## 3 Results and Discussion
The contribution of variable deer in the definition of the PC3 is also evident and amounts to 0.76, where _Ixodes_ sp. was found to be the most intense tick species (factor score of 2.65). The tick species _Haemaphysalis punctata_ (M) and _Dermacentor marginatus_ (M), with factor scores of 14.70 and 7.35, define PC3, and they were the predominant tick species on goats and pigs, with variable contributions of 0.71 and \(-\)0.71, respectively. The relationships between individual tick species and their hosts were also analysed using cluster two-way joining analysis, and the results are shown in Figure 5.  
In the graph, the dependence of tick species (y-axis) and hosts (x-axis) with different colours, from dark green to dark brown, are shown. Certain numerical values within 4 and 124 correspond to specific colours. Our analysis enables the grouping or clustering of data to be displayed simultaneously rather than successively in rows and columns. The most important advantage of such a simultaneous approach is that it is possible to determine the connections between two sets of data. The main goal of the clustering method is data reduction with a minimal loss of information. Categorical predictor/criterion data were used as the target information, which consists of the number of interactions, which is implied by predicting the data input based on categorical row (tick host) and column (tick species) variables. The model is based on the use of correlation analysis of 29 (tick species) \(\times\) 11 (hosts) dependencies according to the data matrix of the situation obtained from the analysis of the tick species identification of an individual host. The results are presented graphically in a \"heat map\". The analysis reveals strong associations of the host  
Figure 5: Distribution of the tick species in all hosts represented using cluster (two-way joining) analysis. dog (cluster of 11 rows) with the tick species _Ixodes ricinus_ (F) and _Rhiphycephalus sanguineus_ (F) (first and third cluster of the column), with a score of 124. Furthermore, strong clusters are observed between the host dog (cluster of 11 rows) and the tick species _Rhiphycephalus sanguineus_ (M) (seventh cluster of the column) as well as between the host horse (cluster of 10 rows) and the tick species _Hyalomma marginatum_ (M) (11 th cluster of the column) with a score of 84. The remaining clusters had weaker associations. Therefore, they were not explained in more detail. In addition, the dependence of the most represented tick species on three hosts (human, dog, and horse) is shown as a three-dimensional (3D) surface plot obtained using PCA (Figure 6). Three-dimensional surface and contour plots represent a response surface with one or more simple maxima. The x-, y-, and z-axes of the plot represent hosts (dog, horse, and human) with the responses between the tick host and tick species shown in different colours that range from lighter to darker, along with the numerical values of the responses (from <-56 to >100). With higher response values, the dependence also increases. Based on the obtained response, it can be concluded that _Ixodes ricinus_ (F) is the most represented tick species on the analysed hosts because the data are grouped in the dark-coloured area (from 84 to 100), representing the maximum values on the 3D surface plot. Combining these datasets, statistical analyses, and spatial distributions via GIS, the results provide a comprehensive understanding of the complex interactions between ticks, hosts, and the environment. As ticks can feed on many different animals, every species has a unique reservoir competence and ability to carry and transmit pathogens. Therefore, the presence of different food sources might affect disease incidences [29; 30]. Furthermore, it is reasonable to assume that if certain types of ticks are present on specific hosts, the possibility of pathogen transmission and the occurrence of a specific disease is greater [31]. Tick-host relationships are influenced by a variety of ecological and environmental factors [32].