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\begin{document}

\title{Stock specific high-seas distribution of maturing sockeye salmon in the
North Pacific}



\author[1]{Boris Espinasse}%
\author[1]{Brian Hunt}%
\author[2]{Bruce Finney}%
\author[3]{Jeffrey Fryer}%
\author[4]{Alexander Rubaev}%
\author[1]{Evgeny Pakhomov}%
\affil[1]{UBC}%
\affil[2]{Idaho State University}%
\affil[3]{Columbia River Inter-Tribal Fish Commission}%
\affil[4]{Kamchatka Research Institute of Fisheries and Oceanography}%


\vspace{-1em}



  
  \date{June 11, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
The stock-specific distribution of maturing and adult salmon in the
Northeast (NE) Pacific has been a persistent information gap that has
prevented us from determining the ocean conditions experienced by
individual stocks. This continues to impede understanding of the role of
ocean conditions in stock-specific population dynamics. We assessed
scale archives for 17 sockeye salmon (Oncorhynchus nerka) stocks
covering the entire North Pacific, from the Columbia River to Kamchatka
Peninsula, to define salmon locations during their last growing season
before returning to their spawning grounds. We used the relationship
between \selectlanguage{greek}δ\selectlanguage{english}13C in salmon scales and sea water temperature to estimate
salmon distribution based on correlation strength. Significant
correlations were found for 13 of the stocks allowing us to define
feeding grounds with confidence. Complementary information from \selectlanguage{greek}δ\selectlanguage{english}15N,
historical tagging studies, and connectivity analysis were used to
further refine distribution estimates. Based on the estimated
distributions of the NE Pacific stocks, we suggest a sequence of steps
that could result in salmon marine distributions. This study is a first
step toward determining stock-specific distributions of salmon in the NE
Pacific, and provides a basis for the application of the approach to
other salmon scale archives. This information will improve our ability
to relate stock dynamics to ocean conditions, ultimately enabling
improved stock management. For example, our estimated distributions of
Bristol Bay and NE Pacific stocks demonstrated that they occupy
different areas with a number of the former being distributed in the
high productivity shelf waters of the Aleutian Islands and Bering Sea.
This may explain while these stocks seem to have responded differently
to changes in ocean conditions, and the long term trend of increased
productivity of Bristol Bay sockeye.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Sockeye salmon (\emph{Oncorhynchus nerka} ) migrate from freshwater to
the coastal ocean as juveniles and rapidly migrate from the coast to the
open ocean where they typically reside for 2 to 3 years before returning
to their natal streams to spawn (Burgner, 1991). Although it is
considered that the early marine life phase is the period most critical
to survival, there are indications that much of the cumulative
juvenile-to-adult mortality of salmon occurs on the high seas
(McKinnell, Curchitser, Groot, Kaeriyama, \& Trudel, 2014; Welch et al.,
2011). It is also during this period, and especially during maturation,
that the fish accumulate most of their mass (Ishida, Soto-o, Ueno, \&
Sakai, 1998) and establish the body condition necessary to undertake
successful spawning migration and reproduction. However, our
understanding of the high seas life phase and its role on determining
stock specific recruitment dynamics remains limited. This knowledge gap
reflects the challenges posed by the massive spatial scale of the North
Pacific, the wide dispersal of fish, and the associated expense and
logistical challenges of sampling.

Many sockeye salmon spawning grounds are located at considerable
distance from the sea (up to 100's km), requiring substantial reserves
for upstream migration. The energy requirements depend on water
temperature, that will affect salmon metabolic rate, distance, and river
flow strength, both of which determine the relative migration duration
(Crossin et al., 2008; J. Stevenson Macdonald, Patterson, Hague, \&
Guthrie, 2010; Rand et al., 2006). It has previously been demonstrated
that high adult returns and high productivity (adult recruits produced
per spawner) are not necessarily linked. In 1997, Fraser River sockeye
returned to the river with poor body condition (smaller size, low lipid
content) and concurrently experienced extremely high river discharge (60
to 100\% greater than mean levels), resulting in the largest en-route
mortality recorded for the Fraser system (Macdonald, 2000). The poor
fish condition when entering the freshwater system that year has been
suggested to be have been due to warmer sea surface temperature (SST),
associated with a record setting El Ni\selectlanguage{ngerman}ño event, and density-dependent
effects (S. M. McKinnell, 2000). Warmer than usual temperatures
experienced by the fish in the high seas were suggested to have resulted
in altered salmon phenology (early maturating) and reduced nutritional
health (low lipid content).

Another direct effect of a warming North Pacific Ocean on salmon is a
reduction in the area of suitable thermal habitat (Abdul-Aziz, Mantua,
\& Myers, 2011; Healey, 2011; Welch, Ishida, \& Nagasawa, 1998). This is
expected to result in an intensification of competition for resources.
Recent studies indicate that competition for food among salmon species
in the open ocean can play a role in regulating populations (Ruggerone
\& Connors, 2015; Springer \& van Vliet, 2014). Increasing pink salmon
abundance (\emph{Oncorhynchus gorbuscha} ), in part due to hatchery
enhancement, combined with the limited carrying capacity of the North
Pacific Ocean could result in unfavorable conditions for sockeye
development. However, the influence of food composition and abundance on
salmon development and condition remains largely unknown, although
Pacific salmon seem to be able to adapt their diet when food conditions
change (Kaeriyama et al., 2004).

A prerequisite to determining the extent to which these different
processes affect sockeye salmon stocks is knowledge of salmon
distributions in the high seas (Chittenden, Beamish, \& McKinley, 2009).
However, this is an extremely challenging task due to the enormous
technical and logistical difficulty associated with tracking and
monitoring salmon populations at the scale of ocean basins. As a
consequence, salmon distributions in the ocean have been only coarsely
defined to date and are almost completely unknown when it comes to
specific stocks, with the exception of intensive tagging studies carried
out by fishery vessels in the 50-60's (French, Bilton, Osako, \& Hartt,
1976). In the future, at-sea genomic analysis may help to resolve this
knowledge gap, however, biogeochemical approaches may provide a means to
retrospective resolve estimate ocean distributions. The Carbon
(\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C) and Nitrogen (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}N) stable
isotope ratios of salmon scales provide a useful tool to estimate the
environmental conditions experienced by fish and their distribution
(MacKenzie et al., 2012; Torniainen et al., 2014; Trueman, MacKenzie, \&
Palmer, 2012). In particular, covariance of SST and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C provide a reliable indicator to track animal
location (MacKenzie et al., 2011; McMahon, Hamady, \& Thorrold, 2013).
This relationship is based on the water temperature control of aqueous
{[}CO\textsubscript{2}{]}, which in turns controls carbon isotope
fractionation by autotrophs. Because autotrophs preferentially take up
the lighter carbon isotope (\textsuperscript{12}C), high
{[}CO\textsubscript{2}{]} concentration (low SST) result in low
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C, and conversely a decrease in concentrations
(high SST) lead to higher \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C as autotrophs have
reduced access to the light isotope (Goericke \& Fry, 1994; Rau,
Takahashi, \& Marais, 1989). Although other mechanisms linked to
phytoplankton physiology and community composition do affect
phytoplankton \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values (Burkhardt, Riebesell, \&
Zondervan, 1999; Riebesell, Burkhardt, Dauelsberg, \& Kroon, 2000),
water temperature is considered to be the main driver of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C variation at high latitude (Magozzi, Yool,
Zanden, Wunder, \& Trueman, 2017). Recently, the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C
/ SST correlation has been used to identify salmon feeding grounds in
the North Atlantic (MacKenzie et al., 2011) and for one sockeye stock in
the North Pacific (Espinasse, Hunt, Doson Coll, \& Pakhomov, 2018).

In this study, we build on the preliminary work of Espinasse et al.
(2018) to assess stock specific sockeye salmon distributions in the
North Pacific through analysis of archived scales for 17 North Pacific
sockeye salmon stocks. These scales were all collected in or near the
freshwater system and differed in the time range covered, sampling
resolution (yearly, every 3\textsuperscript{rd} year, irregular), and
material type (whole scale or last annulus). Our primary objectives were
to determine whether salmon feeding grounds can accurately be described
based on the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C / SST relationship and the
robustness of this method. We discuss the implications of our results
for our understanding of salmon high seas migration history.

\textbf{Materials and Methods}

\emph{Data collection and scale processing}

The literature was reviewed for published SI data of sockeye salmon
scales. Johnson and Schindler (2012) reported on SI data for eight
stocks distributed in Bristol Bay with time series spanning over four
decades of scales collected every three years. Satterfield and Finney
(2002) reported on a 30-year time series with yearly resolution for a
stock located on Kodiak Island in SW Alaska, and Espinasse et al. (2018)
reported on SI data for the Rivers Inlet stock (BC coast) which covered
more than 50 years with irregular sampling resolution. In addition, we
accessed archived scales for two major stocks in the Kamchatka Peninsula
(Ozernaya and Kamchatka) (Bugaev et al., 2008), for two stocks of the
Columbia River (Okanagan and Wenatchee), for two stocks in SE Alaska
(Chilkoot and Chilkat) and for one additional stock on Kodiak Island.
The stock locations can be seen in Figure 1 and the details of the time
series resolution are provided in Table 1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Map of the study area with locations of the sockeye salmon
stocks investigated.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Salmon stock\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Time range\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
Sampling frequency (\# sample yr\textsuperscript{-1})/ Nbr. data
point\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C range (%0)\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N range (%0)\strut
\end{minipage} & \begin{minipage}[b]{0.16\columnwidth}\raggedright\strut
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C vs \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kodiak Island\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1967-1999\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
every year (2-5), last annulus only\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Red Lake\textsuperscript{1}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=32\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-19.05 -- -17.17\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8.29 -- 11.62\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.22\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Upper station\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=31\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.88 -- -17.38\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8.08 -- 10.68\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.57***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SE Alaska\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1968-1998\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
every year (2-5), last annulus only\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Chilkoot\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=29\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.92 -- -17.43\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9.46 -- 11.66\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.67***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Chilkat\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=29\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-19.29 -- -17.18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8.43 -- 10.64\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.12\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Columbia\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1986-2016\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
every year (5)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Okanagan\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=31\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.97 -- -17.82\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9.68 -- 10.96\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.52**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Wenachee\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=31\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.97 -- -17.50\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9.68 -- 11.32\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.57***\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BC coast\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1960-2016\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
irregular (18-135)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Rivers Inlet\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=16\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.26 -- -16.98\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9.57 -- 11.11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.02\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
S Bristol Bay\textsuperscript{3}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1962-2003\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
every 3\textsuperscript{rd} year (3-5)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ugashik\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.13 -- -16.98\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.47 -- 12.03\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.37\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Egegik\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.21 -- -16.75\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.50 -- 11.68\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.29\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Naknek\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=13\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.04 -- -16.79\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.48 -- 12.22\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.69**\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kvichak\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.14 -- -17.32\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.99 -- 11.88\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.07\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
N Bristol Bay\textsuperscript{3}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1962-2003\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
every 3\textsuperscript{rd} year (3-5)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nushagak\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-17.87 -- -16.80\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.89 -- 12.76\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.06\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Wood River\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.07 -- -17.37\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
11.35 -- 12.97\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.29\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Igushik\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.44 -- -16.62\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.79 -- 12.14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.16\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Togiak\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.05 -- -16.88\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
11.15 -- 12.15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.57*\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NW Pacific\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1995-2017\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
every year (3-5)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ozernaya\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=23\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.31 -- -17.46\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.59 -- 11.45\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.24\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kamchatka\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{n}=11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-18.33 -- -17.39\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10.45 -- 11.48\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-0.01\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Table 1. Details of the salmon scale samples analyzed,
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C (corrected for Suess effect) and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N ranges and correlation coefficients between these
two SI ratios. P-values significance is: *** p\textless{}0.001,
**p\textless{}0.01, *p\textless{}0.05.

\textsuperscript{1}Satterfield and Finney,
2002,\textsuperscript{2}Espinasse et al. 2018,
\textsuperscript{3}Johnson and Schindler 2012

Kamchatka Peninsula salmon were collected at the river mouths, while
salmon from Columbia River stocks were collected at Bonneville Dam about
230 km upstream from the sea. The Okanagan and Wenatchee time series
were mixed until 2005 and identified separately afterwards based on tag
data. For four of the stocks processed (see Table 1), only the last
annulus, which grows over the last year at sea, was processed. The last
annulus was excised from the rest of the scale following the description
given by Satterfield and Finney (2002). Most of the scales were
initially glued on gum cards. All the scales were immerged in water and
rubbed thoroughly until the scales were transparent and free of residual
glue. Details on SI analysis of scales from SE Alaska and Kodiak Islands
can be found in Satterfield and Finney (2002).

The scales processed during this study were dried in an oven for 24
hours at 60\selectlanguage{ngerman}°C and sent for SI analysis at UC Davis SIF
(https://stableisotopefacility.ucdavis.edu/13cand15n.html). The samples
were analyzed for \textsuperscript{13}C and \textsuperscript{15}N
isotopes using a PDZ Europa ANCA-GSL elemental analyzer interfaced to a
PDZ Europa 20-20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire,
UK). The system was calibrated using different NIST Standard Reference
Materials. Measurement precision was assessed by running replicates of
these standards and result in standard deviation consistently below 0.1\selectlanguage{english}%0
both for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N. Isotopic
ratios are expressed in the following standard notation:

\selectlanguage{greek}δ\selectlanguage{english}X (%0) = (Rsample / Rstandard -- 1) x 1000

where X is \textsuperscript{13}C or \textsuperscript{15}N and Rsample is
the \textsuperscript{13}C/\textsuperscript{12}C
or\textsuperscript{15}N/\textsuperscript{14}N respectively.
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N were determined in
parts per thousand (%0) relative to external standards of Vienna Pee Dee
Belemnite and atmospheric nitrogen, respectively.

The large amount of anthropogenic carbon dioxide released into the
atmosphere has led to a long term decrease in both atmospheric and
oceanic \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values, known as the Suess effect (Gruber
et al., 1999). The extent of this decrease is directly linked to the
rate of change of CO\textsubscript{2} concentration and therefore has
accelerated in recent decades. Analysis of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C time
series should be corrected by adjusting values to a year of reference.
We applied a correction factor of -0.02 %0 yr\textsuperscript{-1}, in
agreement with recent studies (Espinasse et al., 2018; Williams, Risk,
Stone, Sinclair, \& Ghaleb, 2007) and standardized the time series using
2015 as the year of reference.

C/N ratios are often used to correct \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values for
the presence of lipids in the materials analyzed (Post et al., 2007).
The scales of adult salmon are mainly made out of collagen and as such
show constant C/N values varying between 2.5 to 2.9. However, for some
of the published data (Bristol Bay and Rivers inlet stocks), C/N were
found out of this range. We suggest that the scales which are not rinsed
directly after collection on fish might contain mucus residuals that
will stick to the scale even when washed carefully before analysis. We
applied a correction for the eight Bristol Bay stocks based on the
difference between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C of scale with C/N
\textgreater{} 3.5 and yearly average of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C scales
having a C/N \textless{} 3.5. This resulted in correcting values for 65
scales out of 543 with a maximum correction factor of 1.2 %0. The
correction factor used for Rivers Inlet stock can be found in Boris
Espinasse et al. (2018) and is also based on the differences in
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values between scales with expected C/N and
scales with relatively high C/N. It has been questioned if the scales
should be acidified prior SI analysis as the external layer of the scale
is comprised of mineral apatite that could potentially skew analyses of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C (Tzadik et al., 2017). However, when the scales
grow through the fish life cycle, new layers of collagen are added and
the contribution of the external mineral layer to the total weight of
the scale decreases (Hutchinson \& Trueman, 2006). Furthermore,
Sinnatamby, Bowman, Dempson, and Power (2007) found no significant
differences between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values of Atlantic salmon
scales that were acidified or not. Therefore, none of the scales
processed during this study were acidified.

SST data were extracted from the COBE SST2 dataset, which can be
downloaded freely at https://www.esrl.noaa.gov/psd/. This dataset
provided SST interpolated on a 1 by 1 degree grid, and used a new
analysis scheme to reduce uncertainties in analyzed SST (Hirahara,
Ishii, \& Fukuda, 2013). SST were averaged for each grid cell from
January to June, which generally provides highest correlation
coefficients for both whole and excised scales.

\emph{Time series processing}

Correlation of time series can be caused by similarity in variations
occurring at different frequencies. To be able to identify the relative
effect due to long term trends (low frequency) and interannual
variability (high frequency), three types of data were used: original
data (combination of both effects), detrended data (only interannual
variability) and smoothed data (dominance of long term trend). Time
series subject to low frequency influence are usually strongly
auto-correlated, which violates the assumption of serial independence
required for correlation test. One way to account for autocorrelation is
to re-assess the number of degrees of freedom, which results in higher
p-value for the same correlation coefficient. Pyper and Peterman (1998)
developed the so-called `modified Chelton method' that allows one to
calculate a critical correlation coefficient value associated with a
given p-value. This method was later adapted by Barker, Hannaford,
Chiverton, and Svensson (2016) to account for missing values in time
series. We applied this approach for calculation of the p-values for the
correlation test of original and smoothed data. Smoothing of the data
was conducted using the LOESS method with a span of 0.1-0.2, depending
on the time series length, and one degree polynomial. The map plotting
was done with the Matlab package M\_Map (Pawlowicz, 2019).

\emph{Area restriction and result validation}

Initial plotting of the correlation coefficient between
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and SST can display potential spurious
correlations due to inappropriate time series (small dataset or
irregular sampling) or synchrony in SST variation across the North
Pacific. A range of methods were explored to narrow down the area with
highest correlation and propose the most realistic and accurate salmon
distributions. The main source of information on salmon distributions in
the literature comes from the tagging studies conducted as an
international collaboration between Japanese, Canadian and American
fishery institutes from 1956 to 1970 (French et al., 1976). More than 60
000 sockeye salmon x.2 (two years spent at sea) or older were caught and
tagged over this time period. The data were used to produce distribution
maps of maturing sockeye, a caveat being that since the fish were caught
and tagged between late April to June, they were potentially already
heading to their natal river system. We digitalized these maps and
included them as supplementary materials (Figure S1.1). Another approach
to indirectly validate the proposed feeding grounds is to compare the
average level of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N
values for stocks supposedly distributed in the same area and/or to test
for \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N correlation between stocks (assuming that
stocks with the same at-sea distribution will have correlated
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N series). A pre-requirement for the latter is to
check that \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values are
not correlated, which is stock dependent. Finally, we used an empirical
orthogonal function (EOF) analysis to describe the spatial mode of SST
variability. This provided insight into the connectivity between areas
with high correlation values.

The mean \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N of salmon stocks found to be distributed
in the NE Pacific were compared with zooplankton SI values published in
Espinasse, Hunt, Batten Sonia, and Pakhomov (in press). The zooplankton
SI values were extracted from the isoscapes at a location centered on
the highest likelihood distribution of each stock and averaged over one
degree latitude and longitude. Stock specific trophic level was
estimated assuming a trophic enrichment factor of 3.4%0 (Post, 2002).

\textbf{Results}

\emph{Stock-specific stable isotope values}

Large interannual variations were observed for both
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N (Figure 2). Stocks
that are in close geographic proximity showed similar patterns in SI
values variability at low frequency but their SI values can differ
substantially over small temporal scale. Suess effect-corrected
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values showed more stability over time than
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N and also less variation between stocks with
values mainly distributed between -19 to -17.5%0 while
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values spanned over 3%0 (8.5 to 11.5%0).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2. Yearly mean of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values (corrected for Suess effect) in scales of
eight sockeye salmon stocks from Kodiak Island (Red Lake and Upper
station), SE Alaska (Chillkat and Chilkoot), Columbia River (Okanagan
and Wenachee) and Kamchatka Peninsula (Ozernaya and Kamchatka).

For clarity, only unpublished data (with the addition of the Red Lake
stock) were represented in the Figure 2, however the assessment stands
for all stocks (Table 1) with the absolute range being wider for
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N than \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values, 5.05 vs 2.67,
respectively. Significant correlation between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C appeared random among stocks with no obvious
pattern in region or age. A SI biplot of average values shows clear
separation between the main regions despite the differences in time
range covered (Figure 3). Kodiak Island and SE Alaska stocks together
with Columbia River stocks showed lower \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values. All Bristol Bay stocks grouped together
with higher \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values, while Russian stocks and BC
coast stocks were positioned between these aforementioned groups.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Biplot of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N versus \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C
values (corrected for Suess effect) averaged over different time periods
(see Table 1) for 17 sockeye salmon stocks. The error bars represent the
standard deviation.

\emph{Stock-specific feeding grounds}

Table 2. Correlation coefficients between \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values
in salmon scales of several stocks and SST (averaged for Jan-Jun of the
year of return). The time series were processed in different ways to
disentangle the effect of low and high frequency variability on
correlation estimates (original, detrended and smoothed). SST data for
each stock were retrieved at the location marked in Figures 4, 5 and 6
by a white cross. P-values were calculated using the modified Chelton
method to account for autocorrelation. P-values significance is: ***
p\textless{}0.001, **p\textless{}0.01, *p\textless{}0.05.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Salmon stock & Correlation coefficient & Correlation coefficient &
Correlation coefficient\tabularnewline
\midrule
\endhead
& Original & Detrended & Smoothed\tabularnewline
Kodiak Island & & &\tabularnewline
Red Lake & 0.42* & 0.25 & -\tabularnewline
Upper station & 0.44** & 0.42* & 0.58**\tabularnewline
SE Alaska & & &\tabularnewline
Chilkoot & 0.72** & 0.47* & 0.86*\tabularnewline
Chilkat & 0.58*** & 0.57*** & 0.69*\tabularnewline
Columbia & & &\tabularnewline
Okanagan & 0.59** & 0.55** & 0.73**\tabularnewline
Wenachee & 0.54** & 0.47** & 0.69**\tabularnewline
BC coast & & &\tabularnewline
Rivers Inlet & 0.38 & 0.62* & -\tabularnewline
S Bristol Bay & & &\tabularnewline
Ugashik & 0.69** & 0.72** & 0.71**\tabularnewline
Egegik & 0.31 & 0.29 & 0.46.\tabularnewline
Naknek & 0.68* & 0.51 & 0.67*\tabularnewline
Kvichak & 0.61* & 0.50 & 0.58.\tabularnewline
N Bristol Bay & & &\tabularnewline
Nushagak & 0.37 & 0.28 & 0.51.\tabularnewline
Wood River & 0.50. & - & 0.58*\tabularnewline
Igushik & 0.24 & - & 0.31\tabularnewline
Togiak & - & - & -\tabularnewline
NW Pacific & & &\tabularnewline
Ozernaya & 0.43 & 0.44 & 0.63*\tabularnewline
Kamchatka & 0.41 & 0.43 & 0.60\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

The estimated feeding grounds for stocks with spawning grounds south of
the Aleutian Islands were all located in the NE Pacific itself but with
little overlap between the stocks (Figure 4). The SE Alaska stocks
Chilkat and Chilkoot, which have spawning grounds 10 km away from each
other, showed well separated at-sea locations, with Chilkoot being
distributed 10 degrees further west than Chilkat. While Columbia River
stocks have the most southerly spawning grounds, their high seas
distributions were located in the northern sector of the NE Pacific. The
Rivers Inlet stock was distributed further south than any other stock.
With the exception of Rivers Inlet, all stocks were sampled yearly and
produced significant correlations with highest significance usually
found using the original time series (Table 2, correlation coefficients
were usually higher with smoothed time series but the adjusted degrees
of freedom reduced the

Figure 4. Estimated feeding grounds during the last year at sea of (a)
Red lake, (b) Upper station, (c) Chilkoot, (d) Chilkat, (e) Okanagan,
(f) Wenachee and (g) Rivers inlet sockeye salmon stocks (Gulf of
Alaska). Strongest correlation coefficient between SST and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C value is indicated by a white cross. Locations of
the point of entry in the freshwater system of the different stocks is
shown (red circle).

significance, see Methods). Bristol Bay stocks at-sea distributions
showed interesting patterns with some stocks being distributed in the
Bering Sea and some others along the Aleutian Islands (Figure 5).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 5. Estimated feeding grounds during the last summer at sea of (a)
Wood River, (b) Togiak, (c) Nugashak, (d) Igushik, (e) Naknek, (f)
Kvichak, (g) Egegik and (h) Ugashik sockeye salmon stocks (Bristol Bay
from north to south). Strongest correlation coefficient between SST and
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C value is indicated by a white cross. Locations of
the point of entry in the freshwater system of the different stocks is
shown (red circle).

Correlation coefficients were relatively less significant for the
Bristol Bay stocks, most likely because of the every 3-year sampling
resolution that resulted in a low number of data points that did not
capture the full extent of interannual variability. No smoothing was
applied to the SI values because of the coarse resolution but smoothing
the SST series resulted in a slight improvement in the defined areas.
Both Russian stocks showed similar at-sea distributions despite
originating from opposite side of the Kamchatka Peninsula (Figure 6).
Smoothed time series provided more significant correlations for these
stocks.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 6. Estimated feeding grounds during the last summer at sea of (a)
Ozernaya and (b) Kamchatka sockeye salmon stocks (Russia). Strongest
correlation coefficient between SST and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C value is
indicated by a white cross. Locations of the point of entry in the
freshwater system of the different stocks is shown (red circle).

The first mode of the EOF analysis applied to SST data for 1968-1998
explained 43\% of the total variance and showed that waters from SW
Alaska and North BC coast covaried (Figure 7).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 7. First mode of an EOF analysis using SST averaged each year
from January to June for 1968-1998. This mode explained 43\% of the
variance.

The feeding grounds of nine of the salmon stocks were located in the NE
Pacific. We compared the averaged \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values of these
stocks with \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values extracted from zooplankton
isoscapes (Espinasse et al., in press) (Figure 8). The linear regression
applied to these data gave an almost 1:1 coefficient with a Y intercept
value of 4.6 (with zooplankton as TL=2). Assuming a trophic enrichment
factor of 3.4%0, the trophic levels of the salmon stocks varied between
3.30 and 3.76 (Table 3).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

Figure 8. Salmon \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N plotted against zooplankton
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values. Each point represents an average value
for a sockeye salmon stock that have been assessed to be distributed in
the Gulf of Alaska. Zooplankton \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N estimates were
obtained from isoscapes presented in Espinasse et al., 2019.

Table 3. Trophic level estimates for nine salmon stocks assessed in this
study to be distributed in the Gulf of Alaska during their last year at
sea. The trophic level is based on the difference between
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values in salmon scales and large herbivorous
copepods (TL 2) analyzed for this region, assuming a trophic level
enrichment factor of 3.4%0.\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
Salmon stock & Trophic level\tabularnewline
\midrule
\endhead
Columbia &\tabularnewline
Okanagan & 3.38\tabularnewline
Wenachee & 3.31\tabularnewline
Kodiak &\tabularnewline
Red lake & 3.44\tabularnewline
Upper station & 3.30\tabularnewline
BC coast &\tabularnewline
Rivers Inlet & 3.34\tabularnewline
Bristol Bay &\tabularnewline
Naknek & 3.76\tabularnewline
Wood River & 3.56\tabularnewline
\bottomrule
\end{longtable}

\textbf{Discussion}

In this study we estimated the high seas distribution and foraging
grounds for 17 sockeye salmon stocks, finding significant correlations
for 13 of them. However, the spatial pattern of correlation between SST
and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C did not always result in the identification
of a single well defined foraging region. Aside from the potential
influence of factors other than SST on carbon isotopic composition, the
accuracy of the results relies on the assumptions that 1) the fish go to
a similar place every year and 2) that they do not move across strong
SST/prey \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C gradient during the time period over
which the SI values are integrated.

The first assumption is difficult to assess given that there are limited
data available on high seas stock-specific locations. The question is
probably even more essential for stocks with a strong pattern in age
distribution (one predominant age-class), resulting in little mixing
between cohorts from different brood years. Another aspect that should
be investigated is whether there are differences in SI values between
early and late runs for the same stock. While for some stocks, adult
salmon return predominantly occurs over a short time window, other
stocks have two temporally separated runs identified and it is not
currently known if high seas distributions differ between runs.

The second assumption is also difficult to evaluate as no data are
available for stock-specific movement. A strong gradient in
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values at lower trophic levels is usually located
at the transition between the coastal system (subject to fluvial input,
resuspension, etc.) and the open ocean system (more oligotrophic
conditions). This transition usually occurs above the shelf or
continental slope, depending on the width of the shelf, local
hydrodynamics and water depth, but in all cases occurs at a relatively
short distance from the coast (El-Sabaawi, Trudel, Mackas, Dower, \&
Mazumder, 2012; Kline Jr, 2009). Maturing salmon will likely only swim
through this zone once, just before entering the river system and it is
therefore unlikely to influence their SI values. They could, however,
target areas of higher productivity while in the high seas. For example,
eddies have the potential to enhance local primary production that will
promote development of salmon prey (Crawford, Brickley, \& Thomas, 2007;
Mackas, Tsurumi, Galbraith, \& Yelland, 2005) with higher
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C values (Espinasse et al., in press).
Unfortunately, it is not yet documented whether the salmon display such
behavior.

\emph{Influence of scale sampling method}

Our dataset comprised a mix of SI values measured from whole scales and
the last annulus of the scale. We integrated SST over different time
ranges to evaluate the best match with the potential integration time of
SI values from these different methodologies. The time period from
January to June of the return year gave the best results for the two
scale sampling methods. This suggested a negligible influence of scale
material laid down during the freshwater phase (Hutchinson \& Trueman,
2006) and furthermore that the whole scale SI value was dominated by the
last growing season. This can be explained by the largest increase in
size and weight of the fish occurring during the last year at sea
(Ishida et al., 1998). The mass of collagen produced during this time
period largely overwhelms the rest of the scale mass. Our results
suggest that using the whole scale did not affect time series
comparison, as whole scale and last annulus have very similar SI values
(Finney, unpublished data). Studies focusing on the freshwater phase or
early marine phase of salmon should however be careful in their
interpretations when using scales from maturing fish, as it seems
inevitable that the recent layers deposited will strongly influence the
SI values (Hutchinson \& Trueman, 2006).

We demonstrated that when analyzing time series of \textgreater{} 20
data points with yearly resolution, using the original un-treated data
gave the most significant results. These data retain the influence of
both low and high frequency variations but require correction of the
number of degrees of freedom to adjust the p-value (Pyper \& Peterman,
1998). Some stocks with geographically close spawning grounds showed
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C variations driven by different factors. For
example, Chilkoot salmon \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C data demonstrated a
largely increasing trend from low values in earlier years to high values
in later years, while Chilkat did not show a clear long term trend but
rather greater interannual variability (Figure S1.2). The Suess effect
correction parameter, by changing the overall inclination of the
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C trend, could affect the part of the correlation
driven by the low frequency (long term trend). We tested this using data
for the Chilkoot and Chilkat stocks and additionally the Okanagan stock
for which the data covers more recent years. The Suess effect parameter
was set at -0.015 %0 yr\textsuperscript{-1} and -0.025 %0
yr\textsuperscript{-1}. This resulted in similar distributions and very
little change in correlation significance (Table S1.1). Furthermore, for
the large majority of the stocks for which we found significant
correlations, the three types of series generated the same distribution.
Among the series with yearly resolution, only Red Lake displayed
different high correlation areas between the original series and the
smoothed series. The detrended series, however gave a similar, but
non-significant, distribution to the original one. Overall, using the
three approaches enabled improved evaluation of the ocean area most
likely used by the salmon, and dismissal of the spots which were only
driven by interannual variability or long term trend.

Series with coarser or irregular resolution, usually the smaller
datasets (\textless{} 20), still allowed feeding grounds estimates. Best
results were obtained after smoothing the SST series. We tested the
effect of sampling resolution by downgrading the resolution of the
Okanagan series using data from every 3\textsuperscript{rd} year and
obtained comparable distributions to the full resolution data set
(Figure S1.3). We do not encourage the use of such small datasets, as it
is often hard to estimate if the series meets the basic assumptions for
correlation test, and there is an increased chance of spurious
correlations. Nonetheless, datasets of approximately ten points appear
sufficient to provide a meaningful preliminary indication of salmon
distribution. We recommend that these are subsequently validated with
longer time series.

\emph{Feeding grounds definition and validation}

While for some of the stocks, such as Chilkoot, Okanagan and Wenatchee,
the method resulted in well-defined feeding ground estimates, estimates
for other stocks showed more uncertainty with larger areas showing high
correlation and/or multiple regions with high correlation. Several
approaches were used to assess these areas and to further resolve the
feeding grounds. Data from tagging studies were used to define the
limits of where the salmon are likely to be distributed (Figure S1.1).
However, these limits encompassed large areas as many stocks were
pooled, and so these data were mainly useful to discard high correlation
spots that were located outside the potential distribution range. For
example, for Red Lake and Upper station stocks (Kodiak Island), two
secondary high correlation spots were located eastward of the range
determined by the tagging studies and were therefore not considered. The
EOF analysis of SST data was useful to understand connectivity between
high correlation areas. The Chilkat stock distribution was a good
example, with a high correlation area covering a spot in the middle of
the Gulf of Alaska and extending northwest to Alaska. This may have been
due to the SST of these water masses varying in a similar way during the
time period considered for this stock (Figure 7). Based on the tagging
study, the central Gulf of Alaska region was estimated to be more
realistic.

Another way to validate the inferred stock distributions is to integrate
information provided by the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values. We would
expect that the stocks with similar foraging areas would also show
correlation between their \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N time series, assuming
that they feed at the same trophic level. However, very few stocks
showed significant correlation of \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values (Figure
S1.4). One explanation for this is that there was little overlap between
stock distributions. Differences in return timing may also have been a
factor, since the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N values of prey change rapidly
over the productive season. Based on average levels of
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N and \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{13}C (Figure 3), Bristol
Bay stocks were separated from the others by their higher SI ratios.
This was in agreement with the distributions of the stocks for which the
correlation was significant (Ugashik, Naknek and Wood river). These
stocks were distributed closer to the coast of the Aleutian Islands or
onto the Bering Sea shelf, and both of these areas are more productive
and display elevated SI ratios (Pomerleau, Nelson, Hunt, Sastri, \&
Williams, 2014). Similarly, we found a correlation between the
\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{15}N of salmon prey and salmon for the stocks with
feeding grounds identified in the Gulf of Alaska. This also allowed
estimation of the trophic level for these stocks (Table 3). Salmon are
reported to feed on a large variety of prey such as copepods, squids,
euphausiids and amphipods (Kaeriyama et al., 2004), and, therefore, one
would except their trophic level (TL) to range between 3 and 4, which is
consistent with our estimates (TL = 3.3 to 3.8) and those from Qin and
Kaeriyama (2016) (TL = 3.9).

\emph{Implications}

A broad regional approach, assuming a generalized distribution for all
salmon stocks in the North Pacific, has been demonstrated to be useful
in linking salmon productivity with climate indices (Malick, Cox,
Mueter, Dorner, \& Peterman, 2017; Mantua, Hare, Zhang, Wallace, \&
Francis, 1997). However, other studies have stressed the importance of
stock specific analysis, as stocks can have different productivity
trends despite having geographically close spawning grounds (Quinn,
Rich, Gosse, Schtickzelle, \& Grant, 2012; Rogers \& Schindler, 2011),
and there is a need to differentiate between factors affecting salmon at
local to regional scales (Ohlberger, Scheuerell, \& Schindler, 2016).
The unique SI signatures for each stock implies different feeding
locations, and this study used a recently developed approach based on
SST to infer stock-specific maturing sockeye distributions.
Stock-specific at sea distributions is an important knowledge gap that
may contribute to understanding of stock specific productivity trends.
Changes in environmental conditions due to hydrographic or atmospheric
processes impact the physical environment experienced by salmon and the
quality and / or quantity of prey that they encounter. Because of the
heterogeneity in their prey distribution, the high seas distributions of
salmon can affect their productivity.

At a large spatial scale, the difference in trends of productivity
between Bristol Bay stocks and most of the NE Pacific stocks during the
last decades may be explained by differences in high seas distributions.
In this study, we found Bristol Bay stocks to be primarily distributed
around the Aleutians Island and Bering Sea, while the NE Pacific stocks
were distributed in the off shelf area of the Gulf of Alaska. These
stocks would thus have experienced different environmental conditions
with respect to both annual conditions and long term trends in factors
such as SST (Mueter, Peterman, \& Pyper, 2002) and ocean currents
(Malick et al., 2017).

At a regional scale, stock-specific distributions can help explain
divergence in productivity of stocks with geographically close spawning
grounds. Peterman and Brigitte (2012) investigated similarity in
temporal variation of productivity of several sockeye stocks and found
that among Bristol Bay stocks, Wood River, Naknek, Togiak and Igushik
stocks grouped together while Egegik and Ugashik formed another group,
breaking the usual south/north geographical separation of the stocks.
However, this grouping is consistent with the pattern of salmon at sea
distributions found in this study, with Wood River, Naknek and Igushik
distributed along the south coast of the Aleutian Islands and Egegik and
Ugashik in the Bering Sea. This highlights the uncertainty associated
with assuming that stocks with similar spawning ground locations have
similar at-sea distributions, and the need for at-sea stock ID data to
explicitly connect stocks to high seas conditions.

Another aspect of the high seas life phase that is currently unknown is
what drives salmon at-sea distributions. Feeding ground estimates for
stocks relative to their origin allows the development of a schematic
view of the potential migration sequence determining high-sea salmon
distributions. In the NE Pacific, juvenile sockeye salmon enter the
ocean at the beginning of summer and typically migrate following the
general counter clockwise circulation, northward or westward, depending
on stock location, at a speed of about 14 km d\textsuperscript{-1}(David
W. Welch et al., 2011), although this seems to vary depending on stock
(Tucker et al., 2009). The time of ocean entry is influenced by numerous
factors, including distance between the rearing lake and the ocean
(Carr-Harris et al., 2018) and the seasonal pattern of lake water
warming. The timing of ocean entry and the smolt size will define the
distance that they are able to cover before their first ocean winter.
This, in combination with the entry location, will result in juveniles
from different stocks being distributed in different parts of the coast
of the Gulf of Alaska when winter starts. We suggest that this will
define their high seas distribution for the rest of their marine life
assuming that they are then restricted to seasonal offshore/onshore
migration in fall/spring (Burgner, 1991). This would explain why
Columbia stocks, which have a relatively late ocean entry time due to
the long distance from rearing ground to the river mouth, were
distributed in the northern part of the Gulf of Alaska. The SE Alaska
and Kodiak juvenile salmon seem to move westward and then eventually
southward if time allows. For example, Chilkoot smolts, which rear in a
glacial lake, likely enter the sea later than Chilkat smolts and were
also measured to be significantly smaller in size (Bergander, 1988). As
a result, they end up in the area surrounding the Aleutians Islands,
while salmon from the nearby clearwater systems such as Chilkat, and Red
Lake / Upper station stocks (Kodiak Island) travelled further and were
distributed further south. This strategy may allow sockeye salmon to
more broadly colonize much of the marine domain, utilizing the ocean
carrying capacity over a large area, and minimizing density dependence
and risk of food shortage.

\emph{Conclusions}

Given potential reductions in favourable habitat due to accelerating
global warming, there is an urgent need to better understand the ocean
environment experienced by salmon and how this impacts their health and
condition. Up to now, little information has been available on
stock-specific salmon distribution which hampers our ability to resolve
stock-specific dynamics and develop appropriate management plans. The
marine environment is heterogeneous, and populations foraging in
different areas may be affected in different ways as environmental
conditions change. In this study, we showed that salmon stocks with
geographically close rearing grounds can have different at-sea
distributions, potentially driven by the difference in ocean entry
timing and size. Expanding this approach with data from other major
sockeye stocks and other salmon species would improve understanding of
the mechanisms explaining salmon spatial distribution in the high seas,
and how this distribution interacts with environmental conditions to
affect salmon survival and fitness.

\textbf{Data availability statement}

All stable isotope data supporting the conclusions of this article is
included within the article and supplementary file (Appendix 2).

\textbf{Competing interests}

The authors declare that they have no competing interests.

\textbf{Funding}

BE postdoctoral fellowship was founded by The Monell and Vetlesen
foundation.

\textbf{Authors' contributions}

BE, BPVH, BPF and EAP conceived the project. BPF, JKF and AVB provided
access to new materials (archived scale) or unpublished salmon stable
isotope data. BE carried out the sample and data analysis and wrote the
manuscript. All authors provided editorial advice. All authors read and
approved the final manuscript.

\textbf{Acknowledgements}

We thank the Fisheries and Oceans Canada's Schlerochronology Lab at the
Pacific Biological Station for providing access to the Rivers Inlet
sockeye salmon scale archive, in particular the kind assistance of
Darlene Gillespie and Stephen Wischniowski. We thank the Wuikinuxv
Nation for ongoing support in sample collection from returning sockeye
salmon since 2011, in particular Billie Johnson, Chris McConechy, and
David Rolston. We also thank the Columbia River Inter-Tribal Fish
Commission for providing access to their archive of sockeye salmon
scales; in particular John Whiteaker, Crystal Chulik, and Amber Cate,
and all the specialists working at KamchatNIRO who were involved in the
sampling and archiving of salmon scales.

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