Coordinating Old-Growth Conservation Across Scales of Space, Time, and Biodiversity: Lessons from the US Policy Debate.
Bibliographic record
Abstract
Conservation of old-growth forests along with their unique biodiversity and climate benefits requires coordinated actions across spatiotemporal scales, including immediate restrictions on harvest of old and mature trees and longer-term landscape planning for old-growth restoration and recovery. In 2024, the US government drafted a National Old-Growth Amendment to enhance consideration of old-growth in planning. Although the amendment was never finalized, the policy debate illustrates key questions that remain relevant for future initiatives, while highlighting limitations common to such initiatives in the US and elsewhere as nations work to implement the recently developed Kunming-Montreal Global Biodiversity Framework. More attention must be paid to the policy implications of variation in the context in which old growth occurs across ecosystems. New initiatives must learn from previous efforts, such as the 1994 Northwest Forest Plan’s insights that landscape design including reserves is essential for effective conservation of species, services, and processes associated with old-growth ecosystems. Reserves, conceived as places where extractive uses are restricted but beneficial human activities are supported, are compatible with practical strategies for ecosystem restoration and Indigenous-led conservation. An approach that builds on the NOGA’s proposed adaptive strategies can form a foundation for long-term conservation of forest ecosystems by protecting climate and fire refugia, addressing barriers to connectivity, and enhancing monitoring capacity. Ecosystem-based standards are needed to ensure protection of mature forest so that recruitment into the old-growth stage shifts ecosystems towards historic proportions of old growth. In addition to clarifying goals regarding ecological integrity, comprehensive policy must incorporate specific goals for recovering at-risk species based on empirical relationships across scales of biodiversity between forest habitat and species viability. Land management agencies need to articulate a long-term vision for recovery of depleted ecosystem elements (including both old-growth and naturally disturbed younger stands) via designation of large areas anchored by remaining old-growth stands, surrounded by areas managed for restoration of ecological integrity, native biodiversity, and ecosystem services.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.025 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".