Spatiotemporal patterns of fish community composition in Great Lakes drowned river mouths
Bibliographic record
Abstract
Abstract Freshwater river mouths in large lakes are centres of biological activity, yet little is understood about the spatial and temporal dynamics of fish communities in these systems. In the Laurentian Great Lakes, we sampled littoral fishes over 3 years in six drowned (i.e., protected) river mouths to: (i) quantify spatial (among river mouths) and temporal (among years) variation, (ii) evaluate associations with environmental conditions and (iii) assess spatial patterns of community similarity. We sampled 6787 fish representing 43 species over the course of the study. Multivariate analyses indicated that variation in fish species composition was more strongly partitioned among river mouths than among years. Fish communities across the six river mouths were partitioned into three groups, a pattern we attribute to variability in anthropogenic disturbance and environmental conditions. Canonical correspondence analysis showed that fish species composition was associated with specific conductivity, vegetation cover, turbidity and pH, suggesting species–environment relationships are similar to those shown for Great Lakes coastal wetlands. Finally, we found a negative relationship between geographic distance and community similarity, suggesting that dispersal and/or environmental gradients play a role in shaping these river mouth fish assemblages. We conclude that Great Lakes drowned river mouths can harbour diverse and spatially variable fish assemblages that are driven by a combination of local and regional factors.
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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 | 0.000 | 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".