MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser

Vol. 527: 181–192, 2015doi: 10.3354/meps11260

Published May 7

INTRODUCTION

Populations of many of the ocean’s largest fish havedecreased substantially (Myers & Worm 2003), al -though the magnitudes of the declines are not as uni-versal across regions and species as previouslythought (Sibert et al. 2006). Reducing fishing pres-sure is a necessary condition for recovery of fishstocks, although it may not be sufficient for all spe-cies especially when faced with large changes in

physical conditions or prey abundance (Chavez et al.2003, Lehodey et al. 2003, Vert-pre et al. 2013). Smallpelagic fish such as sardines, herring, and anchoviescomprise ~30% of global fish landings (Alder et al.2008). These fish are critical food resources for largerfish, seabirds, and cetaceans, and due to their abun-dance and trophic position, exert both top-down andbottom-up controls (Cury et al. 2000, Bakun & Broad2003). Over the past 50 yr, landings of small pelagicspecies have scaled with increased demand (Jen-

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

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

*Corresponding author: walter.golet@maine.edu**These authors contributed equally to this work

The paradox of the pelagics: why bluefin tuna can go hungry in a sea of plenty

Walter J. Golet1,2,*,**, Nicholas R. Record3,**, Sigrid Lehuta2, Molly Lutcavage4, Benjamin Galuardi4, Andrew B. Cooper5, Andrew J. Pershing1,2,**

1School of Marine Sciences, University of Maine, College Road, Orono, ME 04469, USA2Gulf of Maine Research Institute, 350 Commercial Street, Portland, ME 04101, USA

3Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA4Department of Environmental Conservation, Marine Fisheries Institute, University of Massachusetts Amherst, PO Box 3188,

Gloucester, MA 01931, USA5School of Resource and Environmental Management, Simon Fraser University, 8888 University Drive, Burnaby, V5A 1S6,

BC, Canada

ABSTRACT: Large marine predators such as tunas and sharks play an important role in structuringmarine food webs. Their future populations depend on the environmental conditions they en -counter across life history stages and the level of human exploitation. Standard predator−prey rela-tionships suggest favorable conditions (high prey abundance) should result in successful foragingand reproductive output. Here, we demonstrate that these assumptions are not invariably validacross species, and that somatic condition of Atlantic bluefin tuna Thunnus thynnus in the Gulf ofMaine declined in the presence of high prey abundance. We show that the paradox of decliningbluefin tuna condition during a period of high prey abundance is explained by a change in the sizestructure of their prey. Specifically, we identified strong correlations between bluefin tuna bodycondition, the relative abundance of large Atlantic herring Clupea harengus, and the energeticpayoff resulting from consuming different sizes of herring. This correlation is consistent withoptimal foraging theory, explaining why bluefin tuna condition suffers even when prey is abundant.Furthermore, optimal foraging principles explain a shift in traditional bluefin tuna foraging areas,toward regions with a higher proportion of large herring. Bluefin tuna appear sensitive to changesin the size spectrum of prey rather than prey abundance, impacting their distribution, reproductionand economic value. Fisheries managers will now face the challenge of how to manage for highabundance of small pelagic fish, which benefits benthic fishes and mammalian predators, andmaintain a robust size structure beneficial for top predators with alternative foraging strategies.

KEY WORDS: Bluefin tuna · Herring · Optimal foraging · Condition · Thunnus thynnus

OPENPEN ACCESSCCESS

Mar Ecol Prog Ser 527: 181–192, 2015

nings et al. 2001, Boyd et al. 2006), but there is growing concern regarding the exploitation of these species (Alder et al. 2008, Tacon & Metian, 2009). Inparticular, management strategies have the potentialto alter food web dynamics and energy flow throughchanges in the size structure and abundance of thesespecies (Furness 2002, Frederiksen et al. 2004). Con-ventional management practices may be inadequateto maintain sufficient biomass of small pelagic spe-cies for fisheries and the predators which depend onthem (Pikitch et al. 2012). The impact of changes inpelagic fish stocks may be particularly high for mar-ine mammals (Spitz et al. 2012) or other large warm-bodied fish such as tunas whose physiology is gearedtoward high energetic returns while foraging.

The Atlantic bluefin tuna Thunnus thynnus is ahigh ly migratory species with an Atlantic-wide dis -tribution (Mather et al. 1995), exploited by multiplecoun tries across the Atlantic basin due to the highvalue of its flesh (Bestor 2004). Maintaining meta-bolic costs for bluefin tuna, like other large marinepredators requires high rates of energy intake (Spitzet al. 2012) and their migratory patterns and foragingbehaviors are adapted to exploit dense aggregationsof lipid rich prey (Chapman et al. 2011).

In the western Atlantic, bluefin tuna are distri -buted across the Northwest Atlantic Shelf from Junethrough to October (Wilson et al. 2005, Galuardi et al.2010), and the Gulf of Maine is one of the most impor-tant foraging grounds (Mather et al. 1995, Wilson etal. 2005, Galuardi et al. 2010). Mid-trophic levels inthis region are dominated by small lipid-rich plankti -vorous fish (Lawson et al. 1998), principally Atlanticherring Clupea harengus, the bluefin tuna’s preferredprey (Chase 2002, Estrada et al. 2005, Pleizier et al.2012). During the summer foraging period, bluefintuna build up large reserves of fat in their muscles andperigonadal mesenteric tissues. The quantity of fatdetermines migratory capacity (Chapman et al. 2011)and reproductive output (Medina et al. 2002, Knappet al. 2014), and is a primary factor that determinesthe market price of an individual fish (Bestor 2004).

The population of herring, the primary forage spe-cies for bluefin tuna in the Gulf of Maine (Chase2002), experienced substantial fluctuations in abun-dance since the 1960s due to harvesting pressureand environmental variability (Overholtz & Friedland2002). Since rapid depletion during the 1970s, theherring stock has rebounded to historically highlevels of abundance beginning ca. 1990 (Overholtz &Friedland 2002). Given that herring is the primary for-age for bluefin tuna and that herring abundance washigh, foraging conditions in the Gulf of Maine should

have been favorable for bluefin tuna thereby increas-ing their lipid accumulation, boosting reproductiveoutput, and increasing their economic value. Duringthis period of high herring abundance, however, bodycondition of bluefin tuna based on market grades de-clined in the Gulf of Maine (Golet et al. 2007).

Based on recent stock assessments by the Interna-tional Commission for the Conservation of AtlanticTunas (ICCAT), bluefin tuna spawning stock biomass(SSB) is down approx. 70% from levels observed inthe early 1970s (ICCAT 2010). Despite 30 yr of regu-lated catches in line with scientific advice, and a pop-ulation rebuilding plan implemented in 1998, therehave been minimal increases in SSB for the westernstock and low levels of recruitment relative to obser-vations in the early 1970s. Assuming these regulatorymeasures were correct, a lack of rebuilding couldsuggest that variables unaccounted for in assess-ments (e.g. changes in ecosystem conditions) may beinhibiting population rebuilding. In fact, the stockassessment for bluefin tuna is run based on 2 differ-ent scenarios; a high and low recruitment scenario.The latter suggests that conditions are no longercapable of sustaining the high recruitment observedin the early 1970s due to ecosystem shifts either onthe foraging or spawning grounds.

We evaluated the somatic condition of bluefin tunathroughout the entire Gulf of Maine over a 30 yr pe-riod of high and low herring abundance to determinemechanisms for the observed paradox of low bluefintuna condition during a period of presumed high for-aging profitability (i.e. the ‘paradox of the pelagics’).We quantified the energetic returns of bluefin tunaforaging on different size spectra of herring acrossdif ferent regions of the Northwest Atlantic shelf usingan optimal foraging model. A time series of bluefintuna catch locations was created from logbook datacollected from the commercial bluefin tuna fisheryto check for shifts in distribution as they related tochanges in foraging conditions.

MATERIALS AND METHODS

Condition analysis

Bluefin tuna

This study is based on body length and weight dataof bluefin tuna individuals that were commerciallyharvested between 1980−2009; data were acquiredfrom the National Marine Fisheries Service (NMFS).Each entry included the date landed, length, weight,

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gear type (i.e. rod and reel, purse seine, longline,harpoon), and catch location (10 statistical areas fromthe Gulf of Mexico to the Gulf of Maine). We definedour research area to include the historical and cur-rent foraging grounds for adult (>185 cm) bluefintuna. These areas included the Gulf of Maine,Georges Bank and waters north of Montauk, NY(NMFS Statistical Areas 1−6) from 1 June to 31 Octo-ber (Fig. 1). All lengths and weights were standard-ized to current reporting requirements for length(curved fork length, CFL) and weight (round weight,fish intact) using conversion factors accepted by theStanding Committee on Research and Statistics with -in the ICCAT convention (Parrack & Phares 1979).Outliers in length-weight relationships (based onquality checks across all fish sizes and gear types)were removed with a standard deviation filter thatcalculated the mean fish size across all years, strati-fied by day (from 1 June to 31 October), and removedall values lying >3 SD from each respective size classmean. To reduce potential bias associated with com-bining different gear types, only rod and reel fish(general category) were included for further analysis.This gear type accounts for approx. 75% of landingsin the region. In this analysis, the lower and upperbounds for lengths of fish were from the current com-mercial min. size (185 cm CFL) to a cut-off point(305 cm CFL), where sufficient numbers of fish al -lowed for statistical comparison. The current min.

size coincides with a point in development wherebluefin tuna transition from a period of rapid growthin length to girth, adding seasonal lipid stores (Ma -ther & Schuck 1960). Collectively, this filtering re -duced our initial database from 127 742 to 70466individual length and weight records.

Atlantic herring

The Maine Department of Marine Resources (USA)and the Canadian Department of Fisheries andOceans have been collecting herring from commercialfishing fleets for over 3 decades to monitor basic bio-logical parameters. Individual entries from thesedatabases for each fish include date sampled, length,weight, age, sex, and gear type. Both the USA andCanadian databases were filtered using the sametemporal boundaries as described for bluefin tuna(June through October). In the case of the Maine her-ring database, spatial boundaries were set accordingto those outlined in the bluefin tuna section for historicand current bluefin tuna distribution. Only herringsampled from mobile fishing gear (e.g. purse seine,trawl) were used in this analysis since the herringfleet’s distribution overlaps with known foraginggrounds of bluefin tuna. Fixed gear (e.g. stop seines,weirs) in the Gulf of Maine occur in areas with limitedbluefin tuna foraging activity and typically catch her-ring absent from diet studies of bluefin tuna (<180 mmstraight fork length). Herring sampled from mobilegears ranged from 73−395 mm straight fork lengthand 2−572 g round weight. The original Maine data-base contained 265 900 individual en tries. We filteredthe database removing herring smaller than 180 mm,and outliers in the length-weight relationship follow-ing the same protocols used in the bluefin tuna data-base. This procedure re duced the number of individ-ual herring to 160 899. For the Canadian herringdatabase, we confined our herring samples to regionswith historic and current distributions of bluefincatches (Hanke et al. 2013). Areas used for this analy-sis included 4W (Scotian Shelf north of Halifax, NS),and 4T (Gulf of St. Lawrence, Prince Edward Island)(Fig. 1). We ex cluded the Bay of Fundy and southernNova Scotia (4X) since this area is adjacent to the Gulfof Maine. Outliers in the length-weight relationshipwere re moved using the same standard deviationmethods described for bluefin tuna. After filtering thisdatabase there was a total of 289 907 individual her-ring length and weight records available for analysis.We acknowledge the potential for gear selectivity inthese data sets given their origin is from the commer-

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Fig.1. Northern foraging grounds for Atlantic bluefin tunaThunnus thynnus in (1) the Gulf of Maine, USA, (2) Scotian

Shelf and (3) the Gulf of St. Lawrence, Canada

Mar Ecol Prog Ser 527: 181–192, 2015

cial fishery. The herring fishery in the Gulf of Mainehas undergone considerable regulatory changeswhich have altered the proportion of the catch by geartype. The biological sampling database does not dis-tinguish between mobile gear types. Thus, it is notpossible to explore potential gear selectivity or relateto which gear these samples came from. However, theMaine De partment of Marine Resources does collectlandings information from commercial herring fisher-men and we used this as a proxy for how the com -position of our samples may have changed. From1980−1994, purse seines accounted for more than80% of the herring captured by mobile gear. Trawllandings began to increase in 1993, and by 1997, ap-prox. equal percentages of herring were landed bytrawl and purse seine. Between 2000−2006, trawl gearlanded ap pro ximately 30% more herring than purseseine and, from 2007−2009, the trend reversed again.However, herring mean weight and length declinedthroughout the time series and did not change as theratio of landings changed between gear types. Giventhe sizes of the mesh, no. of fish, and the time and ar-eas sampled, it is assumed that changes in lengthsand weights of herring are representative of popula-tion level changes and not fleet selectivity. The bio-logical samples collected from the Canadian commer-cial herring fishery are from purse seine gear only.

Seasonal foraging activity in both bluefin tuna andherring lead to seasonal cycles of weight accumu -lation. Since we were interested in year-to-yearchanges in condition, we accounted for these cyclesusing a generalized additive model (GAM) (Ham-ming 1973, Hastie & Tibshirani 1990, Maravelias etal. 2000). The dependent variable (weight) was mod-eled as a function of 2 independent variables (length,day of year). This non-parametric regression is lessre strictive regarding the distribution of the data andweight can be modeled as the additive sum of non-parametric smooth functions of covariates. In thisanalysis, the least squares estimate in the regressionwas replaced with a smoother (s). The general formof the GAM (Hastie & Tibshirani 1990) was:

(1)

where E (y) = the expectation of the random variabley (here bluefin or herring weight as a function oftime), g() = the link function between the responseand additive predictor, β0 + ΣkSk (X), β0 = an interceptterm, Xk = the value of the kth covariate, Sk() = smoothfunctions of the k covariates. Here the de pendentvariable (bluefin or herring weight) is modeled as afunction of day of year and individual fish length. Af-

ter fitting the model to each of the 2 data sets, a meanweight was calculated for a given length by day ofyear across years. To calculate the anomalies we sub-tracted the observed weight of each fish from the ex-pected weight in the length-weight relationship andstandardized by dividing this value by the expectedweight. That is, the anomaly value for a given year isthe mean of [W(y) – E(y)]/E(y) for all weight measure-ments W(y) and expected weights E(y) across all sizeclasses of fish and all days within that year (seeFig. 2A). We applied the same calculation for annualherring weight anomalies (see Fig. 2B).

Foraging model

A payoff-maximizing prey model was used to com-pute the average relative energetic payoff rate for atuna-like predator foraging on a given diet of her-ring. Implicit in the use of this model is the assump-tion that herring, as a prey resource, is not scarce.This assumption breaks down for very low preyabundances; however, herring abundance was rela-tively high in the Gulf of Maine throughout the timeseries. We followed the methodology and notation ofthe ‘Prey Model’ (Stephens & Krebs 1986, §2.2) forthe foraging model described below. Following thatformulation, the payoff rate for a given diet is:

(2)

where n is the number of prey types in the diet, iindexes prey type (in this case, prey size), hi is theexpected handling time per prey item, ei is the netenergy gain per prey item, λi is the encounter rate, sis the energetic search cost per unit time (so that shi

is the energetic search cost per prey item), and pi isthe probability that an item of type i will be attackedupon encounter. Stephens & Krebs (1986) demon-strate that under the payoff-maximizing assumption,pi is either 0 or 1.

Encounter rate is defined as:

(3)

where Ni is the number of individuals of size i(weight, g), NT is the total number of individuals, andλT is the encounter rate for all prey. Energy wasobtained from mass using a constant energy densityconversion, k; that is, ei = kmi. Prey type was binned

E y g S Xk kk

( ) ( )–10 ∑= β +⎛

⎝⎜⎞⎠⎟

P

p e sh

p h

i i i ii

n

i i ii

n

( )

1

1

1

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λ +

+ λ

=

=

N

N

NN

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ii

ni

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TT∑

λ = λ = λ

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Golet et al.: Bluefin tuna foraging

by the mass of the prey in increments of 10 g. Weassume a single handling time h across size classes,and we assume that variability in caloric content ofherring of a particular weight is small (Lane et al.2011) and thus, that the payoff of eating a particularprey is proportional to its mass.

One of the results derived from this model byStephens & Krebs (1986) is that the predator’s dietwill be defined by a threshold prey size x, where thepredator eats any prey larger than x when encoun-tered and ignores fish smaller than x. Using thisresult, and the substitutions in Eqs. (2) & (3), the pay-off for a diet x is then:

(4)

whereby the optimal diet in this case is given by thevalue x* for which P(x) is maximal. The optimal dietand resulting payoff were found by testing all values ofx, using the full range of prey sizes represented in thedata. Parameter estimates were identified using a com-bination of first-hand observations and literature val-ues. The basic assumption is that food is not limiting,and by implication, that foraging decisions are thosemade while in the presence of prey, rather than deci-sions regarding when, where, and how long to searchfor schools of prey. Presumably, the latter would takeon a more substantial role as food be comes scarce;however, current prey levels are extremely high in theGulf of Maine. Correlation analysis was conducted between the time series for bluefin tuna weight anom-alies and herring weight anomalies, herring SSB, mini-mal herring mass (g) and herring profitability.

Parameter estimation and sensitivity

There are 3 parameters to estimate: s (search cost),h (handling time), and λT (encounter rate). The re -sults presented below used best estimates for theseparameters; however, due both to lack of informationand to the complexity of the feeding process, there islarge uncertainty around these estimates. Therefore,upper and lower bounds were also estimated for eachparameter, representing extreme cases, to test thesensitivity of our results to the estimated paramete -rization. These estimates considered tuna activelyforaging within schools of herring (as opposed tosearching for schools or prey patches), with an as -sumed constant energy density (Lawson et al. 1998)for the prey of k = 9.4 kJ g−1.

Search cost was computed as the metabolic expendi-ture during foraging, estimated from values repor tedin the literature for other tuna species. Routine meta-bolic rates of southern bluefin tuna range from a baseline of around 350 mg O2 kg−1 h−1 to upwards of1200 mg O2 kg−1 h−1 in extreme cases during feedingevents (Fitzgibbon et al. 2008). For a 200 kg tuna, thisranges from 70000−240000 mg O2 h−1. Using theconversion factor (Olson 1982) 1 cal = 3.359 mg O2 h−1

and converting cal h−1 to kJ s−1 gives a range of0.02−0.08 kJ s−1. These represent upper and lowerbounds, and s = 0.05 kJ s−1 was used as an intermedi-ate estimate.

Deck-based observations of bluefin tuna feedingsuggest very rapid consumption of fish. Bluefin tunaare ram feeders, using burst swimming to force preyinto their mouth. A lower bound on handling time istherefore on the order of a few seconds. Handlingtime should also consider gut content limitations,however, as a bluefin tuna consuming a 100 g her-ring every 5 s could double its own mass in just a fewhours. A handling time of 10 s is therefore a reason-able estimate for a lower bound. An upper bound onhandling time can be estimated from ingestion rates(I, kJ mo−1) predicted by an energetics model: I = aLb,whereby L is the length of the tuna (cm), and a and bare free parameters. Using the range of values from abluefin tuna bioenergetics model (Chapman et al.2011), and assuming a mean prey of 100 g at 9.4 kJg−1, gives an upper bound for handling time of around20 min, an unrealistically high estimate. Bluefin tunaspend an estimated 30−45% of their time in the Gulfof Maine feeding in prey patches (Gutenkunst et al.2007). Accounting for this factor puts an upper boundfor handling time at 400−600 s, still representing aconservatively high estimate. Bounds for handlingtime are therefore 10−600 s, with h = 30 s as the inter-mediate estimate.

It is difficult to estimate the rate at which anactively feeding bluefin tuna makes a foraging deci-sion. Therefore a range spanning 3 orders of magni-tude was used, from λT = 0.01 herring s−1 to 1 herrings−1, with λT = 0.1 herring s−1 as the intermediate esti-mate. Output from the optimal foraging analysis cre-ated 2 time series: profitability of the herring assem-blage P(x) (kJ s−1) and the mass of the smallest fish inan optimally foraging bluefin tuna’s diet (x*). To testsensitivity of the results on parameter choice, corre-lation coefficients were computed between profit -ability and bluefin tuna weight anomalies, testing thefull parameter space (bounded by upper and lowerbounds for each parameter and subdivided by hun-dredths). Correlations were highly statistically signifi-

P x

N m sN

N h N

i ii x

n

ii x

n( )

–T T

T T

∑

∑=

λ ⎡⎣⎢

⎤⎦⎥

+ λ

=

=

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Mar Ecol Prog Ser 527: 181–192, 2015

cant (p < 0.001) throughout the parameter space,with r2 values ranging from 0.60 to 0.65, suggestingthat the main results are not sensitive to parameterchoice.

RESULTS

From 1980−2009, the body condition of bluefintuna declined. Body condition of bluefin tuna drop -ped rapidly, was at intermediate levels during themajority of the 1980s, and then declined further inthe early 1990s (Fig. 2A).

The decline in bluefin tuna weight anomalies inthe early 1990s tracked a decline in herring weightano malies (Fig. 2B). Although, the correlation be -tween the bluefin tuna and herring weight anomalyseries is significant (r2 = 0.33, p < 0.01), the relation-ship weakens during the last half of the time seriesdue to an increase in herring condition that was notmatched by an increase in bluefin tuna condition.The lack of correlation in the second half of thetime series and the dominance of younger (<4 yrold) but better conditioned herring suggest a syner-gistic interaction between bluefin tuna conditionand herring size. To test this hypothesis, we devel-oped a time series of mean herring weight (g) forthe Gulf of Maine. This time series exhibits astrong and consistent relationship with bluefin tunaweight anomalies (r2 = 0.61, p < 0.01, Fig. 2C). Theassociation between declining bluefin tuna weightanomalies and mean herring weight seems obvious,however, the question still remains as to why a500% increase in herring abundance was insuffi-cient to make up for the decline in mean herringweight.

We interpret the correlation between bluefin tunaweight anomalies and mean herring weight as evi-dence of an optimal foraging strategy occurring insub-optimal conditions. Optimal foraging theorypredicts that predators should always consume themost profitable prey items encountered (Stephens& Krebs 1986); for bluefin tuna, this is the largestsize class of herring. To test this assumption, we

186

Fig. 2. (A) Estimated spawning stock biomass (SSB) for At -lantic herring (Clupea harengus) complex (red line) andweight anomalies for Atlantic bluefin tuna Thunnus thynnus(blue line) (n > 70000) in the Gulf of Maine; these series arenegatively correlated (r2 = 0.47, p < 0.01). (B) Weight anom-alies of Atlantic herring (red line) (n > 260 000) and Atlanticbluefin tuna (blue line) in the Gulf of Maine; these series arepositively correlated (r2 = 0.33, p < 0.01). (C) Atlantic bluefintuna weight anomalies (blue line), mean weight of herringfrom the Gulf of Maine stock complex (red line), and the dietprofitability (black line) from the optimal foraging model.There were significant relationships between bluefin tunaweight anomalies and the mean weight of Atlantic herring(r2 = 0.61, p < 0.01), and between bluefin tuna weight anom-alies and the diet profitability of Atlantic herring (r2 = 0.62,

p < 0.01). Shaded regions represent the 99% CI

Golet et al.: Bluefin tuna foraging

used the Ste phens & Krebs (1986) optimal foragingprey model for bluefin tuna foraging within a patchof herring. The resulting time series of diet prof-itability was strongly correlated with bluefin tunaweight anomalies (r2 = 0.62, p < 0.01, Fig. 2C), andthis relationship was not sensitive to parameterchoices.

While mean herring weight in the Gulf of Mainedeclined by 55% between 1981 and 2010 (Fig. 3A), aherring weight index for the adjacent Scotian Shelfand Gulf of St. Lawrence, and optimal foragingmodel time series were relatively steady and re -mained elevated above the Gulf of Maine indexthroughout the record (Fig. 3). The time series of her-ring weight indicated that larger herring were stillavailable on the Canadian Scotian Shelf and Gulf ofSt. Lawrence, even as their abundance declinedin the Gulf of Maine. The steady decrease in her-ring mean weight in the Gulf of Maine correlateswith the shift in the distribution of adult bluefintuna schools, which moved progressively eastward(Fig. 3B). There was a significant relationship be -tween the difference in herring weight from the Gulfof Maine and Canadian waters (Scotian shelf andGulf of St. Lawrence) and the timing of the eastwardmovement of bluefin tuna catch locations (r2 = 0.40,p < 0.01, Fig. 3B).

DISCUSSION

The results from this study support the observationsof Golet et al. (2007) that the condition of blue fin tunain the Gulf of Maine has declined. In the presentstudy, however, bluefin tuna condition was evaluatedover a much longer time period and throughout theentire Gulf of Maine whereas Golet et al. (2007) onlytracked condition of bluefin tuna from a single spa-tially restricted area. The Gulf of Maine is an impor-tant foraging ground for bluefin tuna (Mather et al.1995) where these fish spend up to 6 mo consuminghigh energy prey such as herring (Chase 2002). Fol-lowing depletion during the 1970s, herring stocksin the Gulf of Maine rebounded to historically high levels of abundance around 1990 as stocks on the offshore banks were rebuilt (Overholtz & Friedland2002, Melvin & Stephensen 2007). The decline ofbluefin tuna condition during a period of such highprey abundance is contrary to expectations, particu-larly since foraging models suggest the proportion ofherring in the bluefin diet has increased (Overholtz2006) across the time series presented in this paper.Conditions on the foraging grounds during the 1990sand 2000s for western bluefin tuna appear favorablegiven the high abundance of their main prey, herring(Chase 2002) and should have resulted in bluefin tuna

187

Fig. 3. Regional differences in herring Clupea harengus mass and profitability. (A) Mean weight of Atlantic herring from theGulf of Maine (red line) and from Canadian waters: Scotian Shelf and Gulf of St. Lawrence, combined (black line). Bluefintuna Thunnus thynnus catch per unit effort (CPUE) in the Gulf of St. Lawrence (blue line) increased rapidly since 2002.(B) Position of bluefin tuna schools observed or captured by the commercial fleet (blue line), the difference in meanherring weight between the Canadian and Gulf of Maine waters (red line), and the diet profitability for Canadian waters(black line). The movement of tuna schools to the east was positively correlated with the difference between the mean herring weight in the Gulf of Maine and the mean weight in Canadian waters (r2 = 0.40, p < 0.01). Shaded regions represent

the 99% confidence intervals

Mar Ecol Prog Ser 527: 181–192, 2015

with higher than average weight anomalies. This sce-nario (i.e. high prey abundance increased predator suc -cess) is typical for many species (Marshall et al. 1999,Öster blom et al. 2008); yet in this study, bluefin tunabody condition is inversely correlated with prey abun-dance. We hypothesize that the decline in blue fintuna condition in the Gulf of Maine is a consequenceof a foraging strategy that depends on larger, moreenergetically profitable prey, in this case herring.

We used several approaches to evaluate this hypo -thesis. First, we developed a time series of herringweight anomalies which was significantly correlatedwith changes in the condition of bluefin tuna. Despitethe significance of this relationship, however, the correlation became weaker in the 1990s which weattribute to the increase in shorter, but better con -dition herring (<3 yr old). We tested this hypo the -sis by constructing a time series of mean herringweights and correlated these with bluefin tunaweight anomalies. The correlation between meanherring weights and bluefin weight anomalies wasmuch stronger and suggests that bluefin require abroad size spectrum of Atlantic herring in order fortheir foraging to be profitable. Still, the questionremains why a 500% increase in herring abundancewould not substitute for the loss of larger individualsin the herring population. We attribute this to a for-aging strategy that relies on optimizing energy in -take by consuming the largest prey in the population.We tested this by developing a foraging model thatpredicts the energetic outcomes of bluefin foragingwithin herring schools composed of different sizes ofherring. The subsequent time series (energy/ time)was highly correlated with the changes in bluefintuna weight anomalies, supporting our hypothesisthat the most successful foraging environments forbluefin tuna are those with the largest prey.

Despite numerous examples of predator success inenvironments with high prey abundance, there isgrowing evidence that predators, particularly oneswith higher metabolic rates, use different strategiesto maximize energy intake (Spitz et al. 2012). This isin line with recent studies which identify benefits togrowth and reproduction for species with foragingstrategies that select a prey base with high energeticvalue and/or a broad size distribution (Pazzia et al.2002, Sherwood et al. 2002, Spitz et al. 2012, Giaco-mini et al. 2013), as opposed to one which relies entirely on abundance. Tunas, particularly bluefin,have elevated metabolic rates relative to other tele -osts (Brill 1996), making them closer (in metabolicdemand) to marine mammals and seabirds in thatthey require high rates of energy intake to satisfy

their basal metabolic rates, large migratory (Block etal. 2005, Wilson et al. 2005, Galuardi et al. 2010) andreproductive capacities (Medina et al. 2002, Knapp etal. 2014). Given these demands, it is not surprisingthat bluefin tuna consume a disproportionate amountof herring, which have high lipid content and ener-getic values relative to other abundant prey re sour -ces on the Northwest Atlantic shelf (Lawson et al.1998). This may be why, despite a decrease in Atlan -tic herring size distribution, substantial chan ges inherring abundance and the availability of alternativeprey resources (sandlance, bluefish, mackerel), blue -fin tuna diet has been stable over time (Chase 2002,Pleizier et al. 2012, Logan et al. 2014).

Results from our foraging model are in line with anumber of other studies which show that foragingstrategies, particularly in pelagic environments, aresensitive to the size of prey. Giacomini et al. (2013)identified foraging costs as the mechanism for differ-ences in prey profitability whereas our model sug-gests handling time is the limiting factor given it doesnot scale with prey size. In other words, given thatbluefin tuna are ram feeders, the time to process alarge or small herring is the same, but the energeticpayoff between the two is quite different (Lane et al.2011). Both our model and Giacomini et al. (2013)demonstrate that particulate feeders in pelagic envi-ronments need to target larger prey. Despite mecha-nistic differences, both studies emphasize the advan-tages of consuming fewer larger prey vs. the moreabundant, smaller ones. Likewise, in the case of blue -fin tuna in this study, animals with similarly highmetabolic demands and high energy content diets(e.g. sea birds and sea lions) have had trouble adjust-ing to changes in prey quality (Diamond & Devlin2003, Rosen & Trites 2004).

There are several alternative explanations forchanges in the condition of bluefin tuna. Consump -tion of other less energetic prey species could reduceenergetic payoffs and results in lower somatic con -dition. Chase (2002) identified sandlance as an im -portant prey item in stomachs collected from bluefintuna in the Gulf of Maine and showed that bluefintuna feed on bluefish, cephalopods and mackerel.However, sandlance were found in 2 primary loca-tions, whereas herring were observed in all stomachsacross the region; and cephalopods, mackerel andblue fish had minor contributions across all areas.Similarly, Logan et al. (2014), using stomach contentanalysis and stable isotopes from bluefin tuna col-lected between 2004−2008, suggest herring is still themost important dietary item for bluefin tuna in theGulf of Maine. Pleizier et al. (2012) identified herring

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as the dominant prey item in the stomachs of bluefintuna in the Gulf of St. Lawrence and Nova Scotia.Herring accounted for >50% wet wt of each stomachwith the next highest contribution (10%) coming frommackerel. The percentage of herring in the diet ap-pears rather consistent both between re gions andacross decades, supporting the idea that bluefin tunarequire high quality prey to satisfy their energeticneeds despite being considered a generalist predator.

Given the consistency of herring in the bluefin dietand the increase in consumption over the past 2 de -cades (Overholtz 2006), it is likely that changes in thequality of herring are contributing to the observedde clines in bluefin tuna condition. The continueddecline in mean weight and length of herring sug-gest changes in their growth patterns which maycome from top-down or bottom-up processes. Thelack of older and/or heavier herring could be a resultof fishing selectivity, removing older age classes;however, assessments indicate that fishing mortalityfor Atlan tic herring is low (TRAC 2009). Observedchanges in body condition of herring has been linkedto density dependence (Melvin & Stephensen 2007),but substantial bottom-up changes have also oc -curred in the Gulf of Maine, which may have affectedherring growth (Greene et al. 2013) by altering forag-ing conditions for herring. Thus, the paradox of lowbluefin tuna body condition and high herring abun-dance results from an interaction between the blue -fin tuna foraging strategy and a shift in the herringsize spectrum towards smaller fish; the exact mecha-nism for this remains unclear.

However, our results suggest bluefin tuna are re-sponding to changes in foraging conditions in the Gulfof Maine. Supporting this hypothesis is the high catchper unit effort in Nova Scotia and the Gulf of St.Lawrence (Hanke & Neilson 2011) and our analysiswhich shows the mean position of bluefin tunacatches in the Gulf of Maine shifting eastward out ofthe Gulf of Maine and across the USA−Canadianboundary (Fig. 3B) (Golet et al. 2013). We hypothesizethat as mean weight of Atlantic herring declined, for-aging profitability declined and fewer bluefin tunahave remained in the Gulf of Maine, analogous to the‘win-stay, lose-shift’ hypothesis (Randall & Zentall1997). As a consequence of lower foraging quality,more individuals are likely to have joined other con-tingents and sought additional regions for larger andpresumably more profitable prey, in this case largerherring. Our time series of herring profitability on theScotian Shelf and Gulf of St. Lawrence illustrates thatforaging conditions were much better there than inthe Gulf of Maine. Our time series of bluefin tuna

school locations correlated with the difference in foraging profitability suggesting bluefin tuna were re-sponding to lower foraging conditions and moving tothese high profitable foraging areas. Electronic tag-ging data also suggests that bluefin tuna tagged in re-gions of high herring profitability (Scotian Shelf andGulf of St. Lawrence) returned annually to those areas(Galuardi et al. 2010) and their migrations largelyavoided the Gulf of Maine during times when herringmean weight (profitability) was low. Furthermore, thedevelopment of a commercial winter (December/January) bluefin fishery off the coast of North Caro -lina in the mid 1990s may have been a result of declin-ing foraging conditions in the Gulf of Maine. In thisregion, bluefin tuna foraged on an other high-lipidand -energy prey, Atlantic menhaden Brevoortia ty -ran nus (Butler et al. 2010), and there appears to be mi-gratory connectivity between blue fin tuna from theGulf of Maine to the North Carolina region (Wilson etal. 2005). Reduced foraging opportunities in the Gulfof Maine may have led to the emergence of the winterbluefin tuna fishery off the Carolinas and the reten -tion of these fish as they sought additional foraging locations to increase lipid stores.

A reduction in lipid stores can have profound ef fectson fish life history, but identifying exactly what willhappen and what aspects of life history will be af-fected is complicated. Faced with reduced ener gy,spawning age fish can reduce growth and re allocatemore reserves to gonadal investment (Kjes bu et al.1991). Such a strategy can maintain fe cun dity in timesof reduced condition, but may also contribute tohigher rates of post-spawning mortality (Lambert &Dutil 2000). Alternatively, fish may skip spawning intimes of reduced condition (Rideout et al. 2005) orchange reproductive output (fewer eggs, lower qual-ity) for females (Lambert & Dutil 2000). Given the ef-fect of total lipid content on egg production (Marshallet al. 1999), the influence of prey quality on preda -tor life history parameters (Rosen & Trites 2004, Jør-gensen et al. 2006) and the relationship betweenbluefin tuna weight and fecundity (Medina et al.2002, Knapp et al. 2014), a reduction in bluefin tunacondition on the foraging grounds could have affec tedreproductive output, and appears to have chan gedhistorical distributions. Although we did not di rectlyestimate potential losses in reproductive out put, givena fecundity of 28 eggs g−1 (Knapp et al. 2014) declinesin the amount of lipids on the foraging grounds islikely to affect reproduction, particularly since well-conditioned fish have a higher weight-specific repro-ductive rate (Scott et al. 2006). Changes in lipid con-tent also affect the sale price of bluefin tu na, with fat

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content having a large influence on selling price (Be-stor 2004). Fat content is also one of the most im por -tant variables determining whether or not a bluefintuna stays on the domestic market or is ship ped toJapan (where returns are typically high er). Markets,particularly those for highly migratory species arecomplicated; fuel, exchange rates, supply and demandamong other things all contribute to the selling priceof a fish. However, given the large in fluence fat con-tent has on the overall quality of the fish and to whichmarket it will be shipped, the loss in lipids for bluefintuna in the Gulf of Maine is likely to have reducedex-vessel prices. In summary, changes in the ability ofbluefin tuna to accumulate lipids on their foraginggrounds can have far reaching consequences.

According to recent assessments, spawning stockbiomass of western Atlantic bluefin tuna has notrecovered to early 1970s levels despite the imple-mentation of a rebuilding plan in 1998. In addition,total allowable catches have been in line with scien-tific advice for over 2 decades. If fishing mortalitywere the sole factor limiting population recovery,levels of fishing mortality set by management shouldhave allowed this stock to rebuild much faster thancurrently observed, even for a species with long generation times like bluefin tuna. A slow recoverymay be related to fishing mortality set too high,despite the scientific advice, or could be the results ofenvironmental factors affecting life history (growth,reproduction, migration) or synergistic effects be -tween the two. At this time, it is difficult to saywhether or not changes in foraging conditions in theGulf of Maine have led to population level effects.

Our results have direct implications for the man-agement of herring and other small pelagic fishwhich serve as energy conduits in many temperatesystems. Approximately one third of marine fish lan -dings are small pelagic species (Alder et al. 2008).Their high energy density, abundance, and schoolingbehavior make them attractive to commercial fish-eries, and critical food resources for top predatorsincluding larger fish, seabirds, and whales (Weinrichet al. 1997, Pikitch et al. 2012, 2014). Ecosystem-based fisheries management requires managers andscientists to quantify trade-offs between managedpopulations by adopting co-management strategies.Standard predator–prey relationships imply that re -ducing fishing pressure on mid-trophic level speciesshould be beneficial for higher trophic levels. In fact,a major recommendation from a recent small pe -lagics review (Pikitch et al. 2012, 2014) suggestedcatch levels for small pelagics should be reduced by50% in some regions to increase abundance and

account for the high volatility of these populations.The decline in bluefin tuna condition, despite highprey biomass in the Gulf of Maine, suggests thatmanaging for high abundance at middle trophic levels does not guarantee the success of all top pred-ators. In fact, it suggests that for some upper levelpredators, the quality of the prey may be more impor-tant than the overall abundance.

Although the interactions are complex, fisheriesmanagement can directly influence the size structureof managed stocks, primarily through minimum sizerequirements typically based on age or size of matu-ration. The traditional management paradigm of sizeselective harvesting is coming under increased pres-sure (Zhou et al. 2010, Garcia et al. 2012) as fisheriesmanagers and scientists look for viable alternatives toshift from single to multi-species management. Agrowing number of empirical (Jul-Larsen 2003) andtheoretical (Law et al. 2012, Jacobsen et al. 2014)studies indicate that unselective balanced harvestingmay be a more desirable strategy which can retain alarger proportion of older individuals in the populationwhile still maintaining high yields. The negative cor-relation between herring abundance and bluefin tunaweight suggest such a balanced, unselective harvest-ing strategy may be beneficial for predators such asbluefin tuna which pursue and consume one preyitem at a time and require high energy intake. In thispaper, we do not attempt to explain the mechanismsleading to changes in size structure of herring in theGulf of Maine. Studies on components of this assem-blage on George’s Bank suggest the shift to a smallerherring community (as indicated in the negative rela-tionship between herring size and abundance) is a direct re sult of density dependent growth (Melvin &Stephen sen 2007). Increased fishing pressure can reduce density dependence and potentially lead tomore positive weight anomalies for these small pelag-ics. However, intense fishing also reduces the abun-dance of older, larger fish, and management strategiesthat produce a high abundance of faster growing butsmaller individuals would shift the size spectrum to-wards smaller fish. By altering the size spectrum andthe abundance of the prey species, different manage-ment options would favor one predator type over an-other. For example, humpback whales that feed onschools of herring rather than individual fish shouldbe more sensitive to total herring biomass. There isevidence that the Gulf of Maine became a more im-portant foraging ground for humpback whales fol -lowing the increase in herring abundance during the1990s (Weinrich et al. 1997). Our work suggests thatindicators of size structure and body condition should

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be considered when managing prey species to ensurehigher predator fitness. The challenge will be imple-menting such a strategy within the context of currentassessments and management models and determin-ing the relative importance of prey abundance andsize for a range of predators with different foragingstrategies and metabolic needs.

Acknowledgements. We thank the New England bluefintuna spotter pilots and commercial bluefin tuna fishermenfor providing us access to their fishing logbooks. We are par-ticularly grateful to Robert Campbell, whose attention todetail and excellent logbooks brought the changes in condi-tion of bluefin tuna to our attention. We thank the NMFS foraccess to the US bluefin tuna landings database, and theMaine Department of Marine Resources for providing uswith the commercial Atlantic herring biological samplingdatabase. We also thank the Canadian Department of Fish-eries and Oceans for access to the Atlantic herring biologicalsampling database. This work was made possible through aComparative Analysis of Marine Ecosystems (CAMEO)grant from the National Science Foundation and the NMFSto A.J.P. (OCE 1041731).

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Editorial responsibility: Jake Rice, Ottawa, Ontario, Canada

Submitted: December 9, 2013; Accepted: February 25, 2015Proofs received from author(s): April 27, 2015