On empty islands and the small‐island effect
Bibliographic record
Abstract
Abstract Aim The small‐island effect (SIE) has become a widely accepted part of the theoretical framework of island biogeography. A major criticism of SIE studies is the exclusion of empty islands from analyses. However, the generality and underlying factors determining the role of empty islands in generating SIEs remain obscure because few published datasets include islands with no species. The aim of this study was thus to evaluate the prevalence and underlying factors determining the role of empty islands in generating SIEs. Location Global. Methods We compiled 278 datasets that included empty islands. For each dataset, we compared the fit of a logarithmic model with two breakpoint models separately for all the islands and for datasets excluding empty islands to determine the role of empty islands in generating SIEs. We then employed multinomial logistic regressions and an information‐theoretic approach to determine which combination of island characteristics was important in determining the role of empty islands in generating SIEs. Results Among 211 datasets with adequate fits, the exclusion of empty islands changed the evidence for an SIE in 68 cases (32.2%). SIEs were quite prevalent, both for all the islands (104 cases, 49.3%) and for datasets excluding empty islands (73 cases, 34.6%). Our results were not consistent with the hypothesis that excluding empty islands would increase the evidence for an SIE. Model selection and relative variable importance indicated that the number of empty islands, the minimum area of empty islands and area ratio were important variables that determined the role of empty islands in generating SIEs. Main conclusions Our study demonstrates that the effect of empty islands in generating SIEs is quite prevalent. The exclusion of empty islands is thus an important methodological shortcoming for the detection of SIEs. We conclude that, for the robust detection of SIEs, empty islands should not be excluded in future studies.
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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 | 0.043 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".