Morphometric Variation in Ixodid Ticks at an Expanding Range Edge
Bibliographic record
Abstract
Ixodid ticks (Acari: Ixodidae) are important vectors of zoonotic disease that can have devastating impacts on human health and well-being. Effective monitoring protocols that map tick abundance and richness can help to identify high risk areas. However, tick identification is complicated by morphological variation among populations and similarities among species. Geometric morphometrics is a powerful tool that allows researchers to compare shape variation between groups and can improve species identification practices. I used geometric morphometrics with machine learning models to compare 518 ticks sampled over a 6-year period from Eastern Ontario. My goals were to identify which tick species are present within the study region, to assess morphological variation within and among species and sex, and to determine if there are morphologically distinct populations of Ixodes scapularis. My collections were primarily composed of two medically important species known to harbour harmful pathogens: Dermacentor variabilis and Ixodes scapularis. Linear discriminant analyses of these two species found that dorsal and ventral landmarks categorized individuals by their respective species and sex with an accuracy of at least 99 %. Individuals of I. scapularis could be accurately assigned to their geographic origin with an accuracy of 65 % to 77 %. Additionally, this analysis identified individuals with abnormal morphologies that may represent novel or cryptic species. Future research should combine both morphometric and genetic approaches to increase the robustness of species identification and provide additional evidence for tick diversity that may lead to better mitigation and management solutions for these critical vectors of zoonotic disease.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".