The small‐island effect: fact or artefact?
Bibliographic record
Abstract
Positive relationships between species richness and sampling area are perhaps the most pervasive patterns in nature. However, the shape of species–area relationships is often highly variable, for reasons that are poorly understood. One such source of variability is the “small‐island effect”, which refers to a decrease in the capacity of sampling area to predict species richness on small islands. Small‐island effects have been attributed to a variety of processes, including spatial subsidies, habitat characteristics and ocean‐born disturbances. Here, we show that small‐island effects can be generated by logarithmic data transformations, which are commonly applied to both axes of species–area relationships. To overcome this problem, we derive several null models to test for non‐random variability in the capacity of island area to predict species richness and apply them to data sets on island plant communities in Canada and New Zealand. Both archipelagos showed evidence for small‐island effects using traditional breakpoint regression techniques on log‐log axes. However, null model analyses revealed different results. The capacity of sampling area to predict species richness in the Canadian archipelago was actually lowest at intermediate island size classes. In the New Zealand archipelago, island area was similarly capable of predicting species richness across the full range of island sizes, indicating the small‐island effect detected by breakpoint regression is an artifact of logarithm data transformation. Overall results show that commonly used regression techniques can generate spurious small‐island effects and that alternative analytic procedures are needed to detect non‐random patterns in 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 | 0.089 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".