Issues and perspectives on the use of exotic species in the sustainable management of Canadian forests
Bibliographic record
Abstract
<p>Plantations offer a high potential to respond to the increasing pressure on forests to deliver social, economic, and environmental services. Exotic tree species have a long history of use in plantation forestry, mostly because of their improved productivity compared with that of native species. Because of their impacts on land management and the environment, questions arise regarding the compatibility of exotic tree plantations with sustainable forest management (SFM), the overarching paradigm driving forest legislations in Canada. Our objectives were thus to i) briefly review the historical and current use of exotic tree species in Canada, ii) identify the social, economic and environmental issues related to the use of exotic tree species in Canadian forestry, based on sustainable forest management criteria, and iii) identify perspectives related to the use of exotic tree species in the sustainable management of Canadian forests. Results show that six out of ten Canadian provinces do not have specific legislations to control the use of exotic tree species for reforestation within their borders. The use of exotic tree species is mainly controlled through third-party certification agencies. Exotic tree species represent a small proportion of the planted seedlings in Canada and Norway spruce is the most common one. The use of exotic tree species is compatible with sustainable forest management criteria used in Canada, but forest managers must take into account several issues related to their use and maintain a social license to be entitled to plant them. Issues are highly dependent upon scale. The zoning of management intensity could provide environmental, economic and social benefits, but costs/benefits analyses should be carried out. The concept of naturalness could also be useful to integrate plantations of exotic species in jurisdiction where SFM strategies are based on ecosystem management principles. Monitoring of hybridization and invasiveness of exotic species must be included in landscape analyses to forestall loss of resilience leading to compromised structural and functional ecosystem states. The use of exotics species is recognized as a tool to sequester carbon and facilitate adaptation of forests to global changes, but it is necessary to carefully identified contexts where assisted migration is justified and disentangle planned novel ecosystems coherent with global changes generated by assisted migration from those emerging from invasive species forming undesired states.</p>
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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".