Global historical trends and drivers of submerged aquatic vegetation quantities in lakes
Bibliographic record
Abstract
Submerged aquatic vegetation (SAV) in lake littoral zones is an inland water wetland type that provides numerous essential ecosystem services, such as supplying food and habitat for fauna, regulating nutrient fluxes, stabilizing sediments, and maintaining a clear water state. However, little is known on how inland SAV quantities are changing globally in response to human activities, where loss threatens the provisioning of these ecosystem services. In this study, we generate a comprehensive global synthesis of trends in SAV quantities using time series (>10 years) in lakes and identify their main drivers. We compiled trends across methods and metrics, integrating both observational and paleolimnological approaches as well as diverse measures of SAV quantities, including areal extent, density, or abundance classes. The compilation revealed that knowledge on SAV is mostly derived from temperate regions, with major gaps in tropical, boreal, and mountainous lake-rich regions. Similar to other wetland types, we found that 41% of SAV times series are largely decreasing mostly due to land use change and resulting eutrophication. SAV is, however, increasing in 28% of cases, primarily since the 1980s. We show that trends and drivers of SAV quantities vary regionally, with increases in Europe explained mainly by management, decreases in Asia due to eutrophication and land use change, and variable trends in North America consistent with invasive species arrival. By providing a quantitative portrait of trends in SAV quantities worldwide, we identify knowledge gaps and future SAV research priorities. By considering the drivers of different trends, we also offer insight to future lake management related to climate, positive restoration actions, and change in community structure on SAV quantities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".