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University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Other Publications in Wildlife Management Wildlife Damage Management, Internet Center for Spring 3-9-2017 Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal Jesse S. Lewis Conservation Science Partners, Fort Collins, CO, [email protected] Mathew L. Farnsworth Conservation Science Partners, Fort Collins, CO Christopher L. Burdett Colorado State University David M. Theobald Conservation Science Partners, Truckee, California Miranda Gray Conservation Science Partners, Truckee, CA See next page for additional authors Follow this and additional works at: http://digitalcommons.unl.edu/icwdmother Part of the Agriculture Commons, Biology Commons, and the Population Biology Commons Lewis, Jesse S.; Farnsworth, Mathew L.; Burdett, Christopher L.; Theobald, David M.; Gray, Miranda; and Miller, Ryan S., "Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal" (2017). Other Publications in Wildlife Management. 79. http://digitalcommons.unl.edu/icwdmother/79 This Article is brought to you for free and open access by the Wildlife Damage Management, Internet Center for at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Other Publications in Wildlife Management by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Authors Jesse S. Lewis, Mathew L. Farnsworth, Christopher L. Burdett, David M. Theobald, Miranda Gray, and Ryan S. Miller This article is available at DigitalCommons@University of Nebraska - Lincoln: http://digitalcommons.unl.edu/icwdmother/79 University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Other Publications in Wildlife Management Wildlife Damage Management, Internet Center for Spring 3-9-2017 Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal Jesse S. Lewis Mathew L. Farnsworth Christopher L. Burdett David M. Theobald Miranda Gray See next page for additional authors Follow this and additional works at: http://digitalcommons.unl.edu/icwdmother Part of the Agriculture Commons, Biology Commons, and the Population Biology Commons This Article is brought to you for free and open access by the Wildlife Damage Management, Internet Center for at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Other Publications in Wildlife Management by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Authors Jesse S. Lewis, Mathew L. Farnsworth, Christopher L. Burdett, David M. Theobald, Miranda Gray, and Ryan S. Miller www.nature.com/scientificreports OPEN received: 04 November 2016 accepted: 03 February 2017 Published: 09 March 2017 Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal Jesse S. Lewis1, Matthew L. Farnsworth1, Chris L. Burdett2, David M. Theobald1, Miranda Gray3 & Ryan S. Miller4 Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. Predicting and mapping species distributions, including geographic range and variability in abundance, is fundamental to the conservation and management of biodiversity and landscapes1. The ecological niche defines species-habitat relationships2–4 and provides a useful framework for understanding the range and abundance of species in relation to biotic and abiotic factors. Further, niche relationships across local scales can provide novel information about the ecology, conservation, and management of species at macro scales5. Most studies evaluating a species’ niche across their distribution focus on presence-absence occurrence data to predict the geographic range6; however, conservation and management plans for species can be improved by understanding patterns of population abundance and density across a species’ range7. In particular, evaluating population density, compared to occurrence, can reveal novel patterns of species distributions in relation to landscape factors8. There is an ongoing paradigm shift in understanding how biotic and abiotic factors shape species distributions. Until recently, it was widely accepted that abiotic factors, such as temperature and precipitation, played the primary role in shaping distributions of species and biodiversity at broad scales (e.g., regional, continental, global extents) and that biotic factors were most important at fine scales (e.g., site, local extents)9–11. It is increasingly recognized, however, that biotic factors are important determinants of species distributions at broad spatial scales, especially when considering biotic interactions12–16. Although interspecific competition can be an important biotic determinant in species distribution models at broad scales, other forms of biotic interactions, such as 1 Conservation Science Partners, 5 Old Town Sq, Suite 205, Fort Collins, Colorado, 80524, USA. 2Colorado State University, Department of Biology, Fort Collins, Colorado, 80524, USA. 3Conservation Science Partners, 11050 Pioneer Trail, Suite 202, Truckee, California, 96161, USA. 4United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, Colorado, 80524, USA. Correspondence and requests for materials should be addressed to J.S.L. (email: jslewis.research@ gmail.com) Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 1 www.nature.com/scientificreports/ Figure 1. Geographic range of wild pigs across their native and non-native global distribution. Areas of white indicate locations in which wild pigs are likely not present. This map was created using ArcGIS 10.3.198. See Supplementary Methods S1 for a description of methods and citations used for creating the map of wild pig global distribution across its native and non-native ranges. predation and symbioses, can also be important determinants15,17, but have received less attention18. In addition, although researchers have evaluated the effects of biotic interactions on geographic range limits18, relatively few studies have evaluated how biotic factors influence population density across a species’ range19,20, which can be more informative in understanding macro-ecological patterns7,21. In addition to species interactions, biotic factors related to vegetation can influence species distributions and abundance at broad scales. In particular, anthropogenic land-use change is rarely considered when evaluating species distributions at broad scales; however, given the human footprint globally22 and projections for expanding human impacts on the environment23,24, biotic factors created by human activities are potentially important predictors that can contribute to a better understanding of species distributions8. For example, agricultural crops are a dominant biotic factor across continents that are facilitated by human engineering and the redistribution of ecological resources and energy, which can have profound impacts on plant and animal populations across broad extents; agriculture can increase populations for some species through increased food, resource availability, and landscape heterogeneity, or decrease populations due to loss of habitat25–27. Ultimately, further evaluation is necessary to understand the relative importance of abiotic and biotic factors in shaping species distributions across broad spatial scales13,15. Invasive species are a primary driver of widespread and severe negative impacts to ecosystems, agriculture, and humans across local to global scales28. These introduced plants and animals often exhibit broad geographic distributions, can be relatively well studied across local scales, and provide novel opportunities to evaluate broad-scale patterns of niche relationships29. Predictions of potential geographic distribution of invasive species can provide critical information that can inform the prevention, eradication, and control of populations, which has been evaluated for many taxa, including plants30, amphibians31, and invertebrates32. However, few studies have predicted the potential ranges and abundance of non-native mammals33. Especially for wide-ranging species that can occur across broad extents of landscapes, predictions of how population density varies spatially can provide important information for prioritizing conservation and management actions. Few species exhibit a global distribution that extends across Europe, Asia, Africa, North and South America, Australia, and oceanic Islands. Besides naturalized animals, such as the house mouse (Mus musculus) and brown rat (Rattus norvegicus), wild pigs (Sus scrofa; other common names include wild boar, wild/feral swine, wild/ feral hog, and feral pig) have one of the widest geographic distributions of any mammal; further, it exhibits the widest geographic range of any large mammal34, with the exception of humans. The expansive global distribution of wild pigs is attributed to its broad native range in Eurasia and northern Africa, widespread introduction by humans outside its native range, and superior adaptability, where it occurs in a wide variety of ecological communities, ranging from deserts to temperate and tropical environments35,36, with a corresponding diverse omnivorous diet37. Across its non-native range (Fig. 1; Supplementary Methods S1), including North and South America, Australia, sub-Saharan Africa, and many islands, wild pigs are considered one of the 100 most harmful invasive species in the world38 due to wide-ranging ecosystem disturbance, agricultural damage, pathogen and disease vectors to wildlife, livestock and people, and social impacts to people and property39–41. Wild pigs are therefore a model species to evaluate biotic and abiotic factors associated with population density because they exhibit a global distribution across six continents, are widely studied across much of their native and non-native (i.e., invasive or introduced) ranges, and previous research has indicated that their population density was related to abiotic factors across a continental scale, although it was ambiguous how biotic factors shape their abundance, warranting further study42. To address these ecological questions and understand the relative importance of biotic and abiotic factors in shaping the global distribution of a highly invasive mammal, we evaluated estimates of population density of wild pigs across diverse environments on five continents. Specifically, we (1) evaluate how biotic (i.e., vegetation and Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 2 www.nature.com/scientificreports/ predation) and abiotic (i.e., climate) factors (Table 1) shape population density across a global scale and (2) create a predictive distribution map of potential population density across the world. We also compare population density between island and mainland populations. Our results contribute novel insight into the relative roles of biotic and abiotic factors in shaping the distribution of species’ population densities across continental and global scales, particularly relating to human-mediated land-use change, which can provide critical information to management and conservation strategies. Results We compiled 147 estimates of wild pig density (# animals/km2), which resulted in 129 estimates of density across their global distribution used in our analyses (Fig. 1; Supplementary Table S2). Some areas contained > 1 density estimate, and these were averaged. Population density of wild pigs was higher on islands (n = 11) compared to on the mainland (n = 118) (t = 4.72, df = 10.93, p < 0.001; Supplementary Figure S3). For the untransformed density estimates, mean population density for on the mainland equaled 2.75 (se = 0.38) and islands equaled 18.52 (se = 4.15). The highest estimates of population density occurred on islands, which reached upwards of 40 wild pigs/km2 (Supplementary Table S2). Due to differences in population density between islands and on the mainland, we used density estimates from mainland populations in our subsequent analyses. Population density was influenced by both biotic and abiotic factors across the global distribution (Tables 2 and 3; Supplementary Table S4). The suite of best models all included combinations of biotic and abiotic factors (Table 2) and the top model (AICc = 237.94; model weight = 0.68; adjusted R2 = 0.55) had > 1,000 times more support as the best approximating model than the top model considering only abiotic factors (AIC c = 311.30; model weight = 7.94 × 10−17) (Supplementary Table S4). The variables with the greatest importance included potential evapotranspiration, large carnivore richness, precipitation during the wet and dry seasons, unvegetated area, and agriculture, which also exhibited 95% confidence intervals that did not overlap zero (Table 3). Density was greatest at moderate levels of potential evapotranspiration and agriculture, decreased with large carnivore richness and amount of unvegetated area, and increased with precipitation during the wet and dry seasons (Fig. 2); percent forest cover was unsupported in models when considering the suite of variables in analyses. Using the full model-averaged results of parameter estimates, we created a predictive map of global wild pig population density (Fig. 3; Supplementary Figure S5). Wild pig populations were predicted to occur at low to high population densities across all continents, including large areas of land where wild pigs are currently absent. The highest predicted densities occurred in southeastern, eastern, and western North America, throughout Central America, northern, eastern, and southwestern South America, western, southern, and eastern Eurasia, throughout Indonesia, central and southern Africa, and northern and southeastern Australia (Fig. 3; Supplementary Figure S5). Results of k-fold cross validation demonstrated that the model had good predictive ability with a mean squared prediction error (MSPE) of 0.22 and a Pearson’s correlation between observed and predicted values of 0.80 (t = 17.711, df = 181, p-value < 0.001). Discussion Population density of an invasive large mammal was strongly influenced by both biotic and abiotic factors across its global distribution. Consistent with the prediction that abiotic factors drive broad-scale patterns of species distribution, potential evapotranspiration (PET) and precipitation variables were important predictors of population density on a global scale. In addition, contributing to growing evidence that biotic factors are also important determinants of broad-scale patterns of species distributions, both biotic interactions and vegetation played important roles in predicting the distribution of wild pig populations globally. Further, land-use change mediated by human activities strongly predicted the broad-scale distribution of an invasive large mammal. Consistent with previous studies evaluating how population density of ungulates varied across broad scales, both bottom-up (resource-related) and top-down (predation) factors influenced the distribution of wild pig populations19,42,43. Ultimately, wild pig populations across their global distribution appeared to respond to biotic and abiotic factors related to plant productivity, forage and water availability, cover, predation, and anthropogenic land-use change. Using both biotic and abiotic factors to evaluate broad-scale species distributions can create more realistic maps of range and density with better predictive ability16,44, which can better inform management and conservation strategies for species. For example, population density of wild pigs was highest in landscapes with moderate levels of agriculture and PET, lower large carnivore richness and amount of unvegetated area, and greater precipitation during the wet and dry seasons. Using these relationships, we created a predictive map of population density across the world, which can be used to manage existing populations and predict areas where wild pig populations are likely to expand or invade if given the opportunity. Ultimately, this information can be used to prioritize management activities in areas at risk of invasion and with expanding populations. Abiotic factors, such as temperature and precipitation, are consistently found to be primary determinants of species distributions at broad scales11. Potential evapotranspiration can be especially informative for understanding broad-scale ecological patterns45, such as species distributions. This was supported in our research where PET was the most important predictor of population density across the global distribution of wild pigs. Potential evapotranspiration is highly correlated with temperature variables, thus indicating that wild pig density was greatest at relatively moderate temperatures and density was lower in areas exhibiting extreme low and high temperatures. In addition, the strong support of precipitation variables in our models is consistent with the association of wild pigs with vegetation cover, forage, and water36. In particular, precipitation likely facilitates rooting behavior by wild pigs by softening the soil substrate46. Biotic factors were among the most supported variables predicting population density across a global scale. Our results indicated that the presence of large carnivores can influence wild pig population density. Large carnivore richness was strongly supported in our models and exhibited a negative relationship with wild pig density; as the number of large carnivore species increased, wild pig density decreased, which is consistent with studies in Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 3 www.nature.com/scientificreports/ Landscape Variable Category, Description of Variable, and Calculation Method Predicted Relationship Supporting Citations for Prediction Data Source Global Land Cover by National Mapping Organizations (GLCNMO) 2008; cropland cover types Agriculture Biotic/Vegetation; all agricultural crop lands; proportional area within 10 km radius buffer Positive, quadratic Geisser and Reyer99, Honda59, Ballari and Barrios-García37, Morelle and Lejeune100 Enhanced Vegetation Index (EVI)* Biotic/Vegetation; plant productivity; mean value within 10 km radius buffer Positive Plant productivity: Melis, et al.42. Google Earth Engine; Landsat 5 TM 32-Day EVI Composite 1984–2012 Forest Canopy Cover Biotic/Vegetation; all forest over 5 m; mean value of canopy cover within 10 km radius buffer Positive Honda59, Morelle, et al.60. Google Earth Engine; Hansen Global Forest Change v1.0 year 2000 Forest Minus Agriculture* Biotic/Vegetation; difference between the proportion of forest and agriculture within 10 km radius buffer Positive See forest (classified as present or absent for this variable) and agriculture descriptions See data sources for forest canopy cover and agriculture Normalized Difference Vegetation Index (NDVI)* Biotic/Vegetation; plant productivity; mean value in 10 km radius buffer Positive Plant productivity: Melis, et al.42. Google Earth Engine; Landsat 5 TM 32-Day NDVI Composite 1984–2012 Unvegetated Area Biotic/Vegetation; cover types lacking vegetation, including bare, snow and ice, and urban; proportion within 10 km radius buffer Negative Plant productivity: Melis, et al.42. Global Land Cover by National Mapping Organizations (GLCNMO) 2008; sparse vegetation, bare area, urban, and snow and ice cover types Large Carnivore Richness Biotic/Predation; number of terrestrial large carnivores presented by Ripple, et al.63, excluding the panda bear and adding the dingo; mean value within 40 km radius buffer Negative Woodall47, Jedrzejewska, et al.50., Sweitzer101, Ickes48, Melis, et al.42., Mayer and Brisbin36, Massei, et al.58. Large carnivore distributions from IUCN79, Dingo distribution in Australia102 Actual Evapotranspiration* Abiotic/Climate; combination of evaporation of water and transpiration from plants; mean value within 40 km radius buffer Positive, quadratic Fisher, et al.45. Global High-Resolution SoilWater Balance: 1950–2000; Trabucco and Zomer103 Potential Evapotranspiration Abiotic/Climate; combination of evaporation of water and transpiration from plants; mean value within 40 km radius buffer Positive, quadratic Fisher, et al.45. Global High-Resolution SoilWater Balance: 1950–2000; Trabucco and Zomer103 Precipitation Annual * Abiotic/Climate; total precipitation during annual period; mean value within Positive 40 km radius buffer Woodall47, Weltzin, et al.104 but see Geisser and Reyer99 Bioclim WorldClim World Climate Data – Bio 12 Annual Precipitation (mm); 1950–2000 Precipitation Driest Season Abiotic/Climate; total precipitation during driest 3 month annual period; mean value within 40 km radius buffer Positive Mortality related to periods of low precipitation, especially during summer105 Bioclim WorldClim World Climate Data – Bio 17 Precipitation of Driest Quarter (mm); 1950–2000 Precipitation Wettest Season Abiotic/Climate; total precipitation during wettest 3 month annual period; mean value within 40 km radius buffer Positive Woodall47, Weltzin, et al.104 but see Geisser and Reyer99 Bioclim WorldClim World Climate Data – Bio 16 Precipitation of Wettest Quarter (mm); 1950–2000 Temperature Annual* Abiotic/Climate; mean temperature over annual period; mean value within 40 km radius buffer Positive, quadratic Jedrzejewska, et al.50. Bioclim WorldClim World Climate Data – Bio 1 Annual Mean Temperature (C); 1950–2000 Temperature Summer* Abiotic/Climate; mean temperature over warmest 3 month annual period; mean value within 40 km radius buffer Positive, quadratic Geisser and Reyer99, McClure, et al.57., but see Groves106 Bioclim WorldClim World Climate Data – Bio 10 Mean Temperature of Warmest Quarter; 1950–2000 Temperature Winter* Abiotic/Climate; mean temperature over coldest 3 month annual period; mean value within 10 km radius buffer Positive, quadratic Bieber and Ruf107, Geisser and Reyer99, Melis, et al.42., Honda59, McClure, et al.57., but see Groves106 Bioclim WorldClim World Climate Data – Bio 11 Mean Temperature of Coldest Quarter; 1950–2000 Table 1. Description of landscape variables considered in analyses evaluating how biotic and abiotic factors influenced wild pig population density across their global distribution. An asterisk (*) indicates landscape variables that were excluded from the final analyses due to high correlation with other variables. Eurasia and Australia42,47,48. In addition, interspecific competition can influence the distribution of species and it has been hypothesized that wild pigs have not extensively invaded wildlands in some regions of sub-Saharan Africa due to the presence of other pig species that exhibit similar niches49. Although competition with other species might influence wild pig populations and their distribution49–51, in other cases wild pigs are reported to spatially and temporally partition habitat use to reduce niche overlap with potential competitors52–54 and not show evidence for interference competition with related mammals (e.g., species within the suborder Suiformes), such as native peccary species55, thus, it is unclear how interspecific interactions influence wild pig populations across their global distribution. Further, understanding potential interspecific competition for invasive species can be especially challenging in non-native habitat because invaders have not coevolved with competitors or predators and thus it is difficult to predict which species will be subordinate or dominant in potential competitive interactions or how competition might influence species distributions in unoccupied habitat17,18,56. Because it was Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 4 www.nature.com/scientificreports/ unknown how competitive interactions between wild pigs and other species might influence their distribution, particularly outside their native range, competition was not included in our analyses. To understand how competition between non-native and native species influences species distributions, field studies evaluating interspecific competition are necessary across the wild pig’s native and non-native geographic range, particularly across local spatial scales. Although biotic interactions between animals are the primary biotic factors evaluated in species distribution models at broad scales, the role of plant communities has received less consideration. In particular, anthropogenic land-use change increasingly influences vegetation communities across continents and warrants a better understanding for how human activities are shaping broad-scale distributions of plant and animal populations22,24. For example, agriculture is a dominating land cover type across continents23,25, which can potentially benefit species distributions in at least two ways. Agriculture can (1) increase population density within areas of a species’ current geographic range through supplemental food and increased resource availability and (2) allow geographic ranges to expand by creating habitat in areas that were previously unsuitable. In contrast, as agriculture increasingly dominates landscape patterns at broad extents, cover and other resources correspondingly decrease, which can negatively impact the geographic range and population density of some species. Our results demonstrate that agriculture can produce both positive and negative effects on populations, depending on the levels of agriculture. At intermediate levels of agriculture, population density of wild pigs was greatest, likely due to an optimal mix of food and cover. Whereas, at high levels of agriculture, population density decreased precipitously, which was likely a result of inadequate cover. Our results indicate that heterogeneous landscapes with a mix of agriculture and cover will support the greatest populations of wild pigs, which is consistent with broad-scale patterns of wild pig populations in North America and Eurasia57–59. Due to relatively high predicted population densities of wild pigs inhabiting heterogeneous landscapes, these regions would likely experience the greatest crop damage, leading to high economic loss to farmers. Forest is considered a key habitat type preferred by wild pigs59,60. In univariate analyses, forest was an important positive predictor of wild pig density (β = 0.170, se = 0.056). When considering additional predictor variables in our models, however, forest was relatively unimportant in predicting wild pig density, which is also consistent when evaluating wild pig occurrence over broad scales57. Thus, the interpretation of how forest influences the distribution of wild pigs must be considered in the context of other variables included in models, where abiotic factors might adequately explain forest distribution (see discussion below). However, as predicted, vegetation and cover play a strong role in predicting wild pig density; as the amount of unvegetated area increased across the landscape, wild pig population density decreased, which is consistent with geographic distribution maps of wild pigs61. In some systems, abiotic factors can be stronger predictors of species distributions, than biotic factors, because of high correlations between these two factors62. Our study indicated that both factors can be important predictors of species distributions, potentially because abiotic factors may poorly predict biotic factors stemming from human activities. In addition, human influences might weaken the correlation between abiotic and biotic factors. For example, humans can significantly reduce the number of large carnivores in an area63, although these species would be predicted to occur across broad areas based on abiotic factors and historic biotic conditions. In addition, human land use change can lead to unpredictable biotic patterns in relation to abiotic factors, such as through agricultural landscape conversion. Although soil types might support crop production, many agricultural areas occur in arid landscapes requiring irrigation of water and application of fertilizer to maintain production25. Thus, agricultural crops could not grow in many areas based on broad-scale climate factors alone, and therefore, abiotic factors can be poor predictors of agricultural practices in some regions. Indeed, there likely are other examples where abiotic and biotic factors may exhibit low correlation in some systems (e.g., location of human activities and development, altered interspecific interactions due to human activities, and other forms of anthropogenic land use change). Ultimately, it can be useful to consider biotic factors in species distribution models that might be poorly predicted by abiotic factors due to human activities. Additional biotic factors that can influences species distributions on a broad scale, particularly invasive species, include the role of humans in distributing the founding individuals of new populations. For example, invasive wild pig populations have arisen across several continents recently through human activities. Illegal translocations by humans for hunting purposes can facilitate the long-distance expansion of wild pig populations into new areas64–66, which is currently a primary source of new populations globally39,41. Further, in countries such as Canada, Brazil, and Sweden, wild pig farms were the propagule source for recent populations of wild pigs across broad regions, which are currently spreading into new areas67–69. Indeed, propagule pressure (i.e., the number of individuals introduced and release events) determines both the likelihood of invasive species becoming established, as well as the rate of geographic range expansion60,70. In addition, invasive species that exhibit r-selected characteristics (e.g., early maturity, short generation time, and high fecundity) can be more likely to successfully invade novel landscapes71. Even at low population densities, invasive species with high reproductive output are more likely to establish populations in areas of lower quality habitat72. Given that wild pigs are one of the most fecund large mammals (e.g., mean litter sizes ranging from 3.0 to 8.4 piglets per sow with the potential for >1 litter annually)36, their reproductive characteristics might increase the probability of establishment and enable them to compensate for small population sizes when introduced into novel environments across a range of habitat qualities. Population density, compared to presence-absence occurrence, can provide more informative conclusions of species distributions in relation to biotic and abiotic factors7,8. For example, although large carnivores likely do not exclude wild pigs from habitat across broad scales, our study revealed they can influence abundance. However, occurrence of species would remain constant across varying population densities, unless it resulted in species exclusion. Ultimately, population densities can provide more detailed information about species distributions, which can better inform conservation and management plans and policy7. Studies analyzing presence-only Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 5 www.nature.com/scientificreports/ Potential Evapotranspiration Large Carnivore Precipitation Wet Season Unvegetated Agriculture Precipitation Dry Season * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Forest * * * * * * * * * * * * * * * * * * * K AICc Δ AICc weight log(L) 10 237.94 0.00 0.68 −108.33 11 240.18 2.24 0.22 −108.32 9 243.00 5.06 0.05 −111.98 10 244.40 6.46 0.03 −111.56 8 246.14 8.20 0.01 −114.65 9 248.20 10.26 0.00 −114.58 9 248.25 10.31 0.00 −114.60 * 10 249.14 11.20 0.00 −113.93 * 9 252.95 15.01 0.00 −116.95 7 253.93 15.99 0.00 −119.64 * Table 2. Model selection results using Akaike Information Criteria (AICc) from analyses evaluating how population density of wild pigs was related to biotic and abiotic factors. A “*” in the covariate columns indicates whether the variable was included in the model. K is the number of variables included in the model. Note that Potential Evapotranspiration and Agriculture include both main and quadratic effects (thus accounting for two parameters for each of these variables). Only the top 10 models are reported. See Supplementary Table S4 for AICc model selection results of all possible variable combinations. Potential Evapotranspiration Large Carnivore Precipitation Wet Season Unvegetated Agriculture Precipitation Dry Season Forest Variable Importance Values 1.00 1.00 1.00 0.99 0.98 0.92 0.25 Parameter Estimate (Standard Error) m: 0.443 (0.056) q: −0.226 (0.046) −0.243 (0.043) 0.233 (0.055) −0.203 (0.061) m: 0.236 (0.076) q: −0.118 (0.038) 0.100 (0.050) −0.001 (0.029) Table 3. Model selection results for parameters evaluating how population density of wild pigs is influenced by biotic and abiotic factors. Variable importance values sum model weights across the entire data set for each variable. Unconditional model-averaged parameter estimates with associated standard errors are based on standardized values. Potential Evapotranspiration and Agriculture include both main effect (m) and quadratic (q) terms, whereas all other covariates report linear relationships. data with logistic regression and Maximum Entropy (MaxEnt) models have examined methods to address spatial sampling bias73–75 and additional evaluations would be useful for studies using population density data with multiple linear regression. Further, global analyses of population genetics could be used to identify groups and the proportion of wild and domestic genes across wild pig populations, which could be used to incorporate population structure into analyses to better understand population characteristics. Predicting species distributions provides critical information to the management and conservation of biodiversity, especially for controlling invasive species. Without intensive management actions, our study predicts that there is strong potential for wild pigs to expand their geographic range and further invade expansive areas of North America, South America, Africa, and Australia. Although wild pigs currently occupy broad regions of predicted habitat in their non-native range, many regions of predicted habitat are currently unoccupied and may be at high risk for future invasion. These areas might warrant increased surveillance by local, state, and federal agencies to counter the establishment of populations. Although attention in unoccupied areas that are predicted to support high densities of wild pigs might warrant priority for countering population introductions, wild pigs can persist in relatively low quality habitat (e.g., arid and/or cold regions) and these areas also warrant attention to halt invasions. Given the potential for wild pig populations to rapidly expand once established36, predictions of potential population density in unoccupied habitat can provide critical information to land managers, which can be used to proactively develop management plans to prevent introductions and control or eradicate populations if they become introduced. Methods Density Estimates. To evaluate the population density (i.e., number of individuals per unit area) of wild pigs throughout their global distribution, we compiled density estimates from the literature throughout its native and non-native ranges across each continent and island for which data were available (Supplementary Table S1). Previous research evaluated how population density of wild pigs varied across western Eurasia42 and we incorporated these 54 estimates of population density into our analysis. In addition, we followed the methodological recommendation of Melis et al.42. to average data when multiple estimates were available for >1 season or year at a study area. Island populations typically exhibit higher population density compared to mainland populations76,77. We thus compared estimates of wild pig population density between island and mainland populations; if population density for islands was significantly higher than on the mainland, we focused on only evaluating mainland populations in subsequent analyses. Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 6 www.nature.com/scientificreports/ Figure 2. Relationships of biotic and abiotic factors with population density (natural log scale; #/km2) of wild pigs, including potential evapotranspiration (a), large carnivore richness (b), unvegetated (c), agriculture (d), precipitation during the wettest season (e), precipitation during the driest season (f), and forest canopy cover (g). Models evaluating and predicting species distributions can be improved by including areas of absence (a.k.a., pseudo-absence or background locations) or zero density to sample the full range of available landscape conditions1 to predict the potential range of a species, absence locations should occur outside the environmental domain of the species, but within a reasonable distance of the species’ geographic range78. Because wild pigs have occurred within their native range for thousands of years, we assumed that populations were at equilibrium and the species had colonized available habitat associated with its geographic distribution. Thus, regions adjacent to its native distribution that were classified as unoccupied were assumed to be unsuitable for population persistence due to unfavorable environmental conditions. In addition, spatial sampling bias (i.e., uneven sampling across geographic extents) can be addressed by increasing the number of background locations in areas with greater sampling73,74. The majority of density estimates used in our study occurred within the wild pig’s native range of Europe and Asia and we focused sampling of background locations associated with this region. To include locations with estimates of zero density in our analyses, we used a three-step approach. First, we created a buffered region that occurred across the area between 100–1000 km around the boundary of the wild pig’s native range79. Next, we calculated the spatial extent of the native range and buffered regions. Lastly, accounting for the area of each region, we selected a random sample of locations within the buffered region that was proportional to the number of estimates used in the native terrestrial range of wild pigs. Based on this approach, we used 65 locations of zero density in our analyses that occurred across central Russia, Mongolia, western China, Saudi Arabia, and northern African countries. Zero density estimates were used in analyses relating wild pig density to landscape variables and excluded when comparing population density between island and mainland populations. Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 7 www.nature.com/scientificreports/ Figure 3. Map of predicted population density of wild pigs for habitat occurring across the world. For terrestrial environments, areas of white represent low density (1 individual/km2), orange moderate density (6 individuals/km2), and dark red high density (≥11 individuals/km2). Maps were created using Google Earth Engine80 and QGIS 2.14.390. See Supplementary Figure S5 for finer scale maps of predicted population density of wild pigs for Europe, Asia, Africa, Australia, North America, and South America. Landscape Variables. We considered a suite of biotic and abiotic landscape variables, which were divided into vegetation, predation, and climate factors (Table 1) that we hypothesized to influence population density of wild pigs. We used landscape variables that were available globally and, where possible, over long time periods (i.e., estimates averaged over several decades) that coincide with the density estimates we compiled for our analyses. Geospatial data layers were acquired through either Google Earth Engine80 or were downloaded from online sources (Table 1). The biotic factors that we evaluated included agriculture, broadleaf forest, enhanced vegetation index (EVI), forest canopy cover, difference in the proportion between forest and agriculture (to characterize landscape heterogeneity), normalized difference vegetation index (NDVI), large carnivore richness, and unvegetated area (Table 1). We expected a positive relationship between density and all vegetation factors, except unvegetated area, due to their association with increased food availability, plant productivity, and cover. In addition, we expected a quadratic relationship between population density and agriculture because we predicted density to be greatest at moderate levels of agriculture (due to a mix of cover and food) and low at high levels of agriculture (due to a lack of adequate cover). Finally, we expected a negative relationship between population density and large carnivore richness. The abiotic factors that we evaluated included two measures of ecological energy regimes, actual evapotranspiration (the amount of water loss from evaporation and transpiration, which is related to plant productivity) and potential evapotranspiration (PET; the amount of evaporation and transpiration that would occur with a sufficient water supply, considering solar radiation, air temperature, humidity, and wind speed;45). Actual evapotranspiration is a measure of water-energy balance and potential evapotranspiration is considered a measure of ambient energy and often highly correlated with temperature variables81. Although evapotranspiration variables can include elements of biotic (i.e., transpiration from plants) and abiotic (i.e., climate and water) factors, they were classified as abiotic for our analyses. In addition, we evaluated precipitation during dry and wet seasons, and annually, and temperature during summer and winter, and annually (Table 1). We predicted a positive relationship between density and precipitation variables due to associated increases in forage, water, and cover and quadratic relationships between density and evapotranspiration and temperature variables due to expected peak densities at intermediate levels and low densities at low and high levels. Modeling. We used data from the wild pig’s native and non-native range in our modeling. Although niche shifts between a species’ native and non-native range appear to be uncommon and it is often assumed that species exhibit niche stasis or conservatism30,82–84 through space and time, models that use data only from a species’ native range can exhibit poor predictive power in the species’ non-native range85–87. Therefore, it is important to include data from the species’ entire distribution to increase the predictive ability of models across both the native and non-native ranges32,88,89. Because wild pigs have been established across much of their non-native range for an extended period of time (e.g., typically greater than a century), we assumed that populations used in our analyses had achieved a localized equilibrium with their environment. Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 8 www.nature.com/scientificreports/ All geospatial data layers were evaluated using QGIS90 and Google Earth Engine80 and statistical analyses were conducted using R91. Because there is uncertainty about the exact location of studies and the scale in which processes might influence wild pig densities, we evaluated multiple scales for each covariate using 10, 20, and 40 km radius buffers around the location of each density estimate (Table 1). Thus a moving window approach was conducted so that each pixel within a spatial layer summarized the landscape within the buffered radius. To determine the best scale for analyses we used a multi-criteria approach. First, variables were centered and scaled to improve model fit92. Next, we considered quadratic relationships for landscape factors that were predicted to exhibit a curvilinear pattern (Table 1). Last, we selected the best scale and relationship for each covariate based on wild pig ecology, model comparisons using Akaike’s Information Criterion corrected for small sample size AICc;93, and plots of residuals. Once the appropriate scale was determined for each variable (Table 1), we evaluated the Pearson correlation among all variables and excluded highly correlated variables (r > 0.70) from our final analysis. We used multiple linear regression to evaluate how population density was influenced by our final suite of biotic and abiotic factors (Table 1). The distribution of density estimates were right skewed, thus we log-transformed density estimates using the natural logarithm42. To compare the relative importance of biotic and abiotic factors and to determine parameter estimates of variables, we ranked all possible models using AICc, model-averaged parameter estimates (i.e., full conditional), and calculated variable importance values93–95. We used model weights and evidence ratios to evaluate if biotic factors improved model fit by comparing models including only abiotic factors to models also including biotic factors. 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Acknowledgements This study was funded and supported by the US Department of Agriculture, Animal and Plant Health Inspection Service, Center for Epidemiology and Animal Health, Veterinary Services, Wildlife Services, the National Wildlife Research Center, the National Feral Swine Damage Management Program, Colorado State University, and Conservation Science Partners. We appreciate the distribution data for wild pigs in Canada provided by R. Kost and R. Brook, synthesis of wild pig distribution in Africa and South America by C. Larson, and the dingo Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 11 www.nature.com/scientificreports/ distribution data in Australia provided by P. Fleming. P. DiSalvo and M. Foley assisted with acquiring literature on density estimates. M. McClure assisted with cross validation of model results. We thank three anonymous reviewers, S. Sweeney and B. Dickson for providing thoughtful feedback that improved earlier versions of this paper. Author Contributions J.L. conceived the ideas, led the analyses, and wrote the manuscript. C.B., M.F., M.G., R.M., and D.T. contributed to the development of ideas, assisted with analyses, and edited the manuscript. CB created the large carnivore richness GIS layer and wild pig global range figure. Additional Information Supplementary information accompanies this paper at http://www.nature.com/srep Competing Interests: The authors declare no competing financial interests. How to cite this article: 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; doi: 10.1038/srep44152 (2017). Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ © The Author(s) 2017 Scientific Reports | 7:44152 | DOI: 10.1038/srep44152 12 Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal Jesse S. Lewis1*, Matthew L. Farnsworth1, Chris L. Burdett2, David M. Theobald1, Miranda Gray3, Ryan S. Miller4 Supplementary Files 1, 2, 3, 4, and 5 1 Conservation Science Partners, 5 Old Town Sq, Suite 205, Fort Collins, Colorado, USA 80524, [email protected], [email protected], [email protected] 2 Colorado State University, Department of Biology, Fort Collins, Colorado, USA 80524, [email protected] 3 Conservation Science Partners, c, Truckee, California, USA 96161, [email protected] 4 United States Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, Center for Epidemiology and Animal Health, Fort Collins, Colorado, USA 80524, [email protected] * Corresponding author: Jesse S Lewis; [email protected]; telephone (970) 484 – 2898 Supplementary Methods S1. Methods and studies used to create the global distribution map of wild pigs across large land areas within their native and non-native range (Figure 1). Methods We mapped the extent of occurrence of wild pigs by first compiling existing spatial datasets depicting Sus scrofa’s geographic range 1,2. Second, we created additional spatial data by digitizing range maps or known occurrence locations from publications. We paid particular attention to range boundaries and areas where their distribution was poorly understood. To upscale fine-grained spatial data digitized from publications into a similar resolution as the existing broad-scale range maps, we buffered point locations by 10 km and then intersected the buffered points or polygons with Pfafstetter Level 6 watersheds obtained from the HydroSHEDS spatial hydrography database 3,4. We used watersheds to spatially filter these data because they provide a biologically meaningful way to depict the presence of wild pigs at a landscape-scale resolution 5. Lastly, we differentiated two categories of occurrence, areas where the presence of wild pigs has been confirmed through published maps or data, and other areas where the occurrence of wild pigs is less certain. All digitizing and map development was performed using ArcGIS software 6. Studies used in constructing distribution Global Oliver and Brisbin 7, Long 8, Meijaard, et al. 9, Barrios-Garcia and Ballari 10 Eurasia Erkinaro, et al. 11, Oliver and Leus 12, Campbell and Hartley 13, Magnusson 14, NBDCI 15, Haaverstad, et al. 16, IUCN 2, Ukkonen, et al. 17, Wilson 18 Africa Blench 19, Phiri, et al. 20, Kisakye and Masaba 21, Pouedet, et al. 22, Ngowi, et al. 23, Githigia, et al. 24, Waiswa, et al. 25, Assana, et al. 26, Kingdon and Hoffmann 27, Thomas, et al. 28, Ouma, et al. 29 Australia West 30 United States and Canada SCWDS 31, Ruth Kost and Ryan Brook, University of Saskatchewan, personal communication Mexico Álvarez-Romero, et al. 32, Solís-Cámara, et al. 33, Hidalgo-Mihart, et al. 34 South America Merino and Carpinetti 35, Merino, et al. 36, Desbiez, et al. 37, Desbiez, et al. 38, Salvador and Fernandez 39, Kaizer, et al. 40, Aravena, et al. 41, Ballari, et al. 42, Pedrosa, et al. 43, Skewes and Jaksic 44 References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Saulich, M. 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Mastozoología Neotropical 22, 113-124 (2015). Supplementary Table S2. Studies used in analyses evaluating the relationship between wild pig population density and biotic and abiotic factors across Europe, Asia, Australia, North America, South America, and several islands. Location coordinates (x,y) are presented in decimal degrees. # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Continent Asia Asia Asia Asia Asia Asia Asia Asia Asia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Europe Europe Country India Malaysia Pakistan Nepal Malaysia India Malaysia Nepal Malaysia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Russia Russia Density (# / km2) 2.46 3.63 3.70 4.00 4.17 4.20 4.62 5.80 37.00 0.40 0.89 1.01 1.60 1.75 1.92 2.00 2.40 2.80 3.30 4.00 5.80 10.00 0.01 0.02 x 13.509 4.533 24.538 28.583 4.623 12.025 4.847 27.551 2.983 -31.110 -35.500 -28.336 -36.718 -35.750 -29.850 -33.481 -29.838 -14.500 -18.184 -14.548 -30.820 -31.006 56.347 53.163 y 75.631 102.429 67.959 81.333 102.068 76.108 102.450 84.471 102.210 145.213 148.999 150.675 148.530 148.991 144.147 149.788 145.358 131.183 145.981 144.144 143.920 147.569 44.012 45.074 Reference Gopalaswamy, et al. 1 Kawanishi and Sunquist 2 Smiet, et al. 3 Dinerstein 4 Kawanishi and Sunquist 2 Karanth and Sunquist 5 Kawanishi and Sunquist 2 Seidensticker 6 Ickes 7 Choquenot, et al. 8 Hone 9 Wilson, et al. 10 Saunders and Giles 11 McIlroy, et al. 12, Hone 13 Choquenot 14, Dexter 15 Saunders and Kay 16 Choquenot, et al. 8 Caley 17 Mitchell 18 Mitchell 19 Choquenot, et al. 8 Saunders and Bryant 20 Fadeev 21 * Fadeev 21 * 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Russia Russia Russia Russia Russia Russia Russia Poland Russia Russia Russia Russia Russia Poland Russia Russia Belarus Russia Poland Russia Russia Kazakhstan Russia Belarus Russia Russia Russia Belarus Russia Poland Russia Poland Poland 0.02 0.02 0.03 0.03 0.03 0.04 0.04 0.05 0.05 0.07 0.08 0.08 0.09 0.09 0.11 0.11 0.12 0.14 0.15 0.16 0.16 0.18 0.19 0.19 0.20 0.21 0.22 0.32 0.35 0.37 0.43 0.46 0.48 53.999 57.020 56.164 56.633 60.000 57.000 57.500 49.383 54.500 58.500 52.669 57.583 52.667 49.487 54.167 57.000 54.691 55.638 49.648 51.703 54.667 43.510 50.500 52.517 54.500 58.000 58.750 53.666 53.167 50.082 51.667 49.834 49.116 44.000 41.068 40.506 59.850 31.000 39.000 61.000 22.420 39.665 31.499 41.512 39.749 39.498 21.735 37.500 36.000 28.383 37.487 19.625 39.216 32.000 72.483 36.497 26.992 36.750 28.500 37.500 28.987 34.500 20.377 36.166 21.501 22.729 Fadeev 21 * Fadeev 21 * Fadeev 21 * Fadeev 21 * Fadeev 21 * Fadeev 21 * Fadeev 21 * Fonseca, et al. 22 Fadeev 21 * Fadeev 21 * Fadeev 21 * Fadeev 21 * Fadeev 21 * Fonseca, et al. 22 Fadeev 21 * Fadeev 21 * Lavov 23 * Fadeev 21 * Fonseca, et al. 22 Fadeev 21 * Fadeev 21 * Fedosenko and Zhiryakov 24 * Fadeev 21 * Kozlo 25 * Fadeev 21 * Fadeev 21 * Tupicina 26 * Kozlo 25 * Fadeev 21 * Pucek, et al. 27 * Fadeev 21 * Fonseca, et al. 22 Kanzaki, et al. 28 * 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Europe Spain Poland Belarus Poland Poland Poland Poland Lithuania Belarus Poland Kazakhstan Poland Poland Italy Poland Poland Poland Poland Poland Poland Germany France Poland Poland France Italy Poland Spain Spain Poland Italy Azerbaijan Poland 0.61 0.64 0.72 0.79 0.88 0.94 1.06 1.10 1.16 1.26 1.50 1.58 1.64 1.70 1.83 1.89 1.99 2.02 2.20 2.21 2.40 2.50 2.65 2.67 2.70 3.00 3.05 3.10 3.50 3.55 3.57 3.59 3.59 41.510 53.885 55.501 50.049 53.833 50.875 54.103 54.868 52.707 53.610 43.501 50.182 49.600 44.500 50.736 50.375 53.511 52.546 50.459 51.408 52.002 43.500 52.735 50.527 43.498 42.628 50.591 42.500 40.005 54.659 43.500 38.921 52.963 -5.488 23.030 28.996 19.640 23.289 15.560 22.277 23.780 24.006 21.549 77.498 19.527 18.832 8.999 18.891 22.197 16.437 17.115 18.955 15.442 13.006 1.748 23.854 16.707 4.519 11.122 17.802 -0.997 -6.334 18.236 11.000 48.849 15.607 Tellería and Sáez-Royuela 29 * Fonseca, et al. 22 Kozlo 25 * Fonseca, et al. 22 Pucek, et al. 30 * Fonseca, et al. 22 Fonseca, et al. 22 Janulaitis 31 * Kozlo 25 * Fonseca, et al. 22 Fedosenko and Zhiryakov 24 * Pucek, et al. 30 * Pucek, et al. 30 * Marsan, et al. 32 * Fonseca, et al. 22 Fonseca, et al. 22 Fonseca, et al. 22 Pucek, et al. 30 * Fonseca, et al. 22 Bobek 33 Kern, et al. 34 * Spitz and Janeau 35 * Melis, et al. 36 Fonseca, et al. 22 Dardaillon 37 * Massei, et al. 38 * Fonseca, et al. 22 Herrero, et al. 39 * Fernández-Llario, et al. 40 * Fonseca, et al. 22 Monaco, et al. 41 * Litvinov 42 * Fonseca, et al. 22 91 92 93 94 95 96 Europe Europe Europe Europe Europe Europe Germany Netherlands Italy Czech Republic Swedan Italy 97 98 99 100 101 Europe Europe Europe Europe North America North America North America North America North America North America North America North America North America North America North America Italy Spain Switzerland Poland USA 102 103 104 105 106 107 108 109 110 111 Ebert, et al. 43 Kuiters and Slim 44 * Mattioli, et al. 45 * Plhal, et al. 46, Plhal, et al. 47 Welander 48 Focardi, et al. 49, Focardi, et al. 4.75 4.80 6.20 6.39 7.50 9.59 49.234 52.000 43.800 49.349 58.972 41.708 7.799 5.339 11.817 16.875 17.534 12.403 9.78 10.00 10.35 12.07 0.65 43.132 37.009 46.185 53.297 30.696 11.166 -6.479 6.021 14.712 -104.094 USA 1.00 38.996 -123.367 Sweitzer, et al. 54 USA 1.10 35.972 -121.233 Pine and Gerdes 55 USA 1.20 38.713 -123.000 Sweitzer, et al. 54 USA 1.30 36.487 -121.854 Sweitzer, et al. 54 USA 1.90 38.537 -123.007 Sweitzer, et al. 54 USA 1.90 35.662 -120.800 Sweitzer, et al. 56 USA 2.37 33.146 -81.685 Kight 57, Sweeney 58, Crouch 59 USA 2.80 28.326 -99.429 Gabor, et al. 60 USA 3.80 37.169 -121.421 Sweitzer, et al. 54 USA 3.80 37.349 -121.641 Schauss, et al. 61 50 Boitani, et al. 51 * Fernández-Llario, et al. 40 * Hebeisen, et al. 52 Fonseca, et al. 22 Adkins and Harveston 53 112 -83.740 Singer 62 USA 4.85 35.584 USA 5.51 40.104 USA 6.13 32.405 -84.729 Hanson, et al. 65 USA 7.00 28.675 -80.737 Singer 62 USA 9.50 28.121 -97.376 Ilse and Hellgren 66 Argentina 3.00 -36.343 Brazil 5.03 -18.999 119 North America North America North America North America North America South America South America Island Sri Lanka 0.90 7.577 120 Island 1.77 -39.985 121 122 Island Island Australia (Flinders Island, Tasmania) Indonesia (Sumatra) USA (Hawaii) 5.02 5.13 -5.250 19.457 123 Island USA (Hawaii) 12.36 20.713 124 Island 26.00 -0.263 -90.747 Coblentz and Baber 79 125 126 127 Island Island Island 26.80 27.75 28.00 35.237 -41.793 33.357 140.092 Osada, et al. 80 172.418 McIlroy 81 -118.422 Baber and Coblentz 82 128 129 Island Island Ecuador (Galapagos) Japan New Zealand USA (Santa Catalina) Indonesia (Java) USA (Santa Cruz) 29.50 40.45 -6.741 34.005 113 114 115 116 117 118 -121.959 Patten 63, Barrett 64 -57.278 Merino and Carpinetti 67, Pérez Carusi, et al. 68 -56.663 Desbiez, et al. 69 80.765 Eisenberg and Lockhart 70, McKay 71, Santiapillai and Chambers 72 148.085 Statham and Middleton 73 104.137 O'Brien, et al. 74 -155.288 Anderson and Stone 75, Scheffler, et al. 76 -156.099 Diong 77, Anderson and Stone 78 105.257 Pauwels 83 -119.766 Sterner and Barrett 84, Parkes, et al. 85 Notes: * cited in Melis, et al. 36 Literature Cited 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Gopalaswamy, A., Karanth, K., Kumar, N. & Macdonald, D. 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Transactions of the Western Section of the Wildlife Society 27, 47-53 (1991). 85 Parkes, J. P. et al. Rapid eradication of feral pigs (Sus scrofa) from Santa Cruz Island, California. Biological Conservation 143, 634-641 (2010). Supplementary Figure S3. Boxplot (including the median, first and third quartiles, minimum and maximum values, and outliers) demonstrating that population density (natural log scale; #/km2) of wild pigs is greater for island (n = 11) compared to mainland (n = 118) populations. Supplementary Table S4. Model selection results using Akaike Information Criteria (AICc) for all possible models evaluating how biotic and abiotic factors influenced population density of wild pigs. A “*” in the covariate columns indicates whether the variable was included in the model. K is the number of variables included in the model. Note that Potential Evapotranspiration and Agriculture include both main and quadratic effects (thus accounting for two parameters for each of these variables). # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Potential Large Precipitation Precipitation Evapotranspiration Carnivore Wet Season Unvegetated Agriculture Dry Season Forest * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * K 10 11 9 10 8 9 9 10 9 7 9 8 10 8 7 8 7 9 8 8 7 8 9 6 9 AICc AICc weight 237.94 0.00 0.68 240.18 2.24 0.22 243.00 5.06 0.05 244.40 6.46 0.03 246.14 8.20 0.01 248.20 10.26 0.00 248.25 10.31 0.00 249.14 11.20 0.00 252.95 15.01 0.00 253.93 15.99 0.00 254.00 16.06 0.00 255.58 17.64 0.00 256.16 18.22 0.00 256.35 18.41 0.00 256.47 18.53 0.00 258.28 20.34 0.00 263.05 25.11 0.00 264.78 26.84 0.00 265.17 27.23 0.00 265.82 27.88 0.00 266.28 28.34 0.00 266.61 28.67 0.00 267.29 29.35 0.00 267.91 29.97 0.00 268.17 30.23 0.00 log(L) -108.33 -108.32 -111.98 -111.56 -114.65 -114.58 -114.60 -113.93 -116.95 -119.64 -117.48 -119.37 -117.44 -119.76 -120.91 -120.72 -124.20 -122.87 -124.17 -124.49 -125.82 -124.89 -124.12 -127.71 -124.56 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 10 8 7 9 8 7 6 6 7 8 7 9 7 8 8 8 6 9 9 7 8 6 7 5 8 7 6 8 5 7 7 6 6 8 268.23 273.12 275.08 275.33 276.81 277.80 279.16 282.40 282.86 283.64 284.04 285.98 289.55 290.88 291.27 291.73 291.82 292.53 293.09 293.56 293.91 293.94 293.95 294.52 295.66 295.98 296.57 296.69 296.84 297.38 297.84 298.03 298.20 300.38 30.29 35.18 37.14 37.39 38.87 39.86 41.22 44.46 44.92 45.70 46.10 48.04 51.61 52.94 53.33 53.79 53.88 54.59 55.15 55.62 55.97 56.00 56.01 56.58 57.72 58.04 58.63 58.75 58.90 59.44 59.90 60.09 60.26 62.44 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -123.47 -128.14 -130.22 -128.14 -129.99 -131.58 -133.34 -134.96 -134.11 -133.41 -134.70 -133.47 -137.45 -137.03 -137.22 -137.45 -139.67 -136.74 -137.03 -139.46 -138.54 -140.73 -139.65 -142.09 -139.41 -140.67 -142.04 -139.93 -143.25 -141.37 -141.60 -142.78 -142.86 -141.78 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 8 5 7 6 4 6 7 5 6 7 7 6 5 6 7 5 6 6 5 8 5 7 6 7 7 6 5 6 4 6 7 8 5 6 302.10 302.23 302.41 302.56 303.12 304.03 304.35 305.03 311.30 313.10 313.39 315.66 320.24 321.06 327.15 328.80 329.10 332.32 341.70 342.80 343.29 346.89 348.35 349.39 349.84 351.44 352.13 353.94 354.89 355.73 356.29 357.46 360.19 361.39 64.16 64.29 64.47 64.62 65.18 66.09 66.41 67.09 73.36 75.16 75.45 77.72 82.30 83.12 89.21 90.86 91.16 94.38 103.76 104.86 105.35 108.95 110.41 111.45 111.90 113.50 114.19 116.00 116.95 117.79 118.35 119.52 122.25 123.45 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -142.63 -145.94 -143.89 -145.04 -147.45 -145.77 -144.85 -147.34 -149.41 -149.23 -149.37 -151.59 -154.95 -154.29 -156.25 -159.23 -158.31 -159.92 -165.68 -162.98 -166.47 -166.12 -167.94 -167.37 -167.60 -169.48 -170.89 -170.73 -173.33 -171.62 -170.82 -170.31 -174.93 -174.45 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * 6 7 5 5 5 7 6 4 7 6 4 4 5 4 5 5 5 6 6 5 4 6 3 4 5 3 5 3 4 4 4 4 3 3 364.46 365.36 366.68 367.68 368.27 368.31 368.90 370.36 370.64 370.68 372.16 372.62 373.43 376.60 376.95 377.07 378.16 379.21 379.42 379.92 381.25 381.81 382.24 384.24 392.46 393.53 393.80 393.98 395.38 395.45 395.52 399.33 413.34 421.30 126.52 127.42 128.74 129.74 130.33 130.37 130.96 132.42 132.70 132.74 134.22 134.68 135.49 138.66 139.01 139.13 140.22 141.27 141.48 141.98 143.31 143.87 144.30 146.30 154.52 155.59 155.86 156.04 157.44 157.51 157.58 161.39 175.40 183.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -175.99 -175.36 -178.17 -178.67 -178.96 -176.83 -178.21 -181.07 -178.00 -179.10 -181.97 -182.20 -181.54 -184.19 -183.30 -183.36 -183.91 -183.36 -183.47 -184.79 -186.51 -184.66 -188.05 -188.00 -191.06 -193.70 -191.73 -193.92 -193.58 -193.61 -193.64 -195.55 -203.60 -207.58 128 2 428.33 190.39 0.00 -212.13 Supplementary Figure S5. Maps of predicted population density of wild pigs for habitat occurring across Europe (a), Asia (b), Africa (c), Australia (d), North America (e), and South America (f). Figure 3. For terrestrial environments, areas of white represent low density (1 individual / km2), orange moderate density (6 individuals / km2), and dark red high density (≥11 individuals / km2). Maps were created using Google Earth Engine 1 and QGIS 2.14.3 2. a. Europe b. Asia c. Africa d. Australia e. North America f. South America Literature Cited 1 2 Google Earth Engine Team. Google Earth Engine: A planetary-scale geospatial analysis platform. https://earthengine.google.com/. ( 2016). QGIS Development Team. QGIS 2.14.3 Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.osgeo.org. (2016).