The utility of DNA metabarcoding for studying the response of arthropod diversity and composition to land-use change in the tropics
Bibliographic record
Abstract
Metabarcoding potentially offers a rapid and cheap method of monitoring biodiversity, but real-world applications are few. We investigated its utility in studying patterns of litter arthropod diversity and composition in the tropics. We collected litter arthropods from 35 matched forest-plantation sites across Xishuangbanna, southwestern China. A new primer combination and the MiSeq platform were used to amplify and sequence a wide variety of litter arthropods using simulated and real-world communities. Quality filtered reads were clustered into 3,624 MOTUs at ≥97% similarity and the taxonomy of each MOTU was predicted. We compared diversity and compositional differences between forests and plantations (rubber and tea) for all MOTUs and for eight arthropod groups. We obtained ~100% detection rate after in silico sequencing six mock communities with known arthropod composition. Ordination showed that rubber, tea and forest communities formed distinct clusters. α-diversity declined significantly between forests and adjacent plantations for more arthropod groups in rubber than tea, and diversity of order Orthoptera increased significantly in tea. Turnover was higher in forests than plantations, but patterns differed among groups. Metabarcoding is useful for quantifying diversity patterns of arthropods under different land-uses and the MiSeq platform is effective for arthropod metabarcoding in the tropics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.000 | 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".