Estimating common dolphin bycatch in the pole-and-line tuna fishery in the Azores

Abstract

Small-scale artisanal fisheries can have a significant negative impact in cetacean populations. Cetacean bycatch has been documented in the pole-and-line tuna fishery in the Azores with common dolphins being the species more frequently taken. Based on data collected by observers on ∼50% of vessels operating from 1998 to 2012, we investigate the influence of various environmental and fisheries-related factors in common dolphin bycatch and calculate fleet-wide estimates of total bycatch using design-based and model-based methods. Over the 15-year study dolphin bycatch occurred in less than 0.4% of the observed fishing events. Generalized additive modelling results suggest a significant relationship between common dolphin bycatch and duration of fishing events, sea surface temperature and location. Total bycatch calculated from the traditional stratified ratio estimation approach was 196 (95% CI: 186–205), while the negative binomial GAM estimated 262 (95% CI: 249–274) dolphins. Bycatch estimates of common dolphin were similar using statistical approaches suggesting that either of these methods may be used in future bycatch assessments for this fishery. Our work shows that rates of common dolphin bycatch in the pole-and-line tuna fishery in the Azores are low, despite considerable variations between years. Dolphins caught were released alive although the fate of these individuals is unknown. Continued monitoring will provide a better understanding of dolphin bycatch and more accurate estimates essential in the development of potential mitigation measures.

Full text

Estimating common dolphin bycatch in
the pole-and-line tuna fishery in the
Azores
Maria Joa
˜o Cruz
1,2
, Miguel Machete
2
, Gui Menezes
1,2
, Emer Rogan
3
and Mo
´nica A. Silva
2,4
1Departamento de Oceanografia e Pescas, Universidade dos Ac¸ores, Horta, Ac¸ ores, Portugal
2MARE—Marine and Environmental Sciences Centre and IMAR—Instituto do Mar,
Universidade dos Ac¸ores, Horta, Ac¸ores, Portugal
3School of Biological, Earth and Environmental Sciences, University College Cork, Enterprise
Centre, Distillery Fields, North Mall, Cork, Ireland
4Department of Biology, Woods Hole Oceanographic Institution, Woods Hole Oceanographic
Institution, Woods Hole, MA, USA
ABSTRACT
Small-scale artisanal fisheries can have a significant negative impact in cetacean
populations. Cetacean bycatch has been documented in the pole-and-line tuna
fishery in the Azores with common dolphins being the species more frequently
taken. Based on data collected by observers on ∼50% of vessels operating from 1998
to 2012, we investigate the influence of various environmental and fisheries-related
factors in common dolphin bycatch and calculate fleet-wide estimates of total
bycatch using design-based and model-based methods. Over the 15-year study
dolphin bycatch occurred in less than 0.4% of the observed fishing events.
Generalized additive modelling results suggest a significant relationship between
common dolphin bycatch and duration of fishing events, sea surface temperature
and location. Total bycatch calculated from the traditional stratified ratio estimation
approach was 196 (95% CI: 186–205), while the negative binomial GAM estimated
262 (95% CI: 249–274) dolphins. Bycatch estimates of common dolphin were
similar using statistical approaches suggesting that either of these methods may be
used in future bycatch assessments for this fishery. Our work shows that rates of
common dolphin bycatch in the pole-and-line tuna fishery in the Azores are low,
despite considerable variations between years. Dolphins caught were released alive
although the fate of these individuals is unknown. Continued monitoring will
provide a better understanding of dolphin bycatch and more accurate estimates
essential in the development of potential mitigation measures.
Subjects Aquaculture, Fisheries and Fish Science, Conservation Biology, Ecology, Marine Biology
Keywords Bycatch, Cetacean conservation, Fishery interaction, Statistical modelling,
Delphinus delphis, Azores
INTRODUCTION
The incidental catch of non-target marine mammals in fisheries, termed bycatch, is a
worldwide problem, with several hundred thousand animals killed per year across the globe
(Lewison et al., 2004;Read, Drinker & Northridge, 2006;Reeves, McClellan & Werner, 2013;
How to cite this article Cruz et al. (2018), Estimating common dolphin bycatch in the pole-and-line tuna fishery in the Azores. PeerJ 6:
e4285; DOI 10.7717/peerj.4285
Submitted 21 August 2017
Accepted 1 January 2018
Published 12 February 2018
Corresponding author
Maria Joa
˜o Cruz,
m.joao83@gmail.com
Academic editor
Sadasivam Kaushik
Additional Information and
Declarations can be found on
page 14
DOI 10.7717/peerj.4285
Copyright
2018 Cruz et al.
Distributed under
Creative Commons CC-BY 4.0

Briscoe et al., 2014). Cetaceans are particularly vulnerable to the effects of bycatch mortality
due to the low rates of potential population growth (Read, Drinker & Northridge, 2006;
Read, 2008;Wade, Reeves & Mesnick, 2012;Geijer & Read, 2013).
Bycatch of marine mammals occurs in nearly all gears, with gillnets being responsible
for the highest mortality, followed by trawls and longlines, particularly in the eastern
Pacific Ocean and Mediterranean Sea (Read, Drinker & Northridge, 2006;Soykan et al.,
2008;Lewison et al., 2014). Most of the bycatch occurs in industrial fisheries although
bycatch in small-scale artisanal fisheries can be substantial (Soykan et al., 2008;Reeves,
McClellan & Werner, 2013;Lewison et al., 2014). About 2,500 small dolphins are captured
annually off the coast of Peru by the artisanal drift gillnet and longline fisheries (Mangel
et al., 2010). In Ecuador, 2,500–5,000 small cetaceans may be killed every year in artisanal
longline and surface gillnet fisheries (Fe
´lix & Samaniego, 1994) and entanglement of
humpback whales in artisanal gillnets may pose a serious threat for the survival of the
population (Alava, Barraga
´n & Denkinger, 2012;Alava et al., 2017). Incidental catches of
dolphins in small-scale fisheries in East Malaysia exceed the maximum 2% annual
anthropogenic removal tolerated by the population according to the potential biological
removal (PBR), and may therefore be unsustainable (Jaaman, Lah-Anyi & Pierce, 2009).
The tuna fishery is one of the most important fisheries in the archipelago of the Azores.
In 2012, 7,000t of tuna were landed by this fishery accounting for 60% of total
landings (Machete, Morato & Santos, 2012) and for 48% of the economic revenue of the
fishing sector (INE, 2013). The tuna fleet represents around 4% of the Azorean fishing
fleet and employs about 17% of fishermen (Carvalho, Edwards-Jones & Isidro, 2011).
This fishery uses pole-and-line, usually with water spray and live bait, and target species
are the bigeye tuna (Thunus obesus), skipjack (Katsuwonus pelamis), albacore (Thunus
alalunga), yellowfin (Thunus albacares) and bluefin tuna (Thunus thynnus)(Morato et al.,
2008;Pham et al., 2013). Up until the late 1990s, dolphins were occasionally hunted by
this fishery and their meat was ground up and used as chum for catching live bait such
as mackerel (Trachurus picturatus) or sardine (Sardina pilchardus) used in tuna fishery.
The Azorean Fisheries Observer Programme (POPA) was created in 1998 to monitor
the fishery and to ensure the validity of the ‘dolphin safe’ certificate for tuna caught in
the Azores. The program covered about half of the tuna fishing vessels and, in general,
>50% of the tuna catches, which is the minimum required to certify the fishery
(Morato et al., 2008).
Silva et al. (2002,2011) used data collected by POPA programme to document
dolphin bycatch in the Azorean tuna fishery between 1998 and 2006. Bycatch was
observed in less than 1% of fishing events with common dolphins (Delphinus delphis)
being the species more frequently taken. There are no records of cetacean mortality
associated with this fishery, because all the animals were released alive by cutting the
fishing line, although their post-release fate was uncertain. These authors used simple
ratio estimators that are commonly used to extrapolate bycatch estimates as the product
of observed bycatch rate (i.e. number of catches per observed fishing set or trip) and total
effort in a fishery (i.e. number of sets or trips). They estimated that about 110 dolphins
were captured by the tuna fishing fleet in the nine-year period (1998–2006).
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 2/18

The rarity of observed dolphin bycatch makes it difficult to conduct a rigorous
statistical evaluation. In recent years, several approaches have been developed to deal
with the analytical challenges posed by rare-event data to ensure that total bycatch
estimated from these few observations is robust. The use of stratification schemes in
ratio estimate methods may improve the precision of bycatch estimates, since, in
addition to fishing effort, they can take into account other factors (e.g. gear
characteristics, time and location of fishing, environmental conditions) believed to
influence dolphin bycatch rates. However, ratio estimates may be biased by the
uneven distribution of observer coverage across space and over time (Murray, 2007;
Lawrence et al., 2009).
Model-based approaches that incorporate variables predicting dolphin bycatch can
reduce the uncertainty of bycatch estimates by better accounting for biases in observer
data (Lawrence et al., 2009). Although describing the relationship between dolphin
bycatch and covariates may be challenging, especially when number of bycatch
observations is low (Barry & Welsh, 2002), predictive models are increasingly used
in bycatch assessments (Rossman, 2010;Thompson, Abraham & Berkenbusch, 2013;
Brown, Reid & Rogan, 2014).
To assess dolphin bycatch in the pole-and-line fishery in the Azores we analysed data
collected by POPA within the exclusive economic zone (EEZ) of the archipelago of the
Azores between 1998 and 2012. We focused on common dolphins as this was the species
involved in most bycatch reports. The objectives of this study were to (i) update
information from Silva et al. (2011) on the frequency of common dolphin bycatch using a
longer time series, (ii) understand which environmental and fisheries-related factors drive
common dolphin bycatch, (iii) compare ratio and model-based estimators of bycatch, and
(iv) provide improved estimates of common dolphin bycatch in the pole-and-line tuna
fishery in the Azores. This knowledge is critical to evaluate the need of management
measures and for the development of mitigation measures.
METHODS
Study area
The Archipelago of the Azores (Portugal) is a group of nine volcanic islands situated
in the North Atlantic, between 37and 41N and 25and 31W, crossing the Mid-Atlantic
Ridge (Santos et al., 1995). The archipelago is divided into three groups: the Eastern
group (Santa Maria and Sa
˜o Miguel), the Central group (Terceira, Sa
˜o Jorge, Graciosa,
Pico and Faial) and the Western group (Flores and Corvo). Due to the scattered
configuration of the islands, the archipelago has an extensive EEZ of almost one million
square kilometres. The islands and their contiguous shelf (<500 m depth) represent about
0.4% of the Azores EEZ (Santos et al., 1995). Therefore, potential fishing grounds are
scarce and limited to the narrow area of shallow water around the islands and to
nearby banks and seamounts. The tuna fishery generally occurs around the island slopes
and at seamounts but distribution of fishing effort varies considerable between years
(Silva et al., 2002;Morato et al., 2008).
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 3/18

Fishery observer data
Data were collected by POPA observers placed onboard commercial tuna vessels (>20 m
length) from 1998 to 2012. A complete description of the tuna fishery and of POPA
program and data collection can be found elsewhere (Silva et al., 2002;Machete & Santos,
2007;Cruz et al., 2016). The fishery occurs between April and November and each boat
spends an average of eight days at sea before landing (Silva et al., 2002;Morato et al., 2008).
Depending on tuna abundance and size of the schools encountered, there may be several
fishing events during a fishing day. Fishing events are defined as when fishers are
effectively capturing tuna. All the tuna fishing vessels operating in the Azores use the
pole-and-line fishing technique. This fishery uses different fishing gear configurations,
depending on the behaviour and morphology of the target species and on the distance
or depth of the school (Feio & Dias, 2000). These gears differ in the length and type of
nylon leader and pole used but all have a hook in the extremity of the line or pole and
potentially represent a risk to cetaceans.
A single observer is assigned to each vessel for a 30-day period, after which
observers rotate between fishing vessels. Observers are required to monitor all fishing
events. For each fishing event, observers record the following information: location,
beginning and end time of the event, number and type of gear used, number, size and
weight of tuna captured, interaction and bycatch of cetaceans, seabirds and turtles.
Tunas are measured at the end of each fishing event. Dolphin bycatch occurs when
animals mix with the tuna school to feed on the live bait and accidentally bite or get
stuck by the hooks in the fishing lines.
Fishing effort was calculated as the number of fishing events per trip (lasting eight days
on average) and per vessel.
Environmental data
The environmental variables selected for this study were sea surface temperature (SST),
depth, distance to coast, and a proxy for prey abundance. These variables were
selected because they are known to affect the abundance and distribution of many marine
taxa, including dolphins, tunas and their potential prey (Brill & Lutcavage, 2001;
Erzini, 2005;Hastie et al., 2005). Chlorophyll was not used since there were no data
available prior to 2002.
Sea surface temperature at each fishing event was obtained from in situ measurements
by the vessel echosounder. When SST values were not available for a fishing event, we used
the average water temperature for the month. The predictor variables depth (m) and
distance to coast (km) of the fishing event were calculated in ArcGIS. Depth was
obtained as bathymetric data composite using multiple sources: GEBCO_08 (Lourenc¸o
et al., 1998), grid (MOMARGIS v2, DOP/UAz), multi-beam surveys (GMRT grids),
point and contour data digitized from nautical charts in the vicinity of the islands.
Original grid resolution varied between 50 m and 0.5′. Distance to coast was calculated
in ArcGIS as the shortest distance between the coordinates point and the closest point
on the coastline.
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 4/18

To investigate the effect of prey availability on the probability of common dolphin
bycatch, we used landings statistics of locally abundant species that likely serve as dolphin
prey, since there are no fisheries-independent abundance estimates of small pelagic
species. Landings of bogue (Boops boops), sardine, chub mackerel (Scomber colias) and
horse mackerel (Lotac¸or, 2014) were pooled together for each month of the study period
and used as a proxy for prey abundance in the region.
Analysis of data
Common dolphin bycatch was recorded mainly during fishing events targeting bigeye
tuna (76% of all bycatch records) therefore we restricted the analysis to events with bigeye
tuna. The software ArcGis 10.0 was used to produce geo-referenced maps to analyse
spatial trends in common dolphin bycatch during the whole study period.
Operational and environmental variables
Generalized additive models (GAMs) were used to investigate the effect of fishing effort,
fishing operations, tuna catches and environmental variables on the probability of
dolphin bycatch and to determine which variables are the most adequate for stratification
when applying ratio estimators (Table 1). GAMs were used to account for non-linear
effects of predictors. GAMs predicting the presence/absence of bycatch used a binomial
error distribution with an identity link function.
The variables fishing duration, distance to coast, number of tuna caught, and average
length of individual tuna were log transformed (+1 for covariates including zero values).
Tuna weight (kg) and average weight of individual tuna (kg) were omitted from the
models because they were strongly correlated.
Before fitting the models homogeneity, potential outliers and amount of zeros were
analysed with Cleveland dotplots, boxplots, and frequency plots. Variance inflation factor
(VIF) analysis and pairwise correlations were computed between all variables to check for
collinearity (Zuur, Ieno & Elphick, 2010). A backward stepwise selection procedure was
used to identify the best fitting model with the lowest Akaike’s Information Criterion
(AIC) score. Model validation was applied on the best fitting model to verify the
underlying assumptions (Zuur, Ieno & Smith, 2007). All calculations were conducted
using R statistical package (R Core Team, 2015).
Bycatch Rates and Total Estimated Bycatch
We compared four different methods to estimate dolphin bycatch in the pole-and-
line tuna fishery: (1) ratio estimate, (2) GAM describing dolphin bycatch rates, (3)
negative binomial GAM, (4) generalized additive models for location, scale and shape
(GAMLSS). Dolphin bycatch rates were calculated as the number of dolphins captured
per observed tonnage of bigeye tuna landed per fishing trip. The fisheries official data
report the landed weight of tuna by fishing trip and vessel but have no information on
number of fishing events. Therefore, the only proxy of fishing effort available for the
non-monitored section of the fleet was total bigeye tuna landed per trip (Silva et al., 2002,
2011). Dolphin bycatch rates calculated from monitored trips were then applied to
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 5/18

total bigeye tuna landings by the whole fleet over the same time period to estimate the
annual bycatch of dolphins in the pole-and-line bigeye tuna fishery.
(1) Ratio estimate method
Ratio estimation was used to extrapolate total bycatch from observed dolphin bycatch
rates, using data stratified by sea surface temperature. SST was selected for stratification
because it was a significant predictor of common dolphin bycatch (see below) and
because information on SST was available in fisheries official data, enabling estimation
of bycatch for the entire fleet. Based on the GAM that showed a decrease in bycatch
probability at SST >18 C, fishing data were grouped into two strata: high temperature
(18 C) and low temperature (<18 C). A bycatch rate was calculated for each SST
stratum as the sum of dolphins captured divided by observed tonnage of tuna landed.
Stratum-specific bycatch rates were then multiplied by the total landings of the tuna
fishery from 1998 to 2012 to calculate bycatch for the study period. Total bycatch over
all strata correspond to the sum of the stratified estimates. Finally, the average annual
bycatch is the total estimated bycatch over all strata divided by the number of years of the
study (n= 15 yr). Confidence intervals (95% CI) of bycatch estimates were calculated
using a non-parametric bootstrap procedure (Efron & Tibshirani, 1993) with fishing
trip as a resampling unit.
(2) Generalized additive model
A GAM with a Poisson distribution was used to model the expected dolphin bycatch rate
based on statistically significant explanatory variables. In order to use this model to
calculate bycatch of the entire tuna-fishing fleet, only variables available in the POPA
dataset and in the fisheries official data could be considered. Thus, explanatory variables
included in the model were year and SST. Year was included in the model as a proxy for
temporal variability to account for interannual differences in bycatch. Initial data
Table 1 Summary of explanatory variables for the GAM predicting the probability of dolphin
bycatch.
Category Explanatory variable Type
Environmental Sea surface temperature () Continuous
Depth (m) Continuous
Distance coast (km) Continuous
Prey abundance (t) Continuous
Fishing operation Hour of day Continuous
Gear Categorical
Latitude, longitude Continuous
Fishing effort Fishing duration (h) Continuous
Number of poles Continuous
Catch Number of tuna Continuous
Baitfish Categorical
Average tuna size (cm) Continuous
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 6/18

exploration indicated that the observed counts were over-dispersed. Usually, models with
a negative binomial (NB) distribution handle overdispersion better than Poisson
regression models (Zuur, Ieno & Smith, 2007) so we decided to fit a second model with a
NB distribution and a log link function to estimate the bycatch rate.
A backward stepwise selection procedure was used to identify the best fitting model
using the AIC and variables with the highest AIC value were excluded. All continuous
variables were considered as smooth terms in the model using the default degrees of
freedom in the fitting procedure. Model validation was applied on the best fitting model
to verify the underlying assumptions. Confidence intervals were calculated as for the ratio
estimate method.
(3) Generalized additive models for location, scale and shape
The GAMLSS approach is a semi-parametric method that allows the relationship between
the predictor variables and the response variable to be modelled either parametrically,
with linear or nonlinear predictors, or non-parametrically, with smooth nonparametric
terms (e.g. cubic splines or loess) and random effects (Rigby & Stasinopoulos, 2010). The
GAMLSS can be considered as an extension of GAMs and generalized linear models.
This method allows for a wide range of skewed and kurtotic distributions by explicitly
modelling various distributional parameters, which may include the location/mean,
scale/dispersion, skewness and kurtosis as functions of predictor variables
(Rigby & Stasinopoulos, 2005).
Generalized additive models for location, scale and shape with a zero-adjusted gamma
distribution (ZAGA) and zero-inflated beta distribution (BEZI) were used to model a
continuous response and to deal with the excess of zeroes. These models were developed
and implemented using gamlss package (Stasinopoulos & Rigby, 2007) in the R 3.1.2
software (R Core Team, 2015).
RESULTS
Observed bycatch
From 1998 to 2012, 23,175 tuna fishing events were observed. Incidental captures
occurred in 86 (∼0.4%) of these fishing events, with a total of 102 dolphins caught.
Typically a single dolphin was captured per fishing event, with a maximum of three
dolphins captured at the same time. Of the 102 dolphins, 92 were common dolphins, nine
were Atlantic spotted dolphins (Stenella frontalis) and one bottlenose dolphin (Tursiops
truncatus)(Table 2). Most (n= 78) of the common dolphin bycatch took place in bigeye
tuna fishing events, mainly from 1998 to 1999 and in 2012. There were no reports of
bycatch in 2003, 2004, 2007 and 2010 (Table 2). The majority of incidental catches took
place in May and June. All dolphins were released alive (by cutting the fishing line) but the
fate of these animals is unknown.
Dolphin bycatch occurred throughout the archipelago but it was more frequent around
the central and eastern islands (Fig. 1), where most of the fishing effort is concentrated
(Cruz et al., 2016). A single dolphin was captured in the western group of islands.
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 7/18

Predictors of common dolphin bycatch
The best fitting GAM showed that fishing duration, SST and geographic position
(latitude and longitude) were significant predictors of bycatch probability
(Tabl e 3 ).Hourofday,gear,depth,distancetocoast,numberoffishingpoles,prey
abundance, number and average size of individual tuna did not influence bycatch
probability.
The final GAM model explained 13.5% of the deviance. The model predicted
that bycatch probability increased almost linearly with fishing duration and
decreased in water temperatures higher than 18 C. The likehood of dolphin
bycatch increased towards the central group of islands (between 36and 38
longitude) (Fig. 2).
Estimation of total common dolphin bycatch
The GAM with a Poisson distribution and GAMLSS with a ZAGA and BEZI provided a
poor fit to the data and gave inconsistent results. Therefore, we will only present results
from the ratio estimate and NB GAM models.
Ratio estimate
This method estimated 73 (95% CI: 66–80) common dolphins captured at SST 18 C
and 123 (95% CI: 116–129) dolphins captured when SST was below 18 C. Combining
data from the two strata gives a total estimate of 196 (95% CI: 186–205) common
dolphins captured by the tuna-fishing fleet between 1998 and 2012, and an average
bycatch of 13 dolphins a year (Ta b le 4).
Table 2 Number of dolphins captured by the pole-and-line tuna fishery, monitored by POPA
observers, from 1998 to 2012.
Year Number of
fishing events
Number of
common dolphins
Number of Atlantic
spotted dolphins
Number of
bottlenose dolphins
1998 13 15 0 1
1999 23 18 7 0
2000 9 7 2 0
2001 1 1 0 0
2002 1 1 0 0
2003 0 0 0 0
2004 0 0 0 0
2005 5 6 0 0
2006 2 2 0 0
2007 0 0 0 0
2008 1 1 0 0
2009 2 2 0 0
2010 0 0 0 0
2011 7 9 0 0
2012 22 30 0 0
Total 86 92 9 1
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 8/18

Negative binomial GAM
The model predicting dolphin bycatch with SST explained 12.8% of the deviance. Results
from the negative binomial GAM agree well with the model predicting bycatch probability
and indicated higher bycatch rates when SST was <18 C(Fig. 3).
Estimated bycatch rates of common dolphins ranged between 0.004 and 0.022 per
metric ton of bigeye tuna landed. Applying these bycatch rates to the entire the tuna fleet
yielded an estimate of 262 (95% CI: 249–274) common dolphins bycaught from 1998 to
2012, and an average annual bycatch of 17 dolphins (CV = 0.23, 95% CI: 9–25). Estimates
of annual bycatch ranged from 2 dolphins in 2003 to 50 animals in 2012 (Table S2).
The estimated dolphin bycatch varied considerably by year for both the ratio estimate
and negative binomial GAM approaches. Although these methods gave similar annual
bycatch estimates, the NB GAM estimates were slightly higher particularly for the years of
2011 and 2012 (Fig. 4).
Figure 1 Location of bigeye tuna fishing events with common dolphin bycatch in the waters around the Archipelago of the Azores (in green)
from 1998 to 2012. Full-size

DOI: 10.7717/peerj.4285/fig-1
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 9/18

Table 3 Summary of parameter estimates from the best-fitting GAM predicting probability of
common dolphin bycatch in bigeye tuna fishery.
Explanatory variable Non-parametric smoothers
edf X
2
P
s(Fishing duration) 1.604 59.10 <0.001
s(SST) 3.431 19.95 <0.001
s(long, lat) 5.818 18.13 0.020
Note:
edf, effective degrees of freedom.
Figure 2 Smoothers estimates for the predictors (A) fishing duration, (B) SST and (C) spatial
location (latitude/longitude) obtained by the GAM predicting probability of common dolphin
bycatch. The grey shading indicates approximate 95% confidence bands. Tick marks on the x-axis
are sampled data points. Full-size

DOI: 10.7717/peerj.4285/fig-2
Table 4 Ratio estimate of common dolphin bycatch for the bigeye tuna fishery from 1998 to 2012.
T(C) Observed
bycatch
Observed
catch (t)
Observed
bycatch rate
Fleet catch (t) Estimated
bycatch
Average estimate
per year
<18 26 1312.54 0.01981 3680.41 73 13 CV = 0.24
(95% CI: -7to9)
18 52 7469.24 0.00696 17622.73 123
Note:
Bycatch rates are stratified by sea surface temperature.
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 10/18

DISCUSSION
The analysis of a longer time series of fishery-observer data supports previous findings by
Silva et al. (2002,2011) that indicated low bycatch rates of small dolphins in the pole-and-
line tuna fishery in the Azores. The large majority of cases of bycatch involved common
Figure 3 Smoothers estimate for the predictor SST obtained by the NB GAM predicting probability
of common dolphin bycatch. The grey shading indicates approximate 95% confidence bands. Tick
marks on the x-axis are sampled data points. Full-size

DOI: 10.7717/peerj.4285/fig-3
Figure 4 Comparison of ratio estimate (grey bars) and NB GAM (black bars) estimates of the annual
common dolphin bycatch. The bars correspond to the 95% CI.
Full-size

DOI: 10.7717/peerj.4285/fig-4
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 11/18

dolphins. Common dolphins frequently interact with this fishery, by sinking the tuna
schools and/or competing with tunas for live bait, namely bogue, sardine, chub mackerel
and horse mackerel, in particular when the fishery targets bigeye tuna (Silva et al., 2002,
2011;Cruz et al., 2016).
The present study revealed that duration of fishing events, sea surface temperature
and location (latitude and longitude) were significant predictors of common dolphin
bycatch. The probability of dolphin bycatch increased with longer fishing events. Longer
fishing events give dolphins increased opportunities for interacting with the fishery as
reported by Cruz et al. (2016), thus increasing the likelihood that dolphins became hooked
in the fishing line. The probability of common dolphin bycatch was higher at lower
SST, which likely reflects the seasonal occurrence of common dolphins, and/or of bigeye
tuna in the region. Common dolphins occur year-round in the Azores with higher
encounter rates from winter to early summer (Silva et al., 2014). Similarly, catches of
bigeye tuna in the Azores generally peak between May and July and decrease after this time
(Da
ˆmaso, 2007). While it is unclear if there is any relationship between the seasonality of
the two species, their higher abundance in the area when SST is lower increases the
likelihood of interactions (Cruz et al., 2016) and of dolphins being captured. The
probability of dolphin bycatch was lower in the western islands and higher in the
central and eastern islands. The combination of higher fishing effort and temporal
occurrence of common dolphins in the latter regions may give rise to a bycatch hotspot.
In spring and early summer, when common dolphins are more abundant in the Azores,
the tuna fishing generally concentrates around the central and eastern group of islands
and surrounding seamounts (Silva et al., 2002;Da
ˆmaso, 2007;Cruz et al., 2016).
According to the ratio estimate, 196 common dolphins were incidentally captured
by the tuna fleet in 15 years, while the results from the NB GAM gave 262 dolphins. The
two methods gave similar annual bycatch estimates, though NB GAM-based estimates
were higher especially in 2011 and 2012. Although we have no way of validating these
estimates, the fact that two distinct analytical approaches provided similar annual
results gives some confidence in their reliability and suggests that either of these
methods may be used in the future to assess bycatch rates in this fishery.
However, these estimates may still be affected by a number of factors and there are a
number of caveats that should be considered in this study. While the GAM indicated
that location and duration of fishing events also influenced bycatch probability, these
could not be used to estimate bycatch rates in the entire fleet because no information on
these variables is available for the unobserved fleet. Given the substantial differences in
bycatch rates between the three groups of islands, with the western islands showing almost
no bycatch, incorporation of a variable describing fishing area could improve both the
ratio and model-based bycatch estimates. Such information should be considered
mandatory and incorporated into fisheries official data. Nonetheless, we do not expect
this limitation to have significantly biased our estimates. First, because there is no reason
to believe that the monitored and non-monitored components of the fleet behave
differently with respect to fishing areas and operations, nor do we expect substantial
differences in distribution of fishing effort between them. Second, because geographic
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 12/18

differences in bycatch rates should, at least partly, reflect fishing effort, and this is
accounted to some extent in our analyses.
A common critique of bycatch estimation methods is the use of landings as a proxy for
fishing effort. Frequently there may be a poor relationship between catches of target
species and bycatch of non-target species, since increasing bycatch may not necessarily be
the result of increasing landings and therefore other units of effort should be considered.
Also, changes in the availability of the target species over the study period may imply
that more effort is needed to land the same amount of fish (Orphanides, 2009;Bjørge,
Skern-Mauritzen & Rossman, 2013). In this study there was a positive relationship between
bigeye landings and common dolphin bycatch, despite large interannual variations in
landings. Although we acknowledge that duration of fishing events is more influential
on dolphin bycatch than tuna catches and would be a more appropriate measure of
fishing effort, this information was not available for vessels not monitored by observers
and the only measure reported in the official data are landings per vessel and trip.
In the ratio estimate approach the paucity of observer data has been considered by
some to bias bycatch rates due to constraints in observer coverage over time or across the
fishing area (Lawrence et al., 2009). This should not be a major source of bias in our
dataset, as POPA covers about 50% of the tuna-fishing vessels for the duration of the
fishing season and all vessels use the same areas.
A number of approaches are available to model bycatch data that is over-dispersed
relative to the Poisson distribution. In this study, the Negative binomial GAM performed
better then the Poisson, GAMLSS ZAGA and BEZI distributions in terms of fit and
predictive capabilities dealing with over-dispersed data and the large number of zeros.
Although estimates of common dolphin bycatch pertain only for the bigeye tuna
fishery, observed bycatch in fishing events for other tuna species was very low (less than
20% of total observed bycatch). Therefore, we assume that number of common dolphins
incidentally captured during events for other tunas do not significantly change estimates
presented here. These bycatch estimates are very low compared with estimates from
dolphin bycatch in fisheries elsewhere like in Peruvian and Ecuadorian artisanal fisheries
(Mangel et al., 2010;Alava et al., 2017). In East Malaysia, where the fishing fleet is
predominantly small-scale and coastal, bycatch was higher with about 527 dolphins taken
annually (Jaaman, Lah-Anyi & Pierce, 2009).
In the Azores, the pole-and-line tuna fishery is the only fishery with records of
dolphin bycatch. Silva et al. (2011) reviewed the interactions between cetaceans and
fisheries in the Azores and reported no bycatch in demersal and swordfish fisheries. In the
artisanal squid fishery there were also no observations or reports of incidental capture of
Risso’s dolphins (Cruz et al., 2014). Yet monitoring of other fisheries, including the
longline fishery, has been scarce. Marine mammal bycatch in the U.S. East Coast pelagic
longline fishery is common, particularly of pilot whales (Globicephala spp.) and Risso’s
dolphins and often results in the mortality or injury of these species (Garrison, 2007).
Similarly, in the Hawaii and American Samoa longline fisheries, dolphins were seriously
injured or died due to interactions with this fishery (Bradford & Foney, 2014). Thus,
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 13/18

implementing an observation program to monitor the longline fishery in the Azores
would be critical to assess bycatch patterns and drivers.
CONCLUSION
Our study shows that bycatch of common dolphins in the Azorean pole-and-line tuna
fishery is low and there is no evidence that bycatch rates have consistently increased over
the 15-year period. Bycatch was primary driven by the spatiotemporal overlap between
common dolphin and fishing effort. The higher bycatch rates occurred in specific
geographic areas and at times favoured by dolphins and the fishery and bycatch
probability increased with duration of fishing events, regardless of the time of day,
fishing gear and local characteristics of the fishing site. As noted by Cruz et al. (2016),
common dolphins and tunas have similar prey and likely exploit the same habitats.
Live bait used by the tuna fishing vessels is an easy and readily available food source
for common dolphins and dolphins are attracted to fishing vessels to prey on it. Under
these circumstances, the most effective way to avoid bycatch may be for fishing vessels to
stop fishing and move away from the area when dolphins are detected nearby. Other
mitigation measures, such as use of acoustic deterrents, should be carefully considered,
as the potential costs (addition of more noise into the marine environment, changes in
tuna behaviour) may outweigh the benefits. Finally, future studies should consider
complementing observer data with logbook data to refine or add new information
about fishing operations in the estimation of bycatch.
ACKNOWLEDGEMENTS
We acknowledge the collaboration of all the observers, captains, crewmembers and
ship-owners of tuna vessels of the Azorean Fisheries Observer Program (POPA). Our
thanks to our colleagues from NPDC for sharing the fishery data used in this study.
We would also like to thank Ricardo Medeiros for help with the maps. We are grateful for
the comments and suggestions of one anonymous reviewer that greatly improved this
manuscript.
ADDITIONAL INFORMATION AND DECLARATIONS
Funding
This work was supported by the Fundac¸a
˜o para a Cie
ˆncia e a Tecnologia (FCT) and the
Fundo Regional da Cie
ˆncia e Tecnologia (FRCT), through research projects TRACE
(PTDC/MAR/74071/2006), MAPCET (M2.1.2/F/012/2011), and the FCT Exploratory
project (IF/00943/2013/CP1199/CT0001), through funds from FEDER, the
Competitiveness Factors Operational (COMPETE), QREN, POPH, the European
Social Fund, the Portuguese Ministry for Science and Education, and the Proconvergencia
Ac¸ores/EU Program. Funds were also provided by FCT to MARE, through the strategic
project UID/MAR/04292/2013. Maria Joa
˜o Cruz is supported by a DRCT doctoral
grant (M3.1.2/F/008/2009-RTF/1) and Mo
´nica Almeida e Silva is supported by an
Cruz et al. (2018), PeerJ, DOI 10.7717/peerj.4285 14/18

FCT-Investigator contract (IF/00943/2013). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Grant Disclosures
The following grant information was disclosed by the authors:
Fundac¸a
˜o para a Cie
ˆncia e a Tecnologia (FCT), research project TRACE:
PTDC/MAR/74071/2006.
Fundo Regional da Cie
ˆncia e Tecnologia (FRCT), research project MAPCET:
M2.1.2/F/012/2011.
FCT Exploratory project: IF/00943/2013/CP1199/CT0001.
FCT to MARE, through the strategic project: UID/MAR/04292/2013.
DRCT doctoral grant: M3.1.2/F/008/2009-RTF/1.
FCT-Investigator contract: IF/00943/2013.
Competing Interests
The authors have declared that no competing interests exist.
Author Contributions
Maria Joa
˜o Cruz analysed the data, wrote the paper, prepared figures and/or tables,
reviewed drafts of the paper, conceptualization and funding acquisition.
Miguel Machete contributed reagents/materials/analysis tools, reviewed drafts of the
paper, data curation.
Gui Menezes reviewed drafts of the paper.
Emer Rogan reviewed drafts of the paper.
Mo
´nica A. Silva analysed the data, wrote the paper, reviewed drafts of the paper,
conceptualization, funding acquisition, project administration and supervision.
Data Availability
The following information was supplied regarding data availability:
Due to the sensitive nature of fisheries data and to confidentiality agreements with data
providers, supporting data cannot be made openly available. Individual requests for the
data can be sent to the POPA Coordination and Data Management (Miguel Machete;
miguel.ag.machete@uac.pt) and will be addressed on a case-by-case basis.
Supplemental Information
Supplemental information for this article can be found online at http://dx.doi.org/
10.7717/peerj.4285#supplemental-information.
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