Caragana arborescens, an emerging invasive shrub in Canada’s southern boreal forests
Bibliographic record
Abstract
Caragana arborescens Lam.(hence: Caragana) is an emerging invasive shrub in Canada's boreal forest, a region formerly considered resistant to biological invasions.Originally introduced to mitigate soil erosion and widely planted as a shelterbelt, hedge, and ornamental, Caragana possesses traits indicative of an ecosystem transformer, including an ability to fix nitrogen, and suppress neighboring plants.It has been reported to spread in natural habitats, yet its general frequency of escape from plantings and rate of spread into natural habitat is unknown.We carried out a field survey in the southern Boreal and Aspen Parkland regions with the aim to ( 1) evaluate the propensity of Caragana to escape from planted shelterbelt and hedges into natural habitats, using historical shipment records from the Prairie Farm Rehabilitation Administration (PFRA), and (2) calculate its local areal rate of spread based on sites identified through PFRA data and road surveys.We found that in 37 out of 40 sites (92.5%)where bordering on natural habitat, Caragana escaped cultivation.Our field survey reveals a local areal rate of spread of 2.23% per year based on an exponential growth model, and 2.95% per year based on a logistic model, with an average invaded area of 1.98 hectares (± 3.2 SD).Our results provide evidence that when natural habitat is nearby, Caragana escapes from plantations with a high probability.Although its local areal spread is relatively slow, the high density of deliberate plantings implies a high rate of spread at the regional scale.Hence, Caragana represents the first case of a successful woody invader in Canada's southern boreal forest.Effective management strategies for Caragana would benefit from a better understanding of its habitat preferences, environmental factors influencing local spread, and impact on native plant regeneration and ecosystem functioning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".