The effect of lake morphology on aquatic vegetation development and changes under the influence of eutrophication

Ecological Indicators 38 (2014) 282–293

Contents lists available at ScienceDirect

Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

The effect of lake morphology on aquatic vegetation development and changes under the influence of eutrophication Agnieszka Kolada ∗ Institute of Environmental Protection – National Research Institute, Kolektorska 4, 01-692 Warsaw, Poland

a r t i c l e

i n f o

Article history: Received 17 May 2013 Received in revised form 9 November 2013 Accepted 15 November 2013 Keywords: Macrophytes Macrophyte metrics Lake morphology Typology Eutrophication indicators

a b s t r a c t Data on aquatic and emergent vegetation, morphology and water quality from 274 Polish lowland lakes surveyed in the years 1996–2009 were used to validate the preliminary typology of Polish lakes based on macrophytes and to indicate the environmental parameters which most significantly determine the vegetation patterns in lakes under various morphological conditions. In highly alkaline lowland lakes representing non-disturbed conditions the key determinants influencing the vegetation patterns were mean depth and the shape of the littoral. Three morphological lake types were distinguished: shallow (<3.5 m), deep, and additionally, within the latter, deep ribbon-shaped, with a clearly elongated base and steep bed slopes. The lake types varied in their vegetation patterns developed under non-disturbed conditions. In the shallow lakes, the share of the phytolittoral in the total lake area (%phytol) was the highest (40–100%, 72.3% on average) and the maximum colonisation depth (Cmax ) the lowest (3.2 m as the maximum) compared to the lakes from both deep types. In the ribbon-shaped deep lakes, %phytol and the plant coverage (%cover) were the lowest, the proportion of submerged vegetation was extraordinarily high (over 90%) and the emergent vegetation was extremely sparsely developed (<6%) compared to the lakes of the two other types. The alterations of aquatic vegetation resulting from the eutrophication process in distinguished morphological lake types were explored. Within the macrophyte variables tested, three groups of indicators were distinguished: (a) metrics performing best in selected lake types, i.e. the type-specific indicators (abundance metrics, %Pota), (b) metrics performing equally well in all the lake types, considered as the universal indicators (e.g. S Chara, %Subm and %Emerg) and (c) metrics performing poorly in all the lake types, with generally limited applicability (most of the metrics on syntaxonomic richness). In the shallow lakes, %cover and %phytol performed notably better than in deep lakes, whereas Cmax worked best in deep lakes and showed the strongest response in the deep regular-shaped lakes. Moreover, in deep regularshaped lakes the number of communities of stoneworts and submerged plants (S Chara and S Subm), and in deep ribbon-shaped lakes the proportion of area inhabited by vascular plant communities (%Pota) performed exceptionally better than in the other two lake types. The most universal metrics, performing equally well in all the lake types, were the proportions of submerged (%Subm) and emergent (%Emerg) vegetation in the total phytolittoral area. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction According to the contemporary approach to evaluating the condition of aquatic ecosystems, as endorsed by the EC Water Framework Directive (WFD, Directive 2000/60/EC), the structure and functioning of aquatic biota should be considered in biological assessment methods. One of the biological elements which should be included in a bioassessment, is aquatic vegetation. The effect of water nutrient enrichment on aquatic vegetation has been recognised for decades and is documented in many previous studies (e.g.

∗ Tel.: +48 22 832 33 01. E-mail address: [email protected] 1470-160X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2013.11.015

Seddon, 1972; Phillips et al., 1978; Newbold and Palmer, 1979; Ozimek and Kowalczewski, 1984; Toivonen and Huttunen, 1995). A decrease in light transmission caused by an increase in water turbidity, leads to a change in the community structure and a reduction in vegetation density and depth limits (Chambers and Kalff, 1985; Duarte and Kalff, 1986; Middleboe and Markager, 1997). The morphological, hydrographical and catchment conditions of a waterbody may influence the pace and direction of changes in aquatic vegetation patterns resulting from water nutrient enrichment. Although the course and impact of eutrophication in shallow and deep lakes are similar, it has been widely recognised that shallow lakes are more resilient to change than deep ones (Scheffer et al., 1993). According to Phillips (2005), two main factors are responsible for this phenomenon. Firstly, in shallow lakes the

A. Kolada / Ecological Indicators 38 (2014) 282–293

return of nutrients lost through sedimentation via sediment release is faster and its effects on the nutrient concentration proportionately greater, relative to the external load (Sas, 1989, after Phillips, 2005). Thus, it can be expected that in very shallow lakes with a limited amount of water, nutrient enrichment would take place more rapidly compared to deep lakes even if the anthropogenic influences affecting them were at a comparable level. Secondly, shallow lakes appear to be more sensitive to trophic interactions such as the top-down control of phytoplankton by grazing zooplankton (Jeppesen et al., 1997, after Phillips, 2005). Most studies aimed at exploring the relationships between environmental predictors and patterns in aquatic biota consider both non-impacted and affected lakes. In fact, in lakes under the influence of anthropogenic pressures the human impact interferes with the natural variety of biological assemblages and vegetation patterns are determined mainly by nutrient availability (Heegaard et al., 2001). In aquatic ecosystems which are not affected by anthropogenic pressures, usually representing lower levels of trophy, the physical and morphological features of a waterbody are the key determinants of natural patterns in the aquatic vegetation. The role of slope, depth, fetch and bottom substrate, and also lake area, in macrophyte development has been demonstrated by Duarte and Kalff (1990), Srivastava et al. (1995) and Rea et al. (1998). Since macrophyte spatial distribution strongly depends on the water depth and light attenuation (Blindow, 1992; Middleboe and Markager, 1997; van den Berg et al., 1999, 2003; Schwarz et al., 2000, 2002), the maximum depth of a lake determines, among other factors, the potential area covered by plants. Other authors also demonstrated water alkalinity and geographical distribution (mainly altitude) to be important factors determining the taxonomic composition of lake macrophytes (Srivastava et al., 1995; Vestergaard and Sand-Jensen, 2000; Smolders et al., 2001; Schaumburg et al., 2004; Free et al., 2006; Alahuhta et al., 2013). Based on previous studies aimed at verifying the abiotic typology of Polish lakes (Kolada et al., 2005) in terms of the variability of the macrophyte community structure, three lake types varying in natural vegetation patterns were considered: shallow, deep regular-shaped and deep ribbon-shaped (Kolada, 2009, 2010). The overall aim of this study was to explore if (and how) the alteration of lacustrine vegetation along with the water eutrophication gradient differ significantly between the distinguished morphological lake types. Firstly, the attempt was made to validate the previously established a priori typologies of Polish lakes based on macrophytes (Kolada, 2009, 2010) using an array of environmental variables, and to indicate the abiotic parameters which most significantly determine the natural vegetation patterns in lakes under various morphological conditions. Next, the responses of the macrophyte community to water eutrophication in different lake types were analysed to ascertain if they vary depending on the lake morphological features. The performance of different macrophyte metrics on richness and composition, including those based on hydrophytes and emergent vegetation, in different types of lakes was tested.

2. Materials and methods 2.1. Data collection Data on vegetation, morphology and water quality from 274 Polish lowland lakes surveyed in the years 1996–2009, representing various morphometric and hydrographic conditions, as well as a wide spectrum of water quality (Table 1), were used in this study. Data on macrophytes were collected within the national lake monitoring programme (180 lakes surveyed in the period 2007–2009), the Polish-Norwegian Research Fund project ‘deWELopment’ (11 lakes surveyed in 2009; Kolada et al., 2011a), and other

283

national projects (83 lakes surveyed in 1996–2006; Ciecierska, 2008; Ciecierska et al., 2010; Kolada, 2010). Data on the water quality of 11 lakes were collected within the PNRF project ‘deWELopment’ (Soszka and Ochocka, 2011), while those for all the other lakes used in the study were collected within the national lake monitoring programme; the latter are owned by the Chief Inspectorate for Environmental Protection in Poland. Lakes were sampled for water quality in the same year as the vegetation surveys were conducted. The submerged, floating-leaved and emergent vegetation of all the lakes was surveyed in the peak of the vegetation season (June–August). For the first two sets of lakes surveyed recently (2007–2009), the field survey procedure involved the belt transect method (Ciecierska et al., 2010; Kolada et al., 2013), whereas for the remaining 83 lakes surveyed earlier (1996–2006), the entire phytolittoral mapping method was applied (Ciecierska, 2008; Kolada, 2010). The use of different sampling techniques had negligible effect on the values of macrophyte metrics tested (Kolada et al., 2013). Within the phytolittoral of each lake the maximum colonisation depth, the mean vegetation coverage and the relative cover of all the aquatic and emergent plant communities (stands) were determined. For identification and classification of aquatic plant communities, the phytosociological approach was applied (BraunBlanquet, 1964). The term ‘community’ was used for homogenous and uniform vegetation areas (phytocoenoses sensu Westhoff and van der Maarel, 1973, after Jensén, 1977), named after the dominant species. All the plant communities were identified and the syntaxonomic systems established by Brzeg and Wojterska (2001) and Matuszkiewicz (2002) were adopted. The classification system and the terminology for stonewort associations as proposed by ˛ (1966) and partly by Brzeg and Wojterska (2001) were Dambska used. For botanical nomenclature the checklists for Polish vascular flora by Rutkowski (2005) and for characeans by Pełechaty and Pukacz (2008) were used. 2.2. Macrophyte variables In the phytolittoral of all the 274 lakes, a total of 98 plant communities: 17 communities of stoneworts (syntaxonomic group ‘Chara’), 27 communities of submerged vascular plants including elodeids, ceratophyllids, isoetids and one moss (group ‘Pota’), 13 communities of floating-leaved plants including nympheids and lemnids (group ‘Nymp’) and 39 communities of emergent plants including rush, sedge rush and shore vegetation (group ‘Emerg’; list of plant communities, dominant species and their life-forms given in Appendix 1 in Supporting Information). As the first part of the study included investigating the effect of variability of lake morphological features on shaping the vegetation patterns in undisturbed conditions, within a pool of all the lakes the nonimpacted ones (reference) were firstly identified. Based on the pressure criteria (lakes which were not affected or only minimally affected by anthropogenic interferences; for the pressure criteria see Kolada, 2010) and the ecological conditions (biological and physico-chemical elements indicating high status), 31 lakes were considered as reference sites: 21 stratified and 10 polymictic, each with highly alkaline, non-coloured waters and water quality parameters indicating an early stage of eutrophy (Table 1). In the phytolittoral of the 31 reference lakes, 75 plant communities were identified (Appendix 1 in Supporting Information). The composition and structure of lacustrine vegetation were analysed with the use of characteristics describing macrophyte abundance (maximum colonisation depth, proportion of the phytolittoral in the total lake area, total plant coverage in the phytolittoral), richness (total number of plant communities and number of plant communities of stoneworts, submerged vascular plants, all submerged plants [=sum of S Chara and S Pota],

284

A. Kolada / Ecological Indicators 38 (2014) 282–293

Table 1 The main morphometric and hydrographic characteristics, and the seasonal mean of the water quality parameters in the non-disturbed (reference) and impacted lakes and the correlations of the variables with the first two components of the PCA in the reference and in all the lakes; variable correlations with the PC1 or PC2 > 0.60 marked in bold; variables excluded from the analyses due to high inter-correlation (R < 0.70) marked with an asterisk; transf. – data transformation; st. dev. – standard deviation, n.a. – data not available, dash – data not tested in PCA. Variable group

Environmental variable (unit)

Transf.

Reference lakes (n = 31) Mean ± st. dev. (range)

PC1

PC2

Mean ± st. dev. (range)

Distribution

Altitude (m a.s.l) Latitude (N) Longitude (E)

– – –

112 ± 37 (26–186) 51.37468–54.20272 14.58004–23.42483

−0.24 −0.59 −0.53

−0.52 −0.54 −0.37

99 ± 41 (0–203) 51.37468–54.71763 14.40657–23.55208

Hydromorphology

Area (km2 ) Mean depth (m) Perimeter (km) Maximum depth (m)* Volume (106 m3 )* Maximum length (L, km)* Maximum width (W, km)* Elongation index (L/W) Mean slope (◦ ) Catchment area (km2 ) Retention time (years)

Log Log Log Log Log Log Log Log – Log Log

2.3 ± 2.2 (0.2–10.4) 7.1 ± 4.0 (0.6–19.3) 9.6 ± 6.3 (2.0–33.9) 22.0 ± 15.7 (3.2–58.8) 16.9 ± 24.6 (0.4–131.3) 3.2 ± 2.0 (0.8–11.1) 1.2 ± 0.6 (0.4–2.9) 3.3 ± 2.2 (1.1–8.5) 5.1 ± 2.9 (0.2–11.6) 24.1 ± 25.0 (3.0–130.0) 6.6 ± 5.7 (0.4–25.0)

0.61 0.72 0.82 – – – – 0.63 0.63 0.24 0.75

0.39 −0.48 0.17 – – – – −0.39 −0.41 0.67 −0.18

Water quality

pH Conductivity (␮S/cm) Alkalinity (meq/L) Total phosphorus (TP, mgP/L) Total nitrogen (TN, mgN/L) Chlorophyll a (Chla, ␮g/L) Secchi disc reading (SD, m)

– – – Log Log Log Log

8.2 ± 0.4 (6.9–8.7) 303 ± 74 (173–481) 2.3 ± 0.5 (1.5–3.2) 0.032 ± 0.015 (0.015–0.077) 0.93 ± 0.34 (0.32–2.02) 5.0 ± 2.7 (0.9–10.8) 3.6 ± 1.3 (1.1–6.5)

0.09 0.19 −0.54 −0.17 0.10 −0.05 0.22

−0.37 0.59 0.36 0.38 0.46 0.56 −0.45

4.2 24.7

3.5 20.7

Eigenvalue % Variance explained

floating-leaved and emergent macrophytes) and syntaxonomic structure (proportion of the area occupied by stoneworts, submerged vascular plants [=sum of %Chara and %Pota], all submerged plants, floating-leaved and emergent macrophytes; list of metrics and abbreviations as in Table 2; indication of life-form of dominant species of each plant community in Appendix 1 in Supporting Information). 2.3. Statistical analyses The links between environmental parameters and taxonomic composition in both reference lakes and lakes representing the

All lakes (n = 274) PC1

PC2

0.39 0.17 0.23

−0.25 0.48 −0.27

2.7 ± 5.7 (0.2–71.4) 6.4 ± 4.3 (0.5–23.9) 10.6 ± 9.0 (1.6–74.4) 18.1 ± 14.6 (1.3–79.7) 22.0 ± 64.7 (0.4–681.7) 3.7 ± 2.7 (0.4–16.4) 1.1 ± 0.9 (0.3–7.6) 4.2 ± 3.1 (1.1–25.6) n.a. 183.4 ± 371.0 (1.1–2809.2) 3.0 ± 4.7 (0.01–25.0)

−0.19 0.53 −0.09 – – – – −0.01 – −0.51 0.56

0.82 0.57 0.86 – – – – 0.34 – 0.47 0.07

8.4 ± 0.4 (6.8–9.5) 418 ± 300 (106–3490) 2.7 ± 0.7 (1.1–4.9) 0.084 ± 0.086 (0.01–0.643) 1.61 ± 0.93 (0.32–7.20) 28.5 ± 30.1 (0.9–182.3) 2.0 ± 1.2 (0.4–6.5)

−0.18 −0.60 −0.45 −0.70 −0.74 −0.86 0.84

0.24 0.33 0.03 0.15 −0.28 −0.11 0.30

4.2 26.3

2.8 17.6

whole spectrum of water quality were tested with the use of canonical community ordination techniques in the CANOCO 4.5 ˇ software (ter Braak and Smilauer, 2002). Ninety-eight plant communities of submerged, floating-leaved and emergent vegetation identified in all the lakes and 75 identified in the reference lakes (Appendix 1 in Supporting Information) were used to estimate the length of a gradient in the standard deviation of species (here, plant communities) turnover using detrended correspondence analysis (DCA). Since in both sets of biological data tested the gradient was relatively long (3.8 SD in all the lakes and 3.3 SD in the reference ones), the unimodal context was assumed. Thus, the canonical correspondence analysis (CCA) with untransformed

Table 2 The main characteristics of the macrophyte metrics in the non-disturbed lakes, all (n = 31) and in the morphological lake types: S – shallow (n = 5), D – deep regular-shaped (n = 23), R – deep ribbon-shaped (n = 3); Abb. – abbreviation; ANOVA results: ***p < 0.001, **p < 0.01, *p < 0.05, ns – difference not significant at p < 0.05. Metric group

Macrophyte metrics

Abundance

Maximum colonisation depth (m) Proportion of phytolittoral in lake area (%) Plant coverage (%)

Syntaxonomic richness

Number of plant communities: All Stoneworts Vascular submerged All submerged Floating-leaved Emergent

Syntaxonomic composition

Abb.

All reference lakes (mean and range)

Morphological lake types (mean)

ANOVA

S

D

R

F(2;28)

p

S: D

S: R

D: R

8.63 23.62 13.42

0.0012 <0.0001 0.0004

*** *** ns

* ** **

ns *** ***

Cmax %phytol %cover

4.7 (1.7–9.2) 38.5 (5.9–99.7) 89.7 (60.9–100.0)

2.5 72.3 93.6

5.2 34.5 92.9

4.5 13.2 72.1

S S S S S S

23 (11–39) 4 (2–13) 7 (3–12) 11 (6–23) 3 (0–5) 8 (2–12)

25 5 8 13 3 8

23 4 7 11 3 8

21 2 7 9 2 9

0.40 1.20 0.60 1.06 0.84 0.23

0.6729 0.3173 0.5555 0.3584 0.4428 0.7977

ns ns ns ns ns ns

ns ns ns ns ns ns

ns ns ns ns ns ns

43.6 (0.2–83.8) 22.9 (0.04–97.3) 66.5 (19.4–97.5) 5.9 (0–31.8) 25.3 (1.9–50.5)

41.3 16.7 58.0 15.0 24.2

43.6 21.1 64.7 4.5 28.2

48.0 46.7 94.7 1.5 4.1

0.09 2.51 6.44 4.71 5.40

0.9150 0.0993 0.0050 0.0173 0.0104

ns ns ns * ns

ns ns * ns **

ns ns ** ns **

tot Chara Pota Subm Nymp Emerg

% Area covered by communities: Stoneworts Vascular submerged All submerged Floating-leaved Emergent

%Chara %Pota %Subm %Nymp %Emerg

3. Results 3.1. Morphological features shaping vegetation patterns in reference lakes Of all the 21 environmental variables tested, four – maximum depth, volume, maximum length and maximum width – were excluded from the analyses due to a high correlation with other parameters (Spearman R > 0.7). The PCA performed for the other 17 environmental variables in the 31 reference lakes produced six

CC2: 18.1%

Cachment Size

Mean slope

CC1: 33.0%

Mean depth Elongation Retention

Perimeter

-1.0

ˇ species data was further applied (ter Braak and Smilauer, 2002). The pre-selection of the environmental variables was performed by applying principal component analysis (PCA). Twenty-one abiotic parameters, including geographical distribution, lake morphology, hydrography and selected water quality indicators (parameters listed in Table 1; all data checked for normal distribution and log-transformed, if required) were tested. The variables not inter-correlated (Spearman correlation test, R > 0.7) and best correlated with the first two principal components (factor loadings > 0.6), were selected and used in the CCA. To determine the relative importance of environmental factors in shaping vegetation patterns in both reference and impacted lakes, the forward automatic selection with Monte Carlo permutation test (499 permutations) was applied (ter Braak and Verdonschot, 1995). Variables significantly contributing to explanation of the macrophyte community at 5% level of significance were identified. The distribution of the reference lakes in ordination space (inter-lake distance) was analysed and lake types were distinguished based on the lake dissimilarity with respect to plant community composition. The composition and structure of vegetation between the established lake types in non-disturbed conditions were compared using ANOVA F-test in STATISTICA 7.1 software (StatSoft Inc., 2005). In a pool of all the lakes representing the gradient of ecosystem degradation, including both the reference and impacted lakes, the distribution of plant communities in ordination space (inter-species distance) was analysed and the most important environmental variables shaping the vegetation patterns were identified. The four water quality indicators (TP, TN, Chla, SD; data log-transformed) were employed in the PCA to derive the eutrophication gradient in all the lakes. The major part of the total lake variance was explained (71.5% by the first component) and the best correlations of the first component sample scores (PC1) with macrophyte metrics (as listed in Table 2) were obtained when all four eutrophication parameters were included. The PC1 sample scores which expressed the progressive water nutrient enrichment (the relationships between PC1 and individual water quality parameters given in Table 3) were further used as a proxy of eutrophication pressure to correlate the macrophyte variables. The response of the macrophyte metrics to the eutrophication gradient in the distinguished lake types was analysed using a linear regression model. The regression slopes and Y-intercepts were compared between lake types for their homogeneity using the relevant techniques of general linear models (separate slope designs, homogeneity of slopes and analysis of covariance ANCOVA) in the STATISTICA 7.1 software. The technique used was chosen according to the procedure described by Stanisz (2007). To illustrate and interpret the response of the macrophyte metrics to water enrichment in distinguished lake types, the PC1 classes, which approximately corresponded to successive stages of eutrophication (mesotrophy, slight eutrophy, eutrophy and hypertrophy; Table 3) were defined and the mean values of macrophyte variables in subsequent PC1 classes in morphological lake types were analysed.

285

1.0

A. Kolada / Ecological Indicators 38 (2014) 282–293

-1.0

1.5

Fig. 1. The ordination plot of the 31 reference lakes based on a canonical correspondence analysis (CCA) of the relative abundance of aquatic vegetation (75 communities of submerged, floating-leaved and emergent plants; rare syntaxa downweighted) with respect to the seven hydromorphological variables preselected on the basis of principal component analysis (PCA); the eigenvalues of axis 1 (CC1) and axis 2 (CC2) are 0.28 and 0.15, respectively, and they together explain 51% of the variance in species–environment relation; symbols indicate lakes with different morphological conditions: triangles – shallow, circles – deep regular-shaped, diamonds – deep ribbon-shaped; the distance between the symbols in the diagram approximates the dissimilarity of the lakes with respect to plant community composition; arrows indicate the relative importance and direction of the environmental variables; each arrow points in the direction of maximum variation in value of the corresponding variable; the length of arrows represents the degree to which each is correlated with CCA axes, i.e. environmental variables important in terms of predicting vegetation composition have longer arrows than less important ones (diagram interpretation after ter Braak and Verdonschot, 1995).

significant principal components (eigenvalues > 1.0), with the first three components together explaining 57.5% of the total lake variance (24.7%, 20.7% and 12.0%, respectively) and the remaining three, 27% (less than 10% each). PC1 presented a clear hydromorphological gradient, PC2 a weak geographical gradient and PC3 a weak water quality gradient. The hydromorphological parameters correlated best (R > 0.6) with the first two principal components (Table 1) were further employed in the CCA. The seven environmental variables used in the CCA explained 27% of the total variance in plant community composition of the reference lakes (total inertia 3.08, sum of all canonical eigenvalues 0.83). The first two axes of the CCA ordination (CC1 and CC2) explained the species–environment relationship well and accounted for 51% of the variation in the 75 plant communities with respect to the environmental variables tested. The first axis was most strongly correlated with elongation index (R = 0.47), mean depth (0.36) and mean slope (0.27) and the second axis with perimeter (−0.15) and retention time (−0.13; Fig. 1). The Monte Carlo permutation test revealed the environmental variables most important in shaping vegetation in reference lakes. The highest conditional effects (A > 0.15) were attributed to elongation index, mean depth, perimeter and lake size, with the first two parameters contributing significantly to the model (elongation index; A = 0.25, p = 0.004; mean depth; A = 0.14, p = 0.045). In the ordination space, the lakes presented a distinct depth and shape gradient, from the very shallow, polymictic lakes with gentle slopes located on the left side of the ordination graph, through a wide range of stratified lakes with diverse morphological conditions in the centre, to deep ones with the steepest slopes (mean > 9◦ ) and high values of the elongation index (>6.0) on the right side of the graph (Fig. 1). Within the group of deep stratified lakes a perpendicular diagonal gradient was revealed, which was not clearly defined by the morphological variables tested in this study.

286

A. Kolada / Ecological Indicators 38 (2014) 282–293

Table 3 The mean and range of values of the main water quality indicators in defined classes of the PC1 sample scores expressing the subsequent stages of water eutrophication process and the relationships between water quality indicators and the PC1 sample scores; all regression coefficients statistically significant at p < 0.001. Class of PC1

Stage of trophy

<−2 −2 to 0 0–2 >2 PC1:variable R2 Regression

Eutrophication indicators (mean and range)

Mesotrophy (M) Slight eutrophy (SE) Eutrophy (E) Hypertrophy (H)

TP (mg/L)

TN (mg/L)

Chla (␮g/L)

SD (m)

0.026 (0.010–0.045) 0.050 (0.010–0.145) 0.090 (0.018–0.288) 0.237 (0.042–0.643) 0.55 y = −1.35 + 39.84*x

0.73 (0.32–0.99) 1.18 (0.53–2.19) 1.88 (0.88–4.19) 3.23 (1.56–7.19) 0.69 y = −4.14 + 10.51*x

4.2 (0.9–9.5) 12.3 (3.3–29.1) 37.4 (12.4–104.7) 86.9 (27.2–182.3) 0.81 y = −4.66 + 3.64*x

4.2 (3.0–6.5) 2.3 (0.7–4.6) 1.2 (0.4–2.8) 0.7 (0.5–1.1) 0.75 y = 3.70 − 8.52*x

the vascular plant communities occupying approximately 20–40% of the phytolittoral on average and relatively scarce rush vegetation (Table 2). The lake types, however, varied significantly in vegetation spatial structure and composition. In the S-lakes %phytol was the highest, whereas Cmax was the lowest compared to the two other lake types. The S-lakes were also the only type with a substantial share of floating-leaved vegetation (Table 2 and Fig. 2). In the lakes from both of the deep types, Cmax was considerably higher than in the shallow type. In the R-lakes %phytol and %cover were the lowest, %Subm was extraordinarily high (over 90% in all the three lakes in this type) and the emergent vegetation was extremely

7,0

120

6,0

100

5,0

80

%phytol

Cmax

As the mean depth and elongation index were also used as the main typological factors in the previous macrophyte-based lake typology (Kolada, 2010), the formerly established three morphological types: shallow lakes (S-lakes, mean depth < 3.5 m), deep lakes (D-lakes), and additionally, lakes with a very steep bed slope (>9◦ on average), were adopted and further explored. The latter were all ribbon-shaped (R-lakes), in contrast to the other deep lakes, which were regular-shaped (the elongation index < 6.0). In all the 31 reference lakes the phytolittoral was dominated by submerged vegetation, with a very high share of Characommunities (up to almost 50% of the total phytolittoral area), with

4,0 3,0

S

110

D

0

R

%Nymp

%cover

80 70

R

20 10 0

60 S

100

D

-10

R

S

60

LAKE TYPE

90

D

R

LAKE TYPE

50

%Emer

80

%Subm

D LAKE TYPE

30

90

70 60 50

40 30 20 10

40 30

S

40

LAKE TYPE

100

50

40 20

2,0 1,0

60

S

D LAKE TYPE

R

0

S

D LAKE TYPE

R

Fig. 2. The comparison of data distribution and means of the selected macrophyte metrics in non-disturbed lakes representing three morphological lake types; S – shallow (n = 5), D – deep regular-shaped (n = 23), R – deep ribbon-shaped (n = 3).

CC2: 15.3%

sparsely developed (%Emerg < 6%) compared to the lakes of both shallow and deep regular-shaped lake types (Fig. 2). In all three lake types, the numbers of plant communities from most of the syntaxonomic groups analysed were very similar. The only significant dissimilarity between the lake types was related to S Chara, as in all the R-lakes not more than three stonewort associations appeared regularly, whereas in the other lakes S Chara was about 4–5 on average.

287

0.8

A. Kolada / Ecological Indicators 38 (2014) 282–293

TN

Chla

TP

Catchment

Size Ret. time

CC1: 50.7%

3.2. Response of macrophytes to eutrophication in different lake types

3.2.1. Abundance The most distinct differences in metric response between the lake types were found in the case of abundance metrics: Cmax , %cover and %phytol (Table 4 and Fig. 4). Cmax performed better in deep lakes than in shallow ones and showed the strongest response to eutrophication in the D-lakes (Table 4). The %cover and %phytol performed well only in S-lakes, whereas in the other two lake types the metric responses to eutrophication were very low or statistically insignificant. In S-lakes a systematic decrease in %phytol from over 60% to about 20% on average along the water deterioration gradient was observed, whereas in deep lakes the response curve was almost flat, with the metric values at the level of approximately 20% irrespective of the water quality (Fig. 5). The slopes of

SD

Elongation

-0.8

The PCA performed for the 16 environmental variables in a pool of all the lakes (regular data on mean littoral slope not available for all the lakes, hence excluded) produced five significant principal components (eigenvalues > 1.0), with the first three components together explaining 54.7% of the total lake variance (26.3%, 17.6% and 10.8%, respectively) and the remaining three less than 10% each. PC1 presented a strong water quality gradient and PC2 a morphological gradient, while the other components were not clearly defined (Table 1). Thus, the hydromorphological parameters tested for their influence on aquatic vegetation in the reference lakes were supplemented with the four eutrophication indicators (TP, TN, Chla and SD) and together 10 environmental variables were further employed in the CCA. The environmental variables applied in the CCA explained 9% of the total variance in lacustrine vegetation of all the lakes (total inertia 3.46, sum of all canonical eigenvalues 0.31). The first axis (CC1) was most important, explaining over 50% of the variance in species–environment relationships, and was most strongly correlated with Chla (R = −0.88), SD (0.86), TN (−0.75) and TP (−0.58). The CC2 explained 15% of variance and was most strongly correlated with mean depth (−0.66) and elongation index (−0.38). The environmental parameters significantly contributing to the model (p < 0.05) included: Chla, SD, mean depth, TN and elongation index. The CCA provided the distinct ordination of the 98 plant communities identified in the phytolittoral of all 274 lakes, arranged mainly along the first canonical axis reflecting eutrophication gradient. The communities of stoneworts were clearly clustered on the right side of the ordination graph and were strongly associated with waters of high transparency, whereas most of the helophyte communities were located on the left side of the graph and were associated with waters with high nutrient and chlorophyll a concentrations (Fig. 3). The communities of vascular submerged and floating-leaved plants were grouped in the centre of the ordination graph. Most of the analysed relationships between pressures and aquatic vegetation measures tested in a linear regression model were statistically significant, the strength of the relationships, however, varied between lake types and metrics (Table 4). In general, the weakest relationships were found in S-lakes (R2 statistically significant at p < 0.05 in the range from 0.08 to 0.37) and the strongest ones in R-lakes (R2 between 0.12 and 0.50).

Perimeter

Mean depth

-1.0

1.0

Fig. 3. The CCA ordination plot of the 98 plant communities of lacustrine vegetation identified in 274 lakes with respect to 10 environmental variables; explanation of the abbreviations of environmental variables as in Table 1; the eigenvalues of axis 1 (CC1) and axis 2 (CC2) are 0.16 and 0.05, respectively, and they together explain 66% of the variance in species–environment relation; symbols indicate plant communities: stars – communities of stoneworts, circles – communities of vascular submerged vegetation, squares – communities of floating-leaved vegetation, triangles – communities of emergent vegetation; syntaxonomic groups enveloped; the distance between the symbols in the diagram approximates the dissimilarity of distribution of relative abundance of those communities across the samples, measured by their Chi-square distance; symbols in proximity correspond to species often occurring together; for arrow explanation see Fig. 1 (diagram interpretation after ter Braak and Verdonschot, 1995).

the regression lines expressing the response of Cmax and %phytol to eutrophication between shallow and deep lakes were statistically significant (Fig. 4). 3.2.2. Syntaxonomic richness The relationships between eutrophication and richness of most of the syntaxonomic groups (S Chara, S Pota, S Subm) in all the lake types, and also total syntaxa richness and number of floatingleaved vegetation communities (S tot, S Nymp) in both deep lake types were significant; however, they were relatively weak and it was only in R-lakes in the case of S Chara and S Subm that the correlation coefficients R2 exceeded 0.30 (Table 4). Although for the significantly responding metrics on syntaxa richness there were no differences of slopes found between the lake types, at the same level of pressure S tot, S Pota and S Subm were lower in shallow lakes than in deep ones (significant differences in Yintercepts, Fig. 4). Neither slopes nor intercepts in the response of S Chara to water enrichment were found to be statistically significant between the three lake types tested (Fig. 4). The average number of stonewort communities decreased systematically with an increase in water trophy in both shallow and deep lakes (Fig. 5). The average number of vascular plant communities decreased linearly in shallow lakes and in lakes from both deep types it vaguely increased between the first two PC1 classes and then decreased rapidly for higher eutrophy (Fig. 5). The range of the values of syntaxonomic richness metrics, mainly the S Pota, was relatively wide in all the water trophy classes and the residuals appeared highly spread along the regression lines, that significantly reduced the overall strength of the relationships (Table 4). In all three lake types the response of S Emerg to eutrophication was statistically insignificant. 3.2.3. Syntaxonomic structure The metrics based on the proportion of an area inhabited by communities from different syntaxonomic groups gave much stronger responses to eutrophication than the richness metrics

288

A. Kolada / Ecological Indicators 38 (2014) 282–293

Table 4 The relationships between the eutrophication pressure (PC1 sample scores) and selected macrophyte metrics in the three morphological lake types: S – shallow (n = 84), D – deep regular-shaped (n = 151), R – deep ribbon shaped (n = 39); explanation of the metric abbreviations as in Table 2; the regression coefficients R2 > 0.30 marked in bold; ***p < 0.001, **p < 0.01, *p < 0.05, ns – relationship statistically insignificant at p < 0.05 (no regression equation). Metric group

Macrophyte metrics

Morphological lake type S

D 2

2

R

R

p

Regression equation

R

p

Regression equation

R2

p

Regression equation

Abundance

Log Cmax %phytol %cover

0.28 0.31 0.15

*** *** **

y = 0.49 − 0.04*x y = 36.69 − 7.21*x y = 80.42 − 3.72*x

0.53 0.17 0.01

*** *** ns

y = 0.57 − 0.07*x y = 19.14 − 3.51*x –

0.35 0.03 0.01

*** ns ns

y = 0.56 − 0.05*x – –

Syntaxonomic richness

S S S S S S

tot Chara Pota Subm Nymp Emerg

0.12 0.26 0.20 0.30 0.11 0.03

** *** *** *** ** ns

y = 17.49 − 1.20*x y = 1.18 − 0.45*x y = 5.05 − 0.76*x y = 6.23 − 1.21*x y = 2.93–0.33*x –

0.16 0.31 0.20 0.35 0.07 0.00

*** *** *** *** ** ns

y = 19.37 − 1.63*x y = 1.47 − 0.70*x y = 6.07 − 0.80*x y = 7.55 − 1.51*x y = 2.47–0.24*x –

0.04 0.24 0.14 0.27 0.00 0.04

ns ** ** *** ns ns

– y = 1.09 − 0.63*x y = 6.52 − 0.80*x y = 7.62 − 1.43*x – –

Syntaxonomic structure

%Chara %Pota %Subm %Nymp %Emerg

0.28 0.08 0.32 0.01 0.37

*** ** *** ns ***

y = 14.88 − 6.16*x y = 22.40 − 3.59*x y = 37.28 − 9.76*x – y = 47.96 + 9.96*x

0.28 0.07 0.36 0.01 0.42

*** ** *** ns ***

y = 8.51 − 6.08*x y = 27.96 − 3.69*x y = 36.47 − 9.77*x – y = 52.26 + 10.08*x

0.12 0.35 0.49 0.00 0.50

** *** *** ns ***

y = 6.83 − 5.31*x y = 21.63 − 9.26*x y = 28.46 − 14.57*x – y = 56.23 + 13.09*x

(Table 4). Particularly, %Subm and %Emerg performed well in all lake types (no significant differences in slopes and intercepts of the regression lines between the lake types, Fig. 4). They demonstrated inverting proportions along with water deterioration, with

a decrease in submerged vegetation, including both stoneworts and vascular plants, and an increase in rushes (Fig. 5). In all the lake types %Chara decreased rapidly between the first classes of water trophy and then it declined systematically up to total extinction

Fig. 4. The comparison of slopes and Y-intercepts of the regression lines expressing the response of selected macrophyte metrics to eutrophication pressure; PC1 gradient – explanation in the text and in Table 3; types: S – shallow lakes (solid line), D – deep regular-shaped lakes (dashed line), R – deep ribbon-shaped lakes (dotted line); upper panel generally includes regressions with varied slopes, middle panel with equal slopes and different intercepts and lower panel with either slopes or intercepts statistically indifferent at the 5% level of confidence; ***p < 0.001, **p < 0.01, *p < 0.05, ns – differences statistically insignificant at p < 0.05; dash – analysis not relevant/inappropriate (regression statistically insignificant or slopes statistically different, see Table 4).

A. Kolada / Ecological Indicators 38 (2014) 282–293

289

Fig. 5. The mean and range of non-outlying values of the best responding macrophyte metrics in subsequent stages of water eutrophication (M – mesotrophy, SE – slight eutrophy, E – eutrophy, H – hypertrophy, established on the basis of the PC1 sample score classes as in Table 3) in lakes representing three morphological lake types; triangles and solid line – shallow lakes (n = 84), circles and dashed line – deep regular-shaped lakes (n = 151), diamonds and dotted line – deep ribbon-shaped lakes (n = 39).

(Fig. 5); the strength of the metric response, however, was relatively weak in all lake types (Table 4). %Pota gave a notably stronger response in R-lakes than in the other lake types. This was the only metric in respect of which the two deep lake types exhibited significant differences in the slopes of regression lines (Fig. 4). In R-lakes the fraction of submerged vascular vegetation declined systematically along the eutrophication gradient (although a high spread of %Pota values in mesotrophic conditions in R-lakes was observed), whereas in S- and D-lakes %Pota increased between the first two classes of water trophy and then decreased in later stages of eutrophy (Fig. 5). In all three lake types the response of %Nymp was statistically insignificant (Table 4).

The %cover and %Nymp in all lake types, and %phytol and S tot in the ribbon-shaped lakes were not examined for the homogeneity of regression lines since the relationships of these metrics with pressure were insignificant (Table 4). 4. Discussion 4.1. The diversity of aquatic vegetation patterns in non-disturbed lakes The intent of typology is to identify groups of lakes that under ideal conditions would have comparable characteristics. To the

290

A. Kolada / Ecological Indicators 38 (2014) 282–293

extent possible, abiotic typology should be restricted to those characteristics of lakes that are intrinsic, or natural, and are not a result of human activities. The results of many studies indicate geology (usually expressed by pH, alkalinity, calcium concentration, water colour) and geographical distribution (mainly altitude) as the factors most strongly determining the taxonomic composition of aquatic vegetation in non-impacted lakes (Srivastava et al., 1995; Vestergaard and Sand-Jensen, 2000; Smolders et al., 2001; Schaumburg et al., 2004; Willby et al., 2009). Particularly, water alkalinity was found to exert a strong influence on the natural distribution of aquatic macrophytes. Certain plant groups, such as stoneworts or elodeids, increase with water alkalinity, due to their capability to use bicarbonate as a source of carbon for photosynthesis, whereas plants which do not have this ability, such as isoetids, are typical of soft-water lakes (Allen and Spence, 1981; Maberly and Spence, 1983; Vestergaard and Sand-Jensen, 2000; Madsen et al., 2002; Alahuhta and Heino, 2013; Alahuhta et al., 2013). In addition to alkalinity, Alahuhta et al. (2013) also found conductivity, water colour and mean altitude as the most important variables explaining macrophyte community composition. Heegaard et al. (2001) and Lacoul and Freedman (2006) demonstrated a strong negative effect of altitudinal gradient on species richness and the diversity of macrophytes showing an approximately linear decrease with increasing altitude. In this study, no effect of water quality parameters (alkalinity, conductivity, pH) nor geographical distribution (altitude, latitude and longitude) on shaping the macrophyte patterns was found. This was probably due to a relatively narrow range of abiotic features represented in analysed reference lakes. They are all lowland (>200 m a.s.l), with highly alkaline (>1.5 meq/L) and non-coloured waters, with low to moderate conductivity (Table 1) and they are representative for all Polish lake resources considered ‘significant’ (see Kolada et al., 2005). It should also be stressed, that the number of reference lakes studied here (n = 31) was rather restricted but this limitation is hard to avoid in the Central European lowlands where human impacts on aquatic ecosystems are relatively intense. In the set of analysed lakes depth and littoral shape appeared to be the most important variables determining the macrophyte community in non-disturbed conditions. Lake depth is one of the key factors most frequently considered in defining macrophyte-based lake typologies (Schaumburg et al., 2004; Free et al., 2006; Mäemets and Freiberg, 2007; Willby et al., 2009). This determines the ability of vegetation to inhabit a specific part of the phytolittoral (Willby et al., 2009; Søndergaard et al., 2013). In Polish lakes under natural conditions the maximum colonisation depth for macrophytes was found as 4–5 m on average and about 9 m as the maximum (Table 2). Thus, it can be expected that in non-disturbed lakes with depth below these values the vegetation will potentially cover the whole lake bed, whereas in deeper lakes the total vegetated area would always be smaller than the total lake area. This was demonstrated in this study, where in shallow lakes the mean %phytol was twice as high (over 70% on average) and Cmax was notably lower than in lakes from the two deep types (Fig. 2). These findings support the results of Søndergaard et al. (2010, 2013), that in deeper lakes Cmax is the more relevant indicator for describing the macrophyte condition while in shallow ones %phytol seems to be a more appropriate parameter. In most studies the average depth of 3.0 m was considered the threshold value for shallow lakes (Scheffer, 1998); this was recommended in Annex II of the WFD and adapted by many Member States in their national typologies (Mäemets and Freiberg, 2007; Willby et al., 2009), and also other WFD-related projects (Wallin et al., 2003; Poikane, 2009). However, in some studies on macrophyte lake typology other depth values were used (e.g. 4 m for Irish lakes; Free et al., 2006) and even the use of mictic conditions instead of depth has been promoted (Schaumburg et al., 2004;

Stelzer et al., 2005). In this study, a 3.5 m mean depth was used to distinguish between shallow and deep lakes, which corresponds to approximately 9.0 m of the maximum lake depth. Littoral slope was found to have a negative effect on vegetation occurrence, abundance and biomass (Duarte and Kalff, 1986, 1990; Srivastava et al., 1995; Rea et al., 1998; Partanen et al., 2009). Duarte and Kalff (1986) demonstrated a rapid decrease in the submerged plant biomass when the littoral slope exceeded 2.24%. Using the Charisma model, van Nes et al. (2002) found a strong effect of the bed slope on light availability, and, in consequence, on the distribution and biomass of macrophytes with significant differences at the threshold value of 2%. In the study presented here, distinct differences in plant cover between lakes with generally gentle slopes (shallow and deep regular-shaped) and ribbon-shaped lakes with much steeper slopes (>9◦ on average, corresponding to app. 16%) were found (90% versus 70%). The shape of the littoral also influenced the taxonomic composition and structure of the vegetation, as in ribbon-shaped lakes only a few species of stoneworts and no rush vegetation found suitable conditions for massive development (Table 2). Hence, in lakes with a very steep bed slope and a specific character of the bottom substrate (no or only a very thin and nutrient-poor layer of sediments) the abundant macrophyte development may be naturally limited, even irrespective of the water quality. The vegetation spatial structure and taxonomic composition is determined there not only by the trophic requirements of macrophytes but also by their ability to colonise such unfavourable habitats. The selected environmental predictors used in this study explained 43.3% of the total variance in macrophyte community in the reference lakes. Thus, there was still over 50% of the macrophyte composition variance remaining unexplained. Also, the relatively wide range of metric values in D-lakes (Fig. 2) points to a relatively high heterogeneity within this type. These results suggest that there is a gradient in the lakes, that is defined by factors other than these tested in this study and that additional criteria can be explored to define more homogenous lake types. 4.2. The effect of eutrophication on aquatic vegetation in different lake types The results of both PCA and CCA demonstrated that, contrary as it was the reference lakes, in a pool of lakes representing the entire spectrum of water deterioration, the eutrophication indicators (namely: water visibility and chlorophyll a concentration followed by total nitrogen, with a relatively weak contribution of total phosphorus) dominated over the hydromorphological variables in shaping the vegetation patterns. This finding is to some extent consistent with the outcome of Alahuhta et al. (2013), who also indicated turbidity and chlorophyll a (but also conductivity and total phosphorus, which was not proved in this study) as the anthropogenic variables most important in explaining macrophytes in boreal lakes. The other important environmental factors found in this study in a set of all the lakes included the mean depth and elongation index, the two main typological factors. Thus, grouping lakes in types was intended to reduce the morphological variability of the analysed lakes. The reaction of lacustrine vegetation to water eutrophication varied between the distinguished lake types mainly in the context of abundance measures, whereas for macrophyte richness and syntaxonomic composition similar trends in pressure–response in all the lake types were recognised, in general. In a set of all the macrophyte variables analysed, three groups can be distinguished: (a) metrics performing best in selected lake types, which can be considered as the type-specific indicators (abundance metrics, %Pota), (b) metrics performing equally well in all the lake types, considered as the universal indicators (e.g. S Chara, %Subm and %Emerg)

A. Kolada / Ecological Indicators 38 (2014) 282–293

and (c) metrics performing poorly in all the lake types, with generally limited applicability (most of the metrics on syntaxonomic richness). Of the type-specific metrics, Cmax and %phytol most significantly discriminated between shallow and deep lakes. Cmax turned out to be the most reliable eutrophication indicator in D-lakes (R2 = 0.53, Table 4), its performance was weaker in R-lakes (R2 = 0.35), whereas in S-lakes the Cmax response was the poorest (R2 = 0.28). Admittedly, in shallow lakes the decrease of Cmax along the eutrophication gradient was also found to be significant, but since the upper anchor point (the reference value) was low there by nature, the changes in colonisation depth were not as evident as in deep lakes and the slope of the regression line was much gentler (Fig. 4). In very shallow lakes the whole surface of a lake may act as the littoral and plants are able to colonise a major part of or even the whole lake bottom to some extent, irrespective of the water quality. Thus, in shallow lakes no clear vegetation zoning but rather mosaic arrangements can be observed and for this reason in their assessments it is the proportion of the vegetated lake area (%phytol) rather than Cmax that is considered to be a more appropriate indicator, as demonstrated in this study, and also discussed in my previous work (Kolada, 2010) and study of Søndergaard et al. (2010, 2013). Mäemets and Freiberg (2004) pointed to the advantage of using average depth limit rather than the maximum, mainly due to a natural inter-annual variability of Cmax , also demonstrated by Søndergaard et al. (2013). The most universal metrics, performing equally well in all lake types, were the proportions of submerged (%Subm) and emergent (%Emerg) vegetation in the total phytolittoral area (Table 4, Fig. 4). The metric %Subm was created as a sum of %Chara and %Pota, which responded individually significantly weaker than %Subm in all lake types. A negative effect of anthropogenic pressure on the occurrence, abundance, biomass and colonisation depth of Characeans was reported e.g. by Ozimek and Kowalczewski (1984), van den Berg (1999), van den Berg et al. (1999) and Blindow et al. (2002). Although stoneworts are considered to occur mainly in waters at a relatively low level of trophy, in fact they can appear over a broad range of nutrient concentration (Blindow, 1992; Kufel and Ozimek, 1994; Kufel et al., 1996). In this study, the range of S Chara and %Chara values at the subsequent stages of water deterioration in all the three lake types was relatively wide (Fig. 5), which weakened the overall metric–pressure relationships and limited the applicability of the metric for status assessment (Table 4). The weak overall response of S Pota and %Pota may be explained by the high heterogeneity of vascular plants in terms of their requirements regarding nutrient conditions, phenomenon commonly used when discriminating between tolerant, sensitive and indifferent taxa (e.g. Melzer, 1999; Schneider, 2007; Penning et al., 2008). As along with the water deterioration the loss in sensitive species is counterbalanced by the increase in the tolerant ones, the metric diagnostic value is limited. Of the three lake types, a surprisingly high correlation between %Pota and the water quality gradient was found only in the R-lakes (R2 = 0.35, compared to R2 = 0.07 and 0.08 found in Dand S-lakes; Table 4). This may be explained by the very low contribution of stonewort communities in reference R-lakes, presumably caused by a negative effect of slope on stoneworts. Since in R-lakes the expansion of dense Chara-meadows was noticeably restricted, mainly the vascular plant communities formed the phytolittoral of these lakes, so they exhibited a clearer response to pressure than stoneworts. Contrary to hydrophytes, which are universally used in macrophyte-based methods, helophytes (emergent vegetation) are rarely included in lake bioassessment (Poikane et al., 2011). Emergent plants are presumed to respond more directly to soil characteristics, exposure or shoreline management that reduces their indicator value, as emphasised by many authors (Penning

291

et al., 2008; Alahuhta et al., 2012; Kanninen et al., 2013). Helophytes are considered effective bioindicators of lake hydromorphological alterations (Hellsten, 2001; Hellsten and Mjelde, 2009; Mjelde et al., 2013) rather than water quality indicators. Nonetheless, some recent studies addressing the applicability of various macrophyte indices for assessing the status of lakes explore the question of whether including helophytes improves or reduces the metric–pressure relationships (Kolada et al., 2011b; Alahuhta et al., 2012; Dudley et al., 2013; Kanninen et al., 2013). The reported findings are equivocal, as results by Kolada et al. (2011b) and Alahuhta et al. (2012, 2013) supported the inclusion of helophytes in bioassessment, whereas those by Dudley et al. (2013) and Kanninen et al. (2013) indicated that limited informative value is added with helophytes. The strong response of helophytes to water deterioration found in the present study was probably a consequence of the use of a relative proportion of an entire syntaxonomic group without taking its taxonomic composition into consideration. Hence, the information on how much of the area potentially available for macrophyte colonisation is occupied by helophytes appeared reliable for bioassessment, whereas the richness of emergent vegetation (as well as most of the other syntaxonomic groups) was of a very little diagnostic value (Table 4). Although the strength and direction of both %Subm and %Emerg and pressure relationships for all three lake types were alike, the underlying mechanisms are considered to be different. In shallow lakes, where in non-disturbed conditions the aquatic vegetation, both submerged and emergent, is usually abundantly developed, the process of water nutrient enrichment restricts the growth of submerged vegetation and promotes the massive rush expansion (Mäemets and Freiberg, 2004; Mäemets et al., 2010). In ribbonshaped lakes, where the steep bed slopes and poor sediment layers, even in non-disturbed conditions, limit extensive plant development, a substantial increase in the contribution of rushes along the eutrophication gradient can be a result of a significant decline in submerged vegetation rather than the massive development of helophytes. Vegetation which was sparsely developed in reference conditions became even poorer when water quality decreased; this was mainly a shift in proportion between submerged and emergent vegetation rather than in their absolute abundance. Since the deep regular-shaped lakes represent a more diverse array of morphological features than shallow and deep ribbon-shaped ones (as already indicated in Section 4.1), the process of aquatic vegetation alteration may unfold there according to more diverse scenarios. All the aforementioned create a common scheme of shifts in vegetation pattern along the deterioration gradient. In lowland temperate highly alkaline lakes this shift proceeds according to the general scenario: Charetea → Potametea → Phragmitetea (Kłosowski et al., 2006), also demonstrated in this study. However, one should keep in mind that in shallow lakes, due to their more complex functioning, this process cannot proceed so directionally and, in situations where the critical turbidity is exceeded, a submerged macrophyte dominated clear-water state can shift rapidly to a phytoplankton dominated turbid-water state, without any transitional state in between (Scheffer, 1998). However, in addition to these two most spectacular and best known alternative stable states, many other less conspicuous shifts seem to occur, e.g. floating-leaved or pondweed dominated states (Scheffer and van Nes, 2007). This was also observed in this study as the shallow lakes were the only ecosystems where phytocenoses of nympheids inhabited a substantial part of the phytolittoral (Fig. 2 and Table 2). However, contrary to that reported by Rooney and Bayley (2012), who included species richness and abundance of floating-leaved vegetation as two of the five best metrics to quantify biological integrity, in this study neither S Nymp nor % Nymp was demonstrated as a reliable bioindicator in any lake type (Table 4).

292

A. Kolada / Ecological Indicators 38 (2014) 282–293

5. Conclusions

References

1. In highly alkaline Polish lowland lakes representing nondisturbed conditions the key determinants influencing vegetation patterns were the depth and shape of the littoral. Thus, based on the mean depth and elongation index three lake types were distinguished: shallow (<3.5 m), deep and, additionally within the latter, deep ribbon-shaped, with a clearly elongated base and steep bed slopes. 2. The lake types distinguished varied significantly in their vegetation patterns developed under non-disturbed conditions. In the shallow lakes, the share of the phytolittoral in the total lake area was the highest and the maximum colonisation depth the lowest compared to the lakes from both deep types. This was also the only type with a substantial share of the floating-leaved vegetation. In the lakes of both deep types, Cmax was considerably higher than in the shallow lakes. In the ribbon-shaped deep lakes, the share of the phytolittoral in the total lake area and the plant coverage were the lowest, the proportion of submerged vegetation was extraordinarily high (over 90%) and the rush vegetation was extremely sparsely developed (<6%) compared to the lakes of both shallow and deep regular-shaped lake types. 3. The lake types varied in the significance and strength of the response of macrophytes to the eutrophication pressure. In the shallow lakes, %cover and %phytol performed notably better than in deep lakes, whereas Cmax worked best in deep lakes and showed the strongest response in the deep regular-shaped lakes. Moreover, in deep regular-shaped lakes the metrics on syntaxonomic richness (S Chara and S Subm) and in deep ribbon-shaped lakes the proportion of vascular plant communities (%Pota) performed exceptionally better than in the other two lake types. The proportions of submerged (%Subm) and emergent (%Emerg) vegetation in the total phytolittoral area were the most universal metrics, performing equally well in all the lake types. 4. In a set of all the macrophyte variables tested, three groups were distinguished: (a) metrics performing best in selected lake types, which can be considered as the type-specific indicators (abundance metrics, %Pota), (b) metrics performing equally well in all the lake types, considered as the universal indicators (e.g. S Chara, %Subm and %Emerg) and (c) metrics performing poorly in all the lake types, with generally limited applicability (most of the metrics on syntaxonomic richness).

Alahuhta, J., Heino, J., 2013. Spatial extent, regional specificity and metacommunity structuring in lake macrophytes. J. Biogeogr. 40, 1572–1582. Alahuhta, J., Kanninen, A., Vuori, K.-M., 2012. Response of macrophyte communities and status metrics to natural gradients and land use in boreal lakes. Aquat. Bot. 103, 106–114. Alahuhta, J., Kanninen, A., Hellsten, S., Vuori, K.-M., Kuoppala, M., Hämäläinen, H., 2013. Environmental and spatial correlates of community composition, richness and status of boreal lake macrophytes. Ecol. Indic. 32, 172–181. Allen, E.D., Spence, D.H.N., 1981. The differential ability of aquatic plants to utilize the inorganic carbon supply in fresh waters. New Phytol. 87, 269–283. Blindow, I., 1992. Decline of Charophytes during eutrophication: a comparison to angiosperms. Freshw. Biol. 28, 9–14. Blindow, I., Hargeby, A., Andersson, G., 2002. Seasonal changes of mechanisms maintaining clear water in shallow lake with abundant Chara vegetation. Aquat. Bot. 72, 315–334. Braun-Blanquet, J., 1964. Pflanzensoziologie. Springer, Wien, New York. Brzeg, A., Wojterska, M., 2001. Plant communities in Wielkopolska: the state of knowledge and threats. In: Wojterska, M. (Ed.), Flora and Vegetation of Wielkopolska and South-Pomerania Lakeland. Guidebook of 52. Reunion of ´ pp. 39–110 (in Polish). Polish Botanical Society, Poznan, Chambers, P.A., Kalff, J., 1985. Depth distribution and biomass of submersed aquatic macrophyte communities in relation to Secchi depth. Can. J. Fish. Aquat. Sci. 42, 701–709. Ciecierska, H., 2008. Macrophyte-Based Indices of the Ecological State of Lakes. Dissertation and Monographs 139. University of Warmia and Mazury in Olsztyn (in Polish with Engl. summary). Ciecierska, H., Kolada, A., Soszka, H., Gołub, M., 2010. A method for macrophytebased assessment of the ecological status of lakes, developed and implemented for the purpose of environmental protection in Poland. In: Proceedings of the 4th BALWOIS Conference on Water Observation and Information System for Decision Support, Orchid, Republic of Macedonia, http://balwois.com/ balwois/administration/full paper/ffp-1502.pdf (accessed September 2013). ˛ Dambska, I., 1966. Communities of stoneworts in Poland. Pozn. Tow. Przyjaciół Nauk, Ser. B.: Biol. 31 (3), 1–75 (in Polish). Duarte, C.M., Kalff, J., 1986. Littoral slope as a predictor of the maximum biomass soft submerged macrophyte communities. Limnol. Oceanogr. 31 (5), 1072–1080. Duarte, C.M., Kalff, J., 1990. Patterns in the submerged macrophyte biomass of lakes and the importance of the scale of analysis in the interpretation. Can. J. Fish. Aquat. Sci. 47, 357–363. Dudley, B., Dunbar, M., Penning, E., Kolada, A., Hellsten, S., Oggioni, A., Bertrin, V., Ecke, F., Søndergaard, M., 2013. Measurements of uncertainty in macrophyte metrics used to assess European lake water quality. Hydrobiologia 704, 179–191. Free, G., Little, R., Tierney, D., Donnelly, K., Caroni, R., 2006. A Reference Based Typology and Ecological Assessment System for Irish Lakes – Preliminary Investigations. Final Report. Pilot Study to Establish Monitoring Methodologies EU (WFD). Environmental Protection Agency, Ireland, http://www.epa.ie/pubs/ reports/research/water/Final%20Report%20%282000-FS1-M1%29.pdf (accessed September 2013). Heegaard, E., Birks, H.H., Gibson, Ch.E., Smith, S.J., Wolfe-Murphy, S., 2001. Species–environmental relationships of aquatic macrophytes in Northern Ireland. Aquat. Bot. 70, 175–223. Hellsten, S., 2001. Effects of water level regulation on aquatic macrophyte stands in northern Finland and options to predict these impacts under varying conditions. Acta Bot. Fenn. 171, 1–47. Hellsten, S., Mjelde, M., 2009. Macrophyte responses to water level fluctuation in Fennoscandinavian lakes – applying a common index. Verh. Int. Verein. Limnol. 30, 765–769. Jensén, S., 1977. An objective method for sampling the macrophyte vegetation in lakes. Vegetatio 33, 107–118. Kanninen, A., Hellsten, S., Hämäläinen, H., 2013. Comparing stressor-specific indices and general measures of taxonomic composition for assessing the status of boreal lacustrine macrophyte communities. Ecol. Indic. 27, 29–43. Kłosowski, S., Tomaszewicz, G.H., Tomaszewicz, H., 2006. The expansion and decline of charophyte communities in lakes within the Sejny Lake District (northeastern Poland) and changes in water chemistry. Limnologica 36, 234–240. Kolada, A., 2009. The evaluation of biocenotic typology of Polish lakes using macrophyte community structure. Verh. Int. Verein. Limnol. 30/5, 775–778. Kolada, A., 2010. The use of aquatic vegetation in the lake assessment: testing the sensitivity of macrophyte metrics to anthropogenic pressures and water quality. Hydrobiologia 656, 133–147. Kolada, A., Soszka, H., Cydzik, D., Golub, M., 2005. Abiotic typology of Polish lakes. Limnologica 35, 145–150. ´ Kolada, A., Ciecierska, H., Ruszczynska, J., Mjelde, M., Dziedzic, J., Dynowski, P., Jasnorzewski, A., 2011a. Macrophytes. In: Soszka, H. (Ed.), Ecological Status Assessment of the Waters in the Wel River Catchment. Guidelines for Integrated Assessment of Ecological Status of Rivers and Lakes to Support River Basin Management Plans. Institute of Inland Fisheries, Olsztyn, pp. 169–186 (in Polish with Engl. summary). Kolada, A., Hellsten, S., Søndergaard, M., Mjelde, M., Dudley, B., van Geest, G., Gold˝ smith, B., Davidson, T., Bennion, H., Noges, P., Bertrin, V., 2011b. Deliverable D3.2-3: Report on the Most Suitable Lake Macrophyte Based Assessment Methods for Impacts of Eutrophication and Water Level Fluctuations, http://www. wiser.eu/results/deliverables/ (accessed September 2013).

Acknowledgments The study was supported by the Polish Ministry of Science and Higher Education. The Chief Inspectorate for Environmental Protection and voivodeship inspectorates of environmental protection in Poland are kindly acknowledged as providers of monitoring data on water quality and anthropogenic pressures in catchments of all the lakes and also on the aquatic vegetation of a substantial share of the lakes analysed in this study. Special thanks go to Prof. Hanna Ciecierska from the University of Warmia and Mazury and to Dr. Szymon Jusik and Dr. Tomasz Zgoła from the University of Life Sciences in Poznan´ for providing some of the biological data used in this study. I also want to thank the two anonymous Referees for valuable comments that significantly contributed to improvement of the paper.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolind. 2013.11.015.

A. Kolada / Ecological Indicators 38 (2014) 282–293 ´ Kolada, A., Ciecierska, H., Ruszczynska, J., Dynowski, P., 2013. Sampling techniques and inter-surveyor variability as sources of uncertainty in Polish macrophyte metric for lake ecological status assessment. Hydrobiologia, http://dx.doi.org/10.1007/s10750-013-1591-9. Kufel, L., Królikowska, J., Kufel, I., 1996. Structure of submerged littoral vegetation in relation to pelagic trophic state indices. Ekol. Pol. 44 (3/4), 299–310. Kufel, L., Ozimek, T., 1994. Can Chara control phosphorus cycling in Lake Łuknajno (Poland). Hydrobiologia 275/276, 277–283. Lacoul, P., Freedman, B., 2006. Relationships between aquatic plants and environmental factors along a steep Himalayan altitudinal gradient. Aquat. Bot. 84, 3–16. Maberly, S.C., Spence, D.H.N., 1983. Phytosynthetic inorganic carbon use by freshwater plants. J. Ecol. 71, 705–724. Madsen, T.V., Olesen, B., Bagger, J., 2002. Carbon acquisition and carbon dynamics by aquatic isoetids. Aquat. Bot. 73, 351–371. Mäemets, H., Freiberg, L., 2004. Characteristics of reeds on Lake Peipsi and the floristic consequences of their expansion. Limnologica 34, 83–89. Mäemets, H., Freiberg, L., 2007. Coverage and depth limit of macrophytes as tool for classification of lakes. Proc. Estonian Acad. Sci. Biol. Ecol. 56 (2), 124–140. Mäemets, H., Palmik, K., Haldna, M., Sudnitsyna, D., Melnik, M., 2010. Eutrophication and macrophyte species richness in the large shallow North-European Lake Peipsi. Aquat. Bot. 92, 273–280. Matuszkiewicz, W., 2002. A Guide Book to Identification Plant Communities in Poland. PWN, Warszawa (in Polish). Melzer, A., 1999. Aquatic macrophytes as tools for lake management. Hydrobiologia 395/396, 181–190. Middleboe, A.L., Markager, S., 1997. Depth limits and minimum light requirements of freshwater macrophytes. Freshw. Biol. 37, 553–568. Mjelde, M., Hellsten, S., Ecke, F., 2013. A water level drawdown index for aquatic macrophytes in Nordic lakes. Hydrobiologia 704, 141–151. Newbold, C., Palmer, M.A., 1979. Trophic Adaptation of Aquatic Plants. CST 18. Nature Conservation Council, Peterborough. Ozimek, T., Kowalczewski, A., 1984. Long-term changes of the submerged macrophytes in eutrophic lake Mikołajskie (North Poland). Aquat. Bot. 19, 1–11. Partanen, S., Luoto, M., Hellsten, S., 2009. Habitat level determinants of emergent macrophyte occurrence, extension and change in two large boreal lakes in Finland. Aquat. Bot. 90, 261–268. Pełechaty, M., Pukacz, A., 2008. The key for determining species of stoneworts ´ Warszawa (Characeae) in rivers and lakes. Biblioteka Monitoringu Srodowiska, (in Polish). Penning, W.E., Mjelde, M., Dudley, B., Hellsten, S., Hanganu, J., Kolada, A., van den Berg, M., Poikane, S., Phillips, G., Willby, N., Ecke, F., 2008. Classifying aquatic macrophytes as indicators of eutrophication in European lakes. Aquat. Ecol. 42, 237–251. Phillips, G.L., 2005. Eutrophication of shallow temperate lakes. In: O’Sullivan, P.E., Reynolds, C.S. (Eds.), The Lakes Handbook. Lake Restoration and Rehabilitation, vol. 2. Blackwell Publishing, pp. 261–278. Phillips, G.L., Eminson, D., Moss, B., 1978. A mechanism to account for macrophyte decline in progressively eutrophicated freshwaters. Aquat. Bot. 4, 103–126. Poikane, S. (Ed.), 2009. Water Framework Directive Intercalibration Technical Report. Part 2: Lakes. JRC Scientific and Technical Reports. European Commission, Luxemburg, p. 176. Poikane, S., van den Berg, M., Hellsten, S., Hoyos, C.d., Ortiz-Casas, J., Pall, K., Portielje, R., Phillips, G., Solheim, A.L., Tierney, D., Wolfram, G., van de Bund, W., 2011. Lake ecological assessment systems and intercalibration for the European Water Framework Directive: aims, achievements and further challenges. Proc. Environ. Sci. 9, 153–168. Rea, T.E., Karapatakis, D.J., Guy, K.K., Pinder III, J.E., Mackey Jr., H.E., 1998. The relative effects of water depth, fetch and other physical factors on the development of macrophytes in a small southeastern US pond. Aquat. Bot. 61, 289–299. Rooney, R.C., Bayley, S.E., 2012. Development and testing of an index of biotic integrity based on submersed and floating vegetation and its application to assess reclamation wetlands in Alberta’s oil sands area, Canada. Environ. Monit. Assess. 184, 749–761. Rutkowski, L., 2005. The key for determining the Polish lowland vascular plants. Wydawnictwo Naukowe PWN, Warszawa (in Polish). Schaumburg, J., Schranz, Ch., Hofmann, G., Stelzer, D., Schneider, S., 2004. Macrophytes and phytobenthos as indicators of ecological status in German lakes – a contribution of the implementation of the Water Framework Directive. Limnologica 34, 302–314.

293

Scheffer, M., 1998. Ecology of Shallow Lakes. Kluwer, Dordrecht. Scheffer, M., Hosper, S.H., Meijer, M.-L., Moss, B., Jeppesen, E., 1993. Alternative equilibria in shallow lakes. Trends Ecol. Evol. 8, 275–279. Scheffer, M., van Nes, E.H., 2007. Shallow lakes theory revisited: various alternative regimes driven by climate, nutrients, depth and lake size. Hydrobiologia 584, 455–466. Schneider, S., 2007. Macrophyte trophic indicator values from a European perspective. Limnologica 37, 281–289. Schwarz, A.-M., de Winton, M., Hawes, I., 2002. Species-specific depth zonation in New Zealand charophytes as a function of light availability. Aquat. Bot. 72, 209–217. Schwarz, A.-M., Howard-Williams, C., Clayton, J., 2000. Analysis of relationships between maximum depth limits of aquatic plants and underwater light in 63 New Zealand lakes. N. Z. J. Mar. Freshw. Res. 34, 157–174. Seddon, B., 1972. Aquatic macrophytes as limnological indicators. Freshw. Biol. 2, 107–130. Smolders, A.J.P., Lamers, L.P.M., Roelofs, J.G.M., 2001. Aquatic macrophytes in assessment and monitoring of ecological quality. In: Karttunen, K. (Ed.), Monitoring and Assessment of the Ecological Status of Aquatic Environments, vol. 563. TemaNord, pp. 23–31. Søndergaard, M., Johansson, L.S., Lauridsen, T.L., Jørgensen, T.B., Liboriussen, L., Jeppesen, E., 2010. Submerged macrophytes as indicators of the ecological quality of lakes. Freshw. Biol. 55, 893–908. Søndergaard, M., Phillips, G., Hellsten, S., Kolada, A., Ecke, F., Mäemets, H., Mjelde, M., Azzella, M.M., Oggioni, A., 2013. Maximum growing depth of submerged macrophytes in European lakes. Hydrobiologia 704, 165–177. Soszka, H., Ochocka, A., 2011. Supporting physico-chemical elements. In: Soszka, H. (Ed.), Ecological Status Assessment of the Waters in the Wel River Catchment. Guidelines for Integrated Assessment of Ecological Status of Rivers and Lakes to Support River Basin Management Plans. Institute of Inland Fisheries, Olsztyn, pp. 249–256 (in Polish with Engl. summary). Srivastava, S.D., Staicer, C.A., Freedman, B., 1995. Aquatic vegetation of Nova Scotian lakes differing in acidity and trophic status. Aquat. Bot. 51, 181–196. Stanisz, A., 2007. The Accessible Course of Statistics with Applying STATISTICA PL Using the Examples From Medical Science. Linear and Non-Linear Models, vol. 2. StatSoft, Kraków (in Polish). StatSoft Inc., 2005. STATISTICA (Data Analysis Software System), Version 7.1, www.statsoft.com Stelzer, D., Schneider, S., Melzer, A., 2005. Macrophyte-based assessment of lakes – a contribution to the implementation of the European Water Framework Directive in Germany. Int. Rev. Hydrobiol. 90 (2), 223–237. ter Braak, C.J.E., Verdonschot, P.E.M., 1995. Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat. Sci. 57/3, 255–289. ˇ ter Braak, C.J.F., Smilauer, P., 2002. CANOCO Reference Manual and CanoDraw for Windows User’s Guide: Software for Canonical Community Ordination (Version 4.5). Microcomputer Power, Ithaca, NY. Toivonen, H., Huttunen, P., 1995. Aquatic macrophytes and ecological gradients in 57 small lakes in Southern Finland. Aquat. Bot. 51, 197–221. van den Berg, M.S., 1999. Charophyte colonization in shallow lakes: processes, ecological effects and implications for lake management. Free University, Amsterdam (PhD Thesis). van den Berg, M.S., Joosse, W., Coops, H., 2003. A statistical model predicting the occurrence and dynamics of submerged macrophytes in shallow lakes in the Netherlands. Hydrobiologia 506–509, 611–623. van den Berg, M.S., Scheffer, M., van Nes, E., Coops, H., 1999. Dynamics and stability of Chara sp. and Potamogeton pectinatus in a shallow lake changing in eutrophication level. Hydrobiologia 408/409, 335–342. van Nes, E.H., Scheffer, M., van den Berg, M.S., Coops, H., 2002. Dominance of charophytes in eutrophic shallow lakes – when should we expect it to be an alternative stable state? Aquat. Bot. 72, 275–296. Vestergaard, O., Sand-Jensen, K., 2000. Alkalinity and trophic state regulate aquatic plant distribution in Danish lakes. Aquat. Bot. 67, 85–107. Wallin, M., Wiederholm, T., Johnson, R.K., 2003. Guidance on Establishing Reference Conditions and Ecological Status Class Boundaries for Inland Surface Waters, CIS Working Group 2.3 – REFCOND. Willby, N., Pitt, J.A., Phillips, G., 2009. The Ecological Classification of UK Lakes Using Aquatic Macrophytes. Environment Agency, Bristol, UK.