Global Distribution and Conservation of Evolutionary Distinctness in Birds
Bibliographic record
Abstract
BACKGROUND: Integrated, efficient, and global prioritization approaches are necessary to manage the ongoing loss of species and their associated function. "Evolutionary distinctness" measures a species' contribution to the total evolutionary history of its clade and is expected to capture uniquely divergent genomes and functions. Here we demonstrate how such a metric identifies species and regions of particular value for safeguarding evolutionary diversity. RESULTS: Among the world's 9,993 recognized bird species, evolutionary distinctness is very heterogeneously distributed on the phylogenetic tree and varies little with range size or threat level. Species representing the most evolutionary history over the smallest area (those with greatest "evolutionary distinctness rarity") as well as some of the most imperiled distinct species are often concentrated outside the species-rich regions and countries, suggesting they may not be well captured by current conservation planning. We perform global cross-species and spatial analyses and generate minimum conservation sets to assess the benefits of the presented species-level metrics. We find that prioritizing imperiled species by their evolutionary distinctness and geographic rarity is a surprisingly effective and spatially economical way to maintain the total evolutionary information encompassing the world's birds. We identify potential conservation gaps in relation to the existing reserve network that in particular highlight islands as effective priority areas. CONCLUSIONS: The presented distinctness metrics are effective yet easily communicable and versatile tools to assist objective global conservation decision making. Given that most species will remain ecologically understudied, combining growing phylogenetic and spatial data may be an efficient way to retain vital aspects of biodiversity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".