Risk factor analysis and spatiotemporal CART model of cryptosporidiosis in Queensland, Australia

Background: It remains unclear whether it is possible to develop a spatiotemporal epidemic prediction model for cryptosporidiosis disease. This paper examined the impact of social economic and weather factors on cryptosporidiosis and explored the possibility of developing such a model using social economic and weather data in Queensland, Australia.Methods: Data on weather variables, notified cryptosporidiosis cases and social economic factors in Queensland were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics, respectively. Three-stage spatiotemporal classification and regression tree (CART) models were developed to examine the association between social economic and weather factors and monthly incidence of cryptosporidiosis in Queensland, Australia. The spatiotemporal CART model was used for predicting the outbreak of cryptosporidiosis in Queensland, Australia.Results: The results of the classification tree model (with incidence rates defined as binary presence/absence) showed that there was an 87% chance of an occurrence of cryptosporidiosis in a local government area (LGA) if the socio-economic index for the area (SEIFA) exceeded 1021, while the results of regression tree model (based on non-zero incidence rates) show when SEIFA was between 892 and 945, and temperature exceeded 32°C, the relative risk (RR) of cryptosporidiosis was 3.9 (mean morbidity: 390.6/100,000, standard deviation (SD): 310.5), compared to monthly average incidence of cryptosporidiosis. When SEIFA was less than 892 the RR of cryptosporidiosis was 4.3 (mean morbidity: 426.8/100,000, SD: 319.2). A prediction map for the cryptosporidiosis outbreak was made according to the outputs of spatiotemporal CART models.Conclusions: The results of this study suggest that spatiotemporal CART models based on social economic and weather variables can be used for predicting the outbreak of cryptosporidiosis in Queensland, Australia

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

This is the publisher’s final pdf. The published article is copyrighted by the Public Library of Science and can be found at: http://www.plosone.org/home.action.Background: Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. \ud \ud Methodology and Principal Findings: A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. \ud \ud Conclusion and Significance: Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level

Population and Land Use Change in the California Mojave: Natural Habitat Implications of Alternative Futures

Demographic and land use dynamics have important implications for the natural environment within both developed and developing nations. Within the context of developed nations, popular and policy debates surrounding contemporary patterns of suburbanization attest to the salience of demographic and development issues. We examine the implications of land-use patterns as related to population and development within the context of the California Mojave Desert ecosystem. In a general sense, we aim to better understand the land requirements inferred by varying levels of population growth and density, as well as the natural habitat implications of those requirements. We develop a GIS including remotely sensed imagery, and demographic, economic, and biophysical data to examine of the implications of various demographic scenarios on species diversity. Spatial and statistical models are designed to develop possible alternative land use \u27futures\u27. Within the context of the California Mojave region, our results suggest that high- density development could reduce conflict with regions providing potential habitat for threatened or endangered species by over 80 percent. The process of model development demonstrates a potentially useful tool for policymakers, allowing for estimation and visualization of the land use implications of policy decisions

Strategies Used by Pet Dogs for Solving Olfaction-Based Problems at Various Distances.

The olfactory acuity of domestic dogs has been well established through numerous studies on trained canines, however whether untrained dogs spontaneously utilize this ability for problem solving is less clear. In the present paper we report two studies that examine what strategies family dogs use in two types of olfaction-based problems as well as their success at various distances. In Study 1, thirty dogs were tasked with distinguishing a target, either their covered owner (Exp 1) or baited food (Exp 2), from three visually identical choices at distances of 0m (touching distance), 1m, and 3m. There were nine consecutive trials for each target. We found that in Exp 1 the dogs successfully chose their owners over strangers at 0m and 1m, but not at 3m, where they used a win-stay strategy instead. In Exp 2 the dogs were only successful in choosing the baited pot at 0m. They used the win-stay strategy at 1m, but chose randomly at 3m. In Study 2, a different group of dogs was tested with their owners (Exp 1) and baited food (Exp 2) at just the 3m distance with two possible targets in 10-10 trials. In Exp 1 the dogs' overall performance was at chance level; however, when analyzed by trial, we noticed that despite tending to find their owners on the first trial, they generally switched to a win-stay strategy in subsequent trials, only to return to correctly choosing their owners based on olfaction in the later trials. In Exp 2, the dogs chose randomly throughout. We also found that dogs who relied on visual information in the warm-up trials were less successful in the olfaction-based test. Our results suggest that despite their ability to successfully collect information through olfaction, family dogs often prioritize other strategies to solve basic choice tasks