Nematode communities, plant nutrient economy and life‐cycle characteristics jointly determine plant monoculture performance over 12 years
Bibliographic record
Abstract
Knowledge from agriculture and ecological field studies suggests that plant monocultures lose productivity over time, but the drivers underlying the long‐term performance of monocultures of grassland species are not completely understood. We examined the performance of 60 grassland species growing in monoculture for 12 years in a biodiversity experiment (Jena Experiment) and studied three groups of biotic drivers potentially affecting plant performance in monocultures over time: 1) soil biota (nematode communities, arbuscular mycorrhizal fungi), 2) leaf traits related to leaf economics spectrum, and 3) plant life‐cycle characteristics related to buffered population growth (viable seeds in topsoil, seedling density, seed survival). Monocultures of 15 out of 60 species increased productivity, while monocultures of the remaining 45 species showed slight to strong losses of productivity over time, resulting in zero biomass in 15 species. All three biotic drivers were related to the varying long‐term performance of monocultures. Their combined influence on monoculture performance could be interpreted as a tradeoff between ‘fast’ versus ‘slow’ life strategies. ‘Fast’ species showed rapid resource use and little buffering of population growth through a viable seed bank, which led to high biomass production in young monocultures but a consecutive loss of biomass production over time. ‘Slow’ species were characterized by positive nematode effects (high abundance of predatory nematodes), conservative use of resources, and a viable seed bank with high recruitment success resulting in gradually increasing productivity over time. In summary, our study highlights the importance of studying long‐term field monocultures to investigate the complex role of different biotic drivers responsible for productivity changes over time. These insights provide an essential baseline for estimating biodiversity effects on productivity as well as to understand and predict long‐term performance of plant populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".