Systematic Parasitology40: 221–227, 1998.© 1998Kluwer Academic Publishers. Printed in the Netherlands.

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Multivariate analyses in the taxonomy of two species ofTylodelphysDiesing, 1850 (Trematoda: Diplostomidae) fromGalaxias maculatus(Teleostei: Galaxiidae)

Veronica Flores and Nora BaccalaLaboratorio de Parasitolog´ıa, Centro Regional Bariloche, Universidad Nacional del Comahue, Unidad PostalUNC, 8400 Bariloche, Argentina

Accepted for publication 5th January, 1998

Abstract

ThreeTylodelphysspecies are known to co-exist in the brain ofGalaxias maculatus: T argentinus, T. barilochensisandT. crubensis.In order to determine the species present in theG. maculatusfrom Lake Gutiérrez, specimenswere captured using baited traps every month from April to December, 1995. A total of 1,526 metacercariae wereseparated for identification, and multivariate techniques were applied on meristic variables measured on stainedmounted metacercariae. Two species were identified through these techniques:T. barilochensisandT. crubensis.

Introduction

It is difficult to determine metacercariae specieswithin the family Diplostomidae Poirier 1886 dueto the limited number of morphological differencesbetween them and also their plasticity at this stage(Niewiadomska & Szymanski, 1991; Graczyk, 1992,Niewiadomska & Niewiadomska-Bugaj, 1995). Muchof the variation in the size of their organs is not propor-tional and not correlated with the variation in body size(Niewiadomska & Szymanski, 1991). Variation canbe caused both by biotic and abiotic factors. Amongthe biotic factors are the influence of the intermedi-ate hosts (snails and fish), including its distributionarea, age and susceptibility to infestation, and factorsmore specifically related to host-parasite interaction,such as host age or whether they has been previouslyinfested. Variations are induced particularly by in-festation intensity, as a result of nutrient availability,space and the inter- and intraspecific competition gen-erated when a certain organ is parasitised, all of whichare crucial for parasite development (Graczyk, 1992;Niewiadomska & Niewiadomska-Bugaj, 1995). Dif-ferences in size have also been found when natural andexperimental infestations of certain parasite specieswere compared, for example inTylodelphys clavata(Nordmann, 1832), which is in the humour of the eye

of several freshwater fishes (Kozicka & Niewiadom-ska, 1960; Niewiadomska, 1963). Among the abioticfactors, temperature and velocity of water are im-portant, because they affect indirectly the infestationintensity (Esch et al., 1977; Doma & Ostrowski deNúñez, 1991). Also, another factor increasing variabil-ity is the methodology by which the metacercariae arefixed, preserved and processed for later microscopicalobservation (Höglund & Thulin, 1992).

Traditionally, species have been described by a setof variables and indices that characterize them by loca-tion and dispersion measurements (mean and standarddeviation) (Kozicka & Niewiadomska, 1960; Yam-aguti, 1975; Ostrowski de Núñez, 1977; Graczyk,1992; Quaggiotto & Valverde, 1992). More re-cently, multivariate techniques as Principal Com-ponent Analysis (PCA) and Cluster Analysis (CA)have been used to distinguish species (Gibson et al.,1985; Höglund & Thulin, 1992; Niewiadomska, 1988;Niewiadomska & Niewiadomska-Bugaj, 1995; PérezPonce de León, 1995). PCA allows multinormality ofmeasured variables to be assumed, particulary whenthe sample is composed of a large number of indi-viduals, and this gives statistical significance to thecomponents, since a measure of significance can beassociated to each of them (Pla, 1986).

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The presence of diplostomid metacercariae par-asitising the brain of autochthonous and introducedfishes has been reported in South America fromVenezuela, Chile, Perú and Argentina (Szidat & Nani1951; Ostrowsky de Núñez, 1977, 1982; Bravo Se-gura, 1981; Heckmann, 1992; Viozzi, 1993; Ortubayet al., 1994; Torres et al., 1996). In the latter coun-try, five species have been reported from Patagonia,three of which have recently been described: i.e.Ty-lodelphys argentinus, Quaggiotto & Valverde, 1992;T. barilochensisQuaggiotto & Valverde, 1992; andT. crubensisQuaggiotto & Valverde, 1992 parasitis-ing the brain ofG. maculatus. These species caneither co-exist or appear in monospecific infestations(Quaggiotto & Valverde, 1992).

The objective of this paper is to identify theTy-lodelphysspecies parasitising the brain ofGalaxiasmaculatusin Lake Gutiérrez (Patagonia, Argentina)using multivariate techniques.

Materials and methods

Lake Gutiérrez belongs to the Araucana Region, and islocated at 41◦12′S and 71◦26′W, at 750 m above sea-level. It is an ultra-oligotrophic lake of glacial origin(Thomasson, 1963) with a surface area of 16.4 km2

and a maximum depth of 111.2 m (Quirós & Drago,1985; Vigliano & Pedrozo, 1995).

The distribution area of the host species,G. mac-ulatus, includes Tasmania, New Zealand, Australiaand southern South America (Ringuelet & Aramburu,1961). Within South America,G. maculatuspopula-tions are found in freshwater bodies from Chile andArgentina, and some of them may migrate towards theAtlantic or Pacific Oceans during their reproductiveperiod (Gosztonyi, 1970, 1974).

Monthly samples of 20 specimens ofG. macula-tus were collected using baited traps from April toDecember, 1995. The fishes were kept alive at con-trolled temperature (6◦C) until they were processed.They were measured, weighed and processed usingthe necropsy technique. The heads were separated andpreserved in 70% ethanol. Then the brains were dis-sected to separate all the metacercariae. A total 140G.maculatusspecimens with a mean length of 45.6± 5.4mm (range 32.6–61.3 mm) and a mean weight of 0.43± 0.21 gr (range 0.1–1.3 gr) were analysed.

The prevalence ofTylodelphysspp. in the brain ofG. maculatusin Lake Gutiérrez during the sampling

period was 92.1% and the mean intensity was 52.9(range 0–409).

From each parasitisedG. maculatus, a maximumof 20 metacercariae were taken at random. The 1,526metacercariae thus taken were stained with hydrochlo-ric carmine and mounted in Canada balsam for mea-surement with an ocular micrometer. The meristicvariables measured on the metacercariae were se-lected according to Kozicka & Niewiadomska (1960),Niewiadomska (1963, 1988), Quaggiotto & Valverde(1992), Höglund & Thulin (1992) and Niewiadom-ska & Niewiadomska-Bugaj (1995). The indices werecalculated according to Niewiadomska (1988), Quag-giotto & Valverde (1992), Höglund & Thulin (1992)and Niewiadomska & Niewiadomska-Bugaj (1995).The meristic variables and the indices used are indi-cated in Table I.

To reduce the dimensions of the metacercarialspace a Principal Component Analysis (PCA) wasperformed. Because the variables were of differentmagnitudes, standardised PCA was used. In orderto verify the grouping obtained with PCA, a ClusterAnalysis (CA) was utilized, using the most importantcomponents obtained from the PCA as variables. Forthis analysis the Ward’s method, an aglomerative hier-archical cluster analysis, was used in order to identifydata partitioning. For each cluster the mean of everyvariable is calculated, then for each case the squaredEuclidean distance to the cluster mean is calculated.These distances are summed for all the cases. At eachstep, the two cluster that merge are those that resultin the smallest increase in the overall sum the squaredwithin-cluster distances (Lebart et al., 1995).

The two analyses mentioned above (PCA and CA)were made, first with eight meristic variables and af-terwards with the eight meristic variables and the 10indices. The data were processed with the SPADNprogram version 2.5 (1994).

Results

The measurements of the variables of 1,526 metac-ercariae were used to construct a correlation matrix(Table II) in which all variables have a positive cor-relation with total body length (TBL). This causes theso-called “size effect” in the PCA. The set of variableshas a common pattern (individuals with low values andothers with high values) and this effect is expressedby the variance explained by the first principal com-ponent (Table III). Because of the characteristics of

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Table I. List of variables used in the multivariate analysis with their corresponding abbreviations.

Variable Abbreviations

Total body length TBL

Total body width TBW

Distance between suckers DOSVS

Distance between ventral sucker and holdfast organ DVSHO

Oral sucker length OSL

Oral sucker width OSW

Ventral sucker length VSL

Ventral sucker width VSW

Total body width/Total body length TBW/TBL

Distance between suckers/Total body length DOSVS/TBL

Distance between ventral sucker and holdfast organ/Total body length DVSHO/TBL

Oral sucker length/Total body length OSL/TBL

Oral sucker width/Total body width OSW/TBW

Ventral sucker length/Total body length VSL/TBL

Ventral sucker width/Total body width VSW/TBW

Oral sucker length× width/Total body length× width OSL× OSW/TBL× TBW

Ventral sucker length× width/Total body length× width VSL× VSW/TBL × TBW

Oral sucker length× width/Ventral sucker length× width OSL× OSW/VSL× VSW

observed variables, this pattern exists even if the totalbody length trait (TBL) is excluded from the analy-sis. The variables showing least correlation with totalbody length (TBL) are total body width (TBW) andoral sucker length (OSL).

The first axis explains 48.26% of the total variationand all variables show important negative correlationwith it. The second axis explains 12.51% of total vari-ation and the variables most correlated with it are totalbody width (TBW), with a positive correlation, andthe distance between oral sucker and ventral sucker(DOSVS), with a negative correlation. The third axisexplains 10.94% of the total variation and the variablesmost correlated with it are oral sucker length (OSL),with a positive correlation, and ventral sucker length(VSL), with a negative correlation (Table IV).

The second axis separates 2 groups. One of thegroups has negative coordinates on the second axis: to-tal body length, distance between suckers and distancebetween ventral sucker and holdfast organ; while theother group has positive coordinates: total body width,oral sucker width and length, and ventral sucker width(Figure 1). So, the distance between suckers and thedistance between the ventral sucker and the holdfastorgan are associated with total body length, while oralsucker width and length and ventral sucker width areassociated with total body width.

The third axis separates oral sucker length and ven-tral sucker length (Figure 2), showing that the lengthof the oral sucker does not have an important corre-lation with the total body width, while the size of theventral sucker, especially the width, is associated tothe total body width.

The CA was carried out with the first 3 factorialaxes as variables, which corroborated the 2 groupsformed by the PCA. Again, the influence of the totalbody length is predominant.

The 2 groups resulting from the classificationwere:

Group I (NI = 838): metacercariae characterised by allvariable having values below the general mean, espe-cially total body length and distance between suckers,while oral sucker length, total body width and ventraloral width are also below the mean, although closer toit (Table V).Group II I (NII = 688): metacercariae characterisedby all variable having values above the general mean,i.e. opposite to Group I (Table V).

Considering the measurements recorded by Quag-giotto & Valverde (1992), when describing fixed spec-imens ofTylodelphysfrom G. maculatusbrain, thespecimens from Group I were assigned toT. bar-ilochensis, while those from Group II were assignedto T. crubensis.

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Table II. Correlation matrix: Pearson correlation coefficient between pairs of variables.

TBL TBW DOSVS DVSHO OSL OSW VSL VSW

TBL 1.0

TBW 0.36 1.0

DOSVS 0.82 0.31 1.0

DVSHO 0.61 0.32 0.57 1.0

OSL 0.36 0.30 0.32 0.21 1.0

OSW 0.41 0.38 0.38 0.37 0.34 1.0

VSL 0.48 0.35 0.44 0.38 0.21 0.30 1.0

VSW 0.44 0.52 0.38 0.41 0.27 0.27 0.55 1.0

Table III. Eigenvalues, percent variance and cumulative percent variance explainedfor the components from the PCA.

Components Eigenvalues Percent variance Cumulative percent variance

1 3.8605 48.26 48.26

2 1.0007 12.51 60.77

3 0.8753 10.94 71.71

4 0.6580 8.22 79.93

5 0.5872 7.34 87.27

6 0.4547 5.68 92.95

7 0.3841 4.8 97.75

8 0.1796 2.24 100.0

Figure 1. First factorial plane from the PCA.

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Figure 2. Second factorial plane from the PCA.

Table IV. Correlation of variables with thethree first component from PCA.

Variables 1 2 3

TBL −0.83 −0.38 0.09

TBW −0.62 0.50 −0.11

DOSVS −0.79 −0.45 0.09

DVSHO −0.72 −0.35 −0.10

OSL −0.51 0.26 0.71

OSW −0.64 0.26 0.24

VSL −0.68 0.07 −0.42

VSW −0.72 0.37 −0.33

Table V. Mean and Standard Error of the eight variables (inmicrometres).

Variables General Group I Group II

TBL 512.5± 77.7 464.9± 56.2 573.2± 55.9

TBW 138.8± 28.4 126.2± 22.5 153.8± 27.3

DOSVS 255.6± 42.9 230.6± 30.4 287.3± 34.9

DVSHO 28.7± 13.7 21.5± 9.7 37.6± 12.5

OSL 40.2± 5.6 38.9± 4.9 42.5± 5.4

OSW 30.9± 5.9 28.1± 4.6 34.4± 5.5

VSL 31.1± 5.1 28.4± 5.1 34.4± 4.3

VSW 27.3± 5.5 24.4± 5.5 30.9± 4.8

The PCA and CA which utilised the indices and themeristic variables did not change the previous results;this means that the correlation matrix kept its structure(high correlations with the total body length), and thetwo clusters were characterised by the same variables.

T. crubensisis longer thanT. barilochensisand, asa consequence, the distance between suckers and thedistance between the ventral sucker and the holdfastorgan are larger. The size of the ventral sucker, espe-cially its width, is related to the total body width inboth species, always being larger inT. crubensis. Thesize of the oral sucker does not show relevant valuesin either of the two species.

Discussion

The separation of species of diplostomid metacer-cariae using morphological features is difficult be-cause many factors increase the range of variability(Niewiadomska, 1963; Gibson et al., 1985; Höglund& Thulin, 1992; Niewiadomska & Niewiadomska-Bugaj, 1995; Pérez Ponce de León, 1995). In thespecific case ofTylodelphysfrom the brain ofG. mac-ulatus, this separation is even more difficult, becauseadults and their natural definitive host have not beenfound.

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The two groups found using the Cluster Analysiswere assigned to the species identified by Quaggiotto& Valverde asT. barilochensisandT. crubensis. Totalbody length and distance between suckers are the mainvariables that characterise the two groups, althoughthe fact that they are correlated must be considered.It should also be pointed out that ventral sucker lengthand width in both groups are more closely associatedwith total body width than total body length. Totalbody length should not be used as an important featurefor taxonomic differentiation because the rest of thevariables are a function of it, giving the set of datathe so-called “size effect”. To make the features thatwere used valid for taxonomic differentiation, indiceseliminating this effect should be constructed. How-ever, in our case the calculation of indices to eliminatethis effect was not satifactory and their influence in thecharacterisation was small.

Although the species from Lake Gutiérrez wereassigned toT. barilochensisandT. crubensis, all thevariables are smaller than the ones in the originaldescription. This was made on a small number ofmetacercariae, N = 19 forT. barilochensisand N = 6for T. crubensis. They were taken fromG. maculatusfrom other lakes and neither the weight nor length ofthe host were recorded. Abiotic factors, such as thephysical and chemical features of the lake and theirinfluence on host size, and biotic factors such as infes-tation intensity and metacercariae age (Niewiadomska& Szymanski, 1991; Graczyk, 1992), are known tocause changes. The way in which the metacercariaewere fixed and stained should not have introduced anyadditional difference, because the methodology usedwas the same as that used by Quaggiotto & Valverde(1992). In this study, variability which could have beencaused by differences in fish size as well as that causedby the different months of year have been considered.

Other studies have been carried out using between40 and 203 specimens (Gibson et al., 1985; Höglund& Thulin, 1992; Niewiadomska & Niewiadomska-Bugaj, 1995; Pérez Ponce de León, 1995). Theirconclusions were supplemented by features only vis-ible in live metacercariae and, in some cases, theadult stage, although these were not considered asvariables in the statistical analyses (Gibson et al.,1985; Höglund & Thulin, 1992; Niewiadomska &Niewiadomska-Bugaj, 1995; Pérez Ponce de León,1995).

Observation of live metacercariae, taking into ac-count shape, size, number and distribution of cal-careous corpuscules in the paranephridial system, also

suggest the presence of two species ofTylodelphys(see Flores, 1997). This agrees with the statisticalanalyses presented here, which show that two speciesof Tylodelphys, T. barilochensisandT. crubensis, co-exist in the brain of theG. maculatuspopulation inLake Gutierrez.

Acknowledgements

We thank Lic. Liliana Semenas for reviewing theoriginal manuscript and anonymous referees for theirsuggestions which improved the paper. Financial sup-port for this study was provided by the UniversidadNacional del Comahue (UNC—B 702/96) Argentina.

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