Recovery time of snowshoe hare habitat after commercial thinning in boreal Quebec
Bibliographic record
Abstract
As short-term effects of partial cuts generally decrease available cover for snowshoe hare ( Lepus americanus Erxleben), most studies have shown negative effects of such treatments on this keystone species in boreal ecosystems. This study aims to determine the long-term impact of commercial thinning on snowshoe hare habitat, and we hypothesized that habitat quality, as well as habitat use, recovers with time since treatment. We selected stands aged 50–90 years dominated by black spruce ( Picea mariana (Mill.) Britton, Sterns & Poggenb.) in Abitibi (Quebec). We used models of habitat parameters to explain the abundance of snowshoe hare tracks and pellets in 20 commercially thinned stands treated between 1989 and 1999 and 12 control stands. Lateral cover was the dominant parameter influencing snowshoe hare habitat use. On average, commercially thinned stands had a lower lateral cover than controls (–18%). We also found that snowshoe hare use of commercially thinned stands increases with time since treatment. However, 11–18 years are needed before commercially thinned stands return to the same level of lateral cover and snowshoe hare signs as control stands. Commercial thinning is generally followed by harvesting all merchantable stems 15 years after treatment. Thus, we suggest that commercial thinning as currently practiced should be avoided if the objective is to maintain quality habitat for snowshoe hare and its associated predators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".