Cage culture effects on mullets (Mugilidae) liverhistology and blood chemistry profile

R. COZ-RAKOVAC*†, I. STRUNJAK-PEROVIC*, N. TOPIC POPOVIC*,M. HACMANJEK*, T. SMUC‡, M. JADAN*, Z. LIPEJ§

AND Z. HOMENk*Ichthyopathology Group – Biological Materials, Division of Material Chemistry, RudjerBoskovic Institute, Bijenicka 54, 10002 Zagreb, Croatia, ‡Laboratory for Information

Systems, Division of Electronics, Rudjer Boskovic Institute, Bijenicka 54, 10002 Zagreb,Croatia, §Croatian Veterinary Institute, Savska 143, 10000 Zagreb, Croatia and

kMinistry of Agriculture, Fisheries and Rural Development, Department of Fishery,Vukovarska 78, 10000, Zagreb, Croatia

(Received 25 September 2006, Accepted 19 February 2008)

A comparative study of blood chemistry and histology was conducted on two groups of mullets

(Mugilidae) living under different conditions with different feed sources. The aquaculture

influenced mullet group (AIM), was collected near fish farms and the control group of mullet

(CM) was caught in the waters without any aquaculture activities. Histological and biochemical

procedures were employed to study liver histomorphology, plasma aspartate and alanine

aminotransferase (AST, ALT), triglyceride (TRIG), cholesterol (CHOL), glucose (GLU) and

total protein (TP) of both AIM and CM. Moderate histological changes (lipid infiltration) were

observed in the liver of AIM. Significant changes in plasma variables were observed in AIM.

Blood chemistry variables measured proved to be good indicators of artificial feed effects.

Classical statistical approaches were applied to the blood chemistry and histopathology data.

For the first time machine learning techniques were used to generate comprehensible

classification models and to explore blood chemistry variable importance, strength, their

mutual interactions or dependencies, and to investigate reliability of particular variables within

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Key words: Adriatic sea; blood chemistry; cage culture; histology; machine learning techniques;

mullet.

INTRODUCTION

Mullets (Mugilidae) are catadromous fishes widely distributed in the coastal,temperate and tropical waters throughout the world. These coastal speciesoften enter estuarine and freshwater areas. The feeding habits of some mullet varywith age. Young mullet feed primarily on small crustaceans and zooplankton,whereas adults ingest plant matter. Six species of mullet occur in the Adriatic

†Author to whom correspondence should be addressed. Tel. and fax: þ385 1 4571232; email:

rrakovac@irb.hr

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doi:10.1111/j.1095-8649.2008.01865.x, available online at http://www.blackwell-synergy.com

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sea, Liza ramado (Risso), Liza aurata (Risso), Lisa saliens (Risso), Mugilcephalus L., Chelon labrosus (Risso) and Oedalechilus labeo (Cuvier) (Jardas,1996). If they live near fish farms, as they are omnivores, they will consumeartificial feed.Blood tissue reflects physical and chemical changes occurring in organisms,

indicating general metabolism and physiological status. Therefore, biochemicalvariables of blood appear to be suitable monitoring tools for environmental in-fluences, stress effects of anthropogenic origin, condition and health.Clinical chemistry analyses are not used extensively due to the lack of stan-

dard values for various fish species (Luskova et al., 1995; Luskova, 1997). Mostof the data on the responses and changes of haematological and biochemicalvariables of fish blood are available for major cultured fish species, however,distinctly less attention has been paid to wild populations or populations livingnear fish farms (Topic Popovic et al., 2006). External factors such as waterquality, diet and culture conditions can affect some blood values (Burtis &Ashwood, 1996). Blood enzyme values, aspartate and alanine aminotransferase(AST and ALT) and levels of the energetic metabolites, tryglicerides (TRIG)and cholesterol (CHOL) of fishes are considered important diagnostic tools.Often their values are used in estimating the health and condition of fishes,as well as in identifying and assessing the effect of stressors in nature. Totalprotein (TP) may be an indicator of liver impairment (Rehulka, 2003).Increased concentrations can be caused by structural liver alterations reducingaminotransferase activity with reduction in the deamination capacity (Burtis &Ashwood, 1996). The most important living condition factors such as nutritionregime (artificial diets or natural foods) and stocking density directly influencethe composition, condition and, consequently, blood and liver modifications(Wood et al., 1990; Christofilogiannis, 1993).In estimation of blood variables, classical statistical analysis techniques are

frequently used. Machine learning methods, however, seem to offer an advan-tage because they do not introduce any prior assumptions about sample distri-bution nor the relationships between the variables (Mitchell, 1997; Duda et al.,2000). Moreover, machine learning methods have an advantage over the clas-sical statistical methods because of their inherent ability to discover complexpatterns in the data. Their application for analysing data sets from diverse bio-logical sources is steadily increasing (McQueen et al., 1995; Mastrorillo et al.,1997; Dzeroski & Drumm, 2003). Decision tree algorithm C4.5 (Quinlan,1992), as implemented within the WEKA data mining suite (Witten & Frank,1999) for generating comprehensible classification models and PARF (Topic &Smuc, 2004; Topic et al., 2005), a parallel implementation of the well knownrandom forest algorithm (Breiman, 2001), were used in order to infer descrip-tive models and gain additional insight about sample distributions and variableimportance, as well as to obtain a template for future differentiation of variousinvestigated fish groups.Therefore, the purpose of this study was to measure differences between

blood biochemical variables and liver histomorphology of two assayed groups,AIM (aquaculture influenced mullets) and CM (control free-living mullets) andcompare the classical statistical approach with machine-learning techniques forestimation of mutual interactions, dependencies or reliability of each analyte.

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MATERIALS AND METHODS

EXPERIMENTAL DESIGN

Collection methods included netting and handling and were the same for bothgroups. The collections were performed during a period of one season (spring). Thestudy included 66 mullets (mean � S.D. 343�2 � 148�3 g and 311�4 � 38�4 mm forklength, LF) collected with hook and line in the immediate vicinity of the cages near fishfarms in the Adriatic sea and 28 mullets (198�4 � 37�2 g; 268�2 � 13�7 mm) caught bya 15 m deep net in the location without aquaculture activities in the radius of at least30 km. After collection, fishes were placed in oxygenated tanks filled with sea water andwere immediately anaesthetized with tricaine methanesulphonate (MS 222; Sandoz,Basel, Switzerland), 1:20 000 solution in order to minimize handling stress. Water tem-peratures and salinity were also recorded (range 11�5–23�2° C; 16–40).

BLOOD COLLECTION AND BIOCHEMICAL ANALYSES

Blood collection was performed immediately after the fishes were captured. Samplesfrom the each fish were collected laterally from the caudal vein or artery, using 2 mlplastic syringes. Approximately 1�5 ml of blood per fish was placed into microvette con-taining lithium heparinate and immediately centrifuged (15 800 g for 95 s). Plasma sam-ples were analysed using a biochemical analyser (Vettest 8008, IDEXX Laboratories,Westbrook, ME, U.S.A.) by the colorimetric method. Samples were consecutivelyrun in duplicate where the volume of withdrawn blood was sufficient. These two valuesare presented as a median value of the two since there were no significant discrepanciesbetween them. Biochemical measurements were carried out for CHOL, TRIG, TP, GLU,AST and ALT. Triplicate analyses conducted on mullet plasma gave reproducible results(unpubl. data), and the analyser is considered to be a high precision instrument forsuitable testing the analytes.

LIVER COLLECTION AND HISTOLOGICAL ANALYSES

Shortly after euthanasia by severing the spinal cord, a complete necropsy of everyfish was performed. During the procedure, entire livers were carefully removed andfixed in 10% buffered formalin for at least 2 weeks. After complete fixation, half ofeach sample was removed from the fixative, dehydrated in increasing concentrationsof alcohol and chloroform and embedded in paraffin blocks. Five micrometre thick sec-tions were cut from the blocks and stained with haematoxylin-eosin. The other half ofthe fixed liver was frozen and sections, 7–8 mm thick, were prepared on a cryo-cutmicrotome. Those sections were stained by oil red in propylene glycol for demonstra-tion of fats in tissues. The methods and techniques used during tissue fixation and his-tological slide preparation are in accordance with generally accepted methodology asdescribed in detail by Luna (1979).

STATISTICS

All data were first assessed using the SYSTAT 11 software package (Systat Software,San Jose, CA, U.S.A.). The normality of the distribution was assessed using the Kolmo-gorov–Smirnov test (KS). Parametric descriptive statistics included measures of centraltendency (arithmetic mean, x�) and dispersion (S.D.). A Mann–Whitney U-test tested allvalues of each variable. Acceptable probability was estimated by 95% CI. Discriminantanalysis was used to elucidate which combination of variables discriminated the bestbetween CM and AIM groups.

Two machine-learning methods, decision trees and PARF [a new implementation ofRF algorithm (Breiman, 2001)] were applied to the problem of fish population model-ling (CM and AIM) based on the blood sample data collected. All plasma biochemical

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variables were analysed and the results were compared with the findings of the classicalstatistical approach, which was used in order to explain which combination of variablesbest demonstrated the differences between examined groups.

RESULTS

The mullet samples belonged to four species: L. aurata, L. ramado, M. cephalusand O. labeo. Classical statistical approaches showed no significant differences invariables between species. Therefore, they were regarded as a single group.Necropsy revealed sporadic haemorrhages of the opercula and petechial hae-

morrhages of the spleen in two AIM mullet, while large amounts of visceral fatand yellowish liver were observed in 64 AIM. In wild populations of mullet(CM) no macroscopic changes were found.Necropsy demonstrated the presence of commercial artificial feed in the

digestive tract of the majority (45 fishes) of mullets captured near aquaculturefacilities (AIM), while guts of wild mullet (CM) contained only small amountsof natural food, and no other contents.

BLOOD CHEMISTRY

The non-parametric Mann–Whitney U-test demonstrated that AST andCHOL were lower in AIM groups than in CM, while TRIG and GLU werehigher (Table I). Differences were found to be significant for AST (P <0�001), CHOL (P < 0�001) and TRIG (P < 0�001), whereas GLU and TP dif-ferences were not significant (P > 0�05). Values of ALT in 90% of sampleswere <5 IU in both CM and AIM groups, therefore they were not taken intoconsideration.Non-parametric Spearman rank order correlation computed correlations and

measured similarity and distance between some measured variables regardlessof fish groups. It showed significant positive correlations (P < 0�05) betweenTRIG and GLU, TRIG and CHOL and CHOL and TP, while AST andCHOL demonstrated significant negative correlation (Table II).Using discriminant function analysis, canonical variables were summarized

(with Wilks’ Lambda at 0�3135, c. F ¼ 17�879, P < 0�001), which demonstratedthat plasma concentration of TRIG, CHOL and AST were the most consistentvariables, while the plasma concentrations of the GLU and TP were less

TABLE I. Blood variables (means and ranges) of randomly sampled aquaculture influ-enced mullets (AIM) and control mullets (CM)

Fishgroups

AST(IU l�1)

ALT(IU l�1)

CHOL(mmol�1)

TRIG(mmol�1)

TP(g l�1)

GLU(mmol�1)

AIM 45 <5 1�05 1�48 3�63 8�02(n ¼ 66) 4–458 0�55–3�55 0�47–13�93 2�0–6�60 2�28–16�79CM 92 <5 2�66 0�98 3�79 7�86(n ¼ 30) 34–296 1�23–4�22 0�55–1�46 1�00–6�90 3�23–13�17

AST, aspartate aminotransferase; ALT, alanine aminotransferase; CHOL, cholesterol; GLU,

glucose; TRIG, triglyceride; TP, total protein.

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consistent variables for group differentiation. CHOL and AST (F ¼ 44�20 and6�40) had the highest values in the discriminant model, while TRIG, GLU andTP accounted for F ¼ 0�16, 3�14 and 4�02, respectively. Tolerance measuresincluded variables in the model and its values may range from 0�0 to 1�0.The values of tolerance were relatively high for all variables TRIG, GLU,AST and TP (0�7526, 0�8042, 0�8225 and 0�7180) except CHOL (0�5946), whichshowed significant correlation (Table III).The model correctly predicted group affiliation of AIM in 92% and CM in

82% of cases. The significant differences between the groups were described bythe classification matrix, which explained 89% of the total variance (Tables IVand V).The three prototype profiles for the CM and AIM classes of samples with

respect to blood chemistry variables generated by the PARF algorithm areshown in Fig. 1. Prototype profiles are represented through distinct normalizedranges for each variable. A somewhat different picture of the sample distribu-tion with respect to blood chemistry is obtained than using decision-tree algo-rithm. This is a consequence of salient properties of random forest approach,which has the capability to more efficiently deal with non-linear dependenciesin data. Also, this figure demonstrates the presence of two different sub-sets ofsamples within the AIM population (16 and 47).

TABLE II. Spearman rank order correlation of plasma chemistry variables (see Table I)(correlations are significant at P < 0�05)

Variable Log10 AST Log10 CHOL Log10 TRIG Log10 TP Log10 GLU

Log10 AST 1�000 �0�2814 �0�0511 0�1620 �0�1958— P < 0�01 P > 0�05 P > 0�05 P > 0�05

Log10 CHOL �0�2814 1�0000 �0�1200 0�2190 0�1310P < 0�01 — P > 0�05 P < 0�05 P > 0�05

Log10 TRIG �0�0511 0�4300 1�0000 �0�1240 0�2199P > 0�05 P > 0�05 — P > 0�05 P ¼ 0�05

Log10 TP 0�1620 0�2190 �0�1240 1�0000 �0�0630P > 0�05 P < 0�05 P > 0�05 — P > 0�05

Log10 GLU �0�1958 0�1310 0�2199 �0�0630 1�0000P > 0�05 P > 0�05 P ¼ 0�05 P > 0�05 —

TABLE III. Discriminant function analysis for the measured plasma biochemical variablesof CM and AIM (see Table I)

Biochemicalvariables(n ¼ 94)

Wilks’Lambda(0�3135)

F-remove(17�8798)

P-level(<0�001) Tolerance

AST 0�8898 6�40 <0�01 0�822 528CHOL 0�5847 44�20 <0�001 0�594 607TRIG 0�8541 0�16 <0�001 0�752 681TP 0�9959 4�02 >0�05 0�718 025GLU 0�9976 3�14 >0�05 0�804 214

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The importance of individual variables as obtained by PARF is shown inTables VI and VII and Fig. 2. They confirm the properties of the decision-tree‘classifier’ by ‘pointing out’ CHOL, TRIG, AST and TP as the most importantmeasurements for discriminating between CM and AIM classes, while GLUand ALT were insignificant for the same classification model.

LIVER HISTOMORPHOLOGY

The livers of the AIM group varied in colour and appearance from yellow toochre, and in some places were mottled and revealed structural modificationsrelated to nutritive imbalance. The histopathological changes included varyingdegrees of hepatocyte lipid infiltration (from slight to excessive) that causedloss of cytoplasmic staining and distortion of hepatic muralia (Figs. 3 and 4).The AIM fish had excessive accumulation of fat in the hepatocyte cytoplasm,while the wild (control) fish had no lipid accumulation in the liver.

DISCUSSION

The instability of the internal environment of the fish organism (poikilother-mia) together with other intrinsic and extrinsic factors (stressors) is the cause ofextensive variability in fish biochemical characteristics. Both experiment groupsof animals had the same common trial procedures, such as netting and han-dling, which may be reflected in plasma biochemical changes. The most impor-tant living condition factors such as nutrition regime (artificial diets or naturalfoods) and stocking density directly influence the composition, condition andconsequently blood and liver modifications (Wood et al., 1990; Christofilogiannis,1993; Coz-Rakovac, et al., 2005). The general lack of information about

TABLE IV. Standardized coefficient for canonical variables (see Table I)

Variables Root 1

TRIG �0�226CHOL 0�235GLU 0�698AST 0�665TP 0�551Cumulative property 1�000

TABLE V. Classification matrix (rows, observed classification; columns, predicted classi-fications) (see Table I)

Group Percentage correctGroup 1-CM

P > 0�05Group 2-AIM

P > 0�05

Group 1-CM 82�10 23 5Group 2-AIM 92�40 5 61Total 89�36 28 66

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biochemical representative values in many species has made it difficult to esti-mate the significance of aquaculture’s influence on physiological status, partic-ularly histological and blood chemical status. Although mullets are the mostwide-spread fishes in the world, surprisingly little is known about the cage cul-ture effects on mullets. In this study the changes in biochemical profiles andhistological alterations were correlated.Non-plasma specific enzymes, like AST and ALT, are localized within tissue

cells of liver, heart, kidneys, muscle and other organs (Bell, 1968; Gaudet et al.,1975) and their levels in plasma may give specific information about organ dys-function (Casillas et al., 1983; Wells et al., 1986). Previous research illustratesthat the AST and ALT enzymes are sensitive indicators; a rise of their activitycan be recognized before appearance of clinical symptoms. Every liver struc-tural modification related to nutritive imbalance or diseases results in liverdysfunction and secondarily causes alterations in the ALT and AST levels(Coz-Rakovac et al., 2005). Aminotransferases are the more specific enzymesof amino acid metabolism and they behave differently in the liver and in theblood. The increased hepatic ALT activity appears to be a directly enhanced

FIG. 1. Prototypes of blood chemistry variables (see Table I) related to two distinct classes [one for control

group mullets – which includes 27 samples ( ), and two for aquaculture influenced mullets with 47

( ) and 16 ( ) samples, respectively]. Values (means � 5th and 95th percentiles) of blood chemistry

variables for the whole sample were standardized to a range 0–1 in order to make this parallel co-

ordinate representation viable.

TABLE VI. Relative importance of variables (see Table I) for the classification problem asinduced by PARF models

Variables Importance Standard score Significance

CHOL 20�60 38�15 0�000TRIG 4�42 12�22 0�000AST 2�36 9�39 0�000TP 0�41 2�70 0�004GLU 0�45 2�64 0�004ALT 0�01 0�10 0�460

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catabolic response to a higher protein diet and hence to higher alanine concen-trations, contributing to increased gluconeogenesis from this amino acid, asdemonstrated by Cowey et al. (1977). Aminotransferase activity increases inthe liver during starvation and triggers increased circulation of amino acids,although of tissue origin. Similar activities were previously observed in two dif-ferent nutritional situations, a high-protein diet and starvation, in cultured (45IU l�1) and wild sea bass Dicentrarchus labrax (L.) (44 IU l�1) (Coz-Rakovacet al., 2005). AST activities were generally higher (92 IU l�1) than values re-ported in English sole Parophrys vetulus Girard, 12�9 IU l�1 (Casillas et al.,1986), but lower than values recorded in reared lake trout Salvelinus namaycush(Walbaum), 690�2 IU l�1 (Edsal, 1999). In the present study, AST activities inthe wild (control) plasma group (with empty digestive tract or filled with smallamounts of natural food), were markedly higher than those of the aquacultureinfluenced mullet group (guts filled with feed in majority of individuals) whichsuggests selective proteolysis because of hepatic use of amino acids for gluco-neogenesis.

TABLE VII. Results of 10-fold cross validation tests performed on blood chemistrydecision-tree model (TP rate, true positive rate; FP rate, false positive rate). Detailed

assessment of the decision-tree model (Fig. 4) accuracy by class

TP rate FP rate Precision Recall Class

0�821 0�076 0�821 0�821 CM0�924 0�179 0�924 0�924 AIM

CHOL

CM (31·33/5·33) TP

AIM (49·67) GLU

AIM (10·0) CM (3·0/1·0)

<= 3·12 >3·12

<= 4·9 >4·9

<= 0·14 >10·14

FIG. 2. Decision-tree classification model of CHOL, TP and GLU of two mullet groups (CM and AIM)

(see Table I) induced by C4.5 algorithm implementation in WEKA package.

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The total plasma protein value is frequently accepted as an indicator ofnutritional status. Fat in liver and total plasma protein increases significantlywith increased feeding rates. Structural liver alterations result in reduced ami-notransferase activity with concurrent reduction in deamination capacity andincreased concentration of total protein (Burtis & Ashwood, 1996). Accordingto Kavadias et al. (2004) TP for cage-cultured fishes ranged from 4�88 to 5�93 g100 ml�1 while in the present study TP of AIM and CM varied from 2�0 to 6�6and from 1�0 to 6�9, respectively. The present research demonstrated resultsthat were opposite to the findings of Goede & Barton (1990), who stated that

FIG. 3. Micrograph of aquaculture-influenced mullet (AIM) liver. Haematoxylin-eosin staining, original

magnification �400. Diffuse vacuolar change is obvious in almost all hepatocytes. Individual

hepatocytes with empty vacuoles in cytoplasm are clearly visible.

FIG. 4. Micrograph of frozen section of liver from aquaculture-influenced mullet (AIM). Stained with oil

red, with propylene glycol method for demonstration of lipid, original magnification �200. Large

numbers of red spheres can be seen distributed evenly throughout the entire section. An individual

lipid droplet is indicated by the arrow.

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nutritional regimes influence TP, since the TP values in different feeding regimegroups were almost equal. These findings could be explained by lower energyproduction and the influence of variable ‘farming’ conditions (Rehulka et al.,2005). In AIM TP concentration tended to increase together with AST (r ¼0�3585, P < 0�01) and CHOL (r ¼ 0�2476, P < 0�05) but with TRIG (r ¼�0�4079, P < 0�001) it tended to decrease. The TP concentrations of CM,however, were positively correlated with TRIG (r ¼ 0�6357, P < 0�001),GLU (r ¼ 0�5005, P < 0�001) and CHOL (r ¼ 0�7253, P < 0�001).A wide range of stressors (starvation, water quality, food and captivity) are

perceived by fishes, which cause a blood GLU increase as a direct consequenceof neuroendocrine response (Melotti et al., 1992). As regards the difference inplasma GLU level of the fish groups observed, a noticeable difference wasfound, but no significant difference emerged with the progression of the statis-tical trial over the study time.Since TRIG are mainly synthesized in the liver from carbohydrates providing

a secondary energy source, and are stored in fatty tissue, they indicate acuteliver disease and a high-fat diet. The work of Kavadias et al. (2004) demon-strated that the high concentrations of plasma TRIG depend on the feedingrate, while low levels can be explained by starvation, which correlates well withthe present findings. The impact of the different living conditions and nutritionregimes (artificial v. natural foods) was considerable since the wild (control)fishes had significantly lower plasma TRIG levels than the aquaculture influ-enced fishes.The influence of the feeding rate on plasma CHOL concentration is already

known in sea bass (Lemaire et al., 1991; Coz-Rakovac et al., 2005) and in perchPerca fluviatilis L. (Vellas et al., 1994). Conversely, in adult farmed Atlanticsalmon Salmo salar L., total plasma CHOL levels showed negative correlationwith feeding rate (Sandnes et al., 1988). These findings were also observed inthe present study where the values were higher in wild populations, althoughfishes were emaciated (guts contained small amounts of natural food or wereempty), than in the well fed AIM group.The histological studies of livers provided an index to the general condition

of the fishes. In this study the pathological syndrome of ‘fatty liver’ occurred.Liver modifications due to artificial feed are a reaction to imbalanced feedingor a nutritional pathological process. According to Casillas et al. (1983),enzyme activity (AST) increased with fatty changes in the hepatocellular com-ponent of the inner zone of the liver, whereas data in this work did not showany increase in AST activity associated with lipid infiltration or degeneration.The authors conclude that the changes in liver, although borderline, were notso overwhelming as to manifest themselves dramatically on liver enzyme inblood, namely aminotransferase. Even though the liver tissue was partly degen-erated, its functional condition was maintained.The results were evaluated through different statistical analyses. Statistical

analysis of multivariable, particularly non-linear systems using conventionalstatistical methods often do not provide solutions that meet the goals of theresearch, so there is always an incentive for exploration of new approachesand techniques. The values of CHOL, TRIG and GLU (relative importancevalues: 15�66, 6�82 and 2�13) seemed to be highly significant for the blood

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chemistry variables classification, while AST and ALT (relative importance:0�34 and �1�69) did not possess any statistical power.Additional insight obtained from machine-learning analysis can be inferred

from the prototypes generated using PARF. The occurrence of two differentsub-sets of samples within the AIM population (16 and 47) suggests that oneminor group of AIM recently came into the vicinity of the cages (Fig. 1).The impact of cages on the minor AIM’s biochemical variables was insignifi-cant. The prototyping procedure in PARF generates distinct clusters withinthe class of samples, using detailed sample distance matrix obtained from theclassification of all samples through the forest of decision trees generated bythe PARF algorithm for classification purposes. Samples which are distantfrom any of the found clusters (prototypes) are not included in the prototypes(represent outliers of the class), which is the reason why the number of samplesin the prototypes (Fig. 1) is not equal to the total number of samples. Further-more, the decision tree classification model induced by C4.5 algorithm imple-mentation in WEKA package proved to be a valuable tool in definition ofstandardized fish blood variables (Fig. 2 and Table VII).The analyses of single variables, multivariate analysis and machine learning

methods indicated that fishes from the control population (CM) exhibited adifferent response from those of aquaculture influenced mullets.As the aquaculture industry expands, tools to monitor the physiological sta-

tus of fishes using standardized non-lethal and low-priced methods will beneeded. Evaluation of biochemical analytes and histological traits may enhanceaquaculture production by facilitating early detection of any homeostatic dis-turbance and identification of living conditions impairment. The biochemicalresults of the present study are useful for understanding how intensively livingconditions affect fish metabolism. Based on the biochemical values and activityestablished, reliable conclusions could be drawn about liver metabolic activity,which may lead to a deficiency in resistance and thus disease.Using this new approach, comprehensive diagnostic classification models

were created for two different populations based on their blood sample meas-urements (AST and ALT, and levels of the energetic metabolites, TRIG andCHOL). The predictive capability of the models, the importance of singlevariable measurements, and the descriptive importance of findings for the reallife applications were also analysed.

This work is the first to demonstrate a new statistical approach in creating a templatefor comparison and differentiation of several variables within fish groups. It will bea helpful tool in designing a standardized data bank of blood variables of different fishspecies in various living conditions.

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Journal compilation # 2008 The Fisheries Society of the British Isles, Journal of Fish Biology 2008, 72, 2557–2569