ICES Journal of Marine Science, 55: 58–66. 1998
Tracking herring schools with a high resolution sonar. Variations in horizontal area and relative echo intensity Ole Arve Misund, Anders Ferno¨, Tony Pitcher, and Bjørn Totland Misund, O. A., Ferno¨, A., Pitcher, T., and Totland, B. 1998. Tracking herring schools with a high resolution sonar. Variations in horizontal area and relative echo intensity. – ICES Journal of Marine Science, 55: 58–66. Fourteen herring schools off northern Norway were tracked for about 1 hour each by the 95 kHz Simrad SA950 sonar onboard R/V ‘‘G.O. Sars’’ in May 1994. The sonar was connected to a work station that contained software for reading the echo telegrams of the sonar, printing of an echogram, automatic detection and measurement of schools, and logging of the sonar data. The horizontal area and relative echo intensity of the schools were recorded ping by ping as well as swimming depth and distance and bearing to the vessel. The position, speed and heading of the vessel were also recorded. Inter- and intra-school events as interpreted from the sonar display were recorded in a separate protocol during the school tracking. The recorded horizontal area and relative echo intensity of the schools varied considerably. Linear models with school area or relative echo intensity as dependent variables, and with range, tilt, speed and swimming angle relative to the sonar beam as continuous effects, did not explain more than 15% and 30% of the observed variations for most schools, respectively. There was a negative correlation between relative echo intensity and range for all schools. Inter- and intra-school events occurred at average rates of about 14 minutes, and inter-school events such as split and joint influenced school size. The sound absorption and the degree to which the sonar beam insonifies the schools in the vertical plane are proposed as the major sources of variation for recorded horizontal area and relative echo intensity of the schools. ? 1998 International Council for the Exploration of the Sea
Key words: sonar, schools, school area, echo intensity, herring. Received 7 May 1996; accepted 1 March 1997. O. A. Misund and B. Totland: Institute of Marine Research, P.O. Box 1870, Bergen N-5024, Norway. A. Ferno¨: Department of Fisheries and Marine Biology, University of Bergen, Bergen N-5020, Norway. T. Pitcher: Fisheries Centre, University of British Columbia, 2204 Main Mall, Vancouver, BC V6T 1Z4, Canada. Corresponding author: O. A. Misund, tel: +47 55 23 85 00; fax: +47 22 23 68 30; email:
[email protected]
Introduction Abundance estimation of pelagic fish stocks with high resolution sonar is under development (Misund, 1993). Acoustic abundance estimation using sonar has two advantages compared with traditional methods using echosounders; the sonar covers a much greater volume, and errors in connection with avoidance of the vessel by pelagic fish schools close to the surface are negligible. All acoustic methods are, however, subject to considerable variations in the echo of fish (MacLennan and Simmonds, 1992). If we are to use sonar as a standard method in abundance estimation, it is crucial that the variations be investigated in detail and that the most important errors are corrected for. 1054–3139/98/010058+09 $25.00/0/jm970228
The variations in backscattered echo intensity are connected both to the properties of sound transmission and reflection and by variations in fish behaviour. The variation can be divided into three sources (Fig. 1). The first possible source of error is connected to acoustics. There exists an inverse relationship between density and horizontal area of a school and the sonar equation assumes a certain relationship between fish density and echo intensity. The sonar equation also compensates for an acoustic target generating less echo with increasing distance. To what extent the applied compensation is correct for a sonar beam guided nearly horizontally has, however, not been systematically studied. Another source of variation in the back scattered echo intensity of schools is connected to changes in aspect angle of the ? 1998 International Council for the Exploration of the Sea
Tracking herring schools with a high resolution sonar fish relative to the sonar beam. According to previous investigations, the backscattered echo intensity of fish in lateral aspects should peak when the fish move normal to the sonar beam and decrease drastically when fish move head on or tail on relative to the sonar beam (Mitson, 1983; MacLennan and Simmons, 1992). As the sonar beam usually is emitted at a slight angle from the horizontal, the influence of aspect angle on the backscattered echo intensity of schools may be a rather complicated function that involves both lateral and dorsal aspect angles of the individual fish in schools (Love, 1980). A second possible source of error is variations in backscattered echo intensity caused by intraschool behaviour (Pitcher et al., 1996). The swimming speed of schools can influence the echo by its effect on fish density (Pitcher and Partridge, 1979; Partridge et al., 1980) and degree of polarization (Foote, 1980; MacLennan et al., 1990). Vertical migrations can influence echo intensity by changes in swimbladder volume (Ona, 1990). Changes in school form, in response for instance to predatory attacks, could also have effects (Fre´on et al., 1992, 1993). At present, we have virtually no information about the effect of intraschool behaviour on backscattered echo intensity, and there is no compensation of such behavioural events in the sonar equations. We could expect that the behaviour of schools has stronger effect on relative echo intensity than on horizontal area, as the area is probably not influenced by variations above a certain threshold level. Up to now we have dealt with variations in backscattered echo intensity from schools assumed to have constant biomass. During interschool events (Pitcher et al., 1996), constituting the third possible source of error, the situation is different. When a school splits or joins with another school, actual changes in biomass take place. The question is whether such events can be recorded as changes in echo intensity. Interschool behaviour thus constitutes a direct test on the accuracy of the sonar method. The aim of this study was to examine the different kinds of variations in backscattered echo intensity with a high resolution sonar by following individual fish schools and continuously recording echo intensity and behavioural events.
Materials and methods A total of 14 herring schools were tracked for approximately 1 hour each during daytime by the 95 kHz Simrad SA950 sonar onboard R/V ‘‘G.O. Sars’’ within an area 68) North, 10) East (approximately 60 nautical miles west of Lofoten, Norway) in the period 3–5 May 1994. When a suitable school for tracking was detected, the vessel was halted at a distance of about 150 m from the school, and then manoeuvred carefully within a
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distance interval of about 100 to 300 m during tracking. The sonar was tilted and trained manually by an experienced operator to have best possible control of the recording trial. For identification of species and fish length, sampling with a medium-sized pelagic trawl (Valdemarsen and Misund, 1994) was conducted on selected schools. The Simrad SA950 is a high resolution sonar (Misund et al., 1995) that transmits pulses in a horizontal sector of 45), and that receives with 32 beams of 1.7) each (between "3 dB points). The vertical beam width is 10) (between "3 dB points). The sonar was operated with full transmission power, frequency-modulated pulse, gainstep 7, and the AGC, PP, and normalization filters set to step ‘‘weak’’. The time-varied gain function was set to 20 log R. Special software for computer-based detection and measurements of schools by this sonar has been developed and implemented on an HP9000/720 work station connected to the LAN communication in the sonar (Misund et al., 1994). The software reads the echo telegrams from the processor to the display of the sonar, and organizes an echo table with 32 columns and 512 rows (one for each distance ring of the sonar). The table contains the colour code values (a number from 0 to 63) for each pixel that is the basis for procedures searching for schools. Targets above a certain threshold and horizontal extent that occur within a minimum number of succeeding pings are identified as schools. During the school recordings, the detection system was operated with a colour code threshold of 15, and minimum lengthwise and crosswise extents (Misund et al., 1994) of 5 and 10 m, respectively. For each ping the horizontal area, range and bearing of the school, together with data on date, time, vessel position (from GPS), heading and speed as well as tilt angle of the sonar were written to a file. The data for each school tracked was logged to a separate file. The colour code is a scaled value based on point sampling of the echo envelope of each pixel. The scaling is done by the formula: Colour code= [64*log (echo envelope)/(327.8)]+6.4*Display gain Colour code values above 63 are truncated, but still the colour code is linearly related to echo intensity within a substantial interval. For each ping, the detection software calculates the coloursum for a school recording by adding the colour code values above the detection threshold of all pixels that constitute the school projection. The coloursum can thereby be considered as an expression of the relative echo intensity of the schools. During the school recordings, intraschool and interschool events (Fig. 1) as interpreted by continuously watching the sonar display, were recorded in a separate protocol. Intraschool events were related to vertical
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O. A. Misund et al. Fish abundance
Fish behaviour
Sonar equation
Packing density
Distance vessel to school
"Split and join"
Intraschool
Backscattering cross section
Split Horizontal area
Tilt angle (dorsal aspect angle)
Swimming speed
Swimming angle rel. beam (lateral aspect angle)
Join
Vertical migration
Aspect angle
Leave Approach
School shape
Vacuole
Dive
Packing density
Surface
Elongate
Pseudopodium
Reorganization Figure 1. Diagram of factors that may influence recording of fish abundance in schools by sonar.
migration (dive, surface) or changes in shape (elongate, reorganization, pseudopodium and vacuole). School splitting, and approaching, leaving or joining of smaller subgroups were scored as interschool events. Postprocessing and statistical analysis of the sonar data were conducted by aid of the SAS software (SAS, 1988). Due to shortcomings in the detection software, schools could temporarily be detected as several units, and the schoolnumber could change during tracking. These shortcomings were corrected by adjusting the enumeration and summing multiple units that belonged to a tracked school. Additional schools or targets detected during trackings were allocated successive school numbers. The speed of the schools was calculated ping by ping on the basis of the GPS position of the vessel, the heading of the vessel and the direction of bearing and range vessel-to-school. To avoid a large fraction of zero speed estimates because of no change in position and time between succeeding pings, which occurred rather frequently, the calculations were made with a lag of 30 pings between succeeding positions. However, the random GPS error will induce a substantial uncertainty in the positions of the vessel and thereby the speed of the schools. We therefore applied simple filtering of the GPS recordings to improve the reliability of estimates of school speed. This was done by restricting the ping-to-
ping calculation of school speed to GPS recordings that did not exceed certain limits. In this procedure, the speed calculation was restricted to positions in which the north and east movements of succeeding GPS recordings were within the 95% and 90% percentiles, and then within 30 and 20 m. With this filtering, the average speed of schools fell from 1.55 m s "1 for the unfiltered data, to 0.90 m s "1 for the 20 m percentile restriction (Fig. 2). The corresponding number of accepted recordings dropped by about 60% on average. Another possibility for reducing the influence of the random GPS error is to smooth the GPS recordings. This was done by calculating the speed on the basis of succeeding positions that were averaged for 50 pings. By this procedure the average speed of the schools was reduced by about 30% to 1.05 m s "1 (Fig. 2). Similar estimates of school speed have been obtained by the authors when tracking herring schools in the Norwegian Sea in spring 1996 with the same sonar system, but using differential GPS position with an accuracy of about 2 m (unpublished data). The smoothing procedure was therefore applied in the further analysis, both to obtain more reliable speed estimates and to smooth transient ping-to-ping variations in horizontal area and relative echo intensity (coloursum) of the school. The swimming angle of the school relative to the sonar beam was calculated by assuming that individuals in schools were swimming
Tracking herring schools with a high resolution sonar
61 6000
3.00 2.75
5000
2.25 4000
2.00 1.75
3000
1.50 1.25
Observations (n)
Average swimming speed (m s–1)
2.50
2000
1.00 0.75
1000
0.50 0.25 0.00
Data
95%
90%
30 m Category
20 m
Aver.
0
Figure 2. Average speed (full line) and number of observations (stippled line) for the recorded herring schools as functions of various filtering and smoothing procedures. 95% – calculations limited to recordings within 95 percentile; 90% – calculations limited to recordings within 90 percentile; 30 m – calculations limited to recordings with less than 30 m movement in north or east direction between succeeding pings; 20 m – calculations limited to recordings with less than 20 m movement in north or east direction between succeeding pings; aver. – calculations based on GPS positions averaged over 50 pings.
polarized, and that the swimming angle will be equal to the lateral aspect angle of the fish relative to the sonar beam. To simplify the analysis, lateral aspect angles >90) were transformed to the first quadrant.
Results Both horizontal area and relative echo intensity as expressed through the coloursum varied substantially during the school recordings (Fig. 3). Typical periodic fluctuations with temporary maximum and minimum values in horizontal area and coloursum occurred during all trackings. In most cases the amplitudes of the fluctuations were larger for coloursum than for horizontal area. During tracking of school no. 4 (Fig. 3), there were about 10 major fluctuations that peaked at a rate varying from about 4.8 min to 11.4 min (6.6 min on average). The ratios between maximum and minimum values in the fluctuations were from about 1.5 to 5.0 for horizontal area and from 1.5 to 7 for the coloursum. Similar fluctuations were recorded for most school trackings. The average area of the schools varied from 100 to 889 m2, and the coefficient of variation of the area from 0.29 to 0.81 (Table 1). The coefficient of variation of the coloursum was in most cases more than 20% higher than
that of the area. The average speed of the schools varied from 0.40 up to 1.79 m s "1 with coefficients of variations ranging from 0.46 to 2.23 (Table 1). There were strong relationships between the area and coloursum of the schools (Fig. 3) with significant correlations between 0.3 and 0.9 (Table 2). The average colour value at the pixel level varied between 20 and 45 for most schools, and the level of saturation (63) was not recorded. The area of the schools was not systematically correlated to range, tilt, speed or relative direction of the schools, although a few negative or positive correlations between the area and these parameters were found for individual schools (Table 2). For most schools there was a significant, negative correlation between coloursum and range. Coloursum and tilt also seemed to be negatively correlated, while speed and relative direction was not systematically correlated to coloursum. A linear model with school area as the dependent variable and range, tilt, speed and relative direction as continuous effects was significant and explained more than 10% of the recorded variation for most schools (Table 2). A similar model with coloursum as dependent variable was significant, and explained more than 30% of the recorded variation for most schools. Interschool events occurred at an average rate of 14 min (Pitcher et al., 1996), and as expected, these
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O. A. Misund et al. 1200
16000
14000 1000 12000
10000
600
8000
Coloursum
School area (m2)
800
6000 400 4000 200 2000
0
0.1
0.2
0.3
0.4
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0.6 0.7 Time (h)
0.8
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Figure 3. Variations in school area (solid curve) and relative echo intensity (coloursum, broken curve) during recording of school no. 4 off Lofoten, northern Norway, May 1994. Table 1. Average area, coloursum, speed, depth and heading of the herring schools.
School
Area (m2)
CVA
Coloursum
CVC
Speed (m s "1)
CVS
Depth (m)
Heading ())
n
1 3 4 5 6 7 8 9 10 11 12 13 14 15
270 255 360 889 100 303 573 489 437 288 177 379 529 137
37 56 45 48 59 29 60 70 75 48 81 46 33 47
2806 2856 4877 16 549 1266 4427 7782 7727 7265 5350 1915 5417 10 611 1607
57 70 56 46 70 38 68 80 95 89 69 59 48 107
1.15 0.99 0.87 0.40 0.71 0.94 0.56 1.65 1.01 1.26 1.23 1.05 0.84 1.79
73 89 92 118 87 46 223 68 82 98 72 73 70 197
44 56 38 21 45 44 34 31 29 30 19 42 26 34
203 209 246 139 261 256 203 206 216 173 177 143 200 172
106 67 114 115 96 82 111 103 116 93 66 86 109 46
CVA: coefficient of variation for the school area; CVC: coefficient of variation for the coloursum; CVS: coefficient of variation for the speed of the herring schools; n: number of observations.
events influenced the size of tracked schools. There were four clear cases of joining of two schools with an increase in horizontal area of 5% to 230% (mean about 90%) when comparing the 6 min before and after the event. In three clear cases of split and leave there was a decrease in horizontal area of 10% to 40% (mean about 20%). No certain effects on school area were found by
intraschool events that also occurred at an average rate of about 14 min (Pitcher et al., 1996).
Discussion Both horizontal area and relative echo intensity as expressed through the coloursum of the schools varied
1 3 4 5 6 7 8 9 10 11 12 13 14 15
0.88* 0.90* 0.83* 0.27* 0.88* 0.88* 0.80* 0.76* 0.77* 0.69* 0.88* 0.81* 0.54* 0.83*
Coloursum
n, number of observations. *p
