African swine fever (ASF) in wild boars (Sus scrofa) poses a significant challenge in Southeast Asia. The transmission of ASF from domestic pigs to wild boars in Southeast Asia highlights the importance of preventing disease transmission in the interface between humans, domestic animals, and wildlife10. Areas with high likelihood of wild boar presence are concentrated in Thailand’s western and northern regions, indicating the fertility of these regions. This suggests that surveillance for ASF in wild boars along the Thailand-Myanmar border is crucial.
In previous studies, wild boar distribution was estimated for managing ASF and conservation purposes using various techniques, such as a generalized linear model21, habitat quality assessment22, logistic regression, and multi-model inference23, maximum entropy (Maxent) models24. Although multiple methods are available for predicting wild boar habitat, we utilized the Random Forest (RF) algorithm to forecast wild boar distribution and abundance across Thailand. This technique stands out for its high accuracy, robustness, ability to identify crucial features, adaptability, and scalability. Its effectiveness lies in addressing overfitting by aggregating multiple decision trees, thereby reducing susceptibility to noise and outliers within the dataset25. This approach not only streamlines the prediction process but also minimizes the time and costs associated with relying solely on expert opinions. Our model is specifically tailored for Southeast Asian countries, aiming to predict wild boar habitats in these regions for enhanced ASF surveillance. This technique was a pilot project that can be easily applied across the region, and we have compiled data sources containing essential geographic and demographic information accessible to each country. Examples include GlobCover for land cover data, water bodies, forests, and both rain-fed and irrigated croplands, elevation data, and human population data. The choice of these factors was based on findings from previous studies that identified them as influential in wild boar habitat determination3,22,23, serving as the foundation for our model.
Our findings emphasize the significance of forested areas and elevation as pivotal factors in detecting wild boar populations. Those were consistent with the previous studies, in which these animals were more prevalent in forested areas3,22 and highlands26. The relationship between the distance to water bodies and the detection of wild boars revealed an initially positive association that decreases beyond a certain point. Wild boars tended to avoid areas too close to water bodies, perhaps due to disturbances from humans or predators27. However, detection decreases when the distance becomes too far; it might limit access to water and reduce their presence. The graph in (Fig. 2f) indicates an optimal distance where wild boars balance, minimizing disturbances while maintaining easy access to water resources. It was different from the previous studies in that wild boar could be detected to decrease when the distance to water increased23. The moderate level of rained cropland was the most incredible regarding wild boar density, consistent with the results of the agriculture area in the previous study in which a relatively high level of rained cropland led to a low possibility of detecting the wild boar21. Additionally, irrigated cropland built by humans and human population density had negative associations in detecting wild boar populations, as shown by our fitted function model (Fig. 2). This might explain why wild boars tend to avoid areas with high human disturbances27. Additional variables may include identifying habitat associations of wild boars, subject to data availability, including seasonal patterns and carnivore diversity21.
The count and binary models showed more accurate predictions than the combination model. This indicates that the count and binary models are suitable for RF wild boar habitat prediction. We estimated the wild boar population using the Smart Patrol System, unlike other techniques that used previously recorded wild boar density data21. However, population density can be calculated based on our estimation method using the wild boar count for each grid area as required. The RF estimation of wild boar population showed that there was a slight variation of approximately 13% in the number of wild boars within a 10-kilometer radius and significant interface areas between wild boar populations and domestic pig farms were identified in the northwest of Thailand, including Chiangmai, Mae Hong Son, and Tak provinces, while Nan province, on the opposite side of Thailand, borders Laos PDR (Fig. 3; Table 4). It emphasizes that the ASF is not only a potential source of transboundary diseases in the Thailand borders but also poses a risk of disease transmission between domestic pigs and wild boars, particularly in areas with backyard pigs, small-holder pig farms, and commercial farms10. Furthermore, high interface areas between wild boar habitats and domestic pig farms are found in north-central Thailand, particularly in Nakhon Sawan, Kamphaeng Phet, Phitsanulok, and the Southern region, especially Nakhon Si Thammarat. Those results are related to the numerous domestic pig farms with wild boar sightings.
In Thailand, measures to prevent and control African Swine Fever (ASF) include effective monitoring systems for both domestic pigs and wild boars. This surveillance enables the quick detection of outbreaks, allowing for prompt action. Collaborative efforts with neighboring countries, particularly Laos PDR and Vietnam, focus on preventing and controlling ASF in border regions. Risk assessments have been conducted on pig farms to evaluate the risk of ASF transmission28. Biosecurity measures have been implemented, such as quarantining and controlling the movement of animals, decontamination of infected animals, and managing vectors. Farmers are also encouraged to prevent direct contact between domestic pigs and wild boars by taking proactive steps, such as building fences around their farms29.
Additionally, the abundance of wild boar populations indicates biodiversity richness in the area, potentially influencing the presence of other species, such as tigers24. Notably, the frequency of wild boar population surveys may vary in certain regions, depending on the monitoring activities conducted by wildlife rangers using the Smart Patrol System. Although this presents a limitation, our RF model focuses on binary data indicating the presence or absence of wild boar in these areas, demonstrating the model’s relatively high performance.
ASFV infection rates in wild boars in Southeast Asia may be underestimated. The accuracy of estimates depends on each country’s surveillance system, especially data from the Smart Patrol System. When there is a lack of information from the Smart Patrol System, we turn to alternative sources. Data on wild boar populations and ASF detection from the National Wildlife Health System (piloted in Cambodia, Lao PDR, and Vietnam), involving protected area rangers, wildlife rescue centers, community members, livestock and human health personnel, and laboratories, can be integrated into our model10. This collaborative surveillance initiative provides a valuable supplementary data stream for refining and enhancing the accuracy of our predictions. However, other species of wild boars, besides Sus scrofa, are found on islands such as the Philippines and Indonesia; a dataset covering different geographic areas may be necessary to estimate wild boar populations.
Although we used a training set to develop the models and a test set to evaluate them, camera traps placed in areas predicted to have high wild boar abundance will confirm the presence of wild boars in areas inhabited by local communities and highlight the likelihood of encounters between wild boars and humans. Our study focused on wild boar information from non-protected areas, where concerns arise regarding interactions between humans, domestic animals, and wildlife in local communities.
One limitation of our data was that the officer conducting the information collection on the Smart Patrol System had difficulty identifying individual wild boars and confirming the uniqueness of each animal. Additionally, the frequency and duration of wild boar surveys on the trails varied by team and did not follow a consistent pattern across the entire country. It might lead to duplicate counting of the number of wild boars. We then used the observed data of live wild boars, footprints, or dung as evidence of the presence of wild boars in those grids (1 km resolution) to model the probability of wild boar distribution using a binary RF and only the observed data (count data) of live wild boars to model the number of wild boars using a quantitative RF. Therefore, the possibility of duplicate counts was relatively low. However, this publication is the first in Southeast Asia to estimate the number of wild boars. Therefore, a well-planned approach to data collection is essential for obtaining more accurate information for future studies.
We expect that our data and techniques would also be relevant and useful to other Southeast Asian countries for conducting comparable studies. Observational data and datasets from the Smart Patrol System of the Department of National Parks, Wildlife and Plant Conservation (DNP) will help validate predictions made using spatial analysis and modeling techniques to estimate the distribution of free-ranging wild boars. The study will also provide information on habitat overlap between domestic pigs and wild boars to mitigate the risk of ASFV transmission, particularly in areas adjacent to forests or bordering countries that have reported ASF outbreaks. Findings can support the strategic planning and risk mitigation for ASF infection in free-ranging wild boars and domestic pigs while providing information for the surveillance of ASF in Thailand and Southeast Asia.
