scholarly journals Exploring the Niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) Infection of the American Dog Tick (Acari: Ixodidae), Using Multiple Species Distribution Model Approaches

2020 ◽  
Author(s):  
Catherine A Lippi ◽  
Holly D Gaff ◽  
Alexis L White ◽  
Heidi K St. John ◽  
Allen L Richards ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Health Planning ◽  
Spotted Fever ◽  
Dermacentor Variabilis ◽  
Distribution Model ◽  
Spotted Fever Group ◽  
Modeling Framework ◽  
American Dog Tick ◽  
Dog Tick

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.

scholarly journals Exploring the niche of Rickettsia montanensis (Rickettsiales: Rickettsiaceae) infection of the American dog tick (Acari: Ixodidae), using multiple species distribution model approaches.

2020 ◽  
Author(s):  
Catherine Lippi ◽  
Holly D Gaff ◽  
Alexis L White ◽  
Heidi K St John ◽  
Allen L Richards ◽  
...  
Keyword(s):  
Species Distribution ◽  
Species Distribution Model ◽  
Health Planning ◽  
Spotted Fever ◽  
Dermacentor Variabilis ◽  
Distribution Model ◽  
Spotted Fever Group ◽  
Modeling Framework ◽  
American Dog Tick ◽  
Dog Tick

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.

scholarly journals A Kunitz Protease Inhibitor fromDermacentor variabilis, a Vector for Spotted Fever Group Rickettsiae, LimitsRickettsia montanensisInvasion

2010 ◽  
Vol 79 (1) ◽  
pp. 321-329 ◽  
Author(s):  
Shane M. Ceraul ◽  
Ashley Chung ◽  
Khandra T. Sears ◽  
Vsevolod L. Popov ◽  
Magda Beier-Sexton ◽  
...  
Keyword(s):  
Protease Inhibitor ◽  
Host Cell ◽  
Spotted Fever ◽  
Dermacentor Variabilis ◽  
Spotted Fever Group ◽  
Kunitz Protease Inhibitor ◽  
Dog Tick ◽  
Interfering Rna ◽  
Tick Midgut

ABSTRACTA defining facet of tick-Rickettsiasymbioses is the molecular strategy employed by each partner to ensure its own survival. Ticks must control rickettsial colonization to avoid immediate death. In the current study, we show that rickettsial abundance in the tick midgut increases once the expression of a Kunitz-type serine protease inhibitor from the American dog tick (Dermacentor variabilis) (DvKPI) is suppressed by small interfering RNA (siRNA). A series ofin vitroinvasion assays suggested that DvKPI limits rickettsial colonization during host cell entry. Interestingly, we observed that DvKPI associates with rickettsiaein vitroas well as in the tick midgut. Collectively, our data demonstrate that DvKPI limits host cell invasion byRickettsia montanensis, possibly through an association with the bacterium.

scholarly journals Reported County-Level Distribution of the American Dog Tick (Acari: Ixodidae) in the Contiguous United States

2019 ◽  
Vol 57 (1) ◽  
pp. 131-155 ◽  
Author(s):  
Aine Lehane ◽  
Christina Parise ◽  
Colleen Evans ◽  
Lorenza Beati ◽  
William L Nicholson ◽  
...  
Keyword(s):  
United States ◽  
Spotted Fever ◽  
Geographical Area ◽  
The United States ◽  
Dermacentor Variabilis ◽  
Accurate Information ◽  
Suitable Habitat ◽  
County Level ◽  
American Dog Tick ◽  
Dog Tick

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.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

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