Environmental drivers influencing the relative abundance of wild boar population in Russia

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Zakharova, Andrey A. Blokhin, Elena A. Liskova, Nadezhda A. Gladkova, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7485754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The examination of wild boar population dynamics plays a crucial role in enhancing our understanding of ecosystem functions and their ability to adapt to changing environmental conditions. Various habitat factors and interactions with other species animals significantly influence the abundance of wild boar populations. In this study, we applied predictive spatial models with a random effect to understand how environmental factors and the frequency of African swine fever outbreaks affect the relative abundance of the wild boar population. We considered different geographical conditions and methods of accounting abundance for animals in hunting farms of subjects of Russia. The number of the wild boar in model region was estimated using two methods, namely, using the method of accounting for traces in snow and noise running of animals. To assess the significance of each factor in the models and their interactions, we used a variation partition method. Our research revealed a close relationship between wild boar numbers and environmental parameters, including snow cover height and vegetation percentage. In addition, a correlation was found between the number of wild boar and the incidence rate of African swine fever virus outbreaks among these animals. However, this relationship was not as strong as the impact of environmental conditions. The integrated model used in this study demonstrated the significance of the environmental drivers considered for dynamic wild boar population abundance across various geographical conditions. This is crucial for developing wildlife management strategies, especially for wild boar, to prevent the spread of infectious diseases. Ecological Modeling Animal Science Population Biology accounting methods environmental factors regression models Russia Sus scrofa Figures Figure 1 Figure 2 Figure 3 1. Introduction Monitoring the dynamics of wild animal populations is a crucial aspect of ecosystem and biodiversity assessment. This approach not only allows for the evaluation of population status but also helps identify potential threats associated with anthropogenic factors, climate change, and other external influences (Engeman et al. 2013 ; Sarasa and Sarasa 2013 ). The wildlife population surveillance involves abundance estimation, demographic analysis, and spatial distribution assessment, considering external factors. As part of the study of wildlife biology, various methods are used, including field studies, remote surveillance of biological sampling analysis, and modeling of population processes. A comprehensive approach of monitoring wildlife allows for identifying current trends and changes in population status. It also enables the prediction of animal population dynamics (Gortazar et al., 2015 ). In particular, a well-founded monitoring system is the foundation for making informed decisions regarding wildlife management and for evaluating the effectiveness of preventive measures against infectious and parasitic diseases (Higashide et al., 2021 ). The relative abundance of wild boar, like those of other wild ungulates, are subject to complex and multifactorial changes over time, which are largely due to both biotic and abiotic factors. Biotic factors include interactions with other ecosystem components, such as predators, competitors, and food resources. Abiotic factors encompass a wide range of external drivers, including climatic, geographic, and anthropogenic impacts. A significant number of studies, both in Russia and abroad, have been devoted to the investigation of factors affecting the distribution and abundance of wild boar and other wild ungulates (Acevedo et al., 2005; Massei et al. 2015 ; Fernández-Llario and Mateos-Quesada, 2003 ; Adams, 2018 ). Hunting and wildlife management activities directly influence the dynamics of wild boar populations, leading to both quantitative and qualitative changes in population characteristics. Reducing human persecution of wildlife can substantially increase the value of areas as refuges and shelters, contributing to higher population densities. This phenomenon is due to adaptive survival strategies for wild boars, which allow them to optimize available resources and minimize the risks associated with predation and other environmental factors (Acevedo et al. 2006 ; Geisser and Reyer, 2004; Keuling and Massei 2021 ; Barrios-Garcia and Ballari, 2012 ; Merli et al., 2017 ). The wild boar ( Sus scrofa ) is a typical representative of wild ungulates, known for its high ecological plasticity and adaptability to new habitats. For the boar general, spatially heterogeneous landscapes may favor higher densities as opposed to lowland ones. This may be due to the greater diversity of food resources and the higher proportion of suitable sheltered areas (Frauendorf et al. 2016 ; Welander 2000 ; Miettinen et al., 2023 ; Garabedian and Kilgo, 2024 ). The main environmental factors affecting wild boar habitat are the availability and quality of food, shelters, water resources, and climatic conditions, including temperature, precipitation, and seasonal fluctuations (Honda 2009 ; Ballari and Barrios-García 2014 ). Some studies have focused on the role of vegetation cover in the life cycle of wild boar (Massolo and Della Stella 2006 ; Fernández-Llario 2004 ; Ferretti et al., 2021 ; Ikeda et al., 2023 ; Vargas-Amado et al., 2023 ). Despite the typical features of the wild boar life cycle (SáAEZ-ROYUELA and TELLERÍIA, 1986), it can be affected by habitat fragmentation, but little is known about this, especially for the territory of Russia. For the analysis of factors regulating wild boar population dynamics, it is optimal to select territories with clear ecological gradients. In this regard, the European part of Russia is particularly suitable due to its diverse range of landscapes. Many ecologists consider harsh climate, fragmented habitats, and hunting to be the most important limiting factors for wild boar distribution. Conversely, factors such as climate warming and supplemental feeding are considered to promote range expansion (Gethöffer et al., 2023 ) (Acevedo et al. 2022 ; Santini et al. 2022 ). Historically, various methods and models have been used in different countries to estimate wild boar population density, including snow-track surveys, hunting harvest data, group counts, and camera trapping. In Russia and some northern European countries, such as Sweden and Finland, snow-track surveys are a common practice, especially in regions with stable snow cover. Methodologically, this approach involves selecting survey areas, establishing straight-line transects through random or systematic sampling, and recording the location of each track or trail intersecting the transect (Bobek et al. 2014 ; D’Eon et al. 2006 ; Keeping and Pelletier 2014 ). However, snow-track surveys are only feasible for a short period, and weather conditions often limit their long-term application. Moreover, climate warming and sharp fluctuations in wild boar populations due to infectious diseases have led to the need for specialized methods and indices for population density estimation (Zancanaro et al. 2019 ; Lange 2015 ; Panel and Health 2015 ). Accounting for wildlife population size for large spatial scales is laborious and requires standardization of methods to obtain correct and accurate data. Therefore, in many European countries, indirect methods are commonly used for estimating the numbers of wild boar, foxes, and wolves (Engeman et al., 2013 ; Llaneza et al., 2023 ; Waller et al., 2024 ). For example, hunting statistics are the most frequently used data source for wildlife monitoring. These indices are often calculated due to their low cost and ease of implementation. Despite their limitations, such indices provide reliable estimates of wild boar population density at both local and broad spatiotemporal scales (Croft S. et al., 2018). In the Russian Federation, wild boar population counts are conducted using two primary methods depending on climatic conditions: the winter route survey (or snow-track method) and the noise-flushing method (Ministry of Natural Resources of Russia,” n.d.; n.d.; Stephens et al., 2006 ). To achieve a comprehensive understanding of the importance of climatic and landscape factors in the context of wild boar population assessment - especially when extrapolating findings to large territories such as the Russian Federation - it is essential to conduct a thorough analysis and select optimal monitoring sites. Currently, this aspect is often overlooked or not given sufficient attention by researchers and natural resource users (Markov et al., 2019 ). When selecting sites or transects for monitoring, decisions are often based on the accessibility of geographic areas and economic considerations related to travel and survey costs. However, this approach neglects an important aspect: the biological suitability and ecological significance of the selected sites for wild boar habitat. As a result, systematic errors in population estimates may arise due to low animal movement along the selected routes during surveys. This leads to biased data and reduced accuracy of monitoring results, which in turn negatively affects the effectiveness of conservation measures and the sustainable use of natural resources. To improve the reliability of wild boar population data, it is necessary to apply an integrated methodology that includes a detailed analysis of landscape and climatic conditions of potential monitoring sites. This will allow for the more accurate identification of optimal routes and locations that align with the ecological and biological characteristics of wild boar populations, thereby increasing the accuracy and reliability of population estimates. In our study, we aimed to identify the factors influencing the relative abundance of wild boar in hunting farms across Russian Federation areas. We also sought to understand how wild boar population dynamics are related to ecological and socio-demographic factors, including the use of different survey methods for this species. This knowledge will help us manage the population and prevent the spread of infectious disease African swine fever virus. This knowledge will support more effective population management and help prevent the spread of infectious disease agents, such as African swine fever (ASF). 2. Materials and methods 2.1 Study area To study the dynamics of wild boar populations, we selected two study regions in the European part of the Russian Federation, representing its central and southern parts (Fig. 1 ). The central region monitoring zone included federal subjects 1 for which the most comprehensive data on relative wild boar abundance at the hunting farm level were available for the period from 2014 to 2024. In total, the dataset for analysis included eight federal subjects from the central region, with 6,856 records collected at the hunting farm level. Wild boar in this model area of registered by the winter route counting method on a trail in the snow. In total, the dataset for analysis included eight federal subjects from the central region, with 6,856 records collected at the hunting farm level. In this model region, wild boar population counts were conducted using the winter route snow-track survey method. The southern model region of the Russian Federation consisted of four federal subjects, where data on wild boar abundance were collected using namely noise-flushing method. Population data for each subject at the hunting farm level were obtained through requests to regional committees for natural resources protection and ministries for each year of monitoring. In total, 4,340 records from hunting farms in the second model region were included in the analysis. Data on the population size of wild boar for each subject in the context of hunting farms were obtained at the request of regional committees for the protection of natural resources and ministries for each year of monitoring. 2.2 Methods for assessing the relative abundance of wild boar populations In our study, the response variable was the total number of wild boar individuals recorded in each hunting farm within the model regions, using methods approved by the Ministry of Natural Resources of the Russian Federation. Depending on the geographic conditions of wild boar habitats, two survey methods are recommended for federal subjects of the Russian Federation: 1) Winter route survey (snow-track method) – for territories with stable snow cover; and 2) Noise-flushing method – for geographic areas where snow cover is either short-lived or absent entirely (Ministry of Natural Resources of Russia,” n.d.)( Ministry of Natural Resources of Russia,” n.d.). Snow-track Survey of Wild Boar One of the primary methods for estimating wild boar abundance over large areas is the winter route survey, which is widely used across the Russian Federation in regions with stable snow cover. The methodology was developed by I.V. Zharkov and V.P. Teplov (Zharkov and Teplov, 1958 ). The snow-track method is based on the assumption that the average number of track intersections recorded along a survey route is directly proportional to the population density of wild boar. In turn, the number of tracks depends on the average length of the animals’ movement paths. When using this method, it is essential to record the number and frequency of tracks per unit area of snow cover to calculate the abundance index of wild boar (Bobek et al., 2014 ). Linear stripes on the snow-covered ground are used to record and estimate population abundance and density from their tracks (Fonseca, 2008 ). The size of the tracks can serve as an indicator of the population structure of wild boar (Bieber and Ruf, 2005 ). The information derived from snow tracks can be influenced by different factors, such as the experience and motivation of the observers, and weather conditions (in very cold weather, the wild boar may stop at resting sites). When applied in individual hunting farms or nature reserves (i.e., on small territories), this method often yields underestimated results. This is because the surveys are typically conducted in the second half of winter, when movement of wild boar is hindered by deep snow and their activity is significantly reduced. During this period, animals tend to remain in specific areas that are richest in forage. Additionally, the likelihood of intersecting tracks is minimal. Noise-flushing method for Wild Boar Counting Accounting for hunting resources using the method of drive counts is carried out in hunting areas where there is no snow cover due to adverse weather conditions (Methodology for accounting for the number of hunting resources by the method of noise run, 2023). The key feature of this method is that several people move sequentially across selected areas, using noise to drive animals toward observers. This method provides not only an estimate of population dynamics but also information on the population structure. The methodology for estimating hunting resources using the noise-flushing method includes the planning of survey plots, field data collection, and the calculation of wild boar population numbers. Although this method provides highly accurate data, its complexity limits its application to the entire territory of a hunting farm. As a result, data are often extrapolated to the remaining areas, which can lead to significant errors. This is because, even when carefully selected, local samples rarely reflect the average population density across the entire hunting farm due to the uneven distribution of wild boar (Borkowski et al., 2011 ). 2.3 Explanatory factors To investigate the relationship between the number of wild boars and factors affecting it in the regions of Russia, environmental, landscape, socio-demographic variables were collected, which are presented in Table 1 . Table 1 Environmental factors included in the GLMM (Generalized Linear Mixed Model) of wild boar population abundance in Russia Variables/Code Variable description Data Type Hunting_farm (HF) Code of hunting farm Categorial Survey Period (SP) Time period of the survey Categorial Alt (A), m Altitude above sea level Continuous Slope (Sl) Terrain slope Continuous Snow Depth (S), m Depth of snow cover Continuous Vegetation (F), % Percentage of vegetation cover Continuous Mixed Forests (MF), % Percentage of mixed forests Continuous Coniferous Forests (CF), % Percentage of coniferous forests Continuous Deciduous Forests (DF), % Percentage of deciduous forests Continuous Hunting Ground Area (HA), km 2 Area of hunting grounds Continuous Number of Boar Hunted (HB) Number of wild boar hunted Continuous ASF Outbreaks in Boar (AO) Number of ASF outbreaks in boar Continuous Number of Boars in Hunting Farm (WB) Number of boars recorded in the hunting farm Continuous The estimate of the relative abundance of wild boar depending on environmental factors in our work was based on simulations with several categories of variables that were formed depending on the grouping characteristics of the variables. One such category included methodological variables (M) related to the organization of hunting and survey activities for wild boar. This category included such factors as hunting farms in which the number of animals was taken into account, the total number, the total area of ​ ​ hunting grounds, as well as the number of wild boars determined for each season. The time period of the population survey using the flushing method and winter route surveys was defined by the duration of the survey activities and included specific months of the year. These methodological predictor variables were obtained through requests to regional authorities, committees for natural resources protection, and the Ministry of Natural Resources of Russia. The environmental category of variables (E) included in the model analysis comprised factors such as elevation above sea level, terrain slope, snow depth, percentage of vegetation cover, percentage of coniferous, deciduous, and mixed forests, and percentage of shrubland. These variables were converted from raster to vector format using the zonal statistics tool in GIS and integrated into a unified database of environmental factors (Brown, 2014 ). A biological factor (A) added to the set of drivers influencing the relative abundance of wild boar was the number of African swine fever (ASF) outbreaks. Data on ASF outbreaks among wild boar in the model regions were obtained from the World Organization for Animal Health (WOAH) database (“OIE-WAHIS,” n.d.). Regression analysis, aimed at evaluating the factors regulating wild boar abundance in the Russian federal subjects, was conducted both using individual factors as fixed effects, and in the form of mixed models (M + A, M + E, E + A and M + E + A), depending on the random effects. The random effects in the models were defined as individual hunting farms where the surveys were conducted. Each hunting farm was assigned a unique code and included in the database of factors. The choice of random effects representing individual hunting farms was influenced by the survey methods used in each farm and the data collection technologies associated with them. 2.4 Generalized linear mixed models with negative binomial distribution To assess the relationship between wild boar population abundance and the selected environmental, climatic, and methodological factors, we applied Generalized Linear Mixed Models (GLMMs) with a random effect (Jamil et al., 2013 , Guisan and Thuiller, 2005 , Harrison et al., 2018 ). Such models are more effective than simple linear models when the data is clustered or grouped in some way. In this study, data clustering lies in the fragmented nature of the distribution of the wild boar population according to the location of the hunting grounds (Brooks et al., 2017 ). In order to improve the performance of models with random effects and the quality of explanation of factors, we performed a procedure for separating variable variations. The method of categorizing variables allowed us to determine the variations of predictors in the final models that were explained by each factor individually. Such models represented net effects and mixed models were also obtained, driven by the interaction of two or more factors and one grouping determining random effects. This approach allowed us to better understand the structure of the relationships between environmental factors and the relative abundance of wild boar, which, in turn, contributed to a more accurate and reasonable interpretation of the modeling results. The choice of regression model type depended on the distribution of the response variable. The data on wild boar population abundance across hunting farms in the model regions followed a negative binomial distribution, due to overdispersion. Therefore, we selected a generalized linear mixed regression model with a random effect. This model is well-suited for handling variable heterogeneity and non-homogeneous variance in the data. The area analyzed the of were identified as random factors. A certain role in the choice of a random factor representing individual hunting farms was played by the methods of accounting for animals used specifically in each of the farms and the accompanying technologies used to collect data. In addition, variables related to hunting methodology and wild boar accounting (period of counting animals, surface area occupied in accounting activities, and number of hunted individuals), environmental variables (composition and habitat structure of wild boar), and frequency of ASF outbreaks were considered as potential covariates. Regression analysis, including the assessment of factors regulating the number of wild boars in the territory of the constituent entities of Russia, was carried out both separately with each of the factors (M, E, A), as fixed variables, and as mixed private models (M + E, E + A, M + A, M + E + A). Each hunting farm was assigned an individual code, which was included in the database. The following statistical measures were used to test the predictive power of the models: the AIC Akaike information test, the coefficient of determination, the Darbin Watson coefficient, and the Houseman test. We ran all the models combining the three factors and their two-way interactions. Following the information-theoretic approach (Burnham and Anderson, 2002 ), we carried out model selection based on Akaike's information criterion (AIC): the smaller the criterion, the better the model (Bozdogan, 1987 , Sutherland et al., 2023 ). The coefficient of determination in our study is used as an indicator characterizing the effectiveness of the obtained models and assessing the quality of the regression model fit. The Darbin-Watson statistical test (DW) was chosen to detect autocorrelation of residuals in regression models (Turner, 2020 ). The Houseman test included in the diagnosis of our models helped us confirm the correctness of our choice from models with random effects. This test helps to choose from all the models used, only those that most effectively reflect the situation are either fixed effects (FE) or random effects (RE) models for data analysis (Amini et al., 2012 ; Fox, 2023 ). All analyses were performed with software R 4.4.1 (The R Project for Statistical Computing, 2025), package 'lme4' (Bates et al., 2015 ). 3. Results 3.1 Generalized mixed models of the relationship between the dynamics of wild boar abundance and environmental factors As a result of the modeling process, three individual regression models and one final model incorporating all three factors (M, E, A) were developed. The statistical metrics of the mixed models, which assess the interaction between variables related to wild boar survey methodology, ecological factors, and ASF prevalence, are presented in Tables 1 – 4 . These models were used to determine the influence of these factors on the population dynamics of wild boar. Preliminary statistical analysis of the response variable, representing the abundance of wild boar in each hunting farm, revealed that the winter route survey method (WRS) produced more evenly distributed data compared to the drive hunting method (Fig. 2 ). This suggests that the WRS method may provide a more reliable representation of population distribution across the study areas. 1. As a result of modeling with methodological type variables and a factor determining the incidence of ASF boar, we identified drivers that play a role in changing the population size of animals. Table 2 Coefficients estimates for the best mixed model (GLMM_1) of relative abundance of wild boar including methodological (M) factor and African Swine Fever cases (A) Variables Estimate Standard error (SE) z-value p-value Survey Period 0.237 0.025 3.214 ≤ 0.002 Hunting Ground Area 1.534 0.635 1.536 ≤ 0.001 Number of Boars Hunted 0.156 0.027 2.824 ≤ 0.623 ASF Outbreaks in Boars 2.342 0.267 1.324 ≤ 0.023 Based on the results of a generalized mixed regression model with methodological factors, it was found that the number of wild boars was more dependent on the area of ​ ​ hunting grounds and the hunting period, which includes maximum data in the spring (Table 2 ). 2. As a result of the modeling of environmental variables with methodological (E + M), environmental determinants were identified that regulate the dynamics of the wild boar population in various regions of the Russian Federation. Among the indicated environmental drivers, the snow cover height and absolute height above sea level were reliably significant (Table 3 ). Table 3 Coefficients estimates for the best mixed regression model (GLMM_2) of relative abundance of wild boar including of environmental (E) factor and methodological (M) factor Variables Estimate Standard error (SE) z-value p-value Survey Period 0.254 0.021 2.423 ≤ 0.024 Hunting Ground Area 0.435 0.321 1.054 ≤ 0.032 Slope -2.135 0.022 -2.364 ≤ 0.001 Snow Depth -3.825 0.654 -0.832 ≤ 0.005 Vegetation 1.124 0.326 1.436 ≤ 0.002 A negative correlation was observed between these environmental parameters and the relative abundance of wild boar, indicating that in regions with higher snow depth and elevation, the relative population abundance of wild boar tends to decrease. 3. As a result of the modeling of environmental variables combined with the frequency of African swine fever outbreaks in wild boar (E + A), key environmental determinants influencing the population dynamics of wild boar were identified. The analysis revealed that vegetation-related drivers have a significant impact on the abundance of wild boar populations. Specifically, it was found that the area of forest cover makes a substantial contribution to the regulatory component that supports optimal population levels within the species' habitat range (Table 4 ). Table 4 Coefficients estimates for the best mixed regression model (GLMM_3) of relative abundance of wild boar including of environmental (E) factor and African Swine Fever cases (A) Variables Estimate Standard error (SE) z-value p-value Snow Depth -1.225 0.412 -2.217 ≤ 0.001 Vegetation 3.248 0.032 0.834 ≤ 0.031 ASF Outbreaks in Boars 0.818 0.634 0.053 ≤ 0.021 Mixed and deciduous forests serve as the primary biotopes determining the geographical distribution and providing the most favorable conditions for wild boar populations across the Russian Federation. 4. The mixed final model of ecological type factors, methodological variables and frequency of ASF outbreaks wild boar incidence identified vegetation and snow cover height as the main factors determining the important role in regulating the number of wild boar population in the territory of model territories of hunting farms (Table 5 ). Table 5 Coefficients estimates for the best mixed regression model (GLMM_4) of relative abundance of wild boar including of all factors Variables Estimate Standard error (SE) z-value p-value Survey Period 0.489 0.023 1.435 ≤ 0.004 Hunting Ground Area 1.567 0.256 3.823 ≤ 0.034 Snow Depth -1.312 0.034 1.923 ≤ 0.001 Vegetation 2.657 0.321 2.243 ≤ 0.01 ASF Outbreaks in Boars 1.563 0.324 2.845 ≤ 0.001 ASF outbreaks played a direct role in regulating the relative abundance of wild boar within hunting farms. Population control measures implemented in response to recorded ASF outbreaks had a significant impact on the relative population density of wild boar, both within the epizootic foci and in adjacent areas. Figure 3 presents the predicted population abundance of wild boar obtained through surveys conducted in hunting farms on model territories, using different survey methods. These estimates show a clear correlation with snow depth, which significantly affects the detectability of animal tracks and the accuracy of wild boar counts in these areas. The graphs clearly illustrate a trend of decreasing relative abundance of recorded individuals as snow depth increases. This phenomenon is of particular importance for the accuracy of population estimates, as it may lead to biased data regarding wild boar population characteristics. Consequently, such biases could result in inaccurate conclusions in the context of wildlife monitoring and resource management. 3.2 Model Diagnostics of GLMMs with Random Effects Statistical criteria and diagnostic metrics of the final models are presented in Table 6 . Table 6 Diagnostic metrics of the mixed models for identifying environmental factors regulating the relative abundance of wild boar in Russia Models / parameters GLMM_1 GLMM_2 GLMM_3 GLMM_4 AIC 1,932 1,929 1,918 1,806 R 2 0.563 0.586 0.643 0.832 DW 1.832 1.887 1.896 1.936 Coefficient of Hausman 0.213 0.325 0.346 0.667 Footnote: AIC – Akaike's information criterion, R 2 – coefficient of determination, DW - Durbin-Watson criterion According to the diagnostic metric indicators presented in Table 6 , among the first three models, the third one is identified as the best specific model. The AIC value for the third model is the lowest at 1.918, it has a high coefficient of determination of 0.643, and the Durbin-Watson statistic suggests the absence of autocorrelation in the model's residuals. These indicators collectively demonstrate that third model provides the best balance of model fit, explanatory power, and assumption validity for understanding the environmental factors regulating the relative abundance of wild boar in Russia. The R 2 increases from 0.563 for GLMM_1 to 0.643 for GLMM_3, showing that third model explains a greater proportion of variance in the relative abundance of wild boar. This metric supports the superior explanatory power of model. The Durbin-Watson values slightly increase from 1.832 (GLMM_1) to 1.896 (GLMM_3). Since values closer to 2 suggest the Durbin-Watson values no autocorrelation in residuals, third model shows less autocorrelation than one mixed model, indicating better model reliability. The coefficient of Hausman increases modestly from 0.213 in GLMM_1 to 0.346 in GLMM_3. A higher value Hausman might suggest greater consistency of the model's assumptions, with three mixed model being more robust than the previous models. However, the final mixed-effects model (GLMM_4), which incorporates all three factors, proved superior across all metrics. It achieved the highest explanatory power (R 2 = 0.832), the lowest AIC value (1,806), and its specification was confirmed by optimal Durbin-Watson and Hausman test results. Discussion This study focused on identifying and analyzing the factors influencing the dynamics of wild boar ( Sus scrofa ) population abundance across the territory of Russia. We aimed to understand how geographical and environmental conditions, as well as the survey methods applied to wild boar in different regions of the country, affect the population size. The research was based on assessing the impact of various environmental factors that characterize different geographical habitats, which vary in terms of natural and climatic conditions (Bosch et al., 2020 ). As a result of the modeling, it was found that the population dynamics of wild boar, estimated using traditional methods applied in Russian hunting farms—such as snow-track counting—can be explained by the influence of a complex set of geographical and environmental factors. In several other studies, environmental characteristics are considered as a key factor influencing the distribution and abundance of wild animals within their habitats (Grebner et al., 2022 ; Teixeira-Santos et al., 2020 ). The results obtained from the work of mixed models with random effects indicate a comprehensive representation of the influence of various factors on the number of wild boars. Four final models were obtained, in which the interdependencies between the number of wild boars and variable factors with a random effect were identified. The results obtained from the random-effects mixed models indicate a comprehensive representation of the influence of various factors on wild boar abundance. Four mixed regression models were developed, in which interdependencies between wild boar abundance and random-effect variables were identified. Hunting farms. Within the scope of this study, an analysis was conducted to determine the significance of the area of hunting grounds and the intensity of wild boar harvesting in relation to population dynamics. Specifically, large hunting grounds with high hunting intensity were found to exhibit positive dynamics in reproduction and recovery of wild boar populations. This phenomenon can be explained by several factors (Lee and Park, 2022 ). First, larger hunting farms typically provide more favorable conditions that support the natural reproduction and growth of wild boar populations. These conditions include the availability of diverse forage resources, shelter, and breeding zones (Ghandri et al., 2024 ; Vajas et al., 2020 ; Vajas et al., 2023 ). Second, on extensive agricultural lands, hunters and biologists can apply more effective monitoring methods, such as remote sensing, unmanned aerial vehicles (UAVs), and modern geographic information systems (GIS) (Quamar et al., 2023 ; Rietz et al., 2023 ). The use of UAVs equipped with multispectral cameras and GIS platforms with integrated spatial analysis tools significantly enhances the efficiency of ecosystem monitoring and wildlife management (Nath Tripathi et al., 2024 ). Thus, it can be concluded that the size of the hunting economy and the level of production of wild boars represent factors that determine the change in the relative abundance of wild boar, visibility and accounting of animals. Differences in methodological approaches to wild boar monitoring in the hunting farms of model regions in the Russian Federation influenced the modeling results. Specifically, in models where hunting farms using the drive method were included as a random factor, the seasonal period, expressed in months of the year, was identified as a significant predictor. This trend can be explained by the increased activity of wild boar during the spring, which in turn enhances their visibility to researchers. Ecological factors. Characterization of the environment in wild boar habitats plays a key role in their distribution and abundance. Environmental characteristics of wild boar (Sus scrofa) habitats play a key role in their distribution and abundance. Numerous studies emphasize that vegetation-related factors are particularly important for the survival and reproduction of the species. Vegetation provides wild boar with essential resources such as food and shelter, and creates conditions that enhance their protection from predators and adverse weather. Wild boar shows significant ecological plasticity, as confirmed by numerous studies (Saez-Royuela и Telleria, 1986; Taylor et al., 1998; Brogi et al., 2023 ). This adaptability allows the species to effectively utilize a wide range of habitats and resources, which likely explains the strong influence of environmental factors observed in our models. The abundance and diversity of plant species shape suitable habitats for wild boar, which is critical for maintaining stable populations. Changes in vegetation cover due to human activity or climatic factors can significantly affect ecological niches, which ultimately affects the number and distribution of wild boars. Based on previous ecological studies of the species, our results revealed a positive correlation between wild boar abundance and the proportion of mixed and deciduous forests in the landscape (Acevedo et al., 2006 ; 2009; Yang et al., 2024 ; Zakharova and Liskova, 2025 ). In our mixed models, vegetation was identified as a key landscape characteristic influencing wild boar habitat. In mixed forests, where both coniferous and deciduous trees grow, all necessary resources for wild boar are available — food and shelter. Such plant biotopes help wild boars maintain a stable population. Such biotopes support the maintenance of stable population levels. Deciduous forests also play an important role in the life of wild boar (Barrios-Garcia and Ballari, 2012 ). During the survey season, wild boar abundance was also associated with territories located at lower or moderate elevations above sea level. This observation is likely due to several factors. First, the absence of snow cover in these regions during winter provides easier access to food resources. Second, these areas offer favorable conditions for reproduction (Fernández-Llario, 2004 ; Acevedo et al., 2006 ). Thus, the results of our study on the factors regulating wild boar population abundance in hunting farms of model regions in Russia indicate that ecological features and the adaptive capacity of wild boar play a central role in their distribution and abundance across different biotopes. African swine fever outbreaks. One of the most significant factors that change the relative size of the wild boar population is the incidence of infectious and parasitic diseases and the natural death of animals. Among these, African swine fever stands out as a particularly devastating disease for wild boar. ASF outbreaks pose a serious threat to wild boar populations due to the high contagiousness of the virus, which can spread rapidly among animals (Thanapongtharm et al., 2025 ). Disease ASF are a serious threat to wild boar populations because the virus that causes the disease is highly contagious and can spread rapidly among animals (Zakharova et al., 2021 ; Ceruti et al., 2025 ). Population regulation in response to ASF outbreaks has become an essential component of wild boar management (Nielsen et al., 2021 ; Jota Baptista et al., 2023 ). Veterinarians and hunting farm managers are often required to implement control and culling measures both within and around the affected areas to prevent the further spread of the virus. These measures may include population reduction, monitoring of infected individuals, and sanitary interventions aimed at limiting the transmission of the disease. As a result of these ASF prevention and control measures, significant changes in the relative abundance of wild boar have been observed, particularly in epizootic foci and adjacent territories. In such contexts, wild boar populations may experience substantial declines, which can lead to ecological imbalances and affect other species and vegetation. Therefore, ASF outbreaks have a complex impact on wild boar populations and require the development of effective strategies for their protection and regulation (Reichold et al., 2022 ; Palencia et al., 2023 ). Conclusion Using a random-effect mixed regression model allowed for variation arising from differences in data collection methods and for more accurate estimates of the influence of factors. The study highlighted the importance of taking into account various methodological and environmental factors when analyzing the number of wild boars, as well as the peculiarity of the links between factors related to both the habitat and the impact of diseases on the population. The patterns identified may be useful for further activities to manage wildlife populations and prevent the spread of disease. Declarations Authors contribuition Olga I. Zakharova: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Writing - Original Draft, Writing - Review & Editing, Visualization. Fedor I. Korennoy: Methodology, Validation, Investigation, Resources, Writing - Original Draft. Elena A. Liskova: Investigation, Writing - Original Draft. Nadezhda A. Gladkova: Investigation, Resources, Writing - Review & Editing. Ivan V. Jashin: Supervision, Project Administration, Funding Acquisition, Writing - Original Draft. Andrey A. Blokhin: Investigation, Resources, Data Curation, Writing - Review & Editing. Funding This study was performed with the support of the Federal Research Center for Virology and Microbiology . This research received no external funding. Data availability statement The data presented in this study are available upon request from the corresponding author. Ethical approval Not applicable. 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Structure and contacts of the Ministry of Natural Resources and Environment of the Russian Federation — Ministry of Natural Resources of Russia. URLhttps://www.mnr.gov.ru/about/ (accessed 5.9.25). Footnotes Federal subject is a first-level administrative division of the Russian Federation Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Zakharova","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-1408-2989","institution":"Federal Research Center for Virology and Microbiology, Branch in Nizhny Novgorod, Nizhny Novgorod, Russia","correspondingAuthor":true,"prefix":"","firstName":"Olga","middleName":"I.","lastName":"Zakharova","suffix":""},{"id":507308581,"identity":"4ac164d4-65ae-4fff-aca4-b7bcbd01bcfd","order_by":1,"name":"Andrey A. Blokhin","email":"","orcid":"https://orcid.org/0000-0001-5161-1184","institution":"Federal Research Center for Virology and Microbiology, Branch in Nizhny Novgorod, Nizhny Novgorod, Russia","correspondingAuthor":false,"prefix":"","firstName":"Andrey","middleName":"A.","lastName":"Blokhin","suffix":""},{"id":507308582,"identity":"035e1e8a-092b-4d22-bfa5-f93b8924f909","order_by":2,"name":"Elena A. Liskova","email":"","orcid":"https://orcid.org/0000-0003-4324-725X","institution":"Federal Research Center for Virology and Microbiology, Branch in Nizhny Novgorod, Nizhny Novgorod, Russia","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"A.","lastName":"Liskova","suffix":""},{"id":507308583,"identity":"235d2611-3eb1-4f6a-8f35-6699874c7d2c","order_by":3,"name":"Nadezhda A. Gladkova","email":"","orcid":"https://orcid.org/0000-0002-2868-5158","institution":"Federal Research Center for Virology and Microbiology, Branch in Nizhny Novgorod, Nizhny Novgorod, Russia","correspondingAuthor":false,"prefix":"","firstName":"Nadezhda","middleName":"A.","lastName":"Gladkova","suffix":""},{"id":507308584,"identity":"d7a61211-ba93-4598-a231-02d703f146dc","order_by":4,"name":"Ivan V. Jashin","email":"","orcid":"https://orcid.org/0000-0001-7359-2041","institution":"Federal Research Center for Virology and Microbiology, Branch in Nizhny Novgorod, Nizhny Novgorod, Russia","correspondingAuthor":false,"prefix":"","firstName":"Ivan","middleName":"V.","lastName":"Jashin","suffix":""},{"id":507308589,"identity":"90d0cadc-0c38-4dc9-bdaf-f15bea82abbb","order_by":5,"name":"Fedor I. Korennoy","email":"","orcid":"https://orcid.org/0000-0002-7378-3531","institution":"Federal Center for Animal Health (FGBI ARRIAH), Vladimir, Russia","correspondingAuthor":false,"prefix":"","firstName":"Fedor","middleName":"I.","lastName":"Korennoy","suffix":""}],"badges":[],"createdAt":"2025-08-29 07:06:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7485754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7485754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90290376,"identity":"7e62637b-c582-4144-9c97-136d3784375e","added_by":"auto","created_at":"2025-09-01 07:19:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":649429,"visible":true,"origin":"","legend":"\u003cp\u003eModel regions of the Russian Federation used to study the relative abundance of wild boar populations, divided into two zones based on the survey methods applied. A – territories where wild boar counts are assessed using the winter route survey method; B – territories where wild boar counts are assessed using the noise-flushing method\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7485754/v1/7cc2b64a3beddb6a9cce407c.png"},{"id":90290374,"identity":"4ff56220-101d-4a7a-bc92-2daee1df4c5d","added_by":"auto","created_at":"2025-09-01 07:19:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":700565,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of relative wild boar abundance in hunting grounds of model regions according to survey methods and the current epizootic status of African swine fever virus\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7485754/v1/f100cf4702a77981a8e7e8af.png"},{"id":90290375,"identity":"8a1388ee-8c24-4255-adf6-0b4e65ef5ac7","added_by":"auto","created_at":"2025-09-01 07:19:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":827289,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of predicted relative wild boar population abundance in relation to snow depth, using different survey methods\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7485754/v1/ddd522deaa6c804a7e5f295b.png"},{"id":90291888,"identity":"8130b739-309a-4d39-befe-3fea89890904","added_by":"auto","created_at":"2025-09-01 07:35:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2941419,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7485754/v1/cc6cfe54-15c6-4ce2-8183-c1990357cafd.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEnvironmental drivers influencing the relative abundance of wild boar population in Russia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMonitoring the dynamics of wild animal populations is a crucial aspect of ecosystem and biodiversity assessment. This approach not only allows for the evaluation of population status but also helps identify potential threats associated with anthropogenic factors, climate change, and other external influences (Engeman et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sarasa and Sarasa \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe wildlife population surveillance involves abundance estimation, demographic analysis, and spatial distribution assessment, considering external factors. As part of the study of wildlife biology, various methods are used, including field studies, remote surveillance of biological sampling analysis, and modeling of population processes.\u003c/p\u003e\u003cp\u003eA comprehensive approach of monitoring wildlife allows for identifying current trends and changes in population status. It also enables the prediction of animal population dynamics (Gortazar et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In particular, a well-founded monitoring system is the foundation for making informed decisions regarding wildlife management and for evaluating the effectiveness of preventive measures against infectious and parasitic diseases (Higashide et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe relative abundance of wild boar, like those of other wild ungulates, are subject to complex and multifactorial changes over time, which are largely due to both biotic and abiotic factors. Biotic factors include interactions with other ecosystem components, such as predators, competitors, and food resources. Abiotic factors encompass a wide range of external drivers, including climatic, geographic, and anthropogenic impacts. A significant number of studies, both in Russia and abroad, have been devoted to the investigation of factors affecting the distribution and abundance of wild boar and other wild ungulates (Acevedo et al., 2005; Massei et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fern\u0026aacute;ndez-Llario and Mateos-Quesada, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Adams, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHunting and wildlife management activities directly influence the dynamics of wild boar populations, leading to both quantitative and qualitative changes in population characteristics. Reducing human persecution of wildlife can substantially increase the value of areas as refuges and shelters, contributing to higher population densities. This phenomenon is due to adaptive survival strategies for wild boars, which allow them to optimize available resources and minimize the risks associated with predation and other environmental factors (Acevedo et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Geisser and Reyer, 2004; Keuling and Massei \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Barrios-Garcia and Ballari, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Merli et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e) is a typical representative of wild ungulates, known for its high ecological plasticity and adaptability to new habitats. For the boar general, spatially heterogeneous landscapes may favor higher densities as opposed to lowland ones. This may be due to the greater diversity of food resources and the higher proportion of suitable sheltered areas (Frauendorf et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Welander \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Miettinen et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Garabedian and Kilgo, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe main environmental factors affecting wild boar habitat are the availability and quality of food, shelters, water resources, and climatic conditions, including temperature, precipitation, and seasonal fluctuations (Honda \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ballari and Barrios-Garc\u0026iacute;a \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSome studies have focused on the role of vegetation cover in the life cycle of wild boar (Massolo and Della Stella \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fern\u0026aacute;ndez-Llario \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ferretti et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ikeda et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vargas-Amado et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the typical features of the wild boar life cycle (S\u0026aacute;AEZ-ROYUELA and TELLER\u0026Iacute;IA, 1986), it can be affected by habitat fragmentation, but little is known about this, especially for the territory of Russia. For the analysis of factors regulating wild boar population dynamics, it is optimal to select territories with clear ecological gradients. In this regard, the European part of Russia is particularly suitable due to its diverse range of landscapes. Many ecologists consider harsh climate, fragmented habitats, and hunting to be the most important limiting factors for wild boar distribution. Conversely, factors such as climate warming and supplemental feeding are considered to promote range expansion (Geth\u0026ouml;ffer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Acevedo et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Santini et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHistorically, various methods and models have been used in different countries to estimate wild boar population density, including snow-track surveys, hunting harvest data, group counts, and camera trapping.\u003c/p\u003e\u003cp\u003eIn Russia and some northern European countries, such as Sweden and Finland, snow-track surveys are a common practice, especially in regions with stable snow cover. Methodologically, this approach involves selecting survey areas, establishing straight-line transects through random or systematic sampling, and recording the location of each track or trail intersecting the transect (Bobek et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; D\u0026rsquo;Eon et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Keeping and Pelletier \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, snow-track surveys are only feasible for a short period, and weather conditions often limit their long-term application. Moreover, climate warming and sharp fluctuations in wild boar populations due to infectious diseases have led to the need for specialized methods and indices for population density estimation (Zancanaro et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lange \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Panel and Health \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAccounting for wildlife population size for large spatial scales is laborious and requires standardization of methods to obtain correct and accurate data. Therefore, in many European countries, indirect methods are commonly used for estimating the numbers of wild boar, foxes, and wolves (Engeman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Llaneza et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Waller et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For example, hunting statistics are the most frequently used data source for wildlife monitoring. These indices are often calculated due to their low cost and ease of implementation. Despite their limitations, such indices provide reliable estimates of wild boar population density at both local and broad spatiotemporal scales (Croft S. et al., 2018).\u003c/p\u003e\u003cp\u003eIn the Russian Federation, wild boar population counts are conducted using two primary methods depending on climatic conditions: the winter route survey (or snow-track method) and the noise-flushing method (Ministry of Natural Resources of Russia,\u0026rdquo; n.d.; n.d.; Stephens et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo achieve a comprehensive understanding of the importance of climatic and landscape factors in the context of wild boar population assessment - especially when extrapolating findings to large territories such as the Russian Federation - it is essential to conduct a thorough analysis and select optimal monitoring sites. Currently, this aspect is often overlooked or not given sufficient attention by researchers and natural resource users (Markov et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen selecting sites or transects for monitoring, decisions are often based on the accessibility of geographic areas and economic considerations related to travel and survey costs. However, this approach neglects an important aspect: the biological suitability and ecological significance of the selected sites for wild boar habitat. As a result, systematic errors in population estimates may arise due to low animal movement along the selected routes during surveys. This leads to biased data and reduced accuracy of monitoring results, which in turn negatively affects the effectiveness of conservation measures and the sustainable use of natural resources.\u003c/p\u003e\u003cp\u003eTo improve the reliability of wild boar population data, it is necessary to apply an integrated methodology that includes a detailed analysis of landscape and climatic conditions of potential monitoring sites. This will allow for the more accurate identification of optimal routes and locations that align with the ecological and biological characteristics of wild boar populations, thereby increasing the accuracy and reliability of population estimates.\u003c/p\u003e\u003cp\u003eIn our study, we aimed to identify the factors influencing the relative abundance of wild boar in hunting farms across Russian Federation areas.\u003c/p\u003e\u003cp\u003eWe also sought to understand how wild boar population dynamics are related to ecological and socio-demographic factors, including the use of different survey methods for this species. This knowledge will help us manage the population and prevent the spread of infectious disease African swine fever virus. This knowledge will support more effective population management and help prevent the spread of infectious disease agents, such as African swine fever (ASF).\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study area\u003c/h2\u003e\u003cp\u003eTo study the dynamics of wild boar populations, we selected two study regions in the European part of the Russian Federation, representing its central and southern parts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The central region monitoring zone included federal subjects\u003csup\u003e1\u003c/sup\u003e for which the most comprehensive data on relative wild boar abundance at the hunting farm level were available for the period from 2014 to 2024. In total, the dataset for analysis included eight federal subjects from the central region, with 6,856 records collected at the hunting farm level. Wild boar in this model area of registered by the winter route counting method on a trail in the snow. In total, the dataset for analysis included eight federal subjects from the central region, with 6,856 records collected at the hunting farm level. In this model region, wild boar population counts were conducted using the winter route snow-track survey method. The southern model region of the Russian Federation consisted of four federal subjects, where data on wild boar abundance were collected using namely noise-flushing method. Population data for each subject at the hunting farm level were obtained through requests to regional committees for natural resources protection and ministries for each year of monitoring. In total, 4,340 records from hunting farms in the second model region were included in the analysis. Data on the population size of wild boar for each subject in the context of hunting farms were obtained at the request of regional committees for the protection of natural resources and ministries for each year of monitoring.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methods for assessing the relative abundance of wild boar populations\u003c/h2\u003e\u003cp\u003e In our study, the response variable was the total number of wild boar individuals recorded in each hunting farm within the model regions, using methods approved by the Ministry of Natural Resources of the Russian Federation. Depending on the geographic conditions of wild boar habitats, two survey methods are recommended for federal subjects of the Russian Federation:\u003c/p\u003e\u003c/div\u003e\n\u003cp\u003e1) Winter route survey (snow-track method) – for territories with stable snow cover; and\u003c/p\u003e\n\u003cp\u003e2) Noise-flushing method \u0026ndash; for geographic areas where snow cover is either short-lived or absent entirely (Ministry of Natural Resources of Russia,\u0026rdquo; n.d.)( Ministry of Natural Resources of Russia,\u0026rdquo; n.d.).\u003c/p\u003e\u003cp\u003e\u003cem\u003eSnow-track Survey of Wild Boar\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOne of the primary methods for estimating wild boar abundance over large areas is the winter route survey, which is widely used across the Russian Federation in regions with stable snow cover. The methodology was developed by I.V. Zharkov and V.P. Teplov (Zharkov and Teplov, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1958\u003c/span\u003e). The snow-track method is based on the assumption that the average number of track intersections recorded along a survey route is directly proportional to the population density of wild boar. In turn, the number of tracks depends on the average length of the animals\u0026rsquo; movement paths. When using this method, it is essential to record the number and frequency of tracks per unit area of snow cover to calculate the abundance index of wild boar (Bobek et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLinear stripes on the snow-covered ground are used to record and estimate population abundance and density from their tracks (Fonseca, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The size of the tracks can serve as an indicator of the population structure of wild boar (Bieber and Ruf, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The information derived from snow tracks can be influenced by different factors, such as the experience and motivation of the observers, and weather conditions (in very cold weather, the wild boar may stop at resting sites).\u003c/p\u003e\u003cp\u003eWhen applied in individual hunting farms or nature reserves (i.e., on small territories), this method often yields underestimated results. This is because the surveys are typically conducted in the second half of winter, when movement of wild boar is hindered by deep snow and their activity is significantly reduced. During this period, animals tend to remain in specific areas that are richest in forage. Additionally, the likelihood of intersecting tracks is minimal.\u003c/p\u003e\u003cp\u003e\u003cem\u003eNoise-flushing method for Wild Boar Counting\u003c/em\u003e\u003c/p\u003e\u003cp\u003eAccounting for hunting resources using the method of drive counts is carried out in hunting areas where there is no snow cover due to adverse weather conditions (Methodology for accounting for the number of hunting resources by the method of noise run, 2023).\u003c/p\u003e\u003cp\u003eThe key feature of this method is that several people move sequentially across selected areas, using noise to drive animals toward observers. This method provides not only an estimate of population dynamics but also information on the population structure.\u003c/p\u003e\u003cp\u003eThe methodology for estimating hunting resources using the noise-flushing method includes the planning of survey plots, field data collection, and the calculation of wild boar population numbers. Although this method provides highly accurate data, its complexity limits its application to the entire territory of a hunting farm. As a result, data are often extrapolated to the remaining areas, which can lead to significant errors. This is because, even when carefully selected, local samples rarely reflect the average population density across the entire hunting farm due to the uneven distribution of wild boar (Borkowski et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Explanatory factors\u003c/h2\u003e\u003cp\u003eTo investigate the relationship between the number of wild boars and factors affecting it in the regions of Russia, environmental, landscape, socio-demographic variables were collected, which are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnvironmental factors included in the GLMM (Generalized Linear Mixed Model) of wild boar population abundance in Russia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables/Code\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVariable description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eData Type\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunting_farm (HF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCode of hunting farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCategorial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Period (SP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTime period of the survey\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCategorial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlt (A), m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAltitude above sea level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope (Sl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTerrain slope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSnow Depth (S), m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth of snow cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation (F), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of vegetation cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed Forests (MF), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of mixed forests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConiferous Forests (CF), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of coniferous forests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDeciduous Forests (DF), %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePercentage of deciduous forests\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunting Ground Area (HA), km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArea of hunting grounds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Boar Hunted (HB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of wild boar hunted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASF Outbreaks in Boar (AO)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of ASF outbreaks in boar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Boars in Hunting Farm (WB)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of boars recorded in the hunting farm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContinuous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c4\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe estimate of the relative abundance of wild boar depending on environmental factors in our work was based on simulations with several categories of variables that were formed depending on the grouping characteristics of the variables. One such category included methodological variables (M) related to the organization of hunting and survey activities for wild boar. This category included such factors as hunting farms in which the number of animals was taken into account, the total number, the total area of ​ ​ hunting grounds, as well as the number of wild boars determined for each season. The time period of the population survey using the flushing method and winter route surveys was defined by the duration of the survey activities and included specific months of the year. These methodological predictor variables were obtained through requests to regional authorities, committees for natural resources protection, and the Ministry of Natural Resources of Russia.\u003c/p\u003e\u003cp\u003eThe environmental category of variables (E) included in the model analysis comprised factors such as elevation above sea level, terrain slope, snow depth, percentage of vegetation cover, percentage of coniferous, deciduous, and mixed forests, and percentage of shrubland. These variables were converted from raster to vector format using the zonal statistics tool in GIS and integrated into a unified database of environmental factors (Brown, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA biological factor (A) added to the set of drivers influencing the relative abundance of wild boar was the number of African swine fever (ASF) outbreaks. Data on ASF outbreaks among wild boar in the model regions were obtained from the World Organization for Animal Health (WOAH) database (\u0026ldquo;OIE-WAHIS,\u0026rdquo; n.d.).\u003c/p\u003e\u003cp\u003eRegression analysis, aimed at evaluating the factors regulating wild boar abundance in the Russian federal subjects, was conducted both using individual factors as fixed effects, and in the form of mixed models (M\u0026thinsp;+\u0026thinsp;A, M\u0026thinsp;+\u0026thinsp;E, E\u0026thinsp;+\u0026thinsp;A and M\u0026thinsp;+\u0026thinsp;E\u0026thinsp;+\u0026thinsp;A), depending on the random effects. The random effects in the models were defined as individual hunting farms where the surveys were conducted. Each hunting farm was assigned a unique code and included in the database of factors. The choice of random effects representing individual hunting farms was influenced by the survey methods used in each farm and the data collection technologies associated with them.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Generalized linear mixed models with negative binomial distribution\u003c/h2\u003e\u003cp\u003eTo assess the relationship between wild boar population abundance and the selected environmental, climatic, and methodological factors, we applied Generalized Linear Mixed Models (GLMMs) with a random effect (Jamil et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Guisan and Thuiller, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Harrison et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSuch models are more effective than simple linear models when the data is clustered or grouped in some way. In this study, data clustering lies in the fragmented nature of the distribution of the wild boar population according to the location of the hunting grounds (Brooks et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn order to improve the performance of models with random effects and the quality of explanation of factors, we performed a procedure for separating variable variations. The method of categorizing variables allowed us to determine the variations of predictors in the final models that were explained by each factor individually. Such models represented net effects and mixed models were also obtained, driven by the interaction of two or more factors and one grouping determining random effects. This approach allowed us to better understand the structure of the relationships between environmental factors and the relative abundance of wild boar, which, in turn, contributed to a more accurate and reasonable interpretation of the modeling results.\u003c/p\u003e\u003cp\u003eThe choice of regression model type depended on the distribution of the response variable. The data on wild boar population abundance across hunting farms in the model regions followed a negative binomial distribution, due to overdispersion. Therefore, we selected a generalized linear mixed regression model with a random effect. This model is well-suited for handling variable heterogeneity and non-homogeneous variance in the data.\u003c/p\u003e\u003cp\u003eThe area analyzed the of were identified as random factors. A certain role in the choice of a random factor representing individual hunting farms was played by the methods of accounting for animals used specifically in each of the farms and the accompanying technologies used to collect data. In addition, variables related to hunting methodology and wild boar accounting (period of counting animals, surface area occupied in accounting activities, and number of hunted individuals), environmental variables (composition and habitat structure of wild boar), and frequency of ASF outbreaks were considered as potential covariates.\u003c/p\u003e\u003cp\u003eRegression analysis, including the assessment of factors regulating the number of wild boars in the territory of the constituent entities of Russia, was carried out both separately with each of the factors (M, E, A), as fixed variables, and as mixed private models (M\u0026thinsp;+\u0026thinsp;E, E\u0026thinsp;+\u0026thinsp;A, M\u0026thinsp;+\u0026thinsp;A, M\u0026thinsp;+\u0026thinsp;E\u0026thinsp;+\u0026thinsp;A). Each hunting farm was assigned an individual code, which was included in the database.\u003c/p\u003e\u003cp\u003eThe following statistical measures were used to test the predictive power of the models: the AIC Akaike information test, the coefficient of determination, the Darbin Watson coefficient, and the Houseman test.\u003c/p\u003e\u003cp\u003eWe ran all the models combining the three factors and their two-way interactions. Following the information-theoretic approach (Burnham and Anderson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), we carried out model selection based on Akaike's information criterion (AIC): the smaller the criterion, the better the model (Bozdogan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1987\u003c/span\u003e, Sutherland et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe coefficient of determination in our study is used as an indicator characterizing the effectiveness of the obtained models and assessing the quality of the regression model fit.\u003c/p\u003e\u003cp\u003eThe Darbin-Watson statistical test (DW) was chosen to detect autocorrelation of residuals in regression models (Turner, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Houseman test included in the diagnosis of our models helped us confirm the correctness of our choice from models with random effects. This test helps to choose from all the models used, only those that most effectively reflect the situation are either fixed effects (FE) or random effects (RE) models for data analysis (Amini et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fox, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAll analyses were performed with software R 4.4.1 (The R Project for Statistical Computing, 2025), package 'lme4' (Bates et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cb\u003e3.1 Generalized mixed models of the relationship between the dynamics of wild boar abundance and environmental factors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs a result of the modeling process, three individual regression models and one final model incorporating all three factors (M, E, A) were developed. The statistical metrics of the mixed models, which assess the interaction between variables related to wild boar survey methodology, ecological factors, and ASF prevalence, are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These models were used to determine the influence of these factors on the population dynamics of wild boar.\u003c/p\u003e\u003cp\u003ePreliminary statistical analysis of the response variable, representing the abundance of wild boar in each hunting farm, revealed that the winter route survey method (WRS) produced more evenly distributed data compared to the drive hunting method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This suggests that the WRS method may provide a more reliable representation of population distribution across the study areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e1. As a result of modeling with methodological type variables and a factor determining the incidence of ASF boar, we identified drivers that play a role in changing the population size of animals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficients estimates for the best mixed model (GLMM_1) of relative abundance of wild boar including methodological (M) factor and African Swine Fever cases (A)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunting Ground Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Boars Hunted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASF Outbreaks in Boars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the results of a generalized mixed regression model with methodological factors, it was found that the number of wild boars was more dependent on the area of ​ ​ hunting grounds and the hunting period, which includes maximum data in the spring (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e2. As a result of the modeling of environmental variables with methodological (E\u0026thinsp;+\u0026thinsp;M), environmental determinants were identified that regulate the dynamics of the wild boar population in various regions of the Russian Federation. Among the indicated environmental drivers, the snow cover height and absolute height above sea level were reliably significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficients estimates for the best mixed regression model (GLMM_2) of relative abundance of wild boar including of environmental (E) factor and methodological (M) factor\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.254\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.024\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunting Ground Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-2.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSnow Depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.825\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.326\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA negative correlation was observed between these environmental parameters and the relative abundance of wild boar, indicating that in regions with higher snow depth and elevation, the relative population abundance of wild boar tends to decrease.\u003c/p\u003e\u003cp\u003e3. As a result of the modeling of environmental variables combined with the frequency of African swine fever outbreaks in wild boar (E\u0026thinsp;+\u0026thinsp;A), key environmental determinants influencing the population dynamics of wild boar were identified. The analysis revealed that vegetation-related drivers have a significant impact on the abundance of wild boar populations. Specifically, it was found that the area of forest cover makes a substantial contribution to the regulatory component that supports optimal population levels within the species' habitat range (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficients estimates for the best mixed regression model (GLMM_3) of relative abundance of wild boar including of environmental (E) factor and African Swine Fever cases (A)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSnow Depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-2.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.031\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASF Outbreaks in Boars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMixed and deciduous forests serve as the primary biotopes determining the geographical distribution and providing the most favorable conditions for wild boar populations across the Russian Federation.\u003c/p\u003e\u003cp\u003e4. The mixed final model of ecological type factors, methodological variables and frequency of ASF outbreaks wild boar incidence identified vegetation and snow cover height as the main factors determining the important role in regulating the number of wild boar population in the territory of model territories of hunting farms (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCoefficients estimates for the best mixed regression model (GLMM_4) of relative abundance of wild boar including of all factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandard error (SE)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurvey Period\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.489\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunting Ground Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSnow Depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.657\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASF Outbreaks in Boars\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eASF outbreaks played a direct role in regulating the relative abundance of wild boar within hunting farms. Population control measures implemented in response to recorded ASF outbreaks had a significant impact on the relative population density of wild boar, both within the epizootic foci and in adjacent areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the predicted population abundance of wild boar obtained through surveys conducted in hunting farms on model territories, using different survey methods. These estimates show a clear correlation with snow depth, which significantly affects the detectability of animal tracks and the accuracy of wild boar counts in these areas.\u003c/p\u003e\u003cp\u003eThe graphs clearly illustrate a trend of decreasing relative abundance of recorded individuals as snow depth increases. This phenomenon is of particular importance for the accuracy of population estimates, as it may lead to biased data regarding wild boar population characteristics. Consequently, such biases could result in inaccurate conclusions in the context of wildlife monitoring and resource management.\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Model Diagnostics of GLMMs with Random Effects\u003c/h2\u003e\u003cp\u003eStatistical criteria and diagnostic metrics of the final models are presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiagnostic metrics of the mixed models for identifying environmental factors regulating the relative abundance of wild boar in Russia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModels / parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGLMM_1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGLMM_2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGLMM_3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGLMM_4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.586\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.832\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.832\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.936\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoefficient of Hausman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.667\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eFootnote: AIC \u0026ndash; Akaike's information criterion, R\u003csup\u003e2\u003c/sup\u003e \u0026ndash; coefficient of determination, DW - Durbin-Watson criterion\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAccording to the diagnostic metric indicators presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, among the first three models, the third one is identified as the best specific model. The AIC value for the third model is the lowest at 1.918, it has a high coefficient of determination of 0.643, and the Durbin-Watson statistic suggests the absence of autocorrelation in the model's residuals. These indicators collectively demonstrate that third model provides the best balance of model fit, explanatory power, and assumption validity for understanding the environmental factors regulating the relative abundance of wild boar in Russia. The R\u003csup\u003e2\u003c/sup\u003e increases from 0.563 for GLMM_1 to 0.643 for GLMM_3, showing that third model explains a greater proportion of variance in the relative abundance of wild boar. This metric supports the superior explanatory power of model.\u003c/p\u003e\u003cp\u003eThe Durbin-Watson values slightly increase from 1.832 (GLMM_1) to 1.896 (GLMM_3). Since values closer to 2 suggest the Durbin-Watson values no autocorrelation in residuals, third model shows less autocorrelation than one mixed model, indicating better model reliability. The coefficient of Hausman increases modestly from 0.213 in GLMM_1 to 0.346 in GLMM_3. A higher value Hausman might suggest greater consistency of the model's assumptions, with three mixed model being more robust than the previous models.\u003c/p\u003e\u003cp\u003eHowever, the final mixed-effects model (GLMM_4), which incorporates all three factors, proved superior across all metrics. It achieved the highest explanatory power (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.832), the lowest AIC value (1,806), and its specification was confirmed by optimal Durbin-Watson and Hausman test results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study focused on identifying and analyzing the factors influencing the dynamics of wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e) population abundance across the territory of Russia. We aimed to understand how geographical and environmental conditions, as well as the survey methods applied to wild boar in different regions of the country, affect the population size. The research was based on assessing the impact of various environmental factors that characterize different geographical habitats, which vary in terms of natural and climatic conditions (Bosch et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs a result of the modeling, it was found that the population dynamics of wild boar, estimated using traditional methods applied in Russian hunting farms\u0026mdash;such as snow-track counting\u0026mdash;can be explained by the influence of a complex set of geographical and environmental factors.\u003c/p\u003e\u003cp\u003eIn several other studies, environmental characteristics are considered as a key factor influencing the distribution and abundance of wild animals within their habitats (Grebner et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Teixeira-Santos et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe results obtained from the work of mixed models with random effects indicate a comprehensive representation of the influence of various factors on the number of wild boars. Four final models were obtained, in which the interdependencies between the number of wild boars and variable factors with a random effect were identified.\u003c/p\u003e\u003cp\u003eThe results obtained from the random-effects mixed models indicate a comprehensive representation of the influence of various factors on wild boar abundance. Four mixed regression models were developed, in which interdependencies between wild boar abundance and random-effect variables were identified.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHunting farms.\u003c/em\u003e Within the scope of this study, an analysis was conducted to determine the significance of the area of hunting grounds and the intensity of wild boar harvesting in relation to population dynamics. Specifically, large hunting grounds with high hunting intensity were found to exhibit positive dynamics in reproduction and recovery of wild boar populations. This phenomenon can be explained by several factors (Lee and Park, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFirst, larger hunting farms typically provide more favorable conditions that support the natural reproduction and growth of wild boar populations. These conditions include the availability of diverse forage resources, shelter, and breeding zones (Ghandri et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vajas et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vajas et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, on extensive agricultural lands, hunters and biologists can apply more effective monitoring methods, such as remote sensing, unmanned aerial vehicles (UAVs), and modern geographic information systems (GIS) (Quamar et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rietz et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The use of UAVs equipped with multispectral cameras and GIS platforms with integrated spatial analysis tools significantly enhances the efficiency of ecosystem monitoring and wildlife management (Nath Tripathi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, it can be concluded that the size of the hunting economy and the level of production of wild boars represent factors that determine the change in the relative abundance of wild boar, visibility and accounting of animals.\u003c/p\u003e\u003cp\u003eDifferences in methodological approaches to wild boar monitoring in the hunting farms of model regions in the Russian Federation influenced the modeling results. Specifically, in models where hunting farms using the drive method were included as a random factor, the seasonal period, expressed in months of the year, was identified as a significant predictor. This trend can be explained by the increased activity of wild boar during the spring, which in turn enhances their visibility to researchers.\u003c/p\u003e\u003cp\u003e\u003cem\u003eEcological factors.\u003c/em\u003e Characterization of the environment in wild boar habitats plays a key role in their distribution and abundance. Environmental characteristics of wild boar (Sus scrofa) habitats play a key role in their distribution and abundance. Numerous studies emphasize that vegetation-related factors are particularly important for the survival and reproduction of the species. Vegetation provides wild boar with essential resources such as food and shelter, and creates conditions that enhance their protection from predators and adverse weather. Wild boar shows significant ecological plasticity, as confirmed by numerous studies (Saez-Royuela и Telleria, 1986; Taylor et al., 1998; Brogi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This adaptability allows the species to effectively utilize a wide range of habitats and resources, which likely explains the strong influence of environmental factors observed in our models. The abundance and diversity of plant species shape suitable habitats for wild boar, which is critical for maintaining stable populations. Changes in vegetation cover due to human activity or climatic factors can significantly affect ecological niches, which ultimately affects the number and distribution of wild boars. Based on previous ecological studies of the species, our results revealed a positive correlation between wild boar abundance and the proportion of mixed and deciduous forests in the landscape (Acevedo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; 2009; Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zakharova and Liskova, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our mixed models, vegetation was identified as a key landscape characteristic influencing wild boar habitat. In mixed forests, where both coniferous and deciduous trees grow, all necessary resources for wild boar are available \u0026mdash; food and shelter. Such plant biotopes help wild boars maintain a stable population. Such biotopes support the maintenance of stable population levels. Deciduous forests also play an important role in the life of wild boar (Barrios-Garcia and Ballari, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring the survey season, wild boar abundance was also associated with territories located at lower or moderate elevations above sea level. This observation is likely due to several factors. First, the absence of snow cover in these regions during winter provides easier access to food resources. Second, these areas offer favorable conditions for reproduction (Fern\u0026aacute;ndez-Llario, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Acevedo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThus, the results of our study on the factors regulating wild boar population abundance in hunting farms of model regions in Russia indicate that ecological features and the adaptive capacity of wild boar play a central role in their distribution and abundance across different biotopes.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAfrican swine fever outbreaks.\u003c/em\u003e One of the most significant factors that change the relative size of the wild boar population is the incidence of infectious and parasitic diseases and the natural death of animals. Among these, African swine fever stands out as a particularly devastating disease for wild boar. ASF outbreaks pose a serious threat to wild boar populations due to the high contagiousness of the virus, which can spread rapidly among animals (Thanapongtharm et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Disease ASF are a serious threat to wild boar populations because the virus that causes the disease is highly contagious and can spread rapidly among animals (Zakharova et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ceruti et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePopulation regulation in response to ASF outbreaks has become an essential component of wild boar management (Nielsen et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jota Baptista et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Veterinarians and hunting farm managers are often required to implement control and culling measures both within and around the affected areas to prevent the further spread of the virus. These measures may include population reduction, monitoring of infected individuals, and sanitary interventions aimed at limiting the transmission of the disease. As a result of these ASF prevention and control measures, significant changes in the relative abundance of wild boar have been observed, particularly in epizootic foci and adjacent territories. In such contexts, wild boar populations may experience substantial declines, which can lead to ecological imbalances and affect other species and vegetation. Therefore, ASF outbreaks have a complex impact on wild boar populations and require the development of effective strategies for their protection and regulation (Reichold et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Palencia et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing a random-effect mixed regression model allowed for variation arising from differences in data collection methods and for more accurate estimates of the influence of factors. The study highlighted the importance of taking into account various methodological and environmental factors when analyzing the number of wild boars, as well as the peculiarity of the links between factors related to both the habitat and the impact of diseases on the population. The patterns identified may be useful for further activities to manage wildlife populations and prevent the spread of disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors contribuition\u0026nbsp;\u003c/strong\u003eOlga I. Zakharova: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Writing - Original Draft, Writing - Review \u0026amp; Editing, Visualization. Fedor I. Korennoy: Methodology, Validation, Investigation, Resources, Writing - Original Draft. Elena A. Liskova: Investigation, Writing - Original Draft. Nadezhda A. Gladkova: Investigation, Resources, Writing - Review \u0026amp; Editing. Ivan V. Jashin: Supervision, Project Administration, Funding Acquisition, Writing - Original Draft. Andrey A. Blokhin: Investigation, Resources, Data Curation, Writing - Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study was performed with the support of the Federal Research Center for Virology and Microbiology\u003cstrong\u003e.\u003c/strong\u003e This research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u0026nbsp;The data presented in this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e The authors have no conflicts of interest to de clare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcevedo, P., Aleksovski, V., Apollonio, M., Berdi\u0026oacute;n, O., Blanco-Aguiar, J., del Rio, L., Ert\u0026uuml;rk, A., Fajdiga, L., Escribano, F., Ferroglio, E., Gruychev, G., Guti\u0026eacute;rrez, I., H\u0026auml;berlein, V., Hoxha, B., Kavcic, K., Keuling, O., Mart\u0026iacute;nez-Carrasco, C., Palencia, P., Pereira, P., Plhal, R., Plis, K., Podg\u0026oacute;rski, T., Ruiz, C., Scandura, M., Santos, J., Sereno, J., Sergeyev, A., Shakun, V., Soriguer, R., Soyumert, A., Sprem, N., Stoyanov, S., Smith, G., Traj\u0026ccedil;e, A., Urbani, N., Zanet, S., Vicente, J., 2022. 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Dis. 2023, 5517000. https://doi.org/10.1155/2023/5517000\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;AEZ-ROYUELA, C., TELLER\u0026Iacute;IA, J.L., 1986. The increased population of the Wild Boar (Sus scrofa L.) in Europe. Mamm. Rev. 16, 97\u0026ndash;101. https://doi.org/10.1111/j.1365-2907.1986.tb00027.x\u003c/li\u003e\n\u003cli\u003eSantini, L., Ben\u0026iacute;tez-L\u0026oacute;pez, A., Dormann, C.F., Huijbregts, M.A.J., 2022. Population density estimates for terrestrial mammal species. Glob. Ecol. Biogeogr. 31, 978\u0026ndash;994. https://doi.org/10.1111/GEB.13476;SUBPAGE:STRING:FULL\u003c/li\u003e\n\u003cli\u003eSarasa, M., Sarasa, J.A., 2013. Intensive monitoring suggests population oscillations and migration in wild boar Sus scrofa in the Pyrenees. Anim. Biodivers. Conserv. 36, 79\u0026ndash;88. https://doi.org/10.32800/abc.2013.36.0079\u003c/li\u003e\n\u003cli\u003eStephens, P.A., Zaumyslova, O.Y., Miquelle, D.G., Myslenkov, A.I., Hayward, G.D., 2006. 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PLoS One 15, e0229459. https://doi.org/10.1371/JOURNAL.PONE.0229459\u003c/li\u003e\n\u003cli\u003eThanapongtharm, W., Wiratsudakul, A., Gilbert, M., Chamsai, T., Pabutta, C., Wiriyarat, W., Oh, Y., Jayme, S., Songsaeng, N., Maneekan, K., Yano, T., Suwanpakdee, S., 2025. Spatial prediction of wild boar distribution in Thailand applications for African swine fever prevention and control. Sci. Rep. 15, 1\u0026ndash;12. https://doi.org/10.1038/S41598-025-94922-1;SUBJMETA=158,2514,255,631,692,699;KWRD=ECOLOGY,VIRAL+INFECTION\u003c/li\u003e\n\u003cli\u003eTurner, P., 2020. Critical values for the Durbin-Watson test in large samples. Appl. Econ. Lett. 27, 1495\u0026ndash;1499. https://doi.org/10.1080/13504851.2019.1691711\u003c/li\u003e\n\u003cli\u003eVajas, P., Calenge, C., Richard, E., Fattebert, J., Rousset, C., Sa\u0026iuml;d, S., Baubet, E., 2020. Many, large and early: Hunting pressure on wild boar relates to simple metrics of hunting effort. Sci. 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Evol. 14, e70747. https://doi.org/10.1002/ECE3.70747\u003c/li\u003e\n\u003cli\u003eWelander, J., 2000. Spatial and temporal dynamics of wild boar (Sus scrofa) rooting in a mosaic landscape. J. Zool. 252, 263\u0026ndash;271. https://doi.org/10.1111/J.1469-7998.2000.TB00621.X\u003c/li\u003e\n\u003cli\u003eYang, G., Peng, C., Yang, X., Guo, Q., Su, H., 2024. Habitat suitability and crop damage risk caused by wild boar in Guizhou Plateau, China. J. Wildl. Manage. 88, e22542. https://doi.org/10.1002/JWMG.22542;PAGE:STRING:ARTICLE/CHAPTER\u003c/li\u003e\n\u003cli\u003eZakharova, O.I., Liskova, E.A., 2025. Risk Factors for African Swine Fever in Wild Boar in Russia: Application of Regression for Classification Algorithms. Anim. 2025, Vol. 15, Page 510 15, 510. https://doi.org/10.3390/ANI15040510\u003c/li\u003e\n\u003cli\u003eZakharova, O.I., Titov, I.A., Gogin, A.E., Sevskikh, T.A., Korennoy, F.I., Kolbasov, D. V., Abrahamyan, L., Blokhin, A.A., 2021. African Swine Fever in the Russian Far East (2019\u0026ndash;2020): Spatio-Temporal Analysis and Implications for Wild Ungulates. Front. Vet. Sci. 8, 1\u0026ndash;13. https://doi.org/10.3389/fvets.2021.723081\u003c/li\u003e\n\u003cli\u003eZancanaro, G., Antoniou, S.E., Bedriova, M., Boelaert, F., Rojas, J.G., Monguidi, M., Roberts, H., Nielsen, S.S., Thulke, H.H., 2019. SIGMA Animal Disease Data Model: A comprehensive approach for the collection of standardised data on animal diseases. EFSA J. 17. https://doi.org/10.2903/J.EFSA.2019.5556\u003c/li\u003e\n\u003cli\u003eZharkov, I.V., Teplov, V.P., 1958. Instructions for the quantitative accounting of game animals in large areas.\u003c/li\u003e\n\u003cli\u003eMethodology for recording the number of hunting resources using the winter route survey method \u0026mdash; Ministry of Natural Resources of Russia, n.d. URLhttps://www.mnr.gov.ru/docs/ metodicheskie_dokumenty/metodika_ucheta_chislennosti_okhotnichikh_resursov_metodom_zimnego_marshrutnogo_ucheta_2023/ (accessed 5.19.25).\u003c/li\u003e\n\u003cli\u003eThe Russian Ministry of Natural Resources has developed four methods for recording the number of hunting resources \u0026mdash; Ministry of Natural Resources of Russia. URL https://www.mnr.gov.ru/press/news/minprirody_rossii_razrabotany_chetyre_metodiki_ucheta_chislennosti_okhotnichikh_resursov/?sphrase_id=1165108 (accessed 5.19.25).\u003c/li\u003e\n\u003cli\u003eStructure and contacts of the Ministry of Natural Resources and Environment of the Russian Federation \u0026mdash; Ministry of Natural Resources of Russia. URLhttps://www.mnr.gov.ru/about/ (accessed 5.9.25).\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Federal subject is a first-level administrative division of the Russian Federation\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Federal Research Center for Virology and Microbiology","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"accounting methods, environmental factors, regression models, Russia, Sus scrofa","lastPublishedDoi":"10.21203/rs.3.rs-7485754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7485754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe examination of wild boar population dynamics plays a crucial role in enhancing our understanding of ecosystem functions and their ability to adapt to changing environmental conditions. Various habitat factors and interactions with other species animals significantly influence the abundance of wild boar populations. In this study, we applied predictive spatial models with a random effect to understand how environmental factors and the frequency of African swine fever outbreaks affect the relative abundance of the wild boar population. We considered different geographical conditions and methods of accounting abundance for animals in hunting farms of subjects of Russia. The number of the wild boar in model region was estimated using two methods, namely, using the method of accounting for traces in snow and noise running of animals. To assess the significance of each factor in the models and their interactions, we used a variation partition method. Our research revealed a close relationship between wild boar numbers and environmental parameters, including snow cover height and vegetation percentage. In addition, a correlation was found between the number of wild boar and the incidence rate of African swine fever virus outbreaks among these animals. However, this relationship was not as strong as the impact of environmental conditions. The integrated model used in this study demonstrated the significance of the environmental drivers considered for dynamic wild boar population abundance across various geographical conditions. This is crucial for developing wildlife management strategies, especially for wild boar, to prevent the spread of infectious diseases.\u003c/p\u003e","manuscriptTitle":"Environmental drivers influencing the relative abundance of wild boar population in Russia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 07:19:01","doi":"10.21203/rs.3.rs-7485754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7eada02d-9974-4019-a407-14f3bbe42a8e","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53882075,"name":"Ecological Modeling"},{"id":53882076,"name":"Animal Science"},{"id":53882077,"name":"Population Biology"}],"tags":[],"updatedAt":"2025-09-01T07:19:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 07:19:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7485754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7485754","identity":"rs-7485754","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}