PS 53-202 - Ticks and tick-borne disease dynamics across the contiguous United States

Background/Question/Methods

Understanding infectious diseases has long been interest for ecologists. Tick-borne diseases in humans have nearly doubled between 1940 and 2004 and the capable of transmitting pathogens to humans. At local scales, dynamics of ticks and tick-borne disease such as Lyme disease are driven by resource pulses in seed abundance that have cascading effects across trophic levels, increasing tick hosts. While these local connections have been shown, our objective is to characterize the spatiotemporal patterns of synchrony and variability in tick and tick-borne pathogen dynamics at subcontinental scale. We hypothesized that synchrony in tick abundance will decrease with geographic distance and ultimately manifest an asynchrony at a subcontinental scale, following patterns revealed in mast-seeding of North American trees. We used datasets on ticks from the family Ixodidae from15 the National Ecological Observatory Network (NEON) sites spanning >2.000km across the contiguous US from 2014- 2020; We used spatial correlograms to quantify synchrony in temporal patterns in tick counts and the prevalence of tick-borne pathogens in sampled ticks across NEON sites. We also characterized relationship between temporal variability and latitude, longitude, and mean annual temperature of NEON sites.

Results/Conclusions

We found that contrary to predictions, spatial correlograms in both tick and tick-borne disease dynamics showed no significant of synchrony across any distance, from between local sites to sites separated by a sub-continental (2,000km) scale. This lack of spatial relationship occurred because patterns of tick abundance and the prevalence of tick-pathogens were both highly variable over time and space. Across the 15 NEON sites, the mean coefficient of variation (CV=standard deviation/mean) of tick counts across years was 1.08 minimum=0.54, maximum=1.39. We used an Akaike Information Criterion (AIC) to test for relationship between CV of nymphs, latitude, longitude, and mean annual temperature of each NEON site. From AIC model selection we dictated that 3 models were the best models with values less than 2, this includes models with longitude only, annual mean temperature only, and null model, with a total AIC weight of 0.75. Uncovering links between abiotic and biotic drivers of local variation in the dynamics of tick and tick-borne pathogens and scaling up to continental scales could be used to inform predictions for disease risk.