In situ target-strength measurements of Chilean jack mackerel (Trachurus symmetricus murphyi) collected with a scientific echosounder installed on a fishing vessel
Institute of Marine Research, PO Box 1870, Nordnes, N-5817 Bergen, Norway;
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Peña, H. 2008. In situ target-strength measurements of Chilean jack mackerel (Trachurus symmetricus murphyi) collected with a scientific echosounder installed on a fishing vessel. – ICES Journal of Marine Science 65: 594–604.The use of commercial fishing vessels for scientific purposes has increased worldwide in the past 10 years. Many such studies have involved the collection of acoustic data from both uncalibrated and calibrated echosounders. However, few studies have involved investigations of in situ target strength (TS). During August/September 2003, in situ TS data on Chilean jack mackerel (Trachurus symmetricus murphyi) were collected on board a commercial purse-seiner during normal fishing operations off Chile, using a 38 kHz, Simrad EK60 scientific echosounder. The single-target detections of Chilean jack mackerel were filtered by depth, off-axis beam angle, and beam-compensation criteria to improve the quality of the data used for the TS calculations. Two methods, using raw data and tracked data, were employed to calculate the mean acoustic-backscattering cross section (
bs) and mean TS of Chilean jack mackerel. The results of the two approaches gave similar results, with a strong mode in the mean TS distribution between –35 and –37 dB for fish lengths ranging from 26 to 34 cm, indicating a b20 value of –66 dB. These results agreed well with most results published for this species and others of the same genus (i.e. T. trachurus, Trachurus t. capensis, and T. japonicus).
Keywords: Chilean jack mackerel, commercial vessels, split-beam echosounder, target strength
Received 21 August 2006; accepted 28 December 2007; advance access publication 1 April 2008.
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Introduction |
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The backscattering cross section (
bs) represents the acoustic intensity response of a fish from the signal transmitted by a transducer, and is a crucial parameter for absolute fish-abundance estimation using echo integration (MacLennan, 1990). The logarithmic (base 10) measure of bs is termed the target strength (TS; Foote, 1997). In situ estimates of TS are preferred because, when properly collected, they can be used to characterize mean TS under natural conditions. In contrast, data collected in controlled laboratory conditions (ex situ) generally use stunned or dead fish attached to a frame (Nakken and Olsen, 1977). The great variability observed under natural conditions is mostly related to the behaviour of a fish during swimming (tilt, roll, and yaw) or to physiological conditions (Ona, 1990); these factors remain difficult to measure accurately during field studies. Among such variables, the tilt angle of the fish is a major source of variability, and, to a lesser degree, so are stomach fullness and fat content (Ona, 1990). Another important source of variability may be associated with compression of the swimbladder during vertical migration, but this has only been studied in herring (Clupea harengus) over an appropriate range of depths (Ona, 2003). The Chilean jack mackerel (Trachurus symmetricus murphyi) supports one of the largest single-species fisheries in the world, with annual landings approaching 2.5 million tonnes (FAO, 2004). The fish aggregate in dense schools and layers, exhibit daily vertical migration, and feed on zooplankton and mesopelagic organisms associated with the upwelling areas off central-south Chile (Córdova, 2003). The swimbladder is responsible for 90–95% of the backscattering energy (Foote, 1980). Physoclists (species with closed swimbladders) such as Chilean jack mackerel (Peña, 2004) can adjust swimbladder volume by resorption or secretion (Harden Jones and Scholes, 1981). Abundance estimates in Chilean waters are determined annually by acoustic methods, using traditional echosounder surveys (Córdova, 1998). The TS data used in the acoustic assessment of Chilean jack mackerel were collected in situ on board a research vessel during annual acoustic surveys carried out between 1991 and 1994. The relationship TS = 20.11 log (L) –68.67 (Córdova, 1998) was developed from these data. Single targets were collected using a split-beam, 38 kHz transducer mounted in the keel. Using the same platform, Lillo et al. (1996) collected data from which they derived a different relationship: TS = 23.31 log (L) –73.26 (b20 between –68.3 and –69.1 dB, where b20 is the intercept of the regression with the slope forced to 20). However, both equations result in similar mean estimates of TS for 24–34 cm fork length (FL) fish, which dominate the stock in Chilean waters.
Over the past 10 years, several vessels operating in the central-south Chilean purse-seine fleet have been equipped with Simrad EK60 scientific echosounders. However, their use has been restricted to acoustic assessment using traditional systematic surveys carried out in collaboration with the government's research vessel. Jack mackerel TS data have not been collected previously by commercial vessels equipped with scientific echosounders. In fact, TS data have been collected only rarely under these conditions for any species (ICES, 2007).
The research objective here was to obtain single-target detections of Chilean jack mackerel from a commercial vessel of a quality similar to that which would be obtained from a research vessel. The results obtained are compared with available information on in situ TS of Chilean jack mackerel and other jack (horse) mackerel species: from the North Atlantic (T. trachurus), the southeast Atlantic (T. t. capensis), and the Northwest Pacific (T. japonicus).
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Methods |
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The acoustic data were collected from the commercial fishing vessel "Ligrunn", a 42 m purse-seiner, during normal commercial operations between 20 August and 5 September 2003. The study area was located ca. 500 miles northwest of the port of Coronel (36°45'S 73°00'W), adjacent to the Juan Fernandez Archipelago (Figure 1), within the fishing grounds of the Chilean purse-seine fleet at that time. The vessel was equipped with a Simrad EK60 echosounder with a 38 kHz, keel-mounted, split-beam transducer (Table 1). Before departure, the system was calibrated using a copper sphere of 60 mm diameter, according to standard procedures (Foote et al., 1987). The calibration was performed at pulse durations of 0.512 and 1.024 ms.
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A major concern with collecting scientific data from commercial vessels is the possibility of encountering high noise levels within the range of frequencies, which may impact echosounder performance. High-frequency noise, more than
10 kHz, may increase the overall signal-to-noise ratio (SNR), reducing the effective depth range of the system, and high levels at the echosounder-operating frequency may contaminate the signal (Mitson and Knudsen, 2003). Using the echosounder in a passive mode, vessel-noise measurements were performed following the procedure proposed by Simrad (2004), and maximum fish-detection depths were then calculated (Mitson and Knudsen, 2003). The echosounder was set to log data continuously for the 3-week survey, on the fishing grounds and during transit to and from port. Detailed scrutiny of all the resulting data files was carried out to allow the selection of sequences suitable for extracting single-target detections. The parameters used for the single-target detection algorithm included in the ER60 software were: minimum TS value, –55 dB; minimum and maximum echolength, 0.8 and 1.8; maximum phase deviation, 6.0 phase steps; maximum gain compensation, 6.0 dB. The resulting data correspond to periods when the vessel speed was reduced to <5 knots, i.e. after the shooting of the net and before pumping the catch.
In split-beam transducers, the four quadrants transmit simultaneously, but the signals are received individually by each quadrant (Ehrenberg, 1983), allowing measurements of phase differences, so obtaining the precise localization of the targets in the beam (Foote et al., 1984). In modern split-beam echosounders, every target detected is corrected according to its position in the acoustic beam (Reynisson, 1999).
The EK60 raw data (Simrad, 2004), defined as the single-target data generated by the echosounder that include all parameters for each detection (TS, target depth, along and athwart angles, and nautical-area scattering coefficient, sA), were analysed initially using the ER60 software and exported as BI500 datagrams (text format file) for detailed analysis. Two methods were used to estimate the TS: (i) using raw data directly (individual targets), and (ii) by tracking individual fish and extracting sequential measurements of TS. For both methods, the single-target detection data were filtered, using software developed in R (www.r-project.org). First, the depth range within which there were a large number of single-target detections was determined by visually inspecting the echograms displayed using the ER60 software. This procedure was used to filter out dense aggregations of fish and plankton layers that were present in most echograms. R software routines were used to extract single targets from the selected data sequences. To select data from a low SNR region within the acoustic beam, two filtering procedures were employed: one based on target-beam compensation, the other utilizing a cut-off angle (Zhao, 1996). A beam-compensation factor, B (
, β), was used to filter out the targets located in a position more than –3 dB from the acoustic axis using the equation B (, β) = TS (0,0)–TS (, β), where TS (, β) is the TS predicted for a target alongship angle () and athwardship angle (β) in the beam, and TS (0, 0) is the TS derived from received-power measurements valid at = 0 and β = 0. All detected single targets were subjected to this filter. The cut-off angle is defined as the angle from the axis in which the single targets are distributed uniformly in the acoustic beam. The methodology compares the relative frequency of occurrence of single-fish echoes within each degree off-axis angle interval with its theoretical counterpart (Zhao, 1996). The reduction of detected targets at increased angles is probably attributable to lower SNR or decreases in the TS at increased beam and tilt angles (Degnbol and Lewy, 1990). Some 40% of the single targets were rejected by a 3°, off-axis angle filter and, from those, some 20% were rejected by a –3 dB compensation filter.
After filtering the data, the bs was calculated for each target as bs= 10TS/10. The bs values were averaged in a linear domain in order to obtain the mean bs. This value was used to calculate the mean TS: TS = 10 log bs (MacLennan et al., 2002).
As an alternative approach, target-tracking methodology was employed to identify and extract repeated detections of single targets (Brede et al., 1990). In that analysis, a multiple target-tracking algorithm was used (Handegard et al., 2005). For effective tracking, only data collected at vessel speeds of <1 knot were selected for analysis. Using time as the common variable, the tracking software screens data from the navigation files and echotrace files. Once a single target is detected, the program searches for the next detection in a volume defined by an ellipse around the last detection. The parameters used to search for a new detection were a maximum range of 1 m between consecutive pings, 4° spherical angles (
, 
) for the ellipse that defines the area within which the next ping could be detected, and a maximum of five missing pings in a track (Handegard et al., 2005). For each target tracked, an estimate of the average acoustic bs was calculated, and the data were grouped according to track length, i.e. number of detections. Subsequent average backscattering computations were carried out for those tracks with 10 or more detections, using the methodology previously described for raw data. This ensured that the quality of the single targets included in the tracks was optimal, by rejecting short tracks (<4 detections) that could have originated from different fish (N. O. Handegard, pers. comm.). The tracking software corrects for vessel movement and utilizes a constant velocity-based prediction for each new detection.
From each purse-seine catch during the survey period, a sample of 200 fish was collected from the fish dryer on the main deck, and a FL distribution by sex was obtained. Also, data on species and size composition in each sample were recorded.
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Acoustic data were collected in three fishing zones (Figure 1). In each, the acoustic sampling period was <24 h, restricted mainly to the hours between dusk and dawn when fish schools disperse (Table 2). The minimum range where most of the single targets were detected was similar in all three zones (30 m), but the maximum range increased from 80 to 120 m from zone 1 to zone 3. The mean depth of single detections was similar, differing by
10 m between zones 1 and 3. The number of single-target detections increased significantly from >30 000 detections in zone 1 to 160 000 in zone 3 (Table 2).
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The noise level increased from 33.5 dB re 1 µPa at a vessel speed of 2 knots to 63.5 dB at 10 knots (Figure 2). The noise level values obtained for "Ligrunn" at 2 knots were as low as for the new Norwegian RV "G. O. Sars", but at increased speeds the noise level was higher than almost all RVs for which data were available. The maximum fish detection depth calculated at 38 kHz was 415 m for a TS target of –40 dB.
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Biological sampling
Nine length-composition samples were collected in zone 1. The length compositions of each of these samples were similar (Figure 3). The overall length composition for that zone demonstrated a unimodal distribution centred at 27 cm FL, with a mean length of 27.2 cm and a standard deviation (s.d.) of 1.5 cm (Table 3). Four samples were taken from zone 2, and these were more heterogeneous, with a wider size distribution ranging from 19 to 50 cm FL (Figure 3, Table 3). A mode of 26 cm was observed from the cumulative distribution, with a higher frequency of fish of 28–37 cm FL. The mean length was 29.2 cm (4.1 s.d.). Five samples were taken from zone 3; these exhibited even greater heterogeneity, and a broader range of lengths, from 17 to 57 cm FL (Figure 3). The cumulative distribution showed a mode at 26 cm, a greater frequency of fish >30 cm, and a small peak at 20 cm. The mean FL was 30.3 cm (6.8 s.d.). In each of the three zones, >85% of the samples were jack mackerel. The other notable species was mackerel (Scomber japonicus), at concentrations of 1% in zone 1 and 3% in zone 2 (Table 3).
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Distribution of targets in the beam and the TS threshold effect
An analysis of the target distribution in the beam showed a uniform distribution of targets from the acoustic axis up to a 3° angle (Figure 4). For longer beam angles, the targets were not uniformly distributed, but were thresholded by acceptance filters. Therefore, this limit, the cut-off angle, was determined to maximize data quality for targets considered for TS processing, rejecting all targets larger than this angle.
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This first analysis considered only the number of targets in the acoustic beam. A second criterion analysed the variation in TS inside the beam, with an increase in the mean TS from the acoustic axes to the outer beam (Figure 5). This example showed that the higher mean TS was due to a decrease in the number of weaker targets accepted, rather than to an increase in the number of stronger targets. Comparing theta cells (beam area bounded by two theta angles) of 2–3° with cells 3–4° clearly shows the decrease in detected weaker targets, below –45 dB, with a very similar probability density function of strong targets, higher than –40 dB (Figure 5c and d).
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Sigma and TS calculations using raw-echo data
Mean
bs was calculated from the raw data for each of the three zones. Values were higher in zone 1 at 0.00301 m2 (Table 4), but similar in zones 2 and 3, 0.00236 and 0.00232 m2, respectively. Low values and a decreasing trend in the standard error of bs in the three zones is attributable to the large number of targets involved in the calculations. A non-parametric, Wilcoxon, signed-rank test revealed significant differences (p < 0.05) in the mean bs among zones.
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The TS distributions for all three zones exhibited bimodal distributions, with a main mode around the –33 dB and a secondary mode around –50 dB (Figure 6). In zone 1, the secondary mode was characterized by a narrow peak with a higher value (close to –32 dB) than in zones 2 and 3, where the distributions were flatter and with peak values between –35 and –37 dB). Bimodal TS distributions for animals with unimodal length distributions have been documented by others (e.g. Williamson and Traynor, 1984; Traynor, 1996). Those authors proposed that such bimodality was the result of a convolution between the single-narrow lobe of the TS tilt-angle distribution, and lesser strength returns from a wide range of angles. Even though no information on the tilt-angle distribution is available in this study, it is possible to infer the same situation.
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The mean values of TS from the three zones varied within the very narrow range of –6.2 to –37.3 dB. Mean TS was higher in zone 1; the mean values of TS in zones 2 and 3 were identical, but lower than in zone 1 (Figure 7).
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TS measurements obtained from target tracking
The TS information extracted from the tracked data demonstrated a different pattern than that observed for raw data (Figure 6). In zone 1, the TS distribution was bimodal, with a pronounced, narrow main mode centred at –32 dB, and a secondary mode centred at –52 dB (Figure 6, top). The TS distribution for zone 2 was also bimodal, with the primary mode centred at –36 dB, and its secondary mode at –48 dB (Figure 6, middle). The TS distribution for zone 3, however, was unimodal, centred at –42 dB (Figure 6, bottom). The number of targets detected through tracking differed markedly among zones, ranging from 342 detections in zone 1 to 16 419 in zone 2 (Figure 6).
The tracking software estimates the swimming angle of the fish for each track detected. However, that angle does not provide information about the tilt angle of the fish, which is a primary factor in TS variability (Ona, 2001). The mean fish swimming angle (
) for detected tracks in zone 1 ranged from +2° to –6° (Figure 8); in the longer tracks the fish tended to be diving, whereas in the shorter tracks the tendency was towards a slight ascent. The swimming angles in zones 2 and 3 were similar for all track lengths, with values close to –2°, indicating that all fish were exhibiting similar diving behaviour, with a slight diving tendency. For track lengths of 10 or more detections the mean angles for the three zones were similar and close to –2° (Figure 8). The s.d. of the swimming angles was lower for fish in zone 3, with values close to 10° for almost all track lengths. Standard deviations for zone 2 were higher, with increasing variability for tracks of six or more detections, and peaking (18°) in tracks with 20 detections. For zone 3, the s.d. demonstrated greatest variability, with a slow increase for longer tracks, ranging from 14° to a maximum of 18°.
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Mean TSs for the tracks included in all three zones fluctuated by <1 dB for all track lengths of 15 detections or less (Figure 8). TS was highest for tracks in zone 1, fluctuating between –34.1 and –33.3 dB, with a high degree of similarity for all track lengths. In zone 2, mean values of TS were lower and more stable than elsewhere, and in zone 3, the mean values of TS were intermediate, showing a slow increase from the shorter tracks up to tracks with 16 detections (Figure 8).
The mean acoustic bs calculations for tracks of 10 or more detections resulted in values of 0.00571, 0.00307, and 0.00345 m2 for zones 1, 2, and 3, respectively (Table 4). The mean TSs derived from the mean acoustic bs were highest for zone 1 (–33.4 dB), and lower and similar for zones 2 and 3: –36.1 and –35.6 dB, respectively (Figure 7).
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Discussion |
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The assumption of uniform target distribution (described by the angle relative to the acoustic axis in the beam,
) is implicit in the calculation of TS distributions or mean values of TS (Degnbol and Lewy, 1990). Processing-system circuitry limitations or the deliberate setting of a detection threshold, or both these factors together, are used to exclude noise or otherwise to constrain target detection, and have the effect of removing weaker targets. Echo amplitude is a function of the scattering cross section of the fish and the beam pattern, so for a fixed threshold, fish with larger scattering cross sections will be accepted as single targets for a wider range of angles than will fish with smaller scattering cross-sections (Weimer and Ehrenberg, 1975). The bias against weaker targets will tend to exclude not only echoes from smaller fish, but also those from larger fish situated off-axis, increasing the overall bias (Soule et al., 1996).
In this study, a TS threshold of –55 dB was used to reject unwanted small targets associated with zooplankton. Although this threshold may be considered adequate after the analysis of the TS distributions, it could be of interest to investigate the effect of a lower limit in future studies. However, probably the best way to increase the SNR would be to lower the transducer closer to the target layer (Ona, 2001).
Whereas most of the studies that use tracked data to estimate TS have used stationary systems, in this case a moving platform was used which, to some extent, reduced the number of sequential detections per track. More detections were reported by authors who conducted their studies inside net pens (Zhao, 1996) or from stationary platforms (Brede et al., 1990; Dawson and Karp, 1990; Huse and Ona, 1996; Axelsen et al., 2003; Ona, 2003). Here, tracked-target sequences selected for analysis were collected only at speeds <1 knot.
The tracking software included a function to correct for movement of the platform, in this case the fishing vessel. Also, a constant-velocity prediction for each new detection in a track was used instead of the zero-velocity prediction, as recommended by Handegard et al. (2005). Correction for transducer tilt/roll was not applied, because this did not improve significantly the tracking results (Handegard et al., 2005), which are consistent with those obtained by McQuinn and Winger (2003), who employed a hull-mounted, 38 kHz transducer to collect single-target data for cod (Gadus morhua) at speeds of 3–5 knots.
Single-target, frequency-distribution plots documented a significant reduction of weak targets (lower than –45 dB) in the tracked data in all three zones in comparison with the raw-echo data (Figure 6). This is particular clear for zone 1, where most of the target TS values exceeded –40 dB and could be due to the rejection of weak targets when the single-echo-detection algorithm was used in the ER60 software. Many of these weak targets could have originated from fish that were not tracked successfully by the tracking algorithm, so that tracks were not identified or, possibly, were split into two or more shorter tracks. Split tracks would in most cases have been excluded from subsequent analysis by the 10 or more detections filter. Both these factors would have reduced the overall number of weak targets included in the averaging calculation. Handegard (2007) proposes a process for including weaker targets, which considers all samples above a threshold as single targets while simultaneously applying phase angle, echo intensity, range, and time criteria.
This study is the first to have provided estimates of Trachurus spp. TS using tracked data. However, several researchers have estimated Trachurus spp. TS from raw-echo data, and their results provide a context for the evaluation of the results obtained here.
The mean bs from the raw-echo data fluctuated between 0.0023 and 0.0030 m2 (Table 4). These values imply a cross section between 23 and 30 cm2 for fish between 27 and 31 cm FL. For the tracked data, these values ranged between 31 and 57 cm2, considerably higher than the values derived from raw data. These relatively large estimates of surface area are fairly consistent with measurements of the dorsal surface of swimbladders obtained during fish dissections and magnetic-resonance imaging (Peña, 2004). Other information on acoustic bs for T. symmetricus murphyi is not available, although Vorobyov and Ivanov (1981) report calculations for T. t. capensis acoustic bs ranging between 6 and 30 cm2 for fish of 13–34 cm FL; these values are comparable with those reported here. In contrast, Axelsen et al. (2003) estimated an acoustic bs of 1–3 cm2 for fish of 17–27 cm total length; those values are approximately one order of magnitude smaller than measurements reported elsewhere.
Mean TS was estimated from the raw- and tracked-echo data (Figure 7). From raw-echo data, the mean TS varied between –36.2 and –37.3 dB, whereas from tracked data the range was –33.4 to –36.1 dB. Mean estimates of TS obtained from raw-echo data can be compared with documented TS estimates for T. symmetricus murphyi and T. t. capensis (Figure 9, Table 5). For T. symmetricus murphyi, the values are in close agreement with the ex situ measurements of Torres et al. (1984), and also with the in situ measurement of Lillo et al. (1996) and Gutierrez (2002). For those results, mean TS estimates fluctuated between –35 and –40 dB, for fish of 26–40 cm TL. Available mean TS data for T. t. capensis apply to smaller fish than for T. symmetricus murpyi (Vorobyov and Ivanov, 1981; Barange and Hampton, 1994; Svellingen and Ona, 1999; Axelsen et al., 2003). In the T. t. capensis studies, the mean TS varied widely (–37 to –50 dB), with the lower estimates almost 10 dB below those obtained for T. symmetricus murphyi. However, if the results of Axelsen et al. (2003) are excluded from this comparison, the mean TS range is reduced to –37 to –43 dB, for fish of 13–34 cm TL, closer to those reported for T. symmetricus murphyi (Figure 9). Finally, ex situ measurements for T. japonicus resulted in mean TS estimates that ranged between –37 and –43 dB for fish of 14–25 cm TL (Kaparang, 1999). Although the fish used in that study were generally smaller than those used in the T. symmetricus murphyi measurements, the mean TS estimates were of the same order.
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It is also important to stress that, although a 38 kHz system was employed during this survey, Vorobyov and Ivanov (1981) used a 20 kHz system, Mukai et al. (1993) used 25 and 100 kHz systems, and Kaparang (1999) used a 70 kHz system. Higher b20 values reported by some of those authors correspond with data collected with lower frequency systems at close range, but overall results are similar to the values obtained at 38 kHz (Table 5).
The relationship developed by Foote (1987) for physoclists [cod, Alaska pollock Theragra chalcogramma, and Pacific whiting (Merluccius productus)] is illustrated in Figure 9. From the data presented, it is apparent that that the TS of T. symmetricus murpyi and T. t. capensis are stronger than the average value calculated by Foote (1987). Foote (1987) also recommends that comparisons be based on the intercept values obtained by fitting a regression model to the TS and (log) length data with a slope of 20 (i.e. b20). That parameter was originally proposed by Love (1977), based on his observation of the approximate proportionality of the bs and the square of the fish length (
2). The b20 values for Trachurus spp. varied between –64 and –75 dB, and those for T. symmetricus murphyi across a narrower range, –66 to –68 dB (Table 5), indicating that the values estimated for this species are consistent regardless of whether the results are based on in situ or ex situ measurement.
Both raw and tracked data demonstrate similar patterns among zones in mean acoustic bs and mean TS, with higher values in zone 1 and similar, but lower, values in zones 2 and 3 (Figure 7). Significant differences (Wilcoxon rank-sum test, p < 0.0001) in the raw-echo data among all the zones were probably a consequence of the larger numbers of samples in zones 2 and 3.
An analysis of the differences in the mean values of TS among the three zones should first consider the size of the fish sampled. In zone 1 the mean size was smaller (27 cm FL) and the distribution unimodal. Fish sampled from zones 2 and 3 demonstrated larger mean FLs of 28 and 31 cm, respectively, and more heterogeneous distributions. The mean TS trends observed are not consistent with this size-distribution information. It should be noted that the depths of the targets involved in the calculations, and information from biological sampling on gonad development and stomach fullness were similar among all three zones. However, the percentage of mackerel (S. japonicus) in the catches was notably higher in zone 1 (14%) than in zones 2 and 3 (4%). The mean TS of S. japonicus (with a swimbladder) is similar to reported values for jack mackerel, with b20 values of –64 and –66 dB for frequencies of 25 and 100 kHz, respectively (Mukai et al., 1993). Those authors also reported that the maximum dorsal-aspect TS in relation to the dorsal aspect of S. japonicus was higher (5 dB) at both frequencies than that of jack mackerel. Gutierrez (2002) also confirmed that the TSs of T. symmetricus murphyi and S. japonicus are comparable, although his results indicate that S. japonicus TS may be slightly lower than T. symmetricus murphyi. If the TS–length relationship for the two species is similar, the size differences observed in the zone 1 catches could, to some extent at least, explain the overall higher mean TS observed there.
This study documents the fact that high-quality acoustic data can be collected from single targets by a commercial vessel. This is one of the first attempts to collect such data for in situ calculations of TS from keel-mounted transducers aboard commercial vessels (ICES, 2007). The high-frequency noise measurements and maximum fish-detection depths calculated for the FV "Ligrunn" were similar to those obtained from some research vessels (Peña, 2004). This result was unexpected, because prior to this study, there was a concern that commercial vessels would be too noisy, and therefore could not be used to provide suitable for TS measurements. It should be noted, however, that the depth range from which the single targets were collected was considerably shallower than the calculated maximum fish-detection depth. The results nevertheless suggest that it would be possible to use such vessels to complement work conducted by government research vessels. This could include, for example, research studies designed to understand the mechanisms involved during daily vertical migration, and the "behaviour" of the swimbladder and its implication in the variability in TS.
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Acknowledgements |
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The work was carried out to meet thesis requirements of the Department of Fisheries and Marine Biology at the University of Bergen. The Norwegian fishing company Austevoll Havfiske AS supported me during my stay in Bergen, and Andrés Daroch and Eduardo Fuica, manager and fleet chief of FoodCorp S.A., provided me with facilities during fieldwork. Capt. Nélson Gutierrez and the officers and crew of PAM "Ligrunn" supported me during data collection, Egil Ona provided excellent guidance, and Bill Karp meticulously reviewed the English language. Finally, I am grateful to the University of Bergen for financing my data-collection trip to Chile.
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References |
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