Do wetland lakes exhibit alternative stable states? Submersed aquatic vegetation and chlorophyll in western boreal shallow lakes
Bibliographic record
Abstract
Shallow lakes often exhibit alternative vegetative states, one a clear‐water state dominated by submersed aquatic vegetation (SAV) and the other a turbid state dominated by pelagic phytoplankton. We determined the nutrient and vegetation status of 148 wetland lakes in boreal Alberta, Canada. The lakes were very shallow (mean depth, 1.3 m), rich in phosphorus (123 µg total P L−−1), and relatively low in available nitrogen (18 µg L−−1 NH4+ + NO3−−), and 62% of them exhibited alternative states. The results of principal components analysis suggested that these lakes could be divided into four categories: (1) phytoplankton‐dominated lakes (25%) with higher concentrations of chlorophyll a (>20 µg L−−1), are more turbid, and have low densities of SAV; (2) SAV‐dominated lakes (37%), with high densities of submersed aquatic plants (>25% cover), are clearer, and have lower phytoplankton concentrations; (3) high SAV and high phytoplankton lakes (12%), with dense populations of both SAV and phytoplankton; and (4) low SAV and low phytoplankton lakes (26%), with low densities of both SAV and phytoplankton. The phytoplankton‐dominated lakes are usually found in hypereutrophic conditions (mean = 205 µg TP L−−1), whereas the SAV‐dominated lakes primarily exist in eutrophic and mesotrophic conditions (mean = 82 µg total P L−−1) and have lower available N (11 µg L−−1 NH4+ + NO3−−). Because most of these lakes lack fish, we expect that nutrient status, depth, and invertebrate predators are probably the most important determinants of vegetative structure and alternative states.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".