INTRODUCTION Top
Effective and successful fisheries management depends greatly on reliable monitoring of fish stocks (Godø 1998Godø O.R. 1998. What can technology offer the future fisheries scientist – Possibilities for obtaining better estimates of stock abundance by direct observations. J. Northwest Atl. Fish. Sci. 23: 105-131., Legović et al. 2010Legović T., Klanjšček J., Geček S. 2010. Maximum sustainable yield and species extinction in ecosystems. Ecol. Model. 221: 1569-1574.). For the purpose of monitoring demersal fish stocks, bottom trawl surveys are most commonly used (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. , Dremière et al. 1999Dremière P.Y., Fiorentini L., Cosimi G., Leonori I., Sala A., Spagnolo, A., 1999. Escapement from the main body of the bottom trawl used for the Mediterranean International Trawl Survey (MEDITS). Aquat. Living Resour. 12(3): 207-217. , Fiorentini et al. 1999Fiorentini L., Dremière P.Y., Leonori I., Sala A., Palumbo V. 1999. Efficiency of the bottom trawl used for the Mediterranean International Trawl Survey (MEDITS). Aquat. Living Resour. 12(3): 187-205.). As trawl efficiency is constantly improving and fishing practice is continuously changing (Lucchetti and Sala 2012Lucchetti A., Sala A. 2012. Impact and performance of Mediterranean fishing gear by side-scan sonar technology. Can. J. Fish. Aquat. Sci. 69: 1806-1816. , Eigaard et al. 2011Eigaard O.R., Rihan D., Graham N., Sala A., Zachariassen K. 2011. Improving fishing effort descriptors: Modelling engine power and gear-size relations of five European trawl fleets. Fish. Res. 110: 39-46., Lucchetti and Sala 2010Lucchetti A., Sala A. 2010. An overview of loggerhead sea turtle (Caretta caretta) bycatch and technical mitigation measures in the Mediterranean Sea. Rev. Fish Biol. Fisher., 20(2), 141-161., Fiorentini et al. 2004Fiorentini L., Sala A., Hansen K., Cosimi G., Palumbo V. 2004. Comparison between model testing and full-scale trials of new trawl design for Italian bottom fisheries. Fish. Sci. 70: 349-359.), this type of standardized survey provides reliable data for describing and quantifying changes in the exploited fish populations (Jukic-Peladic et al. 2001Jukic-Peladic S., Vrgoc N., Krstulovic-Sifner S., Piccinetti C., Piccinetti-Manfrin G., Marano G., Ungaro N. 2001. Long-term changes in demersal resources of the Adriatic Sea: comparison between trawl surveys carried out in 1948 and 1998. Fish. Res. 53: 95-104. ). The most common method used for relative biomass estimation is the swept area method (Saville 1977Saville A. 1977. Survey methods of appraising fishery resources. FAO Fish. Tech. Pap. 171: 76, Sparre and Venema 1998Sparre P., Venema S.C. 1998. Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fish. Tech. Pap. No. 306.1, Rev. 2, 407 pp.). A swept area represents the area covered by the trawl while towing and can be calculated by multiplying trawl wing spread (horizontal net opening) by distance covered, while gear is in contact with the seabed. Relative biomass or catch per unit of area is then calculated by dividing catch by swept area (Sparre and Venema 1998Sparre P., Venema S.C. 1998. Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fish. Tech. Pap. No. 306.1, Rev. 2, 407 pp.) and it is usually expressed in terms of density (e.g. kg km–2). These estimates depend highly on the correct calculation of the swept area, which can easily be miscalculated if the effective tow duration or wing spread are inaccurate (Godø and Engås 1989Godø O.R., Engås A. 1989. Swept area variation with depth and its influence on abundance indices of groundfish from trawl surveys. J. Northwest Atl. Fish. Sci. 9: 133-139. ). The introduction of wireless acoustic trawl monitoring systems in demersal trawl surveys made it possible to monitor trawl performance in real time (Sala et al. 2009Sala A., Prat J., Antonijuan J., Lucchetti A. 2009. Performance and impact on the seabed of an existing- and an experimental-otterboard: Comparison between model testing and full-scale sea trials. Fish. Res. 100: 156-166. , Prat et al. 2008Prat J., Antonijuan J., Folch A., Sala A., Lucchetti A., Sardà F., Manuel A. 2008. A simplified model of the interaction of the trawl warps, the otterboards and netting drag. Fish. Res. 94: 109-117.), generally improving catch per unit of effort and swept area estimates (Zimmermann et al. 2003Zimmermann M., Wilkins M.E., Weinberg K.L., Lauth R.R., Shaw F.R. 2003. Influence of improved performance monitoring on the consistency of a bottom trawl survey. ICES J. Mar. Sci. 60: 818-826. ). The most common net trawl measurement systems used in the central Mediterranean (SCANMAR pers. comm.), use the constant speed of sound of 1500 m/s in their algorithms when calculating the distance between two sensors.
However, the speed of sound in seawater is not constant because it depends on water temperature, pressure, and salinity (Chen and Millero 1977Chen C.-T., Millero F.J. 1977. Speed of sound in seawater at high pressures. J. Acoust. Soc. Am. 62: 1129-1135. , Coppens 1981Coppens A.B. 1981. Simple equations for the speed of sound in Neptunian waters. J. Acoust. Soc. Am. 69: 862-863. , Del Grosso 1974Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091., Leroy et al. 2008Leroy C.C., Robinson S.P., Goldsmith M.J. 2008. A new equation for the accurate calculation of sound speed in all oceans J. Acoust. Soc. Am. 124(5): 2774-2782. , Wong and Zhu 1995Wong G.S.K., Zhu S. 1995. Speed of sound in seawater as a function of salinity, temperature and pressure. J. Acoust. Soc. Am. 97: 1732-1736. ). Hall (2000)Hall J.B. 2000. Principles of naval weapons systems. Kendall/Hunt Publishing, 358 pp., for example, noted that the speed of sound in water might change by approximately 3 m s–1 per 1°C. This is known to affect the performance of the horizontally transmitting sonars (Simmonds and MacLennan 2005Simmonds E.J., MacLennan D.N. 2005. Fisheries Acoustics: Theory and Practice, 2nd edn. Blackwell Science, London, 437 pp. ), and it is reasonable to assume that unless it is properly compensated for, the readings from trawl monitoring systems can be incorrect, resulting in biased swept area estimates. Nowadays, some manufacturers of advanced products for the fishing fleet (e.g. sonars, echo sounders and gear monitoring instrumentation) are designing technology to compensate for this variation and thus ensuring accurate and reliable measured data. By logging data from the combined depth/temperature sensor during the shooting of the equipment, an accurate temperature profile is generated and modern equipment with proper input can automatically compensate for any temperature changes. In this paper we assessed the influence of the seawater temperature and depth on the speed of sound in the central Mediterranean. Moreover, we provided a simple model, which is presented as a valid alternative to the application of the complex formulae reported by Chen and Millero (1977)Chen C.-T., Millero F.J. 1977. Speed of sound in seawater at high pressures. J. Acoust. Soc. Am. 62: 1129-1135. , Coppens (1981)Coppens A.B. 1981. Simple equations for the speed of sound in Neptunian waters. J. Acoust. Soc. Am. 69: 862-863. , Del Grosso (1974)Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091 and Wong and Zhu (1995)Wong G.S.K., Zhu S. 1995. Speed of sound in seawater as a function of salinity, temperature and pressure. J. Acoust. Soc. Am. 97: 1732-1736. for manual correction of horizontal net opening and more precise swept area estimates. The new equation is proposed for the calculation of sound speed in seawater as a function of temperature, and depth and it is of polynomial form with three terms, which is a substantial reduction in complexity compared with the more complex equations using pressure that need to be calculated according to depth and location.
MATERIALS AND METHODS Top
Mediterranean trawl survey (MEDITS)
Bottom trawl surveys in the central Mediterranean have been performed regularly since 1985 (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. ), with the MEDITS project being the most important one. Sampling in this survey covers almost all trawlable areas from 10 to 800 m (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. ) following a depth-stratified regime with strictly defined limits (10-50 m, 50-100 m, 100-200 m, 200-500 m and 500-800 m). A standard tow lasts 30 minutes for depths up to 200 m and 60 minutes for depths over 200 m. Sampling vessels are equipped with both data loggers, that record water temperature and depth at the trawl level, and acoustic net monitoring systems (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. ).
Calculation of the sound compensation coefficient (SCC)
A number of equations for this purpose were developed in the second half of the twentieth century. Synoptical reviews of the available equations have been published by Dushaw et al. (1993)Dushaw B.D., Worcester P.F., Cornuelle B.D., Howe B.M. 1993. On equations for the speed of sound in sea water. J. Acoust. Soc. Am. 93(1): 255-275. and Leroy (2001)Leroy C.C. 2001. The speed of sound in pure and Neptunian water. Handbook of elastic properties of solids, liquids, and gases. Academic, New York, Vol. IV, Chap. 2: 23-81.. The equation developed by Chen and Millero (1977)Chen C.-T., Millero F.J. 1977. Speed of sound in seawater at high pressures. J. Acoust. Soc. Am. 62: 1129-1135. is considered an international standard for the calculation of the speed of sound in seawater. An alternative equation, which has a more restricted range of validity but is preferred by some authors (Dushaw et al. 1993Dushaw B.D., Worcester P.F., Cornuelle B.D., Howe B.M. 1993. On equations for the speed of sound in sea water. J. Acoust. Soc. Am. 93(1): 255-275., Meinen and Watts 1997Meinen C.S., Watts D.R. 1997. Further evidence that the sound-speed algorithm of Del Grosso is more accurate than that of Chen and Millero. J. Acoust. Soc. Am. 102(4): 2058-2062. , Speisberger and Metzger 1991Speisberger J.L., Metzger K. 1991. New estimates of sound speed in water. J. Acoust. Soc. Am. 89(4): 1697-1700. , Speisberger 1993Speisberger J.L. 1993. Is Del Grosso’s sound-speed algorithm correct? J. Acoust. Soc. Am. 93(4): 2235-2237. , Pike and Beiboer 1993Pike J.M., Beiboer F.L. 1993. A comparison between algorithms for the speed of sound in seawater. The Hydrographic Society, Special Publication no. 34.), is the Del Grosso equation (Del Grosso 1974Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091. ). In this study we have used the Del Grosso equation since it is implemented in most trawl monitoring systems used in the MEDITS survey (SCANMAR pers. comm.). Wong and Zhu (1995)Wong G.S.K., Zhu S. 1995. Speed of sound in seawater as a function of salinity, temperature and pressure. J. Acoust. Soc. Am. 97: 1732-1736. reformulated this equation for the new 1990 International Temperature Scale and their version is:
c(S,T,P)=C000+∆CT(T)+∆CS(S)+∆CP(P)+∆CSTP(S,T,P) |
(1) |
where speed of sound in seawater (c [m s–1]) is a function of temperature (T [°C]), salinity (S[ppt]) and pressure (P [kg cm–2]). Since Equation (1) uses pressure instead of depth, conversion had to be made according to Leroy and Parthiot (1998)Leroy C.C., Parthiot F. 1998. Depth-pressure relationship in the oceans and seas. J. Acoust. Soc. Am. 103: 1346-1352. :
P(Z,Φ) = h(Z,Φ) – h0Z |
(2) |
where Z is the depth in metres, Φ is the latitude in radians, h is the pressure in MPa (relative to the atmospheric pressure), and h0Z is the corrective term that has to be subtracted from the value. For the Mediterranean Sea, a fixed latitude of 36° and the following corrective term were used:
h0Z = –8.5 × 10–6 Z + 1.4 × 10–9 Z2 |
(3) |
Equations (1) and (2) are detailed in the Annex 1.
The correct horizontal net openings can be calculated as follows from the data recorded by the acoustic trawl monitoring systems, which use the constant speed of sound:
HNOc = SCC · HNOu |
(4) |
where HNOc[m] and HNOu[m] are the compensated and the uncompensated horizontal net opening, respectively, and the SCC represents the ratio of the real speed of sound (associated with a given depth, salinity and water temperature values) and the constant speed of 1500 m s–1:
SCC = c(S,T,P)/ccost |
(5) |
Since salinity observations during trawl surveys are often unavailable, in agreement with Vargas-Yáñez et al. (2012)Vargas-Yáñez M., Mallard, E., Rixen, M., Zunino, P., García-Martínez, M.C., Moya, F. 2012. The effect of interpolation methods in temperature and salinity trends in the Western Mediterranean. Medit. Mar. Sci., 13/1: 118-125. and Adani et al. (2011)Adani M., Dobricic S., Pinardi N. 2011. Quality Assessment of a 1985–2007 Mediterranean Sea Reanalysis. J. Atmos. Ocean. Tech. 28: 569-589. , a constant value of 38 ppt was assumed. Such fixed value of salinity together a series of theoretical sea depth and water temperature combinations have been used in the Eqs. (1)-(5) for the calculation of a sound compensation coefficients (SCC) dataset.
A generalized linear model (GLM) was applied to this dataset in order to provide coefficient estimates and summary statistics for the prediction regression models of the SCC parameter in function of the independent parameters sea water temperature (SWT) and bottom sea depth (BSD). All the calculations were performed with the use of the SPSS software package.
Spatial data interpolation
SCC was calculated using the BSD and bottom SWT data obtained from MEDATLAS II project (MEDAR Group 2002MEDAR Group. 2002. MEDATLAS/2002 database. Mediterranean and Black Sea database of temperature salinity and bio-chemical parameters. Climatological Atlas. IFREMER Edition (4 Cdroms).). It was gridded with data interpolating variational analysis (DIVA, http://modb.oce.ulg.ac.be/projects/1/diva) and mapped with the Ocean Data View 4 software package (ODV) (Schlitzer 2011Schlitzer R. 2011. Ocean Data View. http://odv.awi.de). A polynomial regression (e.g. simple planar surface) gridding method was used to graphically represent influence of temperature and bottom depth on SCC using the Surfer 9 software package (Golden Software Inc., Golden, Colorado, USA), while the graphical representation of the seasonal variations of the speed of sound for different depth strata was produced using the lattice package (Sarkar 2008Sarkar D. 2008. Lattice: multivariate data visualization with R. Springer, New York. ISBN 978-0-387-75968-5.) in R software (R Core Team 2012R Core Team 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/).
RESULTS Top
In Table 1 we reported the SCC calculated with Equations (1)-(5) for SWT ranging from 0°C to 30°C at BSD from 0 to 1000 m. The variability of the SCC as a function of SWT and BSD is shown in Figure 1.
Table 1. – Sound compensation coefficient (SCC) calculated with the Del Grosso (1974)Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091. equation for different bottom sea depth (BSD) and sea water temperature (SWT) combinations.
| SWT | BSD (m) | |||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| (°C) | 0 | 5 | 10 | 20 | 30 | 40 | 50 | 75 | 100 | 150 | 200 | 250 | 300 | 400 | 500 | 600 | 800 | 1000 |
| 0 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.969 | 0.970 | 0.970 | 0.970 | 0.971 | 0.971 | 0.972 | 0.973 | 0.974 | 0.975 | 0.977 | 0.980 |
| 1 | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 | 0.973 | 0.973 | 0.973 | 0.974 | 0.974 | 0.975 | 0.976 | 0.977 | 0.978 | 0.980 | 0.983 |
| 2 | 0.975 | 0.975 | 0.975 | 0.975 | 0.975 | 0.975 | 0.975 | 0.975 | 0.976 | 0.976 | 0.977 | 0.977 | 0.978 | 0.979 | 0.980 | 0.981 | 0.983 | 0.986 |
| 3 | 0.977 | 0.978 | 0.978 | 0.978 | 0.978 | 0.978 | 0.978 | 0.978 | 0.979 | 0.979 | 0.980 | 0.980 | 0.981 | 0.982 | 0.983 | 0.984 | 0.986 | 0.988 |
| 4 | 0.980 | 0.980 | 0.980 | 0.980 | 0.981 | 0.981 | 0.981 | 0.981 | 0.981 | 0.982 | 0.982 | 0.983 | 0.984 | 0.985 | 0.986 | 0.987 | 0.989 | 0.991 |
| 5 | 0.983 | 0.983 | 0.983 | 0.983 | 0.983 | 0.983 | 0.984 | 0.984 | 0.984 | 0.985 | 0.985 | 0.986 | 0.986 | 0.987 | 0.988 | 0.990 | 0.992 | 0.994 |
| 6 | 0.986 | 0.986 | 0.986 | 0.986 | 0.986 | 0.986 | 0.986 | 0.986 | 0.987 | 0.987 | 0.988 | 0.988 | 0.989 | 0.990 | 0.991 | 0.992 | 0.994 | 0.997 |
| 7 | 0.988 | 0.988 | 0.988 | 0.988 | 0.989 | 0.989 | 0.989 | 0.989 | 0.989 | 0.990 | 0.990 | 0.991 | 0.992 | 0.993 | 0.994 | 0.995 | 0.997 | 0.999 |
| 8 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.991 | 0.992 | 0.992 | 0.992 | 0.993 | 0.993 | 0.994 | 0.995 | 0.996 | 0.997 | 1.000 | 1.002 |
| 9 | 0.993 | 0.993 | 0.993 | 0.993 | 0.994 | 0.994 | 0.994 | 0.994 | 0.994 | 0.995 | 0.995 | 0.996 | 0.997 | 0.998 | 0.999 | 1.000 | 1.002 | 1.004 |
| 10 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.996 | 0.997 | 0.997 | 0.998 | 0.998 | 0.999 | 1.000 | 1.001 | 1.002 | 1.004 | 1.007 |
| 11 | 0.998 | 0.998 | 0.998 | 0.998 | 0.998 | 0.998 | 0.999 | 0.999 | 0.999 | 1.000 | 1.000 | 1.001 | 1.001 | 1.002 | 1.003 | 1.005 | 1.007 | 1.009 |
| 12 | 1.000 | 1.000 | 1.000 | 1.000 | 1.001 | 1.001 | 1.001 | 1.001 | 1.001 | 1.002 | 1.002 | 1.003 | 1.004 | 1.005 | 1.006 | 1.007 | 1.009 | 1.011 |
| 13 | 1.003 | 1.003 | 1.003 | 1.003 | 1.003 | 1.003 | 1.003 | 1.003 | 1.004 | 1.004 | 1.005 | 1.005 | 1.006 | 1.007 | 1.008 | 1.009 | 1.011 | 1.014 |
| 14 | 1.005 | 1.005 | 1.005 | 1.005 | 1.005 | 1.005 | 1.005 | 1.006 | 1.006 | 1.006 | 1.007 | 1.007 | 1.008 | 1.009 | 1.010 | 1.011 | 1.014 | 1.016 |
| 15 | 1.007 | 1.007 | 1.007 | 1.007 | 1.007 | 1.007 | 1.007 | 1.008 | 1.008 | 1.008 | 1.009 | 1.010 | 1.010 | 1.011 | 1.012 | 1.013 | 1.016 | 1.018 |
| 16 | 1.009 | 1.009 | 1.009 | 1.009 | 1.009 | 1.009 | 1.009 | 1.010 | 1.010 | 1.011 | 1.011 | 1.012 | 1.012 | 1.013 | 1.014 | 1.015 | 1.018 | 1.020 |
| 17 | 1.011 | 1.011 | 1.011 | 1.011 | 1.011 | 1.011 | 1.011 | 1.012 | 1.012 | 1.013 | 1.013 | 1.014 | 1.014 | 1.015 | 1.016 | 1.018 | 1.020 | 1.022 |
| 18 | 1.013 | 1.013 | 1.013 | 1.013 | 1.013 | 1.013 | 1.013 | 1.014 | 1.014 | 1.014 | 1.015 | 1.016 | 1.016 | 1.017 | 1.018 | 1.019 | 1.022 | 1.024 |
| 19 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.015 | 1.016 | 1.016 | 1.016 | 1.017 | 1.017 | 1.018 | 1.019 | 1.020 | 1.021 | 1.024 | 1.026 |
| 20 | 1.017 | 1.017 | 1.017 | 1.017 | 1.017 | 1.017 | 1.017 | 1.017 | 1.018 | 1.018 | 1.019 | 1.019 | 1.020 | 1.021 | 1.022 | 1.023 | 1.025 | 1.028 |
| 21 | 1.018 | 1.018 | 1.018 | 1.019 | 1.019 | 1.019 | 1.019 | 1.019 | 1.019 | 1.020 | 1.021 | 1.021 | 1.022 | 1.023 | 1.024 | 1.025 | 1.027 | 1.029 |
| 22 | 1.020 | 1.020 | 1.020 | 1.020 | 1.020 | 1.021 | 1.021 | 1.021 | 1.021 | 1.022 | 1.022 | 1.023 | 1.023 | 1.025 | 1.026 | 1.027 | 1.029 | 1.031 |
| 23 | 1.022 | 1.022 | 1.022 | 1.022 | 1.022 | 1.022 | 1.022 | 1.023 | 1.023 | 1.023 | 1.024 | 1.025 | 1.025 | 1.026 | 1.027 | 1.028 | 1.031 | 1.033 |
| 24 | 1.023 | 1.024 | 1.024 | 1.024 | 1.024 | 1.024 | 1.024 | 1.024 | 1.025 | 1.025 | 1.026 | 1.026 | 1.027 | 1.028 | 1.029 | 1.030 | 1.032 | 1.034 |
| 25 | 1.025 | 1.025 | 1.025 | 1.025 | 1.025 | 1.026 | 1.026 | 1.026 | 1.026 | 1.027 | 1.027 | 1.028 | 1.028 | 1.029 | 1.031 | 1.032 | 1.034 | 1.036 |
| 26 | 1.027 | 1.027 | 1.027 | 1.027 | 1.027 | 1.027 | 1.027 | 1.027 | 1.028 | 1.028 | 1.029 | 1.029 | 1.030 | 1.031 | 1.032 | 1.033 | 1.035 | 1.038 |
| 27 | 1.028 | 1.028 | 1.028 | 1.028 | 1.028 | 1.029 | 1.029 | 1.029 | 1.029 | 1.030 | 1.030 | 1.031 | 1.031 | 1.032 | 1.034 | 1.035 | 1.037 | 1.039 |
| 28 | 1.030 | 1.030 | 1.030 | 1.030 | 1.030 | 1.030 | 1.030 | 1.030 | 1.031 | 1.031 | 1.032 | 1.032 | 1.033 | 1.034 | 1.035 | 1.036 | 1.038 | 1.040 |
| 29 | 1.031 | 1.031 | 1.031 | 1.031 | 1.031 | 1.031 | 1.032 | 1.032 | 1.032 | 1.033 | 1.033 | 1.034 | 1.034 | 1.035 | 1.036 | 1.038 | 1.040 | 1.042 |
| 30 | 1.032 | 1.032 | 1.033 | 1.033 | 1.033 | 1.033 | 1.033 | 1.033 | 1.033 | 1.034 | 1.035 | 1.035 | 1.036 | 1.037 | 1.038 | 1.039 | 1.041 | 1.043 |
|
|
Fig. 1. – Isopleth representation of Sound Compensation Coefficient (SCC) for different sea water temperature (SWT) and sea bottom sea depth (BSD) combinations.
Using the bottom SWT and depth data from the MEDATLAS II project (MEDAR Group 2002MEDAR Group. 2002. MEDATLAS/2002 database. Mediterranean and Black Sea database of temperature salinity and bio-chemical parameters. Climatological Atlas. IFREMER Edition (4 Cdroms).), the seasonal distribution of the speed of sound, c(S,T,P), was calculated for each depth stratum (Fig. 2). Both the high variability in near bottom temperatures, related to the natural temperature fluctuations, and the large depth range in the Mediterranean, resulted in large seasonal differences in the distribution of the speed of sound. Values of the speed of sound in the sea water ranged from 1478 m s–1 in the winter period to 1544 m s–1 in the summer period (Fig. 2). Consequently, SCC (i.e. ratio of the real speed of sound and a constant speed of 1500 m s–1) ranged between 0.985 and 1.029 and unless properly compensated as per Equation 4, the error in the measured horizontal net spread might range from –1.47 to 2.93%.During the summer season, when bottom trawl surveys are usually conducted, low temperatures and high pressures in deeper water resulted in lower sound speeds than in shallower areas (10-50 m), resulting in a spatial bias (Fig. 3). Sound speeds were also lower than 1500 m s–1 in the winter season in the shallow areas (e.g. the North Adriatic Sea), resulting in a source of temporal bias (Fig. 3).
|
|
Fig. 2. – Seasonal variations of the calculated Speed of the sound [m s–1] from different depth strata (A: 0-10 m; B: 11-50 m; C: 51-100 m; D: 101-200 m; E: 201-500 m; F: 501-800 m; G: 801-1000 m). Strata are defined following the MEDITS sampling strategy (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. ).
|
|
Fig. 3. – Temperature and depth data of stations from MEDATLAS project (MEDAR Group 2002MEDAR Group. 2002. MEDATLAS/2002 database. Mediterranean and Black Sea database of temperature salinity and bio-chemical parameters. Climatological Atlas. IFREMER Edition (4 Cdroms).) were used to calculate the near bottom speed of the sound using the Del Grosso (1974)Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091. equation. Seasonal and spatial variation in the temperature, depth and near bottom speed of the sound were gridded with Data Interpolating Variational Analysis (DIVA, http://modb.oce.ulg.ac.be/projects) and mapped with Ocean Data View 4 software package (Schlitzer 2011Schlitzer R. 2011. Ocean Data View. http://odv.awi.de).
The GLM routines applied to these data provided a suitable prediction model for the SCC (Sig. <0.001; Table 2), which is presented as a valid alternative to the application of the complex formulae reported from Equations 1 to 5. Furthermore, the proposed equation agrees more than 99.8% with the values obtained by the complex Del Grosso (1974)Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091. reference equation. As can be seen from the output results of the multiple regression analysis reported in Table 2, the fit of the models (R-square) indicates that more than 98% of the variation in SCC can be accounted for by the SWT and the BSD. Therefore, the use of further variables would not have substantially improved the approximation of data, so the multiple regression relationship is:
SCC = 0.97291+0.02111·(SWT/10)+0.01098·(BSD/1000) |
(6) |
An increase in the SWT or the BSD resulted in a general increase in the SCC. However, the SCC seems more sensitive to changes of SWT than BSD, as shown in Figure 1 and in the parameter coefficients presented in Table 2.
Table 2. – Estimated coefficients (B) and Standard Error (Std. Error) of Generalized Linear Model (GLM) routines, applied to the dependent measure Sound Compensation Coefficient (SCC) in function of the independent parameters sea water temperature (SWT) and bottom sea depth (BSD). ANOVA results: degree of freedoms (DF), R-square (R2), F statistic (F) and Model significance (Mod. Sig.) are also reported.
| Estimated coefficients | ANOVA | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Parameter | B | Sth. Error | t | Sig. | DF | R2 | F | Mod. Sig. | |
| Constant | 0.97291 | 0.00018 | 5364.19 | 0.000 | |||||
| SWT | 0.02111 | 0.00009 | 222.52 | 0.000 | (2, 557) | 0.989 | 25457.9 | 0.000 | |
| BSD | 0.01098 | 0.00029 | 37.44 | 0.000 | |||||
GLM: SCC = 0.97291+0.02111·(SWT/10)+0.01098·(BSD/1000)
DISCUSSION Top
In this study, we have shown how readings from an acoustic trawl monitoring system can be biased to changes in near-bottom water temperature. The results have shown that values of horizontal net opening can be both over- and underestimated, depending on the season and area of the survey. Depth strata between 10 and 50 m have shown the largest variation in speed of sound, which can be explained by the presence of a well-developed thermocline in that layer (Artegiani et al. 1997Artegiani A., Bregant D., Paschini E., Pinardi N., Raicich F., Russo A. 1997. The Adriatic Sea general circulation. Part I: Air-sea interactions and water mass structure. J. Phys. Oceanogr. 27: 1492-1514.). The highest seasonal variations in the speed of the sound were observed in northern Adriatic Sea, which is an area with shallow waters with the highest seasonal thermal gradient (Russo and Artegiani 1996Russo A., Artegiani A. 1996. Adriatic Sea hydrography. Sci. Mar. 60(Suppl. 2): 33-43.). These results emphasize the importance of compensating for the influence of temperature and depth effect on the speed of sound during the trawl survey, assuming that both temperature and depth data are available. Similar results were obtained by Kotwicki et al. (2011)Kotwicki S., Martin M.H., Laman E.A. 2011. Improving area swept estimates from bottom trawl surveys. Fish. Res. 110: 198-206. , who instead of Del Grosso (1974)Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091. used the formula of Coppens (1981)Coppens A.B. 1981. Simple equations for the speed of sound in Neptunian waters. J. Acoust. Soc. Am. 69: 862-863. for the accurate estimate of the speed of sound. Their results have shown that after compensating for the temperature effect, the original measured swept area was overestimated for all tows. The new area swept estimates indicate that the expected corrections will range between –4% and –1%.
Traditionally, changes in sampling procedures and equipment used in bottom trawl surveys are avoided because of the concern that changes could invalidate survey time series (Godø 1998Godø O.R. 1998. What can technology offer the future fisheries scientist – Possibilities for obtaining better estimates of stock abundance by direct observations. J. Northwest Atl. Fish. Sci. 23: 105-131.). Although the methodology of bottom trawl surveys in the central Mediterranean has changed considerably since the mid-1980s (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. ), standardizations have become more rigorous (ICES 2009ICES. 2009. Report of the Study Group on Survey Trawl Standardisation (SGSTS), by correspondence. ICES CM 2009/FTC:09, 127 pp.). Our results suggest that every change (Godø 1998Godø O.R. 1998. What can technology offer the future fisheries scientist – Possibilities for obtaining better estimates of stock abundance by direct observations. J. Northwest Atl. Fish. Sci. 23: 105-131.) in survey methodology, no matter how trivial it may seem to be (e.g. transfer from one trawl monitoring system to another, or from an older to a newer one), must be examined thoroughly to make sure that no bias is being overlooked.
Zimmermann et al. (2003)Zimmermann M., Wilkins M.E., Weinberg K.L., Lauth R.R., Shaw F.R. 2003. Influence of improved performance monitoring on the consistency of a bottom trawl survey. ICES J. Mar. Sci. 60: 818-826. http://dx.doi.org/10.1016/S1054-3139(03)00043-2 described how technical improvements in survey methods can affect fish abundance estimation. They analysed data from U.S. bottom trawl surveys before and after the introduction of new technical equipment, and concluded that changes in abundance indices of some taxa are more likely to be the result of improvements in trawling methods than the real change in abundance. As technology advances it is reasonable to assume that future survey trawls will be equipped with numerous sensors of far better precision then the ones currently being used, providing us with much better net performance monitoring. This is unavoidable, and in many ways desirable but it should be implemented with great caution, because if the goal of the surveys is to monitor changes in abundance of exploited species over a long period of time, all sources of bias must be identified. In our case, influence of temperature and depth on the speed of sound and later on net spread values can easily be retroactively corrected with the proposed model (see Eq. 6), especially knowing that trawl surveys in the study area have been equipped with autonomous temperature and depth recorder since 1998 (Bertrand et al. 2002Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17. ).
FUTURE WORKSTop
In order to prove and support the theoretical statements and calculations provided in the current paper, direct observations of sound-compensated and sound-uncompensated horizontal net openings at different sea water temperatures, salinities and depths have been planned for the forthcoming sea cruises in 2014 within the RITMARE project.
ACKNOWLEDGEMENTSTop
This work was funded by the Flagship Project RITMARE “The Italian Research for the Sea”, coordinated by the Italian National Research Council and funded by the Italian Ministry of Education, University and Research within the 2011-2013 National Research Programme. We would like to thank Joana d’Arc Prat Farran (Universitat Politècnica de Catalunya, Spain) for helpful to translate the abstract into Spanish. Finally, we are also grateful to the editor and the reviewers for their comments, which we feel have improved our manuscript.
REFERENCES Top
Adani M., Dobricic S., Pinardi N. 2011. Quality Assessment of a 1985–2007 Mediterranean Sea Reanalysis. J. Atmos. Ocean. Tech. 28: 569-589.
http://dx.doi.org/10.1175/2010JTECHO798.1
Artegiani A., Bregant D., Paschini E., Pinardi N., Raicich F., Russo A. 1997. The Adriatic Sea general circulation. Part I: Air-sea interactions and water mass structure. J. Phys. Oceanogr. 27: 1492-1514.
http://dx.doi.org/10.1175/1520-0485(1997)027<1492:TASGCP>2.0.CO;2
Bertrand J.A., Gil de Sola L., Papaconstantinou, C., Relini, G., Souplet, A. 2002. The general specifications of the Medits surveys. Sci. Mar. 66 (Suppl. 2): 9-17.
http://dx.doi.org/10.3989/scimar.2002.66s29
Chen C.-T., Millero F.J. 1977. Speed of sound in seawater at high pressures. J. Acoust. Soc. Am. 62: 1129-1135.
http://dx.doi.org/10.1121/1.381646
Coppens A.B. 1981. Simple equations for the speed of sound in Neptunian waters. J. Acoust. Soc. Am. 69: 862-863.
http://dx.doi.org/10.1121/1.385486
Del Grosso V.A. 1974. New equation for the speed of sound in natural waters (with comparisons to other equations). J. Acoust. Soc. Am. 56: 1084-1091.
http://dx.doi.org/10.1121/1.1903388
Dremière P.Y., Fiorentini L., Cosimi G., Leonori I., Sala A., Spagnolo, A., 1999. Escapement from the main body of the bottom trawl used for the Mediterranean International Trawl Survey (MEDITS). Aquat. Living Resour. 12(3): 207-217.
http://dx.doi.org/10.1016/S0990-7440(00)88471-5
Dushaw B.D., Worcester P.F., Cornuelle B.D., Howe B.M. 1993. On equations for the speed of sound in sea water. J. Acoust. Soc. Am. 93(1): 255-275.
http://dx.doi.org/10.1121/1.405660
Eigaard O.R., Rihan D., Graham N., Sala A., Zachariassen K. 2011. Improving fishing effort descriptors: Modelling engine power and gear-size relations of five European trawl fleets. Fish. Res. 110: 39-46.
http://dx.doi.org/10.1016/j.fishres.2011.03.010
Fiorentini L., Dremière P.Y., Leonori I., Sala A., Palumbo V. 1999. Efficiency of the bottom trawl used for the Mediterranean International Trawl Survey (MEDITS). Aquat. Living Resour. 12(3): 187-205.
http://dx.doi.org/10.1016/S0990-7440(00)88470-3
Fiorentini L., Sala A., Hansen K., Cosimi G., Palumbo V. 2004. Comparison between model testing and full-scale trials of new trawl design for Italian bottom fisheries. Fish. Sci. 70: 349-359.
http://dx.doi.org/10.1111/j.1444-2906.2004.00813.x
Godø O.R. 1998. What can technology offer the future fisheries scientist – Possibilities for obtaining better estimates of stock abundance by direct observations. J. Northwest Atl. Fish. Sci. 23: 105-131.
http://dx.doi.org/10.2960/J.v23.a7
Godø O.R., Engås A. 1989. Swept area variation with depth and its influence on abundance indices of groundfish from trawl surveys. J. Northwest Atl. Fish. Sci. 9: 133-139.
http://dx.doi.org/10.2960/J.v9.a12
Hall J.B. 2000. Principles of naval weapons systems. Kendall/Hunt Publishing, 358 pp.
ICES. 2009. Report of the Study Group on Survey Trawl Standardisation (SGSTS), by correspondence. ICES CM 2009/FTC:09, 127 pp.
Jukic-Peladic S., Vrgoc N., Krstulovic-Sifner S., Piccinetti C., Piccinetti-Manfrin G., Marano G., Ungaro N. 2001. Long-term changes in demersal resources of the Adriatic Sea: comparison between trawl surveys carried out in 1948 and 1998. Fish. Res. 53: 95-104.
http://dx.doi.org/10.1016/S0165-7836(00)00232-0
Kotwicki S., Martin M.H., Laman E.A. 2011. Improving area swept estimates from bottom trawl surveys. Fish. Res. 110: 198-206. http://dx.doi.org/10.1016/j.fishres.2011.04.007
Legović T., Klanjšček J., Geček S. 2010. Maximum sustainable yield and species extinction in ecosystems. Ecol. Model. 221: 1569-1574.
http://dx.doi.org/10.1016/j.ecolmodel.2010.03.024
Leroy C.C. 2001. The speed of sound in pure and Neptunian water. Handbook of elastic properties of solids, liquids, and gases. Academic, New York, Vol. IV, Chap. 2: 23-81.
Leroy C.C., Parthiot F. 1998. Depth-pressure relationship in the oceans and seas. J. Acoust. Soc. Am. 103: 1346-1352.
http://dx.doi.org/10.1121/1.421275
Leroy C.C., Robinson S.P., Goldsmith M.J. 2008. A new equation for the accurate calculation of sound speed in all oceans J. Acoust. Soc. Am. 124(5): 2774-2782.
http://dx.doi.org/10.1121/1.2988296
Lucchetti A., Sala A. 2010. An overview of loggerhead sea turtle (Caretta caretta) bycatch and technical mitigation measures in the Mediterranean Sea. Rev. Fish Biol. Fisher., 20(2), 141-161.
http://dx.doi.org/10.1007/s11160-009-9126-1
Lucchetti A., Sala A. 2012. Impact and performance of Mediterranean fishing gear by side-scan sonar technology. Can. J. Fish. Aquat. Sci. 69: 1806-1816.
http://dx.doi.org/10.1139/f2012-107
MEDAR Group. 2002. MEDATLAS/2002 database. Mediterranean and Black Sea database of temperature salinity and bio-chemical parameters. Climatological Atlas. IFREMER Edition (4 Cdroms).
Meinen C.S., Watts D.R. 1997. Further evidence that the sound-speed algorithm of Del Grosso is more accurate than that of Chen and Millero. J. Acoust. Soc. Am. 102(4): 2058-2062.
http://dx.doi.org/10.1121/1.419655
Pike J.M., Beiboer F.L. 1993. A comparison between algorithms for the speed of sound in seawater. The Hydrographic Society, Special Publication no. 34.
Prat J., Antonijuan J., Folch A., Sala A., Lucchetti A., Sardà F., Manuel A. 2008. A simplified model of the interaction of the trawl warps, the otterboards and netting drag. Fish. Res. 94: 109-117.
http://dx.doi.org/10.1016/j.fishres.2008.07.007
R Core Team 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/
Russo A., Artegiani A. 1996. Adriatic Sea hydrography. Sci. Mar. 60(Suppl. 2): 33-43.
Sala A., Prat J., Antonijuan J., Lucchetti A. 2009. Performance and impact on the seabed of an existing- and an experimental-otterboard: Comparison between model testing and full-scale sea trials. Fish. Res. 100: 156-166.
http://dx.doi.org/10.1016/j.fishres.2009.07.004
Sarkar D. 2008. Lattice: multivariate data visualization with R. Springer, New York. ISBN 978-0-387-75968-5.
Saville A. 1977. Survey methods of appraising fishery resources. FAO Fish. Tech. Pap. 171: 76
Schlitzer R. 2011. Ocean Data View. http://odv.awi.de
Simmonds E.J., MacLennan D.N. 2005. Fisheries Acoustics: Theory and Practice, 2nd edn. Blackwell Science, London, 437 pp.
http://dx.doi.org/10.1002/9780470995303
Sparre P., Venema S.C. 1998. Introduction to tropical fish stock assessment. Part 1. Manual. FAO Fish. Tech. Pap. No. 306.1, Rev. 2, 407 pp.
Speisberger J.L. 1993. Is Del Grosso’s sound-speed algorithm correct? J. Acoust. Soc. Am. 93(4): 2235-2237.
http://dx.doi.org/10.1121/1.406686
Speisberger J.L., Metzger K. 1991. New estimates of sound speed in water. J. Acoust. Soc. Am. 89(4): 1697-1700.
http://dx.doi.org/10.1121/1.401002
Vargas-Yáñez M., Mallard, E., Rixen, M., Zunino, P., García-Martínez, M.C., Moya, F. 2012. The effect of interpolation methods in temperature and salinity trends in the Western Mediterranean. Medit. Mar. Sci., 13(1): 118-125.
Wong G.S.K., Zhu S. 1995. Speed of sound in seawater as a function of salinity, temperature and pressure. J. Acoust. Soc. Am. 97: 1732-1736.
http://dx.doi.org/10.1121/1.413048
Zimmermann M., Wilkins M.E., Weinberg K.L., Lauth R.R., Shaw F.R. 2003. Influence of improved performance monitoring on the consistency of a bottom trawl survey. ICES J. Mar. Sci. 60: 818-826.
http://dx.doi.org/10.1016/S1054-3139(03)00043-2