Realistic assumptions about spatial locations and clustering of premises matter for models of foot-and-mouth disease spread in the United States.

Spatially explicit livestock disease models require demographic data for individual farms or premises. In the U.S., demographic data are only available aggregated at county or coarser scales, so disease models must rely on assumptions about how individual premises are distributed within counties. Here, we addressed the importance of realistic assumptions for this purpose. We compared modeling of foot and mouth disease (FMD) outbreaks using simple randomization of locations to premises configurations predicted by the Farm Location and Agricultural Production Simulator (FLAPS), which infers location based on features such as topography, land-cover, climate, and roads. We focused on three premises-level Susceptible-Exposed-Infectious-Removed models available from the literature, all using the same kernel approach but with different parameterizations and functional forms. By computing the basic reproductive number of the infection (R0) for both FLAPS and randomized configurations, we investigated how spatial locations and clustering of premises affects outbreak predictions. Further, we performed stochastic simulations to evaluate if identified differences were consistent for later stages of an outbreak. Using Ripley's K to quantify clustering, we found that FLAPS configurations were substantially more clustered at the scales relevant for the implemented models, leading to a higher frequency of nearby premises compared to randomized configurations. As a result, R0 was typically higher in FLAPS configurations, and the simulation study corroborated the pattern for later stages of outbreaks. Further, both R0 and simulations exhibited substantial spatial heterogeneity in terms of differences between configurations. Thus, using realistic assumptions when de-aggregating locations based on available data can have a pronounced effect on epidemiological predictions, affecting if, where, and to what extent FMD may invade the population. We conclude that methods such as FLAPS should be preferred over randomization approaches.

Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals.

Although habitat fragmentation is often assumed to be a primary driver of extinction, global patterns of fragmentation and its relationship to extinction risk have not been consistently quantified for any major animal taxon. We developed high-resolution habitat fragmentation models and used phylogenetic comparative methods to quantify the effects of habitat fragmentation on the world's terrestrial mammals, including 4,018 species across 26 taxonomic Orders. Results demonstrate that species with more fragmentation are at greater risk of extinction, even after accounting for the effects of key macroecological predictors, such as body size and geographic range size. Species with higher fragmentation had smaller ranges and a lower proportion of high-suitability habitat within their range, and most high-suitability habitat occurred outside of protected areas, further elevating extinction risk. Our models provide a quantitative evaluation of extinction risk assessments for species, allow for identification of emerging threats in species not classified as threatened, and provide maps of global hotspots of fragmentation for the world's terrestrial mammals. Quantification of habitat fragmentation will help guide threat assessment and strategic priorities for global mammal conservation.

Modelling the domestic poultry population in the United States: A novel approach leveraging remote sensing and synthetic data methods.

Comprehensive and spatially accurate poultry population demographic data do not currently exist in the United States; however, these data are critically needed to adequately prepare for, and efficiently respond to and manage disease outbreaks. In response to absence of these data, this study developed a national-level poultry population dataset by using a novel combination of remote sensing and probabilistic modelling methodologies. The Farm Location and Agricultural Production Simulator (FLAPS) (Burdett et al., 2015) was used to provide baseline national-scale data depicting the simulated locations and populations of individual poultry operations. Remote sensing methods (identification using aerial imagery) were used to identify actual locations of buildings having the characteristic size and shape of commercial poultry barns. This approach was applied to 594 U.S. counties with > 100,000 birds in 34 states based on the 2012 U.S. Department of Agriculture (USDA), National Agricultural Statistics Service (NASS), Census of Agriculture (CoA). The two methods were integrated in a hybrid approach to develop an automated machine learning process to locate commercial poultry operations and predict the number and type of poultry for each operation across the coterminous United States. Validation illustrated that the hybrid model had higher locational accuracy and more realistic distribution and density patterns when compared to purely simulated data. The resulting national poultry population dataset has significant potential for application in animal disease spread modelling, surveillance, emergency planning and response, economics, and other fields, providing a versatile asset for further agricultural research.

Summer habitat use and activity patterns of wild boar Sus scrofa in rangelands of central Argentina.

Biological invasions are one of the main components of human-caused global change and their negative impact on invaded ecosystems have long been recognized. Invasive mammals, in particular, can threaten native biodiversity and cause economic impacts in the region where they are introduced, often through a wide range of conflicts with humans. Although the wild boar, Sus scrofa, is considered by the IUCN as one of the 100 invasive species most damaging to biodiversity in the world, in Argentina there have only been a few studies focused on its ecology with most of them conducted in protected areas. In this study, we evaluated the effect of several factors related with human disturbance, landscape composition, degree of fragmentation and the presence of a potential competitor and a predator on the habitat use of wild boar using data from camera traps and site-occupancy modeling. Additionally, we described the daily activity pattern of the species and we studied the level of overlap with both a potential competitor and a predator. The sampling effort totaled 7,054 camera trap days. Farm density, proportion of shrubland and proportion of grassland with bushes were the detection variables included in the most supported model whereas proportion of grassland and capture rate of the Pampas fox Lycalopex gymnocercus were the occupancy variables included in the most supported model. However, the proportion of grassland was the only variable that showed statistically significant support in the averaged model, indicating that habitat use of wild boar in this area was significantly negatively affected by the level of grass cover. Wild boars were mostly nocturnal, with more activity between 21:00 and 3:00 and a peak around midnight. Wild boars showed a high level of overlap with the activity pattern of the Pampas fox and a low overlap with the activity pattern of the puma Puma concolor. Despite wild boar being introduced in Argentina a few decades ago, this study is the first landscape-scale research carried out in an agricultural landscape in Argentina and the first one based on camera-trapping data. Our study contributes valuable information that could be used to design strategies to reduce wild boar population or to minimize the damage caused by this invasive species in Argentina.

Correction: Summer habitat use and activity patterns of wild boar Sus scrofa in rangelands of central Argentina.

[This corrects the article DOI: 10.1371/journal.pone.0206513.].

Publisher Correction: A globally-distributed alien invasive species poses risks to United States imperiled species.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

A globally-distributed alien invasive species poses risks to United States imperiled species.

In the midst of Earth's sixth mass extinction event, non-native species are a driving factor in many imperiled species' declines. One of the most widespread and destructive alien invasive species in the world, wild pigs (Sus scrofa) threaten native species through predation, habitat destruction, competition, and disease transmission. We show that wild pigs co-occur with up to 87.2% of imperiled species in the contiguous U.S. identified as susceptible to their direct impacts, and we project increases in both the number of species at risk and the geographic extent of risks by 2025. Wild pigs may therefore present a severe threat to U.S. imperiled species, with serious implications for management of at-risk species throughout wild pigs' global distribution. We offer guidance for efficient allocation of research effort and conservation resources across species and regions using a simple approach that can be applied to wild pigs and other alien invasive species globally.

Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms.

Livestock distribution in the United States (U.S.) can only be mapped at a county-level or worse resolution. We developed a spatial microsimulation model called the Farm Location and Agricultural Production Simulator (FLAPS) that simulated the distribution and populations of individual livestock farms throughout the conterminous U.S. Using domestic pigs (Sus scrofa domesticus) as an example species, we customized iterative proportional-fitting algorithms for the hierarchical structure of the U.S. Census of Agriculture and imputed unpublished state- or county-level livestock population totals that were redacted to ensure confidentiality. We used a weighted sampling design to collect data on the presence and absence of farms and used them to develop a national-scale distribution model that predicted the distribution of individual farms at a 100 m resolution. We implemented microsimulation algorithms that simulated the populations and locations of individual farms using output from our imputed Census of Agriculture dataset and distribution model. Approximately 19% of county-level pig population totals were unpublished in the 2012 Census of Agriculture and needed to be imputed. Using aerial photography, we confirmed the presence or absence of livestock farms at 10,238 locations and found livestock farms were correlated with open areas, cropland, and roads, and also areas with cooler temperatures and gentler topography. The distribution of swine farms was highly variable, but cross-validation of our distribution model produced an area under the receiver-operating characteristics curve value of 0.78, which indicated good predictive performance. Verification analyses showed FLAPS accurately imputed and simulated Census of Agriculture data based on absolute percent difference values of < 0.01% at the state-to-national scale, 3.26% for the county-to-state scale, and 0.03% for the individual farm-to-county scale. Our output data have many applications for risk management of agricultural systems including epidemiological studies, food safety, biosecurity issues, emergency-response planning, and conflicts between livestock and other natural resources.

Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States.

Wild pigs (Sus scrofa), also known as wild swine, feral pigs, or feral hogs, are one of the most widespread and successful invasive species around the world. Wild pigs have been linked to extensive and costly agricultural damage and present a serious threat to plant and animal communities due to their rooting behavior and omnivorous diet. We modeled the current distribution of wild pigs in the United States to better understand the physiological and ecological factors that may determine their invasive potential and to guide future study and eradication efforts. Using national-scale wild pig occurrence data reported between 1982 and 2012 by wildlife management professionals, we estimated the probability of wild pig occurrence across the United States using a logistic discrimination function and environmental covariates hypothesized to influence the distribution of the species. Our results suggest the distribution of wild pigs in the U.S. was most strongly limited by cold temperatures and availability of water, and that they were most likely to occur where potential home ranges had higher habitat heterogeneity, providing access to multiple key resources including water, forage, and cover. High probability of occurrence was also associated with frequent high temperatures, up to a high threshold. However, this pattern is driven by pigs' historic distribution in warm climates of the southern U.S. Further study of pigs' ability to persist in cold northern climates is needed to better understand whether low temperatures actually limit their distribution. Our model highlights areas at risk of invasion as those with habitat conditions similar to those found in pigs' current range that are also near current populations. This study provides a macro-scale approach to generalist species distribution modeling that is applicable to other generalist and invasive species.

Global patterns of fragmentation and connectivity of mammalian carnivore habitat.

Although mammalian carnivores are vulnerable to habitat fragmentation and require landscape connectivity, their global patterns of fragmentation and connectivity have not been examined. We use recently developed high-resolution habitat suitability models to conduct comparative analyses and to identify global hotspots of fragmentation and connectivity for the world's terrestrial carnivores. Species with less fragmentation (i.e. more interior high-quality habitat) had larger geographical ranges, a greater proportion of habitat within their range, greater habitat connectivity and a lower risk of extinction. Species with higher connectivity (i.e. less habitat isolation) also had a greater proportion of high-quality habitat, but had smaller, not larger, ranges, probably reflecting shorter distances between habitat patches for species with restricted distributions; such species were also more threatened, as would be expected given the negative relationship between range size and extinction risk. Fragmentation and connectivity did not differ among Carnivora families, and body mass was associated with connectivity but not fragmentation. On average, only 54.3 per cent of a species' geographical range comprised high-quality habitat, and more troubling, only 5.2 per cent of the range comprised such habitat within protected areas. Identification of global hotspots of fragmentation and connectivity will help guide strategic priorities for carnivore conservation.

Hematology, serum chemistry, and body mass of free-ranging and captive Canada lynx in Minnesota.

Baseline blood chemistry data could be particularly valuable if reference values from free-ranging populations of rare or endangered species are not available. The Canada lynx (Lynx canadensis) is listed as threatened under the Endangered Species Act in the conterminous United States, even though the species is managed as a furbearer in Alaska and in most provinces of Canada. Body mass, blood chemistry, and hematologic data for free-ranging lynx were collected from 2003 to 2007 and for captive lynx from 1984 to 2007. Up to 2 yr of age, captive lynx were consistently heavier than free-ranging lynx. Body mass of adult free-ranging lynx was similar to body mass of captive adult lynx. Some differences in blood chemistry between free-ranging and captive lynx were statistically significant, but most measured values were within reference ranges for domestic cats. Free-ranging lynx had higher concentrations of aspartate aminotransferase, alanine aminotransferase, and blood urea nitrogen than did captive lynx, and these were outside the reference value ranges for domestic cats. Alkaline phosphatase and phosphorus were higher in juveniles (<12 mo when captured) as compared to adults. Free-ranging lynx maintained body mass between serial captures. Hematologic values, blood chemistry values, and body mass of free-ranging Canada lynx provide support for the hypothesis that Canada lynx in Minnesota, at the southern edge of their range, are in normal physical condition.