The contribution of nitrogen deposition to the photosynthetic capacity of forests
Bibliographic record
Abstract
Global terrestrial carbon (C) sequestration has increased over the last few decades. The drivers of carbon sequestration, the geographical spread and magnitude of this sink are however hotly debated. Photosynthesis determines the total C uptake of terrestrial ecosystems and is a major flux of the global C balance. We contribute to the discussion on enhanced C sequestration by analyzing the influence of nitrogen (N) deposition on photosynthetic capacity (Amax) of forest canopies. Eddy covariance measurements of net exchange of carbon provide estimates of gross primary production, from which Amax is derived with a novel approach. Canopy Amax is combined with modeled N deposition, environmental variables and stand characteristics to study the relative effects on Amax for a unique global data set of 80 forest FLUXNET sites. Canopy Amax relates positively to N deposition for evergreen needleleaf forests below an observed critical load of ~ 8 kg N ha–1 yr–1, with a slope of 2.0 ± 0.4 (S.E.) µmol CO2 m–2 s–1 per 1 kg N ha–1 yr–1. Above this threshold canopy Amax levels off, exhibiting a saturating response in line with the N saturation hypothesis. Climate effects on canopy Amax cannot be separated from the effect of N deposition due to considerable covariation. For deciduous broadleaf forests and forests in the temperate (‐continental) climate zones, the analysis shows the N deposition effect to be either small or absent. Leaf area index and foliar N concentration are positively but weakly related to Amax. We conclude that flux tower measurements of C fluxes provide valuable data to study physiological processes at the canopy scale. Future efforts need to be directed toward standardizing measures N cycling and pools within C monitoring networks to gain a better understanding of C and N interactions, and to disentangle the role of climate and N deposition in forest ecosystems.
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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 | 0.000 | 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".