Project Summary Over the last two decades there has been a dramatic increase in the number and expansion in the geographic range of Lyme disease cases. These are likely caused by increasing temperature's effect on populations of the Lyme disease vector, the blacklegged tick. With expected continued warming and climate change, the geographic range of the blacklegged tick and Lyme disease should continue to change. We need better tools to map the current range of blacklegged tick populations and to forecast future range shifts and year-to-year variations. The development of these tools is hampered by the fact that blacklegged ticks respond to stochastic extreme weather events, while models of tick populations are based on deterministic climate averages. In Aim 1 we will first address this limitation by conducting a series of observations in the field to understand how weather variation and extremes affect life history processes of the blacklegged tick. We will use two types of tick enclosures to monitor tick host-seeking behavior and tick survival. At the same time, we will track temperature and relative humidity in the leaf litter, which is the microclimate that tick's experience. In Aim 2 we will develop a novel tick population model that will be encoded with the results of Aim 1. It will predict how tick populations respond to continuous weather fluctuations rather than just mean climate. This new tool has a stochastic structure so that it can predict how rare events could lead to population bottlenecks or local extinctions. Such a structure is typically very computationally intensive, but we have developed a novel approach which can switch between computationally-intensive, exact population modeling and a computationally-cheap average approximation when necessary. This model will be validated by hindcasting to existing blacklegged and Lyme disease distribution data, and then used to predict future distribution with climate change projections. The code for the model will be shared, as the approach is useful to modeling other arthropod vectors as well.

Project Narrative We need better tools to predict the future distribution of and year-to-year variation in populations of the blacklegged tick, a key Lyme disease vector. Development of these tools is hampered because blacklegged tick populations are affected by stochastic extreme weather events which are poorly captured in current models. We will address this with a joint empirical-theoretical framework to develop better understanding of tick response to weather variation and extremes and a model structure to accommodate that understanding.