Mar Biol

DOI 10.1007/s00227-010-1571-9

ORIGINAL PAPER

Variation in plasma levels of insulin-like growth factor-I (IGF-I) in lingcod: relationships among season, size, and gonadal steroids

Anne H. Beaudreau · Kelly S. Andrews · Donald A. Larsen · Graham Young · Brian R. Beckman

Received: 30 May 2010 / Accepted: 21 October 2010© Springer-Verlag 2010

Abstract The physiological response of a Wsh to its envi-ronment is mediated through the endocrine axis controllinggrowth. Therefore, growth-regulating hormone levels canserve as ecologically relevant indicators of Wsh growth rate.We quantiWed variation in plasma insulin-like growth fac-tor-I (IGF-I) to evaluate its potential as an indicator ofgrowth in lingcod, an economically and ecologically impor-tant bottomWsh species in the northeast PaciWc. An informa-tion-theoretic model selection approach was used to test thehypothesis that variation in lingcod IGF-I is related to sea-son, body size, and gonadal steroid concentration. Seasonand a length £ season interaction were the most importantpredictors of plasma IGF-I among the variables we evalu-ated, suggesting that season and body size should be explic-itly accounted for when interpreting endocrine patterns inwild Wsh populations. This is among the few studies thathave measured and interpreted patterns of IGF-I in wild Wshand the Wrst to describe seasonal endocrine proWles inlingcod.

Introduction

The plasticity of somatic growth in response to biophysicaland environmental factors can have implications for thedynamics and productivity of Wsh populations (Weatherley1990; Sogard 1997). Changes in Wsh growth or body condi-tion related to temperature and food availability may aVectjuvenile survival (e.g., Atlantic salmon Salmo salar,Nicieza and Metcalfe 1997) and stock productivity (e.g.,Atlantic cod Gadus morhua; Ratz and Lloret 2003). Thephysiological response of a Wsh to its environment is medi-ated through the endocrine axis controlling growth; there-fore, growth-regulating hormone levels can serve asecologically relevant indicators of Wsh growth rate (Pichaet al. 2008). SpeciWcally, insulin-like growth factor-I (IGF-I)has been established as an indicator of recent growth(past 2–6 weeks) in Wsh (Beckman et al. 2004a; Tayloret al. 2008). The use of endocrine indicators for assessingWsh growth aVords two major beneWts over traditionalmethods of measuring growth rate (e.g., direct measure-ment from tagging and recapture of individuals, or growthincrement analysis using bone or cartilage). First, plasmaconcentration of growth-regulating hormones may bederived from non-lethal blood sampling, whereas a largenumber of individuals are typically killed for analysis ofotoliths, scales, or other hard structures (Campana andNeilson 1985). Furthermore, a large number of blood sam-ples can be processed quickly in the laboratory (e.g., >200samples/week/technician, Beckman personal observation),while tagging and recapturing Wsh requires extensive Wshery-dependent sampling and often occurs over multiple years(Guy et al. 1996).

Plasma levels of insulin-like growth factor-I (IGF-I) reX-ect an integrated endocrine response to temperature, photo-period, and nutritional status (Pérez-Sánchez et al. 1994;

Communicated by C. Harrod.

A. H. Beaudreau (&) · G. YoungSchool of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, WA 98195, USAe-mail: annebeau@uw.edu

K. S. Andrews · D. A. Larsen · B. R. BeckmanNOAA Fisheries, Northwest Fisheries Science Center, 2725 Montlake Blvd E., Seattle, WA 98112, USA

G. YoungCenter for Reproductive Biology, Washington State University, Pullman, WA 99164, USA

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Pérez-Sánchez et al. 1995; Beckman et al. 1998; Mingarroet al. 2002) and may be modiWed by reproductive hormones(Riley et al. 2002; Larsen et al. 2004; Davis et al. 2008).IGF-I causes the proliferation of target tissues, which ulti-mately leads to body growth in vertebrates (Ohlsson et al.2009). Thus, IGF-I is strongly correlated with speciWcgrowth rate in a number of teleost species (reviewed byBeckman 2010) and shows a weaker positive relationshipwith body size and condition (Beckman et al. 2004a). Theprincipal regulator of the endocrine axis controlling growthin Wsh is nutritional status; laboratory studies have showndepressed plasma IGF-I levels during periods of fasting orreduced ration (Pierce et al. 2001; Uchida et al. 2003;Taylor et al. 2008; Shimizu et al. 2009). Increased plasmaIGF-I levels coincide with warmer water, longer photoperiod,and greater food supply for many species (e.g., Chinooksalmon Oncorhynchus tshawytscha, Beckman et al. 2000;coho salmon Oncorhynchus kisutch, Larsen et al. 2001;gilthead sea bream Sparus aurata, Mingarro et al. 2002;rainbow trout Oncorhynchus mykiss, Taylor et al. 2008).Decreased speciWc growth rate and plasma IGF-I concen-trations in mature female rainbow trout (Oncorhynchusmykiss) have been found to coincide with increased testos-terone levels during maturation (Taylor et al. 2008).

Measurement of IGF-I has been established as a power-ful physiological tool for evaluating growth of Wsh held incontrolled environments; however, the use of IGF-I as anecological tool for identifying diVerential growth condi-tions among groups of wild Wsh occupying diVerent habitatpatches, utilizing alternative food resources, or subject todiVerent population densities is less well developed. In wildWsh, IGF-I levels likely reXect a combined response to sea-sonal changes in environmental conditions (i.e., tempera-ture, photoperiod, food supply) and reproductive status. Inaddition, seasonal changes in the relationship between IGF-Iand speciWc growth rate may complicate interpretation ofseasonal patterns in plasma IGF-I levels (Beckman et al.2004b). Therefore, use of IGF-I as an indicator of growthrate or nutritional status in wild Wsh requires an understand-ing of how intrinsic and extrinsic factors interact to ulti-mately inXuence the plasma concentration of IGF-I.Characterizing variation in IGF-I levels as a function ofenvironmental and physiological factors in free-living Wshis an important step toward this goal, particularly when lab-oratory studies are infeasible.

This study investigated variation in plasma IGF-I in ling-cod (Ophiodon elongatus, Hexagrammidae) to provideguidance on the use of IGF-I as an indicator of lingcodgrowth in the natural environment. Lingcod play a key eco-logical role as top predators in rocky reefs and are har-vested in recreational and commercial Wsheries along thewest coast of North America (Miller and Geibel 1973;Simenstad et al. 1979). Adult lingcod are diYcult to main-

tain in the laboratory due to their large body sizes up to119 cm total length (TL) at 20 years for females and 86 cmTL at 14 years for males (Cass et al. 1990; Jagielo andWallace 2005). Males mature at approximately 52 cm TL(50% mature at 2.5 years) and females at 68 cm TL (50%mature at 4.3 years; Jagielo and Wallace 2005). From lateDecember through February, female lingcod produce a singleegg clutch that is guarded by a male for 5–11 weeks (Millerand Geibel 1973; Withler et al. 2004). Age determination canbe diYcult in this species (Chatwin 1954) although Wn rays,otoliths, and scales have been used as aging structures withvarying success (Beamish and Chilton 1977; McFarlaneand King 2001; Gregg 2003). Growth information is partic-ularly scarce for lingcod in the inshore coastal waters ofWashington State, which are genetically distinct from oce-anic populations (Jagielo et al. 1996; Marko et al. 2007).

A rapid decline in lingcod population abundance overrecent decades (Jagielo and Wallace 2005; Coates et al.2007) initiated harvest reductions and an interest in lingcodenhancement through hatchery production in Puget Sound,Washington (Wespestad et al. 1994). Therefore, develop-ment of non-lethal techniques for evaluating growth in ling-cod, such as measurement of plasma IGF-I levels, maymake a valuable contribution to management and conserva-tion eVorts. In the present study, we evaluated the relativeimportance of physiological and environmental factors indictating variation in IGF-I in lingcod to inform the use ofendocrine tools for evaluating growth of wild Wsh. SpeciW-cally, we used an information-theoretic model selectionapproach to test the hypothesis that variation in lingcodIGF-I is related to season, body size (length), and gonadalsteroid concentration. In addition, we described seasonalvariation in plasma IGF-I and gonadal steroid levels fromlingcod collected in the San Juan Archipelago, Washington,USA.

Methods

Field sampling and laboratory analysis

Lingcod (32–114 cm TL) were collected using hook-and-line from depths of 5 to 52 meters in the San Juan Channel,Washington, USA. Seasonal sampling was conducted in2007–2008: summer (4 August–6 September, N = 149);fall (7–17 November, N = 84); winter (16–23 February,N = 58); and spring (10–19 May, N = 75). Length and masswere recorded for each individual and sex determinedbased on external characteristics (Wilby 1937). Males werechecked for evidence of milt production by gently pressingon their lower abdomen. Each lingcod was sedated with tri-caine methanesulfonate (MS-222), and its stomach contentsretrieved using gastric lavage (Beaudreau and Essington

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2007). A 1.0-ml blood sample was extracted from the cau-dal vein using a heparinized syringe and stored on ice untilreturning to the laboratory (1–4 h). We were required toconduct non-lethal sampling because of the protected statusof lingcod in Washington State, and each Wsh was releasedalive at its capture location.

Blood was centrifuged for 5 min at 3,000 g and plasmawas removed and stored frozen at the Weld station initially at¡20°C and then in the laboratory at ¡80°C until the time ofanalysis. Total plasma IGF-I was measured in all samplesfollowing acid–ethanol extraction (Daughaday et al. 1980)with recombinant salmon IGF-I and anti-Barramundi IGF-Iserum obtained from Gro-Pep (Adelaide, Australia) accord-ing to Shimizu et al. (2000). Parallelism of lingcod plasma inthis assay was demonstrated by Andrews et al. (in review).For androgen analysis, plasma samples were doubleextracted in methylene chloride. Plasma 11-ketotestosterone(11-KT) levels were measured in all males and testosterone(T) in all males and females by an enzyme-linked immuno-sorbent assay (ELISA) according to Cuisset et al. (1994).Antiserum was generously provided by Dr. R.W. Schulz(University of Utrecht, The Netherlands) and Dr. S. Zanuy(CSIC, Institutu de Acuiculturea de Torre la Sal, Castellón,Spain) for 11-KT and T, respectively. Cross-reactivity of theT antibody with 11-KT was less than 5% (data not shown).For estradiol, plasma from all females was double etherextracted and analyzed by radioimmunoassay (RIA) accord-ing to the method of Sower and Schreck (1982).

Statistical analyses

We used a combination of statistical modeling and graphicalapproaches to determine the relationships between environ-mental and physiological factors and plasma insulin-likegrowth factor-I (IGF-I) in wild lingcod. First, the relativeimportance of season, body size, and gonadal steroid levelsin dictating variation in plasma IGF-I concentration wasevaluated using an information-theoretic model selectionapproach. The speciWc nature of the relationships amongIGF-I and these biophysical factors was then examinedusing simple linear regression and graphical methods. Malesand females were analyzed separately because of the poten-tial complex interactions between gonadal steroids and IGF-I (see “Discussion”) and gender diVerences in reproductivebehavior (i.e., nest guarding in males; Withler et al. 2004).

We evaluated the hypothesis that variation in lingcodIGF-I was related to season, length, and gonadal steroidhormones using multiple regression analysis. A set of can-didate models describing the relationship between IGF-Iconcentration in lingcod and environmental and physiologi-cal factors was determined a priori based on the literatureand compared using an information-theoretic (I-T) modelselection approach (Burnham and Anderson 2002). In this

analytical framework, a set of hypotheses represented asalternative models Wtted to data are simultaneously evalu-ated using likelihood-based model selection criteria(e.g., Akaike’s information criterion (AIC); Burnham andAnderson 2002; Hobbs and Hilborn 2006). Thus, it is possibleto evaluate the degree of support for multiple competinghypotheses rather than test a single hypothesis against anull hypothesis; this allows the expression of uncertainty inboth parameter estimates and model formulation (Whitting-ham et al. 2006). The use of I-T approaches has gainedwidespread support for the analysis of ecological systems,in which interactions are complex and it may not be possi-ble to implement true experimental controls (Johnson andOmland 2004; Hobbs and Hilborn 2006; Whittingham et al.2006).

Candidate explanatory models were Wtted to IGF-I dataseparately for females and males using multiple linearregression (Weisberg 1985). Diagnostic residual and proba-bility plots did not reveal heterogeneity of variances ordeviations from normality assumptions (Weisberg 1985).Analyses were performed using R, ver. 2.10.0 (R Develop-ment Core Team 2009). Season was included as a potentialpredictor variable as a proxy for photoperiod, temperature,and food supply. Length was included as a variable toaccount for possible correlations between body size andIGF-I, which have been shown for other teleost species(Beckman et al. 2004a). A length £ season interaction termwas included because the relationship between IGF-I andlength may vary seasonally (Picha et al. 2008). Gonadalsteroid £ season interaction terms were included basedon the expectation that testosterone, estradiol-17�, and11-ketotestosterone would vary seasonally according to theannual reproductive cycle (Roberts et al. 1999; Haukeneset al. 2008).

A total of 128 regression models were evaluated forfemales (N = 122, 34–114 cm TL), including the nullmodel (intercept only) and all possible combinations ofseven predictor variables: lingcod length (length), season,estradiol-17� concentration (E2), testosterone concentration(T), a length £ season interaction (length £ season), atestosterone £ season interaction (T £ season), and anestradiol-17�£ season interaction (E2 £ season). For males(N = 236, 32–90 cm TL), 32 regression models wereevaluated, including the null model and all possible combi-nations of Wve predictor variables: length, season, 11-ketotestosterone concentration (11-KT), length £ season,and an 11-ketotestosterone £ season interaction (11-KT £season). Because T and 11-KT were highly correlated(r = 0.892), T was excluded as an explanatory variablefor males to avoid collinearity in regression modeling(Weisberg 1985).

We compared models using Akaike’s informationcriteria bias-corrected for small sample size (AICc), which

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balances model complexity (number of estimated parame-ters) with the goodness of Wt, as determined by likelihood(Burnham and Anderson 2002). �AICc was calculated foreach model as its AICc minus the lowest AICc across allmodels. The smaller the �AICc, the stronger the empiricalsupport for the given model; by convention, models with�AICc within 2 of the minimum AICc are classiWed as per-forming equivalently to the best approximating model(Burnham and Anderson 2002). We calculated the Akaikeweight (wi) for each model, interpreted as the weight of evi-dence (probability) that model i is the best approximatingmodel for the observed data from among the set of candi-date models (Johnson and Omland 2004). Akaike weightssum to one across the full set of models and provide a wayto scale and interpret �AICc values; as wi approaches one,the weight of evidence in favor of model i increases (Burn-ham and Anderson 2002). The relative likelihood of a givenmodel i compared to model 1, the model with the minimumAICc, was determined as the ratio of w1 to wi (termed evi-dence ratio; Burnham and Anderson 2002). To estimate therelative importance of each predictor variable j, wi weresummed across all models in the set that included variablej; the closer the sum of Akaike weights (w+(j)) is to 1, themore important the variable in predicting the responseacross all models (Burnham and Anderson 2002).

To evaluate general seasonal patterns in hormone levels,we calculated mean (§SE) IGF-I, T, 11-KT (males only),and E2 (females only) levels separately for males andfemales in each sampling period. Seasonal diVerences inmean IGF-I and gonadal steroid levels were tested with aone-way analysis of variance followed by a Tukey multiple

comparison test (Zar 1999). DiVerences were consideredsigniWcant at P < 0.05. We calculated the proportion ofsampled lingcod stomachs containing food items (i.e., non-empty stomachs) in each season as a population-level indexof nutritional status, the principle regulator of the endocrineaxis controlling growth (Taylor et al. 2008). Simple linearregressions of IGF-I on length and gonadal steroid (E2 infemales, 11-KT in males) on length were performed sepa-rately for each season and sex to aid in interpretation of sea-sonal hormone patterns. Diagnostic residual plots revealednonlinear gonadal steroid–length relationships during sum-mer and fall for females and winter and spring for males,and a natural log transformation of the response wasselected by the Box–Cox procedure (Weisberg 1985).

Results

Factors related to IGF-I variation

Multiple explanatory models provided equally good Wts toIGF-I data for males and females, revealing the complexityof factors dictating variation in IGF-I. Season and alength £ season interaction were the most important pre-dictors of plasma IGF-I among the variables we evaluated,based on a strong weight of evidence for their inclusion inthe set of best explanatory models for males and females(Tables 1, 2). Length and gonadal steroids were moderatelyimportant predictor variables, and there was essentially nosupport for including gonadal steroid £ season interactionterms in the models (Tables 1, 2). For males, there was a

Table 1 The 95% conWdence set of candidate models describing the relationship between insulin-like growth factor-I (IGF-I) in male lingcod(N = 236) and environmental and physiological factors

A total of 32 regression models were evaluated, representing all possible combinations of Wve predictor variables: lingcod length (length), season,11-ketotestosterone (11-KT), a length £ season interaction (length £ season), and a 11-ketotestosterone £ season interaction (11-KT £ season).Adj. r2 is the coeYcient of determination adjusted for the number of parameters, K is the total number of estimated parameters, AICc is theAkaike’s information criteria bias-corrected for small sample size, �AICc is the AICc for each model minus the lowest AICc from all possiblemodels, and wi is the model Akaike weighta Best-Wt models based on AICc

Model number

Model parameters Adj. r2 K AICc �AICc wi Evidence ratio

1a Length + Season + Length £ Season 0.297 9 1,440.2 0.0 0.232

2a Season + Length £ Season 0.297 9 1,440.2 0.0 0.232 1

3a Length + Season + 11-KT + Length £ Season 0.299 10 1,440.5 0.3 0.202 1

4a Season + 11-KT + Length £ Season 0.299 10 1,440.5 0.3 0.202 1

5 Length + Season + 11-KT + 11-KT £ Season + Length £ Season 0.295 13 1,445.5 5.3 0.017 14

6 Season + 11-KT + 11-KT £ Season + Length £ Season 0.295 13 1,445.5 5.3 0.017 14

7 Length + Season + 11-KT £ Season + Length £ Season 0.295 13 1,445.5 5.3 0.017 14

8 Season + 11-KT £ Season + Length £ Season 0.295 13 1,445.5 5.3 0.017 14

9 Length + 11-KT + Length £ Season 0.272 7 1,446.1 5.9 0.012 19

10 11-KT + Length £ Season 0.272 7 1,446.1 5.9 0.012 19

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similar level of support for the four best Wtting modelsbased on approximately equal weights of evidence andAICc values within 2 of the minimum score (�AICc · 2;Table 1). Each of the four best models explained a rela-tively low proportion of the total variance in IGF-I (adj.r2 = 0.297–0.299; Table 1). The combined probability ofthese models being the best approximating models for thedata was 0.868, although the weights of evidence wereweak for any of the four models individually (wi = 0.202–0.232; Table 1). Ten models contributed to the 95% conW-dence set of candidate models, or those with a combinedprobability of 0.95 (Burnham and Anderson 2002; Table 1).

For females, six candidate models performed equiva-lently based on AICc (�AICc · 2) and explained a moder-ate proportion of variance in IGF-I (adj. r2 = 0.394–0.400;Table 2). Evidence ratios suggest that the top four modelsare twice as likely to be the best approximating modelscompared to models 5 and 6 and three times as likely com-pared to models 7 and 8 (Table 2). The combined probabil-ity of the six best models was 0.798, but the weights ofevidence were weak for each individually (wi = 0.070–0.166; Table 2). Fourteen models contributed to the 95%conWdence set of candidate models (Table 2). Season and alength £ season interaction were the most important factorsexplaining variation in IGF-I, indicated by their inclusionin the best set of models for both sexes and strong weightsof evidence across all models (w+(j) ¸ 0.957; Table 3).

Table 2 The 95% conWdence set of candidate models describing the relationship between insulin-like growth factor-I (IGF-I) in female lingcod(N = 122) and environmental and physiological factors

A total of 128 regression models were evaluated, representing all possible combinations of seven predictor variables: lingcod length (length), sea-son, estradiol-17� (E2), testosterone (T), a length £ season interaction (length £ season), a testosterone £ season interaction (T £ season), and anestradiol-17� £ season interaction (E2 £ season). Adj. r2 is the coeYcient of determination adjusted for the number of parameters, K is the totalnumber of estimated parameters, AICc is the Akaike’s information criteria bias-corrected for small sample size, �AICc is the AICc for each modelminus the lowest AICc from all possible models, and wi is the model Akaike weighta Best-Wt models based on AICc

Model number

Model parameters Adj. r2 K AICc �AICc wi Evidence ratio

1a Length + Season + Length £ Season 0.394 9 767.6 0.0 0.166

2a Season + Length £ Season 0.394 9 767.6 0.0 0.166 1

3a Length + Season + E2 + Length £ Season 0.400 10 767.7 0.0 0.163 1

4a Season + E2 + Length £ Season 0.400 10 767.7 0.0 0.163 1

5a Season + E2 + T + Length £ Season 0.398 11 769.3 1.7 0.070 2

6a Length + Season + E2 + T + Length £ Season 0.398 11 769.3 1.7 0.070 2

7 Length + Season + T + Length £ Season 0.390 10 769.7 2.1 0.059 3

8 Season + T + Length £ Season 0.390 10 769.7 2.1 0.059 3

9 Length + Season + E2 + E2 £ Season + Length £ Season 0.388 13 774.2 6.6 0.006 27

10 Season + E2 + E2 £ Season + Length £ Season 0.388 13 774.2 6.6 0.006 27

11 Length + Season + E2 £ Season + Length £ Season 0.388 13 774.2 6.6 0.006 27

12 Season + E2 £ Season + Length £ Season 0.388 13 774.2 6.6 0.006 27

13 Length + Season + E2 + E2 £ Season 0.364 10 774.6 7.0 0.005 33

14 Length + Season + E2 £ Season 0.364 10 774.6 7.0 0.005 33

Table 3 Parameter Akaike weights (w+(j)) calculated from all candidateregression models describing the relationships between insulin-likegrowth factor-I (IGF-I) and environmental and physiological factors for(a) male lingcod (32 models), and (b) female lingcod (128 models)

Candidate models were formulated from all possible combinations ofthe predictor variables lingcod length (length), season, estradiol-17�(E2) for females only, 11-ketotestosterone (11-KT) for males only, tes-tosterone (T) for females only, a length £ season interaction (length £season), an estradiol-17� £ season interaction (E2 £ season) forfemales only, an 11-ketotestosterone £ season interaction (11-KT £season) for males only, and a testosterone £ season interaction(T £ season) for females only

Parameter w+(j)

(a)

Length £ Season 0.976

Season 0.957

Length 0.512

11-KT 0.476

11-KT £ Season 0.071

(b)

Season 0.999

Length £ Season 0.983

E2 0.509

Length 0.508

T 0.292

E2 £ Season 0.056

T £ Season 0.032

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Seasonal and size-based patterns in hormone concentration

Season was the most important factor explaining variation inIGF-I for females (w+(j) = 0.999) and second most importantfactor for males (w+(j) = 0.957; Table 3). Mean plasma levelsof IGF-I were lower during fall and winter than in the springand summer for males (Fig. 1a); the seasonal pattern wassimilar for females, but diVerences were not signiWcant(Fig. 2a). The proportion of non-empty stomachs in male andfemale lingcod showed a concordant seasonal pattern withmean IGF-I concentration (Figs. 1a, 2a).

Relationships between length and IGF-I varied seasonallyin both sexes; a length £ season interaction was the mostimportant factor dictating variation in IGF-I for males(w+(j) = 0.976) and the second most important factor forfemales (w+(j) = 0.983; Table 3). In general, IGF-I increasedwith body size in male lingcod except during the fall (sum-mer: r2 = 0.24, F1,95 = 29.26, P < 0.001; fall: r2 = 0.04,F1,57 = 2.10, P = 0.15; winter: r2 = 0.26, F1,36 = 12.86,P < 0.001; spring: r2 = 0.34, F1,40 = 20.27, P < 0.001; Fig. 3).

Fig. 1 Mean levels (§SE) of a insulin-like growth factor-I (IGF-I),b 11-ketotestosterone (11-KT), and c testosterone (T) in male lingcodduring summer (N = 97, mean length = 54 cm total length), fall(N = 59, mean length = 55 cm), winter (N = 38, mean length = 57 cm),and spring (N = 42, mean length = 51 cm). Bars with common lettersare not signiWcantly diVerent (P · 0.05). The proportion of sampledmale lingcod stomachs containing food items (non-empty) is shown assolid points (a)

Summer Fall Winter Spring

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Fig. 2 Mean levels (§SE) of a insulin-like growth factor-I (IGF-I),b estradiol-17� (E2), and c testosterone (T) in female lingcod duringsummer (N = 51, mean length = 60 cm total length), fall (N = 24, meanlength = 51 cm), winter (N = 17, mean length = 48 cm), and spring(N = 30, mean length = 62 cm). Bars with common letters are not sig-niWcantly diVerent (P · 0.05); no signiWcant diVerences in mean IGF-I levels were found among seasons (a). The proportion of sampledfemale lingcod stomachs containing food items (non-empty) is shownas solid points (a)

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IGF-I levels showed a weak positive relationship with bodysize in female lingcod during the summer (r2 = 0.08,F1,49 = 4.00, P = 0.05), no relationship during the fall (r2 =0.02, F1,22 = 0.46, P = 0.51), and a strong positive relationship

during winter (r2 = 0.88, F1,15 = 111.40, P < 0.001) andspring (r2 = 0.42, F1,28 = 20.22, P < 0.001; Fig. 4).

Gonadal steroids (w+(j) = 0.29–0.51) and length (w+(j) =0.508–0.512) were less important and gonadal steroid £

Fig. 3 Seasonal relationships between insulin-like growth factor-I(IGF-I) concentration and body length (cm, total length) for male ling-cod (N = 236; solid and open points). Open points indicate males thatshowed evidence of milt production during the winter sampling period.

Solid black lines show signiWcant regression Wts (� = 0.05). Graydotted vertical lines show the estimated length at 50% maturity formale lingcod (52 cm; Jagielo and Wallace 2005)

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Fig. 4 Seasonal relationships between insulin-like growth factor-I (IGF-I) concentration and body length (cm, total length) for female lingcod (N = 122; solid points). Solid black lines show signiWcant regression Wts (� = 0.05). Gray dotted vertical lines show the estimated length at 50% matu-rity for female lingcod (62 cm; Jagielo and Wallace 2005)

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season interaction terms unimportant (w+(j) · 0.071) inexplaining variation in IGF-I for males and females(Table 3). On average, gonadal steroid levels were highestduring periods of lowest IGF-I (winter for males, fall forfemales; Figs. 1 and 2). Gonadal steroid levels varied sea-sonally, but patterns diVered between males (Fig. 1b, c) andfemales (Fig. 2b, c). In males, mean androgen (11-KT, T)levels were lowest during summer and spring, intermediatein fall, and peaked during the winter. In females, meanestrogen (E2) and androgen (T) levels were intermediateduring summer, peaked in the fall, and were lower in winterand spring. Relationships between length and gonadal ste-roids varied seasonally for males and females. 11-KT con-centration increased with male body size during summer(r2 = 0.29, F1,95 = 38.89, P < 0.001) and fall (r2 = 0.57,F1,57 = 76.34, P < 0.001), showed no relationship withlength in the winter (r2 = 0.08, F1,36 = 3.23, P = 0.08), anddecreased with length in the spring (r2 = 0.14, F1,40 = 6.55,P = 0.01) although 11-KT levels were relatively low duringthis period (Fig. 5). Based on our Weld observations, at least35% of the male lingcod sampled during winter werespermiating (i.e., had free-running milt; denoted by openpoints in Figs. 3 and 5). Strong positive relationshipsbetween E2 levels and body size in female lingcod wereobserved during every season (summer: r2 = 0.67,F1,49 = 100.10, P < 0.001; fall: r2 = 0.83, F1,22 = 104.70,P < 0.001; winter: r2 = 0.69, F1,15 = 32.83, P < 0.001;spring: r2 = 0.75, F1,28 = 83.14, P < 0.001; Fig. 6); how-ever, E2 levels were very low (<2 ng ml¡1) except in largefemales >80 cm TL during the fall.

Discussion

This study provides an improved understanding of the com-plex interactions among intrinsic and extrinsic factors thatoperate on individual Wsh to aVect their plasma IGF-I levelsin the natural environment. It is among the few studies thathave measured and interpreted patterns of IGF-I in wild Wsh(but see Beckman et al. 2000; Beckman et al. 2004a; Lynnet al. 2009; Onuma et al. 2010) and the Wrst to describe sea-sonal endocrine proWles in lingcod, an economically andecologically important bottomWsh species in the northeastPaciWc. Although we did not directly evaluate the strengthof the relationship between lingcod growth rate and plasmaIGF-I level, the positive correlation between percent non-empty stomachs, an index of nutritional status, and meanIGF-I across seasons (males: r = 0.868, females: r = 0.673)strengthens our conWdence that IGF-I might be a usefulindicator of growth in lingcod. In addition, lingcod plasmaIGF-I levels followed expected seasonal and size-basedpatterns according to previous studies of IGF-I in teleostWshes: higher IGF-I during periods of warmer temperature,longer photoperiod, and greater food supply (Mingarroet al. 2002; Taylor et al. 2008; Beckman et al. 1998; Larsenet al. 2001), and a positive relationship between IGF-I andlength (Kajimura et al. 2001; Beckman et al. 2004a).

The high unexplained variance in IGF-I and large numberof explanatory models with equally good Wts to the datarevealed the complexity of factors dictating plasma IGF-Ilevels in wild lingcod. Model results provided support for thehypothesis that variation in lingcod IGF-I is related to

Fig. 5 Seasonal relationships between 11-ketotestosterone (11-KT) concentration and body length (cm, total length) for male lingcod (N = 236; solid and open points). Open points indicate males that showed evidence of milt production during the win-ter sampling period. Solid black lines show signiWcant regression Wts (� = 0.05); back-transformed Wtted values of 11-KT are shown for spring. Gray dotted vertical lines show the estimated length at 50% maturity for male lingcod (52 cm; Jagielo and Wallace 2005)

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season, body size (lingcod length), and gonadal steroid con-centration. Seasonal changes in the relationship betweenIGF-I and lingcod length were revealed by the importance ofthe length £ season interaction term in multiple regressionmodels. In the models, season was a categorical variable thatserves as a proxy for temporal changes in photoperiod, ther-mal environment, and food availability; each of these factorscan individually aVect plasma IGF-I levels (Beckman 2010).The goodness-of-Wt of explanatory models could potentiallybe improved by directly measuring environmental variablesknown to inXuence IGF-I (i.e., food consumption or avail-ability, temperature, photoperiod) and including them ascovariates in multiple regression models. Seasonal eVects onIGF-I related to environmental variables may also be con-founded with changes in gonadal steroid levels throughoutthe reproductive cycle (Campbell et al. 2003). For example,relatively high levels of estrogen (>0.5 ng ml¡1) in largefemale lingcod >80 cm TL coincided with depressed IGF-Ilevels during summer and fall periods (Figs. 4, 6). Addition-ally, the relationship between IGF-I and body size may berelated to changes in feeding ontogeny, underlying lipid lev-els (e.g., coho salmon Oncorhynchus kisutch, Shimizu et al.2009), and maturity stage (e.g., chum salmon Oncorhynchusketa, Onuma et al. 2010). The complexity of these interac-tions and multiple temporal scales of IGF-I response to envi-ronmental and physiological factors make it diYcult toisolate the eVects of season, size, and gonadal steroids onIGF-I levels in free-living Wsh.

Understanding the mechanisms underlying seasonal pat-terns in IGF-I requires insight into lingcod physiology and

behavior in relation to changes in energetic condition.Depressed IGF-I levels during the winter may reXect acombined response to decreased food availability andcolder water temperature leading to slower metabolic ratesin both sexes (i.e., reduced feeding and growth; Beaudreauand Essington 2009). The proportion of non-empty stom-achs in sampled lingcod reXected seasonal changes in feed-ing for males and females (Beaudreau and Essington 2007),with 70% of stomachs containing food items in the sum-mer, 62% in the fall, 29% in the winter, and 71% in thespring. In addition, behavioral changes related to reproduc-tion may contribute to reduced nutrition during the winter.Winter sampling occurred in February, which coincideswith the peak period of nest guarding for male lingcod inPuget Sound and Georgia Basin. Males may incur consider-able energetic costs during parental care activities in thewinter spawning period (LaRiviere et al. 1981; Hinch andCollins 1991), and their nutritional status may be furthercompromised by less frequent feeding while nest guarding(Beaudreau and Essington 2007).

We described seasonal and ontogenetic patterns in ling-cod estrogen and androgen levels; however, our primarymotivation for measuring gonadal steroid levels in lingcodwas to aid in interpreting sources of variation in IGF-I.Mean IGF-I levels were lowest during the fall and winterfor male and female lingcod, coinciding with periods ofhighest mean gonadal steroid levels. Studies on other tele-ost Wshes have shown that relationships between gonadalsteroids and IGF-I can vary across species. For instance, adecrease in plasma IGF-I from peak levels and coincident

Fig. 6 Seasonal relationships between estradiol-17� (E2) concentration and body length (cm, total length) for female lingcod (N = 122; solid points). Solid black lines show signiW-cant regression Wts (� = 0.05); back-transformed Wtted values of E2 are shown for summer and fall. Gray dotted vertical lines show the estimated length at 50% maturity for female lingcod (62 cm; Jagielo and Wallace 2005)

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decline in speciWc growth rate occurred with the Wrst sig-niWcant increase in plasma testosterone in mature femalerainbow trout (Oncorhynchus mykiss; Taylor et al. 2008).In contrast, androgens stimulate IGF-I in both coho salmon(O. kisutch; Larsen et al. 2004) and tilapia (Oreochromismossambicus; Riley et al. 2002). Estrogen depresses IGF-Iin tilapia (Davis et al. 2008) but stimulates growth and IGF-Ilevels in yellow perch (Perca Xavescens; Goetz et al.2009). Further study is needed to resolve the relationshipsbetween gonadal steroids and IGF-I for lingcod; however,gonadal steroids and IGF-I were negatively correlated dur-ing periods when estrogens and androgens were at theirhighest mean levels (E2 for fall females: r = ¡0.298; 11-KTfor winter males: r = ¡0.149).

Seasonal patterns in gonadal steroid concentration may beexplained by the reproduction schedule for lingcod, althoughwe were unable to conWrm this by examining gonadal tissuedue to restrictions on lethal sampling of lingcod. In other Wshspecies, circulating estrogen (E2) and androgens (T and 11-KT) can serve as indicators of reproductive status (Robertset al. 1999). For example, plasma levels of E2, 11-KT, and Thave been found to increase with male and female gonadaldevelopment within the annual reproductive cycle and peakduring spawning for common snook (Centropomus undeci-malis; Roberts et al. 1999), black rockWsh (Sebastes melan-ops; Haukenes et al. 2008), and Atlantic cod (Gadus morhua;Dahle et al. 2003). We hypothesize that the high levels ofgonadal steroids observed during the fall (November) forfemale lingcod and winter (February) for males correspondedwith gonadal maturity. To our knowledge, there is no pub-lished information on lingcod gonadosomatic indices orgonadal histology that might inform our interpretation of sea-sonal gonadal steroid patterns. However, in a Californiastudy, the frequency of female lingcod with mature ovaries(staged macroscopically) increased from October to Januaryand declined to lowest levels in February and March immedi-ately following spawning (Silberberg et al. 2001). The fre-quency of male lingcod with ripe stage testes peaked inMarch, with the immature stage prominent from April toJune (Silberberg et al. 2001).

Variation in androgen levels in males during the wintercould also be related to aggressive behavior, as observed inother nest guarding species (Rodgers et al. 2006). Previousstudies of male nest guarding teleosts suggest that complexrelationships exist among androgen levels, reproductivebehavior, and gonadal maturity. In bluegill sunWsh (Lep-omis macrochirus; Magee et al. 2006) and three-spinedstickleback (Gasterosteus aculeatus; Pall et al. 2002), T and11-KT were highest during courtship and pre-spawningperiods and decreased to lower levels during the parentalcare period before rising again when the eggs hatched. Incontrast, 11-KT levels were signiWcantly higher in maleplainWn midshipman (Porichthys notatus) with empty nests

and nests containing only eggs than in males with nestscontaining embryos (Knapp et al. 1999). Understanding thecomplex mechanisms dictating gonadal steroid levels inlingcod during the fall and winter requires measure-ments of gonadal maturity and observations of reproduc-tive behavior.

As an integrator of the physiology of the organism andits environment, IGF-I can serve as an ecological indicatorof Wsh condition in natural populations; however, the use-fulness of this tool hinges on understanding the conditionsin which comparison of IGF-I levels among groups of indi-viduals leads to ecological insight. Interpreting diVerencesin growth between groups of Wsh, as inferred from IGF-I,may be facilitated by comparing individuals within thesame season, size/age class, or maturity stage (Beckman2010); however, determining the appropriate criteria forgrouping individuals is challenging when maturity and ageare unknown. For lingcod and other long-lived species thathave declined in abundance, the necessity of non-lethalsampling limits our ability to measure age, growth, andmaturity. As we have shown, information-theoretic modelselection provides one way to gain insight into complexsystems from observational data (Johnson and Omland2004). Our modeling results suggest that the use of IGF-Ias a growth indicator requires that season and body size areexplicitly accounted for when interpreting endocrinepatterns in wild Wsh populations (e.g., Andrews et al. inreview). For instance, to evaluate diVerences in growthconditions across habitats, it might be beneWcial to sampleIGF-I from similarly sized Wsh within a non-reproductiveseason (e.g., summer or spring for lingcod). Continueddevelopment of endocrine tools may provide a powerfulalternative to lethal sampling in ecological investigations ofWsh growth.

Acknowledgments This research was funded in part by the InternalGrant Program at the Northwest Fisheries Science Center. The Universityof Washington School of Aquatic and Fishery Sciences (UW-SAFS)and Friday Harbor Laboratories provided funding and facilities forWeld work. A.H.B. was supported by the National Science Foundation,ARCS Foundation, and UW-SAFS. We thank the many volunteersinvolved in the collection of lingcod, especially A. Dufault andC. Sergeant. We thank K. Cooper and J. Dickey (UW-SAFS) forconducting the hormone assays. Thanks to three anonymous reviewersfor providing valuable comments on a draft of the manuscript. Theresearch complies with all applicable United States laws, and theauthors declare that they have no conXicts of interest.

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