In the Wrong Place at the Wrong Time: Identifying Spatiotemporal Co-occurrence of Bycaught Common Dolphins and Fisheries in the Bay of Biscay (NE Atlantic) From 2010 to 2019

Abstract

The first Unusual Mortality Event (UME) related to fishing activity along the Atlantic coast recorded by the French Stranding Network was in 1989: 697 small delphinids, mostly common dolphins, washed ashore, most of them with evidence of having been bycaught. Since then, UMEs of common dolphins have been observed nearly every year in the Bay of Biscay; unprecedented records were broken every year since 2016. The low and unequally distributed observation efforts aboard fishing vessels in the Bay of Biscay, as well as the lack of data on foreign fisheries necessitated the use of complementary data (such as stranding data) to elucidate the involvement of fisheries in dolphin bycatch. The aim of this work was to identify positive spatial and temporal correlations between the likely origins of bycaught stranded common dolphins (estimated from a mechanistic drift model) and fishing effort statistics inferred from Vessel Monitoring System (VMS) data on vessels >12 m long. Fisheries whose effort correlated positively with dolphin mortality areas after 2016 included French midwater trawlers, French Danish seiners, French gillnetters, French trammel netters, Spanish bottom trawlers, and Spanish gillnetters. For the French fleet only, logbook declarations, sales, and surveys carried out by Ifremer were integrated into fishing effort data. Six fleets were active in common dolphin bycatch areas at least twice between 2016 and 2019: gillnetters fishing hake, trammel netters fishing anglerfish, bottom pair trawlers fishing hake, midwater pair trawlers fishing sea bass and hake, and Danish seiners fishing whiting. Except for changes in hake landings in some fisheries, there were no notable changes in total fishing effort practice (gear or target species) based on the data required by the ICES and Council of the European Union that could explain the large increase in stranded common dolphins recorded along the French Atlantic coast after 2016. Small scale or unrecorded changes could have modified interactions between common dolphins and fisheries, but could not be detected through mandatory data-calls. The recent increase in strandings of bycaught common dolphins could have been caused by changes in their distribution and/or ecology, or changes in fishery practices that were undetectable through available data.

Full text

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ORIGINAL RESEARCH
published: 28 April 2021
doi: 10.3389/fmars.2021.617342
Edited by:
Jeremy Kiszka,
Florida International University,
United States
Reviewed by:
Graham Pierce,
Instituto de Investigaciones Marinas
(CSIC), Spain
Maria Grazia Pennino,
Spanish Institute of Oceanography,
Spain
*Correspondence:
Helene Peltier
hpeltier@univ-lr.fr
Specialty section:
This article was submitted to
Marine Megafauna,
a section of the journal
Frontiers in Marine Science
Received: 14 October 2020
Accepted: 19 March 2021
Published: 28 April 2021
Citation:
Peltier H, Authier M, Caurant F,
Dabin W, Daniel P, Dars C, Demaret F,
Meheust E, Van Canneyt O, Spitz J
and Ridoux V (2021) In the Wrong
Place at the Wrong Time: Identifying
Spatiotemporal Co-occurrence
of Bycaught Common Dolphins
and Fisheries in the Bay of Biscay (NE
Atlantic) From 2010 to 2019.
Front. Mar. Sci. 8:617342.
doi: 10.3389/fmars.2021.617342
In the Wrong Place at the Wrong
Time: Identifying Spatiotemporal
Co-occurrence of Bycaught
Common Dolphins and Fisheries in
the Bay of Biscay (NE Atlantic) From
2010 to 2019
Helene Peltier1,2*, Matthieu Authier1,2 , Florence Caurant1,3 , Willy Dabin1, Pierre Daniel4,
Cecile Dars1,2 , Fabien Demaret1,2, Eleonore Meheust1, Olivier Van Canneyt1,
Jerome Spitz1,3 and Vincent Ridoux1,3
1Observatoire Pelagis, UMS 3462-Université de La Rochelle-CNRS, La Rochelle, France, 2ADERA, Pessac, France,
3Centre d’Études Biologiques de Chizé-La Rochelle, UMR 7372-Université de La Rochelle-CNRS, La Rochelle, France,
4Météo-France, DirOP/MAR, Toulouse, France
The first Unusual Mortality Event (UME) related to fishing activity along the Atlantic
coast recorded by the French Stranding Network was in 1989: 697 small delphinids,
mostly common dolphins, washed ashore, most of them with evidence of having
been bycaught. Since then, UMEs of common dolphins have been observed nearly
every year in the Bay of Biscay; unprecedented records were broken every year since
2016. The low and unequally distributed observation efforts aboard fishing vessels in
the Bay of Biscay, as well as the lack of data on foreign fisheries necessitated the
use of complementary data (such as stranding data) to elucidate the involvement of
fisheries in dolphin bycatch. The aim of this work was to identify positive spatial and
temporal correlations between the likely origins of bycaught stranded common dolphins
(estimated from a mechanistic drift model) and fishing effort statistics inferred from
Vessel Monitoring System (VMS) data on vessels >12 m long. Fisheries whose effort
correlated positively with dolphin mortality areas after 2016 included French midwater
trawlers, French Danish seiners, French gillnetters, French trammel netters, Spanish
bottom trawlers, and Spanish gillnetters. For the French fleet only, logbook declarations,
sales, and surveys carried out by Ifremer were integrated into fishing effort data. Six
fleets were active in common dolphin bycatch areas at least twice between 2016 and
2019: gillnetters fishing hake, trammel netters fishing anglerfish, bottom pair trawlers
fishing hake, midwater pair trawlers fishing sea bass and hake, and Danish seiners
fishing whiting. Except for changes in hake landings in some fisheries, there were no
notable changes in total fishing effort practice (gear or target species) based on the data
required by the ICES and Council of the European Union that could explain the large
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Peltier et al. Co-occurence of Bycatch and Fisheries
increase in stranded common dolphins recorded along the French Atlantic coast after
2016. Small scale or unrecorded changes could have modified interactions between
common dolphins and fisheries, but could not be detected through mandatory data-
calls. The recent increase in strandings of bycaught common dolphins could have been
caused by changes in their distribution and/or ecology, or changes in fishery practices
that were undetectable through available data.
Keywords: bycatch, fishing effort, strandings, reverse drift, Bay of Biscay, VMS data
INTRODUCTION
The first multiple stranding event along the French Atlantic coast
was recorded in winter 1989: 697 delphinids, mostly common
dolphins, washed ashore, most of them presenting evidence of
having been bycaught (Figure 1). Since then, severe mortality
events of common dolphins have been recorded almost every
year in the Bay of Biscay. Multiple stranding events, or “Unusual
Mortality Events” (UMEs), are events in which a high number of
strandings occur in a limited area with a common cause of death.
The threshold was set at 30 cetaceans over 10 consecutive days
recorded along a distance of up to 200 km of coastline in the Bay
of Biscay (Peltier et al., 2014).
French driftnet fisheries, fishing for albacore tuna, began
operating in the eastern North Atlantic in the mid-eighties
(Northridge, 1991). This fishery operated between June and late
September, more than 200 NM off the coast of the Bay of Biscay.
The fleet had as many as 64 vessels in 1994, with nets up to 12 km
long (average 7 km) (Antoine et al., 1997). Under NGO pressure,
this fishery was subjected to a high degree of scrutiny: at-sea
monitoring suggested total bycatch estimates of 400 dolphins
per year, including both striped and common dolphins (Stenella
coeruleoalba and Delphinus delphis). High levels of strandings
reported in the Bay of Biscay in winter and the increase of driftnet
fishing effort in summer were two independent processes that
were erroneously confused in the media and public opinion. This
likely influenced the Council of the European Union’s decision to
reduce driftnet length in the Bay of Biscay to 2.5 km in 1991, and
in 1997 to announce a ban of the fishery from January 1, 2002
(Council of the European Union, 1998).
Despite the driftnet ban, continued common dolphin UMEs
in the winter led to the creation of a national working group
on cetacean bycatch in 2004. This group was made up of
scientists, representatives of the fishing industry, officers of the
Ministry in charge of the Environment and the Ministry in
charge of Fisheries. EU funded projects were carried out in
order to estimate bycatch numbers, to explore the circumstances
most associated with dolphin captures, and to test technological
developments (acoustic repellents, or pingers, and excluder
devices) that would reduce bycatch by midwater pair trawlers—
considered at that time to have been responsible for the majority
of cetacean bycatch (Northridge et al., 2006;NECESSITY Report,
2008). The dolphin bycatch issue lost salience in France in the
late 2000s, probably due to the relative decrease in strandings
recorded along the Atlantic coast, and the dedicated national
working group was disbanded.
In 2004, under the Common Fishery Policy (CFP), European
Commission (EC) Regulation no. 812/2004 (Council of the
European Union, 2004) framed the monitoring of cetacean
bycatch through compulsory on-board observers and the
mandatory use of pingers on nets when fishing in latitudes north
of 48◦N. Member States were required to design and implement
monitoring schemes for the incidental capture of cetaceans in
selected fisheries and on fishing vessels with an overall length
≥15 m. By selecting focal fisheries for the implementation of
on-board monitoring programs while excluding others, the EC
Regulation 812/2004 precluded any possibility of providing a
synoptic view of cetacean bycatch in EU fisheries. An observer
program dedicated to marine mammal bycatch was implemented
in France from 2005 until 2009, and was then merged with an
at-sea observer program required by the European Commission
under the Data Collection Framework (DCF; Council of the
European Union, 2008). Designed to collect information on
commercial catch, this revised observer program was no longer
dedicated to cetacean bycatch and was thus less likely to assess
the extent and magnitude of the issue. Within the framework of
the DCF, fishing operations are classified into métiers according
to their target species, gear used, fishing season and fishing area
(Deporte et al., 2012). Of these métiers, those considered to
pose the greatest risk of bycatch are generally under-sampled
by general observer programs, leading to an underestimation
of bycatch (ICES, 2020a). In 2014 in United Kingdom waters,
comparisons between observer programs dedicated to bycatch
and DCF observers highlighted an underestimation of fisheries’
impact on dolphins in non-dedicated programs (ICES, 2016).
Two main biases were identified in non-dedicated fishery bycatch
observation programs with low enforcement: (1) the deployment
effect that results from skippers’ discretion to accept an observer
on board, which produces non-random sampling and non-
representative data and (2) the observer effect, i.e., a change in
fishing practices when an observer is present, which also results
in the collection of non-representative data (Benoît and Allard,
2009;Faunce and Barbeaux, 2011;Amandè et al., 2012;Murphy
et al., 2019). In 2019, EC Regulation 812/2004 was repealed and
replaced by the Technical Conservation Measures, EU Regulation
1241/2019 (Council of the European Union, 2019).
Since 2016, strandings of small cetaceans with evidence of
bycatch occurred in unprecedented numbers along the Bay
of Biscay coast, breaking annual records (Dars et al., 2019).
During the winter of 2019 (January–April), 1,200 strandings of
small cetaceans were recorded by the French Stranding Network
(Réseau National Échouages), 1,089 of which were along the
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1985 1990 1995 2000 2005 2010 2015 2020
1986: French drinet
fishery begins
operaon in the Bay
of Biscay
1989: First UME
in the
Bay of Biscay
Since 1997: Winter UMEs
in the
Bay of Biscay
2004: 1st French
Naonal working group
on bycatch
2004: EC
812/2004
2004-2009: Dedicated
French bycatch observer
program
2009-today: French DCF observer program
2017-today: 2nd French
Naonal working group on
bycatch
2019: EU 1241/2019
2016-today:
Unprecedented winter
UMEs in the Bay of Biscay
2004-2007: EU
funded projects to
reduce bycatch
2018-today: Naonal and
EU funded projects to
reduce bycatch
Stranding events
Fishery involvement
1991: Reducon of
drinet length
2002: Drifnet ban
French iniaves
EU regulaons
FIGURE 1 | Main historical events in common dolphin bycatch management in the Bay of Biscay. DCF, Data Collection Framework; EC, European Commission;
UME, unusual mortality event.
French Atlantic coast.1Between 5,000 and 10,000 bycaught
common dolphins were estimated in French Atlantic waters each
year from 2016 to 2018 (ICES, 2020b;Peltier et al., 2020). Being
one of the most abundant species in the North-East Atlantic
(Hammond et al., 2017;Laran et al., 2017), common dolphins are
among the most vulnerable to being caught in fishing gear (De
Boer et al., 2008;Fernández-Contreras et al., 2010;Peltier et al.,
2016;ICES, 2020b).
In 2017, a new national working group, comprising the same
actors as in 2004, was set up with similar terms of reference: (1)
improve knowledge of cetacean bycatch in the Bay of Biscay, (2)
develop mitigation strategies to reduce bycatch, and (3) involve
fishermen to raise awareness of the bycatch issue.
Today, in addition to Regulation 2019/1241 that set the
obligation to “minimize, and where possible, eliminate” bycatch,
various regulations frame the Member States’ obligations in term
of bycatch mitigation. The objectives of the Common Fisheries
Policy (Regulation 1380/2013, 2013) include implementation
of “the ecosystem-based approach to fisheries management so
as to ensure that negative impacts of fishing activities on the
marine ecosystem are minimized” (Regulation 1380/2013, 2013).
The Habitat Directive (Directive 92/43/EEC) requires Member
States to establish protection measures to prohibit “all forms of
deliberate capture or killing of specimens of these species in the
wild.” The Good Environmental Status (Commission Decision
2017/848) required by the Marine Strategy Framework Directive
(Directive 2008/56/EC) aims to ensure “the mortality rate per
species from incidental bycatch is below levels which threaten
the species, such that its long-term viability is ensured” and “the
population abundance of the species is not adversely affected due
to anthropogenic pressures, such that its long-term viability is
ensured.”
1http://pelagis.in2p3.fr/public/histo-carto/index.php
Faced with the hurdles of enforcing efficient at-sea monitoring
of the impact of fisheries on cetaceans, the use of complementary
data was sought (IWC, 2016;ICES, 2017;Murphy et al., 2019).
Stranding records are an important source of information
on marine mammals, and can provide critical information to
estimate a minimum level of bycatch across fisheries (Lopez et al.,
2003;Leeney et al., 2008;Adimey et al., 2014). Methodological
developments have allowed us to infer the likely origin of
stranded carcasses using drift modeling (Peltier and Ridoux,
2015). Drift modeling, along with a better understanding of drift
conditions, observation pressure and carcass floatation (Hart
et al., 2006;Peltier et al., 2012;Authier et al., 2014;Moore et al.,
2020), has broadened the use of stranding data for quantitative
approaches (Peltier et al., 2016).
Since strandings can be used to model the likely mortality
areas of small cetaceans, the possibility of identifying the potential
fisheries involved has become a prominent topic of debate among
scientists, stakeholders, NGOs and the fishing industry. The
low coverage of non-dedicated observer programs in France
has meant that the collected data could not be used to identify
fisheries posing the greatest risk of bycatch. Therefore, a new
methodology relying on independent data provided by strandings
was developed to detect positive spatial correlations between
common dolphin mortality areas and fishing effort (Peltier et al.,
2020). This methodology, initially tested on the winter UME of
2017, is further developed here and was applied to every UME
recorded along the Atlantic coast of France since 2010. This was
done in order to identify spatial and temporal co-occurrence
between bycaught dolphin mortality areas and fishing effort, and
the change thereof since 2010. The aim of this expanded analysis
is to identify candidate fisheries potentially involved in UMEs,
and to discuss whether the recent evolution of these fisheries
could explain the observed stranding levels.
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MATERIALS AND METHODS
The study area covered the French Atlantic coast along the Bay of
Biscay (ICES sub-areas 27.8a and 27.8b)2(Figure 2).
The analyses, detailed in the following sections and
represented in Figure 3, involved four main steps:
(1) Predicting the reverse trajectories of stranded common
dolphin carcasses with bycatch evidence to estimate the
mortality area of each animal;
(2) Inferring the trajectories of fishing vessels longer than 12 m
from VMS data and estimating fishing effort;
(3) Aggregating the dolphin bycatch areas during an UME
(Figure 3a) and the total fishing effort (Figure 3b) on a
regular grid spanning the Bay of Biscay;
(4) Fitting Spatial Generalized Additive Models within a
Bayesian framework to explore the spatial and temporal
co-occurrence of fishing effort and the origins of stranded
bycaught dolphins.
All analyses were performed in the R software environment,
version 4.0 (Ihaka and Gentleman, 1996;R Core Team, 2020);
bathymetric maps were plotted with the R library marmap
(Pante and Simon-Bouhet, 2013).
Estimating Mortality Areas From
Strandings
Data on marine mammal strandings in France are collected by
the French Stranding Network (Réseau National Echouages),
coordinated by Observatoire Pelagis (CNRS/La Rochelle
University), as mandated by the French Ministry in charge of
the Environment. More than 400 trained volunteers collect
2https://www.ices.dk/data/Documents/Maps/ICES-Ecoregions- hybrid-
statistical-areas.png
information on stranded animals along the entire French coast
following a standardized protocol (Van Canneyt et al., 2015).3
The details of a stranding can be used to infer where the carcass
originated by calculating its reverse drift trajectory from the
location where it stranded.
The common dolphin carcasses retained for analyses included
only fresh or slightly decomposed individuals with evidence of
bycatch and/or which stranded during a UME (Peltier et al.,
2020). Evidence of bycatch includes: net marks; good nutritional
condition; evidence of recent feeding; jaw and rostrum fractures;
froth in the airways; edematous lungs; and amputations of the
dorsal fin, pectoral fins or tail fluke (see details in Kuiken, 1994;
Bernaldo de Quirós et al., 2018). A combination of several of the
above criteria and the lack of evidence for any other cause of
death indicate bycatch to be the cause of death. All diagnoses
are validated by Observatoire Pelagis based on data collected,
examination reports and detailed photographs.
The reverse drift trajectories of stranded common
dolphins with bycatch evidence were calculated using the
drift prediction model MOTHY (Modele Oceanique de Transport
d’HYdrocarbures), initially developed by Météo-France to predict
the drift of oil spills and later adapted to solid object drift
prediction (Daniel et al., 2002). MOTHY predicts the drift of a
floating object under the influence of tides and winds, based on a
global meteorological model validated by observations.
The drift duration of carcasses was inferred from the
decomposition status and visual criteria describing body
decomposition (Peltier et al., 2012). Animals categorized as
“fresh” were estimated to be <5 days post-mortem and animals
classified as “slightly decomposed” to be 5–15 days post-
mortem. One location was retained for each day within the
time window corresponding to the decomposition code of each
3https://www.observatoire-pelagis.cnrs.fr/IMG/pdf/GuideEchouages2015.pdf
FIGURE 2 | North-East Atlantic Ocean including study area (ICES subareas 27.8.a and 27.8.b) in blue boxes. ICES, International Council for the Exploration of the
Sea.
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FIGURE 3 | Schematic representation of methodology used. UME, unusual mortality event; VMS, Vessel Monitoring System. (1) Reverse trajectory of stranded
dolphins; (2) Inferred trajectory of fishing vessel; (3a) Origin of bycaught dolphins found stranded during a UME (n); (3b) Fishing effort at the time of UME (hours);
(4) Bayesian variable prediction and selection using spike-and-slab LASSO | Spatial and temporal correlations.
individual, and locations were weighted in order to obtain a total
weight of 1 animal per drift. Locations of carcass drifts were
summed in each cell of a 0.4◦×0.4◦grid covering the Bay
of Biscay. Locations of fresh and slightly decomposed animals
were summed separately: carcasses found stranded at the same
time but in different decomposition states (in other words with
different drift durations) would have died on different dates and
potentially in different areas.
Fishing Effort Data
European fishing vessels longer than 12 m have been required
under EU regulation to be equipped with VMS transmitters
since 2010. Data from vessels in French waters are collected
and archived by Ifremer’s Fisheries Information System (Système
d’Informations Halieutiques, SIH),4managed by the Directorate
of Marine Fisheries and Aquaculture (Direction des Pêches
Maritimes et de l’Aquaculture, DPMA). The VMS data recorded
includes the date, time and position of the fishing vessels at an
hourly resolution. Ifremer analyzes these locations to compute
fishing effort, mostly from vessel speed: vessels are considered to
be actively fishing if their mean traveling speed is less than 4.5
knots, and in transit otherwise.
Fishing effort is provided at a daily resolution in a
0.05◦×0.05◦grid, and anonymized information related to
fishing vessels is given (flag, size category, harbor, and gear
classification). Only trawls, nets and seines were considered,
4http://sih.ifremer.fr/
because dredges, longlines and pots are not believed to generate
small cetacean bycatch in the Bay of Biscay. Combinations of nine
different gears and nine different flags were tested (Table 1).
Available VMS data of non-French fisheries operating in
French waters (ICES subareas 27.8.a and 27.8.b) could not be
linked to the actual gear used during fishing operations, only to
the gear identified in the initial description of the EU fishing fleet
register. For the French fleet only, VMS data are complemented
by declarative landing statistics (logbooks and sales provided by
the Ministry in charge of Fisheries) and Ifremer survey data in
order to provide fishing effort data related to the species and
quantity of fish landed. Note that fishing effort (in hours) in a
TABLE 1 | Fishing gear included in the study and their ICES abbreviation.
Gear abbreviation Gear
GNS Set gillnets
GTR Trammel nets
OTB Bottom otter trawls
OTM Midwater otter trawls
OTT Otter twin trawls
PS Purse seines
PTB Bottom pair trawls
PTM Midwater pair trawls
SDN Danish seines
ICES, International Council for the Exploration of the Sea.
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dedicated cell is equally allocated to every fish species caught in
that cell, irrespective of the caught quantity.
Several hundreds of gear/species associations can be recorded
within a UME and they cannot all be tested. As a first filter, only
caught species constituting more than 10% of fishing effort in
at least 50% of events recorded within a year were retained. In
the end, 22–37 gear/species combinations were tested for spatial
correlation with strandings (Table 2). Maps of fishing efforts
tested are available online (see Shiny appendix http://pelabox.
univ-lr.fr:3838/pelagis/ShinyApp_v2/). Correlations were tested
at the temporal resolution of the different UMEs in ICES subareas
27.8.a and 27.8.b (Table 3).
Aggregation of Data on a Common Grid
Covering the Bay of Biscay
A “block averaging” method was used in order to spatially smooth
the raw sums computed as above (Petitgas et al., 2014;Peltier
et al., 2020). This method averages the data by block over the
0.4◦×0.4◦grid, spanning 43◦N–47◦N and 1◦W–4◦W. The value
in the first block (block 0, origin = x0) is the sum of the weighted
locations within the block. The point origin (x0) from block 0,
in the lower left corner, was randomized 100 times. At each
randomization k, the grid origin xkvaries and the sum of animals
that died in each cell is calculated. Each block then has 100 sums
associated with it. The mean of all 100 sums was then calculated
and assigned to the location at the center of the block. This block-
averaged dataset is similar to a kernel interpolation (Petitgas
et al., 2014) and was used to smooth the response variable to
mimimize the potential effects of outliers at the boundary of
the study area. This procedure also produced spatial correlation
in the smoothed data, which was considered in downstream
analyses (see section “Co-occurrence Analysis”). Fishing effort
data, initially on a 0.05◦×0.05◦grid, were upscaled to the same
0.4◦×0.4◦grid as the dolphin bycatch data.
Co-occurrence Analysis
Spatial Generalized Additive Models (sGAMs) were used to
explore the spatial overlap between fishing effort and bycatch
locations estimated from strandings. Each year was considered
independently, but could include several UMEs (combinations of
UME dates and decomposition statuses of dolphins) (Table 4).
For each event, a grid of dolphin bycatch locations at sea was
built and analyzed with corresponding fishing effort datasets on
the same grid over the same dates. Each cell in the grid thus
constituted an individual datapoint for the correlation analyses.
The response variable was the density of bycaught dolphins
inferred from strandings, and the explanatory covariate was
fishing effort in hours. All variables were log (1 + x) transformed
before analysis, and a Gaussian likelihood was assumed for the
sGAM. In contrast to the Peltier et al. (2020) study, which
focused on a single event in 2017 and 10 fisheries, we investigated
nine years and also included information on commercial species
targeted by fisheries. This increased the number of covariates
to consider. Consequently, we used a Bayesian framework with
penalized regression and performed automatic variable selection
fitted with spike-and-slab priors (O’Hara and Sillanpää, 2009)
to assess associations between multiple fisheries and dolphin
bycatch. The variable selection and model choice with a spike-
and-slab prior structure enables the selection or deselection
of single coefficients or blocks of coefficients (Scheipl, 2011).
These features are key, since a large number of covariates
can be included in the model, and non-linear relationships
TABLE 2 | Caught fish species included in the study and their ICES abbreviations.
Species abbreviation Species name Species abbreviation Species name
BOG Bogue/Boops boops BRB Black seabream/Spondyliosoma cantharus
BSS European sea bass/Dicentrarchus labrax COE European conger/Conger conger
CPR Common prawn/Palaemon serratus CTC European common cuttlefish/Sepia officinalis
HKE European hake/Merluccius merluccius HMM Mediterranean horse mackerel/Trachurus mediterraneus
HOM Atlantic horse mackerel/Trachurus trachurus MAC Atlantic mackerel/Scomber scombrus
MAS Chub mackerel/Scomber japonicus MGR Meagre/Argyrosomus regius
MNZ Anglerfish/Lophius piscatorius MUR Striped red mullet/Mullus surmuletus
PIL European pilchard/Sardina pilchardus POL European pollock/Pollachius pollachius
SPU Spotted sea bass/Dicentrarchus punctatus SQZ Coastal squids/Teuthida spp.
SOL Common sole/Solea solea WHG Whiting/Merlangius merlangus
Note that some species were only presented in Shiny appendix—http://pelabox.univ-lr.fr:3838/pelagis/ShinyApp_v2/. ICES, International Council for the
Exploration of the Sea.
TABLE 3 | Description of data used, including the temporal and spatial resolution.
Common dolphin strandings Fishing effort (VMS data)
Spatial description French Atlantic coast ICES subareas 27.8.a and 27.8.b
Temporal description Annual –
Winter (January to April) Winter (January to April) (see Shiny appendix)
Co-occurrence UMEs in ICES subareas 27.8.a and 27.8.b
VMS, Vessel Monitoring System; ICES, International Council for the Exploration of the Sea; UME, unusual mortality event.
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TABLE 4 | Details of unusual mortality events (UMEs) used to identify correlations with fishing effort.
Year UME Dates of
stranding
events
Dates of mortality
events – fresh
animals
Dates of mortality
events – slightly
decomposed animals
Common dolphins with
bycatch evidence
Fishing efforts used as covariates
(number of fisheries)
Total fishing effort French fishing effort –
caught species
2010 No multiple stranding event
2011 1 12/01 to 19/01 08/01 to 19/01 28/12/10 to 14/01 30 21 25
2 15/02 to 07/03 11/02 to 07/03 31/01 to 02/03 114
2012 3 21/01 to 25/01 17/01 to 25/01 06/01 to 20/01 40 20 24
4 15/02 to 27/02 11/02 to 27/02 31/01 to 22/02 33
5 04/03 to 21/03 29/02 to 21/03 18/02 to 16/03 89
2013 6 11/01 to 23/02 07/01 to 23/02 27/12/12 to 18/02 316 20 31
2014 7 23/01 to 13/02 19/01 to 13/02 08/01 to 08/02 101 16 27
8 26/02 to 06/03 22/02 to 06/03 11/02 to 01/03 53
2015 9 24/02 to 10/03 20/02 to 10/03 09/02 to 05/03 46 19 28
2016 10 01/02 to 16/02 28/01 to 16/02 17/01 to 11/02 178 19 23
11 04/03 to 12/03 29/02 to 12/03 18/02 to 07/03 65
2017 12 03/02 to 10/02 30/01 to 10/02 19/01 to 05/02 210 18 23
13 28/02 to 14/03 24/02 to 14/03 13/02 to 09/03 182
2018 14 13/02 to 22/02 09/02 to 22/02 29/01 to 17/02 65 18 20
15 12/03 to 23/03 08/03 to 23/03 25/02 to 18/03 145
2019 16 28/01 to 20/02 24/01 to 20/02 13/01 to 15/02 332 14 24
17 03/03 to 22/03 27/02 to 22/03 16/02 to 17/03 345
Dates of the actual mortality events were estimated from the dates of stranding events (stranding/examination date) according to the drift duration inferred from the
decomposition status of carcasses.
between fishing effort and dolphin mortality are allowed and
estimated from data.
These two requirements, namely (i) the need to allow for
non-linear relationships and (ii) the ability to select the most
important covariates among a large number of covariates,
motivated the use of Bayesian variable selection, model choice
and regularized estimation with spike-and-slab priors in an
additive mixed model framework (Scheipl, 2011). Importantly,
within the framework of regularized regression, covariate
selection is achieved in a single model run (Authier et al.,
2017). The expression ‘spike-and-slab’ refers to a mixture prior
distribution for model coefficients. This prior is made up of
a diffuse and vague distribution (the slab) and a degenerate
distribution at 0 (the spike). The spike pulls coefficients for
which there is no information in the data on their effects
to 0, thereby achieving variable selection. The slab allows
important coefficients to escape this pull towards 0 and to
be accurately estimated. Spike-and-slab regression requires
fitting only one model to achieve model selection compared
with alternative procedures based on information criteria.
The interested reader is referred to O’Hara and Sillanpää
(2009) for an overview of Bayesian model selection methods,
and to Scheipl (2011) for all details on GAMs with spike-
and-slab priors. Finally, an important feature of the dolphin
bycatch data to be accounted for is spatial autocorrelation.
SpikeSlabGAM allows for the inclusion of spatially correlated
residuals with a Gaussian Markov Random Field (GMRF) model
whereby the neighboring structure intrinsic to our grid data is
taken into account.
For sGAM modeling, we used the spikeSlabGAM package
(Scheipl, 2011). By default, SpikeSlabGAM uses cubic B-splines
with 2nd order difference penalties to model non-linear
relationships. Data analyses were carried out on a per year
basis: for each dataset, 4 chains were run for 5,000 iterations,
discarding the first 1,000 as burn-in. Thinning was set to 4
(that is, one iteration kept out of 4) yielding a final sample of
1,000 values from the posterior for further inference. All sGAMs
included a GRMF term to account for spatial autocorrelation.
Upon convergence of all parameters in each model, results
were then visualized using the built-in plotting capabilities of
SpikeSlabGAM to visually assess the sign of the (potentially non-
linear) relationship between dolphin bycatch and fishing effort.
For statistical reasons, the low fishing effort of some European
fleets in winter prevented the testing of the co-occurrence of
their fishing effort with the origin of bycaught dolphins. For
brevity, estimated relationships were presented only for gears that
positively correlated with mortality areas in at least two years
between 2016 and 2019.
RESULTS
Strandings
Common dolphin strandings varied greatly over the study
period (Figure 4). The average over the entire study period
(1990–2019) was 340 ±SD 256 stranded individuals
per year, while the average between 2010 and 2019 was
542 ±SD 303, and 860 ±SD 206 between 2016 and 2019.
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FIGURE 4 | Long term time series of common dolphin strandings along the French Atlantic coast, from 1990 to 2009. Dark blue bars represent all common dolphin
strandings, light blue bars represent examined common dolphin strandings (fresh or slightly decomposed) with bycatch evidence. The dark blue box incorporates
the years included in the study to identify positive correlations between mortality areas and fishing effort.
The last four years in the study period had the highest
numbers of strandings.
Numbers of fresh and slightly decomposed carcasses with
evidence of bycatch followed a pattern similar to total stranding
numbers. The average number of strandings with bycatch
evidence was estimated at 205 ±SD 177 common dolphins per
year for the entire study period (1990–2019), while the average in
the last four years increased to 520 ±SD 172.
Since 2011, 17 common dolphin UMEs were recorded along
the French Atlantic coast, all related to bycatch (Table 4). The
magnitude of these events, both in duration and in number of
individuals varied greatly. No multiple stranding events were
recorded in 2010, but one to three events were observed along
the French Atlantic coast each year starting in 2011, with an
average of 138 ±SD 104 common dolphins showing evidence of
bycatch per event.
The proportion of common dolphins with bycatch evidence
recorded during a UME ranged from 49 to 92% of the total
bycaught dolphins found stranded within a year.
Likely Origin of Stranded Common
Dolphins
At least 75% of stranded common dolphins with bycatch evidence
originated from the continental shelf of the Bay of Biscay.
Some came from the Western Channel, but to a lesser extent
(Figure 5). In 2011, 2012, and 2013, more than half of common
dolphin carcasses originated from the southern Bay of Biscay.
Between 2014 and 2016, the origins of bycaught dolphins were
homogeneously distributed between the Spanish border and
southern Brittany. During the last three years of the study
period, the highest densities of dolphin mortality locations (>40
individuals/1,000 km2) were inferred between 46◦N and 47.5◦N.
Co-occurrence of Dolphin Mortality
Areas and Fishing Effort
During UMEs, French trammel netters (GTR) were positively
correlated with dolphin mortality areas in eight of the nine
years tested (Table 5 and Figure 6). French gillnetters (GNS)
and Spanish bottom trawlers (OTB) were positively correlated
with mortality areas in six years. French pair midwater trawlers
(PTM), Spanish gillnetters (GNS), and Danish seiners (SDN) co-
occurred with common dolphin bycatch events in three to four of
the years, mostly in the years with the highest bycatch numbers
(between 2016 and 2019). Six other fishing gears correlated
positively with mortality areas in only one or two years after
2010, and among them.
For the French fleets, fishing efforts of gillnetters catching hake
(Merluccius merluccius) were positively correlated with mortality
areas every year except 2014, and the efforts of midwater pair
trawlers targeting sea bass (Dicentrarchus labrax) were positively
correlated in six of the nine years tested (Table 6 and Figure 7).
The correlations were positive in all of the last four years which
had the highest levels of bycaught dolphin strandings (2016–
2019). Trammel netters catching anglerfish (Lophius spp.) were
positively correlated in six of the nine years too, including 2016–
2018. Midwater pair trawlers were positively correlated when
catching mackerel (Scomber scombrus) in four of the tested years,
and in three years (including 2017 and 2018) when catching
hake. Positive correlations were detected in bottom pair trawlers
catching hake in three of the years, only after 2016. Danish seiners
catching various species [sea bass, cuttlefish (Sepia officinalis),
mackerel and whiting (Merlangius merlangus)] only correlated
positively in two of the years. Nevertheless, this fishing gear
appeared correlated only in recent years, mostly when catching
whiting (2017 and 2018). Midwater otter trawlers, twin otter
trawlers and otter bottom trawlers—all targeting various species,
as well as purse seiners catching pilchards (Sardina pilchardus)
and trammel netters catching sea bass and sole (Solea solea)
were all positively correlated with mortality areas only once in
the time series.
Hake constituted the most recurrent target species in fishing
efforts positively correlated with mortality areas, being targeted
by four different fishing gears. Sea bass and mackerel were also
regularly targeted in positively correlated fishing efforts.
The year 2014 was unusual in that the fishing efforts of
two fisheries—bottom otter trawlers catching mackerel and
midwater otter trawlers catching hake—correlated positively with
dolphin mortality areas, but these two fisheries were never
identified in other years.
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FIGURE 5 | Origin of stranded common dolphins with bycatch evidence. Densities are presented in dead dolphins/1,000 km2. The areas defined by solid lines
included 50% of stranded dolphins, dashed lines included 75% of stranded dolphins and dotted lines 95% of stranded dolphins.
DISCUSSION
General
The recent increase in common dolphin strandings along
the French Atlantic coast occurred mostly in ICES subareas
27.8.a and 27.8.b. The use of reverse drift modeling to infer
the likely at-sea origins of stranded dolphins highlighted
the continental shelf of the Bay of Biscay as a mortality
hotspot, in particular the area between latitudes 46◦N and
47.5◦N in recent years. The expansion of bycaught common
dolphin mortality areas from the southern into the northern
Bay of Biscay and the sharp increase in stranding numbers
happened simultaneously after 2016. The boundaries of the
likely mortality areas must be carefully interpreted as the
grid used is rather coarse (0.4◦×0.4◦). The West-East
extension, mostly in the oceanic area (beyond the continental
slope) can be attributed to reverse drift modeling and
methodological uncertainties such as the absence of current
modeling in coastal areas, time-of-death estimates, or the
grid resolution.
Unusual mortality event detection is based on the analysis
of cetacean carcasses following a standardized protocol. The
decomposition state of carcasses is a major factor in diagnosing
bycatch (Peltier et al., 2020). Net marks can easily be missed
on decomposed carcasses, as the epidermis may be damaged or
missing, and edematous lungs can be indistinguishable due to
decomposition processes. The proportion of carcasses that are
fresh or slightly decomposed is directly related to the distance
to shore and drift conditions at the time of death (Bibby and
Lloyd, 1977;Hlady and Burger, 1993;Peltier et al., 2013).
Because of seasonal changes in fishing practices and dolphin
distribution, the fisheries involved could differ between spring or
summer bycatch events.
Estimates of fishing effort were derived from locations
recorded by VMS transmitters on larger vessels. Fishing effort
must be carefully interpreted because VMS transmitters have
been mandatory only on vessels >12 m long since 2010
(Commission Regulation (EC), 2009). In 2018, 73% of the
French fleet operating from Atlantic harbors was constituted
of vessels <12 m long; 1,090 smaller fishing vessels were
registered in the community fishing fleet register (IFREMER,
2019). Smaller vessels and small scale or artisanal fisheries are
still largely overlooked, despite their potential threat to cetacean
populations having been demonstrated (Zappes et al., 2013;
Cruz et al., 2018).
The calculation based on vessel speed is applied to all gears,
both towed and static. The assumption that vessels under
4.5 knots are actively fishing is mostly relevant for towed
gears, as trawl, seine or dredge operations require slow speeds.
Nevertheless, false-positive results, i.e., vessels traveling at low
speed but not engaged in fishing, can be generated using
this vessel speed filter. However, false-positive results probably
represent a low fraction of estimated fishing time (Bertrand et al.,
2008;Gerritsen and Lordan, 2011). Applying this methodology
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TABLE 5 | Time series of positive correlations (in blue) between fishing effort and origin of bycaught common dolphins in ICES subareas 27.8.a and 27.8.b (2010–2019).
Country Gear 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2011–2019
occurrence (%)
2016–2019
occurrence (%)
France
GTR 89 100
GNS 67 100
OTB 33 0
PTB 22 50
PTM 44 75
SDN 33 75
OTM 22 25
OTT 11 25
PS 11 0
Spain
OTB 67 50
GNS 33 75
PS 11 0
UK
GNS 11 0
Gears positively
correlated
4 4 4 3 3 5 6 5 7
No Multiple Stranding Event
Common dolphin
strandings with
bycatch evidence
(Fresh and slightly
decomposed
animals, during
UMEs)
37
144
162
316
154
46
243
392
210
677
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Total fishing effort of the different fishing gears and flags were tested. Occurrence over the whole time series and during the past four years (with highest stranding
records) were calculated, as well as the percentage of the total number of years in which a positive correlation was found. Gear abbreviations are available in Table 1.
Stranding numbers of common dolphins with bycatch evidence are also presented (2010–2019). ICES, International Council for the Exploration of the Sea; UME, unusual
mortality event. Gray cells indicate neutral or negative correlations.
FIGURE 6 | Functional relationships between the origin of bycaught common dolphins and fishing effort in ICES subareas 27.8.a and 27.8.b for Spanish gillnetters
(GNS_ESP), French trammel netters (GTR_FRA), French gillnetters (GNS_FRA), Spanish bottom trawlers (OTB_ESP), French midwater pair trawlers (PTM_FRA),
French Danish seiners (SDN_FRA). Note that only fisheries which correlated positively at least twice since 2016 are presented. ICES - International Council for the
Exploration of the Sea.
to static gears probably produces fishing effort data that are more
difficult to interpret. The low speed of vessels operating static
gear can be detected when the gears are set or hauled. Still, the
use of VMS data to assess fishing effort is relevant to highlight
changes within years, as long as fishing efforts are not directly
compared between gears.
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TABLE 6 | Time series of positive correlations (in red) between fishing effort and origin of bycaught common dolphins in ICES subareas 27.8.a and 27.8.b (2010–2019).
Gear Caught
species
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2011–2019
occurrence (%)
2016–2019
occurrence (%)
GNS HKE 89 100
GTR
BSS 11 0
MNZ 67 75
SOL 11 0
OTB
CTC 22 0
MAC 11 0
MNZ 11 0
SQZ 11 0
OTM HKE 11 0
OTT CTC 11 25
MNZ 11 25
PS PIL 11 25
PTB HKE 33 75
PTM
BSS 67 100
HKE 33 50
MAC 44 25
SDN
BSS 11 25
CTC 22 50
MAC 22 25
WHG 22 50
Gears positively
correlated
5 5 7 3 3 5 6 8 6
No Multiple Stranding Event
Common dolphin
strandings with
bycatch evidence
(Fresh and slightly
decomposed
animals, during
UMEs)
37
144
162
316
154
46
243
392
210
677
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
The fishing effort of French vessels using different gears related to caught species were tested. Occurrence over the whole time series and during the past four years
(with highest stranding records) were calculated, as well as the percentage of the total number of years in which a positive correlation was found. Gear abbreviations are
available in Tables 1,2. Strandings of common dolphins with bycatch evidence are also presented (2010–2019). ICES, International Council for the Exploration of the
Sea, UME; unusual mortality event. Gray cells indicate neutral or negative correlations.
The long-term analysis highlighted seven different fisheries
whose fishing efforts positively correlated with dolphin mortality
areas in at least two years between 2016 and 2019, the period
with the highest numbers of strandings recorded since the
establishment of the French Stranding Network in the 1980s.
For the French fleet, correlations within the two fishing
effort datasets were consistent overall (total effort per gear
and fishing effort per landed species and gear). Nevertheless,
positive correlations of fishing efforts from these two datasets
with dolphin mortality areas did not necessarily occur
simultaneously (Figure 8).
The joint analysis of VMS data and the origins of
stranded bycaught common dolphins in the Bay of Biscay
provide relevant and realistic information with regards to
fisheries potentially responsible for common dolphin bycatch.
Nevertheless, co-occurrence does not imply causality, merely that
both phenomena occurred at the same place at the same time.
In fact, positive correlations can be identified for fisheries that
cannot generate dolphin bycatch (e.g., longlines, traps, pods,
etc.) but shared common fishing grounds with the fisheries
responsible. Moreover, the degree of correlation does not indicate
the intensity of the interaction, as bycatch events can be intense
and limited in space and time.
Fisheries of the Bay of Biscay
The Bay of Biscay is an important area for the fishing industry,
fishing effort is intense in this region (Kroodsma et al., 2018). The
fisheries operating in ICES subareas 27.8.a and 27.8.b are highly
diverse in terms of their flags, gears used, and species targeted
(SIH—Système d’informations halieutiques, http://sih.ifremer.fr/).
Around 2,000 fishing vessels were active in this area in the year
2018 (Demanèche et al., 2019). The breakdown by flag was as
follows: 1,486 French vessels, including 1,072 vessels <12 m; 300
Spanish vessels (average size of 25 m); 22 Irish vessels (average
size of 33 m); 18 British vessels (average size of 37 m); 12 Belgian
vessels (average size of 37 m); 5 German vessels (average size of
41 m); 3 Dutch vessels (average size of 113 m) and one Danish
trawler (size of 70 m). Because of the great diversity of vessels
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FIGURE 7 | Functional relationships between the origin of bycaught common dolphins and fishing effort in ICES subareas 27.8.a and 27.8.b for French fleets
according to caught species: gillnetters catching hake (GNS_HKE), trammel netters catching anglerfish (GTR_MNZ), bottom pair trawlers catching hake (PTB_HKE),
midwater pair trawlers catching sea bass (PTM_BSS), midwater pair trawlers catching mackerel (PTM_MAC), Danish seiners catching whiting (SDN_WHG). ICES,
International Council for the Exploration of the Sea.
operating in the Bay of Biscay, only fisheries positively correlated
with likely common dolphin bycatch areas will be described.
Most of the French vessels deployed nets (39%; gillnets and
trammel nets). Only 17% of them were equipped with VMS
transmitters (Demanèche et al., 2019). The maximum activity
was recorded in spring and summer. The number of netters in
the Bay of Biscay decreased over the study period, but “fishing
effort” (or “indicator of set and haul time”) remained quite stable
for vessels >12 m after 2010 (Supplementary Figure 1). The
decrease in netters was mostly due to the reduction of smaller
vessels, whereas the number of larger vessels above 15 m was
stable or even increased (>24 m). Fishing activity of smaller
vessels (<12 m) remained mostly coastal, within the 12 NM
limit. The main species landed by smaller netters in winter were
sole, pollock and sea bass, whereas hake, sole and anglerfish were
mostly landed by larger netters, Hake landings doubled between
2013 and 2018 for netters of all size categories (Demanèche
et al., 2019;Supplementary Figure 2). Bottom trawlers (single
and paired) constituted 30% of the vessels operating in the
Bay of Biscay in winter, and 57% of them were equipped with
VMS transmitters. The main species landed by bottom trawlers
were anglerfish and hake. Bottom pair trawlers targeted mostly
hake, and landings remained stable after 2010 (Supplementary
Figure 2). Midwater pair trawlers represented 5% of fishing
vessels in ICES subareas 27.8.a and 27.8.b in 2018; 84% of
which were above 12 m. Catches of sea bass by midwater pair
trawlers decreased greatly after the 2010 winter, whereas hake
landings increased between 2014 and 2017, and decreased in 2018
and 2019. Fishing effort associated with hake catches occurred
mostly in winter along the 100 m isobath, south of 46◦N in
2016 and north of 45.5◦N after 2017 (see Shiny appendix http://
pelabox.univ-lr.fr:3838/pelagis/ShinyApp_v2/). Similar patterns
were highlighted for sea bass catches. French Danish seiners,
operating in the Bay of Biscay since 2009, constituted a small fleet
(<1% of vessels in ICES subareas 27.8.a and 27.8.b) and were
all equipped with VMS transmitters. They fish both demersal
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FIGURE 8 | Theoretical co-occurrence of the origin of stranded dolphins and fishing effort in two distinct scenarios. The red box represents the origin of stranded
bycaught dolphins. In panel (A), the total effort of gear A is presented and only the effort dedicated to catching species 3 correlates with the origin of the strandings.
In panel (B), the origin of the strandings is positively correlated with the total fishing effort including all three species; individually considered, fishing effort for each of
the different species is not correlated.
and pelagic species, mostly whiting, mackerel and sea bass. Their
main fishing grounds were located between 45◦N and 48◦N,
within the 100 m isobath. Depending on the vessel and target
species, the vertical opening of seines can reach several meters
while hauling. Although irregular, fishing effort of Danish seiners
have been increasing, mostly in 2018 and 2019.
The Spanish fleet operated all year in the study area, with
the highest fishing effort recorded between April and November.
Almost half of the Spanish vessels operated purse seines targeting
anchovies in spring (ICES, 2018). The other half of the fleet
included 28% longliners, 21% gillnetters, and 9% bottom trawlers,
both pair and single. The trawl fleet operated across the whole
Bay of Biscay (see Shiny appendix http://pelabox.univ-lr.fr:
3838/pelagis/ShinyApp_v2/), but total fishing effort in winter
decreased slightly after 2010 (Appendix 1). The main species
landed by Spanish trawlers are hake, anglerfish and megrim
(Argyrosomus regius) (ICES, 2019). Winter fishing effort of
Spanish gillnetters was relatively stable between 2010 and 2019.
The main target species of Spanish vessels using static gear was
hake (ICES, 2019).
The fishing effort of British vessels was low but constant
throughout the study period, and quite stable after 2010. Only 5
vessels were registered as gillnetters (Demanèche et al., 2019) and
generally operated over the whole Bay of Biscay.
Relevance of Identified Fisheries
Fisheries whose fishing efforts correlated positively with dolphin
mortality areas were consistent with métiers of concern identified
by ICES in subareas 27.8.a and 27.8.b, based on at-sea
observations (ICES, 2020c,Table 7). French Danish seiners were
the only métier positively correlated with bycatch areas for which
no bycatch was recorded by observers between 2016 and 2018.
The highest bycatch numbers were observed on trammel netters
(GTR) targeting demersal fishes (DEF). Demersal fishes include
hake, sea bass, whiting and anglerfish, all of which were positively
correlated with bycatch areas. Bottom pair trawlers, targeting
a mix of pelagic and demersal species, included a variety of
gears and flags (Spanish bottom pair and otter trawlers, and
French bottom pair trawlers). Common dolphin bycatch has
been documented in bottom pair trawl fisheries targeting hake in
North-Western Spain (Fernández-Contreras et al., 2010), which
is consistent with the above correlation findings. The fishing
efforts of vessels using midwater pair trawls and gillnets to
target demersal fish (Table 7) also correlated positively with
the mortality areas of bycaught dolphins. Interactions between
midwater pair trawls and common dolphins in the Bay of Biscay
were documented 20 years ago (Morizur et al., 1999;Northridge
et al., 2006): various EU funded projects, including PETRACET
(Pelagic TRAwl and CETaceans) and NECESSITY, revealed that
in the early 2000s most bycatch of common dolphins in the Bay
of Biscay and the Celtic Sea occurred in midwater pair trawls
targeting sea bass in winter (Northridge et al., 2006). High levels
of common dolphin bycatch were reported in the early 1990s
on pair trawling fisheries targeting hake (Murphy et al., 2013;
ASCOBANS, 2015).
Midwater pair trawlers targeting large pelagic fish, mostly
tuna, operate in the oceanic waters of the Bay of Biscay during
summer (ICES, 2020b). Distance from the coast as well as
drift conditions in summer make it unlikely that strandings of
common dolphins bycaught in tuna fisheries along the French
Atlantic coast would be detected (Morizur et al., 1999;Peltier
et al., 2013), although high numbers of bycaught common and
striped dolphins were recorded in these fisheries in the late
1990s (Rogan and Mackey, 2007), and more recently by at-
sea monitoring on midwater pair trawlers targeting albacore
(Thunnus alalunga) (ICES, 2020c).
Areas where several predatory fish species were caught - such
as sea bass, hake, anglerfish or whiting—correlated positively
with common dolphin bycatch areas. Of these predatory species,
sea bass, mackerel, hake and whiting have common prey
species, including anchovy, sardine, or sprat (Sprattus sprattus)
(Quéro and Vayne, 1997;Mahe et al., 2007;Murua, 2010;
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TABLE 7 | Summary of the bycatch rate and mortality of common dolphins for métiers of concern from at-sea monitoring (ICES subareas 27.8.a and 27.8.b, data
pooled 2016–2018), raised using the annual mean of available fishing effort from the Regional DataBase (RDB, i.e., data collected in the context of the Data Collection
Framework; in ICES, 2020b).
Gear (Métier 4a) Target species
(Métier 5b)
Fishing effort (RDB,
DaS)
Bycatch rate
(animals/DaS fished)
At-sea monitoring
estimates (95% CI)
% coverage of RDB
fishing effort (DaS)
GTR DEF 58,365 0.035 2,061 (1,203–3,092) 0.194
PTB MPD 5,195 0.149 775 (388–1,163) 0.43
PTM DEF 682 0.71 481 (408–555) 8.2
OTM DEF 243 1.22* 297 (0–890) 0.112
PS SPF 35,564 0.0060 213 (0–532) 0.31
GNS DEF 36,836 0.0037 137 (0–343) 0.49
PTM LPF 510 0.0153 8 (0–23) 4.3
Total 3,973 (1,998–6,598)
ICES, International Council for the Exploration of the Sea; DEF, Demersal Fish; MPD, Mixed Pelagic and Demersal fish; SPF, Small Pelagic Fish; LPF, Large Pelagic Fish.
aSee https://vocab.ices.dk/?ref=1498 for gear description.
bSee https://vocab.ices.dk/?ref=1499 for target species description.
*Bycatch rate calculated from 1 day-at-sea observed.
Spitz et al., 2013). Small pelagic fishes are a large proportion of
the common dolphin diet, suggesting a likely ecological and
spatial overlap between common dolphins and the correlated
catches identified (Pusineri et al., 2007;Meynier et al., 2008;
Spitz et al., 2013;Murphy et al., 2019). Schooling behavior in
small pelagic fishes can lead to high local densities of various
predatory species due to possible mutualism and facilitation
processes in aggregation of both prey and predator species
(Astarloa et al., 2019).
Management and Conservation
Implications
Except for changes in hake landings by various fisheries, no
change in either overall fishing effort or general fishing practice
(gear or target species) was observed, based on ICES and
European Commission required data, that could explain the large
increase in stranded common dolphins along the French Atlantic
coast after 2016. The increase in bycatch could be the result of
changes in fishing practices after 2015 that were not covered
by national or EU data requirements. Fishing data required
by the EU Data Collection Framework include, among other
things: days at sea, fishing days, landed species and weight, and
métiers at level 5 of the ICES classification (gear type +target
species assemblage, see https://vocab.ices.dk/?ref=1498 for gear
description). These data are available at a trimester temporal
resolution. Changes in these parameters at large spatial and
temporal scales have not been detected in the past 10 years (ICES,
2020b), but small scale changes in fishing practices (height of nets,
setting strategies, depth of gear, etc.) could modify interactions
with common dolphins. So far, no long-term data have been
collected to test these hypotheses. Requirements of robust and
relevant at-sea observation data (observer/Remote Electronic
Monitoring, REM), supported by a random sampling strategy,
are crucial in order to refine studies of the interactions between
cetaceans and fishing vessels and therefore to establish effective
mitigation strategies (Dolman et al., 2021).
Another possible explanation for the recent increase in
bycatch could be a change in dolphin distribution relative to
the fishing grounds where fisheries posing the greatest risk of
bycatch operate. Driven by ecological factors—such as changes
in prey behavior or distribution, or a shift of common dolphins
to the coastal waters of the Bay of Biscay - could increase the
risk of bycatch. Possible small-scale changes in common dolphin
prey distribution could be supported by oceanographic data and
fishery surveys in winter.
CONCLUSION
The fisheries identified to have been actively fishing in
the mortality area at the time of bycatch events included
three using towed gear (French midwater trawlers, French
Danish seiners and Spanish bottom trawlers) and three using
static gear (French gillnetters, French trammel netters and
Spanish gillnetters). Among the French vessels, seven fisheries
occurred simultaneously with common dolphin bycatch at
least twice in the years between 2016 and 2019: gillnetters
fishing hake, trammel netters fishing anglerfish, bottom pair
trawlers fishing hake, midwater pair trawlers fishing sea bass,
hake and mackerel, and Danish seiners fishing whiting. Some
of these fisheries’ efforts correlated positively with dolphin
mortality areas nearly each year since 2010, including French
gillnetters (especially those targeting hake), French trammel
netters (especially those targeting anglerfish), Spanish bottom
trawlers, and French midwater pair trawlers in sea bass fishing
areas. The characteristics common to all of these fisheries include
high vertical heights (nets) or openings (trawls) and the targeting
of predatory fishes in winter.
The diversity of the fisheries this study identified as the
potential culprits behind the bycatch events between 2010 and
2019 makes it difficult to suggest specific and adapted mitigation
strategies to implement. At-sea observation of fishing vessels,
either by on-board observers or Remote Electronic Monitoring
(REM), is therefore crucial to further investigate and validate
studies related to the bycatch issue and help mitigate the threat of
bycatch more effectively and precisely in the future. At this point,
it seems that only the closure of certain fishing areas at specific
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Peltier et al. Co-occurence of Bycatch and Fisheries
times following different potential scenarios could achieve EU
requirements in terms of bycatch reduction (ICES, 2020c).
DATA AVAILABILITY STATEMENT
The data analyzed in this study is subject to the following
licenses/restrictions: VMS data used in this article are subject
to specific convention with French ministry in charge of
agriculture and fisheries. Therefore, these data cannot be
freely and fully available online as Supplementary Material.
Agregated data are avalaible online (http://pelabox.univ-lr.fr:
3838/pelagis/ShinyApp_v2/). Stranding data are freely available
online (location, date and species) (http://seamap.env.duke.edu/
dataset/1406). For detailed stranding data, please send request to
corresponding author or Pelagis. Requests to access these datasets
should be directed to pelagis@univ-lr.fr or HP, hpeltier@univ-
lr.fr.
ETHICS STATEMENT
Ethical review and approval was not required for the animal
study because this work required no particular permit, and so,
the authors have adhered to general guidelines for the ethical
use of animals in research, the legal requirements in France and
respective institutional guidelines.
AUTHOR CONTRIBUTIONS
HP: perform analyses, conception, and writing of the paper. WD,
CD, FD, EM, and OV: coordination of the French stranding
network and collect of stranding data. MA: support in analysis,
support in paper conception and correction. PD: conception of
the drift prediction model. JS, FC, VR: support in manuscript
conception and correction. All authors contributed to the article
and approved the submitted version.
ACKNOWLEDGMENTS
We would like to thank the Fisheries Information System
team from IFREMER (http://sih.ifremer.fr/), especially Emilie
Leblond for preparing and providing the data on fishing effort
distribution and for her availability and help in managing and
analyzing this data set. We also thank the Direction des Pêches
Maritimes et de l’Aquaculture for allowing data to be made
available by IFREMER. We are grateful to all the members
of the French stranding scheme for their continuous effort
in collecting data on stranded cetaceans. We thank Auriane
Virgili for creating the Shiny appendix, and Bruno Mansoux
for uploading it. The Observatoire PELAGIS is funded by the
Ministry in charge of the Environment, the French Office for
Biodiversity (Office Français pour la Biodiversité—AFB) and the
Communauté d’Agglomération de la Ville de La Rochelle.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmars.
2021.617342/full#supplementary-material
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Conflict of Interest: HP, MA, CD, and FD were employed by company ADERA.
The remaining 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.
The reviewer GP declared a past co-authorship with one of the author VR to the
handling editor.
Copyright © 2021 Peltier, Authier, Caurant, Dabin, Daniel, Dars, Demaret, Meheust,
Van Canneyt, Spitz and Ridoux. This is an open-access article distributed under the
terms of the Creative Commons Attribution License (CC BY). The use, distribution
or reproduction in other forums is permitted, provided the original author(s) and
the copyright owner(s) are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
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