Benthic diatom communities along pH and TP ... - Springer Link

Journal of Applied Phycology (2006) 18: 105–117 DOI: 10.1007/s10811-006-9080-4

 C Springer 2006

Benthic diatom communities along pH and TP gradients in Hungarian and Swedish streams Csilla Kov´acs1,∗ , Maria Kahlert2 & Judit Padis´ak1 1

University of Veszpr´em, Department of Limnology, 8200 Veszpr´em, Egyetem u. 10. P.O. Box 158, Hungary; Uppsala University, EBC, Department of Limnology, EBC, Erken Laboratory Norr Malma 4200, 761 73 Norrt¨alje, Sweden 2

∗

Author for correspondence: e-mail: [email protected]

Received 15 November 2005; accepted 20 January 2006

Key words: diatoms, ecotypes, pH, streams, TP, water framework directive Abstract Benthic diatoms are widely used in Europe to assess ecological status of running waters. The aim of this study was to develop models to predict average pH and total phosphorus (TP) for European stream monitoring using combined diatom data-sets from Hungary and Sweden. As first step, the relationship between the measured environmental variables and the distribution of the taxa was checked by classification by using cluster-analysis and CCA. Diatom data separated clearly along the main hydrogeochemical parameters (e.g. alkalinity, pH). Concerning nutrients, TP was the most determinant factor. Predictive value of the TP model (r = 0.96) is high only for the Hungarian data and the pH model (r = 0.97) can be used in both phycogeographical regions. Introduction The need for finding appropriate biological indicators for assessing ecological status of surface waters has been increasing. Such biotic measures have advantages against chemical analyses. Most importantly, they integrate environmental effects (Cox, 1991) reflecting the typical conditions instead of momentary values that can be measured precisely with chemical methods. Diatoms are suggested to be indicators of environmental conditions and can be used successfully in biomonitoring (Round, 1991; Whitthon et al., 1991; Rimet et al., 2004). Diatom based impact analyses have a long history (Kolkwitz and Marson, 1908; Butcher, 1947). Diatoms, as pH indicators, were applied to trace the acidification history of lakes in several paleolimnological studies (e.g. Battarbee, 1984; Cameron et al., 1999) and to indicate acidity and pH in running waters (van Dam and Mertens, 1995; Coring, 1996). As a consequence of their relatively short life cycles, they respond rapidly to eutrophication and provide detailed information on nutrient changes. Some paleolimnological studies (e.g. Wunsam et al., 1995, Alefs et al., 1996;

Schmidt et al., 1998) applied TP transfer functions to describe lake eutrophication. Nowadays these methods are becoming widespread in biomonitoring of running waters (Winter and Duthie, 2000; Soininen and Niemela, 2002) especially due to the strong impetus in Europe set by the EU Water Framework Directive (hereafter WFD; EC Parliament and Council, 2000). Accuracy and applicability of diatom/variable models largely depend on the extent how calibration datasets cover the whole environmental range of the variable tested. pH is typically low in Swedish rivers while high in Hungarian ones and opposite can be observed for TP. In this work we used combined data-sets form Sweden and Hungary with the aim that the developed models could be applied to predict pH and TP in a wide geographical scale.

Study areas A total of 102 diatom samples were collected in Hungary and in Sweden (Figure 1). Hungary is located in Central Europe (45◦ 44 –48◦ 35 N;

106

Figure 1. Location of the 102 sampling sites within Sweden and Hungary.

16◦ 07 –22◦ 54 E). The climate of country is affected by three large climatic impacts: Atlantic, continental and Mediterranean. The annual average mean temperature is 9.7 ◦ C. The warmest month is July with mean temperature 20.0 ◦ C and the coldest month is January (2.1 ◦ C). In the central part of the Great Plain the annual average rainfall varies between 470–550 mm, in the mountains 700–800 mm. The number of hours with sunshine varies between 1700–2200 hour year−1 . Most of the investigated Hungarian streams (stream orders 1 to 3 according to Lampert & Sommer, 1997) belong to the catchment of the Danube. The dominance of calcareous bedrock results in dominance of Ca2+ , Mg2+ and HCO2− 3 in surface waters. Sweden occupies the eastern part of the Scandinavian peninsula and is located in Northern Europe bordering the Baltic Sea, Gulf of Bothnia, Kattegat, and Skagerrak. Most of Sweden has a typical continental climate and the northern areas belong to the subarctic climate region. The precipitation during the growing season varies from more than 750 mm in the western part to 350–500 mm in the eastern part and it is less than 400 mm in the northern part. The mean temperature during June–August is typically 13–16 ◦ C.

The coldest month is February with the average temperatures of below 0 ◦ C throughout Sweden. The hours of sunshine are about 1460 hour year−1 . The Swedish dataset was collected in different projects covering a large part of Sweden. Most of the streams are of low stream-order and they are flowing on acid-intermediate bedrock areas with low buffering capacity.

Material and methods Sample collection and chemical analyses In Hungary, dissolved oxygen, water temperature, conductivity and pH were measured in situ with Consort C535 sensor simultaneously with diatom sampling. Water samples at each site were collected and kept at ∼ field temperature until the analyses in the laboratory + − for alkalinity, Ca2+ , Mg2+ , K+ , Na+ , SO2− 4 , Cl , NH4 , − − 3− NO2 , NO3 , PO4 and TP using national standards. The Ca2+ , Mg2+ , K+ , Na+ and Si were measured with ICPOES (Optima 2000 ICP-OES). The alkalinity, SO2− 4 and Cl− were determined by titration (Inc´edy, 1981; − N´emeth, 1998). NO− 2 , NO3 and TP were determined

107 with spectrophotometry (Marczenko, 1976; Incz´edy, 1996; N´emeth, 1998). Each sampling sites represent the “Carpathian-basin” ecoregion in terms of the WFD. The Swedish data (diatom counts and water chemistry) were collected in 42 Swedish streams during several projects initiated by the Swedish Environmental Protection Agency: “AQEM” 2000 (see details in Sandin et al., 2000) and “Al-projectet Cecilia Andr´en” 2000 (see details in Kahlert & Andr´en 2005), national monitoring of streams 2001; Em˚an 2002 (see details in Sandin et al., 2003); Padjelanta 2002 (see details in Wilander, 2003) and acid/eutrophic streams, 2003. The streams covered a broad range (4.8–8) of pH and water colour, and included streams impacted by anthropogenic acidification. Most streams were nutrient poor and pristine (reference sites according to the WFD), some were slightly or moderately impacted. The sites covered all the four Swedish ecoregions that are clearly separated by geochemistry (F¨olster et al., 2004). Region 1: Borealic upland, region 2: Fenno-Scandian shield above 200 m, region 3: Fenno-Scandian shield below 200 m, and region 4: Central Plain. Water samples were taken monthly in most of the streams. The acid streams were sampled every second month, the Al streams were sampled very frequently during the spring flood, after this monthly. Only the Em˚an, AQEM and Padjelanta streams were sampled with low frequency (twice respectively, once a year). Analyses followed the national standards for water chemistry analyses. Diatoms were sampled at 102 stream stations (60 sites in Hungary and 42 in Sweden, Figure 1) in springs (before foliation and avoiding the flooding-periods) in 2001–2004. Sampling was carried out according to the European recommendations (Kelly et al., 1998). Benthic diatoms were collected from at least 3 stones in the lotic part of the river. The stones were scratched by toothbrush and the suspension was put in small plastic bottles then preserved in 3–4% formaldehyde. Diatom preparation, identification and counts The diatom valves were cleaned from organic material using 40% hydrogen peroxide to eliminate organic matter after the calcareous compounds had been removed from the samples with hydrochloric acid. Smear slides were prepared for each sample using synthetic resin c (Zrax ). Total of 400 valves were counted per sample except where diatoms were scarce. If so, we counted each valves on the slide and used the data only if the number of the enumerated valves reached 200. We ensured the comparability of identifications by two di-

atomists. Some randomly chosen slides were analysed and differences in identification were discussed until common decision was made. Diatoms were identified at species level using light microscopy with phase contrast according to Krammer and Lange-Bertalot (1991– 2000), Lange Bertalot (1995–2002) and Krammer (2002). The counts were converted to relative abundance. Data analyses Cluster-analyses were carried out by the widely used (Kwadrans et al., 1999; Owen et al., 2004) Bray– Curtis index and UPGMA (unweighted pair-groups using arithmetic averages) fusion algorithm. The aim of the use of this method was to compare the diatom flora of the two countries to show the main differences and similarities. We used canonical correspondence analysis (CCA; Ter Braak and Smilauer, 2002), a powerful unimodal multivariate technique in ecological studies (Almeida & Gil, 2001; Leira & Sabater, 2005), to examine the distribution of the diatom assemblage along the major environmental gradients. The data processing was performed after logarithmic transformation, except for pH. Si, dissolved oxygen and temperature was not used in the final analyses, because either their regional dissimilarity was minor or their effect on diatom patterns was negligible. Cluster-analysis and CCA was run using SYNTAX (Podani, 2000). The cluster-analysis was based on 442 taxa, CCA on 238 taxa with relative abundance ≥ 2% at any of the 102 sampling sites. Another common bioassay problem in applied ecology is to estimate values, or at least ranges of single environmental variables from species abundance data. The weighted averaging method (WA) is based on the assumption that a taxon with its optimum close to the streamwater’s physical and chemical characteristics is most abundant at that site (Birks et al., 1990). Indicator values of all species present in a site averaged to give an estimate of the value of the environmental variable at the site. The average is weighted by species abundances with absent species having zero weight (Ter Braak & Barendregt, 1986). This method is widely used due to its simplicity and its power for empirical prediction (Ter Braak & Juggins, 1993). Weighted averaging (WA) and weighted averaging without tolerance weighting regression (WAtol) models were developed to infer pH and TP concentrations of the streams. Because of apparently different

108

Figure 2. Dendogram of the cluster-analysis of epilithic diatoms from 102 streams in Hungary and Sweden (Bray-Curtis index).

diatom patterns in the two phycogeographical regions, first we constructed two separate pH models. In the Hungarian and Swedish model we used only those streams that had a trophic status close to the oligotrophic (training set) in order to minimize the influence of other environmental parameters (e.g. nutrients) on diatom structure. Then, in order to test if the model applies in both phycogeographical regions on boarder scale than 1–3 pH unit and if the error will be higher due to the local factors, we developed a combined Hungarian-Swedish model. Regarding phosphorus, it varied in a wide range (from the limit of detection to 30 μmol L−l ) in the Hungarian streams, whereas the Swedish streams in no case did it exceed 4 μmol L−l . Therefore we developed a TP model only for Hungary. The models were cross-validated using a randomly chosen data-set that consisted of 21 sampling sites (test set) and was independent of the training set. Classical and inverse regression was used for dreshrinking. The root mean squared error of prediction (RMSEP) was calculated directly from the calibration set. The calculations were performed with the computer program C2 version 3.1. The total of 246 diatom taxa with relative abundance of ≥1,5% were included in this statistical analysis.

Results In cluster-analysis seven bigger groups were separated and characterized by the most abundant taxa (Figure 2, Table 3). On the right side Swedish streams were segregated (Group 6 and 7) having low alkalinity, pH and nutrient levels (Table 1). Their characteristic species are widely distributed in Sweden (e.g. Cymbella excisiformis, Fragilaria gracilis) and are known to be acidophilus (e.g. Brachysira neoexilis, Eunotia implicata and Tabellaria flocculosa; van Dam et al., 1994). On the left side of the dendogram primarily Hungarian streams were grouped. In these clusters (Group 1,3,4,5) Planothidium lanceolatum, Navicula lanceolata, Gomphonema olivaceum and Nitzschia dissipata are typical examples of the most common species. Group 2 includes some Swedish streams from regions with calcareous hydrogeochemistry and higher nutrient concentrations. Species that are common in both countries characterize this group (for example, Amphora pediculus, Cocconeis placentula and Navicula tripunctata). The CCA analysis (Figure 3) aimed to select the principal environmental parameters responsible for the

109

Alkalinity mmol l−1

mmol l−1 7.75 (5.30−14.10)

Group 1 3.36 (0.03−7.38)

Group 2 6.73 (4.30−8.40)

Group 3

7.33 (3.2−12.8)

Group 4

9.71 (6.1−13.80)

Group 5

0.55 (0.16−0.58)

Group 6

0.21 (0.03−2.03) 0.07

0.24 (0−2.62)

Group 7

Table 1. Means and ranges for the environmental variables in each cluster’ group (1–7).

Ca2+

0.32 (0.14−0.52) 0.08

(0.05−7.59)

1.43 (0.65−2.57) 3.78

0.41

2.26 (0.75−4.44) 2.55

(0.04−0.42)

2.68 (1.43−3.72) 1.81

0.03

1.53 (0.06−2.59) 0.35

(0.57−13.53)

2.04 (0.39−3.67) 2.74

0.25

(0−8.93) 0.23 (0.01−1.53)

mmol l−1

(0.56-9.66)

0.25

Mg2+

0.22

(0.01−0.05) 0.15 (0.02−0.44)

(0.01−0.29) 0.62 (0.25−2.77)

0.09

(0.03−0.16) 0.17 0.16

(0.07−0.65) 3.6 (0.39−7.53)

(0.74−8.68) 6.25

(0.07−5.32)

2.05

(0.64−6.09) 2.67

4.21

(0.05−0.81) 1.64 (0.56−6.09)

(0.82−3.58) 1.04

(0.11−20.66)

1.58

(0.03−1.18) 2.82

0.17

(0.01−0.33) 1.2 (0.17−1.78)

(0.04−4.48) 1.53

mmol l−1

0.96

mmol l−1

K+ mmol l−1

(0.01−9.91) 1.17 (0.02−1.77)

Na+

Cl− 0.37

(0.02−0.53) 0.12 (0−0.19) 0.34 (0.31−0.40)

(0.01−0.39) 1.1 (0.02−7.47)

(0.02−0.22) 0.08 (0.02−0.16) 0.35 (0.31−0.38)

1.33

(0−4.22) 0.12 (0.10−0.13) 0.23 (0.08−0.34)

(0.54−397.57)

77.02 (0.46−493.66) 37.3

mmol l−1

(0.48−5.78) 0.13 (0.03−0.33) 0.25 (0.09−0.38)

(1.79−37.65)

1.51 (0.07−4.42) 13.18

(4.8−8.0)

0.24 (0.06−1.34) 0.61 (0.23−2.12) 6.4

SO42−

(0.09−2.45) 0.11 (0.02−0.33) 0.25 (0.23−0.29)

(7.02−3181.20)

13.63 (4.44−33.28) 678.55

(6.9−8.0)

0.2 (0.03−0.92) 0.55 (0.06−2.47) 7.39

mmol l−1

(0−0.79) 0.12 (0.10−0.16) 0.3 (0.16−0.35)

(12.15−882.26)

6.34 (1.11−28.84) 255.51

1.87 (0.21−5.16) 9.48 (0.97−21.61) 8.03

Si

(0−4.78) 0.08 (0.02−0.33) 0.25 (0.15−0.40)

(13.83−449.60)

30.2 (0−255.14) 183.48

(7.7−8.6)

mmol l−1

173.72 (2.09−364.86) 57.29

2.46 (0.42−16.94) 8.99 (3.23−23.23) 7.9

Dissolved O2

(0−260.71)

(6.4−8.9)

μmol l−1

5.52 (1.11−27.17) 372.05

4.06 (0.21−23.15) 10.54 (1.61−30.00) 7.9

NH4+ -N

(10.37−1418.21)

(6.8−8.6)

μmol l−1

1.29 (0.11−1.89) 3.41 (0.37−6.45) 7.67

(2040−104950)

13537

NO2− +NO3− -N

(6.1−8.0)

(5365−18600)

9939

μmol l−1

1.08 (0.11−12.84) 2.77 (0−13.87) 7.4

(648−2096)

1351

PO43− -P

(5.8−8.3)

(395−1881)

11.3 (4.4−24.2)

980

13.2 (6.7−21.0)

(685−1197)

12.8 (11.8−14.9)

872

9.5 (6.4−15)

36888

11.02 (7.4−15.0)

(833−76333) 10.8 (8.3−16.3)

(156−2319)

μmol l−1

μS cm−1 (20 ◦ C)

12.8 (5.0−27.63)

898

Total P

Conductivity

◦C

pH

Temperature

110 Table 2. Correlation and root mean squared error of prediction (RMSEP) for the weighted averaging and linear regression in the Hungarian (n = 36), Swedish (n = 42) and in the combined Swedish - Hungarian (n = 62) training set. Hungarian model

Swedish model

Combined-model

Method

Meshdrinking

Correlation

RMSEP

Correlation

RMSEP

Correlation

WA

Inverse

0.87

0.47

0.95

0.24

0.97

RMSEP 0.31

WA WAtol WAtol

Classical Inverse Classical

0.87 0.99 0.99

0.49 0.57 0.57

0.95 0.96 0.96

0.25 0.22 0.23

0.97 0.99 0.99

0.3 0.46 0.46

Figure 3. Ordination diagram showing (a) the relative contributions of environmental variables to the total variance (b) the distributions of the sampling sites belonging to designated cluster’s groups (Figure 2) in the CCA space.

observed diatom patterns. The eigenvalues of the first two axes were 0.77 (λ1 ) and 0.41 (λ2 ). The correlation between diatom and environmental variables for axis CCA axis 1 (r = 0.966) and axis 2 (r = 0.875) were high, denoting a strong relationship between diatoms and the measured physical and chemical parameters. Ca2+ , alkalinity, Mg2+ , and pH were the most significant factors to axis 1. These variables are associated primarily with buffering conditions. This axis mainly separated the Hungarian streams with calcareous bedrocks from Swedish streams with acid-intermediate intrusive − + + bedrocks. Correlation with SO2− 4 , Cl , Na , K were weaker. In addition, there was a positive, close corre− lation with TP and weaker with others (NO− 2 -NO3 -N, 3− and PO4 -P). The second axis is correlated with conductivity and NH+ 4. Since diatom species-abundance distributions were markedly different in the two phycogeographical regions, first we developed separate pH models. In the Hungarian model the correlation between observed and diatom inferred pH was the highest (r = 0.99) using weighted averaging with tolerance weighting. The type of dreshdrinking was external. In this case the prediction error was 0.57 (Figure 4A,

Table 2). The weighted averaging without tolerance weighting produced lower correlation between the data, but the prediction error was smaller. In Hungary, the pH value of the streams varied in a narrow range. Some acidic springs with pH ≤7 were included in this analysis, however pH ranged characteristically from 7.5 to 8.5. In the Swedish model the correlation was 0.96 but RMSEP value (0.22) was much better using the same method (WAtol and using inverse deshdrinking). This method produced the best statistical data and smallest differences between bootstrapped and non-bootstrapped (classical and inverse) methods (Figure 4b, Table 2). The pH scale ranged from 5 to 8. In the combined model (Figure 4c; WAtol), the correlation was similar to that of the Hungarian model but the RMSEP was lower due to the decreased training set in the Swedish model where the RMSEP value the best of all of them although the correlation was lower (Table 2). Applying the WA method for the combined data set, the correlation was somewhat weaker (r = 0.97) but the prediction error (0.3) was lower (Table 2.) than in the Hungarian model.

111

Figure 4. Relationship between observed pH and diatom inferred pH using weighted averaging with tolerance weighting (a) in the Hungarian training set, (b) in the Swedish training set, (c) in the combined Swedish–Hungarian training set and (d) in the combined Swedish–Hungarian test set (empty circles: training set; full squares: test set)

We tested the combined model using independent Hungarian and Swedish test samples (Figure 4d). The Swedish test set fit to the model, but some of the Hungarian test samples were obvious outliers. The pH optima and tolerances were calculated for 243 taxa. The optimum of the pH varied between 5.3 (Eunotia naegelii) and 8.1 (Navicula lanceolata) (Table 3). Species indicating acidic waters are members of the Eunotia genus. There are some typical species indicating circumneutral pH (e.g. Achnanthidium minutissimum). At pH values >7, many species with different optima were found (e.g. Fragilaria arcus, Gomphonema olivaceum). In the TP calibration the best results were obtained with a restricted set (n = 56) using weighted averaging with tolerance weighting with inverse regression. The correlation was high (0.96) and the prediction error was lower (6.23 μmol L−l ) than using WA (Figure 5a, Table 4). The only outlier site in the model was characterized by overwhelming dominance of a single species (Gomphonema micropus 76.5%). We tested the Hungarian TP model using independent Swedish test samples (Figure 5b). The Swedish test

set fit to the model and the correlation value did not change. The TP optima and tolerances were also calculated for 211 taxa. Species distribution patterns did not show clear relationships with the concentrations, therefore we selected only the most indicative taxa for eutrophication levels from the training set (Table 5). Nitzschia capitellata indicated unequivocally the hypereutrophic state of the stream. The eutrophic state was clearly indicated by Gyrosigma acuminatum, Cyclotella meneghiniana, Cocconeis placentula var. euglypta. Some species with broad tolerance range (e.g Navicula cryptotenella) can be found from oligotrophic to eutrophic waters. Discussion There is a strong need to develop ecological survey methods that fulfill criteria set by the WFD. Numerous studies (Kelly et al., 1995; Eloranta & Anderson, 1998; Rott et al., 2003; Vilbaste, 2004) supported the usefulness of diatom indices; however, their cross-regional applicability has been rarely dealt with. As presence

112 Table 3. List of the characteristic species, their pH optima, tolerance – ranges as + optimum, number of occurrences (n) and pH classes (according to van Dam et al., 1994). Numbers in square brackets after the name of some species indicate the number of the cluster-group (Figure 2, Figure 3b) in which the species dominant. Optimum

Tolerance

n

pH classes

Achnanthes minutissima var. jackii (Rabenhorst) Lange-Bertalot Achnanthidium biasolettianum (Grunow in Cl. & Grun.) Lange-Bertalot Achnanthidium minutissimum (Ktzing) Czarnecki [1, 3, 7] Amphora fogediana Krammer Amphora inariensis Krammer

AMJA ABIA AMIN AMFO AINA

6.8 7.7 7.1 7.7 7.7

0.4 0.2 0.4 0.4 0.5

14 6 6 8 10

Circumneutral Alkalophil >7 Circumneutral – –

Amphora pediculus (K¨utzing) Grunow [2] Aulacoseira ambigua (Grunow) Simonsen

APED AAMB

8.0 6.9

0.1 0.2

6 2

Alkalophil >7 Alkalophil >7

Brachysira neoexilis Lange-Bertalot [7] Caloneis silicula (Ehrenberg) Cleve

BNEO CSIL

7.8

0.2

25

Acidophil 7

Cocconeis placentula Ehrenberg var. placentula [2] Craticula riparia (Hustedt) Lange-Bertalot

CPLA NRIP

7.9

0.3

5

Cyclotella meneghiniana K¨utzing [4] Cyclotella tripartita Hakansson Cymbella excisiformis Krammer [7] Cymatopleura solea (Brebisson) W.Smith

CMEN CTRI CEXF CSOL

7.9

0.3

18

8.0

0.1

5

Cymbella laevis Naegeli Cymbella sp. Encyonema ventricosum (Agardh) Grunow Encyonopsis cesatii (Rabenhorst) Krammer Encyonopsis microcephala (Grunow) Krammer Eolimna minima (Grunow) Lange-Bertalot Eucocconeis flexella (K¨utzing) Brun Eunotia bilunaris (Ehrenberg) Mills var. bilunaris Eunotia bilunaris (Ehrenberg) Mills var. mucophila Lange-Bertalot & N¨orpel

CLAE CYMS ENVE CCES CMIC NMIN AFLE EBIL EBMU

6.3

0.4

17

7.9 6.9

0.1 0.4

5 12

7.8

0.3

7

6.3 5.4

0.6 0.3

20 5

Eunotia implicata N¨orpel, Lange-Bertalot & Alles [6] Eunotia incisa Gregory Eunotia meisteri Hustedt Eunotia muscicola var. tridentula N¨orpel & Lange-Bertalot Eunotia naegelii Migula

EIMP EINC EMEI EMTR ENAE

6.3 5.5 5.7

0.6 0.5 0.4

19 15 11

5.3

0.5

6

Eunotia rhomboidea Hustedt Eunotia spp. Eunotia tenella (Grunow) Hustedt Fallacia monoculata (Hustedt) D.G. Mann Fallacia pygmaea (K¨utzing) Stickle & Mann Fragilaria arcus (Ehrenberg) Cleve var. arcus Fragilaria capucina Desmazi`eres var. capucina [4] Fragilaria capucina Desmazi`eres var. mesolepta (Rabenhorst) Rabenhorst Fragilaria capucina Desmazi`eres var. vaucheriae (K¨utzing) Lange-Bertalot Fragilaria crassineria (Brebissoni) Lange-Bertalot et Krammer Fragilaria gracilis Oestrup [6, 7] Fragilaria sp. Fragilaria ulna (Nitzsch.) Lange-Bertalot var. ulna [5] Frustulia erifuga Lange-Bertalot & Krammer Gomphonema exillissimum (Grunow) LangeBertalot & Reichardt [6] Gomphonema micropus K¨utzing var. micropus [5]

ERHO EUNS ETEN NMOC NPYG FARC FCAP FCME FCVA FCRS FCGR FRAS FULN FERI GPXS GMIC

5.5 6.6 5.9

0.4 0.6 0.5

6 23 11

8.0 7.4

0.9 0.2

2 3

8.0 7.6 5.9 6.7 7.0

0.2 0.4 0.6 0.5 0.6

6 17 10 24 15

6.0

0.3

10

7.9

0.1

2

Alkalophil >7 Acidophil 7 – – Alkalophil >7 – – – Circumneutral Alkalophil >7 Alkalophil >7 Circumneutral Indifferent Acidophil 7 Circumneutral Alkalophil >7 Circumneutral Circumneutral Alkalophil >7 –

8.1

0.2

17

7.8

0.2

2

8.0 8.0 7.7 7.9

0.3 0.1 0.3 0.1

6 4 33 9

Gomphonema olivaceum (Hornemann) BrÈbisson var. olivaceum [3, 4] Gomphonema parvulum (K¨utzing) K¨utzing var. parvulum [5] Hippodonta capitata (Ehrenberg) Lange-Bertalot Metzeltin & Witkowski Luticula mutica (K¨utzing) D.G. Mann Luticula ventricosa (K¨utzing) D.G. Mann Meridion circulare (Greville) C.A. Agardh var. circulare [5] Microcostatus meceria (Schimanski) Lange-Bertalot Kusber & Metzeltin

GOLI GPAR NCAP NMUT NMVE MCIR NMCE

Navicula lanceolata (Agardh) Ehrenberg [4] Navicula salinarum Grunow in Cleve et Grunow var. salinarum Navicula seminulum Grunow Navicula tripunctata (O.F. M¨uller) Bory [2] Navicula trivialis Lange-Bertalot var. trivialis Nitzschia acicularis (K¨utzing) W.M. Smith Nitzschia amphibia Grunow Nitzschia dissipata (K¨utzing) Grunow var. dissipata [1, 2] Nitzschia fonticola Grunow in Cleve & M¨oller

NLAN NSAL NSEM NTPT NTRV NACI NAMP NDIS NFON

Pinnularia appendiculata (Agardh) Cleve Pinnularia cf. k¨utzingii Krammer

PAPP PKUT

Acidophil 7 Alkalophil >7

Planothidium lanceolatum (Brebisson ex K¨utzing) Lange-Bertalot [1, 5] Psammothidium abundans (Mang. In Bour. & Mang) Bukht. et Round

ALAN AABU

7.8 6.7

0.3 0.3

25 11

Alkalophil >7 –

Psammothidium clidanos (Hohn & Hellerman) Lange-Bertalot Psammothidium subatomides (Hustedt) Lange-Bertalot et Archibald

ACHL ASAT

7.7

0.3

26

– Acidophil 7 Alkalophil >7 Alkalophil >7 Alkalophil >7 –

Tabellaria flocculosa (Roth) K¨utzing [6]

TFLO

6.2

0.6

19

Acidophil 7 Circumneutral Circumneutral Alkalophil >7 Alkalophil >7 Alkalophil >7 Alkalophil >7 Alkalophil >7 Alkalophil >7

Table 4. Correlation and root mean squared error of prediction (RMSEP) for the weighted averaging with tolerance weighting and linear regression in the Hungarian training set (n = 56). Weighted averaging was done with (W Atol) and without tolerance weighting (WA) for the training set. Method

Deshrinking

Correlation

RMSEP

WA WA W Atol W Atol

Inverse Classical Inverse Classical

0.87 0.87 0.96 0.96

5.03 5.17 6.23 6.31

et al., 1990; Ormerod and Jenkins, 1994). On the base of the CCA analyses in this study catchments’ bedrock characteristics also proved to be master-variable (most important variable) separating Hungarian and Swedish

114 Table 5. List of the TP (μmol L−1 ) optima, tolerance – ranges as + optimum, number of occurrences (n) and TP classes (according to van Dam et al., 1994) for common and most indicative taxa. Diatom taxa are ordered according to decreasing TP optimum. Optimum

Tolerance

n

Trophic class

21 13 13 12 11 11 10 9 9 9 9

6 11 6 8 8 7 5 8 10 5 7

4 14 3 18 7 5 7 3 4 5 3

Hypereutrophic Oligo-eutrophic Eutrophic Eutrophic Eutrophic Eutrophic Eutrophic Eutrophic Eutrophic Eutrophic Oligo-eutrophic

Navicula cincta (Ehrenberg) Ralfs Hippodonta capitata (Ehrenberg) Lange-Bertalot Metzeltin & Witkowski

7 7

6 4

3 3

Eutrophic Meso-eutrophic

Gomphonema truncatum (Ehrenberg) Nitzschia fonticola Grunow in Cleve & M¨oller Navicula cryptotenella Lange-Bertalot

6 6 6

3 4 6

5 5 16

Meso-eutrophic Meso-eutrophic Oligo-eutrophic

Gomphonema olivaceum (Hornemann) BrÈbisson var. olivaceum Gomphonema parvulum K¨utzing var. parvulum Hantzschia amphioxys (Ehrenberg) Grunow Staurosira construens Ehrenberg

5 5 5 5

7 7 4 4

33 22 4 28

Eutrophic Eutrophic Oligo-eutrophic Meso-eutrophic

Navicula cari Ehrenberg Fragilaria nanana Lange-Bertalot

4 4

4 5

8 5

Oligo-eutrophic Meso-eutrophic

Nitzschia dissipata (K¨utzing) Grunow var. dissipata Fragilaria capucina DesmaziËres var. rumpens (K¨utzing) Lange-Bertalot

3 3

4 1

41 3

Nitzschia & capitellata Hustedt Achnanthidium frequentissimum (Lange-Bertalot) Round et Bukht Gyrosigma acuminatum (K¨utzing) Rabenhorst Cyclotella meneghiniana K¨utzing Nitzschia acicularis (K¨utzing) W.M. Smith Tryblionella apiculata Gregory Cocconeis placentula Ehrenberg var. euglypta (Ehrenberg) Grunow Fallacia pygmaea (K¨utzing) Stickle & Mann Navicula viridula (K¨utzing) Ehrenberg Cymbella cistula (Ehrenberg) Kirchner Navicula cryptocephala K¨utzing

Meso-eutrophic Oligo-mesotrohic

Figure 5. Relationship between observed TP and diatom inferred TP using weighted averaging with tolerance weighting and inverse deshdrinking (a) in the Hungarian training set (b) with Swedish test set (the unit of TP is μmol L−1 ) (open circles: the training set; full squares: test set).

sites, however, presence of mixed clusters indicates that combined data from the two regions provide a useful tool for enlarging spatial scales of analyses. The developed separate pH models for the Hungarian and Swedish diatom vegetation allowed for highly precise predictions, respectively (r = 0.99

and the RMSEP = 0.57 for Hungary; r = 0.96 and RMSEP = 0.22 for Sweden). The appearance of combined model was similarly good; using weighted averaging without tolerance weighting and classical deshdrinking the correlation was 0.97 and the RMSEP 0.3. This combined model corrected the correlation of

115 the Swedish model and RMSEP value of the Hungarian model, but the correlation was worse than the original Hungarian and the RMSEP of the Swedish model. These changes were not substantial and therefore the combined model covered the pH scale from 4.5 to 9.5 and can be used in different European phycogeographical regions. Our model’s correlation of 0.97 was higher than similar values found in other works (van Dam & Mertens, 1995; Cameron et al., 1999) using data-sets from geographically less extended areas. In the independent test set, the correlation was lower (0.89) because of some outliers. These involved such extraordinary sites like a stream fed by hot-springs where pH is lower than the mean pH of Hungarian streams, however, without development of acidophilous diatom flora. In these cases the model overestimated the pH by values between 0.9−2.04 units. For this reason, we removed these special sites, and then correlation approached the original (0.95). The most common species in the two phycogeographical regions covered the pH scale from 5.3 to 8.7. According to the pH classification by van Dam et al. (1994) species from acidobiontic to alkalibiontic species were found. The lower pH range is indicated by the typical acidobiontic and acidophilous species like e.g. Fragilaria crassineria, Eunotia implicata, E. incisa, E. meisteri, E. naegelii, E. rhomboidea, E. tenella (van Dam et al., 1994). In the Hungarian calcareous streams alkalophilous species like e. g. Planothidium lanceolatum, Amphora pediculus, Cymatopleura solea, Fragilaria capucina were characteristic (van Dam et al., 1994). The calculated optima for some species were different from data suggested in the literature: Psammothidium subatomoides, Brachysira neoexilis, Craticula riparia were listed as acidophil species in van Dam et al. (1994), but in our study they indicated pH higher than 7. Such findings may indicate that different ecotypes of the same species do exist within Europe and provide substantial data for regional re-setting sensitivity and indicator values of such widely used indices like IPS (Indice de Polluosensibilit´e Sp´ecifique or Index of Pollution Sensitivity; Coste in Cemagref, 1982). Although importance of primary nutrients, N and P, in lotic ecosystems has been often doubted, or at least restricted to specific seasons/types (Newbold, 1992), nutrient levels also may prove useful in characterizing the productivity of riverine ecosystems (Wetzel, 1983). In this study, the major nutrient gradient was TP that is generally considered as the most critical variable (Hutchinson, 1967). To avoid problems associated with edge-effects, it is necessary to sample a

sufficient number of streams along the whole environmental gradient. Unfortunately, this may not be fulfilled in some geographic regions like in our case the Swedish ecoregions. In Hungary the TP are commonly considered as driven by human impacts. Nevertheless, sedimentary bedrocks, especially phosphate bearing limestone release substantially more phosphate and nitrate into stream waters than others, dominated, for example by sandstone and shales (Thomas and Crutchfield, 1974). The correlation between the observed and inferred TP in the training set was higher (r = 0.96) than in other studies (0.72 in Winter & Duthie, 2000; 0.91 in Soininen and Niemel¨a, 2002). It was difficult to compare the prediction errors to other studies due to the different scales and range of variation of observed concentrations. In our TP model the RMSEP was 6.23 μmol L−1 and in the above-mentioned studies they were 0.45 μmol L−1 that is much lower, but the scale covered was only 0–5.16 μmol L−1 against our broader scale up to 25 μmol L−1 . The outlier of this model can originate from dominance of one or more week indicator species. As the TP model has comparatively high prediction error, the WA method is much more suitable for finding of the indicator species of different TP levels. The most indicative taxa for eutrophic conditions were Nitzschia capitellata, Gyrosigma acuminatum, Cyclotella meneghiniana and Cocconeis placentula var. euglypta. Low TP concentrations were indicated by Fragilaria capucina var. rumpens, which is listed as oligo-mesotrophic species (van Dam et al., 1994). Using the weighted averaging method and the models developed in this study, prediction of TP and pH was possible. The method allows for predicting quite accurate values of average pH trough phycogeographical regions and for determining the key indicator species of TP levels along a broader TP gradient than was used in previous studies.

Acknowledgement We would like to thank Krisztina Buczk´o for her taxonomical help and Kurt Pettersson for the co´ Sor´oczki-Pint´er operation. We are also grateful to Eva for graphical-, and Zsolt Stenger for the informatical assistance. This research was supported by the Hungarian National Science Foundation (T34414) and by the Swedish Environmental Protection Agency.

116 References Alefs J, M¨uller J, Wunsam S (1996) Die Rekonstruktion der epilimnischen Phosphorkonzentrationen im Ammersee seit 1958. GWF Wasser Abwasser 137/8: 443–447. Almeida SFP, Gil MCP (2001) Ecology of freshwater diatoms from the central region of Portugal. Cryptogamie, Algol. 22: 109– 126. Almer B, Dickson W, Ekstr¨om C, H¨ornstr¨om E, Miller U (1974) Effects of acidification on Swedish lakes. Ambio. 3: 30–36. Battarbee RW (1984) Diatom analysis and acidification of lakes. Philos. Trans. R. Soc. London, Ser. B 305: 451–477. Birks H, Line JM, Juggins S, Stevenson AC, Ter Braak CJF (1990) Diatoms and pH reconstruction. Philos. Trans. R. Soc. London, Ser. B 327: 263–278. Butcher RW (1947) Studies in the ecology of rivers. IV. The algae of organically enriched water. Journal of Ecology 35: 186–191. Cameron NG, Birks HJB, Jones VJ, Berge F, Catalan J, Flower RJ, Garcia J, Kawecka B, Koining KA, Marchetto A, SanchezCastillo P, Schmidt R, Sisko M, Solovieva N, Stefkova E, Toro M (1999) Surface-sediment and epilithic diatom pH calibration sets for remote European mountain lakes with Surface Waters Acidification Programme (SWAP) calibration set. Journal of Paleolimnology 22: 291–317. ´ Cemagref (1982) Etude des m´ethodes biologiques d’appreciation quantitative de la qualit´e des eaux. Rapport Q.E. Lyon. Agence de l’Eau Rhˆone-Medtierrane Corse. Coring E (1996) Use of diatoms for monitoring acidification in small mountain rivers in Germany with special emphasis on diatom assemblages type analysis. (DATA). In: Whitton BA, Rott E and Friedrich G (eds.) Use of algae for monitoring rivers. Institut f¨ur Botanik, Universit¨at in Innsbruck. pp 7–16. Cox EJ (1991) What is the basis for using diatoms as monitors of river quality? In: Whitton BA, Rott E and Friedrich G (eds.) Use of algae for monitoring rivers. Institut f¨ur Botanik, Universit¨at in Innsbruck. pp 33–40. Davies TD, Barthelmie RJ, Varley M, Dorling S, Farmer G, Schaug J (1990) European Precipitation Chemistry Atlas (Volume 2): An atlas of monthly and seasonal maps of precipitation amount, non-marine sulphate, nitrate, ammonium, and hydrogen ion concentrations and depositions based on the EMEP Precipitation Chemistry Network: October 1982 to December 1985. University of East Anglia, Norwich and Norwegian Institute for Air Research, Lillestr¨om. EC Parliament Council (2000) Directive of the European Parliament and of the Council 2000/60/EC establishing a framework for community action in the field of water policy. European Commission PE-CONS 3639/1/100 Rev 1, Luxembourg. Eloranta P, Andersson K (1998) Diatom indices in water quality monitoring of some South-Finnish rivers. Verh. Int. Verein. Limnol. 26: 1213–1215. F¨olster J, Sandin L, Wallin M (2004) A suggestion to a typology for Swedish inland surface waters according to the EU Water Framework Directive. SLU’s report 2004:13. Hutchinson GE (1967) A Treatise on Limnology. Vol. 2. Introduction to lake biology and the limnoplankton, John Wiley and Sons, New York. Inc´edy J (1981) Analitikai laborat´oriumi gyakorlat I. [Manual for chemical analyses I.] Veszpr´emi Egyetem. Veszpr´em [in Hungarian]. 352 pp.

Inc´edy J (1996) Analitikai laborat´oriumi gyakorlat II. [Manual for chemical analyses II.] Veszpr´emi Egyetem. Veszpr´em [in Hungarian]. 289 pp. Kahlert M, Andr´en C (2005) Benthic diatoms as valuable indicators of acidity. Verh. int. Verein. Limnol. 29: 635–639. Kelly MG, Penny CJ, Whitton BA (1995) Comparative performance of benthic diatom indices used to assess river water quality. Hydrobiologia 302: 179–188. Kelly MG, Cazaubon A, Coring E, Dell’Uomo A, Ector L, Goldsmith B, Guasch H, H¨urlimann J, Jarlman A, Kawecka B, Kwadrans J, Laugaste R, Lindstrøm EA, Leit˜ao M, Marvan P, Padis´ak J, Pipp E, Prygiel J, Rott E, Sabater S, van Dam H, Vizinet J (1998): Recommendations for the routine sampling of diatoms for water quality assessments in Europe. J. Appl. Phycol. 10: 215–224. ¨ Kolkwitz R, Marsson M (1908) Okologie der pflanzliche Saprobien. Ber. Deutsche Bot. Gesellsch. 26: 505–5019. Krammer K (2002) Diatoms of Europe. Diatoms of the European Inland Waters and Comparable Habitats. Vol. 1–4 A.R.G. Gantner Verlag K.G. Ruggel. Krammer K, Lange-Bertalot H (1991–2000) Bacillariophyceae 1– 4. Teil: Naviculaceae. In: Pascher A (ed.) S¨usswasserflora von Mitteleuropa. Band 2/1–4. Gustav Fischer Verlag, Heidelberg, Berlin. Kwandrans J, Eloranta P, Kawecka B, Wojtan K (1999) Use of benthic diatom Communities to evaluate water quality in rivers of southern Poland. In: Prygel J, Whitton BA, Bukowska J (eds) Use of Algae for Monitoring rivers III. Institut f¨ur Botanik, Universit¨at in Innsbruck. pp 154–164. Lampert W, Sommer U (1997) Limnoecology. The ecology of lakes and streams. Oxford University Press. New York, Oxford. 382 pp. Lange-Bertalot H (1995–2002) Iconographia Diatomologica. Annotated Diatom Micrographs Vol. 1.–9. Koeltz Scientific Books. K¨onigstein, Germany. Leira M, Sabater M (2005) Diatom assemblages distribution in catalan rivers, NE Spain, in relation to chemical and physiographical factors. Water Res. 39: 73–82. Marczenko Z (1976) Spectrophotometric determination of elements. Ellis Horwood Ltd. Coll House, Westergate, Chichester, Sussex England and Wydawnictwa Naukowo-Techniczne, Warsaw, Poland. 643 pp. N´emeth J (1998) A biol´ogiai v´ızmins´ıt´es m´odszerei [Methods for biological qualification of surface waters]. V´ızi Term´eszet-´es K¨ornyezetv´edelem 7. K¨ornyezetgazd´alkod´asi Int´ezet, Budapest [in Hungarian]. 304 pp. Newbold JD (1992) Cycles and spirals of nutrients. In: Calow P and Petts GE (eds.) The Rivers Handbook. Hydrological and Ecological Principles. Blackwell Scientific Publications, London, pp 379–408. Ormerod SJ, Jenkins A (1994) The biological effects of acid episodes. In: Steinberg CW, Wright RF (eds.) Acidification of Freshwater Ecosystems: Implications for the future., Wiley, Chichester. pp 259–272. Owen RB, Renaut RW, Hover VC, Ashley GM, Muasya AM (2004) Swamps, springs and diatoms: Wetlands of the semi-arid Bogoria-Baringo Rift, Kenya. Hydrobiologia 518: 59–78. Podani J (2000) Introduction to the exploration of multivariate biological data. Backhuys, Leiden, 407 pp. Rimet F, Ector L, Cauchie HM, Hoffmann L (2004) Regional distribution of diatom assemblages in headwater streams of Luxembourg. Hydrobiologia 520: 105–117.

117 Rott E, Pipp E, Pfister P (2003) Diatom methods developed for river quality assessment in Austria and a cross-check against numerical trophical indication methods used in Europe. Algol. Stud. 110: 91–115. Round FE (1991) Use of diatoms for monitoring rivers. In: Whitton BA, Rott E, Friedrich G (eds.) Use of algae for monitoring rivers I. STUDIA Studentenf¨orderungs-Ges.m.b.H., Innsbruck. pp 25–32. Sandin L, Sommerh¨auser M, Stauber I, Hering D, Johnson RK (2000) Stream assessment methods, stream typology, approaches and outlines of a European stream typology. AQUEM Project (Contract No: EVK1-CT1999-00027), 1st deliverable (31/8/00) 43 pp. Sandin L, Andersson B, Bergengren J, Bergquist B, Broberg O, ¨ Dahlberg M, F¨olster J, Jarlman A, Ostlund M (2003) Unders¨okning av p˚av¨axt, bottenfauna, makrofyter och fisk l¨angs en naturlighetsgradient i Em˚ans & M¨orrums˚ans avrinningsomr˚aden Naturv˚ardsverket Rapport 2003: 4 [in Swedish]. Schmidt R, Wunsam S, Brosch U, Fott J, Lami A, L¨offler H, Marchetto A, M¨uller HW, Prazakov M, Schwaighofer B (1998) Late and post-glacial history of meromictic L¨angsee (Austria), in respect to climate change and anthropogenic impact. Aquat. Sci. 60: 56–88. Soininen J, Niemel¨a P (2002) Inferring phosphorus levels of rivers from benthic diatoms using weighted averaging. Arch. Hydrobiol. 154: 1–18. Soininen J, Paavola R, Muotka T (2004) Benthic diatom communities in boreal streams: Community structure in relation to environmental and spatial gradients. Ecography 27: 330–342. Ter Braak CJ, Barendreg LG (1986) Weighted averaging of species indicator values: Its efficiency in environmental calibration. Math. Biosci. 78: 57–72. Ter Braak CJF, Juggins S (1993) Weighted averaging partial least squares regression (WA PLS): An improved method for reconstructing environmental variables from species assemblages. Hydrobiologia 269/270: 485–502.

Ter Braak CJF, Smilauer P (2002) CANOCO reference manual and CanoDraw for Windows user’s guide: Software for canonical community ordination (version 4.5). Biometris, Wageningen and ˇ e Budˇejovice, 500 pp. Cesk´ Thomas GW, Crutchfield JD (1974) Nitrate-nitrogen and phosphorus contents of streams draining small agricultural watershed in Kentucky. J. Environ. Qual. 3: 46–49. Van Dam H, Mertens A (1995) Long-term changes of diatoms and chemistry in headwater streams polluted by atmospheric deposition of sulphur and nitrogen compounds. Freshw. Biol. 34: 579– 600. Van Dam H, Mertens A, Sinkeldam J (1994) A coded checklist and ecological indicator values of freshwater diatoms and ecological indicator values of freshwater diatoms from Netherland. Neth. J. Aquat. Ecol. 28: 117–133. Vilbaste S (2004) Application of diatom indices in the evaluation of the water quality in Estonian streams. Proceedings of the Estonian Academy of Sciences, Biology/Ecology 53: 37– 51. Wetzel R (1983) Limnology, Second edition, Saunders College Publishing, Philadelphia, 858 pp. Whitton BA, Rott E, Friedrich G (1991) Use of algae for monitoring rivers. – Studia Studentenf¨orderungs-Ges. m.b.H. Innsbruck, 193 pp. Wilander A (2003) Institutionen f¨or milj¨oanalys, SLU Boksz 7050 750 07 Uppsala Rapport 2003: 11. Skyddad natur. En unders¨okning av tv˚a sj¨oar och deras utloppsb¨ackar i Padjelanta 2002 [in Swedish]. Winter JG, Duthie HC (2000) Epilithic diatoms as indicators os streams total N and total P concentration. J. N. Am. Benthol. Soc. 19: 32–49. Wunsam S, Schmidt R, Klee R (1995) Cyclotella-taxa (Bacillariophyceae) in lakes of the Alpine region and their relationship to environmental variables. Aquat. Sci. 57: 360–386.