Vertical stratification of insect abundance and species richness in an Amazonian tropical forest
Bibliographic record
Abstract
Tropical forests are among the most biodiverse biomes on the planet. Nevertheless, quantifying the abundance and species richness within megadiverse groups is a significant challenge. We designed a study to address this challenge by documenting the variability of the insect fauna across a vertical canopy gradient in a Central Amazonian tropical forest. Insects were sampled over two weeks using 6-m Gressitt-style Malaise traps set at five heights (0 m-32 m-8 m intervals) on a metal tower in a tropical forest north of Manaus, Brazil. The traps contained 37,778 specimens of 18 orders of insects. Using simulation approaches and nonparametric analyses, we interpreted the abundance and richness of insects along this gradient. Diptera, Hymenoptera, and Coleoptera had their greatest abundance at the ground level, whereas Lepidoptera and Hemiptera were more abundant in the upper levels of the canopy. We identified species of 38 of the 56 families of Diptera, finding that 527 out of 856 species (61.6%) were not sampled at the ground level. Mycetophilidae, Tipulidae, and Phoridae were significantly more diverse and/or abundant at the ground level, while Tachinidae, Dolichopodidae, and Lauxaniidae were more diverse or abundant at upper levels. Our study suggests the need for a careful discussion of strategies of tropical forest conservation based on a much more complete understanding of the three-dimensional distribution of its insect diversity.
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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 | 0.000 |
| 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) | 0.000 | 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".