Ecology of the western bearded dragon (Pogona minor) in unmined forest and forest restored after bauxite mining in south-west Western Australia
Bibliographic record
Abstract
Forest areas restored after mining typically take decades, or longer, before they resemble the original vegetation community. Understanding how fauna succession varies with plant succession requires detailed knowledge of an animal’s ecology. Knowledge of an animal’s ecology can also be used to predict faunal responses to management manipulations and enable techniques to be developed that accelerate the return of fauna to restored sites. We radio-tracked western bearded dragons (Pogona minor) in a mix of unmined forest sites and sites restored after bauxite mining, in the jarrah forest of south-west Western Australia, to determine critical resources and important microhabitats for dragons. Dragons were generalists – utilising a range of microhabitats – and adaptable, adjusting their microhabitat use depending on availability. Individuals also differed significantly in their microhabitat use and did not appear to have a defined home range. We concluded that the species would rapidly recolonise restored sites and that no modifications to current restoration practices were required to accelerate their return. Prescribed burning of restored areas could negatively affect this species but the effect would be short-term (<2 years). The approach used in this study could be used to develop management prescriptions that accelerate the return of late-successional species to restored sites.
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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 | 0.000 |
| 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 | 0.001 |
| 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".