Aggregated dispersal reduces spatial synchrony but promotes instability and extinction risk
Bibliographic record
Abstract
Theory has shown that limited amounts of constant dispersal modeled via fixed probability distributions can promote both coexistence and stability in spatially coupled ecosystems. However, dispersal in nature varies spatiotemporally and only approximates such fixed distributions when averaged over many generations. As a result, assuming constant dispersal introduces an implicit separation of time scales between slow local species interactions and fast regional dispersal. Here, we relax this implicit assumption by using a strategic metacommunity model with spatially aggregated, temporally stochastic dispersal that varies at the same temporal scale as local trophic dynamics in order to reexamine the relationship between synchrony, stability, and persistence. We show that increasing the rate of stochastic dispersal alters the spatiotemporal dynamics of all species, especially relative to what theory has previously demonstrated under constant dispersal. Specifically, regardless of the degree of spatial aggregation, increasing the rate of stochastic dispersal increases the magnitude and the frequency of population fluctuations, while rapidly reducing their spatial synchrony and temporal autocorrelation. Furthermore, increasing spatial aggregation reduces both temporal stability and persistence by inducing boom‐and‐bust cycles that lead to frequent local extinctions, particularly when species disperse in an identical manner. Decreasing the degree of spatial aggregation or allowing species to disperse independently reduces the emergence of such boom‐and‐bust cycles and thus promotes both stability and persistence. Overall, our results demonstrate that relaxing the implicitassumption of separate time scales for local and regional processes can be critical for resolving the relationship between variable dispersal, synchrony and stability in metacommunities.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".