ICES Journal of Marine Science, 59: 445–457. 2002
doi:10.1006/jmsc.2002.1203, available online at http://www.idealibrary.com on
Stock assessments of English Channel loliginid squids: updated
depletion methods and new analytical methods
J. Royer, P. Périès, and J. P. Robin
Royer, J., Périès, P., and Robin, J. P. 2002. Stock assessments of English Channel
loliginid squid: updated depletion methods and new analytical methods. – ICES
Journal of Marine Science, 59: 445–457.
The English Channel squid resource (ICES divisions VIId and VIIe) consists of two
loliginids, Loligo forbesi and L. vulgaris, which are not distinguished by fishers.
Catches are reported almost exclusively by French and British multispecies bottom
trawlers. Monthly sampling in the Port-en-Bessin fish market provided biological data
about exploited stages that allowed evaluation of catch per species, estimation of catch
in numbers and analysis of length frequencies. New species-specific assessments of the
1993–1996 cohorts are presented. Two methods are used with different underlying
assumptions. Depletion estimates of recruitment are based on analysis of an abundance index (cpue) in relation to cumulative catch. An analytical approach is also
developed using polymodal decomposition of length structures in the context of a
given growth curve to obtain catch-at-age data. The estimates derived from both
methods show that recruitment can vary by a factor of five. Estimates of fishing
mortality from cohort analysis indicate a relatively stable exploitation pattern between
years for the dominant species. These parameters are integrated in a Thomson and Bell
model to simulate production and biomass of the exploited populations and to
generate a diagnostic of the impact of the fishery. For both species, observed
exploitation levels were above optimum, but a reduction in fishing mortality would not
have resulted in significantly higher yields.
2002 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd.
All rights reserved.
Keywords: diagnostic, fishery, loliginid squid, recruitment, stock assessment.
Received 23 October 2001; accepted 5 March 2002.
J. Royer, P. Périès, and J. P. Robin: Laboratoire de Biologie et Biotechnologies Marines
I.B.B.A., Université de Caen, Esplanade de la paix, 14032 Caen Cedex, France; tel: +33
231 565395; fax: +33 231 565346; e-mail: [email protected]
Introduction
The English Channel (ICES divisions VIIe and VIId;
Figure 1) is a major fishing ground for long-finned squid
(Loliginidae). Like most worldwide fisheries of shortlived species, catches show high interannual variability.
On average, annual landings from the area (3400 t in
1993–1998; Anon., 2000) represent one-third of the
production from Northeast Atlantic ICES waters (with
other significant fishing grounds off Portugal, Spain, and
Scotland). The resource used to be considered a bycatch
of multispecies demersal trawlers and is mainly landed in
summer and autumn. The fishing season starts with the
first peak of recruits in June and finishes in April–May
the following year.
The English Channel squid resource is a mix of two
loliginids, Loligo vulgaris and L. forbesi, which are not
distinguished by fishers or in fishery statistics. Market1054–3139/02/060445+13 $35.00/0
sampling programmes (Robin and Boucaud-Camou,
1995) revealed Loligo forbesi to be the more abundant
species, but time-lags in the life cycle induce seasonal
changes in the proportion of each. Spawning peaks in
winter for Loligo forbesi (Holme, 1974) and in spring for
Loligo vulgaris (Moreno et al., 2001). During this study,
boundaries for the stock (ICES divisions VIIe and VIId;
Figure 1) are based on two types of observation. Biologically, the life cycle of both populations takes place
entirely within the area. From a fishery point of view,
squid concentrations are high in the English Channel,
abundance indices dropping off in the adjacent waters of
the North Sea and the Celtic Sea (Denis, 2000). Genetic
studies have failed so far to reveal variation in loliginid
squid across Europe’s continental shelf (Brierley et al.,
1995), suggesting that biological populations are likely
more widespread than these two divisions. However,
it is convenient to consider the English Channel as a
2002 International Council for the Exploration of the Sea. Published by Elsevier Science Ltd. All rights reserved.
446
J. Royer et al.
16°W
12°W
55°N
8°W
4°W
8°E
4°E
0°
55°N
53°N
53°N
Port-en-Bessin
47°N
43°N
47°N
43°N
16°W
8°E
12°W
8°W
4°W
0°
4°E
Figure 1. The English Channel study area (shaded; ICES divisions VIId and VIIe).
potential management unit because squid there are
almost exclusively exploited by the United Kingdom and
France, and the national statistics systems of both
countries allow extractions of fishery landings on a
monthly basis.
Preliminary assessments of English Channel loliginids
have been carried out using depletion methods without
distinguishing species (Pierce et al., 1996), and they are
based on analysis of the influence of cumulative catch
(Leslie and Davis, 1939) or cumulative effort (DeLury,
1947) on an abundance index, catch per unit effort
(cpue). Modified versions have been developed for
‘‘open populations’’ taking into account natural mortality and recruitment during the study period (Allen,
1966; Chapman, 1974), and they are currently used for
cephalopod stock assessment. They have been applied
in assessing the two cephalopod stocks caught off
the Falkland Islands, Loligo gahi (Agnew et al., 1998;
Hatfield and Des Clers, 1998) and Illex argentinus
(Beddington et al., 1990; Rosenberg et al., 1990; Basson
et al., 1996), Loligo pealei in the Northwest Atlantic
(Brodziak and Rosenberg, 1993), giant squid (Dosidicus
gigas) in the Gulf of California (Ehrhardt et al., 1983),
and Todarodes pacificus from Japanese waters (Murata,
1989). Analytical methods that require more biological
information about the catch are currently used less
frequently in cephalopod fishery studies, although
Csirke (1987) applied virtual population analysis to Illex
argentinus around the Falkland Islands. Also, a modified Ricker (1958) yield-per-recruit model was used by
Lange (1981) to provide monthly estimates of stock size
and yield for Loligo pealei and Illex illecebrosus in the
Northwest Atlantic. Recently, cohort analysis has been
developed to estimate the stock size of Octopus vulgaris
off Senegal (D. Jouffre, pers. comm.) and to assess the
impact of that fishery.
This study presents a new species-specific assessment
of English Channel loliginids. Two methods are applied
to data from four fishing seasons (1993–1996). Depletion
estimates of recruitment size are based on analysis of
cpue in relation to cumulative catch, as described by
Rosenberg et al. (1990). A cohort analysis (Pope, 1972)
is also developed on a monthly basis, using catch-at-age
data estimated from catch-at-length data. In addition,
monthly stock size and fishing mortality from cohort
analysis are integrated into a Thomson and Bell model
(Sparre and Venema, 1998) to simulate production and
biomass for each fishing season under a range of effort
levels.
The first aim of the study is to estimate recruitment
levels and interannual recruitment variations for each
Loligo population. Results derived from the depletion
Stock assessments of English Channel loliginid squids
method and cohort analysis are compared in the light of
the underlying assumptions of the models. The second
aim is to assess the impact of the fishery on the two
exploited populations. For this purpose, we here provide
the first exploitation diagnostic for a cephalopod stock
in Europe. It covers a series of cohorts that have passed
completely through the fishery. Cohort variability
observed during the four seasons studied provides useful
information about stock response to exploitation.
Models
Depletion methods (extended Leslie–DeLury
model for an homogeneous fleet)
Depletion is estimated by combining two basic submodels. First, the population dynamics are described by
following a first-order difference equation:
Ni+1 =e M(Ni Ci +Ri)
(1)
where the subscript i denotes the month, N the number
of individuals, M the natural mortality, C the total
catch, the total recruitment, and R is the recruitment
index (the proportion of the total recruitment in month
i). This equation assumes that catch and recruitment are
at the start of each time interval. Second, an abundance
index is assumed to be proportional to population size in
numbers:
Ai =q Ni
(2)
where A is the abundance index and q is the catchability.
For one fishing season, input variables are an estimate
of M, series of monthly total catches (Ci) and abundance
indices (Ai), and a monthly recruitment index (Ri). The
outputs of model-fitting are estimates of the catchability
(q), the monthly population size (Ni), and the total
recruitment in the study period (). The fitting procedure
for the combination of equations (1) and (2) is multiple
linear regression applied to a log-transformed abundance index, carried out with the CEDA package,
version 1.0 (MRAG, Imperial College, London). It was
assumed that the residual terms were independent
identically distributed normal random variables with
zero mean and finite variance.
where i denotes the month, a the age-class, C the total
catch (in number), F the fishing mortality, M the natural
mortality, and N is the number of individuals. Second,
the population equation underlying the cohort analysis
(Pope, 1972) is
Na,i =(Na+1,i+1) eM +(Ca,i) eM/2
Na+1,i+1 =(Na,i) e(Fa,i +M)
(4)
(5)
This equation considers that catches are made in the
middle of each time interval.
From Equations (3)–(5), the history of each cohort
(survivors and fishing mortalities) was back-calculated
from the terminal age-class, given a terminal fishing
mortality. This terminal fishing mortality is used in
Equation (3) to generate the abundance of the terminal
age-class which, in turn, is used in Equation (5) to
estimate the abundance of the preceding age-class.
Equation (5) is used step by step to generate the abundances of all age-classes (except the last). Fishing mortalities in each age-class are estimated from abundance
estimates using a rearranged Equation (4).
Input variables are the monthly total catch at age
(Ca,i), natural mortality (M), the fishing mortality of the
terminal age-class (Fta,i), and the fishing mortality in the
terminal month of the study period (i.e. May 1997; Fa,ti).
The outputs of cohort analysis are estimates of the
monthly population numbers at age (Na,i) and monthly
fishing mortality at age (Fa,i).
Thomson and Bell model (Sparre and Venema,
1998)
Yield and average biomass for each fishing season can
be predicted at various levels of fishing mortality using
the classical Thomson and Bell model. This model uses
Equations (3) and (4) and the following relationships
(with the integration of a multiplier factor for fishing
mortalities):
Ya,i =Ca,i Wa
(6)
Ba,i =Na,i Wa
(7)
where a denotes the age-class, i the month, Y the yield in
weight, C the catch in number, B the biomass, N the
number of individuals, and W is the mean weight of a
squid.
Annual production is
Cohort analysis
The basic equations of cohort analysis are a catch
equation and a survival equation:
447
and mean annual biomass is
448
J. Royer et al.
Model inputs are the number of recruits in the smallest age-class, fishing mortalities at age (both derived
from cohort analysis), an estimate of natural mortality,
and a vector of mean weights at age. The model assumes
that the exploitation pattern (total fishing mortalities at
age) and mean weights at age are constant for each
multiplier factor of fishing mortalities.
Each model assumes a closed stock, without migration, so the only significant removals of squid from one
period to the next result from fishing and natural
mortality.
Table 1. Size range of French commercial squid categories.
Commercial
category
Size range
(cm)
1
2
3
4
5
<14
14–20
20–24
24–30
>30
1
Data and input parameters
National databases were interrogated for monthly
records of total squid landings (kg) and of ‘‘catch and
effort’’ data of selected fishing gears. The Scottish
(Scottish Office for Agriculture, Environment and
Fisheries departmental database) and England and
Wales (CEFAS database) data are referred to in text as
UK data. In the case of France (Centre Administratif
des Affaires Maritimes database), landings are sorted
per commercial category (five categories based on individual weight; Robin and Boucaud-Camou, 1995), a
detail that provides information about catch structure in
each rectangle. For both countries (UK and France),
fishery statistics are available on an ICES rectangle basis
(0.5 latitude by 1 longitude).
Species-specific total catches (in number) and catchat-length (total catch in number per 1 cm size-class) were
computed by combining monthly fishery statistics (in kg)
and monthly Port-en-Bessin (Figure 1) fish market sampling data (from June 1993 to May 1997; Robin and
Boucaud, 1995). Biological sampling provided estimates
for three variables: mean body weight, numerical proportion of the catches by species, and length frequencies
(number of squid in each 1 cm size-class).
Each variable estimated at the sampling stage of
‘‘commercial categories’’ was applied to all French landings of the same commercial category (size ranges for
each commercial category are given in Table 1). In the
case of UK landings (which are not divided into commercial categories), mean weight, species proportion,
and length frequencies were applied by area (west coast,
east coast, west offshore and east offshore), assuming
that, for each area, the structure of UK catches was the
same as that in French catches.
Numerical proportion of Loligo forbesi
Total catch and catch-at-age (in numbers)
0.5
Category 1
0
1
0.5
Category 2
0
1
0.5
Category 3
0
1
0.5
Category 4
0
1
0.5
Category 5
0
01
–
05 93
–
09 93
–
01 93
–
05 94
–
09 94
–
01 94
–
05 95
–
09 95
–
01 95
–
05 96
–
09 96
–
01 96
–
05 97
–
09 97
–9
7
Both assessment methods make use of fishery statistics
collected from French and British national databases
and of biological data acquired during the EU-funded
research projects (AIR CT 92–0573 ‘‘Eurosquid’’ and
FAIR CT 96-1520 ‘‘Cephvar’’).
Sample month
Figure 2. Proportion of Loligo forbesi in the five commercial
categories sampled at Port-en-Bessin.
The formulae, given in the Appendix, are based on
two assumptions:
sorting by commercial category is similar in all
French harbours;
species proportions in month i and category c are
those observed at Port-en-Bessin (these proportions
per commercial category are shown in Figure 2).
Differences in species spatial distribution appear in this
calculation via changes in the manner that landings in a
rectangle are split by commercial category.
Catch-at-age was estimated from catch-at-length by
polymodal decomposition with the Normsep module
implemented in the FISAT package (Gayanilo et al.,
1995), using species growth curves based on ages determined on the basis of statolith measurements (Collins
Stock assessments of English Channel loliginid squids
Table 2. Age–length relationship in Loligo forbesi and L.
vulgaris.
Mean age
(months)
6
7
8
9
10
11
12
Loligo forbesi
Mean length
interval
(cm)
Loligo vulgaris
Mean length
interval
(cm)
<13
13–17
17–21
21–26
26–31
31–37
>37
<13
13–16
16–19
19–22
22–26
26–30
>30
et al., 1995 for Loligo forbesi; Natsukari and Komine,
1992 for L. vulgaris). The Normsep decomposition
method assumes that population components have a
normal size distribution. Variability in the age–length
relationship is taken into account and can be summarized, with mean length intervals corresponding to
mean ages (Table 2). These intervals are rather wide, but
it is assumed that growth rates do not vary to such an
extent that the age–length relationships described in the
literature would break down.
Abundance and recruitment indices
The abundance index used in the depletion model is the
adjusted cpue in numbers for a selected homogeneous
fleet.
The basic condition for catch rate to be proportional
to squid abundance is that distribution of fishing effort
must not be related to squid distribution (Hilborn and
Walters, 1992). In the study area, French otter trawlers
operate almost every month in all ICES rectangles
(Denis and Robin, 2001), rather different from the
pattern shown by British trawlers, whose fishing
grounds seem to be more restricted (Dunn, 1999). The
adjusted cpue (catch in number per hour fishing) of
French otter trawlers was therefore considered to be
representative of squid abundance.
Generalized linear modelling (GLM) techniques were
applied to the cpue of French otter trawlers to obtain an
index of relative abundance (adjusted cpue) independent
of changes in spatial fishing patterns and the spatial
distribution of the resource, but assuming stability in
fleet composition through the study period. Indeed,
basic cpue is influenced by several (significant) factors:
the heterogeneous spatial distribution of the fleet and of
the resource, and the different technical characteristics
of boats. Therefore, the factors taken into account in
the GLM process were month, fishing area (ICES
rectangle), and mean engine power of fishing boats (see
Appendix).
449
For each species, the percentage of the annual cohort
recruited per month (R) was derived from the abundance indices of the small squid category for that month
(Ai,5) estimated by GLM. This commercial category is
considered to be representative of recruits because it
contains squid <14 cm dorsal mantle length (DML) and
because the mean length observed for this category is
very stable with time. Therefore, estimation of recruitment index does not require length frequency data from
sampling.
Natural and terminal fishing mortality
Natural mortality was estimated empirically (Caddy,
1996), assuming an annual lifespan in both species and a
mean fecundity of 8500 eggs for Loligo forbesi (Boyle
et al., 1995) and 15 000 eggs for L. vulgaris (Worms,
1980; Mangold, 1989b). For both species, the number of
gnomonic intervals assumed is seven. The final interval
corresponds to the exploited stage (from 6 to 12
months). During this interval, the natural mortality is
considered constant with age. A monthly rate of 0.2
was calculated for both species. Sensitivity to this
assumption was tested by performing species-specific
assessments for natural mortality values of 0.1–0.3.
Terminal fishing mortalities Fta,i and Fa,ti were estimated iteratively. Cohort analysis was initialized first
with arbitrary values and then stabilized by imposing
constraints to the parameters:
Fta,i is equal to the average of the two last age-class
fishing mortalities;
Fa,ti is equal to the average of fishing mortalities in
May from previous fishing seasons.
Mean weight-at-age
For each species, the mean weight-at-age [Wa in
Equation (6), Thomson and Bell model], was estimated
from growth curves (providing length-at-age) and
length–weight relationships.
Length–weight relationships were fitted to English
Channel biological samples collected in Caen between
November 1992 and May 1995 (Robin and Boucaud,
1995).
Wa =0.27L2.26
in Loligo forbesi (n=1911, r2 =0.96)
a
in Loligo vulgaris (n=1087, r2 =0.95)
Wa =0.252.27
a
where a denotes the age-class, W the weight (in g) of the
individual, and L is the dorsal mantle length (in cm) of
the individual.
Sample month
Figure 3. Species-specific abundance index of the French otter
trawler fleet, estimated by general linear modelling (solid line
Loligo forbesi, dashed line L. vulgaris).
Results
Abundance and recruitment indices
The distribution and catches of the French otter trawl
fleet allow temporal changes in abundance of both
loliginid species to be described (Figure 3). Loligo forbesi
seems broadly to be the dominant species in the study
area except in the 1996 season (June 1996–May 1997),
when its abundance was very low. This weak 1996
cohort can also be detected in the plots of species
proportion (Figure 2). The species is absent from all
commercial categories towards the end of that fishing
season. By comparison, 1996 was an average fishing
season for Loligo vulgaris. For both species, abundance
is strongly seasonal, constant between years, but peaking in late summer–early autumn for Loligo forbesi and
in late autumn–early winter for L. vulgaris. The abundance indices of recruits (Ai,5) suggest that interannual
recruitment is highly variable (Figure 4), with a declining trend in the case of Loligo forbesi and less interannual variability in the case of Loligo vulgaris.
However, the seasonal pattern of recruitment indices (R)
seems relatively stable for both species (Table 3).
Recruitment peaks in summer (July–August) for Loligo
forbesi and in autumn (November–December) for L.
vulgaris. Figure 2 shows that Loligo forbesi dominates
commercial category 5 between June and September, but
that it is only a minor component of the same category
between December and February.
Catch
Monthly catch varied from 0 to 2.5 million Loligo
forbesi and from 0 to 2 million Loligo vulgaris according
to month or fishing season (Figure 5). Length at recruitment ranged from 10 to 13 cm which, according to
published growth curves, suggests that both species
4
3.5
3
2.5
2
1.5
1
0.5
0
06
–9
10 3
–9
02 3
–9
06 4
–9
10 4
–9
02 4
–9
06 5
–9
10 5
–9
02 5
–9
06 6
–9
10 6
–9
02 6
–9
7
4
3.6
3.2
2.8
2.4
2
1.6
1.2
0.8
0.4
0
Abundance index of recruitment (number h–1)
J. Royer et al.
06
–9
10 3
–9
02 3
–9
06 4
–9
10 4
–9
02 4
–9
06 5
–9
10 5
–9
02 5
–9
06 6
–9
10 6
–9
02 6
–9
7
Abundance index (number h–1)
450
Sample month
Figure 4. Species-specific abundance index of recruits (smallest
squid category), estimated by general linear modelling (solid
line Loligo forbesi, dashed line L. vulgaris).
Table 3. Squid recruitment indices (Ri) used in the depletion
method listed as the percentage of the annual cohort recruited
in each month of the four fishing seasons.
Loligo forbesi
Loligo vulgaris
Month
1993 1994 1995 1996 1993 1994 1995 1996
June
July
August
September
October
November
December
January
February
March
April
May
0.19
0.22
0.27
0.13
0.04
0.03
0.04
0.02
0.01
0.01
0.01
0.02
0.10
0.28
0.23
0.13
0.04
0.05
0.05
0.02
0.02
0.02
0.02
0.04
0.13
0.30
0.21
0.09
0.05
0.04
0.05
0.03
0.03
0.02
0.02
0.03
0.11
0.15
0.14
0.14
0.11
0.06
0.07
0.04
0.03
0.03
0.04
0.08
0
0
0
0.06
0.13
0.19
0.19
0.17
0.08
0.09
0.07
0.03
0
0
0
0.08
0.19
0.21
0.25
0.10
0.06
0.04
0.05
0.02
0
0
0
0
0.10
0.19
0.34
0.16
0.10
0.06
0.04
0.02
0
0
0
0
0.06
0.18
0.20
0.19
0.13
0.10
0.06
0.07
begin recruiting at an age of approximately 6 months.
Figure 4 shows that the bulk of the catch (60–70%) is
Loligo forbesi of 8–9 months and L. vulgaris of
7–9 months. Next in dominance are the 6-month-old
age-classes of both species. Catches of the final age-class
(12 months old) are very small, <2% of the total catch of
a single cohort.
Interannual trends in recruitment
For each species, assessments were carried out for the
four fishing seasons (1993–1996) using three values of
natural mortality (0.1, 0.2, 0.3).
Stock assessments of English Channel loliginid squids
451
3
(a) Loligo forbesi
2.5
2
1.5
Catch in number (millions)
1
0.5
0
(b) Loligo vulgaris
6 month
7 month
8 month
9 month
10 month
11 month
12 month
2
1.5
1
0.5
02
–9
7
10
–9
6
06
–9
6
02
–9
6
10
–9
5
06
–9
5
02
–9
5
10
–9
4
06
–9
4
02
–9
4
10
–9
3
06
–9
3
0
Sample month
Figure 5. Catch-at-age estimated from polymodal decomposition of observed catch-at-length in (a) Loligo forbesi and
(b) L. vulgaris.
Table 4. Depletion method estimates of recruitment (millions)
in four fishing seasons at three levels of natural mortality (0.1,
0.2, 0.3) in Loligo forbesi and L. vulgaris.
Table 5. Cohort analysis estimates of recruitment (millions) in
four fishing seasons at three levels of natural mortality (0.1, 0.2,
0.3) in Loligo forbesi and L. vulgaris.
Loligo forbesi
Loligo vulgaris
Natural
mortality 1993 1994 1995 1996 1993 1994 1995 1996
Loligo forbesi
Loligo vulgaris
Natural
mortality 1993 1994 1995 1996 1993 1994 1995 1996
0.1
0.2
0.3
0.1
0.2
0.3
15
19
25
12
15
18
9.2
11
14
2.9
3.7
4.5
1.9
2.2
2.4
5.1
5.8
6.6
9.1
10
11
1.9
2.1
2.3
The results of the depletion method and cohort analysis are presented in Tables 4 and 5. With the depletion
method and a mortality of 0.2, initial population size
(recruitment) ranges from 3.7 to 19 million Loligo
15.6
22.3
32.5
13.3
19
27.6
9.4
13.5
19.8
4.2
6.3
9.7
2
2.6
3.5
5.3
7.1
9.7
10.2
14
19.5
1.9
2.4
3.1
forbesi and from 2.1 to 10 million L. vulgaris according
to season. According to cohort analysis, recruitment
ranges from 6.3 to 22.3 million Loligo forbesi and from
2.4 to 14 million L. vulgaris, again according to season.
452
J. Royer et al.
1.6
(a) Loligo forbesi
20
4
1
0
0
(b) Loligo vulgaris
20
4
15
3
10
2
5
1
0
1993
1994
1995
Fishing season
1996
0.8
Fishing mortality
5
2
Cohort analysis
Depletion method
GLM
Also, recruitment for the same season varies between
methods, though the values are always higher from the
cohort analysis. The difference between methods varies
from 0.3 to 4 million squid. However, for both methods,
the trend per season and species is similar, following the
same pattern as French trawler abundance indices
(Figure 6). Loligo forbesi tends to decrease from the
season 1993 on, but there is no obvious trend in Loligo
vulgaris recruitment, which increases between 1993 and
1995 and drops in 1996. Natural mortality influences
estimates of initial stock size but interannual trends
remain similar. Cohort analysis appears to be slightly
more sensitive to natural mortality than the depletion
method. When natural mortality increases by a factor of
3, estimated 1993 recruitment is multiplied by 2 with
cohort analysis and by 1.5 with the depletion model.
0
(b) Loligo vulgaris
2
1.5
0.5
0
0
Figure 6. Comparison between recruitment estimates from
cohort analysis and the depletion method and the recruitment
index from GLM for each fishing season in (a) Loligo forbesi
and (b) Loligo vulgaris.
0.4
1
For each fishing season the exploitation pattern is the
average vector of fishing mortality at age (Fa), each
age-class being observed over a period of 12 months
(June–May). Figure 7 shows that, for both species,
fishing mortality is highest for the last three age-classes
and very low for the youngest age-classes. Loligo
vulgaris is clearly fished harder than L. forbesi. Between
fishing seasons, the exploitation pattern for Loligo
forbesi is more stable than for L. vulgaris. This observation is essentially related to the trend in the 1996
fishing season, when young age-classes were subjected to
high fishing mortality and similar fishing mortalities
were observed for all ages as a result.
7
8
9
10
Age-class (months)
2000
1800
1600
1400
1200
1000
800
600
400
200
0
11
12
(a) 1995
(b) 1996
700
600
500
400
300
200
100
0
Annual exploitation pattern and diagnostic
6
Figure 7. Exploitation pattern of (a) Loligo forbesi and
(b) L. vulgaris over four fishing seasons.
Mean biomass (t)
10
3
Recruitment index (number h–1)
Number (millions)
15
(a) Loligo forbesi
93
94
95
96
1.2
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2
2.2 2.4 2.6 2.8
3
Multiplier factor
Figure 8. Simulated mean biomass of Loligo forbesi (solid line)
and L. vulgaris (dashed line) from the Thomson and Bell model
applied to the (a) 1995 and (b) 1996 fishing seasons.
Simulations of mean biomass and production are
presented for both species for an ‘‘average’’ season in
abundance (1995) and a low season (1996) in Figures 8
and 9, respectively. Fishing intensity is expressed by a
multiplier factor (mf) between 0 and 2 (mf=1 indicates
the present fishing effort).
For fishing season 1995, biomass projections
(Figure 8a) suggest that the total biomass of the unexploited populations (mf=0) is 1730 t for Loligo forbesi
Stock assessments of English Channel loliginid squids
3000
(a) 1995
2500
2000
1500
Production (t)
1000
500
0
(b) 1996
800
600
400
200
0
0.2 0.4 0.6 0.8
1
1.2 1.4 1.6 1.8
2
2.2 2.4 2.6 2.8
3
Multiplier factor
Figure 9. Simulated yield (production) of Loligo forbesi (solid
line) and L. vulgaris (dashed line) from the Thomson and Bell
model applied to the (a) 1995 and (b) 1996 fishing seasons.
and 1480 t for L. vulgaris, and that the actual biomass
(mf=1) is respectively 700 and 675 t (or 41 and 46% of
pristine respectively). For 1996 (Figure 8b), biomass
estimates represent 50% of pristine for Loligo forbesi and
34% for L. vulgaris.
Production curves (Figure 9) seem, however, to be
rather insensitive to changes in the fishing effort multiplier factor in the sustainable yield region. The curves
suggest that increasing current fishing effort would result
in a slight decrease in yield and that decreasing it would
not result in a significant increase in yield. In the 1995
fishing season, the Loligo forbesi population seems to
have been slightly overexploited, whereas in the 1996
season it appears to have been fully exploited. Loligo
vulgaris was seemingly slightly overexploited during
both seasons. The exploitation diagnostics for 1993 and
1994 are similar to that of 1995 for both species.
Discussion
This study has resulted in the first species-specific
estimates of the population sizes of English Channel
loliginid squid and has provided also the first diagnostic
on the effect of fishing. Results show that both assessment methods provide consistent estimates of recruitment and population numbers. They suggest that cohort
analysis, which is more often applied to multi-cohort
finfish stocks, could provide an insight into the dynamics
of exploited squid populations and offers potential for
progress in understanding.
453
A number of factors can influence the quality of an
assessment. For the depletion method, catchability is
assumed to be constant over a given fishing season. This
is a rather unlikely assumption because a cohort is fished
during much of its life and squid behaviour changes as
individuals grow from juveniles to adults (Mangold,
1989a). Catch per unit effort is therefore likely to be
only a rough estimate of abundance in the same way
as constant natural mortality is a preliminary
approximation of natural losses.
Cohort analysis does not require abundance indices,
but it assumes that monthly total catch can be estimated
by age-class. Conversion of catch-at-length to catch-atage has been carried out assuming similar growth rates
for all months and all fishing seasons. For now, squid
age-determination techniques (statolith readings) are too
time-consuming to be applied to monthly market
sampling. Nevertheless, even if the conversion method is
relatively flexible and takes into account some part of
the growth variability, it would be more accurate to use
individual ages than to convert length- into age-classes.
The method also requires estimation of the abundance
of the terminal age-class and of terminal month fishing
mortality, both of which are difficult to evaluate when
fishing intensity is not well known. However, according
to Pope (1972), the terminal fishing mortality and population size of other age-classes converge towards the true
overall values, quicker when exploitation intensity is
high.
Both methods use series of species proportions that
are estimated from a single sampling harbour and are
extrapolated to the total English Channel catches. These
proportions are likely to be only approximations of the
real species proportions, although biases are reduced by
the extrapolation procedure that uses proportions per
commercial category and takes into account geographic
differences in commercial category caught per statistical
rectangle. The methods also consider closed exploited
populations for which total catch is known. Such
assumptions of no migration and no additional
unreported catch are difficult to meet in many other
European cephalopod stocks, limiting opportunities to
carry out assessments. Loliginid squid are neritic and
seem to undertake more limited migrations than oceanic
squid (such as ommastrephids). Migrations could be
taken into account in models by adding to equations a
term that would correspond to the flow of animals
moving into or out of the area of interest, as described
by Brodziak and Rosenberg (1993). Changes in abundance in rectangles adjacent to the study area could
provide an estimate of the flow.
To clarify uncertainties in model input parameters,
sensitivity analysis could indicate those input variables
that have the strongest influence on model outputs.
Whatever the assessment method, the range of variation in interannual recruitment is similar for both
454
J. Royer et al.
species. In each, the large interannual variations in
recruitment and abundance clearly depend more on
the recruitment of the annual cohort than on the
exploitation pattern. Recruitment estimates for the
whole stock (both species) agree with the results of a
previous stock assessment for English Channel squid
(Pierce et al., 1996). The new species-specific assessments
suggest that, in 1993, the English Channel Loligo forbesi
stock was bigger than the stock of the same species off
Scotland, but that the English Channel Loligo vulgaris
stock was smaller than the L. forbesi stock off Scotland.
The English Channel and Scotland are the northernmost
exploited stocks of both species.
High interannual fluctuations in recruitment have also
been observed in populations of other squid species,
including Illex argentinus (Beddington et al., 1990),
Loligo gahi (Agnew et al., 1998), Loligo vulgaris
reynaudii (Roberts and Sauer, 1994) and Loligo pealei
(Brodziak and Rosenberg, 1993). For the cohorts of
English Channel squid analysed here, recruitment
variability does not seem to be linked to the fishing
pressure on the adults. For instance, poor recruitment in
the 1996 fishing season cannot be explained by a high
level of exploitation during the previous fishing season.
We could not identify any stock-recruitment relationship, probably because the time-series is still too short.
However, species-specific assessments carried out on
each annual cohort provide a more accurate measure of
the two variables (recruits and number of adults) than
any raw fishery statistics. Future analyses of recruitment
variability in English Channel squid will have to apply
one of the assessment methods to derive population
numbers of more recent cohorts.
For cephalopod stocks, a stock-recruitment relationship is generally weak and difficult to establish (Pierce
and Guerra, 1994). Changes in abundance between
fishing seasons may be attributable to fluctuations in
environmental conditions that affect the early life history
of several cephalopod populations (Rodhouse et al.,
1992; Dawe and Warren, 1993; Bakun and Csirke, 1998;
Waluda et al., 1999), as suggested for English Channel
loliginids by temperature trends (Robin and Denis,
1999). However, Agnew et al. (2000) suggest that the
level of recruitment and its timing are related to both sea
surface temperature and spawning stock size. This study
is the first evidence that density-dependence affects the
recruitment of a squid population.
In terms of exploitation intensity, the fishing mortality
estimated in the present study is high, but it seems
nevertheless to be realistic in the context of the high level
of fishing activity in the English Channel, as indeed
across the whole European shelf. Brodziak and Rosenberg (1993), also using the depletion method, found
similar values of F over a 14-week period for squid
stocks targeted in the North-West Atlantic. Higher
fishing mortalities in Loligo vulgaris can be considered to
be a consequence of the smaller stock size being
subjected to the same fishing pressure as the larger
Loligo forbesi stock.
The fishery diagnostic shows Loligo forbesi to be
subjected to slight growth-overfishing in some fishing
seasons whereas L. vulgaris is slightly but more consistently overexploited in all seasons. In any case, no cohort
of those studied would have yielded significantly more
by increasing the fishing pressure; this fact needs to be
stressed to those fishing for squid in the English
Channel. In contrast, in terms of biomass curves and
reference points, the annual recruitment would seem not
to have been overfished for either species. The ratios of
exploited biomass to pristine are always above the
recruitment overfishing empirical threshold of 10%
generally accepted for fish stocks.
Although population models do not predict recruitment, the range of recruitment levels observed here
shows how the fishery reacts to recruitment variability.
For Loligo forbesi, simulated production curves show
that the very weak 1996 cohort was less exploited than
other stronger cohorts; it appears that fishers transferred
their interest to other species during that year. The
stability of the exploitation pattern between years supports this assumption. In contrast is the situation for
Loligo vulgaris. The unusual fishing mortality on this
species in 1996 suggests that it can be exploited at
younger ages when the Loligo forbesi cohort is missing.
Two hypotheses could explain this: reduced growth rate
of the 1996 Loligo vulgaris cohort or differences in
spatial distribution of fishing effort. The spatial distribution of the two populations is slightly different, so the
behaviour of fishers could vary between fishing areas.
Loligo forbesi dominates the western English Channel
and L. vulgaris the eastern (Denis, 2000). Clearly, therefore, the status of loliginid squid in the English Channel
(i.e. target or bycatch) can vary according to resource
abundance and fishing area, as would be expected in a
multispecies fishery.
These stock assessment approaches are of value
because they use simple mathematical formulae and
provide original, specific results on the impact on the
stocks of the English Channel squid fishery. However,
they do have certain limitations. Because of high recruitment variability from year to year, the Thomson and
Bell model allows only short-term conclusions to be
drawn. As a result, diagnostics are valid only for the
immediate past fishing season in isolation. No long-term
forecasting and management advice are yet possible.
Long-term projection with an age-structured model
would require a stock-recruitment relationship to be
established. Such relationships are generally weak and
several factors, some of which cannot be predicted, can
affect abundance of juvenile squid. Nevertheless, stock
dynamics can also be modelled taking into account
uncertainty in the level of recruitment (Roel and
Stock assessments of English Channel loliginid squids
Butterworth, 2000). The most common currently
applied procedure used to provide short-term management advice is based on depletion methods, using
real-time fishery statistics to estimate the harvestable
stock size and to limit the fishing pressure, the goal being
to ensure safe levels of spawner escapement. The aim is
to maintain the spawning biomass above an appropriate
threshold that precludes a high probability of very low
recruitment in the following year. A target proportional
escapement has been advised in the Falkland Islands
Illex and Loligo fisheries (Beddington et al., 1990;
Basson et al., 1996; Agnew et al., 1998; Hatfield and Des
Clers, 1998), the most closely and successfully regulated
cephalopod fisheries.
Determination of threshold biomass levels related to
low recruitment in the English Channel will require
longer time-series of estimates. As a further step, evaluation of the risk of overfishing the current cohorts,
real-time access to catch data will be required.
Acknowledgements
Support for this research was provided by the
EU-funded research programmes AIR CT 92-0573
‘‘Eurosquid’’ and FAIR CT 96-1520 ‘‘Cephvar’’ and
also by the Conseil Régional de Basse Normandie. We
thank G. J. Pierce (University of Aberdeen) and M.
Martin (Centre Administratif des Affaires Maritimes,
St Malo) for their help in obtaining British and French
fishery statistics respectively, and Ian Probert for correcting the English. Valuable reviews of the submitted
manuscript were provided by Simeon Hill (MRAG,
London) and Beatriz Roel (CEFAS, Lowestoft); for
both we are very grateful.
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Appendix
Subscripts
i denotes the month, i=June 1993 . . . to May 1997
m denotes the month, m=1 . . . to 12
j denotes the fishing season
r denotes the ICES rectangle
c denotes the commercial category
s denotes the species (Loligo forbesi or L. vulgaris)
z denotes the area (west inshore, east inshore, west
offshore, east offshore)
k denotes the trawler engine power-class
Descriptors and variables
Mean weight: W
Numerical proportion of the species: P
Catch in weight: Y
Catch in numbers: C
Numerical proportion of the commercial category: PC
Fishing effort: E
Catch per unit effort in number: U
Abundance index: AI
Recruitment index: RI
Total catch in number by species
Fish market sampling (at Port-en-Bessin) is used to
convert weights into numbers and to split the loliginids
by species. Monthly observations (i) provided per commercial category (c) yield mean weight (Wi,c) and
numerical proportion per species (Pi,c,s).
French (fr) total catch in number:
English (uk) total catch in number:
where Wi,z is the mean weight of an individual in month
i in area z:
and Pi,z is the numerical proportion of species s in
month i in area z:
The total catch in the whole area is the sum of the
French and the British total catch:
Ci,s =Ci,s(fr)+Ci,s(uk)
Stock assessments of English Channel loliginid squids
Abundance index from French otter trawl data
The catch per unit effort in number (cpue) is calculated
for each species from the catch in number and the fishing
effort by fishing season, month, ICES rectangle, and
engine power-class:
These cpues are used in a generalized linear model to
estimate the abundance index (A) or the adjusted cpue
with the following equation:
457
power-class, and Ey,m,r,k is the normally distributed
residual variation.
Recruitment index
The recruitment index in each species for month i (Ri,s)
is the percentage of all the recruits of the fishing season
that enter the stock in month i. Recruits are juvenile
squid in the category small squid (c=5) observed in
French trawler landings.
lnUs,y,m,r,k =lnAy,m +lnSr +lnPk +lnEy,m,r,k
where Ay,m is the effect of the combined factor fishing
season and month that can be interpreted as a monthly
abundance index (adjusted cpue), Sr the effect of the
factor ICES rectangle, Pk the effect of the factor engine
with months (m=1 . . . to 12) numbered after the first
month in the fishing season, and where As,y,m,5 is the
abundance index of the small squid estimated from the
generalized linear model.