A review of selected ecosystem services provided by coastal wetlands of the Laurentian Great Lakes
Bibliographic record
Abstract
Significant ecosystem services derive from the coastal wetlands of the Laurentian Great Lakes even though two-thirds of the original coastal wetlands have been lost since European settlement, and the remaining 126,000 ha of U.S. coastal wetlands and ≥70,000 ha of Canadian wetlands are affected by anthropogenic stressors. Published information indicates that wildlife habitat, fisheries support, and water quality improvement are significant ecosystem services provided by Great Lakes coastal wetlands that should be strongly considered during management decision making. 30 species of waterfowl, 155 breeding bird species, and 55 species of reptiles and amphibians are supported by coastal wetland habitats across the Basin. Nearly all sport and commercial Great Lakes fish species use coastal wetlands for life-cycle functions, and Great Lakes food webs are supported by wetland export of young sport and forage fish. Biological responses indicate declines in the wildlife and fishery services with increasing levels of anthropogenic disturbance. Extrapolation from a single well-studied system suggests that, Basin-wide, coastal wetlands may retain nearly 4000 tonnes P and 53,000 tonnes N per year, but additional studies are needed to support these estimates and determine stressor effects. Coastal wetlands appear to retain sediments over long time scales, but may either retain or release sediments during storm events. Extrapolation of carbon sequestration from other wetland types suggests that less than 90 g C yr−1 might be retained across the Basin. Wild rice production provides a culturally important ecosystem service, and coastal protection may be locally significant where fringing wetland remain. To support management decisions, quantitative relationships between specific stressors or land use practices and the delivery of ecosystem services are needed, as are ecosystem service indicators to measure those responses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".