# Vermont INBRE Administrative Supplement 2024 AWD 118

> **NIH NIH P20** · UNIVERSITY OF VERMONT & ST AGRIC COLLEGE · 2024 · $180,345

## Abstract

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.

## Key facts

- **NIH application ID:** 11019101
- **Project number:** 3P20GM103449-23S1
- **Recipient organization:** UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
- **Principal Investigator:** CHRISTOPHER S FRANCKLYN
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $180,345
- **Award type:** 3
- **Project period:** 2001-09-30 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11019101

## Citation

> US National Institutes of Health, RePORTER application 11019101, Vermont INBRE Administrative Supplement 2024 AWD 118 (3P20GM103449-23S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/11019101. Licensed CC0.

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