Effects of shading on photosynthesis, plant organic nitrogen uptake, and root fungal colonization in a subarctic mire ecosystem
Bibliographic record
Abstract
Arctic dwarf shrub ecosystems are predicted to be exposed to lower light intensity in a changing climate where mountain birch forests are expanding. We investigated how shading at 0%, 65%, and 97% affects photosynthesis, organic N uptake, C and N allocation patterns in plants, and root fungal colonization in an ericoid dwarf shrub ecosystem. The ecosystem was labeled by injection of [2-13C,15N]glycine into the soil, and the uptake of 15N and 13C in roots and leaves 24 h later was analysed. Fungal colonization in hair roots was determined visually. Hair root 13C:15N ratios showed that dwarf shrub ecosystems are capable of taking up organic N as intact glycine both under high irradiance levels and under shaded conditions when photosynthesis is strongly reduced. The allocation of 15N to green leaves of Rubus chamaemorus L. increased with shading, whereas the allocation of 13C to leaves of both deciduous and evergreen plant species decreased. Species dominance was correlated with uptake of 13C, i.e., the most productive species also took up the highest amount of glycine. The ecosystem exhibited a tendency towards lower colonization by ericoid mycorrhizal fungi and dark septate endophytes in hair roots when shaded. Thus, shading has implications for processes central to both C and N cycling in subarctic ecosystems. This should be considered in projections of ecosystem responses to climate change and expanding mountain birch forests.
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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".