INTEGRATING MOLECULAR AND TRADITIONAL SYSTEMATIC TECHNIQUES TO REDEFINE RED ALGAL (RHODOPHYTE) DIVERSITY IN THE BERMUDA ISLANDS
Bibliographic record
Abstract
Molecular-assisted alpha taxonomy (MAAT) is a groundbreaking methodology that combines molecular tools with traditional morphological investigations. From studies using these methods, researchers can determine whether specimens with different morphologies are actually one entity exhibiting high phenotypic plasticity or are multiple genetic species with convergent morphologies, an important breakthrough for phycologists since algae are notoriously difficult to identify on morphology alone. Molecular-assisted techniques have also significantly increased the rate of novel species discovery among the algae, especially rhodophytes. From our own biodiversity assessments, we have learned that numerous members of Bermuda’s macroalgal flora have been misnamed, overlooked, or have not been identified as accepted species. Seaweed diversity in the islands overall, as well as the percentage of endemic species, is presumably underestimated. To explore this hypothesis, MAAT methods have been applied to extensive collections of Bermuda seaweeds accumulated since 2010 along with robust phylogenetic analyses incorporating comparative sequence data from around the world. This dissertation examines several results of these efforts. Four genera have been added to the Bermuda flora — Hommersandiophycus, Trichogloeopsis, Yamadaella and Laurenciella, and a number of species uncovered that are new reports for the islands – Centroceras gasparrinii, C. hyalacanthum, C. microacanthum, Liagora mannarensis, Trichogloeopsis pedicellata, Laurencia dendroidea, L. catarinensis and Palisada flagellifera. Eight species new to science have also been described — Helminthocladia kempii, Liagora nesophila, Yamadaella grassyi, Chondrophycus planiparvus, Laurenciella namii, Crassitegula laciniata, Centroceras arcii and C. illaqueans. Over the course of this study, we have accumulated 1875 DNA vouchered specimens collected from 157 sites around the Bermuda platform, as well as 317 specimens from the Florida Keys and 236 from St. Croix in the Caribbean Antilles, all paramount for present and future work. What we have learned already from this small archipelago suggests a overwhelming underrepresentation of diversity in historical records of the islands macroalgal flora, and highlights the importance of generating an accurate baseline dataset for future monitoring efforts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".