Fishery Manuscript Series No. 15-05

The Feasibility of Using Sonar to Estimate Adult Sockeye Salmon Passage in the Lower Kvichak River

by April V. Faulkner and Suzanne L. Maxwell

November 2015 Alaska Department of Fish and Game Divisions of Sport Fish and Commercial Fisheries Symbols and Abbreviations The following symbols and abbreviations, and others approved for the Système International d'Unités (SI), are used without definition in the following reports by the Divisions of Sport Fish and of Commercial Fisheries: Fishery Manuscripts, Fishery Data Series Reports, Fishery Management Reports, and Special Publications. All others, including deviations from definitions listed below, are noted in the text at first mention, as well as in the titles or footnotes of tables, and in figure or figure captions. Weights and measures (metric) General Mathematics, statistics centimeter cm Alaska Administrative all standard mathematical deciliter dL Code AAC signs, symbols and gram g all commonly accepted abbreviations hectare ha abbreviations e.g., Mr., Mrs., alternate hypothesis HA kilogram kg AM, PM, etc. base of natural logarithm e kilometer km all commonly accepted catch per unit effort CPUE liter L professional titles e.g., Dr., Ph.D., coefficient of variation CV meter m R.N., etc. common test statistics (F, t, χ2, etc.) milliliter mL at @ confidence interval CI millimeter mm compass directions: correlation coefficient east E (multiple) R Weights and measures (English) north N correlation coefficient cubic feet per second ft3/s south S (simple) r foot ft west W covariance cov gallon gal copyright  degree (angular ) ° inch in corporate suffixes: degrees of freedom df mile mi Company Co. expected value E nautical mile nmi Corporation Corp. greater than > ounce oz Incorporated Inc. greater than or equal to ≥ pound lb Limited Ltd. harvest per unit effort HPUE quart qt District of Columbia D.C. less than < yard yd et alii (and others) et al. less than or equal to ≤ et cetera (and so forth) etc. logarithm (natural) ln Time and temperature exempli gratia logarithm (base 10) log day d (for example) e.g. logarithm (specify base) log2, etc. degrees Celsius °C Federal Information minute (angular) ' degrees Fahrenheit °F Code FIC not significant NS degrees kelvin K id est (that is) i.e. null hypothesis HO hour h latitude or longitude lat or long percent % minute min monetary symbols probability P second s (U.S.) $, ¢ probability of a type I error months (tables and (rejection of the null Physics and chemistry figures): first three hypothesis when true) α all atomic symbols letters Jan,...,Dec probability of a type II error alternating current AC registered trademark  (acceptance of the null ampere A trademark  hypothesis when false) β calorie cal United States second (angular) " direct current DC (adjective) U.S. standard deviation SD hertz Hz United States of standard error SE horsepower hp America (noun) USA variance hydrogen ion activity pH U.S.C. United States population Var (negative log of) Code sample var parts per million ppm U.S. state use two-letter parts per thousand ppt, abbreviations ‰ (e.g., AK, WA) volts V watts W

FISHERY MANUSCRIPT SERIES NO. 15-05

THE FEASIBILITY OF USING SONAR TO ESTIMATE ADULT SOCKEYE SALMON PASSAGE IN THE LOWER KVICHAK RIVER

by

April V. Faulkner

and

Suzanne L. Maxwell

Alaska Department of Fish and Game Division of Commercial Fisheries, Research and Technical Services 333 Raspberry Road, Anchorage, Alaska, 99518-1565 November 2015

This report was prepared by April V. Faulkner and Suzanne L. Maxwell under award NA09NMF4380373 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce, administered by the Alaska Department of Fish and Game. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the U.S. Department of Commerce. The Fishery Manuscript series was established in 1987 by the Division of Sport Fish for the publication of technically-oriented results of several years’ work undertaken on a project to address common objectives, provide an overview of work undertaken through multiple projects to address specific research or management goal(s), or new and/or highly technical methods, and became a joint divisional series in 2004 with the Division of Commercial Fisheries. Fishery Manuscripts are intended for fishery and other technical professionals. Fishery Manuscripts are available through the Alaska State Library and on the Internet http://www.adfg.alaska.gov/sf/publications/. This publication has undergone editorial and peer review.

April V. Faulkner and Suzanne L. Maxwell, Alaska Department of Fish and Game, Division of Commercial Fisheries, 43961 Kalifornsky Beach Rd. Suite B, Soldotna, Alaska, USA

This document should be cited as follows: Faulkner, A. V., and S. L. Maxwell. 2015. The feasibility of using sonar to estimate adult sockeye salmon passage in the lower Kvichak River. Alaska Department of Fish and Game, Fishery Manuscript Series No. 15-05, Anchorage.

The Alaska Department of Fish and Game (ADF&G) administers all programs and activities free from discrimination based on race, color, national origin, age, sex, religion, marital status, pregnancy, parenthood, or disability. The department administers all programs and activities in compliance with Title VI of the Civil Rights Act of 1964, Section 504 of the Rehabilitation Act of 1973, Title II of the Americans with Disabilities Act (ADA) of 1990, the Age Discrimination Act of 1975, and Title IX of the Education Amendments of 1972. If you believe you have been discriminated against in any program, activity, or facility please write: ADF&G ADA Coordinator, P.O. Box 115526, Juneau, AK 99811-5526 U.S. Fish and Wildlife Service, 4401 N. Fairfax Drive, MS 2042, Arlington, VA 22203 Office of Equal Opportunity, U.S. Department of the Interior, 1849 C Street NW MS 5230, Washington DC 20240 The department’s ADA Coordinator can be reached via phone at the following numbers: (VOICE) 907-465-6077, (Statewide Telecommunication Device for the Deaf) 1-800-478-3648, (Juneau TDD) 907-465-3646, or (FAX) 907-465-6078 For information on alternative formats and questions on this publication, please contact: ADF&G, Division of Sport Fish, Research and Technical Services, 333 Raspberry Rd, Anchorage AK 99518 (907) 267-2375

TABLE OF CONTENTS Page LIST OF TABLES...... ii LIST OF FIGURES ...... ii LIST OF APPENDICES ...... iv ABSTRACT ...... 1 INTRODUCTION ...... 1 OBJECTIVES ...... 3 STUDY SITE ...... 3 SITE ASSESSMENT METHODS AND RESULTS ...... 4 METHODS ...... 5 Data collection ...... 5 DIDSON ...... 5 Side-scan sonar ...... 6 Data processing and analyses ...... 7 Manual processing of DIDSON images ...... 7 Developing and evaluating the auto-processor ...... 7 Range distributions ...... 8 Tidal influence ...... 9 Midriver assessment ...... 9 RESULTS ...... 9 Estimating fish passage ...... 9 Developing the auto-processor ...... 11 Evaluating the auto-processor ...... 12 Observer count precision ...... 12 Range Distributions ...... 13 Tidal Influence ...... 13 Midriver Assessment ...... 13 DISCUSSION ...... 14 Can DIDSON be used to assess adult sockeye salmon in the turbid, tidal lower Kvichak river? ...... 14 Did the sonar provide a more accurate sockeye salmon abundance index than the test fishery? ...... 16 Automated versus manual count methods ...... 18 Should the test fishery project be replaced by sonar? ...... 19 ACKNOWLEDGMENTS ...... 20 REFERENCES CITED ...... 21 TABLES AND FIGURES ...... 25 APPENDIX A: DIDSON MANUAL FISH COUNT INSTRUCTIONS ...... 83 APPENDIX B: DIDSON AUTOMATED FISH COUNT INSTRUCTIONS ...... 87 APPENDIX C: ECHOTASTIC INSTRUCTIONS ...... 99 APPENDIX D: HOURLY FISH PASSAGE ESTIMATES ...... 101 APPENDIX E: TIDE TABLES ...... 115

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LIST OF TABLES Table Page 1 Dual-frequency identification sonar specifications and settings used at the Kvichak River by year...... 26 2 Three sets of parameters used for automated processing of DIDSON files to estimate fish passage at the Kvichak River...... 27 3 DIDSON deployment information and dates of operation in the lower Kvichak River, 2010–2013...... 28 4 Environmental data from the Kvichak River sonar site at Levelock, 2011...... 29 5 Environmental data from the Kvichak River sonar site at Levelock, 2012...... 30 6 Environmental data from the Kvichak River sonar site at Levelock, 2013...... 31 7 Daily sockeye salmon indices from test fishery and sonar projects and abundance estimates from the tower project, Kvichak River 2011–2013...... 32 8 Sockeye salmon estimates by zone from nearshore and offshore DIDSON deployed on the right bank of the Kvichak River, 2011...... 33 9 Sockeye salmon estimates by zone from nearshore and offshore DIDSON deployed on the right bank of the Kvichak River, 2012...... 34 10 Sockeye salmon estimates by zone from nearshore and offshore DIDSON deployed on the right bank of the Kvichak River, 2013...... 35 11 Comparison of sonar and test fishery salmon passage indices to abundance estimates from the tower project, Kvichak River, 2011–2013...... 36 12 Selected DIDSON data files from the Kvichak River in 2010 used to test manual, full-auto, and semi- auto fish counting methods ...... 37 13 Comparison of manual, full-auto, and semi-auto counts from 2 observers using the 2010 DIDSON dataset, Kvichak River...... 40 14 Comparison of manual and semi-automated fish counts for the nearshore sonar, offshore sonar, and all zones combined, Kvichak River, 2011–2013...... 40 15 Comparison of manual and semi-automated fish counts by zone, Kvichak River, 2011–2013...... 41 16 The number of tracked fish that were exported as invalid tracks with no data in the automated program, Kvichak River, 2011–2013...... 42 17 Data processing times for the semi-automated and manual methods used to estimate fish passage at the Kvichak River, 2013...... 43 18 Precision of manual and semi-automated fish counts made by 2 observers, Kvichak River, 2011...... 44 19 Range distributions of fish from the designated zero point from DIDSON images processed using the semi-automated method, Kvichak River, 2011–2013 ...... 45 20 Upstream and downstream fish passage by tide cycle in the lower Kvichak River, 2011–2013...... 47 21 Side-scan sonar sampling schedule and corresponding DIDSON fish passage estimates, Kvichak River 2011–2012...... 47

LIST OF FIGURES Figure Page 1 Major river systems, commercial salmon fishing districts, and escapement projects in Bristol Bay, Alaska...... 48 2 Map of the Kvichak River watershed in Bristol Bay, Alaska...... 49 3 Alaska Department of Fish and Game sonar and test fish salmon assessment sites in the Kvichak River near Levelock, Alaska...... 50 4 The side-scan sonar was used to survey the river bottom to search for a deployment site for a shore- based sonar and to quickly survey broad reaches of the river to obtain an idea of the cross-river distribution of salmon...... 51 5 Side-scan sonar image of the left bank test fish site in the Kvichak River...... 52

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LIST OF FIGURES (Continued) Figure Page 6 The river bottom profiles created using DIDSON at the potential sonar sites downriver of the left bank test fish zone, upriver of the right bank test fish zone, and at Levelock on the right bank, where measurements were also taken manually...... 53 7 Dual-frequency identification sonar shown with the multiple beams positioned horizontally to ensonify migrating fish, and vertically to profile the river bottom...... 54 8 The river bottom profile at the Levelock site with the DIDSON beams overlaid...... 55 9 The topside operations along the right bank of the Kvichak River in the village of Levelock consisted of a weatherport to house the sonar electronics and controller computers...... 56 10 Deploying the DIDSON at low tide ...... 57 11 A raw DIDSON image of zone 1 showing the river bottom encountering shore at 20 m from the sonar and the same image with the background subtracted, and a raw DIDSON image of zone 2 and background subtracted image ...... 58 12 Overview of the workflow used to review and process Kvichak River DIDSON files for automated fish counting...... 59 13 Time series of daily fish passage estimates from the nearshore and offshore sonars, Kvichak River right bank, 2011–2013...... 60 14 Time series of daily fish passage estimates from Zones 1 to 4 from both sonars, Kvichak River, 2011– 2013...... 61 15 Time series of daily test fish and sonar indices of sockeye salmon passage, Kvichak River, 2011–2013. ... 62 16 Scatter plots of daily test fish and sonar indices of sockeye salmon passage, Kvichak River, 2011– 2013...... 63 17 Time series of daily fish passage estimates from the tower and right bank sonar index with a 2-day travel time lag applied, Kvichak River, 2011–2013...... 64 18 Scatter plots of daily fish passage estimates from the tower and sonar indices with a 2-day travel time lag applied, Kvichak River, 2011–2013...... 65 19 Scatter plots of daily fish passage estimates from the tower and test fish indices with a 2-day travel time lag applied, Kvichak River, 2011–2013...... 66 20 DIDSON images from Zone 2, where the sonar beam was squeezed between the river bottom and surface aimed toward shore ...... 67 21 Echograms of tracked fish when DIDSON was aimed offshore perpendicular to flow, aimed offshore at an oblique angle downstream, and toward shore perpendicular to flow ...... 68 22 A comparison of fish counts from 2 observers who visually counted fish in DIDSON images and 1 observer who used fully automated and semi-automated counting methods...... 69 23 A comparison of fish counts from a subset of DIDSON data where fish passage was less than 100 fish per 10 min file, made by 2 observers who visually counted fish in DIDSON images and 1 observer who used fully automated and semi-automated counting methods...... 69 24 Comparison of hourly manual and semi-automated counts from the nearshore and offshore sonar, Kvichak River, 2011–2013...... 70 25 DIDSON data processing times for the semi-automated and manual methods used to estimate fish passage at the Kvichak River, 2013...... 71 26 Comparison of manual fish counts by 2 observers from the nearshore and offshore sonars, Kvichak River, 2011...... 72 27 Comparison of semi-automated fish counts by 2 observers from the nearshore and offshore sonars, Kvichak River, 2011...... 72 28 Comparison of fish counts from 30 randomly selected hours of sonar data manually counted by 3 observers in 2012, Kvichak River...... 73 29 Comparison of fish counts from 30 randomly selected hours of sonar data manually counted by 3 observers in 2013, Kvichak River...... 73 30 Range distributions of fish from the designated zero point from DIDSON images processed using the semi-automated method, Kvichak River, 2011–2013...... 74

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LIST OF FIGURES (Continued) Figure Page 31 Relationship between DIDSON hourly fish passage estimates and tide stage, Kvichak River, 2011...... 75 32 Relationship between DIDSON hourly fish passage estimates and tide stage, Kvichak River, 2012...... 76 33 Relationship between DIDSON hourly fish passage estimates and tide stage, Kvichak River, 2013...... 77 34 Relationship between DIDSON fish passage estimates and tide cycle plotted in 0.3 m tide stage bins, Kvichak River, 2011–2013...... 78 35 Relationship between DIDSON hourly fish passage estimates and tide stage on July 4, 2012, when a daily estimated high of 236,488 fish passed along the Kvichak River right bank...... 79 36 Relationship between the percent of upstream and downstream fish passage estimates and tide stage from the Kvichak River right bank sonar, 2011–2013...... 80 37 Side-scan sonar image recorded along the left bank of the Kvichak River at Levelock on June 29, 2011...... 81

LIST OF APPENDICES Appendix Page A1 Instructions for manual fish counting of dual-frequency identification sonar images using Sound Metrics Corporation software...... 84 B1 Instructions for an automated fish counting method of dual-frequency identification sonar images of adult sockeye salmon in the lower Kvichak River...... 88 C1 Instructions for using Echotastic version 2.5.14 software to view and process Edgetech side-scan sonar images and export marked targets...... 100 D1 Nearshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2011...... 102 D2 Offshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2011...... 104 D3 Nearshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2012...... 106 D4 Offshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2012...... 108 D5 Nearshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2013...... 110 D6 Offshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2013...... 112 E1 Tide tables for Levelock, Alaska, during sonar operations in 2011...... 116 E2 Tide tables for Levelock, Alaska, during sonar operations in 2012...... 117 E3 Tide tables for Levelock, Alaska, during sonar operations in 2013...... 118

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ABSTRACT The feasibility of using sonar in the lower Kvichak River to assess adult sockeye salmon Oncorhynchus nerka passage was evaluated as a possible replacement for the existing inriver gillnet test fishery. The test fishery index is used to manage Bristol Bay commercial and subsistence fisheries. A tower project near the Kvichak River’s headwaters where the river is narrow and clear provides accurate sockeye salmon abundance estimates, but fishery managers prefer a timelier estimate for inseason daily management. The test fishery index is timelier but has greater variability and less precision. Two dual-frequency identification sonars (DIDSONs) (1 pointing toward shore and 1 offshore) were deployed along 1 bank of the lower river during the 2011-2013 seasons to assess salmon passage across all tidal stages in an attempt to produce an index with higher accuracy and precision. Compared to the tower estimates, the test fishery index showed a stronger correlation in each of the 3 study years (r = 0.81–0.94) than the sonar index (r = 0.73–0.91). Test fishery and sonar indices were better correlated to each other (r = 0.89–0.95), although we were unable to reject a null hypothesis of no difference between r values from the sonar versus tower and test fishery versus tower correlations for any study year. An automated method of counting fish in DIDSON images was developed, tested, and shown to be highly correlated to manual counts during the first year (R2 = 0.99) with a lower correlation in following years (R2= 0.76–0.94), which suggested the auto-tracking parameters may need annual adjustments. Our recommendation was to continue the test fishery project rather than replace it with sonar because of the similarity between the test fishery and sonar indices and the higher complexity and cost of the sonar project. Key words: sockeye salmon Oncorhynchus nerka, automated processing, sonar, dual-frequency identification sonar DIDSON, hydroacoustics, test fishery, tower, Bristol Bay, Kvichak River INTRODUCTION Bristol Bay, Alaska, supports the largest commercial sockeye salmon Oncorhynchus nerka fishery in the world with a 20-year average (1993–2012) annual harvest of 24.8 million fish (Figure 1; Jones et al. 2014). Prior to a decline that began in 1997, the Kvichak River sockeye salmon stock was the largest contributor on average to this harvest. Historically, the salmon stock had a 5-year cycle with each year designated as peak, pre-peak, or off-cycle with 2 sustainable escapement goals (SEG), 1 for pre-peak/peak years and 1 for off-cycle years (Morstad and Brazil 2012). The largest recorded total run (catch and escapement) was 48 million in 1965, a peak cycle year. From 1955 to 2013, the number of Kvichak River spawners has ranged from 226,000 to 24 million sockeye salmon with a 20-year average (1993–2012) of 3.4 million (Jones et al. 2014). The Kvichak River sockeye salmon stock was found to be a stock of yield concern during the 2001 Bristol Bay Alaska Board of Fisheries (BOF) meeting (Morstad and Brazil 2012). In 2003, the BOF elevated the status to a stock of management concern due to its chronic inability to meet escapement goals. In 2009, a single SEG was adopted (2–10 million) that included the lower and upper ends of both previous goals. It was determined that the salmon production of pre-peak/peak versus off-cycle years shows similarity such that we cannot conclude they are different (Baker et al. 2009). The status was returned to a stock of yield concern because the lower end of this SEG had been met every year since 2004 (Morstad and Brazil 2012). In 2012, the stock of concern status was removed after continued success in achieving the goal every year since 2004. Subsistence users harvest this resource in villages stretching from the lower Kvichak River to Lake Clark (Figure 2). Annual subsistence harvest of sockeye salmon in the Kvichak River drainage averaged 52,000 fish from 1993 to 2002, with recent harvests (2003–2012) averaging 47,000 fish (Jones et al. 2014). The BOF designated 55,000 to 65,000 Kvichak River sockeye salmon for customary and traditional subsistence use described in 5 AAC 01.336 (Morstad and Brazil 2012).

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Fishery biologists manage the Bristol Bay commercial fisheries to meet escapement goal ranges set for individual rivers to provide for sustained yield. Managers use total fish run (catch and escapement) estimates to date to decide when to limit time, area, and gear used by commercial fishermen. Sockeye salmon abundance is assessed in the Kvichak River by counting tower and test fishery projects operated by the Alaska Department of Fish and Game (ADF&G) (Figure 1; Jones et al. 2014). Inriver test fishery data are available to fishery managers approximately 1 day after salmon have passed through the Naknek–Kvichak District commercial fishery. The test fishery project drifts gillnets along both sides of the river for a set time period and uses the catch information to produce catch per unit effort (CPUE) indices. Final salmon escapement in the Kvichak River is determined by visual counts from an observation tower located upriver in the clear headwaters of Iliamna Lake, typically 1–3 days later (West et al. 2012). The tower project has been operational since 1955 producing salmon escapement estimates of which sockeye salmon make up more than 99% of the run (Anderson 1999; West 2009; West et al. 2012). Due to the condensed salmon run timing in Bristol Bay, the escapement goals can be exceeded in 1 to 2 tide cycles. Fishery managers use the timelier test fishery indices to determine when to open and close fisheries to achieve escapement goals and match harvests to run timing to avoid overharvesting portions of the run. The Kvichak River test fishery project has been operational since 1960 (Paulus 1965; West 2009). Test fishery projects also occur on the Egegik and Ugashik rivers in Bristol Bay (West 2009). Assessing fish passage is difficult in the lower reaches of these wide, turbid rivers due to the heavy silt load, amount of debris carried by the river, and the large tidal fluctuations that limit the method of assessment. Because of the large tides, installing sonar systems in the nearshore regions where adult sockeye salmon tend to migrate had not been tried. Test fishery gillnets are drifted for less than 2 h/d capturing ≤500 fish, when >300,000 fish may enter the river each day. Compared to tower estimates, the test fishery indices often have large margins of error that vary by day and year. Early in the season (5–10 days into the project) mean historical (5-year) fish per index point (FPI) values are used to estimate daily inriver abundance (West 2009). The mean percent error of the travel-time relationship (~2 d) between the test fishery and tower sites decreases as the season progresses. Travel-time FPI estimates are more accurate than mean historical FPI values and are used beginning in early July. In 2000 and 2001, all inriver test fishery projects in Bristol Bay were evaluated by examining site location, optimal gillnet mesh sizes, and fishing times along with seasonal factors (site bathymetry, water temperature, water turbidity, river discharge, crew experience, escapement abundance, escapement age composition, and average fish length) to determine how each of these factors affected the test fishery indices and whether the accuracy of the indices could be improved (Schwanke et al. 2003). Fish travel time was also examined using daily and cumulative escapement, maximum likelihood estimation (MLE), and regression techniques to see which method produced the best inseason estimates. Significant relationships occurred between FPI and age composition of the escapement, average length of escapement, and water velocity. The recommendations for the Kvichak River included 1) continued use of the current mesh size gillnet because it selects for the majority of length classes found in the escapement, 2) use of escapement age composition and a univariate time series model of FPI (that incorporates an autoregressive parameter of lag-1 in forecasting models), and 3) continued use of the daily travel time method and compare results with the MLE method to forecast the inriver fish estimates.

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The primary goal of this project was to evaluate the feasibility of installing a sonar system in the lower Kvichak River and design a low-cost project to replace the existing test fishery project. To simplify operations and keep costs down, we limited sonar operations to a single bank with the idea that longer sampling periods and the ability to assess a much greater portion of the salmon run would produce a more accurate index compared to the limited sampling of the test fishery project. A dual-frequency identification sonar (DIDSON1; Belcher et al. 2002) developed by Sound Metrics Corp. (SMC) was selected because of its widespread use in Alaska rivers for the purpose of assessing migrating adult salmon (Buck and Brazil 2013; El Mejjati et al. 2010; Smith and Dunbar 2012; Westerman and Willette 2013). In an early evaluation of the DIDSON, sockeye salmon estimates in a clear river were shown to be very similar to visual counts from a tower and the sonar’s effective range in highly turbid rivers was shown to be sufficient to assess shore-based sockeye salmon (Maxwell and Gove 2007). In the first year of the study, we used side-scanning sonar and DIDSON profiling techniques (Maxwell and Smith 2007) to locate a suitable sonar deployment site. Once a site was selected, we installed a DIDSON for a short time period to work through logistics, assess the quality of the images, and determine optimal settings and sampling parameters. To further simplify the project, we examined automated and semi-automated methods of counting DIDSON fish images. Following the initial assessment, we collected sonar data to obtain a perspective on the annual variation between sonar indices, test fishery indices, and tower estimates. We also compared ease of setup, data collection, crew training, labor intensity, and operational costs of the test fishery and sonar projects. OBJECTIVES 1. Determine the feasibility of using sonar in the lower Kvichak River to provide an index of adult sockeye salmon abundance; 2. If sonar is a feasible option, determine whether the sonar fish index is more accurate and reliable than the test fishing index; and 3. Develop and evaluate automated methods for counting fish in DIDSON images at this site. STUDY SITE The Kvichak River flows approximately 88 km from Iliamna Lake to Kvichak Bay, an arm of Bristol Bay. The test fishery project operates near the village of Levelock approximately 16 km (9.9 mi) upriver from the terminus of the Naknek–Kvichak commercial fishing district and 64 km downriver from the tower site near the mouth of Iliamna Lake (Figure 2). Mean length of sockeye salmon caught in the commercial fishery was 546 mm with a standard error (SE) of 1.0 (West et al. 2012). Fish typically take 1–3 d to migrate from the test fishery site to the counting tower (West 2009). At Levelock, the river is a single channel, highly turbid, and approximately 360–600 m wide depending on tidal stage. A mixed semidiurnal tide cycle is present with the rising tide accompanied by a flood current flowing north and a falling tide accompanied by an ebb current flowing seaward. The river bottom along the right bank (facing downstream) drops off more steeply compared to the left bank of the river, which is shallow with sand bars that emerge at low tide. The Kvichak River has a mean annual discharge of 506 cubic meters per

1 Product names used in this report are included for scientific completeness but do not constitute a product endorsement.

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second based on readings from the U.S. Geological Survey (USGS) gauge located at the outlet of Iliamna Lake (NOAA 2013). SITE ASSESSMENT METHODS AND RESULTS In 2010, ADF&G staff with assistance from Aquacoustics, Inc. surveyed wide swaths of the lower Kvichak River in the vicinity of the test fishery to find a suitable place to deploy the DIDSON and to confirm where fish migrated (Figure 3). To deploy DIDSON, we looked for a site with a single channel, linear slope, laminar current flow, and non-reflective, fine river bottom substrate. An EdgeTech 4125 Tow Fish side-scan sonar with 400/900 kHz simultaneous dual-frequency beams was used to make broad sweeps along the sides and center of the river. Global positioning system (GPS) coordinates were input from a Garmin Map 76CSx handheld unit. The sonar was pole-mounted on the port side of a skiff with the side-looking beams positioned perpendicular to current flow (Figure 4). Sonar images were displayed on a laptop computer using EdgeTech Discover 4125D software that merged incoming GPS coordinates with the images. Sonar configuration settings included the following: 0.3 m aft offset, 15.5 m depth offset, and 0.6 m sonar depth, with sampling ranges set to 40 m per side for high frequency and 75–100 m per side for low frequency. From the high frequency side-scan images, we were able to determine whether the river bottom was linear or smoothly sloping and whether it contained large rocks or other obstructions, mounds, or depressions (Figure 5). With this information, we pared down the number of potential deployment sites and selected 3 for a more detailed look (Figure 3). The first site was along the left bank just downriver of the drift zone of the test fishery. The second and third sites were along the right bank, 1 a short distance upriver of the test fishery drift zone and 1 at the village of Levelock. We used a depth finder mounted to the boat and laser rangefinder to measure distance from shore to create a series of coarse bottom profiles at the selected sites. The profiles showed that the left bank river bottom had a steep slope out to 25 m then flattened and was relatively shallow out to 180 m from shore (Figure 6). The right bank profile just upriver of the test fishery zone had a gradual declining slope out to 80 m from shore, a 0.6 m obstruction at 65 m, and then a flat bottom out to 195 m (Figure 6). We selected the Levelock site which met our deployment requirements and was easy to access from the village (Figure 3; lat 59.11090N, long 156.85701W). At the Levelock site, the river was within a single channel approximately 360–600 m wide with tidal changes ranging in height from 1.6 m to 3.8 m. A sandbar was visible at low tide on the opposite side of the river. A more detailed river-bottom profile below low tide was created at the selected site following methods from Faulkner and Maxwell (2009) using a long-range DIDSON in a vertical position attached to a Remote Ocean Systems (ROS) automated rotator on an aluminum H-mount (Figure 7). Equipment specifications and sonar settings are listed in Table 1. We manually measured the area from the sonar to the bluff that is covered at high tide and combined these measurements with the DIDSON data to create a profile of the entire proposed sampling area. The river bottom consisted of small cobble, sand, and silt with a gradual, 6.5° slope from shore that became steeper (16°) ~25 m offshore and flattened ~45 m offshore (Figure 6). DIDSON was deployed over a 3-day period (June 29–July 1, 2010) to examine image quality and fish distribution in order to determine optimal sampling parameters for the remaining years. Deployed approximately 25.5 m offshore of the bluff at low tide, the DIDSON composite beam was positioned horizontally and initially pointed away from shore, perpendicular to current. The

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automated rotator panned the DIDSON away from shore, toward shore, and at 45° and 60° angles downriver from shore. An optimal tilt angle was found for each position using the river- bottom profiles and DIDSON’s internal tilt sensor. The water temperature was 10.4° C with a corresponding sound speed of 1,449 m/s. Continuous 10–15 min files were recorded, totaling 812 min of data over the 3-day period with sampling alternating between aims, 3 range strata (1–10 m, 10–30 m, and 1–20 m), and the 2 DIDSON frequencies (1.1 and 0.7 MHz). A total of 812 minutes of data, containing 5,446 fish, were manually counted using methods described in Appendix A1 and expanded to hourly counts ranging from 500 to 5,000 fish. The files were processed using the automated procedure described in Appendix B1. In these preliminary data, fish observed with the sonar pointed toward shore in 10–30 m stratum were on average 3.3 m (0.8 m SD) from the transducer. The sonar pointed away from shore which sampled 1–10 m, 1–20 m, and 10–30 m had mean fish ranges of 3.7 m (2.0 m SD), 4.4 m (2.8 m SD), and 15.3 m (3.9 m SD), respectively. From this initial site assessment, we concluded that it was possible to count shore-based adult sockeye salmon at this site using the 2-sonar system, 1 pointing toward shore and 1 pointing away from shore. With this deployment strategy, the constantly changing sampling range caused by the strong tidal fluctuations would not interfere with sampling. METHODS DATA COLLECTION DIDSON Following the site assessment, we began collecting DIDSON data in 2011 to provide an index of salmon abundance. Because we did not know how much the river bottom would change annually due to the dynamic nature of the site, we generated river bottom profiles each year using a vertically positioned DIDSON following methods described by Faulkner and Maxwell (2009). The profiles with model DIDSON beams overlaid were used to determine the best deployment positions and optimal tilt angles of the sonar. The profile was similar to 2010 and showed a change in the river bottom slope from flatter nearshore to steeper offshore at a location that could be reached at low tide by staff wearing chest waders. We deployed 2 DIDSON at this slope change, 1 pointed offshore (offshore sonar) and the other pointed toward shore (nearshore sonar), both aligned perpendicular to current flow (Figure 8). Typically, sonars are deployed nearshore with the beams directed offshore (Maxwell 2007). The constantly shifting shoreline necessitated pointing 1 sonar toward shore to accommodate tidal changes. The selected sonar tilt angles and other specifications are listed in Table 1. In the first year, the nearshore sonar was tilted 13.5° (above level). This was lowered to 4° in the following years to reduce the reverberation in the region closest to shore. Although the DIDSON software’s Background Subtraction function removes static noise, signal from the constantly moving surface made tracking and counting fish more difficult. Lowering the nearshore sonar’s tilt angle reduced but did not eliminate the noise problem. The tilt angle of the offshore sonar remained more constant between years. In 2011 and 2012, 2 long-range DIDSON were used for sampling. In 2013, 1 of the long-range DIDSONs was needed at another project so a standard DIDSON was used in its place. Each

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sonar was attached to an automated rotator (ROS or SMC X2) and deployed on an aluminum H-mount in a horizontal configuration (Figure 7) using methods described in Faulkner and Maxwell (2009). Deployment procedures changed during the course of the study because of the instability of the sonar mounts but deployment location (at the offshore slope change) was the same for all years. In 2011, the 2 sonars were deployed on separate mounts with the offshore sonar 9 m downstream and 0.8 m inshore of the sonar aimed nearshore. The mounts were held in place using sandbags and anchors, but frequently tipped over during strong tidal fluctuations as a result of drag created by debris accumulating on the buoy lines. In 2012, we stabilized the sonar mount by using a longer cross-bar on the H-mount to allow space for both units, attached an additional anchor to the downstream side, and attached the buoy used to mark the mount’s location to a separate line with its own anchor to help eliminate the drag problem. These changes helped the 2-sonar mount to stay erect through the season. In 2013, the deployment was similar to 2012; however, the large spring tides at the start of the season tipped over the sonar mount causing it to slide down the river bottom slope. We attached a third anchor to the mount and set it toward shore to eliminate the problem. A weatherport was constructed to house the topside electronics (Figure 9). The DIDSONs were connected to laptop computers and images were displayed using DIDSON software. The DIDSON was set to automatically record 2 range strata each hour using the Timer Data Entry function, 1–10 m and either 10–30 m or 10–50 m. In the first year, we sampled out to 50 m from the offshore sonar, but reduced the range to 30 m in the following years because few fish were seen beyond 20 m and the shorter range window produced a better resolution for viewing fish. Each range stratum was sampled for 10 min/h, a sample design that has been tested and proven to be satisfactory for assessing migrating adult sockeye salmon in rivers (Becker 1962; Reynolds et al. 2007; Seibel 1967; Xie and Martens 2014), and a method for determining the variance has been developed (Wolter 1984, 1985). For recording fish images, we set the display threshold low (13 decibels [dB]) to produce the best contrast between fish and noise without losing low- intensity fish images. The intensity setting (90 dB) adjusted the color palette of the raw image, brightening the stronger targets. The automatic focus was used, which sets the focus to half the viewing range. Sound speed was calculated based on water temperature (Simmonds and MacLennan 2005). Water temperature was measured daily and if a change of more than 2° C was noted, a new sound speed was input into the DIDSON.ini file. The 2 DIDSONs sampled 4 range strata, referred to as zones, with Zone 1 closest to shore and Zone 4 the most distant (Figure 10). Zones 1 and 2 (which encompassed approximately 25 m at high tide) were completely submerged only during the hours immediately before and after high tide twice a day. A technician monitored DIDSON images throughout the day either adjusting the sonar position or cleaning silt from the sonar lens if the image quality deteriorated. Lens cleaning was limited to low tide periods when the sonar could be reached and redeployed. Side-scan sonar To monitor fish passage across the entire river, we conducted surveys with the side-scan sonar on days when DIDSON images showed high fish passage rates. Equipment setup and configuration settings were the same as in 2010. The images were to be used to determine the cross-river fish distribution and percentage of fish passing beyond the range of the shore-based DIDSON. Technicians ran 3–4 transects depending on tide cycle, each 1.6 km in length (downriver to upriver), 2 approximately 60 m from shore along each bank and 1–2 in the river’s center, while motoring the skiff slowly upstream during recording periods and viewing images in real time to

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ensure adequate image quality. Acoustic images were recorded and viewed on a laptop computer using EdgeTech Discover 4125-D software. For each sample, technicians logged the 1) date and time, 2) location and station number, 3) tidal cycle, 4) range for each frequency, and 5) length of sample period. DATA PROCESSING AND ANALYSES Manual processing of DIDSON images Fish images from both DIDSONs were manually counted using DIDSON software; i.e., staff replayed DIDSON videos counting the number of fish using 2 tally counters, 1 for upstream fish and 1 for downstream fish. The Background Subtraction function was enabled while counting, which removed static images leaving only moving objects (Figure 11). The playback frame rate was reduced from real time during periods of high passage and increased during periods of low passage based on individual preferences to obtain a more accurate count. Detailed instructions for manual counting using DIDSON software were written prior to the field season and used by observers (Appendix A1). Fish counts were entered into a Microsoft Excel spreadsheet. Downriver counts were subtracted from upriver counts, and the results were expanded to obtain an hourly estimate per zone. Missing hourly estimates within each zone were interpolated using maximum likelihood techniques (X. Zhang, Commercial Fisheries Biometrician, ADF&G, Anchorage; personal communication). Zone estimates were summed within each hour, and hourly estimates were summed to obtain a daily estimate (index). The variance for the total estimated fish passage was calculated using Wolter’s (1984, 1985) V5 estimator for systematic sampling. We determined the best lag travel time between the upriver tower site and lower river sonar and test fishery sites to pair the datasets by plotting time series and scatter plots of the lagged data comparing sonar and tower datasets. A qualitative comparison with the test fishery data versus sonar and tower datasets using time series and scatter plots were also completed. We used the correlation coefficient (r) and t-test to determine whether the paired, lagged datasets were correlated and if the correlation was significant, and Fisher’s r to Z transformation to determine whether there was a difference between r values obtained from the paired datasets. For our decision of whether to replace the test fishery project with sonar, we considered several factors: the correlation information, ease of setup, operational complexity, training requirements, labor intensity, and costs. Developing and evaluating the auto-processor The 2010 dataset was used to develop an auto-processor for use in the remaining years of the study. Aquacoustics, Inc. evaluated the DIDSON fish images to determine what portions of the counting process could be automated. Data were sorted into 10 sets, each set combining all files collected at the same location with the same orientation and range window. The file sets were reviewed visually and run through a series of processing steps using DIDSON software (versions 5.25.10 and 5.25.26), Myriax Echoview (versions 4.90, 5.2, and 5.3), and Microsoft Excel. To auto-track fish in Echoview, templates were developed with intensity thresholds (applied to the smoothed image) and auto-tracking parameters based on range strata to produce 3 sets of Echoview parameters (Table 2). Transmission loss corrections were experimented with, but the

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values for the absorption coefficient α and/or the geometric spreading term N did not adequately compensate for the observed range-dependent loss in intensity. The automated procedure included the following steps (Figure 12): 1) organize DIDSON files by zone and day, 2) apply DIDSON software’s Background Subtraction function to remove static images and Export CSOT Frames function to remove empty frames, 3) select a prepared Echoview template and import DIDSON CSOT files, 4) auto-track the data in Echoview, 5) edit auto-tracked data, and 6) export fish tracks into .csv files and open in Microsoft Excel. Downstream fish were subtracted from upstream fish to obtain a fish count for each file. Two types of automated counts were generated without knowledge of the manual counts. For “full-auto counts” only the first and last page of the echogram were checked for tracking errors and only obvious noise were removed throughout the echogram. For “semi-auto counts” auto- tracked fish images were visually reviewed and edited over the entire echogram. In addition to the removal of obvious noise, editing included simple deleting and joining of fish tracks where a comparison with the angle echogram indicated tracking errors. The 10 groups of data were narrowed down to 4 (T1, T2, T3, T4) to compare automatic and manual counts. We used least squares regression to compare manual counts from the 2 observers, and manual counts from observer #1 to the full and semi-auto counts. Once the auto-processor was developed, detailed instructions were written and used for training purposes (Appendix B1). In 2011, DIDSON files were manually counted and auto-processed independently by 2 observers to compare manual versus automated counts. In 2012 and 2013, all data were processed by 1 observer. The manual and automated 10 min counts were compared using least squares regression to determine the variability between years and the 2 methods. No interpolated data were used in these comparisons. To assess the accuracy and precision of the manual counts, the 2011 DIDSON data processed from 2 observers were compared using least squares regression techniques. In 2012 and 2013, 30 randomly selected hours during medium-to-high fish passage were processed by 3 observers to compare manual counts among observers each year. The counts were plotted for each observer and an applied percent error was calculated (Holmes et al. 2006). We also compared the time needed to process DIDSON data using the manual and automated methods. An approximate manual count time was calculated based on the frame rate used to play back each file and summed by zone and by hour to obtain a daily process time. The auto- processing time was based on how long it took staff to pre-process the DIDSON files, track the fish in Echoview, and export the fish data. Range distributions Although the manual count data provided a coarse measure of fish distribution based on zone, a finer range distribution was obtained from the auto-processed data using the mean range of each fish track exported from Echoview. Fish range was condensed into 1 m range bins and used to create frequency distributions. The range data from the nearshore and offshore sonars were combined with offsets to produce a continuous distribution from shore to the farthest range sampled. The first 5 m from shore were excluded (the 25–30 m range of the nearshore DIDSON) because the beam encountered shore at a maximum range of 25 m during high tide. At low tide, Zones 1 and 2 were dry, and only the offshore sonar was sampling.

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Tidal influence To examine the effect of tidal stage on salmon passage we paired hourly fish passage estimates (manual counts) with hourly tide levels for Levelock. The tide stages for Levelock were obtained from Nobeltec Tides & Currents software, which was based on the tide stage from Nushagak Bay at Clark’s Point. For a qualitative analysis, hourly time series plots for each year of data were created. We then split the fish passage estimates into 2 time periods: ebb tide (8–9 h after the high tide) and flood tide (3–4 h before the high tide) to determine if and how tide cycle was related to salmon upstream migration. For our final tidal analyses, we collapsed the tidal stages into 0.3 m bins and plotted hourly fish passage per bin to determine whether a relationship existed between water level and either fish passage or fish direction. Midriver assessment Images from the side-scan sonar were reviewed postseason using EdgeTech Discover 4200-FS software and processed using Echotastic software (developed by Carl Pfisterer, Fishery Biologist, ADF&G, Fairbanks). The EdgeTech software, which allowed visual scanning only with no tools to mark or export fish, was used to examine the acoustic images for presence or absence of fish schools across the river. Echotastic allowed users to click on tracks in the images that appeared to be fish and export the data by range and ping time into a text file. The text file was imported into Microsoft Excel for counting and plotting. Directions for using the Echotastic software are included in Appendix C1. RESULTS ESTIMATING FISH PASSAGE Two DIDSONs, 1 pointed nearshore and 1 offshore, were deployed at the Levelock site during the 2011–2013 field seasons to assess adult sockeye salmon passage rates. Operational dates for each season and initial deployment information are included in Table 3 and environmental data are included in Tables 4–6. Daily water temperature was not collected in 2013 because the data logger malfunctioned. Using the manual count for the DIDSON data and the V5 estimator to calculate a variance, we estimated 456,107 salmon in 2011 (19,654 SE; ±38,521 95% CI), 1,226,542 salmon in 2012 (34,068 SE; ±66,773 95% CI), and 525,103 salmon in 2013 (21,784 SE; ±42,697 95% CI) (Appendices D1–D6). Daily fish passage estimates ranged from a low of -273 salmon (negative due to the subtraction of downriver from upriver fish) to a high of 236,488 salmon (Table 7). Substantially more fish were estimated from the nearshore sonar (pointed toward shore) compared to the offshore sonar (pointed away from shore) in 2011 and 2012, but the 2 estimates were more similar in 2013 (Figure 13, Appendices D1–D6). In 2012, when the largest numbers of fish were estimated, most (84%) were from the nearshore sonar. Subdividing the data into zones showed that the largest percentages of fish were observed in Zone 2 in 2011 (77.4%) and 2012 (71.7%), with a more even percentage across Zones 2 (40.8%) and Zone 3 (44.5%) in 2013 (Tables 8–10). The fewest fish were observed in Zone 4 in all years (0.6–2.2%). A time series of the zonal data showed that the larger peaks from the Zone 2 dataset occurred on different days during the field season with a series of peaks in 2011 and 2 dominant, but not aligned, peaks in 2012 and 2013 (Figure 14). We started with an ensonified range of 10–

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50 m in Zone 4 in 2011 but reduced the range to 30 m for the remaining years because only 8 of 9,795 fish (0.07%) were seen beyond 20 m. The daily estimates of salmon from the sonar and test fishery indices aligned well despite large differences in the number of fish sampled (Figure 15). The 2 indices exhibited a roughly linear trend with more scatter observed in the 2011 and 2012 datasets (Figure 16). The 2 indices were strongly and significantly correlated (r = 0.89 [2011], 0.95 [2012], and 0.92 [2013]; p < 0.05 all years) (Table 11). The daily sonar estimates were lagged 0–3 d and each dataset was compared in time series and scatter plots with the tower estimates truncated to match the sonar sampling dates. A 2 d lag best aligned the sonar and tower estimates and resulted in the highest r values. A 2 d lag was also used to compare the test fishery and truncated tower data. The shape of the time series curves for the lagged sonar and tower estimates were very similar, although the tower estimates were shifted upward due to the higher passage rates (Figure 17). Scatter plots of the sonar versus tower and test fishery versus tower datasets revealed linear relationships with a fair amount of scatter between them (Figures 18–19). The sonar versus tower datasets were strongly and significantly correlated (r = 0.73 [2011], 0.79 [2012], and 0.91 [2013]; p < 0.05 all years) as were the test fishery versus tower datasets (r = 0.81 [2011], 0.82 [2012], and 0.94 [2013]; p < 0.05 all years) (Table 11). We were unable to reject a null hypothesis of no difference between r values from the sonar versus tower and test fishery versus tower correlations for any of the study years (Z = -0.58 [2011], -0.22 [2012], and -0.57 [2013]; p > 0.05 all years), indicating the differences were probably due to sampling error and not the superiority of 1 method over the other. The larger 2013 r value corresponded with an earlier salmon run time than the 2 previous years; 48% of fish passage had occurred at the tower site through July 2 compared to 35% (2011) and 16% (2012). Because the sonar setup only sampled the right side of the river, we were interested in the ratio of salmon assessed along each side of the river and how that ratio changed between years. At the tower site, fewer fish migrated along the right bank compared to the left bank in 2011 and 2012 (39.4% and 37.6%), whereas in 2013 (55%) more fish migrated along the right bank (Table 11). At the lower site, the test fishery CPUE showed a much more even ratio of fish captured along the 2 banks, with a right-bank CPUE of 50.1% (2011), 51.4% (2012), and 49.0% (2013). In 2013, the year with the greatest correlation between the sonar versus tower, the percent of fish passage along the banks of the tower site were more similar. Comparing sonar versus right-bank tower estimates produced lower r values compared to the sonar versus total tower estimate, but once again we were unable to reject a null hypothesis of no difference for any study year (Table 11). The sonar estimate accounted for 21.4% and 27.0% of the total tower estimate in 2011 and 2013 and 54.0% and 48.2% of the right-bank tower estimate (Table 11). In 2012, when fish passage estimates from all 3 projects were the highest, the sonar accounted for 32.1% of the total tower estimate and 81.9% of the right-bank tower estimate, suggesting that in this year, fish may have remained more aligned with the right bank instead of switching between banks as they traveled between the 2 sites. The daily estimates compared by year resulted in a small sample size due to the compressed nature of the salmon run in the Kvichak River, which reduced the power of the statistical tests. Because of the time lag of fish travel between the sonar and tower sites, aligning hourly counts

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rather than daily counts to increase the sample size would not be advisable. To gain more power in the tests, we lumped data from all 3 years and compared the fish estimates again. The tests showed similar results to the yearly data sets with the sonar and test fishery having the highest correlation (Table 11). Each year, missed hourly sonar counts were interpolated. Most of the missed sonar data were due to the instability of the sonar mount(s) and silt accumulation in the sonar lens that degraded and eventually blocked the signal. The number of hours interpolated decreased every year as we were better able to stabilize the mount and keep the lens clear of silt (22% [2011], 10% [2012], and 8% [2013]). DEVELOPING THE AUTO-PROCESSOR Image quality was an important factor in determining whether or not the auto-processor could be used. We discovered that although the image quality from the offshore sonar was good, some of the nearshore sonar images were not because the sonar beam was squeezed between the river bottom and surface as the water level changed with tidal fluctuations. This caused multipath reverberation, which appeared as multiple images of fish and surface reflection (Figure 20). Multiple images were apparent to observers because the fish and its reflections moved in perfect synchrony, which is not how schools of salmon travel. The auto-tracker was not always able to distinguish between multiple images and resulted in mistracked fish. Observers were able to distinguish individual fish from the multiple fish groups and count them, although at high passage rates the counts became less certain. The regions containing multiple images moved inshore and offshore as water levels changed. Images recorded while the sonar was aimed obliquely (45° and 60° angles) resulted in poor quality images (Figure 21), and we were unable to auto-process them. Aside from the multiple image regions and the oblique angle data, image quality in the remaining files was satisfactory and worked well with the auto-processor. We selected files from the 2010 dataset to develop the auto-processor and test-tracking parameters (Table 12). Our initial assessment showed that manual counts from 2 observers and full-auto and semi-auto counts from a single observer were all very similar (Figure 22). Each comparison was strongly correlated (R2 of 1.0) with regression slopes of 1.0–1.1 (p < 0.01) and 95% confidence intervals of 1.08 to 1.10 for the manual 1 versus manual 2 regression, 0.96 to 0.99 for the full-auto versus manual 1 regression, and 0.95 to 0.98 for the semi-auto versus manual 1 regression. Full regression equations are listed in Table 13. Regression slope values for the full-auto versus manual 1 and semi-auto versus manual 1 comparisons were both 1, suggesting that the extra effort of editing occasional fish tracking errors (joined tracks, split tracks) may not be worth the time. Edits of obvious noise events were much faster to make and should remain part of the review routine. For the 3-year study, we decided to use the semi- automated method of reviewing the entire echogram but only editing obvious noise and fish tracking mistakes. This was chosen over the full-auto method of reviewing only the first and last pages of the echogram because of the reoccurring surface noise caused by tidal fluctuations. Files with large numbers of fish appeared to dominate the regression, so we extracted a subset of files with less than 100 fish per file. These regressions showed similar results (Figure 23); each comparison was strongly correlated (R2 of 0.9–1.0) with regression slopes of 0.9–1.0 (p < 0.01) (Table 13).

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EVALUATING THE AUTO-PROCESSOR DIDSON data were manually counted and auto-processed by zone and year. In comparisons of the semi-auto versus manual counts for the nearshore (Zones 1–2) and offshore (Zone 3–4) sonars, there was considerably more scatter in the datasets compared to our initial assessment (Figures 22–24), and regression slopes (b1) and correlation values (R2) were lower (Table 14). In 2011, slope and correlation values were closer to 1.0 (b1 of 0.90 nearshore and 0.93 offshore; R2of 0.96 nearshore and 0.98 offshore) but were substantially lower in the remaining years of the study (b1 of 0.56–0.67 and R2 of 0.75–0.93). The strongest correlation was found in Zone 3 (annual R2 values ranged from 0.83 to 0.96 with a multiyear average of 0.91). We expected a good correlation from this zone because of the high- quality image from the offshore sonar, which had a short window length (WL; 10 m) and no visible surface noise. Zone 2 had strong annual correlations with a multiyear average of R2 = 0.84. This zone also had a short WL but the nearshore sonar had visible surface noise. Annual correlations from Zone 1 were lower. In this zone, the WL was longer (20 m) so the resolution was lower, the sampling range changed based on tidal stage, and the nearshore sonar image quality was poor due to surface noise. The longest window length (40 m) used for Zone 4’s 2011 data (10–50 m) created the poorest resolution image and the auto-processing performed very poorly (R2 = 0.13). This value was removed from the 2011 average. Full regression equations are listed in Table 15. After auto-processing data from all years we found that fish tracks with overlapping echoes on a single ping in the echogram produced invalid tracks. This was caused by a problem in the Echoview software. The percent of invalid fish tracks was greater (3.9–11.5%) from the nearshore sonar where fish passage was highest during all years (Table 16). These targets were not included in the estimates because no direction of travel information was available, so it was impossible to determine whether the fish targets should be added to the count or subtracted. The time it took to process the automated and manual files was logged for the 2013 dataset. The processing time for the 2 methods differed significantly during high fish passage but was similar at low fish passage (Figure 25). Automated processing required 155–355 min/d and manual processing 147–585 min/d (Table 17), with the largest difference occurring during peak passage on July 1 when staff took 292 min longer to process the day’s data using manual methods. Zone 1 data (12% of total fish passage) required the most time to process manually, 968 min longer for the season’s data compared to automated methods. This was due to the multipath issue and the longer window length in the DIDSON viewer, which makes fish appear smaller. Both situations required staff to slow down the frame rate to manually count fish. Observer count precision The 2011 DIDSON dataset was independently counted by 2 observers using manual and semi- automated methods. Paired observer counts from each method showed a strong linear relationship with little scatter in the data (Figures 26–27) and very strong correlations (Table 18). In 2012 and 2013, 30 h of DIDSON data including all 4 zones each hour were randomly selected, manually counted by 3 observers, and summed across zones. Time series plots show the counts from the 3 observers were similar for both years (Figures 28–29). Differences among observers occurred primarily during high passage periods (15,000–18,000 fish/h) in 2012. In sample 30, fish were concentrated in Zone 2 near the shoreline producing numerous double

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reflections, which were difficult to count. In 2013, the largest offsets between observers occurred in samples 10 and 15. In sample 10 (June 30, 1300 hours), 98% of fish passed through Zone 1 just after high tide during the ebb. In sample 15 (July 1, 0400 hours), 99% of fish passed through Zone 2 during an ebb tide. During both samples, fish density was high (10,600–12,300 fish/h). Overall, high fish density combined with surface noise and doubled fish reflections created the largest discrepancies between observer counts. The applied percent error from the 3 observers was low, 1.03% in 2012 and 1.36% in 2013. RANGE DISTRIBUTIONS The average ranges of individual fish were exported from the automated tracked fish files and sorted in 1 m bins (Table 19). The resulting range distributions showed that fish passage was most concentrated between 10 m and 30 m from our designated zero point where the water encountered the bluff (Figures 10 and 30). The 0 frequency from 25 to 26 m was the area not ensonified by either sonar (Figure 30). Because they were pointed away from each other, the dead zone represents the sum of the nearfield regions where the beams are still forming, approximately 2 m. The distributions were similar in 2011 and 2012 with larger peaks in the end of Zone 2 and a smaller peak at the start of Zone 3. In 2013, the larger peak occurred at the start of Zone 3. In all 3 years, 95% of tracked fish were within 31 m from shore (Table 19). TIDAL INFLUENCE Tidal fluctuations up to 4 m were observed at the sonar site during the study period (Appendices E1–E3). Time series plots showed a relationship between tide cycle and the hourly fish passage estimates each year (Figures 31–33). Separating hourly fish passage into ebb and flood tide periods showed that upstream fish passage was considerably higher during ebb tides (Figure 34), ranging from 85% to 98% of the total upriver fish passage (Table 20). The highest fish passage day during the study (July 4, 2012; 236,488 fish) was plotted by hour to show this typical fish movement with the tides (Figure 35). Ebb tides last longer than flood tides at this site, and the number of fish/h was substantially higher during the ebb (Table 20). The majority (97–100%) of total downstream fish passage occurred during flood tides. The largest numbers of total downriver fish were observed in 2013 when 113,374 passed during flood tides, and the lowest was 199 fish in 2012 during ebb tides (Table 20). The relationship between fish direction and tidal stage was examined. No distinct upstream migration patterns were observed, but downstream-moving fish were affected by tidal stage; more fish moved downstream between 1.6 m and 3.1 m tide stages (Figure 36). It can be assumed that this was during the flooding tide. MIDRIVER ASSESSMENT We conducted 5 surveys each year in 2011 and 2012 with the mobile side-scanning sonar. The dates and hours of operation are listed in Table 21. The surveys were conducted during ebb tides (tide stage ranged from 0.0 m to 2.2 m) because that was the only time the test fishery crew was available to operate the boat used for the surveys. Only 3 transects were needed to cover the midriver area that could not be ensonified with shore-based sonar. Fish passage estimates from the DIDSON during the operation hours of the side-scan sonar ranged from 0 to 12,318 fish. The side-scan images of the river bottom topography and shoreline were clearly visible and proved very useful during site selection; however, images of fish were small and difficult to detect and not reliable when we tried to estimate fish passage at the range used (Figure 37). Fish were

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visible along the left bank where the bottom substrate made of sand and silt was not highly reflective in the images. Along the right bank, fish were difficult to see against the bottom background. The side-scan sonar could be useful to determine presence or absence of fish if the sampling ranges were shortened. DISCUSSION This study was designed to answer the questions of whether a sonar project could successfully assess adult sockeye salmon passage in the lower Kvichak River and whether that assessment would provide a more accurate index than the existing test fishery project (West 2009). A more accurate and timely assessment of Kvichak River adult salmon would benefit user groups, fishery managers, fish processors, and agencies interested in the health and status of Bristol Bay salmon by helping to ensure that inriver escapement goals and subsistence needs are met, and harvests are distributed throughout the run in proportion to abundance. The goal was to keep project operations as simple as possible, using sonar on only 1 bank with the idea that the hourly sampling would provide a better index than the more limited sampling of the 2-bank test fishery operation. CAN DIDSON BE USED TO ASSESS ADULT SOCKEYE SALMON IN THE TURBID, TIDAL LOWER KVICHAK RIVER? Although we experienced numerous logistical challenges, we were able to successfully deploy DIDSONs in the lower Kvichak River to assess migrating adult sockeye salmon. The difficulties of finding a suitable deployment site were reduced by using a side-scan sonar to sweep large sections of the river quickly and eliminate sites where the river-bottom topography and substrate were not suited for a fixed, nearshore sonar deployment. The selected Levelock site worked well for sonar deployment. Although a change of slope occurred within the sampling range (Figure 8), the slope change could be reached with chest waders during low tide and proved to be a good position to deploy the 2 sonars. The added bonus to this site was its close proximity to where the crew was staying and the availability to pay for AC power from a nearby resident saved the cost and extra work associated with a generator and 12 V battery system. The large tidal fluctuations (~4 m) made it difficult to deploy the DIDSONs. Incoming tides at the Levelock site were very forceful, especially during spring tides. The problem of the sonar mount tipping over during tidal changes was solved with the double mount, more sand bags, and attaching the buoy marker to its own anchor. The double sonar mount was more stable and quicker to pull and redeploy during low tides. We quickly learned that the speed of pulling and redeploying the sonars was a critical factor. When the mounts tipped over, we were not able to retrieve them until the following low tide. The DIDSON lenses are not sealed and silt rapidly accumulates within the lenses. Without frequent cleaning, silt accumulation blocks the lenses, deteriorating the sonar images. At Levelock, the incoming flood current surges upriver allowed very little time to pull the sonar, clean the lenses, and redeploy. If maintenance took longer than the slack tide allowed, we would have to wait for the next low tide, missing 12 h of data collection. At other sonar projects, we have tested sealing boxes and socks designed by SMC but found they all leak and are difficult and very slow to remove when the lenses need cleaning. They are also bulky, which makes aiming the sonar more problematic. It would be impossible to remove them and finish the maintenance and redeployments in the time allotted. Without the

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sealing boxes or socks, we found it was necessary to pull the DIDSONs and clean the lenses every 2–3 d. Aiming 1 DIDSON toward shore meant that a weir was not needed to divert nearshore fish into the sonar beam. At most riverine sonar sites in Alaska, 1 sonar unit is directed offshore from each bank and a weir is constructed from the bank to ≥1 m in front of each sonar to prevent fish from passing behind or too close to the sonars (Buck and Brazil 2013; El Mejjati et al. 2010; Westerman and Willette 2013). Not having to construct and maintain a weir to direct fish in front of the sonar greatly simplified the project. Installing and maintaining a weir at this site would have been extremely difficult due to the strong tidal fluctuations and heavy debris in the river. In 2010, we experimented with several DIDSON aims including toward and away from shore (perpendicular to current flow) and at angles oblique to current flow. The reason for the oblique angle was to increase the time it takes fish to pass through the beam and thus increase the number of frames in which an individual fish would be seen. The downside was that these fish reflected less sound and produced poor quality images. For auto-processing, we found that the quality of the image was more important than the number of frames per fish. Aiming the DIDSONs perpendicular to current resulted in the reflection of sound off the broadside of fish, producing more distinct images that were easier to track in the automated program. The aims directed toward and away from shore provided usable DIDSON images. Aiming the DIDSON toward shore led to a situation where the sonar beam spreads as the water column narrows. Squeezing the beam into this narrow layer causes the transmitted sound wave to bounce back and forth from bottom to surface before returning to the transducer, creating multiple bottom images which in some cases occluded the range nearest the shoreline. The multi- pathing also created double or triple images of individual fish (known as ghost fish) offset in range from the primary image. The majority of sockeye salmon travel close to shore, so the range of these fish relative to the DIDSONs was constantly shifting with tidal levels. For manual counting, the ghost fish were generally easy to recognize and compensate for because the duplicate or triplicate fish images move side by side in a manner that is different from the natural swimming or schooling behavior of adult salmon. Zone 1 had the most interference as the beam encountered surface constantly somewhere along its range (Figure 10). Also, Zone 1 had a 20 m WL, where fish images appear much smaller. In 2011, the nearshore sonar was tilted 13.5° up from level to place the beam center, the region of strongest energy, just above the river bottom. In 2012, the tilt was lowered to 4° (Figure 8), pushing more of the sonar beam into the river bottom to reduce interaction with the surface in Zone 1. In Zone 2, the amount of sound energy reflecting from the surface and bottom was less regardless of which aim was used. Although recordings made at the higher tilt angle were still usable, the lowered aim in 2012 improved the quality of fish images, making them easier to count. During high fish passage (5,000–22,500 fish/h), a constant stream of salmon traveled close together, which made them more difficult to count. Manually counting fish during these periods occasionally required the observer to use 1 click of the tally counter to represent 10 fish rather than counting fish singly because it was impossible to click fast enough to tally every fish. Slowing down the recordings helped to a point, but slow-moving or still frames were more difficult to assess. By playing the 10 min recording back at recorded speed or slightly increasing the frame rate, the observer’s eye was able to more easily pick out the individual moving fish targets. If large schools of fish moved too slowly across the beam, it was difficult to judge

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whether or not an individual fish had already been counted. Observer error was the highest during high-passage periods (Figures 28–29). Although we encountered numerous obstacles, we were able to operate the 2 DIDSONs at the Levelock site and obtain countable images. We lost a significant amount of data during the first full year of operations, 2011, but once the sonar mount issue was resolved, operations went smoothly in the remaining years of the study. DID THE SONAR PROVIDE A MORE ACCURATE SOCKEYE SALMON ABUNDANCE INDEX THAN THE TEST FISHERY? We were unable to determine whether the sonar indices of abundance were more accurate than test fishery indices. Although the tower versus sonar correlation coefficients were lower than the tower versus test fishery coefficients, differences between the r values were not significant probably because of the small sample sizes and similarity in r values. After all years of data were lumped there was no significant difference in correlations even with the larger sample size. Towers are used throughout Alaska to obtain salmon abundance estimates in rivers for managing fisheries and are considered to be one of the most trustworthy estimates (Anderson 1999; Kohler 2003; Woody 2007). They are located along clear waters where salmon can be viewed by an observer overlooking the river. Although poor viewing conditions may result due to weather conditions, and high passage rates can make real-time counting challenging, for the most part, the counts are very reliable. Because of this reliability, tower counts were used to ground-truth DIDSON counts during our initial evaluation of the DIDSON (Maxwell and Gove 2007). The lower test fishery correlation to tower estimates changes through the salmon migration with the highest agreement starting in early July (West 2009). This lower river sonar project was implemented in an attempt to obtain an estimate with more stable correlation to tower estimates. However, the lower r values of tower versus sonar comparisons suggested that the hourly sampling and single-bank sonar operation was not more reliable than the test fishery index. A lag time of 2 d best aligned the peaks in the daily passage estimates from the tower and sonar and resulted in the highest correlation (Figure 17). The 2 d lag corresponds roughly to a travel time of 1300 m/h (swim speed of 1.2 ft/s) based on a distance of 64 km between the 2 sites. A finer resolution may be achieved using hourly estimates as the unit of comparison rather than daily estimates, but the large changes in current flow would alter fish travel times within a day and probably occlude any relationship at the hourly level. The tower versus sonar and tower versus test fishery r values increased each year of the study (Table 11). Although differences between the lowest 2011 and highest 2013 r values were not significant, the apparent trend was interesting. We have no explanation as to why the 2013 datasets may be more highly correlated, but bank preferences may provide a partial explanation. Bank ratios at the tower site were not the same between years. A smaller percentage of fish traveled along the right bank at the tower site in both 2011 and 2012 (39% and 38%) compared to a larger ratio (55%) in 2013. This is the same year the tower versus sonar r value increased to 0.91. If the bank ratios change by year at the lower river site, the relationship between the tower estimates and the single-bank sonar estimates would not be stable. The test fishery CPUE bank ratios were similar between years with 49–51% of fish along the right bank (Table 11); however, these ratios are less reliable compared to the tower site. Without more conclusive evidence regarding bank preference at the lower river site, it is difficult to determine whether adding a 2-

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sonar operation to the left bank at the lower site would substantially improve the tower versus sonar relationship. The similarity between the tower versus test fishery and tower versus sonar relationships was surprising based on the low sampling power of the test fishery project and the migration behavior of sockeye salmon at this site. The sampling power of the sonar was considerably higher than the test fishery project. The sonar was operated 24 h/d sampling 10 min intervals per strata, a total of 960 min of data per day. Test fishing occurred twice daily starting 1.5 h before high slack tide and conducted 2 drift sessions <15 min along each bank for a maximum sampling time of 120 min/d (West and Brazil 2013). In addition, the numbers of fish counted by the sonar far exceed the number of fish caught in the test fishery operations. Test fishing only occurred during flood tides, but according to the sonar, the majority of salmon migrated upriver during ebb tides (Figure 34). The flood current reverses the river’s flow and an average across the 3 study years showed 99% of the downriver migration occurred during this tidal cycle, so test fishing occurred when fish tended to be moving downriver (Table 20). This migration behavior was reversed for sockeye salmon entering the Frasier River estuary (Levy and Cadenhead 1995) and adult Chinook salmon O. tshawytscha entering the lower Kenai River (Eggers et al. 1995). In these 2 studies both fish species utilized selective tidal stream transport (Forward and Tankersley 2001) to minimize energy expenditures, shown by a high frequency of salmon migrating upstream during flooding tides and fish holding their positions during ebbing tides. Based on the lower sampling power and sampling times, we would expect a lower correlation between the tower and test fishery datasets. The higher sampling power of the sonar and more even sampling across tidal stages did not appear to improve the correlation to tower estimates. This suggests that bank preference may be a larger factor than sampling power in the tower versus sonar relationship. At many tower and sonar sites in Alaska, a “one-percent rule” is used to determine when to stop sampling the salmon run (Suzanne Maxwell, Commercial Fisheries Biologist, ADF&G, Soldotna; personal communication). The rule states that salmon are counted until the daily estimate is <1% of the cumulative estimate for 3 consecutive days. This rule assures that the majority of the salmon run is counted prior to stopping field operations. In the 3 study years, the tower project followed this rule, while the sonar counts did not (Table 7). The sonar operational days were determined by the test fishery project and not by the one-percent rule. Personnel and some equipment were transported upriver by the test fishery crew, and setup and breakdown operations were also completed with their help. Truncation of the field season did not affect the comparison because the tower estimates were trimmed when pairing the datasets. If the sonar project goes forward, it will need to be operated for a longer time span both at the start and end of the project to satisfy this rule. Range distributions from the sonar showed that most fish migrated past the site between 10 and 30 m from shore (Figure 30). Sockeye salmon are known to swim close to shore near the river bottom in large rivers with strong currents to take advantage of the reduced flow velocities (Brett 1995; Hinch and Rand 2000; Hughes 2004; Webb 1995). Other large river sonar assessment sites that use DIDSON to assess sockeye salmon ensonify only a portion (18–67%) of the river’s width (Maxwell et al. 2013). Based on the range distributions, we were confident that the DIDSONs ensonified the majority of salmon passage along the right bank at the Levelock site. An obvious omission is the region 25–26 m from shore where the sonars are positioned (Figure 30). This area was not covered because the DIDSON start range is set at ~1 m from the transducer. Sampling within this 1 m nearfield region is difficult because the beam is still

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forming and has not had a chance to spread, covering an extremely small volume of water. Aiming 1 DIDSON nearshore and the other offshore created a 2 m nearfield between Zones 2 and 3 that were not sampled (Figure 10). An unknown number of fish traveled through this unensonified region as tide level changed. Interpolating this region using the range distributions may be the best way to account for missed fish. Although the side-scan sonar was a very useful tool for finding a deployment site, it was less useful for assessing cross-river fish distribution. Fish in the side-scan images were difficult to distinguish from background objects. Unlike DIDSON images, which are video images that show fish moving upriver against the current on a fixed background, the images produced by the side-scan sonar showed fish as tiny dots or tracks that were sometimes distinguishable from other objects, but often the observers felt they were guessing which images were fish and which were part of the river bottom (Figure 37). Reducing the sampling range and increasing the number of transects may resolve this problem, but considerably more transects would need to be sampled. At other rivers, we have used DIDSON successfully for assessing salmon mid-river by sampling multiple stations and holding the boat stationary while recording data (Maxwell et al. 2013). Using DIDSON to assess mid-river fish passage at the Kvichak River would be very time intensive and require more personnel and funding than were available for this study. The side- scan sonar appeared to be the better option because of its ability to survey large swaths of the Kvichak River quickly where fish passage and tide levels change dramatically. AUTOMATED VERSUS MANUAL COUNT METHODS Auto-processing the DIDSON data was significantly faster than manual counting when fish passage rates were high (Table 17). Using Echoview, it took 10–30 m (depending on fish density, passage rates, and degree of editing) to auto-process 1 full hour of data (Figure 21). This rate could be reduced with additional COM (Component Object Model) automation, which allows third parties to develop scripts and applications that interact with Echoview to reduce the interactive portion of Echoview processing to a single block of time that might take 2–10 min/h of data. The COM automation could be used for automatically applying stratum-specific Echoview templates, consistent file naming, and automatic file management, which would reduce file naming and management errors. The downside to using COM automation is that unless the scripts are written by the people who are operating the sonar units, inseason changes, such as a change in the range setting of the sonar, can render the script files useless unless the writer of the scripts is available. The nearshore sonar data created the most problems for the auto-processor because of the surface reflections and ghost images, especially during high fish density. Unlike reflections from the river bottom, which tend to be static, surface reflections are highly dynamic. The background subtraction algorithm in the DIDSON software works well for removing static bottom images, but unless the water surface is very smooth, surface echoes are difficult or impossible to remove. Lowering the aim to push more of the DIDSON beam into the bottom cleaned up the images somewhat, but Zone 1 remained the most difficult to auto-process. The ghost images from near the shoreline are generally recognized by human observers as duplicated images, but auto- counting these images are difficult. The issue of overlapping echoes that occurred while using Echoview resulted in a significant loss of fish in the automated count of the nearshore sonar, a loss of 3.9–11.5% of tracked fish. Overlapping echoes are 2 echoes that occur in a single ping of the echogram that results in an

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invalid track. We have observed this phenomenon in prior work with Echoview, but the occurrence was rare. The high density and closeness of fish at the Kvichak River made the problem more common. We examined several of these invalid tracks within Echoview and discovered that no information was included with the track other than the number of echoes and a region number. One effect of ‘translating’ tracks from multiple beams in an imaging sonar like DIDSON into a single target echogram is that multiple targets can end up in a single ping, resulting in invalid tracks that will appear in the exports (B. Hutton, Myriax Software Pty Ltd, Tasmania; personal communication). Tracks that have this issue are generally able to be edited to result in a valid track, but this was not feasible with the high numbers of fish passage at this site. Although we were able to export the single targets and count these fish based on the assigned region numbers, it was impossible to know the direction of travel. The direction of travel is crucial because downstream fish are subtracted from upstream fish, and a large number of fish traveled downstream at this site because of the tidal fluctuations (Table 20). The use of a major- axis angle filter and reducing the target thickness in the Echoview processing parameters could help reduce the number of invalid tracks. If Echoview is to be used in the future for auto- processing, the overlapping echo issue will need to be resolved. Regression slope and correlation values from comparisons of automated and manual count data by sonar were closest to 1 in 2011 (both >0.90) and substantially lower during the remaining years (Table 14). In 2012, the nearshore slope value was 0.56 with an R2 of 0.78. This suggested that the auto-count parameters required more adjustments to account for environmental or sampling differences among field seasons. If this is true, using the auto-processor inseason may be difficult because the adjustments would have to be made at the start of the season when few fish are migrating past. If we can determine what situations affect the parameters, the auto- processor may become a viable option. Other studies have been published on an automated method for fish counting using DIDSON data (Han et al. 2009; Handegard and Williams 2008; Kang 2011). Each used a different tracking algorithm for target tracking to meet the objectives of their study. Handegard and Williams (2008) developed an auto-processor in Metlab to track individual fish and quantify behavior (speed, time, direction) in trawls. Three datasets were assessed for a total of 111 min of DIDSON data with 137–208 tracks (mean length 24–47 cm) in each set. Verification was established by comparing manual tracked target versus auto-tracked targets. There was high disagreement at high density and close range. Han et al. (2009) used DIDSON with a WL of 2.6 m to count and size 18 farmed Yellowtail Seriloa quinqueradiata (mean length 83 cm) during 4 net transfers. A sizing error of mean total length was found to be within 2.4 cm. Kang (2011), of Myriax Software, used Echoview to evaluate the utility of a semi-automated method of DIDSON data for fish counting, sizing, and behavior. The dataset used for the analysis consisted of 24 hours of DIDSON data with a total of 248 Chinook salmon tracks (average length 53 cm). No verification was conducted. None of the studies exhibited the fish quantities that were observed at the Kvichak River or the necessary count precision that is needed to estimate a fish species for biological management. SHOULD THE TEST FISHERY PROJECT BE REPLACED BY SONAR? Prior to this study, we had never installed a sonar system in a river with tidal fluctuations as large as the ones experienced at the Levelock site. Although the lower Kenai River Chinook salmon project is located in a tidal zone, the project goal is to ensonify the middle of a much narrower,

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V-shaped river (Miller et al. 2014). The Kvichak River is much wider and more dynamic than the Kenai River. Although we encountered many challenges placing sonars in the rapidly changing river, we were able to successfully ensonify the nearshore region favored by sockeye salmon and produce a lower river index of fish abundance. However, the index proved to be no better than that produced by the existing test fishery project. There are other factors to consider when choosing between the 2 assessment methods. Although the 1-bank operation did not correlate better with the tower estimates, a 2 bank operation would probably do better. Because we were able to successfully run operations along 1 bank, adding another bank could be done potentially at an upriver site examined in 2010 (Figure 3). Ensonifying the opposite bank would require the purchase of 3 additional sonar units. The NOAA grant used for this study purchased 1 sonar. At the time of purchase, SMC was selling an Adaptive Resolution Imaging Sonar (ARIS) that is the DIDSON replacement. The ARIS has better resolution, the lens case can be sealed which supposedly will keep out silt, and the user will be able to look at any range strata from 1 recorded range window. When the unit was purchased, there were still mechanical and software difficulties that needed to be resolved. Rather than using the unit at a remote site like the Kvichak River, we swapped it for a DIDSON and leased a second from ADF&G. Although there are many advantages to using the ARIS, there would be little difference in the resulting fish estimates from the newer sonar. During the study years, the problems with the ARIS system were worked out and the unit already purchased is ready to be used at the Kvichak River. Purchasing 3 additional units for the project would cost approximately $270,000. The operation of a 2-bank sonar site would require a minimum of 3 operators, 1 more person than is needed for the test fishery crew. Overtime costs would be higher because whenever the systems need to be brought to shore for maintenance or position adjustments, it requires 2 people due to safety concerns when working in the river. Operating a sonar project requires more technical skills than the test fishery project and crew training would be more substantial and require longer on-site time for the supervisor. Other costs for the sonar project would include charter flights to transport electronic equipment to and from Levelock, equipment maintenance, data storage media, and reliable power options. At this time, our recommendation for the lower Kvichak River site is to leave the test fishery operations in place. Fish estimates from the 1-bank project were not better correlated to tower estimates than the test fishery indices. Expanding to a 2-bank sonar operation should improve the accuracy of the estimates, but the expansion would be costly both initially and long-term. ACKNOWLEDGMENTS This report was prepared by April V. Faulkner and Suzanne L. Maxwell, under award NA09NMF4380373 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce, administered by the Alaska Department of Fish and Game. The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration or the U.S. Department of Commerce. John Flanagan and Kyle Wilson monitored the DIDSON equipment and conducted the side-scan sonar surveys. Kelsey Romig and Dawson Marchant (test fishery crew) operated the boat during the side-scan surveys and assisted the sonar technician as needed to set up or move equipment. T. D. Hacklin processed DIDSON files and side-scan sonar data postseason each year. Carl

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Pfisterer developed the Echotastic software program used for the side-scan sonar data. Don Degan of Aquacoustics, Inc. assisted with the site assessment during the first year of the study. Anna-Maria Muller of Aquacoustics, Inc. developed the automated counting method used to process DIDSON files. Fred West provided logistical support inseason and editorial comments. Xinxian Zhang provided biometric support. Jack Erickson provided editorial review. REFERENCES CITED Anderson, C. J. 1999. Historic counting tower projects in the Bristol Bay Area, 1955–1998. Alaska Department of Fish and Game, Regional Information Report 2A99-12, Anchorage. Baker , T. T., L. F. Fair, F. W. West, G. B. Buck, X. Zhang, S. Fleischman, and J. Erickson. 2009. Review of salmon escapement goals in Bristol Bay, Alaska, 2009. Alaska Department of Fish and Game, Fishery Manuscript Series 09-05, Anchorage. Becker, C. D. 1962. Estimating red salmon escapements by sample counts from observation towers. Fishery Bulletin 192: Volume 61. Fish and Wildlife Service, Washington. Belcher, E. O., W. Hanot, and J. Burch. 2002. Dual-frequency identification sonar. Pages 187–192 [In] Proceedings of the 2002 International Symposium on Underwater Technology, April 16–19, Tokyo, Japan. Brett, J. R. 1995. Energetics. Pages 3–68 [In] C. Groot, L. Margolis, and W. C. Clarke, editors. Physiological Ecology of Pacific Salmon. University of British Columbia Press, Vancouver, B.C. Buck, G. B., and C. E. Brazil. 2013. Sonar enumeration of Pacific salmon escapement into the Nushagak River, 2007. Alaska Department of Fish and Game, Fishery Data Series No. 13-20, Anchorage. Eggers, D. M., P. A. Skvorc, and D. L. Burwen. 1995. Abundance estimate for Chinook salmon in the Kenai River using dual-beam sonar. Alaska Department of Fish and Game. Alaska Fishery Research Bulletin 2(1):1–22. El Mejjati, S., J. Bell, J. Botz, A. Faulkner, and S. Maxwell. 2010. Using hydroacoustic methods to enumerate migrating salmon in the Copper River, Miles Lake sonar project 2007. Alaska Department of Fish and Game, Fishery Data Series No. 10-98, Anchorage. Foreword, R. B., and R. A. Tankersley. 2001. Selective tidal-stream transport of marine animals. Pages 305–353 [In] R. N. Gibson, M. Barnes, and R. J. A. Atkinson. Oceanography and marine biology: an annual review. Taylor & Francis, New York. Faulkner, A. V., and S. L. Maxwell. 2009. An aiming protocol for fish-counting sonars using river bottom profiles from a dual-frequency identification sonar (DIDSON). Alaska Department of Fish and Game, Fishery Manuscript No. 09-03, Anchorage. Han, J., N. Honda, and A. Asada. 2009. Automated acoustic method for counting and sizing framed fish during transfer using DIDSON. Fish Science 75:1359–1367. Handegard, N. O., and K. Williams. 2008. Automated tracking of fish in trawls using the DIDSON (dual-frequency identification sonar). ICES Journal of Marine Science 65:636–644. Hinch, S. G., and P. S. Rand. 2000. Optimal swimming speeds and forward-assisted propulsion: energy-conserving behaviours of upriver-migrating adult salmon. Canadian Journal of Fisheries and Aquatic Sciences 57: 2470– 2478. Holmes, J. A., M. W. Cronkite, H. J. Enzenhofer, and T. J. Mulligan. 2006. Accuracy and precision of fish-count data from a dual-frequency identification sonar (DIDSON) imaging system. ICES Journal of Marine Science 63:543–555. Hughes, N. F. 2004. The wave-drag hypothesis: an explanation for size-based lateral segregation during the upstream migration of salmonids. Canadian Journal of Fisheries and Aquatic Sciences 61:103–109. Jones, M., T. Sands, C. Brazil, G. Buck, F. West, P. Salomone, S. Morstad, and T. Krieg. 2014. 2013 Bristol Bay area annual management report. Alaska Department of Fish and Game, Fishery Management Report No. 14-23, Anchorage.

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REFERENCES CITED (Continued) Kang, M. 2011. Semiautomated analysis of data from an imaging sonar for fish counting, sizing, and tracking in a post-processing application. Fisheries and Aquatic Sciences 14(3):218–225. Kohler, T. G., and G. L. Todd. 2003. Salmonid escapements into selected Norton Sound drainages using towers and weirs, 2002. Alaska Department of Fish and Game, Regional Information Report 3A03-18, Anchorage. Levy, D. A., and A. D. Cadenhead. 1995. Selective tidal stream transport of adult sockeye salmon (Oncorhynchus nerka) in the Fraser River Estuary. Canadian Journal of Fisheries and Aquatic Sciences 52(1):1–12. Maxwell, S. L. 2007. Hydroacoustics: rivers. Pages 133–151 [In] D. H. Johnson, B. M. Shrier, J. S. O’Neal, J. A. Knutzen, X. Augerot, T. A. O’Neil, and T. N. Pearsons. Salmonid field protocols handbook: techniques for assessing status and trends in salmon and trout populations. American Fisheries Society, Bethesda, Maryland. Maxwell, S. L., and N. E. Gove. 2007. Assessing a dual-frequency identification sonar’s fish counting accuracy, precision, and turbid river range capability. Journal of the Acoustical Society of America 122(6):3364–3377. Maxwell, S. L., and A.V. Smith. 2007. Generating river bottom profiles with a dual-frequency identification sonar (DIDSON). North American Journal of Fisheries Management 27:1294–1309. Maxwell, S. L., A. V. Faulkner, T. D. Hacklin. 2013. Evaluating error in sockeye salmon abundance estimates from salmon traveling outside the sonar beam at the Yentna, Copper, and Kenai rivers. Alaska Department of Fish and Game, Fishery Manuscript Series No. 13-07, Anchorage. Miller, J. D., D. L. Burwen, and S. J. Fleischman. 2014. Estimates of Chinook salmon passage in the Kenai River using split-beam and dual-frequency identification sonars, 2011. Alaska Department of Fish and Game, Fishery Data Series No. 14-18, Anchorage. Morstad, S., and C. E.. Brazil. 2012. Kvichak River sockeye salmon stock status and action plan, 2012; a report to the Alaska Board of Fisheries. Alaska Department of Fish and Game, Special Publication No. 12-19, Anchorage. NOAA (National Oceanic and Atmospheric Administration). 2013. Biological characterization: An overview of Bristol, Nushagak, and Kvichak bays; Essential fish habitat, processes, and species assemblages. National Marine Fisheries Service, Alaska Region: NOAA. Paulus, R. D. 1965. Test fishing in Bristol Bay, 1960–64. Alaska Department of Fish and Game, Division of Commercial Fisheries, Informational Leaflet 67, Juneau. Reynolds, J. H., C. A. Woody, N. E. Gove, and L. F. Fair. 2007. Efficiently estimating salmon escapement uncertainty using systematically sampled data. American Fisheries Society Symposium 54:121–129. Schwanke, C. J., F. West, L. Fair, and N. Gove. 2003. Evaluation of inriver test fishing projects, Bristol Bay, 2000–2001. Alaska Department of Fish and Game, Regional Information Report 2A03-21, Anchorage. Seibel, M. C. 1967. The use of expanded 10-minute counts as estimates of hourly salmon migration past counting towers on Alaska rivers. Alaska Department of Fish and Game, Division of Commercial Fisheries, Informational Leaflet 101, Juneau. Simmonds, E. J., and D. N. MacLennan. 2005. Fisheries acoustics: theory and practice. Blackwell Science Ltd., Oxford, UK. Smith, E. A., and R. D. Dunbar. 2012. Sonar enumeration of Chinook and fall chum salmon passage in the Yukon River near Eagle, Alaska, 2011. Alaska Department of Fish and Game, Fishery Data Series No. 12-52, Anchorage. Webb, P. W. 1995. Locomotion. Pages 71–99 [In] C. Groot, L. Margolis, and W.C. Clarke, editors. Physiological Ecology of Pacific Salmon. University of British Columbia Press, Vancouver, B.C. West, F. W. 2009. Bristol Bay sockeye salmon inriver test fishing, 2007. Alaska Department of Fish and Game, Fishery Data Series No. 09-19, Anchorage.

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REFERENCES CITED (Continued) West, F., T. T. Baker, S. Morstad, K. Weiland, P. Salomone, T. Sands, and C. Westing. 2012. Abundance, age, sex, and size statistics for Pacific salmon in Bristol Bay, 2005. Alaska Department of Fish and Game, Fishery Data Series No. 12-02, Anchorage. West, F., and C. Brazil. 2013. Operational Plan: Bristol Bay sockeye salmon inriver test fish, 2013. Alaska Department of Fish and Game, Division of Commercial Fisheries, Regional Operational Plan ROP.CF.2A.2013.05, Anchorage. Westerman, D. L., and T. M. Willette. 2013. Upper Cook Inlet salmon escapement studies, 2012. Alaska Department of Fish and Game, Fishery Data Series No. 13-30, Anchorage. Wolter, K. M. 1984. An investigation of some estimators of variance for systematic sampling. Journal of the American Statistical Association 79:781–790. Wolter. K. M. 1985. Introduction to variance estimation. Springer, New York. Woody, C. A. 2007. Tower counts. Pages 363–384 [In] D. H. Johnson, B. M. Shrier, J. S. O’Neal, J. A. Knutzen, X. Augerot, T. A. O’Neil, and T. N. Pearsons. Salmonid field protocols handbook: techniques for assessing status and trends in salmon and trout populations. American Fisheries Society, Bethesda, Maryland. Xie, Y., and F. J. Martens. 2014. An empirical approach for estimating the precision of hydroacoustic fish counts by systematic hourly sampling. North American Journal of Fisheries Management 34(3):535–545.

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TABLES AND FIGURES

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Table 1.–Dual-frequency identification sonar (DIDSON) specifications and settings used at the Kvichak River by year.

Year/ DIDSON Serial Frequency Receiver Range Number Individual Sonar position modela number (MHz) Tilt (°) Frames/s gain (dB) strata (m) beams beam width 2010

Nearshore & Offshore Long range 103 1.2, 0.7 -12 to 15 8, 5 40 1-10, 10-30, 1-20 48 0.7°, 0.8° 2011 Nearshore Long range 103 1.2 13.5 8, 5 30 1-10, 10-30 48 0.7° Offshore Long range 479 1.2, 0.7 -16 8, 3 30 1-10, 10-50 48 0.7°, 0.8° 2012 Nearshore Long range 479 1.2 4 8, 5 20, 30 1-10, 10-30 48 0.7° Offshore Long range 103 1.2 -16 8, 5 30 1-10, 10-30 48 0.7° 2013 Nearshore Long range 479 1.2, 0.7 4 8, 5 30 1-10, 10-30 48 0.7°, 0.8°

26 Offshore Standard 243 1.8, 1.1 -12 8, 5 40 1-10, 10-30 96, 48 0.3°, 0.4° Note: Two range strata were recorded with each sonar in 2011–2013; settings reflect those respectively. a Both DIDSON models have a 29° composite beam made up of individual beams, each 17° in the vertical plane (nominal beam size).

Table 2.–Three sets of parameters (P3, P4, P5) used for automated processing of DIDSON files to estimate fish passage at the Kvichak River. P3 P4 P5 DIDSON stratum template parameters 1-10 m 1-10 m 10-30 m, 10-50 m Sound Metrics, Inc. DIDSON software version 5.25.10 5.25.10 5.25.10 Background subtraction default default default

Convolve Samples 4 4 4

Convolve Beams 4 4 4

Foreground Smoothing off off off

Factor A 0.95 0.95 0.95

Factor C 0.65 0.65 0.65

CSOT yes yes yes

Min Cluster Area 200 200 200

Threshold 4.9 4.9 4.9

Persistence 4 4 4

Prequil on on on Myriax Echoview Software (EV) version 4.90.62 4.90.62 4.90.62 EV Kvichak Template P3.EV P4.EV P5.EV

EV Kvichak Auto Processor 4.0.1. P3 4.0.1. P4 4.0.1. P5

DIDSON (Sv) Sound speed (F8>Calibration>Edit file) 1457 1457 1457 Absorption 0 0 0 TVG range coefficient 0 0 0 Smoothing MinThresh (data) 12 15 8-10a MaxThresh (data) none none none Cluster Detection MBDetect Seed/Satellite Link Target Clusters on on on Seed Thresh 350 350 350 Sat Thresh 5 5 5 Link Distance 0.2 0.2 0.2 Link Sat Clusters on on on Cluster Length > x Property Length Length Length Min 6 6 6 Max none none none Track Source Variable (F8>Data) Length (live) Length (live) Length (live) MinThresh none none none MaxThresh none none none DisHeight (multi-beam target thickness) x 1.0 x 0.01 x 0.5 Tracking Algorithm (View>EV File Properties) Alpha Major 0.5 0.5 0.8 Alpha Minor 0.5 0.5 0.5 Alpha Range 1 1 1 Beta Major 0.2 0.2 0.2 Beta Minor 0.2 0.2 0.2 -continued-

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Table 2.–Page 2 of 2. P3 P4 P5 Tracking Algorithm (View>EV File Properties) Beta Range 0.5 0.5 0.5 ExclDist Major 0.4 0.4 0.4 ExclDist Minor 0.1 0.1 0.1 ExclDist Rangeb 0.2 0.2 0.4 missed ping Exp Major 0 0 0 missed ping Exp Minor 0 0 0 missed ping Exp Range 0 0 0 Tracking Weights Major 1.5 1.5 1.5 Minor 0 0 0 Range 1 1 1 TS 0 0 0 Ping Gap 0 0 0 Track Acceptance MinNrST 7 7 7 MnNrPg 7 7 7 MaxGapc 2 10 10 Export Type spreadsheet spreadsheet spreadsheet a Manually adjusted after viewing echogram. b Exclusion Distance Range was used to accommodate sudden changes in range. c Maximum Gap between single targets was used to reduce triggering on forward scatter or blooming acoustical shadows.

Table 3.–DIDSON deployment information and dates of operation in the lower Kvichak River, 2010– 2013.

Operation Number Initial water Sound speed Year dates of days temp (°C) (m/s) Comments 2010 6/29–7/1 3 10.4 1,449 feasibility work - one sonar used 2011 6/25–7/13 19 10.4 1,449 2 separate mounts were used 2012 6/24–7/12 19 9.5 1,445 single mount for both sonars 2013 6/25–7/12 18 12.4 1,457 single mount for both sonars

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Table 4.–Environmental data from the Kvichak River sonar site at Levelock, 2011.

Water Air temp Cloud Date Hour temp (°C) (°C) covera Comments 6/23 1200 10.4 – – 6/26 1700 11.9 20.1 5 6/27 1700 12.2 15.4 5 6/28 1700 11.4 11.3 6 6/28 1700 11.1 10.8 5 6/29 1700 11.7 14.3 5 6/30 1700 12.8 14.1 3 7/1 1700 12.7 15.1 4 7/2 1700 15.3 16.4 5 big increase in water temp

there was a big windstorm late last night and early this morning that also produced very 7/3 1700 13.4 17.9 4 heavy rains. 7/4 1800 13.1 17.2 4 7/5 1800 13.7 18.1 3 winds have been steady the last few days, 7/6 1700 12.8 14.0 5 keeping the river choppy 7/7 1700 11.4 11.0 6 7/8 1700 12.2 16.4 5 7/9 1700 10.9 13.7 6 7/10 1700 11.3 14.2 6 7/11 1700 12.2 16.4 6 7/12 1700 12.5 15.5 4 windy 7/13 1700 12.8 16.0 4 Average – 12.3 15.2 – SD – 1.1 2.4 – a Cloud cover codes: 1 = sunny, bright 2 = sunny, hazy 3 = partly cloudy (<50%) 4 = mostly cloudy (>50%) 5 = overcast 6 = rain

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Table 5.–Environmental data from the Kvichak River sonar site at Levelock, 2012. High Low water water Air temp temp temp Cloud Date Hour (°C) (°C) (°C) covera Comments 6/23 – – 13.2 – – 6/24 – – – – 5 windy & cold 6/25 – – – – – 6/26 1400 – – 12.3 4 slight breeze 6/27 – – – – – 6/27 0900 9.1 11.0 11.0 5 some fog and calm; low tide temp at 1700 6/28 0900 9.4 9.5 7.9 5 light wind; high tide temp at 1100 6/29 0900 10.1 9.3 7.0 5 light wind and fog; high tide temp at 1200 6/30 0900 10.3 10.5 9.4 6 no wind and light rain; high tide temp at 1300 7/1 0910 10.1 10.5 10.1 5 slight breeze; high tide temp at 1300 7/2 0900 10.3 8.9 7.5 6 slight breeze, light rain; high tide temp at 1400 7/3 0900 9.8 10.0 10.3 5 light wind; high tide temp at 1645 7/4 1000 8.9 8.4 7.9 6 cold; low tide temp at 1300 7/5 0950 8.4 10.1 12.0 4 strong wind; low tide temp at 1330 7/6 0910 9.1 12.0 11.5 3 slight breeze; low tide temp at 1300 7/7 0945 11.4 11.8 17.6 3 calm and warm; low tide temp at 1230 7/8 0915 11.5 11.8 10.3 6 slight breeze; low tide temp at 1845 7/9 0900 10.7 11.2 9.0 6 low tide temp at 1600 7/10 1000 9.9 10.2 8.2 6 high tide temp at 1900 7/11 0945 10.5 10.1 9.1 5 very strong wind; low tide temp at 1700 7/12 1000 10.4 11.0 10.8 5 calm; high tide temp at 1230 Average – 10.0 10.6 10.1 – SD – 0.9 1.2 2.5 – a Cloud cover codes: 1 = sunny, bright 2 = sunny, hazy 3 = partly cloudy (<50%) 4 = mostly cloudy (>50%) 5 = overcast 6 = rain

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Table 6.–Environmental data from the Kvichak River sonar site at Levelock, 2013.

Air temp Date Hour (°C) Cloud covera Comments 6/25 1400 22.0 3 6/26 1309 30.7 3 6/27 1053 12.2 5 6/27 1313 13.7 4 6/28 1610 26.4 4 6/29 0951 14.0 4 6/30 0956 11.1 6 windy 7/1 0956 10.1 6 7/2 1032 9.0 6 7/3 0956 10.7 6 light rain 7/4 1609 15.4 3 7/5 1146 12.8 3 7/6 1037 11.4 4 7/7 1117 11.1 5 7/8 1019 11.6 4 7/9 1026 11.1 4 7/10 1119 12.4 5 7/11 1026 14.6 1 7/12 0958 18.6 1 Average – 14.7 – SD – 5.8 – a Cloud cover codes: 1 = sunny, bright 2 = sunny, hazy 3 = partly cloudy (<50%) 4 = mostly cloudy (>50%) 5 = overcast 6 = rain

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Table 7.–Daily sockeye salmon indices from test fishery and sonar projects and abundance estimates from the tower project, Kvichak River 2011–2013.

2011 2012 2013 Date Test fish Sonar Tower Test fish Sonar Tower Test fish Sonar Tower 6/20 – – 0 – – 54 16 – – 6/21 – – 0 – – 138 299 – 894 6/22 – – 150 0 – 132 967 – 60 6/23 – – 24 0 – 180 1,573 – 11,628 6/24 – – 30 15 655 24 520 – 55,410 6/25 – 38 0 575 12,744 78 205 4,745 54,684 6/26 – 162 18 388 8,142 4,836 1,047 -273 24,042 6/27 996 25,584 60 25 666 31,968 1,106 26,031 44,574 6/28 3,059 102,705 24,990 1,068 5,238 28,254 1,444 27,645 71,028 6/29 2,453 51,708 183,318 4,202 134,814 19,476 1,412 38,844 132,828 6/30 542 17,208 237,216 3,045 111,542 168,144 2,812 74,316 142,530 7/1 1,462 35,441 175,224 1,935 57,038 213,024 2,831 116,359 169,140 7/2 2,149 54,134 164,916 605 13,013 193,974 2,074 49,200 304,596 7/3 1,543 14,099 219,726 867 67,578 89,244 1,291 38,604 318,012 7/4 622 22,050 215,136 8,695 236,488 101,022 888 18,720 154,824 7/5 485 19,530 213,144 5,483 143,778 413,994 732 15,318 86,376 7/6 768 3,743 146,418 3,560 106,960 560,958 503 11,415 22,992 7/7 324 9,290 67,788 3,138 68,909 672,996 382 7,410 22,500 7/8 199 4,833 46,344 2,333 67,278 426,888 2,317 53,450 14,682 7/9 752 30,184 16,260 1,544 102,324 282,270 1,397 34,252 78,444 7/10 242 4,635 37,572 1,177 54,338 308,766 306 7,624 232,362 7/11 690 19,843 88,410 285 27,217 126,156 24 947 117,126 7/12 598 27,270 45,966 151 7,819 102,726 0 497 23,538 7/13 1,267 13,650 88,950 354 – 49,308 – – 3,306 7/14 1,198 – 75,312 1,263 – 29,424 – – 2,100 7/15 914 – 80,700 – – 115,530 – – 900 7/16 – – 76,680 – – 92,400 – – – 7/17 – – 43,104 – – 54,762 – – – 7/18 – – 8,544 – – 17,172 – – – 7/19 – – 3,390 – – 5,670 – – – 7/20 – – 4,962 – – 2,700 – – – Total 20,263 456,107 2,264,352 40,706 1,226,542 4,112,268 24,145 525,103 2,088,576 Note: Negative estimates occur when a larger number of fish move downstream than upstream.

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Table 8.–Sockeye salmon estimates by zone from nearshore and offshore DIDSON deployed on the right bank of the Kvichak River, 2011.

Nearshore sonar Offshore sonar Date Zone 1 a Zone 2 Zone 3 Zone 4 Total 6/25 -6 32 12 0 38 6/26 0 48 114 0 162 6/27 72 20,556 4,770 186 25,584 6/28 8,736 88,428 5,134 407 102,705 6/29 12,660 34,728 4,224 96 51,708 6/30 54 14,346 2,562 246 17,208 7/1 30 30,684 3,580 1,147 35,441 7/2 3,156 41,244 8,888 846 54,134 7/3 -660 9,303 5,126 329 14,099 7/4 -1,440 17,424 4,926 1,140 22,050 7/5 78 16,019 2,967 465 19,530 7/6 18 1,529 1,872 324 3,743 7/7 868 7,515 816 91 9,290 7/8 -111 3,888 816 240 4,833 7/9 -96 25,594 4,392 294 30,184 7/10 0 2,721 1,871 43 4,635 7/11 384 12,139 6,420 900 19,843 7/12 2,220 18,570 5,718 762 27,270 7/13 -624 8,418 4,152 1,704 13,650 Total 25,339 353,188 68,360 9,220 456,107 Percent 5.6 77.4 15.0 2.0 100.0 Note: Negative estimates occur when a larger number of fish move downstream than upstream. a Zone 1 is closest to shore and Zone 4 the most distant.

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Table 9.–Sockeye salmon estimates by zone from nearshore and offshore DIDSON deployed on the right bank of the Kvichak River, 2012.

Nearshore sonar Offshore sonar Date Zone 1 a Zone 2 Zone 3 Zone 4 Total 6/24 -15 238 384 49 656 6/25 2,595 8,421 1,584 144 12,744 6/26 1,872 3,726 2,100 444 8,142 6/27 18 102 312 234 666 6/28 204 2,154 2,826 54 5,238 6/29 24,072 106,734 3,792 216 134,814 6/30 21,048 89,688 570 236 111,542 7/1 12,632 41,789 2,618 0 57,039 7/2 2,008 8,014 2,893 99 13,014 7/3 14,376 49,176 3,678 348 67,578 7/4 43,509 167,760 24,619 600 236,488 7/5 19,260 113,964 10,104 450 143,778 7/6 9,166 77,688 19,812 294 106,960 7/7 -1,973 57,312 13,408 161 68,908 7/8 144 54,972 11,892 270 67,278 7/9 3,816 64,026 34,218 264 102,324 7/10 1,212 23,168 29,172 786 54,338 7/11 -420 6,486 19,393 1,758 27,217 7/12 463 3,640 3,285 431 7,819 Total 153,986 879,058 186,660 6,838 1,226,542 Percent 12.6 71.7 15.2 0.6 100.0 Note: Negative estimates occur when a larger number of fish move downstream than upstream. a Zone 1 is closest to shore and Zone 4 the most distant.

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Table 10.–Sockeye salmon estimates by zone from nearshore and offshore DIDSON deployed on the right bank of the Kvichak River, 2013.

Nearshore sonar Offshore sonar Date Zone 1 a Zone 2 Zone 3 Zone 4 Total 6/25 1,139 1,702 1,864 41 4,745 6/26 24 1,860 -2,172 15 -273 6/27 5,286 14,700 5,748 297 26,031 6/28 29 16,722 10,719 174 27,645 6/29 -5,184 25,494 18,234 300 38,844 6/30 14,970 34,284 24,834 228 74,316 7/1 32,220 46,717 37,050 372 116,359 7/2 5,490 18,402 23,784 1,524 49,200 7/3 -174 11,364 25,956 1,458 38,604 7/4 1,920 3,918 12,414 468 18,720 7/5 726 2,808 10,716 1,068 15,318 7/6 2,370 4,068 3,825 1,152 11,415 7/7 588 1,500 4,836 486 7,410 7/8 2,592 21,150 28,150 1,558 53,450 7/9 2,352 7,968 22,229 1,702 34,252 7/10 702 1,578 4,602 742 7,624 7/11 36 90 716 105 947 7/12 -6 31 427 45 497 Total 65,080 214,355 233,932 11,735 525,103 % 12.4 40.8 44.5 2.2 100.0 Note: Negative estimates occur when a larger number of fish move downstream than upstream. a Zone 1 is closest to shore and Zone 4 the most distant.

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Table 11.–Comparison of sonar and test fishery salmon passage indices to abundance estimates from the tower project, Kvichak River, 2011–2013.

2011 2012 2013 All years

Sonara vs. test fishb r 0.89 0.95 0.92 0.94 t-statistic 7.87 12.20 9.16 20.05 p-value <0.05 <0.05 <0.05 <0.05 n 19 19 18 56

Towerc vs. sonard r 0.73 0.79 0.91 0.82 t-statistic 4.39 5.31 9.03 10.64 p-value <0.05 <0.05 <0.05 <0.05 n 19 19 18 56

Towerc vs. test fishd r 0.81 0.82 0.94 0.85 t-statistic 5.73 5.85 11.30 11.66 p-value <0.05 <0.05 <0.05 <0.05 n 19 19 18 56

Tower vs. sonar compared with tower vs. test fish correlations (r) Z-statistic -0.58 -0.22 -0.57 -0.39 p-value 0.56 0.83 0.57 0.70

% Sonar vs. tower 21.4 32.1 27.0 28.0 % Sonar vs. right bank tower 54.0 81.9 48.2 64.4 % Fish passage along right bank tower 39.4 37.6 55.0 43.5 Right bank tower vs. sonar (r) 0.52 0.73 0.77 0.72

Tower vs. sonar compared with right bank tower vs. sonar (r) Z-statistic 1.00 0.40 1.39 1.28 p-value 0.32 0.69 0.16 0.20

Test fish catch 2,791 3,636 3,622 10,049 Test fish total fishing time (h) 19.8 15.6 18.1 53.5 Average test fish CPUE right bank 423 910 515 616 Average test fish CPUE left bank 421 860 535 605 % Test fish CPUE right bank 50.1 51.4 49.0 51.3 a A nearshore region along the right bank was ensonified. No sonar data were collected along the left bank. b Catch per unit effort (CPUE) from combined right- and left-bank fish captures. c Total fish passage at the tower that occurred when the sonar was operational. d 2-day travel time lag applied.

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Table 12.–Selected DIDSON data files from the Kvichak River in 2010 used to test manual, full-auto, and semi-auto fish counting methods. #1 manual count #2 manual count full-auto count semi-auto count Data Sonar Range Tide up- down- up- down- up- down- up- down- file length group direction window cycle Date Hour stream stream total stream stream total stream stream total stream stream total min s T1 offshore 1-10 ebb 6/29 2220 953 4 957 1015 4 1019 904 4 908 901 4 905 9 55 6/29 2240 825 4 829 939 3 942 790 7 797 770 6 776 9 55 6/29 2320 535 4 539 561 4 565 583 10 593 570 6 576 9 55 T2 offshore 10- 30 ebb 6/29 2230 48 19 67 49 16 65 87 12 99 68 10 78 9 55 6/29 2250 43 17 60 47 15 62 60 8 68 56 8 64 9 55 6/29 2330 31 13 44 32 12 44 51 28 79 39 13 52 9 55 T3 nearshore 1-10 ebb/ 6/30 1540 152 0 152 153 0 153 157 5 162 157 5 162 10 0 low 6/30 1550 119 0 119 118 0 118 126 1 127 126 1 127 10 0

6/30 1600 172 0 172 172 0 172 181 0 181 181 0 181 10 0

6/30 1610 67 1 68 67 0 67 71 2 73 71 2 73 3 51

T4 offshore 1-20 full 6/30 1840 15 0 15 16 0 16 15 0 15 15 0 15 10 0 cycle 6/30 1850 1 0 1 0 0 0 1 0 1 1 0 1 10 0

37 6/30 1900 2 0 2 1 0 1 1 0 1 1 0 1 10 0 6/30 1910 0 0 0 0 0 0 0 0 0 0 0 0 2 59 6/30 2054 0 1 1 1 1 2 0 1 1 0 1 1 5 51 6/30 2100 0 3 3 0 3 3 0 1 1 0 1 1 10 0 6/30 2110 0 1 1 1 0 1 0 2 2 0 2 2 10 0 6/30 2120 0 0 0 0 0 0 0 4 4 0 4 4 10 0 6/30 2130 0 0 0 0 0 0 0 4 4 0 4 4 10 0 6/30 2140 3 2 5 4 2 6 3 2 5 3 2 5 10 0 6/30 2150 2 0 2 2 0 2 2 3 5 2 3 5 10 0 6/30 2200 3 0 3 4 0 4 2 1 3 2 1 3 10 0 6/30 2210 3 1 4 3 1 4 3 1 4 3 1 4 10 0 6/30 2220 8 0 8 8 0 8 9 1 10 8 1 9 10 0 6/30 2230 25 0 25 23 1 24 26 2 28 25 2 27 10 0 6/30 2240 17 1 18 15 0 15 18 0 18 18 0 18 10 0 6/30 2250 31 5 36 30 5 35 37 9 46 32 8 40 10 0 -continued-

Table 12.–Page 2 of 3. #1 manual count #2 manual count full-auto count semi-auto count Data Sonar Range Tide up- down- up- down- up- down- up- down- file length group direction window cycle Date Hour stream stream total stream stream total stream stream total stream stream total min s T4 offshore 1-20 full 6/30 2300 77 1 78 80 1 81 84 5 89 83 5 88 10 0 cycle 6/30 2310 87 2 89 90 1 91 90 1 91 90 1 91 10 0 6/30 2320 70 0 70 70 0 70 77 5 82 75 5 80 10 0 6/30 2330 73 3 76 74 0 74 81 6 87 81 6 87 10 0 6/30 2340 74 1 75 74 0 74 84 4 88 82 4 86 10 0 6/30 2350 73 1 74 74 0 74 74 6 80 74 6 80 10 0 7/1 0000 42 0 42 41 0 41 43 7 50 43 6 49 10 0 7/1 0010 42 2 44 46 1 47 40 7 47 39 7 46 10 0 7/1 0020 59 0 59 57 0 57 54 5 59 54 5 59 10 0 7/1 0030 34 0 34 34 0 34 33 3 36 33 3 36 10 0 7/1 0040 40 0 40 41 0 41 44 8 52 42 7 49 10 0 7/1 0050 45 0 45 44 0 44 46 9 55 46 9 55 10 0

38 7/1 0100 64 1 65 62 0 62 64 7 71 62 7 69 10 0 7/1 0110 61 1 62 66 0 66 62 8 70 60 8 68 10 0 7/1 0120 60 0 60 60 0 60 63 12 75 63 12 75 10 0 7/1 0130 64 1 65 64 1 65 69 11 80 69 11 80 10 0 7/1 0140 37 0 37 35 0 35 39 11 50 38 11 49 10 0 7/1 0150 51 1 52 52 1 53 51 12 63 51 11 62 10 0 7/1 0200 81 0 81 82 0 82 88 7 95 87 7 94 10 0 7/1 0210 68 0 68 67 0 67 72 9 81 72 9 81 10 0 7/1 0220 55 0 55 55 0 55 57 12 69 56 12 68 10 0 7/1 0230 65 0 65 65 0 65 70 12 82 69 12 81 10 0 7/1 0240 90 0 90 93 0 93 97 13 110 96 13 109 10 0 7/1 0250 64 0 64 67 0 67 66 12 78 66 12 78 10 0 7/1 0300 39 0 39 42 0 42 37 6 43 37 6 43 10 0 7/1 0310 50 0 50 49 0 49 46 13 59 46 13 59 10 0 7/1 0320 60 0 60 58 0 58 63 12 75 63 12 75 10 0 7/1 0330 33 0 33 33 0 33 35 14 49 33 14 47 10 0 -continued-

Table 12.–Page 3 of 3. #1 manual count #2 manual count full-auto count semi-auto count file Data Sonar Range Tide up- down- up- down- up- down- up- down- length group direction window cycle Date Hour stream stream total stream stream total stream stream total stream stream total min s T4 offshore 1-20 full 7/1 0340 36 0 36 35 0 35 36 13 49 35 13 48 10 0 cycle 7/1 0350 31 0 31 30 0 30 31 11 42 31 11 42 10 0 7/1 0400 38 0 38 40 0 40 39 18 57 39 15 54 10 0 7/1 0410 34 0 34 32 0 32 34 7 41 34 6 40 10 0 7/1 0420 37 0 37 37 0 37 36 6 42 36 6 42 10 0 7/1 0430 31 1 32 29 0 29 30 12 42 30 12 42 10 0 7/1 0440 40 1 41 37 0 37 38 14 52 38 14 52 10 0 7/1 0450 44 0 44 45 0 45 43 16 59 43 16 59 10 0 7/1 0500 35 0 35 34 0 34 36 11 47 35 10 45 10 0 7/1 0510 35 0 35 38 0 38 38 9 47 38 9 47 10 0 7/1 0520 34 0 34 34 0 34 42 8 50 30 7 37 10 0

39 7/1 0530 56 0 56 56 0 56 57 14 71 57 13 70 10 0 7/1 0540 46 0 46 43 0 43 44 8 52 43 8 51 10 0 7/1 0550 37 1 38 37 1 38 36 14 50 36 13 49 10 0 7/1 0600 61 0 61 63 0 63 61 7 68 57 4 61 10 0 7/1 0610 34 0 34 36 0 36 27 1 28 27 1 28 10 0 7/1 0620 4 0 4 4 0 4 7 1 8 7 1 8 10 0 7/1 0630 4 0 4 4 0 4 2 3 5 2 3 5 10 0 7/1 0640 0 0 0 0 0 0 0 0 0 0 0 0 10 0 7/1 0650 0 0 0 0 0 0 1 0 1 1 0 1 10 0 7/1 0700 1 0 1 2 0 2 0 1 1 0 1 1 10 0 7/1 0710 0 0 0 0 0 0 1 0 1 1 0 1 10 0 7/1 0720 2 0 2 2 0 2 0 1 1 0 1 1 10 0 7/1 0730 0 0 0 0 0 0 0 0 0 0 0 0 10 0 7/1 0740 0 0 0 0 0 0 0 0 0 0 0 0 10 0 7/1 0750 0 0 0 0 0 0 1 0 1 1 0 1 10 0 7/1 0800 0 0 0 0 0 0 0 0 0 0 0 0 10 0 7/1 0810 0 0 0 0 0 0 2 0 2 2 0 2 10 0 Total 5353 93 5446 5574 73 5647 5498 520 6018 5382 483 5865 804 491 Note: All DIDSON files were recorded using the 1.2 MHz frequency setting.

Table 13.–Comparison of manual, full-auto, and semi-auto counts from 2 observers (Obs) using the 2010 DIDSON dataset, Kvichak River. Obs #1 95% max Obs #1 Regression confidence p value fish /10 total Fish count comparison equation R² interval (slope) (slope) n a min fish

Fish passage >100 fish/10 min file Manual Obs #1 vs. #2 y=1.09x-2.74 1.00 1.08 - 1.10 <0.01 83 949 5,260 Full-auto vs. manual Obs #1 y=0.97x-1.67 0.99 0.96 - 0.99 <0.01 83 - - Semi-auto vs. manual Obs #1 y=0.96x-1.88 0.99 0.95 - 0.98 <0.01 83 - -

Fish passage <100 fish/10-min file Manual Obs #1 vs. #2 y=1.02x-0.00 1.00 1.00 - 1.03 <0.01 77 90 2,516 Full-auto vs. manual Obs #1 y=0.93x-1.10 0.90 0.86 - 1.01 <0.01 77 - - Semi-auto vs. manual Obs #1 y=0.93x-1.50 0.92 0.87 - 0.99 <0.01 77 - - a n equals the number of 10 min files processed.

Table 14.–Comparison of manual (x) and semi-automated (y) fish counts for the nearshore sonar (Zones 1–2), offshore sonar (Zones 3–4), and all zones combined, Kvichak River 2011–2013.

Fish count Regression 95% confidence p value Max fish comparison equation R² interval (slope) (slope) n a /10 min Total fishb

2011 Zones 1-2 y=0.90x-1.80 0.96 0.88 - 0.92 <0.01 342 2,660 53,036 Zones 3-4 y=0.93x-0.85 0.98 0.92 - 0.94 <0.01 373 401 10,676 All zones y=0.90x+1.67 0.94 0.88 - 0.93 <0.01 288 1,690 41,542

2012 Zones 1-2 y=0.56x +55.00 0.78 0.53 - 0.59 <0.01 416 3,152 170,136 Zones 3-4 y=0.67x+6.72 0.93 0.65 - 0.69 <0.01 423 1,722 30,419 All zones y=0.58x+64.10 0.80 0.55 - 0.60 <0.01 375 3,468 185,699

2013 Zones 1-2 y=0.66x+37.05 0.75 0.63 - 0.70 <0.01 409 3,757 46,403 Zones 3-4 y=0.62x+6.88 0.84 0.60 - 0.65 <0.01 377 1,225 40,494 All zones y=0.64x+46.92 0.76 0.60 - 0.68 <0.01 372 3,760 84,943 a n equals the number of 10 min files processed. When combining all zones, only hours with both nearshore and offshore datasets were used. b Interpolated hourly data not used.

40

Table 15.–Comparison of manual (x) and semi-automated (y) fish counts by zone, Kvichak River 2011–2013.

Max Fish count Regression 95% confidence p value fish /10 Total % fish/ comparison equation R² intervals (slope) (slope) n a min fishb zone c

2011 Zone 1 y=0.66x-2.70 0.60 0.60 - 0.72 <0.01 335 1,148 4,223 5.6 Zone 2 y=0.87x-1.73 0.97 0.86 - 0.89 <0.01 335 1,902 52,401 77.4 Zone 3 y=0.93x-0.07 0.96 0.91 - 0.95 <0.01 380 385 9,783 15.0 Zone 4d y=0.69x-0.33 0.13 0.51 - 0.87 <0.01 380 83 1,325 2.0 average e 0.84

2012 Zone 1 y=0.58x +19.50 0.64 0.54 - 0.63 <0.01 402 1,746 24,999 12.6 Zone 2 y=0.58x+31.55 0.83 0.55 - 0.60 <0.01 402 3,152 145,136 71.7 Zone 3 y=0.67x+7.08 0.93 0.65 - 0.69 <0.01 412 1,719 29,104 15.2 Zone 4 y=0.58x+0.04 0.85 0.55 - 0.60 <0.01 412 64 1,116 0.6 average 0.81

2013 Zone 1 y=0.64x+17.04 0.71 0.60 - 0.68 <0.01 411 3,650 10,900 12.4 Zone 2 y=0.70x+17.07 0.72 0.66 - 0.74 <0.01 411 2,059 35,500 40.8 Zone 3 y=0.62x+7.39 0.83 0.59 - 0.65 <0.01 376 1,221 38,242 44.5 Zone 4 y=0.66x-0.46 0.78 0.62 - 0.70 <0.01 376 106 1,917 2.2 average 0.76 a n equals the number of 10 min files processed. b Interpolated hourly data not used. c Percent fish includes interpolated data from total estimated fish passage. d Sonar sampling range was 10–50 m in 2011; changed to 10–30 m in 2012 and 2013. e Zone 4 was excluded from the average.

41

Table 16.–The number of tracked fish that were exported as invalid tracks with no data in the automated program, Kvichak River 2011–2013.

Year / Stratum Total fish tracks Invalid fish tracks % Invalid fish tracks 2011 Nearshore sonar 53,646 3,522 6.6 Offshore sonar 10,155 205 2.0 2012 Nearshore sonar 157,729 18,108 11.5 Offshore sonar 24,857 954 3.8 2013 Nearshore sonar 82,806 3,226 3.9 Offshore sonar 29,205 381 1.3

42

Table 17.–Data processing times for the semi-automated and manual methods used to estimate fish passage at the Kvichak River, 2013.

Semi-automated process time (min) Manual process time (min) Total data Manual time Auto 10 min Date Zone 1 Zone 2 Zone 3 Zone 4 Total Zone 1 Zone 2 Zone 3 Zone 4 Total processed (h) difference (min) fish estimate 6/25 45 120 30 30 225 127 127 73 50 377 19 152 537 6/26 45 60 75 30 210 77 77 40 29 223 22 13 778 6/27 60 150 75 50 335 150 112 82 44 388 24 53 6,364 6/28 60 105 105 30 300 169 151 102 44 467 24 167 6,369 6/29 30 105 60 30 225 166 145 119 50 480 24 255 9,616 6/30 70 105 95 30 300 208 168 159 50 585 24 285 12,871 7/1 75 90 95 30 290 222 153 160 48 582 24 292 13,984 7/2 70 110 105 45 330 141 117 126 51 435 24 105 10,861 7/3 55 105 105 45 310 111 102 139 52 404 24 94 6,772 7/4 55 145 60 45 305 86 84 106 50 326 24 21 2,635 7/5 120 105 85 45 355 83 89 96 51 319 24 -36 2,763

43 7/6 60 100 85 35 280 70 68 64 40 242 24 -38 1,993 7/7 60 95 85 35 275 64 64 64 37 229 24 -46 1,276 7/8 45 85 85 35 250 65 93 93 35 287 24 37 6,934 7/9 55 75 80 35 245 71 80 77 37 265 24 20 5,783 7/10 30 60 40 35 165 64 64 59 37 223 24 58 1,315 7/11 30 60 50 35 175 64 64 56 35 219 24 44 180 7/12 50 35 35 35 155 45 45 35 22 147 17 -8 43 Total 1,015 1,710 1,350 655 4,730 1,983 1,802 1,651 759 6,195 418 1,465 91,074

Table 18.–Precision of manual and semi-automated fish counts made by 2 observers, Kvichak River 2011. 95% confidence Observer #2 Fish count Regression intervals p value max fish /10 Observer #2 comparison equation R² (slope) (slope) n a min total fish

Manual count Nearshore sonar y =1.01x+8.68 0.98 0.99–1.02 <0.01 343 2,660 53,131 Offshore sonar y=1.03x+0.34 0.99 1.02–1.04 <0.01 386 401 10,912

Automated count Nearshore sonar y =0.95x+9.88 0.97 0.93– 0.97 <0.01 342 2,614 47,223 Offshore sonar y=0.99x+0.72 0.99 0.98–1.00 <0.01 373 355 9,617 a n equals the number of 10 min files processed.

44

Table 19.–Range distributions of fish from the designated zero point (i.e., where the water encountered the bluff) from DIDSON images processed using the semi-automated method, Kvichak River 2011–2013. The 0 frequency at 25–26 m is the 2 m dead zone between the 2 opposite-facing DIDSON. 2011 2012 2013 Range % fish Cum % fish Cum % fish Cum 0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.1 0.0 0.0 3 0.0 0.0 0.2 0.2 0.0 0.0 4 0.0 0.0 0.3 0.5 0.3 0.3 5 0.0 0.1 0.7 1.3 1.0 1.3 6 0.1 0.2 1.5 2.8 2.0 3.3 7 0.2 0.4 2.0 4.8 2.3 5.6 8 0.2 0.7 2.1 6.9 2.4 8.0 9 0.3 1.0 2.0 8.9 2.5 10.6 10 0.3 1.3 1.9 10.8 2.7 13.3 11 0.3 1.6 2.3 13.1 3.1 16.4 12 0.5 2.1 2.8 15.9 4.0 20.4 13 1.0 3.1 3.2 19.1 3.8 24.2 14 1.1 4.2 3.3 22.4 4.1 28.2 15 2.8 6.9 5.3 27.7 6.0 34.3 16 1.7 8.6 3.8 31.5 3.4 37.7 17 2.0 10.7 3.7 35.2 3.3 41.0 18 3.6 14.3 4.8 40.0 3.2 44.2 19 4.7 19.0 5.6 45.6 4.1 48.4 20 5.2 24.2 6.7 52.3 3.6 52.0 21 14.4 38.6 6.5 58.8 4.5 56.5 22 18.2 56.8 9.0 67.8 4.3 60.8 23 20.1 76.8 12.4 80.1 5.6 66.3 24 6.3 83.2 5.4 85.5 2.4 68.8 25 0.0 83.2 0.1 85.6 0.1 68.9 26 0.0 83.2 0.1 85.7 0.2 69.1 27 3.5 86.7 4.3 90.0 14.4 83.5 28 5.1 91.8 5.5 95.4 5.6 89.1 29 2.3 94.1 1.8 97.2 3.6 92.7 30 1.2 95.3 0.9 98.1 2.5 95.2 31 0.9 96.1 0.4 98.5 1.4 96.6 32 0.9 97.1 0.3 98.8 0.8 97.4 33 0.6 97.7 0.3 99.1 0.5 97.9 34 0.6 98.2 0.2 99.3 0.3 98.2 35 0.4 98.6 0.2 99.5 0.3 98.5 36 0.7 99.3 0.2 99.7 0.5 99.0 37 0.3 99.6 0.1 99.8 0.3 99.2 38 0.2 99.8 0.1 99.8 0.2 99.4 -continued-

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Table 19.–Page 2 of 2.

2011 2012 2013 Range % fish Cum % fish Cum % fish Cum 39 0.1 99.9 0.1 99.9 0.2 99.6 40 0.1 99.9 0.1 99.9 0.1 99.7 41 0.0 100.0 0.0 100.0 0.1 99.8 42 0.0 100.0 0.0 100.0 0.1 99.9 43 0.0 100.0 0.0 100.0 0.0 99.9 44 0.0 100.0 0.0 100.0 0.0 100.0 45 0.0 100.0 0.0 100.0 0.0 100.0 46 0.0 100.0 0.0 100.0 0.0 100.0 47 0.0 100.0 0.0 100.0 0.0 100.0 48 0.0 100.0 0.0 100.0 0.0 100.0 49 0.0 100.0 0.0 100.0 0.0 100.0 50 0.0 100.0 0.0 100.0 0.0 100.0 51 0.0 100.0 0.0 100.0 0.0 100.0 52 0.0 100.0 0.0 100.0 0.0 100.0 53 0.0 100.0 0.0 100.0 0.0 100.0 54 0.0 100.0 0.0 100.0 0.0 100.0 55 0.0 100.0 0.0 100.0 0.0 100.0 Note: The 2011 sonar range extended an additional 20 m but was excluded because it contained no data.

46

Table 20.–Upstream and downstream fish passage by tide cycle in the lower Kvichak River, 2011– 2013.

Number Upstream Downstream % Downstream Year / Tide cycle of hours Total fish Fish/h fish fish fish 2011 Ebb 331 396,690 1,198 396,999 309 0.1 Flood 125 59,416 475 70,680 11,264 13.7 %Flood 27.4 13.0 – 15.1 97.3 – 2012 Ebb 323 1,211,988 3,752 1,212,187 199 0.0 Flood 133 14,398 108 103,509 89,111 46.3 %Flood 29.2 1.2 – 7.9 99.8 – 2013 Ebb 314 623,442 1,985 624,278 836 0.1 Flood 118 –98,341 –833 15,033 113,374 88.3 %Flood 27.3 –18.7 – 2.4 99.3 – Total Ebb 968 2,232,120 2,306 2,233,464 1,344 0.1 Flood 376 –24,527 –65 189,222 213,749 53.0 % Flood 28.0 –1.1 – 7.8 99.4 – Note: The tide level across the study period ranged from -0.3 to 3.5 m.

Table 21.–Side-scan sonar sampling schedule and corresponding DIDSON fish passage estimates, Kvichak River 2011–2012.

Date Time Tide cycle Tide stage (m) Fish estimates 6/26/11 1500–1700 ebb 2.0–2.2 0 6/29/11 1900–2100 ebb 0.9–1.5 3,672 7/5/11 1600–1800 ebb 0.2–0.6 1,599 7/8/11 1800–2000 ebb 0.0–0.5 1,248 7/13/11 1900–2100 ebb 1.3–1.7 2,100 6/26/12 1600–1800 ebb 0.8–1.2 426 6/29/12 1700–1900 ebb 0.7–1.6 12,318 7/3/12 1200–1400 ebb 0.4–0.8 5,454 7/7/12 1500 ebb 0.9 2,892 7/10/12 1700–1900 ebb 0.5–0.9 6,576

47

Figure 1.–Major river systems, commercial salmon fishing districts, and escapement projects in Bristol Bay, Alaska.

48

Figure 2.–Map of the Kvichak River watershed in Bristol Bay, Alaska. Note: The test fishery and sonar projects are located at Levelock.

49

Figure 3.–Alaska Department of Fish and Game sonar and test fishery assessment sites in the Kvichak River near Levelock, Alaska. Note: The square markers indicate sites examined for potential sonar deployment. The black lines along the river shoreline represent the area the test fishery nets sample along each river bank.

50

Figure 4.–The side-scan sonar was used to survey the river bottom to search for a deployment site for a shore-based sonar and to quickly survey broad reaches of the river to obtain an idea of the cross-river distribution of salmon. Note: The site assessment took place in the lower Kvichak River near Levelock.

51

Figure 5.–Side-scan sonar image of the left bank test fishery site (top) in the Kvichak River. Note: The river bottom consisted of waves of sand and silt. Drag marks caused by gillnet lead lines can be seen on the left. The right-bank site selected for the DIDSON deployment (bottom) had a smooth sand and gravel bottom with a secondary slope change offshore where the sonar was deployed.

52

Figure 6.–The river-bottom profiles created using DIDSON at the potential sonar sites downriver of the left bank test fishery zone (top), upriver of the right bank test fishery zone (middle), and at Levelock on the right bank (bottom), where measurements were also taken manually.

53

Figure 7.–Dual-frequency identification sonar (DIDSON) shown with the multiple beams positioned horizontally (left) to ensonify migrating fish, and vertically (right) to profile the river bottom.

54

Figure 8.–The river-bottom profile at the Levelock site with the DIDSON beams overlaid. Note: Two sonars were deployed at the slope change, one pointed toward shore tilted 4° above level (left) and one pointed away from shore tilted -12° (right). The vertical line within each beam represents the 10 m range division (from the sonar) between sampling strata (zones). The tide stages shown are the maximum and minimum levels that occurred during project operations.

55

Figure 9.–The topside operations along the right bank of the Kvichak River in the village of Levelock consisted of a weatherport to house the sonar electronics and controller computers.

56

Figure 10.–Deploying the DIDSON at low tide (top left). Note: At high tide, the river rose to the bottom of the bluff, submerging Zones 1 and 2. The top right photo shows the position of the sonar mounts marked by buoys and the weatherport that stored the topside equipment. The 2 sonars sampled 4 range strata, or zones, with Zone 1 closest to shore. The bottom diagram (not to scale) shows a cross section of the Kvichak River looking upstream along the right bank with the sonar beams divided into 4 zones.

57 Figure 11.–A raw DIDSON image of Zone 1 showing the river bottom encountering shore at 20 m from the sonar (top left) and the same image with the background subtracted (top right), and a raw DIDSON image of Zone 2 (bottom left) and background subtracted image (bottom right). Note: Zone 1 fish images are smaller due to the lower resolution from the larger window length and lower sonar frequency.

58 Figure 12.–Overview of the workflow used to review and process Kvichak River DIDSON files for automated fish counting.

59

Figure 13.–Time series of daily fish passage estimates from the nearshore (Zones 1–2) and offshore (Zones 3–4) sonars, Kvichak River right bank, 2011–2013.

60

Figure 14.–Time series of daily fish passage estimates from Zones 1 to 4 from both sonars, Kvichak River, 2011–2013.

61

Figure 15.–Time series of daily test fishery and sonar indices of sockeye salmon passage, Kvichak River 2011–2013.

62

Figure 16.–Scatter plots of daily test fishery and sonar indices of sockeye salmon passage, Kvichak River, 2011–2013.

63

Figure 17.–Time series of daily fish passage estimates from the tower and right-bank sonar index with a 2-day travel time lag applied, Kvichak River, 2011–2013.

64

Figure 18.–Scatter plots of daily fish passage estimates from the tower and sonar indices with a 2-day travel time lag applied, Kvichak River, 2011–2013.

65

Figure 19.–Scatter plots of daily fish passage estimates from the tower and test fishery indices with a 2-day travel time lag applied, Kvichak River, 2011–2013.

66

Figure 20.–DIDSON images from Zone 2, where the sonar beam was squeezed between the river bottom and surface aimed toward shore. It occasionally had multipath propagation issues (reverberation), which caused multiple images of fish. Note: The surface reflection also caused the fish images to appear unfocused as the tide level changed within the stratum (bottom).

67

Figure 21.–Echograms of tracked fish when DIDSON was aimed offshore perpendicular to flow (top), aimed offshore at an oblique angle downstream (middle), and toward shore perpendicular to flow (bottom).

68

Figure 22.–A comparison of fish counts from 2 observeers who visually counted fish in DIDSON images (manual count) and 1 observer who used fully automated (full-auto) and semi-automated (semi- auto) counting methods.

Figure 23.–A comparison of fish counts from a subset of DIDSON data where fish passage was less than 100 fish per 10 min file, made by 2 observers who visually counted fish in DIDSON images (manual count) and 1 observer who used fully automated (full-auto) and semi-automated (semi-auto) counting methods.

69

Figure 24.–Comparison of hourly manual and semi-automated counts from the nearshore (Zones 1–2; left) and offshore sonar (Zones 3–4; right), Kvichak River 2011–2013.

70

Figure 25.–DIDSON data processing times for the semi-automated and manual methods used to estimate fish passage at the Kvichak River, 2013.

71

Figure 26.–Comparison of manual fish counts by 2 observers from the nearshore (left) and offshore sonars (right), Kvichak River 2011.

Figure 27.–Comparison of semi-automated fish counts by 2 observers from the nearshore (left) and offshore sonars (right), Kvichak River, 2011.

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Figure 28.–Comparison of fish counts from 30 randomly selected hours of sonar data manually counted by 3 observers (Obs) in 2012, Kvichak River.

2,500 Obs1 Obs2 2,000 Obs3

1,500

1,000

500 10-min fish counts fish 10-min 0 0 5 10 15 20 25 30

-500

-1,000 Sample number Figure 29.–Comparison of fish counts from 30 randomly selected hours of sonar data manually counted by 3 observers (Obs) in 2013, Kvichak River.

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Figure 30.–Range distributions of fish from the designated zero point (i.e., where the water encountered the bluff) from DIDSON images processed using the semi-automated method, Kvichak River, 2011–2013. Note: The 0 frequency at 25–26 m is the 2 m dead zone between the 2 opposing DIDSON.

74

Figure 31.–Relationship between DIDSON hourly fish passage estimates (right bank) and tide stage, Kvichak River, 2011. Note: Missing hourly data was interpolated.

75

Figure 32.–Relationship between DIDSON hourly fish passage estimates (right bank) and tide stage, Kvichak River, 2012. Note: Missing hourly data was interpolated.

76

Figure 33.–Relationship between DIDSON hourly fish passage estimates (right bank) and tide stage, Kvichak River, 2013. Note: Missing hourly data was interpolated.

77

Figure 34.–Relationship between DIDSON fish passage estimates and tide cycle (ebb and flood) plotted in 0.3 m tide stage bins, Kvichak River, 2011–2013.

78

Figure 35.–Relationship between DIDSON hourly fish passage estimates and tide stage on July 4, 2012, when a daily estimated high of 236,488 fish passed along the Kvichak River right bank.

79

Figure 36.–Relationship between the percent of upstream and downstream fish passage estimates and tide stage (0.3 m bins) from the Kvichak River right-bank sonar, 2011–2013.

80

Figure 37.–Side-scan sonar image (top) recorded along the left bank of the Kvichak River at Levelock on June 29, 2011. Note: Fish images are easily distinguishable with no acoustical noise or bottom image present (top). The bottom image was recorded ~30 min later along the right bank at the sonar site. No fish were visible even though the passage rate was ~1,740 fish/h during the hour the files were recorded.

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82

APPENDIX A: DIDSON MANUAL FISH COUNT INSTRUCTIONS

83

Appendix A1.–Instructions for manual fish counting of dual-frequency identification sonar (DIDSON) images using Sound Metrics Corporation (SMC) software. DIDSON Manual Fish Count Instructions The DIDSON Control and Display (Figure A1) will be used to view the recorded 10-min DIDSON files and count fish images moving upstream and downstream. Counts will be recorded on a hardcopy worksheet and then entered into the Excel spreadsheet. Figure A2 shows the basic controls located in the toolbar used for file playback.

Figure A1.–The DIDSON display used to view recorded files to obtain fish counts. A 10 m range file is shown in the viewing window with rocks visible and two fish between 1–3 m.

Open Previous/ Play Stop Background File Next File Reverse/ Subtraction Forward Figure A2.–DIDSON display toolbar with buttons used for fish counting highlighted. -continued-

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Appendix A1.–Page 2 of 2. Instructions 1. Minimize the DIDSON display that is recording. 2. Open a second DIDSON program by clicking on the DIDSON icon and always keep it open. (Warning: If the second DIDSON program is opened when the live DIDSON view is recording, the recording may transfer to the new window and will record a blank file. This is the reason for keeping the second program open all the time.) 3. Enable Demo Mode so the secondary program will not interfere with the recording (at the bottom of the viewing window it will either state Demo Mode or Connected). Go to Edit>Mode>Demo and check to enable. 4. Turn off the Timer if it appears in the window (red rectangle on lower right of window) by going to Image>Capture>Timer Recording and uncheck. 5. Open a DIDSON file for playback under File>Open or use the button in the toolbar (Figure A2) and navigate to the appropriate directory. Double-click on the file to open it. The files are named with the date, time, and frequency (i.e. 2006-05-13_130000_LF.ddf). 6. Adjust the frame rate in the upper left of the window under Sonar Controls to the maximum rate determined by the project leader. This rate will depend on fish passage rates. You may not exceed the rate but you may go slower. 7. Turn on the background subtraction function under Processing >Background> Background Subtraction or use the toolbar button. 8. Set the Intensity (45) and Threshold (4) located on the left side of the window under Display Controls to the predetermined setting. 9. Click Play. 10. Count upstream and downstream fish using separate tally counters. 11. Hot Keys: Controls on the keyboard can be used in place of some of the display control buttons which makes counting easier during playback because you don’t have to take your eyes off the image (spacebar = stop; up and down arrows = increase/decrease frame rate; left and right arrows = play reverse/forward). Additional keyboard controls can be found under Help>Help Topics>Toolbar. 12. Record counts, intensity, threshold, frame rate settings, and comments on the counts worksheet. Comment on debris, ice, holders, large fish, behavioral changes, etc. that are seen in the images. 13. Advance to the next file by clicking on the forward arrow on the toolbar or going to File>Open and selecting the next file from the folder where it is being recorded. The file name will appear at the top of the display; make sure it is the next file you want to count. 14. Open the electronic spread sheet, enter count, and any comments on the file.

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APPENDIX B: DIDSON AUTOMATED FISH COUNT INSTRUCTIONS

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Appendix B1.–Instructions for an automated fish counting method of dual-frequency identification sonar (DIDSON) images of adult sockeye salmon in the lower Kvichak River. How to generate auto counts from DIDSON data of Kvichak River salmon The method described here uses a combination of three software packages: DIDSON Control and Display Software Version 5.25.10 (Sound Metrics Corp.), Echoview Version 4.90 (Myriax) and Excel 2010 (Microsoft). The DIDSON ddf files are pre-processed in the DIDSON Control and Display Software, which removes the image background and empty frames. The files generated by this process are loaded into Echoview which is used to track the fish. Finally, the Echoview analysis results are imported into Excel for summarization. The end product is a table that summarizes the number of fish by direction of movement (upstream, downstream) and by 10-minute interval or hour. An overview of the process is shown in Figure B1. Before you start processing data, make sure all ddf files are sorted into batches of the same location, range window, system configuration, and date. This can be done, for example, in the Windows File Explorer by searching a given folder for files with the pattern “2014-07- 02_0910*.ddf”. The files returned by the search can be moved to a separate folder.

Figure B1.–Overview of the process used for auto counting adult Kvichak River salmon in DIDSON data.

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Appendix B1.–Page 2 of 11. STEP 1: Background subtraction and empty frame removal (Sound Metrics Corp. DIDSON Viewer) 1.1. Set up the CSOT process: Open the first file of the batch in the DIDSON Control and Display Software and check the following settings. (The software will keep the same settings until they are changed again by the user.) In the menu Image… Capture… Record Options… the settings should read:

N > Threshold = 200 Threshold = 4.9 N = Min Cluster Area (cm2) Persistence (frames): 4 Insert Prequel: checked Save Displayed Data Only: checked

In the DIDSON menu under Processing… Show Parameters… Check : Process N Beams = All Upstream Motion L>R

Figure B2.–Record Options dialog in Sound Metrics Corp. DIDSON Viewer. -continued-

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Appendix B1.–Page 3 of 11. 1.2. Turn on background subtraction: (use DIDSON V5.25.38 to remove 1st frame) In the menu Processing… Background… check Background Subtraction… Fast forward through some of the files to check the result of the background subtraction. If blooming shadows or forward scatter are an issue try checking either Processing… Background… Auto Fix On File Open or Detect Empty Frames. Refresh the background subtraction by turning it off and back on. 1.3. Set up batch mode: In the menu Processing… check Batch Mode. 1.4. Start CSOT batch process: When you are ready to start the batch process go to Processing… CSOT… and check Export CSOT Frames. This will immediately start the batch process. The BS/CSOT process creates a set of new files, one for each original file. The new files have the prefix “CSOT_” and are referred to as “CSOT ddf files”. 1.5. Check results: Briefly review some of the CSOT ddf files to make sure the background subtraction worked.

STEP 2. Cluster detection (cluster = computer-detected fish image = target) (Echoview)

2.1. Create new EV file: In Echoview go to File… New… to create a new EV file. This will open a dialog box that will prompt you for a template. Select one of the prepared Kvichak templates. (If you are not prompted, make sure the files Kvichak Template Px.EV have been copied to the Echoview templates folder. The location of the templates folder depends on the operating system. See Echoview Help.) You should now see the Filesets window, with “DIDSON CSOT BS” as the active fileset.

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Appendix B1.–Page 4 of 11. 2.2. Load CSOT ddf files: In the Filesets window, press the Add… button. This will open a file browser. Select the CSOT ddf files (use the SHIFT key to select multiple files) that you created in Step 1 and press Open. After the files have been loaded you should see them listed in the Filesets window. 2.3. Open dataflow window: Press F7 or the button labeled Open variables and geometry window (run the cursor over the buttons to see their tool tips) to open a chart of the dataflow, also referred to as the dataflow window. You may have to use the scroll bars to see the entire dataflow.

Figure B3.–Dataflow or “Variables and Geometry” window in Echoview. -continued-

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Appendix B1.–Page 5 of 11. 2.4. Open image windows: In the dataflow window double-click “DIDSON image”, “Smoothing”, and “DIDSON Image with Cluster Overlay”. This will open their respective image windows. Minimize the Filesets and dataflow windows. Tile the image windows you just opened (Window… Tile Vertically). 2.5. Auto-synchronize windows: Click in the first image window to make it active, and then press SHIFT+A or the button labeled Autosynchronize windows. Repeat with each of the other two image windows. The three views should now be auto-synchronized. When you play one of them forward, the others should move in synchrony. The SPACE bar toggles between play and pause. The RIGHT ARROW key plays forward (if in pause mode it advances 1 frame at a time). The LEFT ARROW key plays reverse (if in pause mode it reverses 1 frame at a time). CTRL+M toggles between maximum speed supported by the computer and real time speed. Make sure you are using maximum speed otherwise the replay will pause at every CSOT junction (i.e. where empty frames have been removed). If these keyboard shortcuts do not work make sure you copied the keyboard shortcuts file provided with deliverables (Echoview shortcuts.cfg). To find the correct destination for the file (depends on the operating system) go to File… Configuration… Preferences… Keyboard shortcuts Edit… This will open the cfg file in Notepad. In Notepad go to File… Save As… This will automatically open the correct folder. Note its path. This is the location where you have to save the Echoview shortcuts.cfg file.

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Appendix B1.–Page 6 of 11. 2.6. Set smoothing threshold: Click on the “Smoothing” window to make it active. Play forward to a section where you see some fish. Press the + key to raise the threshold or the – key to lower the threshold. Watch what happens to the cluster (= target) outline displayed on the “DIDSON image with Cluster Overlay” window. Compare the orange outline with what you visually perceive to be the fish image in the “DIDSON image” window. Once you have found a “Smoothing” threshold that produces a good match between the outline detected by the computer and what you recognize as the fish image, play through the file to see how well it does on other frames. It will never be perfect on every single frame. The goal is to produce a reasonably good match on most frames. 2.7. Close windows that are no longer needed: Close the “DIDSON image” and “Smoothing” windows. Leave the “DIDSON Image with Cluster Overlay” open for Step 3 (fish tracking). STEP 3. Fish tracking (Echoview)

As a fish moves through the DIDSON beam its image will be recorded over a series of frames. Therefore, the number of fish images detected does not equal the number of fish detected. In order to determine the number of fish detected the images (or echoes) have to be tracked. The term “tracking” refers to the process of grouping images that belong to the same fish into one track.

3.1. Set up workspace: Press F7 to reopen the dataflow window. In the dataflow window double-click “Length (live)” and “Angle (live)” to open their respective echograms. You should now have three windows open: Length echogram, angle echogram and “DIDSON image with cluster overlay” (see Step 2). Arrange the windows to fit your screen, e.g. similar to Figure B4. Depending on the range window of the dataset, you may have to adjust the display limits of the length and angle echogram. To change the display limits click in the echogram, press F8 to open its Variable Properties dialog, go to its Display tab and change the values for the Upper display limit (= start range) and Lower display limit (= stop range).

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Appendix B1.–Page 7 of 11.

Red traces Green traces are larger fish are medium- sized fish

Blue traces are smaller fish

Synch line intersects 3 fish on the echogram, which correspond to the 3 fish on the current frame in the DIDSON

Upstream swimming fish: angle color changes from blue t d Downstream swimming fish: angle colors changes from red to blue

Figure B4.–DIDSON image with cluster overlay (top left), length echogram (top) and angle echogram (bottom). 3.2. Set up auto-synchronization and examine echograms: Click in the length echogram to activate it and press SHIFT+A to auto-synchronize. Do the same for the angle echogram. You should now see a dashed line on each of the two echograms. This line is called the synch line and marks the current “ping”, which corresponds to the frame shown in the DIDSON image. The echograms plot the targets (= clusters = fish images) detected in the DIDSON image over range and time. The echogram essentially “collapses” each frame into one vertical column, along which targets are plotted at the range at which they have been detected. Move the cursor over the synch line until the cursor changes to a hand. Then click, hold down the mouse and move it left to right to move the synch line forward in

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Appendix B1.–Page 8 of 11.

time. You will see the DIDSON image play forward simultaneously. Familiarize yourself with how the cluster outlines displayed on the DIDSON image correspond to the targets displayed on the echograms. In the length echogram the color represents target length. The longer the target the warmer the color. In the angle echogram the color represents the angle at which the target has been detected. As a target moves through the beam it will be seen at different angles. For data collected on the right side of the river, upstream swimming fish will leave traces that run from red to blue. Downstream moving fish or objects can be recognized by their traces changing color in reverse order. The angle echogram is a very efficient tool for seeing which echoes belong to one fish. See Figure B4.

3.3. Adjust angle color scheme if necessary: Depending on which lens type has been used you may have to adjust the color scheme applied to the angle echogram. If you are working with standard lens files open the Variable Properties for the angle echogram (click in the echogram to make it active then press F8 to open its Variable Properties), go to the Display tab and set the Color display minimum to -15 and the Color display range to 30. This will rescale the color scheme so the entire spectrum fits within the angular extent of the big lens. (Alternatively, you can set up a template for standard lens files to have these values preset. To save your current settings to a new template go to File… Save Template….)

3.4. Get length echogram ready for fish tracking: Click on the length echogram to make it active. Press the Home key to make sure you are at the beginning of the fileset. Press the 9 key to activate the fish track tool. The cursor will change to a fish symbol.

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Appendix B1.–Page 9 of 11.

3.5. Test tracking over part of the echogram: Click on the length echogram to make it active. Press the Home key to make sure you are at the beginning of the fileset. On the length echogram, draw a box around a group of fish you want to track and Press K to execute the tracking algorithm over the selected section. The results will be shown as colored outlines around each group of echoes that formed a track. Compare the computer generated tracks with the angle echogram. Looking at the angle echogram you should be able to distinguish between a disrupted trace generated by one fish and two traces generated by two fish following each other head to tail. If the tracking accuracy cannot be judged by a simple comparison with the angle echogram, grab the synch line, move it over the section in question, and watch the corresponding DIDSON images. If necessary you can experiment with some of tracking parameters, in particular the exclusion distance for range and the maximum ping gap (see Kvichak 2010 --- Processing parameters.xlsx). The tracking parameters are accessed through the EV File Properties (F6) which include a tab named Fish Tracks …

3.6. Track entire fileset: To track the entire fileset go to Echogram… Detect Fish Tracks… (make sure the Length Live echogram is active when you do this).

3.7. Look for obvious tracking errors: Look for obvious tracking errors, i.e. tracks generated on forward scatter or other types of noise (e.g. boat noise). Delete false fish tracks by drawing a box around them and pressing CTRL+X. See Echoview Help for more detailed information on additional fish track editing tools.

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Appendix B1.–Page 10 of 11.

3.8. Save EV file and export fish track data: When you have finished tracking a fileset go to Echogram… Export… Analysis by Cells… Fish Tracks… (make sure the length echogram is active when you do this). This will open the Export Fish Tracks dialog. Make sure the export regions are set to “All classes” and press Export. Specify a name and folder for the export file. You may want to use the same name as the EV file so it is easier to keep track of which export came from which EV file. For the purpose of simple enumeration the template is set up to export fish track data in spreadsheet format, which generates 1 csv file.

STEP 4. Fish passage summarization (Excel)

4.1. Convert exported csv file to Excel workbook: Open the csv file you exported in the previous step and save it as an Excel workbook file.

4.2.Add new field for summarization by hour: Insert a new column to the right of the column named “Time_M”. Name the new column “Hour”. In its first record enter the formula “=HOUR(TIMEVALUE(J2))” (you may have to adjust the cell reference) and fill down. This should populate the new field with the correct hour for each record. Scroll down and check.

4.3.Add new field for summarization by 10-minute interval: Insert a new column to the right of the column you created in the previous step. Name the new column “10-min interval”. In its first record enter the formula “=TIMEVALUE(CONCATENATE(I2, ":", MID(H2,5,1)*10))” (you may have to adjust the cell reference) and fill down. This should populate the new field with the correct 10-minute interval for each record. Scroll down and check.

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Appendix B1.–Page 11 of 11.

4.4.Determine direction of movement: See Echoview Help on the definition of Direction_horizontal. This property allows you to distinguish between upstream and downstream movement. An easy way to determine the relevant values (depends on how the DIDSON is oriented and which side of the river you are on) plot the values of “Direction_horizontal”. There will be two groups of values, one for upstream, one for downstream. It is usually obvious which of the two groups is upstream (if most fish move upstream it will be the group that has noticeably more records).

4.5.Add new field for upstream count: Insert a new column to the right of the column labeled “Direction_horizontal”. Name the new column “Upstream”. In its first record enter a formula similar to “=IF(P2<100, 1, 0)” and fill down. You may have to adjust the cut-off value (here 100) based on the values you saw in the previous step.

4.6.Add new field for downstream count: Insert a new column to the right of the column you created in the previous step. Name the new column “Downstream”. In its first record enter a formula similar to “=IF(P2>200, 1, 0)” and fill down. You may have to adjust the cut-off value (here 200) based on the values you saw in the previous step.

4.7.Create a pivot table to summarize the data: Create a pivot table that uses “Hour” or “10-minute interval” as row label (depending on how you want to summarize the data) and Upstream (sum), Downstream (sum) and Target_range (Average) as column values. This will provide you with a summary of fish passage, giving you the number of fish per hour (or 10-minute interval) for each direction (upstream and downstream) and the average range of the tracked fish.

Step 4 can also be done in S-Plus or a database application, which would probably be preferable for large datasets.

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APPENDIX C: ECHOTASTIC INSTRUCTIONS

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Appendix C1.–Instructions for using Echotastic version 2.5.14 software to view and process Edgetech side-scan sonar images and export marked targets. Echotastic Instructions 1. Load a scan-side sonar .jsf file (File > Open). 2. Change the display color palette (View > Color Map > check Brown). 3. Go to Configuration > Settings: a. Under the General Settings tab set the Range Scaler to something fairly small like 0.18 m (default = 1.0 m). The exact value of the scaler may depend upon the range sampled as well. b. Check “Use Voltage” and use the maximum and minimum voltage instead of normal target strength thresholds. Set the maximum to 0.1–0.5. (It really isn’t voltage, it is a linear scale instead of decibel, but it often helps with the dynamic range of the side-scan data.) c. Under the Side-Scan tab, uncheck “Starboard Toward Shore”. 4. Click on targets with the mouse to create data points (Figure C1). Reclick created points to delete if needed. 5. To export data points go to File > click Save and a text file will be created in the same folder as the data file with the file name, total number of marks, total time, date, and start time. Each data point will contain a row of information including, sample, ping, time, range, and amplitude.

Figure C1.–Side-scan sonar image displayed in Echotastic with the top left of the image shown with marked fish targets.

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APPENDIX D: HOURLY FISH PASSAGE ESTIMATES

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Appendix D1.–Nearshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2011.

Hour Date 0 100 200 300 400 500 600 700 800 900 1000 1100 6/25 0 -6 0 0 6 12 0 0 6 0 0 0 6/26 0 0 0 0 0 0 0 -6 0 6 6 6 6/27 0 6 12 0 30 54 174 102 126 114 84 90 6/28 2,808 -180 972 60 -42 6 90 2,712 3,666 6,678 6,012 8,142 6/29 996 132 150 -1,284 1,158 2,760 2,418 2,136 2,598 3,612 1,176 2,616 6/30 246 450 -150 312 144 -300 12 126 1,206 1,056 942 396 7/1 192 180 786 -78 -30 12 48 54 288 348 726 432 7/2 1,044 588 348 5,604 120 -102 -3,828 2,850 1,878 2,610 1,638 1,914 7/3 156 1,278 882 1,104 -930 39 70 256 357 441 386 599 7/4 108 108 48 0 6 -516 -630 -960 174 96 216 738

102 7/5 174 306 108 426 36 48 48 440 614 759 665 1,032 7/6 26 14 10 34 0 0 36 42 59 72 63 99 7/7 126 67 48 169 2 31 56 138 72 42 84 174 7/8 65 35 25 88 1 17 29 107 149 184 162 251 7/9 -66 0 66 510 324 1,026 1,320 3,954 2,394 168 -378 210 7/10 46 24 17 61 1 12 21 75 104 129 113 175 7/11 0 24 366 -12 3 51 91 334 466 575 504 782 7/12 -342 -576 -204 630 1,932 960 780 660 1,236 804 1,626 4,176 7/13 78 -294 -174 -12 -12 120 312 528 342 540 630 1,308 Total 5,656 2,157 3,310 7,612 2,749 4,230 1,048 13,548 15,735 18,234 14,655 23,140 -continued-

Appendix D1.–Page 2 of 2.

Hour Date 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Total 6/25 0 0 0 0 0 0 6 0 0 0 2 0 26 6/26 0 0 6 12 0 0 0 6 0 6 6 0 48 6/27 180 54 0 1,146 3,852 2,226 1,134 2,730 3,000 4,596 852 66 20,628 6/28 5,508 15,780 3,216 486 1,410 876 3,738 5,652 9,354 11,412 7,986 822 97,164 6/29 5,232 2,874 5,712 3,210 1,296 3,504 1,464 1,458 972 2,532 504 162 47,388 6/30 1,074 3,816 54 42 180 234 312 306 432 1,974 1,056 480 14,400 7/1 1,596 1,278 4,656 516 336 330 2,664 1,758 2,238 3,348 4,722 4,314 30,714 7/2 2,796 2,250 10,218 6,012 876 -90 990 1,104 432 1,848 1,992 1,308 44,400 7/3 566 620 656 448 449 138 -60 186 708 132 162 0 8,643 7/4 1,002 762 894 3,822 4,206 3,582 258 78 -96 354 1,194 540 15,984

103 7/5 975 1,068 1,129 771 773 760 824 926 1,198 1,463 1,058 494 16,097 7/6 93 102 108 74 74 73 79 88 114 140 101 47 1,547 7/7 414 486 810 324 666 1,590 1,530 420 636 -168 510 156 8,383 7/8 237 259 274 187 188 288 570 462 192 84 -24 -54 3,777 7/9 18 -6 42 1,296 2,166 1,488 2,172 2,874 3,942 102 1,086 790 25,498 7/10 166 181 192 131 131 129 140 157 204 249 180 84 2,721 7/11 739 809 856 584 586 576 624 702 908 1,109 802 1,044 12,523 7/12 -96 -66 36 198 492 798 1,458 1,146 1,182 2,256 1,014 690 20,790 7/13 1,020 24 -360 210 144 210 378 354 1,002 798 648 0 7,794 Total 21,520 30,293 28,499 19,470 17,825 16,711 18,280 20,408 26,418 32,235 23,851 10,944 378,527

Appendix D2.–Offshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2011.

Hour Date 0 100 200 300 400 500 600 700 800 900 1000 1100 6/25 0 0 6 0 0 0 0 0 6 0 6 0 6/26 0 0 6 0 0 6 12 0 6 0 0 0 6/27 0 0 18 0 18 6 24 48 36 36 6 42 6/28 1,578 0 0 0 6 144 108 159 209 315 130 96 6/29 24 180 0 0 6 6 24 0 24 672 72 48 6/30 210 468 36 -12 -12 0 0 6 18 90 330 18 7/1 162 408 654 -18 -30 126 294 204 119 261 108 107 7/2 668 2,526 1,026 1,974 -6 0 0 0 6 0 30 336 7/3 1,362 510 288 168 270 -18 -120 516 -546 311 128 93 7/4 552 1,158 1,524 828 540 24 0 6 0 6 18 6

104 7/5 150 150 696 486 792 342 -6 0 114 193 80 66 7/6 48 114 156 180 330 318 282 0 0 0 0 30 7/7 30 36 78 84 132 78 18 28 32 51 21 6 7/8 12 24 6 66 114 84 24 36 12 0 0 0 7/9 -6 0 0 12 336 678 102 66 84 6 -12 0 7/10 -18 0 12 99 62 49 36 47 80 110 45 30 7/11 0 0 0 0 24 180 366 396 66 102 132 6 7/12 252 -6 0 -6 12 6 306 828 1,812 1,380 348 318 7/13 324 336 0 0 0 24 96 186 588 846 366 258 Total 5,348 5,904 4,506 3,861 2,594 2,052 1,565 2,525 2,666 4,380 1,809 1,461 -continued-

Appendix D2.–Page 2 of 2.

Hour Date 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Total 6/25 0 -12 0 0 0 0 0 0 0 6 0 0 12 6/26 6 0 0 0 0 0 18 42 0 6 12 0 114 6/27 0 0 0 42 516 78 660 984 234 1,158 858 192 4,956 6/28 92 53 43 75 188 164 265 261 285 353 540 476 5,541 6/29 24 12 6 18 12 138 30 282 960 150 1,170 462 4,320 6/30 54 30 6 -12 6 114 204 168 108 324 402 252 2,808 7/1 88 48 39 76 166 148 226 242 232 280 409 380 4,727 7/2 318 54 48 54 6 12 -12 168 804 360 756 606 9,734 7/3 90 52 42 73 184 161 -6 6 126 198 474 1,092 5,455 7/4 144 384 168 234 18 30 18 -6 0 36 288 90 6,066

105 7/5 60 34 66 12 36 30 72 0 0 0 18 42 3,432 7/6 12 72 114 192 144 30 18 36 0 -6 0 126 2,196 7/7 0 18 42 66 48 60 30 12 42 -6 0 0 907 7/8 6 0 12 78 48 102 90 126 126 90 0 0 1,056 7/9 12 0 24 48 408 90 90 84 228 1,428 1,026 -18 4,686 7/10 31 18 15 25 64 56 92 88 100 125 193 558 1,914 7/11 0 0 6 60 594 762 1,188 660 162 144 894 1,578 7,320 7/12 0 -6 -18 72 204 114 252 156 216 54 84 102 6,480 7/13 396 0 0 -6 12 246 474 426 318 150 210 606 5,856 Total 1,333 757 613 1,107 2,654 2,335 3,708 3,734 3,941 4,849 7,334 6,543 77,580

Appendix D3.–Nearshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2012.

Hour Date 0 100 200 300 400 500 600 700 800 900 1000 1100 6/24 6 12 0 0 5 6 4 0 -1 -6 0 0 6/25 246 237 190 116 6 30 185 -126 114 -144 258 726 6/26 462 276 162 66 270 426 42 264 -36 -246 24 636 6/27 36 12 6 12 0 0 0 0 0 -78 0 24 6/28 -6 18 6 0 0 0 0 0 0 -12 12 0 6/29 462 354 846 486 3,606 6,882 3,342 4,458 6,312 5,922 2,904 1,026 6/30 84 -834 -1,896 3,486 2,574 3,546 5,622 7,296 8,748 7,578 6,042 138 7/1 9,144 4 -76 327 425 4,260 4,362 7,350 5,766 4,920 4,661 2,844 7/2 1,932 1,452 -6 0 -6 6 162 582 1,098 282 183 183 7/3 186 1,032 234 -132 -456 -426 696 972 960 1,524 3,498 1,962

106 7/4 13,002 11,514 8,670 18,522 1,443 -2,604 -636 7,686 10,620 9,342 12,828 17,712 7/5 9,678 15,708 18,912 9,246 -42 -12 -36 -276 72 3,042 2,928 4,212 7/6 7,350 7,632 7,950 5,016 2,598 3,618 -3,054 580 318 4,122 3,948 2,808 7/7 8,550 8,880 6,948 8,604 7,164 576 -966 -234 -396 -95 -72 -126 7/8 2,916 5,538 5,394 2,886 2,850 2,928 1,200 -1,344 -3,558 -2,760 1,314 4,650 7/9 4,872 3,630 2,556 3,912 5,238 7,488 4,230 5,400 -3,018 -4,008 348 2,796 7/10 2,040 714 1,146 1,212 2,142 2,280 2,220 3,264 1,908 -1,620 -1,734 -6 7/11 -924 684 240 570 930 1,230 288 300 60 48 -216 -192 7/12 -102 72 534 168 90 48 348 294 540 180 24 -396 Total 59,933 56,935 51,815 54,497 28,838 30,282 18,009 36,466 29,507 27,992 36,950 38,997 -continued-

Appendix D3.–Page 2 of 2.

Hour Date 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Total 6/24 7 -1 0 10 8 18 0 7 -8 -12 36 132 27 6/25 1,128 978 564 374 289 295 54 864 3,486 678 192 276 1,839 6/26 900 822 852 294 162 54 48 54 60 18 0 -12 2,346 6/27 24 36 6 18 12 0 0 0 6 0 18 -12 12 6/28 120 150 216 300 222 474 540 6 252 42 156 -138 18 6/29 4,596 1,428 2,166 3,966 10,830 6,462 5,766 8,406 7,950 10,938 12,852 18,846 36,600 6/30 -2,652 -1,908 1,932 8,376 6,930 9,894 6,186 5,700 4,974 6,960 13,206 8,754 42,384 7/1 222 -12 -534 306 594 1,668 2,028 858 780 654 1,224 2,646 43,986 7/2 160 85 75 468 257 278 457 440 287 423 565 658 5,869 7/3 2,130 3,312 -54 -4,986 -13,248 4,122 8,772 8,970 16,560 10,632 7,332 9,960 10,050

107 7/4 14,850 13,254 5,958 9,708 486 -7,200 7,266 11,838 6,984 10,296 15,042 14,688 108,099 7/5 6,330 6,210 5,532 7,602 7,842 -228 -48 2,718 9,690 8,556 8,220 7,368 63,432 7/6 4,182 7,434 4,764 4,044 3,906 6,618 -5,388 -4,536 5,346 6,462 4,638 6,498 42,886 7/7 2,442 2,388 4,122 2,022 1,560 144 3,972 -1,692 -2,142 -1,254 1,674 3,270 38,833 7/8 2,946 4,302 6,180 5,526 2,268 1,986 6,804 6,522 -894 -5,400 -1,230 4,092 22,014 7/9 3,156 2,808 5,892 6,546 4,512 4,344 7,044 1,464 696 -186 -1,326 -552 33,444 7/10 1,944 1,860 1,296 2,292 1,728 966 1,782 20 240 60 174 -1,548 13,566 7/11 84 528 204 630 108 438 228 852 84 6 12 -126 3,018 7/12 -120 300 372 156 342 204 348 108 6 19 260 308 1,800 Total 42,449 43,974 39,543 47,652 28,809 30,536 45,859 42,598 54,357 48,892 63,045 75,108 470,223

Appendix D4.–Offshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2012.

Hour Date 0 100 200 300 400 500 600 700 800 900 1000 1100 6/24 12 12 12 -6 12 0 0 12 0 1 30 0 6/25 102 90 102 132 192 140 0 0 0 12 30 12 6/26 150 120 294 240 204 576 24 0 0 -6 48 6 6/27 24 42 72 78 84 18 66 12 0 0 0 6 6/28 0 0 6 0 18 12 18 18 0 0 0 0 6/29 0 114 42 180 222 84 84 36 30 0 -24 -6 6/30 -6 6 18 30 78 114 60 96 72 6 -6 6 7/1 318 161 149 257 294 208 129 47 96 168 12 0 7/2 354 182 172 292 334 239 148 57 20 84 42 12 7/3 858 6 6 0 0 6 18 -6 168 114 48 246

108 7/4 14,677 3,798 -6 -30 -12 -12 72 132 54 228 594 210 7/5 2,610 1,284 2,046 12 0 18 0 24 18 6 24 330 7/6 1,020 1,932 3,240 10,332 -18 -6 -12 12 6 12 6 198 7/7 78 630 1,176 2,028 5,580 6 12 -6 6 42 12 60 7/8 108 798 876 894 3,342 18 -6 -12 0 6 30 36 7/9 1,950 1,068 1,572 2,574 7,122 7,752 -6 6 -12 0 36 66 7/10 540 870 516 594 1,380 3,432 4,236 30 6 -6 0 36 7/11 54 618 630 954 2,256 2,610 4,464 2,610 36 61 -6 30 7/12 6 54 180 288 474 282 258 654 54 0 0 -6 Total 22,856 11,785 11,103 18,848 21,562 15,497 9,564 3,723 554 727 876 1,242 -continued-

Appendix D4.–Page 2 of 2.

Hour Date 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Total 6/24 0 0 6 6 0 6 0 0 12 120 102 96 85 6/25 70 264 48 54 48 336 42 -6 0 -6 30 36 812 6/26 24 84 66 78 138 72 222 204 0 0 0 0 1,656 6/27 6 6 0 24 24 24 24 30 6 0 0 0 402 6/28 30 6 6 66 72 78 300 1,020 1,146 84 0 0 72 6/29 12 36 24 66 72 24 66 18 198 2,736 0 -6 762 6/30 0 36 42 60 48 18 27 31 19 20 16 15 474 7/1 0 0 0 85 136 144 125 79 47 64 53 44 1,840 7/2 0 0 0 48 24 24 90 72 24 222 342 210 1,935 7/3 18 -6 0 0 96 72 90 570 246 348 132 996 1,464

109 7/4 510 216 0 -60 -12 318 390 150 384 456 2,538 624 19,705 7/5 384 402 948 0 0 0 378 642 306 378 300 444 6,372 7/6 498 684 480 1,248 0 -30 -30 96 90 156 132 60 16,722 7/7 162 234 1,596 870 694 17 -78 -60 18 54 156 282 9,624 7/8 174 288 336 378 1,614 2,844 6 -6 -24 6 54 402 6,090 7/9 648 816 312 330 2,172 3,900 4,140 12 12 -18 -6 36 22,128 7/10 1,746 882 3,480 2,376 3,906 1,692 2,136 2,088 24 12 -24 6 11,634 7/11 288 876 726 564 858 684 1,038 744 1,026 30 0 0 14,317 7/12 12 12 138 72 48 300 258 336 65 91 75 65 2,244 Total 4,582 4,836 8,208 6,265 9,938 10,524 9,224 6,020 3,599 4,753 3,900 3,310 118,338

Appendix D5.–Nearshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2013.

Hour Date 0 100 200 300 400 500 600 700 800 900 1000 1100 6/25 0 0 1 0 3 -324 -144 -30 552 30 354 342 6/26 0 42 12 36 0 -738 -48 0 -6 -18 22 0 6/27 792 510 168 6 0 0 -1,224 -1,422 -330 270 4,488 3,618 6/28 2,652 2,292 2,148 612 0 0 0 -780 -1,026 -588 -1,555 1,428 6/29 5,928 4,320 1,950 3,384 1,236 0 0 0 -4,680 -2,850 -642 1,062 6/30 3,798 5,382 4,770 2,868 1,800 924 54 0 0 -2,334 -5,814 -5,874 7/1 -15,564 5,346 22,542 13,212 12,354 9,726 2,724 348 0 0 -10,296 -126 7/2 -1,668 -1,518 1,848 6,144 4,134 3,312 1,986 642 1,380 360 180 -840 7/3 -534 -462 -276 804 1,998 2,748 1,926 972 270 60 84 0 7/4 0 -360 -528 -312 1,506 1,758 1,668 582 672 78 78 18

110 7/5 0 0 -312 -366 -18 546 864 414 456 684 372 42 7/6 0 0 0 -420 -402 -12 1,722 1,926 1,452 876 654 300 7/7 0 0 0 -30 -102 -42 132 372 360 348 348 150 7/8 0 0 0 0 -174 -198 48 1,116 1,524 2,058 402 1,020 7/9 0 0 0 0 -30 -954 -936 -168 2,442 2,388 1,998 1,086 7/10 0 0 0 0 0 -12 -138 0 348 750 570 276 7/11 0 0 0 0 0 0 -30 -36 0 84 24 24 7/12 -6 0 0 0 0 0 0 0 -18 -12 -6 30 Total -4,602 15,552 32,323 25,938 22,305 16,734 8,604 3,936 3,396 2,184 -8,739 2,556 -continued-

Appendix D5.–Page 2 of 2.

Hour Date 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Total 6/25 402 0 731 18 -6 -12 -210 144 294 384 186 126 784 6/26 -6 0 0 6 -28 0 -576 -186 -372 1,578 1,320 846 -698 6/27 3,018 3,012 2,598 822 258 0 0 -858 -3,066 -792 2,646 5,472 6,876 6/28 3,216 3,966 3,906 2,790 1,008 0 -894 0 192 -6,540 -1,638 5,562 5,183 6/29 3,702 4,644 4,770 4,368 3,486 462 0 0 0 -1,614 -3,264 -5,952 9,708 6/30 8,670 10,656 8,574 7,974 5,604 3,678 2,550 192 18 0 120 -4,356 5,574 7/1 84 8,742 9,702 8,382 6,816 372 2,436 1,254 222 2,041 0 -1,380 40,266 7/2 -3,504 -2,670 5,076 3,270 2,928 1,782 576 342 84 48 0 0 15,960 7/3 -300 -5,442 -1,476 3,126 4,038 2,910 348 360 36 0 0 0 7,590 7/4 0 -6 -180 -108 282 330 228 132 0 0 0 0 5,160

111 7/5 0 0 -54 -438 -24 402 396 564 6 0 0 0 2,682 7/6 66 0 66 -6 -54 36 114 78 42 0 0 0 6,096 7/7 18 0 0 108 60 -144 84 210 216 0 0 0 1,536 7/8 330 2,484 156 0 2,580 4,452 438 1,908 3,432 1,764 318 84 5,796 7/9 1,056 750 210 0 0 612 108 -264 66 1,404 552 0 5,826 7/10 258 48 12 0 0 0 24 6 18 126 0 -6 1,794 7/11 18 12 0 0 0 0 0 12 0 0 18 0 66 7/12 18 0 0 0 0 3 1 0 -1 7 0 8 -12 Total 17,046 26,196 34,091 30,312 26,948 14,883 5,624 3,894 1,187 -1,594 258 404 120,187

Appendix D6.–Offshore fish passage estimates by day and hour in the lower Kvichak River at Levelock, 2013.

Hour Date 0 100 200 300 400 500 600 700 800 900 1000 1100 6/25 56 61 29 810 -6 0 0 0 0 0 0 6 6/26 -62 -68 -13 -44 -57 -78 -129 -134 -110 -86 -85 -62 6/27 126 204 426 552 1,080 18 -30 0 -12 18 0 0 6/28 228 144 468 1,308 1,200 2,790 6 0 0 0 0 0 6/29 18 72 156 1,404 2,916 2,022 4,386 582 -72 0 -18 0 6/30 6 36 42 42 396 1,062 1,914 1,968 636 -6 0 0 7/1 -12 24 18 30 114 2,130 5,046 7,350 5,718 12 0 0 7/2 -6 24 18 36 108 480 2,280 2,184 3,516 3,258 -18 84 7/3 12 -18 -60 66 48 132 924 1,638 1,362 2,490 2,880 1,242 7/4 1,302 0 0 0 12 72 378 906 576 900 1,932 1,806

112 7/5 0 546 0 0 6 30 42 246 60 144 738 1,134 7/6 0 0 6 0 -18 12 6 54 474 300 132 456 7/7 84 0 0 -12 0 -6 0 12 84 138 186 240 7/8 480 966 184 -6 0 0 0 0 24 2,640 2,988 1,374 7/9 4,104 4,920 150 453 -6 0 6 -6 6 102 756 840 7/10 810 840 35 95 516 0 0 0 6 90 144 126 7/11 186 174 5 15 21 0 0 0 0 12 12 30 7/12 66 42 48 9 12 16 0 0 0 0 6 6 Total 7,398 7,967 1,513 4,757 6,342 8,680 14,829 14,800 12,268 10,012 9,653 7,282 -continued-

Appendix D6.–Page 2 of 2.

Hour Date 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 Total 6/25 0 61 0 6 68 87 127 149 128 120 96 105 956 6/26 -43 -69 -40 -77 -78 -107 -6 -66 24 6 54 -828 -927 6/27 12 8 106 411 690 642 1,620 -6 -12 18 48 126 2,382 6/28 0 12 24 18 504 511 1,238 2,130 -66 -12 -12 402 6,144 6/29 0 24 24 78 192 1,992 1,074 1,362 2,328 -6 0 0 11,466 6/30 0 0 6 6 810 2,940 4,212 4,542 4,218 2,232 0 0 6,096 7/1 0 0 0 36 2,442 1,878 3,510 3,030 1,338 3,882 876 0 20,430 7/2 0 -6 12 48 162 564 1,092 1,920 2,316 1,812 2,538 2,886 11,964 7/3 -18 -168 -24 1,056 348 1,632 1,908 2,076 1,878 2,076 3,210 2,724 10,716 7/4 354 -6 -48 30 336 54 300 528 894 1,224 114 1,218 7,884

113 7/5 1,530 96 -42 -72 114 534 468 1,362 1,236 1,596 1,488 528 2,946 7/6 708 963 -6 -12 -24 270 300 222 168 210 366 390 1,422 7/7 420 1,008 768 -72 -30 60 144 588 372 348 534 456 726 7/8 2,106 3,876 2,027 2,160 24 -18 246 738 954 1,470 2,682 4,794 8,650 7/9 288 1,734 1,134 4,476 2,706 -18 -54 336 474 342 522 666 11,326 7/10 90 252 486 756 426 54 -18 48 78 84 186 240 2,662 7/11 24 12 36 132 36 30 -6 0 0 6 36 60 455 7/12 0 6 -12 18 54 21 30 36 31 30 26 27 205 Total 5,471 7,802 4,451 8,999 8,781 11,127 16,186 18,995 16,359 15,437 12,764 13,794 105,502

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APPENDIX E: TIDE TABLES

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Appendix E1.–Tide tables for Levelock, Alaska during sonar operations in 2011.

High tides Low tides Date Time (am) ft m Time (pm) ft m Time (am) ft m Time (pm) ft m 6/24 12:14 7.6 2.3 11:58 8.6 2.6 9:06 1.1 0.3 9:45 0.3 0.1 6/25 1:08 7.9 2.4 12:35 8.2 2.5 9:59 1.3 0.4 10:26 0.2 0.1 6/26 2:00 8.2 2.5 1:12 7.8 2.4 10:53 1.5 0.5 11:07 0.1 0.0 6/27 2:50 8.6 2.6 1:51 7.5 2.3 11:46 1.6 0.5 11:49 0.0 0.0 6/28 3:38 8.9 2.7 2:30 7.2 2.2 12:39 1.6 0.5 … … … 6/29 4:24 9.2 2.8 3:12 7.0 2.1 12:31 0.0 0.0 1:31 1.7 0.5 6/30 5:08 9.5 2.9 3:57 6.8 2.1 1:13 -0.1 0.0 2:22 1.7 0.5 7/1 5:51 9.7 3.0 4:44 6.8 2.1 1:56 -0.1 0.0 3:10 1.6 0.5 7/2 6:33 9.9 3.0 5:36 6.8 2.1 2:40 -0.1 0.0 3:57 1.5 0.5 7/3 7:13 10.1 3.1 6:33 6.9 2.1 3:25 -0.1 0.0 4:43 1.3 0.4 7/4 7:53 10.2 3.1 7:33 7.1 2.2 4:13 0.0 0.0 5:28 1.1 0.3 7/5 8:34 10.2 3.1 8:35 7.4 2.3 5:03 0.1 0.0 6:13 0.8 0.2 7/6 9:15 10.2 3.1 9:40 7.9 2.4 5:57 0.3 0.1 6:59 0.4 0.1 7/7 9:58 10.1 3.1 10:44 8.4 2.6 6:53 0.5 0.2 7:46 0.1 0.0 7/8 10:44 9.9 3.0 11:48 9.0 2.7 7:52 0.7 0.2 8:34 -0.2 -0.1 7/9 11:32 9.6 2.9 … … … 8:52 0.9 0.3 9:25 -0.5 -0.2 7/10 12:51 9.6 2.9 12:22 9.3 2.8 9:54 1.0 0.3 10:17 -0.7 -0.2 7/11 1:53 10.0 3.0 1:15 9.0 2.7 10:55 1.2 0.4 11:10 -0.8 -0.2 7/12 2:53 10.4 3.2 2:11 8.7 2.7 11:57 1.2 0.4 … … … 7/13 3:52 10.6 3.2 3:08 8.3 2.5 12:03 -0.8 -0.2 12:58 1.3 0.4 Note: Tide stages were obtained from Nobeltec Tides & Currents software, which was based on the tide stage from Nushagak Bay at Clark’s Point.

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Appendix E2.–Tide tables for Levelock, Alaska during sonar operations in 2012.

High tides Low tides Date Time (am) ft m Time (pm) ft m Time (am) ft m Time (pm) ft m 6/24 9:06 9.7 3.0 9:03 6.7 2.0 5:35 0.4 0.1 6:51 1.0 0.3 6/25 9:41 9.6 2.9 9:59 7.1 2.2 6:21 0.6 0.2 7:30 0.7 0.2 6/26 10:18 9.6 2.9 10:58 7.6 2.3 7:12 0.7 0.2 8:11 0.4 0.1 6/27 10:58 9.5 2.9 11:57 8.3 2.5 8:05 0.9 0.3 8:53 0.1 0.0 6/28 11:41 9.3 2.8 … … … 9:02 1.0 0.3 9:39 -0.2 -0.1 6/29 12:57 9.0 2.7 12:27 9.2 2.8 10:02 1.1 0.3 10:27 -0.5 -0.2 6/30 1:57 9.6 2.9 1:18 9.0 2.7 11:02 1.2 0.4 11:18 -0.7 -0.2 7/1 2:56 10.2 3.1 2:13 8.8 2.7 … … … 12:04 1.2 0.4 7/2 3:54 10.7 3.3 3:11 8.6 2.6 12:11 -0.9 -0.3 1:05 1.2 0.4 7/3 4:52 11.0 3.4 4:12 8.5 2.6 1:06 -0.9 -0.3 2:05 1.1 0.3 7/4 5:47 11.2 3.4 5:15 8.3 2.5 2:01 -0.9 -0.3 3:04 1.0 0.3 7/5 6:42 11.2 3.4 6:20 8.1 2.5 2:58 -0.8 -0.2 4:02 0.9 0.3 7/6 7:34 11.1 3.4 7:26 8.1 2.5 3:54 -0.6 -0.2 4:59 0.7 0.2 7/7 8:25 10.9 3.3 8:31 8.0 2.4 4:51 -0.3 -0.1 5:53 0.5 0.2 7/8 9:13 10.5 3.2 9:35 8.0 2.4 5:47 0.0 0.0 6:45 0.4 0.1 7/9 10:00 10.1 3.1 10:38 8.1 2.5 6:43 0.3 0.1 7:35 0.2 0.1 7/10 10:45 9.6 2.9 11:38 8.3 2.5 7:38 0.6 0.2 8:23 0.1 0.0 7/11 11:28 9.1 2.8 … … … 8:33 0.9 0.3 9:10 0.0 0.0 7/12 12:35 8.4 2.6 12:10 8.5 2.6 9:28 1.1 0.3 9:55 0.0 0.0 7/13 1:30 8.6 2.6 12:51 8.1 2.5 10:22 1.3 0.4 10:39 0.0 0.0 Note: Tide stages were obtained from Nobeltec Tides & Currents software, which was based on the tide stage from Nushagak Bay at Clark’s Point.

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Appendix E3.–Tide tables for Levelock, Alaska during sonar operations in 2013.

High tides Low tides Date Time (am) ft m Time (pm) ft m Time (am) ft m Time (pm) ft m 6/24 6:00 11.2 3.4 5:27 8.3 2.5 2:16 -0.8 -0.2 3:21 1.0 0.3 6/25 6:54 11.5 3.5 6:32 8.3 2.5 3:12 -0.8 -0.2 4:18 0.8 0.2 6/26 7:47 11.5 3.5 7:39 8.4 2.6 4:10 -0.7 -0.2 5:15 0.6 0.2 6/27 8:40 11.4 3.5 8:47 8.5 2.6 5:08 -0.5 -0.2 6:11 0.4 0.1 6/28 9:31 11.2 3.4 9:55 8.6 2.6 6:07 -0.2 -0.1 7:06 0.1 0.0 6/29 10:22 10.8 3.3 11:02 8.8 2.7 7:07 0.1 0.0 8:00 -0.1 0.0 6/30 11:12 10.3 3.1 … … … 8:06 0.4 0.1 8:53 -0.2 -0.1 7/1 12:08 9.0 2.7 12:02 9.8 3.0 9:06 0.7 0.2 9:44 -0.3 -0.1 7/2 1:11 9.1 2.8 12:51 9.2 2.8 10:06 0.9 0.3 10:34 -0.3 -0.1 7/3 2:12 9.3 2.8 1:39 8.6 2.6 11:05 1.1 0.3 11:22 -0.3 -0.1 7/4 3:08 9.4 2.9 2:26 8.0 2.4 … … … 12:03 1.3 0.4 7/5 4:02 9.5 2.9 3:12 7.5 2.3 12:09 -0.2 -0.1 12:58 1.4 0.4 7/6 4:51 9.5 2.9 3:57 7.1 2.2 12:54 -0.2 -0.1 1:52 1.4 0.4 7/7 5:36 9.5 2.9 4:42 6.8 2.1 1:38 0.0 0.0 2:42 1.5 0.5 7/8 6:18 9.5 2.9 5:26 6.6 2.0 2:21 0.1 0.0 3:30 1.5 0.5 7/9 6:56 9.4 2.9 6:11 6.4 2.0 3:03 0.2 0.1 4:15 1.4 0.4 7/10 7:32 9.3 2.8 6:58 6.4 2.0 3:45 0.3 0.1 4:58 1.3 0.4 7/11 8:05 9.3 2.8 7:45 6.4 2.0 4:27 0.4 0.1 5:38 1.2 0.4 7/12 8:38 9.2 2.8 8:35 6.6 2.0 5:09 0.6 0.2 6:17 1.0 0.3 7/13 9:10 9.1 2.8 9:25 6.9 2.1 5:53 0.7 0.2 6:55 0.9 0.3 Note: Tide stages were obtained from Nobeltec Tides & Currents software, which was based on the tide stage from Nushagak Bay at Clark’s Point.

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