Disturbance and diversity in a continental archipelago: a mechanistic framework linking area, height, and exposure
Bibliographic record
Abstract
Abstract Species–area relationships ( SAR s) are among the most general patterns in nature. Yet, significant variation in species richness often remains after accounting for area, especially for small islands. One factor thought to influence species richness on small islands is disturbance from the combined influence of tides and waves. Here, we derive a quantitative framework for determining how ocean disturbance impacts island communities, which we then test in plant communities in a temperate island archipelago. We do so by applying some well‐developed techniques honed in the marine intertidal zone but rarely applied to studies on land. By estimating and adjusting for the effect of wave exposure on habitable island area, we dramatically improved the fit of the SAR for small islands, nearly doubling the amount of variation in species richness explained (from 37% to 69%). Our predictions of island occupancy also improved using this method. Our approach predicts that small islands (<100,000 m 2 ) are the most affected by ocean‐borne disturbance. Given that many archipelagos are susceptible to wave disturbance, future studies should consider how ocean‐borne disturbances arising from the matrix might interact with sampling area to influence patterns of species richness on small 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".