Optimising design and effort for environmental surveys using dung beetles (Coleoptera: Scarabaeidae)
Bibliographic record
Abstract
Abstract In biological monitoring, deploying an effective standardised quantitative sampling method, optimised by trap design and sampling effort, is an essential consideration. To exemplify this using dung beetle (Coleoptera: Scarabaeidae: Scarabaeinae and Aphodiinae) communities, three pitfall trap designs (un-baited (TN), baited at ground level (flat trap, TF), and baited above the trap (hanging trap, TH)), employed with varying levels of sampling effort (number of traps=1, 2, 3 … 10; number of days=1, 2, 3), were evaluated for sampling completeness and efficiency in the Eastern Cape, South Africa. Modelling and resampling simulation approaches were used to suggest optimal sampling protocols across environmentally diverse sites. Overall, TF recovered the greatest abundance and species richness of dung beetles, but behavioural guilds showed conflicting trends: endocoprids preferred TH while paracoprids and telocoprids preferred TF. Resampling simulation of trap type and the two components of sampling effort suggested that six TF traps left for three days was most efficient in obtaining a representative sample and allowed differentiation between trap types, allowing the improved efficiency to be recognised. The effect of trap type on non-target specimens, particularly ants, was also investigated. TF and TH caught almost no by-catch, which is ethically desirable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".