At what scales does aggregated dispersal lead to coexistence?
Bibliographic record
Abstract
Aggregation during dispersal can allow persistence of weak competitors, by creating conditions where stronger competitors are more likely to interact with conspecifics than with heterospecifics. However, aggregation mechanisms operate over a wide range of spatial scales, and species experience space in very different ways. The net effect of dispersal aggregation on coexistence will depend on how these scales interact. We show that it is possible to approximate the effects of aggregated dispersal on coexistence by considering three empirically measurable parameters: the spatial scale of interaction (how strongly competition drops off with distance), the spatial scale of aggregation (how large propagule packets are), and the temporal scale of aggregation (how frequently packets arrive). We use a novel metacommunity moment closure based on this approximation and stochastic simulations to show that aggregated dispersal allows for coexistence only when the stronger competitor is both aggregated and interacts at the same or a smaller spatial scale than the weaker competitor. When species interact and are aggregated at the same scales, coexistence outcomes are only weakly sensitive to the absolute scales of interaction and aggregation, as long as the scale of interaction is smaller than the scale of aggregation. However, coexistence is sensitive to the time‐scale of aggregation: increasing the frequency of packet arrival substantially reduces the region of fitness inequalities where both species persist. Finally, coexistence is less likely and global extinction of both competitors is more frequent when aggregation is fixed (with a constant number of propagules per packet), compared to density‐dependent (number of propagules increases with adult density).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".