Biologia, Bratislava, 61/Suppl. 18: S191—S201, 2006Section ZoologyDOI: 10.2478/s11756-006-0131-8

Classification of the Tatra Mountain lakes (Slovakia) usingchironomids (Diptera, Chironomidae)

Peter Bitušík1, Marek Svitok1, Peter Kološta2 & Marta Hubková3

1Department of Biology and General Ecology, Faculty of Ecology and Environmental Sciences, Technical University inZvolen, SK–96053 Zvolen, Slovakia; e-mail: peter@blatna.cuni.cz2Regional Environmental Office in Banská Bystrica, Nám. Ľ. Štúra 1, SK–97405 Banská Bystrica, Slovakia3State Nature Conservancy of the Slovak Republic, The Low Tatras National Park Authority, Zelená 5, SK–97401 BanskáBystrica, Slovakia

Abstract: Chironomid assemblages in thirty-three mountain lakes situated above tree line in the Slovakian part of theTatra Mountains were studied during 2000–2002. Chironomid species/taxa, collected as pupal exuviae, were correlated withphysical, chemical, and lake morphometry variables of 22 lakes. Two-way indicator species analysis (TWINSPAN) was usedto classify the lakes into four distinct groups: higher situated alpine lakes, lower situated alpine lakes, subalpine lakes andacidified lakes. Presence/absence of eight taxa was identified as indicative for this classification. In discriminant functionanalysis, pH, dissolved organic carbon, altitude and lake area were the most significant variables reflecting differencesamong groups of lakes. This model of four variables allowed 77% success in the prediction of group membership. A multipleregression model with lake area, concentration of magnesium and total phosphorus accounted for 37% of the variance intaxa richness. Lakes with greater area contained more chironomid taxa than smaller ones. Lakes with higher alkalinity andhigher trophic status tend to support more taxa. Canonical correspondence analysis (CCA) indicated that most variationin the composition of chironomid assemblages was related to pH and to altitude. The results can be used as reference datafor long-term monitoring of the Tatra lakes, especially in connection with a recovery from acidification and global climaticchange.

Key words: Chironomidae, mountain lakes, pupal exuviae, classification, Tatra Mountains, Slovakia.

Introduction

Lakes of glacial origin represent more than 90% of alllakes in Slovakia. There are nearly 140 permanent lakesin the High Tatra Mountains (Mts) and West TatraMts. Most of them are situated above the tree-line inthe subalpine and alpine zone of the mountains. Stud-ies on benthic communities of these lakes were startedin the 1930s. Early research was focused on profundalcommunities with attempt to classify the investigatedlakes based on chironomid and oligochaete assemblages(Hrabě, 1939, 1942). Chironomid larvae and pupaefrom Hrabě’s collection were studied by Zavřel (1935a,b, 1937a, b) and Zavřel & Pagast (1935). Later, thestudy of chironomids was a part of a complex limnolog-ical investigation aimed initially on a change of trophicstatus of some lakes as a consequence of increasedtourism (Ertlová, 1964; Juriš et al., 1965), and sincethe 1980s on effects of acidification (Ertlová, 1987).Extensive material of chironomid larvae collected in the1980s and the beginning of 1990s has been determinedlater and the results have not yet been published, withthe exception of Bitušík (1996). Participation of Slo-

vakia in the EU projects (AL:PE, MOLAR, EMERGE,Štefková & Šporka, 2002) enabled to collect chi-ronomids from 49 Tatra mountain lakes. Chironomidfauna has been studied principally with emphasis onacidification and climatic changes. Some results havebeen partly published (Bitušík, 2003a; Kubovčík &Beták, 2004; Kubovčík et al., 2003; Tátosová &Stuchlík, 2003, 2006; Kubovčík & Bitušík, 2006;Hamerlík et al., 2006).Collecting of chironomid pupal exuviae from water

surface of the lakes was a “by-product” of the investi-gations in the framework of the project EMERGE. Al-though, chironomid pupal exuviae technique (CPET)has been developed initially for use in river monitoringand assessment (Wilson & Bright, 1973; Wilson& McGill, 1977) it is applicable to nearly all typesof surface freshwaters (Wilson & Ruse, 2005). Chi-ronomid pupal exuviae have been used as a successfultool for the investigation of lakes (Pinder & Mor-ley, 1995; Bitušík, 1995; Ruse, 2002a, b). A com-prehensive list of chironomid species dwelling in theTatra lakes (Bitušík, 2004) has been based almost ex-clusively on pupal exuviae.

c©2006 Institute of Zoology, Slovak Academy of Sciences

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The aims of present study were: (i) to classify lakeson the basis of pupal exuviae collection, (ii) to reviewwhich environmental variables best describe the lakegroupings, (iii) to define characteristic chironomid as-semblages which could be used as reference data for thefuture monitoring programme of the Tatra lakes.

Study area and sampling sites

Investigated lakes are located in the West Tatra Mts (10lakes) and the High Tatra Mts (22 lakes). The dominantvegetation is dwarf pine (Pinus mugo) between 1550and ∼1800 m a.s.l., and alpine meadow (dry tundra)or rocks in the alpine zone above ∼1800 m a.s.l. Thelakes span wide range of environmental conditions withrespect to altitude, depth, surface area, volume, water-shed characteristics, and present and historical degreeof acidification, as well. The altitude of the lakes rangefrom 1563 m to 2109 m a.s.l., the area from 0.1 to 20ha, and the maximum depth from 1 to 54 m. More de-tails on observed lakes have been presented elsewhere(Vranovský et al., 1994; Kopáček et al., 2004).

Material and methods

Field and laboratoryChironomid pupal exuviae were sampled twice per year(2000–2002): firstly after the melting of ice in June – July,secondly in August or the beginning of September, respec-tively. Floating debris together with pupal exuviae was col-lected skimming the water along the lake shore with 200µm mesh net attached to 1.5 m long pole. Sampled materialwas placed into polythene bottles, labeled and preservedwith 4% formalin. In the laboratory, all pupal exuviae inthe sample were removed using low-power stereomicroscope(× 7–40). Exuviae were mounted on the slides in groupsusing Berlese fluid and identified under high magnification(× 400) to species-level, if possible. Identification was basedon the key by LANGTON (1991) and LANGTON & VISSER(2003).

Numerical analysesAn a priori classification of lakes was conducted in or-der to define groups of lakes that contained similar chi-ronomid assemblages. Two-way indicator species analysis(TWINSPAN; HILL, 1979a) was used to classify studiedlakes into discrete groups. The analysis was based on thepresence/absence of chironomid taxa in each of 22 lakeswith defined environmental conditions. A maximum levelof division was set to 3.

Differences in taxa richness among TWINSPAN groupswere tested by one way analysis of variance (ANOVA) fol-lowed by generalised Tukey’s HSD test for unequal samplesize (STATSOFT, INC., 2001). Kruskal-Wallis test followedby Dunn-type test for multiple comparisons with unequalsample sizes (cf. ZAR, 1999) was used to assess differencesin environmental variables among the groups.

Groups derived in TWINSPAN were used as depen-dent variables in a discriminant function analysis (DA) todetermine the relationship between the environmental vari-ables and the species defined groups of lakes. Non corre-lated environmental variables that showed significant differ-

ences among groups (Kruskal-Wallis, P < 0.05) were chosenas predictors for DA only. Prior to analysis, variables weretested for normality, homoscedasticity (Hartley F-max test)and transformed, if necessary. Best-subset procedure (STAT-SOFT, INC., 2001) based on Wilks’ lambda was used to de-termine subset of predictors comprised of variables that gavethe greatest discrimination among groups of lakes. Jack-knifing procedure was employed to improve validation ofthe classification rates based on discriminant analysis.

Multiple regression analysis was conducted to gain in-sights into the most important environmental factors influ-encing Chironomidae taxa richness. Set of predictor vari-ables was defined by environmental variables measured foreach lake. Prior to analysis, normality of the data distri-bution was assessed by Shapiro-Wilks test and variableswere transformed if necessary. Mean depth and dissolved or-ganic carbon (DOC) were reciprocal cube root transformed.Reciprocal square root transformation was applied for to-tal phosphorus (total P) and concentration of H+ (antilog(−pH)). Conductivity, concentration of total Kjeldahl ni-trogen (total N), and lake area were log10 transformed. Re-ciprocal transformation was used for concentration of mag-nesium (Mg2+). Highly skewed altitude data were trans-formed to the fifth power. Best-subset regression method(STATSOFT, INC., 2001) was used to identify which predic-tors explained significant variation in Chironomidae speciesrichness. Mallow’s Cp was selected as the best-subset crite-rion. Tolerance values were examined to ensure the absenceof multicollinearity among predictors.

Ordination analysis was used to categorise 11 lakeswith unknown environmental conditions into one of thegroup derived through TWINSPAN. As detrended corre-spondence analysis (HILL, 1979b) with detrending by seg-ments revealed gradient length > 2.8 SD units, unimodal re-sponses of species to environmental gradients were assumed.Therefore, canonical correspondence analysis (CCA; TERBRAAK, 1986) with Monte Carlo permutation test (999 un-constrained permutations) of the first and second axis wasemployed. Samples from the 22 lakes with know environ-mental conditions were treated as active samples in the anal-ysis while samples from the remaining 11 lakes with miss-ing environmental variables were treated as passive samples.This encoding enabled us to categorise lakes without envi-ronmental data into TWINSPAN groups. CCA axes wereconstrained to variables that gave the greatest separationamong groups in discriminant analysis.

Results

A total of 9,432 chironomid pupal exuviae from 33Tatra mountain lakes were identified to 55 species/taxa(Appendix 1).TWINSPAN analysis of chironomid assemblages

from 22 lakes resulted in four lake groups (Fig. 1). Theinitial division was based on five indicator species andseparated acidified subalpine and alpine lakes (groupD) from the others. Presence of Zalutschia tatrica andTanytarsus gregarius was indicative for this group. Atthe second division, group A of high alpine lakes wasseparated due to presence of Pseudodiamesa nivosa.The six lakes in group C were characterized by presenceof Microtendipes chloris and Cricotopus (I.) perniger

Classification of mountain lakes using chironomids S193

Fig. 1. TWINSPAN classification of 22 Tatra lakes based on Chironomidae absence/presence data. The indicator species are shown ateach division. The column plot shows mean species richness of Chironomidae (± 1 × SD) in each group of lakes. Columns with the samelowercase letters are not significantly different (Tukey’s HSD test for unequal sample size, P < 0.05). Abbreviations: Bati – Batizovsképleso, Bys2 – Veľké Bystré pleso, Drac – Dračie pleso, Lad1 – Ľadové pleso, Litv – Litvorové pleso, MHin – Malé Hincovo pleso, NJam– Nižné Jamnícke pleso, NTem – Nižné Temnosmrečinské pleso, NTer – Nižné Terianske pleso, Pust – Pusté pleso, PZbo – Prostrednézbojnícke pleso, Rac2 – Vyšné Račkove pleso, Roh4 – Štvrté Roháčske pleso, Sata – Vyšné Satanie pliesko, Sive – Prostredné sivépleso, Slav – Slavkovské pleso, Star – Starolesnianske pleso, VBie – Vyšné Žabie bielovodské pleso, VHin – Veľké Hincovo pleso, VTem– Vyšné Temnosmrečinské pleso, VZbo – Vyšné zbojnícke pleso, VZMe – Veľké Žabie pleso.

Table 1. Best subset multiple regression (Mallow’s Cp = −0.54, F3,18 = 3.504, P = 0.036, R2 = 0.37) of Chironomidae species richnessin Tatra lakes as a function of lake area (log10 transformed), concentration of total phosphorus (reciprocally square root transformed)and magnesium (reciprocally transformed).

Predictor β SE t P Tolerance

Lake area 0.456 0.209 2.182 0.043 0.80TP −0.358 0.213 −1.686 0.109 0.78Mg2+ −0.432 0.194 −2.230 0.039 0.94

Key: β – standardised regression coefficient; SE – standard error.

as indicator species of subalpine lakes. Prodiamesa oli-vacea, Apsectrotanypus trifascipennis, Psectrocladiusoxyura, P. barbatipes and Pagastiella orophila wererecorded exclusively in these lakes. Absence of P.nivosa, presence of Micropsectra radialis, and alsoTanytarsus bathophilus, Zavrelimyia sp., Paratanytar-sus austriacus, Corynoneura arctica/scutellata sep-arated group B of seven alpine lakes. Taxa rich-ness showed significant differences among the fourTWINSPAN groups of lakes (F3,18 = 6.21, P = 0.004).Pairwise comparison revealed significant differences inrichness between groups A and B, and between groupsA and C (Fig. 1).Differences in environmental conditions among

groups of lakes were examined by Kruskal-Wallis test.

Test results indicated that 8 out of 10 environmentalvariables showed significant differences among speciesdefined groups of lakes (Tab. 2). Conductivity and pHshowed the strongest differences among groups. On theother hand, concentration of total phosphorus and ni-trogen did not vary significantly among groups.In DA, the best subset of predictors (Tab. 3) was

determined from eight environmental variables thatsignificantly differ among TWINSPAN groups. Jack-knifing procedure reduced the overall classification suc-cess to 41% with low classification rates for group B(14%), intermediate for groups A (40%) and C (50%)and a relatively high classification rate for group D(75%). Discriminant analysis yielded two significantdiscriminant functions (P < 0.05) which explained

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Table 2. Mean values of environmental variables for each TWINSPAN group of lakes.

Group of lakes

Variable A (n = 5) B (n = 7) C (n = 6) D (n = 4) H

Altitude (m) 1970 a 1907 ab 1738 b 1893 ab 8.267*Lake area (ha) 1.821 ab 4.102 ab 5.025 a 0.479 b 7.954*Mean depth (m) 6.08 ab 7.76 ab 7.86 a 1.36 b 9.462*Conductivity (µS cm−1) 18.18 ab 23.18 ab 27.03 a 10.61 b 10.192*pH 6.72 ab 6.88 b 6.97 b 5.25 a 10.557*Ca2+ (mg dm−3) 2.708 ab 3.166 ab 4.202 a 0.610 b 9.933*Mg2+ (mg dm−3) 0.118 a 0.424 a 0.553 a 0.098 a 7.992*TN (µg dm−3) 77.8 95.0 105.5 236.5 6.423DOC (mg dm−3) 0.180 a 0.405 a 0.356 a 2.008 a 9.157*TP (µg dm−3) 1.640 1.857 2.267 7.250 4.670

Explanations: Differences in environmental variables among the groups were tested by Kruskal-Wallis test. Values of statistics labelledwith asterisks indicate significant difference at P < 0.05. Values with the same uppercase letters are not significantly different (Dunn-type test, P < 0.05).

Table 3. Best-subset models of various effects that gave the greatest separation among lake groups. The percentages correctly classifiedin a discriminant analysis are shown.

VariableLevel of effect % correctly classified

H+ DOC Altitude Lake area Wilks’ lambda P

1 + 0.388 < 0.0006 642 + + 0.215 < 0.0001 683 + + + 0.132 < 0.0001 734 + + + + 0.097 < 0.0001 77

Table 4. Standardised discriminant function coefficients for themodel included four environmental variables (transformed).

Discriminant functionVariable

1 2

H+ 0.79 0.03Lake area 0.59 −0.16Altitude −0.35 0.73DOC 0.34 0.68

Eigenvalue 3.29 1.13% variance explained 72.3 24.9

Explanations: Displayed functions were significant at P < 0.05.Eigenvalues and percentage variance explained by each functionare shown.

97.2% of the between-group variance (Tab. 4). Concen-tration of H+ (pH) contributed most to the first func-tion, whereas the second function was weighted mostheavily by altitude and DOC. Apparently, the first dis-criminant function separated group D and the othergroups (Fig. 2). The second function discriminated wellbetween high alpine lakes (group A) and subalpine lakes(group C) but group B more or less overlapped groupsC and A.Models of higher effects were unwieldy to interpret

and did not considerably improve classification successor even decreased it. Moreover, examination of rela-

tionships among variables revealed that concentrationof calcium, magnesium and conductivity were stronglycorrelated with H+, and mean depth was correlatedwith lake area (Tab. 5). After the addition of inter-correlated variables, strong multicollinearity effect wasobserved (tolerance < 0.1).Multiple linear regression analysis resulted in a

model that explained 37% of the variation in Chi-ronomidae species richness by area of lake, concen-tration of total phosphorus and magnesium (Tab. 1).Lake area was the most important factor predict-ing species richness in the 22 observed lakes. Lakearea (log10 transformed) was positively correlatedwith chironomids species richness, indicating that thelarger lakes tend to contain more chironomids. Al-though concentrations of total magnesium were weakerpredictor than lake area, it did significantly con-tribute to the prediction of species richness of non-biting midges. Lakes with higher concentration ofmagnesium tend to support richer Chironomidae as-semblages. In the regression model, the tolerancevalues are well above the value at which multi-collinearity is of concern (cf. Quinn & Keough,

2002).The results of CCA ordination based on 47 chi-

ronomid taxa and 22 lakes are plotted graphically inFig. 3. Eleven lakes with unknown environmental vari-ables were included in the analysis as passive samplesand therefore they did not influence the ordination.

Classification of mountain lakes using chironomids S195

Table 5. Spearman rank correlation coefficients between environmental variables.

Altitude Lake area Mean depth pH Conductivity DOC TP TN Ca2+

Lake area −0.13Mean depth −0.16 0.86***pH −0.17 0.30 0.41Conductivity −0.26 0.27 0.35 0.97***DOC −0.15 −0.32 −0.43* −0.22 −0.20TP −0.13 −0.38 −0.59** −0.35 −0.28 0.50*TN −0.25 −0.17 −0.28 −0.06 0.00 0.70*** 0.56**Ca2+ −0.19 0.39 0.46* 0.95*** 0.96*** −0.27 −0.38 −0.01Mg2+ −0.25 0.04 0.16 0.85*** 0.87*** −0.04 −0.11 0.15 0.80***

Key: * P < 0.05; ** P < 0.01; *** P < 0.001.

Fig. 2. Plot of discriminant function scores for TWINSPAN groups of lakes (For abbreviations see Fig. 1). Circles represent group A,squares group B, diamonds group C and triangles represent group D.

The first two CCA axis accounted for 13.2% (λ1 =0.46, F = 2.70, P = 0.001) and 8.5% (λ2 = 0.30, F =1.84, P = 0.001) of variance explained. Intra-set corre-lations indicated that the first axis was correlated withpH (r = −0.93) and DOC (r = 0.87), and the secondaxis was most strongly correlated with altitude (r =−0.98). The first CCA axis arranged lakes along a gra-dient of decreasing pH. The group D (polygons in CCAcorrespond to the classes formed by TWINSPAN) isclearly displayed on the right-hand side of the diagram.The other three lake groups are located along a gradi-ent of increasing altitude from the top to the down ofthe diagram.Location of passive samples showed that lakes Prvé

Roháčske pleso, Druhé Roháčske pleso, Tretie Roháčskepleso, Nižné Žabie bielovodské, Vyšné Jamnícke pleso(Roh1, Roh2, Roh3, NBie, VJam) tend to follow thesubalpine lakes of the group C. On the other hand,Ľadové pleso v Zlomiskách (Lad2) shows high affinity to

the group A represented by high alpine lakes, and VyšnéTerianske pleso (VTer) is placed close to the acidifiedalpine lakes. Remaining four lakes with unknown en-vironmental conditions were situated in the middle ofthe ordination space. These lakes may be classified asmembers of the group B.

Discussion

Analysis of chironomid fauna from 33 lakes in the TatraMts demonstrates that chironomid pupal exuviae dataare, generally stated, associated with altitude, ion con-centrations, nutrients, and pH. Only one parameterconnected with lake morphometry (lake area) is sig-nificantly correlated with species/taxa richness.Altitude was one of the most important variables

both in DA and CCA. Altitudinal gradient is closelyconnected with a major climatic gradient in the moun-tains. The harsh climatic conditions exert an impor-

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Fig. 3. CCA ordination of 22 Tatra lakes based on Chironomidae presence/absence data set. Full circles represent group A, squaresgroup B, diamonds group C and triangles represent group D. In addition, 11 passive samples from the lakes with unknown environmentalconditions are projected into the ordination space. Centroids of passive samples are displayed as empty circles and labelled in italic.Abbreviations: VTer – Vyšné Terianske pleso, Roh2 – Druhé Roháčske pleso, Roh3 – Tretie Roháčske pleso, Veli – Velické pleso, Kaca– Zelené Kačacie pleso, Lad2 – ľadové pleso v Zlomiskách, Bys1 – Malé Bystré pleso, NBie – Nižné Žabie bielovodské pleso, Rac1 –Stredné Račkove pleso, Roh1 – Prvé Roháčske pleso, VJam – Vyšné Jamnícke pleso. (for abbreviations of active samples see Fig. 1).

tant influence on many limnological characteristics ofaquatic ecosystems (Michelutti et al., 2002). Lot-ter et al. (1997) found a strong linear relationship be-tween altitude and mean summer temperature within aset of Swiss lakes. The results of Šporka et al. (2006)indicated approximately linear decrease of lake surfacewater temperature with increasing altitude not only insummer, but also in late spring and autumn season inthe Tatra Mts. Faunistic changes in aquatic organismsof remote lakes have been found to correspond to alti-tude/temperature gradient very often (e.g.,Walker &Matthewes, 1989; Krno, 1991; Lotter et al., 1997;Larocque et al., 2001). The changes of temperaturewith the increasing of elevation influence the faunisticcomposition directly and indirectly.In comparison with subalpine lakes, the chirono-

mid assemblages of alpine Tatra lakes are character-ized by lower species richness and total absence of taxafrom the tribe Chironomini. Heterotrissocladius mar-cidus and Procladius tatrensis are widely distributedfrom subalpine to the high alpine zone. H. marciduswas found in all of the observed lakes, with excep-tion of Slavkovské pleso (Slav). It is probably the mostcommon chironomid of the Tatra lakes (cf. Ertlová,1987). P. tatrensis has been considered to be a typicalspecies, perhaps endemic species, for the Tatra Mts and

its taxonomic status was discussed elsewhere (Bitušík,2003b). However, very similar pupal remains have beenfound in the subfossil material from the Alps and ina recent material from the Central Pyrenees (Heiriet al., 2003). Pseudodiamesa nivosa seems to be theonly characteristic species of chironomid assemblagesinhabiting the high alpine Tatra lakes. The species isadapted to harsh physical environment, including freez-ing and drying. It indicates ultra-oligotrophic status (α-oligotrophy, Sæther, 1979) and extremely low watertemperature, without respect to altitude. That is why,the species has been found in small, shallow subalpinelakes (Bitušík & Koppová, 1997; Hamerlík, 2004).P. nivosa is considered to be more of a mountain speciesthan its relative species P. branickii (Serra-Tosio,1976) which occurs in moderate conditions. Actually,the distribution of the both species in the Tatra lakesdisplays an ecologically vicarious distribution pattern.Larvae of P. nivosa tolerate low pH and occur in acidi-fied lakes, too (e.g., Prostredné sivé pleso – Sive, VyšnéTerianske pleso – VTer).Micropsectra radialis can be considered as an-

other species of this assemblage, although it occursin the alpine lakes generally. The species is a charac-teristic member of the ‘Tanytarsus lugens’-community(Brundin, 1956). The larvae are cold stenothermal,

Classification of mountain lakes using chironomids S197

polyoxybiontic, and eurybathic. They inhabit the lit-toral zone under subarctic/arctic conditions, while intemperate lakes they are restricted to the profundalzone (Hofmann, 1988). M. radialis occurs mostly, inthe deep alpine Tatra lakes. Its presence in shallowlakes indicates extremely low temperature conditions(see common occurrence with P. nivosa in Kačacie pleso(Kaca)), on the other hand, its absence in the otheralpine lakes could signalize weaker summer thermalstratification and higher temperatures at the lake bot-tom (e.g., Malé Hincovo pleso – MHin). The species isgenerally absent in the sub-alpine lakes with the excep-tion of some deep lakes (Nižné Temnosmrečinské pleso– NTem) and with cold and well oxygenated water inthe hypolimnetic zone. These data are consistent withearlier studies from prealpine lakes in northern Switzer-land (Reiss, 1968). The species is acid sensitive anddisappeared from acidified lakes (Prostredné sivé pleso– Sive, Vyšné Terianske pleso – VTer). Its absence inpreviously acidified Batizovské pleso – Bati (Kopáček& Stuchlík, 1991) could be a signal that biologicalrecovery in the lake has not been complete yet.More detailed studies based on recent and subfossil

material from Ľadové pleso (Lad1) and Vyšné Wahlen-bergovo pleso revealed predominance of P. nivosa andM. radialis with negligible representation of othertaxa (Tátosová & Stuchlík, 2003; Kubovčík &Bitušík, 2006). Such structure of assemblages resem-bles in some aspects of chironomid fauna of extremeSwiss alpine lake (Lotter et al., 2000). Larocque etal. (2001) found Pseudodiamesa and M. radialis to bemembers of chironomid assemblages restricted to lakescharacterized by cold climatic conditions in alpine-tundra vegetation of northern Swedish Lappland.Most of the taxa collected in some high alpine

lakes, were also present in the subalpine lakes, and forthis reason group B of low-alpine lakes is not separateddistinctly from both subalpine and high alpine lakes.According to these data P. austriacus seems not to besuch cold-stenotherm species as it has been supposed(Fjellheim et al., 1997; Catalan et al., 2002). Sur-prisingly, Tanytarsus lugens, a typical member of the‘lugens’-community was not found in the Tatra lakes,whereas we did collect T. bathophilus. According to themore recent data T. bathophilus is not strictly restrictedto oligotrophic lakes but it can be found in lakes withhigher trophic status (Ekrem, 2004).The subalpine Tatra lakes are inhabited by assem-

blages composed of more thermophilic chironomid taxa.While P. barbatipes and P. orophila are restricted onlyto the subalpine lakes, other species occurred commonlyin reservoirs and streams at low elevations.Not surprisingly, variables connected with lake pro-

ductivity were correlated with the chironomid data.DOC contributed together with altitude to the secondfunction of the discriminant analysis that clearly sep-arated the high alpine lakes from the subalpine lakes.Concentration of TP contributed to explaining the dif-

ferences in the chironomid taxa richness among the lakegroups. The quantities of P and organic C in the Tatralakes were predominantly governed by the soil and vege-tation characteristics of the catchments. Concentrationsof TP and DOC (but also of total organic nitrogen) de-creased with the decline of vegetation and soil coveralong the altitude/temperature gradient (Kopáček etal., 2000). Similar significant positive correlation be-tween DOC concentrations in lakes and amounts of veg-etation in catchments has been found in arctic lakes(Michelutti et al., 2002).Traditionally, relationships between chironomid fa-

una and productivity have been used for classification oflakes (Brinkhurst, 1974;Wiederholm, 1980). Ert-lová (1987) pointed out the changes in trophic condi-tions as a factor influencing both diversity and abun-dance of littoral chironomids in the Tatra lakes. Lot-ter et al. (1998) revealed distinct changes in compo-sition of aquatic organisms, including Chironomidae,along the total phosphorus gradient in investigatedlakes. Under oligotrophic conditions of remote lakes,the chironomid assemblage composition reflected foodavailability rather than oxygen concentration (Linde-gaard, 1995). Thus, the subalpine lakes with higherprimary production and higher input of allochthonousorganic matter utilizing as food are inhabited by morechironomid species than the alpine lakes.Acidification in the Tatra Mts disturbed natural

gradient of changes in the nutrient concentration fromthe forest lakes to the alpine ones, when strongly acidi-fied lakes (e.g., Starolesnianske pleso, Slavkovské pleso,Vyšné Satanie pliesko) had, on average, higher algalproductivity than non-acidified lakes. Species composi-tion of these lakes reflected not only low pH values (Z.tatrica) but the higher trophic status too (e.g., T. gre-garius, Chironomus sp., Procladius choreus, Endochi-ronomus sp.). On the other hand, moderately acidifiedTatra lakes with pH between 5 and 6 became more olig-otrophic. Fott et al. (1994) reported on extinction ofzooplankton by starvation due to low amount of phy-toplankton. The period of acidification resulted in thedecreasing of chironomid productivity but not in taxo-nomic composition changes, as demonstrated from thesubfossil record taken from the Vyšné Wahlenbergovopleso (Kubovčík & Bitušík, 2006). This is in accor-dance with the opinion that chironomids reflect thetrophic status of lakes better than pH (Brodin, 1990)except when pH decreases near or below 5.0 (Brodin& Gransberg, 1993; Olander et al., 1997).In fact, some authors did not find a correlation be-

tween chironomids and pH (e.g., Walker et al., 1991;Olander et al., 1997; Halvorsen et al., 2001). How-ever, pH in this study is shown to be significantly cor-related with chironomid data. Axis 1 of the CCA or-dination reflects a gradient of pH and ionic concentra-tions and the variables are highly positively correlatedwith each other (Tab. 5). Similarly, in DA, concentra-tion of H+ (pH) contributed most to the first discrim-

S198 Bitušík et al.

inant function that explained majority of the betweengroup variance and separates the group D from othergroups of lakes.Acid status of the Tatra lakes is closely related

to their concentrations of base cations, especially cal-cium and magnesium. Both acid neutralizing capac-ity and pH are strongly correlated to the sum ofCa2+ and Mg2+ concentrations (Kopáček et al., 2006).The catchments of strongly acidified lakes (group D)have very low soil base saturation, and their carbon-ate buffering system was depleted a long time beforethe main period of Tatra lakes acidification (Kopáčeket al., 2004). The data from the Tatra lakes confirmthat under conditions of strong acidification, low pHmight override the effects of other environmental fac-tors. Chironomid assemblages tend to be species poorand uniform (“azonal assemblages”), although detailedtaxonomic analysis can reveal some species indicatingthe un-disturbed lake status (e.g., presence of P. nivosain the high alpine acidified lakes, and Endochironomussp., P. choreus in the subalpine acidified lakes).Zalutschia tatrica seems to be a reliable indicator ofacid conditions in the Tatra lakes. In pre-acidificationperiod the larvae were abundant in many shallow, warmpools and the littoral zone of some Tatra lakes (Hrabě,1942). It can be supposed that the species is normallyadapted to humic waters, similarly to the other mem-bers of the genus (Sæther, 1976), and tolerate rela-tively wide range of temperature. In autumn 1993 and1994, Z. tatrica dominated the littoral fauna of thestrong acidified Starolesnianske pleso making up 94.0%and 85.9%, respectively. The findings of some larvae inlittoral samples taken from non-acidified lakes in thesame time indicate that the littoral zone was more in-fluenced by acidification than deeper areas of the lake(Bitušík, unpubl. data).Multiple linear regression analysis revealed the lake

area as the most important variable predicting speciesrichness in the lakes. Most of identified chironomids inthe Tatra lakes are considered to be littoral and sub-littoral inhabitants (Bitušík & Hamerlík, 2003). Lit-toral communities consist of a rich fauna as a conse-quence of greater substratum heterogeneity, and effectof complex factors (Wetzel, 2001). Basin morphom-etry and transparency limit the size of littoral zoneand determine the extension of inshore species (Devol& Wessmar, 1978). The relationship between speciesrichness and lake area has been found for zooplanktonand fish in a number of studies (e.g., O’Brien et al.,2004). The habitat structure and size of littoral zoneof the Tatra lakes have not been measured. It could besupposed that lakes with greater area, under conditionsof high transparency, can have more extensive littoraland support more diverse habitats, consequently higherchironomid diversity. Differences in the species compo-sition suggest that ecological conditions in the littoralzone of seemingly similar lakes (e.g., Nižné Terianskepleso – NTer vs Veľké Hincovo pleso – VHin) could be

different in many aspects. It should be remembered thatmany non-lacustrine species can be found in the littoralzone of the Tatra lakes (cf.Cameron et al., 2002). Mostof these are typical lotic taxa (e.g., Diamesa, Euki-efferiella, Heleniella, Orthocladius, Paratrichocladius)and few usually live in semiterrestrial habitats (Metri-ocnemus, Chaetocladius). These species can contributeconsiderably to the species richness, especially in thelakes that have well developed inflows (e.g., Nižné Teri-anske pleso, Vyšné zbojnícke pleso – VZbo, Prostrednézbojnícke pleso – PZbo).This study presents the second attempt to classify

the Tatra lakes using chironomid data. The first clas-sification was proposed by Hrabě (1939). It containsmany valuable historical data concerning the chirono-mid distribution. Nevertheless the results are hardlycomparable with the scheme presented in this paper dueto taxonomic level of identification. Much later, Krnoet al. (1986) and Krno (1991) outlined comprehensiveclassification of the lakes using littoral macroinverte-brates, however unfortunately, mostly without chirono-mids.Among the lacustrine benthic macroinvertebrates

Chironomidae are the group with the highest number ofspecies, and very often with the highest abundance andbiomass (Armitage et al., 1995). Thus they should notbe ignored in any lake monitoring programme. The nextsurvey on the Tatra lakes will test the accuracy of theproposed classification. Presented results can be used asreference data for further monitoring of the Tatra lakes.The chironomid assemblages will be used as indicatorsof recovery from acidification and changes connectedwith global warming.

Acknowledgements

We wish to thank Dr. T. EKREM for the identification ofpupal exuviae and males of Tanytarsus bathophilus. We alsothank to the Tatra National Park authority for its adminis-trative support and M. BITUŠÍK for linguistic correction ofthe first version of the manuscript. The study was partiallysupported by the EU project EUROLIMPACS (GOCE-CT-2003-505540) and grant No. 1292/04 of the Scientific GrantAgency of the Ministry of Education of Slovak Republic andthe Slovak Academy of Sciences.

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Received August 16, 2005Accepted May 9, 2006

Classification of mountain lakes using chironomids S201

Appendix 1. List of chironomid taxa from the investigated Tatra lakes.

Roh1

Roh2

Roh3

Roh4

VJam

NJam

Rac1

Rac2

Bys1

Bys2

VTer

NTer

VHin

MHin

VZab

Lad2

Drac

Bati

Litv

Lad1

Sive

Pust

VZbo

PZbo

Star

Sata

Slav

Veli

Kaca

NTem

VTem

NBie

VBie

Apsectrotanypus trifascipennis – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – + +(Zetterstedt, 1838)

Macropelopia nebulosa (Meigen, 1804) – – – – – – – – – – + + – – + + + – – – – + + + – – + + + + + + –Procladius choreus (Meigen, 1804) – – – – – – – – – – – – – – – – – – – – – – – – – – + – – – – – –Procladius tatrensis Gowin, 1944 + + + – + + – – – – – + – + + – + + – – + + + – + – + – – + + + +Zavrelimyia sp. + + + + – + – + + – – + – + + – – – – – – – – + – – – – – + + + +Diamesa sp. – – – – – – – – – – – – – – – – – – – – – – + – – – – – – – – – –Diamesa laticauda Serra-Tosio, 1964 – – – – – – – – – – – + – – – – – – – – – – – – – – – – – – – – –Diamesa vaillanti Serra-Tosio, 1972 – – – – – – – + – – + + – – – – – – – – – – – – – – – – – – – – –Pseudodiamesa branickii (Nowicki, 1873) – – – – – + – – – + – + + – – – – – – – – – – – – – – – – – + + –Pseudodiamesa nivosa (Goetghebuer, 1928) – – – – – – – – – – + – – – – + + + + + + + – – – – – – + – – – –Pseudokiefferiella parva (Edwards, 1932) – – – – – – – – + – – – – – – – – – – – + – – – – – – – – – – – –Prodiamesa olivacea (Meigen, 1818) – – – – – + – – – – – – – – – – – – – – – – – + – – – + + + + + –Chaetocladius sp. – – – – – – – – – – – – – – – – – – – – – + – + – – – – – – – – –Corynoneura arctica Kieffer, 1923/ + + + + + + + + + + – + + + + + – – – – – + + + – + + – + + – + +scutellata Winnertz, 1846

Corynoneura lobata Edwards, 1924 – – – – – – – – – – – – – – – – – – – – – – – + – – – + – – – – –Cricotopus (C.) pilosellus Brundin 1956 – – – – – + – – – – – – – – – – – – – – – – + + – – – – – – – – –Cricotopus (I.) perniger (Zetterstedt, 1850) – + + – – – – + + – – – + + – – – – – – – – – – – – – – – + + – +Cricotopus (I.) cf. tricinctus (Meigen, 1818) – – – – – – – – – – – – – – – – – – – – – – – – – – – – – + – – –Diplocladius cultriger Kieffer, 1908 – – – – – – – – – – – – – – – – – – – – – – – – – – – – + – – – –Eukiefferiella minor (Edwards, 1929) – – – – – + + – – – – + + – – – – – – – – – – – – – – – – – – – –Eukiefferiella sp. – – – – – – + – – – – – – – – – – – – – – – – – – – – – – – – – –Heleniella sp. – + – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Heterotrissocladius marcidus (Walker, 1856) + + + + + + + + + + + + + + + + + + + + + + + + + + – + + + + + +Hydrobaenus spinnatis Saether 1976 – – – – – – – – + – – – – – – – – – – – – – – – – – – – – – – – –Krenosmittia boreoalpina (Goetghebuer, 1944) – – – – – – – – – – – – – – – – – – – – – – – + – – – – – – – – –Metriocnemus cf. obscuripes (Holmgren, 1869) – – – – – – – – – – – – + – – – – – – – – – – – – – – – – – – – –Orthocladius (O.) frigidus (Zetterstedt, 1838) – – – – – – – – – – – – + – – – – – – – – – – – – – – – – – – – –Orthocladius (E.) olivaceus (Kieffer, 1911) – + – + – – – – – – – – + + – – – – – – – – + – – – – – + – + – –Orthocladius (E.) cf. fuscimanus (Kieffer, 1908) – – – – – – – – – – – – + – – + – + – – – – – + – + – – – – – – –Parametriocnemus boreoalpinus Gouin, 1942 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 1Paratrichocladius skirwithensis (Edwards, 1929) – – – – – + – + – – – – – – – – – – – – – – – – – – – – – – – – –Psectrocladius (A.) obvius (Walker, 1856) – – – – – – – – – – – – – + – – – – – – – – – – – – – – – – – – –Psectrocladius (A.) platypus (Edwards, 1929) – – – – + – – – – – – – – – – – – – – – – – – – – – – – – – – – –Pcectrocladius (P.) barbatipes Kieffer, 1923 – + + + – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Psectrocladius (P.) octomaculatus Wlker, 1956 + + + + + + – – + + – – – – – – – – – – – – + – – – – – – + + + +Psectrocladius (P.) oxyura Langton, 1985 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – + – –Rheocricotopus effusus (Walker, 1856) – + – – – – – – – – – – – – – – – – – – – – – – – – – – – – + – +Synorthocladius semivirens (Kieffer, 1909) – – – – – – – – – – – – + – + – – – – – – + + – – – – – – – + + –Tvetenia bavarica (Goetghebuer, 1934) – – – – – – – – – – – – – – – – – – – – – – – + – – – – – – – – –Zalutschia tatrica (Pagast, 1935) – – – – – – – – – – + – – – – – – – – – – – – – + + + – – – – – –Chironomus (L.) montuosus – + + – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Ryser, Wulker et Scholl, 1985

Chironomus (C.) sp. – – – – – – – – – – – – – – – – – – – – – – – – – + + – – – – – –Microtendipes chloris (Meigen, 1818) + + + + – – – – – – – – – – – – – – – – – – – – – – + – – + + + +Pagastiella orophila (Edwards, 1929) – + + – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Endochironomus sp. – – – – – – – – – – – – – – – – – – – – – – – – – – + – – – – – –Micropsectra atrofasciata agg. – – – – – – – – – – – – – – – – – – – – – – + – – – – – + – – – –Micropsectra fusca (Meigen, 1804) + – – – – – + – – – – – – – – – – – – – – – – – – – – – – – – – –Micropsectra junci (Meigen, 1818) + + – + – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Micropsectra notescens (Walker, 1856) – + – + – – – – – – – + + + – – + + – – – + + + – – – – + – + – –Micropsectra radialis Goetghebuer, 1939 – – – – – – – – + + – + + – + + + – + + – + + + – – – – + + – – –Paratanytarsus austriacus (Kieffer, 1924) + + + + + + + + + + – – + + + – – – – – + + + + + – – – + + + – +Tanytarsus bathophilus Kieffer, 1911 – – – – – – + + – – – – + + + – – + + – – + + + – – – + + + + + +Tanytarsus gibbosiceps Kieffer, 1922 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – + – – –Tanytarsus gregarius Kieffer, 1909 – + + + – – – – – – – – – – – – – – – – + – – + + + + + – – – – –Tanytarsus pallidicornis (Walker, 1856) – – – – + + – – – – – – – – – – – – – – – – – – – – – – – – – – –

Key: For explanation of the lake name abbreviations see Fig. 1 and Fig. 3; + presence, – absence.