The Experts below are selected from a list of 86490 Experts worldwide ranked by ideXlab platform

Sadie J Ryan - One of the best experts on this subject based on the ideXlab platform.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    Journal of Medical Entomology, 2021
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    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.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    medRxiv, 2020
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    Abstract:

    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.

Holly Gaff - One of the best experts on this subject based on the ideXlab platform.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    Journal of Medical Entomology, 2021
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    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.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    medRxiv, 2020
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    Abstract:

    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.

Heidi K St John - One of the best experts on this subject based on the ideXlab platform.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    Journal of Medical Entomology, 2021
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    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.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    medRxiv, 2020
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    Abstract:

    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.

Ram K Raghavan - One of the best experts on this subject based on the ideXlab platform.

  • assessing the current and future potential geographic distribution of the american dog tick dermacentor variabilis say acari ixodidae in north america
    PLOS ONE, 2020
    Co-Authors: Gunavanthi D Boorgula, Townsend A Peterson, Desmond H Foley, Roman R Ganta, Ram K Raghavan
    Abstract:

    The American dog tick, Dermacentor variabilis, is a veterinary- and medically- significant tick species that is known to transmit several diseases to animal and human hosts. The spatial distribution of this species in North America is not well understood, however; and knowledge of likely changes to its future geographic distribution owing to ongoing climate change is needed for proper Public Health Planning and messaging. Two recent studies have evaluated these topics for D. variabilis; however, less-rigorous modeling approaches in those studies may have led to erroneous predictions. We evaluated the present and future distribution of this species using a correlative maximum entropy approach, using Publicly available occurrence information. Future potential distributions were predicted under two representative concentration pathway (RCP) scenarios; RCP 4.5 for low-emissions and RCP 8.5 for high-emissions. Our results indicated a broader current distribution of this species in all directions relative to its currently known extent, and dramatic potential for westward and northward expansion of suitable areas under both climate change scenarios. Implications for disease ecology and Public Health are discussed.

Catherine A Lippi - One of the best experts on this subject based on the ideXlab platform.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    Journal of Medical Entomology, 2021
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    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.

  • exploring the niche of rickettsia montanensis rickettsiales rickettsiaceae infection of the american dog tick acari ixodidae using multiple species distribution model approaches
    medRxiv, 2020
    Co-Authors: Catherine A Lippi, Holly Gaff, Alexis White, Heidi K St John, Allen L Richards, Sadie J Ryan
    Abstract:

    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.