Long-term patterns in the decay, collapse, and abundance of trees with hollows in the mountain ash (Eucalyptus regnans) forests of Victoria, southeastern Australia
Bibliographic record
Abstract
Large trees with hollows are an important component of stand structural complexity worldwide. Understanding their population dynamics is needed to manage cavity-dependent biota. We quantified long-term rates of collapse of 302 measured trees with hollows in 1939-aged regrowth mountain ash ( Eucalyptus regnans F. Muell.) forest in southeastern Australia. We identified time-dependent dynamics in which the collapse rates of trees slowed from ∼4% annually between 1983 and 1993 to ∼2.2% between 1993 and 2007. Transitions of trees between different decay states (forms) also slowed over time. Nevertheless, during the 24-year period of our study, over half of our marked and measured trees had fallen, but there was no recruitment of new trees with hollows. Under current projections, few trees with hollows will occur on our field sites by ∼2050, although more had been forecast in earlier investigations. Such a paucity of trees with hollows in extensive areas of regrowth mountain ash forests will lead to a shortage of nesting and sheltering sites for cavity-dependent biota. We suggest a short–medium (10- to 100-year) focus on the conservation of old growth and multi-aged stands will be needed to maintain populations of those species strongly associated with trees with hollows in mountain ash forests.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 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".