Aquaculture 315 (2011) 69–77

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Aquaculture

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Application of the Self-Organizing Map to the study of skeletal anomalies inaquaculture: The case of dusky grouper (Epinephelus marginatus Lowe, 1834)juveniles reared under different rearing conditions

T. Russo ⁎, M. Scardi, C. Boglione, S. CataudellaExperimental Ecology and Aquaculture Laboratory — Department of Biology, University of Rome ‘Tor Vergata’, Via della Ricerca Scientifica, 00133 Rome (RM), Italy

⁎ Corresponding author. Tel.: +39 06 72595968; fax:E-mail address: tommaso.russo@uniroma2.it (T. Rus

0044-8486/$ – see front matter © 2010 Elsevier B.V. Aldoi:10.1016/j.aquaculture.2010.11.030

a b s t r a c t

a r t i c l e i n f o

Article history:Received 30 October 2009Received in revised form 1 November 2010Accepted 19 November 2010Available online 26 November 2010

Keywords:QualitySelf-Organizing MapSkeletal anomaliesLarge VolumeGreen WaterDusky grouper

The setting up of effective rearing protocols for domestication of new candidate species for aquaculture and/orto enhance quality in widely reared species requires the availability of appropriate tools to detect patterns ofcovariation among rearing parameters and fish quality. In this framework, the pattern of occurrence ofskeletal anomalies (SAs) in reared lots represents a proxy for quality, since the presence of SAs is associatedwith both a general lowering of performance and a negative image of aquaculture products in consumers. Inthis paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with the analysis ofcorrelations between the pattern of SAs presence and rearing parameters in dusky grouper (Epinephelusmarginatus, Lowe 1834) lots following two different experimental rearing approaches. SOMs were testedbecause the classic multivariate approach failed to producemeaningful results in the same dataset. A SOMwastrained on a dataset containing the mean frequencies of 43 SAs occurring in 20 lots of dusky grouper sampledduring three larval rearing cycles carried out in 2001, 2002 and 2004 in Italy, using two rearing approach:Green Water and Large Volume. A series of well-defined patterns were detected in SAs occurrence withrespect to body regions. When SOM units were grouped into three clusters, a significant relationship wasdetected between lot origin (in terms of rearing approach) and SAs occurrence: The Large Volumemethodology is to be considered more effective in enhancing the quality of dusky grouper larvae. This findingwas independently validated by the superimposition of Hellinger distances obtained from the analysis ofmeristic counts. Finally, SOM visualized coherent and clear patterns of covariation between SAs occurrenceand two crucial aspects or rearing: initial rearing density and final survival rate. We concluded that as a newordination method SOMs afford effective representations of information gathered from patterns of SAsoccurrence in aquaculture lots. Furthermore, SOM appears able to detect subtle but meaningful relationshipsbetween quality, as measured by independent descriptors such as SAs and meristic counts, and rearingparameters. It could be useful for quality assessment in both experimental and productive contexts, ultimatelyhelping to reduce the incidence of SAs in aquaculture products and facilitating the identification of moreeffective approaches to the domestication of new species.

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1. Introduction

Enhancement of quality of aquaculture products and successfuldomestication of new candidate species for aquaculture are likely tobe the result of long-term efforts involving both basic research andobservations gathered from aquaculture. When seeking the mostappropriate rearing methodology by following different approaches,reliable tools are necessary to evaluate the differences among them.

Morphological criteria have been extensively adopted to assess theeffects on fish of experimental diets, rearing protocols, and so on,insofar as they refer to the chronology and conformity of development

(Boglione et al., 1995). Among other morphological criteria, skeletalanomalies (SAs) are considered good descriptors of quality, since itspresence directly affects fish performance (i.e., swimming ability,conversion index, growth and survival rates, and susceptibility tostress, pathogens, bacteria) and external appearance, leading to asubstantial decline in marketing image and therefore in thecommercial value of the reared fish (Hilomen-Garcia, 1997; Boglioneet al., 2001, 2003, 2009; Cahu et al., 2003; Matsuoka, 2003; Lall andLewis-McCrea, 2007; Le Vay et al., 2007; Castro et al., 2008; Başaranet al., 2009; Lijalad and Powell, 2009). SAs are variations in shape(malformations) and/or in the number of skeletal elements as resultsof genetic factors and/or incapacity of homeostatic mechanisms tobuffer stress through development. Objective constraints and incom-plete scientific knowledge in many cases limit aquaculture to producehigh percentages of fish unaffected by morphological anomalies for

70 T. Russo et al. / Aquaculture 315 (2011) 69–77

the absence in controlled conditions of selective pressure culling outphenodeviants (like in wild fish). Multifactorial analysis in order tofind some relationship among morphological quality data of farmedfish and more than one of the rearing parameters or data (growthperformance, survival rates, densities, tank volume/colour/shape,oxygen, temperature, salinity, etc.) is practically unexploited due tothe different typologies (qualitative and quantitative) of data.Consequently, the analyses of these qualitative and quantitativeaspects of rearing conditions remain confined to separate frame-works. At present, some multivariate analyses are applied to data onskeletal anomalies, but they only provide graphical outputs (ordina-tions and histograms), which should be interpreted, and no possibilityto statistically test the observed differences is allowed. This precludesa direct visualization, abstraction and interpretation of the complexrelationships in the original data.

A new approach to pursue these aims could be based on aparticular type of artificial neural networks, known as Kohonen's Self-OrganizingMaps (SOMs). Thismethod has already proved to be usefulin pattern recognition and classification (Park and Kim, 1984;Kohonen, 1995). Furthermore, SOMs are suited in the analysis ofecological and biological data that are often non-linear, complex, andcharacterized by internal redundancy or noise (Park et al., 2003).Recently, we proposed a new approach for achieving this aim ingilthead seabream (Sparus aurata) (Russo et al., 2010) based on theuse of Self-Organizing Maps (SOMs).

In the present study,we applied SOMs to a dataset of 20 lots of duskygrouper (Epinephelus marginatus, Lowe 1834) juveniles, reared underdifferent experimental conditions. In addition, we applied SOMs to thesame subsample of 10 lots used in a previous work (Boglione et al.,2009), in order to compare the capacity of multivariate (Correspon-dence Analysis— CA) and SOM analyses (applied on the same fishes) todiscriminate among groupers reared in different conditions.

The aims of the present study were: (a) to assess whether anysignificant differences can be found in the morphological quality ofdifferently reared individuals by using a more powerful analysis (i.e.SOMs); (b) to ascertain whether any correspondence exists betweenSAs occurrence and meristic counts (MC) variability, which representtwo different descriptors of development in captive conditions; (c) toassess which is the best rearing approach for dusky grouper, betweenthose tested; and d) to explore the advantages of SOM application in

Table 1Characteristics of the 20 lots of dusky grouper used for the present study. a marks lots in th

Name Number ofspecimens

Hatchery Rearing approach used Water(°C)/sa

GW-01 41 Maricoltura Rosignano Solvay Green Water 23–25.

GW-02 50 Maricoltura Rosignano Solvay Green Water 23–25.GW-03a 5 Maricoltura Rosignano Solvay Green Water 23–25.

GW-04a 6 Maricoltura Rosignano Solvay Green Water 23–25.GW-05a 18 Maricoltura Rosignano Solvay Green Water 23–25.GW-06 20 Maricoltura Rosignano Solvay Green Water 23–25.GW-07 5 Maricoltura Rosignano Solvay Green Water 23–25.GW-08 5 Maricoltura Rosignano Solvay Green Water 23–25.GW-09a 48 Maricoltura Rosignano Solvay Green Water 23–25.GW-10 48 Maricoltura Rosignano Solvay Green Water 23–25.GW-11a 5 Maricoltura Rosignano Solvay Green Water 23–25.GW-12 5 Maricoltura Rosignano Solvay Green Water 23–25.GW-13a 5 Maricoltura Rosignano Solvay Green Water 23–25.GW-14 5 Maricoltura Rosignano Solvay Green Water 23–25.GW-15a 98 Maricoltura Rosignano Solvay Green Water 23–25.GW-16a 149 Maricoltura Rosignano Solvay Green Water 23–25.GW-17a 150 Maricoltura Rosignano Solvay Green Water 23–25.LV-01 47 SMEG Farm Large Volumes 25/35LV-02 53 SMEG Farm Large Volumes 25/35LV-03a 122 SMEG Farm Large Volumes 25/35Total 885

comparison to a classic multivariate approach such as Correspon-dence Analysis.

2. Materials and methods

2.1. Sample origin and data collection

The analyses were carried out on 885 juveniles of dusky grouper,belonging to 20 lots (Table 1). 10 of these lots correspond to thosesubmitted to CA in Boglione et al. (2009), and the other 10 werecollected in two parallel experiments carried out in 2001 and 2004.

In the following sections, the different lot sources have beenabbreviated as: LV (Large Volume rearing sensu Cataudella et al.(2002)) and GW (Green Water rearing, sensu Shields (2001)).Detailed information on rearing protocols applied are reported inBoglione et al. (2009) and Russo et al. (2009), while a full descriptionof protocol of SAs identification and count, as well as MCs survey, isreported in Boglione et al. (2009). However, the complete list ofdescribed SAs and their relative codes is reported in Table 2.

2.2. Self-Organizing Map

A SOM is an adaptive unsupervised learning algorithm, that is asequence of instructions that classifies data without human direc-tion (Kohonen, 1989, 2001). The SOM algorithm was first applied tothe dataset containing only the data on the mean SAs frequencies foreach lot. In this way, a matrix was computed which contained, foreach row, the mean value of a given SAs observed in one of theexperimental lot. This means that it was computed by dividing thenumber of individuals in which the given SAs was present by thetotal number of individuals in the lot. Considering that the numberof observed SAs types was 29, the size of this matrix was 20×29, andthe information contained could be imagined as a cloud of 20 pointsin a space of 29 dimensions. The SOM was trained to display thishigh-dimensional dataset in a 2-dimensional space by projecting theoriginal information onto a lattice of hexagons. Kohonen's SOMconsists of two layers: the input, connected to each vector of thedataset, and the output, represented by a two-dimensional networkof neurons (i.e. the units of the map). Each unit of the map, orhexagon, contains a vector of weights, one for each original input

e original dataset used in Boglione et al. (2009).

temperaturelinity

Age (days posthatching)

Total lengthrange (cm)

Initial rearing density(larvae×liter−1)

Final survivalrate (%)

5/38 117 3.9–6.4 30 0.04

5/38 74 2.8–6 30 0.125/38 30 1.1–1.3 30 7.9

5/38 40 1.1–2.2 8.5 7.95/38 50 1.7–2.7 8.5 7.95/38 54 1.8–3.1 21 4.65/38 68 3–4 21 4.65/38 75 3–4 21 4.65/38 78 3.4–7.2 8.5 7.95/38 82 3–4 21 4.65/38 92 6–7.5 8.5 7.95/38 96 6.2–7 21 4.65/38 106 6.9–7.3 8.5 7.95/38 110 1.9–3.4 21 4.65/38 60 1.7–3.4 8 0.25/38 60 1.8–3.5 28 0.15/38 60 4.6–7.8 16 1.1

50 1.7–3.7 7 9.046 2.6–6.7 7 17.570 3–4 7 17.5

Table 2List of the considered anomalies. Bold letters evidence heavy anomalies (affecting theexternal shape of larvae and juveniles).

Region A Cephalic vertebrae (carrying epipleural ribs)B Prehemal vertebrae (carrying epipleural and pleural ribs and with

open hemal arch, without hemal spine)C Hemal vertebrae (with hemal arch closed by a hemal spine)D Caudal vertebrae (with hemal and neural arches closed by

modified spines)E Pectoral finF Anal finG Caudal finH Dorsal spinesI Dorsal soft raysL Pelvic fin

Types S ScoliosisSB Saddle-back1 Lordosis2 Kyphosis3 Incomplete vertebral fusion3* Complete vertebral fusion4 Malformed vertebral body5 Malformed neural arch and/or spine5* Extra-ossification in the neural region6 Malformed hemal arch and/or spine6* Extra-ossification in the hemal region7 Deformed pleural rib7* Extra-ossification of pleural ribs8 Malformed pterygophore (deformed, absent, fused,

supernumerary)9 Malformed hypural (deformed, absent, fused, supernumerary)9* Malformed parahypural (deformed, fused, reduced)10 Malformed epural (deformed, absent, fused, supernumerary)11 Malformed ray (deformed, absent, fused, supernumerary)12 Swim-bladder anomaly13 Presence of calculi in the terminal tract of the urinary ducts14 Prognatism of dental15 Reduced dental16 Dislocation of glossohyal17sx Deformed or reduced left opercle17dx Deformed or reduced right opercle17*sx Deformed or reduced left branchiostegal ray17*dx Deformed or reduced right branchiostegal ray18 Malformed predorsal bones19 Malformed pre-maxillary and/or maxillary

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variable (in this case, the 29 SAs characterizing the dataset). In thisway, the SOM output can be described as a matrix structurallyidentical to the initial dataset but with a different number of rows,since the number of rows in the output corresponds to the number ofunits of the SOM. At its first step, the SOM algorithm proceeds bygenerating a virtual unit (VU — the elements of the output layer) foreach hexagon of the map: the weights of each SOM unit are initiallygenerated as small random numbers. Then, a value of distance wascomputed between each row of the initial (input) dataset and all therow of the output dataset, that represent the SOM units. The unitcharacterized from the smallest value of distance is selected andcalled the best matching unit (BMU). The third step is calledlearning, since the value of the columns of the BMU and itsneighbouring are updated by the SOM learning rule. In this way,the VUs are computed in order to put the sample units (SU — thepattern of SAs occurrence of each specimen which constitutes theinput layer) on the map and preserve the neighbourhood, so that asimilar SAs patterns map closes together on the grid. More in detail,the learning procedure is an iterative sequence of steps repeated fora fixed number of epochs or iterations, measured by the timeparameter t. The procedure could be summed up in 5 steps: 1) t=0,when the VUk are initialized with random samples drawn from theinput dataset; 2) a sample unit SUj is randomly chosen as an inputunit; 3) the distance between SUj and each VUs is computed usingsome distance measurements (in this case, the Euclidean one); 4)

the VUc closest to the input SU is chosen as the best matching unit(BMU); 5) the VUjs are updated by applying the rule:

wikðt + 1Þ = wikðtÞ + hckðtÞ½xijðtÞ−wikðtÞ�

where w is the weight of the VU (in this case w is the vector of SAfrequencies) and h is the neighbourhood function; and 6) t= t+1 andsteps from 2 to 6 are repeated until t= tmax. The neighbourhoodfunction defines the extension of the VU range that was updated instep 5 and, in this study, was chosen to be Gaussian. Moreover,neighbourhood shrinking and learning rate decay were chosen to beexponential. The Euclidean distance was chosen as measure. The timeparameter t was used to stop the learning procedure when t=3000.Usually, this value is calibrated by observing the distance between SUj

and each VUs: this distance initially decreases during the learningprocedure but becomes stable after a certain number of epochs (i.e.,when the learning process is complete and the algorithm can bestopped). However, the training is usually done in two phases: first,rough training for the purpose of ordering, using a large neighbour-hood radius, and then a fine tuning with a small radius. The latter isdone to refine the SOM pattern along the border of the map. The fine-tuning epochs were set to 1000.

The size of the map, that is the number of output units (hexagons),was set to 12 (4×3) at the end of a calibration procedure: maps ofdifferent sizeswere trainedand, for eachone, topological and topographicerrors were computed as described in Park et al. (2003). Then, the mapsize corresponding to the lowest values of both these errormeasureswasselected (Park et al., 2003). Topological and topographic errors are thetwo criteria usually used to evaluate the quality of the trained SOMs andto identify the optimal map in terms of size for a given input data.However, it is important to stress that the size of themapdid not alter theresults, but simply affected the level of detail desired in the analysis.

At the end of the training procedure, the pattern represented bythe SOM units was explored in different ways. On the trained SOMmap, it is difficult to distinguish subsets because there are still noboundaries between possible clusters. Consequently, it is necessary tosubdivide the map into different groups of units according to thesimilarity of the weight vectors of the neurons. Thus, a hierarchicalcluster analysis using the Ward linkage method based on Euclideandistance was implemented to detect clusters in the SOM outputneurons depending on the similarity of the weight vectors of theneurons (Park et al., 2003). A χ2 test was used to assess whether thedistribution of sample lots belonging to the two different rearingapproaches (GW or LV) was statistically linked to the clustersidentified inside the SOMs. The null hypothesis was that the membersof each class randomly segregated into clusters.

It is also important to compute the distances between the units ofthe map: this is useful for the purpose of understanding the degree ofsimilitude among the different patterns evidenced by the SOMs. Thedistances between SOM units were thus computed in order to assessthe degree of similitude between different patterns of SA occurrence.

Further, a series of external descriptors (not used during trainingprocedure) was superimposed onto the trained SOMs using the dataavailable for each of the 20 lots: initial density within the rearing tank,final survival rate, mean size at the end of rearing, typology of rearingapproach, and distances from the pattern of meristic counts of the wildreference. To compute the latter descriptors, the following procedurewas used. Range and median value were computed through the wholematrix for each meristic character. This way, a number of m discreteclasses were defined for each meristic character (MC). The value of thefrequency of individuals was then calculated for each discrete class ofeachMC, and for each lot. The vectors of frequencyobtained at theendofthis procedure were preliminarily compared using Kolmogorov–Smirnov's test to assess the homogeneity of the empirical distributionof MCs. Furthermore, the Hellinger distance (Pollard, 2002) wascomputed between each lot from captive meristic counts and the wild

72 T. Russo et al. / Aquaculture 315 (2011) 69–77

ones, as obtained from the literature (Relini et al., 1999). The five valuesof distance obtained for each lot were assembled in a single measuredefined as the sumof these sevenHellinger distances. Thismeasurewasused to represent thedissimilarity from thewild condition as defined bythe variability ofMCs. Finally, the overall Hellinger distancewas plottedas an external variable on the trained SOMs.

2.3. Visualization of output

The complete sequence of steps involved in data collection, SOMtraining and output visualization is represented in Fig. 1. The output ofthe SOM training procedure was represented in different ways: 1)histograms encapsulated in each hexagon were used to represent thepattern of occurrence of the 16 most frequent SAs (frequency greaterthan 0.1) ; 2) differential occurrences were represented with respect tothe nine body regions used to classify SAs by computing the probabilityvalue of anomaly occurrence for each region and then visualizing it as animage of dusky grouper shape in which the different regions were

Fig. 1. Principle diagram of the procedure applied to model and visuali

identified by boundaries and filled in grey, the darkness of which wasproportionate to the probability of anomaly occurrence; 3) the result ofthe clustering procedure was represented by redrawing SOMs withthick borders to visualize clusters. In addition, themeanpattern for eachcluster was computed and represented; 4) pie charts were used torepresent the relative proportion of lots (from GW or LV) assigned toeach SOM unit; 5) the distances between adjacent neurons of the mapwere visualized by redrawing the trained SOMs in which the thicknessof the hexagon borders was proportional to the distance between twoneighbouringhexagons; and6) external descriptors (as reported above)were represented in grey scale by computing the mean value in eachneuron of the trained SOMs. If the output neuron was not occupiedby input vectors, the value was replaced with the mean value ofneighbouring neurons. These mean values assigned on the SOM mapwere visualized in a grey scale.

Two different SOM trainings were performed: one on all theavailable data (20 lots×29 SAs) and another one only on the 10 lotsanalysed by CA in Boglione et al. (2009).

zed the information stored in the original datasets by using SOMs.

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3. Results

3.1. Analysis of the dataset

The final matrix of overall data contained 20 lots and 29 SAs. Thetrained SOMs had a quantization error of 0.2 and a topographic error of0.01, so that the SOMwas smoothly trained in topology for the selectedsize (4×3). Fig. 2 shows the pattern of SA occurrence for the theoreticalcondition represented by each SOM unit. In the following description,the units of the trained SOM are identified by progressive numbers(from 1 to 12) according to their position along each row and column,

Fig. 2. SOMs in which histograms represent the pattern of presence for the 16most commonthe nine body regions used to classify SA (darkness is proportional to the probability of anomnot been considered into analysis. The hexagon number is shown, in each unit, under the h

starting from the first unit located in the top left corner of the map. Itseems that the absolute level of SAs progressively increases proceedingfrom the bottom left (units 7 and 10), in which only a few SAs showpositive, although low, values of frequency, to the top-right corner(units 3, 5 and 6), inwhich almost all the SAs are representedbypositiveand relatively high values of frequency. More in detail, no values largerthan 0.2 can be found in hexagons 7 and 10, whereas several SAs exceedthis threshold in the remaining hexagons. Moreover, the majority ofvalues on the right side of the map are larger than 0.3.

When the SAs were grouped with respect to the anatomical regionof occurrence, the detailed pattern of each unit revealed that SAs in

SAs observed, while images represent the differential occurrences of SAs with respect toaly occurrence). The region of the pectoral fin is drawn for illustrative purpose but it hasistogram.

Fig. 4. Trained SOMs in which pies were used to represent the total number of lotsassigned to each unit, and the colour of the used rearing approach.

74 T. Russo et al. / Aquaculture 315 (2011) 69–77

the cephalic regionwere present in some units located on the bottom-right side of the map (hexagons 8, 9 and 12, Fig. 2). Anomalies in theprehemal region and dorsal spines are always associated (with theonly exception of hexagons 1 and 12) with higher values in the centralunits (dark grey areas in Fig. 2). The highest frequencies (greater than0.6) can be observed for the prehemal region (B5 in Fig. 2) in the 4top-right units of the map.

In parallel, SAs occurring in the caudal pedunclemainly characterizethe units in the central hexagons of themap (units 2, 3, 5, 6, 8, 9 and 11).Finally, SAson thecaudalfinmainly appear in theunits located along theright side.

Three clusters were formed according to Ward's linkage method(Fig. 3). The representation of the mean different occurrences of SAswith respect to the nine body regions shows that Cluster I represents acondition characterized by the substantial absence of SAs: the meanpattern for this grouping evidences a low (frequency lower than 0.2)or null frequencies for all SAs, except for those affecting the dorsalspines. In contrast, Cluster II contains units characterized by thepresence of SAsmainly occurring in the cephalic, prehemal and caudalregions, particularly in the caudal peduncle. Finally, Cluster III mergesfour units for which the mean profile evidences the presence of SAspredominantly occurring in the prehemal (with a quite highfrequency, anomaly B5), hemal (anomalies C5 and C6) and caudalvertebrae (anomalies D), in the dorsal spines (H8) and caudal fin(anomalies G).

The assignment of each sample lot to the two used rearingapproaches (Fig. 4) evidences that the lots from LV segregate in themiddle-left area of the SOM map (hexagons 7 and 8), whereas thosefrom GW are scattered over the remaining units. The number of lotsassigned to each unit varies from zero to five: the presence of emptyunits suggests that some patterns predicted by SOMs were notexperimentally observed.

Comparing these results (Fig. 4) with those obtained by theclustering procedure (Fig. 3), it seems that LV lots correspondexclusively to Cluster I, while GW lots are associated with all thethree clusters. These observations were confirmed by the χ2 test

Fig. 3. Visualization of the trained SOMs in which Latin numbers (I–III), grey scalecolouration and boundaries display clusters detected by the Ward's Algorithm. Duskygrouper images represent the different occurrences of SAswith respect to the nine bodyregions used to classify SAs (darkness is proportional to the probability of anomalyoccurrence), for each cluster.

computed on the distributions of sample lots from the two rearingconditions: the test rejected the null hypothesis for LV lots(p=0.049), which was found to be significantly associated withCluster I.

Fig. 5a evidences a progressive increase in the inter-hexagonEuclidean distance moving from the left to the right side, without anyspecific discontinuities or full-blown groups. In effect, the minimumdistance corresponds to the first unit of the map, whereas the largestvalues occur for units 7 and 9. This implies that units in the left area ofthe SOM map are more similar to each other than those in the rightregion, and thus that Cluster I represents a more homogeneousgrouping in terms of the units comprising it compared to the othertwo clusters.

Fig 5b shows the result of superimposing the cumulative Hellingerdistance on the trained SOMs on meristic counts. It seems that aprogressive increase in the Hellinger distance can be observed whenmoving from the bottom left corner to the top-right one. It thusfollows that the lots associated with the top area of the SOM map arethe furthest from the wild meristic counts, and that, conversely, lotsassigned to the lower area are very near to the wild pattern.

Finally, Fig. 5c and d shows the distribution gradients for twocrucial aspects related to the rearing approach: initial rearing densityand final survival rate. The rearing density was found to be linked tothe SA (Fig. 2) and MCs (Fig. 5b) gradients, and it increases from thebottom left corner to the top-right one. In particular, the highestvalues correspond to the top-right corner for the units belonging toCluster III. The trend in final survival rates (Fig. 5d) appears to be theopposite: the highest survival rate is located in the bottom-left corner(evidenced by the darker hexagons), whereas the lowest correspondsto the top-right corner, within Cluster III.

The SOM trained on the original dataset of 10 lots (Boglione et al.,2009) returned the pattern shown in Fig. 6a. It seems that, for eight ofthe 10 lots, the positions are identical to those observed in the SOMtrained on the larger dataset (Fig. 6b). The only two exceptions arerepresented by GW13 and GW16. However, these two lots simplyshifted to hexagons adjacent to those occupied in the 20-lot SOM, sothat the overall pattern is substantially respected.

Fig. 5. Trained SOMs on which a) border thickness is related to Euclidean distance between hexagons; b) cumulative Hellinger distance from wild meristic counts; c) initial rearingdensity and d) final survival rates are plotted in grey scale (darkness is proportional to the value of the plotted variable).

75T. Russo et al. / Aquaculture 315 (2011) 69–77

4. Discussion

This paper represents a methodological improvement to theunderstanding of how rearing condition can modulate SA outset in

Fig. 6. Trained SOMs on: a) 10 lots, which are the same used in Boglione et al., 2009, with thlots that change position between the two SOMs, following the paths indicated by the arrowsindicated the lots added in the new dataset and not present in that of Boglione et al., 2009

dusky grouper juveniles. In this study, we used SOMs primarily tomodel the occurrence of SAs in different lots reared using twoalternative approaches. Conceptually, the main goal of this procedurewas to reveal possible patterns concealed in the raw data. The classical

e exclusions of lots younger than 30 days post hatching; underlined label characterizedand b) 20 lots, that is the overall dataset actually available; grey labels between brackets.

76 T. Russo et al. / Aquaculture 315 (2011) 69–77

multivariate approach, reported in Boglione et al. (2009), evidencedseveral important limitations, since no particular SAs patterns (orfate) were clearly identified, as a high variability was observed inmalformation typologies and the affected regions. Moreover, nosignificant differences in the morphological quality between groupersreared using semi-intensive (LV) and Green Water (GW) methodol-ogies were observed. In contrast, the present application allowed thedetection of significant differences between GW and LV lots: the SOMevidenced, in fact, that well-defined patterns of SAs occurrence existdepending on the rearing approach used. More in detail, SOM showedthat the LV lots are characterized by the lowest SAs frequencies,whereas the GW lots display higher values. The clustering procedurefurther emphasized these differences. Cluster I, containing above allthe sample lots from LV, is associated with a high quality level (that islow frequencies of SAs) and a smaller distance from the wildcondition, as measured by means of the Hellinger distance on MCs.This is consistent with a close correspondence with the final survivalrate, which was found to be the highest for the conditions representedby units of this grouping, that is mainly constituted by LV lots.

In contrast, Clusters II and III are associated with a strong presenceof different SAs, mainly affecting the caudal peduncle (in the case ofCluster II) and prehemal region (in the case of Cluster III). Thegradient obtained by the superimposition of initial rearing densitiessuggests that the difference between these two groupings could be atleast partially based on this crucial parameter of the rearing approach.GW lots, in fact, are scattered in all the three clusters: the lowestdensity lots are located in the first one, whilst Clusters II and IIIcontain lots from GW at intermediate and highest densities,respectively. This parameter thus seems to have a crucial effect ondusky grouper larval quality, as described by both SAs patterns andHellinger distance on MCs. Consequently, the pattern obtained for thefinal survival rate indicated that intermediate and highest values ofinitial rearing densities lead to fewer and more deformed duskygrouper juveniles than the low-density-reared lots.

The additional validation of this approach by performing a SOManalysis on the same 10 lots submitted to CA in Boglione et al. (2009),evidenced differences inter and intra rearing approach, not evidencedby CA. However, despite the paucity of used lots (10 against 20), theresults obtained evidenced a pattern coherentwith that achievedwiththe larger dataset, which remains the best choice, given that it offersmuch more information. This statement of coherence between thetwo analyses is justified by the fact that almost all (8/10) the lotsmaintained the same position on the trained SOM, and that the onlytwo exceptions did not alter the overall pattern, since lots are locatedin hexagons adjacent to the corresponding position in the other map.Given that the structure of a SOM is completely determined by thetraining procedure, which associates each initial observation to ahexagon in the output layer, it can be stated that two maps with thesame size and the object located in the same position are substantiallyequivalent. In this way, we did not fully discuss the result for the10-lot SOM, but we argued that SOM was able to perform better thanCA in extracting information and identifying patterns even whenapplied on the same dataset.

Overall, these results indicate that the SOMs are a powerful tool forexploring this particular type of biological data, as they allow thedetection of meaningful patterns of covariation between differentkinds of information. The model obtained in this study revealed: 1)correspondences between the pattern of SA occurrences and the tworearing approaches experimented hitherto in dusky grouper aqua-culture; and 2) substantial agreement between the pattern of SAoccurrence and the distance from the wild MCs. It should be notedthat distances obtained from the analysis of MCs were not used inSOM training, and thus represent a completely independent point ofview. Moreover, a correspondence was detected between thegradients of some fundamental descriptors of rearing conditions(the initial density inside rearing tanks) and of the key production

parameter (the final survival rate). It should be worthwhile to stressthat with SOMs any numerically or categorically representedinformation of what else typology (biomolecular, nutritional, genet-ical, physiological, etc.) can be added as a secondary layer ofinformation in the analysis and any eventual relationship with SAs,MCs, and survival rates checked.

In conclusion, Large Volume seems to represent the morepromising approach for the domestication of this new candidatespecies for aquaculture. This result is in agreement with some recentresults obtained with this technique (Russo et al., 2009), and suggeststhat the approach presented here is reliable for evaluating the effect ofdifferent aquaculture procedures on fish quality, and therefore forassessing the quality of aquaculture products in terms of distancefrom the wild-like phenotype used as reference. This seems ofparticular interest for the image of aquaculture products.

Acknowledgements

This study was funded by a grant from the Italian Ministry ofAgricultural, Forestry and Alimentary Politics (Project “Ottimizzazionedella filiera ittica per il benessere del consumatore: ampliamento etrasferimento delle basi conoscitive dei processi e dei prodotti”). Theauthors would like to thank Dr. Giovanna Marino for providing thedusky grouper eggs for Large Volume rearing and samples from theGreen Water rearing.

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