To better understand tick ecology in Virginia and the increasing Lyme disease incidence in western Virginia, a comparative phenological study was conducted in which monthly collections were performed at twelve sampling locations in southwestern Virginia (high Lyme disease incidence) and 18 equivalent sampling locations in southeastern Virginia (low Lyme disease incidence) for one year. In western Virginia, we also explored the effect of elevation on collection rates of Ixodes scapularis Say (Acari: Ixodidae) and Amblyomma americanum (L.) (Acari: Ixodidae). In total, 35,438 ticks were collected (33,106 A. americanum; 2,052 I. scapularis; 134 Ixodes affinis Neumann [Acari: Ixodidae]; 84 Dermacentor variabilis [Say] [Acari: Ixodidae]; 49 Dermacentor albipictus [Packard] [Acari: Ixodidae]; 10 Haemaphysalis leporispalustris [Packard] [Acari: Ixodidae]; 2 Ixodes brunneus Koch [Acari: Ixodidae]; 1 Haemaphysalis longicornis Neumann [Acari: Ixodidae]). Within southwestern Virginia, Ixodes scapularis collection rates were not influenced by elevation, unlike A. americanum which were collected more frequently at lower elevations (e.g., below 500 m). Notably, I. scapularis larvae and nymphs were commonly collected in southwestern Virginia (indicating that they were questing on or above the leaf litter) but not in southeastern Virginia. Questing on or above the leaf litter is primarily associated with northern populations of I. scapularis. These findings may support the hypothesis that I. scapularis from the northeastern United States are migrating into western Virginia and contributing to the higher incidence of Lyme disease in this region. This comparative phenological study underscores the value of these types of studies and the need for additional research to further understand the rapidly changing tick-borne disease dynamics in Virginia.
The blacklegged tick (
- NSF-PAR ID:
- 10405569
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Molecular Ecology
- Volume:
- 32
- Issue:
- 12
- ISSN:
- 0962-1083
- Page Range / eLocation ID:
- p. 3133-3149
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
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.more » « less
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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"]} -
Lyme disease is the most common vector-borne disease in the United States impacting the Northeast and Midwest at the highest rates. Recently, it has become established in southeastern and south-central regions of Canada. In these regions, Lyme disease is caused by Borrelia burgdorferi, which is transmitted to humans by an infected Ixodes scapularis tick. Understanding the parasite-host interaction is critical as the white-footed mouse is one of the most competent reservoir for B. burgdorferi. The cycle of infection is driven by tick larvae feeding on infected mice that molt into infected nymphs and then transmit the disease to another susceptible host such as mice or humans. Lyme disease in humans is generally caused by the bite of an infected nymph. The main aim of this investigation is to study how diapause delays and demographic and seasonal variability in tick births, deaths, and feedings impact the infection dynamics of the tick-mouse cycle. We model tick-mouse dynamics with fixed diapause delays and more realistic Erlang distributed delays through delay and ordinary differential equations (ODEs). To account for demographic and seasonal variability, the ODEs are generalized to a continuous-time Markov chain (CTMC). The basic reproduction number and parameter sensitivity analysis are computed for the ODEs. The CTMC is used to investigate the probability of Lyme disease emergence when ticks and mice are introduced, a few of which are infected. The probability of disease emergence is highly dependent on the time and the infected species introduced. Infected mice introduced during the summer season result in the highest probability of disease emergence.more » « less
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Abstract Lyme disease is the most common vector‐borne disease in temperate zones and a growing public health threat in the United States (US). The life cycles of the tick vectors and spirochete pathogen are highly sensitive to climate, but determining the impact of climate change on Lyme disease burden has been challenging due to the complex ecology of the disease and the presence of multiple, interacting drivers of transmission. Here we incorporated 18 years of annual, county‐level Lyme disease case data in a panel data statistical model to investigate prior effects of climate variation on disease incidence while controlling for other putative drivers. We then used these climate–disease relationships to project Lyme disease cases using CMIP5 global climate models and two potential climate scenarios (RCP4.5 and RCP8.5). We find that interannual variation in Lyme disease incidence is associated with climate variation in all US regions encompassing the range of the primary vector species. In all regions, the climate predictors explained less of the variation in Lyme disease incidence than unobserved county‐level heterogeneity, but the strongest climate–disease association detected was between warming annual temperatures and increasing incidence in the Northeast. Lyme disease projections indicate that cases in the Northeast will increase significantly by 2050 (23,619 ± 21,607 additional cases), but only under RCP8.5, and with large uncertainty around this projected increase. Significant case changes are not projected for any other region under either climate scenario. The results demonstrate a regionally variable and nuanced relationship between climate change and Lyme disease, indicating possible nonlinear responses of vector ticks and transmission dynamics to projected climate change. Moreover, our results highlight the need for improved preparedness and public health interventions in endemic regions to minimize the impact of further climate change‐induced increases in Lyme disease burden.
