The utility of chemical signals as phylogenetic characters: an example from the Felidae
Bibliographic record
Abstract
Chemical secretions that are explicitly tied to species recognition may potentially be informative for phylogenetic reconstruction, especially when traditional morphological or molecular characters lack resolution. Anal sac secretions from 16 species within the family Felidae (order Carnivora) were chemically analysed and their utility as phylogenetic characters was assessed. Results were generally consistent across the different chemical data types (e.g. glycolipids, neutral lipids, or phospholipids). Two major clades were indicated, falling out according to body size: one for species greater than 30 kg (Panthera, Uncia, and Puma) and another for those less than 12 kg (remaining species). The primary solutions agreed with respect to the species pairs Prionailurus + Leptailurus, Caracal+Lynx, Oncifelis+Leopardus, Otocolobus+Felis, Panthera leo + P pardus, and P. tigris + Uncia. The only area of disagreement between chemical types was the positioning of the mountain lion (Puma concolor); however, this species appears to cluster with the cheetah (Acinonyx jubatus) in the Tjig cat clade. Although our solutions differ from six previously proposed hypotheses of felid phylogeny (morphological and molecular), the previous estimates all differ strongly amongst themselves reflecting the historical uncertainty regarding felid systematics. Phytogenies derived from the lipid data were very robust and decisive. Few equally most parsimonious trees were obtained, consistency indices were much higher than their expected values, and bootstrap and Bremer support values were also high. Thus, our findings illustrate the species-specific nature of chemical signals and their usefulness as phylogenetic characters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".