Kelp-urchin dynamics: stability and thresholds for phase shifts in Newfoundland and the Gulf of St. Lawrence
Bibliographic record
Abstract
In eastern Canada, studies of kelp-urchin systems have been generally restricted to small spatial (few 100s m²) and temporal (<5 years) extents by the traditional scuba-based monitoring techniques employed. Investigation of the drivers of kelp distribution over multiple spatiotemporal scales (including broad spatial [<km²] and temporal [years] extents) and in regions poorly studied is key to assessing the stability of these systems and understanding regional specificities of kelp dynamics across eastern Canada. This thesis investigates the factors controlling kelp distribution and the stability of kelp-urchin systems in southeastern Newfoundland (SEN) and the northern Gulf of St. Lawrence (nGSL) over multiple spatiotemporal scales by applying traditional and novel techniques. In a scuba-based manipulative field experiment in SEN, no significant effect of urchin density was observed on the rate of kelp bed destruction from urchin grazing, suggesting that the minimal urchin density required to maintain destructive feeding on kelp beds may be equal to the lowest density tested (88 urchins·m⁻²) or lower. The suitability of remote sensing and geographic information system (GIS) approaches for mapping kelp in the nGSL was assessed by comparing three image classification methods applied to aerial and satellite imagery. Supervised classification of satellite imagery (89% accuracy) and visual classification of aerial imagery (90% accuracy) were the best methods. Visually classified imagery from the nGSL was used to compute spatial pattern metrics quantifying kelp distribution patterns. These metrics showed that kelp distribution is not uniform, as kelp patches exhibited considerable variation in size and geometric complexity. Kelp presence was negatively correlated with depth, urchin density, and exposure to waves. Investigation of kelp distribution patterns from imagery acquired in six years between 1983 and 2016 in the nGSL revealed an increase in kelp cover since 1999. Harsh oceanographic conditions in late winter and spring were correlated with decreased kelp cover and smaller, more numerous kelp patches. Kelp patches persisting through time were more frequent in shallow areas. Overall, this thesis increases knowledge of scale dependency in the drivers of kelp distribution in eastern Canada. It speaks to the importance of exploring multiple scales to understand, predict, and mitigate changes in in kelp-urchin systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".