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Deep-Sea Research II

0967-06

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journal homepage: www.elsevier.com/locate/dsr2

Bioenergetic model estimates of interannual and spatial patterns inconsumption demand and growth potential of juvenile pink salmon(Oncorhynchus gorbuscha) in the Gulf of Alaska

Jamal H. Moss a,�, David A. Beauchamp b, Alison D. Cross b, Edward V. Farley a, James M. Murphy a,John H. Helle a, Robert V. Walker c, Katherine W. Myers c

a National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Alaska Fisheries Science Center Auke Bay Laboratory,

11305 Glacier Hwy, Juneau, AK 99801, USAb US Geological Survey, Washington Cooperative Fish and Wildlife Research Unit, PO Box 355020, University of Washington, WA 98195, USAc High Seas Salmon Research Program, School of Aquatic and Fisheries Sciences, PO Box 355020, University of Washington, WA 98195, USA

a r t i c l e i n f o

Article history:

Accepted 1 March 2009A bioenergetic model of juvenile pink salmon (Oncorhynchus gorbuscha) was used to estimate daily prey

consumption and growth potential of four ocean habitats in the Gulf of Alaska during 2001 and 2002.

Keywords:

Consumption demand

Growth potential

Bioenergetic model

Pink salmon

Intraspecific competition

Marine survival

45/$ - see front matter Published by Elsevier

016/j.dsr2.2009.03.005

esponding author. Tel.: +1907 789 6609; fax:

ail address: Jamal.Moss@noaa.gov (J.H. Moss)

e cite this article as: Moss, J.H., et alrowth potential of juvenile pink salm

a b s t r a c t

Growth potential was not significantly higher in 2002 than in 2001 at an alpha level of 0.05 (P ¼ 0.073).

Average differences in growth potential across habitats were minimal (slope habitat ¼ 0.844 g d�1, shelf

habitat ¼ 0.806 g d�1, offshore habitat ¼ 0.820 g d�1, and nearshore habitat ¼ 0.703 g d�1) and not

significantly different (P ¼ 0.630). Consumption demand differed significantly between hatchery and

wild stocks (P ¼ 0.035) when examined within year due to the interaction between hatchery verses wild

origin and year. However, the overall effect of origin across years was not significant (P ¼ 0.705) due to

similar total amounts of prey consumed by all juvenile pink salmon in both study years. We anticipated

that years in which ocean survival was high would have had high growth potential, but this relationship

did not prove to be true. Therefore, modeled growth potential may not be useful as a tool for forecasting

survival of Prince William Sound hatchery pink salmon stocks. Significant differences in consumption

demand and a two-fold difference in nearshore abundance during 2001 of hatchery and wild pink

salmon confirmed the existence of strong and variable interannual competition and the importance of

the nearshore region as being a potential competitive bottleneck.

Published by Elsevier Ltd.

1. Introduction

Efforts have been focused on understanding the underlyingcauses of northeast Pacific salmon fluctuations with respect toclimate change and climate variability (Beamish, 1993; Beamishand Bouillon, 1995; Francis and Hare, 1994; Brodeur and Ware,1995; Hare and Francis, 1995). However, much remains to belearned about how environmental conditions influence fishproduction across space and through time (Horne and Schneider,1995; Mackas et al., 1985), and correlating biophysical informa-tion with performance measures of marine organisms may notadequately describe the effect of habitat quality on growth orsurvival. Thus, there is a need to transition from correlativeapproaches to targeted sampling and the modeling of mechanisticprocesses (Ciannelli et al., 1998; Aydin et al., 2005). Insight gainedfrom such inquiries may complement ongoing research by

Ltd.

+1907 789 6094.

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., Bioenergetic model estimon (Oncorhynchus gorbusch

facilitating a more comprehensive understanding of how oceanichabitat influences fish growth, survival, and production.

Salmon experience high mortality during early marine resi-dence (Parker, 1965, 1968; Mathews and Buckley, 1976; Bax, 1983;Hartt, 1980; Furnell and Brett, 1986; Fisher and Pearcy, 2005) andsize-dependent mortality is believed to be concentrated duringspecific life stages (Beamish and Mahnken, 2001) and vary amongregions (Mueter et al., 2002, 2005; Pyper et al., 2005). Survival ofpink salmon (Oncorhynchus gorbuscha) during early marineresidence appears to be determined in two stages, with the firststage characterized by high initial size-selective predation onjuveniles as they enter marine waters (Parker, 1965, 1968; Willetteet al., 1999), and the second by significant size-selective mortalityafter the first summer growing season (Moss et al., 2005). Fishstocks will experience different conditions through time; how-ever, each stock should respond to the same underlying mechan-isms, and this two-stage mortality process can be expresseddifferently. Similar marine survival was reported for pink andchum salmon (Oncorhynchus keta) populations originating withinregions of 100–200 km but survival differed among stocks at

ates of interannual and spatial patterns in consumption demanda).... Deep-Sea Research II (2009), doi:10.1016/j.dsr2.2009.03.005

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greater distances, suggesting that local environmental processesacted on the early life stages in nearshore and coastal marinewaters (Mueter et al., 2002, 2005; Pyper et al., 2005). Therefore,initial size or local conditions affecting growth during the firstsummer in coastal and shelf regions could be used to predict over-winter survival. Interannual and interdecadal changes in physicalforcing on spatial–temporal food web dynamics could influencethe relative importance of initial marine mortality versus over-winter mortality.

Bioenergetics models representing pink salmon in the NorthPacific Ocean (Boldt and Haldorson, 2002; Aydin et al., 2005;Cross et al., 2005) have been used to investigate the relationshipof spatial distribution, environmental conditions, salmon growth(Beauchamp et al., 1989), and survival (Orsi et al., 2004). Thesemodels represent the flow and transformation of energy within anorganism given a specified set of biological and physical condi-tions, and can be used to estimate prey consumption by thepredator. In such applications, bodies of water are commonlytreated as a homogeneous habitat for single or multiple cohorts offish (Cross et al., 2005; Beauchamp et al., 1989). However, thisapproach typically does not account for finer-scale spatialheterogeneity in the ecosystem. Effects of habitat quality onsomatic growth may not be linear (Brandt et al., 1992), and salmonbrood year strength can be determined during critical bottleneckssuch as early marine residence (Parker, 1968) or the first winter atsea (Beamish and Mahnken, 2001). Local conditions rather thanspatially averaged conditions are likely to offer greater insight intothe linkage between fish production and the environment (Horneet al., 1999), and the bioenergetics model accounts for differencesin body size when calculating consumption, thereby providing fora more accurate assessment of competition than abundanceestimates could alone. Applying bioenergetics models to numer-ous relatively small regions over a short period should revealhabitat quality differences with higher resolution and supplementinformation gained from previous studies of consumptiondemand by juvenile pink salmon inhabiting PWS during summerand early fall (Cross et al., 2005; Boldt and Haldorson, 2002).

Models of fish growth potential incorporate environmentalvariables and physiologically important processes occurring at theorganism level (Brandt et al., 1992). Growth potential is anestimate of the expected increase in body weight of an individualfish inhabiting a particular location at a specific time. Thisapproach can offer insight on how climatic shifts can affect thesomatic growth and distribution of consumers (Welch et al.,1998), evaluate direct effects of habitat quality at appropriate timeand space scales, or be used as management tools (Mason andBrandt, 1999). For example, a decline in simulated growthpotential for lake trout (Salvelinus namaycush) was correlatedwith an observed smaller weight at length, suggesting thatspatially explicit models of habitat quality have relevance to theperformance of individual organisms in the field (Luecke et al.,1999). Similarly, a decline in GOA sockeye salmon (Oncorhynchus

nerka) biomass was explained by an order of magnitude decreasein growth potential (Rand, 2002). Foraging and bioenergeticsmodels were integrated to transform environmental variables intomeasures of potential growth for menhaden (Brevoortia tyrannus),which were used to estimate seasonal and spatial patterns ofcarrying capacity in Chesapeake Bay (Luo et al., 2001).

Quantifying the relationship between habitat quality, juvenilesalmon growth, and marine survival is rooted in the premise thatgrowth in coastal regions preconditions juvenile salmon forsurviving their first winter of ocean residence (Beamish andMahnken, 2001; Moss et al., 2005). Faster growth is generallyassociated with higher marine survival for most species ofjuvenile salmon (Holtby et al., 1990; Koenings et al., 1993;Willette et al., 1999; Ruggerone et al., 2003; Ruggerone and

Please cite this article as: Moss, J.H., et al., Bioenergetic model estimand growth potential of juvenile pink salmon (Oncorhynchus gorbusch

Goetz, 2004), and quantifying spatial differences in the ability ofmarine waters to support juvenile salmon growth will helpdecipher the relationship between habitat quality and uppertrophic level production from the standpoint of the processes thatinfluence growth.

The northern coastal Gulf of Alaska (CGOA) is a highlyproductive, down-welling system where freshwater runoff andwinds dominate the physical processes on the shelf. Interannualvariability in physical processes influences the distribution,feeding, growth, and survival of juvenile salmon (Francis andHare, 1994; Mantua et al., 1997), and climatic forcing alterstemperature, salinity, food web structure, and juvenile salmondistribution (Francis and Hare, 1994; Hare and Francis, 1995).Biophysical conditions vary spatially and temporally, and coulddifferentially influence pink salmon growth and survival, depend-ing on timing, location, and life stage. Pink salmon are thepredominant species of salmon in the CGOA in terms of numbers,biomass, and harvest. Hatcheries in Prince William Sound (PWS)have released 500–600 million juvenile pink salmon in May since1988, with annual returns averaging 23.7 million fish (Johnsonet al., 2002) with marine survival rates ranging from 1% to 9%. Allpink salmon released from PWS hatcheries are thermally marked.The thermal mark enables identification of an individual’s releasedate and the average size of the fish released, making them anexcellent species for investigating how ocean habitat affectsgrowth and survival (Farley et al., 1997).

The objectives of this study were two-fold. The first objectivewas to investigate intraspecific competition for zooplankton preyresources between hatchery and wild juvenile pink salmoninhabiting the Gulf of Alaska. The second objective was tocharacterize interannual growing conditions for juvenile pinksalmon and determine if growing conditions are related tosurvival. This analysis was accomplished using a bioenergeticsmodel to quantify interannual differences in daily prey consump-tion demand during July–August of 2001 and 2002. PrinceWilliam Sound hatchery pink salmon survival during these yearsdiffered greatly, with low survival (3%) of the cohort releasedduring 2001 and significantly higher survival (9%) of the cohortreleased during 2002.

2. Methods

2.1. Study location and timing

Biological surveys were conducted during the middle of Julythrough early August 2001 and 2002 aboard the 38-m sterntrawler F/V Great Pacific. Each survey included 11 transectsbeginning with the Ocean Cape transect near Yakutat, Alaska;and ending with the Cape Kaguyak transect near the south-western end of Kodiak Island, Alaska (Fig. 1). Transects sampledduring each survey extended from nearshore to oceanic watersbeyond the 200-m shelf break. Sampling stations along eachtransect were generally spaced 18.5 km apart, with each includinga nearshore station (a shelf station less than 4 km from shore).Habitats were classified as nearshore, shelf (over the continentalshelf), slope (over the continental slope), or offshore (watersbeyond the continental slope).

2.2. Sample collection and biophysical measurements

Fish samples were collected in a 198-m-long mid-water ropetrawl with hexagonal mesh wings and body, and a 1.2-cm meshliner in the codend. The rope trawl was towed at 6.5–9.3 km h�1 ator near the surface, had an average horizontal spread of 40 m, and

ates of interannual and spatial patterns in consumption demanda).... Deep-Sea Research II (2009), doi:10.1016/j.dsr2.2009.03.005

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Fig. 1. Station locations (K) sampled along transects located within the Gulf of Alaska during July–August 2001 and 2002. (1) Ocean Cape; (2) Cape Yakataga; (3) Cape St.

Elias; (4) Cape Cleare; (5) Seward Line; (6) Gore Point; (7) Cape Chiniak; (8) Cape Nukshak; (9) Cape Kekurnoi; (10) Cape Kaguyak.

J.H. Moss et al. / Deep-Sea Research II ] (]]]]) ]]]–]]] 3

an average vertical spread of 15 m. All tows lasted 30 min, covered2.8–5.2 km, and were performed during daylight hours. Thesurface area (km2) of ocean swept by the trawl was calculatedby multiplying the width of the trawl mouth opening by thedistance the net was towed. Juvenile pink salmon and other fishescollected by the trawl were sorted by species, counted, measured(fork length (mm)), and weighed (g). Fish were individuallybagged and frozen whole for laboratory analyses of food habits.Depth profiles of temperature were measured using a Sea-BirdSBE 911+CTD (conductivity–temperature–depth) profiler (refer-ence to trade names does not imply endorsement by the NationalMarine Fisheries Service, NOAA) at each trawl station immediatelyprior to launching the trawl.

2.3. Juvenile salmon diet analysis and stock identification

Fish were thawed in the laboratory and the digestive systemfrom the esophagus to the gut anterior and pyloric caeca removed.The food bolus was removed from the stomach, immediatelypreserved in 10% formalin, and stored in a glass container. Preyitems were categorized to the lowest taxonomic group possible,the percent contribution by weight calculated for each individual

Please cite this article as: Moss, J.H., et al., Bioenergetic model estimand growth potential of juvenile pink salmon (Oncorhynchus gorbusch

stomach, and prey proportions averaged for non-empty stomachs.Left and right sagittal otoliths were removed from juvenile pinksalmon and mounted on a petrographic slide using thermal resinand ground to expose the primordia. Otolith microstructure of theleft sagittal otolith was examined under a compound microscopeand compared to thermal mark patterns from voucher specimenscollected from hatcheries that mark and release pink salmon fry inthe North Pacific Ocean. A second reader independently read allotoliths and disagreements between otolith readers were resolvedby the most experienced otolith reader to ensure accuracy (Hagenet al., 1995). Fish not displaying thermal marks were presumed tobe of wild origin. Up to 50 juvenile pink salmon per haul wereused in the otolith analysis and the numbers of fish belonging to aparticular hatchery stock in a given year reported (Table 1).

2.4. Bioenergetics model

Bioenergetics models describe how energy consumed by anorganism is partitioned among metabolic costs, waste losses, andgrowth. Controlling factors that influence the partitioning areconsumer body mass, ambient temperature, food quality, and foodabundance (Kitchell et al., 1977). These models calculate the level

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of prey consumption required to satisfy the observed growth overa given time interval or the growth rate that should result from aspecified amount of consumption, by calculating consumptionwith the energy-balance equation:

C ¼ GþM þW (1)

where C represents the total energy consumed, G is growth, M ismetabolic costs, and W is waste (excretion and egestion). Theamount of energy acquired by the consumer is calculated from theamount and energy density of the food consumed. After metabolicand waste costs are subtracted from energy acquired throughfeeding, the remaining energy is converted to a mass estimate ofgrowth, which is based on the energy density of the consumer.

The model updates the physiology of the consumer on a dailytime-step according to species-specific functions for M, W, andmaximum specific consumption. Consumption is modeled as aproportion of the maximum daily ration for a fish of a certain size

C ¼ Cmaxpf ðTÞ (2)

where C is the specific consumption rate, Cmax the maximumspecific feeding rate, p the proportion of maximum consumption,and f(T) a temperature-dependent function. Maximum specificconsumption (Cmax) is the greatest mass of food that can bephysiologically consumed during a 24-h period:

Cmax ¼ CA WCB (3)

where CA is the intercept of an allometric weight function, W isthe mass of the fish, and CB the slope of the intercept. Metaboliccosts include respiration (R), specific dynamic action (SDA), andactivity (ACT). Respiration is calculated as

R ¼ RA WRB f ðTÞACT (4)

Table 1Prince William Sound hatchery pink salmon marine survival and sample sizes used

to identify stock of origin in otolith pattern analysis.

Hatchery name 2001 2002

% Marine % Marine

Survival Sample size (n) survival Sample size (n)

Solomon Gulch 2.5 127 8.8 190

Wally Noerenberg 4.4 104 16.8 116

Armin F. Koernig 5.2 68 4.5 20

Cannery Creek 1.1 14 6.0 11

Other Hatchery – 6 – 0

Non-Marked – 145 – 97

Total – 464 – 434

Table 2Literature values of energy content and the indigestible percentage of juvenile pink salm

growth potential.

Prey Percent indigestible Energy conten

Large calanoid copepods 9.04 3810.7

Small calanoid copepods 9.04 3810.7

Hyperiid amphipods 12.99 2906.0

Gammarid amphipods 12.99 2906.0

Euphausiids 10.35 3454.8

Shrimp larvae 10.35 3454.8

Insects 10.00 4531.8

Cladocerans 10.00 2513.5

Larvaceans 10.00 3287.8

Limacina 8.50 2619.8

Larval crab 10.00 3790.4

Fish 8.95 5353.4

Other 10.18 3537.1

Please cite this article as: Moss, J.H., et al., Bioenergetic model estimand growth potential of juvenile pink salmon (Oncorhynchus gorbusch

where RA is intercept of the allometric mass function, RB is theslope of the allometric mass function, f(T) is a temperature-dependent function, and ACT an activity multiplier. Energy lossesdue to SDA, egestion, and excretion are modeled as a constantproportion of consumption. Model parameters are estimated fromlaboratory experiments. Parameters used in this study were fromthe pink/sockeye parameter set (Beauchamp et al., 1989) providedby the Wisconsin bioenergetics model software (Hanson et al.,1997).

2.5. Spatially explicit simulations of consumption demand and

growth potential

The bioenergetics model was used to evaluate the spatialaspects of growth performance and consumption requirements ofjuvenile pink salmon during their first growing season in the CGOAduring contrasting release years of low marine survival (2001,juvenile–adult survival, S ¼ 3%) and three-fold higher survival(2002, S ¼ 9%). Bioenergetics model simulations for growth andconsumption demand of hatchery and wild pink salmon werebased on site-specific diet, temperature, and body mass. Inputs tothe model included the proportional contribution of taxonomicgroups of prey in the diet of fish captured at each station, pinksalmon thermal experience, body weight, and energy density ofprey (Table 2). The surface 15 m of the water column weremeasured with a CTD at 1-m increments, the average of which wasassumed to be the thermal experience of the consumer. Dietcomposition at each survey station was calculated from a randomsubsample of 20 juvenile pink salmon. No differences in dietcomposition between hatchery and wild fish inhabiting the samelocations in the CGOA was detected; therefore, diets for bothhatchery and wild fish captured at the same stations wereassumed to be the same. Consumer body weight was measuredto the nearest 0.01 g and energy density fixed at 4534 J g�1 (Boldtand Haldorson, 2002). The first goal was to estimate the amount ofprey consumed by hatchery and wild stocks in order to assessrelative differences in intraspecific competition while accountingfor effects of body size, thermal experience, and abundance. Whenusing a bioenergetics model to estimate the consumption of apredator, an independent estimate of growth is typically used to fitthe model. Applying a fixed growth rate across a range ofconsumer body sizes could potentially generate artificiallysmaller ration sizes for smaller bodied consumers and artificiallylarger ration sizes for larger bodied consumers. Therefore, a fixed,independent estimate of the proportion of maximum dailyconsumption (P-value) estimated specifically for hatchery or wildpink salmon was applied to all sizes of pink salmon from all

on prey items used in bioenergetics models to estimate consumption demand and

t (J/g wet wt) Literature sources

Davis et al. (1998)

Davis et al. (1998)

Davis et al. (1998)

Hyperiid literature values substituted

Davis et al. (1998)

Euphausiid literature values substituted

Cummins and Wuycheck (1971)

Cummins and Wuycheck (1971)

Healey (1991)

Davis et al. (1998) (values estimated from gastropods)

Nishiyama (1977)

Davis et al. (1998), Ciannelli et al. (1998)

Average value for other prey

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Table 4Mean interannual differences in daily growth potential (g d�1) for juvenile pink

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J.H. Moss et al. / Deep-Sea Research II ] (]]]]) ]]]–]]] 5

locations in a given year. Independent daily P-values were acquiredfrom specimens collected from a separate summer survey thatoccurred within the same study region (Cross et al., 2008). Somaticgrowth increments inferred from scale circuli spacing and bodysize from scales collected from these fish were used to estimateinterannual differences in average consumption rate. Bioenergeticsmodel simulations were limited to a single day, used meanconsumer body weight, and were generated separately forhatchery and wild juveniles occupying a given location. Thermalexperience and diet composition were site-specific and applied toboth hatchery and wild cohorts.

Population-level consumption estimates were calculated bymultiplying daily prey consumption for the mean hatchery or wildjuvenile pink salmon by respective relative estimates of abundancefrom 30-min surface tows, which was the number of fish captured,divided by the surface area of ocean swept by the trawl (km2). Thisprovided regional snap-shops of daily prey consumption across theGulf of Alaska. Biological sampling occurred along oceanographictransects in contrast to grid locations, therefore localized dailyprey consumption estimates from specific habitats were averagedto estimate average localized daily population-level consumption.

Daily growth potential of the mean juvenile pink salmon(combination of hatchery and wild stocks) at each haul locationwas calculated assuming fixed proportions of the theoreticalmaximum consumption (P-value ¼ 1). Spatially explicit models offish growth potential typically involve linking a foraging modulewith a bioenergetics module to estimate potential growth of anindividual based on localized prey availability, prey quality, andthermal conditions. However, foraging models for juvenile pinksalmon were not developed to a state where they could providereliable estimates of prey fields or foraging responses; therefore,the fixed P-value approach was used. Analysis of variance(two-way ANOVA) was used to detect significant differences(alpha level of Pp0.05) in daily growth potential and consumptiondemand for each study year and across habitats. ANOVA was alsoused to detect significant differences in consumption demandbetween wild and hatchery juvenile salmon stocks.

salmon and mean daily prey consumption demand (g d km ).

Year Growth potential Consumption demand

Hatchery Wild

2001 0.743 350.9 646.1

2002 0.871 899.3 619.9

3. Results

The relative abundance of juvenile pink salmon was higherduring 2001 than during 2002, with the highest habitat-specificabundances occurring nearshore (Table 3). Within the nearshore,

Table 3Wild and hatchery juvenile pink salmon density (number km�2) and body size (g) from

2001 Nearshore

No. stations 14

Wild Density (number km�2) 59277(4036)

Hatchery Density (number km�2) 14937(1014)

Wild Body size (g) 21.077(2.37)

Hatchery Body size (g) 15.167(2.48)

Wild P-value (G.P.) 0.86

Hatchery P-value (G.P.) 0.83

Temperature (1C) 11.87(0.2)

2002 No. stations 9

Wild Density (number km�2) 2337(74)

Hatchery Density (number km�2) 727(40)

Wild Body size (g) 11.207(2.29)

Hatchery Body size (g) 19.397(5.12)

Wild P-value (G.P.) 1.06

Hatchery P-value (G.P.) 1.02

Temperature (1C) 11.87(0.4)

Thermal experience (average temperature of surface 15 m) and proportion of maximu

growth potential. All but P-values show the mean and one standard error, latter in par

Please cite this article as: Moss, J.H., et al., Bioenergetic model estimand growth potential of juvenile pink salmon (Oncorhynchus gorbusch

the smallest of the four ocean habitats investigated in terms ofspatial area, relative abundance was more than an order ofmagnitude greater during 2001 than during 2002 for bothhatchery and wild stocks. No significant differences in growthpotential were detected between years at an alpha level of 0.05(P ¼ 0.073) (Table 4). Average differences in growth potentialacross habitats were minimal (slope habitat ¼ 0.844 g d�1, shelfhabitat ¼ 0.806 g d�1, offshore habitat ¼ 0.820 g d�1, andnearshore habitat ¼ 0.703 g d�1); and not significantly different(P ¼ 0.630). Habitat differences within year were also notsignificant (P ¼ 0.648). Marine survival of PWS hatchery pinksalmon increased three-fold for most hatchery stocks between2001 and 2002 (Table 1). Data used in the spatially explicit growthpotential model were inherently noisy, causing interannual andregional estimates to be highly variable.

Consumption demand differed significantly between hatcheryand wild stocks (P ¼ 0.035) (Table 4) when examined within yeardue to the interaction between hatchery verses wild origin, andyear. However, the overall effect of origin across years was notsignificant (P ¼ 0.705). Thus, the total amount of prey consumeddid not vary between years, but the proportion of prey consumedby hatchery verses wild stocks did. Therefore, origin needed to beconsidered when relating intraspecific competition to pinksalmon survival. Habitat differences in daily consumption demandby year were more significant when examined within each year(P ¼ 0.160), but not significant within an alpha level of 0.05.Significant differences in daily consumption demand were notdetected by habitat (P ¼ 0.440) or year (P ¼ 0.686).

4. Discussion

Spatially explicit models of fish growth potential havepreviously been used to quantify habitat quality based on fish

Gulf of Alaska surface trawls.

Shelf Slope Offshore

32 10 19

7337(411) 1037(61) 1737(64)

2787(116) 547(31) 607(37)

17.167(1.12) 23.157(1.77) 17.577(8.07)

16.197(1.25) 21.147(2.02) 13.807(0.27)

0.86 0.86 0.86

0.83 0.83 0.83

12.37(0.2) 12.77(0.2) 12.67(0.2)

37 9 23

1587(74) 4987(458) 3537(306)

1247(50) 2467(191) 387(36)

24.847(3.46) 26.917(5.81) 38.947(8.73)

30.367(2.52) 27.307(3.82) 32.647(4.41)

1.06 1.06 1.06

1.02 1.02 1.02

11.97(0.2) 12.77(0.3) 12.77(0.1)

m consumption (P-value) used in bioenergetics models to estimate average daily

entheses.

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foraging rate and growth (Brandt et al., 1992; Luecke et al., 1999;Mason et al., 1995). Assuming that site-specific diet compositionof juvenile pink salmon reflects the quality of prey available, eachparcel of water sampled could theoretically be evaluated in termsof its ability to support fish growth. Total returns to PWS(hatchery and wild stocks combined) increased by a factor of2.21. Marine survivals of PWS hatchery pink salmon have beenpositively related to early marine growth (Moss et al., 2005),however, results from this analysis did not show that an increasein growth potential, a metric which accounts for biophysicalconditions experienced by the fish and species-specific physiol-ogy, corresponded with increased marine survival. Prince WilliamSound hatchery fish were larger in 2002 (Table 3), which meansthat they enjoyed more rapid growth than in 2001. This isconsistent with the hypothesis that rapid growth (large size atcollection) may be associated with higher survival, as three of thefour PWS pink salmon hatcheries (the exception being the ArminF. Koernig hatchery) had better survival in 2002 than 2001.Therefore, favorable conditions for growth likely enhanced marinesurvival; but, the growth potential model using fixed P-values wasnot able to capture the interannual difference. Perhaps this is sobecause the juvenile pink salmon use of habitat was so clearlydifferent in 2001 and 2002.

The amount of juvenile pink salmon concentrated in nearshorehabitat was more than an order of magnitude greater in 2001 than2002. Nearshore habitat ranked lowest in daily growth potentialrelative to other habitat types, and large numbers of juvenile pinksalmon concentrations nearshore suggests that a bottleneck likelyoccurred in 2001. Furthermore, smaller juvenile pink salmon bodysize in 2001 suggests that higher localized densities nearshoremay have increased intraspecific competition between and amongstocks. Daily juvenile pink salmon prey consumption estimates forthe Gulf of Alaska more than doubled for hatchery stocks butremained relatively constant for wild stocks between 2001 and2002. Wild juvenile pink salmon are typically larger than theirhatchery counterparts in the CGOA and PWS during July andAugust (Boldt and Haldorson, 2004; Cross et al., 2005), andrequire more food on a per capita basis. In 2001, average dailyconsumption demand by the mean-sized hatchery pink salmoninhabiting PWS and adjacent GOA waters was estimated to be1.56 g day�1 while that of a wild salmon was 2.21 g day�1,amounting to a difference of 29% (Cross et al., 2005). Boldt andHaldorson (2002) found that juvenile pink salmon inhabiting PWSconsume only a small proportion of zooplankton biomass orproduction, but that these levels of consumption could represent asubstantial proportion of specific taxonomic groups of zooplank-ton such as large calanoid copepods and amphipods. Consumptiondemand on specific zooplankton prey types was not investigatedin our analysis; however, Cross et al. (2005) found that consump-tion demand by juvenile pink salmon exceeded the averagestanding stock biomass of key prey (large copepods, pteropods,hyperiid amphipods, and larvaceans) during some summermonths in the CGOA. The bioenergetics modeling approachaccounts for prey quality, which has been shown to affect juvenilepink salmon consumption estimates by as much as 68% (Boldt andHaldorson, 2004).

A coincident decline in abundance and body size of wild pinksalmon in conjunction with an increased production of PWShatchery pink salmon during recent years has been interpreted asevidence of density-dependent growth (Hilborn and Eggers, 2000;Wertheimer et al., 2004). Speculations of intraspecific competi-tion between hatchery and wild pink salmon have been published(Hilborn and Eggers, 2000) and challenged (Wertheimer et al.,2001). Wertheimer et al. (2004) concluded that PWS hatcheryreleases affected productivity of wild stocks for the 1975–1998brood years, but hatchery releases did not correlate with as much

Please cite this article as: Moss, J.H., et al., Bioenergetic model estimand growth potential of juvenile pink salmon (Oncorhynchus gorbusch

of the variability in survival as did marine conditions. They arguedthat reduced wild stock production due to intraspecific competi-tion was minimal relative to the net benefits gained fromenhancement. Our findings strongly suggest that there isintraspecific competition between hatchery and wild stocksduring some years due to the spatial overlap of high densities inthe nearshore region. The occurrences of smaller juvenile pinksalmon when these conditions persist indicate density-dependentgrowth.

5. Conclusions

Intraspecific competition for prey resources exists for hatcheryand wild juvenile pink salmon stocks inhabiting the coastal Gulfof Alaska. The highest levels of intraspecific competition occurrednearshore, an area where interannual abundance of hatchery andwild pink salmon can vary by more than an order of magnitude.Sea birds and mammals may be attracted to these high abundanceareas and increase marine mortality of juvenile pink salmon, aninteraction that was not addressed by this study. Estimates ofdaily growth potential that provide information on the quality ofmarine habitat for supporting juvenile pink salmon growth didnot appear to be a useful indicator of marine survival. However,2002 had larger, faster growing fish and higher marine survival;therefore, future investigation into the utility of the growthpotential metric as a forecasting tool is warranted. Incorporatingforaging behavior models based on measured physical andbiological conditions to generate consumption rate estimatesshould provide more accurate estimates of daily consumptiondemand than are possible using fixed theoretical proportion ofmaximum consumption rates (P-values) estimated over relativelylarge spatial and temporal scales.

Acknowledgments

We thank E. Martinson and J. Orsi for thoughtful reviews of thismanuscript, and Captain Jack Bronson and the crew of the FV Great

Pacific for assistance with sample collection. Our manuscript wassignificantly improved by incorporating comments provided by H.Batchelder and two anonymous reviewers. The Bill and MelindaGates Foundation provided a fellowship to the senior author. Thisstudy is contribution 637 of the GLOBEC Project.

References

Aydin, K.Y., Jacquelynne, G.A., King, R., Megrey, B.A., Myers, K.W., 2005. Linkingoceanic food webs to coastal production and growth rates of Pacific salmon(Oncorhynchus spp.), using models on three scales. Deep-Sea Research II 52,757–780.

Bax, N.J., 1983. Early marine mortality of marked juvenile chum salmon(Oncorhynchus keta) released into Hood Canal, Puget Sound, Washington, in1980. Canadian Journal of Fisheries and Aquatic Sciences 40, 426–435.

Beamish, R.J., 1993. Climate and exceptional fish production off the west coast ofNorth America. Canadian Journal of Fisheries and Aquatic Sciences 50,2270–2291.

Beamish, R.J., Bouillon, D.R., 1995. Marine fish production trends off the Pacificcoast of Canada and the United States. Canadian Journal of Fisheries andAquatic Sciences 121, 585–591.

Beamish, R.J., Mahnken, C., 2001. A critical size and period hypothesis to explainnatural regulation of salmon abundance and the linkage to climate and climatechange. Progress in Oceanography 49, 423–437.

Beauchamp, D.A., Stewart, D.J., Thomas, G.L., 1989. Corroboration of a bioenergeticsmodel for sockeye salmon. Transactions of the American Fisheries Society 118,597–607.

Boldt, J.L., Haldorson, L.J., 2002. A bioenergetics approach to estimating consump-tion of zooplankton by juvenile pink salmon in Prince William Sound Alaska.Alaska Fishery Research Bulletin 9 (2), 111–127.

Boldt, J.L., Haldorson, L.J., 2004. Size and condition of wild hatchery pink salmonjuveniles in Prince William Sound, Alaska. Transactions of the AmericanFisheries Society 133, 173–184.

ates of interannual and spatial patterns in consumption demanda).... Deep-Sea Research II (2009), doi:10.1016/j.dsr2.2009.03.005

ARTICLE IN PRESS

J.H. Moss et al. / Deep-Sea Research II ] (]]]]) ]]]–]]] 7

Brandt, S.B., Mason, D.M., Patrick, E.V., 1992. Spatially-explicit models of fishgrowth rate. Fisheries 17 (2), 23–33.

Brodeur, R.D., Ware, T.M., 1995. Interdecadal variability in distribution and catchrates of epipelagic nekton in the northeast Pacific Ocean. Canadian Journal ofFisheries and Aquatic Sciences 121, 329–356.

Ciannelli, L., Brodeur, R.D., Buckley, T.W., 1998. Development and application of abioenergetics model for juvenile walleye pollock. Journal of Fish Biology 52,879–898.

Cross, A.D., Beauchamp, D.A., Armstrong, J.L., Blikshteyn, M., Boldt, J.L., Davis, N.D.,Haldorson, L.J., Moss, J.H., Myers, K.W., Walker, R.V., 2005. Consumptiondemand of juvenile pink salmon in Prince William Sound and the coastal Gulfof Alaska in relation to prey biomass. Deep-Sea Research II 52, 347–370.

Cross, A.D., Beauchamp, D.A., Myers, K.W., Moss, J.H., 2008. Early marine growth ofpink salmon in Prince William Sound and the coastal Gulf of Alaska duringyears of low and high survival. Transactions of the American Fisheries Society137, 927–939.

Cummins, K.W., Wuycheck, J.C., 1971. Caloric equivalents for investigations inecological energetics. Mitteilungen Internationale Vereinigung fur Theore-tische und Angewandte Limnologie 18, 1–158.

Davis, N.D., Myers, K.W., Ishida, Y., 1998. Caloric value of high-seas salmon preyorganisms and simulated salmon growth and prey composition. North PacificAnadromous Fish Commission Bulletin 1, 146–162.

Farley, E.V., Munk, K., Hagen, P., 1997. Incidence of thermally marked pink andchum salmon in the coastal waters of the Gulf of Alaska. Alaska FisheryResearch Bulletin 4, 181–187.

Francis, R.C., Hare, S.R., 1994. Decadal-scale regime shifts in the large marineecosystems of the North-east Pacific: a case for historical science. FisheriesOceanography 3 (4), 279–291.

Fisher, J.P., Pearcy, W.G., 2005. Seasonal changes in growth of coho salmon(Oncorhynchus kisutch) off Oregon and Washington and concurrent changes inthe spacing of scale circuli. Fishery Bulletin 103, 34–51.

Furnell, D.J., Brett, J.R., 1986. Model of monthly marine growth and mortality forBabine Lake sockeye salmon (Oncorhynchus nerka). Canadian Journal ofFisheries and Aquatic Sciences 43, 999–1004.

Hagen, P., Munk, K., Van Allen, B., White, B., 1995. Thermal mark technology forinseason fisheries management: a case study. Alaska Fishery Research Bulletin292, 143–155.

Hanson, P.C., Johnson, T.B., Schindler, D.E., Kitchell, J.F., 1997. Fish Bioenergetics 3.0for Windows. Center for Limnology. University of Wisconsin-Madison andUniversity of Wisconsin Sea Grant Institute, Madison, WI.

Hare, S.R., Francis, R.C., 1995. Climate change and salmon production in thenortheast Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences121, 357–372.

Hartt, A.C., 1980. Juvenile salmonids in the oceanic ecosystem—the first criticalsummer. In: McNeil, W.J., Himsworth, D.C. (Eds.), Salmonid Ecosystems of theNorth Pacific. Oregon State University Press, Corvallis, pp. 25–57.

Healey, M.C., 1991. Diets and feeding rates of juvenile pink, chum, and sockeyesalmon in Hecate Strait, British Columbia. Transactions of the AmericanFisheries Society 120, 303–318.

Hilborn, R., Eggers, D., 2000. A review of the hatchery programs for pink salmon inPrince William Sound and Kodiak Island, Alaska. Transactions of the AmericanFisheries Society 129, 333–350.

Holtby, L.B., Anderson, B.C., Kadowaki, R.K., 1990. Importance of smolt size andearly ocean growth to inter-annual variability in marine survival of cohosalmon (Oncorhynchus kisutch). Canadian Journal of Fisheries and AquaticSciences 47, 2181–2194.

Horne, J.K., Schneider, D.C., 1995. Spatial variance in ecology. Oikos 74, 18–26.Horne, J.K., Smith, P.E., Schneider, D.C., 1999. Comparative examination of scale-

explicit biological and physical processes: recruitment of Pacific hake.Canadian Journal of Fisheries and Aquatic Sciences 56, 1–10.

Johnson, J., Sharp, D., Joyce, T., Moffitt, S., 2002. Prince William Sound Managementarea 2000 annual finfish management report. Regional Information Report2A02-02, Alaska Department of Fish and Game, Division of CommercialFisheries. Anchorage, 164pp.

Kitchell, J.F., Stewart, D.J., Weininger, D., 1977. Applications of a bioenergeticsmodel to perch (Perca flavescens), and walleye (Stizostedion vitreum). Journal ofthe Fisheries Research Board of Canada 34, 1922–1935.

Koenings, J.P., Geiger, H.J., Hasbrouck, J.J., 1993. Smolt to adult survival of sockeyesalmon (Oncorhynchus nerka): effects of smolt length and geographic latitudewhen entering the sea. Canadian Journal of Fisheries and Aquatic Sciences 50,600–611.

Luecke, C., Wengert, M., Schneidervin, R., 1999. Comparing results of a spatiallyexplicit growth model with changes in the length–weight relationship of lake

Please cite this article as: Moss, J.H., et al., Bioenergetic model estimand growth potential of juvenile pink salmon (Oncorhynchus gorbusch

trout (Salvelinus namaycush) in Flaming Gorge Reservoir. Canadian Journal ofFisheries and Aquatic Sciences 56, 162–169.

Luo, J., Hartman, K.J., Brandt, S.B., Cerco, C.F., Rippetoe, T.H., 2001. A spatially-explicit approach for estimating carrying capacity: an application for theAtlantic menhaden (Brevoortia tyrannus) in Chesapeake Bay. Estuaries 24 (4),545–556.

Mackas, D.L., Denman, K.L., Abbott, M.R., 1985. Plankton patchiness: biology in thephysical vernacular. Bulletin of Marine Science 37, 652–674.

Mantua, N., Hare, S.R., Zhang, Y., Wallace, J.M., Francis, R.C., 1997. A Pacificinterdecadal climate oscillation with impacts on salmon production. Bulletinof the American Meteorological Society 78, 1069–1079.

Mason, D.M., Goyke, A., Brandt, S., 1995. A spatially explicit bioenergeticsmeasure of habitat quality for adult salmonines: comparison between lakesMichigan and Ontario. Canadian Journal of Fisheries and Aquatic Sciences 52,1572–1583.

Mason, D.M., Brandt, S.B., 1999. Space, time, and scale: new perspectives in fishecology and management. Canadian Journal of Fisheries and Aquatic Sciences56, 1–3.

Mathews, S.B., Buckley, R., 1976. Marine mortality of Puget Sound coho salmon(Oncorhynchus kisutch). Journal of the Fisheries Research Board of Canada 33,1677–1684.

Moss, J.H., Beauchamp, D.A., Cross, A.D., Myers, K.W., Farley, E.V., Murphy, J.M.,Helle, J.H., 2005. Higher marine survival associated with faster growth for pinksalmon (Oncorhynchus gorbuscha). Transactions of the American FisheriesSociety 134, 1313–1322.

Mueter, F.J., Ware, D.M., Peterman, R.M., 2002. Spatial correlation patterns incoastal environmental variables and survival rates of Pacific salmon in thenortheast Pacific Ocean. Fisheries Oceanography 11, 205–218.

Mueter, F.J., Pyper, B.J., Peterman, R.M., 2005. Relationship between coastal oceanconditions and survival rates of northeast Pacific salmon at multiple lags.Transactions of the American Fisheries Society 134, 105–119.

Nishiyama, T., 1977. Food-energy requirements of Bristol Bay sockeye salmonOncorhynchus nerka (Walbaum) during the last marine life stage. ResearchInstitute North Pacific Fisheries Special Volume, 289–320.

Orsi, J.A., Wertheimer, A.C., Sturdevant, M.V., Fergusson, E.A., Mortensen, D.G.,Wing, B.L., 2004. Juvenile chum salmon consumption of zooplankton inmarine waters of southeastern Alaska: a bioenergetics approach to implica-tions of hatchery stock interactions. Reviews in Fish Biology and Fisheries 14,335–359.

Parker, R.R., 1965. Estimation of sea mortality rates for the 1961 brood year pinksalmon of Belle Coola Area, British Columbia. Journal of the Fisheries ResearchBoard of Canada 22, 1523–1554.

Parker, R.R., 1968. Marine mortality schedules of pink salmon of the Bella CoolaRiver, central British Columbia. Journal of the Fisheries Research Board ofCanada 25, 757–794.

Pyper, B.J., Mueter, F.J., Peterman, R.M., 2005. Across-species comparisons of spatialscales of environmental effects on survival rates of northeast Pacific salmon.Transactions of the American Fisheries Society 134, 86–104.

Rand, P.S., 2002. Modeling feeding and growth in Gulf of Alaska sockeye salmon:implications for high seas distribution and migration. Marine EcologicalProgress Series 234, 265–280.

Ruggerone, G.T., Zimmermann, M., Myers, K.W., Nielsen, J.L., Rogers, D.E., 2003.Competition between Asian pink salmon (Oncorhynchus gorbuscha) andAlaskan sockeye salmon (O. nerka) in the North Pacific Ocean. FisheriesOceanography 12, 209–219.

Ruggerone, G.T., Goetz, F.A., 2004. Survival of Puget Sound chinook salmon(Oncorhynchus tshawytscha) in response to climate-induced competition withpink salmon (Oncorhynchus gorbuscha). Canadian Journal of Fisheries andAquatic Sciences 61 (9), 1756–1770.

Welch, D.W., Ishida, Y., Nagasawa, K., 1998. Thermal limits and ocean migrations ofsockeye salmon (Oncorhynchus nerka): long-term consequences of globalwarming. Canadian Journal of Fisheries and Aquatic Sciences 55 (4), 937–948.

Wertheimer, A.C., Smoker, W.W., Joyce, T.L., Heard, W.R., 2001. Comment: a reviewof the hatchery programs for pink salmon in Prince William Sound.Transactions of the American Fisheries Society 130, 712–720.

Wertheimer, A.C., Heard, W.R., Smoker, W.W., 2004. Effects of hatchery releasesand environmental variation on wild-stock productivity: consequences for searanching of pink salmon in Prince William Sound, Alaska. In: Leber, K.M.,Kitada, S., Svasand, T., Blankenship, H.L. (Eds.), Stock enhancement and searanching II. Blackwell, Oxford, pp. 302–307.

Willette, T.M., Cooney, R.T., Hyer, K., 1999. Predator foraging mode shifts affectingmortality of juvenile fishes during the subartic spring bloom. Canadian Journalof Fisheries and Aquatic Sciences 56, 364–376.

ates of interannual and spatial patterns in consumption demanda).... Deep-Sea Research II (2009), doi:10.1016/j.dsr2.2009.03.005