Geographic, anthropogenic, and habitat influences on Great Lakes coastal wetland fish assemblages
Bibliographic record
Abstract
We analyzed data from coastal wetlands across the Laurentian Great Lakes to identify fish assemblage patterns and relationships to habitat, watershed condition, and regional setting. Nonmetric multidimensional scaling (NMDS) ordination of electrofishing catch-per-effort data revealed an overriding geographic and anthropogenic stressor gradient that appeared to structure fish composition via impacts on water clarity and vegetation structure. Wetlands in Lakes Erie and Michigan with agricultural watersheds, turbid water, little submerged vegetation, and a preponderance of generalist, tolerant fishes occupied one end of this gradient, while wetlands in Lake Superior with largely natural watersheds, clear water, abundant submerged vegetation, and diverse fishes occupied the other. Fish composition was also related to wetland morphology, hydrology, exposure, and substrate, but this was only evident within low-disturbance wetlands. Anthropogenic stress appears to homogenize fish composition among wetlands and mask other fish–habitat associations. Because land use is strongly spatially patterned across the Great Lakes and aquatic vegetation is a key habitat element that responds to both biogeography and disturbance, it is difficult to disentangle natural from anthropogenic drivers of coastal wetland fish composition.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".