ARTICLE

Spawning site fidelity of wild and hatchery lake trout(Salvelinus namaycush) in northern Lake HuronThomas R. Binder, Stephen C. Riley, Christopher M. Holbrook, Michael J. Hansen, Roger A. Bergstedt,Charles R. Bronte, Ji He, and Charles C. Krueger

Abstract: Fidelity to high-quality spawning sites helps ensure that adults repeatedly spawn at sites that maximize reproductivesuccess. Fidelity is also an important behavioural characteristic to consider when hatchery-reared individuals are stocked forspecies restoration, because artificial rearing environments may interfere with cues that guide appropriate spawning siteselection. Acoustic telemetry was used in conjunction with Cormack–Jolly–Seber capture–recapture models to compare degreeof spawning site fidelity of wild and hatchery-reared lake trout (Salvelinus namaycush) in northern Lake Huron. Annual survivalwas estimated to be between 77% and 81% and did not differ among wild and hatchery males and females. Site fidelity estimateswere high in both wild and hatchery-reared lake trout (ranging from 0.78 to 0.94, depending on group and time filter), but wereslightly lower in hatchery-reared fish than in wild fish. The ecological implication of the small difference in site fidelity betweenwild and hatchery-reared lake trout is unclear, but similarities in estimates suggest that many hatchery-reared fish use similarspawning sites to wild fish and that most return to those sites annually for spawning.

Résumé : La fidélité a des sites de frai de haute qualité contribue a faire en sorte que les adultes frayent a répétition dans des sitesqui maximisent le succès de reproduction. La fidélité est également une caractéristique comportementale qu'il importe deprendre en considération quand des individus élevés en écloserie sont ensemencés pour le rétablissement d'espèces, puisque lesmilieux d'élevage artificiels pourraient brouiller les signaux qui guident la sélection de sites adéquats. Nous avons utilisé latélémétrie acoustique combinée a des modèles de Cormack–Jolly–Seber de capture–recapture pour comparer les degrés defidélité au site de frai de touladis (Salvelinus namaycush) sauvages et élevés en écloserie dans le nord du lac Huron. La survieannuelle estimée allait de 77 % a 81 % et était la même pour les mâles et femelles sauvages et issus d'écloserie. Les estimations defidélité au site étaient élevées pour les touladis tant sauvages qu'élevés en écloserie (allant de 0,78 a 0,94, selon le groupe et lefiltre temporel), bien que légèrement plus faibles pour les poissons élevés en écloserie que pour les poissons sauvages. S'il estdifficile d'établir les conséquences écologiques de cette petite différence de fidélité au site entre les touladis sauvages et élevésen écloserie, les similitudes des estimations donnent a penser que de nombreux poissons élevés en écloserie utilisent des sitesde frai semblables a ceux des poissons sauvages et que la plupart retournent a ces sites chaque année pour frayer. [Traduit parla Rédaction]

IntroductionFidelity to spawning sites has been observed in a variety of

fishes (Miller et al. 2001; Bradbury et al. 2008; Skjæraasen et al.2011) and has been particularly well-documented in salmonines(Hendry et al. 2004). High fidelity to spawning sites is an evolu-tionary adaptation for maximizing reproductive success (Leggett1977) and is an important mechanism in the development andmaintenance of genetic diversity and stock structure (Horrall1981). Site fidelity is also an important consideration in restora-tion programs that stock hatchery-reared fish because artificialrearing environments may hinder reproduction of hatchery-reared fish by preventing imprinting to appropriate spawningsites (Dittman and Quinn 1996; Dittman et al. 2010). Wild andhatchery-reared salmonines may show a variety of differences in

spawning behaviour and reproductive success (Fleming and Gross1992; Berejikian et al. 1997; MaClean et al. 2003; Thériault et al.2011), and recent evidence suggests that stocking hatchery-rearedfish may affect the genetic integrity and productivity of naturalpopulations (Buhle et al. 2009; Chilcote et al. 2011; Valiquette et al.2014).

Lake trout (Salvelinus namaycush) are unique among salmoninesin that they spawn primarily in lakes, rather than rivers; however,many aspects of lake trout spawning behaviour are not well un-derstood (Muir et al. 2012a; Binder et al. 2015). For example, thedegree of spawning site fidelity that occurs in lake trout is notknown. Lake trout will use some spawning sites repeatedly(Eschmeyer 1955; Loftus 1958; Martin 1960; Rahrer 1968; Deroche1969; Swanson 1973; Rybicki and Keller 1978; Martin and Olver

Received 1 April 2015. Accepted 4 August 2015.

Paper handled by Associate Editor Josef Michael Jech.

T.R. Binder. Department of Fisheries and Wildlife, Michigan State University, Hammond Bay Biological Station, 11188 Ray Road, Millersburg, MI 49759,USA.S.C. Riley. US Geological Survey, Great Lakes Science Center, 1451 Green Road, Ann Arbor, MI 48105, USA.C.M. Holbrook, M.J. Hansen, and R.A. Bergstedt. US Geological Survey, Great Lakes Science Center, Hammond Bay Biological Station, 11188 RayRoad, Millersburg, MI 49759, USA.C.R. Bronte. US Fish and Wildlife Service, 2661 Scott Tower Dr., New Franken, WI 54229, USA.J. He. Michigan Department of Natural Resources, 160 East Fletcher St., Alpena, MI 49707, USA.C.C. Krueger. Department of Fisheries and Wildlife, Center for Systems Integration and Sustainability, 115 Manly Miles Building, Michigan StateUniversity, East Lansing, MI 48824, USA.Corresponding author: Thomas R. Binder (e-mail: tr.binder@gmail.com).

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Can. J. Fish. Aquat. Sci. 73: 18–34 (2016) dx.doi.org/10.1139/cjfas-2015-0175 Published at www.nrcresearchpress.com/cjfas on 10 August 2015.

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1980), but it is not known whether they return to natal spawningshoals or simply return to sites of first spawning experience(McCrimmon 1958; MaClean et al. 1981). Feral hatchery-reared fishin particular may not show strong site fidelity to spawning areas(Swanson 1973; Pycha and King 1975; Brown et al. 1981; Kruegeret al. 1986). In addition, these fish may not have the ability torecognize good spawning habitat (Bronte et al. 2007) and mayreproduce at different times than wild fish (Krueger et al. 1986).

Restoration of lake trout populations in the Great Lakes hasbeen underway since the 1960s, after extirpation of most wildpopulations by the 1950s (Krueger and Ebener 2004; Muir et al.2012b). Hatchery-reared lake trout have been stocked throughoutthe Great Lakes for over 50 years, but until recently, stock reha-bilitation progressed substantially only in Lake Superior (Hansenet al. 1995; Hansen 1999). Lake trout populations in Lake Huronare the first to show widespread wild reproduction and recruit-ment outside of Lake Superior (Riley et al. 2007; He et al. 2012).Therefore, a better understanding of reproductive behaviour ofthese populations may assist in lake trout rehabilitation through-out the Great Lakes. Specifically, a comparison of spawning sitefidelity in wild and hatchery-reared lake trout might reveal behav-ioural differences that could help explain their slow recoveryin the Great Lakes. For example, low spawning site fidelity inhatchery-reared fish, relative to wild fish, could indicate thathatchery-reared fish (i) have an inability to locate suitable spawn-ing sites, (ii) have an inability to perceive or respond to appropri-ate spawning cues, or (iii) exhibit a higher incidence of skippedspawning (Sitar et al. 2014), all of which would result in lowerreproductive success. Moreover, a better understanding of spawn-ing site fidelity of wild and hatchery-reared lake trout might fur-ther inform potential changes in rearing or stocking practices topromote reproduction and recruitment in the wild (Horrall 1981).

Our objective was to determine if wild and hatchery-reared laketrout differed in site fidelity to a spawning shoal complex innorthern Lake Huron, North America. We used acoustic telemetryover five consecutive spawning seasons at a multishoal spawningsite in the Drummond Island Refuge (DIR) to quantify site fidelityof wild and hatchery lake trout that used the site during theautumn spawning period. We used Cormack–Jolly–Seber (CJS)capture–recapture models to estimate mortality-independent sitefidelity and tested for differences between male and female andwild and hatchery lake trout using information-theoretic modelselection. We hypothesized that rearing lake trout in artificialhatchery environments interferes with normal spawning site se-lection behaviour and therefore predicted that spawning site fi-delity in hatchery-reared lake trout would be lower than that forwild lake trout.

Methods

Study siteThis study focused on a recovering population of lean lake trout

(see Lawrie and Raher 1973; Moore and Bronte 2001; Muir et al.2014 for a description of the lean morphotype) that spawns on ashoal complex within the DIR (Fig. 1). The DIR was established in1985 to protect spawning lake trout from harvest and to assesseffects of predation by invasive sea lampreys (Petromyzon marinus)on a lake trout population without fishing pressure (Madenjianet al. 2008). The study site was a �23 km2 area within the refugelocated adjacent to the south shore of Drummond Island. Thisarea has been the focus of intense monitoring by acoustic telem-etry each autumn between 2010 and 2014. The area contains atleast five distinct lake trout spawning sites (Riley et al. 2014; Fig. 1).However, we treated the entire area as a single “spawning area”because individual acoustic-tagged lake trout often visited severalspawning sites in a single season (T.R. Binder, Michigan StateUniversity, unpublished data). We do not know from telemetryalone which sites (if any) were used by an individual lake trout for

gamete deposition, so we considered an individual trout’s pres-ence in the acoustic telemetry receiver array during spawningseason as evidence that it spawned in the study area.

Acoustic telemetryAdult lake trout were captured in gill or trap nets and were

surgically implanted (N = 390) with an acoustic transmitter (V16 orV16P transmitter; 95 mm (34 g in air) and 98 mm (36 g in air),respectively; VEMCO, Nova Scotia, Canada). Transmitters wereprogrammed to transmit a uniquely coded acoustic signal at ran-dom intervals between 50 and 130 s. Transmitters were implantedas equally as possible among hatchery-reared (identified fromfin-clips) and wild-reared males and females (Table 1); however,wild-reared females were underrepresented because of difficultycapturing sufficient numbers for tagging. Hatchery-reared fishwere raised in raceways at US Fish and Wildlife hatcheries andwere stocked as yearlings (14–16 months) into Lake Huron. We donot know exactly where each fish was stocked, but the presump-tion is that fish used in this study were stocked within or nearby tothe DIR. Because historical wild lake trout populations in themain basin of Lake Huron were extirpated (Eshenroder et al. 1995;Muir et al. 2012b), wild fish in our sample are recent descendantsof hatchery fish (probably F1 or F2 generation). Surgical implanta-tion was done while fish were under general anesthesia (0.8 mL·L–1

of 1:9 clove oil:ethanol solution) using surgical tools sterilized in0.04% chlorhexidine gluconate solution, and incisions were closedusing three to four simple interrupted sutures (2–0 polydiox-anone monofilament; Ethicon, Inc.). Sex was determined by inter-nal examination of gametes through the open incision with alighted, fiber-optic probe (ProVision, model No. PV-618). In addi-tion to the internal transmitter, each fish received two Floy tags(Floy Tag & Mfg., Inc.) with unique identification codes for exter-nal identification of tagged fish by fishermen. The Floy tags of-fered a reward for return of the internal transmitter. Followingsurgery, trout were held and monitored in floating net pens at thetagging site until fully recovered from anesthesia (i.e., able tomaintain equilibrium and swimming freely in the pen), at whichpoint they were released.

Eighty of the 390 transmitter-implanted lake trout (hereinafterreferred to as “early-tagged”) were tagged in August and earlySeptember 2010 at a site just south of False Detour Passage, ap-proximately 5 km east of the Drummond Island acoustic array.The remaining trout (hereinafter referred to as “late-tagged”)were tagged just prior to the spawning period in 2010 or 2011 (148trout in 2010 and 162 trout in 2011) and were released adjacent tothe acoustic telemetry array. To verify that pooling early-taggedand late-tagged trout did not bias site fidelity estimates, we testedwhether 2010 early-tagged trout differed from 2010 late-taggedtrout in number of spawning returns using a linear mixed-effectsmodel (R package “lme4”), with tagging group (early-tagged ver-sus late-tagged) as a fixed factor and treatment group (hatchery–wild × male–female) as a random factor. Individuals not detectedin the array during the spawning period at least one time after theyear of tagging (i.e., individuals with encounter histories 10000 or01000 — see below for details) were excluded from the compari-son because they did not contribute to site fidelity estimates. Nosignificant difference occurred in the number of spawning re-turns between the two tagging groups (linear mixed-effectsmodel, t = 0.436, P = 0.664). Therefore, the two groups were pooledfor analysis.

An array of acoustic telemetry receivers (model VR2W, VEMCO)was deployed at the Drummond Island study site during late sum-mer and autumn each year between 2010 and 2014 to monitorspawning behaviour of acoustically tagged lake trout (Riley et al.2014). In 2010, the array consisted of 108 receivers (�19 km2), butwas expanded to 140 receivers (�23 km2) in subsequent years tofully cover a spawning site that was identified on the boundary of

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the array in 2010. Arrays were designed such that detection rangeof adjacent receivers overlapped, providing full spatial coverageacross the study site. Prior to analysis, detection data were filteredto remove false-positive detections caused by environmentalnoise and signal code collisions (Beeman and Perry 2012), usingthe short-interval criteria described by Pincock (2012). Specifically,detections for a given transmitter on a single receiver not accom-panied by at least one other detection at that same receiver within3 h were removed from the dataset. Ninety-seven percent of alldetections passed the filter.

Modelling apparent survival and site fidelityCJS capture–recapture models (Lebreton et al. 1992) were used

to estimate site fidelity (p) during spawning seasons and annualapparent survival (�) of acoustically tagged lake trout (Fig. 2). Theterm “apparent survival” is typically used instead of “survival”because CJS models cannot distinguish individuals that died fromthose that emigrated from the population (Lebreton et al. 1992).However, in the interest of providing an estimate of annual mor-tality, we assumed that emigration from the Drummond Islandspawning population was negligible, but � could also be inter-preted as a minimum estimate of annual survival probability be-tween successive spawning seasons if any emigration did occur.The exception was the first year after tagging (Fig. 2), where �tagged

was the combined probability that a tagged fish survived the firstyear (reflecting natural, fishing, and tagging mortality, as opposedto latter years where survival reflects only natural and fishingmortality) and that it was part of the Drummond Island spawn-ing population to begin with (because tagging preceded thespawning season by as much as 2 months, a portion of the fishtagged may not have been Drummond Island spawners). By in-cluding this parameter in the CJS models, site fidelity estimateswere based on lake trout that were detected in the array at leastonce after the year it was tagged. In CJS models, p is the probabil-ity of encountering an individual at a study site, given that it wasalive and available to be sampled, and is typically used to account forimperfect sampling when estimating survival rates (i.e., some indi-

Fig. 1. Relief map of the Drummond Island acoustic telemetry site. The broken line demarcates the spatial extent of the acoustic telemetryarray from 2011 to 2014. Ovals indicate the locations of five verified, closely positioned spawning sites (Riley et al. 2014). Spawning sites wereidentified using acoustic telemetry and verified with diver surveys for deposited eggs. Inset: Map of Lake Huron showing the location of theDrummond Island acoustic telemetry array (square — not to scale), along the southern shore of Drummond Island.

Table 1. Number and mean (±standard deviation, SD) length (cm) ofmale (M) and female (F) wild (W) and hatchery-origin (H) lake trouttagged at the Drummond Island Refuge in northern Lake Huron dur-ing 2010 and 2011.

2010 2011

Sex GroupNo.tagged

Mean±SDlength (cm)

No.tagged

Mean±SDlength (cm) Total

M W 69 63.2±3.4 44 68.1±5.2 113H 59 68.4±6.1 45 72.4±6.2 104

F W 36 67.7±3.8 28 71.2±5.0 64H 64 69.9±4.3 45 73.4±6.1 109

Total 228 162 390

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viduals are present but not observed because of gear inefficiency).In our case, however, we assumed that all acoustic-tagged laketrout that entered the array were detected because more than100 receivers with overlapping detection ranges were continuously“listening” for tagged fish. Therefore, we defined p as the proba-bility that an individual fish returned to the spawning area in agiven year (i.e., spawning site fidelity), given that it was alive anddid not emigrate from the population. For example, an estimate ofspawning site fidelity in year t would be 0.50 if 50 of 100 taggedfish detected in the study area in later years (t1+) were detected inthe study area that year (t). In CJS models, � and p are not individ-ually identifiable during the last time interval (Lebreton et al.1992), so � was estimated annually between 2010 and 2013, andp was estimated in 2011, 2012, and 2013. In lieu of separate esti-mates for � in 2013 and p in 2014, we estimated �, the product ofthose probabilities (Fig. 2).

The first step in the analysis was to determine if each fish waspresent in the array during the spawning season in each year. Thespawning period in each season (Table 2) was defined by the be-haviour of male lake trout. Males are usually found on spawningshoals earlier and in greater numbers than females (Muir et al.2012a), so we assumed that movement of males from deep off-shore water into shallow nearshore water marked the start of thespawning period. We used changepoint analysis to objectivelydetermine the start of the spawning period by testing for a changein the mean and variance (“cpt.meanvar” function in R package“changepoint”; Q = 1, test.stat = “Normal”; Killick and Eckley 2014)of swimming depth of male lake trout located within detectionrange of the acoustic telemetry array, determined from pressuresensors in roughly half of implanted transmitters. The end of thespawning period was defined as the day the last receiver waspulled from the water, which was at least 2 weeks after the esti-mated peak of spawning in all 5 years. To limit the possibility offalsely classifying fish that briefly passed by the array on their wayto or from other spawning areas as having used our site for spawn-ing, we used time-based criteria to filter detection data. Each

spawning season was divided into 1 h time bins, and the numberof time bins that each fish was detected in the array that seasonwere summed. Scant behavioural information exists on which todetermine a behaviourally relevant filter cutoff, so we presentresults using a series of time filters, ranging from no filter (detec-tion during at least one 1 h period) to detection during at leasttwelve 1 h periods. This also allowed us to assess the robustness ofour site fidelity and survival estimates. To format the data for theCJS model, the history of each lake trout implanted with an acous-tic transmitter was expressed in compact “encounter history” for-mat (Table 3). For each of the 5 years of study, an individual wasassigned a value of 1 if it was present in the array during thespawning period and a 0 if it was not. For example, encounterhistory 10111 would represent a fish that was tagged in 2010 andwas present in the study area during the spawning season in 2012,2013, and 2014, but not 2011. Because fish must be assigned a valueof 1 on first encounter, some fish were assigned a 1 in the year theywere tagged, even if they were not detected in the array that year.This only affects survival estimates during the first year after tag-

Fig. 2. Structure of Cormack–Jolly–Seber models for lake trout tagged at the Drummond Island Refuge in northern Lake Huron in 2010 and2011. � is apparent survival probability for an individual during a given year. p is the probability that an individual fish visited the spawninggrounds, given that it was alive and part of the Drummond Island spawning population. Because � and p are not individually identifiableduring the last time interval, �, the product of survival and site fidelity, was estimated in lieu of separate estimates for �2013 and p2014.

Table 2. Estimated start and end dates (year-month-day)of the lake trout spawning period at the DrummondIsland Refuge in northern Lake Huron in 2010–2014.

Year Start datea End dateb No. of days

2010 2010-10-16 2010-11-12 272011 2011-10-13 2011-11-29 472012 2012-10-06 2012-11-16 412013 2013-10-19 2013-11-22 342014 2014-10-02 2014-11-23 52

aStart dates determined using changepoint analysis to esti-mate when males moved from deep water onto the shallow-waterspawning sites.

bEnd date was the last day receivers were pulled from thewater, after peak spawning, but before all activity on theshoals ceased.

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ging (i.e., inflates �tagged), as no site fidelity estimate occurred forthe first encounter. Subsequent survival and site fidelity estimateswere based on fish that were detected in the array at least onceduring subsequent spawning seasons.

CJS models were built using the R package “RMark” (Laake2013), which interfaces with program MARK (Colorado State Uni-versity) to fit models using maximum likelihood estimation(Cooch and White 2015). The default CJS model in MARK includedseparate � and p for the last interval, so we imposed a constraintto estimate � (the product of survival and site fidelity during thelast occasion) directly. Specifically, we fixed �2013 equal to 1, mak-ing � = p2014 (Fig. 2). A number of candidate models were con-structed and compared to test if survival (�) and site fidelity (p)differed among years and to test for differences among sex andrearing origin (Table 4). To test whether � differed among years,we compared models in which � differed across all years (“time”models) with models in which � was equal across all years exceptthe first year after tagging (“tagged” models). Survival during thefirst year after tagging was not assumed equal to other years be-cause � in the first year after tagging was the probability that afish survived and that it was part of the Drummond Island spawn-ing population. A similar process was used to test whether p var-ied across years. We compared models in which p differed amongyears (time models) with models in which p was constant in allyears except the last year (“�” models) to test whether p variedamong years.

Akaike information criterion (AIC) was used for model selection(Burnham and Anderson 2002). Program MARK provided AICc (AICcorrected for small sample size) for each model, �AICc, modelweights (wi), and model-averaged parameter estimates withassociated 95% confidence intervals (note that AIC-based modelselection supports additional variables that are “significant” at

approximately the 85% confidence level; Arnold 2010). Modelweights (wi) provided an objective way to assess the weight ofevidence in favor of model i being the best model in the set(Burnham and Anderson 2002). Models with high wi were stronglyfavored over models with low wi (e.g., a model with wi = 0.75 hasthree times as much support as a model with wi = 0.25), but amodel was only weakly supported over other models in the setwhen those models had similar wi. Prior to interpreting theresults, we used the median c goodness-of-fit test on the globalmodel in program MARK to estimate degree of overdispersion (c)in the data, and subsequently adjusted AICc values (QAICc), modelweights, and confidence intervals based on c to correct for over-dispersion (Burnham and Anderson 2002).

To limit the number of models in comparisons and to simplifyinterpretation, model selection was performed in a sequentialfashion (Arnold 2010). First, to test whether a year effect occurredwith survival (�) and site fidelity (p), we compared all combina-tions of four � models (“tagged”, “time”, “tagged + sex + origin +sex × origin”, “time + sex + origin + sex × origin”) and fourp models (“�”, “time”, “� + sex + origin + sex × origin”, “time + sex +origin + sex × origin”). The time variables for �(tagged or time) andp (� or time) that best fit the data were carried through to subse-quent stages of analysis. Next, we determined the most parsimo-nious � model for estimating site fidelity in the Drummond Islandspawning population by comparing all possible � models with thebest time variable from step one, paired with the global p modelwith the best time variable from step one. Lastly, using the �model selected in step two, we then compared all possiblep models (with the best time variable from step one) to test for sexand origin effects and subsequently calculated model-averagedparameter estimates and 95% confidence intervals for both � and p.

ResultsOf the 390 fish tagged in 2010 and 2011, 195 (50%) were not

detected in the acoustic array during spawning periods beyondthe year of tagging (Table 3). The proportion of tagged lake troutdetermined to have died (or emigrated) during the first year aftertagging was higher than in all subsequent years (Table 3). Of fishthat were detected in the array after the year of tagging, 44% (86 of195) were detected in all years and 24% (47 of 195) were detected in

Table 3. Encounter histories of male (M) and female (F) hatchery-reared (H) and wild (W) lake trout detected in the Drummond Islandacoustic telemetry array during spawning between 2010 and 2014.

Taggingyear

Encounterhistory HF HM WF WM Total

2010 10000 30 31 20 28 10910001 0 0 0 0 010010 1 0 0 0 110011 0 0 0 0 010100 3 1 0 0 410101 0 0 0 0 010110 0 1 0 0 110111 2 0 2 1 5

11000 8 8 3 7 2611001 0 0 0 0 011010 0 0 0 1 111011 1 0 0 0 111100 2 2 1 8 1311101 0 2 0 3 511110 6 6 2 4 1811111 11 8 8 17 44

2011 01000 28 22 19 17 8601001 0 1 0 0 101010 0 0 0 1 101011 2 0 0 1 301100 5 4 1 4 1401101 0 0 0 0 001110 6 1 0 8 1501111 4 17 8 13 42

Total 109 104 64 113 390

Note: Encounter histories are for unfiltered detections (i.e., detected duringat least one 1 h period during the spawning season). A one (1) indicates that thefish was released or detected in the array during a given year, and a zero (0)indicates that it was not. Columns to the right indicate the number of fish ineach group that exhibit each encounter history.

Table 4. Variables used to estimate parameters of Cormack–Jolly–Seber models and test for time and group effects on survival (�) andsite fidelity (p) of lake trout spawning at the Drummond Island Refugein northern Lake Huron in 2010–2014.

Parameter Variable Description

� Time Survival differed among all years(2010, 2011, 2012, 2013)

Tagged Survival equal across all years except the yearafter tagginga

Sex Survival differed between male (M) andfemale (F)

Origin Survival differed between wild (W) andhatchery (H)

p Time Site fidelity differed among all years(2011, 2012, 2013, 2014)

� Site fidelity equal across all years except2014b

Sex Site fidelity differed between male (M) andfemale (F)

Origin Site fidelity differed between wild (W) orhatchery (H)

aAccounts for confounding effects of tagging non-Drummond Island spawn-ing fish and possible tagging mortality on the � estimate for the first post-tagging survival interval.

bAccounts for the fact that � and p are not individually identifiable for the lastinterval of the time series.

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all years but one (Table 3). Goodness of fit (median c goodness-of-fittest) of the global CJS model (i.e., �(�time + sex + origin + sex ×origin) × p(�time + sex + origin + sex × origin)) for each level oftime filter returned c values ranging from 1.34 ± 0.04 to 1.78 ± 0.05.These values were subsequently applied (on a per-filter basis) toresults to adjust AICc values (QAICc) and variance estimates foroverdispersion.

Survival and site fidelity were similar among years. Based onmodel weights (wi), CJS models with constant survival in all yearsexcept in the year of tagging (i.e., tagged models) were highlyfavored over models with different survival among years (i.e.,models containing “time”; Table 5). Similarly, models with sitefidelity constant among years (i.e., models containing �) were sup-ported over models with different site fidelity among years (i.e.,models containing time; Table 5). The QAICc ranking of modelscompared in this analysis was fairly consistent across all levels oftime filtration (Table 5; see Appendix A, Table A1 for full QAICcresults).

Survival (�) did not vary among sexes or rearing origin. Thehighest-ranked � model for all levels of time filter contained nogroup covariates (Table 6; see Appendix A, Table A2 for full QAICcresults). Moreover, while QAICc model selection indicated littledifference in model fit among the top three models (Table 6), thefact that the addition of group covariates (i.e., sex and origin) didnot improve model fit indicated that they did not contribute sub-stantially to the explanatory ability of the tagged (i.e., equal sur-vival among groups) model.

Site fidelity (p) differed among lake trout groups, and supportfor this result strengthened as the number of 1 h periods a fish wasrequired to be detected in the array increased (Table 7; see Appen-dix A, Table A3 for full QAICc results). The highest-ranked modelfor all levels of detection filter except the two 1 h time bin filterscontained both origin and sex, as well as their interaction (Table 7;see Appendix A, Table A3 for full AIC results). The top two modelscontained origin as a group covariate, inferring that a differencein site fidelity existed among wild and hatchery trout. Modelscontaining the sex covariate were generally ranked lower thanthe null model (i.e., �), except when the model also included bothorigin and the interaction between sex and origin (Table 7), sug-gesting that site fidelity was consistent between sexes and that themagnitude of the origin effect was dependent on sex.

Model-averaged apparent probability of survival during thefirst-year after tagging (�tagged), which also accounted for taggingmortality and tagging of animals that were not part of the Drum-mond Island spawning population, was consistent across all levelsof time filter, ranging from 0.51 (95% CI = 0.44 to 0.57) to 0.52(95% CI = 0.45 to 0.58). Estimates of survival probability (�) beyondthe first year after tagging were much higher and were also con-sistent across time filters (Fig. 3), ranging from 0.77 (95% CI = 0.69to 0.82) to 0.81 (95% CI = 0.74 to 0.86).

Mortality-independent site fidelity (p) estimates were high forboth wild and hatchery-reared lake trout. Fidelity estimates basedon detection during at least one 1 h time bin (i.e., no time filter)were 0.90 or greater (Fig. 3) for all treatment groups, indicating

Table 5. Akaike weights (w) and relative rank (in parentheses) of Cormack–Jolly–Seber models used for testing for time effects on survival (�) andsite fidelity (p) of lake trout detected within the Drummond Island acoustic telemetry array during spawning period 2010–2014, using twelvedifferent time-based detection filters.

Model

Filter criterion (1 h period)

1 2 3 4 5 6 7 8 9 10 11 12

�(�tagged) × p(�� + sex +origin + sex × origin)

0.34 (1) 0.23 (2) 0.43 (1) 0.46 (1) 0.42 (1) 0.43 (1) 0.54 (1) 0.54 (1) 0.50 (1) 0.45 (1) 0.44 (1) 0.45 (1)

�(�tagged) × p(��) 0.26 (2) 0.28 (1) 0.16 (2) 0.11 (3) 0.16 (3) 0.16 (2) 0.05 (5) 0.05 (5) 0.08 (4) 0.11 (3) 0.15 (3) 0.17 (2)�(�tagged) × p(�time + sex +

origin + sex × origin)0.08 (5) 0.09 (5) 0.10 (4) 0.09 (4) 0.09 (4) 0.10 (4) 0.12 (3) 0.18 (2) 0.15 (2) 0.21 (2) 0.18 (2) 0.14 (3)

�(�tagged + sex + origin + sex ×origin) × p(�� + sex + origin +sex × origin)

0.10 (3) 0.06 (6) 0.15 (3) 0.20 (2) 0.17 (2) 0.15 (3) 0.20 (2) 0.15 (3) 0.15 (3) 0.09 (4) 0.09 (4) 0.09 (4)

�(�tagged) × p(�time) 0.08 (6) 0.15 (3) 0.04 (6) 0.03 (7) 0.04 (7) 0.04 (6) 0.01 (7) 0.01 (7) 0.02 (7) 0.05 (6) 0.05 (5) 0.05 (5)�(�tagged + sex + origin + sex ×

origin) × p(��)0.09 (4) 0.10 (4) 0.07 (5) 0.06 (5) 0.07 (5) 0.07 (5) 0.02 (6) 0.02 (6) 0.04 (6) 0.03 (7) 0.04 (6) 0.05 (6)

�(�tagged + sex + origin + sex ×origin) × p(�time + sex +origin + sex × origin)

0.02 (8) 0.03 (8) 0.03 (7) 0.04 (6) 0.04 (6) 0.04 (7) 0.05 (4) 0.05 (4) 0.05 (5) 0.05 (5) 0.04 (7) 0.03 (7)

�(�tagged + sex + origin + sex ×origin) × p(�time)

0.03 (7) 0.05 (7) 0.02 (8) 0.01 (8) 0.02 (8) 0.02 (8) 0.00 (8) 0.00 (8) 0.01 (8) 0.01 (8) 0.02 (8) 0.01 (8)

�(�time) × p(�� + sex + origin +sex × origin)

0.00 (9) 0.00 (13) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9) 0.00 (9)

�(�time) × p(�time + sex +origin + sex × origin)

0.00 (12) 0.00 (10) 0.00 (10) 0.00 (11) 0.00 (11) 0.00 (10) 0.00 (11) 0.00 (10) 0.00 (10) 0.00 (10) 0.00 (10) 0.00 (10)

�(�time) × p(��) 0.00 (10) 0.00 (11) 0.00 (12) 0.00 (13) 0.00 (12) 0.00 (12) 0.00 (13) 0.00 (13) 0.00 (13) 0.00 (13) 0.00 (11) 0.00 (11)�(�time + sex + origin + sex ×

origin) × p(�� + sex + origin +sex × origin)

0.00 (13) 0.00 (16) 0.00 (11) 0.00 (10) 0.00 (10) 0.00 (11) 0.00 (10) 0.00 (11) 0.00 (11) 0.00 (12) 0.00 (13) 0.00 (12)

�(�time) × p(�time) 0.00 (11) 0.00 (9) 0.00 (13) 0.00 (15) 0.00 (15) 0.00 (14) 0.00 (15) 0.00 (14) 0.00 (14) 0.00 (14) 0.00 (12) 0.00 (13)�(�time + sex + origin + sex ×

origin) × p(�time + sex +origin + sex × origin)

0.00 (16) 0.00 (15) 0.00 (14) 0.00 (12) 0.00 (13) 0.00 (13) 0.00 (12) 0.00 (12) 0.00 (12) 0.00 (11) 0.00 (14) 0.00 (14)

�(�time + sex + origin + sex ×origin) × p(��)

0.00 (14) 0.00 (14) 0.00 (15) 0.00 (14) 0.00 (14) 0.00 (15) 0.00 (14) 0.00 (15) 0.00 (15) 0.00 (16) 0.00 (15) 0.00 (15)

�(�time + sex + origin + sex ×origin) × p(�time)

0.00 (15) 0.00 (12) 0.00 (16) 0.00 (16) 0.00 (16) 0.00 (16) 0.00 (16) 0.00 (16) 0.00 (16) 0.00 (15) 0.00 (16) 0.00 (16)

Note: Filter criterion indicated the number of 1 h periods during which a fish was required to be detected in the array before being considered to have returned tothe site for spawning that season. See Appendix A, Table A1 for full QAICc results table.

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that most lake trout that survived to the spawning period alsoreturned to the vicinity of the acoustic telemetry array during thespawning period. Site fidelity estimates decreased slightly in alltreatment groups as the amount of time a fish was required tospend within the array to be counted as spawning at the siteincreased (Fig. 3). However, the decrease in site fidelity was greaterin hatchery fish (11% and 13% for males and females, respectively)than in wild fish (8% and 5% for males and females, respectively),indicating that a greater proportion of hatchery fish than wild fishspent less than twelve 1 h bins within the acoustic telemetry array.

Overall, site fidelity was greater in wild males and females thanin hatchery males and females, but differences were more pro-nounced when higher time filters were applied (Fig. 3). The highertime filters likely produced more realistic estimates of fidelitybecause they exclude fish that may have passed through the arrayon their way to other nearby spawning sites. Using a twelve 1 htime bin filter, site fidelity estimates were 0.86 (95% CI = 0.77 to0.92) and 0.89 (95% CI = 0.74 to 0.96) for wild males and femalesand 0.82 (95% CI = 0.71 to 0.90) and 0.78 (95% CI = 0.63 to 0.87) forhatchery males and females. Differences in site fidelity estimatesbetween sexes were small.

DiscussionHigh spawning site fidelity estimates in our study indicated a

strong tendency in both wild and hatchery-reared lake trout toreturn to sites of previous experience for spawning. Return rates,especially in wild lake trout, approached those observed instream-spawning salmonines like Chinook salmon (Oncorhynchustshawytscha) (Quinn and Fresh 1984; Hard and Heard 1999; Keeferet al. 2008), sockeye salmon (Oncorhynchus nerka) (Quinn et al.1987), brown trout (Salmo trutta) (Tilzey 1977), and Atlantic salmon(Salmo salar) (Stabell 1984; Jonsson et al. 1991; Youngson et al. 1994).As predicted, fidelity estimates for hatchery-reared lake trout in

our study were lower than those for wild lake trout (recent descen-dants of stocked hatchery fish), particularly in females, which isconsistent (although the difference was not extreme) with lowerrecapture rates observed for hatchery-reared compared with wildlake trout at spawning shoals in Lake Superior (Krueger et al.1986). This finding supports the hypothesis that hatchery experi-ence during early life can hinder imprinting to spawning sites.Nevertheless, hatchery-reared fish showed 78% or greater fidelityto our study site, which indicated that most hatchery-reared indi-viduals that found their way to the spawning sites adjacent toDrummond Island also possessed a strong tendency to return tothose sites annually during spawning period.

The propensity to return to sites where individuals have hadprevious experience (at any life stage) is called “homing”. Our useof the term here is not intended to confer an olfactory natal hom-ing mechanism often associated with Oncorhynchus spp., but sim-ply return to a site of previous spawning. Homing behaviour iswidespread among spawning fishes and is adaptive for at leastthree reasons (Leggett 1977). First, homing synchronizes returnsof mature animals to proven spawning grounds during periodswhen conditions are optimal for egg and larval development. Sec-ond, homing helps to regulate the number of adults using a givenspawning site. Third, homing facilitates reproductive isolationand development of stock-specific adaptations. Wild lake trout inour study may have returned to their natal sites for spawning, butmay also have learned to home to spawning sites they encoun-tered later in life (Gunn 1995). In contrast, hatchery fish, whichwere reared in hatchery raceways for between 14 and 16 months,could not have imprinted on our study site during early stages ofdevelopment, so the learning hypothesis must be true. Evidencefrom Lake Michigan suggests that some form of imprinting mayoccur in lake trout when stocked at 14–17 months of age. About40% of coded-wire-tagged lake trout that were released at many

Table 6. Akaike weights (w) and relative rank (in parentheses) of Cormack–Jolly–Seber models used for determining the most parsimoniousmodel for estimating survival (�) of lake trout detected within the Drummond Island acoustic telemetry array during spawning period 2010–2014,using twelve different time-based detection filters.

Model

Filter criterion (1 h period)

1 2 3 4 5 6 7 8 9 10 11 12

�(�tagged) × p(�� + sex + origin +sex × origin)

0.31 (1) 0.31 (1) 0.31 (1) 0.28 (1) 0.29 (1) 0.30 (1) 0.30 (1) 0.35 (1) 0.32 (1) 0.35 (1) 0.34 (1) 0.35 (1)

�(�tagged + sex) × p(�� + sex +origin + sex × origin)

0.28 (2) 0.28 (2) 0.28 (2) 0.28 (2) 0.28 (2) 0.29 (2) 0.28 (2) 0.27 (2) 0.27 (2) 0.27 (2) 0.27 (2) 0.26 (2)

�(�tagged + origin) × p(�� + sex +origin + sex × origin)

0.18 (3) 0.18 (3) 0.18 (3) 0.17 (3) 0.17 (3) 0.17 (3) 0.17 (3) 0.17 (3) 0.18 (3) 0.18 (3) 0.18 (3) 0.19 (3)

�(�tagged + sex + origin) × p(�� +sex + origin + sex × origin)

0.14 (4) 0.14 (4) 0.13 (4) 0.14 (4) 0.14 (4) 0.14 (4) 0.13 (4) 0.12 (4) 0.13 (4) 0.12 (4) 0.13 (4) 0.13 (4)

�(�tagged + sex + origin + sex ×origin) × p(�� + sex + origin +sex × origin)

0.09 (5) 0.09 (5) 0.10 (5) 0.12 (5) 0.12 (5) 0.10 (5) 0.11 (5) 0.09 (5) 0.10 (5) 0.07 (5) 0.07 (5) 0.07 (5)

Note: Filter criterion indicated the number of 1 h periods during which a fish was required to be detected in the array before being considered to have returned tothe site for spawning that season. See Appendix A, Table A2 for full QAICc results table.

Table 7. Akaike weights (w) and relative rank (in parentheses) of Cormack–Jolly–Seber models used for comparing site fidelity (p) among male andfemale wild and hatchery-origin lake trout detected within the Drummond Island acoustic telemetry array during spawning period 2010–2014,using twelve different time-based detection filters.

Model

Filter criterion (1 h period)

1 2 3 4 5 6 7 8 9 10 11 12

�(�tagged) × p(�� + sex + origin +sex × origin)

0.30 (1) 0.20 (3) 0.42 (1) 0.56 (1) 0.48 (1) 0.43 (1) 0.71 (1) 0.65 (1) 0.44 (1) 0.41 (1) 0.42 (1) 0.37 (1)

�(�tagged) × p(�� + origin) 0.25 (2) 0.31 (1) 0.21 (2) 0.16 (2) 0.19 (2) 0.24 (2) 0.14 (2) 0.15 (2) 0.30 (2) 0.31 (2) 0.27 (2) 0.31 (2)�(�tagged) × p(��) 0.23 (3) 0.25 (2) 0.16 (3) 0.14 (3) 0.18 (3) 0.17 (3) 0.06 (3) 0.06 (4) 0.07 (4) 0.10 (4) 0.14 (3) 0.14 (3)�(�tagged) × p(�� + sex + origin) 0.11 (5) 0.13 (4) 0.10 (4) 0.07 (5) 0.07 (5) 0.09 (4) 0.06 (4) 0.09 (3) 0.13 (3) 0.13 (3) 0.11 (4) 0.12 (4)�(�tagged) × p(�� + sex) 0.11 (4) 0.12 (5) 0.10 (5) 0.07 (4) 0.08 (4) 0.07 (5) 0.04 (5) 0.05 (5) 0.05 (5) 0.06 (5) 0.07 (5) 0.06 (5)

Note: Filter criterion indicated the number of 1 h periods during which a fish was required to be detected in the array before being considered to have returned tothe site for spawning that season. See Appendix A, Table A3 for full QAICc results table.

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onshore and offshore locations were captured as adults during thespawning season at the same locations many years later, andmany other fish were captured at sites close to their stockinglocation (Bronte et al. 2007). This has been observed at the DIR aswell (M. Ebener, Chippewa Ottawa Resource Authority, unpub-lished data).

The specific cues used by lake trout in our study to return tospawning sites in the DIR are not known, but we hypothesize thatthey may have used olfaction, at least during the final stages of

migration, to return to perennial spawning sites (McCrimmon1958; Martin 1960; Wasylenko et al. 2013). Imprinting to site-specific odors and pheromone communication have both beenproposed as orienting mechanisms for olfactory-based homing infishes (Stabell 1984; Nordeng 2009). In lake trout, evidence sup-ports both mechanisms. In Lake Superior, attempts to encouragerecolonization of Devils Island Shoal by stocking with fingerlingand yearling lake trout failed. However, after stocking with fertil-ized eggs in bundles of artificial turf, shoal colonization by wild

Fig. 3. Model-averaged estimates of annual site fidelity (p — top four panels) and survival (� — bottom panel), with associated 95% confidencelimits (dashed lines), for male (M) and female (F) hatchery (H) and wild (W) lake trout. A series of time-based filters were applied to acoustictelemetry detection data to determine the robustness of parameter estimates. The detection filter is the number of 1 h periods a fish wasrequired to have been detected in the acoustic telemetry array before being considered to have returned to the site for spawning that season(see Methods for further details).

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adults was correlated with egg stocking levels in previous years,thereby suggesting that site imprinting occurred during early de-velopment (Bronte et al. 2002). Physiological, biochemical, andbehavioural studies suggest that lake trout might also use phero-mones to locate spawning sites. The olfactory system in lake troutis highly sensitive to bile acids released by conspecifics (Zhanget al. 2001; Zhang and Hara 2009), which have been hypothesizedto serve as migratory pheromones in other salmonines (Hara et al.1984; Groot et al. 1986) and lampreys (Bjerselius et al. 2000). More-over, two-choice laboratory experiments have shown that adultlake trout are attracted to residual odors from fry (Foster 1985) andodors emanating from adult male conspecifics (Buchinger et al.2015).

Lake trout are known to move among different spawning sitesseparated by as much as several kilometres during a single spawn-ing season (Deroche 1969; MaClean et al. 1981; Peck 1986; Kruegeret al. 1986; Bronte et al. 2002), and spawning shoals as far apart as200 m or greater might comprise a single spawning site (Martin1960). In our study area, spawning is known to occur at a mini-mum of five distinct sites, separated by less than 4 km (Riley et al.2014), and tracks from 2D positional telemetry indicated thatmany tagged lake trout visited multiple spawning sites within ourstudy area during a single season (T.R. Binder, Michigan StateUniversity, unpublished data). Unfortunately, an inability to de-termine specific sites where individual lake trout deposited gam-etes meant that we could not accurately estimate site fidelity ratesat the level of individual shoals. Nonetheless, our results sug-gested that most lake trout that survived the prior year returnedto the same complex of spawning shoals year after year. Thosethat did not return to the study site every year either spawned atother spawning sites or did not spawn every year (i.e., skippedspawning; Sitar et al. 2014). Based on encounter histories of troutin our study (Table 3), if failure to visit the spawning site in a givenyear is an indication of skipped spawning, it was not a regularoccurrence in our population. However, we cannot confirm thatall fish spawned in every season, so we cannot discount the pos-sibility that some fish that returned to the array during spawningperiod did not spawn in that season. If this is the case, thenskipped spawning could be more prevalent in our populationthan suggested by our site fidelity results.

The ecological and management relevance of the small differ-ence in spawning site fidelity observed between wild and hatcheryfish is unclear. Site fidelity estimates for hatchery-reared fish weremore greatly affected by time-filtering, indicating that a largerproportion of hatchery fish than wild fish were present in thearray during less than twelve 1 h time bins. This suggests thatthere may be other behavioural differences between wild andhatchery-reared lake trout that are not captured in the presentanalysis. For example, there are many other reef structures alongthe south shore of Drummond Island that may also support laketrout reproduction (see Fig. 1). Fish that were detected in the arrayduring few 1 h time bins may have spawned at nearby spawningsites outside the array. However, differential sensitivity to filter-ing could also reflect differences in the duration of spawningbetween hatchery and wild fish. More detailed behavioural dataare required to fully understand the implications of lower sitefidelity in hatchery-reared fish.

The strong homing tendency we observed in lake trout mayhave contributed to slow recovery of lake trout across the GreatLakes. Until recently (early 2000s), lake trout had only recoveredin Lake Superior and the Parry Sound region of Lake Huron, bothof which had persistent remnant wild populations after popula-tions crashed basin-wide in the 1950s (Hansen 1999; Reid et al.2001). In areas where remnant wild populations did not persist,absence of suitable homing cues on historical spawning shoalsmeant that recolonization of these shoals would require individ-uals to find prime spawning areas with no cues. Therefore, specieslike lake trout that show a strong homing tendency could be

slower to stray and colonize new spawning sites than species thatdisplay weak homing tendencies (Krueger et al. 1986). In a smalllake (64 ha) with abundant spawning habitat, lake trout rapidlycolonized nearby spawning sites when traditional spawning siteswere made unavailable (McAughley and Gunn 1995). However, inAlgonquin Park, Ontario (Martin 1960), and Thunder Bay, LakeHuron, (J.E. Marsden, University of Vermont, personal communi-cation, 2015), where artificial spawning reefs were built nearby totraditional spawning reefs, colonization of the new habitats tookseveral years and radiated outwards from sites where spawningwas already occurring. In contrast, sea lamprey (P. marinus),which use pheromones released by larval lampreys to locate suitablestream habitats (Sorensen et al. 2005; Wagner et al. 2006) andshow no evidence of homing (Bergstedt and Seelye 1995;Waldman et al. 2008), colonized tributaries across all five GreatLakes in less than 20 years (Christie and Goddard 2003). Stockinglake trout on barren reefs appears to remediate this problem, asthe fish tend to return to stocking sites or those nearby (Bronteet al. 2007).

Our use of acoustic telemetry combined with CJS capture–recapture models provided two advantages over traditional mark–recapture methods for estimating site fidelity. First, continuous“listening” for tagged lake trout on acoustic telemetry receiverswith overlapping detection ranges provided complete detection(i.e., encounter) histories for individual lake trout over fivespawning seasons, which is not possible with conventionalmark–recapture methods that depend on sampling gear withvarying efficiencies (Martin 1960; Frost 1963). Second, CJS modelsconcurrently estimated annual survival and capture probabilities,which meant that site fidelity was estimated independently frommortality. For example, mean estimated annual apparent survivalrate in our population was between 77% and 81%, so between 19%and 23% of the population became unavailable to be detected onthe spawning grounds each year (we assume that these individu-als all died, but it's possible that some immigrated to other spawn-ing populations). If we had ignored survival in our analysis,estimated annual site fidelity rates would have been 34% to 44%lower than those estimated using the twelve 1 h time bin filter,ranging from 0.44 in hatchery females to 0.57 in wild males. Liketraditional mark–recapture techniques, acoustic telemetry doesnot provide information on gamete deposition, so spawning mustbe inferred based on presence at a site during spawning period.The main disadvantage of using acoustic telemetry detection datais that detection ranges tend to be great (greater than 1000 m atsome times), so fine-scale behavioural analyses are not possibleusing detection data alone. The Drummond Island acoustic telem-etry array was designed as a positional telemetry array, (i.e., 2Dpositions via time difference of arrival of transmission at three ormore receivers; Smith 2013), but technical issues with the milli-second log files (required for positioning) in 2011 and 2012 pre-vented us from using position data for these analyses. Anotherdisadvantage of acoustic telemetry compared with traditionalmark–recapture techniques is that only large fish can be taggedwith acoustic transmitters, thereby limiting inferences to adultfish (Landsman et al. 2011) and preventing their use in studyinghoming of lake trout to sites of natal origin, which would requiretagging lake trout fry. Manufacturers have made considerableprogress towards miniaturizing acoustic transmitters (McMichaelet al. 2010). However, battery technology has not progressed asquickly, so small acoustic transmitters are limited in their abilityto transmit for an extended period of time (in the case of laketrout first maturity occurs after 5–7 years in Lake Huron).

The finding of a strong homing tendency among lake trout inthe DIR has important implications for lake trout restorationacross the Great Lakes. First, high spawning site fidelity in hatch-ery fish in our study suggests that hatchery fish may be usingsimilar spawning sites to wild fish and that most return thereannually during the spawning period. Moreover, no evidence of a

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difference in survival occurred between hatchery-reared and wildfish. Second, if natal imprinting is a key mechanism, stockingstrategies that encourage imprinting of stocked lake trout to high-quality spawning sites, as has been done in Lake Michigan (Bronteet al. 2007), should promote colonization and help to speed recov-ery. Third, the Great Lakes were once known for their high degreeof diversity among lake trout stocks (Brown et al. 1981; Kruegerand Ihssen 1995). However, because most contemporary lake troutpopulations in the Great Lakes have been maintained throughstocking of the lean morphotype, extant diversity in other GreatLakes outside of Lake Superior is limited (Muir et al. 2014). Stock-ing other morphotypes, as has recently been done in Lakes Erie(Markham et al. 2008) and Michigan (Dexter et al. 2011) with“humper” lake trout (a deep-bodied morphotype), should helprestore diversity in the Great Lakes (Krueger and Ebener 2004).However, because stock diversity is maintained by reproductiveisolation (Leggett 1977; Horrall 1981), we hypothesize that high sitefidelity among lake trout should, over time, naturally lead to ge-netic and morphological diversification. For example, rainbowtrout (Oncorhynchus mykiss) established multiple genetically differ-entiated populations in Lake Superior within only 20 generations(�100 years) of their first introduction to the lake (Krueger et al.1994). A better and more detailed understanding of homing be-haviour in lake trout is needed to fully elucidate the ecological,genetic, and management impacts of high site fidelity on laketrout populations in the Great Lakes. Future studies should focuson determining the geographic scale (i.e., do lake trout home to aspecific shoal or even to specific sites on a given shoal?) and mech-anisms of homing behaviour in lake trout.

AcknowledgementsThanks go to E. Larson, H. Thompson, C. Wright, J. Van Effen,

M. Lancewicz, L. Lesmeister, D. Operhall, B. Lamoreux, S. Farha,Z. Wickert, J. Osga, Z. Holmes, S. Miehls, S. Seegert, J. Hinderer,R. Darnton, K. Smith, and P. Wigren for their assistance in thefield and to P. Barbeaux and the Chippewa Ottawa Resource Au-thority (M. Ebener, R. Reining, A. Handziak, and D. Pine) for theirassistance in procuring lake trout for tagging. Thanks also go totwo anonymous reviewers for their helpful comments on thispaper. This work was funded by the Great Lakes Fishery Commis-sion by way of Great Lakes Restoration Initiative appropriations(GL-00E23010–3). This paper is contribution 15 of the Great LakesAcoustic Telemetry Observation System (GLATOS) and contribu-tion number 1950 of the USGS Great Lakes Science Center. Thefindings and conclusions in this article are those of the authorsand do not necessarily represent the views of the US Fish andWildlife Service. Any use of trade, product, or firm names is fordescriptive purposes only and does not imply endorsement by theUS Government.

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Appendix A. Full QAICc results tablesAppendix tables A1–A3 appear on the following pages.

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Table A1. QAICc results for comparison of Cormack–Jolly–Seber models used for testing for time effects on survival (�) and site fidelity (p) of laketrout detected within the Drummond Island acoustic telemetry array during spawning period 2010–2014.

Filter Model Parameters QAICc �QAICc wi QDeviance

1 �(�tagged) × >p(�� + sex + origin + sex × origin) 7 771.04 0.00 0.34 55.721 �(�tagged) × p(��) 4 771.60 0.56 0.26 62.381 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 773.52 2.48 0.10 52.071 �(�tagged + sex + origin + sex × origin) × p(��) 7 773.78 2.74 0.09 58.461 �(�tagged) × p(�time + sex + origin + sex × origin) 9 773.87 2.83 0.08 54.471 �(�tagged) × p(�time) 6 773.96 2.91 0.08 60.671 �(�tagged + sex + origin + sex × origin) × p(�time) 9 776.20 5.15 0.03 56.791 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 776.36 5.31 0.02 50.781 �(�time) × p(�� + sex + origin + sex × origin) 8 788.70 17.65 0.00 71.341 �(�time) × p(��) 5 789.30 18.26 0.00 78.051 �(�time) × p(�time) 7 789.73 18.68 0.00 74.411 �(�time) × p(�time + sex + origin + sex × origin) 10 789.88 18.84 0.00 68.421 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 790.60 19.56 0.00 67.091 �(�time + sex + origin + sex × origin) × p(��) 8 790.92 19.88 0.00 73.561 �(�time + sex + origin + sex × origin) × p(�time) 10 791.49 20.44 0.00 70.031 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 791.84 20.79 0.00 64.19

2 �(�tagged) × p(��) 4 783.83 0.00 0.28 65.882 �(�tagged) × p(�� + sex + origin + sex × origin) 7 784.26 0.43 0.23 60.212 �(�tagged) × p(�time) 6 785.05 1.22 0.15 63.042 �(�tagged + sex + origin + sex × origin) × p(��) 7 785.99 2.16 0.10 61.952 �(�tagged) × p(�time + sex + origin + sex × origin) 9 786.04 2.21 0.09 57.912 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 786.78 2.95 0.06 56.592 �(�tagged + sex + origin + sex × origin) × p(�time) 9 787.28 3.46 0.05 59.152 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 788.54 4.72 0.03 54.242 �(�time) × p(�time) 7 799.52 15.69 0.00 75.472 �(�time) × p(�time + sex + origin + sex × origin) 10 800.75 16.92 0.00 70.572 �(�time) × p(��) 5 801.00 17.17 0.00 81.022 �(�time + sex + origin + sex × origin) × p(�time) 10 801.30 17.47 0.00 71.122 �(�time) × p(�� + sex + origin + sex × origin) 8 801.38 17.56 0.00 75.302 �(�time + sex + origin + sex × origin) × p(��) 8 802.61 18.78 0.00 76.522 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 802.78 18.95 0.00 66.412 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 803.34 19.51 0.00 71.10

3 �(�tagged) × p(�� + sex + origin + sex × origin) 7 813.12 0.00 0.43 59.293 �(�tagged) × p(��) 4 815.09 1.97 0.16 67.353 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 815.27 2.15 0.15 55.293 �(�tagged) × p(�time + sex + origin + sex × origin) 9 816.11 2.99 0.10 58.183 �(�tagged + sex + origin + sex × origin) × p(��) 7 816.89 3.76 0.07 63.053 �(�tagged) × p(�time) 6 817.69 4.57 0.04 65.893 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 818.26 5.14 0.03 54.163 �(�tagged + sex + origin + sex × origin) × p(�time) 9 819.53 6.40 0.02 61.603 �(�time) × p(�� + sex + origin + sex × origin) 8 830.78 17.66 0.00 74.903 �(�time) × p(�time + sex + origin + sex × origin) 10 832.09 18.97 0.00 72.113 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 832.27 19.14 0.00 70.233 �(�time) × p(��) 5 832.73 19.61 0.00 82.963 �(�time) × p(�time) 7 833.29 20.16 0.00 79.453 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 833.61 20.49 0.00 67.443 �(�time + sex + origin + sex × origin) × p(��) 8 833.91 20.79 0.00 78.033 �(�time + sex + origin + sex × origin) × p(�time) 10 834.59 21.47 0.00 74.61

4 �(�tagged) × p(�� + sex + origin + sex × origin) 7 892.78 0.00 0.46 63.424 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 894.47 1.69 0.20 58.974 �(�tagged) × p(��) 4 895.56 2.79 0.11 72.304 �(�tagged) × p(�time + sex + origin + sex × origin) 9 896.00 3.22 0.09 62.554 �(�tagged + sex + origin + sex × origin) × p(��) 7 896.98 4.20 0.06 67.624 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 897.69 4.91 0.04 58.074 �(�tagged) × p(�time) 6 898.50 5.72 0.03 71.184 �(�tagged + sex + origin + sex × origin) × p(�time) 9 899.95 7.17 0.01 66.514 �(�time) × p(�� + sex + origin + sex × origin) 8 911.25 18.48 0.00 79.864 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 912.19 19.41 0.00 74.634 �(�time) × p(�time + sex + origin + sex × origin) 10 912.80 20.02 0.00 77.304 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 913.75 20.97 0.00 72.064 �(�time) × p(��) 5 913.95 21.18 0.00 88.674 �(�time + sex + origin + sex × origin) × p(��) 8 914.71 21.94 0.00 83.324 �(�time) × p(�time) 7 914.82 22.04 0.00 85.464 �(�time + sex + origin + sex × origin) × p(�time) 10 915.70 22.92 0.00 80.20

30 Can. J. Fish. Aquat. Sci. Vol. 73, 2016

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Table A1 (continued).

Filter Model Parameters QAICc �QAICc wi QDeviance

5 �(�tagged) × p(�� + sex + origin + sex × origin) 7 855.47 0.00 0.42 61.065 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 857.33 1.86 0.17 56.785 �(�tagged) × p(��) 4 857.44 1.97 0.16 69.135 �(�tagged) × p(�time + sex + origin + sex × origin) 9 858.50 3.03 0.09 60.005 �(�tagged + sex + origin + sex × origin) × p(��) 7 859.08 3.61 0.07 64.665 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 860.34 4.87 0.04 55.665 �(�tagged) × p(�time) 6 860.44 4.97 0.04 68.075 �(�tagged + sex + origin + sex × origin) × p(�time) 9 862.08 6.61 0.02 63.585 �(�time) × p(�� + sex + origin + sex × origin) 8 872.97 17.50 0.00 76.525 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 874.11 18.64 0.00 71.495 �(�time) × p(�time + sex + origin + sex × origin) 10 874.46 18.99 0.00 73.915 �(�time) × p(��) 5 874.84 19.37 0.00 84.495 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 875.55 20.08 0.00 68.805 �(�time + sex + origin + sex × origin) × p(��) 8 875.85 20.38 0.00 79.405 �(�time) × p(�time) 7 875.98 20.51 0.00 81.575 �(�time + sex + origin + sex × origin) × p(�time) 10 877.07 21.60 0.00 76.51

6 �(�tagged) × p(�� + sex + origin + sex × origin) 7 880.06 0.00 0.43 63.246 �(�tagged) × p(��) 4 881.97 1.91 0.16 71.246 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 882.16 2.10 0.15 59.196 �(�tagged) × p(�time + sex + origin + sex × origin) 9 882.95 2.89 0.10 62.036 �(�tagged + sex + origin + sex × origin) × p(��) 7 883.81 3.75 0.07 66.986 �(�tagged) × p(�time) 6 884.86 4.80 0.04 70.086 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 885.00 4.94 0.04 57.916 �(�tagged + sex + origin + sex × origin) × p(�time) 9 886.68 6.62 0.02 65.776 �(�time) × p(�� + sex + origin + sex × origin) 8 898.43 18.37 0.00 79.566 �(�time) × p(�time + sex + origin + sex × origin) 10 899.78 19.72 0.00 76.816 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 899.83 19.77 0.00 74.806 �(�time) × p(��) 5 900.22 20.16 0.00 87.476 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 901.09 21.03 0.00 71.936 �(�time) × p(�time) 7 901.24 21.18 0.00 84.416 �(�time + sex + origin + sex × origin) × p(��) 8 901.44 21.38 0.00 82.586 �(�time + sex + origin + sex × origin) × p(�time) 10 902.50 22.44 0.00 79.53

7 �(�tagged) × p(�� + sex + origin + sex × origin) 7 929.72 0.00 0.54 66.327 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 931.69 1.97 0.20 62.157 �(�tagged) × p(�time + sex + origin + sex × origin) 9 932.68 2.96 0.12 65.197 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 934.58 4.86 0.05 60.927 �(�tagged) × p(��) 4 934.59 4.87 0.05 77.297 �(�tagged + sex + origin + sex × origin) × p(��) 7 936.20 6.48 0.02 72.797 �(�tagged) × p(�time) 6 937.77 8.05 0.01 76.417 �(�tagged + sex + origin + sex × origin) × p(�time) 9 939.34 9.62 0.00 71.857 �(�time) × p(�� + sex + origin + sex × origin) 8 948.45 18.73 0.00 83.007 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 949.66 19.94 0.00 78.057 �(�time) × p(�time + sex + origin + sex × origin) 10 950.38 20.66 0.00 80.837 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 951.43 21.71 0.00 75.697 �(�time) × p(��) 5 953.19 23.47 0.00 93.867 �(�time + sex + origin + sex × origin) × p(��) 8 954.15 24.43 0.00 88.717 �(�time) × p(�time) 7 954.92 25.20 0.00 91.527 �(�time + sex + origin + sex × origin) × p(�time) 10 955.89 26.17 0.00 86.34

8 �(�tagged) × p(�� + sex + origin + sex × origin) 7 906.98 0.00 0.54 68.868 �(�tagged) × p(�time + sex + origin + sex × origin) 9 909.23 2.25 0.18 67.028 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 909.57 2.59 0.15 65.308 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 911.72 4.74 0.05 63.338 �(�tagged) × p(��) 4 911.90 4.92 0.05 79.878 �(�tagged + sex + origin + sex × origin) × p(��) 7 914.00 7.02 0.02 75.878 �(�tagged) × p(�time) 6 914.40 7.42 0.01 78.318 �(�tagged + sex + origin + sex × origin) × p(�time) 9 916.43 9.45 0.00 74.228 �(�time) × p(�� + sex + origin + sex × origin) 8 924.88 17.90 0.00 84.718 �(�time) × p(�time + sex + origin + sex × origin) 10 926.07 19.10 0.00 81.818 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 926.78 19.80 0.00 80.458 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 927.78 20.80 0.00 77.328 �(�time) × p(��) 5 929.70 22.72 0.00 95.658 �(�time) × p(�time) 7 930.66 23.68 0.00 92.548 �(�time + sex + origin + sex × origin) × p(��) 8 931.21 24.23 0.00 91.048 �(�time + sex + origin + sex × origin) × p(�time) 10 932.13 25.15 0.00 87.86

9 �(�tagged) × p(�� + sex + origin + sex × origin) 7 867.63 0.00 0.50 67.259 �(�tagged) × p(�time + sex + origin + sex × origin) 9 869.96 2.33 0.15 65.49

Binder et al. 31

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Table A1 (concluded).

Filter Model Parameters QAICc �QAICc wi QDeviance

9 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 870.02 2.40 0.15 63.509 �(�tagged) × p(��) 4 871.20 3.57 0.08 76.919 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 872.30 4.68 0.05 61.669 �(�tagged + sex + origin + sex × origin) × p(��) 7 872.89 5.26 0.04 72.519 �(�tagged) × p(�time) 6 873.84 6.21 0.02 75.509 �(�tagged + sex + origin + sex × origin) × p(�time) 9 875.47 7.84 0.01 71.009 �(�time) × p(�� + sex + origin + sex × origin) 8 882.54 14.92 0.00 80.129 �(�time) × p(�time + sex + origin + sex × origin) 10 883.79 16.16 0.00 77.279 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 884.37 16.75 0.00 75.799 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 885.46 17.83 0.00 72.749 �(�time) × p(��) 5 886.03 18.40 0.00 89.729 �(�time) × p(�time) 7 887.17 19.54 0.00 86.799 �(�time + sex + origin + sex × origin) × p(��) 8 887.19 19.57 0.00 84.779 �(�time + sex + origin + sex × origin) × p(�time) 10 888.30 20.67 0.00 81.78

10 �(�tagged) × p(�� + sex + origin + sex × origin) 7 838.09 0.00 0.45 73.1510 �(�tagged) × p(�time + sex + origin + sex × origin) 9 839.58 1.49 0.21 70.5410 �(�tagged) × p(��) 4 840.95 2.86 0.11 82.1110 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 841.29 3.20 0.09 70.2010 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 842.66 4.57 0.05 67.4410 �(�tagged) × p(�time) 6 842.69 4.60 0.05 79.7810 �(�tagged + sex + origin + sex × origin) × p(��) 7 843.45 5.36 0.03 78.5110 �(�tagged + sex + origin + sex × origin) × p(�time) 9 845.10 7.01 0.01 76.0710 �(�time) × p(�� + sex + origin + sex × origin) 8 852.77 14.67 0.00 85.7810 �(�time) × p(�time + sex + origin + sex × origin) 10 852.98 14.89 0.00 81.8910 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 855.46 17.37 0.00 78.1710 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 855.49 17.40 0.00 82.3410 �(�time) × p(��) 5 855.54 17.45 0.00 94.6710 �(�time) × p(�time) 7 855.59 17.50 0.00 90.6510 �(�time + sex + origin + sex × origin) × p(�time) 10 857.55 19.46 0.00 86.4610 �(�time + sex + origin + sex × origin) × p(��) 8 857.57 19.48 0.00 90.59

11 �(�tagged) × p(�� + sex + origin + sex × origin) 7 802.45 0.00 0.44 69.7011 �(�tagged) × p(�time + sex + origin + sex × origin) 9 804.25 1.81 0.18 67.4111 �(�tagged) × p(��) 4 804.59 2.14 0.15 77.9411 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 805.62 3.17 0.09 66.7211 �(�tagged) × p(�time) 6 806.64 4.19 0.05 75.9311 �(�tagged + sex + origin + sex × origin) × p(��) 7 807.20 4.75 0.04 74.4511 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 807.29 4.84 0.04 64.2611 �(�tagged + sex + origin + sex × origin) × p(�time) 9 809.17 6.72 0.02 72.3311 �(�time) × p(�� + sex + origin + sex × origin) 8 817.06 14.62 0.00 82.2711 �(�time) × p(�time + sex + origin + sex × origin) 10 817.53 15.08 0.00 78.6311 �(�time) × p(��) 5 819.18 16.73 0.00 90.5011 �(�time) × p(�time) 7 819.53 17.08 0.00 86.7811 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 819.74 17.29 0.00 78.7911 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 819.96 17.51 0.00 74.8611 �(�time + sex + origin + sex × origin) × p(��) 8 821.29 18.84 0.00 86.5011 �(�time + sex + origin + sex × origin) × p(�time) 10 821.61 19.16 0.00 82.71

12 �(�tagged) × p(�� + sex + origin + sex × origin) 7 788.88 0.00 0.45 72.1112 �(�tagged) × p(��) 4 790.81 1.93 0.17 80.1412 �(�tagged) × p(�time + sex + origin + sex × origin) 9 791.15 2.27 0.14 70.2912 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 792.09 3.21 0.09 69.1712 �(�tagged) × p(�time) 6 793.30 4.42 0.05 78.5612 �(�tagged + sex + origin + sex × origin) × p(��) 7 793.46 4.58 0.05 76.6912 �(�tagged + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 12 794.24 5.36 0.03 67.1912 �(�tagged + sex + origin + sex × origin) × p(�time) 9 795.87 6.99 0.01 75.0112 �(�time) × p(�� + sex + origin + sex × origin) 8 804.42 15.54 0.00 85.6112 �(�time) × p(�time + sex + origin + sex × origin) 10 805.48 16.60 0.00 82.5712 �(�time) × p(��) 5 806.30 17.42 0.00 93.6012 �(�time + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 11 807.10 18.22 0.00 82.1312 �(�time) × p(�time) 7 807.22 18.34 0.00 90.4512 �(�time + sex + origin + sex × origin) × p(�time + sex + origin + sex × origin) 13 807.94 19.06 0.00 78.8212 �(�time + sex + origin + sex × origin) × p(��) 8 808.42 19.54 0.00 89.6012 �(�time + sex + origin + sex × origin) × p(�time) 10 809.30 20.42 0.00 86.38

Note: Filter is the minimum number of 1 h periods a fish was required to be detected within the array to be considered to have returned to the Drummond Islandsite for spawning that season. AICc values were adjusted for overdispersion (c) using median c goodness of fit of the global model for each filter criterion. Parametersis the number of estimable parameters in model i; QAICc is the adjusted AICc value for model i; �QAICc is the difference between QAICc for model i and the QAICc forthe highest-ranked model (i.e., model with lowest QAICc value); wi is the weight of model i, relative to all other models in the set; QDeviance is the adjusted devianceof model i.

32 Can. J. Fish. Aquat. Sci. Vol. 73, 2016

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Table A2. QAICc results for comparison of Cormack–Jolly–Seber models used for determining the most parsimonious model for estimatingsurvival (�) of lake trout detected within the Drummond Island acoustic telemetry array during spawning period 2010–2014.

Filter Model Parameters QAICc �QAICc wi QDeviance

1 �(�tagged) × p(�� + sex + origin + sex × origin) 7 771.04 0.00 0.31 55.721 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 771.20 0.16 0.28 53.841 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 772.09 1.05 0.18 54.731 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 772.64 1.60 0.14 53.231 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 773.52 2.48 0.09 52.07

2 �(�tagged) × p(�� + sex + origin + sex × origin) 7 784.26 0.00 0.31 60.212 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 784.43 0.18 0.28 58.352 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 785.35 1.09 0.18 59.272 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 785.91 1.65 0.14 57.772 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 786.78 2.52 0.09 56.59

3 �(�tagged) × p(�� + sex + origin + sex × origin) 7 813.12 0.00 0.31 59.293 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 813.32 0.19 0.28 57.443 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 814.21 1.09 0.18 58.333 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 814.78 1.66 0.13 56.853 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 815.27 2.15 0.10 55.29

4 �(�tagged) × p(�� + sex + origin + sex × origin) 7 892.78 0.00 0.28 63.424 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 892.80 0.02 0.28 61.404 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 893.79 1.01 0.17 62.394 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 894.21 1.43 0.14 60.764 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 894.47 1.69 0.12 58.97

5 �(�tagged) × p(�� + sex + origin + sex × origin) 7 855.47 0.00 0.29 61.065 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 855.54 0.07 0.28 59.095 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 856.53 1.06 0.17 60.075 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 856.98 1.51 0.14 58.485 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 857.33 1.86 0.12 56.78

6 �(�tagged) × p(�� + sex + origin + sex × origin) 7 880.06 0.00 0.30 63.246 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 880.12 0.06 0.29 61.266 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 881.19 1.13 0.17 62.336 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 881.63 1.57 0.14 60.716 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 882.16 2.10 0.10 59.19

7 �(�tagged) × p(�� + sex + origin + sex × origin) 7 929.72 0.00 0.30 66.327 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 929.87 0.15 0.28 64.427 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 930.91 1.19 0.17 65.467 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 931.39 1.67 0.13 63.907 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 931.69 1.97 0.11 62.15

8 �(�tagged) × p(�� + sex + origin + sex × origin) 7 906.98 0.00 0.35 68.868 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 907.48 0.50 0.27 67.328 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 908.38 1.40 0.17 68.228 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 909.15 2.17 0.12 66.938 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 909.57 2.59 0.09 65.30

9 �(�tagged) × p(�� + sex + origin + sex × origin) 7 867.63 0.00 0.32 67.259 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 867.92 0.29 0.27 65.509 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 868.74 1.11 0.18 66.329 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 869.37 1.74 0.13 64.909 �(�tagged + sex + origin +sex × origin) × p(�� + sex + origin + sex × origin) 10 870.02 2.40 0.10 63.50

10 �(�tagged) × p(�� + sex + origin + sex × origin) 7 838.09 0.00 0.35 73.1510 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 838.58 0.49 0.27 71.6010 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 839.37 1.28 0.18 72.3810 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 840.14 2.05 0.12 71.1110 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 841.29 3.20 0.07 70.20

11 �(�tagged) × p(�� + sex + origin + sex × origin) 7 802.45 0.00 0.34 69.7011 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 802.91 0.46 0.27 68.1111 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 803.69 1.24 0.18 68.9011 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 804.44 1.99 0.13 67.5911 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 805.62 3.17 0.07 66.72

12 �(�tagged) × p(�� + sex + origin + sex × origin) 7 788.88 0.00 0.35 72.1112 �(�tagged + sex) × p(�� + sex + origin + sex × origin) 8 789.44 0.55 0.26 70.6212 �(�tagged + origin) × p(�� + sex + origin + sex × origin) 8 790.09 1.21 0.19 71.2812 �(�tagged + sex + origin) × p(�� + sex + origin + sex × origin) 9 790.93 2.05 0.13 70.0712 �(�tagged + sex + origin + sex × origin) × p(�� + sex + origin + sex × origin) 10 792.09 3.21 0.07 69.17

Note: Filter is the minimum number of 1 h periods a fish was required to be detected within the array to be considered to have returned to the Drummond Islandsite for spawning that season. AICc values were adjusted for overdispersion (c) using median c goodness of fit of the global model for each filter criterion. Parametersis the number of estimable parameters in model i; QAICc is the adjusted AICc value for model i; �QAICc is the difference between QAICc for model i and the QAICc forthe highest-ranked model (i.e., model with lowest QAICc value); wi is the weight of model i, relative to all other models in the set; QDeviance is the adjusted devianceof model i.

Binder et al. 33

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Table A3. QAICc results for comparison of Cormack–Jolly–Seber models used for comparing site fidelity �p� among male and femalewild and hatchery-origin lake trout detected within the Drummond Island acoustic telemetry array during spawning period 2010–2014.

Filter Model Parameters QAICc �QAICc wi QDeviance

1 �(�tagged) × p(�� + sex + origin + sex × origin) 7 771.04 0.00 0.30 55.721 �(�tagged) × p(�� + origin) 5 771.39 0.34 0.25 60.131 �(�tagged) × p(��) 4 771.60 0.56 0.23 62.381 �(�tagged) × p(�� + sex) 5 772.99 1.94 0.11 61.741 �(�tagged) × p(�� + sex + origin) 6 773.15 2.10 0.11 59.86

2 �(�tagged) × p(�� + origin) 5 783.39 0.00 0.31 63.412 �(�tagged) × p(��) 4 783.83 0.44 0.25 65.882 �(�tagged) × p(�� + sex + origin + sex × origin) 7 784.26 0.87 0.20 60.212 �(�tagged) × p(�� + sex + origin) 6 785.17 1.78 0.13 63.162 �(�tagged) × p(�� + sex) 5 785.22 1.83 0.12 65.24

3 �(�tagged) × p(�� + sex + origin + sex × origin) 7 813.12 0.00 0.42 59.293 �(�tagged) × p(�� + origin) 5 814.50 1.37 0.21 64.733 �(�tagged) × p(��) 4 815.09 1.97 0.16 67.353 �(�tagged) × p(�� + sex + origin) 6 815.98 2.86 0.10 64.183 �(�tagged) × p(�� + sex) 5 816.00 2.87 0.10 66.23

4 �(�tagged) × p(�� + sex + origin + sex × origin) 7 892.78 0.00 0.56 63.424 �(�tagged) × p(�� + origin) 5 895.26 2.48 0.16 69.974 �(�tagged) × p(��) 4 895.56 2.79 0.14 72.304 �(�tagged) × p(�� + sex) 5 896.85 4.08 0.07 71.574 �(�tagged) × p(�� + sex + origin) 6 896.99 4.21 0.07 69.67

5 �(�tagged) × p(�� + sex + origin + sex × origin) 7 855.47 0.00 0.48 61.065 �(�tagged) × p(�� + origin) 5 857.29 1.82 0.19 66.955 �(�tagged) × p(��) 4 857.44 1.97 0.18 69.135 �(�tagged) × p(�� + sex) 5 859.04 3.57 0.08 68.705 �(�tagged) × p(�� + sex + origin) 6 859.20 3.73 0.07 66.82

6 �(�tagged) × p(�� + sex + origin + sex × origin) 7 880.06 0.00 0.43 63.246 �(�tagged) × p(�� + origin) 5 881.24 1.18 0.24 68.496 �(�tagged) × p(��) 4 881.97 1.91 0.17 71.246 �(�tagged) × p(�� + sex + origin) 6 883.20 3.14 0.09 68.426 �(�tagged) × p(�� + sex) 5 883.63 3.57 0.07 70.88

7 �(�tagged) × p(�� + sex + origin + sex × origin) 7 929.72 0.00 0.71 66.327 �(�tagged) × p(�� + origin) 5 933.01 3.29 0.14 73.687 �(�tagged) × p(��) 4 934.59 4.87 0.06 77.297 �(�tagged) × p(�� + sex + origin) 6 934.71 4.99 0.06 73.357 �(�tagged) × p(�� + sex) 5 935.70 5.98 0.04 76.37

8 �(�tagged) × p(�� + sex + origin + sex × origin) 7 906.98 0.00 0.65 68.868 �(�tagged) × p(�� + origin) 5 909.87 2.89 0.15 75.818 �(�tagged) × p(�� + sex + origin) 6 910.90 3.92 0.09 74.828 �(�tagged) × p(��) 4 911.90 4.92 0.06 79.878 �(�tagged) × p(�� + sex) 5 911.99 5.01 0.05 77.94

9 �(�tagged) × p(�� + sex + origin + sex × origin) 7 867.63 0.00 0.44 67.259 �(�tagged) × p(�� + origin) 5 868.41 0.78 0.30 72.109 �(�tagged) × p(�� + sex + origin) 6 870.04 2.42 0.13 71.709 �(�tagged) × p(��) 4 871.20 3.57 0.07 76.919 �(�tagged) × p(�� + sex) 5 872.12 4.49 0.05 75.81

10 �(�tagged) × p(�� + sex + origin + sex × origin) 7 838.09 0.00 0.41 73.1510 �(�tagged) × p(�� + origin) 5 838.66 0.57 0.31 77.7910 �(�tagged) × p(�� + sex + origin) 6 840.33 2.24 0.13 77.4310 �(�tagged) × p(��) 4 840.95 2.86 0.10 82.1110 �(�tagged) × p(�� + sex) 5 842.08 3.99 0.06 81.21

11 �(�tagged) × p(�� + sex + origin + sex × origin) 7 802.45 0.00 0.42 69.7011 �(�tagged) × p(�� + origin) 5 803.35 0.90 0.27 74.6711 �(�tagged) × p(��) 4 804.59 2.14 0.14 77.9411 �(�tagged) × p(�� + sex + origin) 6 805.17 2.72 0.11 74.4611 �(�tagged) × p(�� + sex) 5 806.04 3.59 0.07 77.36

12 �(�tagged) × p(�� + sex + origin + sex × origin) 7 788.88 0.00 0.37 72.1112 �(�tagged) × p(�� + origin) 5 789.24 0.36 0.31 76.5512 �(�tagged) × p(��) 4 790.81 1.93 0.14 80.1412 �(�tagged) × p(�� + sex + origin) 6 791.16 2.28 0.12 76.4312 �(�tagged) × p(�� + sex) 5 792.41 3.53 0.06 79.71

Note: Filter is the minimum number of 1 h periods a fish was required to be detected within the array to be considered to have returned to theDrummond Island site for spawning that season. AICc values were adjusted for overdispersion (c) using median c goodness of fit of the global model foreach filter criterion. Parameters is the number of estimable parameters in model i; QAICc is the adjusted AICc value for model i; �QAICc is the differencebetween QAICc for model i and the QAICc for the highest-ranked model (i.e., model with lowest QAICc value); wi is the weight of model i, relative to all othermodels in the set; QDeviance is the adjusted deviance of model i.

34 Can. J. Fish. Aquat. Sci. Vol. 73, 2016

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