Invasive species can spread rapidly at local and national scales, creating significant environmental and economic impacts. A central problem in mitigation efforts is identifying methods that can rapidly detect invasive species in a cost-effective and repeatable manner. This challenge is particularly acute for species that can spread over large areas (>1 million km2). Wild pigs (Sus scrofa) are one of the most prolific invasive mammals on Earth and cause extensive damage to agricultural crops, native ecosystems, and livestock, and are reservoirs of disease. They have spread from their native range in Eurasia and North Africa into large areas of Australia, Africa, South America, and North America. We show that the range of invasive wild pigs has increased exponentially in Canada over the last 27 years following initial and ongoing releases and escapes from domestic wild boar farms. The cumulative range of wild pigs across Canada is 777,783 km2, with the majority of wild pig distribution occurring in the Prairie Provinces. We evaluate eight different data collection and evaluation/validation methods for mapping invasive species over large areas, and assess their benefits and limitations. Our findings effectively map the spread of a highly invasive large mammal and demonstrate that management efforts should ideally rely on a set of complementary independent monitoring methods. Mapping and evaluating resulting species occurrences provide baseline maps against which future changes can be rapidly evaluated.
The Anthropocene is the current geological age on earth, characterized by the dominant influence of humans on the environment, including impacts on native ecosystems and spread of invasive species globally1. Indeed, invasive species have been identified as one of the greatest threats to global biodiversity2. Human activities can facilitate the spread of invasive species, directly through intentional and unintentional movements of plants and animals, and indirectly through habitat fragmentation and change associated with agriculture, urban expansion, and other anthropogenic land-use changes, climate change, and over-harvest of native species3,4,5. Efforts to mitigate the spread of invasive species have been limited by the lack of timely and accurate maps of occurrences and spatial expansion, especially over very large areas (>1 million km2). Processes required to complete comprehensive national scale mapping of a species are limited by project budgets. New approaches are needed that are cost-effective and repeatable, especially for species expected to expand rapidly over large areas and that are associated with greatest impact.
Wild pigs (Sus scrofa), also referred to as feral swine, wild hogs, or feral hogs6, currently have the largest global range of any non-domesticated terrestrial mammal on earth7. Native to Eurasia and part of North Africa, wild pigs have expanded their range, primarily through human introductions combined with natural dispersal, across all continents except Antarctica7,8. The broad geographic extent of their native range, coupled with the generalist nature of the species has allowed wild pigs to easily adapt and survive in new environments that span a broad range of climate, habitat, and resources9,10. The widespread success of wild pigs is explained by their extremely high fecundity11, early sexual maturity12, plastic diet7, long lifespans13, and highly adaptive nature9.
Wild pigs are an invasive species in North America and are descendants of Eurasian wild boar (S. scrofa scrofa), domestic pigs (S. scrofa domesticus), and hybrids of the two14,15. Long-established populations have existed in southern parts of the U.S. for hundreds of years, with areas of high population densities in Texas, Florida, and California. In the continental United States, there has been a well-documented expansion in the distribution and abundance of wild pigs in recent decades, from 17 to 38 states during the last 30 years16. Wild pigs were first introduced to Canada during a federal and provincial agriculture diversification initiative in the 1980’s and 1990’s to diversify livestock species and supplement producer incomes17,18. Escapes and intentional releases from domestic wild boar farms have led to the feral populations that are established on the Canadian Prairies18,19. Brook and van Beest18 provided a coarse-scale distribution of wild pigs in Saskatchewan at the Rural Municipality level. However, prior to this current study there has not been a comprehensive national scale map of the species range in Canada.
Wild pigs are considered to be the most damaging invasive species in the U.S.20, posing numerous ecological and socio-economic threats within their introduced range. Referred to as ecological train wrecks, wild pigs alter ecosystem processes, vegetation successional stages, nutrient cycles, and cause erosion, sedimentation, and eutrophication to riparian areas and water bodies7,16,20,21,22,23. The generalist nature and plastic diet of wild pigs allows them to utilize and compete for a wide variety of resources, as well as predate small mammals, amphibians, invertebrates, and ground nests16. The significant disturbance of habitat, resources, and ecosystem processes has direct and indirect effects on native wildlife and has the ability to decrease biodiversity and cause extirpations and extinctions20,24,25. Species extirpations and population declines as a result of wild pig presence have been documented in the United States, Galapagos Islands, and Australia16,26,27,28. In some areas within wild pig’s introduced range, populations have expanded to the point where eradication is no longer feasible29. Negative impacts associated with wild pigs have been well-documented across Europe, Australia, and the United States; however, these negative impacts have not been characterized in Canada.
A key challenge in managing rapidly expanding invasive species such as wild pigs at national and continental scales is having up-to-date information on their spatial distribution. Mapping the locations of invasive species is central to guiding effective management and is essential to determine if control efforts are effective at controlling and limiting, or even reducing, their spatial expansion30. However, identifying cost-effective methods to accurately and repeatedly map a species at a national scale represents a significant time and financial commitment. Conventional ecological monitoring used for large mammals such as aerial surveys, trail cameras, and mark-recapture can be effective at relatively small scales (<100,000km2), but become time and cost prohibitive at much larger scales. The use of local and traditional knowledge accumulated by people living and working on the land through personal observations and shared knowledge has been used for documenting species occurrences31,32. Such data can be collected using personal interviews, mail surveys, internet surveys, telephone surveys, and open source mapping33,34. Similarly, citizen science engages large numbers of lay people in collecting species occurrence data, such as the annual Breeding Bird Count and the Christmas Bird Count across the United States and Canada35, though this also requires considerable logistics coordination and has rarely been used for large mammals. Efforts to incorporate local knowledge in data collection using rigorous social science methods have rarely been applied over large areas (>1 million km2). Much work remains to evaluate the efficacy of these methods and determine the benefits and limitations of the different approaches in the face of immediate needs for detailed information on invasive species in general and wild pigs in Canada specifically. As such, the objectives of this study were to: (1) identify the past and current spatial distribution of wild pigs across Canada, and (2) evaluate the benefits and limitations of four different data collection methods and four different evaluation/validation approaches for national-scale, repeatable mapping of wild pig spatial distribution.
Wild pig occurrence data were collected from four independent data collection methods (stakeholder snowball sampling, expert interviews, bounty data, and rural telephone survey) and were used directly to develop maps of wild pig distribution across Canada using all datasets combined for three time periods, 1990–2000, 2001–2010, and 2011–2017 (Fig. 1). An additional four methods (GPS collars, citizen science photos, research trail camera networks, and media search) were used to evaluate benefits and limitations of methods and validate observations. A total of 1,489 occupied watersheds were identified, out of 37,578 watersheds in the study area (3.9% occupied by wild pigs), based on pooled results of all data collection methods across all years. The area of watersheds occupied in Canada has increased exponentially from 1990 to 2017 (Fig. 2). The large majority of the spatial expansion (92%) occurred in the three prairie provinces of Alberta, Saskatchewan, and Manitoba (Fig. 3). Indeed 58% of the national spread of wild pigs occurred within Saskatchewan. Wild pigs are also established in localized populations in British Columbia, Ontario, and Quebec. Of the ten provinces in Canada, only the four eastern provinces in Atlantic Canada (Newfoundland and Labrador, New Brunswick, Prince Edward Island, and Nova Scotia; 5% of Canada combined) have no confirmed sightings of wild pigs.
Based on analysis at the Level 9 watershed, the cumulative range of wild pigs in Canada is 777,783 km2. The average annual cumulative increase in wild pig range from the period 1990–2017 was 40,936 km2. The greatest increase in range expansion has occurred in the current time period (2011–2017), with an average annual cumulative increase of 88,094 km2.
As expected, the number of detected watersheds in Canada occupied by wild pigs was different among sampling methods (Table 1). There was an overall high consistency in the spatial distribution of occupied watersheds for each method. However, specific correspondence of individual occupied watersheds between data collection methods was low overall (<50%) for all methods (Table 2). Correspondence was strongest between the occupied watersheds determined using expert interviews and stakeholder snowball sampling methods (21% correspondence). The number of occupied watersheds was significantly and positively associated with the level of correspondence among all possible pairs of data collection methods (R2 = 0.90, df = 5, p < 0.001).
Overall mean response rate for the expert interviews was 51% (S.E. = 5.9). The mean number of completed districts within provinces was 55% (S.E. = 7.7). Total number of individual participants in the stakeholder snowball sampling method and rural telephone survey was 275 and 3,000 respectively. 272 detections were received from the bounty data. Response from all methods combined provided 95.5% coverage of the study area using the Canadian census sub-division as stratification units (Fig. 4).
Each of the eight research methods assessed to identify and evaluate wild pig distribution had important benefits and limitations and were viewed by participants with varying degrees of creditability and spatial accuracy (Table 3). The three social methods (expert interviews, stakeholder snowball sampling, and rural telephone survey) resulted in the majority (94%) of all watersheds detected with wild pigs in this study, with stakeholder snowball sampling providing the greatest number of occupied watersheds. Total cost of the data collection and mapping methods during the study period was $125,621; with an average of $294 per wild pig occurrence (range $50–$852). Cost of evaluation and validation methods totaled $671,000; with an average of $247 per wild pig occurrence (range $4–$644). Overall cost for all eight methods used was $796,621.
Invasive wild pigs are widespread and rapidly expanding their range in Canada, creating significant risks to the sustainability of native ecosystems and agricultural and livestock production following their escapes and purposeful releases from multiple sources at wild boar meat and penned shoot farms, starting in 1990. This study has produced the first published maps that document the spread of wild pigs across Canada. We show that the majority of the expansion has occurred in the three prairie provinces of Alberta, Manitoba, and especially Saskatchewan. Introduction events and the abundance of agriculture as a high-quality food source exert the greatest effect on the success of wild pig establishment and growth36,37. The Prairie Provinces historically and currently contain the highest number of wild boar farms in Canada19, and are dominated by an agricultural landscape, both of which are expected to have influenced the establishment success and rapid spread of wild pigs. We expect that given the range of available food resources and habitats18, high reproduction rates with an average of 6 young per female38, and the overall absence of national and provincial management plans and control efforts, wild pig populations and range will continue to expand exponentially over the next decade at least. Our results are consistent with the rapid expansion of wild pig populations in the United States16,39 and most other areas of their native and introduced range40,41,42. Many areas in Canada are susceptible to wild pig expansion, especially those that are comprised of ample, energy-rich food resources from agriculture crops10, forest cover37, relatively low predator densities37, and repeated introduction/re-introduction events19.
Our finding of wild pigs mainly concentrated in the Prairie Provinces with some of the coldest winters of all ten provinces and that the species is rare or absent in the warm coastal areas is inconsistent with studies that have found that wild pig distribution is positively associated with warmer climates and have suggested cold winter temperatures to be highly limiting10,37. This likely reflects, in part, that domestic wild boar farms that are known sources of free-ranging wild pigs were, and are, more concentrated on the Canadian Prairies19. The success of wild pigs in western Canada does highlight the capacity for wild pigs to thrive and expand in areas with long and extremely cold winters including some occurrences north of 55° north latitude.
The majority of occupied watersheds throughout Canada are located in agriculture and human-dominated landscapes. The presence of wild pigs in these landscapes creates significant socio-economic risks incurred from economic loss from crop damage43, predation of small and young livestock9,22, vehicle collisions44, structural damage26, and health and safety concerns to humans, livestock, and wildlife16,45. Although these risks have yet to be quantified in Canada, agriculture losses in the United States from wild pig damage have been estimated at $1.5 billion USD per year by Pimental46, based on extrapolation of localized data. Wild pig-vehicle collisions cost $36 million USD per year44, and there is the continued threat of significant economic losses that could be incurred to the livestock industry and international trade if diseases reportable to the Canadian Food and Inspection Agency were identified45,47. In many areas of their introduced and native range wild pigs are considered disease reservoirs and maintenance hosts due to their high densities, complex social behaviors, and ability to maintain the disease without a continued source of infection48,49,50. Wild pigs are host to 89 bacterial, viral, and parasitic diseases which can be transferred to livestock, wildlife, and humans16,45,51. Common diseases of concern transmitted from wild pigs to livestock are swine brucellosis, bovine tuberculosis, pseudorabies, and classical swine fever, while zoonotic diseases include brucellosis, Escherichia coli, salmonellosis, and leptospirosis16,45,52,53. Diseases at the livestock-wildlife interface pose challenges to wildlife managers and livestock producers, as well as towards quantitative risk analyses, as the disease status of wildlife populations and routes of transmission are often poorly understood45,51. Disease threats are an increasing concern to livestock producers and the pork and beef industries, as disease outbreaks are associated with high economic losses9,51,54. The challenges associated with eliminating disease in wildlife populations pose a continued threat of disease introduction to livestock, act as an impediment to disease elimination in livestock populations, and has the potential for spill-over back into wildlife populations49,55,56.
While our mapping efforts provide national scale coverage for an area of 5.5 million km2 at a relatively low cost and the benefits of this approach are evident, there are also limitations that should be considered. Overall, correspondence of wild pig detection between methods was low, with none of the methods came close to detecting all of the occupied watersheds identified by all methods combined. This lack of saturation demonstrates the value of an approach based on multiple methods for identifying a large dataset of novel detections, but also highlights that while stakeholder snowball sampling detected the most wild pig observations within watersheds, they only identify less than half of all watersheds detected in this study. No single method provided comprehensive coverage of the study area due to different sampling designs, stratification units, and response rates of different target groups. The arbitrary stratification units used to determine response for the different sampling methods provided different sizes of coverage between and within methods, with a general trend in unit size increasing northward, and decreasing in size towards the south. The combined use of multiple methods provided comprehensive coverage of the study area. Although it is expected that non-detection errors occurred, the extensive coverage of the study area provides confidence in the overall distribution of wild pigs across Canada.
In presence-absence survey methods non-detection errors and false absences are a common sampling problem57,58. Due to the elusive and often nocturnal behavior of wild pigs, their preferred habitat comprised of thick cover in wetlands and forests, and relatively low density across most of the study area, some false negatives are expected. Using interviews with large numbers of study participants and multiple independent methods helped address challenges in detecting wild pigs and balancing the benefits and limitations of each method59,60. We recognize that our combined map of watersheds identified with wild pigs represents a minimum estimate and that there are likely some undetected watersheds, but given such rapid expansion of wild pigs this is inevitable. Non-detection was addressed in the survey design and implementation with the use of multiple survey methods across the study area and a large sample size59,60. The possibility of false positives by misidentifying species is also a concern in presence/absence surveys that has the potential to overestimate presence60, however, wild pigs are a novel, large, and distinctively shaped species on the landscape that are unlikely to be misidentified. Additionally, the majority of our study participants are individuals who live, work, and pursue recreational activities outdoors, therefore have a higher than average exposure to, and knowledge of, wildlife.
The overall goal of our comparison of different methods was to promote consideration of a wide range of options and how to evaluate and integrate them, not to be prescriptive. Each study will have different budgets, time constraints, and access to existing datasets and so will make different decisions. Expert interviews through systematic sampling provided the most widespread range of independent, unique locations across the study area at moderate cost. The use of expert elicitation is a common method used in identifying species distributions and is a valuable resource61,62, however is potentially limited by non-response bias. Non-response within the expert elicitation survey was found to be affected by the distribution of, and individual’s knowledge regarding, wild pigs; however the most significant factor in determining response rate was the time constraints of experts due to priorities within their profession. Therefore, non-response, while in some cases was determined by the lack of wild pig presence, did not follow the distribution of wild pigs and is not considered to have biased the expert interview method.
Access to social networks of stakeholders using snowball sampling provided the largest number of occupied watersheds across a broad spatial scale at a relatively low cost. The use of social networks allows for large sample sizes to be obtained at a low-cost from hard-to-reach populations with unknown parameters63. However, this technique can introduce selection bias since it involves non-probability sampling of a specific target group, where over or under-representation of a group or specific characteristic can occur due to stronger or weaker social connections64. Inherently, selection bias was present in our sampling design, as only individuals with wild pig knowledge and within subsequent referral chains participated. However, we addressed this in the sampling design through the large sample of participants, as well as by access to several small, discrete referral chains, rather than a few large ones65,66 therefore were able to access a range of participants across a broad geographic and temporal scale.
The rural telephone survey was only moderately effective in terms of the number and range of wild pig detections compared to the high cost. However, the random stratified sampling method and large sample size across all ten Canadian provinces provided some novel data and had limited bias associated with the sampling technique. The stratified random sampling technique reduced bias, as individuals were randomly selected and a set number of interviews was previously identified, therefore non-response bias in provinces without, or with very few, wild pigs was limited by the survey design. The contribution of this sampling method to the overall study design was that it helped examine whether there was any bias in data obtained from the other social science sampling methods. While the overall number of detected watersheds was low for the rural telephone survey, the distribution of occupied watersheds followed the same trend observed in the other social survey methods, validating that bias had been reduced in the expert interview and stakeholder snowball sampling methods. The bounty program in the province of Alberta provided a large number of wild pig locations relative to its small extent, but was limited to specific counties within a single province. While bounties are considered counter-productive to wild pig control effort67 they did help identify the provincial distribution in Alberta.
Methods used in the evaluation and validation of the sampling methods and wild pig locations included GPS collars, citizen science and research trail camera network photos, and media searches. These methods were not suitable for including in the national-scale mapping, but were valuable in evaluating the costs, limitations, and benefits associated with different sampling methods and validating wild pig detections. The use of GPS collars is a common technique in wildlife research, providing multiple locations daily with high spatial accuracy68. However, this was by far the most expensive method evaluated, and while it provided a large number of occurrence points it only occurred at a fine scale, with few watersheds detected overall. Similar research evaluating multiple sampling methods has demonstrated the value of GPS collars and radio telemetry for evaluation or validation of multiple sampling methods, as opposed to an improvement or ‘truth’ in distribution mapping or occupancy estimation31,32. Research trail camera networks and citizen science photos were a low-cost, spatially accurate, and unbiased means of validating wild pig locations. Media searches resulted in very few, broad-scale detections and were difficult to validate through additional contact with the primary information holder.
The combined use of multiple methods that incorporates social and ecological data has been documented in the literature and is an effective tool for large-scale data collection of a species31,32,33,69,70,71. Large sample sizes and the incorporation of multiple sampling methods and survey designs conducted in this study provided a more robust dataset, captured a wider range of information holders, and reduced sampling bias69,70,72. As such, we did not choose one method as ‘truth’ against which to compare all others, but rather to consider all methods as any other dataset to evaluate benefits and limitations and compare with the database of all combined occurrences73. Several studies have found that a strong correlation exists between traditional or local ecological knowledge and western science, with the differences between the two ultimately stemming from differences in temporal and spatial scale31,32,74,75. When used in conjunction with one another, the variation in spatial and temporal scales between local and traditional knowledge with conventional ecological research provides complimentary and novel information and can address gaps in knowledge31,76,77.
Timely and cost-effective mapping of invasive species over large areas (>1 million km2) is essential to developing effective management strategies and responding rapidly to introduced species and rapidly changing distributions. Our study design utilized multiple sampling methods to acquire a large dataset, compare and evaluate results, and identify the national distribution and range expansion of a large, low-density, nocturnal species. The overall approach that we present here can be applied to species distribution studies, with a strong emphasis on invasive species. Although study designs may vary according to the ecology and behavior of the species being studied and the timing of introduction events, the transdisciplinary approach we developed and validated effectively addresses the common challenges and provides a process that is broadly relevant to any invasive species. Each method evaluated has benefits and limitations associated with it, and trade-offs between methods need to be assessed for individual studies. The rapid identification of invasive species distribution provides managers with the ability to implement management strategies to attempt to eradicate the species before population and density levels increase to the point where the cost of eradication becomes unfeasible.
Our map provides the first documented distribution of invasive wild pigs across Canada’s ten provinces. The widespread distribution and significant range expansion identified over the past 27 years, along with the extensive and costly list of risks associated with wild pigs, highlights the need for rapid and aggressive management action. The map offers a baseline against which future range expansion can be compared and a valuable resource for identifying priority management and conservation areas. In the current millennium with ever expanding globalization, land use changes, and climate change, challenges associated with invasive species are likely to only increase5,78. Future research can apply the technique established in this study for rapid and cost-effective identification and understanding of the distribution of an invasive species on the landscape.
Our study area included all ten Canadian provinces. Bordered by the Pacific Ocean to the west and the Atlantic Ocean to the east, Canada’s provincial landmass has an area of 5,499,918 km2 79 and stretches from the U.S./Canada border northward to 60°00′ N latitude. This large expanse of land includes a broad range of biodiversity and variability in ecosystems, topography, temperature, and precipitation that includes mountain ranges, open grassland, temperate rainforest, coniferous and deciduous forest, plains, and boreal shield80. As a result of the heterogeneity across latitude, elevation, and proximity to the moderating effects of large waterbodies, or the more extreme inland climates, considerable variability in temperature and precipitation is observed across Canada’s 15 ecozones81,82.
Our preliminary research based on interviews with researchers and wildlife managers across Northern Canada’s three territories (Yukon, Northwest Territories, and Nunavut) who further consulted with field staff and stakeholders, determined that there was no evidence of wild pigs in these most northerly ecozones, as predicted based on the literature, the extreme cold winter temperatures (<−50 °C) combined with long winters (>7 months), and the absence of past or present domestic wild boar production (except several recent small operations near Whitehorse, Yukon) (Brook unpublished data) which are the source of invasive wild pigs. As such we excluded Taiga Cordillera, Taiga Plans, Taiga Shield, Hudson Plains, Southern Arctic, Northern Arctic, Arctic Cordillera, and Tundra Cordillera ecozones and defined our current study area as including the Pacific Maritime, Montane Cordillera, Boreal Plains, Boreal Shield, Prairies, Mixedwood Plains, and Atlantic Maritime ecozones.
Data collection comparisons
We collected four independent wild pig occurrence datasets and four evaluation/validation datasets using social science and conventional wildlife monitoring to document and evaluate wild pig occurrences across Canada. Our social survey design was approved by the University of Saskatchewan Behavioral Research Ethics Board (BEH# 15–155) and the ecological data was approved by University of Saskatchewan Animal Research Ethics Board (Animal Use Protocol no. 20150024). Both human and wildlife research performed in this study were in accordance with the terms of the ethics approvals above. Informed consent was obtained for all human participants throughout all of the described data collection methods. While the term ‘wild pig’ is now widely used6, for our survey we used the term ‘wild boar’ as we found from preliminary consultations and pre-testing that this term was best understood at the time of the study by rural Canadians. We are now consistently using ‘wild pigs’, recognizing that few, if any, pure bred domestic or free-ranging Eurasian wild boar exist in Canada. The datasets were evaluated against one another to assess the trade-offs that occur from different data collection methods. The total number of wild pig detections and number of occupied watersheds were calculated along with the total cost and cost per wild pig detection for each data collection method.
Expert interview survey design
A stratified sampling design based on provincial wildlife enforcement and management districts was conducted from January 2015 to December 2017. At least one conservation officer (C.O.) and one government wildlife biologist in each district was contacted where possible. Contact information and locations were found through employee directories on provincial government websites. The first positive response received was the individual with which the interview was completed. Three contact attempts were made for each individual, with a message left on the first attempt. Attempts were halted once one interview within the strata had been completed or there was no response after three attempts. If no interviews were completed within a stratum it was considered a non- response. Strata were considered half-complete if both a C.O. and biologist were located in the stratum, but only one of the two completed an interview. Interview questions are included in Supplementary Methods (S1).
Stakeholder snowball sampling design
A snowball sampling survey design83 was implemented to access key participants with knowledge of wild pig presence. Sampling took place from October 2014 to December 2017. Recruitment for this study began with an individual who shared their observations and then provided additional contacts that were also likely to know of wild pig occurrences. This snowball method of accessing additional contacts creates an ever-expanding network of individuals with information64,71. The technique uses social networks; therefore, personal communication between the researchers and members of the social networks was essential64. Non-probability sampling methods typically require the use of advertising and outreach to be successful, compared to the stricter framework of probability sampling, in which letters and phone calls are made to specific individuals84. The snowball sampling method for this study used both techniques for recruiting participants. Multiple methods of recruitment were utilized to gain access to participants. Presentations at wildlife meetings and conventions, information booths, posters, and magazine articles were all forms that were used to recruit participants. Active recruitment such as presentations and information booths were used to make initial contacts and gain access to social networks. Face-to-face contact was primarily used for initial contact and telephone was used to access additional contacts. Passive recruitment in the form of posters and magazine articles were used in an attempt to access an even broader geographic range of participants. Individuals with first-hand wild pig knowledge were also provided by the sample of experts. In order to keep the interest of the participant, and hence response rate, high, interviews were not completed with snowball sample members, only questions relating to wild pig locations were asked.
Rural telephone survey design
We used a stratified random sampling design to conduct a telephone survey of 3,000 individuals, representative of the rural Canadian population. The survey instrument included several themes but for the purposes of this paper we only used the responses to three questions: (1) What is your postal code? (2) Are you aware of wild boar presence or observations in your area in the past five years? (3) If yes, can you provide a date and location of the observation? Data collection was conducted by the Social Science Research Laboratory call center at the University of Saskatchewan. Participants could respond in either official language, English or French, and French responses were translated by individuals with a strong proficiency in that language. Provinces were stratified based on Forward Sortation Area’s (FSA’s) for rural areas as defined by Canada Post. The interview quota for each province was stratified based on the provincial rural population (from Stats Canada 2017 unpublished data) and individual contact information was randomly selected from within the FSA’s. Interviewers asked to speak with a member of the household that was over the age of 18 and was having the next birthday. Interviews were completed in February and March of 2016 to take advantage of the time when farmers were least busy. Individuals with wild pig information were asked if they would be willing to provide their contact information and receive a follow-up call from the primary researcher. Follow-up calls inquired about wild pig locations in greater detail as a means of validation. Interview questions and scripts are included in Supplementary Methods (S1).
Bounty data collection
A bounty on wild pigs was in effect in the province of Alberta from 2003 to 2016. The program was initiated by the provincial government with individual counties signing up to participate. Hunters received $50 for every pair of wild pig ears that were turned into the county, and were required to provide the associated location and date of the kill. All counties participating in the program were contacted and data regarding the location, date, and number of pairs of ears turned in was received. Data were then compiled and verified.
We captured 21 female and 17 male wild pigs by net-gun fired from a helicopter (n = 33) and through trapping using corral-style traps (n = 5) from 2015 to 2017 in southeastern and east-central Saskatchewan. After capture, each wild pig was physically immobilized and fitted with a Global Positioning System (GPS) tracking collar (Telonics, Mesa Arizona, USA). All collars were programmed to record a location every three hours and transmit the data via Iridium satellite link.
Citizen science photos
Citizen science photos consisted of images from trail cameras, digital cameras, and cellular phones. Photos were provided by stakeholders and experts as supplementary information associated with wild pig locations when available.
Trail camera network
We used images from a previous study (O’Brien et al., unpublished data) in our study design as an evaluation/validation method. Over a two-year period from 2011 to 2013 a network of 17 research trail cameras were deployed to capture wild pigs over an area of 275 km2 in east-central Saskatchewan (O’Brien et al., unpublished data).
Media searches were conducted between 2014 and 2017. Searches were conducted in Google, media outlets, hunting forums, and social media and included key words such as “Eurasian wild boar”, “wild boar”, “feral boar”, “feral wild boar”, “feral pig”, and “wild pig”.
Coverage by each method was determined using pre-defined provincial and national units that provided stratified and comparable coverage across the entire study area. Experts were stratified by provincial wildlife management zones, which are designated areas they manage wildlife and work within. The snowball stakeholder and bounty methods used provincial municipal administrative units and the telephone survey used national Forward Sortation Areas. All methods combined used the Canadian census sub-division units, as this stratification unit was consistent across the entire study area and provided a conservative unit size at the national scale with an average area of 1,195 km2 85.
Wild pig occurrence mapping
We followed the MaNIS Georeferencing protocol (2001) and converted all wild pig locations into UTM coordinates using Google Maps (2018) and the Legal Land Description Converter (2017). Estimates of spatial accuracy of the data were based on information from the provider and using map scale. Two Level 9 (North America and Arctic) watershed shapefiles were downloaded from HydroSHEDS (2017) following the Pfafstetter coding system86. The 37,578 Level 9 watershed sub-basins in the study area have a mean area of 267 km2 87. Watershed units were chosen to model wild pig distribution as they are ecologically stratified units and allow for consistency across the landscape based on abiotic and biotic factors10,32,88. Level 9 watershed sub-basins are large enough to include the home range of at least one individual or sounder, which average 3.6 km2 for sows and 4.91 km2 for boar89,90,91,92, while encompassing considerable landscape heterogeneity10, The use of watershed units also maintains confidentiality of exact locations to ensure private land is not easily accessible to hunters, which was a condition of many landowners for providing information. Each wild pig occurrence was buffered by a radius of 10 km, a conservative estimate of annual home range movements and an area larger than the expected spatial error from any of the data collection methods. Watersheds that intersected a wild pig location were selected to create a watershed occurrence layer illustrating wild pig presence by watershed units. Two ten-year interval maps were created from 1990–2000 and 2001–2010. A current wild pig location map was created for the years 2011–2017.
Aggregate data are provided in this paper. Fine-scale point locations are required to remain confidential in accordance with our ethics approval (U of S BEH# 15–155).
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Drizo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
Early, R. et al. Global threats from invasive alien species in the twenty-first century and national response capacities. Nat. Commun. 7, 12485, https://doi.org/10.1038/ncomms12485 (2016).
Crowl, T. A., Crist, T. O., Parmenter, R. R., Belovsky, G. & Lugo, A. E. The spread of invasive species and infectious disease as drivers of ecosystem change. Front. Ecol. Environ. 6, 238–246 (2008).
Hulme, P. E. Beyond control: Wider implications for the management of biological invasions. J. Appl. Ecol. 43, 835–847 (2006).
Hulme, P. E. Trade, transport, and trouble: Managing invasive species pathways in an era of globalization. J. Appl. Ecol. 46, 10–18 (2009).
Keiter, D. A., Mayer, J. J. & Beasley, J. C. What is in a ‘common’ name? A call for consistent terminology for nonnative Sus scrofa. Wildl. Soc. Bull. 40, 384–387 (2016).
Barrios-Garcia, M. N. & Ballari, S. A. Impact of wild boar (Sus scrofa) in its introduced and native range: A review. Biol. Invasions 14, 2283–2300 (2012).
Massei, G. & Genov, P. V. The environmental impact of wild boar. Galemys 16, 135–145 (2004).
Seward, N. W., VerCauteren, K. C., Witmer, G. W. & Engeman, R. M. Feral swine impacts on agriculture and the environment. Sheep Goat Res. J. 19, 12 (2004).
McClure, M. L. et al. Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States. PLoS ONE 10 (2015).
Taylor, R. B., Hellgren, E. C., Gabor, T. M. & Ilse, L. M. Reproduction of feral pigs in southern Texas. J. Mammal 79, 1325–1331 (1998).
Gethöffer, F., Sodeikat, G. & Pohlmeyer, K. Reproductive parameters of wild boar (Sus scrofa) in three different parts of Germany. Eur. J. Wildl. Res. 53, 287–297 (2007).
Gamelon, M. et al. Do age-specific survival patterns of wild boar fit current evolutionary theories of senescence? Evolution 68 (2014).
Goedbloed, D. J. et al. Genome-wide single nucleotide polymorphism analysis reveals recent genetic introgression from domestic pigs into northwest European wild boar populations. Mol. Ecol. 22, 856–866 (2013).
Government of Saskatchewan. Wild boar production: economic and production information for Saskatchewan producers, http://hartkeisonline.com/wp-content/uploads/WildBoarProduction0111.pdf (2001).
Bevins, S. N., Pedersen, K., Lutman, M. W., Gidlewski, T. & Deliberto, T. J. Consequences associated with the recent range expansion of nonnative feral swine. BioScience 64, 291–299 (2014).
Wilkins, W. & Dobbs, S. Have you seen feral wild boar? http://publications.gov.sk.ca/documents/20/83601-agv1212-pdf.pdf (2013).
Brook, R. K. & van Beest, F. M. Feral wild boar distribution and perceptions of risk on the central Canadian Prairies. Wildl. Soc. Bull. 38, 486–494 (2014).
Michel, N. L., Laforge, M. P., van Beest, F. M. & Brook, R. K. Spatiotemporal trends in Canadian domestic wild boar production and habitat predict wild pig distribution. Landsc. Urban Plan. 165, 30–38 (2017).
Vitousek, P. M., D’Antonio, C. M., Loope, L. L. & Westbrooks, R. Biological invasions as global environmental change. Amer. Sci. 84, 468 (1996).
Howe, T. D. & Bratton, S. P. Winter rooting activity of the European wild boar in the Great Smoky Mountains National Park. Castanea 41, 256–264 (1976).
Tegt, J., Mayer, J., Dunlap, J. & Ditchkoff, S. Plowing through North America: Wild pigs leave a trail of depredation and disease. The Wildlife Professional (2011).
Zengel, S. A. & Connor, W. H. Could wild pigs impact water quality and aquatic biota in floodplain wetland and stream habitats at Congaree National Park in South Carolina? In Proceedings of the 2008 South Carolina Water Resource Conference (2008).
Vitousek, P. M. Biological invasions and ecosystem processes: Towards an integration of population biology and ecosystem studies. Oikos 57, 7–13 (1990).
Charles, H. & Dukes, J. S. Impacts of invasive species on ecosystem services in Biological Invasions. Ecological Studies (Analysis and Synthesis), (ed. Nentwig, W.) (Springer, 2008).
United States Department of Agriculture, and Animal Health and Inspection Service. Feral swine: Impacts on threatened and endangered species, https://www.aphis.usda.gov/publications/wildlifedamage/2016/fsc-feral-swine-impacts-tes.pdf (2016).
Cruz, F., Donlan, C. J., Campbell, K. & Carrion, V. Conservation action in the Galapagos: Feral pig (Sus scrofa) eradication from Santiago Island. Biol. Cons. 121, 473–478 (2005).
Fordham, D. A., Georges, A. & Brook, B. W. Indigenous harvest, exotic pig predation and local persistence of a long-lived vertebrate: Managing a tropical turtle for sustainability and conservation. J. Appl. Ecol. 45, 52–62 (2008).
Timmons, H. et al. Feral hog population growth, density, and harvest in Texas. Texas A&M AgriLIFE (2012).
Elith, J. & Leathwick, J. R. Species distribution models: Ecological explanation and prediction across space and time. Annu. Rev. Ecol., Evol. Syst. 40, 677–697 (2009).
Brook, R. K. & McLachlan, S. M. Transdiciplinary habitat models for elk and cattle as a proxy for bovine tuberculosis transmission risk. Prev. Vet. Med. 91, 197–208 (2009).
Polfus, J. L., Heinemeyer, K. & Hebblewhite, M. & Taku River Tlingit First Nations. Comparing traditional ecological knowledge and western science woodland caribou habitat models. J. Wildl. Manage. 78, 112–121 (2013).
Brook, R. K. & McLachlan, S. M. Trends and prospects for Local Knowledge in ecological and conservation research and monitoring. Biodivers. Conserv. 17, 3501–3512 (2008).
Rupp, S. P., Ballard, W. B. & Wallace, M. C. A nationwide evaluation of deer hunter harvest survey techniques. Wildl. Soc. Bull. 28, 570–578 (2000).
Link, W. A., Sauer, J. R. & Niven, D. K. A hierarchical model for regional analysis of population change using Christmas bird count data, with applications to the American black duck. Condor: Ornithological Applications 108, 13–24 (2006).
Tabak, M. A., Webb, C. T. & Miller, R. S. Propagule size and structure, life history, and environmental conditions affect establishment success of an invasive species. Sci. Rep. 8, 10313 (2018).
Lewis, J. S. et al. Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal. Sci. Rep. 7, 44152 (2017).
Koen, E. L., Vander Wal, E., Kost, R. & Brook, R. K. Reproductive ecology of recently established wild pigs in Canada. Am. Midl. Nat. 179, 275–286 (2018).
McClure, M. L., Burdett, C. L., Farnsworth, M. L., Sweeney, S. J. & Miller, R. S. A globally-distributed alien invasive species poses risks to United States imperiled species. Sci. Rep. 8, 5331 (2018).
Morelle, K., Fattebert, J., Mengal, C. & Lejeune, P. Invading or recolonizing? Patterns and drivers of wild boar population expansion into Belgian agroecosystems. Agricult. Ecosys Environ 222, 267–275 (2016).
Ballari, S. A., Cuevas, M. F., Cirignoli, S. & Valenzuela, A. E. J. Invasive wild boar in Argentina: Using protected areas as a research platform to determine distribution, impacts and management. Biol. Invasions 17, 1595–1602 (2015).
Bengsen, A. J., Gentle, M. N., Mitchell, J. L., Pearson, H. E. & Saunders, G. R. Impacts and management of wild pigs Sus scrofa in Australia. Mammal Rev. 44, 135–147 (2013).
Pimental, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econom. 52, 273–288 (2005).
Mayer, J. J. & Johns, P. E. Characterization of wild pig - vehicle collisions. In Proceedings of the Wildlife Damage Management Conference 12, 175–87 (2007).
Miller, R. S., Farnsworth, M. L. & Malmberg, J. L. Diseases at the livestock-wildlife interface: Status, challenges, and opportunities in the United States. Prev. Vet. Med. 110, 119–132 (2013).
Pimental, D. Environmental and economic costs of vertebrate species invasions into the United States. Managing Vertebrate Invasive Species 38 (2007).
Pedersen, K. et al. Pseudorabies in feral swine in the United States, 2009-2012. J. Wildl. Dis. 49, 709–713 (2013).
Naranjo, V., Gortazar, C., Vicente, J. & de la Fuente, J. Evidence of the role of European wild boar as a reservoir of mycobacterium tuberculosis complex. Vet. Microbiol. 127, 1–9 (2008).
Olsen, S. C. Brucellosis in the United States: Role and significance of wildlife reservoirs. Vaccine 285, F73–76 (2010).
Ruiz-Fonz, F., Segales, J. & Gortazar, C. A review of viral diseases of the European wild boar: Effects of population dynamics and reservoir role. Vet. J. 176, 158–169 (2008).
Witmer, G. W., Sanders, B. R. & Taft, A. C. Feral swine - are they a disease threat to livestock in the United States? Mich. Bovine TB 113 (2003).
Gaskamp, J. A., Gee, K. L., Campbell, T. A., Silvy, N. J. & Webb, S. L. Pseudorabies virus and brucella abortus from an expanding wild pig (Sus scrofa) population in southern Oklahoma, USA. J. Wildl. Dis. 52, 383–386 (2016).
Campbell, T. A. et al. Feral swine exposure to selected viral and bacterial pathogens in southern Texas. J. Swine Health Prod. 16, 312–315 (2016).
Siembieda, J. L., Kock, R. A., McCracken, T. A. & Newman, S. H. The role of wildlife in transboundary animal diseases. Anim. Health Res. Rev. 12, 95–111 (2011).
Miller, R. S. & Sweeny, S. J. Mycobacterium bovis (bovine tuberculosis) infection in North American wildlife: Current status and opportunities for mitigation of risks of further infection in wildlife populations. Epidemiol. Infect. 141, 1357–1370 (2013).
Corner, L. A. L. The role of wild animal populations in the epidemiology of tuberculosis in domestic animals: How to assess the risk. Vet. Microbiol. 112, 303–312 (2006).
Gu, W. & Swihart, R. K. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol. Cons. 116, 195–203 (2004).
MacKenzie, D. I. What are the issues with presence-absence data for wildlife managers? J. Wildl. Manage. 69, 849–860 (2005).
Miller, D. A. et al. Improving occupancy estimation when two types of observational error occur: Non-detection and species misidentification. Ecology 92, 1422–1428 (2011).
McClintock, B. T., Bailey, L. L., Pollock, K. H. & Simons, T. R. Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections. Ecology 91, 2446–2454 (2010).
Johnson, C. J., Heard, D. C. & Parker, K. L. Expectations and realities of GPS animal location collars: Results of three years in the field. Wildl. Biol. 8, 153–159 (2002).
Yamada, K., Elith, J., McCarthy, M. & Zerger, A. Eliciting and integrating expert knowledge for wildlife habitat modelling. Ecol. Model. 165, 251–264 (2003).
Kaplan, C. D., Korf, D. & Sterk, C. Temporal and social contexts of heroin-using populations: An illustration of the snowball sampling technique. J. Nerv. Ment. Dis. 175, 556–574 (1987).
Robins Sadler, G., Lee, H. C., Lim, R. S. & Fullerton, J. Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nurs. Health Sci. 12, 369–374 (2010).
Penrod, J., Bray Preston, D., Cain, R. E. & Starks, M. T. A discussion of chain referral as a method of sampling hard-to-reach populations. J. Transcult. Nurs. 14, 100–107 (2003).
Atkinson, R. & Flint, J. Accessing hidden and hard-to-reach populations: Snowball research strategies. Soc. Res. Update (2001).
Ditchkoff, S., Holtfreter, R. W. & Williams, B. L. Effectiveness of a bounty program for reducing wild pig densities. Wildl. Soc. Bull. 41, 548–555 (2017).
Johnson, C. J. & Gillingham, M. P. Mapping uncertainty: Sensitivity of wildlife habitat ratings to expert opinion. J. Appl. Ecol. 41, 1032–1041 (2004).
Huntington, H. P. Using traditional ecological knowledge in science: Methods and applications. Ecol. Appl. 10, 1270–1274 (2000).
Rich, L. N. et al. Estimating occupancy and predicting numbers of grey wolf packs in Montana using hunter surveys. Popul. Ecol. 77, 1280–1289 (2013).
Sandstrom, P. et al. Conflict resolution by participatory management: Remote sensing and GIS as tools for communicating land-use needs for reindeer herding in northern Sweden. AMBIO 32, 557–567 (2003).
Faugier, J. & Sargeant, M. Sampling hard to reach populations. J. Adv. Nurs. 26, 790–797 (1997).
Brook, R. K. & McLachlan, S. M. On using expert-based science to ‘test’ Local Knowledge. Ecol. Soc. 10 (2005).
Anadon, J. D., Gimenez, A., Ballestar, R. & Perez, I. Evaluation of Local Ecological Knowledge as a method for collecting extensive data on animal abundance. Conserv. Biol. 23, 617–625 (2009).
Gagnon, C. A. & Berteaux, D. Integrating Traditional Ecological Knowledge and ecological science: A question of scale. Ecol. Soc. 14, 19 (2009).
Ballard, H. L., Fernandez-Gimenez, M. E. & Sturtevant, V. E. Integration of LEK and conventional science: A study of seven community-based forestry organizations in the USA. Ecol. Soc. 13, 37 (2008).
Ramstad, K. M. et al. Species and cultural conservation in New Zealand: Maori Traditional Ecological Knowledge of Tuatara. Conserv. Biol. 21, 455–464 (2007).
Lockwood, J. L., Cassey, P. & Blackburn, T. The role of propagule pressure in explaining species invasions. Trends Ecol. Evol. 20, 223–228 (2005).
Statistics Canada. Land and Freshwater Area by Province and Territory. (2005).
Liu, J., Chen, J. M., Cihlar, J. & Chen, W. Net primary productivity mapped for Canada at 1-km resolution. Glob. Ecol. Biogeogr. 11, 115–129 (2002).
Marshall, I. B. & Schut, P. A national ecological framework for Canada. Eastern Cereal and Oilseed Research Center (ECORC), Research Branch, Agriculture and Agri-Food Canada. (1999).
Liu, J., Chen, J. M. & Cihlar, J. Mapping evapotranspiration based on remote sensing: An application to Canada’s landmass. Water Resour. Res. 39, 1189 (2003).
Goodman, L. A. Snowball Sampling. Ann. Math. Stat. 32, 148–170 (1961).
Field, L., Pruchno, R. A., Bewley, J., Lemay, E. P. Jr. & Levinsky, N. G. Using probability vs nonprobability sampling to identify hard-to-access participants for health-related reasons. J. Aging Health 18, 565–583 (2006).
Statistics Canada. 2011 Census-Boundary Files. https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-2011-eng.cfm, http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/phys01-eng.htm (2016).
Verdin, K. L. & Verdin, J. P. A topological system for delineation and codification of the earth’s river basins. J. Hydrol. 218, 1–12 (1999).
HydroSHEDS, https://www.hydrosheds.org/ (2017).
Buhlmann, K. A. et al. A global analysis of tortoise and freshwater turtle distributions with identification of priority conservation areas. Chelonian Conserv. Bi. 8, 116–149 (2009).
Kurz, J. C. & Marchinton, R. L. Radiotelemetry studies of feral hogs in South Carolina. J. Wildl. Manage. 36, 1240–1248 (1972).
Singer, F. J., Otto, D. K., Tipton, A. R. & Hable, C. P. Home ranges, movements, and habitat use of European wild boar in Tennessee. J. Wildl. Manage. 45, 343–353 (1981).
Barber, D. W. & Coblentz, B. E. Home range, habitat use, and reproduction in feral pigs on Santa Catalina Island. J. Mammal. 67, 512–525 (1986).
Dexter, N. The influence of pasture distribution, temperature and sex on home-range size of feral pigs in a semi-arid environment. Wildl. Res. 26, 755–762 (1998).
This study was made possible through funding provided by the United States Animal and Plant Health Inspection Service National Feral Swine Damage Management Program, the University of Saskatchewan, Saskatchewan Fish and Wildlife Development Fund, Natural Science and Engineering Research Council (NSERC), and the Cyril Capling Trust at the University of Saskatchewan. This study would not be possible without the help of Raea Gooding, Chantal Jantzen, Dallis Aiken, Annie Rosia, Phillipa Williams, and Marissa Berard, as well as the generous contributions of our study participants.