ARTICLE

Layered effects of parental condition and larval survival on therecruitment of neighboring haddock stocksKevin D. Friedland, Robert T. Leaf, Trond Kristiansen, and Scott I. Large

Abstract: We used remote sensing chlorophyll a concentration data, spring copepod abundance, and individual fish conditioninformation to understand the annual recruitment variability of two neighboring haddock (Melanogrammus aeglefinus) stocks inthe Gulf of Maine region. When we considered the full range of recruitment variability, the abundance of the copepods Calanusfinmarchicus and Pseudocalanus spp. failed to explain the variation in survivor ratio in either stock. However, when we examinedthis relationship with subsets of the data, we found that Pseudocalanus spp. appears to have had an effect on survivor ratio. Thefull range of recruitment variability of the Georges Bank stock was found to correlate with the timing and size of the fall bloomthe year before recruitment, which has been termed the parental condition hypothesis, suggesting that the fall bloom affects thecondition of spawning adults and thus recruitment. The absence of a correlation between fall bloom and recruitment in the Gulfof Maine stock can be attributed to the difference in fall bloom frequency between the two stock areas. It appears that bothparental condition and larval survival affect haddock recruitment; however, the relative impact of these effects depends on thecontrasting nature of ecosystem environmental drivers.

Résumé : Nous avons utilisé des données de télédétection sur les concentrations de chlorophylle a, l’abondance printanière decopépodes et l’embonpoint individuel de poissons pour comprendre la variabilité annuelle de deux stocks voisins d’aiglefins(Melanogrammus aeglefinus) dans la région du golfe du Maine. Quand toute la fourchette de variabilité du recrutement était priseen considération, l’abondance des copépodes Calanus finmarchicus et Pseudocalanus spp. ne pouvait expliquer la variation desrapports de survivants dans l’un ou l’autre des stocks. Cependant, quand cette relation était examinée a la lumière de sous-ensembles de données, nous avons constaté que Pseudocalanus spp. semble avoir un effet sur le rapport de survivants. Lafourchette entière de variabilité du recrutement du stock du banc de Georges était corrélée au moment et a la taille de laprolifération automnale de l’année précédant le recrutement, ce qui a été désigné l’hypothèse de l’embonpoint parental, quiindiquerait que la prolifération automnale a une incidence sur l’embonpoint des adultes en frai et, donc, sur le recrutement.L’absence d’une corrélation entre la prolifération automnale et le recrutement dans le stock du golfe du Maine peut êtreattribuée a la différence des fréquences des proliférations automnales entre les régions des deux stocks. Il semble quel’embonpoint parental et la survie des larves ont tous deux une incidence sur le recrutement d’aiglefins. Toutefois, l’incidencerelative de ces effets dépend de la nature divergente des facteurs environnementaux de l’écosystème. [Traduit par la Rédaction]

IntroductionThe lack of explanatory relationships between spawning stock

biomass (SSB) and recruitment and the complexity of the underlyingprocesses governing recruitment continues to challenge fisheriesmanagers and scientists (Szuwalski et al. 2014). Stock–recruitmentmodels reflect the manner in which populations are controlled byreproductive feedback and the environment regardless of the level offisheries exploitation exerted. These recruitment models have directrelevance to the quality of scientific advice used to set manage-ment quotas for a range of marine species, since stock–recruit-ment relationships are used to define fishing rates and biomassthresholds associated with the maximum sustainable yield (MSY)reference points (Legault and Brooks 2013; Maunder 2012). To im-prove the explanatory power of stock–recruitment relationships,model parameterization has been expanded to include environ-mental covariates (Galindo-Cortes et al. 2010). Comparative stud-ies with multiple models has allowed more confident identificationof stock size drivers, identifying whether these relationships are

primarily related to fishing (Fiksen and Slotte 2002) or ocean climateforcing (Arregui et al. 2006). These modeling exercises often have theadvantage of providing insights on the long-term prospects forrecruitment, thus informing fisheries planning. However, whatcannot be forgotten is that the effective use and evaluation ofenvironmental covariates requires an understanding of the basicunderlying mechanism associated with a particular covariate.

SSB is implicitly a driver of year-class strength; however, thepredictive strength of SSB has been applied with varying degrees ofsuccess, in large measure owing to the governing effects associatedwith the different life stages of a stock (Bradford and Cabana 1997).Perhaps an oversimplification, but we see it as a bifurcation betweeneffects associated with the spawning stock itself, through both bio-mass and condition of the stock and through the survival rates expe-rienced by early life history stages, particularly the first few weeksafter hatching (Leggett and Deblois 1994). It is clear that populationfecundity is related to recruitment variation; however, the linkagebetween condition of the adult population and the number and

Received 10 February 2015. Accepted 2 July 2015.

Paper handled by Associate Editor Michael Bradford.

K.D. Friedland. National Marine Fisheries Service, 28 Tarzwell Dr., Narragansett, RI 02882, USA.R.T. Leaf. Department of Coastal Sciences, Gulf Coast Research Laboratory, University of Southern Mississippi, 703 East Beach Drive, Ocean Springs, MS39564, USA.T. Kristiansen. Institute of Marine Research, 5817 Nordnes, Bergen Norway.S.I. Large. National Marine Fisheries Service, Northeast Fisheries Science Center, 166 Water Street, Woods Hole, MA 02543, USA.Corresponding author: Kevin D. Friedland (e-mail: kevin.friedland@noaa.gov).

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Can. J. Fish. Aquat. Sci. 72: 1672–1681 (2015) dx.doi.org/10.1139/cjfas-2015-0084 Published at www.nrcresearchpress.com/cjfas on 7 July 2015.

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survival probability of the offspring is often less clear (Rickmanet al. 2000). Lambert (2008) provides a review of the modes by whichfish reproduction may be impacted by the condition of spawners,which is often determined by the age composition of the stock(Hsieh et al. 2006) and seasonally variable condition of spawners(Rätz and Lloret 2003). Survival through the early life stages isstrongly related to the overlap in space and time between larvalfish and their prey (Cushing 1996), with reduced survival in yearswhen there is a mismatch. However, the condition of the larvae athatching also plays an important role, as larger eggs and newlyhatched larval fish have a higher chance of survival. Several stud-ies suggest that with a cohort consisting of fish of the same age butvarying sizes, the larger individuals have a higher percent chanceof survival and consequently higher recruitment potential (Hareand Cowen 1997). Several reasons have been invoked to explainwhy “bigger is better”, including (i) larger individuals have moreavailable egg-yolk to sustain them from starvation during the firstcritical days after hatching; (ii) larger larvae are more capable ofcapturing prey as a wider selection of prey sizes are available; and(iii) larger individuals are more mobile, enabling them to betterescape predators (Miller 1997). The condition of individuals com-prising the spawning stock and the survival of the young of theyear seems therefore to be closely linked; however, few studieshave managed to show that this link exists (Campana 1996).

A diversity of ideas have developed around the mechanismscontrolling the recruitment of haddock (Melanogrammus aeglefinus);considering the landscape of these studies, it is necessary to bemindful of the possibility that individual stocks often have uniqueunderlying mechanisms of recruitment control (Houde 2008). Themember–vagrant hypothesis as the mechanism of recruitmentcontrol for haddock has been investigated for the Georges Bankstock, in part owing to the gyre circulation pattern of this ecosys-tem, which has been seen as a unique challenge for the retentionof larval fish (Colton and Temple 1961; Smith and Morse 1985).Though a range of study approaches have been employed, there isno clear evidence that larval transport affects haddock recruit-ment, despite the obvious effect of transport on survival (Boucheret al. 2013; Friedland et al. 2008; Lough et al. 2006). The growth–mortality hypothesis of recruitment control is supported by feed-ing and growth studies, which show that the instantaneousgrowth of haddock larvae is enhanced by the availability of pre-ferred prey (Buckley and Durbin 2006; Mountain et al. 2008). Fur-thermore, a recruitment index based on the ratio of recruited tohatched haddock has been related to shifts in zooplankton com-munity structure on Georges Bank, a finding that explicitly linksthe growth–mortality hypothesis to recruitment (Mountain andKane 2010). The Hjort–Cushing hypothesis, where recruitmentvariation is related to the timing of the spring bloom, is supportedby studies on Scotian Shelf haddock (Platt et al. 2003; Trzcinskiet al. 2013). This theory is explicitly related to the growth–mortalityhypothesis because the bloom is assumed to affect prey speciescomposition and nutritional quality available to haddock larvae.Finally, the parental condition hypothesis (Lloret et al. 2012; Rosaet al. 2010) was applied to haddock based on observations for theGeorges Bank stock, which relates the variation in the fall bloomthe year before spawning to recruitment the following year(Friedland et al. 2008). The premise of this hypothesis is based ona mechanism relating benthic flux from the fall bloom (Kempet al. 2000; Smetacek 2000) as a feeding or provisioning pulse ofenergy to prespawning haddock, thus affecting the fecundity andcondition of gametes. This hypothesis was recently refined withdata that suggests the timing and location of the bloom are criti-cal to the manner in which the adult stock is impacted (Leaf andFriedland 2014). The effect of spawner condition on haddock re-cruitment has been seen in other stocks (Marshall and Frank1999).

Haddock stocks are characterized by episodic recruitments,which complicate efforts to implement sustainable managementapproaches. However, the historical record of recruitment varia-tion in haddock and the availability of long-term environmentaldata allow a comparative approach to understand the relative roleof different forcing factors shaping recruitment patterns. The goalof this investigation was to compare the relative performance (re-cruitment) of two neighboring haddock stocks in the Gulf of Mainearea, the Georges Bank and Gulf of Maine stocks. Recruitment suc-cess, as represented by survivor ratio or recruits-per-spawner, wasconsidered in respect to varying levels of preferred larval prey abun-dances and phytoplankton bloom patterns, including the effects offall and spring blooms, the year before and of recruitment, respec-tively. Hypotheses related to the feeding and condition of spawningfish were extended by developing estimates of relative weight ofhaddock during spring. Finally, the role of environmental contrast asa forcing factor in the relative performance of the two stocks wasconsidered by examining regional-scale fall bloom frequency.

Materials and methods

Haddock stock survivor ratioWe evaluated the effect of zooplankton abundance, phyto-

plankton blooms, and haddock condition on the comparative per-formance of the Georges Bank and Gulf of Maine haddock stocksby calculating survivor ratio based on age-structured populationassessments. The spatial extents of the two stock areas are shownin Fig. 1. Survivor ratios for each recruitment year were calculatedby dividing abundance of age-1 haddock (number of age-1 indi-viduals) by the SSB (t)of the previous year (NEFSC 2012, 2014).Because the survivor ratios were not normally distributed, welog-transformed these data (Friedland et al. 2009).

For comparison with zooplankton abundance, we used survi-vor ratios from the years 1977 to 2010. Survivor ratios were also

Fig. 1. Gulf of Maine and Georges Bank stock assessment areas(Stock Area), areas used to extract zooplankton abundances(Zooplankton), and regions used to extract chlorophyll data tocomputed bloom statistics (Bloom) for the respective stock areas.For the coloured version of this figure, refer to the Web site athttp://www.nrcresearchpress.com/doi/full/10.1139/cjfas-2015-0084.

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compared with phytoplankton bloom data. For comparison withfall bloom data, we used survivor ratios from the years 1997 to2009, whereas for comparison with spring bloom data, we usedsurvivor ratios from the years 1998 to 2010.

Stock area zooplankton abundanceZooplankton abundance data was collected during shipboard

surveys to evaluate the status of the US Northeast Shelf largemarine ecosystem. These surveys began with the MArine Re-sources Monitoring, Assessment, and Prediction (MARMAP) pro-gram that was conducted during the years 1977–1987. MARMAPsample coverage was from Nova Scotia, Canada, to Cape Hatteras,North Carolina. The MARMAP program was discontinued; how-ever, comparative sampling continued, albeit at reduced inten-sity, through the 1990s. Zooplankton sampling was eventuallyincorporated into the Ecosystem Monitoring program (EcoMon),which is ongoing today. Zooplankton collections are made withoblique paired 61 cm bongo trawls constructed with 335 �m meshto a maximum depth of 200 m; samples are distributed each sur-vey using a stratified random sampling design. Zooplankton wereidentified to the lowest taxonomic level possible, resulting intaxa-specific data on abundance and distribution (Kane 2007). Totest the effect of larval feeding opportunity on the survivor ratioof the two subject haddock stocks, we used zooplankton data forwhat we consider to be the key prey taxa for haddock larvae andlimited the sample selections to the respective putative larvaldevelopment. Based on feeding selectivity studies, Pseudocalanus spp.and Oithona spp. were found to be key taxa consumed by larval had-dock (Broughton and Lough 2010). The copepod Calanus finmarchicuswas also consumed by larval haddock, but were not found to bepreferentially selected. Despite this, C. finmarchicus is includedin the analysis because of its dominant role in the ecosystem.Oithona spp. is not well sampled by the bongo trawl gear duringspring, so this taxon was not considered further. We developedindices of C. finmarchicus and Pseudocalanus spp. abundances fortwo larval develop areas (Fig. 1) during April; the selection of these

areas and time frame was in part guided by the distribution of firstfeeding larvae of allied species (Friedland et al. 2013).

The abundances of the copepod taxa C. finmarchicus and Pseudocalanusspp. were correlated with the survivor ratio of the Georges Bankand Gulf of Maine stocks over the time series of spring zooplank-ton abundance data. In addition, an analysis based on subsamplesof the data was also employed. In the report by Mountain andKane (2010), their figure 8b shows the relationship between recruits-per-hatch (eggs) for the Georges Bank stock and ZooX, a variablederived from a multidimensional scaling analysis of the GeorgesBank zooplankton community. This figure shows that recruits-per-hatch were positively correlated with ZooX over a set of yearsassociated with large-scale plankton studies. There were 14 re-cruitment years used in the Mountain and Kane analysis; we tookthe survivor ratio data for the Georges Bank stock and orderedthese data by the size of the recruitment and marked the coordi-nates that were used by Mountain and Kane in their analysis(Fig. 2). The organization of the data shows that the Mountain andKane analysis was drawn from the intermediate recruitments ofthe time series and do not include the five highest or lowestrecruitment years. To explore the effect of reducing the range ofdata to years with intermediate recruitments, we reexamined therelationship between C. finmarchicus and Pseudocalanus abundancesand survivor ratio by analyzing ordered subsamples. For four sub-sample sizes (12, 14, 16, and 18 date pairs), we computed acorrelation using a subsample of the coordinates with the low-est recruitments and progressively adding the coordinate withnext highest recruitment while removing the coordinate with thelowest recruitment of the subsample.

Characterization of the fall and spring bloomsThe relationship between haddock survivor ratio and the tim-

ing and dimension of fall and spring phytoplankton blooms wastested for the two haddock stocks. Bloom dynamics were charac-terized using remotely sensed chlorophyll a concentrations. Thesedata were extracted from the two areas associated with the respective

Fig. 2. Survivor ratio ordered by recruitment for the Georges Bank stock over the period 1977–2010; coordinates within a gray circle arerecruitments analyzed in figure 8b of Mountain and Kane (2010).

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stock areas (Fig. 1); chlorophyll extraction areas are based on pro-duction units used in ecosystem assessments (Lucey and Fogarty2013). Bloom dynamics were also characterized as a gridded spa-tial analysis based on a 0.5° square spatial grid over the full extentof the Northeast Shelf. Chlorophyll a concentrations were derivedfrom level 3 processed data, at 9 km and 8-day spatial and tempo-ral resolutions, respectively, from both the Sea-viewing WideField of View (SeaWiFS) and Moderate Resolution Imaging Spec-troradiometer (MODIS) sensors (oceancolor.gsfc.nasa.gov/cms). Thesetwo sensors provide overlapping data observations that reveal asystemic bias between the two sensors. A bias correction was ap-plied by calculating factors applied to MODIS data to approximatethe mean levels of the SeaWiFS data. The chlorophyll a concentra-tions (mg·m−3) were calculated by taking the mean of the constit-uent pixel values for each spatial–temporal cell. Spring bloomanalyses were based on chlorophyll concentration data for thefirst half of the year, whereas fall bloom analyses were based onthe second half of the year. Only time series with a minimum of12 of the 23 potential observations were considered for analysis.We used linear interpolation to fill missing values within theextent of the data and filled missing values at the beginning andend of the time series with first and last observations, respec-tively, thus completing the time series for the bloom analysis.

We identified the beginning and end of the phytoplanktonblooms using a change-point analysis technique used in previousanalyses of Northeast Shelf bloom patterns (Friedland et al. 2008,2009; Leaf and Friedland 2014) and elsewhere (Friedland and Todd2012). The sequential averaging algorithm called STARS or “se-quential t test analysis of regime shifts” (Rodionov 2004, 2006) wasused to find all change points in a time series. STARS algorithmparameters were specified a priori: alpha level for a change in themean to be considered significant was set to � = 0.1; the lengthcriteria, the number of time steps to use when calculating themean level of a new regime, was set to 5; and the Huber weightparameter, which determines the relative weighting of outliers inthe calculation of the regime mean, was set to 3. A bloom wasconsidered to have occurred if there was a period bracketed by apositive and negative structural change. We ignored structuralchanges (positive or negative) that occurred in the first or last twoperiods (days 1 and 8 and days 169 and 177, respectively). A de-tected bloom could not exceed nine sample periods (approxi-mately 2.4 months), which was based on analyses of climatologicalbloom patterns in the production units. Periods bracketed by pos-itive and negative structural change exceeding nine 8-day periodswere considered to be ecologically different from discrete springor fall blooms. For each detected bloom, we extracted quantitiesto characterize bloom timing and magnitude. Bloom start wasdefined as the day of initiation of the spring bloom, which was thefirst day of the 8-day bloom period that exhibited bloom condi-tions. Bloom magnitude was the integral of the chlorophyll con-centrations during the bloom period.

Relative weight of haddock stocksThe relationship between haddock survivor ratio and the con-

dition of haddock during spring was tested for the two haddockstocks. To compare interannual patterns of haddock condition,we calculated the relative weight (Wr) index, which is a ratiomeasure of the variation in fish weight compared with a standardweight (Blackwell et al. 2000). This approach is an alternative tothe better known morphometric condition index K or the Fultoncondition factor, which is problematic to apply over a range offish sizes, since it assumes isometric growth, which is rarely thecase in fish species (Stevenson and Woods 2006).

Table 1. Recruits (numbers of age-1 recruits, 103), spawning stock bio-mass (SSB, t), and survivor ratio (log, recruits·SSB−1) for the GeorgesBank and Gulf of Maine stocks.

Georges Bank Gulf of Maine

Year Recruits SSBSurvivorratio Recruits SSB

Survivorratio

1977 6 125 49 855 −0.911 1 476 9 438 −0.8061978 83 888 76 793 0.038 6 048 13 392 −0.3451979 10 934 72 409 −0.821 6 435 15 178 −0.3731980 7 364 71 227 −0.986 4 612 14 400 −0.4941981 2 581 61 538 −1.377 774 13 675 −1.2471982 3 284 49 505 −1.178 2 445 13 068 −0.7281983 18 080 38 684 −0.330 1 043 9 895 −0.9771984 2 518 26 978 −1.030 282 6 618 −1.3701985 16 786 20 041 −0.077 265 4 796 −1.2581986 2 614 21 010 −0.905 134 2 735 −1.3101987 19 995 20 829 −0.018 443 1 456 −0.5171988 1 364 19 764 −1.161 187 1 049 −0.7491989 3 406 20 525 −0.780 244 759 −0.4931990 2 716 24 361 −0.953 267 793 −0.4731991 10 741 22 019 −0.312 711 679 0.0201992 15 568 16 501 −0.025 1 318 600 0.3421993 15 420 14 836 0.017 2 903 610 0.6781994 12 687 20 257 −0.203 2 540 1 003 0.4041995 11 778 26 749 −0.356 1 080 1 802 −0.2221996 23 451 35 643 −0.182 2 179 2 962 −0.1331997 14 637 43 585 −0.474 2 276 4 568 −0.3031998 49 156 50 807 −0.014 13 429 5 646 0.3761999 11 668 59 528 −0.708 2 547 5 606 −0.3432000 90 866 73 600 0.092 1 121 6 607 −0.7702001 5 551 87 872 −1.199 1 216 10 840 −0.9502002 2 870 100 258 −1.543 219 13 206 −1.7802003 412 375 119 310 0.539 6 281 11 341 −0.2572004 7 985 108 126 −1.132 386 9 641 −1.3982005 28 833 126 290 −0.641 1 118 8 098 −0.8602006 7 123 225 173 −1.500 1 218 7 443 −0.7862007 9 365 252 065 −1.430 215 6 427 −1.4762008 4 773 238 744 −1.699 301 5 464 −1.2592009 7 605 210 557 −1.442 966 4 771 −0.6942010 748 016 167 279 0.650 6 659 3 904 0.232

Fig. 3. Survivor ratio for the Gulf of Maine stock versus the ratio forthe Georges Bank stock for the period 1977–2010. Dashed line marksequal values. Pearson product-moment correlation (r) and associatedcorrelation probability (p) are given.

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Results

Coherence in survivor ratioDespite differences in overall stock productivity, survivor ratio

between the Georges Bank and Gulf of Maine haddock stocksshow a level of recruitment coherence suggesting they were im-pacted by some common factor. The Georges Bank has been thelarger of the two stocks, averaging 75 668 t spawning stock sizeover the period 1977–2010 (Table 1). Over the same time period, theGulf of Maine spawning stock averaged 6426 t, which is over anorder of magnitude difference between the two stocks. Differ-ences in year-class strength between the two stocks as representedby the stock size of age-1 fish were even larger than the spawningstock size differences. Recruitments have averaged 49 millionage-1 fish for the Georges Bank stocks and 2 million fish in theGulf of Maine, which is over a 20-fold difference. Survivor ratio,taken as the log of the recruitment of age-1 haddock in numbersdivided by the spawning stocks size, ranged from –1.78 to 0.68 forboth stocks (Table 1). Gulf of Maine survivor ratio was significantlycorrelated with the Georges Bank ratio (Fig. 3); however, the dis-tribution of ratio coordinates was not symmetric around the lineof equal ratios. In most years (21 out of 34 years), coordinates wereabove the equal ratio line, indicating a higher ratio was observedfor the Gulf of Maine stocks than the Georges Bank stocks, but themagnitude of orthogonal distances for points above the linetended to be less than those below the line (deviations averaged0.34 versus 0.27, respectively), indicating that there is greater tem-poral variability of the Georges Bank survivor ratio.

Influence of C. finmarchicus and Pseudocalanus abundanceson survivor ratio

The abundances of the copepod taxa C. finmarchicus and Pseudocalanusspp. were uncorrelated to the survivor ratio of the Georges Bankand Gulf of Maine stocks over the time series of spring zooplank-ton abundance data. However, our analysis based on subsamplesof the data suggests that Pseudocalanus abundance appears to playan important role in the pattern of recruitment of Georges Bankhaddock. The scatterplots between C. finmarchicus and Pseudocalanusabundances and survivor ratio for the Georges Bank and Gulf of

Maine stocks are shown in Figs. 4a, 4b and 4c, 4d, respectively;both sets of plots indicate the absence of a relationship betweenthe variables. The analysis based on subsamples of the data showsthat survival ratio for the Georges Bank stock was positively cor-related with Pseudocalanus abundance for subsamples drawn fromintermediate recruitments (Fig. 5b) and complementarily nega-tively correlated with C. finmarchicus abundances over lower re-cruitments (Fig. 5a). The analysis for the Gulf of Maine stockssuggests that survivor ratio was positively correlated with theC. finmarchicus abundances for subsamples drawn from higher re-cruitments (Fig. 6a) and uncorrelated with Pseudocalanus (Fig. 6b).However, it is worth noting that the positive correlation betweensurvival ratio for the Georges Bank stock and Pseudocalanus abun-dance was found in all four subsample sizes (12, 14, 16, and 18 datapairs), whereas the positive correlation between survival ratio forthe Gulf of Maine stock and C. finmarchicus abundance was onlypresent in subsamples of 12 and 14 data pairs.

Influence of fall and spring blooms on survivor ratioThe fall bloom the year before the recruitment year appears to

affect the survivor ratio of the Georges Bank haddock but was notrelated to the survivor ratio for the Gulf of Maine stock. As hasbeen reported elsewhere (Friedland et al. 2008; Leaf and Friedland2014), survivor ratio was significantly correlated with start dayand bloom magnitude of the fall bloom for the Georges Bankstock (Figs. 7a and 7c, respectively). This relationship was firstreported based on a sample size of seven (Friedland et al. 2008);despite nearly doubling the sample size in this analysis, the sam-ple size is still low, suggesting the relationship should continue tobe monitored. Neither start day nor bloom magnitude of the fallbloom was correlated with survivor ratio of the Gulf of Mainestock (Figs. 7e and 7g, respectively).

The spring bloom of the same year as the recruitment yearappears to have had no effect on the survivor ratio of either theGeorges Bank or Gulf of Maine haddock stocks. Survivor ratio wasuncorrelated with spring bloom start day and magnitude for theGeorges Bank and Gulf of Maine stocks (Figs. 7b, 7d and 7f, 7h,respectively).

Fig. 4. Georges Bank and Gulf of Maine stocks survivor ratios versus Calanus finmarchicus (a and c, respectively) and Pseudocalanus spp. (b and d,respectively) abundances for the period 1977–2010. Pearson product-moment correlation (r) and associated correlation probability (p) are given.

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Influence of parental condition on survivor ratioThe condition of haddock during the spring of the recruitment

year was correlated to the survivor ratio for both the GeorgesBank and Gulf of Maine stocks; however, these correlations werestronger for the Georges Bank stock. As has been reported else-where (Leaf and Friedland 2014), the spring condition of femalehaddock on Georges Bank, using the Fulton condition factor, wassignificantly correlated with survivor ratio. We also found thatspring condition of female haddock on Georges Bank was signifi-cantly correlated with survivor ratio, as was male condition andcondition for both sexes, but in this case using a relative weightanalysis (Table 2). The only significant correlation between springfish condition and survival ratio for the Gulf of Maine stock wasfor males; female haddock condition in the Gulf of Maine wasuncorrelated with survival ratio.

Contrast in fall bloom frequency on the Northeast ShelfThe frequency of detectable fall phytoplankton blooms varies

by subareas of the Northeast Shelf ecosystem. A fall bloom wasdetected in most years in the Gulf of Maine with associated bloomfrequencies in excess of 0.7 (Fig. 8). In contrast, over much of theMiddle Atlantic Bight, fall bloom frequencies were less than 0.3.The Georges Bank represented an intermediate area where bloomfrequencies were between 0.4 and 0.6. Hence, an important con-trast can be drawn between the Gulf of Maine, where a fall bloomnearly always occurs, and the Georges Bank, where the occur-

rence of a fall bloom is close to a coin toss probability. The fallbloom is a source of environmental contrast on Georges Bank,which is not the case in the Gulf of Maine.

DiscussionHistorically, two schools of thought have developed in fisheries

sciences with respect to what is important for determining highrecruitment. One school of thought focuses on the importance ofSSB in affecting recruitment levels. MacKenzie et al. (2003) foundthat more than 50% of the variation between interannual biomassrecruitment was explained by SSB, although large variations inspawning per recruit existed within stocks. The reasons for thevariability could be caused by changes in spawning stock struc-ture and the amount of eggs spawned into the water column. It isalso clear that larger and older adults tend to have longer spawningperiods (Kjesbu et al. 1996), and an extended size and age structure ofthe stock would favor higher recruitment potential. The otherschool of thought has focused on the role of environmental con-ditions in determining recruitment success. For example, thesurvival probability during the critical larval stage, and also dur-ing the juvenile stage, is known to have tremendous importanceto recruitment (Cushing 1990; Hjort 1914) and can potentially ex-plain the interannual variability in recruitment. This variability isattributed to changes in the environmental conditions of thespawning habitat, such as availability of prey resources or ocean

Fig. 5. Pearson product-moment correlation between survivor ratioand Calanus finmarchicus and Pseudocalanus spp. abundances versusmean recruitment for data used in the correlation for the GeorgesBank area over the period 1977–2010. Correlations are based on datasubsets of 12, 14, 16, and 18 data pairs (marked 2, 4, 6, and 8,respectively) that were ordered based on recruitment, with subsetsprogressively shifting over the range of recruitment. Coordinatescircumscribed by a magenta square denote a significant correlationat p = 0.05. (For the coloured version of this figure, refer to the Web siteat http://www.nrcresearchpress.com/doi/full/10.1139/cjfas-2015-0084.)

Fig. 6. Pearson product-moment correlation between survivor ratioand Calanus finmarchicus and Pseudocalanus spp. abundances versusmean recruitment for data used in the correlation for the Gulf ofMaine area over the period 1977–2010. Correlations are based ondata subsets of 12, 14, 16, and 18 data pairs (marked 2, 4, 6, and 8,respectively) that were ordered based on recruitment, with subsetsprogressively shifting over the range of recruitment. Coordinatescircumscribed by a magenta square denote a significant correlationat p = 0.05. (For the coloured version of this figure, refer to the Web siteat http://www.nrcresearchpress.com/doi/full/10.1139/cjfas-2015-0084.)

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temperature. Particularly, the duration and timing of the pres-ence and distribution of larval fish to overlap with their preyresources are of key importance to larval fish survival. A recentstudy that combined models and observations suggested that in

addition to the match–mismatch between prey and larvae, theduration of the overlap between larvae and their prey affects thetotal number of survivors during spring (Kristiansen et al. 2011).This is important as observations suggest that although the tim-ing of spring blooms and the presence of zooplankton in the

Fig. 7. Survivor ratio versus fall and spring bloom start day (panels a and b, respectively) and magnitude (mg·m−3 8-day) (panels c and d,respectively) for the Georges Bank stock, as well as survivor ratio versus fall and spring bloom start day (panels e and f, respectively) andmagnitude (panels g and h, respectively) for the Gulf of Maine stock for the bloom years 1997–2009 and recruitment years 1998–2010. Pearsonproduct-moment correlation (r) and associated correlation probability (p) are given.

Table 2. Relative weight (Wr) for haddock captured in the Georges Bankand Gulf of Maine stock areas for sexes combined, females, and males.

Georges Bank Gulf of Maine

N Wr N Wr

Year �� �� � � �� �� � �

1992 73 0.994 1.026 0.962 4 0.968 0.908 0.9741993 120 0.999 1.008 0.989 31 0.955 0.957 0.9591994 81 0.983 0.991 0.966 31 0.986 0.987 1.0111995 199 1.017 1.020 1.010 27 0.974 0.994 0.9191996 313 1.026 1.031 1.022 16 1.055 1.093 1.0061997 324 0.993 1.001 0.981 77 0.998 1.006 0.9821998 148 0.990 1.001 0.975 21 1.006 1.012 1.0021999 209 0.983 0.996 0.973 122 0.993 0.997 0.9972000 214 1.035 1.022 1.043 86 1.012 1.011 1.0132001 266 0.927 0.941 0.911 100 0.988 1.037 0.9552002 388 0.933 0.938 0.930 176 0.949 0.965 0.9352003 254 0.933 0.941 0.927 159 0.957 0.969 0.9392004 388 0.858 0.867 0.852 54 0.960 0.990 0.9172005 428 0.878 0.887 0.871 115 0.933 0.950 0.9192006 163 0.899 0.900 0.902 126 0.967 0.989 0.9402007 453 0.937 0.937 0.939 110 0.960 0.958 0.9672008 712 0.926 0.933 0.922 150 0.945 0.937 0.9552009 373 0.947 0.936 0.957 136 0.952 0.964 0.9312010 600 0.925 0.928 0.922 151 0.955 0.954 0.957

Correlation 0.475 0.501 0.424 0.401 0.046 0.491p 0.040 0.029 0.071 0.089 0.852 0.033

Note: N is the sample size by year and stock area for both sexes. Correlation isPearson product-moment correlation between log recruits per SSB and Wr forrespective stocks with associated correlation probability (p) for the period 1992–2010.

Fig. 8. Fall bloom frequency of detection based on a 0.5° griddeddata over the Northeast Shelf ecosystem based on data from theyears 1997–2013. For the coloured version of this figure, refer tothe Web site at http://www.nrcresearchpress.com/doi/full/10.1139/cjfas-2015-0084.

Frequency

00.10.20.30.40.50.60.70.80.91100m

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water column may change between years, the timing of peak fishspawning is often constant (Pedersen 1984). The comparative ecol-ogy of neighboring haddock stocks appears to illustrate the rangeof population response consistent with both schools of thought.

The recruitment success of two neighboring haddock stocksappears to be driven by distinctly different mechanisms owing tothe range in contrast of the fall bloom in one area versus theother. We see evidence in our data that suggests both growth–mortality and parental condition hypotheses of recruitment con-trol are at work in the subject haddock stocks. However, it wouldappear that the higher degree of contrast in fall bloom patterns onGeorges Bank is reflected in the dynamics of that stock, suggest-ing a more prominent role of the fall bloom provisioning ofprespawning adults on Georges Bank (parental condition hypoth-esis). In both stock areas, there is annual contrast in the growthand survival of larvae related to available zooplankton forage. Inthe Georges Bank stock area, the fall bloom will fail to develop inapproximately half the years, and in the years a bloom occurs itmay not be appropriately timed or in the correct subregion of theBank to impact prespawning haddock. Hence, the flux of particu-late organic carbon to the benthos during fall on Georges Bank isepisodic with a frequency that matches recruitment events. Incontrast, the fall bloom develops in most years in the Gulf ofMaine stock area; thus, it does not produce contrast in particulateorganic carbon flux and would appear to have a minimal effect onthe variability of prespawning haddock condition.

The Hjort–Cushing hypothesis, which appears to be a promi-nent feature of the recruitment of Scotian Shelf haddock, seen asan inverse relationship between bloom timing and survivor ratio,is only partially supported with data for the two subject stocks.This partial support would appear to be related to the dynamics oftheir spring blooms and resultant patterns of zooplankton growthand biomass. Over the same period as this study, Friedland et al.(2015) found that spring zooplankton biomass (as biovolume) wasboth independent and dependent on spring bloom start day onGeorges Bank and in the western Gulf of Maine, respectively. Inneither area was biovolume correlated with Pseudocalanus abun-dance, so we do not see an obvious linkage between zooplanktonbiomass and haddock recruitment, but what is evident is thatbloom start on Georges Bank varies little annually, whereas bloomstart in the western Gulf of Maine is more variable and, as specu-lated by Friedland et al. (2015), may affect bloom succession.Hence, our data for the Gulf of Maine stock is consistent with Plattet al. (2003) and Trzcinski et al. (2013) in that these researchersfound that early blooms on the Scotian Shelf produced highersurvivor ratios, which can be seen in our Gulf of Maine data tosome degree. Gulf of Maine survivor ratio is negatively correlatedwith bloom timing, but the correlation is weak at a significancelevel of p = 0.175. The absence of any indication of a correlationbetween bloom timing and haddock recruitment on GeorgesBank may be related to the lack of contrast in spring bloom timingin that area and the dominant effect of the fall bloom on stockdynamics.

The growth–mortality hypothesis was supported, to some de-gree, by the relationship between the survivor ratio and the abun-dance of key zooplankton species. If the episodic nature ofhaddock recruitment on Georges Bank is due to the variation inreproductive output related to fall bloom variation, the fortuitousselection of study years by Mountain and Kane (2010) and ourintentional subsampling scheme to examine the effects of zoo-plankton on survivor ratio would appear to control for that factor.Years with intermediate recruitment would be years with lowreproductive output per spawner; thus, the contrast in survivorratio for these years would be mostly affected by larval survivor-ship. This conclusion would be consistent with our results andwhat was reported by Mountain and Kane (2010).

The member–vagrant hypothesis as the dominant mode of re-cruitment control has been of particular interest in the context of

the Georges Bank stock for many years (Colton and Temple 1961;Smith and Morse 1985), which may be a reflection of the challengeof larval stage retention suggested by the bathymetry of the Bank.Georges Bank is an elevated portion of the seafloor forming anoblong bank (Brown et al. 2011) surrounded by deeper water onmost of its perimeter. It would be understandable if early researchefforts concerning the retention of fish on this benthic featurewould be mindful of the potential for fish to drift off this structureand be lost from appropriate habitat. The most recent work onretention probability as the driving factor for Georges Bank had-dock recruitment (Boucher et al. 2013) confirms earlier conclu-sions that the adequate retention of larval haddock is a necessaryprerequisite for successful recruitment, but variation in GeorgesBank circulation does not provide much explanatory power toexplain patterns in recruitment.

We suggest that a combination of factors act on the resultantpattern of haddock recruitment, which, depending on the natureof the environmental forcing factors affecting the individual pop-ulation, may play a greater or lesser role. For example, adult pro-visioning, egg and larval retention, and the myriad of additionalfactors affecting growth and survival of larval fish are all at workin all populations; however, regional or local conditions maycause one factor to be dominant compared with other factors andthis confers the ability of that factor to shape recruitment. In asense, these effects are layered upon each other, since there is acertain level of chronology associated with how they impact apopulation. In the western North Atlantic, we have evidence thatpopulations that are in close proximity to each other are alter-nately affected by very different seasonal factors. Successful re-cruitment of the Georges Bank stock is the result of a suite oflayered effects dominated by the effect of the fall bloom provi-sioning of prespawning adults, whereas the Scotian Shelf had-dock stock appears to be dominated by the effect of the timing ofthe spring bloom and its effects on larval feeding and growth.

Recruitment patterns for haddock populations in the northeastAtlantic provide a wide range of responses, some of which may berelated to the parental condition hypothesis. Stocks in the Gulf ofMaine region demonstrate recruitment coherence that would beconsistent with some common factors affecting young-of-the-yearsurvivorship or other life history events. We are also mindful thatthis coherence may also be related to migration between the stockareas to some degree. As we observed for the Gulf of Maine regionstocks, there is also recruitment coherence among Northeast At-lantic haddock stocks, suggesting the existence of common envi-ronmental forcing factors (Fogarty et al. 2001). Northeast Arctichaddock recruitment appears to be related to winter tempera-ture, which was interpreted in the context of the effect of growthconditions on early life history stages (Bogstad et al. 2013). How-ever, with the construction of an egg mortality time series forthese stocks, it was found that this stage-specific mortality ratewas unrelated to recruitment, a finding attributed to low statisti-cal power of the samples (Langangen et al. 2014). Winter condi-tions may also impact the adult stock and the condition ofprespawners; clearly for haddock, like other fish species, seques-tration of energy stores in the liver translate to potential fecun-dity (Skjæraasen et al. 2013). Collectively, these observations areconsistent with the hypothesis that parental condition is an im-portant factor in determining the size of Northeast Arctic had-dock cohorts. However, in other parts of the Northeast Atlantic,such as the Skagerrak–Kattegat, a very different story is emerging,providing a cautionary message for research investigating climateimpacts. Linderholm et al. (2014) found that the correlative rela-tionships between climate forcing and recruitment were not sta-ble and may be more related to fishery effects. In particular, theeffect may be more profound in haddock than for other species inthe region.

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AcknowledgementWe thank J. Kane for assistance with the zooplankton data.

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