Species distribution modelling of Ornithodoros spp. in California with consideration of climate variation and identification of ASFV high-risk areas 

Species distribution modelling of Ornithodoros spp. in California with consideration of climate variation and identification of ASFV high-risk areas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Species distribution modelling of Ornithodoros spp. in California with consideration of climate variation and identification of ASFV high-risk areas Carlos Gonzalez-Crespo, Hélène Jourdan-Pineau, Laura Patterson, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5419700/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 Background African swine fever virus (ASFV) is a highly contagious pathogen affecting domestic and wild pigs, with soft ticks ( Ornithodoros spp.) acting as significant vectors. Understanding the distribution of Ornithodoros ticks in relation to environmental variables is crucial for anticipating ASFV transmission risk areas. Methods This study employed species distribution modeling (SDM) using MaxEnt and Random Forest algorithms to predict the suitable habitat of Ornithodoros coriaceus , O. hermsi , and O. parkeri in California under current and future climate scenarios (2061–2080). The SDMs incorporated climate, edaphic, topographic, and habitat variables, with models evaluated through 10-fold cross-validation using the Area Under the Curve (AUC). Results Key predictors included Isothermality (BIO3), Precipitation Seasonality (BIO15), and soil type (Entisols). The present habitat suitability for Ornithodoros spp. covered approximately 117,208 km², projected to increase by 6,869.5 km² under future conditions. The spatial co-occurrence analysis highlighted an overlap of approximately 37,242.75 km² between Ornithodoros spp. habitats and feral/domestic pig distributions, expanding to 39,213 km² under future scenarios. Conclusions Identifying high-risk areas for ASFV transmission through SDMs provides valuable insights for targeted surveillance and biosecurity measures. The study emphasizes the need for integrated vector-host management and proactive strategies to mitigate ASFV risk in California. African swine fever Ornithodoros spp. species distribution modeling climate change vector-borne diseases feral pigs surveillance biosecurity California Figures Figure 1 Figure 2 Figure 3 Figure 4 Background African swine fever virus (ASFV) is a highly contagious DNA virus that affects both domestic and wild pigs ( Sus scrofa ), causing fever, hemorrhages, and high mortality. Originating from Eastern and Southern Africa, ASFV is maintained in an ancient sylvatic cycle involving African wild suids, predominantly warthogs, and argasid ticks from the Ornithodoros moubata complex [1]. The transmission of ASFV is complex and varies across different geographical regions, reflecting the diverse swine production systems, wild suid populations, and tick vector habitats. Globally, the virus is transmitted through direct contact between infected and susceptible pigs, exposure to contaminated feed or fomites, and notably, through bites from ticks of the genus Ornithodoros (Ixodida: Argasidae) [2]. In Eastern and Southern Africa, the sylvatic cycle involving warthogs (Phacochoerus africanus) and Ornithodoros ticks primarily sustain the virus in the wild, with occasional spillover into domestic pig populations, typically through infected tick bites. In contrast, in parts of Europe and Asia, where the disease has recently spread, transmission has been largely associated with human activities, such as the movement of contaminated pork products and swill feeding practices. In these regions, the role of Ornithodoros ticks in the transmission cycle is less clear, highlighting the importance of understanding local epidemiological contexts to effectively manage and control the spread of ASFV [2–4]. Over the past few decades, ASFV has spread to more than 55 countries across four continents, posing a significant threat to pig production worldwide [5,6]. In recent years, the Dominican Republic has reported a resurgence of ASFV, marking the first time in nearly 40 years that the disease has reached the American continent [7]. Currently, there is no effective vaccine available for ASFV, and control measures focus on biosecurity, rapid diagnosis, and stamping out of infected herds [8]. The devastating economic impact of ASFV on the pork industry, including high morbidity and mortality rates and trade restrictions, has made it a major concern for national and regional governments [8–10]. The US pork industry plays a crucial role in the country's economy, generating over $23 billion in annual economic activity and employing more than 400,000 people [11]. Despite its importance, the swine industry is highly vulnerable to the impacts of diseases such as ASFV. While the US has so far been able to maintain its status as a disease-free country, the threat of ASFV introduction and spread remains a concern for the industry and the country, potentially resulting in significant losses in production, trade, and employment. Across California, outdoor raised pig operations (OPO) (e.g., commercial pork producers, backyard operations) are widely dispersed despite being regarded as a minority production method in the US [12,13]. The potential for these animals to come into contact with wildlife, such as feral pigs, and the risk of pathogen transmission is greater than in conventional farms [13]. This creates a scenario with important consequences for disease transmission at the wildlife-livestock interface. In recent years, the number of feral pigs in the US has significantly expanded, with their range extending from 17 to 41 states. California is among the states with the largest and broadest geographic distribution of feral pigs [13]. These non-domesticated pigs pose a significant threat to the conservation of native ecosystems and serve as a reservoir for diseases. In the context of ASFV, feral pigs play a crucial role in the spread of the disease, increasing the risk of its introduction into new areas and domestic herds. The significance for human and animal health of soft ticks (Acari: Argasidae) like the genus Ornithodoros has typically been underestimated due to the specialized existence in protected microhabitats and characteristic short blood-feeding intervals (15-90 minutes) [14]. As endophilous nidicoles, their niche comprises nests, burrows, and caves of vertebrate animals or human and livestock dwellings [15]. Soft ticks may be less affected by rapidly changing environmental conditions than hard ticks and may instead be more influenced by extreme environmental conditions throughout their lifetime. However, limited information exists on the impact of climate change on soft ticks [14]. Ornithodoros ticks are involved in the original natural cycle of ASFV in Eastern and Southern Africa, where a sylvatic cycle occurs between wild suids, especially warthogs, and O. porcinus ticks. Spillover into domestic swine is typically associated with infected tick bites or ingestion of contaminated warthog meat (Sánchez-Vizcaíno et al ., 2012). Ornithodoros ticks are globally distributed, and five species are present in California: O. coriaceus, O. hermsi, O. turicata, O. parkeri , and O. talaje [16]. Among those O. coriaceus , O. parkeri, and O. turicata have been experimentally infected with ASFV and showed vector competence for the virus [17,18]. Ornithodoros ticks also play a crucial role in the maintenance and spread of ASFV in both wild and domestic populations of pigs [17,19]. Sus scrofa carrying Ornithodoros ticks have already been documented in the US , O. coriaceus has been reported in California [20], while O. turicata in Texas [21]. Ornithodoros ticks are also responsible for transmitting various diseases in the United States, including human tick-borne relapsing fever (TBRF). TBRF is caused by spirochetes of the genus Borrelia and transmitted by four Ornithodoros tick species: O. parkeri, O. hermsi, O. turicata , and O. talaje . These tick species transmit B. parkeri, B. hermsii, B. turicatae , and B. mazzottii , respectively, leading to human disease [22–24]. In addition to ASFV and TBRF, Ornithodoros ticks are also implicated in the transmission of Epizootic Bovine Abortion (EBA), which is caused by Borrelia coriaceus and transmitted by O. coriaceus [14,25,26]. EBA is characterized by late-term abortion and the birth of weak or dead calves. Although documentation is limited, EBA has been reported in California, Oregon, and Nevada, with associated rates ranging from 25 to 75% [25]. Interactions between feral and domesticated pigs, particularly those raised outdoors, are becoming more frequent. Their integration into outdoor environments facilitates their contact with feral pigs, thereby serving as potential conduits for ASFV transmission into commercial and backyard swine operations. Moreover, the presence of Ornithodoros ticks in these outdoor settings, with their proven role in ASFV transmission, further increases the risk, creating a remarkable challenge in controlling the spread of the virus if introduced in the state of California [17,19,20,27]. Species distribution modeling (SDM) are a collection of advanced statistical and machine-learning methodologies that enable the prediction of suitable habitat ranges and ecological niches for various species based on their associations with specific environmental conditions such as temperature, precipitation, and vegetation type [28–30]. These approaches encompass a wide range of techniques, from deterministic methods such as logistic regression to stochastic approaches like Bayesian regression trees. SDMs also employ various model validation strategies to ensure the accuracy and reliability of the predictions generated [31]. By leveraging these powerful tools, researchers can gain valuable insights into species distribution patterns and better understand the impacts of environmental factors on the distribution of organisms [32]. In the context of disease ecology and epidemiology, SDM can be particularly useful for identifying areas at high risk of pathogen transmission by predicting the distribution of vectors and hosts. By considering the role of feral pigs and Ornithodoros ticks in the spread of ASFV, it is possible to identify areas of high risk and prioritize control and surveillance efforts, thus helping to protect the swine industry and prevent the spread of this disease. The primary objectives of this study were to: (1) develop a fine-scale prediction of the distribution of Ornithodoros spp . in California under current and future climate conditions, taking into account the potential effects of climate change on their distribution; (2) assess the spatial co-occurrence of Ornithodoros ticks, feral pigs, and known OPO locations in California to identify areas at high risk for ASFV transmission; and (3) provide recommendations for future tick surveillance efforts, research on host communities, and the identification of suitable habitats that support the maintenance of Ornithodoros spp . Identifying high-risk areas for ASFV in California can support risk-based decision-making processes, enabling the prioritization of surveillance and control measures. Materials and Methods The description of the SDM model follows the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol proposed by Zurell et al ., [33]: a. Overview Model objective: Inference and explanation. Focal Taxon: Ornithodoros spp .- O. coriaceus, O. hermsi and O. parkeri . Location: California, US a.1 Scale of Analysis Spatial extent: -380102.2, 540036.5, -605326.6, 450447.3 (xmin, xmax, ymin, ymax). Spatial resolution: 423,967 km2. Boundary: political. Temporal extent: Present and predicted future (2061-2080) climatic conditions. a.2 Biodiversity data. Observation type: citizen science, field survey. Response data type: presence-only. a.3 Predictors- Predictor types: climatic, edaphic, habitat, topographic. a.4 Hypotheses. The selection of the predictor variables was based on the characteristics of Ornithodoros ticks. While little is known about their ecology and range of mammal hosts (climatic and habitat variables), Ornithodoros spp . always exhibit an endophilous nidicoles lifestyle (edaphic and topographic variables). a.5 Model assumptions. Independence of species observations; Availability of all important predictors; Niche stability/constancy, niche conservatism. a.6 Algorithms Modelling techniques: MaxEnt (MX), Random Forest (RF). Model complexity: Use combinations of SDM algorithms to account for algorithmic uncertainty. Model averaging: Ensembles of SDM algorithms to account for algorithmic uncertainty in order to transfer under scenarios of global change. a.7 Workflow Model workflow: Removal of highly correlated predictor variables. Generation of pseudo-absences. Spatial shorting bias test. MX and RF algorithms fitting. Evaluation by 10-fold cross-validation using the area under the curve (AUC). Selection of variables with a higher relative variable contribution. Ensemble of the models and transformation into binary distribution surface maps. Final models under current and future climatic conditions, by combining shared areas predicted by MX and RF binary maps. a.8 Software Software: SDM models: R Core Team version 4.1.1 (2021) with sdm package. Spatial data preparation: QGIS 3.20.3-Odense. Code availability: The R code used in the present study can be found in: https://github.com/cgonzalezcrespo/SDM_ Ornithodoros _CA. Data availability: All data used in the present study was collected from publicly available online resources (see section b. Data). b. Data b.1 Biodiversity data Taxon names: Ornithodoros coriaceus, Ornithodoros hermsi and Ornithodoros parkeri . Ecological level: species, operational taxonomic units. Data sources: GBIF.org (19 October 2021) GBIF Occurrence. Download https://doi.org/10.15468/dl.jkrgpx. and from Sage et al ., [34]. Sampling design: Due to the scarcity of records, all publicly available data was used in the study. Sample size: Ornithodoros coriaceus (15), Ornithodoros hermsi (65) and Ornithodoros parkeri (7). Clipping: California, US Background data: Pseudo-absences (PA) are generated to act as negative samples in SDM and are crucial for accurately predicting the potential range of a species. The use of pseudo-absences in SDM assumes that the presence of a species is limited by environmental conditions, and these conditions are better represented by areas where the species is absent than by randomly selected points. Massin et al ., [35] proposed a framework for selecting pseudo-absences in SDM. Therefore, 10 000 PA were used for the MaxEnt models and same as number of PA than presences, with 10 runs due to less than 1000 PA were used for the RF models. PA were spatially balanced and distributed evenly throughout the study area to avoid bias in the results. Errors and biases: Presence of spatial sorting bias was tested following the methodology provided by Hijman, [36]. However, the data in this study did not present spatial sorting bias (ssb: 1.039). b.2 Predictor variables and data sources. Bioclimatic predictors present and Elevation (WorldClim2, https://www.worldclim.org/data/worldclim21.html), Bioclimatic predictors 2061-2080 (WorldClim2, https://www.worldclim.org/data/cmip6/cmip6_clim30s.html, CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4, SSP: 370). Predictors: Elevation; BIO1, Annual Mean Temperature; BIO2, Mean Diurnal Range; BIO3, Isothermality; BIO4, Temperature Seasonality; BIO5, Max Temperature of Warmest Month; BIO6, Min Temperature of Coldest Month; BIO7, Temperature Annual Range; BIO8, Mean Temperature of Wettest Quarter; BIO9, Mean Temperature of Driest Quarter; BIO10, Mean Temperature of Warmest Quarter; BIO11, Mean Temperature of Coldest Quarter; BIO12, Annual Precipitation; BIO13, Precipitation of Wettest Month; BIO14, Precipitation of Driest Month; BIO15, Precipitation Seasonality; BIO16, Precipitation of Wettest Quarter; BIO17, Precipitation of Driest Quarter; BIO18, Precipitation of Warmest Quarter; BIO19, Precipitation of Coldest Quarter. Land cover (NLCD 2019 Land Cover (CONUS) Multi-Resolution Land Characteristics (MRLC) Consortium, https://www.mrlc.gov/data/nlcd-2019-land-cover-conus). Predictors: 11, Open Water; 12, Perennial Ice/Snow; 21, Developed, Open Space; 22, Developed, Low Intensity; 23, Developed, Medium Intensity; 24, Developed High Intensity; 31, Barren Land (Rock/Sand/Clay); 41, Deciduous Forest; 42, Evergreen Forest; 43, Mixed Forest; 51, Dwarf Scrub; 52, Shrub/Scrub; 71, Grassland/Herbaceous; 72, Sedge/Herbaceous; 73, Lichens; 74, Moss; 81, Pasture/Hay; 82, Cultivated Crops; 90, Woody Wetlands; 95, Emergent Herbaceous Wetlands. Geologic map (California geologic map data, USGS, (https://mrdata.usgs.gov/geology/state/state.php?state=CA). Predictors: R1, Igneous and Metamorphic; R2, Igneous and Sedimentary; R3, Igneous; R4, Melange; R5, Metamorphic and Sedimentary; R6, Metamorphic; R7, Sedimentary; R8, Unconsolidated. Soils (Digital General Soil Map of the United States-STATSGO2, https://www.nrcs.usda.gov/resources/data-and-reports/description-of-statsgo2-database). Predictors: S1, Alfisols; S2, Andisols; S3, Aridisols; S4, Entisols; S5, Histosols; S6, Inceptisols; S7, Mollisols; S8, Ultisols; S9, Vertisols; S10, Other. Distance to water (Calculated from USA Detailed Water Bodies (https://hub.arcgis.com/datasets/esri::usa-detailed-water-bodies/about) and USA Rivers and Streams (https://hub.arcgis.com/datasets/esri::usa-rivers-and-streams/about). Slope and Orientation (calculated from Elevation). Spatial extent: -380102.2, 540036.5, -605326.6, 450447.3 (xmin, xmax, ymin, ymax). Spatial resolution: 2112, 1840, 3886080 (nrow, ncol, ncell). Coordinate reference system: EPSG:3488 - NAD83(NSRS2007) / California Albers. Temporal extent: Present and predicted future (2061-2080) climatic conditions Data processing: Raster predictors layers were scaled to a resolution of 500x500 m. Hydrology (USA Detailed Water Bodies and USA Rivers and Streams) vector layers were merged in a single vector layer which was subsequently rasterized (500x500 m) and used to calculate the distance of each pixel to water. Slope and Orientation were calculated from the Elevation raster layer using the raster tool provided by QGIS. c. Model c.1 Variable pre-selection. As Ornithodoros spp . are endophilous nidicoles, besides the standard bioclimatic and topographic predictors, information about the geology and soils was also incorporated in the model. c.2 Multicollinearity. Highly correlated predictor variables associated with Ornithodoros presence data were identified and removed using the variance inflation factor (VIF), a measure based on the square of the multiple correlation coefficient (R²). This process ensures that the remaining predictor variables provide valuable and independent information for the model. c.3 Model estimates. Coefficients: The accuracy of the individual models was assessed using the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve, with a threshold >= 0.8. Parameter uncertainty: To evaluate model accuracy, a 10-fold cross-validation process is employed, with 80% of the data used for training and the remaining 20% for testing. Variable importance: In the final model, only variables with a higher relative variable contribution (>0.05) based on AUC were incorporated. c.4 Model selection, averaging and ensembles. Model selection, averaging and ensembles: The present study followed the methodology proposed by Naimi and Araújo, 2016. Each sdm object, created through the 10-fold cross-validation process contained 50 predictive models. The models in the sdm object were ensembled using as weights the true statistic skill (TSS) provided by the SDM package. The ensembled models (12 in total) were transformed into binary (presence/absence) distribution surface maps of California. Suitable areas were selected based on a threshold which maximized the true statistic skill (TSS) for each model, as provided by the SDM package [31]. The MX and RF binary maps for each of the three Ornithodoros species were combined, including only those areas predicted by both algorithms. Finally, the distribution maps for the three species were combined under each set of present and future climatic condition predictors, as a comprehensive overview of potential suitable habitats for Ornithodoros spp . in California. d. Assessment. d.1 Performance statistics- Performance on training data: TSS, AUC. d.2 Plausibility check. Response shapes: The accuracy of the models was assessed using the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve, with a threshold be included of AUC > 0.8. Sus scrofa range: A previously published [13] distribution model of Sus scrofa and known OPO locations in California were utilized for the analysis of the feral swine range. The model provided the necessary information about the current distribution and potential areas where feral pigs could be found within the state (Additional file 1: Fig S1). Vector-Host Spatial Co-Occurrence; Identification of High-Risk Areas for ASFV. In order to identify high-risk areas for ASFV, a spatial co-occurrence analysis of shared areas of suitable habitat for both the Ornithodoros tick vectors and Sus scrofa , their potential hosts, was conducted. By combining the distribution models of the ticks and feral swine, areas where both species were likely to coexist were assessed, representing regions with elevated risk for the transmission and spread of ASFV. The binary distribution maps of Ornithodoros spp . and Sus scrofa were overlaid, and areas where their predicted suitable habitats intersected were identified. Through this analysis, specific locations in California with a higher likelihood of vector-host interactions and, consequently, a higher risk for ASFV transmission were pinpointed. The surface area of the predicted shared habitats under both current and future climatic conditions was calculated, providing insights into potential changes in high-risk areas due to climate change. Results Ornithodoros spp. individual models Some variables (Table 1 ) contributed to predicting the distribution of the Ornithodoros species across different models. Isothermality (Bio3) was the most significant predictor across all species and both modeling methods (MX and RF). For Ornithodoros species, it accounted for a significant proportion of the model's predictive power, ranging from 19.3–22.3% in MX models and 14.2–19.1% in RF models. Precipitation Seasonality (Bio15) played a critical role in RF models for all species, with an importance ranging from 23.1–26.3%. Soil types, represented by predictors S1 to S10, were significant in both models, but they were particularly influential in MX models. Of the soil types, Soil Type 4 (Entisols) stood out in its contribution to the MX models, with its highest importance peaking at 28% for O. coriaceus . Land Cover variables were also influential in model outputs, but they did not consistently emerge as the most important variables. Geological predictors were impactful in some models. Specifically, R8 (Unconsolidated) was notably important in the future distribution model for O. coriaceus , accounting for 12% of the model's predictive power. Table 1 Models’ parameters. Significance of variables in predicting the distribution of Ornithodoros species under different climate scenarios (Future scenario for period 2061–2080, CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4, SSP: 370). The percentages indicate the relative importance of each variable in the model's predictive power. TSS (true statistic skill) threshold for transformation into binary distribution maps. Mx, MaxEnt; RF, Random Forest. Species Model Scenario TSS Threshold Top Variables by Importance Environmental Factors O. coriaceus MX Present 0.3959588 S4 (28%), bio_3P (19.3%), S3 (12.8%) S4: Entisols, bio_3P: Isothermality, S3: Aridisols Future 0.5457646 S4 (25.2%), bio3F (22.3%), R8 (12%) S4: Entisols, bio3F: Future Isothermality, R8: Unconsolidated RF Present 0.3157592 bio_15P (31.7%), bio_3P (26%), Slope (5.3%) bio_15P: Precipitation Seasonality, bio_3P: Isothermality Future 0.5649089 bio15F (20.8%), bio3F (16.2%), bio12F (6.7%) bio15F: Future Precipitation Seasonality, bio3F: Future Isothermality O. hermsi MX Present 0.2120322 Elevation2 (26.5%), DistancetoWater2 (17.1%), R7 (6.7%) R7: Sedimentary Future 0.5669928 Elevation2 (22.3%), DistancetoWater2 (19.9%), R7 (6.9%) R7: Sedimentary RF Present 0.1988138 Elevation2 (22.3%), bio_12P (4.8%), DistancetoWater2 (7.6%) bio_12P: Precipitation of Wettest Month Future 0.5437049 Elevation2 (27%), DistancetoWater2 (11.3%) - O. parkeri MX Present 0.4279793 bio_14P (33.4%), S4 (20.9%), R7 (18.9%) bio_14P: Precipitation of Driest Month, S4: Entisols, R7: Sedimentary Future 0.6628849 bio14F (35%), S4 (22.9%), R7 (18.2%) bio14F: Future Precipitation of Driest Month, S4: Entisols, R7: Sedimentary RF Present 0.4009344 S4 (24%), bio_15P (9.5%), bio_9P (7.8%) S4: Entisols, bio_15P: Precipitation Seasonality, bio_9P: Mean Temperature of Driest Quarter Future 0.646822 bio14F (35%), S4 (22.9%), R7 (18.2%) bio14F: Future Precipitation of Driest Month, S4: Entisols, R7: Sedimentary Ornithodoros spp. suitable area in California After combining first, the individual models of the three species (Additional file 2: Fig S1 , S2, S3 and S4) and second the predictions by both algorithms (Additional file 2: Fig S5), the results predicted areas (Fig. 1 A) within California that could potentially have suitable habitats for Ornithodoros spp ., identifying potential high-risk areas for ASFV. The present suitable area for Ornithodoros spp ., determined by factors such as weather conditions, land cover, edaphic factors, and topographic features, was calculated to be 117,208 km². However, considering future weather scenarios alongside the consistent land cover, edaphic, and topographic factors, the suitable habitat (Fig. 1 B) for Ornithodoros spp. is projected to expand to 124,077.5 km². Consequently, a potential range expansion in suitable area for Ornithodoros spp . is predicted to increase under future (2061–2080) climate scenarios by 6,869.5 km² (Fig. 2 ). Vector-host spatial co-occurrence. Identification of high-risk areas for ASFV Results indicated a significant overlap in habitats considered suitable for both Ornithodoros spp. and feral/domestic Sus scrofa (Fig. 3 A). This overlap, spanning an area of approximately 37,242.75 km², represents a substantial risk area for ASFV transmission due to the concurrent presence of the vector and the host. However, when projecting these observations into the future and incorporating predicted climate scenarios, the overlap of habitats suitable for both species was found to increase (Fig. 3 B), estimated to cover around 39,213 km². Comparing the present and future overlap areas (Fig. 4 ), reveals an increase in shared habitat space by approximately 1,970.25 km² from the current overlap area to the predicted future scenario. Discussion The results of the habitat suitability modeling of Ornithodoros spp. revealed that specific areas in the state of California exhibit a higher suitability for the presence of this vector, consequently increasing the risk of ASFV transmission. These high-risk areas were identified by the co-occurrence of domestic pigs, feral pigs, and suitable climate conditions for Ornithodoros spp. The results of the analysis seem to corroborate with the unique ecological and biological characteristics of Ornithodoros spp ., as soft ticks. As soft ticks, they exhibit endophilous nidicole behavior, with their niche comprising nests, burrows, caves of vertebrate animals, or human and livestock dwellings. Therefore, it is logical that they show sensitivity to variables such as Isothermality (Bio3), Precipitation Seasonality (Bio15), specific soil types like Entisols (S4), and unconsolidated geological features (R8). The fact that Isothermality (Bio3) appeared as a critical variable across all species and models underlines the potential importance of diurnal and seasonal temperature changes for these ticks. This variable represents the diurnal range divided by the temperature annual range, indicating the magnitude of day-to-night oscillation relative to summer-to-winter oscillations. Given their lifestyle in protected microhabitats, they may experience more stable conditions and be less susceptible to sudden environmental changes compared to hard ticks. Nevertheless, these ticks still show sensitivity to extreme temperature conditions, which are reflected in the isothermality measurements. Likewise, the significance of Precipitation Seasonality (Bio15) in the RF models indicates the relevance of moisture levels for these ticks. While soft ticks' existence in protected microhabitats might buffer them against rapid environmental shifts, extreme or prolonged changes in precipitation might still impact their habitat suitability. The preference for Entisols (S4) might be linked to the nidicole behavior of these ticks. As they are typically found in burrows, nests, and similar microhabitats, the soil characteristics of these environments could have a direct impact on their survival and propagation. Entisols, with their minimal horizon development, might offer suitable conditions for these ticks' burrowing habits. Finally, the importance of the unconsolidated geological category (R8) could be attributed to their affinity for burrow-like environments. Unconsolidated materials such as sand or gravel might offer easier burrowing and hiding conditions, thereby providing more secure microhabitats for these ticks. The increasing overlap between the distribution areas of Ornithodoros ticks, feral pigs, and OPOs poses serious implications for animal and public health, particularly concerning the transmission of African Swine Fever Virus (ASFV) [ 6 ], EBA [ 25 ], and human TBRF [ 24 ]. From a disease management perspective, these overlapped areas should be prioritized for targeted surveillance, vector control efforts, and biosecurity measures [ 13 ]. Ornithodoros spp ., which are part of the original ASFV sylvatic cycle in Eastern and Southern Africa [ 1 ], have been found to play a critical role in the worldwide spread and maintenance of ASFV in both wild and domestic populations of pigs [ 17 , 19 ]. The integration of OPOs within the swine industry, while representing a fraction of total production, introduces significant epidemiological complexities at the wildlife-livestock interface, particularly concerning ASFV transmission risks. This tick species, along with the growing interface between feral pigs and OPOs, as identified in recent studies, underlines a critical concern for disease management and control strategies [ 13 , 37 ]. Feral pigs, with their burgeoning populations across the US, present an ongoing risk for the introduction and spread of infectious diseases, including ASFV, given their ability to traverse and inhabit diverse ecosystems, thereby increasing contact opportunities with domestic swine herds [ 13 , 17 , 19 , 20 , 27 ]. Previous research [ 13 , 37 ] underscores the heightened vulnerability of small-scale and pasture-based pig operations, which are increasingly prevalent in California. These systems, often characterized by less stringent biosecurity measures compared to large-scale confinement operations, provide ample opportunities for ASFV transmission through direct interactions between feral and outdoor-raised pigs. The environmental conditions conducive to Ornithodoros tick habitats further compound this risk, facilitating a vector-borne transmission pathway in addition to direct contact [ 13 ]. These studies focusing on diversified small-scale farms have identified risk factors associated with the management practices of outdoor-raised pigs, drawing parallels with the conditions that elevate the risk of Shiga toxin-producing Escherichia coli transmission [ 13 , 37 ]. This research highlights the broader implications of farm management practices at the smallholder level on disease transmission dynamics, offering insights into the interconnectedness of livestock health, farm practices, and public health concerns. The overlap of Ornithodoros ticks, feral pigs with outdoor-raised pig operations not only signifies a direct pathway for ASFV transmission but also underscores the need for comprehensive surveillance and targeted biosecurity enhancements in these systems. The identification of high-risk areas where overlap is most pronounced aids in focusing resources and interventions to mitigate the risk of disease spread effectively. Implementing strategies that address the unique vulnerabilities of small-scale and backyard livestock operations is paramount in safeguarding the national swine industry from the potential devastation of ASFV outbreaks [ 37 ]. Additionally, Ornithodoros ticks are known vectors for diseases beyond ASFV, notably including EBA, a condition caused by Pajaroellobacter abortibovis and transmitted primarily by O. coriaceus [ 14 , 25 , 26 ]. This tick species, integral to the ecosystem in California, has significant implications for the cow-calf ranching industry in the West, where EBA poses a substantial economic burden. The disease's impact on calf mortality rates can profoundly affect the economic viability of affected ranches, underscoring the importance of effective management and control strategies for these tick populations [ 38 , 39 ]. The increased interface between tick habitats and livestock operations exacerbates the risk of EBA, this scenario could pose further challenges to food security, given the role of industry in local and national economies. There is, therefore, a critical need for ongoing surveillance and diagnostic efforts to identify and mitigate the impact of this disease [ 39 ]. Furthermore, the development and deployment of a live Pajaroellobacter abortibovis vaccine have shown promise in protecting cattle against EBA [ 38 ]. In addition to the risks posed by Ornithodoros spp. to swine through ASFV transmission and to cattle via EBA, the cattle industry in California faces parallel concerns with Anaplasma marginale infections. This bacterium causes bovine anaplasmosis, a significant disease impacting cow-calf operations. Research into the seroprevalence of Anaplasma marginale within traditionally managed large beef herds underscores the continuous threat of tick-borne diseases to California's cattle industry [ 40 ]. These challenges exemplify the broader issues of managing vector-borne diseases in livestock operations, critical for the health and productivity of the state's agricultural commodities. The presence of diseases such as bovine anaplasmosis highlights the need for comprehensive disease surveillance and management strategies across livestock species. This necessity is amplified by the broader implications of tick-borne diseases, which extend to public health concerns, including the risk of human TBRF due to their expanding geographical distribution and overlap with human-inhabited areas, could potentially lead to higher incidences of TBRF [ 22 – 24 ]. The overlapping habitats of livestock and tick vectors necessitate integrated pest management and enhanced biosecurity measures to mitigate the risks of disease transmission. This scenario accentuates the need for heightened public health surveillance and appropriate preventative measures in identified high-risk areas to mitigate potential health impacts on local communities. Such multifaceted challenges at the wildlife-livestock-human interface emphasize the importance of collaborative efforts among veterinary health, public health, and agricultural sectors. Addressing these issues is crucial for safeguarding California's agricultural economy, ensuring the sustainability of its livestock industries, and protecting public health. The interconnectedness of these challenges underscores the need for a One Health approach, recognizing the intricate relationships between animal health, human health, and the environment In this study, the future climate model chosen was CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4 (Geophysical Fluid Dynamics Laboratory Earth System Model Version 4), under the Shared Socioeconomic Pathway (SSP) 370 scenario. The rationale behind selecting this particular model and scenario can be explained by several factors: Model Performance: GFDL-ESM4 is a state-of-the-art Earth System Model developed by the NOAA Geophysical Fluid Dynamics Laboratory. It has demonstrated improved performance over its predecessor models, providing more accurate and reliable future climate projections. Representativeness: The GFDL-ESM4 model is part of the CMIP6 (Coupled Model Intercomparison Project Phase 6) initiative, which is a globally coordinated effort to improve climate projections. The CMIP6 ensemble represents a wide range of possible future climate scenarios, increasing the likelihood that the selected model is representative of future climate conditions in California. Shared Socioeconomic Pathway (SSP) 370: The SSP 370 scenario was chosen because it represents a plausible future climate trajectory that is consistent with current greenhouse gas emission trends and socioeconomic development patterns. This "middle-of-the-road" scenario assumes moderate mitigation efforts to reduce greenhouse gas emissions and a medium level of socioeconomic development. It is considered a realistic and useful scenario for assessing potential climate change impacts on the distribution of Ornithodoros spp. and ASFV risk in California. Relevance to the study area: The GFDL-ESM4 model, combined with the SSP 370 scenario, provides projections that are relevant to the study area, considering the specific climate conditions in California. This model captures the regional climate variability and potential changes in temperature and precipitation patterns. It is important to note that the findings of this study should be viewed as an initial step towards proactive ASFV risk management in California. Further research is necessary to validate the results, monitor Ornithodoros spp. population dynamics, and focus surveillance efforts on the high-risk areas identified. Despite some existing knowledge of Ornithodoros tick biology and ecology in the United States, numerous knowledge gaps persist (Gonzalez-Crespo et al ., 2024, in prep), posing challenges for a comprehensive understanding of these organisms. One such limitation is the scarcity of up-to-date information on their distribution throughout various regions of the country. An examination of historical records and literature reveals a dearth of data on the distribution of Ornithodoros ticks. For example, a mere 119 georeferenced historical records of the genus in the United States are accessible via the GBIF (Global Biodiversity Information Facility) database. Although some recent studies have estimated the distribution of specific Ornithodoros species in particular regions, especially in the southern states, using species distribution models and available records [ 17 , 34 ], a substantial portion of our knowledge relies on sampling efforts and publications from several decades ago [ 16 , 41 ]. As the historical data on Ornithodoros ticks in the United States is outdated, it is likely that species distributions have shifted over time due to changes in land cover, development, climate, and other ecological factors. This highlights the need for additional research to accurately determine the current distribution of the genus across the US landscape and address these knowledge gaps. Ultimately, the adoption of a One Health approach is critical to tackling these emerging health challenges. This interdisciplinary strategy acknowledges the intrinsic interconnectedness of human, animal, and environmental health, and advocates for the integration of diverse scientific disciplines and coordinated efforts at multiple levels [ 42 , 43 ]. Conclusions By identifying ASFV high-risk areas in California, the research findings can help guide future tick surveillance efforts, inform risk-based decision-making, and prioritize surveillance and control measures. The results of this study hold implications for understanding the potential distribution of Ornithodoros ticks and the associated ASFV risk in California. This knowledge can assist in devising targeted surveillance efforts and developing effective control strategies, thereby ensuring the swine industry remains resilient in the face of potential ASFV outbreaks and the evolving threat posed by climate change. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding This research was supported by the Coalition for EpiResponse Engagement & Science (CERES). CERES is a collaborative partnership betweenColorado State University (CSU), Iowa State University (ISU), Kansas State University (KSU), Texas A&M University (TAMU), University of California – Davis (UC Davis), University ofNebraska – Lincoln (UNL) and University of Nebraska Medical Center (UNMC). Author Contribution CGC: conception, design of the work, acquisition, analysis, and interpretation of data. BML: conception, design of the work and substantively revised. JB: design of the work. LP: acquisition of data. AP: acquisition of data and substantively revised. HJ: substantively revised. Availability of data and materials All data used in the present study was collected from publicly available online resources (see section b. Data). References Jori F, Bastos ADS. Role of wild suids in the epidemiology of african swine fever. Ecohealth. 2009;6(2):296–310. EFSA. Scientific Opinion on African swine fever. EFSA J. 2014;12(4). Penrith ML, Vosloo W, Jori F, Bastos ADS. African swine fever virus eradication in Africa. Virus Res [Internet]. 2013;173(1):228–46. Available from: http://dx.doi.org/10.1016/j.virusres.2012.10.011 Chenais E, Ståhl K, Guberti V, Depner K. Identification of Wild Boar–Habitat Epidemiologic Cycle in African Swine Fever Epizootic - Volume 24, Number 4—April 2018 - Emerging Infectious Diseases journal - CDC. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5419700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":382419976,"identity":"71a95c7d-e46c-49b9-a11f-9f372131844a","order_by":0,"name":"Carlos Gonzalez-Crespo","email":"data:image/png;base64,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","orcid":"","institution":"University of California- Davis","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Gonzalez-Crespo","suffix":""},{"id":382419977,"identity":"076e550d-802e-40e3-973f-1685cc0c94a7","order_by":1,"name":"Hélène Jourdan-Pineau","email":"","orcid":"","institution":"UMR ASTRE, CIRAD, INRAE","correspondingAuthor":false,"prefix":"","firstName":"Hélène","middleName":"","lastName":"Jourdan-Pineau","suffix":""},{"id":382419978,"identity":"f02e939a-106d-4369-842e-d8545ab94575","order_by":2,"name":"Laura Patterson","email":"","orcid":"","institution":"University of California-Davis","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Patterson","suffix":""},{"id":382419979,"identity":"d9918e09-a10d-417f-ba92-58712e097ebf","order_by":3,"name":"Alda F. 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A.","lastName":"Pires","suffix":""},{"id":382419980,"identity":"cb2b73c5-e0e1-4efb-81c7-854b13f291fa","order_by":4,"name":"Beatriz Martínez-López","email":"","orcid":"","institution":"University of California- Davis","correspondingAuthor":false,"prefix":"","firstName":"Beatriz","middleName":"","lastName":"Martínez-López","suffix":""}],"badges":[],"createdAt":"2024-11-09 04:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5419700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5419700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69996979,"identity":"d7d837b6-4f2d-42c7-bf9e-53b4e260267c","added_by":"auto","created_at":"2024-11-27 10:38:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201955,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted suitable habitat for \u003cem\u003eOrnithodoros spp\u003c/em\u003e. under: (\u003cstrong\u003eA\u003c/strong\u003e) Present climatic conditions; (\u003cstrong\u003eB\u003c/strong\u003e) Future climatic conditions.\u003c/p\u003e","description":"","filename":"F1ab.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/d019441e106f4b4cd02bd858.jpg"},{"id":69996745,"identity":"42f7aa6f-348b-46e1-9992-ddd93e7c2d7c","added_by":"auto","created_at":"2024-11-27 10:30:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":234680,"visible":true,"origin":"","legend":"\u003cp\u003ePotential habitat suitability modification for \u003cem\u003eOrnithodoros spp.\u003c/em\u003e due to climate variation.\u003c/p\u003e","description":"","filename":"F2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/761d369f0f588345d228868a.jpg"},{"id":69996750,"identity":"a256d47d-5c78-436d-8d35-185a6c06c183","added_by":"auto","created_at":"2024-11-27 10:30:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230131,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted overlap of suitable habitat, and between \u003cem\u003eOrnithodoros spp.\u003c/em\u003eand \u003cem\u003eSus scrofa\u003c/em\u003e under: (\u003cstrong\u003eA\u003c/strong\u003e) Present climatic conditions; (\u003cstrong\u003eB\u003c/strong\u003e) Future climatic conditions.\u003c/p\u003e","description":"","filename":"F3ab.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/dae768c3e3d62ee952d9ee89.jpg"},{"id":69998878,"identity":"c56d343d-bd70-4ac3-bdfc-0dfb5d1fdeed","added_by":"auto","created_at":"2024-11-27 10:54:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":205246,"visible":true,"origin":"","legend":"\u003cp\u003ePotential variation in \u003cem\u003eOrnithodoros spp.\u003c/em\u003e - \u003cem\u003eSus scrofa\u003c/em\u003e overlap of habitats and high-risk areas for ASFV due to climate change.\u003c/p\u003e","description":"","filename":"F4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/23235acb2013ac42dcfac777.jpg"},{"id":71423314,"identity":"970e04d5-294f-4b0b-a2a3-1848e2777159","added_by":"auto","created_at":"2024-12-14 21:46:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1407757,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/3d972507-1d6a-44c7-9da3-62cf0746f7fd.pdf"},{"id":69996749,"identity":"d08b7707-8eeb-4d5e-85ef-9bd571f7ab7f","added_by":"auto","created_at":"2024-11-27 10:30:08","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":102982,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/49f99035d669153ae028d18b.docx"},{"id":69997972,"identity":"6b5329d9-2e8c-4650-9f22-4819031bcf37","added_by":"auto","created_at":"2024-11-27 10:46:08","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2154606,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5419700/v1/5f742eef06d0331aa8b5e89c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Species distribution modelling of Ornithodoros spp. in California with consideration of climate variation and identification of ASFV high-risk areas ","fulltext":[{"header":"Background","content":"\u003cp\u003eAfrican swine fever virus (ASFV) is a highly contagious DNA virus that affects both domestic and wild pigs (\u003cem\u003eSus scrofa\u003c/em\u003e), causing fever, hemorrhages, and high mortality. Originating from Eastern and Southern Africa, ASFV is maintained in an ancient sylvatic cycle involving African wild suids, predominantly warthogs, and argasid ticks from the \u003cem\u003eOrnithodoros\u003c/em\u003e moubata complex\u0026nbsp;[1]. The transmission of ASFV is complex and varies across different geographical regions, reflecting the diverse swine production systems, wild suid populations, and tick vector habitats. Globally, the virus is transmitted through direct contact between infected and susceptible pigs, exposure to contaminated feed or fomites, and notably, through bites from ticks of the genus \u003cem\u003eOrnithodoros\u003c/em\u003e (Ixodida: Argasidae)\u0026nbsp;[2]. In Eastern and Southern Africa, the sylvatic cycle involving warthogs (Phacochoerus africanus) and Ornithodoros ticks primarily sustain the virus in the wild, with occasional spillover into domestic pig populations, typically through infected tick bites. In contrast, in parts of Europe and Asia, where the disease has recently spread, transmission has been largely associated with human activities, such as the movement of contaminated pork products and swill feeding practices. In these regions, the role of Ornithodoros ticks in the transmission cycle is less clear, highlighting the importance of understanding local epidemiological contexts to effectively manage and control the spread of ASFV\u0026nbsp;[2\u0026ndash;4].\u003c/p\u003e\n\u003cp\u003eOver the past few decades, ASFV has spread to more than 55 countries across four continents, posing a significant threat to pig production worldwide\u0026nbsp;[5,6]. In recent years, the Dominican Republic has reported a resurgence of ASFV, marking the first time in nearly 40 years that the disease has reached the American continent\u0026nbsp;[7]. Currently, there is no effective vaccine available for ASFV, and control measures focus on biosecurity, rapid diagnosis, and stamping out of infected herds\u0026nbsp;[8]. The devastating economic impact of ASFV on the pork industry, including high morbidity and mortality rates and trade restrictions, has made it a major concern for national and regional governments\u0026nbsp;[8\u0026ndash;10].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe US pork industry plays a crucial role in the country\u0026apos;s economy, generating over $23 billion in annual economic activity and employing more than 400,000 people\u0026nbsp;[11]. Despite its importance, the swine industry is highly vulnerable to the impacts of diseases such as ASFV. While the US has so far been able to maintain its status as a disease-free country, the threat of ASFV introduction and spread remains a concern for the industry and the country, potentially resulting in significant losses in production, trade, and employment. Across California, outdoor raised pig operations (OPO) (e.g., commercial pork producers, backyard operations) are widely dispersed despite being regarded as a minority production method in the US\u0026nbsp;[12,13]. The potential for these animals to come into contact with wildlife, such as feral pigs, and the risk of pathogen transmission is greater than in conventional farms\u0026nbsp;[13]. This creates a scenario with important consequences for disease transmission at the wildlife-livestock interface.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, the number of feral pigs in the US has significantly expanded, with their range extending from 17 to 41 states. California is among the states with the largest and broadest geographic distribution of feral pigs\u0026nbsp;[13]. These non-domesticated pigs pose a significant threat to the conservation of native ecosystems and serve as a reservoir for diseases. In the context of ASFV, feral pigs play a crucial role in the spread of the disease, increasing the risk of its introduction into new areas and domestic herds.\u003c/p\u003e\n\u003cp\u003eThe significance for human and animal health of soft ticks (Acari: Argasidae) like the genus \u003cem\u003eOrnithodoros\u003c/em\u003e has typically been underestimated due to the specialized existence in protected microhabitats and characteristic short blood-feeding intervals (15-90 minutes)\u0026nbsp;[14]. As endophilous nidicoles, their niche comprises nests, burrows, and caves of vertebrate animals or human and livestock dwellings\u0026nbsp;[15]. Soft ticks may be less affected by rapidly changing environmental conditions than hard ticks and may instead be more influenced by extreme environmental conditions throughout their lifetime. However, limited information exists on the impact of climate change on soft ticks\u0026nbsp;[14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOrnithodoros\u003c/em\u003e ticks are involved in the original natural cycle of ASFV in Eastern and Southern Africa, where a sylvatic cycle occurs between wild suids, especially warthogs, and \u003cem\u003eO. porcinus\u003c/em\u003e ticks. Spillover into domestic swine is typically associated with infected tick bites or ingestion of contaminated warthog meat (S\u0026aacute;nchez-Vizca\u0026iacute;no \u003cem\u003eet al\u003c/em\u003e., 2012). \u003cem\u003eOrnithodoros\u003c/em\u003e ticks are globally distributed, and five species are present in California: \u003cem\u003eO. coriaceus, O. hermsi, O. turicata, O. parkeri\u003c/em\u003e, and \u003cem\u003eO. talaje\u003c/em\u003e [16]. Among those \u003cem\u003eO. coriaceus\u003c/em\u003e, O. parkeri, and O. turicata have been experimentally infected with ASFV and showed vector competence for the virus\u0026nbsp;[17,18]. \u003cem\u003eOrnithodoros\u003c/em\u003e ticks also play a crucial role in the maintenance and spread of ASFV in both wild and domestic populations of pigs\u0026nbsp;[17,19]. \u003cem\u003eSus scrofa\u003c/em\u003e carrying \u003cem\u003eOrnithodoros\u003c/em\u003e ticks have already been documented in the US\u003cem\u003e, O. coriaceus\u003c/em\u003e has been reported in California\u0026nbsp;[20], while \u003cem\u003eO. turicata\u003c/em\u003e in Texas\u0026nbsp;[21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOrnithodoros\u003c/em\u003e ticks are also responsible for transmitting various diseases in the United States, including human tick-borne relapsing fever (TBRF). TBRF is caused by spirochetes of the genus Borrelia and transmitted by four \u003cem\u003eOrnithodoros\u003c/em\u003e tick species: \u003cem\u003eO. parkeri, O. hermsi, O. turicata\u003c/em\u003e, and \u003cem\u003eO. talaje\u003c/em\u003e. These tick species transmit \u003cem\u003eB. parkeri, B. hermsii, B. turicatae\u003c/em\u003e, and \u003cem\u003eB. mazzottii\u003c/em\u003e, respectively, leading to human disease\u0026nbsp;[22\u0026ndash;24]. In addition to ASFV and TBRF, \u003cem\u003eOrnithodoros\u003c/em\u003e ticks are also implicated in the transmission of Epizootic Bovine Abortion (EBA), which is caused by Borrelia coriaceus and transmitted by \u003cem\u003eO. coriaceus\u003c/em\u003e [14,25,26]. EBA is characterized by late-term abortion and the birth of weak or dead calves. Although documentation is limited, EBA has been reported in California, Oregon, and Nevada, with associated rates ranging from 25 to 75%\u0026nbsp;[25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInteractions between feral and domesticated pigs, particularly those raised outdoors, are becoming more frequent. Their integration into outdoor environments facilitates their contact with feral pigs, thereby serving as potential conduits for ASFV transmission into commercial and backyard swine operations. Moreover, the presence of Ornithodoros ticks in these outdoor settings, with their proven role in ASFV transmission, further increases the risk, creating a remarkable challenge in controlling the spread of the virus if introduced in the state of California\u0026nbsp;[17,19,20,27].\u003c/p\u003e\n\u003cp\u003eSpecies distribution modeling (SDM) are a collection of advanced statistical and machine-learning methodologies that enable the prediction of suitable habitat ranges and ecological niches for various species based on their associations with specific environmental conditions such as temperature, precipitation, and vegetation type\u0026nbsp;[28\u0026ndash;30]. These approaches encompass a wide range of techniques, from deterministic methods such as logistic regression to stochastic approaches like Bayesian regression trees. SDMs also employ various model validation strategies to ensure the accuracy and reliability of the predictions generated\u0026nbsp;[31]. By leveraging these powerful tools, researchers can gain valuable insights into species distribution patterns and better understand the impacts of environmental factors on the distribution of organisms\u0026nbsp;[32]. In the context of disease ecology and epidemiology, SDM can be particularly useful for identifying areas at high risk of pathogen transmission by predicting the distribution of vectors and hosts. By considering the role of feral pigs and \u003cem\u003eOrnithodoros\u003c/em\u003e ticks in the spread of ASFV, it is possible to identify areas of high risk and prioritize control and surveillance efforts, thus helping to protect the swine industry and prevent the spread of this disease.\u003c/p\u003e\n\u003cp\u003eThe primary objectives of this study were to: (1) develop a fine-scale prediction of the distribution of \u003cem\u003eOrnithodoros spp\u003c/em\u003e. in California under current and future climate conditions, taking into account the potential effects of climate change on their distribution; (2) assess the spatial co-occurrence of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks, feral pigs, and known OPO locations in California to identify areas at high risk for ASFV transmission; and (3) provide recommendations for future tick surveillance efforts, research on host communities, and the identification of suitable habitats that support the maintenance of \u003cem\u003eOrnithodoros spp\u003c/em\u003e. Identifying high-risk areas for ASFV in California can support risk-based decision-making processes, enabling the prioritization of surveillance and control measures.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe description of the SDM model follows the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol proposed by Zurell \u003cem\u003eet al\u003c/em\u003e., [33]:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea. Overview\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eModel objective: Inference and explanation. Focal Taxon: \u003cem\u003eOrnithodoros spp\u003c/em\u003e.- \u003cem\u003eO. coriaceus, O. hermsi\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;O. parkeri\u003c/em\u003e. Location: California, US\u003c/p\u003e\n\u003cp\u003ea.1 Scale of Analysis\u003c/p\u003e\n\u003cp\u003eSpatial extent: -380102.2, 540036.5, -605326.6, 450447.3 (xmin, xmax, ymin, ymax). Spatial resolution: 423,967 km2. Boundary: political.\u003c/p\u003e\n\u003cp\u003eTemporal extent: Present and predicted future (2061-2080) climatic conditions.\u003c/p\u003e\n\u003cp\u003ea.2 Biodiversity data. Observation type: citizen science, field survey. Response data type: presence-only.\u003c/p\u003e\n\u003cp\u003ea.3 Predictors- Predictor types: climatic, edaphic, habitat, topographic.\u003c/p\u003e\n\u003cp\u003ea.4 Hypotheses. The selection of the predictor variables was based on the characteristics of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks. While little is known about their ecology and range of mammal hosts (climatic and habitat variables), \u003cem\u003eOrnithodoros spp\u003c/em\u003e. always exhibit an endophilous nidicoles lifestyle (edaphic and topographic variables).\u003c/p\u003e\n\u003cp\u003ea.5 Model assumptions. Independence of species observations; Availability of all important predictors; Niche stability/constancy, niche conservatism.\u003c/p\u003e\n\u003cp\u003ea.6 Algorithms\u003c/p\u003e\n\u003cp\u003eModelling techniques: MaxEnt (MX), Random Forest (RF). Model complexity: Use combinations of SDM algorithms to account for algorithmic uncertainty.\u003c/p\u003e\n\u003cp\u003eModel averaging: Ensembles of SDM algorithms to account for algorithmic uncertainty in order to transfer under scenarios of global change.\u003c/p\u003e\n\u003cp\u003ea.7 Workflow\u003c/p\u003e\n\u003cp\u003eModel workflow: Removal of highly correlated predictor variables. Generation of pseudo-absences. Spatial shorting bias test. MX and RF algorithms fitting. Evaluation by 10-fold cross-validation using the area under the curve (AUC). Selection of variables with a higher relative variable contribution. Ensemble of the models and transformation into binary distribution surface maps. Final models under current and future climatic conditions, by combining shared areas predicted by MX and RF binary maps.\u003c/p\u003e\n\u003cp\u003ea.8 Software\u003c/p\u003e\n\u003cp\u003eSoftware: SDM models: R Core Team version 4.1.1 (2021) with sdm package. Spatial data preparation: QGIS 3.20.3-Odense. Code availability: The R code used in the present study can be found in: https://github.com/cgonzalezcrespo/SDM_\u003cem\u003eOrnithodoros\u003c/em\u003e_CA. Data availability: All data used in the present study was collected from publicly available online resources (see section b. Data).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb. Data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eb.1 Biodiversity data\u003c/p\u003e\n\u003cp\u003eTaxon names: \u003cem\u003eOrnithodoros coriaceus, Ornithodoros hermsi\u003c/em\u003e and \u003cem\u003eOrnithodoros parkeri\u003c/em\u003e. Ecological level: species, operational taxonomic units. Data sources: GBIF.org (19 October 2021) GBIF Occurrence. Download https://doi.org/10.15468/dl.jkrgpx. and from Sage \u003cem\u003eet al\u003c/em\u003e.,\u0026nbsp;[34]. Sampling design: Due to the scarcity of records, all publicly available data was used in the study. Sample size: \u003cem\u003eOrnithodoros coriaceus\u003c/em\u003e (15), \u003cem\u003eOrnithodoros hermsi\u003c/em\u003e (65) and \u003cem\u003eOrnithodoros parkeri\u0026nbsp;\u003c/em\u003e(7). \u0026nbsp;Clipping: California, US\u003c/p\u003e\n\u003cp\u003eBackground data: Pseudo-absences (PA) are generated to act as negative samples in SDM and are crucial for accurately predicting the potential range of a species. The use of pseudo-absences in SDM assumes that the presence of a species is limited by environmental conditions, and these conditions are better represented by areas where the species is absent than by randomly selected points. Massin \u003cem\u003eet al\u003c/em\u003e.,\u0026nbsp;[35]\u0026nbsp;proposed a framework for selecting pseudo-absences in SDM. Therefore, 10 000 PA were used for the MaxEnt models and same as number of PA than presences, with 10 runs due to less than 1000 PA were used for the RF models. PA were spatially balanced and distributed evenly throughout the study area to avoid bias in the results.\u003c/p\u003e\n\u003cp\u003eErrors and biases: Presence of spatial sorting bias was tested following the methodology provided by Hijman, [36]. However, the data in this study did not present spatial sorting bias (ssb: 1.039).\u003c/p\u003e\n\u003cp\u003eb.2 Predictor variables and data sources.\u003c/p\u003e\n\u003cp\u003eBioclimatic predictors present and Elevation (WorldClim2, https://www.worldclim.org/data/worldclim21.html), Bioclimatic predictors 2061-2080 (WorldClim2, https://www.worldclim.org/data/cmip6/cmip6_clim30s.html, CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4, SSP: 370). Predictors: Elevation; BIO1, Annual Mean Temperature; BIO2, Mean Diurnal Range; BIO3, Isothermality; BIO4, Temperature Seasonality; BIO5, Max Temperature of Warmest Month; BIO6, Min Temperature of Coldest Month; BIO7, Temperature Annual Range; BIO8, Mean Temperature of Wettest Quarter; BIO9, Mean Temperature of Driest Quarter; BIO10, Mean Temperature of Warmest Quarter; BIO11, Mean Temperature of Coldest Quarter; BIO12, Annual Precipitation; BIO13, Precipitation of Wettest Month; BIO14, Precipitation of Driest Month; BIO15, Precipitation Seasonality; BIO16, Precipitation of Wettest Quarter; BIO17, Precipitation of Driest Quarter; BIO18, Precipitation of Warmest Quarter; BIO19, Precipitation of Coldest Quarter.\u003c/p\u003e\n\u003cp\u003eLand cover (NLCD 2019 Land Cover (CONUS) Multi-Resolution Land Characteristics (MRLC) Consortium, https://www.mrlc.gov/data/nlcd-2019-land-cover-conus). Predictors: 11, Open Water; 12, Perennial Ice/Snow; 21, Developed, Open Space; 22, Developed, Low Intensity; 23, Developed, Medium Intensity; 24, Developed High Intensity; 31, Barren Land (Rock/Sand/Clay); 41, Deciduous Forest; 42, Evergreen Forest; 43, Mixed Forest; 51, Dwarf Scrub; 52, Shrub/Scrub; 71, Grassland/Herbaceous; 72, Sedge/Herbaceous; 73, Lichens; 74, Moss; 81, Pasture/Hay; 82, Cultivated Crops; 90, Woody Wetlands; 95, Emergent Herbaceous Wetlands.\u003c/p\u003e\n\u003cp\u003eGeologic map (California geologic map data, USGS, (https://mrdata.usgs.gov/geology/state/state.php?state=CA). Predictors: R1, Igneous and Metamorphic; R2, Igneous and Sedimentary; R3, Igneous; R4, Melange; R5, Metamorphic and Sedimentary; R6, Metamorphic; R7, Sedimentary; R8, Unconsolidated.\u003c/p\u003e\n\u003cp\u003eSoils (Digital General Soil Map of the United States-STATSGO2, https://www.nrcs.usda.gov/resources/data-and-reports/description-of-statsgo2-database). Predictors: S1, Alfisols; S2, Andisols; S3, Aridisols; S4, Entisols; S5, Histosols; S6, Inceptisols; S7, Mollisols; S8, Ultisols; S9, Vertisols; S10, Other.\u003c/p\u003e\n\u003cp\u003eDistance to water (Calculated from USA Detailed Water Bodies (https://hub.arcgis.com/datasets/esri::usa-detailed-water-bodies/about) and USA Rivers and Streams (https://hub.arcgis.com/datasets/esri::usa-rivers-and-streams/about).\u003c/p\u003e\n\u003cp\u003eSlope and Orientation (calculated from Elevation).\u003c/p\u003e\n\u003cp\u003eSpatial extent: -380102.2, 540036.5, -605326.6, 450447.3 (xmin, xmax, ymin, ymax). Spatial resolution: 2112, 1840, 3886080 (nrow, ncol, ncell). Coordinate reference system: EPSG:3488 - NAD83(NSRS2007) / California Albers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTemporal extent: Present and predicted future (2061-2080) climatic conditions\u003c/p\u003e\n\u003cp\u003eData processing: Raster predictors layers were scaled to a resolution of 500x500 m. Hydrology (USA Detailed Water Bodies and USA Rivers and Streams) vector layers were merged in a single vector layer which was subsequently rasterized (500x500 m) and used to calculate the distance of each pixel to water. Slope and Orientation were calculated from the Elevation raster layer using the raster tool provided by QGIS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ec. Model\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ec.1 Variable pre-selection. As \u003cem\u003eOrnithodoros spp\u003c/em\u003e. are endophilous nidicoles, besides the standard bioclimatic and topographic predictors, information about the geology and soils was also incorporated in the model.\u003c/p\u003e\n\u003cp\u003ec.2 Multicollinearity. Highly correlated predictor variables associated with \u003cem\u003eOrnithodoros\u003c/em\u003e presence data were identified and removed using the variance inflation factor (VIF), a measure based on the square of the multiple correlation coefficient (R\u0026sup2;). This process ensures that the remaining predictor variables provide valuable and independent information for the model.\u003c/p\u003e\n\u003cp\u003ec.3 Model estimates. Coefficients: The accuracy of the individual models was assessed using the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve, with a threshold \u0026gt;= 0.8. Parameter uncertainty: To evaluate model accuracy, a 10-fold cross-validation process is employed, with 80% of the data used for training and the remaining 20% for testing. Variable importance: In the final model, only variables with a higher relative variable contribution (\u0026gt;0.05) based on AUC were incorporated.\u003c/p\u003e\n\u003cp\u003ec.4 Model selection, averaging and ensembles.\u003c/p\u003e\n\u003cp\u003eModel selection, averaging and ensembles: The present study followed the methodology proposed by Naimi and Ara\u0026uacute;jo, 2016. Each sdm object, created through the 10-fold cross-validation process contained 50 predictive models. The models in the sdm object were ensembled using as weights the true statistic skill (TSS) provided by the SDM package. The ensembled models (12 in total) were transformed into binary (presence/absence) distribution surface maps of California. Suitable areas were selected based on a threshold which maximized the true statistic skill (TSS) for each model, as provided by the SDM package [31]. The MX and RF binary maps for each of the three \u003cem\u003eOrnithodoros\u003c/em\u003e species were combined, including only those areas predicted by both algorithms. Finally, the distribution maps for the three species were combined under each set of present and future climatic condition predictors, as a comprehensive overview of potential suitable habitats for \u003cem\u003eOrnithodoros spp\u003c/em\u003e. in California.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ed. Assessment.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ed.1 Performance statistics- Performance on training data: TSS, AUC.\u003c/p\u003e\n\u003cp\u003ed.2 Plausibility check.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResponse shapes: The accuracy of the models was assessed using the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) curve, with a threshold be included of AUC \u0026gt; 0.8.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSus scrofa\u003c/em\u003e range: A previously published\u0026nbsp;[13]\u0026nbsp;distribution model of \u003cem\u003eSus scrofa\u003c/em\u003e and known OPO locations in California were utilized for the analysis of the feral swine range. The model provided the necessary information about the current distribution and potential areas where feral pigs could be found within the state (Additional file 1: Fig S1).\u003c/p\u003e\n\u003cp\u003eVector-Host Spatial Co-Occurrence; Identification of High-Risk Areas for ASFV. In order to identify high-risk areas for ASFV, a spatial co-occurrence analysis of shared areas of suitable habitat for both the \u003cem\u003eOrnithodoros\u003c/em\u003e tick vectors and \u003cem\u003eSus scrofa\u003c/em\u003e, their potential hosts, was conducted. By combining the distribution models of the ticks and feral swine, areas where both species were likely to coexist were assessed, representing regions with elevated risk for the transmission and spread of ASFV. The binary distribution maps of \u003cem\u003eOrnithodoros spp\u003c/em\u003e. and \u003cem\u003eSus scrofa\u003c/em\u003e were overlaid, and areas where their predicted suitable habitats intersected were identified. Through this analysis, specific locations in California with a higher likelihood of vector-host interactions and, consequently, a higher risk for ASFV transmission were pinpointed. The surface area of the predicted shared habitats under both current and future climatic conditions was calculated, providing insights into potential changes in high-risk areas due to climate change.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eOrnithodoros spp. individual models\u003c/h2\u003e \u003cp\u003eSome variables (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) contributed to predicting the distribution of the \u003cem\u003eOrnithodoros\u003c/em\u003e species across different models. Isothermality (Bio3) was the most significant predictor across all species and both modeling methods (MX and RF). For \u003cem\u003eOrnithodoros\u003c/em\u003e species, it accounted for a significant proportion of the model's predictive power, ranging from 19.3\u0026ndash;22.3% in MX models and 14.2\u0026ndash;19.1% in RF models. Precipitation Seasonality (Bio15) played a critical role in RF models for all species, with an importance ranging from 23.1\u0026ndash;26.3%. Soil types, represented by predictors S1 to S10, were significant in both models, but they were particularly influential in MX models. Of the soil types, Soil Type 4 (Entisols) stood out in its contribution to the MX models, with its highest importance peaking at 28% for \u003cem\u003eO. coriaceus\u003c/em\u003e. Land Cover variables were also influential in model outputs, but they did not consistently emerge as the most important variables. Geological predictors were impactful in some models. Specifically, R8 (Unconsolidated) was notably important in the future distribution model for \u003cem\u003eO. coriaceus\u003c/em\u003e, accounting for 12% of the model's predictive power.\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\u003eModels\u0026rsquo; parameters. Significance of variables in predicting the distribution of \u003cem\u003eOrnithodoros\u003c/em\u003e species under different climate scenarios (Future scenario for period 2061\u0026ndash;2080, CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4, SSP: 370). The percentages indicate the relative importance of each variable in the model's predictive power. TSS (true statistic skill) threshold for transformation into binary distribution maps. Mx, MaxEnt; RF, Random Forest.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTSS Threshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTop Variables by Importance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnvironmental Factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eO. coriaceus\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3959588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4 (28%), bio_3P (19.3%), S3 (12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS4: Entisols, bio_3P: Isothermality, S3: Aridisols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5457646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4 (25.2%), bio3F (22.3%), R8 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS4: Entisols, bio3F: Future Isothermality, R8: Unconsolidated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3157592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebio_15P (31.7%), bio_3P (26%), Slope (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebio_15P: Precipitation Seasonality, bio_3P: Isothermality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5649089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebio15F (20.8%), bio3F (16.2%), bio12F (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebio15F: Future Precipitation Seasonality, bio3F: Future Isothermality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eO. hermsi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2120322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation2 (26.5%), DistancetoWater2 (17.1%), R7 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR7: Sedimentary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5669928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation2 (22.3%), DistancetoWater2 (19.9%), R7 (6.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR7: Sedimentary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1988138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation2 (22.3%), bio_12P (4.8%), DistancetoWater2 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebio_12P: Precipitation of Wettest Month\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5437049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElevation2 (27%), DistancetoWater2 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eO. parkeri\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4279793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebio_14P (33.4%), S4 (20.9%), R7 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebio_14P: Precipitation of Driest Month, S4: Entisols, R7: Sedimentary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6628849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebio14F (35%), S4 (22.9%), R7 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebio14F: Future Precipitation of Driest Month, S4: Entisols, R7: Sedimentary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4009344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4 (24%), bio_15P (9.5%), bio_9P (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eS4: Entisols, bio_15P: Precipitation Seasonality, bio_9P: Mean Temperature of Driest Quarter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFuture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ebio14F (35%), S4 (22.9%), R7 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ebio14F: Future Precipitation of Driest Month, S4: Entisols, R7: Sedimentary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOrnithodoros spp. suitable area in California\u003c/h2\u003e \u003cp\u003eAfter combining first, the individual models of the three species (Additional file 2: Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2, S3 and S4) and second the predictions by both algorithms (Additional file 2: Fig S5), the results predicted areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) within California that could potentially have suitable habitats for \u003cem\u003eOrnithodoros spp\u003c/em\u003e., identifying potential high-risk areas for ASFV. The present suitable area for \u003cem\u003eOrnithodoros spp\u003c/em\u003e., determined by factors such as weather conditions, land cover, edaphic factors, and topographic features, was calculated to be 117,208 km\u0026sup2;. However, considering future weather scenarios alongside the consistent land cover, edaphic, and topographic factors, the suitable habitat (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) for \u003cem\u003eOrnithodoros spp.\u003c/em\u003e is projected to expand to 124,077.5 km\u0026sup2;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eConsequently, a potential range expansion in suitable area for \u003cem\u003eOrnithodoros spp\u003c/em\u003e. is predicted to increase under future (2061\u0026ndash;2080) climate scenarios by 6,869.5 km\u0026sup2; (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVector-host spatial co-occurrence. Identification of high-risk areas for ASFV\u003c/h3\u003e\n\u003cp\u003eResults indicated a significant overlap in habitats considered suitable for both \u003cem\u003eOrnithodoros spp.\u003c/em\u003e and feral/domestic \u003cem\u003eSus scrofa\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). This overlap, spanning an area of approximately 37,242.75 km\u0026sup2;, represents a substantial risk area for ASFV transmission due to the concurrent presence of the vector and the host. However, when projecting these observations into the future and incorporating predicted climate scenarios, the overlap of habitats suitable for both species was found to increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), estimated to cover around 39,213 km\u0026sup2;.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparing the present and future overlap areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), reveals an increase in shared habitat space by approximately 1,970.25 km\u0026sup2; from the current overlap area to the predicted future scenario.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the habitat suitability modeling of \u003cem\u003eOrnithodoros spp.\u003c/em\u003e revealed that specific areas in the state of California exhibit a higher suitability for the presence of this vector, consequently increasing the risk of ASFV transmission. These high-risk areas were identified by the co-occurrence of domestic pigs, feral pigs, and suitable climate conditions for \u003cem\u003eOrnithodoros spp.\u003c/em\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe results of the analysis seem to corroborate with the unique ecological and biological characteristics of \u003cem\u003eOrnithodoros spp\u003c/em\u003e., as soft ticks. As soft ticks, they exhibit endophilous nidicole behavior, with their niche comprising nests, burrows, caves of vertebrate animals, or human and livestock dwellings. Therefore, it is logical that they show sensitivity to variables such as Isothermality (Bio3), Precipitation Seasonality (Bio15), specific soil types like Entisols (S4), and unconsolidated geological features (R8). The fact that Isothermality (Bio3) appeared as a critical variable across all species and models underlines the potential importance of diurnal and seasonal temperature changes for these ticks. This variable represents the diurnal range divided by the temperature annual range, indicating the magnitude of day-to-night oscillation relative to summer-to-winter oscillations. Given their lifestyle in protected microhabitats, they may experience more stable conditions and be less susceptible to sudden environmental changes compared to hard ticks. Nevertheless, these ticks still show sensitivity to extreme temperature conditions, which are reflected in the isothermality measurements.\u003c/p\u003e\u003cp\u003eLikewise, the significance of Precipitation Seasonality (Bio15) in the RF models indicates the relevance of moisture levels for these ticks. While soft ticks' existence in protected microhabitats might buffer them against rapid environmental shifts, extreme or prolonged changes in precipitation might still impact their habitat suitability. The preference for Entisols (S4) might be linked to the nidicole behavior of these ticks. As they are typically found in burrows, nests, and similar microhabitats, the soil characteristics of these environments could have a direct impact on their survival and propagation. Entisols, with their minimal horizon development, might offer suitable conditions for these ticks' burrowing habits. Finally, the importance of the unconsolidated geological category (R8) could be attributed to their affinity for burrow-like environments. Unconsolidated materials such as sand or gravel might offer easier burrowing and hiding conditions, thereby providing more secure microhabitats for these ticks.\u003c/p\u003e\u003cp\u003eThe increasing overlap between the distribution areas of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks, feral pigs, and OPOs poses serious implications for animal and public health, particularly concerning the transmission of African Swine Fever Virus (ASFV) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], EBA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and human TBRF [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. From a disease management perspective, these overlapped areas should be prioritized for targeted surveillance, vector control efforts, and biosecurity measures [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e \u003cem\u003eOrnithodoros spp\u003c/em\u003e., which are part of the original ASFV sylvatic cycle in Eastern and Southern Africa [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], have been found to play a critical role in the worldwide spread and maintenance of ASFV in both wild and domestic populations of pigs [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The integration of OPOs within the swine industry, while representing a fraction of total production, introduces significant epidemiological complexities at the wildlife-livestock interface, particularly concerning ASFV transmission risks. This tick species, along with the growing interface between feral pigs and OPOs, as identified in recent studies, underlines a critical concern for disease management and control strategies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Feral pigs, with their burgeoning populations across the US, present an ongoing risk for the introduction and spread of infectious diseases, including ASFV, given their ability to traverse and inhabit diverse ecosystems, thereby increasing contact opportunities with domestic swine herds [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] underscores the heightened vulnerability of small-scale and pasture-based pig operations, which are increasingly prevalent in California. These systems, often characterized by less stringent biosecurity measures compared to large-scale confinement operations, provide ample opportunities for ASFV transmission through direct interactions between feral and outdoor-raised pigs. The environmental conditions conducive to Ornithodoros tick habitats further compound this risk, facilitating a vector-borne transmission pathway in addition to direct contact [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These studies focusing on diversified small-scale farms have identified risk factors associated with the management practices of outdoor-raised pigs, drawing parallels with the conditions that elevate the risk of Shiga toxin-producing \u003cem\u003eEscherichia coli\u003c/em\u003e transmission [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This research highlights the broader implications of farm management practices at the smallholder level on disease transmission dynamics, offering insights into the interconnectedness of livestock health, farm practices, and public health concerns.\u003c/p\u003e\u003cp\u003eThe overlap of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks, feral pigs with outdoor-raised pig operations not only signifies a direct pathway for ASFV transmission but also underscores the need for comprehensive surveillance and targeted biosecurity enhancements in these systems. The identification of high-risk areas where overlap is most pronounced aids in focusing resources and interventions to mitigate the risk of disease spread effectively. Implementing strategies that address the unique vulnerabilities of small-scale and backyard livestock operations is paramount in safeguarding the national swine industry from the potential devastation of ASFV outbreaks [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAdditionally, \u003cem\u003eOrnithodoros\u003c/em\u003e ticks are known vectors for diseases beyond ASFV, notably including EBA, a condition caused by \u003cem\u003ePajaroellobacter abortibovis\u003c/em\u003e and transmitted primarily by \u003cem\u003eO. coriaceus\u003c/em\u003e [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This tick species, integral to the ecosystem in California, has significant implications for the cow-calf ranching industry in the West, where EBA poses a substantial economic burden. The disease's impact on calf mortality rates can profoundly affect the economic viability of affected ranches, underscoring the importance of effective management and control strategies for these tick populations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The increased interface between tick habitats and livestock operations exacerbates the risk of EBA, this scenario could pose further challenges to food security, given the role of industry in local and national economies. There is, therefore, a critical need for ongoing surveillance and diagnostic efforts to identify and mitigate the impact of this disease [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, the development and deployment of a live \u003cem\u003ePajaroellobacter abortibovis\u003c/em\u003e vaccine have shown promise in protecting cattle against EBA [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition to the risks posed by \u003cem\u003eOrnithodoros spp.\u003c/em\u003e to swine through ASFV transmission and to cattle via EBA, the cattle industry in California faces parallel concerns with \u003cem\u003eAnaplasma marginale\u003c/em\u003e infections. This bacterium causes bovine anaplasmosis, a significant disease impacting cow-calf operations. Research into the seroprevalence of \u003cem\u003eAnaplasma marginale\u003c/em\u003e within traditionally managed large beef herds underscores the continuous threat of tick-borne diseases to California's cattle industry [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These challenges exemplify the broader issues of managing vector-borne diseases in livestock operations, critical for the health and productivity of the state's agricultural commodities.\u003c/p\u003e\u003cp\u003eThe presence of diseases such as bovine anaplasmosis highlights the need for comprehensive disease surveillance and management strategies across livestock species. This necessity is amplified by the broader implications of tick-borne diseases, which extend to public health concerns, including the risk of human TBRF due to their expanding geographical distribution and overlap with human-inhabited areas, could potentially lead to higher incidences of TBRF [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The overlapping habitats of livestock and tick vectors necessitate integrated pest management and enhanced biosecurity measures to mitigate the risks of disease transmission.\u003c/p\u003e\u003cp\u003eThis scenario accentuates the need for heightened public health surveillance and appropriate preventative measures in identified high-risk areas to mitigate potential health impacts on local communities. Such multifaceted challenges at the wildlife-livestock-human interface emphasize the importance of collaborative efforts among veterinary health, public health, and agricultural sectors. Addressing these issues is crucial for safeguarding California's agricultural economy, ensuring the sustainability of its livestock industries, and protecting public health. The interconnectedness of these challenges underscores the need for a One Health approach, recognizing the intricate relationships between animal health, human health, and the environment\u003c/p\u003e\u003cp\u003eIn this study, the future climate model chosen was CMIP6.CMIP.NOAA-GFDL.GFDL-ESM4 (Geophysical Fluid Dynamics Laboratory Earth System Model Version 4), under the Shared Socioeconomic Pathway (SSP) 370 scenario. The rationale behind selecting this particular model and scenario can be explained by several factors: Model Performance: GFDL-ESM4 is a state-of-the-art Earth System Model developed by the NOAA Geophysical Fluid Dynamics Laboratory. It has demonstrated improved performance over its predecessor models, providing more accurate and reliable future climate projections. Representativeness: The GFDL-ESM4 model is part of the CMIP6 (Coupled Model Intercomparison Project Phase 6) initiative, which is a globally coordinated effort to improve climate projections. The CMIP6 ensemble represents a wide range of possible future climate scenarios, increasing the likelihood that the selected model is representative of future climate conditions in California. Shared Socioeconomic Pathway (SSP) 370: The SSP 370 scenario was chosen because it represents a plausible future climate trajectory that is consistent with current greenhouse gas emission trends and socioeconomic development patterns. This \"middle-of-the-road\" scenario assumes moderate mitigation efforts to reduce greenhouse gas emissions and a medium level of socioeconomic development. It is considered a realistic and useful scenario for assessing potential climate change impacts on the distribution of \u003cem\u003eOrnithodoros spp.\u003c/em\u003e and ASFV risk in California. Relevance to the study area: The GFDL-ESM4 model, combined with the SSP 370 scenario, provides projections that are relevant to the study area, considering the specific climate conditions in California. This model captures the regional climate variability and potential changes in temperature and precipitation patterns.\u003c/p\u003e\u003cp\u003eIt is important to note that the findings of this study should be viewed as an initial step towards proactive ASFV risk management in California. Further research is necessary to validate the results, monitor \u003cem\u003eOrnithodoros spp.\u003c/em\u003e population dynamics, and focus surveillance efforts on the high-risk areas identified. Despite some existing knowledge of \u003cem\u003eOrnithodoros\u003c/em\u003e tick biology and ecology in the United States, numerous knowledge gaps persist (Gonzalez-Crespo \u003cem\u003eet al\u003c/em\u003e., 2024, in prep), posing challenges for a comprehensive understanding of these organisms. One such limitation is the scarcity of up-to-date information on their distribution throughout various regions of the country. An examination of historical records and literature reveals a dearth of data on the distribution of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks. For example, a mere 119 georeferenced historical records of the genus in the United States are accessible via the GBIF (Global Biodiversity Information Facility) database. Although some recent studies have estimated the distribution of specific \u003cem\u003eOrnithodoros\u003c/em\u003e species in particular regions, especially in the southern states, using species distribution models and available records [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], a substantial portion of our knowledge relies on sampling efforts and publications from several decades ago [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As the historical data on \u003cem\u003eOrnithodoros\u003c/em\u003e ticks in the United States is outdated, it is likely that species distributions have shifted over time due to changes in land cover, development, climate, and other ecological factors. This highlights the need for additional research to accurately determine the current distribution of the genus across the US landscape and address these knowledge gaps. Ultimately, the adoption of a One Health approach is critical to tackling these emerging health challenges. This interdisciplinary strategy acknowledges the intrinsic interconnectedness of human, animal, and environmental health, and advocates for the integration of diverse scientific disciplines and coordinated efforts at multiple levels [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy identifying ASFV high-risk areas in California, the research findings can help guide future tick surveillance efforts, inform risk-based decision-making, and prioritize surveillance and control measures. The results of this study hold implications for understanding the potential distribution of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks and the associated ASFV risk in California. This knowledge can assist in devising targeted surveillance efforts and developing effective control strategies, thereby ensuring the swine industry remains resilient in the face of potential ASFV outbreaks and the evolving threat posed by climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Coalition for EpiResponse Engagement \u0026amp; Science (CERES). CERES is a collaborative partnership betweenColorado State University (CSU), Iowa State University (ISU), Kansas State University (KSU), Texas A\u0026amp;M University (TAMU), University of California \u0026ndash; Davis (UC Davis), University ofNebraska \u0026ndash; Lincoln (UNL) and University of Nebraska Medical Center (UNMC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCGC: conception, design of the work, acquisition, analysis, and interpretation of data. BML: conception, design of the work and substantively revised. JB: design of the work. LP: acquisition of data. AP: acquisition of data and substantively revised. HJ: substantively revised.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in the present study was collected from publicly available online resources (see section b. 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Prev Vet Med. 2011;101(3\u0026ndash;4):148\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"African swine fever, Ornithodoros spp., species distribution modeling, climate change, vector-borne diseases, feral pigs, surveillance, biosecurity, California","lastPublishedDoi":"10.21203/rs.3.rs-5419700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5419700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAfrican swine fever virus (ASFV) is a highly contagious pathogen affecting domestic and wild pigs, with soft ticks (\u003cem\u003eOrnithodoros\u003c/em\u003e spp.) acting as significant vectors. Understanding the distribution of \u003cem\u003eOrnithodoros\u003c/em\u003e ticks in relation to environmental variables is crucial for anticipating ASFV transmission risk areas.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study employed species distribution modeling (SDM) using MaxEnt and Random Forest algorithms to predict the suitable habitat of \u003cem\u003eOrnithodoros coriaceus\u003c/em\u003e, \u003cem\u003eO. hermsi\u003c/em\u003e, and \u003cem\u003eO. parkeri\u003c/em\u003e in California under current and future climate scenarios (2061\u0026ndash;2080). The SDMs incorporated climate, edaphic, topographic, and habitat variables, with models evaluated through 10-fold cross-validation using the Area Under the Curve (AUC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKey predictors included Isothermality (BIO3), Precipitation Seasonality (BIO15), and soil type (Entisols). The present habitat suitability for \u003cem\u003eOrnithodoros\u003c/em\u003e spp. covered approximately 117,208 km\u0026sup2;, projected to increase by 6,869.5 km\u0026sup2; under future conditions. The spatial co-occurrence analysis highlighted an overlap of approximately 37,242.75 km\u0026sup2; between \u003cem\u003eOrnithodoros\u003c/em\u003e spp. habitats and feral/domestic pig distributions, expanding to 39,213 km\u0026sup2; under future scenarios.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIdentifying high-risk areas for ASFV transmission through SDMs provides valuable insights for targeted surveillance and biosecurity measures. The study emphasizes the need for integrated vector-host management and proactive strategies to mitigate ASFV risk in California.\u003c/p\u003e","manuscriptTitle":"Species distribution modelling of Ornithodoros spp. in California with consideration of climate variation and identification of ASFV high-risk areas ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 10:30:03","doi":"10.21203/rs.3.rs-5419700/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":"214ceaea-2b09-477d-b9ec-87af00ed3df3","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-14T21:38:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 10:30:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5419700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5419700","identity":"rs-5419700","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}