A modified matrix model captures the population dynamics for the primary vector of Lyme disease in North America

Abstract Lyme disease, the most prevalent tick‐borne disease in North America, is caused by the bacterium Borrelia burgdorferi , and in the eastern and central United States, it is spread to humans by the black‐legged tick ( Ixodes scapularis ). Due to the complex, multiyear and multihost life cycle of this species, a matrix modeling approach is needed to effectively estimate subseasonal, multistage survival and transition dynamics in order to better understand and predict when population growth is high. Of the three questing tick life stages (larvae, nymphs, and adults), nymphs are most often associated with transmitting the bacteria to humans, and previous work suggests a mix of abiotic and biotic drivers are associated with nymph abundance. However, understanding tick population growth requires understanding mortality and transition probabilities for each stage and each stage may be individually and uniquely impacted by climate and host availability. A larval tick, for example, may experience warming temperatures differently than nymph or adults, because they are present on the landscape at different times. Here, we describe and validate a model that accounts for field sampling design and evaluates abiotic (temperature, relative humidity, precipitation) and biotic (host abundance) drivers of variation in tick population growth. To account for the drivers of subseasonal and interannual variability in demography, phenology, and population density, we built stage‐structured population models that account for variability in meteorology and host population abundance throughout the full tick lifecycle. Our model is fit and validated with 11 years of tick and host data from the northeastern United States. In this context, we found that a four‐stage model that includes unique transitions to and from a dormant, overwintering nymph state outperforms a model that only includes the three questing stages, and that incorporating the abundance of the predominant host species, Peromyscus leucopus , and weather variables improved predictions and model fit. Additionally, the model accurately predicted all three questing stages at sites different than where they were calibrated, showing that this model structure is generally transferable. Overall, this model lays a foundation for the real‐time iterative forecasting of tick populations needed to effectively protect public health.