Influences of Thermal and Trophic Heterogeneity on Phenology, Growth and Resource Use by Juvenile Coho Salmon

AN ABSTRACT OF THE DISSERTATION OF

Emily Yvonne Campbell for the degree of Doctor of Philosophy in Fisheries Science presented on June 12, 2017.

Title: Influences of Thermal and Trophic Heterogeneity on Phenology, Growth and Resource Use by Juvenile Coho Salmon.

Abstract approved:

______Jason B. Dunham

Ecological resources available to freshwater fish shift spatially, temporally and across life stages. To better understand how spatial-temporal availability of resources influence fish, I examined the phenologies of hatching and emergence of Coho Salmon (Oncorhynchus kisutch) in streams with contrasting and strongly defined seasonal thermal variability. The study streams included groundwater dominated streams that were characterized by low spatial-temporal thermal variability, and surface-water dominated streams which had much higher variability in temperatures. In these streams, I quantified the timings of emergence and tracked individual fish and fish cohorts to understand the consequences of emergence phenologies for diets, bioenergetics (growth and consumption), body size and condition at the end of the growing season. Despite strong differences in thermal regimes hatch and emergence occurred at the same time among streams in the summer. In an attempt to understand the drivers behind this pattern I then evaluated spatial-temporal variability in thermal and trophic resources available to young-of year Coho Salmon post-emergence. Favorable growth temperatures (based on bioenergetic considerations) only occurred in the warmer surface-water stream in the summer months. However, individual growth

(g·d-1) of young-of year Coho Salmon was not significantly lower in the colder groundwater stream in the summer, despite previous reports indicating such temperatures to be un-favorable for growth. Macroinvertebrate prey availability was highest overall in the groundwater stream in the summer, but there was significant variability among habitats, seasons and years in all streams. Coho Salmon fed on both larval and adult forms of macroinvertebrates from the benthos, drift, and riparian areas; with the dominant prey item being all life stages of the non-biting midges (F. Chironomidae, O. Diptera). In the last chapter, I incorporated the data collected from my earlier chapters into a bioenergetics model to employ a mechanistic approach in understanding how spatial-temporal resource variability affects juvenile salmon growth dynamics. The bioenergetics model was fit to empirical measurements of growth rates, diet composition, energy densities of the predator (fish) and prey (macroinvertebrates), and water temperatures experienced by fish. Estimated consumption rates (g·g-1·d-1) were higher in the surface-water stream in the summertime, due to the warmer temperatures and thus higher metabolic cost compared to fish growing in the groundwater stream. Further, there was a significant positive relationship between fish size and % lipid content in the groundwater stream only, suggesting that size is related to condition for fish surviving in the colder groundwater stream. Through this work I was able to quantify the consequences of synchronous emergence phenologies for young-of-year Coho Salmon in streams with contrasting thermal and trophic resources. Though fish emerged at similar times, they emerged into environments that offered dramatically different conditions for growth. In spite of this, fish in these streams realized similar rates of growth (g·d- 1) and body sizes (mm) at the end of the growing season, with fish in a colder stream exhibiting higher condition. With respect to potential changes to thermal conditions in streams related to regional climate warming, my results highlight a high degree of flexibility in the response of young-of-year Coho Salmon. Understanding this flexibility from a detailed empirical and mechanistic perspective provided important and novel insights into precisely how early life stages of Coho Salmon will potentially respond to changing climates.

Influences of Thermal and Trophic Heterogeneity on Phenology, Growth, and Resource Use by Juvenile Coho Salmon

by Emily Yvonne Campbell

A DISSERTATION

submitted to

Oregon State University

in partial fulfillment of the requirements for the degree of

Doctor of Philosophy

Presented June 12, 2017 Commencement June 2018

Doctor of Philosophy dissertation of Emily Yvonne Campbell presented on June 12, 2017.

APPROVED:

Major Professor, representing Fisheries Science

Head of the Department of Fisheries and Wildlife

Dean of the Graduate School

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

Emily Yvonne Campbell, Author

ACKNOWLEDGEMENTS

There are several people to thank for helping me along this journey. Special thanks to my Ph.D. advisor Dr. Jason Dunham who is a great scientist and devoted mentor; his constant encouragement to think outside the box has made me a better scientist. I am grateful to Dr. Gordie Reeves for funding part of my dissertation, for the amazing trips up to Alaska, and for our interesting conversations about Copper River Delta ecology. Thanks to Dr. Steve Wondzell for our detailed scientific discussions and for providing a hydrologic and riparian perspective of stream processes. I am grateful to Dr. Sherri Johnson for her support and for being an inspiring female mentor. Thanks to Dr. Chris Marshall for serving as my graduate representative and for providing a much needed insect perspective. This project would not have been possible without the support of Oregon State University and the Department of Fisheries and Wildlife; I thank all affiliated professors, post-docs, students, and staff that have given me support during the past 5 years. I am grateful to my funding sources: The National Fish and Wildlife Foundation, the U.S. Department of Agriculture Forest Service PNW Research Station, the Environmental Protection Agency Science to Achieve Results (STAR) Fellowship; as well as the Washington County Flyfishers Marty Day Scholarship, the Neil Armantrout Graduate Fellowship, and the Distinguished Ph.D. Scholarship from the Oregon Chapter of the American Fisheries Society. I am grateful to the many people who assisted me in the field and laboratory. I thank the Pacific Northwest Research Station in Corvallis and the Cordova District of the Chugach National Forest for their assistance. Thanks to the Coho Club, Skeletons, Parr Group, and all other O.S.U. friends. I am thankful to my close friends in Portland, California, and Michigan who helped me during this arduous process. Thanks to my mother Sharon whose beautiful spirit is always with me. Lastly, I thank my entire family for their encouragement, especially my father Douglas, step-mother Joanna, and my little brother Zak.

CONTRIBUTION OF AUTHORS

Jason B. Dunham was an advisor and editor on all manuscripts included in this work.

TABLE OF CONTENTS

Page

CHAPTER 1- INTRODUCTION………………………………...…………….....1

Literature Cited….…………...………………………….……………………….10

CHAPTER 2- SYNCHRONY EMERGES FROM VARIABILITY: PHENOLOGY OF ALASKA COHO SALMON HATCHING AND EMERGENCE IN RELATION TO STREAM THERMAL REGIMES...……...17

Abstract……………………………………………………….…….…….……...18

Introduction …….…………………………………………………..……………19

Materials and Methods ……………………………………………...………...... 21

Results…………………….…………….………………………………….….…26

Discussion …………………….…………………………………...…….…...….30

Acknowledgements…………….…………….…………………………….….…36

Literature Cited…………………………………………………………….….…37

CHAPTER 3- DYNAMICS OF THERMAL AND TROPHIC RESOURCES FOR COHO SALMON IN ALASKA STREAMS WITH CONTRASTING THERMAL REGIMES…………………………...... 43

Abstract……………………………………….………………………..…..…….44

Introduction ………….………………………………………………...... ………46

Materials and Methods ……….…………………………………..………....…...50

Results……………………….…………….…………………………….…….…57

Discussion ……………………….……………………………………...…...…..74

TABLE OF CONTENTS (Continued)

Page

Acknowledgements…………….……………..…………………………………79

Literature Cited…………………………………………………………..………80

CHAPTER 4- SPATIO-TEMPORAL VARIABILITY IN FACTORS INFLUENCING GROWTH RATES, CONSUMPTION, SIZE, AND CONDITION OF YOUNG-OF-YEAR COHO SALMON REARING IN ALASKA STREAMS WITH CONTRASTING THERMAL REGIMES...... 87

Abstract……………… ……………………….……………………..…………..88

Introduction …………...…………………………………………………...….…90

Materials and Methods ...…………………………………………………….....93

Results..…………………….…………….…………………………..…..….….103

Discussion …...………………….……………………………………….….….107

Acknowledgements…...……….……………….…………………….....…....…114

Literature Cited……………………………..…………………………...…...…115

CHAPTER 5- CONCLUSION….……………………..……………..…...... ….123

Literature Cited…………….……………….…….……………..……....……...128

BIBLIOGRAPHY………………………………………………..……….....….134

LIST OF FIGURES

Figure Page

Figure 2.1. Map of the Copper River Delta, Alaska showing the location of the study streams……………………………………...……...………22

Figure 2.2. Water temperatures (°C) of the study streams from Sept 1, 2012 to Dec 31, 2013………………...……………………...…………..…….....26

Figure 2.3. Estimated embryo hatch timing of Coho Salmon in the study stream…………………………………………………………………...... 27

Figure 2.4. Estimated emergence timing of Coho Salmon in the study streams……………………………………………………………………....…...28

Figure 2.5. . Estimated emergence timing of Coho Salmon in the study Streams using a size cut-off of 35mm and below.……….……………………....29

Figure 2.6. Length (mm) of Coho Salmon fry at the end of the growing season (Oct) in the study streams. ………………………...……………..………30

Figure 3.1. Water temperatures (°C) in main-channel and off-channel habitats of the surface-water stream and the groundwater stream…..….……..…58

Figure 3.2. Deviations of the mean daily water temperatures (°C) in the off- channel habitats from mean daily water temperatures in the main-channel habitats of the study streams from 2013-2014…………………………...…..…..59

Figure 3.3. Macroinvertebrate prey biomass in thalweg drift (mg macroinvertebrates·100m3 water/hour) in the study streams from May to August 2014………………………………..……………………………….……70

Figure 3.4. Macroinvertebrate prey biomass in main-channel habitats in the groundwater and surface-water streams in July and September from 2013- 2014……………………………………………………………………………...71

Figure 3.5. Macroinvertebrate prey biomass in off-channel habitats in the groundwater and surface-water streams in July and September from 2013- 2014…………….…………………………………..………………………..…..72

Figure 3.6. Macroinvertebrate prey biomass in riparian habitats in the groundwater and surface-water streams in July and September from 2013- 2014……………………………………………………………………………...72

LIST OF FIGURES (Continued)

Figure Page

Figure 3.7. Proportion of non-biting midges (Family: Chironomidae, Order: Diptera) biomass (mg) in the guts of young-of-year Coho Salmon collected from a groundwater stream and a surface-water stream in 2013...... ………………………………………………………….....73

Figure 4.1. Water temperatures (°C) of the study streams from Sept 15, 2012 to Dec 31, 2014. …………….....…………………………………………104

Figure 4.2. Observed (empirical) mean juvenile Coho Salmon body length (FL, mm) from populations measured each month from June- October 2013 in the study streams...…………………………….….………..…105

Figure 4.3. Box-plots of measured growth rates (grams·day-1; +/-s.e.) of juvenile Coho Salmon during the 2013 growing season (1 July – 15 Oct) in the study streams...... …………………………………………………….…..105

Figure 4.4. Bioenergetic estimates of specific consumption (g·g·d-1) in the study streams over a range of simulated average monthly temperature values during the growing season….………………………………………...... 106

Figure 4.5. Estimated % lipid content of juvenile Coho Salmon dorsal muscle in the study streams in relation to fork lengths of individuals……………………………………………………….…..……….…107

LIST OF TABLES

Table Page

Table 2.1. Thermal conditions during embryo incubation: main- channel water temperatures (°C) from the start of spawning to the start of emergence in the 5 study streams…………………………..……………23

Table 2.2. Estimated hatch timing (Julian dates) of Coho Salmon in the study streams in 2013…..…………………………………….……....…... 25

Table 2.3. Estimated emergence timing (Julian dates) of Coho Salmon in the study streams in 2013...………………………………………..…25

Table 2.4. Size of Coho Salmon in October in the study streams in 2013……………………………………..………………………………………..30

Table 3.1. Descriptive statistics of main-channel thermal regimes from median emergence time to the end of the first growth year (Dec 31st) in 2013 in the study streams………………………………...………...58

Table 3.2. List of all macroinvertebrate taxa found in Coho Salmon guts and various habitats in the study streams from 2013-2014……..…..………60

Table 4.1. Parameters used in bioenergetic simulations of juvenile Coho Salmon in the study streams………………….………………………..….99

LIST OF SUPPLEMENTARY FIGURES

Supplementary Figure: Page

Figure S1A. Mean daily water temperatures (°C) taken in the stream thalweg (main-channel) and off-channel habitats of 18 Mile Creek...…………………………………………………….……...…..…..151

Figure S1B. Mean daily water temperatures (°C) taken in the stream thalweg (main-channel) and off-channel habitats of Blackhole Creek...………………………………………………………...….…151

Figure S1C. Mean daily water temperatures (°C) taken in the stream thalweg (main-channel) and off-channel habitats of Hatchery Creek....…….…152

Figure S1D. Mean daily water temperatures (°C) taken in the stream thalweg (main-channel) and off-channel habitats of Salmon Creek…...…….…152

Figure S1E. Mean daily water temperatures (°C) taken in the stream thalweg (main-channel) and off-channel habitats of 25 Mile Creek...... …….…153

Figure S2. Size-at-emergence of Coho Salmon in the study streams…..….…...153

Figure S3A. Age-length regression for young-of-year Coho Salmon in 18 Mile Creek based on the 2013 sampling season (May-Oct)..………..…...154

Figure S3B. Age-length regression for young-of-year Coho Salmon in Blackhole Creek based on the 2013 sampling season (May-Oct)...... ……….154

Figure S3C. Age-length regression for young-of-year Coho Salmon in Hatchery Creek based on the 2013 sampling season (May-Oct)…....…..…...155

Figure S3D. Age-length regression for young-of-year Coho Salmon in Salmon Creek based on the 2013 sampling season (May-Oct)..….....…..…..155

Figure S3E. Age-length regression for young-of-year Coho Salmon in 25 Mile Creek based on the 2013 sampling season (May-Oct)..…....……….156

Figure S4. Non-metric Multi-Dimensional Scaling ordination of overall diet (young-of-year Coho Salmon gut) content overlap in the study streams from 2012–2014………………………………...... …..…..156

LIST OF SUPPLEMENTARY FIGURES (Continued)

Supplementary Figure: Page

Figure S5. Non-metric Multi-Dimensional Scaling ordination of young-of-year Coho Salmon guts as compared to macroinvertebrates collected in the drift, main-channel, off-channel, and riparian habitats from 2012–2014 in the study streams……………………………………….….157

Figure S6. Non-metric Multi-Dimensional Scaling ordination of young-of-year Coho Salmon gut communities of fish collected from the main-channel (main) and off-channel (pool) habitats considering all streams and years (2012-2014)...……………………….…..….157

Figure S7. Mean invertebrate abundance in young-of-year Coho Salmon guts from June to October 2013 in the five study streams...………..…158

Figure S8. Overall (all guts pooled) invertebrate taxa diversity and relative abundances in young-of-year Coho Salmon guts ...... …………..…..…158

Figure S9. Mean invertebrate abundance in the surface-water stream (18 Mile) and a groundwater stream (25 Mile) from 2012-2014 with samples taken in main-channel benthic habitats in July and September each year (2012-2014)……………………………………………………….…159

Figure S10. Mean invertebrate abundance in the surface-water stream (18 Mile) and a groundwater stream (25 Mile) from 2012-2014 with samples taken in off-channel benthic habitats in July and September each year (2012-2014)…...... …159

Figure S11. Mean invertebrate abundance in the surface-water stream (18 Mile) and a groundwater stream (25 Mile) from 2012-2014 with samples taken in riparian habitats in July and September each year (2012-2014)…....……………………………………………………….………160

Figure S12. Consumed mean invertebrate biomass (mg invertebrates consumed) by young-of-year Coho Salmon from June to October in the surface-water stream (18 Mile) and the groundwater stream (25 Mile) in 2013...…………………………………………………………………………..160

Figure S13. Mean growth rates (mm per day; +/-s.e.) of different young-of-year Coho Salmon size classes (fork length, mm) in a surface-water stream (18 Mile) and a groundwater stream (25 Mile) during the 2013 sampling season May to October……………………….….…161

LIST OF SUPPLEMENTARY FIGURES (Continued)

Supplementary Figure: Page

Figure S14. Mean growth rates (mm per day) of young-of-year Coho Salmon regressed against Coho Salmon size (fork length, mm) in a surface-water stream (18 Mile) and a groundwater stream (25 Mile) during the 2013 sampling season May to October……..….…161

Figure S15. Box-plots of measured growth rates (mm·d-1) of juvenile Coho Salmon during the 2013 growing season in the study streams..………………………………………………………………..…162

Figure S16. Length (fork length, mm) to weight (grams) regressions for Coho Salmon in A) a surface-water stream (18 Mile) and B) a groundwater stream (25 Mile) during the 2013 sampling season from May to October…………………………………………………….…..…162

Figure S17. Mean growth rates (mm per day) of young-of-year Coho Salmon caught in off-channel (pool) habitats, main-channel (main) habitats, and fish that were tagged in one habitat and caught in another (transient) during the 2013 sampling season from May to October………………………………………………………………...…..…163

Figure S18. Mean daily growth rates (mm per day) of young-of-year Coho Salmon during different sampling periods based on when fish were tagged and recaptured………….…………………..…………………..…163

Figure S19. A) Relationship between variation in young-of-year Coho Salmon mean daily growth rates (mm per day) and variation in the water temperatures (°C) fish were exposed to during the growth period. ……………..……………………………………………...………....…164

Figure S20. Observed (empirical) population-level growth A) mean young-of-year Coho Salmon body length (FL, mm), and B) variation in young-of-year Coho Salmon body length from populations measured each month from June-October 2013 in the study streams.……....…164

Figure S21. Median body length (fork length, mm) of young-of-year Coho Salmon during the 2013 sampling season from May to October in the five study streams (18 Mile, Blackhole, Hatchery, Salmon, 25 Mile)...…165

LIST OF SUPPLEMENTARY FIGURES (Continued)

Supplementary Figure: Page

Figure S22. Specific consumption rates (grams per gram per day) for young-of-year Coho Salmon during one simulation year (julian dates) using the Wisconsin Bioenergetics Model. A) Simulated specific consumption in the surface-water stream (18 Mile) and B) the groundwater stream (25 Mile)……...... 166

Figure S23. Specific growth rates (grams per gram per day) for young-of-year Coho Salmon during one simulation year (julian dates) using the Wisconsin Bioenergetics Model. A) Simulated specific growth in the surface-water stream (18 Mile) and B) Simulated specific growth in the groundwater stream (25 Mile)………...... …167

Figure S24. Proportion of maximum consumption (p-values) for young-of-year Coho Salmon in a surface-water stream (18 Mile) and groundwater stream (25 Mile) simulated using the Wisconsin Bioenergetics Model...…….....…………………………………………………168

LIST OF SUPPLEMENTARY TABLES

Supplementary Table Page

Table S1. Estimated size at emergence (mm) based on stream- specific age-length regressions of Coho Salmon in the study streams in 2013…………………………………………………………………168

Table S2. Indicator species analyses showing the taxa that most closely associated with each habitat type in the study streams……..………………………………………………………………...…169

Chapter 1- Introduction

Emily Yvonne Campbell

Department of Fisheries and Wildlife, 104 Nash Hall, Oregon State University, Corvallis, Oregon USA 97331-3803

A major focus in fish ecology is understanding how the complex mosaic of abiotic and biotic factors in the environment influence fish throughout their life cycle. Understanding resource availability for fish through space and time is complicated by the fact that resources occur at multiple scales and resource needs vary by species (MacArthur and Pianka

1966, Pringle et al. 1988, Dunham et al. 2002). The availability of resources can affect timing of life history events (phenology), the growth dynamics of individuals and populations, and also the particular resources that fish can use. In this dissertation, I examined the linkages between environmental resource availability and the phenology, growth and resource use of the freshwater life stages of Coho Salmon (Oncorhynchus kisutch). These processes were examined in a network of streams characterized by strongly defined spatial and seasonal patterns in stream thermal regimes located in south-central Alaska on the Copper River Delta

(CRD). There were streams dominated by surface-water input which showed variable thermographs and ranged from 0 – 16° C annually, in contrast to groundwater dominated streams which had stable thermal regimes and generally reflected the mean annual air temperature (4° C) year-round. Coho Salmon populations occur in both surface-water and groundwater streams on the CRD, thus affording opportunities to examine how these fish respond to spatial and temporal variability in environmental conditions. I considered three integrated aspects of environmental effects on Coho Salmon: 1) hydro-climatic influences on the phenology of Coho

Salmon; 2) hydro-climatic influences on the thermal and trophic resources available to young-of-year Coho Salmon; and 3) linkages between resource availability and Coho Salmon growth using an empirically- informed bioenergetics model.

I addressed the first research goal in chapter two and examined the early life history phenologies of Coho Salmon, including hatch and emergence timings among streams with contrasting thermal regimes. The consequences of these phenologies on fish size during the first summer growing season were then evaluated. I hypothesized that the phenology of different stages in the Coho Salmon life cycle would be linked to the contrasting patterns of thermal variability among streams. The life cycle of

Coho Salmon is complex in that it involves both freshwater and marine environments with multiple transitions that must be timed accordingly to ensure growth opportunities for current and future life stages (Quinn

2005). Coho Salmon adults return from the sea to spawn in these streams from fall through early winter, with incubation occurring over winter and hatching and emergence occurring in the late spring and summer. After a period of freshwater growth, juvenile salmon undergo smoltification and emigrate to the sea. The survival rate of emigrants has been shown to be positively correlated with body size at the time of emigration (Mangel

1996, Thorpe and Metcalfe 1998). During the first year of life, juvenile

Coho Salmon must grow large enough and store enough energy reserves to survive a winter and subsequent year in freshwater before emigrating to

sea; although some individuals may leave after only one year or stay for up to three years, much remains unknown about exact emigration time.

Trophic resources available to young-of-year Coho Salmon in rearing habitats were additionally evaluated to understand potential linkages between Coho Salmon (predator) and the aquatic and terrestrial insects (prey) that they eat. To fully understand trophic resources, it is important to quantify where salmon are acquiring their energy and how these prey resources vary in space and time. Understanding resource use by juvenile fishes can be complicated, however, by high spatial and temporal variability in stream thermal regimes (Armstrong and Schindler

2013, Armstrong et al. 2016, Hines et al. 2017), and corresponding variability in macroinvertebrate prey available to juvenile Coho Salmon in space and time and at multiple scales (Wipfli and Baxter 2010).

Previous studies have shown that juvenile Coho Salmon feed mostly on drifting macroinvertebrates in streams, including the larval and adult stages of both terrestrial and aquatic insects (Nielsen 1992, Robillard

2006, Rosenfeld and Raeburn 2009). To establish if this was true in our study systems, macroinvertebrates in the drift, benthos (main and off- channel habitats), and riparian zones were examined; as well as the guts

(diets) of juvenile Coho Salmon in streams with varying thermal regimes.

This allowed me to determine where juvenile Coho Salmon were acquiring their energy, and how those resources varied through space and time among streams with contrasting thermal regimes. Young-of-year

Coho Salmon consumed prey from all habitats including drift, benthic, and riparian areas. Species in high-latitude environments must be capable of exploiting resources that are episodically available in time and space

(Armstrong et al. 2010, Armstrong et al. 2016) and having a wide breadth of diverse prey on which to feed likely allows young-of-year Coho

Salmon to feed all year.

The dominant prey item for Coho Salmon was the larval, pupal, and adult life stages of Chironomidae (Non-biting midges). Midges are multi-voltine flies which reproduce several times a year in our study streams and thus are available to Coho Salmon year-round. By having a broad range of taxa on which to feed, or prey groups that are multi-voltine and available all year, Coho Salmon are able to exploit variable trophic resources throughout the year depending on what is available each season.

Despite constant availability of prey, there were some patterns of seasonal variability whereby prey abundances were highest in the summertime and particularly in the groundwater stream. The higher prey availability in the summer months may explain, in part, why Coho Salmon emerge in the summer in order to capitalize on this more abundant food resource.

In addition to phenology and resource availability I also examined individual and population-level growth of Coho Salmon during the first year of life. Growth is a key component of fitness for juvenile fish

(Railsback and Harvey 2002) and food availability and consumption have been shown to dramatically affect fish growth rates and development

(Brett 1979, Ali et al. 1998, Hughes and Grand 2000, Sunderström et al.

2005). I predicted that the summer emergence timing observed in Chapter

2 coincided with the onset of favorable growing conditions for the subsequent young-of-year life stage. In Chapter 3, I further evaluated this prediction and examined the thermal and trophic resource conditions pre- and post-emergence to understand how phenology may be linked to environmental conditions.

Studies of the interaction between salmon resources such as food and temperature show that these two resources are not independent, because both influence net energy gain, and ultimately growth (Hanson et al. 1997, Hughes and Grand 2000). Growth is important for the early life history stages of salmon as prior work indicates that larger-bodied individuals are more likely to survive through the winter in freshwaters

(Quinn and Peterson 1996, Berg et al. 2009), as well as within the marine environment (Moss et al. 2005, Wainwright and Weitkamp 2013). Thus juvenile growth is a critical factor determining individual survival to becoming a reproductive adult (Roff 1992, Stearns 1992).

Results from chapter 4 show that the previously determined favorable growth temperatures for young-of-year Coho Salmon do not fully explain the patterns observed whereby fish grew at the same relative rate despite differences in thermal regimes. This difference in ideal growth temperatures between previous research and the work herein may be due to the fact that the previous research was based on laboratory

studies, or that fish in Alaska have thermally adapted to colder temperatures. Growth differences between streams are also non-significant when viewed from the population-level.

Growth rates depend in part on seasonal and spatial variation in resource availability which can alter consumer phenology, behavior and reproduction (Polis et al. 1997, Nowlin et al. 2008, Yang et al. 2010).

From an evolutionary perspective, given our results it is reasonable to assume that natural selection has shaped a summer emergence of salmon which is linked to a common and predictable pulse of resources in summer

(e.g., Townsend and Hildrew 1994). I hypothesized that emergence should be timed for juveniles to exploit seasonal availability of suitable temperatures for growth, peak availability of trophic resources, or both.

We found that overall prey availability was highest in the groundwater stream in the summertime. Temperatures considered by previous research to be conducive to salmon growth were only available in the surface-water stream in the summer and yet individual growth was slower overall in that stream. These results were likely due to relatively low prey availability and the higher consumption requirements as shown in chapter 4 by use of a calibrated bioenergetics model (Hewett and Johnson 1992, Hanson et al.

1997).

The bioenergetics model used in chapter 4 utilized empirical data including fish weight, growth temperatures, diet composition, and prey energy density to estimate consumption. The model accounted for energy

intake by fish, which was simulated by species-specific parameters that balanced the overall equation as the fish grew over time (Brandt and

Hartman 1993). Perhaps more importantly, the model accounted for the non-linear effects on those parameters and coefficients due to variables such as temperature and food intake (Hanson et al. 1997) in ways that are difficult to emulate with purely statistical models. Fish growth rates are known to be depressed by exposure to lower temperatures (Brett 1979,

Nicieza and Metcalfe 1997) and I predicted that growth rates would be lower and less variable in the groundwater stream, as it has a cooler thermal regime thought to limit juvenile Coho Salmon growth. I found however that growth rates in terms of individual mass (g·d-1) or population-level growth did not significantly differ between streams.

Although the surface-water stream reached warmer temperatures in the summertime, when emergence occurs, there was relatively low food availability and with warmer temperatures come higher maintenance costs.

Warmer temperatures can only fuel faster growth if food is not limiting

(Hansen et al. 1993, Hughes and Grand 2000). The groundwater stream had colder temperatures, but higher prey availability and lower physiological maintenance costs. Thus the interactive effects of these resource resulted in no size difference among populations of fish at the end of the growing season in streams with contrasting patterns of available temperatures and prey.

In this dissertation, I show that fish experiencing contrasting thermal regimes can attain the same relative size at the end of the growing season, due in part to variation in consumption, maintenance costs, and differences in the availability of macroinvertebrate prey between streams with contrasting thermal regimes. Taking a mechanistic approach to understand growth dynamics in fishes can elucidate trends in fish growth and consumption patterns. I show that bioenergetics modeling can provide a useful complement to rigorous multi-year field studies as it provides a more comprehensive understanding of how phenology, somatic growth, and resource use can relate to fitness.

The relative costs and benefits of surviving in streams with different thermal regimes translates to life-history and growth tactics that can act in concert with environmental constraints. With respect to potential changes to thermal conditions in streams related to regional climate warming, my results highlight a high degree of flexibility in the response of young-of-year Coho Salmon. Understanding this flexibility from a detailed empirical and phenology-focused perspective provided important and novel insights into precisely how early life stages of Coho

Salmon will potentially respond to changing climates.

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Chapter 2- Synchrony Emerges from Variability: Phenology of Alaska Coho Salmon Hatching and Emergence in Relation to Stream Thermal Regimes

Emily Yvonne Campbell1, Jason B. Dunham2, Gordon H. Reeves3 and Steve M. Wondzell3

1Department of Fisheries and Wildlife, Oregon State University, Corvallis, Oregon; [email protected] 2U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Corvallis, Oregon; [email protected] 3U.S. Forest Service, Pacific Northwest Research Station, Corvallis Forestry Sciences Lab, Corvallis, Oregon; [email protected], [email protected]

Abstract

Phenology can be linked to individual fitness, particularly in strongly seasonal environments where the timing of events can have important consequences for growth, condition and survival. To address this issue, we studied the phenologies of hatching and emergence of Coho Salmon in streams with contrasting and strongly defined seasonal thermal variability. Following emergence, we tracked body sizes of cohorts of fish until the end of the growing season. Hatch and emergence timing occurred at the same time among streams. We demonstrate that this can be explained in part by the thermal units accumulated during embryo development. However, accumulated thermal units alone do not fully describe the phenology patterns we observed and other factors are likely important to predict hatch and emergence timing. At the end of the growing season fish were largest in the colder groundwater streams, despite previous reports indicating that such temperatures are un-favorable for growth. Collectively, these results provide novel insights into the interactions between environmental variability and the early life-history stages of Coho Salmon to further our understanding of the consequences of phenology for individuals within the critical first summer of life.

Introduction

Phenology can be closely linked to an individual’s fitness, particularly in strongly seasonal environments where the timing of events can have important consequences for growth and survival (Einum and Fleming 2000, Love et al.

2010, Johansson et al. 2015). In such cases the phenology of transitions in an individual’s life cycle often coincide with environmental conditions that maximize growth and survival for a given life stage (Crozier et al. 2008, Pörtner and Farrell 2008, Gienapp et al. 2014, Standford et al. 2016). As a result, environmental conditions impose varying constraints on different life stages and a species’ phenology can evolve through natural selection to correspond with a suite of environmental conditions that maximize individual fitness (Stenseth and

Mysterud 2002, Visser and Both 2005). Furthermore, because favorable environmental conditions for one life stage are not necessarily favorable for another (Schluter et al. 1991), it is important to evaluate connections between multiple stages in the life cycle for a comprehensive understanding of phenology.

Species such as salmon and trout are good subjects for the study of linked phenologies as their life cycles include multiple stages. The life cycle can involve both freshwater and marine environments with multiple transitions that must be timed accordingly to ensure growth opportunities for current and future life stages

(Quinn 2005). Growth is important for the early life history of these species as prior work indicates that larger-bodied individuals are more likely to survive through the winter in freshwaters (Quinn and Peterson 1996, Berg et al. 2009), as well as within the marine environment (Moss et al. 2005, Wainwright and

Weitkamp 2013). In this study, we focused on freshwater life stages of Coho

Salmon (Oncorhynchus kisutch), from hatching through the first summer of life.

Within this timeframe we empirically examined multiple, linked phenologies, including hatch and emergence timings as well as the consequences of these phenologies on size near the end of the first summer growing season.

We focused our study on a system characterized by strongly defined spatial and seasonal patterns in stream thermal regimes. In these streams, Coho

Salmon hatch in nearly adjacent streams that can have radically different thermal regimes, ranging from surface-water dominated streams that have high seasonal thermal variability to groundwater-dominated streams that exhibit much lower thermal variability. Water temperatures are a critical factor in all freshwater life stages of salmon and trout (Elliot 1994, Quinn 2005). Temperature determines the developmental rate of embryos and alevins, and regulates metabolic rates and the relative partitioning of energy into growth versus maintenance. Thus, the large differences in the thermal regimes among the streams we studied likely have substantial implications for the overall fitness of populations inhabiting each stream.

Adults return from the sea to spawn in these streams from early fall through early winter, with incubation occurring over winter, hatching in the late- spring, and emergence occurring in summer. We hypothesized that hatching and emergence of salmon fry would coincide with the onset of favorable rearing conditions (e.g., higher prey availability, favorable growth temperatures, longer photoperiods) in spring and summer. Further we predicted that fish would attain

larger sizes at the end of the growing season in the surface-water streams, as only these streams have temperatures that previous reports consider favorable for growth. We addressed the phenologies of the early stages in the salmon life cycle

(hatching and emergence) and their consequences for individuals within the critical first summer of life (Elliott 1994, Fuiman and Werner 2002, Armstrong and Nislow 2006). Collectively, results of this work provide a unique evaluation of linkages between phenologies of successive life stages in a species with a complex life cycle that plays out in a highly variable environment.

Materials and Methods

Our study streams were located on the west Copper River Delta, southcentral Alaska, USA (Figure 2.1), these are: 18 Mile Creek

(N60.45882°W145.29285°), Blackhole Creek (N60.44695°W145.23999°),

Hatchery Creek (N60.59112°W145.63542°), Salmon Creek

(N60.45485°W145.17139°), and 25 Mile Creek (N60.44176°W145.11794°).

Within each stream, a 200m study reach was selected based on the presence of young-of-year Coho Salmon, accessibility to the site; and general similarities between sites including riparian vegetation, stream size, slope, and discharge. We assessed differences in thermal variability among streams with continuous year- round monitoring using water temperature data loggers (HOBO Pro, U-22 model,

Onset Corp., Pocasset, Massachusetts, USA). All loggers were encased in a galvanized pipe (6.3 cm × 15.2 cm) for protection and attached by steel cables to anchors that were driven into the stream bed to withstand frequent storms and

high-flows. At least 4, and up to 10 data loggers that were deployed within the main-channel spawning habitat of each stream and recorded water temperature at hourly intervals from September 1, 2012 to December 31, 2013 in 18 Mile,

Hatchery, Salmon, and 25 Mile Creeks; and from April 1, 2013 to December 31,

2013 in Blackhole Creek.

Figure 2.1. Map of the Copper River Delta, Alaska showing the location of the study streams.

A greater number of water temperature data loggers were placed in streams with higher thermal variation to accurately capture differences in thermal regimes. The mean daily temperature was calculated by summarizing point measurements of water temperatures as daily mean measurements. The incubation

conditions for salmon embryos in each stream were summarized for each stream using basic descriptors of thermal variability over time (Table 2.1). Incubation conditions in each stream were determined by analyzing the thermal conditions from the estimated start of spawning to the start of emergence (0.25 quantile,

Table 2.3) in Julian dates using one-way ANOVAs based on ranks and Dunn’s pair-wise comparisons. The estimated start of spawning for each stream was based on previous field observations as September 1st in 18 Mile and Blackhole

Creeks, and November 1st in Hatchery, Salmon and 25 Mile Creeks; based on previous field observations (Cordova U.S. Forest Service, personal communication). Due to hazardous weather conditions and presence of brown bears (Ursus arctos horribilis) during the spawning season, it was impossible to conduct comprehensive spawning surveys.

Table 2.1. Thermal conditions during embryo incubation: main-channel water temperatures (°C) from the estimated start of spawning (September 1st in 18 Mile and Blackhole Creeks, and November 1st in Hatchery, Salmon and 25 Mile Creeks; based on previous field observations) to the start of emergence (0.25 quantile, Table 2.3) in the 5 study streams (Figure 2.1). C.I. of Stream Mean ATUs St. Dev St. Error mean Range Max Min Median 25% 75% 18 Mile 2.12 593.57 2.85 0.17 0.33 9.2 9.2 0 0.42 0.02 4.24 Blackhole 2.74 789.98 3.15 0.18 0.36 10.93 10.93 0 1.16 0.02 5.5 Hatchery 2.59 596.52 0.94 0.06 0.12 5.31 6.31 1 2.44 1.91 2.97 Salmon 2.88 624.97 0.59 0.04 0.07 3.71 4.47 0.75 2.97 2.6 3.24 25 Mile 3.29 718.31 0.99 0.06 0.13 4.65 6.1 1.44 3.04 2.59 3.9

Recently emerged Coho Salmon were collected twice each month from

April to October in 2013 using baited minnow traps and dip nets. Fish were measured (fork-length, mm) and released, except for a random sub-set of 10 fish/stream/month which were euthanized with tricaine methanesulfonate (MS-

222) and kept frozen for analysis of hatch and emergence dates using otoliths

(Fuiman and Werner 2002). We analyzed a total of 122 otoliths (5 otoliths/stream/month from June-October) to determine hatch and emergence dates among the 5 study streams. Right sagittal otoliths were removed, rinsed in de-ionized water, mounted sulcus side up on a frosted glass slide, and adhered with Crystal Bond© heated thermoplastic cement (SPI Supplies, West Chester,

Pennsylvania, USA). After cooling, otoliths were wet-polished using 0.5µm and then 0.3µm grit paper and buffed with an aluminum oxide micro-polish until daily ring increments were discernible. In larger otoliths, the ring increments were not discernable from polishing one side alone and the other side was wet-polished using the same method as above to form a thin cross-section until daily rings were discernable.

Otoliths were analyzed under a compound microscope (Leica DMLS,

Buffalo Grove, Illinois, USA) using image analysis software (Image-Pro version

7.0, Media Cybernetics, Rockville, Maryland, USA). Ages were estimated by counting each complete daily increment (including increment and discontinuous zones) starting outside the primordia and counting outward in a straight trajectory to the last visible ring. Counts were conducted by two independent readers and if the counts differed by less than 5%, then the average count was used as the age estimate. If the counts differed by more than 5%, then the left otolith was prepared and read to clarify the count estimate. We used the daily increment technique, which is a well-recognized and widely used tool for fish age estimates

(Panella 1971, Jones 1992, Fuiman and Werner 2002). Age validation (Beamish

and McFarlane 1983, Campana 2001) was not possible due to logistical difficulty and the inability to obtain permits to chemically tag wild fish or mark and recapture known age fish. One-way analysis of variance (ANOVA) based on ranks and coupled with Dunn’s pair-wise comparisons were used to assess the significance of differences in dates of hatching (Figure 2.3) and emergence

(Figure 2.4), and fish size (Figures 2.5 & 2.6) among streams. Once the ages were estimated, we subtracted the estimated age from the date of capture to determine hatch and emergence timing (Fuiman and Werner 2002).

Table 2.2. Hatch timing (Julian dates) of Coho Salmon in the study streams (Figure 2.1) in 2013. Stream Mean Std Dev Std Error C.I. of Mean Range Max Min Median 25% 75% 18 Mile 135.04 46.23 9.44 19.52 154 216 62 118 98 185.5 Blackhole 128.57 30.51 6.36 13.19 118 191 73 123 108 142 Hatchery 130.48 28.47 5.69 11.75 117 196 79 135 103.5 147.5 Salmon 124.8 36.33 7.27 14.99 130 205 75 113 98 152 132.56 33.64 6.73 13.89 118 209 91 125 109 150 25 Mile

Table 2.3. Emergence timing (Julian dates) of Coho Salmon in the study streams (Figure 2.1) in 2013. Stream Mean Std Dev Std Error C.I. of Mean Range Max Min Median 25% 75% 18 Mile 193.17 41.63 8.5 17.577 120 242 122 207 157.25 233.75 Blackhole 191.09 33.1 6.9 14.315 103 238 135 190 166 220 Hatchery 195.44 34.75 6.95 14.344 129 258 129 204 169 214.5 Salmon 190.88 49.55 9.91 20.453 167 272 105 208 156 227.5 25 Mile 193.96 43.6 8.7 17.998 167 294 127 200 156.5 221.5

The cumulative frequency distribution (CDF) of emergence timing was based on population-level measurements of body lengths (fork length, millimeters) sampled at regular intervals every two weeks throughout the spring, summer, and fall (Figure 2.5). The CDF was calculated by taking the proportion of fish with measured body lengths of 35mm and below which were considered

recently emerged fish. The threshold of 35mm was selected because that was the size (based on field observations) when the yolk-sac was fully absorbed.

Differences in body size of juvenile salmon were determined by comparing mean lengths of all juvenile salmon measured in each stream in late October (Figure

2.6, Table 2.4).

Results

There were clear seasonal differences in thermal regimes among the five study streams (Table 2.1, Figure 2.2). 18 Mile and Blackhole Creeks were dominated by surface-water and showed higher annual thermal variation relative to the highly stable and groundwater-dominated thermal regimes exhibited by Hatchery,

Salmon, and 25 Mile Creeks.

Figure 2.2. Water temperatures (°C) collected from the main-channel of the study streams (Surface-water: 18 Mile and Blackhole Creeks and Groundwater: Hatchery, Salmon and 25 Mile Creeks) from Sept 1, 2012 to Dec 31, 2013.

18 Mile A

Blackhole A

Hatchery A

Salmon A

25 Mile A

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 Figure 2.3. Embryo hatch timing (based on otolith analysis, n=25 otoliths/stream for n=125 total otoliths analyzed) of Coho Salmon in the study streams. Study streams include: Surface-water streams: 18 Mile and Blackhole Creeks; and Groundwater streams: Hatchery, Salmon, and 25 Mile Creeks. Non-significant (p>0.05) differences are shown by the same letter.

18 Mile × A

Blackhole × A

Hatchery × A

Salmon × A

25 Mile × A

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2013 Figure 2.4. Emergence timing (based on otolith analysis, n=25 otoliths/stream for n=125 total otoliths analyzed) of Coho Salmon in the study streams. Surface- water streams: 18 Mile and Blackhole Creeks and Groundwater streams: Hatchery, Salmon, and 25 Mile Creeks. Non-significant (p>0.05) differences are shown by the same letter. × is the median emergence time in each stream based on a size cut-off (35mm and below).

Neither hatch dates (H=1.701, df=4, p=0.790; Figure 2.3, Table 2.2), nor the fry emergence dates (H=1.20, df=4, p=0.998; Figure 2.4, Table 2.3) differed significantly among streams. There were significant differences in the end of season mean juvenile body lengths among streams; with the largest fish occurring in 25 Mile and Salmon Creeks and the smallest fish occurring in Hatchery Creek

(H=18.753, df=4, p<0.001; Figure 2.6, Table 2.4). The cumulative frequency distribution showing the cohort emergence dates (Figure 2.5) largely corroborated the otolith-determined emergence timing and in each stream and these cohort emergence dates fit within 95% CI of the otolith-determined emergence dates

(Figure 2.4). The exception was Hatchery Creek in which size-determined emergence occurred later than all other streams and young-of-year salmon were smaller than fish in all other streams (Figures 2.5 & 2.6).

Figure 2.5. Estimated emergence timing of Coho Salmon in the study streams using a size cut-off of 35mm and below. Different symbols and lines show different study streams Surface-water: 18 Mile and Blackhole Creeks; and Groundwater streams: Hatchery, Salmon, and 25 Mile Creeks.

Figure 2.6. Length (mm) of Coho Salmon fry at the end of the growing season (Oct) in the study streams. Statistically significant differences (p<0.05) are shown by different letters, non-significant differences are shown by the same letter.

Table 2.4. Size of Coho Salmon in October in the study streams in 2013.

Discussion

We sought to understand the linkages between the widely heterogeneous thermal environments among the surface-water and groundwater streams on the

Copper River Delta, AK and the phenology of hatching and emergence of Coho

Salmon. There were significant differences in the thermal incubation environment

for embryos among the surface-water and groundwater streams. The thermal incubation environment was statistically similar in 18 Mile and Blackhole Creeks and both creeks showed a relatively high thermal range, variance, and standard deviation during embryo incubation, and throughout the rest of the year. In contrast, Salmon and 25 Mile Creeks had very low thermal variation during winter incubation. We predicted that phenologies may vary among streams as species inhabiting a wide range of winter thermal conditions may adopt variable tactics based in part on variable phenologies (Shuter et al. 2012). However, despite the observed thermal differences we found a relatively synchronized spring hatch timing and summer emergence timing among streams. The timing of

Coho Salmon egg hatching ranged from mid-April to June, and fry emergence occurred from June to August.

A similar timing of Coho Salmon hatch and emergence phenology among streams may be a response to the times at which food resources are available

(Godin 1982, Wipfli 1997, Einum and Fleming 2000, Letcher et al. 2004) as well as the need to attain sufficient size (Quinn and Peterson 1996) and condition

(Berg et al. 2009) to survive the winter. There was a pulse of increased macroinvertebrate prey availability in the groundwater stream in the summer as shown in chapters 3 and 4, where the food and temperature resources post- emergence are analyzed in detail. Although we observed the same relative hatch and emergence timing among streams, emergence timing was relatively protracted which may be due to genetic or environmental differences among individuals within and among redds within each stream (Steel et al. 2012, Sear et al. 2014).

In our system, an earlier emergence of juveniles could give them more time to attain size and condition, yet fish emerged well past the summer solstice, when photoperiods are longest and potential feeding opportunities may be greatest. This suggests other potential constraints on earlier emergence in this system, which could include lack of habitat availability due to icing of channels

(Brown et al. 2011) or low availability of insect prey in early spring and higher availability in the summer (Wipfli 1997, Rine et al. 2016). The observed synchronous timing of hatching and emergence for Coho Salmon is a pattern that has been more widely reported in other organisms (Liebhold et al. 2004).

Synchrony of phenological events has also been shown to occur in parallel with the timing of food supplies, for example as in insect-host plant interactions

(Dewar and Watt 1991, VanAsch and Visser 2006, Elzinga et al. 2007), or fledging of birds in concert with high densities of insect prey (Williams et al.

2015). The window for growth of emerging Coho Salmon is relatively short in our system, and it is reasonable to assume the capacity of individuals to grow within this brief window is at a premium.

The cumulative frequency distribution of emergence time based on size showed all streams having the same peak timing of emergence in July (as corroborated by otolith-determined emergence timing). With the exception of

Hatchery Creek, which had a much later estimated emergence time based on size.

The small size of young-of-year Coho Salmon in Hatchery Creek may be due to a later emergence, or due to a lake effect as Hatchery Creek flows into Eyak Lake just below our study reach and we surmise that Coho Salmon may emerge in the

main-stem of the stream and shortly thereafter emigrate to the lake for the majority of their juvenile life stage. Differences in young-of-year body size could additionally be due to among stream variability in food availability, habitat quality, water temperature effects on metabolic rate, or initiation of first-feeding, and other potential biotic or abiotic factors (Björnsson et al. 1989, Jensen et al.1991, Sandercock 1991, Rosenfeld et al. 2005, Quinn 2005).

Though timing of emergence was similar, the largest fish at the end of the growing season were observed in streams with the latest observed timing of emergence. These streams were also among the coldest during summer, and it is possible that other factors such as food availability are driving juvenile growth in these systems (Björnsson et al. 1989, Rosenfeld et al. 2005, Giannico 2000).

Juvenile Coho Salmon may also be using variable movement tactics in these systems to exploit growth opportunities, such as moving vertically or laterally to areas with higher food abundance or better temperatures for growth (Armstrong and Schindler 2013). The spatial and temporal variation in resources available to and used by juvenile salmon are known to mediate foraging opportunities and ultimately drive their growth trajectories (Armstrong et al. 2016).

Our results allowed us to evaluate linkages between multiple stages in the salmon life cycle and understand the consequences of phenology for individuals.

Climate change will continue to alter environmental conditions, shifting the seasonal activities of organisms with potential consequences to their fitness and life history trajectories (Power 2002, Visser and Both 2005). Furthermore, the effect of climate is not consistent across life stages and conflicting selection

pressures are often imposed on individuals in different life history stages

(Schluter et al. 1991). The particular factors that confer fitness are likely different for adults and juveniles; spawning adults may be driven mostly by selection pressures related to reproductive success, whereas embryos may be more driven by survival and rate of development. Our work points to the need for more detailed information on spawn timing as it relates to hatch and emergence timing in streams with contrasting thermal regimes.

We found that previously reported ATUs required for hatching and ATU models (Murray and McPhail 1987) do not fully describe the variability that we observed in hatch and emergence phenology. These previous ATU models were based on laboratory studies and the results therein were not accurate for predicting wild salmon phenology in Alaska. Further, past work reveals plasticity in temperature (ATU) requirements for embryo incubation both among locations, and among individuals (Alderdice and Velsen 1978, Farrell et al. 2008, Eliason et al. 2011, Steel et al. 2012). Accordingly, it seems reasonable that such variability plays out in our study streams, with the potential to explain variability in incubation timing that cannot be explained by ATU requirements alone. More detailed information on spawn timing and incubation (e.g., capping redds to track emergence) is needed to understand the full effects of temperature on that portion of the life cycle.

Potentially conflicting selection pressures within the life cycle are important when considering the costs and benefits of shifts in phenology. Pacific salmonids (Oncorhynchus spp.) have diverse phenological patterns and although

this diversity will aid population survival, it may not be enough to maintain populations in the future (Crozier et al. 2008, Wainwright and Weitkamp 2013,

Flitcroft et al. 2016, Hodge et al. 2016). Stream temperatures are expected to increase (Isaak et al. 2011), however, local factors including groundwater inputs, riparian shading, and channel geomorphology will likely modify these effects

(Arismendi et al. 2012). In this way, climate change will not likely have homogenous effects on temperature within or among streams, and the degree of these effects on fish phenology and demographics will be driven in part by local environmental factors (Penaluna et al. 2015). It is therefore important to understand the roles of phenology in the context of life history tactics that allow species to persist across complex landscapes and thermal regimes.

Acknowledgements

We thank C. Salazar, K. Kirkby, A. Morin, L. Adelfio, L. Hotca, C.

Malstrom, K. Hodges, D. Kuntzsch, S. Meade, M. Sloat, C. Zimmerman, M.

Heck, S. Johnson, C. Marshall, the Fisheries and Wildlife Department at Oregon

State University, and the entire Cordova Ranger District of the Chugach National

Forest. Thanks to K. Christiansen for providing the study map. This research was permitted by Oregon State University ACUP #4364. We are grateful to our funding sources: The National Fish and Wildlife Foundation, the U.S. Department of Agriculture Forest Service PNW Research Station, and the Environmental

Protection Agency Science to Achieve Results (STAR) Fellowship. Use of trade or firm names is for descriptive purposes only and does not constitute endorsement of any product or service by the U.S. Government.

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Chapter 3- Dynamics of Thermal and Trophic Resources for Coho Salmon in Alaska Streams with Contrasting Thermal Regimes

Emily Yvonne Campbell1, Jason B. Dunham2 and Gordon H. Reeves3

Abstract

Ecological resources for fish consumers in stream food webs shift seasonally, spatially and annually, providing a complex template of available resources to use and grow large enough to survive to adulthood and reproduce. We focused on understanding the spatial and temporal dynamics of trophic resources for Coho

Salmon (Oncorhynchus kisutch) in high-latitude streams with contrasting thermal regimes. One system was groundwater-dominated and had low spatial-temporal thermal variability, maintaining relatively constant temperatures all year; the other system was a surface-water dominated stream with greater variability in temperatures. Previous work on Coho Salmon phenology (Chapter 2) in these streams showed that peak fry emergence was in the early summer. In this study, we hypothesized that peak emergence occurred at a time of year when rearing conditions were favorable for growth in terms of the available thermal and trophic resources. To test this, we measured within and between stream thermal variability, macroinvertebrate prey availability, and juvenile Coho Salmon gut contents, both spatially and temporally in the groundwater and surface-water fed streams. Previous literature has determined that temperatures favorable for juvenile Coho Salmon growth range from 10-12°C and these temperatures only occurred in the surface-water stream in the summer months. Macroinvertebrate prey availability was highest overall in the groundwater stream in the summer, particularly in the thalweg drift, however, there was high variability among habitats, seasons, and years in both streams. Coho Salmon diet was composed of larval and adult macroinvertebrates from the drift, riparian, and benthic habitats;

and there was no significant difference in diet composition between streams. The dominant prey item in young-of-year Coho Salmon guts were fly larvae, pupae, and adults of the Family: Chironomidae (Order: Diptera). Chironomidae are multi-voltine and available year-round for Coho Salmon, with the highest consumption rates of Chironomidae occurring in the spring and summer. This study suggests that the synchronous emergence phenology of Coho Salmon observed in the summer (Chapter 2) may be timed to favorable growth temperatures and high prey availability. The thermal and trophic resources available to Coho Salmon can vary both spatially and temporally, here we show that emergence phenology may be timed to capitalize on high resource availability allowing good rearing conditions for subsequent life stages.

Introduction

Species in high-latitude environments must be capable of exploiting resources that are episodically available in time and space (Armstrong et al. 2010,

Armstrong et al. 2016). For example, long summer photoperiods at high latitudes can lead to pulses of ecosystem productivity, but only for a short time. Both seasonal and spatial variation in resource availability can alter consumer phenology, growth, behavior, and reproduction (Polis et al. 1997, Nowlin et al.

2008, Yang et al. 2010). Here we consider temporal and spatial variability in macroinvertebrate prey availability for juvenile Coho Salmon (Oncorhynchus kisutch) rearing in two streams with contrasting thermal regimes. Prior work in these systems indicated that peak hatching and emergence of Coho Salmon occurs in the early summer (July) and is relatively synchronous - even among streams with contrasting thermal regimes (Chapter 2). From an evolutionary perspective, it is reasonable to hypothesize that such synchrony in emergence timing between streams evolved through natural selection and is linked to a common and predictable pulse of resource availability for juveniles in summer (e.g., Townsend and Hildrew 1994).

Understanding resource use by juvenile fishes can be complicated, however, by high spatial and temporal variability in stream thermal regimes

(Armstrong and Schindler 2013, Armstrong et al. 2016), and corresponding variability in macroinvertebrate prey available to juvenile Coho Salmon in both space and time at multiple scales (Wipfli and Baxter 2010). In this study, we sought to quantify spatial and temporal pulses of thermal and trophic resources

available to juvenile Coho Salmon throughout the first year of life to evaluate predictions that a synchronous emergence timing among streams are timed to rearing conditions that allow for suitable growth conditions in the juvenile life stage. Growth is a key component of fitness for juvenile fish (Railsback and

Harvey 2002) and resource availability can be considered in terms of temperature and prey availability, since both can strongly influence bioenergetics and net energy gain (Magnuson et al. 1979, Hanson et al. 1997).

We studied two streams with contrasting thermal regimes: a groundwater- dominated stream with relatively constant temperatures (2-6°C) throughout the year and a surface-water fed stream with much greater seasonality in temperatures

(0-16°C), freezing in the winter and with relatively warm summer temperatures

(Chapter 2). In this study, we sought to understand the spatial-temporal complexity in rearing conditions for juvenile Coho Salmon in terms of both temperature and food availability. If we assume that faster growth is attainable within a thermal range of 10-12°C (assuming unlimited rations; Brett 1952,

Konecki et al. 1995, Hansen et al. 1997), then the only stream reaching these temperatures in summer is the surface-water stream. Seasonal patterns of thermal variability are important for juvenile consumers as cold temperatures characteristic of many streams can limit growth if food is not strongly limiting

(Edsall et al. 1999). In cases where juvenile Coho Salmon inhabit colder systems, some individuals move to exploit warmer temperatures that may allow them to grow faster (Armstrong and Schindler 2013, Armstrong et al. 2013). Accordingly, we examined spatial heterogeneity of temperatures within each stream to evaluate

the degree of opportunity for individuals to behaviorally thermoregulate (i.e., greater spatial heterogeneity should equate to more opportunity).

Temperature can also drive growth rates and development times of aquatic and semi-aquatic macroinvertebrates (Vannote and Sweeney 1980, Merritt et al.

2008) that serve as prey for juvenile Coho Salmon. This relationship varies by taxa and certain groups such as the Chironomidae (midges), a dominant food source for juvenile Coho Salmon, often have greater production in colder waters as these insects evolved in glacial streams (Ferrington 2008, Ferrington et al.

2008). In this way, trophic variability is likely to be high and will depend on the particular community present and the ecological demands that each taxon faces

(Lytle and Poff 2004, Yang et al. 2008, Wipfli and Baxter 2010).

The importance of food availability, like temperature, has been a long- appreciated resource need for juvenile Coho Salmon (Chapman 1965, Lister and

Genoe 1970, Giannico 2000, Baldock et al. 2016). To fully understand trophic resources, it is important to quantify 1) where consumers acquire their energy

(i.e., sources of prey), and 2) how these resources vary in space and time.

Rosenfeld and Raeburn 2009). To establish if this was true in our study systems, we quantified diets of juvenile Coho and compared them to the corresponding availability of macroinvertebrates quantified in the drift, as well as the benthos

(main and off-channel habitats), and riparian zones. This allowed us to determine

where juvenile Coho Salmon were acquiring their energy in different study streams, and how those prey resources varied through space and time.

Specifically, we quantified temporal variability in prey available to juvenile Coho

Salmon in each stream to determine if Coho Salmon fry emergence timing

(Chapter 2) coincided with maximum availability of prey.

Our overall objective was to understand differences in thermal and trophic resource availability for young-of-year Coho Salmon following emergence in two streams with contrasting thermal regimes. We hypothesized that emergence should be timed for juveniles to exploit seasonal availability of suitable temperatures for growth, peak availability of trophic resources, or both. Specific objectives with respect to temperatures were to 1) quantify summer thermal regimes within two proximate streams representing groundwater and surface- water thermal regimes, and 2) within each of these streams, quantify spatial heterogeneity in temperatures (main channel versus off-channel habitats) to evaluate the potential for thermal complementation of habitats (sensu Dunning et al. 1992, Falke et al. 2013). We hypothesized that salmon emergence should occur at a time of year when trophic resources are high, allowing for ideal rearing and growth conditions for the subsequent juvenile life stage. Specific objectives with respect to trophic resources were to 1) identify similarities between diets of juvenile Coho salmon and potential sources of macroinvertebrate prey, 2) evaluate patterns of prey availability over time in relation to emergence timing of juvenile Coho Salmon, and 3) describe spatial patterns of prey availability between and within each of the two study streams.

Materials and Methods

Study Streams

Our study streams were located on the west Copper River Delta, southcentral Alaska, USA, these included: a surface-water dominated stream, 18 Mile

Creek (N60.45882°W145.29285°) and a groundwater dominated stream, 25 Mile

Creek (N60.44176°W145.11794°). From here forward, we refer to 18 Mile Creek as the “surface-water” stream and 25 Mile Creek as the “groundwater” stream.

Both streams have open canopies with riparian zones consisting primarily of alder

(Alnus spp.), willows, Sitka spruce (Picea sitchensis), as well as several species of grasses and sedges. The main-channel habitats were fast-flowing areas in the thalweg of the stream, and the off-channel habitats consisted of relatively shallow areas immediately adjacent to the main-channel with substrates composed of fine organic material, silt, and mud.

Within each stream, a 200m study reach was selected based on the presence of juvenile Coho Salmon, accessibility to the site and general similarities between them including riparian vegetation, stream size, slope, and discharge.

We assessed differences in thermal variability within and between streams with continuous year-round monitoring using water temperature data loggers (HOBO

Pro, U-22 model, Onset Corp., Pocasset, Massachusetts, USA). All loggers were encased in a galvanized steel conduit pipe for protection and attached by steel cables to anchors that were driven into the stream bed to withstand frequent storms and high-flows. Each stream had 10-12 data loggers that were deployed

within main-channel and off-channel habitats and recorded water temperature at hourly intervals from May 15, 2013 to June 1, 2014.

The mean daily temperature, 푇 , was calculated by summarizing point measurements of water temperatures in both main-channel and off-channel

(∑ 푡(°퐶)) habitats in as daily mean estimates defined by 푇 = where, 푇 is the mean 24 daily temperature, and t is the hourly temperature. Descriptive statistics were calculated for main-channel temperatures from the median emergence timing to the end of the first growing year (Dec 31st) in 2013. We tested for significant differences of main-channel temperatures between streams using Mann-Whitney

Rank Sum t-tests. Statistics of thermal regimes calculated included: the mean, variance, maximum temperature, minimum temperature, cumulative degree days, and the 25% and 75% of the cumulative distributions. To understand within stream thermal variability, we calculated residual temperature differences between off-channel and main-channel habitats. We tested for differences in the absolute value of these residuals using Mann-Whitney Rank Sum t-tests from the median emergence timing previously determined (Chapter 2) in each stream which was July 26th in the surface-water stream and July 19th in the groundwater stream, to the end of the first growing year (December 31st) in 2013.

Trophic Resources

Benthic Macroinvertebrates- Benthic prey availability was assessed in main- channel and off-channel habitats within each study stream across seasons and years. Macroinvertebrates were collected from four (n=4) main-channel habitats each sampling period, once in July and once in September from 2012-2014. In

the main-channels, a Hess sampler (diameter 36cm, 250 μm) was used by pushing the sampler into the benthic sediments (approx. 10cm) to minimize water exchange. Samples were collected by disturbing the substratum within the sampler for 1 min as the water flow pushed all benthic macroinvertebrates into the terminal collecting net. The sample was then placed in a tray with stream water, passed through a 250 μm sieve, and placed in a plastic vial with 70% ethanol for later taxonomic identification. In the laboratory, all invertebrates were identified, counted, measured (mm), and biomass (mg wet mass) was determined using published length-weight regressions (Meyer 1989, Sample et al. 1993, Hódar

1996, Burgherr and Meyer 1997, Kawabata and Urabe 1998, Benke et al. 1999,

Sabo et al. 2002, Gruner 2003, Miyasaka et al. 2008, Merritt et al. 2008).

Sampling of benthic macroinvertebrates in off-channel habitats involved sampling a known area of benthic substrates with a PVC quadrat (1m2) and D- frame kick-net (250 μm) to standardize macroinvertebrate densities and biomass to area. Samples were collected by disturbing the substratum within the quadrat for 1 min using a D-net. The collected sample was then placed in a tray with stream water, passed through a 250 μm sieve, and placed in a plastic vial with

70% ethanol for later taxonomic identification in the laboratory to identify, count, measure, and determine biomass as described above. Macroinvertebrates were collected from four (n=4) off-channel habitats each sampling period, once in July and once in September from 2013-2014. Differences in macroinvertebrate prey biomass (mg/m2) were determined using Mann-Whitney rank sum t-tests using

Sigma Plot (version 13.0, San Jose, California, U.S.A.). We plotted these data as

box plots with standard error bars showing significant (p<0.05) differences between streams for each season and year.

Terrestrial Macroinvertebrates- Riparian (terrestrial) macroinvertebrates were collected once in July and once in September from 2012-2014, following methods reported by Wipfli (1997). Eight (n=8) floating pan traps (800cm2 surface area with 10cm high sides) containing approximately 7ml of dish soap (preservative) and 3 L of stream water were placed on stream-water surfaces within each study reach. Traps were placed haphazardly along the study reach approximately evenly on the surface of both main-channel and off-channel habitats. The traps were tied with rope to stable riparian vegetation such as overhanging trees or exposed tree roots. Floating pan traps were left for 3 days and upon collection the sample was passed through a 250 μm sieve to filter out all macroinvertebrates. The sample was then rinsed with stream water to remove excess soap and placed in a plastic vial with 70% ethanol for later laboratory processing. In the laboratory, all invertebrates were identified, counted, measured (mm) under a SMZ445 Nikon stereomicroscope (Nikon Instruments Inc., Melville, New York, U.S.A.), and biomass (mg wet mass) was determined using published length-weight regressions (Meyer 1989, Sample et al. 1993, Hódar 1996, Burgherr and Meyer

1997, Kawabata and Urabe 1998, Benke et al. 1999, Sabo et al. 2002, Gruner

2003, Miyasaka et al. 2008, Merritt et al. 2008). Differences in macroinvertebrate prey biomass were determined using Mann-Whitney rank sum t-tests using Sigma

Plot (version 13.0, San Jose, California, U.S.A.). We plotted these data as box

plots with standard error bars showing significant (p<0.05) differences between streams for each season and year.

Drift Macroinvertebrates- To quantify macroinvertebrate prey availability in the thalweg drift, we collected drift samples once a month from May-August 2014 in the study streams. We evenly spaced two drift nets (0.32m×0.7m, 250 μm mesh) in the thalweg to measure drift at 2-hour intervals for a 24-hour period. The nets were elevated slightly (~5cm) to capture drifting macroinvertebrates only. Stream discharge (m3/s) was recorded at the front of each net. Drift nets were checked and processed every 2 hours. Upon collection, drift nets were rinsed to collect all macroinvertebrates at the terminal end of the net and then the sample was placed in a tray with stream water. Any large debris, sticks or rocks were rinsed and removed. The sample was passed through a 250 μm sieve and then placed in a polyethylene bag with 70% ethanol for later laboratory work. In the laboratory, all collected specimens were identified, counted, measured (mm) under a SMZ445

Nikon stereomicroscope (Nikon Instruments Inc., Melville, New York, U.S.A.), and biomass (mg wet mass) was determined using published length-weight regressions (Meyer 1989, Sample et al. 1993, Hódar 1996, Burgherr and Meyer

1997, Kawabata and Urabe 1998, Benke et al. 1999, Sabo et al. 2002, Gruner

2003, Miyasaka et al. 2008, Merritt et al. 2008). Hourly drift counts were summarized over the entire day and a mean was calculated from the two drift nets to compare drift among months. We calculated drift density (# macroinvertebrates drifting per 1003m of water) as: 퐷푟푖푓푡 퐷푒푛푠푖푡푦 =

[(푁)(100)] where, N is the number of macroinvertebrates collected, t is the [(푡)(푊)(퐻)(푉)(3600)]

time the drift nets set (hours), W is the width of the drift net, H is the height of the drift net, and V is the water velocity at the time the sample was taken (Smock

2007). Drift density was then converted to drift biomass (mg macroinvertebrates drifting per 1003m of water) using published length-weight regressions.

Emergence of Coho Salmon fry occurred in July (Chapter 2) and we sought to quantify the available prey in during the emergence period (from May to August).

We tested for differences in macroinvertebrate drift biomass (mg/100m3) during the time spanning fry emergence between streams with Mann-Whitney rank sum t-tests using Sigma Plot (version 13.0, San Jose, California, U.S.A.). We plotted these data as vertical bar charts with standard error bars showing significant

(p<0.05) differences.

Coho Salmon Diets

Young-of-year Coho Salmon were sampled every two weeks from May to

October in 2013-2014. During each sampling date, fish were collected with minnow traps (40cm long × 23cm wide), each containing a perforated 7 × 17cm plastic bag of salmon eggs as bait. We placed 32 traps in main-channel and off- channel habitats throughout each study reach on each sampling date. Traps were left for approximately 60 minutes and upon collections of the traps, fish were placed in a 20 L bucket of fresh stream water with an aerator. A random sample of 16 Coho Salmon juveniles per capture date were removed in each stream, euthanized with tricaine methanesulfonate (MS-222), and kept frozen for laboratory analysis of gut contents. All remaining Coho Salmon captured were released.

In the laboratory, frozen fish were first thawed and then under a SMZ445

Nikon stereomicroscope (Nikon Instruments Inc., Melville, New York, U.S.A.) a ventral incision was made from esophagus to anus and stomach contents were removed with forceps and placed into a plastic bag with 70% ethanol. All macroinvertebrates in fish guts were identified to the lowest practical taxonomic level, counted, measured to the nearest 0.5mm, and biomass was estimated using published length-weight regressions (Meyer 1989, Sample et al. 1993, Hódar

1996, Burgherr and Meyer 1997, Kawabata and Urabe 1998, Benke et al. 1999,

Sabo et al. 2002, Grunner 2003, Miyasaka et al. 2008, Merritt et al. 2008). For partially-digested prey, actual body measurements were not possible and instead we estimated lengths based on intact individuals of the same taxon and size

(Wipfli 1997). Non-Metric Multi-Dimensional Scaling (NMDS) ordinations were carried out to evaluate spatial-temporal differences in macroinvertebrate community structure in habitats (main-channel, off-channel, riparian, and drift) between streams (Figure S4) and among various habitats (Figure S5) within streams using PC ORD (version 7; MJM software, Glenden Beach, Oregon,

USA). For each ordination, we first log-transformed the data and then ran a total of 250 iterations for the real data with a random seed start. Multiple Response

Permutation Procedures (MRPP) using Sørensen (Bray-Curtis) distance measures were performed to test for significant differences in community structure among different habitats, streams, and years. The mean abundance of prey was additionally calculated between streams from June to October (Figure S7). The proportion of the dominant prey item (Chironomidae larvae) in young-of-year

Coho Salmon guts was quantified during the 2013 growing season from April to

October (Figure 3.7; Figure S8).

Results

There were clear differences in thermal regimes during the first year (from here forward termed ‘growing season’) for young-of-year Coho Salmon (from emergence to the end of the first year of life) in 2013 between the study streams

(Ustat=9636, df=166, p<0.001; Figure 3.1, Table 3.1). Temperatures in the surface- water stream, showed higher annual thermal variation (mean= 6.32°C, variance=21.43) during the growing season compared to the relatively stable thermal regime in the groundwater-dominated 25 Mile Creek (mean= 4.02°C, variance=1.22). The only stream that reached hypothetically favorable growth temperatures for Coho Salmon (based on previous literature values, 10°C- 12°C

(Brett 1952, Konecki et al. 1995, Hansen et al. 1997) was the surface-water stream in the summertime from mid-July to the end of October 2013, whereas the groundwater stream never attained previously determined favorable temperatures for growth (Figure 3.1).

Figure 3.1. Water temperatures (mean daily; °C) in the main-channel and off- channel habitats of the study streams from 2013-2014.

Table 3.1. Descriptive statistics of main-channel thermal regimes from median emergence time to the end of the first growth year (Dec 31st) in 2013 in the study streams.

The surface-water fed stream accumulated more degree days (DD=1049) than the groundwater fed system (DD=667) during the first growth season, from median emergence to the end of the first growing season (Table 3.1). The surface-

water stream showed a wider range of temperatures (max temp= 14.91°C, min temp=0.037°C) than the groundwater stream (max temp= 6.50°C, min temp=1.79°C). There were also clear differences in the within-channel thermal variability between study streams during the first juvenile Coho Salmon growing season in 2013, with significantly higher thermal variability in the surface-water stream (Ustat=3923, df=166, p<0.001; Figure 3.2). Overall, the surface-water stream had higher thermal variation both over time and in space, whereas the groundwater stream had comparatively stable thermal regimes in both dimensions.

Surface-water Groundwater 10 P3 10 P4 P1 P5 P2 P6 P3 P4

5 5

0 0

-5 -5

Mean Temperature Difference Difference Temperature Mean C) Main-channel, - (Off-channel

-10 -10 Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct 2013 2014 2013 2014 Figure 3.2. Deviations of the mean daily water temperatures (°C) in the off- channel habitats from mean daily water temperatures in the main-channel habitats of the study streams from 2013-2014. Symbols represent the four different off- channels sampled in each stream.

A complete list of macroinvertebrate taxa collected in all habitats, Coho

Salmon guts, and streams are listed (Table 3.2) showing Chironomidae (midges) as the dominant taxa collected in all habitats and streams (Table S2).

Macroinvertebrate drift biomass (mg of macroinvertebrates/100m3·hr-1) varied

during the time of fry emergence and throughout the growing season in both study streams (Figure 3.3). In May and June, drift biomass did not significantly differ between streams, however there were significantly more macroinvertebrate prey in the groundwater stream in July (183× greater in 25mi; Ustat=28, df=24, p<0.001) and August (16.3× greater in 25mi; Ustat=68, df=24, p<0.01; Figure 3.3).

Table 3.2. List of all invertebrate taxa found in the surface-water system (18 Mile Creek) and the groundwater system (25 Mile Creek) from 2012-2014.

Surface-water Stream Phylum Sub-Phylum Order Class Family Life- Main- Off- Coho Sub-Class Genus Stage Channel Channel Riparian Drift Guts Annelida Clitellata × Hirudinea Oligochaeta × × × × × Arthropoda Chelicerata × × × × × Arachnida Acari Pseudoscorpiones Araneae × × × × × Arthropoda Crustacea × × × Branchiopoda Cladocera Maxillopoda × × × × Copepoda Ostracoda × × × × × Arthropoda Hexapoda × × × × Entognatha Collembola Poduromorpha Symphypleona × × × ×

Arthropoda

Hexapoda Coleoptera × Insecta Buprestidae A Carabidae A × × Carabidae L × × Chrysomelidae × Donacia L Chrysomelidae A × × Coccinellidae A Curculionidae A × × Dytiscidae × × Hydrotrupes A Dytiscidae A × × Dytiscidae L × × × × Elateridae A × × × Elateridae L Elmidae A × Gyrinidae A × × Haliplidae A × Hydrophilidae L Meloidae A Melyridae A × × × Ptilidae A × × Scarabidae A × Staphylinidae A × × × × Arthropoda Hexapoda Diptera Insecta Bibionidae A Calliphoridae A × × × Calliphoridae L × × × × Ceratopogoniidae × Forcipomyia L Ceratopogoniidae × × × × Probezzia A Ceratopogoniidae × × × × Probezzia L Chironomidae A × × × × × Chironomidae L × × × × × Chironomidae P × × × × × Culicidae A × × × Culicidae L × Dixidae L ×

Dolichopodidae A

Dryomyzidae A Empididae Hemerodromia A Empididae A × × × × Empididae L × × × Empididae P × Muscidae A × × × × × Mycetophilidae A × × × × × Pipunculidae A Psychodidae A × Sarcophagidae A × × Sciomyzidae A × × Sciomyzidae P × Simuliidae × × Simuliium L Simuliidae A × × × × × Simuliidae L × × × × Simuliidae P × × × Syrphidae A Tabanidae A Tipulidae × × Dicranota L Tipulidae Hexatoma L Tipulidae A × × × × × Tipulidae L × × × × Tipulidae P

Arthropoda Ephemeroptera

Hexapoda Ameletidae Insecta Ameletus L Baetidae × × Baetis A Baetidae × × × × Baetis L Baetidae L × × Cinygmula A × Cinygmula L × Heptageniidae L × Leptophlebiidae L × Arthropoda Hexapoda Hemiptera × × × × Insecta Aphididae

Ciccadelliae A × × × × Corixiidae A × × × × × Miridae A × × × × Pentatomidae A × Psyllidae A × × Reduviidae A Arthropoda Hexapoda Hymenoptera × × Insecta Tenthredinidae A Asillidae A Braconidae A × × × × Diapriidae A Eulophidae A Formicidae A × Ichneumonidae A × × × × × Mymaridae A × × × Pteromalidae A × × × Sepsidae A Sphecidae A × Arthropoda Hexapoda Lepidoptera Insecta Gelichilidae L Geometridae L × × × Pterophoridae A Arthropoda Hexapoda × Insecta Neuroptera A Arthropoda Hexapoda Odonata × Insecta Aeshnidae A Libellulidae A Arthropoda Hexapoda Plecoptera Insecta Capniidae A Chloroperlidae Suwallia A Chloroperlidae × × × Suwallia L Chloroperlidae A × Chloroperlidae L × × Nemouridae × Zapada A

Nemouridae × × × Zapada L

Arthropoda Hexapoda Psocoptera Insecta Psocidae A Arthropoda Hexapoda Thysanoptera × × × Insecta Thripidae L/A Arthropoda Hexapoda Trichoptera × Insecta Brachycentridae L Hydroptilidae Micrasema L Limnephilidae × × × × Ecclisomyia L Limnephilidae × × × Onocosmoecus L Limnephilidae × × × × Psychoglypha L Limnephilidae A × × × Limnephilidae L × × × × × Limnephilidae P × Phryganeidae A Arthropoda Myriapoda × Chilopoda Cnidaria Medusozoa Anthomedusae × × Hydrozoa Hydridae Leptolinae Hydra Mollusca × × × × Bivalvia Mollusca × × × × Gastropoda Nematoda × × × Platyhelminthes × × × ×

Groundwater Stream

Phylum Sub-Phylum Order Class Family Life- Main- Off- Coho Sub-Class Genus Stage Channel Channel Riparian Drift Guts Annelida Clitellata Hirudinea

Oligochaeta × × × × × Arthropoda Chelicerata × × × × × Arachnida Acari Pseudoscorpiones × Araneae × × × × × Arthropoda Crustacea × × × Branchiopoda Cladocera Maxillopoda × × × × × Copepoda Ostracoda × × × × × Arthropoda Hexapoda × × × × × Entognatha Collembola Poduromorpha Symphypleona × × × × Arthropoda Hexapoda Coleoptera Insecta Buprestidae A Carabidae A × × × × Carabidae L × Chrysomelidae Donacia L Chrysomelidae A × Coccinellidae A × Curculionidae A × Dytiscidae × × Hydrotrupes A Dytiscidae A × × × Dytiscidae L × × Elateridae A × × × Elateridae L ×

Elmidae A

Gyrinidae A × × × Haliplidae A Hydrophilidae L × Meloidae A × Melyridae A × × × Ptilidae A × × Scarabidae A Staphylinidae A × × × Arthropoda Hexapoda Diptera × Insecta Bibionidae A Calliphoridae A × × × Calliphoridae L × × Ceratopogoniidae × Forcipomyia L Ceratopogoniidae × × × × Probezzia A Ceratopogoniidae × × × × Probezzia L Chironomidae A × × × × Chironomidae L × × × × × Chironomidae P × × × × Culicidae A × × × × Culicidae L Dixidae L × × × Dolichopodidae A × Dryomyzidae A × Empididae × Hemerodromia A Empididae A × × × × × Empididae L × × × × Empididae P × Muscidae A × × × × Mycetophilidae A × × × Pipunculidae A × × Psychodidae A Sarcophagidae A × × × Sciomyzidae A × × × × Sciomyzidae P × Simuliidae Simuliium L

Simuliidae A × × × Simuliidae L × × × Simuliidae P × × Syrphidae A × × Tabanidae A × Tipulidae × × × × Dicranota L Tipulidae × Hexatoma L Tipulidae A × × × × Tipulidae L × × × × × Tipulidae P ×

Arthropoda Ephemeroptera × Hexapoda Ameletidae Insecta Ameletus L Baetidae × × × Baetis A Baetidae × × × Baetis L Baetidae L × Cinygmula A × Cinygmula L Heptageniidae L Leptophlebiidae L Arthropoda Hexapoda Hemiptera × × × × Insecta Aphididae Ciccadelliae A × × × × × Corixiidae A × × × × × Miridae A × × × Pentatomidae A Psyllidae A × × × × Reduviidae A × Arthropoda Hexapoda Hymenoptera × × Insecta Tenthredinidae A Asillidae A × Braconidae A × × × Diapriidae A × Eulophidae A × × Formicidae A × Ichneumonidae A × × × ×

Mymaridae A × × × ×

Pteromalidae A × × × Sepsidae A × Sphecidae A × × Arthropoda Hexapoda Lepidoptera × Insecta Gelichilidae L Geometridae L × × × Pterophoridae A × Arthropoda Hexapoda Insecta Neuroptera A Arthropoda Hexapoda Odonata Insecta Aeshnidae A Libellulidae A × Arthropoda Hexapoda Plecoptera × Insecta Capniidae A Chloroperlidae × × Suwallia A Chloroperlidae × × × Suwallia L Chloroperlidae A × × × Chloroperlidae L Nemouridae × × Zapada A Nemouridae × × × × Zapada L Arthropoda Hexapoda Psocoptera × Insecta Psocidae A Arthropoda Hexapoda Thysanoptera × × × × Insecta Thripidae L/A Arthropoda Hexapoda Trichoptera Insecta Brachycentridae L Hydroptilidae × Micrasema L Limnephilidae × × × × × Ecclisomyia L

Limnephilidae × × Onocosmoecus L Limnephilidae × × × × Psychoglypha L Limnephilidae A × × × Limnephilidae L × × Limnephilidae P × Phryganeidae A × Arthropoda Myriapoda Chilopoda Cnidaria Medusozoa Anthomedusae × Hydrozoa Hydridae Leptolinae Hydra

Mollusca × Bivalvia Mollusca × × × × Gastropoda Nematoda × × × × Platyhelminthes × × × ×

1000 1e+6106 Surface-water 900

Groundwater DaysDegree Cumulative * 800

1e+5105 700

600

4 500 1e+410 * 400 300 3 1e+310 200

100

Thalweg Invertebrate Drift Biomass (mg inverts/100^3/hr)

1e+2102 May June July August

Figure 3.3. Macroinvertebrate prey biomass in thalweg drift (mg macroinvertebrates/100m3 water/hour) in the study streams from May to August 2014. Showing a Log10 scale, +/-s.e. * indicates significant (p<0.05) differences. Dotted line and circles represent the cumulative degree days in each stream.

Between the main-channels in the two streams, there was significantly higher prey availability in the groundwater stream, particularly in September 2013 and July 2014 (Ustat=62, df=4, p=0.023; Figure 3.4; Figure S9). No other significant differences existed, but main-channel prey availability was always higher in the groundwater stream. In comparisons off-channel habitats between the streams, prey availability was significantly higher in the groundwater stream

(Ustat=85, df=4, p=0.02; Figure 3.5; Figure S10), with the exception of September

2013 and 2014 where there was no significant difference. In the riparian zones, prey availability was a significantly higher in the groundwater stream in July 2013 and 2014 (Ustat=66, df=4, p=0.03; Figure 3.6; Figure S11) and there was

significant variability among streams and years with a general decline in riparian invertebrate availability from 2013 to 2014.

106 18 Mile (Surfacewater) 25 Mile (Groundwater)

* 105

104

103

102

Main-Channel Invertebrate Biomass Invertebrate Main-Channel

10 July Sept July Sept 2013 2014 Figure 3.4. Main-channel macroinvertebrate prey biomass (mg per m2) in the groundwater and surface-water streams in July and September from 2013-2014. Showing a Log10 scale, +/-s.e. * indicates significant (p<0.05) differences.

18 Mile (Surfacewater) 25 Mile (Groundwater) 108

7 10 *

106

105

104

103

Off-Channel Invertebrate Biomass Invertebrate Off-Channel 10

10 July Sept July Sept 2013 2014 Figure 3.5. Off-channel macroinvertebrate prey biomass (mg per m2) in the groundwater and surface-water streams in July and September from 2013-2014. Showing a Log10 scale, +/-s.e. * indicates significant (p<0.05) differences.

7 10 18 Mile (Surfacewater) 25 Mile (Groundwater) 106

105

104 * 103

102

Riparian Invertebrate Biomass Riparian Invertebrate

1 July Sept July Sept 2013 2014 Figure 3.6. Riparian macroinvertebrate prey biomass (mg per m2) in the groundwater and surface-water streams in July and September from 2013-2014. Showing a Log10 scale, +/-s.e. * indicates significant (p<0.05) differences.

Surface-water Stream 0.8 Groundwater Stream

0.6

0.4

0.2

Proportion of Chironomidae Larvae in Diet in LarvaeChironomidae of Proportion 0.0 Oct Apr May Jun Jul Aug Sep Figure 3.7. Proportion of non-biting midges (Family: Chironomidae, Order: Diptera) biomass (mg) in the guts of young-of-year Coho Salmon collected from a groundwater stream and a surface-water stream in 2013.

Consumed invertebrate biomass was higher in June and August within the groundwater stream (25 Mile) but not the surface-water stream (18 Mile; Figure

S12). The NMDS ordinations and MRPP revealed no significant differences in macroinvertebrate community structure in Coho Salmon guts between streams

(Figure S4). A total of 78.9% of the variation in gut macroinvertebrate community structure between streams was explained by a three axis solution: 1st axis =

30.4%, 2nd = 31.4%, and 3rd = 17.1% and the mean stress was 15.9. We ran separate ordinations for the gut macroinvertebrate assemblages in various habitats

(Figure S5) among streams and years and did not find a significant difference.

The NMDS and MRPP analyses showed no significant clustering of Coho Salmon gut contents with any particular prey communities in the various habitats

measured (Figure S5). For the surface-water stream: A total of 70.8% of the variation in macroinvertebrate community structure between guts and habitats was explained by a three axis solution: 1st axis = 14.4%, 2nd = 26.8%, and 3rd =

29.6% and the mean stress was 16.2. For groundwater: A total of 78.3% of the variation in macroinvertebrate community structure between guts and habitats was explained by a three axis solution: 1st axis = 16.1%, 2nd = 25.5%, and 3rd =

36.7% and the mean stress was 15.1. In each stream there was overlap of Coho

Salmon diets with macroinvertebrates collected in all habitats (main-channel, off- channel, riparian, and drift). Pooling all guts together the dominant prey item were larval and adult forms of Chironomidae (midges) with the highest consumption of midge biomass occurring in May in the groundwater stream, and peak midge consumption occurring in July in the surface-water stream (Figure

3.7).

Discussion

We considered temporal and spatial variability in resource conditions potentially influencing young-of-year Coho Salmon rearing in strongly seasonal, high-latitude streams. Prior work in these systems indicated that hatching and emergence of Coho Salmon occurs in the summer and is relatively synchronous, even among streams with contrasting thermal environments (Chapter 2). To understand the potential environmental drivers behind this emergence synchrony, we focused on two important resources influencing juvenile Coho Salmon consumers: water temperature and prey availability. These factors were evaluated in rearing habitats within streams characterized by heterogeneous thermal regimes

following emergence in the summertime. Patterns of availability of temperatures suitable for growth, and periods of high prey availability, varied depending on the stream thermal regime. In the surface-water dominated stream, prey availability was relatively low but growth temperatures were favorable (based on previous laboratory studies (Brett 1952, Konecki et al. 1995)) in contrast, the groundwater dominated stream had relatively high prey availability in July but temperatures which were far below those considered to be favorable for growth based on previous literature values (Brett 1952, Konecki et al. 1995).

Thermal resources also varied along the lateral axis (main-channel temperatures and off-channel) between streams. The surface-water stream showed a relatively high degree of thermal variation where the main-channel was warmer than off-channel habitats in the summer but cooler in the winter. In the groundwater dominated stream there was no variation in the main-channel and off-channel temperatures, providing a relatively homogenous thermal environment for fish to grow. Juvenile Coho Salmon may also be using variable movement tactics in these systems to exploit growth opportunities, such as moving vertically or laterally to areas with better temperatures for growth

(Armstrong and Schindler 2013). The spatial and temporal variation in thermal resources available to and used by juvenile salmon are known to mediate foraging opportunities and ultimately drive their growth trajectories (Armstrong et al.

2016).

In terms of trophic resources, it is well known that juvenile Coho Salmon growth is critically dependent on the quality and quantity of the prey available

(Lang et al. 2006, Rinella et al 2012). Previous studies have shown that young- of-year salmon feed mostly on drifting macroinvertebrates in streams, including the larval and adult stages of both terrestrial and aquatic insects (Nielsen 1992,

Wipfli 1997, Nakano et al. 1999, Nakano & Murakami 2001, Baxter et al. 2005,

Robillard 2006, Rosenfeld and Raeburn 2009). These studies corroborate our results which show that Coho Salmon are feeding on larval and adult macroinvertebrates in the drift, benthos, and riparian zone. We examined these prey resources before, during, and after the estimated timing of Coho Salmon fry emergence and found higher overall macroinvertebrate biomass availability in the thermally-stable groundwater stream. This trend was particularly evident in the drift during the summer months (July and August) in 2014, which corresponded with the timing of peak fry emergence.

The dominant prey item, pooling all gut contents (among streams, habitats and years) were larval and adult forms of Chironomidae (non-biting midges) which are multi-voltine and thus were available in varying abundances all year.

Several other studies have found Chironomidae to be a dominant prey item for juvenile Coho Salmon (Dill et al. 1981, Nielsen 1992, Minakawa and Kraft 1999,

Quinn 2005). The Chironomidae evolved in glacial streams and often have greater production in colder waters (Ferrington 2008, Ferrington et al. 2008) and we expected greater abundances of this dominant prey item in the groundwater stream. A recent study conducted in these same streams during same years as ours corroborates this and showed that Chironomidae production was significantly

higher in the groundwater stream with peak abundance for all streams occurring in the winter and early spring (Hertel 2016).

There were also peaks of invertebrate prey availability in September in the off-channel habitats of the surface-water stream, and the riparian habitats of the groundwater stream. By late summer the long days and warmer temperatures are likely boosting production of macroinvertebrates living in riparian areas, thus increasing the number of terrestrial insects falling into streams. Fish consumers can then exploit these peaks in terrestrial prey availability later in the year which are likely important for juveniles and fish that emerge in the late summer and fall

(Giannico 2000, Rosenfeld et al. 2005, Rosenberger et al. 2011). There was however high variability in invertebrate prey abundance for young-of-year Coho

Salmon and the availability of prey varied considerably over both space and time.

In this study, we sought to quantify spatial and temporal pulses of availability of environmental resources available to juvenile Coho Salmon to evaluate predictions that a synchronous emergence between streams is timed to maximize opportunities for growth during the relatively short summer growing season. Ecological resources in stream food webs are known to shift both seasonally and spatially, providing a complex template for consumers to use available resources and grow large enough to survive and pass their genes on to the next generation. Apparent environmental drivers of phenology varied between streams. Favorable growth temperatures (based on previous research) for juvenile

Coho Salmon only occurred in the surface-water stream in the summer months from July-September, whereas macroinvertebrate prey availability was highest in

the groundwater stream during this time. These findings are consistent with the hypothesis that the synchronized emergence phenology (Chapter 2) of Coho

Salmon in these systems may be linked to complementary drivers of growth (prey availability and temperature, depending on the stream). Additional work to more explicitly link these drivers to growth and condition of juvenile Coho Salmon in these systems is needed to more fully understand the fitness consequences of complex interactions between emergence phenology, prey availability, and temperature.

Acknowledgements

We thank A. Morin, L. Hsu, K. Kirkby, C. Salazar, M. Scanlan, L. Hotca, L.

Adelfio, C. Malstrom, D. Kuntzsch, K. Hodges, S. Meade, S. Hertel, B. Gerth, M.

Berg, R. Merritt, K. Cummins, S. Wondzell, S. Johnson, C. Marshall, J. Li, M.

Wipfli, the Fisheries and Wildlife Department at Oregon State University, and the entire Cordova Ranger District of the Chugach National Forest. This research was permitted by Oregon State University ACUP #4364. We are grateful to our funding sources: The National Fish and Wildlife Foundation, the U.S. Department of Agriculture Forest Service PNW Research Station, and the Environmental

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Chapter 4- Spatio-Temporal Variability in Factors Influencing Growth Rates, Consumption, Size, and Condition of Young-of-Year Coho Salmon Rearing in Alaska Streams with Contrasting Thermal Regimes

Emily Yvonne Campbell1, Jason B. Dunham2 and Gordon H. Reeves3

Abstract

Growth models based on bioenergetics are increasingly being used to make significant fisheries management decisions worldwide. It is important to inform these models with empirical data in order to best reflect the natural environment that fish are experiencing and to gain a clear understanding of the mechanisms impacting fish growth. In this study, we sought to understand juvenile Coho Salmon (Oncorhynchus kisutch) consumption, body size, individual and population-level growth dynamics, and the consequences of these consumption and development patterns for fish in two streams with strong contrasts in thermal regimes. One stream is fed by surface-water and showed high thermal variation over space and time, whereas the other stream is fed by groundwater and showed much lower spatial-temporal thermal variability. We expected contrasting thermal and trophic effects on fish growth in these streams. The surface-water stream had hypothetically favorable growth temperatures (based on previous studies), higher physiological maintenance costs, and lower prey availability; whereas the groundwater stream had cooler temperatures, lower maintenance costs, and higher prey availability (Chapter 3). To understand these potentially contrasting effects on young-of-year Coho Salmon growth and consumption, we measured growth and diet composition of wild Coho Salmon in the field as well as using a modified bioenergetics model fit to the empirical data. The data collected in the field included: growth rates, fish weight, diet composition, and water temperatures experienced by fish. Hypothetically favorable growth temperatures only occurred in the surface-water stream in the summer months. These hypothetically favorable growth temperatures do not reflect what we observed in Alaska as growth (g·d-1) of young-of year Coho Salmon was not significantly higher in the surface-water stream as would be expected and instead there was no significant difference. At the population-level body lengths of young-of-year Coho Salmon did not significantly differ between thermally contrasting streams at the end of the first growing season. Estimated consumption rates (g·g-1·d-1) were higher in the surface-water stream in the summertime, due to the warmer temperatures and thus higher metabolic cost compared to fish growing in the groundwater stream.

Further, there was a significant positive relationship between fish size and % lipid content in the groundwater stream only, suggesting that size is related to condition for fish surviving in the relatively cooler groundwater stream. Results from this work elucidate some of the major environmental and mechanistic drivers of salmon phenology, growth, and resource use, providing a foundation for improved species management and future research.

Introduction

Bioenergetics provides a formal framework for evaluating the contributions of factors influencing acquisition of energy by individual fishes

(e.g., via consumption) and losses (e.g., via waste, metabolism, and other physiological processes) in ecological settings where spatial and temporal variability in temperature and food availability, growth rates, and body size can strongly influence net energy gains (Fausch 1984, Hartman and Hayward 2007).

In the case of salmonids, understanding ecological factors driving net energy gain is important because individual growth and condition are ultimately linked to survival, which is typically lowest in the first year of life (Elliott 1994).

Furthermore, an individual’s performance in one life stage can carry over into performance in subsequent life stages (Schluter et al. 1991). This is particularly important for sea-going or anadromous species that transition from rearing in freshwater to grow in estuarine and marine environments (Quinn 2006). In these species, survival of migrants is often positively correlated with body size at marine entry (Mangel 1996, Sogard 1997, Thorpe and Metcalfe 1998).

Stream temperature has received much attention because it directly affects the rates of temperature-dependent physiological processes in juvenile salmon including consumption and metabolism (Elliott 1976, Elliott 1982, Griffiths and

Schindler 2012). In this study, we employed a bioenergetics approach to compare factors influencing acquisition of energy by young-of-the year Coho Salmon

(Oncorhynchus kisutch) in two Alaskan streams with contrasting thermal habitats and prey availability (Chapter 3) on the Copper River Delta (CRD) in south-

central Alaska. There are streams dominated by surface-water input with variable thermal regimes that can range from 0 – 16° C annually, in contrast to groundwater dominated streams which have stable thermal regimes and generally reflect the mean annual air temperature (~4° C) year-round.

Coho Salmon occur in both surface-water and groundwater streams on the

CRD, thus affording opportunities to examine how these fish respond to spatial and temporal variability in environmental conditions. We quantified growth based on overall changes in sizes of fish in these streams across the summer season

(June-October) and measured growth rates of individuals (g·d-1). Growth of individuals was used in conjunction with information on fish diets and temperature (Chapter 3) to estimate patterns of consumption over the summer within the framework of the Wisconsin bioenergetics model (Hansen et al. 1997).

Based on processes captured within the Wisconsin bioenergetics model, we expected greater growth in relation to favorable thermal conditions, higher prey availability, or both (Jobling 1981). We expected consumption rates to be higher for the surface-water stream with warmer temperatures and thus higher metabolic costs (requiring higher consumption) as compared to the groundwater stream.

Previous work in these systems showed that the more thermally variable surface- water stream had temperatures hypothetically considered favorable for growth

(10°C- 12°C; Brett 1952, Konecki et al. 1995) in the summertime, but maintenance costs are generally higher in warmer waters (Jobling 1981) potentially reducing expected growth rates. We examined these variable and

interacting constraints on juvenile Coho Salmon growth using a bioenergetically- based simulation model.

In addition to bioenergetics, growth of individuals be driven by density- dependent resource limitation, which is common in juvenile salmonids (Elliott

1994). We were unable to rigorously quantify abundance in the large, open systems we studied, and opted to quantify more readily measured indicators of density-dependence. Experimental studies have shown that increasing variability in the distribution of body sizes in young-of-the-year cohorts results from the effects of intra-specific competition at this life stage (Keeley 2001, Keeley 2003).

Accordingly, we measured the distribution of body sizes in cohorts of young-of- the-year Coho Salmon to evaluate if variability increased over time, thus providing indirect evidence of intraspecific competition. Alternatively, such variability may simply be the product of extended emergence timing, with addition of new individuals to cohorts or faster growth of larger, dominant individuals contributing to greater variation in body size.

In addition to evaluating hypotheses and predictions linked to growth and consumption, we also considered individual condition. We considered percent lipid content of individuals as a measure of condition and net energy gain that may not be apparent from analyses of growth or consumption alone. Lipids can provide energy reserves that increase the probability of survival of young-of-year salmonids in winter (Metcalfe and Monaghan 2001, Biro et al. 2004, Berg et al.

2009), and fish rearing in colder water may exhibit higher lipid content

(McMillan et al. 2012). Accordingly, we predicted that condition or percent lipid

content of individuals should be higher in fish living in colder temperatures.

Collectively, results of this work provide novel and mechanistic insights into the interplay between the ecological drivers of food and temperature, and fitness related consequences for individual young-of-the-year Coho Salmon as indicated by patterns of growth, consumption, and condition.

Materials and Methods

Thermal regimes in study streams

Our study streams were located on the west Copper River Delta, southcentral Alaska, USA (Chapter 2). The study streams include: a surface-water fed system 18 Mile Creek (N60.45882°W145.29285°), and a groundwater fed system

25 Mile Creek (N60.44176°W145.11794°). From here forward, we refer to 18

Mile Creek as the “surface-water” stream and 25 Mile Creek as the

“groundwater” stream. Within each stream, a 200m study reach was selected based on the presence of Coho Salmon, accessibility to the site, and general similarities between sites including riparian vegetation, slope, and stream size.

Our preliminary sampling of these systems suggested substantial differences in thermal regimes and trophic resources both in terms of seasonal and spatial variability (Chapters 2 & 3).

To more formally quantify thermal variability within and between streams, we conducted continuous year-round monitoring using water temperature data loggers (HOBO Pro, U-22 model, Onset Corp., Pocasset, Massachusetts, USA).

All loggers were encased in a galvanized conduit pipe for protection and attached

by steel cables to anchors that were driven into the stream bed to withstand frequent storms and high-flows. Each stream had 10-16 data loggers that were deployed within main-channel and off-channel habitats and recorded water temperature at hourly intervals from May 15, 2013 to June 1, 2014. The mean daily temperature, 푇 , was calculated by summarizing point measurements of water temperatures in both main-channel and off-channel habitats in as daily

(∑ 푡(°퐶)) mean estimates defined by 푇 = where, 푇 is the mean daily temperature, 24 and t is the hourly temperature.

Salmon Growth, Diet, and Lipids

Juvenile Coho Salmon growth was measured using individually-marked coded-wire tags (Hale & Gray 1998, Munro et al. 2003, Vander Haegen et al.

2005, Brennan et al 2005, Pine et al. 2012). Young-of-the-year Coho Salmon were collected twice each month from May to October in 2013 using baited minnow traps and dip nets. All collected fish were measured (fork-length, mm) and released, except for individuals with body lengths of 36mm and above, that were placed in aerated buckets with fresh stream water and diluted tricaine methanesulfonate (MS-222) until movement slowed. These individuals were then tagged with 1” long, pre-cut, sequentially numbered coded wire tags (CW tags) using a single shot coded wire tag injector (Northwest Marine Technologies, WA,

98501, USA) in the right dorsal muscle. Each tagged fish received an adipose fin clip before release to help with identification of tagged fish upon recapture and to identify potential cases of tag loss.

Tagged fish were later confirmed by examining individuals for adipose fin clips and by using a T-wand coded-wire tag detector (Northwest Marine

Technologies, WA, 98501, USA). Recaptured fish were euthanized with MS-222 and kept frozen for retrieval of the CW tag and gut content analysis. In the laboratory, frozen fish were first thawed and then under a SMZ445 Nikon stereomicroscope (Nikon Instruments Inc., Melville, New York, USA) a ventral incision was made from esophagus to anus and stomach contents were removed with forceps, ensuring that all prey items were included.

All macroinvertebrates in fish guts were identified to the lowest practical taxonomic level (Merritt et al. 2008), counted, measured to the nearest 0.5mm, and biomass was estimated using published length-weight regressions (Meyer

1989, Sample et al. 1993, Hódar 1996, Burgherr and Meyer 1997, Kawabata and

Urabe 1998, Benke et al. 1999, Sabo et al. 2002, Grunner 2003, Miyasaka et al.

2008). For partially-digested prey, actual body measurements were not possible and instead we estimated lengths based on intact individuals of the same taxon and size (Wipfli 1997).

Energy densities of prey items were then calculated based on Cummins and Wuycheck (1971). Coded-wire tags were removed from dorsal muscle tissue, cleaned with deionized water and read under a Nikon SMZ745 Stereo Dissecting

Microscope (Nikon Instruments Inc. Melville, NY, 11747, USA) with a pencil and jig (Northwest Marine Technologies, Olympia, WA, 98501, USA). After the tags were retrieved for all fish, we additionally cut out a portion of dorsal muscle which was then sent to the Colorado Plateau Laboratory (Northern Arizona

University, Flagstaff, ZA, 86011, USA) for carbon (C) and nitrogen (N) stable isotope analysis. These stable isotope values were then converted to percent lipid

(% lipid) content using the methods described in Post et al. (2007). Differences in measured body weights from time of tagging to the time of recapture were used to calculate mean observed growth rates (mm·d-1) of fish in the study streams. Then to get fish biomass stream-specific length-weight regressions were constructed using measurements of length and weight from fish collected in each stream.

Polynomial regression analysis was used to analyze the non-linear relationship observed between length and weight in each stream. Individual growth rates (g·d-

1) were then calculated using these length-weight regressions to understand potential differences in biomass between streams with contrasting thermal regimes.

Growth rates by size-class were calculated (Figure S13), as well as size by growth rate regressions (Figure S14), growth rates shown by habitat (Figure S17), and growth at different times of year (Figure S18). The variability in individual growth rates were also compared to the variability in growth temperatures (Figure

S19) and the mean population-level growth (mean length; FL, mm) was calculated over time in addition to the changes in population-level growth variation over time (Figure S20, S21).

Bioenergetics model

We calibrated a bioenergetically-based simulation model (Fish

Bioenergetics 3.0, widely referred to as the Wisconsin Model; Hewett and

Johnson 1992, Hanson et al. 1997) with empirical data including fish weight,

growth temperatures, diet composition, and diet energy density. The Wisconsin model accounts for the energy intake by fish, which are simulated by species- specific algorithms that balance the energy intake equation as the fish grows

(Brandt and Hartman 1993). The model calculates each component of the energy budget based on species-specific growth coefficients and parameters that have been derived by previous laboratory experimentation and physiological research.

Perhaps more importantly, the model accounts for the non-linear effects on these parameters and coefficients due to variables such as temperature and food intake

(Hanson et al. 1997). These inputs are then used to estimate consumption over a range of temperatures, specific consumption (g·g-1·d-1; Figure S22), specific growth (g·g-1·d-1; Figure S23), and proportion of consumption (i.e. p-values;

Figure S24) for individual fish. Bioenergetic modeling has become an increasingly useful tool to gain insight into the non-linear effects of temperature on fish growth (Kitchell et al. 1977, Brandt et al. 1993, Hansen et al. 1993, Ney

1993, Hanson et al. 1997). The Wisconsin Model assumes that inputs, gains, and losses of energy can be balanced in an energy equation where consumption is the energy input, growth is the net energy gain, and all other uses are losses. The energy equation can be represented in simplified form by the following formula

(Warren and Davis 1967):

C = B + R + A + S + F + U where,

C = rate of energy consumption B = somatic tissue growth R = standard metabolic rate A = active metabolism

S = metabolic rate from specific dynamic action (heat increment) F = waste losses due to egestion (feces) U = waste losses due to excretion (urine)

Diet composition, growth rates, and patterns of thermal variability experienced by the fish were quantified for all re-captured fish which was n=14 in the surface-water stream, and n=34 in the groundwater stream. These data were then used as input parameters in the Wisconsin Bioenergetics Model 3.0 in R (A.

Hansen, University of Washington, personal communication; Eckmann et al.

2016) and bioenergetic simulations were run to evaluate potential consumption rate differences between streams (Hansen et al. 1997). Consumption was estimated over a broad range of simulated temperatures during the growing season.

These specific consumption rates were estimated based on a fish representative in each stream which had an empirical growth rate near the population mean growth rate. The fish selected to run these simulations also had a body size and diet composition which generally represented the ‘average fish’ for that stream. Overlaid on top of these graphs are boxes showing the range of temperatures fish were exposed to in each stream. We additionally calculated the mean temperatures each month during the growing season (June-October) in each stream.

Coho Salmon weights needed for the model were difficult to obtain in the field due to weather conditions so we measured weights of a subset of the population and used this length-weight regression to estimate the mass of all measured fish. Fish lengths (mm, FL) used in the model were converted to mass

(g) using these stream-specific length –weight regressions. Parameter values

(Table 4.1) used in the model were based on a review of previous research that used the model for consumption and growth estimates of Coho Salmon (sources shown in table).

Table 4.1. Parameters used in bioenergetic simulations of juvenile Coho Salmon in the study streams.

Parameter Modeled Description Source Species Value C=Cmax·p·f(T)c Specific consumption rate (g·g-1·d-1) AC 0.303 Intercept of the Hanson et Coho allometric mass al. (1997) Salmon function BC -0.275 Slope of the Hanson et Coho allometric mass al. (1997) Salmon function CQ 5 The lower Hanson et Coho temperature at al. (1997) Salmon which consumption is a small fraction (CK1) of the maximum rate CTO 15 Temperature Hanson et Coho corresponding to al. (1997) Salmon 98% of the maximum consumption rate CTM 18 Temperature Hanson et Coho (>CTO) at which al. (1997) Salmon dependence is still 98% of the maximum CTL 24 Temperature at Hanson et Coho which dependence is al. (1997) Salmon some reduced fraction (CK4) of the maximum rate

100

CK1 0.42 A small fraction of Willey Coho the maximum rate (2004) Salmon CK4 0.03 A small fraction of Willey Coho the maximum rate (2004) Salmon AR R=ARW ·f(T)R·A Specific respiration CT rate (g·g-1·d-1)

AR 0.004 Intercept of the White and Chinoo allometric mass Li (1985) k function BR -0.217 Slope of the Hanson et Coho allometric mass al. (1997) Salmon function RQ 2.1 Approximation of Willey Coho Q10 value over lower (2004) Salmon water temperature RTO 18 The water Brett Coho temperature at (1952) Salmon which respiration is & highest (optimum) Sockey e Salmon RTM 26 The maximum Brett Coho (lethal) water (1952) Salmon temperature & Sockey e Salmon ACT 1 Activity multiplier Kitchell et Various al. (1997) species SDA 0.172 Specific dynamic Hanson et Coho action al. (1997) Salmon OxyConv 13560 O2 in respiration Elliot and conversions (J·g-1) Davidson (1975) AF (GF·p) F=AFT ·e ·C Egestion rate dependent on mass, teperature, and ration AF 0.212 The intercept of the Hanson et Coho proportion of al. (1997) Salmon consumed energy egested versus water temperature BF -0.522 The coefficient of Willey Coho water temperature (2004) Salmon

101

dependence of egestion GF 0.631 The coefficient for Hanson et Coho feeding level al. (1997) Salmon dependence of egestion AU (GU·p) U=AUT ·e ·C-F Excretion rate dependent on mass, temperature, and ration AU 0.021 The intercept of the Willey Coho proportion of (2004) Salmon consumed energy excreted versus water temperature BU 0.38 The coefficient of Willey Coho water temperature (2004) Salmon dependence of excretion GU -0.299 The coefficient for Hanson et Coho feeding level al. (1997) Salmon dependence of excretion ED=α+Βw Predator energy density (J·g-1·d-1) α 4111 The intercept of the Willey Coho allometric mass (2004) Salmon function (J·g-1) Β 155 The slope of the Willey Coho allometric mass (2004) Salmon function Thresh 10 Weight (g) at which Coho predator energy Salmon density equation switches from α and Β to α2 and Β2 α2 7602 The intercept of the Hanson et Coho allometric mass al. (1997) Salmon function (J/g) Β2 0.526 The slope of the Hanson et Coho allometric mass al. (1997) Salmon function

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Statistical analyses

We structured statistical analyses to test predictions from hypotheses regarding factors that influence net energy gain of young-of-the-year Coho

Salmon in the two study streams. The specific set of predictions evaluated included 1) pair-wise differences in size distributions of fish over time, 2) corresponding differences in individual growth, 3) differences in relative consumption (calculated from the bioenergetics model), and 4) differences in condition as indicated by percent lipid composition. Distributions of fish sizes over time were considered in terms of medians and variances of measured fork lengths for all fish captured in each sampling event in 2013. For each sampling event in 2013 (June- Oct), pair-wise differences in both the median and variance of fish sizes were evaluated. Individual growth and consumption were collectively considered for all recaptures in 2013 (all recaptures from June-

October were combined), and similarly compared. All statistical analyses involving pair-wise comparisons used a non-parametric Mann-Whitney Rank sum t-test. Changes in variance over time for each stream were evaluated using non- parametric Spearman rank correlation. To evaluate percent lipid composition, we conducted an analysis of covariance (ANCOVA) using size (fork length, mm) as a covariate and compared differences among streams, and evaluated evidence for a potential stream by size interaction using Sigma Plot (version 13.0).

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Results:

There were significant differences in thermal regimes between the two study streams (Chapter 3, Figure 4.1). The mean monthly temperatures in the groundwater stream were: June = 4.86°C, July = 4.92°C, August = 5.15°C,

September = 4.90°C, and October = 4.34°C. In the surface-water stream, monthly means were: June = 7.29°C, July = 9.82°C, August = 10.23°C, September =

8.26°C, and October = 6.29°C. These differences in thermal regimes did not translate to differences in fish body size as increases in body lengths (mm) of young-of-year Coho Salmon were nearly identical over the season between the two streams (Figure 4.2), with the only significant difference in lengths occurring in July (p<0.01) between streams during the growing season. Overall population- level variance in length did not significantly differ (p=0.347) between streams, but there was a pattern of increasing variance in size over time in both streams

(Figure S19). Pair-wise differences between sites in the variance in length were not significant at any sampling event. Over time the variance in length significantly increased in the groundwater system (rs=1.0, df=4, p=0.02) and although this same trend was apparent in the surface-water system the difference was not significant (rs=0.90, df=4, p=0.08; Figure S19).

The length-weight regressions for both the surface-water and groundwater streams were highly significant (Figure S16). These regressions were: In the surface-water stream: W=1.374-(0.0891*L) + (0.00169*L2), R2= 0.94, p<0.001;

In the groundwater stream: W=0.421-(0.0420*L) + (0.00115*L2), R2= 0.95, p<0.001; where W=weight (g) and L=body length (mm). There was no significant

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difference in the mean overall growth (g·d-1) after using the length-weight regressions to convert length to mass (Figure 4.3).

Bioenergetic simulation runs of a range of temperatures for an average

Coho Salmon in July in each stream showed differences in specific consumption

(g·g·d-1) between streams. In the surface-water stream, specific consumption was much higher than those observed in the groundwater stream, and specific consumption maxima generally occurred at the mean temperature range fish were exposed to in each stream (Fig 4.4).

Fig 4.1. Water temperatures (°C) of the study streams from Sept 15, 2012 to Dec 31, 2014. Box showing the hypothetically favorable growth temperatures (10- 12°C) for young-of-year Coho Salmon based on previous research.

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Surface-water Stream Groundwater Stream

n=195 June n=415

n=369 July n=1076

n=384 August n=537

n=125 September n=247

n=347 October n=231

20 30 40 50 60 70 Coho Salmon Length (mm) Fig 4.2. Observed (empirical) mean juvenile Coho Salmon body length (FL, mm) from populations measured each month from June-October 2013 in the study streams.

0.04

) 1

- 0.03

0.02 Growth Rate (g·d Rate Growth

Growth Rate (g/d) Rate Growth 0.01

0.00 Surface-water Groundwater

Figure 4.3. Box-plots of measured growth rates (grams·day-1; +/-s.e.) of juvenile Coho Salmon during the 2013 growing season (1 July – 15 Oct) in the study streams.

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Surface-water 0.25 Groundwater

0.20

0.15

0.10

0.05

Specific Consumption (g/g/d) Consumption Specific

0.00 0 2 4 6 8 10 12 14 16

Water Temperature (C) Figure 4.4. Bioenergetic estimates of specific consumption (g·g·d-1) in the study streams over a range of simulated average monthly temperature values during the growing season. Estimates apply to an average-sized fish and average estimated consumption in July for each stream. The light grey box and dark grey box indicate the thermal ranges observed during the growing season for the groundwater and surface-water streams, respectively.

Line and scatter plots of an average fish’s specific consumption (g·g·d-1) in each stream over a broad range of simulated temperatures during the growing season revealed much higher rates of consumption in the surface-water stream

(Figure 4.4). Juvenile Coho Salmon showed differences in the relationship between body length and % lipid content (Figure 4.5). In the groundwater stream, there was a significant positive relationship between body length and % lipid content as described by: y= -2.5866 + 0.1875x; R2= 0.3621; p=0.002. In the surface-water stream, there was not a significant relationship between body length and lipid content as described by: y= 6.3225 – 0.08x; R2= 0.003; p=0.7982 (Figure

4.5). We adjusted for the effect of size to determine lipid differences between

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streams using a one-way ANCOVA with size as a covariate and found a significant effect of stream (p=0.006) and a significant interaction term of stream

× size (p=0.006). Thus the slopes were not equal and there is a significant difference in the slope coefficients of the size covariate; for the surface-water stream: Log10 (% Lipids) = 1.01 – (0.004*Size); and for the groundwater stream:

Log10 (% Lipids) = 0.21 – (0.01*Size).

16 Surface-water Groundwater 14

%Muscle Dorsal Contentin Lipid 4

2 35 40 45 50 55 60 65 70 75

Juvenile Coho Length (FL, mm) Figure 4.5. Estimated % lipid content of juvenile Coho Salmon dorsal muscle in the study streams in relation to fork lengths of individuals.

Discussion

In this study, we examined the effects of available thermal and trophic resources on juvenile Coho Salmon growth in two streams with contrasting thermal regimes. Growth was considered in terms of changes in body size within

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cohorts of young-of-the year Coho Salmon, recaptures of individuals, and simulated daily consumption using a bioenergetics model. In terms of trends in body size within cohorts, we expected similar patterns between streams, based on known conditions that can influence growth. This is because previous work

(Chapter 3) showed that there were potential trade-offs during the growing season where the surface-water stream had hypothetically favorable growth temperatures but lower prey availability, and the groundwater stream had unfavorable growth temperatures but high prey availability during the growing season. Individual growth data showed that Coho Salmon did not grow significantly faster in streams with temperatures previously considered favorable for growth (Brett 1952,

Konecki et al. 1995, Hansen et al. 1997). Thus, these previous estimates of favorable growth temperatures do not appear to hold true in these Alaska streams, and it is possible that fish in this area have developed thermal adaptations to the cold stream temperatures which are common in Alaska.

Mean body size was nearly identical between streams during each month of the growing season, with the exception of July, where body sizes were significantly larger in the groundwater stream. July was the same month when prey availability was at a maximum in this stream (Chapter 3) and this likely contributed to elevated growth during this month. In the surface-water stream, pronounced lateral heterogeneity in stream temperatures available to juvenile

Coho Salmon (Chapter 3) could provide opportunities for behavioral thermoregulation, as observed in other studies (Armstrong et al. 2013), and lead to less variability in body size among individuals. Alternatively, if fish are more

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strongly philopatric (e.g., territoriality, Chapman 1966) and essentially grow in place, we would expect increased variability in body size due to the influences of thermal heterogeneity on growth. We observed neither pattern in this study, despite strong differences in thermal heterogeneity we observed between streams.

Clearly temperature always plays a role in driving growth of fish, and in some cases behavioral thermoregulation can be important when fish are mobile

(Armstrong et al 2010, Armstrong et al. 2013), but as shown here many other factors may be important – including bioenergetic costs, food availability, and competition for food or space. Given that all of these factors are likely acting in concert to influence growth, consumption, and condition of juvenile Coho Salmon in the streams we studied, it is perhaps not surprising that temperature alone is insufficient to explain observed variation in growth.

Individual growth and survival can be tightly coupled in early life stages of fish and mortality is typically the highest during the first year of life (Elliot

1994). Surviving juveniles must grow large enough and store enough energy reserves to survive a harsh winter (Metcalfe and Monaghan 2001, Berg et al.

2009). In Alaskan streams, growth and development of Coho Salmon can be limited by colder temperatures (Armstrong et al. 2010, Armstrong et al. 2013).

Under ideal conditions, growth of juvenile Coho Salmon has been shown to hypothetically be greatest when temperatures range between 10°C- 12°C (Brett

1952, Konecki et al. 1995). Only the surface-water dominated stream reached these hypothetical growth temperatures in summer (July through September,

Chapter 3) whereas the groundwater dominated stream never reached

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temperatures exceeding 6°C. This similarity in growth rates between streams is likely due, to in part, to differences in maintenance costs and maximum consumption rates which are both higher in warmer water (Jobling 1981). We found that despite having warmer temperatures, fish in the surface-water stream had less prey availability and a higher consumption requirement due to higher costs of maintenance compared to the groundwater stream. These interacting thermal and trophic effects led to growth rates in the surface-water stream that were similar to those in the groundwater stream in terms of weight. Our results corroborate others which show that contrasting effects of water temperatures and prey availability can explain patterns of juvenile salmonid growth in response to variable hydro-climatic conditions (Jaeger et al. 1999, Clark et al. 2001, Jonsson and Jonsson 2009).

Our bioenergetic simulations show that specific consumption (g·g-1·d-1) is higher for the average fish in the surface-water stream across a broad range of simulated temperatures during the growing season. This result is likely due to the higher maintenance costs in the relatively warmer surface-water stream in the summertime, resulting in a higher consumption requirement. Thus fish in the surface-water stream are having to eat more prey to grow at the same relative rate as fish in the colder groundwater stream during the summertime. This influence may be part of the reason why prey availability was lower in the surface-water stream in the first place, fish in those streams have a bigger appetite (Chapter 3).

Patterns of condition varied strongly in relation to body size and stream type. Lipid content in fish muscle was higher in the colder groundwater stream

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(although this difference was size-dependent) and there was a positive relationship between fish size and lipid content. Lipids can provide energy reserves that increase the probability of survival of juvenile salmonids in winter

(Metcalfe and Monaghan 2001, Biro et al. 2004, Berg et al. 2009), and fish rearing in colder water may exhibit higher lipid content (Cavaletto and Gardner

1999, McMillan et al. 2012). Accordingly, we predicted that condition or percent lipid content of individuals in the summer and fall should be higher in fish living in colder temperatures (i.e., the groundwater stream). Differences in condition in the first year of life can have significant impacts on fitness as juveniles must grow large enough and store enough energy reserves to survive a harsh winter and a subsequent year in freshwater or before emigrating to sea (Folmar and Dickhoff

1980, Metcalfe and Monaghan 2001, Marine and Cech 2004, Berg et al. 2009).

This suggests that fish in the groundwater stream may be in better condition at the end of the growing season and be more likely to survive winter as compared to fish in the surface-water stream. It is worth noting that although the surface-water stream was warmer in summer, it is colder in the winter relative to the groundwater stream, often approaching freezing temperatures (Chapter 2). Thus, although fish in the surface-water system are similar in size to fish in the groundwater system, they may enter the winter in lower condition and face more challenging environmental extremes.

Previous work in Alaska streams has indicated that spatial heterogeneity in thermal regimes is a vital factor for Coho Salmon fitness, as it allows them to behaviorally thermoregulate and potentially exploit favorable growth

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temperatures (Armstrong et al. 2010, Armstrong et al. 2013, Baldock et al. 2016).

In our work, we show that factors affecting growth and development of juvenile

Coho Salmon can be more complex and the hierarchical constraints placed by phenology (i.e., timing of emergence; Chapter 2) ultimately determines the mosaic of resources available for fish to grow. In addition to the phenology of emergence, the template of resources available to fish upon emergence varied depending on the thermal regime (Chapter 3). These contrasting effects as well as differences in the bioenergetic costs for fish rearing in streams with different thermal regimes resulted in differences in individual growth rates but a similar population-level growth pattern over time. Furthermore, the differences in lipid content between fish populations suggests higher condition of fish in the relatively colder groundwater stream. Thus hypothetically favorable growth temperatures based on previous lab research did not translate to increased grow rates or high condition for fish. Further, we find evidence of strong density- dependent effects on the distribution of body sizes, in addition to basic resource availability. The significant contrasts in thermal regimes that we observed did not translate to strong contrasts in fish growth at the population-level.

Collectively, the growing body of empirical studies of juvenile Coho

Salmon within the diverse environments available to them in the relatively undisturbed habitats available in Alaska point to a suite of interacting factors that drive early growth, consumption, and condition. These factors operate within the context of constraints imposed by interactions among life stages, such as the timing of hatching and emergence (Chapter 2), which frames the environmental

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conditions available to juveniles in summer (Chapter 3). In turn, consequences of summer environmental conditions for size, growth, and condition of individuals could influence over-winter survival or the timing of the parr-smolt transformation (emigration of juveniles to sea) in the following year. Ultimately, interactions between life stages and environmental conditions influencing each stage, as well as interactions among stages, should be expected to drive lifetime reproductive success of individuals and population dynamics of Coho Salmon.

Our results, in conjunction with a suite of other recent studies have highlighted the many complexities that are in play, but clearly much work remains to be done.

Understanding these complexities poses a major scientific challenge, but also offers important opportunities to better our understanding of the adaptive capacity of species such as Coho Salmon (Wade et al. 2017) in the context of their sensitivity and exposure to changing climates (Foden et al. 2013). The result herein only represents a few years of data and broader changes in climate (warm versus cold years) are likely to have dramatic effects on development, growth and life-history timing. Assessments of sensitivity and exposure are important first- order approximations for evaluating the future of Pacific salmon in Alaska, and considering additional complexities can offer a more complete understanding of species adaptive capacity (Sloat et al. 2016, Wade et al. 2016). Thus, future investments in further understanding the adaptive capacity of this species would likely lead to more efficient planning for future change and for identifying effective measures for climate adaptation.

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Acknowledgements

We thank A. Morin, C. Galespi, K. Kirkby, C. Salazar, L. Hotca, L. Adelfio, D.

Kuntzsch, K. Hodges, S. Meade, R. Church, M. Heck, S. Wondzell, S. Johnson,

C. Marshall, the Fisheries and Wildlife Department at Oregon State University, and the entire Cordova Ranger District of the Chugach National Forest. This research was permitted by Oregon State University ACUP #4364. We are grateful to our funding sources: The National Fish and Wildlife Foundation, the U.S.

Department of Agriculture Forest Service PNW Research Station, and the

Environmental Protection Agency Science to Achieve Results (STAR)

Fellowship. Use of trade or firm names is for descriptive purposes only and does not constitute endorsement of any product or service by the U.S. Government.

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Chapter 5- Conclusion

Emily Yvonne Campbell

Department of Fisheries and Wildlife, 104 Nash Hall, Oregon State University, Corvallis, Oregon USA 97331-3803

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In this dissertation I considered the patterns of Coho Salmon phenology, growth, condition and resource use in streams with contrasting thermal regimes. I further examined the linkages between these fish demographics and the spatial and temporal variation in thermal and trophic resources important for Coho Salmon. In Chapter 2, I explicitly examined thermal effects on linked phenologies of Coho Salmon including hatch and emergence timings; and then in Chapters 3 & 4, I followed this cohort through time to understand juvenile growth, consumption, condition, and resource use during the first year of life.

I addressed the phenologies of early stages in the salmon life cycle

(hatching and emergence) and their consequences for individuals within the critical first summer of life (Elliott 1994, Fuiman and Werner 2002,

Armstrong and Nislow 2006). Collectively, results of this work provided a unique evaluation of linkages between phenologies of successive life stages in a species with a complex life cycle that plays out in a highly variable environment. Climate change will continue to alter environmental conditions, and it is important to understand the roles of phenology in the context of life history tactics that ultimately effect fitness and allow species to persist across complex landscapes and thermal regimes.

Our results from Chapter 2 demonstrated that hatching and emergence occurred at the same relative time in the summer (July).

Synchronous hatching and emergence timing in the summer among streams may be a response to the times at which food resources are

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available (Godin 1982, Wipfli 1997, Einum and Fleming 2000, Letcher et al. 2004) and the need to attain sufficient size (Quinn and Peterson 1996) and condition (Berg et al. 2009) to survive the winter. I found that prey resources were higher overall in the summertime, particularly in the groundwater stream, which may have had an influence on fry emergence timing. Another potential driver for an earlier emergence is that the window for growth of emerging Coho Salmon is relatively short in our system, and the capacity to grow can depend on the ability of individuals to exploit pulses of resource availability.

In Chapter 3, we considered temporal and spatial variability in conditions potentially influencing net energy gain (growth and condition) of Coho Salmon rearing in high-latitude streams. To understand the potential environmental drivers behind the emergence timing observed, we focused on two important resources influencing net energy gain in juvenile

Coho Salmon consumers: water temperature and prey availability in rearing habitats within streams. We studied these resources in streams with contrasting thermal regimes: a groundwater-dominated stream with relatively stable temperatures throughout the year and a surface-fed stream with pronounced seasonal variability. Warmer (and hypothetically more favorable for somatic growth) summer growing temperatures and lower prey availability occurred in the surface-fed stream; which contrasted with the lower temperature and greater prey availability in the groundwater stream.

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Chapter 4 examined more explicitly the mechanistic link between environmental drivers and the growth and condition of juvenile Coho

Salmon. Previous studies have determined the temperature range between

10°C- 12°C to be favorable for juvenile Coho Salmon to grow (Brett 1952,

Konecki et al. 1995) and the only stream that reached these hypothetically favorable growth temperatures was the surface-water dominated stream in the summer time (July-September); whereas the groundwater dominated stream never reached temperatures above 6°C. We found that, despite having favorable growth temperatures in the summertime, observed growth was not significantly higher in the surface-water stream. This growth pattern was likely due in part to the higher observed consumption rates in the surface-water stream, coupled with higher maintenance costs and lower availability of prey (Chapter 3). Further, at the population-level growth did not differ between streams and fish achieved the same relative size by the end of the first growing season despite clear differences in thermal regimes.

Throughout this dissertation, I have shown that juvenile Coho

Salmon are faced with different trade-offs depending on the stream thermal regime. The synchronized emergence timing in the summer led to stream-specific growing conditions for young-of-year salmon which varied among streams. The groundwater stream had higher availability of invertebrate prey in the summer but relatively cold (and previously considered unfavorable) growth temperatures compared to the surface-

127

water stream. In contrast, the relatively warmer surface-water fed stream had hypothetically favorable growth temperatures but higher maintenance costs and lower prey availability. Fish consumption rates were higher in the surface-water stream due in part to variability in maximum consumption and maintenance costs.

By the end of the first growing season, body sizes in groundwater and surface-water fed streams were not significantly different. However, there was a significant positive relationship between body size and lipid content in the groundwater stream only suggesting fish in this stream may have higher condition. We show that fish living in very different thermal regimes can reach the same relative body sizes by the end of the growing season due in part to trade-offs between thermal and trophic resources therein. In stream systems like those in the Copper River Delta, Alaska where there is significant spatial heterogeneity in micro-environmental conditions, fish will likely experience opportunities to shift their patterns of habitat use in the face of changing climates. It is important for ecologists to track future changes in phenology, development rates, and resource use by salmon in order to best manage our fisheries in the future.

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Supplementary Material:

Off-Channel 16 18 Mile Creek Main-Channel