Ecosystem size and flooding drive trophic dynamics of riparian spiders in a fire-prone Sierra Nevada river system
Bibliographic record
Abstract
Disturbance can play an important role in structuring stream food webs. Although floods have received the greatest attention as a disturbance agent in rivers, wildfire — which can strongly influence fluvial ecosystem structure and function — may also drive consumer trophic dynamics. We measured the relative effects of wildfire, hydrologic disturbance, ecosystem size, and canopy openness (as a proxy for in-stream productivity) on trophic position and reliance on aquatically-derived nutritional subsidies of riparian spiders of the family Tetragnathidae along two rivers on the west slope of the Sierra Nevada in California, USA. Ecosystem size received strong support as an environmental determinant of both trophic measures, with variability in flood magnitude emerging as an important mechanism linking ecosystem size and trophic responses. Piecewise linear regression revealed significant breakpoints in spider trophic position and reliance on aquatically-derived nutritional subsidies that were related to thresholds in fire extent within the catchment. These nonlinear relationships with wildfire may lend additional insight into the potential interactions among ecosystem size, productivity, and disturbance that determine stream–riparian food-web architecture.
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 | 0.000 | 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 | 0.000 | 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".