Assembling productive communities of native grass and legume species: finding the right mix
Bibliographic record
Abstract
Abstract Question Native species have the potential to provide productive, drought‐resistant communities for seeded rangelands and ecological restoration. Little is known, however, about how to identify multispecies mixtures with optimal levels of productivity and stress resistance from the thousands of possible community configurations. Here we examine if empirical models can be used to predict highly productive community configurations of seven native grasses and legumes in controlled conditions from the very large pool of possible communities, and which basic measure of community structure best predicts function. Location Greenhouses in Saskatchewan, Canada. Methods We used a greenhouse experiment, where established communities varied in species and functional group richness, evenness, species and functional group identity, following a response surface design. We measured community productivity and evaluated the predictive power of a range of empirical models linking diversity and productivity. Results Productivity increased with increased functional dispersion, relative growth rate and decreased competitive effect. Selection effects were evident, with the abundance and occurrence of particular species or functional groups and plant traits also linked to increased productivity. Among the strongest predictors of productivity were the presence and abundance of perennial C3 grasses (particularly Pascopyrum smithii), likely because of the high early relative growth rate and strong competitive effect of those species. Conclusions We compiled and compared the ability of a range of empirical models to predict high‐productivity community configurations, and tested the accuracy of the best models in a confirmatory experiment. The relationship between predicted and observed productivity was significantly correlated in the confirmatory experiment, and demonstrates that under controlled conditions, basic measures of community structure can predict community function. This approach has potential, but variability within treatments may limit the accuracy of results. The models developed can be used as a screening tool, narrowing the search window for high functioning seed mixtures for use in ecological restoration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".