Diptera, Chironomidae)

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

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

Peter Bitušík1,MarekSvitok1, Peter Kološta2 &MartaHubková3

1Department of Biology and General Ecology, Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, SK–96053 Zvolen, Slovakia; e-mail: [email protected] 2Regional Environmental Oﬃce in Banská Bystrica, Nám. Ľ. Štúra 1,SK–97405 Banská Bystrica, Slovakia 3State Nature Conservancy of the Slovak Republic, The Low Tatras National Park Authority, Zelená 5,SK–97401 Banská Bystrica, Slovakia

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

Introduction vakia in the EU projects (AL:PE, MOLAR, EMERGE, Štefková & Šporka, 2002) enabled to collect chi- Lakes of glacial origin represent more than 90% of all ronomids from 49 Tatra mountain lakes. Chironomid lakes in Slovakia. There are nearly 140 permanent lakes fauna has been studied principally with emphasis on in the High Tatra Mountains (Mts) and West Tatra acidification and climatic changes. Some results have Mts. Most of them are situated above the tree-line in been partly published (Bitušík, 2003a; Kubovčík & the subalpine and alpine zone of the mountains. Stud- Beták, 2004; Kubovčík et al., 2003; Tátosová & ies on benthic communities of these lakes were started Stuchlík, 2003, 2006; Kubovčík & Bitušík, 2006; in the 1930s. Early research was focused on profundal Hamerlík et al., 2006). communities with attempt to classify the investigated Collecting of chironomid pupal exuviae from water lakes based on chironomid and oligochaete assemblages surface of the lakes was a “by-product” of the investi- (Hrabě, 1939, 1942). Chironomid larvae and pupae gations in the framework of the project EMERGE. Al- from Hrabě’s collection were studied by Zavřel (1935a, though, chironomid pupal exuviae technique (CPET) b, 1937a, b) and Zavřel & Pagast (1935). Later, the has been developed initially for use in river monitoring study of chironomids was a part of a complex limnolog- and assessment (Wilson & Bright, 1973; Wilson ical investigation aimed initially on a change of trophic & McGill, 1977) it is applicable to nearly all types status of some lakes as a consequence of increased of surface freshwaters (Wilson & Ruse, 2005). Chi- tourism (Ertlová, 1964; Juriš et al., 1965), and since ronomid pupal exuviae have been used as a successful the 1980s on effects of acidification (Ertlová, 1987). tool for the investigation of lakes (Pinder & Mor- Extensive material of chironomid larvae collected in the ley, 1995; Bitušík, 1995; Ruse, 2002a, b). A com- 1980s and the beginning of 1990s has been determined prehensive list of chironomid species dwelling in the later and the results have not yet been published, with Tatra lakes (Bitušík, 2004) has been based almost ex- the exception of Bitušík (1996). Participation of Slo- clusively on pupal exuviae.

c 2006 Institute of Zoology, Slovak Academy of Sciences S192 Bitušík et al.

The aims of present study were: (i) to classify lakes ences among groups (Kruskal-Wallis, P < 0.05) were chosen on the basis of pupal exuviae collection, (ii) to review as predictors for DA only. Prior to analysis, variables were which environmental variables best describe the lake tested for normality, homoscedasticity (Hartley F-max test) groupings, (iii) to define characteristic chironomid as- and transformed, if necessary. Best-subset procedure (STAT- OFT NC semblages which could be used as reference data for the S ,I ., 2001) based on Wilks’ lambda was used to determine subset of predictors comprised of variables that gave future monitoring programme of the Tatra lakes. the greatest discrimination among groups of lakes. Jack- knifing procedure was employed to improve validation of Study area and sampling sites the classification rates based on discriminant analysis. Multiple regression analysis was conducted to gain in- Investigated lakes are located in the West Tatra Mts (10 sights into the most important environmental factors influ- lakes) and the High Tatra Mts (22 lakes). The dominant encing Chironomidae taxa richness. Set of predictor vari- vegetation is dwarf pine (Pinus mugo) between 1550 ables was defined by environmental variables measured for and ∼1800 m a.s.l., and alpine meadow (dry tundra) each lake. Prior to analysis, normality of the data distri- or rocks in the alpine zone above ∼1800 m a.s.l. The bution was assessed by Shapiro-Wilks test and variables were transformed if necessary. Mean depth and dissolved or- lakes span wide range of environmental conditions with ganic carbon (DOC) were reciprocal cube root transformed. respect to altitude, depth, surface area, volume, water- Reciprocal square root transformation was applied for to- shed characteristics, and present and historical degree tal phosphorus (total P) and concentration of H+ (antilog of acidification, as well. The altitude of the lakes range (−pH)). Conductivity, concentration of total Kjeldahl ni- from 1563 m to 2109 m a.s.l., the area from 0.1 to 20 trogen (total N), and lake area were log10 transformed. Re- ha, and the maximum depth from 1 to 54 m. More de- ciprocal transformation was used for concentration of mag- 2+ tails on observed lakes have been presented elsewhere nesium (Mg ). Highly skewed altitude data were trans- (Vranovský et al., 1994; Kopáček et al., 2004). formed to the fifth power. Best-subset regression method (STATSOFT,INC., 2001) was used to identify which predictors explained significant variation in Chironomidae species Material and methods richness. Mallow’s Cp was selected as the best-subset crite- rion. Tolerance values were examined to ensure the absence Field and laboratory of multicollinearity among predictors. Chironomid pupal exuviae were sampled twice per year (2000–2002): firstly after the melting of ice in June – July, Ordination analysis was used to categorise 11 lakes secondly in August or the beginning of September, respec- with unknown environmental conditions into one of the tively. Floating debris together with pupal exuviae was col- group derived through TWINSPAN. As detrended corre- ILL lected skimming the water along the lake shore with 200 spondence analysis (H , 1979b) with detrending by seg- > µm mesh net attached to 1.5 m long pole. Sampled material ments revealed gradient length 2.8 SD units, unimodal re- was placed into polythene bottles, labeled and preserved sponses of species to environmental gradients were assumed. TER with 4% formalin. In the laboratory, all pupal exuviae in Therefore, canonical correspondence analysis (CCA; RAAK the sample were removed using low-power stereomicroscope B , 1986) with Monte Carlo permutation test (999 un- (× 7–40). Exuviae were mounted on the slides in groups constrained permutations) of the first and second axis was using Berlese fluid and identified under high magnification employed. Samples from the 22 lakes with know environ- (× 400) to species-level, if possible. Identification was based mental conditions were treated as active samples in the anal- on the key by LANGTON (1991) and LANGTON &VISSER ysis while samples from the remaining 11 lakes with miss- (2003). ing environmental variables were treated as passive samples. This encoding enabled us to categorise lakes without envi- Numerical analyses ronmental data into TWINSPAN groups. CCA axes were An aprioriclassification of lakes was conducted in or- constrained to variables that gave the greatest separation der to define groups of lakes that contained similar chi- among groups in discriminant analysis. ronomid assemblages. Two-way indicator species analysis (TWINSPAN; HILL, 1979a) was used to classify studied Results lakes into discrete groups. The analysis was based on the presence/absence of chironomid taxa in each of 22 lakes with defined environmental conditions. A maximum level A total of 9,432 chironomid pupal exuviae from 33 of division was set to 3. Tatra mountain lakes were identified to 55 species/taxa Differences in taxa richness among TWINSPAN groups (Appendix 1). were tested by one way analysis of variance (ANOVA) fol- TWINSPAN analysis of chironomid assemblages lowed by generalised Tukey’s HSD test for unequal sample from 22 lakes resulted in four lake groups (Fig. 1). The size (STATSOFT,INC., 2001). Kruskal-Wallis test followed initial division was based on five indicator species and by Dunn-type test for multiple comparisons with unequal separated acidified subalpine and alpine lakes (group AR sample sizes (cf. Z , 1999) was used to assess differences D) from the others. Presence of Zalutschia tatrica and in environmental variables among the groups. Tanytarsus gregarius was indicative for this group. At Groups derived in TWINSPAN were used as depen- dent variables in a discriminant function analysis (DA) to the second division, group A of high alpine lakes was determine the relationship between the environmental vari- separated due to presence of Pseudodiamesa nivosa. ables and the species defined groups of lakes. Non corre- The six lakes in group C were characterized by presence lated environmental variables that showed significant differ- of 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 at each division. The column plot shows mean species richness of Chironomidae (± 1 × SD) in each group of lakes. Columns with the same lowercase letters are not significantly different (Tukey’s HSD test for unequal sample size, P < 0.05). Abbreviations: Bati – Batizovské pleso, 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 – Prostredné zbojnícke pleso, Rac2 – Vyšné Račkove pleso, Roh4 – Štvrté Roháčske pleso, Sata – Vyšné Satanie pliesko, Sive – Prostredné sivé pleso, 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.

2 Table 1. Best subset multiple regression (Mallow’s Cp = −0.54, F3,18 = 3.504, P = 0.036, R = 0.37) of Chironomidae species richness in 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 tPTolerance

Lake area 0.456 0.209 2.182 0.043 0.80 TP −0.358 0.213 −1.686 0.109 0.78 Mg2+ −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- Test results indicated that 8 out of 10 environmental vacea, Apsectrotanypus trifascipennis, Psectrocladius variables showed significant differences among species oxyura, P. barbatipes and Pagastiella orophila were defined groups of lakes (Tab. 2). Conductivity and pH recorded exclusively in these lakes. Absence of P. showed the strongest differences among groups. On the nivosa,presenceofMicropsectra radialis,andalso other hand, concentration of total phosphorus and ni- Tanytarsus bathophilus, Zavrelimyia sp., Paratanytar- trogen did not vary significantly among groups. sus austriacus, Corynoneura arctica/scutellata sep- In DA, the best subset of predictors (Tab. 3) was arated group B of seven alpine lakes. Taxa rich- determined from eight environmental variables that ness showed significant differences among the four significantly differ among TWINSPAN groups. Jack- TWINSPAN groups of lakes (F3,18 = 6.21, P = 0.004). knifing procedure reduced the overall classification suc- Pairwise comparison revealed significant differences in cess to 41% with low classification rates for group B richness between groups A and B, and between groups (14%), intermediate for groups A (40%) and C (50%) A and C (Fig. 1). and a relatively high classification rate for group D Differences in environmental conditions among (75%). Discriminant analysis yielded two significant groups of lakes were examined by Kruskal-Wallis test. discriminant functions (P < 0.05) which explained S194 Bitušík et al.

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 (µScm−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 (µgdm−3) 77.8 95.0 105.5 236.5 6.423 DOC (mg dm−3) 0.180 a 0.405 a 0.356 a 2.008 a 9.157* TP (µgdm−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 labelled with 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 classiﬁed in a discriminant analysis are shown.

Variable Level of eﬀect % correctly classiﬁed H+ DOC Altitude Lake area Wilks’ lambda P

1 + 0.388 < 0.0006 64 2 + + 0.215 < 0.0001 68 3 + + + 0.132 < 0.0001 73 4 + + + + 0.097 < 0.0001 77

Table 4. Standardised discriminant function coefficients for the tionships among variables revealed that concentration model included four environmental variables (transformed). of calcium, magnesium and conductivity were strongly correlated with H+, and mean depth was correlated Discriminant function Variable with lake area (Tab. 5). After the addition of inter- 12 correlated variables, strong multicollinearity effect was < + observed (tolerance 0.1). H 0.79 0.03 Multiple linear regression analysis resulted in a Lake area 0.59 −0.16 Altitude −0.35 0.73 model that explained 37% of the variation in Chi- DOC 0.34 0.68 ronomidae species richness by area of lake, concentration of total phosphorus and magnesium (Tab. 1). Eigenvalue 3.29 1.13 % variance explained 72.3 24.9 Lake area was the most important factor predicting species richness in the 22 observed lakes. Lake Explanations: Displayed functions were significant at P < 0.05. area (log10 transformed) was positively correlated Eigenvalues and percentage variance explained by each function with chironomids species richness, indicating that the are shown. larger lakes tend to contain more chironomids. Al- though concentrations of total magnesium were weaker predictor than lake area, it did significantly con- 97.2% of the between-group variance (Tab. 4). Concen- tribute to the prediction of species richness of non- tration of H+ (pH) contributed most to the first func- biting midges. Lakes with higher concentration of tion, whereas the second function was weighted most magnesium tend to support richer Chironomidae as- heavily by altitude and DOC. Apparently, the first dis- semblages. In the regression model, the tolerance criminant function separated group D and the other values are well above the value at which multi- groups (Fig. 2). The second function discriminated well collinearity is of concern (cf. Quinn & Keough, between high alpine lakes (group A) and subalpine lakes 2002). (group C) but group B more or less overlapped groups The results of CCA ordination based on 47 chi- CandA. ronomid taxa and 22 lakes are plotted graphically in Models of higher effects were unwieldy to interpret Fig. 3. Eleven lakes with unknown environmental vari- and did not considerably improve classification success ables were included in the analysis as passive samples or even decreased it. Moreover, examination of rela- and 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 −0.13 Mean depth −0.16 0.86*** pH −0.17 0.30 0.41 Conductivity −0.26 0.27 0.35 0.97*** DOC −0.15 −0.32 −0.43* −0.22 −0.20 TP −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 −0.01 Mg2+ −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 = the group A represented by high alpine lakes, and Vyšné 0.46, F = 2.70, P = 0.001) and 8.5% (λ2 = 0.30, F = Terianske pleso (VTer) is placed close to the acidified 1.84, P = 0.001) of variance explained. Intra-set corre- alpine lakes. Remaining four lakes with unknown en- lations indicated that the first axis was correlated with vironmental conditions were situated in the middle of pH (r = −0.93) and DOC (r = 0.87), and the second the ordination space. These lakes may be classified as axis was most strongly correlated with altitude (r = members of the group B. −0.98). The first CCA axis arranged lakes along a gradient of decreasing pH. The group D (polygons in CCA Discussion correspond to the classes formed by TWINSPAN) is clearly displayed on the right-hand side of the diagram. Analysis of chironomid fauna from 33 lakes in the Tatra The other three lake groups are located along a gradi- Mts demonstrates that chironomid pupal exuviae data ent of increasing altitude from the top to the down of are, generally stated, associated with altitude, ion con- the diagram. centrations, nutrients, and pH. Only one parameter Location of passive samples showed that lakes Prvé connected with lake morphometry (lake area) is sig- Roháčske pleso, Druhé Roháčske pleso, Tretie Roháčske nificantly correlated with species/taxa richness. pleso, Nižné Žabie bielovodské, Vyšné Jamnícke pleso Altitude was one of the most important variables (Roh1, Roh2, Roh3, NBie, VJam) tend to follow the both in DA and CCA. Altitudinal gradient is closely subalpine lakes of the group C. On the other hand, connected with a major climatic gradient in the moun- Ľadové pleso v Zlomiskách (Lad2) shows high affinity to tains. The harsh climatic conditions exert an impor- S196 Bitušík et al.

Fig. 3. CCA ordination of 22 Tatra lakes based on Chironomidae presence/absence data set. Full circles represent group A, squares group B, diamonds group C and triangles represent group D. In addition, 11 passive samples from the lakes with unknown environmental conditions 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 of its taxonomic status was discussed elsewhere (Bitušík, aquatic ecosystems (Michelutti et al., 2002). Lot- 2003b). However, very similar pupal remains have been ter et al. (1997) found a strong linear relationship be- found in the subfossil material from the Alps and in tween altitude and mean summer temperature within a a recent material from the Central Pyrenees (Heiri set of Swiss lakes. The results of Šporka et al. (2006) et al., 2003). Pseudodiamesa nivosa seems to be the indicated approximately linear decrease of lake surface only characteristic species of chironomid assemblages water temperature with increasing altitude not only in inhabiting the high alpine Tatra lakes. The species is summer, but also in late spring and autumn season in adapted to harsh physical environment, including freez- the Tatra Mts. Faunistic changes in aquatic organisms ing and drying. It indicates ultra-oligotrophic status (α- of remote lakes have been found to correspond to alti- oligotrophy, Sæther, 1979) and extremely low water tude/temperature gradient very often (e.g., Walker & temperature, without respect to altitude. That is why, Matthewes, 1989; Krno, 1991; Lotter et al., 1997; the species has been found in small, shallow subalpine Larocque et al., 2001). The changes of temperature lakes (Bitušík & Koppová, 1997; Hamerlík, 2004). with the increasing of elevation influence the faunistic P. nivosa is considered to be more of a mountain species composition directly and indirectly. than its relative species P. branickii (Serra-Tosio, In comparison with subalpine lakes, the chirono- 1976) which occurs in moderate conditions. Actually, mid assemblages of alpine Tatra lakes are character- the distribution of the both species in the Tatra lakes ized by lower species richness and total absence of taxa displays an ecologically vicarious distribution pattern. from the tribe Chironomini. Heterotrissocladius mar- Larvae of P. nivosa tolerate low pH and occur in acidi- cidus and Procladius tatrensis are widely distributed fied lakes, too (e.g., Prostredné sivé pleso – Sive, Vyšné from subalpine to the high alpine zone. H. marcidus Terianske pleso – VTer). was found in all of the observed lakes, with excep- Micropsectra radialis canbeconsideredasan- tion of Slavkovské pleso (Slav). It is probably the most other species of this assemblage, although it occurs common chironomid of the Tatra lakes (cf. Ertlová, in the alpine lakes generally. The species is a charac- 1987). P. tatrensis has been considered to be a typical teristic member of the ‘Tanytarsus lugens’-community species, perhaps endemic species, for the Tatra Mts and (Brundin, 1956). The larvae are cold stenothermal, Classification of mountain lakes using chironomids S197 polyoxybiontic, and eurybathic. They inhabit the lit- ferences in the chironomid taxa richness among the lake toral zone under subarctic/arctic conditions, while in groups. The quantities of P and organic C in the Tatra temperate lakes they are restricted to the profundal lakes were predominantly governed by the soil and vege- zone (Hofmann, 1988). M. radialis occurs mostly, in tation characteristics of the catchments. Concentrations the deep alpine Tatra lakes. Its presence in shallow of TP and DOC (but also of total organic nitrogen) de- lakes indicates extremely low temperature conditions creased with the decline of vegetation and soil cover (see common occurrence with P. nivosa in Kačacie pleso along the altitude/temperature gradient (Kopáček et (Kaca)), on the other hand, its absence in the other al., 2000). Similar significant positive correlation be- alpine lakes could signalize weaker summer thermal tween DOC concentrations in lakes and amounts of veg- stratification and higher temperatures at the lake bot- etation in catchments has been found in arctic lakes tom (e.g., Malé Hincovo pleso – MHin). The species is (Michelutti et al., 2002). generally absent in the sub-alpine lakes with the excep- Traditionally, relationships between chironomid fa- tion of some deep lakes (Nižné Temnosmrečinské pleso una and productivity have been used for classification of – NTem) and with cold and well oxygenated water in lakes (Brinkhurst, 1974; Wiederholm, 1980). Ert- the hypolimnetic zone. These data are consistent with lová (1987) pointed out the changes in trophic condi- earlier studies from prealpine lakes in northern Switzer- tions as a factor influencing both diversity and abun- land (Reiss, 1968). The species is acid sensitive and dance of littoral chironomids in the Tatra lakes. Lot- disappeared from acidified lakes (Prostredné sivé pleso ter et al. (1998) revealed distinct changes in compo- – Sive, Vyšné Terianske pleso – VTer). Its absence in sition of aquatic organisms, including Chironomidae, previously acidified Batizovské pleso – Bati (Kopáček along the total phosphorus gradient in investigated & Stuchlík, 1991) could be a signal that biological lakes. Under oligotrophic conditions of remote lakes, recovery in the lake has not been complete yet. the chironomid assemblage composition reflected food More detailed studies based on recent and subfossil availability rather than oxygen concentration (Linde- material from Ľadové pleso (Lad1) and Vyšné Wahlen- gaard, 1995). Thus, the subalpine lakes with higher bergovo pleso revealed predominance of P. nivosa and primary production and higher input of allochthonous M. radialis with negligible representation of other organic matter utilizing as food are inhabited by more taxa (Tátosová & Stuchlík, 2003; Kubovčík & chironomid species than the alpine lakes. Bitušík, 2006). Such structure of assemblages resem- Acidification in the Tatra Mts disturbed natural bles in some aspects of chironomid fauna of extreme gradient of changes in the nutrient concentration from Swiss alpine lake (Lotter et al., 2000). Larocque et the forest lakes to the alpine ones, when strongly acidi- al. (2001) found Pseudodiamesa and M. radialis to be fied lakes (e.g., Starolesnianske pleso, Slavkovské pleso, members of chironomid assemblages restricted to lakes Vyšné Satanie pliesko) had, on average, higher algal characterized by cold climatic conditions in alpine- productivity than non-acidified lakes. Species composi- tundra vegetation of northern Swedish Lappland. tion of these lakes reflected not only low pH values (Z. Most of the taxa collected in some high alpine tatrica) but the higher trophic status too (e.g., T. gre- lakes, were also present in the subalpine lakes, and for garius, Chironomus sp., Procladius choreus, Endochi- this reason group B of low-alpine lakes is not separated ronomus sp.). On the other hand, moderately acidified distinctly from both subalpine and high alpine lakes. Tatra lakes with pH between 5 and 6 became more olig- AccordingtothesedataP. austriacus seems not to be otrophic. Fott et al. (1994) reported on extinction of such cold-stenotherm species as it has been supposed zooplankton by starvation due to low amount of phy- (Fjellheim et al., 1997; Catalan et al., 2002). Sur- toplankton. The period of acidification resulted in the prisingly, Tanytarsus lugens,atypicalmemberofthe decreasing of chironomid productivity but not in taxo- ‘lugens’-community was not found in the Tatra lakes, nomic composition changes, as demonstrated from the whereas we did collect T. bathophilus. According to the subfossil record taken from the Vyšné Wahlenbergovo more recent data T. bathophilus is not strictly restricted pleso (Kubovčík & Bitušík, 2006). This is in accor- to oligotrophic lakes but it can be found in lakes with dance with the opinion that chironomids reflect the higher trophic status (Ekrem, 2004). trophic status of lakes better than pH (Brodin, 1990) The subalpine Tatra lakes are inhabited by assem- except when pH decreases near or below 5.0 (Brodin blages composed of more thermophilic chironomid taxa. & Gransberg, 1993; Olander et al., 1997). While P. barbatipes and P. orophila are restricted only In fact, some authors did not find a correlation be- to the subalpine lakes, other species occurred commonly tween chironomids and pH (e.g., Walker et al., 1991; in reservoirs and streams at low elevations. Olander et al., 1997; Halvorsen et al., 2001). How- Not surprisingly, variables connected with lake pro- ever, pH in this study is shown to be significantly cor- ductivity were correlated with the chironomid data. related with chironomid data. Axis 1 of the CCA or- DOC contributed together with altitude to the second dination reflects a gradient of pH and ionic concentra- function of the discriminant analysis that clearly sep- tions and the variables are highly positively correlated arated the high alpine lakes from the subalpine lakes. with each other (Tab. 5). Similarly, in DA, concentra- Concentration of TP contributed to explaining the dif- tion of H+ (pH) contributed most to the first discrim- S198 Bitušík et al. inant function that explained majority of the between different in many aspects. It should be remembered that group variance and separates the group D from other many non-lacustrine species can be found in the littoral groups of lakes. zone of the Tatra lakes (cf. Cameron et al., 2002). Most Acid status of the Tatra lakes is closely related of these are typical lotic taxa (e.g., Diamesa, Euki- to their concentrations of base cations, especially cal- efferiella, Heleniella, Orthocladius, Paratrichocladius) cium and magnesium. Both acid neutralizing capac- and few usually live in semiterrestrial habitats (Metri- ity and pH are strongly correlated to the sum of ocnemus, Chaetocladius). These species can contribute Ca2+ and Mg2+ concentrations (Kopáček et al., 2006). considerably to the species richness, especially in the The catchments of strongly acidified lakes (group D) lakes that have well developed inflows (e.g., Nižné Teri- have very low soil base saturation, and their carbon- anske pleso, Vyšné zbojnícke pleso – VZbo, Prostredné ate buffering system was depleted a long time before zbojnícke pleso – PZbo). the main period of Tatra lakes acidification (Kopáček This study presents the second attempt to classify et al., 2004). The data from the Tatra lakes confirm the Tatra lakes using chironomid data. The first clas- that under conditions of strong acidification, low pH sification was proposed by Hrabě (1939). It contains might override the effects of other environmental fac- many valuable historical data concerning the chirono- tors. Chironomid assemblages tend to be species poor mid distribution. Nevertheless the results are hardly and uniform (“azonal assemblages”), although detailed comparable with the scheme presented in this paper due taxonomic analysis can reveal some species indicating to taxonomic level of identification. Much later, Krno the un-disturbed lake status (e.g., presence of P. nivosa et al. (1986) and Krno (1991) outlined comprehensive in the high alpine acidified lakes, and Endochironomus classification of the lakes using littoral macroinverte- sp., P. choreus in the subalpine acidified lakes). brates, however unfortunately, mostly without chirono- Zalutschia tatrica seems to be a reliable indicator of mids. acid conditions in the Tatra lakes. In pre-acidification Among the lacustrine benthic macroinvertebrates period the larvae were abundant in many shallow, warm Chironomidae are the group with the highest number of pools and the littoral zone of some Tatra lakes (Hrabě, species, and very often with the highest abundance and 1942). It can be supposed that the species is normally biomass (Armitage et al., 1995). Thus they should not adapted to humic waters, similarly to the other mem- be ignored in any lake monitoring programme. The next bers of the genus (Sæther, 1976), and tolerate rela- survey on the Tatra lakes will test the accuracy of the tively wide range of temperature. In autumn 1993 and proposed classification. Presented results can be used as 1994, Z. tatrica dominated the littoral fauna of the reference data for further monitoring of the Tatra lakes. strong acidified Starolesnianske pleso making up 94.0% The chironomid assemblages will be used as indicators and 85.9%, respectively. The findings of some larvae in of recovery from acidification and changes connected littoral samples taken from non-acidified lakes in the with global warming. same time indicate that the littoral zone was more in- fluenced by acidification than deeper areas of the lake (Bitušík, unpubl. data). Acknowledgements Multiple linear regression analysis revealed the lake We wish to thank Dr. T. EKREM for the identification of area as the most important variable predicting species pupal exuviae and males of Tanytarsus bathophilus.Wealso richness in the lakes. Most of identified chironomids in thank to the Tatra National Park authority for its adminis- the Tatra lakes are considered to be littoral and sub- trative support and M. BITUŠÍK for linguistic correction of littoral inhabitants (Bitušík & Hamerlík, 2003). Lit- the first version of the manuscript. The study was partially toral communities consist of a rich fauna as a conse- supported by the EU project EUROLIMPACS (GOCE-CT- quence of greater substratum heterogeneity, and effect 2003-505540) and grant No. 1292/04 of the Scientific Grant of complex factors (Wetzel, 2001). Basin morphom- Agency of the Ministry of Education of Slovak Republic and etry and transparency limit the size of littoral zone the Slovak Academy of Sciences. and determine the extension of inshore species (Devol & Wessmar, 1978). The relationship between species References richness and lake area has been found for zooplankton and fish in a number of studies (e.g., O’Brien et al., ARMITAGE, P.D., CRANSTON,P.S.&PINDER, L.C.V. (eds) 1995. 2004). The habitat structure and size of littoral zone The Chironomidae: Biology and ecology of non-biting midges. of the Tatra lakes have not been measured. It could be Chapman & Hall, London, 572 pp. BITUŠÍK, P. 1995. A comparative study of the selected man- supposed that lakes with greater area, under conditions made reservoirs in the Banská Štiavnica mine region (Cen- of high transparency, can have more extensive littoral tral Slovakia) based on chironomid pupal exuviae assemblages and support more diverse habitats, consequently higher (Diptera: Chironomidae). Acta Fac. Ecol., Zvolen 2: 45–52. chironomid diversity. Differences in the species compo- BITUŠÍK, P. 1996. Biologické hodnotenie vybraných plies v sition suggest that ecological conditions in the littoral Západných Tatrách na základe mediálnych spoločenstiev pakomárov (Diptera: Chironomidae), pp. 175–180. In: MID- zone of seemingly similar lakes (e.g., Nižné Terianske RIAK, R. (ed.) Biosférické rezervácie na Slovensku, Technical pleso–NTervs Veľké Hincovo pleso – VHin) could be University, Zvolen. Classification of mountain lakes using chironomids S199

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Received August 16, 2005 Accepted 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 ––––––––––––––––––––––––––––––––1 Paratrichocladius 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, Wülker 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.