Assessing the Potential Impacts of Climate Change on Current Coastal Ecosystems—A Canadian Case Study
Bibliographic record
Abstract
Understanding how climate change affects coastal ecosystems is one of the most important elements in determining vulnerability and resilience for long-term ecosystem management in the face of the increasing risk of coastal hazards (e.g., sea level rise, coastal flooding, and storm surge). This research attempts to undertake a study on the ecosystem–climate nexus in the Canadian province of Prince Edward Island (PEI). Cloud-based remote sensing techniques with Google Earth Engine (GGE) are utilized to identify ecosystem changes over time. In addition, the effects of coastal flooding and storm surge ecosystems under different climate scenarios are examined. The results suggest a reduction in the forest (3%), open water or marsh component (9%), salt water (5%), no open water or marsh component (3%), and salt or brackish marsh (17%) ecosystems from 2013 to 2022. Dune and beach exhibit a non-uniform distribution across the period because of variations in natural processes, with an upward trend ranging from 0% to 11%. Approximately 257 km2 (9.4%) of PEI’s ecosystems would be affected by extreme coastal flooding (scenario 4), compared to 142 km2 (5.2%), 155 km2 (5.7%), and 191 km2 (7%) in scenarios 1, 2, and 3, respectively. Under a 4 m storm surge scenario, around 223 km2 (8.2%) of PEI’s ecosystems would be flooded, compared to 61 km2 (2.2%), 113 km2 (4.1%), and 168 km2 (6.1%) under 1 m, 2 m, and 3 m scenarios, respectively. The findings from this research would enable policymakers to take necessary actions to sustain ecosystem services in PEI while confronting the impacts of climate change.
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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".