DNA metabarcoding reveals fine scale geographical differences of consumed algae in the Galápagos marine iguanas (Amblyrhynchus cristatus)
Bibliographic record
Abstract
Abstract Galápagos marine iguanas are primarily associated with the marine environment and show special nutritional adaptations. They are the only lizards worldwide that forage on marine macroalgae. Until now, consumed algae have been identified by direct observations during their feeding activities and microscopic identification in faeces samples. In this study, we use a novel DNA metabarcoding approach to identify consumed algal species from the faeces of marine iguanas. We developed primers for the ribulose-bisphosphate carboxylase (rbcL) gene and applied a metabarcoding approach to 25 individual faeces samples collected in four representative sites of two subspecies ( Amblyrhynchus cristatus mertensi and A. c. godzilla ), found in the San Cristóbal Island. We detected 18 consistently occurring macroalgal operational taxonomic units (OTUs). Most of the OTUs were assigned to Rhodophyta (red algae) and only one OTU to Chlorophyta (green algae). Despite the number of consumed algal species did not differ between two subspecies (OTU richness; P = 0.383), diet overlap level between A. c. mertensi and A. c. godzilla was low (Schoener index = 0.345), suggesting that both subspecies consumed different algal species in their natural environment. Further studies are needed to understand whether the difference of consumed algae reflects disparities in the abundance of algal species between sites, or whether iguanas of the two genetically differentiated subspecies prefer distinct algal species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.000 | 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".