Nutrient uptake and use efficiency in co‐occurring plants along a disturbance and nutrient availability gradient in the boreal forests of the southwest Yukon, Canada
Bibliographic record
Abstract
Abstract Aim In boreal forest ecosystems plant productivity is typically constrained by mineral nutrient availability. In some boreal regions changes in nutrient availability have led to limited changes in productivity but large changes in plant composition. To determine the impact that a change in nutrient availability has on the plant communities it is important to understand how species use nutrients. Here we explore how plant species and functional types in a cold‐dry boreal forest community use available nutrients by quantifying their respective nutrient utilization and response efficiency. Location Boreal forests in the southwest corner of the Yukon Territory, Canada. Methods We collected soil samples and total plant biomass from 29 plots from nine locations subjected to fire, harvesting or bark beetle disturbances. Nutrient analysis of all vegetation and soil samples were conducted to determine the concentration of macro‐ and micronutrients from both plant biomass and soils collected. Nutrient pools between stands with different disturbance histories are compared. Nutrient uptake, use and response efficiencies were then calculated and nutrient response profiles were developed for each species/functional type. Results We found few differences between nutrient pools in plots with different disturbance histories. A clear separation of species and functional groups in elemental hyperspace suggesting divergent nutrient use in co‐occurring species was identified. The use efficiency analysis highlighted that the species with the highest uptake efficiency have lowest use efficiency and vice versa. Species showed either a monotonic or constant relationship between nutrient response efficiency and N, P, K, reflecting a lack of relationship between plant productivity and resource availability or a linear increase in productivity with increasing nutrient availability, respectively. Conclusions Our findings indicate that species are maximizing nutrient use along different parts of the resource gradient, which has implications for understanding how species respond to changes in nutrient availability. Our findings also show that nutrient use by some species may be governed more by uptake efficiency than use efficiency, allowing them to respond to increases in resource availability by increasing uptake rather than use.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".