The role of transitional areas as avian biodiversity centres
Bibliographic record
Abstract
ABSTRACT Aim With the ever‐increasing threats to biodiversity, efforts are being directed towards identifying hotspots of special importance for conservation. In particular, there has been an effort to identify irreplaceable regions that are especially rich in rare species. Areas of transition between ecological systems in which multiple species coincide are expected, almost by definition, to be species‐rich. Here, we examine whether this is simply a result of an overlap between two communities in boundary regions, or whether boundary areas also hold concentrations of rare (e.g. range‐limited) species. We ask whether an analysis that includes areas of transition may be a useful contribution to the identification of biodiversity centres. Location and methods To address these questions, we examined the relationship between passeriform richness and range size rarity of approximately 2300 bird species in 4889 1° New World grid cells, and the distance of the cells to boundaries between adjacent plant‐based ecoregions. Results Areas nearer to boundary regions between ecoregions not only had more bird species, but also scored more highly in terms of species rarity. The range centres of the rarest 10% of species were distributed significantly closer to boundaries between ecoregions than were species in general. This pattern persisted for rarity when we divided the New World into three latitudinal belts and analysed each separately, and when we excluded the Andes. It also persisted when compared with randomly generated ecoregion polygons. Main conclusions The findings of this work suggest that transitional environments harbour many rare species, in addition to high richness. Consequently areas of biotic transition should be highly valued as biodiversity centres and need to be included in future global conservation analyses and decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".