The Impact of Biometeorological, Demographic, and Ecological Factors on the Population Density of Wild Boar in Slovakia

Abstract

This study examined the factors influencing wild boar population density in Slovakia from 2013 to 2023, focusing on biometeorological, demographic, and ecological variables. Linear regression models were used to analyze spring population figures and the number of hunted animals across eight self-governing regions. Following the African swine fever outbreak in 2019, population dynamics changed significantly. The number of wild boars hunted increased while population densities decreased, particularly in the Presov, Kosice, and Banska Bystrica regions. Biometeorological factors, including monthly air temperature and precipitation, significantly influenced wild boar density at the national level, with soil temperature at a 5 cm depth playing a key role regionally. Demographic factors, such as road network and human population densities, also impacted wild boar populations, with road network density being the most important. Ecological factors, including the presence of brown hares, common pheasants, and grey wolves, had varying effects across regions. Grey wolf predation and interspecies competition were particularly significant in mountainous, less urbanized areas. The joint influence of biometeorological and demographic factors was higher in regions affected by African swine fever. This study highlights the complex interactions between environmental, demographic, and ecological factors and provides insights into more effective wildlife management strategies aimed at the sustainable management of wild boar populations. It advocates for a regionally tailored, integrated approach that considers the influence of biometeorological, demographic, and ecological factors, while also addressing the risks associated with epidemics, such as African swine fever.

1. Introduction

Wild boar (Sus scrofa) populations have expanded significantly worldwide over the past few decades [1,2]. While many wildlife species are experiencing declines due to increasing environmental pressures, wild boars have thrived, primarily due to their remarkable adaptability [3]. In Europe, their numbers have steadily increased since World War II, reaching substantial restoration levels by the mid-20th century [4]. Several factors, including climate change, shifts in agricultural policy, and reduced predation pressure, have further fueled their expansion across the continent [1,5,6].

In Slovakia, the wild boar has long been the most abundant game species, benefiting from favorable environmental conditions, ample shelter, and diverse habitats ranging from floodplain forests to high-altitude spruce forests [7,8]. Their distribution is primarily shaped by winter conditions, as prolonged snow cover and freezing temperatures can critically impact survival. Food scarcity and fat depletion during harsh winters lead to population declines, especially in areas where snow cover persists for more than 140–160 days or reaches depths of 30–50 cm. Although wild boars favor lowland areas, these regions are often deforested or densely populated, limiting suitable habitat. Conversely, high-altitude areas are less favorable due to extended snow cover and frozen soil [9].

In Slovakia, wild boar population density is typically estimated using two theoretical indicators—the standard population (NKS) and spring population (JKS) figures, alongside actual hunting data. According to current legislation, hunting ground users must manage game populations following prescribed standard population targets [10]. These are calculated based on the expected increase from observed spring population figures, which must then be culled during the following season to align with NKS targets. The expected increase is derived from the spring population figures, which are submitted to national statistics by hunters through the year-round monitoring of game conditions. These JKS figures exclude piglets born in the current year but include those born in the previous year. Factors such as hunting ground quality, natural and anthropogenic mortality, predation pressure, flooding, and population sex ratio are considered when determining the coefficient of expected increase, which is influenced mainly by the number of reproductive females [10]. The hunting plan typically comprises 10–15% of adult boars and sows, 20–25% of yearlings, and 60–70% of piglets. Since the emergence of African swine fever (ASF) in 2018, year-round hunting of all age classes has been permitted as a disease control measure [11].

Over the past 50 years, Slovakia has experienced a marked increase in wild boar numbers. In 1968, the NKS was estimated at 8400 individuals, rising nearly fivefold to 42,285 by 2016 [12]. In 2013, 40,941 wild boars were recorded, with 45,172 hunted that year [13]. By 2023, however, the NKS had declined to 29,534, while 60,232 individuals were hunted, amounting to 140% of the planned cull. This discrepancy highlights the largely theoretical nature of NKS prescriptions at the national level, as actual hunting practices often deviate from them [14]. Nonetheless, both indicators reflect a recent decline in population density, primarily due to ASF outbreaks and increased hunting pressure [15]. ASF has impacted wild boar populations through both direct mortality and extensive control measures. In response, the State Veterinary and Food Administration and the Ministry of Agriculture and Rural Development have permitted unrestricted hunting, irrespective of age or sex, to curb ASF transmission [11]. As a result, hunting intensity has doubled compared to pre-ASF levels. Despite these interventions, wild boars remain resilient due to their high reproductive potential and adaptability. Several studies suggest that milder winters and reduced snow cover, driven by climate change, further support population growth by enhancing survival and resource availability [16,17,18,19].

While studies such as Shalom et al. [20] have highlighted the role of invasive exotic mammals and livestock in disrupting wildlife population dynamics and spatial behavior, these findings are not fully applicable to the conditions in Slovakia. In contrast to regions where exotic species may threaten native fauna, non-native ungulates introduced to Slovak ecosystems, such as mouflon (Ovis orientalis musimon) and fallow deer (Dama dama), have demonstrated the ability to coexist harmoniously with native species, including wild boar, red deer, and roe deer. These species have become an integrated part of the game community without exerting significant ecological pressure on wild boar density. Similarly, the disruptive impact of grazing domestic livestock on wildlife is less relevant in the Slovak context [21]. Slovakia remains a predominantly agrarian country, where the open grazing of sheep and cattle is widespread, particularly in highland and submountain areas. Notably, installing electric fencing around grazing areas has not altered the spatiotemporal behavior of wild boar or other game species. The lack of a negative impact of the presence of livestock on the deer (Cervus elaphus) inside the pastures was also partially confirmed in East Germany by Gillich et al. [22].

Field observations [21,23] suggest that wild boars are often attracted to pasture areas due to the presence of livestock, which supports a higher abundance of insects and small mammals, preferred components of the wild boar diet. Behavioral changes in game animals in Slovakia have been observed primarily in red deer populations, especially in the High Tatras region, where large carnivores such as wolves (Canis lupus) and brown bears (Ursus arctos) push deer into suburban zones. Wild boar is not primarily a component of the brown bear diet [24,25]. To date, however, no other factor apart from human pressure has been shown to significantly alter the behavior or distribution of wild boar in Slovakia.

Rising wild boar densities have led to increasing human–wildlife conflicts, including agricultural damage, environmental degradation, and competition with other species, such as brown hares, pheasants, western capercaillie, and black grouse [5,6,26]. In response, Slovak authorities have intensified population control measures. Hunting policies now increasingly aim to mitigate these conflicts, with District Offices cooperating with breeding councils to implement targeted reduction strategies over consecutive years [26]. As wild boar populations continue to expand across Slovakia, effective management is essential to maintaining ecological balance, minimizing economic losses, and preventing disease spread. Future strategies should integrate adaptive hunting regulations, habitat monitoring, and epidemiological surveillance to mitigate negative impacts while preserving biodiversity and ecosystem stability.

This study aims to quantify the effects of biometeorological, demographic, and ecological factors on wild boar population density across Slovakia’s eight self-governing regions from 2013 to 2023. The main goal is to highlight the importance of comprehensive monitoring and data collection in informing wildlife management decisions.

2. Materials and Methods

2.1. Wild Boar Population Data

Wild boar population density was quantified in this study by two indicators—spring population figures and the number of hunted animals across eight self-governing regions in Slovakia from 2013 to 2023 (11 years). A basic description of these regions, including the total acreage and the range of altitudes, is shown in Table S1. The data were obtained from official records in the Hunting Statistical Yearbook of the National Forestry Center [27]. In the evaluated period, 429,843 and 645,837 records were collected for JKS and the number of hunted wild boars, respectively. The number of collected records for each year is shown in Figure 1.

2.2. Biometeorological, Demographic, and Ecological Factors

2.2.1. Biometeorological Data

Data for four biometeorological factors were collected for the tested period—average monthly air temperature (°C), average monthly soil temperature at a depth of 5 cm (°C), average monthly precipitation (mm), and the average number of days with snow cover (from December to March) in the years 2013–2023.

Raw meteorological data provided by the Slovak Hydrometeorological Institute (SHI) [28] were processed into time series representing monthly averages of each factor. Since the SHI does not report annual temperature data, representative meteorological stations in each region were used to estimate average temperatures. The selected meteorological stations in self-governing regions were as follows: Bratislava–Koliba for Bratislava (BA); Dolny Hricov for Zilina (ZA); Prievidza for Trencin (TN); Piestany, Jaslovske Bohunice, and Gabcikovo for Trnava (TT); Sliac and Bolkovce for Banska Bystrica (BB); Presov, Kamenica nad Cirochou, and Tisinec for Presov (PO); Kosice letisko for Kosice (KE); and Hurbanovo for Nitra (NR). The geographical locations of the selected stations are shown in Figure 2. If data from selected meteorological stations were missing, we used available data from the closest neighboring station.

The average monthly air temperature was derived from observations made during climatic periods at the 7th, 14th, and 21st hours of local time and the daily mean air temperature (T) and calculated as average = (T7 + T14 + 2 T21)/4.

Soil temperature was expressed as the average monthly soil temperature at a depth of 5 cm from selected meteorological stations and calculated as the monthly average for the year.

Average annual precipitation was calculated by comparing monthly totals across individual years. Rainfall stations measured atmospheric precipitation levels, duration, snow cover, snow depth, snow water equivalent, and water evaporation. Measurements and observations at rainfall stations were conducted daily at 7:00 AM local time, continuously recording precipitation and notable weather events.

The snow cover period starts on the first day and ends on the last day with snow cover with a consistent snow layer of 1 cm or more observed around the monitoring station at 7:00 AM local time. For the average monthly number of days with snow cover, we used a calculation for the given year that determined the average value based on the number of months in which snow cover occurred. In some years, the average was 4 months; in others, 5 months.

Except for the number of days with snow cover, the monthly averages of all tested meteorological factors for a particular year were calculated as the arithmetic mean of the average monthly temperatures for that year, using the formula average = (sum of average monthly temperatures)/12.

2.2.2. Demographic Data

To analyze the effect of demographic factors on wild boar population density, information about the average altitude above sea level (m), average human population density per 1 km², road network density in km per 1000 inhabitants [29], and number of hunting license holders [30] was collected separately for each self-governing region. Demographic data were provided by the National Statistical Office and the Slovak Hunters Chamber.

2.2.3. Ecological Data

The data about ASF distribution, competence relationships, and predation pressure were collected during the tested period. To evaluate the impact of ASF on hunting management and wild boar population dynamics, we analyzed ASF spread data using a binomial distribution, where ASF-positive cases were coded as 1 and ASF-negative cases as 0. The data were obtained from the State Veterinary and Food Administration of the Slovak Republic [31].

To assess the competitive relationship between wild boar and other small-game species (common pheasant and brown hare), mainly their population dynamics in the East Slovak Lowland, where ASF was first detected in 2019, were analyzed. Since this region supports the highest abundance of common pheasant and brown hare, we examined their population trends not only over the period from 2013 to 2023 but also specifically from 2019 to 2023, when wild boar numbers declined to a minimum due to ASF. Data on brown hare and common pheasant abundance were obtained from the Hunting Statistical Yearbook of the National Forestry Center [27].

Among the potential predators influencing wild boar population dynamics in Slovakia, the predation pressure exerted by large carnivores, specifically the grey wolf (Canis lupus), was tested. Grey wolf population data were obtained from the Hunting Statistical Yearbook of the National Forestry Center [27].

2.3. Effect of Selected Factors on Wild Boar Population Density

To analyze the relationship between selected factors, JKS, and the number of hunted animals, linear regression analysis was conducted using the least squares method in SAS V9.4 [32]. This approach allows a linear function to be fitted to the data and facilitates the evaluation of the effects of the explanatory variables on the dependent variables. The dependent variables, JKS, and the number of hunted animals were analyzed to understand and explain their relationship with one or more independent (explanatory/classification) variables. Explanatory variables include biometeorological (average monthly air temperature, precipitation, soil temperature, and number of days with snow cover), demographic (average population density, altitude, and number of hunting license holders), and ecological factors (competence relationship and predation pressure). The effects of the self-governing region and ASF were analyzed in the models as classification variables.

Analyses were performed in the first step separately for each dependent and explanatory variable to determine their individual effects on a national level. Then, the joint impact of all significant factors was tested by multi-factorial linear regression models. Five different models were used as follows:

• (1) The model equation used for the analysis of biometeorological effects is

$$y=b_1{x}_1+b_2{x}_2+b_3{x}_3+b_4{x}_4+b_5{x}_5+a+e$$

(1)

where y represents the JKS or the number of hunted animals; x_i includes biometeorological factors and the altitude above sea level—average monthly precipitation in mm (x₁), average monthly air temperature in °C (x₂), average number of days with snow cover (x₃), average soil temperature at a depth of 5 cm in °C (x₄), and average altitude above sea level in meters (x₅); b_i represents the regression coefficient of the i-th variable; a is the intercept; and e is the error.

• (2) The model equation used for the analysis of demographic effects is

$$y=b_1{x}_1+b_2{x}_2+b_3{x}_3+a+e$$

(2)

where y is the JKS or the number of hunted animals, x₁ is the human population density per 1 km², x₂ is the road network density in km per 1000 inhabitants, x₃ is the number of licensed hunters, b_i represents the regression coefficient of the i-th variable, a is the intercept, and e is the error.

• (3) The model equation used for joint analysis of biometeorological and demographic effects is

$$y=b_1{x}_1+b_2{x}_2+b_3{x}_3+b_4{x}_4+b_5{x}_5+b_6{x}_6+b_7{x}_7+b_8{x}_8+a+e$$

(3)

where y is the JKS or the number of hunted animals, x₁ is the average monthly precipitation in mm, x₂ is the average monthly air temperature in °C, x₃ is the average number of days with snow cover, x₄ is the average soil temperature at a depth of 5 cm in °C, x₅ is the average altitude above sea level in meters, x₆ is the human population density per 1 km², x₇ is the road network density in km per 1000 inhabitants, x₈ is the number of licensed hunters, b_i represents the regression coefficient of the i-th variable, a is the intercept, and e is the error.

• (4) The model equation used for the analysis of competence relationship and predation pressure is

$$y=b_1{x}_1+b_2{x}_2+a+e$$

(4)

where y is the JKS or the number of hunted animals, x₁ is the number of brown hares, x₂ is the number of common pheasants, b_i represents the regression coefficient of the i-th variable, a is the intercept, and e is the error.

• (5) The model equation used for the analysis of predation pressure is

$$y=bx+a+e$$

(5)

where y is the JKS or the number of hunted animals, x is the number of grey wolves, b represents the regression coefficient, a is the intercept, and e is the error.

The coefficient of determination (R²) describing the impact of each factor on the dependent variable was used as the main result of the regression analyses. The significance of the obtained results was tested by the F-test to produce a p-value in SAS V9.4 [32].

3. Results

3.1. Wild Boar Population Density

As expected due to the spread of ASF in Slovakia in recent years, the analysis of the collected wild boar population data from 2013 to 2023 showed opposite trends in JKS and the number of hunted animals. As can be seen in Figure 3, there has been a significant increase in the number of animals hunted and a decrease in JKS after implementing actions to prevent the spread of ASF across all self-governing regions in 2018.

Before the occurrence of ASF in Slovakia, JKS in different regions remained relatively stable, with minor fluctuations. For example, in the Banska Bystrica region, JKS ranged between 9865 (2014) and 11,072 (2016), reaching 10,776 individuals in 2018. A similar trend was observed in the Kosice and Presov regions, where population size ranged between 5000 and 7000 individuals. In western Slovakia, where wild boar densities are naturally lower, JKS were more consistent across years, with the Nitra region reporting a population size between 3692 and 4250 individuals.

In 2019, the first occurrence of ASF in eastern Slovakia represented a significant event in the dynamics of wild boar populations. In the Kosice region, the wild boar JKS reached a maximum of 5921 individuals. However, by 2020, the population had decreased to 4444, indicating an initial decline due to the ASF outbreak and the subsequent implementation of control measures. In the Banska Bystrica region, the JKS decreased from 12,624 in 2019 to 10,233 in 2020, and in the Presov region, the population declined from 7800 to 6843.

Over the following years, ASF spread to other regions (Banska Bystrica), resulting in a more pronounced and sustained decline in the JKS. The most significant decrease occurred in the Presov region, where the population decreased from 5883 in 2021 to 3307 in 2023, representing a reduction of over 40% within two years. Similarly, in the Kosice region, the JKS declined from 3962 in 2021 to 2070 in 2023. The population in the Banska Bystrica region decreased from 9671 in 2021 to 7186 in 2023. In the Trnava region (Western Slovakia), the JKS initially declined until 2020 but later increased, reaching 2700 individuals in 2023. Conversely, in the Nitra region, population numbers gradually decreased from 3760 in 2020 to 3041 in 2023.

3.2. Biometeorological Factors

In the tested period, the climatic conditions in Slovakia exhibited regional and interannual variability (Table S1). The average monthly air temperature ranged from 9.52 ± 0.15 °C in Zilina to 11.89 ± 0.15 °C in the Nitra region, with a national average of 9.74 ± 0.91 °C. Similarly, the lowest monthly average soil temperature at a depth of 5 cm was found in Zilina (10.00 ± 0.44 °C) and the highest in the Nitra region (12.91 ± 0.15 °C), with an overall average of 10.70 ± 1.14 °C. On the other hand, Nitra showed the lowest monthly average precipitation (50.32 ± 2.85 mm) and the highest was found in the Zilina region (62.20 ± 2.54 mm). The obtained precipitation levels correlated with the number of days with snow cover, ranging from 4.43 ± 0.80 (NR) to 10.00 ± 1.57 (ZA), with a national average of 7.59 ± 0.50.

3.2.1. Effect on Spring Population Figures

On a national level, the average monthly air temperature had a highly significant effect on the JKS (p < 0.0001), explaining 18.12% of the variability in the evaluated period (Table S2). Average monthly precipitation significantly influenced the JKS (p = 0.02), but its explanatory power remained relatively low (R² = 6.04%) compared to the other factors. The number of days with snow cover had a marginal effect on the JKS (R² = 2.85%), and the overall result was non-significant (p = 0.12). The effect of the average monthly soil temperature at a depth of 5 cm was also non-significant (p = 0.13), with a generally low determination value (R² = 2.63%). The average altitude above sea level was a highly significant factor (p < 0.0001) determining a certain proportion of variability in the JKS (R² = 84.42%) (Table S2). Given that analyzed biometeorological factors act in nature simultaneously, a multi-factorial linear regression model was applied to assess their combined influence. This model explains 61.07% of the variance revealed, in contrast to single regression analyses, mainly the significant effect of soil temperature at a depth of 5 cm on, the JKS (p < 0.0001) (Table S3).

Although the analyses of the effect of biometeorological factors were also conducted at the level of self-governing regions, the results did not fully correspond to the overall trends obtained for Slovakia. The average monthly air temperature and precipitation did not significantly affect the JKS in any region. The average number of days with snow cover exhibited a borderline effect only in the Bratislava region (p = 0.056), while in other regions, the effect was non-significant. The average monthly soil temperature at a depth of 5 cm had a significant impact on the JKS in the Zilina region (p = 0.036) and a borderline effect in the Trencin region (p = 0.060). In all other regions, this factor was not statistically significant. The explanatory power of multi-factorial linear regression models ranged across regions from 4.50% (BB) to 58.55% (TN) (Figure 4, Table S3).

3.2.2. Effect on the Number of Hunted Animals

Linear regression analyses revealed that the effect of the average monthly air temperature (R² = 5.05%) and precipitation (R² = 6.83%) on the number of hunted animals on a national level was statistically significant (p = 0.04), whereas the average monthly soil temperature at a depth of 5 cm or the average number of days with snow did not show significant impacts. The most influential factor was the average altitude above sea level, which had a highly significant effect (p < 0.0001) and explained 54.64% of the variance in the number of hunted animals (Table S2). Even if the multi-factorial model with the number of hunted animals showed lower explanatory power (R² = 34.23%) compared to the JKS, it similarly points to the significant effect of soil temperature at a depth of 5 cm on the number of hunted animals (p = 0.02) (Table S4).

At the regional level, the average monthly air temperature and precipitation had a borderline effect only in Bratislava (p = 0.080 resp. 0.038). The average number of days with snow cover and the average soil temperature at a depth of 5 cm did not show statistically significant effects in any region. The coefficient of determination in multi-factorial models ranged from 9.30% in the Trnava region (TT) to 77.66% in the Bratislava region (BA) (Figure 4, Table S4). The obtained results suggest that the influence of biometeorological factors on the number of hunted animals is region-dependent and less pronounced compared to their effect on the JKS.

3.3. Demographic Factors

The road network density, human population density, and the number of hunting license holders revealed a high regional variability similar to the biometeorological factors. Human population density ranged from 324.28 ± 5.98 km (BA) to 67.78 ± 0.56 km (BB) per 1 km², indicating a higher number of inhabitants in more urbanized areas close to the capital city of Slovakia. On the other hand, the road network density ranging from 1.24 ± 0.01 (BA) to 4.98 ± 0.03 (BB) km per 1000 inhabitants reflected not only the degree of urbanization but mainly the total acreage of self-governing regions. The highest average number of licensed hunters was found in the Banska Bystrica (9134.18 ± 505.91) and Presov (8903.00 ± 480.89) regions, whereas the lowest was in Bratislava (5155.91 ± 245.32) (Table S1).

3.3.1. Effect on Spring Population Figures

Regression analyses revealed that all of the tested demographic factors significantly affected the JKS (p < 0.0001). Road network density exhibited the highest explanatory power (R² = 46.38%), followed by human population density (R² = 37.17%), and the number of hunting license holders (R² = 13.69%) (Table S2). The multi-factorial regression model (R² = 47.47%) further emphasized the substantial effect of road network density on the JKS (p < 0.0001) (Table S3).

At the regional level, the statistically significant effect of human population density on the JKS was observed only in the Presov and Kosice regions (p = 0.01 and p = 0.03, respectively). The Kosice region also showed a significant effect of road network density on the JKS (p = 0.04). In the other regions, the effect of demographic factors was non-significant. The coefficient of determination (R²) ranged across self-governing regions from 17.22% (TT) to 88.35% (KE) (Figure 5, Table S3).

3.3.2. Effect on the Number of Hunted Animals

During the tested period, selected demographic factors significantly affected the number of hunted animals (p < 0.0001). However, the explanatory power was lower compared to the JKS. The coefficient of determination ranged from 8.63% (number of hunting license holders) to 32.17% (road network density) (Table S2). The limited explanatory power of the number of hunting license holders suggests that this variable does not necessarily reflect actual hunting activity or its interactions with other factors, such as population density and road infrastructure. Hunting participation is likely influenced by various socio-economic factors that are not fully captured by the number of registered hunters alone. The multi-factorial model (R² = 33.77%) confirmed, similar to the JKS analysis, the significant effect of road network density (p < 0.0001).

Multi-factorial regression analysis by region revealed a significant impact of human population density only in the Bratislava (p = 0.01), Presov (p = 0.02), and Kosice (p = 0.004) regions. The explanatory power of the models varied substantially across regions, ranging from 18.63% in Banska Bystrica to 89.70% in Kosice, highlighting regional disparities in the factors influencing wild boar populations in Slovakia (Figure 5). Given this variability, a joint model incorporating both biometeorological and demographic factors was also tested separately for the JKS and the number of hunted animals. These joint models demonstrated an increased R², explaining 69.16% of the variance in the JKS and 45.17% in the number of hunted animals, with both values exceeding the explanatory power of any single demographic variable. The obtained results suggest that model fit improves when accounting for the combined influence of environmental and demographic factors, providing a more comprehensive understanding of the drivers of wild boar population size and hunting outcomes.

3.4. Ecological Factors

Ecological factors, such as interactions with other wild animal species and the spread of highly infectious diseases, play a crucial role in shaping wild boar populations. To gain deeper insights into these dynamics, we analyzed the populations of brown hare and common pheasant as key prey species sharing agricultural landscapes with wild boar and the grey wolf, a top predator that may influence wild boar through predation and competition. Given the ongoing epidemiological threat, we also examined the occurrence of African swine fever outbreaks, which pose a serious concern not only in Slovakia but across Europe. The brown hare population exhibited substantial differences among the self-governing regions, ranging from 2366.64 ± 31.85 individuals in Zilina to 56,396.91 ± 871.54 in the Trnava region, with a national average of 19,477.93 ± 2137.82 hares per region and year. A similar pattern was observed in the common pheasant population, which averaged 19,103.77 ± 2024.22 individuals per region and year, with the lowest population recorded in the Zilina region (378.00 ± 23.39 individuals) and the highest in Trnava (54,536.73 ± 1756.58 individuals). The grey wolf population showed the most pronounced regional disparity, varying from 0.36 ± 0.15 (BA) to 209.84 ± 14.51 individuals (ZA), with an average of 69.88 ± 8.39 wolves per year and region. In the period evaluated, ASF was positively recorded in four regions—from 2019 to 2022 in Kosice, from 2020 to 2023 in Banska Bystrica and Presov, and from 2021 to 2023 in Zilina (Table S1).

3.4.1. Effect of African Swine Fever

The joint effect of biometeorological and demographic factors was found to be significantly stronger in regions affected by ASF, with R² = 86.32% (p < 0.0001), compared to regions without ASF, where the joint effect explained only R² = 71.24% (p = 0.04). A marked difference was also observed in the coefficient of determination for the number of hunted animals. In regions affected by ASF, the combination of biometeorological and demographic factors accounted for 84.92% of the variation in hunting activity (p = 0.04), whereas in regions without ASF, this model explained only 47.08% of the variation (p < 0.0001).

3.4.2. Effect of Competence Relationship

The regression analysis results indicated a highly significant effect (p < 0.0001) for both the brown hare and common pheasant populations on wild boar densities, with the coefficient of determination ranging from 16.24% to 27.62% (Table S2). The multi-factorial model further confirmed their significant impact, particularly on JKS, explaining 32.83% of the variation (Tables S3 and S4).

The analysis of the competitive relationship between the wild boar JKS and the number of brown hares and common pheasants at the regional level revealed a significant impact, particularly in the Nitra, Zilina, and Presov regions. The Nitra region exhibited the strongest relationship between the JKS and the pheasant population (R² = 64.38%, p = 0.01), suggesting substantial competitive pressure. A significant effect of the number of common pheasants was also observed in Zilina (R² = 77.32%, p = 0.003) and Presov (R² = 58.78%, p = 0.03), both of which also showed the significant impact of the number of brown hares on the JKS (p = 0.09 and p = 0.02, respectively). In Zilina and Presov, there is minimal emphasis on pheasant hunting and, consequently, no active efforts to increase pheasant population sizes. This contrasts with southern Slovakia, where pheasants are intensively managed and artificially reared to maintain higher population densities for hunting purposes. Furthermore, Zilina and Presov exhibit the lowest hare populations in Slovakia due to the high altitude and rugged terrain, which are less suitable for hares that prefer lowland habitats. In other self-governing regions, low R² values (ranging from 8.42% in BA to 27.40% in BB, Figure 6) and the non-significant effects of the brown hare and common pheasant populations on the wild boar JKS (Table S3) suggest that competitive dynamics between these species are less pronounced. This may be attributed to regional differences in habitat characteristics or population densities, potentially reducing interspecies competition intensity.

The Nitra region exhibited the strongest relationship between the number of hunted wild boar and both the common pheasant (p = 0.02) and brown hare numbers (R² = 70.18%, p = 0.02), indicating significant competitive interactions in this area. Similarly, a significant association was observed in the Presov region for the numbers of brown hare (p = 0.02) with an R² value of 49.66%, suggesting moderate interaction with wild boar hunting activity. In the Trnava region, the number of common pheasants demonstrated a borderline effect on wild boar hunting (p = 0.07) with an R² of 56.99%, implying a potential competitive relationship. Although the Bratislava, Zilina, and Trencin regions showed moderate explanatory power in the model (Figure 6), the effects of both the brown hare and common pheasant populations on the number of hunted wild boar were not statistically significant (Table S4).

3.4.3. Effect of Predation Pressure

The effect of the numbers of grey wolf on the wild boar population density in Slovakia was visible mainly in the model assessing the JKS (R² = 15.93%, p < 0.0001) (Table S2). The most significant relationship between grey wolf population density and the JKS was observed in the Presov region (R² = 68.33%). A similar trend was recorded in the Kosice region (R² = 58.40%), suggesting high predation pressure by wolves, particularly in the mountainous areas of Slanske vrchy, Vihorlat, Cergov, and Poloniny. These regions are relatively undisturbed by human activity, characterized by a low road network density, and sparse human populations, which likely support a well-established wolf population capable of exerting significant pressure on wild boar numbers. In addition to predation, other ecological factors may contribute to the observed effects. Notably, the ASF, which has persisted in these regions for five consecutive years, cannot be ruled out.

A moderately strong relationship between grey wolf numbers and the wild boar JKS was observed in the Zilina (R² = 40.76%), Nitra (R² = 33.32%), and Trencin (R² = 31.98%) regions. The significance level of the results suggests that grey wolves have some impact on the wild boar populations in these regions as well; although, as in the case of Presov and Kosice, other factors, including biometeorological and demographic ones, could also play a role (Table S3). In Banska Bystrica, the influence of grey wolves on wild boar populations appears less pronounced (R² = 25.65%, p = 0.112). This may be attributed to intense hunting pressure, as the Banska Bystrica region historically reported the highest wild boar harvest prior to the confirmation of ASF in the area. Additionally, the abundance of alternative prey species, particularly red deer, may divert wolf predation away from wild boar. Similarly to Presov and Kosice, the wild boar population in Banska Bystrica has also been affected by ASF for the past four years. The lowest coefficient of determination (Figure 6) was recorded in the Bratislava and Trnava regions (R² = 1.16% and R² = 0.27%, respectively), indicating minimal or no impact of grey wolf numbers on wild boar density. This result aligns with the urbanized nature of both of them, which is characterized by high human population densities (Table S1) and limited suitable habitats for large carnivores. Urbanization typically leads to habitat fragmentation, restricting wolf migration and territorial expansion, thereby reducing their potential influence on wild boar populations. Lowland areas in Slovakia generally do not support resident wolf packs, meaning predation pressure in these regions is negligible. In recent years, however, golden jackals (Canis aureus) have expanded their range into lowland regions. As an invasive species, golden jackals are not included in hunting statistics and are monitored by the state environmental service, leaving their predation impact on wild boar populations largely unknown.

4. Discussion

The results indicated that average monthly temperature had the most significant impact on wild boar population densities in Slovakia, suggesting that temperature fluctuations influence wild boar behavior and distribution. This effect was more pronounced in the Bratislava and Trnava regions and was weaker in the other areas, likely due to geographical differences. The explanatory power of the model varied between mountainous regions and lowland or urbanized areas, where human activities are more intensive. Vetter et al. [6] modeled the effects of climate change and hunting on wild boar populations, emphasizing the critical role of milder winters and increased food availability due to climate change. These factors likely enhance juvenile survival rates, contributing to population growth and necessitating adjustments in hunting strategies. Similarly, Knape and de Valpine [33] concluded that biometeorological factors influence animal population dynamics, although these effects are often weak and difficult to distinguish from other ecological drivers. They underscored the importance of long-term monitoring of animal populations and weather patterns to fully understand the impact of climate change on biodiversity.

Even if the significance of the joint model was low on a regional level, the one-factor linear regression indicated a significant influence of average monthly precipitation on JKS as well as the number of hunted animals in the tested period. Higher rainfall improves vegetation growth and crop production, which increases food availability and influences wild boar movement and concentration. From practical experience, heavy rains keep boars in fields longer, particularly in meadows where they forage for insects and snails (May–July), facilitating hunting under good light conditions. This hypothesis aligns with findings by Gethöffer et al. [34] in Lower Saxony, where wild boar body weight was negatively impacted by February frosts but positively influenced by higher May and July rainfall. Regions in Slovakia with higher precipitation tend to record larger wild boar populations compared to drier areas, a trend important for planning hunting strategies across regions. Lewis et al. [35] noted that both rainfall and potential evapotranspiration are critical factors for the distribution of wild boar.

Soil temperature at a depth of 5 cm plays a crucial role in wild boar density and behavior, as shown by applied models at both the national and regional levels. This factor may be even more pronounced in regions with intensive agriculture and frequent crop rotation, as wild boars tend to remain in areas with better food availability. Drimaj et al. [36] partially support this by finding that wild boars migrate daily to supplemental feeding sites but return to resting areas for daytime shelter. Moreover, soil temperature at a depth of 5 cm was considered a factor that could affect the survival of piglets, as in the last 10 years, permanent snow cover has not been present in all regions of Slovakia.

The number of days with snow cover significantly affected wild boar density in the Bratislava region. Snow generally reduces boar mobility and concentrates them near feeding spots, especially in lowlands with less rugged terrain. While snow cover can make hunting more challenging by restricting boar movement, its absence often leads to increased boar activity and higher harvest rates. Drimaj et al. [36] also noted that wild boars adapt their movement and spatial behavior to hunting pressure, distinguishing between high-risk and low-pressure refuge areas, even when vegetation and food availability are similar. Most wild boars moved to refuge areas, doubling population density and leading to increased competition for food. Conversely, human activity, such as tourism or cycling, disrupts wild boar behavior, affecting their habitat use. Thurfjell et al. [37] found that hunting methods influence whether wild boars flee or hide. After fleeing to cover, boars reduce movement and prefer habitats offering shelter and food, like agricultural fields. This suggests wild boars reduce detection risk and avoid competition with other boars by exploiting resources that cannot be monopolized in the refuge.

Both simple and multi-factorial regression models demonstrated that demographic factors such as average altitude, human population density, road network density, and the number of hunting license holders have different impacts on the wild boar JKS and hunting activity across self-governing regions in Slovakia. In certain regions, factors such as human population density and road network density may directly influence wild boar distribution and behavior, while in other areas, biometeorological or ecological factors may play a dominant role. This suggests that demographic factors alone may not fully explain variations in their population sizes, and a further exploration of additional explanatory variables is necessary to understand broader patterns. Lewis et al. [35] also state that land use changes caused by human activity significantly impact wild boar spread. Based on their findings, the authors created a predictive map of potential wild boar population density that could assist in population management and optimize conservation strategies. Wild boars may depend more on natural food sources and less on human factors in areas with lower population density. The availability of food and adequate vegetation density are two key factors that influence the occurrence and distribution of wild boars in the environment, despite the intensity of wild boar hunting [38,39,40,41].

The analysis of the impact of selected demographic factors on the wild boar hunting intensity across self-governing regions revealed human population density as the most influential factor in certain regions, particularly those with the largest cities in Slovakia (Bratislava, Presov, and Kosice). This influence may be attributed to better access to hunting infrastructure and more intensive wild boar population management in urban and suburban areas, where conflicts between humans and wildlife are more frequent. In contrast, in regions such as Nitra, Trencin, and Zilina, human population density affected hunting intensity only non-significantly. In these regions, the success and intensity of hunting may have been primarily influenced by environmental factors, such as food availability and habitat characteristics, rather than the presence of human populations. Santilli and Varuzza [41] found that protected areas are the most significant factor positively influencing wild boar hunting levels. This finding is particularly relevant for Slovakia, which has eight national parks, and supports the long-standing assertion by hunters that protected forested areas enhance wild boar hunting opportunities. In fact, this suggests that protected areas, where hunting is prohibited, likely serve as a source from which wild boars expand into surrounding areas where hunting is allowed. This phenomenon is common in many wildlife species, and it is important to consider it when managing wildlife populations.

While road network density showed a significant effect only at the national level, regions with higher urbanization, such as Bratislava, Presov, and Kosice, may have experienced marginal benefits in terms of hunting accessibility. However, at the regional level, the influence of road network density appeared secondary to more impactful factors such as human population density and natural habitat availability. Another notable finding was the minimal impact of the number of hunting license holders. This factor did not exhibit statistical significance in any regions studied, suggesting that the number of hunters does not directly correlate with hunting efficiency. Instead, hunter experience, skill level, access to hunting grounds, and the availability of wild boar populations appear to play more substantial roles in determining hunting success.

This study also examined the competitive interactions between wild boars and other prey species, such as the common pheasant and brown hare. A strong interaction between wild boar density and the number of common pheasants was observed in regions like Nitra, likely due to overlapping habitat use. However, despite relatively small pheasant populations, a statistically significant relationship with the wild boar JKS was also found in the Zilina and Presov regions. This unexpected relationship may be attributed to intense competition for limited resources in these predominantly high-altitude mountainous areas, where scarce pheasant populations may be more vulnerable to the ecological impact of wild boar activity. The significant results suggest that wild boar presence can affect even small pheasant populations, possibly through habitat disturbance, resource competition, or nest predation. In the case of the brown hare, a significant competitive interaction was observed, particularly in Nitra, which had one of the highest brown hare populations, and in Presov, which recorded the lowest average number of brown hares during the study period.

Testing the effect of predation pressure on wild boar density showed significant results, especially in regions with a high abundance of grey wolves. In the self-governing regions such as Presov, Zilina, and Kosice, where mountainous terrain and low human activity support wolf survival and hunting success, a strong interaction was observed between increased wolf abundance and wild boar density. In contrast, regions like Banska Bystrica, Bratislava, and Trnava exhibited a weaker influence of wolf predation on wild boar populations. In Banska Bystrica, the results indicate that factors other than wolf predation, such as hunting intensity, might have historically played a more significant role in regulating wild boar populations, particularly before the spread of ASF. In the urbanized area of Bratislava, the lack of wolf influence can be attributed to habitat fragmentation and limited movement space due to urbanization. Finďo [42] found that wild boar constituted the second most represented prey in the wolf’s diet. However, the ratio of red deer to wild boar in the wolf’s diet varies depending on deer abundance and the season. Notably, the occurrence of wild boar is significantly higher in winter compared to summer. As the wild boar is not the main dietary component of the brown bear diet [24,25], we did not examine its effect on the JKS or hunting as in the case of wolves.

In addition to the above factors, the success of wild boar hunting is also significantly influenced by the experience and skills of the hunters, access to hunting grounds, and the abundance of wild boar. Socio-economic factors, including education, income, and the level of involvement in hunting, also affect hunter motivation. Hunters with lower education and income levels who perceive the wild boar population as increasing are more likely to be motivated by other purposes, such as obtaining meat or controlling the population [43].

The observed regional differences in the impact of selected biometeorological, demographic, and ecological factors on wild boar population density highlight the need for a tailored approach to their management. In urbanized regions, where demographic factors such as human population density and road network density play a more significant role, it is crucial to consider these variables when planning hunts. In contrast, less densely populated and more nature-oriented regions should focus on environmental factors like habitat quality and food availability. These findings emphasize the necessity of a comprehensive management strategy that integrates various factors for effective wildlife and hunting management in Slovakia.

Due to the high population density of wild boars and the significant economic damage they cause to agricultural crops in certain regions of Slovakia, it is often necessary to intensively control their population. However, increased hunting alone may not be sufficient to reduce their numbers. The introduction of alternative methods, such as trapping, has proven particularly effective, especially for capturing one-year-old piglets. Escobar-Gonzáles et al. [44] pointed out that trapping can be very beneficial in urbanized areas. Nevertheless, in Slovakia, this method is more expensive than traditional hunting.

In 2023, a combined hunting and trapping approach was implemented in the Bratislava (BA) region; however, only 10% of the wild boars were captured through trapping. This method was reported as very financially demanding because all management, logistical, and culling expenses were funded from public resources.

Financial incentives can also play an important role in wild boar population management. Although Dickenhoff, Holtfreter, and Williams [45] did not observe a reduction in wild boar populations in response to financial compensation, the Slovak experience suggests otherwise. Financial incentives, such as shooting fees, proved effective in Slovakia, where the Ministry of Agriculture and Rural Development reported a decrease in wild boar hunting of almost 30% during the 2023/2024 season.

Moreover, over 7000 legal baiting sites were installed to lure wild boars away from agricultural areas. However, clear data on their effectiveness are lacking, as the regulations for establishing and locating these sites have only been in place for two years. Additionally, while wild boars are known to visit large fields of crops (e.g., maize, rapeseed, and sorghum) during their growth and maturation stages, hunting at baiting sites remains minimal.

The results of this study provide a foundation for implementing adaptive wild boar management at the level of large-scale hunting units, corresponding to regional divisions, a concept recognized by Slovak legislation but rarely applied in practice. Hunting plans should be developed at the regional rather than individual hunting ground level to better account for factors such as wild boar migration and the fluctuating availability of food resources across the landscape. The quota-setting approach should remain flexible and should be continuously adjusted in response to population dynamics, climatic conditions, food supply, African swine fever (ASF) occurrence, and the presence of natural predators.

A significant issue identified in this study is the partly inaccurate assessment of wild boar population density, particularly in regions such as Nitra, which are primarily oriented toward small-game hunting. In such areas, specific wild boar hunting plans are often lacking. This underreporting may lead to skewed data, masking the true population density of wild boars that is more accurately reflected by hunting records than by spring population estimates. Given that wild boars are natural predators of ground-nesting birds and hares, the observed decline in wild boar abundance, primarily due to ASF, clearly demonstrated a positive effect on small-game populations, highlighting the ecological interactions between wild boar and small-game species.

This underscores the importance of comprehensive monitoring and accurate data collection to support informed decision-making in wildlife management. Without reliable data, the management of both predators (e.g., wolves) and prey (e.g., wild boars) can be compromised, leading to inadequate conservation measures and poorly designed hunting strategies. Furthermore, this study highlights the need for coordinated management across regions and precise population assessments to ensure effective ecological and hunting practices. Future research focusing on the impacts of ASF and intensive hunting on wild boar population structure and genetic diversity could offer valuable insights into population resilience and long-term viability.