Decoupling phylogenetic and functional diversity to reveal hidden signals in community assembly
Bibliographic record
Abstract
Summary Functional traits and phylogeny offer different, and often complementary, information about ecological differences between species, an essential step to uncover biodiversity assembly mechanisms and their feedbacks to ecosystem functions. However, traits and phylogeny are often related due to underlying trait evolution. Consequently, when combined, their shared information can be overemphasized, hindering their complementarity. It is therefore desirable to decouple their unique and overlapping contributions. We propose a conceptual and mathematical framework that produces a set of meaningful measures of ecological differences between species. We test the properties of these measures and the validity of the approach with extensive simulated data to show (i) the information provided by decoupling traits from phylogeny and vice versa, and (ii) that decoupling trait and phylogenetic information can uncover otherwise hidden signals underlying species coexistence and turnover. The application of the approach is further illustrated using a large dataset of Central European meadows as a case study. Decoupling traits and phylogeny particularly reveals the importance of differentiation between phylogenetically related species, which can be essential to understand species replacements along environmental gradients and the combined action of environmental filtering and limiting similarity within communities. Decoupling traits and phylogeny provides an avenue for connecting macro‐evolutionary and local factors affecting coexistence and for understanding how complex species differences affect multiple ecosystem functions. We present an R function called ‘decouple’, which allows a simple and wide application of the framework.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".