Within the past three decades, new bacterial etiological agents of tick-borne disease have been discovered in the southeastern U.S., and the number of reported tick-borne pathogen infections has increased. In Florida, few systematic studies have been conducted to determine the presence of tick-borne bacterial pathogens. This investigation examined the distribution and presence of tick-borne bacterial pathogens in Florida. Ticks were collected by flagging at 41 field sites, spanning the climatic regions of mainland Florida. DNA was extracted individually from 1608 ticks and screened for Anaplasma, Borrelia, Ehrlichia and Rickettsia using conventional PCR and primers that amplified multiple species for each genus. PCR positive samples were Sanger sequenced. Four species of ticks were collected: Amblyomma americanum, Amblyomma maculatum, Dermacentor variabilis, and Ixodes scapularis. Within these ticks, six bacterial species were identified: Borrelia burgdorferi, Borrelia lonestari, Ehrlichia ewingii, Rickettsia amblyommatis, Rickettsia andeanae, Rickettsia parkeri, and Rickettsia endosymbionts. Pathogenic Borrelia, Ehrlichia, and Rickettsia species were all detected in the North and North-Central Florida counties; however, we found only moderate concordance between the distribution of ticks infected with pathogenic bacteria and human cases of tick-borne diseases in Florida. Given the diversity and numerous bacterial species detected in ticks in Florida, further investigations should be conducted to identify regional hotspots of tick-borne pathogens.

Host associations of the tick vector for Lyme Borreliosis, Ixodes scapularis, differ across its geographic range. In Florida, the primary competent mammalian host of Lyme disease is not present but instead has other small mammals and herpetofauna that I. scapularis can utilize. We investigated host–tick association for lizards, the abundance of ticks on lizards and the prevalence of Borrelia burgdorferi sensu lato (sl). To determine which lizard species I. scapularis associates with, we examined 11 native lizard species from historical herpetological specimens. We found that (294/5828) of the specimens had attached ticks. The most infested species were Plestiodon skinks (241/1228) and Ophisaurus glass lizards (25/572). These species were then targeted at six field sites across Florida and sampled from June to September 2020, using drift fence arrays, cover boards and fishing. We captured 125 lizards and collected 233 immature I. scapularis. DNA was extracted from ticks and lizard tissue samples, followed by PCR testing for Borrelia spp. Of the captured lizards, 69/125 were infested with immature I. scapularis. We did not detect Borrelia spp. from tick or lizard tissue samples. Overall, we found that lizards are commonly infested with I. scapularis. However, we did not detect Borrelia burgdorferi sl. These findings add to a growing body of evidence that lizards are poor reservoir species.

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species, but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from nearly one third (34.5% and 30.9%, respectively) of the state where standard surveys predicted the highest likelihood of occurrence. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs of the convenience collections were associated almost exclusively with either adjacency to at least one SDM, or errors in geocoding algorithms that failed to correctly locate geographic locations of convenience samples. These errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for arthropod survey data that are applied to spatial models.

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from 34.5% and 30.9% of the state where standard surveys predicted the highest likelihood of occurrence of the respective vectors. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs, of the convenience collections, frequently were associated with adjacency to at least one SDM or errors in geocoding algorithms that failed to correctly locate convenience samples. These geocoding errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for vector survey data used in spatial models.