Spatial and temporal variation in nitrogen fixation and its importance to phytoplankton in phosphorus‐rich lakes
Bibliographic record
Abstract
Abstract Limnological theory posits that phosphorus (P) limits primary production in freshwater lakes, in part because fixation of atmospheric nitrogen (N 2 ) can compensate for limitations in nitrogen (N) supply to phytoplankton. However, quantitative estimates of the degree to which N 2 fixation satisfies planktonic N demand are rare. Here we used biweekly sampling during summer in seven lakes over 2 decades to estimate both planktonic N 2 fixation and phytoplankton N demand. We further assessed the ability of biologically fixed N to satisfy N needs of primary producers in productive hardwater lakes. Phytoplankton N requirements, derived from estimates of phytoplankton productivity and N content, were moderately synchronous ( S = 0.41) among lakes ( ca . 0.1–9.2 mg N m –3 hr –1 ). In contrast, rates of N 2 fixation determined using isotopic natural abundance method ( NAM ; 0.002–3.2 mg N m –3 hr –1 ), or heterocyte‐based calculations (0.10–1.78 mg N m –3 hr –1 ), varied asynchronously ( S NAM = –0.03 and S Heterocyte = –0.11) among basins, accounted for a median of 3.5% (mean 11.3% ± 21.6) of phytoplankton demand, and were correlated to the abundance of Nostocales cyanobacteria when analysed using generalised additive models. Overall, the total mass of fixed N accounted for a median of only 3.0% of the spring standing stock of total dissolved N in study lakes (mean 7.5 ± 12.1%), with higher relative importance of fixed N in highly productive downstream lakes. Thus, while fixed N helps sustain primary productivity, particularly in years with high rates of N 2 ‐fixation, it does not appear to eliminate N limitation of phytoplankton growth in these P‐rich hardwater lakes.
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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 | 0.001 | 0.001 |
| 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".