Anthropogenic nitrogen loading and risk of eutrophication in the coastal zone of Atlantic Canada
Bibliographic record
Abstract
Nutrient pollution derived from human land-use activities remains a major cause of the eutrophication of coastal ecosystems around the world. In this study, we conduct a nutrient loading analysis for the coastal watersheds bordering the Bay of Fundy and Scotian Shelf of Atlantic Canada, a cold temperate region of the Northwest Atlantic that exhibits strong gradients in oceanographic conditions and human uses of the coastal zone, and where coastal development is expected to increase in the coming decades. Our objective was to apply the Nitrogen Loading Model (NLM) framework to determine the regional distribution and intensity of nitrogen inputs from land-based sources (wastewater, atmospheric deposition, land use, fertilizer applications, and regional industries) to coastal marine environments, and subsequently identify coastal embayments at risk of developing symptoms of eutrophication. Across all 109 watersheds examined, the estimated mean total nitrogen load varied by three orders of magnitude. The largest loads occurred in the most heavily urbanized and densely populated watersheds, where wastewater inputs contributed the most (>80%) to loading rates. Where the footprint of human activity was small, atmospheric deposition of nitrogen was an important contributor to the total load; however, localized point sources (i.e. seafood processing, fur farming) were significant contributors to the total load in smaller sized watersheds. Coastal embayments with small surface areas and low tidal exchange volumes were most likely to have estuarine loading rates that exceeded previously established thresholds for the loss of submerged aquatic vegetation (e.g., eelgrass). In contrast, none of the embayments were deemed likely to develop anoxia. This analysis enables a novel comparison of nitrogen loading from watersheds across a range of land cover types, watershed characteristics, and human land-use activities, while providing a baseline for the detection of future environmental change in the region. Overall, we emphasize the importance of including human activities that occur on the landscape in future marine coastal planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".