Temporal variation in dispersal modifies dispersal-diversity relationships in an experimental seagrass metacommunity
Bibliographic record
Abstract
Dispersal is a key force driving patterns of biodiversity.However, temporal variation in dispersal due to seasonality, weather, and other stochastic forces is an understudied aspect of the dispersal-diversity relationship.Metacommunity theory predicts temporal variation in dispersal can alter species distributions across patches.We empirically tested the hypothesis that variation in dispersal can modify the nonlinear dispersal-diversity relationship.Using a mesocosm experiment, we factorially manipulated variation in and intensity of dispersal in invertebrate grazer communities associated with nearshore seagrass ecosystems.Higher dispersal intensity led to higher grazer abundance, higher species richness (alpha diversity), and -when dispersal was variable -lower compositional similarity among patches (higher beta diversity).Within each dispersal intensity treatment level, temporal variation in dispersal decreased alpha diversity and increased beta diversity, with strongest effects at low-and intermediate-intensity dispersal.Our results provide the first empirical evidence that temporal variation in dispersal can substantially modify the well-described dispersal-diversity relationship for both alpha and beta diversity.Broadly, our results provide increasing evidence for the effects of spatial and temporal variability in modifying the role of the ecological processes driving marine metacommunity 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".