Does habitat structure influence capture probabilities? A study of reptiles in a eucalypt forest
Bibliographic record
Abstract
Pitfall traps are commonly used to examine differences in reptile communities among habitat types and disturbance regimes that differ in structure. However, capture rates and probabilities may be influenced by habitat structure, which invalidates comparisons of relative abundance among habitat types. To assess whether pitfall traps provide accurate reflections of density and whether habitat structure affects capture probabilities, we trapped at six sites in various jarrah-forest habitat types in south-western Australia, then intensively searched 150-m2 total-removal plots around each pitfall grid to obtain absolute densities of reptiles. Pitfall captures were significantly correlated with numbers on total-removal plots for Hemiergis initialis and Lerista distinguenda, indicating that pitfall traps provided accurate reflections of density for these species. Capture probabilities of H. initialis and L. distinguenda and all reptiles combined showed no significant correlations with any structural variables, indicating that capture probabilities were consistent across sites. We conclude that trapping provided accurate estimates of relative abundance for some species and that capture probabilities were not influenced by vegetation structure. Because many studies use trapping to estimate abundances among habitat types, we encourage researchers to investigate how vegetation structure influences capture probabilities, so that general patterns can be determined; we also suggest improvements for any future studies.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".