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Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Remote electronic monitoring as a potential alternative to on-board
observers in small-scale fisheries
☆
David C. Bartholomew
a,d
,Jeffrey C. Mangel
a,b,⁎
, Joanna Alfaro-Shigueto
b,c
, Sergio Pingo
b
,
Astrid Jimenez
b
, Brendan J. Godley
a
a
Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE, UK
b
ProDelphinus, Enrique Palacios, 630-204, Lima 18, Peru
c
Universidad Cientifica del Sur, Facultad de Biología Marina, Lima 42, Peru
d
Department of Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK
ARTICLE INFO
Keywords:
Vessel monitoring systems
Shark
Turtle
Dolphin
Bycatch
Camera
ABSTRACT
Small-scale fisheries can greatly impact threatened marine fauna. Peru's small-scale elasmobranch gillnet fishery
captures thousands of sharks and rays each year, and incidentally captures sea turtles, marine mammals and
seabirds. We assessed the ability of a dedicated fisheries remote electronic monitoring (REM) camera to identify
and quantify captures in this fishery by comparing its performance to on-board observer reports. Cameras were
installed across five boats with a total of 228 fishing sets monitored. Of these, 169 sets also had on-board
fisheries observers present. The cameras were shown to be an effective tool for identifying catch, with > 90%
detection rates for 9 of 12 species of elasmobranchs caught. Detection rates of incidental catch were more
variable (sea turtle = 50%; cetacean = 80%; pinniped = 100%). The ability to quantify target catch from
camera imagery degraded for fish quantities exceeding 15 individuals. Cameras were more effective at quan-
tifying rays than sharks for small catch quantities (x ≤15 fish), whereas size affected camera performance for
large catches (x > 15 fish). Our study showed REM to be effective in detecting and quantifying elasmobranch
target catch and pinniped bycatch in Peru's small-scale fishery, but not, without modification, in detecting and
quantifying sea turtle and cetacean bycatch. We showed REM can provide a time- and cost-effective method to
monitor target catch in small-scale fisheries and can be used to overcome some deficiencies in observer reports.
With modifications to the camera specifications, we expect performance to improve for all target catch and
bycatch species.
1. Introduction
Overexploitation has long been identified as a major threat to global
biodiversity (Diamond, 1984), especially in the marine biome (Knapp
et al., 2017). Monitoring of biodiversity and exploitative activities has
been identified as a major priority in conservation biology (Bawa and
Menon, 1997) and new monitoring tools are being developed for a
variety of biomes (e.g. Bicknell et al., 2016; Rist et al., 2010). Improved
monitoring of the fisheries sector is of particular importance as global
illegal, unreported and unregulated (IUU) fishing practices are esti-
mated at 11–26 million tonnes per annum (Agnew et al., 2009).
Small-scale fisheries make a substantial contribution to global fish
captures (Chuenpagdee et al., 2006), producing more than half of the
world's annual catch and supplying most fish consumed in developing
nations (Berkes et al., 2001). However, despite their importance to
global catches, small-scale fisheries are often largely under-regulated
(Berkes et al., 2001). Moreover, small-scale fisheries remain relatively
unstudied compared to large industrial fisheries due to insufficient re-
sources and poor infrastructure (Berkes et al., 2001; Lewison et al.,
2004; Mohammed, 2003; Pauly, 2006), making it difficult to quantify
their impacts on target and non-target species (Berkes et al., 2001;
Lewison et al., 2004; Pauly, 2006).
Independent on-board observers have traditionally been used to
monitor target catch (Alfaro-Cordova et al., 2017; Haigh et al., 2002;
Mangel et al., 2013) and bycatch (Caretta et al., 2004; Gales et al.,
1998; Rogan and Mackey, 2007)infisheries, including some small-scale
fisheries (Doherty et al., 2014; Mangel et al., 2010; Ortiz et al., 2016).
However, use of on-board observers to quantify fishing activities can
https://doi.org/10.1016/j.biocon.2018.01.003
Received 7 May 2017; Received in revised form 15 December 2017; Accepted 2 January 2018
☆
The work is all original research carried out by the authors. All authors agree with the contents of the manuscript and its submission to the journal. No part of the research has been
published in any form elsewhere, unless it is fully acknowledged in the manuscript.
⁎
Corresponding author at: Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Penryn, Cornwall TR10 9FE, UK.
E-mail addresses: J.Mangel@exeter.ac.uk (J.C. Mangel), b.j.godley@exeter.ac.uk (B.J. Godley).
Biological Conservation 219 (2018) 35–45
0006-3207/ © 2018 Published by Elsevier Ltd.
T
sometimes yield biased information, resulting from deployment effects
(Benoît and Allard, 2009), observer effects (Benoît and Allard, 2009;
Faunce and Barbeaux, 2011) and low fleet coverage (McCluskey and
Lewison, 2008). Monitoring small-scale fisheries through observers
poses a major challenge due to the large number of vessels, limited
number of trained personnel, low enforcement and vigilance, and dif-
ficult working conditions, given the small size of vessels (Salas et al.,
2007).
Some vessel monitoring system (VMS) technologies have been de-
veloped as an alternative or to supplement on-board observers. VMS is
most commonly associated with Geographical Positioning Systems
(GPS), but also incorporates other monitoring technologies. VMS is
capable of providing data at high spatial and temporal resolution and
has been installed in numerous fisheries (Campbell et al., 2014;
Gerritsen and Lordan, 2010; Jennings and Lee, 2012; Witt and Godley,
2007), although to date, VMS has been mostly deployed in industrial
fisheries, where it is sometimes mandatory (Bertrand et al., 2008).
Several aspects of fishing activities can be monitored using VMS, in-
cluding vessel position, operational characteristics, engine operation,
and soak time (Kindt-Larsen et al., 2011; Lee et al., 2010; Vermard
et al., 2010). Simple VMS technologies, such as GPS, have been de-
ployed in some small-scale fisheries to monitor their activities (Metcalfe
et al., 2016), whilst also providing some direct benefits to the fishermen
such as improved navigation (Wildlife Conservation Society
Bangladesh, 2016).
One increasingly popular VMS is the use of Remote Electronic
Monitoring (REM) cameras, and represents one of the many applica-
tions of cameras in marine environmental research (Bicknell et al.,
2016). Studies have been carried out to measure the effectiveness of
REM systems at monitoring industrial fishing activities, including target
catch (Ames et al., 2007; Hold et al., 2015; Kindt-Larsen et al., 2011;
Stanley et al., 2009), bycatch (Kindt-Larsen et al., 2012; Pasco et al.,
(a)
(b) (c)
Fig. 1. The camera system developed by Shellcatch Inc. used in
our study to monitor catch includes (i) a camera and GPS logger,
(ii) a battery pack, (iii) a solar panel to charge the battery, and
(iv) a metal frame to mount the camera to the boat. The position
where the camera was installed depended on the vessel's con-
figuration. Attachment locations included (a) guard rail (vessel
2); (b) cabin (vessel 3); (c) mast A-frame (vessel 5).
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
36
2009) and the use of bycatch mitigation technologies (Ames et al.,
2005). The potential benefits of REM systems to small-scale fisheries
research, surveillance and enforcement is high, as it could help improve
the understanding of these large, vastly understudied fleets by supple-
menting or reducing the need for extensive and costly on-board ob-
server programmes.
Within small-scale fisheries, gillnets represent one of the main
capture methods for elasmobranchs (Alfaro-Shigueto et al., 2010;
Cartamil et al., 2011; Smith et al., 2009). In Peru, it is estimated that
approx. 100,000 km of gillnets are set each year by the small-scale
fishing fleet (Alfaro-Shigueto et al., 2010), and studies have shown it to
have high interaction rates with sea turtles, marine mammals and
seabirds (Alfaro-Shigueto et al., 2011; Mangel et al., 2010; Ortiz et al.,
2016). Monitoring this large small-scale fishing fleet, with approx. 3000
vessels is a major challenge (Alfaro-Shigueto et al., 2010), and any
means of enhancing our ability to understand this small-scale fishery
would greatly improve conservation efforts. Our study aimed to assess
the ability of REM systems to detect and quantify target and incidental
catch in Peru's small-scale elasmobranch gillnet fishery and assess the
advantages and disadvantages of using REM technology compared with
on-board observers.
2. Methods
2.1. The fishery
Our study monitored 30 fishing trips across 5 vessels from the small-
scale fishing ports of San José and Bayóvar in northern Peru from
December 2015 to September 2016. Small-scale fishery vessels are
defined by Peruvian fishery regulations as having a maximum length of
15 m, a maximum storage capacity of 32.6 m
3
, and relying pre-
dominantly on manual labour for all fishing activities (Ley General de
Pesca, 2001). The vessels used in our study had a mean length of 10.8 m
(0.8 m SD; Range 10–12 m; Supplementary Table 1). Our study fishery
uses monofilament and multifilament gillnets that are set in the late
afternoon by the fishing vessels, and left to soak near the surface or
seafloor for approx. 14 h, before being retrieved early the following
morning. The nets stay fixed to the vessel drifting throughout the set
and are typically 1.5 to 3 km long with a stretched mesh size of 8 to
15 cm. The fishery catches multiple species but primarily targets shark
and ray species. The fishery also incidentally captures sea turtles
(Alfaro-Shigueto et al., 2011), cetaceans (Mangel et al., 2010), pinni-
peds (Alfaro-Shigueto et al., 2010), and seabirds (Awkerman et al.,
2006). All fishing vessels and crews were voluntary participants in the
study.
2.2. Camera system
The camera system used to monitor the catches on board vessels was
developed by Shellcatch Inc. (http://www.shellcatch.com), and com-
prised a camera and GPS logger, connected to a portable power pack
charged by a solar panel (Fig. 1). The camera lens was equivalent to a
35 mm full-frame SLR lens, with a fixed focal length of
3.60 ± 0.01 mm and focal ratio (F-stop) of 2.9. The camera's field of
view was set to 53.5 ± 0.1° by 41.4 ± 0.1° and the sensor resolution
was set to 2592 by 1944 pixels. The camera was programmed by
Shelllcatch Inc. to take photos continuously at 40 s intervals to balance
data collection with data management, despite the possibility of
missing discarded catch within this interval. The images were recorded
to a built-in hard drive and were subsequently downloaded to a com-
puter using cloud data storage software developed by Shellcatch Inc.
The entire system was enclosed in a waterproof housing and was in-
stalled on each fishing vessel using a metal mount (Fig. 1).
The camera systems were deployed on five fishing vessels. They
were mounted in a location to provide maximal coverage of the vessel
where catch is processed and to maintain exposure to the sun to charge
the battery through the solar panel. The exact location of the camera
was decided after consultation with the fishermen, to prevent the
camera hindering normal fishing practices, and to ensure some privacy
was provided to the fishermen outside of fishing activities. The in-
stallation process was also dependent on the exact configuration of each
fishing vessel as the fleet is composed of a range of different vessel
types, some containing cabins of varying height. The fishermen were
asked to undertake normal fishing practices and not to alter their be-
haviour in the presence of the camera.
2.3. On-board observer data collection
On four of the five fishing vessels, trained on-board observers were
additionally present (Supplementary Table 1). Observers recorded the
number of individuals captured for all elasmobranch and bycatch spe-
cies. Identification guides were provided to the observers to aid species
identification. Observers also recorded the total length for a subset of
the sharks captured (following Romero et al., 2015) and the disc width
for a subset of the rays captured (following Ebert and Mostarda, 2016).
Catch was recorded using common names, so it was not always possible
to distinguish between closely related species that share a common
name. Consequently, all target catch analysis was done at the genus
level. Participating fishermen were consulted to verify that all common
names correctly matched our interpretation.
2.4. Photo analysis
Photos were analysed using GoPro Studio version 2.5.9. This soft-
ware was used to convert the photos into a time lapse video at 10
frames per second with high image quality (Supplementary Video 1).
An analyst subsequently reviewed the videos using QuickTime Player
version 10.4. Each haul was analysed frame by frame and each captured
animal was recorded. The analyst identified the catch to genus and
consulted an expert for assistance when identification was uncertain.
Identification was aided by identification guides for each taxon. For a
sample of sets (n= 139) we recorded the amount of time necessary to
complete the photo analysis. The mean time per set was
26.5 ± 11 min (mean ± SD; range 8.3–46.3; n = 139).
2.5. Statistical analysis
2.5.1. Target catch
The on-board observer reports were compared to the photo analyst's
observations. The number of individuals of each genus was compared
for each haul and the difference between the two methods was calcu-
lated. For each fishing vessel, the mean and standard deviation of the
number of individuals captured per set was calculated from the ob-
servers' reports and the photo analysis. Ratios were calculated by di-
viding catch quantity from observer reports by catch quantity identified
by the photo analyst. As net length could not be estimated from the
photos and varied between sets, it was not possible to use the catch per
unit effort (CPUE) metric, so catch per set was used in this study. Catch
genera were identified by either the observer, the camera or both in
each set. A percentage occurrence was calculated for each outcome to
determine the ability of the camera to detect each genera.
The mean and standard deviation of the discrepancy between the
two methods was also calculated for sets when either the observer or
the photo analyst reported catch for each genus. All instances when
there was a difference in number of animals landed of the same genus
between the observer report and the photo analyst's observations were
investigated. After subsequent review of the time lapse video, the likely
causes of the discrepancy were identified and attributed to six different
categories: camera failure, camera obstruction, insufficient field of view
(identified by catch being piled on the edge of the camera's field of
view), insufficient light levels, image resolution, or clear deficiencies in
the observer reports.
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
37
To understand which parameters affect the performance of the
cameras, generalised linear mixed effects models (GLMM) with a ne-
gative binomial error structure and log link function were undertaken
(n= 362 species capture events) using package lme4 in R statistical
software, version 3.2.3 (Bates et al., 2015; R Core Team, 2014). A ne-
gative binomial error distribution was used as our dependent variable
(quantity detected by the camera) involved counts with a variance
greater than the mean. Sets where no catch was detected by the ob-
servers and the camera for each genus were removed, as they were not
appropriate for investigations into the factors affecting camera perfor-
mance, especially when considering the sheer number of zeros (1859 of
2028 possible captures) –most hauls capture only a few genera. Initial
models included fixed effect (quantity from observer reports, mean
species size and taxon (i.e. shark or ray) and random effect (haul)
parameters. Vessel was not included as a random effect as all variation
between vessels was accounted for through the inclusion of haul as a
random effect. Catch quantity from observer reports was included as a
quadratic term to test if the camera performed more effectively with
different catch magnitudes. Different genera were divided into three
size categories based on the mean total length (sharks) or disc width
(rays) for each genus calculated from the size measurements the ob-
servers recorded. Genera with a mean length or width ≤100 cm were
classified into size class A, > 100 cm and ≤150 cm as class B, and >
150 cm as class C. Models of all possible combinations of fixed effects
were tested using the dredge function in the R package MuMIn (Bartoń,
2017), after the global model was standardised using the standardize
function in the arm package (Gelman and Su, 2016). The minimal
adequate model was selected based on the lowest Akaike Information
Criterion corrected for small sample size (AICc) value (Sakamoto et al.,
1986). Initial model selection included the quadratic term for quantity
from observer reports in the minimal adequate model, but after model
inspection, a quadratic function was not appropriate due to a high
heteroscadiscity of model residuals. Instead, a stepwise regression
model was undertaken, with an appropriate split point for our dataset
identified using the segmented function in the R package segmented
(Muggeo, 2003). GLMMs were subsequently undertaken using the same
procedure as described above, but with observer quantity included as a
linear term, for both small catches (observer quantity ≤20; n= 296)
and large catches (observer quantity > 20; n= 66). For larger cat-
ches, a Poisson error distribution with square-root link function fitted
our data more efficiently, so was used as the model error family. Fol-
lowing this stepwise regression approach, one anomalous point
(camera = 179, observer = 1200) was shown to be highly influential
on our models, so the stepwise regression procedure was repeated
without this extreme value, identifying a new split point for our data.
GLMMs were again used to model both small catches (observer quan-
tity ≤15; n= 279) and large catches (observer quantity > 15;
n= 82).
2.5.2. Bycatch
A comparison was also made between the observer reports and the
photo analyst's observations for bycatch. The detection rate of bycatch
when recorded by either the observer or the photo analyst was com-
pared for the two methods. A mean and standard deviation for the
detection rates for each vessel was subsequently taken to measure the
ability of detecting bycatch using cameras. Due to the low-resolution
specifications of the camera, the photo analyst was not always able to
identify the bycatch to species level, so all analyses were based on
higher taxonomic groupings (cetaceans, pinnipeds, leatherback turtles
Dermochelys coriacea, hard-shell sea turtles, seabirds). Attempts were
made by the photo analyst and three experts to identify the hard-shell
sea turtles to species level and these were compared to the observer
reports.
3. Results
3.1. Fishing effort
A total of 228 fishing sets from December 2015 to September 2016
across the five fishing vessels were reviewed by the photo analyst and
catch was recorded for each set. 89% of sets took place over the con-
tinental shelf within 50 km of the coastline. A total of 169 sets were
reviewed during the study period across the four vessels with observers
present. Initial studies revealed the position of the camera on vessel 2
was not appropriate as the fishermen piled the nets in front of the
camera, preventing the photo analyst from seeing much of the catch.
Consequently, the camera position was changed and the 12 sets where
the problem occurred were excluded from subsequent analyses. Vessel 1
did not have an observer aboard for the initial 14 sets, so these were
also excluded from subsequent analyses.
3.2. Target catch
Twelve genera of elasmobranchs were captured and identified by
both the observers and the photo analyst across the four vessels with
observers present (Fig. 2). One genus (Sphyrna) was captured by all four
fishing vessels, seven genera (Carcharhinus,Galeorhinus,Mobula,Mus-
telus,Myliobatis,Notorhynchus,Squatina) were captured by three fishing
vessels, one genus (Alopias) was captured by two fishing vessels and
three genera (Prionace,Pteroplatytrygon,Triakis) were captured by only
one vessel. For six genera (Carcharhinus,Notorhynchus,Mustelus,My-
liobatis,Sphyrna,Squatina), the mean catch recorded by the observers
was higher than that identified by the photo analyst (ratios ranging
from 0.52 to 1.00). In contrast, the mean catch recorded by the ob-
servers was lower than that identified by the photo analyst for five
genera (Alopias,Galeorhinus,Mobula,Prionace,Pteroplatytrygon; ratios
ranging from 1.00 to 2.44). There was no discrepancy between the two
methods for Triakis (Table 1a).
The ability of the cameras to identify the genera caught in each set
was investigated for each vessel. For 9 of 12 genera of target catch, the
photo analyst was able to detect its capture for > 90% of instances
when reported by the observer. Only 3 genera (Carcharhinus,
Pteroplatytrygon, and Squatina) were detected by the photo analyst on
≤90% of instances when reported by the observer (85%, 82% and 65%
respectively; Table 1b).
The discrepancy between the number of individuals caught for each
genus was calculated for all sets when either the observer or photo
analyst recorded the genus as captured. All genera of elasmobranchs,
except Mustelus and Sphyrna, had a mean discrepancy of < 5 in-
dividuals (Table 1c). There were 226 instances when there was a dis-
crepancy between the observer and the photo analyst's reports. Six main
problems were identified as the potential cause of the discrepancies:
camera field of view (n= 134), camera obstructions (n= 60), image
resolution (n= 58), observer failing to record all catch (n= 51),
camera failure (n= 45), and low light levels (n= 21; Fig. 3).
GLMMs were undertaken to understand which factors affected the
performance of the cameras (n= 362 species capture incidences). The
effects of quantity, size and whether the catch was a shark or ray were
investigated. The variation between different sets was controlled for by
a random effect in our model. Quantity and size were retained in our
initial minimal adequate model when quantity was included as a
quadratic term (MAM; all other models ΔAICc > 2; see Supplementary
Tables 2a & 3a). Taxon was not retained in the MAM.
A stepwise regression model was subsequently undertaken with
observer quantity as a linear term, with an appropriate split point es-
timated at 20.74 (1.39 SE; n = 362). GLMMs were undertaken for both
small catches (observer quantity ≤20; n= 296) and large catches
(observer quantity > 20; n= 66). Observer quantity and taxon were
retained in our MAMs for both small and large catches, but size class
was no longer identified to influence camera performance. GLMMs
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
38
were re-applied after removal of a highly influential anomaly and a new
split point was identified at 15.73 (1.23 SE, n= 361). For small catches
(observer quantity ≤15; n= 279), observer quantity and taxon were
retained in our MAM (see Supplementary Tables 2b & 3b):
⇒Camera Negative Binomial μ e Camera μ~()()~
iiii
=+× +× +
η
β Observed β Shark Ray a0.673 .
ii
12
=μη
l
og
i
i
where β1=0.160;
=⎧
⎨
⎩=
−=
β
Shark Ray Ray
Shark Ray Shark
0, .
0.327 .
;
2
aN~ (0, 0.117).
i
For large catches (observer quantity > 15; n= 82) observer
quantity and size class were retained in our MAM (see Supplementary
Tables 2c & 3c):
⇒Camera Poisson μ Camera μ~()( )~
iiii
2
=+× +× +
η
β Observed β Size Class a3.216 .
ii
12
Fig. 2. The target species of the San José and Bayóvar fishery includes several shark and ray species: (a) thresher sharks (Alopias spp.), (b) bronze whalers (Carcharhinus brachyurus), (c)
school sharks (Galeorhinus galeus), (d) broadnose sevengill sharks (Notorhynchus cepidianus), (e) Blue sharks (Prionace glauca) (f) Pacific angel sharks (Squatina californica), (g) ham-
merhead sharks (Sphyrna spp.), (h) smoothhound sharks (Mustelus spp.), (i) spotted houndsharks (Triakis maculata), (j) eagle rays (Myliobatis spp.), (k) pelagic stingrays (Pteroplatytrygon
violacea) and (l) spinetail devil rays (Mobula japanica). Images captured using cameras developed by Shellcatch Inc. installed on the fishing vessels involved in our study.
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
39
=μη
ii
where β
1
= 0.041;
=⎧
⎨
⎩=
=
β
Size Class A
Size Class B
0, .
1.782 . ;
2
aN~ (0, 3.470).
i
This suggests that quantity and taxon influence camera performance
for small catches (x ≤15) and that quantity and size category influence
camera performance for large catches (x > 15). The cameras were
more accurate at quantifying catch when catch quantity was low
(GLMM, Z = −12.197, p< 0.001; Supplementary Fig. 1a). For small
catches, the camera performed better for rays than sharks (GLMM,
Z=−3.479, p < 0.001). For large catches, the camera performed
more accurately for species of medium size (category) than for small
species (category A; GLMM, Z = 3.740, p < 0.001; Supplementary
Fig. 1b). It was not possible to measure camera performance for large
species (category C) for large catches as no catches for this size class
exceeded 15 individuals.
3.3. Bycatch
From 172 sets, observers recorded a total of 33 hard shell sea turtles
(19 green Chelonia mydas, 9 olive ridley Lepidochelys olivacea, 5 uni-
dentified) in 20 sets; 7 dolphins (3 common Delphinus spp., 2 dusky
Lagenorhynchus obscurus, 2 Burmeister's porpoise Phocoena spinipinnis)
in 7 sets; and 5 South American sea lions Otaria flavescens in 2 sets as
incidental capture (Fig. 4). The photo analyst recorded a total of 12
turtles, 4 dolphins and 5 seals captured from reviewing the same trips.
The photo analyst recorded 3 dolphins in 3 sets and 5 sea lions in 5 sets
that were not reported by the observers. No leatherback turtles or
seabirds were captured during trips with observers present, but were
detected by the cameras on vessels lacking observers (1 leatherback
turtle, 1 Humboldt penguin Spheniscus humboldti). 48 hard shell sea
turtles in 21 sets, 10 dolphins in 7 sets and 6 South American sea lions
in 5 sets were also detected by the cameras from 47 sets (9 trips)
without an observer present.
The ability of the camera to detect the presence of bycatch was
determined for each vessel. Dividing sets of photo analyst detected
bycatch by analyst and/or observer detected bycatch determined the
camera detection percentage. Sea turtle bycatch had a mean detection
of 50% (26% SD; n= 3 vessels), whilst the mean detection rate of
pinniped bycatch was 100% (0% SD; n= 2 vessels) and cetacean by-
catch was 80% (36% SD; n = 3 vessels; Table 1b).
Attempts were made by the photo analyst and three experts to
identify the 12 hard shell sea turtles detected by the photo analyst for
vessels with on-board observers present. On-board observers were as-
sumed to have correctly identified all individuals to species level as
they were able to manipulate the animal to facilitate identification.
After comparing identifications with those from on-board observers, it
was possible to correctly identify the turtles to species level with a
mean accuracy of 83% (15% SD). It was not always possible to identify
the animal to species level due to limitations in the camera's image
resolution.
4. Discussion
Monitoring catch in small-scale fisheries is vital to understanding
their impact on aquatic ecosystems. In this study, we present a quan-
titative assessment of electronic monitoring using cameras in a small-
scale fishery setting. Our study showed remote electronic monitoring
(REM) to be effective in detecting and quantifying elasmobranch target
catch and pinniped bycatch in Peru's small-scale fishery, but not in
detecting and quantifying sea turtle and cetacean bycatch. When
compared to previous studies looking at similar REM systems in in-
dustrial fisheries, REM performed at similar accuracies in our study for
both target and incidental catch (Ames et al., 2005; Kindt-Larsen et al.,
2011; Pasco et al., 2009; Stanley et al., 2009).
The cameras installed on the fishing vessels were shown to be highly
effective at identifying the genera of target catch. In fact, our study
showed observers were more likely to fail to report genera captured
than the camera failing to detect them. In many of the instances, the
photo analyst noted that much of the unreported catch was consumed
on-board by the fishermen or was of low economic value, e.g. non-
commercial crabs, catfish, rays and small invertebrates. Thus, our study
and wider-scale use of REM, could help improve understanding of the
population-level impacts on species of low economic value that are
consumed by fishermen or discarded, which often remain unreported in
small-scale fisheries (Salas et al., 2007).
From our analysis, three features of the catch composition were
shown to affect the camera performance at quantifying catch: catch
quantity, taxonomic group and mean body size. Firstly, our results show
Table 1a
Mean catch of each genus per set identified by the cameras installed on the boats and recorded by the observers in their reports for the four vessels with observers present. Catch was
measured in terms of numbers of individuals captured.
Species Common name 1 (N = 15) 2 (N=9) 4(N= 44) 5 (N= 101) Mean (N=4)
Camera Observer
reports
Camera Observer
reports
Camera Observer
reports
Camera Observer
reports
Camera Observer
reports
Sharks
Alopias spp. Thresher 1.2 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3
Carcharhinus
brachyurus
Bronze whaler 0.0 0.0 1.9 1.9 0.1 0.1 0.7 0.7 0.7 0.7
Galeorhinus galeus School 0.1 0.0 0.0 0.0 0.3 0.3 0.1 0.4 0.1 0.1
Mustelus spp. Smoothhound 0.0 0.0 3.7 5.9 43.6 84.6 12.5 0.0 10.9 26.6
Notorhynchus
cepidianus
Broadnose
sevengill
0.0 0.0 0.1 0.3 0.0 0.0 0.4 15.9 0.1 0.2
Prionace glauca Blue 0.8 0.7 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.2
Sphyrna spp. Hammerhead 15.3 21.1 5.7 9.8 0.1 0.1 0.8 0.9 5.5 8.0
Squatina californica Angel 0.0 0.0 0.0 0.2 0.6 0.7 1.7 1.6 0.6 0.6
Triakis maculata Spotted
houndshark
0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.0 0.0
Rays
Mobula spp. Devil 0.4 0.0 7.7 4.3 0.0 0.0 0.1 0.1 2.1 1.1
Myliobatis spp. Eagle 0.0 0.0 2.0 2.7 1.7 1.8 5.4 6.9 2.3 2.8
Pteroplatytrygon
violacea
Pelagic stingray 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.3 0.1 0.1
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
40
Table 1b
Probability of detection of each genus of target catch and taxon of bycatch by the cameras and observers for the four vessels with observers present. It should be noted that there may also be instances when both the camera and observers fail to
detect catch, but this cannot be directly measured in our study.
Species Common name 1 2 4 5 Mean (SD)
p
Ba
p
Cb
p
Oc
Np
Ba
p
Cb
p
Oc
Np
B
p
C
p
O
Np
B
p
C
p
O
Np
B
p
C
p
O
N
Target catch
Sharks
Alopias spp. Thresher 63% 25% 13% 8 –––0–––0 100% 0% 0% 2 81% (27) 13% (18) 6% (9) 2
Carcharhinus brachyurus Bronze whaler –––0 83% 17% 0% 6 67% 0% 33% 3 67% 22% 11% 9 72% (10) 13% (12) 15% (17) 3
Galeorhinus galeus School 0% 100% 0% 1 –––0 86% 0% 14% 7 100% 0% 0% 5 62% (54) 33% (58) 5% (8) 3
Mustelus spp. Smoothhound –––0 100% 0% 0% 3 100% 0% 0% 12 86% 5% 9% 65 94% (7) 2% (3) 5% (5) 3
Notorhynchus cepidianus Broadnose sevengill –––0 100% 0% 0% 1 100% 0% 0% 1 60% 24% 16% 25 87% (23) 8% (14) 5% (9) 3
Prionace glauca Blue 100% 0% 0% 4 –––0–––0–––0 100% (0) 0% (0) 0% (0) 1
Sphyrna spp. Hammerhead 83% 0% 17% 6 100% 0% 0% 4 100% 0% 0% 3 79% 14% 7% 14 90% (11) 4% (7) 6% (8) 4
Squatina californica Angel –––0 0% 0% 100% 1 88% 13% 0% 8 85% 12% 3% 33 57% (50) 8% (7) 34% (57) 3
Triakis maculata Spotted houndshark –––0–––0–––0 100% 0% 0% 4 100% (0) 0% (0) 0% (0) 1
Rays
Mobula spp. Devil 0% 100% 0% 4 75% 25% 0% 4 –––0 80% 20% 0% 5 52% (45) 48% (44) 0% (0) 3
Myliobatis spp. Eagle –––0 100% 0% 0% 2 83% 17% 0% 12 78% 15% 7% 67 87% (12) 11% (9) 2% (4) 3
Pteroplatytrygon violacea Pelagic stingray –––0–––0–––0 59% 24% 18% 17 59% (0) 24% (0) 18% (0) 1
Bycatch
Cetacean 50% 50% 0% 4 –––0 100% 0% 0% 1 20% 20% 60% 5 57% (40) 23% (25) 20% (35) 3
Pinniped –––0–––0 0% 100% 0% 2 40% 60% 0% 5 20% (28) 80% (28) 0% (0) 2
Sea Turtle 20% 0% 80% 5 60% 0% 40% 5 –––0 70% 0% 30% 10 50% (26) 0% (0) 50% (26) 3
a
p
B
=probability of detection by both the camera and observers.
b
p
C
=probability of detection by cameras but not observers.
c
p
O
=probability of detection by observers but not cameras.
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
41
that the camera's performance was lower when catch quantity exceeded
15 individuals. With high quantities of catch, the photo analyst was
unable to distinguish between individuals as they became piled up,
reducing the accuracy of catch estimates. Other studies have also pre-
viously found electronic monitoring performance to decline as the catch
magnitude increases in mixed-species net fisheries (Lara-Lopez et al.,
2012; van Helmond et al., 2015). This finding contrasts to longline
fisheries, where several studies showed quantity did not affect catch
estimates generated from electronic monitoring (Ames et al., 2005,
2007; Stanley et al., 2009). Our study further emphasises the difficulty
of quantifying catch in net fisheries where many individuals are hauled
together, unlike longline fisheries where individuals are hauled one by
one and can be counted more easily.
Secondly, our study suggests REM is more effective at detecting and
quantifying ray species than sharks when catch quantity is below 15
individuals, but not for larger catches. This may be a consequence of the
greater surface area of rays compared to sharks, increasing the
likelihood of each individual being detected on camera. However, this
result might simply be a consequence of the positioning of the cameras
on the fishing vessels, with rays more likely to be placed within the
camera's field of view than sharks.
Finally, our study has shown that REM performs differentially for
different sized target catch genera when catches exceed 15 individuals,
with a lower proportion of small-sized animals (size class A: length/
width ≤100 cm) detected. Few studies have investigated the effect of
size on electronic monitoring performance in fisheries (Pasco et al.,
2009; van Helmond et al., 2015). Pasco et al. (2009) studied the effect
of size on cod bycatch recognition in the Northern Irish Nephrops
fishery, whilst van Helmond et al. (2015) investigated the effects of
mesh size, and coincidentally the size of individuals captured, in the
Dutch bottom-trawl fishery on electronic monitoring performance. In
both studies, it was shown that quantifying catch was easier for larger
individuals, corresponding with our findings.
Our cameras performed less well at detecting and quantifying in-
cidentally caught large vertebrate species, corresponding with the re-
sults of previous studies (Ames, 2005; Ames et al., 2007; Pasco et al.,
2009). Our study does, however, contrast with the findings of a pre-
vious study (Lara-Lopez et al., 2012) who found electronic monitoring
to be more effective at quantifying bycatch than target catch in the
southern Australian shark gillnet fishery. However, in previous studies
the cameras were configured to prioritise monitoring of bycatch (Lara-
Lopez et al., 2012), whereas our current study prioritised the location of
target catch processing. The difference in priority could explain the
contrasting outcomes.
Lower rates of detection for bycatch could also be explained by the
length of time catch spends on deck, with unwanted catch released or
discarded relatively quickly after it is hauled. Consequently, bycatch
may not pass into the camera's field of view during this period. Frame
rates have been identified as an issue limiting the effectiveness of
electronic monitoring in other studies (Denit et al., 2016; Needle et al.,
2014), and the 40 s interval between photos could be a cause of lower
performance in our study. Moreover, sea turtles and cetaceans can
damage nets and pose a major challenge to haul aboard for fishermen,
especially those that rely predominantly on manual labour, meaning
much bycatch is not brought on deck. Many incidentally caught in-
dividuals will also drop out of the net before reaching the deck
(Bravington and Bisack, 1996; Kindt-Larsen et al., 2012). Consequently,
these animals that fail to reach the deck will never enter the camera's
field of view, but may still be detected by observers. Following in-
vestigations into the cause of discrepancies between observer and photo
analyst reports, the majority were attributed to aspects of the camera's
specification that were kept low to aid data storage and management. It
Table 1c
Mean discrepancy between catch identified by the cameras and observer reports for each genus for the four vessels with observers present. Catch was measured in terms of numbers of
individuals captured.
Genus Common name Discrepancy
1 N 2 N 4 N 5 N Mean N
Sharks
Alopias spp. Thresher 0.6 8 –0–0 0.0 2 0.3 2
Carcharhinus brachyurus Bronze whaler –0 0.7 6 0.3 3 0.6 9 0.5 3
Galeorhinus galeus School 1 1 –0 0.3 7 0.2 5 0.5 3
Mustelus spp. Smoothhound –0 6.7 3 54.3 37 7.2 66 22.7 3
Notorhynchus cepedianus Broadnose sevengill –0 2.0 1 0.0 1 0.8 25 0.9 3
Prionace glauca Blue 0.5 4 –0–0–0 0.5 1
Sphyrna spp. Hammerhead 14.3 6 9.3 4 0.0 3 1.6 14 6.3 4
Squatina californica Angel –0 0.0 1 1.4 8 0.8 28 0.7 3
Triakis maculata Spotted houndshark –0–0–0 0.0 4 0.0 1
Rays
Mobula spp. Devil 1.5 4 9.0 4 –0 0.4 5 3.6 3
Myliobatis spp. Eagle –0 3.0 2 1.3 12 4.9 67 3.1 3
Pteroplatytrygon violacea Pelagic stingray –0–0–0 0.9 17 0.9 1
Fig. 3. The proportion of incidence of different factors causing discrepancies in the
number of individuals per genus between the observer reports and that identified by the
photo analyst for each vessel (1: n= 15; 2: n= 16; 4: n= 52; 5: n= 143). The mean
proportion of incidence for the 4 vessels was calculated. The causes of the discrepancies
were divided into six different categories: CF –camera failure, CO –camera obstructed by
objects or fishermen, FOV –insufficient field of view, IR –insufficient image resolution,
LL –low light preventing a clear photo, OR –deficiencies in the observer reports. The
camera's field of view was identified as the main factor causing discrepancies between the
photo analyst's and observer reports.
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
42
is expected overall performance will improve through modifications to
the camera's specifications, as found in previous studies (Ames et al.,
2007).
The use of REM could provide a lower cost alternative to the on-
board observer programme. Based on estimated costs of our observer
programme and electronic monitoring systems, including installation,
servicing, data storage and wage costs, REM systems offered savings of
approx. 50% per vessel monitored. Unlike on-board observers who have
to be at sea for the duration of the fishing trip, photo analysts can re-
view a day's fishing in under 30 min. Electronic monitoring also over-
comes other challenges of monitoring small-scale fisheries, such as
space limitation for observers, security at sea in small vessels and large
fleet sizes. Kindt-Larsen et al. (2012) showed electronic monitoring
could provide > 50% savings over observer programmes, although this
is likely attributed to higher wages in the study country. Financial
savings from electronic monitoring could allow for a substantial in-
crease in fleet coverage compared to on-board observers. Advance-
ments in technology and decreasing costs of data storage mean elec-
tronic monitoring is likely to become an even cheaper alternative,
whilst providing more accurate data.
Our study has revealed many advantages and disadvantages of using
REM and on-board observers to monitor the catch of small-scale fish-
eries (Table 2). REM has the potential to replace or supplement on-
board observers to monitor small-scale fisheries, which remain widely
unmonitored and unstudied globally (Berkes et al., 2001; Lewison et al.,
2004; Mohammed, 2003; Pauly, 2006). The potential applications of
electronic monitoring in small-scale fisheries are numerous. When
combined with GPS data it can provide a powerful tool to identify
fishing grounds, areas of high bycatch risk and other important data for
fishery management and conservation (Gerritsen and Lordan, 2010;
Jennings and Lee, 2012; Witt and Godley, 2007). Moreover, recent
studies have identified effective bycatch mitigation technologies for
small-scale fisheries (Mangel et al., 2013; Ortiz et al., 2016; Peckham
et al., 2016) and REM could supplement observer data to improve ac-
curacy, monitor their effectiveness and enforce their use. With an
appropriate regulatory or enforcement structure, REM could also be
used to monitor illegal fishing practices, such as the shark finning trade
(Worm et al., 2013).
Despite its potential to improve fisheries' monitoring, concerns re-
garding the effectiveness of electronic monitoring systems remain
(Association for Professional Observers, 2016). Some of these could
more easily be overcome, such as through modifications to the camera
specifications (e.g. frame rate), but others relate to the inherent nature
of these systems. Camera systems can be manipulated, may be poorly
maintained and are vulnerable to hidden activity outside their field of
view. Consequently, in some cases, actions may be required to over-
come these limitations, such as installation of multiple cameras or pe-
nalties for violations.
Although the use of REM could help increase the coverage of small-
scale fisheries, the vast nature of these fishing fleets remains a great
challenge. Peru's small-scale fisheries alone are composed of nearly
10,000 vessels (Alfaro-Shigueto et al., 2010), meaning full coverage
remains unlikely. Nevertheless, REM has the potential to dramatically
advance our understanding of small-scale fishery interactions with
elasmobranchs and other threatened taxa (a key research priority e.g.
Rees et al., 2016). Any method that increases the quality and quantity
of data can ultimately only help inform and improve conservation ac-
tions.
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.biocon.2018.01.003.
Acknowledgements
We would like to thank the members of Pro Delphinus, including
Francisco Cordova and Hazel Akester, who provided assistance with
data collection and analysis. Advice on statistical analysis was provided
by Prof. Dave Hodgson, Dr. Robert Thomas and Jeremy Smith. Helpful
comments and suggestions on the manuscript were provided by the
editor and three anonymous reviewers. This project would not have
been possible without the gracious cooperation of all the fishermen
(a) (b) (c)
(d) (e) (f)
Fig. 4. Several species are also caught incidentally in the
fishery: (a) common dolphins (Delphinus spp.), (b) dusky
dolphin (Lagenorhynchus obscurus), (c) olive ridley turtle
(Lepidochelys olivacea), (d) leatherback turtle (Dermochelys
coriacea), (e) South American sea lion (Otaria flavescens)
and (f) Humboldt penguin (Spheniscus humboldti). Images
captured using cameras developed by Shellcatch Inc. in-
stalled on the fishing vessels involved in our study.
D.C. Bartholomew et al. Biological Conservation 219 (2018) 35–45
43
involved in this study. We are grateful to Shellcatch Inc. for developing
the camera systems and sharing the camera specifications. This work
was supported by the Darwin Initiative Project EIDP0046 and the
Whitley Fund for Nature Grant 150626 CF15. David C. Bartholomew is
supported by a NERC studentship NE/L002434/1.
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Table 2
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Factor Cameras On-board observers
Boat coverage Dependent on field of view and positioning of the
camera
Whole vessel coverage
Fleet coverage Potentially high Difficult to implement on a large spatial and temporal scale
Bias Independent analyst Fishermen may not report truthfully or may change activity in the presence of
an independent observer
Species identification Analyst can review multiple times and can consult an
expert. Dependent on visual cues
Identification once and in real-time, unless pictures taken. Can use multiple
cues to identify (visual, smell, touch)
Animal manipulation Angle and visual cues dependent on camera Observer can alter position to aid identification
Biological sampling Not possible Possible
Re-analysis Possible Not possible, unless pictures taken
Image quality Camera resolution Human eye
Data intensity Data intensive Data non-intensive
Data processing Same time as analysis Subsequent entry –commonly hand written and then added to electronic
database. Use of apps and computer programs on-board the exception
Automation Potential for artificial intelligence None
Catch per unit effort (CPUE)
calculation
Difficult to estimate net length, but soak time estimate
possible
Possible
Human hours Low–< 30 min to analyse each set High - Observer required to be onboard for duration of trip
Cost Medium High
Vessel accommodation Little space required Space to occupy an extra person on-board required
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