Evidence for passive dispersal of ground beetles (Coleoptera: Carabidae) in the Nearctic boreal forest
Bibliographic record
Abstract
Passive transport has likely contributed to the post-glacial dispersal of species in temperate regions. However, identifying such processes from patterns can be obscured by confounding environmental conditions. We studied the distribution of ground beetles in Ontario’s Far North, a vast (450,000 km2) and largely intact region, to identify mechanisms that aid in species dispersal when confounding factors, such as temperature, are controlled. We tested a model of passive, riverine dispersal using recent records of flightless and flighted ground beetles from 34 sites across the region. The number of species declined with distance from main watershed rivers, as predicted from our model at the site level. Contrary to expectations, this pattern was evident with flighted species but not flightless species. The opposite pattern, with flightless species displaying a decrease with increasing distance from river, was evident at a regional level. These patterns are consistent with ground beetles carried along rivers since glacial retreat. We surmise that northward dispersal of these invertebrates into the boreal forest has been aided by vegetation and other debris flowing down rivers, including soil from eroded banks in which larvae and pupae reside. Biogeographic inferences, although often subtle, can be supported by broad-scale studies of intact landscapes.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".