Trade-offs Between Light and Nutrient Availability Across Gradients of Dissolved Organic Carbon Lead to Spatially and Temporally Variable Responses of Lake Phytoplankton Biomass to Browning
Bibliographic record
Abstract
Abstract Northern lakes are experiencing widespread increases in dissolved organic carbon (DOC) that are likely to lead to changes in pelagic phytoplankton biomass. Pelagic phytoplankton biomass responds to trade-offs between light and nutrient availability. However, the influence of DOC light absorbing properties and carbon–nutrient stoichiometry on phytoplankton biomass across seasonal or spatial gradients has not been assessed. Here, we analyzed data from almost 5000 lakes to examine how the carbon–phytoplankton biomass relationship is influenced by seasonal changes in light availability, DOC light absorbing properties (carbon-specific visual absorbance, SVA420), and DOC–nutrient [total nitrogen (TN) and total phosphorus (TP)] stoichiometry, using TOC as a proxy for DOC. We found evidence for trade-offs between light and nutrient availability in the relationship between DOC and phytoplankton biomass [chlorophyll (chl)-a], with the shape of the relationship varying with season. A clear unimodal relationship was found only in the fall, particularly in the subsets of lakes with the highest TOC:TP. Observed trends of increasing TOC:TP and decreasing TOC:TN suggest that the effects of future browning will be contingent on future changes in carbon–nutrient stoichiometry. If browning continues, phytoplankton biomass will likely increase in most northern lakes, with increases of up to 76% for a 1.7 mg L−1increase in DOC expected in subarctic regions, where DOC, SVA420, DOC:TN, and DOC:TP are all low. In boreal regions with higher DOC and higher SVA420, and thus lower light availability, lakes may experience only moderate increases or even decreases in phytoplankton biomass with future browning.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".