## 3 Results and Discussion
Furthermore, collected data can also be valuable for studying mating behaviours and interactions between male and female ticks within a species [26]. The developmental stage distribution data can indicate the relative abundance of each stage within each tick species and may provide insights into the seasonal activity patterns and host preferences of a particular tick species known to primarily transmit diseases during specific developmental stages (e.g., nymph stage) [11]. The data on the distribution of tick species collected using the dragging/flagging method indicate that this sampling technique effectively captures a representative sample of the tick population in the region of Istria. Valuable information for researchers and public health authorities can be used to understand the local tick ecology and the associated risks of tick-borne diseases. Mapped information on the spatial distribution of different tick species abundance within the region of Istria is presented in Figure 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Species** & **Gender/Development Stage** & **No.** \\ \hline _Dermacentor marginatus_ & M & 2 \\  & F & 1 \\  & M & 16 \\ _Haemphysalis punctata_ & F & 8 \\  & N & 14 \\ _Haemphysalis_ sp. & N & 1 \\ _Haemphysalis sulcata_ & F & 1 \\ _Hyalomuna m. marginatum_ & F & 3 \\ _Ixodes frontalis_ & N & 26 \\  & M & 41 \\  & F & 148 \\ _Ixodes ricinus_ & N & 800 \\  & L & 148 \\  & M & 2 \\ _Ixodes_ sp. & F & 2 \\  & N & 23 \\ _Ixodes vespertilionis_ & F & 1 \\  & M & 6 \\ _Rhiplicaphus sanguineus_ & F & 3 \\  & N & 20 \\ _Rhiplicaphus_ sp. & N & 19 \\ \hline \hline M—male; F—female; N— nymph; L—larva & 1289 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Determined tick species collected using flagging/dragging method distributed according to gender or developmental stage. As ticks are living organisms with particular environmental requirements depending on their species, understanding their global distribution often requires using species statistical models [27; 28]. Therefore, the distribution of tick species according to gender and developmental stage collected using different host methods was analysed via principal component analysis (PCA) (n = 929). [PERSON]'s scree test was used to determine the number of principal components (PCs) to be retained in the analysis. The results of the PCA analysis are presented as projections of the variables (hosts) and cases (active case variable: gender/development; labelling variable: tick species) on the factor plane. According to the mentioned test, four main components (PC1, PC2, PC3, and PC4) were retained in the analysis, which explains 87.38% of the total variance. The PC1 explains 56.09% of the total variance, the PC2 explains 11.49%, the PC3 explains 9.94%, and the PC4 explains 9.85%. Figure 4a shows the 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). Most of the analysed variables (of human, horse, rabbit, cat, roe deer, hedgehog, and dog) are located in the first and second quadrants and define the positive side of the main component PC1 (right side of PC1), while variables (of pig, goat, and deer) are distributed in the fourth quadrant and define the negative side of the principal component PC1 (left side of PC1). The variable donkey is located exactly at the transition from the second to the third quadrant. In order to clarify the involvement of individual variables in the definition of the principal components model, the following parameters were used: associated eigenvectors corresponding to values of the variables (Figure 4a and Table 3) and the variable importance (Table 3). Figure 3: Spatial distribution of different tick species collected using flagging/dragging and animal/human host methods within the region of Istria. Analysing the eigenvector values, it is evident that seven variables define the main component PC1 (Table 3).