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

  • Arizona State Univeristy (Mesa AZ)
  • Ecological Sciences

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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
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@
received: 04 November 2016
Accepted: 03 February 2017
Published: 09 March 2017
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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.
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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.
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).
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
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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
Supporting Citations for
Prediction Data Source
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
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
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
Forest Minus Agriculture*
Biotic/Vegetation; dierence between
the proportion of forest and agriculture
within 10 km radius buer
See forest (classied as
present or absent for this
variable) and agriculture
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
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
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
Mortality related to periods
of low precipitation,
especially during
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);
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
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;
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.
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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.
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.
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.
Wet Season Unvegetate d Agriculture
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.
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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).
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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
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.
Scientific RepoRts | 7:44152 | DOI: 10.1038/srep44152
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.
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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
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
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
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).
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... 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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Background Non-native wild pigs ( Sus scrofa ) threaten sensitive flora and fauna, cost billions of dollars in economic damage, and pose a significant human–wildlife conflict risk. Despite growing interest in wild pig research, basic life history information is often lacking throughout their introduced range and particularly in tropical environments. Similar to other large terrestrial mammals, pigs possess the ability to shift their range based on local climatic conditions or resource availability, further complicating management decisions. The objectives of this study were to (i) model the distribution and abundance of wild pigs across two seasons within a single calendar year; (ii) determine the most important environmental variables driving changes in pig distribution and abundance; and (iii) highlight key differences between seasonal models and their potential management implications. These study objectives were achieved using zero-inflated models constructed from abundance data obtained from extensive field surveys and remotely sensed environmental variables. Results Our models demonstrate a considerable change in distribution and abundance of wild pigs throughout a single calendar year. Rainfall and vegetation height were among the most influential variables for pig distribution during the spring, and distance to adjacent forest and vegetation density were among the most significant for the fall. Further, our seasonal models show that areas of high conservation value may be more vulnerable to threats from wild pigs at certain times throughout the year, which was not captured by more traditional modeling approaches using aggregated data. Conclusions Our results suggest that (i) wild pigs can considerably shift their range throughout the calendar year, even in tropical environments; (ii) pigs prefer dense forested areas in the presence of either hunting pressure or an abundance of frugivorous plants, but may shift to adjacent areas in the absence of either of these conditions; and (iii) seasonal models provide valuable biological information that would otherwise be missed by common modeling approaches that use aggregated data over many years. These findings highlight the importance of considering biologically relevant time scales that provide key information to better inform management strategies, particularly for species whose ranges include both temperate and tropical environments and thrive in both large continental and small island ecosystems.
... However, this relationship has been controversially discussed in the past, as covarying extrinsic factors such as abiotic factors (e.g., resource heterogeneity, disturbance or soil fertility) also have impact on the invasion success and were not always distinguished in previous studies (Levine and D'Antonio, 1999;Grace et al., 2017). Abiotic conditions have been considered as the main predictors for colonization success in various previous studies (Zenni and Nuñez, 2013;Lewis et al., 2017;. ...
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.
... Wild pigs threaten 672 taxa across 54 countries and are expected to contribute to losses in global biodiversity (Risch et al., 2021). Populations of wild pigs are expected to spread in the USA (Snow et al., 2017b;Lewis et al., 2019) and other regions of the world (Lewis et al., 2017) unless more effective tools and techniques for controlling their populations can be applied. Population control often involves baiting wild pigs and attracting them to focal areas (McRae et al., 2019;Snow and VerCauteren, 2019) to ultimately trap, shoot, or deliver toxicants or contraceptives to the animals Faruck et al., 2021;Lavelle et al., 2018b;Snow et al., 2021a,b). ...
Wild pigs (Sus scrofa) are a highly destructive invasive species throughout North and South America, Australia, and many island nations. Where invasive, their populations are targeted for control to reduce damage. Controlling wild pigs often involves baiting to draw them into traps or entice them to consume a toxic bait. However, baiting can have mixed success in congregating wild pigs to focal areas long enough for control measures to ultimately be implemented. We sought to evaluate how environmental conditions (i.e., precipitation) and negative stimuli (i.e., proxy for exposure to previous control efforts) influenced use of bait sites by wild pigs. We compared visitation to bait sites during dry (2019–2020) and wet (2021) years, and between wild pigs that had been previously trapped and released in southcentral Alabama and northcentral Texas, USA. We found that drier years substantially increased use of bait sites by wild pigs (i.e., 119–136% increase over 17 days). Similarly, wild pigs that did not experience negative stimuli had increased use of bait sites (i.e., 30–31% increase over 17 days). We recommend that managers intensify their control efforts during drier periods to take advantage of susceptible behaviors of wild pigs during these times. We also recommend that control efforts focus on eliminating surviving wild pigs which may have experienced negative stimuli (e.g., narrow misses during trapping, shooting some wild pigs from a group, sub-lethal doses from toxic baits) and be educated against future efforts.
... Finally, species have the potential to shift their location to avoid stressful local conditions. Current species ranges are determined by a diversity of biotic and abiotic factors (Cahill et al. 2014;Lewis et al. 2017;Stuart-Smith et al. 2017), and as such, all species may not readily shift in relation to environmental change. Patterns of range expansion are often related to ecological traits (Auer and King 2014;Bates et al. 2014), and a commonly observed pattern is for generalist species to have a greater capacity to shift (Stuart-Smith et al. 2021). ...
... Examinar os padrões de biodiversidade em escalas espaciais menores que englobem estes gradientes naturais pode ajudar na compreensão sobre a sua importância relativa na estruturação de comunidades(Lewis et al., 2017). Estabelecer parâmetros para padrões de biodiversidade em múltiplas ...
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As macroalgas são organismos dominantes e abundantes que atuam como um substrato estrutural biológico com impactos positivos na sobrevivência, riqueza e abundância das espécies locais. Através da sua estrutura física, estas espécies modificam as condições físicas do habitat e influenciam direta e indiretamente as interações biológicas, desempenhando assim um papel importante na estruturação das comunidades macrobentónicas. No entanto, estes habitats são difíceis de amostrar devido ao facto de serem substratos estruturalmente complexos, pelo que a utilização de substratos artificiais se torna uma alternativa válida. A vantagem deste tipo de metodologia é a utilização duma unidade padronizada para comparações quantitativas de amostras separadas espacialmente, bem como elimina a necessidade duma amostragem destrutiva dos substratos naturais. Foram colocados dois substratos artificiais diferentes em ambiente entre marés na Torpedera (2 metros) e subtidal na Enseñada de San Cristovo (9 metros) durante um período de 3 meses (maio a agosto). Em relação às métricas utilizadas, os índices de riqueza, abundância, diversidade e equitabilidade mostraram diferenças significativas entre os substratos colocados na Torpedera, enquanto que na Enseñada de San Cristovo só houve diferenças a nível da abundância de organismos. Na Torpedera o substrato AS2MS contêm mais espécies e menos indivíduos, enquanto que na Enseñada de San Cristovo ocorre o oposto. Em ambiente intertidal, a exposição às correntes e fluxo de água levou ao provisionamento de um maior nicho para organismos filtradores e construtores de casulo no substrato AS1-T e maior assentamento de partículas e sedimentos no substrato AS2-T permitindo um maior número de detritívoros. A profundidades maiores houve preferência de certas espécies pelo substrato AS2-SC devido ao aumento dos espaços intersticiais, o que permite a presença de organismos de maiores dimensões. Os padrões de abundância e riqueza de macroinvertebrados sugerem que as preferências de habitat podem estar relacionadas com diferentes modos de alimentação, estratégias reprodutivas, morfologias e mobilidade, além de confirmarem a dependência de vários grupos de organismos para com a arquitetura do habitat e a sua importância na estruturação das comunidades macrobentónicas, bem como a capacidade dos substratos artificiais suportarem comunidades diversificadas.
African swine fever (ASF) is a hemorrhagic and fatal disease of domestic pigs and wild boars caused by the African swine fever virus (ASFV). There is neither effective treatment nor vaccine at present, and thus this disease has led to major economic losses and adverse impacts on the livelihoods of stakeholders involved in the pork food system in China. In this study, a multi-criteria decision analysis (MCDA) method based on a geographic information system (GIS) was used to identify suitable areas for ASF occurrence in China. Ten spatial risk factors regarding ASF epidemic in China were identified from literature reviews, and the relative importance between them was evaluated by experts based on a pairwise comparison matrix. A numerical weight was calculated for each risk factor using an analytic hierarchy process (AHP) based on the evaluated results. The corresponding geographic data were collected, according to the hypothetical relationship between each factor and the suitability for ASF occurrence, risk factors were converted to standardized geographical layers using suitability relationship and then were combined using a weighted linear combination (WLC) method to produce a map of suitability for ASF occurrence. The results showed that our map has good accuracy in predicting the hot- spots of ASF in China (AUC =0.791; 95% CI [0.741–0.852]). In conclusion, our study provides decision-making aid support for Chinese veterinary services to implement African swine fever surveillance and control measures.
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We assessed the contributions of the Osun-Osogbo sacred grove in supporting bird conservation using point counts between October 2019 and March 2020. The grove was partitioned into three sections based on land use, including the Administrative Block, Forest Area and Osun Shrine. We recorded 3428 birds of 83 species, and then compared the species richness and feeding guilds in each section. A total of 75 (45%), 47 (28%) and 44 (27%), was recorded in Administrative Block, Forest Area and Osun Shrine, respectively. The Forest Area had the highest value of evenness (0.87) compared to the Administrative Block (0.82) and Osun Shrine with 0.81. Six feeding guilds were identified, including 12 granivores, 48 insectivores, 5 frugivores, 6 nectivores and 14 omnivores. Our results suggest that the Sacred Grove still holds substantial avifauna worth protecting, invariably strengthening the traditional conservation approach the site is known for.
Invasive plants are an increasing threat to global biodiversity. Effective management depends on accurate predictions of their spread. However, modelling the geographic distribution of invasive species, particularly with correlative species distribution models (SDMs), is challenging. SDMs assume that species are in equilibrium with their environment (i.e. they occur in all suitable environments); this assumption is likely to be violated for a species that is actively invading new environments. This assumption is rarely assessed, and when violated can have consequences for model reliability. Using the invasive vine Vincetoxicum rossicum, we tested the hypotheses that: 1) invasive species' distribution in environmental and geographic space increase to a plateau over time; 2) this plateau is a useful proxy for equilibrium distribution, a key assumption underlying SDMs. We compare V. rossicum's expansion in environmental and geographic space between historical and current time periods and infer equilibrium when its distribution has remained stable for an extended period. We also compare the performance of SDMs from historical time periods in predicting the current geographic distribution of V. rossicum. We found that V. rossicum has reached equilibrium in environmental space, but is still expanding its geographic distribution. SDM performance was poor in the first 30 years following introduction, but improved as V. rossicum approached environmental equilibrium. Our findings demonstrate the power of including temporal dynamics and the need to consider environmental and geographic equilibrium separately when modelling the distribution of invasive species. In light of our findings, we address shortcomings of the current approach to defining an equilibrium distribution and present a new perspective for reconciling the potentially confounding influence of dispersal limitation when assessing equilibrium distribution.
Wild pigs (Sus scrofa) are one of the most successful invasive species globally and are often implicated in agricultural damage. This damage is expected to increase as ranges of wild pigs expand, impacting the human food supply and increasing costs of food production. Our objective was to evaluate movement behaviors of wild pigs relative to resource availability and landscape features in an agriculture-dominated landscape, with a goal of informing management practices for reducing damage to corn. We monitored hourly movements of adult wild pigs relative to corn crops using GPS collars during the 2019 and 2020 growing seasons (Feb–Sept) in Delta County, Texas, USA. We generated movement metrics, home ranges for space-use analyses, and step selection functions to quantify selection for land cover types and landscape composition for each growth stage (i.e., pre-planting, establishment, vegetative, blister–milk, and dent–mature) and sex of wild pigs. We found that space-use and resource selection by wild pigs was dependent on corn growth stages and landscape composition, with more use as corn matured in fields closer to wooded areas. Most of the pigs had movement patterns that were categorized as residents with site fidelity near corn fields, yet some did make long-distance movements to select for corn. These results suggest that preventing damage is most important during the later stages of growth. If lethal control is not as effective or efficient before or during later growth stages of corn, managers should consider non-lethal methods, such as fencing to account for wild pigs that travel from afar, especially if corn fields are located near landcovers used as shelter for wild pigs.
Worldwide farmers are highly dependent on high-cost chemical fertilizers as a source of plant nutrients. Chemical growth factors are part of inputs to increase the number of micronutrients like phosphorus, potassium, and nitrogen in the soil, which helps more fertile in plants. Hence, these fertilizers are commonly associated with environmental pollution and the degradation of soil. Recently results of using natural organic fertilizers or bio-stimulants showed enhanced fertility of the soil. In this review, we discuss the effect of the natural growth stimulant Moringa oleifera leaf extract and its important role in triggering growth and boosting the economic yield of crops. High performance in the yield of plants by using bio-stimulant showed healthier results in various plants like capsicum, maize and etc. Effect of Moringa oleifera was studied and the enhancement of plant height, early bloom, chlorophyll content, number of vegetables per plant, seedling germination, and nutrient content of shoot tissues. Application of natural plant growth enhancers in the form of diluted Moringa oleifera leaf extracts containing effective micronutrients has been reported and found to be very effective in the growth of various crops.
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A fundamental goal of ecology is to understand the determinants of species' distributions (i.e., the set of locations where a species is present). Competition among species (i.e., interactions among species that harms each of the species involved) is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large-scale effects of competition or other biotic interactions is to fit species' distributions models (SDMs) and assess the effect of competitors on the distribution and abundance of the species of interest. It is often difficult to validate the accuracy of this approach with available data. Here, we simulate virtual species that experience competition. In these simulated datasets, we can unambiguously identify the effects that competition has on a species' distribution. We then fit SDMs to the simulated datasets and test whether we can use the outputs of the SDMs to infer the true effect of competition in each simulated dataset. In our simulations, the abiotic environment influenced the effects of competition. Thus, our SDMs often inferred that the abiotic environment was a strong predictor of species abundance, even when the species' distribution was strongly affected by competition. The severity of this problem depended on whether the competitor excluded the focal species from highly suitable sites or marginally suitable sites. Our results highlight how correlations between biotic interactions and the abiotic environment make it difficult to infer the effects of competition using SDMs.
Biotic interactions can influence the ranges and abundances of species, but no clear guidelines exist for integrating them into correlative models of niches and distributions. Niche/distributional models characterize environmental/habitat suitability or species presence using predictor variables unaffected by (= unlinked to) the population of the focal species. Such variables (termed 'scenopoetic') typically have been considered to include only abiotic factors. In contrast, population-demographic approaches model the abundance of the focal species by including linked predictor variables, which frequently are biotic interactors. Nevertheless, a focal species might hold no, or negligible, population-level effects on its biotic interactors. Hence, contrary to current theory, such interactors would represent unlinked variables valid and potentially very useful for niche/distributional models. Consideration of population-level effects indicates that facilitators and affecting amensals (species that negatively affect another species but are not affected by it) constitute unlinked variables, but commensals and affected amensals do not. For competitors, mutualists, predators/prey, consumers/resources, and parasites/hosts, additional information is necessary. Specifically, available ecological/natural history information for the particular species involved (e.g. regarding specificity) and theory regarding ecological networks can allow identification of interactors that are likely to be unlinked or nearly so. Including an unlinked biotic interactor as a predictor variable in a niche/distributional model should improve predictions when the effects of the biotic interactor vary across the study region, or in another place or time period. Other relevant interactors must be taken into account by post-processing a niche/distributional model, or via population-demographic models that require abundance data over time. This framework should improve current correlative models and highlights areas requiring progress.
Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.
Feral pigs (Sus scrofa) are an exotic ungulate which have been widely introduced worldwide with multiple ecosystem and economic consequences. The author conducted a semi-comprehensive literature review directed at identifying the current state of knowledge related to the effects of feral pigs on island and mainland plant and animal communities. Also, the author describes the situation in California where feral pigs that were introduced in the late 1700s are now widespread due to hunting-related introductions and natural range extensions. Feral pigs on predator-free oceanic islands are a serious conservation problem because they attain high densities and have contributed to near-extinctions and extinctions of multiple endemic plants and vertebrates. In mainland ecosystems, however, feral pigs can have both positive and negative effects depending on the local circumstances. Rooting, for example, can have both positive and negative effects on growth and survival of some trees, soils and soil processes, and the distribution of native and exotic grasses. In general, however, the negative effects of rooting by feral pigs are amplified when population densities are high. Feral pigs may compete with native species for limited resources, but there are limited data relevant to this hypothesis. Based on observations of small amounts of animal matter in their diets, feral pigs eat terrestrial vertebrates and eggs of ground nesting birds, but the importance of predation by feral pigs on native vertebrates is poorly known. Feral pigs also may have important indirect effects in mainland ecosystems by providing a new prey base for native predators which may then increase. In areas of Europe with extant wolf (Canis lupus) populations, wild boar (Sus scrofa) are an important prey species which may be facilitating numerical and geographic recoveries of wolves. Because wild boar are important prey for endangered Amur tigers (Panthera tigris), they are considered important for recovering tiger populations. In Australia, feral pigs are potentially important prey for dingoes (Canis familiaris dingo); whereas, in the United States, endangered Florida panthers (Felis concolor coryi) consumed 23% to 59% feral pigs, and mountain lions (Felis concolor) in Texas and California consumed 5% to 38% feral pigs. Research needs for feral pigs include quantitatively assessing: 1) how acorn foraging by feral pigs limits or influences regeneration of oaks (Quercus sp.); 2) the competitive effects of feral pigs on native species; 3) whether direct predation by feral pigs suppresses small vertebrate populations; and 4) how the availability of feral pigs as prey influences native predator populations.
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.