Do traits and phylogeny support congruent community diversity patterns and assembly inferences?
Bibliographic record
Abstract
Abstract It is now commonplace in community ecology to assess patterns of phylogenetic or functional diversity in order to inform our understanding of the assembly mechanisms that structure communities. While both phylogenetic and functional approaches have been used in conceptually similar ways, it is not clear if they both in fact reveal similar community diversity patterns or support similar inferences. We review studies that use both measures to determine the degree to which they support congruent patterns and inferences about communities. We performed a literature review with 188 analyses from 79 published papers that compared some facet of phylogenetic (PD) and functional diversity (FD) in community ecology. These studies generally report four main cases in which phylogenetic and functional information are used together in community analyses, to determine if: (a) there were phylogenetic signals in the measured traits in communities; (b) PD and FD were correlated with one another; (c) standardized PD and FD measures similarly revealed patterns of community over‐ or under‐dispersion; and (d) PD and FD were both related to other explanatory variables (e.g. elevation) similarly. We found that the vast majority of studies found both strong phylogenetic signals in their traits and positive correlations of PD and FD measures across sites. However, and surprisingly, we found substantial incongruencies for the other tests. Phylogenetic and functional dispersion patterns were congruent only about half the time. Specifically, when communities were phylogenetically over‐dispersed, these same communities were more likely to be functionally under‐dispersed. Similarly, we found that phylogenetic and functional relationships with independent predictors were incongruent in about half of the analyses. Synthesis . Phylogenetic signal tests and PD–FD correlations appear to strongly support the congruence between traits and phylogeny. It is surprising that strong phylogenetic signals appeared so ubiquitous given that ecological studies often analyse phylogenetically incomplete sets of species that have undergone ecological sorting. Despite the largely congruent findings based on phylogenetic signal tests and PD‐FD correlations, we found substantial incongruencies when researchers assessed either dispersion patterns or relationships with independent predictors. We discuss a number of potential ecological, evolutionary and methodological reasons for these incongruencies. Phylogenetic and functional information might reflect species ecological differences unequally with phylogenies better reflecting multivariate conserved elements of ecological similarity, and single traits better able to capture recent divergence, and both elements influence ecological patterns.
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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 | 0.001 | 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 | 0.000 | 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 | 0.000 |
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 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".