Vertical stratification of Diptera abundance and species richness in an Amazonian tropical forest
Bibliographic record
Abstract
Abstract Measuring species richness of tropical forests is a major challenge. Such measurement is a key information in many senses, from an evolutionary perspective to conservation of threatened, fragile habitats. Data has gradually shown that the canopy of tropical forest is a hugely complex component of the forest, but a precise assessment of the diversity of megadiverse groups in the canopy is still wanting. We collected large samples of insects were along a period of two weeks using 6-meter Gressitt-style Malaise traps set at five heights on a metal tower in a tropical forest north of Manaus—one trap at the ground level, one trap above the canopy (32 m) and three traps at intermediate levels (8, 16 and 24 m). The samples contained 37,778 specimens belonging to 18 order of insects. Fifty-seven families of flies (Diptera) were found, 39 of which were identified to 368 genera and 856 species. The species of these 39 families of flies fit into eight patterns of vertical distribution of abundance and species richness of the fauna, with patterns of one or two peaks of species at different levels. A total of 527 (61.6%) of the 856 fly species recognized in the samples were not collected at the ground level. The canopy-associated species of Diptera showed a high species richness and a relatively low abundance indicating that they represent vulnerable components of tropical diversity. The biology of the flies families and genera we collected in the canopy suggest that the evolution of flies went through a unique process: independent clades of Diptera explored in different ways the resources originated along the very evolution of the angiosperm forest canopy along the early Cenozoic. Unlike other primarily phytophagous groups of insects, flies radiated into a large array of biologies associated with the canopies: parasitoids, hematophagous, phytosaprophagous, kleptoparasites, sap feeders, gall-makers etc. The results only stress the need of additional strategies to protect this 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".