Leaving forest strips in large clearcut landscapes of boreal forest: A management scenario suitable for wildlife?
Bibliographic record
Abstract
Riparian forest strips (RS) along lakes and streams have been incorporated in regulations on clearcuts to protect water quality and fish habitat. As well, upland strips (US) are used to limit the size of clearcut patches. We conducted a three-year study to evaluate if RS and US between adjacent cutovers in large clearcut landscapes could be useful for certain terrestrial wildlife. Our study was conducted in southcentral Québec on six landscapes (23–256 km 2 ) originating from black spruce (Picea mariana) forests that were recently logged (≤ 9 years). Residual uncut forest, mostly strips (width = 51–132 m), made up 31% of the productive forest area within these landscapes. RS and US were suitable habitat for red squirrel (Tamiasciurus hudsonicus) and many species of birds including spruce grouse (Falcipennis canadensis). They provided marginal habitat for snowshoe hare (Lepus americanus) and were not preferred by moose (Alces alces) over clearcut areas in winter. One bird species, bay-breasted warbler (Dendroica castanea), was absent in forest strips during the last year. We concluded that leaving RS and US is a management scenario that enables maintaining certain wildlife species within clearcut landscapes, but that larger residual forest patches are needed to accommodate area-sensitive and forest-interior species. A portion of these patches should be allowed to develop into mature and overmature stages for old-growth forest species. Green tree and dead tree retention should also be incorporated in logging practices to accommodate species that need snags for nesting or feeding. In areas where wildlife use has a high priority, large clearcuts should be intermixed with dispersed patch cutting in order to gain social acceptance. Key words: black spruce, forest strip, clearcutting, ecosystem management, Picea mariana, riparian strip
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".