Response of nutrients, biofilm, and benthic insects to salmon carcass addition
Bibliographic record
Abstract
Salmon carcass addition to streams is expected to increase stream productivity at multiple trophic levels. This study examined stream nutrient (nitrogen, phosphorus, and carbon), epilithic biofilm (ash-free dry mass and chlorophyll a), leaf-litter decomposition, and macroinvertebrate (density and biomass) responses to carcass addition in three headwater streams of southwestern Washington State, USA. We used stable isotopes (δ13C and δ15N) to trace incorporation of salmon-derived (SD) nutrients into stream food webs. SD nutrients were assimilated by biofilm, benthic insects (Perlidae and Limnephilidae spp.), and age-1 steelhead (Oncorhynchus mykiss gairdneri). SD nutrients peaked ~2 weeks after carcass addition for insects and fish feeding on carcasses, but indirect uptake of SD nutrients by biofilm and insects was delayed by ~2 months. A strong stable isotope signal did not always correspond with measurable biological change. At reaches 1050 m downstream from carcasses, ammonium concentration, leaf-litter decomposition, and benthic insect density all increased relative to upstream control sites. The strongest responses and greatest SD-nutrient uptake were observed 10 m from decomposing carcasses, with effects generally decreasing to undetectable levels 250 m downstream. Carcass addition to headwater streams can have a transient effect on primary and secondary trophic levels, but responses may be limited to specific taxa near carcass locations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".