Influences of riparian logging and in-stream large wood removal on pool habitat and salmonid density and biomass: a meta-analysis
Bibliographic record
Abstract
We conducted a meta-analysis using data from 37 studies to assess whether the effects of streamside clear-cut logging on large wood (LW), pool size and number, and summertime salmonid density and standing crop biomass were influenced by stream size and gradient, time since logging was last conducted (1–100 years), and removal of in-stream LW. Age-specific (age 0 (fry) and age 1+ (juveniles)) and species-specific (coho salmon ( Oncorhynchus kisutch ), cutthroat trout ( Oncorhynchus clarki ), and steelhead and rainbow trout ( Oncorhynchus mykiss )) comparisons were also made. The majority of studies reported negative postlogging responses for LW and pool habitat but positive responses for salmonid density and biomass, with the greatest reductions in all variables generally associated with a thorough removal of in-stream LW. The magnitude of postlogging responses was largely independent of stream size, gradient, and time since logging last occurred. In terms of density and biomass, juveniles were more negatively affected by logging than fry. Of the surveyed species, steelhead trout appeared to be most resilient to riparian logging. Within the time frame covered by the analyses, streams whose riparian zones have been logged may be able to sustain salmonid populations (and even exceed preharvest levels) as long as rigorous removal of LW is not undertaken.
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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.039 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".