Late-successional riparian forest structure results in heterogeneous periphyton distributions in low-order streams
Bibliographic record
Abstract
Late-successional riparian forests often regulate autotrophic microhabitats in low-order streams through shading provided by canopies. However, few studies have linked forest structure with periphyton microhabitat in adjoining streams. Our hypotheses were that (1) the heterogeneous horizontal structure in old-growth forests creates more spatially variable below-canopy light environments compared with mature forests and (2) site-specific light availability over streams correlates with spatial distributions of periphyton microhabitat. We surveyed 15 low-order stream reaches in late-successional northern hardwood–hemlock forests in the Adirondack Mountains of New York, USA. We measured forest structure and the below-canopy light environment at all reaches and the periphyton chlorophyll a concentration on artificial substrates in eight reaches. While stand age was not statistically significant, multivariate models of horizontal forest structure (e.g., gap distribution) and topography showed strong relationships (R2 > 0.70) with the below-canopy light environment across all late-successional forests. Furthermore, metrics of below-canopy light availability explained a small but statistically significant proportion of the variation in chlorophyll a concentration. This variation in chlorophyll a indicates that complex late-successional riparian forests, both mature and old-growth, create a mosaic of heterotrophic (shaded) and autotrophic (lighted) microhabitats along low-order streams. These results reveal important and previously unrecognized links between stream habitat heterogeneity and the horizontal heterogeneous late-successional forest structure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".