Phylogeny and Evolution of the Brown Algae
Bibliographic record
Abstract
The brown algae (Phaeophyceae) are a group of multicellular heterokonts that are ubiquitous in today’s oceans. Large brown algae from multiple orders are the foundation to temperate coastal ecosystems globally, a role that extends into arctic and tropical regions, providing services indirectly through increased coastal productivity and habitat provisioning, and directly as a source of food and commercially important extracts. Recent multi-locus and genome-scale analyses have revolutionized our understanding of the brown algal phylogeny, providing a robust framework to test evolutionary hypotheses and interpret genomic variation across diverse brown algal lineages. Here, we review recent developments in our understanding of brown algal evolution based on modern advances in phylogenetics and functional genomics. We begin by summarizing modern phylogenetic hypotheses, illuminating the timescales over which the various brown algal orders diversified. We then discuss key insights on our understanding of brown algal life cycle variation and sexual reproduction systems derived from modern genomic techniques. We also review brown algal speciation mechanisms and the associated biogeographic patterns that have emerged globally. We conclude our review by discussing promising avenues for future research opened by genomic datasets, directions that are expected to reveal critical insights into brown algal evolution in past, present, and future oceans.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".