Coastal Wetland Support of Great Lakes Fisheries: Progress from Concept to Quantification
Bibliographic record
Abstract
Abstract Fishery support is recognized as a valuable ecosystem service provided by aquatic systems, but it is harder to quantify than to describe conceptually. In this paper, we combine data on fish inhabiting Great Lakes coastal wetlands (GLCWs) with information on commercial and recreational harvest and the piscivore forage base to develop quantitative understanding of the multiple species involved in direct and indirect fishery support of this complex fishery. We then examine patterns of species co‐occurrence and life history and relationships to GLCW conditions in order to identify fishery support metrics useful in aggregating species patterns and evaluating management outcomes. Our criteria for wetland prevalence (≥10% occurrence) and fishery importance (≥1% of recreational or commercial harvest in one or more of the Great Lakes or having a major forage fish role) yielded 21 wetland‐using, fishery‐relevant species representing multiple taxonomic groups and life history attributes. Wetland‐using species are estimated to make up half the biomass and 60% of the dollar value of the fish landed commercially and ∼80% of the fish numbers harvested recreationally. All of the GLCWs studied supported species of interest to recreational and commercial fishers but with widely varying composition. A few key habitat characteristics (e.g., vegetation structure) are broadly predictive of the types of sport and panfish present, with more degraded GLCWs generally supporting abundant but lower‐value taxa (rough‐fish species) and less degraded GLCWs supporting fewer but higher‐value taxa (sport and panfish species). No single taxonomic or functional metric seems adequate to capture the diversity of fishery‐relevant species supported by GLCWs; fishery support needs to be understood and managed in a multimetric context. Received May 16, 2014; accepted October 15, 2014
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".