Tropical Plant–Herbivore Networks: Reconstructing Species Interactions Using DNA Barcodes
Bibliographic record
Abstract
Plants and their associated insect herbivores, represent more than 50% of all known species on earth. The first step in understanding the mechanisms generating and maintaining this important component of biodiversity is to identify plant-herbivore associations. In this study we determined insect-host plant associations for an entire guild of insect herbivores using plant DNA extracted from insect gut contents. Over two years, in a tropical rain forest in Costa Rica (La Selva Biological Station), we recorded the full diet breadth of rolled-leaf beetles, a group of herbivores that feed on plants in the order Zingiberales. Field observations were used to determine the accuracy of diet identifications using a three-locus DNA barcode (rbcL, trnH-psbA and ITS2). Using extraction techniques for ancient DNA, we obtained high-quality sequences for two of these loci from gut contents (rbcL and ITS2). Sequences were then compared to a comprehensive DNA barcode library of the Zingiberales. The rbcL locus identified host plants to family (success/sequence = 58.8%) and genus (success/sequence = 47%). For all Zingiberales except Heliconiaceae, ITS2 successfully identified host plants to genus (success/sequence = 67.1%) and species (success/sequence = 61.6%). Kindt's sampling estimates suggest that by collecting ca. four individuals representing each plant-herbivore interaction, 99% of all host associations included in this study can be identified to genus. For plants that amplified ITS2, 99% of the hosts can be identified to species after collecting at least four individuals representing each interaction. Our study demonstrates that host plant identifications at the species-level using DNA barcodes are feasible, cost-effective, and reliable, and that reconstructing plant-herbivore networks with these methods will become the standard for a detailed understanding of these interactions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".