Alternative stable states in large shallow lakes?
Bibliographic record
Abstract
Many lakes worldwide are experiencing great change due to eutrophication. Consequently, species composition changes, toxic algal blooms proliferate, and drinking water supplies dwindle. The transition to the deteriorated state can be catastrophic with an abrupt change from macrophyte to phytoplankton domination. This has been shown repeatedly in small lakes. Whether such alternative stable states also exist in large shallow lakes is less clear, however. Here we discuss the characteristics that give rise to alternative stable states in large shallow lakes either in the lake as whole or restricted to specific regions of the lake. We include the effect of lake size , spatial heterogeneity and internal connectivity on a lake's response along the eutrophication axis. As a case study, we outline the eutrophication history of Lake Taihu (China) and illustrate how lake size , spatial heterogeneity and internal connectivity can explain the observed spatial presence of different states. We discuss whether these states can be alternatively stable by comparing the data with model output (PCLake). These findings are generalised for other large, shallow lakes. We conclude that locations with prevailing size effects generally lack macrophytes; and, therefore, alternative stable states are unlikely to occur there. However, most large shallow lakes have macrophytes whose presence remains unexplained when only size effect is taken into account. By including spatial heterogeneity in the analysis, the presence of macrophytes and alternative stable states in large shallow lakes is better understood. Finally, internal connectivity is important because a high internal connectivity reduces the stability of 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".