1503
Anglerfish catchability for swept-area abundance estimates
in a new survey trawl
D. G. Reid, V. J. Allen, D. J. Bova, E. G. Jones, R. J. Kynoch, K. J. Peach, P. G. Fernandes,
and W. R. Turrell
Reid, D. G., Allen, V. J., Bova, D. J., Jones, E. G., Kynoch, R. J., Peach, K. J., Fernandes, P. G., and Turrell, W. R. 2007. Anglerfish catchability for
swept-area abundance estimates in a new survey trawl. – ICES Journal of Marine Science, 64: 1503– 1511.
In 2005, a new trawl survey was launched in Scotland to estimate anglerfish (Lophius spp.) abundance using swept-area estimates. This
required an understanding of the herding of the fish by the gear, particularly in the zone between the doors and wing ends. TV observations at the wing ends and along the sweeps were used to quantify the behavioural reactions of anglerfish. These observations were
analysed to develop a gear efficiency estimate. This paper details the construction of the net and the procedures for data collection on
the survey. In all, 54 reliable observations of anglerfish were recorded at the groundgear, the wing ends, and along the sweep/bridle
combination. Detailed analysis of the recordings showed that all fish in the path of the net were captured, whereas more than half of
the fish between the wings and the doors were not. The fish did not appear to herd and many of the encounters with the wires were
passive. An individual-based particle-tracking model was constructed to use the behavioural observations to simulate the capture process
and generate an efficiency factor. The calculated efficiency factor, based on the behavioural observations, was 1.04, indicating that almost
all fish encountering the sweeps and bridles were lost. The implications and suggestions for development of this work are discussed.
Keywords: anglerfish, catchability, survey trawl.
Received 31 August 2006; accepted 7 June 2007; advance access publication 17 August 2007.
D. G. Reid, D. J. Bova, E. G. Jones, R. J. Kynoch, K. J. Peach, P. G. Fernandes, and W. R. Turrell: Fisheries Research Services Marine Laboratory, PO Box
101, 375 Victoria Road, Torry, Aberdeen AB11 9DB, UK. V. J. Allen: Department of Geography, Kings College, University of London, The Strand,
London WC2R 2LS, UK. Correspondence to D. G. Reid: tel: þ44 1224 876544; fax: þ44 1224 295511; e-mail: [email protected]
Introduction
The northern shelf and North Sea anglerfish (Lophius piscatorius
and L. budegassa) fisheries are of considerable commercial importance to Scotland. Current quotas are set at 9800 t, worth around
E24.5 million. By weight, it is the second most abundant demersal
species landed in Scotland. Until recently, the assessment for these
species has depended on information on effort and landings from
the fishery and on fishery-independent data from bottom-trawl
surveys. However, effort and landings data were considered unreliable, and the ICES Working Group on Northern Shelf Demersal
Species (ICES, 2005, 2006) also examined the available survey
data (from the ICES International Bottom Trawl Surveys in
ICES Areas IV and VIa) and found conflicting signals. As a
result, the ICES Advisory Committee on Fisheries Management
recommended the establishment of a suitable survey that could
provide reliable abundance estimates for these species to be used
as tuning factors in subsequent assessments. The aim was to use
the surveys to establish absolute abundance, using swept-area estimates of fish density and raising these to strata (Sparholt, 1990).
Converting trawl cpue to biomass estimates requires an estimate
of the swept area covered by the trawl and an estimate of the
survey trawl efficiency. Somerton et al. (1999) divided catchability
into three components: vertical herding, horizontal herding, and
escapement under the footrope. In a species such as anglerfish,
the type of vertical herding described by Godø and Totland
(1996) is unlikely to occur, because these fish tend to stay on the
seabed. Escapement under the footrope (Engås and Godø, 1989;
Walsh, 1992) may occur, but is not considered here.
The present study was designed to investigate the degree of
horizontal herding in the survey net, particularly by the bridles,
and to use this to provide a survey trawl catchability estimate
and a corrected swept-area abundance estimate. The aim was to
use direct underwater observations of anglerfish behaviour in the
path of the survey trawl to parameterize an individual-based
model of the capture process and use the model to explore what
effect different behaviour parameters had on estimated efficiency.
Material and methods
The survey
The trawl used in this study and for the subsequent stock estimation surveys was based on standard commercial gear used in
the anglerfish fishery and designed in collaboration with industry
representatives. A drawing of the gear is presented in Figure 1, the
groundgear in Figure 2, and the towing rig in Figure 3. The gear
was also fitted with a 19-mm tickler chain, mounted between
the wings and rigged to run ahead of the groundgear.
The observations were carried out on the Scottish research
vessel FRV “Scotia” in October 2005, immediately before the
Published by Oxford University Press 2007.
For Permissions, please email: [email protected]
1504
D. G. Reid et al.
Figure 1. Net drawing of the adapted commercial trawl used in this study and in the abundance surveys.
stock estimation survey and in the same area to ensure comparability of results. The trawl locations (Figure 4) were based on
information from the commercial fishery and chosen to provide
clear tows with good expected catches of anglerfish and over a
range of depths. In all, 60 trawl stations were completed, totalling
63 h of trawling.
On 43 hauls, low-light cameras (ROS “Navigator”) coupled
with self-recording video tape units were mounted on one or
Figure 2. Illustration of the towing rig used for the trawl. Observations were carried out along the bridles and the wire sweeps.
1505
Anglerfish catchability
Figure 3. Details of the groundgear of the survey trawl.
both of the wing ends, looking down at the junction between the
bridles and the net. When ambient light was insufficient, single
20 W artificial light units were necessary. In good light conditions,
a camera was also mounted on the headline, 5 –10 m away from
the centre and looking down and across the net opening. In
total, there were 85 net camera deployments.
Once the net had been deployed, and where depth and weather
permitted (26 of the 43 hauls), a towed remote-control television
vehicle (RCTV) with real-time monitoring and recording via a
fibre-optic cable was deployed in addition to the net cameras
(Wardle and Hall, 1993). A high-frequency sonar (SIMRAD SM
2000) was used to steer the RCTV to a point above one of the
sweeps (Jones et al., 2001). These could then be monitored
using the colour zoom camera (Kongsberg-Simrad OE14-366)
mounted on a pan-and-tilt unit. The camera required illumination
with artificial light which, depending on water clarity, gave a
working visibility range of 3 –4 m. The RCTV was maintained in
this position for the duration of the tow using a Magnus rotor
system, although it was not always possible to keep the sweeps
in view.
Gear performance was monitored during the tows using
SCANMAR trawl surveillance equipment to provide depth, headline height, door spread, wing spread, bridle angle, and average
speed over the ground. These data were used to set up the gear
geometry parameters for the model described later. The trawl
catches were processed to provide catch numbers, length frequency, weight, and age information on anglerfish and other
species.
Video data analysis
Following the survey, the video recordings from the net-mounted
cameras and the RCTV were examined for sightings of anglerfish;
54 sightings were recorded. Each observation was scored for a suite
of behaviours. These are summarized below.
(i) Fish is inactive and partly buried—run over by the sweep or
herded farther into net path;
(ii) Fish is active (not recessed) and rises off bottom, allowing the
sweep to pass underneath;
(iii) Fish burst-swims on contact with sweep—into or out of the
path of the net;
(iv) Fish swims upwards—into or out of the path of the net.
A full description of these behaviours is presented in Allen (2006).
The fish were also scored for the direction and, where possible, the
distance of any movement, as well as the position on the sweep or
bridle. Fish seen on the headline camera were all observed entering
the net. They were scored on whether they encountered the tickler
chain and/or the groundgear and on how they behaved at that point.
Behavioural analysis and model of trawl
efficiency
For the purposes of this analysis, anglerfish behaviour was divided
into a number of simple classes:
(i) Observation platform—RCTV camera, wing-mounted, or
headline-mounted camera;
(ii) Initial “state” of the fish—partially buried in the sediment or
actively moving above the seabed;
(iii) Outcome—after the encounter, the fish escaped or moved
into path of the net;
(iv) Direction—for fish that moved into the path of the net, the
angle of movement relative to the sweep/bridles (908
represents movement normal to the wires).
Figure 4. Map of the survey area showing trawl areas and positions.
To determine the net efficiency, a simple particle-tracking
individual-based model written in Power Basic was used. The
model’s full operational set-up is described in Allen (2006) and
is summarized briefly here. The model served two purposes.
First, it allowed the use of behavioural observations to quantify
the numbers of fish caught from a known prior distribution.
Second, it allowed a test of the sensitivity of the simulation to
the main input parameters.
The gear was modelled in two parts, the groundgear and the
sweep/bridle section. For simplicity, the model used only one
side of the net, i.e. half the groundgear and one set of bridles
1506
D. G. Reid et al.
and sweeps. The groundgear was positioned at 908 to the direction
of tow. The sweep/bridle was moved at an angle representing the
calculated mean bridle angle, but could be changed to represent a
range of bridle angle options. The fish were represented as individual particles, initially distributed at random across a field representing the swept area of the full gear (i.e. door spread by tow
length). The model then moved the gear through the field of the
fish distribution at 1 s intervals and at a speed representing the
observed speed in the field. At each step, the model would check
for an encounter with a fish “particle”.
Upon encounter, each particle was able to respond to the
arrival of the gear following a set of simple rule-based decisions.
The first decision was whether it had encountered the groundgear
or the bridle/sweep. If it encountered the groundgear, it was
scored as being captured. If it encountered the bridle/sweep, it
was offered a range of possibilities:
(i) A proportion of the fish (initially 25%, based on video recordings) were considered as inactive and partially buried; these
were treated as having been run over and escaped.
(ii) The remainder was programmed to make a burst-swimming
response at an angle and distance determined from the
range of behavioural observations.
The angle of burst-swimming was split into 458 segments. Zero
degrees represented movement in the direction of the tow and generally along the sweeps. Ninety degrees represented movement generally normal to the bridle/sweep and into the net. One hundred
and eighty degrees represented movement in the opposite direction to the tow and generally along the sweeps. Particles moving
at 458, 908, and 1358 were considered as moving into the path of
the net and were able to encounter the gear again. Particles
moving at all other angles were considered as escaping. The
model also included a “stamina” parameter representing the
number of times the fish might react before exhaustion. Once
exhausted, the particle would be run over by the sweeps, unless
it had passed into the path of the groundgear.
The model allowed control of the angle of burst-swimming,
how far the particle moved, how many times it could react
before exhaustion, and how many fish were buried and run over.
We could also vary gear parameters, particularly the bridle
angle. The model was run through a number of repeated cycles
for a given scenario of burst distance, burst angles, number of
bursts, buried fish run over, bridle angle, etc.
Calculation of catch efficiency
Trawl efficiency was calculated following the equation defined by
Somerton et al. (1999). This is based on an expression of trawl
efficiency as a combination of the net efficiency (i.e. the proportion of the fish in the path of the net that are caught) and
the bridle/sweep efficiency (i.e. the proportion of fish from the
area between the net wings and the doors that is herded into the
path of the net) such that
A2
;
Q ¼ e þ eh
A1
where Q is trawl efficiency, e the proportion of fish caught in the
net area, h the proportion of fish caught in the area between the
wing ends and the doors, A1 the area swept by the net, and A2
the area between the wing ends and the doors.
In this model, it is assumed that all fish in the path of the net
will be caught, based on the headline camera observations.
Accordingly, component e of the efficiency calculation will
always be 1. The final trawl efficiency will be 1 or more depending
on how many of the fish in the area between the wing ends and the
doors are herded into the path of the net.
Results
Gear monitoring
Anglerfish were identified in video footage from 17 hauls; Table 1
shows the average gear geometry results recorded. The mean door
spread, wing spread, and net speed were used as fixed input
parameters to the modelling exercise. The mean bridle angle was
also used to set the default for the modelling, and the results
from the individual hauls were used to provide a reasonable
range across which to alter this parameter in the model.
Behavioural observations
Of the 43 hauls where footage was collected, 54 anglerfish were
reliably identified in 17 hauls. Eleven fish were from the headline
cameras, and all passed into the net; 29 were from the wing
cameras, 14 moving into the path of the net and 15 escaping;
and 14 were from the RCTV, of which 7 moved into the path
of the net and 7 escaped. Given the relatively small number of
observations available from the wing and RCTV cameras, it was
decided to use these together to represent behaviour along the
bridles and sweeps. The split by behaviour and camera is presented
in Figure 5, including fish that did not respond upon encounter
with the wires.
Catch rates of anglerfish were relatively low during the survey
(Table 2), and this was reflected in the small number of observations. It should be noted that these catch rates were similar to
those seen in the stock estimation surveys and by commercial
fishers. In addition, the RCTV and wing cameras had limited
fields of view, with footprints of 2 – 4 m2, so were only able to
monitor a small proportion of possible fish/gear encounters.
The angle of movement was recorded for all fish that reacted by
moving into the path of the net. Of these, 22% moved at 458, i.e. in
the same direction as the net movement, but inwards; 56% at 908, i.e.
directly in from the bridle sweep; and 22% at 1358, i.e. back into
the net and against the direction of the net’s travel. Data on the
direction of movement are presented in a kite diagram in Figure 6.
It was not possible to be sure in all cases that a fish observed and
recorded on the RCTV was not also recorded at one of the wing
cameras, except when the RCTV was on the opposite side of the
net. All observations were treated as independent, and the outcome
of the efficiency modelling (see below) would suggest that very few
fish seen at the wings would also have been seen along the wires.
Also, it should be noted that anglerfish observations taken on a
single tow should not be regarded as strictly independent: there
may be interaction between individuals seen and recorded. Given
the low numbers of fish present and the rarity of observations, we
decided to treat each fish observation as independent.
Model runs
Following initial tuning of the model under fixed conditions in
which all particles reacted to the bridles/sweep at a fixed angle
and burst distance (Allen, 2006), the model was run for a series
of scenarios to examine the effect of the different variable input
parameters.
Anglerfish catchability
1507
Table 1. Gear dimensions and towing speed for each of the 17 hauls at various depths, with averages, standard deviations, and 95%
confidence limits.
Haul
Depth (m)
Average headline
Average door
Average wing
Sweepline
Bridle
Average net speed
height (m)
spread (m)
spread (m)
length (m)
anglea
over ground (knots)
451
97.1
5.5
81.5
24.0
137.6
12.5
3.6
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
452
97.1
5.5
78.6
23.0
137.6
11.6
3.4
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
455
99.3
5.3
82.0
23.1
137.6
12.3
3.0
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
457
99.3
5.6
81.5
23.3
137.6
12.3
3.4
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
453
99.8
5.7
79.7
23.4
137.6
11.8
3.3
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
445
99.9
5.0
89.4
25.6
137.6
13.4
3.8
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
456
100.0
5.4
82.2
23.3
137.6
12.3
3.7
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
444
100.9
5.1
87.9
24.9
137.6
13.2
3.8
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
442
101.2
5.5
82.7
24.2
137.6
12.3
3.1
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
440
122.1
4.8
96.1
25.1
137.6
14.9
4.1
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
448
127.8
5.1
91.9
25.8
137.6
13.9
3.7
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
447
128.2
4.7
95.0
25.6
137.6
14.6
4.1
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
433
128.7
5.0
92.3
25.4
137.6
14.1
3.2
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
439
129.0
5.3
91.0
24.7
137.6
13.9
3.6
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
438
129.5
5.1
94.1
25.9
137.6
14.3
3.2
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
446
134.4
5.1
92.0
25.7
137.6
13.9
3.7
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
409
141.7
4.7
94.1
25.9
137.6
14.3
3.2
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Average
113.88
5.2
87.76
24.64
137.6
13.26
3.52
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
s.d.
16.31
0.31
6.06
1.09
0
1.06
0.33
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
95% CL
+7.75
+0.15
+2.88
+0.52
0
+0.5
+0.16
a
Bridle angle was calculated using the length of the sweeps and bridles, and the distance between the paths of the wing ends and the doors.
Figure 5. Number of fish, showing each of the eight principal behavioural categories in relation to the trawl and camera position.
1508
D. G. Reid et al.
Table 2. Catches of anglerfish, with tow durations, wing spread, distance towed, gear-swept area, and catch rates in time and space.
Anglerfish caught
Anglerfish caught
Anglerfish
Duration
Wing spread
Distance towed
Swept area
per min
per km2
caught
(min)
(m)
(m)
(m2)
451
16
60
26
6 494
168 192
0.27
95
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
452
35
153
25
15
403
391
226
0.23
89
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
455
65
140
26
13
814
357
789
0.46
182
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
457
33
151
25
14 864
367 146
0.22
90
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
453
26
90
25
9
291
233
201
0.29
111
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
445
21
58
24
5 617
135 928
0.36
154
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
456
18
73
25
7
057
175
708
0.25
102
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
444
7
84
26
8
185
209
529
0.08
33
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
442
47
60
26
6
055
155
605
0.78
302
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
440
37
60
26
6 377
163 244
0.62
227
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
448
63
73
26
6
951
179
329
0.86
351
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
447
38
133
24
13
056
313
356
0.29
121
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
433
33
136
23
13
290
305
672
0.24
108
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
439
15
79
23
7
964
186
346
0.19
80
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
438
27
137
23
12 742
294 337
0.20
92
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
446
18
90
23
8
669
201
981
0.20
89
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
409
31
116
23
11
285
262
932
0.27
118
. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Average
31
100
25
9 830
241 266
0.34
138
Haul
Default conditions for these parameters for the model runs
were:
Direction of burst—as in Figure 6
Number of burst movements ¼ 5
Length of burst ¼ 2 m (based on estimates from video
observations)
Percentage buried and run over ¼ 25% (based on estimates
from video observations)
Bridle angle ¼ 148 (from gear geometry measurements)
Each parameter could then be varied, whereas the others
retained the default settings.
Base case with all parameters set at default
For the base case, the default conditions were set and 20 runs
carried out. The resulting mean efficiency Q was 1.04 (s.e. 0.01).
Under this scenario, only a very small percentage of the fish that
were initially distributed between the wing ends and doors were
herded into the path of the net.
To allow an estimate of the variance in the model outcomes, the
original burst direction data (into and out of the path of the net)
and numbers not reacting were bootstrapped to obtain the 5 and
95 percentiles of the probability distribution. The model was then
re-run 50 times with these values and the same parameter settings
as the base case. Mean efficiency Q for the lower boundary was
again 1.03 (s.e. 0.003); for the higher boundary, it was 1.12
(s.e. 0.01).
Varying the proportion moving into the path of the trawl
In the base case, the observed percentage of fish reacting into the
path of the net was 39%. Using a range of options, this proportion was varied from 50% to 100% (Table 3). In all cases, the
balance between the three possible directions of burst movement
into the path of the net was maintained, and the proportion escaping outwards was reduced. The results are presented in Figure 7. Q
remained close to 1 for all cases up to 70%. At 80%, it rose to 1.2
(s.e. 0.03), and reached 1.36 (s.e. 0.02) when 100% of the fish
Table 3. Burst direction of 40 individuals for a range of
percentages of fish moving into the path of the trawl.
Burst direction
Figure 6. Kite diagram showing the numbers of fish that exhibited
directional responses to the towing wires. All fish moving in at
458, 908, and 1358 were considered as remaining in the path of
the trawl. The remainder was considered as escaping.
Percentage of fish moving into path of trawl (%)
50
60
70
80
90
100
458
in
5
6
7
8
9
10
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . .
908 in
10
12
14
16
18
20
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . .
1358
in
5
6
7
8
9
10
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . .
Out
20
16
12
8
4
0
1509
Anglerfish catchability
Figure 7. Change in trawl efficiency (Q) from the model with an
increasing percentage of fish herded by the wires. The solid line
represents mean values. The dashed line denotes an efficiency
of 1.0 (no fish are herded into the net).
reacted towards the path of the net. It seems likely that the dominant factor in this set of simulations is the number of burst movements a particle can carry out before it becomes exhausted. Even
with most of the fish reacting into the path of the net, many
escape subsequently through exhaustion.
Figure 9. Change in trawl efficiency (Q) from the model with an
increasing burst distance. Lines as in Figure 7.
tested. The results (Figure 10) show that, for all angles greater
than 108, the efficiency was close to 1. However, for very shallow
bridle angles, efficiency did increase slightly to 1.15 (s.e. 0.03)
at 58. It seems likely that, at such shallow angles, the number of
encounters needed to reach the path of the net is reduced, so a
small gain in efficiency is achieved.
Varying the number buried and run over
The model was tested, allowing the particles between 1 and 10
burst movements before exhaustion. Trawl efficiency remained
close to 1 for all scenarios (Figure 8). The dominant factor in
these scenarios is the high probability of escape upon each encounter. Effectively, given that approximately half of the particles escape
on each encounter, very few of them actually take ten encounters
before escaping.
In all runs, a proportion of the fish (set at 25%, based on the video
recordings) was considered as buried in the sediment and run
over by the net. A range of options between 0% and 50% was
tested, however, and in all cases, trawl efficiency was close to 1
(Figure 11). In these scenarios, there are still substantial numbers
of particles able to escape on each encounter so, in a similar
fashion to the lower percentage scenarios in the base case above,
most particles will have sufficient encounters to escape.
Varying the burst distance
Discussion
The number of times the particle could move was fixed at five, but
the distance moved was varied between 1 and 10 m. As might be
expected, trawl efficiency showed a steady but small increase
over the range of burst distances. At 10 m, it was 1.27 (s.e. 0.02).
The results are presented in Figure 9. In these scenarios, it seems
likely that the longer burst distances mean that the particles
reach the path of the net in fewer bursts, so have a reduced
number of encounters and hence chance of escape.
The aim of this study was to observe anglerfish behaviour directly
at the net and bridle/sweeps to develop an efficiency estimate for
the net to be used in producing swept-area biomass estimates.
There were two clear conclusions from the direct observations.
First, all fish seen in the path of the net itself were caught.
Second, large proportions of fish in the path of the bridle/sweep
combination escaped the path of the gear and were not caught.
The most important finding from direct observation was that
anglerfish do not appear to be herded by the gear, which is the
more classic response seen in species like cod, haddock, and flatfish
(Hemmings, 1973; Walsh and Godø, 2003). Most of the active
Varying the number of possible burst movements
Varying the bridle angle
During the trials at sea, the bridle angle varied with depth of tow
between 128 and 158. Angles of 58, 88, 178, and 208 were also
Figure 8. Change in trawl efficiency (Q) from the model with an
increasing number of possible bursts. Lines as in Figure 7.
Figure 10. Change in trawl efficiency (Q) from the model with
increasing bridle angle. Lines as in Figure 7.
1510
Figure 11. Change in trawl efficiency (Q) from the model with
increasing proportion of buried fish, which are run over by the wires.
Lines as in Figure 7.
responses observed were of fish making short bursts of movement
on contact with the sweeps and bridles, then settling back to the
seabed. However, many fish also simply rose off the seabed, allowing the bridle or sweep to pass under them. Some fish did not
appear to react to the wires at all, and were simply run over by
them. As a result, fish in both categories were not caught by the
trawl. This passive response to the gear has also been reported
for some flatfish species with a similar cryptic, buried habit
(Bublitz, 1996; Ryer and Barnett, 2006). In addition, Winger
et al. (1999) identified the behavioural responses of flatfish as
being important in the capture process. A burst-and-glide behaviour was common in small fish and often resulted in them
being overtaken by the sweeps on exhaustion.
On the basis of these behaviours, more than half the fish would
escape on each encounter. The remainder would move a short distance, most likely encountering the wires again, with the same
probability of escape. Thus, multiple encounters would allow a
high probability of escape.
The modelling work allowed us to calculate the efficiency of the
trawl and to explore how the different behaviours had an impact
on it. The efficiency factors possible from this model ranged
from 1, where all fish in the path of the net were caught, and all
fish in the path of the sweeps and bridles were not, to 3.5 where
all fish in the path of the trawl were caught.
In the base case scenario, the efficiency of the net was close to 1,
and therefore almost all fish (98.5%) in the path of the sweeps/
bridles would escape. The proportions buried and run over, burstmovement direction, distance, and bridle angle were all based on
the observations taken during the study. The only factor that
could not be informed by direct observation was the number of
burst movements a fish could make before exhaustion. Therefore,
it was encouraging that the scenarios where the number of possible
burst movements was varied showed little deviation from the base
case, suggesting that this was not the critical factor.
The behavioural factors that the model suggested could
increase efficiency were distance of burst movement and, critically,
the number of fish reacting away from the trawl on encounter with
the wires. Distance moved could only be estimated from the direct
observations within a limited field of view. Most were in the order
of the 2 m used in the base case, although a small number of fish
were seen making longer excursions. The number of fish moving
into the trawl path was believed to be reliably estimated from
the observations at 40%. The model scenarios suggest that 80%
D. G. Reid et al.
or more of the fish would have to react into the net path to
make any impact on efficiency.
Even with extreme changes in all input parameters (e.g. all fish
that react do so into the path of the net), the efficiency was only
raised to 1.36, meaning that 85% of the fish encountering the
wires would escape.
It is important to note that the behavioural observations were
not obtained under perfect experimental conditions. Visibility
was often poor, and decreased light levels are known to affect
the response behaviour of fish (Glass and Wardle, 1989; Walsh
and Hickey, 1993; Engås, 1994). Artificial light (used in some of
the observations) may have modified the behaviour, although
Weinberg and Munro (1999) found only one species of flatfish
that was affected by artificial light in the capture process. It was
only possible to observe fish at a single point and time, so it was
impossible to track each fish and follow, say, the number of
times it encountered gear components. The observations of the
fish at the bridles and sweeps were taken from two different
platforms, with the RCTV tending to observe an area farther
forward along the wires than the wing-mounted cameras. It was
not possible to be sure in all cases that a fish observed and recorded
on the RCTV was not also recorded at one of the wing cameras,
except when the RCTV was on the opposite side of the net. All
observations were treated as independent, and the outcomes of
the efficiency modelling would suggest that very few fish seen at
the wings would also have been seen along the wires. It should
also be noted that anglerfish observations taken on a single tow
should not be regarded as strictly independent, so there may be
interaction between individuals seen and recorded. However,
given the low numbers of fish present and the scarcity of observations, we believed it was viable to treat each fish observation
as independent.
A critical issue still to be resolved is the efficiency of the net
itself. In this study, we assumed 100% catch rates, based on the
camera observations. However, escape under the groundgear is
highly likely, given previous studies of both flatfish and gadoids
(Somerton et al., 1999; Munro and Somerton, 2002). Future
trials will make use of auxiliary nets behind the groundgear to
test for this possibility (Engås and Godø, 1989; Munro and
Somerton, 2002; Ingólfsson and Jørgensen, 2006).
Although the model proved very useful in understanding the
link between behaviour and net efficiency, there are a range of
improvements possible. Most important would be to include
differences between first and subsequent encounters. It might be
assumed that most fish would be buried in the substratum on
first encounter and would be swimming more actively thereafter.
These encounters could be parameterized and modelled separately.
Differences between the observations farther along the sweep
(RCTV) and those at the wing end could be useful in this
respect. The efficiency of the net component should also be
varied, based on groundgear net trials.
This study was carried out using swept-area estimates from
trawl surveys for stock abundance estimation. Essentially, the
requirement was to learn how efficient the survey net was and to
use this to correct the catch data based on gear spread and tow
distance. When applying such correction factors, it is important
to know if the correction actually improves the estimate. Munro
(1998) suggested applying a correction only when it reduces
mean square error. In this case, the correction factor would be
applied globally across the surveys and would aim to reduce bias
rather than variance.
Anglerfish catchability
In conclusion, the present study showed that it is possible to
observe and quantify the behaviour of anglerfish in a survey
trawl and to use this information to derive a usable net efficiency
factor for use in abundance estimation.
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doi:10.1093/icesjms/fsm106