Dispersal, habitat differences, and comparative phylogeography of Southeast Asian seahorses (Syngnathidae:<i>Hippocampus</i>)
Bibliographic record
Abstract
Four distinct phylogeographical patterns across Southeast Asia were observed for four species of seahorse (genus Hippocampus) with differing ecologies. For all species, genetic differentiation (based on cytochrome b sequence comparisons) was significantly associated with sample site (Phi(ST) = 0.190-0.810, P < 0.0001) and with geographical distance (Mantel's r = 0.37-0.59, P < 0.019). Geographic locations of genetic breaks were inconsistent across species in 7/10 comparisons, although some similarities across species were also observed. The two shallow-water species (Hippocampus barbouri and Hippocampus kuda) have colonized the Sunda Shelf to a lesser degree than the two deeper-water species (Hippocampus spinosissimus and Hippocampus trimaculatus). In all species the presence of geographically restricted haplotypes in the Philippines could indicate past population fragmentation and/or long-distance colonization. A nested clade analysis (NCA) revealed that long-distance colonization and/or fragmentation were likely the dominant forces that structure populations of the two shallow-water species, whereas range expansion and restricted dispersal with isolation by distance were proportionally more important in the history of the two deeper-water species. H. trimaculatus has the most widespread haplotypes [average clade distance (D(c)) of nonsingleton haplotypes = 1169 km], indicating potentially high dispersal capabilities, whereas H. barbouri has the least widespread haplotypes (average D(c) = 67 km) indicating potentially lower dispersal capabilities. Pleistocene separation of marine basins and postglacial flooding of the Sunda Shelf are extrinsic factors likely to have contributed to the phylogeographical structure observed, whereas differences among the species appear to reflect their individual ecologies.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".