Interspecific competition reduces seed dispersal in an annual plant and slows simulated range expansions
Bibliographic record
Abstract
Understanding the factors influencing how fast populations can spread across the landscape will be crucial as species ranges shift due to climate change. While the role of abiotic factors in determining expansion speed has been well studied in theory and empirical research, how competition impacts speed has received far less attention. Here, we investigated how seed dispersal distances change in response to competition and how these changes to distributions of dispersed seeds impact expansion speed. We dispersed four genotypes of the annual plant Arabidopsis thaliana with variation in life history traits into greenhouse mesocosms of either empty habitat or habitat containing the annual grass competitor, Lolium multiflorum . We found that competition decreased both mean and maximum dispersal distance. We then built a simulation model of range expansion with experimental data from this and a prior experiment to explore whether competition slows species expansions primarily through decreasing dispersal or fecundity. We found that competition primarily slows expansion speed through decreases in dispersal, but that when competition impacts both dispersal and fecundity, expansions slow more than with dispersal alone. The genotype with traits associated with longer distance dispersal was the most affected by competition in both experimental dispersal and simulations. This research suggests that not only does competition slow range expansions through decreases in both fecundity and dispersal, but that there may be consequences for evolutionary processes at the leading edge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".