Proximity, benefit transfer and trade-offs: the limits of ecosystem service assumptions in an anthropogenic rural coastal setting
Bibliographic record
Abstract
The Bay of Fundy agricultural dykeland and tidal wetland system in eastern Canada faces sea level rise and increasing storm action. Managed dyke realignment is underway, which will convert some parts of the anthropogenic dykelands – over 400 years old in some places – back to tidal wetlands. The target landforms are small and widely distributed across the rural coastline, making it difficult to identify stakeholders in landscape decisions and understand potential trade-offs. We used a novel survey question set (n = 233, response rate 21%) to understand the ecosystem service (ES) benefits locals feel they receive from dykeland, dyke and tidal wetland landforms and the spatial dynamics of those benefits. Except for safety and activity benefits associated more with dyke infrastructure, respondents seem to think all three landforms (dykes, dykelands and tidal wetlands) provide many of the same benefits, such as experiences of nature, social interaction, time to reflect and a sense of home. This suggests participants might not perceive problematic trade-offs from changing one landform to another. Respondents living close to dykes were statistically more likely than those further away to report four out of the eight most common benefits. However, proximity to tidal wetlands and dykelands was only associated with receiving one benefit, so stakeholders of those landforms may be widely distributed. Uneven distribution of ES benefit hotspots demonstrates the inability to transfer insight about benefits between communities even when they are nearby and biophysically and demographically similar. Findings question conventional assumptions and techniques involved in ES assessments and stakeholder identification.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".