Ixodes scapularis (deer 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. Deer ticks are thought to be expanding in geographic range and population size across the eastern US, leading to concern that tick-vectored illness will correspondingly rise. However, because of wide variability in deer tick monitoring strategies, synthesis efforts may be limited by the sensitivity and reliability of data produced by existing long term studies, especially to inform forecasting and proactive deer tick management. To address this, we explicitly examined the role of how study design parameters affect the likelihood of observing temporal trends in deer tick studies. We used a moving window approach to investigate the temporal stability of deer tick population trajectories across the US. We found several study factors can have an impact on the likelihood of a study reaching stability and the likelihood of tick abundance data leading to misleading results if the study does not reach stability. Our results underscore the need for longer studies of deer ticks when trying to assess long term or broad spatial patterns. Moreover, our results showcase the importance of study length, sampling technique, life stage, and geographic scope in shaping the inferences from deer tick studies. This is especially important for synthesizing across the variety of existing surveys and for potential ecological forecasting.

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.