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Modelling the effects of more selective trawl nets on the
productivity of European hake (Merluccius merluccius)
and deep-water rose shrimp (Parapenaeus longirostris)
stocks in the Strait of Sicily
Sergio Vitale 1, Marco Enea 2, Giacomo Milisenda 3, Vita Gancitano 1, Michele Luca Geraci 1,
Fabio Falsone 1, Gioacchino Bono 1, Fabio Fiorentino 1, Francesco Colloca 1,4
1 Istituto per le Risorse Biologiche e le Biotecnologie Marine (IRBIM), Consiglio Nazionale delle Ricerche (CNR),
via L. Vaccara 61, 91026 Mazara del Vallo, Italy.
(SV) (Corresponding author) E-mail: sergio.vitale@cnr.it. ORCID iD: https://orcid.org/0000-0001-6063-4126
(VG) E-mail: vita.gancitano@cnr.it. ORCID iD: https://orcid.org/0000-0001-9623-6621
(MLG) E-mail: micheleluca.geraci@gmail.com. ORCID iD: https://orcid.org/0000-0002-3143-4659
(FFa) E-mail: falsonefabio@gmail.com. ORCID iD: https://orcid.org/0000-0003-1754-4226
(GB) E-mail: gioacchino.bono@cnr.it. ORCID iD: https://orcid.org/0000-0001-5936-4296
(FFi) E-mail: fabio-fiorentino@cnr.it. ORCID iD: https://orcid.org/0000-0002-6302-649X
(FC) E-mail: francesco.colloca@cnr.it. ORCID iD: https://orcid.org/0000-0002-0574-2893
2 Dipartimento di Scienze Economiche, Aziendali e Statistiche, University of Palermo, Italy.
(ME) E-mail: marco.enea@unipa.it. ORCID iD: https://orcid.org/0000-0002-9281-5746
3 Stazione Zoologica Anton Dohrn, Lungomare Cristoforo Colombo (ex complesso Roosevelt), 90142 Palermo, Italy.
(GM) E-mail: giacomo.milisenda@szn.it. ORCID iD: https://orcid.org/0000-0003-1334-9749
4 Department of Biology and Biotechnology “C. Darwin”, Sapienza University of Rome, Italy.
Summary: Single-species Gadget models were used to assess the effects of using a sorting grid mounted on the traditional
trawl net used by Sicilian trawlers to exploit the deep-water rose shrimp in the Strait of Sicily. The main commercial by-
catch species of this fleet is the European hake (Merluccius merluccius), often caught at sizes well below the minimum
conservation reference size. Selectivity curves based on the results of an experimental survey carried out in the area using a
commercial trawler equipped with an ad hoc-designed sorting grid were incorporated into single-species Gadget models to
forecast the effects of changing fishery selectivity on the performance of the two stocks in terms of catch and biomass. The
models included catch data from the Italian, Tunisian and Maltese fleets as well as MEDITS trawl survey data for the period
2002-2016. Several scenarios were defined to simulate the effect of the Italian trawlers’ adopting the sorting grid under dif-
ferent stock-recruitment assumptions. The results obtained, when compared with status quo simulations of fishing without a
sorting grid mounted on the trawl net, indicated a beneficial effect for both stocks in terms of an increase in biomass and for
the fleets in terms of the amount and size composition of annual landings.
Keywords: Gadget; forecast; selectivity; sorting grids; trawl net; Strait of Sicily.
Modelización de los efectos de redes de arrastre más selectivas sobre la productividad de stocks de merluza europea
(Merluccius merluccius) y gamba blanca (Parapenaeus longirostris) en el estrecho de Sicilia
Resumen: Se usaron modelos monoespecíficos Gadget para evaluar el efecto del uso de una rejilla separadora acoplada a la
red de arrastre tradicional que usan los arrastreros sicilianos para explotar la gamba blanca en el estrecho de Sicilia (SoS).
La principal especie en las capturas accesorias de esta flota es la merluza europea (Merluccius merluccius), que contiene a
menudo tallas muy por debajo de la Talla de Referencia Mínima de Conservación (MCRS). Se incorporaron en Gadget las
curvas de selectividad obtenidas en una campaña experimental en la misma área con un arrastrero comercial equipado con un
modelo de rejillas separadora diseñado específicamente para el estudio, para pronosticar los efectos del cambio de la selec-
tividad pesquera en la evolución de los dos stocks en términos de captura y biomasa. Los modelos incluyen datos de captura
de las flotas italiana, tunecina y maltesa, así como datos de las campañas MEDITS para el periodo 2002-2016. Se definieron
distintos escenarios para simular el efecto de la adopción de la rejilla separadora por parte de los arrastreros italianos bajo
distintas asunciones del modelo stock-reclutamiento. La comparación de los resultados obtenidos con simulaciones de pesca
sin montar la rejilla separadora en la red de arrastre (status quo) indican un efecto neto beneficioso para los dos stocks debido
al incremento de biomasa y, en consecuencia, para las flotas en términos de cantidad y composición de las capturas anuales.
Palabras clave: Gadget; pronóstico; selectividad; rejillas separadoras; red de arrastre; estrecho de Sicilia.
Citation/Cómo citar este artículo: Vitale S., Enea M., Milisenda G., Gancitano V., Geraci M.L., Falsone F., Bono G.,
Fiorentino F., Colloca F. 2018. Modelling the effects of more selective trawl nets on the productivity of European hake
Scientia Marina 82S1
December 2018, 199-208, Barcelona (Spain)
ISSN-L: 0214-8358
https://doi.org/10.3989/scimar.04752.03A
Discards regulation vs Mediterranean fisheries
sustainability
M. Demestre and F. Maynou (eds)
200 • S, Vitale et al.
SCI. MAR. 82S1, December 2018, 199-208. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04752.03A
INTRODUCTION
The Mediterranean basin is affected by a very
high level of different human pressures (Micheli et al.
2013). Fisheries are considered one of the main sources
of impact, with about 22 countries fishing stocks that
are mostly overfished (Colloca et al. 2013, Vasilako-
poulos et al. 2014, Colloca et al. 2017). The increasing
level of fishing effort applied in the last three decades
has led to a profound modification of the marine eco-
system in terms of loss of biodiversity and biomass of
several species (Coll and Libralato 2012, Piroddi et al.
2017). Furthermore, trawl fishing is a non-selective
fishing method resulting in significant quantities of
unwanted catch, including the incidental catch of non-
target species and juveniles, which are either retained
or discarded because of their low economic value or
legal issues (Pravin et al. 2011). Unwanted catch has
been considered a major problem in fisheries manage-
ment as it accounts for a great part of the overall impact
of fishing activities on the environment (Ramsay et al.
1998, Sánchez et al. 2000, Gorelli et al. 2016). Dis-
carding is also considered a moral issue as the waste of
natural resources is considered ethically wrong.
The promotion of a sustainable use of the marine
environment is now the objective of several European
actions, such as the EU’s Common Fisheries Policy
(CFP, reg. EU 1380/2013) and the Marine Strategy
Framework Directive. The CFP prohibits discarding of
the main commercial species through a landing obli-
gation or discard ban. In the Mediterranean Sea, any
discard of species subject to minimum conservation
reference size (MCRS) above 5% of the total catch
is prohibited. Experiences from other countries (e.g.
Alaska, British Columbia, New Zealand, Iceland and
Norway) on the effects of discard bans highlight that a
policy of mandatory landings cannot result in long-term
benefits to stocks unless total removals are reduced,
through the avoidance of undersized, non-commercial
or over-quota catch. Additional management measures
are therefore required to incentivize a switch towards
more selective fishing gear (Condie et al. 2014).
In the last few years, experiments have been car-
ried out on the use of sorting grids mounted on trawl
nets to reduce the discard rate or the catch of under-
sized individuals in poorly selective fisheries such as
those targeting crustaceans (Fonseca et al. 2005). In
Norway lobster (Nephrops norvegicus) fisheries for
example, selective sorting grids have been tested in
many areas and their use is specified by legislation
in some sectors of Kattegat and Skagerrak. Grids
are very selective, but they can lead to loss of com-
mercial Norway lobster and valuable fish species
(Madsen et al. 2016). Trade-offs associated with the
use of sorting grids were investigated in the brown
shrimp (Crangon crangon) fine-mesh trawl fishery in
the North Sea, where a positive reduction of fish by-
catch (>70%) and benthos (65%) was associated with
a reduction of only 15% of brown shrimp catch (Polet
2002). Similarly, experiments in Portuguese waters
have shown that the use of ad hoc-designed grids in
trawl crustacean fishery led to a substantial decrease
in fish by-catch, although the benefits were partially
counteracted by a loss in Norway lobster catch (Fon-
seca et al. 2005). Fishing trials in the Mediterranean
Sea have highlighted that sorting grids can be sub-
stantially beneficial in increasing the size at first cap-
ture of commercial fish and crustaceans, thus making
trawling more selective (Sardà et al. 2006, Bahamon
et al. 2007, Massuti et al. 2009).
Historically, within the Mediterranean basin the
Strait of Sicily has been one of the most important
fishing ground exploited by the fleets of several coun-
tries (Italy, Tunisia, Libya, Malta, Egypt) and features
a high biological diversity, productivity and habitat
heterogeneity. Recently, the General Fisheries Com-
mission for the Mediterranean decided to close three
stable nurseries of deep-water rose shrimp, Parap-
enaeus longirostris Lucas, 1847 (hereinafter DPS) and
European hake Merluccius merluccius Linnaeus, 1758
(hereinafter HKE) in the northern sector of the Strait
of Sicily (REC.CM-GFCM/40/2016/4), although the
measure has not yet been implemented. In this area the
deep-water crustaceans fisheries is the most important
in terms of biomass and commercial value of the land-
ings, although a non-negligible amount of catch comes
from inshore demersal and pelagic fisheries targeting
several fish species (Gancitano et al. 2016).
Among the targeted crustaceans, DPS made up
more than 40% of landings in the Strait of Sicily in
2015, the annual landing being about 6150 t with a val-
ue of €39 million. One of the main by-catches of this
fishery is HKE (Milisenda et al. 2017). However, the
amount of landings of undersized HKE specimens is
considerable. The discarded fraction of DPS trawl fish-
eries ranged between 25% and 40% of the total catch,
being formed mainly by horse mackerel, Trachurus
trachurus Linnaeus, 1758, DPS and HKE specimens
below the MCRS (Milisenda et al. 2017).
The management of these stocks is based on techni-
cal measures such as the prohibition of trawling within
three miles of the coastline and minimum mesh sizes
(MMS, 40 mm square) of trawl cod-end established by
Council Regulation (EC) 1967/2006 and the MCRS of
20 cm total length for HKE and 20 mm carapace length
for DPS.
(Merluccius merluccius) and deep-water rose shrimp (Parapenaeus longirostris) stocks in the Strait of Sicily. Sci. Mar. 82S1:
199-208. https://doi.org/10.3989/scimar.04752.03A
Editor: F. Maynou.
Received: January 17, 2018. Accepted: September 10, 2018. Published: November 9, 2018.
Copyright: © 2018 CSIC. This is an open-access article distributed under the terms of the Creative Commons Attribution
4.0 International (CC BY 4.0) License.
Modelling the effect of a sorting grid on trawlers • 201
SCI. MAR. 82S1, December 2018, 199-208. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04752.03A
The new CFP prohibits discarding of species sub-
ject to an MCRS, such as deep-water rose shrimp and
hake, amounting to more than 5% of the total catch.
As things stand, it is urgent to decrease the amount of
unwanted catch in trawl fisheries through more selec-
tive trawl nets. Since the adoption of a minimum legal
mesh size in trawling does not prevent the catch of
undersized HKE (Bethke 2004, Lucchetti 2008), sort-
ing grids are considered one of the simplest and most
efficient ways to increase the selectivity of trawl nets
among several by-catch reduction devices (e.g. Pravin
et al. 2011).
The objective of this study was to determine whether
the adoption of an ad hoc-designed sorting grid, called
Juveniles Trash Excluder Device, by the Italian trawl-
ers exploiting the deep-water rose shrimp and hake in
the Strait of Sicily can positively contribute to stock
rebuilding and fisheries landings. The new estimated
selectivity curves for DPS and HKE were incorporated
into single-species Gadget (Globally applicable Area-
Disaggregated General Ecosystem Toolbox; Begley
and Howell 2004) models to forecast the grid effects
on the stocks and the fishery. The potential use of sort-
ing grids as a tool for consistently reducing by-catch of
juveniles of the two species is discussed, considering
the requirement of the EU-CFP for more selective and
sustainable fisheries.
MATERIALS AND METHODS
Study area and data collection
An experimental survey was conducted in 2015 on
the continental shelf off the southwestern coast of Sic-
ily (Geographical Sub-Area 16, Fig. 1) using a com-
mercial trawler equipped with three different types of
sorting grids and 40 mm square mesh (SM 40) in the
cod-end. Only the grid constituted by a net of 40 mm
SM (G1-SM40, Fig. 2) was considered in this study
because of its higher efficiency in the reduction of DPS
and HKE juveniles. Three different sources of data
were used to perform the study: i) data collected during
the above mentioned survey, ii) commercial catch data
from the Italian, Tunisian and Maltese fleets (Table
1), and iii) MEDITS trawl survey data for the period
2002-2016.
Gadget models
Gadget is an acronym for the “Globally applicable
Area-Disaggregated General Ecosystem Toolbox”,
which is a statistical model of marine populations and/
Fig. 1. – Map of the study area where fisheries data are collected for the assessment of deep-water rose shrimp and hake. The black square box
indicates the area where the experimental survey was carried out in 2015.
Fig. 2. – Designed sorting grid used in the present study: G1-SM40.
202 • S, Vitale et al.
SCI. MAR. 82S1, December 2018, 199-208. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04752.03A
or ecosystems designed to be multi-fleet and capable of
including predators and mixed fisheries issues (Begley
and Howell 2004). In her review of ecosystem mod-
els, Plagányi (2007) classified Gadget as a “minimum
realistic model” to describe the concept of restricting
a model to those species most likely to have impor-
tant interactions with the species of interest. Gadget
can also be used for single-species assessment, and in
European waters it is currently used to assess stocks in
the ICES area (e.g. southern stock of hake in divisions
8.c and 9.a, and tusk and golden redfish in Icelandic
waters). In the Mediterranean, it has been applied for
the assessment of hake in Geographical Sub-Area 9
(Bartolino et al. 2011).
Gadget is an age-length–structured parametric
forward simulation model coupled with an extensive
set of data comparison and optimization routines. Pro-
cesses are generally modelled as dependent on length,
making Gadget a suitable tool for addressing selectiv-
ity problems. Age is however tracked in the model,
and data can be compared on a length and/or age scale.
Gadget works by running an internal model based on
many parameters and then comparing the data from
the output of this model with observed data to get a
goodness-of-fit likelihood score (Begley and Howell
2004). The parameters can then be adjusted and the
model re-run until an optimum is found, which corre-
sponds to the model with the lowest likelihood score.
The Gadget framework consists of three parts: 1) a par-
ametric model to simulate the ecosystem, 2) statistical
functions to compare the model output with data, and
3) search algorithms to optimize the model parameters.
The internal structure of Gadget and various potential
sub-models and options available are described in de-
tail by Begley (2004) and Begley and Howell (2004).
For the purpose of this study, we used two single-
species Gadget models, for DPS and HKE, with popu-
lations defined by 2 mm carapace length and 2 cm
total length groups, respectively. The year is divided
into four quarters. HKE age range is 0 to 7 years, with
the oldest age treated as a plus group. Recruitment
happens in the second and third quarter. The length at
recruitment is estimated and mean growth is assumed
to follow the von Bertalanffy growth function, with
Linf=100 cm and K estimated by the model. DPS age
range is 0 to 4 years, the latter used as a plus group.
Recruitment takes place in the second and third quar-
ter. Natural mortality was assumed as a vector using
the Prodbiom approach (Abella et al. 1997). The
datasets used by the models (likelihood components)
are listed in Table 2 and include catch data (length
structures and landings) for the Italian, Tunisian and
Maltese trawlers exploiting the two stocks, as well
as catch data for the artisanal vessels exploiting hake
with gillnets. In addition, the two models used time
series of the MEDITS bottom trawl survey (n km2 by
size class) and were updated for the purposes of this
study by adding catch and survey data for 2016, so the
forecast period starts in 2017.
Fleet selectivity curves
Gadget in the Strait of Sicily is designed as a single-
species tool to model interactions between three main
fleets: Italian, Maltese and Tunisian trawlers. Fleets
subtract biomass in different ways from the two popu-
lations and display differences in the exploitation pat-
tern. In the Strait of Sicily, bottom trawlers target DPS
and have HKE as a by-catch (Milisenda et al. 2017).
Native Gadget functions were first used to estimate
the selectivity for HKE and DPS of the Italian and Tuni-
sian trawl fleets using the traditional nets without sorting
grids. For DPS a classical sigmoidal selectivity function
was used (L50=18.92, α=1.16). For HKE a new selec-
tivity function was implemented, considering a reduced
trawl catchability of large specimens (Abella et al. 1997,
Bartolino et al. 2011). The new function is a double lo-
gistic type, which assumes a dome shape but with a con-
stant (at some level) right tail, in order to reproduce the
fish escaping from the net, assuming that only a small,
constant, percentage of the larger HKE are captured
(L50=15.5; R50=35; α. L50=0.7; α. R50=0.7; p=0.1).
Table 1. – Total landings of deep-water rose shrimp (DPS) and hake
(HKE) in the Strait of Sicily by fleet and stock in 2016.
Trawl fleet n. vessels Landings (t)
DPS % HKE %
Italy 468 5293 70.2 1202 45.2
Tunisia 70 2229 29.6 1439 54.0
Malta 14 13 0.2 21 0.8
Total 552 7535 2662
Table 2. – Likelihood components, time period covered and their relative contribution to the final total likelihood (SSF: small-scale fishery).
Likelihood component Period Relative weight
Hake age-length distributions from Italian trawlers 2005-2016 366.1
Hake age-length distributions from Italian SSF 2005-2016 18.8
Hake length distributions from Italian trawlers 2005-2016 1388.2
Hake length distributions from Italian SSF 2005-2016 16.4
Hake length distributions from Italian survey 2002-2016 452.6
Hake length distributions from Tunisian trawlers 2007-2016 501.7
Hake length distributions from Tunisian SSF 2010-2016 13.2
Rose shrimp length distributions from Italian trawlers 2005-2016 31.6
Rose shrimp length distributions from Italian survey 2002-2016 34.8
Rose shrimp length distributions from Tunisian trawlers 2007-2016 44.4
Hake abundance indices 0-20 cm from survey 2002-2016 23.8
Hake abundance indices 20-30 cm from survey 2002-2016 0.8
Hake abundance indices 30-40 cm from survey 2002-2016 0.5
Hake abundance indices >40 cm from survey 2002-2016 0.1
Rose shrimp abundance indices 0-10 mm from survey 2002-2016 2.9
Rose shrimp abundance indices 10-20 mm from survey 2002-2016 0.4
Rose shrimp abundance indices >20 mm from survey 2002-2016 0.4
Modelling the effect of a sorting grid on trawlers • 203
SCI. MAR. 82S1, December 2018, 199-208. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04752.03A
By letting
al, ar, l50, r50 > 0, l50 <r50, 0≤p≤1, L>0 and
=−− >−
lLr xifL
rx
otherwise
,
0,
const
50 50
where x=log((1-p)/p)/ar, we define this new selectivity
function as
S(L; al, ar, l50, r50 , p) =
=
[]
[]
+−− ∗+ −−aL laLr l
1
1exp( ())1exp(
()
)
lr
const50 50
In the above formulation, parameters ar and r50 play
the same role in the right tail as the corresponding pa-
rameters al and l50 for the left side, while p indicates the
proportion of fish captured after length r50+x (Fig. 3).
Estimation of selectivity curves
During the survey, repeated hauls were carried out
without grid (control, ctrl) and with grid (wg). In the
control hauls the number nrctrl,L of specimens per length
class L retained (r) in the cod-end was recorded. In the
hauls with grid both the number nrwg,L of specimens per
length class L retained in the cod-end and the number
negwg,L of specimens of length class L that escaped from
the grid (eg) were recorded.
The available data were, however, not sufficient to
directly estimate the selectivity of the net with grid,
owing to the unknown proportion of specimens that es-
caped through the cod-end. The parameters to estimate
the new selectivity curves were calculated indirectly
through the following ad-hoc procedure.
In order to estimate the selectivity of the trawl net as
determined by the grid g, the proportion p(r)g,L of speci-
mens of length L retained (r) by the net is needed. This
can be calculated from the ratio n(r)g,L/Ng,L, where n(r)g,L
is the number of specimens retained by the net with
grid and Ng,L is the number of specimens that entered
the net. This latter quantity is the sum of the specimens
retained (r) by the net, the specimens that escaped from
the grid (eg) and those that escaped from the cod-end
(ec): Ng,L= n(r)g,L+n(eg)g,L+n(ec)g,L.
Since n(ec)g,L is unknown, Ng,L cannot be directly
obtained from the experimental survey data.
By letting Nctrl,L be the number of specimens of
length L that entered the control net (ctrl), it can be
reasonably assumed that Ng,L=Nctrl,L. Because ctrl was
the same net as that used by the Italian trawl fleet,
the value of Nctrl,L was estimated as nrctrl,L /Prctrl,L with
Prctrl,L obtained from the selectivity curves estimated by
Gadget on the 2002-2016 trawl catch data of the Italian
fleet. Once Nctrl,L had been obtained, having assumed
Ng,L=Nctrl,L, it was possible to estimate the selectivity
of the trawl net with grid from the ratios n(r)g,L/Ng,L by
using the logistic function for DPS and the modified
double logistic for HKE.
This was done externally to Gadget using the R
statistical software. The new estimated proportions
of specimens retained per length class, the selectivity
curve from Gadget and the ad hoc selectivity curve
estimated for the net with grid are shown in Figure 4A
for DPS and in Figure 6A for HKE.
Selectivity scenarios
Forecast scenarios in Gadget were based on the
stock structure, fishing mortality and stock parameters
(growth, maturity, etc.) observed during the last year
of the hind-cast part of the model (2016). Gadget can
be used to run stochastic or deterministic forecasts
predicting a future recruitment on which both biomass
and catch depend. It fits a lag-1 autoregressive model
(AR1) to the fitted recruitment. In particular, AR1 is
a linear regression model where the response (recruit-
ment) at time t (year) depends on the recruitment at
time t–1.
Four selectivity scenarios were considered, as-
suming i) recruitment forecast from the AR1 model,
and ii) a constant exploitation pattern (catch over
the exploitable biomass) set at the 2016 level. In
Scenario I (status quo), all the Italian trawlers were
assumed to be fishing with the traditional trawl net
and the recruitment was forecast by the AR1 model.
In the other three scenarios (II, III, IV), all the Ital-
ian trawlers were assumed to be fishing with sorting
grids mounted on the nets, while the Maltese and Tu-
nisian trawlers were fishing with their traditional nets.
The three scenarios differed only in the assumption
on recruitment. In Scenario II, it was forecast by the
AR1 model. Scenario III assumes an increase of the
recruitment at time t+1 that is linearly proportional
(100%) to the increase of the spawning stock biomass
(SSB). Finally, in Scenario IV recruitment is propor-
tional to a 50% variation of SSB.
Scenarios III and IV incorporate a linear stock-
recruitment relationship assuming that any increases
in SSB should have a positive effect on recruitment,
as observed for other DPS stocks (Colloca et al.
2014).
Fig. 3. – Example of the double logistic selectivity curve, with a
constant right tail, used to reproduce the selectivity of HKE.
204 • S, Vitale et al.
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RESULTS
Deep-water rose shrimp
The use of the sorting grid led to an increase in L50
(length at which 50% of the specimens were retained
in the cod-end) of about 1 mm: from 18.92 to 19.77
mm of the standard trawl net (Fig. 4A). The grid also
increased the steepness of the selectivity curve, reduc-
ing to zero the catch of specimens with carapace length
(CL) below 18 mm (Fig 4A). Over 20 mm CL, the two
nets showed no differences in catchability.
As shown in Figure 4B, the forecast fishing mortal-
ity was the same for the three scenarios adopting a grid.
Independently of the recruitment simulated, the adop-
tion of the grid led to a decrease in fishing mortality of
about 12.5%. The forecast reduction in F occurred in
the first two years of simulations and was constant in
the remaining years. A similar trend was observed for
Scenario I (Table 3).
The SSB was predicted to follow a similar trend for
the three grid scenarios, with an abrupt increase across the
first three years, particularly in Scenario III (Fig. 4C). The
overall average of SSB (from 2016 to 2030) increased by
about 5.9%, 11.7% and 8.8% for Scenarios II, III and IV,
respectively, compared with Scenario I (Table 3).
The prediction of the catch for the three grid sce-
narios indicated a reduction in the first two years and
an increase in the following years, particularly in the
third and fourth year (Fig. 4D). The overall average
catch increase was about 5.6% and 2.9% for Scenarios
III and IV, respectively, while a negligible difference
was recorded for the Scenario II (Table 3).
Fig. 4. – Gadget simulations plots of DPS comparing the trawl nets without grid (Scenario I: black solid line) and with grid (Scenario II, red
dashed line; Scenario III, green dashed line; Scenario IV, blue dashed line). A, selectivity curves of DPS, circles represent the new proportions
of specimens, by length class, retained by the net with grid, forecast up to 2030; B, fishing mortality (F); C, spawning stock biomass (SSB);
D, catch.
Fig. 5. – Proportional change in age composition of DPS catch in
2020 and 2030 in Scenario II (trawl net with grid and recruitment
as in the status quo) when compared with the status quo Scenario I
(traditional trawl net).
Modelling the effect of a sorting grid on trawlers • 205
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The effect of the grid on DPS catch at age is shown
in Figure 5. Here, the catch at age forecast in 2020 and
2030 was compared between status quo (no grid) and
Scenario II. It appeared that the proportional reduction
of catch on juveniles was about 31.5% for age 0 and
16.5% for age 1. Starting from age 2 the use of the grid
would lead to an increase in catch of about 10.8% in
2020 and 11.1% in 2030.
European hake Merluccius merluccius
Selectivity of the trawl net with grid showed an L50
of 17.86 cm, approximately 2 cm higher than L50 (15.5
cm) estimated for the trawl net without grid (Fig. 6A).
The net with the grid displayed an overall escape of all
specimens with total length (TL) up to 13 cm, while
for specimens up to about 28 cm TL the proportion of
Table 3. – Summary of the main Gadget outcomes of DPS after survey with forecast from 2017 to 2030: F, SSB and Catch (expressed in metric
tons) between trawl nets (Sc. I, Scenario I; Sc. II, Scenario II; Sc. III, Scenario III; Sc. IV, Scenario IV).
Year F
Sc. I F
Sc. II F
Sc. III F
Sc. IV SSB
Sc. I SSB
Sc. II SSB
Sc. III SSB
Sc. IV Catch
Sc. I Catch
Sc. II Catch
Sc. III Catch
Sc. IV
2016 0.82 0.82 0.82 0.82 5266.0 5266.0 5266.0 5266.0 8181.1 8181.1 8181.1 8181.1
2017 0.76 0.66 0.66 0.66 5601.6 5705.0 5723.7 5714.3 8296.6 7817.0 7829.4 7823.2
2018 0.74 0.64 0.64 0.64 5454.6 5740.4 5849.2 5794.8 7669.1 7583.1 7682.5 7632.7
2019 0.73 0.63 0.63 0.63 5579.4 5912.7 6164.3 6038.5 7808.0 7808.9 8088.7 7949.4
2020 0.72 0.63 0.63 0.63 5595.1 5957.4 6288.0 6122.7 7892.3 7928.3 8343.0 8135.7
2021 0.72 0.63 0.63 0.63 5622.5 5982.0 6352.9 6167.5 7940.2 7969.6 8453.7 8211.7
2022 0.72 0.63 0.63 0.63 5644.0 6004.0 6385.9 6195.0 7938.8 7977.2 8482.5 8229.9
2023 0.72 0.63 0.63 0.63 5660.8 6030.2 6416.4 6223.3 7951.9 8007.0 8518.6 8262.8
2024 0.72 0.63 0.63 0.63 5602.6 5955.9 6339.8 6147.9 7908.9 7959.9 8472.8 8216.3
2025 0.72 0.63 0.63 0.63 5660.0 6013.9 6396.0 6204.9 7961.8 8000.0 8510.8 8255.4
2026 0.72 0.63 0.63 0.63 5662.6 6018.8 6397.4 6208.1 7983.3 8011.7 8516.8 8264.2
2027 0.72 0.63 0.63 0.63 5696.2 6062.3 6444.6 6253.4 7996.7 8051.3 8558.3 8304.8
2028 0.72 0.63 0.63 0.63 5733.5 6092.5 6479.7 6286.1 8038.2 8072.5 8584.9 8328.7
2029 0.72 0.63 0.63 0.63 5708.2 6073.1 6457.0 6265.0 8009.8 8081.1 8592.9 8337.0
2030 0.72 0.63 0.63 0.63 5646.8 6001.6 6382.3 6192.0 7982.2 8047.8 8557.7 8302.8
Fig. 6. – Gadget simulations plots of HKE comparing the trawl nets without grid (Scenario I, black solid line) and with grid (Scenario II, red
dashed line; Scenario III, green dashed line; Scenario IV, blue dashed line). A, selectivity curves of HKE; circles represent the new proportions
of specimens, by length class, retained by the net with grid, forecast up to 2030; B, fishing mortality (F); C, spawning stock biomass (SSB);
D, catch.
206 • S, Vitale et al.
SCI. MAR. 82S1, December 2018, 199-208. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04752.03A
specimens retained was lower than in the net without
grid (Fig. 6A). The two nets did not differ in selectivity
for specimens over 30 cm TL.
The forecast fishing mortality was the same for the
three grid scenarios, leading to a reduction of about 5%
compared with Scenario I.
The SSB was forecast to decrease from 2017 to
2025. In the last five years of projections, a slight re-
covery (10.9%-18.4%) was predicted in grid scenarios,
while SSB remained stable in Scenario I (Fig. 6B and
Table 4).
A similar pattern was also forecast for the catch
(Fig. 6C): a reduction until 2024 followed by an in-
crease of between 7.7% (Scenario II) and 16.3% (Sce-
nario III, Table 4).
The effect of the grid on hake catch at age is shown
in Figure 7. Here, the catch at age forecast in 2020 and
2030 was compared between status quo (no grid) and
Scenario II. It appeared that the proportional reduction
of catch on juveniles was about 25% for age 0, 13%
for age 1 and 2.5% for age 2. Starting from age 3, the
use of grid would lead to a proportional increase in the
catch of over 20% from age 5 to age 7+ in 2030.
DISCUSSION
In the Mediterranean, the poor selectivity of trawl-
ers is a challenging problem for the reduction of un-
wanted catches. Studies conducted over the past dec-
ade have shown that the selectivity of fishing gear can
be improved through the use of innovative systems
that enable the capture of certain species and of certain
sizes (e.g. Valdemarsen and Suuronen 2003, Hall et al.
2007, Kennelly 2007, Lucchetti 2008). In our study,
we used the results of a selectivity experiment carried
out in 2015 in the south of Sicily (central Mediterra-
nean Sea) where an ad hoc–designed sorting grid was
mounted on a trawl net used by Italian trawlers ex-
ploiting DPS. The experiment outputs were explicitly
included in length-based stock assessment models (i.e.
Gadget) to address the medium-term population ef-
fects on two key stocks for trawl fisheries in the Strait
of Sicily: deep-water rose shrimp and hake. This was
to our knowledge one of the few attempts that have
been made in the Mediterranean Sea to quantitatively
assess the effects of changing gear selectivity on the
productivity of the exploited stocks. Most of the trawl
selectivity experiments carried out on multispecies
fisheries either in the Mediterranean Sea or in other
areas have been limited to analysing the performance
selectivity tools and reducing the catch of juveniles
(i.e. Sardà et al. 2006, Massuti et al. 2009). A proper
evaluation of the effects relative to the goals of these
studies is often not available because of a lack of suit-
able follow-up studies (Suuronen and Sardà 2007).
We have shown that the adoption of sorting grid
led to a substantial improvement in selectivity for DPS
and HKE. Indeed, the estimated L50 for DPS (19.8 mm
CL) was close to the MCRS (20 mm CL). For HKE
the estimated L50 (17.9 cm TL) was noticeably higher
than the L50 for the trawl net without grid (15.5 cm
TL), leading to a consistent reduction in the catch for
specimens below the MCRS (20 cm TL). Mediter-
ranean trawl fisheries are largely multi-specific, with
several species of fish and shellfish contributing to
fisheries landings and profits. Selectivity experiments
have highlighted the issue of improving size-selection
in a multispecies fishery with a single selection tech-
nique (Sardà et al. 2006, Bahamon et al. 2007, Aydın
Table 4. – Summary of the main Gadget outcomes of HKE after survey with forecast from 2017 to 2030: F, SSB and Catch (expressed in
metric tons) between trawl nets (Sc. I, Scenario I; Sc. II, Scenario II; Sc. III, Scenario III; Sc. IV, Scenario IV).
Year F
Sc. I F
Sc. II F
Sc. III F
Sc. IV SSB
Sc. I SSB
Sc. II SSB
Sc. III SSB
Sc. IV Catch
Sc. I Catch
Sc. II Catch
Sc. III Catch Sc.
IV
2016 0.53 0.53 0.53 0.53 4176.8 4176.8 4176.8 4176.8 2922.9 2922.9 2922.9 2922.9
2017 0.33 0.30 0.30 0.30 3389.4 3397.1 3397.1 3397.1 2021.9 2008.3 2008.3 2008.3
2018 0.37 0.34 0.34 0.34 3008.2 3051.6 3052.0 3051.8 1888.0 1925.7 1926.9 1926.3
2019 0.39 0.36 0.36 0.36 2698.8 2778.3 2781.0 2779.7 1705.4 1758.9 1766.8 1762.8
2020 0.39 0.37 0.37 0.37 2496.5 2600.6 2611.0 2605.8 1650.8 1716.8 1735.0 1725.9
2021 0.40 0.37 0.37 0.37 2359.2 2486.7 2506.0 2496.3 1599.5 1687.4 1717.6 1702.5
2022 0.40 0.38 0.38 0.38 2255.2 2401.9 2439.6 2420.8 1586.4 1695.3 1737.4 1716.4
2023 0.40 0.38 0.38 0.38 2162.3 2331.6 2383.5 2357.5 1576.3 1678.3 1741.1 1709.7
2024 0.40 0.38 0.38 0.38 2125.2 2316.7 2387.5 2352.1 1547.4 1644.2 1729.3 1686.8
2025 0.40 0.38 0.38 0.38 2098.3 2295.8 2387.8 2341.5 1574.8 1677.7 1782.7 1731.2
2026 0.40 0.38 0.38 0.38 2134.4 2344.0 2466.3 2405.8 1575.1 1696.4 1814.3 1755.3
2027 0.40 0.38 0.38 0.38 2180.9 2409.5 2557.0 2483.2 1582.1 1711.8 1847.0 1779.4
2028 0.40 0.38 0.38 0.38 2111.0 2357.0 2504.4 2430.7 1592.6 1706.3 1841.9 1774.1
2029 0.40 0.38 0.38 0.38 2236.0 2473.6 2670.8 2572.2 1618.3 1763.7 1923.3 1844.7
2030 0.40 0.38 0.38 0.38 2139.3 2390.2 2589.4 2489.8 1638.0 1762.1 1931.2 1845.2
Fig. 7. – Proportional change in age composition of HKE catch in
2020 and 2030 in Scenario II (trawl net with grid and recruitment
as in the status quo) when compared with status quo Scenario II
(traditional trawl net).
Modelling the effect of a sorting grid on trawlers • 207
SCI. MAR. 82S1, December 2018, 199-208. ISSN-L 0214-8358 https://doi.org/10.3989/scimar.04752.03A
et al. 2008). Clearly, the same mesh size or sorting grid
spacing is not suitable for all species, being too large
for some species and too small for others, and optimal
selection can be achieved for only a few species (Sardà
et al. 2006). Nevertheless, even if a precise optimum is
not achieved for all species, a general increase in the
length at first capture can be obtained for most of the
commercial species, offering general benefits in terms
of fishery sustainability (Guijarro and Massutí 2006,
Bahamon et al. 2007).
In our Gadget simulations, we have shown that the
adoption of sorting grids by a consistent proportion
(84%) of the trawl fleet exploiting DPS and HKE in the
Strait of Sicily is likely to produce long-term positive and
immediate effects on SSB and catch of the two stocks.
In the case of DPS, SSB would increase by between 6%
and 13% by 2030, while the catch in Scenario III would
rise proportionally to 7% in comparison with the status
quo. The simulated data indicated a relevant effect of
the grid in reducing by about 31% and 16% the catch of
ages 0 and 1, respectively, while the model simulated a
significant effect in the catch of age 2+, with an increase
of about 11% in 2030. This prompt reaction of the stock
for both SSB and catch seems to be related to the short
life cycle of the species, which is able to reach the first
maturity during the first year with a length at first matu-
rity in the Strait of Sicily of 20.8-24.0 mm CL and 14.3-
19.0 mm CL for females and males, respectively (e.g.
Fiorentino et al. 2013). In addition, the adoption of the
grid would lead to a reduction of fishing mortality, an
important step towards FMSY ranging between 0.83 and
0.93 (Gancitano et al. 2017).
The predicted effect of sorting grids on HKE was
basically an inversion of the stock decline trend esti-
mated in the period 2002-2015, which was not imme-
diate as in DPS because it occurred after a few years,
leading to a 21% recovery of SSB in Scenario III by
2030. The catch is forecast to follow the same trend of
SSB until 2030. The Scenario III would lead to an aver-
age increase of up to 10% of the total annual landings
of the stock for all the fleets involved in the fishery.
The model predictions indicated a relevant effect of the
grid in reducing by about 25% and 13% the catch of
ages 0 and 1, respectively. The model also simulated
a consistent effect on the catch of older HKE (age>5),
which increased by more than 20% in 2030. The shift-
ed effect of the grid on hake stock is due to the growth
parameters used in the model. According to Vitale et
al. (2016), HKE in the Strait of Sicily is assumed to
follow a slow growth, reaching the first maturity after
the second year of life, with a length at first maturity of
21.5-28 cm TL for males and 31-37 cm TL for females.
As observed for DPS, the adoption of the grid led to
an initial reduction in the catch that was compensated
in the following years by an increase in total catch.
However, unlike DPS, which showed an appreci-
able reduction of the predicted fishing mortality, HKE
showed an overall effect of only a 5% decrease by
2030. This is also the result of the different impact of
the Italian trawl fleet on the two stocks. Indeed, in 2016
the annual landing for DPS produced by this fleet was
70% of the total, while for HKE it was 45% (GFCM
2016). Such predicted benefits are therefore likely to
be higher in scenarios simulating the whole trawl fleet
using sorting grids, particularly for HKE.
The predictions obtained are also the results of the
conditions set in the forecast Gadget routine used. In par-
ticular, constant harvesting, i.e. the proportion of catch
over the available biomass, was assumed, so a variation
in the abundance of length/age classes selected by the
gear led to a proportional variation of the fishing mortal-
ity and catch on those classes. This also implies that the
fishing mortality at age remains constant through time,
with the only variations expected for the length classes
on the left side of the selection curve.
The present study provides a comprehensive un-
derstanding of the effects of sorting grids on Mediter-
ranean trawling. Using two important stocks with very
different life history traits, deep-water rose shrimps
and hake, as case study species, it has demonstrated a
clear improvement in the exploitation pattern, with a
reduction in the catch of undersized juveniles, an over-
all reduction in fishing mortality, and an increase in
stock biomass and annual landings. These results indi-
cate that sorting grids, if appropriately designed, could
be extremely important tools for reducing by-catch
of juveniles in Mediterranean trawl mixed fisheries,
with clear benefits in terms of sustainability (Massutì
et al. 2009, Aydın and Tosunoğlu 2011). Using grids
in trawling can therefore contribute substantially to
moving towards the goal of the CFP for more “eco-
friendly” fisheries in which discards are reduced. In
this perspective, the use of sorting grids, if integrated
with the protection of the main nursery areas, can be a
key step towards minimizing the impact of trawling on
juveniles and promoting more selective trawl fisheries
in the Mediterranean Sea.
ACKNOWLEDGEMENTS
The data were collected in the framework of the
European Commission Horizon 2020 Research and
Innovation Programme under Grant Agreement No.
634495 for the project Science, Technology, and Soci-
ety Initiative to Minimize Unwanted Catches in Euro-
pean Fisheries (MINOUW). The Gadget models used
in this study were developed with the financial support
of the EU FP7 Programme project MAREFRAME
(Grant Agreement No. 613571).
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