Tree symbioses sustain nitrogen fixation despite excess nitrogen supply
Bibliographic record
Abstract
Abstract Symbiotic nitrogen fixation (SNF) is a key ecological process whose impact depends on the strategy of SNF regulation—the degree to which rates of SNF change in response to limitation by N versus other resources. SNF that is obligate or exhibits incomplete downregulation can result in excess N fixation, whereas a facultative SNF strategy does not. We hypothesized that tree‐based SNF strategies differed by latitude (tropical vs. temperate) and symbiotic type (actinorhizal vs. rhizobial). Specifically, we expected tropical rhizobial symbioses to display strongly facultative SNF as an explanation of their success in low‐latitude forests. In this study we used 15N isotope dilution field experiments in New York, Oregon, and Hawaii to determine SNF strategies in six N‐fixing tree symbioses. Nitrogen fertilization with +10 and +15 g N m−2 year−1 for 4–5 years alleviated N limitation in all taxa, paving the way to determine SNF strategies. Contrary to our hypothesis, all six of the symbioses we studied sustained SNF even at high N. Robinia pseudoacacia (temperate rhizobial) fixed 91% of its N (%Ndfa) in controls, compared to 64% and 59% in the +10 and +15 g N m−2 year−1 treatments. For Alnus rubra (temperate actinorhizal), %Ndfa was 95%, 70%, and 60%. For the tropical species, %Ndfa was 86%, 80%, and 82% for Gliricidia sepium (rhizobial); 79%, 69%, and 67% for Casuarina equisetifolia (actinorhizal); 91%, 42%, and 67% for Acacia koa (rhizobial); and 60%, 51%, and 19% for Morella faya (actinorhizal). Fertilization with phosphorus did not stimulate tree growth or SNF. These results suggest that the latitudinal abundance distribution of N‐fixing trees is not caused by a shift in SNF strategy. They also help explain the excess N in many forests where N fixers are common.
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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 | 0.000 |
| 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 | 0.000 | 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".