Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites
Bibliographic record
Abstract
Robust data to refute or support claims of global insect decline are currently lacking, particularly for the soil fauna in the tropics. DNA metabarcoding represents a powerful approach for rigorous spatial and temporal monitoring of the taxonomically challenging soil fauna. Here, we provide a detailed field protocol, which was successfully applied in Barro Colorado Island (BCI) in Panama, to collect soil samples and arthropods in a tropical rainforest, to be later processed with metabarcoding. We also estimate the proportion of soil/litter ant, springtail and termite species from the local fauna that can be detected by metabarcoding samples obtained either from Berlese-Tullgren (soil samples), Malaise or light traps. Each collecting method detected a rather distinct fauna. Soil and Malaise trap samples detected 213 species (73%) of all target species. Malaise trap samples detected many ant species, whereas soil samples were more efficient at detecting springtail and termite species. With respect to long-term monitoring of soil-dwelling and common species (more amenable to statistical trends), the best combination of two methods were soil and light trap samples, detecting 94% of the total of common species. A protocol including 100 soil, 40 Malaise and 80 light trap samples annually processed by metabarcoding would allow the long-term monitoring of at least 11%, 18% and 16% of species of soil/litter ants, springtails and termites, respectively, present on BCI, and a high proportion of the total abundance (up to 80% of all individuals) represented by these taxa.
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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 | 0.035 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".