Earlier feral pig mapping studies by the Southeastern Cooperative Wildlife Disease Study and National Feral Swine Program focused on the entire US and only county level occurrence of feral pigs. A 2015 USDA study overlapped NFSP county-based feral pig locations with data from the 2012 NAHMS study of small-enterprise swine operations, specifically whether these survey respondents had seen feral pigs on their premises or within the same county, to ascertain the level of agreement between the two datasets. They identified five counties in California that were in agreement with our MaxEnt model findings for suitable feral pig habitat: Mendocino, Tehama, Nevada, El Dorado, and San Luis Obispo, and two counties that differed: Ventura and Los Angeles counties. However, their map does not reflect the heterogeneity of feral pig habitat in each county nor identify high-risk contact areas between farms and feral pigs in California, as they did not identify any outdoor-raised domestic swine in California. Although these county-based maps are important to demonstrate the trend of increasing feral pig populations nationwide, stakeholders and feral pig disease surveillance agencies could benefit from targeting outreach and mitigation strategies to specific regions within a county using our maps. The results of our final MaxEnt model indicated five variables that were useful in predicting suitable feral pig areas in California,10 plastic plant pots including three WorldClim layers: BIO6: the minimum temperature of the coldest month, BIO13: precipitation of the wettest month, and BIO15: the coefficient of variation for seasonal precipitation.
Other studies also used WorldClim factors to predict the distribution of wild boar or feral pigs. These bio-climatic variables have been widely used in environmental studies and are now becoming popular for use in epidemiological investigations. These climate variables are 30 year averages and “capture broader biological trends better than the temperature or the amount of precipitation for a given day due to the inherent variability associated with weather.” Bosch et al built a MaxEnt model for wild boar in Spain and their model also contained BIO6 and BIO15 as did regional models built by Pittiglio et al with BIO13 being significant as well. BIO6 is the minimum temperature of the coldest month and is interpreted as being a useful variable when deciding if the species of interest is affected by extreme cold events throughout a year. Hill et al used MaxEnt to predict the distribution of Trichinella and Toxoplasma gondii in feral pigs in the US and also identified BIO6 and elevation as significant predictor variables, along with land cover and other WorldClim factors. The response curve for BIO6 in our model peaks at the predicted ideal range for feral pigs, with both ends indicating extreme cold temperatures that may be avoided by feral pigs. A 2015 study by McClure et al indicated that suitable feral pig habitat may be limited by cold temperatures, precipitation and water availability, which reflects our findings. BIO13 is defined as precipitation of the wettest month and is useful if extreme rainfall patterns influence the range of feral pigs. BIO15 measures the variation in annual precipitation totals per month and reflects the variability of rainfall that may affect a species.
According to the Jackknife graph, the variable with the highest gain when used alone was BIO15, and therefore had the most important information for predicting suitable feral pig habitat. Snow et al used Bayesian methods to predict the expansion of feral pigs in the US , but also detected that temperature and precipitation levels were significant predictors. The final MaxEnt model gain is decreased the most if elevation is ignored and therefore it has significant information that is not available from the other variables in predicting feral pig suitability. Elevation was also significant in the MaxEnt models built by Hill et al . These results combined with the response curve possibly reflect feral pigs preference for lower altitudes in the US. AVGMODIS, a measure of the annual maximum green vegetation fraction on a scale of 0 to 100, was also an important predictor of suitability, which reflects feral pigs’ need for available food and vegetative cover. Garza et al identified NDVI, which AVGMODIS is based upon, and precipitation as important variables in predicting home ranges of feral pigs or wild boar worldwide, using generalized linear models. The significant layers identified in our study to predict feral pig suitability are not unique, and this may be due to the fact that feral pigs are a highly adaptable and opportunistic omnivores. Lobe et al stated that MaxEnt AUC values will be lower for generalist species that are widely distributed. However, the AUCc of our final model was 89.7, which indicates a good model. Additionally, 2017 hunting tags vs. all 5,148 points for 2012-19 provided the best model. MaxEnt is an important method to predict the distribution of rare species, and an upper maximum range for the number of species occurrence points has not been previously determined.
However, our result fits with a study conducted by Chen et al to determine the sample size for the outcome variable in building MaxEnt models. They reported that standard deviation decreased and MaxEnt models became more stable using species occurrence points of 1,000-1,200. Most likely the sample size of the outcome variable that reaches asymptote is dependent on geographic extent and characteristics of the species of interest. Regarding feral pig presence on farms, the most recent NAHMS survey asked participating swine small-enterprise producers in the US about presence of feral swine in their county but did not separate farms based on whether they raised domestic swine indoors or outdoors. However, a 2015 United States Department of Agriculture report regarding overlap of feral and domestic pigs in the US used this NAHMS dataset and reported that of 320 participating US counties, 74% of these counties had small-enterprise swine producers who allowed their pigs some level of outdoor access. The NAMHS results indicated that 52.9% of small-enterprise swine producers in the West/South region, which included California, reported feral pigs in the same county, with 16.2% of those having feral pig presence on their operation, similar to our survey results that showed 15.91% of respondents had seen feral pigs within 500 feet of their pig herd. Another study that measured co-occurrence of feral pigs and agriculture to understand the risk of disease transmission, but did not separate outdoor versus indoor herds, reported that on average, 47.7% of all types of farms had feral pigs in the same counties, including California, showing a significant increase in the decade from 2002-2012. The results of these aforementioned survey-based studies indicated together that more than 45% of farms have feral presence within the same county, which matches the results from our risk map that showed almost half of the identified OPO had suitable feral pig habitat nearby These findings indicate the need for targeted outreach and mitigation strategies for those farms at highest-risk for feral pig contact, due to the potential for disease transmission between these two swine groups. Studies that identified high-risk areas in California between feral pigs and domestic swine raised outdoors are sparse. A 2015 USDA report extracted outdoor operations with NFSP feral swine populations and did not identify any hot spots of overlap in California as seen in our results. However, plastic pots large they did not report the number of OPO per state or county and most likely our state-focused study identified more OPO than their survey-based national study. A 2017 study by Miller et al also assessed possible disease transmission between feral pigs and farm at the county level. They reported that domestic swine, either raised indoors or outside, have been increasing in counties that also had feral pig presence. The lack of maps identifying areas at high-risk for disease transmission between these two swine populations indicates a need for further research. Additionally, the risk map identified eastern counties as having the lowest risk. However, we did not identify OPO in many of these counties, therefore we cannot say there is no risk in these regions. A limitation of this study involves using hunting tags as a proxy for presence of feral pigs to predict suitable habitat. Hunting tags are voluntarily submitted to CDFW by hunters and estimated to account for only 30% of all hunted pigs and most likely biased toward easy to access areas. Also, only half of the land in California is public land and accessible to hunters, therefore feral pigs hunted on private land are not included in our data sets. However, Rutten et al used similar hunting bags and MaxEnt to successfully predict the distribution of wild boar in Belgium. And Alexander et al also used hunting records to predict wild boar habitat in Europe. Additionally, MaxEnt assists in overcoming these challenges by identifying similar habitats in all parts of California and predicting suitable areas. Both the MaxEnt model and risk map are limited because they are static maps that use fixed layers as their foundation; consequently, they do not incorporate dynamic events over various years . Also, feral pigs may migrate seasonally due to shifting weather, resource availability, hunting pressure or wildfire and future research could focus on species distribution modeling that includes dynamic real time variables or remote sensing data; however, seasonal or dynamic spatial data are not available yet for most spatial predictors in California.
However, our approach is valuable as a first step in identifying multiple high-risk areas for future research, where additional data could be collected. Furthermore, future research could add feral pig disease data collected statewide to evaluate if high-risk areas for feral-domestic pig contact equates to those areas with higher prevalence of diseases. For instance, farms and ranches in California, including backyard and commercial operations, are not required to register with state agricultural agencies, therefore, the total number, distribution, and size of OPOs remains unknown and are underrepresented in this study. A majority of the identified OPO in this study were commercial pork producers with an online presence or ones that attend conferences, farmers markets and fairs. If more OPO locations could be identified, than a more comprehensive map of high-risk areas could be generated. Additionally, because we are based at the University of California, Davis in Yolo County, there is selection bias in the OPO identified as our agricultural networks are within the UCCE network. Over represented counties reflected either sampling bias or clustering of these niche operations or both. Nevertheless, the number of OPOs included in this study and the fact that more than 40% of these operations were in highly suitable areas for feral pig contact is relevant as an initial approximation of a likely much larger risk of disease transmission at the feral-domestic swine interface in California. In the future, adding disease cases to this risk map would add additional epidemiological information regarding possible pathogen transmission. The trend to raise domestic swine outside on pasture in the United States , instead of inside confinement barns with high levels of bio-security, has been increasing in the last few decades and is a possible risk factor for the transmission of food borne disease in the wildlife livestock-human interface. Generic Escherichia coli are commensal bacteria living in all mammals; however, pathogenic Shiga toxin-producing Escherichia coli strains can cause severe illness in humans and can be transmitted through consumption of contaminated produce and meat from livestock or wild animals. Both feral and domestic swine are considered a species of concern for the transmission of food borne pathogens, because they are the same taxonomy and therefore can share diseases, and both harbor zoonotic pathogens such as STEC, Salmonella spp., Brucella suis and swine influenza virus. Information regarding the prevalence of STEC in domestic swine raised outdoors is sparse, and more data is needed to understand whether feral pigs that live near these outdoor swine operations may pose a risk for the transmission of pathogens between these two swine populations. Referenced in this study as outdoor-raised pig operations , these type of farms are primarily considered niche market production operations. However, OPO are broadly distributed throughout California, providing an opportunity for the transmission of emerging or reemerging diseases that had been eradicated in confinement swine herds, as each outdoor-based domestic pig could act as an intermediary host between feral pigs and humans. Feral pigs in the US are a mix of introduced Eurasian wild boars and escaped domestically-raised pigs turned feral. Additionally, feral pigs harbor and transmit emerging or transboundary diseases , such as African Swine Fever and pseudorabies.