Occasional long distance dispersal increases spatial synchrony of population cycles
Bibliographic record
Abstract
Spatially separated populations of the same species often exhibit correlated fluctuations in abundance, a phenomenon known as spatial synchrony. Dispersal can generate spatial synchrony. In nature, most individuals disperse short distances with a minority dispersing long distances. The effect of occasional long distance dispersal on synchrony is untested, and theoretical predictions are contradictory. Occasional long distance dispersal might either increase both overall synchrony and the spatial scale of synchrony, or reduce them. We conducted a protist microcosm experiment to test whether occasional long distance dispersal increases or decreases overall synchrony and the spatial scale of synchrony. We assembled replicate 15-patch ring metapopulations of the protist predator Euplotes patella and its protist prey Tetrahymena pyriformis. All metapopulations experienced the same dispersal rate, but differed in dispersal distance. Some metapopulations experienced strictly short distance (nearest neighbour) dispersal, others experienced a mixture of short- and long distance dispersal. Occasional long distance dispersal increased overall spatial synchrony and the spatial scale of synchrony for both prey and predators, though the effects were not statistically significant for predators. As predicted by theory, dispersal generated spatial synchrony by entraining the phases of the predator-prey cycles in different patches, a phenomenon known as phase locking. Our results are consistent with theoretical models predicting that occasional long distance dispersal increases spatial synchrony. However, our results also illustrate that the spatial scale of synchrony need not match the spatial scale of the processes generating synchrony. Even strictly short distance dispersal maintained high spatial synchrony for many generations at spatial scales much longer than the dispersal distance, thanks to phase locking.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".