Road age and its importance in earthworm invasion of northern boreal forests
Bibliographic record
Abstract
Summary Roads are an important conduit for the spread of invasive species. Road age is a key factor that could influence the susceptibility of roads to invasion as older roads are typically subject to higher cumulative levels of human disturbance and propagule pressure than younger roads. We investigated the effects of road age on the spread of non‐native earthworms, which act as ecosystem engineers. We sampled earthworms and habitat variables at 98 roads in the boreal forest of Alberta, Canada, to determine the influence of road age on non‐native earthworm occurrence at the landscape level. The extent and rate of local spread were also assessed at seven sites adjacent to old and young roads. Generalized estimating equations and zero‐inflated negative binomial regression were used to analyze landscape‐ and local‐level results, respectively. We used our models to create maps that predict the current and potential future extent of earthworms in north‐eastern Alberta. Probability of earthworm occurrence and extent of spread increased as road age increased. Areas closer to agriculture and towards the south and west of our study area were also significantly more likely to be invaded by earthworms. Our spread model indicated that approximately 9% of the boreal forest of north‐eastern Alberta is likely invaded by earthworms currently. This is projected to increase to 49% of suitable forest habitat over the next 50 years as human development intensifies in this region. Synthesis and applications. Although the effects of roads and linear features are commonly investigated in relation to native species, our results emphasize the importance of considering the impacts of linear feature creation on the spread of invasive species. We demonstrate that road age in particular can be an important factor affecting the spread of invasive species. In the boreal forest, reducing the number of roads being constructed, restricting traffic, and reclaiming temporary roads will be critical to reduce the future extent of earthworm invasions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".