4. SONAR SURVEYS

4.1 Introduction
4.2 The effective sonar range
4.3 Estimating school sizes
4.4 Sonar biomass estimates
4.5 Improving the precision of sonar biomass estimates
4.6 Example

4.1 Introduction

In fisheries research, sonar surveys have been widely used for biomass estimates of schooling small pelagic fish. In this chapter we discuss in summary the features and survey systems of large-scale surveys. An assumption in the quantitative use of sonar is that of homogeneity which is sufficient for linear extrapolation of observation. This is reliable when a higher degree of uniformity is present.

4.2 The effective sonar range

It has been discussed that, the sampling volume of the sonar system is not uniform; due to the conical shape of the transducer’s beam the sampling volume increases with range. Further, the detection threshold of the system depends, on the one hand, on the size and density of the schools, and on the other, on the distance of the survey schools from the transducer. Big and dense schools will be detected in a higher range than small and light ones. Finally, schools at a close distance from the research vessel are influenced by the noise of the vessel (avoidance effect). These phenomena are reflected in the prepared graphs (Figs. 4.2.1a, b, c) portraying the distance distribution of detected schools within the total sonar range of the research vessel (Morocco, 1979). Specifically, the prepared histograms show the distance distribution of the survey schools in terms of number of schools (sample observations), total area of schools and average area per school.

For estimation purposes, sample data observed only within the “effective sonar range” are taken into account. The technique used for determining the effective sonar range can be summarized as follows:

1. By using the sample observations we prepare the respective histograms indicating the distance distribution of the survey schools within the total sonar range in terms of number of schools, total area of schools and average area per school (see below Figs. 4.2.1a, b, c).

2. The effective sonar range on the x-axis of the histograms is determined by defining the mode domain of the histograms with a low range of variations.

4.3 Estimating school sizes

4.3.1 Estimated school dimensions from sonar traces
4.3.2 Estimation of mean school weight
4.3.3 Estimated PD tolerance limits³

In sections 4.3.1, 4.3.2 and 4.3.3 below we present the methodologies used for the calculations of the school dimensions from sonar traces, mean weight per school and mean packing density per school. It should be emphasized that any consistent errors in measurements of the above magnitudes will clearly give rise to bias in sonar biomass estimates (see also section 5,5).

4.3.1 Estimated school dimensions from sonar traces

When a ship operating a sonar system detects a fish school at a given distance, its horizontal cross-section will be recorded in the produced sonargram (Fig. 4.3.1a).

Figure 4.2.1a Distance distribution of the observed schools within the total sonar range, (Morocco, 1979, Str. 3)

Figure 4.2.1b Distance distribution of the total area of the observed schools within the total sonar range, (Morocco, 1979, Str. 3)

Figure 4.2.1c Distance distribution of the average area per school within the total sonar range, (Morocco, 1979, Str. 3)

Figure 4.3.1a

a) Recorded parameters (sonar measurements)

The recorded sonar measurements for the school are (Fig. 4.3.1a):

D_min: Minimum perpendicular distance of the school from the vessel (m)

D_max: Maximum perpendicular distance of the school from the vessel (m)

B: Breadth of the school (m),

B = D_max - D_min.

L: Length of school (m)

For the estimation of the actual dimensions from the sonar traces, allowance has to be made for the effect of the beam width. This bias has to be substracted from the trace length to find the true school dimensions:

B_C: (adjusted)= B - C₁ (1)

where¹

and

t = pulse duration (sec.)
= sound velocity in the water (m)

¹ If, t = 4 msec and = 1500 m

then

, (2)

or

where

, (3)

For beam angle a = 8°, and . Substituting in the above equations (2) and (3) we get,

(2a),

or

(3a)

For estimating the area (A_s) of the School one can assume that its shape is either circular or elliptical. It should be noted that schools are not all of the same size and shape. Also, it is indicated that as they grow in size they tend to deviate more frequently from the circular shape and become more elliptical, or even rectangular with rounded off ends:

Estimated area:

, (m²) (4)

where

: actual breadth or diameter of the school

p = 3.14

(5)

: short diameter (m)
: long diameter (m)

, (m³) (6)

, (m³) (7)

4.3.2 Estimation of mean school weight

In this Section we discuss some of the methodologies used for the estimation of the mean school weight.

1. Using commercial catch data: Estimates of the weight of the survey schools can be obtained through small-scale catch assessment surveys (purse seine fishery). As an example, a catch assessment survey was conducted covering the purse seine fishery based at two landing centres in the south of Karnataka, i.e., Malpe and Mangalore (India, 1979). In these areas more than 80 purse seiners were operating.

The survey system of the conducted survey was as follows

(i) A survey period of 30 consecutive days was initiated.
(ii) Within each survey day, a pre-selected random sample of landings was obtained.
(iii) For each selected landing/day the following items of information were collected:

a) Total number of observed schools and school sizes (kg), eye-estimates.
b) Actual number of schools caught and observed school sizes (kg), eye-estimates.
c) Actual catches in weight per school caught.
d) Species composition of the catch.

Provided that the obtained information can be considered as a representative sample of the target population, the sample data can be used to provide an estimate of the population mean school weight. In practice, separate estimates of the mean school weight are calculated for the individual areas (= strata) representing different levels of sizes of the surveyed schools.

2. Volume × packing density product: In this method, an estimate of the average weight per school is given by the product of the following two magnitudes

(8)

where

: estimated mean school weight (kg)
: estimated mean school volume (m³)
PD: packing density coefficient (kg/m³, see section 4.3.3)

a) By using the sonar records (effective range) an estimate is calculated of the actual mean diameter per school (, m).

b) Survey echo records (concurrent echo-integrator survey) provide an estimate of the average school thickness (height) per school (, m).

c) An estimate of the average volume per school (m³) is given by (assuming circular shaped schools),

The volumetric packing density coefficient per school can be estimated either by assessing both the formation coefficent of schools and average weight per fish (see section 4.3.3), or by employing available PD values from related surveys.

Example: In an acoustic survey programme the following component values were used for the calculation of the average weight per school of small pelagic fish (Sardina pilcharchus, 1979).

= 22.99 m
= 9.60 m
PD = 1.29 kg/m³

m tons

(9)

where

(kg): weight per school

(kg)/m²): estimated area density

(m²): estimated area of school

² The mean school weight is given by

where, n total number of sample schools.

Estimated area density (kg/m²):

(10)

where

P: total number of pings (= number of transmissions, echo-sounder) per n.mi (= ESDU)

p: number of pings received from a sample school

: estimated integrator deflection of the school (mm)

: estimated calibration coefficient (tons/n.mi²/mm ref. 1 n.mi)

: estimated calibration coefficient in kg/m²

: estimated distance between pings in m, =

(11)

Example: For an individual school the following component values were obtained (echo-integrator survey, Sardina pilchardus, Morocco, 1979)

p: 15 pings, (P = 750 pings per n.mi)
: 117 mm
: 2 tons/n.mi²/nm (after calibration)

, (circular shape of school)

m. tons

4.3.3 Estimated PD tolerance limits³

4.3.3.1 Methodologies used for estimating packing density coefficient (PD)

³ See Bazigos, G.P. and L, Rijavec, The effect of incomplete survey programmes on the accuracy of fish biomass estimates with special reference to Southwest India. Rome, FAO, FIRM-IND/75/038, Report (4)(in preparation)

(i) volumetric unit = kg of fish/m³
(ii) area unit = kg of fish/m²
(iii) formation unit = no, of fish/m³ - The arithmetical value of the formation unit depends on the formation pattern of fish expressed by the average distance between the individual fish in the school; the distance usually expressed in fish length units.

Weichs (1973) has suggested on hydrodynamic grounds that fish should not usually school at a greater density than one fish length apart, here called formation coefficient k = 1.

For our study we used two values of the formation coefficient k, i.e., k = 1, maximum density, and k = 3, minimum density, in order to define the tolerance range of packing densities for pelagic fish schools.

Below, we present the estimators we have developed which can be used for the estimation of the upper and lower limits of packing densities of schools and for various lengths and weights of fish.

Notation:

: average length per fish (cm)
k: formation coefficient (range, k = 1-3)
: formation distance between fish in a school (cm)
N: total number of fish per m

Estimated total number of fish per m³

(1)

where

: expected total number of fish in the longitudinal dimension of the plane

Figure 4.3.3a

Substituting g₁, g₂, g₃ in equation (1), we get

or

(2)

By using empirical data (purse seine fishery off Cochin, India, 1977), we constructed a two-way table (Table 4.3.3b) giving the expected tolerance limits of packing densities of the surveyed fish schools. Specifically, the tabulated data provide the upper limit (k = 1) and lower limit (k = 3) of the packing density coefficients of oil sardine and mackerel schools and for various lengths and weight values per fish.

Figures 4.3.3c, d portray the estimated tolerance curves corresponding to the calculated packing density limits (upper, lower) of oil sardine and mackerel schools, respectively.

Table 4.3.3b Estimated tolerance limits of packing densities of schools of oil sardine and mackerel, expressed in formation and volumetric units, 1977 (purse seine data off Cochin, - INDIA)

Average length per fish (cm) (1)	Average weight per fish (gr) (2)	Month (3)	Minimum packing density (k = 3)		Maximum packing density (k= 1)
			Fish No./m3	kg/m3	Fish No.	Kg/m3
1. Oil sardine
14.32	24	April	9	0.227	170	4.087
14.85	26	March	8	0.220	153	3.970
15.57	31	May	7	0.220	132	4.106
15.62	44	September	7	0.321	131	5.773
16.04	41	October	7	0.276	121	4.968
16.69	41	November	6	0.245	108	4.410
16.71	36	February	6	0.214	107	3.858
17.09	49	July	6	0.273	100	4.908
17.14	43	June	6	0.237	99	4.270
17.24	39	December	5	0.211	98	3.806
17.68	42	January	5	0.211	90	3.780
16.27	37.82	Averages	7	0.242	119	4.359
2. Mackerel
16.81	59	August	6	0.345	105	6.210
17.99	67	September	5	0.320	86	5.753
20.02	80	January	3	0.277	62	4.985
20.17	81	November	3	0.274	61	4.936
21.08	98	March	3	0.291	53	5.231
24.86	158	February	2	0.286	33	5.142
20.16	90.50	Averages	4	0.299	67	5.376

Figure 4.3.3c Estimated tolerance curves of packing densities of schools of oil sardine, expressed in formation units - Fish No./m³

Figure 4.3.3d Estimated tolerance curves of packing densities of schools of mackerel, expressed in formation units - Fish No./m³

4.3.3.1 Methodologies used for estimating packing density coefficient (PD)

For quantitative estimates of the packing density of the surveyed pelagic schools, a variety of methodologies have been used over time. The methodologies employed can be grouped as follows:

1. Estimated average values of PD were expressed in volumetric units (kg/m³) and were calculated as the ratio of the following two primary magnitudes - the arithmetical values of the primary magnitudes were obtained independently:

Estimated PD (kg/m3)	= estimated average total school size in weight (kg)
	- skipper’s eye observations
	+ average school size in volume (m3)
	- sonar/echo-integrator data

Measurements (estimates) of the average arithmetical value of the formation unit of the surveyed schools were calculated by dividing the arithmetical value of the volumetric unit by the average weight per fish ,

(1)

2. Measurements were first obtained of the mean arithmetical value of the formation unit of the surveyed schools either on the basis of visual observations or by using existing auxiliary information (known values of the formation coefficient of similar species in other areas of the world). An estimate of the mean value of the volumetric unit of the surveyed schools was calculated by the formula:

(2)

3. For sample schools, estimate’s were first calculated of their volumes by using observations of the acoustic surveys:

Total volume of school (m3)	= horizontal cross section of school (m3)
	- sonar data
	× vertical extension of school (m)
	- echo-integrator observations

Subsequently, the sample schools were caught by purse seine and weighed. Estimates of the average arithmetical value of PD for the volumetric unit of sample schools was calculated by the formula:

4. For the estimation of PD of the surveyed schools, the procedure used is based on the arithmetical values of the echo integrator deflection given by the subsurface schools, and the estimated values of calibration constant.

Specifically, the methodology employed for the estimation of the arithmetical value of PD expressed in volumetric units is as follows: Reverse the formula for the calculation of the calibration constant when the fish density is a known quantity. The deflection of a school is extrapolated to the standard distance unit of calibration (1 n.mi or 6 min in 10 knots vessel speed). Further, the following formula is used for estimating PD:

(3)

where

= packing density of school (gr/m³)
: average integrator deflection per elementary distance sampling unit (mm, ref. 1 n.mi)
R: integrated layer, equal to the height of the sample school in m
C: acoustic system’s conversion constant (= calibration constant in t/n.mi²)
3.43: unit conversion factor (t/n.mi² into gr/m²)

The above formula (3) is derived from the well-known formula given by Forbes and Nakken which shows that the echo-integrator output signal is proportional to the average fish density within the volume sample by the echo-sounder beam, i.e.,. A slightly modified formula for PD (gr/m³) in comparison with the above (3) is given by:

where

C: calibration constant in t/n.mi²
M: integrator deflection of a given school
D: distance covered by the vessel in min. during the recording period (in m)
H: height of school in m
: true diameter of school (in m)
3.43: conversion factor (converting t/n.mi² into gr/m²)

a) Estimates of the formation unit of PD (Fish, no./m³) are usually calculated from supplementary information which is either very general (data available from other species in various parts of the world) or subjective (visual observations). The statistical power of the method can be improved if objective information based on close photography of the schools can be used.

b) The calculation of the volumetric value of PD is more reliable, provided actual fishing of a good number of schools is accomplished.

c) PD values calculated on the basis of integration deflection are. subject to various sources of errors inherent in the measurement process:

d) Because there are difficulties in practice for obtaining reliable estimates of the vertical dimension of the surveyed schools, a magnitude which is needed for assessing the volume of the schools, another alternative is to estimate PD in area units (kg/m²) instead of in volumetric units (kg/m³).

4.4 Sonar biomass estimates

4.4.1 Introduction
4.4.2 The estimation process

4.4.1 Introduction

The sampling design of large-scale sonar surveys is based on the method of line sample (= regular line transect sample or random line transect sample). Usually sonar surveys are running concurrently with the echo-integrator surveys. Sampling methods of acoustic, surveys are presented in chapter 5 of this report.

4.4.2 The estimation process

It has been discussed that, in sonar surveys the estimated total biomass is a secondary magnitude and is calculated as a product of a series of individual magnitudes which are calculated (estimated) independently. In practice the following two methods are used for sonar biomass estimates:

Method 1: based on the estimated average weight per school

Method 2: based on the estimated packing density of schools

Method 1: Estimated total biomass

In this method of estimation, the estimator used is given by (see also section 4.6 - Example).

(12)

where

: estimated total biomass (m. tons)

A: total survey area (n.mi²)

: average number of schools per n.mi² (sample observations, based on the effective sonar range)

: average weight per school (m. tons)

where

: average number of schools per n.mi²
n: total number of sample ESDU’s (elementary sampling distance unit = 1 n.mi)
l: sonar effective range m, (1852 = length of a n.mi in m).

Method 2: Estimated total biomass

In this method of estimation the calculated total biomass is obtained as a product of a series of magnitudes which are calculated independently. Specifically, we first calculate (estimate) the average total weight per school within the effective sonar range (m. tons),

(1) (average total weight per school) = (mean volume per school) × (packing density per school)

In the above (1), the mean volume per school is a compound magnitude and is given by

(2) (mean volume per school) = (mean horizontal area per school) × (mean vertical extension per school or thickness per school).

In the above (2), estimates of the mean horizontal area per school are calculated through the conducted sonar surveys. Estimates of the mean vertical extension of schools are calculated independently, e.g., visual observations, results of echo-integrator survey.

Estimates of the third component of the above estimator (1), i.e., packing density per school are calculated independently (see section 4.3).

The overall procedure used for the estimation of the total sonar biomass (m. tons) by using method 2 is portrayed in the block diagram (Fig. 4.4.2a, mechanical processing).

Figure 4.4.2a SONAR ESTIMATES: Block Diagram

4.5 Improving the precision of sonar biomass estimates

In the aforegoing sections we have discussed the facts which affect the accuracy of the calculated sonar biomass estimates. Also we presented the sampling methodology required for the estimation of population totals (for the calculation of the sampling errors of the estimates see chapter 5).

We can, however, suggest a more efficient method for the manipulation of the obtained observations which are based on a given sample size, in order to improve the precision of the calculated estimates.

It was observed that schools are not equal in size and shape. Usually, the obtained sample observations of large-scale sonar surveys reveal a great number of small schools and relatively a small number of big schools. One way for improving the precision of the calculated estimates is to group the sample school traces (effective sonar range) into size categories (post-stratification) and worked out sonar biomass estimates within the established size classes. In order to get full benefit from stratification, these estimates should be calculated on a space/depth domain basis.

In order to speed up the numerical processing of the sample observations of small schools, it is convenient to arrange the categories so that the ratio of volumes from one to the next higher ones remains constant, and for small schools the experience suggests that the vertical height (h) of the school is most often increasing with the size of the school at a relatively constant rate. However, because this information (h) is not available in sonar surveys, the diameter of the school should be taken as the criterion for classification.

For estimating the mean value of a category we calculate the sample mean within the category. The Table below (Table 4.5a) portrays the steps to be employed for the estimation of the sonar total biomass by using size school categories.

4.6 Example

For this example we used empirical data (sample observations, daytime) of a conducted sonar survey. One of the objectives of the survey was the calculation of biomass estimates of the survey sardine stocks (Sardina pilchardus),

1. The computer sheet presents a table providing the distance distribution of the schools observed within the total sonar range. The class interval of the distance distribution is 20 m. In the Table the following items of information are given:

a) Total number of schools observed on a class interval basis
b) Total area (m²) of schools observed on a class interval basis
c) Average area per school (m²) on a class interval basis
d) Relative and cumulative relative distribution of the above a, b.

2. By using the empirical data prepare the histograms portraying the above distance distributions (a, b, c). Determine the effective sonar range and discuss critically the obtained results.

3. If the total number of ESDU’s (Elementary Distance Sampling Unit = 1 n.mi) equals n = 600, estimate the average number of schools per n.mi².

4. If the average weight per school is = 3 m. tons and the total survey area equals A = 17,282 n.mi², calculate (estimate) the total biomass of sardines in the survey area.

Table 4.5a Sonar biomass estimates (daytime observations)

School categories		Sample data		Average no. of schools per n.mi2	Total no. of schools	Average weight per school	Estimated total biomass
Category	Diameter of schools	Total no. of schools (effective range)(mi)	Average no. of schools per ESDU (mi/n)
	a1-a2
	a2-a3
	a3-a4
	a4-a5
Total		m			M		B

Space/Depth Domain	C. No.
Area	A (n.mi2)
No. of ESDU’s	n (total sample size)

Computer Sheet

DISTANCE FREQUENCY DISTRIBUTION BASED ON DAYTIME OBSERVATIONS