Plant community structure and nitrogen inputs modulate the climate signal on leaf traits
Bibliographic record
Abstract
Abstract Aim Leaf traits strongly impact biogeochemical cycles in terrestrial ecosystems. Understanding leaf trait variation along environmental gradients is thus essential to improve the representation of vegetation in Earth system models. Our aims were to quantify relationships between leaf traits and climate in permanent grasslands at a biogeographical scale and to test whether these relationships were sensitive to (a) the level of nitrogen inputs and (b) the inclusion of information pertaining to plant community organization. Location Permanent grasslands throughout France. Methods We combined existing datasets on climate, soil, nitrogen inputs (fertilization and deposition), species composition and four traits, namely specific leaf area, leaf dry matter content and leaf nitrogen and phosphorus concentrations, for 15,865 French permanent grasslands. Trait–climate relationships were tested using the following four climatic variables available across 1,833 pixels (5 km × 5 km): mean annual temperature (MAT) and precipitation (MAP), and two indices accounting for the length of the growing season. We compared these relationships at the pixel level using either using community‐level or species’ trait means. Results Our findings were as follows: (a) leaf traits related to plant nutrient economy shift consistently along a gradient of growing season length accounting for temperature and soil water limitations of plant growth (GSL tw ); (b) weighting leaf traits by species abundance in local communities is pivotal to capture leaf trait–environment relationships correctly at a biogeographical scale; and (c) the relationships between traits and GSL tw weaken for grasslands with a high nitrogen input. Main conclusions The effects of climate on plant communities are better described using composite descriptors than coarse variables such as MAT or MAP, but appear weaker for high‐nitrogen grasslands. Using information at the community level tends to strengthen trait–climate relationships. The interplay of land management, community assembly and bioclimate appears crucial to the prediction of leaf trait variations and their effects on biogeochemical cycles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.003 | 0.001 |
| 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 teacher head, 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".