ICES Journal of Marine Science

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1 ICES Journal of Marine Science ICES Journal of Marine Science (2016), 73(4), doi: /icesjms/fsv142 Original Article Changes in weight-at-length and size-at-age of mature Northeast Atlantic mackerel (Scomber scombrus) from 1984 to 2013: effects of mackerel stock size and herring (Clupea harengus) stock size Anna H. Olafsdottir 1 *, Aril Slotte 2, Jan Arge Jacobsen 1, Guðmundur J. Oskarsson 3, Kjell R. Utne 2, and Leif Nøttestad 2 1 Faroe Marine Research Institute, Nóatún 1, 110 Tórshavn, Faroe Islands 2 Institute of Marine Research, Nordnesgaten 33, 5005 Bergen, Norway 3 Marine Research Institute, Skúlagötu 4, 121 Reykjavík, Iceland *Corresponding author: tel: ; fax: ; annao@hav.fo Olafsdottir, A. H., Slotte, A., Jacobsen, J. A., Oskarsson, G. J., Utne, K. R., and Nøttestad, L. Changes in weight-at-length and sizeat-age of mature Northeast Atlantic mackerel (Scomber scombrus) from 1984 to 2013: effects of mackerel stock size and herring (Clupea harengus) stock size. ICES Journal of Marine Science, 73: Received 24 February 2015; revised 14 July 2015; accepted 17 July 2015; advance access publication 25 August Weight-at-length and length-/weight-at-age were analysed for mature 3- to 8-year-old Northeast Atlantic mackerel (Scomber scombrus; n ¼ ) collected annually in autumn (September and October) at the end of the annual feeding season during in the northern North Sea. The age range represented 92% of the mackerel stock size (age 3+). During the most recent decade, mackerel length- and weight-at-age continually declined. In 2013, the average mackerel was 3.7 cm shorter and weighed 175 g less than the average individual in Individual weight-at-length, demonstrating annual summer feeding success, continually declined during the most recent 5 years, whereas somatic growth of cohorts aged 3 8 continually declined for the last 11 of 25 cohorts investigated. Growth of the latest cohort was 34% of the maximum cohort growth recorded. Both weight-at-length and cohort growth were negatively affected by mackerel stock size and Norwegian spring-spawning herring (Clupea harengus) stock size (weight-at-length: r 2 ¼ 0.89; growth (length): r 2 ¼ 0.68; growth (weight): r 2 ¼ 0.78), while temperature was not significant. Conspecific density-dependence was most likely mediated via intensified competition associated with greater mackerel density. Negative effects of herring were likely mediated by exploitative competition for shared food resources rather than direct competition due to limited spatio-temporal overlap between mackerel and herring during the feeding season. Herring begin their seasonal feeding migration at least a month before mackerel; therefore, herring consumption influences prey availability for the later-arriving mackerel. Record low mackerel growth and negative effects of mackerel and herring stock size suggest that the carrying capacity of the Norwegian Sea and adjacent areas for plankton-feeding fish stocks have been reached. However, compounding effects of a less productive Norwegian Sea during the 30-year period cannot be excluded. Keywords: density-dependence, length-/weight-at-age, Northeast Atlantic mackerel, Norwegian spring-spawning herring, Scomber scombrus, weight-at-length. Introduction Northeast Atlantic mackerel (Scomber scombrus) is a widely distributed, highly migratory, schooling pelagic fish that occupies temperate waters from Gibraltar to Svalbard (36 768N) and from Greenland to the Baltic Sea (208W 368E; ICES, 2014a; Berge et al., 2015). Mackerel is currently one of the most valuable commercially harvested fish species in European waters, with annual catches reaching ca.1.4 million tonnes (ICES, 2014a). During the most recent decade, the stock has experienced changes in abundance, density, and distribution (Nøttestad et al., 2010, 2011, 2012, 2013; ICES, 2014a). This includes a westward expansion # International Council for the Exploration of the Sea All rights reserved. For Permissions, please journals.permissions@oup.com

2 1256 A. H. Olafsdottir et al. of mackerel summer feeding range by ca.1500 km and a northward expansion of several hundred km (Iversen, 2002; Nøttestad et al., 2010, 2011, 2012, 2013; Astthorsson et al., 2012; Utne et al., 2012a; ICES, 2013a; Berge et al., 2015). Changes in abundance and distribution of pelagic stocks can be concurrent with changes in the biology of individuals, such as body condition and somatic growth (Winters and Wheeler, 1994; Dragesund et al., 1997). The most mature mackerel undertake an annual migration between southern spawning areas and northern feeding grounds, delineated between 55 and 658N (Lockwood, 1988; Reid et al., 1997; Belikov et al., 1998; Uriarte and Lucio, 2001; Iversen, 2002, 2004; Nøttestad et al., 2007, 2010, 2011, 2012, 2013; Utne et al., 2012a; ICES, 2013a). Spawning is protracted, starting in January/ February and ending in July, and occurs over a large area from the coast of Portugal to the south coast of Iceland and into the Norwegian Sea (36 648N; ICES, 2014b). It begins in the south, advances northward as temperature reaches 9 108C, and is concentrated along the shelf edge, with the major spawning locations in the Bay of Biscay and west of Ireland (Lockwood, 1988; ICES, 2014b). Since the 1980s, mackerel have migrated northward after spawning to feed in the Norwegian Sea and adjacent areas (Walsh and Martin, 1986; Belikov et al., 1998; Uriarte and Lucio, 2001; Iversen 2004; Nøttestad et al., 2010, 2011, 2012, 2013; Utne et al., 2012a). In autumn, they have aggregated in the northern North Sea and on the shelf along the west coast of Norway (ICES Division IVa) before commencing a southward spawning migration in winter (Q1) (Walsh and Martin, 1986; Lockwood, 1988; Walsh et al., 1995; Reid et al., 1997; Uriarte and Lucio, 2001; Jansen et al., 2012; Peña et al., 2012). Mackerel have a pronounced annual feeding cycle. Most of their annual food consumption occurs in summer and autumn, and stored energy is utilized during overwintering and for spawning (Lockwood, 1988). During summer, mackerel feed in the surface layer, typically located within m of the surface (Gødø et al., 2004; Diaz, 2013) in temperatures ranging from 6 to 128C (Nøttestad et al., 2007, 2010, 2011, 2012, 2013; Utne et al., 2012a). Mackerel are opportunist feeders, their main prey being calanoid copepods and, to a lesser extent, euphausiids, amphipods, other crustaceans, pelagic bivalves, and fish (Prokopchuk and Sentyabov, 2006; Debes et al., 2012; Langøy et al., 2012; Óskarsson et al., 2012; Pinnegar et al., 2015). Two other abundant pelagic fish species feed in the Norwegian Sea during summer: Norwegian spring-spawning herring (Clupea harengus) and blue whiting (Micromesistius poutassou) (Utne et al., 2012a). There is a limited diet overlap between mackerel and blue whiting as the latter s main prey is amphipods, euphausiids, and, to a lesser extent, copepods (Langøy et al., 2012; Pinnegar et al., 2015). Diet overlap of mackerel and herring varies between years and areas; however, both species share the same main prey item: calanoid copepods (Dalpadado et al., 2000; Gislason and Astthorsson, 2002; Debes et al., 2012; Langøy et al., 2012; Óskarsson et al. 2012; Pinnegar et al., 2015). This suggests that mackerel and herring compete for shared food resources. Mackerel and herring are estimated to have a zooplankton consumption/biomass ratio of 5.2 to 1, and for the 30-year period , the annual zooplankton consumption ranged from 13 to 26 million tonnes and from 3 to 54 million tonnes for mackerel and herring, respectively (Utne et al., 2012b). Countless studies have proved how indeterminate growth of fish is influenced by density-dependent and density-independent factors. Jenkins et al. (1999) showed how increasing population density caused a lower somatic growth rate of individuals. At the stock level, increasing stock size has been linked to reduced somatic growth of pelagic fish (Winters and Wheeler, 1994; Dragesund et al., 1997) including mackerel (Agnalt, 1989; Overholtz, 1989). Major density-independent factors are environmental conditions such as ambient temperature. Growth increases in warmer years, given that food availability is not limiting, and the opposite occurs when temperature declines (Brett, 1979). Previous studies show that the growth of immature mackerel is negatively correlated with total-stock biomass (Agnalt, 1989) and cohort size (Jansen and Burns, 2015). However, it remains unknown whether growth of the mature stock component is density-dependent. Our objective is to examine the seasonal growth pattern and annual variability in growth of mature mackerel during the 30-year period Furthermore, we explore the potential effects of mackerel stock size, herring stock size, and ambient temperature. Our general hypothesis is that mackerel growth will be greater in years when stock size is smaller and temperature greater, and vice versa. Material and methods Biological samples Data on mackerel weight-at-age (W a ) and length-at-age (L a ) were made available from the Institute of Marine Research (IMR), Bergen, Norway, and were collected on scientific surveys and from commercial fishing vessels during the period in the Norwegian Sea, northern North Sea, and west of Ireland. Samples from commercial vessels were frozen at landing sites and sent to the IMR for biological analysis. A sample was 100 randomly selected mackerel. For each fish, total length (from the tip of the snout to the upper lobe of the pinched caudal fin; +0.5 cm), total weight (+0.5 g), age (years), sex, and maturity (eight stages) were recorded (Mjanger et al., 2012). Gonads and stomach contents were included in the total weight, but have trivial effects on growth estimates as the most individuals (97%) were of maturity stage 8 when gonad weight is,1% of the total body weight (Lockwood, 1988). Furthermore, stomach content averages,1% of the total body weight during peak feeding in July (Óskarssson et al., 2012; annual weight length relationships from the current study were used to convert mackerel mean length to mean weight). Fishing gear types used to catch mackerel included trawl, purseseine, gillnet, longline, and handline. Purse-seine samples were selected for the analysis of annual changes in mackerel growth as this gear type provides unbiased sampling of the mackerel population length frequency (Slotte et al., 2007). These samples were collected annually in the northern North Sea (ICES Division IVa) in autumn (1 September 30 October; Figure 1). The maximum observed age for mackerel sampled was 22 years, but numbers were limited (n, 10) for the oldest age classes in most years. The analysis was thus limited to 3- to 8-year-old mackerel. This age range represents, on average, 92% of the mackerel stock size for age 3+ during (ICES, 2014a). Outliers in individual weight length relationships (data log 10 transformed) were identified using robust regression (Froese, 2006) and were eliminated from analysis (n ¼ 13). Immature individuals (n ¼ 65) and individuals missing information for length, weight, age, sex, or maturity (n ¼ 10) were also excluded. A total of individuals collected at 639 stations during were used for analysis of annual variability in growth (Table 1). To investigate mackerel seasonal growth pattern, it was necessary to include samples from all gear

3 Density-dependent growth of mature mackerel 1257 Table 1. Number of individuals used to calculate length-at-age (L a ), weight-at-age (W a ), and weight-at-length (W L ). Age Figure 1. Mackerel sampling locations [total of 639 stations (+)] by purse-seine net in September and October Also displayed is the mackerel traditional summer feeding distribution including the distribution expansion in recent years as compiled from several resources (Belikov et al., 1998; Uriarte and Lucio, 2001; Iversen, 2002; Nøttestad et al., 2007, 2010, 2011, 2012, 2013; Astthorsson et al., 2012; Utne et al., 2012a; ICES, 2013a), the mackerel traditional feeding grounds in the northern North Sea and the Norwegian Sea (lightest grey), the westward and the northward feeding distribution expansion beginning in 2006 (second lightest grey), a further westward and northward expansion in 2009 (second darkest grey), and finally entering Greenland waters in 2013 (dark grey). The years when different feeding areas were occupied by mackerel are displayed in brackets. types to provide samples from each month of the year. We chose to measure seasonal growth as changes in weight of 36-cm long individuals as this was the most common length (ca. 16%) of the mackerel used to investigate annual variability in growth. A total of individuals sampled at 1898 stations were used to investigate seasonal growth pattern of mackerel (Figure 2a). Stock size Mackerel stock size (N mac ), calculated as the number of individuals (age 2+), was used as a proxy for density-dependence during the summer feeding season (ICES, 2014a). Age 1 mackerel were excluded from the stock size calculation as their occurrence in the feeding migration to the Norwegian Sea and adjacent areas is low (,1%; Nøttestad et al., 2010, 2011, 2012, 2013). The estimate of mackerel stock size at spawning in May during from the analytical assessment in 2014 was applied (ICES, 2014a). Herring stock size (N her ), calculated as the number of individuals (age 3+), was used as a proxy for zooplankton grazing pressure in the Norwegian Sea and adjacent areas in spring before arrival of the mackerel summer feeding migration. Age 3+ was used as that is the age when the most each cohort joins the herring feeding migration in the Norwegian Sea (Dragesund et al., 1980). The annual stock size estimate of herring as of 1 January was obtained from both the analytical assessments in 2007 ( ; ICES, 2007) and 2014 ( ; ICES, 2014a). Ambient temperature Vertical distribution of mackerel during the summer feeding migration in the Norwegian Sea is mostly limited to the upper 40 m of the water column (Gødø et al., 2004; Diaz, 2013). The surface mixed-layer Year Total Total depth during summer in the Norwegian Sea ranges from 15 to 30 m (Bagøien et al., 2012); hence, the sea surface temperature (SST) was assumed to be an appropriate proxy for ambient temperature. SST was derived from monthly optimum interpolation data, based on advanced, very high-resolution radiometer satellite data, Version 2 (product: NOAA_OI_SST_V2). These data have a spatial resolution of 18 latitude/longitude and a temporal resolution of 1 month, and were provided by the NOAA/OAR/ESRL PSD, Boulder, CO, USA, from their website at noaa.oisst.v2.html (downloaded 24 April 2014). For a detailed description of methods used to calculate SST, see Reynolds and Smith (1995). Mackerel summer feeding distribution during , including geographical expansion from 2006 onwards, was estimated from several resources: Belikov et al. (1998), Uriarte and Lucio (2001), Iversen (2002, 2004), Nøttestad et al. (2007, 2010, 2011, 2012, 2013), Astthorsson et al. (2012), Utne et al. (2012a), and ICES (2013a). This is displayed in Figure 1. Temperature data were linked to mackerel summer feeding distribution by assigning monthly temperature values from June to August to every 18 latitude/longitude bin located within the defined mackerel summer feeding distribution in a given year. Finally, annual temperature index (T y ) was calculated as: T y = 1 n n i=1 T i, (1)

4 1258 A. H. Olafsdottir et al. Figure 2. Mackerel sampling locations used to calculate seasonal growth pattern for 36-cm long mackerel in the North Atlantic during In total, individuals were collected at 1898 stations (a). Stations are colour-coded by month of sampling: January April (yellow cross), May and June (black open circle), July and August (red open circle), and September December (blue open circle). Also displayed is the seasonal growth pattern for 36-cm long individuals in the age range 3 8 years calculated as the monthly average weight (b: filled circle: error bars are 95% CI) and the number of individuals used to calculate the monthly averages (grey vertical bars; on log 10 scale). where i is the mean monthly temperature in June, July, and August for spatial bins located within the geographical area defined in Figure 1 for year y. Statistical analysis Multiple linear regression (MLR) was used to investigate effects of N mac, N her, and T on weight-at-length (W L ) of individuals and on cohort growth. Each dependent variable was analysed separately. In the W L analysis, sampling week and individual length were included as covariates to control for their effects. Cohort growth was measured both as an increase in length (C L ) and in weight (C W ), and was calculated as the difference between the cohort s average length-/weight-at-age 3 subtracted from average length/ weight-at-age 8. Before calculating C w, individual weight was standardized to be sampled in Week 1 (defined as the first week of the annual purse-seine sampling period which starts 1 September) using the weight loss of 3.7 g week 21, which was the average weight loss of a 36-cm mackerel from Week 1 to Week 9. The temperature experienced by each cohort (TC) was calculated as: TC y = 1 n (T y+1 + T y T y+5 ), (2) where y is the year when the cohort was 3 years old. The value for the year when the cohort was 3 years old was excluded as growth during age 3 is not included in the cohort growth rate. Equation (2) was also used to calculate mackerel (NC mac ) and herring (NC her ) stock size experienced by each cohort. The parsimony principle was used to choose the best model from all possible combinations of explanatory variables by selecting the model with the lowest Akaike Information Criterion (AIC). If the AIC difference between competing models was,3 (Burnham and Anderson, 2002), analysis of variance was used to compare the nested models, accepting the simpler one if the models were not statistically different (p. 0.05). Correlation of explanatory variables was explored using pairwise scatterplots and by calculating correlation coefficients. For all possible subsets of models, the maximum correlation coefficient was Model assumptions were tested by visually exploring linearity, homogeneity, independence, and normality of model residuals. Modelling was done in R (R CoreTeam, 2014). If model assumptions were violated, a bootstrap method was used to calculatenon-parametric confidence intervalsfor model parameters using the basic bootstrap method with replicas (Davison and Hinkley, 1997; Canty and Ripley, 2014). Results Seasonal weight gain of 36-cm long mackerel began in June and peaked in August followed by a continuous decline in weight until spring the following year (Figure 2b). Therefore, biological samples used for analysis of annual variability in mackerel growth were collected after the annual peak in growth during a period when growth is ceasing and weight-at-length has started to decline. Annual average W a for 3- to 8-year-old mackerel sampled in September and October during ranged from 270 to 720 g (Figure 3a), and average L a ranged from 31 to 40 cm (Figure 3b). All age classes displayed similar annual patterns of increasing and decreasing L a and W a. Mackerel weight and length were high from 1984 to 1995, after which they declined until 1999, before peaking again between 2002 and During the most recent decade, mackerel weight and length declined continually and have been at a record low since In 2013, the average

5 Density-dependent growth of mature mackerel 1259 Figure 3. Mackerel annual average (+95% CI) weight-at-age (W a ;a) and length-at-age (L a ; b) for 3- to 8-year-old individuals sampled in September and October during Figure 4. Mackerel annual average weight-at-length (W L ) for 33- to 38-cm long individuals sampled in September and October during (a). Also displayed is relative mackerel stock size (ICES, 2014a; dark grey bars), relative herring stock size (ICES, 2014a; light grey bars), and annual average mackerel summer feeding ground temperature (open circle) during (b). mackerel was 3.7 cm shorter and weighted 175 g less than the average individual in Average annual length/weight was calculated as an average of age class averages, within each year, to prevent bias from an unequal number of specimens between age classes and years. W L of individuals Mackerel W L varied during the 30-year period, but declined continually from 2009 to 2013 (Figure 4a). The decline in W L lagged ca. 6 years behind the decline in L a /W a. This suggests that mackerel prioritized energy allocation to increasing W L, but not to growth in L a in periods with limited prey. However, as of 2009, they could not compensate anymore, and W L began declining. The rate of W L decline from 2009 to 2013 increased with mackerel length from 4.9% for 33-cm long individuals to 12.2% for 38-cm long individuals. N mac ranged from 7.7 to 20.7 billion individuals. Stock size was high in the 1980s, declined in the early 1990s, and remained low for over a decade, reaching a minimum in 2002 (Figure 4b). From 2002 to 2013, stock size increased 170%. N her ranged from 1.7 to 56.2 billion individuals (age 3+), with two peaks in stock size: and T y ranged from 9.7 to 12.28C, with a distinctive peak in 2002 and 2003 when temperature was ca. 18C warmer than in other years. The best-fitting MLR model for annual variation in W L for an individual fish included N mac and N her and explained 89% of the variance (Table 2). Both N mac and

6 1260 A. H. Olafsdottir et al. N mac had negative effects and explained similar amounts of variation, i.e. standardized regression coefficients were not statistically different (Table 3). Somatic growth of cohorts Mackerel cohort growth from age 3 to 8 ranged from 2.9 to 6.6 cm in length and from 102 to 299 g in weight for the 25 cohorts investigated during (Figure 5a). Cohort growth peaked for the 1997 cohort (cohort labelled by the year when it was 3 years old), but declined continually from 1998 to 2008 (last cohort). Growth of the last four cohorts was at a record low compared with the 25 cohorts investigated. NC mac ranged from 8.5 to 18.9 billion individuals and was a record high for the last four cohorts ( ; Figure 5b). NC her ranged from 15.6 to 47.5 billion individuals and peaked for the cohorts in the early 1990s ( ) and again for the cohorts in the early 2000s ( ). TC ranged from 10.0 to 11.38C, peaked around cohort 2000, and has had a declining trend since. The best-fitting model for C L and C W included NC mac and NC her, both of which had negative effects. The best model for C W explained more variance than that for C L, 78 vs. 68%, respectively. Growth measured as C W incorporates both changes in weight-at-length and growth in length-at-age; hence, the amount of explained variance differs between the best fitting models for C L and C W. Mackerel stock size explained more variance than herring stock size for C L (standardized model coefficients were statistically different), whereas both species explained similar variance for C W. The cohort growth model residuals slightly violated assumption of normality, independence, and homogeneity. The bootstrap method was used to recalculate 95% confidence intervals of model parameters for all models, including models for all possible combinations of explanatory variables. Bootstrap results confirmed statistical significance of parameters as estimated by MLR for all models. Table 2. Model comparison and model selection for weight-at-length (W L ) of individual mackerel and for growth of mackerel cohorts ages 8 3 calculated both for increase in weight (C W ) and for increase in lenght (C L ). Model d.f. AIC DAIC Adj. r 2 Weight-at-length a log(w) ¼ log(l) + S + N mac + N her + T 5; log(w) ¼ log(l) + S + N mac + N her 4; log(w) ¼ log(l) + S + N mac + T 4; log(w) ¼ log(l) + S + N her + T 4; log(w) ¼ log(l) + S + N mac 3; log(w) ¼ log(l) + S + N her 3; , log(w) ¼ log(l) + S + T 3; log(w) ¼ log(l) + S 2; , Somatic growth ages 8 3 : weight C W ¼ NC mac + NC her + TC 3, C W ¼ NC mac + NC her 2, C W ¼ NC mac + T 2, C W ¼ NC her + T 2, C W ¼ NC mac 1, C W ¼ NC her 1, C W ¼ TC 1, Somatic growth ages 8 3 : length C L ¼ NC mac + NC her + TC 3, C L ¼ NC mac + NC her 2, C L ¼ NC mac + TC 2, C L ¼ NC her + TC 2, C L ¼ NC mac 1, C L ¼ NC her 1, C L ¼ TC 1, For W L, effect of mackerel stock size (N mac ), herring stock size (N her ), and temperature (T) on individual fish weight (W) accounting for effects of sampling week (S) and individual fish length (L) were tested. For cohort growth, effects of mean mackerel stock size (NC mac ), mean herring stock size (NC her ), and mean temperature (TC) experienced by each cohort on C W and C L were tested. The best model according to the parsimony principle is displayed in bold. a In all W L models, weight and length were log 10 transformed. d.f., degrees of freedom; DAIC, AIC 2 minaic; Adj., adjusted. Table 3. Standardized model coefficients (+95% CI) for the best-fitting multiple regression model for weight-at-length (W L ) of individual mackerel and for somatic growth of mackerel cohorts, both in weight (C W ) and in length(c L ). Model N mac N her W L ( ) ( ) NC mac NC her C W (260.0 to 222.7) (247.1 to 225.3) C L (21.0 to ) ( to ) Bootstrap methods used to calculate confidence intervals for cohort growth. N mac, mackerel stock size; N her, herring stock size; NC mac, mean of mackerel stock size when cohort was age 4 8; NC her, mean of herring stock size when cohort was age 4 8.

7 Density-dependent growth of mature mackerel 1261 Figure 5. Somatic growth of mackerel cohorts in length (C L ; filled black circle) and in weight (C W ; filled grey circle) during (a). Cohort growth is calculated as the difference in annual average lengthand weight-at-age 3 subtracted from annual average length- and weight-at-age 8. Cohort growth is centred on the year when the cohort was 3 years old. Also displayed is relative mean mackerel stock size (NC mac ; dark grey bars; ICES, 2014a), relative mean herring stock size (NC her ; light grey bars; ICES, 2014a), and mean of annual average mackerel summer feeding ground temperature (open circle) during (b). Mean values are calculated as averages of annual values when the cohort was 4 8 years old, but are centred on the year when the cohort was 3 years old. Discussion The somatic growth of mature mackerel was negatively influenced by mackerel stock size and herring stock size during the 30-year period We measured growth of mature mackerel in the age range of 3 8, which represents on average 92% of the mackerel stock size in numbers (age 3+; ICES, 2014a). Older individuals (age 9+) mix with 3- to 8-year-old mackerel during the summer feeding migration (unpublished data from the international coordinated ecosystem summer survey in the Norwegian Sea and adjacent area; Nøttestad et al., 2010, 2011, 2012, 2013); hence, they compete for the same food resources. It is likely that growth of individuals of age 9+ is affected by density-dependence in a manner similar to the age range studied. Therefore, our results can be considered to represent the mature mackerel stock component that feeds in the Norwegian Sea and adjacent areas during summer. Densitydependent effects of mackerel stock size support results from growth studies on immature Northeast Atlantic mackerel (Agnalt, 1989; Jansen and Burns, 2015) and on mature and immature Northwest Atlantic mackerel (Overholtz, 1989). Mackerel stock size at spawning time in May from the analytical assessment in 2014 was used as a proxy for mackerel density during the annual summer feeding season (age 2+), because data were not available to calculate in situ mackerel density during The mackerel summer feeding range began expanding in 2006 (Astthorsson et al., 2012) and had increased ca. 50% by summer 2013 (Figure 1; Nøttestad et al., 2013). An expanded feeding area did not result in reduced mackerel density. On the contrary, mackerel density during the summer feeding migration increased by an order of magnitude (median density) from 2007 to 2013 (Figure 6; Nøttestad et al., 2007, 2010, 2011, 2012, 2013). An increase in density during a period of expanding feeding area is explained by a 100% increase in stock size from 2006 to 2013 (age 2+; ICES, 2014a). Since mackerel density increased during a period of expanding feeding area, it is likely that population density was positively correlated with stock size before expansion began. This supports our assumption that stock size is an appropriate index for mackerel density during the summer feeding migration in the Norwegian Sea and adjacent areas. Negative effects of mackerel stock size on weight-at-length and growth are likely caused by increasing density of individuals. Studies suggest that when the density of individuals increases, food consumption declines and metabolic cost increases, mediating greater forage effort and intensified competition for limited food resources (Brio et al., 2003; Rosenfeld et al., 2005). It is outside the scope of this paper to estimate how changes in mackerel consumption and metabolic cost affected the observed decline in weight-at-length and growth. Negative effects of herring stock size on mackerel growth are likely mostly mediated via exploitative competition. Spatial distribution of mackerel and herring partly overlaps during their seasonal feeding migration in the Norwegian Sea and adjacent areas (Nøttestad et al., 2007, 2010, 2011, 2012, 2013; Langøy et al., 2012; Utne et al., 2012a). Factors that reduce direct spatial overlap include differences in migration timing and different temperature preferences. Herring begin their feeding migration in April and frequently follow the cold Arctic/Atlantic front northward or westward as summer progresses, usually occupying waters ranging from 2 to 88C (Misund et al., 1998; Utne et al., 2012a). Studies also suggest that the herring feeding migration follows the distribution of the overwintering copepod generation (Broms et al., 2012). On the other hand, mackerel do not arrive in the Norwegian Sea until June, and they generally prefer warmer waters than herring (.88C; Utne et al., 2012a). The earlier-arriving herring graze on copepod overwintering stock, with most of their copepod consumptions occurring in May and June (Dalpadado et al., 2000; Gislason and Astthorsson, 2002). This consequently influences copepod abundance available to the later-arriving mackerel feeding migration (Varpe et al., 2005; Utne et al., 2012b).

8 1262 A. H. Olafsdottir et al. Figure 6. Box-whisker plot of mackerel density from stations with mackerel present during the summer feeding migration in 2007 and Box is the lower and upper quartile, vertical line is the median (value displayed), and whiskers are the interquartile range multiplied by a factor of 1.5. Data are from the coordinated ecosystem survey in the Norwegian Sea and surrounding waters (Nøttestad et al., 2007, 2010, 2011, 2012, 2013). Sampling station numbers are displayed at the top of graph; outliers are not displayed. Note y-axis is displayed as log 10 scale. It was surprising that temperature did not have a significant effect on mackerel growth. However, the temperature range during our study period was small: 2.48C for annual changes in weightat-length, and 1.38C for the 5-year running means used for cohort growth. Limited temperature variability could have limited the study s ability to detect temperature effects. The mackerel feeding migration range expansion from 2006 onwards could have contributed to decreasing weight-at-length and growth in recent years if the longer migration distance was not compensated for by increasing consumption. From 2006 to 2013, the migration range expanded westward by ca km and northward by ca. 300 km (Nøttestad et al., 2007, 2010, 2011, 2012, 2013; Astthorsson et al., 2012; Utne et al., 2012a; Berge et al., 2015). Larger mackerel migrate farther westward and northward during summer feeding; hence, they have a longer summer feeding migration route (Nøttestad et al., 2011, 2012, 2013). From 2009 onwards, weight-at-length of larger individuals has declined proportionally more than for smaller individuals (Figure 4a). This implies that individuals cannot compensate for the inherent higher cost of a longer migration route by increasing their food consumption. The mackerel migration route expansion could have contributed to declining weight-at-length and growth of mackerel observed in recent years. Other factors in combination with stock size and temperature could influence mackerel weight-at-length and growth, such as food abundance (Obradovich et al., 2014) and food quality, i.e. energy content (Rand et al., 2010). Quantitative data on food abundance and food quality during are not available. Data are available on average mesozooplankton abundance (sampled with WP-2 net) for May in the Nordic seas during (ICES, 2013b). During that period, abundance peaked in 2000, then continually declined to a minimum in 2009 with abundance 27% of the peak value, before increasing to 50% of the peak value in 2013 (ICES, 2013b). However, mackerel feed on a wide size range of food items and larger prey, such as euphausiids, amphipods, crustaceans, and fish (Prokopchuk and Sentyabov, 2006; Debes et al., 2012; Langøy et al., 2012; Óskarsson et al., 2012). Larger prey species are poorly sampled by WP-2 nets (Gjøsæter et al., 2000). The proportion of larger prey items in the mackerel diet varies between years and locations, from none to ca. 85% of total stomach content by weight (Prokopchuk and Sentyabov, 2006; Langøy et al., 2012; Óskarsson et al., 2012). No information is available on prey composition from the May mesozooplankton samples; therefore, we cannot track annual changes in prey quality available to mackerel during the feeding migration. Biased species sampling by the WP-2 net in combination with lacking information for species composition limits our ability to quantify the effect of food abundance and food quality on mackerel growth. Negative effects of mackerel and herring stock size on mackerel weight-at-length and growth support the conclusion by Huse et al. (2012) that the carrying capacity of the Norwegian Sea and adjacent areas for plankton-feeding fish stocks is limited and has been reached during the most recent decade. This implies that predation has top down control on zooplankton abundance. Modelling studies suggest that zooplankton abundance in the Norwegian Sea has phases of top down control (Daewel et al., 2014). However, carrying capacity is determined by the productivity of the waters (i.e. bottom up control of zooplankton abundance) and can vary between years (Myers et al., 2001; Perry and Schweigert, 2008). The ecosystem and environmental condition of the Norwegian Sea and adjacent waters shows short- and long-term variability (Skjoldal and Sætre, 2004). Thus, compounding effects of less zooplankton production in the Norwegian Sea during the 30-year period cannot be excluded and requires further study. The observed decline in mackerel weight-at-length could have negative consequences on mackerel recruitment via maternal effects because declining body condition reduces fecundity and increases post-spawning mortality (Lambert and Dutil, 2000; Kurita et al., 2003). There is no indication of declining mackerel recruitment [see Table in ICES (2014a)] despite continually declining condition of individual fish from 2009 onwards. Perhaps, the observed decline in condition is not yet large enough to have significant negative effects on fecundity, or the maternal effects are overwhelmed by other positive factors determining recruitment success. It is not known if expansion of the mackerel summer feeding range has affected the distribution of mackerel in autumn, i.e. is our sampling in the northern North Sea during autumn equally representative for the stock before and after the expansion of summer feeding in the mid-2000s? In other words, is our sampling targeting individuals feeding in the western part to a lesser degree than in the more traditional feeding grounds in the Norwegian Sea and farther north? We cannot answer this adequately. However, we argue that since most of the stock has been feeding within the traditional feeding grounds and farther north in recent years, according to the swept-area trawl survey (Nøttestad et al., 2007, 2010, 2011, 2012, 2013), our results are applicable to the mackerel stock component feeding in the Norwegian Sea and adjacent areas. In conclusion, the data suggest that weight-at-length and growth rate of mature Northeast Atlantic mackerel is negatively influenced by mackerel stock size and herring stock size during

9 Density-dependent growth of mature mackerel 1263 This implies that the carrying capacity of the Norwegian Sea and adjacent areas for plankton-feeding fish is limited and has been reached during the most recent decade. Mackerel length- and weight-at-age declined continually from 2002 to Both weight-at-length and cohort growth of individual 3- to 8-year-old mackerel were negatively influenced by mackerel stock size and herring stock size. Density-dependence is likely mediated via higher forage cost and intensified competition for limited food resources. From 2006 to 2013, the mackerel feeding area expanded ca. 50%; however, mackerel density increased concurrently with an expanding feeding range due to increasing stock size. Expansion of the feeding range caused a longer feeding migration route. Larger individuals migrated farther than smaller ones and weight-at-length of larger individuals declined proportionally more than that of smaller individuals in recent years. This suggests that increasing migration costs related to the extended migration range contributes to declining weight-at-length and probably also to declining growth. Acknowledgements We are grateful to the Institute of Marine Research, Bergen, Norway, for providing us with the biological data which made this study possible. Furthermore, we are grateful to the ICES Working Group on Widely Distributed Stocks and to NOAA/OAR/ESRL PSD, Boulder, CO, USA, for making available SST data and estimated mackerel and herring stock size. We are indebted to Christoph Konrad, Noel Cadigan, and Luis Ridao Cruz for their insightful statistical advice. We thank Clara Penny and Susan Fudge for proofreading the final manuscript and the anonymous reviewers for constructive comments on earlier versions of the manuscript. A.H.O. work was funded by the Danish government through the programme Marine climate in the North Atlantic and its effects on plankton and fish. References Agnalt, A. L Long-term changes in growth and age at maturity of mackerel, Scomber scombrus L., from the North Sea. Journal of Fish Biology, 35 (Suppl. A): Astthorsson, O. S., Valdimarsson, H., Gudmundsdottir, A., and Óskarsson, G. J Climate-related variations in the occurrence and distribution of mackerel (Scomber scombrus) in Icelandic waters. ICES Journal of Marine Science, 69: Bagøien, E., Melle, W., and Kaartvedt, S Seasonal development of mixed layer depths, nutrients, chlorophyll and Calanus finmarchicus in the Norwegian Sea A basin-scale habitat comparison. Progress in Oceanography, 103: Belikov, S. V., Jakupsstovu, S. H., Shamrai, E., and Thomsen, B Migration of Mackerel During Summer in the Norwegian Sea. ICES Document CM1998/AA: pp. Berge, J., Heggland, K., Lønne, O. J., Cottier, F., Hop, H., Gabrielsen, G. W., Nøttestad, L., et al First records of Atlantic mackerel (Scomber scombrus) from the Svalbard Archipelago, Norway, with possible explanations for the extension of its distribution. Arctic, 68: Brett, J. R Environmental factors and growth. In Bioenergetics and Growth, 8, pp Ed. by W. S. Hoar, D. J. Randall, and J. R. Brett. Academic Press, New York. 786 pp. Brio, P. A., Post, J. R., and Parkinson, E. A Density-dependent mortality is mediated by foraging activity for prey fish in whole-lake experiments. Journal of Animal Ecology, 72: Broms, C., Melle, W., and Horne, J. K Navigation mechanisms of herring during feeding migration: The role of ecological gradients on an oceanic scale. Marine Biology Research, 8: Burnham, K. P., and Anderson, D. R Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer-Verlag, New York. 488 pp. Canty, A., and Ripley, B boot: Bootstrap R (S-Plus) Functions. R package version Daewel, U., Hjøllo, S. S., Huret, M., Ji, R., Maar, M., Niiranen, S., Travers-Trolet, M., et al Predation control of zooplankton dynamics: A review of observations and models. ICES Journal of Marine Science, 71: Dalpadado, P., Ellertsen, B., Melle, W., and Dommasnes, A Food and feeding conditions of Norwegian spring-spawning herring (Clupea harengus) through its feeding migrations. ICES Journal of Marine Science, 57: Davison, A. C., and Hinkley, D. V Bootstrap Methods and Their Applications. Cambridge University Press, Cambridge. 594 pp. Debes, H., í Homrum, E., Jacobsen, J. A., Hátún, H., and Danielsen, J The Feeding Ecology of Pelagic Fish in the Southwestern Norwegian Sea-Inter Species Food Competition Between Herring (Clupea harengus) and Mackerel (Scomber scombrus). ICES Document CM 2012/M: pp. Diaz, J. E Schooling Dynamics of Summer Time Migrating Northeast Atlantic Mackerel (Scomber scombrus) in the Norwegian Sea Using Multibeam Sonar. MSc thesis, University of Bergen, Institute of Marine Research, Norway. 59 pp. Dragesund, O., Hamre, J., and Ulltang, Ø Biology and population dynamics of the Norwegian spring-spawning herring. Rapports et Procès-Verbaux des Réunion du Conseil International pour l Exploration de la Mer, 177: Dragesund, O., Johannessen, A., and Ulltang, Ø Variation in migration and abundance of Norwegian spring-spawning herring (Clupea harengus L.). Sarsia, 82: Froese, R Cube law, condition factor and weight-length relationships: History, meta-analysis and recommendations. Journal of Applied Ichthyology. 22: Gislason, A., and Astthorsson, O. S The food of Norwegian spring-spawning herring in the western Norwegian Sea in relation to the annual cycle of zooplankton. Sarsia, 87: Gjøsæter, H., Dalpadado, P., Hassel, A., and Skjoldal, H. R A comparison of performance of WP2 and MOCKNESS. Journal of Plankton Research, 22: Gødo, O. R., Hjellvik, V., Iversen, S. A., Slotte, A., Tenningen, E., and Torkelsen, T Behavior of mackerel schools during summer feeding migration in the Norwegian Sea, as observed from fishing vessel sonars. ICES Journal of Marine Science, 61: Huse, G., Holst, J. C., Utne, K., Nøttestad, L., Melle, W., Slotte, A., Ottersen, G., et al Effects of interactions between fish populations on ecosystem dynamics in the Norwegian Sea Results of the INFERNO project. Marine Biology Research, 8: ICES Report of the Working Group on Northern Pelagic and Blue Whiting Fisheries (WGNPBW), 27 August 1 September 2007, Vigo, Spain. ICES Document CM 2007/ACFM: pp. ICES. 2013a. Report of the Ad hoc Group on the Distribution and Migration of Northeast Atlantic Mackerel (AGDMM), August 2011 and May 2012, ICES Headquarters, Copenhagen. ICES Document CM 2013/ACOM: pp. ICES. 2013b. Interim Report of the Working Group on Integrated Ecosystem Assessments for the Norwegian Sea (WGINOR), August 2013, Bergen, Norway. ICES Document CM 2013/ SSGRSP: pp. ICES. 2014a. Report of the Report of the Working Group on Widely Distributed Stocks (WGWIDE), 26 August 1 September 2014, ICES Headquarters, Copenhagen, Denmark. ICES Document CM 2014/ACOM: pp. ICES. 2014b. Report of the Working Group on Mackerel and Horse Mackerel Egg Surveys (WGMEGS), 7 11 April 2014, Reykjavik, Iceland. ICES Document CM 2014/SSGESST: pp.

10 1264 A. H. Olafsdottir et al. Iversen, S. A Changes in the perception of the migration patterns of Northeast Atlantic mackerel during the last 100 years. ICES Marine Science Symposia, 215: Iversen, S. A Mackerel and horse mackerel. In The Norwegian Sea Ecosystem, pp Ed. by H. R. Skjoldal. Tapir Academis Press, Trondheim. 559 pp. Jansen, T., and Burns, F Density dependent growth changes through juvenile and early adult life of North East Atlantic mackerel (Scomber scombrus). Fisheries Research, 169: Jansen, T., Campbell, A., Kelly, K., Hátún, H., and Payne, M. R Migration and fisheries of North East Atlantic mackerel (Scomber scombrus) in autumn and winter. PLoS ONE, 7: e Jenkins, T. M., Jr, Diehl, S., Kratz, K. W., and Cooper, S. D Effects of population density on individual growth of brown trout in streams. Ecology, 80: Kurita, Y., Meier, S., and Kjesbu, O. S Oocyte growth and fecundity regulation by atresia of Atlantic herring (Clupea harengus)in relation to body condition throughout the maturation cycle. Journal of Sea Research, 49: Lambert, Y., and Dutil, J-D Energetic consequences of reproduction in Atlantic cod (Gadus morhua) in relation to spawning level of somatic energy reserves. Canadian Journal of Fisheries and Aquatic Sciences, 57: Langøy, H., Nøttestad, L., Skaret, G., Broms, C., and Fernö, A Overlap in distribution and diets of Atlantic mackerel (Scomber scombrus), Norwegian spring-spawning herring (Clupea harengus) and blue whiting (Micromesistius poutassou) in the Norwegian Sea during late summer. Marine Biology Research, 8: Lockwood, S. J The Mackerel: Its Biology, Assessment and the Management of a Fishery. Fishing New Books Ltd, Farnham, Surrey, UK. 181 pp. Misund, O. A., Vilhjálmsson, H., Í Jákupsstovu, S. H., Røttingen, I., Belikov, S., Asthorsson, O., Blindheim, J., et al Distribution, migration and abundance of Norwegian spring spawning herring in relation to the temperature and zooplankton biomass in the Norwegian Sea as recorded by coordinated surveys in spring and summer Sarsia, 83: Mjanger, H., Hestenes, K., Svendsen, B. V., and de Lange Wenneck, T Manual for Sampling of Fish and Crustaceans. Ver Institute of Marine Research, Bergen, Norway. 197 pp. Myers, R. A., MacKenzie, B. R., Bowen, K. G., and Barrowman, N. J What is the carrying capacity for fish in the ocean? A meta-analysis of population dynamics of North Atlantic cod. Canadian Journal of Fisheries and Aquatic Sciences, 58: Nøttestad, L., Holst, J. C., Utne, K. R., Tangen, Ø., Anthonypillai, A., Skålevik, Å., Mork, K. A., et al Cruise Report from the Coordinated Ecosystem Survey (IESSNS) with M/V Libas, M/V Finnur Fridi and R/V Arni Fridriksson in the Norwegian Sea and Surrounding Waters, 18 July 31 August, Working Document to Working Group of Widely Distributed Stocks, ICES, Copenhagen, Denmark, 27 August 2 September pp. Nøttestad, L., Jacobsen, J. A., and Sveinbjornsson, S Cruise Report from the Coordinated Ecosystem Survey with M/V Libas, M/V Brennholm, M/V Finnur Fridi and R/V Arni Fridriksson in the Norwegian Sea and Surrounding Waters, 9 July 20 August Working Document to Working Group of Widely Distributed Stocks, ICES, Copenhagen, Denmark, 28 August 3 September pp. Nøttestad, L., Patel, R., Anthonypillai, V., Langøy, H., Nilson, G., Langård, L., Andersen, J., et al Cruise Report on Coordinated Ecosystem Survey with M/V Libas and M/V Eros in the Norwegian Sea, 15 July 6 August Unpublished Cruise Report of Institute of Marine Research, Bergen, Norway. 39 pp. Nøttestad, L., Utne, K. R., Anthonypillai, V., Tangen, Ø., Valdemarsen, J. W., Óskarsson, G., Sveinbjörnsson, S., et al Cruise Report from the Coordinated Ecosystem Survey (IESSNS) with R/V G.O. Sars, M/V Brennholm, M/V Christian í Grótinum and R/V Arni Fridriksson in the Norwegian Sea and surrounding waters, 1 July 10 August Working Document to Working Group of Widely Distributed Stocks, ICES, Lowestoft, UK, August pp. Nøttestad, L., Utne, K. R., Anthonypillai, V., Tangen, Ø., Valdemarsen, J. W., Óskarsson, G., Sveinbjörnsson, S., et al Cruise Report from the Coordinated Ecosystem Survey (IESSNS) with M/V Libas, M/V Eros, M/V Finnur Fríði and R/V Arni Fridriksson in the Norwegian Sea and surrounding waters, 2 July 9 August Working Document to Working Group of Widely Distributed Stocks, ICES, Copenhagen, Denmark, 27 August 2 September pp. Obradovich, S. G., Carruthers, E. H., and Rose, G. A Bottom-up limits to Newfoundland capelin (Mallotus villosus) rebuilding: The euphausiid hypothesis. ICES Journal of Marine Science, 71: Overholtz, W. J Density-dependent growth in the Northwest Atlantic stock of Atlantic mackerel (Scomber scombrus). Journal of Northwest Atlantic Fishery Science, 9: Óskarsson, G. J., Sveinbjörnsson, S., Guðmundsdóttir, Á., and Sigurðsson, Þ Ecological Impacts of Recent Extension of Feeding Migration of NE Atlantic Mackerel into the Ecosystem Around Iceland. ICES Document CM 2012/M: pp. Peña, H., Røttingen, J., Skaret, G., Utne, K. R., and Nøttestad, L Cruise Report on Northeast Atlantic Mackerel (Scomber scombrus) Abundance and Distribution in the North Sea and West of the British Isles from 1st to 20th of October Unpublished Cruise Report of Institute of Marine Research, Bergen. 23 pp. Perry, R. I., and Schweigert, J. F Primary productivity and the carrying capacity for herring in NE Pacific marine ecosystems. Progress in Oceanography, 77: Pinnegar, J. K., Goñi, N., Trenkel, V. M., Arrizabalaga, H., Melle, W., Keating, J., and Óskarsson, G A new compilation of stomach content data for commercially important pelagic fish species in the northeast Atlantic. Earth System Science Data, 7: Prokopchuk, I., and Sentyabov, E Diets of herring, mackerel, and blue whiting in the Norwegian Sea in relation to Calanus finmarchicus distribution and temperature conditions. ICES Journal of Marine Science, 63: R Core Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Version Rand, K. M., Beauchamp, D. A., and Lowe, S. A Longitudinal growth differences and the influence of diet quality on Atka mackerel of the Aleutian Islands, Alaska: Using a bioenergetics model to explore underlying mechanisms. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science, 2: Reid, D. G., Turrell, W. R., Walsh, M., and Corten, A Crossshelf processes north of Scotland in relation to the southerly migration of Western mackerel. ICES Journal of Marine Science, 54: Reynolds, R. W., and Smith, T. M A high-resolution global sea surface temperature climatology. Journal of Climate, 8: Rosenfeld, J. S., Leiter, T., Lindner, G., and Rothman, L Food abundance and fish density alters habitat selection, growth, and habitat suitability curves for juvenile coho salmon (Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences, 62: Skjoldal, H. R., and Sætre, R Climate and ecosystem variability. In The Norwegian Sea ecosystem, 1st edn, pp Ed. by H. R. Skjoldal. Tapir Academic Press, Trondheim. 559 pp. Slotte, A., Skagen, D., and Iversen, S. A Size of mackerel in research vessel trawl hauls versus commercial purse seine catches: Implications for acoustic estimation of biomass. ICES Journal of Marine Science, 64: