How Fishery Collapses: The Case of Lepidopus caudatus (Pisces: Trichiuridae) in the Strait of Sicily (Central Mediterranean)

Abstract

The silver scabbardfish Lepidopus caudatus is a mesopelagic species living on the shelf and slope down to 600 m in temperate seas all around the world. In the Mediterranean, the species is caught mainly by longlines with a marked seasonality. In the early 90s in the Strait of Sicily (Central Mediterranean Sea), a new fishery targeting L. caudatus was developed. This fishery uses an ad hoc pelagic trawl gear called “spatolara.” Vessels using spatolara have increased from 1 in 1993 to 10 in 2007 with a growth of catches of up to 1,200 tons in 2011. Development of this fishery was not regulated by any specific management measures and, due to the progressive reduction of catch to 169 tons, only one vessel was active in 2018. The availability of catch and biomass indices from trawl survey since the beginning of trawling exploitation allowed providing the first assessment of the state of L. caudatus stock in the Central Mediterranean (GFCM Geographical Sub-Area 16) by using data-limited methods. Catch-Maximum Sustainable Yield (CMSY) and Bayesian State Space Schaefer model (BSM) were fitted to landings and abundance indices (2004–2018). The Abundance-Maximum Sustainable Yield model (AMSY) was also applied to survey data from 1994 (1 year after the start of the spatolara fishery) to 2018 to further corroborate the results. BSM prediction of biomass levels was just above 50% of BMSY, whereas AMSY estimated the current stock levels below 50% of BMSY. The BSM was used for forecasting B/BMSY and catches under different fishing scenarios. Although current exploitation was very close to FMSY, more than a decade would be needed to rebuild the stock to biomass levels producing MSY. A faster rebuilding could be achieved by fishing at least 80% of FMSY, with minimal loss in yield over the next 5–8 years. Following the development of a new fishery since the beginning, the study provides a further example of how unregulated exploitation leads to a heavy overfished state of stock and collapse of fishing activities.

Full text

ORIGINAL RESEARCH
published: 20 January 2021
doi: 10.3389/fmars.2020.584601
Frontiers in Marine Science | www.frontiersin.org 1January 2021 | Volume 7 | Article 584601
Edited by:
Natalie Anne Dowling,
Oceans and Atmosphere
(CSIRO), Australia
Reviewed by:
Claudio Vasapollo,
Italian National Research Council
(CNR), Italy
Dimitrios K. Moutopoulos,
University of Patras, Greece
*Correspondence:
Danilo Scannella
danilo.scannella@irbim.cnr.it
Specialty section:
This article was submitted to
Marine Ecosystem Ecology,
a section of the journal
Frontiers in Marine Science
Received: 17 July 2020
Accepted: 11 December 2020
Published: 20 January 2021
Citation:
Falsone F, Scannella D, Geraci ML,
Gancitano V, Vitale S and Fiorentino F
(2021) How Fishery Collapses: The
Case of Lepidopus caudatus (Pisces:
Trichiuridae) in the Strait of Sicily
(Central Mediterranean).
Front. Mar. Sci. 7:584601.
doi: 10.3389/fmars.2020.584601
How Fishery Collapses: The Case of
Lepidopus caudatus (Pisces:
Trichiuridae) in the Strait of Sicily
(Central Mediterranean)
Fabio Falsone 1, Danilo Scannella 1
*, Michele Luca Geraci 1,2 , Vita Gancitano 1,
Sergio Vitale 1and Fabio Fiorentino 1
1Consiglio Nazionale delle Ricerche, Istituto per le Risorse Biologiche e le Biotecnologie Marine, Mazara del Vallo, Italy,
2Laboratorio di Biologia Marina e Pesca di Fano (PU), Dipartimento di Scienze Biologiche, Geologiche ed Ambientali (BiGeA),
Universitá di Bologna, Bologna, Italy
The silver scabbardfish Lepidopus caudatus is a mesopelagic species living on the shelf
and slope down to 600 m in temperate seas all around the world. In the Mediterranean,
the species is caught mainly by longlines with a marked seasonality. In the early 90s in
the Strait of Sicily (Central Mediterranean Sea), a new fishery targeting L. caudatus was
developed. This fishery uses an ad hoc pelagic trawl gear called “spatolara.” Vessels
using spatolara have increased from 1 in 1993 to 10 in 2007 with a growth of catches of
up to 1,200 tons in 2011. Development of this fishery was not regulated by any specific
management measures and, due to the progressive reduction of catch to 169 tons, only
one vessel was active in 2018. The availability of catch and biomass indices from trawl
survey since the beginning of trawling exploitation allowed providing the first assessment
of the state of L. caudatus stock in the Central Mediterranean (GFCM Geographical
Sub-Area 16) by using data-limited methods. Catch-Maximum Sustainable Yield (CMSY)
and Bayesian State Space Schaefer model (BSM) were fitted to landings and abundance
indices (2004–2018). The Abundance-Maximum Sustainable Yield model (AMSY) was
also applied to survey data from 1994 (1 year after the start of the spatolara fishery) to
2018 to further corroborate the results. BSM prediction of biomass levels was just above
50% of BMSY , whereas AMSY estimated the current stock levels below 50% of BMSY .
The BSM was used for forecasting B/BMSY and catches under different fishing scenarios.
Although current exploitation was very close to FMSY , more than a decade would be
needed to rebuild the stock to biomass levels producing MSY. A faster rebuilding could
be achieved by fishing at least 80% of FMSY , with minimal loss in yield over the next
5–8 years. Following the development of a new fishery since the beginning, the study
provides a further example of how unregulated exploitation leads to a heavy overfished
state of stock and collapse of fishing activities.
Keywords: stock assessment, surplus production models, data poor approach, CMSY, BSM, AMSY

Falsone et al. Collapse of a Mediterranean Fishery
INTRODUCTION
The Silver scabbardfish, Lepidopus caudatus, is a mesopelagic
species distributed in warm waters of all oceans and in the
Mediterranean Sea. The species occurs both on the continental
shelf and slope (Nakamura and Parin, 1993) from 100 m to
more than 400 m, on sandy and muddy bottoms (Whitehead
et al., 1986). The bathymetric distribution varies according to
season, the species being more common on the continental
shelf in winter time, and moving to deeper zones in other
seasons (Demestre et al., 1993). L. caudatus forms schools and
migrates vertically from bottom to the water column during night
(Figueiredo et al., 2015). Despite its cosmopolitan distribution,
knowledge on the biology of this species is poor and limited to
growth (Molí et al., 1990;Demestre et al., 1993; D’Onghia et al.,
2000) and reproductive cycle (Karlovac and Karlovac, 1976; Orsi
Relini et al., 1989; Demestre et al., 1993; D’Onghia et al., 2000).
L. caudatus has a moderate commercial value and is caught
mainly as commercial bycatch in several countries worldwide,
i.e., Italy, Morocco, New Zealand, Portugal, and Spain, by bottom
trawler, pelagic trawler, and longline fisheries (Robertson, 1980;
Tuset et al., 2006; Figueiredo et al., 2015,Torre et al., 2019). To
the best of our knowledge, only in New Zealand is the species
sporadically caught as target by pelagic trawling in August–
October off the west coast of the Island (Bentley et al., 2014).
In the Mediterranean Sea, only large specimens of L. caudatus
have an economic value and are landed as commercial catch
in Italy, Spain, Albania, and Tunisia, whereas small individuals
are rejected (Demestre et al., 1993; D’Onghia et al., 2000;
FAO., 2018). In Italy, the longline fishery catches only large
individuals while bottom trawling fishery captures mainly
small and immature specimens (D’Onghia et al., 2000). In
the Mediterranean region, the capture production of silver
scabbardfish reached a peak of almost 5,000 tons in 2011 and then
slowly declined (Torre et al., 2011). In 2018, in the same area,
the total capture production reached 1,675 tons with the 88% of
the total catches belonging to Italy (FAO Fisheries aquaculture
software., 2016). In spite of the amount of landings, the stock
status of the species was never assessed in the Mediterranean.
In this region, there is no targeted fishery for this species with
exception of the Strait of Sicily (Central Mediterranean Sea),
where the filets of the silver scabbardfish are sold in local markets
up to 20 euro per kilo. In this area, until the early 90s, silver
scabbardfish was mainly captured using longlines while it was a
marginal bycatch from bottom trawlers.
At the beginning of the 1980s, the catch of L. caudatus on the
entire Sicilian coasts amounted to 544 tons, out of which more
than 90% was captured by longline (Cingolani et al., 1986). In
the early 1990s, some fishers of Sciacca (south Sicily) developed
a new pelagic trawl net locally called “spatolara” starting a new
fishery for L. caudatus. The number of vessels using spatolara
has progressively increased from 1 in 1993 to 10 in 2007, with a
contextual increase of the catches up to 1,200 tons in 2011 and a
shift in proportion of catch origin, with over 70% due to spatolara
and the remaining 30% to bottom trawling, longline, and purse
seine. The development of the spatolara fishery was not regulated
by any specific management measures and, due to the progressive
reduction of catch to 169 tons, only one vessel was using spatolara
in 2018.
The present study provides the first assessment of the state of
L. caudatus in the Mediterranean basin and more precisely in the
Strait of Sicily (Geographical Sub-Area 16, GSA16 according to
the FAO General Fisheries Commission for the Mediterranean),
in which the most productive L. caudatus fishery of the region
takes place (Supplementary Figure 1). Owing to the limited
amount of data available, the stock status and exploitation
rate of L. caudatus were evaluated using a data-poor approach
by means of a suite of surplus production models (SPMs)
based on commercial landing and abundance indices from trawl
surveys. This stock assessment should be considered as baseline
information for future sustainable fisheries management that
could prevent a new collapse of L. caudatus fishery. Finally, on
the basis of knowledge on biology and fishery L. caudatus and
similar species, some management options for improving the
sustainability of the species exploitation were discussed.
MATERIALS AND METHODS
Data Source
Two different data sources were used for the stock assessment:
(i) commercial landings data by gear from 2004 to 2018 collected
within the EU data collection framework (DCF), and (ii) stock
biomass index from 1994 to 2018 obtained by MEDITS survey
(Mediterranean International Trawl Survey, Anonymous., 2017)
carried out in GSA 16. MEDITS is carried out annually during
late spring/summer in several areas of the Mediterranean Sea
using a standardized sampling methodology (Spedicato et al.,
2019). MEDITS surveys are conducted during daytime according
to a stratified random sampling design with allocation of trawl
stations proportional to strata extension (depth strata: 10–50 m,
51–100 m, 101–200 m, 201–500 m, 501–800 m). The same trawl
stations were sampled each year in May–July using a GOC 73
trawl net characterized by a vertical opening ranging between 2.4
and 2.9 m and a 20-mm stretched mesh size at cod end (Fiorentini
et al., 1999). Although L. caudatus was not a target species
of the MEDITS surveys, its biomass indices were considered
representative of the standing stock at sea due both to the high
vertical opening of the GOC 73 trawl net and to the bento-pelagic
behavior of the species (Figueiredo et al., 2015).
Stock-Assessment Models
SPMs were chosen for estimating the stock status and
exploitation rate of L. caudatus as they need less input data
compared to age-based models to estimate maximum sustainable
yield (MSY) and related reference points for fishery management,
i.e., biomass and fishing mortality at MSY (BMSY and FMSY)
(Hilborn and Walters, 1992; Punt, 2003). Specifically, the stock
status was evaluated by using (i) the Monte Carlo method Catch-
Maximum Sustainable Yield (CMSY) based on catch data and (ii)
the Bayesian State Space Schaefer model (BSM), using catch and
biomass index (Froese et al., 2017, 2018). In addition, considering
that the time series of MEDITS trawl survey started in 1994,
just 1 year after the beginning of the spatolara fishery, stock
status was also assessed by Abundance-Maximum Sustainable
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Falsone et al. Collapse of a Mediterranean Fishery
Yield (AMSY), based on biomass index from scientific surveys
(Froese et al., 2020).
In comparison with other data-limited stock assessment
methods, the requirements of the selected methods appear
very parsimonious with our available information. For example,
the COMSIR (Catch-Only-Model with Sampling-Importance-
Resampling) method (Vasconcellos and Cochrane, 2005) requires
catch, priors for rand k, relative bioeconomic equilibrium, and
increase in harvest rate over time as inputs to assess the stock
status. Then the DCAC (Depletion-Corrected Average Catch)
method (MacCall, 2009) needs information on catch, relative
depletion, natural mortality (M), and FMSY /Mas inputs. On
the other hand, the DB-SRA (Depletion-Based Stock Reduction
Analysis) method (Dick and MacCall, 2011) wants catch, relative
depletion, M,FMSY /M,BMSY/Bvirgin , and age at maturity as
inputs. The SSCOM (State-Space Catch-Only Model) method
(Thorson et al., 2013) requires catch, priors for unexploited
biomass, initial effort, and parameters of an effort-dynamics
model. Additionally, SS-DL (Stock Synthesis Data-Limited)
method (Cope, 2013), in the catch data configuration, requires
several additional basic biological and selectivity assumptions
compared to CMSY.
CMSY and BSM Models
The CMSY model relies on catch time series, an assumed
value of intrinsic population growth rate (r; “resilience”), how
close the biomass is to carrying capacity (k), and qualitative
information on stock status at the beginning and the end of
the time series. The model allows the estimation of the biomass
that can produce MSY (BMSY) and related fishery reference
points such as relative stock size (B/BMSY ), exploitation (F/FMSY),
intrinsic growth rate of a population (r), and carrying capacity (k)
(Froese et al., 2017, 2018).
The BSM, included in the CMSY R-code, needs further relative
abundance data (e.g., biomass index) as input (Froese et al., 2017,
2018) to estimate the same parameters of CMSY.
Both models are based on the dynamic formula of the Schaefer
SPMs, namely:
Bt+1=Bt+r1−Bt
kBt−Ct(1)
where, Bt+1is the exploited biomass in year t+1, Btis the
biomass in year t,ris the intrinsic rate of population increase,
kis the carrying capacity (i.e., the mean unexploited stock size),
and Ctis the catch in year t.
However, when the stock size is severely depleted (Bt/k <
0.25), Equation (1) is modified adding the term 4Bt/k to account
for linear decline of recruitment below half of the biomass that
is capable of producing MSY (Myers et al., 1995) as shown in
Equation (2):
Bt+1=Bt+4Bt
kr1−Bt
kBt−Ct
Bt
k<0.25 (2)
Given a time series of catches and qualitative stock status
information, probable ranges of parameters rand kare filtered
with a Monte Carlo algorithm on the basis of three hypotheses:
(i) compatible with the catch time series, (ii) compatible with
assumed priors on biomass reductions, and (iii) occur within
prior ranges of rand k, corresponding to viable r–kpairs
(Froese et al., 2017).
The biological plausible values of rwere based on the
classification of resilience reported by FishBase and ranging from
0.27 to 0.6 (Froese and Pauly, 2019). The prior ranges for kwere
derived by Equations (3 and 4) for stocks with low and high prior
biomass at the end of the time series, respectively.
klow =max (C)
rhigh
;khigh =4max (C)
rlow
(3)
klow =2max (C)
rhigh
;khigh =12max (C)
rlow
(4)
where klow and khigh are the lower and upper bounds of the prior
range of k,max(C) is the maximum catch in the time series, and
rlow and rhigh are the lower and upper bounds of rrange to be
explored by the Monte Carlo routine of the CMSY.
Both models can incorporate three uniform priors range for
depletion in terms of B/k at the beginning and end of the time
series, and optionally also in an intermediate year.
To detect the effect of the Bstart /k and Bend/k on B/BMSY
estimations, a sensitivity analysis was performed. For this
purpose, the deviations from the “original” value of B/BMSY
estimated by reference model were expressed as percentage
calculated as follows:
1%=
B
BMSY −B
BMSY s.a.
B
BMSY
100 (5)
where B/BMSY s.a. is the value estimated by sensitive analysis.
CMSY was run considering the landing data from the
European Data Collection Framework for time series 2004–2018,
while BSM was run using the same landing data and the biomass
index coming from MEDITS for time series 2004–2018. For both
models, the prior for relative biomass B/k was set to 0.2–0.6
(medium) for the start year (Bstart /K) and to 0.15–0.4 (small) for
the last year (Bend/K ), while the middle prior was set as default
according to the rules provided by Froese et al. (2017). The choice
of these priors was supported by knowledge of fishers and by the
survey biomass index trend for the times series 1994–2018.
AMSY Model
The AMSY is a new data-limited method that estimates fisheries
reference points (F/FMSY ,B/BMSY) when no catch data are
available, using time series of catch rate from commercial
fisheries or scientific surveys combined with prior estimates of
resilience (Froese et al., 2020). In addition to these data, AMSY
needs a prior for relative stock size (B/k, ranging between 0
and 1) for one of the years in the time series. AMSY uses
this information and tests a high number of combinations of
resilience (r) and carrying capacity (k) for their compatibility
with these inputs. All r–k combinations that are compatible
with time series of plausible (never negative, never much too
high) predicted that catches are identified by a Monte Carlo
approach. A detailed description of the theory and equations
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Falsone et al. Collapse of a Mediterranean Fishery
behind AMSY is provided by Froese et al. (2020). The AMSY
model was performed by using biomass index from MEDITS for
the time series 1994–2018. For resilience, the same prior used in
CMSY and BSM was set.
Given that only one spatolara vessel targeting L. caudatus was
active in 1994, the prior for relative biomass B/k in the initial
year was set ranging between 0.7 and 1. As for CMSY and BSM, a
sensitivity analysis was performed to investigate the effect of the
prior (Bstart/k) on the B/BMSY estimation, and the deviation from
the original value was calculated applying (Equation 5).
Forecast
The dynamics of the stock biomass and catch were predicted
applying the dynamic Schaefer model in terms of B/BMSY and
FMSY . Specifically, as reported by Froese et al. (2018), two
different equations were implemented in the model, namely:
Bt+1
BMSY
=Bt
BMSY
+2FMSY
Bt
BMSY 1−Bt
2BMSY −Bt
BMSY
Ft
Bt
BMSY
≥0.5 (6)
Bt+1
BMSY
=Bt
BMSY
+2Bt
BMSY
2FMSY
Bt
BMSY 1−Bt
2BMSY −Bt
BMSY
Ft
Bt
BMSY
<0.5 (7)
where Equation (6) was used to predict next year’s status if
current biomass was equal to or higher than half of BMSY , while
Equation (7) was applied if biomass was lower than half of BMSY.
Stock trajectories from 2019 to 2030 were predicted
considering the stock status estimated by BSM for 2018 and
applying the following four scenarios based on Froese et al.
(2018):
(i) 0.5 scenario: relative fishing impact of 0.5FMSY is considered
if the stock size is equal to or larger than half of BMSY . On the
other hand, no fishing is considered if the biomass is less than
half of BMSY .
(ii) 0.6 scenario: relative fishing impact of 0.6FMSY is considered
if the stock size was equal to or larger than half of BMSY . If
the stock size was lower than half of BMSY , the relative fishing
impact is linearly reduced to zero (Freduced) with decrease in
biomass as shown in the following equation:
Freduced=Bt
BMSY F(8)
(iii) 0.8 scenario: as the ii scenario but it considered a relative
fishing impact of 0.8FMSY .
(iv) Fcurrent scenario: as the ii scenario but it considered
Fcurrent/FMSY estimation of the last year of the temporal series.
This scenario is very close to the 0.95FMSY one proposed by
Froese et al. (2018).
For the forecast scenarios, an ad hoc script based on the modified
methodology proposed by Froese et al. (2018) was applied.
Specifically, the script was modified to calculate the prediction of
B/BMSY and catches for just a single stock. As in the methodology
of Froese et al. (2018), the uncertainty was calculated by means
Monte Carlo simulations based on 1,000 samples expressed as
90% of the confidence interval.
RESULTS
Main Features of Fisheries
Figure 1 shows the landing trend by fishery for the time series
from 2004 to 2018. In the investigated period, L. caudatus was
exploited mainly by spatolara fishery accounting for about 68%
of the total landing, followed by bottom trawler and other
fisheries (longliners and purse seiners) with about 22 and 10%,
respectively. Overall, the total landings increased from 2004 to
2011 with a maximum of 1,150 tons, followed by a progressive
reduction reaching 168 tons in 2018. The fishing effort of
spatolara, expressed as the number of days at the sea, highlighted
a similar dome-shaped pattern with the highest value in 2007.
In addition, some information on the spatolara fishery and gear
features are shown in Supplementary Table 1.
FIGURE 1 | The left y-axis indicates the value of fishing effort expressed as the number of days at sea per year whereas the right y-axis indicates the landings by
fishery typologies expressed in tons. Specifically, fishery typologies are graphically indicated as follows: bottom trawling (long dash black line), longline and purse seine
(dotted black line), and spatolara (two dashed black line). The solid black line and solid red line represent the total landing and spatolara fishing effort, respectively.
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Falsone et al. Collapse of a Mediterranean Fishery
CMSY and BSM Results
The outputs of CMSY and BSM were very similar (Table 1) in
terms of rand kestimations, stock size trend, and exploitation
rate (Figure 2). Both models estimated an overexploited level for
stock size (B/BMSY <1) since 2011 and an overfishing condition
(F/FMSY >1) since 2009, although in the last year, the F/FMSY
value dropped below 1 (Table 1 and Figure 2). The estimated
stock size (0.54 and 0.63 for BMS and CMSY, respectively,
Table 1) indicated an overfished condition of the stock according
to Palomares et al. (2018) (Supplementary Table 2). Moreover,
the Kobe plot based on BSM estimations showed a probability
of 44.8 and 55.1% that the status stock fell in the overfished
(red part) or recovering status (yellow part) of the graph,
respectively (Figure 2).
AMSY Results
Outputs of the AMSY model are shown in
Supplementary Figure 2 and Table 1. The biomass
index from 1994 to 2018 showed a decreasing trend
(Supplementary Figure 2) even if in 2009 a peak of biomass,
due likely to a good recruitment, was recorded, as confirmed
by the highest density index and lower average weight in the
time series (Supplementary Figure 3). The model outputs
(Supplementary Figure 2 and Table 1) underlined that the stock
is both overfished (F/FMSY >1) and, according to Palomares
et al. (2018), “grossly overexploited” (B/BMSY <0.5) from 2012
to 2018, with the stock productivity being severely impaired (0.5
≤C/CMSY <1). The reference point of the last year of the time
series was 0.27 for B/BMSY (95% CI 0.15–0.49) and 2.02 (95% CI
0.66–4.18) for F/FMSY .
The overall dynamics of the stock, showed by the Kobe plot
(Supplementary Figure 2), outlined a progressive worsening
of the stock status from 1994 to 1998, followed by a high
exploitation level associated to a low standing stock biomass for
most of the examined period, with the exception of 2008, 2009,
and 2010, during which a recovery of the stock occurred.
Sensitivity Analysis
The sensitivity analysis showed that the B/BMSY estimations were
more affected by Bend/k prior variation for both CMSY and BSM.
TABLE 1 | Main output of three models in terms of stock size (B/BMSY ),
exploitation rate (F/FMSY ), and r-k prior. Median value ( ˜
x), lower (lci), and upper (uci)
confidential interval are shown.
Model Item lci ˜
x uci
BSM B/BMSY (2018) 0.28 0.53 0.87
F/FMSY (2018) 0.53 0.94 3.29
r0.3 0.44 0.64
k4.09 5.91 8.53
CMSY B/BMSY (2018) 0.32 0.63 0.79
F/FMSY (2018) 0.29 0.37 0.72
r0.33 0.44 0.6
k4.53 6.51 9.36
AMSY B/BMSY (2018) 0.15 0.27 0.48
F/FMSY (2018) 0.66 2.02 4.19
Conversely, Bstart/k influenced mostly the B/BMSY estimation
of the AMSY model (Supplementary Table 2). In light of the
above, to perform trusted estimations of stock size by CMSY and
BSM, a reliable prior of biomass range at end of time series is
crucial. On the other side, for AMSY, the choice of biomass range
relative to unexploited biomass at the start of the time series is of
paramount importance.
Forecast Results
B/BMSY and the predicted cumulative catches of L. caudatus
under the different exploitation scenarios of Fare shown in
Figure 3. By reducing the relative fishing impact to 0.5, 0.6, and
0.8 of FMSY ,L. caudatus stock could reach the BMSY level over a
period between 5 and 8 years. While maintaining the Fcurrent,
which is very close to the FMSY estimated by BSM, the stocks need
12 years to reach a value of 0.94 B/BMSY (Figure 3).
An increasing trend of catches throughout the years for all
considered scenarios was predicted, with only the scenario 0.5
showing a catch decrease in the first year (Figure 3). Overall,
an average increase of catches of about three times more than
those of 2018 was expected according to all scenarios. The
highest values of catches were predicted for the scenarios 0.8 and
Fcurrent with about 555 and 617 tons, respectively. However, both
predicted B/BMSY and catches showed a high uncertainty.
DISCUSSION
Unlike other Mediterranean areas, in the Strait of Sicily, the
spatolara fishery, a specific midwater trawl fishery targeted to L.
caudatus, has been developed from the early 90s. This fishery
started in Sciacca harbor and proved a sudden increase in yield
and fishing effort followed by a progressive decline through time.
The trend in yield and fishing effort was followed, with a shift of
about 4 years, by the reaction of stock in terms of biomass, which
declined considerably after 2011.
According to Palomares et al. (2018), who classified the fish
stock status basing on B/BMSY in the final year of a time series,
results of BSM and CMSY suggested an overfished status of
L. caudatus stock of the Strait of Sicily, while AMSY indicated
a condition of “grossly overfished” (0.2–0.5), being close to
“collapsed” (<0.20). The stock size estimations by the three
models highlighted a very similar trend, even if the AMSY depicts
a more severe overfished condition (Supplementary Figure 4).
The differences between AMSY and the other two models might
be due to the different periods analyzed. Indeed, the decrease of
biomass index from trawl surveys that have occurred from 1994
to 2004 further stresses the importance of having an independent
estimate of stock abundance since the beginning of fisheries
exploitation. The estimated stock size was below BMSY since
the end of the 1990s, with the exception of the years 2009
and 2010, during which signals of strong recruitment events
were recorded (Supplementary Figure 3). Likewise, the BSM and
CMSY estimated a similar trend of B/BMSY for the same period of
AMSY, 2004–2018 (Supplementary Figure 4).
The results of the sensitivity analysis showed that Bstart/k
prior setting for BSM and CMSY affected poorly the B/BMSY
estimation, ranging from 0.50 to 0.63. Conversely, Bend/kprior
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Falsone et al. Collapse of a Mediterranean Fishery
FIGURE 2 | Graphical output of the CMSY (blue lines) and BSM (red lines): (A) catch; (B) Monte Carlo simulations of the best combination of rand k;(C) Equilibrium
curve estimated through Schaefer model, where square and triangle symbols represent the estimates of initial and final years, respectively; (D) stock size (dashed lines
indicate confidence interval); (E) exploitation rate (dashed lines indicate confidence interval); (F) Kobe plot showing B/BMSY against F/FMSY ratios estimated by BSM.
In the Kobe plot, shaded areas indicate the confidence interval at 50% (light gray), 80% (gray), and 95% (dark gray) and quadrants are color-coded, i.e., green (not
overfished, no overfishing), red quadrant (overfished and overfishing), or yellow (recovering status).
setting showed the biggest effect on the outcome of B/BMSY ,
ranging from 0.21 to 0.79.
The only configuration tested for AMSY (Bstart/kranging for
0.4–0.8) indicated a collapsed condition of the stock according
to Palomares et al. (2018). However, this last assessment could
be neglected because that high initial biomass (0.7–1 nearly
unexploited) was deemed highly reliable on the basis that in the
first year of the biomass index, only one spatolara vessel was
active and the fishery was not yet fully developed.
Regarding the exploitation rates, high differences among
the models were recognized. Specifically, although at different
level, CMSY and BSM depicted a no overfishing condition
(F/FMSY <1) in the last year, with the fishing pressure lower than
that giving the maximum sustainable yield. Conversely, AMSY
estimated a condition of high overfishing even in the last year,
with the F/FMSY being equal to 2.02. In this regard, it should
be recalled that estimation of exploitation by AMSY should be
used with caution since this method does not use the information
on catch or fishing effort. Conversely, relative stock size could
be considered suitable for management advice (Froese et al.,
2020).
According to the forecast model, the stock depletion is so
heavy that the recovery of stock biomass to level compatible with
MSY is expected in 2030 if the fishing effort is maintained at
the 2018 level, which is very close to FMSY . The scenarios 0.5
and 0.6 provide fast rebuilding of the stock reaching a value of
biomass higher to that maximum sustainable yield but providing
the lowest levels of catches. Although with high uncertainty in
model estimation, a good compromise between production and
conservation objectives is provided by the scenario reducing
fishing mortality at 80% of FMSY . This scenario produces the
fastest rebuilding of the stock and a minimal loss in catch in a
period of 5–8 years.
Although the data input used to run the models are from
official statistics, the estimation provided in this study may
have some uncertainty due to the lack of discard data. Indeed,
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Falsone et al. Collapse of a Mediterranean Fishery
FIGURE 3 | In the upper panel, B/BMSY trends, while in the lower panel, the predictive catch trends (expressed in tons) both under four different exploitation
Fscenarios: 0.5 (solid line), 0.6 (long dash line), 0.8 (dotted line), and 0.94 (two dashed black line). The shaded areas indicate the range of uncertainty. The dashed
green line represents B/BMSY equal to 1 and the dashed red lines represent the value of MSY and its lower confidence interval (lci MSY).
no studies on discard for this species are available in the
investigated area.
Across the Mediterranean Sea, the knowledge on discarding
of L. caudatus is scattered and scarce (e.g., Sánchez et al., 2004;
Tzanatos et al., 2007; Soykan et al., 2016; Carbonnel and Mallol
2012). The discard rates are affected by gears, target species,
fishing ground depth, as well as the request of the local market.
Tzanatos et al. (2007) in the Aegean Sea and Sánchez et al. (2004)
in the Catalan Sea reported that the whole catch of L. caudatus
was discarded by gillnet and by trawlers, respectively. Conversely,
Carbonell and Mallol (2012) provided discard rate estimation by
trawlers of 7% in Catalonia waters and 100% in Balearic island,
while Soykan et al. (2016) estimated a discard rate of about 30%
in Turkish waters.
Considering the lack of discard data in the investigated area,
the CMSY and BSM assessments were performed using only
official landing data. This might affect the stock status estimation,
giving a more optimistic state of the exploited stock. However,
the absence of discard does not affect the stock perception
by the AMSY, which used only fishery independent data and
showed a clear overfished and overfishing condition of the
L. caudatus stock.
The described pattern of the spatolara fishery, together with
the modeled trajectories of the biomass and the exploitation
rate, reflect the typical phases of development of an uncontrolled
fishery (Hilborn and Walters, 1992). The fast decline of the stock
has been due to the development of specific gear, the spatolara
net, which likely increased the fishery catchability together with a
rapid increase of the fishing effort. These features ensured high
catches and revenues in the short term but, on the other side,
resulted in a progressive decrease in fish abundance and, finally,
fishery collapse. All this happened within a context of lack of
specific management measures for L. caudatus fishery in terms of
catch or fishing effort quota and technical measures such as the
establishment of a minimum conservation reference size. In the
near future, due to its monospecific nature, it would be advisable
to implement the spatolara fishery management measures based
on the Total Allowable Catches (TAC). A management based on
TAC was quite successful in the recovery and maintenance of the
North-East Atlantic stocks (Cardinale et al., 2017) such as the
similar species Aphanopus carbo (ICES., 2020). The effectiveness
of management measure based on TAC for L. caudatus has also
been demonstrated in New Zealand as described by (Bentley
et al., 2014).
Considering that the juveniles of silver scabbardfish represent
an abundant fraction caught by trawling (D’Onghia et al., 2000),
a further management measure to ensure the recovery of silver
scabbardfish could concern the development and adoption of
more selective trawling net, including the use of devices able to
improve the size at first capture of this species.
Eventually, the present study provides a further example on
how the absence of adequate management measures can lead to
a rapid depletion of the resource and, consequently, unprofitable
fishery. Learning from the history of L. caudatus fishery in the
Frontiers in Marine Science | www.frontiersin.org 7January 2021 | Volume 7 | Article 584601

Falsone et al. Collapse of a Mediterranean Fishery
Strait of Sicily, it would be important to monitor both stock
size and fishery pressure and to adopt a multiannual species-
specific management plan to guarantee the fishery sustainability
according to the United Nations sustainable development goals
(United Nations., 2015).
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
Ethical review and approval was not required for the animal
study because no tests or experiments were performed on
vertebrate animals.
AUTHOR CONTRIBUTIONS
FFi and SV: conceptualization and validation. FFa and VG:
formal analysis. FFa, DS, MG, and VG: data curation and
data collection and figures. FFi and FFa: writing—original
draft. FFi, FFa, DS, MG, SV, and VG: writing—review and
editing. All authors contributed to the article and approved the
submitted version.
FUNDING
This work was supported by European Data Collection
Framework (DCF)–Transversal Variables and MEDITS survey
modules funded by the European Union and the Italian Ministry
for Agricultural, Food and Forestry Policies.
ACKNOWLEDGMENTS
This study was carried out within the framework of Italian
National Programs developed according to the European
Data Collection Framework. The authors warmly thank
all the technical staff of the CNR of Mazara del Vallo
who collected data during the MEDITS trawl surveys.
A special thank you to Henning Winker (Department
of Statistical Sciences, University of Cape Town) for his
help in developing the modified script and also for his
useful advice.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2020.584601/full#supplementary-material
Supplementary Figure 1 | Map showing the study area.
Supplementary Figure 2 | Graphical output of the AMSY model: (A) Biomass
index; (B) Monte-Carlo simulations of the best combination of r and k, (C)
Catch/MSY (dashed lines indicate confidence interval); (D) F/FMSY trend (dashed
lines indicate confidence interval); (E) B/BMSY trend (dashed lines indicate
confidence interval), (F) Kobe plot showing B/BMSY against F/FMSY ratios. In the
Kobe plot shaded areas indicate the confidence interval at 50% (light grey), 80%
(grey) and 95% (dark grey) and quadrants are color-coded i.e. green (not
overfished, no overfishing), red quadrant (overfished and overfishing) or yellow
(recovering status).
Supplementary Figure 3 | The left y-axis indicates density index expressed as
number of fish caught on square kilometres (grey line) whereas the right y-axis
indicates the average weight of fish caught per year expressed in grams (black
line).
Supplementary Figure 4 | Stock size (B/BMSY) trends estimated applying
AMSY (blue), BSM (red) and CMSY (green). The coloured areas represent the
confidence interval for each model.
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Falsone, Scannella, Geraci, Gancitano, Vitale and Fiorentino.
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