Conservation de la biodiversité dans les paysages forestiers aménagés : utilisation des seuils critiques d’habitat
Bibliographic record
Abstract
In Canada, as in other large forested countries of the world, managers and scientists alike question what can happen to forest biodiversity under long-term industrial forest management. Recent studies may help us understand how species react when habitat is lost past a certain threshold in the landscape. In the case of population, a “critical threshold for habitat” does exist in forested habitat, which is defined by the minimal proportion of habitat needed to be preserved to avoid drastic population declines or massive species loss. In this paper, two types of thresholds are described, the first refers to population, and the second refers to the community of species. Many ecologists agree with the assumption that the specialist, sensitive species are the first to disappear (local extirpation for specialist species). For most species with large home range (such as birds), the threshold may generally be located between 30% and 40% of the habitat still remaining, compared to the proportion observed under a natural disturbance regime. We suggest, in order to protect the most sensitive species and to deal with uncertainty associated with thresholds, to maintain at least 40% of residual habitats. Although there is still much to understand concerning these thresholds, we nevertheless recommend their use for the diagnostic analysis that must be performed in the context of forest management planning and biodiversity conservation, as these thresholds could represent the minimal proportion of habitat to preserve integrity of the forest ecosystem. However, to be effective, the application of thresholds should be based on detailed knowledge of ecosystem characteristics and dynamics. Key words: ecological threshold, forest management, forest ecosystem, habitat loss, mature and old forests, population, community, biodiversity, conservation
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".