Modelling current and projected niche shifts of the blacklegged tick, Ixodes scapularis, in eastern Canada employing community science data and global climate change drivers
Bibliographic record
Abstract
Climate change rapidly drives species range dynamics, prompting many terrestrial organisms to shift northward to higher latitudes and forcing new species-environment and species-species interactions. The tick vector Ixodes scapularis, commonly known as the blacklegged tick, has historically been endemic to the United States but is establishing a persistent population in Canada, potentially exposing people to a novel zoonotic pathogen, Borrelia burgdorferi, the causative agent of Lyme disease. The collection of tick records (within Canada and the United States) between 2017 and 2022 via citizen-/community-science programs and the usage of high-resolution 1km climate data enabled me to produce robust, ensemble ecological niche models. I carried out 4,704 model iterations across two datasets, 12 algorithms, and 10 climate profiles using 40 environmental variables. I extrapolated select models over three time periods, 2011-2040, 2041-2070, and 2071-2100, across two projected climate scenarios, SSP5-8.5 and SSP3-7.0, incorporating 2,094 future predictions of I. scapularis distribution. My ensembles (AUC: 0.9565 ± 0.0065; TSS: 0.8435 ± 0.0155; Kappa: 0.819 ± 0.014) identified temperature, precipitation, biomass production (NPP), length of the growing season, climate moisture index, and the number of yearly degree days as the variables that best explained the distribution of I. scapularis. Further changes to these climate conditions will result in continued I. scapularis range expansion, with estimates ranging from ~205% (409,475 km² to 1,247,689 km²) up to ~248% (447,532 km² to 1,556,760 km²) before the end of the century. These distributional niche changes coincide with a northern latitude limit reaching as far as ~48°N by 2040, ~50°N by 2070, and ~52°N by 2100. These findings highlight the invasive potential of I. scapularis, with implications for public health and changing ecosystem dynamics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".