Spatial and environmental correlates of fish community structure in Canadian Shield lakes
Bibliographic record
Abstract
We used data on fish species biomass from 38 lakes of the Canadian Shield (Québec) to determine the contribution of environmental (lake and watershed morphometry) and spatial (e.g., hydrographic connectivity and geographic coordinates) variables on fish community structure. By using a combination of multivariate analyses, we show that nearly half of the variation in the fish community structure is explained by the independent contributions of spatial and environmental factors. Walleye (Sander vitreus) and lake whitefish (Coregonus clupeaformis) were significantly associated with the absence of beaver (Castor canadensis) dams, whereas northern pike (Esox lucius) was positively correlated with beaver dam presence. Altitude and longitude, but not current patterns in lake connectivity, were the main explanatory spatial variables accounting for the observed pattern in fish community structure. Large piscivorous fish were associated with a reduced richness and biomass of small prey, suggesting that predation is a structuring factor in these lakes. By showing that geographic coordinates and altitude are better descriptors of fish community structure than hydrographic connections, our study suggests that past colonization routes are relatively more important than current ones in structuring fish communities at the landscape level. This interpretation is supported by recently published genetic data.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".