J. M. Shawn Hutchinson

Connectivity of the American agricultural landscape: assessing the national risk of crop pest and disease spread.

More than two-thirds of cropland in the United States is devoted to the production of just four crop species—maize, wheat, soybeans, and cotton— raising concerns that homogenization of the American agricultural landscape could facilitate widespread disease and pest outbreaks, compromising the national food supply. As a new component in national agricultural risk assessment, we employed a graph-theoretic approach to examine the connectivity of these crops across the United States. We used county crop acreage to evaluate the landscape resistance to transmission—the degree to which host availability limits spread in any given region—for pests or pathogens dependent on each crop. For organisms that can disperse under conditions of lower host availability, maize and soybean are highly connected at a national scale, compared with the more discrete regions of wheat and cotton production. Determining the scales at which connectivity becomes disrupted for organisms with different dispersal abilities may help target rapid-response regions and the development of strategic policies to enhance agricultural landscape heterogeneity.

Identifying highly connected counties compensates for resource limitations when evaluating national spread of an invasive pathogen.

Surveying invasive species can be highly resource intensive, yet near-real-time evaluations of invasion progress are important resources for management planning. In the case of the soybean rust invasion of the United States, a linked monitoring, prediction, and communication network saved U.S. soybean growers approximately

A habitat-based model for the spread of hantavirus between reservoir and spillover species

200 M/yr. Modeling of future movement of the pathogen (Phakopsora pachyrhizi) was based on data about current disease locations from an extensive network of sentinel plots. We developed a dynamic network model for U.S. soybean rust epidemics, with counties as nodes and link weights a function of host hectarage and wind speed and direction. We used the network model to compare four strategies for selecting an optimal subset of sentinel plots, listed here in order of increasing performance: random selection, zonal selection (based on more heavily weighting regions nearer the south, where the pathogen overwinters), frequency-based selection (based on how frequently the county had been infected in the past), and frequency-based selection weighted by the node strength of the sentinel plot in the network model. When dynamic network properties such as node strength are characterized for invasive species, this information can be used to reduce the resources necessary to survey and predict invasion progress.

Extreme Daily Rainfall Event Distribution Patterns in Kansas

Abstract New habitat-based models for spread of hantavirus are developed which account for interspecies interaction. Existing habitat-based models do not consider interspecies pathogen transmission, a primary route for emergence of new infectious diseases and reservoirs in wildlife and man. The modeling of interspecies transmission has the potential to provide more accurate predictions of disease persistence and emergence dynamics. The new models are motivated by our recent work on hantavirus in rodent communities in Paraguay. Our Paraguayan data illustrate the spatial and temporal overlaps among rodent species, one of which is the reservoir species for Jabora virus and others which are spillover species. Disease transmission occurs when their habitats overlap. Two mathematical models, a system of ordinary differential equations (ODE) and a continuous-time Markov chain (CTMC) model, are developed for spread of hantavirus between a reservoir and a spillover species. Analysis of a special case of the ODE model provides an explicit expression for the basic reproduction number, R 0 , such that if R 0 < 1 , then the pathogen does not persist in either population but if R 0 > 1 , pathogen outbreaks or persistence may occur. Numerical simulations of the CTMC model display sporadic disease incidence, a new behavior of our habitat-based model, not present in other models, but which is a prominent feature of the seroprevalence data from Paraguay. Environmental changes that result in greater habitat overlap result in more encounters among various species that may lead to pathogen outbreaks and pathogen establishment in a new host.

Evaluations of hydrologic risk factors for canine leptospirosis: 94 cases (2002-2009).

AbstractThe Rainfall Frequency Atlas (TP40) was last updated for Kansas in 1961, using weather data from 1911 to 1958. Rainfall information contained in the atlas is the basis for important engineering and hydrologic design decisions in the state. With growing concern about the effects of global climate change and predictions of more extreme weather events, it is necessary to explore rainfall distribution patterns using the most current and complete data available. In this study, extreme rainfall frequency was analyzed using daily precipitation data (1920–2009) from 24 stations in Kansas and 15 stations from adjacent states. The Weibull distribution was used to calculate the precipitation probability distribution frequency at each station. Weather station point data were spatially interpolated using kriging. The overall analysis showed an increase in extreme precipitation events in Kansas with extreme event values tending to increase in magnitude from the northwest to southeast part of the state. Comparin...

Neighborhood-level socioeconomic and urban land use risk factors of canine leptospirosis: 94 cases (2002-2009).

Hydrologic and soil-hydrologic variables were evaluated retrospectively as potential risk factors for canine leptospirosis in Kansas and Nebraska using Geographic Information Systems (GIS). The sample included 94 positive and 185 negative dogs for leptospirosis predominantly based on PCR test for leptospires in urine. Hydrologic variables for the region were derived from National Hydrographic Dataset, National Flood Hazard Layer, National Wetlands Inventory; and soil-hydrologic variables from Soil Survey Geographic Database around geocoded addresses of case/control locations. Multivariable logistic models were used to determine association between hydrologic and soil-hydrologic variables and test status. Distance from water features (OR=0.82; 95% CI=0.79, 0.86), hydrologic density (OR=2.80; 95% CI=1.58, 4.96) and frequently flooded areas (OR=4.05; 95% CI=2.17, 7.55) within 2500 m surrounding case/control locations were significant risk factors for canine leptospirosis. Vaccination for dogs that live closer to water features, landscapes dominated by water features and frequent floods should be considered for leptospirosis prevention.

Environmental, climatic, and residential neighborhood determinants of feline tularemia.

Associations of housing, population, and agriculture census variables, and presence near public places were retrospectively evaluated as potential risk factors for canine leptospirosis using Geographic Information Systems (GIS). The sample population included 94 dogs positive for leptospirosis based on a positive polymerase chain reaction test for leptospires on urine, isolation of leptospires on urine culture, a single reciprocal serum titer of 12,800 or greater, or a four-fold rise in reciprocal serum titers over a 2-4 week period; and 185 dogs negative for leptospirosis based on a negative polymerase chain reaction test and reciprocal serum titers less than 400. Multivariable logistic regressions revealed different risk factors among different census units; however, houses lacking complete plumbing facilities [OR=2.80, 95% C.I.=1.82, 4.32 (census unit, block group); OR=1.36, 95% C.I.=1.28, 1.45 (census tract); OR=3.02, 95% C.I.=2.60, 3.52 (county)]; and poverty status by age (18-64) [OR=2.04, 95% C.I.=1.74, 2.39 (block group); OR=1.53, 95% C.I.=1.41, 1.67 (census tract); and OR=1.62, 95% C.I.=1.50, 1.76 (county)] were consistent risk factors for all census units. Living within 2500 m of a university/college and parks/forests were also significantly associated with leptospirosis status in dogs. Dogs that live under these circumstances are at higher risk for leptospirosis and pet owners should consider vaccination.

A statistical approach for predicting grassland degradation in disturbance-driven landscapes

BACKGROUND Tularemia, caused by a Gram-negative bacterium Francisella tularensis, is an occasional disease of cats in the midwestern United States and a public health concern due to its zoonotic potential. Different environmental, climatic, and pet-owners housing and socioeconomic conditions were evaluated as potential risk factors for feline tularemia using Geographic Information Systems (GIS) in a retrospective case-control study. METHODS The study included 46 cases identified as positive for tularemia based upon positive immunohistochemistry, isolation of F. tularensis using bacterial culture, and 4-fold or greater change in serum antibody titer for F. tularensis. Cats with a history of fever, malaise, icterus, and anorexia but no lesions characteristic of tularemia and/or negative immunohistochemistry, no isolation of bacteria in bacterial culture, and less than 4-fold raise in serum antibody titer for F. tularensis were treated as controls (n=93). Candidate geospatial variables from multiple thematic sources were analyzed for association with case status. Variables from National Land Cover Dataset, Soil Survey Geographic Database, US Census Bureau, and Daymet were extracted surrounding geocoded case-control household locations. Univariable screening of candidate variables followed by stepwise multivariable logistic modeling and odds ratios were used to identify strengths of variable associations and risk factors. RESULTS Living in a residence located in newly urbanized/suburban areas, residences surrounded by areas dominated by grassland vegetation, and mean vapor pressure conditions recorded during the 8(th) week prior to case arrival at the hospital are significant risk factors for feline tularemia. CONCLUSIONS Prevention strategies such as acaricide applications in residential backyards during spring and early summer periods and any behavior modifications suitable for cats that will prevent them from contracting infection from ticks or dead animals are necessary. Mean vapor pressure conditions recorded during the 8(th) week prior to case arrival at a diagnostic facility is a predictor for feline tularemia.

Real- or Near-Real Time Monitoring of Military Training Land Sustainability using Geospatial Techniques and Automated Sensor Deployments

The relationship between fire and long-term trends in tallgrass prairie vegetation was assessed at Fort Riley and Konza Prairie Biological Station (KPBS) in Kansas. Linear trends of surface greenness were previously estimated using BFAST and MODIS MOD13Q1 NDVI composite images from 2001 to 2010. To explain trends, fire frequency and seasonality (fire regime) was determined and each site was divided into spatial strata using administrative or management units. Generalized linear models (GLM) were used to explain trends by fire regime and/or stratification. Spatialized versions of GLMs were also computed address unexplained spatial components. Non-spatial models for FRK showed fire regime explained only 4% of trends compared to strata (7-26%). At KPBS, fire regime and spatial stratification explained 14% and 39%, respectively. At both sites, improvements in performance were minimal using both fire and strata as explanatory variables. Model spatialization resulted in a 5% improvement at FRK, but with weak spatial structure in the residuals, and was not necessary at KPBS as the existing stratification most of the spatial structure in model residuals. All models at KPBS performed better for each explanatory variable and combination tested. Fire has only a marginal effect on vegetation trends at FRK despite its widespread use as a grassland management tool to improve vegetation health, and explains much more of the trends at KPBS. Analysis of predictors from spatial models with existing stratification yielded an approach with fewer strata but similar performance and may provide insight about additional explanatory variables omitted from this analysis.

WQTIPS: Water Quality Trading Information Platform System

Military readiness depends on high quality training. High quality training depends on the availability of, and access to, quality training lands. The Integrated Training Area Management program (ITAM) is charged with developing and implementing management and decision-making processes that integrate training with sound natural resources management. At Fort Riley, assessments are made in four areas: training land vegetation condition, training area stability, training land safety and mobility, and Land Rehabilitation and Maintenance (LRAM) project monitoring. In the past, installation training land management decisions were informed through annual quality assessments developed by analyzing data collected from numerous plot and transect studies. Study designs depending upon traditional field methods are time intensive and expensive, and often do not provide results in a timely manner, leaving erosion problem areas unchecked for longer periods of time. By enhancing traditional field data collection methods with established remote sensing techniques and other automated sensor data, it is possible to provide leaders with a variety of assessments multiple times per year and when they are most useful. For example, because military maneuvers can cause extensive damage to vegetation across training areas throughout the calendar year and poor land cover can result in increased soil erosion, vegetation condition is assessed on a 16 day cycle using MODIS imagery. Additionally, areas of high erosion potential are identified using an overland flow energy accumulation model for optimized erosion control BMP placement. Raw data and processed information from the sensor network is accessible through a web portal. The portal represents a training land “sustainability dashboard” for decision makers while providing maps, interpreted information, and raw data feeds to technical users. The portal contains information such as current weather, current vegetation condition, mobility maps, and range usage data. Through continuous monitoring and assessment, and rapid data analysis, installations can better understand the environmental impact of training exercises and reduce safety risks to soldiers and equipment from environmental hazards. Project effectiveness is promoted by employing “enterprise-friendly” approaches for data processing, automated analysis, visualization, and reporting.