Effects of light and water column nutrient availability on eelgrass Zostera marina productivity in Eeyou Istchee, eastern James Bay, Quebec
Bibliographic record
Abstract
Eelgrass Zostera marina meadows provide valuable ecosystem services to coastal communities. These shallow-water ecosystems in Eeyou Istchee (eastern James Bay, Quebec, Canada) support Cree ways of life by providing waterfowl foraging habitat, fish nurseries, and natural storm buffers. In 2019-2021, Eeyou Istchee eelgrass extent and shoot size remained well below historical baseline levels following a major decline in the late 1990s. We experimentally tested the potential roles of present-day nutrient and light conditions in limiting eelgrass productivity during the growing season. We tested the hypothesis that eelgrass growth is limited by water column nutrients using in situ nutrient additions in 2 eelgrass meadows. Eelgrass growth rate did not respond to nutrient addition at either site. We then assessed the ability of eelgrass to grow in low light conditions by producing ex situ production-irradiance curves. Eelgrass at both sites showed no evidence of low light acclimatization with saturating irradiances of 224 and 260 µmol photons m-2 s-1 and compensation points of 31 and 61 µmol photons m-2 s-1. We observed eelgrass growth rates of about 3.8% areal growth per day during the peak growth period, which are high when compared to other rates globally. Together, our results suggest that Eeyou Istchee eelgrass is growing under sufficient water column nutrient levels, but suboptimal growing season water column light conditions. Because Eeyou Istchee eelgrass meadows must endure long periods of seasonal ice-cover, light limitation during the short growth season may have longer-lasting impacts on these meadows than in more temperate ones.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".