Limited recovery following a massive seagrass decline in subarctic eastern Canada
Bibliographic record
Abstract
Over the last few decades, there has been an increasing recognition for seagrasses' contribution to the functioning of nearshore ecosystems and climate change mitigation. Nevertheless, seagrass ecosystems have been deteriorating globally at an accelerating rate during recent decades. In 2017, research into the condition of eelgrass (Zostera marina) along the eastern coast of James Bay, Canada, was initiated in response to reports of eelgrass decline by the Cree First Nations of Eeyou Istchee. As part of this research, we compiled and analyzed two decades of eelgrass cover data and three decades of eelgrass monitoring data (biomass and density) to detect changes and assess possible environmental drivers. We detected a major decline in eelgrass condition between 1995 and 1999, which encompassed the entire east coast of James Bay. Surveys conducted in 2019 and 2020 indicated limited changes post-decline, for example, low eelgrass cover (<25%), low aboveground biomass, smaller shoots than before 1995, and marginally low densities persisted at most sites. Overall, the synthesized datasets show a 40% loss of eelgrass meadows with >50% cover in eastern James Bay since 1995, representing the largest scale eelgrass decline documented in eastern Canada since the massive die-off event that occurred in the 1930s along the North Atlantic coast. Using biomass data collected since 1982, but geographically limited to the sector of the coast near the regulated La Grande River, generalized additive modeling revealed eelgrass meadows are affected by local sea surface temperature, early ice breakup, and higher summer freshwater discharge. Our results caution against assuming subarctic seagrass ecosystems have avoided recent global declines or will benefit from ongoing climate warming.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".