Overyielding in young tree communities does not support the stress-gradient hypothesis and is favoured by functional diversity and higher water availability
Bibliographic record
Abstract
Summary: Biodiversity effects on productivity and other ecosystem functions are strongly dependent on climate and resource availability. Based on the stress-gradient hypothesis, under conditions of greater abiotic stress, diversity effects on plant performance are intensified due to the increased relative importance of positive plant interactions. However, whether this hypothesis is consistently applicable in forest systems remains unclear. A field trial was established to test the stress-gradient hypothesis and examine diversity effects on aboveground biomass production of young trees in mixtures exposed to different water availability. Six native tree species of northern temperate forests (Acer saccharum, Betula papyrifera, Larix laricina, Picea glauca, Pinus strobus, and Quercus rubra) were planted as monocultures and as mixtures of two, four, and six species. For five growing seasons, four replicates of each community were exposed to conditions of either low- or high-water availability created by rainfall exclusion and weekly irrigation, respectively. Growth-years 4 and 5 were significantly different when the climatic water balance of the growing seasons were compared. We tested the effects of functional diversity on: (1) total growth of mixtures under low- and high-water availability, and (2) annual growth in years 4 (higher water availability, 2017) and 5 (lower water availability, 2018). Annual growth of most species in both years was greater under high- vs. low-water availability. Functional diversity had a significant positive effect on total biomass production and annual growth, and this effect was more strongly expressed under high-water availability. Functional diversity effects on annual growth did not differ between years 4 and 5 regardless of their climatic water balance. Functional and species identity were key to understanding productivity responses to mixture and treatment effects. Synthesis: Contrary to the stress-gradient hypothesis, the positive effects of functional diversity on productivity were enhanced by high-water availability and were independent of seasonal water balance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".