Adfluvial salmonids have engineering but not fertilization impacts in tributaries of a central Utah reservoir
Bibliographic record
Abstract
Migratory fishes can affect tributary ecosystem properties given their potential to introduce nutrients (fertilize) and physically modify habitat (engineer) during spawning. Nonetheless, migrant effects are frequently context-dependent, and it is useful to understand their strength relative to other potential ecosystem drivers. We examined whether tributary ecosystem properties varied in response to migrations of two adfluvial salmonids, taking advantage of differences in migration timing and reproductive strategy between species, as well as hydrogeomorphic differences between a pair of tributaries. For analyses, we used a model comparison approach to evaluate migrant effects relative to other possible drivers. We observed that Bonneville cutthroat trout (Oncorhynchus clarkii utah) engineered benthic chlorophyll a in redds, with reduction (51% ± 16% decrease) generally occurring during migrations. Contrary to expectations, migrant fertilization effects were not pronounced even in the more retentive tributary during migration by species (kokanee, Oncorhynchus nerka) that exhibited high postspawning mortality. Based on multimodel comparisons, isolated migrant effects were not the primary influence on measured ecosystem properties. Our findings underscore the need to consider different biotic and abiotic conditions that can mediate migratory fish effects.
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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 | 0.000 | 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) | 0.001 | 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".