Effects of atypical water-level fluctuations on macrophyte species composition, ecological structure, and identification of water-level Indicator Species for coastal wetlands
Bibliographic record
Abstract
Unlike the historical 8-year period oscillations (octennia) of high and low water levels (WLs), Lakes Huron-Michigan recently experienced two octennia of sustained-low WLs (1999 to 2014), followed by an octennium of continuously high WLs. We used macrophyte presence data collected between 2003 and 2021 across 20 sub-regions in Georgian Bay to assess the long-term effects of this atypical pattern of WL fluctuations on the plant communities of 58 wetlands (119 wetland-years). There were significant differences in species composition between Period 1 (2003–2006; low WL) and Period 2 (2015–2021; high WL) (perMANOVA; R 2 = 0.057; p = 0.001), with a significant decrease in α-diversity (17.65 ± 0.72 vs 15.63 ± 0.53, respectively; Wilcoxon signed rank test; p = 0.012), but a significant increase in β-diversity (11.06 ± 0.28 vs 11.86 ± 0.30; Wilcoxon signed rank test; p = 0.002). Indicator Species Analysis identified 10 species that were strongly associated with low WLs and four species with high WLs. We classified these Indicator Species into nine Macrophyte Ecological Groups that reflected their functional roles in wetlands. Low WLs were dominated by nearshore emergent and rosette basal species, whereas unrooted submergent and floating species were more prevalent during high WLs. An unprecedented development of a dead tree zone during Period 2, attributable to these atypical WL fluctuations, potentially prevented nearshore species from colonizing. Such shifts in the wetland plant community will likely have pervasive and cascading effects on the wetland fish community that rely on certain plant species to provide habitat structure.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".