The integration of the small‐island effect and nestedness pattern
Bibliographic record
Abstract
Abstract Aim The small‐island effect (SIE) and nestedness are two important patterns in the fields of island biogeography and community ecology. However, to date, no study has tried to integrate the SIE and nestedness pattern. Therefore, the aim of this study was to integrate these two biogeographical patterns by proposing a new integrative hypothesis. The integrative hypothesis posits that the degree of nestedness of the large island matrix will be larger than that of the small island matrix split by the threshold of the SIE. Location Global. Taxon Plants, invertebrates and vertebrates. Methods We compiled 219 global datasets with both the presence‐absence matrices and the variables of area and species richness. We also collected six island characteristics influencing the SIE and nestedness patterns, that is island type, taxonomic group, area range, the number of islands, species range and matrix fill. We applied breakpoint regressions to detect SIEs and used the metric NODF (Nestedness metric based on Overlap and Decreasing Fill) to quantify nestedness. We then employed logistic regressions and an information‐theoretic approach to determine which combination of island characteristics was important in determining whether the integrative hypothesis was supported. Results Among the 92 datasets in which SIEs were unambiguously detected, nestedness analyses showed that in 64 cases (69.6%) the values of NODFc (nestedness among sites) for the large island matrices were larger than those of the small island matrices. Matrix fill and area range were substantially important in determining whether the integrative hypothesis was supported. By contrast, island type, taxonomic group, the number of islands and species range received considerably less support. Main Conclusions Our study was the first to integrate the SIE and nestedness pattern. Overall, we found prevalent support for our integrative hypothesis. The integration of the SIE and nestedness provides new and interesting insights into these two biogeographical patterns.
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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 | 0.003 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".