Seasonal monopolization of small carrion by a scarab beetle in terra firme Amazonian rainforests
Bibliographic record
Abstract
Small vertebrate carcasses represent an abundant resource in tropical rainforests, where scavengers play a key role in nutrient recycling, enhancing productivity in nutrient-poor habitats such as Amazonian terra firme forests. However, the contributions of vertebrate and invertebrate taxa to carrion decomposition in Amazonia remain understudied. Using motion-triggered and time-lapse cameras, we documented vertebrate species and arthropod groups interacting with 52 guinea pig carcasses in terra firme and floodplain forests in the Peruvian Amazon during dry and wet seasons. We quantified their contributions to carcass removal and examined variation across habitats and seasons, as well as temporal resource partitioning among insect taxa. A single dung beetle species, Coprophanaeus lancifer, removed 93% of carcasses in terra firme during the wet season, by burying them on-site. In contrast, 92% of floodplain carcasses in the wet season were removed by vertebrates. C. lancifer was absent in the dry season, when vertebrates removed most carcasses. Carcasses buried by C. lancifer lasted less than 24 h, reducing detectability for vertebrates and limiting access for other arthropods and necrophagous flies. To our knowledge, this is the first record of a rainforest invertebrate outcompeting vertebrates for carrion under natural conditions, highlighting C. lancifer as a keystone species shaping carrion trophic interactions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".