Among- and within-tributary responses of riverine fish assemblages to habitat featuresContribution of the Group for Interuniversity Research in Limnology (GRIL). We dedicate this paper to the memory of our colleague Réjean Fortin.
Bibliographic record
Abstract
We quantified fish abundance and environmental variables at 170 sites distributed among 11 tributaries of the Ottawa River, Quebec, Canada, to assess the relative importance of among- and within-tributary variation in riverine fish assemblages. Additionally, we determined (i) which environmental variables were most strongly associated with each type of variation and (ii) whether ecomorphological traits in fish assemblages were predictably related to environmental gradients. Partitioning of variation by means of partial ordination indicated that assemblages were less variable among (38.7% of the total variation) than within (61.3%) tributaries. Water transparency singly accounted for 33.3% of the variation among tributaries, whereas macrophyte cover and river width jointly accounted for 8.3% of the variation within tributaries. These results suggests that differences in habitat features among tributaries may account for a substantial fraction of the predictable variation in assemblage structure at the watershed scale, an aspect not emphasized in previous studies of riverine fish assemblages. Mixed regression analyses relating ecomorphological traits to environmental variables showed that the environmental variables most strongly associated with assemblage structure were significantly related to traits associated with predator avoidance or foraging efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".