Tree species identity drives nutrient use efficiency in young mixed‐species plantations, at both high and low water availability
Bibliographic record
Abstract
Abstract Previous studies have demonstrated that tree species diversity increases productivity and may enhance nutrient cycling in forests. The effect of mixing tree species on stand‐level nutrient use efficiency (NutUE) has seldom been studied, and even less so in the context of climate change. Here we present the first study examining how diversity effects on NutUE may be modified by growing season water availability (low vs. high), and importantly, during periodic drought or on water‐limited sites. We tested the interaction of water availability and tree species diversity (i.e. species richness and species identity) on NutUE in two young, experimental plantations located in south‐western France (ORPHEE), and northern Ontario, Canada (IDENT‐SSM). We calculated stand‐level NutUE as above‐ground net primary productivity (ANPP) divided by the product of litterfall mass and macronutrient concentrations, of monocultures and mixed tree communities composed of several temperate tree species, with a focus on birch and pine at both sites. We found significant species richness and water availability effects on NutUE, but they were weakly and inconsistently expressed, detected only for specific nutrients, and differed between the two sites. Species identity had much stronger effects on NutUE when examined using the birch–pine plots at both sites. At ORPHEE, nitrogen use efficiency (NUE) and phosphorus use efficiency (PUE) were significantly greater in the birch–pine mixture than in each monoculture. This was due to an increase in ANPP coupled with a decrease in litter nutrient concentrations in the two‐species plot that was driven by the dominance of pine in the mixture. In the comparatively younger, denser plots at IDENT‐SSM, birch was the dominant species that resulted in positive mixing effects on ANPP and litter nutrient concentrations and a neutral effect of mixing on NUE and PUE in the birch–pine mixture. Overall, the effects of mixing did not differ with water availability treatments, suggesting that species composition of mixtures is more important than water availability for stand‐level NutUE in these young forest communities. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 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".