The impact of feral camels (Camelus dromedarius) on woody vegetation in arid Australia
Bibliographic record
Abstract
Data on the extent of feral camel damage on trees and shrubs in inland Australia are scarce, and there is currently no universally accepted theoretical framework for predicting the impact of a novel large mammal browser on arid vegetation. In other (mainly mesic) grassy systems, large mammal browsers can strongly suppress woody biomass across landscapes by limiting the transition of saplings to adulthood and by significantly thinning adult tree canopies. The recent Australian Feral Camel Management Project provided an opportunity to assess the impacts of camel browsing on woody vegetation in inland Australia. We examined browsing intensity and severity (stunting and canopy loss) in 22 species of woody plants in camel-affected regions across inland Australia prior to camel removal operations. The severity of plant damage increased with camel density as both trees and shrub growth were strongly suppressed where camel densities exceeded 0.25 km–2. In most tree and shrub species tested, camel browsing significantly stunted plants, suggesting that camel browsing has long-term impacts on plant populations. Browsing also reduced canopy volume in several species, including the structurally important Acacia aneura F.Muell. ex Benth. Thus, in this dryland ecosystem, camels can curtail the regeneration and growth of woody species enough to threaten ecosystem health. To avoid adverse impacts on woody plant populations, camel densities should be maintained at 0.25 camels km–2 or less over as much of inland Australia as possible.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".