Habitat filtering not dispersal limitation shapes oceanic island floras: species assembly of the Galápagos archipelago
Bibliographic record
Abstract
Remote locations, such as oceanic islands, typically harbour relatively few species, some of which go on to generate endemic radiations. Species colonising these locations tend to be a non-random subset from source communities, which is thought to reflect dispersal limitation. However, non-random colonisation could also result from habitat filtering, whereby only a few continental species can become established. We evaluate the imprints of these processes on the Galápagos flora by analysing a comprehensive regional phylogeny for ~ 39 000 species alongside information on dispersal strategies and climatic suitability. We found that habitat filtering was more important than dispersal limitation in determining species composition. This finding may help explain why adaptive radiation is common on oceanic archipelagoes - because colonising species can be relatively poor dispersers with specific niche requirements. We suggest that the standard assumption that plant communities in remote locations are primarily shaped by dispersal limitation deserves reconsideration.
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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 | 0.001 | 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".