The Effect of Phylogenetic Uncertainty and Imputation on EDGE Scores
Bibliographic record
Abstract
Abstract Faced with the challenge of saving as much diversity as possible given financial and time constraints, conservation biologists are increasingly prioritizing species on the basis of their overall contribution to evolutionary diversity. Metrics such as EDGE (Evolutionary Distinct and Globally Endangered) have been used to set such evolutionarily-based conservation priorities for a number of taxa, such as mammals, birds, corals, amphibians, and sharks. Each application of EDGE has required some form of correction to account for species whose position within the tree of life are unknown. Perhaps the most advanced of these corrections is phylogenetic imputation, but to date there has been no systematic assessment of both the sensitivity of EDGE scores to a phylogeny missing species, and the impact of using imputation to correct for species missing from the tree. Here we perform such an assessment, by simulating phylogenies, removing some species to make the phylogeny incomplete, imputating the position of those species, and measuring (1) how robust ED scores are for the species that are not removed and (2) how accurate the ED scores are for those removed and then imputed. We find that the EDGE ranking for species on a tree is remarkably robust to missing species from that tree, but that phylogenetic imputation for missing species, while unbiased, does not accurately reconstruct species’ evolutionary distinctiveness. On the basis of these results, we provide clear guidance for EDGE scoring in the face of phylogenetic uncertainty.
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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 | 0.027 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".