Spatial and temporal dynamics of predator-prey species interactions off western Canada
Bibliographic record
Abstract
Abstract Ecosystem models are valuable tools for informing fisheries management due to their ability to simulate the spatial dynamics of modelled species, their trophic interactions, and their responses to fishing in an ecosystem context. In this study, we developed an OSMOSE (Object-oriented Simulator of Marine Ecosystems Exploitation) model for the Pacific North Coast Integrated Management Area (PNCIMA) ecosystem off western Canada, which simulated the entire life cycle of six key species and for the first time integrated spatial population structure and “background” taxa. Background taxa are of secondary importance for the study at hand but have the potential to be important prey or predators of the key species. The primary aim of the study was to explore how the population dynamics of the key species differed over time and different management areas, with results focusing on Pacific Herring (Clupea pallasii) and Pacific Cod (Gadus macrocephalus) that have been assessed on a single-species basis in the last 5 years. Results found that the population dynamics of a specific species varied in different management areas due to differences in species interactions particularly in the form of predation mortality, which supports the current area-specific assessment and management framework. The study also indicated that increasing predation mortality may have caused the decline of the Pacific Cod populations. By contrast, increasing starvation mortality was found to be a limiting factor for the Pacific Herring populations. The discoveries from these OSMOSE simulations provide important information for fisheries management within the PNCIMA ecosystem.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".