Karen C. Poh (@areyoukeddingme.bsky.social)

Incorporating weather and host abundance in an iterative subseasonal-to-interannual ecological forecast system for Ixodes scapularis, the vector of Lyme disease 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. http://dlvr.it/TNVHqj