Soil resources mediate the strength of species but not trait convergence across grassland restorations
Bibliographic record
Abstract
Abstract Ecological restoration is notoriously unpredictable because similar actions can result in different outcomes. Outcomes can also differ for species and functional components of communities depending on how restoration actions and abiotic conditions alter community assembly trajectories. Quantifying variation in community trajectories across restorations for both species and traits is rare, but can help to resolve underlying assembly processes and refine strategies to maximize restoration success. We quantified the importance of soil resources, seed mix richness, and prescribed fire for variation in plant species and functional trait trajectories over 6 years across 20 restored tallgrass prairies in the mid‐western United States. We predicted stronger convergence for traits than species, with species and trait compositions converging more across restorations on resource‐poor soils and with frequent fires due to stronger abiotic filtering. In contrast, we predicted species and trait compositions would converge more slowly or diverge across restorations with resource‐rich soils, with less frequent fires, and seeded with more species due to weaker filtering and more stochasticity. Communities generally converged over time; however, the rate of convergence was determined by soil resources, not restoration actions. Restorations converged more across resource‐rich sites. In contrast to patterns of species convergence, variation in trait composition remained stable over time regardless of soil resources or restoration actions. The unexpected pattern of species, but not trait, convergence during community assembly appears to result from increasing dominance of a native C 4 bunchgrass, Andropogon gerardii that coincides with proportional declines of other C 4 grasses that share similar traits. Synthesis and applications . Restoration outcomes may be more predictable than typically considered. Trait compositions were stable and variation in species compositions decreased over time owing to site conditions, where resource‐rich soils produced more consistent outcomes. Our study shows monitoring multiple facets of biodiversity across restorations can reveal why outcomes vary and inform broad‐scale restoration planning.
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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 | 0.003 |
| 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) | 0.002 | 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".