## 3 Results and Discussion
The variables human (0.37), dog (0.31), cat (0.42), hedgehog (0.38), rabbit (0.44), roe deer (0.39), and horse (0.39) show a positive effect in the definition of the component PC1. The variable donkey defines the main component PC2 with a positive correlation intensity (0.84). In the definition of the PC3, only the variable deer (0.76) is present. Component PC4 is defined by a positive effect of the variable goat (0.71) and a negative effect of the variable pig (\(-\)0.71). Similar behaviour in the definition of the main components as eigenvectors was also obtained using the analysis of the corresponding values for individual variables (Table 3). It was noticeable that the dominant role in the model definition has the variables cat (0.16), rabbit (0.16), and horse (0.15), while the variable dog is the variable that contributes the least (0.09). In the definition of PC2, the variable donkey (0.70) is of significant importance, while the variable deer defines PC3 (0.58), and the same importance in the definition of PC4 is shown with goats and pigs (0.50). Factor scores based on correlations (tick species) represented via PCA (n = 929) are shown in Figure 4b and Table 4. The active case variable of gender/development and the labelling variable of tick species were used,  
Figure 4: Distribution of tick species according to gender and developmental stage collected using different host methods represented via principal component analysis (PCA) (n = 929) with two main components (PC1 and PC2). Projections of (**a**) the analysis variables (hosts) and (**b**) cases (active cases variable: gender/development; labelling variable: tick species) on the factor plane. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multicolumn{4}{c}{**Eigenvectors Spreadsheet**} & \multicolumn{4}{c}{**Variable Contributions**} \\ \cline{2-9}  & **PC1** & **PC2** & **PC3** & **PC4** & **PC1** & **PC2** & **PC3** & **PC4** \\ \hline human & 0.37 & 0.28 & \(-\)0.01 & \(-\)0.00 & 0.14 & 0.08 & 0.00 & 0.00 \\ dog & 0.31 & \(-\)0.21 & \(-\)0.05 & \(-\)0.00 & 0.09 & 0.04 & 0.00 & 0.00 \\ cat & 0.42 & \(-\)0.06 & \(-\)0.01 & \(-\)0.00 & 0.16 & 0.00 & 0.00 & 0.00 \\ hedgehog & 0.38 & \(-\)0.15 & \(-\)0.05 & \(-\)0.00 & 0.14 & 0.02 & 0.00 & 0.00 \\ rabbit & 0.39 & \(-\)0.05 & \(-\)0.02 & \(-\)0.00 & 0.16 & 0.00 & 0.00 & 0.00 \\ roe deer & 0.39 & \(-\)0.09 & 0.08 & 0.00 & 0.15 & 0.01 & 0.01 & 0.00 \\ deer & \(-\)0.04 & \(-\)0.31 & 0.76 & 0.00 & 0.00 & 0.10 & 0.58 & 0.00 \\ goat & \(-\)0.05 & \(-\)0.06 & \(-\)0.43 & 0.71 & 0.00 & 0.00 & 0.18 & 0.50 \\ horse & 0.44 & 0.19 & 0.03 & 0.00 & 0.15 & 0.04 & 0.00 & 0.00 \\ pig & \(-\)0.05 & \(-\)0.06 & \(-\)0.43 & \(-\)0.71 & 0.00 & 0.00 & 0.18 & 0.50 \\ donkey & 0.00 & 0.84 & 0.20 & 0.00 & 0.00 & 0.70 & 0.04 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Eigenvector spreadsheets and variable contribution of active case variables (hosts) represented via principal component analysis (PCA) (n = 929). while the code of the active cases was female (F). Analysing these results presented on the PC1 and PC2 principal components graph, it is evident that the tick species _Ixodes ricinus_ (Acari: Ixodidae) (F, M, and N), _Rhiphycphalus sanguineus_ (Ixodida: Ixodidae) (F and M), and _Hyalomma marginatum_ (Ixodida: Ixodidae) (M) are distributed on the right of PC1. Furthermore, the _Ixodes ricinus_ (Acari: Ixodidae) (F) is placed in the first quadrant of the factor plane together with the variables rabbit, cat, roe deer, and hedgehog, leading to the assumption that this tick species was dominant among these hosts. In the same quadrant, there are also the tick species _Ixodes ricinus_ (M) and _Rhiphycphalus sanguineus_ (Ixodida: Ixodidae) (M and F), which are the most abundant tick species found on dogs.