DNA barcodes reveal striking arthropod diversity and unveil seasonal patterns of variation in the southern Atlantic Forest
Bibliographic record
Abstract
The Atlantic Forest harbors 7% of global biodiversity and possesses high levels of endemism, but many of its component taxa remain unstudied. Due to the importance of tropical forests and the urgency to protect them, there is a compelling need to address this knowledge gap. To provide more information on its arthropod fauna, a Malaise trap was deployed for 12 months in a semi-degraded area of the southern Upper Paraná ecoregion of the Atlantic Forest. All specimens were DNA barcoded and the Barcode Index Number (BIN) system was employed to assign each specimen to a species proxy. DNA barcodes were obtained from 75,500 arthropods that included representatives of 8,651 BINs. Nearly 81% of these BINs were first records, highlighting the high rates of endemism and lack of study of arthropods from the Atlantic Forest. Diptera was the most abundant order, followed by Hemiptera, Lepidoptera and Hymenoptera. Diptera was also the most species-rich order, followed by Hymenoptera, Lepidoptera, and Coleoptera, a result consistent with studies in other biogeographic regions. Insects were most abundant in winter and most diverse in autumn and winter. This pattern, however, was caused mainly by the dynamics of dipteran diversity as other orders differed in their seasonal variation. The BIN composition of the insect community varied sharply through the year and also differed between the two consecutive summers included in the sampling period. The study of the 38 commonest BINs showed that seasonal patterns of abundance were not order-specific. Temperature had the strongest impact on seasonal abundance variation. Our results highlight the striking and understudied arthropod diversity of the highly fragmented Atlantic Forest, the predominance of dipterans, and the fact that abundance and richness in this insect community peak in the coolest months. Standardized studies like this generate fast and reliable biodiversity inventories and unveil ecological patterns, thus providing valuable information for conservation programs.
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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 | 0.001 |
| 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".