1899
Maturity and growth population dynamics of Norway pout
(Trisopterus esmarkii) in the North Sea, Skagerrak, and Kattegat
Gwladys Lambert, J. Rasmus Nielsen, Lena I. Larsen, and Henrik Sparholt
Lambert, G., Nielsen, J. R., Larsen, L. I., and Sparholt, H. 2009. Maturity and growth population dynamics of Norway pout (Trisopterus esmarkii)
in the North Sea, Skagerrak, and Kattegat. – ICES Journal of Marine Science, 66: 1899 –1914.
The population dynamics of the Norway pout stock in the North Sea are investigated by statistical analyses, and GIS of ICES
International Bottom Trawl Surveys (IBTS) and Danish commercial catch data from 1983 to 2006. The stock spawns mainly
around mid-February along the northeastern English and Scottish coasts and between Shetland and Norway. Sex ratios indicate
that males, which mature younger than females (age-at-50%-maturity, respectively, 1.2 and 1.5 years), migrate out of the
Skagerrak – Kattegat to the spawning grounds before females. There is a decrease in the 2+-group maturity ratios as well as in
weight and female length from before to after spawning. The results indicate spawning mortality. Only some 20% of the 1-group
reaches maturity in the first quarter, which is higher than assumed in the stock assessment. Although the maturity ogives are variable
over time, this difference should be taken into account when estimating spawning-stock biomass in routine assessments. Growth is
also variable, with a tendency for male maximum length to be smaller than that of females, and immature fish to be smaller
than mature ones in each age group. The juvenile growth rate is higher when the stock density is low and results in a reduced
age-at-50%-maturity. Besides these intraspecific patterns, the growth rates show interspecific links to stock sizes of the important
predators: cod, haddock, and whiting.
Keywords: density-dependence, intra- and interspecific growth, maturity, North Sea, Norway pout, population dynamics, spawning, spawning
mortality, Trisopterus esmarkii.
Received 12 April 2008; accepted 20 March 2009; advance access publication 8 June 2009.
G. Lambert and J. R. Nielsen: National Institute of Aquatic Resources (DTU AQUA), Technical University of Denmark, Charlottenlund Castle, DK2920 Charlottenlund, Denmark. L. I. Larsen and H. Sparholt: International Council for the Exploration of the Sea, H. C. Andersens-Boulevard 44-46,
DK-1553 Copenhagen V, Denmark. Correspondence to J. R. Nielsen: tel: þ45 33 96 33 81; fax: þ45 33 96 33 33; e-mail: [email protected].
Introduction
The Norway pout (Trisopterus esmarkii) is a small, short-lived
gadoid, which rarely lives longer than 4–5 years (Sparholt et al.,
2002b). It is distributed from the west of Ireland to the Kattegat
and from the North Sea to the Barents Sea, as well as around the
Faroe Islands. In this study, we focus on the North Sea stock as
defined in ICES routine assessments as Norway pout in the North
Sea, Skagerrak, and Kattegat. The distribution of this stock is
given as the northern North Sea (.578N), Skagerrak, and
Kattegat at depths between 50 and 250 m (Poulsen, 1968; Sparholt
et al., 2002a). For the Norwegian Trench, Albert (1994) found
Norway pout deeper than 200 m, but very few deeper than 300 m.
Neither stock separation nor spawning distributions are well
known, but there is no evidence for separate stocks in the North
Sea and the Skagerrak. Preliminary results from an analysis of
regional survey data on Norway pout maturity (ICES, 2001;
Larsen et al., 2001) gave no evidence for stock separation in the
North Sea and the Skagerrak. A number of authors state that the
stock spawns from mid-February/March to April (Raitt and
Mason, 1968; Albert, 1994) in the northern North Sea between
Shetland and Norway. In terms of the distribution of larvae and
juveniles, Norway pout are generally not considered to have
specific nursery grounds, but pelagic 0-group fish have been
reported as being widely dispersed in the northern North Sea
# 2009
close to the spawning grounds (Poulsen, 1968). Most of the
larvae seemingly drift from the more western areas to which they
return mainly during the latter part of their second year of life
before maturing, and adults migrate out of the Skagerrak and
the Kattegat to spawn, because there is no evidence of spawning
there (Poulsen, 1968). Albert (1994) stated that the negative
winter growth in the Skagerrak reported by Ursin (1963) and
Poulsen (1968) could be explained by emigration of the species.
There is no other indication of adult migration.
For the ICES stock assessment, 10% of age group 1 and 100% of
age groups 2 and 3 (ICES, 2007b) are considered mature and to
belong to the spawning-stock biomass (SSB). As the stock
density is driven by age group 1 (ICES, 2007b), the constant
10% of that age group becoming mature is a major parameter in
SSB estimation and influences the perception of SSB levels and
dynamics. However, Raitt (1968) observed that this ratio
reached 60% for the 1964 year class, which indicates that the
maturity ratio at age 1 needs to be studied and interpreted with
care. Moreover, the population was extremely small in the early
1960s, and the 1964 year class showed a remarkably fast growth
rate, so the minimum length at maturity did not decline (Albert,
1994), indicating possible density-dependence in growth and
stability in length-at-maturity. This assumption needs to be investigated with the whole dataset now available.
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1900
Sexual differences in maturity, growth, and numbers are
expected from indications in the literature. The maturity of the
stock has not been studied systematically, and the differences
between the sexes are not known. Female Norway pout are
larger than males (Raitt, 1968), and several authors (e.g. Heesen
and Kuiter, 1982; Cooper, 1983; ICES, 2007b) have reported a
numerical dominance of females which, according to Cooper
(1983), increases with age.
At present levels of fishing mortality, the status of the stock is
determined more by natural processes than by the fishery, and
in general, the fishing mortality on 0-group Norway pout is low
(Sparholt et al., 2002a, b; ICES, 2007b). The population dynamics
of Norway pout in the North Sea and the Skagerrak are highly
dependent on changes caused by recruitment variation and different levels of predation mortality, or other causes of natural mortality, such as spawning mortality (Bailey and Kunzlik, 1984;
Sparholt et al., 2002a, b; ICES, 2007b). However, there is a need
to ensure that the stock remains large enough to provide forage
for a variety of predators, e.g. cod (Gadus morhua), whiting
(Merlangius merlangus), saithe (Pollachius virens), haddock
(Melanogrammus aeglefinus), and mackerel (Scomber scombrus;
ICES, 2007b).
Knowledge of Norway pout life history is needed in terms of
spawning distribution, the short lifespan of the species, and
potential indication of spawning mortality, as well as in relation
to sex-dependent behaviour and intra- and interspecific densitydependence. Density-dependence has important consequences
for fisheries management and recovery, as well as population
maintenance or extinction (Minto et al., 2008). The results of analyses of variability in maturity and growth have been put into integrated perspective to reveal further information and to improve
the assessment of the stock, as well as to give more precise knowledge of the population dynamics in the context of potential
density-dependence and spawning mortality.
The current study is aimed at testing a number of null hypotheses: (H0 1) the exact time and the location of Norway pout spawning is not known; (H0 2) the constant value of 10% is a
good representation of the maturity proportion at age 1 during
Q1 for both sexes, and all older age groups are mature; (H0 3)
there is no area- or sex-dependence of the stock in terms of
growth or maturity; (H0 4) there is no consistency or link
between mortality and growth/maturity trends; and (H0 5) there
is no density-dependence, intra- nor interspecific, on growth or
maturity.
Material and methods
Sex– maturity– age–length keys from surveys
Sex –maturity –age –length keys (SMALKs) were established from
International Bottom Trawl Survey (IBTS) data for the period
1983–2006. From 1983 to 1990, data were only available for
quarter 1 (Q1), from 1991 to 1996 for all quarters, and from
1997 for Q1 and Q3 (Anon., 2004). Norway pout SMALKs and
actual catch rates (catch per unit effort, cpue, in number per trawlhour) by length were used to estimate the sex–maturity –age –
length cpue indices by year, quarter, and roundfish area (RFA;
Anon., 2004) to weight maturity and growth data by cpue.
These data were used to investigate Norway pout stock history
traits.
SMALKs were completed by inserting missing values where no
cpue data by length were available. The southern RFAs 5 and 6
G. Lambert et al.
were directly excluded from the analyses because the cpue values
there were insignificant (Raitt and Mason, 1968). First, we
checked whether ages could be established for the missing values
in the age –length key (ALK). As most empty cells corresponded
to the smaller length classes, we completed the blanks with age 0
for Q3 and Q4, when at least the two next length classes were
determined as age 0, because recruitment takes place in Q3. The
same reasoning was applied to age 1 in Q1 and Q2 because age
group 0 has not yet recruited at that time. For the missing
values in the bigger length classes or the length classes between
two ages, age determination was not that clear and there were
few fish in the dataset, so those data were not considered in the
analysis (Table 1).
Sex and maturity ogives were first evaluated separately.
0-groups were always considered as immature. For the other age
groups, the samples were not always recorded by sex and maturity
stage, making it necessary to select a threshold of relevance (see
below) for the evaluation, to preclude noise in the datasets and
misinterpretation. Data were redistributed according to the percentage of fish examined in the samples, first for the sex–ALK
and then for the maturity –ALK. If a threshold of at least 25% of
the sampled fish of one age – length class had their sex or maturity
recorded, the sample was considered representative of that age –
length class; otherwise the sample was not considered for the
keys (Table 1). Several such percentage thresholds were tested,
and those .25% led to the exclusion from the analysis of many
sex– or maturity – ALKs and, consequently, many length classes
with a high cpue. With 25% as the threshold, ,12% of the total
cpue data (Table 1) were not considered (except areas 4 and 7).
Therefore, applying the threshold of 25% allowed us to provide
improved, but realistic, data on population distribution and
limited the uncertainties without considering the weakest information in the dataset, where basing the output on a few fish
yielded a lot of noise.
The combined SMALKs, i.e. the keys considering both sex and
maturity determination, were established considering only two
stages of maturity: mature and immature. This two-stage maturity
scale was preferred to the four-stage scale available from 1991
because it allowed us to use the full data time-series and to
avoid introducing too many errors and noise in terms of misclassification. The four-stage standard ICES maturity scale (Anon.,
2004) defines M1 as immature, M2 as maturing (either for the
first time or in principle for subsequent spawning), M3 as spawning, and M4 as spent. For the two-stage scale used here, immature
fish are still allocated to M1, but all mature fish to M2, so incorporating all fish assigned to M2, M3, and M4 of the four-stage
scale. We note, however, that the numbers of M3 and M4 fish in
the survey database are anyway very small, an insignificant proportion of the total sample numbers in the database.
Growth data from Danish commercial catches
and survey (IBTS) data
Mean weight-at-age (MWA) is registered from commercial fishery
catch data (1983 –2004, and the second half of 2006), but not
during the IBTS, from which only length data are available. Data
collected by the fishery could not be compared spatially with
survey data because the area stratification differs between the
data time-series. The areas of commercial catch data available
were ICES Areas IVb (North Sea South), IVae (North Sea East),
IVaw (North Sea West), K (Kattegat), and S (Skagerrak).
However, few samples exist for IVb and the Kattegat, so we did
1901
Maturity and growth population dynamics of Norway pout
Table 1. Number of Norway pout sampled for the sex– maturity– age – length key (SMALK) from the 1983 –2006 IBTS by quarter and
area, and the percentages of the cpue undefined (UK) and removed after redistribution.
Quarter
1
2
3
4
Area
1
2
3
4
7
8
9
1
2
3
4
7
8
9
1
2
3
4
7
8
9
1
2
3
4
7
8
Aged
sampled
7 945
4 136
5 289
3 086
1 495
1 112
560
1 748
1 003
839
793
279
163
83
6 837
1 682
2 409
971
282
1 209
510
2 138
989
630
681
121
334
Maturity
determined
5 139
3 177
4 254
2 240
521
953
500
1 367
583
659
450
173
162
82
5 683
1 256
1 769
858
232
891
337
2 030
717
489
387
121
130
Sex
determined
5 989
3 445
4 359
2 570
866
953
500
1 367
717
758
606
205
163
82
5 075
1 084
1 632
921
195
891
331
1 593
751
465
567
67
301
not consider IVb in our analysis, and the Kattegat data were
merged with those from the Skagerrak. Moreover, as there are
few observations of ages 3 –5 and no data at all for age 0 in quarters
1 and 2, we constrained our growth analysis to ages 1 and 2 in
terms of the commercial data.
Mean length-at-age (MLA) information was obtained from
ICES IBTS survey data for the period 1983–2006. Age group 5+
fish were not considered because there were too few data, and
only a few of the fish sampled had attained 3 or 4 years. Using
the completed SMALK, values of MLA were computed by year,
quarter, and area, and by sex and maturity where valid, weighting
the length classes by the cpue of length classes.
Potential spatio-temporal patterns in spawning
and sex distribution
A visual analysis (GIS) of precise locations of spawning fish (M3,
i.e. “running” gonads) from IBTS data for the period 1991–
2006 was carried out to identify where and when spawning takes
place. Mean maturity ratios by age and quarter for all four maturity groups, weighted by the cpue indices by length, were used to
assess maturity trends during the life cycle.
The SMALK combined cpue data were used to provide abundance indices by sex to explore spatio-temporal variation in the
sex ratio and sex-dependent distribution over the seven RFAs
investigated. Weighted mean sex ratios and weighted deviations
were computed and compared (Sokal and Rohlf, 1995).
Revised SSB and stock– recruitment relationships
The SMALK combined cpue data were also used to compute
revised SSB and SSB/R (recruitment) relationships using the
Sum cpue
14 563 613
2 746 510
1 705 302
275 245
257 830
601 516
143 121
3 705 632
1 152 018
467 198
156 995
306 856
156 460
144 833
6 651 579
4 111 794
2 591 472
191 682
977 063
4 402 060
586 018
8 362 485
1 964 999
871 835
180 812
337 772
73 035
Percentage not
aged UK
0
0
0
0.1
5.2
0.1
0
0
0
0
0
4.7
3.4
1.2
0.5
0.1
0.2
0.2
4.5
0
0.6
0
0.2
1.2
0
2.2
0.1
Percentage
maturity UK
4.0
0.3
1.0
15.4
1.5
4.7
8.2
1.0
10.9
1.6
12.3
12.1
0.2
0
0.1
0.6
0.5
1.0
0.4
0.1
0.3
0.2
2.9
4.3
24.8
0
5.3
Percentage sex
UK
11.8
8.1
1.0
8.6
30.0
5.1
8.7
1.0
2.3
0.3
1.1
23.0
0.2
0
0.1
0
0.8
0.4
11.9
0.3
0
0
0
0
0
0.8
0
temporal information on maturity. Revised values of SSB were
calculated using ICES stock numbers-at-age and, first, the mean
values of MWA from the ICES stock assessment (ICES, 2007b)
in quarter 1, and second, the mean values of MWA estimated
from the commercial catch data, both using time-variable
maturity ogives.
To assess the distribution of the population between different
RFAs, mean values of cpue per RFA were multiplied by the size
of the RFA (=number of rectangles of 30 30 nautical miles;
Sparre and Venema, 1989):
cpue ¼
C
qN
¼ qD ¼
, cpue A ¼ qN;
E
A
ð1Þ
where C is the catch, E the effort, D the density, A the area, q the
catchability, and N the number of fish in the stock. Then, the
numbers of mature fish in each RFA were calculated, assuming
constant catchability, and the percentage of mature fish by age
in the whole population applied to the ICES stock numbers to
yield a revised SSB with realistic maturity ratios-at-age.
The ICES SGRECVAP (Study Group on Recruitment
Variability in the North Sea) has fitted different SSB/R relationships for the North Sea pout stock (ICES, 2007c) using the FLR
Library in R. With the same tool for SSB computed from revised
maturity data, Beverton –Holt, Ricker, and quadratic hockey
stick models (http://flr-project.org/), and segmented regression,
were fitted to the revised SSB/R relationships. The advantage of
using FLR is that the starting points are precise points defined
as default by the program for each model. For instance, for
the Beverton – Holt model (hereafter, Bev–Holt) and for the
1902
G. Lambert et al.
segmented regression (Seg–Reg), these points are defined as
follows:
Bev–Holt : a ¼ maxðRÞ þ 0:1ðmaxðRÞ minðRÞÞ
b ¼ 0:5 minðSSBÞ; R
b ¼ meanðSSBÞ:
Seg–Reg : a ¼ mean
SSB
Density-dependence in maturity and growth
ð2Þ
ð3Þ
The resulting fit, obtained after optimization by the maximum
likelihood, was then compared using Akaike’s information criterion (AIC), a statistical test based on the deviance estimates d
and allowing selection of the best fit. The AIC accounts for the
deviance and the number of parameters estimated as follows
(Burnham and Anderson, 2002):
dðy; uÞ ¼ 2 log½ pðyjuÞ;
and AIC ¼ 2k 2 lnðLÞ;
ð4Þ
where k is the number of parameters, and L the likelihood function, p(yju).
Statistical analyses of the variation in growth, sex,
and maturity dynamics
The variability in our percentages of maturity and sex was studied
by GLM analysis in R. The logistic model, where pi is a linear function of the explanatory variables xi, has been used (Sokal and
Rohlf, 1995):
pi
logitð pi Þ ¼ ln
1 pi
¼ b1 x1;i þ þ bk xk;i :
ð5Þ
This produces a linear predictor not for p but for the logit transformation of p, namely ln (p/q) where q = 1 2 p (Sokal and
Rohlf, 1995); the logit is referred to as the link function. The variance is the product of the variance function and the dispersion
parameter, the latter being 1 for the binomial distribution.
However, the models used do allow for estimating overdispersion.
The spatio-temporal variations in MWA and MLA were first
analysed visually to check for any evident patterns. Then, to evaluate variations in weight:
MW Age þ Quarter þ Area þ first-order interactions:
ð6Þ
As the biological data generally do not follow the assumptions of
normality and constant variance, analyses were performed on logtransformed MW, or GLMs with gamma distribution were tested.
The stepAIC function in R returns a stepwise-selected model based
on the AIC, and the best fit is chosen by the analysis of residuals
(Crawley, 2007).
According to the variability in MLA, different analysis of variance (ANOVA) GLM analyses were tested and performed in
relation to quarter, area, and sex- and maturity-dependence. The
von Bertalanffy growth parameters were calculated based on the
equation
LðtÞ ¼ L1 ð1 ekðtt0 Þ Þ;
variable were compared and used in the context of possible correlations with maturation or density-dependence.
ð7Þ
where L(t) is the total length, L1 the maximum theoretical length,
k the growth constant, and t0 the theoretical age at length 0. The
curve was fitted by the method of least-squares to MLA data
split by sex. Values obtained using a fixed t0 or by leaving t0 as a
Intraspecific density-dependence in maturity ratio, MWA, and
MLA were investigated through the possible influence of stock
number (in the previous quarter) on the quarterly maturity
ratio-at-age. This was done partly visually and also through
GLM analyses. To compare the MWA with the occurrence of the
main predators, the SSB estimates of predators from the previous
quarter were used. Predator data originate from the MSVPA (multispecies virtual population analysis) estimates from 2004 (ICES,
2006). The outputs of the MSVPA give quarterly SSB of the
main predators of Norway pout used to study the interspecific
relationships. In relation to the comparison with MLA, SSB data
from the first quarter from the ICES assessments (ICES, 2007b)
were used so that data for more recent years, after 2004, could
also be applied. The SSB estimates were used as further indicators
of possible foraging strategy, because mature Norway pout can be
assumed to be large enough to target small Norway pout. Visual
inspection and GLM analysis were performed to check the significance of the different relationships tested.
Combined patterns in growth, maturity, and density
Following the analyses described above, the relationship between
MLA and maturity ratio was evaluated statistically using a binomial model. Maturity ogives were evaluated to improve life-history
knowledge and to understand and predict the link between growth
and maturity better. They are logistic curves fitted to the percentage maturity data by length class or age group and provide the
length (L50) or age (A50) at which 50% of the fish are mature,
for each sex:
logitð pÞ ¼ a þ b Age
or logitð pÞ ¼ a þ b Length:
ð8Þ
L50 and A50 are given by 2a/b (Heino et al., 2002). The curves,
and specifically values of L50 and A50 by cohort, were compared in
the context of changes in the growth indices (either MLA, the
increment between ages 1 and 2, or the growth rate from the estimated von Bertalanffy parameters) and intraspecific densitydependence (estimation of the correlation with recruitment). To
test L50 and A50 trends and correlations, linear regressions were
performed and after ANOVA, the significance was given by the
p-values.
Results
Spatio-temporal patterns in sex and spawning
distribution
The weighted mean of the Norway pout sex ratio (females/females +
males) does not differ significantly by age group (Table 2). The values
computed for all years where all areas were sampled were: age 1, 0.49
in Q1 and 0.53 in Q3; age 2, 0.49 in Q1 and 0.6 in Q3; age 3, 0.48 in
Q1 and 0.58 in Q3. However, the difference by area was significant,
with a dominance of females in the Skagerrak and the Kattegat in
all quarters (Table 2); the sex ratio there varied from 0.57 to 0.91.
The mean weighted sex ratio at ages 1 and 2 decreased from Q1 to
Q3 in the Skagerrak (including the Kattegat), but increased in the
northwestern North Sea in general (Table 2).
Some spawners (maturity stage 3) were sampled in all RFAs
except 5 and 6 (Figure 1). Moreover, it seems that spawning
takes place mainly in RFAs 1 and 3, around the Viking Bank and
1903
Maturity and growth population dynamics of Norway pout
Table 2. Mean weighted sex ratio (female) by area for each age and quarter, and the standard weighted deviation (NA, ratio not given
when fewer than ten fish were sampled).
Sex ratio age 1
Area
1
2
3
4
7
8
9
Q1
0.44
0.48
0.47
0.64
0.53
0.7
0.71
s.d.w
0.09
0.1
0.1
0.1
0.1
0.07
0.04
Q3
0.49
0.58
0.53
0.63
0.51
0.57
0.7
Sex ratio age 2
s.d.w
0.04
0.12
0.07
0.1
0.2
0.14
0.22
Q1
0.46
0.48
0.53
0.49
0.71
0.86
0.81
s.d.w
0.08
0.1
0.11
0.19
0.25
0.12
0.16
Q3
0.52
0.51
0.66
0.67
0.25
0.67
0.7
Sex ratio age 3
s.d.w
0.07
0.12
0.15
0.16
0.19
0.07
0.09
Q1
0.48
0.49
0.59
0.48
NA
0.78
0.91
s.d.w
0.1
0.27
0.19
0.33
NA
0.07
0.11
Q3
0.42
0.62
NA
0.76
NA
0.69
NA
s.d.w
0.18
0.12
NA
0.33
NA
0.15
NA
The emboldened values reflect high levels in areas 8 and 9.
Figure 1. Distribution and numbers of spawning Norway pout collected during the ICES IBTS surveys (GOV and GRT Trawls) between 1983
and 2007 (values are sums of all fish observed for all quarters).
1904
G. Lambert et al.
Figure 2. Percentage of each maturity stage (1 – 4) of Norway pout per age and quarter, based on data collected in areas 1 – 4 and 7 between
1991 and 1997.
along the Scottish east coast. Spawners were also observed in RF7,
next to the Norwegian coast, at the boundary of RFAs 1 and
2. Throughout the 15 years of surveys, only a few spawning
Norway pout were found in the Skagerrak (RFA 8).
The distribution indicates that there might be some combined
depth- and age-dependent patterns in spawning. None of the
sampled spawning fish aged 1 were in RFAs 1, 7, or 8, though a
few were observed in the more southern areas along the northern
English coast in shallow water of 50 –100 m. Ages 2 and 3 were
found everywhere, including in open-sea areas. However, these
results are only indicative because of the limited number of observations of M3 fish overall. No further comment is provided now
because the aim of our analysis was to obtain information on
spawning time and location and not to seek a hydrographic explanation for the patterns observed. Some 90% of the spawners were
recorded during Q1; the balance were widespread during all three
of the other quarters. From these direct observations, peak spawning is assumed to have taken place mainly in RFAs 1 –3 during Q1
of the year and to a lesser extent in RFA 7 (M3 fish were found
there mainly during 1991).
The combined observations on sex ratio and spawning time
and area indicate that there is a possible migration of males out
of the Skagerrak –Kattegat to spawning grounds before the associated female migration. This indication is confirmed below.
Norway pout maturity
All fish aged 0 in Q3 were immature (Figure 2), but by Q4, a few
were maturing. For Norway pout aged 1 in Q1 –3, the maturity
rate was 20%, but by Q4 the level of maturity had increased to
40%. Age 2 Norway pout were 90% mature in Q1, but just 70%
mature in Q2 and 50% mature in Q3, but then 70% mature in
Q4. Age 3 Norway pout were 95% mature in Q1, 75% mature in
Q2, 65% mature in Q3, and 80% mature in Q4. All age 4 fish
were mature in Q1, but just 5% in Q2, 10% in Q3, and 20% in
Q4. The results in Figure 2 indicate possible spawning mortality
because most spawning fish at each age were recorded in Q1 and
the maturity ratio then decreased. Spent fish (maturity stage 4)
were almost exclusively recorded in Q2 and Q3 (with highest
levels in Q3).
Based on L50 (selectivity), the patterns of declining maturity
ratio over the calendar year and the scarcity of M4 fish are not
expected to result from a size-selective fishery. Once all Norway
pout had recruited (i.e. age 0 in Q4), smaller fish would probably
not be able to escape more effectively than larger fish.
These observations are in accord with the GIS observations and
the observed quarterly variability in the sexual maturity ratio by
age and area: a smaller percentage of fish was mature in areas 8
and 9, and the maturity ratio was low, except for age 3 in Q1 in
area 8 (Figure 3). Statistical analysis revealed the difference by
area to be highly significant (Table 3).
Although these patterns are obvious in our results, variation in
maturity was further investigated to show evidence of density
effects influencing maturation through growth. The significant
increase in the ratio of maturing fish at age 1 (p , 0.001) was
driven by the recent years of low abundance and density (2003–
2005) in the period investigated (Figure 4); estimated values
range from 9 to 36%. If these recent years are removed from the
analyses, the trend is no longer significant. Although there is no
1905
Maturity and growth population dynamics of Norway pout
Figure 3. Maturity ratios for Norway pout in Q1 (left) and Q3 (right) after 1991: interaction age – area for males (bottom) and females (top).
Table 3. p-values of the quasi-binomial distribution of models
fitted to the maturity data for Q1, Q3, females Q1 (F Q1), females
Q3 (F Q3), males Q1 (M Q1), and males Q3 (M Q3).
Age
Area
Age:Area
Q1
,0.001
,0.001
NS
Q3
,0.001
,0.001
,0.1
F Q1
,0.001
,0.001
NS
F Q3
,0.001
,0.001
0.05
M Q1
,0.001
,0.001
NS
M Q3
,0.001
,0.001
NS
NS, not significant.
trend at age 2, Norway pout were not always all mature, as
currently assumed in the ICES assessment. This indicates that
the variations we observed are correlated with stock density
(Figure 5). When density is high at age 0 in Q4, the maturity
ratio tends to be lower in the subsequent quarter, i.e. at age 1 in
Q1 of the succeeding year (p , 0.01; Figure 5). The percentage
of mature fish by sex, weighted by cpue indices, gives an indication
of the ratio of mature fish in the stock. These percentages of
mature females and males aged 2 in Q1 compared well with the
pattern observed at age 1. The slope decreases significantly for
males at age 2 when recruitment increases (p = 0.046). Even if
the other relationships tested (e.g. age group 1 by sex, with recruitment from the previous quarter, for instance) were not significant,
they showed the same trend of decreasing maturity corresponding
to an increase in the levels of recruitment.
The variability in maturity-at-age 1 influences the SSB and
SSB/R estimates. SSB computed with the new estimates of maturity generally results in higher values than estimated with the
Figure 4. Temporal variability in the maturity ratio of Norway pout during Q1 at ages 1 (left) and 2 (right) from 1983 to 2006.
1906
G. Lambert et al.
Table 4. Parameters of four stock – recruitment relationships tested
on the revised SSB data (see also Figure 7).
Relationship
Segmented regression
Ricker
Beverton and Holt
Quadratic hockey stick
a
544.09
796.58
84.56
544.10
b
0.1311
3.7266
0.0408
0.1285
r
NA
NA
NA
0.042
AIC
50.52
51.10
50.19
51.53
NA, not relevant.
Figure 5. Statistically significant intraspecific relationship between
maturity ratio at age 1 in Q1 and the number of fish aged 0 in the
previous quarter (sexes combined), and between maturity ratio at 2
in Q1 and recruitment of the current cohort (subdivided by sex).
Black dots and dashed lines, males; white circles and continuous
lines, females.
traditional 10% mature at age 1 and 100% mature at age 2
(Figure 6). The resulting SSB/R relationship still fits the
Beverton –Holt equation best, followed by the segmented
regression equation (Table 4, Figure 7).
Both the segmented and Beverton – Holt SSB/R relationships
show clearly negative trends in the residuals during the last 10
years of the dataseries, indicating overestimation of the recruitment compared with that estimated by the assessment models
recently. Visually scrutinizing the fit of the curve (Figure 7), we
believe that it is unlikely that the segmented regression yields a
good indication of Blim (currently 90 000 t) in terms of the precautionary approach. Moreover, although Blim is estimated by ICES at
that level for several stocks, there is inconsistency here for Norway
pout: the Blim from the segmented regression is 131 000 t instead
of 77 000 t when considering Bloss as Blim (Figure 6).
Growth analyses
The GLM ANOVA (Table 5) on log-transformed MWA data
showed that the variability is explained by quarter, age, and area
factors, and the interactions quarter–area and quarter–age. Age
2 growth was slower than age 1 growth, and the MWA did not
differ in the two northern areas but was higher in the
Skagerrak –Kattegat (Figure 8). However, the spatial difference
did not explain much of the variability, even if it was significant.
Quarterly growth was faster from Q2 to Q3, and there was no
evidence of an increase from Q1 to Q2 at age 2. Moreover,
growth apparently decreased in area 4aw (northwestern North
Sea; Figure 8), which can be due to either a loss of spawning products or spawning mortality.
In terms of MLA in Q1 and Q3, the factors area, age, sex, and
maturity were significant (not shown). All first-order interactions
except age –sex and age –area explained the variation in Q1, but
not in Q3. Immature fish were smaller than mature fish, both
males and females. Mature females were larger than males at all
ages, and spatial variations are highlighted by their higher MLA
in RFA 4 (not shown), i.e. the southern area. In Q3, in addition
to the higher mean weight observed in the Skagerrak–Kattegat,
there was also a generally lower mean length.
The trajectories of MWA and MLA exhibited similar seasonal
and spatial patterns. Both were higher from Q2 to Q3, and thereafter the values were either stable, with a very small increase, or
even decreasing from Q3 to Q2 the following year (Figure 8).
The difference between the Skagerrak –Kattegat and the North
Sea is clear from the evolution of MLA at ages 2 and 3 supporting
the spawning migration hypothesis. Female MLA also decreased
from Q1 to Q2 in the North Sea, weakly at age 2 and strongly at
age 3. A similar seasonal decrease was clear in the MWA at age 3
(Figure 8). This decrease in the North Sea is not likely to be
explained by the arrival of smaller fish from the Skagerrak –
Kattegat, because of the scarcity of mature fish there and the widespread area to which they would be distributed in the North Sea,
but rather by spawning mortality.
When fitting the von Bertalanffy growth function to MLA, it
was still evident that Linf was higher for females than for males
(Table 6). However, it is difficult to compare the growth rate K
by cohort, because there was correlation between the parameter
estimates, making it difficult to distinguish true differences in parameter estimates, i.e. a variation in K can be influenced by
Figure 6. Comparison between annual trends in SSB from the assessment (ICES, 2007b), with SSB based on maturity data from the ALK and
SSB based on revised values of MWA from Danish commercial catch data and maturity ogives.
1907
Maturity and growth population dynamics of Norway pout
Figure 7. Fits of two stock–recruitment relationships: segmented regression (left), and Beverton and Holt (right).
Table 5. Statistical parameters of the GLM analyses of MWA by
area and season (r2 = 0.87 for the model; see also Figure 8).
Analysis
Intercept
Q2
Q3
Q4
Age 2
Area 4aw
Area SK
Q2: age 2
Q3: age 2
Q4: age 2
Q2: Area 4aw
Q3: Area 4aw
Q4: Area 4aw
Q2: Area SK
Q3: Area SK
Q4: Area SK
Estimate Significance
2.06
,0.001
0.45
,0.001
1.08
,0.001
1.23
,0.001
1.13
,0.001
0.01
0.81
0.14
0.01
20.45
,0.01
20.72
,0.001
20.73
,0.001
20.30
,0.01
0.07
0.38
20.05
0.51
20.04
0.59
0.02
0.79
20.09
0.26
variations in the value of t0 (Figure 9). When fixing t0 at –0.25, Linf
remains higher for females, but the growth rate has limited variability and cannot be distinguished between sexes.
Several indicators of growth were taken into account to analyse
the variability related to density-dependence, covering MLA and K
values from the von Bertalanffy growth equation with or without a
fixed t0 value. With respect to intraspecific density-dependence in
MWA, there was a general trend towards a density-dependent
decrease in MWA, but only the relationship between MWA of
age 2 and the number-at-age 1 in Q4 of the previous year was statistically significant (Figure 10).
The trends in the relationships between K and the indices of
stock abundance are the same with fixed t0 and with t0 considered
as a variable. However, the decrease in female growth rate
induced by stronger cohorts was only significant in the latter
case (p = 0.009). Male growth rate was weakly negatively correlated
with the increase in SSB (p = 0.05) with fixed t0. The same pattern
in MLA was observed for male and female age 1 groups in Q1
Figure 8. Top panels: quarterly evolution of MWA at ages 1 and 2 in areas 4ae (eastern North Sea), 4aw (western North Sea), and SK
(Skagerrak – Kattegat), based on data for the period 1983 – 2004. Bottom panels: evolution of mean length-at-quarterly-age (MLA by quarter,
where age 1 in Q1 = 1.00, and age 1 in Q2 = 1.25, etc.) for males and females in the North Sea and Skagerrak and Kattegat, based on data for
the period 1991 – 1996.
1908
G. Lambert et al.
Table 6. Parameters and confidence intervals of the von Bertalanffy growth curves fitted to the overall dataset by sex with t0 as a variable
or as a constant value equal to 20.25.
Females
Growth curve
t0 non-constant
t0 = 20.25
Age 0.3, length 10 mm
Parameter
K
Linf
t0
K
Linf
K
Linf
t0
2.5%
0.60
197
20.40
0.66
201
1.11
188
0.23
Figure 9. The von Bertalanffy growth equation fitted to data split by
sex and cohort. Blue, males; red, females (fits made to variable t0).
Value
0.71
203
20.21
0.69
204
1.16
191
0.25
Males
97.5%
0.84
210
20.05
0.72
208
1.21
193
0.26
2.5%
0.56
186
20.54
0.70
187
1.19
175
0.24
Value
0.68
192
20.32
0.73
190
1.25
178
0.25
97.5%
0.81
199
20.14
0.76
193
1.30
180
0.26
(Figure 11): MLA was significantly lower for the stronger year
classes (female, p = 0.05; male, p = 0.03). At age 2, that trend was
only significant for females (p = 0.04). Taken together, this
shows weak intraspecific density-dependence in growth, but the
analyses cannot distinguish whether males and females are similarly influenced.
The results of the analyses of interspecific density-dependence
using MWA data from the MSVPA show that whiting SSB is positively correlated with MWA at ages 0 and 1, respectively, in Q3 and
Q4, and that, for cod, SSB is negatively correlated with MWA at
age 0 in Q4 and with MWA at age 1 in Q2 (Figure 12, Table 7).
MLA is correlated with whiting and haddock SSB (data from
ICES, 2007b; Figure 12). When whiting SSB increases in Q1,
Norway pout MLA at age 1 increases in Q2. The same correlation
is negative with haddock SSB.
These results demonstrate that the MLA and MWA values
depend on sex and maturity stage as much as on geographic
area. Moreover, the relationships between growth and population
density and predator biomass have been shown without demonstrating causality. A combined analysis of growth and maturity
in the context of density-dependence has been performed to
explain the causality and the conclusions.
Combined patterns in growth, maturity, and density
Figure 10. Relationship (p , 0.01) between MWA at age 2 in Q1
and the number of fish from the same cohort in the previous quarter
(MWA at age 1 in Q4).
Figure 11. Statistically significant intraspecific density-dependence
between (left) MLA at age 1 in Q1, and (right) MLA at age 2 in Q1,
and recruitment of a cohort. Black dots and dashed lines, males;
white circles and continuous lines, females.
With respect to the relationship between percentage mature and
MLA, it is evident that, early in life (age 1 in Q1), more males
than females are mature at length (Figure 13). In Q1, age 1 and
2 group female maturity and age 1 male maturity were positively
correlated with MLA (Figure 13). The correlation was also slightly
positive in Q3 at age 2 for both sexes, but not at age 1. Therefore,
the correlations for both sexes at age 1 in Q1 are more obvious
indicators that the growth of juveniles is a key factor in maturation
and determines the age at first maturity.
Age- and length-at-50%-maturities are good indicators of this
trade-off between growth and maturity in terms of its relationship
with stock density. Figure 2 shows that the maturity ratio
decreased from Q1 to Q3, so when maturity ogives were fitted
to the overall dataset, values of A50 and L50 were higher than
those if only data from Q1 were used. Fits were based on Q1
data (Figure 14) to perform the analyses over the 23-year timeseries of data available (Q3 data are only available from 1991 to
2006). Visual inspection of the maturity ogives for males and
females (Figure 15) indicates that males mature earlier (A50 =
1.2), and hence smaller (L50 = 11.7 cm) than females (A50 = 1.5;
L50 = 13.1 cm), which explains why the proportion of males
mature at fixed length is greater.
1909
Maturity and growth population dynamics of Norway pout
Figure 12. Statistically significant interspecific density-dependence for other species than Norway pout in MWA (top panels) and MLA
(bottom panels).
Table 7. ANOVA of the significant predator– prey relationships on
the MWA of Norway pout.
Parameter
d.f. Deviance
Age 0 Q3
SSB_Whiting Q2 1
1.16
Age 1 Q2
SSB_Cod Q1
1
0.45
Age 1 Q4
SSB_Whiting Q3 1
0.065
Residuals
d.f.
Residuals
deviance p(>jChij)
18
1.28
,0.001
16
0.84
0.003
18
0.25
0.03
When comparing the maturity ogives fitted to weak and
strong year classes (Figure 15), there was an indication of intraspecific density-dependence for L50 and A50. Although the
values of L50 and A50 for strong year classes were always
slightly higher than for weak year classes (Figure 15), indicating
intraspecific density-dependence, noise in the data precludes
statistical significance. The plots indicate that the effect of
density-dependence is most prevalent for younger fish (age 1;
Figure 15). The significant intraspecific density-dependence for
mature fish at age 1 in Figure 5 supports this statement. We
also investigated whether the growth rate determined the age
at maturity and whether Norway pout matured at a constant
length.
In terms of the relationships between A50, L50, and the growth
increment (Figure 16), it would appear that variations in growth
rate reflect similar variations in the other two parameters
(Figure 16). Although the negative trend in L50 was significant
for males (p = 0.044) and just marginally significant for females
(p = 0.098), the increase in growth and decrease in A50 were not
large. Moreover, A50 and L50 were not strongly correlated with
the increment from ages 1 to 2 (not shown), for which none of
the trends were significant. However, the ratio mature at age 1
was directly correlated with MLA at age 1 (Figure 13), indicating
that when growth is faster for juveniles in their early life stages,
the maturity ratio is greater in the first spawning period, but
that subsequently the growth rate may differ and the relationship
between growth and maturity-at-age is less obvious. Consequently,
the values of A50 and L50 were compared only with MLA at age 1,
which is considered to be representative of juvenile growth rate
(Figure 17). The relationship between A50 and MLA at age 1 in
Q1 decreased significantly for females (p = 0.001) and to a lesser
extent also for males (p = 0.067). The decrease in L50 was not significant (Figure 17). The main conclusion from this finding is that
the length at which Norway pout mature is not affected to a great
extent by the growth rate; instead, they tend to mature at a fixed
length. As growth is related to density and to confirm these
results, correlations between A50 (or L50) and recruitment were
also studied. The general trend was that when the recruitment of
successive cohorts was strong, A50 and L50 tended also to be
high (Figure 18), indicating intraspecific density-dependence.
The trends observed here, even if not significant, confirm that
Norway pout tend to mature at a fixed length, with a weak tendency to mature smaller when they mature younger in the
periods of low stock density. This is more obvious for females,
considering the significance of the correlation between A50 and
MLA at age 1 in Q1 (Figure 17).
Discussion
H0 1: the exact time and location of Norway pout
spawning is not known
Our results have contributed to a more detailed understanding of
spawning time and area for Norway pout. Most of the spawners
sampled were around the Viking Bank and along the eastern
Scottish coast during Q1. However, the area of observation did
1910
G. Lambert et al.
Figure 13. Relationships between the percentage of mature fish and MLA (age 1 Q1 males: p , 0.001, females: p , 0.001; age 2 Q1 males: not
significant, females: p , 0.001; age 1 Q3 males: not significant, females: not significant; age 2 Q3 males: not significant, females: p , 0.01). Black
dots and dashed lines, males; white circles and continuous lines, females.
Figure 14. Maturity ogive functions of age [logit(p) = a + b age] (left) and length [logit(p) = a + b length] (right). Dashed lines, males;
continuous lines, females; LC, length class.
not include the Norwegian coast. Few spawners were found in the
Skagerrak and the Kattegat, indicating that although spawning
may take place in the area, it is certainly not an important
spawning ground. It has been assumed until now that spawning
is negligible there (Poulsen, 1968) and that the adult part of the
stock migrates out of that area to spawn (Poulsen, 1968; Albert,
Maturity and growth population dynamics of Norway pout
1911
Figure 15. Maturity ogives by age (top) and length (bottom), and comparison between weak and strong year classes for each sex. Dotted
lines, weak year classes; long-dashed lines, strong year classes; LC, length class.
1994). Direct observations of spawning are only indicative of
overall general spawning intensity (Figure 1). It appears that age
1 spawning only takes place in shallower water along the north
coast of England. We did not investigate the preferred depth and
temperature range for spawning in the spawning areas we identified, nor was age-group composition studied. A number of
authors (e.g. Poulsen, 1968; Raitt and Mason, 1968) has stated
that the preferred depth of occurrence for Norway pout increases
with age, which could be consistent with our observations. The
actual decrease in the maturity ratio from Q1 to Q3 in age
groups 2+ reinforces the hypothesis of spawning being mainly in
the first quarter, and followed by significant spawning mortality.
If there was no spawning mortality, higher frequencies (than actually observed) of spawning (M3 or M4) fish would be expected
from the fishery or observed at least once during the long timeseries of surveys throughout the North Sea in Q1, which was not
the case. The scarcity of M4 Norway pout in Q2 and Q3 and the
total absence of M4 in Q4 can also be explained by spawning mortality, but a return to M2 cannot be excluded as a potential explanation. This means that the possibility of misidentifying M2 and
M4 gonad stages also has to be considered. The present findings
have been compared with the results from northern North Sea
ichthyoplankton surveys (ICES, 2007a), which confirm the
general spatio-temporal patterns of spawning. Norway pout
larvae have been found in the North Sea during surveys from 18
February to 23 March 2004. Their eggs, observed over a large
area of the northern North Sea, were found for 2 weeks, and
the newly hatched larvae were not caught after 30 d (P. Munk,
DTU-Aqua, pers. comm.). Furthermore, Munk et al. (1999) surveyed juvenile abundance of gadoids in the central North Sea
and Skagerrak –Kattegat during annual surveys conducted in
May from 1991 to 1994 by three international research vessels.
Although there was great variation between years, juvenile
Norway pout were generally abundant everywhere in the surveyed
areas, so it may be assumed that the larvae found in the Skagerrak
were brought there by south-flowing currents from a spawning
area around the Viking Bank (P. Munk, pers. comm.).
Consequently, we believe that it is reasonable to assume that
most spawning takes place in Q1, possibly in mid-February,
because no evidence of later spawning has been found, and
around the 120-m isobath off Norway (along the Norwegian
Trench) and the Scottish Coast (ICES, 2007a; P. Munk, pers.
comm.).
H0 2: the constant value of 10% is a good representation
of the maturity proportion at age 1 during Q1 for both
sexes, and all older age groups are mature
The mean maturity ratio at age 1 in Q1, computed for the period
1991–1997, was 0.21, about twice the estimate used in the ICES
stock assessment, and 2-year-old and older fish are not always
mature. Consequently, H0 2 should be rejected. Spatio-temporal
variation in maturity is an important concept, with high values
1912
G. Lambert et al.
Figure 16. Temporal trends in the growth increment for each cohort from age to age+1 (cm year21), A50, and L50, by sex. Black dots and
dashed lines, males; white circles and continuous line, females.
Figure 17. Correlation between A50 (left) or L50 (right) and MLA at age 1 in Q1. Black dots and dashed lines, males; white circles and
continuous lines, females.
in Q1 for age 1 during low-stock-density periods from 2003 to
2005. These variations in the maturity ratio will result in varying
estimates of SSB. The perception of the stock dynamics does not
change but the SSB would generally be higher, especially also
when considering MWA estimates from commercial catches. If
the fishery is assumed to be unselective, the MWA from the
catches would be representative of those in the stock. In this
context, however, we should note that fishers are not allowed to
fish in a large area along the Scottish east coast, the so-called
Norway pout box (ICES, 2007b). Although variable maturity
ogives do not change the perception of the stock, they would
influence the precautionary reference points used for managing
the stock. The new maturity estimates do not improve the
stock– recruitment relationships significantly, and the Beverton –
Holt equation still fits best.
H0 3: there is no area- or sex-dependence of the stock
in terms of growth or maturity
Spatial differences were observed in both MLA and MWA, with
two distinct patterns: a higher MWA in the Skagerrak and the
Kattegat and a higher MLA in the more southern area of the northern North Sea, RFA 4. This leads to rejection of H0 3. Compared
with age 1, there was a notable decrease in MWA in the western
North Sea from Q1 to Q2 for age 2, which was obviously linked
to spawning. In general, the lack of growth in weight from Q1 to
Q2 and the observed decline in MLA from Q1 to Q2 likely indicate
spawning mortality because the spawning and the loss of spawning
products will affect the largest fish most, resulting in a decreased
MLA. If the loss in weight was due, for instance, to food scarcity
(perhaps leading to mortality), one would not expect a decrease
1913
Maturity and growth population dynamics of Norway pout
Figure 18. Correlations between L50 or A50 and recruitment
number of the previous (top) and current (bottom) cohorts. Black
dots and dashed lines, males; white circles and continuous lines,
females.
in MLA because of greater mortality among the bigger fish. These
analyses did not give strong evidence of spawning mortality, but
the results are still indicative of this, especially for females. Male
Linf was in general smaller than female Linf,, in accord with Raitt
(1968), and immature fish were generally smaller than mature
fish. However, the growth rates, computed with the von
Bertalanffy growth equation, could not be distinguished between
the sexes. This would explain why males attain maturity before
females and why males dominated the maturity ratio at age 1 in
Q1 (to some 70%). This maturity ratio was not spatially equitably
distributed: the Skagerrak and the Kattegat remain the areas with
the lowest maturity ratios, reinforcing our theory of a spawning
migration that is sex-dependent. The migration is especially
obvious when studying the temporal evolution of the MLA from
Q3 to Q2. The decrease in length in the Skagerrak –Kattegat
suggests that mature fish leave for the spawning grounds (Ursin,
1963; Albert, 1994). As males mature before females and the sex
ratio tends to decrease from Q1 to Q3 in the Skagerrak –
Kattegat and to increase from Q3 to Q1, whereas the opposite
phenomenon occurs in the northern North Sea, one hypothesis
would be that males migrate to spawn before females and that
neither return, possibly as a result of spawning mortality. Even if
this suggestion is logical, however, one cannot be definitive
because the weighted mean sex ratios computed here show high
deviance and the results cannot prove what was assumed by
Cooper (1983), who stated that there was an increasing numerical
dominance of females with age. Moreover, these sex ratios can be
skewed by vertical migrations, as already recorded for other
gadoids, such as cod (Armstrong et al., 2004).
H0 4: there is no consistency or link between mortality
and growth/maturity trends
Our combined study of growth and maturity has shown that the
maturity ratio is correlated with the MLA at age 1 during the
Q1. Engelhard and Heino (2004) showed that phenotypic
changes in herring (Clupea harengus) growth can explain a large
part of the variations in maturity of that species. Similarly, the
results of this analysis indicate that Norway pout growth at early
life stages is key to maturation. The trends in A50 confirm this
theory because it is correlated with MLA at age 1, but not with
growth rate determined by the von Bertalanffy growth equation
or with the increment from ages 1 to 2. However, the L50 values
do not show any significant relationship with any of the growth
indices, but just a generally decreasing trend. This means that if
a cohort of Norway pout starts with a fast growth rate, the fish
will mature relatively early at a fixed length, which tends,
however, to be slightly smaller than when they mature later.
Looking at the combined results and also linking to hypotheses
H0 1 –3, there are some indications of possible spawning mortality
from: (i) the decrease in the maturity ratio from Q1 to Q3; (ii) the
patterns in growth, maturity, and migration by sex, which indicate
that fish possibly do not return from their spawning grounds; (iii)
the decrease in MWA in the western North Sea; (iv) the combined
patterns of decreasing MWA and MLA; (v) the greater mortality of
older age groups (Sparholt et al., 2002b), and the fact that we did
not find many 3+ fish in the extensive survey data material
covered; (vi) the fact that there have been very few observations
of spawning (M3) or post-spent (M4) fish, despite extensive
survey effort and fishing activity in Q1; and (vii) the fact that
Norway pout tend to follow an r-strategy (as defined by Pianka,
1970). This r-selection reflects early (young) reproduction, small
body size, and a short lifespan. Although r-strategists are not
necessarily semelparous or total spawners, they generally reproduce just once. The relatively high energy requirement for spawning and developing the spawning products could be the reason for
greater mortality, or perhaps Norway pout become easier prey for
other species when they lose the majority of their energy reserves
during spawning. However, post-spawning fish have been
recorded occasionally and, although it has been only a few such
fish that have been documented and these may die subsequently
from the energy loss through spawning or because of increased
exposure to predation as a consequence of this energy loss, we
do not have firm evidence to prove that they do not mature and
spawn again later. Spawning mortality has also been implicated
in the theory of probabilistic maturation reaction norms (Heino
et al., 2002), which could have been an interesting concept to
study to identify long-term responses of maturation to the
environment as well as unexpected responses to fishing pressure
(Grift et al., 2003). However, we did not have the data to test
this for Norway pout, so we cannot ultimately conclude acceptance or rejection of H0 4. The subject is covered further in a
sister manuscript by some of the present authors, and others, currently under construction.
H0 5: there is no density-dependence, intra- nor
interspecific, on growth or maturity
Our study has shown only weak intraspecific density-dependence
in growth and maturity, as well as in age and length at maturity,
but the general trend is that both these parameters decrease with
the number of fish in a cohort. Although these correlations
could highlight a phenomenon of density-dependence linked to
local aggregation (as for herring; Engelhard and Heino, 2004) or
food availability, perhaps the reductions can be explained by
density- and size-dependent juvenile mortality. The interspecific
density-dependence in growth revealed a positive correlation
between whiting SSB and growth, and a negative one with cod
and haddock SSB. Cod and haddock being larger species probably
1914
target larger prey, whereas whiting likely target smaller Norway
pout. However, other factors can influence these observations.
Raitt and Adams (1965) compared the feeding habits of Norway
pout and whiting and showed an extensive overlap between
what 0-group whiting and adult Norway pout were eating.
Therefore, even if adult whiting are important predators on
small Norway pout (Jones et al., 1954; Daan and Welleman,
1998), the positive correlation between both could be due to
simple food availability and the effects of competition for food
lowering the MWA for Norway pout and whiting recruits.
Depending on the strength of the stock– recruitment relationship
for whiting, this could affect the relationship between Norway
pout growth and whiting SSB.
Acknowledgements
This paper has been produced under equal authorship of the two
first authors. We thank our colleagues at DTU Aqua: Peter Levy for
statistical advice, Morten Vinther for the MSVPA data and discussion of results, Peter Munk for comments on the distribution of
Norway pout larve and juveniles, Marie Storr-Paulsen for GIS
support, Brian MacKenzie for editorial comments, and Mark
Payne for the use of his FLR program for stock–recruitment,
used at the ICES SGRECVAP. We also thank Hans Lassen of the
ICES Secretariat for his valued input on Norway pout population
dynamics. Finally, we thank two anonymous reviewers for valuable
suggestions on the manuscript as well as the editor for editorial
improvements to the text.
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doi:10.1093/icesjms/fsp153