Wildfire and the effects of shifting stream temperature on salmonids
Bibliographic record
Abstract
The frequency and magnitude of wildfires in North America have increased by four‐fold over the last two decades. However, the impacts of wildfires on the thermal environments of freshwaters, and potential effects on coldwater fishes are incompletely understood. We examined the short‐term effects of a wildfire on temperatures and Steelhead/Rainbow Trout ( Oncorhynchus mykiss ) bioenergetics and distribution in a California coastal stream. One year after the wildfire, mean daily stream temperatures were elevated by up to 0.6°C in burned compared to unburned pools. Among burned pools, light flux explained over 85% of the variation in altered stream temperatures, and 76% of the variation in light flux was explained by an index of burn severity based on proximity of the pool to burned streamside. We estimated that salmonids of variable sizes inhabiting burned pools had to consume between 0.3–264.3 mg of additional prey over 48 days to offset the 0.01–6.04 kJ increase in metabolic demand during the first post‐fire summer. However, stomach content analysis showed that fish in the burned region were consuming relatively little prey and significantly less than fish in the reference region. Presumably due to starvation, mortality, or emigration, we found a significant negative relationship between the change in total salmonid biomass over the post‐fire summer and the average energy costs (kJ·g −1 ·day −1 ) within a burned pool. This study demonstrates that wildfire can generate thermal heterogeneity in aquatic ecosystems and drive short‐term increases in stream temperature, exacerbating bioenergetically stressful seasons for coldwater fishes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".