Integrating DNA sequencing and morphological identification enhances understanding of epiphytic diatom diversity and ecology
Bibliographic record
Abstract
Eelgrass ( Zostera spp.) forms productive and biodiverse marine meadows throughout the world. There eelgrass provide living substrata for epiphytes, including diatoms, which contribute substantially to primary productivity. Despite their important role, there are many gaps in our understanding of the diversity and ecology of epiphytic diatoms. Here, we combined morphological identification by scanning electron microscopy and light microscopy with Illumina sequencing of the 18S rRNA and rbcL marker genes to survey the diatom community on eelgrass ( Zostera marina) from Galiano Island, British Columbia across a gradient of tissue age. We detected 76 genera of diatoms, 47 with both molecular and morphological methods. Using a spatially explicit morphological census, we observed a significant increase in density and a moderate increase in diversity across tissue age. We found that changes in community composition are driven by nestedness rather than turnover. The diatom community diversity and composition detected by molecular data did not differ by tissue age. We suggest that this is because molecular data surveyed a larger area (5 cm2 vs. 200 µm2), so rare taxa are more likely to be present. Overall, we found that integrating morphological and molecular datasets enhances detection of diversity and provides context for interpreting ecological patterns.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".