Land-Use Legacies Are Important Determinants of Lake Eutrophication in the Anthropocene
Bibliographic record
Abstract
BACKGROUND: A hallmark of the latter half of the 20(th) century is the widespread, rapid intensification of a variety of anthropogenically-driven environmental changes--a "Great Acceleration." While there is evidence of a Great Acceleration in a variety of factors known to be linked to water quality degradation, such as conversion of land to agriculture and intensification of fertilizer use, it is not known whether there has been a similar acceleration of freshwater eutrophication. METHODOLOGY/PRINCIPAL FINDINGS: Using quantitative reconstructions of diatom-inferred total phosphorus (DI-TP) as a proxy for lake trophic state, we synthesized results from 67 paleolimnological studies from across Europe and North America to evaluate whether most lakes showed a pattern of eutrophication with time and whether this trend was accelerated after 1945 CE, indicative of a Great Acceleration. We found that European lakes have experienced widespread increases in DI-TP over the 20(th) century and that 33% of these lakes show patterns consistent with a post-1945 CE Great Acceleration. In North America, the proportion of lakes that increased in DI-TP over time is much lower and only 9% exhibited a Great Acceleration of eutrophication. CONCLUSIONS/SIGNIFICANCE: The longer and more widespread history of anthropogenic influence in Europe, the leading cause for the relatively pervasive freshwater eutrophication, provides an important cautionary tale; our current path of intensive agriculture around the world may lead to an acceleration of eutrophication in downstream lakes that could take centuries from which to recover.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".