HOW PROXIMITY OF LAND USE AFFECTS STREAM FISH AND HABITAT
Bibliographic record
Abstract
ABSTRACT This study quantified the unique variation in stream fish and habitat and a land use disturbance index (LDI) at a variety of spatial scales: catchment, eight riparian polygons that varied in width and length (e.g. 50 m to all upstream reaches), upstream polygons of 1.6 and 3.2 km and the residual upland area of each site watershed not accounted for by each polygon. The analyses confirmed a hockey stick‐shaped relationship between the fish community and the LDI, with sensitive species only present below an LDI of 11. The largest variation for most metrics was explained by the largest polygons, suggesting that local riparian conditions were not as important predictors of stream condition. LDI in upland areas, where zero‐order streams occur, was also an important predictor of fish biomass and taxa richness. Contrary to expected, additive models with both catchment and riparian corridors provided minimal increases in predictive power, and no improvement in model performance occurred when data sets were stratified into sites below the LDI threshold. Finally, there was considerable covariation in the template and stressor predictor variables that made it difficult to quantify the unique variation in biological and physical responses accounted for by land use. That the 1600‐m proximal polygon provided the best predictor of the fish community and temperature is supportive of there being some proximal effects of land use. Overall, our findings suggest that stream management must consider processes that occur in the entire upstream catchment and the entire riparian corridor, including the headwaters for success. Copyright © 2012 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".