Coastal Wetland Management and Restoration: Importance of Abiotic Factors and Vegetation for Healthy Fish Communities in the Laurentian Great Lakes
Bibliographic record
Abstract
Coastal wetlands in the Laurentian Great Lakes of North America are under increasing stress due to numerous threats. Restoration and management of the remaining wetlands are necessary to ensure that ecosystem functions, critical for fisheries, persist. This study used long-term monitoring datasets for one of the Laurentian Great Lakes, Lake Ontario, including 138 sampling events from 31 different wetlands, to examine the relationship between fish community health and select abiotic and vegetation habitat variables. Eight of 13 habitat variables were found to have significant relationships with fish community health, including total, submerged, and emergent vegetation; submerged aquatic vegetation IBI; water depth; turbidity; conductivity; and water-quality index. Ranges for each significant variable were summarized for each fish community health group to provide guidance when diagnosing impairment or setting restoration goals. An ordination of the fish and environmental data revealed high amounts of variation at sites with poor fish community health relative to excellent health, suggesting a multimetric approach provides valuable insight into community variability. The results from this study provide additional information and alternative methods for assessment of current conditions, target setting, and restoration success assessment for coastal wetland managers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".