TY - JOUR
AU - Espinosa, V
AB - Abstract This study measures the dorsal aspect target strength (TS; dB re 1 m2) and TS–length (standard length, SL; cm) relationships for European anchovy, attained both ex situ and in situ in two different seasons across 7 years in the Bay of Biscay. The measurements were made at three frequently used acoustic frequencies (38, 120, and 200 kHz). A backscattering model for physostome fish was utilized to help interpret the results. The obtained experimental mean TS for anchovies with an SL of 3.5–19.5 cm was −44.6 (±2.3), −46.9 (±3), and −48.4 (±2.7) dB at 38, 120, and 200 kHz, respectively, yielding b20 values of −66.4, −68.7, and −70.4 dB, respectively. The results were consistent across seasons and between in situ and ex situ conditions, presenting TS–length relationships with statistically significant slopes (p-values <0.05) for all frequencies. This research represents part of a series of efforts planned to obtain a comprehensive TS vs. length and depth relationship to update the acoustic assessment methodology of European anchovy in the Bay of Biscay. Introduction European anchovy (Engraulis encrasicolus; Linnaeus, 1758) is one of the main commercial species in the Bay of Biscay, supporting profitable fisheries for both the Spanish and French fleets. Stock assessment of this resource is based on the so-called catch-Bayesian biomass-based model (CBBM) (Ibaibarriaga et al., 2008; ICES, 2015), which depends on the internationally coordinated scientific advice. This advice is based in an abundance index of the adult stock abundance, derived from a combination of commercial and fisheries independent information. The scientific surveys that contribute to the CBBM are BIOMAN (Massé et al., 2018); based on the daily egg production method in spring; and two acoustic-trawl surveys, PELGAS (Massé et al., 2018) in the spring and JUVENA (Boyra et al., 2013) in autumn. These surveys’ methodologies are discussed and evaluated annually at the International Council for the Exploration of the Sea Working Group of Acoustics and Eggs (ICES WGACEGG) and the results are synthetized at the ICES Working Group on Southern Horse Mackerel, Anchovy and Sardine (ICES, 2017) to produce the CBBM index. Acoustic surveys are considered effective methods for quantifying the distribution and abundance of many pelagic marine fauna (Simmonds and Maclennan, 2005). In most cases the echo-integration technique is used to estimate fish density (MacLennan et al., 1990), necessitating information regarding the dorsal aspect target strength (TS; dB re 1 m2) (Maclennan et al., 2002) of the fish that contributes to the received signal (Jech and Horne, 2001). The TS is a measure of the proportion of the incident intensity that is backscattered by the target (Maclennan et al., 2002). To translate acoustic density measurements into biologically more meaningful measures, such as biomass or abundance, the log-linear relationship between the standard length (SL; cm) of the fish and the backscattered acoustic energy is commonly used. This TS to length (TS–L) equation can be expressed as: TS=a log 10(SL)+b,(1) where the slope, a, and the intercept, b, are generally assumed to be species-specific constants. In the case of physostomous fish such as anchovy, a is normally close to 20 (Love, 1977; Foote, 1980) and (1) is often replaced by: TS=20 log 10(SL)+b20.(2) The acoustic surveys that are currently used to estimate anchovy biomass in the Bay of Biscay utilise different b20 values at 38 kHz: the French survey PELGAS uses −71.2 dB, originally obtained from 19 to 35 cm herring Clupea harengus (ICES, 1982), while the Spanish surveys JUVENA and PELACUS use −72.6 dB from 8 to 32 cm herrings (Degnbol et al., 1985). The lack of unique and ad hoc TS–length relationship for Bay of Biscay anchovy presents an obstacle for the development of an absolute index of abundance for this species. This has been acknowledged by ICES WGACEGG, with the attainment of a common TS–length relationship for the region deemed one of the key objectives of the working group (ICES, 2013). Generally, in situ TS measurements are assumed to deliver accurate results when collected with concurrent reliable biological samples and tilt angle information (Torgersen and Kaartvedt, 2001; Madirolas et al., 2016; Zare et al., 2017). However, measuring the TS of fish in their natural environment may be accompanied by difficulties that result in biased TS values. In particular, during the daytime small pelagic fish such as European anchovy aggregate in schools that are too densely packed to resolve individual targets (Sawada et al., 2009). Nevertheless, various strategies exist to overcome this problem. One mitigation tactic is to lower transducers closer to the fish targets, thus reducing the sampling volume (Ona, 2003; Kang et al., 2009; Sawada et al., 2009; Murase et al., 2011; Fernandes et al., 2016). In the Bay of Biscay, a variant of this technique has been applied to estimate the TS of anchovy (Doray et al., 2016), but the methodology stimulated a change in fish behaviour that seemed to positively bias the mean TS values. Other strategies involve working at night, when most species disperse and migrate near the surface (Glass, 2000). These are most useful when a study area is dominated by the target species (Foote et al., 1987; Barange et al., 1996; Peltonen and Balk, 2005; Zhao et al., 2008) but might prove problematic where there are numerous fish species or in the presence of high abundances of plankton. An alternative strategy is to conduct ex situ experiments (Kang and Hwang, 2003; Kang et al., 2009), providing greater control over density and ensuring the isolation of the target species. However, ex situ experiments pose some concerns such as potentially altering the behaviour of the targets (and hence potentially biasing the mean TS values) or the close proximity of the targets to the transducers, which may cause short-range problems [i.e. TS dependence with distance related to measurements performed inside the near field of the transducer (Simmonds and Maclennan, 2005; Foote, 2014; Chu and Eastland, 2015; Pérez-Arjona et al., 2018), or the extended size of fish that produces an uncertainty in the position inside the beam measured by split-beam echosounders (Kieser et al., 2000)]. To gain insights regarding the influence of behaviour on TS, modelling techniques (Fujino et al., 2009) have often been used in combination with ex situ TS measurements (Henderson and Horne, 2007; Sawada et al., 2011). The objective of this work is to measure the TS values and model TS–length relationships for European anchovy at 38, 120, and 200 kHz frequencies, using acoustic data collected both in situ and ex situ. A reduction in potential multiple target bias was attempted by working during the night and applying a high-density filter (Gauthier and Rose, 2001) to the in situ-measured data as well as controlling the influence of the different sampling volumes related to the pulse durations used at the different ex situ experiments. In addition, an ad hoc calibration experiment was conducted to correct the possible bias derived from the short range of some of the ex situ measurements that were done in the transition range between near and far field. Finally, a backscattering model based on the method of fundamental solutions (MFS) (Fairweather et al., 2003; Pérez-Arjona et al., 2018, 2020a) for physostomous fish, simulating the swimbladder as two-chambered prolate spheroids (Andreeva, 1974; Weston, 1966; Love, 1978; Furusawa, 1988; Ye, 1997) plus the backbone, was utilised to help interpret the empirical results. Most of the numerical methods considered for the simulation of TS values are solely valid when estimating TS in the far field of both the emitting transducer and the scatterer (fish) (Jech et al., 2015). Only the finite element method (FEM) (Lilja et al., 2004) and the boundary-element method (BEM) (Foote and Francis, 2002) provide alternatives at arbitrary close distances, but they (especially FEM) have a high and perhaps even unaffordable computational cost. The MFS is a meshless method that has proved to be useful in estimating the measurable TS of fish and the contributions of the different inner structures of fish to TS. It has similar or even greater accuracy than FEM or BEM (Godinho et al., 2012), yet with reduced computational costs, which is a consideration that is especially important when examining fish models with additional fish structures to a swim bladder (e.g. a fish backbone) (Pérez-Arjona et al., 2018). Material and methods In situ data collection Acoustic-trawl data were collected from 2010 to 2017 via two scientific surveys in the Bay of Biscay at two different times of year (Figure 1). JUVENA (Boyra et al., 2013) was conducted in September and mainly focused on the juvenile component of the anchovy population, while BIOMAN (Santos et al., 2016) was undertaken in May (during the peak of the spawning season) and examined the adult component. Two scientific research vessels were used in each survey: RV “Ramón Margalef” (RM, hereafter) and RV “Emma Bardán” (EB, hereafter). Both collected continuous acoustic data using a Simrad EK60 scientific echosounder, with split-beam transducers of 38 kHz (ES38B-7), 120 kHz (ES120-7C), and 200 kHz (200-7C). The echosounders were calibrated at least once a year, typically at the beginning of the survey following standard procedures (Demer et al., 2015), with intercalibration exercises carried out each year between the two vessels following a standard methodology (Simmonds and Maclennan, 2005). The most relevant calibration parameters of the in situ measurements are described in Supplementary Table S1. Figure 1. Open in new tabDownload slide Area of study in the Bay of Biscay, with the locations where the TS measurements were performed, distinguishing the fishing trawls conducted for the in situ measurements in both surveys and the location of the cage for the ex situ ones. Figure 1. Open in new tabDownload slide Area of study in the Bay of Biscay, with the locations where the TS measurements were performed, distinguishing the fishing trawls conducted for the in situ measurements in both surveys and the location of the cage for the ex situ ones. Ground truth trawl hauls were performed based on the interpretation of the echograms, aiming to identify fish species and to determine their size distribution across the whole area of study. Both vessels performed trawl hauls during JUVENA, but only EB performed trawl hauls during BIOMAN due to the regular activities that take place on the RM in association with the daily egg production method. Trawl samples were obtained using a Gloria HOD 352 pelagic trawl of 15-m vertical opening, with a 10-mm mesh size (bar length) at the cod end. Fishing trawls were performed during the day and the night, between 5- and 300-m depth and at a mean speed of 4 knots. Acoustic data recorded during trawl hauls with predominance (>85%) of anchovy in the catch were selected for TS analysis (Supplementary Table S2). The typical recording range was 200 m, yielding 2–3 pings per second. Lengths were obtained from a random sample of >50 individuals of each haul and measured to 0.5-cm SL classes onboard the research vessel. Ex situ data collection Ex situ TS measurements were obtained from two sets of anchovy individuals, captured in December 2011 (set 1) and July 2013 (set 2) in the Bay of Biscay. Both were captured by the purse seiner Itsas Lagunak and transported in live bait fishing tanks onboard the vessel. The first set comprised 120 anchovies that were kept in water tanks (1 m depth × 3 m diameter) in the Aquaculture School of Mutriku for eight months before being moved to the sea culture cage at the mouth of Mutriku harbour (Gipuzkoa, Spain; 43°18′N, 02°22′W) (Figure 1). The cage was cylindrical with ∼8 m depth and 16 m diameter and a mesh size of 0.4 cm. The second set consisted of ∼5000 anchovies and was transported directly from the purse seiner tanks to the harbour cage. After being moved to the cage, anchovies were left at least two days to settle before the experiments commenced. Unfortunately, after dropping the second set of anchovies to the cage, we noticed that it was contaminated by number with ∼2% of horse mackerel. A diver visually inspected the cage periodically to maintain, feed and monitor the fish. Two groups of measurements (N1, N2) were carried out using the first set of anchovies and another (N3) using the second one. At the end of each set of measurements, 50 specimens were weighed and measured for SL. TS measurements were made using a three-frequency (38, 120, and 200 kHz) Simrad EK60 split-beam scientific echosounder system, installed on a floating 0.6 m × 0.6 m platform ∼20 cm below the sea surface. The floating platform was placed about halfway (∼4 m) between the centre and the border of the cage (Figure 2a). The platform was connected to a logistics boat that housed the ancillary electronic equipment and the 12-V batteries used as the power source. Day- and night-time data were registered during the study, but following a preliminary inspection of the data, only night experiments were used in the analysis. Daytime data yielded significantly higher TS values, probably owing to the greater packing densities reported by the diver and hence likely subjected to a higher probability of detecting unresolved multiple echoes. Figure 2. Open in new tabDownload slide (a) Scheme of the experimental set up in the cage. Transducers were installed in a floating platform near the surface pointing downwards towards the scattered anchovies. Different regions are highlighted with reference to the different acoustic fields (near, near-to-far and far field; shown in B) of the used 38 kHz transducer. The dashed lines mark the critical ranges (Rc and 4Rc) that divide the three regions. The TS analysis was done using decreasing pulse durations to achieve increasing sampling resolution (note that neither anchovies nor sampling volumes are at proportional scale). Targets from the grey areas (i.e. depth <2 and >6 m) were not used for analysis. (b) Theoretical range-dependence of the pressure wave of a circular piston simulating the geometry of a Simrad ES38B transducer (see the text for details), showing both the exact solution (black line) and the far field aproximation (i.e. spherical decay, grey line). The different ranges at which the sphere was calibrated during the near-to-far field calibration experiment are also marked (black circles). The calibration values at each range obtained in this experiment were used to correct the anchovy TS measurements of the corresponding range in the cage. Figure 2. Open in new tabDownload slide (a) Scheme of the experimental set up in the cage. Transducers were installed in a floating platform near the surface pointing downwards towards the scattered anchovies. Different regions are highlighted with reference to the different acoustic fields (near, near-to-far and far field; shown in B) of the used 38 kHz transducer. The dashed lines mark the critical ranges (Rc and 4Rc) that divide the three regions. The TS analysis was done using decreasing pulse durations to achieve increasing sampling resolution (note that neither anchovies nor sampling volumes are at proportional scale). Targets from the grey areas (i.e. depth <2 and >6 m) were not used for analysis. (b) Theoretical range-dependence of the pressure wave of a circular piston simulating the geometry of a Simrad ES38B transducer (see the text for details), showing both the exact solution (black line) and the far field aproximation (i.e. spherical decay, grey line). The different ranges at which the sphere was calibrated during the near-to-far field calibration experiment are also marked (black circles). The calibration values at each range obtained in this experiment were used to correct the anchovy TS measurements of the corresponding range in the cage. Measurements at the cage were performed using different pulse durations (64, 128, 256, and 512 µs) to check whether the values obtained varied at increasing sampling volumes (i.e. decreasing vertical resolutions) due to the greater failure probability of the single target discrimination algorithm for larger volumes (see below). Calibrations were made following standard procedures (Demer et al., 2015) and were repeated for all pulse durations and power settings (Table 1). Table 1. Calibration settings of the ex situ data, performed at 4 m from the transducer. Year . Experiment . Frequency . Transducer radius . Pulse duration . Power . Gain . Sa correction . code . (kHz) . (m) . (μs) . (W) . (dB) . (dB) . 2012 N1 38 0.21 256 800 23.62 −0.66 120 0.08 256 200 23.55 −0.59 200 0.05 256 180 25.74 −0.44 2012 N2 38 0.21 256 800 23.51 −0.65 120 0.08 64 200 25.23 −0.58 200 0.05 64 180 24.74 −0.67 2013 N3 38 0.21 512 800 25.4 −0.75 120 0.08 256 200 26.63 −0.61 200 0.05 128 180 26.07 −0.76 Year . Experiment . Frequency . Transducer radius . Pulse duration . Power . Gain . Sa correction . code . (kHz) . (m) . (μs) . (W) . (dB) . (dB) . 2012 N1 38 0.21 256 800 23.62 −0.66 120 0.08 256 200 23.55 −0.59 200 0.05 256 180 25.74 −0.44 2012 N2 38 0.21 256 800 23.51 −0.65 120 0.08 64 200 25.23 −0.58 200 0.05 64 180 24.74 −0.67 2013 N3 38 0.21 512 800 25.4 −0.75 120 0.08 256 200 26.63 −0.61 200 0.05 128 180 26.07 −0.76 Note that the 200-kHz gain values differ from the observed increasing trend with time because there were two different 200-kHz transducers used. Open in new tab Table 1. Calibration settings of the ex situ data, performed at 4 m from the transducer. Year . Experiment . Frequency . Transducer radius . Pulse duration . Power . Gain . Sa correction . code . (kHz) . (m) . (μs) . (W) . (dB) . (dB) . 2012 N1 38 0.21 256 800 23.62 −0.66 120 0.08 256 200 23.55 −0.59 200 0.05 256 180 25.74 −0.44 2012 N2 38 0.21 256 800 23.51 −0.65 120 0.08 64 200 25.23 −0.58 200 0.05 64 180 24.74 −0.67 2013 N3 38 0.21 512 800 25.4 −0.75 120 0.08 256 200 26.63 −0.61 200 0.05 128 180 26.07 −0.76 Year . Experiment . Frequency . Transducer radius . Pulse duration . Power . Gain . Sa correction . code . (kHz) . (m) . (μs) . (W) . (dB) . (dB) . 2012 N1 38 0.21 256 800 23.62 −0.66 120 0.08 256 200 23.55 −0.59 200 0.05 256 180 25.74 −0.44 2012 N2 38 0.21 256 800 23.51 −0.65 120 0.08 64 200 25.23 −0.58 200 0.05 64 180 24.74 −0.67 2013 N3 38 0.21 512 800 25.4 −0.75 120 0.08 256 200 26.63 −0.61 200 0.05 128 180 26.07 −0.76 Note that the 200-kHz gain values differ from the observed increasing trend with time because there were two different 200-kHz transducers used. Open in new tab In addition to the regular calibration at 4 m range, an additional calibration experiment was conducted to determine and correct the bias effect of working at distances where the far field condition of the corresponding transducer was not achieved (see below). Near-to-far field calibration experiment Due to the limited cage dimensions (Figure 2), some of the acoustical measurements were done closer than the far field distance of the 38-kHz transducer, which posed some initial concerns about the validity of these measurements. Depending on the distance from the transducer face, three main regions can be distinguished: the near field, the far field and a near-to-far transition field (Figure 2). The near field is the region of distances closer than RC, the critical range or Fresnel distance, which for the corresponding flat piston transducer is defined as RC= a2/λ, being a the transducer radius and λ its operative wavelength (Medwin and Clay, 1998; Foote, 2014). Inside the near field (i.e. at distances less than 1.14, 0.46, and 0.33 m, respectively, for the 38, 120, and 200 kHz transducers used in this work; radius of each transducer shown in Table 1) the amplitude oscillates with distance (Figure 2) and quantitative acoustic measures are problematic. Immediately beyond RC lies the near-to-far transition field, where the amplitude does not oscillate with distance and the spreading beam directivity pattern is stable, with the on-axis amplitude decreasing monotonously but not yet following spherical spreading. Finally, beyond the transition zone is the far field, where the amplitude decreases spherically with range. The far field starts somewhere between two to four times RC (depending on the maximum deviation allowed from spherical spreading (Foote, 2014)). In this work, taking a rather conservative assumption (4RC), the far field condition was accomplished at different distances for each frequency: 4.55 m at 38 kHz, 1.85 m at 120 kHz, and 1.33 m at 200 kHz. Therefore, as only targets from 2 to 6 m depth were analysed (Figure 2), only those performed at 38 kHz were susceptible to be biased by this effect. Moreover, since the considered measuring distances were greater than RC (1.14 m at 38 kHz) but smaller than 4Rc, measurements followed a stable pattern. It is for this reason that the corrections can be applied not only at the calibration points but also interpolated to intermediate distances. To test the validity of these data and, when necessary, to obtain an extra depth-dependent gain correction, an ad hoc calibration experiment was carried out, in which the TS of a reference target (a tungsten carbide sphere of 38.1 mm diameter) was measured on axis at different distances from a 38 kHz transducer (2, 3, 4, 6, and 12 m), i.e. covering distances from the near-to-far to the far field (Figure 2b). The TS of the sphere was first measured at 12 m (i.e. well beyond the far field range and, thus, where the target measurements were expected to be free from bias caused by range) and then at all the other ranges. We simulated the pressure waves emitted by a 38 kHz transducer using the analytical solution of the pressure radiation of a circular flat piston (Kinsler et al., 1999, pp. 181–185) at different ranges, to visualize (Figure 2b) and help interpret the results of the near-to-far field experiment. Data processing Target selection In situ data To separate swimbladdered fish from fluid-like organisms or macro-zooplankton, a bi-frequency algorithm (Ballón et al., 2011; Lezama-Ochoa et al., 2011) was used on the in situ experiments, based on the differences in mean volume backscattering strength (Sv, dB re 1 m2/m3) at 38 and 120 kHz. It was possible to use this algorithm because of the high percentage of beam overlap within the in situ measurements ranging from 60 to 86% (see here an interactive application illustrating the beam overlap calculations). A binary matrix was created from the data selected by the algorithm and was applied as a mask to the three frequencies of the study using Echoview Software Pty Ltd, 2013, version 5.2 (Echoview Software, 2013). All the echograms were visually inspected to check the correct performance of the algorithm (Figure 3). The TS values were then derived from the selected echoes using a single target detection algorithm (Soule, 1997; Ona and Barange, 1999), the values of which are presented in Table 2. Figure 3. Open in new tabDownload slide Top panel: example of 38 kHz TS echogram showing 10 min of one of the ex situ experiments (N1) targeting ∼10 cm anchovies at 256 µs pulse duration and −70 dB threshold. Anchovies and plankton are easily distinguished by intensity and length of the echotraces. Bottom panels: example of 38 kHz TS echogram showing 2.5 min of the 165 037 in situ haul targeting ∼12 cm anchovies at 1024 µs pulse duration and −70 dB threshold. The left side shows the raw TS echogram where the anchovies (yellow-red echoes) are surrounded by a plankton layer (grey-blue echoes); and the right side shows the same 2.5 min of acoustic data at the same threshold but after the plankton filtering process, where only fish echoes remain (see the text for details). Figure 3. Open in new tabDownload slide Top panel: example of 38 kHz TS echogram showing 10 min of one of the ex situ experiments (N1) targeting ∼10 cm anchovies at 256 µs pulse duration and −70 dB threshold. Anchovies and plankton are easily distinguished by intensity and length of the echotraces. Bottom panels: example of 38 kHz TS echogram showing 2.5 min of the 165 037 in situ haul targeting ∼12 cm anchovies at 1024 µs pulse duration and −70 dB threshold. The left side shows the raw TS echogram where the anchovies (yellow-red echoes) are surrounded by a plankton layer (grey-blue echoes); and the right side shows the same 2.5 min of acoustic data at the same threshold but after the plankton filtering process, where only fish echoes remain (see the text for details). Table 2. Parameters used in the single target detection algorithm. . Units . In situ . Ex situ . TS threshold dB −70 −70 Filter angles (major/minor) Degrees 3.5 3.5 Min. pulse length ms 0.7 0.7 Max. pulse length ms 1.5 1.5 Max. beam compensation dB 6 6 Max. angle SD (minor/major) Degrees 0.6 0.2 . Units . In situ . Ex situ . TS threshold dB −70 −70 Filter angles (major/minor) Degrees 3.5 3.5 Min. pulse length ms 0.7 0.7 Max. pulse length ms 1.5 1.5 Max. beam compensation dB 6 6 Max. angle SD (minor/major) Degrees 0.6 0.2 Open in new tab Table 2. Parameters used in the single target detection algorithm. . Units . In situ . Ex situ . TS threshold dB −70 −70 Filter angles (major/minor) Degrees 3.5 3.5 Min. pulse length ms 0.7 0.7 Max. pulse length ms 1.5 1.5 Max. beam compensation dB 6 6 Max. angle SD (minor/major) Degrees 0.6 0.2 . Units . In situ . Ex situ . TS threshold dB −70 −70 Filter angles (major/minor) Degrees 3.5 3.5 Min. pulse length ms 0.7 0.7 Max. pulse length ms 1.5 1.5 Max. beam compensation dB 6 6 Max. angle SD (minor/major) Degrees 0.6 0.2 Open in new tab Ex situ data Due to the short depth range available in the experimental cage, the overlapping volume between the 38- and 120-kHz frequencies (42%) was not enough to apply the bi-frequency algorithm (link to the interactive beam overlap illustration). Therefore, it could not be used with the ex situ data. Instead, echograms were manually scrutinized to isolate anchovies from unwanted signals based on the intensity and the length of the echo-traces. There were two clearly distinguishable types of traces: intense (TS > −60 dB) and short (<4 consecutive pings, representing typical swimming velocities of small pelagic fish between 7 and 20 cm/s in most cases), attributed to anchovy, and less intense (<−60 dB) and long (typically between 20 and 100 pings, representing velocities between 0.2 and 2 cm/s) were attributed to slow-swimming (presumably planktonic) scatterers. Multiple targets To prevent the inclusion of unwanted multiple targets, two different procedures were followed depending on the type of data collected. In situ To reduce the multiple target bias associated with the in situ data, a high-density filter (Gauthier and Rose, 2001) was applied, for which the density threshold was empirically determined. The echogram of each haul was gridded in cells with a horizontal size of 5 pings (1 ping ≈ 1.8 m) and a vertical length of 5 m. The total number of fish per acoustic reverberation volume (Nv) (Sawada et al., 1993; Ona and Barange, 1999) was then plotted against the number of single targets per sample volume (Tv) in each cell. As in previous studies (Boyra et al., 2018, 2019), the maximum of Tv on Nv was used to determine the target density at which multiple target echoes are likely to be produced. However, in this study, rather than having a maximum, the Tv values were found to monotonously increase for small values of Nv until reaching stabilization at Nv values close to 1. This may have been due to the lower packing density observed, related to the typical dispersed night-time distribution of anchovy. The in situ data set was subsequently limited to cells with Nv values below 1. After filtering the high-density areas, a final quality check consisted on removing the hauls that presented either two similarly pronounced modes or fewer than five targets. Ex situ Measurements at the cage were performed at different pulse durations (Figure 2a). If the proximity between anchovies in the cage caused unresolved multiple targets, the probability of occurrence (and hence the mean TS) was expected to increase at higher pulse durations. In addition, the single target detection performance was tested for values of maximum standard phase deviation between 0.2° and 0.9° (Ona and Barange, 1999). Table 3 summarizes the different procedures performed during the in situ and ex situ data collection and processing. Table 3. Methodological differences between the in situ and ex situ data collection and processing. . In situ . Ex situ .  Sampling period 2010–2017 2012–2013  Season Autumn, spring Winter  Age Juvenile (5.8–16.3 cm) Adult (10–15.94 cm) Juvenile (10.1–10.9 cm)  Survey metdodology Daily Egg Production Metdod and acoustics Acoustics Data processing  Target selection Bi-frequency algoritdm (Ballón et al., 2011; Lezama-Ochoa et al., 2011) Manual selection of anchovy echo signals  Single target detection algoritdm parameters: standard deviation of minor/major axis 0.6° 0.2°  Extra multiple targets filtering High-density filter (Gautdier and Rose, 2001) Measurements at different sampling volumes (pulse durations) Data collection . In situ . Ex situ .  Sampling period 2010–2017 2012–2013  Season Autumn, spring Winter  Age Juvenile (5.8–16.3 cm) Adult (10–15.94 cm) Juvenile (10.1–10.9 cm)  Survey metdodology Daily Egg Production Metdod and acoustics Acoustics Data processing  Target selection Bi-frequency algoritdm (Ballón et al., 2011; Lezama-Ochoa et al., 2011) Manual selection of anchovy echo signals  Single target detection algoritdm parameters: standard deviation of minor/major axis 0.6° 0.2°  Extra multiple targets filtering High-density filter (Gautdier and Rose, 2001) Measurements at different sampling volumes (pulse durations) Data collection Open in new tab Table 3. Methodological differences between the in situ and ex situ data collection and processing. . In situ . Ex situ .  Sampling period 2010–2017 2012–2013  Season Autumn, spring Winter  Age Juvenile (5.8–16.3 cm) Adult (10–15.94 cm) Juvenile (10.1–10.9 cm)  Survey metdodology Daily Egg Production Metdod and acoustics Acoustics Data processing  Target selection Bi-frequency algoritdm (Ballón et al., 2011; Lezama-Ochoa et al., 2011) Manual selection of anchovy echo signals  Single target detection algoritdm parameters: standard deviation of minor/major axis 0.6° 0.2°  Extra multiple targets filtering High-density filter (Gautdier and Rose, 2001) Measurements at different sampling volumes (pulse durations) Data collection . In situ . Ex situ .  Sampling period 2010–2017 2012–2013  Season Autumn, spring Winter  Age Juvenile (5.8–16.3 cm) Adult (10–15.94 cm) Juvenile (10.1–10.9 cm)  Survey metdodology Daily Egg Production Metdod and acoustics Acoustics Data processing  Target selection Bi-frequency algoritdm (Ballón et al., 2011; Lezama-Ochoa et al., 2011) Manual selection of anchovy echo signals  Single target detection algoritdm parameters: standard deviation of minor/major axis 0.6° 0.2°  Extra multiple targets filtering High-density filter (Gautdier and Rose, 2001) Measurements at different sampling volumes (pulse durations) Data collection Open in new tab TS–length and TS–depth relationships Averaged TS values from each haul or cage experiment were plotted against mean length in the logarithmic scale to determine the TS–L relationship from (1) and (2) at the three frequencies. Linear regression models were fit at each frequency, providing probability values and coefficients of determination to measure the goodness of the relationships (p-values <0.05 were considered statistically significant). Obtained TS–length relationships were also compared with those predicted by the MFS model (see below), analysing the agreement between empirical and modelled data in terms of the pairs of mean and standard deviation of fish tilt angle used in the model as a proxy of anchovy behaviour. Finally, TS vs. length and depth relationships were investigated by means of linear models. To avoid the violation of independence of the explanatory variables we studied possible collinearity between depth and length. Acoustic scattering model To interpret the measured TS values, a numerical model of acoustic scattering for European anchovy was applied using the MFS (Fairweather et al., 2003; Pérez-Arjona et al., 2018). Anchovy is a physostomous fish with a dual-chambered swimbladder, which the simulation simplified as two-chambered prolate spheroids (Andreeva, 1974; Weston, 1966; Love, 1978; Furusawa, 1988; Ye, 1997) (PS1 and PS2). The major axis of the PS1 was considered orthogonal with respect to the incident acoustic pulse when fish was swimming horizontally and the major axis of PS2 was tilted α with respect to the major axis of PS1 (Figure 4). The model also considered the backbone’s contribution, which was expected to attenuate the swimbladder signal and/or modify the far-field distance achievement, while discarding the flesh’s contribution which is usually less significant for swimming directions close to the horizontal (Pérez-Arjona et al., 2020b). The prolate spheroids’ dimensions and tilt angle (Figure 4) were based on soft X-ray images (IntechForView CR system). Figure 4. Open in new tabDownload slide Lateral radiograph of a specimen of Engraulis encrasicolus showing the two connected swimbladder chambers (PS1 and PS2) and the backbone. Fish length = 10.1 cm, fish height = 1.04 cm. Figure 4. Open in new tabDownload slide Lateral radiograph of a specimen of Engraulis encrasicolus showing the two connected swimbladder chambers (PS1 and PS2) and the backbone. Fish length = 10.1 cm, fish height = 1.04 cm. Twelve good quality, freshly thawed individuals were radiographed and their swimbladder condition was visually examined from the digitized images. Individuals with ruptured or disfigured bladders were discarded, with the four individuals that appeared to be undamaged ultimately used for the morphological measurements (Figure 4). Calculations were carried out for mean SL = 10.5 cm, with corresponding PS1 and PS2 dimensions given by length (semi-major axes, a1 = 0.625 cm and a2 =0.5 cm), height (semi-minor axes, b1 = 0.2 cm and b2 = 0.2 cm), width (semi-minor axes c1 = b1 and c2 = b2), and relative angle β = 12 degrees, being β the tilt angle with respect to the fish body axis (Figure 5). The two-chambered swimbladder was deemed a pressure-release surface. The fish backbone was modelled as a fluid-filled (ρ = 1100 kg/m3, c = 2270 m/s) (Gorska et al., 2005; Pérez-Arjona et al., 2018) straight cylinder (length = 9 cm and radius = 1 mm) with smooth edges surrounded by a homogeneous host medium (seawater with acoustical properties ρ=1026 kg/m3, c = 1490 m/s). Figure 5. Open in new tabDownload slide Scheme of the TS directivity TS(α), being α the tilt angle of fish body axis with respect to the horizontal. The emitting and receiving echosounder position are fixed and depicted in the diagram. The echosounder’s emitted beam axis is orthogonal to the fish body axis for α = 0°. Figure 5. Open in new tabDownload slide Scheme of the TS directivity TS(α), being α the tilt angle of fish body axis with respect to the horizontal. The emitting and receiving echosounder position are fixed and depicted in the diagram. The echosounder’s emitted beam axis is orthogonal to the fish body axis for α = 0°. MFS was used to solve the three-dimensional Helmholtz equation in the frequency domain (Fairweather et al., 2003). For the 3D case, assuming a point source placed within the propagation domain, at point x0, it is possible to establish fundamental solutions of the Helmholtz equation G, for the sound pressure, and H, for the particle velocity, at a point x, which can be written respectively as: G3D(x,x0,k)=e-ikrr,(3) H3D(x,x0,k,n→)=1−iρω(−ikr−1)e−ikrr2∂r∂n→.(4) In these equations, r corresponds to the distance between the source point and the domain point, given; n→ represents the direction along which the particle velocity is calculated, k=ω/c the wave number, ω=2πf the angular frequency, f the frequency and c the sound propagation velocity within the acoustic medium and the medium density. The basic principle of the MFS (Fairweather et al., 2003; Pérez-Arjona et al., 2018) is that the sound field in a homogeneous region can be simulated by the linear superposition of the effects of a number of virtual sources, each one with its own amplitude. To define the formulation of this problem, let us first consider three sets of virtual sources located: the first within the swimbladder, with NS1 sources; the second within the spine, with NS2 sources; the third in the water, distributed around the spine, also with NS2 sources. The first two sets will allow the simulation of the sound field in the host medium, which, in that case, can be written as p(x,k)Ω1=∑j=1NS1PjG3D(x,x1,j,k1)+∑j=1NS2QjG3D(x,x2,j,k1)+pinc(x,xsource,k1) for x in Ω1,(5) while within the spine the acoustic pressure is given as p(x,k2)Ω2=∑j=1NS3RjG3D(x,x3,j,k2) for x in Ω2.(6) In (2) and (3), Pj, Qj, and Rj are unknown amplitudes of the virtual sources, pinc(x,xsource,k) represents the incident field generated by a source located at xsource and k1 and k2 represent the wavenumber in the host medium (water) and in the spine. To determine the relevant amplitudes, a system of equations must be established by imposing the necessary interface and boundary conditions. A complete description of the MFS application to TS calculation, including the mathematical description of the complete equations system can be found in Pérez-Arjona et al. (2018). To accurately resolve the acoustic wave the MFS virtual sources were considered and located in a number of at least six sources per wavelength. For the sake of comparison with standard experimental measurements, the simulated emitted acoustic field was assumed to be the corresponding to the analytical solution of the pressure created by a circular piston in its far field (Medwin and Clay, 1998), as an idealization of a scientific echosounder transducer. The transducer size was chosen to produce a half-beam angle at −3 dB of 3.5°, following the specifications of Simrad EK60 scientific echosounders at working frequencies. The MFS model was solved at the three frequencies used for the measurements: 38, 120, and 200 kHz. Moreover, convergence tests were carried out for each frequency to guarantee the proper density mesh. The TS directivity was calculated for dorsal incidence with fish tilt angle from α = −90 to α = 90° (Figure 5). To study possible fish swimming orientation, an optimization procedure was applied by running the model with tilt angles following sequences of normal distributions. Simulated TS values obtained using mean tilt angles from −10° to 10° and standard deviation from −20° to 20° (in increments of 1°) were compared to the experimental TS–L relationships, analysing the tilt angles that produced the best agreement. Results Near-to-far field experiment The near-to-far field calibration experiment showed that, when using a gain value obtained by calibrating at the far field, the mean TS measured in the far field were the correct nominal TS value of the sphere (−42.3 dB). However, the mean TS measured at ranges closer than four times the critical range were positively biased by different margins. When using gain values obtained by calibration at each range (Table 4), we were able to measure the same mean unbiased TS values of the target at all ranges. This showed that it was possible to correctly measure the TS of targets (for example anchovies) in the transition from the near to the far field, by applying calibration values at proper ranges (Figure 6). The gain differences obtained between near-to-far and far field were compared against the analytical solution for the circular piston and were then used to apply the corrections to the ex situ anchovy TS measurements. Figure 6. Open in new tabDownload slide Result of the near-to-far field calibration experiment, using a pulse duration of 256 μs. The upper figure shows a scatterplot (grey circles) of measured TS values of the sphere at 38 kHz at different horizontal distances from the transducer face. Black circles mark the mean TS value at each range and the grey dotted line, the nominal TS value of the tungsten carbide sphere at this frequency (−42.3 dB). (a) The values obtained when using for all distances a single set of calibration parameters obtained at 12 m (i.e. well inside the far field). (b) The measured TS values of the sphere using for each distance the calibration parameters obtained at each range (i.e. corrected for the near-to-far field effect). In the figure below, calibrated gain values against range applied in cases a and b are shown. Figure 6. Open in new tabDownload slide Result of the near-to-far field calibration experiment, using a pulse duration of 256 μs. The upper figure shows a scatterplot (grey circles) of measured TS values of the sphere at 38 kHz at different horizontal distances from the transducer face. Black circles mark the mean TS value at each range and the grey dotted line, the nominal TS value of the tungsten carbide sphere at this frequency (−42.3 dB). (a) The values obtained when using for all distances a single set of calibration parameters obtained at 12 m (i.e. well inside the far field). (b) The measured TS values of the sphere using for each distance the calibration parameters obtained at each range (i.e. corrected for the near-to-far field effect). In the figure below, calibrated gain values against range applied in cases a and b are shown. Table 4. Calibration parameters obtained from the TS measurements of the sphere in the near-to-far-field (ntff) at 38 kHz. Depth . Gain_ntff . SACorr_ntff . (m) . (dB) . (dB) . 2 22.4 −0.2 3 22.7 −0.2 4 22.4 −0.2 6 22.3 −0.2 12 22.2 −0.2 Depth . Gain_ntff . SACorr_ntff . (m) . (dB) . (dB) . 2 22.4 −0.2 3 22.7 −0.2 4 22.4 −0.2 6 22.3 −0.2 12 22.2 −0.2 Power = 800 W, pulse duration = 256 s. Open in new tab Table 4. Calibration parameters obtained from the TS measurements of the sphere in the near-to-far-field (ntff) at 38 kHz. Depth . Gain_ntff . SACorr_ntff . (m) . (dB) . (dB) . 2 22.4 −0.2 3 22.7 −0.2 4 22.4 −0.2 6 22.3 −0.2 12 22.2 −0.2 Depth . Gain_ntff . SACorr_ntff . (m) . (dB) . (dB) . 2 22.4 −0.2 3 22.7 −0.2 4 22.4 −0.2 6 22.3 −0.2 12 22.2 −0.2 Power = 800 W, pulse duration = 256 s. Open in new tab These calibration results were in general agreement with the analytical solution of a circular flat piston pressure radiation. As explained below, the analytical solution showed an oscillating pattern at the near field, decaying asymptotically at larger distances (Figure 7a). The exact solution and the analytical far field approximation converged at ∼4.5 m from the source (Figure 7a), the deviation between both curves also describing a decreasing pattern with distance (Figure 7b). The comparison of the far vs. near-to-far field deviation between the model and the empirical measurements of the sphere (Figure 7b) showed good agreement for ranges ≥3 m, but the deviation between model prediction and experimental values was important at 2 m. These results pointed to a larger critical range for the 38 kHz transducer than predicted by the model (1.14 m). According to these results, ex situ TS measurements of anchovy at depths <2.5 m were discarded at 38 kHz for being less reliable: worse agreement between model and measurements and expected faster TS changes with range (Figure 7a). For the rest, we applied bias correction factors according to the interpolated results of the near field experiment. Figure 7. Open in new tabDownload slide (a) Analytical exact on-axis solution for a circular piston simulating the Simrad ES38B transducer pressure (solid line) and far-field approximation (segmented line) relative to the maximum on-axis radiated pressure (pmax) against distance from the source. (b) TS deviation between on-axis pressure and far field analytical approximations (solid curve) vs. distance. The circles represent the mean experimental TS difference of the sphere between measurements done using near-to-far and far field calibration parameters. Deviation between model prediction and experimental values was important only at 2 m (empty circle). Figure 7. Open in new tabDownload slide (a) Analytical exact on-axis solution for a circular piston simulating the Simrad ES38B transducer pressure (solid line) and far-field approximation (segmented line) relative to the maximum on-axis radiated pressure (pmax) against distance from the source. (b) TS deviation between on-axis pressure and far field analytical approximations (solid curve) vs. distance. The circles represent the mean experimental TS difference of the sphere between measurements done using near-to-far and far field calibration parameters. Deviation between model prediction and experimental values was important only at 2 m (empty circle). Measured TS values Following the plankton filtering (Figure 3), the application of the high-density filter to the in situ data retained 30, 52, and 74% of the targets at 38, 12, and 200 kHz, respectively. Concerning ex situ measurements, no significant differences were observed among the mean TS values measured at different pulse durations (Figure 8). This was intended to be a quality control for the single target detection reliability, thus the lack of differences suggested that the single target detection was correctly achieved at all resolutions. Consequently, all three experiments using the ex situ measurement sets were included in the analysis. Figure 8. Open in new tabDownload slide Boxplots summarizing TS distributions against pulse duration used in the ex situ experiments. Dashed boxplots represent the TS distributions of the measurements from set 2, contaminated with 2% of horse mackerel. Pairwise t-test produced p-values >0.05 within pulse durations considering both contaminated and non-contaminated samples. The boxplots are bounded by first and third quartiles, the central line is the median, and the whiskers show the range of values. Figure 8. Open in new tabDownload slide Boxplots summarizing TS distributions against pulse duration used in the ex situ experiments. Dashed boxplots represent the TS distributions of the measurements from set 2, contaminated with 2% of horse mackerel. Pairwise t-test produced p-values >0.05 within pulse durations considering both contaminated and non-contaminated samples. The boxplots are bounded by first and third quartiles, the central line is the median, and the whiskers show the range of values. Combining both in situ and ex situ measurements, a total of 6388, 15,695, and 19,012 targets were finally used for the TS estimates at 38, 120, and 200 kHz, respectively. The filtered data set covered a depth range of 2.5–27.5 m. The fish length values measured from the different experiments ranged from 3.5 to 19.5 cm, with the highest mean (±standard deviation) value obtained in spring 13.4 (±1.5) cm, and smaller mean values in the cage experiments and autumn survey of 10.4 (±1) and 10 (±3.4) cm, respectively. The highest size variability was observed in the autumn JUVENA survey, with a wider distribution than in the spring BIOMAN survey and cage measurements (Figure 9a) caused by presence in this season of a mixture of juveniles and adults. The filtered TS distributions (Figure 9b) were clearly monomodal, except for the 120- and 200-kHz measurements from the cage, where the mode was less pure. Mean TS values obtained from the filtered TS distributions are presented in Table 5. Figure 9. Open in new tabDownload slide Length (a) and filtered TS (b) histograms grouped by in situ (BIOMAN, JUVENA) and ex situ (cage) measurements. Figure 9. Open in new tabDownload slide Length (a) and filtered TS (b) histograms grouped by in situ (BIOMAN, JUVENA) and ex situ (cage) measurements. Table 5. Mean TS values (± SD) obtained from the filtered TS distributions. . 38 kHz . 120 kHz . 200 kHz . BIOMAN −43.3(±1.5) dB −45.4((±2.3) dB −47.1(±2.1) dB CAGE −45.4(±0.9) dB −45.5(±2.3) dB −47.7(±2.3) dB JUVENA −46.2(±2.7) dB −49.4(±2.1) dB −50.3(±2.5) dB . 38 kHz . 120 kHz . 200 kHz . BIOMAN −43.3(±1.5) dB −45.4((±2.3) dB −47.1(±2.1) dB CAGE −45.4(±0.9) dB −45.5(±2.3) dB −47.7(±2.3) dB JUVENA −46.2(±2.7) dB −49.4(±2.1) dB −50.3(±2.5) dB Open in new tab Table 5. Mean TS values (± SD) obtained from the filtered TS distributions. . 38 kHz . 120 kHz . 200 kHz . BIOMAN −43.3(±1.5) dB −45.4((±2.3) dB −47.1(±2.1) dB CAGE −45.4(±0.9) dB −45.5(±2.3) dB −47.7(±2.3) dB JUVENA −46.2(±2.7) dB −49.4(±2.1) dB −50.3(±2.5) dB . 38 kHz . 120 kHz . 200 kHz . BIOMAN −43.3(±1.5) dB −45.4((±2.3) dB −47.1(±2.1) dB CAGE −45.4(±0.9) dB −45.5(±2.3) dB −47.7(±2.3) dB JUVENA −46.2(±2.7) dB −49.4(±2.1) dB −50.3(±2.5) dB Open in new tab TS–length relationships Overall, the measured TS values increased linearly with the logarithm of the fish length. In agreement with the corresponding mean body lengths, the TS values were generally higher for the spring hauls than the autumn hauls and the experimental cage results (Figure 9). In sum, the three types of measurements (ex situ and in situ from both surveys) closely fitted the same TS vs. log-length regression. When the slope of the regression was forced to 20, the b20 values for the in situ-measured data were −66.5, −68.9, and −70.5 dB at 38, 120, and 200 kHz, respectively, and −65.8, −66.4, and −68.7 dB for the ex situ measurements (Table 6). When considering the whole data set, these values were −66.4, −68.7, and −70.4 dB. The free-fitting linear model produced significant in situ regressions, with slopes of 22.5, 20.5, and 22.5 at the three frequencies, respectively. The non-significant TS–L relationship derived from the ex situ data was not considered relevant, instead being attributed to the small number of points (only three) available. When considering the entire data set, significant slopes slightly over 20 at the three operative frequencies, with intercepts at −68.7, −69, and −72.9 dB, respectively. Table 6. Statistics of the empirical and theoretical (by means of the MFS backscattering model) TS–L linear regression parameters. . Frequency (kHz) . a . B . b20 . N . In situ 38 22.2 −68.8 −66.5 53 120 20.5 −69.5 −68.9 53 200 22.5 −73.2 −70.5 53 Ex situ 38 – – −65.8 3 120 – – −66.4 3 200 – – −68.7 3 In situ + ex situ 38 22.2 −68.7 (r2 = 0.7) −66.4 (r2 = 0.8) 56 120 20.2 −69 (r2 = 0.7) −68.7 (r2 = 0.9) 56 200 22.4 −72.9 (r2 = 0.5) −70.4 (r2 = 0.9) 56 Theoretical model 38 15.2 −61.8 (r2 = 0.9) −67.1 (r2 = 0.8) – 120 21.6 −71 (r2 = 0.9) −69.7 (r2 = 0.9) – 200 20.2 −70.1 (r2 = 1) −70.4 (r2 = 1) – . Frequency (kHz) . a . B . b20 . N . In situ 38 22.2 −68.8 −66.5 53 120 20.5 −69.5 −68.9 53 200 22.5 −73.2 −70.5 53 Ex situ 38 – – −65.8 3 120 – – −66.4 3 200 – – −68.7 3 In situ + ex situ 38 22.2 −68.7 (r2 = 0.7) −66.4 (r2 = 0.8) 56 120 20.2 −69 (r2 = 0.7) −68.7 (r2 = 0.9) 56 200 22.4 −72.9 (r2 = 0.5) −70.4 (r2 = 0.9) 56 Theoretical model 38 15.2 −61.8 (r2 = 0.9) −67.1 (r2 = 0.8) – 120 21.6 −71 (r2 = 0.9) −69.7 (r2 = 0.9) – 200 20.2 −70.1 (r2 = 1) −70.4 (r2 = 1) – The number of points available from the ex situ measurements was too small, and the length range too narrow to be considered for estimating the a and b values for ex situ data. Results of the whole dataset (including both in situ and ex situ data) in bold. Open in new tab Table 6. Statistics of the empirical and theoretical (by means of the MFS backscattering model) TS–L linear regression parameters. . Frequency (kHz) . a . B . b20 . N . In situ 38 22.2 −68.8 −66.5 53 120 20.5 −69.5 −68.9 53 200 22.5 −73.2 −70.5 53 Ex situ 38 – – −65.8 3 120 – – −66.4 3 200 – – −68.7 3 In situ + ex situ 38 22.2 −68.7 (r2 = 0.7) −66.4 (r2 = 0.8) 56 120 20.2 −69 (r2 = 0.7) −68.7 (r2 = 0.9) 56 200 22.4 −72.9 (r2 = 0.5) −70.4 (r2 = 0.9) 56 Theoretical model 38 15.2 −61.8 (r2 = 0.9) −67.1 (r2 = 0.8) – 120 21.6 −71 (r2 = 0.9) −69.7 (r2 = 0.9) – 200 20.2 −70.1 (r2 = 1) −70.4 (r2 = 1) – . Frequency (kHz) . a . B . b20 . N . In situ 38 22.2 −68.8 −66.5 53 120 20.5 −69.5 −68.9 53 200 22.5 −73.2 −70.5 53 Ex situ 38 – – −65.8 3 120 – – −66.4 3 200 – – −68.7 3 In situ + ex situ 38 22.2 −68.7 (r2 = 0.7) −66.4 (r2 = 0.8) 56 120 20.2 −69 (r2 = 0.7) −68.7 (r2 = 0.9) 56 200 22.4 −72.9 (r2 = 0.5) −70.4 (r2 = 0.9) 56 Theoretical model 38 15.2 −61.8 (r2 = 0.9) −67.1 (r2 = 0.8) – 120 21.6 −71 (r2 = 0.9) −69.7 (r2 = 0.9) – 200 20.2 −70.1 (r2 = 1) −70.4 (r2 = 1) – The number of points available from the ex situ measurements was too small, and the length range too narrow to be considered for estimating the a and b values for ex situ data. Results of the whole dataset (including both in situ and ex situ data) in bold. Open in new tab Preliminary linear regression results indicated a TS increase with depth at all frequencies (Figure 10), contrarily to expectations of swimbladder compression with pressure increase for a physostomous fish such as anchovy. When building linear models of length against depth, we found a significant (p < 0.05 for adults and p < 0.005 for juveniles) increase of length also with depth, which, as the TS generally increases with fish body length, might explain the unexpected TS–depth pattern observed during the night trawls. However, concurrent multiple lineal analyses of TS against depth and length were not reliable due to the collinearity found between body length and depth of anchovy. To test whether the unexpected TS–depth relationship observed was caused by the increase of length of anchovy, further analyses were conducted: predicted TS values were combined with the observed TS–length relationship and the expected TS–depth relationship from theoretical swimbladder compression following Boylés law (explained in more detail in the corresponding section of the Supplementary material). Figure 10. Open in new tabDownload slide Top panel: relation between anchovy body length and mean depth per haul. Bottom panel: relation between mean TS per haul and mean depth. Both graphs distinguish between age groups and seasons. Figure 10. Open in new tabDownload slide Top panel: relation between anchovy body length and mean depth per haul. Bottom panel: relation between mean TS per haul and mean depth. Both graphs distinguish between age groups and seasons. Theoretical backscattering of anchovy The two-chambered swimbladder and backbone simulations (Figure 11) predicted more directive patterns of TS vs. swimming tilt angle for increasing frequencies, leading to a steeper reduction in TS values with angles for the first ±15°. Thus, although the maximum TS values were similar at all frequencies, the mean values were considerably higher for lower frequencies, due to the narrower directivity pattern decreasing the averages when frequency is increased. This agreed with the empirically-measured TS frequency response of anchovy at the three experimental frequencies. Figure 11. Open in new tabDownload slide Beam directivity patterns obtained with the backscattering simulation of the two-chambered swimbladder plus backbone at the three frequencies of study. The maximum TS values are obtained for a swimming tilt angle of 5° at higher frequencies (120 and 200 kHz), corresponding to more directive TS patterns Although these maximum values are similar at all frequencies, the mean TS values, for a normal distribution of swimming tilt angles around the horizontal (α = 0°), are lower for higher frequencies. Figure 11. Open in new tabDownload slide Beam directivity patterns obtained with the backscattering simulation of the two-chambered swimbladder plus backbone at the three frequencies of study. The maximum TS values are obtained for a swimming tilt angle of 5° at higher frequencies (120 and 200 kHz), corresponding to more directive TS patterns Although these maximum values are similar at all frequencies, the mean TS values, for a normal distribution of swimming tilt angles around the horizontal (α = 0°), are lower for higher frequencies. The model optimization produced the best values at mean tilt angles of 3 ± 9° (mean value ± standard deviation; r2 = 0.9) for the 38- and 120-kHz frequencies (i.e. swimming slightly oriented downwards). However, at 200 kHz, the best fit was obtained at −5 ± 9° (r2 = 0.99) (slightly oriented upwards (Figure 5)). When considering the three frequencies together, the mean tilt angle was practically 0° (i.e. swimming horizontally). Hence, given the discrepancy observed between the individual fits at the different frequencies (mainly due to the highest directivity of the 200-kHz frequency), a mean tilt angle of 0° was chosen. After fixing the mean tilt value at 0°, the optimization process produced the best standard deviation at 9° for 38- and 120-kHz frequencies (r2 = 0.9); 11° (r2 = 0.99) for 200 kHz; and 10° (r2 = 0.9) when considering the three frequencies together. According to these results, the a and b values used in the modelled TS–L relationships (Figure 12) were obtained considering a compromise “optimum” with a mean tilt angle of 0 ± 10° (Table 6). Figure 12. Open in new tabDownload slide Mean TS against total log-length (L) relationship. Dashed line = experimental forced fitting (b20); dotted line = experimental free fitting, bold solid line = theoretical model (b20); and grey solid line = theoretical model free fitting. Red circles correspond to BIOMAN hauls, blue squares to JUVENA hauls, and green triangles to the cage experiments. Figure 12. Open in new tabDownload slide Mean TS against total log-length (L) relationship. Dashed line = experimental forced fitting (b20); dotted line = experimental free fitting, bold solid line = theoretical model (b20); and grey solid line = theoretical model free fitting. Red circles correspond to BIOMAN hauls, blue squares to JUVENA hauls, and green triangles to the cage experiments. The modelled TS–L linear regression agreed better with the experimental results at the highest frequency (Table 6). At 38 and 120 kHz, the modelled b20 value was 0.7–1 dB lower than the experimental value. At 200 kHz instead, there was practically no difference between the modelled and experimental values. Discussion This work presents a comprehensive study of European anchovy TS in the Bay of Biscay. A combination of in situ, ex situ and theoretical modelling results are presented of the species-specific TS–L relationships to be used for biomass estimation and stock assessment of this species. Data were collected during spring and autumn for 7 years, covering a wide range of fish lengths (3.5–19.5 cm) and physiological conditions. The size and TS distributions (Figure 9) at the different sampling periods reflect this seasonality, as the largest specimens were measured during the spring spawning peak (when only adults are present in the area) and the smallest during the autumn survey (when part of the adult stock is absent due to trophic migrations, whereas juveniles predominate) (Boyra et al., 2013). The range of sizes of anchovy used in this experiment practically cover the full range of sizes observed during the acoustic surveys of anchovy in the Bay of Biscay (4–20 cm). The depth range of the anchovies studied (2.5–27.5 m) is in the upper range of the typical 5–120 m of the acoustically sampled depth for assessment (Boyra et al., 2013). When measuring TS values for small pelagic species such as anchovy, two of the key difficulties are to apply a correct lower threshold (Weimer and Ehrenberg, 1975) and to avoid bias by unresolved multiple targets (Soule et al., 1995). The application of an incorrect threshold can affect the lower end of the TS distribution by including small targets such as plankton. In the in situ measurements, this was avoided by applying a bi-frequency mask to distinguish fish from plankton, as has been successfully demonstrated in other studies (Lezama-Ochoa et al., 2011, 2014; Albaina et al., 2015; Gastauer et al., 2017), whereas in the ex situ measurements, this distinction was based on the observed difference between echo-traces according to expected differences in swimming velocity between fish and plankton. Potential bias due to unresolved multiple echoes was mitigated following different procedures. First, all measurements were made at night to facilitate the detection of single fish targets. Second, the high-density filter (Gauthier and Rose, 2001) applied to the in situ measurements further decreased the probability of erroneously assigning to a single fish the echo of multiple targets. As in a previous study (Boyra et al., 2018), the empirically determined Nv value was shown to be independent of the horizontal scale at which it was calculated as well as the initial TS value used, thus avoiding the well-known circularity issue of calculating a TS value based on a previous one. Finally, the ex situ measurement results were similar at different effective resolutions (pulse durations) of the acoustic sensors, supporting the notion that the relatively low fish density at the cage was sufficient to avoid multiple echoes. An additional difficulty encountered was that a large proportion of the ex situ measurements done at 38 kHz were made at distances inside the transition from the near to the far field of the transducer, which, due to the inherent divergence from spherical decay of acoustic beams in this area, were likely to be biased. The ad hoc experiment conducted to address this issue was in agreement with predictions by the analytical model of the circular piston (Figure 7b), thereby showing that it is possible to perform unbiased measurements at distances inside the near-to-far field, provided that calibrations are done at those distances. Moreover, the analytical model also showed that we can safely interpolate gain values at intermediate distances, provided that they are farther than the critical range. The observed deviations between the theoretical and empirical location range of critical range (empirical RC > 1.14 m, Figure 7b) can be attributable to an inaccurate knowledge of the specific geometry of the actual transducers. A better knowledge of the construction characteristics of transducers should improve the application of the proposed method. In future studies, this procedure is recommended when making ex situ acoustic experiments at short ranges, as it may be helpful to increase the range of valid measurements. This study has shown significant positive correlations between TS and fish length and a good consistency both between in situ results from different seasons and between in situ and ex situ results. The optimal slope obtained for the whole data set at all frequencies was consistently close to 20, meaning that the horizontal cross-sectional area of the swimbladder changes proportionally to the square of the fish length, as according to expectations (Simmonds and Maclennan, 2005). Assuming the correct performance of the model and based on the agreement with the experimental results, anchovy swimming orientation tendency should be, on average, not too far from horizontal, but with a rather high variability (standard deviation around 10°). These results should however be taken with care, since the optimization outputs for the different frequencies pointed towards opposite swimming directions, hence making our results not particularly revealing in this matter. Previous studies on similar engraulid species (Aoki and Inagaki, 1988; Madirolas et al., 2016) and other clupeoids (Huse and Ona, 1996) at the night scattering layer described a slightly head up swimming orientation. These results were justified by the need of the fish to compensate a negative buoyancy (Huse and Ona, 1996; Madirolas et al., 2016). Given that anchovy is a physostomous species and thus unable to compensate its swimbladder volume against pressure changes, it can be expected that TS decreases with depth according to Boyle’s law, as has been observed in previous measurements of anchovy (Zhao et al., 2008) and other physostomous species (Ona, 2003). In spite of this, we were unable to find evidence of the swimbladder compression on the observed TS–length relationship. On the contrary, the unexpected increase of TS with depth (Figure 10) was caused by a general depth stratification pattern of anchovies according to body size during night hauls. Our analysis showed that the TS increase produced by this length stratification was able (although not completely, Supplementary Figure S1) to justify the observed TS–depth increase, prevailing over and masking the expected decrease of TS with depth due to swimbladder compression. The failure to find evidence of swimbladder compression in our TS–depth relationships might have owed to the small depth span of our measurements, due to the typical shallower distribution of anchovy at night. An additional factor could be the lack of depth resolution of the sampling collection in the in situ experiments. The vertical opening of the haul was 15–20 m for near-surface trawls, hence the same magnitude of the whole depth range of the study. Consequently, we might be losing part of the length stratification inside each haul, which would explain the smaller predicted slopes. Other factors that might have yield to such inverted TS–depth slope could be for example a higher probability of failure of the single target detection filters with increasing depth or a change of behaviour of anchovy with depth. Consequently, further research is necessary to supplement the measurements obtained in this work at different depth ranges. This is a difficult objective to achieve using echosounders installed on a vessel, because it implies measuring the TS during the day, when anchovies operate near the sea bottom (according to their nycthemeral migrations) and aggregate in schools, precluding the identification of single targets. One possible solution would be to use submersible echosounders inside the trawls at different trawl depths to make these measurements. With respect to the observed increase of anchovy length with depth in night hauls, we consider this an interesting and completely unexpected result in itself. What appears to be happening is that, after the nocturnal migration there occurs a spontaneous stratification of the anchovies by size, perhaps due to change of swimming velocity with body length. Its remarkable to have been able to detect this stratification, given the poor resolution of the pelagic trawl with respect to the full extension of the sampled layer. Therefore, we expect that if this phenomenon is further studied in the future with higher sampling resolution, the observed stratification will be stronger. Our values lie within the range of the latest published TS values obtained for engraulid species. At the most commonly used frequency in fisheries acoustics (38 kHz) (Simmonds and Maclennan, 2005), recent studies have provided b20 = −68.6 dB on similar species Engraulis anchoita (Madirolas et al., 2016), obtained from 11 to 17 cm specimens during night-time in situ TS measurements. Other experiments on Engraulis japonicus (Zhao et al., 2008) have yielded TS–length relationships that have predicted TS values of −65.8 dB (Kang et al., 2009) for lengths ranging 4.8–12.2 cm and −66.5 dB (Sawada et al., 2009) for 10.6 cm at 70 kHz. The only previous work to have examined the TS of European anchovy funnelled the targets through a net with an open cod end, obtaining a b20 of −65.2 dB from 12.5-cm anchovies at ∼60-m depth at 70 kHz (Doray et al., 2016). This methodology avoided multiple target bias, but at the expense of forcing the anchovies to swim almost horizontally (i.e. with a narrower distribution of tilt angles than expected to be their natural behaviour) towards the net mouth. Although the results of that work are not directly comparable with our own due to differences in frequency and behaviour, they can be considered qualitatively consistent. However, these authors predicted a 2-dB decrease at 38 kHz, when increasing the fish tilt angle towards a more “natural” swimming behaviour. Hereby, a b20 value of −67.2 dB was reported. This value is consistent with a TS reduction with the increased range of tilt angles illustrated in Figure 11. The obtained TS trend with frequency, with higher responses at lower frequencies (Figure 9b), was typical of bladder-bearing fish species (Fernandes et al., 2006). This pattern may prove useful in developing multi-frequency masks to discriminate anchovy from plankton and other pelagic species (Lezama-Ochoa et al., 2011). The backscattering model provided some rough explanation of the TS frequency response. According to the model, despite the similarity between the highest TS values across frequencies, the greater directivity of the higher frequencies (Figure 11) produced lower mean TS values when averaged over a range of tilt angles. In general terms, a rather good general agreement was obtained between the simulations and the empirical results (Table 6). Implications for assessment Despite the need for a precise TS value in the acoustic assessment of fish abundance, alongside the recommendation that an empirical TS–L data relationship be established whenever new data are collected (McClatchie, 2003), biomass estimates of European anchovy in the Bay of Biscay have long been obtained with herring TS values published more than three decades ago (ICES, 1982; Degnbol et al., 1985). In response, this study has presented the first TS measurements for European anchovy at the frequency of 38 kHz used for assessment. The obtained b20 values at 38 kHz were 5–6 dB higher than those currently used by acoustic surveys in the assessment of European anchovy in the Bay of Biscay (Boyra et al., 2013). Such values would represent a more than twofold decrease if applied to estimate the acoustic-based biomass of anchovy. Nevertheless, the TS values were derived at a lower depth (z ≈ 13 m) than is typical for anchovy during the daytime (i.e. the period at which acoustic surveys are conducted), especially for adults and larger juveniles that are subjected to nycthemeral migrations. Thus, given the expected decrease of TS with depth for anchovy and other physostomous species (Ona, 2003; Zhao et al., 2008; Fässler et al., 2009; Madirolas et al., 2016), it is likely that the reduction in acoustic-based biomass will be somewhat lower than that inferred solely from this work. Therefore, it is recommended that further research is conducted to determine the TS–depth relationship for anchovy by lowering echosounders at different depths during day and night hauls. The use of this larger depth range should help us avoiding the artefact TS–depth relation observed in this work (Figure 10). The findings could then be combined with the present results to produce a thorough TS–length–depth relationship to update the acoustic-based assessment of this important species. Conclusion This study has provided the first TS–length relationship for European anchovy at the frequency used by assessment acoustic surveys (38 kHz) as well as the frequency response at typical frequencies (38, 120, and 200 kHz), which may be useful for building species discrimination masks. The measurements were done targeting anchovies both in their natural environment and in a harbour cage. Special care was taken to reduce the potential bias associated with measuring anchovies in situ and to guarantee that ex situ measurements are free from bias caused by range. The fact that the linear TS–L regressions using the entire ex situ and in situ data set produced such good fit adds robustness to the estimated values. The values obtained (−66.5, −68.7, and −70.4 dB at 38, 120, and 200 kHz, respectively) are in accordance with recently published values for the TS of anchovy in other areas as well as with backscattering models for bladdered fish. This points towards a general overestimation of current acoustic surveys for assessment, although it is necessary to extend the depth range of measurements beyond the ∼13 m of this study before it will be possible to update the assessment of this species in the Bay of Biscay. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Sobradillo, B., Boyra, G., Pérez-Arjona, I., Martinez, U., and Espinosa, V. 2021. Ex situ and in situ target strength measurements of European anchovy in the Bay of Biscay. – ICES Journal of Marine Science, 00:000–000. Acknowledgements This study was funded by AZTI-Tecnalia and supported by the research projects JAULA for the ex situ measurements and JUVENA for the in situ ones. The project JAULA was funded by the Department of Agriculture, Fisheries and Food of the Basque Country Government; we thank Unai Cotano, the leader of the project, for giving us access to the cage and for providing biological sampling information. We would like to thank the crew from the Itsas Lagunak for providing us with the anchovy specimens, the Aquaculture School from Mutriku for keeping the specimens during the acclimatization period and the technician and diver Gaizka Bidegain from AZTI for the maintenance and underwater inspection of the specimens used throughout the ex situ measurements. Yolanda Lacalle is thanked for the illustration in Figure 2. The project JUVENA was co-funded by the Dirección de Innovación y Desarrollo Tecnológico, Viceconsejería de Política e Industria Alimentaria, Dpto. Agricultura, Pesca y Alimentación of the Basque Government and the Secretaría General del Mar, Ministerio de Agricultura, Alimentación y Medio Ambiente of the Spanish Government. Thanks also to Andrés Uriarte (Azti) for improving this work with his valuable comments and to Mathieu Doray and Laurent Berger (Ifremer) for their helpful suggestions regarding the analysis and the interpretation of the results. Finally, thanks to the anonymous contribution of the reviewers for helping improve the quality and readability of this work. Author contributions BS and GB wrote the main manuscript text and were involved in the acquisition, analysis, and interpretation of data. IP-A and VE contributed to the theoretical modelling application and interpretation. UM collected data for the in situ analysis. All authors have substantially revised the manuscript. Data availability Datasets generated and/or analysed during this study are available from the corresponding author upon reasonable request. References Albaina A. , Irigoien X., Aldalur U., Boyra G., Santos M., Estonba A. 2015 . 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TI - Ex situ and in situ target strength measurements of European anchovy in the Bay of Biscay
JF - ICES Journal of Marine Science
DO - 10.1093/icesjms/fsaa242
DA - 2021-01-23
UR - https://www.deepdyve.com/lp/oxford-university-press/ex-situ-and-in-situ-target-strength-measurements-of-european-anchovy-aTKA6EzL2s
SP - 1
EP - 1
VL - Advance Article
IS - 
DP - DeepDyve
ER -