<scp>DNA</scp> metabarcoding reveals changes in the contents of carnivorous plants along an elevation gradient
Bibliographic record
Abstract
Resource variation along abiotic gradients influences subsequent trophic interactions and these effects can be transmitted through entire food webs. Interactions along abiotic gradients can provide clues as to how organisms will face changing environmental conditions, such as future range shifts. However, it is challenging to find replicated systems to study these effects. Phytotelmata, such as those found in carnivorous plants, are isolated aquatic communities and thus form a good model for the study of replicated food webs. Due to the degraded nature of the prey, molecular techniques provide a useful tool to study these communities. We studied the pitcher plant Sarracenia purpurea L. in allochthonous populations along an elevational gradient in the Alps and Jura. We predicted that invertebrate richness in the contents of the pitcher plants would decrease with increasing elevation, reflecting harsher environmental conditions. Using metabarcoding of the COI gene, we sequenced the invertebrate contents of these pitcher plants. We assigned Molecular Operational Taxonomic Units at ordinal level as well as recovering species-level data. We found small but significant changes in community composition with elevation. These recovered sequences could belong to invertebrate prey, rotifer inquilines, pollinators and other animals possibly living inside the pitchers. However, we found no directional trend or site-based differences in MOTU richness with elevational gradient. Use of molecular techniques for dietary or contents analysis is a powerful way to examine numerous degraded samples, although factors such as DNA persistence and the relationship with species presence still have to be completely determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".