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Susitna-Watana Hydroelectric Project Document
ARLIS Uniform Cover Page
Title:
Implementation plan for the genetic baseline study for selected fish species
in the Susitna River, Alaska SuWa 200
Author(s) – Personal:
Barclay, Andrew W.
Author(s) – Corporate:
Alaska Department of Fish and Game, Division of Commercial Fisheries
AEA-identified category, if specified:
Final study plan
AEA-identified series, if specified:
Regional operational plan ; DF.#R.13-XX
Series (ARLIS-assigned report number):
Susitna-Watana Hydroelectric Project document number 200
Existing numbers on document:
Published by:
[Anchorage : Alaska Energy Authority, 2013]
Date published:
April 30, 2013
Published for:
Date or date range of report:
Volume and/or Part numbers:
Study plan Section 9.14, attachment A
Final or Draft status, as indicated:
[Final]
Document type:
Pagination:
iii, 37, 2 p.
Related work(s):
Implementation plan for and Attachment A to: Genetic baseline
study for selected fish species, Study plan Section 9.14 : Final
study plan
Pages added/changed by ARLIS:
Notes:
The draft of this plan is cataloged as SuWa 72.
All reports in the Susitna-Watana Hydroelectric Project Document series include an ARLIS-
produced cover page and an ARLIS-assigned number for uniformity and citability. All reports
are posted online at http://www.arlis.org/resources/susitna-watana/
Attachment A
Final 2013 Project Operational Plan, Study 9.14
Susitna-Watana Hydroelectric Project
(FERC No. 14241)
Regional Operational Plan DF.#R.13-XX
Implementation Plan for the Genetic Baseline Study
for Selected Fish Species in the Susitna River, Alaska
Prepared for
Alaska Energy Authority
Prepared by
Andrew W. Barclay
Alaska Department of Fish and Game
April 30, 2013
REGIONAL OPERATIONAL PLAN DF.#R.13-XX
IMPLEMENTATION PLAN FOR THE GENETIC
BASELINE STUDY FOR SELECTED FISH SPECIES IN
THE SUSITNA RIVER, ALASKA
by
Andrew W. Barclay
Alaska Department of Fish and Game, Division of Commercial Fisheries, Anchorage
Alaska Department of Fish and Game
Division of Commercial Fisheries
April 2013
25
The Regional Operational Plan Series was established in 2012 to archive and provide public access to operational
plans for fisheries projects of the Divisions of Commercial Fisheries and Sport Fish, as per joint-divisional
Operational Planning Policy. Documents in this series are planning documents that may contain raw data,
preliminary data analyses and results, and describe operational aspects of fisheries projects that may not actually be
implemented. All documents in this series are subject to a technical review process and receive varying degrees of
regional, divisional, and biometric approval, but do not generally receive editorial review. Results from the
implementation of the operational plan described in this series may be subsequently finalized and published in a
different department reporting series or in the formal literature. Please contact the author if you have any questions
regarding the information provided in this plan. Regional Operational Plans are available on the Internet at:
http://www.adfg.alaska.gov/sf/publications/
Andrew W. Barclay,
Alaska Department of Fish and Game, Division of Commercial Fisheries,
333 Raspberry Road, Anchorage, Alaska, 99518-1599
This document should be cited as:
Andrew W. Barclay. 2013. Susitna River Genetic Baseline Study. Alaska Department of Fish and Game, Regional
Operational Plan ROP.DF#R.13-XX, 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
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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
SIGNATURE/TITLE PAGE
Project Title: Susitna River Genetic Baseline Study
Project leader(s): Andrew W. Barclay Fishery Biologist III
Division, Region and Area Commercial Fisheries, Region VI, Anchorage
Project Nomenclature: FERC Project No. 14241; Alaska Energy Authority
Period Covered April 1, 2013 – March 31, 2015
Field Dates: May 1, 2013 – October 30, 2013 and 2014
Plan Type: Category III
Approval
Title Name Signature Date
Project leader Andrew W. Barclay
Biometrician Jim Jasper
Research Coordinator Chris Habicht
Principal Geneticist William D. Templin
Chief Fisheries Scientist Eric C. Volk
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page i April 2013
TABLE OF CONTENTS
1. Purpose................................................................................................................................1
2. Background ........................................................................................................................2
2.1. Existing Information and Need for Additional Information ....................................2
2.2. Study Area ...............................................................................................................5
3. Objectives............................................................................................................................5
4. Methods ...............................................................................................................................5
4.1. Survey Flights ..........................................................................................................5
4.2. Samples to Collect ...................................................................................................6
4.3. Tissue Storage ........................................................................................................10
4.4. Laboratory Analysis ...............................................................................................11
4.5. Data Retrieval and Quality Control .......................................................................12
4.6. Genetic Baseline Development ..............................................................................13
4.7. Mixed-Stock Analysis ............................................................................................15
4.8. Consistency with Generally Accepted Scientific Practice .....................................17
5. Schedule and Deliverables ...............................................................................................17
6. Responsibilities .................................................................................................................19
7. Literature Cited ...............................................................................................................19
LIST OF TABLES
Table 1. FERC recommendations from their Study Plan Determination on 2/1/2013, AEA’s
responses to FERC recommendations, and page number(s) in this document where
each recommendation is addressed (Pages). ......................................................................... 23
Table 2. Area, location, and sub location of desired baseline samples of adult Chinook
salmon spawning aggregates for genetic analysis................................................................. 24
Table 3.- Location, and sublocation of desired baseline samples of adult sockeye salmon
spawning aggregates for genetic analysis. ............................................................................ 27
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page ii April 2013
Table 4. Location, and sublocation of desired baseline samples of adult chum salmon
spawning aggregates for genetic analysis. ............................................................................ 28
Table 5. Location, and sublocation of desired baseline samples of adult coho salmon
spawning aggregates for genetic analysis. ............................................................................ 29
Table 6. Location, and sublocation of desired baseline samples of adult pink salmon
spawning aggregates for genetic analysis. ............................................................................ 29
Table 7. Potential resident and non-salmon anadromous fish species targeted for genetic
tissue sampling in the Susitna River. ................................................................................... 31
LIST OF FIGURES
Figure 1. A generalized flow chart to distinguish among hypotheses of population structure
for Chinook salmon collected over spawning habitat above Devils Canyon in the
Middle and Upper Susitna River. .......................................................................................... 32
Figure 2. Potential baseline sampling locations for adult Chinook salmon. ................................ 33
Figure 3. Potential baseline sampling locations for adult sockeye salmon. ................................. 34
Figure 4. Potential baseline sampling locations for adult chum salmon. ..................................... 35
Figure 5. Potential baseline sampling locations for adult coho salmon. ...................................... 36
Figure 6. Potential baseline sampling locations for adult pink salmon. Circles indicate the
number of samples in the Gene Conservation Laboratory archives. .................................... 37
APPENDICES
Appendix A. Genetic Sampling Instructions
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page iii April 2013
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)
centimeter cm
deciliter dL
gram g
hectare ha
kilogram kg
kilometer km
liter L
meter m
milliliter mL
millimeter mm
Weights and measures (English)
cubic feet per second ft3/s
foot ft
gallon gal
inch in
mile mi
nautical mile nmi
ounce oz
pound lb
quart qt
yard yd
Time and temperature
day d
degrees Celsius °C
degrees Fahrenheit °F
degrees kelvin K
hour h
minute min
second s
Physics and chemistry
all atomic symbols
alternating current AC
ampere A
calorie cal
direct current DC
hertz Hz
horsepower hp
hydrogen ion activity pH
(negative log of)
parts per million ppm
parts per thousand ppt,
‰
volts V
watts W
General
Alaska Administrative
Code AAC
all commonly accepted
abbreviations e.g., Mr., Mrs.,
AM, PM, etc.
all commonly accepted
professional titles e.g., Dr., Ph.D.,
R.N., etc.
at @
compass directions:
east E
north N
south S
west W
copyright
corporate suffixes:
Company Co.
Corporation Corp.
Incorporated Inc.
Limited Ltd.
District of Columbia D.C.
et alii (and others) et al.
et cetera (and so forth) etc.
exempli gratia
(for example) e.g.
Federal Information
Code FIC
id est (that is) i.e.
latitude or longitude lat. or long.
monetary symbols
(U.S.) $, ¢
months (tables and
figures): first three
letters Jan,...,Dec
registered trademark
trademark
United States
(adjective) U.S.
United States of
America (noun) USA
U.S.C. United States
Code
U.S. state use two-letter
abbreviations
(e.g., AK, WA)
Mathematics, statistics
all standard mathematical
signs, symbols and
abbreviations
alternate hypothesis HA
base of natural logarithm e
catch per unit effort CPUE
coefficient of variation CV
common test statistics (F, t, χ2, etc.)
confidence interval CI
correlation coefficient
(multiple) R
correlation coefficient
(simple) r
covariance cov
degree (angular ) °
degrees of freedom df
expected value E
greater than >
greater than or equal to ≥
harvest per unit effort HPUE
less than <
less than or equal to ≤
logarithm (natural) ln
logarithm (base 10) log
logarithm (specify base) log2, etc.
minute (angular) '
not significant NS
null hypothesis HO
percent %
probability P
probability of a type I error
(rejection of the null
hypothesis when true) α
probability of a type II error
(acceptance of the null
hypothesis when false) β
second (angular) "
standard deviation SD
standard error SE
variance
population Var
sample var
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 1 April 2013
1. PURPOSE
The Alaska Energy Authority (AEA) has proposed a hydroelectric project on the Susitna River,
which would involve construction of a dam and reservoir at river mile (RM) 184, approximately
34 miles upstream of Devils Canyon (Figure 2). Construction and operation of the Susitna-
Watana Hydroelectric Project (Project) will modify the flow, thermal, and sediment regimes of
the Susitna River, which may alter the composition and distribution of fish populations.
Genetic analyses can be used in two different ways to assess potential Project impacts. First,
genetic analyses can describe the current genetic relationships among fish populations. These
relationships will be useful in determining relatedness and isolation of spawning aggregates in
the watershed and will serve as baseline for assessing potential Project impacts by species both
before and after construction of the Project. For example, to determine if fish above and below
the proposed dam site part of a single population. Secondly, genetic analyses can be used as tool
(genetic “tag”) to identify population-of-origin for rearing fish sampled in locations and at times
when multiple populations are mixed. For example, this tool can be used examine habitat used
by juvenile Chinook salmon populations within the Susitna River drainage. Understanding of
stock-specific habitat use will provide insights into potential effects of the Project on rearing
areas distant from spawning locations. For this document, a population is defined as a group of
individuals of the same species living in close enough proximity that any member of the group
can potentially mate with any other member (Waples and Gaggiotti 2006).
The usefulness of genetics as a tag depends on the degree of genetic variation among populations
of interest in the Susitna watershed. Genetic variation among populations is governed by
migration, genetic drift (changes in allele frequencies within loci across generations due to
sampling error), and natural selection (non-random process resulting from differential
reproductive fitness among alleles). If breeding isolation (lack of migration) among populations
occurs over sufficient time and population sizes are small enough, genetic drift will result in
variation in allele frequencies at neutral loci (loci not under natural selection) among
populations. Additionally, breeding isolation coupled with differential natural selection will
result in variation in allele frequencies at loci under selection among populations even in the
absence of genetic drift. These variations in allele frequencies at loci among populations (from
either drift or natural selection) create naturally occurring genetic “tags” that can be used to
identify individual spawning populations in mixtures of several populations.
This operational plan describes the first study necessary for the application of genetic
information and methods to evaluate Project effects on fish in the Susitna River. It will begin by
developing a repository of fish tissues from anadromous (defined in this document as Chinook,
chum, coho, pink, and sockeye salmon) and resident (defined in this document as all other
species) fishes. These tissue repositories will be used for future studies necessary to characterize
the genetic legacy and variation for species and populations of interest. It is important to collect
tissue samples before the Project begins to examine possible changes in population structure
associated with the Project. The emphasis of tissue collection will be on samples representing
the five species of Pacific salmon spawning within the Susitna River watershed. Chinook
salmon are a species of particular interest because they are the only anadromous species known
to pass the Devils Canyon impediments, beginning at ~ RM 150, and spawn in areas below and
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 2 April 2013
above the proposed dam site. Understanding the population structure of Chinook salmon
collected above and below Devils Canyon will therefore inform policymakers on the relatedness
and isolation of spawning aggregates. Population structure of Chinook salmon will be measured
within the set of individuals spawning above the canyon, among the groups of individuals
spawning within the Susitna River watershed (with particular emphasis on the Middle River
(~RM 98 – 184) and Upper River (>RM 184; Figure 2)), and in relationship to populations from
nearby drainages in Upper Cook Inlet. Genetic information will be assessed for its utility as a
tool to investigate whether juvenile Chinook salmon originating from the Middle and Upper
River rear in the Lower River; if so, these fish in the Lower River must be added to assessments
of Chinook salmon production upstream.
This work will be conducted through collaboration among Alaska Energy Authority (AEA),
Alaska Department of Fish and Game (ADF&G), and other licensing participants. Information
developed in this study may also assist in the development of protection, mitigation, or
enhancement measures to address potential adverse Project impacts to fish resources, as
appropriate.
2. BACKGROUND
2.1. Existing Information and Need for Additional Information
The genetics samples collected during this study will be used to create a tissue repository for
resident and anadromous fishes in Susitna River with particular emphasis on developing the
genetic baseline for Susitna River salmon populations. Existing tissue collections and genetic
analyses for resident species are limited within the Susitna River. There are few samples in the
tissue archive from resident, non-salmon fish species, because these samples have only been
collected opportunistically. Some genetic/phenotypic analyses have been completed on three-
spine sticklebacks from the Matanuska/Susitna drainages (Cresko et al. 2004), but no population-
structure analyses are available. Population analyses of Bering Cisco indicate that Susitna River
supports a single population (Brown et al. 2012).
Tissue collections and genetic analyses of Pacific salmon stocks elsewhere in Alaska are
relatively well developed and are used for applied research in several watersheds. The baseline
genetic data currently available for the Susitna River is comprehensive only for sockeye salmon;:
data for the other four species vary from moderate (Chinook salmon) to almost non-existent
(pink salmon). Ten Chinook salmon were sampled in 2012 in Kosina Creek in the Upper Susitna
River for genetic analysis.
Samples obtained in this study enable the application of genetic methods in the future to assess
genetic relatedness and isolation of fishes in the watershed and can be used to help determine
potential impacts from the Project. For example, interbreeding by resident fish among areas
might be hindered by Project-imposed barriers, thereby potentially reducing the fitness of some
stocks. Breeding isolation of stocks may be a sign of adapted traits for particular features of the
habitats; such information would alter the impact assessment, and possibly the design of any
proposed mitigation measures. To characterize relatedness and any isolation of particular
resident fishes, tissue samples for genetic analysis must be collected from a range of locations.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
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FERC Project No. 14241 Page 3 April 2013
2.1.1. Assessing Chinook Salmon Population Structure
In 2012, some adult Chinook salmon ascended and remained above Devils Canyon during the
spawning season. This observation led to questions about whether these fish 1) represent a self-
sustaining, genetically isolated, and potentially locally-adapted population (Hypothesis 1a;
Figure 1), 2) are individuals originating from other geographic spawning aggregates below
Devils Canyon (Hypothesis 2; e.g., Portage Creek), or 3) are individuals resulting from
successful reproduction in the Upper River but with a high level of introgression from other
geographic spawning aggregates below Devils Canyon (Hypothesis 1b). Identifying Chinook
salmon originating from above Devils Canyon in mixtures of fish from throughout the Susitna
River drainage will only be possible if these fish represent a self-sustaining population with little
gene flow from populations below the canyon (Hypothesis 1a; Figure 1).
Genetic analysis can help to distinguish among these hypotheses (e.g. Waples and Gaggiotti
2006). Given the small numbers of Chinook salmon that are thought to spawn above Devils
Canyon, genetic drift is expected to be the dominant mechanism for changes in allele frequencies
through time. If gene flow exists, it is likely to go from large populations below the canyon to
the small population(s) above the canyon, just based on demographics.
High genetic divergence between fish spawning above Devils Canyon and fish spawning in
aggregates below the canyon could indicate either a self-sustaining population above the canyon
with little gene flow with other populations (Hypothesis 1a), or recent colonization by small
numbers of successfully-contributing families (Hypothesis 1b). A recent colonization by a small
number of successfully-contributing families, along with high gene flow from straying fish each
generation (Hypothesis 1b), might also be interpreted as an indication of a self-sustaining
spawning aggregate (Hypothesis 1a) with data from only 1 or 2 years. The stability of allele
frequencies across years (cohorts) will provide a means to distinguish between these two
hypotheses (1a and 1b). Assessing stability in allele frequencies across years will need to
account for effective population sizes (Waples and Teel 1990). In addition to temporally stable
allele frequencies, conformance to HWE would also add support for Hypothesis 1a. Conversely,
a lack of temporal stability of allele frequencies and lack of conformance to HWE would support
Hypotheses 1b or 2,
On the other hand, low genetic divergence between fish spawning above Devils Canyon and fish
spawning in aggregates below the canyon would indicate that a large proportion of the fish
ascending Devils Canyon are strays or colonizers, and have not established a self-sustaining
population (support for Hypothesis 2). It may be possible to sample sufficient numbers of fish
from the three years of this study to address Hypothesis 2 (i.e., no divergence seen from a
sufficiently large sample). However, providing evidence for Hypothesis 1 may be difficult with
samples from three return years if the samples do not represent fish from multiple cohorts and/or
if the “signal” is weak, even if a large number of fish can be sampled in locations above and
below Devils Canyon.
Sampling across three years (2012-14) to assess temporal stability in allele frequencies from fish
above Devils Canyon may limit the ability to conclusively distinguish among Hypothesis 1a, 1b,
and 2. The statistical power to detect temporal stability of allele frequencies and conformance to
HWE is only possible with adequate numbers of samples obtained over multiple years and across
cohorts of returning salmon. The adequacy of sample sizes across years depends on the amount
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 4 April 2013
of genetic variation in the population. A small sample size may be adequate to detect large
genetic deviation from populations below Devils Canyon or high inter-annual variation in
samples from each area, but large sample sizes will be required to detect small genetic
deviations. Samples from three calendar years may represent Chinook salmon from as many as 5
or 6 brood years given the multiple ages of maturity in any given year. If large numbers of fish
can be sampled in each of the remaining calendar years (2013 and 2014), it may be possible to
detect instability in allele frequencies if instability exists (some support for Hypothesis 1a). In
summary, the degree of genetic divergence between fish sampled from above and below Devils
Canyon and the stability of allele frequencies across years from 2012–2014 will dictate the level
of support for the existence of a self-sustaining, genetically isolated, and potentially locally-
adapted populations.
2.1.2. Approach to Study Design and Implementation for Chinook Salmon
Above Devils Canyon
The ability to determine the level of genetic divergence of Chinook salmon captured above
relative to below Devils Canyon will be a function of the following:
• Numbers of fish passing through the canyon in 2013 and 2014.
• The ages of fish sampled for genetics.
• The degree of underlying genetic divergence between fish captured above and below
Devils Canyon.
• Temporal stability of allele frequencies within populations.
• Genetics baseline information on any spawning aggregates not currently included in the
baseline.
Given that this information is currently unknown, we propose a comprehensive sampling effort
to help answer as many or all possible hypotheses about the genetic structure of Chinook salmon
in the Middle and Upper River. Some outcomes may preclude or significantly affect the type
and number of samples to analyze. This Operational Plan describes dedicated sampling effort by
field crews for 4 months each year during the spawning period of adult salmon, sufficient to
collect tissue samples over a representative proportion of the entire run of each salmon species.
Additional samples will be collected from other studies, as described Sections 9.5, 9.6, and 9.7 of
the Revised Study Plan (RSP).
To ensure that data sources (and hypotheses) are rigorously examined, AEA will work closely
with geneticists from State and Federal (NOAA and FWS) genetics laboratories. ADF&G’s
Gene Conservation Laboratory (GCL) will be contracted to do the study. Collaboration with
Federal agencies will occur through regular updates to the quarterly Technical Working Group
(TWG) meetings in 2013 and 2014. A draft of this Implementation Plan was provided to the
USFWS and NOAA on 31 March 2013 for their input prior to filing the plan with FERC. Input
from these federal agencies has been addressed in this final Implementation Plan for 2013.
An updated, detailed annual Implementation Plan will be prepared and circulated to TWG
members by April 30 of 2014. The 2014 Genetics Implementation Plan will establish details for
field sampling efforts (including relative priorities, and temporal and spatial sampling
considerations, that take into account the experience from the 2013 field season) and statistical
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 5 April 2013
analysis methods that take into account the success of sampling from the 2013 field season.
FERC’s February 1, 2013 recommendations, which were based on agency consultations and
comments on the RSP are documented, evaluated, and addressed in Table 1 and throughout this
Operational Plan.
2.2. Study Area
The study area encompasses the Susitna River and its tributaries from Cook Inlet upstream to the
Oshetna River confluence (RM 233.4; Figure 2). For baseline data related to stock-specific
sampling, there is an emphasis on tributaries of the Middle and the Upper Susitna River. For
assessing habitat use (juveniles) of fish originating from the Middle (RM 98 – 184) and Upper
Susitna River (RM 184 – 233.4), tissue from juvenile Chinook salmon will be collected in the
Lower River (< RM 98).
3. OBJECTIVES
The goals of this study are to (1) acquire genetic material from samples of selected fish species
within the Susitna River drainage, (2) characterize the genetic structure of Chinook salmon in the
Susitna River watershed, and (3) assess the use of Lower and Middle River habitat by juvenile
Chinook salmon originating in the Middle and Upper Susitna River.
Objectives:
1. Develop a repository of genetic samples for target resident fish species captured within
the Lower, Middle, and Upper Susitna River drainage.
2. Contribute to the development of genetic baselines for chum, coho, pink, and sockeye
salmon spawning in the Middle and Upper Susitna River drainage.
3. Characterize the genetic population structure of Chinook salmon from Upper Cook Inlet,
with emphasis on spawning aggregates in the Middle and Upper Susitna River.
4. Examine the genetic variation among Chinook salmon populations from the Susitna
River drainage, with emphasis on Middle and Upper Susitna River populations, for use in
mixed-stock analyses (MSA).
5. If sufficient genetic variation is found for MSA, estimate the annual percent of juvenile
Chinook salmon in selected Lower River habitats that originated in the Middle and Upper
Susitna River in 2013 and 2014.
4. METHODS
4.1. Survey Flights
Prior to sample collection trips, aerial surveys will be conducted to determine presence and
assess relative abundance of adult salmon at potential sampling locations (Tables 2–6). Chinook
salmon in upper Cook Inlet generally reach spawning grounds between mid-July and early-
August. Each year, survey flights in the Susitna River drainage above the Yentna River
confluence (Susitna River) will begin the first week of July and continue through September.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
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FERC Project No. 14241 Page 6 April 2013
During the 3 week period of July 15 – August 4, when Chinook salmon are usually on their
spawning grounds, additional weekly survey flights will be conducted in the Yentna River
drainage. When conditions allow, Susitna River survey flights will be conducted Monday of
each week and Yentna River survey flights on Tuesday of each week. Populations sampled
elsewhere in Cook Inlet (see Purpose section, above) will be surveyed from the road system or
by separate studies conducted by ADF&G Sport Fish Division.
During survey flights, GPS waypoints will record locations where salmon are present along with
indication of the number of each species observed. In addition, survey flights will be used to
determine potential access to sampling locations (e.g., helicopter, fixed-wing, ATV, boat, etc.).
Information from the survey flights will be recorded in the ADF&G Gene Conservation
Laboratory (GCL) Oracle database, LOKI, and will be used inseason to determine locations and
logistics for directing sampling crew efforts.
4.2. Samples to Collect
Ideal sample size for baseline collections to investigate population structure using genetic
markers is affected by many variables including the generating process, whether the populations
are in equilibrium or not, and the number of markers and alleles associated with them (Landguth
et al. 2010). The upper end of an adequate sample size is 500 individuals, but some researchers
have proposed as few as 20 to 30 individuals (Hale et al. 2012). With information on some of
these variables, a simulation program is available to assess the statistical power of different
sample sizes (Ryman and Palm 2006). However, without the information on these variables, we
cannot perform useful simulations so we propose an idealized sample size of 200 fish per
population for markers with moderate numbers of alleles (i.e. uSATs), and an idealized sample
size of 100 fish per population for markers with two alleles (i.e. SNPs). Small sample sizes of 50
fish per population may be adequate to conduct coarse-scale population structure analyses and
MSA depending on the values of the variables listed above (Landguth et al. 2010; Hale et al.
2012). For mixed stock collections, sample sizes of 200 fish or 100 fish per collection are
adequate to provide stock composition estimates that are within 7% or 10% of the true estimate
95% of the time, respectively (Thompson 1987).
A population is defined as a group of individuals of the same species living in close enough
proximity that any member of the group can potentially mate with any other member (Waples
and Gaggiotti 2006). Functionally, populations will be represented by single or pooled
collections following the “Pooling Collections into Populations” methods below. Based on field
sampling from previous years (Tables 2–6), information gathered from the Catalog of Waters
Important for the Spawning, Rearing or Migration of Anadromous Fishes
(http://www.adfg.alaska.gov/sf/SARR/AWC/), the Susitna Hydro Aquatic Studies (Thompson et
al 1986), and talking with local biologists, we selected possible sites where fish of each target
Pacific salmon species might be spawning. We provide a list of these sites with idealized sample
sizes for each (Tables 2-6). We will make an intensive effort to collect these samples as outlined
in the sections below. However, we are unlikely to obtain the idealized sample size for all of
these sites due to uncontrolled variables (i.e., numbers of fish at a spawning location, number of
fish returning in 2013 and 2014, access issues associated with weather conditions and
mechanical problems, water conditions, and stream characteristics and fish behavior affecting the
catchability of the fish). To reflect the uncertainty in sample collection success, we added a
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column to Tables 2–6 labeled “Expected” that shows the number of fish we reasonably think can
be sampled at each site (or group of sites) in two years, based or previous efforts (and results)
and on and information from the aforementioned catalog and studies. The following sample
collection targets apply only to collections targeted in this study. Some of these samples may be
collected in other program studies, but sample sites that are not targeted in this study are not
listed even if they are proposed to be sampled in other program studies for genetic tissues.
4.2.1. Sample collection targets
1. Collect tissue samples from 50 representative individuals from each of the resident fish
species listed in Table 7, with an emphasis on fish collected in the Lower, Middle and
Upper Susitna River (Objective 1).
2. Collect tissue samples from 100 individuals (total archived and new samples) from at
least 3 spawning aggregates of pink, sockeye, chum, and coho salmon from each of the
following drainages: 1) the Susitna River upstream of the Three Rivers Confluence
(Middle Susitna River), 2) the Talkeetna River, and 3) the Chulitna River (Tables 3–6;
Figures 3–6; Objective 2).
3. Collect sufficient tissue samples from Chinook salmon spawning in Knik Arm and
northwestern Cook Inlet rivers so that at least 2 additional rivers in each region are
represented in the baseline by up to 200 Chinook salmon (total archived and new
samples) (Table 2; Objective 3).
4. Collect sufficient tissue samples from Chinook salmon spawning in Susitna River
tributaries so that each tributary is represented in the baseline by at least 50, but ideally
200 Chinook salmon (total archived and new samples; Table 2; Figure 2; Objectives 3
and 4).
5. Collect tissue samples from a target of 200 juvenile Chinook salmon at each of the
following: Cheechako Creek, Fog Creek, Kosina Creek, Oshetna River (Table 2;
Objectives 3 and 4).
6. Collect tissue samples from 100 juvenile Chinook salmon from 16 sites across 5
mainstem habitat types in the Lower Susitna River (1,600 fish; Objective 5).
4.2.2. Adult Chinook salmon collections
Weekly survey flights will be conducted from June 8 to September 23 to determine the timing
and locations for sampling. Sampling crews will be dispatched when and where Chinook salmon
are observed over spawning habitat. The most intensive sampling of adult Chinook salmon will
occur July 15 – August 4. Because Chinook salmon are generally spread out in streams and in
lower abundance compared to other salmon species, multi-day sampling trips will be required to
get an adequate sample from each location (Table 2; Figure 2). During this time period, each of
the three sampling crews will attempt to collect samples from at least two locations per week
with an average of 2.5 days per trip. The two extra days each week will allow crews to be
relocated and resupplied with sampling gear, food, and other camping supplies, and acquire
information from GCL staff for their next sampling location(s).
During the intensive Chinook salmon sampling period, two crews will be dedicated to sampling
in the Susitna River and one crew will be dedicated for sampling the Yentna River and
northwestern Cook Inlet. Additional GCL staff will collect Chinook salmon samples from
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locations on the road system in the Susitna River and Knik Arm. Because of the large area to be
sampled and short window of opportunity each year to collect Chinook salmon samples, crews in
the Susitna River will have a helicopter (Robinson R-44 II; operated by Alpine Air Alaska, Inc.)
on call for transport to and from sampling locations. Base of operations for the Alpine Air
helicopter will depend on the areas where crews will be sampling and will be determined in
season. The Yentna River crew will charter helicopter (Enstrom F28F) flights, as needed,
through Talaheim Lodge, based on the Talachulitna River.
Chinook salmon will be captured using either hook-and-line, seines, gillnets, or dipnets
depending on the size of the stream and where the fish are located. Upon capture, a single
axillary process will be clipped from each Chinook salmon and placed in a bottle of ethyl alcohol
for preservation (Appendix A1). For Chinook salmon sampled above Devils Canyon, additional
paired samples/data will be collected including scales, length (mid-eye to fork, to nearest 5 mm),
sex, and GPS information (decimal, to the nearest 0.001). Therefore, for these fish, axillary
process and 5 scale samples will be sampled into individually-labeled vials. Scales will be
sampled at a point along the diagonal line from the posterior insertion of the dorsal fin to the
anterior insertion of the anal fish, 2 rows above the lateral line. Length, sex and GPS
information will be recorded on write-in-the-rain notebooks paired with the vial identifier. Fish
will be held in the water as much as possible while hooks are removed and samples are collected,
and released immediately after the sample has been placed in the bottle. If necessary, crews will
hold the fish in the water to make sure they can swim before releasing them.
Chinook salmon collections will not be limited to the three-week intensive sampling period and
may occur as early as the first week of July and as late as the last week of August. In addition to
sampling adult Chinook salmon on these trips, crews may opportunistically collect samples from
juvenile Chinook salmon, other salmon species, and other fish species (Table 7). Collection trips
before and after the three-week intensive sampling period will be performed by two crews, but
trip lengths will be longer (approximately 4 days – one trip per crew per week) due to the lower
anticipated availability of helicopter charters. We will charter helicopter (Enstrom F28F) flights,
as needed, through Talaheim Lodge, mainly to access sites above Devils Canyon and use a jet
boat mainly to access sites below Devils Canyon in the Upper and Middle Susitna River.
4.2.3. Other adult salmon collections
Collections from adult pink, sockeye, chum, and coho salmon will begin in late July and
continue through the end of the field season in late-September. During the Chinook salmon
collection period, collections from these species will be conducted by the 2 Susitna River crews
on an opportunistic basis. After August 4th, each of the 3 sampling crews will be assigned to one
of the following drainages to collect samples from at least 3 locations for each species: 1) the
Middle and Upper Susitna River, 2) the Talkeetna River, and 3) the Chulitna River. Collection
locations and method of transport to sampling locations will be determined after weekly survey
flights (Tables 2–6; Figures 3–6). Capture and sampling of salmon will follow methods used for
adult Chinook salmon.
Previously documented spawning time periods for each species in the Middle Susitna River,
indicated below, will be used as the general time periods for sampling trips (Thompson et al.
1986).
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• Pink salmon – last week of July to third week of August
• Chum salmon – late-August to mid-September
• Sockeye salmon – late-August to mid-September
• Coho salmon – late-August to late-September
4.2.4. Juvenile Chinook salmon collections
4.2.4.1. Above Devils Canyon
Tissue samples from a target (ideal) of 200 juvenile Chinook salmon will be collected at each of
the following: Cheechako Creek, Fog Creek, Kosina Creek, and Oshetna River. We expect to
collect fewer than the ideal sample size per site (Table 2). When possible, these collections will
occur at the same time as adult salmon collection trips.
Methods for capturing juvenile Chinook salmon in minnow traps and seines follow those
suggested by Magnus et al. (2006). Cured salmon roe will be used as bait and several minnow
traps will be set at each location. Minnow traps will be checked at least once a day.
Pelvic fin tissue will be collected from each juvenile Chinook salmon captured and place in an
individual 2ml vial (Appendix A2). Samples will be taken from the same side of each fish to
help prevent resampling of individuals. Total length (snout-to-fork) will be recorded for each
sampled juvenile.
4.2.4.2. Lower River collections
Samples of juvenile Chinook salmon collected in the Lower River will be classified by habitat
type to examine the potential for stock-specific variation in habitat type use. Habitat
classifications will either follow those proposed in Study 9.9 (see Table 9.9-4 of the RSP), or
those used by Murphy et al. 1989; main channels, backwaters, braids, channel edges, and
sloughs). At least 3 locations will be sampled for each habitat type over the 2-year study
period. Crews will begin juvenile collections as early as the first week of May and continue
through early-July. Additional collections may occur between mid-August and the end of
September to meet the yearly sampling goal. Sampling locations will be determined each year
and will be accessed by river boat.
Juvenile Chinook salmon in the Lower River will be captured using the same methods as
described for the juvenile Chinook collections above the Three Rivers Confluence. Minnow
traps will be checked at least once a day and will be reset until the sampling objective (100
samples per location) has been met or few new fish are captured between checks. If the
sampling objective cannot be met at a location, a new one will be selected.
Tissue samples will be collected using the same methods as described for the juvenile Chinook
collections above the Three Rivers Confluence.
4.2.4.3. Species identification of juvenile collections
Species identification will be performed in the field using phenotypic characteristics (i.e. Pollard
et al. 1997). A subset of juvenile putative Chinook salmon collected below Devils Canyon will
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be selected during the season from each collection team and analyzed with DNA markers to
verify correct field species identification. All Pacific salmon captured above Devils Canyon will
be sampled and species will be identified in the field. Species identification using DNA will be
confirmed post season.
4.2.5. Other species collections
Samples of resident fish species will be opportunistically collected while crews are collecting
adult and juvenile salmon samples. Resident fish will be identified to genus or species with a
field key and a picture will be taken. A small piece of fin tissue will be sampled from each fish
and placed into a bottle or vial of ethyl alcohol for preservation (Appendix A1). Samplers will
record on each bottle, or on datasheets for vial collections, which of the following areas the
samples were collected: 1) Chulitna River, 2) Talkeetna River, 3) Upper Susitna River, 4)
Middle Susitna River below Devils Canyon, and 5) Middle Susitna River above Devils Canyon.
Tissues will be placed in separate bottles for each species and the area where they were
collected.
4.2.6. Coordination with other Project studies
As described in the RSP, tissue samples will also be collected by four other studies conducted for
the Project in 2013 and 2014: 9.5 (Upper River Fish Distribution), 9.6 (Middle and Lower River
Fish Distribution), 9.7 (Salmon Escapement); and 9.16 (Eulachon Run Timing, Distribution, and
Spawning). Sampling kits and collection protocols will be distributed to study leads in advance
of the field season, and a weekly communication protocol will be developed to maximize
collections. Collection progress will be updated using a database accessible to all study leads.
4.2.7. Collection trip documentation
Detailed notes will be kept during each collection trip and then entered into the trip report
database in the GCL Oracle database, LOKI, when crews return to Anchorage. The information
that will be recorded for each trip will be: 1) trip logistical information, 2) GPS waypoints where
fish were collected, 3) number of fish and species collected at each location, 4) notes on other
fish species present, 5) life stage of observed fish, 6) fish habitat information, and 7)
recommendations for future collection trips. Collection trip records will be used postseason to
submit Anadromous Waters Catalog nomination forms.
4.3. Tissue Storage
While in the field, tissue samples will be preserved in ethyl alcohol in either a 125–500 milliliter
(ml) bulk sample bottle for each location or individual 2 ml vials (Appendices A1 and A2). After
samples are received by the GCL, collection information will be recorded in LOKI. For long-
term storage, samples will be preserved as follows: 1) sample will be placed into plastic plates
and freeze-dried; 2) once dry, moisture-indicating desiccant beads will be added and the plate
sealed completely with aluminum foil heat-activated tape; and 3) tissue samples will then be
stored at room temperature.
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4.4. Laboratory Analysis
DNA will be extracted from axillary processes using DNeasy 96 tissue kits. Two panels of SNP
markers will be assayed: one to determine species identification for juvenile collections and the
other to genotype Chinook salmon.
For juvenile Chinook salmon samples, species identification will be made by genotyping 5
single nucleotide polymorphism (SNP) markers (OKESSA1-OKE, OTSSSA1-OTS, ONEOGO1-
ONE, OKI1-OKI, OTSOKI1-OKI) using Applied BioSystems’ SNP Taqman assay analysis
methods described below. These five markers differentiate between Pacific salmon species and
rainbow trout. Positive controls for all species will be analyzed along with the unknown fish.
Both adult and juvenile Chinook salmon samples will be analyzed for 96 SNP markers and 12
microsatellite markers for population genetic structure or MSA.
The DNA samples will be analyzed using Fluidigm 96.96 Dynamic Arrays
(http://www.fluidigm.com). The Fluidigm 96.96 Dynamic Array contains a matrix of integrated
channels and valves housed in an input frame. On one side of the frame there are 96 inlets to
accept the sample DNA from each individual fish and on the other are 96 inlets to accept the
assays for each SNP marker. Once in the wells, the components are pressurized into the chip
using the IFC Controller HX (Fluidigm). The 96 samples and 96 assays are then systematically
combined into 9,216 parallel reactions. Each reaction is a mixture of 4 microliters (ul) of assay
mix (1x DA Assay Loading Buffer [Fluidigm], 10x TaqMan SNP Genotyping Assay [Applied
Biosystems], and 2.5x ROX [Invitrogen]) and 5 ul of sample mix (1x TaqMan Universal Buffer
[Applied Biosystems], 0.05x AmpliTaq Gold DNA Polymerase [Applied Biosystems], 1x GT
Sample Loading Reagent [Fluidigm], and 60-400ng/ul DNA) combined in a 6.7 nanoliter (nL)
chamber. Thermal cycling is performed on an Eppendorf IFC Thermal Cycler as follows: an
initial “hot mix” of 30 minutes at 70°C, and then denaturation of 10 minutes at 96°C followed by
40 cycles of 96°C for 15 seconds and 60°C for 1 minute. The Dynamic Arrays are read on a
BioMark Real-Time PCR System (Fluidigm) after amplification and scored using Fluidigm SNP
Genotyping Analysis software.
For some SNP markers, genotyping will be performed in 384-well reaction plates. Each reaction
is conducted in a 5 μL volume consisting of 5–40 ng of template DNA, 1x TaqMan Universal
PCR Master Mix (Applied Biosystems), and 1x TaqMan SNP Genotyping Assay (Applied
Biosystems). Thermal cycling is performed on a Dual 384-Well GeneAmp PCR System 9700
(Applied Biosystems) as follows: an initial denaturation of 10 minutes at 95°C followed by 50
cycles of 92°C for 1 second and annealing/extension temperature for 1.0 or 1.5 minutes. The
plates are scanned on an Applied Biosystems Prism 7900HT Sequence Detection System after
amplification and scored using Applied Biosystems’ Sequence Detection Software (SDS) version
2.2.
For microsatellite markers, samples will be assayed for DNA loci developed by the Genetic
Analysis of Pacific Salmon group funded by the Pacific Salmon Commission for use in U.S.-
Canada Treaty fisheries. Polymerase chain reaction (PCR) will be carried out in 10ul reaction
volumes (10mM Tris-HCl, 50mM KCl, 0.2 mM each dNTP, 0.5 units Taq DNA polymerase
(Promega, Madison, WI)) using an Applied Biosystems (AB, Foster City CA) thermocycler.
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Primer concentrations, MgCl concentrations and the corresponding annealing temperature for
each primer are available upon request. PCR Fragment analysis will be done on an AB 3730
capillary DNA sequencer. 0.5ul PCR product will be loaded into a 96-well reaction plate along
with 0.5ul of GS500LIZ (AB) internal lane size standard and 9.0ul of Hi-Di (AB). PCR bands
will be visualized and separated into bin sets using AB GeneMapper software v4.0.
All genotypes collected will be entered into the GCL Oracle database, LOKI. Quality control
measures include re-extraction and re-analysis of 8 percent of each collection for all markers to
ensure that genotypes are reproducible and to identify laboratory errors and rates of
inconsistencies. Genotypes are assigned to individuals using a double-scoring system.
Scales from Chinook sampled above Devils Canyon will be mounted on gum cards at the GCL
and impressions will be made in cellulose acetates and aged at the ADF&G, should age
information be required.
4.5. Data Retrieval and Quality Control
Genotypes will be retrieved from LOKI and imported into R (R Development Core Team 2011)
with the RODBC package (Ripley 2010). All subsequent analyses will be performed in R, unless
otherwise noted.
Prior to statistical analysis, 4 analyses will be performed to confirm the quality of the data. First,
SNP markers will be identified that are invariant in all individuals or that have very few
individuals with the alternate allele in only one collection. These markers will be excluded from
further statistical analyses. Second, individuals will be identified that are missing substantial
genotypic data because they likely have poor quality DNA. Individuals missing substantial
genotypic data will be identified using the 80 percent rule (missing data at 20 percent or more of
loci; Dann et al. 2009). These individuals will be removed from further analyses. The inclusion
of individuals with poor quality DNA might introduce genotyping errors into the baseline and
reduce the accuracies of mixed stock analyses.
The third QC analysis will identify individuals with duplicate genotypes and remove them from
further analyses. Duplicate genotypes can occur as a result of sampling or extracting the same
individual twice, and will be defined as pairs of individuals sharing the same alleles in 95 percent
of screened loci. The individual sample with the most missing genotypic data from each
duplicate pair will be removed from further analyses. If both samples have the same amount of
genotypic data, the first sample will be removed from further analyses.
The final QC analysis will identify individuals from the juvenile collections that appear to be full
or half siblings. Inclusion of siblings provides inappropriately precise estimates of allele
frequencies. We will use the program ml-relate (Kalinowski et al. 2006) to detect siblings and
exclude from the baseline all but one individual from every set of siblings identified.
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4.6. Genetic Baseline Development
4.6.1. Consultation with other Agencies regarding appropriate statistical
analyses
Below we outline statistical analyses that can be performed to examine population structure and
to develop a baseline for use as a tool in MSA. However, many of these analyses are dependent
on sample sizes and the results from preceding analysis. As this information becomes available,
other analyses may be more appropriate. In January of 2014 and 2015, we will work in
consultation with other Agencies (NOAA and FWS) to fine-tune analyses that are most
appropriate for this genetics project.
4.6.2. Adult and Juvenile collections
Adult collections from all areas will be used for baseline development. However, if inadequate
numbers of adult samples are collected above Devils canyon, juvenile collections may also be
incorporated into the baseline for this area. Before juvenile collections are incorporated into the
baseline, we will test for sibling relationships (see methods in QC, above), test for differences in
allele frequency estimates between the adult collections and juvenile collections (see methods
under pooling collections, below), and examine Hardy-Weinberg equilibrium (HWE) of pooled
adult/juvenile collections (see methods in pooling collections, below). We will delete all but 1
individual from every sibling group, and exclude juvenile collections from the baseline if they
show significant allele frequency differences from adult collections or show deviations from
HWE when pooled with adult collections.
4.6.3. Hardy-Weinberg Expectations
For each locus within each collection, tests for conformance to Hardy-Weinberg expectations
(HWE) will be performed using Monte Carlo simulation with 10,000 iterations in the Adegenet
package (Jombart 2008). Probabilities will be combined for each collection across loci and for
each locus across collections using Fisher’s method (Sokal and Rohlf 1995), and collections and
loci that violated HWE will be excluded from subsequent analyses after correcting for multiple
tests with Bonferroni’s method (α = 0.05 per number of collections).
4.6.4. Temporal Variation
Temporal variation of allele frequencies will be examined with a hierarchical, three-level
analysis of variance (ANOVA). Temporal samples will be treated as sub-populations based on
the method described in Weir (1996). This method will allow for the quantification of the
sources of total allelic variation and permit the calculation of the among-years component of
variance and the assessment of its magnitude relative to the among-population component of
variance. This analysis will be conducted using the software package GDA (Lewis and Zaykin
2001).
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4.6.5. Pooling Collections into Populations
When appropriate, collections will be pooled to obtain better estimates of allele frequencies
following a step-wise protocol. First, collections from the same geographic location, sampled at
similar calendar dates but in different years, will be pooled, as suggested by Waples (1990).
Then differences in allele frequencies between pairs of geographically proximate collections that
were collected at similar calendar dates and that might represent the same population will be
tested. Collections will be defined as being “geographically proximate” if they were collected
within the same tributary (or river for mainstem spawners). Fisher’s exact test (Sokal and Rohlf
1995) of allele frequency homogeneity will be used, and decisions will be based on a summary
across loci using Fisher’s method. Collections will be pooled when tests indicate no difference
between collections (P > 0.01). When all individual collections within a pooled collection are
geographically proximate to other collections within the same tributary, the same protocol will
be followed until significant differences are found between the pairs of collections being tested.
After this pooling protocol, these final collections will be considered to be populations. Finally,
populations will be tested for conformance to HWE following the same protocol described above
to ensure that pooling was appropriate, and that tests for linkage disequilibrium will not result in
falsely positive results due to departure from HWE.
4.6.6. Linkage Disequilibrium
Linkage disequilibrium between each pair of nuclear markers will be tested for in each
population to ensure that subsequent analyses are based on independent markers. The program
Genepop version 4.0.11 (Rousset 2008) will be used with 100 batches of 5,000 iterations for
these tests. The frequency of significant linkage disequilibrium between pairs of SNPs (P < 0.05)
will then be summarized. Pairs will be considered linked if they exhibited linkage in more than
half of all populations.
4.6.7. Hierarchical Log-likelihood Ratio Tests
Genetic diversity will be examined with a hierarchical log-likelihood ratio (G) analysis with the
package hierfstat (Goudet 2006).
4.6.8. Visualization of Genetic Distances
To visualize genetic distances among collections, two approaches will be used. Both approaches
are based on pairwise FST estimates from the final set of independent markers with the package
hierfstat. The first approach is to construct 1,000 bootstrapped neighbor-joining (NJ) trees by
resampling loci with replacement to assess the stability of tree nodes. The consensus tree will be
plotted with the APE package (Paradis et al. 2004). While these trees provide insight into the
variability of the genetic structure of collections, pairwise distances visualized in three
dimensions are more intuitive. In a second approach, pairwise FST will be plotted in a
multidimensional scaling (MDS) plot using the package rgl (Adler and Murdoch 2010).
4.6.9. Testing Among Hypotheses
For the first hypothesis criterion in Figure 1, we will test for panmixia (spawning aggregates
belong to the same population) using Fisher’s exact test of allele frequency homogeneity. For
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the second hypothesis criterion in Figure 1, we will test for temporal stability in allele
frequencies using a three-level analysis of variance (ANOVA). The three levels of the hierarchy
include variation within collections, variation within location among years, and variation among
locations. In addition, we will test between hypotheses 1a and 1b by investigating conformation
to HWE and calculation of effective population sizes and migration rates. Conformance to HWE
across markers will be tested using Fisher’s exact test. Effective population sizes will be
estimated using juvenile collections within cohorts. Juveniles will be binned into cohorts by
total length (snout-to-fork). Finally, we will use the program MIGRATE (Beerli and Felsenstein
2001) to estimate migration rates and direction of migration. All tests will use a significance
level of α = 0.05, adjusted for multiple tests.
4.7. Mixed-Stock Analysis
4.7.1. Assessing Reporting Groups (including above Devils Canyon) for MSA
In response to FERC comments from 2/1/2013, a preliminary analysis of SNP data from 42 loci
using the selected pre-existing baseline and the 2012 collections was proposed to provide some
insight into the potential of genetic data to detect fish from above Devils Canyon in mixtures
(SPD). Subsequent comments from both NMFS and FWS both recommended that such an
analysis was inappropriate given the small sample sizes, and that testing for genetic
differentiation among Chinook salmon above and below Devils Canyon for use in MSA should
wait until more samples are available. We therefore will not analyze these samples until more
samples collected and we can do a more comprehensive analysis.
A comprehensive analysis will be conducted when microsatellite and SNP data are available
from baseline collections sampled through 2014. We will use two methods to assess the utility
of reporting groups for MSA once these data are available: anticipated mixture proof tests and
ONCOR leave-one-out method (Anderson et al. 2008). For the anticipated-mixture proof tests,
we will sample without replacement 400 individuals from reporting groups in proportions similar
to those expected in the Lower River juvenile samples. We will estimate the stock compositions
of these mixed composition proof tests following the BAYES protocol described below and
compare these estimates to the true proportions. To account for sampling error, we replicate this
procedure 10 times in a manner similar to Habicht and Dann (2012a).
For the leave-one-out method, we will use ONCOR, a Windows-based program available at
http://www.montana.edu/kalinowski, to implement the simulations. This program handles only
diploid markers, so we will exclude linked and mtDNA loci from the analysis. The output from
this analysis produces stock proportion point estimates for each population by reporting group.
These two analyses will determine whether the population structure is adequate for MSA to
produce useful results. Generally, correct assignments of 90% to reporting groups are
considered adequate for MSA, but this criterion is dependent on the purpose of the analysis.
Adequate MSA performance will be determined in consultation with Agency (NOAA/FWS)
geneticists and will be based on the reporting groups of interest to and risk tolerance. For an
example of this process, see Habicht et al. (2012b).
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4.7.2. Mixed Stock Analysis of juvenile Chinook salmon
The stock compositions of juvenile Chinook salmon will be estimated using a Bayesian approach
to genetic MSA, the Pella-Masuda Model (BAYES; Pella and Masuda 2001). The Bayesian
method of MSA estimates the proportion of stocks caught within each sample using 4 pieces of
information: 1) a baseline of allele frequencies for each population, 2) the grouping of
populations into the reporting groups desired for MSA, 3) prior information about the stock
proportions of the fishery, and 4) the genotypes of fish sampled from the fishery. We will use a
flat prior for all analyses.
We will run 5 independent Markov Chain Monte Carlo (MCMC) chains of 40,000 iterations with
different starting values and discard the first 20,000 iterations to remove the influences of the
initial start values. We will define the starting values for the first chain such that the first 1/5 of
the baseline populations sum to 0.9 and the remaining populations sum to 0.1. Each chain will
have a different combination of 1/5 of baseline populations summing to 0.9. We will combine
the second halves of these chains to form the posterior distribution and tabulate mean estimates,
90% credibility intervals, the probability of an estimate being equal to zero, and standard
deviations from a total of 100,000 iterations. For each tabulated measure, summary statistics
will be based upon the raw posterior, which will be calculated out to 6 significant digits.
We will also assess the within- and among-chain convergence of these estimates using the
Raftery-Lewis (within-chain) and Gelman-Rubin (among-chain) diagnostics. These values
measure the convergence of each chain to stable estimates (Raftery and Lewis 1996), as well as
measure the variation of estimates within a chain to the total variation among chains (Gelman
and Rubin 1992), respectively. If the Gelman-Rubin diagnostic for any stock group estimate is
greater than 1.2 we will reanalyze the mixture with 80,000-iteration chains following the same
protocol. If the Gelman-Rubin diagnostic for any stock group estimate is greater than 1.2 after
this reanalysis, we will analyze the mixture with the program HWLER (Pella and Masuda 2006).
HWLER is similar to BAYES in that it estimates stock compositions based upon a Bayesian
model, but differs in that it incorporates information about the effect of assigning mixture
individuals to baseline populations with respect to the Hardy-Weinberg and linkage equilibria
conditions observed in the baseline populations. In doing so it allows for the identification of
extra-baseline individuals that contravene these equilibria conditions, but contribute to the
mixture in question. We will incorporate this information into the definition of the posterior for
those mixtures that failed to converge after reanalysis with 80,000-iteration chains in BAYES.
4.7.3. Habitat Utilization in the Lower River by Chinook Salmon Progeny
Originating in the Middle and Upper Susitna River
If the results of the Chinook salmon genetics studies conducted during 2012 are sufficient to
indicate that there is adequate genetic diversity between the Chinook salmon spawning upstream
of Devils Canyon and in the Middle River and its tributaries, ADF&G will characterize the
presence and relative proportion of fish originating from the Upper and Middle River in selected
Lower River habitats. In 2013 and 2014, 100 juvenile Chinook salmon total from each of 16
mainstem locations (across five habitat types) will be collected and preserved as outlined above.
These 1,600 tissue samples will be analyzed and the results will be pooled into a range of spatial
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FERC Project No. 14241 Page 17 April 2013
strata to identify any Middle and Upper River fish, and where feasible, estimate the proportion of
fish originating from upstream of the Three Rivers Confluence (RM 98).
4.8. Consistency with Generally Accepted Scientific Practice
Each method described above employs scientifically accepted principles as noted by regular
citations of peer reviewed methods, where they are presented. The laboratory and analytical
methods to be used for this study are widely applied in North America and Asia to characterize
the origin and genetic variation in salmonid and non-salmonid fish species. GCL is located in
Anchorage, Alaska, has a lot of experience with applied fish genetics and has a long history of
publishing techniques and results from its studies in the peer-reviewed literature. GCL personnel
serve on many multi-national scientific work groups from around the Pacific Rim.
5. SCHEDULE AND DELIVERABLES
• Laboratory analysis of 2012 collections: March to September, 2013.
• Adult Chinook salmon baseline sample collection: May through October 2013 and 2014
(in collaboration with other AEA field studies).
• Other species sample collection: May through October 2013 and 2014 (in conjunction
with other AEA field studies).
• Juvenile Chinook salmon mixture sample collection from the Lower River: May through
October 2013 and 2014.
• Consultation with agencies (NMFS/FWS) to review sample collection results from 2013
in preparation for 2014 field season and project statistical analyses: January 2014
• Preparation of Interim Study Report (ISR). September 2013 – January 2014
• Laboratory analysis of adult Chinook salmon baseline and juvenile mixture samples:
October 2013 to November 2014.
• Statistical analysis of Chinook salmon baseline collections to examine population
structure and potential application of MSA: December 2014
• Consultation with agencies (NMFS/FWS) to review genetic analysis and determine if
adequate genetic variation exists for MSA of juvenile Chinook salmon mixture samples:
January 2015
• Assuming adequate genetic variation for MSA, statistical analysis of juvenile mixture
samples: February 2015.
• Prepare Updated ISR: December – January 2015
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• Deliverables:
o February 1, 2014. Interim Study Report delivered to FERC. Report describes
field effort and collection results. Report will include tables of collections with
associated metadata: Sampling locations, GPS coordinates, sampling dates,
sample species, and sample sizes.
o March 31, 2014. Draft Operational Plan for 2014 Fieldwork to AEA, NMFS and
FWS for review.
o April 30, 2014. Final Operational Plan for 2014 filed with FERC.
o February 1, 2015. Updated Interim Study Report providing analysis results for
population structure and MSA potential. If MSA is useful, MSA results for
juvenile mixtures.
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6. RESPONSIBILITIES
Andrew Barclay, Fishery Biologist III
Duties: Coordinate field and laboratory aspects of genetics project. Perform analysis of
genetic structure and mixed-stock analysis. Write initial and updated study reports to
AEA. Track budgets.
Chris Habicht, Fisheries Geneticist III
Duties: Coordinate with AEA and its contractors to produce genetics project deliverables
on time. Review operational plans and prioritize resources among laboratory projects to
meet deadlines.
Jim Jasper, Biometrician III
Duties: Biometric support. Assist in report writing. Also reviews operational plan and
final report.
Vacant, Fishery Biologist I (3 positions)
Duties: Sampling trip logistics, lead sampling crews, capture spawning adult salmon,
juvenile Chinook salmon, and non-salmon fish species to collect tissue samples for
genetic analysis, write trip reports, and Anadromous Wasters Catalog nominations.
7. LITERATURE CITED
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fishery mixtures. Alaska Department of Fish and Game, Division of Commercial
Fisheries, Regional Information Report 5J12-27, Anchorage.
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Habicht, C., J. R. Jasper, T. H. Dann, N. DeCovich, and W. D. Templin. 2012b. Western
Alaska Salmon Stock Identification Program Technical Document 11: Defining reporting
groups. Alaska Department of Fish and Game, Division of Commercial Fisheries,
Regional Information Report 5J12-16, Anchorage
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genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele
frequencies. PloS one, 7(9), e45170.
Jombart, T. 2008. Adegenet: a R package for the multivariate analysis of genetic markers.
Bioinformatics 24: 1403-1405. doi: 10.1093/bioinformatics/btn129.
Kalinowski, S. T., A. P. Wagner and M. L. Taper. 2006. M‐relate: a computer program for
maximum likelihood estimation of relatedness and relationship. Molecular Ecology
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Koljonen, M.-L., J. J. Pella, and M. Masuda. 2005. Classical individual assignments vs. mixture
modeling to estimate stock proportions in Atlantic salmon (Salmo salar) catches from
DNA microsatellite data. Canadian Journal of Fisheries and Aquatic Sciences 62:1887–
1904.
Landguth, E. L., B. C., Fedy, S. J. Oyler-McCance, A. L. Garey, S. L. Emel, M. Mumma, H. H.
Wagner, M.-J. Fortin, and S. A. Cushman. 2012. Effects of sample size, number of
markers, and allelic richness on the detection of spatial genetic pattern. Molecular
Ecology Resources, 12(2), 276-284.
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Lewis, P. O., and D. Zaykin. 2001. Genetic data analysis: computer program for the analysis of
allelic data. Version 1.0. URL http://lewis.eeb.uconn.edu/lewishome/software.html.
Luikart ,G. and J.-M. Cornuet. 1999. Estimating the effective number of breeders from
heterozygote excess in progeny. Genetics 151, 1211–1216.
Magnus, D. L., D. Brandenberg, K. F. Crabtree, K. A. Pahlke, and S. A. McPherson. 2006.
Juvenile salmon capture and coded wire tagging manual. Alaska Department of Fish and
Game, Special Publications No. 06-31, Anchorage.
Murphy, L. M., J. Heifetz, J. F. Thedinga, S. W. Johnson, and K V. Koski, 1989. Habitat
utilization by juvenile Pacific salmon (Oncorhynchus) in the glacial Taku River,
southeast Alaska. Canadian Journal of Fisheries and Aquatic Sciences 46:1677-1685.
Nei, M. 1978. Estimation of average heterozygosity and genetic distance from a small number of
individuals. Genetics. 89: 583-590.
Paradis, E., J. Claude, and K. Strimmer. 2004. APE: analyses of phylogenetics and evolution in
R language. Bioinformatics 20: 289–290.
Pella, J., and M. Masuda. 2001. Bayesian methods for analysis of stock mixtures from genetic
characters. Fishery Bulletin 99(1):151–167.
Pella J., and M. Masuda. 2006. The Gibbs and split-merge sampler for population mixture
analysis from genetic data with incomplete baselines. Canadian Journal of Fisheries and
Aquatic Sciences 63:576–596.
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juvenile salmonids. Harbour Publishing.
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http://www.R-project.org/.
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S. Richardson, and D.J. Spiegelhalter, editors. Markov chain Monte Carlo in practice.
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Rousset, F. 2008. GENEPOP ' 007: a complete re-implementation of the GENEPOP software for
Windows and Linux. Molecular Ecology Resources 8(1):103–106.
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December 2012) http://www.susitna-watanahydro.org/wp-content/uploads/2012/12/01-RSP-
Dec2012_1of8-Sec-1-5-IntrothroughWaterQuality-v2.pdf
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Ryman N. and S. Palm. 2006. POWSIM: a computer program for assessing statistical power when
testing for genetic differentiation. Mol. Ecol. 6: 600–602.
Sokal, R. R. and F. J. Rohlf. 1995. Biometry. 3rd Edition. Freeman, San Francisco, CA.Thomas, A.,
O'Hara, B., Ligges, U., and Sturtz, S. 2006. Making BUGS Open. R News 6 (1): 12- 17.
SPD: Study Plan Determination for the Susitna-Watana Hydroelectric Project, Project No.
14241-000 (issued Feb. 1, 2013)
Tallmon, D. A., G. Luikart, and M. A. Beaumont. 2004. A comparative evaluation of a new
effective population size estimator based on approximate Bayesian computation. Genetics
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Tallmon, D. A., A. Koyuk, G. Luikart and M. A. Beaumont. 2008. ONeSAMP: a program to
estimate effective population size using approximate Bayesian computation. Molecular
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Thompson, S. K. 1987. Sample size for estimating multinomial proportions. The American
Statistician 41: 42-46.
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Susitna River Aquatics Studies Program. Report # 13, Volume 1: Adult Salmon
Investigations May – October 1985. Alaska Power Authority, Anchorage, Alaska.
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changes in allele frequency. Conservation Biology, 4(2), 144-156.
Waples R.S. 1991. Genetic methods for estimating the effective size of cetacean populations. In:
(ed. Hoezel AR) Report of the International Whaling Commission, pp. 279–300.
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disequilibrium at unlinked gene loci. Conservation Genetics, 7, 167–184.
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Table 1. FERC recommendations from their Study Plan Determination on 2/1/2013, AEA’s responses to FERC
recommendations, and page number(s) in this document where each recommendation is addressed (Pages).
FERC Recommendation AEA Response Pages
We recommend the study plan be modified to include
the following: AEA consult with the FWS and NMFS
prior to preparing the project operational plans;
distribute draft project operational plans to the
agencies by March 31 of each year of study
implementation; allow 15 days for the agencies to
provide comments on the draft plans; file the final
plans with the Commission by April 30 of each year of
study implementation; and include with the final plans,
documentation of agency consultation, description of
how agency comments are incorporated into the final
plans, and an explanation for why any agency
comments are not incorporated into the final plans.
For each year of the study, AEA will submit a draft
operational plan to NMFS, and USFWS for review by
March 31 and agency comments will be returned by
April 15. The final draft will be submitted to FERC by
April 30.
15-16
To the extent feasible, we recommend that AEA collect
tissue samples over a representative proportion of the
entire adult Chinook salmon run.
The field season for this study has been extended to 4
months (June - September), which will include weekly
aerial surveys to confirm the presence or abundance of
adult salmon at potential sampling locations. These
surveys will be used to inform sampling crews where to
focus their efforts.
6-8
We recommend that AEA include in the 2013 project
operational plan, a schedule for when the 2012
genetics studies would be available, and include
provisions for filing those results with the Commission
through either the initial study report, or a
supplemental report in 2013.
Dates for the analysis and reporting of the 2012
collections have been added to the Schedule and
Deliverables section of the Implementation Plan.
15-16
We also recommend that the report on the 2012
preliminary genetics studies clearly describe the
criteria, using current scientific literature, to determine
whether there is sufficient genetic uniqueness to
estimate the percentage of Chinook originating from
Upper and Middle River habitats in areas sampled
downstream.
Criteria for determining if there is sufficient genetic
diversity to estimate the percentage of Chinook salmon
originating from Upper and Middle River habitats has
been added the methods section of the Implementation
Plan.
13-14
Finally, we recommend that the report on the 2012
preliminary genetics studies clearly describe whether
the study results indicate that sufficient genetic
uniqueness is found to characterize the presence and
relative proportion of fish originating from the Upper
and Middle River in selected Lower River habitats as
described in section 9.14.4.7 of the study plan.
The report on the 2012 preliminary genetics studies will
include a test to determine if the allele frequencies of
Chinook salmon collected from Kosina Creek are
significantly different from Chinook salmon populations
spawning below Devils Canyon. A significant
difference in allele frequencies will bode well, but not
guarantee, the usefulness of MSA to separate
populations of juvenile Chinook salmon from the
Middle and Lower River, as proposed. Note that this
analysis has been removed in respose to NMFS and
FWS comments received 4/15/2013.
13
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Table 2. Area, location, and sub location of desired baseline samples of adult and juvenile Chinook salmon for genetic analysis.
Sample sizes show number of samples and sample years for collections already in the Gene Conservation Laboratory archives (Archived), number of samples to
obtain the ideal sample size (Ideal), and the anticipated number to be collected over the two years of this project based on past sampling effort and success and
information from the Anadromous Rivers Catalog and local biologists (Expected). Some of the expected numbers are for groups of locations. Sample collection
targets apply only to collections targeted in this study. Some of these samples may be collected in other program studies, but sample sites that are not targeted in
this study are not listed, even if they are proposed to be sampled in other program studies for genetic tissues. Map numbers (Map No.) correspond to location
numbers on Figure 2.
Map
No.
Sample sizes
This project
Area Location Sublocation Year(s) Collected Archived Ideal Expected
Adult Chinook salmon
West Cook Inlet
1 Chuitna River 2008, 2009 142 58 58
2 Beluga River Coal Creek 2009, 2010, 2011 120 80 80
3 Theodore River 2010, 2011, 2012 189 11 11
4 Lewis River 2011, 2012 86 114 86
Yentna Drainage
5 Clearwater Creek 2012 25 175 50
6 Red Creek 2012 29 171 58
7 Happy River 2012 19 181 38
8 Red Salmon Creek 2012 12 188 24
9 Hayes River 2012 5 195 10
10 Canyon Creek 2012 32 168 64
11 Talachulitna River 1995, 2008, 2010 180 20 20
12 Lake Creek Sunflower Creek 2009, 2011 127 71 71
13 Kahiltna River Peters Creek 2009, 2010, 2011,
2012
110 90 55
Susitna Drainage
15 Chulitna River Middle Fork 2009, 2010, 2011 182 18 18
14 East Fork 200
200
16 West Fork 200
17 Honolulu Creek 200
18 Byers Creek 200
19 Troublesome Creek 200
20 Spink Creek 200
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21 Tokositna River (Bunco Creek) 200
-continued-
Table 2. Page 2 of 3.
Map
No.
Sample sizes
This project
Area Location Sublocation Year(s) Collected Archived Ideal Expected
22 Above Devils Canyon Oshetna River 200
50
23 Kosina Creek 2012 10 190
24 Watana Creek 200
25 Tsusena Creek 200
26 Fog Creek 200
27 Devil Creek 200
29 Middle Susitna River Portage Creek 2009, 2010, 2011 141 59 59
28 below Devils Canyon Chinook Creek 200
75
30 Indian River 1212 1 199
31 Gold Creek 200
32 Lane Creek 200
33 Chase Creek 200
35 Talkeetna River Prairie Creek 1995, 2008 169 31 31
34 Upper mainstem 200
100
36 Iron Creek 200
37 Disappointment Creek 200
38 Sheep River 200
39 Larson Creek 200
40 Chunilna Creek (Clear Creek) 2009, 2012 130 70 65
42 Lower Susitna River, Montana Creek 2008, 2009, 2010 218 0 0
41 upstream of Deshka Birch Creek 200
50 43 Sheep Creek 200
44 North Fork Kashwitna River 200
45 Little Willow Creek 200
46 Willow Creek 1991,1997, 2005,
2009
309 0 0
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47 Deshka River Moose Creek 1995, 2012 103 97 52
48 Deshka River weir 2005 200 0 0
-continued-
Table 2. Page 3 of 3.
Map
No.
Sample sizes
This project
Area Location Sublocation Year(s) Collected Archived Ideal Expected
49 Alexander Creek Sucker Creek 2011, 2012 143 57 57
Knik Arm
50 Matanuska River Kings River 200 25 51 Granite Creek 200
52 Moose Creek 1995, 2008, 2009,
2012
155 45 45
53 Eagle River South Fork 2009, 2011, 2012 73 127 24
54 Meadow Creek 2009 6 194 12
55 Ship Creek 2009 311 0 0
56 Little Susitna River 2009, 2010 125 75 75
Juvenile Chinook salmon
22 Susitna Drainage Above Devils Canyon Oshetna River
2012 35
200
70 23 Kosina Creek 200
24 Fog Creek 200
25 Cheechako Creek 200
Susistna Draniage Lower River 5 habitat types 1,600 1,600
(100 fish/habitat type times 3
or 4 collections)
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Table 3.- Location, and sublocation of desired baseline samples of adult sockeye salmon spawning aggregates for genetic analysis.
Sample sizes show number of samples and sample years for collections already in the Gene Conservation Laboratory archives (Archived), number of samples to
obtain the ideal sample size (Ideal), and the anticipated number to be collected over the two years of this project based on past sampling effort and success and
information from the Anadromous Rivers Catalog and local biologists (Expected). Some of the expected numbers are for groups of locations. Map numbers
(Map No.) correspond to location numbers on Figure 3.
Map
No.
Area
Sample sizes
This project
Location Sublocation Year(s) Collected Archived Ideal Expected
Susitna River
1 Chulitna River East Fork 100 100 2 Middle Fork 100
3 Byers Lake 1993, 2006, 2007 243 0 0
4 Spink Creek 2007, 2008 126 0 0
5 Tokositna River Sloughs 100 100
6 Swan Lake 2006, 2007, 2009 109 0 0
7 Middle Susitna River McKenzie Creek 100 100 8 below Devils Canyon Chase Creek 100
9 Mainstem sloughs
above Three Rivers
Confluence
sloughs 8A,11, and 21 1995, 1996, 1997 156 0 0
10 Talkeetna River Sheep River 2008 190 0 0
11 Stephan Lake 1993, 1994, 2007 346 0 0
12 Iron Creek 100 50
13 Sloughs 1997 79 21 21
14 Larson Creek 1992, 1993 200 0 0
15 Larson Lake - Eastern shore 2011 90 10 10
16 Larson Lake - outlet stream 2011 126 0 0
17 Chunilna Creek 100 100
18 Mama and Papa Bear Lakes 1997, 2007 106 0 0
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Table 4. Location, and sublocation of desired baseline samples of adult chum salmon spawning aggregates for genetic analysis.
Sample sizes show number of samples and sample years for collections already in the Gene Conservation Laboratory archives (Archived), number of samples to
obtain the ideal sample size (Ideal), and the anticipated number to be collected over the two years of this project based on past sampling effort and success and
information from the Anadromous Rivers Catalog and local biologists (Expected). Some of the expected numbers are for groups of locations. Map numbers
(Map No.) correspond to location numbers on Figure 4.
Map
No.
Area
Sample sizes
This project
Location Sublocation
Year(s)
Collected Archived Ideal Expected
1 Susitna River Chulitna River Middle Fork 100
200 2 above Three West Fork 100
3 Rivers Byers Creek 100
4 Confluence Troublesome Creek 100
5 Spink Creek 2007, 2008 45 55 55
6 Tokositna River mainstem 100 50
7 Middle Susitna River sloughs above Three Rivers Confluence 1996 103 0 0
8 below Devils Canyon Indian River
100 100
9 Portage Creek
100 100
10 Talkeetna River Sloughs 1995 50 50 50
11
Upper mainstem 100
200 12
Disappointment Creek 100
13
Sheep River 100
14
Larson Creek 100
15 Chunilna Creek 1993 87 13 13
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Table 5. Location, and sublocation of desired baseline samples of adult coho salmon spawning aggregates for genetic analysis.
Sample sizes show number of samples and sample years for collections already in the Gene Conservation Laboratory archives (Archived), number of samples to
obtain the ideal sample size (Ideal), and the anticipated number to be collected over the two years of this project based on past sampling effort and success and
information from the Anadromous Rivers Catalog and local biologists (Expected). Some of the expected numbers are for groups of locations. Map numbers
(Map No.) correspond to location numbers on Figure 5.
Map No.
Area
Sample sizes
This project
Location Sublocation Year(s) Collected Archived Ideal Expected
1 Susitna River Chulitna River East Fork 100
200
2 above Three Middle Fork 100
3 Rivers
Honolulu Creek 100
4 Confluence
Byers Creek 100
5
Troublesome Creek 100
6 Spink Creek 2008 38 62 62
7 Tokositna River mainstem 100 100 8 Tokositna River (Bunco Creek) 100
9 Tokositna River (Swan Lake) 2009 20 80 80
10 Middle Susitna River Portage Creek 100
200
11 below Devils Canyon Indian River 100
12 Gold Creek 100
13 McKenzie Creek 100
14 Lane Creek 100
15 Chase Creek 100 75
16 Whiskers Creek 100 75
17 Sloughs 100 75
18 Talkeetna River upper mainstem 100 25
19 Prairie Creek 100 75
20 Sheep River 100 50
21 Larson Lake - outlet 2011 84 16 16
22 Chunilna Creek 100 75
Table 6. Location, and sublocation of desired baseline samples of adult pink salmon spawning aggregates for genetic analysis.
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Sample sizes show number of samples and sample years for collections already in the Gene Conservation Laboratory archives (Archived), number of samples to
obtain the ideal sample size (Ideal), and the anticipated number to be collected over the two years of this project based on past sampling effort and success and
information from the Anadromous Rivers Catalog and local biologists (Expected). Some of the expected numbers are for groups of locations. Map numbers
(Map No.) correspond to location numbers on Figure 6.
Map
No.
Area
Sample sizes
This project
Location Sublocation Year(s) Collected Archived Ideal Expected
1 Susitna River Chulitna River Middle Fork 100
100 2 above Three Troublesome Creek 100
3 Rivers Spink Creek 100
4 Confluence Middle Susitna River Portage Creek 100 50
5 below Devils Canyon Indian River 100 100
6 Gold Creek 100
50
7 McKenzie Creek 100
8 Lane Creek 100
9 Chase Creek 100
10 Whiskers Creek 100
11 Talkeetna River upper mainstem 100 25
12 Sheep River 100 25
13 Larson Creek 100 100
14 Chunilna Creek 100 100
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Table 7. Potential resident and non-salmon anadromous fish species targeted for genetic tissue sampling in the Susitna
River.
Common Name Scientific Name
Rainbow trout Oncorhynchus mykiss
Humpback whitefish Coregonus pidschian
Round whitefish Prosopium cylindraceum
Lake whitefish Coregonus clupeaformes
Bering cisco Coregonus laurettae
Eulachon Thaleichthys pacificus
Pacific lamprey Lampetra tridentata
Longnose sucker Catostomus catostomus
Slimy sculpin Cottus cognatus
Prickly sculpin Cottus asper
Coastal range sculpin Cottus aleuticus
Pacific staghorn sculpin Leptocuttus armatus
Burbot Lota lota
Arctic grayling Thymallus arcticus
Dolly Varden Salvelinus malma
Lake trout Salvelinus namaycush
Northern pike Esox lucius
Threespine stickleback Gasterosteus aculeatus
Ninespine stickleback Pungitius pungitius
Alaska blackfish Dallia pectoralis
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 32 April 2013
Figure 1. A generalized flow chart to distinguish among hypotheses of population structure for Chinook salmon collected over spawning habitat above Devils Canyon in
the Middle and Upper Susitna River.
Only a self-sustaining population (Hypothesis 1a) will potentially result in genetic variation suitable for mixed-stock analysis for estimating the proportion of
juvenile Chinook salmon mixtures collected in the Middle and Lower Susitna River that originate from above Devils Canyon.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 33 April 2013
Figure 2. Potential baseline sampling locations for adult Chinook salmon.
Circles indicate the number of samples in the Gene Conservation Laboratory archives. Numbers correspond to map
numbers on Table 2. Call-outs point to divisions between the Lower Susitna River (below river mile (RM) 98),
Middle River (RM 98-184) and Upper River (RM 184=233.4). RM 184 is the location of the proposed dam.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 34 April 2013
Figure 3. Potential baseline sampling locations for adult sockeye salmon.
Circles indicate the number of samples in the Gene Conservation Laboratory archives. Numbers correspond to map
numbers on Table 3.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 35 April 2013
Figure 4. Potential baseline sampling locations for adult chum salmon.
Circles indicate the number of samples in the Gene Conservation Laboratory archives. Numbers correspond to map
numbers on Table 4.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 36 April 2013
Figure 5. Potential baseline sampling locations for adult coho salmon.
Circles indicate the number of samples in the Gene Conservation Laboratory archives. Numbers correspond to map
numbers on Table 5.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Page 37 April 2013
Figure 6. Potential baseline sampling locations for adult pink salmon. Circles indicate the number of samples in the
Gene Conservation Laboratory archives.
Numbers correspond to map numbers on Table 6.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 April 2013
APPENDIX A: GENETIC SAMPLING INSTRUCTIONS
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Appendix A – Page 1 April 2013
Appendix A 1.–Bulk sampling instructions for adult salmon and other adult fish species. Fin tissue will be
sampled when axillary process is not available.
Non-lethal Bulk Sampling Finfish Tissues for DNA Analysis
ADF&G Gene Conservation Lab, Anchorage
I. General Information
We use axillary process samples from individual fish to determine the genetic characteristics and profile of a particular
run or stock of fish. This is a non-lethal method of collecting tissue samples from adult fish for genetic analysis. The most
important thing to remember in collecting samples is that only quality tissue samples give quality results. If sampling
from carcasses: tissues need to be as “fresh” and as cold as possible and recently moribund, do not sample from fungal
fins.
II. Sampling Method
Preservative used: Isopropanol/Methanol/Ethanol (EtOH) preserves tissues for later DNA extraction. Avoid
extended contact with skin.
Sampling instructions are written for (N=100 fish/125ml) bulk bottle. Steps for collecting axillary process tissues:
Axillary process or “spine”
located above pelvic fin.
Using clippers, cut ½-1”
maximum and place in
bulk bottle.
• Wipe dry the axillary process “spine” prior to
sampling to avoid getting excess water or fish
slime into the 125ml bottle (see diagram).
• Clip off the axillary “spine” using dog nail
clippers or scissors to get roughly a ½ - 1”
inch maximum piece and/or about the size of
a small fingernail.
• Place each tissue piece into bulk bottle (place
only one piece of axillary from each fish).
• Repeat: up to 100 fish /125ml bulk bottle (into
same bottle). If you don’t reach this number
of fish per location, that’s ok. Maximum
storage capacity 125ml bulk for proper
preservation of axillary tissue is (N=100).
• Record on each label: Location, sampling date
(mm/dd/yyyy), sampler’s name(s), total
number of fish sampled, latitude/longitude,
and field notes (if any). Use pencil. This insures
correct data with each collection bottle.
• If collection occurs over 4~5 day period,
“refresh” EtOH at end of the collection.
• After the collection is complete and 24 hours
have passed, “refresh” the axillary tissues as
follows: carefully pour off ¾ EtOH and then
pour fresh EtOH into sample bottle
containing axillary clips. Cap and invert
bottle twice mixing EtOH and tissue.
• Freezing not required, store sample bottle in
upright cool location for good tissue quality.
Ethanol
SILLY: ________________
Location: ______________
Sample Date(s):___/___/___
Sampler's name:__________
Total # fish sampled:_______
Latitude:________________
Longitude:______________
Species:________________
Comments:______________
ADF&G:Preserved in EtOH
Return to ADF&G Anchorage lab: ADF&G – Genetics Lab staff: 907-267-2247
333 Raspberry Road Judy Berger: 907-267-2175
Anchorage, Alaska 99518 Freight code: ____________
Supplies included in sampling kit:
1. Clipper- used to cut a portion of one axillary process per fish.
2. Sample target: 100 axillary clips/125ml bulk bottle.
3. Labels on bulk sample bottles: Location, Sample date, Sampler, Total # fish sampled and comments (if any).
4. 1:125ml wide mouth bottle(s) for EtOH “refresh” step.
5. Sampling instructions.
IMPLEMENTATION PLAN – GENETIC BASELINE STUDY FOR SELECTED FISH SPECIES
Susitna-Watana Hydroelectric Project Alaska Energy Authority
FERC Project No. 14241 Appendix A – Page 2 April 2013
Appendix A 2.–Vial sampling instructions for juvenile Chinook salmon.
Non-lethal Juvenile Finfish Tissue Sampling for DNA Analysis
ADF&G Gene Conservation Lab, Anchorage
I. General Information
We use a portion of one pelvic fin tissue sample from individual fish to determine the genetic characteristics and profile of
a particular run or stock of fish. The most important thing to remember in collecting samples is that only quality tissue
samples give quality results. If sampling from carcasses: tissues need to be as “fresh” and as cold as possible and
recently moribund, do not sample from fungal fins.
Preservative used: Isopropanol/Methanol/Ethanol (EtOH) preserves tissues for later DNA extraction. Avoid
extended contact with skin.
II. Sampling Method
III. Supplies included in sampling kit:
1. Scissors - for cutting one pelvic fin/fish.
2. Cryovials - 2.0ml pre-labeled plastic vials.
3. Caps – cap for each vial.
4. Bullet box- box for holding cryovials while sampling.
5. EtOH – ethanol in Nalgene bottle(s).
6. Squirt bottle – to fill and/or “top off” each cryovial with EtOH.
7. Laminated “return address” labels.
8. Sampling instructions.
IV. Shipping: “in commerce” on roadways for return shipment of these samples.
• Wipe excess water and/or slime off the pelvic fin prior to
sampling to avoid getting either water or fish slime into the 2.0ml
vial (see diagram on reverse side).
• Prior to sampling, fill the tubes half way with EtOH. Fill only the
tubes that you will use for each sampling period. The squirt bottle
is for day use only since it will leak overnight when unattended.
• Cut off only one pelvic fin/fish along dotted line (shown in
diagram to left and on reverse side) using scissors to collect tissue
sample from only one pelvic fin.
• Place one pelvic fin tissue into a 2.0ml vial pre-filled with EtOH.
Ethanol/tissue ratio should be slightly less than 3:1 to thoroughly
soak the tissue in the buffer. Not a problem with juvenile samples.
• Top up vials with EtOH and screw cap on securely. Invert vial
twice to mix EtOH and tissue. Periodically, wipe or rinse the
scissors with water so not to cross contaminate samples with any
tissue from the previous fish sampled.
• Only one pelvic fin clip per fish into each vial/location.
• Data to record: Record each vial number to paired data
information (i.e. location, lat./long., sample date(s), etc.).
Electronic version preferred.
• Tissue samples must remain in 2ml EtOH. Store vials
containing tissues at room temperature but away from heat. In the
field: keep samples out of direct sun, rain and store capped vials in
a dry, cool location. Freezing not required.
Return to ADF&G Anchorage lab: ADF&G – Genetics Lab staff: 907-267-2247
333 Raspberry Road Judy Berger: 907-267-2175
Anchorage, Alaska 99518 Freight code: ____________