Dispersal kernels influence the magnitude of environmental, biotic, and stochastic effects on the maintenance of metacommunity diversity
Bibliographic record
Abstract
ABSTRACT Dispersal plays a central role in shaping patterns of diversity in metacommunities. However, a primary focus on emigration rates may mischaracterize dispersal effects that actually arise from dispersal kernels. Kernels describe probabilistic movements between donor and recipient patches, but the influence of kernel shape on metacommunity diversity remains unclear. We used simulations to measure how kernels affect diversity across metacommunity scales and ecological contexts. We disentangled causes of these patterns using a novel approach quantifying the effects of environmental filtering, competition, stochasticity, and dispersal on fitness. Although metacommunities with shallow kernels followed expectations where emigration increased alpha-but decreased beta- and gamma-diversity, metacommunities with steeper kernels did not. Steeper kernels maintained regional diversity by reducing interspecific competition and stochastic extinctions, with dispersal conferring weaker benefits but less homogenization. Our work suggests dispersal kernels and emigration rates jointly regulate exposure to environmental variation and the balance of assembly mechanisms 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".