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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
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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.
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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)
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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
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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
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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
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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
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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.
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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.
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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.
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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. Model averaged parameter estimates were
used to create a predictive global map of wild pig density (1 km2 resolution). This map displays the maximal
potential density of wild pigs in relation to the biotic and abiotic factors used in our modeling and reflects predicted densities that would be achieved if wild pigs had access to all landscapes, their movements were unrestricted, and management activities did not suppress populations. We validated our model using mean squared
prediction error (MSPE)96 and k-fold cross validation and selected the number of bins based on Huberty’s rule
of thumb (k =​ 4)97.
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99. Geisser, H. & Reyer, H.-u. The influence of food and temperature on population density of wild boar Sus scrofa in the Thurgau
(Switzerland). Journal of Zoology 267, 89–96 (2005).
100. Morelle, K. & Lejeune, P. Seasonal variations of wild boar Sus scrofa distribution in agricultural landscapes: a species distribution
modelling approach. European Journal of Wildlife Research 61, 45–56 (2015).
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in California. Proceedings of 18th Vertebrate Pest Conference 18 26–34 (1998).
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103. Trabucco, A. & Zomer, R. Global soil water balance geospatial database. CGIAR Consortium for Spatial Information. Published
online, available from the CGIARCSI GeoPortal at http://cgiar-csi. org (2010).
104. Weltzin, J. F. et al. Assessing the response of terrestrial ecosystems to potential changes in precipitation. Bioscience 53, 941–952
(2003).
105. Massei, G., Genov, P., Staines, B. & Gorman, M. Mortality of wild boar, Sus scrofa, in a Mediterranean area in relation to sex and
age. Journal of Zoology 242, 394–400 (1997).
106. Groves, C. P. Ancestors for the pigs: taxonomy and phylogeny of the genus Sus. 1–96 (Dept. of Prehistory, Australian National
University, 1981).
107. Bieber, C. & Ruf, T. Population dynamics in wild boar Sus scrofa: ecology, elasticity of growth rate and implications for the
management of pulsed resource consumers. Journal of Applied Ecology 42, 1203–1213 (2005).
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
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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
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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).