A newly-developed EwE-based end-to-end model for coastal upwelling systems : insights into the mechanistic linkages between trophic levels under varying environmental conditions
Bibliographic record
Abstract
Coastal ecosystems along the British Columbia coast are impacted by environmental changes, affecting both lower and upper trophic levels, including key commercial species. While 'trophic amplification'—the magnification of primary production changes at higher trophic levels due to climate change—is recognized, few studies span the entire trophic spectrum. This gap hinders our understanding of the mechanisms involved. To address this, I developed an 'end-to-end' model for the west coast of Vancouver Island (WCVI-E2E), integrating a physical-biogeochemical model (NEMURO) with an Ecopath with Ecosim (EwE)-like framework using a two-way coupling approach. The model was built in three stages. First, I optimized the physical-biogeochemical subcomponent using a surrogate optimization algorithm. Second, I constructed an Ecopath model of the WCVI food web, encompassing the entire food chain from primary producers to top predators, and derived higher trophic level parameters. Finally, I explored different coupling strategies between lower and upper trophic levels and evaluated the WCVI-E2E model performance under linear and quadratic mortality formulations. The WCVI-E2E model aims to better represent coastal upwelling dynamics and reconcile lower and upper trophic levels to explore how environmental impacts propagate through the food web. By simulating various upwelling conditions, I quantified the propagation of physical and chemical changes across trophic levels using a trophic amplification metric. The two-way coupling approach showed that a quadratic mortality formulation led to better model performance. This coupling revealed discrepancies in plankton mortality estimates between the standalone NEMURO and WCVI-E2E models, generally leading to lower plankton biomasses in the latter. These changes propagated up the food chain, affecting higher trophic levels and highlighting key feedbacks between trophic levels. Moreover, the findings demonstrated diverse amplification responses to environmental changes, triggered by dynamic changes in trophic transfer efficiencies and trophic levels. An in-depth analysis underscored the importance of temperature variations, physical processes, zooplankton trophodynamics, and nonlinear functional responses in driving trophic amplification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".