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Fish and Fisheries, 2024; 0:1–10
https://doi.org/10.1111/faf.12873
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Fish and Fisheries
ORIGINAL ARTICLE
Opportunity to Leverage Tactics Used by Skilled Fishers to
Address Persistent Bycatch Challenges
LeslieA.Roberson1,2 | ChristopherJ.Brown3 | CarissaJ.Klein1,2 | EdwardT.Game4 | ChrisWilcox3,5
1School of the Environment, The University of Queensland, Brisbane, Australia | 2Centre for Biodiversity and Conservation Science, University of
Queensland, Brisbane, Australia | 3Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia | 4The Nature Conservancy,
Brisbane, Australia | 5Centre for Marine Socioecolog y, University of Tasmania, Hobart, Australia
Correspondence: Leslie A. Roberson (l.roberson@uq.edu.au)
Received: 23 August 2024 | Revised: 14 November 2024 | Accepted: 16 November 2024
Funding: This work was supported by an Australian Research Council Future Fellowship (200100314).
Keywords: blue shark| bycatch mitigation| fisheries management| pelagic longlines| silky shark| tuna fisheries
ABSTR ACT
Effective management of shark bycatch is urgently needed to reverse widespread population declines, especially in longline
fisheries that are estimated to be responsible for half of global shark catch. Management of shark catch typically focuses on the
safe release of landed sharks, with limited attention to reducing the initial catch. Where controls on fishing effort or catch do
exist, management frameworks tend to treat fishing fleets as homogeneous units. The underlying assumption is that fishers have
similar abilities to catch target species and avoid bycatch. We test this assumption by analysing variability in shark bycatch rates
among individual vessels in an industrial tuna longline fleet operating in the Western Pacific. Controlling for factors such as geo-
graphic location, time of day and gear depth, we find that individual vessels drive highly variable bycatch rates of blue (Prionace
glauca) and silky sharks (Carcharhinus falciformis) – two shark species with the highest global catch volumes. Additionally, we
found that the operating company can influence fisher performance. As countries and regional organisations increasingly adopt
shark conservation plans and make international conservation commitments, it is crucial to identify viable new strategies that
do not unduly burden the industry or penalise good actors. Tailoring management actions to individual fishers and companies
– holding high- bycatch fishers accountable and incentivising low- bycatch fishers to continuously improve – presents a crucial
opportunity to address the overfishing of sharks and other global bycatch challenges.
1 | Introduction
Sharks are crucial for marine ecosystem function, but populations
continue to decline – primarily due to overfishing – with rela-
tively few documented conservation successes (Dulvy etal.2021;
Camhi, Fordham, and Fowler2008; Dulvy et al.2017; Herndon
et al. 2010). Sharks are variably classified as targeted catch, by-
products or incidental bycatch, depending on context- specific
factors such as species, season, location, market value, and quo-
tas or catch limits (Oliver etal.2015). Oceanic sharks such as blue
shark (Prionace glauca) and silky shark (Carcharhinus falciformis)
are particularly vulnerable to fishing impacts because their large
ranges overlap closely with industrial fisheries targeting tuna
and other pelagic fish (Dulvy et al. 2008; Pacoureau et al. 2021).
Blue sharks, the most frequently caught elasmobranch in pelagic
fisheries globally, have fast growth rates and high fecundity but
are increasingly overharvested due to emerging markets and
declines of other sharks or target species (Herndon et al. 2010;
Davidson, Krawchuk, and Dulvy 2016). Blue sharks are listed
as Near Threatened on the IUCN Red List and were recently
added to Appendix II of the Convention on International Trade in
Endangered Species of Wild Fauna and Flora (CITES) as part of
the 2022 listing of a group of Carcharhinid species. Silky sharks –
the second largest component of pelagic shark catches – are more
© 2024 J ohn Wiley & Sons Ltd .
2 of 10 Fish and Fisheries, 2024
typical of oceanic sharks and are biologically more vulnerable
to overfishing (Davidson, Krawchuk, and Dulvy 2016; Cortés
etal.2010). They are listed as Vulnerable on the IUCN Red List
and were listed in CITES Appendix II in 2017.
Reversing the alarming declines in the world's shark populations
will require a substantial reduction in total catch mortality, which
is unattainable under the current management framework (Dulvy
et al. 2021). Current regulations concentrate mostly on proto-
cols for releasing entangled sharks or regulating finning (Cronin
etal.2022; Tolotti etal.2015). Proactive measures to reduce initial
catches (e.g., shark sanctuaries, quotas, retention bans, bycatch
reduction devices) are less common and typically focus on char-
ismatic or less valuable species, such as whale sharks (Rhincodon
typus) (Camhi, Fordham, and Fowler2008; Swimmer, Zollett, and
Gutierrez 2020). While existing conservation and management
measures have reduced shark mortality in some contexts, oceanic
sharks remain a persistent problem that will require more inno-
vative, cost- effective and scalable solutions that focus on reducing
catch in addition to increasing surv ival of released animals (Tolotti
etal.2015; Cronin etal.2022).
Environmental impacts of fishing – such as catch of non-
target species – are typically described as f leet- wide problems,
which overlook the significant variability in the operational
practices of individual operators or sub- regional groups of ves-
sels (Roberson and Wilcox 2022; Gilman et al. 2023; Frawley
etal.2021, 2022). Evidence from a range of Australian fisher-
ies indicates some fishers can avoid catching certain species,
including sharks in pelagic longlines, while maintaining high
target catch (Roberson and Wilcox 2022). This ‘fisher effect’
(also called the skipper, vessel or operator effect) appears to be
more complex than simply choosing fishing locations or seasons
to target certain species. It is likely caused by some combination
of motivation and skill at avoiding bycatch, where fishers may
differ in factors such as knowledge of the environment, mainte-
nance of the vessel and equipment, ability to manage crew, and
experience setting and hauling gear.
If this variability is consistent across different species and fisher-
ies, the implication is that some fishers have already developed
effective byc atch reduction strategies, even for problematic species
such as sharks. This suggests there is opportunity to help tackle
bycatch problems that have been difficult and expensive to ad-
dress with current approaches by investigating the practices of
individual operators or groups within a fleet. However, assessing
a fisher's ability to avoid bycatch is challenging due to various en-
vironmental and operational factors influencing shark catchabil-
ity. Bycatch rates alone do not provide a comprehensive picture of
fisher performance. For example, a skilled fisher may fish in high
bycatch risk conditions if these conditions also maximise the tar-
get catch yet still employ strategies that reduce bycatch compared
to others fishing in similar circumstances.
To untangle the fisher effect, we use a unique dataset that in-
cludes logbook, observer and onboard camera data from a project
trialling electronic monitoring systems in the pelagic longline
fleet operating in the Republic of the Marshall Islands (Brown
etal.2021). This fleet, consisting of approximately 40 Chinese-
owned and operated vessels, is emblematic of many tuna fleets
in the Western Pacific and has a history of high shark catches
(Bromhead et al.2012). The fleet targets high- value tuna and
billfish species sold to international markets; less valuable spe-
cies, including sharks, are sold more locally (Frawley etal.2022).
From 2013, licensed shark targeting has been phased out and
two known shark- targeting tactics were banned: use of wire in-
stead of nylon leader lines and ‘shark lines’ that run directly off
the longline floats (WCPFC 2014). Additionally, fisheries were
required to develop management plans aiming to avoid or reduce
incidental catch and maximise live release of highly depleted
species such as silky and oceanic whitetip sharks (Carcharhinus
longimanus) (WCPFC 2014). Retention and sale of sharks was
still permitted, with regulations on trade of CITES Appendix II
species and restrictions on Appendix I species. Although shark
landings decreased following these measures, the fleet's cumu-
lative shark catch rates remain high, especially of blue and silky
sharks (Brown etal.2021).
We hypothesise that the fleet's high cumulative shark catch is
driven by particular vessels or groups of vessels. To test this theor y,
we analyse variability among individual vessels in their silky and
blue shark catch rates, compared to their target catch, accounting
for spatial patterns and other factors that inf luence shark catch-
ability. These two species are a particularly thorny problem in in-
dustrial fisheries globally and are considered challenging to avoid
due to their co- occurrence with high value target species. Thus, if
some fishers are able to consistently avoid them while maintaining
profitable target catch, it would present opportunities for reducing
catches of imperilled sharks on a large scale.
2 | Methods
2.1 | Fisheries Data
We analysed vessel- level variability in silky and blue shark
catches using records from captain's logbook, human observer
and onboard camera datasets that include more than 14,000 sets
(fishing events) over a three- year period. The captain's logbook
data are self- reported and include 13,735 sets from 31 vessels
between January 2016 and December 2018. There are 172,073
records of tuna and billfish and 8765 elasmobranchs (including
2170 silky sharks and 3035 blue sharks). The observer data in-
cludes 864 sets from 30 vessels between October 2016 and July
2018, with 14,166 tuna and billfish recorded and 620 elasmo-
branchs (190 silky sharks and 188 blue sharks). Electronic moni-
toring systems were introduced on six longline vessels as part of
a collaboration between the Marshall Islands Marine Resources
Agency and The Nature Conservancy; these data were assessed
in Brown et al. (2021). The electronic monitoring dataset in-
cludes catch records from 927 sets between February 2017 and
October 2018, with 12,280 tunas and billfish records and 2048
elasmobranchs (407 silky sharks and 198 blue sharks).
For this analysis, we focus on the larger logbook dataset and
compare the results to the human observer and camera data.
We removed records with incomplete data, which appeared to
be unintentional because the proportion of incomplete records
was consistent across vessels and included both high and low
reported bycatch and target catch numbers. The most frequently
missing variable was number of hooks set (3745 sets). These
observations were also missing the number of hooks between
3 of 10
floats. An exploration of hook numbers showed substantial vari-
ability across the fleet and for individual vessels; therefore, we
chose to exclude these records rather than interpolate the miss-
ing data. The final, complete logbook dataset included 9555 indi-
vidual sets from the 31 vessels with more than 100 sets recorded
during the time period.
2.2 | Model Selection
To evaluate the factors driving variations in silky and blue
shark catch, we used generalised additive models (GAMs) with
a Tweedie distribution implemented with the mgcv package in R
(Wood2015). Given that approximately 80%–89% of the sets re-
corded zero catches of silky or blue sharks, we chose the Tweedie
distribution – a mixture of Poisson and gamma distributions –
because it effectively handles zero- inf lated data (Shono 2008).
The objective was to isolate the marginal effect of the skipper
and crew from the many environmental and tactical variables
that could influence the catchability of silky and blue sharks to
capture in longlines. We used the individual vessel to represent
the personnel operating the boat, which seemed to be reason-
ably consistent over this short period. Although historical infor-
mation about the vessel owner and master was not available for
the timeframe of the data, comparison of the current vessel list
to a previous version showed that seven of 41 (17%) vessel mas-
ters changed in four years, with three of the masters moving to
other vessels within the same company.
The model included factors that were recorded in the fisheries
datasets and are typical for models of bycatch or target catch
rates (e.g., fishing location and season), as well as factors that
we derived. First, we derived a categorical variable for target-
ing type because fishers often vary their practices depending
on which target species they prefer. For example, since declines
in yellowfin tuna (Thunnus albacares) catch in the 1990s, the
Chinese longline fleet operating in the Marshall Islands has in-
creasingly used deeper- set gear to target bigeye tuna (Thunnus
obesus) for the sashimi market (Frawley etal. 2022; Bromhead
et al. 2012). While some targeting tactics are captured by the
recorded variables in the dataset (e.g., set start time and number
of hooks between floats, a proxy for depth of fishing gear), there
may be important undocumented factors, such as bait type, or
nuanced fishing techniques that are difficult to measure or de-
scribe (Parsa etal.2020). To capture these unrecorded strategies
used to target different species, we used the package mclust in
R to perform a cluster analysis of the four highest- volume tar-
get species to categorise each fishing event as a targeting type
(Strucca etal.2016).
To test whether the fisher effect was independent of the physical
characteristics of the boat without overfitting the model, we cre-
ated a categorical factor for groups of vessels that had the same
length, tonnage and age. We included the listed operating com-
pany as a factor because we expected company culture and poli-
cies to influence the behaviour of the skipper and crew (Gilman,
Owens, and Kraft2014). We used counts of the two main targets
(bigeye and yellowfin tuna) and two secondary targets (alba-
core, Thunnus alalunga, and black marlin, Makaira nigricans)
as the amount of target catch variable. Each species model was
expressed in terms of bycatch per unit effort, with shark catch
(number of individuals) per fishing event (setting and hauling
the longline) as the response variable, target catch count and the
recorded and derived factors as predictor variables, and number
of hooks as an effort offset.
To determine the best model, we used the Akaike Information
Criterion (AIC) in a stepwise procedure (Roberson and
Wilcox2022). First, we compared a global model that had all fac-
tors included, along with a term for the vessel as a random effect,
to a null model without the vessel factor. If the global model with
the vessel factor improved the AIC score over the null model, we
compared that model to a global model with the vessel factor as
a fixed instead of random effect. We then compared all possible
combinations of factors in the best global model (with vessel as
either a fixed or random effect) using the dredge function from
the mumin package in R (Barton2015), selecting the model with
the lowest AIC as the best model. If there were multiple models
within a 95% confidence interval of the model with the lowest
AIC (ΔAIC < 2), we selected the simpler one with fewer factors.
We tested the best models with various data subsets and varia-
tions. Repeating the analysis with all tuna and billfish species or
with only bigeye and yellowfin did not change the significance of
the target catch variable or the vessel factor. Repeating the pro-
cess with low- effort vessels (less than 100 sets recorded) included,
then with only the two dominant fishing companies included,
confirmed that the vessel factor remained significant.
2.3 | Using Regression Coefficients to Explore
Variability Among Vessels
Once we were confident that the vessel factor improved the best
model, we focused on the regression coefficients for the vessel ef-
fect. When the best model includes the vessel factor as a random
effect, this means that something about the individual vessels as
a group helps explain the observed variations in bycatch. Moving
to a fixed effect tells us how individual vessels drive high or low
bycatch. We re- ran the final models for both species with the ves-
sel as a fixed effect using deviation contrast coding. Deviation
coding compares each level of the vessel factor to the grand
mean, which provides a better picture of the individual vessels
than if the reference level is a randomly selected vessel, as in the
case of the typical default treatment contrasts. Due to the uneven
sample sizes, we applied an additional penalty on the vessel term
to prevent estimates running towards infinity for vessels with no
recorded catch of blue or silky sharks.
Our objective was to use a descriptive model to explore the im-
portance of the fisher effect, as opposed to making predictions
about bycatch rates. For this purpose, the other factors affect-
ing shark bycatch are noise to account for in order to isolate the
variable of interest. Fishing location is consistently found to be
a primary driver of bycatch variability for a wide variety of by-
catch types and fisheries; thus, we were particularly interested in
isolating the vessel effect from the location effect. To do this, we
visually explored the vessel regression coefficients to look for dis-
cernible patterns in fishing locations (geographic coordinates of
where the gear was set). We plotted the two- dimensional spatial
smooth included in the model, which accounts for location effects
on bycatch rates. Including a vessel factor accounts for residual
4 of 10 Fish and Fisheries, 2024
variation; for example, it is possible that high bycatch and low
bycatch vessels are in different locations, so geographic variation
is both to do with availability (a characteristic of the sharks) and
catchability (a characteristic of how the fishing is done).
2.4 | Supplementary Analyses of Vessel
Heterogeneity
The captain's logbooks, human observers and onboard cam-
era datasets each have biases and limitations (Suuronen and
Gilma n 2020). Given the known issues with logbook reporting
accuracy in this f leet (Brown etal.2021), we repeated the analysis
using only the observer records (843 sets from 30 vessels over the
same period). Overall, observers covered only 5.9% of sets – which
is higher than most longline fleets in the Western Pacific – but un-
even across vessels, ranging from 0 to 90 observed sets per vessel.
Electronic monitoring systems consisting of multiple sensors and
onboard cameras were installed on six vessels (Brown etal.2021),
which belong to the same company and have identical specifica-
tions (year built, length, tonnage). We repeated the analysis again
using the camera data (713 sets over two years) to examine the
fisher effect within this group of highly similar vessels.
3 | Results
The individual vessel factor had a significant effect on blue
and silky shark catch rates across all data sources: captain's
logbooks, human observers and onboard cameras (Table1). It
was the only variable, apart from location, that was included in
all best models. Notably, the individual vessel was a more con-
sistent predictor of bycatch rates than the operating company
or the boat's physical specifications (size, tonnage, age), which
were only significant for silky sharks. The operating company
improved the model for the observer data model of blue shark
catch but was not significantly better because it lowered the
AIC score by less than two points, indicating the more com-
plex model was only slightly better. The AIC balances model
fit and complexity by adding a relatively heavy penalty of two
units for each additional parameter. Interestingly, the individ-
ual vessel was significant for both species models from the on-
board camera trials on the six vessels from the same company
with identical specifications. This provides further evidence
that there are unrecorded fishing tactics and behaviours em-
ployed by the people operating the vessel that influence the
amount of shark bycatch.
We used the vessel regression coefficients to explore which
vessels drive high or low bycatch (Figure 1). Both the log-
book and the smaller but more reliable observer dataset show
a steady gradient in the regression coefficients, indicating a
range of performance at avoiding bycatch. The distribution is
close to even, with a slight majority of vessels driving lower
bycatch rates (negative coefficients) compared to the fleet av-
erage and a few coefficients that stand out at either end of the
spectrum. Most of the coefficient estimates from the models
of logbook data are significant (86.7% and 46.7% for blue and
TABLE | Overview of the best generalised additive models of blue and silky sharks selected based on the AIC score in a stepwise procedure.
Blue shark Silky shark
Logbook Observer Cameras Logbook Observer Cameras
Target catch ✓ ✓ ✓ ✓ ✓
Yea r ✓ ✓ ✓ ✓ ✓
Month ✓ ✓ ✓ ✓ ✓
Location ✓ ✓ ✓ ✓ ✓ ✓
Set start time ✓ ✓
Hooks between f loats ✓ ✓
Targeting cluster ✓ ✓ ✓
Company —✓ ✓ —
Vessel specifications —✓ ✓ —
Vessel ✓ ✓ ✓ ✓ ✓ ✓
Number of vessels 31 30 631 30 6
Number obser vations 9955 843 713 9955 843 713
AIC null 13,038.2 467.8 495.2 8625.7 536.4 1141.371
AIC vessel random effect 7102.9 354.4 453.4 5246.4 487.2 1076.442
AIC vessel fixed effect 7103.1 347.8 453.2 5246.3 486.2 1076.35
ΔDeviance explained (%) 26.7 16.3 6.4 17.1 7.2 11.8
Note: Checked va riables were included in the model; dashes indicate variables that were not applicable to that data source. The null model is the best model minus
the vessel factor. Bold AIC values mark the best score (with vessel term as a random versus a fixed effect). Delta dev iance explained is the difference between the null
model (no vessel factor) and the best model.
5 of 10
silky sharks, respectively) and have relatively low standard er-
rors, meaning these vessels differ significantly from the over-
all average across vessels. The observer data has much lower
replication across vessels, and thus the coefficient estimates
have larger standard errors.
Evaluating the distribution of regression coefficients indicates
which vessels are able to better avoid sharks, as well as potential
misreporting. For instance, the vessel with the lowest regression
coefficient for silky sharks is suspiciously good compared to the
rest of the fleet, suggesting potential underreporting of shark
catch (Figure1, top right). It was the only vessel to report zero
silky sharks, had no observer coverage and also reported the
lowest blue shark catch rate (FigureS1).
Comparing the model regression coefficients with the reported
or observed catch of vessels reveals that bycatch performance
cannot be accurately judged simply by looking at a vessel's by-
catch and target catch rates (Figure 2). If context were not im-
portant, the low coefficient values would all have low bycatch
rates. Instead, some high performing fishers successfully mini-
mise shark catch while maintaining high tuna catch, but not all
the low bycatch, high target catch vessels have low regression
coefficient estimates. This illustrates the importance of consid-
ering other factors affecting the likelihood of bycatch (e.g., fish-
ing location, season, year).
The regression coefficients are also useful for exploring spatial
patterns in the data, for instance, whether bycatch hotspots
emerged and how individual vessels distributed their effort in
relation to high- risk areas. The spatial surface corroborated
the patterns observed in the raw data, with most vessels re-
porting higher catch rates for one species in certain parts of
the fishing area (Figure3). Previous studies found similar spa-
tial patterns in catch rates, with catch of blue shark highest in
the northern region and silky shark in the central and south-
ern areas (Brown etal.2021; Bromhead etal.2012). Pearson's
correlation tests of each vessel's coefficients for the two spe-
cies models showed no correlation (r = 0.006), indicating that
vessels driving high blue shark catch did not usually drive
high silky shark catch. This is logical because blue and silky
sharks tend to be most abundant in different conditions (e.g.,
depth, time, location), meaning fishers likely employ a suite
of fishing tactics that result in greater exposure to one species
FIGUR E | Regression coefficients for individual vessels from models of blue and silky shark catch using logbook (top) and observer data (bot-
tom). Vessels are ordered left to right by ascending coefficient value for each species model. Vessels with more positive coefficients are associated
with higher bycatch and more negative coefficients with lower bycatch. Error bars show the standard error of the estimated coeff icient value. Orange
coeff icients were significant (p < 0.05) and grey coef ficients were not. Vessel was included as a fixed effect using deviation coding and a penalt y term.
One vessel coefficient is missing from each model because it is used as the intercept.
p >= 0.05
p < 0.05
BLUE SHARK
Vessel
Regression coefficient
LOGBOOK DATAOBSERVER DATA
SILKY SHARK
6 of 10 Fish and Fisheries, 2024
(Brown et al.2021). Importantly, the lack of pattern in neg-
ative versus positive regression coefficients indicates fishing
location does not explain all of the patterns in bycatch rates. If
it did, the coefficient colours would mirror the colour gradient
in the spatial smooth term (Figure3). The observer data shows
a similar pattern (FigureS2).
Given the known data quality issues with self- reported catch,
we compared the three datasets (logbooks, observers, cameras)
to evaluate the suitability of the captain's logbooks for analys-
ing vessels bycatch rates. Interestingly, most vessels underre-
ported tuna catch in logbooks but did not underreport shark
catch compared to observers (FigureS3) or cameras. In general,
logbooks misidentify species and underreport species diversity.
For example, there are no logbook records of pelagic stingray
(Pteroplatytrygon violacea), yet it is relatively common in the
much smaller observer and camera datasets. Blue shark catch
rates were usually higher in logbooks, but observers recorded
higher rates of thresher sharks (Alopias spp.), probably because
captains often record less common species as blue shark, which
is not a protected species. There is no clear correlation between
observer coverage levels and catch rates reported in logbooks
(FiguresS1 and S3). Although low levels of independent moni-
toring prevented rigorous exploration of the accuracy of logbook
data, this preliminary exploration suggested sufficient record-
ing of sharks in logbooks to analyse general patterns of variabil-
ity among vessels.
4 | Discussion
Anecdotal evidence from various fisheries indicates manag-
ers recognise the variability in fishing skill within their fleet,
but it is difficult to determine vessel performance at avoiding
bycatch (Roberson and Wilcox2022; Gladics et al.2017; Hall
et al. 2007). To isolate the fisher effect on bycatch rates, we
control for factors that confound bycatch rates and find there
is significant variability in shark bycatch rates among vessels
belonging to the foreign longline fleet operating in the Republic
of the Marshall Islands. As expected, we find significant spa-
tial patterns in blue and silky shark rates (bycatch hotspots), as
well as other known drivers of bycatch including time of day
and depth of gear. However, the strong result for vessel identity
means that beyond these commonly recorded factors, there are
other undocumented vessel- level differences in fishing prac-
tices that significantly influence the number of sharks caught.
The variability among vessels indicates that fishers have not re-
sponded uniformly to regulations meant to reduce shark catch
FIGUR E | Average shark and tuna catch rates for individual vessels compared to their regression coefficients from models of logbook, observer
and camera data. Coefficients were standardised −1 to 1 for each model; blue indicates low coefficient values (vessels driving lower than average
bycatch rates) and red indicates high coefficient values (vessels driving higher bycatch rates). The ‘gold- standard fisher’ would be a dark blue dot in
the top left corner. The black dot is the omitted vessel coefficient for each model. Lines show standard errors for target and bycatch. There was high
uncertainty in regression coefficient estimates for the observer and camera data models due to low replication and uneven coverage across vessels.
Blue SharkSilky shark
LogbooksObserversCameras
Vessel
regression
coefficient
–0.5
0
1.0
0.5
–1.0
7 of 10
(Bromhead etal.2012). Certain vessels seem to employ fishing
practices that drive high shark catch rates, but, encouragingly,
there is evidence of low- bycatch vessels that still maintain high
catches of the target tuna species. The importance of the vessel
effect for blue and silky sharks is encouraging because these two
species have been a persistent conservation problem for many of
the world's most valuable fisheries.
Our analysis focused on a large captain's logbook dataset with
known reporting inaccuracies. Supplementary analyses using
smaller but more reliable scientific observer and electronic mon-
itoring datas ets val idated the presence of a f isher effect , although
coverage was too uneven to make reliable estimates of the con-
tribution of individual vessels to the general pattern. While we
use the individual vessel as a rough proxy for the skipper, some
skippers likely left the fleet or switched to a different vessel
during the study period. Furthermore, the lack of information
about the identity and roles of the crewmembers prevents us
from disentangling the effects of different individuals onboard
from the physical vessel itself. Thus, our findings underscore the
importance of a general pattern of variability among fishers that
merits further investigation using more reliable data collection
methods, such as onboard observers or cameras. This variability
presents an opportunity to incentivise effective bycatch avoid-
ance tactics already present in the fishery and develop strate-
gies to discourage high bycatch rates (O'Keefe, Cadrin, and
Stokesbury2014b).
The gold- standard fisher (in terms of bycatch) will be able to
maintain high target catch while avoiding other species. Because
the regression coefficient measures the magnitude of the vessel
effect independent of other factors, vessels with low regression
coefficients do not always have the lowest average bycatch. For
example, fishers may operate in high bycatch conditions for var-
ious reasons, such as maximising tuna catch, market factors,
or proximity to ports or supply vessels (O'Keefe, Cadrin, and
Stokesbury2014b; Pascoe etal.2010). Our approach shows there
are persistent differences among vessels even when they operate
in the same location or conditions. This suggests there are other
important operational tactics or skills that affect bycatch rates
in longline fisheries. These could include use of certain types of
equipment (e.g., hooks, bait, lines, light sticks), quality of echo-
sounders or other sensors, skill at using equipment to identify
certain fish species or underwater features such as thermoclines
or currents, knowledge and ability to respond to environmen-
tal cues not recorded in the data, knowledge of species- specific
responses to environmental factors such as lunar cycles, coop-
eration with other vessels or ability to manage crew (Frawley
et al. 2021; Ward and Hindmarsh 2007). Identification of gen-
eral bycatch risk factors (such as spatial or temporal ‘hotspots’)
remains important; in some cases, fleet- wide measures such
as closed areas, seasons or mandated use of bycatch reduction
devices may be effective (Pons etal. 2022; Doherty et al. 2022;
Gilman etal.2024). However, there is likely untapped opportu-
nity to reduce bycatch while still maintaining profitable target
catch, even in hotspot areas (Roberson and Wilcox 2022). The
significance of the fisher effect implies the existence of additional
undocumented factors that could lead to substantial improve-
ments in bycatch levels, if adopted across the fleet.
Identifying drivers of fisher behaviour in different contexts is
essential for designing effective interventions (Hall etal.2007;
Siders etal. 2023). Fisher performance is likely influenced by
both motivation and skill. In some cases, there may be ample
motivation to avoid bycatch species, but doing so requires
greater skill. For example, reducing oceanic shark catch in
FIGUR E | Map of 11,511 fishing events recorded in captain's logbooks for 31 vessels from 2016 to 2018. Top row shows locations coloured by
each vessel 's regression coeff icient for the best model of (A) blue shark and (B) silk y shark catch. More negative coef ficients (dark blues) are ass ociated
with lower bycatch and more positive coef ficients (light greens) with higher bycatch. Grey dots a re the vessel with no regression coefficient because it
was incorporated into the intercept level. Bottom row shows the same locations plotted over the spatial surface for each model with the vessel factor
removed, with lighter colours showing high shark catch areas and darker colours for lower shark catch.
A) BLUE SHARKB) SILKY SHARK
Vessel
coeff.
2.0
1.0
0.0
–1.0
–2.0
14° N
12° N
10° N
8° N
6° N
4° N
2° N
14° N
12° N
10° N
8° N
6° N
4° N
2° N
1.0
0.5
0.0
–0.5
s(Lat,Lon)
1
0
–1
–2
160° E 165° E 170° E 175° E 160° E 165° E 170° E 175° E
3.0
2.0
1.0
0.0
–1.0
–2.0
–3.0
Vessel
coeff.
s(Lat,Lon)
8 of 10 Fish and Fisheries, 2024
pelagic longlines may be a harder problem than reducing tur-
tle bycatch in bottom trawls, because avoiding sharks is less
straightforward than fitting trawlers with turtle excluder de-
vices (Squires et al. 2021). Additionally, there could be less in-
centive to avoid sharks with market value compared to animals
such as seabirds, which waste time and money. Regulations such
as individual bycatch quotas, levies or penalties – when used ap-
propriately – can help build economic incentives to avoid catch
of certain animals (Pascoe etal.2010; Squires etal.2021; Innes
etal.2015). Social or cultural norms can also be important; scat-
tered examples indicate that in- person fisher engagement can
drive changes in fishing behaviours (Jenkins et al.2022), but
this approach can be difficult to scale in large fleets or distant
water fleets where crews rarely come to shore (Hall etal.2007).
Roll out of large- scale observer or camera programs could also
increase uptake of bycatch avoidance tactics (Battista etal.2018;
Suuronen and Gilman2020). However, for long- term shifts in
fisher behaviour, monitoring should be linked to enforcement
measures (Jenkins 2023). For the safety of human observers,
careful consideration must be given to the design and implemen-
tation of these measures (Suuronen and Gilman2020).
The company operating the boat could be another point of
leverage for changing fisher behaviour. Companies that em-
phasise environmental sustainability, or have explicit cor-
porate social responsibility policies around bycatch, might
formally or informally encourage their fleet to use better
fishing practices. Our findings suggest that the company op-
erating the vessel influences silky shark and potentially blue
shark catch rates. There were two main operating companies
in the dataset analysed here. The first had fewer vessels that
were more active and had greater observer coverage; the sec-
ond had three times more vessels with less overall observer
coverage. Additionally, two minor companies (one with only
one vessel) appear to be related to the primary companies,
based on vessel and company names. Very little information
is available about the structure, beneficial ownership and op-
erations of these companies. Thus, company culture is likely
more important than these results indicate, but we lack the
correct data to isolate this effect. For distant water fleets oper-
ated by subsidiary companies, we suspect the beneficial owner
could be a key inf luencer of fishing culture on each boat (Libre
etal.2015). Another important factor likely driving the perfor-
mance of large tuna vessels is the Master Fisher; this person
hires the crew and makes important tactical decisions such
as when and where to set the gear and often remains in close
communication with the boat owner (Libre etal.2015; Bailey
et al. 2012). Unfortunately, the Master Fisher and owner or
financer of the boat are almost impossible to trace.
These vessel- level differences are an important finding from a
conservation management perspective as they suggest it is worth
exploring vessel- level measures rather than just fleet- level man-
agement measures such as time- area closures of ‘bycatch hotspots’,
which have had limited suc cess (Siders etal.2023; O'Keefe, Cadrin,
and Stokesbury2014a). A range of vessel- level management mea-
sures are plausible, such as individual quotas, certifications or
access to additional fishing areas, but all depend on accurate and
reliable vessel- level data. Broad and effective application of elec-
tronic monitoring approaches are likely to be important for vessel-
level management measures to be viable. Some information about
catch is necessary to design effective incentives, although that in-
formation does not have to be perfect (Booth etal.2021; Squires
etal.2021). Notably, the Marshall Islands is one of several Pacific
Island nations aiming for 100% camera coverage of their distant
water longline fleets in the future, supplemented by continued use
of human observers for data validation.
While evaluating the quality of fisheries logbook reporting was
not a focus of this study, our findings corroborate earlier studies
of this fleet that found frequent misreporting of both target and
non- target species in logbooks (Brown et al. 2021; Bromhead
etal.2012). These results contribute to the large body of evidence
that independent catch monitoring – either by human observers,
cameras or both – is essential for effective fisheries management
(Dennis et al.2015). This is relevant for both target and non-
target species, as logbook records are used to calculate tuna
quotas and catch limits and to assess stock status. For sharks,
accurate species identification is becoming increasingly import-
ant as new conservation and management measures are intro-
duced, but – apart from finning bans – restrictions on retention
remain limited to a small number of species (Cronin etal.2022).
In the Western Pacific tuna management area where this fleet
operates, silky sharks can no longer be retained for sale, but blue
shark catch remains largely unregulated (WCPFC2022).
Tuna fisheries generate the majority of oceanic shark catch,
yet shark management and conservation measures are inad-
equate (Pacoureau et al. 2021). Management of sharks and
other non- target species typically operates at the fleet level;
for instance, bans on shark finning or retention, nylon leader
or other gear requirements, or f leet- wide bycatch limits. While
these approaches have effectively reduced bycatch mortality
in many cases, oceanic sharks are an especially complicated
challenge because they tend to co- occur or associate with
some of the world's major catch species (e.g., tuna) and are
often targeted to some extent (Kirby and Ward2014; Cronin
etal.2022). An alternative approach to managing bycatch –
where variable behaviour and skill are assumed instead of
overlooked – could encourage innovation by rewarding high
performers (low- bycatch fishers) and discouraging low per-
formers (Pascoe et al.2010; Booth etal.2021). Additionally,
it would help inform science- based bycatch quotas, catch lim-
its, and mitigation targets that still allow profitable levels of
target catch (O'Keefe, Cadrin, and Stokesbury 2014b). The
implications for fisheries management are extensive, given
that the importance of variability among vessels in driving
catch of non- target species appears to be a systemic pattern
across multiple gear types, species and countries (Roberson
and Wilcox2022; Pascoe etal.2010). Encouragingly, advances
in catch monitoring technologies, such as onboard cameras,
make more individualised and incentive- based management
interventions feasible.
Acknowledgements
A grant from The Nature Conservancy supported LR. CJK is funded by
an Australian Research Council Future Fellowship (200100314). Thank
you to the Marshall Islands Marine Resources Agency for granting per-
mission to access the onboard camera, logbook and obser ver data and to
Beau Bigler for providing feedback on this manuscript. We greatly ap-
preciate the many Electronic Monitoring analysts from the Pacific Island
9 of 10
EM Coordinators and data analysts from Griffith University who were
instrumental in processing the data sets used in this study. This man-
uscript benefitted from early conceptual input from Jessica Ford at the
Commonwealth Scientific and Industrial Research Organisation and
Klaas Hartmann at the Institute for Marine and Antarctic Studies.
Conflicts of Interest
The authors declare no conflicts of interest.
Data Availability Statement
This an alysis used three c onfidential dataset s belonging to the Marsha ll
Islands Marine Resources Agency. In line with the agreement for ac-
cessing these databases, the data may not be released in any form or
in any output that identifies individual people, vessels or characteris-
tics of those vessels. The data needed to replicate the statistical analy-
ses and the maps of fishing locations (Figure 3 and FigureS2) include
this confidential information. The summarised and fully anonymised
data needed to recreate Figures1 and 2, FiguresS1 and S3 will be made
freely available as CSV files in a public GitHub repository.
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Supporting Information
Additional supporting information can be found online in the
Supporting Information section.
