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International Journal for Parasitology: Parasites and Wildlife logoLink to International Journal for Parasitology: Parasites and Wildlife
. 2026 Sep 17;31:101285. doi: 10.1016/j.ijppaw.2026.101285

Nationwide seroprevalence and spatial distribution of Toxoplasma gondii in wild boar (Sus scrofa) in South Korea, 2022–2025

Garam Kim 1,⁎, So-Jeong Kim 1, Beongchul Shin 1, Seungmin Lim 1, Weon-Hwa Jheong 1,⁎⁎
PMCID: PMC13639601  PMID: 42840683

Abstract

Toxoplasma gondii is a widespread zoonotic parasite that can infect nearly all warm-blooded vertebrates. Wild boars (Sus scrofa) are valuable indicator hosts because of their broad habitat use, omnivorous feeding behavior, and frequent contact with both natural and human-modified environments. However, comprehensive nationwide data on T. gondii exposure in wildlife in South Korea remain limited. This study aimed to investigate the seroprevalence, associated risk factors, and spatial distribution of T. gondii exposure in wild boars in South Korea using nationwide surveillance samples collected between 2022 and 2025. Seroprevalence remained consistently high, ranging from 61.7% in 2022 to 70.0% in 2025. Body weight was the strongest independent predictor of seropositivity, with higher body weights associated with greater odds of seropositivity. At the regional level, metropolitan areas generally had higher odds of seropositivity than non-metropolitan regions. Spatial analysis identified hotspots of high seroprevalence in central and southern South Korea. Local spatial autocorrelation analysis showed that high–high seroprevalence clusters were significantly associated with high densities of both cat and wild boar activity, with a stronger association for wild boar activity. This study provides nationwide baseline data on T. gondii seroprevalence in wild boars in South Korea and supports their use as sentinel hosts for wildlife toxoplasmosis surveillance within a One Health framework.

Keywords: Seroprevalence, Toxoplasma gondii, Wild boar, Zoonosis

Graphical abstract

graphic file with name ga1.webp

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Highlights

  • •

    Overall T. gondii seroprevalence was 65.9% (1314/1993) among wild boars sampled nationwide.

  • •

    Seroprevalence remained consistently high in South Korea from 2022 to 2025.

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    Spatial hotspot analysis identified heterogeneous clustering of seroprevalence.

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    Host activity and landscape composition were associated with high–high (HH) seroprevalence clusters.

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    Forest, cropland, and built-up areas were associated with spatial hotspots.

1. Introduction

Toxoplasma gondii is an obligate intracellular protozoan parasite and one of the most widespread zoonotic pathogens globally, capable of infecting nearly all warm-blooded vertebrates (Dubey, 2008; Tenter et al., 2000). Infection with T. gondii causes toxoplasmosis, which can result in severe disease in immunocompromised individuals and congenital disease following maternal infection during pregnancy (Pappas et al., 2009). In livestock, infections can cause reproductive disorders and associated economic losses (Stelzer et al., 2019). As transmission occurs at the interface of humans, domestic animals, wildlife, and the environment, toxoplasmosis is an important public health concern (Innes et al., 2019). A recent systematic review estimated that approximately one-third of the global human population has been exposed to T. gondii, although seroprevalence varies among regions based on climate, dietary practices, hygiene, and environmental contamination (Molan et al., 2019). Although human and livestock toxoplasmosis have been extensively studied, data on wildlife remain geographically uneven, with most studies conducted in Europe and the Americas (Molan et al., 2019; Ferroglio et al., 2014; Gauss et al., 2005), whereas substantial knowledge gaps persist in Asia.

Felids, including domestic and wild species, are essential to the life cycle of T. gondii as they are the only definitive hosts in which sexual reproduction occurs and environmentally resistant oocysts are shed in feces (Dubey, 2008; Augusto et al., 2021). After excretion, oocysts sporulate under suitable environmental conditions and become infective (Dubey, 1998; Hutchison et al., 1969). Sporulated oocysts can persist in soil, water, and vegetation for prolonged periods and serve as an important source of infection for various intermediate hosts (Jones and Dubey, 2010). Following ingestion, the parasite disseminates as tachyzoites and subsequently forms persistent tissue cysts in various organs. These cysts can be transmitted to other hosts through predation, scavenging, or consumption of raw or undercooked meat, thereby maintaining transmission within natural ecosystems (Dubey, 1998; Hutchison et al., 1969). Wildlife are frequently exposed to soil, water, and vegetation in their natural habitats, resulting in continuous exposure to potential sources of environmental contamination, including infectious oocysts and tissue cysts. Consequently, they can serve as useful indicators of T. gondii transmission (Ferroglio et al., 2014).

Serological surveys of wildlife provide useful evidence of exposure to T. gondii, even when active infection is not directly detected (Hill and Dubey, 2002; Montoya and Rosso, 2005). As antibodies reflect previous exposure to the parasite, wildlife seroprevalence serves as an indirect indicator of T. gondii circulation within ecosystems (González-Barrio et al., 2024; Bokaba et al., 2024). Therefore, wildlife serology is valuable for assessing spatial and temporal exposure patterns and identifying areas where environmental transmission may occur. In particular, wild boars (Sus scrofa) are considered suitable indicator hosts because of their wide distribution, omnivorous feeding behavior, and frequent contact with soil (Massei & Genov, 2004; Rostami et al., 2017). Previous studies have reported substantial T. gondii seroprevalence in wild boar populations, supporting their epidemiological value in wildlife surveillance (Gauss et al., 2005; Rostami et al., 2017; Matsumoto et al., 2011).

Despite growing recognition of wildlife in T. gondii epidemiology, nationwide data on exposure patterns in wild boars in South Korea remain limited. A previous survey using opportunistically collected samples from 2017 to 2020 reported an overall seroprevalence of 34.9% in Korean wild boars (Hwang et al., 2024); however, it used a non-standardized sampling design and covered a relatively short period. Since 2022, the national African Swine Fever (ASF) surveillance program has enabled large-scale and standardized sampling of wild boars across South Korea, providing an opportunity to investigate T. gondii exposure at the national level. Therefore, this study estimated T. gondii seroprevalence in wild boars in South Korea and identified temporal, spatial, and host-related factors associated with seropositivity. We also performed spatial clustering to explore ecological patterns of high-risk areas and their association with wild boar and cat population.

2. Materials and methods

2.1. Study design and sample selection

Although wild boar samples have been collected as part of ASF surveillance since 2019, comprehensive nationwide surveillance with standardized sampling coverage was not fully implemented until 2022. Blood samples were collected from captured wild boars through the National Wild Boar Surveillance Program. Blood samples were centrifuged at 3000 rpm for 10 min to separate serum, which was stored at −80°C until serological analysis. Accordingly, this study included samples collected from 2022 through 2025, when nationwide coverage was sufficient. Serum samples were randomly selected from the available samples for each year. We included at least 450 samples each year to ensure adequate precision for annual seroprevalence estimates and to enable comparisons across years, seasons, and regions.

2.2. Serological testing

Serum samples were tested for antibodies against T. gondii using a commercial enzyme-linked immunosorbent assay (ELISA) kit (Innovative Diagnostics, Grabels, France) according to the manufacturer's instructions. Samples were classified as seropositive or seronegative according to the manufacturer's recommended cutoff.

2.3. Spatial distribution and serological mapping from 2022 to 2025

Sampling locations for wild boars collected between 2022 and 2025 were mapped using ArcGIS Pro (version 3.2; Esri, Redlands, CA, USA) and QGIS (version 3.4). The spatial distribution of sampled animals was visualized using the geographic coordinates of individual sampling locations. Serological grouping was based on T. gondii antibody status (seropositive vs seronegative), and these groups were then overlaid on the spatial dataset for visualization.

2.4. Statistical analysis

Univariate and multivariate logistic regression analyses were performed to identify factors associated with T. gondii seropositivity. Explanatory variables included sex, body weight, year, season, and region. All variables assessed in the univariate analysis were included in the multivariate model based on their biological and epidemiological relevance. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Statistical significance was set at p < 0.05.

2.5. Hotspot analysis

To identify spatial clusters of high and low T. gondii seroprevalence among wild boars, we conducted hotspot analysis using the Getis–Ord Gi* statistic in R version 4.5.3 (R Foundation for Statistical Computing, Vienna, Austria). The study area was divided into 5 × 5 km grid cells, and seroprevalence for each grid was calculated as the proportion of seropositive animals among all animals tested within that cell. A spatial weight matrix was constructed based on neighboring grid cells, and the Getis–Ord Gi* statistic was used to identify significant hotspots (high-value clusters) and cold spots (low-value clusters). Grid cells were classified into five categories according to their z-scores and associated significance levels: very high (p < 0.01, strong hotspot), high (p < 0.05, hotspot), moderate (non-significant), low (p < 0.05, cold spot), and very low (p < 0.01, strong cold spot). All spatial analyses were performed using the sf and spdep packages in R.

2.6. Spatial environmental association analysis

Local Moran's I analysis was conducted using grid-level T. gondii seroprevalence to identify local spatial autocorrelation patterns. Seroprevalence was calculated for each 5 × 5 km grid cell as the proportion of seropositive wild boars among all animals tested within the cell. High–high (HH) clusters represented cells with high seroprevalence surrounded by neighboring cells with similarly high seroprevalence, whereas low–low (LL) clusters represented cells with low seroprevalence surrounded by neighboring cells with similarly low seroprevalence. High–low (HL) and low–high (LH) clusters represented spatial outliers. To investigate the association between spatial clusters and ecological factors, the frequencies of wild boar and cat traces were compared between HH clusters and non-HH areas. Binary variables indicating the presence or absence of cat and wild boar traces and continuous variables representing their trace counts were used as explanatory variables. These data were obtained from the 5th National Ecosystem Survey, a nationwide ecological survey that documents the geographic distribution of wildlife traces through standardized field investigations (National Institute of Ecology, 2024). The analysis included all grid cells with available relevant data. Differences in trace presence between HH and non-HH areas were assessed using Pearson's chi-square test. Logistic regression analysis was then used to evaluate the independent effects of trace variables on cluster formation, with HH cluster membership (HH vs non-HH) as the dependent variable. Separate presence- and count-based models were fitted using a binomial generalized linear model. ORs and 95% CIs were calculated to quantify association strength. To assess whether landscape composition was associated with HH cluster membership, we obtained land-cover data from the ESA WorldCover 10 m 2021 v200 dataset (Zanaga et al., 2022). The original 10-m resolution raster was aggregated to approximately 100-m resolution using modal aggregation. Three land-cover categories—tree cover (class 10), cropland (class 40), and built-up areas (class 50) were extracted, and the proportion of each land-cover type within every 5 × 5 km grid cell was calculated.

3. Results

3.1. Spatiotemporal distribution of T. gondii seroprevalence (2022–2025)

Seropositive wild boars were detected throughout South Korea, with higher concentrations in metropolitan areas and the central inland and southern regions (Fig. 1a). Nationwide seroprevalence varied moderately across years (Fig. 1b), increasing from 2022 (61.7%) to 2023 (67.5%), decreasing slightly in 2024 (64.6%), and peaking in 2025 (70.0%). Overall, seroprevalence remained high throughout the study period. Seroprevalence also varied geographically over the study period (Fig. 1c). Metropolitan areas had the highest seroprevalence (74.1%), followed by Gangwon (71.8%), Jeolla (66.4%), and Gyeongsang (63.9%). In contrast, Chungcheong recorded the lowest seroprevalence (60.0%).

Fig. 1.

Fig. 1

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Spatiotemporal distribution and seroprevalence of Toxoplasma gondii in wild boars in South Korea from 2022 to 2025.

(a) Geographic distribution of seropositive (red dots) and seronegative (sky-blue dots) wild boar samples by year. (b) Annual seroprevalence (%) of T. gondii in wild boars from 2022 to 2025. Values above data points indicate yearly seroprevalence percentages. (c) Regional seroprevalence (%) of T. gondii in wild boars across five geographic regions: Metropolitan areas (Seoul, Incheon, Gyeonggi), Gangwon, Chungcheong (Chungcheongbuk-do and Chungcheongnam-do), Jeolla (Jeollabuk-do and Jeollanam-do), and Gyeongsang (Gyeongsangbuk-do and Gyeongsangnam-do). Bars represent the proportion of seropositive animals in each region.

3.2. Univariate analysis of risk factors

Univariate logistic regression analysis identified several factors associated with T. gondii seropositivity (Table 1). Body weight showed the strongest association, with the odds of seropositivity increasing progressively with increasing body weight. Compared with the reference category (<20 kg), the crude ORs increased progressively from 2.30 (95% CI: 1.66–3.18) in 20–60 kg animals to 7.61 (95% CI: 5.00–11.56) in 100–300 kg animals (all p < 0.001). Associations with year varied. Seropositivity did not differ significantly between 2022 and 2023 (p = 0.064), whereas the odds of seropositivity were higher in 2025 than in 2022 (crude OR = 1.45, 95% CI: 1.10–1.90, p = 0.008). Seasonal analysis revealed lower odds of seropositivity during summer (crude OR = 0.54, 95% CI: 0.40–0.72, p < 0.001), autumn (0.54, 95% CI: 0.41–0.72, p < 0.001), and winter (0.71, 95% CI: 0.53–0.96, p = 0.029) than in spring. Geographic variation was also evident, with lower odds of seropositivity observed in Chungcheong (crude OR = 0.52, 95% CI: 0.38–0.72, p < 0.001), Jeolla (0.69, 95% CI: 0.50–0.96, p = 0.028), and Gyeongsang (0.62, 95% CI: 0.46–0.83, p = 0.001) than in the metropolitan area. No significant differences were observed by sex (p = 0.596) or between the metropolitan and Gangwon regions (p = 0.571).

Table 1.

Univariate analysis of factors associated with seropositivity during 2022–2025.

Variable Category No. tested No. positive (%) Crude OR (95% CI) p-value
Sexa Female 916 600 (65.5) – –
Male 1061 707 (66.6) 1.05 (0.87–1.27) 0.596
Body weight (kg) < 20 200 73 (36.5) – –
20–60 708 403 (56.9) 2.30 (1.66–3.18) < 0.001
60–100 589 455 (77.2) 5.91 (4.18–8.35) < 0.001
100–300 274 223 (81.4) 7.61 (5.00–11.56) < 0.001
Year 2022 454 280 (61.7) – –
2023 462 312 (67.5) 1.29 (0.99–1.70) 0.064
2024 591 382 (64.6) 1.14 (0.88–1.46) 0.325
2025 486 340 (70.0) 1.45 (1.10–1.90) 0.008
Season Spring 373 280 (75.1) – –
Summer 492 304 (61.8) 0.54 (0.40–0.72) < 0.001
Autumn 640 397 (62.0) 0.54 (0.41–0.72) < 0.001
Winter 488 333 (68.2) 0.71 (0.53–0.96) 0.029
Region Metropolitan 313 232 (74.1) –
Gangwon 188 135 (71.8) 0.89 (0.59–1.34) 0.571
Chungcheong 407 244 (60.0) 0.52 (0.38–0.72) < 0.001
Jeolla 381 253 (66.4) 0.69 (0.50–0.96) 0.028
Gyeongsang 704 450 (63.9) 0.62 (0.46–0.83) 0.001
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†Body weight data were unavailable for 222 individuals because body weight was not measured in the field for these animals, and were therefore excluded from the body-weight-specific analysis.

a

Sex data were unavailable for 16 individuals and were therefore excluded from the sex-specific analysis.

3.3. Multivariate logistic regression analysis

In the multivariate logistic regression model (Table 2), body weight remained the strongest independent predictor of T. gondii seropositivity, after adjustment for sex, year, season, and region. A clear positive association was observed, with progressively higher odds of seropositivity in heavier animals. The adjusted ORs were 2.36 (95% CI: 1.65–3.39) at 20–60 kg, 6.03 (95% CI: 4.18–8.77) at 60–100 kg, and 7.77 (95% CI: 5.00–12.24) at 100–300 kg (all p < 0.001).

Table 2.

Multivariate logistic regression analysis of factors associated with seropositivity during 2022–2025.

Variable Category Adjusted OR (95% CI) p-value
Sex Female – –
Male 1.03 (0.84–1.27) 0.766
Body weight (kg) < 20 – –
20–60 2.36 (1.65–3.39) < 0.001
60–100 6.03 (4.18–8.77) < 0.001
100–300 7.77 (5.00–12.24) < 0.001
Year 2022 –
2023 1.19 (0.88–1.61) 0.263
2024 0.98 (0.73–1.32) 0.893
2025 1.23 (0.90–1.69) 0.198
Continuous 1.04 (0.94–1.15) 0.461
Season Spring – –
Summer 0.79 (0.55–1.12) 0.183
Autumn 0.67 (0.48–0.92) 0.013
Winter 0.84 (0.60–1.18) 0.316
Region Metropolitan – –
Gangwon 0.81 (0.52–1.26) 0.341
Chungcheong 0.48 (0.33–0.69) < 0.001
Jeolla 0.65 (0.44–0.95) 0.027
Gyeongsang 0.54 (0.38–0.74) < 0.001
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Sex was not significantly associated with seropositivity (adjusted OR = 1.03, 95% CI: 0.84–1.27, p = 0.766). Among the seasons, autumn showed lower odds of seropositivity than spring (adjusted OR = 0.67, 95% CI: 0.48–0.92, p = 0.013), whereas summer and winter did not differ significantly from spring (p = 0.183 and p = 0.316, respectively).

Regional differences persisted after adjustment. Compared with metropolitan areas, the odds of seropositivity were lower in Chungcheong (adjusted OR = 0.48, 95% CI: 0.33–0.69, p < 0.001), Jeolla (adjusted OR = 0.65, 95% CI: 0.44–0.95, p = 0.027), and Gyeongsang (adjusted OR = 0.54, 95% CI: 0.38–0.74, p < 0.001), whereas Gangwon did not differ significantly from the metropolitan area (adjusted OR = 0.81, 95% CI: 0.52–1.26, p = 0.341).

Year was not significantly associated with seropositivity when modeled either categorically (2023, 2024, and 2025 vs 2022) or continuously (adjusted OR = 1.04, 95% CI: 0.94–1.15, p = 0.461). Overall, body weight showed the strongest and most consistent association with seropositivity, whereas some seasonal and regional differences remained partially significant after multivariate adjustment.

3.4. Spatial hotspot analysis of T. gondii seroprevalence in South Korea

The Getis–Ord Gi* statistic revealed a non-random spatial distribution of T. gondii seroprevalence in wild boars (Fig. 2). Hotspots of high seroprevalence were identified in multiple regions but were primarily concentrated in metropolitan areas, the central inland region, including Chungcheongbuk-do, and the southeastern inland region extending into parts of Gyeongsangnam-do. Additional hotspots were observed in parts of Chungcheongnam-do and Jeollanam-do. Several hotspot cells formed contiguous local clusters rather than isolated locations. Among the five z-score-based categories, statistically significant clustering occurred only in the high (hotspot) and very high (strong hotspot) categories; no cells were classified as significant cold spots (low or very low categories), indicating that low seroprevalence did not show significant spatial clustering.

Fig. 2.

Fig. 2

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Spatial hotspot analysis of Toxoplasma gondii seroprevalence in wild boars across South Korea using the Getis–Ord Gi* statistic. Orange grids represent significant hotspots (p < 0.05), whereas red grids indicate strong hotspots with higher statistical significance (p < 0.01). Uncolored grids correspond to the moderate, low, and very low categories, none of which exhibited statistically significant spatial clustering.

3.5. Local spatial autocorrelation of T. gondii seroprevalence and ecological associations with cat and wild boar traces

A significant spatial clustering of T. gondii seroprevalence was observed, with HH clusters primarily concentrated in the central and southern regions, forming large, contiguous areas that indicate positive spatial autocorrelation. No significant LL clusters were identified, suggesting that areas with low seroprevalence were dispersed rather than spatially aggregated. Conversely, HL and LH clusters occurred sporadically at the boundaries between high- and low-prevalence areas, likely representing transitional zones or localized spatial outliers. Both cat and wild boar traces showed significantly higher densities within HH clusters, with mean trace counts of 0.31 for cats (vs 0.20 in non-HH areas) and 2.46 for wild boars (vs 1.28 in non-HH areas), along with higher occurrence frequencies (cats: 23.0% vs 13.1%; wild boars: 73.0% vs 37.0%). Chi-square analysis confirmed significant associations between trace presence and HH clusters, with stronger effects observed for wild boars (χ2 = 216.08, p < 0.001) than for cats (χ2 = 32.02, p < 0.001).

Logistic regression using continuous variables showed that only wild boar trace counts were associated with HH cluster formation (OR = 1.07, 95% CI: 1.05–1.10, p < 0.001), whereas cat trace counts showed no association (p = 0.085). In logistic regression models using presence/absence variables, cat traces increased the odds of belonging to an HH cluster by 1.35-fold (95% CI: 1.05–1.72), whereas wild boar traces showed a stronger association (OR = 4.39, 95% CI: 3.52–5.51). These findings indicate that both cat and wild boar traces were associated with HH cluster formation, although the association was stronger for wild boar traces.

3.6. Associations of land-cover characteristics and host traces with HH cluster membership

To further evaluate environmental factors associated with HH cluster formation, land-cover characteristics were evaluated using multivariable logistic regression (Table 3). Forest, cropland, and built-up areas were each positively associated with HH cluster membership. The land-cover variables were expressed as percentages; therefore, the ORs represent changes in the odds associated with a 1-percentage-point increase in land-cover proportion. Each 1-percentage-point increase in forest cover was associated with a 4.5% increase in the odds of HH cluster membership (OR = 1.045, 95% CI: 1.036–1.054), while cropland (OR = 1.033, 95% CI: 1.022–1.044) and built-up areas (OR = 1.034, 95% CI: 1.021–1.048) were also positively associated with HH cluster membership. To determine whether host traces remained associated with HH cluster membership after accounting for landscape characteristics, we subsequently included cat and wild boar trace presence in the model. After adjustment for land-cover variables, both variables remained significantly associated with HH cluster formation. Grid cells containing cat traces had 1.40-fold higher odds of being classified as an HH cluster (95% CI: 1.09–1.79), whereas those containing wild boar traces had 1.72-fold higher odds (95% CI: 1.35–2.19). Adding host-trace variables significantly improved model fit compared with the land-cover-only model (likelihood ratio test, χ2 = 31.48, df = 2, p < 0.001).

Table 3.

Logistic regression analysis of land-cover characteristics and host traces associated with HH cluster membership.

Variable Model 1
Land cover only OR (95% CI)		 p-value Model 2
Land cover + host traces OR (95% CI)		 p-value
Forest(%) 1.045 (1.036–1.054) <0.001 1.040 (1.031–1.049) <0.001
Cropland (%) 1.033 (1.022–1.044) <0.001 1.031 (1.020–1.042) <0.001
Built-up (%) 1.034 (1.021–1.048) <0.001 1.033 (1.019–1.046) <0.001
Cat trace presence - - 1.40 (1.09–1.79) -
Wild boar trace presence - - 1.72 (1.35–2.19) -
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HH clusters were identified using Local Moran's I analysis. ORs for land-cover variables represent the change in odds of HH cluster membership associated with a 1-percentage-point increase in land-cover proportion. ORs for host-trace variables represent the odds of HH cluster membership for grid cells with trace presence relative to grids without trace presence. Model 2 significantly improved model fit compared with Model 1 (likelihood ratio test: χ2 = 31.48, df = 2, p < 0.001).

4. Discussion

The seroprevalence of T. gondii antibodies in wild boars in South Korea remained consistently high throughout the study period (Fig. 1). All serum samples in the present study were stored at −80°C until serological analysis. Although storage duration varied among sampling years, previous research has shown that anti-T. gondii immunoglobulins remain sufficiently stable in sera stored at −20°C for several years, with little effect on the interpretation of toxoplasmosis serology (Dard et al., 2017). Therefore, although differences in storage duration cannot be completely excluded, substantial loss of antibody reactivity during frozen storage is unlikely to explain the observed variation in annual seroprevalence. Furthermore, year was not significantly associated with seropositivity after adjustment for sex, body weight, season, and region, providing no evidence of a clear temporal trend during the study period. Overall seroprevalence was 65.9% (1314/1993), higher than estimates reported in neighboring countries, including China and Japan (Zhu et al., 2026; Kobayashi et al., 2021). It was also markedly higher than the 34.9% reported in an earlier Korean survey based on opportunistically collected samples from 2017 to 2020 (Hwang et al., 2024). This difference potentially reflects differences in sampling design, particularly the standardized nationwide ASF surveillance used in the present study compared with opportunistic hunting-based sampling in the earlier study, as well as a possible increase in environmental T. gondii circulation over time. Therefore, direct comparisons among studies should be interpreted cautiously. The elevated seroprevalence observed in Korean wild boars may reflect differences in sampling period, diagnostic methods, study design, and ecological characteristics. Wild boars are omnivorous, and their rooting behavior disturbs the soil during foraging. They also occupy a wide range of habitats, including forests, agricultural lands, and areas surrounding water sources (Massei & Genov, 2004; Barrios-Garcia and Ballari, 2012). These characteristics increase opportunities for repeated contact with contaminated soil, water, and food resources, thereby increasing exposure to the infective stages of T. gondii (Dubey, 2004; Maleki et al., 2021). Previous studies suggest that wild boar populations in South Korea have increased steadily following the loss of large predators and changes in rural landscapes (Lee, 2022). Comparative habitat studies have also reported higher densities in urban areas such as Seoul, suggesting that wild boars are not restricted to forest habitats but may concentrate near human settlements (Lee et al., 2024). Population growth and habitat expansion may further increase contact with contaminated environments, contributing to the high seroprevalence observed in Korean wild boars. Collectively, the elevated antibody prevalence in this study reflects both ecological changes in wild boar populations and the active environmental circulation of T. gondii in South Korea.

Multivariate logistic regression analysis showed a clear positive association between body weight and T. gondii seropositivity. Compared with the <20 kg reference group, the odds of seropositivity increased progressively in the 20–60, 60–100, and 100–300 kg groups (Table 2). Similar age- and body size-related patterns have been reported in other countries. In the Netherlands, seroprevalence increased with age, suggesting cumulative exposure over time (Opsteegh et al., 2011). Similarly, a Slovenian study reported substantially higher seroprevalence in older animals than in juveniles (Bandelj et al., 2021). Although the present study did not directly assess age, the strong association with body weight may reflect greater cumulative exposure among older individuals, assuming body weight is correlated with age in the sampled population.

Getis–Ord Gi* analysis identified spatial clusters of high T. gondii seroprevalence across multiple regions, although their intensity and clustering varied geographically. These spatial patterns should nevertheless be interpreted cautiously because sampling coverage was not uniform across the study area. Wild boars occur at relatively high densities in metropolitan areas adjacent to human settlements, with a stable population maintained around Bukhansan National Park (Lee et al., 2022, 2024). Additionally, T. gondii infections and oocyst shedding have been reported in stray and feral cat populations in the Seoul metropolitan region, suggesting ongoing environmental contamination sources (Lee et al., 2011; Ahn et al., 2019). Together, these conditions may increase exposure opportunities for wild boars and promote hotspot clustering in metropolitan areas. In contrast, Gangwon-do and Gyeongsangbuk-do exhibited weaker hotspot clustering than other regions. ASF causes substantial mortality in wild boar populations, and host density is an important determinant of disease transmission and persistence (Nasiadka et al., 2025; Gervasi and Guberti, 2021). In addition to reducing population size, intensive disease-control measures may also alter wild boar movement, behavior, habitat use, and contact patterns. For instance, hunting and culling may modify wildlife dispersal and space use, with potential consequences on disease dynamics (Mysterud et al., 2020; Miguel et al., 2020). However, the present study did not directly assess ASF occurrence, population density, or control activities as determinants of T. gondii exposure. Therefore, regional differences in T. gondii seroprevalence should not be interpreted as evidence of an association between ASF and T. gondii. Further ecological investigations incorporating wild boar population density, movement, and environmental contamination data are needed to explain these regional spatial patterns.

Local Moran's I analysis was performed using grid-level T. gondii seroprevalence rather than sampling density. However, the serum samples analyzed in this study were obtained through the national ASF surveillance program rather than through a sampling scheme specifically designed for spatial surveillance of T. gondii. Because serological testing was limited to available serum samples, sample availability within the ASF surveillance program may have influenced the spatial distribution of the analyzed samples. Consequently, sampling intensity was not uniform across the study area, and some grid cells contained relatively few samples. Variation in the number of animals tested per grid may have influenced the stability of seroprevalence estimates and, consequently, the detection of HH clusters. In addition, we could not evaluate areas without samples. Therefore, the identified HH clusters should be interpreted as areas of spatially clustered high seroprevalence within the sampled wild boar population rather than as definitive geographic hotspots of T. gondii exposure. The absence of LL clusters should likewise be interpreted cautiously, particularly given the high overall seroprevalence and uneven sampling intensity across grid cells.

Within HH clusters, both the density and frequency of cat and wild boar traces were higher than those in non-HH areas, and wild boar traces showed a stronger association with HH cluster formation (Fig. 3). Environmental contamination is primarily driven by oocyst shedding from felids, the definitive hosts of T. gondii. Wild boars, as intermediate hosts, do not directly contribute to environmental contamination but may indicate local environmental exposure and serve as potential vehicles for foodborne transmission (Shapiro et al., 2019; Hatam-Nahavandi et al., 2021; Rentería-Solís et al., 2024). These findings suggest that T. gondii contamination may be widely established in Korean ecosystems through felid-mediated environmental shedding. Conversely, repeated habitat use and activity of wild boars may contribute to the spatial clustering of seroprevalence. However, wildlife traces provide indirect measures of animal occurrence and do not directly quantify population density, movement, or contact rates. Therefore, caution must be exercised when interpreting these associations. Future studies should integrate direct estimates of host density, movement ecology, and environmental detection of T. gondii oocysts to clarify the ecological mechanisms underlying these spatial patterns.

Fig. 3.

Fig. 3

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Spatial patterns of Toxoplasma gondii seroprevalence and associated animal traces in South Korea.

(a) Local Moran's I cluster map showing high–high (HH), high–low (HL), low–high (LH), and non-significant areas. (b) Spatial distribution of wild boar traces (number of traces per grid cell). (c) Spatial distribution of cat traces (number of traces per grid cell). Local Moran's I analysis revealed significant spatial clustering of T. gondii seroprevalence in wild boars across South Korea.

Our land-cover analysis further demonstrated that landscape composition was independently associated with HH cluster membership. Higher proportions of tree cover, cropland, and built-up areas were each associated with increased odds of HH clusters. Rather than indicating a single high-risk habitat, this pattern may reflect greater exposure in heterogeneous landscapes where natural, agricultural, and human-modified environments occur in close proximity. Such landscapes may facilitate interactions among wild boars, free-ranging cats, domestic animals, and humans, thereby increasing opportunities for environmental contamination and parasite transmission. Importantly, the associations between cat and wild boar trace presence and HH cluster membership remained significant after adjustment for land-cover characteristics, indicating that host occurrence provided information beyond landscape composition alone. Together, these findings support a multifactorial spatial pattern in which both landscape characteristics and host activity are associated with the distribution of T. gondii exposure.

Unlike in several European countries, where consumption of wild boar meat is a recognized route of human exposure to T. gondii, consumption of wild boar meat is relatively uncommon in South Korea. The public health relevance of the present findings may therefore extend beyond direct foodborne exposure to occupational and environmental pathways. Personnel engaged in the national ASF hunting and culling program, including hunters, field veterinarians, and wildlife management staff, handle blood, tissue, and carcasses during sample collection and may be exposed occupationally through skin or mucosal contact. Furthermore, the increasing overlap between wild boar habitats and agricultural and urban-adjacent areas raises the possibility of indirect environmental exposure through contamination of soil, water, and produce. These findings support the continued use of appropriate protective measures during wildlife handling and the incorporation of T. gondii surveillance into broader wildlife and environmental health.

Building on a previous nationwide serosurvey based on opportunistic sampling (Hwang et al., 2024), this study provides a large-scale and more standardized assessment of T. gondii seroprevalence in wild boars in South Korea. Seroprevalence remained consistently high during the study period, and spatial analyses identified significant clustering of high seroprevalence as well as associations with landscape characteristics and host traces. These findings indicate the importance of both environmental and ecological factors in shaping the spatial epidemiology of T. gondii. These findings indicate widespread exposure to T. gondii among Korean wild boars and suggest that its spatial distribution and exposure patterns are associated with multiple ecological and environmental factors. Therefore, wild boars may serve as useful sentinel hosts for monitoring environmental exposure to T. gondii and public health risks, particularly in areas where wildlife increasingly overlaps with human-associated environments. Effective control of toxoplasmosis in South Korea will require a One Health approach that integrates wildlife surveillance, animal management, environmental monitoring, and public health preparedness.

CRediT authorship contribution statement

Garam Kim: Writing – review & editing, Writing – original draft, Visualization, Supervision, Formal analysis, Data curation, Conceptualization. So-Jeong Kim: Methodology, Investigation. Beongchul Shin: Methodology, Investigation. Seungmin Lim: Methodology, Investigation. Weon-Hwa Jheong: Writing – review & editing, Data curation.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Ethics approval and consent to participate

The samples used in this study were collected as part of the national wildlife disease surveillance program conducted by the National Institute of Wildlife Disease Control and Prevention (NIWDC), South Korea. No animals were captured or sampled specifically for this study; therefore, ethics approval and consent to participate were not applicable.

Declaration of generative AI use

The authors declare that no generative AI tools were used in the preparation of this manuscript.

Funding statement

This work was supported by the Ministry of Environment, Republic of Korea, through the National Institute of Wildlife Disease Control and Prevention (grant number NIWDC-2025-RP-05).

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors thank the staff of the National Institute of Wildlife Disease Control and Prevention for their assistance in sample collection and laboratory analysis.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijppaw.2026.101285.

Contributor Information

Garam Kim, Email: garam1204@korea.kr.

So-Jeong Kim, Email: th3555@korea.kr.

Beongchul Shin, Email: nichul2@korea.kr.

Seungmin Lim, Email: lim019419@gmail.com.

Weon-Hwa Jheong, Email: purify@korea.kr.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.pdf (32.9KB, pdf)
Multimedia component 2
mmc2.pdf (70KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.pdf (32.9KB, pdf)
Multimedia component 2
mmc2.pdf (70KB, pdf)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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