Comparing Stability Trends in Long Term Deer Tick Population Datasets

We compiled 133 public Ixodes scapularis datasets that were 9+ years and recorded tick density or count from NY, MA, and NJ with two sampling methods standardized (dragging) and opportunistic (found on a person). Then we ran the ‘bad breakup’ algorithm. This splits long term datasets into different lengths to examine whether the truncated datasets would reach the same conclusions. We recorded years to reach stability and proportion significant right and wrong ( relationships that match/do not match direction of slope). We also ran the regime shift detector which determines when large, sudden changes in tick density/counts occurred within datasets.

SMC is supported by the National Institute of General Medical Sciences of the National Institutes of the Health under Award Number R25GM122672. KSW and CAB are supported by the Office of Advanced Cyberinfrastructure in the National Science Foundation under Award Number #1838807. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Science Foundation.