Most lotic waters provide a variety of habitats that create niches for heterogeneous vegetation. Medium-sized rivers in particular provide perfect conditions for highly diverse hydrophyte vegetation (Hussner & Lösch 2005). Species composition and abundance of freshwater macrophytes in lotic waters are determined by a few physical and chemical factors, such as current velocity (Janauer et al. 2010; Steffen et al. 2014), water depth (Steffen et al. 2014) and water chemistry (Riis et al. 2000; Coops et al. 2007; Maggioniet al. 2009) as well as grain size and nutrient content of the bottom sediments (Riis et al. 2000; Matsui 2014). Moreover, the water regime, described by duration, frequency as well as the rate of filling and drying, is an important factor determining the development of plant communities and patterns of vegetation zonation in aquatic ecosystems (Barrat-Segretain & Cellot 2007). Furthermore, the anthropogenic influence, land use in the riparian zone and bank structure are the most important parameters for the formation of macrophyte communities (Ot’ahel’ová et al. 2007). Ecosystems disturbed by human impact are more prone to invasions of alien species than undisturbed ecosystems, because plant communities in such conditions become more susceptible to invasion of non-native species (Hussner et al. 2010; Kolada & Kutyła 2016).
The study was based on a countrywide survey conducted in Poland, with a dataset for 1135 river sites (Fig. 1). We analyzed 206 sites with
Descriptive statistics of major habitat characteristics of the surveyed rivers (N = 206)
Environmental variables
Units
Min.
Median
Max
Coefficient variation
Physical and chemical parameters of water
pH
-
6.90
7.76
9.30
0.04
Alkalinity
mg CaCO3 l-1
37
165
490
0.36
Conductivity
μS cm-1
192
449
2990
0.60
Total phosphorus
mg P l-1
0.02
0.19
3.47
1.48
Reactive phosphorus
mg PO43- l-1
0.02
0.35
8.58
1.80
Nitrate nitrogen
mg N l-1
0.01
0.57
12.90
1.48
Ammonium nitrogen
0.00
0.16
12.96
2.78
Dimensions of a riverbed
Average width
m
1.5
5.5
45.0
0.97
Average depth
0.13
0.59
1.65
0.54
Hydromorphological metrics
Altitude
m a.s.l.
1
97
464
0.65
HQA index
0-100
15
39
79
0.34
HMS index
0-120
0
29
109
0.93
RHQ index
50-600
107
305
490
0.25
RHM index
0-240
0
34
174
0.98
Shading of riverbed
%
0
12
90
1.15
Bottom sediments
Granulometry index
1-6
1.00
2.09
5.00
0.30
Cobble
0
0
53
2.69
Gravel/pebble
0
10
93
1.21
Sand
%
0
57
100
0.56
Silt
0
7
100
1.35
Peat
0
0
93
5.85
Anthropogenic
0
0
100
3.00
Flow types
Flow type index
1-6
1.00
1.51
4.13
0.42
Chute
0
0
19
1.82
Broken standing waves
0
0
10
3.21
Unbroken standing waves
%
0
0
58
2.21
Rippled
0
13
93
1.04
Smooth
0
63
100
0.49
No perceptible flow
0
3
100
1.79
Land use in a river valley
Forest
0
19
100
0.92
Wetland
%
0
0
74
1.75
Grassland
0
30
100
0.80
Arable land
0
0
81
1.56
Urban area
0
0
100
1.63
Descriptive statistics of concentrations of elements in water of selected Elodea canadensis rivers (based on data from the State Environmental Monitoring)
Elements
Units
Min.
Median
Max
Coefficient variation
Al (aluminum)
μg Al l-1
5
24
370
1.38
As (arsenic)
μg As l-1
0.3
1.3
32.5
1.54
B (boron)
μg B l-1
9
26
409
1.09
Ba (bar)
μg Ba l-1
5
35
108
0.66
Ca (calcium)
mg Ca l-1
19.4
79.6
191.1
0.37
Cd (cadmium)
μg Cd l-1
0.01
0.10
7.70
2.64
Cl (chlorine)
mg Cl- l-1
3.5
17.6
440.1
1.52
Cr (chromium)
μg Cr l-1
0.02
0.63
42.50
2.23
Cu (cooper)
μg Cu l-1
0.20
2.77
101.00
2.08
F (fluorine)
mg F- l-1
0.02
0.18
1.75
1.18
Fe (iron)
μg Fe l-1
1
133
1172
1.06
Hg (mercury)
μg Hg l-1
0.01
0.10
3.28
1.87
K (potassium)
mg K l-1
0.2
4.0
30.0
0.99
Mg (magnesium)
mg Mg l-1
2.0
9.9
35.8
0.57
Mn (manganese)
μg Mn l-1
2
110
492
0.85
Na (sodium)
mg Na l-1
0.7
9.5
247.0
1.67
Ni (nickel)
μg Ni l-1
0.25
2.50
34.73
1.35
Pb (lead)
μg Pb l-1
0.20
2.50
33.00
1.48
S (sulfur)
mg SO42- l-1
4.6
44.9
342.9
0.82
Se (selenium)
μg Se l-1
0.3
2.9
25.0
1.05
Zn (zinc)
μg Zn l-1
0.3
10.0
244.8
1.89
Macrophyte surveys were carried out during the intensive growth of most aquatic plants (from mid-June to mid-September). Field surveys were conducted using the Macrophyte Method for River Assessment (Szoszkiewicz et al. 2010b). This method is currently an official monitoring approach to rivers in Poland (Dziennik Ustaw 2016). The macrophyte assessment was based on the presence of algae, mosses, horsetails, liverworts, monocotyledonous and dicotyledonous plant species that are biological indicators of water quality. All submerged, free floating, semi-terrestrial and emergent plants were considered. The assessment also included macrophytes attached to or rooted in parts of the river bank substrate where they were likely submerged for most of the year. In wadeable survey sites, an aquascope was used to support the observations. The macrophyte survey was conducted over reaches of 100 m length. The survey includes a list of species and estimated ground cover of plants. The presence of each species was recorded with their percentage cover using the following nine-point scale: < 0.1%, 0.1-1%, 1-2.5%, 2.5-5%, 5-10%, 10-25%, 25-50%, 50-75% and > 75%. Based on the collected field data, the numerical index MIR (Macrophyte Index for Rivers) was computed (Szoszkiewicz et al. 2010b). It reflects river degradation, especially eutrophication and ranges from 10 (most degraded rivers) to 100 (highest quality). To assess the ecological status of the river, the calculated values of the MIR index were referenced to the current standards (Dziennik Ustaw 2016).
During plant and hydromorphological surveys, three subsamples of water were randomly collected from each river site in the river midstream at a depth of 0.5-1 m. Water samples were not collected during rainy weather or periods with heavy runoff; if necessary, an additional visit was organized. Prior to analysis, all water samples were filtered using Sartorius cellulose filters with a nominal pore size of 0.45 μm, except for those used for the determination of total phosphorus. Water samples were cooled below 10°C and all parameters were analyzed in a laboratory within a 12h period. Electrical conductivity and pH were measured by digital potentiometers. Alkalinity was measured with sulfuric acid to the end point pH of 4.5 in the presence of methyl orange. Concentrations of phosphate (molybdenum blue method), total phosphorus (molybdenum blue method after microwave mineralization in MARS 5X), nitrate nitrogen (cadmium reduction method), and ammonium nitrogen (Nessler’s method) were determined using a spectrophotometer HACH DR/2800.
Information about concentrations of 21 elements in water was obtained from the State Environmental Monitoring. The research was carried out in laboratories accredited by the Polish Centre for Accreditation. Table 2 shows the average annual values as a mean of twelve measurements (Ca2+, Mg2+, Cl−, F−, and SO42−), or as a mean of four measurements (Al, As, B, Ba, Cd, Cr, Cu, Fe, Hg, K, Mn, Na, Ni, Pb, Se, and Zn).
Calcium and magnesium were determined by titration with EDTA (PN-ISO 6058:1999, PN-ISO 6059:1999) or by atomic absorption spectrometry (PN-EN ISO 7980:2002); sodium and potassium were measured by atomic absorption spectrometry (PN-ISO 9964-2:1994); chlorides were analyzed using the Mohr method, i.e. titration with silver nitrate in the presence of chromate as indicator (PN-ISO 9297:1994); sulfur was determined gravimetrically with barium chloride (PN-ISO 9280:2002); fluorides were measured using ion chromatography (PN-EN ISO 10304-1:2009). Trace elements were determined by atomic emission spectrometry with inductively coupled plasma (PN-EN ISO 11885:2009) and by atomic absorption spectrometry with a graphite tube (PN EN ISO 15586:2005) or with flame atomization (PN ISO 8288:2002). Mercury was determined by atomic fluorescence spectrometry with amalgamation enrichment (PN-EN ISO 17852:2009).
Measurement accuracy was determined by comparing the results of the determination of three separated portions of each sample, which were analyzed using the identical methods. Blank samples were digested in the same manner.
Hydromorphological evaluation was conducted at each site according to the River Habitat Survey (RHS) method (Environment Agency 2003; Szoszkiewicz et al. 2012). The RHS data were collected from 500 m stretches of rivers. The RHS surveys were performed in ten profiles (spot checks) distributed at 50 m intervals. The macrophyte survey section was located inside each RHS site, always between the 6th and 8th spot check. Four numerical metrics based on the RHS protocol were produced: Habitat Quality Assessment– HQA, Habitat Modification Score – HMS (Raven et al. 1998; Szoszkiewicz et al. 2012), River Habitat Quality – RHQ, and River Habitat Modification – RHM (Tavzes & Urbanic 2009). The range of variability of the analyzed hydromorphological indices is given in Table 1. High values of HQA and RHQ indicate an extensive presence of a number of natural river features and high landscape diversity along the river. High HMS and RHM values indicate extensive anthropogenic alteration such as bank and channel re-sectioning and reinforcement or other river engineering constructions. The grain size composition and flow types were derived from the RHS database. Six flow types and six types of riverbed material were distinguished (Table 1). We also calculated the granulometry index and the flow type index (Jusik et al. 2015).
The granulometry index (GMindex) reflects the average grain size composition of the riverbed associated with the kinetic energy of the flow. It is based on the parameter “dominant channel substrate in spot checks” assessed using the RHS method (section E).
The flow type index (FTindex) reflects the average riverbed hydraulic characteristics associated with parameters such as slope, flow velocity and depth. It is based on the parameter “dominant flow type in spot checks” by the RHS method (section E).
Factor analysis (principal components analysis –PCA) with varimax normalized rotation was used to uncover the structure of environmental matrices and reveal environmental gradients. The ecological amplitude of
Principal components analysis resulted in a simplified habitat description of the analyzed matrix. The first three factors were responsible for 43.5% of the sample variance. Table 3 presents three principal components and their corresponding eigenvalues after varimax normalized rotation; each of the three eigenvalues was responsible for more than 10% of the variance. The first principal component was strongly correlated with human impact, especially the degree of hydromorphological modification. It was negatively correlated with a percentage of urban areas, the RHM index, and the HMS index. The second principal component was strongly correlated with physicochemical water parameters reflecting the eutrophication. It was strongly positively correlated with total phosphorous, reactive phosphorous, and ammonia nitrogen. Finally, the third principal component was strongly correlated with high hydromorphological naturalness, forests as a land-use type and shading of a riverbed. The PCA results show that the analyzed database was characterized by a strong human impact gradient associated with hydromorphological modification of a riverbed and changes in land use and eutrophication. In the studied rivers, pH was the most stable environmental variable (CV = 0.04) and ammonium nitrogen – the most diverse one (CV = 2.78) (Table 2).
Factor loadings of the first three principal components factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6 factor loadings > 0.6
Environmental variables
Factorl
Factor 2
Factor 3
Physical and chemical parameters of water
pH
0.198
0.089
0.199
Alkalinity
0.019
0.539
-0.166
Conductivity
-0.057
0.451
-0.149
Total phosphorus
-0.061
0.934
-0.036
Reactive phosphorus
-0.040
0.904
0.005
Nitrate nitrogen
0.041
0.094
0.013
Ammonia nitrogen
-0.035
0.718
-0.033
Dimensions of a riverbed
Average width
0.282
-0.215
0.042
Average depth
0.213
-0.281
-0.248
Hydromorphological metrics
Altitude
0.106
-0.149
0.060
HQA index
0.409
-0.074
0.789
HMS index
-0.759
0.001
-0.361
RHQ index
0.614
-0.174
0.150
RHM index
-0.788
-0.040
-0.322
Granulometry index
-0.423
-0.195
0.432
Flow type index
0.127
-0.192
0.595
Shading of riverbed
0.145
0.036
0.682
Land use in a river valley at a distance of 50 m from the banks
Forests
0.357
-0.070
0.731
Wetlands
0.224
-0.059
0.028
Grassland
0.482
0.050
-0.617
Arable land
-0.567
0.213
-0.085
Urban area
-0.819
-0.127
-0.021
Eigenvalues (λ)
3.546
3.048
2.967
Percentage variance (%)
16.1
13.9
13.5
Canadian waterweed has high light requirements and occurs mainly in unshaded sections of shallow rivers (often flowing through meadows) with an average depth of about 0.6 m (Table 1). It occurs in places with a dominant laminar flow (smooth) or slight turbulence (rippled). The median of the FTindex is 1.51. It avoids highly turbulent flow of high kinetic energy and marginal dead-water zones (Fig. 2). Waterweed occurs mostly in sandy bottom sections (median GMindex = 2.09), with some admixture of coarse mineral (gravel/pebble) and fine organic fraction (silt) (Fig. 3). It prefers moderately natural (HQA = 41 ± 14, RHQ = 307 ± 76) and moderately transformed (HMS = 32 ± 30, RHM = 40 ± 40) river sections, usually with a straightened planform, a uniform bank profile, re-sectioned and reinforced by fascines. It was not found in strongly transformed river sections (reinforced by concrete, sheet piling, cladding, cobblestones, and gabion) and those with reinforced banks and bottoms.
In the present study, the pH of water from
In terms of nutrients,
In the studied rivers, concentrations of trace metals were generally at low levels (Table 2) and were in ranges characteristic of clean water bodies (Kabata-Pendias 2011), although the content of metals in water from some study sites was very high, e.g.: 3.28 μg Hg l−1 (the Meszna river, site Kąty, 2006), 7.70 μg Cd l−1, 32.5 μg As l−1, 33.0 μg Pb l−1, and 101 μg Cu l−1 (the Kania river, site Gostyn, 2006), 245 μg Zn l−1, 409 μg B l−1, and 492 μg Mn l−1 (the Przemsza river, site Będzin, 2006). These metal concentrations were significantly higher than those determined in water of waterweed stands in Poland (Samecka-Cymerman & Kempers 2003) and in north-eastern France (Thiébaut et al. 2010).
The Detrended Correspondence Analysis (DCA) ordination of the macrophyte species is presented in Fig. 5 The first axis (λ1 = 0.303) can be identified with the kinetic energy of water (current velocity), which directly affects the degree of riverbed material fragmentation and the observed types of flow. Aquatic bryophytes and some filamentous algae (e.g.
The average contribution of
In terms of abiotic factors,
In the analyzed Polish lakes, 78 hydrophyte communities were identified.
In summary,