Ecosystem response to changes in water level of Lake Ontario marshes: lessons from the restoration of Cootes Paradise Marsh, a degraded coastal wetland of Lake Ontario
Bibliographic record
Abstract
A general understanding of how aquatic vegetation responds to water-level fluctuations is needed to guide restoration of Great Lakes coastal wetlands because inter-annual and seasonal variations often confound effects of costly remedial actions. In 1997, common carp (Cyprinus carpio) was removed from Cootes Paradise Marsh (L. Ontario) to reduce sediment resuspension and bioturbation, and thus regenerate marsh plants that had declined dramatically since the 1930s. Data from 1934 to 1993 were re-assembled from the literature to relate percentage cover of emergent vegetation to mean summer water level. A non-linear regression equation explained close to 90% of the variation compared with 80% for a non-linear equation, and this trend was confirmed for the dominant species, Typha latifolia. A modest recovery of emergent vegetation in 1999 following carp exclusion could have been predicted from declining water level alone, without invoking any effects of the biomanipulation. An unusually cool spring in 1997 delayed the migration of spawning planktivores into the marsh. This resulted in a grazer-mediated clear-water phase that coincided with a resurgence of the submersed aquatic vegetation (SAV) community in 1997, which declined again in 1999 when low water levels occurred. Even though decrease in water level was significantly related to increased suspended solids and greater light attenuation, light conditions appeared to have been adequate in marsh embayments to support SAV growth, according to a published relationship between maximum depth of SAV colonization and light extinction coefficient. I suggest that wave disturbance and propagule burial associated with shallow water depths may have been the main reasons for the decline of the SAV in 1999 and 2000.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".