Patterns and drivers of species diversity in the Indo‐Pacific red seaweed <i>Portieria</i>
Bibliographic record
Abstract
Abstract Aim Biogeographical processes underlying Indo‐Pacific biodiversity patterns have been relatively well studied in marine shallow water invertebrates and fishes, but have been explored much less extensively in seaweeds, despite these organisms often displaying markedly different patterns. Using the marine red alga Portieria as a model, we aim to gain understanding of the evolutionary processes generating seaweed biogeographical patterns. Our results will be evaluated and compared with known patterns and processes in animals. Location Indo‐Pacific marine region. Methods Species diversity estimates were inferred using DNA ‐based species delimitation methods. Historical biogeographical patterns were inferred based on a six‐gene time‐calibrated phylogeny, distribution data of 802 specimens, and probabilistic modelling of geographical range evolution. The importance of geographical isolation for speciation was further evaluated by population genetic analyses at the intraspecific level. Results We delimited 92 candidate species, most with restricted distributions, suggesting low dispersal capacity. Highest species diversity was found in the Indo‐Malay Archipelago ( IMA ). Our phylogeny indicates that Portieria originated during the late Cretaceous in the area that is now the Central Indo‐Pacific. The biogeographical history of Portieria includes repeated dispersal events to peripheral regions, followed by long‐term persistence and diversification of lineages within those regions, and limited dispersal back to the IMA . Main conclusions Our results suggest that the long geological history of the IMA played an important role in shaping Portieria diversity. High species richness in the IMA resulted from a combination of speciation at small spatial scales, possibly as a result of increased regional habitat diversity from the Eocene onwards, and species accumulation via dispersal and/or island integration through tectonic movement. Our results are consistent with the biodiversity feedback model, in which biodiversity hotspots act as both “centres of origin” and “centres of accumulation,” and corroborate previous findings for invertebrates and fish that there is no single unifying model explaining the biological diversity within the IMA .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".