Massive increase of intertidal seagrass coverage in a large estuarine system revealed by four decades of Landsat imagery
Bibliographic record
Abstract
The ecosystem services and functions of seagrass meadows are indisputable, and knowledge about their coverage is critical for coastal managers worldwide. In this study, the surface area coverage of the foundation species Zostera marina L. (eelgrass) was investigated in four contrasting subregions of the Estuary and Gulf of Saint Lawrence (EGSL), eastern Canada. The meadows in all subregions mainly occupy intertidal zones. Our analysis covered broad spatial (meters to kilometers) and temporal (annual to decadal) scales and revealed unprecedented insights at a local and regional context. We processed surface reflectance products of the Landsat archive through the Google Earth Engine cloud computing platform. The processing scheme only considered emerged areas of intertidal zones from imagery acquired at the lowest tide levels because of inherent limitations imposed by water clarity and the poor radiometric quality for water applications of the early Landsat sensors. The polygons classified as eelgrass encompassed at least 25% coverage of eelgrass for each patch, and the classification scheme showed a very good agreement with coastal ecosystem habitats maps generated by photointerpretation and field validation for the period between 2015 and 2019, with an overall accuracy of approximately 94%. From the 40-year period analyzed (1984–2023), the meadows’ surface area dramatically increased 10- (from approx. 0.3 to 2.5 km 2 ) to 21-fold (from approx. 0.8 to 16.7 km 2 ). The percentage of the intertidal area occupied by eelgrass meadows varied by subregion, ranging between 17% and 82%. In some subregions, meadows expanded landward. Some meadows experienced relatively short-term losses (interannual scale) in three subregions, although these losses differed in their timing. We propose several hypotheses involving hydrodynamic, sedimentological, drift ice and climatic processes that could explain long- and short-term variability of the meadow coverage. However, this complex relationship remains to be investigated. Overall, while showing suitable habitats for eelgrass colonization, this study also revealed the EGSL tidal flats as potentially important areas of biodiversity, carbon storage, and coastal protection against erosion.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".