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  1. This dataset lists 289 blacklegged tick population datasets from 6 studies that record abundance. These datasets were found by inputing keywords Ixodes Scapularis and tick in data repositories including Long Term Ecological Research data portal, National Ecological Observatory Network data portal, Google Datasets, Data Dryad, and Data One. The types of tick data recorded from these studies include density (number per square meter for example), proportion of ticks, count of ticks found on people. The locations of the datasets range from New York, New Jersey, Iowa, Massachusetts, and Connecticut, and range from 9 to 24 years in length. These datasets vary in that some record different life stages, geographic scope (county/town/plot), sampling technique (dragging/surveying), and different study length. The impact of these study factors on study results is analyzed in our research.

    Funding:

    RMC is supported by the National Institute of General Medical Sciences of the National Institutes of the Health under Award Number R25GM122672. CAB, JP, and KSW 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.

    {"references": ["Ellison A. 2017. Incidence of Ticks and Tick Bites at Harvard Forest since 2006. Environmental Data Initiative. https://doi.org/10.6073/pasta/71f12a4ffb7658e71a010866d1805a84. Dataset accessed 6/25/2019", "New York State Department of Health Office of Public Health. 2019. Deer Tick Surveillance: Adults (Oct to Dec) excluding Powassan virus: Beginning 2008. https://health.data.ny.gov/Health/Deer-Tick-Surveillance-Nymphs-May-to-Sept-excludin/kibp-u2ip", "New York State Department of Health Office of Public Health. 2019. Access Nymph Deer Tick Collection Data by County (Excluding Powassan Virus). https://health.data.ny.gov/Health/Deer-Tick-Surveillance-Nymphs-May-to-Sept-excludin/kibp-u2ip", "Ostfeld RS, Levi T, Keesing F, Oggenfuss K, Canham CD (2018) Data from: Tick-borne disease risk in a forest food web. Dryad Digital Repository. https://doi.org/10.5061/dryad.d1c8046", "Oliver JD, Bennett SW, Beati L, Bartholomay LC (2017) Range Expansion and Increasing Borrelia burgdorferi Infection of the Tick Ixodes scapularis (Acari: Ixodidae) in Iowa, 1990\u20132013. Journal of Medical Entomology 54(6): 1727-1734. https://doi.org/10.1093/jme/tjx121", "The Connecticut Agricultural Experiment Station. (n.d.). Summaries of tick testing. CT.gov. Retrieved May 12, 2022, from https://portal.ct.gov/CAES/Fact-Sheets/Tick-Summary/Summaries-of-Tick-Testing", "Jordan, R. A., & Egizi, A. (2019). The growing importance of lone star ticks in a Lyme disease endemic county: Passive tick surveillance in Monmouth County, NJ, 2006 - 2016. PloS one, 14(2), e0211778. https://doi.org/10.1371/journal.pone.0211778"]} 
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  2. Background Understanding how study design and monitoring strategies shape inference within, and synthesis across, studies is critical across biological disciplines. Many biological and field studies are short term and limited in scope. Monitoring studies are critical for informing public health about potential vectors of concern, such as Ixodes scapularis (black-legged ticks). Black-legged ticks are a taxon of ecological and human health concern due to their status as primary vectors of Borrelia burgdorferi , the bacteria that transmits Lyme disease. However, variation in black-legged tick monitoring, and gaps in data, are currently considered major barriers to understanding population trends and in turn, predicting Lyme disease risk. To understand how variable methodology in black-legged tick studies may influence which population patterns researchers find, we conducted a data synthesis experiment. Materials and Methods We searched for publicly available black-legged tick abundance dataset that had at least 9 years of data, using keywords about ticks in internet search engines, literature databases, data repositories and public health websites. Our analysis included 289 datasets from seven surveys from locations in the US, ranging in length from 9 to 24 years. We used a moving window analysis, a non-random resampling approach, to investigate the temporal stability of black-legged tick population trajectories across the US. We then used t-tests to assess differences in stability time across different study parameters. Results All of our sampled datasets required 4 or more years to reach stability. We also found several study factors can have an impact on the likelihood of a study reaching stability and of data leading to misleading results if the study does not reach stability. Specifically, datasets collected via dragging reached stability significantly faster than data collected via opportunistic sampling. Datasets that sampled larva reached stability significantly later than those that sampled adults or nymphs. Additionally, datasets collected at the broadest spatial scale (county) reached stability fastest. Conclusion We used 289 datasets from seven long term black-legged tick studies to conduct a non-random data resampling experiment, revealing that sampling design does shape inferences in black-legged tick population trajectories and how many years it takes to find stable patterns. Specifically, our results show the importance of study length, sampling technique, life stage, and geographic scope in understanding black-legged tick populations, in the absence of standardized surveillance methods. Current public health efforts based on existing black-legged tick datasets must take monitoring study parameters into account, to better understand if and how to use monitoring data to inform decisioning. We also advocate that potential future forecasting initiatives consider these parameters when projecting future black-legged tick population trends. 
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