Mechanistic Links Between Climatic Forcing and Model-based Plankton Dynamics in the Strait of Georgia, Canada
Bibliographic record
Abstract
Large scale climate indices such as the North Pacific Gyre Oscillation (NPGO) have been linked to variability in both phytoplankton and zooplankton, yet the mechanisms by which they are linked remain unknown. We used a three-dimensional coupled biophysical model, SalishSeaCast, to determine the mechanistic links between the NPGO and plankton dynamics in the Strait of Georgia, Canada. First, we compared bottom-up processes during NPGO positive (cold-phase) and negative (warm-phase) years. Then, we conducted a series of model experiments to determine the effects of the NPGO on local physical drivers by switching individual parameters between a typical warm and cold year. The model showed that higher SST and weaker winds contributed to an earlier increase in spring diatom biomass during warm-phase years. Due to the conditions set up during the spring, warm-phase years exhibited lower overall summer diatom biomass and an earlier shift to nanoflagellate-dominance compared to cold-phase years. Our systematic model experiments revealed that variability in wind-driven resupply of nutrients to the surface waters during the summer had the most significant impact on diatom biomass, and ultimately on the food available to zooplankton grazers. The Z1 and Z2 model classes grazed on a higher proportion of nanoflagellates during the summer of warm-phase years, suggestive of a poorer quality diet consumed during warm years. Results from this study are relevant in the context of other climate signals (e.g., El Niño) favouring weaker winds or increased stratification, which would limit the amount of nutrients being replenished to the surface waters.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".