american dog tick
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EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Yuexun Tian ◽  
Cynthia C Lord ◽  
Eva A. Buckner

Ehrlichia and Anaplasma are bacteria that cause diseases, known as ehrlichiosis and anaplasmosis, in humans and other animals. Ehrlichia and Anaplasma are primarily transmitted through the bites of infected hard ticks, such as the lone star tick, the blacklegged tick, and the American dog tick. This publication describes the various forms of ehrlichiosis and anaplasmosis and the tick vectors.


Author(s):  
Catherine A Lippi ◽  
Holly D Gaff ◽  
Alexis L White ◽  
Heidi K St. John ◽  
Allen L Richards ◽  
...  

Abstract The American dog tick, Dermacentor variabilis (Say) (Acari: Ixodidae), is a vector for several human disease-causing pathogens such as tularemia, Rocky Mountain spotted fever, and the understudied spotted fever group rickettsiae (SFGR) infection caused by Rickettsia montanensis. It is important for public health planning and intervention to understand the distribution of this tick and pathogen encounter risk. Risk is often described in terms of vector distribution, but greatest risk may be concentrated where more vectors are positive for a given pathogen. When assessing species distributions, the choice of modeling framework and spatial layers used to make predictions are important. We first updated the modeled distribution of D. variabilis and R. montanensis using maximum entropy (MaxEnt), refining bioclimatic data inputs, and including soil variables. We then compared geospatial predictions from five species distribution modeling frameworks. In contrast to previous work, we additionally assessed whether the R. montanensis positive D. variabilis distribution is nested within a larger overall D. variabilis distribution, representing a fitness cost hypothesis. We found that 1) adding soil layers improved the accuracy of the MaxEnt model; 2) the predicted ‘infected niche’ was smaller than the overall predicted niche across all models; and 3) each model predicted different sizes of suitable niche, at different levels of probability. Importantly, the models were not directly comparable in output style, which could create confusion in interpretation when developing planning tools. The random forest (RF) model had the best measured validity and fit, suggesting it may be most appropriate to these data.


2020 ◽  
Author(s):  
Catherine Lippi ◽  
Holly D Gaff ◽  
Alexis L White ◽  
Heidi K St John ◽  
Allen L Richards ◽  
...  

The American dog tick, Dermacentor variabilis (Say), is a vector for several human disease causing pathogens such as tularemia, Rocky Mountain spotted fever, and the understudied spotted fever group rickettsiae (SFGR) infection caused by Rickettsia montanensis. It is important for public health planning and intervention to understand the distribution of this tick and pathogen encounter risk. Risk is often described in terms of vector distribution, but greatest risk may be concentrated where more vectors are positive for a given pathogen. When assessing species distributions, the choice of modeling framework and spatial layers used to make predictions are important. We first updated the modeled distribution of D. variabilis and R. montanensis using MaxEnt, refining bioclimatic data inputs, and including soils variables. We then compared geospatial predictions from five species distribution modeling (SDM) frameworks. In contrast to previous work, we additionally assessed whether the R. montanensis positive D. variabilis distribution is nested within a larger overall D. variabilis distribution, representing a fitness cost hypothesis. We found that 1) adding soils layers improved the accuracy of the MaxEnt model; 2) the predicted "infected niche" was smaller than the overall predicted niche across all models; and 3) each model predicted different sizes of suitable niche, at different levels of probability. Importantly, the models were not directly comparable in output style, which could create confusion in interpretation when developing planning tools. The random forest (RF) model had the best measured validity and fit, suggesting it may be most appropriate to these data.


PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0237191 ◽  
Author(s):  
Gunavanthi D. Y. Boorgula ◽  
A. Townsend Peterson ◽  
Desmond H. Foley ◽  
Roman R. Ganta ◽  
Ram K. Raghavan

Symbiosis ◽  
2019 ◽  
Vol 79 (3) ◽  
pp. 239-250 ◽  
Author(s):  
Nicholas V. Travanty ◽  
Loganathan Ponnusamy ◽  
Madhavi L. Kakumanu ◽  
William L. Nicholson ◽  
Charles S. Apperson

2019 ◽  
Vol 57 (1) ◽  
pp. 131-155 ◽  
Author(s):  
Aine Lehane ◽  
Christina Parise ◽  
Colleen Evans ◽  
Lorenza Beati ◽  
William L Nicholson ◽  
...  

Abstract In the United States, tick-borne diseases are increasing in incidence and cases are reported over an expanding geographical area. Avoiding tick bites is a key strategy in tick-borne disease prevention, and this requires current and accurate information on where humans are at risk for exposure to ticks. Based on a review of published literature and records in the U.S. National Tick Collection and National Ecological Observatory Network databases, we compiled an updated county-level map showing the reported distribution of the American dog tick, Dermacentor variabilis (Say). We show that this vector of the bacterial agents causing Rocky Mountain spotted fever and tularemia is widely distributed, with records derived from 45 states across the contiguous United States. However, within these states, county-level records of established tick populations are limited. Relative to the range of suitable habitat for this tick, our data imply that D. variabilis is currently underreported in the peer-reviewed literature, highlighting a need for improved surveillance and documentation of existing tick records.


2018 ◽  
Vol 9 (2) ◽  
pp. 354-362 ◽  
Author(s):  
Jordan N. Minigan ◽  
Heather A. Hager ◽  
Andrew S. Peregrine ◽  
Jonathan A. Newman

2017 ◽  
Vol 101 ◽  
pp. 39-46 ◽  
Author(s):  
Andrew J. Rosendale ◽  
Megan E. Dunlevy ◽  
Alicia M. Fieler ◽  
David W. Farrow ◽  
Benjamin Davies ◽  
...  
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2016 ◽  
Vol 7 (6) ◽  
pp. 1155-1161 ◽  
Author(s):  
Andrew J. Rosendale ◽  
David W. Farrow ◽  
Megan E. Dunlevy ◽  
Alicia M. Fieler ◽  
Joshua B. Benoit

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