Use of Geospatial Methods to Characterize Dispersion of the Emerald Ash Borer in Southern Ontario, Canada

Since the introduction of the Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis) to Southern Ontario in 2002, the condition of all species of Ash trees (Fraxinus) in the province is currently at risk. In this research, the effects of positive spatial autocorrelation on the EAB data as a result of sampling bias was addressed by applying a filtering distance threshold. To analyze the impact of environmental and anthropogenic predictors on the distribution of the EAB, logistic regression, Random Forest (RF) and a hybrid of Random Forest and GLM known as the Random Generalized Linear Model (RGLM) were applied to EAB data from 2006-2012 across Ontario. Ultimately, three risk maps were created from the 2006-2012 EAB data to validate the prediction dataset from 2013. In terms of model transferability, RGLM had the best extrapolation accuracy (84%), followed by stepwise backward logistic regression (70%), and Random Forest (52%)

Invasive Species in Forests and Rangelands of the United States

This open access book describes the serious threat of invasive species to native ecosystems. Invasive species have caused and will continue to cause enormous ecological and economic damage with ever increasing world trade. This multi-disciplinary book, written by over 100 national experts, presents the latest research on a wide range of natural science and social science fields that explore the ecology, impacts, and practical tools for management of invasive species. It covers species of all taxonomic groups from insects and pathogens, to plants, vertebrates, and aquatic organisms that impact a diversity of habitats in forests, rangelands and grasslands of the United States. It is well-illustrated, provides summaries of the most important invasive species and issues impacting all regions of the country, and includes a comprehensive primary reference list for each topic. This scientific synthesis provides the cultural, economic, scientific and social context for addressing environmental challenges posed by invasive species and will be a valuable resource for scholars, policy makers, natural resource managers and practitioners

Forest Pathology and Entomology

The 22 papers that make up this Special Issue deal with pathogen and pest impact on forest health, from the diagnosis to the surveillance of causative agents, from the study of parasites’ biological, epidemiological, and ecological traits to their correct taxonomy and classification, and from disease and pest monitoring to sustainable control strategies

New Approaches to Mapping Forest Conditions and Landscape Change from Moderate Resolution Remote Sensing Data across the Species-Rich and Structurally Diverse Atlantic Northern Forest of Northeastern North America

The sustainable management of forest landscapes requires an understanding of the functional relationships between management practices, changes in landscape conditions, and ecological response. This presents a substantial need of spatial information in support of both applied research and adaptive management. Satellite remote sensing has the potential to address much of this need, but forest conditions and patterns of change remain difficult to synthesize over large areas and long time periods. Compounding this problem is error in forest attribute maps and consequent uncertainty in subsequent analyses. The research described in this document is directed at these long-standing problems. Chapter 1 demonstrates a generalizable approach to the characterization of predominant patterns of forest landscape change. Within a ~1.5 Mha northwest Maine study area, a time series of satellite-derived forest harvest maps (1973-2010) served as the basis grouping landscape units according to time series of cumulative harvest area. Different groups reflected different harvest histories, which were linked to changes in landscape composition and configuration through time series of selected landscape metrics. Time series data resolved differences in landscape change attributable to passage of the Maine Forest Practices Act, a major change in forest policy. Our approach should be of value in supporting empirical landscape research. Perhaps the single most important source of uncertainty in the characterization of landscape conditions is over- or under-representation of class prevalence caused by prediction bias. Systematic error is similarly impactful in maps of continuous forest attributes, where regression dilution or attenuation bias causes the overestimation of low values and underestimation of high values. In both cases, patterns of error tend to produce more homogeneous characterizations of landscape conditions. Chapters 2 and 3 present a machine learning method designed to simultaneously reduce systematic and total error in continuous and categorical maps, respectively. By training support vector machines with a multi-objective genetic algorithm, attenuation bias was substantially reduced in regression models of tree species relative abundance (chapter 2), and prediction bias was effectively removed from classification models predicting tree species occurrence and forest disturbance (chapter 3). This approach is generalizable to other prediction problems, other regions, or other geospatial disciplines

Historical GIS Research in Canada

Fundamentally concerned with place, and our ability to understand human relationships with environment over time, Historical Geographic Information Systems (HGIS) as a tool and a subject has direct bearing for the study of contemporary environmental issues and realities. To date, HGIS projects in Canada are few and publications that discuss these projects directly even fewer. This book brings together case studies of HGIS projects in historical geography, social and cultural history, and environmental history from Canada's diverse regions. Projects include religion and ethnicity, migration, indigenous land practices, rebuilding a nineteenth-century neighborhood, and working with Google Earth. With contributions by: Colleen Beard Stephen Bocking Jennifer Bonnell Jim Clifford Joanna Dean François Dufaux Patrick A. Dunae Marcel Fortin Jason Gilliland William M. Glen Megan Harvey Matthew G. Hatvany Sally Hermansen Andrew Hinson Don Lafreniere John S. Lutz Joshua D. MacFadyen Daniel Macfarlane Jennifer Marvin Cameron Metcalf Byron Moldofsky Sherry Olson Jon Pasher Daniel Rueck R. W. Sandwell Henry Yu Barbara Znamirowsk

Ecological differences in the associations between air pollution, greenness, and risk of stroke: The REGARDS study.

The adverse health effects of air pollution have long been recognized, with the majority of morbidity and mortality due to its effects on the cardiovascular system. Alternatively, living in areas with higher greenness has been found to be beneficial to a wide range of health outcomes. However, few studies have considered that these relationships may vary depending on the surrounding ecosystem. The purpose of this dissertation was to examine the effects of long-term exposure to air pollution and greenness on incidence of stroke, and how these relationships vary with the major ecological regions of the United States. We utilized the Reasons for Geographic and Racial Differences in Stroke study (REGARDS), a prospective cohort study of 30,239 participants recruited between 2003 and 2007. One-year and 3-year exposure to PM2.5, PM10, O3, NO2, SO2, and CO were assigned to participants’ census block group. Residential greenspace was estimated by the Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). The risk of incident stroke associated with baseline pollutants and greenness was assessed using adjusted Cox proportional hazards models. Models were stratified by EPA created ecoregions to determine how associations varied by geographic areas with similar environmental features. The hazard ratio (95% CI) for a 2.9 µg/m3 (interquartile range) increase in 1-year PM10 was 1.07 (1.003, 1.15) for risk of stroke in the full study population. We did not find evidence of positive associations for PM2.5, O3, NO2, SO2, and CO in the full population. In our ecoregion specific analysis, we found positive associations for PM2.5 in the Great Plains ecoregion, while associations for PM10 were strongest in the Eastern Temperate Forests region. There was suggestive evidence of a negative association between greenness and stroke incidence (hazard ratio: 0.989; 95% CI: 0.946, 1.033) for a 0.1 increase in NDVI within 250-m. In our analysis by ecoregions, we found negative associations between greenness and stroke incidence in the Eastern Temperate Forests region, but positive associations in the Great Plains and Mediterranean California regions. The associations between exposure to air pollution, greenness and stroke incidence varied by ecoregion, highlighting the importance of considering the complexities of the natural environment