Experimentally enhancing dispersal reveals the outsized importance of transient dynamics in a fluctuating environment
Bibliographic record
Abstract
The size and composition of local species pools are, in part, determined by past dispersal events. Predicting how communities respond to future disturbances, such as fluctuating environmental conditions, requires knowledge of such histories. We assessed the influence of a historical dispersal event on community assembly by simulating various scales of dispersal for 240 serpentine annual plant communities that experienced a large shift from drought to high rainfall conditions over three years. We collected, aggregated, and redistributed the aboveground seed bank (i.e., all loose seed present on the soil surface before the growing season initiates) from 30 sites across five nested spatial scales ([Formula: see text]1 m, 5 m, 100 m, 5 km, and 10 km), and annually censused communities to identify change in community structure among dispersal scales and over time. Our one-time dispersal manipulation diversified aboveground seed banks, provided insurance against temporal variability at intermediate and large dispersal distances ([Formula: see text]100 m), and even supported community reassembly toward a new compositional state at the 10 km scale. We also found evidence that temporal lags directed community responses to environmental fluctuations, preventing rare species extirpations and providing subordinate species discrete windows of time to supplement their seed banks. Our results reveal a joint spatiotemporal equilibrium in this system where dispersal through space interacts with temporal fluctuations in climate to support species' persistence via aboveground seed banks. This experiment underscores the importance of dispersal for diversity maintenance in this global biodiversity hotspot, where the magnitude and frequency of future climate fluctuations is uncertain.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".