AbstractA total of 454 unique sites in western Canada were drag sampled for host-seeking #Dermacentor species. #Dermacentor andersoni Stiles was detected in British Columbia, Alberta, and Saskatchewan, while D. variabilis (Say) was detected in all four provinces. #Dermacentor similis (Lado et al.) was collected from two sites in British Columbia. #Dermacentor andersoni was detected as far east as −103.53° and as far north as 52.38° at elevations from 500 m to 1,231 m. #Dermacentor variabilis was detected as far west as −110.9° and as far north as 54.78° at elevations from 200 to 1,200 m. Logistic regression indicated that prevalence of D. andersoni decreased with latitude, increased in the west, and increased with elevation to 900 m, then, declined. Prevalence of D. variabilis decreased with latitude, increased eastwards, and declined as elevation increased. Relationships with environmental variables were developed and indicated that mean winter temperature (Jan.–Mar.), summer temperature (Jul.–Sept.) and total winter precipitation affected both species. #Dermacentor andersoni occurred at sites with mean winter temperatures between −10.4 °C and 1.4 °C, while most D. variabilis occurred at sites with winter temperatures between −15.0 °C and −7.9 °C. Prevalence of both species increased as summer and winter temperatures increased. Prevalence of D. andersoni also declined as total summer precipitation increased, consistent with it being a xerophilic species. Prevalence was also influenced by ecoregion and soil variables. Models had good fit, low bias, and good discrimination. Results will be useful for forecasting distribution and presence of these #tick species throughout western Canada.

Forecasting the population dynamics of disease vectors is critical for mitigating the risks of vector-borne diseases under a changing climate. We evaluate an iterative Bayesian forecast model of black-legged tick (Ixodes scapularis) phenology and population dynamics at near-term to interannual (e.g. 12 month) scales. The black-legged tick is the vector of Borrelia burgdorferi, the causative agent of human Lyme disease. Our forecasts consistently outperformed seasonal null models based on historical day-of-year averages, particularly during peak questing periods when disease risk is highest. Iterative data assimilation improved forecast performance over time, demonstrating the ability to adaptively learn about climate-driven shifts in demographic parameters, and reinforcing the value of long-term data to support management. Weather and climate variables emerged as the dominant predictors of nymph survival, with daily maximum temperature displacing humidity as the strongest predictor as the iterative forecast cycle evolved over time. Short-term forecasts driven by local weather observations were more accurate than those relying on seasonal climate forecasts, highlighting the importance of fine-scale weather dynamics and data for subannual predictions. At interannual scales, seasonal climate forecasts and vertebrate host (mouse) abundance were important for maintaining strong predictive skill in forecasting nymphal tick abundance, which is often used as a proxy for risk of human exposure to tick-borne disease, but forecasts were largely unaffected by larval abundance. Investment in monitoring efforts should prioritize observations of the nymphal stage to reduce forecast uncertainty. These results offer a path forward for operationalizing ecological forecasts of tick populations under environmental change and underscore the importance of adaptive, process-based models for managing tick-borne disease risk in a changing climate.

The Department of Biology in the College of Arts and Sciences at the University of Kentucky in Lexington, KY seeks to fill a tenure-eligible faculty position at the Assistant or Associate Professor le...

Overview of modelling strategies and packages. Which model do I need for my data?

Meet Katherine Brown, field research manager at the University of South Carolina, professional dancer, and our next "Standout Early Career Professionals" profile.

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A beginners tutorial on Shiny apps. Contribute to JoachimGoedhart/A_Shiny_start development by creating an account on GitHub.

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The American Museum of Natural History is one of the world’s preeminent scientific and cultural institutions, and has as its mission to discover, interpret and disseminate information about human cult...

The transmission of infectious diseases involves complex interactions across multiple biological scales, from within-host immunological processes to between-host transmission dynamics. While multiscale models have the potential to capture these interactions more accurately, they are often hindered by increased complexity and limited data availability. In this study, we develop a multiscale epidemic model linking host-vector population-level transmission dynamics to within-host and within-vector pathogen dynamics. Our model captures key features of within-vector viral progression and allows bidirectional coupling between within-host and between-host processes. The scales are linked under the assumption of dose-dependent transmission, with the functional form informed by empirical viremia-infectiousness data from arbovirus transmission. Focusing on Dengue, Zika, and West Nile viruses as case studies, we assess how different functional forms of the coupling affect the number of equilibria of the epidemic model. We find that when the transmission is modeled using linear coupling functions, the multiscale model yields the same bifurcation structure of the simpler, uncoupled model, indicating that the linking of scales does not alter the range of possible long-term epidemiological states in such cases. However, nonlinear coupling can induce complex behaviors such as multiple endemic equilibria and backward bifurcations, which the uncoupled model does not capture. These results underscore the importance of carefully selecting coupling functions and provide guidance on when multiscale modeling is essential for understanding and managing vector-borne diseases.

Most vector-borne disease cases reported in the United States are caused by pathogens spread by blacklegged ticks, Ixodes scapularis. Of these, a majority are Lyme disease cases caused by Borrelia burgdorferi sen...