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The primary tick vector of major pathogens in the Eastern United States, Ixodes scapularis, maintains Lyme disease (Borrelia burgdorferi) in an ecosystem through a three instar life-cycle. Larval black-legged ticks rise to a peak of activity in the Summer, after Nymphal activity in late spring, and infest hosts that have already been introduced to B. burgdorferi by feeding nymphs. The segregation of instar activity periods is therefore essential in the transgenerational transmission of Lyme disease among ticks. I use mark-recapture data from 1993- 2009 to describe variation in tick activity as a function of temperature and relative humidity (RH) with linear models. These models were ranked using Akaike's Information Criterion, to find the model that best balances parsimony and information loss. I found that the average date of peak larval activity between 1993 and 2009 was August 15th with a range of 26 days. The average date of peak nymphal activity was June 2nd with a range of 32 days. Here, I present low parameter demand models to predict larval and nymphal activity using common meteorological variables available by the end of March. I used these models to forecast future instar behavior based on TAR HadCM3 carbon emission scenario simulations and found that while nymphal activity will not significantly change, larval activity will be delayed by as much as 20 days. I argue that future activity season segregation will increase Lyme disease transmission risk in a way that mirrors Lyme disease emergence in North America during the 20th century.
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Osborn, Samuel Griffiths, "Climate Variability as a Predictor of Ixodes scapularis Annual Peak Activity" (2014). Senior Projects Spring 2014. 196.
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