Phylogenetic signal dynamics during niche filling in food webs
Bibliographic record
Abstract
Abstract Understanding how phylogenetic signal in ecological networks—the tendency for closely related species to resemble one another in ecological roles—emerges and persists remains a central challenge in community ecology. Here, we simulate food web evolution to track how the correspondence between phylogeny and trophic structure changes as communities assemble and niche space fills. By simulating trait evolution coupled with trait-matching for ecological interactions, we quantify how phylogenetic signal in trophic structure changes through time and examine how species’ network positions relate to their phylogenetic distinctiveness and diversification dynamics. We find that the signal declines over time, driven by emergent feedbacks between node extinction, link reorganization, and trait divergence. Species with high phylogenetic distinctiveness tend to be more specialized and occupy peripheral network positions, particularly in late-stage communities. Centrality consistently constrains diversification in intermediate consumers, emerges as a limiting factor for top predators after niche saturation, and shows nonlinear effects in basal species’ diversification. Applying our framework to empirical food webs from the Galápagos Islands, we find partial support for these predictions: phylogenetic signal in foraging and vulnerability roles declines with island age, but shows contrasting trends with island area and elevation. We also detect discrepancies between distance-based and clustering-based measures of phylogenetic signal, highlighting the need for robust methods to compare phylogenetic and network structures. Together, our results reveal how trophic interactions mediate the erosion of phylogenetic structure during community assembly and offer testable predictions for systems at different stages of diversification.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".