Ecological considerations for species distribution modelling of euphausiids in the Northeast Pacific Ocean
Bibliographic record
Abstract
Euphausiids are a keystone species in marine food webs of the Northeast Pacific Ocean, important for forage fish, commercial fish species and marine predators such as seabirds and whales. Ecosystem-based fisheries management relies on realistic lower-trophic-level information for forecasting under future changing climate scenarios. We synthesised information from quantitative modelling studies analysing the relationship between euphausiids and their environment for two species of euphausiids that dominate assemblages in this region — Euphausia pacifica and Thysanoessa spinifera. Studies analysed suggest that variables reflecting the physical and biological environment in situ, features reflecting the geomorphic marine landscape, and large-scale climate indices all significantly affect euphausiid biomass and distribution. Temperature was the most tested predictor variable in the euphausiid models reviewed; however, it was significant in fewer models than other variables tested. We review and compare model structures, predictor variable selection and temporal lag phases to develop recommendations for species distribution modelling of euphausiids in the Northeast Pacific Ocean. We believe the results from this review will be applicable globally across regions with similar climates where euphausiids are numerous and can be adapted for different species and environments.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".