Within‐Year Soil Legacies Contribute to Strong Priority Effects of Exotics on Native California Grassland Communities
Bibliographic record
Abstract
Understanding priority effects, in which one species in a habitat decreases the success of later species, may be essential for restoring native communities. Priority effects can operate in two ways: size‐asymmetric competition and creation of “soil legacies,” effects on soil that may last long after the competitive effect. We examined how these two types of priority effects, competition and soil legacies, drive interactions between seedlings of native and exotic California grassland plants. We established native and exotic communities in a mesocosm experiment. After 5 weeks, we removed the plants from half the treatments (soil legacy treatment) and retained the plants in the other half (priority effect treatment, which we interpret to include both competition and soil legacies). We then added native or exotic seed as the colonizing community. After 2 months, we measured the biomass of the colonizing community. When germinating first, both natives and exotics established priority effects, reducing colonist biomass by 86 and 92%, respectively. These priority effects were predominantly due to size‐asymmetric competition. Only exotics created soil legacies, and these legacies only affected native colonizers, reducing biomass by 74%. These results imply that exotic species priority effects can affect native grassland restorations. Although most restorations focus on removing exotic seedlings, amending soil to address soil legacies may also be critical. Additionally, because native species can exclude exotics if given a head start, ensuring that natives germinate first may be a cost‐effective restoration technique.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".