A UNIFIED MODEL OF AVIAN SPECIES RICHNESS ON ISLANDS AND CONTINENTS
Bibliographic record
Abstract
How many species in a given taxon should be found in a delimited area in a specified place in the world? Some recent literature suggests that the answer to this question depends strongly on the geographical, evolutionary, and ecological context. For example, current theory suggests that species accumulate as a function of area differently on continents and islands. Species richness-climate relationships have been examined separately on continents and on islands. This study tests the hypotheses that (1) the functional relationship between richness and climate is the same on continents and islands; (2) the species-area slope depends on distance-based isolation; (3) species-area relationships differ among land bridge islands, oceanic islands, and continents; (4) richness differs among biogeographic regions independently of climate and isolation. We related bird species numbers in a worldwide sample of 240 continental parcels and 346 islands to several environmental variables. We found that breeding bird richness varies similarly on islands and on continents as a function of mean annual temperature, an area x precipitation interaction, and the distance separating insular samples from the nearest continent (R2 = 0.86). Most studies to date have postulated that the slope of the species-area relationship depends upon isolation. In contrast, we found no such interaction. A richness-environment relationship derived using Old World sites accurately predicts patterns of richness in the New World and vice versa (R2 = 0.85). Our results suggest that most of the global variation in richness is not strongly context-specific; rather, it reflects a small number of general environmental constraints operating on both continents and 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".