Use of Hydroacoustics to Examine Spatial and Temporal Patterns of Pacific Salmon

USE OF HYDROACOUSTICS TO EXAMINE SPATIAL AND TEMPORAL

PATTERNS OF PACIFIC SALMON (ONCORHYNCHUS SPP.) BEHAVIOR

DURING SPAWNING MIGRATIONS IN NUSHAGAK RIVER, ALASKA.

A Thesis

Presented to the Faculty of

Alaska Pacific University

In Partial Fulfillment of the Requirements

For the Degree of

Master of Science in Environmental Science

Samantha C. Simpson

April 2014

UMI Number: 1555183

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Samantha Simpson

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iii

USE OF HYDROACOUSTICS TO EXAMINE SPATIAL AND TEMPORAL

PATTERNS OF PACIFIC SALMON (ONCORHYNCHUS SPP.) BEHAVIOR

DURING SPAWNING MIGRATIONS IN NUSHAGAK RIVER, ALASKA.

Samantha C. Simpson

THESIS

APPROVED: Thesis Committee

______Chairperson Date Bradley Harris, Ph.D. Assistant Professor Alaska Pacific University

______Thesis Committee Date Gregory Buck Assistant Area Research Biologist, Bristol Bay Alaska Department of Fish and Game

______Thesis Committee Date Roman Dial, Ph.D. Assistant Professor Alaska Pacific University

APPROVED: ______Tracy Stewart, Ph.D. Date Academic Dean Alaska Pacific University

ACKNOWLEDGEMENTS

I am deeply indebted to my committee members, Drs. Bradley Harris and Roman

Dial, and Gregory Buck for their persistent guidance, expertise, and encouragement throughout this process. I cannot find words to express my sincere gratitude for my advisor, Dr. Harris, for energetically accepting me as his first graduate student upon his arrival at APU, and his continued and convincing excitement about my project, especially when things seemed hopeless to me. I am grateful for his ideas and positive outlook on the project, as well as all the hours spent on my thesis development and review. Thank you for being so easy and enjoyable to work with. I am tremendously grateful for all the feedback and statistics instruction received from Dr. Dial, and his expertise raised the content of this thesis. While my journey on this thesis is complete, I would not have arrived here if it wasn’t for the Alaska Department of Fish and Game and Greg Buck. His exclusive knowledge of Nushagak River salmon, anadromous fish ecology, and fisheries management in Alaska made for worthwhile application of this thesis.

The Alaska Department of Fish and Game provided invaluable support specifically in the early stages of this project, and I would like to thank Jeff Regnart, Tim

Baker, Lowell Fair, Suzanne Maxwell, and April Faulkner for their guidance and expertise, in addition to the Nushagak River sonar crews who keep the DIDSON project running successfully, year after year.

I am very fortunate to have a wonderful group of colleagues who assisted in the completion and review of this thesis, from both the fisheries lab at APU and in my professional career. Nick Tucker of APU played a vital role in getting echograms ii

processed and worked at incredible speeds, and Sarah Webster and Jenipher Cate conducted expeditious and careful peer reviews of my work. Bill Hanot and Sergio da

Costa, of Soundmetrics Corp., provided considerable technical support when DIDSON questions arose. Thank you all for contributing your valuable time to enhance this project. I am very thankful for the education I have received from both my undergraduate and graduate institutions, and must also thank Dr. Ana irovi for getting me started in the graduate program at APU and encouraging me to develop a project within my personal interests. I would not have found myself in this program without recognizing my love for Alaska and its fascinating environment; Mitch Osborne, Ray Hander, and

Theresa Tanner helped me to come to this realization early on in my fisheries career and have provided moral support throughout the duration of this thesis.

I truly have an incredible group of friends, coworkers, and even some acquaintances who have constantly reminded me over the past several years to get my thesis completed (you know who you are). Words cannot describe the positive influence that your persistent badgering has had on me. Thank you all for listening to my thesis quandaries whether it was at our hockey games, while out on runs, or over a few adult beverages. Additionally, moral support and encouragement from RJM and BRW were indispensable. Thank you. Last but not least, I owe immeasurable gratitude to my parents,

Jeff and Lori, and brother, Jay, for their love and everlasting support of my goals.

iii

ABSTRACT

The Nushagak River, Bristol Bay, Alaska, supports one of the largest wild sockeye salmon (Oncorhynchus nerka) runs in the state and also supports overlapping runs of chum (O. keta) and Chinook (O. tshawytscha) salmon. Each summer, the Alaska

Department of Fish and Game deploys dual-frequency identification sonar (DIDSON) on the river to enumerate salmon to monitor escapement goals; however, salmon behavior and spawning run characteristics at the site are not incorporated into management. We investigated the feasibility of using Nushagak River DIDSON data from 2008-2011 to identify trends in run timing and compare passage rates to local environmental conditions, including tide, light intensity, and water temperature. We also used spatial analysis to examine aggregation behavior of salmon at the DIDSON site at multiple distance scales. Between all four years, run timing only varied within a few days (< 1 week), regardless of water temperature. In 2009 and 2011, more salmon passed upriver during slack and ebb tides than during flood tide. Salmon were never completely spatially random, and distance scales of aggregation behavior were influenced by run period and photo period. When examining all factors together, the interaction between salmon density and run period affected distance at maximum aggregation. This research used hydroacoustics for a multi-level approach to investigating spatiotemporal patterns of

Pacific salmon relative to environmental factors during their spawning migration and may help refine in-season monitoring and management of the fishery.

TABLE OF CONTENTS

ABSTRACT ...... iii LIST OF FIGURES ...... vi LIST OF TABLES ...... viii GENERAL INTRODUCTION ...... 1 CHAPTER 1 ...... 5 1.1 INTRODUCTION ...... 6 1.2 METHODS ...... 9 Site Description ...... 9 DIDSON Recording ...... 11 Species Apportionment ...... 13 Light Intensity ...... 13 Temperature ...... 16 Tide ...... 16 1.3 RESULTS ...... 18 Light Intensity ...... 20 Temperature ...... 27 Tide ...... 31 1.4 DISCUSSION ...... 34 Light Intensity ...... 34 Temperature ...... 35 Tide ...... 35 Conclusion ...... 36 CHAPTER 2 ...... 39 2.1 INTRODUCTION ...... 40 2.2 MATERIALS AND METHODS ...... 44 Site Description ...... 44 Echogram Processing ...... 44 Ripley’s K Analysis ...... 50 Run Period ...... 53 Salmon Density ...... 53 Light Intensity ...... 53 Tide ...... 54 Water Temperature ...... 54 v

Factor Interactions ...... 55 2.3 RESULTS ...... 56 Spatial Structure ...... 56 Run Period ...... 58 Salmon Density ...... 62 Light Intensity ...... 65 Tide ...... 69 Water Temperature ...... 71 Factor Interactions ...... 73 2.4 DISCUSSION ...... 74 Run Period ...... 75 Salmon Density ...... 76 Light Intensity ...... 76 Tide ...... 77 Water Temperature ...... 78 Factor Interactions ...... 78 Conclusion ...... 79 GENERAL DISCUSSION ...... 81 REFERENCES ...... 84 APPENDIX ...... 92

LIST OF FIGURES

CHAPTER 1

Figure 1.1. Nushagak River sonar site study area and geographic location in southwest Alaska (inset).

Figure 1.2. Nushagak River aerial showing DIDSON placement, strata, and river bathymetry. The right bank inshore/right bank offshore strata are on the north bank, and left bank inshore/left bank offshore strata are on the south bank (from Buck et al. 2012).

Figure 1.3. Cumulative daily sonar counts by day of year for Chinook, chum, and sockeye salmon species combined, 2008-2011.

Figure 1.4. 2011 average sonar count and average light intensity per hour. Error bars represent 95% confidence intervals.

Figure 1.5. Influence of average lux on average hourly sonar count for inshore (p = 0.02) and offshore (p = 0.16) strata.

Figure 1.6. Number of fish in each stratum compared to total fish passage.

Figure 1.7. Percent of salmon using the inshore stratum compared to total fish passage.

Figure 1.8. Average daily temperatures from 8 June to 18 July, 2008-2011. Black dots represent the date at which 50% total fish passage occurred. Error bars represent 95% confidence intervals.

Figure 1.9. Regression plots for residuals of dates at which 25%, 50%, and 75% of the run passed, compared to mean temperatures at the early, mid, and late points of the run.

Figure 1.10. Comparison of average hourly salmon passage at slack, ebb, and flood tides in 2008 – 2011. Error bars represent 95% confidence intervals.

CHAPTER 2

Figure 2.1. DIDSON video recording (left) showing two fish near 6 meters from the shore, with corresponding echogram image (right).

Figure 2.2. Echogram display in ImageJ. The two fish from Figure 2.1 are marked with crosshairs of the multi-point tool and echogram dimensions are displayed in vii

pixels (top left). This echogram’s actual dimensions are 10 minutes (x axis) by 10 meters (y axis).

Figure 2.3. Modified Ripley's K L(t)-t curve showing the range of complete spatial randomness (CSR, L(t)-t = 0) and the distance at maximum aggregation tLtmax.

Figure 2.4. Frequency distribution of tLtmax, or distance at maximum aggregation, for Early, Mid, and Late run periods, 2011.

Figure 2.5. Distribution of tLtmax for Early, Mid, and Late run periods. Box plots and symmetrical dot-densities describe the means. Values between 1.5 x (interquartile range) and 3 x (interquartile range) the means are plotted with asterisks. Values beyond 3 x (interquartile range) the means are plotted with circles.

Figure 2.6. Regressions of tLtmax by salmon density and run period.

Figure 2.7. Regression of tLtmax and light intensity (p = 0.37).

Figure 2.8. Regression of tLtmax and water temperature (p = 0.77).

viii

LIST OF TABLES

CHAPTER 1

Table 1.1. Typical illuminance in different environments (from Schubert 2006).

Table 1.2. Average hourly salmon passage as described by inshore and offshore light intensity (lux).

Table 1.3. Summary of mean temperatures at the date where 50% of cumulative salmon occurred, and deviations in mean temperature and 50% passage date from 2008-2011.

Table 1.4. Average hourly salmon passage at each tide stage from 2008-2011 and 95% confidence intervals.

CHAPTER 2

Table 2.1. Index of Dispersion (D) for early, mid, and late run periods. D > 1 indicates significant over-dispersion of tLtmax, or clustering. D increased substantially as the run advanced.

Table 2.2. Linear regressions of fish density on tLtmax during each run period and across the entire run.

Table 2.3. Descriptive non-parametric statistics of tLtmax during dawn, day, dusk, and night photo periods (Kruskal Wallis one-way ANOVA with Dunn’s post hoc tests).

Table 2.4. Median tLtmax during flood, ebb, high, and low tide phases and falling and rising tide phases.

GENERAL INTRODUCTION

In fisheries research, hydroacoustics are used primarily in marine applications, but there is a growing body of work demonstrating their utility in freshwater systems (Daum and Osborne 1998, Grothues et al. 2005, Bernhard et al. 2009, Jones et al. 2013). In freshwater, sonars are generally used when other research methods (e.g. fish capture or photo/video) are not feasible due to logistical, environmental, or biological constraints

(Maxwell 2007). Freshwater acoustic research has focused largely on tracking young fish

(e.g. salmon smolts) with acoustic tags to investigate their behavior near noisy dams or during out-migration (Ehrenberg and Steig 2002, Steig 1999). Sonar has also been used for adult enumeration when a river is too turbid or wide for traditional methods, such as a weir or counting tower (Maxwell 2007).

Newer multi-frequency hydroacoustic tools can be used to sample the local movements of individual fish and thus have the potential to support examinations of in- river spatiotemporal adult salmon distributions, but such investigations are rare. Xie et al.

(2009) used dual-frequency identification sonar (DIDSON) to investigate avoidance behavior of adult sockeye (Oncorhynchus nerka) and pink (O. gorbuscha) salmon during vessel surveys. DIDSON may also be used to measure adult salmons’ distance from the shore by measuring distance from the transducer, in addition to measuring body length

(Burwen et al. 2007), or behavior near commercial fishing activities (Rakowitz et al.

2012). In addition to providing counts for abundance, DIDSON recordings may be useful for investigating relationships between salmon run characteristics and the surrounding environment. 2

Understanding the influences of environmental factors on movement patterns of anadromous fishes during their upriver migrations is important for understanding fish ecology, improving stock assessments and achieving target fish escapement goals (the number of fish that escape to reach spawning grounds). River flow rate may be an important driver of river entry (Banks 1969, Smith et al. 1994), and light intensity, temperature, and tidal cycles may influence upstream migration patterns (Banks 1969), but these relationships are poorly understood and studies report conflicting results. For example, some species’ upriver migration patterns are explained by flow rate or temperature; but in other cases, the linkage between causal environmental factors associated with migratory behavior are reversed or not distinguishable (Northcote 1984,

Bernatchez and Dodson 1987, Quinn and Adams 1996, Jonsson and Jonsson 2002,

Jonsson and Jonsson 2009).

River flow rate can be the dominant environmental factor driving salmon ascension upriver and upstream migration rate, but its influence is most evident in smaller river systems where water levels can reach lows unfavorable for upstream migration

(Banks 1969, Jonsson and Jonsson 2009). However, the effects of flow rate on upstream migrations can be modified by other factors, such as light intensity, temperature, and tidal influences (Banks 1969).

Ringelberg (1995) suggested that light intensity may be a primary mechanism evoking fish migrations in both marine and freshwater environments. Salmon may have a preference to travel in turbid or dark waters in order to avoid predation (Banks 1969).

The lower visual threshold in predatory fish is about 10-2 to 10-3 lux (Blaxter 1970 from 3

Ringelberg 1995), whereas the light intensity at the water surface on a bright summer day is about 105 lux (Licor 1982 from Ringelberg 1995). Many studies use time of day as a proxy for light intensity (Helfman 1986, Daum and Osborne 1998). However, in high latitudes the rapid seasonal daylight changes confound the time of day to light intensity relationship, and thus direct measurements of light intensity, or lux, are more appropriate.

Water temperature influences energetic costs, disease risks, migratory behavior, migration speed, swimming performance, physiology, and survival of Pacific salmonids

(Crozier et al. 2008, Donaldson et al. 2009). Global atmospheric temperatures are increasing, and some models predict up to a 6 °C increase in northern latitudes by 2099

(Sanderson et al. 2011). Increased temperature also has important impacts on metabolism and pathogens in salmon: adult sockeye salmon (O. nerka) usually hold in lakes before they spawn in streams, and increased lake temperatures may induce stress on the adults

(Newell and Quinn 2005). Because large salmon, such as Chinook (O. tshawytscha) and chum (O. keta) travel long distances to spawn (e.g. up 2,750 km – Milligan et al. 1986 from Daum and Osborne 1998), they have high metabolic demands which increase as temperature rises (Bryant 2009). Mantua et al. (2010) estimated that temperatures exceeding 21-22 °C could prevent migration. Moreover, as water becomes warmer, adult salmon are more susceptible to disease and the transmission of pathogens (Newell and

Quinn 2005).

Movements of adult salmon in tidally influenced rivers have not been extensively studied. Smith and Smith’s (1997) analysis of Atlantic salmon entry into the

Aberdeenshire Dee, Scotland, revealed that salmon entered the river during the ebb tide, 4

but once out of the range of tidal influence, there was no significant relationship to tidal phase. Rather, fish traveled more at night. In another study, Atlantic salmon entered into a river during high (slack) tide and the subsequent ebb tide (Karppinen et al. 2004).

In Alaska, the impacts of species, size, sex, and age on salmon migrations have been widely studied (Tracy et al. 2012, Kendall and Quinn 2013, Kovach et al. 2013).

However, diurnal, nocturnal, irregular, and uniform migration patterns have all been observed in Alaskan rivers, and factors such as tide and time of day are poorly understood (Gaudet 1990, Daum and Osborne 1998). The spatiotemporal organizations of fish provide important information for understanding their biology (Pitcher 2001), enhancing management (Doctor et al. 2010), and understanding species exploitation

(Reid et al. 2000). Salmon can adapt their migratory behavior to conserve energy while migrating upriver (e.g. swimming closer to shore to avoid swift water; Quinn 2005), so investigating these point process behaviors in relation to environmental factors may help improve the escapement index. DIDSON echograms allow the enumeration of individual fish as well as an assessment of the spatial structure of salmon schools, facilitating an analysis of the relationships between fish passage rates, spatial structure, and environmental factors such as water temperature, light intensity, and tidal stage.

The aim of this study was to examine: 1) the relations between salmon upstream migration and water temperature, light intensity, and tidal stage, and 2) how the spatial structure of up-migrating fish schools relate to these environmental factors using

DIDSON video collected in the Nushagak River from 2008-2011.

CHAPTER 1

SPATIOTEMPORAL DISTRIBUTION OF MIGRATING PACIFIC SALMON

(ONCORHYNCHUS SPP.) IN THE NUSHAGAK RIVER, ALASKA, RELATIVE

TO LIGHT INTENSITY, TEMPERATURE, AND TIDE.

1.1 INTRODUCTION

Since Alaska’s statehood in 1959, Bristol Bay commercial fisheries have annually harvested an average of 16.5 million sockeye salmon, 900,000 chum salmon, 500,000 pink salmon, 100,000 Chinook salmon, and 100,000 coho salmon (Clark 2005). The

Bristol Bay commercial sockeye salmon fishery comprises about 56% of the total statewide harvest of the species on average, with a record high of 81% in 1965 (Clark

2005), and is the largest sockeye salmon fishery in the world (Rinella et al. 2013).

The Nushagak River supports five species of Pacific salmon (Oncorhynchus spp.): Chinook (O. tshawytscha), coho (O. kisutch), sockeye (O. nerka), chum (O. keta), and pink (O. gorbuscha), and all species contribute to the Bristol Bay salmon fishery.

While commercial harvest of Bristol Bay salmon began in the 1880s, consistent management did not begin until the 1950s (Baker et al. 2009a). For the past 60+ years

State scientists have collected escapement and harvest data in most Bristol Bay rivers.

Even with an extensive dataset of escapement and harvest, the influences of local environmental factors on movement patterns and behavior of these stocks have not been well studied.

From 1979-2002, the Alaska Department of Fish and Game (ADF&G) used a

Bendix echo-counter to enumerate sockeye salmon in the Nushagak River, Alaska (Brazil and Buck 2010). Chinook and chum salmon also passed by the sonar, so in the mid-

1980s, ADF&G began enumerating Chinook at the site (Baker et al. 2009b). In the 2000s,

ADF&G replaced Bendix with dual-frequency identification sonar (DIDSON) to improve 7

escapement estimates; today, these estimates are used by ADF&G under the Nushagak-

Mulchatna Chinook Salmon Management Plan (5 AAC 06.361).

The ADF&G Division of Commercial Fisheries assesses and manages Nushagak salmon stocks. Currently, daily DIDSON sonar-based salmon counts are apportioned into species counts using the proportions of Chinook, sockeye, and chum caught in gillnet test-fishing with 20.6 cm, 15.2 cm, and 13.0 cm mesh (Brazil and Buck 2011). Gillnet sampling is conducted immediately downstream of the two DIDSON units in an effort to represent fish that pass by the sonar. Drift gillnet sets are conducted with each mesh size twice daily for most of the spawning season at 0800-1100 hours and 1600-1900 hours.

During the peak period for sockeye passage, three drift sessions are conducted daily at

0800-1100, 1300-1600, and 1800-2100 hours. Sets typically last 2.5 minutes, but are shortened if a high number of fish are being caught (Brazil and Buck 2011). The catch proportions are applied to the sonar counts to apportion the run into the three species.

Thus, increased knowledge of factors influencing migratory patterns of salmon may help improve in-season monitoring at the sonar site and overall escapement estimation accuracy by characterizing an ideal time frame for test fishing.

Nushagak River salmon are an important food source for subsistence, recreational, and commercial users. Each year, about 6,000 Nushagak Chinook are harvested for recreational, 14,000 for subsistence, and 53,000 for commercial use (Jones et al. 2009). Currently, the stock is labeled as "healthy," but accurate escapement goals must be established and achieved annually to maintain that status (Baker et al. 2009). The

Nushagak-Mulchatna Chinook Management Plan (5 AAC 06.361) has an in-river 8

escapement goal of 75,000 salmon: 65,000 for spawning escapement, and 10,000 for subsistence and recreational harvest upstream of the DIDSON site. If the estimate falls below 75,000, restrictions are placed on recreational and commercial users. When estimates fall below 40,000, restrictions are placed on subsistence users. Therefore, accurate assessments of abundance are necessary to effectively manage the fishery.

This study examines the relationships between the upriver migration patterns of

Pacific salmon and light intensity, temperature, and tide at the Portage Creek DIDSON sonar site. The aim was to determine if local environmental factors were correlated with salmon passage rates to better understand the variations in escapement between years.

We hypothesized that 1) passage rates would be highest during flood tides (Strange 2011,

Walsh et al. 2013), 2) with higher light intensity, fish would move offshore (higher proportion passing the offshore sonar vs. inshore sonar; Banks 1969, Bernhard 2009), and

3) the run timing mid-point would be earlier in warmer years (Hinch et al. 2012). 9

1.2 METHODS

Site Description

The Nushagak River, located in southwest Alaska, drains from the Alaska Range south toward Bristol Bay (Figure 1.1). The sonar site is located 48 km northeast

Dillingham, Alaska; 4 km downriver from the village of Portage Creek. At the site, the river is approximately 300 meters wide with a maximum depth of 6 meters.

Figure 1.1. Nushagak River sonar site study area and geographic location in southwest Alaska (inset).

DIDSON Recording

Up-migrating salmon were sampled using a DIDSON, which creates video-like footage and echogram images stored for playback and analysis (Maxwell 2007, Maxwell et al. 2007). The temperature and tidal analyses used DIDSON recordings from 6 June –

18 July, 2008-2011. The light intensity analysis was only conducted in 2011. For all four years, the ensonified areas were divided into four strata: left bank inshore (LBI), left bank offshore (LBO), right bank inshore (RBI) and right bank offshore (RBO) (Figure 1.2;

Brazil and Buck 2011). One DIDSON (SV model, standard range) was placed on the left bank to ensonify 0-10 meters at high frequency (LBI) and 10-30 meters at low frequency

(LBO). A second DIDSON (LR model, long range) was mounted on the right bank to ensonify 0-10 meters at high frequency (RBI) and 10-50 meters at low frequency (RBO).

For each of the four strata, DIDSON recorded fish passage for the first 10 minutes of every hour. Each 10 minute file was played back and salmon were counted using a tally counter. Counts were then multiplied by 6 in order to obtain an escapement estimate for each hour.

Species Apportionment

In the Nushagak River, chum, sockeye, and Chinook salmon runs overlap in space and time, and DIDSON units cannot distinguish between species (Buck et al. 2012). For the purpose of this study, all salmon species were treated together. Refer to Brazil and

Buck (2011) for specific methods on species apportionment.

Light Intensity

In 2011, two HOBO temperature and light intensity data loggers (Onset HOBO model UA-002-64) were used at the sonar site on the left bank of the river. One was placed on the inshore DIDSON mount, approximately 0.5 m from the river bottom. The other data logger was attached to a buoy line that was used to mark the endpoint of the offshore sonar beam. This second data logger was in deeper water than the inshore logger

(approximately 0.5 m from the river bottom).

From 6 June – 7 July 2011, the light intensity data loggers recorded hourly light intensity values ±1 lumen ft-2; the intensity of light as it reaches a surface1. To make the data compatible with other scientific literature it was converted to lux: one lumen ft-2 is equal to 10.76 lux (Palmer 1999). Lux is the luminous flux incident per unit area.

Average indoor lighting ranges from 100 to 1,000 lux, whereas average outdoor sunlight is about 50,000 lux (Wulfinghoff 1999). For reference, some typical lux values that humans experience in different environments are provided in Table 1.1 (Schubert 2006).

Both lumens ft-2 and lux are sensitive to the data logger’s distance from the light source, and the angle at which the light reaches the sensor (Long et al. 2012); therefore,

1 The amount of illumination the inside surface of a one-foot-radius sphere would be receiving if there were a uniform point source of one candela in the exact center of the sphere 14

inshore and offshore light intensity values were not directly compared to one another, rather the ratio of inshore to offshore lux values was compared against sonar counts.

Since the number of fish passing inshore versus offshore may be influenced by total fish passage, a multiple regression using percent daily lux and percent daily inshore counts as independent variables, and total daily sonar count as the dependent variable was used to determine if light intensity explained fish passage.

Table 1.1. Typical illuminance in different environments (from Schubert 2006).

Illumination condition Illuminance

Full moon 1 lux Street lighting 10 lux Home lighting 30 to 300 lux Office desk lighting 100 to 1,000 lux Surgery lighting 10,000 lux Direct sunlight 100,000 lux

Temperature

Four years (2008-2011) of temperature and fish passage data were used for the temperature analysis. Each year, a HOBO temperature logger (Onset HOBO model UA-

001-64) was fastened to the vertical portion of the RBI DIDSON mount, approximately

0.5 m from the river bottom. The temperature logger recorded temperature hourly during the entire season (June 7 – July 18). Following Hodgson et al. (2006) the day at which

50% of the cumulative total salmon had passed the sonars was calculated for each year.

Hodgson et al. (2006) used the thirty day temperature average surrounding the date at which 50% of the fish had migrated upriver, but because the season on the Nushagak is only about 40 days long, the two-week temperature average surrounding the 50% migration date was used. Average seasonal temperatures were also calculated to determine if there was a warming trend between 2008 and 2011 and related to the date at which 25%, 50%, and 75% of salmon had passed the sonar site.

Tide

The sonar site is slightly influenced by tidal cycles; while flow does not reverse, water levels fluctuate approximately 1-2 meters. There was no water level gauge or flow meter at the sonar site, so tidal heights were used as a proxy for flow. Hourly tidal heights from 2008-2011 were derived for Clark’s Point, near the mouth of the Nushagak River using a tide prediction program (Jtide). Three hours were added to adjust for the time it takes for the tidal influence to reach the sonar site (K. Middlestadt, personal communication, June 2011). Tidal stages were classified as flood, slack, or ebb, based on whether the water level was increasing, constant, or decreasing. Tidal stages are not 17

spread evenly throughout the 24-hour day, so average passage per hour within each of the three stages was calculated to standardize the data. These average hourly passage rates were then compared by tide stage. For each year from 2008-2011, chi-square goodness- of-fit tests were performed to determine whether the proportion of fish passing differed between flood, ebb, and slack tide under an expectation of uniform passage rate (33.3% per stage).

1.3 RESULTS

Salmon passage rates varied between years, but an initial surge always occurred within 1-2 days of summer solstice (day 170-172, Figure 1.3), and was followed by one or more surges later in the season. The highest estimated total passage occurred in 2009

(1,002,327 salmon), followed by 2008 (906,760), 2010 (807,745), and 2011 (781,770).

Figure 1.3. Cumulative daily sonar counts by day of year for Chinook, chum, and sockeye salmon species combined, 2008-2011.

Light Intensity

Light intensity was measured during 2,064 hours from 6 June – 7 July in 2011.

Most lux values were low (>50% of records were at 0-10 lux), and lux was greatest from

1300-1600 hours in both inshore and offshore strata (Figure 1.4). The LBI light intensity values ranged from 0-11,018 lux, while offshore ranged from 0-2,324 lux. Total salmon passage varied, but was greatest at 1000-1100 hours (Figure 1.4). Passage within the light intensity range of 0-10 lux comprised 65% of the total passage inshore. In the offshore stratum, 55% of the passage occurred between 11-2,324 lux. In the inshore stratum, average hourly lux values described 21% of the variance in average hourly sonar counts, but the relationship was not strong (Table 1.2, Figure 1.5, R2 = 0.21, p = 0.02). In the offshore stratum, average hourly lux values explained only 9% of the variance in average hourly sonar count (Table 1.2, Figure 1.5, R2 = 0.09, p = 0.16).

Figure 1.4. 2011 average sonar count and average light intensity per hour. Error bars represent 95% confidence intervals.

Figure 1.5. Influence of average lux on average hourly sonar count for inshore (p = 0.02) and offshore (p = 0.16) strata.

Table 1.2. Average hourly salmon passage as described by inshore and offshore light intensity (lux).

Stratum R2 Upper 95% CI Lower 95% CI P Inshore lux 0.21 0.11 0.01 0.02 Offshore lux 0.09 0.11 -0.02 0.16

Total salmon density (Figure 1.6), but not lux (p = .24), explained the proportion of salmon passing the offshore versus inshore sonars (Figures 1.6 and 1.7, R2 = 0.57,

F(2,43) = 6.51, p < .01). As total salmon in the river increased, the number using the inshore and offshore strata both increased linearly, but 4.7 times more fish passed inshore.

Figure 1.6. Number of fish in each stratum compared to total fish passage.

Figure 1.7. Percent of salmon using the inshore stratum compared to total fish passage.

Temperature

Overall, 2009 was the warmest year with a seasonal average of 13.6 °C, whereas

2011 was the coldest year with a seasonal average of 10.5 °C. From 2008-2011, there was no apparent relationship between temperature and the date at which 50% of the run had passed (Figure 1.8). The difference between each year's seasonal mean temperature at the midpoint of the run and the average temperature for all four years indicated that the midpoint of the run occurs within just a few days (< 7 days between all years) regardless of temperature (Figure 1.9, Table 1.3).

Figure 1.8. Average daily temperatures from 8 June to 18 July, 2008-2011. Black dots represent the date at which 50% total fish passage occurred. Error bars represent 95% confidence intervals.

Figure 1.9. Regression plots for residuals of dates at which 25%, 50%, and 75% of the run passed, compared to mean temperatures at the early, mid, and late points of the run.

Table 1.3. Summary of mean temperatures at the date where 50% of cumulative salmon occurred, and deviations in mean temperature and 50% passage date from 2008-2011.

Mean Temp. Day of Year Deviation from Deviation from at 50% when 50% Mean Temp. at Mean 50% Passage Date Passage 50% Passage date Passage Date Year (°C) 95% CI Occurred (°C) (days) 2008 11.17 0.14 183 -0.48 1.5 2009 13.69 0.21 179 2.04 -2.5 2010 11.18 0.09 185 -0.47 3.5 2011 10.56 0.09 179 -1.09 -2.5

Tide

Over the four year study, the highest tide was 7.27 m, and the lowest was -1.5 m, both of which occurred in 2008. The highest average hourly fish passage occurred during either slack or ebb tides; the lowest passage occurred during flood for all years (Table

1.4, Figure 1.10). In 2009 and 2011, passage rate distributions at tidal phases differed from uniform (2009 2 = 19.81, p < 0.01, 2011 2 = 12.30, p <.01) but not in 2008 and

2010 (2008 2 = 3.43, p = 0.18, 2010 2 = 1.86, p = .40).

Table 1.4. Average hourly salmon passage at each tide stage from 2008-2011 and 95% confidence intervals.

Passage Rate (95% CI) Year Slack Ebb Flood 2008 922 (±75) 945 (±78) 868 (±76) 2009 1069 (±83) 1064 (±90) 893 (±64) 2010 839 (±58) 820 (±57) 785 (±59) 2011 807 (±57) 841 (±65) 707 (±54)

Figure 1.10. Comparison of average hourly salmon passage at slack, ebb, and flood tides in 2008 – 2011. Error bars represent 95% confidence intervals.

1.4 DISCUSSION

The purpose of this study was to examine relationships between salmon upstream migration rate and light intensity, temperature, and tidal phases in the Nushagak River using DIDSON fish passage data. Other workers (e.g. Banks 1969, Daum and Osborne

1998, Bernhard 2009) have argued that better understanding the factors that influence salmon run timing and upriver migration patterns will improve sampling strategies and ultimately management decisions.

Light Intensity

Salmon passage rates varied and peaked several times daily under a range of light conditions (Figure 1.4). While the proportion of fish in the offshore stratum increased slightly at higher lux values, the relationship was weak and a similar, stronger trend was seen inshore where lux was higher (Figures 1.5 – 1.7). As the total number of fish in the river increased, the proportion of fish that used the inshore stratum increased linearly

(Table 1.2). An increasing trend also occurred in the offshore stratum, but the relationship was not as strong (Figure 1.6). We expected salmon to prefer darker habitat for travel

(Ringelberg 1995), but did not find evidence for this behavior. Rather, salmon showed strong preference for the inshore stratum regardless of light intensity, validating existing knowledge that salmon strongly prefer migrating in nearshore habitats. 25% of the lux values used in this analysis were zeroes, potentially due to the data logger’s precision; lumens ft-2 captured light in whole numbers, so any values less than one would have resulted in zeroes when converted to lux. A data logger with increased sensitivity may benefit this limitation. 35

Temperature

While there was variation in run timing and temperature between years, no apparent relationships existed between the dates at which 50% of salmon passed and the mean temperature at the midpoint of the run (Figure 1.8). One noteworthy observation is that in all four years, the midpoint of the run occurred within a 7-day time window between 27 June and 4 July (Figure 1.9). 2009 was the only year where earlier run timing was associated with warmer water temperatures at 25%, 50%, and 75% dates of cumulative passage; however, counter to expectations, 2011 had the same run midpoint as 2009 yet it was the coldest year (Table 1.3, Figure 1.9). These findings suggest that temperature at the date of 50% total passage may influence upriver timing but is not a primary driver. Analysis across multiple years would augment this exploratory research and better describe run timing trends with regard to water temperature.

Tide

In 2009 and 2011, proportions of salmon passing the sonar were greater during slack and ebb tide phases than at flood tide (Figure 1.9, Table 1.4), aligning with findings from research on Atlantic salmon (Smith and Smith 1997, Karppinen et al. 2004). Despite these findings, flow conditions at the DIDSON site may be substantially different than the tidal heights derived from Jtide; storm events and runoff upriver likely cause fluctuations in water levels that could not be captured from the tide prediction program.

Use of a flow meter and water-level gauge at the sonar site would provide more meaningful information. 36

Conclusion

This exploratory study provides a basis for closer examination of the spatial and temporal structure of salmon runs using the DIDSON outputs which are primarily used for escapement estimates. Although the data did not indicate that light intensity, temperature, or tide had much impact on upstream migration patterns, future investigation of these factors in the Nushagak River is still merited and may be of use to

ADF&G’s in-season salmon monitoring and fishery management.

The sonar site is located 48 km northeast of the coastal town of Dillingham, but it may be far enough downriver that conserving energy is not yet a limiting factor for salmon survival (Strange 2013). Salinity measurements at the sonar site would shed light on the potential oceanic influences that may be occurring. Recommendations for future research include calibrating and testing data loggers before conducting fieldwork and at the beginning of the data collection period. Hourly tide and turbidity measurements at the sonar site would also strengthen the tide and light intensity datasets and eliminate the need to forward-calculate tides from Clark’s Point. In rivers where visual observations were difficult, others have used acceleration data loggers, archival tags, radio telemetry, or acoustic telemetry methods and found relationships between salmon movements and turbidity, temperature, and flow (Tsuda et al. 2006, Strange 2007, Hayes et al. 2011).

ADF&G is currently collecting data on Chinook salmon movements using acoustic tags in the Nushagak River (results expected in 2015), as well as in other Alaskan watersheds

(Welch et al. 2013). 37

Other studies on salmon run timing use datasets of 10+ years (Hodgson et al.

2006), or establish multiple observation points along the river system (Smith and Smith

1997). This dataset was limited to only four years and one observation point, the

DIDSON site. A larger sample size would strengthen the analysis explored in this study.

This research may be continued and could be expanded upon at any time, since the data are already collected as part of the annual ADF&G Portage Creek Sonar project.

The Nushagak River salmon run includes Chinook, chum, and sockeye runs that overlap over approximately 40 days duration. Due to the complexity of the multi-species run and the large width of the river, there may be meaningful relationships in the ways that salmon use space and time to migrate upriver that were not captured in this study.

For example, each year there are variable trends in fish passage: based on apportionment findings, Chinook generally arrive at the site first, but sockeye are the first to exhibit a large pulse in daily passage (personal observation; G. Buck, personal communication,

January 2014). In any given season, chum and sockeye might exhibit two to four large pulses throughout the run, while Chinook exhibit only one or two. Further, the timing between each species first large pulse can occur between 0-5 days of one another. For these reasons, migration behaviors may be driven by species-specific patterns, potentially confounding the relationships explored in this study. For further analysis, the data should be sub-sampled for periods that may better identify relationships between the migratory behavior of salmon and their environment on a local scale, in addition to analyzing trends across the entire run and multiple years. Continued knowledge of local river conditions 38

and fish passage characteristics would help management connect future environmental variations with salmon migration patterns. 39

CHAPTER 2

THE EFFECTS OF ENVIRONMENTAL FACTORS ON AGGREGATION

BEHAVIOR OF PACIFIC SALMON IN NUSHAGAK RIVER, ALASKA. 40

2.1 INTRODUCTION

Spatial aggregation occurs in aquatic communities from microorganisms to marine mammals, across a range of spatial scales (Parrish and Edelstein-Keshet 1999,

Parrish et al. 2002). Over 50% of fish species school at one point during their life cycle

(Shaw 1978 from Parrish et al. 2002). Under natural conditions, fishes may self-organize in response to conspecifics, predators, prey or environmental cues, but the evolutionary and ecological drivers of fish aggregation are difficult to assess and are poorly understood (Parrish and Edelstein-Keshet 1999). However, this is a growing field of study because knowledge of species' aggregation tendencies can increase commercial exploitation efficiently (e.g. targeted harvest of fish during schooling) or facilitate improvements in fishery stock assessment (Parrish et al. 2002).

Pelagic and migratory fishes, such as adult Pacific salmon (Oncorhynchus spp.), may be particularly susceptible to human exploitation or environmental change, as they do not rely solely on bottom or coastal habitats during ocean migrations, but instead migrate from a spacious marine environment to relatively small river systems. Spatial modeling approaches have been used to characterize pelagic fish aggregations (e.g.

Capello et al. 2011), but no studies have attempted the same for adult salmon in Alaskan rivers.

The Alaska Department of Fish and Game (ADF&G) has managed salmon in

Alaska since the state’s inception in 1959, using primarily tower, aerial, sonar, or other human observational counts. Dual-frequency identification sonar (DIDSON) was implemented in 2005 on the Nushagak River (Figure 1.1), which flows into Bristol Bay, 41

the largest sockeye salmon (O. nerka) fishery in the world (Rinella et al. 2013), as a sophisticated method to visually count fish in the large, turbid river (Buck et al. 2012).

Chinook (O. tshawytscha) and chum (O. keta) spawning runs overlap with sockeye in the river and are also counted. DIDSON is non-intrusive to fish because it is typically bank- mounted sonar that ensonifies salmon to record acoustic video and generate echograms, which can be used to count fish and observe their morphology and swimming behavior

(Martignac et al. 2014).

Most in-stream salmon assessments employ sub-sampling routines to generate hourly fish passage rates which are extrapolated to daily and ultimately total season escapement estimates. Currently Nushagak River salmon escapement is estimated based on DIDSON sonar recordings collected for 10 minutes once per hour around the clock.

Exploring the influence of environmental factors on fish aggregation patterns may reveal responses to factors that are not detectable at enumeration levels, yet contribute to pulses in salmon runs. Schuerell (2004) found meaningful aggregation behavior in predator and prey fishes in Lake Washington, U.S.A., using small-scale, high-resolution studies, and

Crook et al. (2001) successfully described the influences of spatial scale and habitat arrangement on diel patterns in fish habitat use.

Point pattern analyses are used to determine if the spatial arrangements of events, such as schooling behaviors, are completely random by measuring departures from a homogenous Poisson process (Legendre and Legendre 1998). These methods have been used in ecological and social studies to model the locations of events in a given spatial area. For example, one might be interested in the spatial clustering of trees (Pereira and 42

Turkman 2010), locations of bird and insect nests, locations of city centers or earthquakes

(van Lieshout and Stein 2012), or marine life aggregations (Crook et al. 2001). Biological organisms typically exhibit uniform behavior when competing for a limited resource

(Rohlfs and Hoffmeister 2004), and aggregated behavior when reproducing (Pet et al.

2005), seeking protection (Schuerell 2004), conserving energy (Svendsen et al. 2003), or responding to a heterogenous environment (Post et al. 2008).

We examined the feasibility of using echograms derived from DIDSON video to quantify the distance of maximum spatial aggregation of salmon during their spawning migration. Other studies have used DIDSON video to quantify spatial patterns of fishes

(Hijuelos 2012, Price et al. 2013), but this is the first known study to quantify spatial patterns of salmon using DIDSON echograms. Echograms are the only DIDSON data product with sufficiently large spatial dimension to fully observe the large salmon schools migrating up the Nushagak River.

Using the L-function of Ripley’s K point pattern analysis metric we explored the influence of run period, fish abundance, light intensity, tide, and water temperature on salmon aggregations. Maximum aggregation distances were expected to increase more as the spawning run advanced because later migrants would sacrifice tight aggregations, typically used as a defense mechanism, to arrive at competitive spawning grounds presumably reached by earlier migrants (Hawkins et al. 2005). Maximum aggregation distances were expected to increase as the density of salmon increased, due to effects of space availability. Salmon were expected to aggregate at closer distances during the day and under higher light intensities to avoid threats from visual predation (Schuerell 2004). 43

Maximum aggregation distances were expected to decrease when the tide was falling, because tight aggregations with congeners would be useful for energy conservation against the tide (Svendsen et al. 2003). Lower water temperature inhibits swimming ability, making fish more susceptible to predation and overexploitation (Brown 1998); therefore salmon were expected to aggregate at closer distances in cooler waters.

Knowledge of these aggregation patterns and their potential relationships with local environmental factors may help ADF&G fine tune methods for monitoring salmon runs before, during, and after the spawning season. The incorporation of this research into test fishing methods and DIDSON recording techniques may be of interest to ADF&G.

Developing a baseline understanding of aggregation behavior in the Nushagak River will allow managers to more closely monitor future shifts in run timing or escapement counts and suggest methods to refine apportionment techniques.

2.2 MATERIALS AND METHODS

Site Description

The Nushagak River, located in southwest Alaska, drains from the Alaska Range south toward Bristol Bay (Figure 1.1). The sonar site is located 48 km northeast

Dillingham, Alaska; 4 km downriver from the village of Portage Creek. At the site, the river is approximately 300 meters wide with a maximum depth of 6 meters. For salmon enumeration purposes, ADF&G divides DIDSON-ensonified areas into four strata (Brazil and Buck 2011): left bank inshore, left bank offshore, right bank inshore, and right bank offshore (Figure 1.2). We analyzed recordings only from the right bank inshore stratum; the majority of salmon use this stratum, and the DIDSON unit (LR model, long range) ensonifies from 0-10 meters at high frequency, creating high resolution echograms. In the

Nushagak River, DIDSON currently cannot distinguish between Chinook, chum, and sockeye species with overlapping migrations, so ADF&G apportions the DIDSON counts into species using drift gillnet test fishing (Brazil and Buck 2011). For the present study, we analyzed all salmon as one species.

Echogram Processing

DIDSON echograms collected in 2011 were processed in DIDSON Control and

Display V5.25.24 (Sound Metrics Corp.) and exported as .jpeg-formatted images, producing 10 minute “snapshots” from the video (Figure 2.1). Due to processing time, echograms from 2011 were selected based on three-day windows where 15%, 50%, and

85% of the cumulative salmon passage occurred and categorized as Early run (21-23 45

June), Mid run (28-30 June), and Late run (7-9 July). Of the 216 available echograms for the nine days, 199 were processed; 17 DIDSON recordings were discounted due to intermittent equipment outages in the field.

Figure 2.1. DIDSON video recording (left) showing two fish near 6 meters from the shore, with corresponding echogram image (right).

The light intensity, tide, and water temperature analyses used data collected from

6 June – 18 July, 2011. A HOBO light intensity data logger (Onset HOBO model UA-

002-64) was secured on the left bank inshore DIDSON mount, approximately 0.5 m from the river bottom. It recorded hourly light intensity data in lumen ft-2, a unit that measures the intensity of light as it reaches a surface1. To make the data compatible with other scientific literature we converted them to lux (1 lumen ft-2 = 10.76 lux; Palmer 1999).

Lux is the luminous flux incident per unit area. For reference consider that average indoor lighting ranges from 100 to 1,000 lux, whereas average outdoor sunlight is about

50,000 lux (Wulfinghoff 1999).

The sonar site is slightly influenced by a semidiurnal tidal cycle; while flow does not reverse, water levels fluctuate approximately 1-2 meters. There was no water level gauge or flow meter at the sonar site, so tidal heights were used as a proxy for flow.

Hourly tidal heights from 2011 were derived for Clark’s Point, near the mouth of the

Nushagak River, using a tide prediction program (Jtide). Three hours were added to adjust for the time it takes for the tidal influence to reach the sonar site (K. Middlestadt, personal communication, June 2011).

A HOBO temperature logger (Onset HOBO model UA-001-64) was fastened to the vertical portion of the right bank inshore DIDSON mount, approximately 0.5 m from the river bottom and recorded hourly temperature readings (°C).

Echogram images were individually analyzed in ImageJ (v1.45s), a public domain image analysis program (Rasband 2014, http://imagej.nih.gov/ij/). ImageJ has the ability

1 The amount of illumination the inside surface of a one-foot-radius sphere would be receiving if there were a uniform point source of one candela in the exact center of the sphere 48

to measure two-dimensional data in pixels. To collect x,y positional coordinates in pixels, individual fish in each of the 199 echograms were clicked on by a single operator using the ImageJ multi-point tool (Figure 2.2). To provide the most accurate location, the operator clicked on the centermost point of a fish.

Figure 2.2. Echogram display in ImageJ. The two fish from Figure 2.1 are marked with crosshairs of the multi-point tool and echogram dimensions are displayed in pixels (top left). This echogram’s actual dimensions are 10 minutes (x axis) by 10 meters (y axis).

Echograms have axes of time (10 minutes) along the horizontal axis, and distance

(meters) along the vertical axis. Because the sonar position is fixed, the time units on horizontal axis also reflect the distances between passing fish. However, due to a lack of detailed calibration information (e.g. echogram level water flow rates and fish swimming speeds) we used pixels in this point pattern analysis. A pixel-to-distance conversion was made along the vertical axis for reference. The maximum pixel value in the vertical axis was 512, equivalent to 10 meters, the maximum range of the DIDSON recordings.

Therefore each pixel = 0.019 meters. Additionally, individual salmon were about 30 pixels (0.58 m) long. Spatial analysis results were described in pixels and standard salmon-length units (30 pixels = 1 salmon length) to assist in interpretation.

Ripley’s K Analysis

Coordinates of every fish in each echogram were exported from ImageJ to the

Spatial Analysis in Macroecology software (SAM; v4.0; Rangel et al. 2010), a public domain program used for investigating spatial patterns and processes. The L-function of

Ripley’s K point pattern statistic was calculated for each echogram. The Ripley’s K function (Ripley 1976) examines departures from Complete Spatial Randomness (CSR).

The K(t) function is:

, where λ is intensity, or the density of points per unit area and E is the number of extra points within distance t of a randomly chosen point (Grantham 2012). The simplest form of Ripley’s K function under CSR is:

, (Dixon 2002). 51

The L-function is a variance stabilized formulation of Ripley’s K:

and is commonly used in place of the K-function because its variance is relatively constant under CSR (Dixon 2002, Grantham 2012), thus balancing fluctuations in over increasing distances (t). Echograms were 512 pixels in height but varied in width, from

1721-5952 pixels. Where distance at maximum spatial aggregation was still increasing at t = 256 pixels (half the echogram height), the search radius in SAM was expanded to obtain a spatial structure maximum for each echogram.

The L(t) function reveals the patterns in spatial proximity of fish to one another at increasing search distances within each echogram. Deviations from L(t) at each distance t are used to test departures from CSR and are described as:

– t, (Haase 1995),

or L(t)-t. If L(t)-t = 0, CSR is occurring. L(t)-t > 0 when the point process departs from

CSR and becomes more aggregated. The search radius distance, t, at which L(t) is greatest describes the distance of maximum spatial aggregation along the L(t) curve. Here this point is termed tLtmax (Figure 2.3).

Figure 2.3. Modified Ripley's K L(t)-t curve showing the range of complete spatial randomness (CSR, L(t)-t = 0) and the distance at maximum aggregation tLtmax.

Run Period

To determine if tLtmax became more variable as the run advanced the index of dispersion, the ratio of the tLtmax variance to the mean tLtmax, (D) was calculated for the echograms in Early, Mid, and Late run. We expected D to increase as the run advanced.

A two-sample Kolmogorov-Smirinov (K-S) test was performed to determine if the distribution of tLtmax differed between Early, Mid, and Late run periods. This statistic quantifies the distance between the empirical distribution functions of two samples.

The null distribution is calculated under the null hypothesis that the samples are drawn from the same distribution.

A Kruskal-Wallis one way analysis of variance (ANOVA) was also performed to test the differences in the median values among the distribution of distance at maximum tLtmax. Last, a post-hoc pairwise comparison (Dunn’s method) was performed to isolate run period(s) that differed from the others.

Salmon Density

To determine if salmon abundance (number of fish in each echogram) influenced tLtmax, linear regressions were conducted for each run period and the season overall.

Light Intensity

To determine if light intensity (lux) influenced tLtmax, a linear regression was conducted across the entire run using the mean tLtmax at each lux value. Outliers in lux, including zeroes and one abnormally large reading, were omitted. Since lux values of zero might actually be low light levels that were not captured due to the data logger’s 54

precision (it only recorded lumens ft-2 in whole numbers), we also divided lux into low (0 lux) and high (>10 lux) categories and conducted a two-sample t-test between mean tLtmax at low and high lux. Due to lux data limitations within the selected run periods, a one- way ANOVA was conducted to determine if spatial structure was influenced by four photo periods based on time of day: dawn (0500-1059 hours) day (1100-1659 hours), dusk (1700-2259 hours) and night (2300-0459 hours). A two-sample t-test was also conducted between combined night/dawn (2300-1059 hours) and day/dusk (1100-2259 hours) photo periods, which may better reflect actual light levels in this high-latitude environment.

Tide

To determine if tide had an effect on tLtmax, time adjusted tidal heights (meters) were categorized into flood, ebb, high, and low tidal phases. A Kruskal-Wallis one-way

ANOVA was performed to test the differences in median tLtmax between the four tidal phases. Tidal heights were also categorized into rise and fall, and a Mann-Whitney rank sum t-test was performed to test the differences in tLtmax between the two categories.

Water Temperature

To determine if water temperature influenced tLtmax, a linear regression was conducted over the entire season. Temperatures were also divided into low and high categories (8.5-9.9 °C and 10.0-12.5 °C, respectively), and a two-sample t-test was performed (assuming unequal variances) to test the difference in the mean tLtmax at low and high temperatures. 55

Factor Interactions

To determine which variables were the best predictor of tLtmax, we conducted a multiple linear regression (n=198) using run period (Early, Mid, and Late), salmon density, tide height, and water temperature as predictors of tLtmax. Early run period corresponded to 21-23 June, Mid run period corresponded to 28-30 June, and Late run period corresponded to 7-9 July. One outlier was removed from analysis due to an unusually high level of influence on the regression (Cook’s distance diagnostic). Salmon density and tLtmax were highly and positively skewed and were transformed using natural logarithms (ln); the resulting distributions were far more normal. A full multiple regression model of ln(tLtmax) with intercept, tide, and temperature as coefficients, ln(salmon density), run period (Early, Mid, Late), and the interaction of run period and ln(salmon density) and run period was tested for significance of coefficients using Wald tests on each coefficient.

2.3 RESULTS

Spatial Structure

Positions for 18,614 salmon were collected in 199 echograms during 21-23 June,

28-30 June, and 7-9 July, 2011. Salmon exhibited strong spatial structure in all the echograms we processed and were never spatially random (L(t)-t > 0). Overall the mean tLtmax = 282 (± 95% CI of 46 pixels), or approximately 9 salmon lengths. tLtmax was the most numerous at distances of 90-119 pixels (26%, Figure 2.4), or 3-4 salmon lengths.

Figure 2.4. Frequency distribution of tLtmax, or distance at maximum aggregation, for Early, Mid, and Late run periods, 2011.

Run Period

Salmon tLtmax was over-dispersed (D > 1) in all run periods and D increased substantially during each subsequent run period (Table 2.1). Relative to the Early run period the index of dispersion increased 2.4 times in the Mid and 3.4 times in Late periods.

Table 2.1. Index of Dispersion (D) for early, mid, and late run periods. D > 1 indicates significant over-dispersion of tLtmax, or clustering. D increased substantially as the run advanced.

Run Period D Early 133.98 Mid 322.01 Late 450.33

The distributions of tLtmax differed between Early run and both Mid and Late run

(Kolmogorov-Smirinov test, p < 0.001), but did not differ between Mid and Late run periods (p = 0.315).

Median tLtmax varied by run period (Kruskal-Wallis one-way ANOVA, H(2,198) =

28.507, p < 0.001; Figure 2.5). Median Early run tLtmax was 75 pixels smaller than tLtmax at

Mid run (Dunn’s post hoc Q = 4.475, p < 0.05) and 145 pixels smaller than Late run (Q =

4.808, p < 0.05), but Mid run and Late run were not different (Q = 0.272, p > 0.05).

Figure 2.5. Distribution of tLtmax for Early, Mid, and Late run periods. Box plots and symmetrical dot-densities describe the medians. Values between 1.5 x (interquartile range) and 3 x (interquartile range) the medians are plotted with asterisks. Values beyond 3 x (interquartile range) the medians are plotted with circles.

Salmon Density

tLtmax increased slightly with increasing salmon density during Mid and Late run

(Figure 2.6). However, fish density did not explain tLtmax during any run period or across the entire season (Early run, R2 = 0.00, p = 0.83; Mid run, R2 = 0.00, p = 0.56; Late run,

R2 = 0.04, p = 0.09; entire season, R2 = 0.00, p = 0.80; Table 2.2).

Figure 2.6. Regressions of tLtmax by salmon density and run period.

Table 2.2. Linear regressions of fish density on tLtmax during each run period and across the entire run.

Period F df p Early 0.04 1, 63 0.83 Mid 0.34 1, 66 0.56 Late 2.93 1, 70 0.09 Overall 0.07 1, 199 0.80

Light Intensity

2 Overall light intensity (lux) did not influence tLtmax (R = 0.01, p = 0.37), although tLtmax was slightly larger at higher lux levels (Figure 2.7). There was no difference in mean tLtmax (two-sample t = 0.59, df = 198, p = 0.28) at categorized low lux (mean tLtmax =

291 pixels, ≈ 10 salmon lengths) and high lux (mean tLtmax = 261 pixels, ≈ 9 salmon lengths).

Figure 2.7. Regression of tLtmax and light intensity (p = 0.37).

Photo period appeared to influence tLtmax (H(3,198) = 10.313, p = 0.02; Table

2.3,). Median tLtmax at dawn was significantly smaller than at dusk (Dunn’s post hoc Q =

2.990, p < 0.05). All other pairwise comparisons were similar (Table 2.3). Mean tLtmax at night/dawn (241 pixels, ≈ 8 salmon lengths) was smaller than tLtmax during day/dusk (328 pixels, ≈ 11 salmon lengths) two-sample t = -1.86, p = 0.03.

Table 2.3. Descriptive non-parametric statistics of tLtmax during dawn, day, dusk, and night photo periods (Kruskal Wallis one-way ANOVA with Dunn’s post hoc tests).

Photo Period N Median Dawn 53 120 Day 54 144 Dusk 39 270 Night 53 120

Comparison Difference of Ranks Q P<0.05 Dusk vs Dawn 36.332 2.990 Yes Dusk vs Night 31.964 2.631 No Dusk vs Day 21.697 1.793 No Day vs Night 14.634 1.314 No Day vs Night 10.266 0.922 No Night vs Dawn 4.368 0.390 No

Tide

Median tLtmax did not differ by tidal phase categories (Kruskal Wallis one-way

ANOVA H (3,198) = 1.544, p = 0.672; Table 2.4) and was not different during rising tide

(160 pixels, ≈ 5 salmon lengths) and falling tide periods (120 pixels, ≈ 4 salmon lengths;

Mann-Whitney rank sum test, t(198) = 10,040.5, p = 0.278).

Table 2.4. Median tLtmax during flood, ebb, high, and low tide phases and falling and rising tide phases.

Tide Phase N Median SD Flood 80 160 282 Ebb 88 120 400 High 16 183 214 Low 15 120 202 Falling 103 120 378 Rising 96 160 271

Water Temperature

2 Water temperature did not appear to influence tLtmax (R = 0.00, p = 0.77; Figure

2.8).

Mean tLtmax was not different under high (mean tLtmax 284, ≈ 9 salmon lengths) and low temperature conditions (mean tLtmax 276, ≈ 9 salmon lengths; t(1) = 0.16, p = 0.44).

Figure 2.8. Regression of tLtmax and water temperature (p = 0.77).

Factor Interactions

The interaction between Early run period and ln(salmon density) was not significantly different from the interaction between ln(salmon density) and Late run period (p = 0.11), so we combined Early and Late run periods as a new level in the period variable, called Shoulder. Shoulder corresponded to 21-23 June and 7-9 July combined, the dates we used for Early and Late runs, respectively. Mid run period still corresponded to 28-30 June. Tide and water temperature had no effect on ln(tLtmax) (Wald test, p >

0.88). We removed tide and water temperature from the model, and using the new run period, Shoulder, found that during Mid run, ln(tLtmax) decreased with increasing ln(salmon density) (Wald test, p = 0.038). During the Shoulder run period, ln(tLtmax) increased with increasing ln(salmon density) (p = 0.013) and ln(tLtmax) decreased with decreasing ln(salmon density) (p = 0.006).

2.4 DISCUSSION

Knowledge of aggregation behavior in spawning salmon, which are susceptible to exploitation, is an integral part of understanding run dynamics and improving management. Many fisheries have been managed based on escapement counts and commercial harvest (Rowe and Hutchings 2003), including Nushagak River salmon stocks, yet do not investigate run dynamics at a finer scale. Fine scale examinations are important to fisheries managers because aggregation patterns may relate to the status of the stock, which has implications for in-season management, angling, and commercial activities (Reid et al. 2000). The objective of this study was to examine the utility of

DIDSON echograms for quantifying aggregation patterns of Pacific salmon during their upriver spawning migration. Moreover, in addition to describing salmon aggregations, we incorporated the effects of external factors on aggregation behavior. The present study was the first known attempt to use DIDSON echograms and spatial point pattern statistical analyses to investigate salmon aggregations during their upriver migration.

Within each echogram (a 10 minute x 10 meter snapshot), Ripley’s K (L) function revealed the distances where local salmon spatial structure was strongest. The tLtmax metric described the distance in which more fish are associated with each other compared to any other distance. We investigated whether maximum aggregation distances varied alone and with regard to run period, salmon density, light intensity, tide, and water temperature. While the salmon in all the echograms we sampled displayed significant spatial structure (Figure 2.4), only run period and light intensity appeared to influence their maximum aggregation distances. 75

Run Period

During the Early run period salmon exhibited shorter aggregation distances than in Mid and Late run periods and this may relate to competition to reach spawning grounds (Hawkins et al. 2005). Moreover, Mid and Late run salmon may also sense pheromones released by mature and spawning Early run salmon (Miller et al. 2009), and thus could be triggered to migrate upriver as quickly as possible at the expense of sacrificing any potential benefits of schooling. During in-season monitoring by ADF&G, fishing periods open and close based on daily escapement counts and this finding indicates that Early run salmon may be more vulnerable to exploitation than Mid and

Late run salmon.

We generalized and examined salmon aggregations as one species in a system that supports overlapping spawning runs between three species. Incorporating the species- apportioned counts would help refine this analysis and may yield informative results, as well as provide information to ADF&G on aggregation dynamics by species. For example, apportioned counts indicate that Mid run dates (28-30 June, 2011) were dominated by sockeye salmon (>70% of sonar counts were presumably sockeye), whereas chum and Chinook each comprised ≤ 17%. This indicates that any aggregation patterns observed during Mid run may be representative of behavior specific to sockeye salmon. With a larger dataset, correlations between daily species-apportioned counts and distance at maximum aggregation should be investigated. Any relationships could be used by ADF&G to determine if daily escapement counts and apportionment percentages align with daily means of spatial aggregation metrics. 76

Salmon Density

Salmon aggregation behavior was not affected by the number of salmon present within the same spatiotemporal dimensions (i.e. within the same echogram). We expected salmon to aggregate more loosely as density increased, due to the effects of space availability (i.e. rather than distinct clustering, the run would appear as one continuous stream). The 10 meter range of the echogram may have been too small to capture such an effect. Expanding the DIDSON range beyond 10 meters or using other metrics of spatial point processes, such as nearest neighbor distribution, would provide a useful analysis. If salmon density was found to influence aggregation behavior, fisheries managers could use that knowledge to better understand fine-scale movement patterns relative to daily escapement counts and species apportionments.

Light Intensity

Salmon aggregation distances were not directly linked with light intensity; however, dusk resulted in larger aggregation distances than dawn. In contrast, we expected salmon to aggregate in closer distances during the day when compared to night, to avoid threats from visual predation (Schuerell 2004). The dusk photo period overlaps with daily afternoon test fishing conducted by ADF&G at the DIDSON site; in combination with boat activity from sport fishing, salmon may scatter more with the increase of human activity at this time. Salmon were also found to aggregate at further distances during the combined day/dusk photo period when compared to the combined night/dawn photo period, further suggesting that increased human activity in the river causes salmon clustering to be strongest at farther distance scales. Median aggregation 77

distances were the same between dawn and night photo periods (2300-0500 hours), possibly indicating an effect of tight aggregation during periods of lower light levels, counter to our hypothesis.

This analysis was limited with light intensity readings, which normally record lux values that capture light intensity with the influence of cloud cover, turbidity, and weather patterns. Photo period was a proxy for light intensity and has been used to study migratory fish movements (Daum and Osborne 1998, Berger 2009), but likely does not capture true light levels underwater, especially in this high latitude environment. Future investigations of the effect of light intensity on aggregation behavior of salmon should include long term and consistent light intensity recordings.

Tide

Salmon aggregation distances were not described by tidal phase; we expected aggregation distance to decrease when the tide was falling, or during ebb tide, because tight aggregations with congeners would be useful for energy conservation against the tide (Svendsen et al. 2003). While the DIDSON site is 48 kilometers upriver of

Dillingham, a coastal community, the site may be still far enough downriver that energy conservation is not yet a factor limiting survival to spawning grounds; most salmon observed at the sonar site have only minimally transformed into their spawning morphologies (personal observation).

This analysis was limited by the adjustment of tidal heights derived from Clark’s

Point, as a water level gauge was not available at the DIDSON site in 2011. These measurements may not be an accurate representation of the water movement actually 78

occurring at the site. In 2012, ADF&G began installing water level gauges at the

DIDSON site (G. Buck, personal communication, February 2014). These data will provide a more meaningful representation of water fluctuations and movements and should be analyzed with future escapement counts and aggregation patterns.

Water Temperature

Salmon aggregation distances were not explained by water temperature. Lower water temperature was expected to yield tighter aggregation distances, as cold water inhibits swimming ability, making fish more susceptible to predation and overexploitation (Brown 1998); tighter aggregations would allow salmon to escape more quickly if threatened. Pacific salmon have been documented to prefer migration temperatures between 7.2-14.5 °C (Bell 1986); our data collected temperature readings from 8.5-12.5 °C. This analysis used only nine days of echograms and associated temperature readings; a larger dataset may reveal a relationship and should continue to be examined, especially with ongoing concerns regarding climate change.

Factor Interactions

When examining all variables together, salmon aggregation distances were not affected by tide or temperature; however, they were affected by run period and salmon density. There was no difference in aggregation distances between Early and Late run periods, therefore schooling behavior of salmon is likely similar during these two times, collectively referred to as the Shoulder run period. In the Shoulder period, distance at maximum aggregation increased as salmon density increased, indicating that salmon 79

clustered at further distance scales. In contrast, in the Mid run period, distance at maximum aggregation decreased as salmon density increased, indicating that salmon clustered at nearer distance scales. The species-apportioned data from 21-23 June (Early run period), 28-30 June (Mid), and 7-9 July (Late) show that Early and Late run periods

(and thus, Shoulder period) are the times when Chinook and chum were most prevalent, whereas sockeye dominated the Mid run period. These findings suggest that sockeye exhibit more of a tightly clustered schooling pattern, observed in the Mid run period, and the larger Chinook and chum salmon are more loosely aggregated and reflect the majority of the patterns observed in the Early and Late run periods. Further comparison of apportionment data to aggregation behavior may help ADF&G define species-specific behaviors and corroborate test fishing results.

Conclusion

We set out to quantify salmon aggregation patterns in an Alaskan river during a spawning migration using DIDSON echograms, image analysis, and spatial analysis.

Other studies have used similar methods to spatially examine predator-prey interactions and fish behavior (Hijuelos 2012, Price et al. 2013), but this is the only known study to explore these applications on salmon in Alaska. Our secondary objective was to examine relationships between salmon aggregation patterns and external factors immediately surrounding Pacific salmon in space and time. There were inherent limitations in our analysis, including the use of a human operator for image analysis; however, one operator analyzed all echograms for consistency. Further, the level of distortion that potentially occurs between a raw echogram in DIDSON and analysis in ImageJ pixels is unknown 80

and warrants additional consideration. While we were able to convert pixels into distance units, we were unable to convert pixels into meaningful time units. Incorporating water velocity at the DIDSON site in a 10 minute timeframe would be beneficial for this conversion.

The Nushagak River is relatively pristine and undeveloped, so it is important to accurately characterize salmon run dynamics and environmental conditions prior to change. Small scale analyses of fish behavior are typically overlooked in fisheries management (Rowe and Hutchings 2003); therefore, this study and continued research will help ADF&G and other fisheries managers develop in-season monitoring methods, tailor escapement goals, and manage harvest. 81

GENERAL DISCUSSION

Hydroacoustics, used primarily for enumeration, provided a multi-level approach to investigating the spatiotemporal patterns of Pacific salmon relative to external factors during their spawning migrations. Salmon passage rates at the DIDSON site on the

Nushagak River were greater during slack and ebb tides than during flood tide in 2009 and 2011, but no relationships between salmon passage and tide were found in 2008 and

2010; the location of the sonar site, 48 km from Dillingham, could be far enough upriver that oceanic and tidal influences are no longer a factor in migratory behavior (Smith and

Smith 1997). Light intensity and water temperature had no influence on salmon passage from 2008-2011, yet these exploratory findings lead to insights for future research.

ADF&G monitors salmon escapement on the Nushagak River every year, and plans to expand the operations conducted at the Portage Creek sonar site (G. Buck, personal communication, November 2013). Existing infrastructure and resources thus provide a valuable foundation for future research. Continued data collection on water level, velocity, temperature, and light intensity could be used to build upon the methods presented in this exploratory research. In addition to developing a database of the local environmental conditions at the DIDSON site, trends in run dynamics could be analyzed across multiple years and would provide invaluable insight to fisheries managers when determining run forecasts and escapement goals.

Hydroacoustics allowed for an innovative approach at examining the spatial structure of salmon aggregations during their upriver spawning migrations. Salmon were never randomly distributed, indicating a degree of schooling behavior. Aggregations 82

occurred at closer distances during early run when compared to mid and late run, suggesting that early run salmon have less competition to reach spawning grounds and are selective about their positions relative to their neighbors. Aggregations occurred at closer distances at dawn compared to dusk, suggesting that evening human activity may cause fish to disperse. Salmon density, tide, and water temperature did not affect distances of maximum aggregation, but these external factors should not be discounted from future research. Salmon clearly displayed aggregation behavior, but data limitations and a relatively small sample size restricted the environmental analysis. DIDSON technology provided a fine scale approach to evaluating salmon aggregation behaviors and should be expanded upon with environmental data that is collected each year by

ADF&G.

Drift gillnet sets are conducted by ADF&G with each mesh size twice daily for most of the spawning season at 0800-1100 hours and 1600-1900 hours, which assumes no variability in passage rate or spatial structure. Species-apportioned counts should be compared to hourly passage data and aggregation distances to examine the potential use of fine scale analyses in apportioning salmon by species based on different timing and behavioral characteristics. Varying the times and strata for test fishing may also be beneficial, yet comes with limitations, including worker safety and ability to compare catch data with previous years’ results. The use of other spatial analysis metrics should also be considered, such as nearest neighbor distance. Any methods to assist ADF&G with determining differences between Chinook, chum, and sockeye migrations in the 83

Nushagak River are of extreme interest; aggregation data are already collected as part of the DIDSON escapement counts, thus this research could be continued at any time.

There is a possibility that run timing and aggregation behavior of organisms are not influenced by causal environmental factors within their immediate surroundings

(Northcote 1984, Bernatchez and Dodson 1987, Quinn and Adams 1996, Jonsson and

Jonsson 2002, Jonsson and Jonsson 2009), but are instead driven by genetics and physiological adaptations (Donaldson 2008, Reed et al. 2011). Increased knowledge of the factors underlying salmon passage and schooling behavior relative to local conditions at the DIDSON site may help ADF&G refine its test fishing methods, in-season monitoring goals, escapement index, and overall management of Nushagak River and greater Bristol Bay salmon stocks. 84

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APPENDIX

Figure A1. Log10 transformation of Figure 2.6, provided for visual purposes.

Figure A2. Concept map for echogram processing methods.

Figure A3. Examples from SAM software: settings used for Ripley’s K (top left), an echogram’s spatial pattern (top right), and its L(t)-t function curve alongside data (bottom).

MULTIPLE REGRESSION IN R

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DATA USED IN ANALYSES

Copyright 2014, Samantha Simpson 2008-2011 Sonar Counts, % Daily Passage, and Daily Temperature Averages 2008 2008 Daily 2009 2009 Daily Day of Year 2008 Cumulative Temp 2009 Cumulative Temp 2010 158 0% 234 10.0 0% 870 10.0 0% 159 0% 426 11.6 0% 2098 10.5 0% 160 0% 648 8.2 0% 3280 11.2 0% 161 0% 1530 8.2 0% 4750 11.6 0% 162 0% 2694 8.6 1% 6658 11.5 0% 163 0% 3259 8.2 1% 8110 10.8 0% 164 0% 4074 9.6 1% 9550 10.7 1% 165 1% 4860 10.2 1% 11086 10.7 2% 166 1% 5448 10.2 1% 14848 11.1 3% 167 1% 5964 10.8 2% 17478 11.6 4% 168 1% 8460 10.8 2% 19341 12.1 4% 169 1% 13206 10.5 2% 21945 11.4 5% 170 2% 16686 10.5 4% 39154 10.8 6% 171 2% 17934 10.9 8% 75184 10.5 6% 172 2% 19890 11.9 13% 134291 9.6 7% 173 3% 23136 12.1 18% 178764 9.7 8% 174 4% 34884 11.6 26% 258756 9.5 10% 175 7% 61080 11.6 35% 346784 10.0 17% 176 10% 91565 11.4 42% 419744 10.6 22% 177 14% 125590 11.5 46% 463772 11.0 27% 178 24% 219124 13.9 49% 494984 11.7 31% 179 35% 321771 12.7 52% 518426 12.9 36% 180 44% 403072 12.9 55% 552416 14.5 40% 181 47% 426562 12.5 60% 598219 14.9 42% 182 48% 439342 12.4 64% 643044 15.8 44% 183 52% 468784 11.8 67% 673953 15.4 46% 184 58% 523277 11.4 69% 688871 15.4 48% 185 64% 578710 11.6 72% 722555 16.6 51% 186 69% 625582 12.5 77% 769577 16.7 54% 187 72% 650756 12.9 79% 795864 17.7 56% 188 75% 676407 12.1 83% 831773 18.2 60% 189 78% 708945 11.8 86% 863075 18.5 67% 190 80% 726207 11.4 91% 911148 18.4 77% 191 82% 746350 11.5 93% 935508 18.7 85% 192 85% 773506 10.8 95% 948498 19.4 89% 193 87% 791230 10.9 96% 959256 18.8 92% 194 89% 808708 11.5 97% 969517 17.3 94% 195 92% 830685 11.6 98% 978901 16.5 95% 196 94% 852178 11.0 98% 986977 16.7 97% 197 96% 868541 10.3 99% 991513 16.0 98% 198 98% 889146 10.5 100% 998160 16.3 100% 199 100% 906760 12.9 100% 1002327 13.7 100% 2010 2010 Daily 2011 2011 Daily Cumulative Temp 2011 Cumulative Temp 1332 10.0 0.0% 364 7.8 1914 10.2 0.1% 1028 8.0 2268 10.3 0.2% 1709 8.0 2609 10.0 0.3% 2194 8.1 3299 10.0 0.6% 4462 8.4 3827 10.5 1.3% 10264 8.8 7379 10.0 1.8% 14099 8.6 13996 9.7 2.2% 16965 8.9 24664 8.8 3.6% 28419 9.8 32206 8.3 4.1% 32277 9.8 34384 8.4 4.5% 35205 10.0 39778 8.7 5.2% 40989 11.3 44379 9.7 5.9% 46191 11.4 49875 11.4 6.9% 53919 10.8 55443 12.3 8.7% 68061 10.3 60268 12.1 12.4% 96693 10.8 80638 11.9 22.9% 179343 11.1 140134 11.9 29.5% 230949 11.1 176493 13.0 33.5% 261615 10.9 214677 13.5 37.3% 291213 10.3 248811 12.9 44.8% 350235 9.5 287457 12.6 54.5% 425973 9.4 319599 12.2 57.5% 449691 10.3 340131 11.3 60.9% 476001 11.0 351963 10.8 64.0% 500505 11.5 366891 11.0 68.3% 533763 12.1 385304 11.1 73.7% 576153 12.7 410301 11.6 76.8% 600309 12.9 429255 11.1 78.6% 614741 12.4 445279 10.7 81.0% 633354 11.6 481439 11.2 82.6% 645917 10.9 538505 11.2 84.6% 661229 10.3 614610 10.8 87.3% 682745 10.2 677550 11.3 89.9% 703127 10.5 713370 11.7 91.6% 715812 10.8 740050 12.5 93.1% 727822 11.4 755812 12.6 95.0% 742342 11.5 763767 11.8 96.1% 751534 12.2 776271 11.9 97.6% 763156 12.6 787298 13.0 98.6% 771190 12.3 798241 14.2 99.4% 776922 11.8 801745 11.9 100.0% 781770 12.0 Light intensity (lux) data and inshore/offshore salmon counts for each hour, 2011 Hour offshore inshore sonar ct off sonar ct in sum fish offshore inshore 12:04:00 AM 0 0 61 204 265 0 0 1:04:00 AM 0 0 52 149 201 0 0 2:04:00 AM 0 0 53 148 201 0 0 3:04:00 AM 0 0 53 161 214 0 0 4:04:00 AM 0 0 40 131 171 0 0 5:04:00 AM 0 3 50 190 239 0 3 6:04:00 AM 0 55 76 276 352 0 55 7:04:00 AM 9 165 66 256 322 9 165 8:04:00 AM 22 302 61 84 146 22 302 9:04:00 AM 44 466 76 151 227 44 466 10:04:00 AM 66 661 55 216 272 66 661 11:04:00 AM 111 820 67 399 466 111 820 12:04:00 PM 154 1072 88 228 315 154 1072 1:04:00 PM 203 1392 69 206 275 203 1392 2:04:00 PM 235 1294 50 324 374 235 1294 3:04:00 PM 231 1501 64 265 329 231 1501 4:04:00 PM 218 1430 85 308 393 218 1430 5:04:00 PM 163 1145 60 211 271 163 1145 6:04:00 PM 139 1105 68 262 329 139 1105 7:04:00 PM 103 744 90 297 387 103 744 8:04:00 PM 72 577 68 249 316 72 577 9:04:00 PM 34 325 77 310 387 34 325 10:04:00 PM 11 110 64 253 317 11 110 11:04:00 PM 0 7 71 234 305 0 7 alpha fish CI off CI in CI N light N fish 0.05 11.431442 #NUM! #NUM! 1032 2064 8.6744115 #NUM! #NUM! 8.6864509 #NUM! #NUM! 9.2282254 #NUM! #NUM! 7.3922119 #NUM! #NUM! 10.329834 #NUM! 0.1679364 15.193764 #NUM! 3.3739942 13.875447 0.519076 10.091449 6.2906038 1.328224 18.442466 9.8000985 2.6717149 28.411779 11.720388 4.0457397 40.335261 20.099833 6.7785223 50.029769 13.610579 9.3891694 65.434114 11.858842 12.38149 84.929999 16.13886 14.320392 78.945358 14.188472 14.106654 91.586386 16.975601 13.312773 87.250574 11.696309 9.9235124 69.876794 14.212551 8.5036867 67.403549 16.692674 6.2594463 45.388619 13.640678 4.3663454 35.220835 16.686654 2.0763041 19.831758 13.688835 0.6564785 6.7021876 13.177159 0.0152669 0.4122074 sonar ct off sonar ct in sum fish offshore inshore 61 204 265 0 0 52 149 201 0 0 53 148 201 0 0 53 161 214 0 0 40 131 171 0 0 50 190 239 0 3 76 276 352 0 55 66 256 322 9 165 61 84 146 22 302 76 151 227 44 466 55 216 272 66 661 67 399 466 111 820 88 228 315 154 1072 69 206 275 203 1392 50 324 374 235 1294 64 265 329 231 1501 85 308 393 218 1430 60 211 271 163 1145 68 262 329 139 1105 90 297 387 103 744 68 249 316 72 577 77 310 387 34 325 64 253 317 11 110 71 234 305 0 7 Total fish in the river (both strata) and number of fish in each stratum Total Fish in River In Out 102 48 54 378 88 290 696 364 332 702 444 258 498 290 208 2268 1337 931 5802 2836 2966 3960 2864 1096 2952 2294 658 11454 8017 3437 3858 2557 1301 2928 2211 717 5784 4209 1575 5202 4118 1084 7728 5141 2587 14142 10612 3530 28632 20987 7645 82650 63741 18909 51606 45613 5993 30666 28306 2360 29598 27586 2012 59022 47968 11054 75738 62441 13297 23718 23693 25 26310 21221 5089 24504 18798 5706 33258 26388 6870 42390 36434 5956 24156 18395 5761 14718 10733 3985 18990 14231 4759 12930 9413 3517 15312 11596 3716 21516 17210 4306 20382 16827 3555 12822 9861 2961 12408 9477 2931 14520 12512 2008 9192 7895 1297 11622 10002 1620 8034 6548 1486 7554 6335 1219 6198 4354 1844 3402 2184 1218 Percent of salmon using inshore stratum and total number of fish in the river PctInshore Total 0.472222222 102 0.233531746 378 0.523164683 696 0.631994048 702 0.582936508 498 0.589305471 2268 0.488836239 5802 0.723142104 3960 0.777213092 2952 0.699898626 11454 0.662791915 3858 0.755075452 2928 0.727650867 5784 0.79157236 5202 0.665293008 7728 0.750374641 14142 0.732977566 28632 0.771213403 82650 0.88387417 51606 0.923058029 30666 0.932031238 29598 0.812706391 59022 0.824436232 75738 0.99892852 23718 0.806565755 26310 0.767122057 24504 0.793441251 33258 0.859483645 42390 0.761512902 24156 0.729248202 14718 0.749381049 18990 0.727997229 12930 0.757305851 15312 0.799892247 21516 0.825595817 20382 0.769072326 12822 0.763751133 12408 0.861727923 14520 0.85889037 9192 0.860637116 11622 0.815016206 8034 0.838658854 7554 0.702547185 6198 0.642114709 3402 Daily mean temperatures, 2008-2011 Date Y08Mean Y09Mean Y10Mean Y11Mean 2008 CI 2009 CI 2010 CI 6/8 10.029167 10.025 9.9958333 7.7666667 0.2355617 0.0315435 0.0460703 6/9 11.579167 10.495833 10.245833 7.95 0.3699283 0.0728955 0.0277264 6/10 8.2166667 11.216667 10.320833 7.95 0.1520958 0.0828169 0.0386158 6/11 8.2166667 11.616667 9.9791667 8.1 0.0277077 0.0509606 0.0394646 6/12 8.6 11.5 10.0375 8.3666667 0.0291265 0.0178363 0.0357567 6/13 8.2 10.841667 10.479167 8.8 0.0405424 0.0334141 0.0299666 6/14 9.5625 10.7125 9.9666667 8.6333333 0.083398 0.0397713 0.017712 6/15 10.208333 10.725 9.6666667 8.8666667 0.0454343 0.0312268 0.0188011 6/16 10.216667 11.05 8.75 9.7708333 0.0348106 0.0198583 0.0190056 6/17 10.754167 11.6 8.25 9.8 0.054642 0.0343474 0.0222209 6/18 10.779167 12.1 8.3666667 9.9666667 0.0115103 0.0252243 0.0124358 6/19 10.479167 11.416667 8.65 11.25 0.0175534 0.0286486 0.0380548 6/20 10.533333 10.7625 9.7375 11.366667 0.0214366 0.0235117 0.0557774 6/21 10.9 10.483333 11.358333 10.75 0.0440674 0.016451 0.0610561 6/22 11.908333 9.6166667 12.333333 10.3375 0.1141487 0.0162483 0.0391564 6/23 12.083333 9.65 12.133333 10.795833 0.1039335 0.030732 0.017712 6/24 11.554167 9.4791667 11.916667 11.083333 0.0489476 0.0300769 0.0192367 6/25 11.55 9.9791667 11.916667 11.104167 0.0564914 0.0500523 0.0333521 6/26 11.354167 10.6 13 10.929167 0.1167474 0.0423025 0.0560497 6/27 11.454167 10.991667 13.495833 10.295833 0.1192886 0.0376132 0.0430972 6/28 13.941667 11.7 12.85 9.5 0.2623216 0.0436898 0.0222209 6/29 12.6875 12.875 12.6 9.3666667 0.0985935 0.0955541 0.0196064 6/30 12.854167 14.541667 12.183333 10.316667 0.1100403 0.1307139 0.0205687 7/1 12.466667 14.875 11.25 11 0.0835936 0.1552826 0.0151215 7/2 12.391667 15.770833 10.7875 11.483333 0.0683785 0.0661507 0.0091588 7/3 11.816667 15.445833 10.983333 12.1 0.0224189 0.0268767 0.0271031 7/4 11.416667 15.441667 11.125 12.716667 0.0277077 0.0615696 0.0280986 7/5 11.6375 16.579167 11.583333 12.866667 0.0589827 0.1078195 0.025723 7/6 12.495833 16.708333 11.116667 12.35 0.0708904 0.166355 0.0162483 7/7 12.9 17.708333 10.729167 11.6 0.0178363 0.0612187 0.0134364 7/8 12.133333 18.166667 11.1875 10.8625 0.0191504 0.0580441 0.0495644 7/9 11.783333 18.483333 11.183333 10.333333 0.0317225 0.0491061 0.0246708 7/10 11.4 18.3875 10.775 10.233333 0.0109225 0.0530833 0.0244403 7/11 11.466667 18.683333 11.304167 10.466667 0.0324965 0.0626302 0.0372397 7/12 10.754167 19.375 11.7 10.841667 0.0116533 0.0546805 0.0282017 7/13 10.870833 18.816667 12.466667 11.383333 0.0349916 0.0252024 0.0271642 7/14 11.466667 17.341667 12.6 11.516667 0.0348582 0.0155447 0.0182041 7/15 11.616667 16.5 11.766667 12.216667 0.010244 0.0230266 0.0214366 7/16 11.033333 16.65 11.9 12.55 0.0118909 0.0691755 0.0424589 7/17 10.329167 15.95 12.966667 12.25 0.0131875 0.0283774 0.0314603 7/18 10.541667 16.3125 14.175 11.783333 0.0845837 0.07921 0.0853896 7/19 12.8625 13.741667 11.8875 11.966667 0.3413197 0.1018277 0.0646261 2011 CI 0.0308218 0.0122117 0.015974 0.0209149 0.020488 0.0252243 0.017712 0.0288215 0.0473474 0.0218449 0.0290506 0.0663139 0.0237817 0.0235249 0.0324019 0.0270304 0.0125683 0.0255169 0.0169287 0.0122765 0.0182041 0.0493417 0.0371143 0.0456919 0.0341377 0.0252243 0.0341377 0.0363474 0.0216162 0.0221462 0.013354 0.0118909 0.0191504 0.020488 0.0367216 0.0281821 0.0291076 0.0421324 0.0450712 0.0190056 0.0317225 0.0637576 Residuals of dates at which 25%, 50%, and 75% of the run passed, and temperatures deviations surrounding t Mean Temperature at Year 50% Passage Date SE N Per 50 Yr Dif Temp 2008 11.16775794 0.0693562 1008 183 2008 -0.482664 2009 13.68849206 0.1053193 1008 179 2009 2.0380704 2010 11.18382937 0.0461579 1008 185 2010 -0.466592 2011 10.56160714 0.0464107 1008 179 2011 -1.088814

Overall Mean T 11.65042163 Overall Mean DOY 181.5

Year Mean up to 25 SE N Per 25 Yr Dif Temp 2008 10.57718254 0.3167016 21 178 2008 0.2291246 2009 10.78186275 0.1857 17 174 2009 0.4338049 2010 10.53 0.342137 20 177 2010 0.1819421 2011 9.503186275 0.3096134 17 174 2011 -0.844872

Overall Mean T 10.34805789 Overall Mean DOY 175.75

Year Mean up to 75 SE N Per75 Yr Dif Temp 2008 11.12647849 0.2626103 31 188 2008 0.1152175 2009 12.02758621 0.412692 29 186 2009 1.0163252 2010 10.87739899 0.2312206 33 190 2010 -0.133862 2011 10.01358025 0.26197 27 184 2011 -0.997681

Overall Mean T 11.01126098 Overall Mean DOY 187 those dates

Dif Day50 1.5 -2.5 3.5 -2.5

Dif Day25 2.25 -1.75 1.25 -1.75

Dif Day75 1 -1 3 -3 Hourly passage by tide phase and chi-square test Passage Rate (fish/hour) Year Slack Ebb Flood Total All 2008 921.6 944.9 867.8 2734.2 2009 1068.7 1064.0 893.3 3026.0 2010 839.0 820.3 784.8 2444.1 2011 807.4 841.0 707.4 2355.8 Passage Rate (%) Year Slack Ebb Flood 2008 33.7% 34.6% 31.7% 2009 35.3% 35.2% 29.5% 2010 34.3% 33.6% 32.1% 2011 34.3% 35.7% 30.0% expected values = uniform dist 1/3 of total Standard Yates Correction Standard 2 2 2008 Obs Exp 95 CI ((O-E) )/E ((ABS(O-E)-0.5) )/E χ2 3.43 Slack 921.6 911.41 186.34 0.11316 0.10230 df 2

Ebb 944.9 911.41 118.53 1.23100 1.19453 1-sided p α= 0.05 0.1795 Flood 867.8 911.41 123.09 2.09064 2.04302

Standard Yates Correction Standard 2009 Obs Exp 95 CI ((O-E) 2 )/E ((ABS(O-E)-0.5) 2 )/E χ2 19.81 Slack 1068.7 1008.7 206.95 3.57425 3.51497 df 2

Ebb 1064.0 1008.7 137.31 3.03624 2.98162 1-sided p α= 0.05 0.0000 Flood 893.3 1008.7 100.14 13.19905 13.08491

Standard Yates Correction Standard 2010 Obs Exp 95 CI ((O-E) 2 )/E ((ABS(O-E)-0.5) 2 )/E χ2 1.86 Slack 839.0 814.71 145.91 0.72242 0.69295 df 2

Ebb 820.3 814.71 88.35 0.03860 0.03202 1-sided p α= 0.05 0.3953 Flood 784.8 814.71 92.553 1.09500 1.05865

Standard Yates Correction Standard 2011 Obs Exp 95 CI ((O-E) 2 )/E ((ABS(O-E)-0.5) 2 )/E χ2 12.30 Slack 807.4 785.26 140.91 0.62530 0.59740 df 2

Ebb 841.0 785.26 100.04 3.95266 3.88203 1-sided p α= 0.05 0.0021 Flood 707.4 785.26 83.442 7.72220 7.62336 Yates Correction 1.30 2 0.5229

Yates Correction 6.50 2 0.0388

Yates Correction 0.72 2 0.6959

Yates Correction 4.48 2 0.1065 All L(t)-t bin values and number of occurrences for frequency histogram L(t)-t Bin Frequency 30 2 60 13 90 51 120 26 150 15 180 9 210 6 240 6 270 5 300 6 330 4 360 3 390 2 420 5 450 4 480 8 510 1 540 8 570 1 600 2 630 2 660 1 690 2 720 0 750 1 780 3 810 3 840 1 870 0 900 1 930 1 960 0 990 0 1020 0 1050 0 1080 1 1110 0 1140 0 1170 1 1200 0 1230 0 1260 0 1290 0 1320 1 1350 0 1380 0 1410 0 1440 0 1470 0 1500 0 1530 0 1560 0 1590 0 1620 0 1650 0 1680 1 1710 0 1740 0 1770 1 1800 0 1830 0 1860 0 1890 0 1920 0 1950 0 1980 1 2010 0 2040 1 L(t)-t values divided by Early, Mid, and Late Run period. Frequencies within each bin are also provided Early_K Mid_K Late_K Bins Early Mid Late 24.6 70 40 30 2 0 0 28.7 70 50 60 8 0 5 36.9 70 50 90 25 12 14 40 80 60 120 7 15 4 41 80 60 150 6 5 4 41 90 70 180 4 3 2 41 90 70 210 1 0 5 41 90 70 240 0 3 3 41 90 70 270 2 1 2 50 90 70 300 0 4 2 69.7 90 80 330 0 2 2 69.7 90 80 360 1 1 1 69.7 100 80 390 0 1 1 69.7 100 80 420 1 2 2 70 100 80 450 1 1 2 70 100 80 480 2 4 2 70 100 84 510 0 1 0 73.8 100 90 540 2 3 3 73.8 100 90 570 0 1 0 73.8 110 100 600 0 1 1 73.8 110 120 630 0 0 2 77.9 110 120 660 1 0 0 77.9 120 120 690 0 0 2 77.9 120 130 720 0 0 0 77.9 120 140 750 0 0 1 77.9 120 150 780 0 2 1 80 120 150 810 0 1 2 80 130 160 840 0 0 1 80 130 170 870 0 0 0 82 130 190 900 0 1 0 86.1 140 190 930 0 1 0 90 150 190 960 0 0 0 90 160 192 990 0 0 0 90 170 200 1020 0 0 0 90 170 230 1050 0 0 0 100 220 240 1080 0 0 1 100 230 240 1110 0 0 0 106.6 230 270 1140 0 0 0 110 270 270 1170 0 0 1 110 280 290 1200 0 0 0 120 280 300 1230 0 0 0 120 280 310 1260 0 0 0 130 300 320 1290 0 0 0 131.2 310 340 1320 0 0 1 135.3 330 380 1350 0 0 0 140 340 400 1380 0 0 0 143.5 370 420 1410 0 0 0 143.5 400 440 1440 0 0 0 160 420 444 1470 0 0 0 172.2 440 460 1500 0 0 0 176.3 456 460 1530 0 0 0 176.3 460 520 1560 0 0 0 192.7 470 528 1590 0 0 0 250 480 540 1620 0 0 0 260.61 490 600 1650 0 0 0 340 520 616 1680 0 0 1 410 532 620 1710 0 0 0 430 532 680 1740 0 0 0 451 546 688 1770 0 0 1 460 592 740 1800 0 0 0 540 760 760 1830 0 0 0 540 780 800 1860 0 0 0 633.96 810 800 1890 0 0 0 880 820 1920 0 0 0 920 1080 1950 0 0 0 2040 1160 1980 0 0 1 1320 2010 0 0 0 1680 2040 0 1 0 1750 1980 L(t)-t values by run period and number of fish within echogram Log10 Distance at Distance at Max Max Ripley's Ripley's K Period N = Fish K N = Fish Period 73.8 Early 17 1.86805636 1.230449 Early 176.3 Early 50 2.24625231 1.69897 Early 69.7 Early 9 1.84323278 0.954243 Early 73.8 Early 50 1.86805636 1.69897 Early 131.2 Early 53 2.11793384 1.724276 Early 90 Early 28 1.95424251 1.447158 Early 250 Early 55 2.39794001 1.740363 Early 41 Early 9 1.61278386 0.954243 Early 69.7 Early 24 1.84323278 1.380211 Early 172.2 Early 19 2.23603315 1.278754 Early 192.7 Early 21 2.28488171 1.322219 Early 86.1 Early 41 1.93500315 1.612784 Early 82 Early 36 1.91381385 1.556303 Early 41 Early 15 1.61278386 1.176091 Early 69.7 Early 88 1.84323278 1.944483 Early 143.5 Early 67 2.1568519 1.826075 Early 143.5 Early 66 2.1568519 1.819544 Early 176.3 Early 48 2.24625231 1.681241 Early 41 Early 20 1.61278386 1.30103 Early 77.9 Early 68 1.89153746 1.832509 Early 106.6 Early 36 2.0277572 1.556303 Early 340 Early 31 2.53147892 1.491362 Early 28.7 Early 19 1.4578819 1.278754 Early 41 Early 38 1.61278386 1.579784 Early 36.9 Early 53 1.56702637 1.724276 Early 77.9 Early 70 1.89153746 1.845098 Early 73.8 Early 30 1.86805636 1.477121 Early 77.9 Early 52 1.89153746 1.716003 Early 73.8 Early 33 1.86805636 1.518514 Early 41 Early 28 1.61278386 1.447158 Early 460 Early 31 2.66275783 1.491362 Early 24.6 Early 17 1.39093511 1.230449 Early 69.7 Early 91 1.84323278 1.959041 Early 135.3 Early 214 2.1312978 2.330414 Early 77.9 Early 29 1.89153746 1.462398 Early 260.61 Early 73 2.41599108 1.863323 Early 430 Early 114 2.63346846 2.056905 Early 100 Early 81 2 1.908485 Early 77.9 Early 338 1.89153746 2.528917 Early 90 Early 167 1.95424251 2.222716 Early 110 Early 207 2.04139269 2.31597 Early 120 Early 184 2.07918125 2.264818 Early 110 Early 172 2.04139269 2.235528 Early 70 Early 138 1.84509804 2.139879 Early 50 Early 145 1.69897 2.161368 Early 70 Early 88 1.84509804 1.944483 Early 70 Early 174 1.84509804 2.240549 Early 80 Early 222 1.90308999 2.346353 Early 540 Early 50 2.73239376 1.69897 Early 410 Early 93 2.61278386 1.968483 Early 160 Early 68 2.20411998 1.832509 Early 130 Early 211 2.11394335 2.324282 Early 80 Early 503 1.90308999 2.701568 Early 40 Early 55 1.60205999 1.740363 Early 90 Early 355 1.95424251 2.550228 Early 90 Early 467 1.95424251 2.669317 Early 80 Early 419 1.90308999 2.622214 Early 540 Early 229 2.73239376 2.359835 Early 633.96 Early 174 2.80206186 2.240549 Early 451 Early 217 2.65417654 2.33646 Early 120 Early 480 2.07918125 2.681241 Early 100 Early 354 2 2.549003 Early 140 Early 353 2.14612804 2.547775 Early 100 Intermediate 189 2 2.276462 Intermediate 130 Intermediate 154 2.11394335 2.187521 Intermediate 110 Intermediate 174 2.04139269 2.240549 Intermediate 100 Intermediate 140 2 2.146128 Intermediate 90 Intermediate 185 1.95424251 2.267172 Intermediate 90 Intermediate 244 1.95424251 2.38739 Intermediate 120 Intermediate 72 2.07918125 1.857332 Intermediate 140 Intermediate 128 2.14612804 2.10721 Intermediate 70 Intermediate 197 1.84509804 2.294466 Intermediate 130 Intermediate 176 2.11394335 2.245513 Intermediate 100 Intermediate 295 2 2.469822 Intermediate 120 Intermediate 54 2.07918125 1.732394 Intermediate 90 Intermediate 272 1.95424251 2.434569 Intermediate 80 Intermediate 64 1.90308999 1.80618 Intermediate 810 Intermediate 241 2.90848502 2.382017 Intermediate 420 Intermediate 133 2.62324929 2.123852 Intermediate 80 Intermediate 135 1.90308999 2.130334 Intermediate 470 Intermediate 161 2.67209786 2.206826 Intermediate 532 Intermediate 225 2.72591163 2.352183 Intermediate 2040 Intermediate 296 3.30963017 2.471292 Intermediate 880 Intermediate 105 2.94448267 2.021189 Intermediate 120 Intermediate 101 2.07918125 2.004321 Intermediate 270 Intermediate 77 2.43136376 1.886491 Intermediate 520 Intermediate 134 2.71600334 2.127105 Intermediate 920 Intermediate 45 2.96378783 1.653213 Intermediate 170 Intermediate 75 2.23044892 1.875061 Intermediate 220 Intermediate 80 2.34242268 1.90309 Intermediate 760 Intermediate 17 2.88081359 1.230449 Intermediate 532 Intermediate 23 2.72591163 1.361728 Intermediate 90 Intermediate 25 1.95424251 1.39794 Intermediate 230 Intermediate 100 2.36172784 2 Intermediate 340 Intermediate 13 2.53147892 1.113943 Intermediate 90 Intermediate 41 1.95424251 1.612784 Intermediate 100 Intermediate 74 2 1.869232 Intermediate 280 Intermediate 178 2.44715803 2.25042 Intermediate 310 Intermediate 51 2.49136169 1.70757 Intermediate 120 Intermediate 214 2.07918125 2.330414 Intermediate 300 Intermediate 20 2.47712125 1.30103 Intermediate 456 Intermediate 16 2.65896484 1.20412 Intermediate 160 Intermediate 49 2.20411998 1.690196 Intermediate 110 Intermediate 113 2.04139269 2.053078 Intermediate 440 Intermediate 94 2.64345268 1.973128 Intermediate 280 Intermediate 69 2.44715803 1.838849 Intermediate 130 Intermediate 152 2.11394335 2.181844 Intermediate 780 Intermediate 53 2.8920946 1.724276 Intermediate 230 Intermediate 79 2.36172784 1.897627 Intermediate 100 Intermediate 172 2 2.235528 Intermediate 90 Intermediate 96 1.95424251 1.982271 Intermediate 100 Intermediate 133 2 2.123852 Intermediate 546 Intermediate 49 2.73719264 1.690196 Intermediate 150 Intermediate 68 2.17609126 1.832509 Intermediate 330 Intermediate 48 2.51851394 1.681241 Intermediate 480 Intermediate 10 2.68124124 1 Intermediate 70 Intermediate 17 1.84509804 1.230449 Intermediate 280 Intermediate 42 2.44715803 1.623249 Intermediate 100 Intermediate 91 2 1.959041 Intermediate 170 Intermediate 39 2.23044892 1.591065 Intermediate 120 Intermediate 66 2.07918125 1.819544 Intermediate 370 Intermediate 16 2.56820172 1.20412 Intermediate 90 Intermediate 88 1.95424251 1.944483 Intermediate 400 Intermediate 41 2.60205999 1.612784 Intermediate 70 Intermediate 15 1.84509804 1.176091 Intermediate 490 Intermediate 22 2.69019608 1.342423 Intermediate 592 Intermediate 119 2.77232171 2.075547 Intermediate 460 Intermediate 22 2.66275783 1.342423 Intermediate 110 Intermediate 206 2.04139269 2.313867 Intermediate 84 Late 47 1.92427929 1.672098 Late 1320 Late 41 3.12057393 1.612784 Late 120 Late 52 2.07918125 1.716003 Late 60 Late 13 1.77815125 1.113943 Late 40 Late 5 1.60205999 0.69897 Late 400 Late 18 2.60205999 1.255273 Late 192 Late 39 2.28330123 1.591065 Late 800 Late 43 2.90308999 1.633468 Late 70 Late 22 1.84509804 1.342423 Late 320 Late 11 2.50514998 1.041393 Late 270 Late 12 2.43136376 1.079181 Late 340 Late 28 2.53147892 1.447158 Late 688 Late 127 2.83758844 2.103804 Late 380 Late 75 2.5797836 1.875061 Late 70 Late 45 1.84509804 1.653213 Late 1680 Late 100 3.22530928 2 Late 100 Late 60 2 1.778151 Late 120 Late 68 2.07918125 1.832509 Late 190 Late 13 2.2787536 1.113943 Late 160 Late 23 2.20411998 1.361728 Late 70 Late 39 1.84509804 1.591065 Late 528 Late 27 2.72263392 1.431364 Late 460 Late 37 2.66275783 1.568202 Late 140 Late 67 2.14612804 1.826075 Late 680 Late 87 2.83250891 1.939519 Late 270 Late 14 2.43136376 1.146128 Late 1980 Late 31 3.29666519 1.491362 Late 444 Late 8 2.64738297 0.90309 Late 50 Late 15 1.69897 1.176091 Late 230 Late 23 2.36172784 1.361728 Late 240 Late 34 2.38021124 1.531479 Late 90 Late 27 1.95424251 1.431364 Late 130 Late 48 2.11394335 1.681241 Late 190 Late 32 2.2787536 1.50515 Late 290 Late 43 2.462398 1.633468 Late 440 Late 36 2.64345268 1.556303 Late 460 Late 24 2.66275783 1.380211 Late 520 Late 166 2.71600334 2.220108 Late 80 Late 71 1.90308999 1.851258 Late 80 Late 88 1.90308999 1.944483 Late 760 Late 71 2.88081359 1.851258 Late 420 Late 110 2.62324929 2.041393 Late 740 Late 84 2.86923172 1.924279 Late 70 Late 87 1.84509804 1.939519 Late 170 Late 98 2.23044892 1.991226 Late 1080 Late 49 3.03342376 1.690196 Late 620 Late 86 2.79239169 1.934498 Late 150 Late 49 2.17609126 1.690196 Late 80 Late 78 1.90308999 1.892095 Late 120 Late 59 2.07918125 1.770852 Late 800 Late 53 2.90308999 1.724276 Late 50 Late 34 1.69897 1.531479 Late 616 Late 36 2.78958071 1.556303 Late 200 Late 34 2.30103 1.531479 Late 60 Late 51 1.77815125 1.70757 Late 540 Late 129 2.73239376 2.11059 Late 1750 Late 90 3.24303805 1.954243 Late 90 Late 35 1.95424251 1.544068 Late 80 Late 12 1.90308999 1.079181 Late 310 Late 44 2.49136169 1.643453 Late 70 Late 56 1.84509804 1.748188 Late 240 Late 252 2.38021124 2.401401 Late 820 Late 159 2.91381385 2.201397 Late 600 Late 70 2.77815125 1.845098 Late 150 Late 87 2.17609126 1.939519 Late 1160 Late 93 3.06445799 1.968483 Late 80 Late 49 1.90308999 1.690196 Late 300 Late 107 2.47712125 2.029384 Late 190 Late 19 2.2787536 1.278754 Late 80 Late 94 1.90308999 1.973128 Late L(t)-t values compared to light intensity (lux). All values less than 10 lux were not included. LUX L(t)-t 10.76 110 10.76 70 10.76 470 10.76 920 10.76 100 10.76 160 10.76 130 10.76 480 10.76 70 10.76 270 10.76 270 10.76 230 10.76 130 10.76 150 21.52 250 21.52 73.8 21.52 41 21.52 80 21.52 170 21.52 90 21.52 150 21.52 84 21.52 40 21.52 120 21.52 190 21.52 190 21.52 80 21.52 616 21.52 600 21.52 80 32.28 176.3 32.28 73.8 32.28 70 32.28 400 43.04 160 43.04 100 43.04 546 43.04 460 43.04 80 53.8 36.9 53.8 77.9 53.8 50 53.8 130 53.8 120 53.8 120 53.8 800 53.8 160 64.56 100 64.56 200 64.56 540 75.32 1980 86.08 688 86.08 620 96.84 140 107.6 77.9 107.6 90 107.6 100 129.12 230 139.88 70 161.4 760 204.44 90 L(t)-t by temperature (hourly) and run period Temp Distance at Max Ripley's K Period 10.9 73.8 Early 10.9 176.3 Early 10.6 69.7 Early 10.6 73.8 Early 10.2 131.2 Early 10.2 90 Early 10.2 250 Early 10.2 41 Early 10.2 69.7 Early 10.6 172.2 Early 10.6 192.7 Early 10.6 86.1 Early 10.9 82 Early 11.3 41 Early 11.3 69.7 Early 11.3 143.5 Early 11.3 143.5 Early 11.3 176.3 Early 10.9 41 Early 10.9 77.9 Early 10.6 106.6 Early 10.6 340 Early 10.2 28.7 Early 10.2 41 Early 10.2 36.9 Early 10.2 77.9 Early 9.8 73.8 Early 9.8 77.9 Early 9.8 73.8 Early 9.8 41 Early 9.8 460 Early 9.8 24.6 Early 9.8 69.7 Early 9.8 135.3 Early 9.8 77.9 Early 10.2 260.61 Early 10.6 430 Early 10.6 100 Early 10.9 77.9 Early 10.9 90 Early 10.9 110 Early 10.6 120 Early 10.6 110 Early 10.6 70 Early 10.2 50 Early 10.2 70 Early 10.2 70 Early 10.2 80 Early 10.2 540 Early 10.2 410 Early 10.6 160 Early 10.6 130 Early 10.9 80 Early 10.9 40 Early 10.9 90 Early 11.3 90 Early 11.3 80 Early 11.3 540 Early 11.3 633.96 Early 11.3 451 Early 11.3 120 Early 11.3 100 Early 11.3 140 Early 10.2 100 Intermediate 10.2 130 Intermediate 9.8 110 Intermediate 9.8 100 Intermediate 9.8 90 Intermediate 9.8 90 Intermediate 9.4 120 Intermediate 9.4 140 Intermediate 9.4 70 Intermediate 9.4 130 Intermediate 9.4 100 Intermediate 9.4 120 Intermediate 9.4 90 Intermediate 9.4 80 Intermediate 9.4 810 Intermediate 9.4 420 Intermediate 9.4 80 Intermediate 9.4 470 Intermediate 9.4 532 Intermediate 9.4 2040 Intermediate 9.4 880 Intermediate 9.4 120 Intermediate 9 270 Intermediate 9 520 Intermediate 9 920 Intermediate 9 170 Intermediate 8.6 220 Intermediate 8.6 760 Intermediate 8.6 532 Intermediate 8.6 90 Intermediate 8.6 230 Intermediate 8.6 340 Intermediate 8.6 90 Intermediate 8.6 100 Intermediate 8.6 280 Intermediate 9 310 Intermediate 9 120 Intermediate 9.4 300 Intermediate 9.4 456 Intermediate 9.8 160 Intermediate 9.8 110 Intermediate 9.8 440 Intermediate 10.2 280 Intermediate 10.6 130 Intermediate 10.6 780 Intermediate 10.6 230 Intermediate 10.6 100 Intermediate 10.6 90 Intermediate 10.2 100 Intermediate 10.2 546 Intermediate 10.2 150 Intermediate 10.2 330 Intermediate 9.8 480 Intermediate 9.8 70 Intermediate 9.8 280 Intermediate 9.4 100 Intermediate 9.4 170 Intermediate 9.8 120 Intermediate 9.8 370 Intermediate 9.8 90 Intermediate 10.2 400 Intermediate 10.2 70 Intermediate 10.6 490 Intermediate 10.9 592 Intermediate 11.3 460 Intermediate 11.3 110 Intermediate 12.5 84 Late 12.1 1320 Late 12.1 120 Late 12.1 60 Late 12.1 40 Late 11.7 400 Late 11.7 192 Late 11.7 800 Late 11.7 70 Late 11.7 320 Late 11.7 270 Late 11.7 340 Late 11.3 688 Late 11.3 380 Late 11.3 70 Late 11.3 1680 Late 11.3 100 Late 11.3 120 Late 11.3 190 Late 11.3 160 Late 11.3 70 Late 11.3 528 Late 11.3 460 Late 11.3 140 Late 11.3 680 Late 10.9 270 Late 10.9 1980 Late 10.9 444 Late 10.9 50 Late 10.9 230 Late 10.6 240 Late 10.6 90 Late 10.6 130 Late 10.6 190 Late 10.6 290 Late 10.6 440 Late 10.6 460 Late 10.9 520 Late 10.9 80 Late 10.9 80 Late 10.9 760 Late 11.3 420 Late 11.3 740 Late 10.9 70 Late 10.9 170 Late 10.9 1080 Late 10.9 620 Late 10.9 150 Late 10.6 80 Late 10.6 120 Late 10.6 800 Late 10.6 50 Late 10.6 616 Late 10.6 200 Late 10.2 60 Late 10.2 540 Late 10.2 1750 Late 10.2 90 Late 10.2 80 Late 10.2 310 Late 10.2 70 Late 10.2 240 Late 10.2 820 Late 10.2 600 Late 10.2 150 Late 10.2 1160 Late 10.2 80 Late 10.6 300 Late 10.2 190 Late 10.2 80 Late Daily species-apportioned counts and cumulative totals used in Chapter 2 Discussion

Chinook Chum Sockeye Daily Daily Chinook Daily Chum Daily Sockeye Total 2011 Date DOY Count Cumulative Count Cumulative Count Cumulative Fish 21-Jun 172 1647 11220 4922 29758 7573 27081 14142 22-Jun 173 5893 17113 6713 36471 16026 43108 28632 23-Jun 174 12383 29496 40706 77177 29560 72668 82650 28-Jun 179 9332 55161 12784 134553 53622 236259 75738 29-Jun 180 2355 57516 2008 136560 19355 255614 23718 30-Jun 181 4196 61711 2799 139360 19315 274929 26310 7-Jul 188 3094 84338 4440 192204 5030 369375 12563 8-Jul 189 2177 86515 4787 196991 8348 377723 15312 9-Jul 190 4083 90598 5762 202754 11671 389393 21516

% of Total Daily Count (all species) Average LT- Chum Sockeye Chinook t 35% 54% 12% 110 23% 56% 21% 135 49% 36% 15% 182 17% 71% 12% 302 8% 82% 10% 274 11% 73% 16% 358 35% 40% 25% 358 31% 55% 14% 424 27% 54% 19% 381