Eelgrass (Zostera marina) ecosystems in eastern Canada and their importance to migratory waterfowl
Bibliographic record
Abstract
Seagrasses are marine flowering plants that create some of the most productive coastal habitats globally and play a key role in the functioning of nearshore ecosystems. The most common seagrass genus in Canada is Zostera and the species Zostera marina (eelgrass) is the predominant seagrass in intertidal and subtidal shoreline zones along the Atlantic, Pacific, and eastern James Bay coasts. Eelgrass has specific habitat requirements, with growth and productivity optimized within particular ranges of salinity, temperature, light availability, and nutrient concentrations. Large eelgrass meadows can impact nearshore environments by filtering the water column, stabilizing sediment, buffering shorelines, and providing habitat for various marine and coastal species, including commercially important species like Atlantic cod (Gadus morhua) and lobster (Homarus americanus). Eelgrass is also a vital food resource for migratory waterfowl, notably Canada Geese (Branta canadensis), Pacific Black Brant (Branta bernicla nigricans), and Atlantic Brant (Branta bernicla hrota). Despite their ecological importance, seagrasses are among the most vulnerable coastal ecosystems on the planet. The global loss of seagrass has been linked to a variety of human activities, including pollution, invasive species, and catchment modifications. There is an urgent need to improve monitoring of seagrass responses to environmental change, better document the importance of seagrass meadows to species reliant on them for food and habitat, and advance effective management and conservation of seagrass ecosystems. In this thesis, I investigated the spatiotemporal dynamics of eelgrass meadows in eastern Canada and the importance of eelgrass as a food source for migratory waterfowl, using remote sensing data, long-term monitoring data (biomass, density, and cover), and field observations. In Chapter 3, I used a novel cost-efficient approach for satellite imaging time-series to examine changes in eelgrass distribution and abundance from 1984 to 2017 in a wetland of international importance in northeastern New Brunswick. With minimal ground truth data, the novel time-series approach revealed a slow and steady decline in eelgrass abundance in some areas of the estuary. In contrast, other areas were characterized by highly dynamic shifts in eelgrass cover over time. I demonstrated how time-series analysis can be used to identify potential drivers of seagrass change and the benefits of including time-series analysis in seagrass monitoring programs. In Chapter 4, I contributed to advancing knowledge of migratory waterfowl stopover behaviour by examining the influence of eelgrass and human activities on Canada Geese habitat selection. Combining field observations of Canada Geese and the eelgrass distribution maps produced in Chapter 3, I found that Canada Geese selected areas with high eelgrass availability during periods of low human disturbance, which emphasized the importance of eelgrass as a food source during the fall migration. However, higher levels of human disturbance led to a redistribution of geese away from dense eelgrass meadows. In Chapter 5, I presented new insights into the recent and current state of eelgrass along the eastern coast of James Bay after a drastic and large-scale decline in the late 1990s. By aggregating, synthesizing, and analyzing long-term monitoring data and current surveys, spanning 1982 – 2020, I provided the first quantitative evidence that changes in eelgrass biomass in northeastern James Bay may reflect synergistic impacts of climate change and altered freshwater discharge regimes. Overall, this thesis advances understanding of how temperate and subarctic Zostera marina ecosystems and associated fauna respond to coastal development and climate change
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".