Forestry impacts on stream flows and temperatures: A quantitative synthesis of paired catchment studies across the Pacific salmon range
Bibliographic record
Abstract
Abstract Forestry is pervasive across temperate North America and may influence aquatic environmental conditions such as flows and temperatures, as well as important species such as Pacific salmon ( Oncorhynchus spp.). While there have been many large‐scale forestry experiments using paired catchment designs, these studies have yet to be quantitatively synthesized. Thus, it remains unclear whether forestry impacts are consistent, context‐dependent or unpredictable. This study aims to quantitatively synthesize forestry impacts on streamflow and temperature, through a systematic review and synthesis of paired catchment studies across the range of Pacific salmon. Specifically, we investigated whether generalizable relationships exist between forestry intensity (percent watershed harvested) and impacts to streamflow and temperature. We also examined whether watershed features (climate, hydrology and lithology) and harvest method mediated forestry impacts. We extracted information from 35 unique paired‐catchments from California to Alaska. Forestry had strong impacts on peak and low flows and maximum summer water temperatures, but responses were quite variable. Across all catchments, forestry elevated peak flows ~20% ( n = 31 catchments), reduced low flows ~25% ( n = 13 catchments) and increased maximum summer temperatures ~15% ( n = 35 catchments) on average. However, these impacts were variable and were not predictable based on forestry intensity, thus broader stressor–response relationships were not supported. Forestry impacts on peak flows and maximum summer temperatures varied spatially. Peak flow impacts increased with northward latitude and temperature impacts decreased with eastward longitude. However, the magnitude of impacts were unrelated to other watershed attributes, which included climate (precipitation and aridity), rain versus snow hydrology, elevation and bedrock lithology. Harvest method and riparian buffer presence also had no detected effects on forestry impacts across studies and statistical models explained a low proportion of variation overall. Collectively, our results indicate that forestry can have substantial impacts on key environmental conditions; however, the magnitude of impact was variable and could not be clearly linked to easily measured watershed characteristics. This implies that forestry impacts may not be broadly predictable. Probabilistic risk models based on distributions of potential impacts may therefore be more useful for watershed management in data‐poor situations.
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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".