Host contributions to the force of Borrelia burgdorferi and Babesia microti transmission differ at edges of and within a small habitat patch

In the northeastern United States, the emergence of Lyme disease has been associated, in part, with the increase of small forest patches. Such disturbed habitat is exploited by generalist species, such as white-footed mice, which are considered the host with the greatest reservoir capacity for the agents of Lyme disease ( Borrelia burgdorferi sensu stricto) and human babesiosis ( Babesia microti ). Spatial risk analyses have identified edge habitat as particularly risky. Using a retrotransposon-based quantitative PCR assay for host bloodmeal remnant identification, we directly measured whether the hosts upon which vector ticks fed differed at the edge or within the contiguous small habitat patch. Questing nymphal deer ticks, Ixodes dammini , the northern clade of Ixodes scapularis , were collected from either the edge or within a thicket on Nantucket Island over 3 transmission seasons and tested for evidence of infection as well as bloodmeal hosts. Tick bloodmeal hosts significantly differed by site as well as by year. Mice and deer were identified most often (49.9%), but shrews, rabbits and birds were also common. Ticks from the edge fed on a greater diversity of hosts than those from the thicket. Surprisingly, mice were not strongly associated with either infection at either sampling site (OR<2 for all). Although shrews were not the most common host utilized by ticks, they were highly associated with both infections at both sites (OR= 4.5 and 7.9 B. burgdorferi and 7.9 and 19.0 B. microti , edge and thicket). We conclude that reservoir hosts may differ in their contributions to infecting ticks between edge and contiguous vegetated patches. Importance Habitat fragmentation is thought to be a main factor in the emergence of Lyme disease and other of the deer tick-transmitted infections. The patchwork of forest and edges promotes altered biodiversity, favoring the abundance of generalist rodents such as white footed mice, heretofore considered a key tick and reservoir host in the northeastern U.S. We used tick bloodmeal analyses to directly identify the hosts from which nymphal deer ticks became infected. We demonstrate that there is considerable microfocality in host contributions to the cohort of infected ticks and that shrews, although they fed fewer ticks than mice, disproportionately influenced the force of pathogen transmission in our site. The venue of transmission of certain deer tick-transmitted agents may comprise a habitat scale of 10 meters or fewer and depend on alternative small mammal hosts such as shrews.

Mapping Earthquake Risk Sensitivity of Land Use Plan at Local Level for Sustainable Risk Sensitive Land Use Planning (RSLUP)

Risk-Sensitive Land Use Planning (RSLUP) is the process of mainstreaming disaster risk management parameters in land use planning. To ensure the effectiveness and sustainability of RSLUP, it is necessary to identify and understand the existing risk sensitivity of the land use plan. This research aims to develop a GIS-based multi-criteria zoning approach for mapping earthquake risk sensitivity of the land use plan of a local level area. For this purpose, Uttara Residential Model Town (URMT) (third phase), Dhaka, Bangladesh has been selected as the study area considering its earthquake risk for exposure to a potential earthquake. The methodology applied in this research is comprised of two steps. Firstly, assessment of the spatial earthquake risk sensitivity of the proposed land use plan of the study area based on the risk themes and corresponding risk attributes including both natural characteristics as well as built environment factors. They are macro-form risks (seismic hazard assessment), risks in urban texture (proximity from primary roads), special risk areas (geomorphic suitability and proximity from waterbody), open space scarcity risk, and risks in critical facilities (potential temporary disaster shelter and health facilities). Secondly, preparation of earthquake risk sensitivity zoning map by overlaying the spatial risk attribute maps based on weights determined through Analytical Hierarchical Process (AHP). This research brings out the importance and a methodology to assess risk sensitivity of the land use of an area at the local level, which can further foster sustainable RSLUP reflecting the risk sensitivity accordingly and effectively.

Fuzzy Based Spatial Risk Evaluation of Plastic Pollution: A Case Study of Anambra State of Nigeria

Natural systems, human health, and artistic sensitivities are all threatened by plastic pollution in most developed and developing countries. Plastic has emerged as a major global threat with rivers serving as sink for transported plastics, emanating from the terrestrial environment as a result of human activities. Anambra State in Nigeria is arguably the business hub of the South-eastern part of Nigeria, with a massive output of plastic wastes daily from individuals, commercial activities and industries. Owing to an inefficient waste management system, plastic leakage into her drainage networks is a critical environmental challenge. The aim of this study is to geospatially model the vulnerability associated with the various plastic leakage factors to the environment. To achieve this aim, data on different thematic variables which include plastic waste density, slope, land-use, drainage density and distance to drainage network of the study area were modelled, Geographic Information Systems (GIS) was used to delineate the variables in order to obtain final risk map for the study area. The result of the study indicates that a total area very high risk is 1840.03 km2, this constitutes about 40.11% of the study area. Local Governments Areas (LGA) located in the southern part of the study area is more susceptible to plastic waste leakage, this could be linked to factors like high dense population and increasing rate of urbanization in the region. It is recommended that waste collection should be frequent, strategic and higher priority should be attached to the high risked area from this study. Anambra State Government also needs to work together with plastic recycling companies, for effective collection of plastic wastes in the areas classified as hotspots in plastic litter accumulation as one of the mitigation measures.

Current characteristics of animal rabies cases in Thailand and relevant risk factors identified by a spatial modeling approach

The situation of human rabies in Thailand has gradually declined over the past four decades. However, the number of animal rabies cases has slightly increased in the last ten years. This study thus aimed to describe the characteristics of animal rabies between 2017 and 2018 in Thailand in which the prevalence was fairly high and to quantify the association between monthly rabies occurrences and explainable variables using the generalized additive models (GAMs) to predict the spatial risk areas for rabies spread. Our results indicate that the majority of animals affected by rabies in Thailand are dogs. Most of the affected dogs were owned, free or semi-free roaming, and unvaccinated. Clusters of rabies were highly distributed in the northeast, followed by the central and the south of the country. Temporally, the number of cases gradually increased after June and reached a peak in January. Based on our spatial models, human and cattle population density as well as the spatio-temporal history of rabies occurrences, and the distances from the cases to the secondary roads and country borders are identified as the risk factors. Our predictive maps are applicable for strengthening the surveillance system in high-risk areas. Nevertheless, the identified risk factors should be rigorously considered and integrated into the strategic plans for the prevention and control of animal rabies in Thailand.

Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.