ABIOTIC AND DEMOGRAPHIC DRIVERS OF FLEA PARASITISM ON DEER MICE IN A RECOVERING MIXED-CONIFER FOREST A DECADE POSTFIRE.

With the intensity and frequency of wildfires increasing rapidly, the need to study the ecological effects of these wildfires is also growing. An understudied aspect of fire ecology is the effect fires have on parasite-host interactions, including ectoparasites that might be pathogen vectors. Although some studies have examined the impacts of fire on ticks, studies on other ectoparasites, including pathogen vectors, are rare. To help address this knowledge gap, we examined the abiotic and biotic factors that predict the likelihood and extent of parasitism of deer mice (Peromyscus maniculatus) by fleas within a landscape of unburned and recovering burned (>9 yr postfire) mixed conifer forests. We sampled 227 individual deer mice across 27 sites within the Jemez Mountains of northern New Mexico in 2022 and quantified measures of parasitism by fleas (primarily Aetheca wagneri). These sites were distributed in both unburned areas (n = 15) and recovering burned areas (n = 12), with the latter derived from 2 large fires, the Las Conchas fire (2011) and the Thompson Ridge fire (2013). Using these data, we tested for differences in prevalence, mean abundance, and mean intensity of fleas on deer mice, focusing on the predictive importance of host sex and fire history. We also created generalized linear mixed-effects models to investigate the best host and environmental predictors of parasitism by fleas. Approximately a decade postfire, we found minimal evidence to suggest that fire history influenced either the presence or intensity of fleas on deer mice. Rather, at the current forest-regeneration stage, the extent of parasitism by fleas was best predicted by measures of host sex, body condition, and the trapline's ability to accumulate water, as measured through topography. As host body condition increased, the probability of males being parasitized increased, whereas the opposite pattern was seen for females. Male mice also had significantly greater flea loads. Among potential abiotic predictors, the topographic wetness index or compound topographic index (a proxy for soil moisture) was positively related to flea intensity, suggesting larger flea populations in burrows with higher relative humidity. In summary, although fire may potentially have short-term impacts on the likelihood and extent of host parasitism by fleas, in this recovering study system, host characteristics and topographic wetness index are the primary predictors of parasitism by fleas.

SEASONAL AND HOST-ASSOCIATED VARIATION IN BOT FLY (OESTRIDAE: CUTEREBRINAE) PARASITISM OF LAGOMORPHS ACROSS AN ARID-LAND ECOSYSTEM IN SOUTHERN NEW MEXICO.

Bot flies (Oestridae: Cuterebrinae) are obligate mammalian parasites that complete the larval stage of their life cycle under the skin of their host. Most detailed studies of bot fly larval disease ecology have been conducted in temperate deciduous zone rodent systems. To understand the relative importance of seasonal and spatial factors, as well as factors intrinsic to the host, in underpinning the likelihood and extent of parasitism by bot flies in non-rodent hosts as well as in arid-land ecosystems, we examined the dynamic for black-tailed jackrabbit (Lepus californicus) and desert cottontail (Sylvilagus audubonii) parasitism by bot fly larvae (Cuterebra spp.) across 7 repeatedly sampled sites spread across approximately 500 km of the Chihuahuan Desert ecoregion of southern New Mexico. This environment is characterized by a climate that includes hot dry summers and cool to cold dry winters, as well as strongly seasonal summer monsoonal rains. Lagomorphs are a common mid-sized mammal in these landscapes. Bot fly parasitism was strongly seasonal, with peak prevalence and abundance in the spring, and there was spatial variation in the extent of parasitism between collection sites. Additionally, jackrabbits in better body condition were less likely to be parasitized (as indicated by kidney fat index). We did not find sex-based differences in bot fly parasitism between male and female jackrabbits. Thus, in arid-land ecoregions, abiotic factors are likely the primary driver of the bot fly-host interaction, whereas factors intrinsic to the host were of secondary importance for characterizing the interactions of bot flies and lagomorphs.

Ecological Predictors of Zoonotic Vector Status Among Dermacentor Ticks (Acari: Ixodidae): A Trait-Based Approach.

Increasing incidence of tick-borne human diseases and geographic range expansion of tick vectors elevates the importance of research on characteristics of tick species that transmit pathogens. Despite their global distribution and role as vectors of pathogens such as Rickettsia spp., ticks in the genus Dermacentor Koch, 1844 (Acari: Ixodidae) have recently received less attention than ticks in the genus Ixodes Latreille, 1795 (Acari: Ixodidae). To address this knowledge gap, we compiled an extensive database of Dermacentor tick traits, including morphological characteristics, host range, and geographic distribution. Zoonotic vector status was determined by compiling information about zoonotic pathogens found in Dermacentor species derived from primary literature and data repositories. We trained a machine learning algorithm on this data set to assess which traits were the most important predictors of zoonotic vector status. Our model successfully classified vector species with ~84% accuracy (mean AUC) and identified two additional Dermacentor species as potential zoonotic vectors. Our results suggest that Dermacentor species that are most likely to be zoonotic vectors are broad ranging, both in terms of the range of hosts they infest and the range of ecoregions across which they are found, and also tend to have large hypostomes and be small-bodied as immature ticks. Beyond the patterns we observed, high spatial and species-level resolution of this new, synthetic dataset has the potential to support future analyses of public health relevance, including species distribution modeling and predictive analytics, to draw attention to emerging or newly identified Dermacentor species that warrant closer monitoring for zoonotic pathogens.