# Three-dimensional internal spatial structure of young-of-the-year pelagic freshwater fish provides evidence for the identification of fish school species

Jean Guillard, Paul Fernandes, Thomas Laloë, Patrice Brehmer

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Jean Guillard, Paul Fernandes, Thomas Laloë, Patrice Brehmer. Three-dimensional internal spatial structure of young-of-the-year pelagic freshwater fish provides evidence for the identification of fish school species. Limnology and Oceanography: Methods, 2011, 9, pp.322-328. 10.4319/lom.2011.9.322. hal-00667978

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# Three-dimensional internal spatial structure of young-of-the-year pelagic freshwater fish provides evidence for the identification of fish school species

Jean Guillard<fnref n="1" />, Paul Fernandes<fnref n="2" />, Thomas Laloë<fnref n="3" />, and Patrice Brehmer<fnref n="4" />*

## Abstract

Active acoustic detection and characterization in three dimensions (3D) with multibeam sonars is a powerful technique for ecological studies of schooling fish. The alpine Lake Annecy provides ideal conditions for sampling fish with active acoustic methods: it has calm water, low species diversity, and the density of pelagic fish schools is high. We carried out investigations on the internal 3D morphological characteristics of young-of-the-year (Y-O-Y) pelagic fish schools, using high resolution multibeam sonar in the vertical plane. The objective was to discriminate between the two fish species that school in the lake: perch (*Perca fluviatilis*) and roach (*Rutilus rutilus*). To discriminate between these Y-O-Y fish species, we used only one characteristic of their internal spatial structure: the total number of vacuoles, defined as empty volumes inside the school, relative to the school volume. Two distinct linear relationships between school volume and the total number of vacuoles were determined. These two types were consistent with the proportions of the percentage occurrence of the two different species as obtained from pelagic trawl sampling in 2004. In 2008, perch was dominant (93%) in the lake, and only one relationship was detected: this relationship corresponded closely to the one in 2004 attributed to perch. Thus, we assume that the schooling behavior is a phenotypic expression that could be used for remote fish species identification.

The world's pelagic fisheries are valuable resources that require effective stock assessment methods to manage them sustainably. Some of these stocks are in a poor state (Scheffer et al. 2001; Pauly et al. 2003, 2005) and thus the need for accu-

rate data are paramount. Regular scientific surveys are carried out using scientific trawling and hydroacoustic methods to determine the abundance and distribution of fish populations (e.g., Petitgas et al. 2009). Recent developments in acoustic technology and signal processing (Godø and Tenningen 2009), in addition to decades of experience both at sea and in freshwater (Fernandes et al. 2002), have gradually led to an increased acceptance of acoustic surveys as a reliable technique for abundance estimation, particularly for schooling pelagic fish (Simmonds and MacLennan 2005). However, it is still often difficult to distinguish between the echoes of co-occurring fish species during these surveys. Several studies have been devoted to solving this problem (see Horne 2000), using a variety of methods, such as: i) echo-trace morphology extracted from the two dimensional (2D) echogram (Cushing 1963; Haralabous and Georgakarakos 1996; Moreno et al. 2007); ii) multi-frequency information (Horne et al. 2000; Fernandes 2009; Fernandes and Stewart 2004; Korneliussen et al. 2009); and iii) broad-band echosounders (Reeder et al. 2004).

## Acknowledgments

This work was supported by the ANSON project (INRA, IRD, IFREMER), and we are grateful to the CRH (IRD, IFREMER, UM II; Sète) who provided facilities. The second survey was financially supported by the INRA EFPA department, under the grant 'Innovative Project.' The software used for sonar data extraction was developed during the EU project AVITIS. Special thanks to Yvon Guennegan (IFREMER; Sète) for his help during the first survey, Michel Colon for his help during the two surveys, and Nicolas Givaudan (INRA; Thonon) for previous data analyses. We also thank Marc Taquet (IFREMER; French Polynesia) who attempted to identify species by scuba diving during the 2008 survey. Finally, we are grateful to Keith Mutch of Marine Scotland for producing the 3D graphic.

DOI 10:4319/lom.2011.9.322

<footnote n="1">¹Institut National de la Recherche Agronomique, UMR CARRTEL, BP 511, 74203, Thonon-les-Bains, France</footnote>

<footnote n="2">²Institute of Biological and Environmental Sciences, University of Aberdeen, Tillydrone Avenue, Aberdeen AB24 2TZ, Scotland, United Kingdom</footnote>

<footnote n="3">³University of Nice, Sophia Antipolis, Laboratoire J-A Dieudonné, Parc Valrose, 06 108 Nice Cedex 2, France</footnote>

<footnote n="4">⁴Institut de Recherche pour le Développement, Laboratoire des sciences de L'environnement marin (UMR UBO-CNRS-IRD), BP 70, 29 280, Plouzané, France</footnote>

<footnote>*Corresponding author: E-mail: patrice.brehmer@ird.fr</footnote>
More recently, three-dimensional (3D) observations of
entire fish schools (Gerlotto et al. 1999; Mayer et al. 2002;
Paramo et al. 2010) or Antarctic krill shoals (Brierley and Cox
2010) have been used in ecological and behavioral studies
(Gerlotto et al. 2004; Brehmer et al. 2006; Guillard et al. 2010):
however, these have been seldom used for species identifica-
tion. In this article, we report on investigations into the inter-
nal 3D morphological characteristics of pelagic fish schools
(Partridge 1982), recorded using a high resolution multibeam
sonar, to discriminate between two species of freshwater fish.
An improvement in fish species discrimination has the poten-
tial to greatly improve fisheries management, not only to pro-
vide a better estimate of abundance and distribution, but also
by helping to decrease by-catches and discards, and improving
our knowledge of ecological processes and thus on the ecosys-
tem functioning.

Materials and procedures

Lake Annecy is a deep oligotrophic monomictic lake located at about 600 m above sea level in the French Alps (45°51'24" North, 06°10'20" East) (Perga et al. 2010). It has a maximum depth of 69 m (Fig. 1) and is 14.6 km long, 2 km wide, with a total area of 27.4 km². In autumn, fish populations in Lake Annecy (Guillard et al. 2006b), in common with other lakes in temperate areas, exhibit a vertical distribution that is strongly related to the thermocline (Fig. 2). Under the thermocline, the deeper layers are inhabited by salmonids (Mehner et al. 2010); above it, in the surface layers, juveniles of Cyprinidae (*Perca fluviatilis*) and Percidae (*Rutilus rutilus*) school during the day and disperse at sunset to feed (Masson et al. 2001; Probst et al. 2009).

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Map of Lake Annecy showing the survey design carried out during the two acoustic surveys, and the associated locations of trawl samples (September 2004 and 2008). The black point indicates the position of the temperature profile done during each survey.</figcaption></figure>

Daily acoustic surveys, at a mean speed of 6 km h⁻¹, were
carried out in the larger part of the lake (Fig. 1) on 14 Sept
2004 and 23 Sept 2008. A Reson Seabat 6012 multibeam sonar
operating at a frequency of 455 kHz (Gerlotto et al. 1999;
Nøttestad and Axelsen 1999) was pole-mounted on the side of
a 6.4 m boat with the transducer submerged to a depth of 1 m
(Guillard et al. 2006a). The 60 beams (beam dimensions 1.5°
athwartships and 17° along ships) allowed for a 90°
athwartship observation plane, oriented perpendicularly to
the bottom and parallel to the surface (sonar was orientated at
45° to the vertical perpendicular to the line of travel—*see*
Fig. 3).

The multibeam sonar has been checked several times by the
manufacturer. It was set to operate a 20 Log R Time Varied
Gain (TVG), with a pulse length of 0.06 ms and a range set at
50 m. The system was not calibrated, thus it was not possible
to compare absolute levels of backscatter with other systems;
the study was limited to the analysis of the school spatial
structure. In the case of a nonlinear system, we will not have
an absolute threshold to define the vacuoles, and whatever
biases may be produced by the selection of vacuoles in our
study will be the same across the range of schools that we
studied. Our study was a comparative analysis using the multi-
beam sonar to identify species, not quantify biomass. In this
regard, the calibration required to produce absolute levels of
backscatter is not an essential requirement for our analyses
and should not invalidate our conclusions. The data storage
precision was 8 bits, i.e., 1024 samples per beam at 50 m
range, the range resolution of sonar was 4.9 cm. During each
survey, more than 400 sonar sequences with schools were
recorded. These were then extracted using the SBIViewer 5.01
digital video analysis software (Gerlotto et al. 1999). Only

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Temperature profiles of the Annecy Lake carried out during the two acoustic surveys.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** 3D school representation. Arrows indicate vessel route. a) 3D reconstruction of the school (in blue, the color is not indicative of acoustic intensity). Multibeam sonar receiving beams are shown at the front of the vessel. Cross-sections of the school are shown in the b) horizontal plane, c) vertical plane along ships, and d) vertical plane athwartships. The cross-section pixels are equivalent to the multibeam samples of acoustic intensity according to the legend; in white, the lowest density represents the empty volume, i.e., interstitial spaces known as vacuoles.</figcaption></figure>

sequences without noise and bottom interferences were retained. A total of 12 descriptors were then extracted using image analysis techniques (Gerlotto et al. 1999) for each fish school. Among these descriptors was the number of vacuoles per school 'H' (Partridge 1982; Vabø and Nøttestad 1997; Misund et al. 1998). This parameter was redefined by Gerlotto and Paramo (2003), using the same device and software as in our case study, as empty volumes in the 3D fish school structure, which is very heterogeneous (Partridge 1982; Fréon and Misund 1999). Fish schools with more than 1024 vacuoles were eliminated from the database due to limitations of the image analysis software. Relationships between school volume 'V' and the number of vacuoles 'H' were analyzed (Guillard et al. 2006a), and we set the ratio between 'V' and 'H' using an unsupervised classification method, the quantization (Gersho and Gray 1991). This method consists of representing each point of the data space by a unique element of a finite subset of the data space. Specifically, we used the L1 quantization method proposed by Laloë (2010). Linear regressions for each data set were computed and a Student test was performed using the Gaussian law of the estimators of the slopes to compute a slope comparison test, performed using the R package (Hornik 2010). For each case, the hypothesis of the test was $H_0$: the slopes are different; and the alternative hypothesis $H_1$: the slopes are identical.

Fish samples were collected for species identification at night, using a pelagic frame trawl (Guillard and Gerdeaux

<table><caption>**Table 1.** Descriptive statistics of the total number of fish caught during the two acoustics surveys using a pelagic trawl (total fish length expressed in mm). The occurrences show a dominance of one species (*Perca fluviatilis*) in 2008.</caption><thead><tr><th rowspan="2"></th><th colspan="2">2004</th><th colspan="2">2008</th></tr><tr><th>Perch</th><th>Roach</th><th>Perch</th><th>Roach</th></tr></thead><tbody><tr><td>Number caught (n)</td><td>626</td><td>182</td><td>848</td><td>61</td></tr><tr><td>Mean size</td><td>61.9</td><td>65.4</td><td>55.4</td><td>58.3</td></tr><tr><td>Maximum size</td><td>78.0</td><td>151.0</td><td>68.1</td><td>73.0</td></tr><tr><td>Minimum size</td><td>49.0</td><td>48.0</td><td>45.3</td><td>44.4</td></tr><tr><td>Standard deviation of mean size</td><td>4.7</td><td>8.6</td><td>3.8</td><td>6.0</td></tr><tr><td>Occurrence (%)</td><td>77.5</td><td>22.5</td><td>93.3</td><td>6.7</td></tr></tbody></table>

1993) in the same areas where the schools were previously recorded during daytime. In 2004, 5 trawls where carried out; and in 2008, this increased to 19 trawl operations (Fig. 1). Comparisons of total fish length and mean school volumes per year were made using a Kolmogorov-Smirnov test (Zar 1984).

## Assessment

As expected only young-of-the-year (Y-O-Y) fish were caught in the trawls carried out above the thermocline (Fig. 2). In 2004, 808 fish were caught, 23% of these were *R. rutilus* and 77% were *P. fluviatilis* (Table 1). In 2008, 909 fish were caught, and more than 93% of these were *P. fluviatilis* (less than 7% *R. rutilus*). The mean size of the two species was significantly different (*P* < 0.05), for both surveys (Table 1). The mean sizes of the same species were also significantly different between each year (*P* < 0.05).

In 2004, 277 schools were analyzed and two different relationships were distinguished (Fig. 4). The L1 quantification methods discriminated 2 groups: the first one, comprising 58.8% of the schools (named Fish Group 1, FG1) corresponded to the upper relationship showed in Fig. 4, and the second one (named Fish Group 2, FG2), comprising 41.2% corresponded to the lower relationship in Fig. 4.

The relationships between the numbers of vacuoles and school volume were analyzed for the two sets of data, i.e., FG1, FG2. These two significant relationships (Fisher test, *P* < 0.001), were defined by Eq. 1 for FG1 [V = 0.796 H + 12.89; ($r^2$ = 0.85, *n* = 163)] and by Eq. 2 for FG2 [V = 0.143 H + 2.55; ($r^2$ = 0.63, *n* = 114)]. The two slopes for FG1 and FG2 were significantly different (Student test, *P* > 0.99). The proportions of each group (FG1 and FG2) were similar to the proportions of the species sampled by fishing during our 2004 survey (77% of perch and 23% of roach). The FG1 relationship was, therefore, attributed to perch schools and the FG2 relationship to roach, due to the similarity between the occurrence of the species in the lake and the acoustics data.

In 2008, 379 schools were analyzed using the same protocol and methods but only one Fish Group (FG3) was observed.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Two distinct relationships (FG1, FG2) between the number of vacuoles estimated in a school and its volume were discriminated from the 2004 data, using the L1 quantization method (Laloë 2010). Significant linear relationships between the two parameters were determined for each fish group (black line for FG1; gray line for FG2). FG1's equation [$V = 0.7957 H + 12.89$; ($r^2 = 0.854$, $n = 163$)] and FG2's equation [$V = 0.143 H + 2.55$; ($r^2 = 0.63$, $n = 114$)].</figcaption></figure>

The relationship between the numbers of vacuoles and school volume for FG3 was defined by the equation 3 [$V = 0.997 H + 50.45$; ($r^2 = 0.49$, $n = 379$)]. The FG3 slope was similar to the slope of FG1 (Fig. 5), but the linear relationship for the FG3 was statistically significantly different both from FG1, and obviously FG2 (Student test, *P* > 0.99). Mean school volume of FG3 (222.51 $m^3$) was significantly different from the FG1 mean volume in 2004 (160.56 $m^3$) (Kolmogorov-Smirnov *P* < 0.001).

In 2008, 4% of the school volumes were extreme values (Tukey 1977), and these were removed from the analysis to form a modified dataset named FG3*: these had a mean volume (191.41 $m^3$) that was not significantly different to the FG1 mean volume (Kolmogorov-Smirnov *P* < 0.05). The slope of the relationship for the modified data from 2008 FG3* [$V = 0.785 H + 64.15$; ($r^2 = 0.53$, $n = 370$)] was then not significantly different to the slope of FG1 (*P* = 0.13) indicating that the two relationships FG1 and FG3* were similar.

## Discussion

The vertical structure of the fish assemblage in Lake Annecy is strongly related to the thermodynamic conditions of the water (Appenzeller 1995; Masson et al. 2001; Mehner et al. 2010). At the end of summer, the thermocline is well established, and the only fish occurring in schools above the thermocline are the two species, roach and perch (Guillard et al. 2006b). Other fish present in this upper layer were individuals

<figure><img src="image_6.png" /><figcaption>**Fig. 5.** The relationship FG3 (2008 data) was indicated in gray (dotted and dash gray line), and then, 2008 data without the 9 schools with extreme values of school volume (FG3*) was plotted. A significant linear relationship between the two parameters, school volume, and number of vacuoles was determined for FG3* (gray dotted line). FG3*'s equation [$V = 0.785 H + 64.15$; ($r^2 = 0.53$, $n = 370$)] not significantly different to the slope of FG1 (*P* = 0.13) indicating that the two relationships FG1 and FG3* were similar.</figcaption></figure>

making brief incursions from the deeper layers for foraging: mainly salmonids, older perch, or from time to time, pike (*Esox lucius*). This spatial distribution of fish, coupled with the fact that only two species exhibit schooling behavior, in such a restricted ecosystem, provided ideal conditions to study fish school morphology (versus the open sea).

Schooling in fish (Shaw 1961) is an anti-predator vigilance device, which also offers hydrodynamic advantages (Maguran et al. 1985; Pitcher and Parrish 1993; Pitcher et al. 1996; Parrish et al. 2002). In Lake Annecy, from June to midautumn, above the thermocline, perch and roach live in school structures during the daytime, and disperse at sunset to feed (Thorpe 1977; Azzali et al. 1985), leading to local dispersion throughout the area they occupy (Masson et al. 2001). Our trawl sampling, carried out on the same day, just after the acoustic observations, began during sunset when the schools dispersed. Several attempts were made to catch schools by day, using the same fishing device, but these were all unsuccessful. We were, therefore, unable to check the precise species composition for each school detected (ground truth for each school), and had to determine the species occurrence as the probability for each school to belong to one or another species in the pelagic areas surveyed. The number of trawl samples was increased in 2008 to determine accurately the proportion
of the two species in the pelagic area. The results were patent
in terms of species composition: two species were present in
2004; whereas in 2008, perch was almost the mainly domi-
nant species (93% of the catch).

Schools of Y-O-Y fish are commonly monospecific (Fréon and Misund 1999) and are composed of individuals of the same size. In our case, the two species are predator (*P. fluviatilis*) and prey (*R. rutilus*) (Turesson and Brönmark 2004) and they school in different aggregations. Furthermore, these two species exhibit different behaviors (Eklöv and Persson 1995; Kahl and Radke 2006) and physiology (Thorpe 1977). Eklöv and Persson (1995) have shown that juvenile roach form denser schools in the presence of predators, and have a higher swimming speed in the open water than juvenile perch. Thus, we can expect that these species exhibit different internal school structure and dynamics (Tien et al. 2004). Internal school structure is typically not uniform and is characterized by the presence of many vacuoles (Fréon and Misund 1999; Gerlotto and Paramo 2003), correlated with the school volume (Guillard et al. 2006a). In 2004, the significant positive correlations showed two distinct modes, similar to the occurrence of the two species found in the lake. Whereas in 2008 only one relationship existed, similar to the relationship attributed to perch in 2004, and confirmed by trawl samples that caught 93% of perch. The statistical significance of this correspondence is sensitive to extremes of the data, i.e., very large schools. These larger schools were present due to the great perch density in 2008, where fish densities were up to 150 kg ha⁻¹ (Guillard et al. 2006b).

The relationship between school volume and number of vacuoles in the FG1 and FG3 datasets were close but not identical; this is likely to be due to some environmental variability in schooling behavior. Such variability could be explained by different assumptions. In 2008 (FG3), the relationship could have been affected by the presence of a small number of *R. rutilus* schools (7% of the fish caught) in the sonar dataset. Furthermore, the individual size of perch was smaller in 2008 (FG3) than in 2004 (FG1), and this could have an additional impact on schooling behavior. Lastly, the roach population may have had a different spatial distribution in 2008, distributed more towards the littoral areas during the day, whilst at night, some of them may have moved toward deeper areas (i.e. depths greater than 10 m) as scattered fish available to the trawl. Thus the schools of *R. rutilus* were not observed by sonar, but some individual fish caught in the trawl.

Several studies (Coetzee 2000; Brehmer et al. 2007) have shown that changes in school behavior depend on environmental conditions. The external school morphology can vary according to the variation of the environment; there is, however, a lack of information on the effect of the environment on the internal spatial structure of a school. In our study, the environmental conditions have remained rather stable over the study period: this is typical of an oligotrophic freshwater lake (Domaizon et al. 2010; Perga et al. 2010). It is reasonable

to assume that such small variations have not had an impact
on internal morphological school parameters. Other parame-
ters may also be involved in such changes (Brehmer et al.
2007); nevertheless we assume that they do not play a signifi-
cant role in our case study as they are likely to change at a rate
that is slow enough to not have had any effect. Thus, we pro-
pose that the school internal morphology, as described by the
relationship between the school volume and the number of
vacuoles, is a phenotypic expression therefore characteristic of
a species (Brehmer et al. 2007). Further investigations are
planned, with a focus on ground truth protocols to establish
the true composition of the schools. Using fishing devices
such as a purse seine to catch schools during daylight will be
beneficial. It would also be useful to examine schooling fish in
other ecosystems, particularly marine ones, to extend our
hypothesis to other populations. These relationships between
internal school morphology and their species composition
will be an efficient tool for fish species discrimination and
thus of useful help to study the aquatic ecosystem and so
improve the management of pelagic fish populations.

References

Appenzeller, A. 1995. Hydroacoustic measurement of spatial heterogeneity of European whitefish and perch in Lake Constance. Arch. Hydrol. Spec. Issue Adv. Limnol. 46:261-266.

Azzali, M., G. Buracchi, S. Conti, S. Gambetti, and M. Luna.
1985. Relationship between the forms of pelagic fish distri-
bution and nycthemeral periods. A tentative model of
behaviour. Oebelia 11:471-488.

Brehmer, P., C. Vercelli, F. Gerlotto, F. Sanguinède, Y. Pichot, Y. Buestel, and Y. Guénnegan. 2006. Multibeam sonar detection of suspended mussel culture grounds in the open sea: Direct observation methods for management purposes. Aquaculture 252:234-241 [doi:10.1016/j.aquaculture.2005.06.035].

______, F. Gerlotto, C. Laurent, P. Cotel, A. Achury, and B.
Samb. 2007. Schooling behaviour of small pelagic fish: phe-
notypic expression of independent stimuli. Mar. Ecol. Prog.
Series 334:263-272 [doi:10.3354/meps334263].

Brierley, A. S., and J. M. Cox. 2010. Shapes of krill swarms and fish schools emerge as aggregation members avoid predators and access oxygen. Curr. Biol. 20:1-5 [doi:10.1016/j.cub.2010.08.041].

Cushing, D. H. 1963. The uses of echo sounding for fisher-
men. H.M.S.O.

Coetzee, J. 2000. Use of a shoal analysis and patch estimation system (SHAPES) to characterise sardine schools. Aquat. Living Resour. 13:1-10 [doi:10.1016/S0990-7440(00)00139-X].

Domaizon, I., D. Gerdeaux, J. C. Druart, L. Laine, J. Lazzarotto, M. E. Perga, and F. Rimet. 2010. Suivi Scientifique du lac d'Annecy : rapport 2009. SILA (éd.) et INRA-Thonon.

Eklöv, P., and L. Persson. 1995. Species-specific antipredator capacities and prey refuges: interactions between piscivorous
rous perch (*P. fluviatilis*) and juvenile perch and roach (*R. rutilus*). *Behav. Ecol. Sociobiol.* 37:169-178 [doi:10.1007/BF00176714].

Fernandes, P. G. 2009. Classification trees for species identification of fish-school echotraces. ICES J. Mar. Sci. 66:1073-1080 [doi:10.1093/icesjms/fsp060].

—, F. Gerlotto, D. V. Holliday, O. Nakken, E. J. Simmonds. 2002. Acoustic applications in fisheries science: the ICES contribution. ICES Marine Science Symposia, 215: 483-492.

—, and M. Stewart. 2004. Determining the quality of a multifrequency acoustic identification algorithm. ICES scientific document CM04/R:11.

Fréon, P., and O. A. Misund. 1999. Dynamics of pelagic fish distribution and behaviour: effects on fisheries and stock assessment. Blackwell.

Gersho, A., and R. M. Gray. 1991. Vector quantization and signal compression. Kluwer Academic.

Gerlotto, F., M. Soria, and P. Fréon. 1999. From two dimensions to three: the use of multibeam sonar for a new approach in fisheries acoustics. Can. J. Fish. Aquat. Sci. 56:6-12 [doi:10.1139/cjfas-56-1-6].

—, and J. Paramo. 2003. The three-dimensional morphology and internal structure of clupeid schools as observed using vertical scanning multibeam sonar. Aquat. Living Resour. 16:113-122 [doi:10.1016/S0990-7440(03)00027-5].

—, J. Castillo, A. Saavedra, M. A. Barbieri, M. Espejo, and P. Cotel. 2004. Three-dimensional structure and avoidance behaviour of anchovy and common sardine schools in central southern Chile. ICES J. Mar. Sci. 61:1120-1126 [doi:10.1016/j.icesjms.2004.07.017].

Godø, O. R., and E. Tenningen. 2009. Remote sensing, chapter 5. *In* B. A. Megrey and E. Moksness [eds.], Computers in fisheries research, 2nd ed. Springer.

Guillard, J., and D. Gerdeaux. 1993. In situ determinations of the target strength of roach (*Rutilus rutilus* L.) in Lake Bourget with a single beam sounder. Aquat. Living Resour. 6:285-289 [doi:10.1051/aler:1993028].

—, P. Brehmer, M. Colon, and Y. Guennégan. 2006a. Three dimensional characteristics of young-of-year pelagic fish schools in lake. Aquat. Living Resour. 19:115-122 [doi:10.1051/aler:2006011].

—, M.-E. Perga, M. Colon, and N. Angeli. 2006b. Hydroacoustic assessment of young-of-year perch, *Perca fluviatilis*, population dynamics in an oligotrophic lake (Lake Annecy, France). Fish. Manag. Ecol. 13:319-327 [doi:10.1111/j.1365-2400.2006.00508.x].

—, P. Balay, M. Colon, and P. Brehmer. 2010. Survey boat effect on YOY fish schools in a pre-alpine lake: evidence from multibeam sonar and split-beam echosounder data. Ecol. Freshw. Fish. 19:373-380 [doi:10.1111/j.1600-0633.2010.00419.x].

Haralabous, J., and S. Georgakarakos. 1996. Artificial neural networks as a tool for species identification of fish schools. ICES J. Mar. Sci. 53:173-180 [doi:10.1006/jmsc.1996.0019].

Horne, J. K. 2000. Acoustic approaches to remote species identification: a review. Fish. Oceanogr. 9:356-371 [doi:10.1046/j.1365-2419.2000.00143.x].

—, P. D. Walline, and J. M. Jech. 2000. Comparing acoustic model predictions to in situ backscatter measurements of fish with dual-chambered swimbladders. J. Fish Biol. 57:1105-1121 [doi:10.1006/jfbi.2000.1372].

Hornik, K. 2010. The R FAQ. <http://cran.r-project.org/doc/FAQ/R-FAQ.html>.

Kahl, U., and R. J. Radke. 2006. Habitat and food resource use of perch and roach in a deep mesotrophic reservoir: enough space to avoid competition? Ecol. Freshw. Fish. 15:48-56 [doi:10.1111/j.1600-0633.2005.00120.x].

Korneliussen, R. J., Y. Heggelund, I. K. Eliassen, and G. O. Johansen. 2009. Acoustic species identification of school-ing fish. ICES J. Mar. Sci. 66:1111-1118 [doi:10.1093/icesjms/fsp119].

Laloë, T. 2010. L1-Quantization and clustering in Banach spaces. Math. Methods Stat. 19:136-150 [doi:10.3103/S1066530710020031].

Magurran, A. E., W. J. Oulton, and T. J. Pitcher. 1985. Vigilant behaviour and shoal size in minnows. Z. Tierpsychol. 67:167-178 [doi:10.1111/j.1439-0310.1985.tb01386.x].

Masson, S., N. Angeli, J. Guillard, and B. Pinel-Alloul. 2001. Diel vertical and horizontal distribution of crustacean zoo-plankton and YOY fish in a sub alpine lake: an approach base on high frequency sampling. J. Plankton Res. 23:1041-1060 [doi:10.1093/plankt/23.10.1041].

Mayer, L., Y. Li, and G. Melvin. 2002. 3D visualization for pelagic fisheries research and assessment. ICES J. Mar. Sci. 59:216-225 [doi:10.1006/jmsc.2001.1125].

Mehner, T., S. Busch, I. P. Helland, M. Emmrich, and J. Freyhof. 2010. Temperature related nocturnal vertical segregation of coexisting coregonids. Ecol. Freshw. Fish 19:408-419 [doi:10.1111/j.1600-0633.2010.00424.x].

Misund, O. A., A. Ferno, T. Pitcher, and B. Totland. 1998. Tracking herring schools with a high resolution sonar. Variations in horizontal area and relative echo intensity. ICES J. Mar. Sci. 55:58-66 [doi:10.1006/jmsc.1997.0228].

Moreno, G., E. Josse, P. Brehmer, and L. Nøttestad. 2007. Echotrace classification and spatial distribution of pelagic fish aggregations around drifting fish aggregating devices (DFAD). Aquat. Living Resour. 20:343-356 [doi:10.1051/aler:2008015].

Nøttestad, L., and E. Axelsen. 1999. Herring schooling manoeuvres in response to killer whale attacks. Can. J. Zool. 77:1540-1546 [doi:10.1139/z99-124].

Paramo, J., F. Gerlotto, and C. Oyarzun. 2010. Three dimensional structure and morphology of pelagic fish schools. J. Appl. Ichthyol. 26:1-8 [doi:10.1111/j.1439-0426.2010.01509.x].

Parrish, J., S. Viscido, and D. Nbaum. 2002. Self-organized fish schools: an examination of emergent properties. Biol. Bull. 202:296-305 [doi:10.2307/1543482].
Partridge, B. 1982. The structure and function of fish schools. *Scient. Amer.* 246:90-99 [doi:10.1038/scientificamerican0682-114].

Pauly, D., J. Alder, E. Bennett, V. Christensen, P. Tyedmers, and R. Watson. 2003. The future for fisheries. *Science* 5649:1359-1361 [doi:10.1126/science.1088667].

———, R. Watson, and J. Alder. 2005. Global trends in world fisheries: impacts on marine ecosystems and food security. *Phil. Trans. R. Soc. B* 360:05-12 [doi:10.1098/rstb.2004.1574].

Perga, M. E., M. Desmet, D. Enters, and J.-L. Reyss. 2010. A century of bottom-up- and top-down-driven changes on a lake planktonic food web: A paleoecological and paleoiso- topic study of Lake Annecy, France. *Limnol. Oceanogr.* 55:803-816 [doi:10.4319/lo.2009.55.2.0803].

Petitgas, P., J. Cotter, V. Trenkel, and B. Mesnil. 2009. Fish stock assessments using surveys and indicators. *Aquat. Liv- ing Resour.* 22:119-119 [doi:10.1051/alr/2009014].

Pitcher, T. J., and J. K. Parrish. 1993. Function of shoaling behavior in teleosts, p. 363-439. *In* T. J. Pitcher [ed.], *Behav- iour of teleost fishes. Chapman & Hall*.

———, O. A. Misund, A. Fernö, B. Totland, and V. Melle. 1996. Adaptive behaviour of herring schools in the Norwegian Sea as revealed by high-resolution sonar. *ICES J. Mar. Sci.* 53:449-452 [doi:10.1006/jmsc.1996.0063].

Probst, W. N., G. Thomas, and R. Eckmann. 2009. Hydroa- coustic observations of surface shoaling behaviour of young-of-the-year perch *Perca fluviatilis* (Linnaeus, 1758) with a towed upward-facing transducer. *Fish. Res.* 96:133-138 [doi:10.1016/j.fishres.2008.10.009].

Reeder, D. B., J. M. Jech, and T. K. Stanton. 2004. Broadband acoustic backscatter and high-resolution morphology of

fish: Measurement and modelling. J. Acoust. Soc. Am. 116:747-761 [doi:10.1121/1.1648318].

Scheffer, M., S. Carpenter, J. A. Foley, C. Folke, and B. Walker. 2001. Catastrophic shifts in ecosystems. *Nature* 413:591-596 [doi:10.1038/35098000].

Shaw, E. 1961. The development of schooling behaviour in fishes. *Physiol. Zool.* 34:263-272.

Simmonds, E. J., and D. N. MacLennan. 2005. Fisheries acoustics: Theory and practice, 2nd ed. Oxford [doi:10.1002/9780470995303].

Thorpe, J. E. 1977. Morphology, physiology, behavior and ecology of Perca fluviatilis L. and P. flavescens Mitchell. J. Fish. Res. Board Can. 34:1504-1514 [doi:10.1139/f77-215].

Tien, J. H., S. A. Levin, and D. I. Rubenstein. 2004. Dynamics of fish shoals: identifying key decision rules. *Evol. Ecol. Res.* 6:555-565.

Turesson, H., and C. Brönmark. 2004. Foraging behaviour and capture success in perch, pikeperch and pike and the effects of prey density. *J. Fish Biol.* 65:363-375 [doi:10.1111/j.0022-1112.2004.00455.x].

Tukey, J. W. 1977. Exploratory data analysis. Addison-Wesley.

Vabø, R., and L. Nøttestad. 1997. An individual based model of fish school reactions: predicting antipredator behaviour as observed in nature. *Fish. Oceanogr.* 6:155-171 [doi:10.1046/j.1365-2419.1997.00037.x].

Zar, J. H. 1984. Biostatistical analysis (2nd ed). Prentice-Hall.

Submitted 30 November 2010

Revised 13 May 2011

Accepted 28 June 2011