Effects of salmon on the diet and condition of

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1
ARTICLE
Effects of salmon on the diet and condition of stream-resident
sculpins
Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14
For personal use only.
Noel R. Swain, Morgan D. Hocking, Jennifer N. Harding, and John D. Reynolds
Abstract: Pacific salmon (Oncorhynchus spp.) can subsidize freshwater food webs with marine-derived nutrients from their eggs,
juveniles, and carcasses. However, trophic interactions between spawning salmon and freshwater fish across natural gradients
in salmon subsidies remain unclear. We tested how salmon affected the diets and condition of two dominant freshwater
consumers — prickly and coastrange sculpins (Cottus asper and Cottus aleuticus, respectively) — across a wide gradient of pink
salmon (Oncorhynchus gorbuscha) and chum salmon (Oncorhynchus keta) biomass from 33 streams in the Great Bear Rainforest of
British Columbia, Canada. Sculpin diets shifted from invertebrates and juvenile salmonids to salmon eggs when salmon arrived
in autumn, with salmon-derived nutrient contributions to diets and sculpin condition increasing with increasing biomass of
spawning salmon among streams. Season, habitat, and individual sculpin body size and species also mediated the effects of
salmon on sculpin diet as inferred from their carbon and nitrogen stable isotope signatures. This study shows the timing and
pathways by which spawning salmon influence the diets and condition of freshwater consumers, and some of the individual and
environmental factors that can regulate uptake of salmon nutrients in streams, thus informing ecosystem-based management.
Résumé : Les saumons du Pacifique (Oncorhynchus spp.) peuvent contribuer aux réseaux trophiques d'eau douce par l'entremise
de nutriments d'origine marine issus de leurs œufs, leurs juvéniles et leurs carcasses. Les interactions trophiques entre les
saumons en frai et les poissons d'eau douce le long de gradients naturels d'apports trophiques des saumons demeurent toutefois
mal définies. Nous avons voulu comprendre l'incidence des saumons sur le régime alimentaire et l'état d'embonpoint de deux
consommateurs d'eau douce dominants, le chabot piquant et le chabot côtier (Cottus asper et Cottus aleuticus, respectivement), le
long d'un large gradient de biomasse de saumons roses (Oncorhynchus gorbuscha) et kétas (Oncorhynchus keta) de 33 cours d'eau dans
la forêt pluviale de Great Bear, en Colombie-Britannique (Canada). Les régimes alimentaires des chabots passaient d'invertébrés
et de salmonidés juvéniles aux œufs de saumon après l'arrivée des saumons en automne, les contributions de nutriments
provenant des saumons au régime alimentaire et à l'état d'embonpoint des chabots augmentant parallèlement à l'augmentation
de la biomasse de saumons en frai dans les cours d'eau. La saison, l'habitat, ainsi que la taille du corps et l'espèce des chabots
modulaient également les effets des saumons sur le régime alimentaire des chabots déterminé à la lumière de leurs signatures
d'isotopes stables de carbone et d'azote. Ces résultats font la lumière sur les périodes où les saumons en frai influencent le régime
alimentaire et l'état d'embonpoint de consommateurs d'eau douce et les voies par lesquelles cette influence s'exerce, ainsi que
sur certains des facteurs individuels et du milieu ambiant qui peuvent réguler la consommation de nutriments dérivés des
saumons dans les cours d'eau, ce qui peut éclairer la gestion écosystémique. [Traduit par la Rédaction]
Introduction
In coastal areas of the North Pacific Ocean, marine and aquatic
ecosystems are linked through nutrient subsidies to fresh waters
by annual spawning migrations of anadromous Pacific salmon
(Oncorhynchus spp.) (Gende et al. 2002; Naiman et al. 2002). Salmon
nutrient pulses provide food to freshwater consumers directly
through salmon eggs, juvenile life stages, and adult flesh (Scheuerell
et al. 2005, 2007; Moore et al. 2008). They may also support consumers by affecting primary production and the longer term recycling
of limiting nutrients such as nitrogen in stream food webs (Wipfli
et al. 1998; Verspoor et al. 2010, 2011). Because of the regular periodicity and large magnitude of these nutrient subsidies to coastal
fresh waters, salmon streams are good model systems for understanding the impacts of such processes on recipient ecosystems
and dynamics of consumer populations (Schindler et al. 2003;
Janetski et al. 2009).
Freshwater fish, including sculpins (Cottus spp.) and salmonids,
consume salmon eggs and alevin, as well as invertebrates and
other fish that also benefit from salmon (Foote and Brown 1998;
Chaloner et al. 2002; Denton et al. 2010). This does not mean that
there would necessarily be a strong correspondence between
their dietary composition and the numbers of salmon in the
streams, because this can depend on habitat size and physical
habitat features, as shown recently for other impacts of salmon on
ecosystems (Tiegs et al. 2008; Hocking and Reimchen 2009; Field
Received 14 March 2013. Accepted 30 October 2013.
Paper handled by Associate Editor John Richardson.
N.R. Swain. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A
1S6, Canada.
M.D. Hocking. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby,
BC V5A 1S6, Canada; Hakai Network for Coastal People, Ecosystems, and Management, Faculty of Environment, Simon Fraser University, 8888
University Drive, Burnaby, BC V5A 1S6, Canada; School of Environmental Studies, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W
2Y2, Canada.
J.N. Harding and J.D. Reynolds. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, 8888 University
Drive, Burnaby, BC V5A 1S6, Canada; Hakai Network for Coastal People, Ecosystems, and Management, Faculty of Environment, Simon Fraser
University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada.
Corresponding author: Noel R. Swain (e-mail: swain.noel@gmail.com).
Can. J. Fish. Aquat. Sci. 71: 1–12 (2014) dx.doi.org/10.1139/cjfas-2013-0159
Published at www.nrcresearchpress.com/cjfas on 28 November 2013.
2
and Reynolds 2011). Furthermore, individual characteristics of organisms such as body size can also mediate the diet and abundance of consumers in their response to subsidies (Phillips and
Claire 1966; Sweeting et al. 2007a; Hocking et al. 2013). To date, no
study has tested how salmon may affect the trophic ecology of
freshwater fish across a wide natural range of spawning salmon
densities, and there is little to no research of how individual fish
body size and habitat features of streams may mediate the contribution of salmon to fish diets and condition.
Freshwater sculpins often constitute most of the density and
biomass of freshwater fish assemblages (Adams and Schmetterling
2007; Bellmore et al. 2013) and are known to consume salmon eggs
and juveniles (Hunter 1959; Foote and Brown 1998). Sculpins are
therefore likely to be strongly affected by salmon subsidies and to
play important roles in freshwater ecosystem dynamics. However,
little research has examined their trophic ecology or potential
interactions within freshwater food webs (Bellmore et al. 2013).
Resident freshwater prickly sculpin (Cottus asper) and coastrange
sculpin (Cottus aleuticus) are commonly found in large, sympatric
populations in coastal streams where Pacific salmon spawn. Where
they coexist, prickly sculpins are typically less abundant in lower
reaches of streams and prefer sheltered habitat such as pools and
undercut banks, while coastrange sculpins tend to be smaller and
more abundant riffle-dwelling fish (Mason and Machidori 1976; McPhail
2007).
Here, we test how salmon affect the diets and physical condition of freshwater sculpins (C. asper and C. aleuticus) across a wide
natural gradient in spawning biomass of pink salmon (Oncorhynchus
gorbuscha) and chum salmon (Oncorhynchus keta) from 33 streams in
the Great Bear Rainforest of coastal British Columbia. We account
for mediating effects of habitat, season, and individual body size.
We predicted that salmon-derived nutrient contributions to sculpin diets and lipid storage in sculpin livers (carbon:nitrogen (C:N)
ratios) would increase with increasing salmon spawning density.
We further predicted that these relationships would be stronger
in autumn, when salmon are spawning; would be more apparent
in larger sculpins; and would be mediated by habitat. To test these
predictions we used (i) stomach contents analysis; (ii) tissuespecific nitrogen and carbon isotope analysis (fin versus liver tissues);
(iii) single isotope linear mixed-effects models; and (iv) Bayesian C–N
isotope mixing models to analyze shifts in sculpin diet as well as liver
C:N as a measure of lipid storage and sculpin condition, across the
gradient in salmon nutrient availability.
Materials and methods
Study sites
We conducted our study across watersheds on coastal islands
and mainland inlets near the Heiltsuk First Nation community of
Bella Bella in the Great Bear Rainforest region on the Central
Coast of British Columbia (Fig. 1). These watersheds span several
natural gradients: (i) a wide range of spawning salmon abundance,
from zero to more than 60 000 combined pink and chum salmon
returning within a single year; (ii) watershed size; and (iii) habitat structure (e.g., large wood density) (Table 1). The streams
run through coastal conifer-dominated temperate rainforest in
the Coastal Western Hemlock biogeoclimatic zone, where there
have been relatively few anthropogenic impacts aside from some
selective logging in the 1940s (Hocking and Reynolds 2011). Sampling sites were in the lower reaches of salmon spawning streams,
well above tidal influence, and included one control site located
above an impassable waterfall. Where possible, sampled areas
included a range of stream habitats such as pools, undercut
banks, riffles, runs, and glides.
Sculpin sampling
We collected C. asper and C. aleuticus in the summers of 2007,
2008, and 2010 (before salmon arrived) and in September and
Can. J. Fish. Aquat. Sci. Vol. 71, 2014
October of 2009 and 2010 (while salmon were spawning) using
minnow traps (2007–2009) and backpack electrofishing units
(2010). Twelve minnow traps, baited with perforated canisters
containing a mixture of canned cat food and prawn oil, were set
per stream for 6–24 h. Where possible, half of the traps were set
in shallow high-discharge habitats (riffles, runs, glides), and the
other half in deeper, low-discharge, protected habitats (undercut
banks, pools, log jams). We used Smith-Root LR-24 and 12-B backpack electrofishers (Smith–Root, Vancouver, Washington, USA),
with fish caught by one or two people using dip nets.
We sampled 517 sculpins (258 C. asper and 259 C. aleuticus).
Where possible, we collected 3–10 individuals of each species per
stream per sampling period to analyze fin (n = 517) and liver (n =
352) isotopes, as well as stomach contents (n = 384). In 2007 and
2008, nonlethal fin clips were taken from the anterior portion of
the second dorsal fin. In 2009 and 2010, sculpins were sampled
lethally by clove oil overdose, retained, and stored frozen for later
tissue-specific (fin and liver) isotope as well as stomach content
analysis. We sampled a range of sculpin body sizes (30–155 mm
standard length), and excluded young-of-the-year fish (<30 mm),
because residual maternal signatures and marine inputs during
estuary rearing could confound their tissue isotopic signatures
(e.g., McPhail 2007).
Stream habitat variation
Steam habitat surveys were carried out from 2006 to 2011. Based
on a review of the literature, we hypothesized that there were two
main processes by which physical habitat characteristics of streams
were likely to affect salmon-derived nutrient contributions to sculpin diets: (i) the extent to which a stream was likely to retain or
discharge nutrients from salmon, and (ii) the extent to which
foraging sculpins were able to access these resources during and
after spawning periods. We hypothesized that watershed size and
habitat complexity would be most likely to influence salmonderived nutrient contributions to sculpin diets and subsequently
affect individual sculpin condition (Table 2). Habitat surveys were
conducted for a reach of stream, the length of which was roughly
30 times the mean wetted width, starting just above the estuary
margin. This reach was divided into four sections of equal length,
from which three transects were selected randomly, for a total of
12 transects at which measurements were taken.
Because many potential habitat variables could describe these
processes, we derived a reduced set of variables using methods
described by Zuur et al. (2010). We examined multicollinearity
among habitat variables using variance inflation factors (VIF),
where highly collinear variables (VIF > 4) were either combined
with similar variables through principal component analysis (PCA) or
dropped from subsequent analyses.
We used the first principal component of stream length, bankfull width, and catchment area as a metric of watershed size (Field
and Reynolds 2011; Hocking and Reynolds 2011). This axis explained 87% of the variation in these variables, and all three
variables loaded positively with eigenvalues >0.5. We measured
bankfull width during habitat surveys at each transect and used
the mean for the entire reach. Catchment areas and stream
lengths were determined from area maps and iMapBC (http://
maps.gov.bc.ca/ess/sv/imapbc/). We characterized stream complexity
using the first principal component of large wood per 100 m of
stream channel, percent area of the reach made up of highgradient habitat (falls, riffles, runs, cascades, step pools, and step
runs), percentage of transects having undercut banks, and percentage of the reach area made up of pools, which together explained 52% of the variation in these variables. Large wood per
100 m, percentage of undercut banks, and percent pool area all
loaded positively on this axis with eigenvalues of 0.37, 0.56, and
0.54 respectively, while percent high-gradient habitat loaded negatively with an eigenvalue of –0.51. We also considered stream
discharge, canopy cover, stream gradient, and total alder basal
Published by NRC Research Press
Swain et al.
3
Fig. 1. Locations of the 33 streams (black dots) in the Great Bear Rainforest of coastal British Columbia, Canada.
area per watershed in our analyses, as these variables have been
shown to explain some natural variation in isotope levels in
stream ecosystems and could potentially mediate an effect of
salmon (e.g., Pinay et al. 2003). However, these variables were only
available for a subset of our sites and did not meaningfully change
the patterns in our results, so they were dropped from our final
analyses to maximize sample sizes. Details on the collection of
these data can be found in Hocking and Reynolds (2011) supplementary materials.
Salmon population data
From 2006 to 2010, autumn salmon enumeration was conducted jointly by personnel from Simon Fraser University, the
Heiltsuk First Nation, and Fisheries and Oceans Canada. Spawning
salmon were counted by stream surveys on foot, and where possible, streams were visited at least three times during the spawning season. Abundance estimates were calculated using area
under the curve, or peak live plus dead abundance, which were
highly correlated estimates with comparable means (Hocking and
Reynolds 2011). In the few cases (n = 14) where pink or chum
abundance estimates for a stream were missing for a given
year, we used predicted values from hierarchical linear regressions of abundance estimates based on regional trends for
2006–2010. We derived an index of salmon spawning density
(D) (kg salmon biomass·(m2 spawning area)–1) for each stream:
(1)
D!
!"N × W #
i
i
A
where N is the number of adult salmon, i is the salmon species
(chum or pink salmon), W is the regional adult mean mass (kg)
(Field and Reynolds 2011), and A is salmon spawning area, measured as the distance from the mouth of the stream to the furthest
point upstream where spawning salmon were observed × mean
wetted width of the stream. We tested two salmon metrics: (i) the
single previous year (for summer samples) or current year (for
4
Table 1. Habitat, chum and pink salmon population data (2006–2010), number of sculpins sampled, and other fish species present in the study
streams.
Stream
Spawning
length (m)
Bankfull
width (m)
Chum salmon
mean
Pink salmon
mean
Salmon index
(kg·m−2)
C. aleuticus (n)
C. asper (n)
Other species
present
Ada
Beales Left
Beales Right
Bolin Bay
Bullock Main
Bullock Square
Clatse
Codville
Evans East
Fancy Head
Fancy Right
Fannie Left
Farm Bay
Goat Bushu
Hooknose
Jane
Kill Creek
Kunsoot Main
Lee
Mosquito Bay Left
Mosquito Bay Right
Neekas
Port John
Quartcha
Rainbow
Ripley Bay
Roscoe Main
Roscoe Trib. 1
Sagar
Spiller Trib. 1
Troupe North
Troupe South
Windy Bay
435
300
200
700
622
296
900
250
500
141
298
1500
590
1000
1800
500
453
1280
700
250
500
2100
262
5500
300
0
5800
250
800
80
332
489
2200
11.1
10.9
9.6
17.9
10.9
8.4
22.8
3.3
13.3
5.5
4.8
12.8
6.4
7.5
16.9
4.6
3.5
13.1
12.4
5.7
4
17.7
3.3
21.7
15.1
14.7
23.5
14.1
15.5
7.7
4.4
4.1
13.1
945.4
389.6
72.8
121.8
1416
248.4
3870
5.6
848.6
214
76.4
1935
0.0
75.4
1244.8
8.4
596.6
393.4
1110
91.8
416.8
17280
1.2
2360
189
0.0
10072
461.8
474.2
3.2
2.2
27.6
107
266.8
1187.6
322.2
1871.6
1418.4
119.8
9290
26.2
184.4
107
121.2
3439.4
0.0
662.6
1363.4
0.0
235.8
6238
177.8
202.8
606.2
24070.8
0.4
2040
8.4
0.0
219.8
388
461.2
7.2
0.6
156.4
283.6
0.75
0.93
0.38
0.27
1.25
0.61
1.19
0.06
0.6
1.22
0.28
0.56
0.0
0.19
0.29
0.01
1.25
0.56
0.54
0.54
1.28
3.15
0.05
0.1
0.17
0.0
0.29
0.77
0.2
0.07
0.01
0.12
0.03
5
1
0
0
20
13
7
1
5
4
4
3
9
5
20
7
14
17
14
2
6
10
6
18
3
5
2
10
3
15
14
6
0
8
11
6
6
9
2
15
18
1
15
5
23
0
0
16
7
0
9
2
2
0
26
0
5
0
14
10
0
5
0
15
17
6
CO, CT, RB
CO, CT, DV
CO, CT
CO
CO, CT, DV
CO, RB
CO, RB, DV
CO, SB
CO
CO
CO, SB
CO
CT
CO
CO, CT, DV, LA
CO, CT
CO, CT
CO
CO, RB, DV
CO
CO, CT, RB
CO, RB
CO
CO, CT, RB, DV, LA
CO, RB, DV
DV
CO, RB, DV
NA
CO
CO, CT, DV
CO
CO, CT
CO
Note: CO, juvenile coho; CT, cutthroat trout; RB, rainbow trout; DV, Dolly Varden; SB, stickleback species; LA, lamprey species.
autumn samples) of salmon spawning, reflecting the most recent
contribution of salmon to sculpin diet, and (ii) a 5-year mean
salmon density (2006–2010), which dampens out annual variation
and reflects a more general and temporally robust characterization of the number of salmon spawning in each stream. Although
present in several of these systems, adult steelhead and coho
salmon were excluded from analyses owing to their extremely
low abundance in the reaches of the streams where we worked.
Sculpin diets
The recent diet of sculpins was assessed from the stomach contents of lethally sampled fish. All recognizable prey items were
sorted into broad groups: (i) aquatic invertebrates, (ii) terrestrial
invertebrates, (iii) fish, and (iv) salmon eggs. Aquatic invertebrates
were further sorted into Ephemeroptera, Plecoptera, Trichoptera,
Diptera, and other aquatic invertebrates (e.g., mollusks, worms,
beetle larvae, crustaceans). Diptera were further subdivided into
Chironomidae and other dipterans. All groups were quantified as
number of individuals. These counts were multiplied by mean
individual masses of prey items calculated from our own sampling (for invertebrates and fish) or from published values for our
sampling region (salmon eggs; Beacham and Murray 1993).
Diet characterization using stable isotope analysis
We used stable isotope analysis on both fin and liver tissue to
assess short- versus long-term diets of sculpins. Fish tissues differ
in the rate at which they incorporate the isotopic signatures of
their diet, thereby enabling analyses that represent different diet
windows in time. In this case, fin tissues turn over slowly, incorporating the isotopic signature of a fish’s diet over a long time
period (months), while liver tissues turn over quickly and reflect
diet over shorter time periods (weeks) (Pinnegar and Polunin 1999;
Suzuki et al. 2005; Eloranta et al. 2010). Fin and liver samples were
dried at 60 °C for 48 h and ground manually into a fine powder.
Samples (fin: 0.25–4.4 mg; liver: 0.26–1.5 mg) were assayed for
carbon and nitrogen stable isotopes using a PDZ Europa ANCAGSL elemental analyzer interfaced to a PDZ Europa 20–20 isotope
ratio mass spectrometer (Sercon Ltd., Cheshire, UK) at the University of
California Davis Stable Isotope Facility (http://stableisotopefacility.
ucdavis.edu/). Carbon and nitrogen stable isotope signatures are
expressed in delta notation (") and are measures of a parts per
thousand (‰) difference between the sample isotope ratio and
that of international standards (Pee Dee Belemnite carbon or atmospheric nitrogen, respectively) (Peterson and Fry 1987):
(2)
"13C or "15N !
$
%
Rsample # Rstandard
× 1000
Rstandard
where R = 13C/12C or 15N/14N. We normalized fin and liver "13C
values using a standardized lipid-normalization procedure (Logan
et al. 2008):
(3)
"13Cnormalized ! "13Cuntreated $
" a ×C:NC:N$$c b#
where C:N is the elemental carbon to nitrogen ratio of bulk tissue,
and a, b, and c are averaged tissue-specific model parameters of
7.42, –22.73, and 0.75, respectively, for fin tissue and 6.06, –22.27,
Swain et al.
5
Table 2. A priori predictions for the potential influence of salmon metrics; sculpin body size, species, and season sampled; environmental
variables; and potential interactions between salmon and covariates on salmon nutrients in sculpin diets (tissue "13C and "15N) and individual
sculpin condition (liver C:N).
Variable
Hypothesis
Direction
Metric
Salmon metrics
Most recent salmon Most recent salmon nutrient input Positive
density
and related to direct input in
diets and effect on sculpin
condition
5-year mean salmon Indicates general and temporally Positive
density
robust salmon nutrient inputs
to ecosystem
Most recent autumn pink + chum Verspoor et al. 2011
adult salmon spawning density
(kg·m–2)
Mean 2006–2010 autumn pink +
chum adult salmon spawning
density (kg·m–2)
Sculpin body size, species, and season sampled
Sculpin size
Size is associated with trophic
Positive
level and age, resulting in
differences in tissue isotopic
signatures and condition
Sculpin species
Differences in habitat and ecology See text
among species may affect access
to salmon subsidies
Season
The timing of salmon spawning,
Positive (spawning),
as well as patterns in
Negative
environment and primary
(prespawning)
productivity that can affect diet
and condition of freshwater fish
Environmental variables
Watershed size
Related to level of primary
productivity, terrestrial input,
spawning area, and foraging
area
Habitat complexity Increased habitat complexity and
low channel gradient increase
nutrient retention, primary
productivity, and foraging
habitat for sculpins, with
carryover effects on salmon
nutrients in diet and condition
of sculpins
Interactions between salmon and covariates
Salmon × sculpin
Salmon resources are more
size
accessible to larger fish
Salmon × season
Stronger effect of salmon in
autumn during spawning on
both contributions to diet and
condition
Salmon × watershed Increased terrestrial and in-stream
size
nutrient inputs in large
watersheds reduce the effect of
salmon on stream production,
mediating effects on sculpin
diet and condition
Salmon × habitat
As salmon density and the ability
complexity
for a watershed to retain
nutrients increase, they should
have an additive effect on
dietary contribution of salmon
nutrients and condition
See text
References
Individual sculpin standard
length (mm)
Scharf et al. 2000;
Shannon et al. 2001;
Sweeting et al. 2007a,
2007b
Individual species (C. asper or
Jardine et al. 2005;
C. aleuticus)
Scheuerell et al.
2007.
Season sampled: autumn; salmon Perga and Gerdeaux
spawning (Sept.–Oct.); or
2005
summer (June–Aug.)
PC axis 1 of main stem and
tributary length, bankfull
width, and catchment area
Positive (undercut bank; PC axis 1 of % undercut banks,
large woody debris;
large wood per 100 m, % pool
and % pool area),
area, and % high gradient
Negative (% high
habitat
gradient habitat)
Positive
Verspoor et al. 2011
Lamberti and Steinman
1997; Hocking and
Reynolds 2011;
Verspoor et al. 2011
Quinn and Peterson
1996; Kauffman et al.
2007; Milner et al.
2011
Salmon metrics × sculpin body
size
Salmon metrics × sampling
season (prespawning or
spawning)
Foote and Brown 1998;
Armstrong et al. 2010
Scheuerell et al. 2007
Negative
Salmon metrics × watershed size
PC axis 1
Hocking and Reynolds
2011
Positive
Salmon metrics × habitat
complexity PC axis 1
Milner et al. 2011
Positive
and –1.40, respectively, for liver tissue derived from isotope data
from three species of freshwater fish (see Logan et al. 2008).
Fåhraeus-Van Ree et al. 2003, Heintz et al. 2004), we used liver C:N
as an indirect metric of individual sculpin condition.
Individual fish condition
Tissue C:N ratios provide an indirect measure of lipid content in
tissues (e.g., Post et al. 2007; Fagan et al. 2009). As liver tissue can
be an important site for lipid storage in fishes, and increased lipid
storage is often related to increased physical condition (e.g.,
Data analysis
We used linear mixed-effects (LME) modeling (Zuur et al. 2008)
and model selection via Akaike’s information criterion corrected
for small sample sizes (AICc) to evaluate the relative effects of
salmon density, habitat, sculpin species, size, season, and several
6
Table 3. Results for model selection using Akaike’s information criterion corrected for small sample sizes (AICc) showing top linear mixed-effects
models (%AICc < 3), to a maximum of four models for sculpin fin and liver "13C and "15N isotope signatures and condition (liver C:N).
Response
Model
k
%AICc
wi
Pseudo R2
Fin " N
Salmon × season, body size, species, watershed size
Salmon × body size, salmon × season, species, watershed size
Salmon × season, salmon × watershed size, body size, species
Salmon × season, body size, species, watershed size, habitat complexity
Salmon, body size, species, season, watershed size, habitat complexity
Salmon, body size, species, season, habitat complexity
Salmon × habitat complexity, body size, species, season
Salmon, body size, species, watershed size, habitat complexity
Salmon × body size, salmon × season
Salmon × body size, salmon × season, watershed size
Salmon × body size, salmon × season, species
Salmon × body size, salmon × season, species, watershed size
Salmon × body size, species, season, habitat complexity
Salmon × body size, season, habitat complexity
Salmon × body size, species, season, watershed size
Salmon × body size, season, watershed size
Salmon × season, body size, species
Salmon × season, salmon × watershed size, body size, species
Salmon × body size, salmon × season, species
Salmon × season, body size, species, watershed size
6
7
7
7
6
5
7
5
5
6
6
7
6
5
6
5
5
7
6
6
0.00
1.67
1.90
2.03
0.00
0.27
0.94
1.05
0.00
0.11
0.30
0.41
0.00
0.39
0.81
0.89
0.00
0.62
0.98
1.90
0.29
0.12
0.11
0.10
0.09
0.08
0.06
0.05
0.20
0.19
0.17
0.16
0.14
0.11
0.09
0.09
0.17
0.13
0.11
0.07
0.61
—
—
—
0.48
—
—
—
0.73
—
—
—
0.62
—
—
—
0.28
—
—
—
15
Fin "13C
Liver "15N
Liver "13C
Liver C:N
Note: Candidate sets contained all combinations of parameters — 5-year mean salmon density (kg·m–2) (salmon), sculpin length, species, season sampled (before
or after salmon spawning), watershed size, and habitat complexity (see Table 2) — and were restricted to including only interactions between salmon metrics and
covariates, and up to three stream-level parameters. The value k is the number of parameters (fixed-effects plus interactions) included in a each model.
a priori hypothesized interactions on the isotopic signatures and
liver C:N of sculpins. The following explanatory variables (fixed
effects) were included based on a priori hypotheses (Table 2):
single-year (previous or current autumn) salmon density; multiyear (2006–2010) mean salmon density; season (prespawning or
during spawning); watershed size (PC1 scores); habitat complexity
(PC1 scores); sculpin standard length (mm); sculpin species (C. asper or
C. aleuticus); salmon × season; salmon × watershed size; salmon ×
habitat complexity; and salmon × sculpin length. We included
stream as a random effect to account for the lack of independence
among individual sculpin measurements. Salmon terms were
transformed (log (salmon + 0.1)) to meet model assumptions of
normality.
We evaluated the relative support for these hypotheses using
an approach combining all models (n = 149 models), restricting
models to include up to one salmon term, only prehypothesized
interactions, and a maximum of four stream-level explanatory variables (salmon, watershed size, habitat complexity, and interactions). Model performance and uncertainty were assessed using
AICc, which ranks models based upon the principle of parsimony
(Burnham and Anderson 2002; Anderson 2008). The lower the
AICc score for a given model, the better the trade-off between
complexity and optimal fit for that model. We used the MuMIn
package in R (Barton 2012) to compare competing models based
on %AICc values and AICc weights (wi). Because there was often
little distinction among high-ranking models based on these values (Table 3), we also calculated multimodel averaged parameter
estimates and relative variable importance (the sum of AICc
weights from all models containing the variable of interest) for
fixed effects from 95% confidence sets of models for each of our
five response variables (Burnham and Anderson 2002; Grueber
et al. 2011). These averaged values were then used to assess the
effects of individual independent variables. We also calculated
pseudo R2 as a measure of the explanatory power of top models
(%AICc < 3) (derived from regressions of the observed data versus
fitted values) (see Magee 1990; Nagelkerke 1991; Piñeiro et al. 2008
for details). To test the relative support for our two salmon metrics, we conducted AICc model selection with each salmon metric
separately and compared the top model from each set for each
response variable. Because 5-year mean salmon density performed
considerably better than single-year density, and because multi-
model averaging was not possible when comparing model combinations containing both salmon metrics, we present model-averaged
results for only 5-year mean salmon density.
We used nonstandardized continuous explanatory variables to
maintain the predictive utility of averaged models. To compare
among all parameters and interpret the main effects in conjunction with interaction terms, we also conducted the above analyses
after standardizing continuous explanatory variables by subtracting global means from each value (centering) and dividing by two
times the standard deviation (SD) (scaling) (Gelman 2008). All statistical analyses were conducted in R (R Development Core Team
2012).
Isotope mixing models
While analyses of stomach contents and tissue-specific isotopic
signatures can provide evidence for salmon-derived nutrients in
the diets of freshwater consumers from different time periods,
isotopic mixing models are required to estimate the proportion of
diets attributable to alternative prey sources (Perga and Gerdeaux
2005; Moore and Semmens 2008; Martinez del Rio et al. 2009). We
assessed the contribution of salmon-derived nutrients to sculpin
diets through dual isotope ("13C and "15N), two-source Bayesian
mixing models implemented in the program SIAR (stable isotope
analysis in R; Parnell and Jackson 2011). SIAR takes isotope data
from consumers (sculpins) and sources (diet items) along with
estimates of diet to tissue isotopic fractionation, and then fits
Bayesian models based on Gaussian likelihoods with a Dirichlet
prior mixture on the mean, which provide posterior distribution
estimates of source contributions (Parnell et al. 2010). We used the
isotope signatures from all sampled sculpin individuals and the
mean and standard deviation of isotope signatures from three
potential diet sources: (i) averaged benthic macroinvertebrate values collected from summer and autumn, (ii) prey fish (juvenile
coho salmon) fin tissues, and (iii) dorsal muscle samples from
spawning pink and chum salmon adults collected from streams in
autumn. We categorized streams by salmon spawning density
into three equal bins based on the spread of the data: low (0–
0.03 kg·m−2), medium (0.1–0.2 kg·m−2), or high (0.5–1.3 kg·m−2).
Because isotopic signatures for potential diet sources were not
available for each stream, benthic invertebrate and prey fish values were averaged across several streams in each category, with
Swain et al.
Fig. 2. Mean proportion of diet by mass (g) of identifiable prey
items in stomachs of prickly (C. asper) and coastrange (C. aleuticus)
sculpins sampled in summer before salmon arrived and in autumn
during salmon spawning.
these values representing values for these diet sources in all
streams in that category. The use of separate signatures based on
levels of salmon spawning density accounted for potential indirect salmon inputs through these lower trophic levels. As egg and
juvenile salmon isotopic signatures are very similar to that of
adult flesh (Scheuerell et al. 2007), this latter diet source represents all of the above as direct salmon nutrient inputs. Because no
data on sculpin tissue fractionation values were available, we used
mean values of "13C (fin = 2.2 ± 0.43 (mean ± SD); liver = 0 ± 0.89)
and "15N (fin = 2.5 ± 0.52; liver = 1.75 ± 0.46) for fish (Sweeting et al.
2007a, 2007b). We ran individual models for each season–stream
combination for each tissue. All salmon and juvenile coho tissues
were also lipid normalized (see eq. 3; Logan et al. 2008).
Results
Sculpin stomach contents
Before adult salmon arrived, trichopterans, dipterans (predominantly chironomids), and other aquatic invertebrates made up
the majority of stomach contents in both sculpin species (Fig. 2).
In the autumn, when the salmon were spawning, salmon eggs
became the most common diet items, averaging almost 50% of
diets by mass. While less common in both seasons, juvenile salmonids, when present, outweighed all other prey items in individual stomach samples.
1
7
Fig. 3. Scaled parameter estimates (circles) with 95% unconditional
confidence intervals (lines) from averaged predictive linear mixedeffects models of "13C and "15N isotope signatures in freshwater
sculpins based on fin (slow turnover; A, B) and liver (fast turnover;
C, D) tissues. Parameters (indicated on left) are ordered for each
response variable by their relative importance (indicated on right) to
the averaged model on a scale of 0–1. Only parameters with relative
importance values >0.5 are included in this figure.
Sculpin carbon and nitrogen stable isotope signatures
As predicted, pink and chum salmon spawning density was
typically the best variable at explaining "13C and "15N isotope
enrichment in sculpin fin and liver tissues, with strong positive
effects (Figs. 3, 4). We also found that multiyear mean salmon
density performed considerably better than single-year density
(Table S11). Further in line with our predictions, the combination
of salmon density, season, sculpin body size, species, and either
watershed size or habitat complexity explained much of the variation in sculpin tissue isotopic enrichment among individuals
and streams, particularly for "15N (pseudo R2 = 0.61–0.73) (Table 3).
Sculpins in larger watersheds had higher "13C and "15N enrichment in both tissues (relative variable importance range = 0.42–
0.83; Fig. 3). Contrary to our predictions, increasing habitat complexity
resulted in lower fin and liver "13C enrichment (relative importance = 0.86 and 0.57, respectively), but had a negligible influence
on "15N enrichment (relative importance < 0.3; Fig. 3). Liver "13C
Supplementary data are available with the article through the journal Web site at http://nrcresearchpress.com/doi/suppl/10.1139/cjfas-2013-0159.
8
Fig. 4. Relationship between multiyear mean salmon density (kg·m−2) and tissue-specific "15N and "13C from individual sculpins, with
predicted lines from linear mixed-effects models (A–D). Interaction plots showing relationship between multiyear mean salmon density
(kg·m−2) and tissue-specific "15N and "15C for 40 and 140 mm fish (E–H). Although other covariates and interactions were important (season,
sculpin species, watershed size), we only show body size, as it was consistently important.
signatures were higher during salmon spawning than before they
arrived, consistent with a recent diet of salmon eggs and tissues.
In contrast, and counter to our predictions, fin and liver "15N
signatures were higher in sculpins prior to salmon spawning. Fin
isotope enrichment was also considerably higher in C. asper, while
differences in liver isotopic signatures between the two species
were negligible (Fig. 3). As predicted, apart from salmon spawning
density, sculpin body size was typically the most important variable in describing differences in isotopic signatures among individual sculpins and streams, with enrichment in both isotopes
being consistently higher in larger individuals (Figs. 3, 4).
Stronger positive relationships were observed between salmon
density and isotope signatures in smaller sculpins than in larger
ones in short-turnover liver tissue, but not fin tissue (relative importance for salmon × body size interaction in livers = 1; Figs. 3, 4).
Further, for both fin and liver tissues, the positive relationship
between salmon density and nitrogen isotope enrichment was
stronger in the summer, before adult salmon arrived, than in the autumn, during spawning (relative importance for salmon × season interaction = 0.76–0.87; Fig. 3). An opposite but weaker interaction was
present for "13C (relative importance of salmon × season < 0.18). Finally,
and contrary to our predictions, the interactions with habitat of
salmon × watershed size and salmon × habitat complexity had low variable importance (<0.16).
Sculpin tissue-specific mixing models
"15N and "13C signatures of sculpins predictably followed that of
their diets (Fig. 5). Benthic macroinvertebrates consistently had
the lowest isotopic signatures, regardless of season, with both
"15N and "13C in invertebrates increasing across streams with
higher densities of spawning salmon. Resident prey fish (juvenile
coho salmon) had intermediate isotope signatures, below those of
adult pink and chum salmon in streams with low to medium
salmon densities, and above those of adult pink and chum salmon
in streams with a high densities of spawning salmon.
For fin tissue, representing a longer term diet of sculpins
(months), mean estimates of the contribution of pink and chum
salmon to the diet of !30% changed little with increasing salmon
spawning density regardless of season (Fig. 6). In contrast, liver
tissue, which represents short-term diet (weeks), showed a posi-
tive relationship with salmon spawning density, with mean estimates indicating that up to 80% of the diet is derived from salmon
during spawning in high-density streams. For both long- and
short-term diets, the contribution of prey fish to sculpin diets
increased in summer and decreased in autumn, while the contribution of benthic invertebrates generally decreased, across the
gradient of increasing salmon spawning density.
Sculpin individual condition
As predicted, sculpin liver C:N ratio increased with multiyear
mean salmon density and sculpin body size and was much higher
during pink and chum salmon spawning in autumn (variable importance = !1 for all three variables; Fig. 7). The effect of pink and
chum salmon density on liver C:N ratio was also considerably
stronger for sculpins caught when salmon were spawning than
those caught prior to spawning (salmon × season interaction relative importance = 0.81; Fig. 7). In general, all explanatory variables were poor at explaining differences in liver C:N ratio among
individual sculpins and streams (pseudo R2 < 0.29).
Discussion
We show the timing and pathways by which freshwater resident fish can acquire salmon nutrients, and how individual and
environmental factors may mediate the uptake and effects of
these nutrients, in 33 coastal streams. Freshwater sculpins shifted
from eating invertebrates and fish in the summer to largely
salmon eggs in the autumn, when salmon were spawning. The
relative contribution of salmon-derived nutrients to sculpin diets
rose consistently across the wide gradient of increasing salmon
spawning densities. Sculpin body size, species, season, and physical habitat also affected their isotope signatures, and in some
cases mediated the effects of salmon on these signatures. Distinctions in the responses of different measures of diet and physical
condition highlight the potential utility of using multiple metrics
and analyses in the study of trophic dynamics.
Our results show a range of pathways by which sculpins access
salmon-derived nutrients. One line of evidence was the differing
response of "13C and "15N enrichment of sculpin tissues to increasing salmon spawning densities. "15N enriches considerably with
Swain et al.
9
Fig. 5. Carbon–nitrogen biplots of sculpin long-term, prespawning (fin, summer) and short-term, during spawning (liver, autumn) isotope
signatures. Small circles show mean sculpin values for individual streams. Symbol shading indicates salmon density (white, low; grey,
medium; black, high). Means and standard deviations of isotope signatures of potential diet sources are also shown: pink and chum adult
salmon (large open circle), prey fish (juvenile coho) (triangles), and benthic macroinvertebrates (squares). These are for reference and are the
same in both panels. Adult salmon signatures are averaged across all available streams, while prey fish and invertebrate signatures are
averaged within the three levels of salmon density.
Fig. 6. Relationship between estimated proportional contributions to sculpin diets of pink and chum salmon eggs, carcass tissue or alevins
(panels A and D), resident prey fish (juvenile coho) (panels B and E), and benthic macroinvertebrates (panels C and F) and multiyear mean
salmon spawning density (kg·m−2), prior to salmon spawning (circles, solid line) and during spawning (triangles, dotted line). Estimates are
means of posterior probability distributions from carbon–nitrogen Bayesian mixing models based on isotopic signatures from sculpin fin
tissue (top row, long-term diet) and liver tissue (bottom row, short-term diet).
increasing trophic level compared with "13C (e.g., McCutchan
et al. 2003). Furthermore, much of the sculpin prey base of macroinvertebrates and juvenile fish can have higher "15N in streams
with high salmon densities, because the periphyton base of production is enriched in "15N, but not "13C, from the legacy of past
spawning (e.g., Verspoor et al. 2010). Sculpins that consume lower
trophic levels with elevated "15N signatures would therefore have
even higher "15N themselves, but not necessarily enriched "13C.
Direct consumption of salmon nutrients, however, would result
in similar patterns of enrichment of both isotopes. Our results
provide evidence of both scenarios; the finding that salmon density typically had the highest positive effect on enrichment of
both isotopes in sculpin tissues is consistent with our mixing
models and stomach content analyses showing that salmon eggs
10
Fig. 7. Scaled parameter estimates (circles) with 95% unconditional
confidence intervals (lines) from the averaged predictive linear
mixed-effects model of liver C:N ratio in freshwater sculpins.
Parameters (indicated on left) are ordered for each response variable
by their relative importance (indicated on right) to the averaged
model on a scale of 0–1. Only parameters with relative importance
values >0.5 are included in this figure.
were consumed directly by sculpins. However, the effect of salmon
density was stronger for "15N, suggesting that sculpins were also
obtaining salmon nutrients indirectly through stream invertebrates
and fish. We were also surprised to find that "15N values in both
tissues were usually higher prior to spawning, especially in highdensity streams. This could be due to the legacy of direct consumption of a pulse of pink and chum alevins in spring (e.g., Hunter 1959;
Phillips and Claire 1966; Foote and Brown 1998) or, in line with results from our mixing models, could reflect high consumption of
enriched invertebrates and resident prey fish in summer in highdensity streams. Additional variation in isotopic signatures could
reflect intrinsic differences among streams in geology, inputs from
water or land, or fractionation (Pinay et al. 2003).
As predicted, larger sculpins had higher isotopic enrichment in
both tissues. This was expected owing to trophic level shifts with
body size (e.g., Shannon et al. 2001). Also, gape size is closely
related to length, which determines whether sculpins can feed
directly on large isotopically enriched salmon eggs, alevins, and
juveniles (Foote and Brown 1998; Armstrong et al. 2010). Interestingly, the relationship between salmon spawning density and isotope enrichment also weakened sharply with increasing sculpin
body size for liver tissue, but not fin tissue. This suggests that
larger sculpins always have access to pink and chum salmon eggs
and alevins where available, whereas smaller sculpins either only
have access when salmon spawning densities are high or have
high isotope signatures by feeding on isotopically enriched invertebrates.
We found diet differences between sculpin species. Prickly sculpins had higher fin isotopic enrichment than coastrange sculpins,
suggesting that they feed at higher relative trophic levels. This
matched our findings that prickly sculpins tend to be larger and
more commonly have prey fish in their diets, likely owing to
greater habitat overlap with other fish in pools and sheltered
areas of streams (McPhail 2007). In contrast, the negligible differences in liver isotope enrichment between the two species suggests that both species can access nutrients from salmon eggs and
carcasses.
Habitat played less of a role than we expected in determining
salmon-derived nutrient contributions to sculpin diets. Although
watershed size generally had a positive effect on isotope enrichment in both tissues, this was only consistent in fin "15N. Increased ecosystem size has been linked to higher productivity and
longer food chains (Vander Zanden et al. 1997; Post 2002; Sabo
et al. 2010), which would explain "15N enrichment in sculpin tissues with increasing watershed size.
We confirmed our prediction that sculpins in streams with
higher salmon spawning densities would have higher lipid storage as shown by liver C:N ratios (e.g., Heintz et al. 2004). Although
C:N is only an indirect measure of lipid storage, which in turn may
differ among age classes of fish and season, this result suggests
that increased access to high-quality salmon subsidies carries over
to increased condition of individual sculpins, similar to results
shown for juvenile salmonids in some experimental studies (e.g.,
Wipfli et al. 2003).
Experimental manipulations of salmon subsidies have helped
our understanding of mechanisms by which spawning salmon
influence freshwater fish (e.g., Bilby et al. 1998; Wipfli et al. 2010).
However, there is a need to test these effects under natural conditions to confidently attribute apparent treatment effects to the
presence of salmon (Schindler et al. 2003; Janetski et al. 2009). This
is the first study to test the effects of salmon on freshwater fish
other than salmonids, despite the important ecological roles of
sculpins in salmon spawning streams (Adams and Schmetterling
2007; Janetski et al. 2009). This study is also among the first to test
for such effects across wide natural gradients in salmon density
and habitat. However, future research should aim to access populationlevel effects across such natural gradients to clarify varying results
from recent experimental studies (e.g., Wilzbach et al. 2005;
Denton et al. 2009; Harvey and Wilzbach 2010). While management of wild Pacific salmon stocks has traditionally focused on
maximizing salmon production for commercial fisheries, there is
a need to recognize the importance of salmon subsidies to recreational and nonrecreational fish populations and the overall productivity of freshwater systems. Understanding how salmon
subsidies influence recipient freshwater fish populations across
these natural conditions and gradients is key to implementing
effective ecosystem-based management of wild salmon and associated freshwater systems.
Acknowledgements
We thank N. Dulvy and M. Bradford for constructive input
during this study and we thank all those who assisted us in the
field, including M. Arseneau, T. Brochmeier, D. Brown, C. Bruce,
J. Bruce, R. Field, J. Harding, H. Hermanek, H. Jackson, R. Midgley,
H. Recker, J. Slade, M. Spoljaric, and M. Wagner. We also appreciate help in the lab from M. Chung, G. Gu, L. Ogston, J. Pendray,
M. Segal, and M. Stubbs. We also thank S. Anderson, K. Artelle,
J. Moore, and D. Swain for help in analyzing and presenting our
data. We appreciate the support of our colleagues in the Earth to
Ocean Research Group throughout the study. We thank the Natural
Sciences and Engineering Research Council of Canada (NSERC), the Simon Fraser University Pacific Scholarship Fund, the Tom Buell BC Leadership Chair endowment, and the Pacific Salmon Foundation for
financial support. We also thank the Heiltsuk Integrated Resource Management Department and Fisheries and Oceans Canada staff for logistical support and autumn salmon enumeration and the Heiltsuk First
Nation for allowing us to work with them in their traditional territory.
References
Adams, S.B., and Schmetterling, D.A. 2007. Freshwater sculpins: phylogenetics
to ecology. Trans. Am. Fish. Soc. 136: 1736–1741. doi:10.1577/T07-023.1.
Anderson, D.R. 2008. Model based inference in the life sciences: a primer on
evidence. Springer Science, New York.
Armstrong, J.B., Schindler, D.E., Omori, K.L., Ruff, C.P., and Quinn, T.P. 2010.
Thermal heterogeneity mediates the effects of pulsed subsidies across a landscape. Ecology, 91: 1445–1454. doi:10.1890/09-0790.1. PMID:20503876.
Barton, K. 2012. R Package ‘MuMIn’: multi-model inference.
Beacham, T.D., and Murray, C.B. 1993. Fecundity and egg size variation in North
American Pacific salmon (Oncorhynchus). J. Fish Biol. 42: 485–508. doi:10.1111/
j.1095-8649.1993.tb00354.x.
Bellmore, J.R., Baxter, C.V., Martens, K., and Connolly, P.J. 2013. The floodplain
food web mosaic: a study of its importance to salmon and steelhead with
implications for their recovery. Ecol. Appl. 23: 189–207. doi:10.1890/12-0806.1.
PMID:23495646.
Bilby, R.E., Fransen, B.R., Bisson, P.A., and Walter, J.K. 1998. Response of juvenile
coho salmon (Oncorhynchus kisutch) and steelhead (Oncorhynchus mykiss) to the
addition of salmon carcasses to two streams in southwestern Washington,
U.S.A. Can. J. Fish. Aquat. Sci. 55(8): 1909–1918. doi:10.1139/f98-094.
Burnham, K.P., and Anderson, D.R. 2002. Model selection and multimodel
inference: a practical information-theoretic approach. 2nd ed. Springer Science, New York.
Chaloner, D.T., Martin, K.M., Wipfli, M.S., Ostrom, P.H., and Lamberti, G.A. 2002.
Swain et al.
Marine carbon and nitrogen in southeastern Alaska stream food webs: evidence from artificial and natural streams. Can. J. Fish. Aquat. Sci. 59(8):
1257–1265. doi:10.1139/f02-084.
Denton, K.P., Rich, H.B., and Quinn, T.P. 2009. Diet, movement, and growth of
Dolly Varden in response to sockeye salmon subsidies. Trans. Am. Fish. Soc.
138:1207–1219. doi:10.1577/T09-006.1.
Denton, K.P., Rich, H.B., Moore, J.W., and Quinn, T.P. 2010. The utilization of a
Pacific salmon Oncorhynchus nerka subsidy by three populations of charr
Salvelinus spp. J. Fish Biol. 77: 1006–1023. doi:10.1111/j.1095-8649.2010.02746.x.
PMID:20840627.
Eloranta, A.P., Kahilainen, K.K., and Jones, R.I. 2010. Seasonal and ontogenetic
shifts in the diet of Arctic charr Salvelinus alpinus in a subarctic lake. J. Fish
Biol. 77: 80–97. doi:10.1111/j.1095-8649.2010.02656.x. PMID:20646140.
Fagan, K.-A., Koops, M.A., Arts, M.T., and Power, M. 2011. Assessing the utility of
C:N ratios for predicting lipid content in fishes. Can. J. Fish. Aquat. Sci. 68:
374–385. doi:10.1139/F10-119.
Fåhraeus-Van Ree, G.E., and Spurrell, D.R. 2003. Structure of and energy reserves
in the liver of wild and cultured yellowtail flounder, Limanda ferruginea. Mar.
Biol. 143: 257–265. doi:10.1007/s00227-003-1083-y.
Field, R.D., and Reynolds, J.D. 2011. Sea to sky: impacts of residual salmonderived nutrients on estuarine breeding bird communities. Proc. R. Soc. B
Biol. Sci. 278: 3081–3088. doi:10.1098/rspb.2010.2731. PMID:21325324.
Foote, C.J., and Brown, G.S. 1998. Ecological relationship between freshwater
sculpins (genus Cottus) and beach-spawning sockeye salmon (Oncorhynchus
nerka) in Iliamna Lake, Alaska. Can. J. Fish. Aquat. Sci. 55(6): 1524–1533. doi:
10.1139/f98-034.
Gelman, A. 2008. Scaling regression inputs by dividing by two standard deviations. Stat. Med. 27: 2865–2873. doi:10.1002/sim.3107.
Gende, S.M., Edwards, R.T., Willson, M.F., and Wipfli, M.S. 2002. Pacific salmon
in aquatic and terrestrial ecosystems. BioScience, 52: 917–928. doi:10.1641/
0006-3568(2002)052[0917:PSIAAT]2.0.CO;2.
Grueber, C.E., Nakagawa, S., Laws, R.J., and Jamieson, I.G. 2011. Multimodel
inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24:
699–711. doi:10.1111/j.1420-9101.2010.02210.x. PMID:21272107.
Harvey, B.C., and Wilzbach, M.A. 2010. Carcass addition does not enhance juvenile salmonid biomass, growth, or retention in six northwestern California
streams. N. Am. J. Fish. Manage. 30: 1445–1451. doi:10.1577/M10-022.1.
Heintz, R.A., Nelson, B.D., Hudson, J., Larsen, M., Holland, L., and Wipfli, M. 2004.
Marine subsidies in freshwater: effects of salmon carcasses on lipid class and
fatty acid composition of juvenile coho salmon. Trans. Am. Fish. Soc. 133:
559–567. doi:10.1577/T03-035.1.
Hocking, M.D., and Reimchen, T.E. 2009. Salmon species, density and watershed
size predict magnitude of marine enrichment in riparian food webs. Oikos,
118: 1307–1318. doi:10.1111/j.1600-0706.2009.17302.x.
Hocking, M.D., and Reynolds, J.D. 2011. Impacts of salmon on riparian plant diversity. Science, 331: 1609–1612. doi:10.1126/science.1201079. PMID:21442794.
Hocking, M.D., Dulvy, N.K., Reynolds, J.D., Ring, R.A., and Reimchen, T.E. 2013.
Salmon subsidize an escape from a size spectrum. Proc. R. Soc. B Biol. Sci.
280: 20122433. doi:10.1098/rspb.2012.2433. PMID:23282994.
Hunter, J.G. 1959. Survival and production of pink and chum salmon in a coastal
stream. J. Fish. Res. Board. Can. 16(6): 835–886. doi:10.1139/f59-061.
Janetski, D.J., Chaloner, D.T., Tiegs, S.D., and Lamberti, G.A. 2009. Pacific salmon
effects on stream ecosystems: a quantitative synthesis. Oecologia, 159: 583–
595. doi:10.1007/s00442-008-1249-x. PMID:19132408.
Jardine, T.D., Gray, M.A., McWilliam, S.M., and Cunjak, R.A. 2005. Stable isotope
variability in tissues of temperate stream fishes. Trans. Am. Fish. Soc. 134:
1103–1110. doi:10.1577/T04-124.1.
Kauffman, M.J., Varley, N., Smith, D.W., Stahler, D.R., MacNulty, D.R., and
Boyce, M.S. 2007. Landscape heterogeneity shapes predation in a newly restored predator-prey system. Ecol. Lett. 10: 690–700. doi:10.1111/j.1461-0248.
2007.01059.x. PMID:17594424.
Lamberti, G.A., and Steinman, A.D. 1997. A comparison of primary production in
stream ecosystems. J. N. Am. Benthol. Soc. 16: 95–104. doi:10.2307/1468241.
Logan, J.M., Jardine, T.D., Miller, T.J., Bunn, S.E., Cunjak, R.A., and
Lutcavage, M.E. 2008. Lipid corrections in carbon and nitrogen stable isotope
analyses: comparison of chemical extraction and modelling methods. J. Anim.
Ecol. 77: 838–846. doi:10.1111/j.1365-2656.2008.01394.x. PMID:18489570.
Magee, L. 1990. R2 measures based on Wald and likelihood ratio joint significance tests. Am. Stat. 44: 250–253. doi:10.2307/2685352.
Martinez del Rio, C., Wolf, N., Carleton, S. A, and Gannes, L.Z. 2009. Isotopic
ecology ten years after a call for more laboratory experiments. Biol. Rev.
Camb. Philos. Soc. 84: 91–111. doi:10.1111/j.1469-185X.2008.00064.x. PMID:
19046398.
Mason, J.C., and Machidori, S. 1976. Populations of sympatric sculpins, Cottus
aleuticus and Cottus asper, in four adjacent salmon-producing coastal streams
on Vancouver Island, BC. Fish. Bull. 74: 131–141.
McCutchan, J.H., Lewis, W.M., Kendall, C., and McGrath, C.C. 2003. Variation in
trophic shift for stable isotope ratios of carbon, nitrogen, and sulfur. Oikos,
102: 378–390. doi:10.1034/j.1600-0706.2003.12098.x.
McPhail, J.D. 2007. The freshwater fishes of British Columbia. The University of
Alberta Press, Edmonton, Alta.
Milner, A.M., Robertson, A.L., Brown, L.E., Sønderland, S.H., McDermott, M., and
11
Veal, A.J. 2011. Evolution of a stream ecosystem in recently deglaciated terrain. Ecology, 92: 1924–1935. doi:10.1890/10-2007.1.
Moore, J.W., and Semmens, B.X. 2008. Incorporating uncertainty and prior information into stable isotope mixing models. Ecol. Lett. 11: 470–480. doi:10.
1111/j.1461-0248.2008.01163.x.
Moore, J.W., Schindler, D.E., and Ruff, C.P. 2008. Habitat saturation drives
thresholds in stream subsidies. Ecology, 89: 306–312. doi:10.1890/07-1269.1.
PMID:18409419.
Nagelkerke, N.J.D. 1991. A note on a general definition of the coefficient of
determination. Biometrika, 78: 691–692. doi:10.1093/biomet/78.3.691.
Naiman, R.J., Bilby, R.E., Schindler, D.E., and Helfield, J.M. 2002. Pacific salmon,
nutrients, and the dynamics of freshwater ecosystems. Ecosystems, 5: 399–
417. doi:10.1007/s10021-001-0083-3.
Parnell, A.C., and Jackson, A.L. 2011. R package SIAR: Stable Isotope Analysis in R.
Parnell, A.C., Inger, R., Bearhop, S., and Jackson, A.L. 2010. Source partitioning
using stable isotopes: coping with too much variation. PloS ONE, 5: e9672.
doi:10.1371/journal.pone.0009672. PMID:20300637.
Perga, M.E., and Gerdeaux, D. 2005. “Are fish what they eat” all year round?
Oecologia, 144: 598–606. doi:10.1007/s00442-005-0069-5. PMID:15891838.
Peterson, B.J., and Fry, B. 1987. Stable isotopes in ecosystem studies. Annu. Rev.
Ecol. Syst. 18: 293–320. doi:10.1146/annurev.es.18.110187.001453.
Phillips, R.W., and Claire, E.W. 1966. Intragravel movement of the reticulate
sculpin, Cottus perplexus and its potential as a predator on salmonid embryos.
Trans. Am. Fish. Soc. 95: 210–212. doi:10.1577/1548-8659(1966)95[210:IMOTRS]2.
0.CO;2.
Pinay, G., O’Keefe, T., Edwards, R., and Naiman, R.J. 2003. Potential denitrification activity in the landscape of a western Alaska drainage basin. Ecosystems,
6: 336–343. doi:10.1007/s10021-002-0169-6.
Piñeiro, G., Perelman, S., Guerschman, J.P., and Paruelo, J.M. 2008. How to evaluate models: observed vs. predicted or predicted vs. observed? Ecol. Model.
216: 316–322.
Pinnegar, J.K., and Polunin, N.V.C. 1999. Differential fractionation of "13C and
"15N among fish tissues: implications for the study of trophic interactions.
Funct. Ecol. 13: 225–231. doi:10.1046/j.1365-2435.1999.00301.x.
Post, D.M. 2002. The long and short of food-chain length. Trends. Ecol. Evol. 17:
269–277. doi:10.1016/S0169-5347(02)02455-2.
Post, D.M., Layman, C.A., Arrington, D.A., Takimoto, G., Quattrochi, J., and
Montaña, C.G. 2007. Getting to the fat of the matter: models, methods and
assumptions for dealing with lipids in stable isotope analyses. Oecologia,
152: 179–189. doi:10.1007/s00442-006-0630-x. PMID:17225157.
Quinn, T.P., and Peterson, N.P. 1996. The influence of habitat complexity and
fish size on over-winter survival and growth of individually marked juvenile
coho salmon (Oncorhynchus kisutch) in Big Beef Creek, Washington. Can. J. Fish.
Aquat. Sci. 53(7): 1555–1564. doi:10.1139/f96-092.
R Development Core Team. 2012 R: a language and environment for statistical
computing. R Foundation for Statistical Computing, Vienna, Austria.
Sabo, J.L., Finlay, J.C., Kennedy, T., and Post, D.M. 2010. The role of discharge
variation in scaling of drainage area and food chain length in rivers. Science,
330: 965–967. doi:10.1126/science.1196005. PMID:20947729.
Scharf, F.S., Juanes, F., and Rountree, R.A. 2000. Predator size - prey size relationships of marine fish predators: interspecific variation and effects of ontogeny
and body size on trophic-niche breadth. Mar. Ecol. Prog. Ser. 208: 229–248.
doi:10.3354/meps208229.
Scheuerell, M.D., Levin, P.S., Zabel, R.W., Williams, J.G., and Sanderson, B.L.
2005. A new perspective on the importance of marine-derived nutrients to
threatened stocks of Pacific salmon (Oncorhynchus spp.) Can. J. Fish. Aquat. Sci.
62(5): 961–964. doi:10.1139/f05-113.
Scheuerell, M.D., Moore, J.W., Schindler, D.E., and Harvey, C.J. 2007. Varying
effects of anadromous sockeye salmon on the trophic ecology of two species
of resident salmonids in southwest Alaska. Freshw. Biol. 52: 1944–1956. doi:
10.1111/j.1365-2427.2007.01823.x.
Schindler, D.E., Scheuerell, M.D., Moore, J.W., Gende, S.M., Francis, T.B., and
Palen, W.J. 2003. Pacific salmon and the ecology of coastal ecosystems. Front.
Ecol. Environ. 1: 31–37. doi:10.1890/1540-9295(2003)001[0031:PSATEO]2.0.CO;2.
Shannon, J.P., Blinn, D.W., Haden, G.A., Benenati, E.P., and Wilson, K.P. 2001.
Food web implications of "13C and "15N variability over 370 km of the regulated Colorado River USA. Isot. Environ. Health. Stud. 37: 179–191. doi:10.
1080/10256010108033294.
Suzuki, K.W., Kasai, A., Nakayama, K., and Tanaka, M. 2005. Differential isotopic enrichment and half-life among tissues in Japanese temperate bass
(Lateolabrax japonicus) juveniles: implications for analyzing migration. Can. J.
Fish. Aquat. Sci. 62(3): 671–678. doi:10.1139/f04-231.
Sweeting, C., Barry, J., Barnes, C., Polunin, N., and Jennings, S. 2007a. Effects of
body size and environment on diet-tissue "15N fractionation in fishes. J. Exp.
Mar. Biol. Ecol. 340: 1–10. doi:10.1016/j.jembe.2006.07.023.
Sweeting, C., Barry, J., Polunin, N., and Jennings, S. 2007b. Effects of body size
and environment on diet-tissue "13C fractionation in fishes. J. Exp. Mar. Biol.
Ecol. 352: 165–176. doi:10.1016/j.jembe.2007.07.007.
Tiegs, S.D., Chaloner, D.T., Levi, P., Rüegg, J., Tank, J.L., and Lamberti, G.A. 2008.
Timber harvest transforms ecological roles of salmon in southeast Alaska
rain forest streams. Ecol. Appl. 18: 4–11. doi:10.1890/07-0655.1. PMID:18372551.
Vander Zanden, M.J., Cabana, G., and Rasmussen, J.B. 1997. Comparing trophic
position of freshwater fish calculated using stable nitrogen isotope ratios
12
southeastern Alaska, U.S.A. Can. J. Fish. Aquat. 55(6): 1503–1511. doi:10.1139/
f98-031.
Wipfli, M.S., Hudson, J.P., Caouette, J.P., and Chaloner, D.T. 2003. Marine subsidies in freshwater ecosystems: salmon carcasses increase the growth rates of
stream-resident salmonids. Trans. Am. Fish. Soc. 132: 371–381. doi:10.1577/
1548-8659(2003)132<0371:MSIFES>2.0.CO;2.
Wipfli, M.S., Hudson, J.P., Caouette, J.P., Mitchell, N.L., Lessard, J.L., Heintz, R.A.,
and Chaloner, D.T. 2010. Salmon carcasses increase stream productivity more
than inorganic fertilizer pellets: a test on multiple trophic levels in streamside experimental channels. Trans. Am. Fish. Soc. 139: 824–839. doi:10.1577/
T09-114.1.
Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A., and Smith, G.M. 2008. Mixed
effects models and extensions in ecology with R. Springer Science, New York.
Zuur, A.F., Ieno, E.N., and Elphick, C.S. 2010. A protocol for data exploration to
avoid common statistical problems. Methods. Ecol. Evol. 1: 3–14. doi:10.1111/
j.2041-210X.2009.00001.x.
("15N) and literature dietary data. Can. J. Fish. Aquat. Sci. 54(5): 1142–1158.
doi:10.1139/f97-016.
Verspoor, J.J., Braun, D.C., and Reynolds, J.D. 2010. Quantitative links between
Pacific salmon and stream periphyton. Ecosystems, 13: 1020–1034. doi:10.
1007/s10021-010-9371-0.
Verspoor, J.J., Braun, D.C., Stubbs, M.M., and Reynolds, J.D. 2011. Persistent ecological effects of a salmon-derived nutrient pulse on stream invertebrate
communities. Ecosphere, 2: art18. doi:10.1890/ES10-00011.1.
Wilzbach, M.A., Harvey, B.C., White, J.L., and Nakamoto, R.J. 2005. Effects of
riparian canopy opening and salmon carcass addition on the abundance and
growth of resident salmonids. Can. J. Fish. Aquat. Sci. 62(1): 58–67. doi:10.
1139/f04-177.
Wipfli, M.S., Hudson, J., and Caouette, J. 1998. Influence of salmon carcasses on
stream productivity: response of biofilm and benthic macroinvertebrates in

Effects of salmon on the diet and condition of

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