Tree species partition N uptake by soil depth in boreal forests
Bibliographic record
Abstract
It is recognized that the coexistence of herbaceous species in N-depleted habitats can be facilitated by N partitioning; however, the existence of such a phenomenon for trees has not yet been demonstrated. Here, we show from both foliage and soil 15N natural abundance values and from a 12-year in situ 15N addition experiment, that black spruce (Picea mariana) and jack pine (Pinus banksiana), two widespread species of the Canadian boreal forest, take up N at different depths. While black spruce takes up N from the organic soil, jack pine acquires it deeper within the highly N-depleted mineral soil. Systematic difference in foliar 15N natural abundance between the two species across seven sites distributed throughout the eastern Canadian boreal forest shows that N spatial partitioning is a widespread phenomenon. Distinct relationships between delta15N and N concentration in leaves of both species further emphasize their difference in N acquisition strategies. This result suggests that such complementary mechanisms of N acquisition could facilitate tree species coexistence in such N-depleted habitats and could contribute to the positive biodiversity-productivity relationship recently revealed for the eastern Canadian boreal forest, where jack pine is present. It also has implications for forest management and provides new insights to interpret boreal forest regeneration following natural or anthropogenic perturbations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".