Boston University Boston, Massachusetts, United States
The human incidence of tick-borne diseases is on the rise, which is attributed to the spread of ticks and increased tick densities, and land-use changes that have been advantages for generalist species, such as mice, to thrive. Furthermore, mice are excellent hosts for ticks and act as reservoirs for pathogens. However host-use patterns, when and where ticks are attached to mice, are complex across space and time, and previous studies, do not explore the intra-annual variation in host-use across large spatial scales. Thus, the motivation for this study is to quantify the parameters responsible for the phenology of host-use for the Lyme disease vector (Ixodes scapularis) at the regional scale. To accomplish this, we built two interconnected models using National Ecological Observatory Network (NEON) data. . The first is a multistate model for mice, which explicitly estimates the presence or absence of ticks on mice, and the daily probability of transitioning from one state to the other. The second is a matrix population model for ticks collected off-host. The estimated tick density was used to constrain the state of mice in the multistate model, and the estimated mouse states were used to constrain demographic parameters in the tick population model.
Models were validated by forecasting the 2020 field season, which was not used for calibration. For the mice, we were able to predict the phenology of the increase in the presence of ticks on mice, using cumulative growing degree days, which generally coincides with tick nymphal peak abundance. Our analysis indicates that, in general, mice survive at a higher rate when ticks are attached, and that host-use is more prevalent at northern latitudes than southern. For the ticks, we found that survival rates varied greatly across land cover classes and that the effect of the mouse population is more significant at northern latitudes. We ran the validation predictions with coupled and uncoupled (i.e. mouse states not used in tick model) scenarios, and for a majority of sites, the coupled models are more accurate in forecasting the 2020 data. This study reaffirms that mouse and tick populations are intrinsically linked at the local scale and that incorporating knowledge about one species can help predict the other. Furthermore, this work represents the next step in developing a regional forecasting system to aid the public in understanding the risk of encountering a potential disease vector.