Inherited hypoxia: A new challenge for reoligotrophicated lakes under global warming
Bibliographic record
Abstract
Abstract The Anthropocene is characterized by a worldwide spread of hypoxia, among other manifestations, which threatens aquatic ecosystem functions, services, and biodiversity. The primary cause of hypoxia onset in recent decades is human‐triggered eutrophication. Global warming has also been demonstrated to contribute to the increase of hypoxic conditions. However, the precise role of both environmental forcings on hypoxia dynamics over the long term remains mainly unknown due to a lack of historical monitoring. In this study, we used an innovative paleolimnological approach on three large European lakes to quantify past hypoxia dynamics and to hierarchies the contributions of climate and nutrients. Even for lake ecosystems that have been well oxygenated over a millennia‐long period, and regardless of past climatic fluctuations, a shift to hypoxic conditions occurred in the 1950s in response to an unprecedented rise in total phosphorus concentrations above 10 ± 5 µg P L−1. Following this shift, hypoxia never disappeared despite the fact that environmental policies succeeded in drastically reducing lake phosphorus concentrations. During that period, decadal fluctuations in hypoxic volume were great, ranging between 0.5 and 8% of the total lake volumes. We demonstrate, through statistical modeling, that these fluctuations were essentially driven by climatic factors, such as river discharge and air temperature. In lakes Geneva and Bourget, which are fed by large river systems, fluctuations in hypoxic volume were negatively correlated with river discharge. In contrast, the expansion of hypoxia has been related only to warmer air temperatures at Annecy, which is fed by small river systems. Hence, we outline a theoretical framework assuming that restored lake ecosystems have inherited hypoxia from the eutrophication period and have shifted to a new stable state with new key controls of water and ecosystem quality. We suggest that controlling river discharge may be a complementary strategy for local management of lakes fed by large river systems.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".