From land to deep sea: A continuum of cumulative human impacts on marine habitats in Atlantic Canada
Bibliographic record
Abstract
Abstract Effective management and mitigation of multiple human impacts on marine ecosystems require accurate knowledge of the spatial patterns of human activities and their overlap with vulnerable habitats. Cumulative impact (CI) mapping combines spatial information and the intensity of human activities with the spatial extent of habitats and their vulnerabilities to those stressors into an intuitive relative CI score that can inform marine spatial planning processes and ecosystem‐based management. Here, we mapped potential CIs of 45 human activities from five sectors (climate change, land‐based, marine‐based, coastal, commercial fishing) on 21 habitats in Atlantic Canada's Scotian Shelf bioregion. We applied an uncertainty and sensitivity analysis to assess the robustness of results and identify hot and cold spots of CIs. Nearly the entire Scotian Shelf bioregion experiences the CIs of human activities, and high CIs were frequently associated with multiple stressors. CIs varied widely across habitats: CI scores in habitats >30 m deep were dominated by climate change and commercial fishing, while nearshore habitats were influenced by a much wider range of activities across all five sectors. When standardized by area, coastal habitats had among the highest CI scores, highlighting the intensity of multiple stressors in these habitats despite their relatively small spatial extent and emphasizing the importance of a multisector approach when managing coastal ecosystems. Robust hot spots of CIs (i.e., areas with high CI scores that were insensitive to alternative modeling assumptions and simulated data quality issues) occurred mostly in coastal areas where multiple high‐intensity activities overlapped with highly vulnerable biogenic habitats. In contrast, robust cold spots of CI mostly occurred offshore. Overall, our results emphasize the need to consider CIs in management and protection and demonstrates that, in many areas, targeting only one activity will be insufficient to reduce overall human impact. The CI map will be useful to highlight areas in need of protection from multiple human impacts, provide information for ecological indicator development, and establish a baseline of the current state of human use in the bioregion.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".