On the relationship between phylogenetic diversity and trait diversity
Bibliographic record
Abstract
Niche differences are key to understanding the distribution and structure of biodiversity. To examine niche differences, we must first characterize how species occupy niche space, and two approaches are commonly used in the ecological literature. The first uses species traits to estimate multivariate trait space (so-called functional trait diversity, FD); the second quantifies the amount of time or evolutionary history captured by a group of species (phylogenetic diversity, PD). It is often-but controversially-assumed that these putative measures of niche space are at a minimum correlated and perhaps redundant, since more evolutionary time allows for greater accumulation of trait changes. This theoretical expectation remains surprisingly poorly evaluated, particularly in the context of multivariate measures of trait diversity. We evaluated the relationship between phylogenetic diversity and trait diversity using analytical and simulation-based methods across common models of trait evolution. We show that PD correlates with FD increasingly strongly as more traits are included in the FD measure. Our results indicate that phylogenetic diversity can be a useful surrogate for high-dimensional trait diversity, but we also show that the correlation weakens when the underlying process of trait evolution includes variation in rate and optima.
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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 | 0.008 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".