The dynamics of community assembly under sudden mixing in experimental microcosms
Bibliographic record
Abstract
Landscape connectivity has been shown to alter community assembly and its consequences. Here we examine how strong, sudden changes in connectivity may affect community assembly by conducting experiments on the effects of "community mixing," situations where previously isolated communities become completely connected with consequent community reorganization. Previous theory indicates that assembly history dictates the outcome of mixing: mixing randomly assembled communities leads to a final community with random representation from the original communities, while mixing communities that were assembled via a long history of colonizations and extinctions leads to strong asymmetry, with one community dominating the other. It also predicts that asymmetry should be stronger in the presence of predators in the system. We experimentally tested and explored this theory by mixing aquatic microcosms inhabited by a complex food web of heterotrophic protists, and algae. Our results confirm the prediction that long assembly history can produce asymmetry under mixing and suggest these dynamics could be important in natural systems. However, in contrast to previous theory we also found asymmetry weaker under mixing of communities with more complex trophic structure.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".