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ORIGINAL ARTICLE Fisheries
Deep water longline selectivity for black spot seabream
(Pagellus bogaraveo) in the Strait of Gibraltar
Ivone Alejandra Czerwinski Æ
Karim Erzini ÆJuan Carlos Gutie
´rrez-Estrada Æ
Jose
´Antonio Hernando
Received: 30 April 2008 / Accepted: 17 November 2008 / Published online: 4 March 2009
ÓThe Japanese Society of Fisheries Science 2009
Abstract Species and size selectivity of the deep water
longline traditionally used in commercial fishing of the
black spot seabream (Pagellus bogaraveo) were studied in
the Strait of Gibraltar with four sizes of hooks. Black spot
seabream contributed up to 88% of the catch by number.
Catch and by-catch rates differed for the different hooks
and fishing trials. Significant differences in average fish
length between all hooks, except in one case, were found.
The comparison of two experimental fishing trials within
4 years indicates a displacement towards smaller sizes in
the size frequency distributions. The results of this study
show that the fishing gear can be size selective depending
on hook size. The fitted selectivity models for each
experiments were very different despite having two hooks
in common. This is probably due to the very different catch
size distributions in the two periods, which suggests that
the population size structure changed significantly between
2000/2001 and 2004/2005.
Keywords Hook Longline Pagellus bogaraveo
Selectivity
Introduction
The black spot seabream (Pagellus bogaraveo) in the Strait
of Gibraltar is an economically important fishery species,
with an average value of 5.2 million Euros per year (1990–
1994) [1]. Approximately 40 Mt per year were landed in the
port of Tarifa between 1972 and 1978, increasing to 100 Mt
in 1980. The fishery grew rapidly in the 1980s, reaching a
maximum of 850 Mt of landings in 1984. The decrease in
landings since 1995, which was particularly severe in 1998
[2], has lead to management actions by the state and
regional administrations to stop this decline and to keep the
resource within sustainable limits. In the state administra-
tion regulation zone (005°47095 W to 005°20070 W), black
spot seabream fishery is carried out exclusively by a vertical
deep water longline called ‘‘voracera’’, with a maximum
legal length of 120 m. The legal dimensions of the hooks
are a minimum length of 3.95 ±0.39 cm and a minimum
width of 1.4 ±0.14 cm. The minimum landing size in the
regulation zone was changed from 25 to 33 cm in 2003. The
allowed fishing season has also been regulated, with a
maximum of 140 days per year and a closed season from
January 15 to March 31. There is a total annual quota for the
Spanish fleet of 270 Mt.
Scientists have studied the relationships between the
physical properties of fishing gear and the species and size
composition of catches for many years [3]. Estimates of
size selectivity of fishing gear provide important informa-
tion for use in programs focusing on both the conservation
and optimum exploitation of fisheries resources [4–6].
While the size-selective nature of gears, such as trawls and
gill nets is well known, there is still no clear consensus on
the form of the size selection curve for hooks on longlines.
Both logistic type models, typically used to describe the
selectivity of trawls, and the unimodal models used in
I. A. Czerwinski (&)J. A. Hernando
Biology Department, Marine and Environmental Faculty,
Campus of Puerto Real, Cadiz University, Puerto Real, 11510
Cadiz, Spain
e-mail: ivone.czerwinski@uca.es
K. Erzini
Centre of Marine Sciences (CCMAR), Campus of Gambelas,
University of Algarve, 8005-139 Faro, Portugal
J. C. Gutie
´rrez-Estrada
Agroforestry Sciences Department, Polytechnic University
College, Campus of La Ra
´bida, University of Huelva,
Palos de la Frontera, 21819 Huelva, Spain
123
Fish Sci (2009) 75:285–294
DOI 10.1007/s12562-009-0071-7
gillnet selectivity studies have been used in hook selec-
tivity studies [3,7–19].
Semi-pelagic longline selectivity for the black spot
seabream has been already studied in the Azores [18]as
well as for other Pagellus species caught with demersal
longlines off the south of Portugal [10]. In both cases, the
logistic type model seems to be most adequate for
describing longline size selectivity for P. bogaraveo and
Pagellus and other small Sparidae species in general.
In this paper, we present the results of two longline
selectivity studies in which four sizes of hooks were used.
We fitted the logistic size selectivity model to catch size
frequency distributions from the different sizes of hooks.
Catch size frequency distributions, estimated catches based
on the selectivity models, catch species composition and
P. bogaraveo average length for the different hooks were
compared.
Materials and methods
Fishing was carried out from a commercial fishing boat
(12 m in length). The ‘‘voracera’’ main line is 80 m long,
consisting of a 1.2-mm-diameter monofilament with 1.0-m
long and 0.6-mm diameter monofilament gangions spaced
1.10 m apart. Each main line consists of 70 gangions and is
attached to a small sinker and a 3-mm-diameter monofil-
ament line on a hydraulic bobbin. A 20-kg concrete ballast
is attached to the end of the longline and is released and left
on the bottom when the longline is hauled (Fig. 1). Fishing
was carried out on rocky bottoms at depths of up to 850 m.
Normal fishing practices in terms of setting, setting time
and duration of set were observed. Hooks were baited with
standard sized pieces of sardine (Sardina pilchardus) in all
longline sets.
Two experimental fishing trials were carried out. In the
first experiment (Exp. 1), 50 longline sets were placed
between November 2000 and May 2001, with three sizes of
round-bent, eyed ‘‘Siapal’’ brand hooks (numbers 9, 10 and
11) and 3500 hooks of each size. In the second experiment
(Exp. 2), 106 longline sets were placed between October
2004 and July 2005, with 7420 hooks of each size (num-
bers 9, 9.5 and 10). The different sized hooks were
randomly distributed along the mainline, allowing us to
consider the number of contact fish of each size class equal
for all hook sizes. Catch rate for each hook size was esti-
mated as number of fish per 100 hooks. Hook dimensions
(length, width and depth) are given in Fig. 2.
Using the product of width and length to represent
overall hook size [20], we calculated that hook numbers 10,
9.5 and 9 are 1.12-, 1.26- and 1.40-fold larger than the
number 11 hook. Differences between average dimensions
of the four hooks were analyzed using a ttest. Only the
depth dimensions of hooks 10 and 9.5 were not signifi-
cantly different (t=2.359, df =198, P= 0.019).
The choice of a size selectivity model is less obvious for
longlines than it is for gillnets and trawl experiments [21],
although the logistic selectivity model has been found to be
the most appropriate for a variety of Sparidae, including
Pagellus acarne and P. erythrinus, and a logistic-type
selection curve was also obtained for P. bogarveo in the
Azores using a versatile model that can be used to describe
a wide range of selection curves, from bell-shaped to
asymptotic [10,18]. Therefore, it was decided to fit logistic
selection curves to the experimental catch data:
Sij ¼1
1þebiðljL50iÞð1Þ
where S
ij
is the size selectivity for hook size iand size class
j,b
i
is a parameter determining the slope of the selection
curve for hook size i,l
j
is the mid point of the size class j
and L50
i
is the length at 50% selection.
To estimate the parameters of the selection curves, it is
assumed that the parameters of the selection curve are a
function of hook size [22,23]. In our case, mean length,
depth, width and overall hook size [20] were used to
Fig. 1 Schematic
representation of the ‘‘voracera’’
longline
286 Fish Sci (2009) 75:285–294
123
estimate the parameters of the logistic curves as linear
functions of hook size:
bi¼AHiþBð2Þ
and
L50i¼CHiþDð3Þ
where A,B,Cand Dare parameters of the linear functions.
H
i
is the mean of each dimension such as width, length and
overall hook size for hook size i.
The methodology of Wulff [23] and Kirkwood and
Walker [22] was used to fit the model. If it can be assumed
that the probability of catching a fish of size jwith gear size
ifollows a Poisson distribution, the parameters of the
selection curve can be estimated by maximizing the fol-
lowing likelihood function, with the assumption of equal
fishing power of the different sized hooks:
X
iX
j
Cij log Sij
PiSij
ð4Þ
where C
ij
is the observed catches for hook sizes iand size
classes j. If fishing power is not assumed to be constant, a
scaling factor can be included to model optimal selectivity,
and the resulting selectivity curves would not be of equal
height. A total of 72 models (36 for each experiment) were
fitted using SAS (ver. 1998; SAS Institute, Cary, NC),
following the six model types of Table 1, for each hook
dimension (mean length, depth, width and overall hook
size) and for each fish length dimension (total, fork and
standard). Expected catches are calculated from the
following relationship:
^
Cij ¼Sij ^
Njð5Þ
where
^
Nj¼PiCij
PiSij ð6Þ
where ^
Cij is the expected catch and ^
Njis the expected
number of contact fish.
There are many measures of forecasting accuracy that
one may use to compare different models [24]. The cor-
relation between observed and predicted catches was
expressed by means of the correlation coefficient r. The
coefficient of determination (R
2
) describes the proportion
of the total variance in the observed data that can be
explained by the model. Other measures of variances
applied were the percentage standard error of prediction
(%SEP) [25], the coefficient of efficiency (E
2
)[26] and the
average relative variance (ARV) [27]. These four estima-
tors are unbiased estimators that are employed to see how
far the model is able to explain the total variance of the
data.
The %SEP is defined by:
%SEP ¼100
CRMSE ð7Þ
RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
PiPjCij ^
Cij
2
n
sð8Þ
where
Cis the average of the observed catches, ^
Cij is the
estimated catch of the same size class jand hook iand nis
the total number of observations. The RMSE is the square
root of the mean square error. The principal advantage of
%SEP is its non-dimensionality, which allows comparison
on the same basis of forecasts given by different models.
Fig. 2 Shape and dimensions
of each type of hook (numbers
9, 9.5, 10 and 11) used in the
selectivity study. Means and
standard errors (in cm) are
based on a sample size of 100
hooks for each type
Table 1 Different model types according to the parameters of the
logistic curves, estimated as linear functions of hook dimension
Model type b
i
L50
i
1AH
i
CH
i
2AH
i
CH
i
?D
3AH
i
?BCH
i
4AH
i
?BCH
i
?D
5BCH
i
6BCH
i
?D
H
i
, Hook size; b
i
, determines the slope of the logistic curve; L50, size
at 50% selection; A,B,Cand D, constants
Fish Sci (2009) 75:285–294 287
123
The coefficient of efficiency E
2
and the ARV are used to see
how the model explains the total variance of the data and
represents the ‘‘proportion’’ of the variation of the observed
data considered by the model. E
2
and ARV are given by:
E2¼1:0PiPjCij ^
Cij
2
PiPjCij
C
2ð9Þ
ARV ¼1:0E2ð10Þ
The sensitivity to outliers due to the squaring of the
difference terms is associated with E
2
or, equivalently, with
ARV. A value of zero for E
2
indicates that the observed
average
Cis as good a predictor as the model, while
negative values indicate that the observed average is a
better predictor than the model [28].
For a perfect match, the values of R
2
and of E
2
should be
close to 1 and those of %SEP and ARV close to 0.
In addition, it is advisable to quantify the error in the
same units of the variables. These measures, or absolute
error measures, included the RMSE and the mean absolute
error (MAE), given by:
MAE ¼PiPjCij ^
Cij
nð11Þ
Results
During the Exp. 1 fishing trials, the black spot seabream
catch rates for hook numbers 9, 10 and 11 were 3.42, 13.04
and 4.48%, respectively. In comparison, during the Exp. 2
fishing trials, the catch rates for hook numbers 9, 9.5 and 10
were 1.93, 6.93 and 7.21%, respectively. Overall, the
number 9 hook had the lowest catch rate while the number
10 hook had the highest.
The species caught and fish numbers were different for
each experiment (Table 2). In Exp. 1, more than 99% of
the fish caught were P. bogaraveo and Helicolenus dac-
tylopterus, with only two fish of two other species.
Pagellus bogaraveo contributed up to 89% of the catch in
number in Exp. 1 and up to 92% in Exp. 2. In Exp. 2,
all hooks caught fish of P. bogaraveo,H. dactylopterus,
Trachurus mediterraneus and Scomber japonicus, and
hook 10 also caught one individual of Lepidopus caudatus.
There was a strong overlapping of the catch size fre-
quency distributions in both experiments. However, a ttest
found significant differences in average fish length between
all hook sizes in Exp. 1 [ANOVA, F=15.28, df =2,
P\0.01; least significant differences (LSD) post hoc
analysis with a=0.01) and in Exp. 2 between hooks 9 and
9.5 and between hooks 9 and 10, but there was no significant
difference between average fish size for hooks 9.5 and 10
(ANOVA, F=0.08, df =2, P\0.01; LSD post hoc
analysis with a=0.01).
To compare fish size frequency distribution of each
hook, a Kolmogorov–Smirnov (K–S) test was performed.
In Exp. 1, there were significant differences between all
hooks (9 and 11: K–S =2.779, P\0.05; 10 and 11:
K–S =2.445, P\0.05; 9 and 10: K–S =1.476, P[0.05).
In Exp. 2, the catch size frequency distribution of hook
number 10 was not significantly different from that
of hook number 9.5 (K–S =0.749, P[0.05), but
there was a significant difference between hook number 9
and the other two hooks (9 and 10: K–S =1.543,
P\0.05; 9 and 9.5: K–S =1.842; P\0.05). The catch
size frequency distributions by hook size are given in
Fig. 3.
In both fishing trials, black spot seabream of a wide
size range were caught (Fig. 4). Fish ranging from 23.5
to 52.0 and from 19.5 to 51.5 cm in fork length were
caught in the fishing trials of Exp. 1 and Exp. 2,
respectively. While the catch size distributions of Exp. 1
can be seen to be essentially uni-modal but skewed to the
right, those of Exp. 2 are clearly multi-modal, with
several smaller modes to the right of the main mode
corresponding to catches of larger black spot sea-
bream. All comparisons between the distributions of the
two experiments showed significant differences (K–S =
8.497, P\0.05).
The estimated parameters and accuracy measures for a
selection of selectivity models for each experiment are
given in Tables 3and 4. Models that did not give reason-
able parameter estimates or failed to converge are not
included in the tables.
For Exp.1, only 11 of type 1 and 5 models gave good
fits. Simple proportional functions were in all cases
adequate for describing the relationships between L50
and hook size (H
i
). For Exp. 2, six of type 1, 2 and 5
models gave good fits, with proportional functions
describing the relationships between L50 and hook size in
most cases. The slope of the selection curves (b
i
) was
described as a constant or proportional function of hook
size in both experiments. While total length was the most
adequate fish dimension for describing selection curves
as a function of hook dimension in Exp. 1, which in most
cases was the overall hook size, in Exp. 2, standard
length and in most cases hook depth were the measure-
ments that resulted in the best fits. The L50 values
obtained for Exp. 1 varied from 19.29 to 37 cm and from
34.15 to 49.46 cm in Exp. 2, while the selection curve
slopes (b
i
) varied from 0.21 to 3.55 in Exp. 1 and from
0.18 to 0.30 in Exp. 2.
Comparing the forecasting accuracy measures of Exp. 1
models, model No. 10 [type 5 model with constant slope
(b
i
) and L50
i
proportional to overall hook size, based on
fork length measurements] showed the highest values of
%SEP, E
2
and R
2
, and the lowest value of ARV. In Exp. 2,
288 Fish Sci (2009) 75:285–294
123
Table 2 Number of fishes
caught of each species for the
three hooks used in each
experiment
Species Hook size numbers Total catch Percentage of
total catch
91011
Experiment 1
Pagellus bogaraveo 120 484 157 761 89.8
Brama brama 1 1 0.1
Helicolenus dactylopterus 5 71 8 84 9.9
Trachurus mediterraneus 1 1 0.1
Experiment 2 9 9.5 10
P. bogaraveo 143 514 535 1192 92.1
H. dactylopterus 8 18 39 65 5.0
T. mediterraneus 3 20 6 29 2.2
Scomber japonicus 2 3 2 7 0.5
Lepidopus caudatus 1 1 0.1
Fig. 3 Pagellus bogaraveo
catch size frequency
distributions for different hook
sizes in the two experiments
(Ex)
Fig. 4 Comparison of the
P. bogaraveo total catch size
frequency distributions for the
two experiments (Ex)
Fish Sci (2009) 75:285–294 289
123
model No. 2 [type 1 model with slope (b
i
) and L50
i
pro-
portional to depth hook dimension, based on fork length
measurements] had the best values for %SEP, E
2
, ARV and
R
2
. In both experiments, models with the lowest RMSE and
MAE values were not the models with the best %SEP, E
2
,
ARV and R
2
values.
Table 3 Estimated parameters for logistic selectivity curve fitted by maximum likelihood, for each experiment using different fish and hook size
dimensions
Experiment Model
no.
Model
type
Fish
length
Dimension
of H
i
ABb
9
b
10
b
11
CDL50
9
L50
10
L50
11
1 1 5 Total Overall 0.356 0.36 0.36 0.36 6.131 35.96 28.61 24.13
2 1 Total Overall 0.053 0.31 0.25 0.21 6.309 37.00 29.44 24.83
3 5 Total Width 0.413 0.41 0.41 0.41 23.325 34.99 30.35 27.90
4 1 Total Width 0.247 0.37 0.32 0.30 23.914 35.87 31.11 28.60
5 1 Total Depth 0.284 0.48 0.41 0.36 20.308 34.04 29.67 25.79
6 5 Total Length 0.568 0.57 0.57 0.57 8.369 32.72 30.02 27.53
7 1 Total Length 0.135 0.53 0.48 0.44 8.496 33.22 30.48 27.95
8 5 Total Depth 1.900 1.90 1.90 1.90 17.745 29.74 25.93 22.54
9 1 Standard Overall 0.606 3.55 2.83 2.38 4.901 28.74 22.87 19.29
10 5 Fork Overall 0.410 0.41 0.41 0.41 5.483 32.16 25.59 21.58
11 1 Fork Overall 0.062 0.36 0.29 0.24 5.632 33.03 26.28 22.16
b
9
b
9.5
b
10
L50
9
L50
9.5
L50
10
2 1 5 Total Depth 0.242 0.24 0.24 0.24 29.506 49.46 43.44 43.11
2 1 Fork Depth 0.120 0.20 0.18 0.18 28.709 48.12 42.26 41.94
3 5 Fork Depth 0.268 0.27 0.27 0.27 26.736 44.81 39.36 39.06
4 1 Standard Depth 0.130 0.22 0.19 0.19 25.477 42.70 37.50 37.22
5 2 Standard Depth 0.153 0.26 0.23 0.22 19.100 10.767 42.78 38.88 38.67
6 5 Standard Depth 0.301 0.30 0.30 0.30 23.374 39.17 34.41 34.15
H
i
, Dimension of the hook i(i=9, 9.5, 10 and 11);b
i
, equation of the slope of the logistic curve; b
9–11
, values of b
i
for each hook and model no.;
L50
i
, size at 50% selection for hook i,L50
9–11
, values of L50 for each hook and model no. A,Band C,D: parameters relating the slope and L50 to
hook size, respectively
Table 4 Accuracy measures for logistic selectivity curve fitted, for each experiment
Experiment Model no. RMSE MAE %SEP E
2
ARV R
2
1 1 6.975 4.662 85.349 0.573 0.427 0.574
2 7.473 5.181 91.450 0.510 0.490 0.510
3 7.142 4.808 87.394 0.553 0.447 0.553
4 7.224 4.882 88.404 0.542 0.458 0.542
5 7.337 4.984 89.780 0.528 0.472 0.528
6 7.382 5.049 90.333 0.522 0.478 0.522
7 7.560 5.226 92.512 0.499 0.501 0.499
8 7.582 5.089 92.784 0.496 0.504 0.496
9 8.070 4.921 95.563 0.603 0.397 0.602
10 7.303 4.842 83.598 0.615 0.385 0.608
11 7.372 4.833 84.389 0.608 0.392 0.601
2 1 2.211 1.587 21.698 0.976 0.024 0.975
2 2.339 1.759 18.837 0.978 0.022 0.979
3 2.361 1.759 19.018 0.978 0.022 0.978
4 2.990 1.984 21.826 0.974 0.026 0.974
5 2.992 1.983 21.840 0.974 0.026 0.974
6 3.002 2.010 21.912 0.974 0.026 0.974
RMSE, Square root of the mean square error; MAE, mean absolute error; %SEP, percentage standard error of prediction; E
2
, the coefficient of
efficiency; ARV, average relative variance; R
2
coefficient of determination
290 Fish Sci (2009) 75:285–294
123
Models No. 10 of Exp. 1 and No. 2 of Exp. 2, were
selected as the best based on their values of %SEP, ARV
and R
2
. Differences in the chosen models can be appreci-
ated in the L50 values (in Exp. 1, 32.2-, 25.6- and 22.4-cm
fork length for hooks 9, 10 and 11, respectively; in Exp. 2,
48.1-, 42.3- and 41.9-cm fork length for hooks 9, 9.5 and
10, respectively) as well as the slopes of the curves (0.41
for all three hook sizes in Exp. 1; 0.20 for hook 9 and 0.18
for hooks 9.5 and 10 in Exp. 2).
The selection curves for both experiments are shown in
Fig. 5. The observed catches and the expected catches
based on these models are given in Fig. 6. In Exp. 1,
observed frequencies for hook 11 were lower than the
expected, while observed frequencies for hook 10 were
rather higher than the expected. However, such differences
were not observed in Exp. 2. As can be seen, better fits
were obtained for Exp. 2 than for Exp. 1. This was con-
firmed by the results of forecasting accuracy measures.
Fig. 5 Logistic selection curves
of the longline, obtained from
the selectivity model No. 10 of
Exp. 1 and No. 2 of Exp. 2
Fig. 6 Comparison of the
observed and predicted catch
size frequency distributions for
the three hook sizes according
to the logistic selectivity model
of each experiment
Fish Sci (2009) 75:285–294 291
123
Discussion
The longline used in this study can be considered to be
highly species selective given the small proportion of non-
target species caught, confirming that hook and line gear
can be highly species selective through a manipulation of
hook size and bait type [29]. The number of captured
species (nine) is lower than that reported in other similar
longline fisheries, such as the black spot seabream fishery
in the Azores, where 27 species were caught, with the most
important species being H. dactylopterus followed by
P. bogaraveo. This difference could be due to geographic
differences, gear characteristics and fishing strategy dif-
ferences, since the fishing experiments in the Azores were
carried out with semi-pelagic longlines and the hooks were
of different sizes from the ones used in the Strait of
Gibraltar (hooks numbers 12, 9, 6 and 4 in the Azores
study). In Algarve (south Portugal), 35 species of fish and
cephalopods were reported in a similar study in which three
smaller hooks (numbers 15, 13 and 11) were used in much
shallower depths (between 13 and 20 m) [9].
Our comparison of the catch size frequency, distributed
over both experiments, revealed a displacement of the
distribution towards smaller sizes over time. Similar
changes have been observed in the size composition of
exploited populations of Squalus acanthias [30], sharks,
rays and chimaerae [31], Ammodytes marinus [32], Sander
lucioperca [33] and different species from the communities
of coral reefs [34]. Variations in fish population size fre-
quency distributions have been identified as possible
indicators of the impact of fishing [35]. In the case of the
black spot seabream, the results of our comparison of the
two experimental fishing trials indicate a displacement
towards smaller sizes in the size frequency distributions.
Very similar size frequency distributions were observed
from 1990 to 1994 [36]. Nevertheless, the catch size
composition began to change after 1995, with diminishing
catches of larger individuals and significant increases in
smaller sized black spot seabream.
The efficiency and the size selectivity of hook gears are
influenced by numerous factors, such as the distribution of
the fish, competition between fish, size and design of hook,
size and forms of bait, combination of baits, duration of
sets and the time of the day of fishing [19,37–41]. Previous
studies of selectivity of hooks have used a wide range of
sizes and types of hooks and have reached different
conclusions.
Otway and Craig [20] evaluated the size selectivity of
Pagrus auratus using hooks of three sizes. With differ-
ences in the absolute hook size of only 26.5 and 65%, they
found that the smaller hook caught more illegal size fishes
(\20 cm in fork length), whereas the larger hook caught
relatively bigger fishes. In some studies, hook size
selectivity was only demonstrated using hooks that were
different by more than 200% in absolute size, and even
then only small differences in the catch size structures of
small species were found [11–13,19]. These studies
showed that the efficiency decreased with increasing size
of the hook and that all hook sizes captured the same size
range of fish. In other cases, the conclusion drawn by
researchers was that there was no difference in the size
selectivity with maximum differences in the absolute hook
size of 72 and 96% [17,42]. The results of this study show
that the ‘‘voracera’’ fishing gear can be selective in terms of
sizes based on the size of hook used. The catch size fre-
quency distributions of different sizes of hooks used in this
study also show a high degree of overlap, but even though
differences in hook size did not surpass 43.7%, there was
evidence of size selectivity. It has been reported that bait
size can be more important that hook size in determining
the size of captured fish [40]. However, in this study
standardized pieces of sardine were used as bait.
Although there is no consensus on the form of the hook
selection curve, the logistic model of selectivity was cho-
sen as the most adequate for the black spot seabream
longline fishery, as in other largely seabream-based long-
line fisheries [10,18,43]. A decrease in the selectivity for
larger sizes may be expected [17], but it was also found
that the population size structure can limit the possibilities
of a total recognition of selectivity effects. A considerable
ambiguity in the form of the selection curve (normal type
or sigmoidal curves) indicates that fish catch size frequency
distributions may not be sufficiently informative to allow a
meticulous evaluation of the real shape of the hook selec-
tion curve [14]. Nevertheless, the black spot seabream
caught in this study had a wide size range, with the largest
fish (57.8 cm in total length) approaching the population
L?of 58 cm [36].
Total catches of the two experiments showed differences
in the size frequency distributions, implying that the pop-
ulation size structures for the two periods differed. In the
same way, the fitted selectivity models for each experiment
were very different despite having two hooks in common.
The differences in the selectivity curves for the two periods
(2000/2001 and 2004/2005) probably reflects the different
population size structures. A simulation study revealed that
size selectivity and fishing mortality strongly influenced
the mean size dynamics of the black spot seabream and that
logistic type selectivity had a greater effect on the popu-
lation mean size than normal type selectivity [43]. This
result suggests that in this fishery, there is an interrela-
tionship between population size structure and the longline
size selectivity.
A multicriteria performance assessment based on dif-
ferent accuracy measures was appropriate for selecting the
best models [28]. In some cases, the explained variances
292 Fish Sci (2009) 75:285–294
123
were significantly high, indicating good model perfor-
mance, but the values of RMSE, %SEP, E
2
, ARV and
MAE were significantly worse than those obtained with
others models. In this way, high correlations can be
achieved by mediocre or poor models. Similar conclusions
were obtained in the forecasting of different kinds of time
variables [44–48].
The obtained accuracy measures were different for the
two experiments. In Exp. 1, estimated catches were more
different from the observed ones than in Exp. 2. The main
reason for these differences and the lack of accuracy in
Exp. 1 is that, in all cases, the estimated catch rate
decreases with increasing hook size, but in Exp. 1 the
intermediate sized hook shows the highest observed catch
rate. It can be assumed that all hook size selection curves
have the same maximum height if the fishing power of the
different hook sizes is equivalent [14]. If the discordance of
the model in Exp. 1 is due to the assumption of equal
fishing power for all size hooks, this is not the case in
Exp. 2. The fishing intensity of hooks can be affected by a
variety of factors, such as hook size, hook model, bait type
or bait size. In this experiment only hook size was varied.
While it is to be expected that larger hooks have lower
catch rates [41], in Exp. 1 the catch rate of the smallest
hook (hook 11) was significantly lower. This decrease in
the catch rate of the smallest hook undermines the
assumption of fishing intensity as a function of hook size.
In the Azorean fishery of H. dactilopterus and P. bogara-
veo, a similar pattern with the smallest of four hook sizes
was observed, suggesting that loss of efficiency could be
due to bait loss, since both hook size and sardine bait
texture could contribute to bait loss for the smallest hooks
[18]. The main reason for the lack of accuracy in Exp. 2
could be due to the overlapping of the catch size frequency
distributions of different hook sizes. A possible deduction
that could be drawn from these differences in both exper-
iments is that the assumption of equal fishing power for all
size hooks could be more correct when the difference
between hook sizes is small, as is the case for Exp. 2, or if
the size range of the hooks does not include small hooks
from which the bait is lost.
In Exp. 2, there were no significant differences between
the size frequency distributions of hooks 9.5 and 10 cat-
ches, which did not differ significantly in terms of average
depth dimension. In all of the fitted models, size selectivity
was a function of hook depth dimension, a parameter that
differed little between hooks 9.5 and 10. This could be a
key hook size parameter for management purposes in this
fishery.
The importance of hook selectivity in a black spot
seabream fishery was noted in a previous study, and the
effects of hook size selectivity on the size structure were
reported for a simulated black spot seabream population.
As this is a protandric hermaphrodite species, where the
biggest individuals are females, a decrease in the average
size would contribute to the decline of spawning biomass,
thereby increasing the probability of recruitment failure
[43]. This could indicate that one of the reasons for the
observed decline in recruitment in the Strait of Gibraltar is
the over-fishing of first mature females (4?age class
females with an average total length of 33.1 ±2.23 cm
[36]). In the Strait of Gibraltar, the sexual inversion of the
black spot seabream takes place at an estimated size of
32.5 cm total length [36], and the minimum legal size is
currently 33 cm total length. Based on the fitted selectivity
models, only hook 9 in Exp. 1 and all hooks in Exp. 2 have
aL50 greater than the size of sexual inversion. Neverthe-
less, smaller individuals exceed 50% of landings, even in
Exp. 2. This suggests that although hooks are selective, the
sizes used in this study catch a high number of illegal sized
fishes, given the current population size structure. Even if
the selectivity model fitted in Exp. 2 shows low selectivity
for illegal sizes, the higher relative number of smaller fish
sizes produce high catches of these sizes.
Acknowledgments This work has been partly financed by the Agro
alimentary and Fishery Research and Formation Institute (IFAPA)
(project: C03-007-2003-110), the General Direction of Fishery and
Aquaculture of the Council of Andalucı
´a and the Provincial Depu-
tation of Cadiz. The University of Cadiz provided the necessary
facilities for a stay at the Universidade do Algarve. We would like to
express our gratitude to our colleagues Dr. Mila C. Soriguer,
Dr. Cristina Zabala, Dr. Eva Velasco, M
a
Carmen Go
´mez Cama,
Remedios Cabrera, Javier Llorente and Jose M. Garcı
´a Rebollo for
the assistance they willingly provided during the samplings. We also
thank two anonymous reviewers for their helpful comments and
suggestions for improving the manuscript.
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