Revealing large‐scale parasite ranges: An integrated spatiotemporal database and multisource analysis of the winter tick
Bibliographic record
Abstract
Abstract Concerns that climate warming may drive the spread of ectoparasites into previously uninhabited areas have increased the need for baseline knowledge of their distributional history. For species of wildlife health concern, presence data are often lacking or outdated, thus limiting our ability to assess range changes and subsequent host impacts. We reconstructed the past and present distribution of the winter tick, Dermacentor albipictus, through the compilation of a spatiotemporal database to create the first full baseline map of its occurrence throughout its North American range. The ongoing impacts of winter tick epizootics in moose (Alces alces) and recent mortality events in elk (Cervus canadensis) and white‐tailed deer (Odocoileus virginianus) have led to a resurgence in interest in the future of this parasite in a warming climate. Over 3400 unique records of winter tick occurrence were compiled from multiple data sources, dating from 1869 to 2020 and spanning from 16.5 to 66.2° N latitude. Both conventional published sources and natural history records were included along with new records from previously unpublished datasets and citizen science observations, to make this a comprehensive occurrence dataset for this species. Along with standardized location information and year of observation, the dataset includes associated host species and descriptive categorization of the type and source of each record, providing new opportunities to examine host–parasite interactions in the winter tick system over time and space. In presenting these data, we discuss the potential sampling biases and lacunas in our integrated database records, particularly at the winter tick's northernmost range. We also document changes in the types and sources of winter tick information from past to present, highlighting potential issues that should be considered before using these data in further analyses and when collecting ongoing records. Our database demonstrates that the collation and synthesis of records beyond conventional sources can shed light on the distributional history of tick species and serve as a useful baseline for prioritizing future research and management decisions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".