MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 537: 217–232, 2015doi: 10.3354/meps11460

Published October 14

INTRODUCTION

The life cycle of anadromous salmonids involves anumber of discrete habitats, within which mortalityoccurs. Different combinations of biotic and abioticprocesses control mortality during the embryonicstages in the gravel, throughout life as free-livingjuveniles in lotic and lentic freshwater environments,

along migration routes, and at sea prior to return asadults (Quinn 2005, Jonsson & Jonsson 2011). Themajority of lifetime mortality typically takes placeduring the freshwater stages, but the mortality at seais substantial, variable among years, and occurs laterin the life cycle, so there are fewer opportunities forcompensation compared to mortality that occurs dur-ing earlier, freshwater stages (Bradford 1995, Quinn

© The authors 2015. Open Access under Creative Commons byAttribution Licence. Use, distribution and reproduction are un -restricted. Authors and original publication must be credited.

Publisher: Inter-Research · www.int-res.com

*Corresponding author: megan.moore@noaa.gov

Multi-population analysis of Puget Sound steelheadsurvival and migration behavior

Megan E. Moore1,*, Barry A. Berejikian1, Frederick A. Goetz2, Andrew G. Berger3, Sayre S. Hodgson4, Edward J. Connor5, Thomas P. Quinn6

1Environmental and Fisheries Sciences, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration Fisheries, PO Box 130, Manchester WA 98353, USA

2U.S. Army Corps of Engineers, 4735 East Marginal Way South, Seattle WA 98134, USA3Puyallup Tribe Fisheries Department, Puyallup Tribe of Indians, 3009 East Portland Avenue, Tacoma WA 98404, USA

4Department of Natural Resources, Nisqually Indian Tribe, 4820 She-Nah-Hum Drive, Olympia WA 98513, USA5Seattle City Light, City of Seattle, 700 Fifth Ave., Seattle WA 98104, USA

6School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle WA 98195, USA

ABSTRACT: Until recently, research on mortality of anadromous fishes in the marine environmentwas largely limited to estimates of total mortality and association with group characteristics or theenvironment. Advances in sonic transmitter technology now allow estimates of survival in discretemarine habitats, yielding important information on species of conservation concern. Previoustelemetry studies of steelhead Oncorhynchus mykiss smolts in Puget Sound, Washington, USAindicated that approx. 80% of fish entering marine waters did not survive to the Pacific Ocean.The present study re-examined data from previous research and incorporated data from addi-tional Puget Sound populations (n = 7 wild and 6 hatchery populations) tagged during the sameperiod (2006−2009) for a comprehensive analysis of steelhead early marine survival. We usedmark-recapture models to examine the effects of several factors on smolt survival and to identifyareas of Puget Sound where mortality rates were highest. Wild smolts had higher survival proba-bilities in general than hatchery smolts, with exceptions, and wild smolts released in early Apriland late May had a higher probability of survival than those released in early and mid-May. Steel-head smolts suffered greater instantaneous mortality rates in the central region of Puget Soundand from the north end of Hood Canal through Admiralty Inlet than in other monitored migrationsegments. Early marine survival rates were low (16.0 and 11.4% for wild and hatchery popula-tions, respectively) and consistent among wild populations, indicating a common rather thanwatershed-specific mortality source. With segment-specific survival information we can begin toidentify locations associated with high rates of mortality, and identify the mechanisms responsible.

KEY WORDS: Steelhead · Survival · Smolts · Migration · Telemetry

OPENPEN ACCESSCCESS

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2005). Most studies of marine mortality have investi-gated (1) environmental factors related to ‘smolt-to-adult’ survival (Mantua et al. 1997, Coronado &Hilborn 1998), (2) patterns of survival or recruitmentover various spatial areas (Pyper et al. 2005, Sharmaet al. 2013), (3) scale growth patterns to assess size-selective mortality (Ward et al. 1989, Holtby et al.1990, Henderson & Cass 1991, Moss et al. 2005), or(4) the effects of specific predators on populations ofinterest (Beamish et al. 1992, Willette et al. 2001).More recently the use of sonic transmitters and re -ceiver stations has helped to partition mortality asso-ciated with initial entry into the marine environmentfrom that which occurs at later stages (e.g. Halfyardet al. 2013, Lacroix 2013, Melnychuk et al. 2014).

Telemetry studies have been useful in estimatingsmolt mortality via specific routes and migratory seg-ments (Perry et al. 2013, Romer et al. 2013), and de -scribing smolt migratory behavior within and amongpopulations (Plantalech manel-la et al. 2011, Renka -witz et al. 2012). To reveal ecosystem-scale processesinfluencing smolt survival, it is important to assessvariation in migratory behavior and survival of smoltsamong habitats, populations, and years. Large-scalecomparisons of both geographically similar and moredistant populations can help determine whether mor-tality vectors are local or shared through out the eco-system. Furthermore, patterns that emerge from mul-tiple years of study are more likely than patternsfrom single year studies to reflect consequential asopposed to transient effects on survival. In this study,we report the results of a multi-year, multi-popula-tion study examining patterns of migration and sur-vival of steelhead, the anadromous form of rainbowtrout Oncorhynchus mykiss, in riverine, estuarine,and marine waters of Puget Sound, a fjord complexunique in North America in its combination of physi-cal features and human development.

Marine survival rates (smolt-to-adult returns [SARs])of steelhead smolts originating in Washington Stateand British Columbia have declined substantially inthe last 25−30 yr (Ward 2000, Scott & Gill 2008). WhileSARs from populations originating along the PacificOcean coast of Washington have increased in recentyears from very low levels in the early 1990s, popula-tions from Puget Sound and the greater Salish Sea re-gion (the complex of inland marine waters includingPuget Sound, the Strait of Georgia, and associatedother straits and inlets) continue to experience lowSARs (N. Kendall unpubl. data). Puget Sound steel-head were listed as threatened under the US Endan-gered Species Act in 2007 (NOAA 2007). The earlymarine experiences of steelhead smolts depend on

where they enter marine waters. Coastal steelheadpopulations experience ocean conditions immediatelyupon saltwater entry, whereas steelhead smolts origi-nating in the Salish Sea experience an inland marineecosystem much more influenced by terrestrial andfreshwater processes. Unless steelhead from coastalpopulations migrate to different ocean regions, someaspects of the inland sea migration likely explains theobserved difference in coastal and Puget SoundSARs.

Very low early marine survival through PugetSound was documented for steelhead from popula-tions that enter Hood Canal (Moore et al. 2010,Moore et al. 2012) and from the Green River (Goetzet al. 2015), which feeds the main basin of PugetSound. The goal of the present study was to combinetelemetry data from these Hood Canal and GreenRiver studies with data from 3 additional PugetSound populations (the Nisqually, Puyallup, andSkagit rivers) for a more comprehensive regional sur-vival analysis. We investigated temporal, biologicaland geographic factors that may affect survival ofsteelhead smolts, comparing the performance of wildand hatchery-reared fish over 4 years. By comparingsurvival rates among populations and examiningspatial patterns of mortality, we sought to identifyspecific sub-regions, or ‘hotspots’ for mortality inPuget Sound, and assess interactions between bodysize and wild or hatchery origin, in the context ofinterannual variation.

METHODS

Fish collection and tagging

Natural-origin (hereafter ‘wild’, but with no as -sumptions regarding their genetic background) steel-head smolts were collected from 3 Hood Canal rivers(Big Beef Creek, Dewatto River, and South ForkSkokomish River), 2 rivers in central Puget Sound(Puyallup and Green rivers), the Nisqually River insouth Puget Sound, and the Skagit River in northPuget Sound (Fig. 1) during the smolt migration peri-ods (April−June) of 2006 through 2009. Hatcherysteelhead smolts were obtained from rearing facilitiesassociated with the Duckabush River (LilliwaupHatchery), the Green River (Soos Creek Hatchery),the Hamma Hamma River (Lilliwaup Hatchery), thePuyallup River (White River Hatchery), the Sko ko -mish River (McKernan Hatchery), and the SkagitRiver (Marblemount Hatchery) (Fig. 1). Hatcherysmolts from the Duckabush, Hamma Hamma, and

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Moore et al.: Early marine survival of steelhead smolts

Sko ko mish rivers, and the Puyallup River (2008 and2009 smolts), were raised from natural-origin localbroodstock, whereas hatchery smolts from the Greenand Skagit rivers, and Puyallup River (2006 smolts),originated from a multi-generation, non-native brood -stock (Chambers Creek). A total of 771 wild and616 hatchery smolts were surgically implanted witheither a V7-2L-R64K (7 × 17.5 mm [diameter × length],1.4 g), V7-4L-R64K (7 × 18.5 mm, 1.6 g) or V9-2L-R64K (9 × 20 mm, 3.5 g) 69 kHz acoustic transmitter(VEMCO) (Table 1). Tagging occurred at the smoltcollection sites and hatcheries-of-origin. Each smoltwas anesthetized using MS-222 prior to surgery, andsupplied with anesthetic throughout the procedure.Incisions were made anterior to the pelvic girdle, the

transmitter was placed within the body cavity, andthe opening was closed using 2 or 3 interruptedstitches using monofilament sutures. Smolts wereheld for variable time periods ranging from 1 h to 30 dbefore release into their respective streams (Table 1).

Receiver arrays

VEMCO VR2 receivers were deployed at each rivermouth to detect tagged smolts entering sea water, and6 main receiver ‘lines’ of VR2 and VR3 re ceivers weredeployed across-channel in Puget Sound and theStrait of Juan de Fuca to detect smolts along theirearly marine migration (Fig. 1). During the 2006−2009

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Fig. 1. Map of Puget Sound showing the locations of receiver arrays (circles) at the Hood Canal Bridge (HCB), in central PugetSound (CPS), and in the Strait of Juan de Fuca (JDF) during all study years (2006−2009). Arrays were also deployed at AdmiraltyInlet (ADM) in 2008 and 2009, at Deception Pass (DP) in 2006, 2008 and 2009 and at the Tacoma Narrows (NAR) in 2006 and 2009.One or more receivers were deployed at river mouths to determine time of saltwater entry. Triangles denote locations of hatcheries

Mar Ecol Prog Ser 537: 217–232, 2015

smolt outmigration periods, receiver arrays spannedcentral Puget Sound (CPS; 7−8 re ceivers), northernHood Canal at the Hood Canal Bridge (HCB; 4−7 re-ceivers), and the Strait of Juan de Fuca (JDF; 31 re-ceivers). In 2006, 4 receivers were deployed in south-ern Admiralty Inlet (too far south to detect HoodCanal populations). In 2008 and 2009, a 13-receiverline was deployed across northern Admiralty Inlet in-tersecting the migration path of both Puget Soundand Hood Canal populations (ADM). Two receiverscovered Deception Pass (DP) in 2006, 2008, and 2009,and a group of receivers was also deployed in south-ern Puget Sound near the Tacoma Narrows Bridge(NAR) in 2006 (3 receivers) and in 2009 (10 receivers)(Fig. 1).

Survival and detection probability estimation

Cormack-Jolly-Seber (CJS) statistical models (Le -breton et al. 1992) were created and compared in 2separate analyses of the smolt detection data. Oneset of models was constructed to estimate the proba-bility of survival (ϕ) of both hatchery and wild smoltsthrough multiple migration segments, and the detec-tion probability (p) at each receiver line, and to eval-uate possible geographic and population effects onsegment-specific ϕ and p (‘multi-segment analysis’).A second ‘2-segment’ analysis compared models thatbroke the migration into only 2 segments (freshwaterand saltwater), and used data from wild smolts onlyto identify factors affecting survival over the broaderPuget Sound migration. The multi-segment analysisallowed for estimation of hatchery and wild smoltsurvival at a fine scale (i.e. segment-specific survival

probabilities), while the 2-segment analysis focusedon the entire early marine migration of wild smoltsexclusively.

Models for ϕ and p were constructed using theRMark package (Laake & Rexstad 2007) in R (R De -velopment Core Team 2007) for the program MARK(White & Burnham 1999). Models in the multi- segment analysis incorporated data from all 1466tagged individuals, and models in the 2-segmentanalysis used data from a subset of those (850 wildsmolts). Goodness-of-fit of the detection data to theglobal CJS model for each analysis was tested usingthe median c method (within MARK) and the vari-ance inflation factors were found to be satisfactory(c < 2). The CJS model cannot distinguish betweenmortality and emigration, so in this study, 1 − ϕ re -presents both animals that died and those that didnot migrate. This issue generally tends to causeunder estimation of survival, and may specificallyinfluence freshwater survival rates of hatchery smoltsas some delay migration or remain indefinitely infreshwater after release (see Hausch & Melnychuk2012). However, all evidence indicates that oncesteelhead smolts enter Puget Sound they migrate tothe ocean rather than remaining in Puget Sound, assome Pacific salmon species do.

Several combinations of factors and covariateswere used to construct a series of models to be testedin RMark. Akaike’s Information Criteria (AIC) wereused to identify the set of variables that parsimo-niously explained the variation in the survival anddetection data (Burnham & Anderson 2010). Model-ing results were adjusted using the estimated vari-ance inflation factor (c) to compute QAICc values,which are adjusted AIC values that compensate for

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Population Origin 2006 2007 2008 2009 Total Average Average Release length (mm) weight (g) site (rkm)

Big Beef Creek W 37 (V9) 26 (V7) 27 (V7) 32 (V7) 122 182 ± 2 56 ± 2 0.1Dewatto River W 40 (V7) 40 175 ± 3 53 ± 3 0.3Duckabush River H 30 (V7) 30 211 ± 2 92 ± 3 1.9Green River W 50 (V7) 40 (V7) 48 (V7) 50 (V7) 188 185 ± 1 60 ± 1 55Green River H 50 (V7) 49 (V7) 50 (V7) 149 192 ± 1 66 ± 1 54.5Hamma Hamma River H 33 (V9) 47 (V7) 80 188 ± 2 45 ± 2 2.0Nisqually River W 19 (V7) 35 (V7) 3 (V7) 34 (V7) 188 198 ± 2 81.1 ± 3 0−21.2

37 (V9) 14 (V9) 11 (V9) 35 (V9)Puyallup River W 25 (V7) 25 198 ± 5 77 ± 3 17.0

H 25 (V7) 90 (V7) 66 (V7) 181 190.1 ± 1.1 72.6 ± 1.1 55.8Skokomish River W 27 (V9) 51 (V7) 41 (V7) 23 (V7) 142 182.5 ± 1.6 56.7 ± 1.8 13.5

H 42 (V7) 29 (V7) 71 187.6 ± 2.8 67.2 ± 3.4 13.5Skagit River W 23 (V7) 47 (V7) 50 (V7) 25 (V7) 145 170.0 ± 1.2 52.1 ± 1.1 10.0

H 50 (V7) 55 (V9) 105 178.1 ± 1.2 62.7 ± 1.4 102

Total 326 349 412 379 1466

Table 1. Number of individual steelhead smolts tagged with V7 and V9 transmitters between 2006−2009, and average length, weight and release site for each tagged population. W = wild, and H = hatchery populations. rkm: river kilometer

Moore et al.: Early marine survival of steelhead smolts

extra-binomial variation. The model with the lowestQAICc was considered the best fit, given the detec-tion data, though models with ΔQAICc <2 were alsoconsidered to have substantial support. Models withΔQAICc >4 have considerably less support, and aΔQAICc >10 suggests a failure of the associatedmodel to explain substantial variation (Burnham &Anderson 2010). A 95% confidence set of models wascompiled for each of the 2 mark-recapture analysesby adding up the model Akaike weights from largestto smallest until the sum exceeded 0.95. Estimates ofthe relative importance of predictor variables werecalculated by summing the Akaike weights across allmodels in the confidence set where that variable wasincluded (Burnham & Anderson 2010).

In the multi-segment analysis, detection data foreach smolt at each receiver line were used to con-struct individual encounter histories, which allowedestimation of ϕ and p through either 4 migration seg-

ments (Hood Canal, Nisqually, Puyallup and GreenRiver smolts) or 3 migration segments (Skagit Riversmolts): ϕ1 = point of release (PR) to river mouth (RM),ϕ2 = RM to either the HCB (Hood Canal Rivers), CPS(Nisqually, Puyallup, and Green River smolts), or DP(Skagit River smolts), ϕ3 = HCB/CPS to the ADM (noSkagit), ϕ4a = RM to JDF (Skagit only), or ϕ4 = ADM toJDF (Fig. 2). In 2008, 5 Skagit River smolts migratedsouth through Possession Sound and were detectedat the ADM line; these detections were combinedwith the DP detections to calculate one estimate of ϕand p. During years when receiver lines were absent(ADM in 2006 for Hood Canal smolts and 2007 for allsmolts, and DP in 2007 for the Skagit smolts), the 2migration segments using that line became one(Fig. 2). A separate CJS model was set up to estimateϕ for Nisqually smolts during the years when re -ceivers were deployed near the Narrows Bridge toget a survival probability estimate for RM to NAR

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Fig. 2. Migration segmentsfor which survival probabilitywas estimated by year, de -fined by annual variation inreceiver array locations. ϕ1 =segment from point of re lease(PR) to river mouth (RM), ϕ2 =RM to either the Hood CanalBridge (HCB; Hood CanalRivers), the Central PugetSound line (CPS; Nisqually,Puyallup, and Green Riversmolts), or the Deception Passline (DP; Skagit River smolts)segments, ϕ2a = RM to Nar-rows Bridge (NAR; 2006 and2009 only) segment, ϕ3 =HCB/CPS to the AdmiraltyInlet line segment (ADM; noSkagit), ϕ4 = the ADM or DPto the Strait of Juan de Fucaline (JDF) segment, and ϕ4a =RM to JDF (Skagit only) seg-ment. See ‘Materials andMethods: Survival and detec-tion probability estimation’ for

additional explanation

Mar Ecol Prog Ser 537: 217–232, 2015

(ϕ2a) for years 2006 and 2009 (Fig. 2). Including datafrom the NAR line in the larger CJS model wouldhave resulted in a mismatch of comparisons betweenmigration segments and was not available for allyears.

The survival probability portion of the multi-seg-ment models included linear and multiplicativeeffects of the following factors alone and in combina-tion: ‘migration segment’, ‘year’, ‘release date’ (levels= late April [16−30 April], early May [1−15 May], lateMay [16−31 May], or early June [1−15 June]), ‘popu-lation’, ‘rear type’ (i.e. hatchery or wild), or a ‘reartype:population’ variable that represented each reartype × population combination (e.g. SkokomishHatchery, Skokomish Wild, Nisqually Wild, etc.). A‘region’ factor was substituted for ‘population’ in aset of models to determine whether survival of geo-graphically similar populations covaried. Regionswere defined as Hood Canal (Big Beef Creek, De -watto, Hamma Hamma, and Skokomish rivers), Cen-tral and South Sound (Green, Puyallup, Nisquallyrivers), and North Sound (Skagit River). The detec-tion probability portion of the multi-segment modelswas parameterized to produce separate estimates ofp for each river mouth and for each receiver line. A‘year’ factor was included in all candidate modelssince receiver lines were often redeployed each year.A ‘tag type’ factor was also included in each detec-tion model to estimate different p for V7 and V9tagged smolts.

The 2-segment models used detections from rivermouth receivers and the JDF line to create a conden -sed encounter history for each wild steelhead smolt.Models were constructed to estimate smolt survivalfrom PR to RM, and from RM to JDF only. The mainpurpose of this second analysis was to examine howwild Puget Sound survival rates were affected bysmolt size (fork length) and release date, 2 factorshypothesized to work over a longer migration dis-tance to affect overall survival through Puget Sound.As we sought broad rather than isolated or variableeffects in the 2-segment analysis, candidate modelsincluded only additive effects of ‘year’. Interactionsbetween ‘segment’ and ‘release date’, and ‘segment’and ‘fork length’ were modeled because we wereinterested in possible differences in the effects ofrelease date and body size in freshwater and marineportions of the route. Terms for the linear and inter-action effects of ‘population’ were also included insome candidate models to examine possible effects ofgeography. The detection portion of these 2-segmentmodels included the same parameters (‘year’ and ‘tagtype’) as the multi-segment models.

The CJS model uses detections at subsequent en -counter occasions to estimate p for each previous oc -casion; therefore, ϕ and p are confounded for the lastreceiver line. To circumvent this problem, empiri-cally derived estimates from similarly sited and con-figured receiver lines were used to fix p at the JDFline (Melnychuk 2009). Melnychuk (2009) calculatedmean and 95% confidence limit estimates of p for V7and V9 VEMCO tags passing a receiver line span-ning the Strait of Georgia in 2004, 2005, 2006, and2007, so we used an average of the 2005−2007 values(2004 was an anomalous year) for all years to fix thevalue of p for the JDF line in our models (pJDF(V7) =0.685, pJDF(V9) = 0.909). An empirical estimate of theJDF line was also calculated for V7 tags by compar-ing detections of V9H (‘high power’) and V7 tags inspring 2014. Equal numbers of V9H and V7 tagswere surgically implanted in Skagit River hatcherysmolts, treated identically, and released at the sametime into the Skagit River. In 2013 studies of Skagithatchery smolts, each V9H tag at the JDF line wasdetected an average of 216 times, and always on atleast 2 receivers, indicating 100% detection of thosetags. In 2014, 6 out of 50 V9H tags were detected atJDF an average of 48 times, always on at least 2 re -ceivers, compared to only 4 out of 50 V7 tags de tec -ted (E. J. C. unpubl. data). The relative rate of detec-tion for V7 tags was therefore calculated as 66.67%,a rate very similar to the mathematically derived esti-mate of 68.5%.

Survival curves were constructed for each popula-tion using survival probabilities derived from thesegment-specific survival model in the 95% candi-date set that defined the most variation in segmentsurvival estimates (ϕ (segment + rear type:population+ year), ΔQAICc = 2.761; Table 2). Survival througheach migration segment was compared betweenpopulations by distance-based instantaneous mortal-ity rates:

(−ln ϕs) / ds

where ϕs is the model-derived survival probability ofa migration segment s, and d is the distance from thebeginning to the end of migration segment s.

Migration behavior

Travel rates and travel times were calculated for allmarine segments to examine migratory behavior.Travel time was calculated as the time in daysbetween the last detection at the first detection linein the migration segment and the first detection at

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Moore et al.: Early marine survival of steelhead smolts

the subsequent detection array. Travel rate was thestraight line in-water distance (km) between seg-ment detection arrays divided by the travel time.Segments were categorized as either initial marine(‘initial’) (RM to HCB/DP/CPS), Admiralty Inlet(ADM) (HCB/CPS to ADM), or Strait of Juan de Fuca(JDF) (ADM/DP to JDF). Travel rates were log trans-formed to achieve normality. Analysis of covariancewas used to test for effects of region (Hood Canal,Central and South Sound, or North Sound) and rear-ing type on initial and ADM travel rate ([log]travelrate ~ region + region:rear type + fork length), andto test for differences in travel rate by segment([log]travel rate ~ segment + fork length; all regionspooled). Factors were considered significant at the0.05 level and the Tukey’s HSD method was used tocompare levels of region and segment. The datawere not sufficient to analyze the effect of rearingtype on JDF travel rate, so only the effect of regionwas tested ([log]travel rate ~ region + fork length).

RESULTS

Survival

Marine survival probabilities (RM to JDF) rangedfrom 0.8% (Skokomish hatchery population in 2009)to 39.3% (Big Beef Creek wild population in 2006),and averaged 16.0% for wild smolts and 11.4% forhatchery smolts over the 4 years of the study. RM toJDF survival was generally highest in 2006, similarbetween 2007 and 2008, and lower in 2009. Fresh-water survival probabilities were high relative to ear -ly marine estimates, ranging from 63.6% (Skokomish

Hatchery population, 2009) to 94.9% (Big Beef Creekin 2006), though, for most populations, distances be -tween PR and RM distances were much shorter thanRM to JDF distances (Table 1, Fig. 1).

The survival portion of the multi-segment modelwith the lowest QAICc (ϕ (segment + rear type:popu-lation + year)) estimated separate survival probabili-ties for each population × rear type combinationthrough each migration segment, and adjusted thoseprobabilities with an additive year effect (Table 2).The population × rear type interaction indicated thatthe relationship between hatchery and wild smoltsurvival varied by river. That is, hatchery smolt sur-vival was lower than wild smolt survival in some pop-ulations but not others. In general, Skagit, HammaHamma, and Duckabush river hatchery smolts hadhigher estimated survival probabilities than Green,Skokomish, and Puyallup river hatchery smolts(Fig. 3). The rear type factor was present in everymodel in the 95% confidence set, giving that variablethe highest relative weight possible (1.0). Populationand year were also important variables in estimatingsurvival of steelhead smolts, with weights >0.965(Table 3). Regional patterns of survival were not sup-ported by the data (weightregion = 0.009); the modelwith the lowest AICc that included the region vari-able had a fairly high ΔAICc value (8.228) (Table 2).Release date effects were less important than reartype, population, and year effects in the multi-seg-ment analysis (weightrelease date = 0.281) (Table 3).

Release date was associated with survival in the2-segment analysis. The top model simply esti-mated a separate survival rate for each release date(ϕ (segment + release date)) (Table 4), leaving outeffects of both population and year. The second

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Model QAICc ΔQAICc Weight

ϕ(segment + rear type × population + year) 2590.389 0.000 0.433ϕ(segment + rear type × population + release date + year) 2591.889 1.500 0.205ϕ(segment × rear type × population + year) 2593.15 2.761 0.109ϕ(segment × release date + rear type × population + year) 2594.719 4.330 0.050ϕ(segment × rear type + population + year) 2594.968 4.579 0.044ϕ(segment × population + rear type + year) 2595.635 5.246 0.031ϕ(segment + rear type + population + year) 2595.963 5.574 0.027ϕ(segment + population × rear type) 2596.006 5.617 0.026ϕ(segment × rear type × region + release date + year) 2598.287 7.898 0.008ϕ(segment × population + release date + rear type + year) 2598.603 8.214 0.007ϕ(segment × rear type + year + region) 2598.617 8.228 0.007ϕ(segment + release date + rear type × population) 2598.753 8.364 0.007

Table 2. Confidence set for the multi-segment mark-recapture analysis, which tested effects of population, rearing type, population, region, and year on survival of steelhead smolts through 3 or 4 migration segments (depending on population). Allsurvival portions of the model are paired with a receiver line, tag type, and year-specific detection probability portion (p (line

+ tagtype + year))

Mar Ecol Prog Ser 537: 217–232, 2015

best model, however, had a negligible ΔQAICc

(0.815) and in cluded both a size and a year effect(ϕ (segment + release date + fork length + year))(Table 4). Both models indicated substantial varia-tion in freshwater and marine survival probabilities,and considerable variation in survival by releasedate in all populations in all years. The probabilityof survival was lowest for fish released in earlyMay and highest in late April (Fig. 4). Though forklength was included in the second most parsimo-nious model, relative weight ana lysis did not pro-vide evidence of substantial size selective mortalityin rivers or in Puget Sound (weightfork length = 0.299)(Table 5).

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Nisqually (W)

Puyallup (W)

Puyallup (H)

Green (W)

Green (H)

Skagit (W)

Skagit (H)

0

20

40

60

80

100

Sur

viva

l pro

bab

ility

(%)

Skokomish (W)

Skokomish (H)

Dewatto (W

)

Hamma (H)

Duckabush (H)

Big Beef Creek (W

)0

20

40

60

80

100

A

B

Sur

viva

l pro

bab

ility

(%)

Model variable Weight

Rearing type (hatchery or wild) 1.00Population 0.984Year 0.966Release date 0.281Region 0.009

Table 3. Relative importance (sum of Akaike weights ofmodels in the 95% confidence set containing each variable)of each variable tested in the multi-segment mark-recapture

analysis of steelhead smolt survival

Fig. 3. Average freshwater and early marine survival prob-abilities ± SE for (A) all Hood Canal and (B) Puget Soundwild (W) and hatchery (H) tagged steelhead populations.Black bars = freshwater stage (point of release to rivermouth), grey bars = early marine stage (river mouth to the

Strait of Juan de Fuca line)

Model QAICc ΔQAICc Weight

ϕ(segment + release date) 838.709 0.000 0.347ϕ(segment + release date + fork length + year) 839.5243 0.815 0.231ϕ(segment + release date + year) 839.9426 1.234 0.187ϕ(segment + year) 844.0033 5.294 0.025ϕ(segment × release date + fork length) 844.1769 5.468 0.023ϕ(segment × fork length + release date + year) 844.1841 5.475 0.022ϕ(segment × release date) 844.5654 5.856 0.019ϕ(segment + population + release date) 844.856 6.147 0.016ϕ(segment × release date + fork length + year) 844.8575 6.149 0.016ϕ(segment × fork length + release date) 844.901 6.192 0.016ϕ(segment + fork length + year) 844.9576 6.249 0.015ϕ(segment × release date + year) 845.4483 6.739 0.012ϕ(segment) 845.4484 6.739 0.012ϕ(segment × population + release date) 845.6098 6.901 0.011

Table 4. Confidence set for the 2-segment mark-recapture analysis, which tested the effects of population, release date, bodysize (fork length), and year on freshwater (point of release to river mouth) and Puget Sound (river mouth to the Strait of Juan

de Fuca line) wild steelhead smolt survival using the same model for p (p(line + tagtype + year + distance))

Moore et al.: Early marine survival of steelhead smolts

The highest distance-based instantaneous mortal-ity rates were estimated for Big Beef Creek smoltsfrom HCB to ADM during 2008 (4.8% mortality perkm) and 2009 (5.5% mortality per km), (Fig. 5). Otherrelatively high mortality rates were estimated forsmolts migrating from the Green River to the CPS inall study years (range = 3.2−4.4% mortality per km),for Skokomish River smolts through HCB to ADM in2008 (1.7% mortality per km) and 2009 (2.0% mortal-ity per km), and for Nisqually River smolts through

225

Model variable Weight

Release date (factor) 0.946Year 0.534Body size (fork length) 0.299Population 0.028

Table 5. Relative importance (sum of Akaike weights ofmodels in the 95% confidence set containing each variable)of each variable tested in the 2-segment mark-recapture

analysis to explain the survival of steelhead smolts

Late April16–30 Apr

Early May1–15 May

Late May16–31 May

Early June1–15 Jun

0.0

0.2

0.4

Sur

viva

l pro

bab

ility

0.6

0.8

1.0

Fig. 4. Freshwater and early marine survival probabilityestimates ± SE by release date, derived from the top 2-seg-ment model (ϕ (segment + release date + year)). Black bars =freshwater stage (point of release to river mouth), grey bars= early marine stage (river mouth to the Strait of Juan de

Fuca line)

0501001502002500

20

40

60

80

100

Sur

viva

l pro

bab

ility

(%)

050100150200250

050100150200250 050100150200250

0

20

40

60

80

100

Distance from JDF line (km)

0

20

40

60

80

100

0

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60

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2006 2007

2008 2009

NAR

CPS

HCB

DP

HCB

CPS

CPS

HCB

DP

ADM

NAR

CPSADM

HCB

DP

ADM

Fig. 5. Estimated survival probabilities for steelhead plotted at each receiver array along the marine migration pathway foryears 2006 to 2009. Survival estimates are shown for only wild smolts from Big Beef Creek (red), Green River (green),

Nisqually River (dark blue), Skagit River (purple), Skokomish River (orange), and the Puyallup River (light blue)

Mar Ecol Prog Ser 537: 217–232, 2015226

RM to CPS in 2008 (1.0% mortalityper km) and NAR to CPS in 2009(0.9% mortality per km).

Migratory behavior

Steelhead smolts migrated quicklythrough Hood Canal and Puget Sound.Average population-specific traveltimes from RM to the next marine lineranged from an average of 1.8 d forhatchery and wild Green River smolts(RM to CPS; 11 km) to 12.8 d for theSkokomish River smolts (RM to HCB; 75 km) (Table 6).Average RM to ADM travel time ranged from 5.7 d forhatchery and wild Green River smolts (47 km) to13.2 d for hatchery and wild Puyallup River smolts(86 km) (Table 6). Green River smolts exhibited theshortest population-specific travel times (6.2 d) overthe entire early marine migration (RM to JDF;157 km). The longest average travel time was 18.1 d(Skokomish River smolts, 210 km).

Travel rates differed significantly by migration seg-ment (F = 28.93, p < 0.001) (Fig. 6). Smolts traveledsignificantly slower through initial marine migrationsegments in Puget Sound and Hood Canal (x =12.04 km d−1) than through Admiralty Inlet (x =21.50 km d−1) (Tukey’s HSD, p < 0.001). Travel ratesthrough the Strait of Juan de Fuca (x = 34.14 km d−1)were significantly faster than Admiralty Inlet travelrates (Tukey’s HSD, p < 0.001), indicating increasingtravel rates as smolts neared the Pacific Ocean.

Initial marine travel rates differed by region (F =16.75, p < 0.001). Hood Canal smolts travelled moreslowly (x = 10.11 km d−1) than south and central PugetSound smolts (x = 15.22 km d−1) (p < 0.001), but notsignificantly more slowly than Skagit smolts (x =15.20 km d−1, p = 0.360). South and central PugetSound smolts and Skagit smolts did not travel at sig-nificantly different rates through the initial marinesegment (p = 0.401) (Fig. 6A). Hatchery and wildsmolt initial marine travel rates did not differ (p =0.440). Fork length had a slight effect on initial marinetravel rate, with larger smolts travelling faster thansmaller smolts (F = 4.88, p = 0.027). Neither Admiralty

Fig. 6. Comparison of average (± SE) steelhead smolt travelrates for hatchery (H) and wild (W) smolts from either theHood Canal (HC), South and Central Sound (SCS), or Skagit(SK), through (A) the initial marine segment, (B) AdmiraltyInlet, and (C) Strait of Juan de Fuca. Different letters denote

statistically significance differences (α = 0.05)

Population RM to CPS RM to DP RM to HCB RM to ADM RM to JDF

Nisqually 5.7 ± 0.6 na na 8.8 ± 1.1 12.2 ± 0.7Puyallup 5.9 ± 0.8 na na 13.2 ± 1.2 10.3 ± 0.4Green 1.8 ± 0.3 na na 5.7 ± 0.8 6.2 ± 0.5Skagit na 2.9 ± 0.4 na na 7.2 ± 1.0Skokomish na na 12.8 ± 1.4 12.2 ± 1.9 18.1 ± 1.5Dewatto na na 12.4 ± 1.5 na 16.0 ± 1.8Hamma na na 9.8 ± 1.6 na 11.3 ± 1.2Big Beef na na 6.5 ± 0.6 6.1 ± 1.9 15.0 ± 1.4

Table 6. Average travel time (d ± SE) for all populations through cumulativemigration segments for all 4 years of the study (2006−2009). Hatchery and wild

smolt travel times are pooled. na = not applicable

HC H HC W SCS H SCS W SK H SK W0

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20

25

30

HC H HC W SCS H SCS W0

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HC SCS SK0

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30

40

Trav

el r

ate

(km

d–1

)Tr

avel

rat

e (k

m d

–1)

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el r

ate

(km

d–1

)

a

ab

b

a

a

aa

a

A

B

C

Moore et al.: Early marine survival of steelhead smolts

Inlet nor JDF travel rates differed by region or reartype (all p > 0.258) (Figs. 6B,C), and were not affectedby fork length (F = 0.468, p = 0.628, and F = 0.842, p =0.481 for ADM and JDF, respectively).

DISCUSSION

The high mortality estimated for steelhead smoltsmigrating through Puget Sound is consistent with es-timates of low overall marine survival of these popula-tions over the past several decades. Our analysis ofPuget Sound populations indicated that only about16% of wild and 11% of hatchery smolts that left themouths of their natal rivers survived to reach the Pa-cific Ocean. Previous analyses indicated similar earlymarine survival rates for steelhead migrating to seafrom the Strait of Georgia, British Columbia (Melny-chuk et al. 2007, Welch et al. 2011). Low survival rateshave been reported widely for steelhead and othersalmonids entering various coastal habitats, especiallyestuaries. Halfyard et al. (2012) re cently found thatmigrating Atlantic salmon smolts survived at low ratesthrough estuaries in relation to freshwater and bayhabitats in a Nova Scotia river system. In the AlseaRiver, Oregon, 78% of wild steel head smolts surviveda 74 km downstream migration, followed by 60% sur-vival in only 9 km of estuary habitat (Johnson et al.2010). Chinook sal mon Oncorhynchus tshawytschaand steelhead smolts sustained low mortality rates inthe upper Columbia River estuary (0.1% mortality perkm), but died at a much higher rate (1.2% mortalityper km) when they neared the river mouth (Harnish etal. 2012). A study conducted on the Napa River in Cal-ifornia contradicts the low early marine survival pat-tern documented in several coastal environments;85% of steelhead smolts survived through the estuaryand San Francisco Bay (~ 42 km), and continued to ex-perience high survival (at least 60%) after traveling58 km along the California coast (Sandstrom et al.2013). Finer scale research into the mechanisms ofearly marine mortality may explain why some geo-graphic locations promote more successful migrationto the open ocean than others.

Survival probabilities varied substantially by mi -gration segment, though patterns were not alwaysconsistent among populations. Green and Skagit riversmolts experienced their highest mortality rates in theinitial marine segment (RM to CPS and RM to DP, re-spectively), which is similar to other steelhead studiesdocumenting relatively high mortality upon saltwaterentry (Johnson et al. 2010, Clements et al. 2012). Mel-nychuk et al. (2013) found that coho salmon and steel-

head smolt mortality was greater within 5 km of theSquamish River mouth than anywhere else in HoweSound, British Columbia. In contrast, Hood Canalpopulations generally survived better through the ini-tial marine segment, and sub sequently experiencedhigh mortality in a short distance over the secondmarine survival segment from the Hood Canal Bridgethrough Admiralty Inlet. A substantial portion of theHCB to ADM mortality may be associated with theHood Canal Bridge itself. Detailed behavioral analysissuggests that the Hood Canal Bridge causes migrationdelays and abnormal movement patterns that may in-crease predation on steelhead smolts in the vicinity(Moore et al. 2013). Although steelhead smolt popula-tions typically experience their highest mortality ratesduring their initial marine migration segment, habi-tat-specific conditions can contribute substantially tovariation in mortality rates among regions.

The broader (‘2-segment’) mark-recapture analysisidentified release date as an important factor ex -plaining variation in freshwater and saltwater sur-vival. Smolts released in early May, regardless ofpopulation of origin, experienced very low RM toJDF survival each year of the study (see Fig. 4). Nat-ural processes such as harmful algal blooms or dis-ease outbreaks would seem unlikely to contribute toextra mortality during a specific time period on aninterannual basis. A predator response to the consis-tently timed release of hatchery steelhead in earlyMay could explain the increased mortality. Approxi-mately 70−95% of all 1−2 million hatchery steelheadsmolts released into Puget Sound were released dur-ing the first week of May, and 1.9−2.6 million cohosmolts were released in very late April/early Mayduring the 2006−2009 study period (K. Hendersonunpubl. data), coinciding with the lowest smolt sur-vival rates. Predators respond to large releases ofhatchery salmon by increasing local density, i.e. anaggregative response (Wood 1985, Collis et al. 1995),which may increase consumption of otherwise lessdensely congregated wild conspecifics. Higher sur-vival rates observed before and after major hatcheryreleases may be attributed to low smolt densities thatescape the attention of opportunistic predators.Steelhead migrating with large groups of hatcheryfish could be more vulnerable if predators have thecapacity to consume large numbers of smolts.

Though release date was an important factor in the2-segment analysis, it did not substantially explainvariation in survival patterns in the multi-segmentanalysis. The difference in the outcomes of the 2analyses stems from the inclusion of hatchery smoltsin the multi-segment analysis. The hatchery smolts

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used in this study were released within a short win-dow of time, while wild smolt release dates weremore variable throughout the outmigration period.For example, hatchery smolts from the Soos Creekhatchery (Green River) and the Marblemount hatch-ery (Skagit River) were all released on one day.There were few early- and late-released smolts with -in the hatchery groups with which to test the effect ofrelease date, and release date was closely associatedwith population, which was an important factor in themulti-segment analysis. If there had been more vari-ation in release timing of hatchery smolts, the effectof release date may have been stronger in the multi-segment analysis.

Fork length was not a significant predictor of sur-vival probability, so early marine survival in PugetSound seems to be somewhat indiscriminant of bodysize. However, analysis of Skagit River steelheadscales indicated size selective smolt-to-adult survival(Thompson & Beauchamp 2014), and smolt size waspositively correlated with adult survival of bothColumbia River (Evans et al. 2014) and Keogh Riversteelhead (Ward et al. 1989). Size selective mortalityhas been observed in other species of salmon as well;adults that had been large as smolts were over-repre-sented, relative to the abundance of that size class,among the smolts (Healey 1982, Holtby et al. 1990,Henderson & Cass 1991). Our study focused only onsurvival within Puget Sound rather than on survivalover the entire (smolt-to-adult) marine period, sobody size may play a larger role in survival patternsonce smolts enter the Pacific Ocean. The time scaleover which the process of size selectivity was meas-ured was also short since steelhead spend little timein nearshore habitats, and patterns may take longerto resolve. In a telemetry study of early marine sur-vival of British Columbian coho, Chinook, steelhead,or sockeye salmon, Welch et al. (2011) found no evi-dence of a difference in size between released smoltsand those determined to be survivors. Halfyard et al.(2013) demonstrated that the shape and direction ofthe body size to survival relationship can changewith watershed and habitat type. Ward (2000) re -ported size selective mortality of Keogh River (BritishColumbia) steelhead for several years up to the year1990, when population abundance began to decline,suggesting that density-dependent factors affect sizeselective mortality. Size-selective mortality of PugetSound steelhead was not evident during the migra-tion to the open ocean in the present study, but mayoperate at a later life history stage, over a differenttime scale, or during periods of higher populationabundance.

Rearing history accounted for substantial variationin smolt survival across multiple migration seg-ments. Hatchery-smolt survival probabilities weregenerally less than for wild smolts, though smoltsfrom some hatchery populations had comparable orbetter survival than wild smolts in some migrationsegments. The higher survival of Skagit, Duck-abush, and Ham ma Hamma River hatchery smoltsin relation to survival of Skokomish, Green, andPuyallup River hatchery smolts cannot be explainedby differences in broodstock type (locally derived,natural origin, vs non-local multi-generation). Forexample, Skagit Ri ver smolts (Chambers Creekbroodstock) survived at higher probabilities thanmost wild populations in this study, and local wildbroodstock hatchery smolts from the Skokomishand Puyallup. Skagit River smolts were smaller andDuckabush smolts larger than average, so body sizedoes not appear to explain differences amonghatcheries. Rearing practices and hatchery condi-tions (i.e. rearing density, feeding frequency) proba-bly influenced survival differences between Skoko -mish River wild and hatchery smolts, and betweenSkokomish and Duckabush River hat chery smolts(Moore et al. 2012), and are likely to have played arole in the current expanded study as well.

Steelhead smolts travelled quickly through theestuary and Puget Sound. Nisqually River smoltstravelled the longest distance from their rivermouth to JDF (233 km) in as little time as 10 d(average 12.2 d). Some Pacific salmon species rearin nearshore environments extensively as juveniles(not ably Chinook and chum salmon) before migrat-ing to the ocean (Quinn 2005). A considerable frac-tion of the Chinook and, to a lesser extent, cohosalmon, re mains in Puget Sound waters to maturitywithout migrating to the coastal ocean. In contrast,steelhead migrate directly out of Puget Sound andoffshore (Hartt & Dell 1986). Smolts originating inHood Canal rivers travelled more slowly thoughHood Canal than Nisqually, Puyallup, and Greenriver smolts travelled through their initial marinesegment in Puget Sound. Perhaps current patternsor better foraging opportunities retained smolts inHood Canal longer than smolts in other areas. Allpopulations travelled through the final ADM to JDFmigration segment faster than through any othermeasured segment, averaging about 34 km d−1

(approx. 2 body lengths [BL] s−1 for a 200 mmsmolt). The range of maximum sustainable swim-ming speeds for similarly sized sock eye salmon is0.8−2.0 BL s−1 (Hinch et al. 2006), suggesting thatsteelhead smolts approaching the Pacific Ocean

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were travelling very fast, even if aided by tidal cur-rents.

Short residence times, coupled with the high fresh-water and low Puget Sound survival probabilitiesobserved in this study, suggest a source of mortalitythat acts quickly on a large number of smolts in theearly marine environment. If predators are abundant,predation fits this pattern and may explain the lowRM to JDF segment survival probabilities measuredover less than 2 wk. Bottom-up processes (e.g. lack ofsuitable prey) that may cause starvation in migratingsmolts are unlikely to act on such time-scales. PugetSound Chinook salmon populations have experi-enced declines in recent decades, and juvenile sur-vival has been linked to rapid growth in the earlymarine environment (Duffy & Beauchamp 2011).However, Chinook salmon typically spend 2−3 moin estuaries and nearshore environments, where assteel head use these habitats very briefly and prima-rily as migration corridors. It is also un likely thatdirect mortality from disease affected large numbersof smolts, as neither overt signs of distress nor exter-nal signs of disease were observed on any of thetagged steelhead from the 6 wild populations. How-ever, it is possible that asymptomatic fish with chronicinfections would demonstrate decrea sed swimmingperformance, resulting in their predisposition to cap-ture by predators (P. Hershberger pers. comm.).Hostetter et al. (2011) found that migrating steelheadsmolts with poor body condition were more likely tobe prey for birds than healthy smolts in the ColumbiaRiver. Whether acting on healthy or diseased fish,predation most plausibly explains the rapid mortalityrates evident in steelhead smolts migrating throughPuget Sound, al though the primary predators onsteelhead in Puget Sound are essentially unknown.

Healthy populations of marine mammals in the Sal-ish Sea have the potential to consume a large propor-tion of steelhead migrants, and may account for thehigh rates of mortality sustained in Puget Sound. AsPuget Sound steelhead populations have declined,marine mammal populations have steadily increased.Harbor seal Phoca vitulina populations increasedrapidly from the 1970s until just before the turn ofthis century (Jeffries et al. 2003), and harbor porpoisePhocoena phocoena have expanded distribution andincreased in abundance as well. Harbor seals forageopportunistically and shift diet seasonally based onprey availability (Lance et al. 2012) and prey onsteel head smolts in some locations (Laake et al.2002). In the Puntledge River, British Columbia, har-bor seals adapted their foraging behavior to ambushmigrating salmon smolts under a lighted bridge

(Yurk & Trites 2000). Diet information on harbor por-poises in the Salish Sea indicates little or no preda-tion on salmonids at any life stage, though data aretemporally limited and only represent stranded ani-mals (Walker et al. 1998, Nichol et al. 2013).

Some bird species prey on juvenile salmonids, andmay target smolts exiting rivers. Columbia River predation studies found steelhead to be particularlyvulnerable to birds, presumably due to their ten-dency to migrate near the surface of saline waterbodies (Collis et al. 2001). However, tagged steel-head smolts were not eaten by birds in the NehalemRiver (Oregon) estuary, where more than half of cohohatchery smolts were consumed by double crestedcormorants Phalacrocorax auritus and Caspian ternsHydroprogne caspia (Clements et al. 2012). PugetSound supports populations of several seabirds cap -able of consuming a 150−200 mm smolt, which in -clude cormorants, Caspian terns, loons Gavia sp.,common murres Uria aalge, but none of these specieshave dramatically increased in abundance since the1980s, when steelhead populations began to decline.

The present study supports the general under-standing that anadromous salmonid mortality ratesduring the early marine period exceed those duringlater periods when fish are larger and in differentenvironments (Ricker 1976). Partitioning of marinemortality rates in nearshore and offshore environ-ments has typically been inferred by estimating earlymarine mortality rates and considering them in thecontext of overall smolt-to-adult mortality. The lim-ited empirical evidence suggesting higher mortalityin the early marine period than in later marine peri-ods comes from conventional marking studies, one.g. pink salmon Oncorhynchus gorbuscha (Parker1968), chum salmon Oncorhynchus keta (Wert heimer& Thrower 2007), and has recently been corro boratedby acoustic telemetry studies that estimate mortalityin inland marine waters (Welch et al. 2011, Halfyard etal. 2012). Data from this analysis suggests that wildsteelhead smolts survive the migration from rivermouth to the Strait of Juan de Fuca at rates between2.4 and 39.3%. Using an instantaneous mortality rate(based on the above estimated survival rates andaverage population- specific travel times) we can pro-ject forward the percentage of smolts remaining afteronly one month at sea (range = 0−3.2%). Mortalityrates after open ocean entry must decrease thereafterfor there to be any adult steelhead returns. Under-standing the specific mechanisms causing high earlymarine mortality rates of Puget Sound steelheadtrout populations could be critical in predictingtheir viability over the long term and identifying

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management measures to improve the status of pop-ulations.

Summary

Comparing the similarities and differences in sur-vival among Puget Sound steelhead populationshelps resolve patterns and identify trends in steelheadmigration behavior and mortality. Hood Canal popu-lations survived with higher probability than centraland south Puget Sound populations through the initialmarine migration segment, but experienced highmortality per kilometer between the Hood CanalBridge and the Admiralty Inlet line. Rearing type wasthe most important predictor of survival through mul-tiple segments of Puget Sound; hatchery populationsgenerally survived poorly but some comprised similarsurvival rates to wild populations. Early marine sur-vival of wild smolts depended on release date, withlow estimated survival rates coincident with large-scale hatchery releases in early May. There was also adecrease in early marine survival over the 4 yearstudy period. Short Puget Sound residence times andhigh rates of mortality implicate predation as the mostlikely mortality mechanism acting on smolts betweensaltwater entry and arrival in the Pacific Ocean.

Acknowledgements. This is Publication Number 2 from theSalish Sea Marine Survival Project: an international, collab-orative research effort designed to determine the primaryfactors affecting the survival of juvenile chinook, coho andsteelhead survival in the combined marine waters of PugetSound and Strait of Georgia (marinesurvivalproject.com).Funding was provided by Washington State with equal in-kind contributions by those participating in the research.Additional funding was provided by the Steelhead TroutClub, Wild Steelhead Coalition, Nisqually Tribe, PuyallupTribe, Squaxin Tribe, H. Mason Keeler Endowment to theUniversity of Washington, Washington Department of Fishand Wildlife, King County Department of Natural Re -sources, Seattle City Light, US Army Corps of Engineers,NOAA Fisheries, Pacific Ocean Shelf Tracking. The studycould not have been successful without the help of severalindividuals, to whom we are grateful, especially Skip Tezak,Joy Lee Waltermire, Rick Endicott, Teresa Sjostrom, SeanHildebrandt, Mat Gillam, Eric Jeanes, Catherine Morello,Bob Leland, Kelly Kiyohara, Pat Michael, Brody Antipa, PeteTopping, Deborah Feldman, Kelly Andrews, John Blaine,Jim Deveraux, Correigh Greene, Shawn Larson, Jeff Chris-tiansen, John Rupp, Chuck Ebel, Hal Boynton, Nate Man-tua, John Kelly, Ed Conroy, Jose Reyes-Tomassini, JenniferScheurell, Chris Ewing, Dawn Pucci, Kurt Dobszinsky, PaulWinchell, David Welch, Debbie Goetz, Jose Gimenez,Aswea Porter, Emiliano Perez, Craig Smith, Tim Willson,Florian Leischner, Christopher Ellings, and Scott Steltzner.We also thank Mike Melnychuk and Jeff Laake for crucialsupport of the mark-recapture modeling process.

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Editorial responsibility: Myron Peck, Hamburg, Germany

Submitted: March 9, 2015; Accepted: August 13, 2015Proofs received from author(s): September 24, 2015

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