Acoustic identification, classification and structure of ...

South African Journal of Marine Science

ISSN: 0257-7615 (Print) (Online) Journal homepage: http://www.tandfonline.com/loi/tams19

Acoustic identification, classification and structure of biological patchiness on the edge of the Agulhas Bank and its relation to frontal features M. Barange To cite this article: M. Barange (1994) Acoustic identification, classification and structure of biological patchiness on the edge of the Agulhas Bank and its relation to frontal features, South African Journal of Marine Science, 14:1, 333-347, DOI: 10.2989/025776194784286969 To link to this article: http://dx.doi.org/10.2989/025776194784286969

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Date: 13 January 2016, At: 07:18

S. Afr. J. mar. Sci. 14: 333-347 333

1994 ACOUSTIC IDENTIFICATION, CLASSIFICATION AND STRUCTURE OF BIOLOGICAL PATCHINESS ON THE EDGE OF THE AGULHAS BANK AND ITS RELATION TO FRONTAL FEATURES

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M.BARANGE* Echo-signals were collected with a 38-kHz transducer across frontal zones and shelf-break features off the south-east coast of South Africa, with the objectives of (a) developing a method for automatically characterizing biological patterns at appropriate spatial scales and (b) quantifying the extent, intensity, structure and identity of patches by analysis of their acoustic signal. Patches were identified and isolated according to interactive criteria based on intensity thresholds of the signal and continuity of the echoes above and below those thresholds. Statistical analysis indicated that the dispersion patterns of organisms within and between patches, and patch size and shape, explained most of the observed variability. Patchiness was more intense where frontal gradients were strongest. The technique gave good separation between zooplankton and fish aggregations and it should prove beneficial for small-scale distributional studies of fish and their prey. Eggoseine is met 'n oordraer van 38 kHz oor frontale sones en verskynsels van die platrand heen versamel teenoor die suidooskus van Suid-Afrika met die doel om 'n metode te ontwikkel waarvolgens (a) biologiese patrone outomaties op gepaste ruimtelike skale gekarakteriseer en (b) die omvang, intensiteit, struktuur en identiteit van kolle deur ontleding van hul akoestiese sein gekwantifiseer kan word. Kolle is geeien en ge'isoleer votgens interaktiewe maatstawwe wat op intensiteitsdrempets van die sein en onafgebrokenheid van die eggo's bo en benede daardie drempels berus. Statistiese ontleding het aangetoon dat die verspreidingspatrone van organismes binne en tussen kolle, asook kolgrootte en -fatsoen, die meeste van die waargenome variabiliteit verklaar het. Kol-kol-verspreiding het meer voorgekom waar frontale gradiente die sterkste was. Die tegniek het goed tussen soiiplankton- en visversamelings onderskei en behoort vir studies van die kleinskaalse verspreiding van vis en hul prooi van waarde te wees.

Patchiness is one of the most prominent features of the pelagic marine environment (Steele and Henderson 1992). It is defined in the present context as the non-random distribution of organisms in aggregations that exceed a certain density. Patchiness intensity in the pelagic environment appears to increase in the vicinity of hydrographic discontinuities, such as fronts and eddies, as a result of passive concentration of biota by physical dynamics (Owen 1981, Mackas et al. 1985, Olson and Backus 1985, Haury and Pieper 1988, Nash et al. 1989. Nero et al. 1990, Aoki and Inagaki 1992). It is the general belief that behavioural factors, such as swarming and schooling, and differential rates of production and mortality are likely to influence the formation, maintenance and structure of biological patches (Haury and Wiebe 1982, Simard and Mackas 1989, Smith et al. 1989). Also, patches are often multispecific (Haury and Wiebe 1982), and the hierarchical structure of aggregations may result in levels responding differently to the dominant driving forces, so generating patterns not easily linked to the causal mechanism. Whether patches are originated and maintained by their own volition, or by the complex dynamics of the physi-

* Sea

cal environment, remains uncertain, because paucity of data on spatial patterns hinders the construction and verification of ecological models of formation and maintenance of patches. Reasons for this constraint lie in the lack of biological sampling methods capable of collecting data at appropriate scales, over a wide range of organism and patch sizes. It is therefore necessary to develop techniques capable of sampling a large variety of patches at their relevant spatial and temporal patterns, and independent of their species composition. Hydroacoustic methods offer a practical solution to these problems because of their capability to analyse abundance distributions over a wide range of organism and aggregation size in a non-destructive and near real-time manner. Whereas defmitive species identification by acoustic techniques is not yet possible, such techniques have been useful in describing fine-scale patchiness over large areas (Nash et al. 1989, Aoki and Inagaki 1992). This paper describes a patch-recognition system, based on continuous acoustic back-scattering data, which identifies and analyses patchiness and patch structure in the ocean. The area selected for testing the patch-recognition algorithms was the boundary zone between the Agulhas

Fisheries Research Institute, Private Bag X2, Rogge Bay 8012, South Africa

Manuscript received: January 1994

334

1994

South African Journal of Marine Science 14


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1m' S-1 (Fig. 4). Vertical ADCP profiles (data not shown) indicate that the Current was essentially barotropic in the upper 200 m of the water column. As the SST never exceeded 21°C, it is assumed that sampling was inshore of the core of the Current. Over the shelf, particularly towards the 'western sector of the survey grid, currents were weakest and generally south-eastwards, but counter-currents were often present. This flow patttern, typical of the

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337

Barange: Acoustically Identified Biological Patchiness on Agulhas Bank

1994

Rg. 4: Distribution of water currents 30 m deep according to ADCP measurements during the October 1992 cruise

study area (Lutjeharms et al. 1989, Boyd et al. 1992), generated strong frontal systems over the shelf-break in the eastern sector (Transects NOI and N03), where-

as the western sector (Transects N06 and NlO) was characterized by less-intense current shear and weaker frontal systems. A body of cool water was present

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PING NUMBER Fig. 5: Echogram of Line N03. indicating the position of the sea bed and the biological targets analysed

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1994


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PING NUMBER Fig. 6: Echogram of Line N10, indicating the position of the sea bed and the biological targets analysed

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PING NUMBER Fig. 7: Representation of the patches identified on Line N03, and superimposed sea temperature profiles. Each circle indicates the position of an aggregation, regardless of its size or echo intensity

1994


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PING NUMBER Fig. 8: Representation of the patches identified on Line N10, and superimposed sea temperature profiles. Each circle indicates the position of an aggregation, regardless of its size or echo intensity

inshore between 23 and 25°E (incorporating Transect NI0), a feature perhaps indicative of recent upwelling. The influence of these physical features on the structure and distribution of biological patches will be discussed in following sections.

Patch identification and description Echo-charts of the two transects studied in most detail (N03 and NlO) are presented in Figures 5 and 6. Transect N03 was characterized by a dense layer of zooplankton (A) in the upper 100 m of the water column, extending over the slope and shelf-break regions. Bongo nets towed offshore of the front at the shelf-break showed that small copepods (Ca/anus sp.) were numerically dominant (82%: 105 individuals' m-3) in the region, followed by euphausiid larvae (7,1 %), chaetognaths (5,8%) and fish eggs (4,6%). The zooplankton assemblage in midshelf was similar to that at the shelf-break, small Ca/anus sp. dominating (93%: 61 individuals' m-3), followed by euphausiid larvae (2,9%), chaetognaths (1,6%) and fish larvae (1, 1%). Fish targets (B) were close to the bottom at the shelfbreak, appearing as a cloud-like layer over the outer

shelf, and as sparse, plume-like targets (C) in the lower half of the water column over the shelf. Trawl catches taken over the outer shelf showed that the fish targets near the shelf-break were mainly horse mackerel, averaging 41,2 cm total length (TL). Targets over the shelf were probably also horse mackerel, but they were slightly larger (43,7 cm TL) than those at the shelf-break. Transect NlO had a more complex structure than Transect N03, layers of zooplankton being more diffuse and extending from over the slope in the upper 200 m to the upper 50 m over the shelf. Zooplankton collected over the outer shelf in 130 m of water were indicative of a more mixed zooplankton community than along Transect N03. Small Calanus sp. were still numerically dominant (67%), albeit in lower densities (8 individuals' m-3), followed by chaetognaths (18,6%) euphausiid larvae (11 %) and fish eggs (1 %). Target morphology indicated that pelagic fish were present within the zooplankton layer. Fish targets were present over the entire shelf, appearing as plume-like patches between the thermocline (at approximately 50 m) and the bottom. Trawl catches taken in this region showed that the near-bottom and midwater targets (Fig. 6) consisted entirely of horse mackerel with a mean TL

1994


340

Table II: Mean and standard error (SE) of selected aggregation parameters for the three groups of targets identified on Transect N03. Threshold factor was 250 units and continuity factor was five pings. Units of length and height are in pings and metres respectively, all other parameters having relative values. One ping is equivalent to approximately 6,7 m Zooplankton targets


Aggregation parameters

Horse-mackerel targets (shelf-break)

Horse-mackerel targets (shelf)

Mean

SE

Mean

SE

Mean

SE

Length (pings) Height (m) Area Perimeter Fractal dimension Nearest-neighbour distance Acoustic intensity Horizontal acoustic roughness'" Vertical acoustic roughness'" Skewness

10,58 4,67 28,92 22,98 0,89 7,32 424,20 4,89 4,22 1,33

0,75 0,18 3,40 2,08 0,01 0,20 0,49 0,02 0,04 0,04

8,26 3,89 17,54 15,44 0,86 8,74 871,50 5,83 5,19 1,63

0,56 0,15 1,60 0,94 0,02 0,51 43,86 0,04 0,09 0,08

6,18 3,84 13,81 13,13 0,83 109,17 1799,00 6,31 5,44 1,49

0,44 0,70 2,01 1,37 0,04 60,19 248,50 0,41 0,62 0,65

Number of aggregations

316

136

15

'" Average of logarithmically transformed variables

of 28 cm. Patch-identification algorithms were applied to Transects N03 and NlO. The positions of the patches identified by the use of a threshold factor of 250 units and a continuity factor of five pings, relative to temperature, are presented in Figures 7 and 8. For clarity in the Figures, each aggregation is denoted by a single circle, which indicates the spatial centre of the aggregation regardless of patch size or density. On Transect N03, aggregations were grouped into three large layers, denoted as A, B and C in Figures 5 and 7. Layer A corresponded to zooplankton in the upper mixed layer of the water column, offshore of the frontal region which separated the warm AgulhaS Current from the

cooler, shelf waters. Horse-mackerel targets over the shelf-break and outer shelf are depicted by the letter B, and the more structured, plume-like shelf targets are denoted by the letter C. Patch classification on Transect NlO was more difficult as a result of the lack of a frontal zone over the shelf-break (Fig. 8). Temperature proflles, however, showed the presence of a mixed layer in the upper 50 m of the water column and a strong thermocline between 50 and 80 m deep. Patches were allocated subjectively by dividing the aggregations into three broad groups (A, B and C). Group A depicted shelfbreak aggregations and included the patches recorded in the upper 40 m of the water column around ping

Table III: Mean and standard error (SE) of selected aggregation parameters for the three groups of targets identified on Transect N10. Threshold factor was 250 units and continuity factor was five pin~s. Units of length and height are in pings and metres respectively, all other parameters having relative values. One ping is equivalent to approximately 6,7 m

Aggregation parameters Length (pings) Height (m) Area Perimeter Fractal dimension Nearest-neighbour distance Acoustic intensity Horizontal acoustic roughness'" Vertical acoustic roughness'" Skewness Number of aggregations

Fish targets (bottom)

Zooplankton/fish targets (offshore)

Fish targets (midwater)

Mean

SE

Mean

SE

Mean

SE

8,34 4,74 20,92 18,57 0,91 10,40 2099,04 6,00 5,54 1,84

0,34 0,15 1,32 0,98 O,QI 0,73 317,31 0,08 0,09 0,07

11,35 6,39 35,07 26,03 0,99 9,15 2625,01 5,35 5,69 1,46

0,69 0,32 2,88 1,66 O,QI 0,37 1 173,34 0,14 0,Q7 0,09

11,96 6,08 38,06 29,48 0,98 11,65 1547,18 6,52 6,24 2,62

0,89 0,25 3,45 2,39 0,02 0,62 78,86 0,Q7 0,09 0,10

284

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168

184

1994

341


Table IV: Variable weightings on first, second and third principal component axes (1, 2 and 3) for peA of patches along Transects N03 and N10. All variables were logarithmically transformed, except for fractal dimension and nearestneighbour angle. Matrix rotated using the Varimax method Transect N03 components

Transect N10 components

Variables


I Length Height Area Perimeter Fractal dimension Nearest-neighbour angle Nearest-neighbour distance Average acoustic intensity Maximum acoustic intensity Intensity variance Horizontal acoustic roughness Vertical acoustic roughness Skewness Kurtosis Variance explained

2

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2 000 (Fig. 8). These targets roughly encompassed the shelf-break (55 m) and even over the shelf (41 m). , Values of nearest-neighbour distance and acoustic zooplankton layer and pelagic fish aggregations shown in Figure 6. The remaining targets, which were intensity indicate that horse-mackerel patches on the assumed to consist of horse mackerel on the basis of shelf are denser, but farther apart than at the shelftrawled samples from the outer shelf, were divided break, suggesting behavioural differences between between upper (B) and lower (C) layers of the water these two populations of horse mackerel. Differences column. It should be noted that the aggregations were in acoustic roughness, which is a measure of the disclassified to compare values of some aggregation parpersion pattern of organisms within patches (Nero et ameters between target groups. Whereas certain assumpal. 1990), provide further evidence of internal organizations regarding species homogeneity between and tional differences between the two populations. Although within patches have to be made, oversimplification in not normalized by intensity, the differences in acoustic the allocation of aggregations would reduce the effecroughness may indicate that fish over the shelf have more internal patchiness than those at the shelf-break. tiveness of variable comparisons, reducing the usefulness of the technique as a predictive tool. Patch statistics for Transect NIO are difficult to Averages of the statistical variables generated for interpret. A very high acoustic intensity in the zoothe six target groups on Transects N03 and N 10 are plankton/fish offshore patches (Group A) suggests that presented in Tables II and III respectively. Acoustic this layer is not homogeneous, probably as a result of intensity, which is associated with the density of scatthe presence of fish schools within the zooplankton terers, shows clear patterns in the target groups of patches. A threshold factor increase from 250 to 500 Transect N03. Zooplankton aggregations averaged units predicts a decrease of up to 92% in the number one-half of the intensity of that estimated for horse of Group A patches (data not shown). This result indimackerel at the shelf-break, whereas the latter avercates that only a few patches in that group have very aged less than one-half of the intensity of their shelfhigh acoustic intensities, which increases the overall dwelling counterparts. Although comparisons between mean intensity of the group and suggests the presence the back-scattering intensity from zooplankton and of fish schools within the zooplankton layer. Patch fish may be inconclusive, because of large, unknown statistics of Groups B (midwater) and C (bottom) in differences in target strength, discrepancies between Figure 8 are comparable, indicating the similarity in the two fish layers probably reflect differences in their their composition, despite other notable differences. schooling density and behaviour. Despite some ap- Although similar in length (approximately 85 m), parently large variability, the length, height, perimeter midwater schools have lower average acoustic intenand area of the patches suggest that zooplankton patches sity (lower density) than bottom schools, perhaps indiare relatively long (71 m) and thin (5 m), and that they cating differences in their packing densities. This are slightly larger than horse-mackerel patches at the would suggest that schools of horse mackerel tend to

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343

Barange: Acoustically 1dentified Biological Patchiness on Agulhas Bank

1994

Table V: Classification predictions according to the discriminant functions generated by Discriminant Analysis on data from Transects N03 and N10 Predicted Observed

Horse mackerel (shelf-break)

Horse mackerel (shelf)

95,6% 3,4% 11,8%

8,5% 64,4% 26,5%

13,9% 32,2% 61,8%

Zooplankton! fish (offshore)

Bottom fish (shelf)

Midwater fish (shelf)

10,8% 77,5% 22,2%

31,6% 8,5% 53,9%

Zooplankton Transect N03

Zooplankton Horse mackerel (shelf-break) Horse mackerel (shelf)


Transect NIO Zooplankton/fish (offshore) Bottom fish (shelf) Midwater fish (shelf)

57,5% 13,9% 23,8%

disperse more in the thermocline region and group closer together near the bottom. An increase in the threshold factor from 250 to 500 scarcely changed the average intensity of the bottom aggregations of horse mackerel (from 2 625 to 2 536), but it substantially increased the intensity of midwater schools (from 1 547 to 3 371), suggesting different packing densities between midwater and bottom schools. Patch classification and statistical anlysis In order to establish which variables best describe differences among patches, Principal Component Analysis (PCA) was applied to the data for both transects. The eigenvalues of the original variables for the fIrst three components are presented in Table IV, after the exclusion of highly correlated variables. The first component in Transect N03 was highly correlated to the variables nearest-neighbour distance and acoustic roughness, indicating the importance of the spatial distribution between and within aggregations as classifIcation indices. The second component mainly reflected those variables related to aggregation size and shape, such as area and fractal dimension. The third component reflected a combination of factors, of which acoustic intensity was considered the most important for describing the density of the aggregations, on the proviso that target-strength data were not used to convert echo intensity to density. The fIrst component in Transect NIO was highly correlated with acoustic roughness and average acoustic intensity, crudely separating aggregations according to their echo intensity and distribution within the aggregations. The second component was correlated to area and fractal dimension, and, as in Transect N03, the size and the shape of

the aggregation. The third component was a combination of factors, most importantly the nearest-neighbour angle and distance, indicating the two-dimensional spatial distribution between aggregations. The first two components explained approximately 88% of the variance in both transects. The distribution of the aggregations on Transect N03, classifIed according to the fITst two components of the PCA, showed interesting patterns (Fig. 9). The first component separated the aggregations classifIed as Group A (zooplankton) from those classifIed as Groups B and C (fIsh). As the fIrst component was correlated (Table IV) with the dispersion pattern of organisms within (acoustic roughness) and between aggregations (nearest-neighbour distance), the indication is that aggregations of zooplankton and horse mackerel have notably different organizations, zooplankton patches having less internal patch variability and being closer together than horse-mackerel patches. This result shows that relevant biological information in aggregation structure can be measured acoustically. It is apparent from Figure 9 that fIsh aggregations are more variable along the X-axis and zooplankton aggregations more variable along the Y-axis. From this, it seems that the internal structure and packing density of fish schools may be more variable than in zooplankton patches, but zooplankton aggregations vary more in size and shape than fIsh schools. The large number of patches recorded along Transect NlO (636) complicated visual interpretation of the data. Therefore, for clarity, a threshold of 500 units was used instead of 250 units. This doubling of threshold units reduced the number of patches to 304, and their position relative to the first two components of the PCA are shown in Figure 10. The zooplankton/fish aggregations did not appear as a distinct group, which

344

1994

South African Journal of Marine Science 14 ,""'VV' I

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is not surprising owing to their mixed nature, but the horse-mackerel group near the bottom was well separated from the mid water group. As the first component was related to acoustic roughness (Table IV), aggregations in the mid water group appeared to have more internal patchiness than those near the bottom. However, this result may be a consequence also of the influence of target depth on acoustic roughness, as a result of an increase of insonifIed targets with depth and overlapping between pings. The second component did not provide any additional information, suggesting that there were no marked differences in size and shape between horse-mackerel aggregations near the bottom and those in midwater (see Table ill). In order to analyse the consistency of the target groups described above, a Discriminant Analysis was applied to the same data used in the peA analysis. The classification predictions are presented in Table V. For Transect N03, almost all the zooplankton aggregations were predicted as being part of a single group, indicating that the structure of these aggregations could be quantitatively linked to the type of scatterer. For horse-mackerel targets, approximately 60% were

classified correctly, both for the shelf and shelf-break subgroups, and only between 4 and 12% were wrongly classified as zooplankton aggregations. Owing to the zooplankton/fish mix on Transect NlO, predictive patterns were less clear. Nevertheless, the results were promising, with successes ranging from 54 to 78%, demonstrating that fairly subjectively defined groups can form consistent, homogeneous units. Success rates in the classification of acoustically recorded fish schools vary between 60 and 100% (Rose and Leggett 1988, Richards et ai. 1991, Weill et ai. 1993). However, classification successes have been shown to be strongly temporally and spatially dependent and therefore should be regarded as guidelines only (Richards et al. 1991).

Influence of frontal boundaries on patchiness intensity An important objective of this study was to obtain an index of patchiness to compare temporal and spatial differences between water masses quantitatively,


1994


particularly in relation to the strength of frontal systems. The fIrst 4 000 echo returns and continuous SST records, collected along four transects, were analysed to compare the horizontal distribution and frequency of aggregations across the shelf-break, under different development of the thermal front. As patchiness along these transects can be influenced by a number of factors, for instance temporal variability in species distribution, the analysis was qualitative. Therefore, no rigorous statistical analyses were applied. The cumulative number of aggregations along the first 4000 pings of Transects NOI, N03, N06 and NlO relative to SST (see Fig. 3) are shown in Figure 11. Transects N01 and N03 were characterized by strong thermal gradients with temperature decreases of up to 4 DC in less than 2 miles (approximately 700 pings). Patchiness was greatest at the outer edge and within the front (up to 90% of the number of aggregations occurring in the fIrst 2 000 pings - see also Fig. 7). In contrast, Transects N06 and N 10 were characterized by weak thermal boundaries with patches occurring throughout the transects. Only 54 and 44% of the aggregations were recorded in the fIrst 2 000 pings of Transects N06 and NlO respectively (see also Fig. 8). Whether these observations indicate a causal relationship between frontal structure and patch formation or an interaction of common controlling factors is uncertain. Nevertheless, because continuous indices of patchiness can be measured and compared to hydrographic variables, biological variability can be apportioned to environmental and spatial components, offering new perspectives in the study of patchiness.

DISCUSSION The Agulhas Current is the main forcing mechanism of water structure off the south-east coast of South Africa. The core of the Current follows the edge of the continental shelf (200 m), close inshore at the easternmost region of the study area, but farther from the coast as the shelf widens towards the south to form the Agulhas Bank (Schumann and Van Heerden 1988). As sampling commenced at 500 m, the acoustic coverage of the frontal zone decreased towards the western part of the study area. This fact may be partially responsible for the differences in patchiness along the frontal zone. The inshore boundary of the Agulhas Current fluctuates south of Algoa Bay, sometimes generating eddies and plumes which cross the shelf-edge and disperse over the shelf (Goschen and Schumann 1988, Lutjeharms et al. 1989). These shoreward intrusions of Agulhas Current water and associated fronts would presumably transport biological patches onto the shelf.

345

It is postulated that the strong degree of physical forcing and cross-shelf activity along the frontal zone affects the intensity of patchiness. The mechanisms through which cross-shelf flow and shear friction affect aggregation of organisms as diverse as zooplankton and fIsh are probably species-dependent (Mackas et al. 1985, Smith et al. 1989) and scale-dependent (Greenlaw and Pearcy 1985), but they are currently obscure. This study has attempted to identify the causal or circumstantial agreement between the position and the intensity of the physical borders and the intensity of biological scattering. Similar observations have been described qualitatively (Aoki and Inagaki 1992) and quantitatively (Nash et at. 1989, Nero et al. 1990) for other frontal areas. Biomass in the ocean is distributed in a discontinuous manner, especially at frontal zones (Owen 1981, Haury and Pieper 1988, Nero et al. 1990). However, fInesc'ale distributions of organisms has not been described in much detail because of methodological problems (e.g. the inability of nets to resolve small-scale biological structures). The methodology described in the present study offers new perspectives in the use of acoustic techniques to resolve biological oceanographic problems. The results. show that acoustic data from patches contain certain quantitative biological information which was not previously obtained by traditional methodologies (Nero et ai. 1990), or could not be identifIed from echo-charts. High-resolution high-frequency acoustic data have been employed in the past to characterize patchiness in the ocean (Nash et al. 1989, Nero and Magnuson 1989, Nero et ai. 1990, Weill et at. 1993) and to identify fIsh-school echoes to species (Rose and Leggett 1988, Richards et al. 1991, Scalabrin and Masse 1993). These studies showed the value of patch-fInding algorithms for quantifying patchiness. By making use of every echo-return, rather than averaging them to reduce fluctuations in the reflected sound, the present method maintained a fIner structure than was used in other studies. Also, by means of an interactively defmed continuity factor, the scale of the study can be specifIed by defIning the minimum size of the patches. However, these techniques should be viewed as parallel efforts for extracting higher quality hydroacoustic information than was previously available. Further developments of the present system could include a second continuity factor, which would permit specifIcation of interactively defIned minimum distances between patches. Both continuity factors could be applied for hierarchical studies of patchiness (Ulanowicz and Platt 1985), leading towards a better understanding of aggregation processes. Additionally, algorithms could be applied to correct for artifacts introduced by the pulse length and the beam width in the horizontal


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and vertical dimensions of the aggregations (Barange et al. 1993, Reid and Simmonds 1993). Using conventional nets, Wiebe (1970) provided one of the first direct estimates of zooplankton patch size (mainly copepods, euphausiids and chaetognaths). He estimated a median patch length of between 23 and 27 m during daylight and between 66 and 110 m during the night, values comparable to the present estimates of between 60 and 80 m for night-time zooplankton aggregations (Tables III and IV). Based on the observation that patch structure was similar between different zooplankton species, Wiebe (1970) concluded that aggregations were physically induced. Similar patch-size dimensions to those estimated herein were reported by Sameoto (1983), who estimated euphausiid patches to be between 25 and 150 m long. Pelagic fish shoals are generally tens of metres in length (Squire 1978, Scalabrin and Masse 1993), similar to the present estimates of between 40 and 90m for horse-mackerel aggregations. However, results from one of the most extensive data bases for a single aggregating species, the euphausiid Euphausia superba, suggest that their patch dimensions are variable and depend on a number of factors (Miller and Hampton 1989). In the present analysis, the data were not corrected for biases resulting from variability of beam width and depth. If such corrections were to be applied, a patch estimated to be 10 m long at a depth of 20 m and surveyed at 10 knots, would effectively be reduced to a length of 6,5 m. Present patch statistics should therefore be used with caution. The PCA results identified acoustic roughness as the factor which provided most information on the internal structure of patches. Nero et al. (1990) suggested that internal structure was the factor that best separated patches. The addition of nearest-neighbour distance as a major discriminant between patches expands on this concept by indentifying the distribution of the patches, i.e. patchiness within patches as a major factor characterizing patches. However, the influence of the differences in the vertical distribution of targets on some of the statistical parameters requires further study. In conclusion, the algorithms developed herein provide a rapid and high-resolution view of the distribution of fish and zooplankton aggregations, providing the ability to resolve fine-scale patterns in complex· and dynamic areas. The statistical variables generated may also prove useful for the eventual acoustic identification and classification of biological aggregations, providing a tool for testing hypotheses concerning the generation and maintenance of patchiness in the ocean. Scrutiny of echo-charts as a means of identifying target types has been a method used routinely by both fishermen and scientists. However, present results demon-

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strate the usefulness of descriptors of patchiness, such as the acoustic roughness, information which cannot be obtained from echo-charts. The use of such an objective and semi-automated technique could prove beneficial for future studies on patch recognition and classification.

ACKNOWLEDGEMENTS I thank my colleagues Dr A. J. Boyd and Mr G. P. J. Oberholster for providing the ADCP data and Mr 1. Hampton for his continuous guidance during the study. I am also grateful to Drs Boyd and S. C. Pillar, Mr Hampton and an anonymous reviewer for their comments on an early draft of the manuscript. The officers and crew of the F.R.S. Africana are thanked for their cooperation during the cruise.

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Sci. 77(5): 231-236. LUTJEHARMS, 1. R. E., CATZEL, R. and H. R. VALENTINE 1989 - Eddies and other boundary phenomena of the Agulhas Current. Continent. Shelf Res. 9(7): 597-616. MACKAS, D. L., DENMAN, K. L. and M. R. ABBOTT 1985Plankton patchiness: biology in the physical vernacular. Bull. mar. Sci. 37(2): 652-674. MILLER, D. G. M. and 1. HAMPTON 1989 - Biology and ecology of the Antarctic krill (Euphausia superba Dana): a review. BIOMASS scient .. Ser. 9: 166 pp. NASH, R. D. M., MAGNUSON, 1. J., STANTON, T. K. and C. S. CLAY 1989 - Distribution of peaks of 70 kHz acoustic scattering in relation to depth and temperature during day and night at the edge of the Gulf Stream - EchoFront 83. Deep-Sea Res. 36(4A): 587 -596. NERO, R. W. and J. J. MAGNUSON 1989 - Characterization of patches along transects using high-resolution 70-kHz integrated acoustic data. Can. J. Fish. aquat. Sci. 46(12): 2056-2064. NERO, R. w., MAGNUSON, J. J., BRANDT, S. B., STANTON, T. K. and J. M. JECH 1990 - Finescale biological patchiness of 70 kHz acoustic scattering at the edge of the Gulf Stream -EchoFront 85. Deep-Sea Res. 37(6A): 999-1016. OLSON, D. B. and R. H. BACKUS 1985 - The concentrating of organisms at fronts: a cold-water fish and a warm