8 2.27x10-8 5.86x10-5 5.86x10-5 5.86x10-5 High microplastic abundance 4.36x10-8 4.92x10-8 4.92x10-8 2.26x10-8 5.89x10-5 5.89x10-5 5.89x10-5 99% elimination 1.66x10-8 2.71 x10-10 2.71 x10-10 1.15 x10-10 7.26 x10-9 7.2 x10-9 7.26 x10-9 Baseline 4.36x10-8 4.92x10-8 4.92x10-8 2.26x10-8 5.90x10-5 5.90x10-5 5.90x10-5 BCF Low microplastic abundance 4.36x10-8 4.92x10-8 4.92x10-8 2.27x10-8 5.86x10-5 5.86x10-5 5.86x10-5 High microplastic abundance 4.36x10-8 4.92x10-8 4.92x10-8 2.26x10-8 5.89x10-5 5.89x10-5 5.89x10-5 99% elimination 1.66x10-8 2.71x10-10 2.71x10-10 1.15x10-10 7.26x10-9 7.26x10-9 7.26x10-9 Baseline 4.36x10-8 4.92x10-8 4.92x10-8 2.26x10-8 5.90x10-5 5.90x10-5 5.90x10-5 BMFTL Low microplastic abundance 3.50 6.84 3.06 11.4 1232.5 1784 2300 High microplastic abundance 3.50 6.85 3.06 11.3 1238 1794 2305 99% elimination 1.40 0.10 0.02 0.12 27.7 43.5 49.6 Baseline 3.50 6.84 3.06 11.3 1239 1796 2308 110 B) Bolivar Channel Ecosystem (BCE) Model Metric Scenario Predatory zooplankton with prey set to herbivorous zooplankton Planktivorous fish with prey set to predatory zooplankton Planktivorous fish with prey set to herbivorous zooplankton Mullet with prey set to detritus Seabirds with prey set to planktivorous fish Seabirds with prey set to mullet Seabirds with prey set to lobster BAF Low microplastic abundance 7.34x10-8 6.62x10-8 6.62x10-8 1.14x10-8 5.84x10-7 5.84x10-7 5.84x10-7 High microplastic abundance 7.34x10-8 6.62x10-8 6.62x10-8 1.14x10-8 5.85x10-7 5.85x10-7 5.85x10-7 99% elimination 2.83x10-8 3.50 x10-10 3.50 x10-10 8.45x10-11 3.55x10-11 3.55x10-11 3.55x10-11 Baseline 7.34x10-8 6.62x10-8 6.62x10-8 1.14x10-8 5.85x10-7 5.85x10-7 5.85x10-7 BCF Low microplastic abundance 7.34 x10-8 6.62 x10-8 6.62 x10-8 1.14 x10-8 5.84 x10-7 5.84 x10-7 5.84x10-7 High microplastic abundance 7.34 x10-8 6.62 x10-8 6.62 x10-8 1.14 x10-8 5.85 x10-7 5.85 x10-7 5.85x10-7 99% elimination 2.83 x10-8 3.50 x10-8 3.50 x10-10 8.45 x10-11 3.55 x10-11 3.55 x10-11 3.55 x10-11 Baseline 7.34 x10-8 6.62 x10-8 6.62 x10-8 1.14 x10-8 5.85 x10-7 5.85x10-7 5.85x10-7 BMFTL Low microplastic abundance 4.58 -2.73 -2.73 18.56 8.59 36.84 76.14 High microplastic abundance 4.58 -2.73 -2.73 18.52 8.62 36.93 76.05 99% elimination 1.77 -0.04 -0.04 0.89 0.10 0.30 76.71 Baseline 4.57 -2.73 -2.73 18.32 8.61 36.93 75.81 111 Simulation outcomes of the different scenarios yielded interesting results for TMF calculations, where all scenarios, except for the 99% elimination rate, predicted the plausible trophic biomagnification of microplastics in the biomass species\u2019 functional groups (Table 3.18; Figures 3.18 A and B). For the 99% elimination rate, some slopes were negative (i.e., year 1 in the GP model and years 1 to 100 in the BCE model), indicating lack of trophic magnification, however regressions were not significant (p > 0.05) in the GP modelled scenario (Figure 3.18 A) or the BCE (Figure 3.18 B). A negative slope, if significant, would indicate trophic dilution of microplastics (i.e., a decline in microplastic concentrations as the trophic level increases, TMF < 1 [slope < 0]). The highest TMF value was predicted at year 100 in the baseline and high abundance scenarios running in the GP model, i.e., TMF = 61.7 (Table 3.18). The highest TMF predicted in the BCE model was 16.6 in the high abundance and baseline scenario. Across both models under the 99% elimination rate scenario, there was very low TMF values from year one to year 100 (ranging from 0.48-2.57) for the GP model, while the lowest TMF values, ranging from 0.19-0.37 were predicted from year one to 100 for the BCE model (Table 3.18). TMF rose substantially from year 1 to year 25 and then gradual increases in TMF through to year 100 were predicted for the other scenarios tested (Figures 3.18 A and B). 112 Table 3.18: Apparent trophic magnification factors (TMF) and regression statistics for the linear regression models of the log of microplastic (MP) concentrations versus trophic level for the Gal\u00e1pagos penguin (GP) model and Bolivar Channel Ecosystem (BCE) model, for year 1, 25, 50, and 100, under four different scenarios including low microplastic abundance in seawater, high microplastic abundance in seawater, 99% elimination rates for all functional groups, and the baseline scenario which includes elimination rates based on available literature. Model Years Scenario Slope (b) P-value TMF (=10b) Biomagnification metric outcome GP 1 Low Abundance 0.85 p < 0.05 7.08 Potential biomagnification 25 Low Abundance 1.62 p < 0.05 41.7 Potential biomagnification 50 Low Abundance 1.76 p < 0.05 57.5 Potential biomagnification 100 Low Abundance 1.78 p < 0.05 60.3 Potential biomagnification 1 High Abundance 0.85 p < 0.05 7.08 Potential biomagnification 25 High Abundance 1.61 p < 0.05 40.7 Potential biomagnification 50 High Abundance 1.74 p < 0.05 55.0 Potential biomagnification 100 High Abundance 1.79 p < 0.05 61.7 Potential biomagnification 1 99% Elimination -0.32 p > 0.05 0.48 Not significant\/No biomagnification 25 99% Elimination 0.28 p > 0.05 1.91 Not significant\/No biomagnification 50 99% Elimination 0.39 p > 0.05 2.45 Not significant\/No biomagnification 100 99% Elimination 0.41 p > 0.05 2.57 Not significant\/No biomagnification 1 Baseline 0.85 p < 0.05 7.08 Potential biomagnification 25 Baseline 1.61 p < 0.05 40.7 Potential biomagnification 50 Baseline 1.74 p < 0.05 55.0 Potential biomagnification 100 Baseline 1.79 p < 0.05 61.7 Potential biomagnification BCE 1 Low Abundance 0.79 p < 0.05 6.17 Potential biomagnification 25 Low Abundance 1.19 p < 0.05 15.5 Potential biomagnification 50 Low Abundance 1.22 p < 0.05 16.6 Potential biomagnification 113 100 Low Abundance 1.21 p < 0.05 16.2 Potential biomagnification 1 High Abundance 0.79 p < 0.05 6.17 Potential biomagnification 25 High Abundance 1.19 p < 0.05 15.5 Potential biomagnification 50 High Abundance 1.22 p < 0.05 16.6 Potential biomagnification 100 High Abundance 1.22 p < 0.05 16.6 Potential biomagnification 1 99% Elimination -0.72 p > 0.05 0.19 Not significant\/No biomagnification 25 99% Elimination -0.44 p > 0.05 0.36 Not significant\/No biomagnification 50 99% Elimination -0.43 p > 0.05 0.37 Not significant\/No biomagnification 100 99% Elimination -0.43 p > 0.05 0.37 Not significant\/No biomagnification 1 Baseline 0.79 p < 0.05 6.17 Potential biomagnification 25 Baseline 1.20 p < 0.05 15.8 Potential biomagnification 50 Baseline 1.22 p < 0.05 16.6 Potential biomagnification 100 Baseline 1.22 p < 0.05 16.6 Potential biomagnification 114 A) 115 B) Figure 3.18: Linear regressions showing the significant relationship between predicted concentrations of microplastics (log-transformed data) and trophic levels for the (A) Gal\u00e1pagos penguin (GP) model and (B) Bolivar Channel Ecosystem (BCE) model, for year 1, 25, 50, and 100, under four different scenarios including low microplastic abundance in seawater, high microplastic abundance in seawater, 99% elimination rates for all functional groups, and the baseline scenario which includes elimination rates based on available literature. The antilog of the regression slope was used to determine TMF. This panel was made to visualize the overall trends, not to display the fine detail of each graph 116 3.3.4 Model Bias The projected concentration of MPs in the environment, and biota including zooplankton, anchovies, and mullet were compared to empirically collected data from similar abiotic (environmental) and biotic compartments. The outcomes of the MB ratio analysis revealed systematic under prediction (MB < 1) of microplastic concentrations in zooplankton, across all scenarios, with a MB ranging from 0.002 to 0.1 at low abundance scenario and high abundance scenario, respectively while using the GP model (Figure 3.19 A and Figures 3.20 A, B). Similarly, under prediction for zooplankton occurred in the BCE model, with MB ranging from 0.002 to 0.08 in the low and baseline scenarios, respectively (Figure 3.19 B and Figures 3.20 C, D). Conversely, the baseline, low, and high abundance (1, 2, 3) scenarios revealed over-prediction of microplastic concentrations (MB > 1) in fish ranging from 5.4 (low abundance) to 105.1 (high abundance) for anchovies in the GP model and 9.2 (low abundance) to 72.8 (baseline) for anchovies in the BCE model, as well as, 13.0 (low abundance) to 252.3 (high abundance) for mullets in the GP model and 11.8 (high abundance) to 65.5 (baseline) for mullets in the BCE model (Figure 18B: Figure 19). Results for fish are different in the 99% elimination rate scenario, namely, fish had the closest concentration values to the observed data (MB= ~1) (Figure 3.20 Biv and Div). In the GP model, the 99% elimination rate scenario yielded MB of 0.29 and 0.65 and the BCE yielded MB of 0.38 and 0.48 for the anchovies and mullets, respectively. 117 A) B) Figure 3.19: Assessment of the model bias (MB = CBP,iMP\/CBO, iMP; where CBP,iMP and CBO,iMP are the model predicted and observed, respectively, microplastic concentrations in zooplankton, anchovies, and mullets) and performance of the (A) Gal\u00e1pagos penguin (GP) and (B) Bolivar Channel Ecosystem (BCE) models by comparing the simulations of microplastic concentrations averaged from year 1 \u2013 100. Four different scenarios are compared for model bias, including low microplastic abundance in seawater, high microplastic abundance in seawater, 99% elimination rates for all functional groups, and the baseline scenario which includes elimination rates based on available literature. The dotted line indicates MB =1 (i.e., predicted microplastics data is equivalent to the observed microplastic concentrationsMB=1 118 A) 119 B) 120 C) 121 D) 122 Figure 3.20: Assessment of the model bias using the (A-B) Gal\u00e1pagos penguin (GP) and (C-D) Bolivar Channel Ecosystem (BCE) models run with Ecotracer to project microplastic concentration simulations from year 1 to 100 as predicted microplastic concentrations in the abiotic and biotic compartments. Results are presented by seawater microplastic abundance, microplastic concentrations in zooplankton, microplastic concentrations in anchovies, and microplastic concentrations in mullets. Predicted data is compared to empirically measured data on anthropogenic particle concentrations in Gal\u00e1pagos seawater and ingestion rates for zooplankton, anchovies, and mullets collected in October 2021. Two scenarios are presented for each model: GP model baseline (A), GP 99% elimination rates (B), BCE model baseline (C), BCE 99% elimination rates (D) 3.4 Discussion Ecosystem modelling simulation results in this study revealed bioaccumulation potential in all predator-prey combinations in both GP and BCE models across all scenarios. Biomagnification of microplastics was apparent in all simulations, except for the 99% elimination rate scenario, which showed a lack of trophic magnification with insignificant negative slopes (p > 0.05) in both GP and BCE models. Biomagnification was highly dependent on microplastic egestion rates. As well, the microplastic concentration in zooplankton was systematically under-predicted in both GP and BCE models, whereas microplastic concentrations in fish were over-predicted, with the closest concentration values to the observed data being seen in the 99% elimination rate scenario and the baseline scenario for fish and zooplankton respectively (Figure 3.19 & Figure 3.20). These results are comparable to limited existing microplastic bioaccumulation and biomagnification simulations using EwE, which is not surprising given similar methods and parameters used (Alava et al., in prep.; Ma & You, 2021). Ma and You\u2019s (2021) EwE simulations found microplastics bioaccumulate quickly in fish food webs of Baiyangdian Lake, China. Likewise, Alava et al. (in prep.) ran Ecotracer through an extensive list of 20 marine ecosystem models available through EcoBase. The projections of microplastic concentrations in biota revealed that top predators are likely exposed to higher levels of microplastics accumulated through their diet (e.g., prey items). However, the elimination or egestion rate was likewise a key factor in determining the bioaccumulation behavior of microplastics. Emphasis was placed on better understanding of the role of retention times and elimination rates of microplastics in different functional groups. Likewise, microplastic bioaccumulation modelling in a cetacean food 123 web (Alava, 2020) was comparable to EwE results, indicating that species-specific bioaccumulation of microplastics is likely, while biomagnification is highly dependent on species-specific elimination rates. According to a comprehensive meta-analysis carried out by Miller et al. (2020), evidence has been found for bioaccumulation of microplastics in marine species; however, biomagnification of these pollutants in the food web has yet to be confirmed by field data. A select number of field studies have observed trophic transfer of microplastics (Chagnon et al., 2018; Farrell & Nelson, 2013; Nelms et al., 2018), but there is disagreement as to whether microplastics are retained in the GI tract or gut (Chagnon et al., 2018). Microplastic dilution, for example, has been found in mussels and fish in the Persian Gulf (Akhbarizadeh et al., 2019). To better understand this phenomenon, Miller et al. (2023) explored microplastic movement through a coral reef food web and found bioconcentration evident in zooplankton, crustaceans and fish, but no bioaccumulation or biomagnification. Bioaccumulation and biomagnification currently represent an important debate in the field of microplastic science and uncertainly remains. While conducting research to investigate these ecotoxicological factors, it is important to consider (1) microplastics are a class of contaminants and may move differently within a food web depending on their characteristics (Rochman et al., 2019), (2) microplastic input into the ocean is increasing year over year, and (3) evidence suggest ingestion of microplastics provides no advantage to marine organisms in a changing ocean. Thus, though microplastic biomagnification is not agreed upon, it is still important to reduce the input of microplastics to the sea. First, it is critical to consider microplastics as a class of contaminants (Rochman et al., 2019); it is reasonable that their behaviour in the GI tract is varied based on their characteristics (e.g., size, shape, microbial biosphere, additives, dyes), as different microplastics and associated chemicals will react differently in the gut (Moncl\u00fas et al., 2022; Thornton Hampton et al., 2022). There is evidence to suggest that small microplastics and nanoplastics (<1\uf06dm), not measured in this study, can pass through tissue membranes, translocate to tissues, and enter the blood stream (Leslie et al., 2022; Ragusa et al., 2021; Ragusa et al., 2022; Ramsperger et al., 2023). Chemical additives or 124 harmful bacteria on microplastics can adsorb within the biota (Bakir et al., 2016; Naik et al., 2019) and may or may not bioaccumulate, biomagnify, or multiply within the organism (Beiras et al., 2019; Koelmans, 2015). Specific shapes and sizes may be more toxic (Thornton Hampton et al., 2022) and prone to lower or higher GI tract retention times, influencing microplastic bioaccumulation (Alava, 2020). Secondly, plastic and microplastic pollution inputs into the ocean is increasing in the Plasticene (Andrady, 2022; Everaert et al., 2018, 2020; Haram et al., 2020; Thompson et al., 2004; UNEP, 2021). The increase in these anthropogenic particles will inevitably lead to accumulation in ecosystems, whether it be marine ecosystem accessible to researchers and the public, or ecosystems at deep sea depths not commonly sampled. Lastly, numerous studies note prominent ingestion of microplastics when organisms are exposed to plastic particles in the environment or in laboratory conditions. Microplastic ingestion has been linked with several primarily sublethal health effects (Anbumani & Kakkar, 2018; Cole et al., 2013; Wright et al., 2013; Yin et al., 2021), thus, it is reasonable to assume ingested microplastics do not offer advantages in stressful changing oceanic conditions due to overfished resources and climate change effects (Alava et al., 2022). Instead, the presence of microplastics or nanoplastics is more problematic as a threat for marine organisms, as in the case of nanoplastics and ocean acidification with Antarctic krill (Rowlands et al., 2021). Overall, future research should prioritize laboratory assessments of microplastic accumulation across trophic levels, with particular attention on egestion rates and GI tract retention times, physical-chemical characteristics of retained microplastics, and ecotoxicological health effects. Future modelling work should explore different interactions with microplastic at the primary consumer level. For example, microplastics can be added as a functional group in EwE and mediation applied to increase plastic consumption when phytoplankton abundance is low and vice versa (Christensen, 2021). It would also be prudent to create model scenarios with varying rates of increase for microplastics in the specific ecosystem. Future modelling should likewise consider multiple-anthropogenic stressors; for example, the impact of climate change forcing and El Nino Southern Oscillation (ENSO) events in the Gal\u00e1pagos penguin food web when there are drastic 125 changes on sea surface temperature (negative anomalies), disruption of primary production and pronounced food shortages as fewer fish serving as prey for penguins during El Ni\u00f1o are available (Vargas et al., 2006, 2007). These multifactorial stressors may well affect the exposure pathways, bioaccumulation and elimination of microplastics in marine top predators, including seabirds like the Gal\u00e1pagos penguins. 3.5 Conclusion It is imperative to continue prioritizing efforts to reduce the input of microplastics into vulnerable ecosystems and food webs, such as that of the endangered Gal\u00e1pagos penguin. Despite ongoing research, the biomagnification of microplastics remains unclear, and additional studies are needed to fully understand this phenomenon. This study identified a knowledge gap in microplastic elimination rates, which are needed to determine biomagnification potential. As microplastic research continues, it is key to continue efforts raising awareness and mitigating microplastic pollution into waterways, while not losing sight of other pressing threats to the world\u2019s ocean. It is critical to adopt a balanced, holistic approach when considering oceanic threats, including plastics, but also overfishing and climate change, in order to effectively protect and preserve iconic species like the Gal\u00e1pagos penguin and our precious ocean environment. Ultimately, this trophic-dynamic ecosystem modelling work provided insights on the potential bioaccumulation and biomagnification risks of microplastics as a global pollutant of concern to support regional marine plastic pollution management efforts for the conservation of native and endemic species of the Gal\u00e1pagos Islands and the Gal\u00e1pagos Marine Reserve. 126 Chapter 4: Marine litter and social inequities entangle Ecuadorian mangrove communities: Perceptions of plastic pollution and well-being concerns in Puerto Hondo and Isla Santay, Ecuador \u201cTechnical knowledge means nothing in a vacuum, and by vacuum, I mean without local knowledge of what\u2019s happening on the ground.\u201d - Dr. Asha de Vos, IMPAC5 2023 Keynote (paraphrased) 127 Graphical Abstract 128 4.1 Introduction: Call to include coastal community input in marine litter management Despite being on the front lines of marine debris, coastal communities impacted by marine litter have rarely been involved in policy and decision making on marine litter management (Barragan-Paladines & Chuenpagdee, 2017; McMullen, 2022; Monnier et al., 2020; Vandenberg & Ota, 2022). Literature calls researchers to include community input when evaluating the impact of plastics on ecosystem services, equity, and well-being (Broocks, 2021; Malizia & Monmany-Garzia, 2019; Stedman, 2016; Thiagarajah et al., 2015; Vandenberg & Ota, 2022), relative to plastics\u2019 positive contributions to way of life. Malizia & Monmany-Garzia (2019) urges scientists to include non-scientific groups on this research front. Global mangrove forests cover an estimated 1.7x105 km2 (Valiela et al., 2001) and are ecologically and socially valuable to local people living within them, yet they are threatened by marine litter. These natural regions provide coastal protection from storms, water filtration, sinks for carbon dioxide, sources of oxygen (Morocho et al., 2022; Estrella Benavides, 2007), and economic opportunities such as small-scale fishing and ecotourism (Hamilton & Collins, 2013). They foster unique biodiversity as well as provide refuge, habitat, and nursing areas for economically important species, including fish (e.g., mullets, drums or croakers, snook, and sharks), crustaceans (e.g., shrimps and crabs) and shellfish (e.g., cockles and mussels) (Hamilton & Collins, 2013; Martinez-Alier, 2001; Morocho et al., 2022; Shervette et al., 2007). Nonetheless, macro (>5mm) and micro (<5mm) plastics are pervasive in these regions, which pose risks to mangrove ecosystem sustainability and biodiversity as well as local communities\u2019 reliance on mangrove services (Cole et al., 2011; Galgani et al., 2010; Ryan, 2015; Wright et al., 2013). Research on marine anthropogenic litter (e.g., plastic pollution) has explored the distribution of marine debris, biological impacts, and socioeconomic impacts such as human health and economic implications (Lusher, 2015). Marine litter has direct and indirect effects on ecosystems and may be intensifying the negative effects of climate change, commercial overfishing, and other chemical pollutants in mangrove regions (Alava et al., 2020; Calle et al., 2018; Castro et al., 2012; Fern\u00e1ndez-Cadena et al., 2014). Plastic ingestion is inevitable by aquatic invertebrates and marine 129 or estuarine fish species inhabiting mangrove ecosystems (Garc\u00e9s-Ord\u00f3\u00f1ez et al., 2020; Mieles Ch\u00e1vez et al., 2020; Riascos et al., 2019) and can lead to falsified satiation, reduced growth rates, reproductive failure, absorption of toxic chemicals, among other impacts (Cole et al., 2011; Galgani et al., 2010; Galloway et al., 2017; Ryan, 2015; Wright et al., 2013). The accumulation of plastics or microplastics in fish may compromise the human health and food safety of small-scale fishers, aquaculture, coastal communities, and Indigenous people who strongly rely on commercial fish and traditional seafoods (Domenech & Marcos, 2021; Lusher et al., 2017; O\u2019Neill & Lawler, 2021; Revel et al., 2018; Santillo et al., 2017; United Nations Environment Programme, 2021). Plastics may also physically or chemically restrict the growth of mangrove trees (Makowski & Finkl, 2018) or bring in invasive species (Barnes, 2002; Beaumont et al., 2019). While most marine litter research has focused on coastal areas (Haarr et al., 2022), few studies have conducted research within mangrove regions, and fewer studies have explored local communities\u2019 perceptions and input on the issue of marine litter. Beyond our current framing of economics, human health, and ecosystem sustainability, marine litter has wider well-being implications that are unique to the geographies and cultures of those living on coasts where marine litter accumulates. Mangroves are intrinsically important to local communities through culture and heritage and other dimensions of well-being (Haines-Young & Potschin, 2010; Martinez-Alier, 2001; Morocho et al., 2022; Potschin, 2008; Schreckenberg, 2018). Not only can marine litter interfere with seafood provisioning and livelihood in mangrove communities (Beaumont et al., 2019; Domenech & Marcos, 2021; O\u2019Neill & Lawler, 2021; Revel et al., 2018; Santillo et al., 2017; United Nations Environment Programme, 2021), but marine litter also challenges the natural aesthetic of culturally significant places and may interfere with well-being, defined as a multi-dimensional positive state of being, fulfillment, and positive functioning (Donatuto et al., 2016; Well-Being Concepts, 2018). The factors which determine perceived positive well-being are subconsciously or consciously determined by the agents themselves, thus, when we consider the impact of plastic pollution, we must consider the multi-level and multi-dimensional impacts on ecosystem services and well-being, all of which are subject to the perception of those living within the region. 130 This transdisciplinary study addresses the above outlined research gap by exploring human well-being and marine litter impacts in two mangrove-dependent communities in the Gulf of Guayaquil: Puerto Hondo and Isla Santay, Guayas, Ecuador. Ecuador is home to the largest region of mangrove forests in the Pacific South American coast (Carvajal & Alava, 2007; CLIRSEN-PMRC, 2007; Morocho et al., 2022), the majority of which is in the Gulf of Guayaquil (Carvajal & Alava, 2007; CLIRSEN-PMRC, 2007; Morocho et al., 2022). In addition, the Gulf of Guayaquil holds the largest abundance of marine litter observed in Ecuador, mainly comprised by marine plastics from consumer items and fishing activities (Gaibor et al., 2020; Mestanza et al., 2019), and possibly due to the number of mangrove trees acting as traps for marine debris (Martin et al., 2019). With the aim to understand the impacts of marine litter on well-being, public health, and equity in Puerto Hondo and Isla Santay, we contribute baseline data on perceptions and insights gathered from in-person surveys conducted in 2019 and 2021. Doing so, we illustrate the importance of understanding the local complexities of human plastic entanglement and how this information can directly influence more equitable and effective marine litter and waste management governance strategies. If marginalized groups are excluded from solution ideation and plastic pollution regulations, the trade-offs imposed onto them are likely to be overlooked (Schreckenberg, 2018). It is of utmost importance to include coastal communities in the decision-making process that directly affects them. The findings of this work are envisioned to support local policy to improve solid waste management strategies and mitigation actions to reduce marine plastic pollution and foster the well-being, equity, and health of the most exposed coastal communities in Ecuador's continental coast. 4.2 Methods 4.2.1 Location justification and historical context \u201cViene con las mareas\u201d [It comes in with the tides] - multiple survey participants in Puerto Hondo and Isla Santay, referring to plastic pollution. 131 Puerto Hondo and Isla Santay have faced numerous anthropogenic threats over the past four decades including urban sprawl, industrialization, and encroaching shrimp farms, resulting in rapid mangrove deforestation in tandem with high environmental and social-marginal or socioeconomic costs for the community (see Appendix D; Carvajal & Alava, 2007; Carvajal & Alava, 2007; Calle et al., 2018; Fern\u00e1ndez-Cadena et al., 2014; Martinez-Alier, 2001; Morocho et al., 2022; Twilley et al., 2001). Ana Tirap\u00e9 and Paola Calle, affiliated with Escuela Superior Polit\u00e9cnica del Litoral (ESPOL) University, Guayaquil, Ecuador, have maintained strong relations with both communities and have historically connected with community members on topics not limited to marine debris. Having witnessed the rise in marine plastics in these mangrove communities and the other socioeconomic stressors inhibiting well-being, these areas were selected as survey sites to amplify community member\u2019s stories and inputs. Both Puerto Hondo and Isla Santay are located on the periphery of Ecuador\u2019s second largest city, Guayaquil (2\u00ba11'S, 79\u00ba53'W) (See Figure 4.21). With a population exceeding 2.3 million (from a 2010 census) (INEC Instituto Nacional De Estad\u00edstica y Censos, 2022), Guayaquil city has seen an increase in unplanned housing beyond the city borders throughout recent decades. Puerto Hondo - 17 kilometers to the west of Guayaquil, along \u201cVia a la Costa\u201d [Coast Road], is Puerto Hondo (2\u00b012'S, 80\u00b01'0W), hidden among the riverine mangroves remaining in the interior estuary of the Gulf of Guayaquil. Roughly 1,000 to 1,700 inhabitants live in the coastal town (Guevara, 2007; Rocio Sarmiento Arias, 2011; INEC, pers. comm., 2021). Puerto Hondo mangroves are a subset of the Manglares El Salado Fauna Production Reserve (RPFMS; 2\u00ba10'S, 79\u00ba56'W) (National System of Protected Areas, 2015) in the Gulf of Guayaquil (Carvajal et al., 2006). This reserve bears 9,748 hectares (~97.5 km2) of remnant mangroves southeast of the city of Guayaquil. These mangroves act as breeding grounds and homes for unique species; including endemic and threatened birds (Guevara, 2007; Alava et al., 2011; Berg & Angel, 2006), mammals (Andrade Ojeda, 2014), amphibians, reptiles, and commercially relevant fish, mollusks, and crustaceans (Alava & Barrag\u00e1n-Paladines, 2017; Andrade Ojeda, 2014; Carvajal et al., 2005; Estrella Benavides, 2007; Carvajal et al., 2006). Bottlenose dolphins (Tursiops truncatus) are among the charismatic marine mammal species inhabiting the estuarine waters nearby Puerto 132 Hondo mangrove creeks, providing dolphin watching\/ecotourism (Jimenez & Alava, 2014; Jimenez & Alava, 2015; Alava et al., 2019; Alava et al., 2020). Despite generational dependence on artisanal fishing (Puerto Hondo Beach Resort and Recreational Center, n.d.), the number of fishers has been declining since the 1950s coinciding with a decrease in abundance of mangrove fish and shellfish (Puerto Hondo Beach Resort and Recreational Center, n.d.). As of the early 2000s, there has been an occupational shift towards ecotourism as another means of economic growth and enhanced livelihood for the Puerto Hondo community (Andrade Ojeda, 2014; Guevara, 2007; Rocio Sarmiento Arias, 2011). A recreational center was constructed in 2006 by the Municipality of Guayaquil (Puerto Hondo Beach Resort and Recreational Center, n.d.), including a water park, viewpoint platform, beach, facilities, small grocery stores and shops, as well as local and traditional food vendors (Puerto Hondo Beach Resort and Recreational Center, n.d.). The ecotourism push has prompted an increase in shops and restaurants around the recreational center as well as near the Guayaquil-Salinas highway (Guevara, 2007). In 1976, the Puerto Hondo community was subject to land displacement (Meyfroidt et al., 2013); the community\u2019s land, previously owned by a Peruvian man, was invaded and taken. Citizens living on the land were forced to move to the opposite side of the estuary to make way for a newly declared national park (Andrade Ojeda, 2014; Guevara, 2007). After eviction, however, the national park did not materialize and shrimp farms were built on the land instead (Guevara, 2007). An Association of Small Farmers was inaugurated and left in charge of the area. Isla Santay - While only 3 kilometers from central Guayaquil, Isla Santay (2\u00ba13'S, 79\u00ba51'W) is located in the inner estuary of the Guayaquil Gulf (Guayas River Estuary Basin) and is separated from the city by two rivers, Rio Guayas and Rio Duale. Made up of accumulated sediment, the island is home to approximately 210 to 250 people (Guayaquil Es Mi Destino., n.d.; National System of Protected Areas, 2015; Diaz-Christiansen et al., 2016) who live on boardwalks atop a plain that floods during high periods of rain (National System of Protected Areas, 2015). 133 SI harbors 4,705 hectares (47.05 km2) of flooding forest and tropical dry forest and 2,224 hectares (22.24 km2) of mangroves, creating a suitable habitat to several threatened animal species (Diaz-Christiansen et al., 2016). The island is now renowned for bird watching and biodiversity which has attracted tourists from around the world (Alava & Haase, 2011). Isla Santay has been called \u201cThe Lung of Guayaquil\u201d and \u201chidden in plain view\u201d (MAE, 2013) referring to the juxtaposition between the small community and vast green space located next to a bustling Guayaquil city (Diaz-Christiansen et al., 2016). In the seventeenth and eighteenth centuries, as the city of Guayaquil was developing, Isla Santay was the site of pirate attacks, a quarantine site for boats heading to the city, and later it was used for ship maintenance and anchorage (National System of Protected Areas, 2015). Rich soil on Isla Santay sparked rice and cattle farming peaking in the 1940s (Travel.Earth, 2018) and led to the pronounced ring of mangroves along the circumference of the island (National System of Protected Areas, 2015). In 1990, the land was transitioned to a protected area and a Convention on Wetlands of International Importance Especially as Waterfowl Habitat (RAMSAR) site in 2000 and was declared a National Recreation Area by the Ministry of Environment, Water and Ecological Transition of Ecuador (MATE) in 2010 (MATE, 2011; National System of Protected Areas, 2015). The community and the Ecological Committee of the Littoral, a natural non-legislative association of Guayaquil, started the foundation of \u201cSan Jacinto de Santay,\u201d a community association dedicated to fishing and tourism on the island (Travel.Earth, 2018; Diaz-Christiansen et al., 2016). 134 Figure 4.21: Location of two Ecuadorian mangrove communities surveyed in 2019 and 2021 to gain an understanding of the perception of marine pollution and its impact on well-being in the Gulf of Guayaquil (Ecuador): Puerto Hondo (2\u00b012'S, 80\u00b01'0W) and Isla Santay (2\u00ba13'S, 79\u00ba51'W). 4.2.2 Study instrument: Surveys A team of local, international and interdisciplinary scholars conducted 59 surveys between 2 phases. Phase 1 and phase 2 surveys were created by the Nippon Foundation-Marine Litter Project (Institute for the Oceans and Fisheries, University of British Columbia). Thematic findings from the phase 1 pilot study were used to locally ground the follow-up 2021 survey instrument. Survey design and timelines are illustrated in Figure 4.22. 135 Figure 4.22: Flow-chart diagram illustrating the survey phase 1 and phase 2 respondent data with location and survey design used to deploy in person surveys in Puerto Hondo and Isla Santay mangrove communities from the Gulf of Guayaquil (Ecuador) in 2019 and 2021. The phase 2 survey was comprised of quantitative measures whereby respondents could choose responses that were later coded as a number, e.g. \u201cIn the last few months, how often have you felt stress from marine litter? 1 - almost never to 5 - frequently\u201d. Unless otherwise stated, mean scores close to 5 indicate \u201cfrequently\u201d or \u201cstrongly agree.\u201d Close-ended questions included \u201cselect all that apply\u201d questions where responses were later totalled to assess which factors are most significant to the majority of respondents. In both phases, respondents provided socio-demographic information. Respondents were asked which gender they identify with most. Note, because all individuals responded either male or female, only these two genders are reported on. In both phases, respondents were surveyed on an opportunistic basis outside their residence or place of work and no compensation was received by participants. In the questionnaire presentation, 136 the aims of the study were explained, and the confidentiality of information collected assured. Individuals over 18 years old were asked if they were willing to participate in a survey about marine litter and were read a consent form. An ethics certificate was approved by the University of British Columbia Research Ethics Board (REB) [ref: H21-02114] and local community access permits to conduct the surveys were obtained by ESPOL-Polytechnic School in Guayaquil, Ecuador, and approved by the Research Commission of ESPOL. 4.2.3 Data treatment and analysis Completed surveys were translated from Spanish to English. ATLAS.ti Scientific Software Development GmbH\/Qualitative Data Analysis Software was used to analyze open-ended questions; this software allows for quick textual coding, theme analysis, and response comparisons between demographic cohorts. Flourish.studio software was used to visualize and compare close-ended response data. Following large scale studies on marine litter perceptions (e.g., Hartley et al., 2021) and social science literature, we report means (x\u0304) and standard deviations (\u03c3) to quantitative responses. Means close to 5 indicate a response of \u201cstrongly agree,\u201d whereas means closer to 1 indicate \u201cstrongly disagree\u201d. Where applicable, multiple responses that followed a common theme were reported quantitatively as n=x and quotes were included to amplify respondents\u2019 points. Radar plots and regression models were also presented for the data analysis to illustrate trends in responses between different demographic cohorts. 4.2.4 Limitations A limited number of surveys were conducted due to time, financial, and logistic constraints. Most respondents were assisted in reading and writing to complete the survey. Opportunistic sampling resulted in more females surveyed in Isla Santay. 4.3 Results and discussion Results are presented in four sections. (1) Phase 1 2019 Puerto Hondo survey analysis is presented as initial exploratory data. Next, we present phase 2 survey results: (2) We provide an assessment 137 on perceptions of well-being, basic needs, and contextualize community connection to mangroves. (3) We present a concerted assessment of plastic pollution perceptions, including plastic usage, solid waste management, marine litter, governing bodies, and plastic pollution solutions. Finally, (4) we compare the results across demographic data, including age and gender groups. 4.3.1 Preliminary survey insights: Puerto Hondo Mangrove dependence and concerns - In open-ended responses, 14 of 29 respondents expressed livelihood and sustenance dependance on mangroves, including the following statements: \u201cmuchas familias dependen de este estero\u201d [many families depend on this estuary] (male, age 43, fisher\/driver), \u201chay menos pescado, mi familia vive del pescado... comemos lo que sale del estero\u201d [there are less fish, my family lives off the fish...we eat what comes from the estuary] (female, age 26, mother), \u201cSi el estuario est\u00e1 sucio, los turistas no vendr\u00e1n\u201d [if the estuary is dirty, the tourists won\u2019t come] (male, age 54, driver), \u201chay falta de trabajo, la comunidad depend\u00eda de la pesca\u201d [there is a lack of work\u2026the community depended on fishing] (male, age 75, fisher). While time at current occupation ranged from 1 month to 60 years (x\u0304=21.8 years, n=29), the majority of those having worked in their current occupations for more than 50 years were fishers (n=4). Three individuals, including an 80-year-old retail worker, a 46-year-old fisher, and a 47-year-old mother whose husband is a fisher, noted visible and serious declines in cockles, mussels, and clams. One individual noted that white shrimp, which were frequently abundant in previous years, have completely disappeared from the area (female, age 80, retail). Theft at sea has reportedly increased as a result of the decreased abundance of fish. As the community\u2019s dependence on fishing has become less reliable, individuals have shifted focus toward Guayaquil City to look for jobs, and respondents reported decreased community cohesion. Survey analysis revealed a lack of community connection and cohesion on the issue of marine litter, and the unbalanced responsibility in addressing the issue. The surveys revealed a group of community members who feel they are left to work out this issue on their own despite the significant implications for the community at large. 138 Marine litter concerns - When asked general thoughts on marine litter, Puerto Hondo respondent (age 46, male, fisher) replied: \u201cla basura marina es lo que est\u00e1 destruyendo las especies en el mar. Antes hab\u00eda abundancia de berberechos, mejillones, almejas, y ahora ya no vemos y para seguir pescando [los pescadores] tienen que ir cada vez m\u00e1s lejos por la contaminaci\u00f3n\u201d [marine litter is the thing that is destroying the species in the sea. Before there was an abundance of cockles, mussels, clams, and now we do not see anymore and, to keep harvesting, [fishers] need to go farther and farther because of the pollution.]. The majority of Puerto Hondo respondents (n=26, 89%) expressed concern over marine debris and its economic or health implications for the community. Respondents shared what items they consider marine debris, where it accumulates, who produces it, and what their concerns are over marine litter in the mangroves. Responses are amalgamated on Figure 4.23. Marine debris was most commonly associated with plastics, which match studies in Ecuador that found marine debris is primarily plastics from consumer goods and fishing material (Gaibor et al., 2020). Respondents also shared anecdotes of having viewed marine life entangled with plastics and the common presence of plastic bags and other plastic items entangled within mangrove roots. Puerto Hondo respondents also noted waste flowing into the estuary and marine plastic coming into the town from the city. Respondents critiqued the nearby foreign franchised concrete factory and shrimp farms for pollution, not necessarily limited to plastics. The town is surrounded by a plastic manufacturer, fish exporting company, and other smaller businesses, which, in addition to Guayaquil city, likely contribute to the marine debris in the estuary. 139 Figure 4.23: Preliminary Survey Insights: Puerto Hondo in 2019. (a) Results from free list analysis (Bernard et al., 2011) of what items are considered marine litter. In the event that multiple items or themes were listed in a single response, each word or theme was coded individually for consistency. For example, \u201cplastics, bags, bottles\u201d would be coded as \u201cplastics\u201d, \u201cbags\u201d, \u201cbottles\u201d - three individually listed items, rather than one list \u201cplastic, bags, bottles\u201d or \u201cplastic bags\u201d and \u201cplastic bottles.\u201d This was necessary given that some respondents reported \u201cbags and bottles\u201d without specifying \u201cplastic.\u201d The category \u201cOthers\u201d includes \u201cwaste from shrimp factories\u201d (e.g., \u201cgreen slime\u201d) (n=2), \u201cdead animals\u201d (n=2), \u201cfeces\u201d (n=2), and \u201corganic waste\u201d (n=3), as well as \u201cmangrove tree branches'' (n=3). (b) Respondents reports of where marine litter accumulates and (c) who produces marine litter. The category \u201cShrimp industry\u201d includes shrimp farmers and processing\/seafood packing factories, the \u201cResidential community\u201d refers to those living just outside the mangrove areas, closer to the highway, and \u201cOthers\u201d includes cars (n=1), fishers (n=1), and street food vendors (n=1). Lastly, (d) what specific 140 concerns respondents have over marine litter. The category \u201cWorry over health conditions\u201d refers to reported concern over plastics causing skin or throat conditions, or the flu. 141 Literature suggests marine debris impacts are perceived differently among varying occupations and life experiences (UNEP, 2021; Simon et al., 2021; Schreckenberg, 2018). For example, women are said to be more motivated by collective\/social then individual\/private benefits (e.g., food security and safety is more important than money) (Schreckenberg, 2018). In congruence with literature, more males in Puerto Hondo were concerned about marine litter\u2019s impact on the economy, whereas females were more likely to note human health concerns. In addition, retail workers and drivers reported concerns regarding ecotourism (n=2, 7%), whereas all fishers reported concerns regarding fish stocks (n=7, 24%). Concern over marine litter was felt more strongly among senior age cohorts. The average age of individuals concerned about marine litter\u2019s impact on economic prospects and community health was fifty-seven years of age (x\u0304=56.5, \u03c3=17), while the three individuals who expressed no concern regarding marine litter had an average age of thirty-eight (x\u0304=37.6, \u03c3=23.7). All respondents over the age of seventy (n=6, 21%) expressed strong concerns over the impact of marine litter on the economy and community health. The younger generation, under forty years of age, was also more likely to state that the problem was worse before (n=7, 24%). Marine litter governance - Multiple respondents noted that no one is doing anything about the issue (n=23, 79%). Respondents stated that solutions should come from the government or non-governmental organizations. Fundaci\u00f3n Pro-Bosque and Fundaci\u00f3n Natura, volunteer-based non-profit organizations aiming to conserve the environment, have been known to undertake community cleanup efforts (known as mingas) in the area and to conduct educational campaigns for youth on the adverse impact of littering. Organizations such as Fundaci\u00f3n Natura and were noted to have helped in the past. More responsibility was attributed to government entities due to respondents\u2019 perceived feelings that taxes paid should cover cleanliness of the mangrove area, though one individual noted loss of faith in \u201cMinisterio del Ambiente\u201d [Ministry of Environment, Water and Ecological Transition of Ecuador] (MATE). \u201cMingas\u201d or community cleanups were urged as a reactive solution. Many respondents noted major barriers to sustaining a clean estuary centered around the difficulty of changing human behaviour. 142 4.3.2 Mangroves and well-being in Puerto Hondo and Santay Island: livelihood, community and seafood security Well-being - The well-being aspects of surveys were framed by the Center for Disease Control and Prevention (CDC) and Donatuto et al. (2016)\u2019s definition of well-being, including the presence of positive emotions, fulfilment, and satisfaction with life, resulting in a positive view of life. The World Health Organization (WHO) recognizes health as a fundamental right, but human health extends beyond physical health. In compliance with WHO\u2019s theoretical framework, authors approach health as \u201ca state of total physical, mental, and social well-being not merely the absence of disease or infirmity\u201d (WHO, 1948). Respondents were presented with a list of twenty-three well-being parameters and were asked to select which factors contribute most to their well-being. For Puerto Hondo and Isla Santay, nature was a dominant factor in well-being for respondents (Figure 4.24). Isla Santay respondents reported connection to nature (n=13, 87%), historic heritage (n=11, 73%), culture (n=11, 73%), community physical health (n=11, 73%), housing (n=11, 73%), and proximity to nature (n=11, 73% contributed most to well-being (Figure 4.24). One Isla Santay respondent (age 44, female, business owner) stated, \u201cme encanta caminar en la playa, es tranquila.\u201d [I love walking on the beach; it is calming]. In Puerto Hondo, proximity to nature (n=12, 80%) was most commonly associated with well-being, followed by housing (n=11, 73%), public services (n=10, 67%), and connections to nature (n=10, 67%). 143 Figure 4.24: Distribution pattern of the most important factors influencing well-being by Puerto Hondo and Isla Santay community respondents. Respondents were presented with a list of twenty-three well-being parameters and were asked to select which factors contribute most to their well-being. Frequency indicates the number of individuals who deemed the parameter essential to well-being. 144 Basic needs - Basic needs are the minimum requirement for good quality of life. To assess basic needs fulfilment in Puerto Hondo and Isla Santay, we presented needs parameters (e.g., money, leisure, water, etc.) and asked respondents to report a score of 1 to 5 where 1 is a score of \u201cnever available\u201d and a score of 5 is a score of \u201calways available.\u201d Basic needs were more readily accessible in Puerto Hondo, across all parameters (Figure 4.25). Of particular concern in Isla Santay was not having enough income to meet basic needs (x\u0304=2.13, \u03c3=0.74) and education limitations (x\u0304=2.33, \u03c3=0.62). In Puerto Hondo, primary concerns were not having enough income to meet basic needs (x\u0304=2.66, \u03c3=1.05) and no available leisure activities (x\u0304=3.43, \u03c3=1.40). Figure 4.25: Results from self-assessment survey of basic needs as an average across individuals in Isla Santay and Puerto Hondo mangrove communities. Frequency refers to whether or not basic needs parameters are available in day-to-day life, where a score of 1 is a mean score of \u201cnever available\u201d and a score of 5 is a mean score of \u201calways available.\u201d Livelihood - Despite heavy historical reliance on small-scale fishing, the fishing industries in both communities are declining. This trend is apparent in six other Ecuadorian mangrove regions as 145 well (Hamilton & Collins, 2013). General responses revealed concern in Puerto Hondo and Isla Santay over decreasing catches, decreasing fish sizes, longer times at sea to yield adequate catch, oyster death from increased rains and increased freshwater entering the estuary, increased robberies of fishing material and catches, and catching plastic items. Puerto Hondo respondent (age 64, male, fisher\/kayak guide) noted: \u201cNo hay mucho pescado. Recibimos $ 2 - $ 3 USD por d\u00eda\u201d [There is not a lot of fish. We get $2 - $3 USD a day]. He also explained net size has changed to account for physically smaller individuals in catches. In the last 20 years, the nets have changed from 2-inch mesh size to 1 inch. Similar to a previous study in the region (Hamilton & Collins, 2013), respondents attributed this change to shrimp farming, mangrove deforestation, and pollution including plastics. Ecotourism is another important sector in both communities, however, ecotourism necessitates a healthy environment. Puerto Hondo residents noted mangrove changes inhibit ecotourism growth. On average, respondents felt \u201cneutral\u201d or \u201cagreed\u201d that each respective community environment was healthy (x\u0304=3.62, \u03c3=0.90 in both Puerto Hondo & Isla Santay). Puerto Hondo respondent (age 44, female, business owner) explained: \u201cLa gente viv\u00eda de los manglares y hab\u00eda m\u00e1s turismo de naturaleza, pero hace diez a\u00f1os eso cambi\u00f3 a medida que los manglares se vieron afectados\u201d [People used to live from the mangroves and there was more nature tourism, but ten years ago that changed as the mangroves became impacted]. New economic opportunities were deemed low in both Puerto Hondo and Isla Santay. Both communities traditionally relied on fishing but have recently observed occupational shifts. Fishers have added additional streams of revenue to supplement their income, such as ecotourism and food vending. Deforestation of mangroves have also resulted in occupational shifts, away from natural resources, in other coastal regions of Ecuador, including Muisn\u00e9 and Chone estuaries (Hamilton & Collins, 2013). Respondents in Puerto Hondo reported an average 40% (x\u0304=0.4, \u03c3=31) economic dependence on marine resources, whereas Isla Santay reported an average 60% (x\u0304=0.6, \u03c3=36) dependence. Food security - 100% of respondents (n=30) in Isla Santay and Puerto Hondo rely on seafood for their own sustenance. In Isla Santay, self-caught seafood is eaten by 93% (n=14) of respondents. 146 Seafood is also purchased directly from fishers (n=5) or from fish markets (n=3). No respondents reported purchasing seafood from a grocery store. In Puerto Hondo, 20% of respondents reported consuming self-caught seafood (n=3), the remaining respondents purchase directly from fishers (n=11) or from the fish market (n=6). Two Puerto Hondo respondents buy from grocery stores (n=2). Since mangrove seafood is key to food security, we assessed what types of seafood are relied on in Puerto Hondo and Isla Santay. Puerto Hondo respondents eat a variety of seafood, including fish (e.g. \u201cMojarras\u201d flatfish) (n=15), shellfish (n=10), shrimp (e.g. Pacific whiteleg shrimp, Litopenaeus vannamei) (n=9), and crab (e.g. Mangrove crabs, Ucides occidentali) (n=7). Isla Santay respondents rely more on fish (n=15) including \u201ccorvina\u201d drum\/croaker fish (Cynoscion spp.) and \u201cbagre\u201d catfish (Bagre spp.; Ariopsis) and crab (n=12) including swimming crabs (Callinectes arcuatus) and mangrove crabs (U. occidentalis). Some respondents eat shrimp (e.g., Pacific whiteleg shrimp, L. vannamei) (n=8) and shellfish (e.g. \u201ccockles,\u201d Anadara spp.) (n=4). See Figure 4.26 for seafood breakdown. 147 Figure 4.26: Self-reported seafood consumed by respondents from (a) Puerto Hondo and from (b) Isla Santay mangrove communities in the Gulf of Guayaquil (Ecuador), based on in person surveys conducted in November 2021. Similar to results from the preliminary survey, eight respondents noticed a change in the types of seafood they eat (Isla Santay n=5; Puerto Hondo n=3). Respondents reported a lower abundance of marine life in the mangroves, smaller fish, crabs, and shrimp, and more time needed to fish. Some reported specific species have disappeared. For example, there are reportedly no more mangrove mussels (Mytella strigata) or \u201cJurel\u201d jack\/horse mackerel (Trachurus murphyi) and there are fewer mangrove\/red crabs (U. occidentalis), fewer shrimp (Litopenaeus spp.), and fewer cockles (Anadara spp.). Consumption of drum fish \u201ccorvina\u201d (Cynoscion spp.) and swimming crabs (C. arcuatus) increased, as a result. Changes in seafood consumed is perceived to be a result of decreased access to resources (Isla Santay n=2; Puerto Hondo n=3), contamination (Isla Santay n=2; Puerto Hondo n=3), less fish or fished out resources (Puerto Hondo n=3), noise from vessels forcing fish farther into mangroves (Isla Santay n=1), and warming water and urbanization (Puerto Hondo n=1). Community connection - Puerto Hondo and Isla Santay both revealed high attachment to place, sense of belonging, and support within the community Isla Santay scored slightly higher (x\u0304=4.61, \u03c3=0.88) relative to Puerto Hondo (x\u0304=4.22, \u03c3=0.99). There was a slight dip in Puerto Hondo community cohesion. Puerto Hondo respondents noted an increase in robberies and that new individuals (labelled \u201cnon-fishers\u201d) move into Puerto Hondo to rent housing. Renters reportedly only stay one year and thus care less about community and place. Heightened community cohesion is commonly experienced in small-scale fishing communities, where there are no formal governing bodies, rather the community must co-manage fishing resources themselves (Alexander et al., 2018). This may explain why community connection in both Puerto Hondo and Isla Santay is high, however, Isla Santay shows higher levels of social connectedness. Small islands off the coasts of Ireland were found to have a strong sense of community and social cohesion, \u201cdictated by geography.\u201d Islanders remarked that they felt a sense of belonging, of safety, and felt positive about the continuation of culture and heritage (Broocks, 2021). A similar island-effect may increase Isla Santay\u2019s community connection. 148 Lack of community cohesion is a common finding in \u201cperi-urban\u201d communities. \u201cPeri-urbanization\u201d refers to mixed urban and rural characteristics as a result of sporadic growth from urban cities (Schreckenberg, 2018); These changes result in livelihood changes, land use changes, and institutional and socioeconomic changes (Schreckenberg, 2018). Intrinsic mangrove values (cultural heritage and spiritual or religious values) also decrease with urbanization and there is a decreased connection to place among individuals physically farther from the mangroves (Thiagarajah et al., 2015). I suggest \u201cperi-urban\u201d phenomena can explain some of the discrepancies between Puerto Hondo and Isla Santay on the issue of marine debris. At the core of Puerto Hondo and Isla Santay is a fishing culture and heritage, however, ties to a fishing heritage are decreasing in \u201cperi-urban\u201d Puerto Hondo. Puerto Hondo respondents reportedly look inland towards the city to supplement their livelihood, and while Isla Santay respondents celebrate their heritage through \u201cEl Festival de la Corvina,\u201d (\u201cCorvina Festival\u201d or \u201cCroaker Festival\u201d) a fishing festival, and \u201cSan Jacinto de Santay\u201d a religious festival, Puerto Hondo respondents did not report formal marine related festivals, aside from day-to-day fishing and tourism. Puerto Hondo shows \u201cperi-urban\u201d trends as illustrated throughout these results, which plays a role in how marine plastic is perceived in Puerto Hondo versus Isla Santay (Figure 4.27). 149 Figure 4.27: Geographic location of Puerto Hondo and Isla Santay relative to Guayaquil city, illustrating Puerto Hondo\u2019s \u201cperi-urban\u201d geographic landscape and Isla Santay\u2019s isolation from the major city. 4.3.3 Plastic pollution, waste management, litter in mangroves, governing bodies and solutions Plastic usage - On average, respondents \u201cagreed\u201d or \u201cstrongly agreed\u201d that plastic made their lives easier (x\u0304=4.35, \u03c3=0.91 in Puerto Hondo &Isla Santay). Respondents noted plastics make easy food containers, can be recollected and sold, and they are less expensive for business owners to stock than glass products sold in their store. On average, respondents felt \u201cneutral\u201d or \u201cagreed\u201d that plastic improved safety (x\u0304=3.44, \u03c3=1.15 in Puerto Hondo &Isla Santay). For example, plastics improved household safety as an alternative to glass; glass is especially problematic around children. Plastics are also used to shelter fishing canoes from rain (reported in both Isla Santay and Puerto Hondo) and plastic bottles are used as buoys for fishing nets (Figure 4.28). Plastics\u2019 safe properties were also related to secure food storage, building of huts\/shelter, and a safer alternative to glass. Surprisingly, access to resources was the least important factor when compared to plastic making life easier and plastic improving safety. The average response to plastic improving access to resources was just above \u201cneutral\u201d in both communities (x\u0304=3.15, \u03c3=1.28). Respondents noted everything comes to the town in plastic packaging and most goods are made of plastic, including the tourism boardwalk in Isla Santay (Figure 4.28). Alternatives to plastics known in the communities are glass and cardboard, but glass is too expensive and dangerous for children. \u201cViandas\u201d, an aluminum container similar to an Indian Tiffin, is a known alternative to food containers, however respondents reported the hassle of having to clean and return these items. 150 Figure 4.28: Marine plastic repurposed and reused in Isla Santay. (a) Plastic bottles used as buoys\/floatation devices for fishing nets. (b) Plastic fragments recycled into boardwalks used for ecotourism. (c) Plastic sheets used as tarps for shelter on fishing canoes. Photo credits: K. McMullen. Solid waste management - Even though Puerto Hondo has formal municipal solid waste management that extends into the town and Isla Santay does not, waste management practices were similar in both communities. In both communities, the majority of waste is reused (24%Isla Santay; 21% Puerto Hondo) or disposed of in an open space (18% Isla Santay; 21% Puerto Hondo). 17% of waste from Isla Santay respondents is brought to Guayaquil city (Figure 4.29). Puerto Hondo respondents return waste for cash (18%) more than Isla Santay (4%), which is likely due to limited access to resources (e.g., transportation to the city from the island). Figure 4.29 shows the solid waste disposal breakdown. 151 Puerto Hondo Isla Santay Figure 4.29: Self-reported solid waste disposal in (a) Puerto Hondo and (b) Isla Santay mangrove communities in the Gulf of Guayaquil (Ecuador), based on in person surveys conducted in November 2021. Numbers within the bars indicate the number of respondents who reported the method of waste disposal. 152 40% of Puerto Hondo respondents noted a positive change in waste management (n=6). Solid waste management services are a recent addition to the community. Reported challenges include inconsistent timing due to damaged trucks or trucks that are too full. Trucks pass quickly and respondents often throw their waste into a passing truck, resulting in some waste spilling into the street. Respondents requested larger trucks (n=2), an additional collection schedule (n=4), cleaning of sidewalks and roads (n=2), and that waste collection trucks reduce speed when driving through the community (n=1). Despite this and the self-reported disposal methods, most respondents reported satisfied (n=8) or very satisfied (n=5), which aligns with \u201cperi-urbanization\u201d characteristics in which these areas tend to downgrade the value of public services like water provision and waste removal (Marshall et al., 2009). In Isla Santay, solid waste management has reportedly changed for the worse. 47% of Isla Santay respondents noted a change in waste management (n=7). Six people reported that a single individual (n=2) or a company (n=4) used to collect garbage, now each family is responsible for their own waste. There is no formal collection and challenges include distance from resources (n=3), access (n=3), and geography (n=3). Those who use government services bring the garbage in boats to Guayaquil (n=2) and are satisfied with this. One respondent asked for Urvaseo, the solid waste management company in Guayaquil, to come to the island. Marine plastic is collected and reused by some in Isla Santay and Puerto Hondo. Around 33% (n=5) of respondents in Isla Santay and 33% (n=5) of respondents in Puerto Hondo reuse or repurpose plastic litter collected from the sea. In Isla Santay, a senior person is also known to collect marine plastic. The plastic litter in Isla Santay is reported to come from drink sellers (n=3), tourists (n=1), the city (n=1), and the tides (n=1) and is either sold in the city (n=2), sent to recycling depot (n=1), or given to \u201cchamberros\u201d [informal waste collectors] (n=1). Marine plastic is also used for fishing net flotation and in the Isla Santay tourism boardwalk (Figure 4.28). In Puerto Hondo, most respondents reported plastic waste comes from their own daily consumption (n=6), businesses including their own (n=1), community (n=1), children (n=1), and wind (n=1) and in turn, they recycle it (n=2), sell to those who recycle (n=2), use it as garbage bags in the house (n=1), give it to a collector (n=1), use it for different liquids (n=1), or use it for flower pots (n=1). 153 4.3.4 Marine litter and mangroves SI survey respondents demonstrated more concern over marine litter than Puerto Hondo across all categories and reported that marine litter impacts their basic needs, far more than Puerto Hondo respondents. Isla Santay respondents on average \u201cagreed\u201d that marine litter affects the conditions of their living space (x\u0304=3.8, \u03c3=1.26), whereas Puerto Hondo community members \u201cdisagreed\u201d on average (x\u0304=1.4, \u03c3=0.74), as illustrated in Figure 4.30 a. In the figure, note that the majority of Isla Santay respondents indicated by the red area points to extremely, whereas Puerto Hondo respondents point to not at all. 33% (n=5) respondents in Isla Santay noted that marine litter is an extreme issue in the community Isla Santay respondents on average \u201cagreed\u201d marine litter is a serious issue in the community (x\u0304=4, \u03c3=1), whereas Puerto Hondo respondents felt less than \u201cneutral\u201d (x\u0304=2.73, \u03c3=1.53), as shown in Figure 4.30 b. Marine litter also added more stress to Isla Santay respondents (Figure 4.30 c). I suggest this is another symptom of \u201cperi-urbanization\u201d illustrated through the case study of Puerto Hondo. As residents retreat from the mangroves and seek basic needs in the city, marine litter in the mangroves is seen less and urban litter in the streets may be largely ignored. De Veer et al., (2022) found \u201clitter blindness\u201d occurs in school age children when viewing litter in an urban landscape, however, the same does not occur in a natural environment (De Veer et al., 2022). As communities spend less time in the mangroves, marine litter is effectively \u201cout of sight, out of mind.\u201d Isla Santay is isolated on an island and thus, marine litter is seen against a natural landscape frequently. As an example, only one individual in Isla Santay reported marine litter does not affect anything, whereas nine individuals in Puerto Hondo reported marine litter has no effects. 154 a) Does marine litter affect your living area? b) Is marine litter a serious issue in your community? c) Does marine litter add stress to your life? 155 Figure 4.30: Radar plots showing the outcomes of perception responses related to the stress caused by marine litter on surveyed members from the Isla Santay and Puerto Hondo mangrove communities in the Gulf of Guayaquil, Ecuador. Responses close to \u201cnot at all (1)\u201d indicate no reported stress from marine debris, whereas responses close to \u201cextremely (5)\u201d indicate the high stress. Circumference values (e.g., 0-10) indicate the number of respondents who selected the response. Respondents in Isla Santay were concerned about marine litter due to contamination implications (n=1), damage to the environment (n=1), more litter with rainy season (n=1), litter with tides (n=2), bad smells (n=1), plastics in fish (n=1), effects on children (n=1), health effects (n=1), and its origin from Guayaquil via the river (n=1). Isla Santay respondent (age 67, male, fisher) added marine litter impacts the community\u2019s ability to care for their family due to impacts on fish, because many members of the family rely on income from fishing. Some respondents noted marine litter makes it more difficult to fish, stating that nets collect plastic (n=2). In Puerto Hondo, concerns over marine litter included leaching substances from plastic (n=1), impact on the environment (n=1), litter at the beach\/creeks (n=2), the \u201cisland of plastic\u201d (n=1), and entangled fish (n=1). One respondent noted seeing plastic in fish (n=1). That said, in Puerto Hondo, more individuals reported that general stress, not directly related to marine litter, hindered their ability to obtain work (n=6) and obtain food (n=4), more so than stress from marine debris. 4.3.5 Gender and age considerations Gender \u2013 Schreckenberg (2018) suggests gender plays a key role in how people perceive ecosystem benefits. As outlined in Section 4.3.1 women are said to be more motivated by collective\/social outcomes then individual\/private benefits, to value mangroves more than men (Schreckenberg, 2018), and to be more concerned about marine litter impacts (Hartley et al., 2018; Davison et al., 2021). Converse to the preliminary survey (Section 4.3.1), in phase two, more male respondents were concerned about marine litter and considered it a serious issue. In both Isla Santay and Puerto Hondo, men were overall more concerned about marine litter (see brown area in Figure 4.31). Isla Santay males (n=3) reported more concern over marine litter\u2019s impact on obtaining food (100% of Isla Santay male respondents) and providing a safe home 156 (100% of Isla Santay male respondents). PI males (n=7) were primarily concerned with marine litter\u2019s impact on their ability to care for their family (42%). The concerns of Isla Santay female respondents varied from marine litters\u2019 impact on obtaining food (50%), providing a safe home (33%), and obtaining work (25%). Puerto Hondo women were least concerned overall; female respondents were concerned about marine litter\u2019s impact on obtaining food (12.5%) and access to leisure activities (12.5%) (Figure 4.31). We suggest that males are more concerned due to the theory that connections to nature and concern for a resource is bound by access and control (Schreckenberg, 2018). In Puerto Hondo andIsla Santay, the act of fishing in the estuary arms has largely been a male activity, suggesting access and control to mangroves has been dominated by men. These results suggest men in Puerto Hondo and Isla Santay feel a heightened responsibility to steward the mangroves they have fished in for generations. a) Puerto Hondo 157 b) Isla Santay Figure 4.31: Radar plots showing the outcomes from perception responses by gender to the individual and community impacts or effects by marine litter in and Puerto Hondo (PH, a) and Isla Santay Island (SI, b) mangrove communities (Gulf of Guayaquil, Ecuador), where a circumference score of 0 is equivalent to 0% respondents agree and a score of 1 is equivalent to 100% of respondents agree. Age - Additionally, previous studies have found that older adults (46-64 years) tend to be more concerned about ocean health compared to young adults (Hartley et al., 2018; Potts et al., 2016), \u201cpossibly indicating something unique about the marine environment that warrants further investigation\u201d (Davison et al., 2021). Older respondents in European countries were more concerned than younger respondents about plastic litter\u2019s impact on health (Hartley et al., 2018). Both phase one and phase two survey analysis revealed that the cognitive burden of concern over marine litter was felt more strongly among senior age cohorts. Similar to the preliminary study, in phase 2, older demographics associated marine litter with more negative impacts on basic needs (e.g., obtaining food, obtaining work, providing a safe home, caring for family, enjoying leisure activities, and engaging in meaningful relationships). 158 As shown in Figure 4.32, while the Puerto Hondo community shows a statistically significant relationship with a moderate correlation between basic needs impacted by marine letter and age (r = 0.53; p < 0.05), Isla Santay exhibited a high degree of correlation with a significant relationship between the two variables (r = 0.74; p = 0.0014). According to the outcomes of the linear regression analysis, respondent\u2019s age in both communities may well be considered a plausible predictor variable explaining the variation of responses concerning the marine litter impacts on basic needs in Puerto Hondo and Isla Santay, where 28% (i.e., r2 = 0.28) and 55% (i.e., r2 = 0.55) of responses were explained by age, respectively (Figure 4.32). a) Puerto Hondo b) Isla Santay Figure 4.32: Regressions showing trends of the community concerns over marine litter impacts on basic needs as a function of respondents\u2019 age. The concern increases with age in both Puerto Hondo and Isla Santay mangrove communities in the Gulf of Guayaquil, Ecuador. Industrialization and investment in shrimp farming resulted in mangrove deforestation beginning around forty years ago (see Appendix D). It is speculated that those who saw and experienced the changes over these years, have higher concern about the impacts of marine debris and other pollutants on the mangrove environment; they have seen the communities\u2019 increased dependence on plastic products and the increased amount of litter accumulating in estuary arms. One Puerto Hondo respondent (age 64, male, fisher\/kayak guide) notes: \u201cantes no hab\u00eda recolecci\u00f3n porque no hab\u00eda pl\u00e1sticos\u201d [Before there was no solid waste collection because there were no plastics]. At the same time as the rise of plastics, the abundance of species in the estuary dwindled. 159 The concept of \u201cshifting baselines\u201d (Pauly, 1995) in fisheries science refers to the phenomenon of a new accepted standard of fisheries yield, much less than generations ago, and yet, new generations are increasingly ignorant of the abundance that once was, and unknowingly accept a large extent of change. As a result, concern for the loss of biodiversity is lacking (Pauly, 1995). In this case study, despite advanced education at a secondary level, younger generations expressed less concern over the marine debris and its impacts on the estuary and the community\u2019s basic needs (Figure 4.33). A \u201cshifting baseline\u201d may be conceptualizing. The generations younger than 40 years of age were not present for the rise of plastic and the decrease in mangrove forest coverage, thus the younger generation may pose a barrier to change, given their acceptance of marine debris. Older demographics may well be more concerned with preserving a heritage closely linked to fishing in the mangroves. In Broocks (2021) study of Irish coastal islands, older demographics aged over 60 were found to actively preserve traditional aspects of living on the islands. Similarly, older survey respondents indicated an increased dependency on seafood from the mangroves, thus suggesting harm to the mangroves is more deeply felt among this cohort (Figure 4.33). For example, most respondents reported eating seafood weekly (Isla Santay n=10, Puerto Hondo n=12), but all who consume seafood daily (n=4) were above age 50. With the exception of one respondent, all of those who noticed a change in seafood were over forty years of age (n=7). The reported timeframe of the change ranged from 20 years ago (Isla Santay n=1; Puerto Hondo n=1), to 15 years ago (Puerto Hondo n=1), to 7-10 years (Isla Santay n=1; Puerto Hondo n=1) prior to 2021. 160 Figure 4.33: Images from Puerto Hondo. Left: view from homes near mangroves, where fishing canoes can be seen. Right: View from the road near the highway, where a more urban landscape is viewed. This figure illustrates vast differences in sense of place in Puerto Hondo. Older generations of fishers spent the majority of the working day in the mangroves (left). Younger respondents now tend to work in a more urban landscape (right). The differences in place illustrate how marine litter can be \u201cout of sight, out of mind.\u201d Photo credits: K. McMullen. 4.3.6 Solutions There are associations in both communities that oversee community affairs: \u201cSan Jacinto de Santay\u201d [Saint Jacinto of Santay] in Isla Santay and \u201cAsociaci\u00f3n de Peque\u00f1os Agricultores Puerto Hondo\u201d [Association of Small Farmers] in Puerto Hondo. Puerto Hondo respondent (age 41, female, tourism) notes the irony: \u201ctenemos una asociaci\u00f3n de peque\u00f1os agricultores pero no fincas\u201d [We have an association of small farmers but no farms]. These organizations were not mentioned to manage marine litter. Because \u201cperi-urban\u201d communities fall at the juxtaposition between city and rural, they receive waste outflow from the city (Schreckenberg, 2018) and are often seen as \u201cfuzzy zones\u201d in terms of regulations (Marshall et al., 2009). In terms of which organizations or individuals should take responsibility, Isla Santay placed highest emphasis on solid waste management services (x\u0304=2.54, \u03c3=1.61, where lower number, e.g. close to 1, indicates higher responsibility), local community (x\u0304=2.54, \u03c3=1.55), and self (x\u0304=2.54, 161 \u03c3=1.46), whereas Puerto Hondo placed highest emphasis on the national government (x\u0304=2.71, \u03c3=1.68) and local government (x\u0304=2.71, \u03c3=1.54). In Isla Santay, lack of collaboration (n=2), lack of action (n=2), lack of resources (n=1), lack of solid waste management system (n=1), and pressure on children to do the work (n=2) are among the barriers to resolving the marine litter issues. In Puerto Hondo, the perceived barriers are different; respondents noted lack of information and awareness (n=2), shyness (n=1), lack of cooperation (n=1), and not viewing marine litter as an issue (n=2). One Puerto Hondo respondent (age 64, male, fisher & tourism) noted conflict between groups: \u201cellos dicen que no soy pescador, \u00bfpor qu\u00e9 deber\u00eda importarme?\u201d [People say \u2018I am not a fisher, why should I care\u2019]. SI expressed more self-efficacy in terms of being in a position to raise awareness about marine litter as an issue (x\u0304=3.36, \u03c3=1.28), whereas in Puerto Hondo self-efficacy was lower (x\u0304=2.07, \u03c3=1.28). Self-efficacy and agency, an individual\u2019s values, power, and control, can advance well-being, provided the individual perceives the freedom, capacity, and self-efficacy needed to address an issue (Alkire, 2008). Likely abetted by a history of forced displacement, Isla Santay and Puerto Hondo respondents perceived little control over marine litter and thus are at a disadvantage for prompting solutions. Both Puerto Hondo and Isla Santay respondents provided solutions, as illustrated in Figure 4.34. Solutions varied from external support, improving solid waste management, \u201cMingas\u201d [community cleanups], action from MAE, and education and awareness. 162 163 Figure 4.34: Suggested solutions reported in an open-ended question from (a) Puerto Hondo and (b) Isla Santay communities (Gulf of Guayaquil, Ecuador), where a larger rectangular area indicates a higher number of respondents mentioned the solution. Extending beyond Puerto Hondo and Isla Santay, the focus for marine litter solutions have been primarily on the symptoms, rather than the root of the causes (Davison et al., 2021; Pahl et al., 2017; Vandenberg & Ota, 2022). Cleanups have positive sentiment because they stop the symptoms (e.g., littered beaches), but positive waste management does not have the same sentiment (Pahl et al., 2017). Ecuador has implemented an Organic Law and Regulation for the Rationalization, Reuse, and Reduction of Single Use Plastics to regulate and restrict the use of single-use plastic products (Ley Org\u00e1nica y Reglamento para la Racionalizaci\u00f3n, Reutilizaci\u00f3n y Reducci\u00f3n de Pl\u00e1sticos de Un Solo). They entered into force in December 2019 and May 2021, respectively (Registro Oficial N\u00b0 354. 2020; Registro Oficial N\u00b0 459. 2021). In addition to this, two recent taxes have been passed for the use and consumption of the most common single-use plastics (plastic bags and PTE plastic bottles). On July 1, 2019, Ecuador\u2019s Internal Revenue Service (SRI) established the Redeemable Plastic Bottle Tax (\"Impuesto Redimible a las Botellas Pl\u00e1sticas no Retornables\") of USD $0.02 for each polyethylene (PET) plastic bottle, equivalent to USD $0.30 per kilogram (kg) of PET plastic bottles (i.e., 15 PET plastic bottles per kg). Under the Resolution No NAC- DGERCGC19-00000029 of June 19, 2019 (Ministerio de Producci\u00f3n, Comercio Exterior, Inversiones y Pesca., 2022), this tax was devised to reduce environmental pollution and stimulate the recycling process. Similarly, On May 9, 2020, Ecuador enacted the Special Consumption Tax (\u201cImpuestos a los Consumos Epeciales,\u201d ICE) to plastic bags. Plastic bags now incur an additional charge USD $0.04 for each plastic bag, with an increase of USD $0.02 each year until the year 2023. This is according to Ecuador\u2019s Internal Revenue Service (SRI), which established the rules for the declaration and payment through Resolution No. NAC-DGERCGC20-00000033 as of May 6, 2020 (Romero, 2020; Servicio de Rentas Internas, 2020). Of note, few respondents, however, suggest laws to reduce plastic consumption by consumers. Proposed plastic taxes have received backlash (Municipality of Guayaquil, 2018; El Universo, 164 2021a; El Universo, 2021b; El Universo, 2020a; El Universo, 2020b; El Universo, 2020c; El Universo, 2020d; El Universo, 2012). One Puerto Hondo business owner reported that the business cannot afford a plastic tax, especially, with the economic decline resulting from the COVID-19 pandemic. As we seek to create solutions, it is key to understand how management of these resources will impact marginalized communities who are not seated at the decision-making table. 4.4 Towards community-led bottom-up frameworks for plastic use and waste management The complex socio-marginal costs perpetuated by the Guayaquil Gulf urbanization and industrialization may be less apparent in the day-to-day activities of local Guayas River Estuary\u2019s community members, but marine litter is a constant reminder of the human intrusion on the area\u2019s precious ecosystem. As the United Nations 2030 Sustainable Development Goals (SDGs) seeks to \u201cleave no-one behind\u201d (Schreckenberg, 2018), we are ethically and inherently responsible to ensure voices of marginalized communities are heard and have equitable access and support (Vandenberg & Ota, 2022). To do so, we need co-production of mitigation strategies designed to integrate diverse knowledge systems of nature (Bennett et al., 2015; Alkire, 2013; Dawson & Martin, 2015). The trade-offs between conserving the biodiversity of these regions and the anthropogenic environmental implications of shrimp farming, artisanal fishing, ecotourism, and plastic-use in the community, are significant and complicated. Disposable plastic offers retail and food vendors economical transfer materials; the communities feel plastic adds to their ease of life and safety; as well, \u201cchamberos\u201d (i.e., informal garbage collectors) rely on plastic collection for their income. At the same time, plastic litter accumulating among the mangroves is perceived as a threat to the economy and health of the community. Few studies focus on the impact of plastic litter in these mangrove communities (Mieles, 2020; Pernia et al., 2019). Like many aspects of living in the Anthropocene, the situation is complex, especially given the different stakeholder interests (e.g., shrimp industry vs. ecotourism operators) and how different stakeholders have varying levels of influence and power over policy-making decisions. The need for community agency with external support is apparent. 165 Integrative collaboration across sciences is critical to account for the complexities and impacts of plastic pollution on the environment and well-being. Figure 4.35 illustrates the complexities of human nature and the imposed cognitive burden of using plastic out of necessity, yet feeling its impacts within the natural environment. As demonstrated in other areas of the world, mangrove communities, even if close in proximity to each other, can have vastly different needs within the mangrove environment (Seary et al., 2021). This is the case with Puerto Hondo and Isla Santay and emphasizes the need to look at micro-and macro-levels when considering the impacts of plastic pollution and addressing these complex problems. Figure 4.35 illustrates the cognitive complexities associated with \u201cto use or not to use\u201d plastics. Each individual within the context of their respective community, with its own set of needs and challenges, faces unique challenges and decision points throughout life, each shaped by their personal traits and experience. When it comes to deciding whether to use plastic or not, personal traits and experiences can significantly impact an individual\u2019s behaviour and choice. While some individuals may intend to minimize plastic use due to the environmental impact, plastic use may in some cases be deemed necessary and can, therefore, lead to cognitive dissonance, the psychological harm that occurs when an individual experiences inconsistency between their beliefs and actions (Festinger, 1957). Each decision point has an outcome, which may be a negative consequential outcome should cognitive dissonance occur. Each set of personal traits and experience is dynamically changing in time and space (e.g., different situational contexts). In sum, this diagram illustrates that there is no \u201cone size fits all\u201d solution to plastic pollution. Societal pressure and cognitive burden must be considered, and solutions will only be successful if they are dynamic and aligned with local perceptions and information. Furthermore, the people living in isolated, rural and continental coast areas, mainly ancestral and Indigenous communities, have unique needs and health and equity issues in the face of pervasive ocean pollution by marine plastics (Vandenberg & Ota, 2022). New collaborative frameworks and solutions-oriented research to champion community-led bottom-up policies are vital to ensure the health, environmental protection and socioeconomic needs are met for those living in Isla Santay and Puerto Hondo mangrove communities, as well as other coastal, rural and remote communities along the Ecuadorian coast. 166 Figure 4.35: Conceptual framework highlighting the complexities involved in the decision-making process of using plastic items. Framework was conceptualized based on themes through survey responses at Puerto Hondo and Isla Santay communities. 4.5 Conclusions This work contributed to uplifting knowledge of marginalized coastal communities and revealed complexities of marine litter in two coastal mangrove communities in mainland Ecuador. Overall, marine litter in Isla Santay and Puerto Hondo, affects community cohesion and well-being, most predominantly in terms of livelihood, food security and natural resource security. Traditional economic opportunities (fishing and ecotourism) are confined by the health and success of the mangrove ecosystem. In a community threatened by unemployment, the need to enhance new economic avenues, increase access to education, and create inclusive solutions to marine debris is critical. Puerto Hondo and Isla Santay mangroves have been a cornerstone of the communities, however, as these mangroves are damaged, the historical heritage and connection to the mangroves 167 are at risk. Supporting agency in local governance may lead to more socially equitable, economically viable, and environmentally sustainable outcomes that ensure equal access to resources, livelihood, public health, and pollution prevention measures. 4.6 Acknowledgements First and foremost, we humbly express our gratitude to the local people of Puerto Hondo and Isla Santay, without whom we would not have gained such insights. We thank them for their willingness to share knowledge and connect with us, and will cherish our time spent there. Thank you Geovanny Parrales, of Isla Santay National Recreation Area, for guiding us in Santay. Karly McMullen and Juan Jos\u00e9 Alava thank the Nippon Foundation for providing funding to support fieldwork and surveys in Ecuador via the Nippon Foundation-Marine Litter Project at the Institute for the Oceans and Fisheries, University of British Columbia. Dr. Wilf Swartz proactively helped to manage funding and financial resources allocation and administration to support the Nippon Foundation-Marine Litter Project. We also thank the Escuela Superior Polit\u00e9cnica del Litoral (ESPOL) for coordinating accommodation, logistics and transportation, contributing to the survey effort, as well as permits for the surveys. Surveys could not have been conducted without the help of surveyors, including coauthors and Carlos S\u00e1nchez Monserrate. We thank Madeleine Calle for helping to coordinate and contact the Puerto Hondo and Santay Island communities as well as for providing demographic data. Karly McMullen thanks Dr. Iria Garcia Lorenzo, Roshni Mangar, and Julia Adelsheim for assistance and guidance, as well as Nippon Foundation Marine Litter Project team members for thoughtful questions and feedback. We also thank the late H\u00e9ctor Ay\u00f3n, who was a well-known Ecuadorian geophysicist expert, for kindly providing historical maps of Puerto Hondo for this study. Human ethics certification to conduct the fieldwork and interviews were provided by the University of British Columbia\u2019s Ethics Committee (H21-02114). The Ministry of Environment, Water and Ecological Transition of Ecuador (MATE) granted official permits to have access to and deploy surveys in Puerto Hondo and Isla Santay communities. The University of British Columbia\u2019s Go Global: International Learning Programs approved travel to Ecuador based on risk management procedures. COVID-19 field protocols and risk assessment and mitigation were concertedly practiced and followed in all surveys. This chapter has been submitted for publication and is currently under review. Thank you kindly to coauthors: Ana Tirap\u00e9, Paola Calle, Jessica Vandenberg, Omar Alvarado-Cadena, Yoshitaka Ota, Juan Jos\u00e9 Alava 168 Chapter 5: Conclusion - Transdisciplinary Microplastic Thesis for Equitable, Solutions-Oriented Research \"We can no longer afford to ignore the impacts of plastic on wildlife, ecosystems, and our own health. We must take action now to create a better future for all.\" - Jane Goodall Plastics are an entirely human issue (Pahl et al., 2017). Unlike other naturally occurring marine pollutants, plastics are entirely anthropogenic, and the impacts are our responsibility alone. This work concludes that microplastic pollution in the oceans is a relevant and rising concern in the Gal\u00e1pagos archipelago, even in light of recent, global efforts to reduce plastic use (United Nations Environment Programme, 2021). This project sought to advance knowledge and science on microplastic pollution in the tropical waters in Ecuador\u2019s Gal\u00e1pagos Islands, using the Gal\u00e1pagos penguin as a sentinel and indicator species for seabird exposure to microplastic pollution in the remote, UNESCO World Heritage site and Gal\u00e1pagos Marine Reserve (GMR). The study assessed the concentration of microplastics in penguin colony surface seawaters, penguin prey, zooplankton and Gal\u00e1pagos penguin scat, as well as microplastic bioaccumulation and biomagnification potentials in the Gal\u00e1pagos penguin using trophodynamic Ecopath with Ecosim (EwE) ecosystem modeling using the Ecotracer contaminant tracing routine. The study also explored Ecuadorian mangrove communities' attitudes towards plastic pollution's impact on the environment, economy, and human health, providing localized context for effective strategies to tackle this pervasive problem. The chapters can be summarized as follows: \u2022 Chapter 2 provided evidence of microplastic and anthropogenic particle concentration levels in waters near Gal\u00e1pagos penguin colonies, specifically in coastal Santa Cruz, south and southeastern Santiago, eastern Isabela, and northern Floreana, as well as the level of microplastic and anthropogenic particles found in zooplankton, the ingestion rate of microplastics and other anthropogenic particles in the Gal\u00e1pagos penguins' prey (anchovies and mullets), and penguin scat. \u2022 Chapter 3 used trophodynamic ecosystem modelling (EwE) to assess the potential bioaccumulation and biomagnification of microplastics in Gal\u00e1pagos seabirds, particularly the Gal\u00e1pagos penguin. 169 \u2022 Chapter 4 employed social psychology methods to measure and amplify local perceptions towards plastic pollution in Ecuadorian mangrove communities, namely Puerto Hondo and Santay Island, to provide policy recommendations based on the findings. Chapter 2 revealed an average of 0.54\u00b10.49 anthropogenic particles\/L or 4.11x10-6\u00b13.72x10-6 g\/L, mostly 10 to 1250 \uf06dm in size, primarily blue, fibers, and polypropelene (PP), polyethylene terephthalate (PET) and cellulose (CE), in waters surrounding the Santa Cruz, Santiago, Isabela, and Floreana islands. Likewise, the chapter provided no evidence of microplastics in Gal\u00e1pagos penguin scat, but potential for consumption as supported by calculation of anthropogenic particles per day through diet items (e.g., fish). Of 11 anchovies assessed, 45% had anthropogenic particles ranging from 0 to 3 microplastics per fish, the majority of which were 750 to 1250 \uf06dm, were black, fibers, and PET or CE. All 6 mullets and 1 milkfish assessed had anthropogenic particles ranging from 3 to 27 particles per fish (with an average 7.27), most of which were between 10 to 2000 \uf06dm. There were a mix of fibers, fragments, and foam as well as a wide variety of colours in the mullets and mostly fibers, blue and black, in milkfish. Polymers were predominantly PP, PE, and CE in mullets, and milkfish had mostly PET and CE. The study suggests that there is an urgent need for further research and intervention to reduce the exposure of marine organisms to microplastics, including the Gal\u00e1pagos penguin, which is already facing various anthropogenic threats including plastics, other water-soluble pollutants, oil, light pollution (Gilmour et al., 2023), and ENSO (Boersma, 1998; Vargas et al., 2006, 2007; Wolff et al., 2012). Though there were many challenges with sampling for microplastics in this remote region, it is still critically necessary to monitor the amount of microplastics in these areas and others around the world to provide baselines for the amount of microplastic pollution in waterways. Efforts need to be standardized so results can be compared, and monitoring studies should be balanced with laboratory efforts to determine the impacts of environmentally relevant microplastics (including, relevant in size, abundance, shape, weathered and aged, including microbioms, etc.) (Anbumani 170 & Kakkar, 2018; Kooi et al., 2017; Lambert et al., 2017; Lusher et al., 2020; Mohan et al., 2016; Provencher et al., 2020). Chapter 3 concluded that microplastics can bioaccumulate in all predator-prey combinations, and biomagnification of microplastics was observed in most simulations. However, biomagnification was not observed in the 99% elimination rate scenario, indicating that the elimination or egestion rate of microplastics is a key factor in determining their bioaccumulation behavior, which supports findings that elimination and retention time are critical values needed to determine biomagnification of microplastics (Alava, 2020). Compared to empirical data collected in chapter 2, the modelling efforts in this study under-predicted microplastic concentrations in zooplankton and over-predicted concentrations in fish. The closest microplastic concentration values to the observed data were seen in the 99% elimination rate scenario and the baseline scenario for fish and zooplankton, respectively. Future research with more substantial sample sizes may be able to deduce retention times by comparing empirical and predicted data from microplastic accumulation modelling. The behaviour of microplastics in ecosystems and their bioaccumulation and biomagnification potential is still not agreed upon (Akhbarizadeh et al., 2019; Alava, 2020; Goswami et al., 2020; Li et al., 2022; Miller et al., 2020, 2023; Yang et al., 2019). A major meta-analysis of field data found evidence of bioaccumulation of microplastics in marine species, but the biomagnification of these pollutants in the food web was not supported by field data (Miller et al., 2020). Some studies have observed trophic transfer of microplastics (Chagnon et al., 2018; Farrell & Nelson, 2013; Nelms et al., 2018; Sotton et al., 2014), but there is disagreement as to whether microplastics are retained in the gut (Alava, 2020; Miller et al., 2020). Bioaccumulation and biomagnification are widely significant factors to help evaluate the potential treat of microplastics to marine organisms and ecosystems (Chormare & Kumar, 2022). Since it is difficult and often not practical to examine gut contents of species at high tropic levels, incorporating empirical data from microplastics found in prey items and fecal matter into modeling can be a useful approach to trace the movement of microplastics through food webs. 171 Given that microplastic ingestion has been linked to several primarily sublethal impacts (Lusher, 2015), it is necessary to continue assessing exposure rates and movements of the microplastics in the organism themselves. It is suggested that ingested microplastics do not offer advantages in stressful changing oceanic conditions (e.g., overfished resources and climate change effects). Instead, the presence of microplastics or nanoplastics is more likely disadvantaging organisms, and thus, assessing their impacts is necessary and critical to aid in the conservation of species in a changing ocean. Chapter 4 investigated perceptions of plastic pollution in Ecuadorian mangrove communities. The Gal\u00e1pagos and continental Ecuador are home to thousands of hectares of mangrove forests (Carvajal & Alava, 2007; CLIRSEN-PMRC, 2007; Morocho et al., 2022), which have recently been cited as potential traps for marine debris (Martin et al., 2019). The mangrove communities of Ecuador's Gulf of Guayaquil are important for the economic and cultural well-being of local communities. However, anthropogenic impacts including marine litter have caused significant issues within these ecosystems. This chapter explored the social narrative of plastics and community perceptions based on surveys conducted in Puerto Hondo and Santay Island, Ecuador, in 2019 and 2021. The study found that Puerto Hondo, in which the younger generation is moving from historical work in the mangroves (e.g., artisanal fishing) to work in the neighbouring city, is becoming less connected to the mangroves in day-to-day activities and subsequently were less concerned with impact of marine litter. Conversely, Santay Island revealed significant impacts of marine litter on their livelihood, health and well-being, which may be associated with their close connection to the mangroves, namely, directly living within them. Surveys in both communities revealed strong ties between a positive sense of well-being and a close connection to healthy nature, and thus, this study advocates for locally grounded solid waste management policies to reduce marine plastic and the associated cognitive and physical burden to local people to uplift and empower the well-being of the coastal mangrove communities in Ecuador. The Guayaquil Gulf urbanization and industrialization have resulted in complex socio-marginal costs that are perpetuated by marine litter. The trade-offs between conserving biodiversity and anthropogenic environmental implications are significant and complicated. Integrative collaboration across sciences is critical to account for the complexities and impacts of plastic 172 pollution on the environment and well-being. Community agency with external support is vital, and new collaborative frameworks and solutions-oriented research are necessary to ensure health, environmental protection, and socioeconomic needs are met for marginalized coastal communities. Marine litter affects community cohesion and well-being, particularly in terms of livelihood, food security, and natural resource security. Supporting agency in local governance may lead to socially equitable, economically viable, and environmentally sustainable outcomes. Overall, this thesis represents a comprehensive, transdisciplinary outlook on marine debris and microplastic pollution in Ecuador mangrove communities, including the Gal\u00e1pagos Islands. It combines traditional scientific techniques including empirical data collection and modelling, as well as social-psychology science. The Gal\u00e1pagos penguin served as a unique and charismatic case study for microplastics pollution. The goal was to use a case study that can raise awareness in the public eye. Subsequently, this study was picked up by media outlets including the University of British Columbia \u201cFocus\u201d Science, CTV News, CBC, Vancouver Sun, and Canada Today. Outcomes from mangrove community surveys were included in the Nippon Foundation and Ocean Nexus equity and marine plastic report (2022) \u201cTowards and Equitable Approach to Marine Plastic Pollution.\u201d Disseminating knowledge and breaking science, public, and policy barriers is a critical step towards generating marine pollution solutions. This thesis highlights the pervasive and harmful nature of plastic pollution in our oceans, which has significant implications for marine ecosystems, biodiversity, and human health. Seabirds are a key indicator species for marine ecosystem health (Avery-Gomm et al., 2018; Dee Boersma, 1978; Fortunato et al., 2013; Provencher et al., 2018; Ryan, 2008; van Franeker et al., 2011), and their health and population dynamics can reflect the state of the wider marine environment. Modelling efforts reveal that under certain circumstances microplastic pollution can have cascading effects throughout the food web, potentially affecting seafood availability and security. Finally, the study can inform policy and management decisions aimed at reducing plastic pollution, in the Gal\u00e1pagos Marine Reserve, the Gal\u00e1pagos National Park, and mangrove communities across Ecuador, to protecting marine ecosystems and the services they provide. 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Marine mammals and microplastics: A systematic review and call for standardisation. In Environmental Pollution (Vol. 269). Elsevier Ltd. https:\/\/doi.org\/10.1016\/j.envpol.2020.116142 212 Zettler, E.R., Mincer, T.J., & Amaral-Zettler, L.A. (2013). Life in the \u201cPlastisphere\u201d: Microbial communities on plastic marine debris. Environmental Science & Technology 47, 7137\u20137146. Zhao, S., Zhu, L., & Li, D. (2015). Microplastic in three urban estuaries, China. Environmental Pollution, 206, 597\u2013604. https:\/\/doi.org\/10.1016\/j.envpol.2015.08.027 Zhang, Y., Gao, T., Kang, S., Shi, H., Mai, L., Allen, D. and Allen, S. (2022). Current status and future perspectives of microplastic pollution in typical cryospheric regions. Earth-Science Reviews, 226, 103924. https:\/\/doi.org\/10.1016\/j.earscirev.2022.103924 213 Appendix A Plastic Ingestion by Zooplankton Source \u2022 Intertidal and epipelagic cnidaria, benthic macroinvertebrates such as arthropods (crustaceans), and Echinodermata all have evidence of plastic ingestion. (in review Macali & Bergami, 2020) \u2022 Microplastics have been found in all of the following organisms collected from marine environments: Salpida spp., Amphiphoda spp., Chaetognatha spp., Cladocera spp., Copepoda spp., Euphausiacean spp., Heteropada spp., Luciferidea spp., Medusozea spp., Pteropoda spp, Luciferidea spp., Medusozea spp., Siphonophorea spp., Thaliacea spp., Brachyura Larvae, Ichthyoplankton (Fish larvae), and Stomatopoda larvae. (in review Botterell et al., 2019) \u2022 Jellyfish have also been reported to ingest plastic. (Macali & Bergami, 2020; Sun et al., 2017) Zooplankton have been suggested as a potential global marine plastic pollution indicator species. (Macali & Bergami, 2020) \u2022 Zooplankton play an important role in the marine ecosystem as a fundamental trophic level. They are critical food sources for secondary consumers and have been suggested to transfer microplastics up trophic levels. (Botterell et al., 2019; Mahara et al., 2022; Sotton et al., 2017). Prey selection, abundance of bioparticles (phytoplankton) and feeding type likely impact microplastic uptake. (Botterell et al., 2019; Cole et al., 2011; Mahara et al., 2022). Plastic Ingestion by Fish Source In the 2,364 reported occurrences of ingested plastic assessed by the \u201cLitterbase\u201d team, fish (Actinopteri) were the second most studied taxa group (25%). Litterbase (Tekman et al., 2023) \u2022 Plastic ingested by fish has been documented across the globe. (Azevedo-Santos et al., 2019; Jovanovi\u0107, 2017; Macali & Bergami, 2020) \u2022 Plastic particle ingestion in 8 different species of fish was established early on in literature. (Carpenter & Smith, 1972) \u2022 As of 2019, there were at least 108 studies on plastic ingestion in fish, covering 427 species of both adult and larval fish from various trophic guilds, including Elasmobranchs such as the basking shark (Cetorhinus maximus), Portuguese dogfish (Centroscymnus coelolepis), velvet belly lantern shark (Etmopterus spinax), pelagic stingray (Pteroplatytrygon violacea) and teleosts such as long snouted lancetfish (Alepisaurus ferox), big- and small- eye moonfish (Lampris (Azevedo-Santos et al., 2019; Macali & Bergami, 2020) 214 sp.) (Choy & Drazen, 2013), pollock (Pollachius virens) (Carpenter & Smith, 1972), boop boops, Imperial blackfish (Schedophilus ovalis), and Atlantic horse mackerel (Trachurus trachurus). \u2022 Filter-feeding sharks are susceptible to ingest ocean plastics. (Abreo et al., 2019; Germanov et al., 2018) \u2022 It is common to find microplastic debris in planktivorous fish such as Myctophidae, Stomiidae and Scomberesocidae in the North Pacific Gyre. (Boerger et al., 2010) \u2022 High incidences of plastic ingestion by mesopelagic fish not necessarily feeding at the surface, have been reported. (Choy & Drazen, 2013) Plastic Ingestion by Seabirds Source Seabirds make up 27% of recorded marine litter interactions in literature, some examples include: Litterbase (Tekman et al., 2023) \u2022 The first evidence: in the late 1960s synthetic plastics were found in stomachs of Laysan Albatross chicks (Phoebastria immutabilis). (Kenyon & Kridler, 1969) \u2022 Various seabirds in California, including short-tailed shearwater (Puffiinus tenuirostris), sooty shearwater (Puffiinus griseus), northern fulmar (Fulmarus glacialis), and more were found to contain polystyrene, polyethylene, and food wrappers. (Baltz & Morejohn, 1976) \u2022 Plastic was identified in 81% to 96% of great shearwater (Puffinus gravis) gizzards and white-faced storm petrels (Pelagodroma marina) in the Southern Atlantic Ocean. (Furness, 1985) \u2022 Plastic was identified in 100% of collected surface-feeding birds, storm petrels (Oceanodroma furcata, O. leucorhoa), albatross (Diomedea nigripes), petrels (Pterodroma Iongirostris), and fulmar (Fulmarus glacialis), and in 75% of shearwaters (Puffinus griseus) in the Eastern North Pacific. (Blight & Burger, 1997) \u2022 Plastics have been identified in prions (Pachyptila desolata) remote polar regions in the Sub-Antarctic and Southern Indian Ocean. (Auman et al., 2003) \u2022 In the southeastern tropical Pacific, several seabirds\u2019 species from the Humboldt Current Ecosystem, including diving species such as the Peruvian diving petrel (Pelecanoides garnotii), common diving petrel (P. urinatrix), Guanay cormorant\/shag (Leucocarbo bougainvilliorum), and Humboldt penguin (Spheniscus humboldti) and a plunge diver species, the Peruvian pelican (Pelecanus thagus), have been reported to ingest plastic debris. (in review Thiel et al., 2018) 215 \u2022 Plastic fragments and pellets were found in 74% of fulmars (Fulmarus glacialis) in the Canadian Arctic. (Bourdages et al., 2020) Increases in plastic ingestion rates have been noted in seabirds: \u2022 Plastic ingestion by fulmars in the Netherlands has increased from 91% to 98% between the 1980s and 2000s. (van Franeker et al., 2011) \u2022 Plastic ingestion by northern fulmars (F. glacialis) has increased by 34% over the past 40 years. (Avery-Gomm et al., 2012) Low ingestion rates among some birds have been established, specifically in the Northeast Pacific: \u2022 Some seabird species contain low rates of plastics, such as common murres (Uria aalge) where only 2.7% of the birds sampled between British Columbia, Canada, and Washington had plastics. (Avery-Gomm et al., 2012; Ivar Do Sul & Costa, 2014) Ingestion rates differ based on ecology: \u2022 Plastic ingestion differs between surface-feeding seabirds and diving seabirds. For example, diving thick-billed murres (Uria lomvia) ingest plastic at lower quantities than their surface-feeding relatives. (Provencher et al., 2014) \u2022 Procellariiforms or tubenose seabirds (Order: Procellariiformes) are among the most critically affected because these species ingest and accumulate large quantities of ocean plastics in the gastrointestinal (GI) tract. (K\u00fchn and van Franeker, 2020) Seabirds are often used as indicator species for policy on plastic pollution: \u2022 Northern fulmars (Fulmarus glacialis) are used by the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) as well as the North Pacific Marine Science Organization (PICES) (WG 42 - 2019, 2019) to assess environmental health and abundance of plastic debris at sea. (Cole et al., 2011; van Franeker et al., 2011) Relevant to this thesis, there are reported incidents of penguin species ingesting plastics: \u2022 While several penguin species from the Antarctic and surrounding circumpolar regions such as ad\u00e9lie (Pygoscelis adeliae), chinstrap (Pygoscelis antarcticus), gentoo (Pygoscelis papua), and King (Aptenodytes patagonicus) have been reported to ingest microplastics no evidence of microplastic ingestion was observed for Emperor penguin chicks (Aptenodytes forsteri) from the Atka Bay colony (Dronning Maud Land, Antarctica). (Bessa et al., 2019; Frag\u00e3o et al., 2021; le Guen et al., 2020; Leistenschneider et al., 2022) Regurgitating plastics to chicks is a concern: \u2022 45 of 50 albatross chicks collected in Hawaiian Islands during the mid-1980s had plastics. (Fry et al., 1987; Pettit et al., 1981) \u2022 In the Eastern North Atlantic, 84% of fledgling Cory\u2019s shearwaters (Calonectris borealis) were found to contain (Rodr\u00edguez et al., 2012) 216 plastics in their guts, since these chicks feed solely from parental regurgitation, the contaminants were likely passed on by parents. Literature on seabird and marine plastic interactions has expanded beyond ingestion, with research now focused on seabirds incorporating plastic into their nests: (Claro et al., 2019; Grant et al., 2022; Lato et al., 2021) \u2022 At least 12 seabird species in the southeastern Pacific use and incorporate plastics debris and pieces as material for nest construction, including both continental and oceanic seabirds (e.g., cormorants, Phalacrocorax spp., frigatebirds Fregata minor, and albatrosses, Thalassarche spp.). (Thiel et al., 2018) \u2022 Plastics were found in a variety of 50 seabird nests in the eastern Pacific, in what appeared to be both aesthetic (blue most frequent) and construction purposes. (Claro et al., 2019) \u2022 Plastics were incorporated in 24.5% to 80% of nests of five seabird species in Lady Isle, Scotland. (Thompson & Kerr, 2020) \u2022 In the remote Gal\u00e1pagos Islands, the flightless cormorant (Phalacrocorax harrisi) also utilizes blue-colour plastic debris and pieces as construction items into their nests in Isabela Island. (see Figure 4D in Alava et al., 2022) Plastic Ingestion by Sea Turtles Source Though sea turtles account for only 7 species globally, there have been a number of incidents of plastic interactions with sea turtles. Plastic and sea turtle incidents make up 6% of marine litter litature assessed by the \u201cLitterbase\u201d team. Litterbase (Tekman et al., 2023) \u2022 A recent review of 131 studies covering five decades of research showed that sea turtle species such as hawksbill (Eretmochelys imbricata) and green turtles (Chelonia mydas) are heavily affected by plastic ingestion, with hotspots in the Central and Northwest Pacific and Southwest Atlantic Oceans. (Lynch, 2018) \u2022 Plastic debris and in some cases microplastics have been found in loggerheads (Caretta caretta), green turtles (C. mydas), olive ridleys (Lepidochelys olivacea), hawksbills (E. imbricata), and leatherbacks (Dermochelys coriacea). (Duncan et al., 2018; Lynch, 2018; Macali & Bergami, 2020; L\u00f3pez-Mart\u00ednez et al., 2021; Schuyler et al., 2014; Yaghmour et al., 2021) \u2022 18.9% of ingested plastics were polystyrene, based on 79 sea turtles assessed in the Pacific near Hawaii. (Balazs, 1985) Microplastics have been documented in sea turtles as well: \u2022 Though comparatively, microplastics specifically have been rarely documented in sea turtles, two individual green turtles (C. mydas) collected in North Australia had microplastics. (Caron et al., 2018) 217 \u2022 Evidence suggests microplastic ingestion is prevalent in all seven species of sea turtles across 102 individual samples from the Atlantic, Mediterranean, and Pacific. (Duncan et al., 2018) Sea turtles interacting with plastic have triggered public concern on the matter of ocean pollution. (Robinson & Figgener, 2015) As convergence zones tend to attract both juvenile sea turtles and plastic debris, a continued trend of plastic ingestion can be expected by sea turtle offspring. (Baker et al., 2008) Plastic Ingestion by Marine Mammals Source Only 0.3% of all studies of ingested plastic assessed by the \u201cLitterbase\u201d team were related to marine mammal species, but this is increasing. Litterbase (Tekman et al., 2023) (Baker et al., 2008; Hernandez-Gonzalez et al., 2018; Lusher et al., 2018; L\u00f3pez-Mart\u00ednez et al., 2021) \u2022 Between 1963 and 1986, a number of plastic ingestion incidents in marine mammals, including incidents with sperm whales (Physeter macrocephalus), dwarf sperm whales (Kogia sima), pygmy sperm whales (Kogia breviceps), Cuvier\u2019s beaked whales (Ziphius cavirostris), Blainville\u2019s beaked whales (Mesoplodon densirostris), Gervais\u2019 beaked whales (Mesoplodon europaeus), short-finned pilot whales (Globicephala macrorhynchus), rough-toothed dolphins (Steno bredanensis), Pacific white-sided dolphins (Lagenorhynchus obliquidens), common dolphins (Delphinus delphis), bottlenose dolphins (Tursiops truncatus), Risso\u2019s dolphins (Grampus griseus), Striped dolphins (Stenella coeruleoalba), Northern Right Whale dolphins (Lissodelphis borealis), harbour porpoises (Phocoena phocoena), and Dall\u2019s porpoise (Phocoenoides dalli). (Claro et al., 2019; Simmonds, 2012) \u2022 One Dall\u2019s porpoise was described as \u201cjammed with debris\u201d as it had 13 pieces of clear plastic sheets, 3 heavy clear plastic bags, 2 plastic bread bags, and 2 plastic sandwich bags. (Simmonds, 2012) Microplastics in marine mammals is a developing field: (Baker et al., 2008) \u2022 The first recorded incident of microplastic ingestion by a marine mammal appears to be in the Common dolphin (Delphinus delphis), on the Galician coast. Of 35 dolphins analyzed, all contained microplastics. (Hernandez-Gonzalez et al., 2018) \u2022 There were reported microplastic ingestion and prevalence in several species of stranded marine mammals along the British coast by analysing the GI tracts of 50 individuals, (Nelms et al., 2019) 218 representing 10 species (i.e., 8 cetacean species, n\u202f=\u202f43; 2 pinniped species, n\u202f=\u202f7). \u2022 Stranded bottlenose dolphins (T. truncatus) from in the eastern Pacific, had more microplastics per individual reported than previous studies on microplastic ingestion by dolphins, which could be explained by methods used to detect microplastics. (Battaglia et al., 2020) \u2022 Microplastics were detected in all GI tracts of seven beluga whales (Delphinapterus leucas) in the Northwest Territories, Canada, with quantities of 18 to 147 microplastics in each whale\u2019s GI tract. (Moore et al., 2020) \u2022 East Asian finless porpoises (Neophocaena sunameri) were also found to contain microplastics in all specimens sampled, as well. (Xiong et al., 2018) \u2022 Microplastics were found in the stomachs of 13 grey seals (Halichoerus grypus) from Ireland. (Hernandez-Milian et al., 2019) \u2022 In the Northern Pacific, microfibers (55%) and microfragments (41%) were found in scats of 44 northern fur seals (Callorhinus ursinus). (Donohue et al., 2019) \u2022 Microplastic particles with sizes < 5 mm were detected in the GI tracts of 14 seals from Ireland\u2019s southern coast. (Lusher et al., 2022) There is significant variation in debris ingestion among different species and locations: \u2022 Only 8.5% of 528 marine mammals that were stranded or caught accidentally in Irish waters had microplastics, macroplastics, or both, which is quite lower than the studies mentioned. (Lusher et al., 2018) \u2022 In free-ranging pinniped species, including the ringed seal (Pusa hispida), bearded seal (Erignathus barbatus), and harbour seal (Phoca vitulina) from the Canadian Arctic, no microplastic ingestion or accumulation was documented. (Bourdages et al., 2020; Jardine et al., 2023) \u2022 There was no evidence of microplastic ingestion as revealed by scats\u2019 analysis of Antarctic fur seals (Arctocephalus gazella) from the western Antarctica. (Garcia-Garin et al., 2020) Trophic transfer has been documented: \u2022 Plastic pieces have been found in fur seal (Arctocephalus spp.) scat in Macquarie Island in the Pacific Ocean. Authors suggested contamination was due to trophic level transfer. (Eriksson & Burton, 2003) \u2022 Microplastics were found in captive grey seals who were fed solely wild-caught Atlantic mackerel. Approximately 48% of scat and 32% of fish contained microplastics. (Nelms et al., 2018) Plastic ingestion rates vary based on ecology and behaviour: \u2022 Variances in plastic ingestion could be attributed to differences in feeding habits, such as filter versus non-filter feeding. (Besseling et al., 2015) 219 \u2022 Microplastics were found in the Indo-Pacific humpback dolphin (Sousa chinensis), which authors suggested was due to play or exploration, not just trophic transfer. (Battaglia et al., 2020; Zhu et al., 2019) There is also the potential for larger plastic debris to breakdown once ingested: \u2022 One study found an estimated 3,600 to 4,500 foam pieces in one dolphin's stomach and suggested this was a larger foam piece that had broken down. (Battaglia et al., 2020) 220 Appendix B Pictured polyproplene (PP) fragment found in airborne field control. It was the only PP found in controls. There was no evidence of PP matching this colour and morphology in samples. Since this was a very specific piece of PP found in the control and it was not found in samples, it was not deemed appropriate to remove all PP from sample data. PP data was included in the study. 221 Appendix C Table AC1: Functional group input data for the Gal\u00e1pagos penguin (GP) model based on the baseline scenario. Group name Direct absorption rate Prop. of contaminant excreted 1 Gal\u00e1pagos Penguin 0.333 2 Barracuda 0.02 3 Mullet 0.03333 4 Anchovy, Herring, Sardines, Salema 0.02 5 Decapods 0.07 6 Predatory zooplankton 7.38x10-7 0.14 7 Herbivores zooplankton 7.38x10-7 0.14 8 Macroalgea 9 Microalgea\/phytoplankton 10 Detritus Table AC2: Functional group input data for the Bolivar Channel Ecosystem (BCE) model based on the baseline scenario. Group name Direct absorption rate Prop. of contaminant excreted 1 Phytoplankton 2 Macroalgae + others 3 Surgeonfishes, chubs and giant damiselfishes 0.02 4 Sea cucumbers and other 0.14 5 Herbivorous zooplankton 7.38x10-7 0.14 6 Sea turtles and marine iguanas 0 7 Small hervivorous gastropods 0.14 8 Sponges and polichaetes 0.14 9 Gorgonians 0.14 10 Parrotfish 0.02 222 11 Mullets 0.033 12 Benthic omnivorous fish 0.02 13 Anemones and zoanthids 0.14 14 Sea stars and sea urchins 0.14 15 Planktivorous reef fish (>15cm) 0.02 16 Small planktivorous reef fish 0.02 17 Lobsters 0.07 18 Predatory zooplankton 7.38x10-7 0.14 19 Big gastropods and other sea stars 0.14 20 Small predator gastropods 0.14 21 Benthic predatory fish (<30cm) 0.02 22 Benthic predatory fish (>30cm) 0.02 23 Barracudas 0.02 24 Groupers 0.02 25 Jacks and mackerels 0.02 26 Rays 0.02 27 Predatory marine mammals 0.33 28 Seabirds 0.33 29 Sharks 0.02 30 Detritus 223 Appendix D Historical maps illustrating the landscape change in Puerto Hondo, Ecuador from map 1 (created in 1967) to map 2 (created in 2008). Puerto Hondo is located in the top-right of the map. The town location is highlighted by a yellow arrow. Likewise, the Puerto Hondo estuary is highlighted via a flowing yellow line. Before the 1970s, Puerto Hondo was called Hacienda Palobamba and was covered by shrubbery and low bushes (\u201cmatorral monte bajo\u201d). The green area in map 1 indicates the green land surrounding the Puerto Hondo estuary at the time. In contrast, map 2 highlights the industrialization of the region and shrimp farms encroaching on the mangrove regions. Shrimp farms are highlighted in map 2 via blue rectangular boxes. Maps were provided by Hector Ayon, a well-known expert in Ecuador and retired marine geologist and geophysics engineer. 224 Map 1: Created in 1967 225 Map 2: Created in 2008 ","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/hasType":[{"value":"Thesis\/Dissertation","type":"literal","lang":"en"}],"http:\/\/vivoweb.org\/ontology\/core#dateIssued":[{"value":"2023-11","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/isShownAt":[{"value":"10.14288\/1.0432346","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/language":[{"value":"eng","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeDiscipline":[{"value":"Oceans and Fisheries","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/provider":[{"value":"Vancouver : University of British Columbia Library","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/publisher":[{"value":"University of British Columbia","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/rights":[{"value":"Attribution-NonCommercial-NoDerivatives 4.0 International","type":"literal","lang":"*"}],"https:\/\/open.library.ubc.ca\/terms#rightsURI":[{"value":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/","type":"literal","lang":"*"}],"https:\/\/open.library.ubc.ca\/terms#scholarLevel":[{"value":"Graduate","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/contributor":[{"value":"Pakhomov, Evgeny A.","type":"literal","lang":"en"},{"value":"Alava, Juan Jos\u00e9","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/title":[{"value":"The Gal\u00e1pagos penguin as the \"canary in the coal mine\" for microplastics research in the Gal\u00e1pagos Marine Reserve & plastic pollution perceptions in Ecuadorian mangrove communities","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/type":[{"value":"Text","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#identifierURI":[{"value":"http:\/\/hdl.handle.net\/2429\/84664","type":"literal","lang":"en"}]}}