Producing wood at least cost to biodiversity: integrating <scp>T</scp>riad and sharing–sparing approaches to inform forest landscape management
Bibliographic record
Abstract
Forest loss and degradation are the greatest threats to biodiversity worldwide. Rising global wood demand threatens further damage to remaining native forests. Contrasting solutions across a continuum of options have been proposed, yet which of these offers most promise remains unresolved. Expansion of high-yielding tree plantations could free up forest land for conservation provided this is implemented in tandem with stronger policies for conserving native forests. Because plantations and other intensively managed forests often support far less biodiversity than native forests, a second approach argues for widespread adoption of extensive management, or 'ecological forestry', which better simulates natural forest structure and disturbance regimes - albeit with compromised wood yields and hence a need to harvest over a larger area. A third, hybrid suggestion involves 'Triad' zoning where the landscape is divided into three sorts of management (reserve, ecological/extensive management, and intensive plantation). Progress towards resolving which of these approaches holds the most promise has been hampered by the absence of a conceptual framework and of sufficient empirical data formally to identify the most appropriate landscape-scale proportions of reserves, extensive, and intensive management to minimize biodiversity impacts while meeting a given level of demand for wood. In this review, we argue that this central challenge for sustainable forestry is analogous to that facing food-production systems, and that the land sharing-sparing framework devised to establish which approach to farming could meet food demand at least cost to wild species can be readily adapted to assess contrasting forest management regimes. We develop this argument in four ways: (i) we set out the relevance of the sharing-sparing framework for forestry and explore the degree to which concepts from agriculture can translate to a forest management context; (ii) we make design recommendations for empirical research on sustainable forestry to enable application of the sharing-sparing framework; (iii) we present overarching hypotheses which such studies could test; and (iv) we discuss potential pitfalls and opportunities in conceptualizing landscape management through a sharing-sparing lens. The framework we propose will enable forest managers worldwide to assess trade-offs directly between conservation and wood production and to determine the mix of management approaches that best balances these (and other) competing objectives. The results will inform ecologically sustainable forest policy and management, reduce risks of local and global extinctions from forestry, and potentially improve a valuable sector's social license to operate.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".