Seasonality, dung specificity and competition in dung beetle assemblages in the Australian Wet Tropics, north-eastern Australia
Bibliographic record
Abstract
A trapping study of five mammal species in wet sclerophyll forest adjacent to rain forest in the Australian Wet Tropics was used to examine the seasonal diversity, abundance and dung-specificity of dung beetles associated with mammal dung. A total of 542 dung beetles from 11 species within three genera was recovered from beneath the traps of 1104 mammal captures. The diversity of beetles associated with the dung of the northern bettong (Bettongia tropica), a mycophagous marsupial, differed significantly from the diversity predicted by a null model. Numbers of beetles varied significantly with type of dung, indicating preference by beetles. Beetle numbers were related positively to a 1-mo lag in monthly mean minimum temperature and less strongly to maximum temperature and rainfall. Significantly more beetles per mammal capture were detected in the wet season than in the dry season. Dung beetles showed a strong preference for either the Eucalyptus woodland (six species) or the adjacent Allocasuarina forest (four species), with only one species occurring in both habitat types. Beetle species from the Eucalyptus woodland were typically only detected in the late wet and early dry seasons, while species in the wetter Allocasuarina forest were generally collected during the late dry and early wet seasons. A significant ‘checkerboard’ species effect was detected in both time and space in both habitat types, suggesting that competition for dung was strong.
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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 | 0.001 | 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".