Theory predicts that a primary impact of global climate change on vector-borne disease transmission will be shifts in or expansion of current vector ranges. However, few empirical studies have investigated potential mechanisms by which this could occur. Knowledge of current tick and pathogen distributions and their determinants is essential to develop accurate predictive models of the impacts of climate change on tick-borne disease. The consequences of climate change for tick distributions in Central America have received scant attention, despite the presence of several important tick-borne diseases. This study evaluates relative contributions of abiotic and biotic factors in controlling current tick and tick-borne pathogen distributions in Panama, which will inform predictions of how pathogen exposure risk will be impacted by climate change. Field efforts were conducted throughout twelve months at three sites spanning a natural precipitation gradient across Panama, which provides a proxy for future climate change, wherein conditions at the driest site represent the predicted result of climate change for wetter regions. Tick abundance and survival were monitored weekly at each of these sites, and local mammal species richness was estimated using camera traps. Pathogen screening of ticks was performed using polymerase chain reaction and reverse line blot hybridization.
Results/Conclusions
Preliminary findings indicate that overall tick abundance is negatively correlated with weekly rainfall, and the lowest abundance of nymphs and adults occurred at the wet site. Phenological differences in tick abundance were observed for several species, highlighting the importance of seasonality in tick-borne disease (TBD) transmission. Tick mortality was highest at the dry site, suggesting that future potential increases in abundance may be tempered by a corresponding increase in mortality. Preliminary pathogen screening indicated that 12.3% of ticks tested were infected with Spotted Fever Group Rickettsiae. Ultimately, I will use the data generated by camera trapping in combination with concurrent abundance surveys, survival assays, abiotic data, and pathogen screening to evaluate the relative contributions of abiotic and biotic factors on current TBD risk. I will construct a structural equation model to estimate relationships among both independent variables and covariates (abiotic factors, biotic factors) and dependent variables (tick survival, abundance, distribution and phenology, and pathogen prevalence). By applying various IPCC climate change scenarios to the model I will evaluate how climate change may impact TBD risk. The applicability of this model extends beyond this disease system and can serve as a framework for studies of climate change-vector interactions in other regions.