Decision-making and ecosystem service dimensions of managed dyke realignment in the complex coastal landscapes of the Bay of Fundy
Bibliographic record
Abstract
The Bay of Fundy coast in Atlantic Canada hosts a particularly complex landscape including tidal wetlands, agricultural land reclaimed from tidal wetland (locally called dykeland), and a dyke system that holds back sea water to maintain that agricultural land. Sea level rise and storm surges can overtop dykes, and rainwater can get trapped behind, causing flooding of dykeland, towns and other infrastructure. Climate change is exacerbating these flooding events, and the dyke system is no longer adequately engineered to protect the land behind it. Several strategies for dyke maintenance are applied in the region, such as dyke reinforcement, aboiteau upgrades, drainage improvement, and managed dyke realignment. Managed dyke realignment (MDR) is a hybrid adaptation strategy which entails breaching and/or relocating a dyke landward and restoring tidal wetlands to enhance coastal protection. MDR frequently leads to the conversion of agricultural land back to wetland. Making decisions about when to implement MDR is complex for a variety of biophysical, social, and institutional factors. We present a conceptual model based on evidence from previous studies, co-developed and validated through a participatory approach, to explore ecosystem service trade-offs and synergies and institutional dynamics, in the context of MDR. We then test the practical use of this model by exploring changes under different environmental scenarios. Results reveal that the main ecosystem service trade-offs are associated with changes in the extent of agricultural land versus the extent of restored wetland areas, and most synergies comprise non-material ecosystem services. Administrative complexity, multiple funding streams and MDR-specific policy are the variables playing key roles within the decision-making process. The operationalization of the conceptual model through exploring environmental scenarios helped us examine the compromises involved in managing the dyke system. • Main trade-offs are associated with changes in the extent of agricultural land versus the extent of restored wetland areas. • Main ecosystem service synergies comprise non-material services and values. • Administrative complexity and multiple funding streams are some variables playing key roles in the decision-making process. • The model can be used to explore social-ecological dynamics from diverse adaptation strategies and environmental scenarios.
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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 | 0.001 | 0.007 |
| 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".