Evidence for Geographic Variation in Life-Cycle Processes Affecting Phenology of the Lyme Disease Vector Ixodes scapularis (Acari: Ixodidae) in the United States
Bibliographic record
Abstract
The seasonal activity pattern of immature Ixodes scapularis Say (Acari: Ixodidae) varies geographically in the United States, which may affect the efficiency of transmission cycles of pathogens transmitted by this species. To study the factors that determine seasonality, a multiyear study at seven sites across the geographic range of I. scapularis systematically collected questing ticks by flagging/dragging, and feeding ticks by capture of their hosts. The observed phenology patterns were consistent with previous studies reporting geographic variation in seasonal tick activity. Predictions of seasonal activity for each site were obtained from an I. scapularis simulation model calibrated using contemporaneous weather data. A range of scenarios for life-cycle processes-including different regimes of temperature-independent behavioral and developmental diapause, variations in temperature-development rate relationships, and temperature-dependent tick activity-were used in model formulations. These formulations produced a range of simulations of seasonal activity for each site and were compared against the field observed tick data using negative binomial regression models. Best fit scenarios were chosen for each site on the basis of Akaike's information criterion and regression model parameters. This analysis suggests that temperature-independent diapause mechanisms explain some key observed variations in I. scapularis seasonality, and are responsible in part for geographic variations in I. scapularis seasonality in the United States. However, diapause appears to operate in idiosyncratic ways in different regions of the United States, so further studies on populations in different regions will be needed to enable predictive modeling of climatic and climate change effects on I. scapularis seasonal activity and pathogen transmission.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".