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The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Consequences of climate change for species assemblages and biodiversity may depend on how the relative importance of biotic and abiotic interactions shifts with the environment. Thus, disentangling the relative impact of biotic and abiotic factors on species composition, and how these vary with environmental conditions, are urgently needed to understand how climate change affects ecological processes and biodiversity across regions [ 1 ].
This is a recent restatement of the classical debate within ecological theory about the determinants of plant community species composition [ 2 , 3 ]: Abiotic factors, such as soil moisture, nutrients and pH are traditionally predicted to more directly affect species establishment and survival under stressful environmental conditions, such as in the alpine [ 4 ]. Biotic interactions, on the other hand, such as competition from denser and higher vegetation and a larger amount of litter, are often predicted to play a more important role for species coexistence in more productive habitats [ 5 , 3 , 6 , 7 , 8 ], but see [ 9 , 10 , 11 ].
Negative biotic interactions are also found in stressful environments e. Thus, biotic interactions may be equally important in stressful and more productive habitats, although the relative importance of different types of biotic interactions positive vs.
Empirical assessment of trends in the impact of species interactions, whether negative or positive, along broad-scale productivity or stress gradients are typically based on meta-analyses of experimental manipulation of neighbours and quantification of responses in terms of plant growth, or less commonly, survival [ 20 , 16 ]. However, such individual-level data cannot necessarily be scaled up to consequences of these interactions at community-level.
To take an extreme and unlikely example: if all species were strongly reduced in growth and survival by competition, but suffered similar reductions, their relative abundance would not be affected [ 21 ]. Thus, approaches to assess effects of biotic interactions on entire communities are needed. In this paper we take a non-experimental approach disentangling the relative importance of interactions among plants vs.
We first assess the variation in fine-scale vegetation accounted for by local biotic vs. We then compare the relative variation accounted for by local biotic vs. While this observational approach cannot unambiguously isolate cause and effect, it does avoid experimental artefacts and can more easily focus on the entire community rather than simple measures of individual growth for one or a few component species. The use of observational, rather than experimental data also facilitates comparison across different sites and scales.
Hierarchical multivariate variation partitioning approaches [ 22 ], see also [ 23 ] are often used to assess the relative importance of different local- and regional-scale environmental factors for community composition e. However, these approaches estimate variation without taking into account differences in responses across environmental gradients, i.
We hypothesize that the relative importance of biotic and abiotic factors, and possibly the direction of biotic effects positive vs. Thus, we supplement the variation partitioning approach with replicated local-scale ordination analyses to allow assessment of the relative importance of different environmental factors within each local community along broader-scale climate gradients.
In our study, we thus combine broad- and fine-scale ordination analyses of vegetation data from local sites within a regional-scale grid of sites to compare the effect of biotic vs. The impacts of biotic and abiotic factors on plant communities along elevation gradients have primarily been studied, or interpreted, as temperature gradients [ 16 ], but see [ 28 ]. Temperature and precipitation are, however, often correlated along elevation gradients, and recent studies have pointed at the importance of precipitation, soil moisture, and their interactions with temperature and local environmental factors as drivers of community dynamics [ 29 , 30 , 31 ].
Both temperature and precipitation regimes have changed worldwide during the last century, and are predicted to increase by 2. Towards higher elevations, there is also an increasing harshness and decreasing productivity. Higher temperatures and precipitation rates may increase productivity in these areas and thereby alter the relative role of biotic and abiotic interactions.
In accordance with this, vegetation canopy height and litter cover have increased in alpine and arctic plant communities during the last 20—30 years, both in climate warming experiments [ 33 , 29 ] and in unmanipulated monitoring plots [ 30 ].
These studies and others e. Graminoids are better competitors for nutrients and light than most forbs [ 35 ], and consequently, the decline of forbs may be due to biotic interactions rather than a direct climate effect.
To separate the effects of temperature and moisture on the relative importance of biotic and abiotic factors, we established twelve study sites along natural temperature and precipitation gradients in southern Norway, such that these two main climate variables varied independently Table 1 and all factors other than climate were as identical as possible; including vegetation type, bedrock, aspect, slope, and land use [ 36 ].
We used these data to test the following hypotheses: 1 The community response to local-scale variation in biotic and abiotic variables is not consistent across broad-scale bioclimatic gradients. We conclude by discussing the implications of these results for grassland species composition under warmer and wetter future climates. Our results show that the importance of abiotic vs biotic variables for species composition shift along the broad scale temperature and precipitation gradients and we propose that both local and regional scales analyses is crucial to be able to detect and disentangle the different drivers of local vegetation-environment relationships.
The study was conducted at twelve sites situated from the continental east to the oceanic west and from the alpine to the boreal climatic zones in the fjord landscape of southern Norway. The sites are distributed across a unique climate grid with three levels of summer temperature mean of four warmest months replicated at four levels of mean annual precipitation Fig 1 , Table 1. The climate data are interpolated with m resolution [ 37 ] from the normal period — [ 38 ]. The sites were selected to be as similar as possible with respect to all factors other than climate to facilitate comparisons between sites.
The sites are all grasslands associated with calcareous bedrock. Most of the sites are south-west exposed slopes of ca twenty degrees inclination, except one Boreal 3 that is east exposed. All sites are moderately grazed. Geographical distance between sites is on average 15 km and ranges from km Boreal 1 and Boreal 4 to m Boreal 2 and Sub alpine 2; these are also m a.
Within sites, all plots were situated in the selected grassland within 5 blocks with a total area of ca. All the plant communities are within the plant sociological association Potentillo-Festucetum ovinae [ 39 ].
In the alpine, this type tends towards Potentillo-Poligonium vivipari, and in some of the lowland sites, they tend towards Nardo-Agrostion tenuis [ 39 ]. Location of the twelve study sites along temperature and precipitation gradients in the fjord landscape of southern Norway. Vegetation sampling was conducted in 25 x 25 cm plots randomly positioned within five blocks at each site. The number of plots differs between sites due to the design of a transplant experiment conducted between the sites Table 1.
We estimated percentage cover of all vascular species, and the total cover of bryophytes, litter, and bare soil in each plot. We used a ruler at four fixed points in each plot to measure the mean height of the vegetation.
All of these variables are important proxies for the intensity of positive as well as negative biotic interactions among plants in boreal and alpine grasslands. Height of the vascular vegetation may indicate the intensity of competition for light. Increasing vascular cover may also indicate increasing competition for light or belowground , but under harsh environmental conditions increasing cover has also been shown to facilitate recruitment [ 40 , 41 ] and plant growth [ 35 ].
Bryophytes may limit seedling emergence and growth by limiting access to soil, shading or allelopathic interactions [ 15 , 42 ], limit growth of the vascular vegetation [ 43 ], or facilitate other species due to their water holding capacities [ 44 ]. Similarly, litter cover may limit plant recruitment by shading, act as a physical barrier to seedlings and shoots e.
All the sites are on non-protected privately owned land, and permits for doing field sampling has been given from the twelve landowners. No protected species were sampled. To quantify the local abiotic environment, we measured soil pH, moisture, and organic content proxy for nutrient availability in each plot. These are important determining factors for plant species composition and diversity in this system [ 24 , 40 ]. We were able to take the soil samples from directly underneath the sampled vegetation in each of the plots, because, after the vegetation was sampled, all plots were dug up and transplanted to a new site for another experiment.
The soil samples were stored cold and brought to the freezer directly after sampling. Before analyses the soil was thawed and put through a two mm sieve. Soil pH was measured after adding 50 ml distilled water to 25 g soil and mixing for two hours.
Soil moisture was positively correlated between the soil sampled from the field and the direct field measurements, and to obtain more sampling points on a time scale, we used the average of the soil sample and field measurements in the analyses. To determine whether the design of the grid with orthogonal temperature and precipitation gradients corresponded to clear vegetation patterns with respect to these two factors, we first examined the regional-scale pattern in vegetation-environment relationships by means of a canonical correspondence analysis CCA; [ 49 ] with all sites, climate variables, and local environmental variables included.
We then partitioned the community data into graminoid and forb species composition and conducted a series of analyses on these two datasets to determine the relative importance of the biotic and abiotic variables for local community composition within each dataset along the bioclimatic gradients.
First, a standard hierarchical variation partitioning [ 22 ], see also [ 23 ] was used to assess the overall importance of the different groups of explanatory variables on regional among-site and local within-site scales. This was done in three steps: 1 We assessed the total variation accounted for by each local abiotic soil moisture, pH, LOI or biotic vegetation height and cover, bryophyte cover, litter cover, bare soil variable.
Second, to test whether variation explained by biotic and abiotic variables differed among the 12 local sites i. Other species are generalists: species which live in a wide variety of geographic areas; the raccoon, for example, is native to most of North and Central America.
Species distribution patterns are based on biotic and abiotic factors and their influences during the very long periods of time required for species evolution; therefore, early studies of biogeography were closely linked to the emergence of evolutionary thinking in the eighteenth century. Some of the most distinctive assemblages of plants and animals occur in regions that have been physically separated for millions of years by geographic barriers. Biologists estimate that Australia, for example, has between , and , species of plants and animals.
Figure 1. Australia is home to many endemic species. The a wallaby Wallabia bicolor , a medium-sized member of the kangaroo family, is a pouched mammal, or marsupial. The b echidna Tachyglossus aculeatus is an egg-laying mammal.
Sometimes ecologists discover unique patterns of species distribution by determining where species are not found. Hawaii, for example, has no native land species of reptiles or amphibians, and has only one native terrestrial mammal, the hoary bat. Most of New Guinea, as another example, lacks placental mammals. Check out this video to observe a platypus swimming in its natural habitat in New South Wales, Australia.
Note that this video has no narration. Plants can be endemic or generalists: endemic plants are found only on specific regions of the Earth, while generalists are found on many regions. Isolated land masses—such as Australia, Hawaii, and Madagascar—often have large numbers of endemic plant species. Some of these plants are endangered due to human activity. The forest gardenia Gardenia brighamii , for instance, is endemic to Hawaii; only an estimated 15—20 trees are thought to exist. Figure 2.
The spring beauty is an ephemeral spring plant that flowers early in the spring to avoid competing with larger forest trees for sunlight.
Energy from the sun is captured by green plants, algae, cyanobacteria, and photosynthetic protists. These organisms convert solar energy into the chemical energy needed by all living things.
Light availability can be an important force directly affecting the evolution of adaptations in photosynthesizers. For instance, plants in the understory of a temperate forest are shaded when the trees above them in the canopy completely leaf out in the late spring. Not surprisingly, understory plants have adaptations to successfully capture available light. One such adaptation is the rapid growth of spring ephemeral plants such as the spring beauty Figure 2.
These spring flowers achieve much of their growth and finish their life cycle reproduce early in the season before the trees in the canopy develop leaves.
In aquatic ecosystems, the availability of light may be limited because sunlight is absorbed by water, plants, suspended particles, and resident microorganisms. Toward the bottom of a lake, pond, or ocean, there is a zone that light cannot reach. Photosynthesis cannot take place there and, as a result, a number of adaptations have evolved that enable living things to survive without light. For instance, aquatic plants have photosynthetic tissue near the surface of the water; for example, think of the broad, floating leaves of a water lily—water lilies cannot survive without light.
In environments such as hydrothermal vents, some bacteria extract energy from inorganic chemicals because there is no light for photosynthesis. Figure 3. Ocean upwelling is an important process that recycles nutrients and energy in the ocean. As wind green arrows pushes offshore, it causes water from the ocean bottom red arrows to move to the surface, bringing up nutrients from the ocean depths.
The availability of nutrients in aquatic systems is also an important aspect of energy or photosynthesis. Many organisms sink to the bottom of the ocean when they die in the open water; when this occurs, the energy found in that living organism is sequestered for some time unless ocean upwelling occurs. Ocean upwelling is the rising of deep ocean waters that occurs when prevailing winds blow along surface waters near a coastline Figure 3.
As the wind pushes ocean waters offshore, water from the bottom of the ocean moves up to replace this water. Carbon dioxide is also the byproduct of burning fossil fuels. Ultraviolet is often shortened to UV. The audio, illustrations, photos, and videos are credited beneath the media asset, except for promotional images, which generally link to another page that contains the media credit. The Rights Holder for media is the person or group credited.
Tyson Brown, National Geographic Society. National Geographic Society. For information on user permissions, please read our Terms of Service.
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Any interactives on this page can only be played while you are visiting our website. You cannot download interactives. An abiotic factor is a non-living part of an ecosystem that shapes its environment.
This map was created using ArcGIS 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. To address these ecological questions and understand the relative importance of biotic and abiotic factors in shaping the global distribution of a highly invasive mammal, we evaluated estimates of population density of wild pigs across diverse environments on five continents.
Specifically, we 1 evaluate how biotic i. We also compare population density between island and mainland populations. For the untransformed density estimates, mean population density for on the mainland equaled 2. Due to differences in population density between islands and on the mainland, we used density estimates from mainland populations in our subsequent analyses. Population density was influenced by both biotic and abiotic factors across the global distribution Tables 2 and 3 ; Supplementary Table S4.
Density was greatest at moderate levels of potential evapotranspiration and agriculture, decreased with large carnivore richness and amount of unvegetated area, and increased with precipitation during the wet and dry seasons Fig. Using the full model-averaged results of parameter estimates, we created a predictive map of global wild pig population density Fig.
Wild pig populations were predicted to occur at low to high population densities across all continents, including large areas of land where wild pigs are currently absent. The highest predicted densities occurred in southeastern, eastern, and western North America, throughout Central America, northern, eastern, and southwestern South America, western, southern, and eastern Eurasia, throughout Indonesia, central and southern Africa, and northern and southeastern Australia Fig. Results of k-fold cross validation demonstrated that the model had good predictive ability with a mean squared prediction error MSPE of 0.
Population density of an invasive large mammal was strongly influenced by both biotic and abiotic factors across its global distribution. Consistent with the prediction that abiotic factors drive broad-scale patterns of species distribution, potential evapotranspiration PET and precipitation variables were important predictors of population density on a global scale.
In addition, contributing to growing evidence that biotic factors are also important determinants of broad-scale patterns of species distributions, both biotic interactions and vegetation played important roles in predicting the distribution of wild pig populations globally. Further, land-use change mediated by human activities strongly predicted the broad-scale distribution of an invasive large mammal.
Consistent with previous studies evaluating how population density of ungulates varied across broad scales, both bottom-up resource-related and top-down predation factors influenced the distribution of wild pig populations 19 , 42 , 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 ability 16 , 44 , which can better inform management and conservation strategies for species.
For example, population density of wild pigs was highest in landscapes with moderate levels of agriculture and PET, lower large carnivore richness and amount of unvegetated area, and greater precipitation during the wet and dry seasons.
Using these relationships, we created a predictive map of population density across the world, which can be used to manage existing populations and predict areas where wild pig populations are likely to expand or invade if given the opportunity. Ultimately, this information can be used to prioritize management activities in areas at risk of invasion and with expanding populations. Abiotic factors, such as temperature and precipitation, are consistently found to be primary determinants of species distributions at broad scales Potential evapotranspiration can be especially informative for understanding broad-scale ecological patterns 45 , such as species distributions.
This was supported in our research where PET was the most important predictor of population density across the global distribution of wild pigs. Potential evapotranspiration is highly correlated with temperature variables, thus indicating that wild pig density was greatest at relatively moderate temperatures and density was lower in areas exhibiting extreme low and high temperatures.
In addition, the strong support of precipitation variables in our models is consistent with the association of wild pigs with vegetation cover, forage, and water In particular, precipitation likely facilitates rooting behavior by wild pigs by softening the soil substrate Biotic factors were among the most supported variables predicting population density across a global scale. Our results indicated that the presence of large carnivores can influence wild pig population density.
Large carnivore richness was strongly supported in our models and exhibited a negative relationship with wild pig density; as the number of large carnivore species increased, wild pig density decreased, which is consistent with studies in Eurasia and Australia 42 , 47 , In addition, interspecific competition can influence the distribution of species and it has been hypothesized that wild pigs have not extensively invaded wildlands in some regions of sub-Saharan Africa due to the presence of other pig species that exhibit similar niches Although competition with other species might influence wild pig populations and their distribution 49 , 50 , 51 , in other cases wild pigs are reported to spatially and temporally partition habitat use to reduce niche overlap with potential competitors 52 , 53 , 54 and not show evidence for interference competition with related mammals e.
Further, understanding potential interspecific competition for invasive species can be especially challenging in non-native habitat because invaders have not coevolved with competitors or predators and thus it is difficult to predict which species will be subordinate or dominant in potential competitive interactions or how competition might influence species distributions in unoccupied habitat 17 , 18 , Because it was unknown how competitive interactions between wild pigs and other species might influence their distribution, particularly outside their native range, competition was not included in our analyses.
Although biotic interactions between animals are the primary biotic factors evaluated in species distribution models at broad scales, the role of plant communities has received less consideration. In particular, anthropogenic land-use change increasingly influences vegetation communities across continents and warrants a better understanding for how human activities are shaping broad-scale distributions of plant and animal populations 22 , For example, agriculture is a dominating land cover type across continents 23 , 25 , which can potentially benefit species distributions in at least two ways.
In contrast, as agriculture increasingly dominates landscape patterns at broad extents, cover and other resources correspondingly decrease, which can negatively impact the geographic range and population density of some species. Our results demonstrate that agriculture can produce both positive and negative effects on populations, depending on the levels of agriculture.
At intermediate levels of agriculture, population density of wild pigs was greatest, likely due to an optimal mix of food and cover. Whereas, at high levels of agriculture, population density decreased precipitously, which was likely a result of inadequate cover. Our results indicate that heterogeneous landscapes with a mix of agriculture and cover will support the greatest populations of wild pigs, which is consistent with broad-scale patterns of wild pig populations in North America and Eurasia 57 , 58 , Due to relatively high predicted population densities of wild pigs inhabiting heterogeneous landscapes, these regions would likely experience the greatest crop damage, leading to high economic loss to farmers.
Forest is considered a key habitat type preferred by wild pigs 59 , 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 scales Thus, the interpretation of how forest influences the distribution of wild pigs must be considered in the context of other variables included in models, where abiotic factors might adequately explain forest distribution see discussion below.
However, as predicted, vegetation and cover play a strong role in predicting wild pig density; as the amount of unvegetated area increased across the landscape, wild pig population density decreased, which is consistent with geographic distribution maps of wild pigs In some systems, abiotic factors can be stronger predictors of species distributions, than biotic factors, because of high correlations between these two factors Our study indicated that both factors can be important predictors of species distributions, potentially because abiotic factors may poorly predict biotic factors stemming from human activities.
In addition, human influences might weaken the correlation between abiotic and biotic factors. For example, humans can significantly reduce the number of large carnivores in an area 63 , 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 production Thus, agricultural crops could not grow in many areas based on broad-scale climate factors alone, and therefore, abiotic factors can be poor predictors of agricultural practices in some regions.
Indeed, there likely are other examples where abiotic and biotic factors may exhibit low correlation in some systems e. Ultimately, it can be useful to consider biotic factors in species distribution models that might be poorly predicted by abiotic factors due to human activities. Additional biotic factors that can influences species distributions on a broad scale, particularly invasive species, include the role of humans in distributing the founding individuals of new populations.
For example, invasive wild pig populations have arisen across several continents recently through human activities. Illegal translocations by humans for hunting purposes can facilitate the long-distance expansion of wild pig populations into new areas 64 , 65 , 66 , which is currently a primary source of new populations globally 39 , 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 areas 67 , 68 , Indeed, propagule pressure i.
In addition, invasive species that exhibit r-selected characteristics e. Even at low population densities, invasive species with high reproductive output are more likely to establish populations in areas of lower quality habitat Given that wild pigs are one of the most fecund large mammals e.
Population density, compared to presence-absence occurrence, can provide more informative conclusions of species distributions in relation to biotic and abiotic factors 7 , 8.
For example, although large carnivores likely do not exclude wild pigs from habitat across broad scales, our study revealed they can influence abundance.
However, occurrence of species would remain constant across varying population densities, unless it resulted in species exclusion.
Ultimately, population densities can provide more detailed information about species distributions, which can better inform conservation and management plans and policy 7. Studies analyzing presence-only data with logistic regression and Maximum Entropy MaxEnt models have examined methods to address spatial sampling bias 73 , 74 , 75 and additional evaluations would be useful for studies using population density data with multiple linear regression.
Further, global analyses of population genetics could be used to identify groups and the proportion of wild and domestic genes across wild pig populations, which could be used to incorporate population structure into analyses to better understand population characteristics.
Predicting species distributions provides critical information to the management and conservation of biodiversity, especially for controlling invasive species. Without intensive management actions, our study predicts that there is strong potential for wild pigs to expand their geographic range and further invade expansive areas of North America, South America, Africa, and Australia. Although wild pigs currently occupy broad regions of predicted habitat in their non-native range, many regions of predicted habitat are currently unoccupied and may be at high risk for future invasion.
These areas might warrant increased surveillance by local, state, and federal agencies to counter the establishment of populations. Although attention in unoccupied areas that are predicted to support high densities of wild pigs might warrant priority for countering population introductions, wild pigs can persist in relatively low quality habitat e.
Given the potential for wild pig populations to rapidly expand once established 36 , 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.
To evaluate the population density i. Previous research evaluated how population density of wild pigs varied across western Eurasia 42 and we incorporated these 54 estimates of population density into our analysis.
In addition, we followed the methodological recommendation of Melis et al. Island populations typically exhibit higher population density compared to mainland populations 76 , We thus compared estimates of wild pig population density between island and mainland populations; if population density for islands was significantly higher than on the mainland, we focused on only evaluating mainland populations in subsequent analyses.
Models evaluating and predicting species distributions can be improved by including areas of absence a. Because wild pigs have occurred within their native range for thousands of years, we assumed that populations were at equilibrium and the species had colonized available habitat associated with its geographic distribution.
Thus, regions adjacent to its native distribution that were classified as unoccupied were assumed to be unsuitable for population persistence due to unfavorable environmental conditions. In addition, spatial sampling bias i. To include locations with estimates of zero density in our analyses, we used a three-step approach. Next, we calculated the spatial extent of the native range and buffered regions. Lastly, accounting for the area of each region, we selected a random sample of locations within the buffered region that was proportional to the number of estimates used in the native terrestrial range of wild pigs.
Based on this approach, we used 65 locations of zero density in our analyses that occurred across central Russia, Mongolia, western China, Saudi Arabia, and northern African countries. Zero density estimates were used in analyses relating wild pig density to landscape variables and excluded when comparing population density between island and mainland populations.
We considered a suite of biotic and abiotic landscape variables, which were divided into vegetation, predation, and climate factors Table 1 that we hypothesized to influence population density of wild pigs.
We used landscape variables that were available globally and, where possible, over long time periods i. Geospatial data layers were acquired through either Google Earth Engine 80 or were downloaded from online sources Table 1.
The biotic factors that we evaluated included agriculture, broadleaf forest, enhanced vegetation index EVI , forest canopy cover, difference in the proportion between forest and agriculture to characterize landscape heterogeneity , normalized difference vegetation index NDVI , large carnivore richness, and unvegetated area Table 1.
We expected a positive relationship between density and all vegetation factors, except unvegetated area, due to their association with increased food availability, plant productivity, and cover. In addition, we expected a quadratic relationship between population density and agriculture because we predicted density to be greatest at moderate levels of agriculture due to a mix of cover and food and low at high levels of agriculture due to a lack of adequate cover.
Finally, we expected a negative relationship between population density and large carnivore richness. The abiotic factors that we evaluated included two measures of ecological energy regimes, actual evapotranspiration the amount of water loss from evaporation and transpiration, which is related to plant productivity and potential evapotranspiration PET; the amount of evaporation and transpiration that would occur with a sufficient water supply, considering solar radiation, air temperature, humidity, and wind speed; Actual evapotranspiration is a measure of water-energy balance and potential evapotranspiration is considered a measure of ambient energy and often highly correlated with temperature variables Although evapotranspiration variables can include elements of biotic i.
In addition, we evaluated precipitation during dry and wet seasons, and annually, and temperature during summer and winter, and annually Table 1. We predicted a positive relationship between density and precipitation variables due to associated increases in forage, water, and cover and quadratic relationships between density and evapotranspiration and temperature variables due to expected peak densities at intermediate levels and low densities at low and high levels. Because wild pigs have been established across much of their non-native range for an extended period of time e.
Thus a moving window approach was conducted so that each pixel within a spatial layer summarized the landscape within the buffered radius. To determine the best scale for analyses we used a multi-criteria approach. First, variables were centered and scaled to improve model fit Next, we considered quadratic relationships for landscape factors that were predicted to exhibit a curvilinear pattern Table 1.
We used multiple linear regression to evaluate how population density was influenced by our final suite of biotic and abiotic factors Table 1. The distribution of density estimates were right skewed, thus we log-transformed density estimates using the natural logarithm To compare the relative importance of biotic and abiotic factors and to determine parameter estimates of variables, we ranked all possible models using AIC c , model-averaged parameter estimates i.
We used model weights and evidence ratios to evaluate if biotic factors improved model fit by comparing models including only abiotic factors to models also including biotic factors.
This map displays the maximal potential density of wild pigs in relation to the biotic and abiotic factors used in our modeling and reflects predicted densities that would be achieved if wild pigs had access to all landscapes, their movements were unrestricted, and management activities did not suppress populations.
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