Mapping accessibility zones to salmon fishing in Newfoundland and assessing the potential impacts of spruce budworm outbreaks using GIS
Bibliographic record
Abstract
Fishing in Newfoundland represents both a cultural tradition and an important contributor to community well-being and local economies. In this project, we endeavoured to understand salmon fishing as a cultural ecosystem service and explore the relationships and the influence of natural disturbances, specifically Spruce Budworm (SBW) outbreaks. Our primary objectives included (1) mapping accessibility zones to salmon fishing in Newfoundland and (2) Identifying and assessing areas of SBW defoliation impact on salmon fishing areas. Road networks, towns, HydroSHEDs data, SBW defoliation data, scheduled salmon river networks and flow direction raster acted as foundation data for our comprehensive analyses. The study area covered Newfoundland's diverse watersheds, emphasizing the 14 salmon fishing areas designated by the Angler's Guide of Newfoundland and Labrador. By identifying zones of access and overlapping with SBW defoliation data, we aimed to uncover potential impacts on salmon fishing and fishers in the region. The first study mapped out accessibility zones to salmon fishing using a Multi-Criteria Decision-Making (MCDM) approach with GIS, integrating proximity to towns, roads, and scheduled rivers. The weighted overlay model revealed that 13.66% of the area was highly suitable, 31.32% moderately suitable, and 33.46% suitable for salmon fishing accessibility. The second study assessed spatially the potential impacts of SBW defoliation on our ecosystem service using a predicted SBW defoliation model by Zhang et al. (2023) and HydroSHED data resulting in a potential impact matrix. Results indicated that high impact zones represented 3.63% of the study area but included key salmon rivers like the Humber and Exploits that contribute the most to salmon fishing indicating that the continued studied impacts and effects of SBW defoliation on hydrological bodies will most likely have a high occurrence on these rivers. These outcomes aim to inform fisheries management strategies in response to SBW outbreaks and contribute valuable insights for broader ecosystem services quantification and conservation in Newfoundland. Through this approach, we aspired to contribute to sustainable management of Newfoundland's invaluable fishing resources, ensuring their resilience amidst a changing environmental landscape by providing actionable knowledge for stakeholders, policymakers, and the community.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".