Salmon carcasses influence genetic linkages between forests and streams
Bibliographic record
Abstract
Biodiversity at many scales (functional group, species, genetic) can result in emergent ecological patterns. Here we explore the influence of tree genotypic variation and diversity on in-stream ecosystem processes and aquatic communities. We test whether genetically diverse inputs of leaf litter interact with a keystone organism, anadromous salmon, to influence in-stream ecosystem function. We used reach-level manipulation of salmon carcasses and leaf litter bags to examine how nutrient inputs interact with genetic variation in leaf litter decomposition. Genotypic variation in black cottonwood (Populus balsamifera ssp. trichocarpa) significantly influenced leaf litter chemistry, litter mass loss, and fungal biomass, but these variables were only weakly influenced by salmon carcass presence or a genotype × salmon (G × E) interaction. Mixtures of genotypes tended to demonstrate antagonistic effects (slower than expected decomposition) in the absence of salmon, but synergistic effects (faster than expected decomposition) when salmon were present. Our findings suggest that the influence of plant genotypic variation in linking aquatic and terrestrial ecosystems may be altered and in some cases intensified in the presence of a keystone vertebrate species.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".