Ixodes scapularis density and Borrelia burgdorferi prevalence along a residential-woodland gradient in a region of emerging Lyme disease risk.

The environmental risk of Lyme disease, defined by the density of Ixodes scapularis ticks and their prevalence of Borrelia burgdorferi infection, is increasing across the Ottawa, Ontario region, making this a unique location to explore the factors associated with environmental risk along a residential-woodland gradient. In this study, we collected I. scapularis ticks and trapped Peromyscus spp. mice, tested both for tick-borne pathogens, and monitored the intensity of foraging activity by deer in residential, woodland, and residential-woodland interface zones of four neighbourhoods. We constructed mixed-effect models to test for site-specific characteristics associated with densities of questing nymphal and adult ticks and the infection prevalence of nymphal and adult ticks. Compared to residential zones, we found a strong increasing gradient in tick density from interface to woodland zones, with 4 and 15 times as many nymphal ticks, respectively. Infection prevalence of nymphs and adults together was 15 to 24 times greater in non-residential zone habitats. Ecological site characteristics, including soil moisture, leaf litter depth, and understory density, were associated with variations in nymphal density and their infection prevalence. Our results suggest that high environmental risk bordering residential areas poses a concern for human-tick encounters, highlighting the need for targeted disease prevention.

Knowledge, protective behaviours, and perception of Lyme disease in an area of emerging risk: results from a cross-sectional survey of adults in Ottawa, Ontario.

BACKGROUND: The number of Lyme disease risk areas in Canada is growing. In regions with emerging tick populations, it is important to emphasize peridomestic risk and the importance of protective behaviours in local public health communication. This study aims to identify characteristics associated with high levels of Lyme disease knowledge and adoption of protective behaviours among residents in the Ottawa, Ontario region. METHODS: A geographically stratified web survey was conducted in November 2020 (n = 2018) to determine knowledge, attitudes, and practices regarding Lyme disease among adult residents. Responses were used to calculate: (i) composite scores for knowledge and adoption of protective practices; and (ii) an exposure risk index based on reported activity in woodlands during the spring-to-fall tick exposure risk period. RESULTS: 60% of respondents had a high knowledge of Lyme disease, yet only 14% indicated they often use five or more measures to protect themselves. Factors strongly associated with a high level of Lyme disease knowledge included being 55 or older (Odds Ratio (OR) = 2.04), living on a property with a yard (OR = 3.22), having a high exposure index (OR = 1.59), and knowing someone previously infected with Lyme disease (OR = 2.05). Strong associations with the adoption of a high number of protective behaviours were observed with membership in a non-Indigenous racialized group (OR = 1.70), living on a property with a yard (OR = 2.37), previous infection with Lyme disease (OR = 2.13), prior tick bite exposure (OR = 1.62), and primarily occupational activity in wooded areas (OR = 2.31). CONCLUSIONS: This study highlights the dynamics between Lyme disease knowledge, patterns of exposure risk awareness, and vigilance of personal protection in a Canadian region with emerging Lyme disease risk. Notably, this study identified gaps between perceived local risk and protective behaviours, presenting opportunities for targeted enhanced communication efforts in areas of Lyme disease emergence.

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada.

Lichen mapping is vital for caribou management plans and sustainable land conservation. Previous studies have used random forest, dense neural network, and convolutional neural network models for mapping lichen coverage. However, to date, it is not clear how these models rank in this task. In this study, these machine learning models were evaluated on their ability to predict lichen percent coverage in Sentinel-2 imagery in Québec and Labrador, Canada. The models were trained on 10-m resolution lichen coverage (%) maps created from 20 drone surveys collected in July 2019 and 2022. The dense neural network achieved a higher accuracy than the other two, with a reported mean absolute error of 5.2% and an R2 of 0.76. By comparison, the random forest model returned a mean absolute error of 5.5% (R2: 0.74) and the convolutional neural network had a mean absolute error of 5.3% (R2: 0.74). A regional lichen map was created using the trained dense neural network and a Sentinel-2 imagery mosaic. There was greater uncertainty on land covers that the model was not exposed to in training, such as mines and deep lakes. While the dense neural network requires more computational effort to train than a random forest model, the 5.9% performance gain in the test pixel comparison renders it the most suitable for lichen mapping. This study represents progress toward determining the appropriate methodology for generating accurate lichen maps from satellite imagery for caribou conservation and sustainable land management.

Risk factors for Lyme disease resulting from residential exposure amidst emerging Ixodes scapularis populations: A neighbourhood-level analysis of Ottawa, Ontario.

Lyme disease is an emerging health threat in Canada due to the continued northward expansion of the main tick vector, Ixodes scapularis. It is of particular concern to populations living in expanding peri-urban areas where residential development and municipal climate change response impact neighbourhood structure and composition. The objective of this study was to estimate associations of socio-ecological characteristics with residential Lyme disease risk at the neighbourhood scale. We used Lyme disease case data for 2017-2020 reported for Ottawa, Ontario to determine where patients' residential property, or elsewhere within their neighbourhood, was the suspected site of tick exposure. Cases meeting this exposure definition (n = 118) were aggregated and linked to neighbourhood boundaries. We calculated landscape characteristics from composited and classified August 2018 PlanetScope satellite imagery. Negative binomial generalized linear models guided by a priori hypothesized relationships explored the association between hypothesized interactions of landscape structure and the outcome. Increases in median household income, the number of forest patches, the proportion of forested area, forest edge density, and mean forest patch size were associated with higher residential Lyme disease incidence at the neighbourhood scale, while increases in forest shape complexity and average distance to forest edge were associated with reduced incidence (P<0.001). Among Ottawa neighbourhoods, the combined effect of forest shape complexity and average forest patch size was associated with higher residential Lyme disease incidence (P<0.001). These findings suggest that Lyme disease risk in residential settings is associated with urban design elements. This is particularly relevant in urban centres where local ecological changes may impact the presence of emerging tick populations and how residents interact with tick habitat. Further research into the mechanistic underpinnings of these associations would be an asset to both urban development planning and public health management.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge.

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Species distribution models for the eastern blacklegged tick, Ixodes scapularis, and the Lyme disease pathogen, Borrelia burgdorferi, in Ontario, Canada.

The blacklegged tick, Ixodes scapularis, is established in several regions of Ontario, Canada, and continues to spread into new geographic areas across the province at a rapid rate. This poses a significant public health risk since I. scapularis transmits the Lyme disease-causing bacterium, Borrelia burgdorferi, and other pathogens of potential public health concern. The objective of this study was to develop species distribution models for I. scapularis and B. burgdorferi to predict and compare the potential distributions of the tick vector and the Lyme disease pathogen as well as the ecological factors most important for species establishment. Ticks were collected via tick dragging at 120 sites across southern, central, and eastern Ontario between 2015 and 2018 and tested for tick-borne pathogens. A maximum entropy (Maxent) approach was used to model the potential distributions of I. scapularis and B. burgdorferi. Two independent datasets derived from tick dragging at 25 new sites in 2019 and ticks submitted by the public to local health units between 2015 and 2017 were used to validate the predictive accuracy of the models. The model for I. scapularis showed high suitability for blacklegged ticks in eastern Ontario and some regions along the shorelines of the Great Lakes, and moderate suitability near Algonquin Provincial Park and the Georgian Bay with good predictive accuracy (tick dragging 2019: AUC = 0.898; ticks from public: AUC = 0.727). The model for B. burgdorferi showed a similar predicted distribution but was more constrained to eastern Ontario, particularly between Ottawa and Kingston, and along Lake Ontario, with similarly good predictive accuracy (tick dragging 2019: AUC = 0.958; ticks from public: AUC = 0.863. The ecological variables most important for predicting the distributions of I. scapularis and B. burgdorferi included elevation, distance to deciduous and coniferous forest, proportions of agricultural land, water, and infrastructure, mean summer/spring temperature, and cumulative annual degree days above 0°C. Our study presents a novel application of species distribution modelling for I. scapularis and B. burgdorferi in Ontario, Canada, and provides an up to date projection of their potential distributions for public health knowledge users.

A novel low-cost method for assessing intra-urban variation in night time light and applications to public health.

Night time lighting (NTL) pollution is a public health concern given its known impact on a range of health outcomes. The daily cycle of the hue of natural ambient light shifting from relatively blue-white light at noon to relatively yellow-red light at sunset is important for human functioning. Disruptions of the circadian clock can result in melatonin suppression, sleep and mood disorders, and increased risks of cancer in adults. Current measures of intra-urban variation in NTL are based on costly in-person or coarse satellite image-based assessments. The central objective of the current study is to validate a novel low-cost measure of intra-urban NTL variation. Estimates of red, green and blue NTL intensity were derived from a cloud-free night time image of the city of Montreal, Canada, taken from the International Space Station (ISS). The new measures are shown to converge with in-person assessed NTL and to predict known child health-related outcomes. Specifically, the results suggest that ISS-assessed blue NTL is associated with feelings of safety and self-reported health. In conclusion, ISS-based measures of NTL, particularly of blue NTL, are valid indicators of intra-urban variation in NTL for applications in public health. Limitations of, and future directions for, the method are discussed.

Quantifying Airborne Lidar Bathymetry Quality-Control Measures: A Case Study in Frio River, Texas.

Airborne Lidar Bathymetry (ALB) is an advanced and effective technology for mapping water bodies and measuring water depth in relatively shallow inland and coastal zones. The concept of using light beams to detect and traverse water bodies has been around since the 1960s; however, its popularity has increased significantly in recent years with the advent of relatively affordable hardware, supplemented with potent software applications to process and analyze resulting data. To achieve the most accurate final product, which is usually a digital elevation model (DEM) of the bottom of a water body, various quality-control (QC) measures are applied during and after an airborne mission. River surveys, in particular, present various challenges, and quantifying the quality of the end product requires supplemental surveys and careful analysis of all data sets. In this article, we discuss a recent ALB survey of the Frio River in Texas and summarize the findings of all QC measures conducted. We conclude the article with suggestions for successful ALB deployments at similar survey locations.

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5.

Fine particulate matter (PM2.5) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM2.5, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM2.5, especially in areas with high spatiotemporal variability of PM2.5. In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 µg/m3, CV-R2 = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation. In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

A linked land-sea modeling framework to inform ridge-to-reef management in high oceanic islands.

Declining natural resources have led to a cultural renaissance across the Pacific that seeks to revive customary ridge-to-reef management approaches to protect freshwater and restore abundant coral reef fisheries. Effective ridge-to-reef management requires improved understanding of land-sea linkages and decision-support tools to simultaneously evaluate the effects of terrestrial and marine drivers on coral reefs, mediated by anthropogenic activities. Although a few applications have linked the effects of land cover to coral reefs, these are too coarse in resolution to inform watershed-scale management for Pacific Islands. To address this gap, we developed a novel linked land-sea modeling framework based on local data, which coupled groundwater and coral reef models at fine spatial resolution, to determine the effects of terrestrial drivers (groundwater and nutrients), mediated by human activities (land cover/use), and marine drivers (waves, geography, and habitat) on coral reefs. We applied this framework in two 'ridge-to-reef' systems (Ha'ena and Ka'upulehu) subject to different natural disturbance regimes, located in the Hawaiian Archipelago. Our results indicated that coral reefs in Ka'upulehu are coral-dominated with many grazers and scrapers due to low rainfall and wave power. While coral reefs in Ha'ena are dominated by crustose coralline algae with many grazers and less scrapers due to high rainfall and wave power. In general, Ka'upulehu is more vulnerable to land-based nutrients and coral bleaching than Ha'ena due to high coral cover and limited dilution and mixing from low rainfall and wave power. However, the shallow and wave sheltered back-reef areas of Ha'ena, which support high coral cover and act as nursery habitat for fishes, are also vulnerable to land-based nutrients and coral bleaching. Anthropogenic sources of nutrients located upstream from these vulnerable areas are relevant locations for nutrient mitigation, such as cesspool upgrades. In this study, we located coral reefs vulnerable to land-based nutrients and linked them to priority areas to manage sources of human-derived nutrients, thereby demonstrating how this framework can inform place-based ridge-to-reef management.

Spatiotemporal analysis of regional socio-economic vulnerability change associated with heat risks in Canada.

Excess mortality can be caused by extreme hot weather events, which are increasing in severity and frequency in Canada due to climate change. Individual and social vulnerability factors influence the mortality risk associated with a given heat exposure. We constructed heat vulnerability indices using census data from 2006 and 2011 in Canada, developed a novel design to compare spatiotemporal changes of heat vulnerability, and identified locations that may be increasingly vulnerable to heat. The results suggest that 1) urban areas in Canada are particularly vulnerable to heat, 2) suburban areas and satellite cities around major metropolitan areas show the greatest increases in vulnerability, and 3) heat vulnerability changes are driven primarily by changes in the density of older ages and infants. Our approach is applicable to heat vulnerability analyses in other countries.

The Heat Exposure Integrated Deprivation Index (HEIDI): A data-driven approach to quantifying neighborhood risk during extreme hot weather.

Mortality attributable to extreme hot weather is a growing concern in many urban environments, and spatial heat vulnerability indexes are often used to identify areas at relatively higher and lower risk. Three indexes were developed for greater Vancouver, Canada using a pool of 20 potentially predictive variables categorized to reflect social vulnerability, population density, temperature exposure, and urban form. One variable was chosen from each category: an existing deprivation index, senior population density, apparent temperature, and road density, respectively. The three indexes were constructed from these variables using (1) unweighted, (2) weighted, and (3) data-driven Heat Exposure Integrated Deprivation Index (HEIDI) approaches. The performance of each index was assessed using mortality data from 1998-2014, and the maps were compared with respect to spatial patterns identified. The population-weighted spatial correlation between the three indexes ranged from 0.68-0.89. The HEIDI approach produced a graduated map of vulnerability, whereas the other approaches primarily identified areas of highest risk. All indexes performed best under extreme temperatures, but HEIDI was more useful at lower thresholds. Each of the indexes in isolation provides valuable information for public health protection, but combining the HEIDI approach with unweighted and weighted methods provides richer information about areas most vulnerable to heat.

Delineation of Spatial Variability in the Temperature-Mortality Relationship on Extremely Hot Days in Greater Vancouver, Canada.

BACKGROUND: Climate change has increased the frequency and intensity of extremely hot weather. The health risks associated with extemely hot weather are not uniform across affected areas owing to variability in heat exposure and social vulnerability, but these differences are challenging to map with precision. OBJECTIVES: We developed a spatially and temporally stratified case-crossover approach for delineation of areas with higher and lower risks of mortality on extremely hot days and applied this approach in greater Vancouver, Canada. METHODS: Records of all deaths with an extremely hot day as a case day or a control day were extracted from an administrative vital statistics database spanning the years of 1998-2014. Three heat exposure and 11 social vulnerability variables were assigned at the residential location of each decedent. Conditional logistic regression was used to estimate the odds ratio for a 1°C increase in daily mean temperature at a fixed site with an interaction term for decedents living above and below different values of the spatial variables. RESULTS: The heat exposure and social vulnerability variables with the strongest spatially stratified results were the apparent temperature and the labor nonparticipation rate, respectively. Areas at higher risk had values ≥ 34.4°C for the maximum apparent temperature and ≥ 60% of the population neither employed nor looking for work. These variables were combined in a composite index to quantify their interaction and to enhance visualization of high-risk areas. CONCLUSIONS: Our methods provide a data-driven framework for spatial delineation of the temperature--mortality relationship by heat exposure and social vulnerability. The results can be used to map and target the most vulnerable areas for public health intervention. Citation: Ho HC, Knudby A, Walker BB, Henderson SB. 2017. Delineation of spatial variability in the temperature-mortality relationship on extremely hot days in greater Vancouver, Canada. Environ Health Perspect 125:66-75; http://dx.doi.org/10.1289/EHP224.

A comparison of urban heat islands mapped using skin temperature, air temperature, and apparent temperature (Humidex), for the greater Vancouver area.

Apparent temperature is more closely related to mortality during extreme heat events than other temperature variables, yet spatial epidemiology studies typically use skin temperature (also known as land surface temperature) to quantify heat exposure because it is relatively easy to map from satellite data. An empirical approach to map apparent temperature at the neighborhood scale, which relies on publicly available weather station observations and spatial data layers combined in a random forest regression model, was demonstrated for greater Vancouver, Canada. Model errors were acceptable (cross-validated RMSE=2.04 °C) and the resulting map of apparent temperature, calibrated for a typical hot summer day, corresponded well with past temperature research in the area. A comparison with field measurements as well as similar maps of skin temperature and air temperature revealed that skin temperature was poorly correlated with both air temperature (R(2)=0.38) and apparent temperature (R(2)=0.39). While the latter two were more similar (R(2)=0.87), apparent temperature was predicted to exceed air temperature by more than 5 °C in several urban areas as well as around the confluence of the Pitt and Fraser rivers. We conclude that skin temperature is not a suitable proxy for human heat exposure, and that spatial epidemiology studies could benefit from mapping apparent temperature, using an approach similar to the one reported here, to better quantify differences in heat exposure that exist across an urban landscape.

A Spatial Framework to Map Heat Health Risks at Multiple Scales.

In the last few decades extreme heat events have led to substantial excess mortality, most dramatically in Central Europe in 2003, in Russia in 2010, and even in typically cool locations such as Vancouver, Canada, in 2009. Heat-related morbidity and mortality is expected to increase over the coming centuries as the result of climate-driven global increases in the severity and frequency of extreme heat events. Spatial information on heat exposure and population vulnerability may be combined to map the areas of highest risk and focus mitigation efforts there. However, a mismatch in spatial resolution between heat exposure and vulnerability data can cause spatial scale issues such as the Modifiable Areal Unit Problem (MAUP). We used a raster-based model to integrate heat exposure and vulnerability data in a multi-criteria decision analysis, and compared it to the traditional vector-based model. We then used the Getis-Ord G(i) index to generate spatially smoothed heat risk hotspot maps from fine to coarse spatial scales. The raster-based model allowed production of maps at spatial resolution, more description of local-scale heat risk variability, and identification of heat-risk areas not identified with the vector-based approach. Spatial smoothing with the Getis-Ord G(i) index produced heat risk hotspots from local to regional spatial scale. The approach is a framework for reducing spatial scale issues in future heat risk mapping, and for identifying heat risk hotspots at spatial scales ranging from the block-level to the municipality level.

Modeling the distribution of Geodia sponges and sponge grounds in the Northwest Atlantic.

Deep-sea sponge grounds provide structurally complex habitat for fish and invertebrates and enhance local biodiversity. They are also vulnerable to bottom-contact fisheries and prime candidates for Vulnerable Marine Ecosystem designation and related conservation action. This study uses species distribution modeling, based on presence and absence observations of Geodia spp. and sponge grounds derived from research trawl catches, as well as spatially continuous data on the physical and biological ocean environment derived from satellite data and oceanographic models, to model the distribution of Geodia sponges and sponge grounds in the Northwest Atlantic. Most models produce excellent fits with validation data although fits are reduced when models are extrapolated to new areas, especially when oceanographic regimes differ between areas. Depth and minimum bottom salinity were important predictors in most models, and a Geodia spp. minimum bottom salinity tolerance threshold in the 34.3-34.8 psu range was hypothesized on the basis of model structure. The models indicated two currently unsampled regions within the study area, the deeper parts of Baffin Bay and the Newfoundland and Labrador slopes, where future sponge grounds are most likely to be found.