<Abstract Centered> an ABSTRACT of the THESIS OF

AN ABSTRACT OF THE THESIS OF

Katelyn M. Qualls for the degree of Master of Science in Ocean, Earth, and Atmospheric Sciences presented on April 2, 2019.

Title: Drivers of Euphausiid Abundance and Biomass in the Kitimat Fjord System, BC Canada.

Abstract approved:

______Kim S. Bernard

Euphausiids are recognized as essential components of marine food webs throughout the world’s oceans due to their role as prey for many species including whales, seabirds, and commercial fishes. The Kitimat Fjord System is an important fisheries area and is the only fjord habitat on the British Columbia coast that is designated as Critical Habitat for humpback whales.

Despite this, very little was previously known about the area’s composition of euphausiid species, temporal and spatial patterns in these species’ abundances and distributions, and the environmental drivers of those patterns. In this study, zooplankton samples were collected and

CTD casts were conducted at a repeated network of stations in the Kitimat Fjord System in summer 2015 (May-September). The relationships between euphausiid abundance and a suite of spatial, temporal, and oceanographic variables were analyzed in order to examine the potential drivers of euphausiid abundances in this important coastal fjord environment. Six species of euphausiids were present in the study area, with Euphausia pacifica and Thysanoessa spinifera being the most abundant (52.6% and 46.3%, respectively). The following rare species were also captured: Thysanoessa longipes, Thysanoessa gregaria, Tessarabrachion oculatum, and

Nematoscelis difficilis. However, due to the scarcity of these rare species, (1) it was difficult to establish patterns in their distribution and (2) there was not enough data available to examine the drivers of these species’ abundances. Results indicated that while E. pacifica and T. spinifera were typically found to be sympatric, E. pacifica was more dominant in the inner fjords and T. spinifera was more dominant near the mouth of the fjords. Strong temporal changes in the abundance of early furcilia suggest that peak E. pacifica spawning occurred about one month earlier than peak T. spinifera spawning in this region. Competing generalized additive model

(GAM) formulations, each representative of a different hypothesis for euphausiid distribution, were used to investigate the effects of (1) temporal, (2) spatial, (3) oceanographic, and (4) combined temporal, spatial, and oceanographic covariates on E. pacifica and T. spinifera abundance and biomass. Results of these GAMs suggested some species-specific responses to environmental factors, indicating that niche differentiation is occurring in this dynamic habitat.

Combined models were consistently more parsimonious than others, suggesting that the controls on euphausiid abundance are complex. Model results indicated that E. pacifica biomass is significantly positively related to sea surface temperature and highest around seafloor depths of

375 m. T. spinifera biomass was found to be positively affected by day of the year and negatively affected by distance into the fjordland. Determining the drivers of species-specific spatiotemporal patterns in euphausiid abundance is (1) crucial to understanding how environmental variables shape the distribution of euphausiid species and (2) necessary for understanding how oceanographic change could impact euphausiid populations. The results of this study have implications for understanding the distribution patterns of top predators in the

area and can be used to predict future changes in euphausiid abundance and distribution in the

Kitimat Fjord System.

Drivers of Euphausiid Abundance and Biomass in the Kitimat Fjord System, BC Canada

Katelyn M. Qualls

A THESIS

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Master of Science

Presented April 2, 2019 Commencement June 2019

Master of Science thesis of Katelyn M. Qualls presented on April 2, 2019.

APPROVED:

______Major Professor, representing Ocean, Earth, and Atmospheric Sciences

______Dean of the College of Earth, Ocean, and Atmospheric Sciences

______Dean of the Graduate School

I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request.

______Katelyn M. Qualls, Author

ACKNOWLEDGEMENTS

I find myself indebted foremost to my advisor, Kim Bernard, for her guidance, helpful critiques, insight, and support. Her suggestions, edits, and assistance during the preparation of this thesis were invaluable and without her mentorship this project would not have been possible.

Her excitement for science rubbed off on me and she kept me motivated and pointed in the right direction during the many points in my degree at which I felt stressed and overwhelmed. I am also immensely appreciative of the knowledge of modeling analyses that my committee member

Lorenzo Ciannelli shared with me, and am grateful for the help with modelling in R that he provided. His expertise in modelling and guidance in designing and verifying statistical analyses greatly improved the statistical methods in this thesis. I am also indebted to my graduate committee member Jim Lerczak, who assisted with interpreting environmental data and provided valuable feedback and advice on the oceanographic component of my thesis. My research was supported by the National Science Foundation Graduate Research Fellowship under Grant No.

1314109-DGE. I am grateful for my mentors and friends who wrote reference letters and provided advice that helped me receive this grant. I am also thankful for my friends and family who were always rooting for me and filled my years at Oregon State with laughter, fun, and adventure.

This research also would not have been possible without the tirelessness, persistence, and adventurous spirit of Eric Keen, the captain of the RV Bangarang and PI who organized the collection of zooplankton and CTD data analyzed in this thesis. His extensive and impressive work collecting, organizing, and processing data, in addition to his help with analyzing data in R, made this thesis possible. Fieldwork was conducted under federal permit (DFO XR 83 2014) in

collaboration with the North Coast Cetacean Society and under a formal research agreement with the Gitga’at First Nation. Funding for fieldwork was provided by the Gitga’at First Nation

Guardian Watchmen, Canadian Department of Fisheries and Oceans, Cascadia Research

Collective, NSF Graduate Research Fellowship program (DGE-114086), Lewis and Clark Fund, and private donations from the Watson, Ayres, Cunningham, Barlow, Wright, and Keen families.

The CTD used in this research was provided by Gitga’at Guardian Watchmen and Tides Canada.

Fieldwork would not have been possible without the friendship and support of J. Wray and H.

Meuter, or without the dedication, skills and friendship of RV Bangarang crew: W. and K.

Watson, D. and L. Padgett, M. Irwin, N. Pierson, K. Beach, J. Carpenter, M. Keen, K.-L.

Thompson, W. Bostwick, J. Barlow, B. Taylor, A. Simonis, S. Watson, E. Ezell, S. Keen, N.

Bruns, and J. Garretson.

This thesis, in part, is a reprint of a manuscript that has been submitted to Progress in

Oceanography: Qualls, K.M., Bernard, K.S, Keen, E.M., Ciannelli, L., Picard, C.R. “Drivers of

Euphausia pacifica and Thysanoessa spinifera Abundances in the Kitimat Fjord System, BC

Canada”. The thesis author was the primary investigator and author of this paper.

TABLE OF CONTENTS

Section Page

Chapter 1: Introduction ...... 1

1.1 Euphausiids ...... 1

1.2 Drivers of Spatial and Temporal Variability in Euphausiid Abundance ...... 4

1.3 Common Euphausiid Species Found on the British Columbia Coast ...... 5

1.4 Study Area: The Kitimat Fjord System ...... 7

1.5 Objectives ...... 13

Chapter 2: Material and Methods ...... 16

2.1 Data Collection ...... 16

2.2 Calculations ...... 24

2.3 Statistical Modeling ...... 25

Chapter 3: Results ...... 35

3.1 Oceanographic Results...... 35

3.2 Euphausiid Abundance, Biomass, and Distribution ...... 41

3.3 E. pacifica and T. spinifera Abundance Model Results ...... 49

3.4 E. pacifica and T. spinifera Biomass Model Results ...... 58

Chapter 4: Discussion ...... 64

4.1 Euphausiid Species ...... 64

4.2 Drivers of E. pacifica and T. spinifera Abundance ...... 65

4.3 Drivers of E. pacifica and T. spinifera Biomass ...... 71

4.4 Implications for Whale Distributions ...... 73

4.5 Limitations ...... 73

TABLE OF CONTENTS (Continued)

Section Page

4.6 Recommendations for Future Work ...... 75

Chapter 5: Conclusions ...... 76

Bibliography ...... 77

Appendices ...... 90

Appendix A: Discussion of Temporal Differences between Paired CTD-Zooplankton Samples ...... 91

Appendix B: Example R Code for Models ...... 100

LIST OF FIGURES

Figure Page

1. Euphausiid life cycle...... 3

2. Map of the study area with channel names ...... 9

3. Map of the study area with regional context and stations ...... 10

4. Locations of CTD profiles taken in each survey ...... 24

5. Correlation matrix for all initially considered covariates ...... 30

6. Interpolated maps of oceanographic properties ...... 37

7. Depth profiles of temperature, salinity, and chlorophyll-a ...... 40

8. Log-transformed abundances of Euphausia pacifica life stage groups ...... 43

9. Log-transformed abundances of Thysanoessa spinifera life stage groups ...... 45

10. Abundances of Thysanoessa longipes life stage groups...... 47

11. Abundances of total Thysanoessa gregaria ...... 48

12. Log-transformed Euphausia pacifica biomass and Thysanoessa spinifera biomass .. 49

13. Effects of the covariates in the Abundance Combined Model ...... 55

14. Effects of the covariates in the Biomass Combined Model ...... 62

LIST OF TABLES

Table Page

1. Number of samples taken in each geographic channel ...... 17

2. Competing initial model structures for abundance and biomass models ...... 27

3. Comparison of means of oceanographic properties in each channel and survey ...... 38

4. Covariate significances in each different Abundance competing model structure ...... 51

5. Comparison of competing models that explain abundances ...... 55

6. Covariate significances in the Abundance Combined Model...... 54

7. Covariate significances in each different Biomass competing model structure ...... 60

8. Comparison of competing models that explain biomass ...... 62

9. Covariate significances in the Biomass Combined Model ...... 63

LIST OF APPENDIX FIGURES

Figure Page

A1. Map of the study area with channel names and stations ...... 93

A2. Location and time of the 44 paired CTD and zooplankton tows ...... 95

A3. Wind speed, precipitation, and Kitimat River discharge data ...... 97

A4. Depth profiles of temperature and salinity ...... 99

LIST OF APPENDIX TABLES

Table Page

A1. Samples with time gaps between CTD and zooplankton tows exceeding 1 day ...... 94

A2. Columns in the dataframe ...... 101

1 CHAPTER 1: INTRODUCTION

1.1 Euphausiids

Euphausiids, commonly referred to as “krill,” are small shrimp-like crustaceans (Phylum

Arthropoda; Subphylum Crustacea; Class Malacostraca, Order Euphausiacea) that can be found throughout the world’s oceans (Mauchline and Fisher, 1969; Brinton et al., 1999). Currently, 86 species of euphausiids have been identified (Brinton et al., 1999; Baker et al., 1990). These crustaceans hatch from free-floating eggs and, by molting, pass through a series of larval stages before maturing into adults (Figure 1). Some euphausiid species follow a direct pathway of development without variation, while others may follow a variety of pathways which may vary (1) between geographical regions, (2) in the same region in different years and/or (3) in the same region at different times of year (Mauchline and Fisher, 1969; Brinton and Townsend,

1984; Feinberg et al., 2006). Small larval euphausiids, especially those in the nauplius, metanauplius, calyptopis, and very early furcilia stages, are considered true members of the zooplankton and are primarily dispersed by water circulation (Mauchline and Fisher, 1969).

However, as they grow larger and develop more efficient swimming appendages, euphausiids become more able to move through their environment and maintain their position in particular areas (Mauchline and Fisher, 1969). Euphausiids are usually herbivorous or omnivorous and feed while swimming, using their modified front legs to form a food basket that strains food from the water around them (Todd et al., 1996; Mauchline and Fisher, 1969). Euphausiids are important in coastal and pelagic ecosystems as consumers of phytoplankton and as prey for a diverse assemblage of predators. Feeding on phytoplankton and small zooplankton, euphausiid species in the northwest Pacific commonly grow to sizes of 15-30 mm and provide a food source

2 for animals at higher tropic levels (Smiles, 1968; Tanasichuk, 1998a, 1998b). Euphausiids are especially accessible to predators because they are larger and live longer than micro- and mesoplankton, and often form aggregations (Tanasichuk, 1998b). Due to the importance of euphausiids in the diets of seabirds, fish, whales, and other predators, they are often considered key species (Mauchline and Fisher, 1969). Marine organisms in the northeastern Pacific that do not feed directly on euphausiids are rarely more than two trophic links removed from them at some point in their life history (Field et al., 2006). Due to this high trophic connectivity, variability in euphausiid reproductive timing, distributions, and abundances can have far- reaching impacts throughout the marine food chain, including consequences on the productivity, reproductive responses, and survival of many species at higher tropic levels.

Figure 1. Euphausia pacifica life cycle. Different euphausiid species may have differing numbers of stages and instars. Not to scale. After Suh, 1993, Boden, 1950, and Baker et al., 1990.

4 1.2 Drivers of Spatial and Temporal Variability in Euphausiid Abundance

Euphausiids, like most plankton, are not distributed randomly in space and time. Instead, they are often observed to be patchily distributed and exhibit spatiotemporal patterns in abundance and distribution (Quetin et al., 1996; Haury et al., 1978). Euphausiids are often found in large, concentrated patches and are known to occasionally form swarms with as many as

100,000 individuals per cubic meter of ocean water (Mauchline and Fisher, 1969). On the British

Columbia coast of Canada, patchy aggregations of euphausiids with 1 to 10 individuals per cubic meter of ocean water are more common (Tanasichuk, 1998a, 1998b). Euphausiids are often found in vertically compressed layers in the ocean, although their position in the water column can change daily as they migrate to the surface at night to feed on plankton and retreat to deeper waters during daylight hours (Bollens et al., 1992; Taki, 1998).

Spatiotemporal patterns in euphausiid abundance are the result of a complex suite of both abiotic and biotic factors that affect euphausiid dynamics over a wide range of spatial and temporal scales (Quetin et al., 1996; Haury et al., 1978). At small spatial (meters - 100s of meters) and temporal (within-day) scales, biological process including predator avoidance behavior, such as diel vertical migration, and social/reproductive behavior, such as swarming, affect local euphausiid density (Zhou et al., 2005; Smith and Adams, 1998; Endo, 1984). At regional spatial (100s of meters - 100s of kilometers) and seasonal (within-year) temporal scales, euphausiid growth, fecundity, and survival are directly affected by environmental factors such as water temperature, water salinity, and the quantity/quality of food sources (Yoon et al., 2000;

Iguchi et al., 1993; Iguchi and Ikeda, 1995; Taki, 1998, 2008; Taki et al., 1996; Taki and

Ogishima, 1997; Endo and Yamano, 2006; Sogawa et al., 2016; Ressler et al., 2014; Murase et

5 al., 2009; Regan 1968). Life history characteristics of euphausiid species such as growth rate, life span, and varying ontogenetic behaviors, in addition to the timing and duration of reproduction, affect seasonal euphausiid abundance and population structure (Quetin et al., 1996; Pinchuk and

Hopcroft, 2007). Physical processes occurring on scales of kilometers - 1000s of kilometers, such as currents, fronts, tides, buoyancy-driven plumes, and coastal upwelling in conjunction with euphausiids’ preferences for certain light depths (and thus certain positions in the water column) can cause the advection of individuals into or out of a system, or cause dispersion or accumulation of euphausiids (Taki et al., 1996; Cotte and Simard, 2005; Lindsey and Batchelder,

2011). Physical processes that cause mixing or upwelling often also affect local oceanographic conditions, including the availability of nutrients, and thus determine the quality and quantity of prey available for euphausiids (Garçon et al., 2001; Gonzalez-Gil et al., 2015; Tanasichuk,

1998a, 1998b). Euphausiid biomass, species composition, and species diversity at interannual scales are significantly correlated with basin-scale modes of climate variability such as the North

Atlantic Oscillation (NAO), El Niño Southern Oscillation (ENSO) and the Pacific Decadal

Oscillation (PDO) (Marinovic et al., 2002; Brinton, 1981; Mackas et al., 2001; Tanasichuk,

1998a, 1998b).

1.3 Common Euphausiid Species Found on the British Columbia Coast

Euphausia pacifica Hansen and Thysanoessa spinifera Holmes are the two most abundant euphausiid species in the coastal waters of British Columbia (Fulton and LeBrasseur,

1984; Simard and Mackas, 1989; Mackas 1992, 1995; De Robertis, 2002; Regan, 1968;

Tanasichuk, 2002). There is spatial (latitudinal) and temporal (inter-annual) variability in the relative abundances of these two species (Brinton 1962; Feinberg and Peterson, 2003). E.

6 pacifica is broadly distributed across the North Pacific and is the most abundant species found from the California Current west across the Pacific to Japanese waters (Mauchline and Fisher,

1969; Brinton, 1976). T. spinifera occurs in upwelling zones along the western coast of North

America from the southeastern Bering Sea to as far as mid-Baja California in cool years

(Brinton, 1962). E. pacifica is a strong vertical migrator and has been found at depths of 1000 m, although this species is usually distributed above 300 m (Brinton, 1962). E. pacifica is found above 125 m in fjords (Bollens et al. 1992). T. spinifera is restricted to depths of less than 100 m throughout its range and undertakes a limited diel migration (Brinton, 1962). E. pacifica is considered the more ‘oceanic’ species and is most abundant in outer shelf environments, whereas

T. spinifera occurs most abundantly in coastal waters but can be found offshore as well (Brinton

1976; Tanasichuk, 1998a, 1998b; Brinton et al., 1999, Coyle and Pinchuk, 2005; Brinton and

Townsend, 2003; Feinberg and Peterson, 2003; Lu et al., 2003; Dorman et al., 2005). These two abundant species are particularly important along British Columbia’s productive coast as prey for seabirds (Ainley et al., 1996; Abraham and Sydeman, 2004), baleen whales (Croll et al., 2005;

Nemoto, 1957; Fiedler et al., 1998; Ponomareva, 1963), and commercially important fishes including salmon (LeBrasseur, 1966; Armstrong et al., 2005), rockfish (Genin et al., 1988; Reilly et al., 1992), cod and pollock (Yamamura et al., 1998), herring (Brodeur, 1998; Robinson, 2000), and hake (Alton and Nelson, 1970; Tanasichuk et al. 1991; Ware and McFarlane, 1995).

Other euphausiid species including Thysanoessa inspinata, Thysanoessa longipes,

Thysanoessa gregaria, Thysanoessa raschii, Thysanoessa inermis, Nematoscelis difficilis,

Nematobrachion flexipes, and Tessarabrachion oculatum may also form significant components of the euphausiid community in the northeastern Pacific (Coyle and Pinchuk, 2005; Gómez-

7 Gutiérrez et al. 2005; De Robertis, 2002; Simonsen et al., 2016; Regan, 1968; Tommasi et al.,

2013; Mackas et al., 2001). T. inspinata and T. longipes were once considered a single species, but it is now understood that their ranges are divided to the south and north, respectively, along the highly productive subarctic frontal zone near 50 °N, a major oceanographic boundary in the mid-latitude Pacific (Nemoto, 1963). While T. inspinata and T. longipes occur along the North

Pacific coast from California to Japan, they are virtually absent from the open ocean

(Ponomareva, 1963; Afanasiev, 1982; Zhuravlev, 1977). T. gregaria is found in subtropical oceans around the world (Brinton, 1962). T. raschii and T. inermis are cold-adapted species that are found in waters in the Arctic Circle (Brinton, 1962). T. raschii is a shallow-water, neritic species that occupies coastal habitats but T. inermis is not restricted to coastal habitats (Brinton,

1962). N. difficilis is a subtropical species that inhabits mid-latitudes in the North Pacific, mainly in the Pacific Drift and California Current (Brinton 1962). N. flexipes is mainly found south of 40

°N throughout most of the Pacific, but is rarely caught in large numbers (Brinton, 1962). T. oculatum is confined to the subarctic North Pacific (Brinton, 1962).

1.4 Study Area: The Kitimat Fjord System, British Columbia

Few studies have examined the euphausiid communities in British Columbia’s coastal fjords. One reason for this may be that these fjords, which are estuaries that form when glacially carved valleys are flooded by rising seas, are relatively difficult to study compared to adjacent continental shelf environments. The narrow channels and shallow sills in fjords can pose hazards to large research vessels and limit maneuverability. However, fjords are interesting coastal environments in which zooplankton are influenced by the open ocean, the surrounding coastal terrain, and freshwater runoff (Gorsky et al., 2000). Fjords can be especially productive coastal

8 areas because they receive nutrients from both coastal upwelling and runoff from land. Their steep-sided channels often contain basins that are deeper than the adjacent continental shelf

(Farmer and Freeland, 1983). Shallow sills, which are glacial moraines, divide fjords into basins and act as barriers to the free interchange of deep oceanic water and deep fjord basin water

(Matthews and Sands, 1973). Since fjords are semi-enclosed systems that are restricted from interactions with open waters by shallow sills and narrow entrances, they are relatively isolated and their water properties are often distinct from open shelf waters (Burrell, 1986; Crawford et al., 2007; Keen, 2017b).

1.4.1 Location and Physical Characteristics

The study area is located within the Kitimat Fjord System of northern mainland British

Columbia, Canada and is centered at 53 °N and 129 °W (Figures 2 and 3). The study area comprises a broad inshore-offshore transect of the Kitimat Fjord System that is approximately 68 km long and extends southwest-northeast from Caamano Sound to the northern end of Gribbell

Island (Figure 2). This area consists of a complex network of channels and is also bathymetrically complex (Figure 3). Fjords within the study area have steep bedrock walls and relatively smooth sediment-floored basis that are separated by sills of rugged relief. In the southwestern part of the study area, a 200 m deep sill at Otter Channel and a broader 170 m sill at the entrance to Caamano Sound separate the fjord basin from Hecate Strait (MacDonald et al.,

1983). The shallowest sills in the study area occur in the northeastern part of the study area, where 31 m and 35 m sills cross Verney Passage and Ursula Channel, respectively (MacDonald et al., 1983). In addition, a 130 m sill crosses the entrance to Fraser Reach/Princess Royal

Channel and a 200 m sill crosses Whale Channel at the southern end of Gil Island (MacDonald et

9 al., 1983). With seafloor depths exceeding 690 m in some areas, the Kitimat Fjord System is deeper than the adjacent continental shelf and among the deepest inlets on the British Columbia coast (Figure 3) (MacDonald et al., 1983).

Figure 2. Map of the study area with channel names. After Keen 2017b.

Figure 3. Map of the study area (right) with regional context (left). Black dots in the study area represent stations at which pairs of zooplankton tows and CTD cases were conducted. Color represents seafloor depth. After Keen 2017a.

1.4.2 Oceanography and Circulation

Like many of British Columbia’s mainland fjords, the Kitimat Fjord System is characterized by high inputs of fluvially-transported detrital sediment, high discharge, fjord- estuarine circulations that are well-developed in the summer months, strong winds and tides that control circulation in the outer fjords, and basin waters that seldom go anoxic (Shaw et al.,

11 2017). The Kitimat Fjord System is located within the Kitimat Coastal Ranges of the Great Bear

Rainforest, the largest temperate coastal rainforest in the world (Thompson, 1981). As such, the

Kitimat Fjord System is subject to high precipitation and high rates of snowpack melt, which drive estuarine circulation in the fjords (Thompson, 1981; Freeland and Farmer, 1980;

MacDonald et al., 1983). Discharge from Gardner Canal, which empties into Gil Basin at the north end of Gribbell Island, is second highest among British Columbia's fjords (Pickard, 1961).

High amounts of seasonal freshwater input lead to seasonally strong seaward surface flow that is countered by a landward bottom current and a strong surface salinity gradient inshore-offshore

(Freeland and Farmer, 1980, MacDonald et al., 1983). This perennial estuarine circulation is modulated by punctuated events of wind-driven circulation (e.g., from katabatic outflows) and strong tidal currents, all of which exhibit strong seasonal signals (MacDonald et al., 1983; Keen,

2017b).

Since Gil Basin opens directly to the Pacific, it is also influenced by oceanographic processes that occur on the continental shelf. The central British Columbia coast is at the northern end of the Northeast Pacific coastal upwelling domain and near the upstream end of the equatorward California Current (Mackas et al., 2001). However, it is also a transition region sharing some biological and physical characteristics with the downwelling-favorable, cold, poleward-flowing Alaska Current to the north (Mackas et al., 2001). The degree to which local conditions resemble one or the other of these two domains fluctuates both seasonally and from year to year (Mackas et al., 2001). Much of the deep current flow on the southern half of the

British Columbia coast comes from the California Undercurrent (Mackas et al., 2001). During El

12 Niño events and warm periods, the California Current can transport zooplankton species northward onto the British Columbia shelf (Mackas, 1992; Mackas et al., 2001).

Atmospheric and oceanic patterns are strongly seasonal, driven by annual cycles in solar heating and in position and intensity of the Aleutian Low and North Pacific High atmospheric pressure centers (Mackas et al., 2001). In winter, the Aleutian Low is strong and drives prevailing southerly coastal winds and poleward, downwelling-favorable surface currents

(Mackas et al., 2001). In summer, the North Pacific High moves northward, accompanied by frequent upwelling-favorable northerly winds, fewer clouds, and equatorward surface currents along the shelf break (Mackas et al., 2001). Over the inner continental shelf, coastal inputs of low-salinity water drive a year-round poleward coastal current (Mackas et al., 2001). Subsurface water properties including temperature, salinity, oxygen, and nutrient concentrations have strong annual cycles caused by summer upwelling of deep water onto the shelf and winter downwelling of surface water (Mackas et al., 2001). Productivity and upper ocean biomass of both phytoplankton and zooplankton cycle annually between a spring–summer maximum and a winter minimum (Mackas et al., 2001). There are strong between-year modulations of the amplitudes and phasing of seasonal cycles, especially the timing of the spring and fall transitions between winter and summer current patterns (Mackas et al., 2001). In any single year, these circulation changes are abrupt and synchronous to within a week over a broad latitude range, but the date of onset varies between years by several weeks (Strub et al. 1987; Thomson and Ware 1996).

Warming and stratification of the surface layer, timing of the spring phytoplankton bloom, and timing of peak zooplankton recruitment also show year-to-year timing shifts (Mackas et al.,

2001).

13 1.4.3 Importance of Studying the Kitimat Fjord System

The Kitimat Fjord System is a productive commercial fisheries area (Fisheries

Management Area 6), supports multi-million-dollar recreational fishing and ecotourism industries, and contains one of only four areas on the coast of British Columbia that are designated as critical habitat for humpback whales (Fisheries and Oceans Canada, 2013). It is also proposed as critical habitat for fin whales (Nichol and Ford, 2011). In addition, in 2006

Fisheries and Oceans Canada identified the entire study area as an Important Area (IA) for fin whales, resident killer whales, and humpback whales (Clarke and Jamieson, 2006a). The more oceanic half of the study area was also identified as an IA for Stellar sea lions, Whale Channel was identified as an IA for walleye pollock, and Caamano Sound was identified as an IA for herring (Clarke and Jamieson, 2006a). Caamano Sound is now identified as an Ecologically and

Biologically Significant Area (EBSA) by Fisheries and Oceans Canada for (1) its particularly high productivity that occurs as a result of tidal mixing and (2) its importance as killer whale habitat (Clarke and Jamieson, 2006b). Since euphausiids are integral members of the food web in this area, it is important to understand their local distribution patterns. In addition, this remote and productive area is slated for the development of liquid natural gas (LNG) export facilities which would dramatically increase shipping traffic through the Kitimat Fjords as early as 2025

(LNG Canada, 2014; LNG in Northern B.C., 2018). Therefore, it is also important to establish an ecological baseline for euphausiid abundance that could be useful in future monitoring efforts.

1.5 Objectives

The primary objectives of this study are (1) to describe patterns in euphausiid abundance and biomass throughout Gil Basin in the Kitimat Fjord System and (2) to investigate the

14 potential influence of a suite of oceanographic, spatial, and temporal variables on these patterns using a generalized additive modelling (GAM) approach. The use of GAMs to examine associations between euphausiid abundances and environmental predictors has been employed previously in other systems (e.g. Trathan et al., 2003; Ressler et al., 2014; Lawson et al., 2008;

Simonsen et al., 2016; Letessier et al., 2009) with interesting ecological conclusions. In this study, competing null, temporal, spatial, oceanographic, and combined models are explored in order to understand the relative importance of these external drivers and examine the effects of temporal, spatial, and oceanographic variables on euphausiid abundance and biomass in a coastal fjord system.

Understanding what drives spatiotemporal patterns in euphausiid abundance is both a fundamental ecological question and a requirement for understanding and predicting deviations in existing patterns that are likely to occur as ocean environments continue to change. Several studies have examined mesoscale to basin-scale drivers of euphausiid abundance and species richness (e.g. Lindley, 1977 in the north Atlantic and the North Sea; Gibbons, 1997 in the south

Atlantic; Letessier et al., 2009 in the Atlantic; Tarling et al., 1995 in the southwest Atlantic;

Lawson et al., 2008 in the Southern Ocean, and Simonsen et al., 2016 in the Gulf of Alaska and eastern Bering Sea), and a few studies have focused on distributions and drivers of Euphausia pacifica and Thysanoessa spinifera abundances in particular (e.g. Gomez-Gutierrez et al., 2005 off the Oregon coast; Kim et al., 2009 in the western subarctic Pacific; Mutase et al., 2009 off

Japan; Mackas et al., 2001 off Vancouver Island; and Marinovic et al., 2002 off central

California). However, no previous studies have examined the drivers of euphausiid abundances along the remote central coast of British Columbia, nor have any previous studies compared

15 the relative importance of spatial, oceanographic, and temporal drivers of euphausiid abundances. In addition, few studies have focused on understanding variations in euphausiid abundances on small spatial and temporal scales.

16 CHAPTER 2: MATERIALS AND METHODS

2.1 Data Collection

Fieldwork was conducted between May and September 2015 over four repeated surveys of the study area. Surveys occurred May 24th - June 10th (“June”), June 22nd - July 4th (“July”),

August 5th - August 26th (“August”) and August 31st - September 20th (“September”). During each survey, between 22 and 24 paired conductivity-temperature-depth (CTD) profiles and zooplankton net tows were conducted at stations in eight channels in the study area (Figure 3,

Table 1). Station placement was informed to a degree by logistics (e.g., position relative to viable anchorages, protection from prevailing winds, etc.), which is an inevitable result of working from a small vessel in a remote area. Nevertheless, stations were positioned randomly with respect to oceanography and occurred at a variety of depths and proximities to shore, allowing for representative sampling of euphausiid abundance with respect to many different oceanographic and geographic factors.

17 Table 1. Number of samples taken in each geographic channel. Channel abbreviations: CAA = Caamano Sound; CMP = Campania Sound; EST = Estevan Sound; MCK = McKay Reach and south Ursula Channel; SQU = Squally Channel; VER = Verney Passage and north Ursula Channel; WHA = Whale Channel; WRI = Wright Sound. The three zooplankton samples from Verney Passage taken during the 4th survey were removed from modelling analyses due to missing GPS information for these tows.

Zooplankton tows Station All zooplankton Zooplankton tows All CTD profiles after pseudo- tows used for modeling Survey Channel replicates averaged June WRI 3 3 2 2 WHA 3 3 2 2 VER 3 3 2 2 SQU 3 3 2 2 MCK 3 3 1 1 EST 3 3 2 2 CMP 3 3 2 2 CAA 1 1 1 1 Total 22 22 14 14

July WRI 3 3 1 1 WHA 3 3 2 2 VER 3 3 1 1 SQU 3 3 2 2 MCK 3 3 1 1 EST 3 3 2 2 CMP 3 3 2 2 CAA 3 3 2 2 Total 24 24 13 13

August WRI 3 3 1 1 WHA 3 3 1 1 VER 3 3 1 0 SQU 3 3 1 1 MCK 3 3 1 1 EST 3 3 2 2 CMP 3 3 1 1 CAA 3 3 1 1 Total 24 24 9 8

September WRI 3 3 1 1 WHA 3 3 1 1 VER 3 3 1 1 SQU 3 3 2 2 MCK 3 3 1 1 EST 3 3 1 1 CMP 3 3 1 1 CAA 3 3 1 1 Total 24 24 9 9

GRAND 94 94 45 44

18 2.1.1 Zooplankton Sampling

A target number of three plummet-style zooplankton tows were conducted at stations within each of eight geographic channels in the study area during each survey. However, two or more zooplankton tows were sometimes repeated consecutively at the same station instead of being spread between the stations in that channel. As a result, all 19 stations were not sampled in each survey. This caused sampling effort to be divided unequally among stations, although it was divided approximately equally among the channels in the study area. Since replicates taken at the same station consecutively were not independent, they were averaged before models were fit, yielding a total of 44 independent zooplankton samples across the four surveys. While averaging yielded a smaller number of samples, it served to minimize unresolved small-scale zooplankton patchiness.

Zooplankton samples were collected during daylight hours using a custom manufactured cylindrical-conical plummet net with 333 µm mesh and a 0.7 m diameter ring. Sampling did not occur at night due to hazards associated with navigating the research vessel, a 12 m motorsailer, in remote confined channels in the dark. Plummet-style nets are known to effectively capture euphausiids, as euphausiid escape responses have a significant upward component (Bartle, 1976;

Daly, 1990; Daly and Macaulay, 1988; O’Brien, 1987). The zooplankton net used in this study was designed by Eric Keen and was based on the net described in Heron (1982) (Keen 2015).

This design eliminates the three‐point bridle and the long wire that is present in front of many other vertically hauled net designs, which are components that can cause avoidance reactions in active zooplankton such as euphausiids (Heron, 1982). The zooplankton net used in this study was appropriate given that the research vessel did not have large winches for net operation and

19 had limited space onboard. The net ring was weighted to 30lbs to ensure a relatively constant and rapid drop speed. The zooplankton net was released when the vessel was stationary and sampled in free fall as it descended to depths between 70 and 250 m, dependent on bottom depth.

Maximum target net depth was 250 m where bottom depth exceeded 250 m and within 25 m of the seafloor elsewhere. Actual net drop distances were calculated by correcting line payout according to vessel drift distances measured using GPS, as no depth logger was available. In all instances where bottom depth was shallower than 250 m, an average of 87% of the water column was sampled with all tows sampling more than 70% of the water column. In instances where bottom depths were greater than 250m, an average of 58% of the water column was sampled and the net fell at least 180 vertical meters during each tow. Since E. pacifica is usually restricted to the top 125 m in British Columbia fjord environments (Boden and Kampa, 1965), T. spinifera is distributed above 100 m (Brinton, 1962), and the larval stages for all euphausiids are usually confined to the upper 100 m of the water column (Boden 1950), the vertical tows conducted in this study should have given a representative sample for each location. Because the net was allowed to freefall vertically and begin sampling immediately once it hit the water, the problem of drift in strong currents was minimized. While there were many advantages to the net design, this sampling method yielded zooplankton samples that were vertically integrated, relatively small (mean sample volume 81 m3; median sample volume 88 m3; sample volume range 27-96 m3), and possibly vulnerable to the lateral patchiness of euphausiids.

Since euphausiids have been known to exhibit net avoidance behavior (Hovekamp, 1989;

Heron, 1982), net fall rates were measured in order to compare the fall velocity of the zooplankton net to euphausiid abundance and ensure that net fall rate did not bias sampled

20 euphausiid abundances. In order to evaluate net fall rates, time and GPS location were recorded when 25 m marks on the tow line were submerged during pay out. The zooplankton net fall rate was 0.94 m/s on average (Keen 2017b), which is greater than the recommended speed for vertical tows outlined by UNESCO Working Party No. 2 (0.75 m/s, Tranter and Smith, 1968) and greater than the 0.7 m/s fall rate used for capturing euphausiids in plummet tows in

Hovekamp (1989). However, the fall rate of the net was slightly slower than the 1 m/s fall rate achieved by Daly and Macaulay (1988) and Daly (1990) and below the 1.5m/s fall rate recommended in Heron (1982).

The net used in this study was equipped with a General Oceanics flowmeter mounted off- center at the entrance of the net (after Tranter and Smith, 1968) in order to determine the volume of water sampled during each tow. When mounted on the net, the forward orientation of the flowmeter was maintained with wound elastic. When the net was cinched shut the flowmeter was turned perpendicular and held against the net by tension in the wound elastic to halt flowmeter spin. Unfortunately, when flowmeter-derived sample volumes were checked against calculated sample volumes based on calculated drop distances and net mouth area, it was clear that the flowmeter readings where not accurate. Since all flowmeter-derived sample volumes were much lower than expected (12 flowmeter readings indicated drop distances of less than 9 m while 250 m of line was paid out and no anomalous line angles were observed), it is likely that flowmeter spin was impeded during the tow or the flowmeter malfunctioned due to internal resistance to spin. As a result, tow volumes derived from line payout, fall rate, drop duration, and net mouth area were considered to be more accurate than tow volumes derived from flowmeter readings.

21 Euphausiid counts were divided by tow volume to yield euphausiid density (individuals m-3), then multiplied by tow depth to integrate over the water column sampled and yield euphausiid abundance (individuals m-2). This study is focused on euphausiid abundance rather than euphausiid density because krill-like backscatter (200 kHz) was observed mainly above 150 m during the daytime in this area (Keen, 2017b) and all zooplankton tows passed through this layer or, if tows were shallower than 150 m, sampled at least 70% of the water column. Hence, each of the vertical tows should have provided a representative euphausiid sample for their location although tows achieved widely differing maximum depths. Thus, examining euphausiid abundance was preferred.

Immediately after zooplankton net retrieval at stations, zooplankton samples were preserved in a buffered 5% formaldehyde-seawater solution. After the field season, euphausiids in each sample were identified to species and staged with the aid of dissecting and compound microscopes. Adults were identified using the Baker at al. (1990) key to euphausiids while furcilia larvae were identified and staged according to the descriptions and sketches presented by

Boden (1950) for Euphausia pacifica, Summers (1993) for Thysanoessa spinifera, Ponomareva

(1959) and Endo and Komaki (1979) for Thysanoessa longipes, Gurney (1947), Sheard (1953), and Casanova (1974) for Thysanoessa gregaria, and Endo (1980) for Tessarabrachion oculatum.

In all samples, all euphausiids > 7 mm in total length were counted and identified. All individual euphausiid furcilia and post-larvae were identified and staged in all samples that contained less than 700 individual euphausiids. For the three largest samples, which had euphausiid abundances of over 1000 individuals per sample, a subsample of ¼ of euphausiids < 7 mm in total length was counted and identified. For the following four largest samples, which had abundances greater

22 than 700 individuals per sample, ½ of individuals < 7 mm were counted and identified. Calytopis larvae and nauplii were observed in some samples, but these smallest life stages are not likely important as prey for whales or seabirds and thus were not counted or identified to species in this study.

All post-larvae and the first 20+ randomly-selected individuals of each furcilia stage were photographed using a Leica MC120 HD 2.5-megapixel digital microscope camera attached to a

Leica M125 stereomicroscope. Leica Application Suite (LAS) V4.4 software was calibrated by a professional prior to photograph collection and scale bars were added to images automatically by the software. Lengths of individual euphausiids were measured digitally to the nearest micrometer using ImageJ software, with total length (TL) defined as the dorsal distance between the anterior tip of the rostrum and the posterior end of the uropods, excluding the terminal setae

(Mauchline 1980). In cases where all individuals of a life stage were not measured in a sample

(e.g. if 20 E. pacifica F1 were photographed and measured of 50 in a sample), the measured lengths were replicated and/or length values were chosen from measured lengths at random without replacement using a random number generator until a length value existed for each individual euphausiid captured.

2.1.2 Oceanographic Sampling

CTD casts were conducted at the same stations as zooplankton tows in each survey in order to gather oceanographic data to link with euphausiid abundance data, and an additional 50

CTD-only casts were conducted at stations in order to produce more accurate maps of oceanographic properties (Figure 4). CTD data were reduced to stations that were repeated in every survey (n=88 CTD tows) for average calculations of oceanographic variables to avoid

23 biasing means. However, all available CTD data were used to create interpolated maps of oceanographic variables. The CTD unit was composed of a Seabird Electronics 25plus CTD profiler (SBE-1070, temperature sensors SBE-3 and conductivity sensor SBE-4) with attached

WetLabs ECO-FL fluorometry sensor (ECOFL-3486), was pump controlled, and sampled at a 16

Hz rate. The CTD and fluorometer were cast to depths between 67 m and 237 m, depending on bottom depth. Maximum target CTD depths were 250 m where bottom depth exceeded 250 m and within 25 m of the seafloor elsewhere. To achieve a higher resolution sample of surface waters, where gradients in oceanographic parameters were greatest, the unit was lowered hand- over-hand at roughly 0.3 m s-1 for the first 25 m then allowed to free fall at around 1.5 m s-1 for the remainder of the cast. Most CTD casts occurred either immediately prior to or immediately after zooplankton tows. However, some CTD casts occurred 1-18 days after the zooplankton tow at stations. Nonetheless, pairs of matched CTD casts and zooplankton tows at stations in each survey occurred within the duration of each of the four survey periods. Variability in the physical environment at the few stations where there were large temporal gaps between CTD casts and zooplankton tows was examined, and it was determined that each of these periods coincided with quiescent times in terms of physical forcing (Appendix A). It was therefore assumed that the physical environment as described by the CTD data had not changed significantly since the zooplankton tow had been conducted in these instances. Thus, all pairs of matched CTD casts and zooplankton tows were considered in the modeling analysis.

Figure 4: Locations of CTD profiles taken in each survey.

2.2 Calculations

2.2.1 Euphausiid Biomass

Body allometry equations published by Pinchuk and Hopcroft (2007) were applied to E. pacifica and T. spinifera length data in order to calculate the dry weight of each euphausiid in mg. These biomass data were summed to obtain total E. pacifica and total T. spinifera dry weight

(biomass) for each zooplankton sample. Values were then standardized by volume filtered and multiplied by tow depth to yield biomass in mg m-2.

2.2.2 Spatial, Temporal, and Oceanic Covariates

Spatial, temporal, and oceanographic variables were calculated or extracted from collected data so that they could be included in models. The following oceanographic variables were calculated or directly extracted from CTD data: (1) sea surface salinity (SSS), (2) salinity at

50 m (Salinity_50m), (3) sea surface temperature in °C (SST), (4) temperature at 50 m in °C

(Temp_50m), (5) thermocline depth in meters (Therm_depth) and (6) thermocline strength

(Therm_str) calculated using the variable representative isotherm method described in Fiedler

25 (2010), (7) water column stratification (Stratification), the difference between density at surface and 50 m, (8) maximum chlorophyll-a (Chl_max), calculated from fluorometer readings, and (9) integrated chlorophyll-a (Chl), calculated from fluorometer readings and integrated using trapezoidal integration from the surface to 70 m depth, the depth of the shallowest CTD cast.

Both salinity and temperature at the surface and at depth were considered due to the fact that E. pacifica is known to undertake extreme diel vertical migration (De Robertis, 2002) and is thus subjected to both surface temperatures and temperature at depth. T. spinifera also may undertake a limited diel vertical migration (Brinton, 1962). Temporal variables included (10) day of the year (DOY) and (11) time of day (Time, hh:mm:ss), which were recorded when zooplankton tows began. Spatial variables consisted of (12) distance into the fjord system (Distance), the great circle distance calculated between a fixed point offshore at (-129.66, 52.73) and the GPS point taken at the beginning of each zooplankton tow, (13) proximity to the nearest sill

(Sill_prox), the marine distance between the closest sill and the GPS point taken at the beginning of each zooplankton tow, and (14) average seafloor depth during the duration of each zooplankton tow (Seafloor), extracted from Canadian Hydrographic Service's 3-arc second bathymetry data set. To ensure that measured euphausiid abundances were not biased by zooplankton tow conditions, the average drop speed of the net during each zooplankton tow was additionally calculated using times that were recorded when 25 m marks on the tow line were submerged during pay out.

2.3 Statistical Modeling

Statistical analyses and generalized additive model (GAM) fitting were carried out using the ‘mgcv’ package (version 1.8-25; Wood 2018) in R (version 3.5.1; R Development Core

26 Team 2018). GAMs are non-parametric regressions which assume that the effects of the predictors are additive. These models are flexible regarding the statistical distribution of the data and proceed by fitting smoothing functions (penalized regression splines; Wood and Augustin,

2002) to the relationship between the response and each predictor variable, and thereby allow for non-linear relationships (Swartzman et al., 1995; Hastie and Tibshirani, 1990). Before performing modelling analyses, it is important to ensure that assumptions of the statistical technique are met (Zurr, 2010). Therefore, prior to conducting GAM analyses, (1) outliers in the data were examined, (2) heterogeneity of variance was ensured, (3) collinearity was resolved, and (4) it was confirmed that there were not too many zeros in the data. Since including all explanatory variables in a single model for each abundance and biomass group would have resulted in a model with too many degrees of freedom, variables were organized into three competing model structures: a Temporal Model, a Spatial Model, and an Oceanographic Model

(Table 2). A Null Model, in which abundance or biomass was predicted only by mean abundance

(in Abundance Models) or mean biomass (in Biomass Models), was also considered (Table 2).

27 Table 2. Competing initial model structures for abundance and biomass models. In Abundance -2 Models, E represents log-transformed euphausiid abundance (log10(individuals m ) +1) and in -2 Biomass Models, E represents log-transformed euphausiid biomass (log10(biomass in mg m ) +1). Intercept terms are represented by αi, si are nonparametric smoothing functions, and εi indicates the independently and identically distributed normal residuals. All terms were allowed 3 degrees of freedom with the exception of DOY, which was allowed 5. Terms in bold were retained in the final competing models. Combined Model formulations differ between Abundance vs Biomass models, and these Combined Model formulations are explained in the text.

Model Formulation Null E ~ α1 + 1 Temporal E ~ α3 + s1(DOY) + s2(Time) + ε1 Spatial E ~ α4 + s3(Seafloor) + s4(Distance) + s5(Sill_prox) + ε2 Oceanographic E ~ α5 + s6(SSS) + s7(Salinity_50m) + s8(SST) + s9(Temp_50m) + s10(Stratification) + s11(Therm_depth) + s12(Therm_str) + s13(Chl_max) + s14(Chl) + ε3

2.3.1 Data Exploration

Although six euphausiid species were present in the study area, only E. pacifica and T. spinifera were common, and thus models were only fit for these two species. Due to low abundances of some life stages of E. pacifica and T. spinifera and the presence of many zeros in the data, abundance data were pooled into three groups per species: E. pacifica post-larvae (E. pacifica adults and juveniles), E. pacifica late larvae (E. pacifica larger furcilia, stages VII-IV),

E. pacifica early larvae (E. pacifica smaller furcilia, stages I-III), T. spinifera post-larvae (T. spinifera adults and juveniles), T. spinifera late larvae (T. spinifera larger furcilia, stages III-V), and T. spinifera early larvae (T. spinifera smaller furcilia, stages I-II). Furcilia of each species were pooled by stage such that the two resulting groups of furcilia (early and late) were approximately equal in total abundance. Since E. pacifica and T. spinifera were represented by at least one life stage per sample, biomass data contained no zeros and did not need attention.

28 To improve the normality of the response variables (abundance and biomass) and thus avoid any problems that would arise during modeling where normality was assumed, a log- transformation was applied to both the abundance and biomass data. Pooled abundance data were log-transformed according to the equation log10(abundance + 1). The logarithmic transformation makes the most sense ecologically as it turns ecologically plausible multiplicative models into mathematically attractive additive ones and also makes the outliers less extreme (ter Braak and

Šmilauer, 2015). It was necessary to add a constant before taking the logarithm of pooled abundance values because some zeros were still present in the data even after summing abundances into post-larval, late furcilia, and early furcilia groups. A natural choice for this constant is the minimum non-zero value in the data (ter Braak and Šmilauer, 2015), which for our data was near 1. Biomass data were also transformed according to the equation log10(biomass + 1) in order to ensure that resulting log-transformed values were positive.

The collinearity of all variables was investigated using a correlation matrix (Figure 5) and the collinearity of variables in each competing model structure was examined using generalized variance inflation factors (GVIF). Appropriate variables were removed from model structures in order to minimize collinearity among the explanatory variables. In the Oceanographic Model, three sets of variables were strongly collinear: SSS/Stratification, Temp_50m/Chl_max, and

Chl_max/Chl. Of these pairs, only SSS, Temp_50m, and Chl were retained for further analysis.

GVIFs were examined for each model structure and if any extremely collinear variables were still present, the variable with highest collinearity was removed until the GVIFs were less than 2 for each model. In this fashion, Therm_depth and Therm_str were additionally removed from both the Abundance and Biomass Oceanographic Model. Because salinity at 50 m displayed

29 extremely low variability (<1 PSU), it was deemed unlikely to affect euphausiid abundance or biomass and was subsequently removed from the remaining considered variables. Both SST and

Temp_50m were retained in the model, as these two temperature variables were not highly collinear with one another nor any other retained variable. All variables were continuous (not categorical). Due to the relatively low sample size, all variables in all model structures were additive (i.e. no interaction terms were included). Therefore, each of these models assumed that the direction of relationships between abundance or biomass and covariates did not change over time or space. The resulting final competing model structures included only the bold terms in

Table 2.

Figure 5. Correlation matrix for all initially considered covariates. Both bubble size and bubble color correspond to degree of collinearity. Largest and darkest bubbles represent most highly collinear relationships.

To determine the maximum degrees of freedom that should be allowed in models for each covariate, univariate GAMs were inspected visually with a maximum of five, four, and three degrees of freedom (k) between each continuous candidate explanatory variable and the response variable. Most relationships between the dependent variable and each predictor were best fit with spline smoother functions constrained to three degrees of freedom in order to allow for potential non-linearities but restrict large and unrealistic fluctuations in the shape of the resulting response function over small changes in predictor variables. However, relationships

31 between day of the year (DOY) and the dependent variables were allowed five degrees of freedom as abundances and biomass of some groups displayed more complex nonlinear relationships with this explanatory variable.

2.3.2 Generalized Additive Modeling

After (1) ensuring that model assumptions were met and (2) determining the maximum degrees of freedom that were appropriate for each covariate, modelling analyses were conducted.

GAMs with a Gaussian error distribution and identical link function were applied to the log- transformed (normalized) euphausiid abundance and biomass data. While many combinations of

GAM distributions and data transformations were initially examined, a logarithmic transformation with the addition of a constant and a Gaussian model family with identical link function were selected as they provided satisfactory residual plots, intuitive scaling of the partial plot axes, and lowest Akaike information criterion (AIC) scores. AIC is a statistic used to measure goodness of fit for a model (Akaike, 1974). Minimum AIC is often used as a model selection criterion, with the best model being the one that has minimum AIC among all other models (Burnham and Anderson, 2002). The smoothing parameter selection criterion in models was generalized cross validation (GCV).

All covariates in each of the competing models highlighted bold in Table 2 were considered initially for each abundance and biomass group. Effective covariates were selected by removing covariates, one at a time, which caused the greatest decrease in AIC scores until the model with lowest AIC was reached. Each time a variable was dropped from the model, the

‘mgcv’ fitting algorithm determined whether a linear or smoothing function best described the relationship between the response variable and each remaining predictor. If the confidence

32 interval about the smoothed function did not exclude the possibility of a linear relationship, then a linear function was adopted. Since GAMs are very flexible and are capable of fitting many types of relationships, over-fitting can occur. To test the robustness of the model predictions, leave-one-out cross-validation was employed on each of the four model structures implemented for each abundance and biomass group. Example R code for these analyses is included in

Appendix B. Since the sample size was relatively small, one sample was iteratively left out of the models, remaining samples were fit, and summary model statistics were calculated in order to compare the relative performance of competing null, temporal, spatial, and oceanographic models. For each iteration, mean squared prediction error (MSPE) was computed as a measure of the model's ability to predict the remaining abundances, AIC was computed as a measure of goodness of fit, and fraction of deviance explained by the models was computed as a measure of the proportion of variation that the model accounted for. Percentages of times that each covariate was significant at three p-value thresholds, 0.01, 0.05, and 0.10, in cross-validated competing models were calculated for each explanatory variable in every abundance and biomass group.

For the purpose of this work, I have considered modeled relationships as significant only if they produced significant results at the p < 0.05 level in more than 20% of the 44 cross-validated models ran.

The performance of all explanatory variables in the competing temporal, spatial, and oceanographic models was examined in order to select important variables for Combined Model structures. In Abundance Models, explanatory variables that were significant in any cross- validated abundance model at the p < 0.05 threshold for any of the abundance groups (DOY,

33 Seafloor, Distance, SSS, SST, Temp_50m, and Chl) were included in an Abundance Combined

Model, with formula

A~ α6 + s15(DOY) + s16(Seafloor) + s17(Distance) + s18(SSS) + s19(SST) + s20(Temp_50m) + s21(Chl) + ε4,

-2 where A is log-transformed euphausiid abundance (log10(individuals m ) +1), α6 is the intercept term, si are nonparametric smoothing functions, and ε4 indicates the independently and identically distributed normal residuals. In Biomass Models, explanatory variables that were significant in any cross-validated model at the p < 0.05 level for either of the biomass groups

(DOY, Seafloor, Distance, SST, and Chl) were similarly included in a Biomass Combined Model with formula

B~ α7 + s22(DOY) + s23(Seafloor) + s24(Distance) + s25(SST) + s26(Chl) + ε5, where B is log-transformed euphausiid biomass (log10(biomass) +1), α7 is the intercept term, si are nonparametric smoothing functions, and ε5 indicates the independently and identically distributed normal residuals. Collinearity was again examined prior to fitting Combined Models, but GVIF values were found to be below 3.2 and no further variables were removed. As in the analysis of other model structures, the performances of all Combined Models were examined with leave-one-out cross-validation and summary statistics were calculated.

Average AIC, the fraction of deviance explained ([null deviance - residual deviance]/null deviance) and MSPE of all five model structures for Abundance Models and all five model structures for Biomass Models were then compared to evaluate the explanatory power and performance of each. The best model structure among all Abundance Models for each life stage group and among Biomass Models for each species was fit to all 44 samples in order to (1)

34 evaluate directions of relationships between all response variables (abundance/biomass groups) and predictor variables of interest, and (2) to inspect model residuals and partial residuals to verify the underlying assumptions of normality and homogeneity, as well as to test for indications of temporal autocorrelation.

35 CHAPTER 3: RESULTS

3.1 Oceanographic Results

Sea surface temperatures ranged from 9.9 °C to 15.8 °C at stations where zooplankton were collected. In general, the inner channels had higher sea surface temperatures than outer channels (Figure 6, Table 3, Figure 7). However, sea surface temperatures were higher in the outer channels in the September survey (August 31st - September 20th) due to the intrusion of a warmer water mass from offshore. A similar pattern was detected in the study area in 2014

(Keen 2017b). Since upwelling generally relaxes around September offshore of the Kitimat Fjord

System (Foreman et al. 2011), warmer waters observed in the outermost channels at the end of the study season are likely a result of relaxation of upwelling in the region. Patches of warmer water at the sea surface may be the result of local insolation of stagnant waters trapped by tidal fronts (Keen 2017b). Mean sea surface temperatures increased between June and July in all channels, then decreased through the remainder of the study period (Figure 6, Table 3, Figure 7).

The average temperature at 50 m displayed much lower variability and an opposite pattern to sea surface temperature over the season: in all channels with the exception of the innermost channel,

Verney Passage, where the temperature at 50 m remained relatively stable after the first survey, temperatures at 50 m decreased from June to July but increased through the remainder of the summer (Figure 6, Table 3, Figure 7).

Sea surface salinities ranged between 21.5 and 30.8 at stations where zooplankton were sampled. In general, the inner channels had lower sea surface salinities than the outer channels

(Figure 6, Table 3, Figure 7). Mean sea surface salinity and salinity at 50 m remained relatively consistent throughout the study season (Figure 6, Table 3, Figure 7). While sea surface salinity

36 displayed a strong inshore-offshore gradient, salinity at 50 m was relatively consistent throughout the study area (Figure 6).

Figure 6. Interpolated maps of oceanographic properties. SST = sea surface temperature; Temp_50m = temperature at 50 m; SSS = sea surface salinity; Salinity_50m = salinity at 50 m; Therm_depth = depth of the thermocline; Therm_str = strength of the thermocline; Chl = chlorophyll-a integrated from 1 m to 70 m. After Keen 2017b.

Table 3. Comparison of means of oceanographic properties in each channel and survey. For these calculations, CTD data were reduced to stations which were sampled in every survey (n=88 tows). Channels are organized from innermost to outermost, left to right. S= survey number; SD = standard deviation. No means or standard deviations could be calculated for Caamano Sound because only one station was sampled in each survey. Overall minimum and maximum values for each variable are displayed on the scale bars of Figure 6 (n=94 tows). SST = sea surface temperature; Temp_50m = temperature at 50 m; SSS = sea surface salinity; Salinity_50m = salinity at 50 m; Therm_depth = depth of the thermocline; Therm_str = strength of the thermocline; Chl = chlorophyll-a integrated from 1 m to 70 m; Chl_max = maximum chlorophyll-a.

Variable Verney McKay Wright Whale Squally Campania Estevan Caamano S (Unit) Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Value SST June 13.06 0.27 12.78 0.43 13.13 1.03 12.99 0.60 12.86 0.69 11.73 1.58 11.72 0.96 11.39 (°C) July 14.20 0.78 14.30 0.73 14.90 0.92 14.06 0.32 14.83 1.09 13.64 0.63 12.21 0.92 11.83 Aug. 12.20 0.64 13.75 0.62 14.76 0.87 13.51 0.41 13.58 0.60 12.31 0.41 12.17 0.08 11.92 Sept. 11.01 0.44 11.24 0.17 11.99 0.50 11.10 0.44 12.31 0.52 12.44 0.88 12.39 0.37 12.83 Temp_50m June 8.52 0.03 8.45 0.16 8.34 0.07 8.32 0.06 8.51 0.02 8.47 0.11 8.56 0.12 8.56 (°C) July 8.15 0.09 8.21 0.11 8.17 0.07 8.06 0.08 8.37 0.17 8.21 0.26 8.94 0.19 8.40 Aug. 8.13 0.10 8.38 0.21 8.37 0.16 8.49 0.36 8.96 0.35 8.83 0.49 8.75 0.11 9.10 Sept. 8.14 0.06 8.66 0.06 8.58 0.09 8.76 0.39 9.73 0.68 10.68 0.47 10.67 0.13 11.40 SSS June 21.60 0.71 26.21 0.54 25.41 1.89 25.05 1.00 27.71 0.22 28.31 1.64 27.88 3.22 29.26 (psu) July 24.16 0.98 26.25 0.85 25.92 1.38 25.44 0.87 28.19 0.38 27.54 0.08 29.15 1.70 29.59 Aug. 22.02 3.39 25.37 0.61 21.94 2.29 26.56 0.32 28.09 0.33 28.64 0.52 30.25 1.37 30.83 Sept. 25.92 1.31 28.51 0.17 24.98 1.90 28.16 3.20 26.44 1.22 28.05 2.74 28.86 2.59 31.38 Salinity_50m June 31.74 0.12 31.89 0.26 32.02 0.07 32.29 0.11 32.03 0.03 32.06 0.19 32.02 0.11 32.08 (psu) July 32.15 0.14 32.15 0.15 32.19 0.11 32.37 0.08 32.22 0.00 32.30 0.17 32.01 0.16 32.19 Aug. 32.06 0.14 32.11 0.04 32.13 0.05 32.23 0.20 32.24 0.11 32.37 0.22 32.41 0.05 32.58 Sept. 32.14 0.05 31.97 0.10 31.90 0.19 32.12 0.32 31.96 0.07 31.90 0.04 31.79 0.09 32.07 Therm_depth June 7.97 4.30 10.93 2.43 5.81 2.48 5.76 1.88 8.28 2.11 12.92 6.36 21.21 12.72 27.03 (m) July 9.20 1.75 8.84 2.06 6.47 3.82 6.09 2.45 6.86 4.66 5.60 0.39 14.33 10.09 17.03 Aug. 10.82 6.01 7.95 5.15 3.69 1.80 8.98 3.95 17.20 4.61 13.36 2.86 21.54 8.22 19.42 Sept. 19.27 7.58 21.29 5.51 24.96 13.40 30.97 6.32 36.94 7.09 53.08 4.28 53.04 1.63 61.90 Therm_str June 0.54 0.24 0.50 0.21 0.74 0.41 0.68 0.24 0.88 0.17 0.19 0.19 0.26 0.34 0.09 (°C m-1) July 0.93 0.37 0.46 0.07 1.18 0.38 0.79 0.19 0.80 0.10 0.81 0.14 0.23 0.23 0.08 Aug. 0.26 0.13 0.35 0.16 1.18 0.57 0.34 0.17 0.18 0.11 0.10 0.03 0.10 0.02 0.06 Sept. 0.08 0.02 0.07 0.01 0.11 0.06 0.06 0.01 0.08 0.01 0.09 0.03 0.08 0.04 0.07 Stratification June 8.61 0.50 5.13 0.57 5.93 1.53 6.41 0.79 4.09 0.27 3.45 1.69 3.73 2.75 2.65 (kg m-3) July 7.23 0.50 5.63 0.46 6.05 1.13 6.40 0.58 4.27 0.09 4.62 0.11 2.78 1.46 2.59 Aug. 8.44 2.74 6.15 0.53 8.99 1.98 5.26 0.26 4.03 0.15 3.49 0.67 2.26 1.07 1.84 Sept. 5.30 1.06 3.11 0.16 5.92 1.44 3.46 2.35 4.68 0.92 3.30 2.03 2.58 1.94 0.80 Chl June 130.1 65.8 135.5 20.7 190.3 42.0 115.6 33.1 132.6 40.0 100.4 46.8 220.4 82.0 212.7 (mg m-2) July 62.9 14.2 61.0 25.7 55.8 10.3 63.7 29.2 18.4 3.5 38.7 7.7 266.0 116.1 21.7 Aug. 49.3 6.9 40.6 3.8 36.4 5.9 79.3 22.0 40.6 13.1 30.7 11.8 42.8 15.6 57.0 Sept. 39.0 1.7 37.2 3.0 63.5 52.0 34.5 8.0 49.7 10.2 52.5 4.4 81.9 4.2 45.2 Chl_max June 11.42 1.33 10.20 1.11 23.92 3.88 14.7 5.04 19.03 3.52 9.34 6.97 14.38 2.78 12.20 (mg m-3) July 5.43 1.72 4.65 2.56 3.42 0.35 5.10 2.39 1.33 0.60 2.72 0.66 16.36 4.28 1.10 Aug. 8.70 2.93 6.59 6.95 3.92 1.51 10.47 9.57 2.61 1.11 1.72 0.61 2.33 0.83 3.81 Sept. 3.57 1.80 1.85 0.16 10.01 14.73 1.43 0.43 2.66 0.41 2.25 0.69 2.20 0.16 1.61

Verney McKay Wright Whale Squally Campania Estevan Caamano

C) °

Temperature(

) psu

Salinity(

) 1

- a (µg L a

Chlorophyll

Figure 7. Depth profiles of temperature (top), salinity (middle), and chlorophyll-a (bottom) by channel. Colors represent different surveys in which tows were conducted. Profiles taken within the same geographic channel are plotted together, and channels are organized from innermost (left) to outermost (right). See Figure 2 for a map of channels.

41 The thermocline developed at 2-64 m and was generally shallower in the inner channels

(Figure 6, Table 3). Changes in mean thermocline depth through the summer were complex, but in every channel the depth of the thermocline was shallower at the beginning of the study season than at the end, when the thermocline abruptly dropped tens of meters in all but the innermost channels. Cold storms and high winds that developed late in the summer were likely responsible for this change in thermocline depth. Innermost channels were likely not as strongly affected due to their more protected positions further inland. The thermocline generally weakened through the summer, but strengthened in some channels between the first and second surveys. The thermocline was typically strongest in the central waters of the study area and consistently weakest in the outer channels (Figure 6, Table 3). Stratification strength (referenced to 50 m depth) remained relatively stable until the fourth survey, when average stratification strength dropped (Figure 6, Table 3). Stratification strength displayed a strong inshore-offshore gradient, with stronger stratification in the inner channels.

Average integrated chlorophyll-a (Chl) was highest in June (Figure 6, Table 3, Figure 7).

Chl values dropped drastically in most channels in July and remained lower in the following surveys. Measurements of maximum chlorophyll-a concentrations (Chl_max) were also highest in June and dropped to much lower concentrations as the season progressed (Figure 6, Table 3,

Figure 7).

3.2 Euphausiid Abundance, Biomass, and Distribution

Six euphausiid species were identified from the 17,038 individual euphausiids collected in the Kitimat Fjord System, but only two were common: Euphausia pacifica and Thysanoessa spinifera. E. pacifica was slightly more abundant overall, composing 52.6% of the euphausiid

42 community, but T. spinifera also composed a large fraction of captured euphausiids (46.3%).

Together, these two species made up 99% of the euphausiid community in the study area. The following rare species were encountered: Thysanoessa longipes, Thysanoessa gregaria,

Tessarabrachion oculatum, and Nematoscelis difficilis.

Total E. pacifica abundances varied between 5 ind. m-2 and 1645 ind. m-2 at sampled stations with an average abundance of 253 ind. m-2. E. pacifica post-larval abundances did not display any inshore-offshore patterns and total E. pacifica post-larval abundance was relatively consistent between months (Figure 8). E. pacifica early and late furcilia were most abundant in

June and their abundances generally decreased throughout the study season (Figure 8). However, very early E. pacifica furcilia were detected throughout the summer season. Both early and late

E. pacifica furcilia were generally more numerous inshore in May and early June, but were more abundant in the outer channels by the end of the study season (Figure 8).

Figure 8. Log-transformed abundances of Euphausia pacifica post-larvae (top), late furcilia (middle) and early furcilia (bottom) from the 91 zooplankton samples taken during the four surveys. Bubble size represents log-transformed abundance, calculated as log10(number of individuals m-2 +1). Bubbles are slightly transparent so that euphausiid abundances in pseudo- replicate samples can be compared. PL = post-larvae; LF = late furcilia; EF = early furcilia.

44 Total T. spinifera abundances varied between 0 ind. m-2 and 4074 ind. m-2 at sampled stations with an average abundance of 222 ind. m-2. The total number of T. spinifera post-larvae increased over the summer, with peak abundances occurring in September (Figure 9). Post-larvae were generally more numerous in the outer channels, although abundances in the middle channels increased in August and September. Numbers of T. spinifera early and late furcilia peaked during late June - early July (Figure 9). Early T. spinifera larvae were consistently more abundant in the outer channels. While late T. spinifera larvae were also most common in outer channels in July, August, and September, they were absent in the very outermost channels in

June and were found to be fairly evenly distributed throughout the remainder of the study area during this survey (Figure 9).

Figure 9. Log-transformed abundances of Thysanoessa spinifera post-larvae (top), late furcilia (middle) and early furcilia (bottom) from the 91 zooplankton samples taken during the four surveys. Bubble size represents log-transformed abundance, calculated as log10(number of individuals m-2 +1). Bubbles are slightly transparent so that euphausiid abundances in pseudo- replicate samples can be compared. PL = post-larvae; LF = late furcilia; EF = early furcilia.

46 Of the rare species captured, T. longipes was the most numerous, with a maximum abundance of 47 ind. m-2. All life stages of T. longipes were represented, but only 6 - 40 total individuals of each life stage were captured over the entire study season. Adult T. longipes were captured only in late May, early June, and September in the central and inner channels in the study area (Figure 10). T. longipes late furcilia (furcilia stages IV-VI), which were also distributed in central and inner channels, were most abundant in June and July, rare in August, and absent in September (Figure 10). T. longipes early furcilia (furcilia stages I-III) were most abundant in the innermost channels in June, although a few individuals were also captured on the southern and eastern sides of Gil Island in June and July. Early T. longipes furcilia were absent from the study area in August and September (Figure 10).

Figure 10. Abundances of Thysanoessa longipes post-larvae (top row), late furcilia (middle) and early furcilia (bottom) during the four surveys. Note that these abundances have not been log- transformed. Bubble size represents abundance in individuals m-2. PL = post-larvae; LF = late furcilia; EF = early furcilia.

Only 24 total post-larvae and two Furcilia IV of T. gregaria were captured. T. gregaria were captured in the middle and outer channels in June, in outer, middle, and inner channels in

48 July, in Wright Channel in August, and in Wright Sound and Squally Channel in September

(Figure 11). Greatest abundances of T. gregaria were captured in the first survey (Figure 11). T. oculatum and N. difficilis were represented by only two individuals each. One T. oculatum post- larvae, one T. oculatum Furcilia V, and two N. difficilis post-larvae were captured. The T. oculatum specimens were captured in Campania Sound in late July. One N. difficilis was captured in Campania sound in early June and the other was captured in McKay Reach in

August.

Figure 11. Abundances of total Thysanoessa gregaria during the four surveys. Note that these abundances have not been log-transformed. Bubble size represents abundance in individuals m-2.

E. pacifica biomass remained relatively consistent across the study area and across surveys (Figure 12). T. spinifera biomass was concentrated toward the fjord mouth in June and

July, but was spread throughout the study area in August and September (Figure 12).

Figure 12. Log-transformed Euphausia pacifica biomass (top row) and Thysanoessa spinifera biomass (bottom row) during the four surveys. Bubble size represents log-transformed dry mass, -2 calculated as log10(biomass (in units of mg m ) +1).

3.3 E. pacifica and T. spinifera Abundance Model Results

3.3.1 Identification of Important Variables

Due to low abundances of rare species, only E. pacifica and T. spinifera abundances were modeled. An examination of the significance of all of the initially considered spatial, temporal, and oceanographic variables in their respective Abundance Model structures (Table 4) indicated that sill proximity and time of day were not significantly correlated with abundance of any life stage group for either species at the p < 0.05 level and these covariates were thus removed from

50 the variables of interest. Net drop velocity initially appeared to significantly affect the quantity of

T. spinifera post-larvae captured, but an examination of the modeled relationship confirmed that the significance of this relationship was likely an artifact of over-fitting. Net drop velocity was also deemed unimportant and was excluded from the variables of interest because (1) the modeled relationship was not logical for this variable (with peaks in abundance at the extremes of fall velocities), (2) a linear fit of this variable was not significant, and (3) this variable was never significant in more than 3% of 44 cross-validated models conducted for each life stage group and significance level. Thus, the Abundance Combined Model included only the following variables: day of the year, seafloor depth, distance into the fjord, sea surface temperature, sea surface salinity, temperature at 50 m, and integrated chlorophyll-a.

51 Table 4. Covariate significances in each different Abundance initial competing model structure at three significance levels (Sig. level). For competing model structure forms, see Table 2. Values are percentages of times a p-value was above the corresponding significance level in 44 backwards selected leave-one-out models for each model structure. M = model in which variable was included; PL = post-larvae; LF = late furcilia; EF = early furcilia.

Sig. Euphausia pacifica Thysanoessa spinifera M Covariate level PL LF EF PL LF EF DOY p < 0.01 0 100.0 100.0 2.3 100.0 100.0

p < 0.05 0 100.0 100.0 13.6 100.0 100.0 p < 0.10 0 100.0 100.0 72.7 100.0 100.0

Time p < 0.01 0 0 0 0 0 0

Temporal p < 0.05 0 0 0 0 0 0 p < 0.10 0 2.3 0 0 0 0

Seafloor p < 0.01 100.0 0 0 84.1 0 0 p < 0.05 100.0 0 0 97.7 0 0 p < 0.10 100.0 0 0 97.7 0 0

Distance p < 0.01 0 0 0 100.0 4.5 0 p < 0.05 0 0 0 100.0 90.9 0

Spatial p < 0.10 0 0 0 100.0 97.7 18.2

Sill_prox p < 0.01 0 0 0 0 0 0 p < 0.05 0 0 0 0 0 0

p < 0.10 0 0 2.3 0 2.3 13.6

SST p < 0.01 0 6.8 95.5 0 97.7 61.4 p < 0.05 22.7 79.5 97.7 0 100.0 97.7

p < 0.10 97.7 97.7 100.0 0 100.0 100.0

SSS p < 0.01 0 0 15.9 0 100.0 100.0

p < 0.05 6.8 0 100.0 2.3 100.0 100.0 p < 0.10 86.4 0 100.0 2.3 100.0 100.0

Temp_50m p < 0.01 0 0 27.3 4.5 0 4.5 p < 0.05 0 2.3 100.0 97.7 0 95.5 Oceanographic p < 0.10 0 40.9 100.0 100.0 0 100.0

Chl p < 0.01 0 0 100.0 4.5 0 95.5 p < 0.05 0 6.8 100.0 86.4 0 100.0 p < 0.10 0 18.2 100.0 97.7 0 100.0

52 3.3.2 Comparison of Competing Abundance Model Structures

For each combination of euphausiid group abundance and model structure, AIC was computed as a measure of goodness of fit of each model, the fraction of deviance explained by models was computed as a measure of the proportion of variation that each model accounted for, and mean squared prediction error (MSPE) was computed as a measure of each model's ability to predict the remaining abundances (Table 5). The Combined Model was the best-performing model structure for every euphausiid abundance group, with lowest mean AIC and highest deviance explained among all of the competing model structures. While the MSPE was not always lowest for the Combined Models, Combined Model MSPE was always within 20% of the lowest model MSPE for each life stage group (Table 5). Significances of variables within the

Combined Models for each euphausiid life stage group are reported in Table 6. To visualize the effect of each covariate in the Combined Model on each euphausiid abundance group, additive effects of covariates were plotted (Figure 13). The y-axis in each covariate plot displays the conditional effect that the covariate has on the response variable. The conditional effect is the effect that each variable has when the other variables are also included in the model. The cumulative effect at any given point is given by the sum of all partial effects plus a constant, which is the average of the response variable (average abundance for each euphausiid group, the intercept αi in each model formula). Therefore, the y-axes display deviances from average abundance, or anomalies of abundance. The x-axes for covariate effect plots display the values of covariates and include tick marks which show the sampling intensity over the range of variation of each covariate. The points in covariate plots are partial residuals.

53 Table 5. Comparison of competing models that explain abundances of E. pacifica and T. spinifera post-larvae, late furcilia, and early furcilia. AIC = Akaike’s information criterion; Dev. Exp. (%) = deviance explained expressed as a percent; MSPE = mean squared prediction error. Each value is a mean of each statistic from 44 backwards selected leave-one-out models per life stage group.

Mean Mean Dev. Mean Species/Stage Model AIC Exp. (%) MSPE

Euphausia pacifica Post-Larvae Null 80.75 0.00 0.60 Temporal 80.71 8.15 0.63 Spatial 63.83 38.32 0.51 Oceanographic 75.28 24.83 0.63 Combined 61.26 46.65 0.61

Late Furcilia Null 95.39 0.00 0.72 Temporal 77.01 37.80 0.60 Spatial 96.46 2.14 0.78 Oceanographic 87.31 29.70 0.79 Combined 67.88 56.62 0.66

Early Furcilia Null 116.80 0.00 0.92 Temporal 61.52 76.63 0.51 Spatial 116.71 4.72 0.92 Oceanographic 94.85 54.14 0.84 Combined 57.83 81.88 0.57 Thysanoessa spinifera Post-Larvae Null 74.01 0.00 0.56 Temporal 72.61 7.90 0.57 Spatial 59.39 37.69 0.48 Oceanographic 65.14 30.03 0.58 Combined 52.34 53.18 0.51

Late Furcilia Null 109.51 0.00 0.84 Temporal 93.13 40.76 0.71 Spatial 106.18 11.78 0.83 Oceanographic 96.04 33.40 0.73 Combined 82.86 59.72 0.71

Early furcilia Null 129.78 0.00 1.07 Temporal 78.95 73.97 0.60 Spatial 128.97 6.33 1.14 Oceanographic 111.76 48.60 0.94 Combined 70.69 80.94 0.71

54 Table 6. Covariate significances in the Abundance Combined Model. Values are percentages of times a p-value was above the corresponding significance level in 44 backwards selected leave- one-out models. Sig. level = significance level; PL = post-larvae; LF = late furcilia; EF = early furcilia.

Covariate Sig. E. pacifica T. spinifera level PL LF EF PL LF EF DOY p < 0.01 0.0 100.0 100.0 93.2 100.0 100.0 p < 0.05 2.3 100.0 100.0 100.0 100.0 100.0 p < 0.10 2.3 100.0 100.0 100.0 100.0 100.0

Seafloor p < 0.01 100.0 0.0 0.0 0.0 0.0 0.0 p < 0.05 100.0 4.5 72.7 2.3 0.0 0.0 p < 0.10 100.0 4.5 95.5 22.7 0.0 0.0

Distance p < 0.01 0.0 2.3 2.3 100.0 0.0 6.8 p < 0.05 0.0 77.3 65.9 100.0 0.0 11.4 p < 0.10 0.0 86.4 90.9 100.0 0.0 11.4

SST p < 0.01 0.0 9.1 90.9 0.0 2.3 0.0 p < 0.05 29.5 13.6 100.0 0.0 2.3 0.0 p < 0.10 38.6 20.5 100.0 0.0 4.5 2.3

SSS p < 0.01 0.0 0.0 86.4 0.0 2.3 63.6 p < 0.05 4.5 9.1 97.7 0.0 9.1 86.4 p < 0.10 11.4 9.1 100.0 2.3 9.1 86.4

Temp_50m p < 0.01 0.0 2.3 2.3 0.0 90.9 0.0 p < 0.05 0.0 79.5 59.1 0.0 93.2 2.3 p < 0.10 4.5 81.8 93.2 0.0 95.5 2.3

Chl p < 0.01 0.0 95.5 2.3 4.5 2.3 2.3 p < 0.05 0.0 100.0 13.6 18.2 90.9 6.8 p < 0.10 0.0 100.0 75.0 97.7 95.5 25.0

* = significant at p < 0.05 level in 20-50% of 44 leave-one-out models for each life stage ** = significant at p < 0.05 level in 50-80% of 44 leave-one-out models for each life stage *** = significant at p < 0.05 level in 80-100% of 44 leave-one-out models for each life stage Figure 13. Effects of the covariates in the Abundance Combined Model on E. pacifica and T. spinifera abundance groups. Shading around fitted relationships represents 95% confidence intervals for the curve. When the slopes of the functional forms are positive, the covariates are related positively to the dependent variables, and vice versa. Note that graphs for each abundance group (each row) have matching y-axis scales while scales may differ between rows. PL = post- larvae; LF = late furcilia; EF = early furcilia.

56 3.3.3 Temporal Relationships

The variable day of the year (DOY) was significant at the p < 0.05 level in 80-100% of

44 backwards selected leave-one-out Combined Models for all life stage groups of both species, except E. pacifica post-larvae, which were present in approximately equal average abundances throughout the summer (Table 6, Figure 13). T. spinifera post-larva were the only group whose abundances were significantly greater at the end of the summer than at the beginning (Figure 13).

Models based only on day of the year explained low amounts of the variation in E. pacifica and

T. spinifera post-larval abundances (8.15% and 7.90%, respectively) but explained increasingly larger amounts of variation in earlier life stage groups (37.80% of the variation in E. pacifica late furcilia and 76.60% of the variation in E. pacifica early furcilia; 40.76% of the variation in T. spinifera late furcilia and 73.97% of the variation in T. spinifera early furcilia). Abundances of both groups of E. pacifica larvae displayed significant, nonlinear relationships with DOY, with highest abundances in June (Figure 13). Both groups of T. spinifera larvae were also found to have significant, nonlinear relationships with DOY, as their numbers increased between June and

July but fell to lower values in August and September than were encountered in June (Figure 13).

3.3.4 Spatial Relationships

While the Temporal Model performed poorly for E. pacifica and T. spinifera post-larval abundances, the Spatial Model performed nearly as well for these two groups as the Combined

Models (Table 5). Spatial Models accounted for 38.32% of deviance E. pacifica post-larval abundance due to the strong, nonlinear relationship between E. pacifica post-larval abundance and seafloor depth, and 37.69% of deviance in T. spinifera post-larval abundances due to strong relationships with both distance into the fjord and seafloor depth. However, the strictly Spatial

57 Model performed poorly for furcilia of both species, where it explained <12% of deviance.

Relationships with distance into the fjord system were negative for all T. spinifera groups but positive for all E. pacifica groups in the Combined Models (Figure 13). Relationships with distance into the fjord were statistically significant for E. pacifica early furcilia (significant in

65.9% of models), E. pacifica late furcilia (significant in 77.3% of models), and T. spinifera post-larvae (significant in 100% of models) (Table 6). Most modeled relationships between abundance groups and seafloor depth were linear or nearly linear and negative in the Combined

Models, with the exception of E. pacifica post-larval abundance, which peaked around seafloor depths of 375 meters (Figure 13). Seafloor depth was significantly related only to abundances of

E. pacifica post-larvae (significant in 100% of cross-validated models at the p < 0.05 level) and early furcilia (significant in 72.7% of cross-validated models at the p < 0.05 level).

3.3.5 Oceanographic Relationships

The Oceanographic Models performed relatively poorly in comparison to the Combined

Models (Table 5). All E. pacifica life stage groups displayed positive relationships with sea surface temperature in Combined Models although the relationships were only significant for E. pacifica post-larvae (significant in 29.5% of models) and early furcilia (significant in 100% of models) (Figure 13). The relationship between sea surface temperature and late T. spinifera furcilia was also positive. In contrast, T. spinifera post-larvae and early larvae were found to be most abundant at the extremes of sea surface temperature ranges, but these relationships were not significant and may be an artifact of overfitting. All E. pacifica life stage groups and both T. spinifera furcilia groups were positively correlated with sea surface salinity, but the relationship between sea surface salinity and post-larval T. spinifera was weakly negative (Figure 13). Only

58 the early furcilia of each species displayed relationships with sea surface salinity that were statistically significant (Table 6). All E. pacifica groups displayed positive relationships with temperature at 50 m while this relationship was negative for T. spinifera post-larvae, peaked around 9.5 °C for T. spinifera late furcilia, and was non-existent for T. spinifera early furcilia

(Figure 13). Relationships with temperature at 50 m were significant for both groups of E. pacifica furcilia and the late furcilia of T. spinifera. Relationships with integrated chlorophyll-a were positive for post-larvae of both species, but negative for both E. pacifica early and late furcilia and T. spinifera late furcilia (Figure 13). T. spinifera early furcilia were most abundant at integrated chlorophyll-a levels around 175 mg m-2. Relationships with integrated chlorophyll-a were only significant for the late furcilia of each species.

3.3.6 Statistical Model Validation

Residual deviance was calculated and the observed and predicted values were plotted and visually inspected to examine the fit of the model to the data. No indications of violations of assumptions of normality and homogeneity were detected for any competing model in any abundance group, and there were no indications of significant temporal autocorrelation.

3.4 E. pacifica and T. spinifera Biomass Model Results

3.4.1 Identification of Important Variables

An examination of the significance of all of the initially considered spatial, temporal, and oceanographic variables in their respective model structures (Table 7) indicated that sill proximity, time of day, sea surface salinity, and temperature at 50 m were not significantly correlated with biomass of either species at the p < 0.05 level in any cross-validated iteration.

Therefore, these covariates were removed from the variables of interest. In addition, net drop

59 velocity was never significant at any p-value threshold, and was deemed unimportant and excluded from the variables of interest. Thus, the Biomass Combined Model included only the following variables: day of the year, seafloor depth, distance into the fjord, sea surface temperature, and integrated chlorophyll-a.

60 Table 7. Covariate significances for E. pacifica and T. spinifera biomass in the initial competing model structures. For competing model forms, see Table 2. Values are percentages of times a p- value was above the corresponding significance level in 44 backwards selected leave-one-out models for each model structure. M = competing model in which variable was included; Sig. level = significance level.

E. pacifica T. spinifera M Covariate Sig. level Biomass Biomass DOY p < 0.01 0 2.3

p < 0.05 0 43.2 p < 0.10 0 97.7

Time p < 0.01 0 0 Temporal p < 0.05 0 0

p < 0.10 0 2.3

Seafloor p < 0.01 100.0 0 p < 0.05 100.0 0 p < 0.10 100.0 4.5

Distance p < 0.01 0 11.4 p < 0.05 0 97.7

Spatial p < 0.10 0 100.0

Sill_prox p < 0.01 0 0 p < 0.05 0 0 p < 0.10 0 0

SST p < 0.01 29.5 0 p < 0.05 95.5 0 p < 0.10 97.7 2.3

SSS p < 0.01 0 0

p < 0.05 0 0 p < 0.10 2.3 0

Temp_50m p < 0.01 0 0 p < 0.05 0 0 Oceanographic p < 0.10 0 43.2

Chl p < 0.01 0 0 p < 0.05 0 4.5 p < 0.10 0 29.5

61 3.4.2 Temporal, Spatial, and Oceanic Relationships

Temporal Models were only able to explain 8.37% of deviance in E. pacifica biomass and 10.12% of deviance in T. spinifera biomass (Table 8). E. pacifica biomass did not significantly change over the study season, although it did slightly increase (Figure 14). T. spinifera biomass increased significantly over the study season (Figure 14, Table 9). Spatial

Models performed relatively well for both E. pacifica and T. spinifera biomass, and were only less parsimonious than Combined Models (Table 8). E. pacifica biomass displayed a nonlinear, strongly significant relationship with seafloor depth, with biomass peaking at stations with seafloor depths around 375 m. Seafloor depth had a negative, nonsignificant effect on T. spinifera biomass (Figure 14). While E. pacifica biomass did not display any significant relationship with distance into the fjord system, T. spinifera biomass was much greater in the middle and outer fjord channels than in the more inland ones (Figure 14). Oceanographic Models performed poorly relative to Combined Models for biomass of both species. However, E. pacifica biomass displayed a strongly significant, positive relationship with sea surface temperature. Although biomass of both species increased as integrated chlorophyll-a increased, neither of these relationships were significant at the p < 0.05 level in more than 3% of cross- validated models performed for each species.

62 Table 8. Comparison of competing Biomass Model statistics for E. pacifica and T. spinifera. AIC = Akaike’s information criterion; Dev. Exp. (%) = deviance explained expressed as a percent; MSPE = mean squared prediction error. Each value is a mean of each statistic from 44 backwards selected leave-one-out models. Mean Mean Dev. Mean Species Model AIC Exp. (%) MSPE Euphausia pacifica Null 72.04 0.00 0.55 Biomass Temporal 71.53 8.37 0.58 Spatial 55.31 38.06 0.47 Oceanographic 67.16 15.21 0.53 Combined 51.51 46.36 0.48

Thysanoessa spinifera Null 113.49 0.00 0.88 Biomass Temporal 111.48 10.12 0.94 Spatial 108.62 19.05 0.91 Oceanographic 110.74 17.57 1.05 Combined 105.44 30.54 0.92

* = significant at p < 0.05 level in 20-50% of 44 leave-one-out models for each life stage ** = significant at p < 0.05 level in 50-80% of 44 leave-one-out models for each life stage *** = significant at p < 0.05 level in 80-100% of 44 leave-one-out models for each life stage Figure 14: Effects of the covariates on E. pacifica biomass (top row) and T. spinifera biomass (bottom row) in the Biomass Combined Model. Shading around fitted relationships represents 95% confidence intervals for the curve. When the slopes of the functional forms are positive, the covariates are related positively to the dependent variables, and vice versa.

Table 9. Covariate significances in the Biomass Combined Model. Values are percentages of times a p-value was above the corresponding significance level in 44 backwards selected leave- one-out Combined Models. Sig. level = significance level. Temp_50m was originally considered in the Biomass Combined Model but was removed after a first round of Combined Modeling revealed that it was never significant at any p-value threshold.

Covariate Sig. level E. pacifica T. spinifera Biomass Biomass p < 0.01 0 0 DOY p < 0.05 0 72.7 p < 0.10 0 100.0

p < 0.01 100.0 0 Seafloor p < 0.05 100.0 0 p < 0.10 100.0 52.3

p < 0.01 0 11.4 Distance p < 0.05 0 100.0 p < 0.10 0 100.0

p < 0.01 2.3 0 SST p < 0.05 88.6 0 p < 0.10 97.7 0

p < 0.01 0 0 Chl p < 0.05 2.3 2.3 p < 0.10 11.4 4.5

3.4.3 Model Validation

64 CHAPTER 4: DISCUSSION

4.1 Euphausiid Species

The euphausiid species Euphausia pacifica and Thysanoessa spinifera composed 99% of the euphausiid community in the Kitimat Fjord System. E. pacifica and T. spinifera have been found to be extremely dominant euphausiid species in other areas along the coast of British Columbia

(Fulton and LeBrasseur, 1984; Simard and Mackas, 1989; Mackas 1992, 1995; De Robertis

2002; Regan 1968; Tanasichuk 2002), and it was thus not surprising that these two species were the only common euphausiids captured in the Kitimat Fjord System during our study. Observed maximum E. pacifica abundances were similar to those observed off Vancouver Island

(Tanasichuk, 2002; Simard and Mackas, 1989) but less than those observed in Indian Arm,

British Columbia (Regan, 1968). Maximum abundances of T. spinifera observed in this study were greater than those captured off Vancouver Island by Simard and Mackas (1989) but similar to those captured off Vancouver Island by Tanasichuk (2002). T. spinifera was found in greater abundances in the Kitimat Fjord System than in Indian Arm by Regan (1968).

Lower numbers of Thysanoessa longipes were captured in this study. T. longipes commonly comprises a small amount of euphausiid biomass along the northern coast of British

Columbia and has been previously recorded in fjords in British Columbia (Tommasi et al., 2013;

Regan, 1968) and Alaska (Coyle and Pinchuk, 2005). In Alaska, T. longipes is almost completely restricted to the deeper regions of coastal fjords (Coyle and Pinchuk, 2005). In this study, T. longipes was observed in both middle and inner regions of a fjord system. One record of

Tessarabrachion oculatum was made in this study. This mesopelagic subarctic species has been found from the Gulf of Alaska to California (Brinton, 1962; Coyle and Pinchuk, 2003), but had

65 not previously been recorded in a British Columbia fjord. Small numbers of Thysanoessa gregaria and Nematoscelis difficilis were also present in the Kitimat Fjord System. These two species are usually found south of 50 °N and 51 °N, respectively (Banner, 1950; Mauchline and

Fisher, 1969; Brinton, 1962; Ponomareva, 1963). The records of T. gregaria and N. difficilis from around 53 °N in this study are among the most northerly records for these two species.

These warmer water species may have occurred in the study area in 2015 due to anomalously high ocean temperatures in the northeast Pacific that developed in the winter of 2013-2014, reached coastal waters in spring and summer 2014, and persisted through 2015 (Bond et al.,

2015; Cavole et al., 2016). Thysanoessa raschii, a neritic arctic and subarctic species, has historically been found along the coast of British Columbia (Brinton, 1962; Regan, 1968) but this species was absent from my zooplankton samples. Since T. raschii prefers cooler water temperatures, its absence from the study area in 2015 could also be due to the anomalously warmer ocean conditions of 2015.

4.2 Drivers of E. pacifica and T. spinifera Abundance

Given the sensitivity of many predatory species, including whales, seabirds, and fish, to changes in euphausiid abundance and the fact that many ecosystems are already responding to climate change, there is an increasing need to understand how euphausiids are distributed in space and time and how their abundances are affected by environmental variability (Walther et al., 2002). I examined competing Temporal, Spatial, Oceanographic, and Combined Models in order to understand some of the complex drivers of E. pacifica and T. spinifera abundances in the Kitimat Fjord System. A key finding from this study is that spatial and temporal heterogeneity in the abundance of these dominant euphausiids is not driven exclusively by

66 spatial or by temporal factors, but is instead a consequence of complex interactions between spatial, temporal, and environmental dynamics, as has also been shown for other planktonic species in different systems (Klais et al., 2016; De Robertis, 2002). I determined that E. pacifica and T. spinifera post-larval and larval abundances are the result of a combination of (1) timing of reproductive behavior, (2) geographic forcing, and (3) oceanographic properties including temperature, salinity, and the availability of phytoplankton prey. The effects of these driving forces were sometimes species-specific (e.g. E. pacifica life stage groups displayed positive relationships with distance into the fjord while T. spinifera displayed negative relationships with this variable) but sometimes aligned (as in the positive relationships between integrated chlorophyll-a concentrations and post-larval abundance and the negative relationships between integrated chlorophyll-a concentrations and late furcilia abundances of both species). In some cases, effects of variables were relatively consistent within-species (e.g. all life stage groups of

E. pacifica showed positive relationships with sea surface salinity, sea surface temperature, and temperature at depth). However, some effects were more life-stage-specific (e.g. T. spinifera post-larvae displayed negative relationships with both sea surface salinity and temperature at depth while furcilia displayed positive relationships with these variables). These relationships and the prevalence of Combined Models (which included temporal, spatial, and oceanographic terms) as the most parsimonious speak to the complexity of the controls on euphausiid abundance in dynamic coastal habitats.

4.2.1 Temporal Relationships

E. pacifica and T. spinifera life stage groups did not demonstrate any significant relationships with the time of day that zooplankton samples were taken. I did not expect this

67 variable to have an effect on captured euphausiid abundances because all zooplankton tows occurred during daylight hours (6:47-19:56 PST). However, since other studies have demonstrated radical differences in sampled euphausiid abundance between repeated daytime and nighttime sampling at coastal stations in British Columbia (Shaw and Robinson, 1998) and some of the zooplankton sampling occurred near sunset in this study, I determined that it was necessary to include this variable in modelling to confirm that the time of day at which samples occurred did not affect the numbers of euphausiids captured.

All life stage groups of both species, with the exception of E. pacifica post-larvae, demonstrated strongly significant relationships with day of the year. This variable alone was able to explain much of the deviance in abundances of early and late furcilia, but only explained very low amounts of deviance in post-larval abundances. This result is likely due to the high temporal variability observed in furcilia of both species, while post-larvae displayed much smaller changes in abundance over the season. High temporal variability is typical of small organisms that reproduce quickly (Klais et al., 2016) and it has commonly been observed at similar seasonal scales in other populations of these species (Kim et al., 2009; Marinovic et al., 2002; Regan,

1968).

Although my observations are limited to May-September, seasonal changes in the abundances of E. pacifica furcilia in the Kitimat Fjord System indicate that there is a large pulse in E. pacifica reproduction in May or earlier in this region, although reproduction appears to continue, albeit at a lower rate, throughout the summer. These results were consistent with the reproductive behavior of E. pacifica observed in a Washington fjord (Bollens et al., 1992). Peak abundances of early T. spinifera larvae were encountered later in the study season (late June –

68 early July) than peak abundances of E. pacifica larvae. It is therefore likely that peak T. spinifera spawning occurs after that of E. pacifica in this region. However, T. spinifera reproductive activity usually begins earlier in the spring than that of E. pacifica along the Oregon coast

(Gómez-Gutiérrez et al., 2007), and the presence of some early T. spinifera larvae at the beginning of the study season does not rule out the possibility that the initiation of T. spinifera spawning may occur before that of E. pacifica in the Kitimat Fjords. Declines in the numbers of furcilia captured in late summer, especially the sharp decreases in numbers of early furcilia, can be explained by a combination of waning reproduction, growth and molting into later life stages, and mortality (including predation). Post-larval abundances of both species remained comparatively stable over the season likely due to a near balance between predation/mortality and maturation of furcilia.

4.2.2 Spatial Relationships

E. pacifica post-larvae and furcilia life stage groups displayed abundances that increased slightly with distance into the fjords. Although E. pacifica is generally regarded as an oceanic species and is usually most dominant in outer shelf environments, it has also been previously encountered in coastal fjord habitats in the northeast Pacific including Prince William Sound,

Alaska (Coyle and Pinchuk, 2005), Rivers Inlet, British Columbia (Tommasi et al., 2013), and

Saanich Inlet, British Columbia (De Robertis, 2002). The Kitimat Fjord System contains some of the deepest areas on the coast of British Columbia, parts of which are deeper than the adjacent inner shelf. It is possible that the deep nature of this fjord system may simulate conditions found on the outer shelf and thus harbor especially high abundances of E. pacifica. Since E. pacifica is a strong vertical migrator (De Robertis, 2002), it is likely that this species prefers areas with

69 bottom depths that are capable of allowing for its large vertical migrations. The observation that fewer E. pacifica post-larvae were captured at stations with the shallowest depths supports this hypothesis. Maximum abundances of E. pacifica post-larvae were found at stations with seafloor depths around 375 m, which is similar to depths in outer shelf environments found offshore of the study area. At depths greater than 375 m, E. pacifica post-larval abundances decreased. This suggests that deeper seafloor depths in these fjords create a physical environment that is not ideal for E. pacifica post-larvae. Abundances of all T. spinifera life stage groups and E. pacifica early and late furcilia decreased with increasing seafloor depth. Although most of these relationships were not significant, the consistency in direction of the relationship indicates that some aspect of deep seafloor depth exerts a negative control on euphausiid abundance.

T. spinifera is considered a coastal species and has been observed to be abundant in both inner shelf and fjord environments in the north Pacific (Tanasichuk, 1998b; Brinton et al., 1999), although it occurs offshore as well (Mauchline and Fisher, 1969; Coyle and Pinchuk, 2005).

Since T. spinifera are considered to be coastally distributed, I expected to find high numbers of

T. spinifera throughout the fjord system. However, I found that T. spinifera were mainly distributed near the fjord mouth. This distribution pattern may also be related to the extreme depth of these particular fjords, and it is possible that this species preferred the shallower area near the fjord mouth since it is usually encountered in shallow inner shelf environments.

Neither species displayed significant relationships with proximity to sills in the fjords. Other studies reported large concentrations of euphausiids over sills in fjord environments (Ianson et al., 2011) and over other abruptly sloping bathymetry during upwelling events (Simard et al.,

1986; Mackas et al., 1997; Simard and Lavoie, 1999). In all cases, these aggregations were

70 attributed to upward water flow forced by bathymetry and resulting downward swimming of euphausiids as they maintained their preferred light level and/or reacted to small-scale velocity shear. There are several possible reasons why aggregations were not detected over sills in the

Kitimat Fjord System: (1) the sampling stations were not located directly over these features, (2) it is possible that the sills in the study area are not shallow enough to cause significant upward water flow, (3) tidal and estuarine circulation may have been too weak to create currents at sills that had significant vertical components, or (4) the complexity of the Kitimat Fjord System may prohibit the development of stable and predictable currents over sills.

The spatial variables, seafloor depth (Seafloor) and distance into the fjord (Distance), were able to explain about 38% of deviance in E. pacifica and T. spinifera post-larval abundances but explained less than 12% of deviance in abundances of furcilia for both species.

This indicates that adult and juvenile euphausiid abundances are strongly influenced by geographical factors in the Kitimat Fjord System while larval abundances are not.

4.2.3 Oceanographic Relationships

Physiology often determines the limits of temperature and salinity that euphausiid species can endure, but the ranges of temperature and salinity observed in this study fell within the ranges that are normally associated with E. pacifica and T. spinifera, and were therefore not limiting (Taki et al., 1996; Taki and Ogishima, 1997; Taki, 2007; Gilfillan, 1972; Regan, 1968).

Considering that both of these species have wide geographic ranges from the Gulf of Alaska to

California (Brinton, 1981) and the Kitimat Fjord System is nearly centered in between the extremes of these geographical ranges, this result was not surprising. However, consistencies in the direction of the effects of modeled temperature and salinity terms suggest that these

71 environmental factors are exerting some control on euphausiid abundances in this region, although these effects may be indirect rather than direct. Model results were consistent with positive correlations found between E. pacifica abundance and upper temperature in the water column, lower temperature in the water column, and salinity in the Gulf of Alaska (Coyle and

Pinchuk, 2005). However, while Coyle and Pinchuk (2005) found T. spinifera abundance to be related positively to temperature terms and negatively to salinity terms, the relationships between

T. spinifera abundance and temperature/salinity terms in this study were harder to decipher.

Directions of some relationships differed between life stages, which could indicate that T. spinifera post-larvae and furcilia have different ecological niches and habitat requirements in fjord environments.

E. pacifica and T. spinifera post-larvae were related positively to integrated chlorophyll-a

(Chl), while their late furcilia displayed negative relationships with this variable. E. pacifica early furcilia also displayed a negative relationship with Chl. These results could suggest that euphausiid post-larvae have the swimming ability to seek out areas with higher chlorophyll-a concentrations while furcilia do not. However, post-larvae of E. pacifica are known to be omnivorous while furcilia mainly consume phytoplankton (Nakagawa et al., 2002; Dilling et al.,

1998; Park et al., 2011). An alternative explanation is that furcilia were grazing heavily on phytoplankton, thereby reducing phytoplankton chlorophyll-a concentrations in areas where furcilia were most abundant.

4.3 Drivers of E. pacifica and T. spinifera Biomass

Because baleen whales do not target individual euphausiids, but instead capture many thousands of individuals by filtering large portions of water, they are more impacted by overall

72 euphausiid biomass rather than euphausiid abundance (Nemoto, 1954). Due to the fact that captured post-larvae differed greatly in size (E. pacifica post-larvae were 5-26mm, T. spinifera post-larvae were 6-31mm), species abundances may have been relatively equal between samples although species biomass greatly differed. Therefore, modelling biomass in addition to abundance was important and informative.

E. pacifica and T. spinifera biomass model results were most similar to those of E. pacifica and T. spinifera post-larval abundance, respectively. Since adults have far greater body mass than furcilia, this result is not surprising. While many similarities existed between GAM results for post-larval abundance and corresponding species biomass, some interesting differences were present as well. Although E. pacifica post-larval abundance was nearly equal at the beginning and end of the study season and abundances of furcilia decreased throughout the season, E. pacifica biomass increased over time, albeit not significantly. This result is likely due to growth of individuals over the summer season that outweighed the effects of mortality and predation. T. spinifera biomass also increased over the season, mirroring the trend in abundances of T. spinifera adults. Relationships between biomass of both species and the covariates seafloor depth, integrated chlorophyll-a, and sea surface temperature were similar to those of respective post-larvae abundances. See the previous section for a discussion of these relationships.

However, while abundances of all life stage groups of E. pacifica were somewhat positively correlated with distance into the fjord, E. pacifica biomass was slightly negatively correlated with this variable. Although this relationship was not significant, this result suggests that larger

E. pacifica post-larvae are more common in outer channels. Since all T. spinifera abundance

73 groups displayed negative relationships with distance into the fjord, it was not surprising that T. spinifera biomass was strongly negatively correlated with this variable.

4.4 Implications for Whale Distributions

The Kitimat Fjord System is an important summer feeding ground for many vertebrate species that either directly or indirectly depend on euphausiid prey (Fisheries and Oceans

Canada, 2013). Humpback whales mainly occupy the outer Kitimat Fjord System in early summer but migrate inland as summer progresses into fall (Keen et al., 2017). GAMs revealed that humpback distribution in the Kitimat Fjord System is not strongly associated with euphausiid biomass, as indicated by 200 kHz backscatter data (Keen et al., 2017). A comparison of my euphausiid abundance data with humpback whale distributions in the Kitimat Fjord

System recorded in 2015 by Keen et al. (2017) confirmed this finding, suggesting that humpback whale distributions were not strongly coupled with E. pacifica, T. spinifera, or total euphausiid abundance or biomass. Since baleen whale foraging success depends not only on prey abundance/biomass but also on the vertical distribution of prey and the density of prey patches, the patchiness of euphausiids may have a larger impact on predator distribution than euphausiid abundance/biomass (Benoit-Bird et al., 2013; Santora et al., 2009; Mangel and Switzer, 1998). In addition, humpback whales are also known to consume small schooling fish such as herring

(Clupea harengus pacifica), sand lance (Ammodytes hexapterus), and smelt (f. Osmeridae), and it is possible that humpback whale distribution was more strongly affected by the abundance and distribution of these species or more strongly driven by other cues (Keen et al., 2017).

Fin whales almost exclusively occupy Squally Channel and Campania Sound in the KFS

(Keen et al., 2018). Their distribution overlaps with some of the greatest abundances of T.

74 spinifera that were observed in this study. T. spinifera has a greater lipid content than E. pacifica, and is thus a more energy-dense and superior food source (Abraham and Sydeman 2004). While fin whale distribution may be more strongly controlled by factors other than prey patch characteristics (Keen 2017a), it is possible that fin whales are specifically targeting T. spinifera in the Kitimat Fjord System.

4.5 Limitations

Combined Models, which included important temporal, spatial, and oceanographic terms, were able to predict between 46 and 80% of deviance in the abundance of E. pacifica and T. spinifera life stage groups and 30.6% and 45.7% of E. pacifica and T. spinifera biomass, respectively. The remaining deviance in the abundance and biomass of E. pacifica and T. spinifera may be due to (1) sampling limitations including euphausiid spatial patchiness and sample size and (2) additional environmental and biological variables that we were not able to measure but that may exert some control over euphausiid distribution and abundance in this system, including predation upon the euphausiid population, advection by currents, and euphausiid swarming behavior (Zhou et al., 2005; Mackas, 1992). Spatial and temporal distributions of euphausiids are often complicated by current advection and behavior of zooplankton (Zhou et al., 2005), but I was not able to measure or include the strength or direction of currents, nor the effects of euphausiid behavior in models. Estuarine circulation was likely strong at some points in the study season and euphausiids may have been advected into or out of the fjord system depending on their position in the water column. Vertical positions of individuals in the water column were likely reliant on behavioral responses such as diel vertical migration, which is known to vary seasonally and by size (stage) class (Bollens et al., 1992).

75 The species abundance and biomass data also have several limitations. In particular, due to the low number of independent zooplankton samples, models were forced to assume that the direction of relationships between response variables and predictor variables did not change over the season nor over the spatial scale of the fjord length (i.e. all predictor variables in models were fixed as no interaction terms could be included). This is potentially troublesome because the relative importance of spatial, temporal, and environmental factors affecting heterogeneity in the abundance of planktonic species are often different at varying spatial and temporal scales (Klais et al., 2016; De Robertis, 2002). However, since this study focused on relatively small scales, both spatially and temporally, the results are still informative. In addition, model validation did not show patterns in model residuals, so it is unlikely that any interactions were important.

4.6 Recommendations for Future Work

Further sampling over multiple years would provide valuable information on whether the responses I observed on a seasonal scale are consistent at annual or interannual temporal scales in this region. Since major climate indices shift on time scales of decades to centuries, long-term observations are necessary to capture specific ecosystem changes related to climate shifts and to identify and document the mechanisms driving the changes. I hope that data from this study will be used in long-term monitoring efforts to better understand changes to euphausiid distribution and abundance relative to season, geography, and oceanographic properties that are influenced by climate.

76 CHAPTER 5: CONCLUSIONS

I have presented a description of the distribution and abundance of all euphausiid species encountered in the Kitimat Fjord System during May-September 2015, and a description of the distribution of Euphausia pacifica and Thysanoessa spinifera biomass in this area. Using a series of competing model structures, I have investigated how a suite of temporal, spatial, and oceanographic factors influence the abundance and biomass of E. pacifica and T. spinifera in this region. Results suggest that the abundance of post-larval euphausiids is driven strongly by spatial factors, while the abundance of furcilia larvae is strongly affected by time of year. E. pacifica biomass in the Kitimat Fjord System is strongly affected by seafloor depth and sea surface temperature, and T. spinifera biomass is correlated with day of the year and distance into the fjord system. While separate Spatial, Oceanographic, and Temporal Models could each explain some of the deviance in observed abundances and biomass, Combined Models that included temporal, spatial, and oceanographic terms were consistently more parsimonious. In conclusion, abundance and biomass of E. pacifica and T. spinifera in the Kitimat Fjord System are driven by complex interactions between environmental forces that are often spatial, temporal, and oceanographic in nature. This study highlights the complexity of environmental forcing behind spatial and temporal heterogeneity in euphausiid abundance and is of ecological relevance as euphausiids are amongst the most important links in coastal and oceanic food webs.

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APPENDICES

91 Appendix A: Discussion of Temporal Differences between Paired CTD-Zooplankton Samples

Introduction

This appendix outlines the steps taken to evaluate potential oceanographic changes that could have occurred between paired CTD and zooplankton sampling at stations in the Kitimat

Fjord System, northern British Columbia, Canada. Data from these paired samples were used in models to evaluate oceanographic drivers of euphausiid abundance. Therefore, temporal gaps between paired CTD and zooplankton tows at stations could have presented problems if oceanographic conditions had changed between the time that CTD data and zooplankton data were collected. In this supplementary material, summarized information on precipitation, wind speed, and flow of freshwater into the study area is provided in order to identify storm events that could have caused changes in temperature, salinity, mixing, or estuarine circulation in the

Kitimat Fjord System during temporal gaps between CTD and zooplankton tows. CTD data are also summarized to evaluate within-survey oceanographic changes.

Potentially Problematic Data

Zooplankton tows and CTD casts were conducted at a grid of 19 stations within the

Kitimat Fjord System (Figure A1) repeatedly over the summer season of 2015 with the intent of gathering both euphausiid abundance and environmental data at specific locations across the fjord at different points in the summer in order to populate models. Four repeated surveys of the study area were made over the season: (1) May 24th - June 10th (“June”), (2) June 22nd - July 4th

(“July”), (3) August 5th - August 26th (“August”), and (4) August 31st - September 20th

(“September”). The surveys occurred approximately monthly with durations of 17, 12, 21, and

11 days. However, zooplankton were not sampled at all 19 stations in each survey and in some

92 cases up to three pseudo-replicates were taken consecutively at individual stations during a survey. These pseudo-replicates were taken within one hour of one another and, since they were not independent, were averaged for modeling. This yielded a total of 44 independent sample points where both CTD and zooplankton data were available. While the majority of paired CTD- zooplankton tows occurred within two hours of one another and it is unlikely that oceanographic conditions would have changed during this small amount of time, the remaining 14 paired samples occurred within the same survey period but more than one day apart (Figure A2, Table

A1). For these samples, it is necessary to examine the environmental conditions that existed between paired tows and casts in order to evaluate whether oceanographic conditions may have significantly changed over these times.

Figure A1. Map of the study area with channel names (left) and stations at which pairs of zooplankton and CTD samples were taken with station names (right). After Keen 2017b. Station names consist of a three-letter channel code followed by a number: CAA= Caamano Sound; CMP= Campania Sound; EST = Estevan Sound; SQU = Squally Channel; WRI = Wright Sound; WHA = Whale Channel; VER = Verney Passage; MCK = McKay Reach).

94 Table A1. Samples with time gaps between CTD and zooplankton tows exceeding 1 day, organized by increasing time between CTD cast and zooplankton tow.

Time of sampling Time difference Survey Station ID Zooplankton tow CTD cast (days) 3 CMP 6 8/17/2015 14:37 8/16/2015 09:10 1.23 4 WRI 4 9/04/2015 17:48 9/03/2015 08:24 1.39 3 EST 12 8/23/2015 13:30 8/21/2015 07:21 2.26 4 EST 12 9/15/2015 15:25 9/12/2015 08:34 3.29 4 SQU 5 9/15/2015 17:21 9/11/2015 13:55 4.14 1 CMP 9 6/02/2015 14:30 5/28/2015 14:53 4.98 3 MCK 10 8/19/2015 18:46 8/11/2015 07:56 8.45 3 WRI 4 8/20/2015 19:37 8/11/2015 15:04 9.19 4 SQU 12 9/15/2015 13:12 9/05/2015 15:05 9.92 3 WHA 3 8/18/2015 10:48 8/07/2015 16:18 10.77 4 MCK 6 9/13/2015 11:22 9/01/2015 17:31 11.74 4 VER 6 9/14/2015 12:18 9/02/2015 14:03 11.93 4 WHA 6 9/12/2015 17:53 8/31/2015 13:40 12.18 3 SQU 5 8/23/2015 11:58 8/05/2015 12:42 17.97

Figure A2. Location and time of the 44 paired CTD and zooplankton tows. Matched pairs are plotted on top of one another if they occurred consecutively or are attached by a horizontal line if they occurred more than two hours apart. Vertical lines are drawn at the 1st day of each month. The first number in the y-axis labels corresponds to the survey number in which the samples were collected and the following code of letters and numbers corresponds to the station at which the samples were collected.

96 Evaluation of Hydrographic Data

Precipitation, river flow, and wind data were extracted from the Environment and

Climate Change Canada Historical Hydrometric Data web site

(https://wateroffice.ec.gc.ca/mainmenu/historical_data_index_e.html) for the nearest hydrographic stations to the study area that had available historical data for 2015. Data were graphed with R in order to identify any storm events that may have caused changes in mixing or estuarine circulation in the Kitimat Fjord System (Figure A3). Several precipitation events occurred during time gaps between paired samples and it appears that the three largest of these precipitation events, which occurred on August 3rd, August 30th, and September 7th and 8th, resulted in sharp increases in Kitimat River discharge (Figure A3). However, from CTD data it appears that these increases did not cause significant changes to estuarine flow or oceanography.

High wind events did not often coincide with time gaps between paired zooplankton and CTD tows, although anomalously high wind speeds occurred on 8/30 and 9/9 in between zooplankton and CTD samples taken in survey 4 at stations WHA 6, MCK 10, VER 6, and SQU 12 (Figure

A3). However, since fjords are relatively protected with respect to high winds, especially in inner channels, I did not expect that wind significantly impacted mixing between any two pairs of zooplankton and CTD data.

Figure A3. Maximum daily wind speed at Bella Bella 136 km SE of the study area (extracted from hourly wind data), total precipitation recorded at Kitimat Townsite 104 km NE of the study area (recorded daily), and Kitimat River discharge data (recorded daily) for 5/28/2015 – 9/15/2015. All data were extracted from the Environment and Climate Change Canada Historical Hydrometric Data web site (https://wateroffice.ec.gc.ca/mainmenu/historical_data_index_e.html) on 1/18/2019.

98 Evaluation of CTD Data

All CTD data, which consisted of the 44 tows that were paired with zooplankton tows and an additional 50 casts that were conducted at stations, were used to check for evidence of extreme changes in oceanography within the duration of surveys. Large differences in the shapes of salinity and temperature profiles existed between inner and outer channels in all surveys, and shifted slightly over time within channels, but profiles taken within the same channel and survey period were similar (Figure A4).

Conclusion

Due to the apparent within-survey oceanographic stability, I conclude that temporal differences between pairs of CTD casts and zooplankton tows (which were limited to within- survey durations) were insignificant.

Figure A4. Depth profiles of temperature (top) and salinity (middle) from CTD casts. Colors represent different surveys in which tows were conducted. Profiles taken within the same geographic channel are plotted together, and channels are organized from innermost (left) to outermost (right).

100 Appendix B: Example R Code for Models

Introduction

This appendix contains example R code that was written to conduct modeling analyses.

The first section of this code automatically performs backwards selection of GAM models without the need to inspect model outputs after each term is dropped in order to decide which, if any, further variables should be dropped. The second section of code contains a “for loop” that repeats models with jackknife (leave-one-out) sampling. The third section of code combines the previous two sections, conducting backwards selected models with leave-one-out sampling. The fourth section of code can be used to find the percent of leave-one-out models in which each variable was significant at different p-value thresholds (e.g. p < 0.01, p < 0.05, p < 0.1).

Data

For simplicity, this appendix outlines only the modelling of E. pacifica post-larval abundance. Modelling of other abundance and biomass groups (E. pacifica late furcilia abundance, E. pacifica early furcilia abundance, E. pacifica biomass, T. spinifera post-larval abundance, T. spinifera late furcilia abundance, T. spinifera early furcilia abundance, and T. spinifera biomass) involved a similar code, but with all occurrences of “E.pacifica_PL” replaced with column names of other groups.

Data used in modeling of E. pacifica post-larvae consisted of a data frame with the columns of variables that I was interested in listed in Table A2. The response variable in the following examples is E.pacifica_PL, and other covariates are predictor variables. The following section of code have been formatted such they it can be directly copied into a script in R

101

Table A2. Columns in the data frame.

Column Name Meaning and Units Source E.pacifica_PL E. pacifica post-larval abundance, krill m-2 zooplankton samples Circuit Survey number (1,2,3, or 4) Recorded at time of sampling DOY Day of year sample was taken Recorded at time of sampling Time Time of sample, PST Recorded at time of sampling Seafloor Seafloor depth at station, meters Echosounder Sill_prox Marine distance to closest sill, m GPS Distance Distance into fjord, measured from random point offshore, m GPS SST Sea surface temperature, °C (temperature at shallowest reading) CTD SSS Sea surface salinity (salinity at shallowest reading), PSU CTD Temp_50m Temperature at 50 meters depth, °C CTD Chl Integrated chlorophyll readings (1 to 70 m), mg m-2 CTD

PART 1: Backwards Selection Code # assume that the data is saved as a .csv filed named “krill.data.csv” and is located in # folder C:/Users/Katie/Desktop/ResearchData setwd("C:/Users/Katie/Desktop/ResearchData") # set working directory krill.data<-read.csv('krill.data.csv', header = T, stringsAsFactors = F) # load data

################################################### # Perform backwards stepwise selection using AIC as criteria # (this code can be edited to use cross-validation as criteria instead) ################################################### library(mgcv) # load library for fitting GAMs

# store variables as individual vectors (makes next step less confusing) abundance<-krill.data$E.pacifica_PL DOY <- krill.data$DOY

102 Seafloor <- krill.data$Seafloor Distance <- krill.data$Distance Sill_prox <- krill.data$Sill_prox SSS <- krill.data$SSS SST <- krill.data$SST Temp_50m <- krill.data$Temp_50m Chl <- krill.data$Chl

############################## # Run loop for backwards selection ############################## # start with the full model. Define the formula you wish to model. Here, I have defined the model according to the Oceanographic competing model structure full.formula <- formula(abundance ~ s(SSS, k=3) + s(SST, k=3) + s(Temp_50m, k=3) + s(Chl, k=3)) # add whichever variables # you are interested in counter <- 1 # set counter to a value of 1. This is used later in the code to help us keep track of removed/ dropped terms full <- gam(full.formula) # run a GAM of the full model and store the model output AIC(full) # find AIC of the full model scopevars <- attr(terms.formula(full.formula), "term.labels") # store list of model predictors with knots n <- length(full$residuals) # store sample size (in my data, this was 44 independent samples) current_best <- full # store the model that is currently best: here, the full model since we have not run others while(T) { # create a while loop process that repeats until we meet the conditions “if (update_AIC > best_AIC) break” found # in code below – i.e. the loop is broken once all variables have been dropped that will lower AIC

best_AIC <- AIC(current_best) # store the AIC of the current best model temp <- summary(current_best) # store summary output of the current best model cms <- dim(temp$edf) # extract current model's size from summary output rnames <- (attr(terms.formula(temp$formula), "term.labels")) # store list of terms in the current model

103

compare <- matrix(NA,cms,1) # create a matrix to store AIC information rownames(compare) <- rnames # name the rows of the matrix # create a for loop within the while loop that drops one predictor at a time and calculates the AIC of the regression when that # predictor is dropped. for (i in 1:cms) { drop_var <- rnames[i] # variable to be deleted (each variable is deleted once and remaining variables are modeled) f <- formula(current_best) # current best formula f2 <- as.formula(paste(f[2], "~", paste(f[3], drop_var, sep=" - "))) term.labels<- attr(terms.formula(f2), "term.labels") new.f <- formula(paste(f[2], paste(term.labels, collapse="+"), sep=" ~ ")) # new formula new_fit <- gam(new.f) # fit the modified model (model with one term dropped) compare[i] <- AIC(new_fit)[1] # store modified model AIC in compare matrix that we created above } # end the for loop once all terms have been dropped once and resulting AICs have populated our compare matrix

# Next, we want to remove the variable from the model formula that caused the largest drop in AIC when it was dropped. remove_var <- rownames(compare)[which.min(compare)] # store variable name that we want to remove (variable that causes # the lowest resultant AIC when it is removed) update_AIC <- compare[which.min(compare)] # store AIC value that results when the variable above is dropped if (update_AIC > best_AIC) break # if update_AIC is lower than the current best AIC, proceed and remove variable. But break # the loop / do not continue if dropping variables if it will not improve AIC

write(paste("--- Dropping", counter, remove_var , update_AIC, "\n"), file="") # output the variables we are dropping so we can # watch as the loop drops terms and model AIC is improved

f2.1 <- formula(current_best) # store current formula f2.2 <- as.formula(paste(f2.1[2], "~", paste(f2.1[3], remove_var, sep=" - "))) # to modify the formula to completely remove the # chosen variable run this line and the following two term.labels<- attr(terms.formula(f2.2), "term.labels") new.f <- formula(paste(f2.1[2], paste(term.labels, collapse="+"), sep=" ~ "))

104 current_best <- gam(new.f) # we now have a new best model counter <- counter + 1 # update our counter and if “(update_AIC > best_AIC)” was true, continue the while loop to see which other variables should be dropped } # end the while loop print(current_best) # print the best, backwards selected model AIC(current_best) # print the AIC of the best model

PART II: Jackknife (Leave-One-Sample-Out) Code # assume that the data is saved as a .csv filed named “krill.data.csv” and is located in folder # C:/Users/Katie/Desktop/ResearchData setwd("C:/Users/Katie/Desktop/ResearchData") # set working directory krill.data<-read.csv('krill.data.csv', header = T, stringsAsFactors = F) # load data library(mgcv) # load library for fitting GAMs

# as in the previous code, store variables as individual vectors abundance<-krill.data$E.pacifica_PL DOY <- krill.data$DOY Seafloor <- krill.data$Seafloor Distance <- krill.data$Distance Sill_prox <- krill.data$Sill_prox SSS <- krill.data$SSS SST <- krill.data$SST Temp_50m <- krill.data$Temp_50m Chl <- krill.data$Chl

# define the formula you wish to model. As in Part 1, I have defined the model according to the Oceanographic competing # model structure model.form<-formula(abundance~s(SSS, k = 3) + s(SST, k = 3) + s(Temp_50m, k = 3) + s(Chl, k = 3)) # add whichever variables

105 # you are interested in dim( krill.data)[1] # There are 44 samples/rows in my data result<- vector("numeric", 44) # prepare a vector for storing AIC values

# use a “for loop” to remove one row/ sample, perform model, find AIC, store AIC in a vector for (i in 1:44){ # for each of the 44 rows, perform the following subset.data<- krill.data[-c(i), ] # remove this row/sample from the data frame (do this 44 times) model<-gam(model.form, data = subset.data) # model the subset of data (43 samples) AIC(model) # compute AIC of modeled subset result[i] <- AIC(model) # store the AIC value for this ith iteration in the ith term of the empty vector we created } # end the for loop result # inspect the 44 AIC values mean(result) # this is our mean AIC value

PART III: Jackknife (Leave-One-Sample-Out) with Backwards Selection # assume that the data is saved as a .csv filed named “krill.data.csv” and is located in folder # C:/Users/Katie/Desktop/ResearchData setwd("C:/Users/Katie/Desktop/ResearchData") # set working directory krill.data<-read.csv('krill.data.csv', header = T, stringsAsFactors = F) # load data library(mgcv) # load library for fitting GAMs

# define the model formula (here, the Oceanographic model is used for E. pacifica post-larval abundance) model.form<-formula(E.pacifica_PL~s(SSS, k=3) + s(SST, k=3) + s(Temp_50m, k=3) + s(Chl, k=3))

# Use loops to remove one row/ sample, BACKWARDS SELECT best model, find summary statistics, and store summary # statistics in vectors counter <- 1 # set counter to 1

106

# prepare vectors for storing model statistics for each leave-one-sample-out model modelAIC<- vector("numeric", 44) # prepare a vector for storing AIC values modelDevianceExp<- vector("numeric", 44) # prepare a vector for storing Deviance explained error.sq<-vector("numeric", 44) # prepare a vector for storing prediction error squared for (i in 1:44){ subset.data<-all.data[-c(i), ] # remove one row/sample from the data frame (do this 44 times) model<-gam(model.form, data = subset.data) # model the subset of data current_best <- model # store the current best model formula (that would be our full model for now)

while(T) { # process until we break the while loop/ drop all variables that will lower AIC best_AIC <- AIC(current_best) # store the AIC of the current best model temp <- summary(current_best) # store summary output of the current best model cms <- length(temp$edf) # store the current model's size if (cms==1) break # stop dropping terms if there is only one explanatory variable left rnames <- (attr(terms.formula(temp$formula), "term.labels")) # list of terms in the current model

compare <- matrix(NA,cms,1) # create a matrix to store AIC information rownames(compare) <- rnames # name the matrix rows

for (j in 1:cms) { # for each variable in the current model perform the following drop_var <- rnames[j] # store variable to be deleted (each variable is dropped and model is run with the remaining others) f <- formula(current_best) # store the current formula f2 <- as.formula(paste(f[2], "~", paste(f[3], drop_var, sep=" - "))) # modify the formula to completely remove the chosen # variable with this and the following two lines of code term.labels<- attr(terms.formula(f2), "term.labels") new.f <- formula(paste(f[2], paste(term.labels, collapse="+"), sep=" ~ ")) # new formula with jth term dropped new_fit <- gam(new.f, data=subset.data) # fit the modified model compare[j] <- AIC(new_fit)[1] # store AIC of modified model } # end the for loop

107

remove_var <- rownames(compare)[which.min(compare)] # drop the variable that causes the lowest (best) resultant AIC update_AIC <- compare[which.min(compare)] # store the AIC value that results when the chosen variable is dropped if (update_AIC > best_AIC) break # we should not continue if dropping a variable will not improve AIC write(paste("--- Dropping", counter, remove_var , update_AIC, "\n"), file="") # output the variables we are dropping

f2.1 <- formula(current_best) # store current formula f2.2 <- as.formula(paste(f2.1[2], "~", paste(f2.1[3], remove_var, sep=" - "))) # modify the formula to completely remove the # chosen variable using this line of code and the following two term.labels<- attr(terms.formula(f2.2), "term.labels") new.f <- formula(paste(f2.1[2], paste(term.labels, collapse="+"), sep=" ~ ")) # new formula

current_best <- gam(new.f, data=subset.data) # we now have a new best model counter <- counter + 1 # update our counter and continue until only one variable remains or dropping any variable will not # improve AIC } # end the while loop

print(current_best) # print the current best model modelAIC[i] <- AIC(current_best) # store the AIC value for this ith iteration in the ith term of the empty vector we created dev<- summary(current_best)[14]$dev.expl[1] # find deviance explained modelDevianceExp[i] <- as.numeric(dev) # store deviance explained for this iteration in the prepared vector error.sq[i]<-(predict(current_best,newdata=all.data[c(i),])- all.data$E.pacifica_PL[c(i)])^2 # store prediction error squared of best model for this iteration in the prepared vector } # end the for loop once all 44 leave-one-out models have been fit mspe<-sqrt(mean(error.sq)) # calculate mean MSPE ave.AIC<-mean(modelAIC) # calculate mean AIC perc.ave.dev<-mean(modelDevianceExp)*100 # calculate mean Deviance Explained as a percent round(rbind(ave.AIC, perc.ave.dev, mspe), digits=2) # output summary statistics in a table

108 PART IV: Jackknife Model Structures to Find % of 44 Models in which Each Variable was Significant at Different Levels (e.g. p < 0.01, p < 0.05, p < 0.1) # assume that the data is saved as a .csv filed named “krill.data.csv” and is located in folder # C:/Users/Katie/Desktop/ResearchData setwd("C:/Users/Katie/Desktop/ResearchData") # set working directory krill.data<-read.csv('krill.data.csv', header = T, stringsAsFactors = F) # load data library(mgcv) # load library for fitting GAMs

# define the model formula (here, the Oceanographic model is used for E. pacifica post-larval abundance) model.form<-formula(E.pacifica_PL~s(SSS, k=3) + s(SST, k=3) + s(Temp_50m, k=3) + s(Chl, k=3)) counter <- 1 # store counter as 1 (for later use)

# prepare empty vectors to store values for edf, F, and p for each variable. I have included only the variables here that are relevant to the Oceanographic model, but similar vectors can be prepared for any variable. edf.SST<-vector("numeric", 44) F.SST<-vector("numeric", 44) p.SST<-vector("numeric", 44)

edf.SSS<-vector("numeric", 44) F.SSS<-vector("numeric", 44) p.SSS<-vector("numeric", 44)

edf.Temp_50m<-vector("numeric", 44) F.Temp_50m<-vector("numeric", 44) p.Temp_50m<-vector("numeric", 44)

edf.Chl<-vector("numeric", 44)

109 F.Chl<-vector("numeric", 44) p.Chl<-vector("numeric", 44)

# use loops to run models and calculate variable statistics for (i in 1:44){ # for each sample (each row of the data), perform the following operations subset.data<-all.data[-c(i), ] # remove one row/sample from the data frame (do this 44 times) model<-gam(model.form, data = subset.data) # model the subset of data current_best <- model # store the current the best model (which is just our full model for now)

while(T) { # process until we break the loop/ drop all variables that will lower AIC best_AIC <- AIC(current_best) # store the AIC of the current best model temp <- summary(current_best) # store the summary output of the current best model cms <- length(temp$edf) # extract the current model's size if (cms==1) break # stop dropping terms if there is only one explanatory variable left rnames <- (attr(terms.formula(temp$formula), "term.labels")) # store list of terms in the current model compare <- matrix(NA,cms,1) # create a matrix to store AIC information rownames(compare) <- rnames # name the matrix rows

for (j in 1:cms) { # create a “for loop” that drops one predictor at a time and calculates the AIC of the model when that # predictor is dropped drop_var <- rnames[j] # store name of variable to be deleted f <- formula(current_best) # store current best model formula f2 <- as.formula(paste(f[2], "~", paste(f[3], drop_var, sep=" - "))) # modify the formula to completely remove the chosen # variable with this line of code and the following two lines term.labels<- attr(terms.formula(f2), "term.labels") new.f <- formula(paste(f[2], paste(term.labels, collapse="+"), sep=" ~ ")) # store new formula new_fit <- gam(new.f, data=subset.data) # fit the new model formula compare[j] <- AIC(new_fit)[1] # put the AIC of the new model into the "compare" matrix that we made above } # end for loop once compare matrix has been completely populated

# drop the variable that causes the largest drop in AIC when it is dropped

110 remove_var <- rownames(compare)[which.min(compare)] # store the variable name that when removed, results in the lowest # model AIC update_AIC <- compare[which.min(compare)] # store AIC value of model when the chosen variable from above is dropped if (update_AIC > best_AIC) break # we should not continue if dropping a variable will not improve AIC write(paste("--- Dropping", counter, remove_var , update_AIC, "\n"), file="") # output the variables we are dropping

f2.1 <- formula(current_best) # store current best formula f2.2 <- as.formula(paste(f2.1[2], "~", paste(f2.1[3], remove_var, sep=" - "))) # modify the formula to completely remove the # chosen variable using this line of code and the following two lines term.labels<- attr(terms.formula(f2.2), "term.labels") new.f <- formula(paste(f2.1[2], paste(term.labels, collapse="+"), sep=" ~ ")) # new best formula current_best <- gam(new.f, data=subset.data) # store new current best model counter <- counter + 1 # update our counter } # end the while loop if only one variable remains or dropping any further variables would not improve AIC table<-summary(current_best)$s.table # store each variable’s edf, F, and p-value

# if SST is included in the best model, store edf, F, and p-value for this variable in the vectors that were prepared at the beginning # of this code if (any(grepl("s(SST)", rownames(summary(current_best)$s.table), fixed=TRUE))) { row.SST<-table["s(SST)",] edf.SST[i]<-row.SST[1] F.SST[i]<-row.SST[3] p.SST[i]<-row.SST[4] }

# if SSS is included in the best model, store edf, F, and p-value for this variable in the vectors that were prepared at the beginning # of this code if (any(grepl("s(SSS)", rownames(summary(current_best)$s.table), fixed=TRUE))) { row.SSS<-table["s(SSS)",] edf.SSS[i]<-row.SSS[1] F.SSS[i]<-row.SSS[3]

111 p.SSS[i]<-row.SSS[4] }

# if Temp_50m is included in the best model, store edf, F, and p-value for this variable in the vectors that were prepared at the # beginning of this code if (any(grepl("s(Temp_50m)", rownames(summary(current_best)$s.table), fixed=TRUE))) { row.Temp_50m<-table["s(Temp_50m)",] edf.Temp_50m[i]<-row.Temp_50m[1] F.Temp_50m[i]<-row.Temp_50m[3] p.Temp_50m[i]<-row.Temp_50m[4] }

# if Chl is included in the best model, store edf, F, and p-value for this variable in the vectors that were prepared at the # beginning # of this code if (any(grepl("s(Chl)", rownames(summary(current_best)$s.table), fixed=TRUE))) { row.Chl<-table["s(Chl)",] edf.Chl[i]<-row.Chl[1] F.Chl[i]<-row.Chl[3] p.Chl[i]<-row.Chl[4] }

} # end the for loop once all 44 leave-one-out models have been backwards selected and all summary statistics have been stored

#### Calculate means of edf, F, and p-values, and find out what percentage of jackknifed models each variable was significant in # at the three different p-value thresholds (p<0.01, p<0.05, p<0.1)

# For SST edf.SST2<-edf.SST[edf.SST>0] # remove zero values (where variable was not included in best model) mean(edf.SST2) # find mean of statistic from instances where variable was included in the best model F.SST2<-F.SST[F.SST>0] # remove zero values (where variable was not included in best model) mean(F.SST2) # find mean of statistic from instances where variable was included in the best model

112 p.SST2<-p.SST[p.SST>0] # remove zero values (where variable was not included in best model) mean(p.SST2) # find mean of statistic from instances where variable was included in the best model p.SST_0.01<-p.SST2[p.SST2<0.01] # store p-values that were <0.01 where the variable was included in the best model p.SST_0.05<-p.SST2[p.SST2<0.05] # store p-values that were <0.05 where the variable was included in the best model p.SST_0.1<-p.SST2[p.SST2<0.1] # store p-values that were <0.1 where the variable was included in the best model

SST_1<-(length(p.SST_0.01)/44)*100 # calculate % of 44 jackknifed models in which SST was significant at p<0.01 level SST_2<-(length(p.SST_0.05)/44)*100 # calculate % of 44 jackknifed models in which SST was significant at p<0.05 level SST_3<-(length(p.SST_0.1)/44)*100 # calculate % of 44 jackknifed models in which SST was significant at p<0.1 level

# repeat this process for the other variables # SSS edf.SSS2<-edf.SSS[edf.SSS>0] mean(edf.SSS2) F.SSS2<-F.SSS[F.SSS>0] mean(F.SSS2) p.SSS2<-p.SSS[p.SSS>0] mean(p.SSS2) p.SSS_0.01<-p.SSS2[p.SSS2<0.01] p.SSS_0.05<-p.SSS2[p.SSS2<0.05] p.SSS_0.1<-p.SSS2[p.SSS2<0.1]

SSS_1<-(length(p.SSS_0.01)/44)*100 SSS_2<-(length(p.SSS_0.05)/44)*100 SSS_3<-(length(p.SSS_0.1)/44)*100

# Temp_50m edf.Temp_50m2<-edf.Temp_50m[edf.Temp_50m>0] mean(edf.Temp_50m2)

113 F.Temp_50m2<-F.Temp_50m[F.Temp_50m>0] mean(F.Temp_50m2) p.Temp_50m2<-p.Temp_50m[p.Temp_50m>0] mean(p.Temp_50m2) p.Temp_50m_0.01<-p.Temp_50m2[p.Temp_50m2<0.01] p.Temp_50m_0.05<-p.Temp_50m2[p.Temp_50m2<0.05] p.Temp_50m_0.1<-p.Temp_50m2[p.Temp_50m2<0.1]

Temp50_1<-(length(p.Temp_50m_0.01)/44)*100 Temp50_2<-(length(p.Temp_50m_0.05)/44)*100 Temp50_3<-(length(p.Temp_50m_0.1)/44)*

# Chl edf.Chl2<-edf.Chl[edf.Chl>0] mean(edf.Chl2) F.Chl2<-F.Chl[F.Chl>0] mean(F.Chl2) p.Chl2<-p.Chl[p.Chl>0] mean(p.Chl2) p.Chl_0.01<-p.Chl2[p.Chl2<0.01] p.Chl_0.05<-p.Chl2[p.Chl2<0.05] p.Chl_0.1<-p.Chl2[p.Chl2<0.1]

Chl_1<-(length(p.Chl_0.01)/44)*100 Chl_2<-(length(p.Chl_0.05)/44)*100 Chl_3<-(length(p.Chl_0.1)/44)*100

# Make table of percentages of times variables were included in models and below p-value thresholds SST<-rbind(SST_1, SST_2, SST_3)

114 SSS<-rbind(SSS_1, SSS_2, SSS_3) Temp50<-rbind(Temp50_1, Temp50_2, Temp50_3) Chl<-rbind(Chl_1, Chl_2, Chl_3) round(cbind(SST, SSS, Temp50, Chl), digits=1)

&lt;Abstract Centered&gt; an ABSTRACT of the THESIS OF

<Abstract Centered> an ABSTRACT of the THESIS OF