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

BackgroundThe 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.MethodsA 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.Results60% 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).ConclusionsThis 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

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

Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between  ∼ 2 and  ∼ 15&thinsp;cm horizontal resolution and accuracies of ±10&thinsp;cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along  ∼ 50&thinsp;m transects, ranged from 1.58 to 10.56&thinsp;cm for weekly SD and from 2.54 to 8.68&thinsp;cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71&thinsp;cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.</p

Borehole water level response to barometric pressure as an indicator of aquifer vulnerability

The response of borehole water levels to barometric pressure changes in semiconfined aquifers can be used to determine barometric response functions from which aquifer and confining layer properties can be obtained. Following earlier work on barometric response functions and aquifer confinement, we explore the barometric response function as a tool to improve the assessment of groundwater vulnerability in semiconfined aquifers, illustrated through records from two contrasting boreholes in the semiconfined Chalk Aquifer, East Yorkshire, UK. After removal of recharge and Earth tide influences on the water level signal, barometric response functions were estimated and aquifer and confining layer properties determined through an analytical model of borehole water level response to barometric pressure. A link between the thickness and vertical diffusivity of the confining layer determined from the barometric response function, and groundwater vulnerability is proposed. The amplitude spectrum for barometric pressure and instrument resolution favor determination of the barometric response function at frequencies to which confining layer diffusivities are most sensitive. Numerical modeling indicates that while the high frequency response reflects confining layer properties in the immediate vicinity of the borehole, the low frequency response reflects vertical, high diffusivity pathways though the confining layer some hundreds of meters distant. A characteristic time scale parameter, based on vertical diffusivities and thicknesses of the saturated and unsaturated confining layer, is introduced as a measure of semiconfined aquifer vulnerability. The study demonstrates that the barometric response function has potential as a tool for quantitative aquifer vulnerability assessment in semiconfined aquifers

Improving spatial prioritisation for remote marine regions: optimising biodiversity conservation and sustainable development trade-offs

Creating large conservation zones in remote areas, with less intense stakeholder overlap and limited environmental information, requires periodic review to ensure zonation mitigates primary threats and fill gaps in representation, while achieving conservation targets. Follow-up reviews can utilise improved methods and data, potentially identifying new planning options yielding a desirable balance between stakeholder interests. This research explored a marine zoning system in north-west Australia–abiodiverse area with poorly documented biota. Although remote, it is economically significant (i.e. petroleum extraction and fishing). Stakeholder engagement was used to source the best available biodiversity and socio-economic data and advanced spatial analyses produced 765 high resolution data layers, including 674 species distributions representing 119 families. Gap analysis revealed the current proposed zoning system as inadequate, with 98.2% of species below the Convention on Biological Diversity 10% representation targets. A systematic conservation planning algorithm Maxan provided zoning options to meet representation targets while balancing this with industry interests. Resulting scenarios revealed that conservation targets could be met with minimal impacts on petroleum and fishing industries, with estimated losses of 4.9% and 7.2% respectively. The approach addressed important knowledge gaps and provided a powerful and transparent method to reconcile industry interests with marine conservation

Use of Species Distribution Modeling in the Deep Sea

In the last two decades the use of species distribution modeling (SDM) for the study and management of marine species has increased dramatically. The availability of predictor variables on a global scale and the ease of use of SDM techniques have resulted in a proliferation of research on the topic of species distribution in the deep sea. Translation of research projects into management tools that can be used to make decisions in the face of changing climate and increasing exploitation of deep-sea resources has been less rapid but necessary. The goal of this workshop was to discuss methods and variables for modeling species distributions in deep-sea habitats and produce standards that can be used to judge SDMs that may be useful to meet management and conservation goals. During the workshop, approaches to modeling and environmental data were discussed and guidelines developed including the desire that 1) environmental variables should be chosen for ecological significance a priori; 2) the scale and accuracy of environmental data should be considered in choosing a modeling method; 3) when possible proxy variables such as depth should be avoided if causal variables are available; 4) models with statistically robust and rigorous outputs are preferred, but not always possible; and 5) model validation is important. Although general guidelines for SDMs were developed, in most cases management issues and objectives should be considered when designing a modeling project. In particular, the trade-off between model complexity and researcher’s ability to communicate input data, modeling method, results and uncertainty is an important consideration for the target audience

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value

Models of marine fish biodiversity : assessing predictors from three habitat classification schemes

Prioritising biodiversity conservation requires knowledge of where biodiversity occurs. Such knowledge, however, is often lacking. New technologies for collecting biological and physical data coupled with advances in modelling techniques could help address these gaps and facilitate improved management outcomes. Here we examined the utility of environmental data, obtained using different methods, for developing models of both uni- and multivariate biodiversity metrics. We tested which biodiversity metrics could be predicted best and evaluated the performance of predictor variables generated from three types of habitat data: acoustic multibeam sonar imagery, predicted habitat classification, and direct observer habitat classification. We used boosted regression trees (BRT) to model metrics of fish species richness, abundance and biomass, and multivariate regression trees (MRT) to model biomass and abundance of fish functional groups. We compared model performance using different sets of predictors and estimated the relative influence of individual predictors. Models of total species richness and total abundance performed best; those developed for endemic species performed worst. Abundance models performed substantially better than corresponding biomass models. In general, BRT and MRTs developed using predicted habitat classifications performed less well than those using multibeam data. The most influential individual predictor was the abiotic categorical variable from direct observer habitat classification and models that incorporated predictors from direct observer habitat classification consistently outperformed those that did not. Our results show that while remotely sensed data can offer considerable utility for predictive modeling, the addition of direct observer habitat classification data can substantially improve model performance. Thus it appears that there are aspects of marine habitats that are important for modeling metrics of fish biodiversity that are not fully captured by remotely sensed data. As such, the use of remotely sensed data to model biodiversity represents a compromise between model performance and data availability

Global challenges for seagrass conservation

Seagrasses, flowering marine plants that form underwater meadows, play a significant global role in supporting food security, mitigating climate change and supporting biodiversity. Although progress is being made to conserve seagrass meadows in select areas, most meadows remain under significant pressure resulting in a decline in meadow condition and loss of function. Effective management strategies need to be implemented to reverse seagrass loss and enhance their fundamental role in coastal ocean habitats. Here we propose that seagrass meadows globally face a series of significant common challenges that must be addressed from a multifaceted and interdisciplinary perspective in order to achieve global conservation of seagrass meadows. The six main global challenges to seagrass conservation are (1) a lack of awareness of what seagrasses are and a limited societal recognition of the importance of seagrasses in coastal systems; (2) the status of many seagrass meadows are unknown, and up-to-date information on status and condition is essential; (3) understanding threatening activities at local scales is required to target management actions accordingly; (4) expanding our understanding of interactions between the socio-economic and ecological elements of seagrass systems is essential to balance the needs of people and the planet; (5) seagrass research should be expanded to generate scientific inquiries that support conservation actions; (6) increased understanding of the linkages between seagrass and climate change is required to adapt conservation accordingly. We also explicitly outline a series of proposed policy actions that will enable the scientific and conservation community to rise to these challenges. We urge the seagrass conservation community to engage stakeholders from local resource users to international policy-makers to address the challenges outlined here, in order to secure the future of the world’s seagrass ecosystems and maintain the vital services which they supply