Patterns in habitat and fish assemblages within Great Lakes coastal wetlands and implications for sampling design
Bibliographic record
Abstract
Discerning fish–habitat associations at a variety of spatial scales is relevant to evaluating biotic conditions and stressor responses in Great Lakes coastal wetlands. Ordination analyses identified strong, geographically organized associations among anthropogenic stressors and water clarity, vegetation structure, and fish composition at both whole-wetland and within-wetland spatial scales. Lacustrine-protected wetlands were generally internally homogeneous in fish composition, whereas riverine or barrier-beach lagoon wetlands could be more heterogeneous, especially if they had large tributaries and complex morphology or if the mouth area was more directly exposed to the adjacent lake than were other areas. A tendency towards more turbidity-tolerant fish but fewer vegetation spawners, nest guarders, or game and panfish differentiated both more-disturbed from less-disturbed wetlands and open-water from vegetated areas within wetlands. Variation in vegetation structure related to wetland hydromorphology and anthropogenic impacts makes standardizing fish sampling protocols by microhabitat impractical across broad spatial or disturbance gradients. We recommend distributing sampling effort across available microhabitats and show that both fish and habitat can be adequately characterized with a single field day of effort.
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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 | 0.031 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".