Dispersal and establishment filters influence the assembly of restored prairie plant communities
Bibliographic record
Abstract
Community assembly filters, which in theory determine the suite of species that arrive at and establish in a community, have tremendous conceptual relevance to restoration. However, the concept has remained largely theoretical, with a paucity of empirical tests. As such, the applicability of assembly filters theory to ecological restoration remains incompletely known. We tested the relative strengths of dispersal and establishment filters by comparing the plant species composition, measured by species' presence/absence, in 29 restored prairies with the seed mixes used to restore each prairie. We found that both establishment and dispersal filters limited prairie similarity to the seed mix. Sown species responded differentially to filters, with a few species limited only by dispersal (seed density), many others limited only by establishment conditions (i.e. organic matter and sand content of soils, land use history, and fire frequency), and others limited by both dispersal and establishment filters. A few species, typically those sown most often, were not restricted by dispersal or establishment filters, likely because they were sown in high enough densities and all sites had suitable environmental conditions. Finally, one group of species established poorly, but we could not attribute this to either dispersal or establishment filters. This information can help land managers select species likely to establish in restorations when sown at sufficient densities. These results illustrate that dispersal and establishment filters limit the establishment of species in restored communities and these filters are species‐dependent. Identifying the most limiting filter(s) for species will inform strategies to increase their establishment success.
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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) | 0.001 | 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".