Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
McEwan, Nicole
Master of Science - MSc
University of British Columbia
The Strait of Georgia (SoG) is a productive, temperate, and coastal ecosystem in the Northeast Pacific Ocean. Throughout a year in the SoG, plankton productivity and community structure have strong seasonal variations. However, the diverse range of plankton species, trophic processes, and interactions are often over-simplified in food web models. The objectives of this study were to: a) describe the seasonal trends in plankton community composition, biomass, and vertical distribution; b) represent the seasonal food web structure of the plankton community; and c) compare this complex and seasonal modelling approach to a more common and simplified approach. In the spring, diatoms dominate the plankton biomass in the SoG, leading to high primary productivity and fueling zooplankton growth. In summer, primary production is nutrient limited, leading to a decline in diatom biomass and increased mixotrophic flagellate dominance. Zooplankton biomass, dominated by large crustaceans including copepods, amphipods, and euphausiids, peaks and their diets shift to become more omnivorous. In winter, plankton biomass and productivity are low. Based on these seasonal trends, I constructed a food web model for each season (spring, summer, and winter), with the plankton community represented by ten mesozooplankton groups, two mixotrophic microzooplankton groups, one phytoplankton group, one heterotrophic bacteria group, and one detrital group. The spring food web is mostly fueled by high diatom primary production. However, the microbial loop, consisting of detritus, heterotrophic bacteria, and microzooplankton, shifts to become an important energy source for the system under limiting conditions in the summer and winter food webs. These structural changes throughout a year could have important implications and insights for higher trophic levels, such as into the seasonal food availability and quality for plankivorous fish. Finally, I compared this detailed seasonal approach to plankton modelling with an aggregated and year-averaged approach. The microbial loop is often excluded from coastal ecosystem models but is an important component, influencing trophic positions and transfer efficiencies. However, aggregating plankton groups appears to be an adequate approach to ecosystem modelling in the SoG, but modelling decisions should be driven by the research question.
Seasonality in the community composition and food webstructure of plankton in the Strait of GeorgiabyNicole McEwanB. Engineering, University of Guelph, 2017A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Oceanography)The University of British Columbia(Vancouver)August 2022© Nicole McEwan, 2022The following individuals certify that they have read, and recommend to the Faculty of Gradu-ate and Postdoctoral Studies for acceptance, the thesis entitled:Seasonality in the community composition and food web structure of plank-ton in the Strait of Georgiasubmitted by Nicole McEwan in partial fulfillment of the requirements for the degree of Masterof Science in Oceanography.Examining Committee:Dr. Richard Pawlowicz, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisorDr. Maria Teresa Maldonado, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisory Committee MemberDr. Evgeny Pakhomov, Professor, Earth, Ocean and Atmospheric Sciences, UBCSupervisory Committee MemberDr. Brian Hunt, Assistant Professor, Earth, Ocean and Atmospheric Sciences, UBCAdditional ExamineriiAbstractThe Strait of Georgia (SoG) is a productive, temperate, and coastal ecosystem in the NortheastPacific Ocean. Throughout a year in the SoG, plankton productivity and community structurehave strong seasonal variations. However, the diverse range of plankton species, trophic pro-cesses, and interactions are often over-simplified in food web models. The objectives of thisstudy were to: a) describe the seasonal trends in plankton community composition, biomass,and vertical distribution; b) represent the seasonal food web structure of the plankton commu-nity; and c) compare this complex and seasonal modelling approach to a more common andsimplified approach. In the spring, diatoms dominate the plankton biomass in the SoG, leadingto high primary productivity and fueling zooplankton growth. In summer, primary productionis nutrient limited, leading to a decline in diatom biomass and increased mixotrophic flagellatedominance. Zooplankton biomass, dominated by large crustaceans including copepods, am-phipods, and euphausiids, peaks and their diets shift to become more omnivorous. In winter,plankton biomass and productivity are low. Based on these seasonal trends, I constructed a foodweb model for each season (spring, summer, and winter), with the plankton community repre-sented by ten mesozooplankton groups, two mixotrophic microzooplankton groups, one phyto-plankton group, one heterotrophic bacteria group, and one detrital group. The spring food webis mostly fueled by high diatom primary production. However, the microbial loop, consistingof detritus, heterotrophic bacteria, and microzooplankton, shifts to become an important energysource for the system under limiting conditions in the summer and winter food webs. Thesestructural changes throughout a year could have important implications and insights for highertrophic levels, such as into the seasonal food availability and quality for plankivorous fish. Fi-nally, I compared this detailed seasonal approach to plankton modelling with an aggregated andyear-averaged approach. The microbial loop is often excluded from coastal ecosystem modelsbut is an important component, influencing trophic positions and transfer efficiencies. However,aggregating plankton groups appears to be an adequate approach to ecosystem modelling in theSoG, but modelling decisions should be driven by the research question.iiiLay SummaryPlankton are crucial organisms in marine ecosystems. They are the base of the food web andfood sources for many ecologically and economically important animals, including fish and ma-rine mammals. Despite this importance, the plankton community is typically highly simplifiedin food web studies. In this thesis, I investigate the consequences of this simplification in theStrait of Georgia (SoG) food web. First, the variations in the plankton community throughoutthe year in the SoG are investigated. Then, the interactions between various plankton groupsduring different seasons are represented using a series of models. This thesis provides insightsinto the seasonal food web structure of plankton in the SoG, which could have important im-plications on their predators. Finally, this complex modelling approach was compared to amore simplified approach. Both approaches gave similar results when considering the entireecosystem, but my models allowed for a deeper understanding of seasonal behaviours.ivPrefaceThis thesis represents the original, independent, and unpublished work of Nicole McEwan,with input and guidance from R. Pawlowicz, M. T. Maldonado, and E. Pakhomov. This workcontributes to the goals of the UBC component of Metro Vancouver’s Liquid Waste AmbientMonitoring Program.The biomass data used in this thesis came from long-term sampling programs. HPLC datawas processed by N. Nemcek at Fisheries and Oceans Canada and is available from their archiveat waterproperties.ca, along with associated CTD data. Species-level zooplankton data wasprovided by K. Young and the Plankton Ecology group at Fisheries and Oceans Canada. Aversion of this database, split into major taxa, is available under conditions of the Open Gov-ernment Licence - Canada at https://open.canada.ca/data/en/dataset/2822c11d-6b6c-437e-ad65-85f584522adc.Chapter 2 has been prepared with the intent of publication, and will be submitted for peer-review after the completion of this thesis as N. McEwan, E. Pakhomov, R. Pawlowicz & M. T.Maldonado, Seasonality in the diel vertical migration of mesozooplankton in the Strait of Geor-gia, Canada. I performed the analyses within and the writing of the manuscript with guidanceand input from the co-authors.Chapter 3 has been prepared with the intent of publication, and will be submitted for peer-review after the completion of this thesis as N. McEwan, R. Pawlowicz, E. Pakhomov, & M.T. Maldonado, Characterizing the seasonality of the planktonic food web structure in the Straitof Georgia, Canada using different modeling approaches. I performed the analyses within andthe writing of the manuscript with guidance and input from the co-authors. I completed all themodelling, except for the existing Ecopath model (Preikshot, 2007). This model was used aspublished in EcoBase (http://ecobase.ecopath.org/), an open-access database of EwE models.This model was further modified in Preikshot et al. (2013), but was not available in EcoBase.I coded, plotted, and created all figures and tables shown in this thesis, except for Figures2.1 & 3.2. The code for these figures was originally written by T. Suzuki and B. Boufford andmodified by me for use in this thesis. Finally, this thesis was written by me with feedback andedits from R. Pawlowicz and M. T. Maldonado.vContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vContents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiList of Common Terms and Abbreviations . . . . . . . . . . . . . . . . . . . . . . . xiii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 The Strait of Georgia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Marine food web modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2.1 NPZ Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.2.2 Ecopath modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.2.3 Biological modelling in the Strait of Georgia and the simplification ofplankton . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.3 Thesis Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Seasonality in the diel vertical migration of mesozooplankton in the Strait ofGeorgia, Canada . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.3.1 Seasonal and diel vertical migration . . . . . . . . . . . . . . . . . . . 132.3.2 Capture efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.4.1 General trends in vertical distribution . . . . . . . . . . . . . . . . . . 142.4.2 Seasonal and diel vertical migration behaviours of zooplankton taxa . . 172.4.3 Night to day biomass ratios (N:D ratios) . . . . . . . . . . . . . . . . . 212.4.4 Implications on food web structure . . . . . . . . . . . . . . . . . . . . 22vi2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Characterizing the seasonality of the planktonic food web structure in the Straitof Georgia, Canada using different modelling approaches . . . . . . . . . . . . . 253.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.3.1 Study Area and Biomass Data . . . . . . . . . . . . . . . . . . . . . . 273.3.2 Ecopath Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.3.3 Model Parameterization and Balancing . . . . . . . . . . . . . . . . . . 323.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.4.1 Plankton Community Composition . . . . . . . . . . . . . . . . . . . . 343.4.2 Balanced Model and Flow Diagrams . . . . . . . . . . . . . . . . . . . 373.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.5.1 Ecosystem Functioning . . . . . . . . . . . . . . . . . . . . . . . . . . 453.5.2 System Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.5.3 Bacteria and the microbial loop . . . . . . . . . . . . . . . . . . . . . 463.5.4 Nutritional content of zooplankton . . . . . . . . . . . . . . . . . . . . 473.5.5 The use of simplified community dynamics in numerical models . . . . 483.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.1 Summary, analysis, and implications of research findings . . . . . . . . . . . . 514.2 Limitations of thesis research and future directions . . . . . . . . . . . . . . . 53Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57A Supplemental tables and figures for Chapter 2 . . . . . . . . . . . . . . . . . . . 68B Chemtax Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76B.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76B.1.1 Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76B.1.2 Depth Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78B.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80C Ecopath Model Input Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 82C.1 Justification of Diet Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . 87viiD Regional Variations in Plankton Community Composition . . . . . . . . . . . . . 90viiiList of TablesTable 3.1 Summary values of spring (Sp), summer (Sm), and winter (Wn) model inputsand outputs: trophic level, biomass (gC m-2), production to biomass ratio(P/B, day-1), consumption to biomass ratio (Q/B, day-1), and ecotrophic ef-ficiency (EE). Values estimated by Ecopath are in italics. For further detailsand sources, see Appendix C. . . . . . . . . . . . . . . . . . . . . . . . . . 37Table 3.2 Ecopath input parameters for aggregated plankton groups. TM is trophicmode (where 0 is fully heterotrophic, 1 is fully autotrophic, and between 0and 1 is mixotrophic), B is biomass, P/B is production/biomass, and Q/B isconsumption/biomass. Parameters are a weighted average over a year, withgroups aggregated as detailed in Fig. 3.7. . . . . . . . . . . . . . . . . . . . 42Table 3.3 Diet matrix for aggregated plankton groups averaged over a year. Groupswere aggregated as detailed in Fig. 3.7. . . . . . . . . . . . . . . . . . . . . 42Table 3.4 Comparison of productivity estimates in the Strait of Georgia. All numbersare in units of gC m-2 d-1. . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Table A.1 Number of tows used in analysis of seasonal diel vertical migration (Fig.2.2). Depth refers to the depth of the net tow. . . . . . . . . . . . . . . . . 68Table A.2 Number of tows used in calculation of N:D ratios. Depth refers bottom depth,where only tows that sampled the entire water column were included. . . . 68Table A.3 N:D ratios for full water column tows in areas with bottom depth > 200 m.Significant results (p-value < 0.05) are bolded. . . . . . . . . . . . . . . . . 68Table A.4 One-way analysis of variance (ANOVA) results for comparisons of taxabiomass in deep water (bottom depth > 200 m) by twilight (day or night).Cases where p-value < 0.05 are noted. . . . . . . . . . . . . . . . . . . . . 72Table B.1 HPLC marker pigments, the abbreviations used here, and the phytoplanktongroups in the Strait with which they are associated. Based on the groupingsin Higgins et al. (2011). . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Table B.2 Separation of samples into seasonal bins. . . . . . . . . . . . . . . . . . . . 78Table B.3 Initial seeding matrix used in Chemtax. . . . . . . . . . . . . . . . . . . . . 78Table C.1 Values, sources, and assumptions for model input parameter P/B in thespring, summer, and winter Strait of Georgia Ecopath models. . . . . . . . 82ixTable C.2 Values, sources, and assumptions for model input parameter Q/B in thespring, summer, and winter Strait of Georgia Ecopath models. . . . . . . . 83Table C.3 Diet matrix for spring Ecopath model. . . . . . . . . . . . . . . . . . . . . 84Table C.4 Diet matrix for summer Ecopath model. . . . . . . . . . . . . . . . . . . . 85Table C.5 Diet matrix for winter Ecopath model. . . . . . . . . . . . . . . . . . . . . 86Table C.6 Diet proportions (σ ) for each consumer in diet matrix set-up. . . . . . . . . 88xList of FiguresFigure 1.1 Map of the Strait of Georgia and surrounding area. . . . . . . . . . . . . . 2Figure 2.1 Sampling frequency of zooplankton net tows plotted through time at a ver-tical location that coincides with its location in the map at left. Colourcorresponds to tow depth. Symbol refers to time of sampling (day or night). 12Figure 2.2 Seasonal variations in daytime and nighttime zooplankton biomass (in mgdry weight (d.w.) m-3) at three depth intervals, where bottom depth is >200 m. Bar width corresponds to size of depth interval, so area correspondsto total biomass (in mg d.w. m-2). Error bars correspond to standard error.Errors bars are omitted where sample size was very small (n≤ 7). . . . . . 15Figure 2.2 (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Figure 3.1 Map of the Strait of Georgia, British Columbia, Canada, with the modelledecosystem outlined in red. . . . . . . . . . . . . . . . . . . . . . . . . . . 28Figure 3.2 Sampling frequency of zooplankton net tows and pigment samples, plot-ted horizontally through time at a vertical location that coincides with itsvertical location in the map at left. . . . . . . . . . . . . . . . . . . . . . . 29Figure 3.3 Average monthly phytoplankton biomass composition, as measured by HPLCpigment analysis using Chemtax. This excludes ciliate and heterotrophicflagellate biomass. ‘n’ refers to the number of bottle samples analysed ineach month. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Figure 3.4 Average monthly zooplankton biomass composition. ‘n’ refers to the num-ber of net tow samples averaged in each month. . . . . . . . . . . . . . . . 35Figure 3.5 Community composition of phytoplankton and flagellate functional groupsin spring, summer, and winter models. . . . . . . . . . . . . . . . . . . . . 36Figure 3.6 Trophic pathways between functional groups in the planktonic food websfor the Strait of Georgia in spring, summer, and winter. The area of thenodes are scaled to represent biomass (B) in gC m-2. The thickness of thelines represents flux between groups in gC m-2 d-1, where the green end isthe consumer and the red end is the prey (only flows≥5% for each group areshown). Position along the y-axis corresponds to trophic level, as calculatedby the model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38xiFigure 3.7 Summarised trophic pathways between aggregated functional groups in theplanktonic food webs for the Strait of Georgia in spring, summer, and win-ter. The area of the nodes are scaled to represent proportion of total systembiomass (B). The thickness of the lines represents proportion of consump-tion flows, where the green end is the consumer and the red end is the prey.The semi-circles around a single group indicate flux within the groups ag-gregated together. Only flows accounting for ≥1% of the total system fluxare shown. Position along the y-axis corresponds to the weighted averagetrophic level of the aggregated groups. . . . . . . . . . . . . . . . . . . . . 41Figure 4.1 Size spectrum of plankton for each season, based on the typical range ofbody sizes of each group. Dashed line is demonstrating a slope of -1. Thesolid black line represents the total size spectrum (sum of all groups). . . . 54Figure A.1 Seasonal variations in daytime and nighttime zooplankton normalised biomassat three depth intervals, where bottom depth is > 200 m. . . . . . . . . . . 69Figure A.1 (continued) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70Figure A.2 Community composition in daytime and nighttime seasonally at three depthintervals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Figure B.1 Fluorescence profiles of all samples with an offset subtracted. The thickblack line shows the monthly mean calculated every 1 m. . . . . . . . . . . 79Figure B.2 Composition (bars) and total chlorophyll biomass (black dots) of all sam-ples, ordered from North to South for each cruise. Note the axis limits aredifferent for April 2019 and March 2020. . . . . . . . . . . . . . . . . . . 81Figure D.1 Boundaries for regional plankton analysis. . . . . . . . . . . . . . . . . . 90Figure D.2 Seasonal zooplankton community composition in each region. Error barsshow the standard deviation of total biomass. . . . . . . . . . . . . . . . . 91Figure D.3 Seasonal phytoplankton community composition in each region. . . . . . . 91xiiList of Common Terms and AbbreviationsANOVA analysis of varianceC carbonchl a chlorophyll aDFO Department of Fisheries and OceansDVM diel vertical migrationd.w. dry weightEwE Ecopath with EcosimFG functional groupHPLC high-performance liquid chromatographyNPZ nutrient-phytoplankton-zooplanktonNPZD nutrient-phytoplankton-zooplankton-detritusOPC optical plankton counterPP primary productivitySTRATOGEM Strait of Georgia Ecosystem MonitoringSoG Strait of GeorgiaTB total biomassTC total consumption fluxTL trophic levelTM trophic modeTTE trophic transfer efficiencyUBC University of British ColumbiaxiiiChapter 1: IntroductionA food web is a branching network of feeding relationships, and therefore energy transfer,among many different organisms. The basic pelagic food web structure is typically divided intofive trophic levels. Phytoplankton, as primary producers, are the base of the food web and formthe first trophic level. Herbivorous and carnivorous zooplankton form the subsequent secondand third trophic levels, respectively, and are the key links between primary producers and theupper trophic levels. Fish, marine mammals, and seabirds form the upper levels and decom-posers of organic matter recycle nutrients. In reality, however, the food web is rarely a simplechain, even within the plankton community.As the base of the food web, plankton are a crucial component of marine ecosystems. Theyare key in energy transfer through the food web and have a major influence on the biomassand composition of higher trophic levels. Additionally, many zooplankton undergo extensivevertical migrations on seasonal and diel timescales, influencing the vertical flux of organic mat-ter through the water column (Forward and Cohen, 2010). However, marine ecosystem studiesoften fail to detail the large range of taxonomic, functional, and trophic diversity in plankton(Mitra et al., 2014; D’Alelio et al., 2016). While there have been many model-based ecosystemstudies for the Strait of Georgia (SoG) (i.e. Venier, 1996; Li et al., 2000; Martell et al., 2002;Preikshot, 2007; Collins et al., 2009; Li et al., 2010; Pen˜a et al., 2016), none of these have fo-cused exclusively on the plankton community, despite the extensive sampling that has occurredin the past (i.e. Stockner et al., 1979; Harrison et al., 1983) and more recently (i.e. Mackas et al.,2013; Perry et al., 2021). When modelling, aggregating the plankton community into a smallnumber of groups with simple interactions can lead to misrepresentations and missed detailsin a system, which could have cascading impacts up the food web. Further, food-web modelsare often constructed on a year-averaged approach (Christensen et al., 2005), missing the largevariations in plankton community composition and trophic behaviour throughout a year in tem-perate regions like the SoG.This thesis will describe the seasonal community and trophic structures for plankton in theSoG more comprehensively than previous studies, by taking advantage of the extensive datasetsthat are now available. The description includes such details as mixotrophy and the microbialloop, which have previously been neglected. In the rest of this chapter, I will introduce the phys-ical, chemical, and biological properties of the SoG, types of food web modelling, examples ofmodelling that have been done in the SoG, and provide the thesis objectives.11.1 The Strait of GeorgiaThe SoG is a large and productive semi-enclosed basin situated between Vancouver Island tothe west and the British Columbia mainland to the east (Fig. 1.1). It is a coastal marine ecosys-tem about 200 km in length and 30 km in width, with a maximum depth of 420 m. The SoGis connected to the Pacific Ocean through Johnstone Strait to the north and Juan de Fuca Straitto the south. The Fraser River is the largest source of fresh water to the SoG, with peak flowsduring the spring freshet (Masson, 2006). It is an ecologically, economically, and culturallyvaluable ecosystem, and an important fishery and rearing area for herring and Pacific salmon(Harrison et al., 1983).Figure 1.1: Map of the Strait of Georgia and surrounding area.2The physical circulation in the SoG is estuarine, affected by river flows, winds, and tidalmixing (Masson, 2002). There are three distinct vertical layers in the SoG: surface (0-50 m),intermediate (50-200 m), and deep (200-420 m) waters, each with distinct seasonal properties(Pawlowicz et al., 2007). Most of the exchange with Pacific Ocean occurs through the Juande Fuca Strait, where a surface outflow of brackish water is balanced by deep-water inflow ofnutrient-rich waters (Pawlowicz et al., 2007).The deep water seasonal cycle is marked by incoming pulses of dense and oxygenated Pa-cific Ocean water in summer (Masson, 2002; Pawlowicz et al., 2007). During the rest of theyear, the deep layer density decreases due to diffusion of lower salinity seawater from the layerabove. Renewal of this deep layer occurs about once a year (Pawlowicz et al., 2007). The inter-mediate layer is less affected by the seasonal cycle. Water enters the intermediate layer throughhorizontal diffusion from Haro Strait, upwells and mixes with the surface layer (Pawlowiczet al., 2007). This water is replaced about twice a year (residence time ∼ 160 days) (Pawlowiczet al., 2007). Surface layer properties are highly variable and seasonal. Water temperature isclosely correlated with surface heating and cooling, with warmest temperatures in August andcoolest in January (Pawlowicz et al., 2007). Similarly, surface salinity varies with the magni-tude fresh water inflows, particularly from the Fraser River, whose flow peaks during the freshetin late spring to early summer (Masson, 2006). In the Fraser River plume, the residence time offreshwater is about 1 day (Pawlowicz et al., 2007). In the rest of the Strait, the residence timeof the surface layer is about 10-40 days (Pawlowicz et al., 2007).These seasonal variations in physical properties influence the timing and extent of phyto-plankton growth. Estuarine circulation is the main input of nutrients to the system, with smallerinputs from river discharge, sewage, and atmospheric sources (Mackas and Harrison, 1997). Inwinter, mixing and upwelling brings nutrient-rich water from the deep to surface water (Harri-son et al., 1983; Pen˜a et al., 2016; Olson et al., 2020). However, light irradiance is low, limitingphytoplankton growth. In spring, high irradiance and water stability, combined with the highinitial nutrient levels, trigger a diatom bloom and a decline in nitrate concentrations (Mackasand Harrison, 1997; Olson et al., 2020). In summer, nitrate levels are at a minimum, which isthe main factor limiting phytoplankton growth in this season (Mackas and Harrison, 1997; Pen˜aet al., 2016). Surface stratification is strong in summer, further limiting nutrient levels, althoughmixing from tides and wind events can sporadically introduce nutrient-rich water to the surfacefrom below (Pen˜a et al., 2016). Within the turbid Fraser River plume, surface nitrate concentra-tions are relatively high but light attenuation is high, limiting growth (Pen˜a et al., 2016).3The combination of these favorable physical and chemical properties make the SoG a highlyproductive ecosystem with a strong seasonal cycle. Phytoplankton biomass and primary pro-ductivity (PP) peaks in the spring across the northern and central SoG. PP has been estimatedin the SoG using a variety of methods. Based on 14C experiments in the 1960-70s, Harrisonet al. (1983) estimated an annual Strait-wide PP of 280 gC m-2 yr-1. More recent estimates,using modelling approaches have mostly agreed with this estimate. Using a biophysical model,Pen˜a et al. (2016) estimated an annual mean PP of 268 gC m-2 yr-1. Using a nitrogen budgetapproach, Sutton et al. (2013) estimated annual PP to range from 260-300 gC m-2 yr-1.The phytoplankton community in the SoG is characterised by a diatom bloom in the spring,although the exact timing and magnitude of the bloom are variable (Belluz et al., 2021). Outsideof bloom conditions, the community is more diverse, consisting of a wide range of species andcell sizes including cyanobacteria, cryptophytes, dinoflagellates, and prasinophytes (Stockneret al., 1979; Belluz et al., 2021). Smaller blooms of diatoms or flagellates are common in thesummer.Zooplankton are relatively well studied in the SoG, with long-term monitoring of abundanceand biomass (e.g. Harrison et al., 1983; Mackas et al., 2013; Perry et al., 2021). Zooplanktonbiomass and community composition vary seasonally, but are dominated by crustaceans year-round, including copepods, amphipods, and euphausiids. Gelatinous zooplankton, includingmedusae, siphonophores, and ctenophores, also make up an important component of the systembut to a lesser degree (Mackas et al., 2013). In general, zooplankton biomass is low in winterand peaks in summer, ranging between about 4 and 12 g dry weight (d.w.) m-2, with a yearlymean of 10.1 g d.w. m-2 (Mackas et al., 2013).These seasonal variations in plankton biomass and community composition impact the over-all structure of the food web. Many zooplankton in the SoG can utilize a wide range of diets,including bacteria and detritus, and their dietary modes change through the year as the availabil-ity of food changes, shown through fatty acid and stable isotope analyses (i.e. El-Sabaawi et al.,2009a, 2010; Costalago et al., 2020). Additionally, many zooplankton undergo diel and seasonalvertical migrations. Generally, diel vertical migration (DVM) involves movement from deeperwaters during the day, where there is a lower risk of predation, to surface water at night forgrazing (Hays, 2003), and seasonal vertical migrations involve changes in vertical distributionthroughout a year.41.2 Marine food web modellingFood web modelling is commonly used to quantify and characterise the trophic structure andfunctioning of an ecosystem. Ecological models can take on many different forms, dependingon the research questions. They are primarily in the form of single species models, populationmodels, biomass models, or models combining any, or all, of these three parameters (Fenneland Neumann, 2004). Some require an integration of physics, chemistry, and biology. Thisstudy will focus on biomass models.Biomass models must obey the fundamental law of conservation of mass. As such, changein the total mass, M, of an ecosystem can be expressed by,dMdt= sources− sinks (1.1)Biomass models have taken many forms in their application to marine ecosystem. Here,I describe two approaches: the nutrient-phytoplankton-zooplankton (NPZ) approach and theEcopath (https://ecopath.org/) approach.1.2.1 NPZ ModelsThe NPZ model (sometimes nutrient-phytoplankton-zooplankton-detritus (NPZD)) is a com-mon tool used by oceanographers to predict simple dynamics of oceanic plankton (Franks,2002). NPZ models are set up as simple box models, with three state variables, a limiting nu-trient N, total phytoplankton P, and total zooplankton Z. The model is put together using aseries of transfer functions that link the state variables together. There are 5 transfer functionsto consider (Franks, 2002):i. Phytoplankton response to light, f (I)ii. Phytoplankton nutrient uptake, g(N)iii. Zooplankton grazing, h(P)iv. Phytoplankton losses due to death, excretion, and predation, i(P)v. Zooplankton losses due to death, excretion, and predation, j(Z)5Using these transfer functions and assuming conservation of mass, Franks (2002) providesequations for the general set up of an NPZ model:dNdt=− f (I)g(N)P+(1− γ)h(P)Z + i(P)P+ j(Z)Z (1.2a)dPdt= f (I)g(N)P−h(P)Z− i(P)P (1.2b)dZdt= γh(P)Z− j(Z)Z (1.2c)Note: γ is zooplankton assimilation (typically a linear function of food ingested).This is evidently a simple approach to a complex system. While, NPZ models can be goodtools for coupling the biological ecosystem dynamics to physical processes (Franks, 2002), thesimplification of plankton into two groups with two trophic levels ignores a vast range of speciesand trophic processes (D’Alelio et al., 2016).1.2.2 Ecopath modellingThe Ecopath model was first developed by Polovina (1984) to estimate mean annual biomass,production, and consumption for constituents of an ecosystem. In this manifestation, Ecopathwas a box model for an ecosystem under static and equilibrium conditions. Today, Ecopath hasdeveloped into an ecological modelling software with more consideration of theoretical ecol-ogy (Christensen et al., 2005). It is still based on a mass-balance approach but has the abilityto consider non-equilibrium (but still static) states. In the years since its development, twomore models have been integrated with Ecopath: (1) Ecosim, a time dynamic model, and (2)Ecospace, a spatial dynamic model.The model is set up by aggregating ecologically important species into functional groups(FGs), linked by trophic interactions. Ecopath assumes a mass balance over an arbitrary period,using two ‘master equations’ (Christensen et al., 2005). The first master equation describes theproduction for each FG, i, represented by the following equation,Bi · (P/B)i ·EEi = Yi +n∑j=1B j · (Q/B) j ·DC ji +Ei +BAi (1.3)6where Bi and B j are the biomasses of prey and predator, respectively; (P/B)i is the produc-tion/biomass ratio; EEi is the ecotrophic efficiency; Yi is the total fishery catch rate; (Q/B)i isthe consumption/biomass ratio; DC ji is the contribution fraction of prey i in the diet of predatorj; n is the number of consumer groups; Ei is the net migration rate; and BAi is the biomassaccumulation rate (Christensen et al., 2005). These parameters are explained in more detail inSection 3.3.The second master equation describes the energy balance within each FG, described using,Qi = Pi +Ri +(1−GSi) ·Qi (1.4)where Qi is consumption rate; Pi is production rate; Ri is respiration rate; and GSi is the fractionof unassimilated food (loss due to feces and directed to detritus). When all necessary parametersand diet compositions are entered, the software solves a series of linear equations (Christensenet al., 2005). The result is a picture of energy flows, biomasses, and production.1.2.3 Biological modelling in the Strait of Georgia and the simplificationof planktonThere have been several biological models built for the SoG, with varying degrees of detail andresearch objectives. The models generally fall into two classes: coupled biological-physicalmodels and food web ecosystem models (i.e. Ecopath). Here, I briefly describe some of thesemodelling exercises.Biophysical modelling in the SoG has generally focused on nutrient uptake dynamics andthe physics processes that govern them. Many of these models structure the biological compo-nent based on the NPZ or NPZD approach, although some have more details than others. Torepresent the biology in a Salish Sea box model, Li et al. (2000) used the simplest form of thisapproach, with just one parameter for each of N, P, and Z in each physical box. In the SoGbiophysical model developed by Pen˜a et al. (2016), the NPZD scheme had two phytoplank-ton groups (diatoms and flagellates) and two zooplankton groups (herbivorous and omnivorouszooplankton).The biophysical model developed by Collins et al. (2009) aimed to predict spring bloomtiming in the SoG. In its original implementation, the biological component of the model fol-7lowed the simple NPZD approach. Since then, this model has been improved, modified, andapplied to different research questions (Allen and Wolfe, 2013; Moore-Maley et al., 2016; Olsonet al., 2020). In it’s latest form, the biological component has evolved to include three primaryproducer groups: diatoms, flagellates, and the mixotrophic ciliate Mesodinium rubrum; twozooplankton groups: microzooplankton and mesozooplankton; and more detailed interactionsbetween these groups (Olson et al., 2020).All of these models aim to describe biological or plankton dynamics using physical forcing.However, the interactions within the plankton community are represented very simply. Com-putational power is a key consideration in these models, driving the choice for simplification(Olson et al., 2020). Food web models, on the other hand, are capable of including more com-plex trophic interactions between functional groups. However, this complexity is again rarelyextended to the plankton community. There have been several implementations of this type ofmodelling for the SoG ecosystem, typically using Ecopath.The first Ecopath model for the SoG ecosystem, compiled by Venier (1996) comprised of 15FGs, with plankton represented by 3 three groups: primary producers, herbivorous zooplank-ton, and carnivorous zooplankton. This model set the framework for most subsequent Ecopathmodelling in the SoG (i.e. Dalsgaard et al., 1998; Martell et al., 2002; Preikshot, 2007; Li et al.,2010; Preikshot et al., 2013). More details were added over the years, driven by research inter-ests. For instance, in Dalsgaard et al. (1998), three models were built to represent the SoG 500hundred years ago, 100 years ago, and in the present day. The number of FGs increased from15 to 25 - 27, with most of the increase consisting of fish and marine mammal groups. Oneadditional plankton group was added, gelatinous zooplankton.The next development, by Martell et al. (2002), further built on this model. The most sig-nificant addition was the development of a time-dynamic Ecosim model, which focused on theimpacts of fishing. However, the plankton community remained relatively simple. Next, Preik-shot (2007) further developed the Ecopath with Ecosim (EwE) model to investigate changes introphic interactions, fisheries, and climate from the 1950s to the early 2000s. This model had atotal of 53 FGs, yet plankton were still highly aggregated (into five groups: phytoplankton, her-bivorous zooplankton, carnivorous zooplankton, krill, and jellies). Input parameters for theseplankton groups were primarily based on values from the previous model versions (Preikshot,2007). An updated version of this model was later published (Preikshot et al., 2013).8Finally, another EwE model was built for the SoG to investigate the competition betweenharbour seals and fisheries (Li et al., 2010). In this model, the Ecopath component was based onconditions in 2005, and had 39 FGs. Since the focus of the study was on higher trophic levels,the plankton community was, again, highly aggregated. There were 5 groups: phytoplankton,omnivorous zooplankton, copepods, krill, and jellyfish, with parameter values mostly based onthose in Preikshot (2007).1.3 Thesis ObjectivesAs outlined above, the SoG has had several model-based ecosystem studies. However, none ofthese have focused exclusively on the plankton community, and the included plankton groupsand interactions are generally highly simplified. Thus, the main objective of this thesis is todevelop a more comprehensive description of the planktonic ecosystem in the SoG by charac-terising the seasonal community composition and food web structure. In particular, I investigatethe seasonal trends in phytoplankton and zooplankton community composition, biomass, andvertical migration.In order to build food web models, information about the biomass of zooplankton is re-quired. However, zooplankton are capable of vertical migrations on both seasonal and diel timescales, affecting biomass estimates from net tows. Some of the larger zooplankton are also ca-pable of avoiding nets even when they are towed over the depth ranges in which they reside.Thus, in Chapter 2, I first investigate the possible biases that arise from these effects in the SoGusing a multi-year dataset of net tow biomass data, so that they may be properly accounted for.Using this information, in Chapter 3, I construct food web models for three seasons in theSoG: spring, summer, and winter, that focus exclusively on the plankton community. Thesetaxonomically diverse, seasonal models give insights into how energy pathways and trophicinteractions within the plankton community vary between the seasons. These results have im-plications through the food web, such as in energy transfer to plankivorous fish. Finally, I testsome of the consequences of model simplification on the SoG ecosystem.9Chapter 2: Seasonality in the diel verticalmigration of mesozooplankton in the Straitof Georgia, Canada2.1 OverviewThe seasonal and diel vertical migration of major mesozooplankton taxa in the Strait of Georgiawas investigated using a multi-year dataset of net tow biomass data, comprised of 1448 towsover 11 years (2006 - 2017). The vertical distribution of zooplankton biomass varied on sea-sonal and diel timescales, likely driven by changes in food availability and life histories. Dielvertical migration (DVM) was strongest in spring, primarily driven by copepods, amphipods,and euphausiids.Changes in total sampled water column biomass between day and night were also investi-gated by calculating night to day (N:D) captured biomass ratios. Euphausiids had an N:D ratioof 2.59 (± 0.23, p = 0.0005) in summer, implying that daytime net avoidance had a signifi-cant effect on sampling results. This analysis of vertical distribution and capture efficiency willinform the biomass estimates used in the food web modelling in Chapter 3.2.2 IntroductionZooplankton biomass in the SoG is dominated by large-bodied crustaceans that undergo strongvertical migrations (Harrison et al., 1983; Mackas et al., 2013; Perry et al., 2021). Generally,there are three types of migration that zooplankton can undergo: diel, seasonal, and ontogeneticmigration. Diel vertical migration (DVM) most commonly involves daily movement betweenthe surface water at night, where organisms feed, and deeper waters during the day for preda-tor avoidance, also called nocturnal vertical migration (Hays, 2003). These patterns could alsobe reversed, where plankton reside near the surface during the day and descend to depth atnight (Forward and Cohen, 2010). Seasonal migrations relate to changes in vertical distribu-tion throughout a year, while ontogenetic vertical migrations involve movement of different lifestages. Seasonal and ontogenetic migrations are not always related to each other.The extent and timing of zooplankton vertical migrations are influenced by a number ofphysical and biological factors, including tidal forcing, light levels, food availability, and the10presence of fish or other zooplankton predators (Forward and Cohen, 2010). For instance, zoo-plankton in the surface layers could be subjected to higher rates of advection compared to whenthey reside in deeper layers (Tommasi et al., 2014). The extent of these advective losses arefurther affected by seasonal physical forces. These migrations then have many implications onecosystem functioning, population dynamics, and the vertical flux of organic matter.The vertical distribution of zooplankton through the day and seasons also has implicationson the long-term monitoring of their biomass and abundance, as estimated using net hauls.While the sampling methodology (e.g. vertical or oblique tows) and gear types (e.g. net types,mesh size) have remained relatively consistent through the years of monitoring in the SoG(Perry et al., 2021), sample collection has occurred over 24-hour cycles, covering both day andnight time periods, and to various depths. Thus, biases could have been introduced into the data.Typically, investigations of DVM focus on individual species and rarely on major taxonomicgroups. Furthermore, seasonal aspects of group-level DVMs are also not well documented. Forthe purpose of modelling, when mesozooplankton biomass values are taken from the literature,the DVM is often not considered. Thus, averaged data could be underestimated simply becausemost samples were collected during the daytime. Here, I used a multi-year dataset to quantifygroup-specific and season-specific vertical migration patterns of the SoG zooplankton and tocalculate night to day (N:D) biomass correction factors.2.3 MethodsThe biomass dataset used in this analysis spans 11 years, from 2006-2017, and consists ofspecies-level biomass data in g d.w. m-3 and the associated metadata from 1448 net tows. Anextended version of this dataset was described in detail in Mackas et al. (2013) & Perry et al.(2021). Zooplankton data were collected using vertical net tows from various depths to thesurface across the SoG, and throughout the 24-hour cycle (Fig. 2.1). Samples were collectedusing various, but inter-comparable (McKinnell and Mackas, 2003), net types (Bongo, NorPac,Ring, and SCOR) with a mesh size of 200 to 335 µm and a mouth diameter of 0.42 to 0.58 m.Samples were labeled as either ‘daytime’ or ‘nighttime’ tows in the original dataset, with onlyabout 10% of tows occurring at night.Eleven major zooplankton taxa and species were considered in this study. Copepods weredivided into 4 groups based on differences in their vertical distributions: 1) large copepods11Figure 2.1: Sampling frequency of zooplankton net tows plotted through time at a vertical lo-cation that coincides with its location in the map at left. Colour corresponds to tow depth.Symbol refers to time of sampling (day or night).(including Neocalanus plumchrus, Eucalanus bungii, Paraeuchaeta elongata); 2) the mediumsized copepod Metridia sp.; 3) medium-sized copepod Calanus sp.; and 4) other small-mediumcopepods (e.g. Gaetanus minutus, Pseudocalanus minutus). Although some of the large cope-pod species are known to have different migration strategies, they showed similar trends in ver-tical distributions across the three seasons when considered separately. So, they were combinedinto one group in this analysis. The large copepod species were grouped because they showedsimilar trends when considered separately. The other groups were amphipods, euphausiids, de-capods, gelatinous zooplankton (including cnidaria and ctenophora), gastropods, chaetognaths,and other zooplankton (including ostracods, annelids, and cirripedia).The SoG water column was divided into three distinct layers, as described in Pawlowiczet al. (2007): surface (0 - 50m), intermediate (50 - 200 m), and deep (200 - 420 m). The surfacelayer is characterised by high chlorophyll biomass, low nutrients and high irradiance in sum-mer. Water enters the intermediate layer from the south, where it upwells and mixes with thesurface layer. The intermediate layer has a residence time of about 160 days (Pawlowicz et al.,2007). The deep layer receives dense and oxygenated water from the Pacific Ocean in earlysummer, which then diffuses into the intermediate layer during the rest of the year (Pawlowicz12et al., 2007). Although these three layers are based on physical circulation parameters, the sur-face layer roughly corresponds with the euphotic zone (which is typically in the upper 30 m).The intermediate layer contains the extent of many diel migrations, and the deep layer extendsto the depth of the majority of ontogenetic migrations (i.e. depth of the diapause). The yearwas divided into three seasons: spring (March - June), summer (July - October), and winter(November to February). These seasons were defined based on the annual cycle of the zoo-plankton community, and our knowledge of zooplankton dynamics (i.e. Harrison et al., 1983).2.3.1 Seasonal and diel vertical migrationThe data were analysed in two ways. First, seasonal and diel vertical migrations of zooplank-ton groups were investigated by examining general trends in vertical distribution in three layersover seasonal time scales. This analysis was restricted to samples taken where bottom depth≥ 200 m, to capture full migrations in the offshore regions of the SoG. This limited the datasetto 760 samples.The available samples were binned by tow depth and season (Fig. 2.1 & Table A.1). Datawere averaged after first being log transformed using log10(B+ c), where B is biomass in mgm-2, and c is a constant equal to 1 mg m-2, used to account for zero observations. The averagewas then reverse transformed to get bi. Average biomass density (B) for each depth interval (i)was calculated using,Bi =bi−bi−1∆d(2.1)where Bi is the biomass of depth interval i in mg m-3; bi is the average biomass from the surfaceto the maximum depth of i, in mg m-2; ∆d = di− di−1 is the size of the depth interval in m; irefers to the depth interval (surface, intermediate, or deep) and i− 1 refers to the layer above.For the surface interval (0 - 50 m), bi−1 = 0.2.3.2 Capture efficiencyIn the second analysis, day and night capture efficiency was evaluated using differences inbiomass density sampled during the day versus the night. Here, instead of examining differentlayers in the water column, I considered the entire water column.The analysis was restricted to samples that captured the entire water column for areas with13a bottom depth > 200 m. Only whole water column tows were included in this analysis, whichwas defined as tows with a tow depth within 15 m or 10% of the bottom depth, in order to reducethe impacts of vertical migration. The 402 available samples were binned by season (Fig. 2.1& Table A.2). For each taxa and season, a one-way analysis of variance (ANOVA) model wasperformed to test the effects of daylight (n = 2) on log-transformed biomass density (in mg m-3).For the groups with a significant difference, a night to day (N:D) biomass ratio was calculated.2.4 Results and DiscussionFirst, I describe the general seasonal trends in the vertical distribution of the overall zooplank-ton community. Next, I describe the seasonal and diel trends of each group individually andhypothesize the potential ontogenetic or other mechanisms explaining these migrations. Then,I discuss differences in capture efficiency between day and night. Finally, I explore the impli-cations these migrations have on the development of food web models.2.4.1 General trends in vertical distributionIn all seasons, zooplankton are distributed throughout the water column, with most (> 50%)in the deep layer (> 200 m) during both the day and night (Fig. A.1A). This deep water com-munity is dominated by crustaceans (i.e. copepods, amphipods, euphausiids, decapods). Whilethe community composition in this layer varies seasonally, it appears to stay relatively constantbetween the day and night within each season.Between the surface (0 - 50 m) and intermediate (50 - 200 m) layers, however, many zoo-plankton taxa ascend in the water column at night, indicating DVM behaviour (Fig. 2.2). Thestrength of this migration varies seasonally, but is generally strongest in spring. Overall, totalsurface zooplankton biomass density increases by a factor of 1.7 to 4.7 at night (Fig. 2.2A), andis mostly driven by copepods, amphipods, and euphausiids (Fig. A.2).During the day in spring, surface biomass is low and dominated by other zooplankton (Fig.A.2A). At night, biomass density of all groups increases in the surface layer. This increase isprimarily driven by copepods (Metridia sp. and Calanus sp.) and amphipods, but also euphausi-ids and decapods, mostly migrating from the deep layer.14Figure 2.2: Seasonal variations in daytime and nighttime zooplankton biomass (in mg d.w. m-3)at three depth intervals, where bottom depth is > 200 m. Bar width corresponds to size ofdepth interval, so area corresponds to total biomass (in mg d.w. m-2). Error bars correspondto standard error. Errors bars are omitted where sample size was very small (n≤ 7).15Figure 2.2: (continued)16In summer, there is some migration from the intermediate layer during the day to the surfacelayer at night (Fig. A.2B). During the day, the surface community is comprised mostly of otherzooplankton, followed by amphipods and gastropods, while the intermediate layer is mostlylarge crustaceans (amphipods and euphausiids). At night, some of these crustaceans migrateupwards. This increase in surface density at night is largely driven by increases in amphipod,Metridia sp., Calanus sp., and gastropod biomass at the surface, complemented by a decrease inbiomass of these groups in the intermediate layer. Decapods and gelatinous zooplankton werethe only groups to have a decrease in biomass density at the surface from day to night.In winter, total biomass is low across the entire water column (Fig. 2.2Aiii). During the day,intermediate density is higher than surface density, and is mostly comprised of Metridia sp.,Calanus sp., amphipods, and euphausiids (Fig. A.2C). At night, there is an increase in surfacebiomass density, largely driven by migrations of Metridia sp., amphipods, gastropods, and otherzooplankton. Again, most of this surface increase appears to be from the intermediate layer.Overall, most community-wide diel migrations occur in the top 200 m of water column,with deep layer biomass and community composition staying relatively constant on these dielscales. The strongest migrators appear to be amphipods and copepods, particularly Metridia sp.On seasonal scales, there are changes to both biomass and community composition in each ofthe layers, however crustaceans dominate year-round.2.4.2 Seasonal and diel vertical migration behaviours of zooplanktontaxaCopepodsThrough the year, copepod biomass is generally most dense in the deep layer (200 - 420 m)(Fig. 2.2, rows B to E). Of the four copepod groups, Metridia sp. appears to have the strongestDVM, particularly in spring, although there is still DVM behavior in summer and winter. Thismigration appears to be mainly between the intermediate and surface layers. Metridia sp. re-produce continuously throughout the year and spawn at the surface (Padmavati et al., 2004),potentially explaining these high surface densities. During the winter, Metridia sp. remain ac-tive, contributing to the continued strong DVM in winter (Tommasi et al., 2021). Thus, in thiscase, seasonal and ontogenetic vertical migration behaviours seem to be closely related.17The other copepod groups (Calanus sp., large copepods, and small-medium copepods) dis-play strong DVM behaviour in spring, and some DVM in summer (Fig. 2.2C-Ei, ii). In spring,the entire Calanus sp. population seems to have a general upward trend at night (Fig. 2.2Ci).Calanus pacificus and Calanus marshallae are the dominant species in this group, but haveslightly different life histories (Osgood and Frost, 1994). They both spawn throughout spring,but C. marshallae enters diapause in early summer, earlier than C. pacificus. C. pacificus contin-ues to reproduce through summer and enters diapause in fall (Osgood and Frost, 1994). Thesecombined migration patterns can be seen in this analysis, where the majority of the biomassis present in the deep layer (63− 85% during the day, 56− 87% at night) during summer andwinter (Fig A.1Cii-iii).Large copepods show a similar trend in summer and winter, with > 85% of total biomassremaining in the deep layer during both the day and night (Fig. A.1Dii-iii). This behaviourfollows the known ontogenetic vertical migration of large copepods (i.e. Neocalanus plum-chrus, Eucalanus bungii), where they migrate to the surface layer during the productive seasonto graze and grow (Mackas and Tsuda, 1999; Tommasi et al., 2021). They then descend to thedeep layer, where they remain dormant for several months.In summary, the copepods in the surface layer during the day are likely early life stageswhile the deep populations are probably dominated by later life stages (Tommasi et al., 2014).Thus, surface biomass estimates may be under estimated by net tows because copepod nau-plii and early stages can have body sizes too small to be captured by nets with a mesh size ≥236 µm (Tommasi et al., 2013). This is corroborated by the findings of Campbell and Dower(2008), where abundance estimates of Neocalanus plumchrus measured using an optical plank-ton counter (OPC) were compared with those made using depth-stratified net tows (mesh sizeof 236 µm). In deep water (> 200 m), the two methods were comparable, but closer to thesurface (< 100 m), the OPC measured higher abundances than the nets.AmphipodsAmphipods show strong DVM behaviour between the intermediate and surface layers (Fig.2.2F), with surface density increasing by a factor of 11 (spring) to 17 (summer) at night. Thestrong DVM in spring is likely correlated with the spring phytoplankton bloom and the subse-quent increase in reproduction (Abe et al., 2016). The most abundant amphipod, Cyphocarischallengeri, reproduces throughout the year but reproduction may peak during the spring bloom18(Yamada and Ikeda, 2006; Abe et al., 2016). Amphipod vertical distribution through the yearcould also be correlated with copepods (i.e. Metridia sp.), which are their main prey (Cornetand Gili, 1993; Harvey et al., 2009; Mackas et al., 2013).EuphausiidsIn spring, most (51%) euphausiid biomass is in the deep layer during the day (Fig. A.1Gi), butthere is a strong DVM, with surface density increasing by a factor of 12 at night (Fig. 2.2Gi).This DVM is likely correlated with the large increase in surface chlorophyll during the springbloom (Sogawa et al., 2016). In summer and winter, there is also a nighttime increase of sur-face density (Fig. A.1Gii-iii). These intra-annual changes in vertical distribution and strengthof DVM are likely related to life stages, as nauplii and early stages of euphausiids, which spawnin surface water during late spring to early fall, do not vertically migrate (Harrison et al., 1983).Despite the strong DVM in summer and winter, the largest migration appears to be fromthe intermediate layer during the day to the deep layer at night in these seasons. During theday, ∼ 100% of biomass is in the intermediate layer, while 62% is in the deep layer at nightin summer and 97% in winter (Fig. A.1Gii-iii). However, these trends are within the estimateuncertainties and must be read with caution. At night, there are low sample numbers, so theimpacts of sampling inefficiencies are heightened. Some of these inefficiencies are discussed inSection 2.4.3. Additionally, the low surface biomasses could be a result of their visual avoid-ance of nets.Finally, using echo sounders, euphausiids have been observed to rapidly ascend to the sur-face at night and then asynchronously swim downward after feeding (Sourisseau et al., 2008;Vestheim et al., 2014). As the nighttime tows in this analysis were at different times of thenight, some may have missed an earlier descent.Remaining zooplankton groupsGelatinous zooplankton show some migration in spring, primarily from the intermediate layerto the surface and deep layers (Fig. 2.2Ii). In summer and winter, the population is distributedthroughout the water column, with minimal changes from day to night (Fig. 2.2Iii-iii). Despitethis, gelatinous zooplankton are known to be diel vertical migrators with an upward trend atnight (i.e. Kaartvedt et al., 2007; Nogueira Ju´nior et al., 2015). Based on echo sounder obser-19vations, jellyfish have been observed to undergo asynchronous DVMs, where deep scatteringlayers remain in deep water apparently non-migrating, but when zoomed to an individual level,migration is actually occurring (Kaartvedt et al., 2007). Gelatinous zooplankton may ascend atnight to feed and then move downwards once satiated (Nogueira Ju´nior et al., 2015). Through-out the year, the length of feeding time may vary depending on food availability. As witheuphausiids, this behaviour could be missed by the net tows used in this analysis because thenighttime tows were taken at various times.In spring, decapod biomass is high relative to the rest of the year, and distributed throughoutthe water column (Fig. 2.2Hi). At night, there is some vertical movement from the deep layerto the layers above. In summer and winter, however, biomass is low and most (> 85%) remainsat depth during both the day and night (Fig. A.1Hii-Hiii).Gastropods show strong DVM behaviour year-round between intermediate and surface lay-ers, with surface density increasing at night year-round (Fig. 2.2J). This migration is likelymotivated by nighttime grazing of phytoplankton and a return to safety at depth during theday (Ringelberg and Gool, 2003), although the vertical distribution is size and season depen-dent (Kobayashi, 1974). Deep water biomass stays relatively constant between day and night.Chaetognaths also show DVM behaviour between the deep layer and the layers above (Fig.2.2K). Again, this migration is likely driven by predation avoidance during the day, and huntingat night (i.e. Lie et al., 2012).ConsiderationsThe patterns seen here were determined by averaging long term biomass data across the entireSoG, meaning they show only general population-wide trends. For some groups (i.e. copepods,gelatinous zooplankton), they indicate that there is little migration or that much of the popu-lation stays in the deep layer continually. However, this may not be the case for individuals.Based on echo sounder measurements, it has been noted that, while the scattering layer may notdisappear completely at night, individual zooplankton still undergo DVM (i.e. Kaartvedt et al.,2007; Sourisseau et al., 2008; Vestheim et al., 2014). However, due to the nature of this datasetand the analysis done, these individual migrations are not captured. Additionally, individualspecies and life stages exhibit varying vertical migration behaviour, which were aggregated to-gether in this analysis. Thus, this analysis only gives indications of general population-widetrends.202.4.3 Night to day biomass ratios (N:D ratios)The capture efficiency in day and night was investigated using tows that captured the entire wa-ter column. Only tows that had a bottom depth > 200 m were included in this analysis, in orderto capture the most biomass (Sec. 2.4.2). Log-transformed day and night biomass density, sep-arated by season, only showed significant differences in 4 out of 36 cases (Table A.3 and A.4),all of which were during the summer period (July to October).Of the four significant cases, euphausiids had the highest N:D ratio (± standard deviation)at 2.59 ± 0.23 (one-way ANOVA, p = 0.0005), indicating that the summer captured biomassdensity over the whole water column is higher at night by this factor. This implies that daytimesampling could be underestimating euphausiid biomass, which could be due to a number offactors. Daytime net avoidance is a major consideration when sampling euphausiids (Sameoto,1980; Everson and Bone, 1986). Additionally, euphausiid populations are extremely patchy,and the extent of patchiness varies with body size and, therefore, time of year (Fulton et al.,1982; Dec´ima et al., 2010). As discrete samplers, results from nets may vary depending onwhether a patch is sampled or missed (Shaw and Robinson, 1998). This estimated N:D ratio iscomparable to the average ratio (2.2) measured by Shaw and Robinson (1998) for Euphausiapacifica abundance in the SoG, however they found that the ratio varied substantially with lifestage, station location, and interannually.Of the copepod groups, both Metridia sp. and the other small-medium copepod groupsshowed significant differences between day and night captured biomass density in summer(one-way ANOVA, p = 0.04 for Metridia sp. and p = 0.005 for other small-medium cope-pods). These translated into N:D ratios of 1.46 ± 0.16 for Metridia sp. and 0.76 ± 0.29 forother small-medium copepods in this season. Both of these estimates are within the rangesmeasured by Shaw and Robinson (1998) in the SoG, which were 0.53 to 1.64 (average = 0.89)for Metridia sp., 0.42 to 2.64 (average = 0.46) for the medium-sized copepod Pseudocalanussp., and 0.49 to 0.72 (average = 0.41) for the small-sized copepod Paracalanus sp. In general,Shaw and Robinson (1998) found that N:D ratios increased with increasing copepod life stagesize, although, like euphausiids, the ratios varied substantially between species, life stages, andover the sampling period (1993-1995). Similar to euphausiids, patchiness in horizontal distri-butions is a possible reason for the discrepancies between night and day (Harris et al., 2000).Net avoidance could also be a factor, but this is typically more of a concern with euphausiids.21Gelatinous zooplankton had a summer N:D ratio of 0.52 ± 0.41 (one-way ANOVA, p =0.0004), implying that more biomass is captured during the day. This day to night differenceis likely not due to net avoidance, but rather changes in horizontal dispersion, along with in-effective sampling. Aggregations of gelatinous zooplankton can occur due to physical forcing(Graham et al., 2001). This spatial patchiness can greatly impact biomass estimates for thistaxa, depending on whether an aggregation was sampled. Additionally, estimates of gelatinousbiomass based on net tow data must be used with caution, as nets tows can damage them, lead-ing to ineffective sampling of these plankton (Purcell, 1981). Using a different sampling methodfor gelatinous taxa, such as echo sounders, would be beneficial.All remaining zooplankton groups and seasons had non-significant differences between dayand night biomass (p> 0.05; Table A.4) and N:D ratios≈ 1 (Table A.3). Overall, it appears thatbiomass estimates using full water column tows have good capture efficiency in both day andnight, except in these few instances. However, this study focused primarily on data grouped bytaxon. Different results might be expected if individual species and life stages were considered(Shaw and Robinson, 1998). When considering only taxa-level communities (e.g. for mod-elling purposes), these statistically significant N:D ratios could be used as correction factors forsamples collected using nets in the associated season and bottom depth.These statistics were performed using biomass density. Total biomass was initially testedfor significance, but I believe results were skewed due to the wide range of tow depths beingaveraged together. Biomass density was chosen instead of depth integrated biomass so thatresults weren’t skewed by depth. When the statistics were done with total biomass, the samegroups showed significant differences, but the N:D ratios varied. Using total biomass, N:Dratios were 1.90 for Metridia sp. (p = 0.05), 0.79 for other small-medium copepods (p = 0.05),3.99 for euphausiids (p = 0.001), and 0.51 for gelatinous zooplankton (p = 0.0003).2.4.4 Implications on food web structureThe extent of vertical migration may have important implications on the food web. The in-tent of this Chapter was to explore the extent of the migrations occurring and their importance,so that an appropriate method to estimate biomasses for use in food web models can be assessed.Based on this analysis, it seems most appropriate to use biomass estimates from net towsthat capture as much of the water column as possible. Additionally, a correction factor of 2.5922will be applied to estimates of euphausiid biomass taken during the day in summer to accountfor net avoidance. When this correction factor is applied to the data, average summer euphausiidbiomass increases by 144% and yearly biomass increases by 22%.A correction factor will not be applied in the other significant cases (summer estimates ofMetridia sp., other small-medium copepods, and gelatinous zooplankton) because the N:D ra-tios were not substantially different than 1. Additionally, for gelatinous zooplankton, I expectthe errors associated with using nets to be greater than the differences in day to night capture.When these correction factors are applied to the copepod data, summer small-medium copepodbiomass varies only slightly. It increases by 13% using the Metridia sp. ratio and decreases by6% using the other small-medium copepod ratio. The summer estimate of gelatinous zooplank-ton decreases by 47% using an N:D ratio of 0.52.When building the food web models, I will assume that all zooplankton biomass is partic-ipating in the food web. This is a reasonable assumption because, even though this analysisdoesn’t show entire populations migrating synchronously to the surface at night, the zooplank-ton must still be consuming food. Several issues may be important. First, some zooplanktonmay still be migrating but it wasn’t captured by this analysis, for the reasons described in Sec-tion 2.4.2. Second, some groups may be consuming zooplankton more than they are consumingphytoplankton, reducing the need for migrations. Finally, even if one assumes no vertical mi-gration, the production that eventually becomes detritus would be consumed. However, moreresearch is needed into vertical migration behaviours and their impact on trophic flows and foodweb structure and functioning in the SoG.2.5 ConclusionsThe purpose of this study was to investigate general population-wide trends in vertical distri-bution of major zooplankton taxa on diel and seasonal scales. Additionally, I discussed thepotential reasoning behind these observations and their implications on the food web. In sum-mary, diel vertical migration in the SoG varies seasonally and is dominated by large-bodiedcrustaceans, namely copepods, amphipods, and euphausiids. These seasonal variations in verti-cal distribution could be due to changes in physical characteristics, or due to ontogenetic factors,such as spawning and maturation timing. Such factors should be considered in future analyses.These migrations could affect results from net tow sampling, if the full depth of the water23column is not sampled. When the entire water column is sampled, there are few differencesbetween day and night captured biomass, with the exception euphausiids in deep waters (> 200m), and the possible exception of Metridia sp., other small-medium copepods, and gelatinouszooplankton. The differences that are seen between day and night are likely due to net avoidanceor dispersion of plankton. Overall, when sampling plankton in the SoG, it appears that samplingareas of deep water in either the day or night gives representative results, with the exception ofeuphausiids during the summer months. In this instance, a correction factor can be used toadjust daytime results. Inputs of group biomass are a key component of food web models, andthis analysis will inform the set-up of the models in Chapter 3.24Chapter 3: Characterizing the seasonalityof the planktonic food web structure in theStrait of Georgia, Canada using differentmodelling approaches3.1 OverviewFood web models often over-simplify the diverse range of trophic interactions and taxonomyin plankton communities. Here, I explore the implications of this simplification on a modelledecosystem, by using Ecopath to model the plankton food web in the Strait of Georgia (SoG), atemperate coastal system on the west coast of Canada. The food web is defined by fifteen func-tional groups representing autotrophic phytoplankton, the microbial loop, and mesozooplanktonin three seasons, differentiated by node biomass, productivity, and diet. Variations in the com-munity composition lead to shifts in trophic behaviour and food-web structure in each season. Inspring, autotrophic phytoplankton (i.e. diatoms) are the main energy source for the system. Butin summer and winter, when primary production is limited by nutrients and light, respectively,the microbial loop shifts to serve a more important role. These structural changes within theplankton food web throughout a year have implications for higher trophic levels, including theseasonal availability and quality of food for plankivorous fish. Finally, I compared this detailedseasonal approach to plankton modelling with a simplified approach and examined its impactson the entire ecosystem. The microbial loop is often excluded from coastal ecosystem modelsbut is an important component, influencing trophic positions and transfer efficiencies. However,aggregating plankton groups appears to be an adequate approach to plankton modelling in theSoG, but modelling decisions should be driven by the research question.3.2 IntroductionThe construction of accurate food web models is critical for quantifying the importance of dif-ferent components of oceanic ecosystems and assessing the potential commercial yield. In theStrait of Georgia (SoG), on the west coast of Canada (Fig. 3.1), such models have been widelyused. Examples include Dalsgaard et al. (1998); Li et al. (2000); Martell et al. (2002); Preik-shot (2007); Collins et al. (2009); Li et al. (2010); Allen and Wolfe (2013); Pen˜a et al. (2016);Olson et al. (2020), described in Section 1.2.3. However, these studies are mostly focused on25higher trophic levels (e.g. fish, marine mammals) and biophysical interactions. Consequently,representations of ecological interactions within the plankton community have been greatlysimplified. Nevertheless, plankton are a critical ecosystem component and provide a crucialecosystem service by linking primary producers to fish.Historically, linking primary producers and higher trophic levels has often been done bysimplifying a diverse range of plankton into just two groups, i.e. the nutrient-phytoplankton-zooplankton (NPZ) approach (Franks, 2002). Yet, SoG copepods are known to have a widerange of diets, including prey ranging from bacteria and detritus to phytoplankton, proto- andmetazoans (El-Sabaawi et al., 2009a, 2010; Costalago et al., 2020). Furthermore, mixotrophicplankton and the microbial loop are often not considered at all.On the other hand, more complex food web models are often constructed on a year-averagedapproach. This approach ignores the large variations in plankton composition and trophic be-haviour throughout the year in temperate regions, as the light-driven spring bloom is very dif-ferent in nature from nutrient-limited summer productivity (Harrison et al., 1983).Thus, these overly simplified models lack details that can potentially lead to misrepresenta-tions and erroneous results, which could have cascading impacts in understanding the dynamicsof the whole ecosystem. Here, I investigate whether oversimplification of the planktonic com-ponent is important in the SoG ecosystem by first developing a detailed 15-group, quasi-steadybut seasonally-varying plankton food web model, using the Ecopath framework (Christensenand Walters, 2004). I then simplify the model by aggregating the plankton groups with aver-aged rate parameters, to reduce the plankton component complexity in an existing SoG Ecopathmodel (Preikshot, 2007). Finally, I examine the effects of this aggregation on the model overallperformance and on the higher trophic levels in particular.These taxonomically diverse, seasonal models provide insights into how energy pathwaysand trophic interactions within the plankton community vary between the seasons, which hasimplications higher in the food web, such as in nutrient transfer to plankivorous fish (Costalagoet al., 2020).263.3 Methods3.3.1 Study Area and Biomass DataThe SoG is a large semi-enclosed basin situated between Vancouver Island to the west and theBritish Columbia mainland to the east (Fig. 3.1). It is connected to the Pacific Ocean throughpassages on the north and south ends. This study focused on the deep offshore portions in theNorth and Central Strait (outlined area in Fig. 3.1). The surface area of interest is approximately4600 km2 with a maximum depth of 430 m, and average depth of ∼ 330 m. Physical circula-tion is estuarine, with a surface outflow of brackish water mostly through the southern channelsbalanced by nutrient-rich upwelling of deeper waters whose residence time in the Strait is morethan half a year (Pawlowicz et al., 2007; Olson et al., 2020). Although the freshwater input hasa large seasonal component, the estuarine circulation is only weakly dependent on this compo-nent and is relatively steady year-round (Riche and Pawlowicz, 2014).High-resolution biomass data are available for both the phytoplankton and zooplanktoncommunities in recent years (Fig. 3.2). The zooplankton biomass dataset, maintained by De-partment of Fisheries and Oceans (DFO) and described in detail in Mackas et al. (2013) & Perryet al. (2021), spans 2006-2017 and consists of species-level biomass data in g d.w. m-3. Zoo-plankton data were collected using vertical net tows from various depths across the SoG, and inboth daytime and nighttime. Samples were collected using Bongo and SCOR nets with a meshsize of 200 to 335 µm and a mouth diameter of 0.42 to 0.58 m. After a careful evaluation, Ionly considered tows to depths of > 200 m, to capture known diel vertical migrators (Harrisonet al., 1983; Mackas et al., 2013) in both day and night tows, resulting in a dataset of 399 tows(Fig 3.2).I examined the regional variability of the mesozooplankton community by dividing the SoGinto two regions (Fig. D.1). Although some regional variations do exist in the SoG (Fig. D.2),they were generally smaller than the seasonal variations. So, I considered the SoG as a singlegeographic region with seasonal variation. The Strait-wide average community compositionwas weighted by the area of each region. Similarly, interannual differences were not consideredas, although there have been long term changes in both biomass and zooplankton communitycomposition, yearly anomalies were only on the order of ± 20% in the sampled years (Perryet al., 2021).27Figure 3.1: Map of the Strait of Georgia, British Columbia, Canada, with the modelled ecosys-tem outlined in red.Zooplankton species were aggregated into 10 functional groups (FGs) (described in Sec.3.4.2) and converted from g d.w. to g carbon (gC) using conversion factors of: 0.1 gC:g d.w.for gelatinous zooplankton, 0.2 gC:g d.w. for gastropods, and 0.4 gC:g d.w. for all other groups(Harris et al., 2000).Monthly and seasonal averages were calculated following the methods described in Mackaset al. (2013): total sample biomass for each functional group was log-transformed as log10(B+c), where B is biomass in gC m-2, and c is a constant to account for zero observations with anorder of magnitude related to the detection threshold (either 0.01 or 0.001 g d.w. m-2 convertedto gC m-2 using the conversion factors above).Daytime estimates of euphausiids in July to October were increased by a multiplicative fac-tor of 2.59 to account for net avoidance and diel vertical migration. This factor was calculatedusing an expanded version of the dataset, and is the average night:day (N:D) euphausiid biomass28Figure 3.2: Sampling frequency of zooplankton net tows and pigment samples, plotted hori-zontally through time at a vertical location that coincides with its vertical location in themap at left.ratio for July to October (see Chapter 2). Of all zooplankton groups in areas with bottom depth> 200 m, this was the only instance of a statistically significant difference between day andnight biomass when analysed seasonally. Note that this factor is different than the value of 3used year-round in Mackas et al. (2013) and Perry et al. (2021), based on Shaw and Robinson(1998); I use the calculated value because it is based on the more extensive dataset used in myanalysis.Phytoplankton data were derived from 5 years (2015-2017 and 2019-2020) of pigment datameasured using high-performance liquid chromatography (HPLC), collected on routine cruisesby DFO. Pigment samples were collected in Niskin bottles between 0 to 5 m depth. There were4 to 9 cruises per year between February and November, with 5 to 15 stations per cruise (Fig.3.2). Cell count data were also provided for ∼ 25% of the stations, used to determine speciesdominance.The 311 pigment samples were analysed using Chemtax (version 1.95), which derives es-timates of biomass contributions of phytoplankton taxa, relative to total chlorophyll a (chl a)(Mackey et al., 1996). Eight expected taxa were selected based on the associated microscope29counts and from a literature review (i.e. Stockner et al., 1979; Belluz et al., 2021): diatoms,dinoflagellates, cyanobacteria, prasinophytes, crytophytes, dictychophytes, haptophytes, andraphidophytes. Biomass data were depth integrated using CTD fluorometry profiles taken at thetime of sampling. Methods regarding Chemtax analysis and depth integration are detailed inAppendix B.These phytoplankton data were converted to carbon biomass using a C:chl a ratio of 50 gg-1 (Welschmeyer et al., 1991; Booth et al., 1993; Kirchman et al., 1993; Boyd et al., 1995). Al-though recent data shows that the C:chl a ratio varies seasonally (i.e. Jakobsen and Markager,2016), this constant ratio was used year-round. Again, I examined the regional variability of thephytoplankton community (Appendix D). The slight variations (Fig. D.3) were taken into ac-count by weighting the Strait-wide average community composition by the area of each region.Ciliate and heterotrophic flagellate biomass were not sampled and not presented in eitherof the datasets described above. Both are too small to be retained by zooplankton nets, andheterotrophic flagellates are not pigmented, thus missed by HPLC analysis. The estimates ofthese two groups were derived from sampling completed during the Strait of Georgia Ecosys-tem Monitoring (STRATOGEM) program (Pawlowicz et al., 2003). Samples were collected atstation S4-1 (Fig. 3.1) during surveys carried out weekly to monthly between April 2002 andJune 2005. 52 phytoplankton samples were collected at a depth of 5 m using Niskin bottles.The detailed composition of these samples was obtained through observation, identifica-tion, and enumeration of the settled particles under a Zeiss Axiovert 10 inverted microscope.Cell abundance was converted to carbon biomass using estimates of cell biovolume from Haighet al. (1992) and converted to carbon using the equations of Strathmann (1967). Biomass wasdepth integrated using the median m-3:m-2 ratio from the previous pigment analysis. The ciliateMesodinium rubrum (also know as Myrionecta rubra) was an important component of the phy-toplankton system in this sampling and was assumed to represent the ciliate population in thestudy area.M. rubrum is a mixotrophic plankton than can photosynthesize by ingesting cryptophytesand retaining their chloroplasts (Gustafson et al., 2000), meaning they have some pigmenta-tion. This pigmentation would be caught by the HPLC analysis but recognised as cryptophytebiomass, leading to a slight overestimation of crytophyte biomass. However, in comparison tothe cell counts described above, the relative contribution of cryptophytes to total phytoplankton30biomass is comparable (within ∼ 10%), so this contamination is minimal.Recent and high-resolution bacterial biomass data were not available in the SoG, so the Eco-path model was used to estimate the biomass of heterotrophic bacteria as part of the modellingprocess. The limited literature estimates (Seki and Kennedy, 1969; Bell and Albright, 1981)were reserved for comparison with model outputs.3.3.2 Ecopath ModelI developed seasonal steady state food web models using Ecopath 6.6.5 (https://ecopath.org/)for the plankton community to investigate trophic interactions at different times of the year.Ecopath is a widely used ecological modelling software that creates a snapshot of an ecosystemby aggregating ecologically important species into functional groups (FGs), linked by trophicinteractions. The model assumes a mass balance within the system over an arbitrary period(in this case, a season) such that the fluxes into a FG must equal the fluxes out of that group(Christensen et al., 2005). As implemented for this study, production for the ith FG is describedusing:Bi · (P/B)i ·EEi−n∑j=1B j · (Q/B) j ·DC ji = 0 (3.1)where Bi and B j are the biomasses of prey and predator, respectively (gC m-2); (P/B)i is theproduction/biomass ratio (d-1); EEi is the ecotrophic efficiency, which represents the propor-tion of mortality that is explicitly modelled, where EEi = 1 implies that all losses are due topredation and EEi = 0 implies that all losses are due to natural processes and the FG mass getsconverted to detritus; (Q/B)i is the consumption/biomass ratio (d-1); DC ji is the contributionfraction of losses in the ith group as prey in the diet of the jth group; and n is the number ofconsumer groups (Christensen et al., 2005).The model requires any three of the four basic input parameters (B, P/B, Q/B, EE) to be de-fined for each group, and calculates the fourth missing parameter by enforcing the mass balanceamong the groups. A diet composition is also required for each consumer group (Appendix C,Tables C.3 - C.5). The model can also include terms for losses due to fisheries, migration, andbiomass accumulation but these have been excluded in this study.Equation (3.1) is used to enforce a mass balance between the FGs. Within each FG, mass31balance is described using:Qi = Pi +Ri +(1−GSi) ·Qi (3.2)where Qi is consumption rate (gC m-2 d-1), with Qi = Bi · (Q/B)i; Pi is production rate (gC m-2d-1), with Pi = Bi · (P/B)i; Ri is respiration rate (gC m-2 d-1); and GSi is the fraction of unas-similated food (loss due to feces and directed to detritus). Qi and Pi are calculated from Eq. 3.1and GSi is a model input, making Ri an output of the model.Once the food web parameters have been determined, the Ecopath software can calculatetrophic level (TL) for each FG (Lindeman, 1942; Odum and Heald, 1975). Producers and detri-tus are assigned a TL of 1, and consumers have a TL of 1 + the weighted average of the prey’sTL (Christensen et al., 2005).3.3.3 Model Parameterization and BalancingAutotrophic phytoplankton and heterotrophic zooplankton FGs and their biomasses were de-fined based on the major taxa seen in the analysis of biomass data, and aggregated based onthe role they play in the ecosystem (Sec. 3.4.1). Two mixotrophic microzooplankton FGs wereincluded: ciliates and flagellates. In addition, there was a heterotrophic bacteria group and adetritus group.P/B estimates for the autotrophic groups were calculated using measurements of primaryproductivity in the SoG, based on 13C measurements in 2017-2018 by the Maldonado Lab atUBC. Other P/B and Q/B estimates varied seasonally and were based on values from literatureor assumptions (see Appendix C).Mixotrophic production is modelled in Ecopath using the ‘proportion of production fromprimary production’ parameter (or trophic mode (TM)). The TM ranges between 0 (for het-erotrophs) and 1 (for autotrophs). TM for ciliates was assumed to be 0.90 year round (D’Alelioet al., 2016). Flagellate TM was calculated based on the ratio of heterotrophic flagellate pro-duction to pigmented flagellate (mixotrophic and autotrophic) production and the associated Band P/B values, and varied seasonally.Diet links between predator and prey were identified in the literature and, when not avail-able, using expert opinion with consideration of size classes and feeding strategy (e.g. filter32feeders vs. selective hunters). Using these links, the proportion of each prey group to a preda-tor diet was initially weighted proportionally to prey biomass to define the diet matrices (Ap-pendix C.1). During the model balancing process, these proportions were varied slightly but thechanges were not large enough to change the general structure of the food web. Imported food(i.e. terrestrial detritus) is not considered, as it’s known to be only a small part of this food web(Costalago et al., 2020).A microbial loop was represented by the trophic interactions between the two mixotrophicmicrozooplankton groups, heterotrophic bacteria, and detritus. Only one detritus group wasincluded, representing flows from unassimilated food and dead organic matter. A value ofGSi = 0.3 was assumed for all groups (Christensen et al., 2005; Heymans et al., 2016). Bacte-rial biomass was left unknown, but an EE of 0.90 was assumed for all seasons.For each model, three of the four basic input parameters were provided for each FG, usuallyB, P/B, Q/B. The Ecopath software then estimated the missing parameter for each group (EEfor all but bacteria).An Ecopath model is considered valid under two general conditions. First, all P/Q values(P/Q = (P/B)/(Q/B)) should be between 0.2 and 0.4 for bacteria and mesozooplankton groups(Christensen et al., 2005). For the mixotrophic groups, P/Q is greater than 1 because most ofthe production comes from autotrophic activity.Second, for the model to be considered balanced, EE should be less than one for all groups.If the model was unbalanced, it was then balanced manually by adjusting parameters with thehighest uncertainty, namely the diet matrix. Specifically, the diets of predators of groups withEE > 1 were adjusted to reduce predation, until EE was less than one on these unbalancedgroups.3.4 ResultsFirst, I describe the plankton community composition in the Strait of Georgia in an averageyear using monthly averages. Using these results, I separate the year into three seasons (spring,summer, and winter), define functional groups (FGs) and construct mass-balanced food webmodels for each season. These food web models thus represent our best knowledge regard-ing the functioning of this planktonic ecosystem. Then, I aggregate these models in a similar33Figure 3.3: Average monthly phytoplankton biomass composition, as measured by HPLC pig-ment analysis using Chemtax. This excludes ciliate and heterotrophic flagellate biomass.‘n’ refers to the number of bottle samples analysed in each month.manner as those in NPZ system models, by averaging biomasses and rate parameters. The sim-plified model is then inserted into an existing Ecopath model that includes higher trophic levelsto examine the effects of our improved knowledge.3.4.1 Plankton Community CompositionThe monthly mean phytoplankton community (Fig. 3.3) shows that a spring bloom occursin March - April, with a strong dominance of diatoms (> 80%) (Skeletonema costatum, Tha-lassiosira spp., Chaetoceros spp.) and a peak phytoplankton biomass of 11.6 gC m-2. Sum-mer (June to October) biomass is lower than the spring (average of 3.71 gC m-2, includingheterotrophic flagellates), and more variable, with episodic blooms dominated by flagellates,including cryptophytes (i.e.Telaulax spp.) and dictyochophytes (i.e. Dinobryon spp.). Raphi-dophytes (Heterosigma akashiwo) are a harmful algae that bloom occasionally, but only insummer. Phytoplankton biomass in winter (November to February) is low (average of 2.44 gCm-2, including heterotrophic flagellates), and dominated by small cells (i.e. cryptophytes).34Figure 3.4: Average monthly zooplankton biomass composition. ‘n’ refers to the number ofnet tow samples averaged in each month.HPLC analysis only captured pigmented phytoplankton biomass. Cell counts from theSTRATOGEM project were used to estimate the proportion of heterotrophic flagellate biomasscompared to pigmented flagellate biomass for each season. The heterotrophic flagellate:pigmentedflagellate ratios are 0.54, 0.17, and 0.15 in spring, summer, and winter, respectively. This resultsin an estimated heterotrophic flagellate biomass of 0.70, 0.22, and 0.14 g m-2 in spring, summer,and winter, respectively.The fraction of heterotrophs is low in winter, but this might be underestimated becausewinter is dominated by small cells (< 20µm), which could be missed by microscope counts.However, these ratios are similar to the average heterotrophic:autotrophic nanoflagellate ratiofound in the northeastern Pacific, of 0.63 in May and 0.25 in February (Doherty, 1995).Turning to larger zooplankton, on average, total zooplankton biomass peaks in July at 9.49gC m-2 (Fig. 3.4). This biomass is dominated by crustaceans year-round, primarily small-medium copepods (30-44 % of zooplankton biomass; i.e. Metridia pacifica, Calanus pacificus,35Figure 3.5: Community composition of phytoplankton and flagellate functional groups inspring, summer, and winter models.and Calanus marshallae), followed by amphipods (i.e. Cyphocaris challengeri), euphausiids(primarily Euphausia pacifica), and large copepods (i.e. Neocalanus plumchrus and Eucalanusbungii). The relative proportion of most groups is roughly constant year-round, so that theirbiomass increases rapidly after the spring bloom and remains high through the summer, untildeclining again through the winter.On the basis of these monthly breakdowns, I identify 3 different seasonal community struc-tures: a winter (November - February) ecosystem with low plankton biomass, a summer (June -October) ecosystem with high zooplankton biomass and a intermediate phytoplankton biomassdominated by flagellates, and a spring (March - May) period with high diatom-dominated phy-toplankton biomass and an intermediate zooplankton biomass.36Table 3.1: Summary values of spring (Sp), summer (Sm), and winter (Wn) model inputs andoutputs: trophic level, biomass (gC m-2), production to biomass ratio (P/B, day-1), con-sumption to biomass ratio (Q/B, day-1), and ecotrophic efficiency (EE). Values estimated byEcopath are in italics. For further details and sources, see Appendix C.Group Name Trophic Level Biomass P/B Q/B EESp Sm Wn Sp Sm Wn Sp Sm Wn Sp Sm Wn Sp Sm Wn1 Gelatinous 2.99 3.13 3.20 0.15 0.14 0.04 0.088 0.103 0.077 0.293 0.343 0.258 0.22 0.25 0.242 Chaetognaths 3.04 3.06 3.13 0.05 0.19 0.1 0.025 0.040 0.028 0.085 0.140 0.097 0.58 0.30 0.353 Amphipods 2.75 2.82 2.84 0.76 2.07 0.68 0.017 0.019 0.016 0.071 0.080 0.066 0.75 0.47 0.314 Decapods 2.76 2.84 2.90 0.26 0.12 0.07 0.016 0.018 0.014 0.065 0.080 0.050 0.74 0.45 0.665 Euphausiids 2.23 2.43 2.43 0.47 0.48 0.59 0.023 0.029 0.016 0.075 0.082 0.068 0.45 0.27 0.316 Large copepods 2.25 2.42 2.41 0.62 0.69 0.21 0.110 0.072 0.040 0.367 0.240 0.133 0.25 0.45 0.587 Other zooplankton 2.35 2.51 2.56 1.10 0.91 0.43 0.049 0.053 0.026 0.164 0.178 0.086 0.76 0.99 0.988 Gastropods 2.23 2.35 2.37 0.12 0.19 0.05 0.037 0.053 0.020 0.122 0.178 0.066 0.53 0.63 0.809 Small-medium copepods 2.15 2.26 2.27 1.75 2.94 1.29 0.093 0.105 0.049 0.309 0.350 0.163 0.49 0.64 0.8510 Appendicularians 2.17 2.26 2.34 0.11 0.03 0.02 0.347 0.434 0.260 1.147 1.434 0.859 0.06 0.13 0.0711 Ciliates 1.12 1.13 1.29 0.63 1.3 0.36 0.496 0.363 0.233 0.077 0.090 0.064 0.25 0.68 0.6112 Flagellates 1.40 1.39 1.31 2.00 1.5 1.1 0.416 0.375 0.229 0.153 0.200 0.045 0.25 0.82 0.5113 Heterotrophic Bacteria 2 2 2 1.09 1.46 0.36 0.183 0.200 0.166 0.441 0.483 0.400 0.90 0.90 0.9014 Phytoplankton 1 1 1 7.65 2.21 1.34 0.531 0.378 0.244 - - - 0.22 0.79 0.3715 Detritus 1 1 1 1 1 1 - - - - - - 0.11 0.55 0.313.4.2 Balanced Model and Flow DiagramsThe phytoplankton taxa were aggregated into two FGs based on their role in the ecosystem(Fig 3.5). Fully autotrophic plankton were aggregated into the ‘phytoplankton’ FG, containingdiatoms, prasinophytes, haptophytes, and cyanobacteria. Flagellates comprise a large selectionof auto-, mixo-, and heterotrophs, but as prey they are mostly indistinguishable and so were putinto a second group, which is then “mixotrophic” in nature. This ‘flagellate’ FG contains cryp-tophytes, dinoflagellates, raphidophytes, dictyochophytes, and heterotrophic flagellates (Jeonget al., 2010; Stoecker et al., 2017). The community composition within these FGs varied sea-sonally (Fig. 3.5).Mesozooplankton were divided into 10 FGs, ordered by TL from highest to lowest: 1)gelatinous zooplankton, 2) chaetognaths, 3) amphipods, 4) decapods, 5) euphausiids, 6) largecopepods, 7) other zooplankton, 8) gastropods, 9) small-medium copepods, and 10) appendicu-larians. Additionally, there were FGs representing ciliates, heterotrophic bacteria, and detritus.Seasonal ModelsUsing seasonally-varying biomass data, as well as other input parameters like growth rates (seeAppendix C), food web models were set up and then modified until balanced (Fig. 3.6). Thespring model had the highest total biomass (TB) of the three models (16.76 gC m-2), primarilydue to the high phytoplankton biomass (46% of TB). This phytoplankton biomass proportiondecreased to 16% and 20% of the TB in summer and winter, respectively. The decrease in phy-37Figure 3.6: Trophic pathways between functional groups in the planktonic food webs for theStrait of Georgia in spring, summer, and winter. The area of the nodes are scaled to representbiomass (B) in gC m-2. The thickness of the lines represents flux between groups in gC m-2d-1, where the green end is the consumer and the red end is the prey (only flows ≥5% foreach group are shown). Position along the y-axis corresponds to trophic level, as calculatedby the model. 38toplankton dominance after the spring was complemented by a relative increase in mixotrophicmicrozooplankton biomass (FGs 11-12), from 16% in spring to 20% and 22% of TB in summerand winter, respectively, consistent with the community composition analysis in Section 3.4.1.This leads to a shift in trophic structure, as a higher proportion of carbon flow passes throughthe microbial loop (i.e., between detritus and bacteria) before reaching mesozooplankton.Estimated heterotrophic bacteria biomass ranges from 0.36 gC m-2 (in winter) to 1.46 gCm-2 (in summer). Mesozooplankton biomass (FGs 1-10) is highest in summer (7.76 gC m-2),with small-medium copepods (FG 9) making up the largest proportion in every season.The models indicate that the planktonic component of the food web in the Strait of Georgiaspans 3 trophic levels (Fig. 3.6). The highest trophic levels seen in the models are 3.04 inspring (chaetognaths), 3.13 in summer (gelatinous zooplankton), and 3.20 in winter (gelatinouszooplankton). All mesozooplankton have lower trophic levels in the spring than the other twoseasons (Table 3.1), indicative of the shift to more herbivorous activity in this season. After thespring bloom, microzooplankton increase in dominance and zooplankton diets shift to reflectthis change in food availability, increasing their trophic position. Flagellates and ciliates arebetween TLs 1 and 2 because of their mixotrophic behaviour.Flows beginning with primary producers and detritus form the main energy pathways in thesystem. Total system flow is defined as the sum of all flows in the system, i.e. consumption,respiration, and total flows to detritus (Christensen et al., 2005). The proportion of this systemflow that originates from detritus, as opposed to primary producers, accounts for 50%, 48%, and53% of system flow in spring, summer, and winter, respectively. This indicates that system pro-duction is highly dependent on the detritus pool year-round, which highlights the importance ofthe microbial loop in the system, where energy is transferred up the food web through detritus,bacteria, and microzooplankton.Total consumption flux (TC) was highest in summer (Fig. 3.6). In all three seasons, small-medium copepods and heterotrophic bacteria had the highest consumption rates (∼ 25−35% ofTC, each). In spring, flagellates also accounted for a large proportion of TC (15%), mainly withtheir consumption of phytoplankton and flagellates. Of the other mesozooplankton groups, largecopepods, amphipods, and other zooplankton had the largest TCs, aside from small-mediumcopepods. Zooplankton respiration was also highest in summer. The models estimated zoo-plankton respiration to be 0.50, 0.70, and 0.17 gC m-2 d-1 in spring, summer, and winter, re-39spectively.Primary productivity (PP) ranges from 0.63 (in winter) to 5.01 (in spring) gC m-2 d-1, witha year average of 2.19 gC m-2 d-1. In spring, phytoplankton productivity (FG 14) contributes81% of the total PP, due primarily to the diatom bloom. After spring, this dominance of the PPpool declines as mixotrophic microzooplankton groups (FGs 11-12) increase in importance. Insummer, 48% of PP comes from FG 14 and 52% comes from FGs 11-12. In winter, 51% of PPcomes from FG 14 and 49% comes from FGs 11-12.Lower trophic level groups (i.e. primary producers) had the highest ecotrophic efficiencies(EEs) in summer (Table 3.1). A high EE means that most of their production is being consumedby higher groups, indicating that there is less excess food available (low natural mortality) inthis season. This highlights the importance of these producers as food sources to omnivorousand carnivorous zooplankton when system biomass is limited.The low EEs for primary producers in spring indicate that there is a large amount of car-bon exported into detritus, thus fueling the detrital food chain. Detrital EE is also higher insummer, indicative of less material flowing into detritus in this season and the higher amount ofcarbon moving through the detrital chain. The high EE of grazing groups (i.e. small-mediumcopepods, gastropods, other zooplankton) in summer and winter emphasizes their importanceas a critical link between primary producers and carnivorous zooplankton. Top predators (FGs1-3) may have artificially low EEs because predation by fish was not explicitly included in themodels. I return to this later.Using these EEs, the amount of mesozooplankton production unused in the plankton sys-tem, and therefore theoretically available to higher trophic levels, is 54.2 gC m-2 yr-1, which is43% of total zooplankton production.Group AggregationThese seasonal models highlight the complexities of the trophic interactions within the planktoncommunity in the SoG. However, when groups are aggregated and the flows combined, one canget a picture of the overall relative structure of the system (Fig. 3.7). I aggregated the systeminto six functional groups: carnivorous zooplankton (CZ, FGs 1-4), omnivorous zooplankton(OZ, FGs 5-10); mixotrophic microzooplankton (MZ, FGs 11, 12), heterotrophic bacteria (HB,40Figure 3.7: Summarised trophic pathways between aggregated functional groups in the plank-tonic food webs for the Strait of Georgia in spring, summer, and winter. The area of thenodes are scaled to represent proportion of total system biomass (B). The thickness of thelines represents proportion of consumption flows, where the green end is the consumer andthe red end is the prey. The semi-circles around a single group indicate flux within thegroups aggregated together. Only flows accounting for ≥1% of the total system flux areshown. Position along the y-axis corresponds to the weighted average trophic level of theaggregated groups.41Table 3.2: Ecopath input parameters for aggregated plankton groups. TM is trophic mode(where 0 is fully heterotrophic, 1 is fully autotrophic, and between 0 and 1 is mixotrophic),B is biomass, P/B is production/biomass, and Q/B is consumption/biomass. Parameters area weighted average over a year, with groups aggregated as detailed in Fig. 3.7..Group name TM B P/B Q/Bt km-2 yr-1 yr-11 Carnivorous zooplankton 0 13.8 9 332 Omnivorous zooplankton 0 34.1 25 833 Microzooplankton 0.89 13.9 124 414 Heterotrophic bacteria 0 10.0 67 1625 Phytoplankton 1 19.7 136 -Table 3.3: Diet matrix for aggregated plankton groups averaged over a year. Groups were ag-gregated as detailed in Fig. 3.7.Prey\Predator 1 2 3 41 Carnivorous zooplankton 0.06 0.01 0 02 Omnivorous zooplankton 0.55 0.09 0 03 Microzooplankton 0.21 0.38 0.17 04 Heterotrophic bacteria 0.00 0.05 0.44 05 Phytoplankton 0.09 0.41 0.30 06 Detritus 0.08 0.06 0.09 1Sum 1 1 1 1FG 13), phytoplankton (PH, FG 14), and detritus (D, FG 15). Rate parameters for each groupare calculated as a biomass-weighted average of the rates for all FGs combined.In spring, PH are the main energy source for OZ, who then transfer energy to CZ (Fig. 3.7).In summer and winter, MZ shift to become the more dominant energy source for these groups.While system biomass and magnitude of carbon flows are much higher in summer than winter,the relative structure of the system is very similar. This structure could represent the systemunder limiting conditions, where nutrient limitation is occurring in summer and light limitationis occurring in winter. Interestingly, the structure of the microbial loop (consisting of D, HB,and MZ) is remarkably similar between the three seasons, and makes up a large proportion ofsystem carbon flow (∼ 30%).Many food web models aggregate the plankton community in a similar way, often sim-plifying into just a few groups averaged over a year (Christensen et al., 2005). With this inmind, I calculated parameters that could be used in a typical year-average model with a sim-42plified plankton structure. Input parameters and diet matrices were averaged over the threeseasons, weighted by season length (Tables 3.2 and 3.3). Biomass was converted using a wetweight:carbon ratio of 8.33 g g-1 for CZ and OZ (Harris et al., 2000), 6 g g-1 for MZ and PH(Preikshot, 2007), and 10 g g-1 for HB (Romanova and Sazhin, 2010). Average P/B and Q/Brates were converted to yearly values. These aggregated average parameter estimates are de-rived from a more quantitative analysis of the plankton community than most models to date.To evaluate the utility of this approach, these aggregated groups and calculated parameterswere put into an existing Ecopath with Ecosim (EwE) model for the SoG (Preikshot, 2007;Preikshot et al., 2013), replacing their plankton groups. The objective of the Preikshot (2007)model was to investigate changes in fished species, marine mammals, and bird populationsby running a time-dynamic simulation between 1950 and 2002. This Preikshot (2007) modelrepresents the SoG ecosystem using 53 functional groups, 5 of which represent plankton (phy-toplankton, krill, herbivorous zooplankton, carnivorous zooplankton, and jellies).My CZ group replaced the original carnivorous zooplankton (FG 41) and jellies (FG 43)groups. OZ replaced the original herbivorous zooplankton group (FG 42) and krill group (FG40). Predation of these original groups were summed for the new group. Parameter values forPH replaced those used in the original phytoplankton group (FG 51). MZ and HB groups wereadded. I only ran the static Ecopath part of the model.With these changes to the plankton community, the model still nearly balances (except theEE of carnivorous zooplankton was 1.01). A minor adjustment to fish consumption of zoo-plankton would allow the model to balance. The original model had higher zooplankton B’sbut lower P/B’s compared to my annual average model, resulting in comparable productivity(P). Total zooplankton productivity was 1078 t km-2 yr-1 and 967 t km-2 yr-1 in the originaland updated models, respectively. In the updated model, 13% of zooplankton productivity isconsumed by whales and fish (128 t km-2 yr-1) and 6% is consumed by other non-zooplanktongroups (i.e. benthic filter feeders; 57 t km-2 yr-1).PP from phytoplankton was also similar at 2860 t km-2 yr-1 and 2669 t km-2 yr-1 in the orig-inal and updated models, respectively. However, PP from the new mixotrophic group increasedtotal PP substantially, by 1521 t km-2 yr-1.The inclusion of the microbial loop also increased the proportion of flow originating from43detritus (as opposed to primary producers) from 20% to 30%. Trophic levels generally increaseddue to less strictly herbivorous feeding by zooplankton and the inclusion of the microbial loop.The model included fisheries in the SoG, and the mean trophic level of the catch increased from3.48 to 3.60.3.5 DiscussionIn this study, I constructed three food-web models using Ecopath for the plankton communityin the Strait of Georgia, corresponding to average (1) spring, (2) summer, and (3) winter condi-tions. The spring period is characterised by high productivity and begins with the spring diatombloom, known to be triggered by increased light levels and surface layer stability (Stockneret al., 1979; Collins et al., 2009), and the subsequent increase in copepod reproduction (Har-rison et al., 1983). As nutrients are depleted and grazing increases, the diatom bloom crashesand flagellates increase in dominance (Fig. 3.3), represented in the summer model. Zooplank-ton biomass peaks in mid-summer (typically June-July; Fig. 3.4), largely driven by increasedcopepod reproduction (Harrison et al., 1983). In winter, primary production is light limited andthe zooplankton community is relatively stable.Despite the large variations in plankton community composition and biomass between theseasons, I assumed quasi-steady state conditions within each season. Plankton populations re-mained relatively stable in summer and winter states, making this a reasonable assumption.This is less true in spring, which is highly dynamic with large changes in both phytoplanktonand zooplankton biomass. Despite this, the seasonal breakdown still provides more informationthan incorporation into a year average. Similarly, as part of the steady state assumption, I as-sumed that net migration and biomass accumulation were both zero. As the SoG is a relativelyisolated system, losses due to circulation within seasonal time scales are minimal relative toother terms (i.e. consumption fluxes) (Li et al., 2000).Although I do not include trophic levels above plankton in these models, this will only bea problem if the flow of carbon to these other higher levels is significantly larger than thosewithin the planktonic ecosystem. Fortunately, the updated whole ecosystem model (Sec. 3.4.2)showed that only 19% of zooplankton productivity is consumed by groups not included in mymodels. In the plankton models, these losses flow to detritus instead. This additional flux todetritus is within the uncertainty of the models and does not impact ecosystem functioning. So,consideration of the plankton system alone is an acceptable approximation.443.5.1 Ecosystem FunctioningThe weighted average trophic level (TL) of mesozooplankton was 2.36, 2.51, and 2.50 in spring,summer, and winter, respectively. The increase in trophic level from spring to summer is consis-tent with the findings of Flores-Ruiz (2020), where zooplankton in the SoG had a mean (± onestandard deviation) TL of 2.14 (± 0.09) in April, 2.26 (± 0.07) in August, and 2.53 (± 0.28) inDecember, based on stable nitrogen isotope analysis. It is also consistent with the stable isotopeanalysis by El-Sabaawi et al. (2010), where copepods had lower trophic positions in the springdue to herbivorous feeding.The changes in trophic position from season to season affect the total food chain length, andtherefore efficiency of energy transfer (Hairston and Hairston, 1993; Post, 2002). The meanpath length, defined as the average number of groups an inflow or outflow passes through (Finn,1976), varies seasonally. In spring, when the system is primarily supported by the phytoplank-ton group, the mean path length is short (2.71), indicating that the system is more resilient toperturbations in this season (Post, 2002). The mean path length was longer in summer (6.22)and winter (4.70), when flagellates and the microbial loop play a larger role in supporting thesystem, increasing the complexity of trophic interactions. These longer chains indicate that thesystem may be less stable than in the spring.3.5.2 System ProductivityIn the models, seasonal conditions were represented by changes in node biomass (B) and P/B &Q/B rates, which allowed for investigation into productivity (P) fluctuations throughout a year.Productivity in the SoG has been estimated using a variety of methods (Table 3.4). Whileestimates of gross and net primary productivity, new primary production, and net communityproduction are not easily comparable, they give an indication of ecosystem functioning. Theprimary productivity (PP) estimates and seasonal trends from the models are slightly larger thanthe other known estimates, but within reason.Crustacean productivity ranged from 0.09 (winter) to 0.42 (summer) gC m-2 d-1, with ayearly average of 0.27 gC m-2 d-1. These results are supported by estimates of crustacean45Table 3.4: Comparison of productivity estimates in the Strait of Georgia. All numbers are inunits of gC m-2 d-1.Source Peak Range Yearly Mean Estimation MethodThis study 5.01 0.63 - 5.01 2.19 Ecosystem modellingSuchy et al. (2016) 5.01 - 9.17 0.18 - 9.17 1.90 13C tracerWang et al. (2019) 16.2 1.7 - 16.2 - GP derived from ferry based O2 measurementsWang et al. (2019) 1.24 -0.02 - 1.24 0.46 NCP derived from ferry based O2 measurementsPen˜a et al. (2016) 1.51 0.013 - 1.51 0.73 Three-dimensional coupled biophysical modelPawlowicz et al. (2007) - - 0.60 NPP from box modelLi et al. (2000) - - 1.03 PP from box modelStockner et al. (1979) 8 - 0.95 14C methodGP = gross productivity, NCP = net community productivity, NPP = net primary production,PP = primary productivitysecondary productivity using the chitobase method (i.e. Suchy et al., 2016; Sastri and Dower,2009)) and empirical model estimates (Kwong et al., 2020). In Suchy et al. (2016), crustaceanproductivity in nearby Saanich Inlet ranged between 0.01 and 0.65 gC m-2 d-1. The annualaverage crustacean productivity in the SoG, measured by Sastri and Dower (2009) was 0.21 gCm-2 d-1.3.5.3 Bacteria and the microbial loopA novel aspect of my modelling is the attempt to include a microbial loop. The role of bac-teria in the SoG is not well studied, yet these models indicate that a substantial proportion ofcarbon biomass is cycled through this part of the food web. Because limited heterotrophic bac-terial biomass data were available, I allowed the models to estimate bacterial carbon, assumingan ecotrophic efficiency of 0.90. Bacterial biomass estimates ranged from 0.36 to 1.46 gC m-2.These estimates were within the range (0.04 to 3 gC m-2) measured by Seki and Kennedy (1969)and the summer range (0.8 to 1.7 gC m-2) reported by Bell and Albright (1981) (from 8 to 17mgC m-3, assuming depth integration to 100 m). The model estimates are also comparable tomeasurements in other coastal regions. Bacterial biomass within the upper 50 m of the watercolumn ranges from 0.303 to 0.925 gC m-2 in Reloncavı´ Fjord, Chile (Gonza´lez et al., 2010).In the polar fjord Kongsfjorden, Svalbard, bacterial biomass ranged from < 0.4 to 2.7 gC m-2(Iversen and Seuthe, 2011).There may be other significant consumers, such as benthic filter feeders (i.e. glass spongereefs; Kahn et al., 2018), which may suggest that bacterial biomass is underestimated in mymodels. Indeed, glass sponges are abundant in the SoG, forming reefs covering hundreds of46square kilometers (Conway et al., 2005; Kahn et al., 2018), and removing bacterial carbon athigh rates (i.e. 0.96 gC m-2 d-1 over the reefs; Chu and Leys, 2010). On the other hand, glasssponge reefs only cover a small percentage of the SoG’s total area (6,800 km2), so althougheffects might be large in some places, overall it is likely low.Detritus and phytoplankton are the two primary sources of organic carbon in the system.Since bacterial production is less affected by seasonal changes than phytoplankton, bacteriamay form an important alternate food source for filter feeders in the winter (Seki and Kennedy,1969; Albright, 1983). Bell and Albright (1981) estimated the ratios of bacterial productionto phytoplankton production as 0.25:1 in the Strait, and 0.04:1 during the spring bloom. Thisis consistent with my results of 0.10-0.17:1 in summer and winter, and 0.04:1 in spring. Thehigher ratio outside of the spring bloom highlights the increased importance of the detrital car-bon pathway as a food source.Outside of the spring bloom, both mixotrophic groups (FGs 11, 12) played a large role inthe transfer of energy from bacteria to the higher trophic levels, because mixotrophy is advanta-geous when the availability of light and/or nutrients is low (Pa˚lsson and Grane´li, 2004; Stoeckeret al., 2017; Leles et al., 2018). By consuming these smaller particles (bacteria) that are lessavailable to zooplankton due to their small size, mixotrophic microzooplankton became an im-portant pathway, enhancing carbon flow to mesozooplankton consumers (Ward and Follows,2016; Stoecker et al., 2017). This enhancement then translates into an increase in trophic trans-fer efficiency (TTE) (Landry and Calbet, 2004; Ward and Follows, 2016; Leles et al., 2018),demonstrated in the modelling exercise in Section 3.4.2. When the microbial loop was includedin the Preikshot (2007) model, TTE increased from 16% to 25%.3.5.4 Nutritional content of zooplanktonThe plankton community in the Strait of Georgia is temporally highly variable due to highturnover rates and changing conditions (i.e. temperature, nutrients, light availability) through-out a year. As many zooplankton are opportunistic feeders, this variability translates to con-siderable changes in food availability, and therefore diet composition, for many zooplankton(El-Sabaawi et al., 2009b, 2010).Changes in diet from autotrophic phytoplankton (mostly diatoms) in spring to flagellates insummer and winter also has implications on the nutritional quality of zooplankton as prey,47in terms of their fatty acid composition. A common indicator of nutritional quality is theDHA/EPA ratio, meaning the ratio of the fatty acids (FAs) docosahexaenoic acid (DHA), amarker for flagellates, and eicosapentaenoic acid (EPA), a marker for diatoms (Mayzaud et al.,1989; Dalsgaard et al., 2003; Kainz et al., 2004). Nutritional quality in each season can beestimated using,DHAEPA∣∣∣∣∣est=Bd ·DHAd +B f ·DHA fBd ·EPAd +B f ·EPA f (3.3)where Bd and B f are the biomasses of diatoms (d) and flagellates ( f ), respectively (mg d.w.m-2); DHA and EPA are the average FA content of each group (µg FA mg d.w.-1), from Pel-tomaa et al. (2019).Because of the shift from diatom dominated production in spring to flagellate dominatedproduction in summer and winter, the DHA/EPA ratio available is 70% higher in summer whencompared to spring. This higher nutritional value in summer over spring will transfer to zoo-plankton, making them higher quality food and impacting the dynamics, growth, and survival oftheir consumers (i.e. juvenile Pacific salmon and Pacific herring) (John et al., 2001; Costalagoet al., 2020).3.5.5 The use of simplified community dynamics in numerical modelsStandard applications for food web models include fisheries management questions (Heymanset al., 2014) or investigating biological-physical interactions (i.e. Collins et al., 2009; Pen˜a et al.,2016; Olson et al., 2020). Due to lack of knowledge or computational resources, planktonicecosystems are often set up in a rather ad-hoc manner, with rate parameters undergoing sometuning so that results (e.g. timing of blooms, magnitude of production) are “reasonable”. Analternative approach would be to derive a simpler model of a planktonic ecosystem by explic-itly aggregating a more complex (and realistic) model, such as mine. But does this alternativeprovide any advantages in practice?I investigated this question by aggregating the 15-group, 3 season system derived here into6 groups averaged over a year. I then replaced the plankton community in an existing SoGEcopath model with this aggregate (Section 3.4.2). My parameters were defined quantitatively,using the relative contributions of individual plankton taxa throughout the year. But interest-ingly, this quantitative and more detailed approach did not make a substantial difference to theoverall ecosystem (Fulton et al., 2003). While the input parameter values differed (i.e. B, P/B,48Q/B), phytoplankton and zooplankton productivity only varied slightly (∼ 10%).The inclusion of the microbial loop, specifically mixotrophy, had the biggest impact on ourunderstanding of food web dynamics. It’s inclusion increased PP, which then increased thedelivery of organic material to higher trophic levels. However, few food web models includegroups representing the microbial loop (i.e. heterotrophic bacteria, mixo- or heterotrophic flag-ellates, and microzooplankton).3.6 ConclusionsWithin the SoG, the trophic structure of the plankton food web varies seasonally. During thespring bloom, diatoms are the primary driver of energy flows through the system. Outside ofthese bloom conditions however, the microbial loop plays an important role in the food web.Despite this importance, many components of the microbial loop are not well studied in theSoG, including heterotrophic bacteria and, to a lesser degree, microzooplankton. This is a keygap in the understanding of bottom-up controls in the SoG. As productivity is heavily influ-enced by environmental forcing, understanding the small-scale interactions at the bottom of thefood web will only become more important as the climate varies (Mitra et al., 2014). Addition-ally, there are indications that primary productivity is a key variable in leading to predictableoutcomes, particularly in time-dynamic simulations (Christensen and Walters, 2011). The in-clusion of mixotrophic plankton in the modelled system allows for a better definition of it’simpacts over time.Along with the changing trophic structure, many other ecosystem functions also vary sea-sonally. The nutritional quality of plankton as prey to fish, for instance, is closely tied to thecomposition of the phytoplankton community. Additionally, seasonal estimates of primaryand secondary productivity, respiration, and trophic transfer efficiency are important factorsin ecosystem functioning. And thus, these models are a valuable new resource for future SoGstudies.All modelling exercises are a balance between complexity and effectiveness, where the bal-ance point depends on the modelling goal. My results indicate that complex models are notalways necessary to understand certain fisheries system dynamics, such as general fish yields.Indeed, it has been suggested that, beyond the most basic model structures, predictability de-creases as models become more complex (Fulton et al., 2003, and references within). However,49in these simple models, many details are lost, which may be important for some research inter-ests. For instance, these intricate details become important when investigating the impacts ofsmall-scale processes that may cascade up the food-web, such as seasonal nutrition availabilityor contaminant transfer. Overall, the modelling approach requires a thoughtful consideration ofthe research question and scope.50Chapter 4: Conclusions4.1 Summary, analysis, and implications of researchfindingsThe primary objective of this study was to investigate changes to the plankton community inthe Strait of Georgia (SoG) throughout a year, in terms of both community composition andtrophic structure. This analysis was based on multi-year datasets of phytoplankton and zoo-plankton biomass and community composition. First, I investigated the vertical distribution ofzooplankton groups on diel and seasonal scales. Crustaceans, namely copepods, amphipods,and euphausiids, which also dominate the biomass year-round, exhibit the strongest diel verti-cal migration (DVM) behaviour. During the day, most zooplankton biomass is in deep water(> 200 m), with low densities in surface water (< 50 m). At night, surface biomass tends toincrease. For most groups, this migration is strongest in spring.These migrations must be considered when sampling and using biomass estimates from towdata in modelling. When the entire water column is sampled, few differences were observedbetween catches in the day and night. In deep water, euphausiids showed a significant differ-ence between day and night biomass during summer. This difference was likely due to netavoidance (Sameoto, 1980; Everson and Bone, 1986), meaning daytime sampling during thesummer probably underestimates euphausiid biomass, consistent with previous analyses for theSoG (Shaw and Robinson, 1998). A correction factor was calculated, which was then used tocorrect taxa-level zooplankton biomass estimates in the SoG. Although there were few signifi-cant differences between day and night in full tows of deep water, this would likely not be thecase for tows that did not sample the entire water column.Many zooplankton reside at depth during the day and migrate to the surface at night tograze on phytoplankton. Thus, these migrations impact energy flows and, therefore, food webstructure. Consequently, in the application of these data to a food web model, it’s importantto consider the total potential grazing pressure. This analysis informed the decision to usebiomass estimates from only deep water tows (tow depth > 200 m) in the model set-up. There-fore, the food web models constructed in Chapter 3 considered all available feeding pressureby zooplankton, by using total water column biomass. However, these models did not considerdiapause, which some zooplankton (e.g. large copepods) undergo during the winter months51(Harrison et al., 1983; Mackas and Tsuda, 1999).The plankton community had three distinct seasonal states, defined by changes in groupbiomass, productivity, and diet composition. This study suggests that, in the SoG, the trophicstructure of the plankton food web varies seasonally. During spring bloom conditions, diatomsare the primary driver of energy flows through the system. Outside of these bloom conditionshowever, when primary productivity is limited by nutrients or light, the microbial loop shifts toserve a more important role.These structural changes within the plankton food web throughout a year have implicationsfor higher trophic levels. For one, the variations in trophic level (TL) and trophic transfer effi-ciency (TTE) within the plankton community influence the efficiency by which carbon is trans-ferred to consumers (Hairston and Hairston, 1993). My models also provide insights into theseasonal availability and quality of food for plankivorous fish. Although I didn’t include higherconsumers in the seasonal models, these insights may help in predicting the use of planktonproduction by planktivorous fish, seasonally (D’Alelio et al., 2016).The models also provide estimates for important ecological parameters where limited fieldmeasurements exist, including primary and secondary productivity, respiration rates, TTE, andbacterial biomass. Although my model estimates are generally consistent with field measure-ments, the development of my food web models highlight the generally overlooked importanceof mixotrophy and the microbial loop for ecosystem functioning in coastal waters.Finally, I compared this detailed seasonal approach to plankton modelling with the typicalsimplified approach by inputting my values into an existing Ecopath model for the SoG (Preik-shot, 2007). This allowed for a comparison of the impacts of the two approaches on the entireecosystem. The microbial loop is often excluded from ecosystem models but is an importantaddition, influencing TTE and trophic positions. However, aggregating plankton groups may bean adequate approach to plankton modelling, depending on the research question.One particular implication that can be drawn from these food web models is an estimate ofthe particle size spectrum in the SoG, which is an important factor in determining the eventualfate of contaminants that adsorb onto particles. Using the biomass estimates and typical bodysize range of each functional group, I developed a size spectrum (Fig. 4.1). Size spectra forparticles are typically presented in terms of a biomass density, i.e., the amount of biomass per52unit length, where the length characterizes their size. Typically, in a log-log format we expectthese to follow an approximately linear decay to larger sizes (i.e., a slope of −1). This is equiv-alent to saying that the biomasses of different trophic levels (or roughly of groups in differentsize ranges) are about equal.Here, we see a relationship where a slope of about -1 does in fact approximately characterizethe biogenic particles in the SoG. At the low end (under 10 µm) the spectrum is dominated byheterotrophic bacteria. It is possible that their mass is underestimated because the ecotrophicefficiency of 0.9 is too large; if that was the case then the sum would come in closer agreementwith this slope. From 10-100 µm, diatoms dominate, although in summer and winter they arejust barely the majority. For sizes more than 1 mm, copepods, amphipods, and euphausiids arethe most important organisms. In the mid-range, between 100 and 1000 µm, our classificationsuggests a gap. However, this may just be an artifact of sampling.4.2 Limitations of thesis research and future directionsThis study looked into the plankton community in a great level of detail, but there were a fewlimitations stemming from assumptions made due to a lack of available information in the liter-ature. First, although these models include the highest level of taxonomic detail for the planktoncommunity than any other food web model for the SoG, the system could be further disaggre-gated so that the roles played by individual species or size-classes could be explored. Thiswould be particularly thought-provoking for the phytoplankton and microzooplankton groups,which are still highly aggregated in my models. However, current in situ knowledge is toolimited to build a model of this resolution at this time. Second, it would be interesting to dis-aggregate the ‘mixotrophic’ group to better understand the role of each trophic mode in theecosystem. However, biomass and productivity estimates of the auto-, mixo-, and heterotrophyof microzooplankton are lacking, which resulted in them being grouped into one ‘mixotrophic’group. Additionally, recent estimates of bacterial biomass are not available, yet the carbon flowthrough bacteria is one of the largest in the system. This is a significant gap in the understandingof bottom-up controls in the SoG.An additional limitation of the model set-up is that the phytoplankton and zooplanktondatasets were collected over different time periods: 2015-2017 & 2019-2020 for phytoplankton,and 2006-2017 for zooplankton. The aim of the modelling was to get a baseline understandingof the system, which was best achieved using the most amount of data available. Zooplankton53Figure 4.1: Size spectrum of plankton for each season, based on the typical range of body sizesof each group. Dashed line is demonstrating a slope of -1. The solid black line representsthe total size spectrum (sum of all groups).community structure is known to vary over 5 to 10 year periods (Perry et al., 2021), which werenot considered in the averaging procedures. In the future, efforts could be made to stratify thedata to periods of these known long term variabilities, and give insights into whether the foodweb structure also varies over those time periods.On a similar note, little is known about in situ zooplankton diet compositions. To definediets here, I used literature and expert knowledge about their feeding strategy (i.e. filter feeder,raptorial) to define prey groups. Then, I used the proportion of food available to them (B ofprey groups) to define diet compositions. These were then adjusted manually until the modelbalanced. I also did not consider any imported food (i.e. terrestrial detritus), which could be54a small, but potentially important part of this coastal and estuarine food web (Costalago et al.,2020). More investigation into zooplankton diet compositions and consumption rates in theSoG would be beneficial.Finally, I assumed steady-state conditions within each seasonal period. However, the Eco-path framework allows for non-steady state conditions through the use of migration and biomassaccumulation terms, which may be important especially in spring during the bloom. Accumu-lation of dissolved oxygen from primary productivity is known to be an important part of theoxygen budget at these times (Wang et al., 2019) and this may be true for biomass budgets aswell. Since plankton are highly susceptible to physical processes such as currents, there willinevitably be some migration in or out of the study area, despite the SoG being a relatively en-closed basin. Pen˜a et al. (2016) estimated the total export of plankton from the SoG to be∼ 7%of primary production for phytoplankton and ∼ 18% of secondary production for zooplankton.Similarly, the changes in biomass throughout the season could be included in the biomass ac-cumulation term.Despite these limitations, the models are valid for their intended purpose, which was to rep-resent the carbon flows through the plankton system seasonally based on biomass data fromrecent years. These models could be further applied to other research questions. For instance,it would be insightful to model the system as a time-dynamic simulation (i.e. using ‘Ecosim’)with daily or monthly time-steps, to further refine the system seasonality and understandingof the mechanisms behind seasonal transitions. Further, time-dynamic simulations in Ecopathalso have the ability to simulate the transport of contaminants through the food web, using the‘Ecotracer’ tool (Walters and Christensen, 2018). 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Depth refers bottom depth, whereonly tows that sampled the entire water column were included.Mar-Jun Jul-Oct Nov-FebDay Night Day Night Day NightBottom depth > 200 m 209 32 96 34 22 9Table A.3: N:D ratios for full water column tows in areas with bottom depth > 200 m. Signifi-cant results (p-value < 0.05) are bolded.Group Spring Summer Winter(Mar-Jun) (Jul-Oct) (Nov-Feb)Total Biomass 1.07 ± 0.04 1.00 ± 0.04 0.96 ± 0.10Metridia sp. 1.26 ± 0.16 1.46 ± 0.15 0.68 ± 0.45Calanus sp. 1.10 ± 0.14 0.97 ± 0.14 0.74 ± 0.30Large copepods 1.06 ± 0.20 0.95 ± 0.26 1.17 ± 0.58Other small-medium copepods 0.96 ± 0.30 0.76 ± 0.29 1.13 ± 0.61Amphipods 1.00 ± 0.13 0.97 ± 0.11 1.07 ± 0.27Euphausiids 0.93 ± 0.31 2.59 ± 0.23 1.80 ± 0.47Decapods 1.27 ± 0.27 0.91 ± 0.64 0.80 ± 0.85Gelatinous zooplankton 0.85 ± 0.26 0.52 ± 0.41 1.13 ±0.54Gastropods 1.03 ± 0.27 0.89 ± 0.52 0.91 ± 0.76Chaetognaths 1.08 ± 0.47 0.96 ± 0.40 0.92 ± 0.73Other zooplankton 1.22 ± 0.11 0.81 ± 0.16 0.85 ± 0.2868Figure A.1: Seasonal variations in daytime and nighttime zooplankton normalised biomass atthree depth intervals, where bottom depth is > 200 m.69Figure A.1: (continued)70Figure A.2: Community composition in daytime and nighttime seasonally at three depth inter-vals.71Table A.4: One-way ANOVA results for comparisons of taxa biomass in deep water (bottomdepth > 200 m) by twilight (day or night). Cases where p-value < 0.05 are noted.Season Source Sum Sq. d.f. Mean Sq. F Prob>Ftwilight 0.02 1 0.023 0.385 0.54Error 14.12 239 0.059SpringTotal 14.14 240twilight 0.00 1 0.000 0.002 0.97Error 5.56 128 0.043SummerTotal 5.56 129twilight 0.00 1 0.001 0.024 0.88Error 1.77 29 0.061Total BiomassWinterTotal 1.77 30twilight 0.20 1 0.197 2.491 0.12Error 18.89 239 0.079SpringTotal 19.08 240twilight 0.52 1 0.518 4.128 0.04Error 16.06 128 0.125SummerTotal 16.58 129twilight 0.08 1 0.082 1.146 0.29Error 2.09 29 0.072Metridia sp.WinterTotal 2.17 30twilight 0.03 1 0.034 0.368 0.54Error 21.89 239 0.092SpringTotal 21.92 240twilight 0.00 1 0.004 0.034 0.85Error 14.27 128 0.112SummerTotal 14.28 129twilight 0.07 1 0.069 0.947 0.34Error 2.10 29 0.072Calanus sp.WinterTotal 2.17 3072Table A.4: continued from previous pageSeason Source Sum Sq. d.f. Mean Sq. F Prob>Ftwilight 0.01 1 0.012 0.088 0.77Error 33.72 239 0.141SpringTotal 33.74 240twilight 0.01 1 0.007 0.035 0.85Error 24.80 128 0.194SummerTotal 24.80 129twilight 0.01 1 0.009 0.217 0.64Error 1.17 29 0.040LargecopepodsWinterTotal 1.17 30twilight 0.00 1 0.003 0.130 0.72Error 5.86 239 0.024SpringTotal 5.86 240twilight 0.16 1 0.156 8.164 0.005Error 2.44 128 0.019SummerTotal 2.59 129twilight 0.00 1 0.003 0.419 0.52Error 0.21 29 0.007Othersmall-mediumcopepodsWinterTotal 0.22 30twilight 0.00 1 0.000 0.000 0.99Error 25.33 239 0.106SpringTotal 25.33 240twilight 0.00 1 0.003 0.037 0.85Error 9.82 128 0.077SummerTotal 9.82 129twilight 0.00 1 0.003 0.063 0.80Error 1.55 29 0.053AmphipodsWinterTotal 1.55 3073Table A.4: continued from previous pageSeason Source Sum Sq. d.f. Mean Sq. F Prob>Ftwilight 0.02 1 0.017 0.093 0.76Error 44.03 239 0.184SpringTotal 44.05 240twilight 2.86 1 2.862 12.843 0.0005Error 28.52 128 0.223SummerTotal 31.38 129twilight 0.22 1 0.215 0.800 0.38Error 7.79 29 0.269EuphausiidsWinterTotal 8.01 30twilight 0.18 1 0.175 1.442 0.23Error 29.09 239 0.122SpringTotal 29.26 240twilight 0.01 1 0.012 0.124 0.73Error 12.35 128 0.097SummerTotal 12.36 129twilight 0.01 1 0.012 0.236 0.63Error 1.50 29 0.052DecapodsWinterTotal 1.51 30twilight 0.10 1 0.099 0.495 0.48Error 47.63 239 0.199SpringTotal 47.73 240twilight 1.18 1 1.181 13.395 0.0004Error 11.29 128 0.088SummerTotal 12.47 129twilight 0.01 1 0.005 0.109 0.74Error 1.46 29 0.050GelatinouszooplanktonWinterTotal 1.46 3074Table A.4: continued from previous pageSeason Source Sum Sq. d.f. Mean Sq. F Prob>Ftwilight 0.00 1 0.002 0.015 0.90Error 30.43 239 0.127SpringTotal 30.44 240twilight 0.03 1 0.031 0.289 0.59Error 13.75 128 0.107SummerTotal 13.78 129twilight 0.00 1 0.002 0.061 0.81Error 0.95 29 0.033GastropodsWinterTotal 0.95 30twilight 0.01 1 0.007 0.126 0.72Error 14.25 239 0.060SpringTotal 14.26 240twilight 0.00 1 0.003 0.032 0.86Error 12.42 128 0.097SummerTotal 12.42 129twilight 0.00 1 0.002 0.051 0.82Error 1.04 29 0.036ChaetognathsWinterTotal 1.04 30twilight 0.17 1 0.174 2.279 0.13Error 18.25 239 0.076SpringTotal 18.42 240twilight 0.16 1 0.157 2.793 0.10Error 7.21 128 0.056SummerTotal 7.36 129twilight 0.02 1 0.018 0.465 0.50Error 1.13 29 0.039OtherzooplanktonWinterTotal 1.15 3075Appendix B: Chemtax AnalysisB.1 MethodsB.1.1 SamplesPhytoplankton biomass and community composition was estimated using a combination of mi-croscopy and high-performance liquid chromatography (HPLC) derived pigment data collectedin the SoG in 2015-2017 and 2019-2020. Both (pigment and microscope) samples were col-lected in Niskin bottles between 0 to 5 m depth on cruises in February to November. Therewere 4 to 9 cruises per year, with 5 to 15 stations per cruise. Over the time series, there was 311pigment samples collected (Fig. 3.2).The pigment samples were analysed using Chemtax (version 1.95), which derives estimatesof biomass contributions of phytoplankton taxa, relative to total chlorophyll a (Mackey et al.,1996). Thirteen pigments were used as marker pigments to analyse phytoplankton biomass andcomposition (Table B.1). Eight expected taxa were selected based on the associated microscopecounts and from a literature review (i.e. Stockner et al., 1979; Belluz et al., 2021): diatoms 1-2 (diat), dinoflagellates-1 (dino), cyanobacteria-2 (cyan), prasinophytes-3 (pras), cryptophytes(cryp), dictyochophytes-1 (dict), haptophytes6-8 (hapt), and raphidophytes (raph).Data were separated into 6 bins based on their sampled time or possible presence of raphi-dophytes (Table B.2). Bins 1-5 were each run through Chemtax twice. The first run used theinitial seeding matrix in Table B.3. Except where specified in the table, values in the seedingmatrix were taken from the culture mean ratios in Higgins et al. (2011). Where groups werecombined (i.e. diatoms 1-2), the value was a mean of the culture means of each individualgroup. The second run used the output ratio of the first run as the initial matrix. The ratio limitsin Chemtax varied between the bins, based on the seasonality (Table B.2). The LL (low light)ratio limits were used for the winter samples (bins 1, 5) and were 500 for all pigments. TheHL (high light) ratio limits were used for the summer samples (bins 2-4) and were 300 for allpigments. The ratio limit for TChl a was 100 for both LL and HL.Bin 6 aimed to separate the samples with raphidophytes present. The first run used the seed-ing matrix (Table B.3) and included all the samples from May-September in every year, except76Table B.1: HPLC marker pigments, the abbreviations used here, and the phytoplankton groupsin the Strait with which they are associated. Based on the groupings in Higgins et al. (2011).Pigment Abbreviation GroupsChlorophyll c3 Chl c3 Diatoms, haptophytesChlorophyll c1 + c2 Chl c1+c2 Diatoms, dinoflagellates, cryptophytes,dictochophytes, haptophytes, raphido-phytesPeridinin Peri Dinoflagellates-119’-butanoyloxyfucoxanthin BF Dominant in dictyochophytes; present inhaptophytesFucoxanthin Fuco Dominant in diatoms; present in dicty-ochophytes, haptophytes, raphidophytesPrasinoxanthin Pras Prasinophytes-319’-hexanoyloxyfucoxanthin HF HaptophytesZeaxanthin Zea Dominant in cyanobacteria; present indiatoms, prasinophytes, raphidophytesAlloxanthin Allo CryptophytesLutein Lut PrasinophytesChlorophyll b Chl b PrasinophytesViolaxanthin Viola Prasinophytes, diatomsTotal Chlorophyll a TChl a All photosynthetic groups2015 (no Heterosigma bloom was observed in 2015). From the results of this run, samples withraphidophyte biomass less than 1% were removed (assumed to be absent) and samples withraphidophyte biomass greater than or equal to 5% were kept (assumed to be present). Sampleswith biomass between 1 and 5% were looked at individually; most were kept for Run 2 but ifthe error was > 100%, they were removed. Run 2 ran the remaining samples with the seedingmatrix again. A few more samples were removed, if the biomass was very low and/or the errorwas very high. Runs 3 and 4 ran this final subset of samples in the same methodology as theother bins (seeding matrix, then output matrix).The composition output from the final run for each bin was used for the biomass analysis.Results for samples included in Bin 6 replaced the results of the previous binned run if theyhad raphidophyte presence. Errors for each sample and run were analysed and considered to bewithin reason.77Table B.2: Separation of samples into seasonal bins.Bin Sampling Time Ratio LimitBin 1 Feb 1 - Mar 31 LLBin 2 Apr 1 - May 15 HLBin 3 May 16 - July 15 HLBin 4 Sep 1 - Oct 15 HLBin 5 Nov LLBin 6Subset of samples from May-Sep 2016,2017, 2019, 2020, run with raphidophytes. HLTable B.3: Initial seeding matrix used in Chemtax.Group Chl c3 Chlc1+c2 Peri BF Fuco Pras HF Zea Allo Lut Chl b Viola TChlaDiat 0.042 0.281 0 0 0.81 0 0 0.011 0 0 0 0.0045 1Dino 0 0.22 0.56 0 0 0 0 0 0 0 0 0 1Cyan 0 0 0 0 0 0 0 0.64 0 0 0 0 1Pras 0 0 0 0 0 0.25 0 0.058 0 0.021 0.70 0.054 1Cryp2 0 0.05 0 0 0 0 0 0 0.07 0 0 0 1Dict 0 0.11 0 0.103 0.35 0 0 0 0 0 0 0 1Hapt 0.174 0.20 0 0.054 0.26 0 0.42 0 0 0 0 0 1Raph4 0 0.077 0 0 0.49 0 0 0.062 0 0 0 0.16 11. The culture mean of Chl c1+c2 for diatom-1 was anomalously low, and was absent fordiatom-2, so the value used is based on the field means (Higgins et al., 2011).2. The initial values for cryptophytes were taken from Belluz et al. (2021), derived frompigment concentrations measured during cryptophyte blooms in the northern SoG.3. A BF pigment ratio for dictyochophytes is not reported in Higgins et al. (2011), but one isused in Belluz et al. (2021) based on measurements during events with high dictyochophyteabundance in the northen SoG. This value was used in this initial matrix.4. Raphidophytes were only included in Bin 6.B.1.2 Depth IntegrationBiomass data were depth integrated using CTD fluorometry profiles taken at the time of sam-pling. CTD data were binned in 1 metre intervals, but often began 2-4 m below the surface. TheHPLC data is taken in bottles on the upcast, and all are within the top 5 m. Where possible, Iincorporated the near-surface bottle fluorescence data with the CTD fluorescence data, to get amore complete profile from 0 - 400 m. To do this, I checked if there were bottle samples in 1 mintervals above the first CTD point. If there was a bottle sample for an interval, the fluorescencefor that depth was set to the bottle data (or a mean of them if there was more than 1 point within1 m). For example, if there’s a bottle sample at 1.5 m, the data would be set at the profile depthof 1 m. Where there was no CTD or bottle data, the fluorescence for that metre was set to the78Figure B.1: Fluorescence profiles of all samples with an offset subtracted. The thick black lineshows the monthly mean calculated every 1 m.previous value (from the metre below).Profiles were depth integrated to 200 m for the February and November samples, and to 100m for the rest. These were roughly the depth that fluorescence went to zero. An offset for eachprofile was subtracted from the entire profile. This offset was calculated as the median valuewithin ± 15 m of the max depth (100 m or 200 m). If there were any negative values after thissubtraction, they were set to 0. All fluorescence profiles (after the offset subtraction) and themonthly means are shown in Figure B.1.79These profiles were then depth integrated, to get the total fluorescence in the water columnin mg m-2. The depth integrated fluorescence was converted to depth integrated Chl biomassusing the biomass and fluorescence of the HPLC sample (Equations B.1).A =∫ ymax0f (y)dy (B.1a)B = A∗ bfd(B.1b)where A is the area under curve (mgflu m-2); ymax = maximum depth of integration (100m or200m); f (y) = fluorescence at depth y; b = Chl a biomass from HPLC sample (mgchla m-3); fd =fluorescence at depth of HPLC sample (mgflu m-3); B = depth integrated Chl a biomass (mgchlam-2).B.2 ResultsThe total chlorophyll biomass and composition of all samples is shown in Figure B.2. Thebloom can be seen in March and April, with a strong dominance of diatoms. February andNovember seem to be relatively consistent, with low total biomass. The summer is a little morevariable, with episodic blooms that may or may not have been captured by the sampling. In2016-2017, a dominance of diatoms is still (or again) seen in early June. Diatoms are the dom-inant group in the Strait, but cryptophytes and prasinophytes are abundant when diatoms arenot blooming. Raphidophyte blooms can be seen in July 2019 and June 2020. Cyanobacteriaseems to be more present in the summer (June-September). Haptophytes contribute relativelylittle to the total biomass but seem to be somewhat constant throughout the year, except for a fewcases (late-June 2015, early-April 2019, May 2019). Dinoflagellates and dictyochophytes alsogenerally have low relative biomasses, except in a few cases (i.e. dichychophytes in late-June2016). Since raphidophytes are not a consistently found group in the Strait (rather they bloomto varying degrees some years and are absent others), they will not have their own functionalgroup in the Ecopath model. However, their biomass is still important, so they will be groupedwith other flagellate groups.80Figure B.2: Composition (bars) and total chlorophyll biomass (black dots) of all samples, ordered from North to South for eachcruise. Note the axis limits are different for April 2019 and March 2020.81Appendix C: Ecopath Model Input ParametersTable C.1: Values, sources, and assumptions for model input parameter P/B in the spring, summer, and winter Strait of GeorgiaEcopath models.P/B (day-1)Spring Summer WinterValue Source Value Source Value Source1 Gelatinous 0.088 Hansson (1997) 0.103 Hansson (1997) 0.077 Hansson (1997)2 Chaetognaths 0.025 Huo et al. (2020) 0.040 Huo et al. (2020) 0.028 Huo et al. (2020)3 Amphipods 0.017 Yamada and Ikeda (2006) 0.019 Yamada and Ikeda (2006) 0.016 Yamada and Ikeda (2006)4 Decapods 0.016 a 0.018 Cartes et al. (2001) 0.014 Cartes et al. (2001)5 Euphausiids 0.023 a 0.029 Iguchi and Ikeda (1999) 0.016 Iguchi and Ikeda (1999)6 Large copepods 0.110 Suchy et al. (2016) 0.072 Suchy et al. (2016) 0.040 Suchy et al. (2016)7 Other zooplankton 0.049 b 0.053 Hirst et al. (2003) 0.026 b8 Gastropods 0.037 a 0.053 Hirst et al. (2003) 0.020 Fabry (1989)9 Small-medium copepods 0.093 Liu and Hopcroft (2006, 2007) 0.105 Suchy et al. (2016) 0.049 Liu and Hopcroft (2006, 2007)10 Appendicularians 0.347 a 0.434 Pave´s et al. (2015); Deibel and Lowen (2012); Tomita et al. (1999) 0.260 Uye and Ichino (1995)11 Ciliates 0.496 a 0.363 Aydin et al. (2003) 0.233 Aydin et al. (2003)12 Flagellates 0.416 Kang et al. (2020) 0.375 Kang et al. (2020) 0.229 Kang et al. (2020)13 Heterotrophic Bacteria 0.183 a 0.200 Wilhelm et al. (2002); Sherry et al. (1999) 0.166 Bell and Albright (1981)14 Phytoplankton 0.531 c 0.378 c 0.244 c15 Detritus - - - - - -a. Average of summer and winter valuesb. Average of other crustacean inputsc. Calculated using biomass and primary productivity measurements using 13C in 2017-2018 by the Maldonado Lab at UBC.82Table C.2: Values, sources, and assumptions for model input parameter Q/B in the spring, summer, and winter Strait of GeorgiaEcopath models.Q/B (day-1)Spring Summer WinterValue Source Value Source Value Source1 Gelatinous 0.293 d 0.343 d 0.258 d2 Chaetognaths 0.085 Huo et al. (2020) 0.140 Huo et al. (2020) 0.097 Huo et al. (2020)3 Amphipods 0.071 Yamada and Ikeda (2006) 0.080 Yamada and Ikeda (2006) 0.066 Yamada and Ikeda (2006)4 Decapods 0.065 a 0.080 Fileman et al. (2014) 0.050 Fileman et al. (2014)5 Euphausiids 0.075 a 0.082 Iguchi and Ikeda (1999) 0.068 Iguchi and Ikeda (1999)6 Large copepods 0.367 d 0.240 d 0.133 d7 Other zooplankton 0.164 d 0.178 d 0.086 d8 Gastropods 0.122 d 0.178 d 0.066 d9 Small-medium copepods 0.309 d 0.350 e 0.163 d10 Appendicularians 1.147 Pave´s et al. (2015) 1.434 Pave´s et al. (2015) 0.859 Pave´s et al. (2015)11 Ciliates 0.077 Aydin et al. (2003) 0.090 Aydin et al. (2003) 0.064 Aydin et al. (2003)12 Flagellates 0.153 f 0.200 f 0.045 f13 Heterotrophic Bacteria 0.441 D’Alelio et al. (2016) 0.483 D’Alelio et al. (2016) 0.400 D’Alelio et al. (2016)14 Phytoplankton - - - - - -15 Detritus - - - - - -a. Average of summer and winter valuesd. Assumed P/Q = 0.30 (Christensen et al., 2005)e. Assumed P/Q = 0.25 (Christensen et al., 2005)f. Assumed same P/Q as ciliates83Table C.3: Diet matrix for spring Ecopath model.Predator1 2 3 4 5 6 7 8 9 10 11 12 13Prey1 Gelatinous zooplankton 0.021 0.021 0.0052 Chaetognaths 0.007 0.012 0.0023 Amphipods 0.108 0.005 0.0264 Decapods 0.011 0.018 0.0095 Euphausiids 0.040 0.005 0.095 0.0086 Large copepods 0.088 0.142 0.087 0.126 0.007 0.0227 Other zooplankton 0.156 0.251 0.155 0.077 0.038 0.0378 Gastropods 0.017 0.016 0.0049 Small-medium copepods 0.248 0.398 0.245 0.353 0.110 0.073 0.060 0.058 0.02910 Appendicularians 0.015 0.015 0.00411 Ciliates 0.089 0.143 0.088 0.127 0.040 0.044 0.022 0.105 0.071 0.10012 Flagellates 0.052 0.050 0.045 0.041 0.119 0.119 0.145 0.135 0.152 0.145 0.176 0.10413 Heterotrophic Bacteria 0.050 0.050 0.050 0.050 0.050 0.100 0.100 0.40014 Phytoplankton 0.098 0.205 0.159 0.681 0.680 0.555 0.515 0.698 0.555 0.674 0.39615 Detritus 0.050 0.100 0.050 0.050 0.100 0.100 0.050 0.100 1Sum 1 1 1 1 1 1 1 1 1 1 1 1 184Table C.4: Diet matrix for summer Ecopath model.Predator1 2 3 4 5 6 7 8 9 10 11 12 13Prey1 Gelatinous zooplankton 0.014 0.015 0.0032 Chaetognaths 0.018 0.029 0.0043 Amphipods 0.200 0.011 0.0434 Decapods 0.003 0.003 0.0025 Euphausiids 0.028 0.004 0.066 0.0106 Large copepods 0.067 0.107 0.076 0.096 0.004 0.0147 Other zooplankton 0.088 0.142 0.100 0.052 0.038 0.018 0.0088 Gastropods 0.019 0.025 0.0089 Small-medium copepods 0.284 0.457 0.322 0.408 0.208 0.169 0.122 0.057 0.05410 Appendicularians 0.003 0.008 0.006 0.00211 Ciliates 0.126 0.203 0.143 0.181 0.092 0.075 0.054 0.126 0.238 0.20012 Flagellates 0.056 0.050 0.101 0.067 0.269 0.291 0.242 0.222 0.242 0.202 0.303 0.14113 Heterotrophic Bacteria 0.050 0.050 0.050 0.150 0.050 0.150 0.200 0.55014 Phytoplankton 0.044 0.099 0.083 0.331 0.359 0.358 0.328 0.358 0.298 0.447 0.20915 Detritus 0.050 0.100 0.050 0.050 0.050 0.050 0.100 0.050 0.150 0.050 0.100 1Sum 1 1 1 1 1 1 1 1 1 1 1 1 185Table C.5: Diet matrix for winter Ecopath model.Predator1 2 3 4 5 6 7 8 9 10 11 12 13Prey1 Gelatinous zooplankton 0.009 0.011 0.0042 Chaetognaths 0.023 0.037 0.0103 Amphipods 0.158 0.009 0.0354 Decapods 0.005 0.010 0.0045 Euphausiids 0.103 0.011 0.168 0.0316 Large copepods 0.049 0.078 0.055 0.060 0.005 0.0227 Other zooplankton 0.100 0.159 0.113 0.055 0.045 0.0308 Gastropods 0.012 0.011 0.0059 Small-medium copepods 0.302 0.480 0.342 0.369 0.211 0.165 0.135 0.091 0.05210 Appendicularians 0.004 0.005 0.00211 Ciliates 0.085 0.135 0.145 0.104 0.089 0.075 0.057 0.129 0.148 0.15012 Flagellates 0.080 0.100 0.110 0.090 0.270 0.293 0.225 0.225 0.358 0.303 0.270 0.15813 Heterotrophic Bacteria 0.050 0.050 0.050 0.100 0.050 0.200 0.350 0.55014 Phytoplankton 0.020 0.090 0.110 0.330 0.357 0.275 0.275 0.292 0.247 0.330 0.19215 Detritus 0.050 0.100 0.050 0.050 0.050 0.100 0.150 0.100 0.100 0.050 0.100 1Sum 1 1 1 1 1 1 1 1 1 1 1 1 186C.1 Justification of Diet MatricesDiet matrices were determined in two steps. First, by identifying trophic connections (i.e. whoeats who). Then, scaling the degree of interaction. The trophic connections were determinedusing a combination of knowledge of feeding mechanism, literature resources, and discussionswith a zooplankton researcher (E. Pakhomov, pers. comm.). The trophic links were assumed tobe identical across the three seasons, but with variations in the weight of interaction.Generally, zooplankton were divided into filter feeders and predators. Filter feeders wereassumed to be omnivorous and able to feed on a range of groups but generally limited to thosewith smaller body sizes than the consumer. Copepods, euphausiids, appendicularians, and gas-tropods are filter feeders (Harris et al., 2000). All filter feeders consumed phytoplankton, flag-ellates, ciliates, and detritus (Costalago et al., 2020). Large copepods also consumed large andsmall-medium copepods and other zooplankton (El-Sabaawi, 2008). Small-medium copepodsconsumed copepods in the same size class, other zooplankton, and bacteria (El-Sabaawi, 2008;Costalago et al., 2020). Euphausiids consume small-medium copepods and bacteria (Costalagoet al., 2020). Gastropods were assumed to be mainly herbivorous with the exception of micro-zooplankton, small-medium copepods, and other zooplankton (Pasternak et al., 2017). Appen-dicularians only consumed microzooplankton, phytoplankton, bacteria, and detritus (Schein-berg et al., 2005).The remaining zooplankton groups were assumed to be higher predators with a variety offeeding strategies. All consumed ciliates and flagellates, and all except chaetognaths consumedphytoplankton (Costalago et al., 2020). Gelatinous zooplankton catch their prey with stingingcells (Harris et al., 2000). It was assumed that this feeding strategy was not very selective, so,in the modelling, they consumed all groups except heterotrophic bacteria. Chaetognaths areambush predators (Harris et al., 2000), which is a more selective feeding style. They consumedchaetognaths, euphausiids, copepods (all sizes), and other zooplankton (Harris et al., 2000; Huoet al., 2020). Amphipods are predators but with a large range of prey items (Harris et al., 2000;Haro-Garay, 2004). They consumed all groups except chaetognaths and euphausiids, whichwere assumed to be too large, and heterotrophic bacteria, which were assumed to be too small(Haro-Garay, 2004). Decapods had a range of prey items, and consumed euphausiids, largecopepods, small-medium copepods, and bacteria (Fileman et al., 2014; Costalago et al., 2020).The other zooplankton group contained a wide variety of zooplankton taxa and feeding strate-gies so they were assumed to consume all groups.87Table C.6: Diet proportions (σ ) for each consumer in diet matrix set-up.Detritus, σ1 Bacteria, σ2 Herbivory, σ3 (FGs 12 & 14) Carnivory, σ4 (FGs 1-11)Group Spring Summer Winter Spring Summer Winter Spring Summer Winter Spring Summer Winter1 Gelatinous zooplankton 0.05 0.05 0.05 0 0 0 0.15 0.1 0.1 0.8 0.85 0.852 Chaetognaths 0 0 0 0 0 0 0.05 0.05 0.1 0.95 0.95 0.93 Amphipods 0.1 0.1 0.1 0 0 0 0.25 0.2 0.2 0.65 0.7 0.74 Decapods 0.05 0.05 0.05 0.05 0.05 0.05 0.2 0.15 0.2 0.7 0.75 0.75 Euphausiids 0 0.05 0.05 0.05 0.05 0.05 0.8 0.6 0.6 0.15 0.3 0.36 Large copepods 0 0.05 0.05 0 0 0 0.8 0.65 0.65 0.2 0.3 0.37 Other zooplankton 0.05 0.05 0.1 0.05 0.05 0.05 0.7 0.6 0.5 0.2 0.3 0.358 Gastropods 0.1 0.1 0.15 0.05 0.15 0.1 0.65 0.55 0.5 0.2 0.2 0.259 Small-medium copepods 0 0.05 0.1 0.05 0.05 0.05 0.85 0.6 0.65 0.1 0.3 0.210 Appendicularians 0.1 0.15 0.1 0.1 0.15 0.2 0.7 0.5 0.55 0.1 0.2 0.1511 Ciliates 0.05 0.05 0.05 0.1 0.2 0.35 0.85 0.75 0.6 0 0 012 Flagellates 0.1 0.1 0.1 0.4 0.55 0.55 0.5 0.35 0.35 0 0 013 Heterotrophic Bacteria 1 1 1 0 0 0 0 0 0 0 0 0Ciliates and flagellates were mixotrophic and consumed flagellates, bacteria, phytoplank-ton, and detritus. Heterotrophic bacteria consumed only detritus.Once these links were identified, the weight of interaction (DC ji) was scaled primarily bythe amount of biomass available, with a few exceptions and adjustments. First, the proportionof detritus, bacteria, herbivory (FGs 12 and 14), and carnivory (FGs 1-11) in the diet of eachpredator was defined as σ (Table C.6). These were estimated based on knowledge of feedingstrategy and preferences, as well as the amount of biomass available.Based on the extent of herbivory and carnivory defined for each group, the consumption ofeach prey item i in the diet of consumer j was scaled using their biomass using,DC ji =σ1 i = 15σ2 i = 13σ3 αiBi∑i=12,14αiBi i = 12,14σ4 αiBi∑i=1−11αiBi i = 1−11(C.1)where DC ji is the contribution fraction of the ith group as prey in the diet of the jth group,where n is the number of prey groups; σ is the feeding type proportion (from Table C.6); αi isthe weighting factor for each prey group i; and Bi is the biomass of group i.On the first modelling attempt, all prey items had a weight (αi) of 1, with exceptions whena group only consumes the eggs or nauplii of another group. This was the case for: chaetog-naths consuming euphausiids, amphipods consuming amphipods, and large copepods consum-ing large copepods, and small-medium copepods consuming small-medium copepods. For thesegroups, α was reduced to 0.05 to 0.15.88After the first model run, groups that were unbalanced were identified. On most modellingattempts, these groups were ones consumed by many other groups, namely small-medium cope-pods and other zooplankton. In the event of an unbalanced model, the α of these unbalancedgroups or other, less exploited groups, was adjusted.89Appendix D: Regional Variations in Plank-ton Community CompositionFigure D.1: Boundaries for regional plankton analysis.90Figure D.2: Seasonal zooplankton community composition in each region. Error bars show thestandard deviation of total biomass.Figure D.3: Seasonal phytoplankton community composition in each region.91
Thesis/Dissertation
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Oceanography
Vancouver : University of British Columbia Library
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Pawlowicz, Rich
Seasonality in the community composition and food web structure of plankton in the Strait of Georgia
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