Data from: Dispersal and diversity in experimental metacommunities: linking theory and practice
Bibliographic record
Abstract
There has been a recent rise in the number of experiments investigating the effect of dispersal on diversity, with many of the predictions for these tests derived from metacommunity theory. Despite the promise of linking observed relationships between dispersal and diversity to underlying metacommunity processes, empirical studies have faced challenges in providing robust tests of theory. We review experimental studies that have tested how dispersal affects metacommunity diversity to determine why shortcomings emerge, and to provide a framework for empirical tests of theory that capture the processes structuring diversity in natural metacommunities. We first summarize recent experimental work to outline trends in results and to highlight common methods that cause a misalignment between empirical studies and the processes described by theory. We then identify the undesired implications of three widely used experimental methods that homogenize metacommunity structure or species traits, and present alternative methods that have been used to successfully integrate experiments and theory in a biologically relevant way. Finally, we present methodological and theoretical insights from three related ecological fields (coexistence, food web and priority effects theory) that, if integrated into metacommunity experiments, could help isolate the independent and joint effects of local interactions and dispersal on diversity, and reveal the mechanisms underlying observed dispersal–diversity patterns. Together, these methods can provide stronger tests of existing theory and stimulate new theoretical explorations.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".