Fire coral clones demonstrate phenotypic plasticity among reef habitats
Bibliographic record
Abstract
Clonal populations are often characterized by reduced levels of genotypic diversity, which can translate into lower numbers of functional phenotypes, both of which impede adaptation. Study of partially clonal animals enables examination of the environmental settings under which clonal reproduction is favoured. Here, we gathered genotypic and phenotypic information from 3,651 georeferenced colonies of the fire coral Millepora platyphylla in five habitats with different hydrodynamic regimes in Moorea, French Polynesia. In the upper slope where waves break, most colonies grew as vertical sheets ("sheet tree") making them more vulnerable to fragmentation. Nearly all fire corals in the other habitats are encrusting or massive. The M. platyphylla population is highly clonal (80% of the colonies are clones), while characterized by the highest genotype diversity ever documented for terrestrial or marine populations (1,064 genotypes). The proportion of clones varies greatly among habitats (≥58%-97%) and clones (328 clonal lineages) are distributed perpendicularly from the reef crest, perfectly aligned with wave energy. There are six clonal lineages with clones dispersed in at least two adjacent habitats that strongly demonstrate phenotypic plasticity. Eighty per cent of the colonies in these lineages are "sheet tree" on the upper slope, while 80%-100% are encrusting or massive on the mid slope and back reef. This is a unique example of phenotypic plasticity among reef-building coral clones as corals typically have wave-tolerant growth forms in high-energy reef areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".