Distribution and Performance of the Nonnative Seagrass <i>Zostera japonica</i> Across a Tidal Height Gradient on Shaw Island, Washington
Bibliographic record
Abstract
In the Northeast Pacific the nonnative seagrass Zostera japonica frequently exists at the same sites as the native seagrass Zostera marina. Although at some sites their vertical distributions overlap, at most sites in the Pacific Northwest there is a distinctive unvegetated zone between them. The objective of this study was to better understand why a gap between the lower limit of Z. japonica and the upper limit of Z. marina exists. To address this issue we carried out transplant experiments, conducted in situ monitoring of existing Z. japonica patches, and collected sediment samples at South Beach on Shaw Island, Washington, during the spring and summer of 2006. Transplant and in situ monitoring data indicate that survival and performance of Z. japonica are reduced lower in the intertidal zone. In addition, Z. japonica patches tended to be smaller and more spaced out at lower tidal heights. Although we found no Z. japonica seeds within or outside extant Z. japonica patches, high transplant mortality indicates that Z. japonica dispersal limitation is an unlikely cause of the unvegetated gap zone. Our field observations further suggest that herbivory, biotarbation, and epiphytes are unlikely causes of the gap pattern at our study site. Instead, we hypothesize that light limitation prevents Z. japonica from occurring lower in the intertidal. A review of published vertical distribution data for both Zostera species indicates that the lower limit of Z. japonica is relatively invariant among sites. In contrast, the upper limit of Z. marina is highly variable, ranging by more man 4 m within some subregions in Washington State. Consequently we hypodiesize that intersite variability in the vertical distribution of Z. marina is the primary driver of spatial variability in the presence of the unvegetated gap.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".