Molecular characterization of Amblyomma geoemydae using CO1 mitochondrial gene to validate phenotypic taxonomical evaluation

Animal ectoparasites are linked to the spread of serious medical and veterinary important pathogens. Our research intends to close the knowledge gap concerning the numerous ectoparasites that inhabit animals in Wayanad. Ectoparasites in animals brought to the veterinary dispensaries in Wayanad were retrieved and identified morphologically and molecularly. Using a high-quality stereomicroscope, the taxonomic features of the four following species were examined and identified: Haemaphysalis bispinosa, Rhipicephalus annulatus, R. microplus, and Amblyomma geoemydae. The important disease vector A. geoemydae was reported for the first time in Kerala. The important phenotypic characters of the highlighted species A. geoemydae are the edge of the basis capituli is circular without cornua, and the hypostomal dental formula is 2/2. The taxonomically identified four species were subjected to CO1 gene sequence analysis. The evolutionary relationship was inspected through the neighbour-joining method, and the phylogenetic tree was built through the Maximum Likelihood method. The present study has also estimated the diversity index of R. microplus, R. annulatus, H. bispinosa, and A. geoemydae. Among them, R. microplus 0.36638 have reported with the maximum diversity index score. The significance of the study is the presence of Lyme disease vector A. geoemydae, in the Wayanad District of Kerala, and it is the first report of the species from where an outbreak of Lyme disease occurred in 2013.

Estimating disease vector population size from citizen science data

Citizen science projects have the potential to address hypotheses requiring extremely large datasets that cannot be collected with the financial and labour constraints of most scientific projects. Data collection by the general public could expand the scope of scientific enquiry if these data accurately capture the system under study. However, data collection inconsistencies by the untrained public may result in biased datasets that do not accurately represent the natural world. In this paper, we harness the availability of scientific and public datasets of the Lyme disease tick vector to identify and account for biases in citizen science tick collections. Estimates of tick abundance from the citizen science dataset correspond moderately with estimates from direct surveillance but exhibit consistent biases. These biases can be mitigated by including factors that may impact collector participation or effort in statistical models, which, in turn, result in more accurate estimates of tick population sizes. Accounting for collection biases within large-scale, public participation datasets could update species abundance maps and facilitate using the wealth of citizen science data to answer scientific questions at scales that are not feasible with traditional datasets.

Climate impacts on blacklegged tick host-seeking behavior

The nymph of the blacklegged tick (Ixodes scapularis), the primary North American vector of the causative agent of Lyme disease, must attach to a host by the end of its questing season in order to feed and subsequently molt into an adult. The proper timing of this behavior is critical both for the tick's survival and for perpetuating the transmission of tick-borne pathogens. Questing also depletes limited nymphal lipid reserves and increases desiccation risk. Given this tradeoff, questing behavior and its environmental influences can be expressed in a dynamic state variable model. We develop what we believe to be the first such model for a tick, and investigate the influence of climate on nymph fitness predictions. We apply these results to the hypothesized inland migration of I. scapularis from island refugia, evaluating fitness under suboptimal questing strategies and uncertain environmental conditions.