Seed dynamics linked to variability in movement of tidal water
Bibliographic record
Abstract
Abstract Question: Community structure may be influenced by patterns of dispersed seeds (seed rain) because they contribute to the template of plant community development. We explored factors influencing seed rain in a system dominated by tidal water, where direction and magnitude of water flow are difficult to predict, unlike many other hydrochorous systems where water flow is directional. We posed three main questions: (1) are patterns in seed rain linked to effects of hydrodynamic variability; (2) do these patterns in seed rain reflect distribution of seed sources and seed production; and (3) what are the implications for the assembly of tidal communities? Location: Salt marshes on the Wadden island of Schier‐monnikoog, The Netherlands. Methods: Species compositions of vegetation, seed rain, seed production and driftlines along a chronosequence of communities were compared. We also studied seed movement by sowing Astroturf® mats with seeds and checking for seeds remaining after a single tidal inundation. Results: Storm surges had a significant effect on seed‐rain patterns as the highest density and diversity of captured seeds were found during a stormy period. Seed rain of the youngest communities was more influenced by storms than that of older communities. Patterns in seed rain generally followed similar patterns in the distribution of established plants, and seed production. Conclusions: Results suggested mostly local dispersal of seeds. However, there was some evidence of long‐distance dispersal occurring during storm surges in younger communities that are regularly inundated with tidal water. The possible role of seed retention in constraining community development, rather than dispersal per se, is further discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".