Spatial variation of nitrogen fixation in lakes of the northern Great Plains
Bibliographic record
Abstract
Cyanobacterial blooms are a regular feature of lakes in central North America, but little is known of their importance to the nitrogen (N) cycle and nutrition of aquatic food webs. We hypothesized that N2‐fixing cyanobacteria constitute a significant source of N to prairie lakes, that fixed N is effectively transferred to primary and secondary consumers, and that the importance of fixed N is structured on a landscape scale due to spatial gradients of lake chemistry and catchment characteristics. These hypotheses were tested using stable isotope analyses and mass budgets in six chained lakes of the northern Great Plains that were sampled biweekly during summers of 1994‐2004. Mean annual N isotope signatures of particulate organic matter (δ15N‐POM) were highly correlated to the abundance of N2‐fixing cyanobacteria (r2 = 0.75, p , 0.001), but exhibited no marked spatial organization. Instead, the temporal variability of δ15N‐POM was greatest in downstream lakes where N2‐fixing cyanobacteria were abundant. Furthermore, seasonal declines in δ15N‐POM were correlated with reductions in δ15N of individual zooplankton taxa (Daphnia spp., Diacyclops thomasi, Leptodiaptomus siciloides, Leptodora kindtii), especially in downstream eastern lakes. N mass balances revealed that the importance of biological N2 fixation relative to total N inputs (up to 77%) and initial N standing stock (up to 201%) were significantly linearly correlated with landscape position (0.59 ≤ r2 ≤ 0.83, p ≤ 0.07) and effective drainage area (0.61 ≤ r2 ≤ 0.98, p ≤ 0.07), as were volumetric and areal estimates of N2 fixation during summer (r2 ≥ 0.87, p ≤ 0.007). These patterns demonstrate that fixed N is spatially structured, highly predictable based on landscape position, and readily assimilated into aquatic food webs.
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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 | 0.000 |
| 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 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".