Fish Assemblage Structure in Great Lakes Coastal Wetlands Over Ten Years
Bibliographic record
Abstract
Coastal wetlands in the Laurentian Great Lakes are important habitats for many fish species. The geographic scale of the watershed and the diversity of land uses in the region result in substantial environmental variation among coastal wetlands. During 2011-2020, annual surveys were conducted as part of the Great Lakes Coastal Wetland Monitoring Program (GLCWMP) to better understand the status and trends of coastal wetlands. Fish sampling consisted of fyke netting in monodominant vegetation zones. During this time, 1225 unique monodominant plant zones in coastal wetlands were sampled, resulting in 584,125 fishes captured that consisted of 113 different species. Yellow Perch (Perca flavescens) was the most abundant species collected (30.9% of the catch), and Bluegill/Pumpkinseed (Lepomis macrochirus/Lepomis gibbosus) were collected the most frequently (present in 68.4% of sampling events). The purpose of the study was to evaluate how fish assemblages in Great Lakes coastal wetlands are associated with Great Lake basin, monodominant vegetation type, hydrogeomorphic type, and sampling year. Fish characterized based on size in the field as young-of-year (YOY) comprised 69.7% of the total catch and 95.7% of the Yellow Perch collected, suggesting coastal wetlands may be important nursery habitats. I found that basin, hydrogeomorphic type, monodominant vegetation type, and sampling year influenced variation in fish assemblages with basin and monodominant vegetation type showing the strongest association with fish assemblages among the variables investigated. While significant patterns were identified, the variables evaluated explained small amounts of variation associated with fish assemblages in Great Lakes coastal wetlands. Fish assemblages in the oligotrophic Lakes Michigan, Huron, and Superior appeared more similar to each other than the more mesotrophic Lakes Erie and Ontario. Yellow Perch was the strongest indicator of coastal wetland by basin, being most abundant in the catch of Lake Michigan and least abundant in the catch of Lake Erie. Fish assemblages were significantly associated with monodominant vegetation type, but the associations varied depending on basin. While fish assemblage structure in Great Lakes coastal wetlands have substantial amounts of variation, patterns were identified that further define how fish assemblages vary across the Great Lakes basin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.054 | 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 teacher head, 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".