Tree hollow decline in new forest reserves with a long history of logging
Bibliographic record
Abstract
Abstract In many parts of the world, achieving a target of 30% of land managed for conservation under the Kunming‐Montreal Global Biodiversity Framework will require the protection of land with a long history of management for production. In newly protected forests, past logging practices will have impacted key aspects of stand structure, including hollow‐bearing trees that provide critical habitat for vertebrate fauna. The impacts of past silvicultural practices on hollow density, distribution, type and longevity may necessitate targeted ameliorative actions. We investigated tree hollows in the largest river red gum (Eucalyptus camaldulensis Denh.) forest in the world, which had undergone logging‐induced woody thickening prior to being converted to a conservation reserve in 2010. We recorded stem diameters and hollows in living and dead trees in 66 two‐hectare plots. Our sites sampled two productivity states and a wide range of total tree densities. On all sites, we found that hollow‐bearing tree densities were lower than reference values for unlogged stands and average density had halved relative to reference values. We found no relationship between the density of hollow‐bearing trees and total tree density, but we did find a weak positive relationship with site productivity. Larger trees had more hollows, bigger hollows and a greater diversity of hollow sizes. However, of the 1254 hollow‐bearing trees recorded, 43% were dead, 48% of the dead trees had been ringbarked. The proportion of hollow‐bearing trees that were dead was positively correlated with tree size, with 60% of trees in the largest quartile (>105 cm) recorded as dead. The prevalence of dead hollow‐bearing trees suggests that the density and diversity of hollows will continue to decline and ameliorative actions should be considered. These results highlight the need to consider the legacy of past silvicultural practices in the management of newly created conservation reserves.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".