## 3 Material and Methods  
The research on the shore vegetation of Lake Piaseczno was conducted in 2015 and 2018, in spring, summer and autumn, several times in each season. The plant species composition was determined in the shore belt of the lake, depending on the topographic conditions, with a width of approximately 10 m and along the entire shoreline of approximately 3800 m (38,000 m\({}^{2}\)). The research was conducted in stages by designating sections with a length of 50 m corresponding to the range of particular sectors of the catchment (length of the shoreline in sectors (m): peatbog-forest-recreational (PFR): 799; recreational (R): 607; recreational-agricultural (RA): 1098; and forest-recreational (FR): 1288). Botanical analyses were focused on a multidimensional analysis of the species composition of the flora in the study area. Species were identified by means of the Rutkowski's key [39], and the botanical nomenclature was adopted after [PERSON] et al. [40]. The identified species were ascribed to different life forms according to Raunkieaer [41; 42], syntaxonomic groups [41] and historical-geographic groups. This permitted the calculation of the following indicators of anthropogenic transformations in the flora [43; 44]:  
* Total synanthropisation \[-\text{WS-c}=\frac{\text{Ap}+\text{A}}{\text{Sp}+\text{A}}\times 100\%\] (1)
* Total anthropopphytisation \[-\text{WAn-c}=\frac{\text{A}}{\text{Sp}+\text{A}}\times 100\%\] (2)
* Total apopphytisation \[-\text{WAp-c}=\frac{\text{Ap}}{\text{Sp}+\text{A}}\times 100\%\] (3) where Ap denotes apophytes (taxa of synanthropic plants of local origin that constitute part of the native flora and occur in anthropogenic habitats developed as a result of human activity); Sp denotes spontaneophytes (taxa developed in or imported into a given area that are able to function with no human interference and exist in nature) plus apophytes; and A denotes anthropophytes (taxa developed in a given area as a result of human pressure, taxa imported into a given area by man, and species surviving at sites under constant human pressure). The [PERSON]'s species similarity coefficient of the shore flora of Lake Piaseczno was also calculated in 2015 and 2018 [45]. The species composition was also referred to index numbers of higher plants \begin{table}
\begin{tabular}{c c} \hline \hline
**Lake** & **Lake Density Ratio** \\
83.2 (ha) & 3.4 \\
**Schindler’s Ratio** & **Forests** \\
0.3 & 78.1 (ha, 27.42\%) \\
**Arable Land** & **Shrubs, Tree Stands** \\
82.15 (ha, 28.8\%) & 4.28 (ha, 1.52\%) \\
**Recreational Areas, Rural** & **Arable Land, Meadows,** \\
**Building Development** & **Pastures** \\
76.6 (ha, 26.9\%) & 37.57 (ha, 13.19\%) \\
**Water Bodies** & **Swamps** \\
0.31 (ha, 0.11\%) & 6.2 (ha, 2.17\%) \\
**TOTAL** = 284.9 (ha, 100\%) & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The surface area and land use structure in the catchment of Lake Piaseczno in 2010 (adapted from [38]), and its division into sectors: 1, peatbog–forest–recrereational (PFR); 2, recreational (R); 3, recreational–agricultural (RA); 4, forest–recreational (FR). (moisture, acidity, trophic status, content of organic matter and granulometric composition of the soil [42]) for the purpose of ascribing species with preferences for particular habitat properties. Shore vegetation was treated as a complex phytocoenotic system that interacts with the catchment and lake and is often transformed anthropogenically [46]. Therefore, no analyses concerning the syntaxonomic classification of plant communities were performed. The identified species were only described as characteristic of a given phytosociological unit. Statistical analyses were carried out in the R environment for statistical computing v. 3.5.1 [47] with the vegan v. 2.5-3 library [48]. Based on the Jaccard's species similarity coefficient of the phytocoenoses, a classification analysis was performed as a measure of distance with the application of the average linkage classification algorithm (UPGMA).