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 and their biodiversity and climate benefits requires coordinated actions across spatiotemporal scales, including restrictions on harvest of old and mature trees and longer-term landscape planning for old-growth recovery. In 2024, the US government drafted a National Old-Growth Amendment (NOGA) to enhance consideration of old growth in planning. The amendment was never finalized due to a change in administrations. However, the debate associated with the proposed NOGA remains relevant for several reasons. The key points of debate also underpin discussion regarding ongoing US regional policy initiatives such as amendments to the Northwest Forest Plan (NWFP). The NOGA policy debate illustrates questions that should inform US national old-growth initiatives under future administrations. Lastly, the debate highlights challenges common to such initiatives globally as nations implement the Kunming-Montreal Global Biodiversity Framework. Although the proposed NOGA provides a useful foundation for future initiatives, 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 regional efforts, including the NWFP’s insights that landscape design including reserves is essential for 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 strategies for ecosystem restoration and Indigenous-led conservation. An approach that builds on the NOGA’s adaptive strategies can form a foundation for long-term conservation of forest ecosystems by protecting climate refugia, addressing barriers to connectivity, and enhancing monitoring capacity. Ecosystem-based standards are needed to ensure protection of mature forest so 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 goals for recovering at-risk species based on 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 recovery of ecological integrity, native biodiversity, and ecosystem services.
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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".