Detecting sorghum aphid infestation in grain sorghum using leaf spectral response.

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected.

Estimating environmental vulnerability in the Cerrado with machine learning and Twitter data.

Estimating vulnerability is critical to understand human-induced influenceimpacts on the environmental system. The purpose of the current study was to integrate machine learning algorithm and Twitter data to estimate environmental vulnerability in the Brazilian Cerrado for the years 2011 and 2016. We first selected six exposure indicators and five sensitivity indicators to build an environmental vulnerability model and applied an Autoencoder algorithm to find the representation of exposure and sensitivity, respectively.

Hyperspectral Analysis of Leaf Pigments and Nutritional Elements in Tallgrass Prairie Vegetation.

Understanding the spatial distribution of forage quality is important to address critical research questions in grassland science. Due to its efficiency and accuracy, there has been a widespread interest in mapping the canopy vegetation characteristics using remote sensing methods. In this study, foliar chlorophylls, carotenoids, and nutritional elements across multiple tallgrass prairie functional groups were quantified at the leaf level using hyperspectral analysis in the region of 470-800 nm, which was expected to be a precursor to further remote sensing of canopy vegetation quality.

Heterogeneous Associations of Ecological Attributes with Tick-Borne Rickettsial Pathogens in a Periurban Landscape.

The variations in prevalence levels of two tick-borne rickettsial pathogens, Ehrlichia chaffeensis and Ehrlichia Ewingii, in a periurban environment were evaluated along with their ecological determinants. Tick life stage and sex, month of tick collection, landscape fragmentation, and ecological covariates specific to pasture and woodland sites were considered as explanatory covariates. Questing lone star ticks (Amblyomma americanum) were collected by flagging for an hour once every week during mid-April through mid-August in years 2013 and 2014.

Serologic Evidence of Mammarenaviruses among Wild Rodents in Brazil.

We screened blood samples from 560 wild rodents collected in southeastern Brazil for antibodies to a recombinant nucleoprotein (rN) of Junín virus. Six rodents were antibody positive (1.1%), demonstrating evidence of infection with mammarenaviruses in several species of Brazilian rodents.

Bayesian Spatiotemporal Pattern and Eco-climatological Drivers of Striped Skunk Rabies in the North Central Plains.

Striped skunks are one of the most important terrestrial reservoirs of rabies virus in North America, and yet the prevalence of rabies among this host is only passively monitored and the disease among this host remains largely unmanaged. Oral vaccination campaigns have not efficiently targeted striped skunks, while periodic spillovers of striped skunk variant viruses to other animals, including some domestic animals, are routinely recorded. In this study we evaluated the spatial and spatio-temporal patterns of infection status among striped skunk cases submitted for rabies testing in the North Central Plains of US in a Bayesian hierarchical framework, and also evaluated potential eco-climatological drivers of such patterns.

Hierarchical Bayesian Spatio-Temporal Analysis of Climatic and Socio-Economic Determinants of Rocky Mountain Spotted Fever.

This study aims to examine the spatio-temporal dynamics of Rocky Mountain spotted fever (RMSF) prevalence in four contiguous states of Midwestern United States, and to determine the impact of environmental and socio-economic factors associated with this disease. Bayesian hierarchical models were used to quantify space and time only trends and spatio-temporal interaction effect in the case reports submitted to the state health departments in the region. Various socio-economic, environmental and climatic covariates screened a priori in a bivariate procedure were added to a main-effects Bayesian model in progressive steps to evaluate important drivers of RMSF space-time patterns in the region.

Maximum Entropy-Based Ecological Niche Model and Bio-Climatic Determinants of Lone Star Tick (Amblyomma americanum) Niche.

The potential distribution of Amblyomma americanum ticks in Kansas was modeled using maximum entropy (MaxEnt) approaches based on museum and field-collected species occurrence data. Various bioclimatic variables were used in the model as potentially influential factors affecting the A. americanum niche.

Bayesian spatio-temporal analysis and geospatial risk factors of human monocytic ehrlichiosis.

Variations in spatio-temporal patterns of Human Monocytic Ehrlichiosis (HME) infection in the state of Kansas, USA were examined and the relationship between HME relative risk and various environmental, climatic and socio-economic variables were evaluated. HME data used in the study was reported to the Kansas Department of Health and Environment between years 2005-2012, and geospatial variables representing the physical environment [National Land cover/Land use, NASA Moderate Resolution Imaging Spectroradiometer (MODIS)], climate [NASA MODIS, Prediction of Worldwide Renewable Energy (POWER)], and socio-economic conditions (US Census Bureau) were derived from publicly available sources. Following univariate screening of candidate variables using logistic regressions, two Bayesian hierarchical models were fit; a partial spatio-temporal model with random effects and a spatio-temporal interaction term, and a second model that included additional covariate terms.

Differentiation of springtime vegetation indices associated with summer anthrax epizootics in west Texas, USA, deer.

Anthrax outbreaks in white-tailed deer, Odocoileus virginianus, are frequent in west Texas, USA, particularly across the Edwards Plateau. However, the outbreak severity varies among years. We summarize the outbreak history in white-tailed deer at a ranch north of Del Rio, Texas, from 2001 to 2010 and compare mortality rates to remotely sensed vegetation indices derived from Moderate Resolution Imaging Spectroradiometer satellite data.

Correlation between normalized difference vegetation index and malaria in a subtropical rain forest undergoing rapid anthropogenic alteration.

Time-series of coarse-resolution greenness values derived through remote sensing have been used as a surrogate environmental variable to help monitor and predict occurrences of a number of vector-borne and zoonotic diseases, including malaria. Often, relationships between a remotely-sensed index of greenness, e.g.

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

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.

Microhabitat characteristics of Akodon montensis, a reservoir for hantavirus, and hantaviral seroprevalence in an Atlantic forest site in eastern Paraguay.

Hantaviruses may cause serious disease when transmitted to humans by their rodent hosts. Since their emergence in the Americas in 1993, there have been extensive efforts to understand the role of environmental factors on the presence of these viruses in their host rodent populations. HPS outbreaks have been linked to precipitation, but climatic factors alone have not been sufficient to predict the spatial-temporal dynamics of the environment-reservoir-virus system.

Stratifying land use/land cover for spatial analysis of disease ecology and risk: an example using object-based classification techniques.

Landscape epidemiology has made significant strides recently, driven in part by increasing availability of land cover data derived from remotely-sensed imagery. Using an example from a study of land cover effects on hantavirus dynamics at an Atlantic Forest site in eastern Paraguay, we demonstrate how automated classification methods can be used to stratify remotely-sensed land cover for studies of infectious disease dynamics. For this application, it was necessary to develop a scheme that could yield both land cover and land use data from the same classification.

Phylogenetic and geographical relationships of hantavirus strains in eastern and western Paraguay.

Recently, we reported the discovery of several potential rodent reservoirs of hantaviruses in western (Holochilus chacarius) and eastern Paraguay (Akodon montensis, Oligoryzomys chacoensis, and O. nigripes). Comparisons of the hantavirus S- and M-segments amplified from these four rodents revealed significant differences from each another and from other South American hantaviruses.