Managing tree plantations as novel socioecological systems: Australian and North American perspectives
Bibliographic record
Abstract
Novel ecosystems occur when new combinations of species appear within a particular biome. They typically result from direct human activity, environmental change, or the impacts of introduced species. In this paper, we argue that considering commercial tree plantations as novel ecosystems has the potential to help policy makers, resource managers, and conservation biologists better deal with the challenges and opportunities associated with managing plantations for multiple purposes at both the stand and landscape scales. We outline five inter-related issues associated with managing tree plantations, which are arguably the largest form of terrestrial novel ecosystem worldwide. This is to ensure that these areas contribute significantly to critical ecosystem services, including biodiversity conservation, in addition to their wood production role. We suggest that viewing tree plantations as novel socioecological systems may free managers from a narrow stand-based perspective and having to compare them with natural forest stands. This can help promote the development of management principles that better integrate plantations into the larger landscape so that their benefits are maximized and their potential negative ecological effects are minimized.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".