An Experimental Test of Dispersal Limitation of Species Diversity in Eelgrass Meadows
Bibliographic record
Abstract
Abstract Biogenic habitats, including eelgrass, support productive and diverse species assemblages in spatially heterogeneous seascapes. Eelgrass-associated faunal diversity depends not only on conditions within each eelgrass meadow but also on their probability of dispersing among eelgrass meadows in a seascape in which meadows are separated by soft sediments, kelp or rocky habitats. Experiments are required to establish dispersal limitation in communities. Here, we experimentally tested the hypothesis that dispersal limitation and edge effects influence local faunal diversity in eelgrass meadows. We manipulated habitat spacing and exposure of standardized substrate and quantified invertebrate colonization. We predicted that invertebrate species richness would decline with increasing distance from the meadow edge and that mobility traits would covary with species’ responses to distance. We also predicted that eelgrass edges would harbor communities distinct from the interior of the meadow and that these differences would covary with species’ trophic traits. We found that species richness decreased with increasing distance from meadows and this pattern was not explained by reduced abundance or filtering of species based on traits, consistent with dispersal limitation of diversity. Edges did not affect total community richness or abundance, and the only trait that varied with meadow-position was average body size, which was larger within the meadow interior. These results suggest that even short-distance dispersal limitation can structure the eelgrass invertebrate community but that communities of similar richness, trait profiles and abundance will colonize all available habitat in the absence of dispersal barriers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".