Can ecological thinning deliver conservation outcomes in high-density river red gum forests? Establishing an adaptive management experiment
Bibliographic record
Abstract
Newly protected areas often have land-use legacies that affect their capacity to deliver conservation outcomes into the future. The management actions required to achieve conservation outcomes may be uncertain. This uncertainty may be resolved through experimental adaptive management that draws on knowledge of the ecology and history of the ecosystem. In New South Wales, Australia, river red gum (Eucalyptus camaldulensis) floodplain forests were gazetted as National Park in 2010, including Murray Valley National Park. Land-use legacies had resulted in one-third of river red gum forests and woodlands occurring as high-stem-density (>400 stems ha-1) stands at the time of gazettal. High-stem-density stands are characterised by dominance of narrow straight trees, a paucity of large and hollow-bearing trees, modified understorey vegetation and reduced coarse woody debris. A simple state-and-transition process model captured knowledge of the processes that led to the high-stem-density river red gum forest state being widespread. We describe the establishment of a manipulative experiment to evaluate whether ecological thinning can achieve conservation outcomes in high-stem-density stands of river red gum floodplain forest. The experiment was designed to reduce intrastand competition for water and other resources, and encourage development of spreading tree crowns. Future results will inform management decisions in high-stem-density stands of river red gum floodplain forests. The adaptive management approach employed provides a template for using knowledge of the ecosystem to resolve uncertainty about management, particularly in newly protected areas.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".