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Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal

Authors:
  • Arizona State Univeristy (Mesa AZ)
  • Ecological Sciences

Abstract and Figures

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.
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Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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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 t 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 fun-
damental to the conservation and management of biodiversity and landscapes1. e ecological niche denes
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 evaluat-
ing 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.
ere is an ongoing paradigm shi in understanding how biotic and abiotic factors shape species distribu-
tions. 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 ne scales (e.g., site, local extents)9–11. It is increas-
ingly recognized, however, that biotic factors are important determinants of species distributions at broad spatial
scales, especially when considering biotic interactions12–16. Although interspecic competition can be an impor-
tant biotic determinant in species distribution models at broad scales, other forms of biotic interactions, such as
1Conservation 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)
received: 04 November 2016
Accepted: 03 February 2017
Published: 09 March 2017
OPEN
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predation and symbioses, can also be important determinants15,17, but have received less attention18. In addition,
although researchers have evaluated the eects of biotic interactions on geographic range limits18, relatively few
studies have evaluated how biotic factors inuence 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 inuence 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 expand-
ing 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 nec-
essary 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. ese introduced plants and animals oen 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. e expansive global distribu-
tion 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 ve continents. Specically, we (1) evaluate how biotic (i.e., vegetation and
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. is 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.
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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 den-
sity 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 den-
sity estimates, mean population density for on the mainland equaled 2.75 (se = 0.38) and islands equaled 18.52
(se = 4.15). e highest estimates of population density occurred on islands, which reached upwards of 40 wild
pigs/km2 (SupplementaryTableS2). Due to dierences in population density between islands and on the main-
land, we used density estimates from mainland populations in our subsequent analyses.
Population density was inuenced by both biotic and abiotic factors across the global distribution (Tables2
and 3; SupplementaryTableS4). e 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 (AICc = 311.30;
model weight = 7.94 × 1017) (SupplementaryTableS4). e variables with the greatest importance included
potential evapotranspiration, large carnivore richness, precipitation during the wet and dry seasons, unvege-
tated area, and agriculture, which also exhibited 95% condence 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 sea-
sons (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. e highest predicted densities occurred in southeastern, eastern, and western North America, through-
out 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 inuenced 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 popu-
lation density on a global scale. In addition, contributing to growing evidence that biotic factors are also impor-
tant 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 inuenced 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 conserva-
tion 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 pre-
cipitation 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 understand-
ing broad-scale ecological patterns45, such as species distributions. is was supported in our research where PET
was the most important predictor of population density across the global distribution of wild pigs. Potential evap-
otranspiration 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 soening 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 inuence wild pig population density. Large carni-
vore 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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Eurasia and Australia42,47,48. In addition, interspecic competition can inuence 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 inuence 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 interspecic interactions inuence wild pig populations
across their global distribution. Further, understanding potential interspecic 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 dicult to predict which species will be subordinate or dominant in potential competitive
interactions or how competition might inuence species distributions in unoccupied habitat17,18,56. Because it was
Landscape Variable
Category, Description of Variable, and
Calculation Method
Predicted
Relationship
Supporting Citations for
Prediction Data Source
Agriculture
Biotic/Vegetation; all agricultural crop
lands; proportional area within 10 km
radius buer
Positive, quadratic
Geisser and Reyer99,
Honda59, Ball ari and
Barrios-García37, Morelle
and Lejeune100
Global Land Cover by National
Mapping Organizations
(GLCNMO) 2008; cropland
cover types
Enhanced Vegetation Index
(EVI)*
Biotic/Vegetation; plant productivity;
mean value within 10 km radius buer 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 buer
Positive Honda59, Morelle, et al.60.
Google Earth Engine; Hansen
Global Forest Change v1.0 year
2000
Forest Minus Agriculture*
Biotic/Vegetation; dierence between
the proportion of forest and agriculture
within 10 km radius buer
Positive
See forest (classied as
present or absent for this
variable) and agriculture
descriptions
See data sources for forest canopy
cover and agriculture
Normalized Dierence
Vegetation Index (NDVI)*
Biotic/Vegetation; plant productivity;
mean value in 10 km radius buer 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 buer
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 buer
Negative
Woodall47, Je drzejewska,
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 buer
Positive, quadratic Fisher, et al.45.
Global High-Resolution Soil-
Water 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 buer
Positive, quadratic Fisher, et al.45.
Global High-Resolution Soil-
Water Balance: 1950–2000;
Trabucco and Zomer103
Precipitation Annual *
Abiotic/Climate; total precipitation
during annual period; mean value within
40 km radius buer
Positive 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 buer
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 buer
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 buer
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 buer
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 buer
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 inuenced wild pig population density across their global distribution. An asterisk (*) indicates
landscape variables that were excluded from the nal analyses due to high correlation with other variables.
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unknown how competitive interactions between wild pigs and other species might inuence their distribution,
particularly outside their native range, competition was not included in our analyses. To understand how compe-
tition between non-native and native species inuences species distributions, eld studies evaluating interspecic
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 inuences vegetation communities across continents and warrants a better under-
standing 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 benet species
distributions in at least two ways. Agriculture can (1) increase population density within areas of a species’ cur-
rent 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 increas-
ingly 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 eects 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, lead-
ing to high economic loss to farmers.
Forest is considered a key habitat type preferred by wild pigs59,60. In univariate analyses, forest was an impor-
tant 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. us, the interpretation of how forest inuences 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 predic-
tors of species distributions, potentially because abiotic factors may poorly predict biotic factors stemming from
human activities. In addition, human inuences might weaken the correlation between abiotic and biotic factors.
For example, humans can signicantly 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. us,
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 interspecic 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 inuences 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 becom-
ing 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 inuence 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 distribu-
tions, which can better inform conservation and management plans and policy7. Studies analyzing presence-only
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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 pop-
ulation structure into analyses to better understand population characteristics.
Predicting species distributions provides critical information to the management and conservation of bio-
diversity, 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. ese 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 incor-
porated 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 popula-
tions76,77. We thus compared estimates of wild pig population density between island and mainland populations;
if population density for islands was signicantly higher than on the mainland, we focused on only evaluating
mainland populations in subsequent analyses.
Potential
Evapotranspiration
Large
Carnivore
Precipitation Wet
Season Unvegetated Agriculture
Precipitation Dry
Season Forest KAICcΔ AICcweight 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 eects
(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 Unvegetate d 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
inuenced 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 eect (m) and
quadratic (q) terms, whereas all other covariates report linear relationships.
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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 con-
ditions1 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. us, regions adjacent to
its native distribution that were classied 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. e majority of density estimates used in our study occurred within the wild pigs native range of
Europe and Asia and we focused sampling of background locations associated with this region. To include loca-
tions with estimates of zero density in our analyses, we used a three-step approach. First, we created a buered
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 buered regions. Lastly, accounting for the area of
each region, we selected a random sample of locations within the buered 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.
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).
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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 inuence 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 analy-
ses. Geospatial data layers were acquired through either Google Earth Engine80 or were downloaded from online
sources (Table1).
e biotic factors that we evaluated included agriculture, broadleaf forest, enhanced vegetation index (EVI),
forest canopy cover, dierence in the proportion between forest and agriculture (to characterize landscape het-
erogeneity), normalized dierence 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.
e abiotic factors that we evaluated included two measures of ecological energy regimes, actual evapotran-
spiration (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
sucient water supply, considering solar radiation, air temperature, humidity, and wind speed;45). Actual evap-
otranspiration is a measure of water-energy balance and potential evapotranspiration is considered a measure of
ambient energy and oen 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 classied 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 rela-
tionship 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
shis between a species’ native and non-native range appear to be uncommon and it is oen assumed that spe-
cies 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. erefore, 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.
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 ner scale maps of predicted population density
of wild pigs for Europe, Asia, Africa, Australia, North America, and South America.
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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 inuence wild pig densities, we evaluated multiple scales for each covariate using 10, 20, and
40 km radius buers around the location of each density estimate (Table1). us a moving window approach
was conducted so that each pixel within a spatial layer summarized the landscape within the buered radius. To
determine the best scale for analyses we used a multi-criteria approach. First, variables were centered and scaled
to improve model t92. 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 eval-
uated the Pearson correlation among all variables and excluded highly correlated variables (r > 0.70) from our
nal analysis.
We used multiple linear regression to evaluate how population density was influenced by our final
suite of biotic and abiotic factors (Table1). e 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 t 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). is map displays the maximal
potential density of wild pigs in relation to the biotic and abiotic factors used in our modeling and reects pre-
dicted densities that would be achieved if wild pigs had access to all landscapes, their movements were unre-
stricted, 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.
References
1. Franlin, J. Mapping species distributions: spatial inference and prediction. (Cambridge University Press, 2009).
2. Grinnell, J. e niche-relationships of the California rasher. e Au 34, 427–433 (1917).
3. MacArthur, . H. In Population Biology and Evolution (ed . C. Lewontin) 159–186 (Syracuse University Press, 1968).
4. Hutchinson, G. E. Concluding remars. Cold Spring Harbor Symposium on Quantitative Biology 22, 415–427 (1957).
5. Brown, J. H. Macroecology: progress and prospect. Oios 87, 3–14 (1999).
6. Elith, J. & Leathwic, J. . Species distribution models: ecological explanation and prediction across space and time. Annual eview
of Ecology, Evolution, and Systematics 40, 677–697 (2009).
7. Brown, J. H., Mehlman, D. W. & Stevens, G. C. Spatial variation in abundance. Ecology 76, 2028–2043 (1995).
8. andin, C. F., Jaccard, H., Vittoz, P., Yoccoz, N. G. & Guisan, A. Land use improves spatial predictions of mountain plant
abundance but not presence-absence. Journal of Vegetation Science 20, 996–1008 (2009).
9. Pearson, . G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope
models useful? Global Ecology and Biogeography 12, 361–371 (2003).
10. Benton, M. J. e ed Queen and the Court Jester: species diversity and the role of biotic and abiotic factors through time. Science
323, 728–732 (2009).
11. Wiens, J. J. e niche, biogeography and species interactions. Philosophical Transactions of the oyal Society of London B: Biological
Sciences 366, 2336–2350 (2011).
12. Van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why
it is essential to include biotic interactions across trophic levels. Philosophical Transactions of the oyal Society B: Biological Sciences
365, 2025–2034 (2010).
13. Meier, E. S. et al. Biotic and abiotic variables show little redundancy in explaining tree species distributions. Ecography 33,
1038–1048 (2010).
14. Guisan, A. & uiller, W. Predicting species distribution: oering more than simple habitat models. Ecology Letters 8, 993–1009
(2005).
15. Wisz, M. S. et al. e role of biotic interactions in shaping distributions and realised assemblages of species: implications for species
distribution modelling. Biological eviews 88, 15–30 (2013).
16. Leach, ., Montgomery, W. I. & eid, N. Modelling the inuence of biotic factors on species distribution patterns. Ecological
Modelling 337, 96–106 (2016).
17. Anderson, . P. When and how should biotic interactions be considered in models of species niches and distributions? Journal of
Biogeography, doi: 10.1111/jbi.12825 (2016).
18. Sexton, J. P., McIntyre, P. J., Angert, A. L. & ice, . J. Evolution and ecology of species range limits. Annual eview of Ecology,
Evolution, and Systematics 40, 415–436 (2009).
19. Melis, C. et al. Predation has a greater impact in less productive environments: variation in roe deer, Capreolus capreolus,
population density across Europe. Global Ecology and Biogeography 18, 724–734 (2009).
20. Pasanen-Mortensen, M., Pyyönen, M. & Elmhagen, B. Where lynx prevail, foxes will fail–limitation of a mesopredator in Eurasia.
Global Ecology and Biogeography 22, 868–877 (2013).
21. Boulangeat, I., Gravel, D. & uiller, W. Accounting for dispersal and biotic interactions to disentangle the drivers of species
distributions and their abundances. Ecology Letters 15, 584–593 (2012).
22. Sanderson, E. W. et al. e Human Footprint and the Last of the Wild. Bioscience 52, 891–904 (2002).
23. Laurance, W. F., Sayer, J. & Cassman, . G. Agricultural expansion and its impacts on tropical nature. Trends in Ecology & Evolution
29, 107–116 (2014).
24. Newbold, T. et al. Global eects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
25. Alexandratos, N. & Bruinsma, J. World agriculture towards 2030/2050: the 2012 revision. (ESA Woring Paper No. 12-03, ome,
FAO, 2012).
26. Green, . E., Cornell, S. J., Scharlemann, J. P. & Balmford, A. Farming and the fate of wild nature. Science 307, 550–555 (2005).
27. Bengtsson, J., Ahnström, J. & Weibull, A.-C. e eects of organic agriculture on biodiversity and abundance: a meta-analysis.
Journal of Applied Ecology 42, 261–269 (2005).
28. Mac, . N. et al. Biotic invasions: causes, epidemiology, global consequences, and control. Ecological Applications 10, 689–710
(2000).
www.nature.com/scientificreports/
10
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
29. Parmesan, C. et al. Empirical perspectives on species borders: from traditional biogeography to global change. Oios 108, 58–75
(2005).
30. Peterson, A. T. Predicting the geography of species’ invasions via ecological niche modeling. e Quarterly eview of Biology 78,
419–433 (2003).
31. Ficetola, G. F., uiller, W. & Miaud, C. Prediction and validation of the potential global distribution of a problemat ic alien invasive
species—the American bullfrog. Diversity and Distributions 13, 476–485 (2007).
32. Sánchez-Fernández, D., Lobo, J. M. & Hernández-Manrique, O. L. Species distribution models that do not incorporate global data
misrepresent potential distributions: a case study using Iberian diving beetles. Diversity and Distributions 17, 163–171 (2011).
33. auhala, . & owalczy, . Invasion of the raccoon dog Nyctereutes procyonoides in Europe: history of colonization, features
behind its success, and threats to native fauna. Current Zoology 57, 584–598 (2011).
34. Oliver, W. L. . & Brisbin, I. In Pigs, peccaries and Hippos: status survey and conservation action plan (ed W. L. . Oliver) 179–195
(IUCN, 1993).
35. Oliver, W. L. ., Brisbin, I. L. & Taahashi, S. In Pigs, peccaries and Hippos: status survey and conservation action plan (ed W. L. .
Oliver) 112–120 (IUCN, 1993).
36. Mayer, J. & Brisbin, I. L. Wild pigs: biology, damage, control techniques and management. (Savannah iver Site Aien, SC, USA,
2009).
37. Ballari, S. A. & Barrios-García, M. N. A review of wild boar Sus scrofa diet and factors aecting food selection in native and
introduced ranges. Mammal eview 44, 124–134 (2014).
38. Lowe, S., Browne, M., Boudjelas, S. & De Poorter, M. 100 of the world’s worst invasive alien species: A selection from the global
invasive species database. 1–12 (Auland, New Zealand, 2000).
39. Barrios-Garcia, M. N. & Ballari, S. A. Impact of wild boar (Sus scrofa) in its introduced and native range: a review. Biological
Invasions 14, 2283–2300 (2012).
40. Courchamp, F., Chapuis, J.-L. & Pascal, M. Mammal invaders on islands: impact, control and control imp act. Biological eviews 78,
347–383 (2003).
41. Bevins, S. N., Pedersen, ., Lutman, M. W., Gidlewsi, T. & Deliberto, T. J. Consequences associated with the recent range
expansion of nonnative feral swine. Bioscience 64, 291–299 (2014).
42. Melis, C., Szafrańsa, P. A., Jędrzejewsa, B. & Bartoń, . Biogeographical variation in the population density of wild boar (Sus
scrofa) in western Eurasia. Journal of Biogeography 33, 803–811 (2006).
43. Danell, ., Bergström, ., Duncan, P. & Pastor, J. Large herbivore ecology, ecosystem dynamics and conservation. Vol. 11 (Cambridge
University Press, 2006).
44. González-Salazar, C., Stephens, C. . & Marquet, P. A. Comparing the relative contributions of biotic and abiotic factors as
mediators of species’ distributions. Ecological Modelling 248, 57–70 (2013).
45. Fisher, J. B., Whittaer, . J. & Malhi, Y. ET come home: potential evapotranspiration in geographical ecology. Global Ecology and
Biogeography 20, 1–18 (2011).
46. Sandom, C. J., Hughes, J. & Macdonald, D. W. ooting for rewilding: quantifying wild boar’s Sus scrofa rooting rate in the Scottish
Highlands. estoration Ecology 21, 329–335 (2013).
47. Woodall, P. F. Distribution and population dynamics of dingoes (Canis familiaris) and feral pigs (Sus scrofa) in Queensland, 1945-
1976. Journal of Applied Ecology 20, 85–95 (1983).
48. Ices, . Hyper-abundance of native wild pigs (Sus scrofa) in a lowland Dipterocarp rain forest of peninsular Malaysia Biotropica
33, 682–690 (2001).
49. Oliver, W. & Fruzinsi, B. In Biology of Suidae (eds . H. Barrett & F. Spitz) 93–116 (Institute Nat ional de echerche Agronomique,
Castanet, France, 1991).
50. Jedrzejewsa, B., Jedrzejewsi, W., Bunevich, A. N., Milowsi, L. & rasinsi, Z. A. Factors shaping population densities and
increase rates of ungulates in Bialowieza Primeval Forest (Poland and Belarus) in the 19th and 20th centuries. Acta eriologica 42,
399–451 (1997).
51. Corbett, L. Does dingo predation or bualo competition regulate feral pig populations in the Australian wet-dry tropics? An
experimental study. Wildlife esearch 22, 65–74 (1995).
52. Ilse, L. M. & Hellgren, E. C. esource partitioning in sympatric populations of collared peccaries and feral hogs in southern Texas.
Journal of Mammalogy 76, 784–799 (1995).
53. Desbiez, A. L. J., Santos, S. A., euroghlian, A. & Bodmer, . E. Niche partitioning among white-lipped peccaries (Tayassu pecari),
collared peccaries (Pecari tajacu), and feral pigs (Sus scrofa). Jour nal of Mammalogy 90, 119–128 (2009).
54. Gabor, T. M., Hellgren, E. C. & Silvy, N. J. Multi-scale habitat partitioning in sympatric suiforms. The Journal of Wildlife
Management 65, 99–110 (2001).
55. Oliveira-Santos, L. G., Dorazio, . M., Tomas, W. M., Mourao, G. & Fernandez, F. A. No evidence of interference competition
among the invasive feral pig and two native peccary species in a Neotropical wetland. Journal of Tropical Ecology 27, 557–561
(2011).
56. Louthan, A. M., Doa, D. F. & Angert, A. L. Where and when do species interactions set range limits? Trends in Ecology & Evolution
30, 780–792 (2015).
57. McClure, M. L. et al. Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States.
PLoS ONE 10, e0133771 (2015).
58. Massei, G. et al. Wild boar populations up, numbers of hunters down? A review of trends and implications for Europe. Pest
Management Science 71, 492–500 (2015).
59. Honda, T. Environmental factors aecting the distribution of the wild boar, sia deer, Asiatic blac bear and Japanese macaque in
central Japan, with implications for human-wildlife conict. Mammal Study 34, 107–116 (2009).
60. Morelle, ., Fattebert, J., Mengal, C. & Lejeune, P. Invading or recolonizing? Patterns and drivers of wild boar population expansion
into Belgian agroecosystems. Agriculture, Ecosystems & Environment 222, 267–275 (2016).
61. Oliver, W. & Leus, . Sus scrofa. e IUCN ed List of reatened Species 2008: e.T41775A10559847. http://dx.doi.org/10.2305/
IUCN.U.2008.LTS.T41775A10559847.en. (2008).
62. Godsoe, W., Franlin, J. & Blanchet, F. G. Effects of biotic interactions on modeled species’ distribution can be mased by
environmental gradients. Ecology and Evolution 7, 654–664 (2017).
63. ipple, W. J. et al. Status and ecological eects of the world’s largest carnivores. Science 343, 1241484 (2014).
64. Spencer, P. B. & Hampton, J. O. Illegal translocation and genetic structure of feral pigs in Western Australia. Journal of Wildlife
Management 69, 377–384 (2005).
65. Sewes, O. & Jasic, F. M. History of the introduction and present distribution of the european wild boar (Sus scrofa) in Chile.
Mastozoología Neotropical 22, 113–124 (2015).
66. Gipson, P. S., Hlavachic, B. & Berger, T. ange expansion by wild hogs across the central United States. Wildlife Society (USA)
(1998).
67. Broo, . . & van Beest, F. M. Feral wild boar distribution and perceptions of ris on the central Canadian prairies. Wildlife
Society Bulletin 38, 486–494 (2014).
68. Pedrosa, F., Salerno, ., Padilha, F. V. B. & Galetti, M. Current distribution of invasive feral pigs in Brazil: economic impacts and
ecological uncertainty. Natureza & Conservação 13, 84–87 (2015).
www.nature.com/scientificreports/
11
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
69. Lemel, J., Truvé, J. & Söderberg, B. Variation in ranging and activity behaviour of European wild boar Sus scrofa in Sweden. Wildlife
Biology 9, 29–36 (2003).
70. Locwood, J. L., Cassey, P. & Blacburn, T. e role of propagule pressure in explaining species invasions. Trends in Ecology &
Evolution 20, 223–228 (2005).
71. Saai, A. . et al. e population biology of invasive specie. Annual eview of Ecology and Systematics 32, 305–332 (2001).
72. Warren, . J., Bahn, V. & Bradford, M. A. e interaction between propagule pressure, habitat suitability and density-dependent
reproduction in species invasion. Oios 121, 874–881 (2012).
73. Syfert, M. M., Smith, M. J. & Coomes, D. A. e eects of sampling bias and model complexity on the predictive performance of
MaxEnt species distribution models. PLoS ONE 8, e55158 (2013).
74. Barbet-Massin, M., Jiguet, F., Albert, C. H. & uiller, W. Selecting pseudo-absences for species distribution models: how, where
and how many? Methods in Ecology and Evolution 3, 327–338 (2012).
75. ramer-Schadt, S. et al. e importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and
Distributions 19, 1366–1379 (2013).
76. Adler, G. H. & Levins, . e island syndrome in rodent populations. Quarterly eview of Biology 69, 473–490 (1994).
77. rebs, C. J., eller, B. L. & Tamarin, . H. Microtus population biology: demographic changes in uctuating populations of M.
ochrogaster and M. pennsylvanicus in southern Indiana. Ecology 50, 587–607 (1969).
78. Lobo, J. M., Jiménez-Valverde, A. & Hortal, J. e uncertain nature of absences and their importance in species distribution
modelling. Ecography 33, 103–114 (2010).
79. IUCN. e IUCN ed List of reatened Species. Version 2014.1. http://www.iucnredlist.org. Downloaded on 26 February 2016.
(2014).
80. Goog le Earth Engine Team. Google Earth Engine: A planetary-scale geospatial analysis platform. https://earthengine.google.com/.
(2016).
81. Hawins, B. A. et al. Energy, water, and broad-scale geographic patterns of species richness. Ecology 84, 3105–3117 (2003).
82. Wiens, J. J. et al. Niche conservatism as an emerging principle in ecology and conservation biology. Ecology Letters 13, 1310–1324
(2010).
83. Wiens, J. J. & Graham, C. H. Niche conservatism: integrating evolution, ecology, and conservation biology. Annual eview of
Ecology, Evolution, and Systematics 36, 519–539 (2005).
84. Alexander, J. M. & Edwards, P. J. Limits to the niche and range margins of alien species. Oios 119, 1377–1386 (2010).
85. Fitzpatric, M. C., Weltzin, J. F., Sanders, N. J. & Dunn, . . e biogeography of prediction error: why does the introduced range
of the re ant over-predict its native range? Global Ecology and Biogeography 16, 24–33 (2007).
86. Mau-Crimmins, T. M., Schussman, H. . & Geiger, E. L. Can the invaded range of a species be predicted suciently using only
native-range data?: Lehmann lovegrass (Eragrostis lehmanniana) in the southwestern United States. Ecological Modelling 193,
736–746 (2006).
87. Loo, S. E., Nally, . M. & Lae, P. Forecasting New Zealand mudsnail invasion range: model comparisons using native and invaded
ranges. Ecological Applications 17, 181–189 (2007).
88. Broennimann, O. & Guisan, A. Predicting current and future biological invasions: both native and invaded ranges matter. Biology
Letters 4, 585–589 (2008).
89. Be aumont, L. J. et al. Dierent climatic envelopes among invasive populations may lead to underestimations of current and future
biological invasions. Diversity and Distributions 15, 409–420 (2009).
90. QGIS Development Team. QGIS 2.14.3 Geographic Information System. Open Source Geospatial Foundation Project. http://qgis.
osgeo.org. (2016).
91. . : a language and environment for statistical computing, Version 3.2.3.  Foundation for Statistical Comput ing. Vienna, Austria.
(Development Core Team 2016).
92. Schielzeth, H. Simple means to improve the interpretability of regression coecients. Methods in Ecology and Evolution 1, 103–113
(2010).
93. Burnham, . P. & Anderson, D. . Model selection and multimodel inference: a practical information-theoretic approach. Second
Edition., (Springer Verlag, 2002).
94. Doherty, P. F., White, G. C. & Burnham, . P. Comparison of model building and selection strategies. Journal of Ornithology 152,
317–323 (2012).
95. Luacs, P. M., Burnham, . P. & Anderson, D. . Model selection bias and Freedman’s paradox. Annals of the Institute of Statistical
Mathematics 62, 117–125 (2010).
96. Murtaugh, P. A. Performance of several variable-selection methods applied to real ecological data. Ecology Letters 12, 1061–1068
(2009).
97. Boyce, M. S., Vernier, P. ., Nielsen, S. E. & Schmiegelow, F. . Evaluating resource selection functions. Ecological Modelling 157,
281–300 (2002).
98. ESI. ArcGIS Destop: Version 10.3.1 Environmental Systems esearch Institute, edlands, CA, USA. (2015).
99. Geisser, H. & eyer, H.-u. e inuence of food and temperature on population density of wild boar Sus scrofa in the urgau
(Switzerland). Journal of Zoology 267, 89–96 (2005).
100. Morelle, . & Lejeune, P. Seasonal variations of wild boar Sus scrofa distribution in agricultural landscapes: a species distribution
modelling approach. European Journal of Wildlife esearch 61, 45–56 (2015).
101. Sweitzer, . A. Conservation implications of feral pigs in island and mainland ecosystems, and a case study of feral pig expansion
in California. Proceedings of 18th Vertebrate Pest Conference 18 26–34 (1998).
102. Fleming, P. J. et al. In Carnivores of Australia: past, present and future (eds A. S. Glen & C. . Dicman) (CSIO Publishing, 2014).
103. Trabucco, A. & Zomer, . Global soil water balance geospatial database. CGIA Consortium for Spatial Information. Published
online, available from the CGIACSI 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. & uf, 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
is 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
www.nature.com/scientificreports/
12
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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 gure.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing Interests: e authors declare no competing nancial 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 aliations.
is work is licensed under a Creative Commons Attribution 4.0 International License. e 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
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© e Author(s) 2017
... Furthermore, an understanding of the distribution and abundance of wild pig populations in relation to environmental conditions may shed light on the potential for pig populations to expand into favorable surrounding areas (McClure et al. 2015(McClure et al. , 2018Snow et al. 2017). Most studies have taken place within the continental United States, Europe, or Australia (Hone 2002;Mitchell et al. 2007;Morelle and Lejeune 2015;McClure et al. 2015;Lewis et al. 2017;Froese et al. 2017;Amendolia et al. 2019) and few studies have addressed these issues in island environments (Risch et al. 2020). ...
... To date, the application of distribution models to help manage wild pig populations has been limited. There have been several large-scale modeling attempts that identified distribution and abundance of wild pigs at the country or global scale (McClure et al. 2015(McClure et al. , 2018Snow et al. 2017;Lewis et al. 2017) and these efforts have shed light on critical issues regarding current wild pig populations, their potential for expansion, and species at-risk. However, due to their large-scale (country, continent, or global), the coarse resolution of these outputs, they may not be useful to decision-making for smaller municipalities or regions. ...
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Protected areas are among the key elements of global biodiversity conservation strategies and aim to conserve native species, habitats and ecosystems. Globalisation has led to increased introductions of species outside their natural range. In their new environment, some of these non-native species have the potential to affect ecosystems and compete with or threaten native species. The environment in close proximity to protected areas is likely to be the stepping stone for non-native species to become established in protected areas. However, little is known about the role that protected area surroundings play in the permeability of protected areas to non-native species. In this thesis, I focused on protected areas and their surrounding belts to address the issue of permeability to non-native species. Examining protected areas in Norway, I showed that non-native species surrounding protected areas have a qualitative impact on the community of non-native species in protected areas. Moreover, the proportion of invasive species was higher in protected areas (40 %) compared to their belts (12 %). The number of non-native species in the surrounding areas also significantly determined the number of non-native species in protected areas. I have also highlighted the dynamics of colonization from the belts to the protected areas by showing that non-native species were detected in the protected areas on average several years after they were recorded in the belts. In addition, I showed in four European countries that the type of land use and land cover in the proximity and within protected areas plays a central role in the establishment of non-native species in protected areas. Anthropogenic land use and land cover around protected areas promoted the establishment of non-native species inside protected areas, regardless of the land use and land cover present in them. Finally, I investigated the colonization dynamics of Acacia dealbata, an invasive t ree species, in and around protected areas in central Portugal over the last twenty years. I showed that disturbances by fires and the loss of tree cover had a significant positive effect on the presence of the species. This thesis highlights the importance of the protected area surroundings for the colonization of non-native species. This is particularly relevant for future management strategies for non-native species in protected areas.
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Aim The use of species distribution models (SDMs) to predict biological invasions is a rapidly developing area of ecology. However, most studies investigating SDMs typically ignore prediction errors and instead focus on regions where native distributions correctly predict invaded ranges. We investigated the ecological significance of prediction errors using reciprocal comparisons between the predicted invaded and native range of the red imported fire ant (Solenopsis invicta) (hereafter called the fire ant). We questioned whether fire ants occupy similar environments in their native and introduced range, how the environments that fire ants occupy in their introduced range changed through time relative to their native range, and where fire ant propagules are likely to have originated. Location We developed models for South America and the conterminous United States (US) of America. Methods We developed models using the Genetic Algorithm for Rule-set Prediction (GARP) and 12 environmental layers. Occurrence data from the native range in South America were used to predict the introduced range in the US and vice versa. Further, time-series data recording the invasion of fire ants in the US were used to predict the native range. Results Native range occurrences under-predicted the invasive potential of fire ants, whereas occurrence data from the US over-predicted the southern boundary of the native range. Secondly, introduced fire ants initially established in environments similar to those in their native range, but subsequently invaded harsher environments. Time-series data suggest that fire ant propagules originated near the southern limit of their native range. Conclusions Our findings suggest that fire ants from a peripheral native population established in an environment similar to their native environment, and then ultimately expanded into environments in which they are not found in their native range. We argue that reciprocal comparisons between predicted native and invaded ranges will facilitate a better understanding of the biogeography of invasive and native species and of the role of SDMs in predicting future distributions.