Acoustic information applied to implement environmental

Transcription

Acoustic information applied to implement environmental
Not to be cited without prior reference to the author
ICES ASC 20-24 September 2005, Aberdeen, Scotland, UK
ICES CM 2005/U:05
Session on Acoustic Techniques for Three-dimensional Characterization and
Classification of the Pelagic Ecosystem (U)
Acoustic information applied to implement environmental studies in the
Baltic
Andrzej Orlowski.
Sea Fisheries Institute, ul. Kollataja 1, 81-332 Gdynia, Poland,
Corresponding author: [email protected]
Abstract
Since 1981, acoustic information, collected in a form of calibrated measurements of
integrated echoes energy is applied in Sea Fishery Institute to observe the relationships among
fish distribution and environmental factors. Data were collected during different seasons for
each elementary distance units (EDSU) in standardized depth intervals to be compared to
values of selected environmental parameters, measured parallel. Acoustic, biological and
hydrological data were correlated in space and transferred to the complex data base, enabling
4D analysis of numerous factors, characterising wide range of the fish behaviour. Few
methods and standards of comparisons are described to explain how to improve recognition of
relationship between 3D spatial environmental gradients and fish distribution. Results of
various case studies, including the influence of hydrologic and seabed characterising factors
illustrate practical application and validity of the methods. Particular attention was given to
indicate dependence of local fish biomass density on temperature structure in the sea.
Keywords: acoustics, environment, marine ecosystem, Baltic.
INTRODUCTION
Understanding the functioning of marine ecosystem, especially in the context of fisheries
science, demands quite strong enhancing of the data base describing spatial and temporal
structure of main and critical abiotic and biotic factors in this area. Very attractive methods to
survey marine ecosystems were based on satellite optical and infrared sensors (Yoder and
Garcia-Moliner, 1993). Minimal depth penetration range resulting from high attenuation of
electromagnetic waves limited their application to surface layers only. Due to very low
attenuation in water – sound waves are most promising tools for complex observations of
marine ecosystem in the scale adequate to its dimensions. In response of the gradients of
physical properties (density and sound speed) acoustic waves are reflected and deflected.
Application of the echo sounding afforded possibilities of localization of such areas of
physical instabilities and gave an opportunity to estimate their effect on echo received. In a
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consequence distribution of received echoes characterises 3D qualitative and quantitative
components of the marine ecosystem (Clay and Medwin, 1977, Holliday, 1993, Orlowski,
1989a-b, Sherman, 1993). Echoes represent all types of acoustic instability of the media and
in a case of the marine ecosystem are associated with seabed, fish and plankton organisms,
gas bubbles, hydrologic gradients, and many other sources. Application of different
frequencies and directional characteristics of acoustic systems enable detection of different
spectra of the objects (Clay and Medwin, 1977, Holliday, 1993, Holliday and Pieper, 1995).
Providing systematic surveys and additional processing of the data by computer give an
opportunity to observe important ecosystem characteristics in an appropriate spatial and
temporal scale (Jech and Luo, 2000, Kemp and Meaden, 2002, Massé and Gerlotto, 2003,
Orlowski, 1989c, 1990, 1998, Peltonen et al., 2004, Socha et al., 1996, Szczucka, 2000).
Results of interdisciplinary measurements, unified in space by acoustic localization of all
elements can be easily collected in huge data base and visualized for better interpretation of
analysed processes (Bertrand et al., 2003, Orlowski, 1989c, 1998, 2003a, 2003b.). The
importance of such studies for widely understand fisheries is obvious (Barnes and Mann
1991; Helfman et al. 1997). The significance of results is very important for all marine
scientists and ecologists causing reversible linkage in estimation of models and interpretations
applied.
The paper describes few case studies illustrating practical application of acoustic information
to implement knowledge on selected elements of the Baltic Sea marine ecosystem. Examples
are selected from two last decades of the author's research work at the Sea Fisheries Institute
at Gdynia (Orlowski 1989a, 1989b, 1989c, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003a,
2003b, 2004, Orlowski and Kujawa 2005). That period has been started in 1989 by
publication of doctor’s of science dissertation titled “Application of acoustic methods for
study of distribution of fish and scattering layers vs. the marine environment” (Orlowski,
1989).
1. MATERIALS
During the period 1981-2004 ships of Sea Fisheries Institute in Gdynia (RV "Profesor
Siedlecki" and RV "Baltica") carried out series of research cruises, collecting acoustic,
biological and environmental data in the southern Baltic. Three cruises (July 1981, August
1983, 1988) were conducted during the summer, two cruises (May 1983, May 1985) during
the spring. Since 1989 all cruises have been carried out during the autumn (October 1989,
1990, 1994 - 2004), being the part of international ICES programme of the Baltic pelagic fish
stock assessment. Each cruise lasted approximately two-three weeks, and had a potential to
collect data from 1-2 thousands nautical miles of acoustic transect. Samples were collected
continuously, and integrated every one nautical mile, 24h a day. The time distribution of
samples in relation to the whole period 1981-2004 was quasi-homogeneous what gave a good
base to estimate 4D characteristics of fish behaviour in the southern Baltic.
In early eighties EK 38 echosounder and QMkII echo-integrator were used. Since 1989
EK400 and a QD echo integrating system were applied with proprietary software. In 1998 an
EY500 scientific system was introduced to fulfil international standards of acoustic
measurements, enabling research to continue. Both systems were using a frequency 38 kHz
and the same hull-mounted transducer of 7.2˚x 8.0˚. Calibration has been performed with a
standard target in Swedish fjords in 1994 to 1997 and in Norway from 1998 to 2000. Cruises
were carried out in October and lasted 2 to 3 weeks, giving a possibility of collecting samples
over 1 to 1.5 thousands of n.mi.(approximately 450 transmissions per nmi). Survey tracks of
all cruises were on the same grid to obtain high comparability of measurements.
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Biological samples were collected over the period from 1994 to 2004 by the same
pelagic trawl, on average every 37 n.mi. of the transect. Fish observed during all surveys were
mostly pelagic, herring and sprat (Clupeidae). Hydrographic measurements (temperature-T,
salinity-S, and oxygen level-O2) were made by a Neil-Brown CTD system. These were
mostly at sample haul positions, with a similar biological sampling space density. Each
hydrological station was characterized by its geographical position and values of measured
parameters at 2m depth intervals (slices).
2. METHODS AND RESULTS
Application of acoustic measurements as the tool for 4D arrangement of data collected by
biologists and oceanographers is given in Figure 1.
Figure 1. Scheme of acoustically co-ordinated transfer and compiling common
interdisciplinary data base on marine ecosystem.
Due to limited sampling possibilities of each of three channels shown in Fig.1. survey strategy
has to be well matched to spatial characteristics of the surveyed area. Geographical structure
of the surveyed area has a significant influence on strategy of its sampling. Example of such a
strategy applied in October 2004 is presented in Fig. 2. Sampling density is differentiated
according to methods of research.
Distance of one nautical mile was considered as an elementary unit (record) of the data base.
For each record values of remain factors characterizing the biological and hydrological
parameters were estimated. Enhancing the record length by depth structure of acoustic
scattering Sv (z), biological and hydrological components, and by introducing time factor we
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can produce 4D data base, called 4D-ABO, covering wide range of parameters. Due to
limited possibilities of 2D sampling density of biological and hydrological parameters
estimate of their values per each EDSU had to be done within some standardized statistical
areas (ICES rectangles in the Baltic). Detail description of the methods applying acoustic
sounding for producing 4D inter-disciplinary data base in the Baltic is given by the author in
Orlowski, 1989a, 1989c, 1990, 1997, 1998, 1999, 2000, 2001, 2003a, 2003b, 2004, Orlowski
and Kujawa, 2005.
Figure 2. Area of research of R.V. Baltica in October 2004, sampled in a similar way since
1981. Geographical structure of basic components of 4D-ABO inter-disciplinary data base.
2.1. INFLUENCE OF THERMAL CONDITIONS ON FISH DISRIBUTION
Temperature belongs to the factors of the first order from the point of view of fish horizontal
and vertical distribution (Barnes, et al., 1991, Helfman et al., 1997). Variability of this factor
is associated with the season of the year but from the fishery research point of view the most
important is to correlate its instability to fish distribution anomalies. For the same season
(month), the temperature can vary in very big range. The example of such a fluctuation is
given in Figure 3.
The patterns in the Figure 4 show strong fluctuations in depth structure, gradients, and
absolute values of the temperature.
Fish vertical distribution during the day-time strongly differs from the night-time one (Fig. 3).
It means that the hydrological variability observed in Fig.3 will modulate independently day
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and night vertical pattern of fish distribution. In Orlowski (1999) were introduced standards to
characterize ranges of hydrological factors associated with fish night and day distributions.
Figure 3. Average temperature-depth structure in the southern Baltic observed in October
1994-2004 in the southern Baltic.
Figure 4. Night and day average depth distribution of Sv (z) as an average for period 19942003 in the southern Baltic.
Characteristic points were associated with cumulative empirical distribution of given factor at
fish main depth (25%, 50%, and 75% - quartiles), weighted by sA. Values of TF25%, TF50%
TF75%, SF25%, SF25%, SF75%, and O2F25%, O2F50% O2F75% for years 1989-2004 are shown in Figure
5.
Example of strong temperature pressure on herring distribution in the southern Baltic is given
in Figure 6. Comparison of herring distribution was made against the temperature one at the
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Figure 5. Acoustically determined temperature, salinity, and oxygen level limits, characterizing day and night distribution of fish biomass for years 1989-2004 in the southern Baltic.
Figure 6. Comparison of herring distribution and temperature structure of area in spring 1983
and 1985.
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depths characteristic for day-time (50m) and night-time (20m). Presence of very cold waters
(< 3ºC) in 1985 reduced drastically the biomass of herring in the Polish EEZ from 217
thousands t in 1983 up to 10.7 thousands t in 1985.
Similar phenomenon was observed for the autumn, when absence of warmer water in the
Polish EEZ was directly correlated to decrease of the total biomass in the area. Illustration of
that relationship is given in Figure 7. Total biomass of fish is quite strongly correlated in the
period 1989-2001 to the temperature at fish main depth during the day-time. Detail analysis of
this phenomenon is published in Orlowski (2003b).
2.2. CHARACTERIZING DIEL CYCLE OF THE FISH
Diel cycle of fish is one of the most fundamental processes regulating fish biological
condition (Helfman et al., 1997). Activity patterns of fish represent its response to widely
understand environment. In a consequence research on characterizing diel cycle of the fish
can strongly improve the knowledge on interactions between the fish and surrounding marine
ecosystem. Acoustic methods are one of the most effective to provide observations of fish and
fish schools in incomparable big space volumes, as it is shown by Aoki and Inadaki, 1992,
Bertrand et al., 2003, Cassini et al., 2004, Castillo et al., 1996, Fréon et al.,1996, Gauthier
and Rose, 2002, Massé, 2003, Orlowski, 1997, 2000, 2001, Pelotonen et al., 2004, Pieper et
al., 2001, Szczucka, 2000, Tameshi et al., 1996. On the other hand detail knowledge on diel
cycle of the fish can strongly minimize the errors in estimating their scattering properties.
Application of data collected in 4D-ABO data base give the chance to estimate many
single or cross-correlated characteristics of the marine ecosystem. One of the first steps has to
be made by complex visualization of the cycle over environmental background. The example
of such visualization is given in Figure 8. The measurements were carried out during lasting
two days experiment at south Gotland Deep, in which integration of echoes was provided
along the sides of the square track The process of construction of visualization is described
by Orlowski (2003) and the experiment is characterized in detail in Orlowski (2005). It can
be easy seen from Fig. 8 that few different phases of fish behaviour (clupeoids) can be
differentiated. The phases are closely related to characteristic time and spatial limits. Each
phase gives different pattern of the echoes.
Diel cycle characteristics can be expressed by time dependent functions describing average
values of basic factors joint with fish vertical migrations, as depth, temperature, salinity, and
oxygen level. One of the most useful for approximation of those periodic relationships are
trigonometric polynomials (Clay and Medwin, 1977). Their application for this purpose was
introduced in Orlowski (1998). In Figure 9 are given examples of mathematical models of
diel variability of fish main depth and temperature at fish main depth. Models are estimated
on the basis of the 4D-ABO October data base for consequent years 1989-2003.
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Figure. 7. Regression between temperature at fish depth during the day and total biomass of
fish in the Polish EEZ for period 1989-2000 and 1989-2001.
Figure 8. TDS visualization of diel cycle of fish distribution (sv) in area 4 x 4 nmi in south
Gotland Deep in October 2001. Diagrams of average temperature, salinity and oxygen level
are enclosed below.
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Figure.9. Diel cycle of clupeoids characterized by approximation curves expressing
relationship between time of a day and fish main depth (left panel), and temperature at fish
main depth (right panel) for period 1989-2003, and for each single year.
Diel dependence of main depth of the fish on time of the day seems quite regular over the
period investigated. Pattern averaged over the period 1989-2003 has a regular quasi-sinus
shape associated with the variability of light factor (position of the sun). Time dependence of
the temperature at fish main depth (centre of gravity of scattering strength) is strongly
influenced by vertical structure of the temperature (Figure 9, right panel), significantly
variable from year to year (Fig. X). It possible to distinguish the years of clearly regular
differentiation between day and night () and the years of low day and night differences.
In the Figure 10 approximation of empirical relationships between the time of a day fish and
fish main depth (left panel), and temperature at fish main depth (right panel) for the spring
time and the summertime are shown. The patterns were calculated for the period 1983-1985
(spring season) and for period 1989-2003 (autumn).
During the spring diel fish vertical migrations are much stronger that during the summer. The
variability of temperature at fish depth is much stronger during the summer.
If we consider the average pattern of diel dependence of environmental factors at fish depth as
the standard pattern a comparison of each year characteristic to that standard can be made and
analysed. Such comparisons between functions of the 1994-2004 period and 2004 are given in
Figure 11.
2.3. CHARACTERIZING RELATION SEABED-DEMERSAL FISH
The new method applied for acoustic bottom classification was introduced for this purpose
(Orlowski and Kujawa, 2005b). Previously the author introduced application of multiple
echoes for evaluation of seabed. The main intention of the method presented below is to
simplify classification procedure by limiting the output to one-parameter values.
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Figure 10. Spring and summer dependence of diel cycle of clupeoids characterized by
approximation curves expressing relationship between time of a day and fish main depth (left
panel), and temperature at fish main depth (right panel)
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Figure 11. Approximation curves modelling diel dependence of temperature, salinity, and
oxygen levels at fish characteristic depths (upper depth limit, main depth, lower depth limit)
for period 1994-2004 and 2004.
The parameter is defined as a hypothetical effective angle Θ corresponding to received seabed
echo. The angle is defined as below:
arc cos (Θ’/2) = ( 1 + c(τs - τ1)/d) –1
where: Θ’/2 τs τ1 c
d
-
parameter characterizing acoustic seabed properties,
superposition of all seabed echo time components,
component dependent on pulse length,
sound speed,
bottom depth
Comparing the values of Θ’/2 within regular statistical rectangles of the surveyed area and
the values of bycatch of fish available from 4D-ABO data base we can generate relationship
between those two factors. In Figure 12 are given patterns of statistical correlation between
bycatch of herring, sprat and cod and seabed classes, characterized by Θ’/2 intervals.
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It is quite easy to realize that bycatch (percentage) of pelagic fish does not show any
correlation over differentiated by Θ’/2 values bottom areas. Cod as a typical demersal fish
shows very clear and strong correlation to the seabed type.
Figure 12. Bycatch of pelagic (A -herring and sprat) and bottom fish (B-cod) in the areas
characterized by Θ’/2 parameter.
3. CONCLUSIONS
Selected examples of methods and results obtained show how application of acoustic data can
effectively improve understanding functioning of the marine ecosystem. The most important
part of this process is producing acoustically co-ordinated transfer and compiling common
interdisciplinary data base on marine ecosystem (4D-ABO). Value of this data base is higher
when data are collected by similar time and spatial strategy and technical means. Each new
parameter included to the data collection strongly enhances the range of possible analyses.
Relations and trends between environment and distribution of life organisms in 4D structure
can be estimated and formulated by mathematical models for further comparisons and multidimensional modelling.
Analyses of case studies presenting selected applications indicate very wide range of possible
characterization of the marine ecosystem by acoustically produced 4D database (4D-ABO)
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describing the distribution of life organisms and environmental gradients and limits. In
examples shown following elements of marine ecosystem dynamics were characterized:
-
depth geographical structure of marine life organisms,
year, season, and diel dynamics of biological cycles in relation to environmental factors,
environmental pressure on horizontal distribution of fish,
comparisons of defined standards of fish behaviour for determined periods and areas,
association of fish species to seabed characteristics.
A lot of similar studies were made by other scientists cited in this paper. Application of
acoustic information to describe 4D functioning of the marine ecosystem can be significantly
improved if normalization of standards, functions and magnitudes characterizing its elements
can be precisely determined and kept by other researchers. It can significantly improve
comparability and allow significantly enhance a possibility of supplementing joint data bases
produced in 4D-ABO format. The significance of this fact does not need any discussion.
REFERENCES
Aoki I., Inagaki T., 1992. Acoustic observations of fish schools and scattering layers in a
Kuroshio warm-core ring and its environs. Fisheries Oceanography, Vol.1, No 1, pp. 137-142.
Barnes, R. S. K., Mann, K. H., 1991. Fundamentals of Aquatic Ecology, Blackwell Science
Cambridge, 270 p.
Bertand, A., Josse, E., Massè, J., Bach, P., Dagorn L., 2003.Acoustics for ecosystem research:
lessons and perspectives from a scientific programme focusing on tuna-environment
relationships, Aquatic Living Resources, 16, pp.197-203.
Castillo, J., M. Barbieri, A., Gonzalez, A., 1996. Relationships between sea surface
temperature, salinity, and pelagic fish distribution off northern Chile. ICES Journal of Marine
Science, 53, pp. 139-146.
Clay, C. S., Medwin, H., 1977. Acoustical Oceanography: Principles and Applications, John
Wiley & Sons, New York, pp. 122-150.
Féon., P., Gerlotto, F., Soria, M., 1996. Diel variability of school structure with special
reference to transition periods, ICES Journal of Marine Science, 53, pp. 459-464.
Gauthier, S., and G.A. Rose, 2002. Acoustic observation of diel vertical migration and
shoaling behaviour in Atlantic redfishes, Journal of Fish Biology, 61, pp. 1135-1153.
Helfman, G. S., B. B. Colette, D. E. Facey.1997. The Diversity od Fishes, Blackwell Science,
Oxford, 528 p.
Holliday, D. V., 1993. Application of Advanced Acoustic Technology in Large Marine
Ecosystem Studies, pp. 301-319, [in:] Large Marine Ecosystems, Sherman, K., Alexander, L.
M., Gold. B. D., (eds.), AAAS Press, Washington.
13
Holliday, D. V., and Pieper R.E., 1995. Bioacoustical oceanography at high frequencies,
ICES Journal of Marine Science, 52, pp. 279-296.
Jech, J. M., Luo, J., 2000. Digital echo visualization and information system (DEVIS) for
processing spatially explicit fisheries acoustic data, Fishery Research, 47, pp. 115-124.
Kemp Z., Meaden, G. 2002. Visualization for fisheries management from spatio-temporal
perspective, ICES Journal of Marine Science, 59, pp. 190-202.
Massè, J., 1989. Daytime detected abundance from echo-surveys in the Bay of Biscay. Proc.
I. O. A., Vol. 11, Part 3, pp. 252-259.
Massè, J., Gerlotto F., 2003. Introducing nature in fisheries research: the use of underwater
acoustics for an ecosystem approach of fish population, Aquatic Living Resources, 16, pp.
107-112.
Mayer, L., Yanchao, L., Melvin, G., 2001. 3D visualization for pelagic fisheries research and
assessment, ICES Journal of Marine Science , 10, pp. 1-10.
Orłowski., A., 1989a. Application of acoustic methods for study of distribution of fish and
scattering layers vs. the marine environment, Stud. Mat. Morsk. Inst. Ryb., ser. B, 57, 134p.
(in Polish).
Orłowski, A., 1989b. Seasonal fluctuations of biomass distribution based on results of
hydroacoustic surveys of the Polish fishery zone, Fisheries Research, 8, pp. 25-34.
Orłowski A., 1989c. Application of acoustic methods to correlation of fish density
distribution and the type of sea bottom, Proc. I.O.A., Vol 11, Part 3, pp. 179-185.
Orłowski, A., 1990. Macrosounding as a new concept in the study of marine ecosystems,
Coll. Phys. c2 (n 2), Tome 51, pp. 69-72.
Orłowski., A., 1997. Diel and lunar variations in acoustic measurements in the Baltic Sea,
ICES Rep. C. M. 1997/S:29, 14 p.
Orlowski A., 1998. Acoustic methods applied to fish environmental studies in the Baltic Sea,
Fisheries . Research., 34(3), pp. 227-237.
Orlowski A., 1999. Acoustic studies of spatial gradients in the Baltic: Implication for fish
distribution, ICES Journal of Marine Science, 56, pp. 561-570.
Orlowski A.,2000. Diel dynamic of acoustic measurements of Baltic fish, ICES Journal of
Marine Science, 57, pp. 1196-1203.
Orlowski, A., 2001. Behavioural and physical effect on acoustic measurements of Baltic fish
within a diel cycle, ICES Journal of Marine Science, 58 , pp. 1174-1183.
Orlowski, A., 2003a. Acoustic semi-tomography in studies of the structure and the function of
the marine ecosystem, ICES Journal of Marine Science, 60, pp. 1392-1397.
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Orlowski, A., 2003b. Influence of thermal conditions on biomass of fish in the Polish EEZ,
Fishery Research., 63(3), pp. 367-377.
Orlowski, A., 2004. Acoustic reconnaissance of fish and environmental background in
demersal zone in southern Baltic. Annual Journal: Hydroacoustics Volume 7, Polish
Acoustics Society, Gdańsk, pp. 183-194.
Orlowski, A., 2005. Experimental verification of the acoustic characteristics of the clupeoid
diel cycle in the Baltic, ICES Journal of Marine Science, xx, pp.xxxx-xxxx.
Orlowski, A., Kujawa, A. 2005. Acoustic reconnaisance of fish and evironmental background
in demersal zone in southern Baltic- Part 2 - Seabed. Annual Journal: Hydroacoustics, 8,
Polish Acoustics Society, pp. 137-147.
Peltonen, H., Vinni M., Lappalainen A., and Pönni J., 2004. Spatial feeding patterns of
herring (Clupea harengus L.) sprat (Sprattus sprattus L.), and three spinned stickback
(Gasterous aculeatus L.) in the Gulf of Finland. ICES Journal of Marine Science, 61, pp. 966971.
Pieper, R.E., McGehee D.E., Greenlaw C.F., Holliday D.V., 2001. Acoustically measured
seasonal patterns of zooplankton in the Arabian Sea, Deep Sea Research II 48, pp.1325-1343.
Sherman, K., Alexander, L. M., Gold, B. D., 1993. Large Marine Ecosystems. AAAS Press,
Washington, 376 p.
Socha, D. G., J. L. Watkins, A. S. Brierley, 1996. A visualization-based post-processing
system for analysis of acoustic data, ICES Journal of Marine Science, 53(2), pp. 335-338.
Szczucka, J., 2000. Acoustically measured diurnal vertical migration of fish and zooplankton
in the Baltic Sea – seasonal variations, Oceanologia, 42(1), pp. 5-17.
Tameshi, H., Shinomiya, H., Aoki, I., Sugimoto, T., 1996. Understanding Japanese sardine
migrations using acoustic and other aids. ICES Journal of Marine Science, 53, pp. 167-171.
Yoder J., Garcia-Moliner, G., 1993. Application of satellite remote sensing and optical buoys
to LME studies. [in:] Large Marine Ecosystems, Sherman, K., Alexander, L. M., Gold, B. D.
(eds.), AAAS Press, Washington, pp. 353-358.
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