Creating entrances to tree cavities attracts hollow‐dependent fauna: proof of concept
Bibliographic record
Abstract
Redressing the paucity of tree hollows is essential for conservation of hollow‐dependent fauna in many landscapes around the world. We describe a method of accelerating availability of natural hollows in regenerating landscapes by mechanically creating entrances in tree stems that have existing voids or internal decay but have not yet developed entrances. We trialed this method in woodland and forest of south‐eastern Australia in 39 stems in the closely related tree genera Eucalyptus and Angophora. Exploratory drilling of 10‐mm diameter holes was used to detect the presence of internal decay or voids. We then drilled 40‐, 65‐, or 90‐mm diameter entrance holes, depending on the size of the potential cavity, 2.4–4.8 m above ground level. Camera traps showed that drilled entrances were investigated or used within hours of creation. A diverse suite of invertebrates, reptiles, mammals, and birds were recorded entering or leaving entrances, including threatened species. All 39 holes were used by animals with up to six vertebrate taxa using some entrances. Two bird species excavated material from within cavities, and three species of marsupial were recorded taking nesting material into the cavities. This trial provides evidence that adding entrances to currently inaccessible internal cavities in trees has potential to accelerate development of habitat for hollow‐dependent fauna, particularly in regenerating vegetation.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".