Large-scale comparison of biomass and reproductive phenology among native and non-native populations of the seagrass Zostera japonica
Bibliographic record
Abstract
Large-scale analysis along latitude or temperature gradients can be an effective method for exploring the potential roles of light and temperature in controlling seagrass phenology. In this study, we investigated effects of latitude and temperature on seagrass biomass and reproductive seasonality.Zostera japonicais an intertidal seagrass with a wide latitudinal distribution expanding from tropical to temperate zones in its native range in Asia, with an additional non-native distribution in North America. We collated available data on phenological traits (timings of peak biomass or reproduction, durations of biomass growth and reproductive season, and maximum biomass or reproductive ratio) from publications and our own observations. Traits were compared among geographic groups: Asia-tropical, Asia-temperate, and North America-temperate. We further examined relationships between traits and latitude and temperature for 3 population groups: Asian, North American, and all populations. Our analysis revealed significant variation among geographic groups in maximum biomass, peak reproductive timing, and maximum reproductive ratio, but not in other traits. Maximum biomass and peak reproductive timing for Asian and all populations were significantly correlated with latitude and temperature. Maximum biomass was highest at mid-latitudes or intermediate temperatures and decreased toward distribution range limits, and peak reproductive timing occurred later in the year at higher latitudes or cooler sites. North American populations showed shorter growth durations and greater reproductive ratios at higher latitude. Different responses observed for North American populations may reflect effects of introduction. Our study demonstrates potential variation among geographic regions and between native and non-native populations.
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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 | 0.001 | 0.001 |
| 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".