Conservation priority of global Galliformes species based on phylogenetic diversity
Bibliographic record
Abstract
In this study, based on phylogenetic diversity (PD), I develop a conservation strategy for Galliformes species around the world. A cladogram of 197 Galliformes species derived from a previous study was used for calculating PD metrics. Branch length is an important aspect of the phylogenetic information a tree can convey, but 2 traditionally-used metrics, the number of phylogenetic groups to which a taxon belongs (I) and the proportion that each taxon contributes to the total diversity of the group (W), are fully node-based and do not take branch length into account. Therefore, to measure PD more appropriately, I combined a branch-related metric, pendant edge (P), in addition to I and W. A final combined rank for Galliformes species was obtained by summing the ranks of the 3 metrics. My results showed that the 5% top priority species for conserving evolutionary potential were Galloperdix lunulata, Haematortyx sanguiniceps, Margaroperdix madagarensis, Syrmaticus soemmerringii, Coturnix pectoralis, Polyplectron napoleonis, Alectoris melanocephala, Xenoperdix udzungwensis, Afropavo congensis and Syrmaticus reevesii. The current species priority ranking based on pylogenetic diversity and the official International Union for Conservation of Nature (IUCN) ranking of Galliformes species was significantly correlated when considering the 5 categories of IUCN (critical endangered, endangered, vulnerable, near threatened and least concern). This indicated the feasibility of introducing the PD index into the network of IUCN regional Red List assessment. The 5% top priority countries selected using the complementarity principle possessing diversified Galliformes genetic resources were China, Indonesia, Mexico, India, Colombia, Australia, Brazil, Angola, Congo and Japan (in descending order). China, Indonesia, Mexico, Brazil, India and Colombia are consistently selected among the 4 top priority sets of richness, rarity, endemicity and PD. This result indicated that the priority result from PD is highly congruent with conventional measures. Along with other conventional ecological attributes, the alternative conservation scenario based on PD is reasonable and can be adopted in systematic conservation planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".