Colonization and diversification of Galápagos terrestrial fauna: a phylogenetic and biogeographical synthesis
Bibliographic record
Abstract
Remote oceanic islands have long been recognized as natural models for the study of evolutionary processes involved in diversification. Their remoteness provides opportunities for isolation and divergence of populations, which make islands remarkable settings for the study of diversification. Groups of islands may share a relatively similar geological history and comparable climate, but their inhabitants experience subtly different environments and have distinct evolutionary histories, offering the potential for comparative studies. A range of organisms have colonized the Galápagos Islands, and various lineages have radiated throughout the archipelago to form unique assemblages. This review pays particular attention to molecular phylogenetic studies of Galápagos terrestrial fauna. We find that most of the Galápagos terrestrial fauna have diversified in parallel to the geological formation of the islands. Lineages have occasionally diversified within islands, and the clearest cases occur in taxa with very low vagility and on large islands with diverse habitats. Ecology and habitat specialization appear to be critical in speciation both within and between islands. Although the number of phylogenetic studies is continuously increasing, studies of natural history, ecology, evolution and behaviour are essential to completely reveal how diversification proceeded on these islands.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".