Simulating riparian disturbance: Reach scale impacts on aquatic habitat in gravel bed streams
Bibliographic record
Abstract
Large wood governs channel morphology, as well as the availability of in-stream habitat, in many forested streams. In this paper, we use a stochastic, physically based model to simulate wood recruitment and in-stream geomorphic processes, in order to explore the influence of disturbance history on the availability of aquatic habitat. Specifically, we consider the effects of fire on a range of stream sizes by varying the rate of tree toppling over time in a simulated forest characterized by a tree height of 30 m. We also consider the effects of forest harvesting with various riparian buffer sizes, by limiting the lateral extent of the riparian stand. Our results show that pulsed inputs of wood increase the availability and variability of physical habitat in the postfire period; reach-averaged pool area and deposit area double in small streams, while side channels increase by over 50% in intermediate-sized channels. By contrast, forest harvesting reduces the availability of habitat within the reach, though the effects diminish with increasing buffer size or stream width; in laterally stable streams the effects are minimal so long as buffer width is large enough for key pieces to be recruited to the reach. This research emphasizes the importance of natural disturbance in creating and maintaining habitat heterogeneity and shows that scenario-based numerical modeling provides a useful tool for assessing the historical range of variability associated with natural disturbance, as well as changes in habitat relevant to fish. It can be also used to inform forest harvesting and management.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".