Major shifts in species’ relative abundance in grassland mixtures alongside positive effects of species diversity in yield: a continental‐scale experiment
Bibliographic record
Abstract
Summary Increased species diversity promotes ecosystem function; however, the dynamics of multi‐species grassland systems over time and their role in sustaining higher yields generated by increased diversity are still poorly understood. We investigated the development of species’ relative abundances in grassland mixtures over 3 years to identify drivers of diversity change and their links to yield diversity effects. A continental‐scale field experiment was conducted at 31 sites using 11 different four‐species mixtures each sown at two seed abundances. The four species consisted of two grasses and two legumes, of which one was fast establishing and the other temporally persistent. We modelled the dynamics of the four‐species mixtures, and tested associations with diversity effects on yield. We found that species’ dynamics were primarily driven by differences in the relative growth rates ( RGR s) of competing species, and secondarily by density dependence and climate. The temporally persistent grass species typically had the highest RGR s and hence became dominant over time. Density dependence sometimes induced stabilising processes on the dominant species and inhibited shifts to monoculture. Legumes persisted at most sites at low or medium abundances and persistence was improved at sites with higher annual minimum temperature. Significant diversity effects were present at the majority of sites in all years and the strength of diversity effects was improved with higher legume abundance in the previous year. Observed diversity effects, when legumes had declined, may be due to (i) important effects of legumes even at low abundance, (ii) interaction between the two grass species or (iii) a store of N because of previous presence of legumes. Synthesis . Alongside major compositional changes driven by RGR differences, diversity effects were observed at most sites, albeit at reduced strength as legumes declined. This evidence strongly supports the sowing of multi‐species mixtures that include legumes over the long‐standing practice of sowing grass monocultures. Careful and strategic selection of the identity of the species used in mixtures is suggested to facilitate the maintenance of species diversity and especially persistence of legumes over time, and to preserve the strength of yield increases associated with diversity.
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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 | 0.000 | 0.000 |
| 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".