Negative or positive effects of plantation and intensive forestry on biodiversity: A matter of scale and perspective
Bibliographic record
Abstract
Terrestrial biodiversity is closely linked to forest ecosystems but anthropogenic reductions in forest cover and changes in forest structure and composition jeopardize their biodiversity. Several forest species are threatened because of reduced habitat quality and fragmentation or even habitat loss as a result of forest management activities. In response to this threat, integrated forest management (IFM) was developed in the early 1990s and has been applied over large spatial scales ever since. While IFM seeks to satisfy both human resource demands and ecosystem integrity, the whole forest matrix is affected and this may also have negative impacts on biodiversity. The concept of forest zoning (e.g., Triad) avoids these issues by physically separating land uses from each other. The zoning approach has been developed in the same period as IFM, but there are still very few examples of large-scale applications. This may be because its distinctiveness from IFM may not always seem clear and because forest zoning is not easily implemented. Here we explain these differences and show that IFM and the zoning approach are indeed different management paradigms. We advocate the use of high-yield plantations within the zoning paradigm as a means for biodiversity conservation and review the literature (with an emphasis on the northern hemisphere and on plantation forestry within a land-zoning approach) on impacts of forest management activities on biodiversity. Furthermore, we give advice on issues that require consideration when implementing forest zoning at both the stand and the landscape levels. We recommend several small changes in design and management of forest plantations as a means to significantly increase their biodiversity value. We conclude that while forest zoning seems an adequate strategy for the Canadian forestry sector, a shift in paradigm must carry over to policy-makers and legislation if this approach is to succeed. Key words: biodiversity, landbase zoning, forest management, intensive silviculture, plantation forests
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".