Contrasting patterns in growth and survival of Central Valley fall run Chinook salmon related to hatchery and ocean conditions

Abstract

The objective of this study was to determine important ocean and hatchery covariates influencing early growth and survival of Central Valley fall run Chinook salmon. We used a dataset of recaptured coded wire tagged hatchery Chinook salmon to estimate early growth and cohort survival. Ocean conditions during the period of early ocean entry were based on output from a coupled physical-biogeochemical model configured for the broader California Current region. We built generalized additive and generalized linear models to describe growth and survival and used Akaike Information Criterion (AICc) model selection to determine which hatchery and ocean covariates related best to response variables. With regards to hatchery covariates, growth was best explained by release location, while survival was best explained by release weight and hatchery of origin. The ocean conditions included in the best models for both growth and survival included diatoms, predatory zooplankton, temperature, and currents. We observed the highest rates of salmon survival when in situ physical ocean conditions were indicative of relaxation events. For all four ocean covariates, the response curves illustrated opposite patterns between growth and survival models. This result implies that during periods of low survival, juvenile salmon were either 1) growing at a faster rate, or 2) growth appeared to increase because smaller fish had a higher mortality rate than larger fish. The first explanation would imply density-dependence, whereas the second explanation would imply size-selective mortality. These alternatives have implications on hatchery practices including salmon size at release and number of salmon in release groups.

Full text

Contrasting patterns in growth and survival of Central
Valley fall run Chinook salmon related to hatchery
and ocean conditions
Megan C. Sabal &David D. Huff &Mark J. Henderson &
Jerome Fiechter &Jeffrey A. Harding &Sean A. Hayes
Received: 5 February 2016 /Accepted: 14 October 2016
#Springer Science+Business Media Dordrecht 2016
Abstract The objective of this study was to determine
important ocean and hatchery covariates influencing
early growth and survival of Central Valley fall run
Chinook salmon. We used a dataset of recaptured coded
wire tagged hatchery Chinook salmon to estimate early
growth and cohort survival. Ocean conditions during the
period of early ocean entry were based on output from a
coupled physical-biogeochemical model configured for
the broader California Current region. We built general-
ized additive and generalized linear models to describe
growth and survival and used Akaike Information
Criterion (AICc) model selection to determine which
hatchery and ocean covariates related best to response
variables. With regards to hatchery covariates, growth
was best explained by release location, while survival
was best explained by release weight and hatchery of
origin. The ocean conditions included in the best models
for both growth and survival included diatoms, preda-
tory zooplankton, temperature, and currents. We ob-
served the highest rates of salmon survival when in situ
physical ocean conditions were indicative of relaxation
events. For all four ocean covariates, the response
curves illustrated opposite patterns between growth
and survival models. This result implies that during
periods of low survival, juvenile salmon were either 1)
growing at a faster rate, or 2) growth appeared to in-
crease because smaller fish had a higher mortality rate
than larger fish. The first explanation would imply den-
sity-dependence, whereas the second explanation would
imply size-selective mortality. These alternatives have
Environ Biol Fish
DOI 10.1007/s10641-016-0536-3
Electronic supplementary material The online version of this
article (doi:10.1007/s10641-016-0536-3) contains supplementary
material, which is available to authorized users.
M. C. Sabal :M. J. Henderson
Santa Cruz, Cooperative Institute for Marine Ecosystems and
Climate (CIMEC), University of California, Santa Cruz, USA
M. C. Sabal (*):M. J. Henderson :J. A. Harding
Southwest Fisheries Science Center, National Marine Fisheries
Service, National Oceanic and Atmospheric Administration, 110
Shaffer Road, Santa Cruz, CA 95060, USA
e-mail: msabal@ucsc.edu
D. D. Huff
Point Adams Research Station, Northwest Fisheries Science
Center, National Oceanic and Atmospheric Administration, PO
Box 155, Hammond, OR 97121, USA
J. Fiechter
Institute of Marine Sciences, University of California, Santa Cruz,
Santa Cruz, CA 95064, USA
S. A. Hayes
Northeast Fisheries Science Center, National Oceanic and
Atmospheric Administration, 166 Water Street, Woods Hole, MA
02543, USA
M. J. Henderson
United States Geological Survey, California Cooperative Fish and
Wildlife Research Unit, Department of Fisheries Biology,
Humboldt State University, 1 Harpst Street, Arcata, CA 95521,
USA

implications on hatchery practices including salmon size
at release and number of salmon in release groups.
Keywords Growth .Survival .Chinook salmon .
California .Size-selective mortality.Density-
dependence
Introduction
Migratory species encounter a diversity of environments
across space and time, making it difficult to determine
which factors influence overall survival. Because en-
hancing and predicting survival is often a primary goal
of fishery and wildlife managers, environmental com-
plexity presents an enormous challenge for managing
migratory species (Martin et al. 2007). Successful con-
servation must recognize the spatial connections across
life histories to understand how factors that affect one
life stage can subsequently impact future life stages
(Heppell 1998;Klaassenetal.2014). Furthermore, a
mechanistic understanding of how individuals interact
with their environment can contribute to individual-
based models that typically incorporate more realistic
environmental conditions and population dynamics than
statistical models (McLane et al. 2011). Therefore, it is
important to examine effects of various factors across
life stages on individual-based biological responses.
Pacific salmon (Oncorhynchus spp.) are born in
freshwater, migrate through streams and rivers to the
ocean, and spend 1–4 years dispersing in coastal waters
before returning to spawn in their natal rivers. They
experiencediverse environments and stressors over their
lifetime which cumulatively impact adult survival.
Previous studies have examined the influence of a vari-
ety of hatchery, river, and ocean conditions on salmon
survival such as trucking of smolts (Holsman et al.
2012), water temperature (Mueter et al. 2002), climate
(Martins et al. 2012; Sharma et al. 2013), predators
(Holsman et al. 2012), prey (Wells et al. 2012; Losee
et al. 2014), and migration timing (Scheuerell et al.
2009; Satterthwaite et al. 2014). However, the relative
importance of these and other factors is highly variable
and depends on the nature of the population under study
(Martins et al. 2012), the location of the study
(Quiñones et al. 2014), and the temporal and spatial
scale over which variables are measured (Sharma et al.
2013). For example, warm ocean temperatures during a
cohort’s first year at sea have been associated with
increased survival of Alaskan salmon populations,
while cool ocean temperatures have been associated
with increased survival in more southern populations
(Mueter et al. 2002).
One consistently important factor influencing adult
survival is early ocean growth (Holtby et al. 1990;
Friedland et al. 2000; Beamish and Mahnken 2001;
Duffy and Beauchamp 2011). The first few months after
juvenile salmon enter the ocean is a time of high mor-
tality when salmon must avoid starvation and predation.
Hence, salmon must enter the ocean when prey is plen-
tiful to persist and grow to larger sizes and escape
predation by gape-limited predators (Satterthwaite
et al. 2014;Fiechteretal.2015). In California, strong
early season (Jan-Mar) upwelling is important to pre-
condition areas with nutrients that support nekton pro-
duction in the summer, which relates to positive marine
growth of juvenile salmon (Wells et al. 2008; Schroeder
et al. 2013; Fiechter et al. 2015; Wells et al. 2016).
Likewise, warm or cool ocean temperatures, depending
on the location and scale over which they are measured,
have also been related to salmon growth (Friedland et al.
2000; Martins et al. 2012; Agler et al. 2013). Similarly,
in the Pacific Northwest winter preconditioning and
early season upwelling have shown to influence salmon
survival, although relationships with spring upwelling
are variable (Nickelson 1986; Ryding and Skalski 1999;
Logerwell et al. 2003).
Our study examines early growth and cohort survival
of Central Valley fall run Chinook salmon
(O. tshawytscha). Due to California’s arid and variable
Mediterranean climate, freshwater stages of this popu-
lation are periodically exposed to elevated river temper-
atures and reduced river flows relative to Chinook salm-
on populations north of California. These potentially
more stressful freshwater migration conditions may alter
the relative importance of hatchery and ocean conditions
on survival. In addition, California coastal waters un-
dergo intense upwelling and relaxation events, but are
not influenced by large river plumes (De Robertis et al.
2005;Morganetal.2005; Burla et al. 2010)orproduc-
tive downwelling regions (Mueter et al. 2002) which are
important features in northern areas. In the Pacific
Northwest, large river outflows have been shown to
influence both nutrient inputs into the coastal ecosystem
(Davis et al. 2014) and retention of zooplankton on the
shelf (Banas et al. 2009). Therefore, we may expect to
see different dynamics influencing juvenile salmon
growth and survival relative to other regions.
Environ Biol Fish

Many Pacific salmon populations are heavily supple-
mented byhatcheryoperations, some of which use coded
wire tags (CWTs) to monitor harvest and adult survival.
CWTs are small pieces of wire inscribed with unique
codes which are injected into the snouts of juvenile
salmon. It is common to use CWT recoveries from adult
salmon to estimate cohort survival. This study uses CWT
recoveries from juvenile salmon during their first ocean
year, providing information about the specific growth
and ocean conditions salmon experienced during the
critical period of early ocean entry. These fine-scale data
were used to explore relationships with early growth and
survival to age 3. This information is particularly valu-
able because local oceanographic conditions are impor-
tant in driving salmon biological responses such as
growth and survival (Mueter et al. 2002;Sharmaetal.
2013), and because it may generate more mechanistic
hypotheses than broader scale correlations.
Our objective was to determine important factors
influencing early growth and survival to age 3 of
Central Valley fall run Chinook salmon. Specifically,
we fit a series of statistical models that included various
combinations of potential explanatory variables through
Akaike Information Criterion (AICc) model selection.
We further examined model response curves to identify
patterns that may signify mechanisms. Finally, we qual-
itatively compared covariates and model response
curves from our early growth model with our survival
model. Since previous studies suggest that early ocean
growth is important for overall salmon survival, we
expected similar covariates to be included in both
models with similar response curves. However, differ-
ences among covariates or response curves may provide
insights into other processes such as size-selective mor-
tality or density-dependence.
Methods
Study system
The Sacramento-San Joaquin Rivers support the largest
salmon populations in California, and Central Valley fall
run Chinook salmon are the most numerous contributing
race. Five hatcheries supplement fall run Chinook salm-
on populations producing over 32 million smolts per
year; differences in rearing practices among hatcheries
produce smolts that differ in release size, timing, and
location within the watershed (Huber and Carlson 2015).
Approximately 25 % of hatchery released juvenile salm-
on are implanted with unique CWTs which identify
salmon released in a given group and the characteristics
of each release group. CWT release and recapture data
are stored in the Regional Mark Identification System
(RMIS, www.rmpc.org) (Nandor et al. 2010). Fall run
Chinook salmon juveniles enter the ocean at the Golden
Gate Bridge from April–June and encounter ocean con-
ditions driven by upwelling and relaxation events which
have distinct physical ocean characteristics and dictate
productivity and food availability (Pringle and Dever
2009). Chinook salmon disperse along the coastal ocean
rim for 1–4 years, and return to rivers as adults to spawn
in the fall. Adult salmon with CWTs are recovered from
commercial and recreational fisheries, spawning ground
surveys, and hatchery returns.
Data collection
Growth
To investigate salmon early growth, we utilized tag
recoveries of CWT juvenile fall run Chinook salmon.
Juvenile salmon during their first ocean year were sam-
pled from NOAA Fisheries salmon ocean surveys from
1999 to 2012 (excluding 2006–2009), and were recov-
ered from Pigeon Point, CA to Astoria, OR (Fig. 1).
Cruise dates varied across years, but surveys were either
conducted in the summer (mid-June to mid-August) or
in the fall (mid-September to late-October) with three of
the ten years completing both summer and fall cruises
(Table 1). Salmon were captured using a surface trawl
(264 Nordic Rope Trawl) that samples the upper 20 m of
the water column for ~30 min tows (Harding et al.
2011). Biological data including weight, length, and a
DNA sample were taken for all salmon, and these fish
were brought back to the laboratory where salmon miss-
ing their adipose fins were electronically scanned for a
CWT, and if present, the tag was extracted and read.
Of the coded wire tagged, age 1 Central Valley fall
run Chinook salmon recovered in the juvenile salmon
trawl surveys, 171 had sufficient data to estimate
growth. We excluded fish that had less than 10 days
between release and recapture because they would not
have had sufficient time to exhibit differential growth
due to exogenous conditions, and salmon from CWT
groups that were released over a range of dates spanning
greater than 5 days. Salmon age was determined by
Environ Biol Fish

subtracting the brood year, as provided from RMIS,
from the recapture year. Early growth was calculated
for each fish as the difference in recapture weight (g)
from the ocean survey and mean release weight (g) from
the CWT release group. Because growth is estimated by
subtracting a group mean from an individual measure-
ment, there is the potential to over or under estimate
growth if a salmon was released above or below the
119 ° W120° W121° W122° W123° W124° W125° W126° W
46° N
45° N
44° N
43° N
42° N
41° N
40° N
39° N
38° N
37° N
Coleman FH
Feather FH
Nimbus FH
Mokelumne FH
Merced FH
013026065 Kilometers
Battle Creek
Sacramento River
Feather River
American River
Mokelumne River
Merced River
San Joaquin River
Recapture locations
Release locations
Fish hatcheries
Pigeon Point, CA
Astoria, OR
Fig. 1 Locations of juvenile salmon coded wire tag group releases (yellow circles), recoveries of salmon in their first ocean year on NOAA
ocean surveys (red circles), and fish hatcheries (blue triangles)
Environ Biol Fish

mean release weight. Our measure of early growth in-
cludes growth that occurred during river emigration, in
the estuary, and in the coastal ocean.
Survival
Of juvenile salmon recovered with CWTs from the ocean
surveys, 45 unique CWT release groups were represent-
ed between 1999 and 2010 from a dataset of 98 salmon.
Again, we excluded salmon from CWT groups that were
released over a range of dates spanning greater than
5 days. More recent years of recaptured juvenile salmon
(2011–2012) did not have sufficient adult recaptures
from subsequent years to estimate survival. We estimated
survival to age 3 for each CWT release group using tag
recoveries from multiple life stages from RMIS in a
Virtual Population Cohort Analysis (VPA) (Magnusson
and Hilborn 2003). For each CWT release group, we
determined the total number of salmon released and the
total number and dates of adult fish (age 1+) recovered
from the RMIS database (Supplementary Table 1). The
range of number of fish per release group was 15,770 to
396,000 (mean: 124,200). To estimate survival rates, we
followed the methods of Magnusson and Hilborn (2003)
and estimated the number of individuals from a release
group that survived to age 3 (N
3
):
N3¼C2s2þC3þC4
s3
þC5
s3s4
þC6
s3s4s5
Where C
a
is the number offish recovered atage a and
s
a
is the adult survival rate at age a. As with Magnusson
and Hilborn, we assume that the adult survival rates
were constant (s
2
=0.6,s
3
=0.7,s
4
=0.8,s
5
=0.9)
and relatively minor components of the smolt-to-adult
survival rate. The survival rate to age 3 for each release
group was then estimated by dividing N
3
by the number
of individuals released.
It is important to note that survival was estimated for
each release group based only on recoveries of adult
tagged fish (age 1+), not those recaptured in the juvenile
ocean survey (Supplementary Table 1). We then used
the survival rate estimated for the release group as the
response for the individual fish recovered in the juvenile
survey (Table 2). As a result, if more than one fish from
a single release group was recaptured in the juvenile
survey they would have the same survival response.
Explanatory variables
We collected data on hatchery and ocean conditions that
we hypothesized could affect salmon growth and survival
(Table 2). Hatchery conditions were taken from the RMIS
database for each CWT release group. We considered
release date, release weight (g), release location, and
hatchery of origin as potential important hatchery factors.
The first two variables were treated as continuous and last
two as categorical. Release location was described as the
distance upstream (km) from the Golden Gate Bridge, and
was calculated using linear referencing tools in ArcGIS
10.2. Locations that were less than 10 km apart were
grouped together at a middle distance value, combining
17 release locations into 10 distance groups (35, 50, 70,
105, 126, 143, 167, 183, 215, 525 km). We treated release
location as a categorical variable because locations may
have multiple attributes that uniquely affect juvenile salm-
on; however, we were also able to visually assess trends
related to distance upstream from the Golden Gate Bridge
by defining the categories by distance.
To determine early ocean conditions likely encoun-
tered by individual salmon, we estimated where, and
over which dates, salmon traveled in the coastal ocean.
We estimated ocean entry dates following the methods
from Fisher et al. (2014) with the exception that instead
of using a constant mean value, we predicted river
migration rate using a linear mixed-effects model and
AICc model selection using CWT juvenile salmon
recaptured between May 15th and June 16th from
NOAA San Francisco Bay trawl surveys over 10 years
Tabl e 1 Dates of NOAA salmon ocean surveys where juvenile
Chinook salmon with CWTs were recovered
Cruise Year Dates
IW9901 1999 8/3–8/9
IW9902 1999 10/20–10/22
IW0001 2000 6/20–6/30
IW0101 2001 7/24–8/5
IW0201 2002 6/19–6/27
IW0202 2002 9/17–9/26
IW0301 2003 7/8–7/14
FR0401 2004 7/26–7/30
CA0501 2005 10/5–10/13
FR1001 2010 6/30–7/13
FR1101 2011 6/29–7/15
FR1102 2011 9/7–9/16
OS1201 2012 6/11–6/25
Environ Biol Fish

(n= 121). We considered four fixed variables (mean
release weight (g), release date, distance released up-
stream, and May river flow), included release year and
release location as random effects, and did not allow
correlated variables (> 0.7) to occur in the same model
(Dormann et al. 2012). We excluded fish where the
linear mixed-effects model estimated negative down-
stream migration rates (3 % of all growth data, 2 % of
all survival data). These covariates come from the RMIS
database, therefore ocean entry dates were estimated for
each CWT release group.
To determine ocean travel routes for each salmon, we
used ArcGIS cost distance and cost path tools to generate
least-cost routes for juvenile salmon traveling from the
Golden Gate Bridge to recapture locations. Based on
previous research (Hassrick et al. 2016), we assumed
salmon prefer depths less than 200 m in generating the
least-cost routes. We extracted points along these routes
every 10 km. For each salmon, we assigned a sequence
of dates from ocean entry to recovery at each point along
its travel route and assumed a constant rate of travel.
From these specific locations and dates, we extracted
oceanographic data from a coupled physical-
biogeochemical model for the broader California
Current region (Fiechter et al. 2014), including concur-
rent water temperature (°C), eastward and northward
currents (m/s), predatory zooplankton (e.g., krill)
(mmol/m
3
), and diatoms (mmol/m
3
). The physical model
is an implementation of the Regional Ocean Modeling
System (ROMS) (Shchepetkin and McWilliams 2005;
Haidvogel et al. 2008) and the biogeochemical model is
based on the NEMURO model (Kishi et al. 2007)witha
spatial resolution of 10 km. We chose to use a physical-
biogeochemical model instead of empirical data because
cloud cover along coastal waters was common generat-
ing many missing values in satellite datasets, we wanted
values for zooplankton which are not available via satel-
lite, and the ROMS and NEMURO models have been
shown to correspond well with empirical oceanographic
and biological data (Centurioni et al. 2008; Ivanov et al.
2009; Santora et al. 2013). Values from all points within a
travel route were averaged for each fish.
Data analysis
Growth analysis
We used a generalized additive model (GAM) to deter-
mine how variables describing the oceanic and release
conditions were related to the early growth of juvenile
salmon. Although we considered using more traditional
growth models (e.g., von Bertalanffy), we chose to use a
GAM because it was a better fit to these short-term early
life history growth patterns. The growth dataset
consisted of 171 recovered juvenile salmon that repre-
sented 78 CWT releases from 1999 to 2012. Potential
explanatory variables included in the GAM were: hatch-
ery of origin, release location, mean release weight,
release date, ocean temperature, predatory zooplankton,
diatoms, and simultaneously smoothed eastward and
northward currents (m/s). Prior to fitting the model, we
examined the relationship between the different vari-
ables to reduce redundancy due to variables that were
related or collinear. Hatchery and release location were
related to each other because hatcheries had specific
Tabl e 2 Description of covariates for growth (G) and survival (S) models of Central Valley juvenile Chinook salmon
Covariate Level Units Source Models
Recapture weight
a
Individual grams NOAA cruise G
Survival CWT group proportion of individuals
surviving to age 3
RMIS S
Pred zooplankton Individual mmol/m
3
NEMURO G, S
Diatoms Individual mmol/m
3
NEMURO G, S
Currents Individual m/s ROMS G, S
Temperature Individual °C ROMS G, S
Mean release weight
a
CWT group grams RMIS G, S
Hatchery of origin CWT group - RMIS S
Release location CWT group km upstream from
Golden Gate Bridge
RMIS G
a
Recapture weight –mean release weight = growth
Environ Biol Fish

locations where they released fish. Thus, during model
selection we excluded any models that contained both
hatchery and release location. Likewise, release date
was collinear with release location because hatcheries
farther upstream released fish earlier than downstream
hatcheries. In this case, we only retained release location
as a candidate variable because we were more interested
in the potential connection of time spent in freshwater
on growth. We did not include any interactions in the
model because we had no a priori hypotheses regarding
how different explanatory variables may interact.
Because our primary goal was to examine the effect of
the environment on fish growth, we needed to control for
the duration an individual was at large prior to recapture.
To do this, we used a separate GAM to predict salmon
growth given the number of days between release and
recapture (days at large). We then included the predic-
tions from this model as an offset in the full model
containing the explanatory variables. In other words,
we were testing the hypothesis that the ocean conditions
and release variables could help explain if fish grew more
or less than expected for a given duration at large. We
used the predicted growth as an offset, rather than simply
using days at large as an offset, because there was a non-
linear relationship between days at large and growth.
We tested all possible combinations of the explana-
tory variables (96 possible models) and compared
models using Akaike’s Information Criterion corrected
for small sample size (AICc; Burnham and Anderson
2002). We used the ‘dredge’function in the ‘MuMIn’
package in R (Barton 2015), and averaged the models
with ΔAICc values <2. Model diagnostics, such as the
QQ plot and residual plots, were used to assess normal-
ity and homogeneity of variance. To examine how well
the model fit the data we used k-fold cross-validation, in
which we split the data into equal-sized parts and then
iteratively used part of the data to fit the model and a
different part to test it (Hastie et al. 2009). K-fold cross-
validation is a valuable tool to assess the predicative
capabilities of a given model when challenged with a
new dataset. This is an iterative process, thus, we re-
peated each k-fold cross validation process 500 times
and examined the distribution of the r
2
for the test data
set based on the calibration model.
Survival analysis
We examined variables influencing survival to age 3
with a dataset of 98 recovered juvenile salmon, which
represented 45 CWT release groups from 1999 to 2010.
Cohort survival was estimated for each CWT group
using recoveries from the RMIS database. We described
the relationship of survival to age 3 to hatchery and
ocean variables with a generalized linear model
(GLM). We fit the models using a beta error distribution
in the R package ‘betareg’because our survival esti-
mates were a proportional response (Ferrari and Cribari-
Neto 2004). Prior to selecting the fixed effects, we used
the fully parameterized model to select the most appro-
priate link function. The cauchit link with no bias re-
duction was overwhelmingly supported as the best link
function based on AICc.
We considered the same suite of potential explanato-
ry variables as the growth model, and tested all possible
model combinations (360 possible models). The surviv-
al dataset had a smaller sample size (n= 98) than the
growth dataset (n= 171) without years 2011 and 2012.
As with the growth data set, each hatchery primarily
released fish at one location; therefore, we restricted the
model from including both variables at the same time.
Release date was again excluded because it was collin-
ear with release location. We built a full model, tested all
possible model combinations, selected the top models
with ΔAICc value <2, and averaged those top models.
We performed cross validation as described above.
Results
Growth analysis
In our dataset, growth ranged from 0.58 to 213 g from
salmon who were at large from 31 to 169 days from
release to recapture. Ocean sea surface temperatures
ranged from 9.89 to 12.27 °C, eastward currents ranged
from −0.02 to 0.04 m/s, and northward currents ranged
from −0.08 to 0.03 m/s. Diatom densities ranged from
0.25 to 1.27 mmol/m
3
and predatory zooplankton
ranged from 0.14 to 0.36 mmol/m
3
(Table 3). The juve-
nile salmon were released from CWT groups with mean
release weights ranging from 3 to 19.2 g from locations
that ranged from 35 to 528 km upstream from the
Golden Gate Bridge. The top model for predicting
downstream migration rate, which was then used to
estimate ocean entry date and specific locations and
dates to extract ROMS data for individual salmon, in-
cluded mean release weight, release date, and random
effects with a normal error distribution (conditional
Environ Biol Fish

R
2
= 0.93, k = 6, df = 6, AICc = 696.5, deviance = 683.8,
weight of evidence = 0.86).
Model selection for the growth model resulted in two
top models with ΔAICc values less than two (Table 4).
The averaged top model included the variables, preda-
tory zooplankton, currents, temperature, release loca-
tion, and diatoms, and had a median cross-validated
adjusted R
2
value of 0.81. Model response curves illus-
trate the shape of the nonlinear relationship between
early growth and individual variables over the range of
the selected covariate while keeping all other covariates
constant at average values. We also plot the cross-
validation model predictions to illustrate factor variance
(Fig. 2). When all of the cross-validation lines (gray) fall
close to the top model line (black) and exhibit the same
pattern, this indicates more certainty in that covariates
contribution to explaining patterns in growth or surviv-
al. We observed decreased juvenile salmon growth as
the concentration of diatoms increased (Fig. 2a).
However, some of the cross-validation lines show hor-
izontal (no effect) or a U-shaped parabolic relationship,
indicating considerable uncertainty in the relationship
between diatom density and growth. The model re-
sponse curve for predatory zooplankton indicated a
parabolic relationship with lowest predicted growth
values occurring at middle predatory zooplankton den-
sity with higher predicted values at high and low densi-
ties (Fig. 2b). Temperature was negatively related to
growth (Fig. 2c). We display the relationship between
growth and two directions of currents simultaneously.
Growth was highest with southwest flowing currents
(Fig. 2d). Growth was greatest in fish released at loca-
tions nearest to the Golden Gate Bridge (Fig. 3).
Survival analysis
In our dataset, survival to age 3 ranged from 0.0058 to
0.0563. Ocean sea surface temperatures ranged from
9.89 to 12.25 °C, eastward currents ranged from −0.01
to 0.04 m/s, and northward currents ranged from −0.08
to 0.03 m/s. Diatom densities ranged from 0.25 to
1.27 mmol/m
3
and predatory zooplankton ranged from
0.14 to 0.34 mmol/m
3
(Table 5). The juvenile salmon
were released from CWT groups with mean release
weights ranging from 4.3 to 19.2 g from locations that
ranged from 35 to 528 km upstream from the Golden
Gate Bridge.
AICc model selection resulted in three top models
with ΔAICc values less than two (Table 6). The aver-
aged top model included the variables, predatory zoo-
plankton, diatoms, currents, temperature, hatchery of
origin, and mean release weight, and had a median
cross-validated adjusted R
2
value of 0.75. Model re-
sponse curves indicated opposite relationships between
survival and growth models. Diatom biomass was pos-
itively related to survival (Fig. 4a). For predatory zoo-
plankton, survival peaked at middle zooplankton densi-
ty with much lower survival estimated at high and low
densities (Fig. 4b). Temperature showed a positive as-
sociation with survival although there is a wide margin
Tabl e 3 Physical and biological covariate summary statistics for early growth dataset of Central Valley juvenile Chinook salmon from
1999–2012 (excluding 2006–2009)
Covariate Mean SD Minimum Median Maximum
Temperature (°C) 11.08 0.70 9.89 11.38 12.27
Eastward current (m/s) 0.01 0.02 -0.02 0.01 0.04
Northward current (m/s) -0.02 0.02 -0.08 -0.02 0.03
Diatoms (mmol/m
3
) 0.66 0.22 0.25 0.69 1.27
Pred zooplankton (mmol/m
3
) 0.25 0.06 0.14 0.22 0.36
Tabl e 4 Top models for early growth from AICc model selection of Central Valley juvenile Chinook salmon from 1999–2012 (excluding
2006–2009)
Top growth models AICc weight ΔAICc LogLik df Deviance explained
Pred zooplankton + Currents + Temperature + Release location + Diatoms 1418 0.52 0 -684 21 47.1 %
Pred zooplankton + Currents + Temperature + Release location 1419 0.29 1.19 -688 19 44.6 %
Environ Biol Fish

of uncertainty and some cross-validation lines indicated
a horizontal (no effect) or negative relationship (Fig. 4c).
The currents contour plot illustrated that survival was
greatest with primarily northward flowing currents
which were slightly westward (Fig. 4d). As for hatchery
effects, mean release weight showed a strong positive
relationship with survival, and Feather and Coleman
hatcheries produced the greatest survival, followed by
Nimbus, Mokelumne, and Merced hatcheries respec-
tively (Fig. 5).
Discussion
Our results indicate that both release conditions at the
hatchery and early ocean conditions influence growth
and cohort survival of Central Valley fall run Chinook
salmon. We built two separate models for growth and
survival with the expectation that similar covariates
would be important because of the strong evidence that
early growth is a critical component of adult survival
(Holtby et al. 1990; Friedland et al. 2000; Beamish and
Mahnken 2001; Duffy and Beauchamp 2011). Hatchery
effects varied between survival and growth models, with
release location included in the growth model, and
a)
12
16
20
24
28
0.50 0.75 1.00 1.25
Diatoms (mmol/m
3
)
Growth (g)
b)
12
16
20
24
28
0.15 0.20 0.25 0.30 0.35
Predatory zooplankton (mmol/m
3
)
Growth (g)
c)
0
10
20
30
40
50
60
10.0 10.5 11.0 11.5 12.0
Temperature (°C)
Growth (g)
d)
−0.06
−0.03
0.00
0.03
−0.01 0.00 0.01 0.02 0.03 0.04
Eastward current
(
m/s
)
Northward current (m/s)
Growth
−47
−34
−22
−9
3
16
28
41
53
66
78
91
Fig. 2 Growth across the range of adiatoms (mmol/m
3
), b
predatory zooplankton (mmol/m
3
), ctemperature (°C), and d
currents (m/s) while keeping all other model covariates constant
at average values. Solid black lines represent the mean and
individual k-fold runs were also plotted (light gray lines)toillus-
trate variability. Rug plots along the horizontal axis of plots a-c
indicate the distribution of data points. dCurrents, blue colors
represent the highest modeled growth and dark pink the lowest
0
10
20
30
35 50 105 183 215 525
Km released upstream
of the Golden Gate Brid
g
e
Growth (g)
Fig. 3 Growth values from the top model across release location
categories while keeping all other model covariates constant at
average values. Release location categories with sample sizes less
than five are not plotted
Environ Biol Fish

release weight and hatchery of origin included in the
survival model. The same four ocean variables (currents,
temperature, diatoms, and predatory zooplankton) were
included in both models; however, covariate relation-
ships were opposed in the growth and survival models.
Two non-mutually exclusive hypotheses that may
explain the opposing patterns between growth and sur-
vival model responses are size-selective mortality and
density-dependence mechanisms (Miller et al. 2013).
Under conditions in which survival is low, smaller fish
may die at a greater rate than larger fish. Thus apparent
high growth conditions could result from demographic
shifts in size rather than actual growth differences.
Evidence for size-selective mortality has been observed
during early ocean entry for juvenile salmon in
California (Woodson et al. 2013), the Pacific
Northwest (Claiborne et al. 2011), and Alaska (Moss
et al. 2005). Likely predators include piscivorous birds
in the estuary and ocean (Anderson et al. 2004;Adrean
et al. 2012; Tucker et al. 2016), predatory fish (Emmett
et al. 2006; Emmett and Krutzikowsky 2008), and ma-
rine mammals (Yurk and Trites 2000; Kvitrud et al.
2005). Predation pressure can also vary with respect to
oceanographic conditions (Emmett et al. 2006)making
an assessment of the overall extent of predation impacts
on juvenile salmon populations difficult.
The density-dependence hypothesis would argue that
under conditions in which survival is high, salmon may
occur at higher densities, and intraspecific competition
may lead to reductions in growth. Density-dependence
may be important for salmon during freshwater life
stages (Jonsson et al. 1998), but has been difficult to
document in the ocean. In Alaska, density-dependence
in the ocean has been observed to impact salmon growth
and survival in multiple species (Ruggerone et al. 2003;
Beamish et al. 2008; Martinson et al. 2008; Agler et al.
2013), although signals were much weaker for Chinook
salmon in the Puget Sound, WA (Greene and Beechie
2004). Furthermore, juvenile salmon catches in Alaska
are 10–100 times higher than standardized catches in
central California, potentially making conspecific
density-dependence more likely in northern areas
(Fisher et al. 2007). In the California Current, there has
been little work evaluating density dependence, al-
though the potential exists as resource limitation has
been documented for juvenile salmon in the ocean
(Daly et al. 2009). Miller et al. (2013) found Columbia
River juvenile Chinook salmon growth to be negatively
related to juvenile salmon abundance; however, they did
not find further support for density-dependence. There
is potential for density-dependence to impact salmon in
the ocean especially when populations are robust or
habitat is restricted (Greene and Beechie 2004).
Furthermore, it is important to emphasize that size-
selective mortality and density-dependence hypotheses
are not mutually exclusive, and may interact if larger
salmon out-compete smaller individuals at high densi-
ties (Reinhardt et al. 2001).
Tabl e 5 Physical and biological covariate summary statistics for cohort survival dataset of Central Valley juvenile Chinook salmon from
1999–2010 (excluding 2006–2009)
Covariate Mean SD Minimum Median Maximum
Temperature (°C) 11.08 0.70 9.89 11.38 12.27
Eastward current (m/s) 0.01 0.02 -0.02 0.01 0.04
Northward current (m/s) -0.02 0.02 -0.08 -0.02 0.03
Diatoms (mmol/m
3
) 0.66 0.22 0.25 0.69 1.27
Pred zooplankton (mmol/m
3
) 0.25 0.06 0.14 0.22 0.36
Tabl e 6 Topmodels for cohort survival from AICc model selectionof Central Valley juvenile Chinook salmon from 1999–2010 (excluding
2006–2009)
Top survival models AICc weight ΔAICc LogLik df Psuedo R
2
Pred zooplankton + Diatoms + Release weight + Hatchery + Currents -679 0.375 0 354 13 0.36
Pred zooplankton + Diatoms + Release weight + Hatchery + Temperature -678 0.269 0.66 353 12 0.35
Pred zooplankton + Diatoms + Release weight + Hatchery + Currents + Temperature -677 0.154 1.79 355 14 0.36
Environ Biol Fish

Productivity and food
The diatoms and predatory zooplankton covariates were
chosen to represent productivity and food at two differ-
ent trophic levels. Diatoms are food for predatory zoo-
plankton such as krill which may be an important com-
ponent of juvenile salmon diets. We expected increasing
productivity, represented by diatoms, and increasing
food resources, represented by predatory zooplankton,
to be positively related to increased juvenile salmon
growth and survival.
Our model results indicated survival increased with
diatoms, as hypothesized, but the relationship with zoo-
plankton was more complex. Our model indicated a
parabolic relationship between survival and predatory
zooplankton in which the highest survival occurred at
moderate zooplankton concentration. Previous studies
have shown that measures of productivity and food
correlate with increased salmon survival, and discuss
mechanisms falling into two categories: (1) via an in-
crease in growth which allows salmon to avoid
a)
0.01
0.02
0.03
0.04
0.50 0.75 1.00 1.25
Diatoms (mmol/m3)
Survival
b)
0.005
0.010
0.015
0.020
0.15 0.20 0.25 0.30
Predatory zooplankton (mmol/m3)
Survival
c)
0.018
0.020
0.022
0.024
10.0 10.5 11.0 11.5 12.0
Temperature (°C)
Survival
d)
−0.075
−0.050
−0.025
0.000
0.025
0.00 0.01 0.02 0.03
Eastward current (m/s)
Northward current (m/s)
Survival
0.011
0.015
0.019
0.022
0.026
0.03
0.033
0.037
0.04
0.048
0.051
0.055
0.059
Fig. 4 Modeled survival values across the range of adiatoms
(mmol/m
3
), bpredatory zooplankton (mmol/m
3
), ctemperature
(°C), and dcurrents (m/s) while keeping all other model covariates
constant at average values. Individual k-fold runs were also plotted
(light gray lines) to illustrate variability. Rug plots along the
horizontal axis of plots a-c indicate the distribution of data points.
dCurrents, blue colors represent the highest modeled survival and
dark pink the lowest
0.00
0.02
0.04
0.06
5101520
Mean release wei
g
ht
(g)
Survival
Coleman
Feather
Merced
Mokelumne
Nimbus
Fig. 5 Modeled survival values across the range of release weight
values while keeping all other covariates constant for each hatch-
ery category. The plotted range for mean release weight for each
line represents the observed release weights in our survival dataset
Environ Biol Fish

starvation and escape gape-limited predators, and (2) via
indirect interactions between forage, salmon, and pred-
ators that mediate juvenile salmon losses due to preda-
tion (Willette et al. 2001; Emmett and Sampson 2007;
Wells et al. 2012;Dalyetal.2013). Therefore, there is
the potential for complex bottom-up and top-down
mechanisms to explain relationships between produc-
tivity and salmon survival. The relationship we ob-
served between survival and diatoms was consistent
with previous studies. However, the parabolic pattern
observed between predatory zooplankton and survival
deviates, and may be explained by either foraging effi-
ciency relative to prey density or predator aggregations.
Many species of zooplankton use schooling behavior as
an anti-predator tactic, and foraging on high densities
may be less efficient for visual feeders like salmon,
while at low densities salmon may have reduced en-
counter probabilities (Hamner and Hamner 2000;
Goldbogen et al. 2011; Crook and Davoren 2014). In
this case, foraging behavior may impact survival via
growth. Also, predators and competitors may aggregate
to areas with abundant food (Ainley et al. 2009; Santora
et al. 2011; Santora et al. 2012), which may increase the
risk for salmon predation or create a competitive disad-
vantage that reduces salmon feeding and growth
(DeCesare et al. 2009). For example, common murres
(Uria aalge) may aggregate at frontal features where
there is abundant forage where they may compete with
juvenile salmon for zooplankton and/or directly prey on
salmon, both which may negatively affect salmon
growth and survival (Ainley et al. 2009). Alternatively,
although not consistent with our results, abundant for-
age may serve as alternative prey and dampen juvenile
salmon losses due to predation (Cooney et al. 2001;
Willette et al. 2001; Emmett and Sampson 2007). At
low forage abundances, juvenile salmon may be forced
to venture further to feed into habitats where they are
more likely to be predated upon (Cooney et al. 2001;
Willette et al. 2001).
Contrary to our original hypothesis, we observed that
the relationships between growth and productivity were
the opposite of those observed for survival. Generally,
growth declined as diatom density increased, however
some cross-validation lines showed no effect or a u-
shaped parabolic relationship. The sizeable variability
in cross-validation lines indicates uncertainty in diatoms
explaining patterns in growth. This is also evident in that
diatoms were not included in the second best model
(Table 4). The important, consistent pattern is the model
always predicts high growth at low diatom densities. We
observed the highest growth at low and high densities of
zooplankton. In the current literature, we failed to find
an ecological mechanism that explains this u-shaped
relationship between growth and zooplankton abun-
dance, and negative relationship between growth and
diatom density. It is possible that our modeled outputs of
productivity could have missed zooplankton patchiness
clouding patterns at low densities. However, as we
previously discussed, our apparent estimate of growth
may be affected byfactors influencing survival of small-
er fish, such as size-selective mortality or intraspecific
competition through density-dependence.
Limitations in the ROMS-NEMURO model are
worth noting. Because the ROMS solution was not for-
mally constrained by observations (i.e., data assimila-
tion) during the model run, spatial and temporal mis-
matches between actual and simulated physical fields
(e.g., ocean currents and temperatures) are bound to
occur, which will translate to similar discrepancies in
the biological fields (e.g., diatom and krill concentra-
tions). However, Santora et al. (2013) found reasonable
spatial and temporal agreement between observed krill
abundances (from acoustic and trawl surveys) and sim-
ulated large zooplankton concentrations from a coupled
physical-biological model similar to the one used here.
Because the predatory zooplankton component of
NEMURO was parameterized to represent krill in a
broad sense, the model captures more accurately the
spatial distribution and temporal variability associated
with Euphausia pacifica, the most abundant species off
of Central California. Hence, simulated krill
concentrations may miss some of the characteristics
associated with the less numerous, nearshore species,
Thyanoessa spinifera, which could in turn impact
predicted salmon growth during periods when
T. sp i n i fera is an important prey item. Both krill species
occur in salmon diets, and Wells et al. (2012) have
shown T. spinifera to correlate with increased salmon
body condition (Wells et al. 2012). While it is difficult to
assess the exact impact of these potential discrepancies
on the model results, earlier studies have demonstrated
that the coupled ROMS-NEMURO model is capable of
reproducing the temperature, diatom and krill conditions
that modulate juvenile salmon growth off of central
California (Fiechter et al. 2015). Hence, the relationships
identified here for growth and survival are generally
expected to hold over the same spatial (> 10 km) and
temporal (> days) scales. However, these relationships
Environ Biol Fish

may differ (or at least not hold as strongly) under condi-
tions when salmon growth is significantly influenced by
coastal dynamics and biological responses occurring at
spatiotemporal scales not adequately represented by the
model (e.g., krill diel vertical migration or patchiness
associated with sub-mesoscale frontal dynamics).
Physical Ocean conditions
Ocean currents are tied to ocean productivity via wind-
driven, offshore Ekman transport that causes deep, cold,
nutrient-rich water to be upwelled to the surface near the
coast (Pringle and Dever 2009). In the summer, the
California Current coastal waters typically alternate be-
tween phases of upwelling and relaxation. During relax-
ation events, winds weaken and currents shift to along-
shore and northward while temperatures increase
(Melton et al. 2009). Upwelling conditions are impor-
tant to pre-condition areas for productivity (Vander
Woud e et al. 2006; Thompson et al. 2012); however,
we expect a period with many relaxation events to
benefit salmon growth and survival. Warmer in situ
temperatures associated with relaxation events may in-
crease growth (Beckman et al. 2004), and weaker,
alongshore currents may promote retention of prey
(Wing et al. 1998; Vander Woude et al. 2006; Wilson
et al. 2008) and reduce the energetic cost for salmon to
swim against advection offshore. Therefore, increased
growth and survival of juvenile salmon should be more
strongly correlated with frequent relaxation events, rath-
er than periods of prolonged, active upwelling.
Although coastal upwelling and relaxation events also
occur in the Pacific Northwest, the Columbia River
interacts with upwelling dynamics to increase retention
of upwelled waters along the coast (Banas et al. 2009)
and subsequent productivity and zooplankton (Peterson
et al. 1979), which potentially explains why Pacific
Northwest studies show positive trends of winter pre-
conditioning with salmon survival (Logerwell et al.
2003), but relationships with in situ upwelling are var-
iable (Nickelson 1986). Contrastingly, in California
there is not the influence of a large river plume, and
strong upwelling has been shown to increase advection
of productivity off the shelf (Jacox et al. 2016). Since
retention of productivity on the shelf where salmon
reside is important for salmon growth and survival, the
absence of a large river plume may explain why we
observe distinct dynamics in coastal California com-
pared to more northerly regions.
Physical environmental covariates that best ex-
plained growth and survival were water temperature
and surface currents. Model response curves indicated
greatest survival during northward currents and a posi-
tive relationship with temperature, which correspond
with relaxation events when weakened winds allow for
alongshore, northward transport of warm water.
Although, temperature exhibited the most uncertainty
in explaining survival patterns as evident in widely
spread cross-validation lines (Fig. 4c) and that it was
included only in the second and third top models
(Table 6). These survival patterns with respect to phys-
ical ocean conditions are consistent with our expectation
that relaxation events are important for juvenile salmon.
Previous studies indicated increased salmon growth and
survival in years with strong early season upwelling
(Schroeder et al. 2013), and cool water temperatures
(Mueter et al. 2002; Wells et al. 2008). These studies
examined physical ocean conditions at broad spatial and
temporal scales (averages over 100 s to 1000s of km and
multiple months), whereas other studies that examined
finer scales failed to find a strong influence of upwell-
ing on salmon survival (Scheuerell et al. 2009;
Holsman et al. 2012). Therefore, it is possible that
years with strong early upwelling and cool tempera-
tures pre-condition regions for high productivity, but
on finer spatial and temporal scales, relaxation events
are important.
Opposing our original hypothesis and our findings
relative to survival, the growth model indicated greatest
growth in active upwelling conditions. We saw greatest
growth during southwest flowing currents and a nega-
tive relationship with temperature. There is an expected
time lag of weeks to months between upwelling and
bottom-up processes that produce food for salmon
(Croll et al. 2005;Thompsonetal.2012). Therefore,
we would not expect abundant prey to be available in
active upwelled waters, unless they were transported
from a different area or concentrated in eddies or up-
welling shadows adjacent to upwelling centers (Santora
et al. 2012). Furthermore, areas of persistent, strong
upwelling are expected to have reduced productivity
due to physical processes that advect nutrients offshore
or below the euphotic zone (Jacox et al. 2016). Once
again, our two models offered contradictory results:
conditions related to high growth correspond to
those with low survival, indicating that this pattern
may be influenced by size-selective mortality or
density-dependence.
Environ Biol Fish

Hatchery release effects
The influence of hatchery of origin and release location
differed between growth and survival models. For release
location, fish released at locations lower in the river had
more growth than fish released at locations higher in the
river. Salmon released farther downstream likely spent
less time in the river between release and recapture, and
reached the ocean more quickly than salmon released
farther upstream in the watershed. There is ample evi-
dence for large growth potential in the ocean (Atcheson
2010; Duffy and Beauchamp 2011; Woodson et al.
2013), and due to the great reduction of floodplain habitat
in the Sacramento and San Joaquin watersheds
(Opperman 2012), there may be less growth opportuni-
ties for juvenile salmon in the freshwater environment
than there was historically (Sommer et al. 2001). Also,
salmon that reach the ocean more quickly might have
more energy reserves to help them actively find prey in
the marine environment. Alternatively, our measure of
growth may still be confounded, which, following our
previous logic, might suggest salmon released further
downstream have experienced more size-selective mor-
tality or density-dependence. However, since release lo-
cation was not an important covariate in the survival
model, we speculate this is a less compelling hypothesis.
Mean release weight was positively correlated with
survival which is consistent with previous studies (Irvine
et al. 2013; Zeug and Cavallo 2013; Satterthwaite et al.
2014). Larger salmon may be less susceptible to preda-
tion or starvation in both freshwater (Sogard 1997)and
marine environments (Moss et al. 2005; Claiborne et al.
2011; Woodson et al. 2013). Our results also indicated
that survival was greatest among fish produced at the
Feather River Fish Hatchery. The hatchery primarily
releases large smolts, which they transport and release
close to San Francisco Bay, potentially avoiding high in-
river mortality (Buchanan et al. 2013)(Table7). Among
the remaining hatcheries, we saw a latitudinal gradient
with more northerly hatcheries exhibiting greater surviv-
al. The Merced River Hatchery is the most southerly
hatchery in our study, and salmon produced there must
travel down the San Joaquin River to reach the sea. The
San Joaquin River was designated one of the nation’s
most endangered rivers in 2014 due to degraded habitat
(Saiki et al. 1992; Baker et al. 1995) and excessive water
diversion (Newman and Brandes 2010) which may con-
tribute to the low survival estimates.
Conclusions
Our findings suggest that both oceanic and hatchery
release conditions influence juvenile salmon early
growth and survival. Interestingly, all four ocean covar-
iates included in both growth and survival models
showed opposite patterns. One possible mechanism to
explain this result is size-selective mortality. Under con-
ditions when survival is low, smaller fish may die at a
greater rate than larger fish, and apparent high growth
may actually result from demographic shifts in size.
Size-selective mortality has previously been document-
ed in Central Valley fall run Chinook salmon (Woodson
et al. 2013). If juvenile salmon size influences survival,
managers may be tempted to increase the size at which
they release juvenile salmon. However, increased
growth in hatcheries could have unintended conse-
quences. Selection of large sizes and fast growth rates
in hatcheries could result in domestication and alteration
of other behaviors such as reduced predation defenses
(Alvarez et al. 2003), increased metabolism, thus, great-
er fitness cost in the wild where food is less available
(Metcalfe et al. 2003), and increased aggression towards
wild salmon (Weber and Fausch 2005). Additionally,
growth in the hatchery can affect maturation rate, with
too much growth resulting in an increase of precocious
Tabl e 7 Range of Central Valley juvenile Chinook salmon CWT group release attributes by hatchery from 1999–2010 (excluding 2006–2009)
Hatchery Release dates Release locations Mean release
(km upstream) weight (g)
Coleman FH Apr 7 - May 24 48–528 3.0–10.0
Feather FH Apr 25 - Jun 8 35–126 4.2–9.1
Merced FH Apr 18 - May 25 105–167 4.3–19.2
Mokelumne FH Apr 20 - Jun 7 104–143 7.1–13.3
Nimbus FH May 4 - Jun 18 48–215 6.8–8.7
Environ Biol Fish

salmon returning years earlier than normal (Larsen et al.
2006), and in steelhead (O. mykiss) populations can
increase the incidence of residualism with a host of
ecological effects (Berejikian et al. 2012). Perhaps a
promising conservation strategy would be to increase
available floodplain or estuarine habitat within a hetero-
geneous landscape to increase growth potential of juve-
nile salmon of both origins before they enter the ocean
(Magnusson and Hilborn 2003).
Another potential mechanism to explain the oppos-
ing patterns between growth and survival covariates is
density-dependence. Under conditions when survival is
high, salmon may occur at higher densities, and intra-
specific competition may cause reduced growth. No
studies have documented density-dependence in
California salmon populations in the ocean, although
this is a research area that deserves further examination.
Density-dependence has been observed in northern
salmon populations, creating concerns about the density
of salmon and the time interval of hatchery releases
(Beamish et al. 2008). Competition resulting from un-
natural crowding and other artifacts of hatchery produc-
tion may suppress growth and lower survival, and re-
leasing many salmon at once may also amplify density-
dependence effects. Hatchery and natural juvenile salm-
on exhibit spatial, temporal, and trophic overlap during
early marine residency, setting the stage for potential
competition (Daly et al. 2011).
Across geographic regions, unique local conditions
are important to that stock’s survival (Sharma et al.
2013). The effect of ocean conditions on juvenile salm-
on growth and survival appear to be sensitive to the
scale at which measurements are taken. Unique to this
study, we examined ocean conditions at spatial and
temporal scales of 10 km resolution specific to individ-
ual fish migration routes and dates in the ocean. In our
study, warmer ocean temperatures appeared to enhance
salmon survival. This result contradicts other studies
conducted at larger basin scales (Mueter et al. 2002;
Wells et al. 2008; Schroeder et al. 2013), and deviate
from regional patterns where the Pacific Northwest
shows a negative relationship with SST (Mueter et al.
2002; Sharma et al. 2013). We also saw a signal of
relaxation events being important for survival, while
most previous studies fail to observe a consistent pattern
with upwelling (Nickelson 1986;Scheuerelletal.2009;
Sharma et al. 2013). This may be because the influence
of upwelling on biological productivity is reliant on
intermittent relaxation events and it is difficult to select
an appropriate metric for analyses that captures this
dynamic. It is possible that within broader climatic
regimes, finer-scale relationships are important for un-
derstanding salmon population dynamics because they
relate more closely to specific mechanisms affecting
salmon responses. A mechanistic understanding of
salmon ecology is important for building life cycle and
individual-based models which can arguably perform
better than statistical models to understand salmon pop-
ulation dynamics, predict changes under future condi-
tions, and assess conservation strategies. This paper
explores the importance and relationships of various
oceanic and hatchery release conditions on salmon
growth and survival. It also lays a foundation for many
future hypothesis-driven studies to ultimately under-
stand mechanisms that affect salmon responses at vari-
ous life stages to incorporate into a holistic understand-
ingofsalmonpopulationdynamics.
Acknowledgments The authors thank B. Wells for scientific
insight, B. Lehman for extracting tags, the NW Fisheries Science
Center and Fisheries and Ocean Canada for sharing data on tagged
juvenile salmon, and Northwest Marine Technology for reading
coded wire tags. This project would not have been possible with-
out the efforts from current and past members of the salmon
ecology team and crews of the vessels AR4 Jensen, Bell Shimada,
Cassandra Anne, David Starr Jordan, Frosti, Irene’s Way, Long
Fin, Ocean Starr, Shana Rae, and Whitsel. Comments from four
reviewers were valuable and greatly improved the quality of this
manuscript. Funding was provided by the National Oceanic and
Atmospheric Administration, and the collection procedures were
conducted under IACUC guideli nes.
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