The structure of food webs along river networks
Bibliographic record
Abstract
Do changes in the species composition of riverine fish assemblages along river networks lead to predictable changes in food‐web structure? We assembled empirical “fish‐centered” river food webs for three rivers located along a latitudinal gradient in the South Saskatchewan River Basin (SSRB) that differ in land‐use impacts and geomorphology but flow through similar mountain, foothill, and prairie physiographic regions. We then calculated 17 food‐web properties to determine whether the nine river food webs differed according to physiographic region or river sub‐basin. There were no statistically significant differences in the 17 food‐web properties calculated among the rivers. In contrast, fish species richness, connectance, the proportion of herbivores, and the proportion of cannibals changed longitudinally along the river network. Our results suggest that regional changes in river geomorphology and physicochemistry play an important role in determining longitudinal variation in food‐web properties such as fish species richness and connectance. In contrast, the overall structure of river food webs may be relatively similar and insensitive to regional influences such as zoogeography. Further explorations of river and other food webs would greatly illuminate this suggestion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".