Salmon nutrients are associated with the phylogenetic dispersion of riparian flowering‐plant assemblages
Bibliographic record
Abstract
A signature of nonrandom phylogenetic community structure has been interpreted as indicating community assembly processes. Significant clustering within the phylogenetic structure of a community can be caused by habitat filtering due to low nutrient availability. Nutrient limitation in temperate Pacific coastal rainforests can be alleviated to some extent by marine nutrient subsidies introduced by migrating salmon, which leave a quantitative signature on the makeup of plant communities near spawning streams. Thus, nutrient-mediated habitat filtering could be reduced by salmon nutrients. Here, we ask how salmon abundance affects the phylogenetic structure of riparian flowering plant assemblages across 50 watersheds in the Great Bear Rainforest of British Columbia, Canada. Based on a regional pool of 60 plant species, we found that assemblages become more phylogenetically dispersed and species poor adjacent to streams with higher salmon spawning density. In contrast, increased phylogenetic clumping and species richness was seen in sites with low salmon density, with steeper slopes, further from the stream edge, and within smaller watersheds. These observations are all consistent with abiotic habitat filtering and biotic competitive exclusion acting together across local and landscape-scale gradients in nutrient availability to structure assembly of riparian flowering plants. In this case, rich salmon nutrients appear to release riparian flowering-plant assemblages from the confines of a low-nutrient habitat filter that drives phylogenetic clustering.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".