Why phylogenies do not always predict ecological differences
Bibliographic record
Abstract
Abstract The merger of phylogenies with ecology has given rise to the field of “community phylogenetics,” predicated on the assumption that ecological differences among species can be estimated from phylogenetic relationships (the phylogenetic distance/ecological difference, or PDED , hypothesis). A number of studies have failed to find strong support for this assumption, thus challenging the utility of phylogenetic approaches. This gap might highlight the fact that the PDED relationship is not useful for community assembly, but it is difficult to know because the lack of a relationship might also be due to a number of biological or methodological reasons, including inappropriate phylogenies, skewed distributions of phylogenetic distances, the lack of consideration of models of trait evolution, or the absence of sufficient niche space in experimental and observational venues. Each of these limitations, separately or combined, may confound recent experimental or observational results that examine relationships between phylogenetic distance and ecological differences. Notably, common evolutionary models can support alternative conclusions about the relationship between evolutionary distances and ecological differences than typically assumed and can change interpretations of community‐based phylogenetic analyses. Here we review a number of issues that may lead to confounded effects in community phylogenetic analyses. In light of these potential pitfalls, we provide a number of guidelines for researchers to follow and stress that they need to address methodological shortcomings before concluding that ecological differences are unrelated to phylogenetic distances.
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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 | 0.019 | 0.111 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".