Search

Established tick control strategies often involve methods that can be damaging to existing environmental conditions or natural host ecology. To find more environmentally friendly methods, biological controls, like predators of ticks, have been suggested. There are natural predators of ticks, but most are generalists and not expected to control tick populations. Helmeted guinea fowl (Numida meleagris(L.) (Galliformes: Numididae)) have been suggested to be biological controls of ticks, and therefore, tick-borne pathogens, but their potential role as hosts for ticks complicates the relationship. A study was conducted to assess whether guinea fowl reduces the abundance of lone star ticks, Amblyomma americanum(L.) (Acari: Ixodidae), or whether they are hosts of ticks. Using mark–recapture techniques, painted lone star ticks were placed into 3 different treatments: penned, excluded, and free range. The recapture rates of painted ticks were compared. There was a significant difference between excluded and free-range treatments, but not between excluded and penned or between free range and penned. To investigate the role of guinea fowl as hosts of ticks, coop floors were examined for engorged ticks. Engorged lone star nymphs that had fed on guinea fowl were found. Lastly, ticks collected were tested to identify the potential reduction in risk of tick-borne pathogens. This study found no evidence that guinea fowl are an effective biological control of lone star ticks or tick-borne pathogens, but they are hosts of lone star nymphs. Future studies are needed to assess the complex ecology of a biological control of ticks that is also a host.

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. variabilisand R. montanensisusing 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. montanensispositive D. variabilisdistribution is nested within a larger overall D. variabilisdistribution, 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.