Preferences of individual macrophyte species for specific environmental conditions make it possible to determine the degree of flowing water degradation (Szoszkiewicz et al. 2017). In every ecosystem, nutrient concentrations change under the influence of contaminants. Changes may also result from fluctuations in river water flow, seasons, weather conditions and the changing self-purification capacity of rivers (Westlake 1975; Dawson 1988). Water quality assessments based on biological characteristics, physical and chemical parameters are complementary, and reflect the state of an aquatic ecosystem. Aquatic organisms are permanently exposed to environmental pressure. If their sensitivity to a specific contaminant has been determined, then the degree of degradation of an aquatic environment resulting from a given pressure can be determined based on field studies, with macrophytes (similarly as fish) facilitating the identification of changes over a longer timeframe, as opposed to phytoplankton and zooplankton, which tend to respond promptly (Wiegleb 1979; Haslam 1982; Holmes et al. 1999; Ceschin et al. 2010; Szoszkiewicz et al. 2017).
Ecological assessment methods for rivers based on macrophytes are applied in many European countries. In the UK, the Mean Trophic Rank (MTR) is commonly used (Holmes et al. 1999). The research conducted in France resulted in the development of the Indice Biologique Macrophytique En Rivière (IBMR) method (Haury et al. 2006). The Polish macrophyte method for rivers is largely based on the British MTR method and the French IBMR method. Their Polish counterpart, using the Macrophyte Index for Rivers (MIR), was developed in 2006 (Szoszkiewicz et al. 2007; Szoszkiewicz et al. 2010) and implemented in the monitoring of flowing waters.
Regardless of the type of biological characteristic (index), in each case the primary problem relates to accurate estimation of the sample size. Spatial and temporal variability are the most important sources of uncertainty affecting the variance of indicator values and classification results for various groups of organisms (Staniszewski & Szoszkiewicz 2006; Staniszewski et al. 2006; Carvalno et al. 2013). The main sources of variability related to macrophytes are associated with the method of on-site data collection (Dudley et al. 2013; Kolada et al. 2011) and the accuracy of macrophyte identification and classification, particularly the estimation of their abundance and cover. Another important source of errors, so far neglected in the literature on the subject, is the underestimation of the sample size – meaning that not all the taxa present were found. On the other hand, new species records are sometimes discovered during on-site studies, which do not provide relevant information, because they may occur anywhere.
The development of the information theory initiated by Shannon (1948) was of great importance for the progress of the probability theory and mathematical statistics. Furthermore, it was also applied in ecological studies (Kullback 1959; Sherwin et al. 2017). The creator of information theory, Eryomin (1998), proposed new scientific directions of research related to information ecology, such as research into the value of information or identification of quantitative and qualitative criteria of information. The concept of entropy in ecology means the expected value of a discrete variable, which is the sum of products of frequencies of species at surveyed sites and their corresponding information values (or relative total percentage cover of a species over the entire study area and its corresponding information value). The amount of information obtained while finding a species at a site may be considered as one of the aspects of diversity.
The aim of this study was to characterize selected macrophyte species in rivers in terms of their indicator value, related to the information they provide in the assessment of the ecological status of rivers. In practice, this will facilitate the assessment of the site completeness in terms of reported taxa, based on the informative value of indicator species for the Macrophyte Index for Rivers (MIR). On this basis, a decision can be made as to whether studies at a given site should be repeated or whether they may be considered complete.
The paper evaluates macrophytes found in medium-sized lowland rivers in Poland in terms of their informative value reflecting the quality of an aquatic ecosystem. A criterion for the sample completeness required for the determination of MIR is proposed.
The research was conducted in Poland at 100 river sites on medium-sized lowland rivers covered by the national environmental monitoring system, which were classified as representing a single abiotic type of sandy lowland rivers. These rivers are located below 200 m a.s.l. Their catchments are less than 1000 km2 in area (small and medium-sized rivers according to WFD; WFD Intercalibration 2011; Fig. 1).
Research on macrophytes, including vascular plants, algae, mosses and liverworts (of the total number of 153 indicator taxa reported in the literature), was conducted at water sampling sites in the period 2008–2013 from July to early September (once in the analyzed period) along 100 m sections of river channels. Macrophytes were identified to the species level (except for six algae taxa:
The Macrophyte Index for Rivers (MIR) is a biological quality indicator for flowing waters (Szoszkiewicz et al. 2010). This index was calculated for each site based on the formula:
where:
The MIR value was determined based on macrophytes identified at each site, with specific
Maximum, minimum, mean and median MIR values in five quality classes
MIR | class I | class II | class III | class IV | class V |
---|---|---|---|---|---|
MIRmax | 56.11 | 46.67 | 46.90 | 38.00 | 38.70 |
MIRmin. | 35.77 | 34.09 | 32.41 | 20.78 | 18.29 |
MIRmean | 43.32 | 39.29 | 37.94 | 32.02 | 29.01 |
MIRmed | 42.98 | 39.31 | 38.06 | 33.13 | 29.06 |
To determine the information value of each species, the probability
Indicator values of
Taxa | |||||||
---|---|---|---|---|---|---|---|
2 | 3 | 1 | 0.007 | 4.955 | 0.006 | 5.053 | |
4 | 2 | 1 | 0.012 | 4.465 | 0.019 | 3.953 | |
1 | 1 | 1 | 0.003 | 5.902 | 0.006 | 5.170 | |
4 | 2 | 1 | 0.048 | 3.028 | 0.039 | 3.242 | |
5 | 2 | 1 | 0.012 | 4.412 | 0.014 | 4.253 | |
6 | 2 | 1 | 0.000 | 7.955 | 0.001 | 7.249 | |
8 | 2 | 2 | 0.001 | 6.586 | 0.001 | 7.249 | |
6 | 2 | 1 | 0.003 | 5.869 | 0.003 | 5.863 | |
5 | 1 | 2 | 0.004 | 5.570 | 0.009 | 4.684 | |
4 | 1 | 4 | 0.006 | 5.050 | 0.010 | 4.610 | |
5 | 1 | 2 | 0.002 | 6.143 | 0.005 | 5.303 | |
4 | 2 | 1 | 0.017 | 4.074 | 0.015 | 4.205 | |
6 | 3 | 5 | 0.003 | 5.658 | 0.001 | 7.249 | |
6 | 2 | 2 | 0.002 | 6.334 | 0.002 | 6.151 | |
5 | 1 | 2 | 0.003 | 5.875 | 0.004 | 5.640 | |
2 | 3 | 1 | 0.033 | 3.397 | 0.018 | 4.030 | |
2 | 3 | 1 | 0.003 | 5.843 | 0.002 | 6.151 | |
6 | 2 | 2 | 0.004 | 5.583 | 0.005 | 5.303 | |
1 | 2 | 3 | 0.025 | 3.678 | 0.018 | 3.991 | |
7 | 1 | 5 | 0.002 | 6.254 | 0.001 | 7.249 | |
6 | 2 | 2 | 0.001 | 7.309 | 0.001 | 6.556 | |
5 | 2 | 2 | 0.070 | 2.666 | 0.039 | 3.242 | |
1 | 2 | 2 | 0.001 | 7.563 | 0.001 | 7.249 | |
6 | 2 | 2 | 0.017 | 4.093 | 0.014 | 4.305 | |
5 | 2 | 2 | 0.003 | 5.794 | 0.006 | 5.052 | |
6 | 2 | 1 | 0.007 | 4.923 | 0.006 | 5.170 | |
5 | 2 | 2 | 0.012 | 4.436 | 0.011 | 4.477 | |
3 | 1 | 2 | 0.036 | 3.324 | 0.041 | 3.189 | |
5 | 1 | 2 | 0.001 | 7.090 | 0.001 | 7.249 | |
4 | 1 | 1 | 0.000 | 9.306 | 0.001 | 7.249 | |
6 | 2 | 1 | 0.013 | 4.329 | 0.011 | 4.477 | |
5 | 1 | 2 | 0.001 | 7.170 | 0.001 | 7.249 | |
6 | 2 | 2 | 0.021 | 3.880 | 0.021 | 3.882 | |
1 | 3 | 7 | 0.021 | 3.879 | 0.009 | 4.764 | |
2 | 2 | 2 | 0.075 | 2.590 | 0.053 | 2.945 | |
4 | 2 | 1 | 0.011 | 4.482 | 0.014 | 4.305 | |
7 | 3 | 1 | 0.001 | 7.313 | 0.001 | 7.249 | |
4 | 1 | 1 | 0.001 | 6.805 | 0.004 | 5.640 | |
5 | 1 | 1 | 0.014 | 4.245 | 0.029 | 3.536 | |
9 | 3 | 1 | 0.001 | 7.444 | 0.001 | 7.249 | |
4 | 1 | 1 | 0.015 | 4.219 | 0.033 | 3.399 | |
3 | 2 | 1 | 0.005 | 5.226 | 0.005 | 5.303 | |
5 | 2 | 1 | 0.001 | 7.550 | 0.001 | 7.249 | |
4 | 2 | 2 | 0.030 | 3.513 | 0.021 | 3.848 | |
6 | 2 | 1 | 0.001 | 7.137 | 0.001 | 6.556 | |
4 | 1 | 1 | 0.002 | 6.166 | 0.005 | 5.303 | |
5 | 1 | 2 | 0.001 | 6.869 | 0.003 | 5.863 | |
5 | 2 | 1 | 0.001 | 7.550 | 0.001 | 7.249 | |
2 | 1 | 2 | 0.050 | 3.004 | 0.050 | 3.001 | |
4 | 1 | 1 | 0.001 | 6.592 | 0.005 | 5.303 | |
3 | 1 | 2 | 0.002 | 6.011 | 0.007 | 4.947 | |
2 | 2 | 1 | 0.003 | 5.717 | 0.005 | 5.303 | |
7 | 2 | 1 | 0.000 | 8.748 | 0.001 | 7.249 | |
4 | 2 | 2 | 0.002 | 6.120 | 0.002 | 6.151 | |
4 | 2 | 1 | 0.017 | 4.095 | 0.013 | 4.359 | |
4 | 3 | 6 | 0.005 | 5.391 | 0.003 | 5.863 | |
4 | 1 | 3 | 0.006 | 5.130 | 0.009 | 4.684 | |
3 | 2 | 1 | 0.002 | 6.290 | 0.002 | 6.151 | |
5 | 2 | 1 | 0.000 | 8.161 | 0.001 | 7.249 | |
1 | 1 | 3 | 0.020 | 3.897 | 0.016 | 4.114 | |
4 | 2 | 3 | 0.008 | 4.788 | 0.006 | 5.052 | |
6 | 3 | 3 | 0.005 | 5.284 | 0.004 | 5.457 | |
4 | 2 | 2 | 0.001 | 6.558 | 0.001 | 6.556 | |
5 | 3 | 2 | 0.009 | 4.689 | 0.004 | 5.640 | |
5 | 2 | 1 | 0.003 | 5.870 | 0.004 | 5.640 | |
7 | 2 | 2 | 0.009 | 4.659 | 0.005 | 5.303 | |
8 | 2 | 1 | 0.004 | 5.621 | 0.003 | 5.863 | |
4 | 3 | 2 | 0.001 | 6.908 | 0.001 | 7.249 | |
2 | 1 | 1 | 0.001 | 6.727 | 0.004 | 5.640 | |
6 | 2 | 4 | 0.002 | 6.201 | 0.002 | 6.151 | |
1 | 1 | 1 | 0.013 | 4.363 | 0.011 | 4.477 | |
5 | 1 | 2 | 0.001 | 7.550 | 0.001 | 7.249 | |
3 | 1 | 2 | 0.014 | 4.264 | 0.028 | 3.586 | |
4 | 1 | 2 | 0.010 | 4.616 | 0.028 | 3.586 | |
4 | 2 | 3 | 0.047 | 3.052 | 0.035 | 3.357 | |
4 | 2 | 1 | 0.001 | 7.376 | 0.001 | 6.556 | |
5 | 2 | 2 | 0.015 | 4.190 | 0.014 | 4.253 | |
4 | 1 | 2 | 0.008 | 4.772 | 0.018 | 4.030 | |
7 | 1 | 1 | 0.010 | 4.592 | 0.019 | 3.953 | |
4 | 2 | 2 | 0.069 | 2.668 | 0.041 | 3.206 | |
3 | 1 | 1 | 0.030 | 3.498 | 0.036 | 3.317 | |
2 | 2 | 1 | 0.016 | 4.162 | 0.023 | 3.783 | |
2 | 1 | 1 | 0.008 | 4.771 | 0.021 | 3.882 | |
1 | 1 | 2 | 0.000 | 7.844 | 0.001 | 7.249 | |
3 | 2 | 2 | 0.002 | 6.040 | 0.003 | 5.863 | |
2 | 2 | 3 | 0.014 | 4.287 | 0.013 | 4.359 | |
2 | 1 | 1 | 0.003 | 5.904 | 0.009 | 4.764 | |
4 | 2 | 2 | 0.029 | 3.538 | 0.028 | 3.560 | |
4 | 1 | 1 | 0.005 | 5.277 | 0.013 | 4.359 | |
9 | 1 | 1 | 0.000 | 8.412 | 0.001 | 7.249 |
The occurrence of the
In order to determine whether a sample is representative of a macrophyte population in rivers of a given type, entropy was defined as average information. It was compared with the maximum value of entropy for the complete pool of taxa found.
The entropies H(
Based on the matrix of six characteristics for the indicator species recorded in the study (Table 2), the synthetic Perkal index was constructed (Smith 1972; Parysek et al. 1979; Chojnicki et al. 1991; Sobala-Gwózdz 2004). This is a sum of standardized partial values in two versions: (Pe
Species profiles for macrophytes and the Perkal indices
Taxa | Perkal indices | |
---|---|---|
Pe |
Peln(1/ |
|
−2.94 | 0.23 | |
−2.94 | −1.85 | |
−2.94 | −1.28 | |
−2.94 | 3.04 | |
−2.39 | −3.26 | |
−2.39 | 1.12 | |
−2.39 | −1.46 | |
−2.39 | −0.06 | |
−1.85 | −2.91 | |
−1.85 | 0.14 | |
-1.85 | -2.00 | |
−1.85 | −2.70 | |
−1.42 | −2.09 | |
−1.42 | 2.86 | |
−1.31 | −0.73 | |
−1.31 | 3.99 | |
−1.31 | 1.17 | |
−1.31 | −2.17 | |
−1.31 | 0.51 | |
−1.31 | 0.78 | |
−1.31 | −0.63 | |
−1.31 | −1.78 | |
−1.31 | −1.35 | |
−1.31 | −0.77 | |
−0.88 | −3.57 | |
−0.88 | 0.21 | |
−0.88 | −1.92 | |
−0.88 | −1.42 | |
−0.77 | −0.34 | |
−0.77 | 0.49 | |
−0.77 | 0.57 | |
−0.77 | 2.55 | |
−0.77 | 2.60 | |
−0.77 | −2.05 | |
−0.77 | 1.38 | |
−0.77 | 2.85 | |
−0.33 | −0.11 | |
−0.33 | 1.21 | |
−0.33 | 0.84 | |
0.10 | −1.38 | |
0.21 | -1.60 | |
0.21 | −3.06 | |
0.21 | −1.67 | |
0.21 | −1.33 | |
0.21 | −2.30 | |
0.21 | 1.10 | |
0.21 | −1.54 | |
0.21 | −0.58 | |
0.21 | 1.69 | |
0.21 | −2.96 | |
0.21 | 2.22 | |
0.21 | −3.33 | |
0.21 | −2.50 | |
0.32 | 2.00 | |
0.32 | −1.52 | |
0.64 | −0.47 | |
0.64 | −2.24 | |
0.64 | 0.92 | |
0.75 | −1.41 | |
0.75 | −3.30 | |
0.75 | 0.08 | |
0.75 | −1.23 | |
0.75 | 2.85 | |
0.75 | 2.85 | |
0.75 | 3.25 | |
0.75 | 0.56 | |
0.75 | −1.56 | |
1.29 | 3.11 | |
1.29 | 0.73 | |
1.29 | 1.24 | |
1.29 | 0.13 | |
1.29 | 2.18 | |
1.29 | −1.58 | |
1.29 | −0.40 | |
1.29 | −1.30 | |
1.29 | −2.04 | |
1.29 | 2.07 | |
1.29 | 1.16 | |
1.40 | 3.41 | |
1.73 | 0.42 | |
1.73 | 2.43 | |
1.83 | 3.63 | |
1.83 | −0.48 | |
2.27 | −0.21 | |
2.38 | 2.22 | |
2.38 | 0.56 | |
2.81 | 1.62 | |
2.81 | 0.04 | |
3.35 | 2.69 | |
4.44 | 2.78 |
Pe
For the newly determined characteristics from Table 2, Pearson’s correlation coefficient r was used to determine the linear correlation between the variables ln(1/
To verify the hypothesis that there is no significant difference between the investigated entropies H(
These values were used to determine the information threshold required for a site to be considered sufficiently surveyed, regardless of its quality class. Furthermore, based on literature data, it is assumed that a thorough study requires on average at least eight or nine indicator species (Szoszkiewicz 2013; Budka 2018).
As a consequence, a criterion was proposed to indicate whether a given site was sufficiently surveyed. The total arithmetic mean of the informative value for the recorded species at a given site may not be lower than the arithmetic mean of the informative value determined separately based on all sites belonging to each of the river quality classes.
Statistical analyses were performed using the R computational platform. The available packages, i.e. “VennDiagram” v.1.6.20 , “ggplot2” v.1.0.0, “gplots” v. 2.14.1, “graphics” v.3.1.1, were used.
A total of 90 indicator species with specific indicator values
It is estimated that the total aquatic flora in the analyzed types of watercourses (lowland, with sandy bottom substrate) comprises approx. 115 vascular plant species (Bernatowicz et al. 1969; Rutkowski 2008; Jusik 2012).
It should be noted that this study identified 19 strongly stenotopic species that were reported in only one quality class. Six species were reported in quality class I:
The basic characteristics of the MIR index (maximum, minimum, mean and median values) in five quality classes were determined for the obtained database (Table 1) in order to present a general description of the environment.
The largest range (20.3), i.e. the MIR values were the most diverse at sites with waters of the highest eutrophic levels. Furthermore, the range for quality class IV was 17.2, followed by 14.4 for class III and 12.6 for class II. It can be observed that the highest disproportions occurred among the MIR values for the sites in class I. This may be due to the fact that at some sites mainly rare and scarce species of high indicator value were identified, while other sites were dominated by species typical of all water quality classes. Furthermore, it should be noted that the decreasing trend of the MIR mean value in the trophic gradient was maintained. Values of the above-mentioned measures are typical of medium-sized lowland rivers. It can be assumed that the data characterize well ecoregions established by the EU WFD (2000/60/EC): Central Plains (Ecoregion no. 14) and Eastern Plains (Ecoregion no. 16). As a good representation of lowland rivers in Europe, they constitute an adequate group of bioindicators for Europe (Klijn 1989; 1994; Groen et al. 1993; Kondracki 1995).
The
The highest recorded incidence of the
Taxa found in only one quality class (19 species) had the lowest
Such results are consistent with the scale of species occurrence and dynamic trends (Rutkowski 2008). The abundance score denotes the abundance of species on a nationwide scale of five classes: 1 – very rare (1–10 sites), rare (10–100 sites), quite frequent (more than 100 sites), frequent in many regions, common throughout (or nearly throughout) the territory of Poland. Macrophytes that are highly ecologically specialized, such as
Entropy for all macrophytes found in all examined river sections was H(
The correlation coefficient between the information vectors ln(1/
The next step consisted in the standardization of data from Table 2, which resulted in the so-called species profiles for individual taxa. Standardization involves converting measurements expressed in different units into a scale expressed in the same measurement unit (the variable obtains an average equal to 0 and standard deviation equal to 1). The transformed data are presented in Table 3.
Two synthetic Perkal indices were constructed based on the species profiles (Table 3). The maximum value of the Perkal index Pe
The highest values of the Pe
The proposed approach allowed for more precise ecological characteristics of macrophyte species. Species with the same values of
Table 4 presents basic descriptive statistics for information values of all macrophyte species found in the respective quality classes.
Descriptive statistics: maximum, minimum, mean and median values for the information value ln(1/Di) of plants found in five quality classes
ln(1/ |
class I | class II | class III | class IV | class V |
---|---|---|---|---|---|
ln(1/ |
4.80 | 4.40 | 4.70 | 4.10 | 4.20 |
ln(1/ |
3.60 | 3.40 | 3.40 | 3.50 | 3.50 |
ln(1/ |
4.19 | 4.03 | 3.94 | 3.78 | 3.80 |
ln(1/ |
4.20 | 4.00 | 3.90 | 3.70 | 3.80 |
For the selected representative characteristic
Information mean values for ln(1/D) at the study sites in five quality classes
class I | class II | class III | class IV | class V | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ST | SP | MI | ST | SP | MI | ST | SP | MI | ST | SP | MI | ST | SP | MI |
32 | 8 | 4.6 | 30 | 12 | 4.0 | 3 | 28 | 4.3 | 4 | 14 | 3.6 | 79 | 17 | 3.8 |
36 | 19 | 4.4 | 38 | 14 | 3.9 | 14 | 12 | 3.7 | 8 | 14 | 3.7 | 82 | 18 | 3.6 |
37 | 17 | 4.1 | 43 | 10 | 3.8 | 58 | 23 | 3.9 | 63 | 12 | 3.8 | 84 | 10 | 3.9 |
39 | 10 | 3.6 | 49 | 14 | 4.0 | 66 | 17 | 3.7 | 200 | 14 | 3.9 | 95 | 10 | 4.2 |
224 | 19 | 4.4 | 51 | 14 | 3.9 | 109 | 13 | 4.1 | 238 | 14 | 4.1 | 170 | 13 | 3.5 |
237 | 9 | 4.0 | 137 | 9 | 3.4 | 141 | 6 | 3.4 | 259 | 12 | 3.7 | 171 | 21 | 4.1 |
529 | 6 | 4.3 | 140 | 13 | 3.8 | 210 | 12 | 4.7 | 295 | 12 | 3.8 | 176 | 14 | 3.9 |
591 | 11 | 3.9 | 143 | 14 | 3.8 | 226 | 14 | 4.0 | 427 | 11 | 4.0 | 190 | 16 | 4.0 |
598 | 10 | 4.8 | 151 | 14 | 4.4 | 236 | 19 | 3.9 | 429 | 8 | 3.7 | 243 | 18 | 3.8 |
599 | 15 | 3.8 | 153 | 9 | 4.1 | 435 | 12 | 3.7 | 432 | 12 | 3.6 | 260 | 17 | 4.0 |
601 | 10 | 4.7 | 201 | 14 | 3.9 | 439 | 17 | 3.8 | 444 | 11 | 3.7 | 262 | 13 | 3.8 |
616 | 21 | 4.5 | 256 | 14 | 3.6 | 451 | 8 | 4.4 | 474 | 7 | 3.5 | 277 | 11 | 3.5 |
639 | 12 | 3.7 | 423 | 14 | 4.4 | 462 | 17 | 3.9 | 475 | 10 | 3.6 | 426 | 18 | 3.9 |
640 | 28 | 4.5 | 452 | 7 | 4.4 | 469 | 10 | 3.7 | 530 | 14 | 4.0 | 430 | 6 | 3.6 |
645 | 24 | 4.3 | 600 | 11 | 4.3 | 531 | 16 | 3.6 | 562 | 11 | 3.5 | 437 | 6 | 3.8 |
647 | 18 | 4.6 | 602 | 13 | 4.0 | 552 | 11 | 4.3 | 573 | 8 | 4.0 | 438 | 7 | 3.6 |
686 | 22 | 3.9 | 625 | 14 | 4.4 | 634 | 21 | 4.0 | 628 | 10 | 3.7 | 443 | 9 | 3.6 |
693 | 21 | 4.0 | 630 | 11 | 4.1 | 694 | 14 | 3.7 | 670 | 25 | 4.0 | 549 | 9 | 3.7 |
703 | 10 | 3.9 | 641 | 14 | 4.2 | 697 | 20 | 3.9 | 671 | 14 | 3.9 | 650 | 13 | 4.1 |
723 | 14 | 3.8 | 699 | 14 | 4.1 | 707 | 14 | 4.0 | 713 | 10 | 3.7 | 667 | 14 | 3.6 |
ST – site number,SP – numberof species,MI – meanln(1/D)
It is assumed that a reliable macrophyte study requires the incidence of at least nine indicator species, but the number of bioindicators may be lower if they are the most sensitive ones, i.e. mostly stenobiotic species (Szoszkiewicz 2013; Budka 2018). The analysis of the number of taxa at the investigated lowland river sites and the mean value of information introduced by those taxa showed considerable variability in individual water quality classes (Fig. 3).
Figure 3 shows the relationship between the number of species and the mean information value at a given site.
Based on the conducted study, the mean information value at thirteen sites in river quality class I exceeded 4.0, with 6 to 28 species recorded at a site. It should be noted that at some sites, despite the large number of species found (e.g. 22 species at site 686), the information value was insufficient. On the other hand, there are sites where 6, 8 or 9 species were reported, but this was sufficient in terms of information to conclude that a given site was complete. Twelve sites in quality class II exceeded the threshold for the mean information required for a complete inventory (including two sites with only 7 and 9 species). The other sites did not have a sufficient number of species or a satisfactory information value. Eight sites in class III reached a sufficient information level, including one site with only 8 species. At the other sites, a sufficient information level was obtained following a considerable effort to complete the study (as many as 23 species recorded at a site). Only five sites in quality class IV were completely inventoried (including 8 species at one site), while the information threshold at the other sites was too low to consider the sites as completely inventoried. Five sites in quality class V achieved a high information value (from 4.2 to 4), while in the other cases the species identification must be considered incomplete. Consequently, these sites are not reliable for further analysis (Table 4).
The slow natural response of biological indicators to changing environmental conditions and the relatively small possibility of increasing the sampling frequency for biological parameters are an issue for all European water monitoring systems. Therefore, an attempt to solve some of the methodological dilemmas related to sampling, necessary to obtain reliable assessment results is very relevant and may be the first step toward solving numerous ecological issues, such as the likelihood of misclassification of biological elements in surface waters (Loga & Wierzchołowska-Dziedzic 2017). In specific cases where the indicator value is close to the threshold value between good and moderate status classes, these analyses are of particular importance as their results are crucial for water management and water protection decisions. It should be noted that statistical uncertainty in the evaluated ecological status of homogenous waters based on biological parameters may be much higher than the uncertainty based on frequently measured physicochemical indicators. The requirements of the Water Framework Directive (WFD) regarding the determination of the status of watercourses based on biological indices (in particular MIR) may contribute to an increasing risk of errors in decision making related to water management based on the WFD. This study addresses an unresolved bioethical problem by attempting to answer the question “how many species are required to obtain a reliable assessment result with a minimum level of uncertainty”. Monitoring data used for this analysis represent a relatively large database of lowland rivers in Poland. Considering the results obtained while determining the criterion for lowland rivers, the proposed method could be easily extended to rivers of other types in Poland and Europe. The analyses could be performed for other indicators, such as the phytoplankton index (IFPL, Błachuta et al. 2012; Mischke et al. 2011), the diatom index (IO, Błachuta et al. 2010; Rimet et al. 2012) and the multimetric benthic invertebrate index (MMI, Bis et al. 2013; Lewin et al. 2013). With regard to these elements, there is no problem of insufficient data collection, as the relevant data have been collected by all EU countries for several decades.
Determining the completeness of a sample is consistent with the necessity to integrate many monitoring actions in order to systematize and make rational use of environmental monitoring data. The general concept of a hierarchical perspective was proposed by Loga (2012) and assumed that elementary measurement errors would be considered via a hierarchical structure of procedures applied to define the water status in compliance with WFD.