Dispersal limitation and roughening of the ecological interface
Bibliographic record
Abstract
Limited dispersal distance, whether associated with vegetative growth or localized reproduction, induces spatial clustering and, in turn, focuses ecological interactions at the neighborhood scale. In particular, most invasive plants are clonal, cluster through vegetative propagation, and compete locally. Dispersal limitation implies that invasive spread occurs as advance of an ecological interface between invader and resident species. Interspecific competition along the interface produces random variation in the extent of invasive growth. Development of these random fluctuations, termed stochastic roughening, will often structure the interface as a self-affine fractal; a series of power-law scaling relationships follows as a result. For a diverse array of local growth processes exhibiting both forward and lateral propagation, the extent of invader advance becomes spatially correlated along the interface, and the width of the interface (the area where invader and resident compete directly) increases as a power function of time. Once roughening equilibrates statistically, interface width and the location of the most advanced invader (the "front-runner") beyond the mean incursion should both increase as a power function of interface length. To test these predictions, we let white clover (Trifolium repens) invade ryegrass (Lolium perenne) experimentally. Spatial correlation developed as anticipated, and both interface width and the front-runner's lead scaled as a power law of length. However, the scaling exponents differed, likely a consequence of clover's growth morphology. The theory of kinetic roughening offers a new framework for understanding causes and consequences of spatial pattern in between-species interaction, and indicates when interface measures at a local scale predict properties of an invasive front at extended spatial scales.
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 | 0.000 | 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) | 0.000 | 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 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".