Jointly modeling niche width and phylogenetic distance to explain species co‐occurrence
Bibliographic record
Abstract
Abstract Competitive exclusion is most likely when there are large differences in competitive ability and the strength of competitive interactions between species is high, but predicting competitive outcomes is not straightforward. Assuming a trade‐off between competitive ability and ecological generalism, we would predict larger competitive differences between species with different niche widths. Community phylogenetic theory predicts that competition will be stronger among more closely related species, assuming that phylogenetic distance reflects ecological similarity. We would therefore expect the probability of competitive exclusion to be highest among closely related species with different niche widths. Here, we assess how well differences in niche width and phylogenetic distance correlate with co‐occurrences among 34 species of Cyperaceae (sedges) in the eastern Canadian subarctic. The Cyperaceae is a species‐rich family, with many species sharing similar niches and environmental tolerances, making it a model clade for evaluating the importance of niche width differences and phylogenetic distances on co‐occurrence. Consistent with both hypotheses, we found that higher co‐occurrence scores correlated with species pairs that were distantly or only intermediately related and that had similar niche widths. Furthermore, we show that this correlation is stronger when considering only more recently diverged species pairs and that there is a triangular relationship between phylogenetic distance and species co‐occurrence, suggesting that distantly related species might have both strong and weak competitive interactions. Using co‐occurrence as a proxy for competitive outcomes, our results support both a negative correlation between phylogenetic distance and strength of competitive interactions, and a trade‐off between niche width and competitive ability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".