Phylogenetic diversity–ecosystem function relationships are insensitive to phylogenetic edge lengths
Bibliographic record
Abstract
Summary For experiments that link manipulated biodiversity to ecosystem function ( EF ), phylogenetic diversity ( PD ) has been an especially powerful form of biodiversity that explains variation in EF . PD represents the total amount of evolutionary history or genetic changes represented by a suite of species and potentially the total accumulation of species niche and functional differences. Analyses often use PD in linear models and assume that ecological differences are proportional to phylogenetic distances. Yet, it is unclear whether alternative models of evolutionary change would improve both the statistical fit and conceptual understanding of how PD influences EF . Here, I use a PD – EF relationship and systematically alter models of evolution including changes in the rate of evolution from phylogenetic root–to‐tip and constancy of evolutionary rate across clades. I also compare the PD – EF relationship to several randomization procedures that sequentially remove aspects of the observed phylogeny. I show that changing edge lengths with evolutionary models does not strongly affect PD – EF relationships. Moreover, the observed relationship was not substantially different than the explanation provided by a phylogenetic tree with edge lengths randomized, but was significantly better than randomizations that affected the topology of the phylogenetic trees. These results reveal that changes to the edge lengths have little effect on PD – EF relationships, and it is the topology that really matters. Further, placing species in their correct phylogenetic positions is much more important than developing better estimates of edge lengths.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 teacher head, 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".